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NASA NTRS2020

An Inter Planetary Network Enabled by SmallSats

This paper describes the implementation of an unparalleled large sensor and communications platform along the solar system that we call the inter planetary network (IPN). The proposed IPN consists of thousands of small spacecraft (e.g. CubeSats) strategically deployed around planetary bodies in the solar system where each spacecraft is furnished with fast communications and sensor systems. The IPN concept is being proposed as an avant-garde science and communications platform that could allow for continuous fast communications and remarkable science returns. The IPN spacecraft, furnished with suitable miniaturized sensors, could form an amazing deep space platform for unique observation of the solar system, stars, galaxies and universe. A key feature of the IPN architecture is the use of swarms of spacecraft as network units. Super-high-speed intra-swarm communications could be achieved via omnidirectional optical links. The swarms act as autonomous network nodes and are capable of forming large synthetic apertures that enable high data rate communications among the IPN nodes. Depending on the sensors they carry, these swarms may also be capable of forming large synthetic sensors by rapidly combining data among the spacecraft. We envision distances between spacecraft forming a swarm to be in the range of 102-104 kilometers, whereas distances of 106-108 kilometers among swarms (IPN nodes) are expected. We provide an example of an initial IPN implementation in the inner solar system where swarms of spacecraft are deployed around the Earth, Moon, Mars, Venus and Mercury. Placing swarms around 3 Lagrange points per planet yield a total of 12 possible IPN network nodes. We present communications link calculations among IPN nodes that allow high data rate communications between Earth and Mars while Mars is in solar conjunction (behind the Sun). A second example of an IPN platforms includes a giant spaceborne radio telescope behind the moon.

Velazco, Jose↗
NASA NTRS2020

Using Networked Pandora Observations to Capture Spatiotemporal Changes in Total Column Ozone Associated with Stratosphere-to-Troposphere Transport

Accurately capturing the evolution of episodic stratosphere-to-troposphere transport is critical due to the potential impacts on both climate and air quality. Until now, investigating associated spatiotemporal gradients in total column ozone (TCO) has primarily been the task of observations from polar-orbiting satellites as well as high-resolution models. We explore how a network of five ground-based Pandora spectrometer systems can be utilized in a similar fashion. The passage of a strong mid-latitude cyclone in March 2018 and its associated stratospheric intrusion is used as a case demonstrating the ability of networked Pandora observations to contextualize these regions of transport across space and time. Results show that the high temporal resolution of Pandora observations and the networked approach were able to resolve increases in TCO associated with stratosphere-to-troposphere transport and to capture the spatial context of the chosen episode. The use of networked Pandora observations shows promise for additional transport studies and for supporting future geostationary atmospheric composition satellite missions and modeling efforts.

J. Robinson↗
NASA NTRS2019

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Simone Lolli↗
NASA NTRSDate not supplied

Status and Future of the FACT Network in the Northern Indian River Lagoon (Abstract for 2022 Indian River Lagoon Symposium)

Continued expansion of passive acoustic telemetry networks is allowing the movements of aquatic animals to be monitored over ever-larger distances and extended time periods. From modest beginnings in east-central Florida, the collaborative FACT Network has grown into one of the largest animal tracking networks in the world, with partners working throughout the southeastern US, Bahamas, and Caribbean. In the IRL region, tracking stations deployed across a continuum of habitats are revealing the behavior and habitat needs of our region’s most valuable sportfish, shark, and sea turtle species. This talk will summarize the status of the FACT Network as of 2022. Several ongoing and upcoming studies in the Canaveral region will be highlighted including those designed to understand the export of economically valuable sportfish from protected waters of Kennedy Space Center, inshore-offshore movements of spawning red drum, seasonal migrations of coastal sharks, and habitat use of juvenile IRL sea turtles.

Indian River Lagoon↗
NASA NTRS2021

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

This paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers.

Dominique Carrer↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗
NASA NTRS2020

An Inter Planetary Network Enabled by SmallSats

This paper describes the implementation of an unparalleled large sensor and communications platform along the solar system that we call the inter planetary network (IPN). The proposed IPN consists of thousands of small spacecraft (e.g. CubeSats) strategically deployed around planetary bodies in the solar system where each spacecraft is furnished with fast communications and sensor systems. The IPN concept is being proposed as an avant-garde science and communications platform that could allow for continuous fast communications and remarkable science returns. The IPN spacecraft, furnished with suitable miniaturized sensors, could form an amazing deep space platform for unique observation of the solar system, stars, galaxies and universe. A key feature of the IPN architecture is the use of swarms of spacecraft as network units. Super-high-speed intra-swarm communications could be achieved via omnidirectional optical links. The swarms act as autonomous network nodes and are capable of forming large synthetic apertures that enable high data rate communications among the IPN nodes. Depending on the sensors they carry, these swarms may also be capable of forming large synthetic sensors by rapidly combining data among the spacecraft. We envision distances between spacecraft forming a swarm to be in the range of 102-104 kilometers, whereas distances of 106-108 kilometers among swarms (IPN nodes) are expected. We provide an example of an initial IPN implementation in the inner solar system where swarms of spacecraft are deployed around the Earth, Moon, Mars, Venus and Mercury. Placing swarms around 3 Lagrange points per planet yield a total of 12 possible IPN network nodes. We present communications link calculations among IPN nodes that allow high data rate communications between Earth and Mars while Mars is in solar conjunction (behind the Sun). A second example of an IPN platforms includes a giant spaceborne radio telescope behind the moon.

Velazco, Jose E↗
NASA NTRS2020

Scheduling NASA's Deep Space Network: Priorities, Preferences, and Optimization

NASA's Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. Growth in mission demand, both in number of spacecraft and in data return, has led to increased loading levels on the network, and actual demand frequently exceeds network capacity. The DSN scheduling process involves peer-to-peer collaborative negotiation, which consumes significant time and resources in order to reach a baseline version of the schedule, and then to manage and agree to changes. Process delays are exacerbated by the high level of oversubscription experienced by the DSN: it is not unusual for the scheduling process to start with 20-40\% more requested time can be accommodated on the available antennas. The other NASA networks make use of a static mission priority list to address a similar problem: missions are ranked in priority order, then the schedule is populated by priority from highest to lowest. Such a mechanism would not work for DSN due to the heterogeneity of the mission set, and to the time-varying mission requirements with mission phase. This paper describes an alternative approach for the DSN that addresses key problems inherent in the current process --- oversubsubscription and how to "fairly'" reduce it to a manageable level. The main characteristics of the new approach are the use of loading-based limits based on balancing requested time, along with priorities and user preferences as the basis for optimization criteria that can be used by new algorithms.

Johnston, Mark D↗
NASA NTRS2021

NASA’s Deep Space Network: Automation in the Follow-the-Sun Era

The Deep Space Network (DSN) comprises three sites, located in California, Spain, and Australia; each site operates one 70m and multiple 34m antennas that provide communications and navigation services to NASA and international partners. As part of a multiyear upgrade in automation of the network, the DSN has undertaken a series of operations efficiency improvements which has fundamentally changed the operations paradigm of the network. At the start of this effort, each site operated only their local antennas and equipment 24 hours/day, 7 days/week, and each spacecraft activity (link) had a single dedicated Link Control Operator. In its final stage, realized in 2020, each one of the three sites operates the entire network during their local day shift, handing off control to the next site as their day ends, and tracks can be run in a fully automated manner with no operator intervention. This paradigm change has realized significant operations cost savings, but has posed some major challenges along the way. This paper describes the system changes that enabled the operations efficiency improvements, some the challenges, and plans for the future.

Levesque, Michael↗
NASA NTRS2021

User Preference Optimization for Oversubscribed Scheduling of NASA’s Deep Space Network

NASA’s Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. Growth in mission demand, both in number of spacecraft and in data return, has led to increased loading levels on the network, and actual demand frequently exceeds network capacity. The DSN scheduling process involves peer-to-peer collaborative negotiation, which consumes significant time and resources in order to reach a baseline version of the schedule, and then to manage and agree to changes. Process delays are exacerbated by the high level of oversubscription experienced by the DSN: it is not unusual for the scheduling process to start with 20-40% more requested time can be accommodated on the available antennas. The other NASA networks make use of a static mission priority list to address a similar problem: missions are ranked in priority order, then the schedule is populated by priority from highest to lowest. Such a mechanism would not work for DSN due to the heterogeneity of the mission set, and to the time-varying mission requirements with mission phase. This paper describes an alternative approach for the DSN that addresses key problems inherent in the current process — oversubsubscription and how to “fairly” reduce it to a manageable level. The main characteristics of the new approach are the use of loading-based limits based on balancing requested time, along with priorities and user preferences as the basis for optimization criteria that can be used by new algorithms.

Johnston, Mark D↗
NASA NTRS2022

Developing High Performance Space Networking Capabilities for the International Space Station and Beyond

A performance optimized implementation of Delay Tolerant Networking (DTN) with the capacity of gigabit-per second rates is developed for the International Space Station (ISS) and missions demanding large amounts of communications bandwidth. An overview of the High-rate Delay Tolerant Networking (HDTN) architecture and support for different convergence layers is provided. This paper then presents an overview of the testing and integration efforts to evaluate interoperability and capability in relevant environments. The first was interoperability testing with DTN Marshall Enterprise (DTNME) which resulted in near-gigabit per second data rates. This was followed by ISS emulation testing with the Software Development and Integration Laboratory (SDIL) at the Lyndon B. Johnson Space Center (JSC) and local testing based on the ISS DTN network topology. The local tests resulted in the discovery of potential sources of performance loss in the network and demonstrated near-gigabit rates between HDTN and DTNME.

Daniel Raible↗
NASA NTRSDate not supplied

The Space Superhighway: A Cost Analysis of an In-Space Logistics Resupply Network

The Space Superhighway is a concept for an in-space logistics network for payload delivery and propellant resupply within the Earth-Moon system. It is expected that in the next several years the number of spacecraft in cislunar space will increase significantly. Commercial launch service providers have significantly reduced the cost of access to space and reduced the barrier to entry for private companies to build and operate their own satellites. Many of these newer satellites are being designed to take advantage of future In-space Servicing, Assembly, and Manufacturing (ISAM) capabilities. In addition, the National Aeronautics and Space Administration (NASA) intends to develop and maintain a sustained human presence in cislunar space and on the surface of the Moon. The sustained lunar presence will then serve as a steppingstone for human missions to Mars. The combined demand for propellant resupply and payload delivery in cislunar space for NASA's Moon to Mars campaign in addition to commercial ventures will likely exceed 1,000 t in the upcoming decades. This work presents a concept for an in-space logistics network to fulfill that demand. The network consists of commercial launch vehicles, propellant tankers, orbital depots, and in-space electric propulsion tugs. This work specifically analyzes the cost of developing, producing, and operating such a network at varying levels of customer demand.

nrho↗
NASA NTRSDate not supplied

The Space Superhighway: A Cost Analysis of an In-Space Logistics Resupply Network

The Space Superhighway is a concept for an in-space logistics network for payload delivery and propellant resupply within the Earth-Moon system. It is expected that in the next several years the number of spacecraft in cislunar space will increase significantly. Commercial launch service providers have significantly reduced the cost of access to space and reduced the barrier to entry for private companies to build and operate their own satellites. Many of these newer satellites are being designed to take advantage of future In-space Servicing, Assembly, and Manufacturing (ISAM) capabilities. In addition, the National Aeronautics and Space Administration (NASA) intends to develop and maintain a sustained human presence in cislunar space and on the surface of the Moon. The sustained lunar presence will then serve as a steppingstone for human missions to Mars. The combined demand for propellant resupply and payload delivery in cislunar space for NASA's Moon to Mars campaign in addition to commercial ventures will likely exceed 1,000 t in the upcoming decades. This work presents a concept for an in-space logistics network to fulfill that demand. The network consists of commercial launch vehicles, propellant tankers, orbital depots, and in-space electric propulsion tugs. This work specifically analyzes the cost of developing, producing, and operating such a network at varying levels of customer demand.

space superhighway↗
NASA NTRSDate not supplied

Diving into the Data Plane of Delay Tolerant Networks

This presentation details early research towards applying data plane programming and software defined networking (SDN) to delay tolerant networking (DTN). This involved using the Programming Protocol-Independent Packet Processors (P4) language to program the data plane to handle DTN bundles natively in hardware. The results presented include a proof of concept DTN Bundle Protocol (BP) translator network middlebox, the challenges of programming a data plane to process BP, a software-defined DTN (SDDTN) architecture, and potential future applications of SDDTN. It is demonstrated that SDN and the P4 language has high potential for improving the scalability of DTN networks, although further research must be done to enable this.

Dominick Ta↗
NASA NTRS2024

Interoperability Review of Wireless Hardware in Emergency Air-To-Ground Mesh Networks

The air-to-ground network has the goal of advancing the capabilities and versatility of communications in wildfire and emergency scenarios. To ensure compatibility and keep costs low for emergency services interoperability between hardware is an important step in development of the mesh network. For the purposes of this evaluation interoperability of hardware is described as the ability for hardware from different vendors to be interchangeable within the network with minimal initial setup and no functional change from an operator’s viewpoint. This report will also focus on the features and limitations in addition to the interoperability of the major wireless hardware systems core to the function of the proposed air-to-ground mesh network. The hardware systems involved are ADS-B receivers, 3GPP Sidelink devices, mesh radios, and commercial ISM band radios. For early testing the mesh radios will be in the 900 MHz frequency range using the DoodleLabs RM-1700-22W Mesh Rider Radio as the lab reference. This report will consider and categorize desirability of features, the amount of setup required before deployment, the effect on performance characteristics from nominal and maximum load, and the ability to interface with other vender devices.

Mesh Radio↗
NASA NTRS2024

Envisioning an Optimal Network of Space-Based Lasers for Orbital Debris Remediation

The rapid increase in resident space objects, including satellites and orbital debris, poses a significant threat to the safety and sustainability of space missions. This paper explores orbital debris remediation using a network of collaborative space-based lasers, leveraging laser ablation for momentum transfer on debris. A novel delta-v vector analysis framework quantifies the e↵ects of multiple simultaneous laser-to-debris (L2D) engagements by using vector composition of the imparted delta-v vectors. The paper introduces the Concurrent LocationScheduling Problem (CLSP), which optimizes the placement of laser platforms and the scheduling of L2D engagements to maximize debris remediation capacity. Due to the computational complexity of the CLSP, it is decomposed into two sequential subproblems: (1) optimal laser platform locations are determined using the Maximal Covering Location Problem, and (2) a novel integer linear programming-based approach schedules L2D engagements within the network configuration to maximize remediation capacity. Computational experiments are conducted to evaluate the proposed framework’s e↵ectiveness under various mission scenarios, demonstrating key network functions such as collaborative nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further examines how varying the number and distribution of laser platforms a↵ects debris remediation capacity, providing insights into optimizing the performance of space-based laser networks.

David O Williams Rogers↗
NASA NTRS2011

The Impact of Traffic Prioritization on Deep Space Network Mission Traffic

A select number of missions supported by NASA's Deep Space Network (DSN) are demanding very high data rates. For example, the Kepler Mission was launched March 7, 2009 and at that time required the highest data rate of any NASA mission, with maximum rates of 4.33 Mb/s being provided via Ka band downlinks. The James Webb Space Telescope will require a maximum 28 Mb/s science downlink data rate also using Ka band links; as of this writing the launch is scheduled for a June 2014 launch. The Lunar Reconnaissance Orbiter, launched June 18, 2009, has demonstrated data rates at 100 Mb/s at lunar-Earth distances using NASA's Near Earth Network (NEN) and K-band. As further advances are made in high data rate space telecommunications, particularly with emerging optical systems, it is expected that large surges in demand on the supporting ground systems will ensue. A performance analysis of the impact of high variance in demand has been conducted using our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) simulation tool. A comparison is made regarding the incorporation of Quality of Service (QoS) mechanisms and the resulting ground-to-ground Wide Area Network (WAN) bandwidth necessary to meet latency requirements across different user missions. It is shown that substantial reduction in WAN bandwidth may be realized through QoS techniques when low data rate users with low-latency needs are mixed with high data rate users having delay-tolerant traffic.

prioritization↗
NASA NTRS2011

Deep Space Networking Experiments on the EPOXI Spacecraft

NASA's Space Communications & Navigation Program within the Space Operations Directorate is operating a program to develop and deploy Disruption Tolerant Networking [DTN] technology for a wide variety of mission types by the end of 2011. DTN is an enabling element of the Interplanetary Internet where terrestrial networking protocols are generally unsuitable because they rely on timely and continuous end-to-end delivery of data and acknowledgments. In fall of 2008 and 2009 and 2011 the Jet Propulsion Laboratory installed and tested essential elements of DTN technology on the Deep Impact spacecraft. These experiments, called Deep Impact Network Experiment (DINET 1) were performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. The DINET 1 software was installed on the backup software partition on the backup flight computer for DINET 1. For DINET 1, the spacecraft was at a distance of about 15 million miles (24 million kilometers) from Earth. During DINET 1 300 images were transmitted from the JPL nodes to the spacecraft. Then, they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. The first DINET 1 experiment successfully validated many of the essential elements of the DTN protocols. DINET 2 demonstrated: 1) additional DTN functionality, 2) automated certain tasks which were manually implemented in DINET 1 and 3) installed the ION SW on nodes outside of JPL. DINET 3 plans to: 1) upgrade the LTP convergence-layer adapter to conform to the international LTP CL specification, 2) add convergence-layer "stewardship" procedures and 3) add the BSP security elements [PIB & PCB]. This paper describes the planning and execution of the flight experiment and the validation results.

automated data communication↗