esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 2 Materials and Methods
### Test Methods  
#### 2.6.1 Expansion Measurement  
The durability of the cement-based material structure is closely related to its volume stability because the volume of the specimen will change due to the effect of sulphate in the material; thus, the measurement of the deformation performance of the mortar specimen has some significance for the study of internal sulphate attacks. In this test, the nail head has been buried in the axial at both ends of the specimen in advance, and the lengths of mortar specimens at different ages during the process of sulphate attack have been measured using BC-II ratio meter to calculate the expansion rate of the specimens. Measurement and calculation method: After the specimen is demoulded, it is placed in water for 30 min to measure its initial length \(L_{0}\), and then its length \(L_{t}\) is measured at different ages of attack. The expansion rate of the specimen is calculated according to the Formula (1), and the average expansion rate of each group of three specimens is taken as the expansion value of the group, accurate to 0.001%.  
\[P_{t}=\frac{L_{t}-L_{0}}{L}\times 100\% \tag{1}\]  
where \(P_{t}\): expansion rate of the specimen at age \(t\), 100%.  
\(L_{0}\): the initial length of the specimen, mm.  
\(L_{t}\): length of the specimen at age \(t\), mm.  
\(L\): the effective length of the test piece and the effective length of the mortar is 250 mm.
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 4 Open Problems and Ongoing Trends
### Semantic Mapping of Dynamic Environments  
Although many single-robot semantic-mapping systems have been proposed, most of the works focus on static environments. The majority of them propose a process to create the initial map of the environment, but they do not consider maintaining it. Only a few works designed a system to update the map to take into account moving objects, newly introduced objects, and deleted objects. To perform this process, these systems are not autonomous and require the assistance of an operator. Moreover, only a few works considered collecting semantic data about humans in the environment, which can be interesting in a collaborative context. Similarly, in collaborative semantic-mapping systems, the majority of works focus on static scenes, and only a few works considered updating the dynamic feature representation after the mapping process. Updating the map through an autonomous process is a challenging task that could be addressed in future works on semantic mapping. Indeed, such a system is very important for mapping a dynamic environment, where the structure changes at a high frequency. For example, in an industrial environment, where the human and the robot share the same workspace and where objects can be introduced and removed every second [64], a real-time semantic map is needed to give the robot real-time information about its environment and to ensure the safety of the human-robot collaboration.
###### Abstract  
On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-I SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards.  
AI Earth cloud platform; InSAR processing system; glacial lake outburst flood; Sentinel-I; time-series InSAR; deformation monitoring
# Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
## 4 Results
### Performance of the SG-RL Method
#### 4.1.1 Performance on Static Maps  
We evaluated the SG-RL method with four maps described in Figure 6. We trained the robot to learn to avoid obstacles with LSPI in Figure 6a. The three maps for navigation tasks (Figure 6b-d) were between 250 and 521 times larger than the training map. Since the resolution of these three original maps (i.e., 2.5 cm per pixel) was too high for doing experiments on them, a down-sampling method was used to compress them. These original maps were composed of grayscale information, and wetransformed them into grid maps (if there existed an obstacle in a cell, then the value of this cell was set to 1, otherwise 0).  
Table 3 shows the action-switching frequency of the robot completing the whole path on different maps. Since the learned policy was deterministic, this indicator was also fixed given the same start-goal location pair. All these values were below 10%, which indicates that the robot maneuvered by SG-RL can work well in large and realistic environments with low action-switching frequencies. In Table 4 the path length of SG-RL in Building 1 was almost equal to that in Building 3; however, in Table 3, the action-switching frequency of Building 3 was larger than that of Building 1, since there were more narrow passages in Building 3, where the robot needed to quickly change its actions for adapting to the complex environment (i.e., the robot changed its action to traverse many left and right turns). As a consequence, as the complexity of test maps increased, the action changes that were essential for the environment accordingly increased in number, which finally, increased the action-switching frequency. Action-switching frequency is also related to the performance of learned policy. If the performance of learned policies improves, the evaluation factor will decrease. Therefore, in Section 4.2, we present two ways to improve the performance of learned policies (i.e., by modifying reward functions and by choosing suitable training maps).  
Figure 6: (**a**) Training environment—50 by 50. The training environment was a simplified office. (**b–d**) Given the start position and the goal position, the blue line is the path planned by the simple subgoal graph (SSG), and the black line is the trajectory planned by SG-RL. The dark-gray region is unpassable while the light-gray region is traversable for robots. Red regions deemed close to obstacles denote alert areas. (**b**) Building 1—820 by 820, (**c**) Building 2—1142 by 1142, (**d**) Building 3—792 by 792.
## 6 Comparison Between KANs and Classical Models
### Classical Formulas, Machine Learning, and KANs  
The Usui Model represents a class of classical cutting models that express relationships between machining variables. These models share key similarities. They are intuitive, interpretable, and static. Meanwhile, their forms do not evolve with the data.  
Crucially, they ignore the operational data generated by CNC machines, tool systems, and materials during runtime. For example, the same formula is applied to new vs. aged machines of the same model for identical tasks, despite differences in wear states and system dynamics.  
Recent machine learning and deep learning approaches have proven effective for machining process modeling, but their black-box nature remains an inescapable drawback. Even with cutting-edge methods and satisfactory test results, humans struggle to derive new knowledge from these opaque relationships. Technical innovation and engineering applicability alone cannot substitute for the discovery of fundamental insights.  
KAN-based machining variable modeling bridges the strengths of both approaches: it treats machine runtime data as dependent variables and targets physical variables of interest, which enables researchers to establish quantifiable input-target relationships, while deriving interpretable mathematical formulas. It also represents dynamic modeling that evolves with the data. In other words, machine operational and degradation processes (e.g., tool wear and spindle drift) are directly encoded into the derived formulas.  
By embedding system dynamics into interpretable equations, KANs transcend static classical formulas and opaque machine learning models. They offer a dual-purpose framework for both academic discovery and industrial innovation.
## 3 A Security-Enhanced Authenticated Key-Agreement Protocol: SE-UAVAKA
### SE-UAVAKA Authentication Key-Agreement Phase
#### 3.4.1 Detailed Steps of Authentication Key-Agreement Phase
* Calculate \(H_{b}^{{}^{\prime}}=H(ID_{B}^{{}^{\prime}}||TS_{b}||N_{b}^{{}^{\prime}})\) using a one-way hash function. * Compare with the obtained \(H_{b}\): \(H_{b}=H_{b}^{{}^{\prime}}\)? If the comparison is successful, A authenticates B successfully. * Calculate the session key: \(SSK_{AB}=H(r_{a}\cdot ID_{A}\cdot N_{b}^{{}^{\prime}})\). Through these steps, the UAV and ground station complete a secure agreement for the session key and achieve ID authentication.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 2 Deployable Mesh Antenna Design  
### SAR Considerations  
In a SAR system, the antenna size must be determined at an early stage of the design, as antenna length and width define the half-power beamwidth (HPBW) of the antenna pattern, from which many other SAR parameters are derived.  
Volume constraints from the launcher envelope allowed a maximum antenna diameter of 3.6 m. This gives a \(PRF_{min}\) of 4.2 kHz, and defines a range of valid PRFs and subsequent SAR system parameters. When accounting ambiguities from blind ranges and nadir returns, non interference PRF values are limited to 4.2-7.5 kHz within 15\({}^{\circ}\)-35\({}^{\circ}\). That corresponds to an access area of 216 km on both sides of the nadir track. Figure 2 plots usable PRF values (white area) as a function of incidence angle for a 3.6 m-diameter parabolic antenna, accounting eclipsing from transmitter pulse (yellow) and nadir echos (blue). In full polarimetric mode, the PRF must be doubled, leading to a useful range of 8.4-15 kHz. Even for high PRF values, the Ambiguity Signal Ratio (ASR) is below \(-18\) dB considering targets with uniform reflectivity. For transmission peak power of 1500 W, ground resolution of 30 m with a standard image quality NESZ of \(-20\) dB can be achieved from 500 km altitudes. Swaths of 15-30 km wide can be achieved to cover the remote sensing applications considered for the mission, as listed in Table 1. That way, at the cost of smaller swath widths, a 3.6 m diameter sized antenna in combination with low orbit would allow good SAR sensitivity with reasonable power budget. A summary of the mission and antenna's specifications can be seen in Table 3.  
Next, details of the design of the parabolic reflector antenna are discussed.  
Figure 2: Dependency of usable PRFs (white area) as incidence angles vary. Unavailable PRF values due to blind ranges and nadir returns are depicted in yellow and blue.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 3 Methods
### Rapid Update of Cropland Extent Based on LUILC  
#### 3.1.1 Automatic Generation of Sample Library of Cropland  
To obtain high-quality cropland sample points, this study integrates multi-source, time-series LULC maps to identify temporally and spatially stable sample points. This approach aims to minimize uncertainties caused by differences in category definitions, image data sources, and production methods across various products. Specifically, the process involves three main steps. First, all collected time-series LULC maps are reclassified into binary categories--cropland and non-cropland. Raster computation is then performed to retain only those pixels with consistent values across all seven rasters. Second, spatial filtering is applied by conducting a convolution operation within a 5 \(\times\) 5 window to identify homogeneous points; the center pixel is preserved only if all pixels within the window share the same value. Finally, stratified sampling is performed to ensure sample balance during the training process, with 5000 samples each for cropland and non-cropland selections. The workflow is illustrated in Figure 3.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 2 Sensor Node Energy Model
### Analytical Energy Model
#### 2.3.2 Wireless Transceiver and Sensor
The latter is determined for a given bit error rate (BER) or reliability target of the application. To this end, a realistic path loss model is indispensable in order to estimate the received signal power. It is introduced in Section 3.3 of this paper.
# A Cost-Efficient Approach towards Computational Fluid Dynamics Simulations on Quantum Devices
## 3 Preliminaries
### Reducing Model Complexity  
Problems in CFD often lead to large systems of equations that are computationally too expensive to solve on classical computers. Model order reduction methods aim to solve this problem by decreasing the complexity and computational cost of a system by removing \"redundant\" information from the high fidelity model. The main idea is to generate computationally efficient ROMs that work with a relatively high accuracy at points of interest and to remove any unnecessary complications in the model otherwise. One example is the proper orthogonal decomposition (POD) method, which is a widely used snapshot-based reduction method for CFD applications. The details of the POD method are discussed next in Section 3.3.1.  
Snapshot-based model order reduction methods require a set of solutions that are computed or measured in advance. Standard MOR techniques assume that the snapshots, which are solutions sampled at different and evenly spaced times, use a constant spatial mesh. Adaptive snapshot computation methods also exist, where the mesh could be different in the case of each snapshot [44]. The solution of the ROM is represented as a linear combination of the snapshots, with the coefficients determined by a Galerkin projection based on a weak formulation of the governing equations. The two most commonly used MOR methods for nonlinear systems are the proper orthogonal decomposition (POD) [45; 46; 47] and trajectory piecewise-linear techniques (TPWL) [48; 49; 50; 51], which are discussed in Section 3.3.1 and Section 3.3.2, respectively.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 5 Verification of the NOMA Simulator
### Exemplary Simulated Results
#### 5.1.3 Throughput Analysis  
The throughput of a system is a measure that determines the successful transmission over a communication channel with channel impairments. NOMA is believed to improve throughput compared to OFDMA [40; 41; 42]. However, the throughput of NOMA depends on the pairing of users and the power allocation for each UE along with the modulation and coding scheme. For link-level analysis, the throughput \(\epsilon\) of the system can be expressed as [38]  
\[\epsilon=\frac{(1-PER)\times n_{b/pk}}{T_{pk}(s)} \tag{7}\]  
where PER denotes the packet error rate, \(n_{\frac{b}{pk}}\) is the number of bits per packet, and \(T_{p}k\) is the duration of a single packet. Figure 11 demonstrates the throughput performance of NEU and FEU with perfect SIC in the Rayleigh fading channel in the presence of AWGN. Convolutional coding was also applied at different coding rates. Although with channel coding, the system can detect and correct more errors, which improves the BER performance, but at the same time, redundant bits lower the throughput of the individual user.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 4 Performance Evaluation
### Jitter Ratio  
Figures 8 and 9 show the average jitter ratios of the two buffering schemes when varying the total number of peers and the number of chunks per buffermap. As expected, the jitter ratios of the adaptive buffering scheme are lower in all numbers of peers and chunks per buffermap. When the number of peers are 1200, 1800, and 2400, the jitter ratios of the adaptive buffering scheme are 32.5%, 32.4%, and 30.6% lower than those of the fixed buffering scheme, respectively. When the numbers of chunks per buffermap are 256, 512, and 1024, the adaptive buffering scheme achieves performance improvements of 42.1%, 32.5%, and 28.3%, respectively. The reason for this is that in the adaptive buffering scheme, each peer can provide relatively more data to others because peers can share a relatively larger amount of data with each other with increase in data duplication among peers.  
It is noted that the difference in the jitter ratio between the two schemes increases as the buffermap size decreases. This is because even though buffer size is small, the adaptive buffering scheme can still increase the overlap in buffered data among peers by properly adjusting the buffer ratio of each peer.
## 1 Introduction
), flexural yielding damper (e.g., added damping and added stiffness (ADAS) [36, 37, 38], pipe damper (PD) [39, 40], U-shaped energy dissipation damper (UEDD) [41, 42, 43], etc. ), and combined metallic hysteretic dampers [22, 25, 26, 44], etc. The literature reviews show that metallic dampers with steel brace could significantly enhance the lateral stiffness, strength, and deformation capacity of RC frames, which indicates that these metallic dampers could play a positive role in strengthening RC frames [22, 23]. However, some researchers [45, 46] also found that it is precisely due to the increase in structural overall stiffness that these retrofitting devices might have a potential adverse effect on the structure. To this end, a novel adaptive hysteretic damper has been developed by [PERSON] et al. [46], and it effectively improved this unacceptable situation. Nonetheless, it is worth noting that the aforementioned studies mainly conducted the retrofitting analysis of new-built or existing structures or structural members, and few studies for the post-mainshock damaged structures [20, 22]. In other words, most of experimental or numerical studies focused on structural members, and the retrofit effect of the entire structures was rarely investigated, especially for MD-RC frames that might suffer from adverse failure or collapse under aftershocks. It is generally believed that retrofitting analysis of the damaged structure belongs to the category of secondary force, and the stiffness and strength of the structural material will decrease significantly after the first excitation (i.e., mainshock). Because of the randomness of earthquakes (or aftershocks), it is obviously insufficient to only consider the perfect structures, which might overestimate the actual bearing capacity of structure. To this end, some investigations [1, 20, 22] have aimed at the retrofitting study of earthquake-damaged structures through experiments or numerical simulation techniques in recent years. The objective of this research aims at studying the retrofitting analysis of the MD-RC frames under aftershock excitations. In order to understand the seismic performance of MD-RC frames with and without retrofitting schemes, a typical low-standard designed RC frame in the rural area is taken as the research object and a numerical evaluation framework of retrofitting analysis of MD-RC frame is also proposed that is based on the \"element live and death\" technology of ABAQUS program [47]. In addition, a retrofitting device that is based on combined soft steel dampers (CSSD) and three layout schemes is designed for MD-RC frames. Finally, through nonlinear dynamic time history analysis of MD-RC frame with and without retrofitting schemes, the retrofit effects of the CSSD retrofitting devices on MD-RC frames under three layout schemes are evaluated in terms of natural frequency, displacement response, interstory drift response, and shock absorption rate of MD-RC frames.
# A Data-Driven Situational Awareness System for Enhanced Air Cargo Operations Emergency Control
## 3 ULD Remote Monitoring System  
Every sensor placed on the ULD is connected to the Main Board, which is responsible for collecting and processing data. The Main Board is powered by the Power Supply Board which provides the necessary voltage levels to the Main Board and the sensors (Table 1). The Main Board also includes a wireless communication module to transmit the sensors' data. Figure 5 illustrates the ULD Remote Monitoring System assembled.  
The ULD is designed to be at one of three states: Idle, Normal, and Lock. Regardless of the state, it will respond to every command sent by the HMI.  
* Idle: data from sensors are collected, although nothing is reported. If a smoke alarm happens, it will be reported to HMI. The system can still receive commands.
* Normal: ULD will periodically report its measurements to the HMI, depending on its \"report period\" setting. It will also spontaneously send alarms to the HMI.
* Lock: designed for the loading-unloading phase. ULD sends every 100 ms lock feedback to the robotic platform through HMI.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Feature** & **Specification** & **Quantity/ULD** \\ \hline \multirow{2}{*}{RMS Processing Unit} & System clock & 16-bit RISC up to 20-MHz & \\  & Ultra-low power consumption & 295 \(\mu\)A/MHz at 8 MHz, 3.0 V & 1 \\  & Analog-to-Digital Converter (ADC) & 12-bit & \\ \hline \multirow{2}{*}{Wireless Communication} & Wi-Fi & 802.11 n (2.4 GHz) & \\  & Communication rate & up to 150 Mbps & 1 \\ \hline Structural Health Monitoring & \multirow{2}{*}{Output voltage} & \multirow{2}{*}{100 mV to 100 V} & 3 \\ (SHM) sensor & & & \\ \hline Fire detection sensor & Siemens PCM1103-03 & Temperature-humidity & 1 \\ \hline \multirow{2}{*}{ULD lock sensors} & Contact switches & 30 operations/min (max) & 8 \\  & Position sensors & Sensitivity: 0.90 mV/Gauss & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote Monitoring System technical specifications.  
Figure 5: The Remote Monitoring and Control (RMC) system inside its Housing.  
The ULD's power supply is provided by two packs of batteries. A battery pack manager board is responsible for measuring the voltage of every cell of each battery every ten seconds. The charge percentage of each pack is calculated into a running average to smooth readings. At ULD Normal state, at the report, the charge percentage is sent to HMI. The switching between the two battery packs is done by a power source selector board. The first battery is always the first to deplete, after which comes the second. The convention used is that, at 0%, the pack does not provide power for the ULD to function properly while, at 100%, the battery pack is at its maximum charge capacity. The battery pack manager boards, the power source selector board, and the battery packs are shown in Figure 6.  
RMC system can control three basic operations: