esa-sceva/satcom-chunk-collection huggingface dataset:
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 6 Planning Algorithm of IPCB Geometry Configuration  
Based on the analysis above, it can be inferred that if fans and valves can be controlled properly, IPCB geometry configuration can be achieved by utilizing winds at different altitudes effectively. So, the IPCB geometry configuration planning problem can be considered a flight altitude combination problem that can be solved by heuristic algorithms.  
WOA is a famous heuristic algorithm that has achieved success in many applications because of its strong robustness, effective searchability, and convenient parameter settings [54; 55; 56; 57; 58]. Compared with PSO (particle swarm optimization) or DE (differential evolution algorithm), WOA does not consider subjective parameter settings, such as the inertial coefficient, acceleration coefficient, and other parameters in PSO, scale factor, crossover probability, and other parameters in DE [59; 60]. In addition, the performance of WOA, PSO, and DE are compared in reference [54], and WOA displays its excellent capability [54]. Therefore, this paper adopts WOA to realize IPCB geometry configuration planning.  
In the planning algorithm, the flight altitudes of all the IPBs in an IPCB can be treated as a whale agent. The general procedure of the algorithm can be described as follows:  
Initialize all the whale agents in the current whale population randomly (i.e., initialize the flight altitudes of all the IPBs randomly);Acquire horizontal winds corresponding to the flight altitudes (i.e., the whale agents just initialized) by Equation (17) or from other data sources;  
Calculate horizontal trajectories of all the IPBs in the IPCB by Equation (16);  
Adjust the flight altitudes and horizontal trajectories of the IPCB by approaches defined in Section 4;  
If the flight altitudes, horizontal trajectories, or adjusted time intervals (calculated by Equation (14) or (15)) do not meet the constraints listed in Section 5, the fitness of the whale agent is defined as 0, meaning that the corresponding IPCB geometry configuration is not feasible in the assumed conditions;  
To a whale agent complying with the constraints listed in Section 5, calculate its fitness by Equation (9);  
Calculate the fitness of all the whale agents and select the best whale agent in the current whale population;  
Update all the whale agents in the current whale population by strategies defined in WOA, such as the \"encircling prey\" strategy, \"bubble-net attacking\" strategy, and \"search for prey\" strategy [54];  
Implement updating iteration according to the procedure of WOA, which has been described in detail in reference [54];  
When the iteration ends, an IPCB flight altitude can be obtained from the best whale agent. The flight trajectory and constellation GDOP can also be calculated from the best whale agent, forming a complete IPCB geometry configuration.
## 2 Superiorities of Near-Space Radar Platform
### Inherently Survivable  
Near-space platforms, especially stationary vehicles, are inherently survivable. They have extremely small radar cross and thermal cross sections, making them relatively invulnerable to most traditional tracking and positioning methods. Estimates of stationary vehicle's radar cross sections (RCS) are on the order of hundredths of a square meter. In fact, at near-space altitude, stationary vehicles will be small optical targets, only showing up well when the background is much darker than them. Consequently, the acquisition and tracking problem will be very difficult, even without considering what sort of weapon could reach them. Surface-to-air missiles (SAMs) may be a threat, but they are most likely not designed to engage a non-maneuvering target at that altitude. Economics also discourage such as exchange because near-space vehicles are inexpensive.  
Moreover, even if the acquisition and tracking problems are overcome, near-space sensors are difficult to destroy, particularly for the stationary vehicles. They are normally manufactured in two ways: zero-pressure and super-pressure. Zero-pressure ones are similar to hot air balloons. They are less vulnerable to puncture, since significant amount of the lifting gas must diffuse through the holes before the lift is lost. Imaging an inflated, lightweight plastic garment bag floating on the wind; even if there are many holes in such a bag, it can still float in the air for a long time. Super-pressure ones are inflated and sealed, much like a child's toy helium balloon. However, most of them are generally constructed by strong and rip-stop material, which do not catastrophically deflate.
## 3 Information-Centric Networking
### Named Data Networking: Basics
#### 3.2.1 Node Architecture  
As shown in Figure 2, each NDN node maintains three tables at the data plane:  
* The Content Store (CS) is a cache that temporarily stores Data packets passing through the node. If an Interest requests a content available in the CS, the node can respond directly, without forwarding the Interest further, reducing network load.
* The Pending Interest Table (PIT) tracks pending interests that the node has forwarded but has not yet been satisfied. When a Data packet arrives, the PIT ensures that it is sent back to all downstream consumers who requested it and then removes the corresponding entry.  
* The Forwarding Information Base (FIB) stores name-based forwarding rules. It is used to direct Interests to the appropriate next hops, similar to a routing table in IP networks.  
In addition, the forwarding strategy module dynamically determines how and when to forward Interests, i.e., over which network or application interface, considering multiple factors such as network congestion, link quality, and application requirements [55]  
At the control plane, the Routing Information Base (RIB) maintains information about routing prefixes, typically received from one or more routing protocols. The entries in the RIB indicate possible paths to reach specific name prefixes and include metrics such as cost, delay, or link quality. The RIB dynamically updates the FIB with the best routes based on these metrics. NDN routing protocols operate similarly to their IP counterparts but use named prefixes instead of IP addresses.
## 2 Materials and Methods
### Laboratory Phase  
The laboratory phase of the study was carried out at the Laboratory of Pesticides' Toxicology of Benaki Phytopathological Institute, Greece. Stock and working solutions (used for the validation of analytical method) of the analytical standards tebufenozide (obtained from ChemService 99.5% purity) and bupirimate a.s. (acquired from ChemService, 99.5% purity) were prepared as described in previous related publication [12]. All the aforementioned solutions were stored at \(-18\)\({}^{\circ}\)C.  
For both pesticides used, the a.s. residues from the dosimeters were extracted with methanol according to the procedure described earlier [10]. Especially for the outer gloves (nitrile), the extraction had already been performed in the field after the end of each application based on the procedure defined in the aforesaid study, since it was known that recovery of the a.s. reduces over time with this matrix [23]. The chemical analysis (liquid chromatography electrospray mass spectrometry, LC-ESI/MS) was conducted using previously described conditions and the same instrumentation [12].  
Considering that no matrix effect from fabric and glove dosimeters was observed during the validation process (for this purpose, a series of calibration standard solutions was fortified with 40% concentrated extract of cotton matrix and analyzed, showing no evidence of influence on the detector response), all the standard solutions prepared and used in the analyses were prepared in methanol. The calibration standards were mixtures of equivalent concentration of both a.s. in this solvent. As the analytical routine procedure for completion of all the sample extraction and measurements took several weeks, calibration curves were prepared every two weeks, during this period, to ensure that possible baseline drifting and instrumental variations were detected. Acceptable linearity (r\({}^{2}>\) 0.997) was observed in the range of 0.001 \(\upmu\)g/mL to 0.050 \(\upmu\)g/mL for bupirimate and up to 1 \(\upmu\)g/mL for tebufenozide. The rest of validation criteria (accuracy, precision, specificity) were fulfilled as well.  
The limit of quantification (LOQ) values for fabric (inner, outer) and glove dosimeters were equal to the lowest amount of a.s. that could be extracted and analyzed from a given dosimeter surface area with satisfactory recovery (>70%), providing a final extract solution concentration not less than the lowest point of the calibration curve (i.e., 0.001 \(\upmu\)g/mL). Thus, LOQs corresponded for both a.s. to 0.007 \(\upmu\)g/cm\({}^{2}\) for overall fabrics and 0.6 \(\upmu\)g/dosimeter for cap and for nitrile glove.
# Orbit Determination of Korean GEO Satellite Using Single SLR Sensor
## 3 GK-2B Orbit Determination Using SLR Data
### Orbit Determination  
OD of GK-2B is performed by iterating the forward sequential filter and backward smoothing process. Initial orbits are perturbed to 1 km in each direction for convergence in orbit. The solar radiation parameter included in the dynamic model is estimated, along with the six orbit states. Figure 9 shows the resultant position uncertainties of estimated orbit and orbit errors for Scenario 1. The diagonal term of the orbital covariance matrix indicates the orbit uncertainty. The estimated orbits are directly compared with the pre-generated true orbits in the simulation and represented in radial, in-track, and cross-track (RIC) frame. As a result of the characteristics of SLR observations, the orbital errors in radial direction are the smallest with respect to those of in-track and cross-track directions. The RMS errors in radial, in-track, and cross-track direction are 4.21 m, 8.94 m, and 42.32 m, respectively, and 43.46 m in three-dimension. While the errors in each direction show oscillation within some error bounds, the orbit uncertainty of in-track direction gradually increases over time while the position errors remain within \(\pm\)20 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model/Parameter** & **Description** \\ \hline Earth gravity & GGM03C 30 \(\times\) 30 [14] \\ Solid earth tides/Ocean tides/ & IERS conventions 1996 [15] \\ General relativity correction & JPL DE405 ephemeris [16] \\ Third body gravity & None \\ Solar radiation pressure & Penumbral cone model [12] \\ Central body radiation & Earth albedo with thermal radiation pressure \\ Empirical forces & One-cycle-per-rev model [17] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dynamic models, observation models, and estimation parameters for GK-2B OD. IERS—International Earth Rotation and Reference Systems Service; JPL—Jet Propulsion Laboratory; GGM—Global Gravity Model.  
Figure 9: Estimated orbit uncertainty and orbital errors for Scenario 1 in radial (**upper left**), in-track (**upper right**), and cross-track (**lower**) directions.  
Figure 10 shows the estimated orbit errors and uncertainty for Scenario 2. The errors in all directions show a slight decrease when compared with those of Scenario 1. The RMS errors are 2.40 m, 4.79 m, and 24.01 m for radial, in-track, cross-track direction, respectively. Three-dimensional orbit error is 24.60 m for the whole period. It is reduced from that of Scenario 1 by 56.6%. These results indicate that the OD precision could be enhanced with more frequent SLR observations.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology
### Mask-Conditioned Feature Fusion Network
#### 3.1.3 Spatial-Temporal Aggregation Sub-Network  
The spatial-temporal aggregation sub-network constitutes the final processing stage that transforms frame-level fusion features into a comprehensive tracklet representation. The network operates on the quality-weighted features \(F_{fused}^{t}\) from the Adaptive Feature Fusion Sub-Module. It establishes spatial-temporal relationships through a graph neural network. The aggregation process initiates a spatial-temporal graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\). Each node \(v_{t}\in\mathcal{V}\) corresponds to the frame's feature \(F_{fused}^{t}\), and each edge \(e_{ij}\in\mathcal{E}\) encodes pairwise relationships between frames. We utilized a learned affinity function to compute the edge weights:  
\[\mathcal{A}_{ij}=\text{softmax}\left(\frac{W_{q}F_{fused}^{i}(W_{k}F_{fused}^ {j})^{\top}}{\sqrt{d}}+\phi(\Delta t_{ij})\right) \tag{4}\]  
where \(W_{q}\) and \(W_{k}\) are learnable projection matrices, \(d\) is the feature dimension, and \(\phi(\cdot)\) encodes the time interval as a position embedding. This formulation captures similarity in appearance and temporal proximity simultaneously. For frames with low quality scores, the network activates the memory augmentation mechanism. It retrieves historically reliable features from the memory bank \(\mathcal{M}\) through content-based addressing. The expanded features are then processed in the transformer encoder layer to produce a tracklet representation:  
\[F_{memory}^{t}=\sum_{k=1}^{K}\text{softmax}((F_{fused}^{t},\mathcal{M}_{k})) \cdot\mathcal{M}_{k} \tag{5}\]  
\[F_{transformer}^{t}=\text{Transformer}(F_{fused}^{t}+F_{memory}^{t}\cdot\mathbb{ I}(\alpha_{t}<\tau)_{t=1}^{N}+\text{PE}(t)) \tag{6}\]  
where \(\text{PE}(t)\) is the standard sinusoidal position coding and \(\mathbb{I}(\cdot)\) is the indicator function. The transformer encoder employs multi-head self-attention and layer normalization to model long-range dependencies across tracklets effectively.  
Eventually, output features are dimensionally reduced by mean pooling and fully connected layers. The final output encompasses both the pattern of occlusion and the temporal motion characteristics of the target.
## 2 Materials and Methods
### The Hardware Development  
The hardware architecture of each station is shown in Figure 7. The Arduino UNO control board was used as the core to communicate with the 13.56 MHz high-frequency RFID reader through the SPI communication protocol. When the CDT started and ended his work, the corresponding tag was scanned. When the scan was complete, the controller sent a high-level electric potential to trigger the buzzer, giving a sound to inform the CDT that the data had been completed. Moreover, the controller transmitted the packed data to the Wi-Fi module through UART communication every 20 s, and then the packet was transmitted to the ThingSpeak website for storage and display.  
The control circuit block is shown in Figure 8. The system power supply performed the conversion between AC and DC through the transformer. After conversion, the output voltage was 5 V. Then the LDO stepped down the 5 V to 3.3 V for the RFID Reader. The core of the control circuit was the Arduino UNO control board, in which the ATmega328P 8-bit microcontroller was the core. The buzzer was self-stimulating. When the output exceeded the high electrical potential level, it emitted a sound to inform the CDT about the status of the station. The Wi-Fi module was ESP8266, which supported 802.1 b/g/n communication protocols. It connected the RXD pin with D1 pin for data transmission and was mainly responsible for transmitting data of the CDT and the station to the cloud of ThingSpeak.  
Figure 7: The hardware architecture of the process site.
# Investigation of the Behaviors of Methanol Spray Impingement and Wall Wetting
## 2 Experimental Setup and Methodology
### Spray Image Processing  
In this study, the spray penetration and area were extracted from spray images by using MATLAB codes developed by our group. First, an original image was converted to a grayscale image. After background subtraction, and Wiener filtering [27], the image was binarized using the threshold obtained by the Otsu algorithm [28], and finally, the image parameters were calculated. For all measurement parameters, the averaged results with a statistical error bar calculated from five repeated events were depicted. Figure 3 showed a diagram of typical impinging spray and defined impinging spray height \(H_{i}\) and width \(W_{i}\).  
For the quantitative study of the adhesive fuel film, the RIM method was used. In a gray image, the adhesive fuel film thickness was obtained with the calibration results of RIM. And adhesive fuel film mass \(M_{f}\) was calculated by Equation (1),  
\[M_{f}=\frac{\rho}{1000}\times\sum_{x,y\in f}T(x,y)\times\tau^{2} \tag{1}\]  
where \(\rho\) is methanol density (0.792 g-cm\({}^{-3}\)), \(f\) is adhesive fuel film area, \(T(x,y)\) is film thickness (um) of the \((x,y)\) point, and \(\tau^{2}\) is image scale (mm\({}^{2}\)-pixel\({}^{-1}\)).
## 4 XFEM-Based Two-Scale Simulation on RC Columns under Cyclic Loading
### Computational Efficiency Analysis  
The original intention of two-scale analysis is to improve computational efficiency with guaranteed calculation precision. In this study, all of the numerical studies were performed using Dell workstation equipped with 8 central processing units (CPUs) and 32 gigabytes memory. In hysteretic performance analysis simulated with solid element model and two-scale models, the element numbers and the corresponding CPU times of different models are compared in Table 4.  
The element number will be significantly reduced by using two-scale modeling method. However, the reduction of CPU time is not strictly proportional to that of element quantity since the main program of two-scale analysis has to call the external material subroutine developed in this study. The element quantities in a solid element model and two-scale model are 2258 and 1144, respectively. The ratios of CPU times between a two-scale model and solid element model corresponding to specimens C1 to C3 are 0.79, 0.8 and 0.79, which are closer to 0.80. Generally, the calculation speed can be improved by 20%. The time ratio corresponding to a CDP-based two-scale model and solid element model subjected to cyclic loading is 0.590, presenting higher computational efficiency. In solid element models and two-scale models of specimens C1-C3, the nodes of concrete elements and the longitudinal bars are connected using non-linear spring elements. It should be stated that the non-linear degree of numerical models and the element number will increase significantly while considering the bond-slip effect. Compared with simple RC columns investigated in this study, the improvement of calculation efficiency of the two-scale modeling approach will be more remarkable for large-scale RC frame structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen No.** & **Comparison Parameter** & \begin{tabular}{c} **Solid Element** \\ **Model (T1)** \\ \end{tabular} & \begin{tabular}{c} **Two-Scale** \\ **Model (T2)** \\ \end{tabular} & **Time Ratio (T1/T2)** \\ \hline C1 & \begin{tabular}{c} Number of elements \\ Wall clock time(s) \\ \end{tabular} & 2258 & 1144 & 0.79 \\ \hline C2 & \
\begin{tabular}{c} Number of elements \\ Wall clock time(s) \\ \end{tabular} & 2258 & 1144 & 0.80 \\ \hline C3 &
\begin{tabular}{c} Number of elements \\ Wall clock time(s) \\ \end{tabular} & 23572 & 2825 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of element numbers and computation time of XFEM-based numerical models.
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 5 Results and Discussion
### Multiple vs. Individual Classification Methods  
For the experiments, the BU-3 DFE and JAFFE data sets were used to construct the training and test sets, respectively. The training and test sets contained six expression classes--anger, disgust, fear, happiness, sadness, and surprise--which were common in both data sets. The experimental results presented in Table 9 show that the fusion method R/W/B/P/G+MP achieved an accuracy of 0.54 and an F1-score of 0.721, higher than those of the other methods used for comparison. [PERSON] et al. (2018) presented a method to combine multiple datasets and conducted an exhaustive evaluation of a proposed system based on a CNN that analyzed and compared performance using single- and cross-dataset approaches with other architectures. They reported that they trained with BU-4 FDE and tested with JAFFE, obtaining an accuracy of 0.4317. In our current proposal, we study the most important features by applying different feature extraction methods for facial expression representation, transforming each obtained representation into a sparse representation (SR) domain, and training combination models to classify signals, using the extended Cohn-Kanade (CK+), BU-3 DFE, and JAFFE data sets for validation. In general, combining the feature methods increased the system performance. This supports the theory that trainable fusion methods can find experts for each subspace of features and efficiently combine them. In all cases, the combination rules were better at identification than the individual methods. However, the values of the metrics were lower than in the case in which face images from a single data set were used for both the training set and test set. This performance degradation can mainly be attributed to the fact that the face images were collected under two different controlled conditions. For a better generalization ability across image acquisition conditions, it is necessary to collect large training data sets under various image acquisition conditions ([PERSON] et al., 2019).  
In summary, our findings confirm the increased accuracy of the proposed combination system, with respect to the individual systems. The best schemes (R/W/H/P/G/F+MP and R/W/B/P/G+MP) combined the best and the worst individual schemes. This increases the diversity of the system, offering the opportunity to select the best representation subspaces for each class.
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 3 Model Test Results  
### Stress State at the Bottom of the Embankment  
Figure 6 depicts the changing process of soil pressure at the embankment's bottom during tests. Curves of soil pressure above pile caps and subsoil are shown in Figure 6a,b, respectively.  
We can know from Figure 6a: (1) The soil pressure above the pile cap is not evenly distributed, and it is higher at the corner of the cap than it is in the middle. (2) As filling cohesiveness increases, the differential in soil pressure between the pile caps' corner and center reduces. (3) The soil pressure above the pile caps always maintains an enormous growth rate.  
It is evident from Figure 6b that: (1) Compared to the soil pressure above the pile cap, the soil pressure above the subsoil is substantially lower. (2) The soil pressure is a little higher in the center of the subsoil between two piles than it is between four piles. (3) The soil pressure above the subsoil increases rapidly before the embankment height reaches 0.6 m and then gradually tends to be gentle.  
The redistribution of stress is a sign of soil arching, and soil arching is essential for enlarging the embankment load shared by the piles. The load distribution between the pile caps and the subsoil eventually differs significantly. The distinction is caused by: At first, the difference in stiffness between the piles and the subsoil is what causes the pile-subsoil settlement to differ; the shear stress in the fill is induced to hinder the relative movement in embankment filling. As a result, the pile caps are bearing the majority of the embankment's weight. The load distribution at the bottom of the embankment changes dramatically as a result of the modified load-transfer mechanism in the embankment. At the same time, the cohesion change will affect the value of shear stress on the slip surface as a non-negligible part of the shear stress. The load transfer and distribution are both impacted by the change in primary stress direction that occurs when shear stress changes. Lastly, a presentation of the variation in soil pressure above the pile cap is made.