esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Configuration of the Proposed Tilt Sensor  
Figure 1 shows the simple architecture of the optical path of the design of the sensing system, in which the proposed fiber tilt sensor observes the two FBGs reflecting the center wavelength shift amount to determine the tilt of the azimuth and angle. We use the C + L band broadband light source (BBS) and a three-port optical circulator as the input, sense, and output ports. The first port of the circulator was connected to the broadband source; the second port was connected to the coupler and FBGs. When the sensing signals are reflected by the FBGs into the third port of the optical circulator, they will be displayed on the optical spectrum analyzer (OSA).  
The proposed tilt sensor is composed of two cylindrical floats suspended in water, connected with FBG fiber gratings. Because the cylindrical float is attracted by gravity, it is always facing the center of the earth. When the sensor is tilted, it will drive the cylindrical float to move, causing the buoyancy force to change. That will cause the FBG to be stretched or contracted to change the center wavelength of the reflection light. Figure 2 shows the configuration of the proposed FBG tilt sensor. In this configuration, the size of the container and cylindrical float will affect the sensitivity. As the container and cylindrical float become larger, the sensitivity of the sensing will also rise. In this study, we used a rectangular container that has a length and width of 34 cm, a height of 14 cm, and two cylindrical floats with a radius of 4.3 cm and a height of 6 cm. In order to sink the cylindrical float to the liquid level 3.5 cm, we put some sand in the cylindrical float to increase the weight. Then, two aluminum bars were fixed and orthogonal above the container containing the liquid, and the FBG and the cylindrical float were connected to the north and west positions of the container, respectively  
Figure 1: The optical path diagram of the proposed tilt sensor.  
(see Figure 2). The FBGs are made of photosensitive boron and germanium co-doped optical fiber (Fibercore PS1250/1500), with numerical aperture of 0.14 and cut-off at 1159 nm. At room temperature without external force, the reflection center wavelengths of FBG1 and FBG2 are 1548.877 nm and 1551.84 nm, respectively.  
When the fiber grating is connected with the cylindrical float, the sustained weight is 0 g of FBG, as shown in Figure 3a. In order to produce a pre-stretched effect of the fiber grating, we reduced the height of the liquid in the container by 0.9 cm, to reduce the buoyancy of the liquid on cylindrical float 52 grams, in order to increase the tension on the FBG fiber grating, as shown in Figure 3b. The advantage of this pre-stretched operation is that only one fiber grating can be used to do one-dimensional tilt sensing. Therefore, the cost can be reduced, and the production is simple. Compared with the tilt detectors described in the literature previously, we only need two fiber gratings to do 2D tilt sensing, not four. The proposed tilt sensor can also maintain better sensitivity.
## 3 The Proposed Method
### The Constraints for Particularity Representation
#### 3.2.2 The Group Regularization  
Furthermore, to promote the consistency of particularity representation from the same class, we introduce the group regularization on \(Z_{p}^{i}\). In light of the label information of training samples, assume one sample is related to one vertex; the vertices corresponding to the same class samples are connected and neighboring each other; thus, each class forms a densely connected sub-graph. Considering the training samples \(X_{i}\) and their coding coefficients \(Z_{p}^{i}\), we first define \(k_{i}\) graph maps with mapping each graph to a line that consists of \(N_{i}\) points, as follows  
\[f_{k}^{i}=\left[\begin{array}{c}z_{1}^{i}(k)\\ z_{2}^{i}(k)\\ \vdots\\ z_{N_{i}}^{i}(k)\end{array}\right]\in\mathbf{R}^{N_{i}\times 1},\ \ \forall\ \ k=1,2,\cdots,k_{i};\ \ i=1,2,\cdots,C. \tag{10}\]Here \(z_{n}^{i}(k)\)\((n=1,2,\cdots,N_{i})\) is the \(k\)th component of \(z_{n}^{i}\), which corresponds to the \(k\)th atom in the \(i\)th class sub-dictionary \(D_{i}\).  
Then, we can calculate the variation for these \(k_{i}\) graph maps as follows.  
\[V_{i}=\sum_{k=1}^{k_{i}}f_{k}^{\rm{T}}L_{i}f_{k}^{i}=\mathcal{T}\!\left(\left[ \begin{array}{c}f_{1}^{\rm{T}}\\ \vdots\\ f_{k_{i}}^{\rm{T}}\end{array}\right]L_{i}[f_{1}^{i},\cdots,f_{k_{i}}^{i}] \right)=\mathcal{T}\!r(Z_{p}^{i}L_{i}Z_{p}^{i\intercal}), \tag{11}\]  
where \(L_{i}\in R^{N_{i}\times N_{i}}\) denotes the normalized Laplacian of the overall graph for the \(i\)th class, which can be derived as  
\[L_{i}=\frac{1}{N_{i}-1}\left[\begin{array}{cccc}N_{i}-1&-1&\cdots&-1\\ -1&N_{i}-1&\cdots&-1\\ \vdots&\vdots&\vdots&\vdots\\ -1&-1&\cdots&N_{i}-1\end{array}\right]. \tag{12}\]  
For the different classes, the vertices related to their samples should not be connected; thus, the graphs of \(C\) classes are isolated from each other. Therefore, we can obtain the total variation for \(K_{p}\) graph maps of all the \(C\) classes as \(\sum_{i=1}^{C}\mathcal{T}\!r(Z_{p}^{i}L_{i}Z_{p}^{i\intercal})=\mathcal{T}\!r (Z_{p}L_{p}Z_{p}^{\intercal})\), where \(L_{p}=\text{diag}(L_{1},\cdots,L_{C})\). Keeping this group regularization small will promote the consistency of the representation for the same class samples. Moreover, by combing it with the label-aware constraint, the coding coefficients for the different classes will be remarkably distinct with the large coefficients locating in the different areas, which is very favorable for the classification task.
## 3 Proposed System Model
### Neural Network Architecture in DQN
#### 3.5.5 Performance Enhancements via Neural Networks  
The incorporation of DNN in Q-learning provides several advantages:  
* **Function Approximation**: This contrasts with naive tabular Q-learning which faces the curse of dimensionality, because DQNs effectively generalize across state spaces, and learn efficiently even in high-dimensional environments.
* **Improved Convergence Stability:** This allows to stabilize training and reduces oscillation due to high weight updates.
* **Efficient Representation Learning:** The hidden layers extract hierarchical representations of states, enabling the model to capture complex patterns in the environment.
* **Non-Linear Decision Boundaries:** The ReLU activation function allows the network to model highly non-linear Q-functions, improving decision-making capabilities in dynamic scenarios.  
A key component of our approach is the DQN neural network architecture that will help us to approximate the Q-value function, which is essential for our decision making process. With the use of multi-layer neural networks with ReLU activations, backpropagation, and fixed target networks, DQN is able to significantly stabilize and converge to a policy, as well as scale in size with increased complexity of the state representation. This is exactly why it has adaptive behavior for improving practical applications like adaptive communication protocols in dynamic environments.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Influence of Preparation Conditions
#### 3.1.2 Impregnation Ratio  
Figure 2 shows the effect of impregnation ratio on methylene blue removal. It can be seen that with the increase in impregnation ratio, the removal of methylene blue by CuO, ZnO, and TiO\({}_{2}\) composite materials first increased and then decreased. Additionally, under the impregnation mass ratios of 0.2:1, 0.05:1, and 0.3:1, the removal effect of methylene blue reached 63.61%, 46.74%, and 74.64%, respectively. Then, with the increase of these impregnation ratios, the removal effect of methylene blue decreased. This may be attributed to the further increase in the impregnation ratio. CuO, ZnO, and TiO\({}_{2}\) were loaded into the pores of the pyrolysis residue, leading to pore blockage, and then the mass transfer resistance of methylene blue increased [29].  
The composite materials prepared under the optimal conditions mentioned above were named AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\), and were used to further explore the effect of removal conditions on the removal of methylene blue.  
Figure 1: Effect of (**a**) CuO; (**b**) ZnO; (**c**) TiO\({}_{2}\) composite materials’ impregnation time on methylene blue removal (impregnation ratio = 0.1:1, initial methylene blue concentration = 5 mg/L, composite materials dosage = 1 g/L, contact time = 60 min, temperature = 25 \(\pm\) 0.5 \({}^{\circ}\)C, initial pH = 7.00).
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 3 Methods
### Spatial Attention Mechanism  
Spatial attention allows us to focus on regions of the frames that contribute to a higher classification accuracy; in this case, to B-line classification, hence it should be able to focus on B-line artefacts. The feature map output by the CNN network (explained in the previous section) is passed as an input to our spatial attention module. Attention is achieved by learning a map \(M_{i}\) that masks the image feature \(X_{i}\) of the \(i\)th frame to obtain attended image features, as in [26]:  
\[\tilde{X}_{i}=X_{i}\odot\ M_{i}, \tag{1}\]  
where \(\odot\) denotes element-wise multiplication; for \(1\leq i\leq n\), \(n\) is the number of frames, and each entry of \(M\) lies in [0, 1]. This operation attenuates certain regions of the feature map based on their estimated importance. Figure 4 illustrates the spatial attention mechanism.  
In order to learn the importance mask \(M_{i}\), we apply three convolutional layers (architecture details are in Table 1) on the image feature \(X_{i}\). Finally, the attended image feature \(\tilde{X}_{i}\) represents a region highlighted (or suppressed) version of \(X_{i}\) that is globally pooled, hence delegating any ability to resolve features spatially to the attention mask.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Operation** \\ \hline (1) & Conv (N = 16, K = 3, S = 1, P = 1, BN, Relu) \\ (2) & Conv (N = 8, K = 3, S = 1, P = 1, BN, Relu) \\ (3) & Conv (N = 1, K = 3, S = 1, P = 1, Sigmoid) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details for spatial attention (N: number of output channels, K: kernel size, S: stride, P: padding, BN: Batch Normalization).  
Figure 4: Spatial attention module. Several layers of convolutional networks (for details see Table 1) are used to learn the importance mask \(M_{i}\) for the input image feature \(X_{i}\), the output is the element-wise multiplication \(\tilde{X}_{i}=X_{i}\odot M_{i}\). GAP: Global Average Pooling.  
Figure 3: The CNN architecture of the proposed model. It consists of four convolution layers with Relu activation function and; Maxpooling followed by the second and forth convolution layer.
## 4 Results
### Anomaly Detection Performance
#### 4.3.1 RGB Camera Anomaly Detection  
The RGB camera anomaly detection model demonstrated excellent performance in distinguishing between normal operation and attack conditions. Figure 14 shows the anomaly scores for different operational states in Episode 1.  
Statistical analysis of the RGB anomaly detection results revealed the following:  
* **Normal Operation**: Mean anomaly score of 0.000000 with a standard deviation of 0.000000, establishing a clear baseline for normal behavior.
* **RGB Noise Attacks**: Mean anomaly scores of 0.378064 (with defense) and 0.389462 (without defense), both significantly higher than normal operation. This demonstrates the model's ability to reliably detect RGB camera attacks regardless of whether defenses are active.
* Our RGB camera anomaly detection model is specifically designed to detect anomalies in RGB camera data only and does not process data from other sensors.  
These results demonstrate that our RGB anomaly detection model achieves high sensitivity to relevant attacks while maintaining specificity against unrelated attack vectors.
# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
## 4 Experiments  
### Dataset  
#### 4.1.1 Traffic Police Gestures Data  
In a real pathway, five cameras from five directions were used to collect traffic police gesture video data, whose video window size was \(840\times 480\), and which was stored in the format of AVI. In total, 20,480 videos were captured; each camera filmed 512 videos for each gesture among them. Eight kinds of gestures were used by traffic police: stop, straight ahead, turn left, turn left waiting, turn right, lane change, slow down, and pull over. Eight volunteers were invited to participate during data collection, and their age, gender, and height were different; each of the participants were labeled separately in the dataset. Specifically, we used five cameras set in the same horizontal height to capture five different horizontal views for one gesture action, which were 90 degrees left, 45 degrees left, 0 degrees front, 45 degrees right, and 90 degrees right. Furthermore, each of the volunteers were asked to only complete each traffic police gesture action once. In the dataset, each camera was assigned a consistent number, and each number had its own filming angle. To further increase the diversity of the data, different conditions with regard to distances between cameras and participants, lighting conditions, junction scene, and clothes of the participants were added. In the dataset, the gestures used by traffic police were divided into a training set, validation set, and a test set in the proportion of approximately 7:2:1 for each gesture. Since there are only eight kinds of gestures in Chinese traffic police gesture, taking top-1 and top-3 to validate our method is better (top-1 means to take the largest probability vector as the predicted result. If the classification result is correct, then the prediction is correct; otherwise, the result is wrong. Top-3 means if the correct result appears in the top 3 largest probability vectors, then the prediction is correct, otherwise the result is wrong). Figure 12 shows some data samples in the dataset; Table 1 shows exact condition settings in data collection.  
Figure 12: Part of the traffic police gestures dataset.
## 3 Parallel Fuzzy Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings
### Coupling Multi-Precision and Parallel Programming  
The evaluation of the correlation, support, and generation of gradual pattern candidates are tasks that require huge amounts of processing time, memory consumption, and load balance. In order to reduce memory consumption, each matrix of _fuzzy concordance degrees mcc(i,j)_ is represented and stored according to the _Binary Fuzzy Matrix Multi-precision Format_, where each \(\tilde{c}c(i,j)\in\) [0, 1] is represented with a precision of 2, 3, or more up to 52 bits. In order to reduce processing time we propose to parallelize the program using OpenMP, a shared memory architecture API, which is ideally suited for multi-core architectures [9].  
Figure 5 shows an overall view of the parallel version of two regions of our fuzzyMGP algorithm where, in the first region, the extraction process of gradual patterns of size \(k=2\) is parallelized. In the second region, we show the parallelization of the extraction cycle of gradual patterns of size \(k>2\).  
In the experiments reported below, we aim at studying how multi-precision impacts performances, regarding the trade-off between high precision but high memory consumption and low memory consumption with low precision. The behavior of the algorithms is studied with respect to the number of bits allocated for storing the precision. The question raised is to study if there exists a threshold beyond which it is useless to consider allocating memory space. This threshold may depend on the database.
## 2 Materials and Methods
### Methods
#### 2.2.7 Parametric Predicative Modeling  
The following research employs a random forest regression model, an ensemble learning method that combines multiple decision trees to create a robust and accurate prediction system. The choice of the random forest was motivated by several key factors: its ability to handle non-linear relationships, robust performance with smaller datasets, and inherent feature importance analysis capabilities. The random forest regression model used in this study was implemented using the Scikit-learn 1.4.2 library. After rigorous testing, we established an optimal configuration utilizing 200 decision trees, with each tree maintaining a minimum of 2 samples per leaf and requiring at least 5 samples for node splitting. This configuration strikes a balance between model complexity and generalization ability.  
The model optimization was performed using GridSearchCV 1.4.2, a grid search method with 5-fold cross-validation to tune hyperparameters. The parameters optimized include  
n_estimators: [50; 100; 200] (number of trees in the forest);  
min_samples_split: [2; 5] (minimum number of samples required to split a node);  
min_samples_leaf: [1; 2] (minimum number of samples required in a leaf node).  
The best combination of parameters was selected based on negative mean squared error (MSE) as the scoring metric. No Bayesian optimization tools like Optuna were used; instead, GridSearchCV systematically evaluated all parameter combinations.
## 2 Reliability Analysis Method
### Method Validation
#### 2.4.2 Example 2  
The structure considered in this example [32; 33] is a cantilever beam with a rectangular cross section, which bears uniformly distributed loads. The relevant information of the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & \(\mathbf{N_{call}}\) & \(\mathbf{\beta}\) & \(\mathbf{P_{f}(10^{-3})}\) & \(\mathbf{\zeta(\%)}\) \\ \hline MCS & \(10^{6}\) & 2.8434 & 2.232 & 0 \\ Binomial response surface [29] & 50 & 3.0879 & 1.008 & 54.36 \\ IS [30] & 4750 & 2.8480 & 2.200 & 1.43 \\ IS-Kriging [31] & 146 & 2.8800 & 2.000 & 10.40 \\ Kriging-MCS & 200 & 2.8256 & 2.360 & 5.74 \\ ISSA-Kriging-MCS & 200 & 2.8300 & 2.300 & 3.05 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of reliability calculation results for Example 1.  
Figure 3: Example 1 real response surface. (**a**) Real response surface. (**b**) Limit state surface.  
random variables is shown in Table 2, and the real response surface is shown in Figure 4. \(COV\) is represented by the ratio of the standard deviation to the mean. The limit state function is  
\[G(X_{1},X_{2})=18.46154-7.476923\times 10^{10}\frac{X_{1}}{X_{2}^{3}} \tag{18}\]  
As shown in Table 3, the failure probability and reliability index estimated using the ISSA-Kriging-MCS model were the closest to the results obtained by the MCS model. When comparing the characteristics of the number of calls and the relative error, it can be seen that the ISSA-Kriging-MCS model has certain advantages over other methods described in the table.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 5 Multi-Source Data and Data Assimilation
### Combination of Remote Sensing and Other Data Sources
[241] constructed the Evaporative Stress Index (ESI) as a water stress indicator for drought monitoring:  
\[\text{ESI}=1-f_{\text{PET}}=1-\frac{\text{E}}{\text{PET}} \tag{21}\]  
where E is the actual ET fluxes from the system (canopy and soil), and PET is the potential ET rate. ESI shows strong correlations with Palmer's Drought Severity Index (PDSI) and monthly precipitation anomalies. ESI is constructed based on surface energy balance and accounts for the effects of available energy and atmospheric conditions on LST. This makes it applicable in both water-limited and energy-limited situations [241; 242].
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 1 Introduction
### Main Contributions and Structure of the Paper  
Within this line of study, we aim to shed light on the potential of a non-orthogonal resource distribution, as well as to trigger additional research on the topic in a satellite-IoT context. In particular, in our work, we do the following:  
* We characterize some fundamental trends for the performance of an overlay allocation of a QoS-constrained service and of mMTC traffic, composed of short packets. Based on practical considerations, we assume the receiver to first attempt decoding the former traffic. In the case of success, its contribution is removed (interference cancellation), and retrieval of the underlying mMTC packets is attempted;
* In this setup, we derive the exact analytical expressions for the maximum aggregate rate that can be granted to mMTC traffic as a function of the requirements in terms of the rate and packet error rate set for the QoS-constrained service;
* Going beyond the approach followed in [28], the analysis relies on information theoretic arguments to capture the impact of short packets transmission, as well as the impact of the length of frames in which the uplink communication is organized.  
The remainder of this paper is structured as follows. After introducing the system model in Section 2, we provide in Section 3 the initial insight by comparing the performance of the overlay and orthogonal allocations in a setting characterized by packets long enough to justify the use of asymptotic information theoretic tools, considering both an ergodic and a non-ergodic case. The study is then complemented in Section 4 by exploring the impact of transmission of short packets commonly encountered in mMTC applications, relying on the normal approximation [31; 32]. The numerical results are presented and discussed in Section 5, highlighting some fundamental trends and comparing the effectiveness of orthogonal and overlay allocations in all of the considered cases. Finally, Section 6 draws the conclusions, offering some relevant open issues and future research directions.