esa-sceva/satcom-chunk-collection huggingface dataset:
# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
## 4 Network Deployment Model
### Adaptive Topology Based on Power Consumption  
As mentioned previously, power consumption is a critical aspect in the operation of the TWUs, since it determines the time period in which these devices will be able to provide the communication service. We assume that the dynamics of the power required for each TWU is proportional to both the number of retransmitted messages and the distance to the linked neighbors. In order to balance the power consumption of the TWUs when using the network, it is important to make sure that the higher the communication load of a link, the lower the distance between the joined nodes.  
Assuming that the increase of the load of different links is considerable and the information loading process lasts for relatively long periods, we propose a distributed model in which the network adjusts its topology according to these variations, and where each TWU must update its position following Equations (3) and (4):  
\[\vec{v}_{i}=b\cdot\vec{v}_{i}-\sum_{j\in links}k_{j}(\vec{x}_{i}-\vec{x}_{j}), \tag{3}\]  
\[\vec{x}_{i}=\vec{x}_{i}+\vec{v}_{i}, \tag{4}\]  
where \(\vec{x}_{i}\) is the position of the \(i\)-th TWU, and \(\vec{v}_{i}\) models the attractive forces that act on each TWU and point towards the neighboring nodes. The values of the constants \(k_{j}\) must be calculated according to the data rate through the \(j\)-th link. The \(b\) constant allows adjustment of the simulation establishment time in order to stabilize the positions of the TWUs as quickly as possible.  
Figure 5 shows an example of a network deployment, where an increased load in the sending of messages between team \(S1\) and the IC makes it so that TWUs \(f1\) and \(f2\) have to move to the left and slightly down. This reduces the distances \(d1\), and in greater quantity \(d2\), in order to keep the communication capability among the nodes. Naturally, the distances \(d3^{\prime}\) and \(d4^{\prime}\) will be greater than the original distances \(d3\) and \(d4\).  
Figure 4: Network deployment situations: (**a**) more nodes using minimal paths and (**b**) less nodes with longer paths. IC: incident commander.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### Overview of the Ionospheric Delay Concept  
The effect of the ionospheric layer on signals has been known for a long time. Even the first satellite-based positioning system included ways to mitigate its impact in their system's designs. The easiest applicable solution for ionospheric delay compensation is to transmit signals on multiple Radio Frequency (RF) frequencies, as the ionosphere effect is a frequency-dependent delay [24]. Nowadays, high-grade GNSS receivers can track signals from two or more frequency bands and, more recently, some mass-market GNSS receivers are also multi-band receivers [34; 35; 36; 37]. Such receivers are typically called Dual Frequency Receivers (DFRs).  
From Equation (6), we can see that the ionospheric delay \(I\) is inversely proportional to the carrier frequency \(f_{c}\). Therefore, the two different frequencies \(i\) and \(j\) will experience two different delays. DFRs record pseudorange measurements \(\rho_{i}\) and \(\rho_{j}\) on the \(f_{c_{i}}\) and \(f_{c_{j}}\) frequencies, respectively, and then calculate the ionospheric delay corresponding to each frequency using the following formulas:  
\[I_{f_{c_{i}}}=\ \left(\frac{f_{c_{i}}^{2}}{f_{c_{j}}^{2}-f_{c_{i}}^{2}}\right)( \rho_{i}-\rho_{j})\quad\text{and}\quad I_{f_{c_{j}}}=\ \left(\frac{f_{c_{i}}^{2}}{f_{c_{j}}^{2}-f_{c_{i}}^{2}}\right)(\rho_{i}-\rho_ {j}) \tag{1}\]  
However, dual-frequency reception requires more advanced hardware (antenna and receiver), which makes it harder to embed DFRs in applications with limited size. Therefore, DFRs are typically used for high-precision receivers, whereas single Frequency Receivers (SFRs) are usually utilized for mass-market GNSS receivers. Note that nowadays, dual-frequency support has been reported even in smartphones [38], yet it still does not concern the majority of smartphones today.  
Contrary to DFRs, SFRs rely on two widely used models to estimate ionospheric delay, namely the Klobuchar [23; 24; 25] and NeQuick [23; 25] models. The first is typically used in GPS and BeiDou, while the latter is more encountered in Galileo signals. This is mostly due to how systems provide access to the corrections within their signals [24]. Additional ionospheric models also include IRI, NeQuick2, IGS, BDGIM, etc. [23; 39]. Choosing an adequate ionospheric model generally depends on many factors, including real-time availability and computational complexity. In the following subsections, we will discuss the most promising models we found in the literature and their ways of correcting the errors induced by the ionosphere.
## 3 Results
### Preliminary/Comparative Experiments
#### 3.1.2 Photolysis with H\({}_{2}\)O\({}_{2}\)/S\({}_{2}\)O\({}_{8}\)\({}^{2-}\)  
As observed, the effect of UV radiation was ineffective for dye degradation within the prescribed application time (1 h). Therefore, the effect of adding oxidizing agents on the photodegradation of Nile Blue dye was studied, utilizing the following two broadly utilized oxidizing reagents: hydrogen peroxide (HP) and persulfate ions (PS). These preliminary experiments were carried out using 200 mg L\({}^{-1}\) of oxidant and the results are presented in Figure 2.  
Figure 2: Photolytic degradation of Nile Blue dye in the presence of H\({}_{2}\)O\({}_{2}\) (HP) and S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) (PS). Inset: % degradation achieved after 1 h of treatment (C\({}_{0}\) = 15 mg L\({}^{-1}\), C\({}_{oxidant}\) = 200 mg L\({}^{-1}\)).  
It is evident that with the addition of an oxidant, the degradation of the dye was faster compared to the effect of radiation alone. Over a period of one hour, the degradation efficiency of the dye reached 21% in the presence of H\({}_{2}\)O\({}_{2}\), while in the presence of S\({}_{2}\)O\({}_{8}\)\({}^{2-}\), it increased to 71%.  
The UV/H\({}_{2}\)O\({}_{2}\) method was more effective than simple photolysis in decomposing the dye due to the generation of reactive hydroxyl radicals. On the other hand, the UV/S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) approach proved to be even more effective. The superior efficiency of this process, as indicated by the steep decline in the curve shown in Figure 2, can be attributed to the high oxidizing activity of the sulphate radicals (E\({}^{0}\) = 2.5-3.1 V), since they are stronger oxidants than hydroxyl radicals (E\({}^{0}\) = 1.89-2.72 V), especially at a neutral pH [30]. Furthermore, the higher degradation rates may be also attributed to the O-O bond energy of H\({}_{2}\)O\({}_{2}\) and S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) which is estimated to be 33.5 kcal mol\({}^{-1}\) for persulfate, while for hydrogen peroxide, it is 51 kcal mol\({}^{-1}\). This suggests that the former is more easily decomposed than the latter, and, thus, the resulting sulfate radicals can be formed more easily and rapidly than the corresponding hydroxyl radicals [31].
## 6 Security Analysis
### Key Space Analysis  
For the convenience of analysis, noise is not considered in the communication scheme. If a third party intercepts the signals of transmission channel such as \(s(t)\), as \(s^{r}_{1}(t)=h^{r}_{1}g_{1}(y)^{r}-h^{i}_{1}g_{1}(y)^{i}\) or \(s^{i}_{1}(t)=h^{r}_{1}g_{1}(y)^{i}+h^{i}_{1}g_{1}(y)^{r}\), the message signal \(H\) cannot be decrypted without the private keys such as the function \(g(y)\). In particular, when the amplitude of \(H\) is much smaller than that of \(g(y)\), \(s(t)\ll g(y)\). It is impossible to decrypt the message signal \(H\) using only the signals of transmission channel. Therefore, for private keys \(g(y)\) include parameters, initial conditions and time lag \(\tau\), the key space of our algorithm is infinite.
## 1 Introduction
### Objective  
In this study, climate change and reservoir operation were considered. First, the climate of the Aidoghmoush region of Iran was simulated. Maximum and minimum temperatures and precipitation amounts were simulated based on the Long Ashton Research Station Weather Generator (LARS-WG5) model, which can effectively simulate climate change. Notably, [PERSON] et al. (2019) investigated the effect of climate change for the period 2011-2040 using this model. Results indicated a reduction in the normal water level in the reservoir. Additionally, LARS-WG5 was used to simulate precipitation for the period 2040-2070 ([PERSON] et al., 2019). In the current study, LARS-WG5 was used for climate change predictions, and the volume of runoff was calculated using IHACRES software based on simulated temperature and precipitation. Runoff volume was considered as the inflow to the reservoir, and reservoir operation was simulated with the objective of minimizing the irrigation deficit. The shark metaheuristic algorithm is proposed for reservoir operation, and the decision variable was the water release rate. There was a need to identify the reservoir's future inflow pattern in order to generate the operation rule for the dam and reservoir water system. In fact, the available parameter for downscaling of the LARS-WG was 'rainfall', which could be used to estimate or predict reservoir inflow. Hence, the procedure carried out in this study downscaled precipitation, so that reservoir inflow could be generated using the rainfall-runoff model 'IHACRES'.
# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
## 3 Algorithm Simulation and Results Analysis
### Simulation Results
The proposed hierarchical resource allocation scheme provides a better fairness index due to its proportional allocation based on Nash bargain game.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 3 Method
### A Novel Pixel Sampling Method
In SGCS sampling, the overall sampling variance is calculated by taking a weighted average of the sampling variances of all groups:  
\[\text{Var}[\hat{P}_{SGCS}]\leq\frac{1}{M^{2}}\sum_{m=1}^{M}n_{m}\sigma_{m}^{2}. \tag{8}\]  
The contrast learning loss function is based on SupCon [33], aiming to bring the positive samples closer together in the feature space while pushing the negative samples farther apart. The loss function for contrastive learning can then be defined as:  
Figure 3: Schematic diagram of SGCS (semantic grouped contrastive sampling) showing the division of images into grids and the selection of representative pixels from each group for efficient contrastive learning. This method reduces variance while maintaining consistency in feature representation. In the diagram, the red stars represent the sampled pixels, which are selected based on their semantic relevance within each grid. The green stars denote pixels within the same grid that are semantically similar to the sampled pixels and spatially antithetic, ensuring a balanced and comprehensive representation of the local feature space. \[L^{CL}=\sum_{i=1}^{2M}L_{i}^{CL}. \tag{9}\]  
\[L_{i}^{CL}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{N}M_{ij}\log\left(\frac{e^{z_{ij }/T}}{\sum_{k=1}^{N}e^{z_{ik}/T}}\right). \tag{10}\]  
In this context, \(N\) represents the total number of pixels, while \(i\) and \(j\) denote the indices of the samples to be compared. \(M_{ij}\) is a mask that determines whether samples \(i\) and \(j\) are to be considered as positive samples. \(z_{ij}\) is the cosine similarity between the features of sample \(i\) and those of sample \(j\), used to measure their similarity. Finally, \(T\) is the temperature parameter that controls the sensitivity of the loss function. As the temperature increases, the impact of similarity on the loss function is reduced. The objective of this loss function is to minimize the distance between similar samples while maximizing the distance between dissimilar samples. This facilitates the distinction of different samples in the feature space. The pseudo-code of the SGCS method is as shown in Algorithm 3:  
```
1:\(pixels,M,sample\_size\_per\_group\)
2:\(sampled\_pixels\)
3: Divide \(pixels\) into \(M\) groups based on features
4: Initialize \(sampled\_pixels=[]\)
5:for each group in \(M\)do
6: Identify the center pixel \(c_{m}\) of the group
7: Sort pixels in the group by distance to \(c_{m}\)
8: Select the closest \(sample\_size\_per\_group/2\) pixels to \(c_{m}\)
9: Select the farthest \(sample\_size\_per\_group/2\) pixels from \(c_{m}\)
10: Add selected pixels to \(sampled\_pixels\)
11:endfor
12:return\(sampled\_pixels\)
```  
**Algorithm 3** Semantic Grouping Contrastive Sampling Algorithm
# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 3 Proposed Methodology
### Enhanced Feature Extraction Network  
After image feature extraction by the backbone network, considering global image information can enhance the model's performance and processing efficiency. Therefore, in this study, the Transformer structure [36] is incorporated into the model framework. Since the output of the main feature extraction network consists of two feature maps, and the Transformer operates on tokens, it is necessary to transform the feature maps into token structures with high-dimensional semantic information. To represent the varying parts between the two input images using a single token, the feature maps obtained from VGG16 network processing are converted into tokens using the Semantic Tokenizer, which extracts compact semantic tokens from each temporal feature map. The processing procedure of the Semantic Tokenizer is illustrated in Figure 8. Initially, the obtained feature maps are divided into multiple submaps, each of which is mapped to a token. To incorporate spatial information, spatial attention is employed to compress the channels and extract spatial information, resulting in compact tokens with enriched semantic information.  
To obtain concise tokens, a set of spatial attention mappings is learned using the Tokenizer, aiming to concentrate the spatial feature mapping into a set of tokens. Let \(X^{1}\) and \(X^{2}\)\(\in\)\(R^{HW\times C}\) represent the input dual-temporal feature maps, where \(H\), \(W\), and \(C\) are the height, width, and channel dimensions of the feature maps, respectively. Let \(T^{1}\) and \(T^{2}\)\(\in\)\(R^{L\times C}\) denote two sets of tokens, with \(L\) being the size of the token vocabulary set. The feature map of size \(C\times W\times H\) is compressed into a \(1\times W\times H\) feature map, allowing the aggregation of average and maximum information along the dimension of the feature map. This aggregated information serves as the convolutional layer for extracting attention information, enabling the extraction of image attention information. This process yields \(L\) semantic groups, each representing a unique semantic concept. Subsequently, a softmax operation is applied to the \(HW\) dimensions of each semantic group, ensuring non-negative attention weights and thus obtaining spatial attention mappings. Finally, the attention mappings are employed to compute the weighted average of pixels in \(X^{i}\), resulting in a compact vocabulary set of size \(L\), referred to as semantic tokens \(T^{i}\).
## 4 Discussion
The successful integration of geophysical techniques provides a blueprint for addressing similar infrastructure challenges in geologically complex regions, ensuring the stability and resilience of critical transportation networks. In summary, highway pavement subsidence poses a significant threat to transportation infrastructure, impacting road safety, economic sustainability, and long-term structural integrity. The findings of this study demonstrate how a combination of advanced geophysical techniques--InSAR, GPR, and ERT--can provide a comprehensive understanding of the underlying causes of subsidence along Road 431. The detection of surface subsidence rates reaching \(-2.7\) cm/year, alongside the identification of subsurface anomalies such as karstic voids, disintegrated fill layers, and groundwater fluctuations, underscores the complexity of the problem. Borehole validation confirmed the presence of voids and weakened subsurface materials, reinforcing the critical role of geophysical monitoring in infrastructure assessment. By directly linking subsurface instability with observed surface subsidence, this study highlights the necessity of a multi-method approach for diagnosing and mitigating subsidence-related hazards. These results provide a scientific foundation for improving road stability assessment techniques and guiding preventative maintenance strategies, ultimately reducing the risks associated with subsiding roadways.
# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 3 Methodology
### The Evaluation Metrics  
The final results and performance of the pretrained models are demonstrated using evaluation metrics, namely accuracy, loss, precision, F1-score, Hamming loss, and AUC. The accuracy, precision, and F-score are defined in Equations (1)-(3).  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{1}\]  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[F-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{3}\]  
Here, TP (true positive) is the number of instances correctly predicted as positive, TN (true negative) is the number of instances correctly predicted as negative, FP (false positive) is the number of instances incorrectly predicted as positive, and FN (false negative) is the number of instances incorrectly predicted as negative.  
Figure 6: Design of the ConvNeXt block.  
Figure 7: General architecture of the models.  
Accuracy (Equation (1)) can be defined as the proportion of correct classifications to the total number of predictions. Precision (Equation (2)) is the proportion of TP predictions to the total number of positive predictions. To calculate the F-score, recall must also be defined. Recall is the proportion of TP predictions to the positive instances on the dataset. The F-score (Equation (3)) is the harmonic mean of the precision and recall metrics.  
For the loss metric, binary cross-entropy loss has been employed. This loss function is applicable for not only binary classification tasks but also multilabel classification tasks. It evaluates the dissimilarity between the predicted probabilities of positive labels and actual labels.  
Hamming loss measures the percentage of labels that are incorrectly predicted over all samples in the dataset and it is defined as Equation (4). Hamming loss offers an evaluation of how well the model performs in correctly classifying each sample for all its relevant labels.  
\[\textit{Hamming Loss}=\frac{\textit{Number of incorrectly predicted labels}}{\textit{Total number of labels}} \tag{4}\]  
The last metric is AUC which stands for area under the receiver operating characteristic curve (ROC). To define the AUC, the ROC curve must be explained. The ROC curve is the depiction of the performance of the model across different thresholds. The ROC curve plots a graphic with the TP rate against the FP rate with different values of thresholds. The AUC score is the area under this ROC curve and it demonstrates the distinguishing power of the model among classes. The AUC score can be between 0 and 1. An AUC score over 0.5 is interpreted as the model having performed better than random guessing. If the AUC score is close to 1, it can be interpreted as it categorizes almost perfectly.
# Flexible Null Broadening Robust Beamforming Based on JADE
## 3 Proposed Algorithm  
### Preprocessing Technology Based on Blind Source Separation  
Blind source separation (BSS) is a new technology to find statistically independent components in multi-channel signals. It can estimate the information of the signal source from the mixed signal when both the mixing process and the signal source are unknown. As a classical algorithm of BSS, JADE is widely used in beamforming and DOA estimation. The mixing matrix separated by JADE contains the gain-phase errors, which can improve the robustness of beamforming. The mixing matrix obtained by JADE  
\[\hat{\mathbf{B}}_{JADE}=[\mathbf{b}_{1},\mathbf{b}_{2},\ldots,\mathbf{b}_{L+1}] \tag{12}\]  
Taking the first element of each column as the reference element, the column vectors in the mixing matrix are normalized as  
\[\begin{array}{l}\overline{\mathbf{B}}=\left[\frac{\mathbf{b}_{1}}{\mathbf{b}_{1}(1)^{ \prime}},\frac{\mathbf{b}_{2}}{\mathbf{b}_{2}(1)^{\prime}},\cdots,\frac{\mathbf{b}_{L+1}} {\mathbf{b}_{L+1}(1)}\right]\\ =[\overline{\mathbf{b}}_{1},\overline{\mathbf{b}}_{2},\cdots,\overline{\mathbf{b}}_{L+1}] \end{array} \tag{13}\]
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 4 Applications of MMP to Biology and Agriculture
### Further Development of MMP Diagnostics Using Fiber Optics  
Methods of non-destructive optical diagnostics, including those based on MMP, are constantly being developed. Fiber-optic devices have a special place in modern MMP instruments. To measure the MM, a multichannel PS-OCT with dynamic calibration improved by the use of a fiber-optic system was developed [90]. In the design of MMP devices, it is important to know the intrinsic polarization characteristics of the fiber-optic systems used. A virtual, generalized MM method for the characterization of a fiber-optic system with polarization-dependent elements and amplification was proposed [121]. This method allows obtaining data on polarization mode dispersion with high resolution and reduced noise level. An approach to measuring the MM for an optical fiber system with both birefringence and polarization-dependent loss or gain was tested [122]. Design examples of Mueller polarimetric endoscope and fiber Mueller polarimeter were described in [123].
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## Preliminaries
### Support Vector Machine  
Support vector machine (SVM) [PERSON] (1975) is a supervised learning model for pattern recognition and data analysis and is mainly used for classification and regression analysis. When a binary classification problem is given, SVM creates a non-probabilistic binary linear classification model for classifying data depending on the category it belongs to. SVM constructs a hyperplane that best separates the training data points with the maximum-margin. In addition to linear classification, SVM can efficiently perform nonlinear classification using a kernel trick that maps data to a high dimensional space.