esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Operation and Control
### Fault Detection and System Protection  
In order to improve _QoS_, it is necessary to locate faults faster so that they can then be isolated. Potential subareas will be explained:  
* A key factor is to have intelligent systems for the detection of faults and their subsequent recovery automatically.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Summary of Communications**} & **Operation and Control: Communications and Automation** \\ \cline{2-3}  & **Communications Solutions for** & **The Standardization of** \\  & **Mass Automation** & **Communication with** \\  & **Automation Elements Protocols** \\ \hline
**Big Data** & [28; 33; 34; 36] & [27; 29; 32; 35] \\ \hline
**Standardization of protocols** & [43; 48] & [41; 42; 44; 45; 46; 47; 49; 50] \\ \hline
**Measurement for Automation** & [53] & [51; 52] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Communications and Automation.  
* A second factor has to do with the use of devices for the improvement and quality control of wavelength, in order to give a personal service (from the perspective of quality) to individual consumers.
* Wide Area Monitoring Systems (WAMS)-based area protection systems are necessary. This is necessary because the protection system must also be seen as an extensive network infrastructure, and for this reason, this system can be supported by WAMS.  
After the review of the works, the author has considered grouping them in the following subsections: Fault Detection, System Protection and Fault Isolation and System Restoration.
## 4 Analysis of Test Results
### Ductility Factor Analysis  
Figure 7 displays the ultimate load of the specimens compared with the ductility factor.  
From Table 4 and Figure 7, it is clear that:  
1. The ductility coefficient of specimen C4 was the largest. Using epoxy resin interface-agent treatment best improved the ductility coefficient. Compared to specimen C1, it increased by 53.7%.
2. When the C5 and C6 specimens with post-installed rebar were used, the ductility coefficients were enhanced by 44.7% and 28.3%, respectively. It can be seen that the ductility of the strengthened column significantly improved by using epoxy interface-agent reinforcement.
3. Specim C3 (strengthened with LC50 ceramsite concrete) improved the ductility coefficient by 8.3% compared to specimen C2 (strengthened with C50 ordinary concrete). Therefore, ceramsite concrete as a reinforced solid could achieve greater ductility than ordinary concrete.  
Figure 7: Comparison of ultimate load and ductility factor.
## 2 Related Works
### Vanishing Gradient  
Neural networks are universal approximators, with deep neural networks being more expressive and providing better data representations than shallow neural networks [64]. However, training deep neural networks is challenging due to the vanishing gradient problem. This occurs when the derivative slope does not propagate backward during training [65]. In recent years, various methods have been introduced to solve the vanishing gradient problem.  
One method is better model initialization, which helps the optimization algorithm converge faster. Different initialization methods have been introduced, such as zero initialization, one initialization, Dirac initialization, Kaiming initialization [66], and Xavier initialization [67], with Xavier initialization being the most used in practice. A better initialization method accelerates the training stage and the model convergence, especially for deep neural network models.  
The second method uses ReLU activation, which is linear in the positive dimension and zero otherwise. Compared to other activation functions, ReLU provides a large and consistent derivative [68]. This makes training deep neural network models more plausible without suffering from the vanishing gradient problem. The second derivative of ReLU is zero everywhere and one when the ReLU is active, making learning more efficient compared to other activation functions.  
The third method to solve the vanishing gradient problem is the use of a batch normalization layer. The batch normalization layer reduces the internal covariate shift, which is the change in activation distribution during training. This layer accelerates the training of deep neural network models. Batch normalization layers fix the mean and variance of the input layer [69]. They also affect the gradient flow of the neural network model, accelerating the training of deep neural network models. Batch normalization can be seen as a regularizer that removes the need for dropout layers in deep neural network models [70].  
The fourth method to solve the vanishing gradient problem is the use of residual blocks in a very deep neural network model. This idea was first presented in the ResNet architecture [71]. Residual blocks help the gradient backpropagate, then train a deeper network. The core idea of residual blocks is the use of an identity mapping when the model gets very deep, making it easier to learn identity mapping between the input and the output.  
In this paper, we use different methods to avoid the vanishing gradient problem, especially when training decomposed binary neural network models. In the ablation studies and comparative analysis section, we explain, in detail, the different initialization and activation functions we use to improve model accuracy and train the model to converge to better minima.
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 5 Conclusions  
In this paper, a Context-Aware Trust and Reputation Routing protocol for OppiIoT networks (called CATR) is proposed, in which the trust values of nodes are computed dynamically. In such a scheme, before forwarding any data packet to its neighbor node, a source node computes the trust of that node in two phases--Direct Trust and Indirect Trust. These trust values serve as key parameters in deciding on suitable relay nodes to carry the message from its source to its eventual destination. Simulation experiments conducted using the ONE simulator demonstrate that CATR outperforms the Epidemic protocol, the beta-based Trust and Reputation Evaluation System (BTRES), and the secure and privacy-preserving scheme structure in opportunistic networks (PPHB+). Specifically, CATR achieves improvements of 22%, 15%, and 9% in terms of average latency, number of messages dropped, and average hop count, respectively. These results hold under varying conditions, including the number of nodes, buffer size, time to live (TTL), and message generation intervals. In a future study, this model can be extended by incorporating a mechanism to enhance the performance metrics.  
Conceptualization, J.S. and S.K.D.; methodology, J.S.; software, J.S.; validation, J.S., S.K.D. and I.W.; formal analysis, H.-C.C.; investigation, J.S.; resources, I.W.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, S.K.D., I.W. and H.-C.C.; visualization, J.S.; supervision, S.K.D., I.W. and H.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData will be made available on request.  
The authors declare no conflicts of interest.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 1 Introduction
However, in practical application, since the update rates of GNSS and INS are generally asynchronous, the choices of different integration time and aiding information update time will result in different tracking errors and the integration time adjustment will also increase the complexity of the tracking loop implementation, therefore we need to consider the influences of various factors on the INS-aided tracking loop to design an adaptive tracking method. This paper focuses on an INS-aided PLL tracking method for GNSS receiver in harsh environments with high dynamics and signal attenuation. The PLL is aided with delta Doppler from the integrated system. Based on a comprehensive theoretical analysis of the relation between INS-aided PLL tracking error and \(C/N_{0}\), vehicle dynamics, aiding information update time, noise bandwidth and integration time, a method to choose the optimal integration time, noise bandwidth for a given application under the minimum tracking error criterion has been proposed. By means of the adjustments of both integration time and noise bandwidth, the adaptive ability of the INS-aided GNSS carrier tracking loop for the harsh environment can be effectively improved. The rest of this paper is organized as follows: Section 2 gives an overview of the principle of delta Doppler-aided PLL. In Section 3, the main error sources of the INS-aided PLL are analyzed and the phase errors are derived by the discrete-time domain approach. Section 4 describes the method for choosing the optimal noise bandwidth and integration time. In Sections 5 and 6 software and hardware simulations are conducted to test the adaptive tracking method. In Section 7, the method is evaluated through a field test using a vehicle driven in urban environments. The final conclusions of this paper are given in Section 8.
## 2 Materials and Methods
### Modeling  
We used generalized additive models (GAMs; [54; 55]) with a binomial distribution (logit link function) to model the relationship between binary nightly occurrence of tree bats and the variables. GAMs are an extension of generalized linear models (GLMs; [56]) such that the expected response is a link transformed summation of an intercept and the product of slope coefficients and variables, however, some or all variable effects may be specified as semi- or non-parametric real functions denoted as splines (hereafter \"smooths\" or throughout, \"_f_(\(x\))\"; [54; 55; 56; 57; 58; 59; 60]). Smooths are created by a series of coefficient scaled basis functions \"tied\" together at knots--evenly spaced segments along the variable range. These smooths then, can take on complex, non-parametric shapes in the relationship between the variable effect and the variable as opposed to the generally linear, or parametric relationships in GLMs that may not reflect actual biological patterns. We performed all analysis in program R [61] and fit GAMs using the R package _mgcv_[53]. To test our first hypothesis and the subsequent competing hypotheses, we used a model selection process that tested three a priori models to select the appropriate model that best accounted for a potentially nonlinear seasonal effect. These three models contained smooth non-parametric function(s) of the ordinal date that took on factor level-specific shapes depending on the factor provided in the model. We supplied one model with no factor variable, one with roosting habitat availability that varied its intercept with site, and one with site only (Table 1). We used the minimum change in Bayesian information criterion (\(\Delta\)BIC; [62]) as the basis for model selection because BIC outperforms [PERSON]'s information criterion (AIC) when \(n\) is large (\(n>3000\); [63]) and tends to select more parsimonious models because the penalty for complexity is larger than AIC (for \(n>e^{2}\)). We calculated each a priori model BIC using [PERSON]'s method of BIC = -2 \(log(\mathcal{L})+K\,log(n)\) implemented in the _model.sel_ function of the R package _MuMIn_[64]. We used the top a priori model structure in all further models as a baseline (i.e., this model structure was nested within any other further competing model). To include atmospheric conditions into the model, we first reduced variables by omitting those of high correlation (Pearson correlation coefficient > 0.7). We then created a global model that included all variables as smooth functions. We performed a dredge (i.e., fit all possible additive model combinations). We compared these models by BIC. We considered models
## 3 Materials and Methods
### Machine Translators and GenAI  
For this experiment, it is essential to ensure that all conditions remain consistent, even if they could be optimized in a real-world application or production environment. To achieve this, the study has deliberately relied on the most basic implementations, avoiding associated costs and utilizing only fundamental resources. This approach ensures an objective and fair comparison of GenAI tools, eliminating external factors that could introduce bias into the results. Furthermore, by maintaining uniform testing conditions, the findings provide a reliable baseline for evaluating the inherent capabilities of each tool, independent of any additional optimizations that might be applied in a practical setting.  
The tools evaluated in the study were ChatGPT, Bing Translator, DeepL, Google Translate, and Copilot. In Table 6, some of the most representative features of each GenAI are presented.  
\begin{table}
\begin{tabular}{c c} \hline
**Characteristics** & **Content** \\ \hline Id & Identifier \\ CVEId & Common Vulnerabilities and Exposures \\ Title & Alert title \\ Description & Description in Spanish \\ Description (English) & Description in English \\ Dates & April 2006–6 June 2024 \\ Instances & 205,534 \\ Characters in English & 355.5990 per description (average) \\ Characters in Spanish & 302.9201 per description (average) \\ \hline \end{tabular}
\end{table}
Table 4: Information of each vulnerability in the database.  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **D** \\ \hline Sample size (Instances) & 7514 \\ Total characters in English descriptions & 1,958,403 \\ Average characters per English description & 260.6339 \\ \hline \end{tabular}
\end{table}
Table 5: Sample information.  
The free versions of all the tools mentioned above were used.  
Although the inclusion of the Gemini tool was initially considered, it was ultimately excluded from the final tests due to the requirement of creating an account and its significant limitations.  
For the purpose of comparing the efficiency of machine translators versus GenAI tools, we have created two distinct groups. The Translators Group consists of Google Translate, Bing Translator, and DeepL, all of which are specialized tools for machine translation. On the other hand, the GenAIs Group includes ChatGPT and Copilot, both of which leverage GenAI technologies that extend beyond just translation tasks, allowing them to adapt to a variety of linguistic needs.
## 3 System Model
### Data Transmission Model  
Let \(S_{i}=\{j|d_{i,j}\leq R,j\in N\}\), where \(R\) is the radius of the node \(i\)' transmission range. According to the geometric routing, only those neighbors that are closer to the sink node s can serve as the downstream nodes. Let us denote this set of downstream neighbors as \(\overrightarrow{S_{i}}=\{j|d_{j,s}<d_{i,s},j\in S_{i}\}\). Similarly, the set of upstream neighbors is denoted as \(\overleftarrow{S_{i}}=\{j|d_{j,s}\geq d_{i,s},j\in S_{i}\}\). Note that in case a node has no neighbors that are closer to the sink node than itself, we encounter a problem known as \"local maximum\" where the node fails to find routing path to the sink node according to geometric routing. A few solutions have been proposed for this problem [67; 68]. However, the consideration of these solutions is beyond the scope of this paper. In the following, we assume that the downstream neighbor set \(\overrightarrow{S_{i}}\) non-empty for all \(i\in N\).  
To further illustrate the process of data aggregation, an example is provided, which is shown in Figure 4. The data aggregation and routing work as follows. A sensor node \(j\) is ready to send data to its downstream neighbor node \(s\). The data from node \(j\) contains two parts, one parts is aggregation data or transit data passed from node \(i\) (which has been encoded by node \(i\) or its upstream nodes), no further encoding is performed, which is directly forwarded to the downstream neighbors. Another part is the  
\begin{table}
\begin{tabular}{c c} \hline
**Weather Condition (at 12:00 noon)** & **The Power Density** \\ \hline cloudy day inside & 4000–7000 lux \\ cloudy day outside & 8000–12,000 lux \\ sunny day inside & 30,000–50,000 lux \\ sunny day outside & \textgreater{}100,000 lux \\ \hline \end{tabular}
\end{table}
Table 1: The power density in different weather condition.  
raw data of node \(j\), it is encoded with the local information. All this transit traffic is forwarded to the downstream neighbors.
## 3 Research Method
### Model Introduction
#### 3.2.3 LightGBM Model  
LightGBM (Light Gradient Boosting Machine) is an efficient gradient-based Machine Learning framework developed by Microsoft, which is particularly suitable for regression and classification tasks of large-scale datasets [79]. In our study, it is used to mine the deep features of PM\({}_{2.5}\) concentration changes and capture complex nonlinear patterns.  
Core calculation formula:  
(1) Objective function  
\[L=\sum_{i=1}^{N}l\left(yi,\hat{y}i\right)+\Omega(f)\]  
\(L\) is the objective function value, which is used to measure the difference between the model prediction result and the actual value, and to constrain the complexity of the model to balance the model's fitting ability and generalization ability.  
\(N\) is the number of PM\({}_{2.5}\) concentration data samples used to train the LightGBM model.  
\(l(yi,\hat{y}i)\) is the loss function for the actual value \(yi\) and the predicted value \(\hat{y}i\) of the \(i\)-th sample. Common loss functions include mean square error, mean absolute error, etc. In our study, we select a suitable loss function to evaluate the model performance according to the task requirements.  
\(\Omega(f)\) is a regularization term, which is used to control the complexity of the model, prevent the model from overfitting, and make the model perform better on both training data and new data.  
(2) Additive Model  
\[\hat{y_{i}}^{(m)}=\hat{y_{i}}^{(m-1)}+\
u\cdot f_{m}(x_{i})\]  
\(\hat{y_{i}}^{(m)}\) is the predicted value of the \(i\)-th sample in the \(m\)-th iteration. As the number of iterations increases, the predicted value of the model is continuously updated and optimized.  
\(\hat{y_{i}}^{(m-1)}\) is the predicted value of the \(i\)-th sample in the \((m-1)\)th iteration, that is, the result of the previous iteration. The new predicted value is updated based on the result of the previous iteration.  
\(\
u\) is the learning rate, which controls \(f_{m}(x_{i})\) the contribution of the newly generated weak learner to the final prediction result at each iteration. A suitable learning rate can make the model converge stably during the training process and improve the accuracy of the prediction.  
\(f_{m}(x_{i})\) is the predicted value of the \(m\)-th weak learner for the \(i\)-th sample \(x_{i}\). LightGBM constructs multiple weak learners through continuous iteration, and accumulates their prediction results to finally form a strong learner for prediction.
###### Abstract  
In recent times, 3D electrical resistivity and induced polarization tomographies are being used more frequently. However, it is often not possible to have regular grids of electrodes due to irregular topography, difficulty accessing urbanized or industrialized places, and other environmental and health problems. In these cases, the use of unconventional arrays is necessary, arranging the electrodes around the inaccessible area according to one or more open or closed polygonal traces. In this work, three different perimeter arrangements of electrodes are considered, and, for each, three different electrode array configurations are tested by calculating their apparent resistivity and solving the inverse problem on a three-dimensional model with resistive and conductive blocks. The comparison of the results showed that the dataset that produces the most realistic inverse model consists of electrodes arranged in concentric squares and the use of the Full Range Gradient (FRG) Array. This combination was evaluated in the field on a waste landfill, in which electrical resistivity and induced polarization tomographies were carried out, exploiting the access paths to the various sectors of the landfill to arrange the electrodes on approximately concentric polygons. The 3D models of electrical resistivity and induced polarization allowed the detection of zones of high concentration of leachate, defining their extension, and monitoring the functioning of the waterproofing membrane at the bottom of the landfill. The results proved that when it is not possible to arrange a regular grid of electrodes, the use of perimeter disposals of electrode joined to the FRG array provide a sufficiently homogeneous resolution below the area to be investigated.  
electrical resistivity tomography; induced polarization; unconventional electrode arrays; 3D models; landfill; leachate +
Footnote †: journal: Applied Physics Letters  
Article
## 4 Analysis of Test Results for Diversion Function Pipe Segment Models
### Analysis of Pore Water Pressure on Both Sides of the Pipe Segment under Different Pressures  
#### 4.3.1 Distribution and Change in Pore Water Pressure on Both Sides of the Pipe Segment  
As illustrated in Figure 17, when the pressure differential is identical, the pore water pressure value at a depth of 0.3 m from the bottom on the left side is greater than the pore water pressure values at the other five measurement points. The pore water pressure around the pipe segment at 0.3 m, 0.5 m and 0.7 m from the bottom on the left side decreases with the increase in pressure difference between the two sides. When the pressure difference is increased from 0.5 kPa to 2.5 kPa, the pore water pressure decreases by 1.7%, 1.4%, and  
Figure 16: Variation in earth pressure. (**a**) Pressure difference 0.5 kPa. (**b**) Pressure difference 1.0 kPa. (**c**) Pressure difference 1.5 kPa. (**d**) Pressure difference 2.0 kPa. (**e**) Pressure difference 2.5 kPa. (**f**) Changes in earth pressure difference on both sides of pipe segments.  
1.5%, respectively. It can be observed that the pore water pressure loss on the left side is smaller. The pore water pressure around the pipe segment at 0.3 m, 0.5 m and 0.7 m from the bottom on the right side also decreased with the increase in pressure difference between the two sides. When the pressure difference increased from 0.5 kPa to 2.5 kPa, the pore water pressure decreased by 74.6%, 75.4% and 73.5%, respectively. The pore pressure loss on the right side was larger.  
The pore water pressure values at the three measurement points on the left side remain essentially unchanged with an increase in pressure difference, whereas those at the three measurement points on the right side decrease as the pressure difference between the two sides increases. This can be interpreted as the first two water level heights being equal. When adjusting the pressure difference between the two sides of the water level, the left side exhibits slight fluctuations, but remains essentially unchanged. Consequently, the change in pore water pressure on the left side is minimal, while on the right side, it increases as the water level decreases. As the water level on the right side continues to decline, the pressure difference between the two sides of the water column also remains constant.