esa-sceva/satcom-chunk-collection huggingface dataset:
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## 4 Numerical Experiments  
A variety of numerical experiments have been carried out in order to compare the performance of the developed techniques with other well-known PF solvers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solver** & **Analogy** & \(\mathbf{A}\) & \(\mathbf{b}\) **or \(\mathbf{B}\)** \\ \hline NR (2) & Explicit Euler & \(\left[\begin{array}{cc}0&0\\ 1&0\end{array}\right]\) & [1, 0] \\ NRJ (6) & Explicit Midpoint & \(\left[\begin{array}{cc}0&0\\ 1/2&0\end{array}\right]\) & [0, 1] \\ (23) & Explicit Heun & \(\left[\begin{array}{cc}0&0\\ 1&0\\ 0&0\\ 1&0\end{array}\right]\) & [1/2, 1/2] \\ (24) & Embedded & \(\left[\begin{array}{cc}1/2&1/2\\ 1&0\end{array}\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of some PF solvers based on the developed solution paradigms using matrix/vector notation.  
The developed Explicit Heun and Embedded Heun-Euler solution approaches (Equations (23) and (24), respectively) are compared with the standard NR, the Reverse Bulirsch-Stoer solver (RBS) [9], the Newton-Raphson with Jacobian adjustments (NRJ) [6], and the implicit implementation of the Continuous Newton's method using the Backward-Euler technique (BEM) [12]. In this latter case, we have used the implementation labelled 'Implicit-Continuous Newton Method with approximated Hessian and the solution of the inner loop is approximated by its first iteration (ICNM-J1)' in reference [12].  
All simulations have been obtained using Matpower [22] and running on a 3 GHz Intel Core i5-8500 personal laptop (8.00 GB RAM). In all experiments, a convergence tolerance of \(10^{-5}\) and \(k^{\max}~{}=~{}2000\) have been considered. The maximum number of iterations was tuned high enough to only discriminate the divergence cases. In order to avoid the impact of parallel computational activities, the reported execution times have been calculated taken as the average values of 1000 simulations. To determine the goodness of the solutions calculated, they have been compared with those obtained using NR and the default starting point provided in Matpower. Thereby, if both solutions differ more than 0.1 per unit (p.u.), the obtained solution is considered inaccurate.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.2.2 Interference Signal Classification  
Interference signal classification (also known as interference detection or interference recognition) is actually a typical application of pattern recognition, which includes three steps: data pre-processing, feature extraction and classification.  
The first is to pre-process the input data, that is, to preliminarily process the original signal data and map the signal from the signal space to the observation space. The process mainly includes: orthogonal down conversion, filtering, in-phase orthogonal decomposition, carrier estimation and carrier frequency component elimination. This step is to prepare for the feature extraction.  
The second step is feature extraction. The purpose is to map the signal data of high-dimensional observation space to low-dimensional observation space, and extract the time domain characteristics, frequency domain characteristics, time frequency domain characteristics and various statistical characteristics of the received signal. Selecting useful features is a very important step in the process of interference signal classification.  
The third step is classification decision-making. The basic concept of classification decision-making is to classify the identified targets into certain categories through pattern  
Figure 2: Interference monitoring process.  
recognition in feature space. Therefore, it is necessary to input the extracted feature parameters into the designed classifier, and then identify the corresponding signal categories. The process is divided into two steps: one is learning, which finds appropriate decision rules through known samples; The second is recognition, which uses the rule to design a classifier to realize the classification and recognition of unknown types of signals.  
The data preprocessing is a relatively simple part, which will not be introduced here. And this paper focuses on feature extraction and classification decision-making.
## 2 Related Research
### Intelligent Transportation System
#### 2.1.3 Security Threats
Even then, if it is not possible to prove the update was not properly carried out at the time of the accident, the manufacturer or service provider could be held liable, and damages due to nonrepudiation may occur. Additionally, such nonrepudiation can apply the other way around. A manufacturer might deceive the consumer and avoid liability for a problem that occurred when a software update was not provided, claiming that the problem occurred because the consumer did not perform an update. When utilizing the cloud with a V2N service, an attacker can forge or alter a message sent between nodes in the vehicle cloud and transfer it to another node. Later, when the fact becomes apparent, an attacker can deny it and is vulnerable to the repudiation of a cloud-based attack. Privacy Invasion  
In a typical attack, personal information can be collected or leaked through session sniffing, traffic analysis, etc. In particular, a vehicle terminal used for vehicle-to-vehicle communication, including vehicle speed and position information, will broadcast the vehicle position to other terminals in the vicinity in a basic safety message (BSM). By continuously tracking geolocation records, it can locate the vehicle. Similar to the V2V method, personal information can be collected and leaked through session sniffing, traffic analysis, etc. In particular, probe vehicle data (PVD) messages discussed in ISO TC204 include vehicle status information (engine status, ABS resin functioning, etc.) and vehicle position information (latitude, longitude, altitude), and vehicle operation information (route, heading, etc.). Therefore, the interception of these PVD messages can lead to privacy breaches. V2V and V2I privacy breaches can occur because of the variety of services provided and the implementation of personalized services if the main problem is the capture of location information. Personal information can be collected or leaked through general session sniffing, traffic analysis, etc., and the information stored on the cloud server can be stolen because of mismanagement of the cloud service connected via V2N. There is also the possibility that personal information will be leaked after bypassing other services or from threats such as an attacker stealing the personal information of a service user because of a bug or vulnerability in software related to the V2N service or from the installation of malware.
## 5 Discussion
### Performance Compared to Existing Fundamental Diagrams  
By comparing the fitting results between different models, it can be seen that our model achieves smaller statistical errors and higher fitness both in comparison with the classical fundamental diagram models [14,21] and with the queueing theory-based models [23,24]. In addition, our model performs better than other models in capturing the system's capacity and its sudden drop.  
This is due to the fact that in the actual flow-density data, the value of the flow is approximately plummeting after reaching the maximum, and the model function is limited by the mathematical form, which cannot take better care of the fitting of both the maximum flow point and the plummeting flow in the middle-density area. The other models, for comparison, also face this difficulty, and they choose to fit the middle-density area instead of the maximum flow point. Therefore, the fitness of the middle-density area in them is higher than the model in this paper.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects
#### 6.1.9 Temporal Instability  
Both angle and non-angle crashes showed temporal instabilities during the analysis periods. As [PERSON] [22] indicated, the causes of instability are diverse, particularly those involving changes in driver behavior. On the one hand, changes in the external environment (road, weather, etc.) can impact driver behavior. On the other hand, injury severity changes as drivers become more experienced and familiar with the road. Of course, part of the instability may come from accidental factors such as deviations in data records.
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 2 Related Works
### Geospatial Semantic Web
GeoSPARQL has some impressive features for geospatial implementation on the SW; this language did not accommodate analytical queries and has no extension for spatiotemporal and mobility data representation and querying. Another proposal that is similar to GeoSPARQL is the development of stRDF/stSPARQL. The stRDF is a data model with an RDF extension and represents geospatial information that changes over time [47]. stRDF defines spatial and temporal dimensions that have literal spatial and spatial data types. stRDF also uses OGC standards such as WKT and GML for serialization. stSPARQL was developed as an extension query language for SPARQL by adding functions defined in the OpenGIS standard Simple Feature Access. These functions are in the form of basic functions (get, set), basic function access functions (equals, disjoint, intersect, touches, crosses, within, contains, overlaps, and relate), Eigenhofer, RCC8, spatial analysis functions, spatial metric functions (distance and area), the spatial aggregate function (union, intersection, and extent). Strohon was then developed as an RDF store that supports semantic geospatial query languages in stSPARQL and GeoSPARQL. Strabon has expressive power in its use of these query languages with the stRDF model data. The testing of performance at strategic scales, for vast volumes of data, yielded good results [47]. stRDF/stSPARQL accommodates significant spatial data representation and query implementations, but it still lacks spatiotemporal data adoption and analytical capabilities. [PERSON] et al. proposed a SPARQL extension for modeling and querying a quantitative spatiotemporal relationship. The extension model adds an event model with time and space. The query extension created 30 new query operators that could effectively find the unseen connections between event ontology entities and have excellent performance and effectiveness [48]. This approach comprehensively adds SPARQL functionality, but this approach still does not handle the spatiotemporal analytical query. On the other hand, [PERSON] et al. proposed QB4 SOLAP, a generic and extensible vocabulary (meta-model) for spatial DW on the SW. QB4 SOLAP extends the QB4 OLAP vocabulary with spatial concepts. They also give a formalization of QB4 SOLAP. This paper defined critical spatial cube concepts, spatial hierarchies and levels, spatial measures, spatial aggregation functions, and topological relations among spatial dimension and hierarchy level members. They also defined several analytical spatial OLAP operators over QB4 SOLAP, these operators' formal semantics, and algorithms to generate spatially extended SPARQL queries [3]. This work provides a significant result in terms of the modeling and querying of spatial analytical data, but it is only limited to spatial data and does not include MOs data with more dynamic characteristics. In this study, we enhanced the QB4 SOLAP capability to support mobility analysis for MOs data.
## 3 VRIS Image Steganography Model
### Basic Results of the VRIS Basic Model
#### 3.3.1 Visual-Masker
Finally, the Conv22 layer converts the final feature map into the output image. This convolutional operation reduces the feature map's channels to three, corresponding to the RGB color channels, and applies the tanh activation function to normalize the output values within the range of \(-1\) to \(1\), satisfying image data representation requirements. The design of Visual-Masker follows an end-to-end principle, enabling it to learn the mapping relationship between the input image and its corresponding first-level encrypted output image. Each convolution layer is followed by a batch normalization layer to accelerate the training process and reduce internal covariate shifts. Additionally, at several critical points in the network, we utilize the LeakyReLU activation function, which introduces non-linear features and maintains effective gradient propagation throughout the network. This approach aids the model in learning complex mapping relationships. With the above design, the Visual-Masker module is able to learn the deep features of the input image and also enables efficient steganography of secret images while maintaining the visual naturalness of the cover images. This design allows secret images to be hidden in the cover images without attracting the attention of the human eye, providing an efficient and stealthy solution to the image steganography task.
# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 2 Background Knowledge
### RNN Encoder  
RNN is a type of neural network that is designed to process sequential data by considering the temporal dependencies within the input sequences [29]. However, initial vanilla RNNs have limitations in capturing long-term dependencies, due to the vanishing gradient problem. As the gradients back-propagate through time, they can become exponentially small, causing the network to struggle in preserving information over long sequences. To address this issue, LSTM (long short-term memory) was introduced.  
LSTM is a variant of RNN that incorporates memory cells to selectively store and forget information over time [30]. It consists of different gates, including input, forget, and output gates, which control the flow of information within the cells. These gates enable LSTM to capture long-term dependencies more effectively and to alleviate the vanishing gradient problem. Another variant of RNN that addresses the vanishing gradient problem is GRU (gated recurrent unit). GRU simplifies the architecture compared to LSTM by combining the memory update and reset operations into a single gate. This results in a more computationally efficient model while still maintaining the ability to capture long-term dependencies [31].  
The RNN encoder is used when we need to transform an input sequence into a fixed-length vector representation [32; 33]. The operation of the RNN encoder can be described as follows. Starting from the first step of the input sequence, it receives an input and updates its hidden state. Then, it moves on to the next step, combining the previous hidden state with the current input, to compute a new hidden state. This process is repeated for each step of the sequence, gradually encoding the information into the hidden states. At the end of this process, the final hidden state serves as the context vector. This context vector representation summarizes the input sequence with the shape of the fixed-length vector. The RNN encoder has been widely used in various tasks, such as natural language processing, speech recognition, and action recognition, demonstrating its effectiveness in encoding sequential data and extracting meaningful features.
# Towards Automatic Depression Detection:
## 6 Results
### Results of Experiment 2  
The results of the second experiment are compared with the baseline results in [47], which are shown in Table 4. Compared with the baseline performance on the test set, it can be seen that our method can effectively improve the assessment accuracy with MAE and RMSE values equal to 9.30 and 11.55, respectively. It should be noted that the language used in AViD-Corpus is German, while the language in DAIC-WoZ is English. This strongly demonstrates that our proposed 1D CNN model based on Mel spectrograms features can work properly even if the patients speak different languages.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Classification**} & \multicolumn{3}{c}{**Regression**} \\ \cline{3-8}  & & **F1 Score** & **Recall** & **Precision** & **MAE** & **RMSE** \\ \hline \multirow{8}{*}{Audio} & CNN-Augm [32] & 0.67 & 0.58 & 0.78 & - & - \\  & [PERSON] et al. [17] & 0.50 & - & - & 5.36 & 6.74 \\  & [PERSON] et al. [33] & 0.52 & 1.00 & 0.35 & - & - \\  & [PERSON] et al. [19] & 0.63 & 0.56 & 0.71 & 5.13 & 6.50 \\  & [PERSON] et al. [50] & - & - & - & 4.63 & 5.52 \\  & [PERSON] et al. [20] & - & - & - & 5.78 & - \\  & SVM & 0.40 & 0.50 & 0.33 & 5.93 & 6.74 \\  & Decision Tree & 0.57 & 0.50 & 0.57 & 5.70 & 6.79 \\  & Proposed 1D CNN model & **0.81** & **0.92** & 0.73 & **4.25** & **5.45** \\ \hline \multirow{8}{*}{Text} & Trf-Augm [32] & 0.78 & 0.75 & 0.82 & - & - \\  & [PERSON] et al. [20] & - & - & - & 6.57 & - \\  & [PERSON] et al. [19] & 0.67 & 0.80 & 0.57 & 5.18 & 6.38 \\  & [PERSON] et al. [29] & 0.55 & 0.89 & 0.40 & 3.87 & 4.98 \\  & [PERSON] et al. [17] & 0.84 & - & - & **3.34** & **4.46** \\  & SVM & 0.53 & 0.42 & 0.71 & 5.58 & 6.71 \\  & Decision Tree & 0.50 & 0.67 & 0.40 & 6.09 & 7.84 \\  & Proposed BiLSTM model & **0.83** & 0.83 & 0.83 & **3.88** & **5.44** \\ \hline \multirow{8}{*}{Audio \& Text} & [PERSON] et al. [19] & 0.77 & 0.83 & 0.71 & 5.10 & 6.37 \\  & Trf+CNN-Augm [32] & **0.87** & 0.83 & 0.91 & - & - \\ \cline{1-1}  & Proposed multi-modal fusion network & **0.85** & **0.92** & 0.79 & **3.75** & **5.44** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the experiments on the Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WoZ) dataset.
## 3 Literature Classification and Analysis
### Mathematical Models  
Reinforcement Learning Policy (MAPPO) Equation. In the RL framework, an agent selects joint actions based on the current environmental state to learn how to control the manipulator. The policy NN utilizes the MAPPO algorithm to predict the optimal steps needed to navigate the manipulator toward a specified position while avoiding both obstacles and joint limits. The agent maximizes a cumulative reward function, which encourages precise hitting, significant movement minimization, and collision-free navigation. This approach enables the manipulator to learn sophisticated motion sequences and inverse kinematics through interaction with the environment rather than through purely analytical methods.  
The policy that maps the current state to an action  
\[a_{t}=\pi_{\theta}(s_{t}) \tag{41}\]  
where \(s_{t}\) is the state at time t, \(\pi_{\theta}\) is the neural network policy parameterized by \(\theta\), and \(a_{t}\) is the action (joint angle adjustments).  
And the optimization objective (expected cumulative reward)  
\[J(\theta)=E_{s_{t},a_{t}}\Big{[}\sum_{\ell=0}^{T}\gamma^{t}r(s_{t},a_{t}) \Big{]}, \tag{42}\]represents the expected cumulative discounted reward in a reinforcement learning framework, parameterized by the policy \(\pi_{\theta}\). Here is a breakdown: \(J(\theta)\) is the objective function is to be maximized, representing the expected total reward under the policy with parameters \(\theta\). \(E_{s_{t},a_{t}}\sim\pi_{\theta}\) is the expectation over state-action trajectories generated by following the policy \(\pi_{\theta}\). \(\sum_{t=0}^{T}\gamma^{t}r(s_{t},a_{t})\) is the sum of discounted rewards collected from time step t = 0 to T, where \(r(s_{t},a_{t})\) is the reward received at time t, when the agent acts in state \(s_{t}\). \(\Gamma\in[0,1]\): The discount factor that prioritizes immediate rewards over distant future rewards.  
These two equations capture the core mathematical models for the manipulator's kinematics and the reinforcement learning control strategy.  
The RL equation, particularly the policy function \(a_{t}=\pi_{\theta}(s_{t})\), outlines how the agent (controller) moves from the current state \(s_{t}\) (containing information about the manipulator's pose and the surrounding environment) to action \(a_{t}\) (joint angle adjustments). The aims are, as described by the cumulative reward function, to predict an optimal outcome and retrieve maximum value by improving policy parameters \(\theta\). By means of interactions governed by these equations, an inverse kinematics problem is solved, which is learning to identify the joint angles for a specified end-effector position [53].
# FANT: Flexible Attention-Shifting Network Telemetry
## 1 Introduction
Now, the metrics required for the application have expanded to include a variety of new metrics, such as specific protocol rates, queue waiting times, and port/queue packet loss rates. In addition, as the inband network telemetry protocol evolves, we can also see an increase in the number of measurement metrics, with newer versions (INT specification v0.5 [8], INT specification v1.0 [9], INT specification v2.0 [10], INT specification v2.1 [11]) generally supporting more metrics. The increase in the number of monitored metrics will inevitably increase the bandwidth consumption of measurements, which subsequently affects the performance of business flows. FANT does not monitor all metrics continuously across all locations. Instead, it categorizes measurements into two groups: basic (low attention) and detailed (high attention). Based on the analysis from the previous cycle's measurements, FANT determines which links in the next cycle require comprehensive metrics and which can function with simplified metrics. FANT dynamically adjusts its attention to offer either comprehensive or simplified measurements for the upcoming cycle, based on the decisions. To ensure measurement precision, we propose a strategy for measurement metrics categorization. To further reduce overhead, we model the problem of dynamic attention shifting as a weighted set cover problem (WSCP), which is NP-hard. We introduce a heuristic algorithm to address this challenge. By flexibly shifting its focus, FANT conservstill providing comprehensive, multi-dimensional measurements. Our major contributions are summarized as follows:  
1. We propose the FANT system (the proposed method, encompassing the system architecture, probe-switching mechanism, metrics categorization strategy, attention switching strategy, and experimental validation, was all developed entirely by the authors), a network-wide measurement system based on an attention mechanism that reduces the increase in measurement costs caused by a surge in measurement metrics through flexible attention shifting. 2. We introduce a measurement metrics category and an attention-shifting strategy to enhance the effectiveness of the aforementioned mechanism, thereby avoiding impacts on accuracy and promoting cost savings. 3. Our experiments validate that our approach reduces costs significantly, consuming only 42.6% of the bandwidth used by state-of-the-art solutions. For scenarios requiring high-speed computation, our algorithm can accelerate by \(10^{5}\times\) with a 6.4% performance loss, keeping computation times under 10 ms.