esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Wargame Decision-Making Method Based on Multi-Agent Deep Deterministic Policy Gradient
## 2 Related Work
### Markov Decision Process  
The arcane and complex world of Markov processes has been the focus of relentless research since [PERSON]'s seminal paper. The evolution of Markov processes is listed in Table 1. The introduction of environmental factors into classical Markov chains was a watershed moment for scholars, as it opened up new avenues for exploration. In the groundbreaking work conducted by [PERSON] [30], researchers gave formulas for Markov chains in stochastic environments, exploring special cases such as branching processes, queues, life and death chains, and random wandering in stochastic environments. Based on this study, [PERSON] [31] presented various limit theorem theories for Markov processes in the general context, while [PERSON] [32] compiled the limit theorems concerning the transfer probability of Markov chains. [PERSON] [33] tackled the daunting task of providing general expressions for the stochastic model of Markov chains in stochastic environments, analyzing the dependence between environmental factors and controlled Markov chains. They also derived the ergodic theory of Markov chains in stochastic environments and established the conditions for the existence of finite invariant measures, a formidable undertaking to say the least. Under the premise of a finite state space and the existence of finite invariant ergodic measures and mixed conditions, [PERSON] [34] established the central limit theorem for the function of a Markov chain in a stochastic environment. It has been demonstrated that these conditions are always satisfied when the state space is finite, which is a pioneering result that will undoubtedly determine the future of research in this field.  
Underlying the above studies, the Markov decision process (MDP) was formulated, which consists of a tuple \(M=<S,A,P,R,\gamma>\). Here, \(s\in S\) describes the true state of the environment. In the MDP, the agent needs to select the action \(a\in A\), which depends on the agent's own policy. This may lead to the state of the environment changing to \(s^{\prime}\), which is determined by the state transition function \(P(s^{\prime}|s,a)\). In addition, the environment will give the reward \(r=R(s,a)\). \(\gamma\in[0,1)\) is a discount factor.
## 3 Results and Discussion
### Model Application Analysis  
The enhanced ChebNet model proposed in this paper has far-reaching implications for reservoir management and industrial operations in the oil and gas industry. In reservoir management, accurately predicting the high-pressure physical property parameters of reservoir fluids can optimize production plans. For example, the precise prediction of bubble point pressure helps engineers determine the optimal time for gas injection. If the bubble point pressure is accurately predicted, operators can inject gas at the right moment to maintain reservoir pressure, which is crucial for enhancing oil recovery. This not only improves production efficiency, but also reduces the costs associated with unnecessary gas injection or premature production decline. In terms of reservoir dynamic analysis, the model's accurate prediction of parameters like crude oil viscosity and compressibility provides a more reliable basis for understanding fluid flow behavior within the reservoir, enabling better reservoir management strategies.  
In industrial operations, such as oil field production and pipeline transportation, the model also plays an important role. The prediction of crude oil viscosity is directly related to pipeline transportation costs. By accurately predicting the viscosity, appropriate pipeline diameters and pumping equipment can be selected in advance, ensuring smooth fluid transportation and reducing energy consumption.  
This method also has strong generality in other industries. In the chemical engineering field, when dealing with multicomponent systems, similar to the complex relationships between reservoir fluid parameters, the enhanced ChebNet model can analyze the relationships between the properties of various chemical components. For example, in the process of petroleum refining, it can predict product properties based on feedstock characteristics, helping to optimize the refining process and improve product quality. In environmental science, when studying the complex interactions between pollutants in the atmosphere or water bodies, this model can be applied to analyze the relationships between different pollutant concentrations and environmental factors. By regarding pollutants and environmental factors as nodes in a graph, the model can capture their complex relationships and predict environmental quality changes, providing a new approach for environmental monitoring and pollution control.
# PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software
## 4 Experiments and Results
### Experimental Setup  
In terms of collection programs, the Software Assurance Reference Dataset (SARD) [42] serves as the standard dataset to test vulnerability detection tools with software security errors, and the National Vulnerability Database (NVD) [43] contains vulnerabilities in production software. In the SARD,each program case contains one or multiple common weakness enumeration Identifiers (CWE IDs). In the NVD, each vulnerability has a unique common vulnerabilities and exposures identifier (CVE ID) and a CWE ID to identify the vulnerability type. Therefore, we finally collected the programs with CWE IDs that contained vulnerabilities.  
We chose two types of vulnerabilities as detection object: buffer overflow (CWE-119) and resource management error (CWE-399). We also collected some other C/C++ programs on NVD as an extended corpus for pre-training. Table 1 summarizes statistics on training data and pre-training data. The datasets were preliminarily processed by [13]. We collected data from the 10,440 programs related to buffer error vulnerabilities and 7285 programs related to resource management error vulnerabilities from the NVD; we also collected 420,627 programs as an extended corpus to improve code representation. The extended dataset focuses on 1591 open-source C/C++ programs from the NVD and 14,000 programs from the SARD. It includes 56,395 vulnerable samples and 364,232 samples that are not vulnerable.  
Regarding training programs vs. target programs, we randomly chose 80% of the programs that were collected as training programs and 20% as target programs. This ratio is applied when dealing with one or both types of vulnerabilities. We also used 10-fold cross-validation over the training set to select the model and used the test set to test the obtained model.  
For the deep learning model, we implemented the deep neural network in Python with Keras [44]. We ran experiments on a Google Colaboratory [45] with Nvidia K80, T4, P4, or P100 graphics processing unit (GPU). Genism [46] Word2 vec was used to train the word embedding layer. Scikit-learn [47] provides KNeighborsClassifier, RandomForestClassifier, MultinomialNB, and LogisticRegression algorithm as classifiers. Every experiment monitored valid F1 as a condition of early stopping in 10 epochs. Table 2 shows the parameters in the representation learning phase.
# Business Process Configuration according to Data Dependency Specification
## 4 Conf-BP Framework: A Configurable System to Create Imperative BP
### Conf-BP Declarative Specification (ConfD-BP). A Formalization of the Language
#### 4.1.4 Specification Applied to the Trip Planner Example
The constraints between the input data of the sub-process and the activities are specified as the set of \(\mathbb{INPUT\_CONSTRAINT}\) at design time. In the same way, the sub-process returns different data with respect to those returned by the activities, and it is necessary to define, at design time, how these data are calculated and related with the output data of the activities. Therefore, the set of \(\mathbb{OUTPUT\_CONSTRAINT}\) define these relationships between the output data of the activities and the data returned by the sub-process to the customer. Although in the experimental evaluation all the constraints have been included, only the most representative constraints have been formulated in this section. Regarding the relationships between the data input and output that belong to the sub-process and the activities, the most representatives \(\mathbb{INPUT\_CONSTRAINT}\) and \(\mathbb{OUTPUT\_CONSTRAINT}\) are detailed below:  
There are several numerical constraints that relate data input and output belonging to the process and the activities. Some of the constraints are defined below:
* The constraints that establish the values of departure date (C1) and return date (C2) of the flights have to coincide with the input data proposed by the customer. * The constraints that describe the values of the departure airport (C3) and arrival airport (C4) of the flight have to coincide with the input data proposed by the customer. * The date of check-in into the hotel should coincide with the arrival date of the outward flight (C5). * If the flight does not depart from the departure location (C6), then the rental of a car (\(CR_{1}\)) is necessary. * \(A_{F}.goingTo=a_{F}.departingFrom\to A_{CR1}.departingFrom= departingFrom\wedge A_{CR1}.goingTo=A_{F}.departingFrom\wedge A_{CR1}.departDate=A_{F}.departDate\wedge A_{CR1}.returnDate=A_{F}.returnArrivalDate\)
* If the flight arrives at the destination city (C7), then it is not necessary to rent a car at the destination city. * \(A_{F}.goingTo=goingTo\to A_{F}.goingTo=A_{H}.location\)
* If the flight does not depart from the departure location (C8), then the rental of a car (\(CR_{2}\)) is necessary. * \(A_{F}.goingTo\
eq goingTo\to A_{CR2}.departingFrom=A_{F}.goingTo\wedge A_{CR2}.goingTo=A_{H}.location\wedge A_{CR2}.departDate=A_{F}. outwardArrivalDate\wedge A_{CR2}.returnDate=A_{F}.returnDate\)  
* The total price is the sum of all the prices returned by the activities, as presented in constraint (C9). \(totalPrice=A_{F}.fightPrice+A_{H}.hotelPrice+A_{CR1}.carRentalPrice+A_{CR2}.carRentalPrice\)  
In this example, the optimization involves the minimization of the total price of the trip, which is composed of the cost of buying flight tickets, staying in a hotel room, and renting cars for the departure and arrival cities.
# Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck
###### Abstract  
At CRYPTO 2019, [PERSON] proposed the neural differential distinguisher using the residual network structure in convolutional neural networks on round-reduced Speck32/64. In this paper, we construct a 7-round differential neural distinguisher for Speck32/64, which results in better than [PERSON]'s work. The details are as follows. Firstly, a new data format \((C\_r,C\_r^{\prime},d\_l,C_{l},C_{r},C_{l}^{\prime},C_{r}^{\prime})\) is proposed for the input data of the differential neural distinguisher, which can help the distinguisher to identify the features of the previous round of ciphertexts in the Speck algorithm. Secondly, this paper modifies the convolution layer of the residual block in the residual network, inspired by the Inception module in GoogLeNet. For Speck32/64, the experiments show that the accuracy of the 7-round differential neural distinguisher is 97.13%, which is better than the accuracy of [PERSON]'s distinguisher of 9.1% and also higher than the currently known accuracy of 89.63%. The experiments also show that the data format and neural network in this paper can improve the accuracy of the distinguisher by 2.38% and 2.1%, respectively. Finally, to demonstrate the effectiveness of the distinguisher in this paper, a key recovery attack is performed on 8-rounds of Speck32/64. The results show that the success rate of recovering the correct key is 92%, with no more than two incorrect bits. Finally, this paper briefly discussed the effect of the number of ciphertext pairs in a sample on the training results of the differential neural distinguisher. When the total number of ciphertext pairs is kept constant, the accuracy of the distinguisher increases with \(s\), but it also leads to the occurrence of overfitting.  
deep learning; differential cryptanalysis; Speck; key recovery attack +
Footnote †: journal: Applied Sciences
# Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System
## 3 Proposed Scheme
### Receiver
#### 3.3.2 Vehicle Localization
When the front vehicle was on the left side of the FLV, the distance was calculated as follows  
\[\frac{o}{O}=\frac{f}{O-f}. \tag{2}\]0When the front vehicle is on the same line as the FLV, the distance was calculated as  
\[\frac{o}{O}=\frac{f}{O-f}. \tag{2}\]1  
Then, the angular distance of the left and right LEDs, and the center of the vehicle were calculated as  
\[\frac{o}{O}=\frac{f}{O-f}. \tag{2}\]2  
\[\frac{o}{O}=\frac{f}{O-f}. \tag{2}\]3  
\[\frac{o}{O}=\frac{f}{O-f}. \tag{2}\]4  
The overall algorithm to localize the FWVs is shown in Algorithm 1. ```
0: captured images, training.weights, classes.txt, testing.cfg. 0:\(loc_{i}\), \(dir_{L}\), \(dir_{R}\). 1: assign FWV-ID;
2: execute CNN using training.weights, classes.txt, testing.cfg;
3:for\(i=0:N\)\(\epsilon\) number of detected transmitter do
4:while\(N_{IS}\geq a^{2}\)do
5: calculate S;
6:if\(\delta S_{L}=0\)then
7: continue;
8:else
9: check bending direction;
10:if\(S_{LL}>S_{RL}\)then
11: assign \(dir_{R}\);
12:else
13: assign \(dir_{L}\);
14: (X, Y) \(\leftarrow\) append coordinates (x,y);
15: demodulate received signal;
16: calculate distance, \(D=\sqrt{D_{L}^{2}-\left\{\frac{(D_{R}-D_{L})^{2}-D_{LR}^{2}}{D_{LR}}\right\}^ {2}}\);
17: calculate angle, \(\theta_{L}=\tan^{-1}(\frac{C}{D})\);
18: calculate \(loc_{i}\);
19: update \(loc_{i}\);
20:endif
21:endif
22:endwhile
23:endfor
```  
**Algorithm 1** Vehicle localization and communication scheme.
# Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
## 3 Proposed Methods
### Hybrid Map  
In this paper, we describe the structure of Hybrid RC-Map and show that it can be used to quickly patrol a given space by making a path plan. Figure 1 shows the difference between RC-Map and an occupied grid map. Hybrid RC-Map divides the free area of the occupied grid map into squares and expresses each square as a node. Since RC-Map maintains only the coordinate information of squares, it can express the environment using less memory than a cell-based occupied grid map.  
Figure 2a shows a visual representation using the data structure of RC-Map. In the actual implementation, RC-Map is stored as a graph-type data structure as shown in Figure 2b. This structure was chosen for its low memory usage and fast search. RC-Map uses V and A nodes as internal data structures. V node means an empty space in the hybrid map where the robot can move. TV-nodes maintain node numbers, coordinates on the environmental map, and pointer information pointing to a list of adjacent nodes to accurately represent environmental information. An A node means a node adjacent to a V node. A node has the node number, AP (Adjacent Point), V node number, and a pointer to the next A node. The proposed hybrid map RC-Map functions as follows from the existing topology map. In a general topology map, a node only maintains basic information about the area it wants to express, but in RC-Map, a square is referred to as a node, and coordinate information about the area is maintained to maintain the accurate expression of the grid map. In addition, an AP is used to indicate the connection between nodes and to generate a movement path between nodes. AP is explained in the next subsection, Path Generation.  
Figure 1: Hybrid RC-MAP based on a grid map.
# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 3 Dataset
### Data Collection
#### 3.1.2 Article Collection and Processing  
We crawled data from each media outlet on the list obtained through the previous process. Using the Newspaper3k(v0.2.8) ([[https://github.com/codelucas/newspaper](https://github.com/codelucas/newspaper)]([https://github.com/codelucas/newspaper](https://github.com/codelucas/newspaper)), accessed on 15 December 2023), we extracted all URLs linked within the media outlets' websites. We applied URL matching to exclude advertisements, as these links often point outside of news websites. After filtering out suspected advertisements, we downloaded the HTML files of the remaining news URLs. The BeautifulSoup library was then employed to parse the HTML and extract the corresponding article titles, texts, image URLs, authors, and publication dates.  
However, due to anti-crawling measures on certain websites, we were unable to collect content from them. As a result, our dataset comprises 242 multilingual news media outlets and 2620 monolingual ones. Despite these limitations, we hope that, for academic purposes, these sites may eventually offer open public interfaces to facilitate deeper research in this area.  
Our data are stored in JSON format. The JSON file of xMP contains the media outlet URL and name, the factuality label, the political bias label, the language category, and the country of origin for each media outlet. For each article, the JSON file includes the title, content, image URLs, publication date, and the article URL. We believe the image URLs in our dataset will enable future research into multimodal media profiling.  
Given our focus on cross-lingual media profiling, we further split the dataset, as detailed in Table 1. The English data were divided into training, validation, and test sets using the standard 8:1:1 ratio. For the cross-lingual media profiling task, the multilingual data were split into cross-lingual test sets, each evaluated separately based on language.
## 3 Results
### Subgroup Analysis  
#### 3.4.1 Aerobic Capacity  
Subgroup analysis based on the intervention type revealed that both aerobic exercise (WMD, 2.33; 95% CI, 1.31 to 3.34; \(p<0.00001\); I\({}^{2}\) = 80%) and combined exercise (WMD, 3.39; 95% CI, 2.03 to 4.75; \(p<0.00001\); I\({}^{2}\) = 87%; Figure 4) significantly improved aerobic capacity in HF patients, with combined exercise demonstrating a greater improvement effect.  
Additionally, interventions lasting less than 60 min (WMD, 2.07; 95% CI, 1.12 to 3.02; \(p<0.0001\); I\({}^{2}\) = 73%) and those lasting 60 min or longer (WMD, 3.61; 95% CI, 2.53 to 4.69; \(p<0.00001\); I\({}^{2}\) = 82%; Figure 5) significantly improved aerobic capacity in HF patients, with longer durations (\(\geq\)60 min) showing superior effects.  
Figure 2: Meta-analysis results of the effect of exercise on aerobic capacity in HF patients.  
Figure 3: Meta-analysis results of the effect of exercise on QOL in HF patients.  
Figure 4. Meta-analysis results of the effect of types of intervention on aerobic capacity in HF patients.  
Figure 5. Meta-analysis results of the effect of duration of intervention per session on aerobic capacity in HF patients.  
Figure 7: Meta-analysis results of the effect of duration of intervention per week on aerobic capacity in HF patients.  
Figure 8: Meta-analysis results of the effect of supervision status on aerobic capacity in HF patients.  
Finally, exercise significantly improved aerobic capacity in both middle-aged (WMD, 3.28; 95% CI, 2.39 to 4.18; \(p<0.00001\); I\({}^{2}=76\%\)) and elderly HF patients (WMD, 2.94; 95% CI, 1.67 to 4.21; \(p<0.00001\); I\({}^{2}=84\%\); Figure 9). Exercise had a greater effect on improving aerobic capacity in middle-aged HF patients.
## 3 Regression Analysis
### Determination of the Regression Coefficients  
In order to determine the regression coefficients, the least square method is used based on the thought that the regression coefficients can be estimated when the error sum of squares \(Q\) is at its minimum, which can be expressed as Equations (5) and (6).  
\[Q(\beta_{0},\beta_{1},\cdots\beta_{\rm P})=\sum_{i=1}^{n}{(y_{\rm i}-\beta_{0 }-\beta_{1}x_{\rm i1},\cdots,\beta_{\rm P}x_{\rm ip})^{2}} \tag{5}\]  
\[Q(\hat{\beta}_{0},\hat{\beta}_{1},\cdots\hat{\beta}_{\rm P})=\min\{Q(\beta_{0 },\beta_{1},\cdots\beta_{\rm P})\} \tag{6}\]  
where \(\hat{\beta}_{0},\hat{\beta}_{1},\cdots\hat{\beta}_{\rm P}\) are the estimated values of the regression coefficients; \(n\) is the number of the experiments; \(p\) is the number of independent variables. The regression coefficients can be acquired when the partial differential of \(Q\) to \(\beta\) equals to zero, as given by Equation (7).  
\[\frac{\partial Q(\beta)}{\partial\beta}=0 \tag{7}\]  
The regression coefficients are obtained through the computer-aided calculation software, and the developed regression equations are given by Equations (8)-(11).  
\[TS=304.988+37.9527f_{\rm L}+1.8339f_{\rm H}+2.9213\psi-11.2392f_{\rm L}{}^{2} -0.0395f_{\rm H}{}^{2}-0.1002\psi^{2}-0.073\psi f_{\rm H} \tag{8}\]  
\[YS=195.7674+17.3403f_{\rm L}+1.0875f_{\rm H}+0.2276\psi+1.3576f_{\rm L}{}^{2} -0.0248f_{\rm H}{}^{2}+0.0768\psi^{2}-0.0203\psi f_{\rm H} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **Factor** & **A:\(f_{\rm L}\)/kHz** & **B:\(f_{\rm H}\)/kHz** & **C: \(\psi\)** & **_TS/MPa** & **YS/MPa** & **_PE/\%_ & **_RA/\%_** \\ \hline
1 & 1 (0.1) & 1 (5) & 1 (0.6) & 311 & 198 & 7.7 & 8.4 \\
2 & 1 (0.1) & 2 (20) & 2 (2.17) & 335 & 211 & 7.7 & 8.6 \\
3 & 1 (0.1) & 3 (40) & 3 (7.33) & 318 & 204 & 8.1 & 8.6 \\
4 & 2 (0.5) & 1 (5) & 2 (2.17) & 339 & 213 & 7.6 & 10.9 \\
5 & 2 (0.5) & 2 (20) & 3 (7.33) & 340 & 215 & 7.7 & 9.5 \\
6 & 2 (0.5) & 3 (40) & 1 (0.6) & 334 & 210 & 7.5 & 9.6 \\
7 & 3 (1) & 1 (5) & 3 (7.33) & 356 & 226 & 8.4 & 9.9 \\
8 & 3 (1) & 2 (20) & 1 (0.6) & 358 & 229 & 9.7 & 12.8 \\
9 & 3 (1) & 3 (40) & 2 (2.17) & 334 & 213 & 7.6 & 8.9 \\ \hline \end{tabular}
\end{table}
Table 4: Tensile properties of AA 5456 Al alloy welded joints by orthogonal experiment.  
\[PE=8.3494-0.8327f_{\rm L}+0.0518f_{\rm H}-0.7894\psi+1.9906{f_{\rm L}}^{2}-0.0018{ f_{\rm H}}^{2}+0.0756\psi^{2}+0.0072\psi f_{\rm H} \tag{10}\] \[RA=8.7995+8.7482f_{\rm L}+0.0534f_{\rm H}-1.0729\psi-4.7774{f_{\rm L }}^{2}-0.0029{f_{\rm H}}^{2}+0.0719\psi^{2}+0.0168\psi f_{\rm H} \tag{11}\]
# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
## 2 Methodology
### SMA-MLP Model  
The powerful modeling capabilities, flexibility, and broad applicability of MLP make it a crucial tool in ML methods. Particularly when traditional ML approaches struggle to effectively model nonlinear problems, MLP often demonstrates superior performance. However, MLP typically relies on gradient descent algorithms during training, which may lead to local optima and degrade model performance. Additionally, MLP suffers from issues such as overfitting and insufficient global optimization capabilities. SMA, with its robust global search and parameter optimization capabilities, can significantly address these shortcomings. Therefore, this study introduces SMA to optimize MLP, constructing a hybrid ML model (the SMA-MLP model). This combination not only enhances MLP's optimization efficiency but also reduces the complexity of manual parameter tuning, enabling the MLP model to adapt more effectively to complex problems.  
Figure 4 illustrates the modeling process of the SMA-MLP framework. By optimizing the connection weights and biases of MLP, SMA enhances the model's predictive accuracy. Specifically, SMA leverages its strong global search capability to prevent MLP from getting trapped in local optima during training. Additionally, it autonomously adjusts the hyperparameters and weight distributions, reducing the need for manual tuning. This optimization strategy not only improves the generalization ability of MLP but also enhances model stability and computational efficiency. Therefore, the integration of MLP's powerful nonlinear modeling capacity and SMA's global optimization capability results in an efficient and flexible hybrid modeling approach.  
Figure 4: Framework of the proposed SMA-MLP model.