esa-sceva/satcom-chunk-collection huggingface dataset:
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 5 Proposed Method
### Full Pipeline  
The final threat detection pipeline is illustrated in Figure 7. We preprocessed all images using [37], wherein we cropped out the unnecessary air space in the image, leaving only the relevant information in the data. The cropped image was fed to the detection model, which predicted the locations of the predicted threat objects. In our final pipeline, we improved the backbone of the Faster R-CNN model by attaching FPN. The predicted regions were cropped out of the image and then fed to the segmentation model, which predicted the classification of each pixel in the region. In our final pipeline, we improved the segmentation model by using the next iteration of the algorithm, called DeepLabV3+ [12]. This latest version of the algorithm introduces atrous depthwise convolution that combines the ideas from atrous convolution and depthwise separable convolution [38] to drastically reduce the network parameters while maintaining, or even achieving better, performance. Furthermore, we also enhanced the model used in the encoder by using ResNext-50 [39] architecture with squeeze-and-excitation (SE) blocks [40]. We made the pixel level predictions threshold to be at 95% so that only the pixels with strong detections were considered. The regions without a pixel-level annotation were discarded. In this sense, the segmentation model acts as a verification model that ensures all predicted regions contain threat objects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIoU** & **DC** & **Precision** & **Recall** \\ \hline UNet & 83.32 & 87.64 & 94.16 & 87.04 \\ PSPNet & 81.54 & 86.71 & 94.68 & 85.05 \\ MANet & 80.81 & 84.46 & 95.87 & 83.69 \\ PANet & 82.28 & 86.62 & 96.38 & 84.82 \\ FCN & 84.42 & 88.48 & **96.48** & 86.86 \\ DeepLabV3 & **86.77** & **90.54** & 93.89 & **91.52** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation performance on the validation set using various evaluation metrics (%).
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 6 Conclusions  
This paper focuses on the dynamic orchestration of service function chains in LEO satellite networks, proposing a DRL-based dynamic SFC orchestration algorithm called PPOSFC. The algorithm takes a service provider profit maximization approach, establishing mathematical models for network node resources and link states, and presents  
Figure 6: Cumulative training rewards with different hyperparameters: (**a**) cumulative training rewards with different learning rates; (**b**) cumulative training rewards with different batch sizes.  
a PPO-based reinforcement learning method. By incorporating time-varying topology information and adjusting reward mechanisms, it effectively achieves joint optimization of SFC deployment and readjustment. Simulation results demonstrate that, compared to existing algorithms, PPOSFC achieves significant improvements across multiple metrics including operator profit, readjustment costs, and deployment success rates. The algorithm can flexibly adjust deployment strategies based on user service request intensity, maximizing operator benefits while ensuring service quality. Future research will explore distributed training, multi-objective weight adaptation, and other technologies to further enhance the algorithm's performance and scalability. Simultaneously, the load balancing of physical resources among satellite nodes requires further attention, particularly regarding the trade-off between load balancing, service quality, and operating costs.  
Conceptualization, Z.Z., H.H., and Y.W.; formal analysis, Z.Z. and H.H.; writing--review and editing, Z.Z.; supervision, H.H. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundations of China, grant number 61821001.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Numerical Simulations and Discussion
### Uniform Linear Array (ULA)  
This simulation parameters are SNR = 10 dB, number of samples \(N\) is 10, and the inter-element spacing is \(d=0.5\lambda\). The scanning step angle is \(0.5^{\circ}\). Figure 3 shows an example of two signals incident on a ULA with \(M=10\) sensors, arriving from different directions (\(\theta=0^{\circ}\) and \(8^{\circ}\)). As can be seen from Figure 3, the PDDA method estimates the angles of the arrival signal accurately, and also provides sharp peaks in the directions of arrival, with negligible side lobes.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Computational Complexity** \\ \hline Capon & O(\(M^{2}\)\(N+M^{3}+M^{2}(180/\delta)\)) \\ MUSIC & O(\(M^{2}\)\(N+M^{3}+M^{2}(180/\delta)\)) \\ ESPRIT & O(\(M^{2}\)\(N+M^{3}+D^{3}\)) \\ Min-norm & O(\(M^{2}\)\(N+M^{3}+M(180/\delta)\)) \\ Pisarenko & O(\(M^{2}\)\(N+M^{3}+M\)\((180/\delta)\)) \\ ME & O(\(M^{2}\)\(N+M^{3}+M\)\((180/\delta)\)) \\ Propagator & O(\(M^{2}\)\(N+M^{2}\)\(D+M^{2}\)\((180/\delta)\)) \\ OGSBI & O(max(\(M\)\((180/\delta)^{2}\), \(M\)\(N\)\((180/\delta)\)) per iteration \\ PDDA & O(\(M\)\(N+M\)\((180/\delta)\)) \\ \hline \end{tabular}
\end{table}
Table 4: Showing the overall computational complexity comparison between PDDA and popular AOA methods.  
Figure 3: Performance estimation of the PDDA method, \(M=10\), SNR = 10 dB, \(N=10\), \(D=2\).
# An Improved CNN Model for Within-Project Software Defect Prediction
## 2 Background  
### Software Defect Prediction  
Figure 1 presents a typical file-level software defect prediction process, which is the prediction process adopted in most recent studies. As the figure shows, the first step of the process is to extract program modules (i.e., source files) from software repositories. The second step is to label program modules as buggy or clean. The labeling criteria are based on post-release defects collected from bug fixing information or bug reports in bug tracking systems (i.e., Bugzilla). A source file is labeled buggy if it contains at least one bug. Otherwise, it is labeled clean. The third step is to extract features from the labeled program modules to form training instances. Traditional features consist of code metrics such as Halstead features [7], McCabe features [8], and CK features [9], and process metrics such as change histories. Recently, AST-based features have also been presented [23; 24]. The fourth step is to build a classification model and use training instances to train the model. Researchers have proposed various machine learning models, such as the decision tree [12], random forest [13], logistic regression [14], naive Bayes [15], and dictionary learning [16] models, as well as several deep learning models including DBN [23] and CNN [24]. The last step is to feed new program feature instances into the trained classifier to predict whether a source file is buggy or clean.  
In the following we will explain some terminologies regarding software defect prediction. A **training set** refers to a set of instances used to train a model, whereas a **test set** refers to a set of instances used to evaluate the learned model. When performing **WPDP**, the training set and the test set come from the same project. WPDP can be performed if defect information regarding current version or previous versions can be obtained. More specifically, in **within-version WPDP** the training set and the test set come from the same version of the same project, whereas, when performing **cross-version WPDP**, the training set and the test set come from different versions of the same project. We focused on WPDP in this study to compare our improved model with existing baseline methods [23; 24; 29; 31]. Specifically, we performed cross-version WPDP on the SPSC dataset in the first experiment, and within-version WPDP on the PSC dataset in the second experiment.
## 4 Results and Discussion
### Complex Viscosity and Loss Factor (Tan \(\delta\)) Results
The 5% WCO-modified sample (Tables 7-10) shows the lowest complex viscosity among the three samples, particularly at 25 \({}^{\circ}\)C, indicating that this concentration provides the highest level of flexibility. The tan \(\delta\) values reflect more viscous behavior at 25 \({}^{\circ}\)C, indicating improved flow characteristics, and more elastic behavior at 15 \({}^{\circ}\)C. The 5% WCO concentration helps the bitumen retain flexibility at higher temperatures, reducing the risk of block cracking, while still offering sufficient stiffness at lower temperatures. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Bitumen** & & & & & & & & & \\
**Type** & **Unmodified** & **4\% SBS** & **5\% SBS** & **4\% AB** & **5\% AB** & **6\% AB** & **3\% WCO** & **4\% WCO** & **5\% WCO** \\ \hline
**Angular** & & & & & & & & & \\
**Frequency** & & & & & & & & & \\
**[rad/s]** & & & & & & & & & \\ \  
In conclusion, as the concentration of WCO increases from 3% to 5%, the bitumen becomes more flexible and less prone to cracking at higher temperatures. The 5% WCO sample offers the best overall balance between flexibility and stiffness, showing the lowest complex viscosity and more viscous behavior at 25 \({}^{\circ}\)C, while maintaining adequate elastic behavior at 15 \({}^{\circ}\)C. This makes 5% WCO the most effective concentration for enhancing the bitumen's performance across a range of temperatures and stress conditions. In summary, the 5% SBS-modified bitumen delivered the best overall performance, offering excellent flexibility and resistance to block cracking across various temperatures and stress conditions. The 5% WCO-modified bitumen also showed improved flexibility, particularly at higher temperatures, but was more prone to cracking under stress compared to SBS. Animal bone ash-modified bitumen, while improving stiffness, increased the risk of cracking at lower temperatures. Overall, 5% SBS proved to be the most effective modifier for enhancing bitumen performance
## 2 Methods
### Analog to Photon-Counting Signal Gluing
#### 2.3.1 \(\chi^{2}\)-Based Gluing
Collect results: Once all batches have been processed, the results from each batch are stored and flattened into individual results, which contain the minimization results. The absolute minimum reduced \(\chi^{2}/NDF\) obtained is then searched and its absolute offset value \(|O|\) tested to lie below a predefined threshold, until a reference minimum \(\chi^{2}/NDF\) is found. From that result on, the algorithm tries to enlarge the window, as long as the \(\chi^{2}/NDF\) does not exceed the reference by 10% or falls anyhow below 1.1 and \(|O|\) remains within one standard deviation of the PC background. The result of that procedure is then a range that is as large as possible within the given constraints and has yielded an acceptable \(\chi^{2}\) from the minimization. The centre of that range is used to switch from the AN to the PC signal (see the black points labeled \"photon-counting limit\" in Figure 10). Figure 10: _Cont._  
The corresponding gluing constants \(g\) and \(O\) are further used to construct the amplitude part of the signals (Equation (15)) and their uncorrelated variances (Equation (16)). Additional correlated uncertainties \(>3\%\) or even \(\gg 3\%\) (depending on the final gluing range chosen) must be assumed due to the uncertainties of the gluing parameter \(g\). For the photon-counting part, Equations (13) and (14) are used. Here, a correlated uncertainty from the selected PC efficiency \(\epsilon\) of \(>3\%\) applies. These correlated uncertainties can be greatly reduced by averaging gluing results over many data sets and studying and correcting their temperature dependencies and general ageing over time [9]. However, such a study is beyond the scope of this work.
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
###### Abstract
Therefore, from the perspective of RPs, the system's evolution over time can be more deeply understood. Recurrence quantification analysis (RQA) technique developed by [PERSON] and [PERSON] [21] is the most commonly used tool in non-stationary time series analysis. It can obtain quantitative information on nonlinear dynamical systems by analyzing the distribution of structures, such as diagonal lines and vertical lines in RPs. Compared with traditional nonlinear analysis methods, RQA has apparent advantages in sensitivity to changing dynamic characteristics, including non-stationarity. In recent years, RQA has been widely used in various fields, such as life sciences, earth sciences, finance and physics [22; 23; 24; 25; 26]. In the field of pathological voice detection that we are interested in, [PERSON] and [PERSON] et al. directly extracted recurrence quantification measurements (RQMs) from the original voice signal to identify normal and distinct pathological voices, with an average recognition of over 90% [27]. [PERSON] et al. [28] introduced the embedding dimension and delayed time into the RQMs and analyzed the accuracy of the combination of different RQMs in distinguishing individuals with and without voice disorders. The results show that RQA has substantial discriminative potential in pathological voice detection. In addition, some studies have pointed out the effectiveness of multi-scale features in pathological voice detection [29]. The perceptual characteristics of the auditory system prompt researchers to explore auditory features for pathological voice detection. Typically, the extracted auditory feature is MFCC, which meets the principles of human auditory perception in the mel scale. According to pathological voice energy distribution characteristics, [PERSON] [30] used the frequency division method to improve the accuracy rate in the bark scale. [PERSON] [31] extracted multi-scale nonlinear features GTSLs in the equivalent rectangular bandwidth scale. These multi-scale features provide better resolution on frequency scales. In this paper, we propose Multi-scale Recurrence Quantification Measurements (MRQMs). The proposed feature can decompose non-stationary, nonlinear complex sequences into a set of frequency subsequence features by multi-scale auditory analysis. MRQMs can effectively resolve the significant fluctuations in nonlinear representations caused by the non-stationarity of speech. Furthermore, it can use the acoustic characteristics of the auditory system to improve the accuracy of its pathological voice detection.
## 2 Experimental Work
### Investigation into Homogeneous Dispersion
#### 2.4.2 FTIR Analysis  
FTIR was used to check for the complete removal of solvent from the modified binder. The test was performed according to ASTM E1552. For FTIR (6700 Nicolet, Thermo Science, Waltham, MA, USA) spectrometers was used to get the asphalt spectra in the range of 400-4000 cm\({}^{-1}\) wavenumber. It can be seen from the FTIR spectra shown in Figure 6 that no new chemical functional groups were formed with the addition of MWCNTs, and all different concentrations of MWCNTs showed a similar trend. Thus, the MWCNTs did not chemically react with bitumen. Methanol (CH\({}_{3}\)OH) was used as a solvent in wet mixing technique to disperse the CNTs in bitumen. In methanol, functional group -OH is present and in FTIR spectrum the peak of -OH alcohol group appears at 3200-3700 cm\({}^{-1}\) wavenumber spectra. In Figure 6, it is clear that, in the FTIR spectra, no peak appears at that range of -OH functional group, which means the solvent was completely evaporated and no effect of methanol was left. Any change in the properties of asphalt can only be attributed to the addition of CNTs in asphalt binder.  
Figure 4: CNTs dispersion in bitumen using mechanical stirrer.  
Figure 5: CNTs dispersion in bitumen using high shear mixer at different magnifications.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 3 Compact Data Structures
### Wavelet Tree  
The wavelet tree is a very useful data structure that serves several purposes. It can be used to adaptively change the entropy measures of the data when it encodes, thereby enabling compressed data representations. It is considered useful for three tasks: (i) adaptive representation of a sequence, (ii) reordering of data elements, and (iii) for the grid of a point's representation [40].  
A wavelet tree is constructed by assigning 0 to half of the symbol and 1 to the other half of the symbol. The same process for symbol assignment is performed recursively on each half of the children, splitting the \(\alpha\)ets at each level. The splitting and symbol assignment process stops when each 03B1 bet set consists of only two symbols.  
In Figure 4, we demonstrate a simple wavelet tree as an example. The representation shows how to assign binary values to the 03B1 bets by dividing them into lower and upper halves. In practical implementations, we only store the assigned binary values; the symbols are shown here only to facilitate understanding of the wavelet tree decomposition process.  
To adaptively change the entropy measure during encoding, different variants can be used to obtain satisfactory compression and space results. The symbol assignment strategy can be changed to obtain better compression results. The wavelet tree is useful in many ways for data compression, especially with Huffman coding. By using a wavelet tree, we can obtain the desired bit pattern by using a specific encoding that change the tree shape.  
Figure 4: Wavelet tree example, with 0 for first half and 1 for second half of the 03B1 bet.  
Inserting or removing 03B1 bet symbols in the original sequence requires changing the shape of the wavelet tree and the bitmaps that are stored at each level. This dynamism can be achieved when we combine the K\({}^{2}\)-tree with the wavelet tree and it is very helpful for data security and compression.
# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
## 1 Introduction
When the traffic tends to be congested during rush hours, the network load of a single RSU is bound to increase sharply, resulting in that the Qos of VUEs cannot be guaranteed. In addition, the services that the VUEs request from the RSUs can be divided into safety services and nonsafety services [14]. Safety services are used to send safety messages, for example, various warning messages the assist vehicles to prevent accidents. Nonsafety services are used for entertainment purposes. Obviously, safety services have a higher priority than nonsafety services. Hence, considering the limited resource of RSU, we must  
Figure 1: Network access in Vehicular Ad hoc Network-Cloud. first satisfy the requirements of safety services. On the other hand, the services requested by VUEs have different requirements for transmission characteristics provided by RSUs, such as delay, jitter and packet loss rate [15]. Therefore, in order to solve the resource allocation problem in the VANET in a more reasonable way, we must take the differences of services requested by VUEs into account, while ensuring the fair allocation of resources. In this paper, we proposes a hierarchical resource allocation scheme based on Nash bargaining game to achieve a fair and efficient resource allocation in VANET. In the first layer, we study the resource allocation between the central cloud and the RSUs' cloud. In the second layer, we study resource allocation between RSUs and VUEs. The major contributions of this paper are as follows:  
* Considering the characteristics of VANET, we propose a hierarchical resource allocation architecture based on Nash bargaining game to ensure the proportional fairness and effectiveness of resources allocation in VANET. * Considering the tidal phenomenon caused by the random movement of vehicles and the limited resources of a single RSU, we study the resource allocation strategy of the central cloud by establishing the Nash bargaining model between RSUs. * Considering the selfishness of VUEs and the difference of services requested by VUEs, we study the resource allocation of RSUs, by establishing the Nash bargaining game model of VUEs and constructing the matching degree index between VUEs and RSUs. * To reduce computational overhead, we transform the resources allocation problem between VUEs and RSUs into two sub-problems: power allocation and slot allocation, according to the time division multiplexing mechanism. The rest of this paper is organized as follows. We formulate this hierarchical resource allocation scheme based on Nash bargaining game in Section 2. Simulations are performed and results are analyzed in Section 3. Finally, Section 4 concludes this paper.
## 3 Management of Electrical Grids by Means of WSANs
### Application of WSANs to Manage the Whole Electrical Grid
#### 3.2.3 WSANs Applied to End Users Management  
In a SG scenario, power grid operators need tools capable of communicating with the energy consumers in order to collect online information concerning their energy consumption behavior [42]. These tools can also be used to remotely control the energy production of distributed generation systems, such as solar panels or wind turbines, placed at the customer sites. All these collected data, issued from the direct communication link with the end user, are needed to enhance the reliability of the electrical grid and to better manage its energy balance by means of different procedures applied at the customer side and, more precisely, DR.  
In general, the main customer side applications that can be cited are Wireless Automatic Meter Reading (WAMR), Advanced Metering Infrastructure (AMI), residential energy management, distributed generation management, building automation, demand-side load management, process control monitoring, and equipment management and monitoring.  
A number of new applications that employ WSANs, such as smart meters [43; 44; 45] or AMI [46; 47; 48], are proposed to establish a link between the network operator and the end users. WAMR [49], which is a typical SG application, offers the ability to remotely monitor the customer energy consumption in order to prevent power disturbances during peak hours, using customer appliances advanced control and load shedding. As mentioned in [50], low-cost and low-power WSANs that use in example ZigBee or Machine-to-Machine (M2M) communications are the most suitable technology for WAMR systems. Moreover, customers are able to access the information issued from these WSANs through web services [51], allowing not only the analysis of their energy generation or consumption (with the aim of helping end users to optimize their consumption habits), but also to remotely control different appliances. Optimization-based Residential Energy Management (OREM) and in-Home Energy Management (iHEM) are also proposed for residential demand management [52]. These solutions, whose aim is to minimize the end-user energy consumption, utilize a central Energy Management Unit (EMU), WSANs and smart appliances.  
Finally, it is important to highlight the increasing number of renewable generation and MGs installed at the end users sites. Thus, the implementation of WSANs to monitor and control customer wind turbines or solar panels, along with energy storage solutions, seems to be necessary in order to increase the reliability of these distributed generation systems [53]. Once again, wireless sensors and controllers using specific communication protocols can be used at MG level to monitor and to connect or disconnect distributed sources or electric loads in order to improve DSM, by adapting the energy production to the customer consumption. In addition, this type of WSANs could also be used for islanding detection, and their main challenge is to provide to the MG the capability to maintain their efficiency after islanding.