esa-sceva/satcom-chunk-collection huggingface dataset:
# New Nonlinear Second-Order Phase-Locked Loop with Adaptive Bandwidth Regulation
## 4 Discussion and Conclusions  
This paper proposes a new second-order PLL structure coupled with one nonlinear element based on the nonlinear controlling idea. The proposed nonlinear second-order PLL conducts adaptive regulation of the loop bandwidth through the nonlinear element and balances between tracking speed and accuracy of traditional PLL. The results confirm that the new PLL not only can accelerate the tracking process, but also can increase the acquisition bandwidth by four times under some configuration of nonlinear element parameters compared with traditional PLL. The simulation results show the advantages of the new PLL. The proposed PLL changes the characteristics of the traditional second-order PLL itself to balance noise characteristics and fast locking performance. It will maintain good compatibility with the traditional communication systems because the proposal requires almost no additional cost. The improved PLL can be used to upgrade the traditional phase-locked receivers. The proposed new phase-locked loop will be able to adapt to large dynamic scenarios such as aerospace communications requisition and tracking, navigation, and energy harvest. Additionally, the proposal can be applied in cellular networks as well as real-time motion detection to improve the comprehensive performance of the modern communication networks. During practical applications in different fields, the parameters of the nonlinear components (e.g., turning point and slope) must be adjusted depending on the actual needs.  
[PERSON] and [PERSON] proposed research ideas and methods; [PERSON] performed the simulations; [PERSON] analyzed the data; [PERSON] wrote the paper.  
This research was funded partly by the National Natural Science Foundation of China under Grants 61871302 & 61473228, National Defense Basic Scientific Research Program of China Grant CKY2016110C040, and Shaanxi National Natural Science Foundation under Grant 2016 JM06.  
The authors declare no conflict of interest.
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 1 Introduction
### Motivation  
EV plays a key role in the advanced transportation system. Furthermore, a rapid increase of EV will not only reduce CO2 emission but also dependency on fossil fuels [8]. However, EV users are still facing some challenges in practical drawbacks, such as the long duration of recharging the battery, limited driving range on a single charge and frequent requirement for recharging battery, higher operating cost, stochastic charging pattern and selecting convenient and appropriate CS for the requested vehicle [9; 10]. There has been some work conducted relating to the charging management of EV in the parking mode and on-the-move mode [11; 12; 13; 14; 15; 16]. These solutions in plug-in charging services have shown their effectiveness and minimized recharging time. However, fast charging technology is still taking around 15 min on battery recharging.  
Therefore, BS can be a better alternative to provide more options in a cost-effective way. The whole process of BS takes 3-5 min in which depleted battery is replaced by a full charge battery [17]. As such, works considering BS have included scheduling technique, BSS placement and deployment [18; 19; 20; 21] issues. However, the research works has been done in a homogeneous BS in which one type of battery is assumed in BSS. Thus, it leads to a lack of compatibility problem because each type of EV battery is different. Therefore, we employ a heterogeneous BS [22] where multiple types of EV battery are available in BSS. Along with this proposing efficient charging mechanism for mobile Battery EV (\(BEV\)) and scheduling technique for depleted batteries recharging.
## 5 Recent Progress
[PERSON] _et al_[39] proposed an energy-efficient multicast routing protocol that was specially designed to minimize the total energy used by the multicast tree. [PERSON] _et al_[40] developed a data transport protocol that reduces the energy consumption associated with data transmission while meeting the QoS requirements in timeliness domain. [PERSON] _et al_[41] proposed a delay-energy aware routing protocol that enables a flexible range of tradeoffs between the packet delay and the energy use. A power-aware many-to-many routing scheme has been proposed in [42]. A low-energy and delay-sensitive TDMA based MAC protocol has been presented in [43]. In line of supporting tools development, an interesting attempt is TrueTime [44], a Matlab/Simulink-based simulator developed at Lund University. TrueTime facilitates co-simulation of controller task execution in real-time kernels, network transmissions, and continuous plant dynamics. It was designed primarily for simulating networked and embedded control systems, and has supported sensor network applications. Another notable attempt is the Agent/Plant [45] module developed at Case Western Reserve University. The module extends NS-2 to interface network dynamics with physical behaviors, making it possible to simulate physical systems that are attached to a network.
## 3 Materials and Methods
### Problem Statement  
Designing and managing quality inspection systems in modern assembly processes presents a complex decision-making challenge. Manufacturers must determine which inspection methodologies to adopt, where to implement them along the assembly line, and how frequently to perform inspections--all while balancing competing costs and operational constraints. These decisions are further complicated by the presence of multiple products and quality characteristics, each potentially requiring different inspection technologies with varying detection capabilities and resource requirements.  
One of the central issues in this context is how to allocate inspection activities to minimize the total cost of quality control. This cost includes the investment and operational costs associated with inspection technologies and the financial impact of undetected defects, which may lead to product rework or penalties. Importantly, the choice of inspection frequency--defined by the sampling rate--plays a critical role in this trade-off: increasing the number of inspected products raises variable costs but lowers the risk of defective items reaching customers.  
Compounding this challenge is the probabilistic nature of defects and the varying effectiveness of inspection technologies depending on the product and characteristic under examination. Moreover, manufacturers must operate within real-world constraints, such as limited time availability for inspection and rework tasks during each production period. As a result, achieving an optimal inspection strategy requires careful coordination across multiple decision layers, including technology selection, resource allocation, and inspection intensity.  
Figure 1 illustrates an assembly station's typical structure where quality controls must be performed. According to the figure, a total demand \(D\) enters the system, and at each assembly station, a subset of products (\(N^{contr}\)) can be selected for quality inspection. Non-compliant units (\(NC\)) may still be present among those not selected for inspection, with a probability equal to \(Def^{prob}\). Moreover, even among inspected units, some defects may remain undetected, depending on the defect probability (\(Def^{prob}\)) and the detection capability (\(Det^{prob}\)) of the inspection methodology employed. All non-compliant units continue through the assembly process and are delivered to the customers. In contrast,  
units found to be defective during inspection (\(N^{rep}\)) are sent to rework to address the detected non-compliance.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
###### Abstract  
Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. However, the complexity of the marine environment and the difficulty in target acquisition limit the detection accuracy of side-scan sonars. To address these issues, this study proposes a small-sample target detection method of side-scan sonar images using the Cycle-Consistent Generative Adversarial Network (CycleGAN) model and the improved YOLOv8 model. First, considering the difficulty in obtaining side-scan sonar target images, the proposed method uses the CycleGAN model to generate pseudo-side-scan sonar images from optical images for data augmentation. Second, the original YOLOv8 model is revised by adding attention mechanisms, employing deformable convolution networks, and updating the loss function to improve the target detection accuracy of side-scan sonar images. Experimental results demonstrated the effectiveness of the CycleGAN model in generating pseudo-side-scan sonar images and better performance of the improved YOLOv8 model in the target detection of side-scan images. Moreover, the combination of data augmentation and the improved YOLOv8 model can remarkably increase the target detection accuracy of side-scan images. The proposed methods can effectively improve the target detection efficiency of underwater sonars in marine surveying.  
s +
Footnote †: journal: Applied sciences  
_Article_  
A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 5 Analyzing the Selected Features
### Heating and Air Conditioning Equipment  
The analysis of HAC usage reveals distinct patterns across various clustering methods, as depicted in Figure 11. This pattern underscores the significant influence of HAC equipment on energy-consumption profiles.  
In the low-consumption profile, most dwellings do not use HAC. In k-means, 66.42% of dwellings fall into this category, a behavior maintained across other clustering algorithms: 71.74% in AHC and 66.37% in SOM.  
Conversely, the medium-consumption profile is characterized by a substantial increase in HAC presence. The proportion of dwellings without HAC significantly drops to around 20% in k-means and SOM and to 25% in AHC. This shift suggests an escalating dependence on HAC as consumption levels rise.  
The trend becomes more pronounced in the high-consumption profile, where the percentage of dwellings without HAC diminishes further. Notably, the proportion of households using more than two HAC units increases markedly in this category, jumping from 13.39% to 48.78% in k-means, 10.96% to 46.51% in AHC, and 12.90% to 40.35% in SOM.  
The decreasing trend of dwellings without HAC in the medium- and high-consumption profiles indicates a significant shift towards increased HAC usage. Consequently, households within these profiles that do not use HAC can be considered out-profiled, deviating from the typical pattern of their respective consumption categories.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Method** \\ \hline ARE & RFE-MLR/FRFS-FRNN \\ TYP & CHI2/FRFS-FRNN \\ BED & CHI2/RFE-MLR/RF/FRFS-FRNN \\ HAC & CHI2/RFE-MLR/RF/FRFS-FRNN \\ DHW & CHI2/RFE-MLR/RF/FRFS-FRNN \\ COE & CHI2/RFE-MLR \\ WPU & RFE-MLR \\ NPE & CHI2/RFE-MLR/RF \\ U18 & CHI2/RF \\ PET & RFE-MLR \\ INC & CHI2/RFE-MLR/RF \\ \hline \hline \end{tabular}
\end{table}
Table 10: Features and the methods by which they were selected.  
Figure 11: Cumulative bar chart of the HAC percentage in each consumption profile. (**a**) K-means, (**b**) AHC, (**c**) SOM.
## 2 Materials and Methods  
### Sample Preparation and Drying  
Apples ('Starkrimson' (S), 'Golden Delicious' (G) and 'Florina' (F) cultivars, Figure 1) were procured from a local apple orchard of Botosani, Romania. Fresh apples were selected, washed and sliced longitudinally, following their vertical axis, into slices 4 mm and 3-7 cmthick using a kitchen knife (after removing the apple core) and then dipped in a lemon salt solution (4%) for 7 min to inactivate browning enzymes, according to the methods described by [PERSON] et al. (2018) and [PERSON] (2019) with minor modifications. The dipping was performed under normal conditions (light and room temperature). The apple slices were removed from the solution and washed with distilled water. After that, the adhering water was removed at the end of immersion by using paper towel. Samples were then loaded in perforated trays and subjected to drying, up to constant weight. Drying was performed by using two types of dryers: oven (Indesit on gas) and dehydrator (Gorenje, electric). The dehydrator controls and keeps the internal temperature constant, while the oven tends to fluctuate the temperature. The oven door was kept slightly open (1 to 2 inches) during drying, and a thermometer was used to check the oven temperature (2017). The drying temperature was 65 \({}^{\circ}\)C; all samples were dried to constant weight (Figure 1). There are numerous studies on apple chip drying at temperatures between 40 and 90 \({}^{\circ}\)C (convective drying) (2018) but also between 35 and 85 \({}^{\circ}\)C (microwave, infrared or vacuum freeze-drying) (2019). According to [PERSON] et al. (2019), drying apples at 65 \({}^{\circ}\)C seems to be a good process to produce healthy, high-quality snacks for both consumers and the food industry.  
Dried samples were cooled down and packed in plastic bags (HDPE) and stored at room temperature. Approximately 194 g of dried apple chips (172 g 'Golden Delicious' cultivar, 201 g 'Florina' cultivar and 209 g 'Starkrimson' cultivar) can be obtained from 1 kg of apples dehydrated in the oven, while from 1 kg of apples dried in dehydrator, an average of about 167 g of dried apple chips (158 g 'Starkrimson' cultivar, 169 g 'Florina' cultivar and 175 g 'Golden Delicious' cultivar) can be obtained. After removing the apple core, between 12 and 16% of the apple content can be lost, depending on the cultivar of apple (10.36-12.91% 'Starkrimson' cultivar, 15.44-16.33% 'Golden Delicious' cultivar, 11.79-19.21% Florina' cultivar).
# Root System and Its Relations with Soil Physical and Chemical Attributes in Orange Culture
## 2 Materials and Methods
### Data Mining
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictive Attributes** & \multicolumn{2}{c}{**Description**} & **Unit** \\ \hline \multirow{6}{*}{**Physical**} & SH & Volumetric soil moisture & \% \\ \cline{2-3}  & MWD & Mean Weighted-Diameter & mm \\ \cline{2-3}  & Macro & Macroporosity & m\({}^{3}\) m\({}^{-3}\) \\ \cline{2-3}  & Micro & Microporosity & m\({}^{3}\) m\({}^{-3}\) \\ \cline{2-3}  & TP & Total porosity & m\({}^{3}\) m\({}^{-3}\) \\ \cline{2-3}  & BD & Bulk density & kg dm\({}^{-3}\) \\ \cline{2-3}  & SPR & Soil resistance to penetration & MPa \\ \hline \multirow{6}{*}{**Chemical**} & pH & pH & - \\ \cline{2-3}  & H\({}^{+}\) + Al\({}^{3+}\) & Potential acidity & cmol\({}_{\text{c}}\) dm\({}^{-3}\) \\ \cline{2-3}  & Ca\({}^{2+}\) & Exchangeable calcium & cmol\({}_{\text{c}}\) dm\({}^{-3}\) \\ \cline{2-3}  & Mg\({}^{2+}\) & Exchangeable magnesium & cmol\({}_{\text{c}}\) dm\({}^{-3}\) \\ \cline{2-3}  & K\({}^{+}\) & Exchangeable potassium & cmol\({}_{\text{c}}\) dm\({}^{-3}\) \\ \cline{2-3}  & P & Available phosphorus & mg dm\({}^{-3}\) \\ \cline{2-3}  & B & Exchangeable boron & mg dm\({}^{-3}\) \\ \cline{2-3}  & Cu & Exchangeable copper & mg dm\({}^{-3}\) \\ \cline{2-3}  & Fe & Exchangeable iron & mg dm\({}^{-3}\) \\ \cline{2-3}  & Mn & Exchangeable manganese & mg dm\({}^{-3}\) \\ \cline{2-3}  & Zn & Exchangeable zinc & mg dm\({}^{-3}\) \\ \cline{2-3}  & CEC & Cation exchange capacity & cmol\({}_{\text{c}}\) dm\({}^{-3}\) \\ \cline{2-3}  & SB & Sum of bases & cmol\({}_{\text{c}}\) dm\({}^{-3}\) \\ \cline{2-3}  & V\% & Base saturation & \% \\ \cline{2-3}  & C org & Organic carbon & g kg\({}^{-1}\) \\ \cline{2-3}  & SOM & Soil organic matter & g kg\({}^{-1}\) \\ \hline
**Goal attribute** & Root & Root volume & cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the physical and chemical predictive attributes and the goal attribute used in the composition of the database aiming at decision tree induction to predict root volume of orange crops. \begin{table}
\begin{tabular}{c c} \hline \hline
**Kappa Statistics** & **Quality** \\ \hline \multirow{2}{*}{0.00} & Very poor \\
0.00–0.20 & Poor \\
0.21–0.40 & Reasonable \\
0.41–0.60 & Good \\
0.61–0.80 & Very Good \\
0.81–1.00 & Excellent \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification based on Kappa statistics. the root volume attribute of both soils was ordered in ascending order and divided into three classes: low, medium and high root volume of orange plants, where root volume values of up to 7.10 cm\({}^{3}\) were classified as low, values from 7.15 to 22.11 cm\({}^{3}\) were classified as medium and values from 22.14-225.9 cm\({}^{3}\) were classified as high.
# Tree-Based Classifier Ensembles for PE Malware Analysis: A Performance Revisit
## 3 Materials and Methods
### Datasets  
One of the most problematic aspects of using machine learning to solve malware detection problems is producing a realistic feature set from a large variety of unidentified portable executable samples. In essence, the dataset used to train machine learning models determines their level of sophistication. Hence, developing a solid, labeled dataset that represents all analyzed samples is more helpful for malware detection. In light of this, we utilize more recent public datasets that depict the characteristics and attack behaviors of contemporary malware:  
1. BODMAS [27] The dataset contains 57,293 malicious and 77,142 benign samples (134,435 in total). The malware samples were arbitrarily picked each month from the internal malware database of a security company. The data were collected between 29 August 2019 and 30 September 2020. The benign samples were gathered between 1 January 2007 and 30 September 2020. In order to reflect benign PE binary distribution in real-world traffic, the database of the security company is also processed for benign samples. In addition, SHA-256 hash, the actual PE binary, and a pre-extracted feature vector were given for each malicious sample, whereas only SHA-256 hash and the pre-extracted feature vector were provided for each benign sample. BODMAS is comprised of 2381 input feature vectors and 1 class label feature, of which 0 is labeled as benign and 1 is labeled as malicious.
2. Kaggle ([[https://tinyurl.com/22z7u898](https://tinyurl.com/22z7u898)]([https://tinyurl.com/22z7u898](https://tinyurl.com/22z7u898)), access on 25 August 2022) The dataset was developed using a Python library called \(pefile\) ([[https://tinyurl.com/w75](https://tinyurl.com/w75) zewvr]([https://tinyurl.com/w75](https://tinyurl.com/w75) zewvr), accessed on 25 August 2022), which is a multi-platform module used to parse and work with PE files. Kaggle dataset contains 14,599 malicious and 5012 benign samples (19,611 in total). The dataset is comprised of 78 input features, denoting PE header files and one class label attribute.
3. CIC-MalMem-2022 [28] Unlike the two above-mentioned datasets, CIC-MalMem-2022 is an obfuscated malware dataset that is intended to evaluate memory-based obfuscated malware detection algorithms. The dataset was designed to mimic a realistic scenario as accurately as possible using reowned malware. Obfuscated malware comprises malicious software that conceals itself to escape detection and eradication. The dataset consists of an equal ratio of malicious and benign memory dumps (58,596 samples in total). In addition, CIC-MalMem-2022 is made up of 56 features that serve as inputs for machine learning algorithms.
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 5 Conclusions  
A novel complexity analysis algorithm called HLZC was proposed for fault diagnosis of rotating machinery. The proposed HLZC can extract the fault information hidden in both low and high frequency components through the hierarchical decomposition. After the fault extraction, we utilized the SVM classifier to recognize different fault types of rotating machinery. To evaluate the performance of the proposed HLZC-SVM method, we used one simulated signal and two experimental signals with different fault types to verify the effectiveness of the HLZC-SVM method in FDRM. The comparison results demonstrated that the proposed HLZC-SVM method yielded the highest average classification accuracy of 94.3% and 94.72% for two cases, which was significantly higher than that of the LZ-SVM method (63.47% and 41.22%) and the MLZC-SVM method (91.72% and 87.82%). This further reinforces the fact that HLZC has certain advantages in fault feature extraction of rotating machinery. The main contributions of this paper include:  
1. LZC was extended to hierarchical decomposition analysis, namely, HLZC;
2. HLZC considered the fault information hidden in both low-frequency and high-frequency components through conducting the averaging and differencing operations;
3. A novel fault diagnosis scheme was proposed by combining HLZC and SVM;
4. The proposed method was verified using both simulated and experimental signals.  
There were some limitations for the proposed HLZC in fault diagnosis applications. First, although the proposed HLZC was demonstrated to be effective for the fault diagnosis of rotating machinery, it requires a large amount of labeled data for feature extraction and training the intelligent model for classification, which is difficult to meet sufficient labeled requirement in real industrial application scenarios. This limitation can be overcome by combing the feature knowledge transfer strategy with HLZC in future work. Second, the proposed HLZC lacks the denoising process to remove strong background noises so that it is difficult to extract the weak fault features from the strong noisy signal, especially at the early fault stage. This issue can hopefully be solved by applying the symbolic dynamic filtering to remove the noise-related fluctuations and reverse the fault-related information in future work.  
[PERSON] conceived the paper. [PERSON] and [PERSON] processed the data and wrote the paper. [PERSON] and [PERSON] edited the manuscript. [PERSON] helped to refined the whole paper and complete the final submission. All authors have read and agree to the published version of the manuscript.  
This research was funded by the Key Project of National Natural Science Foundation of China (grant no. 51535009), the 111 Project (grant no. B13044), and the National Natural Science Foundation of China under grant 51805434, and in part by the Key Research Program, Shaanxi Province under grant 2019 KW-017.  
The authors declare no conflict of interest.All authors have read and agreed to the published version of the manuscript.