esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### Ranging Performance  
The purpose of the RNSS signal is to provide accurate ranging information to the user. The position accuracy at a receiver is determined by the pseudorange error and the user-satellite geometry. The pseudorange error can be modeled as a user equivalent range error (UERE) budget consisting of a delay-locked-loop (DLL) error, multipath error, ionosphere/tropospheric errors, satellite clock/orbit errors, and so on [39]. Among them, the DLL error and the multipath error depend on the signal modulation scheme.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 4 Experimentation and Analysis  
### Description of Experimental Data  
In this paper, fault diagnosis experiments were conducted on Deep Groove Ball Bearings. Through processing methods such as Electrical Discharge Machining (EDM) and drilling, crack damage and spalling damage were created on the surfaces of the inner ring, outer ring, and rolling element of the bearings. The different types of damage were categorized into a total of six types according to the fault type and fault location, as shown in Table 3. The sampling frequency was set to 12,800 Hz, and vibration data from the faulty bearings were collected using an acceleration sensor under three distinct working conditions. The experimental setup is depicted in Figure 10, and the parameters of the working conditions are detailed in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Labels**} & **Bearing Health** & \multirow{2}{*}{**Fault Type**} & \multirow{2}{*}{**Damage Volume**} \\  & **Status** & & \\ \hline
0 & Inner ring fault & Crack Fault & 0.2 mm \(\times\) 0.2 mm \(\times\) 0.2 mm \\
1 & Inner ring fault & Spalling Fault & Calibre: 0.6 mm, Depths: 0.5 mm \\
2 & Outer ring fault & Crack Fault & 0.2 mm \(\times\) 0.2 mm \(\times\) 0.2 mm \\
3 & Outer ring fault & Spalling Fault & Calibre: 0.6 mm, Depths: 0.5 mm \\
4 & Rolling body fault & Spalling Fault & Calibre: 0.6 mm, Depths: 0.5 mm \\
5 & Healthy & None & None \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bearing failure information.  
Figure 9: Schematic of ECA-MRANet.  
In order to adapt to the input of the network, in this paper, the original one-dimensional time domain signal collected by the acceleration sensor is divided into samples of 1024 points each. There are a total of 750 samples of data for each fault type, including 600 samples for the training set, 75 samples for the validation set, and 75 samples for the test set. Data Sets A, B, and C are constructed according to the above method for the data collected under operating conditions I, II, and III, respectively. Specific details are shown in Table 5, in which the label columns are consistent with the corresponding bearing information in Table 3.
## 5 Conclusions  
In this paper, we consider a MISO-NOMA-CR network assisted by SWIPT with the presence of EH users and an eavesdropper. To improve the security of the secondary network, we investigate a beamforming design and optimal PS ratios to minimize the transmit power of the secondary transmitter. Moreover, AN is considered in the proposed scheme to disrupt eavesdropping. The formulated non-convex optimization problem is subject to satisfying QoS constraints of the SUs and EH users, and maximum allowable power of the SU-Tx and maximum acceptable interference with the PUs. We provide a solution based on SDR, and develop performance comparison with baseline schemes such as SDMA, ZF, and a conventional approach without AN. Numerical results show that the proposed AN-aided beamforming and PS-ratio design furnishes secure transmissions of confidential information, and provides better secrecy than that obtained by the scheme without AN. Furthermore, it was shown that the application of NOMA in the proposed network overcomes the drawbacks of the traditional SDMA and ZF baseline schemes.  
[PERSON] conceived and proposed the research idea; All authors designed the experiments; [PERSON] performed the experiments. [PERSON]; [PERSON] and I.K. analyzed the experimental results; [PERSON] wrote the paper under the supervision of I.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) through the Korean Government and the Ministry of Science and ICT (MSIT) under Grant NRF-2018R1A2B6001714.  
The authors declare no conflict of interest.
# Simulation and Design of Three 5G Antennas
## 6 Conclusions and Future Outlook  
This study has presented the design of three high-performance, high-isolation array antennas suitable for 5G communication, including a 5G microstrip array antenna and two MIMO antennas designed for mobile devices with metal frames. While the research has achieved significant milestones, several challenges and areas for improvement have been identified:  
The 5G microstrip array antenna designed in this study, though successful in covering 5G LTE band 42 (3.4-3.6 GHz) with notable improvements in bandwidth and gain, could be further developed into a multi-band antenna to cater to the varying frequency requirements in different scenarios.  
As technology continues to advance, the trend towards larger screens in mobile phones has become more prominent. For metal-frame antennas, it will be necessary to design smaller ground clearance areas and ensure compatibility with a wider range of network generations, including 2G, 3G, 4G, 5G, as well as LTE/WLAN frequency bands. Subsequent adjustments and refinements should be made to address these specific requirements.  
Conceptualization, D.W.; writing--original draft preparation, K.L.; writing--review and editing, D.C.; supervision, L.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 6 Implementation
### CoAP Network  
CoAP networks have been deployed using the CoAP++ framework [41], a CoAP framework which enables the definition of CoAP applications with extended capabilities such as support for group communication, ACLs, observe operation and conditional observe developed by the IDLab research group. The CoAP++ framework allows a run-time definition of ACLs that is used to manage which user can access which resources. This allows custom control, as for example, only enable the data access of the resource (GET method) or the interaction with (PUT method). The ACL can also support cipher suites for DTLS secured communication, including the certificate and public key and virtual resources in the CoAP server. An example of resource virtualisation is the location resource which typically is a virtualised resource that is hosted in the gateway, and not on the device itself. The communication process usually involves high-requirements in terms of communication, processing and memory footprint, which are not available in resource-constrained equipment, such as class 1 devices. The CoAP++ framework can be deployed in more powerful devices such as Raspberry Pi or BeagleBone and allows the use of pre-shared keys in order to avoid using public key operations in DTLS communications and reducing the requirements for performance-constrained environments [42].  
The group communication was presented as an RFC in [43], nevertheless the CoAP group and the observe specification required to observe groups, have not been defined to work together [44]. This means that if a client needs an up-to-date representation of the resources belonging to a group, it has to continuously pull the group or observe the resources individually. In both cases, the client loses the benefits of the observe option and the benefits of the group, respectively. In the CoAP++ framework, the observe option and groups have been integrated and enable the possibility to apply different operations on groups of resources (e.g., average, minimum, maximum and list of values). Although group operations can be optional in some use cases, like, for example, actuating environments, and in other environments such as continuous monitoring of set of resources, the group communication in the CoAP++ framework provides an optimal solution. Any standard-based CoAP server and RD implementation could be used in Appdaptivity, however some optional functionalities not available in other implementations such as the group management and access control available in the CoAP++ framework could be required in some behaviours.
## 6 Analysis of Group 2 Performance
### General Trends: Group 2  
As shown in Figure 8, the moderate negative correlation between plasma intensity and 1-Ui for both CH2F2 and CHF3 suggests that while higher intensities tend to reduce the unused rate, plasma intensity alone is not a strong predictor. Other factors, such as gas composition, chamber conditions, and temperature, may also impact 1-Ui, warranting further investigation to understand these influences better.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gas** & **F-Statistics** & _p_**-Value** \\ \hline CH2F2 & 10.18 & 0.00 \\ CHF3 & 19.15 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 10: ANOVA results for plasma intensity on CH2F2 and CHF3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gas** & **Comparison** & **Mean Difference** & _p_**-Value** \\ \hline CH2F2 & 700 vs. 500 & 0.028 & \textless{}0.001 \\ CH2F2 & 600 vs. 500 & 0.015 & 0.046 \\ CHF3 & 700 vs. 500 & 0.105 & \textless{}0.001 \\ CHF3 & 700 vs. 600 & 0.066 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Pairwise test of mean in plasma intensity levels for CH2F2 and CHF3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gas** & **Correlation Type** & **Correlation Coefficient** & _p_**-Value** \\ \hline CH2F2 & Pearson (Linear) & \(-\)0.124 & 0.000 \\ CH2F2 & Spearman (Rank) & \(-\)0.360 & 0.000 \\ CHF3 & Pearson (Linear) & \(-\)0.172 & 0.000 \\ CHF3 & Spearman (Rank) & \(-\)0.379 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Correlation between plasma intensity and 1-Ui for CH2F2 and CHF3.  
Extreme plasma levels reveal potential non-linear relationships with 1-Ui, where minor intensity shifts have limited effects, but more significant changes produce notable impacts. Future research can consider incorporating non-linear regression or machine learning models to capture these patterns, offering a more detailed understanding of plasma intensity's role.  
These findings can help optimize semiconductor plasma processes, particularly for CHF\({}_{3}\), where higher intensities significantly affect 1-Ui. Carefully calibrating plasma levels with attention to this tendency can improve control over the unused ratio, enhancing the efficiency of reducing exhaust gas.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.4 Xception  
Xception, a significant innovation in deep learning architecture, was a watershed moment in the evolution of convolutional neural networks (CNNs). The letter \"X\" in Xception refers for \"Extreme\", underlining its radical divergence from regular CNNs. This advancement was created to solve the shortcomings of prior models and improve their performance on complicated image analysis jobs.  
The Xception concept is based on tremendous depth and has an innate capacity to catch intricate visual details. The \"Xception Block\" is the network's primary building component. It employs depth-wise separable convolutions in a revolutionary manner to learn features. Xception implements a depth-wise separable convolution, which divides the operation into two steps: depth-wise convolution followed by point-wise convolution. This division improves the model's ability to detect complicated patterns and correlations in data.  
Xception has exceptional capabilities, making it well suited for a variety of tasks in the context of the SFSS, including picture categorization, object identification, and intrusion detection. Its distinguishing characteristics include its capacity to extract fine-grained information from pictures, making it an excellent option for detecting complicated patterns and objects. Furthermore, Xception has a great track record of beating other models on difficult picture recognition tests.  
Xception's deep architecture and new design concepts allow it to excel in identifying intruders in agricultural fields, whether they be animals or people, within the agricultural Guard System. Its extraordinary capacity to record complex visual information corresponds to the system's requirements, guaranteeing precise and efficient detection. When integrated with cloud architecture, this advanced model offers a dependable way of protecting agricultural land from possible dangers.
## 2 Materials and Methods
### Data Collection and Processing  
Data on gas pipeline network emergencies are typically stored as textual or numerical records, especially safety-related documents, which are too extensive for manual processing. Advances in natural language processing technologies have significantly improved text processing techniques, enabling automated handling of textual data and facilitating the digitization of domain-specific knowledge [27]. Numerous countries have established dedicated agencies or websites for managing emergency event data. These platforms provide access to high-quality structured or semi-structured data. Examples include the Pipeline and Hazardous Materials Safety Administration (PHMSA) [28], the Major Accident Hazards Bureau (MAHB) of the Joint Research Centre [29], and the European Gas Pipeline Incident Data Group (EGIG) [30], each maintaining specialized databases. Table 1 provides examples of various data sources related to gas pipeline network accidents.  
At present, China lacks a dedicated regulatory authority or department tasked with developing systems or platforms specifically designed for the collection and analysis of gas pipeline accident data. Emergency data are mainly held by relevant gas companies and specialized investigation teams, with most detailed investigation report data remaining unpublished. This paper extends existing databases by incorporating diverse sources, such as investigation reports from Chinese gas pipelines, legal statutes, emergency plans, training documents, scholarly articles, standards, and news, creating a dataset specifically for emergencies in Chinese gas networks. Investigation reports mainly come from pub  
Figure 2: Framework for constructing and validating a domain-specific lexicon for emergency response in gas pipeline networks.  
lic documents issued by emergency management departments in various provinces and cities, supplemented by additional data from web scraping. Information regarding laws and regulations is sourced from the national legal database. Emergency plans and training materials mainly come from industry websites and specific gas companies, collected through web scraping. The standards literature is obtained from the National Standard Information Public Service Platform using web scraping. News data are extensively collected from Sina Weibo using scraping techniques, while scholarly articles are sourced from open-access academic websites.
## 2 Results and Discussion
### Isolation of APs from Water-Soluble Protein Hydrolysate (RSH) of Red Stingray Cartilages
#### 2.1.3. Gel filtration Chromatography of RSH-I-4  
As shown in Figure 3A, RSH-I-4 was subsequently separated into two subfractions (Frac.1 and Frac.2) by a Sephadex G-15 column. The radicals scavenging rates of Frac.1 on DPPH\(\bullet\) and OH\(\bullet\) were \(81.1\pm 1.98\%\) and \(89.5\pm 2.37\%\) at the concentration of 10 mg protein/mL, which were significantly (\(p<0.05\)) higher than those of RSH-I-4 and Frac.2 (Figure 3B). Smaller peptide fractions generally exhibited a stronger antioxidant activity than their larger counterparts [32; 33]. However, Frac.1 with bigger MW showed a higher activity than Frac.2 did, which indicated that some other factors, such as amino acid residue composition and spatial conformation, also played important roles in the bioactivity of APs.
## 3 Performance Evaluation
### Simulation Study
To guide the damage detection decision, an OC-SVM model was trained using the reconstruction errors for all the TFs as outlined by the methodology depicted in Figure 1. The detection results, based on AE versus PCA reconstruction errors, are summarized in Table 2 in confusion matrix format together with the associated performance metrics. The results demonstrate that the AE outperforms PCA across all critical performance metrics, particularly in recall and precision for the damaged class. While PCA correctly identifies all damaged instances, it struggles with healthy instances due to its difficulty in generalizing the dispersion of the training data. As a result, PCA tends to classify anything outside the training data as \"damaged\". In contrast, the AE not only maintains perfect recall for the damaged class but also significantly improves recall for the healthy class. With its improved ability to distinguish between healthy and damaged conditions, the AE provides a more balanced and reliable model. The damage localization performance of the AE is compared with that of PCA in Table 3. The table clearly shows that the AE consistently predicts the correct damaged floor with perfect accuracy across all damage cases significantly and outperforms PCA in this task. PCA, although it demonstrates some success in identifying the damage location in a few cases, often fails to pinpoint the exact damaged floor, with the incorrect localization being close to the damaged floor. The bar chart in Figure 7 displays mean values of DI along with the standard deviations obtained from the reconstructed transmissibilities by AE. The distinct localization performance for the damaged floor, except for Case 2, is clearly observed in this figure. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**PCA**} & \multicolumn{4}{c}{**AE**} \\ \hline  & Predicted & Predicted & & Metric: & Predicted & Predicted & & Metric: \\  & Healthy & Damaged & Total & Recall (\%) & Healthy & Damaged & Total & Recall (\%) \\ True & 18 & 22 & 40 & 45 & 38 & 2 & 40 & 95 \\ Damaged & 0 & 180 & 180 & 100 & 0 & 180 & 180 & 100 \\ Predictions & 18 & 202 & 220 & & 38 & 182 & 220 & \\ Metric (\%) & Precision: & Precision: & Accuracy: & & Precision: & Precision: & Accuracy: & \\  & 100.0 & 89.1 & 90.0 & & 100.0 & 98.9 & 99.1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Damage detection performance of PCA vs. AE: confusion matrix and the performance metrics. Figure 6: Distribution of the first two DSFs for the training and validation data: (**a**) PCA; (**b**) AE.
###### Abstract  
The discrete tonal bands generated by an electric machine stator current spectrum for the fixed Space Vector Pulse Width Modulation (SVPWM) control have side effects on the acoustic noise and vibration. Thus, reducing the magnitude of dominant harmonics and spreading the harmonic spectrum provide a remedy for this problem. A well-established concept has involved the application of the randomized Pulse Width Modulation (PWM) strategy in power converters to spread the power spectrum of the stator current to minimize its amplitude. Therefore, Random SVPWM (RSVPWM) is a new switching method applied for power converters. For the control of the three-phase inverter, three different RSVPWM approaches are suggested: Random Switching Frequency (RSF), Random Zero Vector (RZV) and Random Pulse Position (RPP). This paper investigates the effect of the fixed and random SVPWM strategies on acoustic noise emitted by an Induction Machine (IM) fed by a three-phase inverter. Simulation results exhibit the validity and efficiency of the RSF_SVPWM strategy to reduce sideband harmonics at different modulation indexes. The proposed PWM strategies evaluate the results of the equivalent experiments on an IM fed by a two-level voltage source inverter. The experimental results of the harmonic current and acoustic noise spectra demonstrate also that the acoustic noise is attenuated and dispersed totally for the proposed RSF_SVPWM compared to the RZV_SVPWM and, RPP_SVPWM strategies and the random PWM modulation.  
a Article  
Academic Editors [PERSON], [PERSON] and [PERSON] Received: 22 February 2025  
Revised: 27 March 2025  
Accepted: 30 March 2025  
Submitted: 23 April 2025  
**Citation**: [PERSON]; [PERSON] Acoustic Noise of Induction Motor Drive with Voltage-Source Inverter by Random Space Vector PWM: Simulation and Experimentation Analysis. _Appl. Sci._**2025**, 15, 4646. [[https://doi.org/10.3390/appr1509464](https://doi.org/10.3390/appr1509464)]([https://doi.org/10.3390/appr1509464](https://doi.org/10.3390/appr1509464))  
**Copyright**: 02 2025 by the authors. Liconsse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Detecting Fake Reviews in Google Maps--A Case Study
### Detection of Fake Reviews  
Detection of fake reviews using an ML model was shown to be effective by many researchers: for example, by [PERSON] et al. [14] and [PERSON] et al. [15]. However, ML algorithms need a dataset of meaningful features to build a good model. To do so, many existing researchers followed the work of [PERSON] et al. [16]. They separated the detection of fake reviews into two approaches and described features that can be used in both:  
* \"Review-centric\", which mostly concentrates on the content of the review itself. This relies on natural language processing (NLP) methods to separate fake reviews from real ones.
* \"Reviewer-centric\", which looks more broadly at the reviewer's account and considers the general statistics of the account, such as the percentage of positive reviews or number of reviews written in a given time.  
The already mentioned research [7] studied the psycho-linguistic differences between real and fake reviews. The researchers observed that fake reviews were more complex and contained more self-references and a greater number of references to the hotel brand. They also contained a greater percentage of positive words.  
In [11], it was shown that algorithms such as support vector machines (SVMs) with Naive Bayes features (NBSVM) and the fine-tuned large neural language model RoBERTA [17] called \"fakeRoBERTA\" [11] models gave a very high performance. Neural networks (NNs) were also  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Authors**} & **No. of** & \multirow{2}{*}{**Fake Percentage**} & **Labeled** & **Natural** & **Public** & **Reference** \\ \hline Amazon & Jindal and Liu & 5.8 M & 0.95\% & Some & Yes & No & [5] \\ TripAdvisor & [PERSON] et al. & 800 & 50\% & Yes & No & Yes & [6] \\ TripAdvisor (Marriott subset) & Yoo and Gretzel & 82 & 51\% & Yes & No & No & [7] \\ Trustpilot & Trustpilot & 9000 & “Balanced” & Yes & Yes & No & [8] \\ Yelp reviews & Yelp & 6.99 M & Unknown & Some & Yes & Yes & [12] \\ Yelp users & Yelp & 1.99 M & Unknown & Some & Yes & Yes & [12] \\ Amazon review dataset & Amazon & 83.68 M & Unknown & No & Yes & Yes & [9] \\ Amazon review dataset & [PERSON], [PERSON], [PERSON] & 233.1 M & Unknown & No & Yes & Yes & [10] \\ GPT-2 dataset & [PERSON] et al. & 40,000 & 50\% & Yes & Half & Yes & [11] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing review datasets with fake reviews.  
shown to be effective, e.g., in [PERSON] et al. [18]. On the other hand, [PERSON] et al. [19] showed that even simple ML algorithms, such as Naive Bayes, SVM, and decision trees, can perform well.