esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process.  
acoustic emission (AE); composite material; carbon fiber-reinforced polymer (CFRP); monitoring; abrasive waterjet cutting; trimming; industry 4.0; smart manufacturing; digitalization; sensor integration; data analytics +
Footnote †: journal: Applied sciences
## 2 Characterization of Wind-Solar Power Generation for Earth Regions
Estimation of renewable resources: To support this analysis, historical datasets of wind and solar resources for the different climatic regions under consideration are compiled to estimate the resources available for each of the main environmental conditions on Earth. This provides patterns on the availability of these renewable sources under specified conditions. Accurate characterization of solar irradiance and wind speed patterns in various geographic zones is vital for estimating energy generation potential and identifying suitable locations for renewable installations. Therefore, a section has been dedicated to the complexities of our data sources and methodologies that underpin the assessment of solar and wind resources. 2. Characterization of the renewable generation system: Typical performance values of the generation systems considered within this study have been taken. It is important to note that it has been assumed that there are no significant disparities in performance between different current commercial models, so typical characteristics have been assumed for solar PV and wind generation. Specifically, a conventional wind turbine and standard photovoltaic solar panels have been used in current simulations, acknowledging their suitability in renewable energy projects worldwide. Renewable energy, namely solar and wind sources, is a cornerstone of contemporary energy strategies. This document comprehensively evaluates the technical specifications, efficiency metrics, and maintenance requirements associated with the solar and wind generation equipment used in our analyses. These details are crucial for assessing renewable energy sources' performance and seamless integration into the broader energy grid. 3. Finally, it is essential to analyze the differences between different climates, considering their annual generation capacity for resources in all climate regions and the peculiarities over the different timeframes. Thus, to estimate the strengths and weaknesses of each resource for the different regions and during the different phases of the year, especially paying attention to Dunkelflauten (DF) periods. The DF phenomenon is well known in countries or regions that rely on energy mixes and is heavily comprised of wind and solar power. Ambitious energy transitions to more renewable energy have made these periods a critical challenge, for example, in Germany in the last few years. Since the decommissioning of nuclear power, this country has suffered peaks in energy prices and relied more on fossil fuels, especially during winter [31]. The following subsections will discuss how data on available solar and wind resources are collected and, secondly, how the potential generation for each defined climatic region is estimated. This calculation will not only be conducted on an annual basis but also across various temporal intervals for further analysis.
## 3 Results and Discussion
### E-Nose Analysis  
The E-nose was employed to differentiate the dry-cured hams and compare the results with those from the GC-MS-O analysis. The examples of the E-Nose response curves in the collection stage were shown in Figure 7. The data obtained from each sensor from 81-100 s was used as the stable value for the subsequent analysis. Figure 8 presents a radar chart depicting the responses (stable value) of the 10 sensors to the volatile compounds in the five ham samples. Sensors W1W, W1S, and W5S exhibited stronger responses to the volatile compounds in BHCH and FZWH compared to the other three samples, suggesting that these hams may contain higher levels of higher sulfur-containing compounds, terpenes, alkanes, and other volatile substances that triggered stronger sensor responses.  
To enhance the interpretation of the data, a PCA was performed on the stable values recorded by the 10 sensors for the ham samples. The PCA results are shown in Figure 9a. The x-axis represents PC1, which accounts for 85.6% of the variance, while the y-axis represents PC 2, which accounts for 6.0% of the variance. The cumulative variance explained by PC 1 and PC 2 was 91.6%, indicating minimal information loss. As shown in Figure 9a, the BHCH group (the most expensive ham sample) and FZWH group (the cheapest ham sample) were clearly distinguished from the other groups by the PCA analysis, while the other three ham groups (SXZH, FXZH, and LTWH) showed a relatively high overlap. Furthermore, the projections of the three primary components of PCA are shown in Figure 9b. The cumulative variance of PC 1, PC 2, and PC 3 was 96.3%, showing a smaller loss of information compared with the projections of the two primary components. However, the data distribution among the five ham samples remained similar, suggesting that while PCA combined with E-nose can effectively distinguish between the most and least expensive hams, it is less effective for distinguishing between all five dry-cured hams. The E-nose,  
Figure 6: The PCA of the aroma-active compounds in the different dry-cured hams based on PC1 and PC2. (The dots with different colors represent the five different dry-cured hams; the ellipses with different colors represent the 95% confidence ellipse for the different hams; the triangles represent the aroma-active compounds.).  
therefore, serves as a quick analysis tool for broad classifications but does not provide the comprehensive and detailed evaluation achievable with a GC-MS-O analysis.  
Figure 8: The radar chart of E-nose response data.  
Figure 7: Examples of the 10 sensors’ response curves. (**a**) BMCH; (**b**) SXZH; (**c**) FXZH; (**d**) LTWH; (**e**) FZWH.  
Figure 9: The PCA results of the of the ham samples by E-nose. (**a**) Projection of the first two principal components of the PCA; (**b**) projection of the first three principal components of the PCA. (The dots with different colors represent the five different dry-cured hams; the ellipses with different colors represent the 95% confidence ellipse for the different hams.).
## 4 Experimental Investigations
### Subjective Experiment for Auditory Localization
#### 4.3.3 Results and Analysis
Under the significance level of \(\alpha=0.05\), the result of the post-hoc was shown in Table 9 and Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**HRTF Species** & **Item** & **Subject** & **Subject** & **Subject** & **Subject** & **Subject** & **Subject** & **Subject** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{Individualized HRT} & Accuracy & \multirow{3}{*}{39.3} & \multirow{3}{*}{62.1} & \multirow{3}{*}{30.3} & \multirow{3}{*}{25.8} & \multirow{3}{*}{39.4} & \multirow{3}{*}{42.4} & \multirow{3}{*}{16.7} & \multirow{3}{*}{12.1} \\  & rate/(\%) & & & & & & & \\ \cline{1-1} \cline{6-10}  & Average & & & & & & & \\ \cline{1-1}  & error/(\({}^{\circ}\)) & 26.97 & 12.80 & 32.58 & 42.88 & 37.88 & 15.83 & 28.71 & 81.71 \\ \cline{1-1}  & Standard & & & & & & & \\ \cline{1-1}  & deviation/(\({}^{\circ}\)) & 30.86 & 20.97 & 29.79 & 38.56 & 47.51 & 23.98 & 29.59 & 56.55 \\ \hline \multirow{3}{*}{General HRT} & Accuracy & \multirow{3}{*}{28.8} & \multirow{3}{*}{18.2} & \multirow{3}{*}{24.2} & \multirow{3}{*}{21.2} & \multirow{3}{*}{37.9} & \multirow{3}{*}{28.2} & \multirow{3}{*}{16.7} & \multirow{3}{*}{3.0} \\  & rate/(\%) & & & & & & & \\ \cline{1-1} \cline{6-10}  & Average & & & & & & & \\ \cline{1-1}  & error/(\({}^{\circ}\)) & 37.8 & 38.94 & 53.94 & 51.80 & 44.62 & 56.21 & 46.21 & 80.76 \\ \cline{1-1}  & Standard & & & & & & & \\ \cline{1-1}  & deviation/(\({}^{\circ}\)) & 43.27 & 49.08 & 50.96 & 49.04 & 53.30 & 57.23 & 45.31 & 52.16 \\ \hlineIt can be found from Table 9 that under the significant level of \(\alpha=0.05\), the localization errors of the individualized HRTFs and the non-individualized ones are significantly different. For the reason that the mean localization error of the individualized HRTFs was less than that of the non-individualized ones, the individualized HRTFs were considered performing better than the non-individualized ones in the binaural localization experiment.
## 1 Introduction
Principal component analysis (PCA) is applied to the composited file. The principle components are classified by the thresholding technique, and the result of the change detection for the lake is obtained. But these methods are only based on spectral information. When they are applied to cases using different sensors, the ability of the method can vary with the changes of spectral characteristics. Recently, context information, especially spatiotemporal context information, has attracted more attention and proven to bring much improvement in monitoring the water surface. It is combined with other techniques to generate chains of processing for better representation of an event. [PERSON] et al. [22] proposed a water surface monitoring method using contextual information. First, permanent water/non-water pixels were detected by judging the statistical consistency between an image point and its neighbourhood. Then, a distance-based classifier was used to map the other pixels with the obtained permanent pixels. Experiments on Moderate Resolution Imaging Spectroradiometer (MODIS) proved its validity and superiority over other unsupervised methods. However, the proposed definition of the statistical equality depended on simple one-dimensional features, which were the means and mediums of temporally adjacent pixels. A pixel was considered to be permanent if it had more than five spatially adjacent and statistically equal pixels. This simple count strategy can reduce the robustness of the method. Moreover, the low spatial resolution of MODIS data also limited its performance in spatial dimension. To resolve these issues, in this paper, we introduce a spatiotemporal context learning (STCL) method and propose a novel work flow for flood mapping. The main objective is to delineate the water surface in an accurate and automatic way. First, a statistical model is built for the contextual information of multi-temporal NDWI. Then, permanent pixels are extracted according to their contextual consistency confidence values calculated from the model. Finally, a Modest AdaBoost (MADB) classifier, trained with the permanent pixels and a variety of spectral characteristics, is adopted to map the image into water and non-water categories. Through making full use of the spatiotemporal and spectral information, the proposed approach improves the ability to map inundated surfaces. The uncertainty caused by the sensor and scene differences is also reduced. Two different multispectral datasets with medium resolution, HJ-1A CCD (30 m) and GF-4 PMS (50 m), are employed for the validation.
## 4 Performance Results
### Performance Indicator
#### 4.1.4 Recall Score  
The recall score represents the proportion of data that the model predicted to be true among data that were actually positive. The formula for calculating the recall score is as follows.  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
A model that always predicts only true positives will have a recall value close to 1. Therefore, the precision score should be compared with the recall score.
## 5 Discussion
### Validation Limitations  
The \(Z_{sd}\) estimations were validated for the in situ dataset ranging from 0.3 to 4.5 m, with a lack of validations for highly turbid or clear waters. Considering that the semi-analytical method has been tested with \(Z_{sd}\) values of 0.1-30 m in coastal and oceanic waters [16], we must further validate its applicability to AHSI data with more turbid waters (i.e., \(Z_{sd}\) \(<\) 0.3 m) in the future.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 4 Numerical Results
### The Impact of Link/Hop Selection Strategies  
We now proceed to investigate the impact of link selection strategies and last hop selection strategies. The former can be utilized to establish a multi-hop connectivity with a given data rate while the latter ensures that the last link in the multi-hop bridge does not produce a bottleneck.  
We start with the link selection strategies in Figure 8a, where the bridge availability is shown as a function of ISD for different target data rates, represented by MCSs. Here, we observe three adjacent lanes with normal traffic conditions. We see that trying to achieve the required data rate over the multi-hop bridge leads to miserable results decreasing the bridge availability drastically to unacceptable values. Thus, we may conclude that the multi-hop bridges even in relatively regular vehicular deployment are inherently opportunistic in terms of the data rate.  
Figure 8b shows the impact of the last hop selection strategy as a function of ISD with the observed environment of three unidirectional lanes with normal traffic conditions. Here, we see that, contrarily to forcing all the links in a bridge to achieve a prescribed CQI, performing it for the last hop results in extreme gains in terms of the data rate as compared to the simplest \"first opportunity\" strategy. Furthermore, we observe that the difference between the advanced last hop strategies is rather mild lying within 5-10%, while the difference between them and the \"first opportunity\" strategies reaches 100% for large ISD and 400-500% for small ISDs. Furthermore, note that the \"best quality\" strategy selecting the last hop based on the best CQI shows the best results. However, quite comparable results are achieved by utilizing MCS 6. For practical implementation, however, lower values are recommended as they lead to better bridge availability.  
We specifically note that the gains of utilizing comprehensive last hop selection strategies are highly sensitive to the system parameters as shown in Figure 9, where the mean data rate is illustrated as a function of the ISD for different vehicles densities and two last hop selection strategies, BQ and PQ with MCS 3. Here, we report an interesting behavior. First of all, for the BQ strategy, the data rates coincide for all three considered vehicle densities. Alternatively, when MCS 3 is enforced, we see the significant impact of \(\lambda\) on the data rate. Specifically, the data rate corresponding to \(\lambda=1/30\) maximized the data rate, while lower and higher values of vehicle densities lead to a lower data rate. This is principally different as compared to the increased data rates for lower values of \(\lambda\) reported for the FO strategy in Figure 6a.  
Figure 8: Bridge availability and data rate. (**a**) different MCSs; (**b**) different last hop selection strategies.
## 1 Introduction  
Today, society is on the threshold of a \"digital\" revolution. Digitalization is happening everywhere and concerns almost all spheres of activity. Trends related to the globalization of remote access technologies (e.g., telemedicine and distance learning) are developing. More and more professions are becoming remote. In such an information environment, it is extremely important to prove the authenticity of the virtual image of a remote user (employee, partner, student, etc.).  
Modern security systems increasingly use biometric parameters as a basis, but their effectiveness and convenience remain questionable: special equipment is required to recognize a fingerprint or iris, and the accuracy of facial identification can be affected by numerous parameters, from lighting, posture, and emotions, to changes in appearance by wearing accessories or a mask.  
One option for solving the problem of reliable contactless authentication is based on the use of the speaker's voice parameters. However, speech parameters are variable depending on the psychophysiological state (PPS), which can negatively affect accuracy.  
A voice image can be associated with a long cryptographic key or password directly used for authentication. Such a binding is conducted using special artificial intelligence (AI) models that allow us to not only perform authentication (recognition of a biometric image) but at the same time protect the cryptographic key or password and the biometric image during storage and transmission over communication channels.  
Biometric authentication has become a critical component of ensuring secure access to systems and data. This approach requires resistance to attempts to extract knowledge from it. The field of biometric template protection (BTP) has evolved to encompass various approaches to hiding, encrypting, and transforming biometric images. The main ones are homomorphic encryption, which allows for encrypted data processing, revocable biometrics, and AI models for linking a key and a biometric image, such as a fuzzy extractor (vault, commitment, embedder) and a neural fuzzy extractor.  
The aim of this study is to develop a neural network model for biometric-based key generation and user authentication based on voice images. In this paper, a new type of neuron called trigonometric correlation neurons is proposed. A correlation neuron is a neuron that can record deviations in the correlation dependence of input data [1]. Thus, a correlation neuron does not analyze the values of features, but instead, the correlation relationships between them. Moreover, the knowledge of such a neuron can be stored in open form since it is resistant to key extraction and biometric data extraction attacks.  
The first correlation neuron model [1] showed high results only for the case when the input features had a strong mutual correlation dependence (positive or negative). In this paper, we propose a neuron model based on trigonometric functions, which allows for working with features that have both weak and strong dependence.
# Well-Being and Social Media: A Systematic Review of Bergen Addiction Scales
###### Abstract  
Does social media addiction impair the well-being of non-clinical individuals? Despite the Internet being able to be considered as a promoting factor for individual empowerment, previous literature suggests that the current massive availability of Information and Communication Technologies (ICT) may be dangerous for users' well-being. This article discusses the relationship between the most used social media addiction measures (i.e., the Bergen Facebook Addiction Scale--BFAS, the Bergen Social Media Addiction Scale--BSMAS) and well-being. A systematic review considering all the publications indexed by PsyInfo, PsyArticles, PubMed, Science Direct, Sociological Abstracts, Academic Search Complete, and Google Scholar databases was performed to collect the data. Ten of 635 studies were included in the qualitative synthesis. Overall, most of the included works captured a negative but small relationship between BFAS/BSMAS and well-being, across multiple definitions and measurement.  
well-being; Bergen Facebook addiction scale; Bergen social media addiction scale; social media addiction [1  
compulsive use and withdrawal, craving, tolerance, interpersonal and health-related problems [19; 20]. The Internet and social media users may expect a lower quality of life and well-being [21] due to having problems with self-care, difficulty in performing daily routines, suffering from pain and discomfort, anxiety, and depression [22].  
Nonetheless, the contributions concerning the relationship between social media addiction (especially Facebook) and well-being have not yet been systematized accounting for the multiple conceptualizations and, thus measures, of well-being adopted across studies. The aim of this article is to offer a systematic review of empirical evidence connecting the Bergen Facebook Addiction Scale--BFAS [23] and the Bergen Social Media Addiction Scale--BSMAS [24], with well-being. Moreover, the size and direction of the relationship between well-being and social media addiction measured through Bergen's addiction scales will be identified and discussed. As it emerges from the literature, we expect a negative relationship between the two constructs [3; 25; 26; 27].  
The paper is organized as follows. In the \"method and procedures\" section, BFAS and BSMAS measures will be presented together with the rationale behind their development. The systematic review methodology and inclusion/exclusion criteria will be described after. In the results section, the included studies will be analyzed and presented in two separate paragraphs. The first one will include the studies that relied on well-being definitions that fall within the dichotomy hedonic-eudaimonic well-being. The second one will instead encompass all the works that are not attributable to this classification. Finally, the results will be critically discussed in the last section.
## 3 Application to Experimental Data
### Evaluation
#### 3.4.4 Influence of Individual Sensors  
The effect of individual sensors on the fused performance is assessed in this study. To this end, fusion is carried out three times, while leaving out the hits of one of the sensors in each run. The results are compared to fusing the full data set. Again, the _product_ fusion rule is applied, and hits are weighted. The results are presented in Figure 14. Each two-sensor subset of inspection methods shows slightly different effects. Apparently, the thermographic data mainly help in detecting the shallow grooves 12-14. However, the same inspection seems to have missed the flaws 8 and 9, because the information from both MFL and ET is crucial for detection here. On the other hand, TT is required to identify most of the shallow grooves in this evaluation. The same observation holds for ET. In contrast, by purposely degrading the MFL data (see Section 3.2), we reduced the positive effect this inspection method has on multi-sensor detection. Still, although this low-quality data has a large set of false alarms, it impairs multi-sensor detection for none of the grooves. On the contrary, MFL improves the detection quality of grooves 9, 13 and 14. Among the deeper grooves, nrs. 1, 4, 5, 6 and 7 are perfectly found using any two-sensor configuration, thus indicating that they are clearly represented in all three measurements. Overall, the evaluation demonstrates that the full set of sensors is required for optimal performance with our data set. Yet, with the right choice of sensors two-source fusion already has the potential to outperform individual detection.