esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the sfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature's 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification.  
title: 2023 by the authors. Liconse 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/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 3 Proposed Biometric System
### Emotion Detector from EEG  
The K-nearest neighbors (KNN) algorithm is a supervised learning classifier, meaning that a training set containing multiple input-output data observations determines the inference of the output of an unseen input object, the test set. In practice, KNN maps objects into images, given a collection of previously memorized training object-image pairs (instances). The principle of KNN is to find the \(K\) closest memorized instances to the recently observed set of features, in other words, to find the known instances that are the most similar to the feature set to be classified. Once the most suitable instances are assessed, the emotion classes that each instance is associated with are registered. The modal class is declared as the predicted emotion of the queried test set.  
Measuring the similarity of training and test sets is the distance between their points, considering that the feature sets can be viewed as arrays. This KNN version uses the method of the Canberra distance, mathematically defined in Equation (5) as \(d_{C}\), where \(u\) and \(v\) denote two points in \(n\)-dimensional space:  
\[d_{C}(u,v)=\sum_{i=1}^{n}\frac{|u_{i}-v_{i}|}{|u_{i}|+|v_{i}|} \tag{5}\]  
The input objects of the classifier are EEG features that are normalized to \([0,1]\). This way, the distances between test and training instances are not biased by a dominant feature. Normalization methods vary, but a common approach is the rescaling from minimum and maximum values, as stated in Equation (6). There, \(x\) represents the whole feature set to be normalized; \(x_{ij}\) is the \(j\)-th element of the \(i\)-th array of EEG features; and \(f_{ij}\) denotes a normalized EEG feature. The equation applies a linear transformation to the vector space containing the set of EEG features:  
\[f_{ij}=\frac{x_{ij}-\text{min}(x)}{\text{max}(x)-\text{min}(x)} \tag{6}\]  
In short, to classify an unobserved test instance, the algorithm determines its \(K\) most similar instances from the observed training set. This step implies two tasks: the computation of the Canberra distance, \(d_{C}\), between the test and every training instance, and then sorting those distances to obtain the \(K\) shortest distances. The \(K\) training instances that present more similarity with the test instance correspond to the \(K\) shortest Canberra distances. The wider the training set, the more Canberra distances are calculated and compared, and thus the higher the computational cost. Once the \(K\) shortest Canberra distances are found, the corresponding \(K\) training instances are selected to proceed with the algorithm. The next step is to register the emotion classes associated with the selected \(K\) instances, finding the most common class. In other words, the \(K\) training instances vote for a class. The most voted class determines the emotion prediction output. Figure 4 shows the mapping of emotions into the [PERSON]'s Cartesian model, where emotions are obtained combining three levels of intensities of valence and arousal.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 3 Results and Discussions  
### LULC Maps  
Figure 4 shows the spatial distribution and visual representation of the LULC changes that have occurred over seven years. In Figure 4a, the 2016 LULC map predominantly shows vegetation cover and also identifies pockets of urban development, indicating the early phases of urbanization. Conversely, the LULC map for 2023 (Figure 4b) indicates an obvious change in the landscape of Celephu. The growth of urban areas has been substantial, with an increase in buildings, roads, and other infrastructure, indicating a rapid pace of urbanization and migration that has taken place in Celephu in recent years. A prominent shift from green space to urbanized areas is notable as demand for residential and commercial space soared. In addition, the implementation of local area plans (LAP) and policies were reflected in the LULC map for 2023.  
Figure 4: LULC maps of Celephu (**a**) LULC map of 2016; (**b**) LULC map of 2023.  
Gelephu LULC maps from 2016 and 2023 showed major land-use changes, concentrated in the southern zones, which comprise the Gelephu Thromde sub-administrative unit. The implementation of the LAP falls into this area. In order to specifically assess the urban extent of the Gelephu Thromde, the study additionally focused on this administrative boundary. Gelephu Thromde LULC maps for 2016 and 2023 are shown in Figure 5. The Gelephu Thromde 2023 LULC map in Figure 6b shows a relatively high concentration of construction activities resulting from the construction of buildings and road networks compared to the 2016 LULC map. The change in vegetation cover up to 2023 is clearly pronounced in the land use class, suggesting a widespread shift in land-use dynamics toward barren land and built-up areas, including minor observations of water body cover.
## 4 Mitigation Methods for Disinfecting AMR Pathogens and Genes in Wastewater Treatment
### Photocatalysis  
Photocatalysis has been widely employed for the removal and inactivation of ARGs in wastewater. In WWT, during photocatalysis, reactive oxygen species (ROS) are generated in the presence of light, which results in the increased permeability of the cell membrane and the oxidation of cell contents such as DNA, RNA and proteins, resulting in the inactivation of microorganisms or their AMR genes [119, 120]. Due to its enhanced efficacy and stability, the production of nontoxic by-products and affordability, TiO\({}_{2}\)-based photocatalysts has been the most popularly applied photocatalysts method for the removal of ARGs from WWT plants [96, 97, 99, 100, 101, 102, 109]. For instance, [PERSON] et al. [109] employed a photocatalytic ozonation process in combination with TiO\({}_{2}\)-coated Raschig rings (in the presence of LED light) for the removal of ARGs from urban MWW in comparison with the single ozonation method. The authors reported that this method was effective for the removal of ARGs such as _blatTEM_, _qnrS_ and _sull_ conferring resistance against antibiotics such as beta-lactam, quinolones and sulfonamides. The efficiency of this method for ARG degradation was found to be -98% under UV irradiation. In another study, [PERSON] et al. [100] aimed for the elimination of ARGs such as _sull1_, _ampC_, _mecA_ and _ermB_ associated with resistances against sulfonamide, ampicillin, Methciillin and macrolide from urban wastewaters using a graphene-based TiO\({}_{2}\) composite photocatalyst in the presence of a solar radiation. The results revealed that this method efficiently removed the _ampC_ gene, but the ARGs were detected to be persistent throughout the treatment process. Likewise, a combination of H\({}_{2}\)O\({}_{2}\)/TiO\({}_{2}\)-based photocatalysts under UV irradiation was employed for nullifying the
# Detailed Design and Aerodynamic Performance Analysis of a Radial-Inflow Turbine
## 3 Preliminary Design
### Preliminary Design Outputs  
Table 4 shows the final geometric dimensions of the turbine stage computed by an iterative process between the preliminary design code and the mean-line analysis code. These parameters will then be used in rotor detailed design code to finalize the radial-inflow turbine geometry for CFD simulation. The final design dimensions for the volume are shown in Table 5. The reference point for the azimuth angle, \(\theta\), was illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline
**Volute** & & \\ Volute inlet area, \(A_{1}\) & mm\({}^{2}\) & 414.8 \\ Volute inlet radius, \(r_{1}\) & mm & 69.0 \\ Volute discharge radius, \(r_{2}\) & mm & 57.9 \\
**Nozzle** & & \\ Nozzle inlet radius, \(r_{2}\) & mm & 55.1 \\ Nozzle discharge radius, \(r_{3}\) & mm & 48.8 \\ Nozzle exit throat width, \(o_{3}\) & mm & 3.6 \\ Nozzle passage width, \(b_{2}=b_{3}\) & mm & 2.9 \\ Nozzle inlet blade angle, \(\beta_{2}\) & deg. & 28.6 \\ Nozzle exit blade angle, \(\beta_{3}\) & deg. & 7.0 \\ Number of nozzle blade, \(N_{N}\) & - & 20 \\
**Rotor** & & \\ Rotor inlet radius, \(r_{4}\) & mm & 40.6 \\ Rotor exit hub radius, \(r_{h5}\) & mm & 7.5 \\ Rotor exit hub radius, \(r_{s5}\) & mm & 21.8 \\ Rotor throat width, \(o_{s5}\) & mm & 4.7 \\ Rotor inlet blade angle, \(\beta_{4}\) & deg. & 90.0 \\ Rotor exit blade angle, \(\beta_{5}\) & deg. & 45.9 \\ Rotor blade thickness, \(t_{r}\) & mm & 1.0 \\ Number of rotor blades, \(N_{R}\) & - & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Output dimensions from preliminary design.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of Loss** & **Empirical Correlation** \\ \hline
**Volute** & \\ Circumferential distortion loss & \(Y_{\theta}=\ \left[{}_{r^{1}:C_{s/r_{3}}-C_{\alpha S}/C_{3}}\right]^{2}\) \\
**Nozzle** & \\ Incidence loss coefficient & \(Y_{inc}=sin^{2}(\alpha_{1}-\alpha^{*})\frac{P_{1}-P_{1}}{P_{3}-P_{3}}\) \\
**Rotor** & \\ Blade loading loss & \(Y_{BL}=\frac{1}{24}[\frac{AW}{W_{3}}]^{2}\) \\ Hub-to-shroud loading loss & \(Y_{HS}=\frac{1}{24}[\frac{k_{B}W_{3}}{W_{3}\sin\alpha_{3}}]^{2}\) \\ Incidence loss & \(Y_{inc}=sin^{2}(\alpha_{1}-\alpha_{1}^{*})\frac{P_{1}-P_{3}}{P_{0}-P_{3}}\) \\ Clearance loss & \(Y_{CL}=\hat{m}_{CL}\Delta P/[\hat{m}(P_{t3}^{\prime}-P_{3})]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Loss models used in the mean-line analysis.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 2 Network Traffic Modelling
### Proposed Traffic Modelling Approach  
The main goal of the presented work is to model the network traffic dynamics to identify various communication states and profile their characteristics. For this purpose, we propose a two-step approach, Figure 2, and detail the methodology in the following.  
Previous studies and literature indicate the projection of the operational work-cycles in the repeated transmission and throughput patterns of the aggregated traffic [10; 14]. It is reasonable to deduce that the repeated patterns provide similar information. That is a large dimension without any significant information gain, which can result in inaccurate modeling due to overfitting. Therefore, the proposed approach aims to exclude similar information and then to model the network traffic and profile the communication states. Accordingly, the goal of modeling the network traffic dynamics can be achieved by defining the following objectives:  
1. Recognizing repeated communication patterns to identify work-cycles with respect to bandwidth consumption and communication intensity.
2. Profiling distinguishable transmission states for identifying the possible state-space in each work-cycle.  
Figure 2: The proposed two-step approach overview; it builds on the network-level modeling method proposed in [10].  
The two objectives are closely linked as the first objective serves as a prerequisite of the second objective. The state-space of the model can be reduced by identifying the work-cycles boundaries and consequently eliminating the dynamic imposed by insignificant and inconsequential variances in different work-cycles.  
The following section details the proposed approach and presents the results for the realization of the two objectives on the data collected from brownfield with aggregated traffic classes.
## 2 Materials and Methods
### Field Data Collection and Laboratory and Numerical Analysis
#### 2.2.3 Numerical Analysis  
A numerical model previously developed and calibrated for the Ria de Aveiro lagoon [43] was used to determine hydrodynamic variables relevant to the establishment of halophytic vegetation (Table 1). A numerical simulation covering the annual period in which the campaigns took place, from 1 October 2021 to 31 September 2022, was performed, and sea surface elevation time series outputted at the nearest point of each salt marsh were considered. In detail, sea surface elevation and soil elevation were combined to identify when each point emerged or submerged, and to determine the hydrodynamic variables:  
1. The mean flood duration (\(FD\)), which denotes the time in hours during which the sampling point is flooded [28], was calculated: \[FD=\sum_{i=1}^{n}d_{i}/n\] where \(d_{i}\) is the duration of a single flood and \(n\) is the number of floods.
2. The frequency of flooding (\(F\)), which estimates the average number of floods that occurred during a reference interval of time [28], was determined: \[F=\ n/T_{\tau}\ \times 100\] where \(n\) represents the number of floods and \(T_{\tau}\) is the time elapsed between surveys.
3. The water depth (\(WD\)), defined as the difference between the level of mean high water (\(MHW\)) and soil elevation, \(z\), was calculated [44]: \[WD=MHW-z\] The hydrodynamic variables were determined every month for the monospecific stands considering the sea surface elevation covering the time between each campaign,while for the topographic profile, the hydrodynamic variables were calculated considering the sea surface elevation annual time series. The annual time series were also used to determine several tidal levels for each salt marsh, namely the highest tidal level (_HL_), the mean high water springs (_MHWS_), the mean high water (_MHW_), the mean high water (_MHW_), the mean high water neaps (_MHWN_), and the mean sea level (_MSL_).
# PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
## 3 Fabric Defect Detection Based on PRC-Light YOLO
### HardSwish Activation Function  
Activation functions are significant in neural networks, introducing non-linearity and enabling the networks to learn and represent complex functions, thereby enhancing the network's representational capacity [34]. In the absence of activation functions, deeper neural networks would be limited to handling linearly differentiable problems [35]. Commonly used activation functions are Sigmoid, ReLU, Swish, Mish, GELU and so on. The YOLOv7 model employs the SiLU activation function, which is expressed as follows:  
\[SiLU(x)=x\cdot sigmoid(x)=\frac{x}{1+e^{-x}} \tag{4}\]  
The computation process of the Sigmoid function involves exponentiation, which undoubtedly increases the computational complexity of the SiLU activation function. However, the Sigmoid activation function can be approximated using a piecewise linear function called HardSigmoid [36], which significantly reduces the computational cost. The HardSigmoid activation function and HardSwish activation function expressions are as follows:  
\[HardSigmoid(x)=\left\{\begin{array}{cc}0,&x\leq-3\\ 1,&x\geq 3\\ (x+3)/6,&otherwise\end{array}\right. \tag{5}\]  
\[HardSwish(x)=\left\{\begin{array}{cc}0,&x\leq-3\\ (x^{2}+3x)/6,&-3<x<3\\ x,&x\geq 3\end{array}\right. \tag{6}\]  
Figure 4: Upsampling process on CARAFE.  
The HardSwish and SiLU activation function output curves are shown in Figure 5. HardSwish and SiLU are differentiable at all points and have no upper bounds, enabling them to avoid overfitting while making the model more generalizable. Furthermore, the segmented function reduces the number of memory accesses, which significantly reduces the latency cost.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 6 Conclusions  
In this paper, we have undertaken an intensive review. At first, we concentrated on MoM and reviewed the studies of other researchers related to MoM and even to other computational methods. We tried to reflect the reasons why MoM investigations are still relevant. We also provided a considerable amount of research related to the use of MoM, as well as various methods used to accelerate the modeling and calculating processes. In addition, we mentioned some of the methods that are used when MoM is combined with other methods. Furthermore, some research in the review contained remarkable examples of using MoM for modeling antennas. Various optimization techniques have also been considered in this review as they are among the most important aspects in modeling antennas. Among these techniques, we considered genetic algorithms (GA) and the studies that employ the combination of MoM and GA in modeling antennas. In addition, we provided a review of reconfigurable and smart antennas followed by wire-grid sparse antennas. We also presented a new method that can be used in both development and modeling of antennas and scatterers, and demonstrated its capabilities.  
Conceptualization, A.A.H. and S.P.K.; methodology, T.R.G. and S.P.K.; software, T.M.N. and A.A.H.; validation, T.M.N. and A.A.H.; formal analysis, S.P.K. and T.R.G.; investigation, A.A.H. and T.R.G.; resources, T.R.G.; data curation, T.M.N.; writing--original draft preparation, S.P.K.; writing--review and editing, A.A.H. and T.R.G.; visualization, T.M.N.; supervision, T.R.G.; project administration, A.A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation project FEWM-2023-0014 and FEWM-2022-0001.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the funding organization policy.  
The authors declare no conflict of interest.
###### Abstract  
In the last 20 years, several articles related to the use of fluorescence excitation-emission matrices--parallel factor analysis (EEM-PARAFAC) to monitor dissolved organic matter (DOM) in drinking- and wastewater treatment plants were published. Noteworthy, its use in respective quality control laboratories remains scarce. To extend its popularisation, in this work, EEM-PARAFAC was employed to analyse the DOM composition changes along the different stages of the drinking water treatment plant administrated by _Societa Metropolitana Acque Torino_. The best PARAFAC model was the one of three components, indicating that the Po River is constituted, mainly, by humic acid-like (HA-L) and tryptophan-like (Try-L) substances, the tyrosine-like ones being negligible (Tyr-L). Results indicated that physical treatments (sedimentation) did not produce a reduction in the PARAFAC scores; however, a 50% decay in 254 nm absorbance was observed. Fluorescent DOM was only removed with chemical treatments, obtaining ca. 70% HA-L scores decay with ozonation and 40% with chlorination. Furthermore, although ozonation degraded HA-L substances, the Try-L scores increased by 25%, indicating the transformation of HA-L into smaller molecules. On the contrary, total organic carbon measurements only exhibited a significant change when comparing the treatment plant's inlet and outlet (approximately a 45% decrease), but not within intermediate processes.  
chemometrics; chlorination; fluorescence spectroscopy; ozonation; quality control
## 6 Practical Implications  
The current article is based on a systematic literature review of selected studies from industry and academic journals. First, the findings presented a list of most potential determinants with the frequency of use in prior studies. However, determinants were utilized in different contexts of each sector for the BIS adoption and acceptance in organizations, but same determinants might lose their significance in other business scenarios. Thus, selection of determinants according to the business and industry requirement is crucial for attaining the better results from BIS projects. Second, the findings of the current study show that the BIS enhances the business value majorly in banking and SME sectors and generally in other industries such as health-care sector, telecommunication, insurance companies, academic, supply chain, and retail chain companies etc. For instance, telecommunication and financial companies' chief concerns are better marketing strategies and promotions with prediction of cost and revenues; they can use the relative advantage and management support. Competitive pressure and regulatory body determinants are main concerns for retail, supply chain, and manufacturing companies. This review would contribute greatly to decision-making processes before the BIS integration in companies. Particularly, this study empowers the industry experts and policy-makers to attain a deeper understanding of the determinants with different dimensions for the successful BIS adoption. Various determinants are highlighted here; can attract managerial attention such as management support, manager's innovativeness/ awareness and managers involvement. To follow these study findings, managers need to consider the crucial measures that lead to the successful adoption of BIS in firms. Organizations can also be aware of the critical threats and risks associated with the BIS adoption in terms of environmental determinants. This study can guide the BI vendors and cloud service providers to pay attention in solving the compatibility, cost, and complexity issues especially for medium to small organizations in developing countries for the BIS investments. According to the findings, firms would consider the organization readiness, perceived usefulness, and compatibility determinants before the BIS integration and refrain from the BI projects' failure.