esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Binary White Shark Optimizer
### Problem Formulation and Solution Representation  
In this study, the feature selection problem is addressed as a binary optimization task. The primary objective is to identify the optimal subset of features that maximizes classification accuracy while minimizing the number of selected features.  
Each candidate solution is represented as a binary vector with a length equal to the number of features in the dataset, as shown in Equation (1).  
\[X=[x_{1},\ x_{2},\...,x_{n}],\ \ \ \ x_{i}\ \in\{0,1\} \tag{1}\]  
where \(n\) represents the total number of features; a value of \(x_{i}=1\) indicates that the \(i\)th feature is selected, while \(x_{i}=0\) indicates that the th feature is not selected. An example of the solution representation is shown in Figure 2. Figure 2 illustrates the binary vector representation of a candidate solution, where each element corresponds to a feature in the dataset. A value of 1 indicates that the corresponding feature is selected, whereas a value of 0 means the feature is not selected. For example, in the given illustration, features 3, 7, 8 and 19 are included in the selected subset.  
Fitness Function: In feature selection problems, the goal is to identify an ideal subset by minimizing the number of selected features while maximizing classification performance. The features selected by each search agent in the algorithm are evaluated using a fitness function based on the K-nearest neighbor (KNN) classifier to estimate classification accuracy. The fitness function is used by optimization algorithms to measure the performance of solution vectors. The fitness function used in this study is shown in Equation (2).  
\[\downarrow Fitness=\alpha\gamma_{R}\ (D)+\beta\frac{|M|}{|N|} \tag{2}\]  
The fitness function represents the fitness value of the White Shark; \(\gamma_{R}\ (D)\) denotes the classification error rate; \(|M|\) is the number of selected features; and \(|N|\) is the total number of features. The parameters \(\alpha\) and \(\beta\) correspond to the importance of the number of selected features and classification accuracy, respectively. In the literature, they are  
Figure 2: An example of solution representation [41].  
commonly used as \(\alpha\in[0,1]\), \(\beta=(1-\alpha)\)[11; 28; 33; 38]. In this study, alpha was selected as 0.99 and beta as 0.01.  
Classification Process: The K-nearest neighbor (K-NN) classifier is a commonly used method in feature selection problems [11; 28; 33]. The Euclidean distance is used in the K-NN classifier, as shown in Equation (3). The value of K was set to 5 to obtain the optimal feature subset.  
\[distance(x_{a}-x_{b})=\left(\sum_{i=1}^{N}\left(x_{ai}-x_{bi}\right)^{2} \right)^{0.5} \tag{3}\]  
where \(x_{a}\) and \(x_{b}\) denote two instances represented as \(N\)-dimensional feature vectors, where \(N\) corresponds to the total number of features. The term \(x_{ai}\) refers to the value of the \(ith\) feature of instance \(x_{a}\), and similarly \(x_{bi}\) corresponds to the \(ith\) feature of instance.
# Designing a User-Centered Inspection Device's Handle for the Aircraft Manufacturing Industry
## 2 Materials and Methods
### Physical Workload--EMG of the recruited Upper Limb  
The EMG signals of different muscles of the upper limb of the participants were recorded to assess the level of physical workload of the arm. The recording was carried out using wireless 8-channel biosignals Flux HUB (Plux wireless biosignals technologies, Lisboa, Portugal). The sampling frequency was 1000 Hz. The skin preparation and the fixation of bipolar Ag/AgCl electrodes to the participants' bodies were made according to the surface EMG for the non-invasive assessment of muscles SENIAM guidelines [36]. EMG electrodes were placed parallel to the muscle fibers with an inter-electrode distance of 20 mm. A reference electrode was placed on the olecranon. The electrodes were placed on the arm of the subject's dominant side on a selected set of muscles, namely _Deltoideus anterior_ (DA), _Extensor carpi ulnaris_ (ECU), and _Flexor carpi radialis_ (FCR) (Figure 4). The selection of these muscles was based on their functions during the inspection task performance; namely, the DA acts in glenohumeral joint mobilization, as well as scapular abduction and arm abduction, and ECU and FCR are responsible for wrist extension with ulnar deviation and wrist flexion with radial deviation, respectively [37; 38; 39]. Before the experiments of each subject, the maximum voluntary contraction (MVC) value for the three muscles was collected: the subject performed the three contractions lasting three seconds each, with an interval of a three-second break in between; the maximum value of the EMG along this acquisition was considered the MVC value; to measure the MVC of the DA muscle, the subject raised the armed against a fixed object; for the ECU, the subject extended the wrist against a fixed object; and for the FCR, the subject flexed the wrist against a fixed object.  
MATLAB R2022a (version R2022a, Mathworks Inc., Natick, MA, USA) was used to process and analyze the EMG data. The raw EMG signals were amplified, high-pass filtered at 10 Hz and low-pass filtered at 450 Hz, rectified, and smoothed through the digital algorithm Root Mean Square (RMS). EMG data (mean values for each task) were normalized to the MVC and collected for each participant at the beginning of the task performance, according to the recommendations of [38].
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Model of the Propagation
#### 2.1.1 Free Space Propagation  
We speak of propagation in free space when there is direct visibility between receiving and transmitting antennas in the absence of any obstacle. The following formula provides the attenuation [1; 2; 3]:  
\[\text{L}=32.4+20\text{log(f)}+20\text{ log(d)}, \tag{4}\]  
With:  
f: operation frequency (MHz)  
d: distance between receiver and transmitter (km).
## 3 Results and Discussion
### Principal Component Analysis (PCA) of Volatile Profiles during the Dehydration Process  
As an overview of the profile of volatile compounds during the entire dehydration process, the raw peak table was statistically analyzed using unsupervised PCA. The score plot clearly revealed stepwise alterations and distinct differences of volatile compounds from S-0 to S-240 (Figure 2). An obvious difference was observed between S-0 and S-30, and this could be due to the evaporation loss of some volatile aldehydes, alcohols etc., arising from the increased temperature. For example, aldehydes including 2,4-decadienal, 2,4-nonadienal, octanal and 3-methyl-butanal, and alcohols including1-octen-3-ol existed in fresh cane juice, whereas all these aldehyde contents experienced a significant drop from S-0 to S-30.  
While S-30 to S-120 clustered, which perhaps indicates a relatively stable volatile profile with no formation of new volatile compounds during this period, water evaporation is the dominant activity at such a relatively lower boiling temperature (~90 \({}^{\circ}\)C, 225 Torr). Further volatile profiles development was gradually observed from S-120 to S-180, obviously the concentration typical sulfur containing compound dimethyl sulfide increased eight times during this period, which could significantly contribute to the characteristic aroma. The most significant visible profile variation was observed from S-180 to S-210, with a continuous development of sulfur containing compounds from 26.12 to 46.73 \(\upmu\)g/g, and aldehydes from 3.73 to 22.72 \(\upmu\)g/g, ketones from 11.81 to 24.38 \(\upmu\)g/g, heterocyclic oxygen compounds from 6.58 to 20.27 \(\upmu\)g/g, and the initiative formation of nitrogen heterocyclic compounds, such as 1-(1-H-pyrrolo2-yl)-ethanoneone (0 to 0.54 \(\upmu\)g/g) and 2,6-dimethyl-pyrazine (from 0 to 0.58 \(\upmu\)g/g).  
At the final dehydration stage, i.e., from S-210 to S-240, the profiles completely changed with significant increases in oxygen heterocyclic compounds (20.27 to 108.11 \(\upmu\)g/g) and nitrogen heterocyclic compounds (1.12 to 12.27 \(\upmu\)g/g). This provides a clear indication of the further development of these compounds and the reduction in the reactive aldehydes and ketones.  
Figure 1: Venn diagram of 111 differential volatile compounds in nine interval samples from the NCS (non-centrifugal sugar) formation process. (Outer circle represents the volatile compounds that exist only in each sample, and central circle represents the shared volatile compounds that exist in all samples).
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 2 Experiment
### Stimuli  
Stimuli for the hazard identification task were presented in the form of images against a black background on a computer screen. These images, sourced from coal mining enterprise safety training material, depicted various scenes from coal mines, and were categorized into hazardous and safe types, as shown in Figure 2. Hazardous images depicted potential dangers within the coal mining scenes, such as personnel without safety helmets, loose equipment screws, exposed electrical wires, and obstructions in conveyor belts. Safe images, on the other hand, depicted scenes free of any potential hazards.  
Due to the specific requirements of EEG experiments, the coal mine work scene images used for experimental stimuli were standardized to avoid inconsistencies in the difficulty of hazard recognition and disputes over the presence of hazards in the images. A pre-experiment was conducted with 10 participants recruited from the same population as the main study. Participants evaluated 60 hazardous and 60 safe coal mine images sourced from official safety training materials. The evaluation criteria included recognizability and difficulty. Each image was rated on a 5-point scale (1 = very easy to identify, 5 = very difficult to identify).  
Based on these ratings, images with an average score between 5 and 6 were selected to ensure a moderate and consistent level of challenge across trials. Images with extreme ratings (e.g., overly obvious or overly ambiguous hazards) were excluded. Ultimately, 30 images depicting various hazardous situations were selected, involving common issues, such as violations by personnel, hidden dangers at the workplace, and abnormal conditions of production facilities and equipment, ensuring these situations were broadly representative. Additionally, 30 safe scene images were matched to these hazardous situations, each representing the rectification of one of the 30 hazardous conditions, to ensure consistency of the scenarios.  
Numerous studies provide compelling evidence that noise significantly contributes to workplace distraction [25; 29]. To assess distraction, our experiment involved two distinct groups: a control group and a noise group. Participants in the control group performed their tasks in a quiet setting, whereas those in the noise group were exposed to simulated disruptions via noise. According to \"Coal Mine Safety Regulations,\" miners exposed to noisy environments for more than eight hours daily should not exceed 85 dB [10]. Consequently, in our study, participants in the noise group were exposed to 85 dB, ensuring their auditory health while replicating real-world coal mining conditions. The noise, sourced from typical mining equipment, such as ventilation fans, coal cutters, and scraper conveyors, was delivered to participants via headphones at the specified dB level.  
Figure 2: Sample images of hazard recognition task. (**a**) Safe image. (**b**) Hazardous image.
## 3 Results Analysis and Discussion
### Pile Strain and Bending Moment
According to the theory of elastic beam, the curvature of the pile body \(\varphi\) can be obtained using:  
\[\varphi=\frac{1}{\rho}=\frac{\Delta\varepsilon}{h}, \tag{1}\]  
where  
\(\rho\)--curvature radius of the pile body (m);  
\(\Delta\varepsilon\)--difference value of the strain between the two symmetrical sides of the cross-section pile body;  
\(h\)--cross-section height of the pile body (m). Then, the bending moment \(M\) of the pile body can be obtained as:  
\[M=EI\times\varphi, \tag{2}\]  
where  
\(EI\)--flexural rigidity of the cross-section pile (kN-m\({}^{2}\)). The measured pile body strain was converted into a bending moment in accordance with Equations (1) and (2). Then, the result was compared to the pile-body bending moment calculated through finite element analysis after the excavation, as exhibited in Figure 16.  
Figure 16 presents that, after the foundation pit excavation, the maximum negative bending moment of the pile body occurred at 4 m below the ground surface (at \(-129\) kN-m). By contrast, the maximum positive bending moment occurred at 1.5 m below the undersurface of the foundation pit (at 51.9 kN-m in the finite element analysis result). The analysis of the bending moment distribution laws of the pile body after the excavation revealed that the pile-body bending moment above the undersurface of the foundation pit had a negative value. Moreover, the maximum negative bending moment occurred at 0.62 \(H\) below the ground surface. Below the undersurface of the foundation pit, the bending moment of the pile-body was positive, and its maximum bending moment occurred at 0.23 \(H\) below the undersurface of the foundation pit. The pile-body stress-bearing deformation laws in the finite element analysis result agreed with the field test measured result. Therefore, the finite element analysis model was reasonable.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 3 The Software-Defined Media Ontology
### Design Overview  
The following is an overview of the SDM Ontology design. The SDM Ontology defines the media, recorder, processor, player, and context as the primary objects that constitute spatio-temporal media. Five classes, the **Media**, **Recorder**, **Processor**, **Player**, and **Context** are defined to express the above objects. These are called the primary classes in the SDM Ontology. The **Recorder**, **Processor**, and **Player** classes describe the devices and systems that perform the recording, editing/processing, and playback. In contrast, the **Media** class describes the spatio-temporal media data. The **Context** class is the primary class for the general metadata expression to provide semantic information for the primary class objects. The **Context** class is used not only to describe the semantic information defined in the SDM Ontology but also to describe objects that serve as relay nodes for linking various metadata related to external ontologies. Since spatio-temporal information is important metadata to be handled in the SDM Ontology, it is described using the **Geometry** class and **CoordinateSystem** class separately from the **Context** class. The **Geometry** class represents the spatio-temporal information of the primary class object. The **CoordinateSystem** class is used to specify the coordinate system of the spatio-temporal coordinates described in the **Geometry** class object.  
Figure 2 shows the structural outline of the SDM Ontology. Each node in the figure represents a class, with red and blue letters representing the primary and other classes, respectively. The vocabulary written within the nodes represents the data properties, and the black arrows connecting the nodes represent the object properties. The following subsections describe each class in detail. In the following text, \"**class-name** + object\" means \"instance data described according to the **class-name** class definition\".
###### Abstract  
The beneficial effects of adopting a triangular lattice on phased arrays with regular and periodic grids for high-altitude platform station (HAPS) systems are presented in the scenario of massive MIMO communications operating within the 5G NR n257 and n258 frequency bands. Assessment of a planar array with 64 elements (\(8\times 8\)) is provided for both a triangular lattice and a square one in terms of array gain, average sidelobe level (ASLL), and mutual coupling. Particular attention is devoted to illustrating the impact of the antenna array lattice at the system level by evaluating its significant merits, such as its spectral efficiency (SE) and signal-to-interference ratio (SIR). The better performance exhibited by the triangular lattice array in comparison to the square one makes it appealing for the 5G massive MIMO paradigm.  
p 2021 21 32021 5G Massive MIMO Systems for High-Altitude Platform Stations by Using Triangular Lattice Arrays.
# Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
## 3 Model and Experiment
### Performance Indicators  
This paper uses the Mean Absolute Percentage Error (MAPE) to evaluate the magnetic moment modulus. MAPE represents the percentage of the error relative to the true value, is dimensionless, and provides an intuitive measure of the prediction error in relation to the actual value. The calculation equation for MAPE is given in Equation (10), where represents the true value of the modulus and represents the predicted value of the magnetic moment.  
\[E_{M}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{M_{i}-\hat{M}_{i}}{M_{i}}\right| \times 100\% \tag{10}\]  
The Mean Absolute Error (MAE) is a simple and intuitive metric that directly reflects the average difference between the predicted and true values. It effectively measures the performance of the algorithm, as it provides the same impact for the same error magnitude, regardless of the actual value [32]. Therefore, MAE is used to measure the errors of declination and inclination. The calculation of the prediction error for the deflection and inclination of the magnetic moment is represented by Equation (11).  
As in previous sections, \(D_{i}\) and \(I_{i}\) represent the true values of the magnetic moment deflection and inclination, while \(\hat{D}_{i}\) and \(\hat{I}_{i}\) represent the predicted values of these parameters.  
\[E_{D}=\frac{1}{n}\sum_{i=1}^{n}\left|D_{i}-\hat{D}_{i}\right|,\quad E_{I}= \frac{1}{n}\sum_{i=1}^{n}\left|I_{i}-\hat{I}_{i}\right| \tag{11}\]
## 4 Survey of Existing Works
### Ant Colony Optimization
#### 4.2.1 ACO Based Routing Algorithms
The simulation results show that the routing algorithm implemented by ACO can reduce effectively energy consumption. ACO proves to be an effective way to reduce the energy consumption and maximize the lifetime in WSNs. In [80] a routing protocol defined as Biological inspired self-organized Secure Autonomous Routing Protocol (BIOSARP) is proposed to enhance the limitations of Secure Real-Time Load Distribution (SRTLD). SRTLD uses broadcast packets to perform neighbor discovery for every packet transfer every hop, and thus consumes high energy. The BIOSARP routing protocol depends on the optimal forwarding decision obtained by ACO. The pheromone value in ACO is computed based on end-to-end delay, residual energy, and packet reception rate metrics similar to SRTLD. The proposed BIOSARP has been designed to reduce overhead broadcast packet in order to minimize the delay, packet loss, and power consumption in WSN. In simulation study BIOSARP normalized overhead is 12.1% less as compared to E&D ANTS and achieves 14% higher delivery ratio with 9% less power consumption when compared to SRTLD. The demand for real-time application in WSN is increasing day by day. So the quality of service (QoS)-based communication protocols are becoming a hot research area, specially in the case of wireless multimedia sensor networks (WMSNs). In WMSNs the transmission of imaging and video information requires both energy efficiency and QoS assurance (e.g., bandwidth, packet loss and delay constraints). In order to achieve the balance between energy-efficiency and QoS improvements [PERSON] _et al._[81] presented a multiple QoS metrics hierarchical routing protocol (2 ASenNet), with a combination of an improved ACO and artificial fish swarm optimization (AFSO) [82]. It adopted hybrid ant behavior to produce diverse original paths, while adding AFSO for the iterative process of the improved ACO and the optimization path was explored according to multiple QoS constrained. Experimental results indicated the efficiency of this novel approach, while simultaneously reducing the consumption of constrained resources as much as possible.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### The Major Classification of Digital Watermarking Techniques
#### 2.4.1 Application's Perspective  
The watermark could be source-based or destination-based. The source-based watermark could be used to verify the ownership and recognize authorized people, trace the marked document's publishing through the network, inform users about the rights-holder or the permissible use of the data evidence [57]. The buyer-seller identification is a type of destination-based watermark [58; 59] where each distributed copy of the product obtains a unique identification of the watermark that relates to a particular purchaser. Further, the destination-based watermark traces illegal reselling in case a buyer is caught to commit such an action. This act is being used in fingerprinting procedures.
## 1 Introduction
While SiN offers very appealing passive properties, it lacks active functionalities. There is therefore a clear advantage to integrating both Si and SiN within the same integrated platform to benefit from the active properties of silicon, such as, for example, high-speed modulation. We review here the various integration processes and applications of SiN in a silicon photonic platform using the fully CMOS-compatible fabrication line at CEA-LETI based on 200 mm Silicon-On-Insulator (SOI) wafers. Depending on the targeted application and the operation wavelength, different types of SiN are considered: A low-stress, low-deposition temperature, Plasma Enhanced Chemical Vapor Deposition (PECVD) SiNx process is developed for (de-)multiplexing operation of optical transceivers for data center applications in the O-band. An annealing-free, crack-free, close to stoichiometry Si\({}_{3}\)N\({}_{4}\) deposition process is developed for nonlinear generation of a frequency comb in the C-band. Both types of SiN are used to demonstrate two-dimensional (2D) beam steering with OPA for LIDAR application at \(\sim\)900 nm. In the first part, we present the fabrication processes leading to the integration of both types of SiN to the photonics platform and we show optical characterizations of basic properties of the SiN waveguides (propagation losses, thermo-optic coefficient and Si-SiN interlayer transition). Then we detail the main results for the previously cited applications: thermally stable coarse-WDM (de-)multiplexer and broadband grating fiber coupler for optical transceivers, comb generation by a micro-resonator for Kerr-based broadband sources, and 2D beam steering with OPA for LIDAR applications.