esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article evaluates two essential non-destructive electromagnetic techniques, magnetic flux leakage (MFL) and eddy current (EC) methods, and their effectiveness in assessing the basic parameters of reinforced concrete (RC). The study compares both systems' hardware and software components, emphasizing the adaptations implemented to tailor the methods for evaluating RC structures. Subsequently, the measurement results are analyzed, and association rules are extracted to demonstrate the relationships between variations in the physical parameters of the tested structure and the features of the measured waveforms. Finally, similar identification models are implemented, and the obtained identification results are compared. The paper documents and details all phases of this research. The findings indicate that while the operational principles of both methods are similar, the techniques differ significantly in terms of their measurement systems' complexity and usability. The eddy current (EC) method exhibits superior spatial resolution, whereas the magnetic method is more straightforward and offers a greater effective range and favorable association rules. Consequently, it is recommended that both techniques be utilized for different structures and in varying contexts. The techniques' advantages, disadvantages, and limitations are discussed in this work and supported by the measurement results.  
Keywords:reinforcement bar evaluation; rebar; reinforced concrete; machine learning; feature extraction; association rule extraction; parameter identification; nondestructive testing NDT; magnetic flux leakage (MFL) method; anisotropic magneto resistance (AMR) sensor +
Footnote †: journal: Applied Sciences
## 4 Simulation Configurations
### Simulation Setup for Pathological Tremors Control  
The model defined in Section 2.2, Equation (2)--which is a synthetic signal similar to [PERSON]'s tremor--was used in order to generate the synthetic signal. This signal simulates the tremor of [PERSON]'s disease, as can be seen in Figure 4. The frequency values used for the sinusoidal sum were \(5.2\) Hz and \(4.6\) Hz.  
Figure 5 represents the Fourier transform of the synthetic [PERSON]'s tremor signal shown in Figure 4. Note the occurrence of the peaks at approximately \(4.5\) Hz and \(5.5\) Hz.  
Unlike linear systems which can be uniquely identified by their impulse response, nonlinear systems need additional steps. In this way, we require nonlinear processing. Therefore, we need a transformation in input space to guarantee the modeling of the nonlinear part of the system.  
Figure 4: [PERSON]’s tremor signal in the time domain.  
The linear filter is \(\mathbf{S}(z)=[0.51423\ 0.92868]^{\top}\). The desired signal \(d(n)\) was generated according (2). The input signal \(x(n)\) is the white Gaussian noise transformed by sinusoids. We clarify that, in this case, P(\(z\)) is not displayed because it is a non-linear system composed of the sum of sinusoids. The need to change the signals of the primary and secondary paths by sinusoids was due to the nonlinear characteristic of the desired signal \(d(n)\). This is an adaptation, called here the FsinX-LMS algorithm, given that the dynamics of the [PERSON]'s disease signal are non-linear and deterministic. A Gaussian white noise generator would not be able to generate a control signal for [PERSON]'s tremors, given its linear nature. We used the step-size, \(\mu=0.05\) in the primary path and \(\mu=0.08\) in the secondary path.
## 3 Validation
### Case 1  
A well-known case used in many studies [1; 6; 10; 43; 44; 45; 46], is preferred for the statistical validation of the numerical model. Parameters of the model are given in Table 1 and Figure 1.  
Coordinates of points L and M on a cable under catenary action are (121.92 m, \(-\)29.276 m) and (152.4 m, \(-\)30.48 m), respectively. An external load 35.586 kN is applied at point L of the cable after taking its catenary form under self-weight. Displacement of point L after the application of external load is used as the validation parameter in the literature.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Total cable length & 312.73 m \\ Distance between supports & 304.8 m \\ Unstretched length A-L & 125.88 m \\ Unstretched length A-M & 156.365 m \\ Elastic modulus & 131 GPa \\ Cross-sectional area & 548.4 mm\({}^{2}\) \\ Self-weight & 46.12 N/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in case 1.  
Figure 1: Model of case 1.  
For the numerical model, the number of particles is found to be 312 after a convergence test. The cable is initially defined as linear and the right support is initially located at 0 m and 312.73 m. By moving the right support to its original position (0 m, 304.80 m), the cable is allowed to sag to its natural catenary form. After oscillations vanish and the cable reaches its final form under self-weight, the external point load shown in Figure 1 is applied at point L. Figure 2 shows the time history of the positions of L in y direction. External load is applied at the 150 th second when the oscillations completely vanish. Final displacement of point L can be seen in Table 2. According to the table, calculated final displacement of point L is consistent with the data in the literature. Since the support is moved instantly at the beginning of the simulation, the initial oscillations are very high as can be seen in Figure 2. High initial oscillations may be avoided by defining the initial configuration of the particles similar to the natural catenary form of the cable. However, the oscillations do not create a convergence problem. In addition, they completely vanish thanks to the damping parameter proposed in the present study.
# The Effect of 3D TVs on Eye Movement and Motor Performance
## 3 Results
### Eye Movement
#### 3.1.4 Time to First Fixation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Fixations**} \\ \hline  & **Level** & **Mean** & **Group \({}^{\mathbf{a}}\)** & **SD** & **F\({}_{\mathbf{n,m}}\)** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Environment} & 2D Screen Displays & 2.686 & A & 0.481 & \multirow{3}{*}{F\({}_{1,11}\) = 1.726} & \multirow{3}{*}{\(p\) = 0.216} \\  & 3D Stereoscopic Displays & 2.899 & A & 0.822 & \\ \hline \multirow{3}{*}{Depth} & 210 cm & 2.813 & A & 0.791 & \multirow{3}{*}{F\({}_{2,22}\) = 0.064} & \multirow{3}{*}{\(p\) = 0.938} \\  & 190 cm & 2.777 & A & 0.657 & & \\ \cline{1-1}  & 140 cm & 2.745 & A & 0.583 & & \\ \hline \multirow{3}{*}{ID} & 2.8 bits & 2.580 & A & 0.430 & \multirow{3}{*}{F\({}_{5,55}\) = 15.022} & \multirow{3}{*}{\(p\) = 0.000} \\  & 3.3 bits & 2.543 & A & 0.587 & & \\ \cline{1-1}  & 3.7 bits & 2.883 & B & 0.449 & \\ \cline{1-1}  & 4.2 bits & 2.875 & B & 0.518 & \\ \cline{1-1}  & 5.1 bits & 2.627 & A, B & 0.871 & & \\ \cline{1-1}  & 6.1 bits & 3.249 & C & 0.831 & & \\ \hline \multicolumn{2}{l}{Environment \({}^{*}\) Depth} & & & & \multicolumn{1}{c}{F\({}_{2,22}\) = 1.055} & \multirow{3}{*}{\(p\) = 0.365} \\ \hline \multicolumn{2}{l}{Environment \({}^{*}\) ID} & & & & \multicolumn{1}{c}{F\({}_{5,55}\) = 3.171} & \(p\) = 0.014 \\ \hline \multicolumn{2}{l}{Depth \({}^{*}\) ID} & & & & \multicolumn{1}{c}{F\({}_{10,110}\) = 0.973} & \(p\) = 0.471 \\ \hline \multicolumn{2}{l}{Environment \({}^{*}\) Depth \({}^{*}\) ID} & & & & \multicolumn{1}{c}{F\({}_{10,110}\) = 0.660} & \(p\) = 0.759 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Means, SDs, ANOVA, and Tukey HSD test results of number of fixations.
## 1 Introduction  
The (E)-1,3-diphenylpropen-1-ones, best known as chalcones, belong to the flavonoids family and are an important class of natural products across the plant kingdom [1]. Structurally, these compounds contain two aromatic rings, bonded by a three-carbon \(\alpha\),\(\beta\) unsaturated carbonyl bridge (Figure 1), which are synthesized in plants as the C15 key intermediate in the biosynthesis of the other flavonoids [2]. Flavanones are also naturally occurring compounds and are chalcones' isomeric forms. In fact, the equilibrium between chalcones and flavanones is common in nature and is regulated by chalcone-isomerase [3].  
Besides their natural occurrence, both chalcones and flavanones can be obtained synthetically and are often used as the preferred starting material for the synthesis of other polycyclic aromatic compounds [4]. Furthermore, they present great pharmacological potential with a wide variety of biological activities, including antioxidant [5], anticancer [6; 7; 8], and antimicrobial activities [9; 10; 11; 12], and also the ability to treat cardiovascular diseases and their risk factors [13; 14; 15; 16], among others [17].  
On the other hand, cancer, neurodegenerative diseases, oxidative stress-related diseases and multi-resistant bacterial infections are, after cardiovascular diseases, the top four health problems that cause the most victims every year, leading to higher medicine consumption and putting great pressure on the national health systems of many countries [18; 19; 20; 21; 22; 23; 24]. These problems occur either due to a lack of effective medicines to treat diseases, such as neurodegenerative ones, or due to the increasing drug resistance presented by numerous pathogenic bacteria and by some cancers. Therefore, exploring well-known scaffolds as lead compounds will help in the battle against diseases that affect humanity.  
Putting together the facts stated above, the synthesis of chalcone-based functionalized derivatives remains a popular research objective. The most common and efficient approach to obtain the chalcone nucleus is the aldol condensation of substituted acetophenones with proper substituted benzaldehydes in the presence of a base, namely sodium or potassium hydroxide [25; 26; 27; 28]. Despite the efficiency of this method, when planning a synthesis some drawbacks should be considered. For instance, the protection of the reagents' hydroxyl groups should be done previously, the acetophenone hydrogen \(\alpha\) acidity should be analyzed, and by-products can be obtained if the bases are also good nucleophilic species [29; 30].  
In this regard, the objective of this work is to synthesize hydroxy- and/or methoxychalcones by aldol condensation, using the less common bases sodium hydride and lithium bis(trimethylsilyl)amide. Also, this work studies their antioxidant, anticholinesterase, antibacterial and antitumor activities, aiming to establish some potential medicinal applications. Simultaneously, a structure/activity relationship was established, and the isomeric equilibrium chalcone-flavanone was also studied.
## 1 Introduction
The applicability of the method proposed is demonstrated through the AlSi10 Mg case study, which is employed in Sections 2 and 3 to provide concrete examples of its utility. Section 2 introduces the underlying experimental dataset for AlSi10 Mg, the BWMD Ontology and a flexible methodology for digitalizing MSE domain knowledge, supported by representative examples. An interview template is also included to facilitate interaction with domain experts. The resulting data structures and datasets are summarized in Section 3, presented through real-life use cases for knowledge extraction. A first step toward AI-assisted prediction of material properties is taken based on the generated knowledge base, demonstrating its machine readability. Finally, the results are discussed in Section 4 and concluded in Section 5.
## 2 Materials and Methods
### Methods  
The proposed apnea/hypopnea detection algorithm involved using the oronasal thermal airflow signals with a sampling rate of 51.2 Hz, including annotations by clinicians (Figure 1). The algorithm scans airflow signals by applying a windowing function, W:  
\[\text{W(t)}=\text{rect}\bigg{[}\frac{\text{t}-65}{130}\bigg{]}; \tag{1}\]  
where rect represents a rectangular pulse \(-\frac{\text{T}}{2}<t<\frac{\text{T}}{2}\) and T is 130 s. Generally, in apnea/hypopnea episodes, the drop in blood oxygen level lasts from 10 s to 1 min or even longer [28; 29]. Due to the fact that apnea/hypopnea events can last as long as 120 s [30], a 130 s window frame is an appropriate choice for clear observation of apnea/hypopnea episodes [25; 31; 32; 30]. The observing window frame shifts 5 s each time as \(\text{W(t}-5\text{n}),\text{ n}\in\text{Z}\). Next, it finds all the peaks of airflow signals within a 130 s window. Then, fuzzy logic is applied to compute the risk scores by fusing the SD of airflow signals (SDAs) and ACP [25] indexes. The AHI value is determined according to risk scores. The details of each block are as follows.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 5 Building the Cloud Detector
### CNN Architectures for Cloud Detection  
One real-world example of a satellite-borne CNN-based cloud detector is Cloud-Scout [5]. As shown in Figure 4, the architecture is made up of two core layers: feature extraction and classification. The feature extraction layer is made up of 4 blocks of convolutional layers, each having a different number of filters, kernel sizes, batch normalisation and pooling operators, and rectified linear unit (ReLU) activations, whereas the classification layer is made up of two fully connected layers with ReLU activations. The CNN takes as inputs 3 bands of the preprocessed data cube and outputs a binary response of whether the data cube is _cloudy_ or _not cloudy_.  
The use of 3 bands to perform cloud detection was simply due to a limitation in the compiler for the EoT AI board, where only inputs with a maximum of 3 bands are supported. However, in Section 8.1, we investigate the effects of the domain gap by  
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline \multicolumn{6}{c}{**Sentinel-2 (13 Bands)**} & \multicolumn{6}{c}{**Landsat 9 (11 Bands)**} \\
**Spectral Bands** & **CW** & **BW** & **SR** & **Spectral Bands** & **CW** & **BW** & **SR** \\  & **(nm)** & **(nm)** & **(m)** & & **(nm)** & **(nm)** & **(m)** \\ \hline B01 - Coastal Aerosol & 442.7 & 21 & 60 & B01 - Coastal Aerosol & 443 & 16 & 30 \\ B02 - Blue & 492.4 & 66 & 10 & B02 - Blue & 482 & 60 & 30 \\ B03 - Green & 559.8 & 36 & 10 & B03 - Green & 561.5 & 57 & 30 \\  & & & & & & \\  & & & & & & \\ B04 - Red & 664.6 & 31 & 10 & B04 - Red & 654.5 & 37 & 30 \\ B05 - Red Edge 1 & 704.1 & 15 & 20 & & & \\ B06 - Red Edge 2 & 740.5 & 15 & 20 & & & \\ B07 - Red Edge 3 & 782.8 & 20 & 20 & & & \\ B08 - NIR & 832.8 & 106 & 10 & & & \\ B08A - Narrow NIR & 864.7 & 21 & 20 & B05 - NIR & 865 & 28 & 30 \\ B09 - Water Vapour & 945.1 & 20 & 60 & & & \\ B10 - SWIR - Cirrus & 1373.5 & 31 & 60 & B09 - Cirrus & 1373.5 & 21 & 30 \\ B11 - SWIR 1 & 1613.7 & 91 & 20 & B06 - SWIR 1 & 1608.5 & 85 & 30 \\ B12 - SWIR 2 & 2202.4 & 175 & 20 & B07 - SWIR 2 & 2200.5 & 187 & 30 \\  & & & & B10 - Thermal & 10895 & 590 & 100 \\  & & & & B11 - Thermal & 12005 & 1010 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spectral bands of Sentinel-2 (equipped with Multispectral Instrument) [7] and Landsat 9 (equipped with Operational Land Imager 2 and Thermal Infrared Sensor 2) [78]: blue text indicates the 8 bands that closely overlap between the two datasets in terms of central wavelength (CW) and bandwidth (BW).
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### CFAR Detector Based on K-Distribution
#### 2.2.2 CFAR Ship Detection Based on the K-Distribution  
The CFAR algorithm determines the threshold based on the statistically determined characteristics of the image pixels. The basic principle is that, for each pixel \(x_{i}\), a certain reference window around it is defined and threshold \(l_{c}\) is determined based on the characteristics of the pixels in the window. Target detection can be achieved by comparing the value of the pixel with the value of the threshold. The sea background statistical model uses the K-distribution, and the specific process is as follows:  
1. The scale parameter a and shape parameter \(v\) of the K-distribution are calculated using Equations (3)-(5).
2. The parameter estimations are used in Equation (1) to obtain the probability density function that is used to solve Equation (7) for \(l_{c}\), as follows: \[P_{fa}=1-\int_{0}^{l_{c}}p(x)dx,\] (7) where, \(P_{fa}\) is the false alarm rate.
3. According to the detection threshold \(l_{c}\) obtained by Equation (7), we can determine whether the pixel \(x_{i}\) to be detected in the target window is a target pixel based on Equation (8): \[x_{i}>l_{c},\] (8) if the equation is satisfied, then it is categorized as a target; otherwise, it is categorized as background.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 2 Operation Mode and Coordination Control of Microgrid in Forest Area
### Operation Mode of the Forest Microgrid
#### 2.2.4 Load-Shedding Mode  
When the power deficiency lasts and the load demand is still unable to be met, even if the energy storage is depleted, the wind turbine releases its kinetic energy to temporarily extend the operation time of the load. Under long working conditions of low power input, the load needs to be shed according to priority.
# Application of Machine Learning Methods for Pallet Loading Problem
## 6 Conclusions  
This paper shows an application of ML methods in a warehouse for the PLP. Because of the global competition faced by large companies such as DHL, shipment issues must be addressed in an optimal manner to save costs, time, and energy. In logistics, storage and transportation are two key elements, as they increase the company's expenditure. Reducing the storage and transportation costs may increase the profitability of the company. Subsequently, the use of pallets plays an important role in the transportation and storage of many products, and this process facilitates the efficient shipment of any product(s) by allowing them to be stored in any given facility. To solve the PLP for better facilitation, various studies have been carried out over time. In these studies, different methods have been attempted for efficient pallet loading consisting of boxes of the same or different sizes. The PLP involves many different factors, thereby making the solution NP-hard. Therefore, various methods have been developed for the solution of the PLP in the literature. In this paper, we developed a method to speed up the pallet-loading process. We utilized the three basic pallet classifications using ML. Our results provide a framework for shipment designers to decide which pallet to use when the box dimensions and demand are given. These results are significant in terms of saving considerable costs, time, and other resources.  
However, our study has some limitations: it can be applied to only three of the most commonly-used standard pallet sizes. Consequently, our algorithm can be applied to only 1 in 15 real instances that satisfy these criteria.  
For future research, we aim to develop an AI-based model that can efficiently predict a solution for pallet loading with variable-sized boxes. In addition, several other factors, such as moisture and PUV, will be considered. We also envision the application of neural networks to help us determine pallet sizes in the case involving more than three classes.  
[leftmargin=*]  
B.L.A. conceptualization, methodology, M.I. and O.O.: software, M.I. and O.O.: methodology, M.S.: data acquisition, writing--original draft preparation, B.L.A. and N.A.: writing--review and editing, G.S.: supervision, writing--review and editing, B.S.: funding acquisition, writing--original draft preparation. All authors have read and agreed to the published version of the manuscript.  
This study received funding from King Saud University through researcher-supporting project number RSP-2021/145, and the APC was funded by King Saud University through researcher-supporting project number RSP-2021/145.  
Not applicable.  
Figure 13: A comparison of PUV values according to pallet sizes for the 1 K instances for which the algorithm can be applied.  
Data Availability StatementThe data used to support the findings of this study are included within the article.
# Tin Whisker Growth Inhibition in RE-Doped Sn-Zn Soldered Joints
## 1 Introduction  
With the increasing requirements of environmental protection in the electronics industry, Sn-Pb series solder were banned in electronic packaging products, therefore the development of lead-free solders has attracted special attention in the past years [1; 2; 3]. Among all the lead-free solders, Sn-Zn series solders have the advantage of relatively low melting temperature, higher mechanical properties, and a relatively low cost [4; 5]. However, poor wettability limits the application of Sn-Zn solders in industry due to the easy oxidation of zinc [6]. In the previous research, a small addition of rare earth elements (RE) can evidently improve the wettability and mechanical properties of Sn-Zn solders [7], however, an excessive addition of RE would be harmful to the solder which may induce the tin whisker growth phenomenon.  
Tin whiskers are 10-500 \(\upmu\)m long pure tin crystal filaments which can cause short circuits in electronic devices. It is reported that the tin whisker growth phenomenon has caused great losses in satellites, missiles, radars, and other equipment during the past decades [8]. Although a tin whisker would bring immense risk of device failure, the uncertainty of whisker growth under various conditions brings great difficulties to the study. The compressive stress has been considered as the main reason which induced the tin whisker growth, nevertheless, the mechanism of tin whisker growth remains unclear. In consideration of the high tin content in lead-free solders, tin whisker growth in lead-free soldered joint should be paid sufficient attention.  
Recent research has discovered that tin whiskers grow in solder with excessive addition of rare earth element, which provides a novel approach to explain the mechanism of whisker growth [9; 10]. In this paper, the behavior and mechanism of tin whisker growth in Sn-Zn series soldered jointsbearing rare earth Nd were investigated. The results could provide a feasible method to inhibit tin whisker growth, and the method has been successfully tested over a long time aging treatment.
## 5 Conclusions  
This paper aims to design an electro-optical up-converter based on a sampling SOA-MZI used as an optical mixer by applying the modulation of the differential mode, for the first time, to a top frequency up to 195.5 GHz. We have propounded important results obtained by utilizing a VPI simulator based on an electro-optical SOA-MZI sampling mixer. The efficiency of this optical transmission system has been assessed by the most important parameter called conversion gains. Moreover, high conversion gains that have positive values are reached for the entire range of mixing frequencies. The frequency conversion of QPSK data offers excellent EVM values at 5 Gbit/s. Furthermore, the EVM of the QPSK signal to up-converted attains 14% at 195.5 GHz, relevant to the tenth harmonic of the sampling OPC signal at 5 Gbit/s. In order to confirm the behavior of the simulation results, the experimental study is inspired. The comparison between them shows that they have the same performance. Finally, this novel designed system is efficacious, low cost, and can be stratified to a variety of modernistic applications. Our future work is dependent on the investigation of the frequency down-conversion process based on an electro-optical sampling SOA-MZI and compared to the outstanding performance of this system.  
All the authors, [PERSON] and [PERSON], contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.