esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.2.2 Evaluation of Autoencoder  
The online learning activity sequence obtained by the proposed representation directly has the problem of high dimensions. In order to alleviate this issue, we design an autoencoder to transform the two-dimensional vector \(\mathbf{R}^{m,m\times c}\) into \(\mathbf{R}^{m,code\_size}\), and code_size should be much smaller than \(\mathbf{n\times c}\). In this group of experiments, we set code_size to 3 and used visualization to evaluate the autoencoder. The experimental dataset contains sequences of students' online learning activities over multiple semesters. In order to avoid redundancy, we only selected the results of one semester of each course to show the performance of the autoencoder. The experimental results are shown in Figure 7.  
In Figure 7, each data point represents a student, and the color of the data point represents the final performance of the student. As can be seen from Figure 7, the autoencoder we designed can compress students' high-dimensional features into a smaller feature space, which is more suitable for clustering, classification, and other tasks. In Figure 7, students with the same performance in a course have a closer distance, which shows that the au  
Figure 7: Visualization of the results of the autoencoder.  
toencoder can extract latent features that have a significant impact on performance. Since extracting latent features from the original high-dimensional features using an autoencoder is an unsupervised learning task, it also has the advantage of not requiring the special annotation of students.  
In addition, we set the code_size to 6, 9, 12, 18, 24, 36, 48, respectively. The purpose of this was to evaluate the influence of latent features with different sizes on the prediction results. In order to avoid the influence of random factors, we compared the average value of the prediction results of all courses. The experimental results are shown in Figure 8.  
As can be seen from Figure 8, the accuracy of prediction using the latent features extracted by the autoencoder generally improved; when code_size is 18, the improvement in prediction accuracy is the most significant, reaching 25%. This also shows that the autoencoder can extract more essential features, which can affect the performance of the original online learning activity sequence. However, when using latent features, the Pre, Rec, and F1 of the prediction model do not significantly improve, which indicates that the accuracy of the prediction model for non-major grades is unsatisfactory. The bias of the prediction model may be caused by an imbalanced training dataset.
## 4 Experimental Results
### Experimental Setup for Benchmark Problems
Here, \(p\) is the number of features describing each instance from the dataset, and \(P\) is the number of variables optimized by the NL-SHADE-RSP algorithm. It should be noted that for the original PFuzzND algorithm, parameters \(k_{class}\) and \(k_{sm}\) are fixed during the offline phase and the novelty detection procedure, namely exactly these numbers of clusters are generated for each known class and short memory, respectively. Thus, initially the number of clusters is the same for all known classes, but can later be different for classes due to the procedures executed during the online phase. Moreover, in this study, the maximum number of clusters created for one class by PFuzzND is limited. Namely, this number cannot exceed 8 or be less than 2. Values for parameters used during the online stage (for both PFuzzND and HPFuzzNDA) were chosen according to the previously conducted experiments. Experiments were evaluated using the incremental confusion-matrix, which evolves as soon as a new data point is classified [86]. This matrix is composed of rows and columns that represent known classes, concept drift of known classes and new classes (concept evolution), and finally unlabelled data instances (those removed from short memory). So, for each dataset, three values were calculated over 10 program runs: the accuracy, the macro \(F\)-score metric and the unknown rate measure \(UnkR\)[11]. The accuracy was calculated only for the labelled data instances (new classes were not considered), while the macro \(F\)-score values were calculated considering not only the examples classified with already existing labels but also with new class labels, determined by the algorithm during the novelty detection process.
# Sugar Maple and Red Maple Face-Off: Which Produces More and Sweeter Sap?
## 1 Introduction
A recent study compared these two species regarding sap yield, sugar content, and physicochemical properties [6]. According to this study, sugar maple generally yield more and sweeter sap than red maples, with a mean difference of 0.51 \(\pm\) 0.58\({}^{\circ}\) Brix in sugar [6]. This difference highlights the potential advantages of sugar maple for syrup production but still suggests that red maple could contribute to the maple industry expansion, particularly in regions where sugar maple is less adapted [22]. A better understanding of the difference between the saps produced by sugar maple and red maple would allow the maple syrup industry to develop new strategies to address the challenges posed by climate change, including harvest optimization, maintaining production profitability, and preserving a high quality of maple syrup. Three characteristics are typically assumed to give sugar maple an advantage over red maple in syrup production: (i) sugar maple exhibits a longer sap production season, primarily due to a later cessation of sap exudation compared to red maple; (ii) this extended season should result in a higher sap yield; and (iii) sugar maple should have a higher sugar content compared to red maple [10]. This study aims to test the abovementioned differences between the two species using high-resolution temporal measurements of sap production collected on sugar maple and red maple growing under the same environmental conditions. We challenge the hypotheses that there are significant differences in sap season, sap yield, and sugar content between the two species.
# Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
## 2 Materials and Methods
### Magnetic Anomaly Detection
#### 2.1.1 Principles and Methods
The simulation results are shown in Figure 4, which illustrates the impact of changes in the magnetic moment direction on the magnetic anomaly signal while keeping the magnetic moment magnitude constant. Examples are highlighted in the figure, with the red dashed lines representing the magnetic anomaly signal. When the total magnetic moment is orthogonal to the geomagnetic field and parallel to the observation surface, the magnetic anomaly vector exhibits symmetry along the target center, with peaks and troughs symmetrically distributed around the center. In contrast, when the magnetic moment is parallel to the geomagnetic field in the same direction, the magnetic anomaly vector at the center of the observation surface aligns with the geomagnetic field but points in the opposite direction. This results in the largest negative projection of the magnetic anomaly field along the geomagnetic direction, and the angle between the magnetic anomaly vectors on either side of the center and the positive direction of the geomagnetic field is less than \(90^{\circ}\), with symmetry around the center. As a result, the total magnetic anomaly field forms a single trough distribution. \[|\mathbf{H}_{T}| =\sqrt{|\mathbf{H}_{E}|^{2}+|\mathbf{H}_{r}|^{2}-2|\mathbf{H}_{E}|| \mathbf{H}_{r}|\cos(\pi-\varphi)}\]0 Illustration of magnetic moment direction information. When the magnetic moment is orthogonal to the geomagnetic field and perpendicular to the observation surface, the magnetic anomaly vector at the center is also perpendicular to the geomagnetic field, resulting in a zero projection along the geomagnetic field direction. This configuration generates a magnetic anomaly field with peaks and troughs symmetrically distributed around the target origin. Lastly, when the magnetic moment is angled relative to the geomagnetic field, the total magnetic anomaly field exhibits a double peak--one positive and one negative--where the peak line is offset from the target origin. These simulation results confirm that the vector relationship between the magnetic anomaly field and the geomagnetic field primarily governs the distribution of the total magnetic anomaly field signal.
# Determination of Arsenic Species Distribution
###### Abstract  
This study describes the distribution of arsenic mobile species in the tailings of Cu-Co-Ni-arsenide using the sequential extraction and determining the contents of arsenate (As\({}^{\text{V}}\)) and arsenite (As\({}^{\text{III}}\)). The object of this study is the tailings ponds of the Tuvakobalt plant, which contains waste from the hydrometallurgical arsenide ore processing of the Khovu-Aksy deposit (Republic of Turoa, Russia). A procedure of sequential extraction for arsenic was applied, and it includes the extraction of the following forms: water-soluble, potentially water-soluble and exchangeable, easily sorbed on the surface of carbonates, associated with Fe/Mn oxides/hydroxides, associated with easily oxidized minerals, and accounted for by non-oxidized arsenic minerals. This procedure, which takes into account the peculiarities of the physical and chemical composition of the waste, was supplemented by the analytical determination of the arsenite and arsenate content by using the methods of inductively coupled plasma atomic emission spectrometry (ICP-AES) combined with the hydride generation technique (HG-ICP-AES). The content of the most mobile forms of arsenic, which are water-soluble, potentially water-soluble, and exchangeable species, is equal to 56% of the total arsenic content, 23% and 33% of which are arsenite and arsenate, respectively. Unlike arsenic, the mobile forms of metals have been determined in small quantities. The largest proportion of water-soluble and exchangeable forms is formed by Mg, Ca, and Sr at 11, 9.4, and 20%, respectively (residual and redeposited carbonates). The proportion of water-soluble forms of other metals (Cu, Zn, Co, and Ni) is \(<\) 1% or 0. The main part of the metals is adsorbed on the surface of Fe and Mn hydroxides, enclosed in easily and hardly oxidized minerals. In addition to geochemical studies, the presence of leaks from the tailing ponds into ground waters was determined by using electrical resistivity tomography. The data obtained indicate a high environmental hazard of tailings and the possibility of water-soluble and highly toxic arsenic compounds entering ground waters and aquifers.
## 2 Laser-Driven Very-High-Energy Electron/Photon Beam Radiation Therapy System
### Laser Beam Stabilizing System  
The misalignment in the laser transport optics due to movement of the rotary gantry can be retrieved by means of a laser beam stabilizing system, as shown in Figure 2. The drive laser pulse delivered from a fixed mirror inside the pulse compressor chamber is guided by a mirror system comprising M1, M2, M3, and M4 to the entrance O of the laser plasma accelerator. A position of an alignment beam provided by a laser S is monitored at a charge coupled device (CCD) camera, C1 to C4, viewing each mirror, M1 to M4. Generally, once movement of the gantry causes displacement \(\Delta C_{x,y}\) of the position of the alignment laser beam on the \(i\)th mirror for \(x\)- and \(y\)-direction, respectively, a correction angle of the \(i\)th mirror is given by \(\Delta\theta_{a,y}=\Delta C_{i-1x,y}\) / \(s_{i,-1}-\Delta C_{i+1x,y}\) / \(s_{i}\), where \(s_{i}\) is a distance between the \(i\)th mirror and the \(i+1\)th mirror in order to keep the final focus position O fixed. These angular corrections of each mirror are set by means of two precision actuators, e.g., piezoelectric actuators, integrated into a  
Figure 6: A schematic of the gantry head, comprising (**a**) a final focus mirror; (**b**) two-stage gas cell; (**c**) multi-axis positioning stage for the gas cell, e.g., miniature 6-axes hexapod; (**d**) vacuum chamber containing the laser plasma accelerator and drive laser optics; (**e**) vacuum window thin film, e.g., beryllium, for separating vacuum and air; (**f**) beam focusing system assembled three permanent quadrupole magnets; (**g**) current transformer for measuring electron beam charge; (**h**) Bremsstrahlung conversion target; (**i**) photon beam collimator; (**j**) pure He gas fed to the accelerator; (**k**) N\({}_{2}\) and He mixed gas fed to the injector; (**l**) injector gas cell; and (**m**) accelerator gas cell.  
mirror holder, installed in a vacuum box. A process measuring the laser beam position on each mirror and then correcting angles of the mirror may be repeated until the laser beam positions are stabilized.
## 4 Representative Machine Learning Models for IONM-Related Research
### Neural Networks
#### 4.1.5 Bayesian Neural Networks  
Most neural networks provide prediction results as fixed values without an indication of uncertainty. Without uncertainty modeling, it is challenging to determine the appropriate level of confidence in the output. This obstacle causes reluctance among medical experts to accept the results of ML models.  
Bayesian neural networks provide the posterior distribution of the output value rather than a single deterministic value [48]. In general, the prediction and its uncertainty are provided as the mean and variance of the output distribution, respectively.  
Bayesian neural networks model one or both of two types of uncertainties: aleatoric uncertainty, which captures variation inherent in the data, and epistemic uncertainty, which accounts for uncertainty in the model [49]. Epistemic uncertainty is modeled by estimating the posterior distribution of the model parameters, whereas aleatoric uncertainty is modeled by a neural network that estimates the density of the output quantity. Bayesian neural networks can be implemented with various types of neural networks including CNN, RNN, and transformers.
## 2 Configurations and Design Principles  
### Architecture of the Shared-Aperture CP Array  
The array lattice arrangement is crucial to achieving a good radiation performance for a dual-band phased array [16]. Within the scanning range, the appropriate element arrangement should meet the demand of avoiding grating lobes first. In addition, the element spacing should be large enough to allow element implementation in engineering, which can effectively help to improve the isolation performance between co-frequency elements and different-frequency elements. To avoid the generation of grating lobes, the element spacing should meet the requirement as shown in the following expression:  
\[\mathrm{d}\leq\frac{\lambda}{1+\sin\theta_{0}} \tag{1}\]  
The maximum scanning angle is \(\theta_{0}\). \(\lambda\) is the wavelength of the corresponding frequency in free space. The shared-aperture array has a small frequency ratio of about 1:1.8 between two operating bandwidths. To arrange as many elements as possible under the same aperture and avoid grating lobes, the element interval is chosen to be \(\mathrm{d}_{\mathrm{s}}=0.58\,\lambda_{\mathrm{s}}\) for the S-band array and \(\mathrm{d}_{\mathrm{c}}=0.52\,\lambda_{\mathrm{c}}\) for the C-band array, as shown in Figure 1a.  
One can see that the spacing \(\mathrm{d}\) between the S-band and the C-band elements is only \(0.37\,\lambda_{\mathrm{c}}\), and it is really difficult to implement both S- and C-band elements using one radio frequency (RF) printed circuit board (PCB) substrate with the same dielectric constant (normally with a dielectric constant less than 10) because the elements of high frequency are partially touched with the elements of low frequency already, as illustrated in Figure 1b. Two high-permittivity ceramic antenna elements are thus chosen to address this issue.
## 4 Psha
### Cumulative Probability Distribution of the PGA  
It is necessary to determine the probability distribution of the PGA in the PSHA and judge the accuracy of the BPNN in supplementing the PGA data through the probability distribution results that were previously obtained through fitting. In this section, all the California data were divided into two categories: Sample A only contains the measured PGA and Sample B contains the measured data and the BPNN prediction data.  
Four commonly used asymmetric probability models, namely the Gumbel, Frechet, Weibull, and generalized Pareto distribution (GPD), were selected to model the PGA probability distribution for analysis. The cumulative distribution function (CDF) of the above four models is shown in Equations (9)-(12), and the values of the parameters in the probability distribution function through regression analysis are listed in Table 1.  
\[\text{Gumbel}:\ F(x)=\exp\biggl{[}-\exp\biggl{(}-\frac{x-a}{b}\biggr{)}\biggr{]} \tag{9}\]  
\[\text{Frechet}:\ F(x)=\exp\biggl{[}-\biggl{(}-\frac{x-a}{b}\biggr{)}^{-c}\biggr{]} \tag{10}\]  
\[\text{[PERSON]}:\ F(x)\!=\!1\!-\exp\biggl{[}-\biggl{(}\frac{x-a}{b}\biggr{)}^{c} \biggr{]} \tag{11}\]  
\[\text{GPD}:\ F(x)=1-\left[1+c(x-a)/b\right]^{-\frac{1}{c}} \tag{12}\]  
The closer that the determinants of the coefficients (R\({}^{2}\)) in Table 1 are to one, the better their goodness-of-fit values. [PERSON] displayed the best goodness-of-fit value in both samples. BPNN prediction had less impact on the regularity of the data, which still conforms to the original probability distribution. The CDFs of the two samples are plotted in Figure 8.  
It is not difficult to interpret that inflexion points appeared in the regions with more concentrated PGA values. Combining Figures 3 and 5, the known PGAs were often larger ground motion events, while the more frequent earthquakes were relatively concentrated at lower magnitudes. This indicates that the PGA results obtained via the BPNN prediction will increase the number of medium and low PGAs, and the change of this data volume eventually affects the CDF of the PGAs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **CDF** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{Sample A} & [PERSON] & 122.4 & 145.5 & — & 0.9797 \\  & [PERSON] & \(-\)157.6 & 268.8 & 2.179 & 0.9942 \\  & [PERSON] & 10.17 & 223.5 & 0.9294 & 0.9971 \\  & GPD & 6.825 & 215.6 & 0.09101 & 0.9969 \\ \hline \multirow{4}{*}{Sample B} & [PERSON] & 46.7 & 42.6 & — & 0.9724 \\  & [PERSON] & \(-\)3.254 \(\times\) 10\({}^{4}\) & 3.259 \(\times\) 10\({}^{4}\) & 765.3 & 0.9723 \\ \cline{1-1}  & [PERSON] & 55.5 & 136 & 2.958 & 0.9755 \\ \cline{1-1}  & GPD & 6.944 & 73.03 & 2.676 \(\times\) 10\({}^{-7}\) & 0.9591 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regression results of the CDF of the PGA.  
Figure 8: Cumulative distribution of the PGA. (**a**) Sample A. (**b**) Sample B.
## 3 Experimental Tests and Results Analysis
### Implementation of an IoT Solution  
Furthermore, an IoT implementation was developed as a proof-of-concept using an ESP32-CAM with a OV2640 camera and a 65\({}^{\circ}\) FOV lent to capture images and send them to a local computer that has the processing power to run the algorithm. Then, the information is sent to a web application to display the data as an interface for visualization. The communications are made via Wi-Fi using the Hypertext Transfer Protocol (HTTP), and the architecture is illustrated in Figure 17.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gamma (\%)** & **Contrast (\%)** & **Gaussian Noise (\%)** & **Brightness (\%)** \\ \hline
81.5 & 77.3 & 43.3 & 78.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proportion of gauges correctly read for each image adjustment.  
Figure 16: Algorithm robustness assessments with image adjustments.  
The camera was installed inside a cabinet pointing to two gauges of a gas cylinder. One of the gauges was selected to be monitored, so it was set with a QR code, as shown in Figure 18. Finally, a dashboard was developed to indicate the gauge value and tell-tale pointer value, if applied.
## 2 Materials and Methods  
### Extraction Analysis  
In order to study the kinetics of the dissolution of LiCoO\({}_{2}\) particles, a number of extractions were carried out on LiCoO\({}_{2}\) powder (Alfa Aesar, 97%) in HCl 0.1 M solution. Batch-extraction experiments were carried out in well-stirred 50 mL polypropylene vessels containing LiCoO\({}_{2}\) powder suspended in the extracting solution using a liquid/solid ratio of \(L\):\(S\) = 200, namely 125 mg of solid powder suspended in 25 mL of 0.1 M HCl solution. The vessels with the suspensions were stirred in a rotatory shaking table at room temperature, and they were withdrawn and analyzed at different times (up to 6 days) to obtain the transient concentration profile of Co\({}^{2+}\) and Li\({}^{+}\) in the aqueous phase. With the aim of determining the initial amount of metals, microwave-assisted acid digestion was carried out. All the samples were filtered using 0.60 \(\upmu\)m glass-fiber (Macherey-Nagel (MN) GF-3) and analyzed for Co and Li using atomic absorption spectrophotometry (Varian SpectrAA 1101).  
In hydrometallurgical processes, different extracting agents (organic and inorganic acids) have been tested [3; 17; 23; 24; 25]. In this study, 0.1 M HCl was used to extract Co and Li from LiCoO\({}_{2}\). The choice of the extracting agent was done to evaluate the capability of chloride ions as extraction agent, as it may act as a reducing agent of Co\({}^{3+}\) to Co\({}^{2+}\).
###### Abstract  
The incorporation of Internet of Things (IoT) edge nodes into assistive technologies greatly improves the daily lives of individuals with disabilities by facilitating real-time data processing and seamless connectivity. However, the increasing adoption of IoT edge devices intended for individuals with disabilities presents significant security challenges, particularly concerning the safeguarding of sensitive data and the heightened risk of cyber vulnerabilities. To effectively mitigate these risks, advanced cryptographic protocols, including those based on elliptic curve cryptography, have been proposed to establish robust security measures. While these protocols are effective in reducing the risk of data exposure, they often demand considerable computational resources, which poses challenges for cost-effective IoT devices. Therefore, it is essential to prioritize the effective execution of cryptographic algorithms, as they rely on finite field operations such as multiplication, inversion, and division. Among these computations, field multiplication is particularly critical, serving as the backbone for the other operations. This study intends to create an innovative hybrid systolic array design for the Dickson basis multiplier, which integrates both serial and parallel inputs to enhance overall performance. The proposed design is anticipated to significantly reduce space and power consumption, thereby enabling the secure execution of complex cryptographic algorithms on resource-limited IoT devices designed for disabled people. By addressing these pressing security issues, the study inspires to fully leverage IoT technologies to enhance the living standards of individuals with disabilities, while ensuring that their privacy and security are meticulously maintained.  
disabled individuals; internet of things (IoT); assistive technologies; cryptographic protocols; systolic array architecture; dickson basis multiplier; resource-constrained IoT devices +
Footnote †: journal: Applied Sciences  
1 Computer Engineering Department, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia 12 King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia 2