esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.7.5 Sample Plot Selection  
Six random plots were sampled without adjusting for variation in slope, aspect or relative soil hydrology of the site (xeric, mesic, or hydric). Each sample plot provided 900 square meters of taxonomic field data with a total survey area of 5400 m\({}^{2}\). The survey was conducted by quantifying species richness and abundance within the plot perimeter. Absolute quantitative numbers were not the focus; species richness was estimated in areas of significant vegetation abundance. Surveyed taxa included visible vascular plants, bryophytes and canopy lichens. Overall accuracy of species identification was high, with few visible species remaining unidentified or uncounted. The data are representative of the forest vegetation in the study area, but likely understate actual richness and abundance.  
The plot map consisted of a web feature service published to a hosted cloud server, downloaded and synchronized to a field data collection tablet paired with a map grade GNSS receiver. The mobile field data collection device utilized a satellite based real-time kinematic (RTK) network for plot position corrections (or SBAS if the RTK network was unavailable). This method achieved an estimated horizontal plot accuracy of approximately 2 m with respect to the LiDAR survey data used to produce the web-gis map service.
# LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
## 3 Materials and Methods
### AutoRegressive Model with eXogenous Input (ARX) Model
#### 3.3.2 Model Structure Estimation  
In order to estimate the initial model orders and delays for the system, several ARX models can be estimated within a range of orders and delays, and then the performances of these models can be compared. The model orders that correspond to the best model performance are selected and used as an initial guess for the next phase.  
To guarantee a fair comparison between models having different degrees of freedom, a simple validation approach is used [35]. According to this technique, the data are split into two sets: identification and validation. Each model is identified using the first set, while the performance of the model is evaluated on the second set. Then, the best model structure is the one that minimizes the prediction error computed on the validation dataset.  
The ASM model family [3; 4; 5] was used to determine the ranges of the desired structure parameters. According to these models, the number of states ranges from 12 to 15, no delay is involved, and the proposed model is strictly proper. Starting from this consideration, the parameter space was determined. The main idea is that the research space has to include the structure proposed by the physical models. In particular, the degree of \(A(q)\) is connected to the order of the system; then, it was supposed that \(N_{a}>15\), and for each input, the sum of delay and order should also include the same limit, \(d_{m}+N_{bm}>15\). Table 1 summarizes the proposed ranges.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal** & **Component** & **Range** & **Selected** \\ \hline \(O(t)\) & order (\(N_{a}\)) & 10–17 & 12 \\ \(q_{oss}(t)\) & order (\(N_{b1}\)) & 2–12 & 6 \\  & delay (\(d_{1}\)) & 0–5 & 2 \\ \(q_{in}(t)\) & order (\(N_{b2}\)) & 2–12 & 3 \\  & delay (\(d_{2}\)) & 0–10 & 5 \\ \(q_{ol}(t)\) & order (\(N_{b3}\)) & 3–9 & 7 \\  & delay (\(d_{3}\)) & 0–10 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compared ARX structures.
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 3 Thermodynamic Analysis Model
### Energy Analysis Model  
#### Conventional CAES  
The energy analysis of the ST-CAES system is based on the first law of thermodynamics. It describes the process of energy transfer and conversion of each component. Outlet temperature \(T_{out}^{c,i}\) of the compressor is obtained via:  
\[T_{out}^{c,i}=T_{in}^{c,i}\bigg{(}1+\frac{1}{\eta_{com}}\bigg{(}\beta_{i}^{ \frac{k-1}{k}}-1\bigg{)}\bigg{)}, \tag{1}\]  
where \(k\) is the polytropic index, \(\beta\) is the pressure ratio, and \(\eta_{com}\) represents the adiabatic efficiency of the compressor, which is calculated by  
\[\eta_{com}=\frac{h_{out,s}-h_{in}}{h_{out}-h_{in}}. \tag{2}\]  
The required power of the compressor is equal to the enthalpy difference between the outlet and inlet streams multiplied by its mass flow rate, and is obtained by  
\[W_{com}=\sum_{i=1}^{4}m_{ARin}(h_{out}^{i}-h_{in}^{i}), \tag{3}\]  
where \(W_{com}\) is the compressor power consumption and \(m_{ARin}\) is the mass flow rate of the compressor.  
Energy balance allows for computation of the amount of hot water power produced in HEX1-HEX4:  
\[Q_{heat}=m_{AR}(h_{AR,in}-h_{AR,out})=m_{WA}(h_{WA,out}-h_{WA,in}), \tag{4}\]  
where \(Q_{heat}\) is the recovery compression heat power that can supply heat to residents.  
The charge and discharge time of the SPT are decided by the maximum and minimum allowable air pressures:  
\[\tau_{ch}=\frac{(p_{\max}-p_{0})V_{SPT}}{m_{AR9}RT}, \tag{5}\]  
\[\tau_{dch}=\frac{(p_{\max}-p_{0})V_{SPT}}{m_{AR10}RT}, \tag{6}\]  
where \(\tau_{ch}\) and \(\tau_{dch}\) are the charge and discharge time, respectively; \(V_{SPT}\) is the volume of SPT; \(R\) is the gas constant of air under normal circumstances; and \(T\) is the temperature in SPT.  
To calculate the outlet temperature of the air turbine, \(T_{out}^{c,i}\) (streams AR13, AR15, and AR17), the following is used:  
\[T_{out}^{c,j}=T_{in}^{c,i}\bigg{(}1-\eta_{e}\bigg{(}1-\pi_{j}^{\frac{1-k}{k}} \bigg{)}\bigg{)}, \tag{7}\]where \(\pi\) is the expand ratio, and the isentropic efficiency of the turbine \(\eta_{e}\) is defined by  
\[\eta_{e}=\frac{h_{out}-h_{in}}{h_{out,s}-h_{in}}. \tag{8}\]  
An STC subsystem is used to collect and store solar radiation energy to heat the inlet air of the turbine. The temperature of the heated air is obtained from the energy balance as shown by  
\[m_{AR10}(h_{ARin}-h_{ARout})=m_{O}(h_{Oout}-h_{Oin}). \tag{9}\]  
The output power of the air turbine generator \(W_{Thr}\) is  
\[W_{Thr}=\sum_{j=1}^{3}m_{AR10}(h_{out}^{j}-h_{in}^{j}). \tag{10}\]
## 7 Current Challenges and Future Research Directions
### Unauthenticated Application  
Many applications at the application layer can access network resources for better management. Furthermore, many unauthorized applications obtain this access through instances of approved applications. An attacker 1 might take advantage of this vulnerability and utilize these programs to launch LDDoS attacks, reducing network performance. As a result, there is a need to identify a reliable application authenticator to validate the application before authorizing access to network resources.
## 3 Asymptotic Homogenization Method: Homogeneous Problem, Local Problems and Effective Coefficients
### Random NTC Reinforced Composites
The CNWs are simulated by ellipsoidal inclusions with different orientations, as seen in Figure 10. In Figure 11, the behavior of \(E_{11}^{*}\) as a function of the CNW weight content (CNW wt content)(\(\%\)) for different CNW orientations \(\theta=0^{\circ}(\text{aligned}),\,10^{\circ},\,20^{\circ},\,30^{\circ}\) and \(35^{\circ}(\text{Random})\) is reported. In addition, a comparison between the SAFEM-3D results and experimental data [10] is shown. For the numerical computation, the material constituents of the matrix (polystyrene) and the CNW inclusions are assumed to be isotropic materials, such as \(E=3.349\) GPa and \(\
u=0.34\) for polystyrene [10] and \(E=169\) GPa and \(\
u=0.185\) for CNW [51]. As can be observed, the theoretical results using the SAFEM-3D show that \(E_{11}^{*}\) decreases as \(\theta\) increases; i.e., \(E_{11}^{*}\) for aligned CNW inclusions (\(\theta=0\)) is greater than that for random CNW inclusions (\(\theta=35^{\circ}\)) in the \(y_{1}\) axial direction. It is remarkable that the SAFEM-3D values for random inclusions are within the expected error range when the CNW inclusions are between 1% and 5% CNW wt content. From a physical point of view, as the CNT wt content increases, \(E_{11}^{*}\) should grow, as can be seen in the SAFEM-3D results (Figure 11). The difference between the SAFEM-3D values and the experimental values when the CNW inclusions have 5% CNW wt content is also reported in [10]. Figure 9: Effective longitudinal Young’s modulus (\(E_{11}^{*}\)) as a function of the inclusion’s aspect ratio \(\alpha\) for a composite with different fiber orientations and 1% SWNT volume fraction. Figure 11: Effective longitudinal Young’s modulus \((E_{11}^{*})\) as a function of the carbon nanowire (CNW) weight content(%). A comparison between the SAFEM-3D results and experimental data is shown.
# Feature Selection Algorithms as One of the Python Data Analytical Tools +
## 2 Background
### Traditional Feature Selection Algorithms Categorization  
Traditionally, feature selection methods were divided into three main groups: wrapper, filter, and embedded methods [15]. Wrappers try to build optimal feature subset by the evaluation of the quality measure \(Q_{c}\) for the predefined machine learning algorithm:  
\[F^{*}=\underset{F^{\prime}\subseteq F}{argmax}\ Q_{c}(F^{\prime}),\]  
where \(C\) is a machine learning model and \(Q\) is the quality measure for the model. For this, a wrapper algorithm works iteratively; on each step, it takes some feature subset and passes it to the model and then, depending on the model quality, it decides to pick another subset or stop the process. The picking procedure and the optimization stopping criteria basically define the wrapper algorithm. The main problem of this approach is that it is too slow for high-dimensional datasets as the number of possible subsets is equal to \(2^{N}\) and on each step we need to build a model to evaluate the subset quality.  
Embedded methods usually use some intrinsic properties of the classifier to get features subset. Feature selection with random forest [16] is an illustrative example of such approach, where the out of bag error for each feature on each tree is aggregated into resulting feature scores and features that most often result in the elimination of bad classification results. Some of these methods can work even with really high-dimensional data, but their main restriction is the model itself. Basically, features that were selected with one model can result in bad performance if they are used for another model.  
Filters are the third traditional group of feature selection algorithms. Instead of evaluating the feature sets with some models, they take into consideration only intrinsic properties of the features themselves. If a filter does not consider any dependencies between features themselves, thus assuming that they are independent, it is called univariate, otherwise it is multivariate. For multivariate filters, the problem is stated as follows:  
\[F^{*}=\underset{F\subseteq F}{argmax}\ \mu(F^{\prime}),\]  
where \(\mu\) is the feature subset quality measure. On the other hand, for the univariate filters, the feature selection problem is stated without optimization. Instead, every feature is evaluated with feature quality measure \(\mu\) (which for this case should be defined only on \(f_{i}\in F\), but not for the whole set of features subsets) and then some cutting rule \(\kappa\) is applied.
## 6 Undesired Effects
### Survey Repeatability Errors  
Repeatability errors are generated by inexact positioning and/or orientation of the receivers and the source in time-lapse surveys. In a surface-to-borehole survey, wireline receivers are installed in a vertical monitoring well. Replacing aged borehole receivers may change the location of the receivers while their orientation remains fixed. Regarding the surface source, the location, azimuth, and frequency may change. The authors of [45] added 1% error relative to the signal amplitude to mimic repeatability errors in surface-to-surface surveys. Here, we adopt a different approach to study the impact of 1% repeatability errors on a surface-to-borehole CSEM. We changed the source location, azimuth, and frequency by 10 m, 1\({}^{\circ}\), and 0.01 Hz, respectively, and arbitrarily changed the position of the receivers by 5 m.  
Figure 6 shows that the difference resulting from the survey errors was typically in the range of 1-10%, and even exceeded 10% in the 200 m around the zero-crossing. This high error near the zero-crossing was caused by the source repeatability error, as the error decreased when moving away from the source. As the source was 700 m from the edge of the resistivity change, it would still be possible to detect a difference with moderate confidence due to the resistivity change. This implies that the impact of the source repeatability error is reduced when the source is at an adequate distance from the borehole receivers. Next, we examined data errors caused by seasonal near-surface changes.  
Figure 5: The influence of 1% recording noise when monitoring the 300 m radius change. The (**left plot**) shows the relative difference due to the random noise, while the (**right plot**) shows the relative difference between the base and monitoring fields in the presence of the recording noise. The black dashed rectangle indicates the boundaries of the resistivity change.
## 1 Introduction  
Children's dental anxiety is a common health problem which has a negative impact on the quality of a child's life and oral health. Moreover, it may cause psychological problems and sleep disorders [1]. Dental anxiety is characterized as a particular fear that predisposes a person to a negative or unpleasant experience during dental procedures.  
Dental anxiety can affect people of all ages, with many experiencing it in childhood and adolescence [2]. According to [3], dental phobia and anxiety are widespread among both dentists and the general population in Saudi Arabia. This often leads many individuals to postpone or avoid routine dental care, which can have detrimental effects on their oral health. Furthermore, the study found that dental phobia is more prevalent among females than males in Saudi Arabia.  
Another study found a high degree of fear and anxiety among Saudi children towards dental practices, with injections being the most feared practice [4].  
Children's dental anxiety is a significant concern that stops patients from cooperating completely during dental treatment, which may result in a lack of time for the doctor, needless complications during the treatment, and increases dental services [5]. There are some methods used by dentists to distract children during treatment, but these methods have not completely resolved the problem, which is still widespread among children [6].  
Virtual reality is quickly emerging as one of the most promising tools for managing stress and anxiety [7; 8; 9; 10]. Moreover, several studies have presented evidence for the effectiveness of VR in rehabilitation [11; 12]. By providing an immersive and interactive experience, creating a familiar and comfortable environment, providing educational experiences, and offering relaxation and distraction techniques, these systems can help to make dental visits less intimidating for young patients. A recently published review paper [10] stated that various studies have shown that virtual reality glasses are successful in distracting pediatric dentistry patients; however, to date, few systems have been developed to serve this domain, especially for young dental patients.  
Motivated by this, we developed an interactive AR/VR-based system for managing the dental anxiety of young children. Specifically, the work presented here aims to manage children's dental anxiety by using 360\({}^{\circ}\) VR video and AR technology. Additionally, it aims to improve the relationship between children and the dental clinic environment to make any future procedures more comfortable.
## 3 Performance of IWP with Steel Anchor Box
### FE Analysis Results and Performance Comparison
#### 3.1.1 FE Model  
As shown in Figure 20, three FE models were established in ANSYS 14.5 for the anchoring zone of the cable pylons with internal, exposed, and IWP details with a steel anchor box, based on the geometries of an actual bridge. The model encompasses the top six segments of the cable pylon anchoring zone, numbered sequentially from 1 to 6 from top to bottom. The bottom of the pylon wall is fully constrained in the vertical direction, and symmetry constraints are applied along the longitudinal and transverse symmetry axes. In the model, concrete is simulated using Solid65 elements; steel plates are simulated using Shell63 elements; anchor bearing plates are simulated using Solid45 elements; prestressed tendons are simulated using Link8 elements. Connectors are simulated using three-directional spring elements, and contact elements are established at the steel-concrete interface to account for pressure transfer effects at the interface. The material properties and connector properties are listed in Table 4. The design cable force is applied, with a maximum force per cable of 8528 kN, as a surface load on the anchor bearing plates. No prestressing is applied for internal and IWP type models, while two bundles of circumferential prestressed tendons are applied for the exposed type in every segment. The tendons were modeled as truss elements and were embedded in the concrete elements. The prestress was introduced by the cooling method, and the temperature change was calculated based on the defined thermal expansion coefficient.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Output and Audit Module
#### 4.4.1 Result Aggregation and Output  
After completing the approximate matching tasks, computing nodes upload their partial hamming distance shares to IPFS and record the corresponding CIDs on the blockchain via smart contracts [30]. Participants query the blockchain using the task identifier to obtain the CIDs of the \(t\) shares \(\left[D_{i,j}\right]_{k}^{A}\) (\(k=1,\ldots,t\)) for record pair \((i,j)\). They download the shares from IPFS, restore the original order using the index table \(\pi_{\text{gid}}\) (\(\left[D_{i,j}\right]_{A}^{\pi_{\text{gid}}^{-1}(k)}\)), and reconstruct the Hamming distance with the following formula:  
\[D_{i,j}=\sum_{k=1}^{t}\left[D_{i,j}\right]_{A}^{k}\text{mod}\ p \tag{15}\]  
Participants only obtain \(D_{i,j}\), without access to intermediate values or original Bloom filters, ensuring privacy. The Hamming distance can be directly output or further processed, such as applying thresholds to filter similar record pairs.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 2 Materials and Methods  
### Divided Solar Cell Circuit Modeling  
A passive emitter and rear cell (PERC) were used in this study. The M2 size (156.75 mm) solar cell was divided into five sections and used to manufacture a shingled PV module, which was modeled first. The divided cell was modeled using a double diode model, and the required parameters were measured using a solar simulator (WXS-155S-L2, Wacom, Fukaya, Japan) and current-voltage analyzer (DKSCT-3T, Denken, Yufu, Japan) under standard test conditions (STC, AM 1.5, solar irradiance: 1000 W/m\({}^{2}\), temperature: 25 \({}^{\circ}\)C). The voltage-current characteristics of the double-diode model are expressed by Equation (1) [12; 13; 14]. The parameters used to conduct the modeling were the short-circuit current (I\({}_{\text{sc}}\)), open-circuit voltage (V\({}_{\text{oc}}\)), maximum power (P\({}_{\text{m}}\)), maximum power voltage (V\({}_{\text{pm}}\)), maximum power current (I\({}_{\text{pm}}\)), series resistance (\(R_{s}\)), and parallel resistance (\(R_{sh}\)) [15; 12]. Figure 1 shows the circuit of the double-diode model that was used for modeling the divided cell. To simulate the output loss resulting from shading, the light generated current (\(I_{L}\)) of the modeled divided cell was adjusted with a voltage source (V\({}_{\text{input}}\)) and simulated as though shading had occurred. V\({}_{\text{input}}\) replaces the current source of the double diode model. The control range of V\({}_{\text{input}}\) is 0 V-1000 V, and when it is set to 1000 V, the amount of light in the simulation is the same as 1000 W/m\({}^{2}\). A comparison of the measurement efficiency and output values of the double diode model with those of actual divided cells showed that the two are similar. Table 1 presents the characteristics of the modeled split solar cell and the simulated solar cell and Figure 2 presents the I-V curve of the modeled split solar cell and the simulated solar cell. This confirms that the modeled divided cell has almost the same characteristics as the actual divided solar cell, verifying that the loss analysis according to the shading of the shingled string was successfully simulated using the double-diode model.  
\[I=I_{L}-I_{\text{s1}}\bigg{(}exp\frac{V+IR_{s}}{V_{T}}-1\bigg{)}-I_{\text{s2}} \bigg{(}exp\frac{V+IR_{s}}{2V_{T}}-1\bigg{)}-\frac{V+IR_{s}}{R_{sh}} \tag{1}\]  
Figure 1: Double diode model and divided cell circuit diagram.
# Effect of Extraction Method and Thermosonication on Apple Juice Quality
###### Abstract  
This paper presents the results of a study on the influence of juice extraction and thermosonication methods on the content of microorganisms in juices stored under refrigeration and in ambient temperature. It was determined that the type of extraction device used influenced the content of microorganisms in juice. The lowest total content of mesophilic microorganisms was obtained in juice extracted on a piston press, and the highest was obtained using a centrifuge. The additional purpose was to evaluate the influence of ultrasonic treatment on selected parameters of apple juice quality: cloud value, antioxidant activity, total phenolic content, flavonoid content, and anthocyanin content. The ultrasound treatment of juice from the piston press was conducted at two levels of radiated sonic power so as to obtain sublethal and lethal temperatures of the juice. It was demonstrated that only ultrasound treatment within the range of lethal temperatures was sufficient to deactivate microorganisms. It was determined that thermosonication did not influence the deterioration of chemical properties of juices, with the exception of decreased anthocyanin content. The described method enables prolongation of the shelf life of fresh apple juice under refrigeration to 168 hours (7 days).  
thernosonication; apple juice; extraction methods; bioactive compounds Article