esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The geomechanical characteristics of soil guarantee adequate soil-structure interactions [1]. However, more information is needed regarding technical aspects, such as the appropriate period to store soil samples for long periods without altering their characteristics.  
The permissible timeframe between sample collection and laboratory testing is a subject of considerable uncertainty, and it remains unclear whether extending this period will impact the outcome of the tests. Some studies have focused on sampling methodologies, such as (i) the use of Shelby tubes to obtain better-quality samples [2], (ii) soil characterization with the establishment of new correlations [3], (iii) the use of in situ methods in conjunction with the Cone Penetration Test Unit (CPTU) and dilatometers, which is a crucial aspect of geotechnical engineering [4], and (iv) soil characterization at different depths and loading levels [5]. The duration for which the samples could be stored is not specified.  
Soil behavior is fundamental to engineering projects [6]. Therefore, it is crucial to emphasize the importance of soil characterization beyond particle size classification [7]. The Atterberg limit test, for instance, quantifies the alterations caused by differences in the MC [8].  
Challenges arise during silt sampling due to the modifications occurring during extraction, leading to sample densifications caused by equipment, handling, storage, or transportation [9]. Shelby tube extraction causes a more significant disturbance at the time of penetration because of the diameter of the sampling tube, method, and extraction speed based on small-scale modeling [10]. Conversely, the use of block sampling in various studies, such as odometers and computerized scanners, has demonstrated superior preservation of soil structure and properties [11]. [PERSON] et al.'s [12] study on unaltered soil samples demonstrated that silty and organic soils experience a reduction in MC and saturation levels over extended periods. These findings imply that the water content of the samples plays a crucial role in assessing the test outcomes.  
The complexity of comparing results from different studies arises from the absence of standardized soil sample collection and storage techniques [13]. While soil has been classified by extraction method and mineralogical composition, no studies have been conducted to assess the fluctuation of soil properties over time, specifically in response to changes in humidity and temperature during the storage period.  
The main objective of this study was to assess fluctuations in the UCS, MC, and ME of silt and organic soil at different time intervals following sampling. To achieve this, a controlled methodology was used to analyze the behavior of these geomechanical properties at various time intervals. This study aimed to provide recommendations for the maximum duration for which a sample can be stored without compromising the accuracy of the results. The hypothesis examined was that the strength of the soil would increase, and the MC would decrease with an increase in the storage period.
# Initial Results from SQUID Sensor: Analysis and Modeling for the ELF/VLF Atmospheric Noise
## 5 Conclusions  
A low-temperature SQUID receiver was adopted to observe the ELF/VLF atmospheric noise for the first time in this paper. Symmetric wavelet decomposition and LS estimation were adopted to preprocess the data to suppress baseline wandering caused by geomagnetic activity and harmonics radiated from power line. Analysis for the temporal atmospheric noise show that the ELF/VLF noise have non-Gaussian characteristic, especially for the narrowband noise. Modeling of the ELF/VLF noise provides the insight that, Hall model is the optimal choice to depict the APD of the ELF noise, while SaS can fit the heavy-tail characteristic of the VLF noise more precisely. As \(\alpha\) fitted by SaS distribution are mainly in the range of 1.9 to 2, the ELF noise can be regarded as Gaussian distribution for simplicity after preprocessing the impulses by a hole puncher.  
The initial results of this study may allow for the modeling of wideband ELF/VLF communication performances, given the improved knowledge of the characteristics of the natural background noise for the design of an optimal SQUID based receiver. The parameters derived in this work can also be used as input parameters when trying to simulate low-frequency wideband communication. Longer observation time and more receivers in different districts will be adopted in the future to derive a more general conclusion.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 2 Target Identification
### Charging Technologies
#### 2.3.4 Interoperability Issues  
As observed in Section 2.3.1, there are many competing charging standards in use currently. The variety and incompatibility between these standards represent a barrier against an easy adoption of EV technology among vehicle owners: cross-border trips are currently virtually impossible, and integration of EV charging technologies to provide ancillary services to the power grid is jeopardized among different communication standards [96].  
To summarize the problems in interoperability currently existing in electric mobility, the following can be highlighted [97]:  
* Mismatch of the hardware interfaces;
* Multiple versions of different protocols covering the same communication link;
* Low level of standardization between high level actors.  
The impact of the lack of interoperability can be described at different levels. In terms of user comfort, it has hindered the possibility of charging anywhere, across large areas; from an economic point of view, interoperability enables an easier maintenance of the charging infrastructure by avoiding potential conflicts in technology updates; finally, from a smart grid perspective, a low level of standardization between electric mobility companies (EV manufacturers, charging operators, etc.) and grid operators hinders the implementation of a scalable vehicle-to-grid infrastructure, and the full set of benefits that are foreseen in terms of integration of the electric transportation within the smart grid concept [98].
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 4 Simulation Verification  
CST Microwave Studio was been used to simulate and optimize the antenna design. Figure 3 demonstrates the mutual connection between the antenna azimuth gain and the total number of slots along the antenna's circumference. Each slot radiates a directed pattern. With each increment in the number of slots along the antenna's circumferential axis, these directional radiations widened, as shown in Figure 3. Four slots made the radiation patterns combine and generate a low-ripple horizontal polarized omnidirectional radiation pattern.
## Appendix A  
The calculation formula of the Global Moran's I is as follows:  
\[I_{g}=\frac{N\sum_{i}\sum_{j}w_{ij}(x_{i}-\mu)(x_{j}-\mu)}{\left(\sum_{i}\sum_ {j}w_{ij}\right)\sum_{i}(x_{i}-\mu)^{2}}\]where \(w_{ij}\) is the row-standardized contiguity matrix, \(x_{i}\) and \(x_{j}\) are the EQI at grids \(i\) and \(j\), respectively, and \(\mu\) is the average level of EQI. N is the total number of the grids in the study area. Moran's I \(I_{g}\) ranges from approximately +1 (for positive spatial autocorrelation) to \(-1\) (negative autocorrelation), and zero expresses the absence of spatial autocorrelation.  
Local Moran's I was used to present LISA results, and its formula is as follows:  
\[I_{l}=\frac{x_{i}-\mu}{\sum_{l}(x_{i}-\mu)^{2}}\sum_{j}w_{ij}(x_{i}-\mu)\]  
where \(w_{ij}w_{ij}\) is the row-standardized contiguity matrix and \(x_{i}\) are the EQI at grids \(i\), and \(\mu\) is the average level of EQI. The statistical quantity which examines the Local Moran's I \(I_{l}\) is as follows:  
\[z_{i}=\frac{I_{l}-E(I_{l})}{\sqrt{VAR(I_{l})}}\]  
where \(VAR(I_{l})\) and \(E(I_{l})\) denote the variance and expectation of \(I_{l}\), respectively. If \(|z_{i}|<1.96\), \(p>0.05\), then the test of significance is considered not passed regardless of whether \(I_{l}\) is greater than 0 or less than 0. According to the results of \(I_{l}\) and \(z_{i}\), five clustering patterns were eventually created in the EQI assessment results: high-high (hot spots), low-low (cold spots), low-high, high-low, and \"not significant\".
## 1 Introduction
Also, to improve video quality, Forward Error Correction (FEC) is suggested to be used in data link layer for high priority data. Finally, a hierarchical single-path routing protocol and a dropping scheme are invented in network layer. The proposed single-path routing protocol finds a proper path between source and Sink in the network. In order to send data bursty over a reliable path, this approach negotiates with the selected parent hop-by-hop. The dropping scheme uses probability functions for discarding packets along with considering priority level of data packets and hierarchy level of video source node. Also, EQV-Architecture can be used on Mobile Ad-hoc Network (MANET) by applying some little changes in parameters of the architecture. The rest of this paper is organized as follows: Section 2 presents the related works on the topics of WVSN in all the related layers. The overall design of the architecture is presented in Section 3. The corresponding layers of proposed architecture, the proposed methods and protocols in the application, transport, and network layers are introduced in Sections 4, 5, and 6 respectively. Section 7 provides simulation and experimental results and finally, in Section 8 the paper's conclusions are presented.
## 3 Results and Discussion
### GS/MS Analysis  
A typical chromatogram of the six standard THMs (100 \(\upmu\)S. L\({}^{-1}\)) by SPME vial sampling is shown in Figure 6 and illustrates that good peak shape and resolved components were obtained. Individual THM components in the water samples studied were identified by spiking the samples with standards and by MS identification. Quantification of the six THMs analytes were performed using the peak area ratios of the analyte relative to the calibration curve (with no salt added) from 3 to 800 \(\upmu\)S. L\({}^{-1}\). The sampling range was found to be linear and was chosen to conform to THM regulatory limits, for total THMs to be less than 100 \(\upmu\)S. L\({}^{-1}\)[15], and to provincial reports that have shown that total THMs have been found in natural waters up to 600 \(\upmu\)S. L\({}^{-1}\)[4]. Each sample and standard was analyzed in triplicate.  
Figure 6: (**a**) Top extracted MS of chloroform (1); (**b**) Bottom: GC/MS chromatogram of the 6 THM target components (and GC retention time): 1 = chloroform (1.88), 2 = 1,2-dichloroethane (2.21), 3 = bromodichloromethane (2.92), 4 = dibromochloromethane (4.59), 5 = bromoform (6.01), 6 = 1,2-dibromoethane (6.26).  
GC THM peaks areas were obtained and quantified by selected ion monitoring (SIM) mode for the optimized selected fragment ions given in the experimental section. Assessment of the largest GC peak area counts obtained for all six THM standards, was used to determine the optimized column conditions, which were found to be: 40 \({}^{\circ}\)C, held for 4 min, to 160 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C\(\cdot\) min\({}^{-1}\), and held for 4 min, then ramped to 210 \({}^{\circ}\)C at a rate of 50 \({}^{\circ}\)C\(\cdot\) min\({}^{-1}\), and held for 1 min, giving a total run time of 10 min. Under such conditions, all THM components, including those from real samples, were separated by the 6.8 min in the GC 10 min run time. To preclude any column contamination, the GC run included an extra time at 210 \({}^{\circ}\)C before the next run. Directly after injection, the SPME fiber was then desorbed into a heater port at 250 \({}^{\circ}\)C for a period of 2 min to negate sample carry over from the fiber. THM GC retention data tends to follow an increased retention time with increasing halo substitution, and with substitution of chlorine for bromine (chloroform elutes earlier than bromoform). Similar comparisons can be made with the compound Henry's constant and to compound substitution, which suggests that there is a volatility decrease with increased halo substitution. For example, bromoform and dibromochloromethane give larger peak areas than do chloroform and bromodichloromethane (See Figure 6).
## 2 Materials
### Winter Wheat Planting Area Dataset
For scale consistency, the binary mask was resized to produce a final area mask with 1 km resolution by computing the percent of the wheat value (Pct) from the Sentinel-2 binary mask (10 m resolution) map within each 1 km pixel footprint (Figure 2) [3]. When the Pct was >80%, the pixel was pure for the winter wheat. When the Pct was distributed from 0.5 to 0.8, the pixel was a mixed-pixel. If the Pct was <0.5, the pixels were neglected for the verification of the field data below. Six pure pixels and eleven mixed pixels were at the experimental sampling plots of exp2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Sentinel-2**} \\ \cline{3-5}  & & **Winter Wheat** & **Non-Wheat** & **Total** \\ \hline \multirow{3}{*}{**Worldview2**} & **Winter Wheat** & 144 & 6 & 150 \\  & **Non-Wheat** & 7 & 43 & 50 \\  & **Total** & 151 & 49 & 200 \\ \hline \multicolumn{5}{c}{Overall accuracy = 100 \(\times\) (144 + 43)/200 = 93.5\%} \\ \hline \multicolumn{5}{c}{Producer’s accuracy} & \multicolumn{1}{c}{Omission error} & User’s accuracy & Commission error \\ \hline
**Winter wheat** & 100 \(\times\) 144/151 = 95.36\% & 4.64\% & 100 \(\times\) 144/150 = 96\% & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error matrices of the winter wheat area classification results at Hengshui City in 2017.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Neural Network Model Performance
#### 5.3.5 Model Performance Across Shot Types  
The model's performance varied across different shot types, as shown in Table 5. Smashes were predicted with the highest accuracy (89.2%, SD = 1.1%), followed by drops (84.5%, SD = 1.3%) and clears (83.4%, SD = 1.5%). This variation in performance may be attributed to the different QE patterns and biomechanical requirements of each shot type.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 2 Background
### IoT Use Cases in Healthcare  
We often think of intelligent sensors and connected hospitals when considering the IoT in the healthcare sector. However, the answers go beyond this. Spending on IoT solutions in healthcare is expected to reach USD 1 trillion by 2025 [24]. The IoT has the potential to assist healthcare providers in offering high-quality, cost-effective, and individually tailored healthcare services to a broader population. Several applications exist for healthcare IoT, such as remote health monitoring and transmitting real-time alarms [25]. Some compelling use cases of IoT in healthcare are remote patient monitoring, emergency care, staff monitoring, inventory tracking, and virtual monitoring, etc. [26; 27; 28; 29].  
Figure 2: 5G, IoT, cloud, and AI use cases for healthcare.
## 2 Research Method
### FPHFA Model
#### 2.2.2 Bi LSTM for Long-Term Series Learning  
To overcome the problem of gradient reduction or gradient explosion, the LSTM is designed with a special cell storage structure. There are three gate structures for each LSTM cell structure, namely, the input gate, the output gate, and the forget gate. The specific derivation of the LSTM layer is as follows.  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{3}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{4}\]  
\[\widetilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{5}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{6}\]  
\[O_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}\ ]+b_{o}) \tag{7}\]  
\[h_{t}=O_{t}*\tanh(C_{t}) \tag{8}\]  
Here \(W_{f}\), \(W_{i}\), \(W_{C}\), and \(W_{o}\) are the input weights, \(b_{f}\), \(b_{i}\), \(b_{c}\) and \(b_{o}\) are the deviation weights, \(t\) is the time state at the moment, \(t-1\) is the last time condition, \(x_{t}\) is the input vector, and \(h_{t}\) is the output vector. The forget gate, \(f_{t}\), determines which data from the cell state should be deleted. The input gate, \(i_{t}\), determines what new data should be logged in the cell state. \(\widetilde{C}_{t}\) is a neuron with a self-recurrent cell like an RNN. \(C_{t}\) is the internal storage unit of the LSTM block. The feature matrix \(H=[h_{1},h_{2},\cdots,h_{t}]\) is the LSTM layer's output.  
LSTM has the limitation that it can perform work with previous content but cannot use predictions from future data. [PERSON] and [PERSON] [33] introduced the idea of the bidirectional regression neural network (BRNN), which was combined with the LSTM to form the Bi LSTM. It has two distinct hidden LSTM layers with contrasting output directions. With this structure, the output layer can make use of both past and future information.  
\[\overset{\rightarrow}{f}_{t}=\sigma(\overset{\rightarrow}{W}_{f}\cdot[ \overset{\rightarrow}{h}_{t-1},\overset{\rightarrow}{x}_{t}]+\overset{ \rightarrow}{b}_{f}) \tag{9}\]  
\[\overset{\rightarrow}{i}_{t}=\sigma(\overset{\rightarrow}{W}_{i}\cdot[ \overset{\rightarrow}{h}_{t-1},\overset{\rightarrow}{x}_{t}]+  
\[\stackrel{{\leftarrow}}{{O}}_{t}=\sigma(\stackrel{{ \leftarrow}}{{W}}_{o}\cdot[\stackrel{{\leftarrow}}{{h}}_{t-1}, \stackrel{{\leftarrow}}{{x}}_{t}]+\stackrel{{ \leftarrow}}{{b}}_{o}) \tag{19}\] \[\stackrel{{\leftarrow}}{{h}}_{t}=\stackrel{{ \leftarrow}}{{O}}_{t}\ast\tanh(\stackrel{{ \leftarrow}}{{C}}_{t})\] (20) \[h_{t}=\stackrel{{\rightarrow}}{{h}}_{t}\ast\stackrel{{ \leftarrow}}{{h}}_{t} \tag{21}\]  
The above formulas show the Bi LSTM layer function. The positive and negative directions of the process are each represented by a separate directional arrow. The variable \(h_{t}\) is concatenated by \(\stackrel{{\rightarrow}}{{h}}_{t}\) and \(\stackrel{{\leftarrow}}{{h}}_{t}\), which represents the final result of the Bi LSTM cell. Through the process described above, the Bi LSTM enables the acquisition of the characteristics of past and future time series data and generates prediction outputs based on past and future contexts.
## 2 Problem Statement
### Extended Bounded Real Lemma  
This section extends the bounded real lemma to polytopic-LPV system, consider the following systems  
\[G_{y\omega}\left(z,\rho\right):\left\{\begin{aligned} x\left(k+1 \right)&=A\left(\rho\right)x\left(k\right)+B\left(\rho\right) \omega\left(k\right),\\ y\left(k\right)&=C\left(\rho\right)x\left(k\right)+D \left(\rho\right)\omega\left(k\right),\end{aligned}\right. \tag{15}\]  
where \(x\left(k\right),\omega\left(k\right),y\left(k\right)\) denote the state variables, disturbances and measurements, respectively. \(\rho\in\mathcal{P}_{\rho}\) is a time-varying parameter vector with \(\rho_{i}\in\left[\rho_{i},\tilde{\rho}_{i}\right]\). Assuming that parameter space \(\mathcal{P}_{\rho}\) is a convex hull, the system (15) can be presented as a polytopic form  
\[\left[\begin{array}{cc}A\left(\rho\right)&B\left(\rho\right)\\ C\left(\rho\right)&D\left(\rho\right)\end{array}\right]\overset{\Delta}{=} \sum\limits_{i=1}^{N}\eta_{i}\left[\begin{array}{cc}A_{i}&B_{i}\\ C_{i}&D_{i}\end{array}\right], \tag{16}\]  
where \(N\) is the number of vertices, \(\sum\limits_{i=1}^{N}\eta_{i}=1\) and \(\eta_{i}\geq 0\). The \(\mathcal{H}_{\infty}\) performance is defined as Equation (17) to guarantee the asymptotically stability of system (15)  
\[\left\|G_{y\omega}\left(z,\rho\right)\right\|_{\infty}=\sup\limits_{\left\| \omega\left(k\right)\right\|_{2}\
eq 0}\frac{\left\|y\left(k\right)\right\|_{2}}{ \left\|\omega\left(k\right)\right\|_{2}}. \tag{17}\]  
An extended Bounded real lemma can be derived from the results in [23; 24].  
**Lemma 1**.: _Given the system (15) and for all \(\rho\in\mathcal{P}_{\rho}\), \(G_{y\omega}\left(z,\rho\right)\) is asymptotically stable with \(\left\|G_{y\omega}\left(z,\rho\right)\right\|_{\infty}<\gamma\), if there exists a symmetric positive definite matrix \(\mathcal{P}\) satisfying that_  
\[\left[\begin{array}{cccc}-\mathcal{P}&0&\mathcal{P}A\left(\rho\right)& \mathcal{P}B\left(\rho\right)\\ *&-\gamma I&C\left(\rho\right)&D\left(\rho\right)\\ *&*&-\mathcal{P}&0\\ *&*&*&-\gamma I\end{array}\right]<0. \tag{18}\]  
Lemma 1 can be proved by definition of a Lyapunov function  
\[V\left(x\left(k\right),\rho\right)=x^{T}\left(k\right)\mathcal{P}x\left(k\right)\]  
such that  
\[V\left(x\left(k+1\right),\rho\right)-V\left(x\left(k\right),\rho\right)+\gamma ^{-1}\|y\left(k\right)\|_{2}^{2}-\gamma\|\omega\left(k\right)\|_{2}^{2}<0 \tag{19}\]  
for all \(k=0,1,\cdots,k+1\). In this paper, a parameter-independent Lyapunov function is defined. In order to achieve a less conservative solution, the parameter-dependent matrix \(\mathcal{P}\left(\rho\right)\) is designed in [25; 26].
## 1 Introduction  
Astronomy is an ancient science that has far-reaching and wide-ranging effects on human beings. However, further development in this field requires new advanced experimental equipment. Astronomical telescopes are an indispensable tool for observing distant planets, galaxies, and other astronomical objects. Aspherical and freeform optics have been extensively used in astronomical telescopes to improve imaging performance, reduce size, and minimize weight [1]. For example, our research team produced a SiC aspherical mirror with a form accuracy and a root-mean-square (RMS) of 18 nm over a 4 m aperture [2, 3]. However, aspheric surfaces cannot be measured directly using an interferometer. Instead, it is necessary to use a corrector to change a spherical wavefront to an aspherical one, and the interferometer can then be adopted for aspherical testing, as for a general sphere [4].  
In recent years, diffractive optical elements have played an important role in the field of imaging systems [5], X-ray focusing [6], laser beam shaping [7] and optical system testing [8]. Computer-generated holograms (CGHs) are specially designed diffractive optical elements that can create reference wavefronts for arbitrary aspherical surfaces. They are convenient and efficient corrections used to test the form accuracy of aspherical mirrors. To some extent, the accuracy and validity of interferometric measurements depend on the accuracy of the CGHs. Therefore, CGH fabrication errors must be budgeted or calibrated accurately.  
The duty cycle and etching depth are two important geometric parameters of CGHs, which determine the diffraction efficiency and phase modulation. These geometric parameters of CGHs can be measured directly using a white light interferometer [9] or a scanning electron microscope [10]. However, these methods represent local micro-investigations, and they cannot be used to evaluate the accuracy of CGHs directly. Diffractive optics calibrators (DOCs) are a promising method for the accurate measurements of CGHs based on spectroscopic scatterometry [11; 12; 13]. According to scalar approximation theory, the duty cycle and etching depth of the CGHs can be determined simultaneously by fitting the measured intensities from spectroscopic scatterometry to the grating parametric model [14]. However, the scalar approximation has imperfections for small-period structures that led to phase errors, which will cause the fitting results to be inaccurate in the DOCs as well.  
This study aims to ascertain the measurement errors in DOCs using rigorous finite-difference time-domain (FDTD) simulations for cases when scalar approximation fails to accurately describe CGHs in optical testing. Measurements of the fabrication errors of two gratings will be used to verify the imperfections in the scalar approximation for the evaluation of CGHs. The results will show that the effects of imperfections cannot be ignored in small-period structures. There is great significance in the development of rigorous evaluation methods to improve the accuracy of CGH measurements.