esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In Implantable Biomedical Microsystems (IBM), a wireless interface is used for transmission of power and data between the internal and external parts of the system (Figure 1). The most important issues in a wireless link for IBM are data transfer rate, power consumption, and chip area [1; 2; 3]. It is evident that in a forward data transfer, the maximum data rate is desired, especially in applications such as visual prostheses in which the implanted microsystem is in direct contact with the central neural system [4]. Furthermore, due to the power loss in the power-transmission circuitry and power dissipation in the tissue [5], the maximum frequency of the carrier signal for the IBMs is limited to a few tens of megahertz [1; 2; 3; 4; 5].  
There have been proposed various modulation techniques for wireless data transfer [1; 4; 6]. Making comparisons between the various digital modulation schemes from different point of views, reveals that binary phase-shift keying (BPSK) structure is more appropriate in most IBMs [7; 8; 9; 10; 11; 12; 13]. Generally, this scheme provides proper insensitivity to amplitude noise, high data rate, and good power transfer efficiency. Moreover, if a capacitive link is utilized for data transmission, fast phase variations can be attained within every carrier cycle and data-rate-to-carrier-frequency (DRCF) ratios of as high as 100% are achievable in this scheme [1].  
BPSK demodulation can be realized in two ways: coherent and non-coherent structures. In coherent detection, phase synchronization between the received signal and the receiver is essential [8]. Most of the digital coherent demodulators consume lots of power and suffer from circuit complexity. Consequently, non-coherent BPSK demodulators are widely used in radio frequency applications and IBMs due to their lower circuit complexity, occupying less chip area, and lower power consumption, despite higher bit error rates [9; 10].  
Most of the non-coherent BPSK demodulation schemes are designed based on edge-detection technique [1; 14], pulse width measurement [15], and filtering techniques [16]. Among all the benefits of these circuits, there are some disadvantages, like high power consumption and occupying a large area. This article introduces a new high-data-rate, low-power, non-coherent BPSK demodulator for fixed-in position implantable biomedical applications such as cochlear implants and visual prostheses, where the link properties such as distance between receiver and transmitter, orientation, and alignment are easily available [1]. The proposed demodulator is based on using a delayed digitized format of the input signal for the detection of the received data as well as for recovering the clock signal. The operation of the proposed circuit is closely comparable in terms of power consumption, occupied active area, and reliability to most of the existing similar works.
## 5 Stability Evaluation of Three-Dimensional Numerical Simulation
### Simulated Conditions and Parameters  
To study the slope stability under different water impoundment conditions, the following conditions are established to analyze the slope changes:  
1. Condition I: water level (2785 m) + cable support;
2. Condition II: water level (2815 m) + cable support;
3. Condition III: water level (2845 m) + cable support.  
The numerical simulation uses the back analysis results during the second stage of water impoundment as the parameters, as shown in Table 6. The material model used is Mohr Coulomb.
## 1 Introduction
Numerous experimental studies using particle image velocimetry have analyzed relationships between mechanical parameters and horizontal displacements along walls, characterizing slip surface geometries, displacements, and shear strains in passive zones [27; 28; 29; 30; 31]. Discrete element numerical simulations [32; 33; 34; 35] have further reproduced the deformation characteristics of cohesionless backfills under different displacement patterns. Nevertheless, critical knowledge gaps persist:  
1. Simplistic critical displacement thresholds: Existing theories arbitrarily define critical displacements as fixed values (e.g., 0.01% \(H\)-0.05% \(H\)) based on [PERSON]'s assumptions [12], neglecting their dependence on wall displacement modes. 2. Methodological limitations: Horizontal/slope differential element methods frequently employed in theoretical derivations disregard compatibility with actual soil failure mechanisms under specific displacement patterns. Moreover, assumed relationships between mobilized friction angles and wall displacements lack experimental validation. 3. Neglected displacement effects in seismic conditions: Few studies have systematically integrated displacement modes/states into seismic earth pressure calculations, despite backfill soils rarely reaching limit states during earthquakes. To address these issues, this study develops a computational method for seismic non-limit active earth pressure under RB mode. Incorporating nonlinear depth-dependent variations in the soil internal friction angle and wall-soil interface friction angle, the proposed approach combines pseudo-static principles with the inclined slice method. Validation through experimental comparisons and parametric analyses of seismic acceleration, wall displacement, and friction characteristics provides theoretical support for optimizing seismic design of retaining walls.
## 3 Plenoptic Camera Based Image Processing  
Plenoptic or light-field camera collects the light-field information of a scene by employing specialized design. Unlike the conventional camera, plenoptic camera captures light as well as associated direction information. It contains a micro-lens array (MLA) between the main lens and the image sensor [36], as depicted in Figure 1. Whereby \(\mathrm{L_{i}}\) represents a micro-lens in the grid with a unique identifier \(\mathrm{i}\in\{1,2,3,\dots\}\); \(d_{L}\) corresponds to the diameter of the micro-lens, \(f_{m}\) is the focal length of the main lens, \(a_{m}\) is the distance of the object from the main lens and \(b_{m}\) is the distance between virtual image and the main lens. Moreover, \(b_{Lm}\) is distance between MLA and the main lens, and \(b_{L}\) is the distance of the corresponding virtual image from the MLA, \(b_{Ls}\) is the distance between MLA and the image sensor. The image is formed behind the image sensor, hence it is virtual.  
Within the plenoptic camera, a micro-lens in the MLA focuses a segment of main-lens image on the sensor with its own perspective. These micro-lenses either have similar focal lengths, or they consist of different focal lengths (multi-focus) [37]. Hence, each micro-lens captures a part of the scene corresponding to its position and perspective. As a result, an image that consists of a grid of micro-images with distinct perspectives is captured,  
Figure 1: A setup of focused light-field camera including a main lens, an image sensor and an MLA.  
as shown in Figure 2. As the scene is captured with several focal planes, the output image can be refocused even after it has been captured. In this way, a scene is recorded with several perspectives just by taking a single picture. Moreover, depth of the scene is computed by evaluating this data, that too can be estimated just by single recording of the scene. The depth-of-field (DOF) of the plenoptic camera is higher in comparison to same configuration conventional camera [7]. It also reduces occlusion regions [1] because of its large array of micro-lenses with small field view, which are closely interlaced together.
## 3 Results and Discussion
### Influence of Extraction Solvent and Salt Type  
The selection of the most appropriate and efficient extraction solvent is a crucial parameter affecting the extraction performance of the analytes [22]. The extraction solvent should normally show appropriate extraction efficiency and it must be compatible with the analytical technique. Furthermore, it must form a consistent layer after the step of phase separation. Regarding the agents used for phase formation, they must demonstrate high solubility in water and negligible solubility in the extraction solvent. In our study, different organic solvents were initially examined including methanol, isopropanol, acetonitrile and acetone. Moreover, the phase-forming agents such as sodium chloride, magnesium sulfate, zinc sulfate, ammonium chloride, ammonium sulfate and sodium sulfate were examined for their performance and suitability. Such experiments were performed by using aliquots of 500 \(\mu\)L of artificial urine mixed with 500 \(\mu\)L of the examined extraction solvents. An aliquot of 200 \(\mu\)L of the aforementioned salts was added in each mixture (C\({}_{\mathrm{salt}}\) = 2 mol L\({}^{-1}\)).  
When methanol was used, no phase separation was observed, while phase separation was observed using isopropanol, acetonitrile and acetone in the presence of sulfate salts. However, the addition of magnesium sulfate and zinc sulfate in the salt solution resulted in an organic upper layer that contained a high water amount and was almost twice its initial volume. This phenomenon has also been reported elsewhere [19], and it can be attributed to the simultaneous extraction of polar interferences that could result in a significant matrix effect. Based on these results, both zinc sulfate and magnesium sulfate were excluded from the consequent trials. Analogous results were also obtained in the cases of isopropanol and acetone when mixed with sodium sulfate. On the other hand, the combination of acetonitrile/sodium sulfate resulted in an organic layer with low water content (460 \(\pm\) 30 \(\mu\)L), providing a more suitable option for sample clean-up and the effectiveness of the SI-HLIME procedure. Additionally, acetonitrile exhibited compatibility with the mobile phase used during HPLC analysis, and it was finally adopted for the method development.
## 3 Data Preparation and Analysis
### Data Sources  
The DEM used in the experiment is ASTER GDEM V2 data product with a resolution of 30 m. The optical image data used included Gaofen-2 (GF-2) with a resolution of 1 m and Google imagery. Landsat 8 data were used to obtain the Normalized Vegetation Index (NDVI), and the Sentinel-1A data with a resolution of 5 m \(\times\) 20 m and polarization mode of VV were adopted to obtain the surface deformation information of the study area. Detailed data sources are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Categories** & **Data Scale** & **Data Time Phase** & **Data Sources** \\ \hline DEM & 30 m & 2021 & ASTER GDEM V2 \\ Slope, slope direction, curvature & 30 m & 2021 & Elevation data acquisition \\ GF-2 remote sensing imagery & 1.0 m & 2020, 2021 & Yunnan Remote Sensing Center \\ \hline \end{tabular}
\end{table}
Table 1: Data sources.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Categories** & **Data Scale** & **Data Time Phase** & **Data Sources** \\ \hline Google remote sensing imagery & – & 2018–2021 & Google Earth \\ Quantity of rainfall & 30 m & 2016–2020 & Yunnan Provincial Bureau of Statistics \\ Rivers and roads & – & 2020 & Data from the Third National Land Survey \\ Stratigraphic lithology and faults & 1:50,000 & 2015 & Natural Resources Bureau (NRB) \\ NDVI & 30 m & 2020 & Landsat 8 data \\ Sentinel-1A data & 5 m \(\times\) 20 m & 2018.7–2021.5 & European Space Agency (ESA) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Geographic location of the study area.
## 3 Test Results and Discussion
### Fe-SMA Stress  
Figure 12 shows the trend in strain variation with temperature at the boundary between the activated and non activated regions of the Fe-SMA plate (strain gauge 1) during elevated-temperature activation. The reason for selecting the strain measurement at the boundary is that the activation region cannot accurately monitor the strain in the Fe-SMA due to the elevated temperatures involved. It can be observed that the strain of the Fe-SMA does not change significantly when the temperature is below 100 \({}^{\circ}\)C. When the activation temperature exceeds 100 \({}^{\circ}\)C, the strain of the Fe-SMA begins to increase, and the growth rate gradually increases. After the activation temperature reaches 250 \({}^{\circ}\)C, the temperature begins to decrease. During the cooling process, the strain further increases. It can be found that compared with the heating process, the strain of the Fe-SMA increases more during the cooling process, which further confirms that the metallographic phase transformation of the Fe-SMA is mainly concentrated in the cooling process after temperature activation.  
The magnitude of the recovery stress of Fe-SMA plates in different connection methods is calculated according to Equation (1). Among them, the elastic modulus of the Fe-SMA (\(E_{SMA}\)) is shown in Table 1.  
\[\sigma_{SMA}=E_{SMA}\times\epsilon \tag{1}\]  
Finally, as shown in Figure 12, the recovery stress of the Fe-SMA in the adhesive-bolted hybrid connection is 246 MPa, the recovery stress of the Fe-SMA in the bolted connection is 227.2 MPa and the recovery stress of the Fe-SMA in the adhesively bonded connection is 184.6 MPa. The recovery stress of the Fe-SMA in the adhesively bonded connection is smaller compared to the other two connection methods, because the softening and slip of the structural adhesive during the activation process reduce the total recovery force.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Anchoring Methods**} & **Activated Boundary** & **Stress outside the** & **Stress inside the Bolt** \\  & **Stress/MPa** & **Bolt Hole/MPa** & **Hole/MPa** \\ \hline Adhesive-bolted & & & \\ hybrid connection & 246.02 & 183.81 & \(-\)165.66 \\ Bolted connection & 227.21 & 245.36 & \(-\)414.65 \\ Adhesively bonded & 184.64 & 130.35 & - \\ connection & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stress of Fe-SMA.  
Figure 11: Specimens after activation and reinforcement.
## 3 Background
### Convolutional Neural Network  
Originally, convolutional neural network was designed for computer vision tasks such as image classification, image segmentation, or face recognition. The data format for these tasks is two-dimensional data, such as images and videos. Therefore, the CNNs processing 2D data are often referred to as 2D CNNs. Due to the need to process 1D (one-dimensional) signals, 1D CNNs, a modified version of 2D CNNs, have been developed [30].  
The main component of CNN is convolution kernels, which can extract various features or patterns in the input using convolution operations. The convolution operation uses a convolution kernel, which contains kernel weights to slide over the input sequence and compute a weighted average. The operation convolution with a kernel \(k\) on a univariate time series \(x\) is defined as:  
\[s_{t}=(x\oplus k)[t]=\sum_{i=1}^{n_{k}}x_{t-i+1}k_{i} \tag{1}\]  
where \(s_{t}\) is the output of time step \(t\), \(\oplus\) is the convolution operator, and \(n_{k}\) is the size of the kernel \(k\). The output result is one-dimensional and its length is \(n_{x}-n_{k}+1\), where \(n_{x}\) is number of time steps in \(x\). To obtain an output which has the same length as the input, zero padding is applied to the input sequence before performing the operation convolution. As multivariate time series data contains multiple metrics (channels), the kernel k with only one channel will not be meaningful to apply across different channels because of the nature of each metric. Thus, a multi-channel kernel k is created to treat each channel of the input independently. The convolution operation with a multi-channel kernel \(k\) on multi-channel data \(x\) is:  
\[s_{t}=(x\oplus k)[t]=\sum_{c=1}^{n_{c}}\sum_{i=1}^{n_{k}}x_{c,t-i+1}k_{c,j} \tag{2}\]  
where \(n_{c}\) is number of channels in the data, \(x_{c,t-i+1}\) is the measurement of \(c\)th channel at time step \(t-i+1\). The convolution on the multi-channel data is almost similar as on the single channel, and the difference between these two is an additional summation accross all channels. This summation is the one that implicitly learns the dependency among different metrics in the multi-channel data. Figure 3 illustrates the 1D convolution operation for multivariate time series data.
## 3 Methodology
### Detailed Procedure
#### 3.3.3 Phase 3: Morphology Matrix Construction  
Traditional MA relies on intuition of experts since MA has no systematic method that identifies the dimensions and shapes that are used to fill in the content of the nodes in an MA-based TRM. To overcome these limitations, this study adopted keyword-based MA to analyze the inner mechanisms of technology, the overall structures of products and the relationship between the technology and product layers in a semi-autonomous way. Based on the keyword lists derived in Phase 2, the technology morphology, the product morphology and the hybrid morphology of the technology and products were developed to describe the main characteristics of the target research field hierarchically. The configurations of different shapes were used to fill in the content of the nodes in the MA-based TRM.  
This process was implemented through two steps. In the first step, a data-driven k-means clustering approach was used to analyze the interrelationships of the innovation elements. In this step, the technology keywords and product keywords were classified into different groups automatically according to the k value in a specific interval, which represents the distance between the keywords and the central point. However, the clustering results obtained with the k-means algorithm are insufficient for combining the keywords with the technology or product features. For this purpose, in the second step, a process of qualitative analysis--a similarity-seeking and topic analysis process--was carried out to construct the morphology matrix. During the process of this step, expert judgement played a crucial role in defining the topic of each cluster. Experts selected proper keywords and clustered the keywords by using their professional knowledge. The overly nonspecific and unimportant keywords were excluded in each cluster. The morphology matrix was constructed after the two steps.  
A traditional k-means algorithm must set the k value manually, and this value has great impacts on the clustering results. In order to weaken this influence and to find the best k value in a certain interval, our approach employed the research of [PERSON] (2018), an optimized algorithm based on k-means and the low-energy adaptive clustering hierarchy (LEACH) protocol through simulation experiments, which can determine the best k value in an interval to ensure the overall distance between different clusters is as great as possible while the distance between the vectors in each cluster is as small as possible.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TF-IDF Matrix: (\(k\), m + n)** & **Patent 1** & **Patent n** & **Product 1** & **Product m** \\ \hline Keyword 1 & 0.022 & 0.15 & 0.059 & 0.214 \\ Keyword 2 & 0 & 0.247 & 0.48 & 0.59 \\ Keyword \(k\) & 0.169 & 0.01 & 0.159 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of a term frequency-inverse document frequency (TF-IDF) matrix based on patents and product manuals.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values  
In this section, we first construct dynamic vehicle clusters and then propose a cooperative caching strategy (called THCS) based on temporal convolutional networks and hybrid content value to solve the above problem.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 6 Uncertainties of Reconstructed Wind Characteristics
### Example for a Two-Beam Nacelle Lidar
#### 6.2.3 Uncertainty Budget, Results and Scale Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combined Uncertainty** & \multicolumn{4}{c}{**Uncertainty Term and Order of Magnitude**} \\ \hline \multirow{2}{*}{\(u_{V_{x}}^{2}\) in \([\text{m}^{2}\cdot\text{s}^{-2}]\)} & \(\frac{u_{1}^{2}}{(2\cos\varphi\cos\alpha)^{2}}\) & \(\frac{u_{2}^{2}}{(2\cos\varphi\cos\alpha)^{2}}\) & \(\frac{2r_{1,2}u_{1,2}}{(2\cos\varphi\cos\alpha)^{2}}\) & \(V_{x}^{2}\tan^{2}\varphi\cdot u_{\varphi}^{2}\) & \(V_{x}^{2}\tan^{2}\alpha\cdot u_{x}^{2}\) \\ \cline{2-5}  & \(10^{-3}\) & \(10^{-3}\) & \(10^{-3}\) & \(10^{-7}\) & \(10^{-5}\) \\ \hline \multirow{2}{*}{\(u_{V_{y}}^{2}\) in \([\text{m}^{2}\cdot\text{s}^{-2}]\)} & \(\frac{u_{1}^{2}}{(2\cos\varphi\sin\alpha)^{2}}\) & \(\frac{u_{2}^{2}}{(2\cos\varphi\sin\alpha)^{2}}\) & \(\frac{-2r_{1,2}u_{1,2}}{(2\cos\varphi\sin\alpha)^{2}}\) & \(V_{y}^{2}\tan^{2}\varphi\cdot u_{\varphi}^{2}\) & \(\frac{V_{y}^{2}u_{x}^{2}}{\tan^{2}\alpha}\) \\ \cline{2-5}  & \(10^{-1}\) & \(10^{-1}\) & \(-10^{-1}\) & \(10^{-9}\) & \(10^{-4}\) \\ \hline \multirow{2}{*}{\(u_{V_{H}}^{2}\) in \([\text{m}^{2}\cdot\text{s}^{-2}]\)} & \(\frac{V_{x}^{2}u_{V_{x}}^{2}}{V_{H}^{2}}\) & \(\frac{V_{y}^{2}u_{V_{y}}^{2}}{V_{H}^{2}}\) & \(\frac{2r_{xy}V_{x}u_{V_{x}}u_{V_{y}}}{V_{H}^{2}}\) & \\ \cline{2-5}  & \(10^{-2}\) & \(10^{-4}\) & \(10^{-3}\) & \\ \hline \multirow{2}{*}{\(u_{\theta_{r}}^{2}\) in \([\text{rad}^{2}]\)} & \(\frac{V_{y}^{2}u_{V_{x}}^{2}}{V_{H}^{2}}\) & \(\frac{V_{y}^{2}u_{V_{y}}^{2}}{V_{H}^{2}}\) & \(\frac{-2r_{xy}V_{y}V_{y}u_{V_{x}}u_{V_{y}}}{V_{H}^{2}}\) & \\ \cline{2-5}  & \(10^{-6}\) & \(10^{-4}\) & \(10^{-6}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scale analysis of uncertainty components contributing to uncertainties on \(V_{x}\)\(V_{y}\), \(V_{H}\) and \(\theta_{r}\).