esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 3 Methodology
### Construction of the GPP Estimation Model  
By exploring a range of machine learning algorithms, we investigated the complex interplay between input variables and GPP to develop the estimation model. These algorithms include LightGBM [45], CatBoost [46], PyTorch neural network model [47], ExtraTrees [48], XGBoost [49], and Random Forest [50], all of which are well-established tools for GPP estimation, each with its own set of strengths and characteristics. We leveraged AutoGluon's automated feature engineering and hyperparameter tuning capabilities to split the dataset into training and validation sets, optimizing the parameter settings for each model to achieve the best possible performance. A total of 11,222 records from 72 sites were used for model training and validation. The training set and validation set were divided into an 80:20 ratio, and ten-fold cross-validation was employed for training. The final estimates were derived from the mean outcomes obtained through the ten-fold cross-validation procedure. The remaining data were reserved for accuracy testing and comparison. Following a series of experiments and evaluations, we identified LightGBM as the top-performing method. LightGBM utilizes gradient boosting methodologies, which entail sequentially training a series of weak learners, usually decision trees, and subse  
Figure 3: Steps of constructing spatial weights matrix and extracting eigenvectors.  
quently aggregating them to form a robust model. Furthermore, as depicted in Figure 4, we incorporated spatial factors into our modeling approach and developed a spatial autocorrelation LightGBM-based model (SA-LGBM) to enhance GPP estimation. This model fully accounts for the similarities and spatial correlations between different locations, allowing for a better capture of GPP's spatial distribution.
## 4 Evaluation of Biological Properties
### Hypolipidic Activity  
In the objective to investigate the lipolysis effect of phytosterols isolated from mulberry (_Morus alba_) leaves, [PERSON] et al. observed that daucosterol exhibits a concentration-dependent lipolysis activity with very high potency. They also hypothesized that doctors could use daucosterol to manage certain types of disease, which can confer medicinal principles [35]. [PERSON] et al. [129] screened for natural resources with hypolipidemic activity in _Bauhinia racemosa_ leaves using high-fat diet (HFD)-fed hamsters. The results showed that _Bauhinia racemosa_ leaf ethanolic extract exhibits a strong hypolipidemic action due to its content of sitosterol-\(\beta\)-D-glucoside and other phytoconstituents, which could act additively or synergistically. The authors also indicated that _Bauhinia racemosa_ leaf alcoholic extract rich in daucosterol could be used as a herbal drug to treat hypercholesterolemia and hypertriglyceridemia, a comprehensive approach to hypercholesterolemia and hypertriglyceridemia dyslipidemia management [129].  
In addition, red yeast rice (RYR) used in traditional Chinese medicine could be an important product to prevent hyperlipidemia and dyslipidemia due to its content of more than 101 chemical agents, including daucosterol. According to experiments, RYR has been shown to significantly reduce total cholesterol, triglyceride, and low-density lipoprotein cholesterol (LDL-C) levels and increase high-density lipoprotein cholesterol (HDL-C) levels [130]. The underlying pathways of drug action involve increased mRNA levels of the farnesoid X receptor and peroxisome-proliferator-gamma-activated receptor, key receptors in cholesterol metabolism and bile acid homeostasis [131].  
[PERSON] and [PERSON] extracted two bioactive molecules, \(\beta\)-sitosterol glycoside and scopoletin, from _Ipomoe digitate_ root ethanolic extract. The tuberous root of this plant, mainly used in traditional medicine, has been shown to exhibit hypolipidemic action, potently lowering serum total cholesterol and LDL-C [45]. In the in vivo study performed by [PERSON] and [PERSON] [132] on animals suffering from hypercholesterolemia, treatment with \(\beta\)-sitosterol \(\beta\)-d-glucose reduced \(\beta\)-lipoproteins by 46% and blood cholesterol by 31% while normalizing the cholesterol/phospholipid level, whereas administration of \(\beta\)-daucosterol did not affect the \(\beta\)-lipoprotein and cholesterol levels in the blood serum in control animals. In experimental hypercholesterolemia, the suspected mechanism of lipid-lowering activity of the drug is the stimulation of phospholipid synthesis [132]. Further studies investigating the hypolipidemic capacity of daucosterol are needed to clarify the different pathways implicated in this pharmacological property to prevent the various diseases associated with lipid disorders.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 4 Infrared and Visible Image Fusion Dataset
### MSRS Dataset  
The MSRS dataset, a novel multispectral dataset of infrared and visible picture fusion built on the foundation of the MFNet dataset, has 1444 pairs of highly aligned infrared and visible image pairs. After removing 125 pairs of unaligned image pairs from the MFNet dataset, [PERSON] et al. first collected 715 pairs of daytime images and 729 pairs of nighttime images. Next, they optimized the contrast and signal-to-noise ratio of the infrared images by using a dark channel-based a priori image enhancement algorithm. A previous algorithm based on dark channels was first proposed by [PERSON] (2016) et al. According to this theory, for any image J, its dark channel can be expressed as:  
\[J^{dark}(x)=\min_{y\in\Gamma_{(x)}}(\min_{\tau\in\{
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 1 Introduction
### Understanding Audience  
Having said that, video plays a major role in evaluating the progress of the online meeting as it reveals how an audience engages with the content, giving the meeting hosts insight and confidence. For the employers, examiners, teachers, and online meeting hosts, the need to understand the behavior and attentiveness of the online audience naturally arises.  
First, understanding the behavior and attentiveness of the audience can help to improve the effectiveness of the meeting or exam. One study [16] observed that individuals who pay attention to the information are more likely to retain it and perform better on tasks compared to those who are likely to be distracted (also [17; 18]). By using video analytics to track attentiveness, employers, examiners, and teachers can identify any issues that may be impacting the ability of the audience to engage with the material, and take steps to address them.  
Second, understanding the behavior and attentiveness of the audience can help to ensure the integrity of the meeting or exam. For example, in an online exam setting, video analytics can be used to detect and prevent cheating by identifying behaviors that are inconsistent with an individual working alone [19].
# Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface
## 3 Results and Discussion
### Reflection  
To make a direct comparison of the reflection effect between laser-treated and untreated region, we collected the reflectance spectra. Figure 5 shows the surface reflectance spectra of the untreated sample and the sample after laser processing. It can be seen from the figure that the reflectivity of the untreated sample is -50% between 800 and 200 nm. This means about half of the light can be reflected. The reflection of IR light may result in loss half of the light energy irradiated on the surface. In this case, the photothermal conversion rate is not high. On the contrary, the reflectivity of the laser-treated sample from 300 to 2000 nm is less than 8%. The lowest reflectivity around 500 nm is only 2%. At the same time, it can be seen from the figure that the reflection intensity of the laser-processed sample is much lower than that of the initial sample. It can be preliminarily judged that the sample after laser processing shows much higher anti-reflection effect, which promotes light absorption and the light-heat conversion. Therefore, under stable light source conditions, more light energy can be harvested from the laser-treated titanium alloy surface.
# Study of Effective Length Factor of Frame-Core Wall Structure with Cross-Layer Columns
## 2 Effective Length Modification for Frame-Core Wall Structures with Cross-Layer Columns
### Modification of Effective Length Factor  
The frame column linear stiffness was obtained using the D-value method, and the effective length factor of the frame column was then obtained using GB 50017-2017, so as to derive the ultimate bearing capacity of the column using the Euler formula. This method only considers the column end constraints, and ignores the distribution and interaction of frame columns; in particular, for the frame-core wall structure with cross-layer columns, it ignores the sudden change in lateral stiffness caused by the cross-layer column and the supporting effect of the core. Therefore, this calculation method has a large deviation from the actual engineering results.  
For the frame-core wall structure with cross-layer columns, firstly, the ultimate bearing capacity \(P_{F,\sigma}\) of the single floor can be calculated according to GB 50017-2017. Combined with the finite element calculation results, the percentage of vertical load on a single cross-layer column in the vertical load on all columns of this floor can be obtained. Then, Equation (7) is used to obtain the modified ultimate bearing capacity \(P_{F,\sigma}\)i of a single cross-layer column on the basis of GB 50017-2017. \(R_{FW}\), \(R_{F}\) are the design values of gravity loads at the bottom of the frame-shear wall and frame, respectively.  
\[P_{F,\sigma}=\frac{N_{i}}{\sum\limits_{i=1}^{n}N_{i}}\cdot\frac{R_{FW}}{R_{F} }\cdot P_{F,\sigma}=\frac{N_{i}}{\sum\limits_{i=1}^{n}N_{i}}\cdot P_{F,\sigma} \tag{7}\]  
The corrected effective length factor \(\mu^{\prime}\) of the cross-layer column is inverted by [PERSON]'s formula, shown in Equation (8).  
\[\mu^{\prime}=\sqrt{\frac{\pi^{2}EI_{i}}{h^{2}\cdot P_{F,\sigma}i}}=\sqrt{\frac {\pi^{2}EI_{i}}{h^{2}\cdot P_{F,\sigma}r}}\cdot\frac{\sum\limits_{i=1}^{n}N_{i} }{N_{i}} \tag{8}\]  
Figure 2: Column effective length factor study ‘seven-rod model’. (**a**) Analytical model with lateral shift frame; (**b**) lateral shift-free frame analysis model.
## 2 Autonomous Driving Control Algorithm
### Path-Tracking Control Algorithm
Details of the equations to calculate the coordinate of a target point using the enclosed-based LOS guidance algorithm can be found in [22]. The difference from the method in [22] is if the distance between the current vehicle's location and the current waypoint's location is smaller than radius R, the coordinate of the target point is set to the coordinate of the current waypoint. Figure 4: Enclosed-based line-of-sight (LOS) guidance principle with radius R [11]. In the final step, to compute the control parameter is to calculate the value of the left and right track speed to reach a target point. The method of calculating the control parameters is explained through two cases. The first case is that the vehicle rotates in situ when the distance between the current vehicle's location and the current waypoint's location is smaller than radius R. In this case, the same constant value is assigned to the left and the right track speeds, and the sign is assigned differently according to the rotation direction. For example, in the case of a left rotation in situ, the signs of the left and right track speeds are assigned as plus and minus, respectively. In the case of a right rotation in-situ, the signs of the left and right track speeds are assigned as minus and plus, respectively. The second case is for calculating the velocity of the left and right track to drive the vehicle to a target point when the distance between the vehicle's current location and the current waypoint's location is larger than radius R. To calculate the velocity of the left and right track, the target angle and the target speed are computed. The target angle (\(\alpha_{TP}\)) is the difference between the current vehicle's yaw and the azimuth of the straight line generated by the vehicle's current location and the target point's location, which is calculated by:  
\[\alpha_{TP}=\text{atan2}\bigg{(}\frac{TP_{East}}{TP_{North}}\bigg{)}-\psi, \tag{9}\]  
where \(TP_{North}\) and \(TP_{East}\) are the north and the east coordinates of a target point, which are defined by the NED relative coordinate system with the origin at the vehicle's current location, and \(\psi\) is the yaw of the vehicle. The target speed (\(V_{TP}\)) is defined based on the target angle as follows:  
\[V_{TP}=V_{min}+(V_{max}-V_{min})e^{-\frac{\alpha_{TP}2}{\sigma^{2}}}, \tag{10}\]  
where \(V_{min}\) is the lower bound of the speed, \(V_{max}\) is the upper bound of the speed, and \(\sigma\) is the design parameter to alter the slope of the speed curve. The velocities of the left and right tracks are calculated as:  
\[\left[ \begin{array}{c}v_{left}\\ v_{right}\end{array} \right]=\left[ \begin{array}{cc}1&1\\ T_{r}/2&T_{r}/2\end{array} \right]\left[ \begin{array}{c}V_{TP}\\ \alpha_{TP}/\Delta t\end{array} \right], \tag{11}\]  
where \(v_{left}\) and \(v_{right}\) are the left track velocity and the right track velocity, respectively; \(T_{r}\) is the distance between the left track and the right track; and \(\Delta t\) is the control time interval.
## 1 Introduction
### Critical Drilling Operations
#### 1.2.3 Counter-Action Support  
Besides the detection of already occurring or possible upcoming crises, there is also a need for counter-action support. This means that the system provides guidelines and suggestions for the particular handling of crises to system operators. This can involve the use of simple electronic crisis handling checklists (similar to the practices used by aircraft crew). In addition, more sophisticated methods, e.g., semantic decision tables or even offline trained classifiers based on the experience of a large amount of rig data (aggregating offline knowledge from multiple rigs and boreholes), might be feasible [3].
## 7 Conclusions
Calibrations can be provided in the internationally accepted IGS ANTEX standard, or directly applied as corrections to RINEX files in the event that the chosen processing software does not support, or only partly supports, the ANTEX format. Examining different binning widths of 0.1\({}^{\circ}\), 1\({}^{\circ}\), and 5\({}^{\circ}\) for the elevation-based averaging method revealed, in some cases, a slight performance decrease for the standard 5\({}^{\circ}\) resolution over the 1\({}^{\circ}\) and 0.1\({}^{\circ}\) binning widths. Given the presence of the higher amount of potential outliers in the 0.1\({}^{\circ}\) and the increased computational costs, the 1\({}^{\circ}\) resolution appears to be a good trade-off between effectiveness and smoothing. The reported PCOs of the PPK process (Figure A3) are given with respect to the combined \(L_{1}+L_{2}\) PCO and are close to the \(L_{1}\) PCO. They are consistent with using no PCV corrections, while the estimated PCVs are with respect to the same phase center as obtained without calibration. The presented PCO estimations are subject to improvement due to uncertainty introduced by the manual height measurement. However, even without respecting the PCO, the estimated PCVs are strongly beneficial for deformation or atmosphere monitoring. Further testing using multiple frequencies could widen the application from the current GPS-only to all GNSS frequencies. The provided PCVs and PCOs and the online service to perform the antenna calibrations in the field can be helpful for the widespread application of low-cost antenna and receiver setups for high-precision applications. Conceptualization, A.K., H.v.d.M., N.v.d.G. and M.-C.t.V. ; methodology, A.K., H.v.d.M. and M.-C.t.V. ; software, A.K. and H.v.d.M. ; validation, A.K., H.v.d.M., N.v.d.G. and M.-C.t.V. ; data curation, A.K. and H.v.d.M. ; writing--original draft preparation, A.K. ; writing--review and editing, H.v.d.M., N.v.d.G. and M.-C.t.V. ; visualization, A.K. ; supervision, H.v.d.M. and M.-C.t.V. ; funding acquisition, N.v.d.G. and M.-C.t.V. All authors have read and agreed to the published version of the manuscript. The work received funding from the European Community's Horizon 2020 Program (2014-2020) under grant agreement No. 776691 (TWIGA). This project has also received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 700699 (BRIGAID). Data Availability Statement:Data are made available via the TU Delft/4 TU data repository: [[https://doi.org/10.4121/14743014](https://doi.org/10.4121/14743014)]([https://doi.org/10.4121/14743014](https://doi.org/10.4121/14743014)) (accessed on 9 February 2022). The authors declare no conflict of interest.
# Instantaneous Networking Service Availability for Disaster Recovery
## 5 Restoration of Networking Service Availability for Users in Multiple Areas
### Network Restoration Based on LI-Cloud
\\ \hline \(PC_{CaseAA}\) & The emergence probability of CaseAA \\ \hline \(PC_{CaseBB}\) & The emergence probability of CaseBC \\ \hline \(PC_{CaseAC}\) & The emergence probability of CaseAG \\ \hline \(PC_{CaseBG}\) & The emergence probability of CaseBG \\ \hline \(PC_{CaseCG}\) & The emergence probability of CaseCG \\ \hline \(F_{AA}\) & Probability that X is in A  
Let CaseCG denote that one user in C communicates with one user over the global Internet. Let Probability of (CaseCG) be \(PL_{CaseCG}\). We have  
\[PL_{CaseCG}=F_{CG}\cdot P_{C\sim G}(t). \tag{21}\]  
The availability probability of \(X\sim Y\) is then expressed as  
\[ \begin{split} PL(X\sim Y)&=PL_{CaseAA}+PL_{CaseAB}+PL_ {CaseAC}\\ &+PL_{CaseAG}+PL_{CaseBB}+PL_{CaseBC}+PL_{CaseBG}\\ &+PL_{CaseCC}+PL_{CaseCG}.\end{split}  \tag{22}\]  
Consequently, the average improvement on the I-NSA by utilizing multiple local clouds can be expressed as  
\[ \begin{split} Improve_{Multi}&=PL(X\sim Y)-PC(X \sim Y)\\ &=PL_{CaseAA}+PL_{CaseBB}+PL_{CaseCC}-PC_{CaseAA}-PL_{CaseBB}-PC_ {CaseCC}\\ &=F_{AA}\cdot P_{A}(t)\cdot(1-P_{AG}(t))+F_{BB}\cdot P_{B}(t) \cdot(1-P_{BG}(t))+F_{CC}\cdot P_{C}(t)\cdot(1-P_{CG}(t)).\end{split}  \tag{23}\]  
To summarize the previous statement and equation, the average improvement of I-NSA for any two users, in all cases of their location, depends on the user location, and the probabilities of the local and global connectivity at the time.
## 3 Definition of Main Design Parameters for Three-Phase Six-Switch Converter Example
### Pre-Design of 10 kW SiC Light-Load Converter Stage  
As already mentioned in Section 1 and discussed in Section 2 of this manuscript, the equipped additional WBG-based semiconductor components should be designed for considerably lower rated power, in order to support the system with outstanding efficiency during the light-load operation. Furthermore, it benefits from already existing DC-link and input inductors. In a best-case scenario, the light-load converter should be rated for at least \(<\)10% \(P_{\text{N}}\). For the current example, a light-load rating of 8% regarding the SiC converter stage was chosen, which results in a rated power of 10 kW. In order to keep the rule of thumb from [15] and allow comparable results, Infineon's IMZ120R030M1H [17] 1200 V/30 m\(\Omega\) with a current rating of 45 A for a case temperature of 100 \({}^{\circ}\)C is utilized for the WBG-based hookup. It is worth noting that the choice of semiconductors and components such as input/output inductors always highly dependent on the application and dedicated parameters. The chosen parameters in Sections 3.1 and 3.2 should give the reader an idea of relevant effects and considerations for such light-load converter stages and can thus vary for final product design.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 2 Proposed Model
### Data Pre-Processing  
The raw HRRP data of radar targets have aspect sensitivity, amplitude sensitivity, and time-shift sensitivity [26], which have a significant impact on model recognition performance. Therefore, before using the model to recognize, this paper applies Euclidean norm to the raw ship target HRRP to remove the amplitude sensitivity. Suppose the HRRP data after amplitude normalization is:  
\[\mathbf{p}=[p_{1},p_{2},\dots,p_{L}]^{T} \tag{1}\]where \(L\) is the number of distance units and \(p_{i}\) is the normalized amplitude of the \(i\)th distance unit. When using HRRP data for target recognition, the time domain HRRP is converted into sequence data and then input into the model for target recognition.  
We use the sliding window method proposed in the literature [27] to transform the HRRP data into serial data after amplitude normalization in the following steps. The amplitude normalized HRRP data is intercepted as a short sequence containing multiple range cells by means of sliding window.  
\[\mathbf{X}=[\mathbf{x_{1}},\mathbf{x_{2}},\dots,\mathbf{x_{T}}] \tag{2}\]  
\[\mathbf{x_{l}}=\left[p_{(l-1)<1},p_{(l-1)<2},\dots,p_{(l-1)<d}\right] \tag{3}\]  
where \(\mathbf{x_{l}}\in\mathbb{R}^{d\times 1}\) is the input at moment \(l\) with length \(d\). The matrix \(\mathbf{X}\in\mathbb{R}^{d\times T}\) corresponds to \(T\) time steps with data of dimension \(d\) at each time step.
## 2 Study on the Compressive and Tensile Mechanical Properties of Shale  
### Uniaxial Compression and Brazilian Splitting Tests  
The compressive and tensile mechanical properties of shale are often measured through uniaxial compression tests and Brazilian splitting tests. In this study, shale outcrops were obtained from Changing District, Yibin City, Sichuan Province. Drilling core samples following the direction of parallel bedding planes and vertical bedding planes (Figure 1a), uniaxial compression (Figure 1b), and Brazilian splitting samples (Figure 1c) were prepared according to international rock mechanics standards. Basic physical parameter measurements were conducted on parallel bedding and vertical bedding shale samples, and the results are shown in Table 1.  
The experiments were conducted using a TAR-1500 rock mechanics testing machine (Changchun, China). The TAR-1500 testing machine features full servo control, with a maximum vertical loading capacity of 600 kN. The axial displacement measurement range is \(\pm\)100 mm, while the lateral displacement measurement range is 0-200 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **Bedding** & **Sample** & **Thickness \(H\)** & **Diameter \(D\)** & **Mass \(M\) (g)** & **Volume \(V\)** & **Density \(\rho\)** \\
**Type** & **Bedding** & **Number** & **(mm)** & **(mm)** & **Mass \(M\) (g)** & **(cm\({}^{3}\))** & **(g/cm\({}^{3}\))** \\ \hline \multirow{4}{*}{Uniaxial Compression} & \multirow{2}{*}{Parallel} & sc-H-1 & 50.12 & 25.04 & 62.62 & 24.67 & 2.54 \\  & & sc-H-2 & 50.38 & 25.03 & 63.49 & 24.78 & 2.56 \\  & & sc-H-3 & 49.71 & 25.28 & 63.38 & 24.94 & 2.54 \\ \cline{2-8}  & \multirow{2}{*}{Vertical} & sc-V-1 & 49.42 & 25.22 & 63.84 & 24.68 & 2.59 \\  & & sc-V-2 & 49.41 & 25.25 & 63.38 & 24.73 & 2.56 \\  & & sc-V-3 & 49.88 & 25.23 & 63.49 & 24.92 & 2.55 \\ \hline \multirow{4}{*}{Brazil Splitting Test} & \multirow{2}{*}{Parallel} & sc-H-1 & 15.37 & 24.88 & 18.52 & 7.47 & 2.48 \\  & & sc-H-2 & 15.29 & 24.96 & 18.43 & 7.48 & 2.46 \\ \cline{1-1}  & & sc-H-3 & 15.33 & 24.87 & 18.48 & 7.44 & 2.48 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{vertical} & sc-V-1 & 15.26 & 25.20 & 20.01 & 7.61 & 2.63 \\ \cline{1-1}  & & sc-V-2 & 15.48 & 25.21 & 19.95 & 7.72 & 2.58 \\ \cline{1-1}  & & sc-V-3 & 15.21 & 25.20 & 20.03 & 7.58 & 2.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Determination results of basic physical parameters of shale samples.  
Figure 1: Diagram of core drilling method and shale sample preparation.