esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 2 Materials and Methods
### Rf  
Several machine learning models are available for predicting the distribution of the soil contaminants. However, when it comes to predicting regression data, such as heavy metal concentrations in soil, a RF model tends to have the best predictive performance [28]. This is the reason for the selection of RF models among the machine learning models in this study. RF is an ensemble method commonly used to process both regression and classification data. It constructs multiple decision trees and outputs either a class (for classification tasks) or an average prediction (for regression tasks), depending on the type of individual trees. In the classification tasks, RF outputs the class that receives the majority vote among all the created decision trees. In contrast, when dealing with regression data, RF calculates the average prediction of all the decision trees [29]. This approach improves the predictive accuracy and controls overfitting by aggregating the results from multiple trees, with each contributing to the final outcome.  
RF models typically make predictions for unsampled locations using patterns learned from training data. In this study, auxiliary data, including elevation, slope, aspect, curvature, and flow accumulation, were added to the location coordinates (latitude and longitude) of the 40 sampling data points when the RF model was applied. The RF model trained with data from 40 sampling locations was then applied to the location coordinates and five types of auxiliary data for the entire area. This enabled the prediction of heavy metal concentrations across the entire sampling region.  
In the RF model, hyperparameters, such as the number of trees in the forest or the maximum depth of each tree, significantly impact the model's predictive performance. Therefore, this study optimized the model by adjusting the available options and parameters using GridSearchCV. A grid was defined to set the options and parameters, and GridSearchCV was used to determine the optimal choices. This process was repeated until the RMSE was minimized. The number of trees in the forest (n_estimators) increased from 10 to 300 in intervals of 50, whereas the max_depth increased from 2 to 10 in steps of 2, along with the option of none. For the min_samples_split parameter, the value increased from 2 to 10. Similarly, the min_samples_leaf parameter was set to a range of 1 to 5. The bootstrapping option was tested for both True and False values. Table 1 summarizes the defined grid of options and parameters for the RF model.
# SDToW: A Slowloris Detecting Tool for WMNs
## 1 Introduction
WMNs are also susceptible to DoS attacks, which can jeopardize network resources [14]. This work presents a Slowloris behavior analysis on WMN. Additionally, we provide a new tool to detect and prevent this attack. Our tool, SDToW, blocks Slowloris attacks without limiting the number of clients' connections. Therefore, our solution avoids blocking legitimate users. We performed our analysis using network traffic logs and by analyzing traffic behavior. This analysis provides useful information concerning the attack behavior. We investigated a real scenario using a Web server and legitimate clients and attackers. Our tool uses filters to extract crucial information from the traffic logs and detect a Slowloris attack. These filters extract the information from the Web server and analyze it on a different node called Concentrator. After that, considering a correct attack identification, our tool blocks the attacker MAC address. The tool blocks the attacker on its associated access point (AP) and avoids further malicious traffic, from that specific attacker, on the WMN. Our tool is separated into three different modules to act on different locations and perform different roles concerning the attack identification process in a WMN. We compared our tool with Snort to analyze its efficiency in blocking attacks and resource consumption. The work is organized as follows. Section 2 presents related work concerning the defense strategies against Slowloris attacks. Section 3 shows an examination of the Slowloris attack behavior. Section 4 describes the proposed tool and details its modules. Section 5 presents the results of the experiments and compares our tool with Snort. Section 6 presents a discussion regarding how SDToW can work with other security applications. The conclusion is presented in Section 7.
## 6 Discussion
### Advantages and Disadvantages of Laboratory-Based Hyperspectral Imaging  
Compared with point spectrometer, laboratory-based hyperspectral imaging has several advantages in extraction of mineralogical information, which are attributed to the high spatial resolution. First of all, the pixel size in hyperspectral image is much smaller than commonly used point spectrometer. For example, the probe size of the ASD FieldSpec-3 pro spectrometer used in this paper is 2 cm in diameter, whereas the pixel size of the HySpec image is 0.3 mm \(\times\) 0.3 mm. The relatively small pixel size makes it possible to detect relatively small amounts of minerals whose spectral features are usually completely masked when measured by point spectrometer. The relatively small pixel size also provides spectra of pure minerals in hyperspectral imaging. Another advantage of hyperspectral imaging is that a large number of spectra are acquired simultaneously. By recording the number of pixels per mineral in the image, the mineralogy of samples can be quantified. This allows HySpex image to more accurately observe the details of minerals in the hand specimen, including mineral types, grain size, crosscutting, spatial distribution, paragenesis, etc., which can be used to determine the structure and texture of the rocks.  
The disadvantage of laboratory-based hyperspectral imaging is relatively low S/N compared to point spectrometer. If a better S/N is required, it will take a long time to collect the images. Even in the same sensor, the different mineral combinations have a certain impact on the S/N. If low-reflectivity minerals are accompanied by the minerals of high-reflectivity (such as quartz), the S/N of image is also higher and the spectra of minerals can be identified. If most of the minerals are dark with low-reflectivity, the low S/N makes it difficult to identify. [PERSON] also noted that it is difficult to identify minerals with a content of less than 5%, unless the sample is a mixture with quartz and the mineral is highly reflective. However, where low reflectance minerals are present, recognition may require 20% or more of the mineral in the sample [48]. Therefore, a smoothing method should be used in actual operations to improve the S/N of the hyperspectral images.
## 4 Discussion  
### LAI and GPP Trend and Correlation Analysis  
[PERSON] et al. [31] used satellite remote sensing data to determine that from 2000 to 2016, the world was becoming greener. Most notably, in China, which accounted for 25% of the total global increase in leaf area, human land use was the key to greening the Earth. The results of this study support this thesis, and a more detailed analysis of the Chinese region was conducted. The LAI and GPP exhibited increasing trends after 2000. This pattern was mainly due to the combined effects of the initiation of national-scale plantation projects (external forcing) and the physiological conditions of the vegetation (intrinsic factor) [32].  
In recent years, forestry projects implemented in China have played a key role in the sequestration of carbon dioxide from land greening and the atmosphere. From 1982 to 2014, the LAI of the plantation area increased from 22% to 23.4%, and there was a major growth trend after 2000 [33]. The carbon sequestration capacity of the Grain for Project could offset about 3%-5% of China's annual carbon emissions (based on emissions in 2010) and about 1% of global emissions. Regarding the global carbon sink, from 2000 to 2010, the projects of returning cropland to forest-grassland have contributed 25% [34]. All these studies indicate that since 2000, with the launch of large-scale national projects, land use management has had a great impact on surface energy allocation, and the LAI and GPP have increased rapidly.  
The surface energy partitioning propagates the changes in the land surface properties into the atmosphere by regulating land-atmosphere feedback effects and influencing the global water and energy cycles [35]. With the change in the LAI, the related vegetation structure and physiological changes affect the canopy conductance, latent sensible heat flux, and surface albedo. Then, the energy exchange between the land and atmosphere is affected [36; 37]. The consequent variations in the climate modulate the interplay between the LAI-related biophysical processes and the surface energy partitioning, and ultimately, the vegetation productivity changes [38]. The process-based diagnostic system modeling in [PERSON] et al. found that the increase in the global LAI was responsible for 12.4% of the cumulative terrestrial carbon sink [39]. [PERSON] et al. found that large-scale ecological engineering improved the environment in northern China, the GPP increased significantly on the Loess Plateau and northeast plain, and the increase in the LAI was the main reason for the increase in the GPP in these areas [40]. In this study, we also found that the correlation between the LAI and GPP was stronger in northern China. Implementing large-scale ecological projects such as the Three-North shelterbelt forest may be an important reason for this phenomenon.
## 4 The Proposed DC-CAAE Method
### The Benchmark Methods  
#### 4.7.1 PCA-Based Methods  
PCA [28] is a common data dimension reduction method, which compresses high-dimensional data by extracting the main feature vectors. PCA maps \(n\)-dimensional features to \(k\)-dimensions, where \(n>k\), where \(k\)-dimensional features become the principal components. In the early research stage of network traffic classification system, PCA was widely used as a convenient and efficient dimension reduction algorithm. In this paper, to ensure the fairness of the comparative experiment, the dimensions of the original data were reduced to 100.
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 3 Materials and Methods: A Pipeline for Dynamic Range Expansion of Video Sequences
### Dynamic Range Expansion
In Equation (5), parameter \(p\) serves as a \"damping\" factor. A larger \(p\) dampens abrupt changes in the mid-level output, thus preventing flickering artifacts in the resulting HDR video sequence. However, an increased \(p\) also introduces a \"delay\" in the expansion operator's responsiveness to subtle scene shifts, which might be perceptible to an observer. For 24 fps video sequences, we determined an optimal \(p\) value range between 0.1 and 0.3, effectively mitigating flickering artifacts without introducing noticeable delays.
## 4 Experiments and Analyses
### Implementation Details  
The experiments in this paper were conducted under the Linux system, based on Python 3.8 and PaddlePaddle 2.3.2 implementation. The hardware configuration was Nvidia-Quardo-P5000 16G with CUDA11.0 and CUDNN8.0. For selecting hyperparameter values, a random set of values was first initialized. Then, we used these initial values to train the model on the training data, continuously adjust the hyperparameter values, and record the combination of hyperparameter values when the model performance reached an optimal level. The same hyperparameter value selection method was applied throughout Section 3. For the key information extraction module, RVI-LayoutXLM, the number of layers \(N\) of the RVI-LayoutXLM coding layer is 12, and the number of attention heads per layer \(n\) is 12. It has a model depth \(d_{model}\) of 768, a depth \(d_{k}\) of 64 for each header, and a maximum input sequence length \(L\) of 512. The attention refinement module has a convolution kernel size \(k\) of 5 and an intermediate size \(d_{c}\) of 32. For training, the learning rate (\(lr\)) is initially set at 0.000025 and the batch size is 4. For RIFD-Net, the input image size is 640, the initialized \(lr\) is 0.00125, and the CosineDecay \(lr\) schedule is used. The weight decay is set to 0.0005.  
In this study, DBNet++ incorporates spatial self-attention to enhance the fusion of features at various scales in the segmentation network. In the training phase, the initial \(lr\) is set to 0.007 and the batch size is set to 8. After 2000 training epochs, the model with the highest accuracy in the validation set is obtained and used to capture the text layout.  
CRNN is used for text recognition. In CMERD, there are 66,054 text images (47,615 in the training set and 18,439 in the validation set) and corresponding text content labels. In CRNN training, the initial \(lr\) is set to 0.001 and the batch size is 32. After 10,000 iterations, the accuracy of the validation set reaches 98%.  
The text detection and recognition model with the highest validation accuracy is saved and converted into an inference model for subsequent key information extraction. The specially trained model leads to superior OCR results, minimizing potential performance issues in subsequent tasks.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 3 V2I Communication
### Communication Technologies  
The method and efficiency of data communication is an important aspect of the Intelligent Transport System (ITS). As a part of the development of the Smart Mobility concept, the standards used have gradually changed in order to increase the speed of information transfer, reliability and efficiency of communication [22]. V2I communication takes place via wireless communication technologies, namely dedicated short-range communications (DSRC) and cellular vehicle-to-everything (C-V2X).
## 3 Theoretical Framework
### Simulation
\begin{split} u_{z}(r)&=\sum\limits_{n=1}^{N}a_{n}e^{ \mathrm{i}Z_{n}r}/\sqrt{\zeta r}+O((\zeta r)^{-1}),\quad\zeta r\to\infty,\ r \sqrt{x^{2}+y^{2}}\\ a_{n}&=\sqrt{\zeta/(2\pi i\zeta_{n})}\operatorname{ res}K_{33}(\alpha_{1},0,0)|_{\alpha_{1}=\zeta_{n}}Q(\zeta_{n},0)\end{split}  \tag{4}\]  
Here \(N\) is the number of real and closest to the real axis complex poles \(\zeta_{n}\) held in the asymptotics, \(\zeta\) is a characteristic wavenumber; for definiteness, the amplitude factors are shown for the SAWs propagating in the \(x\)-direction (\(y=0\), \(\alpha_{2}=0\), \(r=x\)). Each term of expansion (4) describes the guided wave, for which the pole \(\zeta_{n}\) is the wavenumber. With real \(\zeta_{n}\), they are traveling waves propagating with the phase and group velocities \(c_{n}=\Omega/\zeta_{n}\) and \(v_{n}=d\Omega/d\zeta_{n}\) or slownesses \(s_{n}=\zeta_{n}/\Omega\). The imaginary part \(\mathrm{Im}\zeta_{n}\) of a complex \(\zeta_{n}\) results in the exponential attenuation \(O(e^{-\mathrm{i}\mathrm{Im}\zeta_{n}|r})\) as \(r\to\infty\), and the corresponding waves are leaky or pseudo-surface acoustic waves (PSAW). The poles \(\zeta_{n}\) are the roots of the \(K_{33}\) denominator, therefore, the characteristic equation that relates the wavelength \(\Lambda=2\pi/\alpha_{1}\) with frequency \(f\) can be written in the form  
\[K_{33}(\Lambda,f)=0, \tag{5}\]  
where \(K_{33}\) is treated as a function of \(\Lambda\) and \(f\) at \(\alpha_{2}=0\) and \(z=0\). With a fixed wavelength \(\Lambda\), the roots of this equation \(f_{n}\) are the frequencies of SAW/PSAW modes, which should coincide with the TGM measured frequencies of the sample's resonance response, e.g., peak frequencies in Figure 4a. And vice versa, with a fixed frequency \(f\), the roots \(\Lambda=\Lambda_{n}(f)\) yield the SAW wavenumbers \(\zeta_{n}=2\pi/\Lambda_{n}\).
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 3 Parametric Analysis
### Independently Controllable Notch X Band (9.8-10.4 GHz)  
A C-shaped parasitic resonator was placed near the feed line for mitigating interference at X-band (9.8-10.4 GHz). Similar to the monitoring parameters of the AWS notch band and \"C\" notch band, parameter \(C_{2}\) was the controlling parameter for the X band.  
By altering the values of the parameter \(C_{2}\), the third frequency notch band was shifted, whereas the first and second frequency notch bands remained unaltered. As is depicted in Figure 8, reducing the value of parameter \(C_{2}\) displaced the third notch band towards higher frequencies. The third frequency notch band present at 10.38 GHz for \(C_{2}\) = 2.2 mm was displaced to 10.22 GHz for \(C_{2}\) = 2.3 mm and was shifted to 10 GHz for \(C_{2}\) = 2.4 mm. From the parametric study it was found that C\({}_{2}\) had a considerable influence on localizing the third frequency notch band, whereas the remaining notch bands were unaltered. At the intended notched frequency, the length of the parasitic resonator is calculated using the expression given below in Equation (7).  
\[L_{\text{{parasitic resonator}}}=\frac{\lambda_{g}}{2}=\frac{\lambda_{0}}{2 \sqrt{\varepsilon_{eff}}}=\frac{c}{2f_{n notch}\sqrt{\varepsilon_{eff}}} \tag{7}\]  
\[\varepsilon_{eff}=\frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}-1}{2} \bigg{[}1+12\frac{h}{W}\bigg{]}^{-\frac{1}{2}} \tag{8}\]  
The length of the parasitic resonator is equal to \(C_{1}+2C_{2}+2C_{3}-4C_{T}\), where \(C_{1}\) = 5 mm, \(C_{2}\) = 2.4, \(C_{3}\) = 1.6 mm, \(C_{T}\) = 0.54 mm, \(C_{G}\) = 0.6 mm, \(\lambda_{0}\) = _cf\({}_{netch}\)_ is the free space wavelength, \" _c\"_ is the speed of light, \" _f\({}_{netch}\)\"_ is the center frequency of the notched band, \"\(\varepsilon_{eff}\)\" is the effective permittivity, \" \(\varepsilon_{r}\)\" is the dielectric constant of the substrate, \" _h\"_ is the height of the substrate, and \(W_{F}\) is the width of the feed line. The theoretically calculated C-shaped resonator value for 10 GHz is 11.05 mm, while the adjusted value for the resonator is 10.84 mm.
## 2 Datasets and Methodology
### GNSS PWV Inversion  
When GNSS signal passes through the atmosphere, the signal delay is caused by the atmosphere, namely Zenith Total Delay (ZTD). ZTD is mainly composed of ionospheric delay and tropospheric delay, among which, the ionospheric delay can eliminate 99% of the influence via a dual-frequency receiver. tropospheric delay is mainly composed of Zenith Hydrostatic Delay (ZHD) and Zenith Wet Delay (ZWD), that is, ZWD = ZTD \(-\) ZHD.  
\[ZTD=10^{-6}\times k_{1}\times R_{d}\int_{H_{0}}^{H}rdh+10^{-6}\times\int_{H_{ 0}}^{H}\left(k_{2}\times\frac{p_{w}}{T}\times Z_{w}^{-1}+k_{3}\times\frac{p_{ w}}{T_{2}}\times Z_{w}^{-1}\right)rdh \tag{4}\]  
where \(k_{1}\), \(k_{2}\) and \(k_{3}\) represent atmospheric refraction constants, respectively. \(R_{d}\) is dry air gas constant; \(P_{w}\) and \(Z_{w}\) are the local pressure and compressibility coefficient of water vapor, respectively. \(H\) and \(H_{0}\) represent the top tropospheric height and the height of the station, respectively. ZHD can be obtained from site information and ground pressure.  
\[ZHD=\left[(2.279\mp 0.0024)\times\frac{P_{0}}{f(\phi,H)}\right] \tag{5}\]  
\[f(\phi,H)=1-0.0026 cos(2\phi)-0.00028H \tag{6}\]  
where \(P_{0}\) is the ground pressure of the station, in units of hPa; \(\phi\) is the geographical latitude of the station, and \(H\) is the altitude of the station, in km.  
\[PWV=\frac{10^{5}}{R\left(\frac{k}{T_{m}}+k^{\prime}\right)\rho_{w}}\times ZWD \tag{7}\]  
where \(\rho_{w}\) represents water density; \(R=461\)\(J\cdot kg^{-1}\cdot K^{-1}\); \(T_{m}\) is the weighted average temperature of the atmosphere, which can be obtained via the IGPT2w model proposed by [PERSON] [38].
## 2 Results and Discussion
### C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) Mag-dSPE Procedure and Its Optimization
#### 2.2.4 Optimization of Volume of Water Sample  
To obtain reliable analytical results and a high enrichment factor, obtaining satisfactory recoveries of the target substance from a large volume of sample solutions is very important. Maximum volumes ranging from 20 mL to 200 mL were studied. Following the experimental procedure, the recoveries of 11 food additives at different volumes were obtained, and the results indicate that the maximum volume was as high as 100 mL with a recovery >88.7% (Figure 11). Therefore, 100 mL of the sample solution was adopted for the preconcentration of 11 food additives from sample solutions. A high enrichment factor of 100 times was obtained because the final elute solution was 1.0 mL in these experiments.  
Finally, the optimum conditions were determined as follows: 40 mg C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was used as an adsorbent to enrich 11 kinds of food additives by 100 times, then 20 mmol/L ammonium acetate solution was used as an eluent to elute the nanoparticles adsorbed with food additives, and then these were analyzed by HPLC. The obtained chromatogram is shown in Figure 12.
# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 6 Results
#### Comparison with SOTA Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy** & **F1-Score** & **AUC-ROC** & **Inference Time** \\ \hline \multirow{3}{*}{FERONET (Ours)} & FER-2013 & 92\% & 92\% & 0.98 & 15 ms \\ \cline{2-5}  & CK+ & 95\% & 95\% & 0.99 & 18 ms \\ \cline{2-5}  & RAF-DB & 95\% & 95\% & 0.99 & 18 ms \\ \hline \multirow{3}{*}{DFEM [7]} & FER-2013 & 89\% & 89\% & 0.95 & 33 ms \\ \cline{2-5}  & CK+ & 91\% & 91\% & 0.96 & 30 ms \\ \cline{2-5}  & RAF-DB & 89\% & 88\% & 0.95 & 30 ms \\ \hline \multirow{3}{*}{CF-DAN [16]} & FER-2013 & 91\% & 91\% & 0.97 & 25 ms \\ \cline{2-5}  & CK+ & 89\% & 88\% & 0.94 & 25 ms \\ \cline{2-5}  & RAF-DB & 90\% & 90\% & 0.92 & 32 ms \\ \hline \multirow{3}{*}{HCSFNet [21]} & FER-2013 & 90\% & 90\% & 0.93 & 22 ms \\ \cline{2-5}  & CK+ & 94\% & 94\% & 0.95 & 20 ms \\ \cline{2-5}  & RAF-DB & 92\% & 92\% & 0.95 & 20 ms \\ \hline \multirow{3}{*}{CSE-GResNet [22]} & FER-2013 & 91\% & 91\% & 0.96 & 27 ms \\ \cline{2-5}  & CK+ & 91\% & 92\% & 0.92 & 22 ms \\ \cline{1-1} \cline{2-5}  & RAF-DB & 91\% & 92\% & 0.92 & 33 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative performance of facial expression recognition models across benchmark datasets. Figure 8: Comparison chart highlighting how the removal of each major component affects FERONet’s performance. It demonstrates that the full model outperforms all ablated variants, with the temporal decoder and multimodal fusion being particularly critical.