esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
As represented in Figure 1, our proposed WGA-SWIN is based on a transformer-based architecture that has been modified with a novel module that enhanced the swin transformer and window token grouping. The importance behind WGA-SWIN lies in extracting information-rich inter-view features with high-quality 3D object reconstruction. The proposed method is capable of processing a variety number of view images with dimensions of 224 \(\times\) 224 \(\times\) 3, denoted as the image set \(I=\{I_{1},~{}I_{2},~{}...,~{}I_{N}\}\), and generating the corresponding voxel representation \(V\) with dimensions of 32\({}^{3}\). To enhance feature integration across views, we introduce a Window Grouping Attention (WGA) mechanism, which segments tokens into groups for independent window attention operations.  
The detailed process for the encoders is described in Section 3.1. After feature extraction, the proposed method integrates a progressive hierarchical decoder based on swin transformer blocks with a 3D CNN. The decoder progressively upscales the feature map into a voxel grid, preserving fine-grained details and ensuring visualization efficiency, as described in Section 3.2. The voxel representation \(V\) is constructed through successive decoding operations. The overall transformation process is summarized as follows:  
\[V=\text{WGA-SWIN}(I)=De(En(I)) \tag{1}\]  
where En and De represent the processes of feature extraction by the encoder, multi-view feature processing through the WGA-SWIN mechanism, and the generation of the 3D output V through the decoder, respectively.
## 4 Challenges in Rainfall Estimation by CMLs
### Data Acquisition
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Challenges**} & \multicolumn{1}{c}{**Highlights**} \\ \hline Baseline estimates & 1. & Dry and wet period classification methods can still produce misclassification due to the effects of coarse quantization, the multipath effect, wet antenna effect, etc. \\  & 2. & Collecting the calibrated training set required for dry and wet period classification methods is time consuming due to the uneven rainfall distribution and unbalanced datasets may lead to model overfitting or performance degradation. \\  & 3. & Limited by the sampling strategy and quantization resolution of the CML data, researchers have to ignore, to varying degrees, changes in the baseline during wet periods [138]. \\ \hline WAA Quantification & 1. & The characteristics of WAA are highly dependent on the antenna properties of CMLs and existing empirical models may be less portable for CMLs with different characteristics [159]. \\ WAA Quantification & 2. & Due to the uncertainty in the baseline estimation, the WAA model may no longer simply compensate for WAA but include corrections for WAA and other unknown sources of bias. \\  & 3. & The properties of WAA induced by fog and dew have not been studied specifically. \\ \hline \multirow{3}{*}{\(\gamma\)–\(R\) Relationship} & 1. & Nonlinearity of the \(\gamma\)-\(R\) relationship leads to systematic bias between rainfall estimates and path-averaged rain rates [160]. \\  & 2. & Dependence of the \(\gamma\)-\(R\) relationship on DSD leads to reduced applicability of the power-law approximation to the \(\gamma\)-\(R\) relationship [18]. \\ \hline Reconstruction of the Rainfall Spatial Distribution & 1. & Most studies directly treat CML-based rainfall estimates as the rain rate at the midpoint location of the path, ignoring the variability of rainfall on the CML path [45; 126; 127]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._To ensure communication quality during rainfall, some CMLs are configured with automatic transmit power control (ATPC) to minimize RSL fluctuations. However, it can introduce additional errors, especially under low-temporal-resolution sampling conditions or with only RSL data available [158]. Currently, only a few research groups have shared their CML datasets [168; 169; 170], which may be related to the commercial confidentiality of mobile network operators. However, open access to the data may bring more insights. Finally, in some developed countries, operators are planning to replace CMLs with fiber optic communications, which undoubtedly hurts the availability of CML data.
# Load Forecasting with Machine Learning and Deep Learning Methods
## 3 Methodology
### Modeling  
Training was conducted through a cross-validation procedure to ensure the quality of the models applied. This technique is used to guarantee that in each of the splits, the results are independent of the training, thereby minimizing overfitting in the modeling [46]. Thus, the training process is performed in one subdivision, corresponding to 80% of the training data, and the efficiency of the model is evaluated in another, which includes the rest of the training data. This process is carried out repetitively, exchanging the position of the validation throughout the training set.  
In addition, the training process is conducted with mini-batch gradient descent, which avoids standstill at a local minimum and increases the level of convergence of the model [47]. Once the training process with these techniques has been carried out, the model is evaluated on the test sample.  
This section explores the capabilities and reports the suitability of the different ML and DL models evaluated in this manuscript for the electricity load forecasting. Thus, the basics of the ML models are presented, starting with RF, and followed by SVR and XGBoost. Next, the characteristics of the DL models are introduced, starting with MLP and followed by LSTM and Conv-1D.
# Effects of Freeze-thaw Cycles on the Mechanical Properties and Microstructure of a Dispersed Soil
## 3 Result and Discussion
### Soil Sample Dispersion Identification Test
#### 3.1.2 Crumb Test  
Figure 5 displays crumb test images of each soil sample following freeze-thaw cycles. The disintegration condition of the crumb test showed that each soil sample started to react severely when it was put into distilled water, and the disintegration condition of the soil sample hardly changed after one hour. A large amount of cloudy, thick, viscous particle suspension could be observed around the soil block, and the mist spread more than 10 mm at the bottom of the cup. The soil sample was judged as strongly dispersed soil of grade 4 according to the criteria of the crumb test specification. And as the number of freeze-thaw cycles increases, the cloudy suspension spreads more and more (the most apparent observation is at 2 min), and the solution becomes increasingly turbid.  
As the number of freeze-thaw cycles increases, the interaction forces between colloidal particles gradually weaken, leading to more severe damage to the soil structure. Therefore, colloidal particles that act as connectors between soil particles interact with water and dissolve, leading to an increased degree of fine particle precipitation in the soil sample. Simultaneously, the turbidity of the surrounding water gradually increases. As colloids continue to dissolve, the connections between particles gradually weaken, ultimately enhancing soil dispersibility.  
Figure 4: Effect of freeze–thaw cycles on the pinhole test: (**a**) variation curve of final flow rate and (**b**) variation curve of final pore diameter.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of F–T Cycles** & **Water Head** & \begin{tabular}{c} **Duration at a** \\ **Given Water** \\ **Head (min)** \\ \end{tabular} & \begin{tabular}{c} **Final Flow** \\ **Rate (mL/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Turbidity of** \\ **Water** \\ \end{tabular} &
\begin{tabular}{c} **Final Pore** \\ **Diameter (mm)** \\ \end{tabular} & **Results** \\ \hline
0 & 50 & 5 & 2.00 & Turbid & 2.0 & D1 \\
1 & 50 & 5 & 1.83 & Turbid & 2.4 & D1 \\
3 & 50 & 5 & 1.67 & Turbid & 2.3 & D1 \\
5 & 50 & 5 & 2.00 & Turbid & 3.5 & D1 \\
7 & 50 & 5 & 2.03 & Turbid & 3.6 & D1 \\
9 & 50 & 5 & 2.33 & Very turbid & 4 & D1 \\
12 & 50 & 5 & 2.5 & Very turbid & 4.5 & D1 \\
15 & 50 & 5 & 3.17 & Very turbid & 4.8 & D1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification results of the pinhole test.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 2 The KMALL Data Format  
Before describing the structure of a KMALL file, we shall provide five definitions related to the echosounder's performance. We define a _ping_ as the set of acoustic pulses transmitted at approximately the same time. Each of these pings is formed by pulses directed into different angles, which are called _beams_. The number of beams per ping depends on the sonar model. The number of samples per beam depends on the angle and the depth. Each transmit pulse defines an across sector called a _transmit sector_. Simultaneously, a _receiver fan_ is defined as the set of received soundings from one unit. Then, based on the transmit sectors and the receiver fans, a _swath_ is defined as the complete set of across-track data.  
The KMALL format is the successor of the Kongsberg Maritime.all format. KMALL is a generic format with high-resolution data and is also structured in datagrams. This structure is designed to avoid breaking existing decoders when updating the format. Datagrams are usually stored in binary little-endian format with a 4-byte alignment [13]. Each datagram starts with a generic header containing the datagram size in bytes and a 4-byte identifier. KMALL datagrams do not have any size constraints, as opposed to the.all format, which is limited to 64 kB due to the maximum size of UDP packets. The main advantage of this feature is that pings are not split between datagrams.  
The size of a KMALL file is not limited either. The echosounder operator decides when the file being recorded should end. Each file contains several datagrams, the most relevant ones being Multibeam Water Column (MWC), Multibeam Raw Range and Depth (MRZ) and Sensor Attitude Data (SKM) datagrams. This mixture of data types in the same file makes it difficult for general-purpose data compressors such as _gzip_ to identify data statistics. For this reason, they do not perform as well as one could expect.  
In this study, we analyze multibeam data acquired with Kongsberg EM2040, EM712 and EM304 MBES models, kindly provided by Kongsberg Maritime and Fugro. EM2040 is a high-resolution shallow water multibeam system operating at sonar frequencies in the 200-400 kHz range with an angular coverage up to 200\({}^{\circ}\) and pulse lengths as short as 14 us. EM712 can survey depths up to 3500 m and operates at frequencies in the 40 to 100 kHz range with a swath coverage sector up to 140\({}^{\circ}\) and pulse lengths up to 0.2 ms, while EM304 allows mapping waters as deep as 8000 m with an operating frequency between 26 and 34 kHz and a pulse length of 0.4 to 200 ms.
## 3 Experiments
### Absolute Nonlinearity Parameter Measurement
The tone-burst current output, I\({}_{out}(\omega)\), was detected using the same current probe (Lecroy, CP030, NY, USA) that was used for calibration. The nonlinear measurements were repeated by increasing the input voltages. Figure 3b shows the tone-burst received current signals. The rectangular-marked tone-burst received current signal was processed via FFT excluding the transient and ringing parts to obtain I\({}_{out}(\omega)\). Figure 3c shows the FFT results of the received signals. As the roughness increased, the amplitude of the received signal in Figure 3b and the magnitude of the FFT result in Figure 3c decreased. Through calibration and nonlinear measurements, the displacement of the fundamental and second-order frequencies components after propagating through a material, A(\(\omega\)), was obtained using the calibration function, H(\(\omega\)), and current output spectrum, I\({}_{out}(\omega)\), as follows [13]:  
\[\text{A}(\omega)=\big{|}\text{H}(\omega)\big{|}\big{|}_{out}(\omega)\big{|} \tag{4}\]  
The absolute nonlinearity parameter is generally measured using a contact transducer, namely a LiNbO\({}_{3}\) or PZT transducer. The LiNbO\({}_{3}\) transducer is a single-crystal material; thus, its nonlinearity is lower than that of the PZT transducer [24, 25]. Therefore, the LiNbO\({}_{3}\) transducer is widely used for measuring the absolute nonlinearity parameter to suppress the nonlinearity generated by the transducer [12]. However, the LiNbO\({}_{3}\) transducer was not suitable for this experimental setup because it is fragile when used for measurements involving rough surfaces. Hence, we conducted experiments using the PZT transducer because the type of transducer does not affect the experiments performed to analyze the influence of the surface roughness on the parameter measurements. Figure 2: (a) Experimental setup and (b) calibration curve. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**H(\(\omega\))** & **0.5 \(\upmu\)** & **1.0 \(\upmu\)** & **1.5 \(\upmu\)** & **2.2 \(\upmu\)** & **2.7 \(\upmu\)** \\ \hline
5 MHz & \(3.69\times 10^{-8}\) & \(4.03\times 10^{-8}\) & \(4.49\times 10^{-8}\) & \(5.26\times 10^{-8}\) & \(5.34\times 10^{-8}\) \\
10 MHz & \(1.33\times 10^{-8}\) & \(1.61\times 10^{-8}\) & \(1.97\times 10^{-8}\) & \(2.49\times 10^{-8}\) & \(2.84\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: H(\(\omega\)) value according to the surface roughness.
# Research Developments of Aerostatic Thrust Bearings: A Review
## 4 Advances in Dynamic Performance
### Flow Characteristics of ATBs  
The performance of ATBs is closely related to the gas flow characteristics. It is necessary to investigate flow characteristics from a microscopic perspective. [PERSON] found that aerostatic bearings inherently have small vibrations in the order of nanometers. These vibrations have a very negative impact on the accuracy and stability of the bearing [92]. To study the mechanism of this phenomenon and suppress the harmful vibration, a more accurate and real-time solution of the flow field needs to be obtained. [PERSON] applied RANS simulation to the research. The numerical results validated adequate accuracy in predicting flow characteristics [93]. [PERSON] investigated the flow characteristics of a circular ATB by solving the Navier-Stokes equations directly [30]. [PERSON] solved  
Figure 12: Photo of a recessed aerostatic bearing with AMR [64].  
the Navier-Stokes equation for the three-dimensional turbulent flow in a circular ATB by RANS simulation [94]. The results showed that the pressure drop phenomenon was caused by the interaction between compression waves and the boundary layer, as depicted in Figure 13. The pressure recovered due to the flow separation at the upper wall. The flow state turned from laminar to turbulence in the region where rapid pressure recovery occurred. [PERSON] applied the large eddy simulation to investigate the transient flow field and the vortex shedding phenomenon in ATBs [95], as shown in Figure 14. Although many investigations have been carried out on various phenomena in the flow field of ATBs, there has not been a consensus on the mechanisms of the flow characteristics in ATBs.
## 2 Electrothermal Model of Spiral Structure GISA
### Temperature and Flow Field Equation  
The energy absorption process of the arrester is transient, and the heat transfer process includes heat conduction, heat convection, and heat radiation [27; 28]. The heat conduction equation satisfies the following formula:  
\[\frac{1}{r}\frac{\partial}{\partial r}\left(kr\frac{\partial T}{\partial r} \right)+\frac{1}{r^{2}}\frac{\partial}{\partial\varphi}\left(k\frac{\partial T }{\partial\varphi}\right)+\frac{\partial}{\partial z}\left(k\frac{\partial T }{\partial z}\right)+Q_{V}=\rho C_{P}\frac{\partial T}{\partial t} \tag{7}\]  
where \(r\) is the radial distance of the calculation point from the center of the variator; \(k\) is the thermal conductivity of materials; \(T\) is the absolute temperature; \(Q_{V}\) is the input energy; \(C_{P}\) is the specific heat capacity; and \(t\) is time.  
As there is a temperature difference between the ZnO variator of the arrester, SF\({}_{6}\) gas, tank, and ambient air, the heat is transmitted between solids and gas by convection. The internal gas flow and convection heat transfer process need to meet the mass conservation equation, momentum conservation equation, and energy conservation equation [29]:  
\[\
abla\cdot(\rho V)=0 \tag{8}\]  
Figure 4: Three-dimensional model of a \(\pm 550\) kV DC GIS arrester.  
\[\rho(v\cdot\
abla)v=\
abla\cdot\left[-pI+\mu\left(\
abla v+\left(\
abla v \right)^{T}\right)-\frac{2}{3}v(\
abla v)I\right)\right]+g\Delta\rho \tag{9}\] \[\rho C_{p}\rho\
abla T=\
abla\cdot(k\
abla T)+Q \tag{10}\]  
where \(v\) is the velocity of SF\({}_{6}\) gas; \(k\) is the material thermal conductivity; \(p\) is the SF\({}_{6}\) gas pressure; \(\mu\) is the dynamic viscosity of the fluid; \(Q\) is the heating power per unit volume; \(T\) is the temperature of the materials; \(g\) is the gravitational acceleration; and \(\
abla\rho\) is the difference in density due to the heating of the gas.  
The internal gas is considered an ideal gas and satisfies the ideal gas law:  
\[\rho=\frac{Mp}{RT} \tag{11}\]  
where \(M\) is the molar mass and \(R\) is the universal gas constant.  
The convective heat transfer satisfies the following equation [29]:  
\[q_{0}=h(T_{1}-T_{ext}) \tag{12}\]  
where \(q_{0}\) is convective heat flux, \(T_{1}\) is the surface temperature, Text is the external temperature, and \(h\) is the heat transfer coefficient. \(h\) is related to the specific shape of the object, and it is generally calculated using the following formula [30]:  
\[h=\frac{k}{L}C\left(Gr\cdot Pr\right)^{n} \tag{13}\]  
where \(G_{r}\) is the Grashof number, \(P_{r}\) is the Prandtl number, \(C\) and \(n\) are related to the object's shape and the surface's condition, and \(L\) is the characteristic length.  
The thermal radiation satisfies the Stefan-Boltzmann law [31]:  
\[Q_{r}=\varepsilon\sigma A(T_{1}^{4}-T_{ext}^{4}) \tag{14}\]  
where \(Q_{r}\) is radiant heat flux, \(\varepsilon\) is the emissivity, \(\sigma\) is the Stefan-Boltzmann constant, and \(A\) is the radiant surface area.
## 4 Experiments and Results
### Experiment Results and Analysis
#### 4.4.2 Ablation Experiments  
In order to validate the effectiveness of incorporating domain-specific dictionaries into our method, we conducted three experiments: one without dictionary incorporation, one with integrating Tencent's public dictionary, and one with integrating a domain-specific dictionary.  
As depicted in Figure 5, with an increase in the number of iterations, a significant improvement in the F1 scores was observed across all three experiments. This is attributed to the model's ability to learn more feature information through continuous iterations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **P** & **R** & **F1** \\ \hline BiLSTM & 38.45\% & 40.33\% & 39.37\% \\ BiLSTM-CRF & 68.27\% & 64.24\% & 66.20\% \\ BERT & 83.27\% & 91.98\% & 83.27\% \\ BERT-BiLSTM-CRF & 89.22\% & 94.37\% & 91.73\% \\ LEBERT & 97.01\% & 98.20\% & 97.60\% \\ Method of this study & 97.33\% & 98.39\% & 97.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relevant comparison experiment results.  
A comparison was made between the F1 scores at the final iteration. The experiment incorporating Tencent's public dictionary showed a 4.87% increase in the F1 score compared to the experiment without dictionary incorporation. This improvement is due to the semantic information in Tencent's public dictionary, underscoring the importance of dictionary incorporation for the semantic representation of text. In the experiment incorporating the domain-specific dictionary, the F1 score improved by 1.16% compared to the experiment incorporating Tencent's public dictionary. This is because the domain-specific dictionary contains semantic information and entity boundary information specific to the field of mine hoist faults, resulting in better recognition of entities related to mine hoist faults.
## 1 Introduction
### Contributions of the Study  
* Initially, the data were collected and stored in a network.
* Next, an elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) was developed for the enhancement of security and energy in IoT healthcare 5.0.
* Finally, the performance of the system was analyzed.  
The remainder of this paper is structured as follows: Section 2 presents the related studies and problem descriptions. The recommended method is illustrated in Section 3. Section 4 includes the findings and analyses. Section 5 highlights the conclusions.
# XLNet-Based Prediction Model for CVSS Metric Values
## 1 Introduction
### Contributions  
This study provides a vulnerability metric value prediction method based on the XLNet model to enable rapid vulnerability metric value prediction. The method discovers contextual characteristics from vulnerability descriptions in order to forecast potential metric values. As compared to previous work, the paper's main contributions are as follows:  
1. The concept of transfer learning [24] is presented in the realm of vulnerability assessment. The existing pre-trained model has increased maturity. Compared with the traditional model, the performance has been greatly improved. However, the application is not popular. The study extends the pre-trained model to the subject of vulnerability assessment, therefore generating novel ideas for cyber security research.
2. Traditional machine learning techniques simply assess the influence of word frequency on the outcomes, ignoring the context-based improvement on the final output. This paper employs the XLNet model, which incorporates contextual information and enhances the classification impact of the model.
3. This paper constructs two versions of the CVSS v2 and v3.1 datasets. It concurrently investigates CVSS v2 and v3.1, providing assessment experts with reasonable metric value suggestions and reducing the workload of assessors, therefore speeding up the vulnerability severity assessment.
# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Basic Image Augmentation Methods
#### 2.1.4 Geometric Deformation  
Geometric deformation is suited to image segmentation tasks where the region of interest may shift position and shape [31]. The position of the foreground in an image can be modified or harmonized using simple geometric deformation techniques such as affine or perspective transformation, which will help the segmentation network truly understand the segmentation targets. When the original image changes, the corresponding truth value result should also change. Examples include RandomScale, Affine, ElasticTransform, Perspective, and Flip. Since microscopic images can be acquired at various magnifications and scaling angles during the process of microscopic image acquisition, the geometric deformation method is frequently utilized in this field of segmentation.