esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Auto-Insurance Systems Based on Cloud or Blockchain
### Cloud-Based Auto-Insurance
Similarly, using cloud computing techniques, the research presented in [45] provided solutions for life-critical systems following an accident. Sensors in an automobile system were employed to increase safety and relax the driver. Another mobile application called MATCH [46] offered a simple method for taking pictures of a damaged car and sending an initial notification to the insurance company from a smartphone. Additionally, a map with contact information for neighboring hospitals was displayed. The authors claimed that this application would be quite helpful in reducing human labor by allowing people to file insurance claims with just one click. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt}} \hline
**Ref.** & **Advantages** & **Issues/Limitations** \\ \hline
[45] & i. The project focused on the application of cloud computing in accident response systems, such as hospitals and drivers. ii. The web browser could provide a variety of services that are necessary in vehicles, hospitals, and on the roadside. \\ \hline
[46] & i. MATCH provided a straightforward technique for using a smartphone to take images of a damaged car and transmit the initial notification report to the insurance company. ii. Additionally, a map with the phone numbers of neighboring medical facilities and towing companies was shown. \\ \hline
[47] & The car instantly uploads its auto-insurance history to the cloud. More importantly, a car involved in an incident must immediately provide the insurance provider with the necessary paperwork and vehicle information, so that it may be processed. \\ \hline
[48] & i. AES is a faster and more secure algorithm because it has a comparatively large secret key. ii. AES is open-source and resistant to hacking attempts due to its longer key size (128, 192, and 256 bits). \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._In addition to accidental information storage, [PERSON] [47] proposed a centralized cloud-based smart parking system in addition to the storage of accident information for insurance reports. If necessary, real-time reporting can be used when autonomous vehicles require repair work or assistance. Using either roadside infrastructure or a cellular network, an intelligent vehicle can establish a connection to the cloud. Essentially, a vehicle involved in a collision must send the necessary paperwork and vehicle information to the insurance provider right away, and only once, for processing. The car immediately uploads its auto-insurance history to the cloud. More importantly, a vehicle involved in a collision must immediately provide the insurance company with the required documentation and vehicle details for processing. Cloud-based infrastructure has security threats that cannot be ignored. To ensure data security, a vehicle information management system [48] stores data in an encrypted form in the cloud. The client component provides a variety of forms and reports to present various plans and satisfy the needs of vehicle insurance management. The data are encrypted with an AES algorithm so that no one else can read them.
## 2 Constructing the Evaluation Index System
### Screening of Evaluation Factors  
Due to the complex interrelationships among evaluation factors and their varying degrees of correlation, directly inputting selected factors into a model may introduce noise. Redundant factors can increase model instability and decrease predictive accuracy. Common methods for screening evaluation factors include correlation analysis, collinearity analysis, Relief-F, recursive feature elimination, the information gain ratio (IGR), and the geographic detector (GD) [37]. Table 3 lists the relevant studies. [PERSON] et al. [37] employed the Pearson correlation coefficient and a geographic detector to screen the evaluation factors in the study area. The results showed that compared to using all factors, the AUC values increased by 0.023 and 0.034, respectively. [PERSON] et al. [38] utilized the recursive feature elimination algorithm to remove factors such as the water flow dynamics index and aspect. Afterward, the model's AUC value had increased by 0.019. [PERSON] [39] employed entropy theory and correlation analysis to screen the evaluation factors. Before and after screening, the accuracy of the deterministic coefficient model, frequency ratio model, information content model, and evidence weight model increased from 0.881, 0.911, 0.895, and 0.886 to 0.910, 0.913, 0.920, and 0.906, respectively. This indicates that entropy theory and correlation analysis are advantageous for constructing susceptibility evaluation systems.  
Figure 1: Geographic location (**a**) and statistical chart of the landslide number and relative density of NDVI classification in Baiyun District (**b**) [36].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Author** & **Title** \\ \hline
2024 & [PERSON] et al. [37] & A study on the landslide susceptibility of LightGBM-SHAP, based on different factor screening methods. \\
2023 & [PERSON] et al. [38] & Evaluation of landslide susceptibility in gentle hill-valley areas, based on an interpretable random forest-recursive feature elimination model \\
2023 & [PERSON] [39] & Landslide susceptibility assessment, based on an optimal computing cell and ulti-model coupling \\ \hline \hline \end{tabular}
\end{table}
Table 3: The relevant literature discussed in Section 2.3 on evaluation factor selection.
## 1 Introduction
### Environmental Impact on FSO Communication System  
The impact of the environment on the beam propagation may reduce the availability of the FSO system, mainly due to inhomogeneity. This inhomogeneity is mainly due to the different composition over the entire length. As a result, the beam is degraded and, thus, the quality of the received optical signal is reduced. This means that a change in weather can cause a connection deterioration and, in some cases, even a loss of communication [6]. Here is a list of several influences that have a major impact on the quality of the transmitted signal:  
* Visibility;
* Temperature;
* Humidity;
* Wind speed;
* Rain;
* Fog;
* Concentration of particles in the air;* Haze;
* Atmospheric turbulence and scintillation;
* Molecular absorption and scattering [7; 8].
## 2 Algorithm and Implementation
### PHOTON Hash Function  
The PHOTON hash function [39] is a lightweight security primitive designed for low-resource devices. It has a sponge-based construction, as shown in Figure 5, and its permutation function is an AES-like function, following the SP network shown in Figure 6. Sponge construction has two phases: the absorbing phase and the squeezing phase. The original input message is fully absorbed by the absorbing phase whereas the output hash is produced by the squeezing phase.  
PHOTON-80/20/16 has an output hash size (\(n\)) of 80 bits, an input rate (\(r\)) of 20 bits, an output rate (\(r^{\prime}\)) of 16 bits, and a (\(5\times 5\)) state matrix size (\(t\)) of 100 bits with a 4-bit cell  
Figure 5: PHOTON sponge construction.  
Figure 6: PHOTON permutation.  
size (\(s\)). Figure 7 shows the architecture of this variant of the PHOTON hash function. \(M\) is the input message, \(IV\) is the initialization vector, and \(h\) is the output _hash_. It takes 12 rounds for the absorbing phase for each 20-bit absorbed message, and another 12 rounds for the squeezing phase for each 16-bit squeezed output, as shown in the sponge construction in Figure 5 and the PHOTON control flow in Figure 8. Each cell within the matrix corresponds to an element from the Galois field GF (\(2^{4}\)) with the irreducible polynomial \(X^{4}+X+1\).
## 3 Results and Discussion
### Nanocoated Fabric Pull-Out Properties
#### 3.2.1 Single and Multiple Nanocoated Fabric Pull-Out Force-Displacement Results
This behavior is likely attributed to the delayed response induced by the incorporation of nanosized boron carbide (B4C) and multiwalled carbon nanotubes (MWCNTs), which can be critical during the energy absorption process in soft fabrics [34]. Consequently, the main contributing factors include the surface roughening induced by nanoparticles, which intensifies filament-to-filament friction within the interlacement zone, alongside the cumulative frictional interactions among the nanoparticles [1; 10; 24]. This mechanism is expected to play a pivotal role in increasing energy absorption levels, a key attribute for prospective soft ballistic applications [38; 42; 65]. As exhibited in Figure 6(b1-b4,c1-c4), the first multiple (two ends and three ends)-yarm pull-out tests exhibited remarkably elevated pull-out forces in comparison to the subsequent second and third multiple yarns (Figure 6(b1-b4,c1-c4)). In particular, the nanocoated B4C structure (Figure 6(b1,c1)) exhibited extraordinary high values in the crimp extension region, surpassing those of MWCNT- and GNP-coated and uncoated control structures. The results of the experiments are summarized in Table 1.  
Figure 6: Single-yarm and multiple-yarm (2 ends and 3 ends) consecutive pull-out force (N)–displacement (mm) graphs for nanocoated p-aramid fabric structures. (**a1**,**b1**,**c1**) Nanocoated B4C-paramid fabric (BPO). (**a2**,**b2**,**c2**) MWCNT-coated p-aramid fabric (MPO). (**a3**,**b3**,**c3**) GNP-coated p-aramid fabric. (**a4**,**b4**,**c4**) Control fabric (KPO). structures (Figure 6(b2-b4,c2-c4)). Furthermore, the stick-slip zone of the n-B4C-coated structure displayed more irregular and broad-range declining force values in contrast to the MWCNT and GNP structures. Additionally, the pull-out forces observed in the subsequent second and third multiple yams for the n-B4C-coated structures were remarkably higher than those recorded for the MWCNT, GNP, and control structures. Despite the reduction in areal fabric density, it is posited that the incorporation of nanomaterials facilitates the entrapment of filament-to-filament stick-slip interactions within localized surface areas, thereby generating an accumulative resistance effect. This phenomenon is deemed critical for maintaining structural integrity to a certain extent under multiple impacts in soft ballistic applications [30,38]. The pull-out forces obtained in multiple-yarn tests demonstrate a pronounced non-linear response in contrast to single yarn. This behavior is a significant phenomenon, particularly in relation to bullet-structure interactions within the domain of soft ballistic applications. For example, the engagement of the projectile with multiple warp-filling yams, such as in the case of 12 ends, exemplifies this effect [65].
# Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations
## 1 Introduction
Being able to adapt to different sorts of data allows ML to be applied across various fields, from medicine and engineering to finance or marketing. So, it only makes sense that it can also be used to boost techniques such as AFM, mainly in the postprocessing stage. Although contact mechanics models have proven to be great tools for the study of surface properties from AFM nanoindentations, particularly in the domain of soft biological tissues, this traditional approach can be a time-consuming task. Hence, new strategies have been explored to overcome this limitation. Physics-informed ML models represent an alternative capable of taking advantage of contact mechanics theories to guide their development. By splitting AFM F-I curves in their two ramp cycle stages--approach and withdraw--and selecting an appropriate contact theory for each, it is expected that the synthetically generated data will have a fair resemblance of experimental curves. Another drawback associated with many contact models is their assumption of linear elastic behaviour. This limitation can also be addressed by employing ML models, which can easily capture material nonlinearity. In recent years, several works have introduced different approaches that utilize ML methods to assist in diverse types of AFM analyses. A ML approach based on artificial neural networks (ANNs) is used in [28] to classify bladder cancer cells into different grades, based on cellular mechanical properties obtained with AFM. For an application more related to AFM intrinsic properties, Convolutional neural network (CNN) models have been developed to determine the tip sharpness directly from indentation images [29]. A different study [30] has presented a quasi-recurrent neural network to identify the coupling of vibrating modes in dynamic AFM (intermittent contact between probe and sample). At last, ML regression models were used to predict the elastic modulus based on nanoindentation curves, without having to fit them with a contact model, in the work presented in [31], where the ML models were trained with experimental F-I curves from AFM analyses. This last work shares some similarities with the current project, in the sense that they both focus on determining the sample's stiffness without contact model fitting. Nevertheless, the work in [31] does not cover the development of deep learning strategies and it requires experimental data to train the models, unlike the current framework. Despite the several ML applications in the AFM field, there is not yet a deep learning-based approach to determine surface properties related to the elastic modulus and to the sample's adhesion, from both the approach and retraction curves, without requiring the model to be trained on actual data from AFM experiments. This study aims to address this gap by examining the effectiveness of a similar method. This is performed by training the models only on synthetically generated data and then analysing if they can accurately predict surface properties from AFM-studied samples.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 4 Results and Discussion
### Machine Learning Algorithm Results
The statistical metrics for the RFR model are 0.00324 (MSE), 0.0125 (MAE), 0.0569 (RMSE), 0.922 (R\({}^{2}\)), and 0.920 (EVS) on the test set, as listed in Figure 5:0 The value of the R\({}^{2}\) for the DTR, SVM, GPR, and ANN models was 0.711, 0.685, 0.843, and 0.789, respectively, compared to the R\({}^{2}\) value of 0.922 for the RFR model on the test set, as presented in Figure 5:1  
Figure 7a-e shows the scatter plots for the predicted versus experimental raster angle using ML models. Generally, all developed ML models showed a good correlation between  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training Dataset**} & \multicolumn{4}{c}{**Test Dataset**} \\ \hline  & **RFR** & **DTR** & **SVM** & **GPR** & **ANN** & **RFR** & **DTR** & **SVM** & **GPR** & **ANN** \\ \hline
**MSE** & **0.002589** & 0.010384 & 0.014762 & 0.003698 & 0.006395 & **0.003248** & 0.012935 & 0.016287 & 0.005723 & 0.009137 \\
**MAE** & **0.010358** & 0.030198 & 0.413681 & 0.018356 &  
the experimental and predicted raster angle. Among the models, SVM demonstrated the least predictive performance on both test sets, while RFR demonstrated the highest predictive performance, as can be seen in Figure 5:2 The predicted raster angles are well concentrated around the diagonal line, representing an excellent match between the predicted raster angle and the corresponding experimental values (Figure 7e). Figure 5:3 Experimental versus predicted raster angle based on ML models.
## 4 Laser Multilateration in Machine Tool Verification
### Traditional Trilateration Technique  
Trilateration aims to obtain a point with less involvement of the measurement uncertainty. Therefore, it uses only the radial component of each LT to determine points' coordinates. To do this, the new point is defined as the intersection of the three spheres that define the radial component of each LT, with (\(x_{i}\), \(y_{i}\), \(z_{i}\)) defining the center of a sphere LTi (\(i\) = 1, 2, 3).  
\[R_{i}^{2}=(x-x_{i})^{2}+(y-y_{i})^{2}+(z-z_{i})^{2}\quad\text{with }i=0,\ldots,3 \tag{9}\]  
The equations system defined by LT coordinates can be solved analytically. However, the most useful technique, thanks to its accuracy, is trilateration using optimization [14; 15]. This technique is based on an iterative process of parameter identification. The producer minimizes the difference between distances measured for each LT (Equation (10)).  
\[Rest^{2}=\sum_{j=1}^{n}\sum_{i=1}^{4}\Bigg{\{}\Big{[}\big{(}x_{mj}-X_{i}\big{)} ^{2}+\big{(}y_{mj}-Y_{i}\big{)}^{2}+\big{(}z_{mj}-Z_{i}\big{)}^{2}\Big{]} \frac{1}{2}-(d_{i})\Bigg{\}}^{2} \tag{10}\]  
The value _Rest_ is the difference between the radial coordinate of point \(P_{j}\) measured with LTi (\(d_{ij}\)) and the distance between the coordinate system of point \(P_{j}\) (\(x_{mj}\), \(y_{mj}\), \(z_{mj}\)). The difference is calculated in the LT coordinates system, with (\(X_{i}\), \(Y_{i}\), \(Z_{i}\)) coordinates of the LT in the reference coordinate system (Figure 4). Parameters to be identified in the optimization process can be reduced if a new reference system of the LT is created as shown in Figure 4[14; 15].  
Therefore, parameters to be identified are the LT positions \(X_{1}\), \(X_{2}\), and \(Y_{2}\), and the multilateralized coordinates \(x_{mj}\), \(y_{mj}\), and \(z_{mj}\) with \(j\) = 1 \(n\) (\(n\) = number of points). These parameters are obtained by non-lineal optimization techniques based on the Levenber-Marquadt algor  
Figure 4: Trilateration and multilateration coordinate system.
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
## 5 Conclusions  
In this paper, the fluctuate-volume-current (FVC) method is used to calculate the near-field back radiative heat transfer from the disk to the magnetic head in heat-assisted magnetic recording (HAMR). And the thermal deformation of the slider on the magnetic head caused by the back radiative heat from the hot magnetic recording layer is then analyzed.  
The formulation for calculating the near-field radiative heat transfer by the fluctuate-surface-current method is based on the fluctuation-dissipation theory, which was brought in to solve [PERSON]'s equations to obtain the electromagnetic fields on the target position in the magnetic head, and then the heat flux using the Poynting's theory is obtained.  
Materials Fe and Al\({}_{2}\)O\({}_{3}\) are chosen to represent the magnetic recording film and the slider on the magnetic head, respectively. The radiative heat flux from the hot magnetic recording layer to the cold slider is calculated. It is shown in the spectral heat flux graph that the fluctuations of the heat flux are caused by the electromagnetic properties of the materials, and the frequencies reached the surface phonon-polariton resonance.  
It is shown in the graph of heat flux varying with flying height that, under the small flying height band, the contribution of the far-field heat flux can be ignored. And even under the extremely small flying height from 1 nm to 10 nm, the heat flux has almost two orders of magnitude changes.  
The thermal analysis and the structural analysis of the slider on the magnetic head are used to discuss the influence of the back radiation on the structure of the slider. It is shown that when the distance between the head and disk gets larger, the deformation rate decreases and becomes small enough to be ignored when the distance is larger than 5 nm. Now that the flying height is reaching 2 nm, the thermal deformation may cause an obvious decrease in the air bearing (i.e., the gap between slider and disk), leading to the fluctuation of the head flying. More research on eliminating the negative effects of the back radiation thermal effects should be conducted in the future, such as how to reduce the near-field radiative heat transfer, how to achieve ultra-fast heat dissipation, and how to maintain the dynamic balance of the flying height while the slider is deformed by the back heating from the disk. The analysis of the thermal effect of the back radiation in this paper will be a preparation for these studies.  
Figure 11: (**a**) The deformation and (**b**) the deformation rate under varying flying height.
## 2 Data Enhancement  
The original training set data in this paper comes from the recorded field microseismic waveform in the tunneling process of a working face in Xingdong Mine of Jizhong Energy Group from October 2018 to April 2019. After identification and classification, the data set contains three classes of microseismic signals, the typical waveforms of each class are demonstrated in Figure 1, and the numbers of each class are 2319, 361, and 314.  
The three classes of events are groundwater movement, continuous rupture of coal seam, and tectonic activation-induced microseismic events.  
It is observed that there are two problems when the data set is used as the training set data. On the one hand, the total number of training set samples is relatively small. On the other hand, the distribution is very unbalanced: the number of samples of the first class of data is large, and the number of samples of the other two types of data is relatively small, so the ratio of the three classes is close to 7:1:1. It is very difficult to obtain a classifier with high accuracy using these training data. The role of data enhancement is mainly reflected in two aspects:  
1. Increase the amount of training data to improve the generalization ability of the model.
2. Increase the noise data to improve the robustness of the model.  
We designed the following process to achieve the purpose of data augmentation:  
1. Bandpass filtering.
2. Upsampling.
3. Window warping and window slicing.
## 2 Materials and Methods
### Hyperspectral Image Acquisition
#### 2.2.2 Spectra Extraction and Preprocessing  
After image acquisition and calibration [14], spectral information was extracted from the hyperspectral images. Pixelwise spectra within each flower were extracted. The head and the end of pixelwise spectra were removed due to the obvious random noises, and only the spectra at the range of 975-1646 nm were analyzed. Pixelwise spectra were firstly smoothed by wavelet transform (WT) (wavelet function Daubechies 8 and decomposition level 3 for both fresh and dry flowers) [12]. To reduce the influence of light variations caused by sample shape, an area normalization was then applied to pixelwise spectra. Then, preprocessed pixelwise spectra of flowers in one sample were averaged as the spectrum of the sample.