esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Wireless Sensor Network Simulations
The amplitude of the signal and the interference were calculated with the optical power of the LED matrix; and the Silicon responsivity and the Bayer filter response of the dome camera. The affected pixels were obtained from the NPSIR calculated in the previous experiment. Figure 17: Synchronizing procedure. (**a**) The average pixel value of the second frame is \(k_{max}\), therefore the first two frames are decoded as “1” (\(k>k_{max}/2\)) and the third is decoded as “0” (\(k<k_{max}/2\)), so the signal is synchronized with the camera and no process is needed. (**b**) The average pixel value of the third frame is \(k_{max}\), therefore the first two frames are decoded as “0” (\(k<k_{max}/2\)) and the third is decoded as “1” (\(k>k_{max}/2\)), so the signal is synchronized with the camera as in case (**a**). (**c**) The average pixel value of the second frame is \(k_{max}\), therefore the first frame is decoded as “0” (\(k<k_{max}/2\)) and the other ones are decoded as “1” (\(k>k_{max}/2\)), so the signal is not synchronized with the camera and type I process is needed; the first frame is discarded in order to synchronize the signal. (**d**) The average pixel value of the second frame is \(k_{max}\), therefore the first and third frames are decoded as “0” (\(k<k_{max}/2\)) and the second is decoded as “1” (\(k>k_{max}/2\)), so the signal is not synchronized with the camera and type II process is needed; the emitter restart the transmission. Another simulation was also held. In this case, for each projected distance, the corresponding pixels SIR (\(Pix_{S}/Pix_{I}\)) was calculated using Equation (4) with the NPSIR values obtained in the previous experiment and the parameters of the simulated camera and emitters. Finally, the BER was calculated in function of the SIR. As shown in Figure 19, the sensors on the table (S1 and S3) had a wider disk representation than the sensor over the autonomous robot (S2) due to the height's difference.
## 2 Modeling and Theory
### Working Principle Based on Periodic Modulation  
The thermal conductivity and temperature of the infrared light source can be derived as G and \(T_{b}\), respectively. G is the thermal conductivity, and the thermal energy of the infrared light source is represented by the product of the temperature difference and the thermal conductivity. \(H\) is the heat capacity, and the hot wire obtains the heat energy to generate the capacity of heating up. As shown in (5).  
\[H\frac{dT_{b}}{dt}+\text{G}(T_{b}-T_{a})+A_{b}\varepsilon_{b}\sigma T_{b}^{t }=P_{e} \tag{5}\]  
According to [PERSON]'s law, we can state the temperature variations in the heating and cooling processes as \(\Delta T_{r}\) and \(\Delta T_{f}\) and the heating and a cooling period can be derived as \(t_{h}\), \(t_{c}\) in (6) and (7), separately.  
\[\Delta T_{r}=\frac{P_{e}t_{h}}{H}-\frac{\text{G}(T_{b}-T_{a})t_{h}}{H}-\frac{A _{b}\varepsilon_{b}\sigma T_{b}^{4}t_{h}}{H} \tag{6}\]  
\[\Delta T_{f}=-\frac{\text{G}(T_{b}-T_{a})t_{c}}{H}-\frac{A_{b}\varepsilon_{b} \sigma T_{b}^{4}t_{c}}{H} \tag{7}\]  
After \(m\) times of heating within \(N\) times cycles, the working temperature of IR light source can be expected to reach a stable value followed by \(m\cdot\Delta T_{r}+(N-m)\cdot\Delta T_{f}=0\). The working temperature can be approximated by the following expression in (8).  
\[T_{b}\cong\left(\frac{m}{N}\frac{P_{e}}{A_{b}\varepsilon_{b}\sigma}\right)^{1 /4} \tag{8}\]  
The Beer-Lambert law serves as the foundation for the NDIR working concept. The light intensity received by the IR detector, as well as the radiation of the light, would be transmitted to an electronic output, which can be equated with \(V_{th}\) in (9). That means the output signal will be a complex function of the concentration of the gas, which is denoted as C. There are two coefficients related to the amplification factor and offset of the circuit,  
Figure 3: Architecture of the sensing and driving circuit of the direct modulation.  
denoted as \(X\) and \(Y\). \(S\) is the Seebeck coefficient which describes the transfer of temperature rise to voltage.  
\[V_{th}=XS\bigg{(}A\frac{m}{N}\frac{P_{e}}{A_{b}}\times\mathrm{e}^{-\lambda Cd}-B \varepsilon_{a}\sigma T_{a}^{4}\bigg{)}+Y \tag{9}\]
# Line Clipping in 3D: Overview, Techniques and Algorithms
## 3 Line-Clipping Algorithms in Three-Dimensions
### Three-Dimensional Cyrus-Beck Line Clipping  
The Cyrus-Beck algorithm is another classic line-clipping algorithm in two-dimensional space. With simple modifications, it can also clip lines in three-dimensional space. According to the algorithm, a straight line, intersecting the interior of a convex set, can intersect the boundary of the set in, at most, two places. In the case where the convex set is closed and bounded, the straight line will intersect it in exactly two places [6].  
Each side of the view volume is considered as a convex polygon in three-dimensional space and it is used as a clipping plane. This means that the algorithm examines all six planes of the view volume and performs clipping when necessary. For each clipping plane, the following steps are applied:  
1. The inward normal of the plane (perpendicular vector to the plane) is calculated by using three out of the four known vertices that form the plane.
2. The vector of the clipping line is calculated.
3. The dot product between the difference of one vertex per edge and one selected end point of the clipping line and the normal is calculated (for all edges).
4. The dot product between the vector of the clipping line and the normal is calculated.  
The former dot product is divided by the latter dot product and multiplied by \(-1\) (this is \(t\)).
6. The values of \(t\) are classified as entering or exiting (from all edges) by observing their denominators (latter dot product).
7. One value of \(t\) is chosen from each group and it is put into the parametric form of a line to calculate the coordinates.
8. If the entering \(t\) value is greater than the exiting \(t\) value, then the clipping line is rejected.
# Influence of Welding Parameters on the Weld Pool Dimensions and Shape in a TIG Configuration
## 4 Discussion
### Effect of the Arc Length  
The effect of the arc length is made through the comparison of case n\({}^{\circ}\)1 (five millimeters) and case n\({}^{\circ}\)3 (three millimeters). Figure 4a describes the weld pool width evolution from 1 to 20 s of interaction in Arcal.37 with a welding current of 100 A.  
Figure 3: (**a**) Effect of the current intensity on the evolution of the weld pool width; (**b**) Effect of the current intensity on the evolution of the weld pool shapes; (**c**) Effect of the current intensity on the depth-to-width ratio.  
Figure 4: _Cont._  
The weld pool expands in the same way as described above. The stabilization of the weld pool width happens after 8 s in case n\({}^{\circ}\)3, against 12 s for case n\({}^{\circ}\)1. Values of the weld pool width are slightly higher in case n\({}^{\circ}\)1, especially after 8 s of interaction. Figure 4b displays the weld pool shapes for both cases from 1 to 20 s.  
Comparison of these profiles shows that the melted area is modified by the decrease of the arc length. While the weld pool shape looks like a 'bowl' in case n\({}^{\circ}\)1, some bulges appear on both sides of the profile in case n\({}^{\circ}\)3. The weld pool depth is more important as the arc length increases. Due to a higher distance between the cathode tip and the anode material, the drop voltage increases, leading to an augmentation of the applied power. In the same time the width increases, leading to a bigger weld pool. A stagnation of the weld pool depth is observed in both cases. Maximal width reaches 8.00 \(\pm\) 0.02 mm (respectively 7.16 \(\pm\) 0.02 mm) and maximal depth reaches 3.95 \(\pm\) 0.02 mm (respectively 3.40 \(\pm\) 0.02 mm) in case n\({}^{\circ}\)1 (respectively case n\({}^{\circ}\)3). Figure 4c presents the evolution of depth-to-width ratio. This ratio evolution is almost the same in the two cases with stabilization around 0.50.  
In these configurations, one diminution of the arc length modifies the balance of the different forces. As mentioned in Introduction, an increase the arc length would lead to a rise of the shear forces resulting in a wider weld pool, and to a rise of the pressure forces leading to a deeper weld pool [9]. Our experimental work is consistent with those reports: wider and deeper pools are observed for the longer arc. It is interesting to note that up to 12 s the weld pool area remains almost the same. The evolution of the front of fusion given in Figure 4c can be interpreted by the curve of the diffusivity versus temperature Figure 4d. During the first second, the material is not heated much by the flux components and the diffusivity takes place at low temperature. Thus, the'mean' diffusivity is very high and the position of the front of fusion changes rapidly. Then progressively the workpiece is heated, leading to a diminution of the diffusivity. We can assume that from time t = 12 s the temperature is higher than 1000 K and that the diffusivity is low, leading to a weak evolution of the shape of the weld pool.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 5 Experimental Verification  
In order to verify the effectiveness of the proposed method in engineering applications, the data of Case Western Reserve University Bearing Data Center were used for experimental verification [47]. The test bench shown in Figure 7 mainly includes a test gearbox, a console, a motor, and a piezoelectric acceleration sensor. The motor has a power of 2 horsepower.  
The fault of the rolling bearing is achieved by machining the inner ring of the bearing with a spark. The bearing type is 6205-2 RS JEM SKF (Svenska Kullargerfabriken). During the experiment, the motor speed is 1797 RPM, the sampling frequency is 48,000 Hz, and the sampling point is 4096. The fault frequency can be obtained by calculation, as shown in Table 2.  
Figure 8 shows the time-domain diagram and envelope spectrum of the fault signal. It can be seen from the above Figure 8a that the collected vibration signal contains fault information, but the fault information is not clearly located. The envelope spectrum analysis of the time domain signal is shown in Figure 8b, and only one tip pulse is extracted. The fault frequency cannot be accurately described. The vibration signal is processed separately by the traditional MOMEDA and the ARMA-MOMEDA proposed in this paper, and the effects of each method are compared.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AR** & **ARMA** & **MED** & **ARMED** & **ARMAMED** & **ARMAMOMEDA** \\ \hline Permutation Entropy & 4.6870 & 4.5623 & 4.7073 & 4.7069 & 4.7033 & 4.3100 \\ Fault Energy Ratio & 0.0211 & 0.03971 & 0.0263 & 0.0286 & 0.02674 & 0.06707 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of simulation results. ARMA, autoregressive moving average; MOMEDA, multipoint optimal minimum entropy deconvolution adjusted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rotation Speed** & **Rotational Frequency** & **Fault Frequency of Inter Ring** \\ \hline
1797 rpm & 30 Hz & 162.4 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault frequency.  
Figure 7: Bearing fault diagnosis test bench.
###### Abstract
Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive EXPlanations) analysis. The findings reveal five key thematic areas: AI's role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI's positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI's impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector. artificial intelligence; telemedicine; systematic review; machine learning; prediction algorithms; SHAP Analysis +
Footnote †: journal: Applied sciences
# Performance of Stone Masking Asphalt Mixtures Fabricated by Different Compaction Methods
## 4 Conclusions  
The mechanical properties, road performances, anti-aging performances and fatigue performances of SMAs designed by the Marshall method, the SGC method, and VVTM were analyzed in a comparative study. The following conclusions can be drawn on the basis of the experimental results:  
(1) The asphalt-aggregate ratio was 5% lower in the VVTM-designed SMA-13 than in the Marshall- and SGC-designed SMA-13s. Moreover, the VVTM-designed SMA-13 improved the density, mechanical properties, high-temperature rut resistance, low-temperature crack resistance, and water stability by 2.4%, 32%, 30%, 20% and 4% respectively, relative to the Marshall-designed SMA-13 sample, and by 2.2%, 13%, 8%, 17% and 3% respectively, relative to the SGC-designed sample.  
(2) Relative to the Marshall-designed SMA-13 sample and SGC-designed sample, the short-term aging degree of the VVTM-designed SMA-13 was deep, but the long-term aging degree was smaller than in the other mixtures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Design Methods**} & \multicolumn{2}{c}{_P Value (\%) in the List Corresponding to Fatigue Life at S = 0.45_} \\ \cline{2-3}  & **5** & **50** \\ \hline Marshall & 1665 & 2968 \\ SGC & 2032 & 3747 \\ VVTM & 2221 & 4125 \\ \(N_{\text{V}}\)/\(N_{\text{M}}\) & 1.33 & 1.39 \\ \(N_{\text{V}}\)/\(N_{\text{S}}\) & 1.09 & 1.10 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Comparison of the fatigue lives of specimens designed by different methods.  
(3) The fatigue life of SMA-13 was subjected to two-parameter Weibull distribution, and the fatigue equation could accurately reflect the fatigue life of SMA13.The fatigue life was at least 33% and 9% higher in the VVTM-designed sample than in the Marshall- and SGC-designed samples, respectively.  
This study provides new ideas for the design of SMA mixtures. The VVTM method offers many more advantages than the traditional method for the design of SMA mixtures, and can solve the early degradation problems of SMA pavements to a certain extent. Therefore, it improves the prospects and economic benefits of SMA reading.  
Conceptualization, Y.J. and J.X.; data curation, Y.Z. and J.X.; formal analysis, Y.Z., J.X. and C.D.; funding acquisition, Y.J.; C.D. and J.X.; experiment operation and writing--original draft preparation, Y.Z. and J.X.; writing--review and editing, Y.J., Y.Z.; C.D. and T.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Doctoral Research Foundation of Hubei University of Arts and Science (grant number 2059084), Youth Science and technology new star project of Shaanxi innovation talents promotion plan (grant number 2019 KJXX-035) and the Scientific Research of Central Colleges of China for the Chang'an University (grant number 300102218212). The author gratefully acknowledges its financial support.  
The authors declare no conflicts of interest.
# A Two-Stage Anomaly Detection Method Based on User Preference Features and the Deep Fusion Model
## 4 Results
### Environment  
The experimental environment is as follows: Window10 \(\times\) 64 operating system, Intel Core i7-8850H 2.6 GHz CPU, NVIDA GeForce GTX 1050 Ti with Max-Q Design GPU, 32 GB RAM, 1 TB SSD. We use Vmware WorkStation 16 Pro to create a 64-bit Ubuntu16 virtual machine as part of the Spark Working cluster. Each virtual machine is installed with Spark version 3.0.1 and Hadoop version 3.3.0. The memory of the virtual machine is 4 GB, the number of computing cores is 1, the IP address of the Master host is 192.168.118.129, and the IP address of the Worker host is 192.168.118.130-192.168.118.138.  
We use the well-known CIC 2017 App-DDoS Dataset [15] as the experimental data set. The data set was obtained by deploying Apache Linux v.2.2.22, PHP5, Drupal v.7 web servers in a real network environment and using a network sniffer to capture network traffic on the server. For the attack data, four different types of network attacks were carried out on the server at different times through seven network attack tools such as Hulk, DDoSim, and Rudy, and a total of 24 h of network data were recorded, with a total file size of 4.6 GB.  
The CIC 2017 App-DDoS Dataset contains the network traffic of eight different application-layer DDoS attacks, and the network characteristics of each attack traffic are different. Using this dataset could well reflect the detection capability of the model for network attacks with different characteristics.
## 2 Methodologies
### Bidirectional Generative Adversarial Networks  
In order to use GAN's advantages to capture data distribution for inference and classification tasks, [PERSON] et al. [19] proposed the bidirectional generative adversarial network (BiGAN) by introducing an additional encoder \(E\) on the basis of GAN, which gives BiGAN the ability to extract data features and to perform semi-supervised learning.  
As shown in Figure 1b, the obvious difference between BiGAN and GAN is the encoder \(E\). In addition to the generator \(G\) that maps the latent space to the data space, BiGAN introduces an encoder \(E\) to inversely map the data space to the latent space, i.e., \(\tilde{z}=E(x)\). Thus \(\tilde{z}\) can be used as low-dimensional features extracted from data samples by the encoder.  
Besides, the structure of discriminator \(D\) needs to be modified as well. Instead of taking \(x\) or \(\tilde{x}=G(z)\) as the input of \(D\), the discriminator discriminates joint pair \((x,\tilde{z})\) or \((\tilde{x},z)\) from data space and latent space, respectively.  
Similarly to GAN, the goal of BiGAN can be defined as a minimax objective:  
\[\begin{split}\min_{G,E}\max_{D}V(D,E,G)=\mathbb{E}_{x\sim p_{X}} [\log D(x,E(x))]+\\ \mathbb{E}_{z\sim p_{Z}}[\log\left(1-D(G(z),z)\right]\end{split} \tag{5}\]  
The goal of \(D\) is to maximize the value function \(V(D,E,G)\), and the goal of both \(G\) and \(E\) is to minimize it. In addition, BiGAN also uses the alternative loss function of \(G\) as mentioned in Equation (4). So we can obtain the following loss functions for the training of BiGAN:  
\[\begin{split}\mathcal{L}_{D}=&-\mathbb{E}_{x\sim p _{X}}[\log D(x,E(x))]\\ &-\mathbb{E}_{z\sim p_{Z}}[\log\left(1-D(G(z),z)\right)]\\ \mathcal{L}_{EG}=&-\mathbb{E}_{x\sim p_{X}}[\log \left(1-D(x,E(x))\right)\\ &-\mathbb{E}_{z\sim p_{Z}}[\log D(G(z),z)]\end{split} \tag{7}\]  
The encoder \(E\) helps BiGAN extract features from data to perform classification and makes BiGAN suitable for semi-supervised learning tasks.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 4 Correctness and Security Analysis
### Security Analysis
#### 4.2.1 Conditional Anonymity  
In this scheme, only the trusted entity TA knows the EV users' real identities, while other entities only know the pseudonymous identities. Apart from the TA, no one can deduce the real identity of the EV user from the pseudonymous identity. This ensures the conditional anonymity of the EV user.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 6 Resin Adhesive Systems
### Composition of the Adhesive Systems
#### 6.1.1 Acidic Components
The dentist should clean the tooth surface and get it ready for bonding by using H3 PO4 (liquid or gel). Compared to liquid acid, gel acid has advantages such as improved control during application and longer retention on the tooth surface [154] (Figure 10). Figure 9: 37% orthophosphoric acid on dentin. (Courtesy of Prof [PERSON]). Since the advent of lasers in dentistry in 1960, there has been fast advancement, and the application field of lasers has grown dramatically [157]. The laser interacts with the target tissues through absorption, transmission, scattering, and reflection. Chromophores found in both soft and hard tooth tissues include hemoglobin, melanin, water, and HAp. The choice of laser is determined by tissue characterization since they have an affinity for specific wavelengths [158]. In addition to soft tissue applications, dentists frequently use lasers on dental hard tissues for cavity preparation, treating dentin hypersensitivity, caries removal, and surface conditioning [157]. Erbium lasers are often used for these hard tissue applications because their wavelengths are selectively absorbed by the HAp and water content of enamel and dentin. Another application for lasers is enamel and dental conditioning before the bonding process. On one hand, erbium lasers act on dentin by making a visible rough surface, opening dental tubules without a smear layer, and producing micro-irregularities caused by selective ablation of ITD [159]. Choosing the correct laser parameters is critical, since it may create changes in dentin collagen, which may have an unfavorable effect on adhesion. On the other hand, treating the enamel surface by means of an erbium laser produces an irregular, uneven surface that permits the adhesive to penetrate. As a result, the production of RTs enhanced the micro-mechanical retention [157]. Some studies have indicated that laser irradiation improves micro-mechanical retention by increasing surface roughness and surface area at the enamel-resin interface [160; 161]. Prior investigation discovered that surfaces conditioned with erbium lasers or H\({}_{3}\)PO\({}_{4}\) exhibit similar micro-irregularities [162]. Overall, there has been extensive research on the use of lasers in conjunction with or as an alternative to H\({}_{3}\)PO\({}_{4}\) etching [163; 158; 164; 155]. Although the ideal strategy for adhering to enamel and dentin remains unknown, clinicians prefer to employ H\({}_{3}\)PO\({}_{4}\).
## 1 Introduction
This attenuation study benefits from the recently released dataset of GP microearthquakes [27; 31] and from a 3D mapping approach based on sensitivity kernels [15]. The 3D tomography of \(Q_{c}\) was obtained by combining the new dataset with the previous one [23], used for the 2D mapping of \(Q_{c}\). With respect to the previous estimate of \(Q_{c}\) in the GP [23], the used dataset is enlarged and consists of earthquakes with magnitudes ranging between 1 and 2.8, and the sampled area is extended to the NE, thanks to a change in the OT network geometry. All the details regarding the data and the network can be consulted in [27]. Moreover, 3D images of \(Q_{c}\) for the GP, using a projection method based on weighting functions derived from sensitivity kernels [15] have been performed for the first time in this area. The present \(Q_{c}\) tomography depicts the S-wave attenuation structure down to a 32-km depth, in three frequency bands centered at 3, 6 and 12 Hz, respectively. We will show how our present images provide evidence that the strongest attenuation contrasts are well correlated with the geotectonic setting of the GP.