esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Due to the presence of a relatively high or low permeability layer in some cases [12], several studies have analysed the hydraulic response of soils. Some studies have been conducted on slopes containing relatively permeable layers which are interspersed with or underlain by a low permeability or an impermeable layer [47; 48; 49]. The changes of pore pressures or the propagation of excess pore pressures around the interface of layers have been investigated along with the influence on the slope stability. These include some cases in which the upper layer is more permeable than the lower layer, or the case in which the lower layer with a greater permeability is located in a very steep slope. The mechanisms of the effect of the difference in permeability of soil layers on the occurrence of landslide events have not been analysed in detail for these cases. In the case of the Ludoialm landslide investigated in this contribution, the permeability of the lower layer is presumed to be much greater than that of the upper layer based on geological surveys. We show the influence of permeability difference between layers on the Ludoialm landslide in Tyrol, Austria, in order to explain the formation of an exceptionally flat dipping basal shear zone (about 12\({}^{\circ}\)). Therefore, we provide a comprehensive analysis of the initial triggering factors of this landslide by analysing the temporal relationship between precipitation, snowmelt, and the events. Photogrammetric methods were used to investigate the historical development of the landslide. A geological and geomorphological mapping was carried out to develop a geometrical-geological model of the slide. A field survey and various laboratory analyses on collected soil samples are performed to obtain the geotechnical properties, i.e., soil cohesion, friction angle, and permeability coefficient. Several slope stability analyses such as LEA, SRFEM, and FELA were applied to identify and validate a plausible initial failure mechanism of the landslide event. We introduce the study area in Section 2. The identification of the soil parameters is presented in Section 3. We present the numerical studies in Section 4 and the results in Section 5. Finally, we have a discussion in Section 6 and conclude with the key messages of this study in Section 7.
## 6 Results and Discussion
### Effect of Traffic Variation  
The impact of a possible increase or decrease in total road traffic was also taken into account. This allowed us to assess the sensitivity of the infrastructure to possible future variations and the resilience of the planned intervention. An increase/decrease in traffic of \(\pm\)20% (in intervals of 5 to 10%) compared to the value initially assumed for the simulation of Scenarios A and B was considered. The results are illustrated in Figure 5. It can be seen that the sensitivity to traffic is lower in the case of Scenario B (with redesign intervention). In fact, the overall number of predicted accidents on segments and intersections is lower independently from a traffic increase/decrease. Moreover, the slope of the trend line expressing the crashes increase with increasing traffic is lower. This means that the implementation of rehabilitations and bypasses make the infrastructure more resilient and less vulnerable to potential traffic variation.  
In fact, this sensitivity analysis describes the influence of traffic volume on the number of predicted crashes and, thus, on the effectiveness of the redesign intervention. A similar approach can be adopted to assess the impact of the other variables of the predictive model (e.g., curve radius, lane width, shoulder width and type), affecting the CMFs values. This additional sensitivity analysis could be the scope of a further agenda.
# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 7 Conclusions and Future Works  
In the last few years, we have been witnessing an increase in mobile malware, with particular regard to malicious payloads targeting the Android platform, the most diffused one. Considering the inefficiency of the currently adopted signature-based approach in the detection of zero-day malicious behaviors, the research community, from both the academic and industrial sides, is focused on proposing new techniques to mitigate malware, mainly exploiting deep learning. In this last context, deep learning recently has introduced quantum computing to train models using the integration of quantum algorithms within Machine Learning algorithms. For these reasons, we proposed to apply quantum Machine Learning models for Android malware detection. We considered a comparison between five state-of-the-art deep learning models, i.e., AlexNet, MobileNet, EfficientNet, VGG16, VGG19, and a deep learning network developed by the authors, which we called Standard-CNN, and two quantum models, the first one being a hybrid quantum model and the second one a fully quantum model: all these (convolutional and quantum) models were trained to perform the malware detection in the Android environment. The input for the models was a set of images obtained from the conversion of Android applications into grayscale images. In the experimental analysis, 7386 applications belonging to six different malicious families and 1060 legitimate applications were considered. As a result of the experiments, we obtained that the architecture obtaining the best performance was the Standard-CNN model, with an accuracy equal to 0.970. Relating to the quantum models, the best model in terms of accuracy was the Hybrid-QCNN model, with an accuracy of 0.905. In future work, more complex quantum models (in terms of PQC layers) will be considered. Furthermore, considering that the proposed method is not operating as system-dependent (as a matter of fact, the images are generated directly from the application bytes), we will evaluate whether the proposed approach is working in ransomware detection. Additionally, to provide more explainability, different images will be proposed, for instance an interesting improvement of the proposed method is represented by the adoption of an image obtained from the application code; in this case, Grad-CAM will be able to highlight areas that are immediately traceable to code snippets and, presumably, the malicious payload.  
Methodology, F.M.; Software, G.C., G.I. and M.S.; Validation, F.M., G.C., G.I. and M.S.; Formal analysis, F.M. and A.S.; Writing--original draft, F.M. and F.M. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the EU DUCA,EU CyberSecPro, and EU E-CORRIDOR projects and PNRR SERICS_SPOKE1_DISE, RdS 2022-2024 cybersecurity. The authors declare no conflict of interest.
## 3 Results
### Mechanical Strength  
To study wound dressings' ultimate tensile strength (UTS) and elongation-at-break were gathered as essential factors for biomedical dressings. Many aspects, such as the temperature, and physical and chemical conditions, could influence the mechanical properties of the films [25].  
The results indicate that the materials' (S1-S4) UTS changes from 6.14 (without nZnO) to 15.33, 16.61, 15.94, and 16.31 MPa, respectively, after adding ZnO nanoparticles to the starch-based/polyvinyl alcohol/citric acid/glycerol solution (SPGC), as shown in Figure 9. There are no remarkable changes in the ultimate tensile strength after increasing the nZnO content in the solutions (S1-S4). The film elongation-at-break (Figure 10) decreased gradually as the ZnO nanoparticles were added to the solution. The increase in the tensile strength of the films could be due to the uniform dispersion of the ZnO nanoparticles in the composite matrix since their presence creates unique surface properties. In addition, the moisture content of the material directly influences the film's mechanical properties. By adding nZnO particles to the solution, the free water molecules content decreases, which reduces flexibility and increases the strength of the resulting films. The reported observations are in good agreement with open literature [17; 12; 25]. Since the average thicknesses of the different samples used in calculating the UTS were relatively close, the effect of sample thickness on the dressings' UTS is negligible in this study.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## 5 Brief Case Studies
### Pauli X Quantum Gate  
The ideal matrix \(A_{XI}\) of the Pauli X quantum gate operator is obtained from the matrix \(A_{RX}\) of the \(R_{X}\) operator [22]:  
\[A_{RX}=\begin{bmatrix}cos\frac{\theta}{2}&-jsin\frac{\theta}{2}\\ -jsin\frac{\theta}{2}&cos\frac{\theta}{2}\end{bmatrix} \tag{44}\]  
by forcing the rotation angle of the qubit state vector \(\theta=\pi\) and by factorizing and neglecting the global phase term \(-j\):  
\[A_{XI}=\begin{bmatrix}0&1\\ 1&0\end{bmatrix} \tag{45}\]  
Let us suppose that the actual rotation angle is affected by an error \(\varepsilon\), so that \(\theta=\pi+\varepsilon\), because the electronics circuitry, designed to control the qubit, causes errors in the amplitude of \(s_{RF}\) and/or in the duration \(\Delta t\). From Equation (44), the actual matrix \(A_{XR}\) of the Pauli X quantum gate operator is:  
\[A_{XR}=\begin{bmatrix}cos\left(\frac{\pi}{2}+\frac{\varepsilon}{2}\right)&- jsin\left(\frac{\pi}{2}+\frac{\varepsilon}{2}\right)\\ -jsin\left(\frac{\pi}{2}+\frac{\varepsilon}{2}\right)&cos\left(\frac{\pi}{2}+ \frac{\varepsilon}{2}\right)\end{bmatrix}=-\begin{bmatrix}sin\left(\frac{ \varepsilon}{2}\right)&jcos\left(\frac{\varepsilon}{2}\right)\\ jcos\left(\frac{\varepsilon}{2}\right)&sin\left(\frac{\varepsilon}{2}\right) \end{bmatrix} \tag{46}\]  
Since the entries of the matrix \(A_{XI}\) are real values, \(A_{XI}=A_{XI}^{+}\), and thus from Equations (45) and (46), you obtain:  
\[A_{XI}^{+}A_{XR}=-\begin{bmatrix}0&1\\ 1&0\end{bmatrix}\begin{bmatrix}sin\left(\frac{\varepsilon}{2}\right)&jcos \left(\frac{\varepsilon}{2}\right)\\ jcos\left(\frac{\varepsilon}{2}\right)&sin\left(\frac{\varepsilon}{2}\right) \end{bmatrix}=-\begin{bmatrix}jcos\left(\frac{\varepsilon}{2}\right)&sin \left(\frac{\varepsilon}{2}\right)\\ sin\left(\frac{\varepsilon}{2}\right)&jcos\left(\frac{\varepsilon}{2}\right) \end{bmatrix} \tag{47}\]  
from which:  
\[\left|Tr\left\{A_{XI}^{+}A_{XR}\right\}\right|^{2}=\left|-2 jcos\left(\frac{ \varepsilon}{2}\right)\right|^{2}=4 cos^{2}\left(\frac{\varepsilon}{2}\right) \tag{48}\]  
The substitution of Equation (48) into Equation (42) yields:  
\[F=\frac{2+4 cos^{2}\left(\frac{\varepsilon}{2}\right)}{2(2+1)}=\frac{1+2 cos^{2 }\left(\frac{\varepsilon}{2}\right)}{3} \tag{49}\]  
where it has been emphasized that n = 2 for a one-qubit quantum gate. As expected, Equation (49) yields \(F=1\) when \(\varepsilon=0\).
## 2 The Designing and Assembling of the New Modular Connection  
The connection is composed as follows. The locating plate and locating tube are successively welded into the square steel tube. The locating tube is the inscribed circle for the square steel tube. The giant bolt is placed into the locating tube, and the prefabricated end plates are welded to the upper and lower ends of the column. This process is completed in the factory. It is assembled as shown in Figure 2f, and the assembly is completed in the way shown in Figure 2g. To avoid the bolt tilting or even reversing, the upper part of the bolt will be made into a cylinder whose diameter is slightly less than that of the positioning cylinder. For the convenience of centering, a certain space is reserved in front of the screw thread. To ensure firm welding, the upper-end plate with the screw thread on the top of the column will be divided into three parts, which are welded one after another. The bottom-end plate with no screw thread at the bottom of the column is welded twice successively; groove welding is adopted. The other assembly processes of the modules in the factory stay unchanged. The beams and the columns are welded together, and V grooves are made on the four beam edges to strengthen the welding, as shown in Figure 3, and the yellow part of the figure shows the weld seam.  
The prepared modules are hoisted and assembled on the construction site. After hoisting and assembling the lower modules, the base plate is installed as shown in Figure 4. Then, the upper modules are hoisted and assembled. A tailored twisting tool as shown in Figure 5 is stretched through into the column from above, to screw down the giant bolt. The bolt will join the vertical modules together, and the base plate transverses the horizontal modules together. Finally, eight modules are connected as a whole. Figures 6 and 7 show the connection location in the eight-module building.  
Figure 1: Difficulties in the last module installation.  
Figure 4: Base plate.  
Figure 5: Twisting tool.  
Figure 3: Beam–column joint.  
Figure 2: Diagram of the new connection structure. (**a**) Locating plate (**b**) locating tube (**c**) bolt (**d**) upper-end plate (**e**) bottom-end plate (**f**) assembly (**g**) assembly completed.
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
## 4 Discussion  
_Ablation Studies of IDM and CCAM_  
To quickly demonstrate the effect of the information distillation module (IDM) and contrast-based channel attention mechanism (CCAM), we remove the IDM between IDB and/or CCAM, so the IDMF-SR becomes the basis of a deep network, which we named IMDN-Basic, as described in Figure 11. Firstly, we use four IDB to certify the effect of IDM and CCAM. In Table 4, when both IDM and CCAM are removed, the PSNR on Set5 at the scale factor of 4 is 32.48 dB as the first column. When CCAM is added, the PSNR value reached 32.56 dB. This is because CCAM can improve the information about structures, textures, and edges that are propitious to enhance image details. The PSNR value reaches 32.62 dB with the contribution of IDM and CCAM. This indicates that IDM and CCAM are essential for improving SISR performance.
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 4 Experiments
### Effects of Contrastive Prototype Learning
#### 4.5.4 Visualization of Action Prototypes  
For better understanding of the effectiveness of **CPL** in learning action prototypes, closer examination of the embedding features from the two domains is studied using t-SNE [22]. Figure 3 illustrates the action clusters of the two respective domains (NTU RGB+D and PKU-MMD) encoded on the last convolutional layer of \(\mathcal{M}\) as interpreted by the two action recognition models (\"vanilla\" supervised pretrained on the upper row and \"**CPL**\" on the bottom row). First, even though the \"vanilla\" supervised pretrained model presents well-separated action clusters when tested on the source domain (upper left feature map), which reflects the model's ability to interpret feature representations from the source domain, it presents less separable action clusters when tested on the target domain (upper right map) due to the impacts of domain shift. Such a discrepancy on action cluster interpretation leads to performance degradation across the two domains (from 69.07% to 57.32% in Table 1). Second, when applying the proposed **CPL** method, the model (bottom row) demonstrates more effective adaptation to the target domain whose action clusters are improved in terms of separability. Such improvement is observable on both test cases considering the source domain (bottom left map) and the target domain (bottom right map).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**NTU RGB+D to PKU-MMD Part II** & **Top1 Acc** \\ \hline [PERSON] et al. [19] & 44.8\% \\ [PERSON] et al. [20] & 45.8\% \\ [PERSON] et al. [21] & 45.9\% \\ CPL (proposed) & 47.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison to state-of-the-art works.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Full Supervision** & **CPL** \\ \hline
**NTU and 10\% PKU** & **NTU and 10\% PKU** \\
**(Fine-Tuning)** & **(Fine-Tuning)** \\ \hline Accuracy & 45.97\% & 72.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of “vanilla” fully supervised learning vs. semi-supervised learning. “NTU” stands for “NTU RGB+D”, and “PKU” stands for “PKU MMD Part I”.
## 2 Materials and Methods
### Measures and Procedures
#### 2.2.4 Physical Activity  
A questionnaire for self-assessment of physical activity--the _International Physical Activity Questionnaire Short Form_ (IPAQ-SF) [44]--was used for the assessment of the physical activity (PA) level. This questionnaire was created to address the challenge of identifying the amount of PA at a worldwide level so that active and inactive respondents could be distinguished [45], as well as for the comparison of different populations. A short form of the IPAQ questionnaire estimates the frequency, duration, and intensity of PA in four domains as follows: (a) leisure-time PA, (b) household PA, (c) occupational PA, and (d) transportation PA. The questions in the IPAQ-SF questionnaire are structured to allow separate scoring for walking/low-intensity PA, moderate-intensity PA, and vigorous PA. The standardized scoring system for the IPAQ-SF was used to perform the data processing in this study (www.ipaq.ki.se, 12 June 2022). A metabolic equivalent of task (MET) was used to calculate the results for each of the three levels of PA listed with respect to minutes per day. Total weekly MET-minutes (MET-min/week) were calculated by adding MET-minutes for each level of PA intensity (walking/low intensity = 3.3 METs; moderate intensity = 4.0 METs; vigorous intensity = 8.0 METs). The reliability and validity of the IPAQ-SF questionnaire were found to be satisfactory [46; 47].
# Altering the Supply of Shielding Gases to Fabricate Distinct Geometry in GMA Additive Manufacturing
## 2 Results and Discussion  
### Arc Characteristics  
The droplet cycle (initiation to the detachment) for the binary and ternary blend is shown in Figures 2 and 3. The droplet is not visible in the image due to the low intensity of the lighting used during the high-speed imaging; however, the droplet cycle can be observed as the metal transfer is typical to the surface tension transfer or short-circuit transfer. The period of each droplet cycle was 21.4 ms for the ternary blend and 33 ms on average for the binary blend.  
The figures also show the non-uniformity of the arc shape. The bright region is dominated by metal vapor; references [19; 20; 21] have shown that there is very little difference between the net emission coefficients for the 50% Fe-50% binary mixture and 50% Fe-50% ternary mixture. Only in the fringe regions will the Fe concentration be low; in this region, the larger net emission coefficient of the binary mixture will influence the brightness of the arc. The image analysis of the bright region arc shows the average width of the arc was 11.4 mm for the binary blend and 8.2 mm for the ternary blend. Similarly, the average length of the arc was 5.9 mm for the binary blend and 3.1 mm for the ternary blend. The ternary blend arc appears to be more symmetrical than the binary blend with respect to the electrode axis.  
Figure 2: The arc appearance, labeled with the timing from the droplet initiation to detachment, for the ternary blend.  
These results indicate that the shorter droplet cycle that corresponded with the ternary blend was due to the addition of helium gas, which has a higher thermal conductivity and ionization potential than the other gases, which led to a higher power (current) density and smaller arc width, despite identical set parameters. Similarly, it can be hypothesized that the larger arc length in the binary blend led to the longer droplet cycle and the widening of the bead. In support of these interpretations, a higher heat flux density has been previously observed when helium was added to an Ar-CO\({}_{2}\) mixture [14].
## 1 Introduction
However, the 3 mm outside diameter of the sensor has become a major defect, making it unsuitable for high-flow debris detecting. Currently, research related to debris sensors mainly focuses on improving the recognition accuracy of wear debris, and exciting debris sensors are generally small in size of the oil path. The height of the oil path of image-based sensors is about 1 mm [21], which results in poor passability of the lubricating oil and easy jamming. The inductive debris sensor has a relatively large oil path size and is currently up to 34 mm [22]. Most inductive debris sensors are driven by high-frequency voltage, and the recognition accuracy of the sensor is directly related to the frequency of the drive voltage. However, the frequency of the drive voltage is too high, which will aggravate the skin effect of the excitation coil, causing an increasing temperature of the sensor and changing the magnetic field in the oil path. Moreover, for larger-diameter lubricating oil circuits, such as ships and steam turbines, only one bypass can be set for sampling and monitoring. This may change the flow resistance of the original lubricant oil circuit and affect the lubrication effect. Accordingly, one possible solution is to develop a large-diameter debris sensor to match the size of the lubricating oil circuit based on the previously proposed inductive sensor [26]. However, the following problems exist: (a) compared with the small-diameter sensors, after increasing the diameter, it cannot be ignored that the magnetic field strength in the radial direction of the oil pipe is changing; and (b) when the pipe diameter is small, the entire oil pipe is in a strong magnetic field, and all the wear debris passing through the sensor can be detected. However, after the diameter of the oil pipe is increased, only the local region of the oil pipe is in a strong magnetic field, which causes some significant induced voltages not to be generated when wear debris passing through a portion of the tubing flow. In this paper, a large-diameter inductive debris sensor is proposed. The sensor is driven by a constant current and generates a high-gradient magnetic field in a limited region near the wall of the sensor tube. When a piece of metal wear debris flows through the region, a pulse output signal similar to a sine wave is generated to achieve the on-line monitoring of the wear debris in larger-diameter lubricant lines.