esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Kappa carbides expected in austenitic Fe-21.3 Mn-7.6 Al-4.3 Cr-1C low-density stainless steels due to the high Al and C content [4] were also not identified. The type of carbide that evolved after hot working is unknown; thus, future work will focus on the evolution of carbides in hot-worked alloys. As breaking down the as-cast dendritic structure was achieved under certain deformation conditions, it is important to evaluate the pitting corrosion resistance of the deformed alloys compared to the as-cast samples in simulated body fluids. This is being investigated by our research group. The hardness results (Figure 7) indicate that hot working improved the hardness of the two low-density stainless steels compared to the as-cast dendritic structure. This can be ascribed to gain refinement and carbide redistribution in the alloys. [PERSON] et al. [31] reported that grain refinement was caused by DRX in hot-worked Fe-30 Mn-11 Al-1C-0.1 Nb-0.1V alloy at relatively low deformation temperatures (850 to 950 \({}^{\circ}\)C) and strain rates of 1 to 10 s\({}^{-1}\). Their alloy's refined grains and hardness were sensitive to deformation temperatures and strain rates. Higher deformation temperatures and low strain rates caused an increase in the average grain size of the austenite grains, decreasing the hardness. Although, in our work, grain refinement and microstructural reconstitution are prominent at 800 and 900 \({}^{\circ}\)C as in [PERSON] et al. [31], the hardness increased slightly with deformation temperature and was insensitive to increasing strain rate. The discrepancy between this work and that of [PERSON] et al. [31] can be attributed to the difference in alloy compositions, as well as different initial microstructures. Furthermore, [PERSON] et al. [32] established that grain refinement was optimum at a deformation temperature of 1050 \({}^{\circ}\)C and 10 s\({}^{-1}\) strain rate in hot-worked Fe-35 Mn-10 Al-1C steel. The disagreement between the optimum deformation temperature for grain refinement in our work, [PERSON] et al. [31], and [PERSON] et al. [32] implies that the different compositions and initial microstructures influence the response of low-density steels to deformation parameters. Therefore, alloys with unique compositions must be subjected to various testing conditions to understand their behavior.
## 3 Simulation Description
### Building Characteristics  
The characteristics of the dwellings analysed are described in this section. The median area of urban dwellings in Madrid is 90 m\({}^{2}\)[81].  
As a square enclosure measuring 9.5 m on each side, the dwelling simulated here had an area of 90.25 m\({}^{2}\), very close to the aforementioned median value. The storey height applied was 3 m. The dwellings were assumed to be located in one of two positions: at mid-building, with just one outer enclosure (hereafter 'interior dwelling'); or on the top storey, in a corner location (hereafter 'corner dwelling') (Figure 8).  
IIn pursuit of a wide variety of constructional characteristics, four variables were defined: glazed area, envelope insulation, overall heat transfer coefficient (U-value), and heat capacity and orientation, summarised below and substantiated in greater detail in Appendix B.  
* Glazed area: 15%, 30%, or 80%;
* Envelope insulation (U-value): window, outer wall, and roof insulation were rated for the simulations as low, medium, or high depending on the respective U-values (Appendix B, Table A2), with high meaning better U-values than required by current legislation; medium meaning compliance-level; and low meaning non-conformity, i.e., as a rule, buildings 20 years old or over;
* Heat capacity: the three levels of heat capacity applied, low, medium, and high, were defined as per standard ISO 52016-1:2017 [82]);
* Orientation: the orientations adopted for interior dwellings were south, east, and west, and for corner dwellings, southeast, southwest, northeast, and northwest.  
Consequently, the number of dwellings analysed was the result of combining the above parameters, namely two in-building locations, three percentages of glazing, three insulation levels, three levels of heat capacity, and four orientations, for a total of 216. The dwellings were coded as summarised in Figure 9.  
Figure 8: Dwelling locations used in simulations.
# Thermal Accelerometer Simulation by the R-Functions Method
## 4 Generalized Numerical Procedure
\begin{array}{l}a_{m,n}=\frac{1}{\Pr}\int\limits_{\Omega}f_{m}\
abla^{2}f_{ n}\mathrm{d}\sigma=-\frac{1}{\Pr}\int\limits_{\Omega}\
abla f_{m}\
abla f_{n} \mathrm{d}\sigma,\\ \widetilde{a}_{m,n}^{(k-1)}=-\int\limits_{\Omega}f_{m}\bigg{(}U^{(k-1)}\frac{ \partial f_{n}}{\partial X}+V^{(k-1)}\frac{\partial f_{n}}{\partial Y}\bigg{)} \mathrm{d}\sigma;\\ b_{m}=-\frac{1}{\Pr}\int\limits_{\Omega}f_{m}\
abla^{2}\Phi\ dXdY=\frac{1}{\Pr} \int\limits_{\Omega}\
abla f_{m}\
abla\Phi\ \mathrm{d}\sigma,\\ \widetilde{b}_{m}^{(k-1)}=-\int\limits_{\Omega}f_{m}\bigg{(}U^{(k-1)}\frac{ \partial\Phi}{\partial X}+V^{(k-1)}\frac{\partial\Phi}{\partial Y}\bigg{)} \mathrm{d}\sigma;\\ m,n=0,\dots,N.\end{array}  \tag{19}\]  
Some optimization of Equation (19) can be done to decrease the time for computations. Then we pass to finding coefficients of expansion (14). Analogously to the previous case, we write the corresponding residual for the second equation of the System (6) as:  
\[\delta_{\zeta}^{(k)}=\sum\limits_{n=1}^{N}d_{n}^{(k)}\bigg{(}\
abla^{2}\
abla ^{2}(\omega f_{n})-U^{(k-1)}\frac{\partial}{\partial X}\
abla^{2}(\omega f_{n })-V^{(k-1)}\frac{\partial}{\partial Y}\
abla^{2}(\omega f_{n})\bigg{)}-F_{ \zeta}^{(k)}, \tag{20}\]  
where  
\[F_{\zeta}^{(k)}=-\mathrm{Gr}\cdot\bigg{(}\frac{\partial\theta^{(k)}}{\partial Y }+\frac{\partial\Phi}{\partial Y}\bigg{)}.\]  
To find coefficients \(d_{n}^{(k)}\), we make an orthogonal projection of the residual (20) to all functions of the basis \(\{f_{n}\}_{n=0}^{N}\) that is the main idea of the Petrov-Galerkin method [6]:  
\[\int\limits_{\Omega}f_{m}\delta_{\zeta}^{(k)}\mathrm{d}\sigma=0,\ m=0,\dots,N. \tag{21}\]  
Finally we obtain the following SLAE for \(\mathbf{d}^{(k)}\):  
\[(\mathbf{W}+\widetilde{\mathbf{W}}^{(k-1)})\mathbf{d}^{(k)}=\mathbf{z}+ \widetilde{\mathbf{z}}^{(k)}, \tag{22}\]where  
\[ \begin{array}{l}w_{m,n}=\int\limits_{\Omega}f_{m}\
abla^{2}\
abla^{2}(\omega f_{ n})\,\mathrm{d}\sigma;\\ \widetilde{w}_{m,n}^{(k-1)}=-\!\!\!\!\!\!\!\int\limits_{\Omega}f_{m}\big{(}U^{( k-1)}\frac{\partial}{\partial X}\
abla^{2}(\omega f_{n})+V^{(k-1)}\frac{ \partial}{\partial Y}\
abla^{2}(\omega f_{n})\big{)}\mathrm{d}\sigma;\\ z_{m}=-\mathrm{Gr}\!\!\!\!\!\!\!\!\!\!\int\limits_{\Omega}f_{m}\frac{ \partial\Phi}{\partial Y}\mathrm{d}\sigma;\\ \widetilde{z}_{m}^{(k)}=-\mathrm{Gr}\!\!\!\!\!\!\!\!\int\limits_{\Omega}f_{m} \frac{\partial\theta^{(k)}}{\partial Y}\mathrm{d}\sigma,\;m,n=1,\ldots,N.\end{array}  \tag{23}\]  
Integrals in Equations (19) and (23) can be evaluated numerically, and differential operators are approximated with their finite-difference analogs.
## 4 Field Application
### Determination of Drilling Parameters
Gas concentration in the 4# group of boreholes sharply decreases to 4.57% when \(D_{v}\) grows to 21.5 m, consistent with that of the 1# group of boreholes. These boreholes are set in the continuous zone with the medium permeability, where it is difficult for gas to migrate due to few fractures. To sum up, gas concentration in the 2# and 3# groups of boreholes is more significant than that in the 1# and 4# groups of boreholes. Thus, the favorable gas extraction zone ranges from 13 m to 19 m of \(D_{v}\), where an acceptable extraction efficiency may be achieved. Moreover, gas concentration in every group of boreholes indicates pronounced zoning with the advancement of the working face. It rises to its peak when \(D_{v}\) exceeds 20 m, equal to the distance of the periodic roof pressure. This verifies that the permeability change lags behind the coal and rock mass failure. Notably, the favorable gas extraction zone identified through the on-site experiments is considerably smaller than that determined through the numerical simulation. This indicates that when guided by numerical simulation results, on-site experiments can provide a more precise determination of the favorable gas extraction zone with minimal engineering. Therefore, combining numerical simulation with on-site experiments can achieve the most accurate favorable extraction area at the lowest cost, thereby improving engineering efficiency and economic benefits.
# V-SOC4 AS: A Vehicle-SOC for Improving Automotive Security
###### Abstract
The first experiment has been done considering two state-of-the-art attacks (DoS and fuzzing). * The use of security information and event management (SIEM) to monitor each subsystem for intra-vehicle communication management, namely the controller area network (CAN), local interconnect network (LIN), FlexRay, and media oriented systems transport (MOST), and Ethernet. Each of them has vulnerabilities and security issues [6; 20]. The research goal is to define a solution that can improve and redefine security controls in the three functional areas: detection, response, and prevention. Specifically, in this first step, the work focuses on the CAN protocol and the use of IBM QRadar as a SIEM. * The possibility to identify not only the attack but also the type and the pattern of attack. This allows work on the threat, attack vector, and risk associated with a given vehicle component. In the literature, most works use binary classifiers such as IDSs for detection without defining the attack type. Therefore, V-SOC4 AS allows for the investigation of how different attack types can be identified and how AI models can be integrated (in the future) to improve the security lifecycle in this context. The vehicle was simulated using an open-source dataset, specifically the exchange of CAN messages between an open-source tool and the SIEM recreating the V-SOC4 AS. The proposed method can detect attacks without changing the CAN protocol. Therefore, it is also applicable to any vehicle that uses the CAN. The paper is organized as follows: Section 2 outlines related works on identifying attacks to the CAN; Section 3 briefly describes the CAN protocol; Section 4 presents the Vehicle-SOC and how to set up the architecture to identify the attacks; Section 5 highlights the proposed architecture in conjunction with a SIEM to identify two types of attacks and experimental evaluation; Section 6 sets out the conclusions.
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
## 4 Ultrasound-Assisted Osmotic Dehydration
### Synergic Effect of US and Other Innovative Technics/Treatments in Osmotic Dehydration
[103] observed a significant increase in water loss in combined vacuum (100 mbar)-US pretreated garlic samples (21.12%) dehydrated in 25% NaCl solution, in comparison to the untreated (10.67%), vacuum treated (14.18%) and US-treated in multi-frequency mode (11.20-13.56%) samples. Moreover, [PERSON] et al. [48] observed that vacuum-assisted osmosonication (UVOD) of garlic resulted in the shortest drying time (280 min) and lower energy consumption in comparison to the untreated samples (570 min) or those treated with US (330 min) or UOD (300 min). Therefore, the reduction of the drying time of the pretreated samples with respect to the control samples was 57.90%, 52.63% and 49.12% for UVOD, UOD and US, respectively. [PERSON] et al. [96] studied the effect of different pretreatments, such as OD (50% sucrose and 10% (\(w/v\)) NaCl solution), vacuum-assisted OD (VOD) (200 mbar, 15 min), UOD (35 kHz, 130 W, 15 min) and UVOD on drying time and physicochemical characteristics of dried carrots. The results demonstrate that the application of US and vacuum alone, as well in combination, could reduce the drying time of carrots in comparison to the OD treated samples (470 min). The lowest drying time was exhibited in the UOD-treated samples (355 min), followed by UVOD- (422 min) and VOD-treated samples (441 min). Recently, the synergic effect of the US application along with the edible coating has been studied, with the aim of increasing mass transfer rate and to modify some properties of plant tissue by US while decreasing the solid uptake due to the application of coating film. [PERSON] and [PERSON] [73] observed that the combined application of US (40 kHz, 150 W, 10 min) and 3% sodium alginate (SA) coating to the pumpkin resulted in a high WL/SG (5.18), better structure preservation and decreased time of the OD process (from 12 to 9 h) compared to the samples pretreated with the SA alone. Usually, the dried products show moderate or low rehydration capacity because, during the drying process, there is cellular and structural rupture of the tissue, therefore a combination of US with firming agents was proposed by [PERSON] and [PERSON] [104]. They studied the combined effect of application of UOD in solution containing firming agents (calcium chloride and calcium lactate) on strawberry physical parameters and rehydration of freeze-dried samples. They showed that freeze-dried samples pretreated with UOD with firming agents presented an enhanced rehydration capacity, better color retention, improved texture and better preserved microstructure.
## 3 Experiments and Results
### Validity Analysis of Body Parameter Measurements  
The detection results for each key point and the heatmap representation results obtained after inputting the horse images to be measured into the algorithmic model proposed in this study are shown in Figure 11, from which it is evident that the algorithm proposed in this study can effectively locate and classify the location and category of each measurement key point. Finally, the automated calculation of individual body measurements is predicated on the coordinate data associated with keypoints across distinct categories.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Pixel Accuracy** & **Intersection over Union** & **Average** \\  & **PA (\%)** & **MIoU (\%)** & **DetectionTime (s)** \\ \hline Mask R-CNN [37] & 91.46 & 84.72 & 0.76 \\ YOLACT & 92.33 & 85.51 & 0.57 \\ SOLO & 93.44 & 86.46 & 0.48 \\ [PERSON] et al. [32] & 90.15 & 85.37 & - \\ [PERSON] et al. [47] & 98.26 & 94.97 & 0.96 \\ Our research & 98.72 & 95.36 & 2.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of segmentation performance for Mongolian horses yielded by different segmentation algorithms.  
Figure 10: Comparison of Mongolian horse segmentation performance for Mask R-CNN and our algorithm.  
As shown in Figure 11, by comparing the automatically acquired and manually measured values of each physical parameter with the actual values, the following results were obtained: the average relative error of shoulder height of the 20 Mongolian horses was 4.01%, the average relative error of witness height was 2.98%, the average relative error of best depth was 4.86%, the average relative error of body length was 2.97%, the average relative error of group height was 3.06%, the average relative error of shoulder angle was 4.91%, and the average relative error of group angle was 5.21%. Among these values, the errors of shoulder angle and group angle are significantly larger than the errors of linear parameters. This difference may be due to the obvious changes in the pectoral and caudal end muscles of individual horses when walking at slow speeds, and there is a certain degree of difference compared with the real values measured manually when the horses were at rest, leading to the bias in the measurement results.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 5 Evaluations  
All the five different communication models referenced in the previous section are evaluated against each other for their efficiency in terms of data loss, packet latency, control overhead, energy consumption, time needed to obtain occupancy data for all the parkingslots and the time taken for the occupancy data to reach the consumers. To this effect, an experimental study is carried out in a simulated IPv6 mesh network with 100 nodes and one BR. The Cooja simulator is a widely accepted simulator for conducting experimental studies of IEEE 802.15.4 based IoT networks [45].
## 1 Introduction  
Skin analysis, particularly of facial skin, is very important in many cosmetic and medical applications. In this paper, we present our latest research on skin characterizations by two novel skin imaging technologies--i.e., Contact Capacitive Imaging and High-Resolution Ultrasound Imaging. The aim is to measure the skin water content and skin layer thickness of different skin sites, mainly for facial skin, and to perform skin image analysis by using Machine Learning Algorithms.  
Contact Capacitive Imaging is a novel imaging technique based on the dielectric constant measurement principle. It was originally developed for biometric applications, and has found applications for skin water content as well as skin texture measurements [1; 2; 3; 4; 5]. Our previous studies show that with Contact Capacitive Imaging we can analyze skin water content, skin solvent penetrations, skin texture and skin micro-relief analysis [6; 7; 8; 9].  
Ultrasound imaging [10; 11; 12] is a well-established technique that has been applied in medicine since the 1950s. Over the years, it has significantly improved with advancements in technology, such as miniaturization, faster data processing and improved image quality. Little changes in the imaging system make it applicable to aesthetic beauty, wound care and dermatology. The EPISCAN I-200 High-Resolution Ultrasound (HRUS) [13] imaging system is a state-of-the-art ultrasound system, with frequencies ranging from 20 MHz to 50 MHz. These high frequencies help to generate high-resolution skin images which make it possible to differentiate different skin layers such as stratum corneum, epidermis, dermis and subcutaneous [14; 15].  
Comprehensive detailing with a friendly interface is EPISCAN's main feature. Additionally, it also allows the tiling of multiple images on the scanner screen along with an illustration of the progression of conditions, thus making comparison quick and flexible. Any suspected damage to skin tissues can be diagnosed using EPISCAN. high frequency ultrasonic waves facilitate estimation of changes and damages to the skin. Clinical skin assessment has become easier and quicker with such advanced tools.  
Contact Capacitive Imaging and High-Resolution Ultrasound Imaging provide complementary information to other skin diagnostic devices, such as skin water content, skin trans-dermal water loss, skin elasticity, skin sebum measurement devices and so on. The advantage of the two technologies is that they are imaging based, which provides more information, whilst the disadvantages are that they are expensive and need complex analysis methods to extract information from the images. Therefore, to develop new Machine Learning algorithms to analyze skin capacitive images and high-resolution ultrasound images is very important, and is the main novelty and the main contribution of this research.
## 4 Engineering Application
### Verification of Discontinuity Sets' Clustering Effect
Moreover, based on the clustering calculation via the FCM-ABC method using the dip direction and dip angle of the engineering structural plane in this study, the empirical thresholds of the three clustering effectiveness indicators \(H_{\text{m}}\), \(F_{\text{m}}\), and \(F_{\text{h}}\) could be recommended as \(H_{\text{m}}\) < 0.15, \(F_{\text{m}}\) > 0.5, and \(F_{\text{h}}\) < 1000, respectively, to evaluate whether the clustering calculation has reached the optimal solution. This may need to be adjusted according to different data types, specific engineering conditions, and actual requirements. In addition, the selection scheme for an appropriate number of clusters is generally predetermined with different numbers of partition groups (usually between two and ten) [34], and the corresponding  
Figure 10: Process of searching for optimal solutions during the structural plane clustering calculation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \(H_{m}\) & \(F_{m}\) & \(F_{h}\) & **Iterations** & **Convergence Time/s** \\ \hline FCM-ABC & 0.10 & 0.58 & 998.47 & 125 & 0.02 \\ FCM & 0.15 & 0.27 & 1195.91 & 308 & 8.99 \\ Genetic algorithm & 0.18 & 0.21 & 1453.83 & 562 & 306.69 \\ Neural network & 0.20 & 0.13 & 1501.55 & 40037 & 509.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clustering results obtained by different clustering algorithms. grouping result is the best grouping result when the clustering effectiveness indicator values are optimal. Notably, the above method is also used to cluster analyze the structural plane data (all of which are two groups of dominant structural planes) investigated at other depth levels in this gold mine, and good recognition results are also reached. Due to the space limitation, the clustering calculation results of the dominant grouping for these structural planes are not provided. Nevertheless, it should be emphasized that only the sample data containing two groups of dominant structural planes are used to analyze and verify the proposed FCM-ABC method in this study. To further verify the applicability of the proposed method, the sample data containing at least three groups of dominant structural planes will be used for the clustering analysis in the next step, which is also conducive to optimizing and perfecting the method. In addition, only two parameters, i.e., dip direction and dip angle, are employed to group the dominant structural planes of rock masses, and the proposed method should be used to identify the dominant structural planes based on more parameters in the future because more parameters can more accurately characterize the distribution and development laws of structural planes.