esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Design
### Mechanisms of Sulfate Weathering Damage  
The mechanisms of sulfate weathering damage suggest that test samples immersed in salt solutions continuously absorb moisture until saturation is reached. Under high-temperature and low-humidity conditions, anhydrous mirabilite forms in the pores and fractures. The accumulation of mirabilite in the cracks leads to compressive damage to the crack walls. When the mirabilite exceeds the critical tensile strength of the pore walls, instantaneous damage affects the pore structure. During periodic immersion and exposure to a given environment, mirabilite continuously precipitates, dissolves, regenerates, and migrates to the interior of the rock. The evolution process is illustrated in Figure 3[22]. Furthermore, the hydrophilic minerals present in sandstone (such as calcite, albite, and microcline) and limestone (calcite) undergo complex physicochemical reactions, resulting in the detachment and loss of surface particles in the rock samples. The physical and chemical effects of sulfate weathering cause the widening of existing surface pores, as well as the formation of new pores and microcracks. The accumulated microscopic damage eventually leads to macroscopically visible instantaneous fractures, resulting in a reduction in rock mass, decreased wave velocity, and weakened strength [23].  
Figure 3: Microstructural damage mechanism of rock cultural heritage sites undergoing sulfate erosion.  
Figure 2: Operational steps of the cyclic experiment. (**a**) Soaking in saturated sodium sulfate solution; (**b**) salt crystallization under constant temperature and humidity conditions; (**c**) salt efflorescence on the surface of sandstone.
## 3 Results
### Microbiological Analyses
To confirm the presence of the targeted bacteria, suspect colonies were analyzed using MALDI/MG/MS, which ultimately did not validate their presence. In summary, despite all the experimental samples exhibiting pH ranges from 5.9 to 6.16 and water activity (a\({}_{w}\)) values consistently between 0.95 and 0.96--conditions known to promote bacterial growth and proliferation in meat--the natural additives employed demonstrated efficacy in inhibiting or reducing microbial growth in the analyzed artif-sanal mortalellas compared to the control (MTC). Notably, the analysis of the microbial load trends (Table 3) revealed that among the tested additives, the oregano and common lambsquarter powders were more effective than the mandarin peel powder. With the exception of the MTS1 sample, our findings are perfectly consistent with those reported by [PERSON] et al. [54], who analyzed the effects of incorporating orange dietary fiber (1%), rosmary essential oil (0.02%), and thyme essential oil (0.02%) on the quality and shelf lives of Bologna-type sausages. No specific studies on mortaldella samples made with the other two natural additives investigated in our study were found. Despite this, similar microbiological counts have been reported by [PERSON] et al. [55], who evaluated the effects of another natural substance, which was black quinoa applied either as whole seeds and as the fibre-rich fraction obtained from its wet-milling process, at a concentration of 2.5 g/100 g, on the shelf lives of Bologna-type sausages during cold storage (for 21 days). Except for the MTS1 sample, which microbial count exceeded 4 Log CFU/g, reaching 5.70 Log CFU/g at T2, their findings for microbial counts, ranging between 2 and 4 Log CFU/g over the storage period, are in accordance with the results shown below (Table 3). Comparable microbiological loads during refrigerated storage have also been observed by [PERSON] et al. in cooked sausages with almond nuts (15 g/100 g) and emmer wheat (30 g/100 g) added [56]. These comparisons highlight the importance of our findings, as the concentration (0.1 g/100 g) of each natural additive used in our artisanal mortalellas is substantially lower than those reported in the other two works, which ranged from 2.5 [55] to 30 g/100 g [56]. Finally, our data, except for those for the sample made with the mandarin peel powder (MTS1), align also with those reported by [PERSON] et al. [57], who made Bologna-type sausages with different concentrations of jabuticaba peel extract (0.5-1 g/100 g) combined with the natural additive nisin and other chemical additives, including sodium nitrite, sodium tripolphosphate, and sodium erythorbate. This result is particularly encouraging for the development of a healthier alternative produced exclusively with natural additives. In fact, in our study, we achieved comparable outcomes to those of [PERSON] et al. [57], using only a single untreated natural additive and at a significantly lower concentration (0.1 g/100 g) for each sample. Moreover, unlike in their approach, we did not combine the additive with any other natural or chemical additives.
## 4 Experimental Results and Analysis
### Feature Size Measurement Experiment  
Two actual \"measurement unit\" with weld groove widths of 4.80 mm and 4.60 mm were selected, containing four weld seams (a), (b), (c), and (d), The four key dimensions \(B_{w}\), \(B_{l}\), \(h\), and \(h_{f}\) were measured using both manual measurements by highly experienced experts and measurements by the measurement system, with the expert measurement distance defined as \(l_{1}\), the measurement system measurement distance defined as \(l_{2}\), the absolute error mean defined as \(\overline{\Delta}\), and the relative error means defined as \(\overline{\delta}\). The obtained measurement results are shown in Figure 12 and Table 5.  
As can be seen from Table 4, the average absolute error in the measurement of the maximum sidewall fusion width \(B_{w}\) is 0.08 mm, and the average relative error is 1.77%; the average absolute error in the measurement of the bottom sidewall fusion width \(B_{l}\) is 0.06 mm and the average relative error is 1.27%; the average absolute error in the measurement of the weld height \(h\) is 0.10 mm and the average relative error is 2.65%; the average absolute error in the measurement of the weld fusion depth \(h_{f}\) is 0.15 mm and the average relative error was 3.12%. The results show that the absolute error of measurement is between 0.06 and 0.15 mm, the relative error is between 1.27% and 3.12%, the error is in the acceptable range, and the accuracy of the visual measurement system meets the requirements. The measurement efficiency was improved by one order of magnitude.  
Figure 12: Line graph display of measurement results: (**a**) results of the weld, (**b**) results of the weld, (**c**) results of the weld, (**d**) results of the weld. **Note**: The weld height of (**b**) weld and (**d**) weld is replaced by 0.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement Parameters**} & **Unit Pixel** & \multicolumn{3}{c}{**(a)**} & \multicolumn{3}{c}{**(b)**} & \multicolumn{3}{c}{**(c)**} & \multicolumn{3}{c}{**(d)**} & \multicolumn{3}{c}{**-**} & \multicolumn{3}{c}{-**} \\  & **Length/\(\mu\)m** & \(l_{1}\)/mm & \(l_{2}\)/mm & \(l_{1}\)/mm & \(l_{2}\)/mm & \(l_{1}\)/mm & \(l_{2}\)/mm & \(l_{1}\)/mm & \(l_{2}\)/mm & \(l_{1}\)/mm & \(l_{2}\)/mm & \(\overline{\Delta}\)/mm & \(\overline{\delta}\)/\% \\ \hline \(B_{W}\) & & 5.50 & 5.40 & 5.47 & 5.42 & 4.89 & 4.80 & 5.01 & 5.10 & 0.08 & 1.77 \\ \(B_{l}\) & 4.80 & 4.78 & 4.89 & 4.96 & 4.22 & 4.23 & 4.26 & 4.39 & 0.06 & 1.27 \\ \(h\) & 2.51 & 2.59 & & & & 3.12 & 3.19 & & & 0.10 & 2.65 \\ \(h_{f}\) & & 4.31 & 4.49 & 4.38 & 4.25 & 4.17 & 4.29 & 4.32 & 4.51 & 0.15 & 3.12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measurement results of the two methods.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 9 Conclusions  
A security protocol for smart cards based on the elliptic curve signcryption has been developed and presented in this paper. It has been shown that the presented protocol satisfies mutual authentication, non-repudiation, forward security, anonymity, confidentiality, integrity, and availability attributes. Furthermore, it has the capability to defend against known key attacks, replay attacks, user impersonation, server impersonation, insider attacks, de-synchronization attacks, man-in-the-middle attacks, and password guessing attacks. The analysis of computational time has shown that only two ECPM operations are to be executed by the user. This protocol consumes the least computational cost and communication bandwidth in comparison to the other smart card security protocols. ECSSP-SC reduces the communication overhead by 34.5% and computational time by 6% as compared to other efficient protocols. Since the presented protocol satisfies all the required security functions with the least computational cost and communication bandwidth for the smart card, it outperforms the existing smart card security protocols. The possible future improvements in the research work presented in this paper could be to use hyper elliptic curve cryptography (HECC) for designing efficient signcryption based security protocol for smart cards, because hyper elliptic curve cryptography (HECC) is the upcoming area in cryptography, which delivers an equal level of security to 160 bit ECC using only 80 bits, i.e., the parameter size in HECC is halved compared to ECC.  
Conceptualization, A.K.S., A.S. and A.N.; methodology, A.K.S. and A.S.; software, A.S. and A.K.S.; validation, A.N., A.K.S. and A.S.; formal analysis, A.N., A.K.S. and A.S.; investigation, A.N. and A.S.; resources, A.N. and B.Q.; data curation, A.K.S., A.S. and A.N.; writing--original draft preparation, A.K.S. and A.S.; writing--review and editing, A.N., A.K.S. and B.Q.; visualization, A.S. and A.N.; supervision, A.N. and B.Q.; project administration, A.N. and B.Q.; funding acquisition, B.Q. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank Prince Sultan University, Riyadh, Saudi Arabia for partially funding this research work.  
The authors declare no conflict of interest.
## 5 Discussion  
For remote sensing image object detection tasks, we use an improved LSKNet to complete the object detection task on long-tail datasets. Using LSKNet as the backbone of the network, more accurate features are obtained by adding rotational convolution to the RPN network. By using a simple regression network to predict the true rotation angle of feature maps and extending the idea of 2D convolution to rotation convolution, experiments have shown that our proposed rotation convolution is more universal and has better detection performance. In response to the problem of the imbalanced distribution of datasets, we draw on the commonly used techniques for processing long-tailed datasets in object detection. We prevent overfitting of tail features by adding pseudo feature collection and generation modules and guide the network to correctly classify tail samples through loss function weighting. We can conclude from the experimental results that both adding rotational convolution and the long-tail data detection module can improve the detection accuracy of the network. As shown in Figure 8, the network detection performance we proposed is better than the baseline network LSKNet in terms of detection performance. But overall, although rotation convolution solves the problems of universality and computational complexity, it has a much larger number of parameters compared to 2D convolutions. In the solution for long-tailed datasets, there may still be some overfitting of the tail samples. Therefore, we will continue to attempt to reduce the number of parameters in the rotation convolution while maintaining detection accuracy and explore the use of data augmentation methods to improve the problem of imbalanced data categories.
## 2 Research Aims, Scope and Novelty  
As mentioned above, the Poisson ratio of concrete has been investigated for elements under pure compression, but the problem has not been studied for bending. The present research aimed to investigate the Poisson ratio in the compressed zone of real high-performance reinforced concrete beams. An analysis of the Poisson ratio at the elastic and elastic-plastic stages up to a failure was carried out. For this purpose, the experimental results obtained by the authors were used.  
The novelty of the present study was that, for the first time, the dependence of the Poisson ratio was obtained for the compressed zone of real RC bending elements at the elastic and elastic-plastic stages. The results of this study form a basis for a more accurate design of such elements, enabling analyses to be performed without empirical coefficients.
# Propospheric Attenuation in GeoSurf Satellite Constellations
## 4 LEO Probability Distributions of Rain Attenuation
In any constellation, the switchover between satellites is obviously mandatory for providing a continuous service. However, the number of switchovers and the interval between them, which is a function of the orbit altitude, increasing with altitude, should  
Figure 11: Results predicted with the Global SST model. (**a**) LEO annual probability distributions (%) of rain attenuation at the reference sites, obtained by weighing the probability distribution of rain attenuation predicted for each elevation angle larger than 20\({}^{\circ}\) according to the probability of the elevation angle shown in Figure 10; 39.6 GHz, at the reference sites. (**b**) The difference \(A_{LEO}-A_{Z}\), vs. annual probability (%), between the rain attenuation exceed in the LEO path, \(A_{LEO}\), and the rain attenuation exceed in the GeoSurf path, \(A_{Z}\), i.e., at the zenith at the reference sites. Figure 10: Probability density of the Metop\(-\)C satellite elevation angle seen from Spino d’Adda for elevation angles larger than \(\sim\)20\({}^{\circ}\). not change the results of our investigation in any significant way because rainfall and switchovers are, of course, uncorrelated.
## 3 Results and Discussion  
### Weldment Structures and Hardness  
Figure 5 reveals the macro- and microstructure of the specimen's welded joint. There are three zones of interest, namely, the weld metal (WM), heat-affected zone (HAZ), and base metal (BM), as illustrated in Figure 4(a). The weld metal microstructure, as shown in Figure 4(b), displays a microstructure characterized by grain boundaries of the acicular, polygonal form of ferrite and perlite, with an average grain size of 21.5 \(\upmu\)m. The weld metal's (Figure 4(b)) varied microstructure, with large and small grains, stems from the complex thermal cycles in Shielded Metal Arc Welding (SMAW). Rapid heating and cooling due to the welding arc's high heat cause differing cooling rates across the weld. Slower cooling fosters larger grains in the center, while faster cooling near the edges forms smaller grains. This heterogeneous microstructure results from the SMAW's localized high-heat input, influenced by the welding parameters and base metal composition. Furthermore, a microstructural inspection of the heat-affected zone, as shown in Figure 4(c), is a mostly acicular ferrite structure and has small amounts of perlite with a grain size of 12.8 \(\upmu\)m. The base metal zone contains ferrite and perlite phases with an approximation size of the grain of 10.3 \(\upmu\)m as shown in Figure 4(d). The WM zone acquires the lowest average hardness at  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Batch & 64 & Angle & 0 \\ Subdivision & 32 & Learning rate & 0.001 \\ Width & 352 & Burn\_in & 1000 \\ Height & 352 & Max\_batches & 6000 \\ Channels & 3 & Policy & Steps \\ Momentum & 0.949 & Steps & 4800; 5400 \\ Decay & 0.0005 & Scales & 0.1:0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model configuration of YOLOv4.  
Figure 4: (a) Example image of bubbles in metal weldment: start and end of corrosion process, and (b) video acquisition timestamp on common potentiodynamic polarization curve [6].  
167.1 VHN and then is followed by the HAZ at 173.1 VHN and the BM zone at 192.0 VHN. A Vickers hardness profile of the specimen is presented in Figure 6.
## Appendix A Model of Diesel Nozzle Flow  
This model offers a simple method to correct the velocities and initial droplet diameters of injected diesel fuel droplets caused by cavitation. The principal schematic of the direct-injection diesel nozzle is presented in the figure below.  
The phenomenological equations are used to compute the discharge coefficient \(c_{d}\). In addition to the flow field conditions, the nozzle geometrical features, such as the length-to-diameter ratio (L/D) and inlet hole rounding (inlet radius R) of the nozzle hole, also influence the \(c_{d}\) coefficient. These are included in the equation through input parameters \(c_{1}\) and \(c_{2}\). By this method, the inlet pressure \(p_{1}\) of turbulent flows is computed as:  
\[p_{1}=p_{2}+\frac{\rho}{2}\times\left(\frac{U_{geo}}{c_{d}}\right)^{2} \tag{1}\]  
where \(\rho\) is the diesel oil density. From this moment, the turbulent flow can be determined to know whether the nozzle hole is suffering from cavitation under this condition. With an assumption that the velocity profile of the flow through the nozzle hole is flat, by employing the [PERSON]'s equation [59; 60] for the expression of the contraction coefficient \(c_{c}\), the continuity equation will provide the velocity at the smallest flow cross-sectional area at point \(c\):  
\[U_{c}=\frac{U_{geo}}{C_{c}} \tag{2}\]  
\(U_{geo}\) is the theoretically calculated velocity of the laminar flow through the nozzle hole with the above assumed flat velocity profile. The \(c_{c}\) coefficient is calculated from the inletradius R of the nozzle hole. In the case of cavitation, the theory of potential flow allows for the application of the Bernoulli equation from point 1 to point \(c\) without any energy losses:  
\[p_{c}=p_{1}-\frac{\rho}{2}\times{U_{c}}^{2}\] (A3)  
If \(p_{c}\) is under \(p_{vapor}\), it implies that the flow inside the nozzle hole must be fully cavitating. In this case, a new inlet pressure and \(c_{d}\) coefficient are calculated by:  
\[p_{1}=p_{vapor}+\frac{\rho}{2}\times{U_{c}}^{2}\] (A4)  
\[c_{d}=c_{c}\times\sqrt{K}=c_{c}\times\sqrt{\frac{p_{1}-p_{vapor}}{p_{1}-p_{2}}}\] (A5)  
Now, the new effective conditions at the exit of the nozzle hole are computed as:  
\[U_{eff}=U_{c}-\frac{p_{2}-p_{vapor}}{\rho\times U_{gao}}\] (A6)  
\[A_{eff}=A_{gao}\times\frac{U_{gao}}{U_{eff}}\] (A7)  
\[D_{eff}=\sqrt{\frac{4\times A_{eff}}{\pi}}\] (A8)  
For more detailed information of this model, please refer to [60].
# Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator
## 3 Kinematics Analysis of 5-DOF Manipulator  
### Construction of D-H Model and Parameter Table  
The research object is a manipulator with 5 rotational DOF. Due to the complexity of the mechanical arm structure, there are many bends and offsets between each joint axis. The bending occurs at the joints of the manipulator. If the traditional D-H parameter table is used to build the forward kinematics model, it will be difficult to measure the parameters accurately. The problem is that when the machining or installation accuracy is insufficient, the adjacent parallel joints will be close to parallel with a certain angle. Therefore, the virtual joint is introduced; that is, an unchangeable joint is added between the actual two joints to change the need to measure when building the D-H parameter table, so as to reduce the difficulty of parameter measurement and improve the modeling accuracy. After introducing the virtual joint, the manipulator model has six joints, one of which is located between the elbow joint and the wrist pitch joint. The structure diagram and the establishment of coordinate system after the introduction of virtual joint are shown in Figure 3.  
Figure 3: Structure sketch of the manipulator containing a virtual joint.  
Figure 2: Prototype model diagram of the robot manipulator.  
Based on the coordinate system of each joint in the figure above, the D-H parameter table can be obtained, as shown in Table 1. In the table, \(\text{d}_{i}\), \(\text{a}_{i}\), \(\text{a}_{i}\) and \(\theta_{i}\) represent the joint offset, connecting rod length, connecting rod angle and joint angle, respectively [14].
## 4 Non-Vision-Based HAR
### HAR Using Deep Reinforcement Learning
From their experiments, the baseline model had an accuracy of 89.16% on the opportunity dataset, while the DKFSN had an accuracy of 89.74%. On the UCI HAR dataset, the baseline model had an accuracy of 92.06%, while the DKFSN had an accuracy of 93.82%. In 2019, [PERSON] and [PERSON] proposed a DRL-based human activity prediction algorithm in a smart home environment [85]. They proposed the use of Deep Q-Network (DQN) for recognising and predicting human activities in a smart home environment. They compared the performance of their proposed methodology to more traditional methods (bag, set and list approaches) and found that the DQN neural network performed better than the others. In 2019, [PERSON] and [PERSON] proposed a DRL framework that adaptively positions the home robot in a way that does not distract or discomfort the user [86]. They used a DNN with LSTM for activity recognition which had a 97.4% accuracy on the PAMAP2 dataset. Their proposed DRL framework had an 82% success rate for the simulated episodes. [PERSON] et al. in 2020 also proposed using DRL to control the movement of a home robot to maximise its recognition of human activities [61]. They used a deep Q-network (DQN) to automatically control the movement of the home robot. The agent was trained to maximise the confidence value of the HAR model. The HAR model used in their work is a dual-stream network that takes skeleton data and cropped images fed into LSTM and VGG-16 streams respectively. The streams are then concatenated, and the recognised activity is outputted with a confidence value. A virtual environment was used to evaluate the proposed DRL method. In the same vein, [PERSON] et al. also proposed a DRL framework that aligns the action of a robot based on the recognised action of the human [87]. DRL is a promising technique for improving HAR. It has been used to improve the results for the fusion of multistream models [69], adjust the movement of robots in smart homes for better HAR [61; 86], improve human-robot interactions [87] and increase the efficiency of feature extraction [32; 83; 84]. There are ethical challenges to the use of DRL in real-world applications such as its compliance with legal standards and issues around privacy and algorithmic bias. It also has a black-box nature, which makes interpretability of the algorithm difficult, and this limits its clinical applicability. While DRL trained in simulated environments might perform well, they may not perform well in real-world environments. These challenges highlight the need for more research in this area to improve the applicability of DRL in HAR.