esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 New Challenge: Safety
### Robustness of HMI to Failures
#### 5.2.2 The Evolution of AV Safety Standards  
Historically, the Vienna Convention on Road Traffic of 1968 [56] is a treaty, signed by 101 countries in March 2025. It is an international standard that was implemented to harmonize international road traffic and improve general safety. Article 8 of this treaty initially required that \"Every moving vehicle or combination of vehicles shall have a driver\" and also that \"Every driver shall at all times be able to control his vehicle\". The treaty was amended in 2014, allowing \"Systems influencing the way vehicles are driven\" and thus facilitating the legal operation of AVs in signatory countries.  
In Europe, the safety standards for AVs have been assessed and rules exist governed primarily by the European Union through a combination of regulations, directives, and international agreements. These rules ensure that AVs are safe for occupants, pedestrians, and other road users.  
As of today, Regulation (EU) 2019/2144 [57] adopted on 27 November 2019 and applicable since 6 July 2022 is considered the General Safety Regulation for vehicle safety in the European Union. It mandates ADASs (Advanced Driver Assistance Systems) for all new vehicle types and sets the legal foundation for approving automated and driverless vehicles. It encompasses modern features such as Intelligent Speed Assistance (ISA), Autonomous Emergency Braking (AEB), Driver Drowsiness and Attention Warning (DDAW), and Emergency Lane-Keeping Systems (ELKSs). Further expansions are planned until 2029, including external user protection.  
Regulation (EU) 2022/1426 [58], effective from 5 August 2022, complements the previously mentioned General Safety Regulation by providing uniform procedures and technical specifications for the type-approval of Automated Driving Systems in fully automated vehicles (SAE Level 4, such as urban shuttles or robotaxis). It includes requirements for testing, cybersecurity, data recording, safety performance monitoring, and incident reporting, ensuring that fully driverless vehicles meet rigorous safety standards before market entry.
## 5 Results and Discussion
Since an average salinity of \(35\,\mathrm{g/L}\) and a depth \(d_{UW}=6\,\mathrm{cm}\) can be assumed for the lagoon test, the laboratory test configuration used as comparison is #9 (\(36.7\,\mathrm{g/L}\) and \(d_{UW}=6\,\mathrm{cm}\)). For clarity, both the nominal receiver sensitivity and the noise floor levels for SF 9 are added in magenta [45], along with the RSSI estimation according to Equation (27) assuming salinity of \(35\,\mathrm{g/L}\) and \(d_{UW}=6\pm 4\,\mathrm{cm}\) (dashed red lines). It can be pointed out that the outcomes of the lagoon tests are quite consistent with the model-based prediction and with the assumed error of \(\pm 4\,\mathrm{cm}\). Moreover, the proximity to the sensitivity and noise limits is the main reason determining the very high observed packet loss. Finally, from Figure 10 also the trend due to the tide can be appreciated, determining a drift in the RSSI of the lagoon tests, which was absent during the laboratory measurement campaign. In conclusion, the performed experimental tests prove the feasibility of the LoRaWAN transmission with saline water from a transmitter node positioned just below the water surface. The critical context characterized by severe attenuation partially compromises the reliability of the transmission in operating conditions far from those in the laboratory. It is also true that, depending on the considered application, the high packet loss may not be a critical issue, for example in high-water or fill-level alarm systems advising just the crossing of a certain water point. Hence, according to the application, it is advisable to choose the best radio settings guaranteeing the optimum trade off between energy saving  
Figure 10: (**a**) RSSI and (**b**) SNR trends for SF 9 during field tests (red plots) and laboratory tests in configuration #9 (blue plots) together with the nominal receiver sensitivity and the noise floor levels for SF 9 (magenta lines) and the estimated RSSI according to Equation (27) assuming salinity of \(35\,\mathrm{g/L}\) and \(d_{UW}=6\pm 4\,\mathrm{cm}\) (dashed red lines). and reliability of the transmission, and the proposed link budget model can be a valid tool to orient this choice. Of course, the obtained results are very context-specific and by changing the test conditions, greater or smaller depths can be reached.
## 2 Spectral Analysis
### Numerical Evidence: When the Hypotheses Are Violated  
Here, we check the clustering at zero of the imaginary part of the eigenvalues of \(\hat{A}_{\mathbf{n}}\) and the relation \(\{\hat{A}_{\mathbf{n}}\}\sim_{\lambda}t_{\alpha}\) for a moderate matrix size as \(2^{12}\), for \(\gamma=3\), and for \(\alpha=1.2,1.4,1.6,1.8\), see Figures 9-12. We stress that the condition \(\gamma<\alpha+1\) in Theorem 3 is violated. Nevertheless, the clustering at 0 of the imaginary part is present and the agreement between an equispaced sampling of \(t_{\alpha}\) and the real parts of the eigenvalues of \(\hat{A}_{\mathbf{n}}\) is still striking.  
However, the number and the magnitude of outliers in the preconditioned matrices start to become significant as reported in Table 7 and Figure 13 and hence, the number of preconditioned GMRES iterations starts to grow moderately with the matrix-size as can be seen in Table 8.  
Figure 11: Eigenvalues of the matrix \(\hat{A}_{\mathbf{n}}\) for \(\gamma=3\), \(\alpha=1.6\) and \(n^{2}=2^{12}\). The left panel reports the eigenvalues in the complex plane. The right panel reports in blue the real part of the eigenvalues and in red the equispaced samplings of \(t_{\alpha}\) in nondecreasing order, in the interval \([\min t_{\alpha}=0,\max t_{\alpha}=2^{3\alpha/2}]\).  
Figure 10: Eigenvalues of the matrix \(\hat{A}_{\mathbf{n}}\) for \(\gamma=3\), \(\alpha=1.4\) and \(n^{2}=2^{12}\). The left panel reports the eigenvalues in the complex plane. The right panel reports in blue the real part of the eigenvalues and in red the equispaced samplings of \(t_{\alpha}\) in nondecreasing order, in the interval \([\min t_{\alpha}=0,\max t_{\alpha}=2^{3\alpha/2}]\).  
Figure 12: Eigenvalues of the matrix \(\hat{A}_{\mathbf{n}}\) for \(\gamma=3\), \(\alpha=1.8\) and \(n^{2}=2^{12}\). The left panel reports the eigenvalues in the complex plane. The right panel reports in blue the real part of the eigenvalues and in red the equispaced samplings of \(t_{\alpha}\) in nondecreasing order, in the interval \([\min t_{\alpha}=0,\max t_{\alpha}=2^{3\alpha/2}]\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \(\mu(x,y)=1/(x+iy)^{\gamma}\), \(\gamma=3\) & & \\ \(n\) & \(N_{\alpha}(0.1)\) & \(\boldsymbol{Percentage}\) & \(N_{\alpha}(0.01)\) & \(\boldsymbol{Percentage}\) \\ \hline  & & \(\alpha=1.8\) & & \\ \(2^{4}\) & \(15\) & \(5.859375\times 10^{0}\) & \(85\) & \(3.320312\times 10^{1}\) \\ \(2^{5}\) & \(26\) & \(2.539062\times 10^{0}\) & \(162\) & \(1.582031\times 10^{1}\) \\ \(2^{6}\) & \(50\) & \(1.220703\times 10^{0}\) & \(303\) & \(7.397461\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of outliers \(N_{\alpha}(\epsilon)\) with respect to a neighborhood of \(1\) of radius \(\epsilon=0.1\) or \(\epsilon=0.01\) and related percentage for increasing dimension \(n^{2}\).  
Figure 13: Eigenvalues of the preconditioned matrix of size \(n^{2}=2^{12}\) for \(\gamma=3\) and \(\alpha=1.8\).
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 4 The Utility-Based Ontology Recommendation System
### The Concept Evaluation Component  
The concept evaluation component receives a ranked list of candidate ontologies from the ontology evaluation component and the user's input class (i.e., the class from the input ontology that the user needs to extend). Then, it checks each ontology in the list for a class(es) that has a corresponding match with the user's input class. For each matching concept, it determines the concept utility \(CUI(O,c)\), which aggregates the scores provided by two measures (the concept context semantic matching and the concept semantic richness) to generate the concept utility score for each candidate concept \(c\) in the candidate ontology \(O\). Next, we will explain the two measures in detail.
## 4 Liquefaction Mitigation Using Colloidal Silica Grout
### Field Tests for Soils Gruoted with CS
For each ground motion, the vibratory hammer was employed as the steel mandrel and was driven into the ground to a maximum depth of 6 m. As the mandrel is driven to produce a combination of compression waves and shear waves, complex shearing in deeper soil deposits is initiated, and liquefaction can be achieved in larger depths. Shear strain was measured by liquefaction sensors in both the horizontal (X-Y) and vertical (Y-Z and X-Z) planes. Each liquefaction sensor consisted of 3 accelerometers that formed a triangular array. For each accelerometer, motions were recorded in the X, Y, and Z axes. In this way, shear strains were obtained in X-Y, Y-Z, and X-Z directions. The shear strains, which predominantly occurred in the X-Y and Y-Z axes in the untreated area, reached 0.05% in both directions. Even the shear strain in the X-Z direction increased beyond 0.01% in the original area. However, after being grouted with CS, the shear strains measured in the treated area reduced significantly in all three of these directions. Except for the shear strain in the X-Y direction at 12 s, which arrived at 0.025%, the others remained below 0.01% throughout the whole experiment process. The results indicate that permeation of CS  
Figure 26: Elevation profile prior to and after blasting in full-scale test [33]. gel will aid in decreasing the shear strain occurred during cyclic loading and improving liquefaction resistance for liquefiable ground. The experimental element tests, centrifuge tests, and field tests performed in the literature have shown that CS has potential application in improving ground conditions and mitigating liquefaction in an earthquake event. By grouting CS, both the settlement and shear strain during the cyclic loading process decrease, and the generation of pore water pressure is inhibited. However, some problems still exist in the grouting process. The field test conducted by [PERSON] et al. (2007) [PERSON] et al. (2007), for example, suffered some grouting failures in three injection holes. These failures lead to a significant reduction in the injected CS volume compared to the other holes. Post-treatment analysis showed that the failure could be attributed to the gelation of CS grouted in other boreholes and the return of grout around the annulus. Nevertheless, the treated area still produced less settlement in a blast-induced liquefaction, indicating the remarkable effect of CS in mitigating liquefaction.
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 3 Effect of the Modification with Nanomaterials in the Asphalt Binder
In the cases that authors studied the same dosage and nanomodification in different control binder, they reported that the effects in softer binders are stronger than those in harder binders [93; 94]. [PERSON] et al. [79] studied the effects of the modifications with an organically modified nanoclay and nanosilica on the properties of a PG 58-28 asphalt binder. The results obtained by the authors were dependent of the type and dosage of the nanomaterial. For example, regarding viscosity, 2% nanoclay and 4% nanoclay increased the viscosity by 22% and 36%, respectively, and 2% nanosilica and 4% nanosilica increased the viscosity by 13% and 10%, respectively. Regarding complex shear modulus, 2% nanoclay and 4% nanoclay presented an increase of 19% and 40%, respectively, and 2% nanosilica and 4% nanosilica presented an increase of 21% and 35%, respectively. A possible drawback of the modification of asphalt mixtures with nanomaterials is underperformance at low temperatures (PG low temperature). [PERSON] et al. [79] tested a PG 58-28 with 2% and 4% nanosilica modifications with bending beam rheometer (BBR) and found the modifications to present 6% and 14% higher creep stiffness and equal and 2% lower m-value, respectively. In the same study, the authors also evaluated the effects of 2% and 4% nanoclay modification, using the same control binder (PG 58-28) and an organically modified nanoclay (cloisite 30B), and found the modifications to present 8% and 14% higher creep stiffness and 2% and 4% lower m-value, respectively. Regarding nanoclays, other studies [38; 55; 90] reported similar underperformance of the modified binders in BBR or direct tensile test (DTT) at PG low temperature. However, in another study [41], the modifications of the 60/70 control binder with 5% nanoclay bentonite and 5% organically modified bentonite presented similar m-values but, 19% and 22% lower creep stiffness, respectively, indicating enhanced low temperature performance. On the one hand, the effects of the modifications with nanomaterials in the low temperature performance are not entirely understood and deserve further investigation. On the other hand, besides the worsening in low temperature performance, authors reported that the modified binders still passed the respective Superpave(tm) specification (maximum 300 MPa creep stiffness and minimum 0.300 m-value) presenting the same PG low temperature of the control binder. Thus, the use of such modifications in cold regions can be possible, but further investigation is recommended.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 2 Related Work
### Over the Air Update of IoT Devices
In contrast to [15], in our scenario, the use of a TDMA protocol is not feasible due to two specific reasons: first, it would require a synchronization mechanism; and second, in our system, there are some asynchronous events that cannot wait for much time. To the best of our knowledge, there is no standard procedure for firmware exchange in an OTA update over LoRa. In our study, the focus is on the comparison between three different modes that can be employed in firmware delivery. This happens during a concrete stage of the update, but it is usually the most time-consuming one, as it is carried out using a very slow wireless technology.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 2 Methods  
### Establishing Forklift Detection Model and Situation Analysis  
Figure 1 shows the considered situation detection model of approaching a pallet. The model consists of a forklift, RGB-D sensor, goods, and pallets. Multiple pallets are used to load differently sized goods, and the pallets are located on the ground, goods, or shelves. Two coordinate frames were constructed: world coordinate frame {W}, where the _X-Y_ plane is the ground, and sensor coordinate frame {C}, where the _X-Y_ plane is parallel to the ground. The detection algorithm aims to obtain the center position of the pallet surface in {C} and the angle of {C} to _Y-Z_, which is represented as (C x_p, C y_p, C z_p, 0). The pallet has four degrees of freedom: moving along the \(X\), \(Y\), and \(Z\) axes of {W} and rotating along the \(Z\) axis of {W} (Figure 1).  
Figure 1: Schematic of forklift detection situation. (**a**) Side view schematic. (**b**) Overhead view schematic.  
In previous studies, scholars attempted to recognize single pallets without taking the goods, the ground, and shelves into account. However, not only pallets but also goods and the ground must be detected during the process of engaging a pallet. Moreover, the combination of pallets and goods must always be predetermined such that information in respect of goods and the ground can be utilized as the detection target. With more target information, a detection algorithm can be more robust in complicated situations.
## 3 Results
### Influence of Sociodemographic Data on Emotion Recognition for the Depression Group (H4)
#### 3.4.3 Influence of Educational Level  
In order to study the influence of educational level on emotion recognition, the sample of the depression group was divided into three groups: basic (\(n=17\)), medium (\(n=21\)), and high (\(n=16\)) education. The mean score for the basic education group was 69.00%, while it was 73.72% and 79.69% for the medium and high education groups, respectively. Regarding the reaction times, the mean for the basic group was 5.42 s, 4.48 s for the medium, and 4.27 s for the high. Despite these differences, the Kruskal-Wallis test revealed no differences for emotional recognition in relation to educational level, neither for recognition scores (\(X^{2}(2)=3.274\), \(p=0.195\)) nor for the reaction time (\(X^{2}(2)=1.248\), \(p=0.536\)).
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
## 4 Parametric Study  
After demonstrating the efficacy of the proposed framework to detect and locate damage, we conducted a parametric study to evaluate the impact of different parameters on the damage estimates of the proposed framework. Accordingly, we repeated the numerical analysis by varying the following parameters: (1) Damage location and amount (2) vehicle speed (3) road roughness level (4) boundary conditions and (5) multiple damage.
## 5 Conclusions  
In the E2 EA scheme, it is important to determine an appropriate \(m\) value that represent how many times the SAPs and WAPs will be executed when the LAP is executed. This can maintain a specific level of security and reduce the authentication signaling traffic cost. In this paper, we proposed an analytical model based on the Poisson process for E2 EA to derive the authentication cost function and compute the optimal values of \(m\) according to the overhead transmission of authentication messages that minimize the signaling traffic cost. We observed from the numerical analysis of the proposed model that the optimal value \(m\) increased when the value of the authentication request ratio \(\gamma\) increased. For any specific \(\gamma\) value, the optimal \(m\) value decreased when the overhead of the authentication messages \(\alpha\) transmitted through the communication channels increased. Hence, the service provider of the E2 EA scheme-based healthcare IoT system should use an \(m\)-selection algorithm to determine its optimal value dynamically according to the authentication request ratio of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(\lambda=\) & \(\mu\) & \(2\mu\) & \(3\mu\) & \(4\mu\) & \(5\mu\) & \(10\mu\) & \(20\mu\) \\ \hline \(\alpha\) & \(\beta\) & \(\gamma=0.5\) & \(\gamma=0.667\) & \(\gamma=0.75\) & \(\gamma=0.8\) & \(\gamma=0.833\) & \(\gamma=0.909\) & \(\gamma=0.952\) \\ \hline  & 1 & 2 & 3 & 3 & 3 & 4 & 5 & 7 \\  & 2 & 2 & 2 & 3 & 3 & 3 & 4 & 6 \\
20 & 5 & 2 & 3 & 4 & 4 & 5 & 6 & 9 \\  & 10 & 2 & 3 & 3 & 4 & 4 & 6 & 8 \\  & 15 & 2 & 3 & 3 & 4 & 4 & 6 & 8 \\  & 20 & 2 & 3 & 3 & 3 & 4 & 5 & 7 \\ \hline  & 1 & 3 & 3 & 4 & 5 & 5 & 7 & 10 \\  & 10 & 2 & 3 & 4 & 4 & 5 & 7 & 9 \\
100 & 15 & 2 & 3 & 4 & 4 & 5 & 7 & 9 \\  & 50 & 2 & 3 & 3 & 4 & 4 & 6 & 8 \\  & 75 & 2 & 3 & 3 & 4 & 4 & 6 & 8 \\  & 100 & 2 & 3 & 3 & 3 & 4 & 5 & 7 \\ \hline \end{tabular}
\end{table}
Table 1: The optimized \(X^{\star}\) of the cost function C(\(m\)) for different values of (\(\alpha\)) and (\(\beta\)) with respect to a fixed ratio (\(\gamma\)) when \(\lambda=z\mu\), where \(z=1\), \(2\), \(3\), \(4\), \(5\), \(10\), and \(20\).  
the physician when it executes the LAP and SAP for a specific patient to reduce the cost of authentication signaling traffic. Therefore, investigating of our analytical model using a complement simulation tool, and designing a dynamic algorithm to determine the optimal values of (\(m\)) with variant authentication request ratio are our future works.  
Conceptualization, S.N.; methodology and writing--original draft preparation, S.N., I.I.H.N.; writing--review and editing, S.N. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We are very grateful to the reviewers for their valuable comments that helped improve the paper. We wish to express our gratitude to all members of our colleges, Jouf University and Al Quds Open University, for their support.  
The authors declare no conflict of interest.
# ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
## 4 Experimental Results and Analysis
### Analysis of Results
(2) Since the fine-grained features of airplanes and cars are easier to be focused by the network model than those of birds, the classification network generally has higher accuracy in classifying airplanes and cars, and therefore provides fewer difficult samples for the model prediction, so the improvement in accuracy of the model on these two datasets is less than that of the bird dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**Accuracy Rate/\%**} \\ \cline{2-4}  & **CUB200-2011** & **FGVC-Aircraft** & **Stanford Cars** \\ \hline ConvNeXt-Tiny & 85.1 & 91.8 & 92.9 \\ ResNet50 & 81.6 & 88.9 & 92.0 \\ SE-ResNet50 & 80.9 & 88.7 & 91.7 \\ CBAM-ResNet50 & 81.2 & 89.2 & 92.0 \\ ECA-ResNet50 & 79.5 & 88.9 & 91.5 \\ BCNN [19] & 84.1 & 84.1 & 91.3 \\ HBP-FNet [29] & 85.8 & 90.2 & 92.2 \\ UPM [30] & 81.9 & 85.9 & 89.2 \\ RA-CNN [31] & 85.3 & 88.2 & 92.5 \\ MA-CNN [32] & 86.5 & 89.9 & 92.8 \\ GP-256 [33] & 85.8 & 88.1 & 91.7 \\ Bilinear CBAM & **87.8** & **92.1** & **93.3** \\ ConvNeXt & **87.8** & **92.1** & **93.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of bilinear CBAM ConvNeXt and traditional network in classification task. For the different fine-grained classification networks used in the above experiments, the advantages and disadvantages are relatively obvious: the advantage is that basically all the network models can achieve an accuracy of more than 80% on all three types of datasets, and the classification accuracy of the car and airplane classes can even reach more than 90%, which already meets the classification requirements in many cases; however, the disadvantage is that there is still a lot of room for improving the accuracy of fine-grained classification. In addition, except for the algorithm proposed in this paper, other algorithms to achieve fine-grained classification have high hardware requirements and cannot achieve the classification task under general hardware conditions. In summary, combining the experimental results to compare the different network models, it is concluded that the improved ConvNeXt network is able to outperform the traditional fine-grained classification network accuracy in terms of classification accuracy.