esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The distribution of the data was examined for normality using the Kolmogorov-Smirnov test, while the homogeneity of variances was assessed through the Brown-Forsythe test. A two-way analysis of variance (ANOVA) was performed on the physico-chemical, microbiological, texture, and sensory properties of pork meat. The model was designed with treatment type and cold storage duration treated as fixed effects, while batch was included as a random factor to account for variability between batches. For the sensory evaluations, batch, panelists, and their interactions were considered, with batch serving as the error term to evaluate the significance of the main effects and the interactions between factors. Significant differences between means were determined using [PERSON]'s post hoc tests with a significance level of \(p<0.05\). The Statistica 13.3 PL software (STATISTICA v. 10; StatSoft, Krakow, Poland) package from TIBCO Software Inc. (Palo Alto, CA, USA) was utilized for this analysis. The results, including means and standard errors (SEM), are presented in Tables 1-4 and Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specification**} & \multirow{2}{*}{**Treatments**} & \multicolumn{4}{c}{**Cold Storage Period (Days)**} & \multirow{2}{*}{**Standard Error of the Mean**} \\ \cline{3-3} \cline{5-6}  & & **1** & **7** & **10** & **the Mean** \\ \hline Fat (\%) & K & 4.76 & 4.65 & 4.24 & 0.02 \\  & MS & 4.13 & 4.83 & 4.38 & 0.37 \\  & SEM & 0.29 & 0.46 & 0.53 & \\ \hline Water (\%) & K & 74.23 & 73.23 & 72.19 & 3.13 \\  & MS & 74.06 & 73.43 & 72.65 & 4.29 \\  & SEM & 2.26 & 5.38 & 3.48 & \\ \hline Protein (\%) & K & 20.11 & 20.53 & 20.45 & 1.03 \\  & MS & 20.36 & 20.26 & 20.46 & 1.34 \\  & SEM & 0.62 & 1.09 & 1.85 & \\ \hline Minerals (\%) & K & 1.79 & 1.93 & 1.63 & 0.28 \\  & MS & 1.83 & 1.72 & 1.55 & 0.29 \\  & SEM & 0.38 & 0.35 & 0.13 & \\ \hline Salts (\%) & K & 0.53 & 0.43 & 0.35 & 0.05 \\  & MS & 0.51 & 0.41 & 0.36 & 0.05 \\  & SEM & 0.05 & 0.06 & 0.05 & \\ \hline \hline \end{tabular}  
* K represents the control sample; MS refers to the combined treatments of both methods used; No statistically significant differences were observed between the groups (\(p>0.05\)).  
\end{table} 
Table 1: The chemical profile of refrigerated pork loin subjected to the processing techniques used.
## Literature Review
### DEA for Road Safety Analysis  
Road safety performance analysis of highways is an important task for the safety of travelers. To analyze the safety performance of certain attributes, a benchmarking mechanism has remained a basic procedure to be adopted by researchers [16; 17; 18]. With reference to the applied techniques for this purpose, DEA has been a popular technique with its theoretical basis in linear programming. Evolving the concept of DEA from research work in 1978, [PERSON] et al. [19] applied a linear program to estimate an empirical production technology frontier (bench marking) for the first time [19]. In the basic DEAmodel, the definition of the best practices relies on the assumption that inputs have to be minimized and outputs have to be maximized (such as in the economics field). However, to use DEA for road safety risk evaluation, the target becomes the output, i.e., the number of traffic crashes, to be as low as possible with respect to the level of exposure to risk. Therefore, the DEA frontier based Decision Making Units(DMUs) or the best-performing road segments are those with minimum output levels given the input levels, and other segments' risk is then measured relative to this frontier [20].  
Mathematically, to use DEA for road safety evaluation, the model is shown as follows:  
\[\begin{array}{rcl}\min R_{0}&=&\sum\limits_{r=1}^{s}u_{r}y_{r0}\\ s.t.&\sum\limits_{i=1}^{m}v_{i}x_{i0}=1,\\ &&\sum\limits_{i=1}^{m}v_{i}x_{ij}-\sum\limits_{r=1}^{s}u_{r}y_{rj}\leq 0, \quad j=1,\cdots,n\\ &&u_{r},v_{i}\geq 0,\quad r=1,\cdots,s,\quad i=1,\cdots,m\end{array} \tag{2}\]  
where _vrj_ and _xij_ are the \(r\)th output and \(i\)th input respectively of the \(j\)th DMU, \(u_{r}\) is the weight given to output r, and \(v_{i}\) is the weight given to input \(i\).  
In view of the model applications for road safety analysis, road safety condition was compared to 21 European countries [16] and an ideal trauma management record score was also calculated by using DEA [21]. Furthermore, using population, passenger-kilometers, and passenger cars as inputs, and the number of fatalities as output, DEA was used for the evaluation of risk level of countries [22]. Monitoring of yearly progress in road safety was also conducted by utilizing the DEA technique [23]. Adding to road safety determination on a national level for 27 Brazilian states, two fundamental indicators accessible in Brazil: death rate ( fatalities per capita) and casualty rate ( fatalities per vehicle and fatalities per vehicle kilometer traveled) were focused upon [24]. From the literature review on DEA application in the field of road safety, it was confirmed that DEA is one of the established techniques to evaluate the risk level of road safety.
## 3 Numerical Simulations of Gas Explosion along Pipelines with Abrupt Cross-Section Area Changes
### Analysis of Pressure Wave Transmission Process in the Pipes  
Taking the simulation results for a 0.18 m\({}^{3}\) fuel volume and a 0.36 m middle pipe diameter as an example, the whole propagation process of a gas explosion is shown in Figure 6.  
It can be seen from Figure 6 that, during the gas explosion process, the changes in explosion pressure are complicated. When the pressure wave propagates from a small cross-section pipe to a large cross-section pipe, the pressure wave is disturbed by n expansion cavity at an abrupt change in diameter, and the pressure wave decreases in intensity. When the pressure wave propagates from a large cross-section pipe to a small cross-section pipe, the air flow turns, and the pressure wave exhibits a reflection phenomenon.  
Figure 6: Diagram of explosion pressure in a pipe with abrupt changes in cross-section area at different times (V = 0.18 m\({}^{3}\), d = 0.36 m).  
Here, the incident wave and reflection wave become superposed, and the reflection wave becomes stronger.  
The shock wave overpressure value was calculated for a 0.18 m\({}^{3}\) fuel volume and middle pipe diameters of 0.2 m, 0.24 m, 0.28 m, 0.32 m, and 0.36 m. From Figure 7, it is evident that the numerical simulations qualitatively match the experimental results. However, the values from the numerical simulations are typically larger than those from the experiments, leading to larger attenuation coefficients and increase coefficients than those obtained experimentally. The main reasons are as follows:  
(1) Due to the lack of consideration of factors such as wall heat loss and wall roughness in simulation results;, the overpressure obtained by numerical simulation is often greater.  
(2) In the experiment, the pipeline is not completely sealed, and the propagation of gas explosion propagation to the pipeline connection and ball valve is often blocked, resulting in energy loss, so numerical simulation results will be larger than experimental results.
## 3 Results and Discussion  
Universal Validity: Reliability of Previous Protocol for Predicting the Dielectric Strengths of Non-PFC Materials  
The dielectric strength prediction protocol, PIM, was previously developed and validated for a selected set of PFC compounds [21]. In the current study, PIM was employed to predict the dielectric strengths of both PFC and non-PFC compounds with the aim of investigating the universal applicability of this protocol to a broad range of organic compounds (Figure 3a,b). Although PIM provided reasonable predictions for the dielectric strengths of the compounds with a trend line of \(y=0.993x+0.035\) (Figure 3a; Table 1), the accuracy level was slightly lower than that in our previous study. Specifically, for the compounds in this study, this protocol gave a relative error of 19.83% with a root mean squared deviation (RMSD) of 0.329, and a relative error of 14.69% with an RMSD of 0.338 was obtained for the 137 PFC compounds in our previous study (Figure 3b; Table 1). Further analysis of the distributions of the core factors (i.e., polarizability, IE, and MW) constituting the PIM protocol revealed that the compounds of interest in this study cover a broader range for each factor than those in our previous study (Figure 3c). This difference may be responsible for the slight deterioration in the protocol accuracy. Furthermore, the increased structural diversity of the database containing the additional 13 PFC and 18 non-PFC compounds could also contribute the decreased accuracy.  
Figure 3: (**a**) Comparison of PIM-predicted and experimental dielectric strengths for 168 PFC and non-PFC compounds. (**b**) Distribution of relative errors for the PIM-predicted dielectric strengths. (**c**) Distributions of polarizability, ionization energy, and molecular weight for both the 137 PFC compounds in our previous study and the 31 newly added PFC/non-PFC compounds in this study. (**d**) PIM-predicted dielectric strengths as functions of the core factors (polarizability, ionization energy, and molecular weight).
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
## 4 Model Evaluation and Comparison
### Comparison with Other Models  
The fatigue life prediction abilities of the GB and RF models were compared with those of other models. The prediction results of the ANN algorithm for AM stainless steel 316L [36] and five models considering the effect of the mean stress for 2.2 Ni-1 Cr-0.5 Mo-0.1 V steel [37] are presented in Figures 9 and 10. The prediction results of the GBR algorithm for aluminum alloy [38] are shown in Figure 11. In terms of R\({}^{2}\) and MSE, the prediction accuracy of the GB model was higher than those of the MORROW, SWT, and FS models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Stress Amplitude (MPa)** & **490** & **490** & **480** & **480** & **470** & **470** & **470** & **470** & **460** & **460** & **460** & **450** \\ \hline Relative error of the GB model (\%) & 4.92 & 3.55 & 1.4 & 5.24 & 10.56 & 9.31 & 5.42 & 1.96 & 0.76 & 4.17 & 14.75 & 0.69 \\ Relative error of the RF model (\%) & 16.84 & 15.31 & 23.78 & 18.95 & 18.15 & 16.81 & 12.65 & 8.96 & 1.93 & 3.06 & 13.76 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative errors of the predicted and experimental data for GCr15 bearing steel.  
Figure 8: Predicted fatigue life values for GCr15-bearing steel at 450 MPa based on the GB and RF models.  
The models of Figure 10 used twelve input parameters, whereas the models used in this work used eight input parameters. Therefore, the models proposed in this analysis ensured better prediction accuracy with lower input parameters. In addition, the model developed in this study has wider applicability. The ANN model developed by [PERSON] et al. applies to 316L, while the GBR model developed by [PERSON] et al. applies to aluminum alloys.  
Figure 11: Comparison of the predicted and experimental fatigue life values for aluminum alloy based on GBR [38].  
Figure 10: Comparison of the predicted and experimental fatigue life values for 2.2 Ni–1 Cr–0.5 Mo–0.1 V steel [37].  
Figure 9: Comparison of the predicted and experimental fatigue life values for AM stainless steel 316L based on ANN [36].
## 2 Novel Measurement Strategy
### CAD (Computer-Aided Design) Model Definition  
Researchers usually try to reuse the CAD sections which are used to build the airfoil surfaces for planning the inspection [4; 7; 8]. This paper presents another, more modern method, not relying on those sections. In the case used here, the defining CAD sections are related to gas flow and are 3D splines defined by advanced flow tools to control the shape at ideal gas path positions. Their count is strictly related to shape curvature and can vary between three and nine for very long parts relative to platform size. CMM sections are not required to be 3D, to simplify the process they can be 2D splines, too, which will simplify their reparameterization during measuring.  
There is a long way from prototyping the part to proceeding to build the final shape. That is why at least the first and last sections are to be located outside the final platforms. This will allow the CAD engineer to build extended airfoils without worrying about continuity and configuration changes during the process (Figure 2). The airfoil should be designed in such a way that the ends will be cut by the platform surfaces, as extruding these ends results in unpredictable curvature shapes. Another important fact is that boundaries should be limited/restricted by the inner and outer gas-path lines. Both are necessary to build component platforms or to calculate the space for additional coating. Additionally, there is a need to re-calculate the shape from hot working conditions into cold production shapes. This is the reason why different sections are chosen for profile measurement at the end.  
For the qualification process, CMM sections should be in a specific order. There is also a need for both platforms to have massive blends for reducing stresses. To be more specific, it can be mostly assumed that 10-20% of the airfoil span calculated from the platform surface is enough to properly locate the first section and, similarly, the last one. Other sections should be placed with equal spacing, and extra sections can also be added here.  
It was decided to decrease the number of measuring sections to three, and keep them at 10-20%, 50%, and 80-90% of the span (Figure 3) as these are the most significant sections for small vanes. More complex turbine vanes on the last stages of big turbines should have an increased number of sections. Other, non-aero shaped, surfaces should also be included in the CMM process, but these do not require too many control points, as they will be machined later. Those surfaces only need to comply with looser tolerance requirements.  
Figure 2: Airfoil created in space between gas path surfaces.  
The fully-defined CMM input can be translated into a neutral format like the older IGES or newer STEP standards, and every modern machine should be capable of reading those formats without distortions.
## 5 Overview of Traditional and DL Approaches for Object Detection in AVs
### DL Approaches
While Faster R-CNN algorithms demonstrate satisfactory accuracy on COCO and ImageNet datasets, they encounter difficulties when it comes to detecting small objects and objects at varying scales. This approach, while being more memory-efficient and faster, faces challenges in achieving consistent performance during both training and test-time inferences. [PERSON] [100] implemented YOLOv8; however, that work has not yet been published in a journal. The structure of YOLOv8 is shown in Figure 12. Different versions are available with different scales of YOLO (N, S, M, L, and X) according to the scaling coefficient. The two main differences are the variety of kernels at different locations in the network and the number of feature extraction modules. These modifications are intended to meet the unique requirements of various situations. The backbone, neck, and head are the three primary modules that make up the YOLOv8 networks. First, the backbone module extracts features from the input photos. The neck component then performs feature fusion to create features in three different sizes: large, medium, and small. The final output is the detection results. Eventually, these characteristics of various sizes are sent to the head section for identification of the object. To improve deep image extraction features, the YOLOv8 system uses a layered C2f technique inside the backbone section. The main function of the SPPF layer, which is the tenth layer in the backbone module, is to transform input feature maps--regardless of their original size--into a fixed-size feature vector. The goal of this change is to increase the responsiveness of the network. YOLOv8 uses the traditional top-down as well as bottom-up feature fusion technique referred to as PAN (Path Aggregation Network) in the neck component. This tactic successfully combines elements from several scales. YOLOv8 adopts the widely used decoupled head architecture in the head section, which divides the categorization head from the identification head. Furthermore, there is a noticeable change in the network's OD technique: the anchor-free strategy replaces the anchor-based method. Figure 11: The architecture of Faster R-CNN [90]. The distinction between traditional and DL approaches is justified by their fundamentally different methodologies and performance in various weather conditions. Traditional methods, while effective in ideal conditions, struggle with adverse weather due to their sensitivity to lighting, occlusion, and the need for precise feature engineering. DL methods, on the other hand, have shown superior performance in handling these challenges by learning robust representations from large datasets, which include diverse weather conditions. This allows DL-based systems to generalize better and maintain high detection accuracy even under challenging circumstances.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 2 Related Work
### Acoustic Side Channels  
Acoustic side channels involve a malicious user or a program using computer speakers to transmit sonic and ultrasonic signals to a nearby receiver. For example, _Fansmitter_[7] is malware that can leak data out from audio-gapped computers by utilizing the CPU or casing fan noise to encode information. The malware regulates the device's fan speed to enable a vector for information leakage through the acoustic side-channel. These acoustic patterns can be captured using a remote microphone, a nearby smartphone, or a similar electronic device.
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 4 Experimental Procedure
### Analysis of the Experimental Results
(**b**) Sample 2. As can be seen from Figure 10, the error of the proposed algorithm is smaller than those in Figures 8 and 9. In Figure 10a, the fine-tracking region of the algorithms described in [5,6] drifts and is even gradually lost due to the large change in the target attitude of the image, while the algorithm proposed in this paper shows a high consistency. The reason for this is that, through the optimization of the weight selection for particle filtering with  
Figure 8: Fine-tracking effect of weak and small multi-targets in infrared images using the algorithm reported in [6]. (**a**) Sample 1. (**b**) Sample 2. Figure 10: Multi-target fine-tracking error curves of the sample image. (**a**) Sample image 1. Figure 9: Fine-tracking effect of weak and small multi-targets in infrared images using the algorithm proposed in this paper. (**a**) Sample 1. (**b**) Sample 2.  
the algorithm used in this paper, particles with large weights are copied more than once, while particles with small weights are copied less often or are rejected, providing effective data to the PigNet network for the fine tracking of targets. This prevents the appearance of multiple distinct peaks, as observed in [6]. In Figure 9b, we can see that the visual fine-tracking effect of the algorithms in [5,6] becomes very poor due to the partial occlusion of the target caused by the illumination change in the infrared image, but the algorithm proposed in this paper is always in the stable fine-tracking state. As can be seen in Figure 10, the two examples use different image frames/frames to better verify that the improved algorithm described in this paper has a wide range of adaptability. Ideal results can be achieved at different image frame rates, with small fluctuations in the tracking process and no significant peak changes. The improved algorithm proposed in this paper targets images that are infrared images. The frame rate of the thermal imaging camera is generally in the range of 25-50 Hz, and the experimental image frame rate used also varies within this range, proving that the algorithm has some degree of generality. However, image data with a high frame rate were not analyzed, which might mean that this algorithm has some limitations in the application process. The experimental results show that the proposed algorithm is better than the algorithms reported in [5,6] in precision tracking in a complex environment, which proves that the proposed algorithm is more effective.
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 3 Model Design and Implementation
### Introduce FPN Feature Pyramid Structure  
In order to alleviate the unsatisfactory detection ability of the Faster RCNN algorithm for small-sized targets, the FPN feature pyramid network model is introduced into the Faster RCNN target detection algorithm. The FPN network model is divided into two network routes. One of the network routes produces multi-scale features from bottom to top, connecting the high-level features with high semantics and low resolution and the low-level features with high resolution and low semantics [33]. Another network route is from top to bottom; after some layer changes, the rich semantic information contained in the upper layer is transferred layer by layer to the low-layer features for fusion [34].  
Figure 6: Schematic diagram of CBAM attention mechanism.  
Compared with the SSD algorithm, FPN also uses multi-level features and multi-scale anchor frames. SSD predicts the low-level data alone, where it is difficult to ensure strong semantic features, and the detection effect is not ideal for small targets.  
Figure 7 is a schematic diagram of the FPN feature pyramid structure. In the figure, the bottom-up feature map with computed order on the left is {C2, C3, C4, C5}, and the top-down feature pyramid structure on the right is {P2,P3,P4,P5}. The CF-RCNN algorithm uses the above VS-ResNet as the backbone extraction network of the FPN feature pyramid structure. The image part on the left side of Figure 7 is a downsampling model. During the feature extraction operation of this model, the value of the step size is set as {4,8,16,32}. In the upsampling model of the right image, the upper feature map is convolved with a convolution kernel of 1 \(\times\) 1 size, the step size is set to 1, and the number of channels is 256, to adjust the dimension to be consistent so that it can be fused with the lower feature. Then, after 3 \(\times\) 3 size convolution, the aliasing situation in the 2-fold upsampling process is eliminated to obtain the feature map. {P2, P3, P4, P5} share the weight of RPN and Fast RCNN, and use a different anchor size of {322, 642, 1282, 2562} and the anchor ratio of {1:2, 1:1, 2:1} on the {P2, P3, P4, P5} feature map to select candidate boxes.