esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Step 2: Attack Information Identification
MITRE ATT&CK supports performing an attack by utilizing normal functions of a service or system through attack techniques such as native API. Otherwise, if a specific attack technique, such as firmware modification, is mapped to an asset, the CVE enabling the attack should also be assigned to the asset. By allocating vulnerability information to assets, MITRE ATT&CK's weakness can be supplemented, which does not provide detailed information on attack techniques. If multiple vulnerabilities are identified for one asset, to minimize the complexity of the scenario, the single most appropriate vulnerability can be selected by considering the attack purpose, attack target, and impact. Table 3 is an example of an event message that can detect an attack technique used in a scenario causing damage to the PLC's control process. To use cyberattack scenarios in exercises, information enabling to identify or respond against cyberattacks should be included. For example, if an attacker attacks an ICS asset remotely over the network, events associated with the attack may be observed in the network packets. In this case, by monitoring the network traffic, the attack can be detected. To this end, we identify logs and event messages that can be observed by the attack techniques previously assigned to assets as information for recognizing and responding to cyberattacks. Since the proposed method utilizes MITRE ATT&CK, log or event messages necessary for detecting cyberattacks can be adopted through detection items provided by ATT&CK. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Asset** & **Attack Tactic** & **Attack Technique and Vulnerability** \\ \hline \multirow{4}{*}{Internal network PC} & Initial Access & Hardware additions \\  & Execution & Exploitation for client execution \\  & & (CVE-2012-0158) \\  & Discovery & Remote system discovery \\  & Lateral Movement & Exploitation of remote services \\ \hline \multirow{4}{*}{EWS} & Initial Access & Exploitation of remote services \\  & & (CVE-2017-0144) \\  & Execution & Exploitation for client execution \\  & Execution & (CVE-2020-7315) \\  & & API for Memory Allocation and Code \\  & & Execution/Injection \\  & & Program download \\  & & (CVE-2015-7937) \\  & Lateral Movement & Program download \\  & & (CVE-2015-7937) \\ \hline \multirow{4}{*}{PLC} & Lateral Movement & Program download \\  & Lateral Movement & (CVE-2015-7937) \\ \cline{1-1}  & Impair Process & Unauthorized command message \\ \cline{1-1}  & Control & Unauthorized command message \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An example of the attack techniques causing damage to the PLC’s control process.
## 3 Scattering Parameters
### Bias Lines  
The proposed unit cell is the elementary unit of an immense transmitarray; it requires more bias lines to accommodate many unit cells, such as 10 bias lines (for 20 \(\times\) 20 array) on each cell and 8 bias lines (for 16 \(\times\) 16 array) on each unit cell transmitarray.  
A simulation study was accomplished to understand the impact of bias lines on the unit cell and array structure results. The proposed unit cell is modified by adding ten bias lines into the unit cell in different configurations. Firstly, we added ten bias lines on the bonding film presented, five on the first bonding film, and five on the second bonding film. The simulation results are shown in Figure 7. Consequently, the effect of bias line modification is negligible on the scattering parameters of the unit cell. This effect is due to the decoupling capacitive circuit, and parallel lines have a capacitive impact on the behavior of the unit cell. Bias lines also pass through the second bonding film in the second configuration; these are attached through metallic via with decoupling capacitive plates. There is less impact on insertion losses due to bias lines; we add 10 bias lines on  
Figure 5: (**a**) E-plane oblique incident performance. (**a**) Transmission coefficient (S21). (**b**) Reflection coefficients S11, S22.  
unit cell to analyze the effect of bias lines on whole array is shown in Figure 8. The bias line has less impact so it is also has less impact on gain of the array that is no more than 0.2-0.3 dB approx.  
Figure 8: Scattering parameters with and without bias lines.  
Figure 6: (**a**) E-plane oblique incident performance of transmission coefficient (S21). (**b**) E-plane oblique incident performance of transmission coefficient (S21).  
Figure 7: Bias lines in the proposed unit cell.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 3 Algorithm Performance Test
### Multiple Pallet Recognition Algorithm Comparison  
To illustrate the performance of the proposed recognition algorithm in recognizing multiple and different pallets in a warehouse environment where different goods are stored, the proposed algorithm is compared with the deep learning-based yolov5 approach in this paper.  
A pallet dataset was photographed with a Bumblebee XB3 camera in the XiZhou agriculture material warehouse (located in XinTang, Guangdong, China). The test dataset contained 21 pallet pictures with different distances and arrangements. The training set contains 122 images captured in the warehouse.  
Since the dataset is too small, yolov5 uses mosaic data enhancement, which is stitched with 4 images, randomly scaled, randomly cropped, and randomly lined up, to enrich the dataset. It also uses a pre-trained model to speed up the training speed. yolov5 takes the input images through the focus structure, downsamples the images, and then sends them to the backbone. The backbone uses CSPDarknet53, and resizes all the input images into 640 \(\times\) 640 size, and the training batch size is 64, with 300 epochs of training, a learning rate of 0.01, a decay of 0.0005, and a momentum of 0.937.  
To verify the performance of the proposed method, precision and recall are used as evaluation metrics in this study.  
Precision indicates the proportion of the correct detection results in the total detection results, and recall indicates the proportion of the correct detection results in all the ground truths. They can be denoted as the following equations:  
\[recision=\frac{TP}{TP+FP} \tag{17}\]  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
where _TP_, _FP_, and _FN_ represent the number of correctly detected objects, falsely detected objects, and missed detected objects, respectively.
# Interface Shear Performance between Porous Polyurethane Mixture and Asphalt Sublayer
## 6 Conclusions  
This study investigated shear performance of composite specimen with different adhesive materials using the inclined shear test. The following conclusions can be concluded:  
1. It is necessary to apply adhesive material to the interface between porous polyurethane mixture and asphalt mixture, since the untreated interface shear strength is smaller than the ones between two asphalt mixtures.
2. The interface shear strength increases as the thickness of adhesive layer increases, although the trends vary depending on the material. In general, the greatest interface shear strength was achieved by using epoxy resin followed by polyurethane and then SBS modified asphalt at 25 \({}^{\circ}\)C as the adhesive layer thickness is the same.
3. The interface shear strength decreases as the temperature increases or after freezing-thaw cycles. The interface shear strengths of composite specimen with three adhesive materials are similar to each other at high and low temperatures (60 \({}^{\circ}\)C and \(-\)18 \({}^{\circ}\)C) or after freezing-thaw cycles, which is different from the trend at 25 \({}^{\circ}\)C.  
Figure 13: Response curves of (**a**) interface displacement vs. number of loading cycles; and (**b**) shear stiffness vs. number of loading cycles.  
The composite specimen with epoxy resin as adhesive material has the longest fatigue life; while the SBS modified asphalt has the least fatigue life at 25 \({}^{\circ}\)C. Further work will be conducted to evaluate fatigue life of interface at different temperatures and freezing-thaw conditions.  
The research findings can help select the appropriate adhesive materials to enhance the interface bonding between porous polyurethane mixture and asphalt layers. Future research should be conducted to investigate the cost-effectiveness of different adhesive materials using field performance data. This will significantly increase the durability and service life of pavement and reduce the life-cycle cost when porous polyurethane mixture is used as road surface layer for safety and noise reduction.  
The research presented herein was partially sponsored by the National Natural Science Foundation of China (No. 51208178) and the Fundamental Research Funds for the Central Universities (No. 2015B17014).  
[PERSON] and [PERSON] designed the experiment and wrote the paper; [PERSON] and [PERSON] conducted experiments; [PERSON] and [PERSON] helped analyzing experimental data.  
The authors declare no conflicts of interest.
# Mining Pool Selection under Block WithHolding Attack +
## 4 Case Study with Two Mining Pools
### Both-Side Attack Case  
Next, we consider the both-side attack case, where the two pools attack each other. According to (2), the revenue densities of both pools can be expressed as follows:  
\[r_{1}=\frac{\frac{Nx_{1}\omega_{1}(1-a_{12})\alpha}{Nx_{1}\omega_{1}(1-a_{12})+Nx _{2}\omega_{2}(1-a_{21})}+Nx_{1}\omega_{1}a_{12}r_{2}}{Nx_{1}\omega_{1}+Nx_{2} \omega_{2}a_{21}}, \tag{29}\]  
\[r_{2}=\frac{\frac{Nx_{2}\omega_{2}(1-a_{21})\beta}{Nx_{1}\omega_{1}(1-a_{12})+ Nx_{2}\omega_{2}(1-a_{21})}+Nx_{2}\omega_{2}a_{21}r_{1}}{Nx_{2}\omega_{2}+Nx_{1} \omega_{1}a_{12}}. \tag{30}\]  
Note that (29) and (30) form an equation system in terms of \(r_{1}\) and \(r_{2}\). Solving this equation system, we obtain the expressions of \(r_{1}\) and \(r_{2}\), which are given by (31) and (32).  
\[r_{1}=\frac{x_{1}\omega_{1}(x_{2}\omega_{2}(a_{12}(a_{21}\beta+\alpha-\beta)- \alpha)+\alpha(a_{12}-1)a_{12}x_{1}\omega_{1})}{N((a_{12}-1)x_{1}\omega_{1}+( a_{21}-1)x_{2}\omega_{2})\big{(}a_{12}x_{1}^{2}\omega_{1}^{2}+x_{2}\omega_{2}(a_{ 21}x_{2}\omega_{2}+x_{1}\omega_{1})\big{)}}, \tag{31}\]  
\[r_{2}=\frac{x_{2}\omega_{2}(x_{1}\omega_{1}(a_{21}(a_{12}\alpha-\alpha+\beta) -\beta)+(a_{21}-1)a_{21}\beta x_{2}\omega_{2})}{N((a_{12}-1)x_{1}\omega_{1}+( a_{21}-1)x_{2}\omega_{2})\big{(}a_{12}x_{1}^{2}\omega_{1}^{2}+x_{2}\omega_{2}(a_{ 21}x_{2}\omega_{2}+x_{1}\omega_{1})\big{)}}. \tag{32}\]  
According to (1), (29) and (30), the expected payoffs of miners in both pools can be given by  
\[y_{1}=\omega_{1}(r_{1}-p), \tag{33}\]  
\[y_{2}=\omega_{2}(r_{2}-p). \tag{34}\]  
However, in the both-side attack case, the expressions of the rest points are difficult to obtain due to the complexity of the replicator dynamics. Thus, we resort to numerical calculations to obtain the values of the rest points.
###### Abstract
The findings indicate a hysteresis of 3.279% and an average sensitivity of 0.046 kPa\({}^{-1}\) across a broad pressure range of 0-30 kPa when the Fe\({}_{3}\)O\({}_{4}\) content is 0.665 g, the carbon nanotube (CNT) content is 1.098 g, the multilayer graphene (MLG) content is 0.99 g, and the moulding temperature (MT) is 67 \({}^{\circ}\)C. The simulation outcomes for the hysteresis and sensitivity closely align with the experimental test values, exhibiting relative errors of 0.765% and 0.434%, respectively. Furthermore, the sensing performance in this study shows a significant enhancement compared to prior research, with the hysteresis performance improved by 31% and sensitivity increased by 26%. This approach enhances the experimental efficiency and reduces costs. It also offers a novel strategy for the large-scale, rapid fabrication of high-performance flexible pressure sensor materials.
# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 2 Methods
### Dataset
#### 2.2.1 Neural Network Input Data
For convex probes like the one used in this work, an active sub-aperture is used for generating each beam, typically with between 32 and 64 elements. That sub-aperture is moved along the array in one or several elements step, forming the B-Scan. For example, a 128 elements array with a 32 element aperture can generate up to 96 scan lines, while a 196 elements array with an aperture of 64 elements generates 132 scan lines. Based on these typical numbers, an input width of 128 lines was selected, being power of 2 for operations optimization. The size of the vertical direction is related to the frequency content of the signal and the sampling rate. For an ideal 100% bandwidth array, the maximum frequency content of the signal envelope is equal to half the array center frequency. To reconstruct the envelope without aliasing, the pixel density in the vertical direction should be, at least, able to sample the signal at double of that frequency (Nyquist criteria). For the array used in this work with 3.5 MHz center frequency and 70% bandwidth, the number of pixels required for sampling up to 70 mm and 90 mm is 210 and 294, respectively. Based on these numbers, a height of 256 was selected for the network, which imposes a trade-off between training and inference cost and image quality. In cases when larger images are used, they should be scaled down using compression algorithms that preserve the artifacts' information. For example, in the scanner used in this work, a data reduction algorithm without peak information losses is available [26] and was used to accommodate the B-Scan height to the network size. In Figure 3, a comparison example between sectorial and B-scan images is shown.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### Feature Fusion and Applications  
In many deep learning tasks, such as object detection and image segmentation, integrating features from different scales is a crucial technique for improving performance. Low-level features have higher resolution and contain more positional and fine-grained information, but they have lower semantic meaning and may include more noise. High-level features, on the other hand, carry stronger semantic information but have lower resolution and may lack the ability to perceive fine details. Efficiently fusing these two types of features while leveraging their respective strengths and mitigating their weaknesses is the key to enhancing object detection models.  
To address this challenge, the authors of FPN [30] and other researchers proposed that by fusing low-level features with high-level features, it is possible to construct a detection system that can accurately recognize and precisely locate objects. Unlike previous feature fusion methods, predictions in FPN are performed separately on each fused feature layer. Subsequent experiments have shown that this approach can achieve excellent prediction results. Following this, some researchers developed new algorithms based on FPN, such as NAS-FPN [31] and AUTO-FPN [32], among others. In contrast, PANet was the first to introduce a two-stage fusion approach, incorporating both bottom-up and top-down fusion pathways on top of the FPN architecture. BiFPN is a more efficient feature pyramid network compared to PANet. The introduction of bidirectional flow and skip connections in BiFPN enhances the passage of features and information flow. It is precisely because BiFPN is better at handling multiscale features that we incorporated BiFPN into the baseline model to achieve better object detection performance.
## 1 Introduction
Many NDT techniques, such as visual inspection (VT) [9; 22], tap testing (TT) [23; 24; 25; 26; 27], impact echo testing (IE) [28; 29; 30; 31; 32], microwave testing (MW) [33; 34; 35; 36; 37; 38], ground penetrating radar (GPR) [39; 40; 41; 42; 43; 44; 45; 46; 47], ultrasonic testing (UT) or phased array ultrasonic testing (PAU) [48; 49; 50; 51; 52; 53; 54; 55], infrared thermography testing (IR) [56; 57; 58; 59; 60; 61; 62; 63; 64], acoustic emission testing (AE) [65; 66; 67; 68], laser testing (LT) [69; 70; 71; 72; 73; 74; 75], radiographic testing (RT) [76; 77; 78; 79; 80], global structural response testing [82; 83; 84; 85; 86; 87], etc., have been studied for detecting damages in the externally applied FRP composites. However, the inspection of FRP-reinforced concrete members are limited to detection of debonding between internal FRP bars and concrete [88; 89] or initiation of breakage in FRP [14; 90] rather than the detection of the bars themselves. Hence, among several available NDTs, this paper aims to determine the feasibility of using commercially available GPR and PAU for detecting embedded FRP bars in concrete as they are the most common methods in the NDT practice for steel RC elements [16; 91; 92]. In 2019, [PERSON] et al. confirmed that GPR and PAU have limited detectability of non-metallic reinforcement (FRP reinforcements), but they had only conducted tests on the fiber meshes of FRPs [93]. However, this paper will further explore the potential of these devices in detecting the FRP bars and strands used in FRP-reinforced concrete. The results of this study show that lower frequency GPR devices are able to detect larger FRP bars/strands at shallower depths with improvements in their detectability with the use of higher antenna center frequency devices. The results also indicate that PAU devices are only effective for detecting GFRP and CFRP prestressing strands.
# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 4 Result
### BPNN-PSO Integration Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Predicted Weight (g)** & **Predicted Thickness (mm)** & **Actual Weight (g)** & **Actual Thickness (mm)** \\ \hline
1 & 67.3 & 4.2 & 68.1 & 4.3 \\
2 & 66.4 & 4.4 & 67 & 4.4 \\
3 & 67.5 & 4.1 & 68.5 & 4.2 \\
4 & 66.7 & 4.3 & 67.4 & 4.3 \\
5 & 67.1 & 4.2 & 67.9 & 4.2 \\
6 & 66.8 & 4.4 & 67.6 & 4.5 \\
7 & 67.2 & 4.1 & 68 & 4.2 \\
8 & 66.9 & 4.3 & 67.8 & 4.4 \\
9 & 67 & 4.2 & 67.9 & 4.3 \\
10 & 67.4 & 4.1 & 68.3 & 4.2 \\ \hline \end{tabular}
\end{table} 
Table 6: BPNN-PSO prediction and actual production comparison. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Optimal Value** \\ \hline Pressure-holding switch position & 20.99 mm \\ Melt endpoint & 96.19 mm \\ Injection endpoint & 21.75 mm \\ Holding pressure & 39.8 bar \\ Nozzle temperature & 224.7 \({}^{\circ}\)C \\ Melt temperature & 234.8 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table} 
Table 5: Optimal process parameters from BPNN-PSO. predicts weight within the desired target range of 66-70 g, although with slightly larger deviations than thickness. These results confirm that the BPNN-PSO model exhibits robust and stable predictive performance across different datasets. The model achieves particularly high thickness prediction accuracy while maintaining acceptable weight accuracy, thereby validating its applicability for optimizing the injection molding process parameters.
## 2 Materials and Methods
### Questionnaire  
According to questionnaires used in previous studies [6; 7; 13], individual factors (sex, age, body height and weight, fitness level, skill level, risk-taking behavior, preferred skiing speed), equipment-related factors (ski length, sidecut radius, standing heights) and environmental factors (snow and weather conditions, difficulty of the preferred slopes) of the participants were evaluated.  
Participants had to rate their current perceived fitness level on a five-point Likert scale (very good, good, medium, weak, very weak) which was further divided into three fitness categories (very good/good vs. medium vs. weak/very weak). Skiers were also asked to self-report their skiing skill level (expert, advanced, intermediate and beginner) according to [PERSON] et al. [14] and risk-taking behavior (more risky vs. more cautious) according to [PERSON] et al. [15]. Furthermore, study participants were divided into more skilled (expert and advanced) and less skilled (intermediate and beginner) skiers as a tendency was shown to underestimate individual skiing skills, especially among female skiers [14].  
According to [PERSON] et al. [16] skiers had to rate their preferred skiing speed on a five-point Likert scale (very fast, fast, moderate, slow, very slow) which was further divided into three speed categories (very fast/fast vs. moderate vs. slow/very slow).  
Ski geometry data (ski length and sidecut radius as well as waist width) were directly recorded from the carving ski of the subject. Additionally, ski length was relativized by body height as literature displays evidence of a positive link between ski length and risk of falling [7] and/or sustaining an injury [17]. The standing height (i.e., the distance between the bottom of the running surface of the ski and the ski boot sole) at the front and rear component of the ski binding was measured using a digital sliding caliper. In addition, a percentage ratio between front and rear component heights of the ski binding was calculated. This standing height ratio relates to the angle of the boot sole when inserted into the ski binding. A previous study has shown that ski boot orientation can influence knee kinematics and would be likely to alter the loads experienced at the knee [18].  
Participants' self-rated perceived environmental factors in the skiing day consisted of snow conditions (fresh snow, grippy, icy, slushy/soft), weather conditions (sunny, overcast, snowfall) and difficulty of the preferred downhill slopes (easy, moderate, hard according to the blue, red and black signs on the ski slopes) [19].  
Finally, skiers were asked about their frequency of falling (never, 1-3 times, more than 3 times) during the skiing day.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Feature Extraction
#### 2.3.3 Spectral Features  
The reason for including the spectral features in this study was an observation made, among others, by such researchers as [PERSON] et al. [49] and [PERSON] at al. [50]. They found that in order to build reliable models accounting for spatial quality of sound, it is not sufficient to extract only spatial metrics of the signals, but it is also necessary to derive the timbral descriptors. The timbral characteristics of sound are often correlated with the spectral features [51]. Hence, it was assumed that in order to build a classifier of the spatial audio scenes, the spatial metrics should be supplemented by the spectral features.  
The following 14 spectral features were included in the calculations: centroid, spread, brightness, high-frequency content, crest, decrease, entropy, flatness, irregularity, kurtosis, skewness, roll-off, flux, and variation. They all constituted the standard metrics commonly used in ASC algorithms and music information retrieval applications. Their mathematical definitions can be found elsewhere [51; 52; 53; 54]. Although they are commonly referred to as \"spectral features\", some of them also account for temporal characteristics of sound, e.g., flux. Therefore, these features, along with the metrics described below, were categorized in this paper using a joint term of \"spectro-temporal\" features (see Table 3).  
In summary, the following descriptors were estimated:  
1. means and standard deviations of 14 spectral features calculated for \(x\) signal (\(2\times 14\) features),
2. means and standard deviations of 14 spectral features calculated for \(y\) signal (\(2\times 14\) features),
3. means and standard deviations of 14 spectral features calculated for \(m\) signal (\(2\times 14\) features),
4. means and standard deviations of 14 spectral features calculated for \(s\) signal (\(2\times 14\) features).  
Hence, in total 112 metrics were calculated. This number was doubled by including delta features. Therefore, the overall number of the spectral features amounted to 224.