esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 4 Cross-Border Use Case Implementations
### Vehicle QoS Support  
This use case is transverse and intrinsically related to compensate network issues by finding new ways to complement or substitute the under-performing radio link, as rendered in Figure 8. Thus, multi-radio [14] communications, broadcast and multicast [24] flows, mmWave [27] spectrum bands and network analysis at local edge [11] will lead to QoS slicing [29], ensuring a seamless and steady QoS. Even if it is not key, a multi-SIM [17] support would also be interesting to increase the reliability of the service.  
From the latency perspective, as this use case is transverse, the manner that multi-radio, broadcast and network slicing technologies may help to reduce the use case latency was already described.  
This use case is wider in terms of multi-modal communications, adding satellite communications--for widening coverage--mmWave communications, to increase bandwidths in a short-range area and vehicle traffic tethering, allowing a vehicle to act as a gateway for another vehicle connected through V2V connection.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 4 Results
### Experimental Results
#### 4.2.3 Pixel-Level Anomaly Location  
Table 3 shows the anomaly location results of our method compared to the current state-of-the-art methods on the MVTec dataset. Our method achieves the second-best result, only 0.3% lower than PaDiM, 0.2% higher than CutPaste, and 2% higher than RIAD. The visualization of the anomaly location is shown in Figure 4. To demonstrate the generality of our method, we also compared it with some current state-of-the-art methods (PatchCore [7] and VT-ADL [46]) on BTAD, the results are shown in Table 4 and the visualization is depicted in Figure 5. We achieved the best anomaly detection and location performance on BTAD. Although the single-class performance was not particularly impressive, the highest average AUC score was achieved, further illustrating the generalization performance of our model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{
\begin{tabular}{c} **SPADE** \\ [5] \\ \end{tabular} } & **RIAD** & **P-SVDD** & **P-SVDD-C** & **PaDiM** & **CutPaste** & **SCGAN** & \multirow{2}{*}{**Ours**} \\  & & [5] & [15] & [31] & [32] & [6] & [10] & [19] & \\ \hline \multirow{8}{*}{Texture} & Carpet & 92.8 & 84.2 & 92.9 & 94.4 & 99.9 & 93.1 & 97.0 & 98.9 \\  & Grid & 47.3 & 99.6 & 94.6 & 95.6 & 95.7 & 99.9 & 96.3 & 96.9 \\  & Leather & 61.5 & 100.0 & 90.9 & 96.1 & 100.0 & 100.0 & 94.7 & 100.0 \\  & Tile & 96.5 & 98.7 & 97.8 & 93.5 & 97.4 & 93.4 & 97.4 & 97.3 \\  & Wood & 95.8 & 93.0 & 96.5 & 98.0 & 98.8 & 98.6 & 100.0 & 97.8 \\  & Average & 85.6 & 95.1 & 94.5 & 95.5 & 98.4 & 97.0 & 97.1 & 98.2 \\ \hline \multirow{8}{*}{Object} & Bottle & 97.2 & 99.9 & 98.6 & 99.5 & 99.8 & 98.3 & 98.3 & 99.9 \\  & Cable & 84.8 & 81.9 & 90.3 & 97.8 & 92.2 & 80.6 & 98.2 & 94.9 \\  & Capsule & 89.7 & 88.4 & 76.7 & 88.7 & 91.5 & 96.2 & 83.2 & 90.3 \\  & Hazelnut & 88.1 & 83.3 & 92.0 & 97.9 & 93.3 & 97.3 & 97.5 & 94.2 \\  & Metal nut & 71.0 & 88.5 & 94.0 & 96.5 & 99.2 & 99.3 & 90.1 & 99.1 \\  & Pill & 80.1 & 83.8 & 86.1 & 91.9 & 94.4 & 92.4 & 89.4 & 89.7 \\  & Screw & 66.7 & 84.5 & 81.3 & 83.3 & 84.4 & 86.3 & 100.0 & 87.5 \\  & Toothbrush & 88.9 & 100.0 & 100.0 & 95.6 & 97.2 & 98.3 & 100.0 & 94.7 \\  & Transistor & 90.3 & 90.9 & 91.5 & 92.1 & 97.8 & 95.5 & 91.3 & 95.7 \\  & Zipper & 96.6 & 98.1 & 97.9 & 95.9 & 90.9 & 99.4 & 92.4 & 98.2 \\  & Average & 85.3 & 89.9 & 90.8 & 93.9 & 94.1 & 94.3 & 94.0 & 93.9 \\ \hline Average & & 85.4 & 91.7 & 92.1 & 94.4 & 95.5 & 95.2 & 95.1 & 95.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of image-level AUC% with different methods on MVTec. Figure 4: Anomaly visualization of our method on the MVTec dataset. The figure shows the anomaly detection results for a number of categories, for each of which from left to right are test images, ground truth, and predicted heat maps.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 2 Methods  
### Design of SAR Small-Satellite Constellation  
#### 2.1.1 Constellation Configuration  
In this method, the Walker Constellation (WC) method was applied in the first step in designing SAR small-satellite constellations. The WC method consists of three integer parameters \(T/P/F\), where \(T\) denotes the total number of satellites, \(P\) is the number of orbit planes, and \(F\) is the relative phase difference between satellites in adjacent planes [14; 15]. The details are described in Appendix
# Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media
## 3 Results
### Classification of the Length and Time Scales of Photon Transport
From these results, the crossover length and time from the scattering to diffusive regimes are evaluated as approximately \(10^{1}/\mu_{t}^{\prime}=10/\mu_{t}^{\prime}\) and \(10^{1.6}/\mu_{t}^{\prime}v\sim 40/\mu_{t}^{\prime}v\), respectively. The crossover length from the scattering to diffusive regimes has been extensively discussed so far, and our evaluation is consistent with the previous studies such as a comparative study of the measurement data and the PDE-results for 3D slab media [10], and a comparative study of the numerical results using the RTE and PDE for 2D square media [9]. This consistency implies that boundary conditions less influence the crossover length because the previous studies consider the finite media, while this study the infinite media. Meanwhile, the crossover time from the scattering to diffusive regimes is longer than that evaluated by the previous study [9], which is approximately \(20/\mu_{t}^{\prime}v\). The difference probably comes from the dimension of the medium and boundary effects; the previous study considers 2D finite media, while this study the 3D infinite media. The dependence of the \(t_{peak}\)-results on the parameter sets is observed in the diffusive regime as shown in Figure 6a. This is because that in the diffusive regime, the normalizations of \(t_{peak}\) by \(\mu_{a}v\) and of \(r^{2}\) by \(\mu_{a}/D\) are appropriate, which are suggested from the form of \(t_{peak}^{diff}\) (Equation (15)). As a preliminary study, we confirmed that when the above normalizations are employed, the \(t_{peak}\)-results  
Figure 5: Normalized peak times \(t_{peak}\) by \(\mu_{t}^{\prime}v\) as a function of the normalized SD distances \(r\) by \(\mu_{t}^{\prime}\) for the RTE, dE0, and PDE in the ballistic and scattering regimes. \(\mu_{t}^{\prime}=\mu_{s}(1-g)+\mu_{a}\) represents the reduced transport coefficient and \(v\) the speed of light in a medium, respectively. Colors of the figures correspond to the results for the parameter sets A to F listed in Table 1 or in the legend at the right-side of the figure. The dashed line represents the linear relation between \(t_{peak}\) and \(r\). The yellow colored area corresponds to the scattering regime. Figure 6: **(a)** Normalized peak times and (**b**) relative errors \(E_{peak}\) (Equation (16)) for the dE0 and PDE as a function of the normalized SD distances in the scattering and diffusive regimes. The other details are the same as Figure 5.  
for all the parameter sets are collapsed on a single curve in the diffusive regime. However, for the evaluation of the crossover length and time from the scattering to diffusive regimes, the above normalizations are not appropriate because the length regime, where the \(E_{peak}\)-values are larger than 2%, depends on the parameter sets, so that we can not evaluate the crossover length uniquely for all the parameter sets.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 2 Materials and Methods
### Proposed Model
#### 2.2.2 Depthwise Separable Convolution
Compared to the traditional convolution, the computational amount and model complexity of the depthwise separable convolution are greatly reduced, making it a more efficient convolution method that is able to be used to learn representative features better with less data. These advantages of the depthwise separable convolution structure are especially important for our crack detection model: our task is to train the model from scratch on a crack dataset, and the depthwise separable convolution allows us to learn the full feature representation of the data efficiently, thereby improving the model's cracks detection performance.
## 5 Discussion
### Comparison of the Two Eye Movement Tasks  
Why were the two eye movement tasks so similar to each other? A noteworthy finding was that they produced highly similar attention maps and thus overlapped with Grad-CAM to a similar extent. Only for indoor scenes, gaze-pointing was more similar to Grad-CAM than spontaneous gaze, and even this was only the case for the Dice scores. Perhaps participants instructed to look at relevant areas might intentionally fixate several objects when categorization depends on object arrangements, even though a single diagnostic object would be sufficient [15]. Moreover, gaze-pointing might lead them to intentionally fixate more diagnostic but less salient objects (e.g., not only fixating the person in an office but also pointing out a printer). In any case, these differences were minor and thus it seems more important to explain the high similarity between the two eye movement tasks. Two potential explanations concern the similarity of task goals and systematic tendencies in scene viewing.  
The first explanation focuses on the similarity of task goals. Spontaneous gaze during categorization is likely to target the same category-defining areas that participants would also point out intentionally because the ultimate categorization requirement remains the same. This could also explain why our results diverged from previous studies on eye movements during free viewing. Such eye movements were more dispersed than when pointing with gaze [60] or when explaining the suitability of category labels [51]. However, free viewing is much less restrictive than categorization. Participants can inspect whatever image areas may seem interesting, whereas in the present study, they still had to select the correct response and thus focus on response-relevant areas.  
Additionally, the similarity between our two eye movement tasks might result from systematic tendencies in scene viewing. For one, both were affected by task-irrelevant biases. However, a high similarity between them was also evident for landscapes. Given that landscapes encourage exploratory eye movements [25], we observed a high dispersion of fixations. Interestingly, even these small dispersed areas often coincided between the two eye movement tasks, highlighting the prominent role of scene guidance [20; 21; 22]. This has practical implications when choosing a task to elicit attention maps. For images without clearly identifiable relevant areas, eye tracking might be a better choice than manual selection, as the selected areas are less arbitrary and thus more likely to produce consistent attention maps. The specific eye movement task seems less relevant. Our results suggest that researchers can simply use spontaneous gaze during categorization as the outcomes hardly differ from those obtained with more gaze-based intentional selection.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 3 Results
### Temperature Dependence of Dielectric Properties of Samples  
Under the condition that the sample density was level II, the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) of the samples were examined at a measurement frequency of 10 kHz under various moisture contents (5-35%). The results are displayed in Figure 6.  
The figure reveals that the \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) of the samples increased with the increase in the temperature at the same moisture content. The higher the moisture content of the samples at the same temperature, the larger is the value of the dielectric properties and the more significant is the changing trend. The increase in the temperature accelerates the polarization of molecules and the Brownian motion of free water in the material, which results in an increasing trend of dielectric properties [36].  
Figure 6: Temperature dependence of dielectric properties at various moisture contents: (**a**) dielectric constant, (**b**) loss factor.
# Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve
## 3 Tracking Control Method Using Clothoid Curve
### Algorithm Process  
The overall process of the trajectory tracking control method based on the clothoid curve is shown in Algorithm 1. Firstly, the reference path is obtained from the path planning module. Then, it is checked whether the point number of the reference path is more than one. If not, the target steering angle calculated from the previous cycle is output; otherwise, the state of the vehicle after \(t_{1}\) is predicted according to the current state of the vehicle, where \(t_{1}\) is the communication delay time of the control system. The point closest to the predicted vehicle position on the reference path is found and set as the first point on the preview point selection interval; then, the last point of the preview point selection interval on the reference path is determined. The clothoid curve is calculated from the predicted vehicle position to the preview point, which is selected from the preview point selection interval. The clothoid curve obtained is composed of three curves. Since the distance traveled by the vehicle in the control cycle is covered by the first curve, only the parameters of the first \(S[0]\) curve need to be verified. If \(S[0]\) meets the vehicle motion and safety constraints, it would be used as the candidate curve; then, the process would jump to Step 6 to calculate the next clothoid curve. If the \(S[0]\) curve obtained does not satisfy the constraints, the validation of the existing candidate curve is checked. If it is valid, it is selected as the control curve; otherwise, the maximum curvature change rate under the limit is used as the change rate of the output curve, and its symbol should be consistent with \(S[0]\). Taking the change rate of output curve curvature as a parameter, and taking the curvature of the corresponding position of the output curve after the preview \(t_{2}\) time of the current vehicle speed as the target curvature, the target angle of the front wheels of the vehicle is calculated and then converted to the steering wheel angle. Finally, the steering wheel control target angle is processed by the moving average filtering of the calculated steering wheel target angles from the processes above.
###### Abstract  
Cup-plant (_Silibrium perfolatum_ L) stalks were investigated as a potential wood-replacement in particleboards (PBs). Two types of PBs were produced--(1) single-layer and (2) three-layer boards. In the three-layer cup-plant PB, the core layer was made from cup-plant, while the surface layer consisted of spruce particles. The cup-plant as well as spruce control panels were produced with polymeric methylene diphenyl disocyanate (pMDI) as the adhesive, with the physical and mechanical properties measured to meet class P1 of the European EN 312 standard. For the intrinsic morphology of the particleboards, scanning electron microscopy was applied. Wood-based and cup-plant-based particleboards indicated significant differences in morphology that affect the resulting properties of particleboards. Furthermore, an innovative approach was used in the determination of the pMDI bonding morphology. With a compact Time-of-Flight Secondary Ion Mass analyser, integrated in a multifunctional focused-ion beam scanning-electron-microscope, it was possible to show that the Ga+ ion source could be detect and visualize in 3D ion molecular clusters specific to pMDI adhesive and wood. Mechanical performance data showed that cup-plant particleboards performed well, even though their properties were below the spruce-made controls. Especially the modulus of rupture (MOR) of the cup-plant PB was lowered by 40%, as compared to the spruce-made control board. Likewise, thickness swelling of cup-plant made boards was higher than the control. Results were linked to the specific porous structure of the cup-plant material. In contrast, it was shown that three-layer cup-plant PB had a higher MOR and also a higher modulus of elasticity, along with lower thickness swelling, compared to its single-layer cup-plant counterpart. The industry relevant finding was that the three-layer PB made from cup-plant stalks fulfilled the EN 312 standard, class P1 (usage in dry conditions). It was shown that raw material mixtures could be useful to improve the mechanical panel performance, also with an altered vertical density profile.  
p ARTICLEBOARD; three-layer particleboard; cup plant; TOF-SIMS; biomass; bioresources +
Footnote †: journal: Applied Sciences
## 4 Simulation and Analysis
### Critical Creeping Speed Analysis
As is displayed in Figures 6-14, the critical creeping velocity of our dual linear-motor differential-drive system is lower, which can provide the table with a comparatively normal and stable working condition upon reaching the linear-motor single-drive system to its critical state. In addition, our system exhibits superior dynamic response characteristics to the linear-motor single-drive system, which thus outperforms it in terms of low-velocity stability. Comparing Figures 7 and 11, it can be seen that due to the influence of frictional nonlinearity, the system undergoes multiple oscillations before reaching a steady state. when the feed rates of the linear-motor single-drive system and the dual linear-motor differential-drive system are both 2 mm/s, the adjustment time for the linear-motor single-drive system to reach steady state (error = 2%) is 2.553 s, while the adjustment time for the dual linear-motor differential-drive system to reach steady state (error = 2%) is 1.896 s. From a quantitative analysis perspective, the response speed of the dual linear-motor differential-drive system is 25.73% higher than that of the linear-motor single-drive system. At the same time, comparative analysis shows that the oscillation frequency of the dual linear-motor differential-drive system is 23.1% less than that of the linear-motor single-drive system.
## 6 Performance Results  
This section provides details of performance results. Each combination of options, which is supported by the equipment, was tested. These include the following options: encoding algorithm, transport protocol, encryption on or off and encrypted string length. The motivation for such comprehensive testing lies in the desire to achieve the following goals: draw conclusion about the effect of encryption on video and network latencies; determine a set of parameters which result in optimal performance of encrypted video in terms of video latency; compare the latencies of encrypted and unencrypted video streams; compare obtained results to our previously published work [12]. For the purpose of neat result presentation, the tests are divided based on different scenarios, groups and cases.  
Scenario based on hardware platform: This scenario is based on a hardware platform, used for running camera client/camera server and encrypt/decrypt applications. This scenario is called symmetrical (Scenario I) for the case when the same equipment was used on both ends of the test setup and called non-symmetrical for the case when different equipment was used on both ends (Scenario II). Detailed results are provided for the symmetrical scenario only, while for the non-symmetrical scenario, the main conclusion and figures are shown.  
Groups based on encryption string length: Each scenario is further divided into groups based on encryption string length (Group 1/Group 2/Group 3 correspond to 128/256/512 bytes, respectively).  
Cases based on encoding algorithm and transport protocol: Each group consists of four cases which were derived by using different combinations of options for the encoding algorithm (H264/MJPEG) and the transport protocol (TCP/UDP). For each case, a pair of tests were conducted, one without encryption and the other with encryption. For each test, two sets of results were obtained. One set represents video latency, while the other set represents network latency. The configuration without encryption is shown in Figure 10. All tests were conducted with equipment connected over the Internet.  
Two scenarios (symmetrical and non-symmetrical) were necessary in order to show the portability of the developed system to different hardware platforms. The total number of cases in both scenarios was 20, of which 12 cases pertain to scenario I and 8 cases to scenario II. The total amount of tests and presented data may seem large, but this amount of detail is necessary in order to explore all possible options and the measurements supported by the proposed system. This thorough analysis helps in the conclusions drawn about the best approach.  
In the symmetrical scenario, two compactRIO devices were used as hardware platforms to run the camera client/camera server together with the encrypt/decrypt application nodes. Here, three groups of tests based on string length (128/256/512 bytes) were used. Both ends of the testing setup were located in Limerick, Ireland for the tests.