esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 5 Sensor Fusion for Relative Vehicle Positioning
Furthermore, communication outages due to shadowing, line-of-sight obstruction or packet collisions can be overcome with on-board ranging sensors. In this way, a cooperative/non-cooperative solution is able to offer higher availability and continuity with an increase in accuracy for safety critical applications. Unfortunately, regarding the fusion of on-board ranging sensors and cooperative approaches based on V2V, only a few publications exits. [PERSON] et al. have worked on augmenting the capabilities of radar sensors with absolute positions exchanged over V2V communication in [26; 139]. They proved an increase in availability with their approach in experimental measurements. A simulative evaluation of a multi-target tracking approach based on an automotive radar sensor is presented in [89]. [PERSON] et al. suggest a plausibility checking of CAM via a monocular camera in [13]. [PERSON] et al. proposed a sensor fusion framework, where cooperative relative positioning is supported by a radar sensor in [114]. This setup was tested in both highway and rural environments showing that the cooperative approach profits from the higher accuracy of the radar sensor, whereas the radar sensor's poor availability due to obstructions is enhanced by the availability of V2V communication. Figure 5 shows the accuracy in the relative position in both the longitudinal (blue) and lateral (green) direction on the highway. Figure 5c demonstrates that an increased availability and accuracy is achieved when compared to each solution alone. In [140], [PERSON] et al. performed a simulative evaluation of an intersection where vehicles were possibly equipped with ranging sensors and/or V2V communication. They showed that by equipping vehicles with V2V communication, the average cooperative positioning accuracy could be increased. [PERSON] et al. propose in [141] a decentralized filter that fuses inter-vehicle UWB ranges with GPS-based observations from each vehicle that achieves sub-meter relative positioning accuracy. Figure 5: The figures show the relative positioning error in longitudinal (blue) and lateral (green) direction for a radar sensor (**a**), a cooperative approach (**b**) and the fusion of both (**c**) while driving on a highway [114]. The radar sensor has a good longitudinal accuracy, but a limited availability of 75%. The cooperative approach has a larger error, similar in both directions and continuous availability. The continuous lines in (**c**) show the fusion of both, where an increased precision and availability are achieved.
## 2 Adaptive Filtering Systems for Active Control
### Least Mean Squares Method
Therefore, \(e[k]^{2}\) is chosen as an estimate of \(\xi\), and the correlation matrix and cross correlation vector are assumed to be:  
\[e[k]=d[k]-y[k] \tag{2}\]0  
\[e[k]=d[k]-y[k] \tag{2}\]1  
\[e[k]=d[k]-y[k] \tag{2}\]2  
Next, the estimation of the gradient \(\hat{\
abla}\xi\) of the cost function is obtained from Equations (8) and (10)-(12):  
\[ \begin{array}{c}\hat{\
abla}\xi=2\mathbf{u}[k]\cdot\mathbf{u}[k]^{T}\cdot \mathbf{\hat{w}}[k]-2\mathbf{u}[k]\cdot d[k]\\ =-2\mathbf{u}[k](d[k]-y[k])=-2\mathbf{u}[k]\cdot e[k]\end{array}  \tag{13}\]  
Finally, substituting Equation (13) into Equation (7), the final form of the LMS algorithm is derived. \[\hat{\mathbf{w}}[k+1]=\hat{\mathbf{w}}[k]+\mu\mathbf{u}[k]\cdot e[k] \tag{14}\]  
Notably, the filter coefficients \(\hat{\mathbf{w}}[k]\) cannot trace the actual steepest descent surface because the changes of the weights are based on estimated or simplified gradients, which produce noisy tracking.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 6 Simulation Performance and Analysis
### Result Discussion
#### 6.2.1 Constant Bit Rate Traffic
Different from DBPR, which adopts the distance from intermediate satellites to destination satellites as link weights, DHBP utilizes the destination hops delay-based differential backlog between neighboring satellites, which can balance the congestion and delay flexibly. Simulation results show that DHBP achieves the best data delivery ratio, especially when the network traffic becomes heavy. The quantitative analysis of the network performance of different routing protocols under heavy load (CBR = 4 Mbps) is shown in Table 4. The data in the table are the promotion rate of DHBP compared with OSPF, DBPR, and SALB. We can see that, when the network load is heavy, DHBP has a significant improvement in throughput. Compared with OSPF and SALB, the introduction of the back-pressure mechanism allows for the diversion of network traffic to take into account the load and transmission delay. As a result, both back-pressure-based routing, i.e., DHBP and DBPR, perform better. Compared with DBPR, DHBP has improved delay performance. This is due to the fact that DHBP utilizes destination hops count delay as a backlog metric. In satellite networks, hops count reflects the link delay more accurately than the distance between satellites. Figure 9: The performance under CBR traffic.
## 3 Results
### Identification  
The thickness of the concrete cover is a continuous parameter. Consequently, regression and statistical analysis should be applied to ascertain the precision with which this parameter can be identified. The experiments show that the most significant separation, accompanied by a relatively low standard deviation, is observed for the spatial component \(B_{\text{x}}\). However, identification may also be based on other spatial components. The correlation parameter is particularly sensitive to noise and disturbances. Therefore, it is recommended not to include it in the analytical model. For smaller cover thicknesses, the identification accuracy utilizing the parameter \(A(B_{\text{x}})\) can exceed 1 mm, while for greater thicknesses, this accuracy approximates 2 mm. Additionally, due to its non-linear characteristics, the offset  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline _Feature_ & **A (\(B_{\text{x}}\))** & **C (\(B_{\text{x}}\))** & **O (\(B_{\text{y}}\))** & **A (\(B_{\text{y}}\))** & **C (\(B_{\text{y}}\))** & **O (\(B_{\text{y}}\))** & **A (\(B_{\text{2}}\))** & **C (\(B_{\text{2}}\))** & **O (\(B_{\text{2}}\))** \\ \hline _change_ & \(\uparrow\) & - & \(\uparrow\) & \(\downarrow\) & - & \(\downarrow\) & \(\uparrow\) & - & \(\uparrow\) \\ _confidence_ (\%) & 94 & 76 & 92 & 52 & 58 & 57 & 99 & 61 & 100 \\ _support_ (\%) & 29 & 29 & 29 & 29 & 29 & 29 & 29 & 29 & 29 \\ \hline \end{tabular}
\end{table}
Table 4: Basic association rules between change in rebar class and signal features. Class \(\uparrow\).  
Figure 23: The influence of the change in the _class_ on the measurements—normalized waveforms for P1 and P2 at \(h=40\): (**a**) \(B_{\text{x}}\), (**b**) \(B_{\text{y}}\), and (**c**) \(B_{\text{z}}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline _Correctness of_ & **73\%** & **80\%** & **80\%** & **79\%** & **76\%** & **70\%** & **71\%** &  
parameter offers a decent identification accuracy and can be used to identify the higher \(h\) (Figure 24).  
The diameters and classes of reinforcing bars are regarded as discrete parameters. An InfoGain filter utilizing Kullback-Leibler divergence and a Naive Bayes classifier (NB) were used to select the attributes. The results obtained for various models are summarized in Table 5 (diameter identification) and Table 6 (class identification).  
The five attributes that ranked highest in the context of specific parameters were subsequently evaluated using a range of standard classifiers, including the Naive Bayes (NB) classifier, the C4.5 decision tree, and neural network architectures such as Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks. All possible combinations of the selected attributes were tested utilizing a wrapper method. The results obtained from this analysis are presented in Table 7.  
The C4.5 decision tree algorithm emerged as the most effective identification method. The classification accuracy achieved for all three parameters concurrently was 86%.
## 3 Results and Discussion
### Validation of the Multi-Mycotoxin Determination Method in Vegetable Drinks
#### 3.1.2 Linearity  
The method showed a linear behavior within the 100 LQ-LQ working range of analyte concentrations. It was calculated by means of a least squares adjustment for the relationship between the response obtained in the mass spectrophotometer and the concentration of the analyte both in solution and in a matrix solution. The acceptance criterion implies a test of the goodness of fit, using a correlation coefficient (r) between 0.9 and 0.999 as a criterion.
## 5 Conclusions
### Limitations of the Study
#### 5.1.3 Sample Size and Generalizability  
The study's sample size and composition also present limitations. The study involved a relatively small and homogeneous group of 17 university students from a single institution, which may limit the generalizability of the findings. To strengthen the external validity of IVR-based learning outcomes, future research should include a more diverse population across different educational contexts, age groups, and academic backgrounds. Expanding the participant pool would provide a more comprehensive understanding of the applicability of IVR-based serious games in physics education.
# Minimization of Material Waste Through Maintenance Interval Optimization in Transport Systems
## 3 Materials and Methods  
### Input Data  
The data used in the studies were gathered using the maintenance system documentation (DSU) for the specific rail vehicles included in the project. This documentation contains, among other things, information about the vehicle, details on the structure of the service cycle types, descriptions of the tasks scheduled for each cycle, as well as the measured characteristics. The maintenance system documentation covers all aspects related to the scope of technical reviews and repairs that occur during the operation of rail vehicles.  
The documentation includes the scope of maintenance tasks (control technical reviews and periodic technical reviews) defined by maintenance levels P1, P2, and P3 for rail vehicles, as well as levels P4 and P5 (overhaul and major repairs), in both cases including the intervals at which they are performed. Additionally, it specifies requirements concerning the equipment of workshops and the competencies of the personnel performing maintenance on the given rail vehicle. The DSU establishes a set of regulations that all entities operating rail vehicles on the public railway network are obliged to follow.  
The first step in data collection was to analyze the DSU for the most common types of rail vehicle in Poland. After this, we created a dataset containing the requirements of this regulation. This dataset served as the core input data for the general assumptions of the optimization model presented in this paper. We also analyzed statistical data from rail operators regarding services to prepare datasets with the most common failures and related parameters, such as actual mileage and operating hours.  
As part of the task, a review of the DSU was conducted for the following types of vehicles:  
* EN57/EN57 AL;
* ED78/EN78;
* EN76/EN76A Elf;
* EP07;
* SU45/SP45;
* ST44 in version ST40 (Newag 311D).  
IoT (Internet of Things) can significantly assist in collecting operational data from rail vehicles by utilizing various sensors that track specific performance indicators such as mileage, distance travelled, engine runtime in hours, and vehicle location on the route. These capabilities can help in route identification, understanding the wear and tear on key components, and assessing the usage of major systems. By gathering these data in real time, operators can better optimize maintenance schedules, predict component failures, and improve overall safety, efficiency, and performance of rail operations. Actually, this kind of data are collected in a narrow range by internal vehicle systems and data transfer usually is possible in service stations, not in real time. In this paper, it was not used because this type of system is not used by Polish rail operators.
## 5 Experiment and Validation Results
### Advantages of the IWO-NN Compensator  
In this work, the robot configurations are divided into several datasets that are compensated by the NN and the proposed IWO-NN. From this experimental application, the NN appear to be usually fallen into the local minima, and a reinstalling of random weights and bias is required to reach the global minima. On the other hand, the IWO-NN seems to archive the global minima quite efficiently. For that reason, the proposed IWO-NN can be said to have better convergence capability. Moreover, an accuracy consistency of experimental calibration (0.345 mm) and validation (0.4662 mm) confirms the abilities of the suggested calibration method.
# A 28 GHz 5G Phased Array Antenna with Air-Hole Slots for Beam Width Enhancement
###### Abstract  
In this paper, a 28 GHz fifth-generation (5G) phased array antenna with air-hole slots for beam width enhancement is proposed. The proposed antenna consists of eight dipole radiators on a mobile handset-sized ground with air-hole slots between the two adjacent elements for enhancing the half power beam width (HPBW) in the elevation plane. The dimensions of the proposed antenna are 130 mm \(\times\) 42 mm \(\times\) 0.127 mm. The proposed array antenna satisfies a \(-\)10 dB reflection coefficient in the frequency range from 27.2 to 29.2 GHz with a peak gain of 10.33 dBi and a side lobe level (SLL) of 13 dB. In addition to its good performance, the proposed antenna has a very wide HPBW (measured) in the elevation plane, up to 219 degree with a scan coverage of \(\pm\)45 degree in the azimuth plane. The proposed antenna demonstrates excellent hemispheric beam coverage for 5G mobile handset devices and can enable cost-effective mass production.  
Keywords:fifth-generation (5G); millimeter-wave; phased array; air hole; beam width +
Footnote †: journal: Applied Sciences
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 4 Experiments and Results
### Fusion Results
#### 4.3.1 Subjective Evaluation  
In order to elaborate the fusion effects of different methods clearly and have an intuitional vision experience, firstly, the fusion results obtained by different methods of six infrared and visible image pairs from the TNO database are shown in Figure 7. Moreover, two groups of single image comparisons are given, and we can easily judge the fusion quality by subjective assessment. Figures 8 and 9 show the fusion results of \"Marne_04\" and \"lake\" of the TNO dataset.  
Figure 7: Fusion results of six infrared and visible image pairs. From top to bottom: infrared images, visible images, the results of curvelet transform (CVT), dual-tree complex wavelet transform (DTCWT), Laplacian pyramid (LP), nonsubsampled contourlet transform (NSCT), two-scale image fusion based on visual saliency (TSIFVS), guided filtering based fusion (GFF), convolutional neural network based fusion (CNN), Dense-add, Dense-L1, and the proposed method.  
In Figure 8, the fused images obtained by CVT, DTCWT, LP, NSCT, TSIFVS although contain the information of two source images, but are not very clear and have low contrast. The images obtained by GFF and Dense-L1 tend to approximate to just one kind source image. As to the other fused images, the image obtained by the proposed method looks smoother than the image obtained by CNN which has some undesirable regions around the eave and the front window of the jeep. The image obtained by Dense-add is good, but has a lower contrast than the image obtained by the proposed method.  
Figure 8: Fused images of “Marne_04”.  
Figure 9: Fused images of “lake”.  
The gaps of branches and the spot of the jeep can be seen in the colored frames of the image obtained by the proposed method. In Figure 9, the characteristics of the fused images are similar to Figure 8. We can easily distinguish the plants, riverbank, and other objects in the colored frames of the image obtained by the proposed method.  
As mentioned above, the fused images obtained by the proposed method always contain more details, and there is less noise added to the fused image which can guarantee a high correlation with source images. What is more, the results fused by our method have high visual information fidelity and look more natural and comfortable.
## 2 Materials and Methods
### Landing Error Scoring System (LESS)  
Subsequently, we used LESS to identify possible landing errors that may increase the risk of ACL injury. The LESS was performed after a five-minute rest from the SLTH test. The LESS is a valid and reliable tool [19; 20] that assesses the participant's landing pattern during a drop-jump task. The athletes jumped from a 30 cm box with both feet, landed at a distance equal to half of their height, and performed a maximum vertical jump. Beyond the information on the proper execution of the test, no other information was given regarding the proper landing technique. Athletes performed 1-2 trials for familiarization with the test, and then we recorded three successful jump-landing trials. No unsuccessful trials were observed. Two conventional cameras (Panasonic HC-V770 (Panasonic, Kadoma, Osaka, Japan) and Sony HDR-CX625 (Sony Corporation, Minato, Tokyo, Japan)) captured the trials from the frontal and sagittal view. The recorded video of three successful trials was stored on a personal computer and analyzed using the Kinovea software (0.8.26 experimental version). We used the scoring form and the guidelines of [PERSON] et al. [20] to evaluate the landing technique. According to standardized guidelines, the scoring form contains 19 rating items that evaluate knee, hip, and trunk flexion angles from the side view, and knee valgus, base of support width, and toes position from the frontal view. The first fifteen items were assessed at initial contact and maximum knee flexion and given the value of \"zero\" for no error, thus a biomechanically correct landing, or \"one\" if landing error was observed. The prevailing value of the three attempts was representative of the specific item. The last two items contained the rater's sense of the landing technique in terms of overall joint displacement and whole-body control, and there were three options: \"zero\" for no error, \"one\" for average and \"two\" for poor technique. More strict rules apply for those two items, and \"two\" is the final value even in case it presents only once during the three attempts. For the rest of the scenarios, the previous principle for the predominant value applies. The LESS total score can range from 0 to 19, with a higher value indicating a worse landing technique [20]. The average LESS score from the three trials was used for statistical analysis. Athletes with a total LESS score > 6 were considered to have a high-risk landing pattern for ACL injury [20,38]. The same examiner (NIL) who rated all videos had clinical experience in sports injury assessment and rehabilitation and the proper use of the LESS scoring instrument [39,40]. Test-retest reliability was examined in a random subgroup of 37 players and proved to be excellent (ICC = 0.927, 95% CI = 0.859-0.982).
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Braced Frames
Accurately predicting the behavior of steel braced frames under PEF conditions necessitates the use of advanced analytical tools that can capture the complex interactions between seismic and thermal loads, as well as material and geometric nonlinearities. Numerical simulations conducted using software like OpenSees and ABAQUS are essential for understanding the performance of these structures under PEF scenarios and for developing effective design solutions [92; 93]. These simulations provide a comprehensive analysis that is crucial for designing steel braced frames capable of withstanding the dual challenges of seismic activity and fire exposure. The vulnerability of steel braced frames to PEF conditions is a significant concern, particularly in regions prone to both seismic and fire hazards. The reduced fire resistance resulting from residual deformations, material degradation, and weakened braces underscores the importance of targeted design strategies. By employing advanced analytical techniques and incorporating PEF scenarios into design standards, structural engineers can develop safer and more resilient buildings capable of withstanding the combined impacts of earthquakes and fire.
## 4 Conclusions  
In this study, a new method was proposed to predict soil shear strength parameters. The main intellectual merits of this work include the new attempt to combine CPT data with soil properties for modeling, the construction of a portable and low-cost CPT device, and the evaluation of the effectiveness of three ML models (BPNN, PLSR, and SVR) for predicting soil shear strength parameters using a combination of data. Compared with the laboratory direct shear test, this method is convenient to use, and the data are easy to obtain. The test results demonstrate that: (a) CPT data and soil properties can be combined to predict soil shear strength parameters, and (b) the BPNN model has an advantage in predicting soil cohesion, while the SVR model is optimal for predicting the soil's internal friction angle. According to numerical and evaluation index tradeoffs, the BPNN model with a 4-7-1 neuron structure (\(R_{C}\)\({}^{2}\) = 0.89, \(RMSE_{C}\) = 3.39, \(R_{V}\)\({}^{2}\) = 0.87, and \(RMSE_{V}\) = 4.08) is the most suitable prediction model for soil cohesion, while the SVR model with punishment factor 32.4 and kernel parameter 0.20 (\(R_{C}\)\({}^{2}\) = 0.87, \(RMSE_{C}\) = 1.78, \(R_{V}\)\({}^{2}\) = 0.86, and \(RMSE_{V}\) = 2.40) is used to predict the soil's internal friction angle.  
This study effectively improves the determination efficiency of shear strength parameters for arable soil; however, in our work, soil water content and soil bulk density are measured using an oven-drying method, which is not conducive to real-time detection. In addition, it should be stressed that the models used can be reliably applied when the parameter values range between the minimum and maximum values of each parameter (as presented in Table 2); otherwise, the predicted value is unreliable; therefore, future research into a method that can quickly obtain soil water content and soil bulk density is necessary, and the number of soil samples should also be increased in order to increase the range of suitable models. Moreover, the model optimization algorithms should also be studied in order to reduce the computation complexity of the used models.  
Conceptualization, L.Z. and Q.Z.; methodology, Q.L.; software, Z.W., J.C. and Z.C.; validation, L.Z., Q.L. and Q.Z.; formal analysis, Q.B., J.C., and Z.W.; investigation, Q.Z. and Z.C.; resources, Q.L.; data curation, Z.W., Q.B. and J.C.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z., Q.L. and Q.Z.; supervision, Q.Z.; project administration, Q.L.; funding acquisition, L.Z. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (32001427), the China Postdoctoral Science Foundation (2021M701341), and the National Rape Crop Industry System Special Project Funding (CARS-12).  
Not applicable.  
The authors declare no conflict of interest.