esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 4 Results
### Stage 1: Selection of Data Collection Equipment  
The technological solution is an autonomous and interoperable detection system that operates online and in real time. This system uses cameras and artificial intelligence to identify and discriminate between equipment of different sizes or functions and people. It assigns specific conditions that allow recording the actions of the equipment and issuing alarms when sectors or perimeters with prohibited entry are transgressed, preventing quasi-accidents. This paper presents a mobile station (See Figure 5), which is energy autonomous and operates efficiently in the mining environment.
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 4 Result and Discussion
### Impact of the Number of Reservations and Cars on the Utilization Ratio  
Both relocation models had significant impacts on the vehicles used. Relocation means that high demand stations get additional cars, increasing the vehicles used. Increasing vehicles used means an increased utilization ratio, and increased profit.  
The simulation was run for 10 stations and 100 cars for a week. The input for comparison was the average number of reservations per day (x-axis), and the output was the utilization ratio. Figure 10 shows that for all scenarios, increasing the total number of reservations increases the utilization ratio (more cars are rented by customers). Forecasting relocation was significantly superior to rebalancing relocation with an increasing number of reservations. Thus, if an operator implements forecasting relocation in a real case, as the number of reservations increases, profit will increase compared to rebalancing relocation. The forecasting model will help the operator to predict the demand level for each station, allowing higher vehicle use. Both relocation models showed better utilization compared to one-way carsharing without relocation.  
Figure 11 shows the utilization ratio for the different numbers of cars. The simulation assumed an average of 1000 daily reservations and 10 stations and was run for a week. The input for comparison was the total number of cars (x-axis), and the output was the utilization ratio. As the number of cars increased, the utilization ratio decreased for all models. Forecasting relocation showed a better utilization ratio than rebalancing relocation, but one-way services with rebalancing relocation still had better utilization performance than without relocation.  
Figure 9: Impact of the number of cars on relocation cost.  
Thus, an operator must consider the ratio of their total number of customers and total number of cars to maximize their profit. Increasing customers or reservations increases utilization, since more cars are used by the customers, but increasing the number of cars reduces utilization (most cars are parked). If the operator allows one-way service, they need to consider relocation models, as these increase the vehicles used (utilization ratio) compared to the no relocation scheme. Forecasting relocation is the superior option due to allocating cars to high demand stations, enhancing the utilization ratio.
# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 5 Results and Discussion
Each arrow represents a single cataloged event, beginning from the predicted epicenter and pointing to the catalog epicenter. The colors represent the misfit ratio over the model posterior's 95 percent confidence interval. As a result, the blue color denotes that the catalog epicenter is within the 95 percent confidence interval, while the red color denotes that it is not. We can compare the posterior's confidence intervals with the actual epicenter location error because we can compute the posterior distribution for each event. A model error ratiometric, which measures the distance between predicted and observed epicenters, is used to illustrate the model uncertainty. This metric is normalized by the model posterior's 95% confidence interval. A score of 1 indicates that the genuine epicenter location falls within a 95 percent confidence interval, while a value of 0 indicates that the true epicenter location does not lie within the confidence interval at all. In most cases, the error ratio is less than one. This means that the model posterior distribution's expected aleatoric uncertainties are substantially smaller than the actual epistemic uncertainties. Because regions with the highest density of seismic stations also have the lowest prediction error (see Figure 8), it appears that the spatially interpolated prediction error is somewhat related to the local density of seismic stations. The highest systematic errors are seen in the northwest and southeast corners of the selected region, where the station density is low and the model appears to be unable to obtain the boundary values of latitude and longitude. In this case, the behavior of the tanh activation function can be explained by the fact that it asymptotically approaches the value \(\pm 1\), which corresponds to the range of latitudes and longitudes represented by the training samples. As a result, ever greater activations are necessary to push the final location predictions toward the boundaries of the domain, resulting in findings that are biased toward the interior of the domain. This shows a basic trade-off between resolution (prediction accuracy) in the interior of the  
Figure 8: Prediction errors of the GNN from (**a–d**) and BNGCNN from (**e–h**) models to predict latitude, longitude, depth, and magnitude of a seismic event without and with using station location information. With station spatial information, the black line shows the training error on the training subset while the yellow line shows the validation error on the validation set. The blue line shows the prediction error on combined training and validation subsets without location information. data domain and the greatest amplitude of the predictions (which also applies to linear activation functions).
# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR2: A Systematic Review of Publications on Racket Sports
#### 3.2.6 Analysis of Citations  
Table 8 shows the twenty most cited documents. The top two publications conduct research on padel tennis, which is a surprising result given the sport's low popularity when compared to other sports. Padel tennis is not even an Olympic sport, like tennis, table tennis, or badminton, and it is the most recent racket sport to be introduced to the world.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Paper** & **Total Citations** & **TC per Year** & **Normalized TC** \\ \hline [PERSON] et al. [143] & 42 & 8.4 & 3.761 \\ [PERSON] et al. [146] & 38 & 5.429 & 2.676 \\ [PERSON] et al. [177] & 36 & 4.5 & 2.25 \\ [PERSON] et al. [168] & 19 & 3.167 & 1.754 \\ [PERSON] et al. [161] & 18 & 2 & 1.636 \\ [PERSON] et al. [163] & 17 & 0.68 & 1 \\ [PERSON] and [PERSON] [172] & 16 & 1 & 1 \\ [PERSON] et al. [170] & 15 & 2.143 & 1.056 \\ [PERSON] et al. [144] & 14 & 4.667 & 2.676 \\ [PERSON] and [PERSON] [162] & 14 & 1.4 & 1 \\ [PERSON] et al. [169] & 13 & 2.167 & 1.2 \\ [PERSON] et al. [57] & 13 & 2.167 & 1.2 \\ [PERSON] et al. [134] & 12 & 3 & 3.529 \\ [PERSON] et al. [155] & 11 & 1.571 & 0.775 \\ [PERSON] et al. [141] & 10 & 5 & 3.243 \\ [PERSON] et al. [154] & 10 & 2 & 0.896 \\ [PERSON] et al. [135] & 10 & 1.667 & 0.923 \\ [PERSON] et al. [164] & 10 & 1.25 & 0.625 \\ [PERSON] et al. [97] & 9 & 3 & 1.721 \\ [PERSON] et al. [140] & 9 & 4.5 & 2.919 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The 20 most-cited documents on a global scale.  
Figure 8: Dendrogram.  
**Summary:** A look at this aspect of the review revealed that the top two most cited writers think of padel tennis as a research topic. This result demonstrated a general interest in the newly-formed sports across a broad spectrum of disciplines. To achieve a significant number of citations, it is recommended that future studies examine novel sports (for example, padel squash, or outdoor badminton) or develop performance analysis (which may incorporate machine learning, artificial intelligence, or fuzzy neural networks).
###### Abstract  
In our frequency scanning interferometry-based (FSI-based) absolute distance measurement system, a frequency sampling method is used to eliminate the influence of laser tuning nonlinearity. However, because the external cavity laser (ECL) has been used for five years, factors such as the mode hopping of the ECL and the low signal-to-noise ratio (SNR) in a non-cooperative target measurement bring new problems, including erroneous sampling points, phase jumps, and interfering signals. This article analyzes the impacts of the erroneous sampling points and interfering signals on the accuracy of measurement, and then proposes an adaptive filtering method to eliminate the influence. In addition, a phase-matching mosaic algorithm is used to eliminate the phase jump, and a segmentation mosaic algorithm is used to improve the data processing speed. The result of the simulation proves the efficiency of our method. In experiments, the measured target was located at eight different positions on a precise guide rail, and the incident angle was 12 degrees. The maximum deviation of the measured results between the FSI-based system and the He-Ne interferometer was 9.6 \(\upmu\)m, and the maximum mean square error of our method was 2.4 \(\upmu\)m, which approached the Cramer-Rao lower bound (CRLB) of 0.8 \(\upmu\)m.  
frequency scanning interferometry; adaptive filtering method; mosaic algorithm +
Footnote †: journal: Applied Physics Letters
## 5 Results and Analysis
### Test Results  
#### 5.2.1 Comparison of the Effect between K-Means++ and K-Means Algorithms  
To examine the effect of the K-means++ clustering algorithm on the model alone, the attention module was not embedded in this experiment.  
The change in the target detection performance of the YOLOv4 algorithm for pavement cracks after optimizing the Anchor box using the K-means++ clustering algorithm was first verified using the test dataset established above. The test results are shown in Table 1. From Equations (3) and (4), it can be seen that accuracy and recall are mutually constrained; the higher the accuracy, the lower the recall will be in relative terms. Therefore, the recall obtained by using the K-means++ algorithm is substantially increased, and instead the accuracy will be slightly decreased. The F1-Score as a comprehensive index is to balance the influence of accuracy and recall; the higher the F1-Score, the higher the accuracy of the model. A total of 11.5% improvement in F1-Score indicated that the model was more comprehensive after optimizing the Anchor box. It was also because the K-means++ algorithm optimized the parameters of the Anchor box that the recall rate of the model was improved significantly. Moreover, the mAP was improved by 0.93%, which also indicates that the improved network model also had good accuracy improvement.
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 4 Simulation and Experimental Results
### LMSED Algorithm Simulation
#### 4.1.1 Feasibility Verification and Convergence Speed Analysis
However, an excessive step factor may cause the parameters to change drastically, causing the entire convergence process to exhibit oscillating changes and prolong convergence time. Figures 7 and 9 are the convergence processes of the demodulation parameters after determining the design parameters. They correspond to different design parameters. Better design parameters can be chosen by controlling a single variable. Since the demodulation method is essentially an iterative algorithm, the relationship between the convergence speed and the design parameters does not have an explicit expression, and can only be qualitatively evaluated. Therefore, the SoC is designed to support software-based configuration of parameters.
## 3 UAV Power Supply and Charging Mechanisms
### Grid Powered UAVs: Tethering  
Another technique for powering UAVs is called tethering, which allows the connection to be established between the UAV and power supply on the ground using a fiber optic cables [48]. Fiber optic connections allow several kilowatts of power to be transferred using high-intensity light. This technique provides more autonomy to UAVs and allows secure and quick data transfer in real time. However, the tethered UAVs (TUAV) are only allowed to hover within a certain hovering region since the tether has a maximum length and the launching point of the TUAV is typically located on a rooftop. A study in [51] investigated the best location for tethered UAVs (TUAVs) to reduce the average path-loss between the TUAV and a terrestrial receiver. They calculated the upper and lower bounds for appropriate tether length and inclination angles in order to reduce average path-loss. Aside from the maximum tether length, the heights of the buildings surrounding the TUAV rooftop determine the hovering region, which necessitates that the tether's inclination angle does not fall below a certain minimum value to avoid tangling and to ensure safety. It is worth noting that if the tethered cable delivers constant power, the UAV flight is expected to last a few days or possibly a few months [52].  
Tethered UAVs have been considered for various wireless network applications in the literature. The work in [53] presents a comparative performance analysis of untethered-UAV (U-UAV) and T-UAV-assisted cellular traffic offloading in a traffic-heavy location. They employed stochastic geometry methods to calculate joint distance distributions between users, the terrestrial BS (TBS) and UAV. A user association strategy is presented, and the relevant association areas are analytically discovered to optimize the end-to-end signal-to-noise ratio (SNR). The total coverage probability of U-UAV/T-UAV-assisted systems is then calculated for specified TBS and U-UAV/T-UAV locations. The authors in [54] introduced a novel UAV-based post-disaster communication system where U-UAVs are employed to offer cellular service in disaster-affected areas, while T-UAVs provide backhaul for the U-UAVs.
## 2 Materials and Methods
### Characterization of the PAN-NF HF Membrane  
The inner surface morphology of the PAN-NF HF membrane was analyzed by high-quality field emission scanning electron microscopy (FE-SEM, Nova-SEM, FEI, Hillsboro, OR, USA). The inner surface charge (zeta potential) of the PAN-NF HF membrane was measured using an electrophoretic apparatus ELS_2000Z (OtsuKa Electronics, Otsuka, Japan) using 10 mM NaCl solution. The water contact angle of the PAN-NF HF membrane was measured by the sessile drop method (Phoenix-300 analyzer with video capturing equipment; SEO Corporation). The results were determined as the average of at least five measurements for each sample.
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 2 Basic Theory of Random Field
### Discretization of Random Field
#### 2.1.1 1D Random Field  
\(H(x)\) represents a 1D uniform random field, which is discretized by linear elements, as shown in Figure 1, and then the random field is discretized into \(n\) random variables. \(L_{i}\) denotes a linear element with a midpoint coordinate of \(x_{i}\) and a length of \(l_{i}\). The local average of \(H(x)\) defined on the element is expressed as follows:  
\[H_{i}=H_{i}(x_{i})=\frac{1}{l_{i}}\int_{x_{i}-l_{i}/2}^{x_{i}+l_{i}/2}H(x) \mathrm{d}x. \tag{1}\]  
\(H_{i}\) represents the local average of \(H(x)\) defined on the element \(L_{i}\), and the degree of correlation between \(H_{i}\) and \(H_{i}\) is expressed in terms of covariance:  
\[Cov(H_{i},H_{j})=\frac{\sigma^{2}}{2l_{i}l_{j}}\sum_{k=0}^{3}{(-1)^{k}l_{k}^{2 }\Gamma^{2}(l_{k})}, \tag{2}\]  
where \(l_{k}\) (\(k\) = 0, 1, 2, 3) indicates the relative position between the elements, \(L_{i}\) and \(L_{j}\) (see Figure 1). Parameter \(\Gamma^{2}(l_{k})\) is the reduction coefficient of variance, which represents the reduction degree of variance of the local average with respect to the point variance of the random field and is related to the correlation model, \(\rho(\tau)\). There are four main types of \(\rho(\tau)\), including the triangular (T) model, exponential (Exp) model, second order auto-regressive (AR) model, and Gaussian (G) model, whose specific expressions and corresponding reduction coefficients of variance can be found in [29]. Figure 2 illustrates the curve of \(\Gamma^{2}(l_{k})\) changing with \(l_{k}/L\), in which \(L\) is the correlation length of the random field. Figure 2 shows that the variation in \(\Gamma^{2}(l_{k})\) with \(l_{k}/L\) is basically identical under different correlation models and that the differences between the curves are small. This indicates that the LAM of random field discretization is insensitive to the type of correlation models, which is another reason for the wide application of this method in practical engineering.
## 3 Results
### Availability Maps  
The annual availability map for Germany and surrounding areas reveals that the lower mountain ranges and the alpine region generally show availabilities which reach the critical level of 80% (used, for example, in [1]) or even below Figure 7). Hotspot areas of critical availability are the lower mountain ranges of the Black Forest, Vosges mountains, the Swiss Jura, and the Swabian Alps in the south, but also some more northerly mountain ranges like the Thuringian Forest, Rohn, Spessart, the Harz Mountains, as well as the northwestern mountain areas of Hunsruck, Taunus, and the Rothaar Mountains. While the lowlands of Germany generally show higher availability \(\geq\) 80%, it should be kept in mind that the maps only show the expected availability with regard to the frequency of conditions with clouds of base altitude of 100 m and below. Other factors, such as exceptional clean air periods (low aerosol backscatter), can further reduce the availability of lidar wind measurements even under cloud-free conditions [16].  
Figure 6: Monthly mean of 10-min availability for the measurement height bin of 152.5 m.  
Figure 7: Map of estimated availability based on CBA \(\leq\) 100 m frequency (2006–2017) and assumed 10-min availability threshold of 80% at measurement heights of 130 m and above.  
In order to reduce the data loss due to FLS during a complete measurement period, a monthly resolution of spatial availability information is required. Here, the availability reveals a clear seasonal course. The monthly availability maps (see Figure 8) show that during the summer months availability might reach the problematic threshold only at the highest elevations. On the other hand, during all months of the winter half of the year (Oct-Mar) the highest elevations are all below the critical threshold and the availabilities at middle elevations already reach close to critical values of \(\sim\)85%. Therefore, special care regarding the temporal representativity must be taken when designing measurement strategies for these regions, especially during the winter time. In particular, lower availabilities due to FLS are widely found in central and southern Germany in November and December. This is especially important if the currently necessary 12-month measurement period of the TR-6 (Technische Richtlinie 6) issued by the FGW e. V. (Fordergesellschaft Windenergie und andere Dezentrale Energien) [1] used in Germany might be relaxed in the future in favor of shorter measurement periods combined with timely extrapolation.  
Figure 8: Monthly maps of estimated availability based on CBA \(\leq\) 100 m frequency (2006–2017) and assumed 10-min availability threshold of 80% at measurement heights of 130 m and above.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSS Based on Array Elements
#### 4.1.3 Fractal Based FSSs
Sierpinski fractals with dissimilar patch elements on a single-layer substrate have been used to improve the multiband frequency response and for the trimness of the FSS [84]. It is noticed that the integration of dissimilar fractal motif geometries enables the realization of multi-stop band filtering characteristics, also allowing for the adjustment of resonant frequency and bandwidth. Such multiband FSSs have potential use in modern designs of civil construction with optimized propagation for the indoor environments. Furthermore, Table 3 details different types of fractal and meandered FSSs when comparing structure dimensions and performance parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FSS Ref.** & **Layers** & **f\({}_{\text{r}}\) (GHz)** & **Array Element Size** & **Total Thickness** & **FBW \%** & **Iteration** &
\begin{tabular}{c} **Patch and Aperture Element** \\ **Element** \\ \end{tabular} \\ \hline
[29] & 1 & 15 & 0.377\(\lambda\) & 0.075\(\lambda\) & 37 & 1 & Minkowski fractal patch \\ \hline
[64] & 1 & 4 & 0.058\(\lambda\) & 0.013\(\lambda\) & 101 & – & Meandered spiral lines \\  & & & & & & & nested with neighborhood \\ \hline
[85] & 1 & 0.96 & 0.11\(\lambda\) & 0.005\(\lambda\) & 14 & 1 & Double square loop (first iteration) with folded \\  & & & & & & & strips at the four corners \\ \hline
[86] & 1 & 2.73 & 0.293\(\lambda\) & — & 23.8 & 1 & Circular and cross dipole \\  & & & & & & & conductive fractal patches \\ \hline
[87] & 1 & 11.95 (stop Band) & 0.2\(\lambda\) & 0.002\(\lambda\) & 4.2 & 1 & Concentric double \\  & & & & & & & Minkowski fractal \\ \hline
[88] & 1 & 2.02 & 0.012\(\lambda\) & 0.162\(\lambda\) & 31 & SFD = 1.8617 & Teragon fractals \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different types of fractal and meandered FSSs. Figure 9: (**a**) FSS pre-fractal based patch element showing reconfiguration possibilities for k = 1 with switch positions (**b**) Number of resonances versus resonant frequency for different fractal levels. (**c**) Effect Inter-element spacing on resonance frequency. Effect of changing the angle of incidence for different fractal levels on (**d**) resonance frequency (**e**) Bandwidth [82]. (**f**) The effect on the resonant frequency by varying the dielectric constant [81].