esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the driving vehicle was detected using the drone and the vehicle velocity was calculated based on the detection information. The driving vehicle velocity estimation was calculated from a distance and passing the time by detecting the driving vehicle with the drone LiDAR sensor at the two measurement points on the road. The drone's position for measuring the driving vehicle was stationary flying above the outside of the road, and the driving vehicle detected the first lane and the second lane at the position of the drone. The vehicle velocity estimation of the developed drone was compared with the speed gun of the existing equipment. The speed gun and drone's RMSEs were 0.75 km/h in the first lane and 1.31 km/h in the second lane. The performance of measuring the driving vehicle's velocity was similar between the drone and the existing equipment.  
The existing driving vehicle velocity measuring equipment requires measuring equipment for each lane. However, the developed drone system can measure vehicles in all lanes with a single drone. Drone velocity measurement can reduce the risk of roadside traffic accidents and there is no need to install a gentry structure. In addition, vehicle information can be obtained in a dark environment. The velocity of the vehicle can be measured at a maximum wind speed of 12.1 m/s. The drone's total weight is about 12.7 kg and the hovering range is within 1 m.  
This research and development (R&D) drone system has developed a driving vehicle detection system from one to three lanes, but there is no test road environment with roads from one to three lanes. Thus, the driving vehicle velocity measurement was tested in the  
Figure 12: This is the wind resistance of the developed drone: (**a**) wind speed of 6.6 m/s at a distance of 8 m from the fan to the drone; (**b**) wind speed 7.7 m/s at a distance of 6 m from the fan to the drone; (**c**) wind speed 8.7 m/s at a distance 4 m from the fan to the drone; (**d**) wind speed 10.1 m/s at a distance 2 m from the fan to the drone; (**e**) wind speed 12.1 m/s at a distance 1 m from the fan to the drone.  
first and second lanes. It was impossible to test the driving vehicle's velocity in the urban road environment with up to three lanes because drone flight regulations and laws restrict it. In addition, South Korea is limited in the area and time of drone flights due to neighboring countries' environment. It is impossible to sufficiently consider the irregularities in all situations since the measurement of the driving vehicle velocity cannot be tested in various road topologies and night environments.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data availability in a publicly accessible repository.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Research Methodology and Model Construction
#### 2.3.3 SCV-RF Model  
This model uses the random search method to determine the optimal hyperparameters for the RF model, with the average accuracy from five-fold cross-validation serving as the model evaluation metric. This approach is used to construct the SCV-RF model for assessing heavy rain disaster risk in the Beijing-Tianjin-Hebei region. The process for optimizing model parameters is as follows:  
1. Selection of Hyperparameters for Optimization: To balance the complexity and generalization ability of the SCV-RF model, the following hyperparameters are chosen for optimization: number of decision trees (n_estimators), maximum depth of the decision trees (max_depth), minimum number of samples required to split an internal node (min_samples_split), and minimum number of samples required at a leaf node (min_samples_leaf) [31].
2. Setting the Range for Hyperparameter Optimization: The ranges are set as follows: n_estimators [between 1, and 100], max_depth [from 1, to 20], min_samples_split [from 2, to 10], and min_samples_leaf [between 1, and 10].
3. Random Search and Model Training: Within the specified hyperparameter ranges, a set of hyperparameters is randomly sampled and evaluated for simulation accuracy using five-fold cross-validation. This random sampling and model training process is repeated 100 times, with the set of hyperparameters yielding the highest model accuracy selected as the final optimized parameters for the SCV-RF model.
## 4 Discussion
The NACP-02 chamber showed a variation of about 1.01% compared with the cylindrical chambers. Cylindrical chambers are recommended for photon beam calibrations, whereas parallel-plate chambers are recommended for photon beam energies \(\leq\)6 MV. Thus, large uncertainties were expected for the 10 and 20 MV photon beams. Different components of the dosimetry measuring system contribute to some uncertainties, including entrance window and chamber positioning, as demonstrated by [PERSON] et al. [19; 20]. Compared with the findings of similar studies, [PERSON], [PERSON], and [PERSON] [17] reported deviations of at least 0.23% in \(D_{w}\) measured using the TG-51 and TRS-398 protocols compared with the DIN 6800-2 protocol [2]. Recently, [PERSON] et al. [8] compared \(D_{w}\) values measured using the TRS-398 and TG-51 protocols. Using the TRS-398 protocol, the mean \(D_{w}\) values varied only by 0.13%-0.2% on average compared with TG-51 protocol. Table 6 shows the relative uncertainties (%) associated with measurements made using the IAEA TRS-398 and DIN 6800-2 protocols. The estimated combined uncertainties for TRS-398 and DIN 6800-2 were 1.24% and 1.25%, respectively. For a particular protocol, the  
Figure 2: The absorbed dose to water ratios obtained with three ionization chambers: (**a**) AAPM TG-51/IAEA TRS-398, (**b**) IAEA TRS-398/DIN 6800-2, and (**c**) AAPM TG-51/DIN 6800-2.  
estimated uncertainties in absorbed dose to water, measured using cylindrical ion chambers are slightly lower than those measured using plane-parallel chambers. As demonstrated in this study, a significant contribution to the overall uncertainty came from the beam quality correction factor, kQ, which amounted to 1.0%, followed by the calibration factor, which amounted to 0.55%. In a similar study, [PERSON] and [PERSON] [9] compared the IPEM 1990 protocol for photon dosimetry with the IAEA TRS-398 and AAPM TG-51 protocols. The combined uncertainty (k = 1) for the absorbed dose measurements was 1.4 (range: 1.53-1.88), which is comparable to our results. Our findings are thus consistent with the most recent IAEA recommendations, as uncertainties for clinical high-energy photon beams were estimated to be about 1.5% [15].
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 4 Aircraft Wake Recognition
### Metrics of Proposed Method for Wake Recognition  
To assess the efficacy of the YOLOv8n-CBAM-EfficientNetV2 model proposed in this study for wake detection, the precision rate, recall rate, and F1-score have been selected as the evaluation metrics for the experimental outcomes. The underlying calculation formulas are presented in Equations (5), (6) and (7), respectively:  
\[P_{Precision}=\frac{TP}{TP+FP}\times 100\%, \tag{5}\]  
\[P_{Recall}=\frac{TP}{TP+FN}\times 100\%, \tag{6}\]  
\[P_{F1-score}=2\times\frac{P_{Precision}\times P_{Recall}}{P_{Precision}+P_{ Recall}} \tag{7}\]  
where wake samples are designated as positive samples and samples devoid of wake are classified as negative samples. In addition, _TP_ represents the number of samples identified as positive with a positive truth value, _FP_ denotes the number of samples identified as positive with a negative truth value, _FN_ is the number of samples identified as negative with a positive truth value, and _TN_ is the number of samples identified as negative with a negative truth value. Furthermore, the precision rate is associated with the false alarm rate of the wake, which decreases as the precision rate increases. The recall rate is linked to the missed detection rate of the wake, which decreases as the recall rate increases. The F1-score is a harmonic average of precision and recall used to comprehensively evaluate the performance of the model.  
Additionally, the experiments employed giga floating-point operations per second (GFLOPs) and frames per second (FPSs) as evaluation metrics for the model performance. GFLOPs measure the computational complexity of a model. A higher GFLOP value indicates that the model requires greater computational resources, which may result in a reduced rate of target detection. FPS is a metric that is used to assess the real-time performance of a model, and it denotes the number of image frames processed per second.
# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 3 The Design and Implementation of DSCSD  
Usually, the links among data centers are highly probable for reuse, and the burst of instantaneous traffic leads to the instability and dynamicity of the network. Traffic scheduling and congestion control among multiple data centers can be well addressed with DSCSD by taking advantage of the flexibility and agility of SDN. Data centers are always interconnected through wide area networks. The network traffic is not constant at all times and has an unbalanced distribution. The traffic during the peak hours could increase by twice that of the average link load. However, the traditional routing protocols route and forward flows abide by the shortest paths algorithm, without shunting away flows to balance the link load. To satisfy the requirement of bandwidth during peak hours, we have no choice but to purchase 2-3\(\times\) bandwidth as well as upgrade to large-scale routing equipment. As a result, the cost of network operation, administration, and maintenance are increased sharply, and also causes the waste of bandwidth in ordinary times. If we can meet the requirements of network services, the average link utilization rate of wide area networks can only achieve 30-40% [30]. Saying that some malicious flows extend the occupation of links, congestion might bring about interruptions in the transmission of their services [31; 32; 33; 34]. In this paper, we propose DSCSD, a scheme of dynamic traffic scheduling and congestion control with a multilevel feedback queue mechanism that can address the problems above-mentioned to some extent. The following sections explain the design in detail and its implementation process.
## 3 Intelligent Tuning Based On DDPG  
### Preliminaries  
The observed states from the VNA are S-parameters curves, as shown in Figure 3. Since the cavity filter is a passive device, all signals passing through it come from external sources. During the test, the cavity filter device under test (DUT) is connected to a VNA through input/output ports. The VNA then sends the signal to the DUT to observe the received signal in the form of scattering parameter, or S-parameter for short. To meet the designed requirements of a cavity filter product, it is necessary to examine some test data, including the Return Loss, the Insertion Loss, the Pass-band Fluctuation, and the Out-of-band Rejection. \(S_{ij}\) is the common notation of S-parameters, indicating the energy measured at port \(i\) injecting to port \(j\). The return loss of port 1 is represented by \(S_{11}\), indicating the energy measured at port 1 after injecting from port 1 and returning to port 1. Similarly, the insertion loss of port 1 is always represented by \(S_{12}\), representing the energy measured at port 1 after injecting from port 1 to port 2 and returning to port 1. It is always required that the value of the return loss should be minimized within the passband (always less than or equal to \(-20\) dB). In other words, the values of \(S_{11}\) in Figure 3 must be under the black dashed benchmark line within the specified frequency range. For insertion losses, the value should be maximized within the same passband (always close to 0 dB).  
In this work, we only consider the value of return loss, namely, the \(S_{11}\) curve in the passband, as the state of RL, and ignore the other specifications, such as insertion loss. Therefore, the goal of tuning is to adjust the screw to control the \(S_{11}\) curve to the target line. It is important to note that our tuning method can be applied to any filter class of radio frequency (RF) devices, including cavity filters, combiners, duplexers, and multiplexers.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 7 Location-Based Techniques for Enhancing PHY Layer Security
### Machine-Learning and Deep-Learning-Based Approaches  
As mentioned in the previous section, various machine-learning and DL-based approaches are used more and more for RF fingerprinting, as well as for device localization [159]. The same trend is evident in the field of the localization of FBSs. [PERSON]. [160] introduced a DL-based solution employing a CNN to locate cell towers. This approach utilized crowdsourced smartphone measurements and operator-side tower licensing data.  
The study demonstrated that the proposed classifier can effectively geolocate eNodeBs from other metropolitan areas or mobile operators. The authors of [107; 161] demonstrated that Maximum Likelihood (ML)/DL-based approaches can effectively detect FBSs by utilizing various parameters measured at the PHY layer of a wireless link. In contrast, [PERSON] et al. [162] introduced an ML-based solution, named FBSDetector, which detects FBSs in cellular networks by analyzing network traces at Layer 3. This work also contains a good introduction and discussion about the challenges and importance of FBS detection. The results obtained from these studies, along with ongoing research in this field, indicate that ML/DL-based approaches hold significant potential for enhancing the accuracy of FBS detection and localization in the future.
## 2 Experimental Investigations
### Experimental Results and Discussion
#### 2.3.2 FT-IR Analysis  
FT-IR spectroscopy measures a sample's characteristic vibrational absorption bands to determine its chemical structure [32]. The infrared spectra of the powder coatings are shown in Figure 7. The absorption peaks of the functional groups were in the wavenumber range of 4000-500 cm\({}^{-1}\) (with a corresponding wavelength range of 2.5-20 \(\upmu\)m). The most intense absorption peaks were located in the wavenumber range of 2000-500 cm\({}^{-1}\) (with a corresponding wavelength range of 5-20 \(\upmu\)m). Therefore, the infrared radiation panel's infrared radiation range covered the absorption wavelengths of the powder coating's functional groups.  
Moreover, most absorption peaks were located in the range of 5-10 \(\upmu\)m, matching the most intense infrared radiation wavelengths emitted by the radiation panel. Under the radiant panel, the functional groups of the coating absorbed infrared radiation energy and converted it into vibrational energy. At the same time, intermolecular energy transfer and whole-molecule resonance led to a crosslinking reaction and accelerated the curing of the powder coating.
## 4 Discussion
### Parameter and Optimization Strategy of the Proposed Method for Real-Time Applications
As a result, the optimized parameter ranges used in our study not only enhance energy efficiency but also enable rapid reconfiguration in real-time systems. This demonstrates that selecting pre-optimized values eliminates the need for continuous parameter tuning, making them sufficient for practical applications.
# Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
## 3 Materials and Methods  
### ILE Algorithm  
The idea of graph theory is introduced with the LE algorithm in this paper. By constructing weighted neighborhood graphs, the relation of the same positions of sample points in local neighborhoods is maintained in high and low dimensional space [14]. The neighborhoods \(k\) in LE is the key parameter in reducing dimensions. In view of the different characteristics of different manifold surfaces, it is easy to include the traditional fixed \(k\) value with the points in the same neighborhood graph. This results in the \"short circuit phenomenon\", as these points do not belong to the same manifold area. To solve this problem, an improved Laplace Eigen-mapping method dimensionality reduction algorithm is proposed and is shown in Figure 2. Detailed step-by-step instructions are as follows:  
Figure 1: The fault diagnosis method based on a manifold learning and a neural network.  
Step 1: Construct weighted neighborhood graphs.  
From the \(k\)-nearest neighbors of the high-dimensional spatial data point \(X_{i}\), the adjacency matrix of the data set is obtained.  
1. Set the initial neighborhood value \(k\), according to the principle of KNN for each of the sample points to determine the neighborhood, \(\left\{X_{i}^{1},\ X_{i}^{2},\ X_{i}^{3}\dots\ X_{i}^{k}\right\}\), \(i=1,\ 2,\ 3\dots N\);
2. For each of the resulting neighborhoods, we can calculate the tangent space coordinates that correspond to the neighborhood points [20]. \[\Phi_{i}^{(j)}=V^{T}N_{i}\left(I-\frac{1}{k}ee^{T}\right)\] (1) where Mapping function of neighborhood points to local tangent space coordinates can be expressed as a Equation (2) based on Taylor series expansion. \[\left\|x_{ij}-\overline{x_{i}}-V_{i}V_{i}^{  
Step 3: Calculate the feature map.  
Compute the eigenvectors and eigenvalues of the Laplace matrix \(L\):  
\[Ly=\lambda Dy \tag{6}\]  
where \(D\) is a diagonal matrix, the eigenvectors are outputted as the result of the reduced dimension that corresponds to the smallest m nonzero eigenvalue.
# Non-Classic Atmospheric Optical Turbulence: Review
## 6 Influence in Applications
### Wireless Optical Communications
#### 6.1.2 Probability of Fade  
For a given probability density function (PDF) of the irradiance fluctuations, the probability of fade describes the percentage of time, and the irradiance of the received signal is below some prescribed threshold value, for example, \(I_{t}\). Hence, the probability of fade as a function of a set threshold level is defined by the cumulative probability as follows:  
\[Prob(I<I_{t})=\int\limits_{0}^{I_{t}}P_{I}(I)dI, \tag{47}\]  
where \(P_{I}(I)\) is the PDF of the fluctuating instantaneous irradiance. In weak turbulence, the log-normal PDF model is most frequently used for description of the optical intensity fluctuations, leading to the following expression for the probability of fade [95]:  
\[Prob(I<I_{t})=\frac{1}{2}\left[1+\text{Erf}\left(\frac{\sigma_{I}^{2}/2-0.23F_ {t}}{\sqrt{2}\sigma_{I}}\right)\right], \tag{48}\]  
where \(\text{Erf}(x)\) is the error function. Here, threshold (fade) parameter \(F_{t}\) represents the intensity margin in decibels (dB) from the threshold \(I_{t}\), which is usually the sensitivity of the receiver.  
On using the scintillation flux (from [100]) in Equation (48), one can deduce the probability of fade as a function of slope \(\alpha\) for a fixed fade threshold parameter (say \(F_{t}=6\) dB) and fixed structure function at the Fresnel distance, in a particular horizontal case scenario and for different values of anisotropy parameters along the \(x\) and \(y\) axes (shown as \(z_{x}\)eff and \(z_{y}\)eff in the legend). The curves in Figure 18 indicate that the decrease in scintillation due to anisotropy reduces the probability of fade.  
Figure 17: Scintillation flux with fixed structure function at the Fresnel distance for six pairs of anisotropic factors along the \(x\) and \(y\) axes (shown as \(z_{x}\)eff and \(z_{y}\)eff in the legend). Reprinted with permission from Reference [100].
## 5 Conclusions  
Conventional direct shear tests were implemented to investigate the shear behavior of undisturbed loess and paleosol with distinct initial moisture contents under various normal stresses. The SWCCs of loess and paleosol samples were measured separately and used to predict their unsaturated shear strength formulas. On the basis of this work, the following conclusions can be drawn:  
* There are significant discrepancies in basic physical properties between loess and paleosol samples, particularly in natural moisture content, Arterberg limit, grain size distribution, and mineral composition.
* As the initial moisture content and normal stress increase, different shapes of shear stress-shear displacement curves for loess and paleosol are observed, and their shear failure behavior gradually transitions from strain softening to strain hardening. The strain softening phenomenon of loess samples at a low initial moisture content is not as significant as that of paleosol samples under the same conditions.
* The shear strength of loess and paleosol samples is negatively correlated with an increased initial moisture content and decreased normal stress. The sensitivity of the internal friction angle to water is much less than that of the cohesion. There is an exponentially decreasing relationship between cohesion and an increased initial moisture content, while the internal friction angle shows a decreasing trend with a limited attenuation range, along with augmenting the initial moisture content. This is close to the weakening action of water on soil's inter-particle cementation.
* On the basis of the Vanapalli model, formulas for predicting the unsaturated shear strengths of undisturbed loess and paleosol were put forward respectively. The applicability of the prediction models proposed in this paper and the accuracy of their fitting parameters were verified by using experimental data.
* This study has offered scientific guidance on how we should understand the mechanism of loess landslides in the South Jingyang tableland. However, due to the size and boundary effects of our indoor experiments, further in situ direct shear tests can more accurately reflect the shear characteristics of undisturbed, unsaturated loess and paleosol.  
Conceptualization, F.D. and Z.H.; data curation, P.L. and J.W.; formal analysis, P.L.; funding acquisition, Z.H. and P.L.; methodology, P.L. and F.D.; writing--original draft preparation, P.L.; writing--review and editing, F.D. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Key Research and Development Project of Henan Province (No. 221111321500) and the Ph.D. Student Innovation Fund of the North China University of Water Resource and Electric Power (No. NCWUBC202235).  
Not applicable.  
Data Availability Statement: Data are contained within the article.
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 4 Materials and Methods
### Model and Methodology
#### 4.3.3 Model for Calculating the Vegetation Restoration Potential Index  
Vegetation recovery potential epitomizes the pinnacle condition for optimal vegetation growth attainment [63]. Additionally, due consideration should be afforded to the disparity between the current state of vegetation growth and the optimal growth condition. To encapsulate this differential aspect, this paper computes the vegetation restoration potential index as per Equation (4). To mitigate statistical bias, the 90 th percentile value of vegetation cover was adopted as the maximum recoverable cover (Vcm). By utilizing the prevailing vegetation cover (VC, %) and the maximum recoverable cover, the vegetation recovery potential index (IVCP, 0-1) was appraised. A value of IVCP ranging from 0 to 1 signifies varying degrees of capacity for prospective vegetation restoration, with 0 signifying the absence of such potential.  
\[Ivcp=(VC_{m}-VC)/VC_{m} \tag{5}\]  
where \(Ivcp\) was the index of vegetation restoration potential, \(VC_{m}\) was the maximum cover, and \(VC\) was the current vegetation cover.