esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 HEPD Response to the August 2018 Storm  
Figure 4 illustrates a comparison between the HEPD count rate maps before (20-23 August; upper panel) and after the impact of the storm (25-27 August; lower panel). In the top panel, the southern polar region presents a larger trigger rate than the northern one. This is due to the dipole tilt angle, which, in August, allowed the CSES-01 to explore higher geomagnetic latitudes in the Southern Hemisphere than in the Northern Hemisphere. In the bottom panel, an increase in the count rate is evident at both northern and southern latitudes--especially in the southern region--as a consequence of the storm's arrival. Both maps are related to trigger configuration T in Section 2, which requires an above-threshold signal only in the trigger plane and allows detecting the lowest energetic electrons (\(>\)3 MeV). As concerns protons, their contribution to the trigger rate increase is negligible due to the absence of direct injection from solar energetic particles (SEPs) during this specific storm event [48]. For visualization purposes, we excluded the South Atlantic Anomaly (SAA) region, which is characterized by extremely high particle rates. For this purpose, we selected magnetic field values larger than 23,000 nT. For this analysis, we calculated magnetic field values by using the International Geomagnetic Reference Field (IGRF) series of mathematical models, in particular the IGRF-12 candidate [49].  
The increased particle rate, during the storm time, is also visible as a function of the L-shell and time in Figure 5. The first three panels show the HEPD count rates for three different trigger configurations: from top to bottom, T, T & P1, and T & P1 & P2. The increase in the number of calorimeter planes used for trigger generation resulted in a higher energy threshold for electron detection (\(>\)3 MeV, \(>\)4.5 MeV, and \(>\)8 MeV, respectively), thus reducing the particle rate. For comparison, the time evolution of the Dst index is shown in the bottom panel of Figure 5. As can be inferred by a strong decrease of the Dst down to \(\sim\)\(-\)190 nT, the start of the storm's main phase was on late 25 August, exactly in coincidence with the increase of the HEPD particle rates.  
Figure 4: Comparison between an HEPD trigger rate map before the occurrence of the geomagnetic storm, from 20–23 August (upper panel), and after the impact of the storm, from 25 August to 27 (lower panel). The maps are related to trigger configuration T, requiring an above-threshold signal just in the trigger plane and providing the lowest energy threshold for electron detection (\(>\)3 MeV). For visualization purposes, we excluded the South Atlantic Anomaly region, characterized by extremely high particle rates.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### PCA-Based Weight Determination Framework
#### 2.4.2 PCA-Based Weight Calculation Process  
The PCA-based weight calculation follows five key steps [37]:  
1. Standardize the matrix \(X_{t}\) for each time point:  
\[Z_{t}=(X_{t}-\mu_{t})/\sigma_{t} \tag{14}\]  
2. Calculate the correlation coefficient matrix:  
\[R_{t}=\frac{1}{n}Z_{t}^{T}Z_{t} \tag{15}\]  
3. Solve the eigenvalue equation:  
\[|R_{t}-\lambda_{t}I|=0 \tag{16}\]  
4. Calculate provincial weights based on eigenvalues and eigenvectors:  
\[w_{i,t}=\frac{\sum_{j=1}^{3}\lambda_{j,t}e_{ij,t}}{\sum_{j=1}^{3}\lambda_{j,t}} \tag{17}\]  
5. Apply temporal smoothing to ensure stability:  
\[\hat{w}_{i,t}=\theta w_{i,t}+(1-\theta)\frac{1}{k}\sum_{j=t-k}^{t-1}w_{i,j} \tag{18}\]  
where \(\theta\) is a smoothing coefficient (typically 0.6-0.8) and \(k\) is the smoothing window length. The constraint \(\hat{w}_{i,t}\leq w_{i}^{set}=\alpha\cdot s_{i}^{fin}\) is applied to ensure no single province dominates the forecast, where \(s_{i}^{fin}\) represents the final score for the \(i\)-th province, and \(\alpha\) is a coefficient for the final score (set at 1.2).
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 3 Results
### Comparison of Accuracy in Yield Prediction Algorithms for Individual Growth Periods  
This study focused on winter wheat yield as the target variable, utilizing different modeling factors for each growth stage as the feature variables. High-precision machine learning algorithms, including gradient boosting, random forest, ridge, and ensemble voting, were employed for modeling and analysis at each growth stage throughout the  
Figure 6: Scatter plots and residuals of the validation set models for the entire growth period. Subfigures (**a–d**) depict the scatter plots and residual information for ensemble voting, ridge, gradient boosting, and random forest models, respectively.  
Figure 7: Scatter plots and residuals of the test set models for the entire growth period. Subfigures (**a–d**) depict the scatter plots and residual information for ensemble voting, ridge, gradient boosting, and random forest models, respectively.  
entire winter wheat growing season. Figure 8 illustrates the accuracy of the four algorithm models in predicting winter wheat yield on the validation and test sets. For the validation set, considering the entire growth period, the ranges of R\({}^{2}\), RMSE, and MAE across different models were 0.20-0.81, 585.34-1211.34 kg/ha, and 468.07-1040.78 kg/ha, respectively. On an individual growth stage basis, the differences in R\({}^{2}\), RMSE, and MAE for each model ranged from 0.05 to 0.30, 48.32 to 260.84 kg/ha, and 3.01 to 198.16 kg/ha, respectively. Ensemble voting showed higher yield prediction accuracy. For the test set, considering the entire growth period, the ranges of R\({}^{2}\), RMSE, and MAE across different models were 0.33-0.77, 645.66-1101.03 kg/ha, and 534.93-877.08 kg/ha, respectively. On an individual growth stage basis, the differences in R\({}^{2}\), RMSE, and MAE for each model ranged from 0.16 to 0.42, 268.3 to 372.83 kg/ha, and 263.02 to 403.70 kg/ha, respectively. The heading stage exhibited the highest yield prediction accuracy.  
In both the validation and test sets, the ensemble voting model consistently exhibited stable predictive capabilities. Figure 9 illustrates the scatter plot and residual information for the ensemble voting model during the heading stage. The curve in the graph indicates a well-fitted linear relationship between the predicted and actual yields during the wheat heading stage.
## 5 Test Systems and Simulation Cases
### Simulation Cases  
To validate the effectiveness and robustness of the proposed MI-SOCP model, we consider two simulation cases as follows:  
* **Case-1**: This case considers the possibility of installing three DERs. For the IEEE 33-bus system, the capacity of DERs is from 0 kW to 1.2 MW for each one. While for the IEEE 69-bus system, the capacity of DERs is from 0 kW to 2 MW for each one. In addition, each DER is assumed to operate with a unit power factor;
* **Case-2**: This case also considers the possibility of installing three DERs with the capacity to generate reactive power, i.e., variable power factor.
## 5 Conclusions  
In this study, considering the problem of multiple meshing pairs and complex load distribution of the concentric face gear split-torque transmission system, a quasi-static analysis model with coupled elastic support was constructed based on the multi-point constraint method. To balance the solver time and solution accuracy, the meshes of the driving side and coast side were controlled with different dimensions using the grid non-coordination principle. Then, the trend analysis of the load-sharing and contact characteristics was carried out using the single factor analysis method. The effects of the elastic support stiffness parameters, face gear installation error, and backlash on the load-sharing and contact characteristics were studied. By analyzing the calculation results, the conclusions were as follows:  
1. The support stiffness had less influence on the contact characteristics of the input face gear pair. Therefore, in the adjustment of the contact performance of the concentric face gear drive, the other gear pairs could be adjusted under the premise of ensuring proper contact characteristics of the input face gear pair.
2. Adjusting the pinion support stiffness could modify the load-bearing of each gear pair to make it more balanced; a larger support stiffness of the idler gear and tail gear and a smaller support stiffness of the input gear could reduce the load-sharing coefficient.
3. Increasing the support stiffness of the upper face gear was beneficial for lowering the load-sharing coefficient of the idler gear, and the load-sharing coefficient of the input gear did not change significantly, whereas it would be more appropriate when the factor of the support stiffness of the lower face gear was approximately 1.0.
4. The increase in the axial installation error of the upper and lower face gears obviously deteriorated the contact characteristics, where such a deterioration needs to be properly limited; the backlash of the input gear, idler gear, and tail gear had little influence on the load-sharing characteristics of the system.  
Conceptualization, F.G. and R.Z.; methodology, F.G.; software, P.L.; investigation, G.J.; resources, R.Z.; data curation, F.G.; writing--original draft preparation, F.G.; writing--review and editing, R.Z. and G.J.; visualization, F.G.; supervision, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFB2001300; National Science and Technology Major Project, grant number J2019-IV-0001-0068; and the National Natural Science Foundation of China, grant numbers 51975273 and 51775265.  
Not applicable.  
Figure 19: Influence of the backlash on the load-sharing coefficient.  
**Acknowledgments:** This project is supported by the National Key R&D Program of China, grant number 2018 YFB2001300, National Science and Technology Major Project, grant number J2019-IV-0001-0068 and the National Natural Science Foundation of China (Grant 51975273 and 51775265).  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The research and development of unmanned aerial vehicles (UAVs) is progressing rapidly, and they are expected to be used in a wide range of applications. In this paper, we evaluated the propagation characteristics of air-to-ground (A2G) communications used by UAVs. Specifically, we investigated the Rician K-factor, which is one of the indicators representing the impact on communication quality. We carried out radio wave propagation measurements for A2G communications at low altitudes in propagation environments with simple (S environment) and complex (C environment) structures within the measurement area and then performed a detailed evaluation of the effect of the distance from buildings, UAV altitude, and antenna installation on the Rician K-factor and propagation characteristics. The measurement and analytical results reveal that the Rician K-factor in an S environment was observed to be high due to the strong dominance of the direct wave. On the other hand, the Rician K-factor in a C environment decreased because of complex multiple reflected and diffracted waves caused by surrounding buildings. In addition, dummy fading signals generated from the useful path calculated with the ray-tracing method using a simple 3D analytical model showed a high degree of agreement with the experimental results. These outcomes provide key parameters for the optimal design of UAV-based A2G communication systems, contributing to the practical application of UAV operations.  
rician K-factor; channel measurement and analysis; small-scale fading; air-to-ground; low altitude; UAV Article  
2020
## 4 System Performance Discussion  
In order to verify the correctness of the single RF link MIMO radar theory and its superiority compared to traditional MIMO radar, this section will compare the performance of target detection in the single RF link MIMO radar system and the traditional MIMO radar system under the ideal environment, which will focus on the speed and distance detection capability.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 2 The European Framework
\begin{table}
\begin{tabular}{l l l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**C-ITS**} & \multicolumn{3}{c}{**Interlocutors Involved**} & \multicolumn{3}{c}{**Standardised Messages**} & \multicolumn{3}{c}{**Communication Technologies**} & \multicolumn{3}{c}{**Application Field**} & \multicolumn{3}{c}{**Safety**} \\ \cline{3-14}  & & **Services** & **V2I** & **V2V** & **CAM** & **DENM** & **SPaT** & **MAP** & **IVI** & **ETSI-G5** & **Cellular (SG/4G)** & **Urban** & **Motorway** & **SC** & **nSC** \\ \cline{2-14}  & & EBL & & x & & x & & x & & x & x & x & x & **1** \\  & EVA & & & x & x & & x & & x & x & x & x & **1** \\  & SSV & & x & x & x & & x & & x & x & x & x & **1** \\  & TJW & & x & & x & & & x & & x & x & x & **1** \\  & RWW & x & & & x & & & x & x & x & x & **1** \\  & WTC & x & x & & x & & & x & x & x & x & **1** \\  & CCRW & x & x & x & x & & x & & x & x & x & **1.5** \\  & MCA & & x & x & & & x & & x & x & x & **1.5** \\  & WWD & x & & & x & & x & & x & x & x & **1.5** \\ \hline  & VGGN & x & & & & & x & x & & x & x & x & **1** \\  & VSPD & x & & x & & & x & x & & x & x & **1** \\  & VPD & x & & x & & & & x & x & x & x & **1** \\  & SWD & x & & & & & x & & x & x & **x** & **1** \\  & GLOSA & x & & x & x & x & & x & & x & **1** \\  & SigV & x & & & x & x & & x & & x & **1** \\  & TSP & x & & & x & x & & & x & & **1** \\ \hline  & Info1 & x & & x & & & & x & x & x & **x** & **1.5** \\  & LZM & x & & x & & & x & x & & & **x** & **1.5** \\  & ZAC & x & & x & & & & x & & **x** & **1.5** \\  & VRU & V2P 2 & x & & & & x & & & & **1.5** \\  & CCN 3 & x & & & & & & & & **1.5** \\ \hline \hline \end{tabular}  
* Within infolaiaiment tasks, the Traffic Information and Smart Routing service may also involve vehicle-to-vehicle (V2V) communication and decentralized environmental notification messages (USEMs). \({}^{1}\) Vulnerable road over (VRU) services are based on vehicle-to-pedestrian (V2P) communication. \({}^{1}\) No definitive description is given. Note EBL, electronic brake light, EVA, emergency vehicle approaching; \({}^{2}\)SW, slow water stationary vehicle; TJW, traffic jam warning; RWW, road works warning; WTC, weather condition; CCRW, cooperative collision risk warning; MCA, motorcycle approaching WWD, wrong wray driving VSCN, in-vehicle signage; VSPD, in-vehicle speed; IVP, probe vehicle data; SWD, shockwave damping; GLOSA, green light optimal speed advisory; SigV, signal videofrontfront sensor; SigV, signal videofront sensor; SF, traffic signal priority, LZM, loading zone management; ZAC, zone access control; CCN, connected and cooperative navigation; V2V, vehicle-to-Infrastructure; CAM, cooperative awareness; SPaT, signal phase and timing. MAI; map data; IVU, in-vehicle information; FTEI, European Telecommunications Standards Institute; SC, safety critical; and nSC, nonsafety critical.  
\end{table} 
Table 1: Classification of C-ITS services (source: [40]).
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 6 Legislation
### AV Testing Possibilities  
As explained in the previous section, the various factors involved in the regulation of AV circulation means the development of legislation that regulates this activity becomes a complex and slow task. This difficulty largely hampers the deployment of a technology that has already reached a high degree of maturity.  
One of the main issues presented by the lack of legislation is limitation in the testing of autonomous vehicles on roads open to traffic.  
As summarized in Table 9, only a small set of 17 countries/states around the world allow partial access to public roads by autonomous vehicles for testing purposes and, among them, only 11 allow unrestricted access.  
Legislation limiting liability for possible accidents caused by autonomous vehicles is also concerned with establishing standards for the development of tests in real traffic conditions. Thus, institutions such as the European Road Transport Research Advisor Council (ERTRAC, Europe), the National Highway Traffic Safety Commission (NHTSA, USA), the Center for Connected and Autonomous Vehicles (CCV, UK) or the National Transport Commission (NTC, Australia) establish conditions under which the tests must be carried out. In general, the established rules are very restrictive in that it is necessary to communicate in advance the nature of the tests (details of the tests to be performed, equipment, routes, safety measures adopted, etc.).  
All this generates great complexity, both in the administrative procedures and necessary security deployment, which causes the tests to be expensive and not as abundant as would be desirable by the developers of this technology. That is why companies make great use of simulation techniques for the development of systems for autonomous driving.
### Cantonese and Mandarin Tonal Systems  
Although Cantonese and Mandarin are both varieties of Chinese, they have different phonological systems and are not mutually intelligible [18]. Like other Chinese languages, both Mandarin and Cantonese are tonal; that is, lexical tones contribute to the differentiation of words in Mandarin and in Cantonese. Table 1 presents the tonal systems in Cantonese and Mandarin, and it lists the name, the category, the letter and an example for each tone of these two languages. Tone letters are used to represent the relative values of the tones, with '1' and '5' representing the lowest and highest pitch levels, respectively. As shown in Table 1, there are six lexical tones in Cantonese and four in Mandarin [19; 20]. Cantonese has a complex tonal system, with three level tones (T1, T3 and T6), two rising tones (T2 and T5) and one falling tone (T4). Following the convention of Cantonese phonology, the three entering tones in Cantonese are grouped into the three level tones. In Mandarin, there is one level tone (T1), one rising tone (T2), one dip-rising tone (T3) and one falling tone (T4). In addition, a Mandarin T3 syllable undergoes tone sandhi when it is not a sentence-final syllable [21]. In terms of potential mapping of the tones, Cantonese and Mandarin share similar tone levels and values for T1 and T2, as T1 is a high-level tone (55 in both languages) and T2 is a high rising tone (25 in Cantonese and 35 in Mandarin).  
According to recent studies, several tone pairs (i.e., T2 and T5, T3 and T6, and T4 and T6) are merging in either the production or the perception of some native speakers [22; 23], which is probably part of a sound change in contemporary Cantonese [24]. If immigrants in Hong Kong are exposed to different types of Cantonese input, namely some merged tones and some unmerged tones, it would be interesting to explore whether they can maintain the contrasts in the six tones in their L2 Cantonese.
## 4 Closed-Loop Control Algorithms
### Single-Neuron-Based Self-Pre-Optimization Algorithm
\begin{cases}w_{1}(k)=w_{1}(k-1)+\eta_{p}e(k)u(k)x_{1}(k),\\ w_{2}(k)=w_{2}(k-1)+\eta_{i}e(k)u(k)x_{2}(k),\\ w_{3}(k)=w_{3}(k-1)+\eta_{d}e(k)u(k)x_{3}(k),\end{cases}  \tag{19}\]  
where \(K\) represents the scale learning rate of a single neuron; \(x_{1}(k)\), \(x_{2}(k)\), and \(x_{3}(k)\) correspond to the three components of the PID algorithm without the weighting parameters; while \(\eta_{p}\), \(\eta_{i}\), and \(\eta_{d}\) signify the learning rates corresponding to the proportional, integral, and derivative components of the neuronal PID, respectively. According to Equation (7) of the positional PID algorithm, \(x_{j}\) (\(j=1,\ 2,\ 3\)) can be expressed as follows:  
\[\left\{ \begin{array}{l}x_{1}(k)=e(k),\\ x_{2}(k)=\sum\limits_{j=0}^{k}e(j),\\ x_{3}(k)=e(k)-e(k-1).\end{array} \right. \tag{20}\]  
Similarly, the three key parameters of the positional PID algorithm can be expressed using the signal neuron PID parameters as follows:  
\[ \begin{cases}K_{p}=K\hat{v}_{1}(k),\\ K_{i}=K\hat{v}_{2}(k),\\ K_{d}=K\hat{v}_{3}(k).\end{cases}  \tag{21}\]  
The single-neuron PID controller described above is essentially a variable coefficient proportional-integral-derivative compound controller. Through its own learning process, a corresponding change is made in the control parameters, and therefore, it has strong robustness [39]. In general, the neuronal PID algorithm has more parameters than the conventional PID algorithm, including the neuronal learning rate \(K\), the weighting coefficients for each of the three PID components, and their respective learning rates. Among them, \(\eta_{p}\), \(\eta_{i}\), and \(\eta_{d}\) can be selected by the method of the conventionality PID parameters [38]. In addition, the selection of \(K\) is very important. If the overshoot is more significant, the scale coefficient \(K\) may be decreased; otherwise, it may be increased [37]. In the practical parameter tuning process, the first step is to ensure that the system will stabilize. Then, increasing \(K\) can improve the speed at which the system reaches stability. When the system reaches the steady state through self-learning of the single neuron, the relative optimal positional PID parameters for the system have been identified. This process is also known as the single-neuron-based self-pre-optimization process.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 3 Proposed Method  
It is critical to inform customers about IoT security and protection and to adamantly state that there would be no real risk to the integrity of their data, secrecy, or the confidentiality of a professional in the medical system. This will allow businesses to profit from IoT innovation. The deciding factor for IoT to anchor the transmission of medical images is its quick advancement on a large scale.  
This research develops a hybrid encryption method for IoT security where the computation suggested has unique features in encryption and decoding in terms of speed, even with optimal keys. The quality of cryptographic primitives for safeguarding sensitive data heavily relies on random numbers, which are represented by encryption keys. The integrity and confidentiality of the image must be preserved because the processing and communication of digital medical images raises various security issues. The verification of medical images will serve as the main means of ensuring the prevention, stability, which is shown in Figure 1. The original image's blocks are randomly deleted and reassembled inside the image. The edited image is again put through a twofold encryption procedure for medical image security that first uses RSA-AM encryption and then also uses an obstruction bloom breeding optimization-based signcryption technique. To strengthen the encryption security of the sender's and receiver's private and public keys, this hostile orchestration search key generation is carried out. The yielding image and the initial image are then contrasted to see how well they performed using the PSNR after the decryption procedure. With stronger PSNR, this tactic aims to raise the security level of encrypted images.