esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conventional Active Disturbance Rejection Control Problem  
In ADRC, the model of the nonlinear system is extended with an additional virtual state variable, which lumps all of the unwanted dynamics, uncertainties, and disturbances that remain unobserved in the standard system into a single term known as \"generalized disturbance\". In addition to estimating the states of the nonlinear system, the ESO performs online estimation and cancellation of this virtual state. In this scenario, the nonlinear system is converted into a chain of integrators, which allows the control system design to be simpler. Figure 1 demonstrations the structure of a Conventional ADRC, (C-ADRC) which contains three key parts: the Tracking Differentiator (TD), an Extended State Observer (ESO), and Nonlinear State Error Feedback (NLSEF) [24]. The tracking differentiator generates the required signal profile, which is the signal itself, free from noise, and a set of signal derivatives (first derivative, second derivative...). The NLSEF acts as a nonlinear combination of the error profile. The ESO function is as discussed in the introduction section [25].
## 3 Mechanism of the Shutter-like Fairing Structure
### Shutters' Characteristics and Working Mechanism  
Shutters are characterized by their ability to guide flow and reduce flow dimension, which is similar to the mechanism of a polarizer. The air flow passing through the shutters shows a strong tendency of vorticity reduction and pressure enhancement due to these characteristics. As shown in Figure 8, the finite span airfoil induces significant wingtip vortices under the incoming flow velocity of 15 m/s. The vorticity decreases significantly, the air flow pressure increases, and the flow field changes from a low-pressure turbulent one to a favorable one after passing through the airfoil cross-section fins of shutters shown in Figure 7.  
Fins on two ends of the shutters and fins in the center area are not equivalent in function. The ideal number of fins is \(n=\infty\). Although the fins in the center are cross-sectioned airfoils, they mainly play a guiding role due to the influence of adjacent fins. On the contrary, fins at the top and bottom ends show the characteristics of a single airfoil, resulting in unfavorable positive lift, which can completely offset the improvement of side wing efficiency. The addition of top and bottom endplates effectively reduces the adverse effects caused by fins on the ends.  
At a certain incoming flow velocity, the performance of shutters deteriorates if the distance between fins is far, that is, the blocking ratio \(p\) is less than a certain threshold. The threshold is also different in accordance with the cross-sectional shape of fins because the interaction between the fins disappears, and a single fin is exposed in free flow. When the arrangement of fins is extremely dense and \(p\) is larger than a certain threshold, the blocking effect decreases sharply, resulting in the degradation of shutters' performance. Therefore, the blocking ratio \(p\) has an optimal value under a given free flow velocity.
# Throughput Capacity of Selfish Wireless _Ad Hoc_ Networks with General Node Density
## 2 System Assumption  
In this paper, we construct a random SWANET with the general node density \(\zeta\in[1,n]\). The general node density includes the case of random dense networks where \(\zeta=n\) and random extended networks where \(\zeta=1\). The other features of the system model we considered are as follows:1. We assume \(n\) static nodes uniformly and independently placed over an area \(\mathbb{A}=[0,\sqrt{A}]\times[0,\sqrt{A}]\), where \(A=n/\zeta\).
2. The node is classified by whether it will forward other transmission. As shown in Figure 1, if a node will forward other transmission, we define the node as an altruistic node (AN, empty points in Figure 1). Otherwise the node is a Selfish Node (SN, solid points in Figure 1). In this work, we assume each node may be selfish, and define the probability that a node \(i\) will forward other transmission as \(p_{i}(n)\). For simplicity, we assume the probability that each node forwards other transmission is \(p(n)\).
3. Each node randomly chooses a destination node and each node is the destination of exactly one node.
4. The transmission model we adopted is General Physical Model [4], where the channel gain ignores shadowing and fading, and only depends on the distance between the transmitter and receiver. Let \(S\) denote the subset of nodes transmitting simultaneously. Based on the point-to-point coding and decoding [4], the transmission rate \(R_{i,j}\) between node \(i\) to node \(j\) is: \[R_{i,j}=\log\left(1+\frac{P_{i}\cdot d_{ij}^{-\alpha}}{N_{0}+\sum_{k\in S \setminus\{i\}}P_{k}\cdot d_{kj}^{-\alpha}}\right)\text{bit/s}\] (1) where \(P_{i}\) is the transmission power of node \(i\). We assume each employs identical power \(P\) to transmit. \(d_{ij}\) denotes the distance between an arbitrary pair of nodes \(i\) and \(j\). \(N_{0}\) is the ambient noise power at the receiver. \(\alpha\) is the path loss exponent, and \(\alpha>2\). The notations of this paper are summarized in Table 1.
5. We say that the throughput capacity of a network [4] is of the order \(O(f(n))\) bits per second if there is a deterministic constant \(c_{1}<+\infty\) such that \[\liminf_{n\rightarrow+\infty}\text{Pro}(T(n)=c_{1}f(n)\text{ is feasible})<1\] and is of order \(\Theta(f(n))\) bits per second if there are deterministic constants \(0<c_{2}<c_{3}<+\infty\) such that \[\liminf_{n\rightarrow+\infty}\text{Pro}(T(n)=c_{2}f(n)\text{ is feasible})=1,\] \[\liminf_{n\rightarrow+\infty}\text{Pro}(T(n)=c_{3}f(n)\text{ is feasible})<1\]  
Figure 1: Network model of selfish wireless _ad hoc_ networks (SWANETs). Solid points are selfish nodes, and empty points denote altruistic nodes.
## 4 Results and Discussions
### Analysis of Train Energy Consumption
Calculation conditions 1-3 in Table 5 indicate that reducing the FAV can simultaneously reduce the OSV while maintaining the indoor O\({}_{2}\) PP. Therefore, reducing the FAV can significantly reduce the energy consumption of the oxygen supply. Calculation conditions 3-5 further explain the importance of reducing the oxygen supply to save energy while maintaining FAV. When the OSVs were 0.138, 0.124, and 0.102 m\({}^{3}\)/s, the oxygen energy consumption was reduced by 21.59, 29.55, and 42.07%, respectively, compared to the basic condition. When the OSV was 0.102 m\({}^{3}\)/s, the O\({}_{2}\) PP in the carriage reached the lowest limit specified in the standard [12]. In Figure 12, it can be seen that the energy consumption of the ACS was the lowest under calculation condition 1. However, the ACS energy consumption accounted for a small proportion of the total energy consumption, indicating that the oxygen supply energy consumption had a greater impact on the total energy consumption. The importance of reducing OSS energy consumption was also revealed in reference [8], and measures were provided to reduce OSS energy consumption by reducing the FAV. Basically, the total energy consumption of the system achieved its minimum value under calculation condition 5, in which the oxygen energy consumption was the lowest. Therefore, when the train was running in oxygen supply areas, the adjustment of the FAV should be based on reducing the OSS energy consumption as measures to reduce the ACS energy consumption had little effect on the total energy consumption. Figure 11: Effect of fresh air volume on partial pressure of CO\({}_{2}\) and the energy consumption of air-conditioning (Wudaoliang).
# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
## 4 Case Study
### Effect of Transition Resistance  
The effectiveness of the proposed scheme is verified in different transition resistance cases, respectively.  
As shown in Figure 15, in the case of a low transition resistance, the amplitude of the wave head is large and easy to identify; with the increase of the transition resistance, the peak value of the wave head gradually decreases, the reflected wave head is difficult to identify, and the single-ended method of traveling wave ranging scheme faces failure. So, for high-resistance faults, the traveling wave method faces challenges.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 1 Introduction
The three levels (also known as sub-models), as described in the literature, build the HBM model's functions. [PERSON] and [PERSON] [18] developed a hierarchical Bayesian approach, using three sub-models: data model, process model, and parameter model in its hierarchy. The results of their research support the space-time and air-pollution pair datasets to predict the daily 8 hour maximum ozone concentration [18]. The performance evaluation of their study using GP, AR, and GPP models shows high prediction accuracy with the used data. Utilizing their approach with different covariance matrices to predict the traffic flow data should make an essential contribution to the domain of spatiotemporal analysis. In this paper, we apply the HBM approach in the traffic domain, using the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) approximation models. The estimation of model parameters is carried out using Bayesian inference, and different traffic data modeled for the experiments analysis. We apply these three different models to obtain accurate spatial prediction and temporal forecasting, using the HBM approach. We test four different spatial correlation matrices: _Exponential, Gaussian, Spherical,_ and _Matern_ in the GP model. Constructing the temporal forecasting is based on two different units of time: _day_ and _month_. We use two different datasets collected by the Chicago Transit Authority (CTA) about bus traffic counts to apply our model and conduct a spatiotemporal traffic prediction. The first dataset has its spatial points relatively close to one another, while in the second dataset, the spatial points distribution is widely spaced from each other.
## 8 System Design
### Open Spacecraft Design
#### 8.6.2 Subsystem/Payload Board Receivers  
The configuration of the OPEN base plate allows four OPEN-compliant boards to be incorporated into the spacecraft, simultaneously. This base plate configuration, which also leaves the requisite 5 cm \(\times\) 5 cm payload area clearance, is depicted in Figure 14. A top plate, not shown, provides the other aspect of the electrical stacking (without requiring physical stacking); the two plates (interconnected via wires run adjacent to the corner posts) and the corner posts are the key structural members of the spacecraft.  
Figure 12: Diagram of OPEN Board Specifications implemented and demonstrated by OpenOrbiter.  
Figure 13: OPEN/OpenOrbiter Board Clearances (top view).  
The OPEN design seeks to maximize the volume available for other subsystems and payload elements while minimizing mass consumed by the structure. The base plates provide structural rigidity; they hold the corner posts in place. The subsystem and payload modules' printed circuit boards (PCBs) also provides rigidity against certain types of torques.  
The corner posts hold the top and base plate together. They also secure the subsystem and payload PCBs via the slot that runs the full length of each corner post. A diagram of the corner post configuration is shown in Figure 16. Note that while this is oriented for the lower left position, the same part can be utilized (in different orientations) for all four corners. The corner post is affixed with a screw to the top and base plates. Figure 15 shows the assembled corner posts. It also shows the overhang clearance which is allowed in all areas (per Section 2.2.6 of [24]), except for the corner posts, which must be in direct contact with the P-POD rails. The corner posts exceed the minimum required width of 8.5 mm (per Section 2.2.9 of [24]). They must also have their outward-facing corner rounded to a radius of at least 1 mm (per Section 2.2.11 of [24]), be composed of aluminum 7075 or 6061 (per Section 2.2.19 of [24]) and be hard anodized to preclude cold welding with the P-POD rails (per Section 2.2.20 of [24]).  
Figure 14: Receiver (bottom face) plate for OPEN board design.
## 3 Methodology
### Model Selection  
The decision tree model is commonly used for categorization purposes. It effectively sorts data by learning rules based on characteristics. When fine tuning, setting parameters, such as maximum tree depth and minimum sample size splits, are important. In a CART model, attributes are assigned based on their importance. The structured layout identifies factors for classification or regression. Essentially, decision trees make predictions by splitting the input features into subsets, refining these splits to reduce uncertainty in the class variable. This process uses the Gini index as a measure of uncertainty [37].  
The Gini index is used to quantify homogeneity in data by computing the proportion of sample that belongs to a class. This index is defined in the following Equation (1):  
\[\text{Gini index}=1-\sum_{i=1}^{n}p_{i}^{2}, \tag{1}\]  
where \(p_{i}\) denotes the probability of an element being classified in a distinct class.  
Nonparametric techniques, such as CART, have become popular in predicting the severity of traffic crashes. In contrast to regression models that heavily rely on the relationships between dependent and independent variables, CART works without predefined assumptions. The results of CART models are presented in a tree format, making it easier to understand and interpret the outcomes. When developing machine learning models for classification, such as decision trees, it is essential to divide the data into training, validation, and testing sets. While there is no rule for the percentage allocation between these sets, common practices include using 60% to 80% of the data for training and 10% to 20% for validation, and having a testing set similar in size to the validation set [37].  
The RUMC technique is a method used to tackle imbalanced datasets by reducing samples from the majority class. In analyzing crash severity, the application of RUMC with CART involves adjusting the training dataset. Traditional CART models face challenges with imbalanced data where one class dominates (such as slight injuries) resulting in biased predictions. By undersampling the majority class, RUMC-CART enables focus on  
Figure 2: Methodology flowchart for the CART and RUMC-CART models.  
minority instances (like more severe crashes) improving the model's predictive capabilities for underrepresented cases. When determining the model configurations, a thorough grid analysis is conducted to assess levels of complexity, leaf arrangements, and division strategies [32].
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Seeds** & \(W_{2}\) **(\%)** & \(D_{g}\) **(mm)** & \(R\) **(\%)** & \(\Phi\) **(\%)** & \(m_{D}\) **(g\(\cdot\)m\({}^{-1}\))** \\ \hline \multirow{6}{*}{Untreated winter} & 9.5 & 4.35 a,b  & 43.41 a  & 53.02 a  & 10.94 b  \\ \cline{2-5}  & 11.5 & 4.25 b  & 42.83 a  & 52.70 a  & 10.85 b  \\ \cline{2-5}  & 13.5 & 4.41 a  & 42.14 a  & 52.67 a  & 11.56 a  \\ \cline{2-5}  & 15.5 & 4.36 a  & 42.14 a  & 52.58 a  & 11.33 a, b  \\ \cline{2-5}  & 17.5 & 4.38 a  & 42.59 a  & 52.88 a  & 11.64 a  \\ \hline \multirow{6}{*}{Dressed winter} & 9.5 & 4.16 b  & 45.85 a,b  & 56.77 a  & 10.28 b  \\ \cline{2-5}  & 11.5 & 4.16 b  & 46.24 a,b  & 56.99 a  & 10.36 b  \\ \cline{2-5}  & 13.5 & 4.07 b  & 45.11 b  & 56.45 a  & 9.97 b  \\ \cline{2-5}  & 15.5 & 4.28 a  & 46.61 a  & 57.41 a  & 11.12 a  \\ \cline{2-5}  & 17.5 & 4.34 a  & 46.76 a  & 57.38 a  & 11.22 a  \\ \hline \multirow{6}{*}{Untreated winter barley} & 9.5 & 4.63 c  & 40.29 a,b  & 50.69 a  & 11.02 b  \\ \cline{2-5}  & 11.5 & 4.71 b,c  & 38.15 c  & 48.91 b  & 10.70 b  \\ \cline{2-5}  & 13.5 & 4.76 b  & 39.59 b,c  & 50.13 a,b  & 11.36 a  \\ \cline{2-5}  & 15.5 & 4.73 b  & 40.96 a,b  & 51.26 a  & 11.59 a,b  \\ \cline{2-5}  & 17.5 & 4.87 a  & 41.57 a  & 51.43 a  & 11.96 a  \\ \hline \multirow{6}{*}{Dressed winter barley} & 9.5 & 4.51 a,b  & 41.46 a  & 50.41 b  & 10.76 b  \\ \cline{2-5}  & 11.5 & 4.46 b  & 51.12 a  & 55.56 a  & 10.89 b  \\ \cline{2-5}  & 13.5 & 4.57 a  & 42.07 a  & 50.73 b  & 10.70 b  \\ \cline{2-5}  & 15.5 & 4.52 a,b  & 42.00 a  & 51.02 b  & 10.79 a  \\ \cline{2-5}  & 17.5 & 4.52 a,b  & 41.64 a  & 50.61 b  & 10.86 a  \\ \hline \multirow{6}{*}{Untreated winter vye} & 9.5 & 3.69 c  & 36.28 a  & 49.38 a  & 9.04 a,b  \\ \cline{2-5}  & 11.5 & 3.77 a,b  & 34.94 a  & 48.37 a  & 8.74 b  \\ \cline{2-5}  & 13.5 & 3.82 a  & 34.33 a  & 48.15 a [FOFigure 6: _Cont_.
## 2 Methods
### Dataset Preparation  
The datasets used in this study were created using the data of 66 patients (62 patients with eye diseases and 4 normal patients) with an image size of 768 \(\times\) 768 pixels using Spectralis HRA equipment (Heidelberg Engineering, Heidelberg, Germany) between August 2011 and June 2019 at the Third People's Hospital of Changzhou (Jiangsu, China). The fundus structure images were acquired at the 785 nm wavelength with a probing power of 1.2 mW, and the FFA images were acquired at the 488 nm wavelength with a probing power of 1.4 mW. The field of views of these images included 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). The image capture operating procedure involved system parameters selection, head placement,alignment of the imaging system, eye fixation, focusing, and pressing the shutter. The three-phase FFA images were captured from the arterial phase (11 to 15 s), venous phase (16 to 20 s), and late phase (5 to 6 min). Thus, we created one image group, which contains fundus structure image and the corresponding three-phase FFA images, for each patient. In each group, three-phase FFA images were manually registered to fundus structure image through translation and rotation, respectively. Figure 2 shows an example of a manually registered image group that contains the fundus structure image and the corresponding three-phase FFA images.  
We used 51 image groups for the training set, 2 image groups for the validation set, and the remaining 13 image groups for the test set. Because the deep learning network requires a large dataset, we augmented the registered dataset with the data. The training set was generated by randomly cropping images with the image size of 512 \(\times\) 512 pixels from 51 image groups during the training process. The validation set was generated by randomly cropping 50 images with the image size of 512 \(\times\) 512 pixels from 2 image groups. The test  
Figure 1: Diagram of our SPFSAT algorithm.  
Figure 2: Example of the manually registered images group containing the fundus structure image and the corresponding three-phase FFA images.  
set was generated by randomly cropping 100 images with the image size of \(512\times 512\) pixels from 13 image groups, and final testing results with different fields of view are generated by randomly cropping from these images. Because the diseased areas appear in different locations in the FFA images and are not very large, the augmented dataset that is generated by random cropping is balanced--nearly half of the images contain diseased areas and nearly half do not.
# Discussion on the Design of Sprayed Eco-Protection for Near-Slope Roads Along Multi-Level Slopes
## 4 Planar Design of Near-Slope Roads
### Minimum Curve Radius Design  
Differences in excavation methods and ecological restoration techniques lead to variations in the required types of construction machinery. Table 1 provides a data sheet for common construction equipment. During the initial construction phase, multi-level near-slope roads are typically partially leveled to ensure vehicular accessibility and efficiency. In such cases, the road surface is often irregular concrete or gravel, with lateral friction coefficients exceeding 0.3 [18; 19]. The design lateral force coefficient (0.10-0.17) accounts for a small proportion of the ultimate lateral friction coefficient [20], ensuring high safety margins that effectively mitigate lateral sliding risks [21]. Additionally, given the low average operating speeds of common equipment listed in Table 1, the design adopts minimum curve radii with single-sided road crowns instead of superelevation.  
If berms or access ramps on the multi-level slope include curves, this method can universally apply to determine the minimum curve radius. The formula is as follows (The formulas are derived from AASHTO):  
\[R_{min}=\frac{v^{2}}{127(\mu+i)}. \tag{1}\]  
where \(R_{min}\) is minimum curve radius with a single-sided road crown (m); \(i\) is cross slope of the single-sided road crown, taken as 0.015-0.020; \(\mu\) is lateral force coefficient, taken as 0.10-0.17.  
The minimum curve radius for roads without superelevation but with a single-sided road crown is calculated using Equation (1), with results rounded to the nearest integer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equipment** & **Length (mm)** & **Width (mm)** & **Height (mm)** & \
\begin{tabular}{c} **Average Operating** \\ **Speed (Site) (km/h)** \\ \end{tabular} \\ \hline Excavator (Light Vehicle) &
\begin{tabular}{c} 9625–11,250 \\ (with extended arm) \\ \end{tabular} & 2990–3245 & 3100–3500 & 4–6 \\ Water Truck (Medium/Large Vehicle) & 7650–9880 & 2280–2500 & 2780–3210 & 8–10 \\ Earth Mover (Medium/Large Vehicle) & 7320–9680 & 2350–3240 & 2520–3140 & 8–10 \\ Loader (Light Vehicle) & 5860–6880 & 1960–2230 & 2570–2710 & 4–6 \\ Pickup Truck (Light Vehicle) & 4820–5370 & 1780–1918 & 1450–1880 & 10–12 \\ Soil Sprayer (Large Vehicle) & 9000–12,000 & 1500–2600 & 3800–4500 & 8–10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common Construction Equipment Data Sheet.  
and listed in Table 2. The designed minimum curve radii must comply with Table 4.1.3 in the <<Code for Design of Parking Garage Buildings>>.
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
## 2 Circuit Analysis in the Air
However, when the transmission system is working in an underwater environment, eddy current loss will be generated, which will cause changes in the various parameters of the transmission system and the conditions that enable the system to achieve the maximum energy transmission efficiency have changed.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
###### Abstract  
In this paper, we propose a novel multi-user access in wireless optical communication based on the quantum detection of the coherent state. In this case, the coherent states are used as the signal carrier and a technique of quantum detection is applied to distinguish between signals from different users. To accomplish this task, two main quantum measurement methods are introduced; one is minimum error discrimination (MED), and the other is unambiguous state discrimination (USD). The theoretical derivation implies that the two methods can both distinguish between the signals from different users efficiently when the average photon number is large enough. Typically, the numerical result shows that in the two-user case, the channel capacity will approach the theoretical maximum limit when the average photon number is greater than 2.5 for MED and 5 for USD in the absence of noise. The MED gains more channel capacity than the USD at the same average photon number. However, the USD wins the error-correction scene with its free-error capability. Furthermore, the detection error probability and channel capacity for the USD with the thermal noise are examined. The result shows that increasing the signal average photon number can continue the USD's advantage of error-free detection even if in the presence of thermal noise. In addition, compared with non-orthogonal multiple access (NOMA), the bit error rate (BER) against signal-to-noise rate (SNR) performance of USD has been improved.  
multi-access; wireless optical communication; multi-user detection; quantum measurement; quantum detection 202241044. [[https://doi.org/10.3390/e24081044](https://doi.org/10.3390/e24081044)]([https://doi.org/10.3390/e24081044](https://doi.org/10.3390/e24081044))