esa-sceva/satcom-chunk-collection huggingface dataset:
## 3. Ising Experiment Device and Three-Dimensional Scanning of Blade  
### 3.1. Blade Icing Test  
The blade used in this test was produced by a company in Jiangsu Province, China. It is made of glass fiber composite. Its length is 1170 mm. Its maximum chord length is 170 mm and its weight is 2.14 kg. Two different types of icing were used for the icing test, namely the horizontal and vertical icing of the blade. The horizontal icing of the blade is divided into windward and leeward icing, which are divided into four different icing mass cases. The vertical icing is divided into seven different angle of attack icing cases, as shown in Figure 2.  
The icing tests were carried out at Inner Mongolia Agricultural University in the Inner Mongolia Autonomous Region, where the average outdoor temperature was in the range of \(-12\)\({}^{\circ}\)C to \(-19\)\({}^{\circ}\)C. The test instrument parameters are shown in Table 2.  
To quantitatively study the icing characteristics of the blade under different icing conditions, the blade is placed horizontally in the blade windward and leeward icing test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Density/(kg\(\cdot\)m\({}^{-3}\))** & **Color** & **Adhesion** \\ \hline Glaze ice & 900 & Transparent & Strong \\ Heavy rime ice & 600–900 & Nontransparent & Strong \\ Mild rime ice & 200–600 & White & Medium \\ Mixed ice & 600–800 & Strong & Semi-transparent \\ \hline \hline \end{tabular}
\end{table}
Table 1. Main physical characteristics of various icing types.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instrument** & **Parameters** & **Value** \\ \hline Temperature and relative humidity detector & Temperature range & \(-40\)–\(80\)\({}^{\circ}\)C \\ \hline \multirow{2}{*}{Electric nebulizer Precision electronic platform scale} & Temperature accuracy & \(\pm 0.2\)\({}^{\circ}\)C \\ \cline{2-3}  & Humidity range & 0–100\% RH \\ \cline{2-3}  & Humidity accuracy & \(\pm 2\)\% RH \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blade icing instrument parameters.  
Figure 2: Blade icing test program.  
The blade windward side and leeward side are facing upwards to be covered in ice. In the vertical icing test, the blade is placed vertically to be covered in ice under different angles of attack. An eight-hole high-pressure electric sprayer is placed in front of the blade to spray water mist. The diameter of the atomized particles is up to 0.3 \(\upmu\)m. When the water mist from above is falling freely, the blade windward side, leeward side, and different angle of attack positions will be gradually covered by ice as is shown in Figure 3.  
As shown in Figure 4, the density of ice in an icing environment needs to be measured to determine the type of ice in a trial. Each set of tests was repeated 3 times each, with 5 in each group.  
According to the measurement, the average density of ice is 920.16 kg/m\({}^{3}\), and the ice type is glaze ice.
## 1 Introduction
Identifying the correlations between standing balance performance and dynamic balance control during gait would be helpful for evaluating the risk of imbalance during locomotion from standing balance evaluations alone, which are easier to obtain in a clinical setting. Patients with degenerative lumbar disease present poor postural control ability and tend to rely on visual feedback and a wide-base standing posture [12]. Previous studies also showed that postural sway was increased in patients with spondylosis and other degenerative lumbar diseases [12; 35; 36]. [PERSON] et al. [13] found that the root-mean-squared distance of the COP in anteroposterior and mediolateral directions was decreased in patients with LS after lumbar surgery. These findings may be indicative of impairment of neuromuscular feedback loops at a different level of motor activation within the central nervous system [37], which could be evaluated through static balance using force plates. However, it remains unclear how LS may affect the dynamic balance control in terms of the body's motion relative to the center of pressure during level walking, and what the relationship is between dynamic balance control during gait and static standing balance in such patients. The purpose of this study was to quantify the dynamic balance control in patients with LS in terms of COM-COP IA and RCIA in comparison with healthy controls and to identify the correlations of IA and RCIA between dynamic gait balance and static standing balance in LS. It was hypothesized that, compared to the controls, patients with LS would show compromised balance control with increased IA and RCIA during gait and increased COP total distance and sway area during quiet standing, and that the IA and RCIA during walking would have a significant correlation with COP total distance and COP sway area during quiet standing in patients with LS.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 5 Final Construction: TSKT-ORAM
### System Initialization  
To initialize the system, the client acts as follows. It first encrypts each real data block \(d_{i}\) to \(D_{i}\), as in TSBT-ORAM, and then randomly assigns it to a leaf k-node on the \(k\)-ary tree maintained at each server. The rest of the DA spaces on the tree shall all be filled with dummy blocks.  
For each k-node, its EI entries are initialized to record the information of blocks stored in the node. Specifically, the entry for a real data block shall record the block ID to the \(ID\) field, the ID of the assigned leaf k-node to the \(IID\) field and the ID of an arbitrary leaf b-node within the k-node to the \(bn1D\) field. In an entry for a dummy data block, the block ID is marked as \"\(-1\)  
Figure 3: An example TSKT-ORAM scheme with a quaternary-tree storage structure. Each k-node in the physical view (**a**) corresponds to a two-tier binary subtree shown in the logical view (**b**). Bold boxes represent the k-nodes accessed when a client queries a target data block stored at k-node \(u_{3,21}\); that is, nodes \(u_{0,0}\), \(u_{1,1}\), \(u_{2,5}\) and \(u_{3,21}\) need to be accessed. (**a**) Quaternary tree: physical view of the server storage; (**b**) binary tree: logical view of the server storage.  
while the \(1 ID\) and \(bn1D\) fields are filled with arbitrary values. The \(ts\) field of the EI shall be initialized to zero.  
For the client-side storage, the index table \(\mathcal{I}\) is initialized to record the mapping from real data blocks to leaf k-nodes, and the keys for data and index table encryption are also recorded to a permanent storage space. Finally, the client initializes its counter \(\mathcal{C}\) to zero.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 4 Results
### Model Assessment
#### 4.2.2 Comparison of Different Attention Modules  
In this study, two attention modules, SE and SK, are integrated into the baseline model for comparative analysis. Subsequently, the most suitable attention module is selected to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **F1 (\%)** \\ \hline YOLOv8 & 69.2\% & 63.1\% & 62.8\% & 66.0\% \\ YOLOv8-CW1 & 76.7\% & 65.9\% & 68.2\% & 71.0\% \\ YOLOv8-CW2 & 80.1\% & 69.1\% & 73.8\% & 74.0\% \\ YOLOv8-CW3 & 79.0\% & 66.8\% & 70.3\% & 72.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of detection performance for different models.  
Figure 9: Landslide detection results ((**a**–**d**) is the mark location of old landslides; (**a**–**d**1) indicates the detection results of the original YOLOv8 model; (**a**–**d**2) indicates the detection results of the YOLOv8-CW1 model; (**a**–**d**3) indicates the detection results of the YOLOv8-CW2 model; (**a**–**d**4) indicates the detection results of the YOLOv8-CW3 model).  
construct the optimal model [34; 35]. Table 3 shows the results of the experiment using the same old landslide dataset.  
It is obvious from the table that the model with a CBAM attention mechanism has better performance. The accuracy and mAP of the YOLO-CW2 model are much higher than those with the SE and SK attention mechanism. In terms of recall rate, the model with the SE attention mechanism has better performance. In short, the YOLOv8-CW2 model is selected as the improved model in this experiment.
## 3 Results and Discussion
### Fast Fourier Transform (FFT) Analysis
The test results contained in [34] carried out for the AZ91D alloy allowed for the formulation of important conclusions: all values of the vibration displacement and acceleration increased with the cutting speed and feed per tooth (except \(v_{c}\) 1000 m/min--a decrease in the vibration displacement). For a heat shrink holder, several times lower values of the vibration acceleration and the lowest entropy value. For \(v_{c}\) 400 m/min, the lowest values of vibration displacement and amplitude (below 0.02 mm) were observed. However, an increase in \(f_{z}\) caused an increase in the value of vibration displacement and amplitudes. For the range 0.05-0.2 mm/tooth, the vibration displacement range and amplitudes were equal to approx. 0.015-0.025 mm. The maximum values of vibration acceleration were the lowest for \(v_{c}\) 400 m/min and for heat shrink chuck--4.9 m/s\({}^{2}\). The lowest a\({}_{y}\) vibration values were obtained for \(f_{z}\) 0.05 mm/tooth, and for heat shrink chuck--5.7 m/s\({}^{2}\). However, in the case of the AZ31B alloy [35], it was observed that the increase in \(v_{c}\) and \(f_{z}\) had a significant impact on the vibrations. A vibration increase with an increasing \(v_{c}\) (approx. linear) was observed; however, \(f_{z}\) caused the greatest increase in vibrations, especially visible and noticeable for vibration acceleration. A relatively low level of signal irregularity for CMSE was observed, and the value of vibration acceleration entropy decreased for higher values of the scale factor. During FFT analysis, a high stability of milling processes was observed; however, for the highest \(v_{c}\) and low \(f_{z}\), the process showed susceptibility to chatter vibration. However, the use of trochoidal paths [19] when milling with a tool with a shrink holder allowed us to notice that with the increase in cutting speed, the values of the analysed machinability indicators decreased. Better stability and milling effectiveness (lower values of vibrations) were obtained for high-speed machining (\(v_{c}\) 1000-1200 m/min). The character of vibrations depends on the milling parameters; moreover, stable milling conditions may be obtained by increasing the \(v_{c}\) and reducing the trochoidal step.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 4 Dynamic Service Placement Framework Based on Deep Reinforcement Learning
### Dynamic Service Placement Based on Deep Reinforcement Learning
```
0: Sets of edge nodes \(\mathbf{M}\), services \(\mathbf{V}\), and users \(\mathbf{U}\);
0: Dynamic service placement scheme \(\mathbf{X}\);
1: Randomly initialize the actor network \(\mu(s|\theta^{\mu})\) and critic network \(Q(s,a|\theta^{Q})\) with weight \(\theta^{\mu}\) and \(\theta^{Q}\);
2: Initialize the target networks with weights \(\theta^{\mu^{\prime}}\leftarrow\theta^{\mu}\) and \(\theta^{Q^{\prime}}\leftarrow\theta^{Q}\);
3: Initialize replay buffer \(B\);
4:for episode from \(1\) to \(\kappa\)do
5: Initialize environmental parameters for edge servers and users, and generate an initial state \(s_{1}\);
6:for each time slot \(t\) from \(1\) to \(T\)do
7: Select an action \(a_{t}=\mu(s_{t}|\theta^{\mu})+\delta_{t}\) to determine the destination of migration by running the current policy network \(\theta^{\mu}\) and exploration noise \(\delta_{t}\);
8: Detect migration conflicts and resolve via Algorithm 1;
9: Execute action \(a_{t}\) of each user agent independently, and observe reward \(r_{t}\) and new state \(s_{t+1}\) from the environment;
10: Store the transition tuple \((s_{t},a_{t},r_{t},s_{t+1})\) into replay buffer \(B\);
11: Randomly sample a mini-batch of \(\mathbf{I}\) transitions \(\{(s_{w},a_{w},r_{w},s^{{}^{\prime}}_{w})\}\) from replay buffer \(B\);
12: Update the critic network \(Q(s,a|\theta^{Q})\) by minimizing the loss function \(L\) in Equation (16);
13: Update the actor network \(\mu(s,a|\theta^{\mu})\) by using the sampled policy gradient \(\bigtriangledown_{\theta^{\prime}}J\) in Equation (17);
14: Update the target networks: \(\theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}\), \(\theta^{Q^{\prime}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\);
15:endfor
16:endfor
```  
**Algorithm 2** Dynamic service placement based on DRL
###### Abstract  
In order to prevent joints from being damaged by impact force in a space robot capturing satellite, a spring-damper device (SDD) is added between the joint motor and manipulator. The device can not only absorb and attrition impact energy, but also limit impact force to a safe range through reasonable design compliance control strategy. Firstly, the dynamic mode of the space robot and target satellite systems before capture are established by using a Lagrange function based on dissipation theory and Newton-Euler function, respectively. After that, the impact effect is analyzed and the hybrid system dynamic equation is obtained by combining [PERSON]'s third law, momentum conservation, and a kinematic geometric relationship. To realize the buffer compliance stability control of the hybrid system, a reinforcement learning (RL) control strategy based on a fuzzy wavelet network is proposed. The controller consists of a performance measurement unit (PMU), an associative search network (ASN), and an adaptive critic network (ACN). Finally, the stability of system is proved by Lyapunov theorem, and both the impact resistance of SDD and the effectiveness of buffer compliance control strategy are verified by numerical simulation.  
space robot; spring-damper device; buffer compliance control; fuzzy wavelet network; reinforcement learning Article  
A Fuzzy Logic Reinforcement Learning Control with Spring-Damper Device for Space Robot Capturing Satellite. April 2022122662. [[https://doi.org/10.3390/app12052662](https://doi.org/10.3390/app12052662)]([https://doi.org/10.3390/app12052662](https://doi.org/10.3390/app12052662))
# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 4 Experimental Results and Performance Analysis
### Evaluation Metrics of Vehicle Detection  
To quantitatively analyze the performance of the proposed vehicle detection model, this paper adopts the same evaluation metrics as in the literature [20], which mainly include the following three metrics:  
Average Precision (\(AP\)). Precision is the proportion of true positive samples predictions among all positive samples predictions, while Recall is the proportion of true positive samples predictions among all actual positive instances. The calculation equation is shown in Equations (6) and (7), respectively:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{6}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{7}\]  
where \(TP\) represents true positive samples, \(FP\) represents false positive samples, and \(FN\) represents false negative samples. \(AP\) is the area under the Precision-Recall curve, and the higher the \(AP\) value, the better the model's accuracy and performance.  
Log Average Miss Rate (\(MR^{-2}\)) [41]. The vehicle detection index is measured by simultaneously calculating the Miss Rate (\(MR\)) and the number of False Position Per Image (\(FPPI\)). The equation for \(MR\) is shown in Equation (8):  
\[MR=1-TP/N \tag{8}\]  
where \(TP\) represents true positive samples. By plotting the \(MR\)-\(FPPI\) curve, the \(MR^{-2}\) is the average value of \(MR\) calculated for nine \(FPPI\) values. The logarithmic intervals of the nine points are averaged samplings within the range of [10\({}^{-2}\), 100]. \(MR^{-2}\) represents the Miss Rate of the vehicle detector at a specified false positive rate, and the lower the value, the better the detection performance.  
Jaccard Index (\(JI\)). The equation for \(JI\) is shown in Equation (9):  
\[JI(D,G)=\frac{|IOUMatch(D,G)|}{|D|+|G|-|IOUMatch(D,G)|} \tag{9}\]  
where \(D\) is a set of detection boxes, and \(G\) is a set of ground-truth boxes. \(JI\) is more suitable for detection tasks in dense scenes. \(JI\) represents the degree of overlap between the predicted box and the ground-truth box, and the higher the value, the better the detection performance.
## 2 Materials and Methods
### Health Risk Quotient (HQ) Method  
The main methods used to assess health risk included the health quotient (HQ), health risk index (HRI), and lifetime cancer risk index (ILTR). These risk assessment methods can only be used to evaluate the health risk of a single metal(loid). In this study, a target hazard quotient (THQ) was used to evaluate the health risks of multiple paths, such as soil, wheat, and corn. The THQ method is a USEPA human health risk assessment method for external substances intake [44; 45]. When the THQ value is less than 1, there is no significant health risk in the exposed population. However, a THQ value over 1 indicates a health risk. The HQ for a single metal(loid) is calculated as follows:  
\[\mathrm{HQ}=\frac{\mathrm{EF\times ED\times IR\times C}}{\mathrm{RfD\times BW \times AT}}, \tag{2}\]where EF is the exposure frequency with a value of 365 d/a; ED is the exposure range with a value of 70 a; IR is the intake rate (kg person/d); C represents the concentration of the soil and crop (mg/kg), and RfD is a reference control (Cd, Cu, Ni, Pb, Cr, Zn, As, and Hg are 0.001, 0.04, 0.02, 0.004, 1.5, 0.03, 0.0003, and 0.0003 mg/kg/d, respectively) [44; 45; 46; 47], BW is the average body weight (61.6 kg for adults and 18.7 kg for children 0-6 years old) [48]; and AT is the average exposure time for non-carcinogenic effects (ED \(\times\) 365 d\({}^{-1}\).y\({}^{-1}\)).  
The THQ formula is calculated as follows:  
\[\text{THQ}=\sum_{i=0}^{n}\text{THQ}=\text{THQ}_{\text{Cu}}+\text{THQ}_{\text{Zn }}+\text{THQ}_{\text{Pb}}+\text{THQ}_{\text{Cd}}+\text{THQ}_{\text{Cr}}+\text {THQ}_{\text{As}}+\text{THQ}_{\text{Ni}}+\text{THQ}_{\text{Hg}}. \tag{3}\]  
The total target hazard quotient (TTHQ) is calculated via three pathways. Typically, potential exposure routes for humans to metal(loid)s include the consumption of food and soil. TTHQ is equal to the sum of THQ for each exposure route, as described below:  
\[\text{TTHQ}=\sum_{i=0}^{n}\text{TTHQ}=\text{THQ}_{\text{com}}+\text{THQ}_{ \text{wheat}}+\text{THQ}_{\text{soil}}, \tag{4}\]  
where THQcom, THQwheat, and THQsoil represent THQ for a specific metal(loid) encountered during the consumption of corn and wheat by humans, and the oral ingestion of soil by humans.
# Analysis of the Development Status of eLoran Time Service System in China
## 6 Signal System
### Eurofix Principles  
The Eurofix data link uses 3S-PPM (Tri-State Pulse Position Modulation); 3S-PPM modulates pulses 3-8 in each pulse group. The modulated signal consists of a 1 \(\upmu\)s pulse transmission phase shift relative to the unmodulated pulse. The three possible states of modulation are given in Table 4 and Figure 8 [18].  
In the 3S-PPM pulse group, the numbers of advanced and delayed pulses in a channel are equal. The modulation of six pulses in a pulse group will produce 141 possible modulation modes, of which 128 modes represent valid data. The 3S-PPM mode is shown in Figure 9. Each of the 128 effective modulation modes uniquely represents a seven-bit binary data block.  
The frame length of Eurofix message design is 210 bit, and a frame requires 30 GRIs to complete data modulation. The information body occupies 56 bit, the CRC code occupies 14 bit, and the RS code occupies 140 bit.
## 3 Results
### Model Testing of Complex Scenarios
In addition, a comparison was conducted between the results of ROI binarization hyperbola region extraction for network structures with and without skip connections, as shown in Figure 12. In Figure 12, extracting a complete hyperbola morphology proves challenging when using a structure without skip connections. However, in Figure 11d, a clearer and more complete hyperbolic reflection feature can be observed. Nonetheless, it is noteworthy that the characterization in the second and third layers of the pipes appears clearer than that in the first layer. This difference may arise from the smaller spacing between the pipelines, leading to the three pipeline hyperbolas in the first layer capturing the rough reflection region without proper separation. Therefore, the convolutional neural network with a skip connection structure can enhance the transmission of information in the deep network structure and obtain better results.
## 2 Delay-Free Phase Noise QRNG Scheme
### Principle of Scheme
\[V_{mix} = [V_{D1}]_{AC}[V_{D2}]_{AC}\] \[\propto -4T(1-T)E_{1}^{2}E_{2}^{2}\cos\left[(\omega_{1}-\omega_{2})(t+t_{1}) +\varphi_{1}(t+t_{1})-\varphi_{2}(t+t_{1})\right]\] \[\cos\left[(\omega_{1}-\omega_{2})(t+t_{2})+\varphi_{1}(t+t_{2})- \varphi_{2}(t+t_{2})\right]\] \[= -4T(1-T)E_{1}^{2}E_{2}^{2}\cos\left[\Delta\omega(2t+t_{1}+t2)+ \Delta\varphi(t+t_{1})+\Delta\varphi(t+t_{2})\right]\] \[-4T(1-T)E_{1}^{2}E_{2}^{2}\cos\left[\Delta\omega(t_{1}-t_{2})+ \Delta\varphi(t+t_{1})-\Delta\varphi(t+t_{2})\right]\]  
where \(\Delta\omega=\omega_{1}-\omega_{2}\), \(\Delta\varphi(t)=\varphi_{1}(t)-\varphi_{2}(t)\). It is clear that from (10), the final signal includes several frequency components, in which three of them are dominating. Two of them are from the phase term \(\Delta\varphi(t)\), where \(\Delta\varphi^{\text{fiber}}\) is due to the fiber jitter, and \(\Delta\varphi^{\text{phase}}\) comes from phase noise of laser diode, from which we expect to extract randomness. Another part of frequency term \(2\Delta\omega t\) is due to the difference of center wavelength of two laser diodes. In fact, there exist a crucial relationship between these three frequency components in our scheme:  
\[f(2\Delta\omega)>>f(\Delta\varphi^{\text{phase}})>>f(\Delta\varphi^{\text{fiber}}) \tag{11}\]  
and all the implementation settings in our experimental setup is based on (11).
## 1 Introduction
### Organization and Notations  
Organization: The rest of the paper is organized as follows. The system model of the proposed RIS-CF network and corresponding weighted sum-SINR maximization problem formulation are discussed in Section 2. A two-layer iterative algorithm is proposed in Section 3 to solve the nonconvex problem. Section 4 provides numerical results to validate the proposed schemes. Finally, Section 5 summarizes the conclusions.  
Notations: Bold symbols in capital letters and small letters denote matrices and vectors, respectively. The conjugate transpose of a matrix is denoted as \((\cdot)^{H}\). \(\mathbb{C}^{n}\) denotes the set of complex vectors of length \(n\). For a complex number \(s\), \(|s|\) and \(\angle s\) denote its amplitude and phase, respectively, and \(s\sim\mathcal{CN}(\mu,\sigma^{2})\) means that it is a circularly symmetric complex Gaussian (CSCG) random variable with mean \(\mu\) and variance \(\sigma^{2}\). For a vector \(\mathbf{a}\in\mathbb{C}^{n}\), \(\text{diag}(\mathbf{a})\) denotes an \(n\times n\) diagonal matrix whose entries are the elements of \(\mathbf{a}\). \(\mathbb{E}[\cdot]\) denotes the expected value of random variables. \(\mathbf{I}_{n}\) denotes an \(n\times n\) identity matrix. \(j\) denotes the imaginary unit, i.e., \(j^{2}=-1\). \(\mathcal{O}(\cdot)\) denotes the Landau's symbol to describe the order of convergence as well as complexity. In addition, the major variable definitions in Section 2 are summarized in Table 2.