esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance when Using CCIS in FSO Applications
Figure 7: BER vs. FSO range for the proposed system using CCIS under different haze conditions. Figure 12 shows the eye diagrams that obtained from the simulation of our proposed model at an FSO range of 3.7 km under low haze, 2.5 km under moderate haze, and 1.5 km under heavy haze. The wider eye opening shows the good reception for the information signal at the receiver. Figure 10: BER vs. FSO range for the proposed system using CCIS under different fog conditions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Weather Condition** & **BER** & **Q-Factor** \\ \hline Low haze (3.7 km) & 2.08 \(\times\) 10\({}^{-7}\) & 4.99 \\ \hline Medium haze (2.5 km) & 1.55 \(\times\) 10\({}^{-6}\) & 4.60 \\ \hline Heavy haze (1.5 km) & 5.16 \(\times\)10\({}^{-6}\) & 4.36 \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of BERs and Q-factors under different weather conditions. Figure 9: Eye diagrams for the proposed model using CCIS under (**a**) low haze, (**b**) medium haze, and (**c**) heavy haze. Table 4 summarizes the log(BER) and Q-factor values for our suggested model under low haze at 1.6 km, medium fog at 1 km, and heavy fog at 0.8 km. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Weather Condition** & **BER** & **Q-Factor** \\ \hline Low haze (3.7 km) & 3.42 \(\times\)\(10^{-7}\) & 4.44 \\ \hline Medium haze (2.5 km) & 1.19 \(\times\)\(10^{-6}\) & 4.60 \\ \hline Heavy haze (1.5 km) & 1.89 \(\times\)\(10^{-6}\) & 4.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of BERs and Q-factors under different weather conditions. Figure 11: Q-factor vs. FSO range for the proposed system using CCIS under different fog conditions. Figure 12: Eye diagrams for the proposed model using CCIS under (**a**) low haze, (**b**) medium haze, and (**c**) heavy haze.
## Appendix B Graphical Representation of Experiments Results  
### Machine Learning Models  
This section will present the confusion matrix corresponding to the highest accuracy achieved by the ML models for each dataset. The confusion matrix provides valuable insights into the model's performances by highlighting where errors occur, specifically identifying false positives (FPs) and negatives (FNs). For instance, Figure A1 reveals that Extra Tree classifier incorrectly classified 57 phishing emails as safe_emails (missed phisheing emails, FNs) and misclassified 40 safe emails as phishing_emails (false alarms, FPs). The Extra Tree classifier achieves the highest accuracy and F1-Score among the traditional classifiers on the Enron dataset. It also maintains a strong balance between precision and recall, as shown in Figure A1. The SGD Classifier outperforms other traditional models with the highest accuracy and F1-Score in the SpamAssassin dataset. Its precision and recall are well-balanced, as shown in Figure A2. The SGD Classifier achieves the highest accuracy and a perfect precision score, indicating it correctly identified all emails that were marked as phishing in the Ling dataset, as shown in Figure A3. The logistic regression classifier provides the highest accuracy and F1-Score among the traditional classifiers, reflecting a strong balance between precision and recall in TREC_05 dataset, as presented in Figure A4. As shown in Figure A5, the SGD Classifier attains the highest accuracy and F1-Score among the ML seven models in TREC_06 dataset. The SGD Classifier attains the highest accuracy and F1-Score among the ML seven models in TREC_07 dataset, as shown in Figure A6. The Extra Tree classifier excels with the highest accuracy and F1-Score among traditional models on the CEAS_08 dataset. The confusion matrix is shown in Figure A7. In Nazario_5 dataset, naive Bayes achieves the highest accuracy and a perfect recall, meaning it identified all phishing emails correctly, as shown in Figure A8. In Nigerian_5 dataset, the SGD Classifier attains the highest accuracy and F1-Score, making it the top performer among traditional classifiers, as presented in Figure A9. Among the traditional machine learning classifiers, the Extra Tree Classifier demonstrates the best performance on the merged dataset as illustrated in Figure A10. Among the traditional machine learning classifiers, the Extra Tree Classifier shows the best performance on the balanced merged dataset, as presented in Figure A11.  
Figure A6: Confusion Matrix of SGD Classifier in TREC_07 Dataset.  
Figure A7: Confusion Matrix of Extra Tree Classifier in CEAS_08 Dataset.
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 4 The Proposed Intelligent Control Method
### The Structure of the Control Method
#### 4.1.3 Temperature Coefficient and Its Loss Function  
The temperature coefficient \(\alpha\) controls the relative importance between the instantaneous reward and strategy entropy in each time step and affects the randomness of the optimal strategy. \(\alpha\) is a learnable parameter, so only an initial value needs to be provided at the beginning of training, and then the value of \(\alpha\) can be dynamically adjusted during the course of the algorithm. It can be adjusted using the objective function in Equation (8).  
\[J(\alpha)=E_{(s_{t},g_{t})\sim\mathcal{D},\mu_{t}\sim\pi}[-\alpha\log\pi(a_{t }|h_{t},s_{t},g_{t})-\alpha\mathcal{H}_{0}] \tag{8}\]  
where \(\mathcal{H}_{0}\) represents the target value of entropy, and is set to the negative of the duty cycle dimension in this paper. The temperature coefficient is updated by iteration, similar to that for the value network and the actor network. In each network-weight-updating process, the value network, the actor network, and the temperature coefficient are trained sequentially, and all the used samples are drawn from the replay buffer. When the entropy of the current strategy is lower than the target value, the objective function will increase the value of \(\alpha\). Otherwise, the value of \(\alpha\) will be reduced. In this way, the relative importance of policy entropy in the objective function of the policy network is adjusted to control the direction of policy improvement.  
The pseudocode of the proposed method is summarized in Algorithm 1.  
Figure 5: The structure of the strategy network.  
```
0: Parameters of value network \(\theta_{1}\) and \(\theta_{2}\), parameter of policy network \(\varphi\), and experience pool \(\mathcal{D}\).
1: for each iteration do
2: for each environment step do
3: obtain the duty cycle \(a_{t}\) according to the state \(s_{t}\), the current target \(g_{t}\) and the sampling strategy \(\pi_{\varphi}\);
4: execute duty cycle \(a_{t}\) and then obtain the next state \(s_{t+1}\), the next target \(g_{t+1}\) and the reward \(r_{t}\);
5: store sample (\(s_{t}\),\(a_{t}\),\(r_{t}\),\(s_{t+1}\),\(g_{t}\)) into experience pool \(\mathcal{D}\);
6: for each gradient step do
7: evenly extract a batch size sample from \(\mathcal{D}\) for training;
8: update value network parameters \(\theta_{1}\) and \(\theta_{2}\);
9: update policy network parameter \(\varphi\);
10: update temperature coefficient \(\alpha\);
11: end for
12: end for
13: end for
```  
**Algorithm 1** The proposed intelligent control method
## 2 Materials and Methods
### Data Source Selection  
Hourly concentrations of particulate matter (PM\({}_{2.5}\) and PM\({}_{10}\)), sulfur dioxide (SO\({}_{2}\)), nitrogen dioxide (NO\({}_{2}\)), ozone (O\({}_{3}\)), and carbon monoxide (CO) in Chenzhou City were assiduously collected from 1 March 2022 to 1 March 2023. This invaluable dataset was procured from the prestigious China Environmental Monitoring Station and is accessible at [[http://www.cnemc.cn](http://www.cnemc.cn)]([http://www.cnemc.cn](http://www.cnemc.cn)) (accessed on 16 October 2023). Meteorological data, crucial for the backward trajectory model, PSCF, and CWT analyses, were obtained from both the China Meteorological Data Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 16 October 2023)) and the Global Data Assimilation System (GDAS) archives, courteously provided by the U.S. National Centers for Environmental Prediction (NCEP) ([[ftp://ftp.arl.noaa.gov/pub/archives/gdas1](ftp://ftp.arl.noaa.gov/pub/archives/gdas1)]([ftp://ftp.arl.noaa.gov/pub/archives/gdas1](ftp://ftp.arl.noaa.gov/pub/archives/gdas1)) (accessed on 16 October 2023)). The geographical expanse of the study was meticulously delineated into grids, characterized by a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). For the purposes of this investigation, the seasonal categorization is articulated as spring, extending from March to May; summer, spanning June to August; autumn, embracing September to November; and winter, from December to February of the subsequent annum.  
The delineation of the study period from March 2022 to March 2023 is grounded in several pivotal considerations. Firstly, this timeframe adeptly captures the seasonal nuances of climatic variables influencing PM\({}_{2.5}\) concentrations. Secondly, the stringent implementation of specific atmospheric environmental policies during this epoch is expected to exert a discernible impact on air pollutant emissions and transmission pathways, thereby shedding light on the environmental ramifications following policy recalibrations. Lastly, the robustness and fidelity of the data throughout this interval instill confidence in the reliability and precision that underlie the study. As a result, the choice of this timeframe not only facilitates the exploration of seasonal and policy-induced effects but also upholds the integrity and coherence of the data throughout the research.
## 3 Results and Discussion
### Practical applications of our results  
Although the range of environmental conditions during the trials was a subset of the broader range that occurs in the Mojave Desert, conditions were representative of those during which tortoises are active in the spring and fall ([2], [3]). It is not currently possible to extrapolate our detection distance results to tortoises in burrows or to small tortoises less than 100 mm MCL since we have no data representing either of these categories, although based on our observations from [38], scent transport and detectability of small tortoises on the surface should be consistent with our findings here. The movement of vapor from tortoise burrows into open air is unstudied and to assume that scent would be transported as readily or in the same manner as from surface tortoises is simply too great a leap to make. However, we have observed dogs perform alerts at the mouth of occupied burrows ([3], [20]) that suggests burrow screening by dogs is another valid and useful application. Finally, the method we developed was successful for determining detection distances and could be applied for other wildlife, wildlife sign or other targets.
# Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences
## 2 Proposed Method
### 3D Human Pose Estimation Based on a Single Frame Image
#### 2.1.1 SMPL Model  
This SMPL model contains 6890 vertices (\(N\)) and 23 key points (\(K\)). The initial state of this model contains the human-body average template: \(\overline{T}\in\mathbb{R}^{3N}\) (T-pose initial pose) and the blend weight: \(\mathcal{W}\in\mathbb{R}^{N\times K}\) (Figure 2a). The human-body shape blend deformation function of this model: \(B_{S}(\vec{\beta}):\mathbb{R}^{|\vec{\beta}|}\mapsto R^{3N}\), and human-body key-point prediction function: \(J(\vec{\beta}):\mathbb{R}^{|\vec{\beta}|}\mapsto\mathbb{R}^{3K}\), their inputs are human-body shape coefficients: \(\vec{\beta}\in\mathbb{R}^{|\vec{\beta}|}\) (Figure 2b). Human pose blend deformation function of this model: \(B_{P}(\vec{\theta}):\mathbb{R}^{|\vec{\theta}|}\mapsto\mathbb{R}^{3K}\), the input is the axial angle of the human-body pose parameters: \(\vec{\theta}\in\mathbb{R}^{3K}\) (Figure 2c). Finally, the standard linear blend skinned function: \(W\) (\(\cdot\)) is used to drive the model deformation.  
In summary, the SMPL model is defined as: \(M(\vec{\beta},\vec{\theta},\Phi):\mathbb{R}^{|\vec{\beta}|\times|\vec{\theta }|}\mapsto\mathbb{R}^{3N}\), where \(\Phi=[\overline{T},\mathcal{W},\mathcal{S},\mathcal{J},\mathcal{P}]\) is the parameter obtained by solving the SMPL model, including: human-body average model, blend weight, orthogonal basis of human-body shape principal components: \(\mathcal{S}=\left[\mathcal{S}_{1},\mathcal{S}_{2},\dots,\mathcal{S}_{|\vec{ \beta}|}\right]\in\mathbb{R}^{3N\times|\vec{\beta}|}\), human-body key-point regression matrix: \(\mathcal{J}\in\mathbb{R}^{3K\times 3N}\), and a vcpose blend deformation vector: \(\mathcal{P}=[\mathbf{P}_{1},\mathbf{P}_{2},\dots,\mathbf{P}_{9K}]\in\mathbb{R}^{3N\times 9K}\). Through the method of grid alignment [33], the multi-pose dataset [11] and multi-shape dataset [34] obtained from 3D scanning are aligned with the grid of SMPL model, then the aligned dataset is used to train and solve the model parameters: \(\Phi\).  
Figure 1: The pipeline of the proposed 3D human pose recovery approach.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 3 The Proposed Method
### Spatial Feature Extraction  
Surveillance video contains a series of frames. To detect persons in each frame and avoid the influence of surveillance background, Yolo network [39] is employed to determine the bounding boxes of persons. Compared with Fast RCNN [40], the Yolo network can provide a faster detection speed, which is more suitable for surveillance videos. Let \(V=\{v_{1},v_{2},\cdots,v_{N}\}\) be a collection of \(N\) frames in the video, and the \(i\)-th frame is represented as \(v_{i}\). After the frame \(v_{i}\) is processed by the Yolo network, the bounding box is detected and represented as \(r_{i,j}=\{x_{i,j},y_{i,j},l_{i,j},w_{i,j}\}\), where \(r_{i,j}\) is a bounding box of the \(j\)-th person in the frame, \(v_{i}\), \(x_{i,j}\), and \(y_{i,j}\) are the central locations of the bounding box \(r_{i,j}\), and \(l_{i,j}\) and \(w_{i,j}\) are the height and width of the bounding box \(r_{i,j}\).  
Based on the bounding boxes of persons in each frame, Resnet-50 model [41] is used to extract the corresponding spatial features of persons. Each bounding box is passed through the Resnet-50 model by using conv4_4 to conv5_3, and then sent into a global average pooling layer to generate a 2048-dimensional feature vector. After that, this high-dimensional feature vector is projected into an L2-normalized 256-dimensional subspace. Finally, each bounding box in one frame of the video can be represented by a 256-dimensional spatial feature.  
Figure 1: The framework of the proposed person search method.  
For the bounding box \(r_{i,j}\), the Resnet-50 model can be regarded as a non-linear function \(f_{RES}(\cdot)\). After the bounding box \(r_{i,j}\) is processed by the Resnet-50 model, the spatial feature \(s_{i,j}\) can be represented as:  
\[s_{i,j}=f_{RES}(v_{i}) \tag{1}\]  
where \(s_{i,j}\) is a 256-dimensional spatial feature of the \(j\)-th person in the frame \(v_{i}\). All the spatial features of persons can be represented as \(S=\{s_{i,j}\}(1\leq i\leq n_{j},1\leq j\leq M)\), in which \(n_{j}\) is the number of frames containing the \(j\)-th person, and \(M\) is the total number of persons in the video.
## 3 Results
### Checking of Dataset Missing Rate  
For 2764 original images, the SMR of each subregion was calculated first, and a total of 795 images were selected in which the SMRs of all subregions were less than 95%. Subsequently, only 139 images representing the lowest SMR for each distinct date were selected to maintain a relatively consistent time interval for further analysis. Then, a TMR check was conducted on all marine pixels, and 15,908 invalid points with a TMR over 95% were identified and marked as missing values (NaN) in all 139 images. Subsequently, the SMR for each subregion was rechecked to ensure that the SMR of each sub-dataset in the 139 images remained within acceptable limits.  
The refined dataset, extracted from the original dataset passing all of the checks, included 139 images and 284,456 marine pixels, with the OMR reduced from 79.79% to  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subregion** & **Chl-a (mg/m3)** & **Pixels** & **OMR (\%)** & **Subregion** & **Chl-a (mg/m3)** & **Pixels** & **OMR (\%)** \\ \hline
1 & 0–1.3 & 5472 & 75.50 & 6 & 1.9–2.0 & 20,527 & 77.62 \\
2 & 1.3–1.5 & 10,353 & 75.76 & 7 & 2.0–2.1 & 19,932 & 78.64 \\
3 & 1.5–1.7 & 90,095 & 75.07 & 8 & 2.1–2.2 & 19,  
34.06% compared to the original dataset. The statistics of the dataset missing rate are presented in Figure 5. As shown in Figure 5a, the SMR per image ranges from 6.96% to 89.42%, with the majority of SMRs lower than 50%. Figure 5b indicates that the distribution of pixels TMR is similar to the original dataset (Figure 2), showing a gradual decrease from the coastal water to the open sea. However, those critically missing points have been excluded and most pixels now have TMRs below 40%, with some even lower than 20%. Figure 5c further verifies the fact that missing data in coastal areas are more severe than those in open sea areas, but the OMRs of each sub-dataset significantly decrease. In summary, compared to the original dataset, the missing data in the refined dataset were effectively restricted in both spatial and temporal dimensions.  
Figure 5: Statistics of data missing from 2019 chl-a refined dataset of Bohai Sea region. (**a**) SMR of each chl-a image in the refined dataset. (**b**) Spatial distribution of TMR for each pixel point in the refined dataset. (**c**) Comparison of OMR for each sub-dataset before and after missing-data checks.  
Notably, the number of images was reduced to 139, representing distinct dates. This reduction was considered acceptable when compared to the valid image numbers for each hour in the original dataset, which ranged from 110 to 184, as shown in Table 2.
## 3 Formalization of Dima Optimization
### Notation  
We introduce notations \(\mathcal{F}\), \(\mathcal{D}\) and \(\mathcal{L}\) to represent functions, devices and locations in the DIMA system as follows:  
* Functions are denoted as \(\mathcal{F}=\{F_{1},\ldots,F_{n}\}\), where \(n\) is the number of functions;
* Communication between functions are defined by virtual links \(\mathcal{V}\);
* Devices are denoted as \(\mathcal{D}=\{D_{1},\ldots,D_{m}\}\), where \(m\) is the number of devices;
* Installation locations are denoted as \(\mathcal{L}=\{L_{1},\ldots,L_{l}\}\), where \(l\) is the number of locations.  
In addition, some sets of binary indicator variables are introduced to represent the utilization of devices, cables and locations as well as the mapping of functions and devices as follows:  
* \(\mathbb{K}_{\mathcal{D}}\) indicates the usage of device \(D\in\mathcal{D}\), where \(\mathbb{K}_{\mathcal{D}}=1\) represents the device is used, and \(\mathbb{K}_{\mathcal{D}}=0\) otherwise;
* \(\mathbb{K}_{\mathcal{F},\mathcal{D}}\) describes function mapping, where \(\mathbb{K}_{\mathcal{F},\mathcal{D}}(F,D)=1\) represents if function \(F\in\mathcal{F}\) is allocated to device \(D\in\mathcal{D}\) and \(\mathbb{K}_{\mathcal{F},\mathcal{D}}(F,D)=0\) otherwise;
* \(\mathbb{K}_{\mathcal{D},\mathcal{D}}\) indicates the physical cables between devices, where \(\mathbb{K}_{\mathcal{D},\mathcal{D}}(D,D^{\prime})=1\) if \(D\) and \(D^{\prime}\) are connected and \(\mathbb{K}_{\mathcal{D},\mathcal{D}}(D,D^{\prime})=0\) otherwise;
* \(\mathbf{V}=\{V_{ij}^{s}\}\) denotes whether \(D_{i}\) and \(D_{j}\) are connected with respect to the virtual link \(\mathcal{V}(F_{\mathrm{s}})\) of function \(F_{\mathrm{s}}\).
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 5 Simulation Results and Discussion
### Training and Testing of the Model  
The color image dataset generated by Model 1 was divided into a new training set and a validation set at a ratio of 4:1. There were 2016 sets of images in the training set and 504 sets of images in the validation set. Subsequently, the MCECA-CloFormer model was constructed, with training samples from the training set being fed into the model for training. The training process was deemed complete when the accuracy rate stabilized. At this point, the optimal hyperparameters and weights of the model were saved. Employing the pretrained model, tests were conducted on the test set. Experimental validation confirmed that the line selection neural network based on MCECA-CloFormer achieved an accuracy rate of 100% in both the resonant grounding distribution networks and the ungrounded neutral point distribution networks of Model 2. This led to the conclusion that the method proposed in this paper was insensitive to variations in operating conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline faulty line & L\({}_{1}\), L\({}_{2}\), L\({}_{3}\), L\({}_{4}\) \\ fault location & 10\%, 20\%, 30\%, 40\%, 50\%, 60\%, 70\%, 80\%, 90\% \\ fault ground resistance & 0.1 \(\Omega\), 1 \(\Omega\), 10 \(\Omega\), 100 \(\Omega\), 1000 \(\Omega\), 3000 \(\Omega\), 5000 \(\Omega\), Emanuel \\ initial fault phase angle & 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\), 180\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario settings for simulation training set.  
Figure 8: Emanuel model.  
The testing results on the NES and NUS datasets were visualized using t-SNE, with the outcomes presented in Figure 9.  
In Figure 9, four distinct colors correspond to the four feeders, demonstrating that the MCECA-CloFormer model possesses remarkable clustering capability.  
In order to verify the influence of inverter interfaced distributed generation (IIDG) on the algorithm proposed in this paper, the IIDG model was added to the distribution network model, which adopts PQ control during normal operation and employs a low voltage crossing and negative sequence suppression strategy during fault operation [26]. Because IIDG grid-connected transformer adopts star triangle connection, that is, IIDG is not in zero-sequence network, it has little influence on the method presented in this paper. In Figure 7, 50% of lines L\({}_{1}\) and L\({}_{2}\) of the resonant grounding system are connected to the IIDG with a capacity of 2 MW. The fault points were set at 25% and 75% of each line, respectively. After testing, the accuracy rate was 100%.  
In order to verify the influence of fault phase angle, fault resistance, and fault location on the algorithm, the test scenario shown in Table 6 was set up. After testing, the accuracy rate was 100%.
## Appendix B  
Table 1 summarizes the results of the power-shift method without noise or a linear ramp added to the injected signal. This table provides a way to access the known attractor in LIS solely power shifting.  
We discuss the results of the study of the power- shift method without noise by starting with the two simplest goal attractors, AI and AII. There is no coexisting attractor for AI and only one attractor (AIV) with a narrow domain of existence coexists with AII for the region \(1.275<Y<1.6\). One can select any base attractor (AI-AVIII) using any \(Y_{0}\) and TAP, then power shift at \(Y_{PS}\leq 1\) to access AI and the power shift at \(Y_{PS}=1.54\) to access AII.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Goal Attractor** & **Base Attractor** & **Base Period (\(\tau\))** & \(Y_{\theta}\) & \(Y_{PS}\) & **TAP \(\pm\) Leeway** \\ \hline AI & Any & Any & & \\ AII & Any & & Any & 1.54 & Any \\ AIII & AII & 108.025 & 1.51 \({}^{*}\) & 1.8 & \(8\pm 52\) \\ AIV & AI & 63.132 & 0.854 & 1.114 & \(3\pm 4\) \\ AV & AIII & 163.551 & 1.63 \({}^{*}\) & 1.84 & \(151\pm 50\) \\ AVI & AIII & 158.396 & 1.72 \({}^{*}\) & 1.96 & \(154\pm 27\) \\ \hline AVII & AIII & 156.964 & 1.8 & 1.269 & \(138\pm 2\) \\ \hline \end{tabular} \({}^{*}\)\(Y_{0}\) optimized using the method performed in Figure 7.  
\end{table}
Table 1: LIS Attractor Access Via a Power Shift \(Y_{PS}\).