esa-sceva/satcom-chunk-collection huggingface dataset:
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 4 Numerical Results
### Exclusion Zone
#### 4.1.2 The Exclusion Zone When Using Beamforming Antenna Array  
The omnidirectional antenna provides a maximum gain in all directions. Herein, we consider a massive MIMO antenna array with beamforming. For the coexistence study, the worst interference case is considered for both the horizontal and vertical domains.  
* Azimuth plane: By electronic steering, a planar URA can generate the main lobe beam, which can sweep in all horizontal directions. For each BS, the interference from every beam direction (\(360^{\circ}\)) is checked, and the maximum interference is considered as the BS interference.
* Elevation plane: The beam angle changes with the user location, and the worst case is obtained for a user at the cell edge. Such a user has the smallest beam off-boresight angle (Figure 13). Having a larger number of elements in the antenna array enables a higher gain for the main-lobe beam. This increases the interference from the BS at the ES, thus resulting in a larger exclusion zone. Using the beamforming antenna array, the exclusion zone becomes larger, owing to the increased number of antenna elements. Figures 14-17 show the exclusion zones in the presence of the BS beamforming antenna array for the RMa, UMa, and UMi scenarios.  
Figure 11: UMa scenario with ES antenna elevation angle of \(5^{\circ}\).  
Figure 12: UMi scenario with ES antenna elevation angle of \(5^{\circ}\).  
Figure 14: RMa, UMa, and UMi scenario with an ES antenna elevation angle of \(5^{\circ}\) for a \(4\times 4\) beamforming antenna array.  
Figure 16: RMa, UMa, and UMi scenarios with ES antenna elevation angle of \(5^{\circ}\) for a \(16\times 16\) beamforming antenna array.  
Figure 13: The elevation plane.  
Figure 15: RMa, UMa, and UMi scenarios with ES antenna elevation angle of \(5^{\circ}\) for an \(8\times 8\) beamforming antenna array.  
Comparing the results in Figure 14 with those in Figure 17, it is observed that the exclusion zone is larger at the north side of the ES when the antenna array is used. This is because the maximum gain of the antenna array is higher than that of the omnidirectional antenna, i.e., max 20 dB of a 4 \(\times\) 4 array \(>\) 15 dB of the omnidirectional antenna. On the south side of the ES, however, the exclusion zone is much smaller in the case of the antenna array; this is attributed to the directional attenuation pattern of the antenna array. Comparing the results in Figures 14-16, It can be found that the exclusion zone becomes larger when the number of elements increases. Because more elements in antenna array leads to higher antenna array gain of main lobe beam, higher interference from BS to ES, and bigger exclusion zone.
## 3 Results
### Example of Horizontal Wind Speed Gradient on 25 August 2019  
Unlike the previous case on 6 April 2018, this case illustrated in Figure 6d,e shows an unexpected behaviour for the horizontal wind speed. On 25 August 2019 at 05:48 UTC, the observed 10 m SAR wind field (Figure 6d) indicates higher amplitudes near the land rather than far offshore, even though the wind direction comes from the land (wind arrows in Figure 6e). In other words, the wind speed decreases with distance away from the coast. The wind speed amplitude at the coast is about 6 m/s and drops below 4 m/s beyond 100 km offshore. The decrease of the horizontal wind speed observed in SAR is confirmed by the 10 m wind speed from DWD data (Figure 6e), which does not include any wind-farm parametrizations to take into account wakes generated behind the offshore wind farms. One has to mention that the analysis of the synoptic conditions reveals no precipitation and frontal structures that could potentially explain this unusual wind speed gradient. Higher values of stable stratification are found in the west, where the wind speed is also low (Figure 6f). The highest stability values are also found in this area of low wind speed. To understand the behaviour of the wind on 25 August 2019, we take a look at the vertical wind speed profiles measured by a wind LIDAR located at Norderney (Section 2.5.1, and Figure 1). The wind speed from SAR and DWD at Norderney is 5 m/s and 4.4 m/s, respectively, which is close to the lowest level's LIDAR measurement. For further analysis, temporal evolution of the LIDAR wind profiles are also investigated. Figure 8a-c show the wind speed evolution (10 min time-step) for a period before, during and after the SAR overflight on 25 August 2019. Prior to the morning transition, a low-level jet (LLJ) with an average jet core height of about 200 m above ground is observed  
Figure 7: **(a,b)**: Corresponding horizontal wind speed gradient of 10 m wind speed from SAR (purple) and DWD (blue) with superimposed curves for bathymetry (green) (**a**) and for thermal stability (green) (**b**). The orange dashed line at \(x_{95}\) marks the adjustment distance according to Equation (12). (**c,d**): Wind speed increase \(\delta u_{10}\) (**c**) and normalised wind speed increase \(R_{u_{10}}\) (**d**). The purple and blue curves, respectively, refer to SAR and DWD. The red curves in (**a–d**) corresponds to the fitted model given by Equations (10), (14) and (15), respectively.  
(Figure 8a), decreasing to 120 m around SAR acquisition time (Figure 8b). The jet is spread over a wider altitude band after 6:30 UTC and finally starts to disappear around 8:00 UTC (Figure 8c). This phenomenon is found to have a high frequency of occurrence between evening and morning at these latitudes [54].
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 3 Results and Analysis
### Experimental Setup  
All audios were resampled to 16 kHz for the present work. The training set audio was divided into 2-s segments, while the test set retained its original length without segmentation. The STFT module employed a Hamming window (of a length of 512) with a window shift of 256 samples, resulting in a 50% overlap. After STFT processing, the generated spectrogram contained 257 frequency components, and the time dimension was determined by the actual duration of the audio. In the generator, the number of DeConformer modules was 5, the batch size was 4, and the number of channels was 64. For the metric discriminator, the number of channels was set to 16, 32, 64, and 128. During training, the AdamW optimizer was employed to optimize the generator and the discriminator over 100 epochs in total. The learning rate for the generator and the discriminator was set to 0.0005 and 0.001, respectively, with dynamic adjustments based on the training loss. In the generator loss \(L_{G}\); the weight was set as \(\{\alpha=1,\beta=0.05,\gamma=2\}\).
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 2 Related Work
### Guided Crowd Evacuation
#### 2.2.4 Multiple Robot Leaders  
The study presented in [15] investigated a framework for using multi-cooperative robots in the evacuation of pedestrians. Another study, [16], introduced a robot-assisted crowd evacuation system to provide dynamic instructions on hazards to the crowd during the evacuation process. Even though this study revealed that using robot-assisted evacuation is beneficial in both normal and panic scenarios, this study did not focus on different approaches to guidance, such as partial guidance and full guidance. Another study [17], introduced a heuristic method to deploy evacuation robots and assign them guidance roles as either attractive or repulsive entities to humans. In [18], a UAV-based crowd evacuation was proposed. However, this study mainly focused on UAV scheduling in a polluted industrial area.  
Finally, a few studies [19; 20; 21] have explored cooperation among multiple UAVs in a crowd evacuation scenario. The study presented in [19] used multi-UAV cooperation in the evacuation where multiple drones support disaster evacuation guidance. The UAV agent shares information about the UAVs to decide on the evacuation guidance, rerouting if the UAVs encounter any problems. The study presented in [20] generated a plan to evacuate pedestrians to the safest point and guide them using multi-UAV cooperation. The study presented in [21] provided safe evacuation guidance for when tsunamis occur using multiple UAVs. The study identified a safe route using information such as disaster situations and regional characteristics, through the cooperation of multiple UAVs.
# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
## 3 Materials and Methods
### Model Selection
#### 3.2.4 Artificial Neural Network (ANN)  
ANNs are derived from neurons from the brains of living organisms that perform the calculations. ANNs are based on several neurons or nodes along with each other, which form a layer called the hidden layer. By placing these hidden layers sequentially, the neural network forms. All neurons of each layer are connected to the front and back layers; those connections are called weight and such networks are called fully connected layers. Each neuron has an activation function. The activation function is a function that takes the sum of data as input and generates output data [56]. Another important component of ANNs is the loss function that calculates the gradient (prediction error) of the model. The loss function is used for optimizing the weight and improves the performance of the model. The ANN network has many parameters and is capable of discovering complex relationships between data. Therefore, an ANN model is fitted on the existing dataset. Different numbers of neurons, hidden layers and various types of loss function were employed in the current study. Finally, the most accurate and optimum performance was provided by our designed network. The designed network has two hidden layers plus input and output layers. The arrangement of layers consists of 35 neurons with a linear activation function, and in the next layer, there are 105 neurons with a linear activation function that forms the first hidden layer. Then, a dropout exists between this layer and the next layer that eliminates 40 percent of non-necessary connections. In the second hidden layer, there are 70 neurons with linear activation functions that are connected to the four neurons with a SoftMax activation function in the output layer. The most optimal loss function was the mean squared error and the most optimal optimizer was the Adam optimizer with a 0.0005 learning rate. The network was run with a batch size equal to 47 in 200 epochs.
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 3 MPCR-Net
### Training  
#### 3.5.1 Preprocessing of Training Data  
The original data used for training was the ModelNet40 [45] dataset, which contains 12468 CAD models in 40 categories. We randomly designated 20 categories of models as the training set and the other 20 categories of models as the test set. The creation process of a global template point cloud and a partial point cloud is illustrated in Figure 6.  
For each original point cloud in ModelNet40, the global template point cloud was obtained through voxel grid downsampling [46; 47], consisting of 1024 data points.  
The process of creating a partial point cloud was as follows:  
1. Each initial partial point cloud contained 568 data points, which were \"cut\" from a global template point cloud using the farthest point sampling (FPS) [42] algorithm.
2. Gaussian noise of 0.0075 level to the initial partial point cloud was added to simulate the deviation of the coordinate value between the scanned data point and the data point in the global template point cloud under noisy conditions.
3. 284 outlier noise points were randomly added to the initial partial point cloud to simulate the disturbance of the scanned point cloud structure by environmental noise and sensor error. This increased the structural difference between the initial partial point cloud and the template point cloud.
4. A random rigid body transformation matrix was created, through which the initial partial point cloud was subjected to random rotation transformation (\(\pm\)45\({}^{\circ}\) around each Cartesian coordinate axis) around the origin of the coordinate and a random translation transformation (\(\pm\)0.5 unit along each Cartesian coordinate axis) to obtain the partial point cloud.
## 2 Problem Formulation
### Capacity Configuration Model of Energy Storage
#### 2.1.4 Energy Storage System Constraints  
The energy storage system is allocated in distribution networks connected to the main grid by tie lines and a medium/low voltage transformer. The energy storage system may be operated by power system operators (PSOs) or the DG owners. There are three different energy storage control strategies for different operators. For a DG owner, the energy storage system supplies the load demand when load level is lower than the specified proportion of wind power, and the output power of the wind power and energy storage is limited in a specified proportion of power. Strategy 2 is similar to strategy 1. The difference is that when the load demand is higher than a specific proportion of wind power,energy storage can be utilized to satisfy the load. Strategy 3 is planned for PSO. When the available capacity cannot satisfy the load demand, energy storage will be used. In this paper, strategy 3 is utilized. The constraints related to the energy storage system are given as follows.  
The power of energy storage system at time \(t\) is illustrated as  
\[P_{ES}(t)=\left\{\begin{array}{l}max\{P_{G}(t)+P_{W}(t)-P_{LOAD}(t),-P_{ch\_ max}\},\,if\,\,P_{G}(t)+P_{W}(t)-P_{LOAD}(t)\leq 0\\ \min\{P_{G}(t)+P_{W}(t)-P_{LOAD}(t),P_{ch\_max}\},\,if\,\,P_{G}(t)+P_{W}(t)-P_ {LOAD}(t)>0\end{array}\right. \tag{20}\]  
The energy stored in the energy storage system at time \(t+1\) is  
\[E_{bat}(t+1)=\left\{\begin{array}{l}E_{min},\,if\,E_{bat}(t)+TP_{ES}(t)\leq E _{min}\\ \min\{E_{bat}(t)+TP_{ES}(t),E_{max}\},\,if\,E_{bat}(t)+TP_{ES}(t)>E_{min}\end{array}\right. \tag{21}\]  
The available power of the energy storage system at time \(t\) is  
\[P_{Available}(t)=\min\Bigl{(}T^{-1}(E_{bat}(t)-E_{min}),\,\,P_{ch\_max}\Bigr{)} \tag{22}\]  
where \(P_{ch\_max}\) is the maximum permissible discharging power, \(P_{ch\_max}\) is the maximum permissible charging power, \(P_{G}(t)\) is the power of the fuel generators, \(P_{W}(t)\) is the power of the wind turbines, \(P_{LOAD}(t)\) is the power of the load demand at time \(t\), \(E_{min}\) and \(E_{max}\) are the minimum and maximum energy of the energy storage, respectively.  
Furthermore, it is assumed that the maximum discharge power of energy storage is less than the maximum charge power.  
\[0<P_{ch\_max}<P_{ch\_max} \tag{23}\]  
The initial energy of the energy storage system is the minimum permissible stored energy of the energy storage system.  
Energy Storage State of Charge Constraints  
The state of charge of the \(k\)th energy storage device is constrained by  
\[SOC_{k,min}\leq SOC_{k}(t)\leq SOC_{k,max} \tag{24}\]  
\[SOC_{k}(t)=P_{ES}(t)/P_{ES\_max} \tag{25}\]  
where \(SOC_{k,min}\) and \(SOC_{k,max}\) are the minimum and maximum capacity allowed to be stored in the \(k\)th energy storage units.  
Energy Storage Output Energy Constraints  
\[E_{bat}=n_{ES}E_{bat\_unit} \tag{26}\]
# Estimating Optical Flow with Streaming Perception and Changing Trend Aiming to Complex Scenarios
## 3 Method
### Structure Overview  
This section describes the training and inferencing process of our Streaming Perception and Changing Trend (it will be referred to as SPCT later in this paper) method.  
In the training phase, as shown in Figure 1, the network receives three image frames \((f_{t-2},f_{t-1},\ f_{t})\), which are input to the backbone in the groups of \((f_{t-2},f_{t-1})\) and \((f_{t-1},f_{t})\). The backbone has the same structure as in RAFT, and employs a share-weight \(1\times 1\) convolution layer to reduce channels for both output flow features. In the next step, the 2D features output from the two consecutive optical flow frames are encoded into two feature streams, one continuous and the other momentary. These two streams are input into the Continuous-Momentary Feature Fusion (Hereinafter referred to as C-MFF) module simultaneously, and it integrates the two input streams into one fused optical flow feature through a simple concatenate operation. It decodes them through the GRU structure to obtain the optical flow estimation at frame \(t\), compares it with the ground truth optical flow value as the supervision, then calculates the losses and updates the parameters in the network structure by backpropagation.  
Figure 1: Training pipeline. The whole training architecture consists of the RAFT backbone, a fusion module for continuous-momentary flow features, and a combination of the three types of losses.  
For the forward inference stage, compared to the traditional optical flow estimation algorithm that only needs to extract the optical flow frames once, our proposed structure requires the extraction of two optical flow frames from three consecutive image inputs before post-processing. In order not to double the computation of estimating the optical flow, as depicted in Figure 2, a historical buffer is used to store the last extracted optical flow feature so that only the current optical flow feature needs to be extracted at one time. After this, all the network needs to do is input the feature of the current optical flow, along with the one cached in the historical buffer, into the C-MFF module rather than re-extract the same feature that the previous operation has already processed. For the beginning of the image sequence, we directly copy the first optical flow feature \(f_{0}^{{}^{\prime}}\) as a pseudo-historical buffer to fit the input form (\(f_{0}^{{}^{\prime}},f_{0},\ f_{1}\)).
# Application of Iron-Modified Activated Carbon for Phosphate Removal in Aqueous Systems
## 3 Results and Discussion
### Prevention of Phosphate Release by Soil
The experimental results show that the phosphate leaching rates and concentration of the water increase with rpm, but rise more gradually in the SMAC-applied P-Soil with SMAC. In Figure 5a, phosphate leaching rates were 8.90%, 19.27%, and 39.56% at 0, 40, and 80 rpm, respectively. The increase in leaching rate at 80 rpm reached 444.50% compared to the rate at 0 rpm, where there is no hydraulic flow. This is due to the fact that the hydraulic stress and turbulent flow conditions induce the agitation of the P-Soil, resulting in the higher degree of detachment of phosphorous from the soil. This can also be explained as turbulence-driven. In contrast, Figure 5b showed phosphate leaching rates of 4.08%, 4.90%, 6.44%, 6.88%, and 8.73% at 0, 20, 40, 60, and 80 rpm, respectively, maintaining leaching rates below 10% across the entire range tested. At 80 rpm, SMAC-applied soil showed 22% phosphate leaching compared to the untreated sample, similar to the leaching rate observed in a stagnant environment under natural conditions. These results indicate that SMAC application effectively suppresses phosphate leaching even in environments with hydraulic flow, with its effectiveness further amplified in stronger flow conditions. The reduction in phosphate leaching with SMAC is due to the sorption of phosphate, and the fact that the additional carbon layer reduces the turbulent conditions the soil is exposed to, leading to lower phosphate leaching. This suggests that SMAC can be effective not only in stagnant water bodies but also in environments with weather-induced inflows or mixing, such as during rainfall or wind-induced waves, as well as in flowing rivers and coastal areas. Furthermore, it indicates that SMAC may be even more effective in these conditions than in situations with no hydraulic flow.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 1 Introduction
With a unique combination of WSN, MANET, and DRONET networks, it is possible to preserve the functionality of urgent applications, such as public health and safety, in different adverse situations. Based on this motivation, the multilayered model was build in order to use the advantages of mentioned networks and also empower the potential of new generation 5G networks and IoT solutions. The simulations of the proposed multilayered model show that it is able to provide a fully functional backup solution that preserves functionality and service demands required by 5G standards.
# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 2 Materials and Methods
### Approach
#### 2.2.2 Exoskeleton Device  
Trunk flexion tasks were performed while wearing a BSIE. Among the several types, passive ones are affordable, compact, and are more likely to be adopted by the workforce. Thus, we used a passive rigid BSIE, specifically the BackX Model AC (SuitX, Emeryville, CA, USA), with actuators located on each side of the hip. The BSIE selected for our study, as shown in Figure 1, consisted of a chest pad to support the trunk when performing trunk flexion, specifically offloading upper-body weight to the front region of the thigh using mechanical linkages. Among the three provided support levels, we selected the device at medium (\(\sim\)25 lbs.), which was kept consistent across participants.  
\begin{table}
\begin{tabular}{c c c} \hline
**Demographic Information** & **Mean (SD)** & **Range of Values** \\ \hline Age (yrs.) & 20.21 (2.6) & 18–28 \\ Height (cm) & 179.1 (3.7) & 172–186 \\ Weight (kg) & 72.9 (6.2) & 65.68–82.44 \\ Body mass Index (kg/m\({}^{2}\)) & 22.7 (2.4) & 19.42–27.56 \\ Chest Circumference (cm) & 89.5 (3.9) & 84–96 \\ Hip Circumference (cm) & 86.6 (6.0) & 80–98 \\ \hline \end{tabular}
\end{table}
Table 1: Demographics of participant pool showing the Mean, standard deviation (SD), and the ranges (expressed as Maximum–Minimum) for anthropometric dimensions of participant age, height, weight, body mass index, chest/hip circumference.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 6 Conclusions  
This paper focuses on the optimal joint processing of B1I and B1C signals in the upcoming BeiDou global system. We propose to view these two signals as one navigation signal with asymmetric dual sidebands, named B1-ADS, whose Gabor bandwidth is much broader than that of B1I and B1C alone. To take full advantage of its broader Gabor bandwidth, the ASYM-DBT method has been designed to achieve optimal ranging accuracy with low hardware complexity. Theoretical analysis and experimental results based on the software receiver have proved the performance of the proposed method. Future research would extend the ASYM-DBT method to process other asymmetric dual sideband signals and to study the multipath mitigation algorithm under this method.  
The authors gratefully acknowledge the efforts of [PERSON] and [PERSON] in discussing the simulated experiments and collaborating in the manuscript preparation. This work is supported by the National Science Foundation of China (Grant U1333203).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.