esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 4 Problem Solution
### Low-Complexity Subcarrier Scheduling and User Pairing Algorithm  
An exhaustive traversal is applied in Algorithm 1 to obtain both the optimal user pairing and subcarrier schedule. When the numbers of waiting users and subcarriers are both small, the complexity is tolerable, and the optimal solution can be achieved simply. However, when the numbers of waiting users and subcarriers are significantly boosted, a low-complexity algorithm is required, as shown in Algorithm 3. Compared to Algorithm 1, bipartite graph and distributed queue are applied to simplify the complexity.  
```
1:Input:\(N_{c}\), \(U\), \(\left|I_{n_{c},k_{n_{c}}}\right|^{2}\), \(P_{1,n_{c}}\) and \(P_{2,n_{c}}\).
2:Output: Subcarrier scheduling \(M_{\text{low}}\) and user pairing \(k_{n_{c}}\).
3:Initialize: \(M_{\text{low}}=\emptyset\), \(i_{1}=1\) and \(\varsigma=1\), where \(M_{\text{low}}\), \(i\) and \(\varsigma\) denote the subcarrier mapping, \(i\)th element of \(\mathbb{U}_{s}\) and \(\varsigma\)th user pairing result, respectively;
4:Arrange the elements of \(\mathbb{U}_{s}\) in descending order, known as \(\overline{\mathbb{U}_{s}}\);
5:while\(i\in\left[1,\left\lfloor\frac{U}{2}\right\rfloor\right]\)do
6: Denote the subset of the use pairing as \(\overline{\mathbb{U}_{s}^{\text{sub}}}\);
7:if\(i+\left\lceil\frac{U}{2}\right\rceil=U\)then
8:\(\overline{\mathbb{U}_{s}^{\text{sub}}}=\left[\left(\overline{\mathbb{U}_{s}}(i),\overline{\mathbb{U}_{s}}\left(i+\left\lceil\frac{U}{2}\right\rceil\right) \right),\left(\overline{\mathbb{U}_{s}}\left(i+\left\lceil\frac{U}{2}\right \rceil\right),\overline{\mathbb{U}_{s}}\left(i\right)\right)\right]\);
9:else
10:\(\overline{\mathbb{U}_{s}^{\text{sub}}}=\left[\left(\overline{\mathbb{U}_{s}}(i),\overline{\mathbb{U}_{s}}(i)\right),\left(\overline{\mathbb{U}_{s}}(i), \overline{\mathbb{U}_{s}}(i)\right)\right]\);
11:endif
12:while\(\varsigma\in[1,2]\)do
13: Select the \(\varsigma\)th element of \(\overline{\mathbb{U}_{s}^{\text{sub}}}\);
14: Determine the 1 th and 2 th users;
15: Set \(\mathbb{C}=\left\{\widetilde{n_{c}}|\widetilde{n_{c}}\in M_{\text{low}}\right\}\), \(\widetilde{n_{c}}\) is the unsaturated points;
16:if\(\mathbb{C}\
eq\emptyset\)then
17: Find a new augmented branch \(\chi\);
18:\(M_{\text{low}}=M_{\text{low}}\bigoplus\chi\);
19:else
20: Calculate \(E_{\text{total}}\) as (16);
21: Record \(E_{\text{total}}\), subcarrier scheduling \(M_{\text{low}}\) and user pairing \(k_{n_{c}}\);
22:\(\varsigma=\varsigma+1\);
23:endif
24:\(i=i+1\);
25:endwhile
26:\(i=1\);
27:endwhile
28:Search the maximum \(E_{\text{total}}\) and corresponding subcarrier scheduling \(M_{\text{low}}\) and user pairing \(k_{n_{c}}\).
```  
**Algorithm 3**Low-complexity subcarrier scheduling and user pairing scheme.
# Evaluating Earthworms' Potential for Remediating Soils Contaminated with Olive Mill Waste Sediments
## 3 Results and Discussion
### Microcosm: Impact of Earthworms on Soil Physicochemical Variables  
The addition of OMW sediments significantly increased the concentrations of Na, K, P, total organic carbon and nitrogen, total phenols and oxidized carbon; and the electrical conductivities and pH values of the spiked soils, in a dose-dependent manner (ANOVA test with Welch's homogeneity correction, \(p<0.05\)) (Figure 6). The inoculation of OMW-amended soils with earthworms had a strong impact on total phenol concentrations, which significantly decreased in all treatments after 30 d of soil incubation (\(p<0.05\), Games-Howell post-hoc test). However, the percentage of total phenol decrease depended on their initial concentration; the more phenols were concentrated in soil, the lesser the degradation rate achieved (96%, 87.4%, 73.2% and 58.6% decreases for 0%, 10%, 20% and 40% _w/w_ OMW sediments, respectively). The negative relationship may be attributed to the toxic effects of phenol compounds on earthworms, as showed by the biomarker and avoidance response outcomes, which would reduce their burrowing and feeding activities and thereby their potential remediation capacity. Likewise, TOC and total N and Na concentrations increased in the soils treated with the highest OMW concentration after 30 d incubation with respect to their initial values (Figure 6). Regular feeding of earthworms during the microcosm study could account for this finding because of exogenous organic matter input. In addition, earthworm-induced microbial mineralization of organic matter could be depressed because of toxic effects derived from high OMW concentrations on earthworms. Although these assumptions require further research, current data suggest that co-application of \(L\). _terrestris_ and leaf litter (as a food supply to earthworms) to OMW-amended soils improves soil quality in terms of enhanced exoenzyme activity as long as concentrations of OMW sediments do not exceed 20% (_w/w_).  
Figure 5: Mean (\(\pm\) SEM) enzyme activity in olive mill waste (OMW)-amended soils at \(t=0\) d and after 30 d of incubation. Different letters indicate significant differences between OMW treatments (\(p<0.05\), [PERSON]’s \(t\) post-hoc test), whereas asterisks denote differences between soils inoculated with earthworms and earthworm-free (\(p<0.05\)). Inset plots show the mean (\(\pm\) SEM) enzyme activity in casts collected from the soil surface after 30 d of incubation.
# How Human Mobility Models Can Help to Deal with COVID-19
## 3 Mobility Models
### Real Traces  
Real mobility traces are obtained from observing people and their mobility in real scenarios. These traces provide accurate information, especially when collected from a large number of participants and over a lengthy period of time. Two different traces can be found: contact-based and location traces.  
A contact-based trace collects the contact time and durations between pairs of nodes. There are well-known traces or data sets, where the contacts of people or vehicles have been captured, in congresses or cities such as INFOCOM [52], Cambridge [53], Milan [54], MIT [55], among others, and their statistical data have been studied in depth in [56; 57]. The simplicity of contact-based traces allows the analytical evaluation and simulation of OppNet. Their main drawback is that they do not provide any location information and, what is more important, the collected contacts are based on the communication technology used (for example Bluetooth, WiFi). Thus, as contacts are already measured, these traces are not suitable for evaluating epidemic contacts, which have other patterns.  
Location-based traces are the result of obtaining the location of individuals, and in general, they are usually GPS coordinates obtained periodically, or whenever they move. There are several types of these traces, such as Shanghai taxis [58], or students on the university campus [59], among others. In the Crawdad repository [60] we find most of the publicly available traces, and it could be considered the first place to find the type of traces required for simulations.  
Although trace-based mobility models can represent very realistic human mobility patterns, they are restricted to specific scenarios. In this sense, the use of these traces, although useful, clearly has some drawbacks when trying to extend the results obtained to other similar scenarios or when dealing with a new network environment, from which traces have not yet been collected. Additionally, these traces have a fixed set of nodes, and the renewal (if it exists) is very low. Finally, their use can be computationally intensive, because of the huge quantity of information that these traces contain.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 3 Methodology
### Overall Architecture  
As shown in Figure 1, our FreANet utilizes the popular U-shaped encoder-decoder architecture to achieve rich hierarchical representations. Specifically, given an input image \(I\in\mathbb{R}^{H\times W\times 3}\), FreANet projects it into the embedding space using a single \(3\times 3\) convolution, producing features of size \(H\times W\times C\), where \(H\) and \(W\) indicate the original resolution sizes, and C represents the number of channels. These features are then fed into FreABlock as input to capture high-level representations. FreABlock consists of the MDFE, MPA, and a feed-forward network (Figure 1b). Furthermore, the input features of the MDFE are flexibly enhanced through the residual deformable block (RDB) using DCNv3 [35], allowing a focus on critical features. As the features pass through deeper layers, the number of channels doubles, and their resolution halves. Upsampling and downsampling are performed using transposed and strided convolutions, respectively. Finally, the restored image \(\hat{I}\in\mathbb{R}^{H\times W\times 3}\) is estimated using a single \(3\times 3\) convolution.
## 4 Results
### Landslide Hazard
#### 4.1.3 Evaluation of the LHA Models  
Based on the validation set, we evaluated all the LHA models using the ROC curve. Figure 8a shows that the ROC curves of the different models in this study have apparent discrepancies. In terms of the types of FRs, the AUC values of the models using the DFR method are higher than those of the same types of models using the FR method, which does not consider landslide aggregation. This indicates that the DFR method, considering landslide spatial aggregation, can effectively improve the prediction performance of the LHA model. Overall, the AUC value of the RF model is higher than that of the AHP model, suggesting that the former performs better in LHA studies. Meanwhile, the AUC value (0.913) of the DFR-RF model is also the highest among all models.  
In addition, the distribution characteristics of the LHIs and the quantities of landslides under different landslide hazard levels can reflect the rationality and reliability of each LHA model. Firstly, as shown in Figure 8b, the coverage of each landslide hazard grade decreases with an increase in the hazard level on the whole. Moreover, the zones with very  
Figure 8: The characteristics of different LHA models’ results: (**a**) the ROC curves of different LHA models’ results; (**b**) the distribution characteristics of the LHIs.  
high and high landslide hazards only cover a very small part of the study area, ranging from 6.75% to 7.68% and 9.97% to 13.72%, respectively. Secondly, the frequency of landslide occurrence increases with an increase in the landslide hazard level. In the evaluation results of each model, the number of landslides in the zones with very high and high hazards accounts for 79.41% to 91.76% of the total number. Meanwhile, the share of landslides in the low-hazard zone is only 0.59% to 7.059%. The number of landslides in the zone with a very low landslide hazard is the lowest, not exceeding 0.59% of the total. Notably, there are fewer landslides in the zones with very low and low hazards predicted by the LHA models using the DFR method. Overall, all LHA models in this study show good predictive performance. However, the DFR method, which considers landslide aggregation, can significantly improve the predictive ability of the LHA models.
## 4 Methods
### Anomaly Detection Based on CNN  
After generating the vehicle sensor CWTS, the CNN was trained to obtain detection models to classify a vehicle that is normal or anomalous. Specifically, we used the sliding window to maintain the sensor data that was converted to the CWTS. Then, these observations after conversion to the CWTS are fed into the CNN for model training and testing. We used the labeled input, i.e., CWTS, to train the CNN models for the classification of normal/anomalous. That is, if anomalies occur in a CWTS, the corresponding input is set to 1; otherwise, it is set to 0.  
The CNN's structures and its parameter values were selected and tuned through a large amount of experiments in order to enhance the anomaly detection capability on a validation set. In this study, the structure of the CNN is shown in Figure 4. To illustrate the structure clearly, the convolution layer and activation unit are visually combined into a convolution stage. We used three convolution layers with a set of kernels to extract the relevant high-level features. The weights and bias of the convolution kernels were trained using the backpropagation (BP) error algorithm. The leaky rectified linear unit (LeakyRelu) was chosen as the activation function to achieve nonlinear capabilities, and the Adam optimizer was used to minimize the cross-entropy. Finally, the feature map's output was transformed into a one-dimensional array, called the fully connected layer, for input to the Softmax classifier to provide the detection results. The model parameters of the CNN are shown in Table 1.  
During model training, the learning rate of the optimizer was set as 0.001. Furthermore, a batch size of 128 and epoch number of 500 were used. In order to reduce the risk of overfitting, we used a dropout method with 0.2 probability and the early stopping strategy. The process of hyperparameter tuning in the training phase is illustrated in Table 2.  
Figure 3: The time domain waveforms and CWTS of the normal and anomaly in-vehicle sensor signals. (**a**) is the normal driving state. (**b**) is the hijack scenario. (**c**) is the bias scenario. (**d**) is the injection scenario. (**e**) is the DOS scenario. (**f**) is the replay scenario.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 5 Experimental Results
### Very Complicated  
The image showed an inverted U-shaped structure and had the most rooms, entrances, and obstacles among all maps considered. Compared with the average of 759,311.4 nodes visited with the conventional image map for pathfinding to the destination, when the same path search algorithm was performed on the preprocessed topology map, an average of 380.8 nodes were visited (decrease of 99.95%). Given its higher resolution, more obstacles, and longer moving distance compared with the map with lower complexity, the time required for path search with an image map increased greatly. When A* was applied to the image map, the average path search time was 3342.1 ms, and when preprocessing was performed through the CNA, the average time was 1137.2 ms. When the same pathfinding algorithm was applied to the topology map generated by the CNA, the search took 9.7 ms (99.71% less time when preprocessing and pathfinding were performed together versus alone).
## 3 Motor d-q Current Signature Analysis  
Motor Current Signature Analysis (MCSA) relies on examining a motor's stator current to find specific patterns associated with faults. The stator's current is analyzed in the frequency domain in the interest of extracting specific frequencies associated with motor's faults. For a healthy motor, the form of stator currents is as follows:  
\[\begin{split} I_{A_{b}}(t)&=Asin(2\pi f_{s}t)\\ I_{B_{b}}(t)&=Asin\big{(}2\pi f_{s}t-\frac{2\pi}{3})\\ I_{C_{b}}(t)&=Asin\big{(}2\pi f_{s}t+\frac{2\pi}{3}) \end{split} \tag{5}\]  
Using the Park transform matrix in the synchronous rotational reference system (\(\theta=\theta_{e}\)):  
\[\begin{bmatrix}I_{q}\\ I_{d}\end{bmatrix}=\frac{2}{3}\begin{bmatrix}cos(\theta_{e})&cos(\theta_{e}- \frac{2\pi}{3})&cos(\theta_{e}+\frac{2\pi}{3})\\ sin(\theta_{e})&sin(\theta_{e}-\frac{2\pi}{3})&sin(\theta_{e}+\frac{2\pi}{3} )\end{bmatrix}\cdot\begin{bmatrix}I_{a}\\ I_{b}\\ I_{c}\end{bmatrix} \tag{6}\]  
It follows that:  
\[\begin{split} I_{q_{h}}&=\frac{A}{3}\\ I_{d_{h}}&=0\end{split} \tag{7}\]  
With the presence of a fault in the motor, additional harmonics appear in the current's spectrum. The frequency patterns for MCSA related to eccentricity, demagnetization, and bearings faults are presented in Table 1.  
Figure 1: PMSM mathematical model.  
Currents under faulty PMSM state can be expressed as follows:  
\[\begin{split} I_{A}(t)&=Asin(2\pi f_{s}t)+\sum\limits_{ x}A_{x}sin(2\pi f_{k}t)=I_{A_{h}}(t)+I_{A_{f}}(t)\\ I_{B}(t)&=Asin\big{(}2\pi f_{s}t-\frac{2\pi}{3} \big{)}+\sum\limits_{x}A_{x}sin\big{(}2\pi f_{k}t-\frac{2\pi}{3}\big{)}=I_{B_{ h}}(t)+I_{B_{f}}(t)\\ I_{c}(t)&=Asin\big{(}2\pi f_{s}t+\frac{2\pi}{3} \big{)}+\sum\limits_{x}A_{x}sin\big{(}2\pi f_{k}t+\frac{2\pi}{3}\big{)}=I_{C_{ h}}(t)+I_{C_{f}}(t)\end{split} \tag{8}\]  
The above-mentioned quantities are expressed in the synchronous rotational reference system as:  
\[\begin{split} I_{q}&=I_{q_{h}}+I_{q_{f}}=\frac{A}{ 3}+I_{q_{f}}\\ I_{d}&=I_{d_{h}}+I_{d_{f}}=I_{d_{f}}\end{split} \tag{9}\]  
From the above equations, we can observe that currents in the d-q rotating system contain two quantities, the dc quantities \(I_{q_{h}}\), \(I_{d_{h}}\), which refer to the healthy operational state, and the oscillating quantities \(I_{q_{f}}\), \(I_{d_{f}}\), which refer to additional harmonics associated with the fault. By analyzing the above quantities in the frequency domain, the frequencies that occur beyond dc can be used to detect and identify PMSM faults.
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 5 Conclusions  
A SAC-OCDMA system is proposed including a DCF to compensate dispersion and an EDFA to compensate attenuation in the communication channel. MDW codes and EDW codes were utilized as the signature sequences and a single photodiode was used. The SAC-OCDMA system performance was investigated, and its performance was evaluated in terms of BER, Q-factor, optimum received power, and eye opening, using different codes and detectors at different transmission distances and bit rates.  
The obtained results assure that the use of DCF and EDFA allows longer transmission distances with a BER under\(10^{-9}\), especially at bit rates greater than 1 Gbps. This implies the feasibility of the proposed system as a candidate for future optical access network. The simulation results show that the use of an APD in SPD detection in the SAC-OCDMA system with EDW codes as signature codes, yields adequate BER and Q-factor. The system using the APD achieves transmission distances 40 and 30 km longer that that using a PIN detector at data rates of 622 Mbps and 1 Gbps, respectively.  
Conceptualization, H.A.F., A.A.E.-A. and M.H.A.; Data curation, H.A.F. and M.A.M.; Formal analysis, S.A.A.E.-M. and M.H.A.; Investigation, S.A.A.E.-M.; Supervision, H.A.F., A.A.E.-A. and M.H.A.; Validation, M.H.A.; Writing-original draft, S.A.A.E.-M.; Writing-review & editing, A.A.E.-A. and M.H.A.  
This research received no external funding.  
The authors declare no conflict of interest.
# Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes
## 3 Method
### Improved Yolov5s Model
#### 3.2.2 CA Attentional Mechanisms  
In order to maintain high accuracy even after extreme compression, we have opted to incorporate the CA mechanism, which encompasses both spatial and channel attention. This mechanism significantly aids in precise object localization and identification. It leverages 1D global pooling to gather directional feature maps in both horizontal and vertical directions. This enhances the representation of learned features within mobile networks by capturing location coordinates. The CA attention mechanism has garnered considerable attention in the realm of mobile networks. Its flexibility and lightweight nature mean that it can be easily integrated into classic building blocks of mobile networks, such as MobileNetV3 modules.  
Figure 2: MobileNetV3 network structure.  
The encoding process of the CA attention mechanism consists of two main processes: information embedding and attention generation. Firstly, in the process of coordinate information embedding, the global pool is decomposed to ensure the long-range interaction capture, and the channels are encoded along two horizontal vertical coordinates from two spatial ranges of (\(H\),1) and (\(1\),\(W\)), respectively, and the output between two directions is given by:  
\[z_{m}^{h}(h)=\frac{1}{W}{\sum}_{0\leq i\leq W}x_{m}(h,i) \tag{2}\]  
\[z_{m}^{w}(h)=\frac{1}{H}{\sum}_{0\leq j\leq H}x_{m}(j,w) \tag{3}\]  
Afterwards, a one-dimensional direction-aware feature map is generated. This transformation captures the long-range dependencies in one spatial direction, while preserving the precise position information in another. In the process of coordinate attention generation, the output results of both directions are first sent to a shared \(1\times 1\) convolutional transform function:  
\[f=RELU\Big{(}F_{conv1\times 1}\Big{[}z^{h},z^{w}\Big{]}\Big{)} \tag{4}\]  
After that,\(f\) is divided into two independent tensors along the spatial dimension, and a tensor of the same channel count as the input \(X\) is obtained by a \(1\times 1\) convolutional transformation to obtain:  
\[g^{h}=\sigma\Big{(}F_{conv1\times 1}\Big{(}f^{h}\Big{)}\Big{)} \tag{5}\]  
\[g^{w}=\sigma(F_{conv1\times 1}(f^{w})) \tag{6}\]  
Finally, the output expansions are used as attention weights to obtain the final weighted attentional feature map:  
\[y_{m}(i,j)=x_{m}(i,j)\times g_{m}^{h}(i)\times g_{m}^{w}(j) \tag{7}\]  
The CA attention mechanism avoids the compression of the tensor channel bits compared to CBAM and retains more features, which will enable the model to achieve further improvements in accuracy.
## 5 Definition of Main Aspects Related to Space Mission Planning
Space missions can have various environmental impacts, including the release of pollutants and emissions during launches, the creation of space debris, and the potential contamination of celestial bodies with biological or chemical substances. Understanding and mitigating these impacts is crucial for ensuring sustainable space exploration and minimizing harm to both Earth and space environments [59]. Space mission planning and risk assessment: Space mission planning and risk assessment involve the systematic process of developing mission objectives, defining mission requirements, and evaluating potential risks and uncertainties. It includes identifying and analyzing risks related to mission design, technology development, operations, and safety. By conducting comprehensive risk assessments, mission planners can make informed decisions, develop contingency plans, and allocate resources effectively to ensure mission success and minimize potential hazards [60; 61]. Space mission safety and risk analysis: Space mission safety and risk analysis involve evaluating and managing the safety aspects and risks associated with space missions. It includes assessing potential hazards, such as launch failures, onboard emergencies, radiation exposure, and crew health risks. Risk analysis involves quantifying and prioritizing risks, identifying potential mitigation measures, and developing safety protocols and procedures. The objective is to protect the well-being of astronauts, ensure mission success, and prevent accidents or critical failures during space missions [62].