esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### A Fast Constraint Evaluation Mechanism
#### 4.1.2 Memory Constraint  
The evaluation of memory constraint is relatively easy since it is not related to transition activity. Let \(M(v)\) be the memory level at current window \(v\) and \(MV(v)\) be the violation of memory constraint. For an insertion which inserts \(v_{2}\) after \(v_{1}\), the memory level and violation at \(v_{2}\) can be calculated as  
\[MV(v_{2})=MV(v_{1})+\triangle MV \tag{15}\] \[M(v_{2})=M(v_{1})-MC+\triangle MV \tag{16}\]  
where \(\triangle MV=max(0,MC-M(v_{1}))\). MC is the memory consumed by \(v_{2}\). \(MC=d_{2}p_{a}\) if \(v_{2}\) is an observation window. Note that download activity inevitably increases the memory level onboard and \(MC=-t_{l}b_{a}\)(negative value) if \(v_{2}\) is a download window.
## 1 Introduction
### Previous Work
[22] proposed a novel three-stage methodology for a cooperative FSO transmission network, where closed-form expressions for the conditional error probability were derived. Ref. [23] considered MIMO relay-assisted FSO communication, where the performance of different methods was compared via outage probability analysis in the case of independent fading among the different apertures of communication nodes. Note that all the above references either derived closed-form expressions or used iterative algorithms to optimize the performance. While noting that the optimization problem for fairness-awaresecrecy is extremely complex, the approaches adopted in these references are not suitable for optimizing such a metric. Since an UAV can be flexibly deployed in some hard-to-reach locations, they have recently been applied to FSO communication networks to forward information [24; 25; 26; 27]. Ref. [24] demonstrated that the usage of a UAV relay can considerably improve the performance of conventional FSO communication networks. In [25; 26; 27], the performance of UAV-aided FSO relaying transmission networks was analyzed and optimized. However, to the best of our knowledge, an optimal fairness-aware design scheme for a UAV-assisted secure FSO relay communication network has not been fully investigated to date.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 2 Background
### Vehicle Detection and Segmentation
#### 2.1.3 Convolutional Neural Network (CNN)-Based Detection Methods  
Convolutional neural networks (CNNs) are predominantly used as learning structures for image understanding. The benefits of using CNNs are that fewer learning parameters might be used, which greatly improves the learning time and reduces the amount of data required to train the model. A convolution is a weighted sum of the pixel values of an image as the window slides across the whole image. Basically, the convolution process throughout an image with a weight matrix produces another image. The CNN-based approach may be considered one of the best ways to precisely detect a vehicle's position in an image, and so far, it includes sophisticated methods such as the single shot multiBox detector (SSD) [10], the faster regional CNN (faster R-CNN) [11], and you only look once (YOLO) [12]. All these methods have a similar average precision on detections. However, there is a significant difference in the inference time with each method. Although the faster R-CNN (a region proposal network) was the best detection method on Pascal Visual Object Classes (VOC) and Common Objects in Context (COCO) dataset challenges in 2012 and 2015, respectively, we decided to use the YOLOv3 network due to the speed performance.  
As Figure 4 shows, all the compared methods have similar mean average precision, but with a huge gap between them for processing times. Since our project is highly reliant on fast processing, we chose to use YOLOv3-416 which took 29 ms to process the COCO dataset.  
Figure 4: Performance and inference time of detection methods [13].  
Figure 3: Detection with optical flow using the Lucas–Kanade method.
## 7 System Performance Optimization
### CSI-Based Localization for Wireless Transmitters
#### 7.2.1 Noise Removal Using DWT  
As mentioned in Section 3.2.2, the obtained CSI values contain much noise, so noise needs to be removed from received CSI values. In this paper, we apply a two-level discrete wavelet packet decomposition and Symlets wavelet filter to remove noise.  
Figure 5: CSI measurements for different positions.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 4 The Composition of Ground Demonstration and Verification System
### Image Simulator  
The image simulator is used to simulate payload imaging and its main function is to provide remote sensing images for the entire system. It includes two different methods to generate the required images. One is to directly use the real remote sensing data as input data, and the other one is to combine the task requirements and the image synthesis method in Reference [42] to generate simulation images for application scenes. The latter method is applied to deal with the unusual scenes and enhance the generalization of the proposed algorithm.
## 4 The Proposed Method for Predicting the Reactive Power Adjustment
### Load Similarity  
In the same period of different years, the daily load curves are similar. To measure the similarity of forecasting load and the historical load quantitatively, load similarity (LS) is defined to measure the similarity level of the length quantitatively. To reflect the fluctuation of daily load cures in the same network of two days, the load similarity \(s\) is defined.  
According to Equations (2) and (3), the historical load and forecasting load of the day ahead can be represented with random matrix. \(P_{H}\) and \(Q_{H}\), respectively, represent the historical active load and reactive load. \(P_{F}\) and \(Q_{F}\), respectively, represent the active load and reactive load and reactive load of the day ahead.  
Structure the load augmented matrix \(A_{H}=\left[\begin{array}{c}P_{H}\\ Q_{H}\end{array}\right]\), \(A_{F}=\left[\begin{array}{c}P_{F}\\ Q_{F}\end{array}\right]\) Combining with the method to obtain the length of the matrix in Equation (5) and the method to obtain the covariance of random matrix in Equation (9), the load similarity can be listed as:  
\[s=\left(1-\frac{\left|d_{A_{H}}-d_{A_{F}}\right|}{d_{A_{H}}+d_{A_{F}}}\right) \frac{\text{cov}(A_{H},A_{F})}{\sqrt{\text{cov}(A_{H},A_{H})}\cdot\sqrt{\text {cov}(A_{F},A_{F})}} \tag{32}\]  
where \(0\leqslant 1-\frac{\left|d_{A_{H}}-d_{A_{F}}\right|}{d_{A_{H}}+d_{A_{F}}}\leqslant 1\), \(-1\leqslant\frac{\text{cov}(A_{H},A_{F})}{\sqrt{\text{cov}(A_{H},A_{H})}\cdot \sqrt{\text{cov}(A_{F},A_{F})}}\leqslant 1\), so the load similarity ranges from -1 to 1. As load similarity \(s\) approaches 1, the similarity of matrix \(A_{H}\) and \(A_{F}\) rises, indicating the similarity of historical load and forecasting load rises. Only when \(A_{H}=A_{F}\), load similarity is \(s=1\), indicating historical load and forecasting load are the same.
## 4 Classification of MAC Protocols Based on Channel
### Cluster-Based Multi-Channel MAC Protocols  
Basically, the cluster-based multi-channel MAC protocols are proposed to minimize channel contention, to increase network capacity by reusing resources, to provide fair access to channels within the cluster, and to efficiently control the network topology.
# Applications of Magnesium and Its Alloys: A Review
## 4 Magnesium and Its Alloys as a Biomaterial
### Biomedical Applications
#### 4.5.2 Cardiovascular Applications  
Presently, there are limited applications for magnesium and its alloys in cardiovascular medicine, as they are primarily experimental or preclinical [79]. Magnesium and its alloys are being investigated for their suitability to replace bio-inert metallic implants in typical cardiovascular therapeutic applications. These include prosthesis implantation and stent insertions, such as artificial valves, stents, pacemaker cases, and stent-grafts [80].  
Figure 4: Examples of magnesium applications in orthopedics. **(A)** Use of MAGNEZIX® MgYREZr alloy bioassay compression screw to treat hallux valgus fracture in 13 patients. Reproduced with permission from [PERSON], et al. (2020). \"Biodegradable Magnesium-Based Implants in Orthopedics—A General Review and Perspectives.”; published by Wiley, 2020 [73]; **(B)** Use of [PERSON] MgCaZn alloy bioresorbable bone screw to treat distal radius fracture in 53 patients. Reproduced with permission from [PERSON], et al. (2016). \"Long-term clinical study and multiscale analysis of in vivo biodegradation mechanism of Mg alloy.”; Copyright 2016, National Academy of Sciences [76].  
As with any medical application, metallic implants are preferred over ceramics and synthetic polymers for their biocompatibility and superior mechanical properties [81]. Similar to musculoskeletal and orthopedic applications, its ability to biodegrade is critical. It can reduce the need for risky and expensive surgical operations to remove or replace the metal at each stage of the patient's recovery. Specific to cardiovascular medicine, it will reduce the likelihood of thrombogenesis, restenosis, endothelial dysfunction, and other clinical complications [80; 81].  
However, bare magnesium alloy stents are notorious for rapid corrosion or biodegradation, leading to devastating effects described in Section 4.3. Hence, as magnesium-based biodegradable coronary stent implantation is highly regarded as a potential therapeutic application to coronary heart disease, one of the leading causes of death globally [79; 80; 81], extensive ongoing investigations centered around strategies to control its degradation rate [82; 83]. The strategies proposed are consistent with our earlier discussion in Section 4.3. It includes improving the design of the alloy and modifying the surface with coating technologies. Thus far, the literature has suggested that incorporation of both strategies would work [82], although early in vivo trials have demonstrated mixed clinical outcomes [81]. Nonetheless, more research and advancements in this direction will likely persist in the coming years. Technological breakthroughs are critical for magnesium-based cardiovascular applications to be widely adopted for clinical uses.
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 4 The Remarkable Physical Features of Solitons
### Solitons in Healthy EEG Traces  
The occurrence of solitons in EEG traces from healthy individuals has been conjectured. Sen [48] generated action potential-like solitons resembling EEG-like surface waves on a synthetic brain connectome in response to surface deformations produced by local pinch. Yet, part of the electric activity of the healthy human brain is characterized by nervous electric spikes that are reminiscent of solitons. For instance, the neuronal activity in the medial entorhinal cortex displays peculiar ultraslow periodic oscillations from tens of seconds to minutes. Further, electrocorticographic studies suggest that alpha and theta oscillations generate traveling waves propagating in a posterior-to-anterior direction at \(\sim\)0.25-0.75 m/s [49, 50]. The last, but not the least significant, electrical recordings and spectral analysis from many brain regions exhibit neural oscillations at multiple spatial scales that, like solitons, are non-sinusoidal [50].  
Therefore, theoretical mathematical findings, as well as implicit cues from neurophysiological studies, suggest the possibility that at least part of the normal electric EEG activity might involve solitons.
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 4 Experiments
### Specific Realizations
#### 4.1.2 Implementation Details  
The model in this experiment was implemented using Detectron 2. The model was trained using the AdamW optimizer [15] with an initial learning rate of 0.0001. Because the Cityscapes dataset has a relatively small number of training images, weights pre-trained on the ImageNet dataset [16] were used to initialize the model. During training, input images underwent multi-scale augmentation, with the shorter side randomly scaled between 512 and 1024 pixels and the longer side constrained to a maximum of 2048 pixels. Data augmentation techniques, including random horizontal flipping and random cropping, were also employed. During testing, the shorter side of the input images was uniformly resized to 1024 pixels while preserving the original aspect ratio. The ResNet-50 architecture served as the backbone network. Using the same standard backbone allows the performance improvements to be more clearly attributed to our proposed architectural innovations rather than differences in backbone capacity. While more advanced backbones like Swin Transformers might yield higher absolute scores, exploring such combinations was left for future work.Training was conducted for 200 epochs with a batch size of 16, utilizing a polynomial learning rate decay schedule with a warm-up period. The loss function described in Section 3.5 was used, with loss weights \(\lambda_{edge}\), \(\lambda_{ce}\), \(\lambda_{dice}\), and \(\lambda_{cls}\) set to 0.8, 2.0, 5.0, and 5.0, respectively. The experiments were performed on a single NVIDIA Tesla A100 GPU with 40 GB of VRAM, using PyTorch 2.1.0 and CUDA version 12.1.  
Regarding statistical validation across multiple training runs, we acknowledge the importance of such analyses for ensuring result reliability. However, due to significant computational resource and time constraints associated with training these large models, the reported results are based on carefully conducted single training runs using fixed random seeds and standardized protocols to ensure reproducibility of these specific runs. Consequently, we were unable to perform multiple runs to calculate margins of error or conduct statistical significance tests (e.g., \(t\)-tests) for this submission. While, we followed standardized initialization procedures and training protocols to ensure representativeness of our results, including fixed random seeds for reproducibility and adherence to established hyperparameter settings from relevant literature. We recognize this limitation (meaning standard deviations are not reported and statistical significance cannot be claimed) and plan to conduct more comprehensive statistical validation with multiple runs in future work, resources permitting, to further strengthen the reliability of our findings.
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 4 Results
### Performance Assessment
#### 4.4.2 Efficiency Comparison  
In addition to considering accuracy and stability, another important factor to verify the performance of an algorithm is computational efficiency, namely the computational time. Therefore, a comparative evaluation was conducted between the GA and the GA-SIRT where the _RMSE_ varied with the number of iterations. As shown in Figure 9, the _RMSE_ of the GA-SIRT was reduced to 25 ms after 1150 iterations within 4 min. However, the GA performed 4000 iterations to reach the same error within 17 min, in the same computing environment. This is due to the fact that the convergence rate of the GA becomes slower after 1000 generations, and the GA wastes much time on local optimum values. Although the SIRT only needed 30 s to reach the same error, it is not considered herein, due to its instability.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**PCBs**} & \multicolumn{2}{c}{**PCB Recovery by Method 1 * (\%)**} & \multirow{2}{*}{**PAHs**} & \multicolumn{2}{c}{**PAH Recovery by Method 2 **(\%)**} \\ \cline{2-5}  & **Method 3** & & **Method 4** & & **Method 3** & **Method 4** \\ \hline PCB 52 & 24.7–330.9 & 30.4–127.5 & ACL & 65.7–112.7 & 55.7–211.5 \\ \hline PCB 101 & 202.7–342.3 & 253.0–374.6 & AC & 78.4–92.7 & 63.8–233.2 \\ \hline PCB 81 & 0–559.3 & 0.0–768.4 & FA & 71.3–83.9 & 52.7–124.3 \\ \hline PCB 77 & 35.7–212.9 & 32.2–305.0 & PHE & 60.5–80.0 & 80.1–94.1 \\ \hline PCB 118 & 27.6–220.3 & 14.7–233.7 & AN & 62.1–79.6 & 89.9–150.9 \\ \hline PCB 114 & 105.4–338.3 & 50.2–238.1 & FL & 68.7–88.7 & 42.7–83.5 \\ \hline PCB 153 & 83.9–417.3 & 60.9–361.0 & PY & 59.2–112.1 & 47.0–124.9 \\ \hline PCB 105 & 0.0–78.2 & 0.0–55.9 & BaA & 79.6–187.9 & 39.4–129.9 \\ \hline PCB 138 & 29.6–141.7 & 33.1–215.6 & CHR & 61.5–162.1 & 37.3–75.9 \\ \hline PCB 126 & 330.7–358.1 & 260.6–320.8 & BbFA & 79.2–116.2 & 31.8–125.3 \\ \hline PCB 156 & 0.0–138.3 & 0.0–100.4 & BkFA & 115.8–242.8 & 102.5–280.7 \\ \hline PCB 157 & 0.0–39.8 & 15.7–35.2 & BaP & 70–222.9 & 61.0–513.1 \\ \hline PCB 180 & 123.9–5284.0 & 60.9–4119.1 & IP & 45.9–480.4 & 57.9–1152.2 \\ \hline PCB 169 & 0.0–18.5 & 0.0–19.8 & DBahA & 537.2–1323.0 & 408.1–1177.0 \\ \hline \multicolumn{5}{c}{BghiP} & 61.3–371.5 & 33.7–2828.7 \\ \hline \end{tabular}  
* Values obtained with method 1 assumed to be 100%; ** Values obtained with method 2 assumed to be 100%.  
\end{table} 
Table 4: Value ranges of PCB and PAH determinations in the fruits tested with methods 3 and 4 in comparison to method 1 (expressed in %). observed for fluorene and phenanthrene. Recovery with method 3 was 93.6-133.4% for PCBs and 18.4-94.2% for PAHs. As with PCBs, the values of all 16 PAHs determined with methods 2, 3, and 4 differed depending on the method. Differences among results obtained with method 2 and methods 3 and 4 revealed that, especially with heavy PAHs, their contents were overestimated (Figure 4, Table 4). The contents of BaP determined with methods 3 and 4 were three and five times higher than that determined with method 2. Figure 3: Recovery of PCBs congeners (**a**) and PAHs (**b**) (method 3—according to [PERSON] and [PERSON] [32]; method 4—according to [PERSON] et al. [33]).
## 5 Simulation Outcomes and Analysis
### Energy Consumption Analysis  
Figure 12 shows the comparison of elapsed time and energy consumption for each localization algorithm. At this point, there are 25 anchor nodes and 150 unknown nodes in the network evenly distributed in a 100 \(\times\) 100 area. Elapsed time is simply the amount of time that passes from the beginning of a localization process to its end. Energy consumption is the amount of energy consumed when 150 unknown nodes are located. As can be seen from the figure, the localization scheme in this paper has a shorter elapsed time and less energy consumption compared to the improved algorithm of DV-Hop.  
Figure 11: Comparison of various localization algorithms with communication radius: (**a**) localization error and (**b**) average localization error.
## 3 Mathematical Models of the Induction Generator and the Three-Blade Horizontal Axis Turbine
### The Mathematical Model of the Turbine
Since the power of the wind turbine is zero when operating under no load \[\frac{dP_{WT}}{d\omega}=\frac{d}{d\omega}\left[a\left(\frac{V}{\omega}-b \right)\cdot e^{-c(\frac{V}{c})}\cdot V^{3}\right]=-V^{4}\cdot\frac{a}{\omega ^{3}}\cdot e^{-V\hat{c}}\left(\omega-V\cdot c+b\cdot c\cdot\omega\right)=0 \tag{24}\]3 (35) at MAS \[\frac{dP_{WT}}{d\omega}=\frac{d}{d\omega}\left[a\left(\frac{V}{\omega}-b \right)\cdot e^{-c(\frac{V}{c})}\cdot V^{3}\right]=-V^{4}\cdot\frac{a}{\omega ^{3}}\cdot e^{-V\hat{c}}\left(\omega-V\cdot c+b\cdot c\cdot\omega\right)=0 \tag{24}\]4 (36) By substituting \(\omega_{MAXIM}\) into the ratio \(\omega_{OPTIM}/\omega_{MAXIM}\), we obtain \[\frac{dP_{WT}}{d\omega}=\frac{d}{d\omega}\left[a\left(\frac{V}{\omega}-b \right)\cdot e^{-c(\frac{V}{c})}\cdot V^{3}\right]=-V^{4}\cdot\frac{a}{\omega ^{3}}\cdot e^{-V\hat{c}}\left(\omega-V\cdot c+b\cdot c\cdot\omega\right)=0 \tag{24}\]5 (37) and it results in \[\frac{dP_{WT}}{d\omega}=\frac{d}{d\omega}\left[a\left(\frac{V}{\omega}-b \right)\cdot e^{-c(\frac{V}{c})}\cdot V^{3}\right]=-V^{4}\cdot\frac{a}{\omega ^{3}}\cdot e^{-V\hat{c}}\left(\omega-V\cdot c+b\cdot c\cdot\omega\right)=0 \tag{24}\]6 (38) Thus, the system in the unknowns \(a\), \(b\), and \(c\) was obtained: \[ \begin{cases}\frac{c}{1+b\cdot c}=32.028\\ \frac{a}{c}\cdot e^{-1-c\cdot b}=55\\ \frac{4}{9}=\frac{b\cdot c}{1+b\cdot c}\end{cases} \] (39)with the solution \(a=\) 19,182, \(b=\) 1.3877 \(\times\) 10\({}^{-2}\), and \(c=\) 57.65. The mathematical model of \(WT\) was obtained in the form  
\[P_{WT}(\omega,V)=19182\bigg{(}\frac{V}{\omega}-1.3877\cdot 10^{-2}\bigg{)}\cdot e ^{-57.65(\frac{V}{\omega})}\cdot V^{3}\left[\text{W}\right] \tag{40}\]  
The maximum power point, \(MPP\), is achieved by setting the derivative of power with respect to \(\omega\) to zero, resulting in the following:  
\[\frac{dP_{WT}(\omega,V)}{d\omega}=\frac{d\Big{[}19182\Big{(}\frac{V}{\omega} -1.3877\cdot 10^{-2}\Big{)}\cdot e^{-57.65(\frac{V}{\omega})}\cdot V^{3}\Big{]}}{ d\omega}=0 \tag{41}\]  
or  
\[1.1058\cdot 10^{13}\cdot V-3.4528\cdot 10^{11}\cdot\omega=0 \tag{42}\]  
with the solution \(\omega_{OPTIM}=\) 32.026\(\cdot V\). The maximum power value of the \(WT\) corresponds to the optimal \(MAS\) and is  
\[P_{WT-MAX}(V)=55\cdot V^{3} \tag{43}\]  
depending on the wind speed:  
\[P_{WT-MAX}(\omega)=55\cdot\Big{(}\frac{\omega}{32.026}\Big{)}^{3}=1.6744\cdot 1 0^{-3}\cdot\omega^{3} \tag{44}\]  
or the mechanical angular speed.
## 3 Results and Discussion  
### Terahertz Optical Properties of LCP  
Figure 2a,b show the refractive index and absorption coefficient of the LCP, respectively. The sample was rotated such that its direction was perpendicular and parallel to the polarization of the THz beam. The optical properties (refractive index and absorption coefficient) for both the ordinary and extraordinary polarization can be determined, with the THz electric field polarized perpendicular or along the molecular long axis [12]. The measurement bandwidth was limited to 0.2-2.2 THz due to the attenuation loss of the sample. As shown in Figure 2a, we observed a birefringence \(\triangle n=0.1\) across this frequency range. The refractive index for the extraordinary axis was higher than for the ordinary axis, similar to the other liquid crystal materials [11; 12; 13]. The absorption coefficient for the ordinary axis was slightly higher than for the extraordinary axis, similar to the other liquid crystal materials [11; 12; 13].