esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 3 Performance Analysis
### Multihop MI Communication with Statistically Independent and Identical Hops (Balanced Hops)  
First, we develop a system model with IID of channel states between hops. To analyse a basic multihop communication model and its performance, we assume the basic multihop case with two hops (\(H=2,~{}K=3\)). If \(h_{1}\) and \(h_{2}\) are the first and second hops, we obtain errors at the sink for the following two cases.  
Case 1: \(h_{1}\) is correctly received but \(h_{2}\) has an error.  
Case 2: \(h_{1}\) has an error but \(h_{2}\) is correctly received.  
Let \(E_{h}\) be the probability that a bit is received in error at hop \(h\). Then, the probability that \(h_{1}\) is correctly received can be defined using (14) as \((1-E_{h1}(\gamma_{h1}))\), and the probability that \(h_{2}\) is correctly received can be defined by \((1-E_{h2}(\gamma_{h2}))\). For multiple hops \(i\), these definitions can be generalised to \((1-E_{i-1})\) and \((1-E_{i})\), respectively. Thus, the error probability at the \(ith\) hop (for _two_ statistically independent hops) can be given by the joint recursive probability [37]  
\[E_{i=2}(\gamma_{i-1},\gamma_{i})=(1-E_{i-1}(\gamma_{i-1}))E_{i}(\gamma_{i})+( 1-E_{i}(\gamma_{i}))E_{i-1}(\gamma_{i-1}). \tag{15}\]  
Let us denote the end-to-end BER in a multihop network with IID of channel characteristics between hops by \(E_{H}\). By using the analogy employed in (15), we obtain  
\[E_{H}=E(H)(1-E_{H-1})+E_{H-1}(1-E(H)). \tag{16}\]  
By rearranging this equation, we can obtain  
\[E_{H}=E(H)+E_{H-1}(1-2E(H)). \tag{17}\]By using the substitution method that is used to solve linear ordinary differential equations, we can determine \(E_{H-1}\) from (17):  
\[E_{H-1}=E(H)+E_{H-2}(1-2E(H)). \tag{18}\]  
Substituting the expression for \(E_{H-1}\) in (18) into the recurrent relation (17), we obtain  
\[E_{H}=E(H)+E(H)(1-2E(H))+E_{H-2}(1-2E(H))^{2}. \tag{19}\]  
Expanding this series over \(H\) hops yields  
\[E_{H}=E(H)+E(H)(1-2E(H))+\ldots+E_{1}(1-2E(H))^{H}. \tag{20}\]  
Under the assumption of IID of all hops, the following equality holds true:  
\[E_{1}=E_{2}=\ldots=E_{i},\forall i\in 1,2,\ldots,H. \tag{21}\]  
Using (21) and applying summation to (20), we have the final BEP of an end-to-end multihop network under the assumption of IID of channel states between hops:  
\[E_{H}=\sum_{i=0}^{H-1}E_{i}(1-2E_{i})^{i} \tag{22}\]  
According to [38], an upper bound for this end-to-end BER is  
\[E_{H}=1-(1-E_{i})^{H} \tag{23}\]
###### Abstract  
Active myofasical trigger points (MTrPs) in masticatory muscles are associated with a reduced range of motion and muscle weakness within the stomatognathic system. However, it is hard to identify the most effective treatment technique for disorders associated with MTrPs. The objective of this study was to analyze the acute effect of the compression technique (CT) on active maximal mouth opening (MMO) and electromyographic activity of the masseter (MM) and temporalis anterior (TA) muscles in subjects with active myofasical trigger points in the MM muscles. The study group comprised 26 women (mean age \(22\pm 2\)) with bilateral active myofasical trigger points (MTrPs) in the MM. The control group comprised 26 healthy women (mean age \(22\pm 1\)) without the presence of MTrPs in the MM. Masticatory muscle activity was recorded in two conditions (during resting mandibular position and maximum voluntary clenching) before and after the application of the CT to the MTrPs in MM. After the CT application, a significant decrease in resting activity (3.09 \(\upmu\)V vs. 2.37 \(\upmu\)V, \(p=0.006\)) and a significant increase in clenching activity (110.20 \(\upmu\)V vs. 139.06 \(\upmu\)V, \(p=0.014\)) within the MM muscles were observed in the study group, which was not observed within TA muscles. Controls showed significantly higher active MMO values compared to the study group before CT (50.42 mm vs. 46.31 mm, \(p=0.024\)). The differences between the study group after CT and controls, as well as among the study group before and after CT did not reach the assumed level of significance in terms of active MMO. The compression technique appears to be effective in the improvement of the active maximal mouth opening and gives significant acute effects on bioelectric masticatory muscle activity. Therefore, CT seems to be effective in MTrPs rehabilitation within the stomatognathic system.**  
**Keywords: musculoskeletal rehabilitation; masticatory muscles; surface electromyography; trigger points; masseter muscle; temporal muscle**
## 3 The Proposed Solution Algorithms
### Genetic Algorithm (GA)
#### 3.2.3 Mutation Operator  
The mutation operator is responsible for abstaining local optimal solutions and helps to prevent the recessive population. This operator randomly changes one or more genes values 1 to 0 or 0 to 1. The mutation constant is defined at the beginning of the optimization process.  
The genetic algorithm-based optimization process begins with the transportation path (tp) relation base matrix creation. Each tp matrix involves a combination of three different relation matrices, which are UKS, UKP, and UKW. The initial population is involving four different randomly uniform distributed individuals. After the base population creation phase, all of the transportation paths (tp\({}_{1}\) to tp\({}_{4}\)) are evaluated by using GAMS optimization package, respectively, and objective values are obtained. The roulette wheel selection method is used for elitism and provides transferring more powerful individuals' features to the next generations. Selected paths are used for generating new individuals by using single point cross-over operation. The crossing point is defined as the middle position of the path. After the crossing operation, each bit of the new individuals is controlled with the mutation constant. The mutation constant is defined as 1% before the optimization process at the beginning of the script. If randomly produced variable for each bit is greater than mutation value, it is changed to 1 to 0 or 0 to 1 randomly. After the mutation process, a new population is created and the optimization process continues while the iteration limit or deserved objective value is not reached. The optimization script is evaluated several times with 500-1000 iteration. The GA process pseudo-code for maximization problem is given in Table 2.  
\begin{table}
\begin{tabular}{l} \hline \hline Begin; \\ Define iteration limit, mutation constant, cross-over position, \\ Generate initial Transportation Relation Population tp\({}_{\text{Pop}}\) \\ Generate UKS \\ For \(i=1\) to \((s*p)\) \\ For j = 1 to \((t*k)\) \\ tp\({}_{(i,j)}=\text{rand}[0\;1]\) \\ End \\ End \\ Generate UKP \\ For \(i=(s*p+1)\) to \((s*p+p*w)\) \\ For \(j=1\) to \((t*k)\) \\ tp\({}_{(i,j)}=\text{rand}[0\;1]\) \\ End \\ End \\ Generate UKP \\ For \(i=(s*p+p*w+1)\) to \(((s*p+p*w+w*r)\) \\ For \(j=1\) to \((t*k)\) \\ tp\({}_{(i,j)}=\text{rand}[0\;1]\) \\ End \\ End \\ Evaluate each individual solution using GAMS and save fitness values \\ For \(i=1\) to Iteration Limit \\ Select parental individuals by Roulette Wheel Selection operator \\ Generate new candidate offspring solutions by Crossover operator \\ Improve candidate solutions using Mutation Operator \\ Evaluate each offspring solution using GAMS and save new fitness values \\ End \\ Return Best Solution \\ End \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pseudo-code for GA.
## 2 Materials and Methods
GPP is thus expressed in gC/m\({}^{2}\). \[\text{GPP}=\text{IPAR}\times\text{FPAR}\times\varepsilon \tag{7}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**dNBR** & **Severity** \\ \hline \(>\)0.66 & High \\ \([0.44;0.66]\) & Moderate–high \\ \([0.27;0.44]\) & Moderate–low \\ \(<\)0.27 & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification of fire severity based on dNBR. \begin{table}
\begin{tabular}{c c c} \hline \hline
**NDVI** & **Classification** & **Land Cover** \\ \hline \([0.5;1.0]\) & High & Dense vegetation (Pinus and eucalyptus) \\ \([0.2;0.5]\) & Moderate & Moderate vegetation (Pinus and eucalyptus) \\ \([0.1;0.2]\) & Low & Sparse vegetation (shrubs and grassland) \\ \(\leq\)0.1 & None & Bare soil, urban areas, and water bodies \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification of normalized difference vegetation index (NDVI) and definition of vegetation cover. CO\({}_{2}\)flux average values were calculated per GPP pixel for pre- and postfire scenarios immediately before and after the wildfires of 2003, 2007, and 2019. The 1991 wildfires were not included in the statistical analysis, as MODIS Aqua only started to operate from 2002. Linear regressions were established (Equation (8)), where m is the slope and b is intersect X = 0. The \(p\) value, R\({}^{2}\) of the model, and the four assumptions of the residuals (normality, homogeneity of variance, independence, and Cook's distance) were analysed for a confidence level of 95%. \[\text{GPP}=\text{m}\times\text{Mean CO}_{2}\text{flux}+\text{b} \tag{8}\]  
These linear-regression equations allowed for creating 8 days of accumulated GPP maps with 30 m resolution for the pre- and postfire scenarios in 2003, 2017, and 2019.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 5 Results
### ICI Aware Progressive Iterative Receiver
#### 5.2.1 Simulation Results
Now we further compare the complexity of the two iterative receivers, which we claim to be the main advantage of the proposed model. Based on the time spent in computer simulation, the complexity is compared in Figure 9. The simulation is performed using the computer, with Intel(R) Core(TM) i7-7700 [EMAIL_ADDRESS] GHz 3.60 GHz processor and the MATLAB version 2015a used. Corresponding to each signal-to-noise ratio, the average running time of multiple cycles of an OFDM symbol is plotted for the fourth iterations of both receivers, i.e., D = 3 and Dmax = 3. It can be seen that progressive iterative receiver has significantly less runtime, which leads to the conclusion that progressive iterative receiver has lower complexity as compared to non-progressive iterative receiver.
## 5 Experimental Results and Analysis
### Subsection Experimental Setting  
The experiment used eight imaging satellites from the simulation scenario set up by the Satellite Toolkit (STK), and the simulation time was set from 18 June 2024 04:00:00 (UTCG) to 19 June 2024 04:00:00 (UTCG). The orbital parameters of the eight imaging satellites and the parameters of the payloads are listed in Table 1. Among them, the satellite orbital parameters include the half-length axis length (\(a\)), eccentricity (\(e\)), orbital inclination (\(i\)), perigee spoke angle (\(\omega\)), ascending node ruddy longitude (\(RAAN\)), and true proximity angle (\(\varphi\)), and the payload parameters include the field-of-view (\(\theta_{j}\)), single longest online (\(t\max_{S_{j}}\)), maximum side-swinging angle (\(A_{j}\)), average side-swinging rate (\(w_{j}\)), and optimal resolution (\(r_{j}^{s}\)). The parameters of the POM-PSAIDEA algorithm were set with a crossover rate of 0.5, a mutation rate of 0.6, initial population size of 100 (adaptively adjusted according to the method in Section 4.2), and maximum number of iterations of 1000 generations.
## 3 Congestion-Aware EFL-Based Model Aggregation Policy in Edge Computing  
### System Architecture  
In this section, we present the system architectures used to implement the proposed software-defined approach, which are organized into three tiers as follows:  
1. **Participant tier** refers to local devices with image datasets, computing models, and service type indicators.
2. **Edge tier** involves a heterogeneity of multi-service image processing for industrial applications, model labels/features, and allocatable virtual capacities.
3. **Controller tier** comprises three primary processing phases. First, the modified LSTM congestion prediction (MLSTM-CP) module assists in predicting congestion levels. Next, the offloading decision-maker module executes to facilitate the selection of the optimal edge aggregator for offloading. Lastly, the policy installation for model aggregation concludes the tier's functionalities. Comprehensive descriptions of the primary notations are given in Table 1.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 3 Results
### Drought Impacts on Vegetation Growth  
#### 3.3.1 Response of NDVI and SIF to Drought  
The difference between NDVI and SIF responses to drought from annual and seasonal time scales are shown in Figure 9. Annual NDVI and SPEI are significantly positively correlated (R \(>0,p<0.05\)), accounting for 12.67%, mainly distributed in the northwest. The  
Figure 7: Spatial variation of average NDVI and SIF in different seasons (**a**–**h**). (**a**) Annual average NDVI; (**b**) Spring average NDVI; (**c**) Summer average NDVI; (**d**) Autumn average NDVI; (**e**) Annual average SIF; (**f**) Spring average SIF; (**g**) Summer average SIF; (**h**) Autumn average SIF.  
area with significant negative correlation (R \(<\) 0, \(p\)\(<\) 0.05) between annual SIF and SPEI accounted for 3.25%, mainly distributed in the southwest; significant positive correlation area accounts for 5.92%, which is mainly distributed in the northwest, which is relatively consistent with the spatial distribution of NDVI and SPEI. In spring, SIF and SPEI are significantly positively correlated (7.00%) and mainly distributed in the middle of the study area. In spring, NDVI and SPEI are significantly positively correlated (1.79%) and relatively scattered and irregular. Obviously, the area of SIF and SPEI (28.49%) in summer (R \(>\) 0, \(p\)\(<\) 0.05) is significantly higher than that in other seasons, and is higher than that of NDVI and SPEI in summer (20.72%). In autumn, SIF and SPEI showed a significant positive correlation (R \(>\) 0, \(p\)\(<\) 0.05), and the area (1.87%) was higher than that of NDVI and SPEI in autumn (1.13%). In autumn, NDVI was significantly negatively correlated with SPEI (R \(<\) 0, \(p\)\(<\) 0.05), and the area (19.26%) was significantly higher than that of SIF and SPEI in autumn (1.87%). The regions with significant positive correlation between SIF and SPEI in spring, summer, and autumn (R \(>\) 0, \(p\)\(<\) 0.05) were 7.00%, 28.49%, and 2.28% respectively, which were higher than the regions with significant positive correlation between NDVI and SPEI (spring: 1.79%, summer: 20.72%, autumn: 1.13%). Therefore, SIF is more sensitive to drought than NDVI.  
Figure 8: Significance test of seasonal vegetation (**a**–**h**). (**a**) Annual NDVI; (**b**) Spring NDVI; (**c**) Summer NDVI; (**d**) Autumn NDVI; (**e**) Annual SIF; (**f**) Spring SIF; (**g**) Summer SIF; (**h**) Autumn SIF.
## 4 Experimentation and Results
### Experiment System
#### 4.2.2 Implemented Code  
Using the Roboflow dataset, Python code was developed for the DeepSORT tracking and YOLOv5 product identification algorithms. The YOLOv5 model was trained, photos were pre-processed, and DeepSORT was integrated for real-time tracking. The code was created to simplify consumer tracking and product identification while preserving high accuracy.  
The subsequent Algorithm 1 can be used to represent the proposed system.  
```
import cv2 from yolov5 import YOLOv5 from deep_sort import DeepSort # Initialize models yolo = YOLOv5(\"path/to/yolov5s.pt\", device = \"cuda\") deep_sort = DeepSort(\"path/to/deep_sort_checkpoint\") cap = cv2.VideoCapture(\"path/to/video.mp4\") # Change to 0 for webcam def track_objects(): while cap.isOpened(): ret, frame = cap.read() if not ret: break img = cv2.resize(frame, (640, 640)) results = yolo.predict(img) detections = [[\"box, conf, cls] for box, conf, cls in zip(results[\"boxes\"], results[\"confidences\"], results[\"class_ids\"])] track_ids = deep_sort.update(detections, frame) for track_id, box in zip(track_ids, detections): x1, y1, x2, y2, conf, = box label = \"ID: [track_id], Conf: [conf.zf]\" cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2))), (0, 255, 0), 2) cv2.putText(frame, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), cv2.imshow(\"Tracking\", frame) if cv2.waitKey(1) & 0 xFF == ord(q\({}^{\prime\prime}\)): break cap.release() cv2.destroyAllWindows() track_objects()
```  
**Algorithm 1** Custom Object Detection and Tracking with YOLOv5 and DeepSORT
# An Abstract Summarization Method Combining Global Topics
## 5 Experiments
### Experimental Setup  
The experiments in this paper are conducted with the PyTorch deep learning framework on a graphics device, NVIDIA GeForce RTX 3090 TI. Training is performed using the ADAGRAD [34] optimizer, with a learning rate set to 0.15. For CNN/DM dataset, we follow the processing method of [PERSON] et al., and use the non-anonymized version of the data. Word separation is performed using the Stanford University Toolkit core NLP pair, setting  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Set**} & \multicolumn{2}{c}{**Text, Summary) Pairs**} \\ \hline part I & & 2,400,591 & \\ \hline \multirow{6}{*}{part II} & number of pairs & 10,666 \\ \cline{2-3}  & manual score 1 & 942 \\ \cline{2-3}  & manual score 2 & 1039 \\ \cline{2-3}  & manual score 3 & 2019 \\ \cline{2-3}  & manual score 4 & 3128 \\ \cline{2-3}  & manual score 5 & 3538 \\ \hline \multirow{6}{*}{part III} & number of pairs & 1106 \\ \cline{2-3}  & manual score 1 & 165 \\ \cline{2-3}  & manual score 2 & 216 \\ \cline{2-3}  & manual score 3 & 227 \\ \cline{2-3}  & manual score 4 & 301 \\ \cline{2-3}  & manual score 5 & 197 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of LCSTS.  
the original text length to 400, the summary length to 100 for training, and 120 for testing, and the preset vocabulary is set to 50 k. For LCSTS dataset, four types of characters are inserted into the document first, including <PAD> as a complementary character, <UNK> as an OOV words, 
 and 
 as sentence start and end identifiers. The vocabulary size is set to 40 k at (CHARACTER-BASED) and 50 k at (WORD-BASED) using the JIEBA word splitting tool. In the coverage mechanism, the weight of coverage loss is set to 1.  
In our approach, we use a bidirectional LSTM on the encoder and a unidirectional LSTM on the decoder, with the hidden layer dimension being 256 in both. Additionally, our model has 128 dimensional word embeddings. The batch size is set to 16. We use beam search to get the summaries and the beam size is 4.
## 2 Materials and Methods
### Chemometric Modeling  
#### 2.3.1 Classification Approach  
Partial Least Squares-Discriminant Analysis (PLS-DA) is a discriminant classifier developed in order to overcome the issued encountered by [PERSON]'s Linear Discriminant Analysis when handling ill-conditioned data matrices [15]. This approach exploits PLS [16; 17] and a so-called Dummy Y (a response matrix binary encoding the class-belonging) in order to convert a classification problem into a regression one (whose solution is achieved by PLS).  
Very briefly, for a two-class problem (as the one discussed in the present study), given a data matrix \(\mathbf{X}\) (e.g., constituted by the N\({}_{\text{tr}}\) instrumental measures used as calibration set), the dummy \(\mathbf{y}\) is a partitioned binary vector made of N\({}_{\text{tr}}\) elements: each object belonging to Class 1 is represented by the value \(y_{1}=1\), while samples belonging to Class 2 are encoded by \(y_{2}=0\)[18].  
Once the dummy response vector \(\mathbf{y}\) is generated, the model is built by solving Equation (1) and estimating the regression coefficients \(\mathbf{b}\):  
\[\mathbf{y}=\hat{\mathbf{y}}+\boldsymbol{e}=\mathbf{X}\boldsymbol{b}+ \boldsymbol{e} \tag{1}\]  
\(\hat{\mathbf{y}}\) being the vector of predicted responses and \(\boldsymbol{e}\) collecting the residuals.  
After solving the regression problem in Equation (1) by PLS, a further step is needed to achieve classification, since the predicted response \(\hat{\mathbf{y}}\) is not binary anymore but real-valued: accordingly, a suitable classification rule has to be built based on the values of \(\hat{\mathbf{y}}\). For a two-class problem, such as the one in the present study, this usually translates in setting a threshold \(y_{thr}\) to \(\hat{y}\), so that if \(\hat{y}_{i}>y_{thr}\), the sample is assigned to class 1, and otherwise it is predicted to be class 2. In particular, in the present work the threshold was calculated based on the probabilistic approach proposed by [PERSON] and coworkers [19].  
When new, unknown, samples have be to classified, their predicted responses \(\hat{\mathbf{y}}_{new}\) are calculated based on the measurements \(\boldsymbol{X}_{new}\) and the regression coefficients \(\boldsymbol{b}\) estimated on the training set, and the classification rule described above is then applied to assign each individual to one of the categories under study.
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 1 Introduction
Considering at least two NLOS paths are required to locate targets behind the corner, the multipath returns with the two largest echo amplitudes are selected to obtain candidate target positions, and the target true position is further derived by a correlation coefficient function [29; 30]. Researchers also attempted to solve the NLOS target localization problem under real-world conditions with the latest deep learning methods [31; 32; 33]. The studies above based on analytical geometry modeling of multipath propagation implied prior information of the propagation model determined by the specific scenario, while the propagation model mismatching leads to significant localization error in the actual complex scene, especially in dense clutter environments. A widely used solution to predict EM wave propagation characteristics precisely is the ray-tracing technique based on physical optics (PO) which approximates the EM wave propagation in terms of rays [34; 35]. The reflection, diffraction and transmission paths from the radar to the target can be computed with known scene information. A sequential Monte Carlo PHD filter based on real-time ray-tracing prediction is implemented to track mobile terminals in a large-scale urban scene [36]. For simple scenarios, validation of a 2D ray-tracer has been proved by comparison with measured data [37]. The ray-tracing technique is also used to generate multipath fingerprints for localization methods based on DOA, time difference of arrival or received signal strength [38; 39; 40]. However, real-time ray-tracing calculation is time-consuming in complex environments because of its high computational complexity. This paper concentrates on a preliminary offline calculation for ray-tracing prediction of multipath signals combined with high-accuracy real-time online localization in dense clutter environments. The main contributions of this paper are summarised as follows:Derivation of the likelihood of cluttered multipath measurement with respect to the reference multipath parameters predicted by the ray-tracing technique and the approximation of multipath likelihood;
2. Proposal of an accurate target localization method based on NLS optimizations of associated multipath measurements;
3. Simulation results in various conditions validating the robustness and performance of the proposed method. The remainder of this paper is organized as follows. Section 2 introduces the multipath propagation model and the multipath measurement model used in this paper. Section 3 describes the proposed localization algorithm in detail. In Section 4, simulations are carried out in different conditions to analyze the performance of the proposed method. Finally, conclusions are given in Section 5.