esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
By international consensus, cancer cachexia has been clarified as \"a multifactorial syndrome defined by an ongoing loss of skeletal muscle mass with or without loss of fat mass that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment\" [38]. It has been suggested that elevated lipolysis might account for fat loss in cancer cachexia, although the precise underlying physiopathological mechanism is not fully understood [6; 39]. Interestingly, fat loss seems to precede muscle loss and it has been associated to shorter survival [3; 40]. Thus, abdominal fat as a novel biomarker of cancer cachexia in the present study was chosen because it has not previously been investigated in ccRCC and is potentially of great clinical importance as it might show changes earlier than muscle tissues. Moreover, this is the first study linking peritumoral collateral vessels adjacent to ccRCC, a finding that can be present in CT or MR studies, to cancer cachexia which is a clinically important diagnosis to make because it is associated to prognostic impact. Thus, the presence of peritumoral collateral vessels associated to the reduction of SAT should be further tested as potential marker of cancer cachexia with prognostic implications. Our results are not likely to be influenced by the age of the patients since no statistically significant difference between groups was present in terms of age. Moreover, by subgrouping the patients according to the history of previous malignancy or neoadjuvant treatment, we found no statistically significant differences suggesting no significant impact of these potentially confounding variables on our results. Of note, only 4 out of 54 ccRCCp patients received neoadjuvant therapy, making it highly unlikely as a bias for the evaluation of collateral vessels. Finally, no difference in TAT, VAT and SAT areas and VAT/SAT ratio between low and high Fuhrman grade groups was found, suggesting a lack of association between histopathological signs of aggressive disease and abdominal fat distribution. However, a high Fuhrman grade has been recently reported as significantly more prevalent in patients with ccRCC and peritumoral collateral vessels [13]. The retrospective design of this study was associated with limitations such as a lack of some clinical data including BMI, hormonal status, histological data related to staging, progression-free survival (PFS) or overall survival (OS). PFS and OS data would have been important to show the impact of SAT reduction on the prognosis of ccRCC patients as well as the impact on surgery or neoadjuvant medical treatments. Further studies, including larger samples more representative of the whole society, will evaluate SAT loss in ccRCC patients to clearly validate this potential biomarker for cancer cachexia.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 5 Experiment Results and Analysis  
### Dataset Processing  
The proposed approach was verified in simulation experiments. The Shanghai Telecom base station dataset [21; 22; 23], which contains millions of data elements consisting of location and user Internet logs, etc., was used for these experiments. User Internet-access start and end points were provided by this dataset. Therefore, we preprocessed the dataset and selected the number of users accessing the Internet and their locations at 04:00, 08:00, 12:00, 16:00, 20:00, and 24:00 in a single day.
## 3 Results
### Simulation of 'Real' \(f^{esc}\) in Discontinuous Forest Canopies
For the estimations using high FVC (i.e., FVC equal to 0.8), however, the estimation accuracy of SIF escape probability using fAPAR\({}_{leaf}\) as input was slightly better than using canopy interceptance as input. Figure 4: Comparison of SIF escape probability estimated using SIT (i.e., \(f_{SIT}^{\text{esc}}\)) with reference values that were simulated by DART (i.e., \(f_{DART}^{\text{esc}}\)). The calculation formula of \(f_{SIT}^{\text{esc}}\) was shown in each sub-plot. \(f_{SIT}^{\text{esc}}\) in the first column (**a,c**) were estimated using \(NIR_{T}\); while in the second column (**b,d**) using \(NIR_{\text{p}}\). \(f_{SIT}^{\text{esc}}\) in the first row (**a,b**) were estimated using \(i_{o}\); while in the second row (**c,d**) using fraction of absorbed photosynthetically active radiation (fAPAR). BS means black soil (i.e., non-reflecting background). Relative error was calculated as \((f_{SIT}^{\text{esc}}-f_{DART}^{\text{esc}})/f_{DART}^{\text{esc}}\). Figure 3: DART canopy interceptance (\(i_{o}\), in blue color) and fraction of absorbed photosynthetically active radiation only by leaves (fAPAR\({}_{leaf}\), in red color). Three simulation results for each of them are shown here: FVC = 0.3, 0.6, and 0.8. We also compared the estimated SIF escape probabilities using \(NIR_{T}\) as input and using \(NIR_{v}\) as input that was mentioned in Table 1 (i.e., comparison between Figure 4a,b, or Figure 4c,d). The accuracy of the SIF escape probability was better when using \(NIR_{v}\) than using \(NIR_{T}\). This was especially true with high FVC (i.e., FVC equal to 0.8) as illustrated by the comparison of the estimation accuracy between 0% and 50% with \(NIR_{v}\) (Figure 4b) compared to the accuracy between 50% and 180% with \(NIR_{T}\) (Figure 4a). We further calculated total emitted SIF (\(SIF^{SIT}_{tot}\)) using the estimated SIF escape probabilities (\(f^{esc}_{SIT}\)) mentioned above. Here only the results of \(\frac{NIR_{v}}{i_{v}\cdot\omega_{\lambda}}\) were used to estimate \(SIF^{SIT}_{tot}\) (corresponding to the \(f^{esc}_{SIT}\) in Figure 4b). \(SIF^{SIT}_{tot}\) in Figure 5a was calculated using specific SIF escape probabilities (\(f^{esc}_{SIT}\)) that were estimated at varying solar zenith angles from 20\({}^{\circ}\) to 70\({}^{\circ}\). While for Figure 5b, \(SIF^{SIT}_{tot}\) was calculated using constant SIF escape probabilities (\(f^{esc}_{SIT}\)) estimated at a fixed solar zenith angle (i.e., SZA = 70\({}^{\circ}\) and SZA = 20\({}^{\circ}\) for (b) and (c), respectively). In other words, SIF escape probabilities (\(f^{esc}_{SIT}\)) estimated at SZA = 70\({}^{\circ}\) or SZA = 20\({}^{\circ}\) were used to assess other cases when solar zenith angles change from 20\({}^{\circ}\) to 70\({}^{\circ}\). It is clear that \(f^{esc}_{SIT}\) estimated at high solar zenith angles produces higher accuracy (R\({}^{2}\) = 0.95) of \(SIF^{SIT}_{tot}\) than that estimated at varying solar zenith angles (R\({}^{2}\) = 0.85) and low solar zenith angles (R\({}^{2}\) = 0.81).
# Retracted: W-GPCR Routing Method for Vehicular Ad Hoc Networks
## 1 Introduction
* It does not work well in a sparse network, and can easily fall into a routing hole, resulting in routing death. In order to solve these shortcomings, a new Weighted-Greedy Perimeter Coordinator Routing (W-GPCR) method is developed in this study to address the following aspects of the GPCR approach:  
* The node's movement direction and density are also taken into consideration, and the optimal next-hop node is selected through weight calculation. * Through the weight calculation, the direction of data packet delivery will always converge towards the target node, which solves the drawbacks of the right-hand rule. * Using a restricted forwarding strategy, weight selection can prevent data packets from being delivered again in the direction of routing holes, and find other hidden paths to deliver data packets to target nodes.
# Korean Sign Language Recognition Using Transformer-Based Deep Neural Network
## 5 Experimental Evaluation
### Performance with the Benchmark KSL and Proposed Dataset  
We considered two benchmark datasets in the experiment: KSL and our proposed dataset. KSL is the famous Korean sign language dataset we used to evaluate the proposed model. Figure 5 shows the confusion matrix showing that most of the Korean alphabet produces 100% accuracy; two signs achieved more than 95%, and two signs achieved in the range 84% to 90% accuracy. Figure 6 demonstrates the proposed model's precision, recall, F1-score and accuracy with the proposed 20 classes of Korean sign language datasets. The figure demonstrates 98.50%, 98.35%, 98.40%, and 98.30% for precision, recall, F1-score and accuracy, respectively, which proves the effectiveness of the proposed model. Figure 7 visualizes the proposed model's precision, recall, F1-score and accuracy with the KSL dataset with 20 classes. The figure demonstrates 94.80%, 87.20%, 90.25%, and 89.05% for precision, recall, F1-score and accuracy, respectively, which proves the effectiveness of the proposed model.  
Table 1 shows the performance accuracy of the proposed model with the proposed and KSL datasets. For the KSL dataset, our model produced 89.00% accuracy, and for the proposed dataset, our model achieved 98.30% accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset Name** & **Accuracy [\%]** & **Parameters** & **Flops** \\ \hline Proposed Dataset & 98.30 & 1.52 M & 1.17 GMac \\ \hline KSL & 89.00 & 1.5 M & 245.5 MMac \\ \hline \end{tabular}
\end{table}
Table 1: Performance accuracy of the proposed model.  
Figure 5: Confusion matrix of the proposed dataset.  
Figure 6: Precision, recall, F1-score and accuracy of the proposed dataset.  
Figure 7: Precision, recall, F1-score and accuracy of the KSL dataset.
## 3 Proposed Methodology
### Customization of Loss Functions
#### 3.3.2 Regression Stage  
In the regression stage of the proposed framework for crash frequency analysis, the focus shifts towards accurately predicting the actual number of crashes, given that a crash is predicted to occur. This necessitates a nuanced approach to modeling count data, which inherently possesses unique characteristics and challenges. CatBoost, by default, utilizes the Mean Squared Error (MSE) loss function for regression tasks. Given the specific nature of crash frequency data, which predominantly comprise count data, MSE might not be the most effective loss function. Count data, especially in the context of crash frequency, can exhibit overdispersion, where the variance exceeds the mean, or underdispersion, where the variance is less than the mean. To address this and better model the count nature of crash data, the utilization of the Poisson loss function is proposed for the regression stage of the framework. The Poisson distribution is a natural choice for modeling count data, particularly for its capacity to handle the discreteness and non-negative nature of such data. The Poisson loss function, often referred to as the log-likelihood of the Poisson distribution, is given as follows:  
\[L_{Poisson}=-{\sum_{i=1}^{n}(y_{i}\text{log}(\hat{y}_{i})-\hat{y}_{i}-\text{ log}(y_{i}!))} \tag{10}\]  
where \(y_{i}\) represents the actual count value, \(\hat{y}_{i}\) is the predicted count value, and \(n\) is the total number of observations. The term \(\text{log}(y_{i}!)\) in the Poisson loss function does not vary with the predictors and is solely dependent on the actual counts \(y\). This independence from the model's inputs means that, during optimization, the term does not influence the model's ability to learn from the data. Consequently, for model training and optimization, this constant term can be omitted without affecting the relative evaluation of different model configurations. Therefore, the final Poisson loss function becomes  
\[L_{Poisson}=-{\sum_{i=1}^{n}(y_{i}\text{log}(\hat{y}_{i})-\hat{y}_{i})} \tag{11}\]  
The utilization of the Poisson loss function aligns the model's optimization process with the distributional properties of count data. Unlike the MSE, which penalizes deviations from the actual values uniformly, the Poisson loss introduces a dependence on the actual counts, making the penalty for deviation more sensitive to the scale of the data. This is particularly beneficial in crash frequency modeling, where the range of counts can vary significantly across observations.
## 3 Trajectory Optimization Design
### Adaptive Genetic Algorithm
#### 2.2.1 Cross Operation  
The cross operation is an operation of partially exchanging the coding of two individuals to be optimized to generate new individuals. Since the time population is small and the single-point cross operation is simple [25], the cross operation is realized by a single point crossing method. The working principle of the cross operation is shown in Figure 5.  
Two pairs of individuals are respectively set as: \(A=(a_{1},a_{2},\ldots,a_{n-1},a_{n})\),  
\(B=(b_{1},b_{2},\ldots,b_{n-1},b_{n})\). Single point cross operation is carried out on individuals A and B, and the two newly encoded individuals produced are:  
\[\left\{\begin{array}{l}A_{1}=(a_{1},a_{2},\ldots,a_{k-1},a_{k},b_{k+1},b_{k+2 },\ldots,b_{n-1},b_{n})\\ B_{1}=(b_{1},b_{2},\ldots,b_{k-1},b_{k},a_{k+1},a_{k+2},\ldots,a_{n-1},a_{n}) \end{array}\right., \tag{21}\]
# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 3 Results
### Joint Performance of Expert and ML  
In Table 4, it is evident that incorporating experts' predictions as a feature has led to a significant improvement in the performance of all models. LR and XGB have benefitted the most from this addition, displaying notable enhancements across all metrics and demonstrating a stable performance over different iterations. While DT also exhibits improvement, its performance varies considerably across iterations. Upon selecting the best iteration for each algorithm and comparing mean scores, based on a five-fold CV, it is apparent that LR and DT have a similar level of performance. Table 5 compares the performance of the best iteration of each algorithm with that of the expert. Although DT and LR marginally outperform the expert, it is worth noting that they were heavily reliant on the expert feature. On the other hand, XGB significantly outperformed the expert, achieving a very good mean \(R^{2}\) score of 0.72. Figure 6 presents a comparison of the mean MSE and its standard deviation for the estimated injury duration between the expert, XGB, and XGB with the expert feature. The figure clearly shows that the combination of XGB and expert feature has the lowest mean MSE and standard deviation compared to a regular XGB and the expert using the LOO test. This indicates that incorporating expert estimations as a feature in the XGB model greatly improves the accuracy and consistency of the injury duration predictions. To better demonstrate the benefits of adding the expert feature to the XGB model, a more detailed inspection is necessary. Figure 7 shows the estimated recovery duration in days on the x-axis and the true recovery duration in days on the y-axis. It is clear that the XGB model with the expert feature produces more accurate predictions compared to the regular XGB and the expert estimations alone. The XGB model's predictions are better aligned with the ideal prediction line. Figure 5: Comparison of the estimations made by the expert (blue circles) with the predictions of the best performing algorithm, XGB (orange asterisks). The black dotted line shows ideal predictions, while green and red backgrounds indicate cases where the recovery was faster or slower than expected.
## 2 Results and Discussion
### Oil Absorption Performance Analysis  
Figure 4a shows the oil absorption rate of the P(St-BMA) microspheres at different monomer ratios. When the monomer ratio was 2:8, the oil absorption rate was 1.45. In addition, when the monomer ratio was 6:4, the oil absorption rate reached a maximum. As the amount of BMA with flexible long chain lipophilic groups became too large, the structure was easily dissolved in the hydrophobic phase, resulting in disruption of the three-dimensional network structure. More importantly, in the presence of too many long-chain groups, the three dimensional network structure was found to be relatively \"loose\", reducing the dissolution properties in hydrophobic media [25]. This latter finding is the reason why the oil absorption ratio was low when the BMA ratio was high. However, St exhibiting a rigid benzene ring structure could improve the rigidity of the three-dimensional network structure, thereby enhancing the oil absorption performance. Therefore, by adjusting the flexible structure and the rigid structure for BMA and St, the oil absorption rate reached a maximum at a monomer ratio of 6:4. However, the oil absorption rate decreased as the St monomer ratio continued to increase. This latter finding was due to the microspheres exhibiting too many rigid groups, leading to a structure that was too rigid, resulting in a three-dimensional network structure that was too tight, ultimately reducing the oil absorption rate.  
Figure 4b shows the oil absorption rate of P(St-BMA) microspheres at various amounts of DVB crosslinking agent. The oil absorption rate first increased and then decreased as the amount of DVB increased. When the amount of DVB was in the range of 0.5% to 2.0%, the three-dimensional network  
Figure 4: **(a)** Oil absorption characteristics of the P(St-BMA) microspheres at different monomer ratios and (b) oil absorption characteristics of the P(St-BMA) microspheres at different dosages of the DVB crosslinking agent.  
Figure 3: **(a)** TGA curves of TTO, P(St-BMA) microspheres, and TTO microspheres, and (b) TGA and DTG curves of the TTO microspheres.  
structure of the microspheres was found to be loose to moderate, resulting in a great increase in the oil absorption rate. According to the Flory theory [26], after reaching a certain degree, the oil absorption rate was inversely proportional to the crosslinking density. As the amount of DVB increased further, the oil absorption rate decreased. This result was mostly due to the crosslinking density that further increased due to the three-dimensional network structure that was too tight, which was not favorable to the adsorption of TTO.
## 4 Experimental Verification  
### Experimental Instruments  
We designed an experiment in the laboratory to verify the statistical model built in Section 2. Figure 7 shows the schematic diagram. An optical fiber collimator is linked to a fiber-output laser as the signal laser source. The divergence angle is not small enough so that telescope is applied as the beam expander. Transmitting though the turbulence generated by equipment 3, the laser beam is received by a telescope and then split into two beams by a BS. One is reflected into a wavefront sensor to calculate \(r_{0}\), the other beam passes though the BS and incident into the coupling lens. An SMF is placed behind the lens on a six-axis stage to maximize the CE.  
The layout of the experiment is shown in Figure 8a. A fiber collimator linked to a fiber-output laser with the wavelength of 1550 nm is put at position 1, while the light source and its divergence angle is 1.2 mrad. Expanded by a beam expander consisting of a set of lenses placed in position 2 and 4, the emergent beam transmits though the turbulence. A heat gun with a designed air outlet is set at position 5 as the turbulence generator. The wind speed and temperature are adjustable to control the intensity of the turbulence. The lenses placed at positions 10 and 15 form the receiving telescope. A BS is laid at position 13 to split the incident beam. One of the split beams is received by a Hartmann-Shack wavefront sensor placed behind the BS at position 14 to measure \(r_{0}\), and the other is reflected by some mirrors and transmitted into a set of lenses used as the coupling lens at position 19. An SMF is set at a six-axis stage behind the coupling lenses and linked to an optical power meter.  
Figure 6: PDF of coupling efficiency as \(\beta\) varies from 2.7 to 3.76.  
Figure 7: Schematic diagram of the experiment. 1—optical fiber collimator, 2—beam expander, 3—turbulence generator, 4—receiving telescope, 5—beam splitter, 6—coupling lens, 7—SMF, 8—wavefront sensor.  
The data are essential to calculate the CE and analyze its statistical distribution. Figure 8b shows the details of the coupling parts, marked with a green dashed line in Figure 8a. All the mirrors shown in Figure 8a are used for bending the optical path. Moreover, the experiment is also set to verify the tracking algorithm designed for an FSO system, so some FMSs are laid in the optical path. During the experiment for this paper, all the FMSs were frozen at a certain position and will not introduce extra errors.  
Table 5 shows the key parameters of the equipment related to the experiment.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### Conclusion  
The analysis highlights that researchers aim to build adaptable solutions capable of functioning across diverse environments, leading to minimal focus on specific deployments. In these general cases, high-quality data take precedence, often overshadowing considerations related to network infrastructure.  
Nevertheless, some architectural patterns stand out. For well-established technologies, SDNs, Edge, and Edge-IIoT emerge as popular choices. SDNs have been widely adopted due to their scalability and programmability. Similarly, edge computing has emerged as a vital component in distributed computing by bringing computation closer to data sources, reducing latency, and improving efficiency. Additionally, WSNs have been instrumental in industrial contexts, supporting distributed sensor networks for real-time data collection and analysis. While these technologies are widely known, blockchain has been an emerging topic in research in recent years. Apart from its origins, blockchain and the ledger system's use cases have been explored since, and evolved into a versatile, decentralized framework  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Infrastructure** & **\# of Papers** & **Citations** \\ \hline General IoT network, dataset-focused & 32 & [28; 29; 30; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82] \\ Software-Defined Networks (SDNs) & 9 & [10; 31; 32; 33; 34; 35; 36; 37; 38] \\ Edge, Edge-IIoT, Wireless Sensor Network (WSN) & 6 & [39; 40; 41; 42; 43; 44] \\ Blockchain-integrated networks (BINs) & 5 & [45; 46; 47; 48; 49] \\ Vehicle-related, Internet of Vehicles & 2 & [52; 53] \\ Medium-sized networks & 2 & [50; 51] \\ Home-built network & 1 & [54] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Studies categorized by infrastructure.  
across various domains. It offers promising enhancements in security, but introduces performance trade-offs, as it is costly to monitor all transactions.