esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Post Hoc Analysis  
In the post hoc analysis, three subgroups of the WOMAC score were analyzed, including the WOMAC pain score, the WOMAC stiffness score, and the WOMAC physical function score. There are three studies with three RCTs reporting the aforementioned outcomes [34; 37; 38]. In the group of WOMAC pain score, Figure 6 reports that there was a significant difference in pain relief between willlow bark and analgesic drugs or placebo for patients with arthritis (SMD: \(-\)0.30; 95% CI: \(-\)0.61, 0.00, \(p\) = 0.05; I2: 77%; quality of evidence: very low). In the group of WOMAC stiffness score, Figure 7 reports that there were no significant differences in symptom relief between willlow bark and analgesic drugs or placebo for patients with arthritis (SMD: \(-\)0.06; 95% CI: \(-\)0.36, 0.24, \(p\) = 0.68; I2: 63%; quality of evidence: very low). In the group of WOMAC physical function score, Figure 8 reports that there were no significant differences in physical function improvement between willlow bark and either analgesic drugs or placebo for patients with arthritis (SMD: \(-\)0.24; 95%, CI: \(-\)0.54, 0.07, \(p\) = 0.13; I2: 76%; quality of evidence: very low).  
Figure 5: Forest plot demonstrating overall adverse events between willlow bark and placebo. A lower adverse event is shown in favor of willlow bark over placebo. The study conducted by [PERSON] et al. comprised two distinct trials. The first trial focused on osteoarthritis and was designated as ”C Biegert 2004”. The second trial, which centered on rheumatoid arthritis, was labeled “C Biegert 2004”. See refs. [34; 35; 36; 37; 38].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Willow Bark** & **Placebo** \\ \hline Skin and appendage disorders & 9 & 7 \\ GI system disorders & 14 & 26 \\ Central and peripheral nervous system & 14 & 19 \\ disorders & 7 & 4 \\ Psychiattic & 1 & 0 \\ Cardiovascular disorder & 1 & 1 \\ Urinary system disorder & 1 & 2 \\ Vision disorders & 1 & 0 \\ Vascular disorders & 0 & 1 \\ Infections & 2 & 2 \\ Change in hemogram & 2 & 2 \\ Hypertriglyceridemia & 1 & 1 \\ Musculoskeletal system disorders & 4 & 5 \\ Disc prolapse & 1 & 0 \\ Other adverse events & 3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adverse events comparing willlow bark and placebo.
## 4 Calibration Comparison of Landsat 8 and Sentinel 2
### Summary of Results  
In order to combine all methods of validating the calibration of MSI with OLI, the ratios of the MSI and OLI results are taken. This metric should serve to remove any systematic errors within the models and provide for a per-model comparison of OLI and MSI. Results are shown in Figure 11. The DIMITRI-PICS model predicts that OLI and MSI are within 1% for all VNIR bands except the CA, which is likely due to the use of Landsat pre-collection data. The APICS model indicates the instruments are within 2% for all bands. The UAz RRV comparison shows that MSI and OLI are within 2% across all bands, though with MSI consistently brighter than OLI. Even the SDSU vicarious results, which indicate the largest absolute calibration errors, suggest the instruments are calibrated to within 4% of each other.  
Figure 10: Vicarious calibration results for University of Arizona (UAz) at the Railroad Valley Playa, Nevada and South Dakota State University (SDSU) at the Brookings vegetated site for OLI and Sentinel-2A MSI.
## 3 Results
### Supervised Machine Learning Models
#### 3.5.2 Model Evaluation  
Although very often regression models (especially linear regression) are evaluated indirectly, by defining the model only by statistically significant variables, we decided to implement direct evaluation of our models. For this purpose, we used error metrics. In other words, the statistical significance of the variables in the created prediction models was not important to us, but the main criterion for evaluating the models was the predictive accuracy. The evaluation of the models therefore consisted of a comparison between the actual value and the value predicted by our models. We used the accuracy metrics based on residual characteristics. We calculated the residuals as follows  
\[e_{t}=y_{t}-\hat{y}_{t} \tag{4}\]  
This error was defined as the difference between the predicted value of \(\hat{y}_{t}\) and the actual value of \(y_{t}\). Using residuals, we then calculated the residual accuracy metrics. We used mean square error (MSE) and mean absolute percentage error (MAPE) metrics to determine the accuracy of our created models. We calculated the MSE as follows:  
\[MSE=\frac{1}{n}SSE=\frac{1}{n}\sum_{t=1}^{n}{(y_{t}-\hat{y}_{t})^{2}}=\frac{1 }{n}\sum_{t=1}^{n}{e_{t}^{2}} \tag{5}\]  
where \(\hat{y}_{t}\) was the predicted value, \(y_{t}\) was the actual value and \(n\) was the number of observations in the defined set of observations. We calculated MAPE as follows:  
\[MAPE=\frac{1}{n}\sum_{t=1}^{n}{\frac{|e_{t}|}{y_{t}}\times 100} \tag{6}\]  
We always calculated the MSE and MAPE error on both the training set and the test set. During cross-validation, MAPE was a criterion function on the validation set, according to which we optimized our models. We considered the best model to be the model in which the MAPE metric was the lowest on the validation set.
## 5 Discussion of Simulations and Results
### Comparative Study
From Table 9, it can be seen that, from the perspective of economics, our proposed GDCAC approach is the most promising approach for EM modeling considering the renewable power penetration among the three approaches. The reason for low performance when applying Q-learning approach is due to both the discrete action and state sets within it. In addition, simulation with the Q-learning approach takes only about 3.1 min to reach a final result, which is the lowest among the three approaches. However, the time complexity of our proposed GDCAC approach (about 3.2 min) is also acceptable for hour-ahead EM modeling so that we can extend it to the modeling and simulation of more realistic and complex EM systems.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
###### Abstract
Although the above methods have achieved performance improvements for recommendations to some extent, the following problems still exist: how to supplement the missing information of meta-paths; how to learn the display representation of paths or meta-paths in recommendation tasks; how to fuse the direct and indirect information in heterogeneous information networks' assisted recommendations. To solve the above problems, this paper proposes a recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). It uses the neighbor node information of users and items to mine and supplement the missing information of meta-paths. By introducing the attention mechanism in DL, the representation vectors of users and items under different meta-paths are fused, and the feature vector representation capability of the user and item node sequence is enhanced to generate the final representation of users and items. The algorithm captures and fuses indirect information in the HIN at the node level and path level to achieve more accurate recommendations.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 8 Challenges and Opportunities
### Sports Data Ethics, Privacy and Security
However, despite the widespread adoption of WTs in the daily routines of both amateur and professional athletes, most sports leagues exhibit reservations regarding their in-game use [11]. This reluctance is primarily attributed to significant apprehensions surrounding cyberattacks and potential data breaches that could impact the sports industry and sports in general. In addition to the aforementioned issues, other challenges pertaining to privacy, security, and confidentiality are posed by wearable technologies in the domain of sports. * **Data Breach and Leaks:** WTs are frequently employed for the collection and processing of personal health and performance data, encompassing details such as physical condition, location information, and other personal particulars. However, should this data fall into the hands of malicious actors or be disseminated through unauthorized access, it could lead to severe breaches of privacy, increasing the risk of misuse or undesirable utilization of users' personal and health information. * **Data Security:** The deployment of wearable technologies introduces security vulnerabilities concerning users' health and performance data. Unauthorized access, data manipulation, or data loss could compromise data protection, thereby endangering the accuracy, integrity, and confidentiality of the information. * **Legal and Regulatory Issues:** The integration of wearable technologies in sports engenders legal and regulatory complexities. Adherence to various legal regulations is imperative throughout the processes of data collection, processing, and sharing, particularly when handling sensitive information such as health data. Violations of these regulations may result in legal repercussions and expose companies or organizations to criminal or legal consequences. * **Ethical Concerns:** Concurrent with the adoption of wearable technologies in sports, ethical considerations assume paramount importance. Ethical principles governing the collection, use, and dissemination of personal data should be meticulously observed to safeguud athletes' privacy, autonomy, and rights. Furthermore, equitable and unbiased utilization of these technologies is crucial, ensuring equal opportunities for all. Addressing these issues is imperative to maintain trust among athletes, sports organizations, and technology providers. Appropriate technical safeguards, adherence to legal regulations, and the application of ethical principles should be rigorously enforced to fortify privacy, security, and confidentiality within the realm of wearable technologies in sports.
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 2 Methods
### Construction of the ResNet1D-AttLSTM Model
#### 2.2.3 Trajectory Classification Based on Multilayer Perceptron  
First, to effectively integrate the features extracted by the ResNet1D module and the AttLSTM module, we applied global average pooling (GAP) to the feature maps output by the ResNet1D module. By calculating the average value of each channel across the time dimension, we obtained a fixed-length one-dimensional vector. For the output feature  
Figure 5: Architecture of an LSTM cell unit.  
of the AttLSTM module, we selected the state at the last time step as its one-dimensional representation. These two processing methods not only reduce the number of parameters but also retain the main information of the features, thereby helping to prevent overfitting.  
Next, we concatenated the two aforementioned one-dimensional feature vectors to form a comprehensive feature vector. This vector was then fed into a multi-layer perceptron (MLP) for classification tasks. The configuration of the MLP in this study is as follows:  
* **Number of layers:** 2 hidden layers.
* **Number of neurons per layer:** i. The first hidden layer has twice the number of neurons as the dimensionality of the comprehensive feature vector. ii. The second hidden layer has half the number of neurons as the first hidden layer.
* **Activation Function:** ReLU activation function is used for all hidden layers.  
Finally, the MLP outputs a one-dimensional vector of length \(s\), where \(s\) equals the number of ship types. For each value \(z_{i}\) (\(1\leq i\leq s\)) in the output vector, we apply the softmax function for probability normalization to obtain the probability distribution across categories, as shown in Equation (12):  
\[P_{i}=\frac{e^{z_{i}}}{\sum_{k=1}^{s}\ e^{z_{k}}} \tag{12}\]  
where \(P_{i}\) represents the probability that the ship trajectory is predicted to be of the \(i\)-th type and satisfies \(\sum_{i=1}^{s}P_{i}=1\). The category with the highest probability value is chosen as the predicted class for the corresponding ship trajectory.
## 3 Anaerobic Digestion of Food Waste
### Key Parameters  
#### 3.2.1 Temperature  
Temperature is one of the most significant parameters, as it influences the activity of enzymes and co-enzymes responsible for hydrolysis development, methane yield, and digestate quality [88; 89; 112]. Anaerobic microbes and bacteria can grow in psychrophilic (10-30 \({}^{\circ}\)C), mesophilic (30-40 \({}^{\circ}\)C), and thermophilic (50-70 \({}^{\circ}\)C) conditions. As a general rule, the performance of AD increases with temperature, as well as the bacteria growth rate, metabolic rate, and biogas production rate [113; 114]. Reference [115] pointed out that thermophilic conditions produce double biogas than phychrophilic conditions. As reported by [116], thermophilic reaction suffers less from ammonia inhibition.  
However, higher temperatures also represent some disadvantages. While endogenic reactions (acetylogenesis) are enhanced with temperature, exergonic reactions, such as methanogenesis, reduce their performance. That is the statement for the events reported by [117], where in mesophilic conditions, 70% of the biogas is formed via acetoclastic methanogenesis, and the rest through hydrogenotrophic methanogenesis. In psychrophilic conditions all methane is produced by acetoclastic methanogens, hydrogen is oxidized and transformed, together with CO\({}_{2}\) into acetate, and then into methane. However, in thermophilic conditions, homoacetogenic bacteria are capable of transforming acetate into hydrogen and carbon dioxide (In the opposite direction), and the methane inengenerated, almost entirely by hydrogenotrophic methanogenesis means.
## 3 Methods and Data
### PS-MTI Processing
This set-up assures minimum influence of thermal effects and stability with respect to wind and snow cover (that, by the way, was removed from the tetrahedral element during GPS surveys and at the times of SAR acquisitions). Due to the overall lightweight construction and the use of screws instead of welded connections, the 16 CRs were easily transported in a backpack and installed in early 2013 according to a specific orientation function of CSK track and swath directions (Figure 1b,c). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Month and Day of Acquisition** & **Total** \\ \hline
2013 & 7 October; 23 October; 8 November; 24 November; 10 December & 5 \\ \hline
2014 & 27 January; 20 February; 8 March; 5 April; 21 April; 3 May; 4 June; 20 June; 22 July; 7 August; \\  & 23 August; 8 September; 24 September; 10 October; 26 October; 11 November; 13 December & 17 \\ \hline
2015 & 14 January; 15 February; 3 March; 19 March; 4 April; 20 April; 6 May; 22 May; 7 June; 23 June; \\  & 10 July; 25 July; 10 August & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of available COSMO-SkyMed (CSK) data.
## 7 Performance Analysis
### Simulation Results  
In the sun outage scenario in LEO satellite networks, two characteristics are more important. The first is the duration of sun outage. A longer duration of the sun outage is a challenge to the adaptability of the algorithm. As time goes by, the link information and traffic information before failure become unreliable. The longer the failure time, the worse the adaptability of the algorithm. The second is the average load of the links in the network. Link average load reflects the degree of network congestion and can evaluate the effect of the algorithm in different network environments. In the simulation, we set the duration of sun outage to 120 s and the reference value of the average network load to 0.33. We fixed the value of one feature and analyzed the impact of the other feature on the algorithm performance.
## 2 Generation of Information Granules and Design of the Granular Model
### CFCM-Based Multi-Granular Fuzzy Model (MGFM) with Hierarchical Tree Structure  
The tree-structured CFCM-based MGFM is proposed to resolve the problems that arise when a single-granular model processes a large-scale multivariate database. The CFCM-based MGFM is designed as a multi-granular model in the form of a tree by stacking  
Figure 1: Hierarchical tree structure: (**a**) incremental structure, (**b**) aggregated structure, (**c**) cascade structure.  
models in aggregated and incremental structures. Figures 2 and 3 show the hierarchical tree structure and diagram of CFCM-based MGFM, respectively. This structure uses correlated input variables and non-correlated input variables by dividing them. First, the low-level granular model calculates the output by grouping the correlated input variables in an aggregated form. Then, the calculated output and the uncorrelated input variables are calculated through the granular model designed in an incremental structure. Figure 4 shows the process of generating fuzzy rules in CFCM-based MGFM. The context is created in the output variable, and the clusters are created in the input areas corresponding to each context. Fuzzy rules are created using context and clusters. Correlation determines the rank of input variables based on their correlation with the output attributes of the database, and it uses them as the inputs to the low-level granular model based on descending order of positive and negative correlations. The procedure of the tree-structured CFM-based MGFM is as follows:  
1. Calculate the positive and negative correlations of the input variables based on the correlations with the output variables in the database to be used.
2. By using the positive and negative correlation ranks, designate input variables (input variables with high correlation ranks) to be input into the low-level granular model of the aggregated structure and input variables (input variables with low correlation ranks) to be input into the granular model of the incremental structure.
3. Sets the context's number to create in the low-level granular model and the cluster's number to be produced per context. In addition, initialize the membership matrix \(U\).
4. Create contexts and clusters using context-based FCM clustering to generate fuzzy rules automatically. The generated fuzzy rules are then used to calculate the output of the lower-level granular model.
5. Process the output of the low-level granular model, the output variables of the database, and the input variables with low correlation ranks that will be used in the incremental structure so that they can be used as inputs to the high-level granular model.
6. Use the processed database as input to the high-level granular model and generate fuzzy rules using CFCM clustering to calculate the final output.  
Figure 4: Diagram for fuzzy rule generation in design of CFCM-based MGFM.  
Figure 3: Diagram of tree-structured CFCM-based MGFM.
# Synthesis of Benzoxazine-Based N-Doped Mesoporous Carbons as High-Performance Electrode Materials
## 2 Experimental Section
### Electrochemical Measurements  
A three-electrode system was adopted to study the electrochemical behavior of carbon materials. In the three-electrode system, a saturated calomel electrode was the reference electrode and a platinum electrode was the counter electrode, the prepared carbon materials was the working electrode, and 0.5 M H\({}_{2}\)SO\({}_{4}\) was the electrolyte. The preparation of the working electrode involved a carbon: acetylene black: Nafion perfluorinated resin solution at an 8:1:1 mass ratio, where the three materials were mixed in ethanol. The mass of materials coated on each working electrode was 0.2 mg. Galvanostatic charge-discharge (GCD) and cyclic voltammetry (CV) were conducted using an electrochemical workstation (CHI660E, Shanghai, China) at room temperature. The potential scan rates of cyclic voltammetry (CV) tests were 5, 10, 20, 50, and 100 mV s\({}^{-1}\), and the voltage window was \(-\)0.2 to 0.8 V. Electrochemical impedance spectroscopy (EIS) measurements were recorded from 10 mHz to 100 kHz. The galvanostatic charge-discharge (GCD) experiments were conducted under current densities of 0.25, 0.5, 1, 2.5, and 5 A/g. The specific capacitance (\(C\), F/g) of the active material (galvanostatic discharge process) could be calculated using the following equation:  
\[C=\frac{I\times\Delta t}{m\times\Delta V},\]where \(I\) (A) is the constant discharge current, \(\Delta t\) (s) is the discharge time, \(\Delta V\) (V) is the voltage difference during discharge, and \(m\) (g) is the mass of carbon loaded into the working electrode.  
In order to investigate the application of supercapacitors, a two-electrode system was also adopted. The carbon: acetylene black: poly(tetrafluoroethylene) (PTFE) binder at a 8:1:1 mass ratio was mixed. The slurry of the mixture was painted on the nickel foam (area of 1 cm\({}^{2}\)) and formed a symmetrical supercapacitor device. The mass loading of the active materials on each electrode was 2.5 mg/cm\({}^{2}\). Galvanostatic charge-discharge (GCD) and cyclic voltammetry (CV) were conducted using an electrochemical workstation (CHI660E, Shanghai, China) at room temperature. The voltage window was \(-0.2\) to 0.8 V. The electrolyte was 6 M KOH. The specific capacitance (\(C\), F/g), energy density (\(E\), Wh/kg) and power density (\(P\), \(W\)/kg) were calculated from the discharge curves as follows [30]:  
\[C=\frac{2\times I\times\Delta t}{m\times\Delta V},\]  
\[E=\frac{C\times\Delta V^{2}}{2\times 4\times 3.6},\]  
\[P=\frac{3600\times E}{\Delta t},\]  
where \(I\) (A) is the constant charge-discharge current, \(m\) (g) is the mass of active material per electrode, \(\Delta t\) (s) is the discharging time, and \(\Delta V\) (V) is the potential range for the discharging process excluding the voltage drops (IR) value.