esa-sceva/satcom-chunk-collection huggingface dataset:
# CoSimulating Communication Networks and Electrical System for Performance Evaluation in Smart Grid
## 5 Simulation Study
### Simulation Model
* The energy management system model is a node model which includes the ESM process, and each ESM process reports the response time of application traffic to the CoSim module;
* The communications between the gateway system and each energy management system are configured with a server/client model. As the operational scenario, each energy management system sends a request message whose size is 1 KB every second, and the gateway sends a response message whose size is 5 KB to the energy management system. When the energy management system receives the response message, it calculates the response time and inform the result to the CoSim module. **Smart Grid Application:** We also implemented the application model in the CoSim module. In Section 4, we formulated the energy management system operation. We simply assumed that electricity forecasting is performed at each five minute, and the price of electricity is inversely proportional to the output power of the wind generator. Therefore, \(T\) in Equation (3) is set to 300 s, the \(k\) is set to 1 s, and \(C(t)=\alpha\cdot\frac{1}{G_{\text{wind}}(t)}\). The forecasted electricity price is delivered to the gateway energy management system, and it decides the second-level electricity load of each energy management system by solving Equation (3). Every second, each energy management system requests its actual load to the gateway energy management system and it adjusts their load. Since the gateway energy management system calculates the load of each customer according to the forecasted output power of the wind generator, it might recalculate the load value if the actual output power is changed. However, we simply assume that the forecasted wind power is correct and the load calculation is performed only once every five min. The retrieval of the actual load of each customer is performed through the network infrastructure, there might exist the network delay and packet error. Therefore, the CoSim module should schedule the load adjustment event according to the following equation,  
\[ \begin{cases}P_{L,i}(t+d_{i}(t))=P_{L,i}^{determined}(t)&\text{if $no$ packet error}\\ P_{L,i}(t)=P_{L,i}(t-k)&\text{if $packet error$}\end{cases}  \tag{4}\]  
where \(P_{L,i}^{determined}(t)\) is a determined load of customer \(i\) for \(t\) second, and \(d_{i}(t)\) is a communication delay of the load decision message for customer \(i\) at time \(t\). Figure 9 summarizes the operation flow of the DR application. Note that traditionally, DR is performed for a long period of time and as a prearranged plan, for instance, 1-day early or 1-hour early. However, as the advanced computing and networking technologies are applied to the power system, the scale of the DR is being shortened. Recently, many studies have proposed and verified the impact of load scheduling at minute-level or second-level [34; 35]. Therefore, we do not regard the defined application model as an unrealistic model but as a future application model in the smart grid.
# Fabricating Sustainable All-Cellulose Composites
## 3 Dissolution of Cellulose
### Derivatising Solvents  
The most widely used and important industrial-scale method to dissolve and process cellulose is the viscose process, which is used to create viscose fibres and cellophane films [39; 67]. During the viscose process, cellulose, often from wood pulp or cotton linters, is treated with sodium hydroxide and then with carbon disulfide, resulting in a product called cellulose xanthate. The alkaline cellulose xanthate forms a thick solution, which is forced through small openings into acid, where it will eventually coagulate as regenerated cellulose [40]. In the traditional viscose process, the usage of carbon disulfide in the solution is a cause of environmental pollution, and the development of volatile sulphur-containing compounds (e.g., H\({}_{2}\)S, COS) requires complex recovery technologies [80; 81; 82].  
The cellulose carbamate process is an attractive, more sustainable alternative to the viscose process, and it can utilize conventional viscose spinning equipment [83; 84; 85]. Cellulose carbamate is a cellulose derivate with carbamate groups substituted on the hydroxyl groups along chain molecules [86], and it can be prepared through various routes. In the conventional processes, the formation of cellulose carbamate begins when cellulose is treated with urea at a temperature above its melting point in an alkali medium with an organic solvent, such as xylene (the CarbaCell process) or toluene [87; 88]. Other ways to produce cellulose carbamate include using supercritical CO\({}_{2}\)[89; 90], microwave heating [83; 91], electron beam irradiation [92], liquid ammonia [88; 93] or deep eutectic solvents [94] as part of the manufacturing processes. Recent developments in process operation, increased understanding of physical chemistry, and lower cost of raw materials have increased the competitiveness of cellulose carbamate processes. Further improvements need to be focused on handling the rigorous processing conditions (e.g., pre-treatments, reaction times, temperatures and specific equipment requirements) and efficient chemical recycling [83; 88].
###### Abstract  
The subgrade structure of high-speed railways is an important foundation for the safe and smooth operation of high-speed trains, and the scientific design of the subgrade structure provides a fundamental guarantee of its durability and technical economy. As, in the development of high-speed railways in China, higher speeds are being pursued, more requirements have been put forward for the dynamic stability of subgrade structures. To address this issue, this article focuses on the control requirements for the long-term stability of subgrade deformation, and various design methods for high-speed railway subgrade structures are presented. Considering the energy dissipation and dynamic stability characteristics of subgrade filling materials, the dynamic performance of coarse-grained soil filling materials in the bottom layer and graded crushed stones in the surface layer are revealed. The methods for determining the values of dynamic parameters such as the dynamic modulus and damping ratio are provided. Based on the dynamic shakedown theory, the stress-strain hysteresis characteristics of fillers and the variation law of dissipated energy are revealed. The correlation between unit volume dissipated energy and shakedown state under cyclic loading conditions is identified. A criterion for determining the critical shakedown state of high-speed railway subgrade structures based on equivalent unit volume dissipated energy is proposed, and a method for determining the design threshold of dynamic stress and dynamic strain is also proposed. The results show that the shakedown design critical values of equivalent unit volume dissipated energy in the bottom and surface layers of the foundation were between 0.0103\(\sim\)0.0133 kJ/m\({}^{3}\) and 0.0121\(\sim\)0.0149 kJ/m\({}^{3}\), respectively. The critical dynamic strain range was 0.8 \(\times\) 10\({}^{-3}\)-1.3 \(\times\) 10\({}^{-3}\). On this basis, a high-speed railway subgrade design method based on energy dissipation and dynamic shakedown characteristics was developed. The results can provide theoretical support for the design of high-speed railway subgrade structures with different filling material alternatives and control standards.  
high-speed railway; subgrade filler; dynamics parameters; shakedown theory; dissipated energy; long-term deformation; design method +
Footnote †: journal: Applied sciences
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Forecasts Combination  
Wind speed forecasting presented in this paper requires prediction of very accurate forecasts. One way to do this is through forecast combination of forecasts from individual models, ANN, SGB and GAM. Combining of forecasts was first introduced by [PERSON] [36] as a viable way of achieving improved forecasts. Forecasts combination is an approach for ensuring increased forecast accuracy and error variability [36]. The theoretical justification for forecasts combination involves testing and averaging individual forecast models according to their probabilities when the problem is viewed from the perspectives of Bayesian model average (BMA) [37].  
Forecast combination is important because it provides a means of compensating for the drawbacks in component forecasts. It helps to avoid the risk involved in using one forecast and also provides a way of benefiting from various interactions among component forecasts [18]. The motivation for forecasts combination lies in the following: possibility for insufficiency of forecasts from individual component models, forecasts from the components are from different and complementary perspectives, considerable grounds are covered from component forecasts which gives a complete picture of the forecasts when combined, improved forecasts accuracy due to the effects of model uncertainties, structural breaks and model mis-specification being forestalled by forecasts combination [18]. Depending on the forecasting intervals, the loss function is an important parameter in forecasts combination. It is the main performance evaluation criterion and the sole ingredient in forecasts combination formulae [38]. Forecasts combination schemes, involving adaptive forecast combination schemes and regression-based combinations make use of a loss function for combining forecasts [38]. While convex combination of models make use of various algorithms based on minimising losses from pinball, absolute error, percentage error and square losses, a regression-based approach on the other hand is quantile regression averaging (QRA), used for combining forecasts and computingprediction intervals [38]. The forecasts combination method presented in this paper is according to those discussed in [31; 38] and given in Equation (6)  
\[y_{i,q}=\sum_{k=1}^{K}w_{i,k,q}\hat{y}_{i,k}+\varepsilon_{i,q}\quad q\in(0,1) \quad i=1,\cdots,m, \tag{6}\]  
where \(y_{i,q}\) is the combined forecast for the wind speed; \(k\) is the number of forecasting methods used to predict the next observation \(i\) of \(y_{i,q}\), in our case \(k=3\), since forecasts combination method employed combines three methods; \(m\) is the total number of points used for each of the forecast models; \(w_{i,k,q}\) is the weight assigned to each forecast model \(\hat{y}_{i,k}\); and \(\varepsilon_{i,q}\) is an error term.
## 3 Proposed Methodology
### Data Preprocessing
#### 3.2.3 Data Segmentation  
Considering the substantial amount of signal data captured by wearable sensors, it becomes impractical to input them into the HAR model simultaneously. Therefore, a crucial step is to conduct sliding-window segmentation before providing data to the model. This technique is widely employed in HAR to segment data and identify periodic and static activities, making it a popular method for this purpose [39]. The sensor signals are partitioned into fixed-length windows, employing a strategic approach to ensure comprehensive coverage of adjacent windows. This maximizes the number of training data samples and captures the smooth transition between different activities.  
The sample data that are segmented by a sliding window with size \(N\) is a size of \(K\times N\). The sample \(W_{t}\) is denoted as:  
\[W_{t}=[a_{t}^{1}a_{t}^{2}\dots a_{t}^{K}]\in\mathbb{R}^{K\times N} \tag{2}\]  
We represent the signal data for sensor \(k\) at window time \(t\) as column vector \(a_{t}^{k}=(a_{t_{1}}^{k},a_{t_{2}}^{k},\dots,a_{t_{N}}^{k})^{T}\). The transpose operation is denoted by \(T\). \(K\) stands for the number of sensors and \(N\) represents the length of the sliding window. In Equation (2), \(K\) denotes the count of sensor channels supplying input time-series data streams. Each sensor mode serves as one feature dimension for the dataset. For instance, a triaxial accelerometer embedded in wearable devices would supply \(K=3\) input acceleration features, representing the \(x\)-, \(y\)-, and \(z\)-axes. Likewise, a sensor consisting of nine IMUs, each with 3-axis gyroscope, accelerometer, and magnetometer data, would encompass \(K=9\) overall features (three for each modality). To effectively leverage the relationships between windows and streamline the training process, the window data are segmented into sequences of windows, as shown in Figure 2, as follows:  
\[S=(W_{1},y_{1}),(W_{2},y_{2}),\dots,(W_{T},y_{T}) \tag{3}\]  
where \(T\) represents the length of the window sequence and \(y_{t}\) signifies the corresponding activity label for window \(W_{t}\). When windows encompass multiple activity classes, the window label is determined based on the most prevalent activity among the sample data.
# Multi-Objective Optimization Study of Annular Fluid Flow Structure in Cordless Core Drilling Tools
[30] used NSGA-II to obtain the optimal values of process variables such as wheel speed, workpiece speed, dressing depth, and dressing lead in order to improve process performances such as production cost, productivity, and surface finish. [PERSON] et al. [31] proposed a novel multi-objective optimization algorithm for wind turbine blades, which has a good advantage in dealing with turbine multi-objective optimization with better performance. The objective of this study is to perform a multi-objective optimization of the annulus fluid flow structure of the cordless core drilling tool while optimizing the back pressure ratio of the spool to overcome the spring and the drilling fluid flow rate, as well as to obtain a low back pressure ratio and a high drilling fluid flow rate in hard rock formations to ensure high-quality core samples. In this work, at first, the structural parameters of the spearhead and spool are simulated, followed by designing orthogonal experiments to derive the spool back pressure and drilling fluid flow rate under different structural parameters. Secondly, a prediction model is also established to achieve the prediction of spool back pressure and drilling fluid flow rate under different structural parameters. A mathematical model of the relationship between decision variables and optimization objectives is then fitted by the BP neural network. Then, the optimal spool back pressure and drilling fluid flow rate in hard rock formation, as well as the solution space of structural parameters of spearhead and spool, are derived from the established nonlinear optimization model and the solution of the improved NSGA-II multi-objective optimization algorithm [32; 33; 34; 35]. Finally, simulation and experimental verification are carried out to verify the multi-objective optimization results and achieve the improvement of coring efficiency and coring quality, as well as the reduction of energy consumption.
## 3 Results and Discussion
### Correlation of AD Catalytic Effect to Structural Evolution during Pyrolysis
#### 3.2.1 Aliphatic Structure Activation by AD  
Comparing the sub-curves and area percentage assigned to the aliphatic structures in PADC with those in PC, PADC had a higher area percentage at 15, 30, 72, 80, and 101 ppm, as shown in Figure 5. The pyrolysis mechanism was summarized using biomass models by other researchers [38; 39]. The initial step for catalytic pyrolysis by metallic oxide was the formation of active intermediates, with the oxidized clusters of C-O-M following the intermediate hybrid mechanism [40; 41; 42]. The oxygen functional groups were more easily activated by metallic oxide [40; 41]. AD and pine melt mixed well with each other to accelerate mass transfer and generate active radicals. The oxygen-containing functional group of the aliphatic structure in biomass components, such as alcohol hydroxyl group and ether, could form C-O-M clusters of C-O-M with the metallic oxide in AD. The area percentage of the signal at 72 ppm assigned to C2,3,5 from hemicellulose or cellulose in char, which linked with the alcohol hydroxyl group, increased from 3.00% to 10.13% with AD addition at 500 \({}^{\circ}\)C. The signal at 15 ppm assigned to CH\({}_{3}\)-COO- from hemicellulosees increased from 0 to 3.49%. When the temperature increased to 700 \({}^{\circ}\)C, these oxygenated structures were still present in char due to the strong conjugation to the aromatic structure and the percentage increased. These structures contributed to the formation of the aggregated structure in char. This was evidenced by the increase in pyrolysis residue weight with AD addition, as shown in Figure 2. More glycosidic bonds linked with oxygen functional groups were activated by AD during pyrolysis and trapped in molten clusters to pyrolysis. At 500 \({}^{\circ}\)C, the correlating signal for C4 in amorphous cellulose at 80 ppm increased from 0 to 4.21% and that for C1 in hemicellulose at 101 ppm increased from 0.02% to 5.07%. These active intermediate structures were then facilely broken with further increase in temperature and were no longer detected in the composite at 700 \({}^{\circ}\)C. Meanwhile, the bridging aliphatic structure (CH\({}_{2}\)) corresponding to the signal at 30 ppm was also trapped in the intermediate structure during primary pyrolysis; an area percentage of 7.78% was observed in PADC-500. This structure disappeared with higher temperature and the area percentage became 0 in PADC-700. However, C1 in cellulose assigning at 109 ppm, correlating to the crystalline structure, decreased from 14.00% to 9.66% due to catalytic pyrolysis. This was in accordance with the slightly larger peak areas for peaks 2 and 3 in the DTG sub-curves. The structural evolution indicated that the AD had a significant influence on the pine pyrolysis rate, favoring the formation of active radicals containing an alcohol hydroxyl group and glycosidic bonds.
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
###### Abstract  
Wearable devices are gaining prominence in a variety of applications, including athletic and physical activity monitoring. Their effectiveness often relies on the ability to deliver instantaneous feedback, enabling timely responses and supporting their integration into fast-paced, interactive contexts. Based on these aspects, this paper proposes a wearable system that collects accelerometer and gyroscope data using the MPU6050 sensor from three points on a human arm. The collected data are compared to a predefined set of values using Dynamic Time Warping. Each wearable device provides live feedback via a vibration motor if a stroke deviates from the desired motion. The entire system enables swimming coaches to monitor individual strokes in real time using Wi-Fi connectivity while also allowing athletes to receive immediate feedback when their strokes are executed incorrectly. This system aids in identifying improper swimming techniques and enables faster correction, ultimately improving performance. Experiments will be performed in water environment supervised by a specialized swimming coach.  
swimming; correction; strokes; embedded; accelerometer and gyroscope sensor; Wi-Fi; ESP32; MPU6050; Dynamic Time Warping; live feedback Article
## 3 Results and Discussions
### Photocatalysis for Congo Red Dye Removal
#### 3.2.2 Efficiency of Photocatalysts  
A comparison between the photocatalysis efficiency developed by the materials EthylAmAcid, BenzylAmAcid and P-CHO is presented in Figure 6. Starting from the initial concentration of 50 mg/L CR, the photolysis efficiency was 47.7% after 240 min of irradiation. Aldehyde support (sample P-CHO) has poor photocatalyst characteristics; after 240 min of reaction, a CR removal efficiency of 60.5% was achieved. However, samples EthylAmAcid and BenzylAmAcid have developed very good efficiencies, of 95.3% and 85.1%, respectively. It can be concluded that by grafting the aldehyde support with the [(diethyl)](phosphono)methylene] glycine pendant groups/[(diphenyl)(phosphono)methylene] glycine pendant groups, the photocatalyst characteristics of the synthesized materials are improved. The two studied materials (EthylAmAcid and BenzylAmAcid) presented a low adsorption capacity, but in the meantime developed a very good photocatalysis efficiency. These underline their efficiency to be used as catalytic materials in the degradation process of dyes from water using photocatalysis treatment.  
Figure 5: Variation in CR concentration versus time in dark adsorption. \(c_{0}=25\) mg/L. Catalyst concentration = 1 g/L.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Makespan for DRFTSA Using Large Dataset by Using Variable Number of VMs  
In this subsection, we present the generated makespan for DRFTSA using the large dataset by using fixed numbers of VMs and 700 to 1000 tasks. Variable numbers of VMs are used in this scenario: 50 VMs are assigned to 700 tasks, 60 VMs are assigned to 800 tasks, 70 VMs are assigned to 900 tasks, 80 VMs are assigned to 1000 tasks. We compared our proposed DRFTSA with PSO, GA, and ACO approaches. Generated makespan for PSO with 700, 800, 900, 1000 tasks are 634.87, 685.12, 709.18, 725.88, respectively. Generated makespan for GA with 700, 800, 900, 1000 tasks are 653.02, 674.11, 722.98, 763.46, respectively. Generated makespan for ACO with 700, 800, 900, 1000 tasks are 688.18, 724.37, 766.91, 843.99, respectively. Generated makespan for DRFTSA with 700, 800, 900, 1000 tasks are 421.57, 463.14, 521.66, 540.28, respectively.  
The generated results from Table 7 and Figure 7 revealed that DRFTSA minimized makespan compared to state-of-the-art approaches when a large dataset is used while varying the number of VMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No. of Tasks** & **PSO** & **GA** & **ACO** & **DRFTSA** \\ \hline
700 & 784.27 & 767.96 & 812.98 & 624.78 \\
800 & 811.26 & 827.19 & 853.36 & 645.87 \\
900 & 943.78 & 958.14 & 876.17 & 723.18 \\
1000 & 1048.86 & 1127.36 & 1176.17 & 767.99 \\ \hline \end{tabular}
\end{table}
Table 6: Makespan for DRFTSA using large dataset by using fixed number of VMs.  
Figure 7: Calculation of makespan for large dataset using variable number of VMs.