esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Properties of Multifunctional Cemertitious Composites
### Conductive Fillers
#### 3.1.3 Graphene  
Unlike CNTs, graphene is derived from unrolled sheets of carbon. The sheet of this two-dimensional conductive filler forms a hexagonal honeycomb lattice structure. Based on the number of unrolled layers, graphene can be classified into single-layer graphene and multi-layer graphene [50]. For example, graphene nanoplatelets (GNPs) consist of multiple graphene sheets stacked together [51]. Furthermore, graphene can be categorized based on its chemical modification into graphene oxide (GO), reduced graphene oxide (rGO), functionalized graphene, and pristine graphene, which is free from oxidation [33]. Depending on the production method, graphene can also be classified into exfoliated graphene, graphene nanoribbons (GNRs), and chemical vapor deposition (CVD) graphene [52; 53]. The incorporation of graphene into cementitious composites enhances their mechanical strength, electrical conductivity, thermal conductivity, and fire resistance [54]. However, as with other carbon-based conductive fillers, issues such as agglomeration and high production costs pose significant challenges that can hinder large-scale applications [33]. Toaddress these challenges, effective dispersion techniques and strategies to reduce production costs are essential. In this context, various dispersion methods can be employed, with the combined dispersion technique being particularly effective for achieving optimal dispersion [55]. Additionally, the production of graphene-based conductive cementitious composites from waste materials has been proposed as a cost-effective solution [56].
## 1 Introduction
### Thermal Energy Storage  
Thermal energy storage (TES) is a temporary capture of thermal energy in the form of hot or cold substances for later utilisation [2]. The stored thermal energy may be used for heating or cooling applications. So, TES is useful for addressing the mismatch between the supply and demand of energy [3]. The principle of TES is based on a temperature difference (sensible TES) or change of phase (latent TES). The initial state is a solid state when the heat is added, the solid substance is heating up (sensible heat), after that, a solid-to-liquid phase change follows (latent heat). When the heat is still added, a liquid is heating up (sensible heat) and then, a liquid-to-vapour phase change occurs (latent heat) and after that, sensible heating of the vapour occurs (sensible heat) [4]. As can be seen, two forms of heat are recognized: sensible heat and latent heat. The principle of sensible heat storage is based on the material's change of heat capacity and temperature during the process of charging and discharging [5]. In case of the sensible heat, the specific heat capacity is the primary parameter. It determines the amount of energy needed to change the temperature of 1 kg of the substance by 1 K. The amount of sensible heat energy can be calculated by Equation (1).  
\[Q=m\cdot\int_{T_{i}}^{T_{f}}c_{p}\,\mathrm{d}T, \tag{1}\]  
where  
\begin{tabular}{c l l} \(m\) & mass & (kg), \\ \(T_{i}\) & initial temperature & (K), \\ \(T_{f}\) & final temperature & (K), \\ \(c_{p}\) & specific heat capacity & (J kg\({}^{-1}\) K\({}^{-1}\)). \\ \end{tabular}  
Latent heat storage is reliant on the storage material absorbing or releasing heat as it undergoes a solid to solid, solid to liquid or liquid to gas phase change or vice versa [5]. During the phase change, the substance temperature is constant. Even so, itsability of storing and releasing a large amount of thermal energy. So, this is the reason why latent heat storage is a most efficient method of storing thermal energy [6]. The quantity of latent heat is derived from the difference in enthalpy of the two relevant states.  
Thermal energy storage realised by phase change materials (PCMs) exploits especially latent heat [7]. PCM can be packaged in specialised containers (panels, tubes, and plastic bags), or it can be contained in ordinary building elements (ceiling and wallboards), or encapsulated as self-contained elements [8].  
The amount of energy is given by the enthalpy difference during the phase change, according to the following Equation (2).  
\[\Delta Q=m\cdot\Delta h, \tag{2}\]  
where  
\[\Delta h\quad\text{enthalpy difference}\quad\text{(J$\cdot$kg${}^{-1}$)}.\]  
Figure 1 shows the principle of thermal energy storage in the form of latent and sensible heat in the PCMs.
## 6 Numerical Results
### Performance of OSNR-LA-PSA vs. MIP Model
The above results show that the MIP model has a low scalability, especially, at the presence of crosstalk. Concurrently, the processing times of the OSNR-LA-PSA method are low and the quality of solutions generated is high in the network scenarios evaluated. These results indicate that OSNR-LA-PSA is an effective and efficient method for solving the OSNR-aware lightpath planning problem. Therefore, in the next section, we apply this method for analysis of larger network and traffic scenarios.
## 3 Results and Discussion
### Establishment and Validation Calibration Model  
#### 3.3.1 Establishment Calibration Models  
After hyperspectral image acquisition, 120 and 30 spectral data were involved in the training set and test set, respectively. The regional growing method was used to calibrate the model. Different pretreatment methods were used to obtain the optimal model for four quality parameters of rapeseed. The light scattering correction was used to reduce the difference caused by the light source drift, whereas the first or second derivative (1 st Der or 2 nd Der) by the Savitzky-Golay algorithm was employed to eliminate the interference of baseline and other backgrounds. KPLS was employed to build the models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. As shown in Table 2, the best processing method was MSC and the first derivative for crude protein content, oil content, erucic acid, and glucosinolates.  
Figure 3: **(a)** The raw image of rapeseed; (b) ROI selected by regional growing method; (c) the raw spectra of rapeseed.  
As shown in Figure 4, the prediction model for crude protein content was built and obtained acceptable results (R\({}^{2}\)c = 0.9692, RMSEC = 0.4500%, R\({}^{2}\)cv = 0.9548, RMSECV = 0.5522%), whereas the prediction models for oil content, erucic acid content, and glucosinolate content also showed satisfactory results. R\({}^{2}\)c = 0.9653, RMSEC = 0.5399%, R\({}^{2}\)cv = 0.9528, RMSECV = 0.6285% for fat, R\({}^{2}\)c = 0.9774, RMSEC = 2.5018%, R\({}^{2}\)cv = 0.9603, RMSECV = 3.2581% for erucic acid, and R\({}^{2}\)c = 0.9451, RMSEC = 9.5087 \(\upmu\)mol/g, R\({}^{2}\)cv = 0.9182, and RMSECV = 11.7602 \(\upmu\)mol/g for glucosinolates.
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
## 5 Simulation Experiments
### Quadrotor with Loss of Effectiveness  
A typical actuator partial failure scenario was constructed in the simulation: at \(t=10\) s, a sudden 50% effectiveness degradation occurred in actuator 1, represented by the effectiveness of the actuator \(a_{1}^{F}=50\%\).  
Figure 5 compares the trajectory tracking errors of the quadrotor under actuator effectiveness degradation using ADRC, CADRC, and PPC-CADRC controllers. The root mean square errors (RMSEs) were 0.27 (PPC-CADRC), 0.30 (CADRC), and 0.52 (ADRC) for the respective control systems. Before fault occurrence, all three controllers demonstrated stable tracking capability, while PPC-CADRC exhibited smaller overshoot and faster convergence. When actuator effectiveness degradation occurred after \(t>10\) s, PPC-CADRC maintained smaller error amplitude, demonstrating superior disturbance rejection capability.  
Figure 6 demonstrates the dynamic constraint effect of the prescribed performance function on system error. By introducing the prescribed performance function, the tracking error was strictly confined within boundaries throughout the control process. The error trajectory maintained compliance with all performance requirements throughout both transient and steady phases. This outcome confirms the effectiveness of the performance function while demonstrating its ability to bound system dynamic errors.  
The event-triggered mechanism of the observer was analyzed by taking the displacement \(z\)-axis as an example. Figure 7 shows the event-triggered moments and sampling errors for the \(z\)-axis observation channel. It can be seen that when the sampling error between the sampled value and the actual value was greater than the threshold value, the observer performed a state sampling and information transfer. The event-triggered mechanism reduced 76% of data transfer compared to the continuous triggering mechanism.  
Figure 8 shows the event-triggered instant and sampling error for the control variable \(U_{1}\). The results show that a smaller triggering threshold was adopted when the tracking error was large, while a larger threshold was set for smaller errors to reduce event-triggered frequency. This time-varying event-triggered mechanism reduced 93.2% of data transfer compared to a continuous triggering mechanism. From Figures 7 and 8, it can be observed that when \(t=10\) s, the system state changed after the actuator failed, and the frequency of event triggering increased at this time to ensure the control accuracy.  
Figure 5: The trajectory tracking errors of the quadrotor.  
increased, significant growth in trajectory tracking error was observed. Following fault occurrence, the RMSEs were 0.05 (10%), 0.06 (30%), 0.09 (50%), and 0.12 (70%), respectively. The steady-state error exhibited progressive enlargement with increasing fault severity. However, the controller maintained fundamental tracking capability without complete failure, thereby validating its passive fault-tolerant characteristics.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 4 Case Study: Benchmark Simulation Model for Sewer Networks
### Fault-Tolerant Control System for the Sever Network
- The MPC reconfiguration depends on the element that presents the fault, so different strategies will be applied to reconfigure the MPC controller to minimize the effects caused by the faults: - Faults in level sensors: when a level sensor fails, the level value of that tank \(h_{i}(k)\) can be estimated at instant \(k\) if its output flow \(u_{i}(k)\), its discharge coefficient \(c_{0i}\) and the gate opening degree \(v_{i}(k)\) are known by Equation (20): \[h_{i}(k)=\left[\frac{u_{i}(k)}{c_{0i}v_{i}(k)}\right]^{2},\ 0<v_{i}\leq 1,\ i=1,2, \ldots,5,\] (20) - Faults in gates: in this case, the MPC algorithm is reconfigured by adding to the QP optimization problem an equality constraint for the calculation of the reference output flow rate of the tank affected, \(u_{\text{irref}}(k)\) since its output flow \(u_{i}(k)\) can be measured at instant \(k\), so: \[u_{\text{irref}}(k)=u_{i}(k),\ i=1,2,\ldots,5,\] (21)  
To have normal online performance data, a neural network trained with fault-free data extracted from the benchmark will be used. The neural network is composed of 25 neurons in the hidden layer and 19 in the output layer, as shown in Figure 3. To improve the performance of the neural network, it is fed back with the system outputs from the previous instant. Thus, the disturbances (\(d_{1},\ldots,d_{6}\)) and the previous outputs are applied to the input (\(i_{1},\ldots,i_{25}\)), and the process variables of interest are obtained at the output (\(o_{1}\), \(\ldots\), \(o_{19}\)): state variables (\(x_{1}\), \(\ldots\), \(x_{9}\)), flow references (\(u_{\text{ref1}}\), \(\ldots\), \(u_{\text{ref5}}\)) and output flow rates of every tank (\(u_{1}\), \(\ldots\),\(u_{5}\)).
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Experimental Progress and Result Analysis
#### 4.2.2 Ablation Experiments
The introduction of the improved SPP module in the Scheme SR-3s-SPP could extract feature with different receptive fields, which can further improve P, R, F1 by 0.7%, 0.8%, 0.8%, respectively. Based on the Scheme SR-3s-SPP-SE, the channel attention mechanism was introduced into the Sandglass-Residual module, which could pay attention to useful feature, improving P, mAP and F1 by 2.0%, 1.1%, 1.0%, respectively. Further, CIoU loss was utilized in the final Scheme SR-3s-SPP-SE-CIoU to promote positioning accuracy, which could improve P by nearly 1%. Due to the combination of the above improved methods, compared with the original YOLOv3-tiny, SAS-YOLOv3-tiny had advantages on model performance and complexity. Specifically, it improved P by 2.5%, improved R by 6.9%, improves mAP by 7.9%, improved F1 by 4.5% over the original algorithm on the validation set and had a smaller number of parameters than the original algorithm at a sacrifice of only 0.8 ms.  
To further demonstrate the effectiveness of different schemes, we presented the curves in the training process for six groups of experiments. Two critical performance indicators are mAP and F1, the curves of F1 and mAP in the different models are shown in Figure 6a,b. The horizontal axis in the Figure 6a,b represents the training time, while the vertical axis represents the value of F1 and mAP, respectively. The YOLOv3-tiny represents the training curves of the original algorithm, in which the mAP value and F1 value are the lowest. The SR represents the training results of the Scheme SR, the main reason for the promotion of performance is utilization of the Sandglass-Residual module. The SR-3s represents the training process of the Scheme SR-3s, in which the Sandglass-Residual module and the three-scale feature prediction are applied simultaneously, promoting further enhancement in terms of the mAP and the F1. The SR-3s-SPP represents the training curves of the Scheme SR-3s-SPP, in which not only the Sandglass-Residual module and the three-scale feature prediction are adopted, but also the SPP module is employed. The application of the SPP module showed that the training process was easier to converge and the results were more robust. The SR-3s-SPP-SE represents the training process of the Scheme SR-3s-SPP-SE, in which the channel attention mechanism was introduced on the basis of the above three improvement methods, indicating that the training model had reached a better level. The SR-3s-SPP-SE-CIoU represents the results of the Scheme SR-3s-SPP-SE-CIoU, in which CIoU was utilized on the basis of the previous methods, proving that the CIoU could promote the positing accuracy. As shown in Figure 5(a),b, we can intuitively see that the final model is better than the original algorithm.
## 3 Methods for Obtaining Plant Extracts  
Obtaining plant extracts involves separating the desired bioactive compounds from the plant material. Various methods are used to achieve this, each having its own advantages and disadvantages. The choice of method depends on the properties of the target compounds and the desired quality of the final extract [53]. Macceration involves soaking the plant material in a solvent for an extended period to allow the extraction of the desired compounds. Common extractants used in macceration include ethanol, water and a mixture of both (hydroalcoholic solvents). This method is relatively simple and inexpensive, which makes it suitable for large-scale operations. However, it may not be optimal for extracting certain heat-sensitive or polar compounds [54,55]. Solvent extraction is a widely used method in which a suitable solvent, such as hexane, ethanol or supercritical CO2, is used to selectively dissolve the target compounds. This method can produce high-quality extracts and is efficient in extracting a wide range of compounds. However, it requires careful handling of flammable or toxic solvents and may leave trace amounts of solvents in the final product [56,57,55]. Supercritical fluid extraction (SFE) involves using a supercritical fluid, usually CO2, to extract plant compounds. Under specific conditions of pressure and temperature, CO2 behaves as both a liquid and a gas, allowing it to penetrate plant material and extract the desired compounds. SFE is a clean and efficient method that produces high-quality extracts, but it requires specialised equipment and can be expensive [58]. Soxhlet extraction is a continuous extraction method that uses a combination of macceration and distillation. The plant material is placed in a thimble and the solvent is repeatedly cycled through the material, allowing for efficient extraction. This method is suitable for extracts with a high concentration of compounds, but can be time-consuming [54]. The choice of extraction method and the process parameters play a vital role in obtaining high-quality plant extracts that can be used in various industries, such as pharmaceuticals, cosmetics and food production. Each method has its strengths and weaknesses, so it is essential to carefully evaluate the specific requirements and goals of the extraction process to determine the most suitable approach.
## 3 Results and Discussion  
### GC-MS Analysis Results  
From the comparison between samples chromatograms, substantial differences were found. The main difference between 12-months and 24-months ripened grated PR lies in the amount of fatty acids that characterize this product. They are acetic acid, butanoic acid, hexanoic acid, octanoic acid, n-decanoic acid, and their presence is much greater in 24-months PR. In Figure 2, histograms for each of the aforementioned compounds are shown: results are presented in terms of mean \(\pm\) standard deviation of the mean with an arbitrary unit. This result is widely confirmed in the literature. Indeed, it is well known that these fatty acids are the results of fermentation processes, especially in butter and seasoned cheese. Some studies revealed that the amount of acetic acid and butanoic acid doubles in the period between 12 and 24 months [51; 52]. The same trend was observed in the other compounds, since biochemical processes that lead to their formation are very similar.  
Figure 2: _Cont._  
Differences were also found in comparing samples with different percentages of rind and the same rind working process; the same trend is valid both for 12-months and 24-months ripened PR. It turned out that in increasing the quantity of rind, the presence of three compounds increases. Besides butanoic and hexanoic acid, 2-nonanone has the same behavior. It is a member of the class of methyl ketones and it can be found in several foods, such as milk and cheese [53]. It is produced by the oxidative degradation of fatty acids [54]. These results suggest that both the fermentation and the degradation happen closer to the rind than in the central part of the cheese.
# Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite
## 5 Verification of Thermal Design
Compared to Figure 7b, the testing results exhibit a high degree of agreement with the simulation analysis, achieving a temperature prediction error of less than 3 \({}^{\circ}\)C. This demonstrates that each component possesses relatively accurate thermal condition inputs. Furthermore, this achievement relies on the highly implemented thermal control components and precise space heat flux simulation methods utilized during the thermal testing. Overall, the temperature results for T/RMs and other components on the satellite during the TVAC testing are quite satisfactory. During the test, auxiliary temperature measurements is arranged on the antenna mounting plate. Upon observing the temperature variations, it became evident that the aluminum honeycomb mounting plate exhibited superior thermal diffusion capabilities than anticipated. Consequently, the heat pipe positioned beneath the T/RMs may not be essential, and we intend to simplify the design in subsequent satellites. Figure 11: Temperature curve of the support plate: (a) cold case; (b) hot case. \begin{table}
\begin{tabular}{c c c} \hline
**Equipment** & **Hot Case [\({}^{\circ}\)C]** & **Cold Case [\({}^{\circ}\)C]** \\ \hline PS & 17.0\(\sim\)19.2 & 16.7\(\sim\)17.2 \\ Battery & 15.1\(\sim\)16.8 & 13.9\(\sim\)14.3 \\ DSU@1@2 & 11.4\(\sim\)17.4 & 0.3\(\sim\)4.2 \\ DCU & 32.2\(\sim\)33.3 & 20.1\(\sim\)20.7 \\ MCU & 23.3\(\sim\)24.0 & 9.4\(\sim\)9.7 \\ RW@1@2@3@4 & 22.6\(\sim\)24.9 & 2.5\(\sim\)6.2 \\ PCDU & 30.4\(\sim\)32.3 & 25.1\(\sim\)25.6 \\ DTCU@1@2 & 13.8\(\sim\)21.2 & 2.8\(\sim\)4.3 \\ ACU & 15.3\(\sim\)19.4 & 1.9\(\sim\)2.1 \\ NRU & 13.8\(\sim\)14.4 & \(-\)1.5\(\sim\)\(-\)1.3 \\ \hline \end{tabular}
\end{table} 
Table 5: Platform components’ temperature during the TVAC testing.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 3 Performance Prediction Model
### Step 5: Final Model Preparation with Complete Dataset  
The final model was made to predict the probable output on the new data. The models were finalized by training with the complete available dataset, which was saved for operational use in later cases. In Table 5, the parameter values of the finalized models are tabulated.  
To visually inspect the performance of the regression process, the actual values vs. predicted values of the finalized model were plotted. Figures 3 and 4 show the actual values vs. values predicted by the finalized model for ASE and CP, respectively, where it could be seen that the model fits linearly with the trend line. The performance of the finalized models has been quantitatively evaluated based on the residue over the predicted output of the dataset, and the results are given in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Parameters** & **CP** & _ASE_ \\ \hline \% of error margin & 12.06 & 11.32 \\ R\({}^{2}\) score & 0.754 & 0.749 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the finalized models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Evaluation Parameters** & **k = 1** & **k = 2** & **k = 3** & **k = 4** & **k = 5** \\ \hline \multirow{3}{*}{\(CP\)} & \% of error margin & 13.18 & 13.46 & 13.62 & 12.08 & 12.90 \\  & R\({}^{2}\) score & 0.7355 & 0.749 & 0.7272 & 0.7797 & 0.7624 \\  & \% of error margin & 13.47 & 12.84 & 12.35 & 13.19 & 13.31 \\ \multirow{2}{*}{\(ASE\)} & R\({}^{2}\) score & 0.6782 & 0.692 & 0.7274 & 0.6917 & 0.7531 \\ \hline \hline \end{tabular}
\end{table}
Table 4: 5-fold cross validation evaluation results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coverage Probability** & **Area Spectral Efficiency** \\ \hline \(\hat{\beta}_{0}^{CP}=+0.355020\) & \(\hat{\beta}_{0}^{ASE}=-12.3185\) \\ \(\hat{\beta}_{1}^{CP}=+0.001934\) & \(\hat{\beta}_{1}^{ASE}=+0.4574\) \\ \(\hat{\beta}_{2}^{CP}=-0.010107\) & \(\hat{\beta}_{2}^{ASE}=+1.8500\) \\ \(\hat{\beta}_{3}^{CP}=+0.008203\) & \(\hat{\beta}_{4}^{ASE}=+1.0308\) \\ \(\hat{\beta}_{4}^{CP}=-0.016797\) & \(\hat{\beta}_{4}^{ASE}=-2.5567\) \\ \(\hat{\beta}_{5}^{CP}=+0.013265\) & \(\hat{\beta}_{5}^{ASE}=+1.1783\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter values of the finalized models.