esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wind Turbine Model Validation and Disturbances
### Addition of External Periodic Disturbances  
The simplified model presented in the previous subsection is specifically designed for conducting free decay tests. This process involved initializing the system with conditions that deviate from the platform's equilibrium state, while ensuring the absence of external disturbances. Such an approach facilitates the analysis of the system's inherent dynamics and control design without introducing additional complexities. Moreover, it enables the determination of the natural frequency, which is dictated by the mass and stiffness characteristics of the wind turbine.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(k_{t}\) (N/m) & \(1.4635\times 10^{10}\) \\ \(k_{b}\) (N/m) & \(2.0016\times 10^{9}\) \\ \(d_{t}\) (N/m) & \(2.5415\times 10^{7}\) \\ \(d_{b}\) (N/m) & \(5.6431\times 10^{7}\) \\ \(l_{t}\) (kg\(\cdot\)m\({}^{2}\)) & \(3.4523\times 10^{9}\) \\ \(l_{b}\) (kg\(\cdot\)m\({}^{2}\)) & \(2.1613\times 10^{9}\) \\ \hline \end{tabular}
\end{table}
Table 1: FOWT identified parameters.  
Figure 2: Validation of the simplified model of the FOWT.  
In the context of designing semi-active control strategies, it is important to adjust the turbine dynamics to generate oscillations at different frequencies. This method enables the assessment of the system's behavior not only during free decay but also when subjected to simulated external forces. To implement this, a vertical force is exerted at one end of the platform, replicating the impact of wave motion. The applied force follows a sinusoidal pattern, with its amplitude (A) and frequency (\(\omega\)) being customizable.  
\[F_{ext}=A\times\sin\omega t, \tag{9}\]  
As a result of applying the external force at the edge of the barge, a moment is induced:  
\[M_{Fext}=F_{ext}\times(20\times\cos\theta_{b}+(R_{b}+3.718)\times\sin\theta_{ b}), \tag{10}\]  
Applying small angle approximations, this becomes  
\[M_{Fext}=F_{ext}\times 20+F_{ext}(R_{b}+3.718)\times\theta_{b}, \tag{11}\]  
The expression in (11) can now be incorporated into the platform's angular acceleration Equation (1) to account for the torque generated by the external force, yielding the revised form of Equation (12). Equations (2) and (3) of the simplified model remain unaltered.  
\[\begin{split} I_{b}\ddot{\theta}_{b}=-d_{b}\dot{\theta}_{b}-k_{b} \theta_{b}-& m_{b}gR_{b}\theta_{b}+k_{t}(\theta_{t}+\theta_{b})+ d_{t}\Big{(}\dot{\theta}_{t}+\dot{\theta}_{b}\Big{)}\\ +& F_{ext}(20+(R_{b}+3.718)\theta_{b})\end{split} \tag{12}\]  
In linear systems theory, it is well known that a periodic input force applied to a linear system induces a steady-state response with an oscillation frequency that matches the frequency of the applied force. This fundamental property of linear systems forms the basis for the development of a frequency-dependent adaptive control strategy, which will be detailed in the following sections.
## 4 Industry Perception Study
### Feedback about Ability of DePaaS to Address SDP Challenges
#### 4.3.1 Solving SDP Problems Related to SDP Objectives and Focus  
The sample pool was briefed about SDP problems related to SDP objectives, SDP focus, and was asked whether DePaaS could help solve those problems.  
About 62% of software practitioners indicated that DePaaS could help predicting defects across-projects (\(\chi^{2}\) = 8.91, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0028). Majority of software practitioners believed that DePaaS does not help estimating defect severity (78% negative, \(\chi^{2}\) = 12.45, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0004), predicting security vulnerabilities (78% negative, \(\chi^{2}\) = 12.45, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0004), and highlighting risky code changes (83% negative, \(\chi^{2}\) = 17.29, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0000).
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 5 Experiments
### CIFAR Classification  
To further evaluate the performance of FSRFNets, we conduct experiments on CIFAR-10 and CIFAR-100 [57]. The CIFAR-10 [57] dataset consists of 60,000 \(32\times 32\) color images in 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images. The CIFAR-100 [57] dataset resembles the CIFAR-10 [57], except that it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 [57] are grouped into 20 superclasses. Each image comes with a \"fine\" label (the class to which it belongs) and a \"coarse\" label (the superclass to which it belongs).  
We use the same approach as above to integrate FSRF blocks into several popular baseline frameworks (ResNeXt-29 [9], ShuffleNetV2 [38], and MobileNetV2 [39]). Each baseline and its FSRFNet counterpart were trained using standard data enhancement strategies [58; 59]. During the training process, the image is flipped horizontally, filled with four pixels on each side, and then randomly \(32\times 32\) cropped. We report the performance of each baseline and its FSRFNet counterpart on CIFAR-10 and CIFAR-100 [57] in Tables 5 and 6. From the results, we can find that FSRFNets outperform the baseline architectures in every comparison, suggesting that the benefits of FSRF blocks are not confined to the Tiny ImageNet [54] dataset. Remarkably, FSRFNet-29 outperforms ResNeXt-29, 16\(\times\)64d by above absolute 0.21% accuracy, and almost halves the number of parameters, which is extremely efficient. For Lightweight models, we compare FSRF blocks with the ShuffleNetV2 [38] and MobileNetV2 [39] baseline models based on SE blocks and SK blocks. ShuffleNetV2\(\_0.5\times+\) FSRF and ShuffleNetV2\(\_10\times+\) FSRF achieve better performance than other models with corresponding scaling.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**ShuffleNetV2** & **CIFAR-10** & **CIFAR-100** \\ \hline
0.5 \(\times\) (our impl.) & 6.98 & 39.31 \\
0.5 \(\times+\)SE(our impl.) & 6.73 & 37.87 \\
0.5 \(\times+\)SK(our impl.) & 6.61 & 36.38 \\
0.5 \(\times+\)FSRF(ours) & 6.46 & 34.64 \\
1.0 \(\times\) (our impl.) & 6.29 & 33.24 \\
1.0 \(\times+\)SE(our impl.) & 6.17 & 32.32 \\
1.0 \(\times+\)SK(our impl.) & 5.93 & 30.71 \\
1.0 \(\times+\)FSRF(ours) & 5.85 & 29.68 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top-1 errors (%) on CIFAR, with all models based on the corresponding ShuffleNetV2.  
Figure 7: Relationship between performance and the number of parameters: FSRFNet compared with the corresponding lightweight model MobileNetV2.
## 3 Results and Discussion
### Sensory Characteristics of Distilled Soju Samples by Descriptive Analysis (DA)  
To depict the sensory characteristics of the distilled _soju_ samples, sensory descriptive analysis was performed. The mean intensity ratings of the 18 samples are presented in Table 5. The ANOVA performed on the 28 sensory attributes of the 18 samples revealed that all attributes except'metal', 'bitter', 'alcohol flavor', 'astringent','spicy', and'swallow' were significantly different across the samples (\(p<0.05\)). Because the alcohol concentration of each sample was adjusted to the same level, a similar intensity of alcohol and mouthfellated sensory attributes could be expected. This outcome was also reported in the descriptive analysis of eleven distilled spirits also adjusted to the same alcohol level [15].  
Figure 1: Principal component analysis loading for 56 volatile compounds (red) and scores for the 18 distilled _soju_ samples (blue). The samples and volatile compound codes are defined in Table 3.  
To examine the relationships among the sensory terms and separations of samples at a glance, principal component analysis (PCA) was performed using the mean ratings of 22 sensory characteristics showing significant differences across the 18 samples (Figure 2). PC1 and PC2 explained 69% and 13% of the variance across the samples, respectively. PC1 showed a contrast between 'earthy'/'yeasty'/'barley'/'soy_A' and'sweet aroma'/'fruity'/'fruit-related aroma attributes, as shown in Figure 2. PC2 seemed to show a contrast between 'woody_A'/'bleach_A' and'sour_A'. Examining the sample configuration (Figure 2), along PC1, there was a strong separation between samples shown by DA to be fruity and sweet and those which were low in fruitiness but high in sensory attributes related to yeasts and earthy. MSO, PJU, MIR, and GSO were located to the far right along PC1, indicating high levels in 'earthy', 'yeasty', 'barley', and'soy_A'. Likewise, these samples were made using atmospheric distillation, except for MIR. These typical sensory characteristics of atmospherically distilled liquors were also revealed in sensory studies of commercial distilled liquor products [15; 17]. Conversely, DJA, R25, and R40 appeared on the negative side of PC1 with strong fruit- and sweet-related sensory characteristics. Along PC2, CTO and OAK, both aged in oak, were located on the far positive side, which showed significantly high intensities in 'woody_A' and 'bleach_A'.
## 3 Results and Discussion
### TFC Membrane Performance in Conditions Imitating Flue Gas Separation
#### 3.5.2 Influence of Temperature  
Figure 9 represents the measured CO\({}_{2}\) permeance and CO\({}_{2}\)/N\({}_{2}\) mixed-gas selectivity values in dry conditions (a) and in 90 % RH conditions (b) at 1.2 bar and varied process temperatures for pure P[DADMA][Tf\({}_{2}\)N] (**P9 IL0 Zn0**) and PIL/IL/Zn\({}^{2+}\) composite materials (**P9 IL6 Zn0**, **P9 IL6 Zn1**, and **P9 IL6 Zn9**) based membranes. Both in dry and in humidified process conditions, CO\({}_{2}\) permeance increased, and CO\({}_{2}\)/N\({}_{2}\) mixed-gas selectivity decreased at higher temperatures. In dry conditions,  
Figure 8: Influence of the feed pressure on the separation performance of PIL/IL/Zn\({}^{2+}\) composite-based TFC membrane at 26 \({}^{\circ}\)C in dry (**a**) and humidified (**b**) conditions of the sweep test set-up. The relative humidity of feed gas was ca. 90 % for humidified conditions.  
the performance trends for all materials were similar, while the pure PIL-based membranes (\(\mathbf{P_{9}IL_{0}Zn_{0}}\)) broke at increased process temperatures (\(>30\,^{\circ}\)C).  
In humidified conditions, the pure PIL-based membrane continued to perform in a way resembling the performances of composite materials-based membranes in the dry process conditions. This suggests that a minimum humidity in the membrane matrix is required to preserve the CO2 transport. Additionally, the plasticization effect of water vapor might improve the matrix stability of the PIL by decreasing its rigidity. The composite materials showed a two-fold permeance increase from the initial value observed for the parent PIL. This confirms the results obtained in the tests with varied feed pressures.  
Humidity presence in the feed stream also positively affected \(\alpha_{\text{CO2}/\text{N2}}\) for all investigated materials with an increase of least 10 %. CO2 permeance and \(\alpha_{\text{CO2}/\text{N2}}\) were most stable for \(\mathbf{P_{9}IL_{6}Zn_{9}}\) over the broad range of tested temperatures. This observation supports the idea that while the sorption of CO2 in other materials considerably decreases with increasing temperature, the \(\mathbf{P_{9}IL_{6}Zn_{9}}\) strong affinity towards CO2 molecules in Langmuir sites compensates these losses. Thus, these results provide a sensitive measure of \(\mathbf{P_{9}IL_{6}Zn_{9}}\)-based membrane robustness in terms of process conditions in a wide range of feed pressures, temperatures and humidities imitating the flue gas CO2 capture.
## 3 Experimental Results  
The experimental procedure consisted of first mounting the chip with a precision (dovetail) rail that enabled its precise and repeatable positioning in and out of the beam path of the THz spectrometer. The next step was obtaining the \"background\" power spectrum, with the chip nanochannels pre-wet by the TPE buffer solution. The pre-wetting was carried out by depositing a \(\sim\)100-\(\mu\)L drop of buffer in the chip reservoir, and then waiting about 20 min, which was the typical time required for the channels to fill. The filling could be observed visibly by a slight darkening of the channel region under test. Then, the same volume of nucleic acid sample was added to the reservoirs, allowed to disperse, and the THz \"sample\" power spectrum was measured. Finally, the THz beam was blocked to get the \"noise floor\" power spectrum, and the transmittance was calculated at each frequency using Equation (1). The measurement was often repeated to check for reproducibility.
## 4 Experiment
### Dosimetry Methods  
Absorbed dose rate measurements of X-rays generated were performed using alanine dosimeters. Alanine pellets (E2203, Aerial, France) had a diameter of 4 mm, a thickness of 2.35 mm and an average mass of 36.35 mg. The irradiated alanine dosimeters were placed in a quartz measuring tube and were measured using an MS-5000 EPR spectrometer (Magnettech, Germany) equipped with a rectangular TE 102 cavity and operating in the X-band at approximately 9.2-9.6 GHz. Dosimeters were read out, setting up the following parameters: microwave power 10 mW, modulation amplitude 0.7 mT, sweep time 120 s, magnetic field range 330-333 mT. Taking into account the dependence of the EPR signal amplitude on the orientation of each sample inside the resonant cavity, each sample was read out two times by rotating it 90\({}^{\circ}\). The peak-to-peak amplitude of the central line of the spectrum was applied as a measure of the signal intensity, being proportional to the absorbed dose. EPR spectrum measurements were performed using ESR-studio software supplied by the manufacturer.  
Cellulose triacetate (CTA) is 125 \(\upmu\)m film dosimeters (FuijiFilm) which can be used for dose measurement, especially where an electron beam was used. The dose range in which CTA can be used cover doses from 5 up to 300 kGy. A CTA dosimeter is an accurate and precise film of which the optical density increases by radiation exposure. Before the irradiation net absorbance of CTA was measured. In order to determine depth dose cure of the electron in experimental conditions, the stack of CTA dosimetry foil was irradiated with a surface dose of about 50 kGy. Thirty minutes after exposure to irradiation the same dosimeters were read-out at 280 nm using a UV-Vis spectrophotometer (Jasco V-650). The results of the depth dose curve were normalized to the surface dose and presented as a percent of the surface dose.
## 4 Analysis and Result
### Ensemble Analysis Using Adaboost  
The ensemble analysis was performed using the AdaBoost method, a type of boosting method. The ensemble analysis is a method for creating an ensemble model suitable for data classification using various weak learners. The Adaboost is a method that increases the reliability of classification through the next learner by weighting misclassification. Therefore, the optimal number of learners and the accuracy of classification were verified by increasing the number of learners. The region images for distribution of the range of data according to the ensemble analysis are shown in Figure 11. As the number of learning increased, it was possible to classify the area more accurately and precisely and predict with high accuracy. The prediction accuracy was 100% and the learning time was 0.28 s when seven learners were used. However, in the future, if a larger dataset is used, inefficiencies may occur, for example, with respect to model development time. Therefore, using the training count ratio instead of the entire dataset would increase the efficiency of prediction time and prediction degree.  
Figure 11: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Cristobalite** & **Alumina** & **Defect** & **Boundary** \\ \hline Cristobalite & 72 (97.3\%) & - & - & 2 (2.7\%) \\ Alumina & - & 36 (97.3\%) & - & 1 (2.7\%) \\ Defect & - & - & 6 (100\%) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction ratio by nonlinear separation with multiple SVMs.
# 3D-Printed Lightweight Foamed Concrete with Dispersed Reinforcement
## 2 Materials and Methods
### Materials
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Oside Composition** & \multicolumn{4}{c}{**Material**} \\ \cline{2-5}
**(\%)** & **Fly Ash** & **Sand** & **CEM IV 42.5** & **Coal Slag** \\ \hline Na\({}_{2}\)O & 0.69 & - & - & - \\ MgO & 2.02 & - & 1.54 & 1.20 \\ Al\({}_{2}\)O\({}_{3}\) & 30.71 & 13.01 & 13.44 & 31.94 \\ SiO\({}_{2}\) & 49.85 & 81.50 & 28.46 & 48.38 \\ P\({}_{2}\)O\({}_{5}\) & 0.71 & - & - & 0.60 \\ SO\({}_{3}\) & 0.97 & - & 3.10 & 3.41 \\ Cl & - & - & 0.09 & 0.02 \\ K\({}_{2}\)O & 3.58 & 2.16 & 1.46 & 2.61 \\ CaO & 2.95 & 1.20 & 45.61 & 1.73 \\ Sc\({}_{2}\)O\({}_{3}\) & - & - & 0.10 & - \\ TiO\({}_{2}\) & 1.48 & 1.08 & 0.79 & 1.71 \\ V\({}_{2}\)O\({}_{5}\) & 0.08 & - & 0.02 & 0.10 \\ Cr\({}_{2}\)O\({}_{3}\) & 0.03 & - & 0.03 & 0.03 \\ MnO & 0.05 & - & 0.08 & 0.02 \\ Fe\({}_{2}\)O\({}_{3}\) & 6.43 & 1.05 & 5.01 & 7.66 \\ CoO & - & - & 0.02 & - \\ NiO & 0.02 & - & 0.02 & 0.03 \\ CuO & 0.02 & - & 0.02 & 0.02 \\ ZnO & 0.02 & - & 0.11 & 0.01 \\ Rb\({}_{2}\)O & 0.03 & - & 0.01 & 0.02 \\ SrO & 0.13 & - & 0.06 & 0.20 \\ Y\({}_{2}\)O\({}_{3}\) & 0.01 & - & - & 0.01 \\ ZrO\({}_{2}\) & 0.04 & - & 0.03 & 0.09 \\ In\({}_{2}\)O\({}_{3}\) & - & - & - & 0.01 \\ BaO & 0.15 & - & - & 0.11 \\ Ta\({}_{2}\)O\({}_{5}\) & - & - & - & 0.01 \\ WO\({}_{3}\) & - & - & - & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical composition of the raw materials. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fibers** & **Diameter [\(\mu\)m]** & **Length [mm]** & **Density [g/cm\({}^{3}\)]** \\ \hline Coconut (CO) & 70 & 10 mm & 2.41 \\ Basalt (B) & 14 & 10 mm & 4.88 \\ Glass (G) & 20 & 12 mm & 1.88 \\ Polypropylene (PP) & 18 & 12 mm & 1.39 \\ Merino wool (M) & 18.20 & 10 mm & 4.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fibers characteristics. The specific compositions and proportions of all mixtures are outlined in Table 4. The water-to-cement (w/c) ratio was maintained between 0.24 and 0.27, depending on the mixture composition. This range was selected to ensure consistency and optimize the mechanical properties of the foamed concrete. A mixing station aligned with the idea developed as part of the \"Implementation doctorate\" was used to blend the material. Figure 1: Representative view of (**a**) coconut (CO) fibers, (**b**) basalt (B) fibers, (**c**) glass (G) fibers, (**d**) polypropylene (PP) fibers, and (**e**) merino wool (M) fibers. \begin{table}
\begin{tabular}{c c c c c} \hline
**Component** & **Dosage (\%)** & **Density (kg/m\({}^{3}\))** & **Compressive Strength After 7 Days (MPa)** & **Compressive Strength After 28 Days (MPa)** \\ \hline Synthetic foaming agent & 2 & 1602 & 5.2 & 9.7 \\ \hline \end{tabular}
\end{table} 
Table 3: Data from the product specification sheet of the foaming agent manufacturer.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 6 Simulation Results and Analyses
### Initial Analysis
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \(R_{i}^{\text{min}}(kbps)\) & \(R_{i}^{\text{max}}(kbps)\) & \(Style\) \\ \hline Akiyo & 1.5119 & 84.5447 & slow motion and smooth scene \\ Carphone & 20.2554 & 322.0153 & medium motion and smooth scene \\ Table & 12.7781 & 481.1014 & medium motion and smooth scene \\ Foreman & 17.8168 & 388.7091 & medium motion and smooth scene \\ Coastguard & 28.4987 & 878.8011 & medium motion and complex scene \\ Football & 286.311 & 1720 & fast or complex motion \\ Mobile & 225.0682 & 1610 & fast or complex motion \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of different videos. Figure 3: The frames of Coastguard after being encoded by H.265 in different rates. The encoding rate of each UAV plays a crucial role in this paper. It is both the cause and the result of strategy update. Thus, the rate range of each video in Table 1 is firstly segmented into some discrete values and all of them can form the strategy space \(\{R\_space\}\) for each video. On one hand, different rate selection from the strategy space represents the application of different video encoding rates, which will lead to the change in the total utility function, in other words, in the potential function. On the other hand, the change of the potential function will drive the algorithm to update the allocation scheme. That means to change the bandwidth for each UAV. As a result, each encoders have to resort to a new encoding rate and the scheme will help to look for a new one from the strategy space \(\{R\_space\}\) to replace the old--wherein, it follows the distributed self-learning algorithm mentioned above. Until the potential function is no longer changed, the system reaches equilibrium.
## 3 Results
### Thermogravimetric Analysis
[31] study showed an increase in HHV of torrefied sugarcane bagsase from 17.3-20.2 MJ/kg as torrefaction temperature rose from 200-300 \({}^{\circ}\)C. Similarly, [PERSON] et al. [11] study on torrefaction of sewage sludge recorded an HHV that varied from 15-22 MJ/kg as torrefaction temperature increased from 200-600 \({}^{\circ}\)C. In their study, an increasing trend in HHV from around 200 \({}^{\circ}\)C was first noticed, before a decline at a temperature above 350 \({}^{\circ}\)C. The decline is associated with the occurrence of pyrolysis reaction at higher torrefaction temperature. In a recent study that evaluated the characteristic of wood pellet mixed with torrefied rice straw, a decrease in heating value was first observed before an increase as torrefaction temperature increased from 220-280 \({}^{\circ}\)C [41]. This back and forward trend can be associated with the unique fuel property that results from blending two biomass, which is the case of the present study. The highest HHV of 22.70 MJ/kg and 23.62 MJ/kg determined for the M\({}_{\rm e350}\) sample and lowest HHV (12.25 MJ/kg and 14.59 MJ/kg) of the M\({}_{\rm a250}\) sample can be attributed to the percentage concentration of VM, AC and FC (thermochemical parameters) in the samples when compared to other samples. Similarly, [PERSON] et al. [22] study reported the highest HHV of 20.28 MJ/kg for 50:50 blend ratio of sewage sludge and _Leucaaena_ torrefied at a microwave power of 150 W and least HHV of 13.39 W for 75:25 blend ratio of sewage sludge and _Leucaa_ torrefied at a microwave power of 350 W. Enhancing the FC content of any solid fuel produces a fuel with higher HHV and lower reactivity while a higher VM gives a lesser HHV and more reactive fuel [26; 42]. Notably, increasing the torrefaction temperature enhanced the HHV of some samples as expected, although not in all cases, which could be linked to the complexity of sewage sludge composition as well as the unique fuel property of the blended sample. The recorded increase in HHV as torrefaction temperature increased is due to reduced oxygen content.