esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## 2 Model of Wind Turbine Systems  
Modeling of the electromagnetic dynamics of the doubly-fed induction generator (DFIG) system is based on assumptions 1-3:  
1. All of the stator and rotor quantities are transformed into a stationary frame (the d-q frame) using the Park transformation in terms of the stator's and rotor's voltage-pulsation frequencies \(\omega_{\text{s}}\) and \(\omega_{\text{r}}\), respectively. In addition, the Park transformation is based on an equivalent power principle.
2. The stator flux is constant. The q-axis is aligned with the stator flux vector. The d-axis is aligned with the stator voltage vector.
3. The stator's resistive voltage drop is neglected. Modeling of the wind turbine dynamic drive-train is based on assumptions 4-5:
4. The rotor speed, the equivalent rotor speed, and the generator speed are always positive when wind turbines work normally. The aerodynamic torque is positive towards the accelerating direction of the drive-train. The electromagnetic torque is positive towards the decelerating direction of the drive-train.  
The moment of inertia of the drive-train shafts and the gearbox is neglected.
## Results  
### The Assessment of Taste of Flat Peaches from Xinjiang  
#### 3.1.1 The SSC and TA of Flat Peaches from Xinjiang  
It has been properly recognized that the SSC and TA of fruits are vital factors affecting their taste and, additionally, are important determinants of customer acceptability [48]. In this study, the SSC and TA of Qingpan (QP), Ruipan 2 (R2), Ruipan 4 (R4), and Wanpan (WP) were investigated. As is shown in Figure 2a, the SSC of the flat peaches ranged from 9.90% to 15.67%. WP showed the highest SSC (15.67%), while QP had the lowest SSC (9.90%). The TA of the flat peaches ranged from 0.43% to 0.90%. QP (0.43%) had the lowest TA and R2 (0.90%) had the highest TA. Of note, the SSC of the flat peaches ranged from 10% to 15% for all flat peaches except QP, and the TA of all tested flat peaches was less than 0.70%, except for R2. It has been documented that fruit in this range of SSC and TA are of high quality [49; 50; 51]. The results showed that the flat peaches from Xinjiang generally had a higher quality, broadly recognized by the industry. The SSC of nectarines was found to range from 9.5% to 14.4% and TA from 0.63 to 0.93% [52], and the \"Xinchuanzhongdao\" peach cultivar's SSC ranged from 6.4% to 0.93% [53]. It is easy to observe the differences in SSC and TA as a result of the different cultivars. The SSC/TA ratio is also considered to be an important parameter to evaluate the taste equilibrium of fruits [54; 55]. The SSC/TA ratio of the flat peaches was 17.04 to 22.98 (Supplementary Table S2). QP (22.98) and WP (22.36) had higher SSC/TA ratios, while R4 (19.46) and R2 (17.04) had slightly lower SSC/TA ratios. In general terms, this meant that the flat peaches had an excellent taste with high sugar and low acidity.  
Figure 2: The soluble solid content, titratable acidity (**a**), total flavonoid content, and total phenol content (**b**) of flat peaches from Xinjiang. Significant variations at a level of \(p\leq 0.05\) (Duncan’s test) are shown by different letters for each sample.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 2 Faster Region-Based Convolutional Neural Networks (RCNN) in an Automatic Detection Framework  
Region-based convolutional neural networks (RCNN) use a selective search to generate region proposals [15]. These region proposals are then used by CNN to extract features. Finally, these features are used by a support vector machine (SVM) to classify features in RCNN. The detection accuracy of the RCNN method on the pattern analysis, statistical modeling and computational learning visual object class (PASCAL VOC) dataset is much higher than the traditional methods [16], however, its training time and space overhead are huge. Each training image results in a large number of regions of interest (ROI). Moreover, we need to extract features for each ROI, and write the output to disk. Similarly, during testing, it is also necessary to extract ROI from the test samples and complete the detection after extracting the features from each ROI.  
Faster RCNN is an improved algorithm based on RCNN and fast RCNN [17]. The main improvements of faster RCNN are: the SVM does not need to be trained; it combines multiple loss functions for a single level training process and for reducing time overhead; layers update during training and there is no need to write features to save disk space. The region proposal network (RPN) is proposed to generate candidate regions in a computationally efficient manner. It is noticeable that through alternate training, RPN and fast RCNN network share parameters which greatly improves the detection speed.  
Faster RCNN consists of three parts: (1) feature extraction network, (2) region proposal network, (3) fast RCNN.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 4 Experiment
### Results  
#### 4.3.1 Segmentation Evaluation Results  
Tables 1 and 2 show the segmentation evaluation results for two different datasets: the S3 IDS and ShapeNet datasets, respectively. In the S3 IDS dataset, we use the same configuration of sample size and training parameters as models such as GACNet [8], DGCNN [6] and PAConv [42], e.g., 4096 samples and 100 epochs of input for training. This careful configuration is used in order to perform a robust comparative analysis. Compared to PAConv, our new method provides a significant improvement, increasing the mIoU by 0.21%. In addition, our method achieves significant progress in terms of overall accuracy (OA), mAcc, and mIoU by 1.3%, 0.3%, and 4.1%, respectively, compared to the BIDEL model. Similarly, with respect to the ShapeNet Part dataset, we conducted an evaluation using consistent sampling points to assess the performance of multiple networks such as AGNet [43], DGCNN, and PAConv. Through rigorous analysis, we found an improvement in mIoU for instance segmentation 0.7% compared to the SBSNet network.  
PointNet++ [5], SegGCN [44] and our method represent point-based, graph-based and hypergraph-based methods, respectively. The analysis in Figure 3 shows that our method achieves the best results in all three indicators, OA, mAcc and mIoU, and it proves the effectiveness and advancements of our method using a hypergraph for point cloud segmentation. Specifically, PointNet++ directly deals with the raw point cloud data, maximizing the preservation of the spatial features of the point cloud. However, this method ignores the geometric relationships between points, resulting in a lack of local features. SegGCN is to process graph-structured data by using graph convolution, which performs convolution operations on nodes and edges, thus realizing feature extraction and representation learning on graph-structured data. However, it is difficult to capturethe complex structures present in 3D data because the higher-order relationships between objects are not taken into account. Our approach utilizes hypergraph convolution models that act directly on the raw feature data of the point cloud, which naturally preserves the spatial position information of the point cloud and helps to better capture the local feature information, thus improving the performance of the model in the point cloud data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **OA** & **mAcc** & **mIoU** \\ \hline PointNet++ [5] & 86.4 & 61.2 & 56.0 \\ PointWeb [45] & 87.0 & 66.6 & 60.3 \\ KPConv [46] & - & 72.8 & 67.1 \\ GACNet [8] & 87.8 & - & 62.9 \\ SegGCN [44] & 88.2 & 70.4 & 63.6 \\ PAConv [42] & - & - & 66.6 \\ GAPointNet [47] & 85 & - & 51.2 \\ CBL [48] & 87.6 & 71.9 & 64.1 \\ AGNet [43] & 85.9 & - & 59.6 \\ BIDEL [49] & 88.0 & 73.6 & 64.7 \\ SuperCluster [50] & - & - & **68.1** \\ \hline
**Ours** & **89.3** & **73.9** & 66.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Semantic segmentation results on Area 5 in the SSIDS dataset (%).  
Figure 3: Accuracy of different methods.
## 4. SoSCO-Based Architectural Steps and Application Example
### Step 4: Organize Capabilities Based on Their Relationships  
This step involves organizing and representing the capabilities through the composition relationship to establish the top-level capability and sub-capabilities that must be achieved together to attain the overall capability. To this end, a cluster of capabilities based on _OperationalFactor_ can complement the ones with the same _TemporalFactor_ or _StructuralFactor_ in the generalization relationship. The Strategic Structure (St-Sr) view of the  
Figure 11: Strategic Taxonomy (St-Tx) of capabilities.  
Figure 10: Excerpt of facts of SoSCO.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Comments** & **SWRL Rules** \\ \hline Generalization reasoned by TemporalFactor & ScS(?sos) \({}^{*}\)TemporalFactor(?t) \({}^{*}\) owns(?sos,?t) \({}^{*}\) correspondsToCapability(?sos,?c) \({}^{*}\) \\  & correspondsToCapability(?t,?tc) \  
UAF, implemented by the SysML BDD, can be used to display the class diagram of the capability clusters.  
To support the analysis of the SoS context, capability correlations need to be identified. Dependencies between capabilities demonstrate that one capability cannot achieve its full potential without the presence of other capabilities [29]. Based on the capability structure obtained in the St-Sr view, domain experts can establish the dependency relationship between capabilities using knowledge about the relationships between _OperationalFactors_. We propose using the Strategic Connectivity (St-Cn) view of the UAF implemented by the SysML internal block diagram (IBD) to address the logical grouping of capabilities within a specific context and their dependencies.  
This Strategic Connectivity (St-Cn) view helps group capabilities and display their dependencies, as shown in Figure 12, where SAR command and control (C2) depends on the military C2 capability. Similarly, the assistance, search, and recovery capabilities depend on the SAR C2 capability, which relies on the distress signal monitoring capability. The SysML IBD defines capabilities in a specific context, and the dependencies are scoped accordingly.
## 3 Results and Discussion
### Performance Tests of VRLA Batteries
#### 3.2.4 Water Loss Performance  
During the cycle life of VRLA batteries for e-bikes, one of the main reasons for the failure of VRLA batteries is dry failure due to lack of water. The main reason for this is that the float charge voltage is high, causing the battery to lose water faster, so that the battery cannot work normally because of the excessive polarization in the charging and discharging process. At the end of charging, the decomposition of water leads to the separation of hydrogen and oxygen; this is the main factor of water loss. The oxygen gas can perform the oxygen recombination reaction on the negative plate, while the hydrogen cannot be absorbed and can only gather at the top of the battery [1; 37]. When the hydrogen gas reaches a certain amount, the safety valve automatically opens and releases gas, causing water loss. The addition of additives to the negative active material can improve the battery's charge acceptance and low temperature performance. However, the presence of some carbon additives may reduce the hydrogen evolution over-potential of the negative electrode, leading to an increase in the hydrogen evolution of the negative electrode. Carbon black as an active additive of the negative electrode has an important effect on water loss of the batteries.  
The water loss of the four batteries during the cycle is shown in Figure 6. In the figure, with the increase in cycle number, the water loss of the control batteries increased from 6.5 g to 17.1 g. In addition, the water loss of the carbon black batteries increased only slightly between 200 cycles of the control batteries, indicating that the water loss of the batteries containing carbon black was within a controllable range and there was no obvious difference from the control batteries. The water loss of the batteries with carbon black reached 30.8-31.2 g after 350 cycles. Moreover, the CB3 battery water loss reached 36.1 g after 400 cycles. The water loss of the battery can increase the acid concentration, aggravate the sulfation degree of the negative plate, magnify the internal resistance, depress the battery capacity, shorten the battery service life and even lead to the thermal runaway of the battery [16; 38].
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
###### Abstract  
In this study, the feasibility of using Lamb waves in functionally graded (FG) nano copper layered wafers in nondestructive evaluation is evaluated. The elastic parameters and mass densities of these wafers vary with thickness due to the variation in grain size. The power series technique is used to solve the governing equations with variable coefficients. To analyze multilayered structures, of which the material parameters are continuous but underivable, a modified transfer matrix method is proposed and combined with the power series method. Results show that multiple modes of Lamb waves exist in FG nano copper wafers. Moreover, the gradient property leads to a decrease in phase velocity, and the absolute value of the phase velocity variation is positively correlated with the gradient coefficient. The phase velocity variation and variation rate in Mode 2 are smaller than those in other modes. The findings indicate that Mode 4 is recommended for nondestructive evaluation. However, if the number of layers is greater than four, the dispersion curves of the Lamb waves in the multilayer structures tend to coincide with those in the equivalent uniform structures. The results of this study provide theoretical guidance for the nondestructive evaluation of FG nanomaterial layered structures.  
functionally graded nano copper; lamb wave; power series technique; modified transfer matrix method +
Footnote †: journal: Applied Sciences  
Article  
Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]
## 2 Preliminaries
### Chaotic Maps
This simple, nonlinear function is defined using a trigonometric formulation, specifically involving the sine of the preceding state variable. By employing Equation (4), the system effectively incorporates the influence of the previous state variable in its dynamic evolution. The sine map is known for its sensitivity to initial conditions and its ability to generate complex, irregular behavior. It is often used as a testbed for studying the properties of chaotic systems, such as Lyapunov exponents and fractal dimensions. \[x_{n+1}=k\sin(\pi\times x)\ x\ \in\ [0,\ 1],\ k>0 \tag{4}\]  
When the value of parameter \(k\) approaches 1, the sine map exhibits chaotic behavior, with the values of \(x_{n}\) appearing randomly as non-convergent and aperiodic when \(x\in[0,1]\) and \(k\in(0,1]\). The sensitivity of the system to the parameter \(k\) makes it a useful tool for generating chaotic behavior and has been applied to various fields, such as cryptography and secure communication. By adjusting the value of parameter \(k\), the sine map can be used to generate random sequences that can be utilized for encryption purposes. Another objective of the proposed study is to integrate the logistic map and the sine map to form a hybrid map called the logistic sine map that generates a sequence of random numbers used as secret keys. The logistic sine map is intended to be utilized as a key during the image encryption process to enhance the security and confidentiality of the image data. The resulting hybrid map exhibits more complex and unpredictable behavior than either map alone, making it a more secure option for encryption.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.3 Outpatient Clinics  
The application of DES in outpatient clinics has grown considerably, as highlighted by [PERSON] et al. [39]. Simulation models in these settings focus on optimizing appointment scheduling, managing patient flow, and improving resource allocation to better align capacity with demand. A systematic review of 22 outpatient clinic implementations showed that DES-optimized scheduling reduced patient waiting times by 15-30%, illustrating its effectiveness in improving operational efficiency and patient experience.
## 3 Results
### Bunker Silage
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Chemical Composition 2**} & \multicolumn{3}{c}{**45 d**} & \multicolumn{3}{c}{**105 d**} \\ \cline{2-7}  & **Control** & **DPI** & \(p\) **Value** & **Control** & **DPI** & \(p\) **Value** \\ \hline DM (\%) & 31.7 \(\pm\) 1.2 & 31.7 \(\pm\) 0.8 & 0.495 & 30.3 \(\pm\) 0.9 & 30.4 \(\pm\) 0.5 & 0.44 \\ CP (\%) & 7.83 \(\pm\) 0.20 & 7.96 \(\pm\) 0.33 & 0.378 & 7.59 \(\pm\) 0.03 & 7.47 \(\pm\) 0.10 & 0.16 \\ ADE (\%) & 28.3 \(\pm\) 0.8 & 28.9 \(\pm\) 0.5 & 0.289 & 29.5 \(\pm\) 0.8 & 30.7 \(\pm\) 0.8 & 0.18 \\ NDF (\%) & 56.7 \(\pm\) 0.9 & 58.0 \(\pm\) 0.4 & 0.129 & 56.7 \(\pm\) 0.5 & 58.8 \(\pm\) 1.4 & 0.11 \\ WSC (\%) & 1.86 \(\pm\) 0.24 & 1.69 \(\pm\) 0.17 & 0.301 & 1.02 \(\pm\) 0.40 & 1.43 \(\pm\) 0.40 & 0.26 \\ Starch (\%) & 20.7 \(\pm\) 0.9 & 20.4 \(\pm\) 2.1 & 0.450 & 19.3 \(\pm\) 0.7 & 17.1 \(\pm\) 1.8 & 0.15 \\ pH & 3.76 \(\pm\) 0.04 & 3.77 \(\pm\) 0.05 & 0.423 & 3.79 \(\pm\) 0.01 & 3.81 \(\pm\) 0.06 & 0.42 \\ Lactic acid & 1.51 \(\pm\) 0.23 & 1.41 \(\pm\) 0.17 & 0.367 & 3.28 \(\pm\) 0.14 & 2.90 \(\pm\) 0.20 & 0.095 \\ (\% of DM) & 3.79 \(\pm\) 0.34 & 4.73 \(\pm\) 0.36 & 0.065 & 3.43 \(\pm\) 0.10 & 3.08 \(\pm\) 0.22 & 0.11 \\ LA-AA ratio & 0.40 \(\pm\) 0.05 & 0.30 \(\pm\) 0.03 & 0.087 & 0.96 \(\pm\) 0.02 & 0.96 \(\pm\) 0.13 & 0.50 \\ Butyrate & ND & ND & ND & ND & ND & \\ (\% of DM) & 1.80 \(\pm\) 0.64 & 2.83 \(\pm\) 0.88 & 0.199 & 2.07 \(\pm\) 0.05 & 1.62 \(\pm\) 0.60 & 0.249 \\ LAB (log10 cfu/g) & 8.1 \(\pm\) 0.1 & 8.2 \(\pm\) 0.1 & 0.294 & 7.0 \(\pm\) 0.3 & 7.6 \(\pm\) 0.3 & 0.11 \\ YMC (log10 cfu/g) & 3.8 \(\pm\) 1.8 & 3.8 \(\pm\) 1.8 & 0.493 & 2.0 \(\pm\) 1.1 & 1.5 \(\pm\) 0.8 & 0.37 \\ Aerobic stability (h) & 53 \(\pm\) 13 & 59 \(\pm\) 14 & 0.389 & 53 \(\pm\) 11 & 113 \(\pm\) 29 & 0.062 \\ \hline \hline \end{tabular}  
* Values were presented as mean \(\pm\) SEM. ND: not detected. 1 Control (_n_ = 3) = no inoculant added; DPI (_n_ = 3) = 2.5 \(\times\) 10\({}^{5}\) cfu/g of _Lactobacillus plantarum_ and 5.0 \(\times\) 10\({}^{5}\) cfu/g of _Lactobacillus buchneri_. 2 WSC: water soluble carbohydrates; LA-AA ratio: lactate-to-acetate ratio; LAB: lactic acid bacteria; YMC: yeast and mold counts.  
\end{table} 
Table 3: Effects of inoculant application 1 on the quality and chemical composition of bunker silage at different ensiling periods.
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 4 Activation of Anti-Tumor Immunity by LTP  
The development of cancer immunotherapy provides patients with better hope for treatment. Studies have shown that LTP has an effect on the activity of immune cells and immune response [19; 20; 21]. Two lines of research are currently pursued to disentangle the effects of plasma treatment in anti-cancer immunity: the ability of LTP to affect immune cells directly [22], and indirect activation of immune cells via LTP-mediated tumor cell death and pro-inflammatory signals in the microenvironment [23].
# A Combined Electromagnetic and Mechanical Approach for EU-DEMO Toroidal Field Coils
## 7 Conclusions  
A methodological approach is presented that explores the analyses performed on the TF coil system and its support structures. Two sequentially coupled parts are identified: the electromagnetic section (EMS) and the structural section (SS). Concerning the electromagnetic field, the full set of loading conditions included in the reference scenario is computed with a dedicated 3D finite element model. To this aim, EMS is further split into two sequentially coupled steps, the first one dealing with an electrostatic study, the second one with a magnetostatic analysis. The magnetic field, the magnetic flux and the Lorentz force exerted by the magnets are accurately investigated to have a better insight into the behavior of the machine. A consistent 3D structural model is then defined, comprising the three main magnetic systems and their support structures. To model the winding packs of the CS, PF and TF coils, a homogenization procedure is implemented, considering a proper representative volume element for each magnet and, for the TF winding pack, a different RVE for each cable of the twelve layers. The periodic boundary conditions applied are also detailed, together with the virtual tests performed to obtain the homogenized materials. Finally, the mechanical evaluation is completed by the fatigue assessment, via the (S-N) approach. To this aim, two time points (the TF energization and the end of flattop of the plasma scenario) are used to define the minimum and the maximum of the stress cycle. By considering the 20,000 plasma pulses scheduled for the machine, the equivalent Goodman stress is compared to the allowable limit for the AISI 316 LN at the operating temperature. The final \(S_{eq}\) field shows that there are only a few, very narrow areas where the limit is  
Figure 21: Detail of the Goodman equivalent stress considering the base metal (**a**) or the weld metal (**b**). The region in grey exceeds the allowable limit of 462 MPa.  
Figure 20: Plot of the Goodman equivalent stress considering a residual stress of 250 MPa.  
exceeded. The smoothing of those stress peaks can be easily obtained by an improvement of the design.  
Conceptualization, L.G. and D.P.B.; methodology, L.G. and D.P.B.; software, L.G.; validation, L.G. and D.P.B.; formal analysis, D.P.B.; investigation, L.G. and D.P.B.; resources, L.G., D.P.B. and V.C.; data curation, L.G.; writing--original draft preparation, L.G.; writing--review and editing, L.G., D.P.B. and V.C.; visualization, L.G.; supervision, D.P.B.; project administration, V.C.; funding acquisition, L.G., D.P.B. and V.C. All authors have read and agreed to the published version of the manuscript.  
Euratom research and training programs 2014-2018 and 2019-2020 under grant agreement No 633053.  
Not applicable.  
This work was carried out within the framework of the EUROfusion Consortium. The views and opinions expressed herein do not necessarily reflect those of the European Commission.  
The authors declare no conflict of interest.