esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Recharging Scheme for the Nodes in the RCCH
### Wireless Recharging Model  
As shown in Figure 8, there is a WCV on the center line of each annulus, and the initial position of each WCV is the center of the first RCCH of each annulus. During the recharging process, each WCV only stays at the center of each RCCH and recharges the CH and CCHs wirelessly. After finishing the recharging task of one RCCH, it moves along a straight line to the next RCCH for recharging, and finally it returns to the initial position.  
Taking the WCV in the \(i\)-th annulus as an example, the wireless recharging strategy is described as follows. \(T_{i}^{r}\) is defined as a round of recharging time, that is the time duration from the moment the WCV leaves from the initial position to the moment it returns back again. In addition, \(t_{ij}^{c}\) is defined as the time duration for the WCV to recharge nodes in the \(j\)-th RCCH of the \(i\)-th annulus. Moreover, the time spending on moving from the \(j\)-th RCCH to the \((j+1)\)-th RCCH is denoted as \(t_{ij}^{m}\). Thus:  
\[T_{i}^{r}=\sum_{j=1}^{n}\left(t_{ij}^{c}+t_{ij}^{m}\right) \tag{29}\]  
and, the total moving path length of WCV during \(T_{i}^{r}\) is:  
\[l_{i}=n(2i-1)(R/k)\sin\alpha \tag{30}\]  
The recharging scheduling strategy in this paper should mainly solve the following two problems:  
How to set the most appropriate energy threshold of the recharging request?  
How to balance the energy consumption of nodes as much as possible while maximizing the recharging efficiency?  
The detailed analysis is as follows.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring
### Signal Pre-Processing  
#### 5.1.1 Sources of Noise  
The main sources of noise in EBI measurements are associated with an electrical contact between the sensor and the skin. This contact itself is affected by properties of skin, such as state of hydration and roughness [60], as well as properties of electrodes, for example, electrode area and material [61]. It is well-known that the smaller the area of electrodes, the higher the electrode-to-skin impedance and the lower the signal-to-noise ratio of the measured bioimpedance signal [62]. Sources in the literature explore self-adhesive materials (e.g., 2D layer graphene, ultra-thin gold electronic-tattoo) for bioelectronic measurements [63; 64]. However, a major issue with these materials is establishing a suitable connection between the flexible substrates and electronics which are rigid, and the connection often becomes the source of failure [18]. The pressure with which the electrodes are pressed to the skin is also of importance. A faulty attachment of electrodes to the skin can be detected as a relatively high-frequency perturbation in the measured signal [65; 66]. Moreover, if unsuitable or inappropriate electrode configuration relative to the artery is used, the result may be fully interfered or wrongly interpreted [67].  
In the case of PPG signals, MAs, electromagnetic interference, spatial stray light interference, variations due to baseline drift, noise in the light signal due to variations in the sensor position [9], and local perfusion changes [34] are the most profound causes of noise. Out of these, the most challenging to deal with for accurate HR estimation are MAs if the frequency of the MA lies inside the permissible HR frequency band.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 3 Origin of Digestate  
Digestate or anaerobic effluents arise when carbon of organic material is converted to biogas under anaerobic conditions, mainly consisting of methane (CH\({}_{4}\)--energetic component) and CO\({}_{2}\). Most macro- (N, P, K, S, Mg, Ca) and micronutrients (B, Cl, Cu, Fe, Mn, Mo, Ni, Zn) are concentrated in digestate [46]. However, digestate also contains solids (suspended matter) and colorants. The raw material affects the TS (total solids--fiber, particles, soluble solids) and nutrient content (N, P, K) of the digestate [17; 46; 47; 48]. An overview of the nutrient composition of different digestates in terms of their origin is summarized in Table 2. On the basis of the used raw material, biogas plants can be categorized into agricultural, industrial, and waste plants [17; 46; 47; 48]. Agricultural plants use agricultural residues as well as renewable materials, industrial plants use various organic residues ranging from whey to slaughterhouse residues and wastewater, whereas different kinds of food wastes are used in waste plants [17]. Agricultural residues and renewable raw materials such as maize silage result in high dry weight contents (6-24%), while food waste and liquid industrial residues usually result in rather low TS concentrations [17]. High protein contents in the raw material such as in slaughterhouse wastes lead to high NH\({}_{4}\)-N concentrations [17]. Colorants, high solid and NH\({}_{4}\)-N concentrations may cause severe problems in microalgae cultivation since light is absorbed over the whole visible spectrum [18], and too high NH\({}_{4}\)-N concentrations may have toxic effects on microalgae [49]. Digestate also contains inorganic (CaCO\({}_{3}\)) and organic carbon (volatile fatty acids), whose concentrations depend on pH value and on the degree of digestion [17].  
During the anaerobic digestion process, trace elements (microelements) are often added, especially in the case of mono-digestion [17]. Furthermore, additives such as iron chloride are sometimes used to remove sulfur compounds. These additives might influence microalgal growth if present in the digestate.
## 3 Winding Simulation at Strands Level
### Effect of Strand Transposition  
In stranded windings, the location of each conductor is hard to control. That is why the conductors are placed randomly inside the stator slot, resulting--most of the time--in higher losses. In order to investigate the effect of strand transposition on AC losses of  
Figure 10: Impact of PWM on the core losses at different frequency levels.  
Figure 9: Impact of PWM on the copper losses for the 10-strand coil at different frequency levels.  
windings, two samples are prototyped using traditional stranded round wire. In both samples, a coil support is used to have a defined location for each strand, as shown in Figure 11. As shown in this figure, the first sample has five turns and seven strands per turn with a fixed strand location. In the second sample, the same number of turns and strands is used. The only difference is that the strands are transposed to different locations using a special pattern. In this pattern, each strand is moved one location after the transition to the next turn.  
Both of the aforementioned arrangements are simulated using the FEM under the same current level at a frequency of 1 kHz, as shown in Figure 12. It is clear that the maximum current density in the case of transposed locations is 26% lower than its value in the case of fixed strand locations. Therefore, the strand transposition will limit the AC losses, especially at a high frequency.  
Figure 11: Strand transposition inside a 2-slot magnetic core with the aid of a 3D-printed thermoplastic coil support.  
Figure 12: Current density distribution at 1 kHz under different strand arrangements. (**a**) Fixed strand location. (**b**) Transposed strands.  
For additional verification, both samples are prototyped using the same wire gauge, as shown in Figure 13. These plastic supports are intended only for accurately studying the effect of controlled transposition and not for inserting in real machines because of the low fill factor and bad heat evacuation. The coil resistances are measured using an RLC meter. Also, the measured resistances are compared with the simulated values up to 5 kHz. A very good agreement is obtained between the simulation and measurements, as shown in Figure 14. It is also clear that the transposed strand coil can provide remarkably lower AC losses, especially at high frequencies. For instance, at 2 kHz, the coil resistance is nearly 50% lower in the case of the transposed stranded coil. With this being said, a flat lift wire is used as one of the test samples to compare its performance with the conventional stranded windings.  
Figure 14: AC resistance at different frequencies: comparison between fixed strand location and transposed strand location.  
Figure 13: Prototyping of stranded coils with controlled strand locations.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 4 Discussion
### Role of GANs in Load Profile Generation  
GANs offer a powerful framework for modeling the complex and varied nature of energy consumption patterns. Compared to rule-based or statistical models, GANs can learn non-linear, multimodal distributions directly from historical data, preserving key behaviors. However, conventional GAN architectures often suffer from instability, mode collapse, or unrealistic outputs. As this work demonstrates, GANs are not only viable but also effective for generating synthetic energy data across multiple domains, enabling privacy-preserving data sharing, benchmarking of forecasting models, and scenario testing in grid simulations.
# Mmpal _sciences_
## 4 Experiments and Results
### Experiments on the AR Database
In testing set I, the recognition rate of MMPCANet is 7.9%, 3.21%, 5.68%, 3.91%, and 2.9% higher than ESRC, NNAODL, PCANet, 2 DPCANet, and L1-2 DPCANet, respectively. In testing set II, the recognition rate of MMPCANet is 7.42%, 4.12%, 6.08%, 5.67%, and 0.68% higher than that of ESRC, NNAODL, PCANet, 2 DPCANet, and L1-2 DPCANet, respectively. In testing set III, the recognition rate of MMPCANet is 12.59%, 5.54%, 9.57%, 4.33%, and 2.8% higher than that of ESRC, NNAODL, PCANet, 2 DPCANet, and L1-2 DPCANet, respectively. It is easy to see that the recognition rate of MMPCANet in self-occlusion (testing set I) is higher than that in scarf occlusion (testing set III), and the recognition rate of scarf occlusion (testing set III) is higher than that of mirror occlusion (testing set II). In these three cases, we suspected that eye features are more recognizable than mouth features. After further verification, we added testing set IV, under which we manually added occlusion to face images. As shown in Figure 7, we divided images into eight occlusion levels, namely, 0% occlusion, 10% occlusion, 20% occlusion, 30% occlusion, 40% occlusion, 50% occlusion, 60% occlusion, and 70% occlusion. Considering 80%,  
From the test samples and experimental results, it can be seen that, when the face image is unshielded or when the occlusion rate is 10%, each deep learning network can achieve a very high face recognition rate. From 20% occlusion to 60% occlusion, the face recognition rates of other deep learning networks decrease rapidly. The more occlusions that there are, the fewer useful face recognition features that there are that can be extracted. However, in the case of 20%-60% occlusion, eye features can still be extracted, so the proposed method can also effectively capture eye features for face recognition. However, when the occlusion is increased to 70%, the recognition rates of all networks are approximately low (only about 12%), and it can be seen that almost no facial features can be learned by the network.
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 4 Experiments
### Data Set  
To evaluate our proposed method, we used three public data sets: the extended Cohn-Kanade (CK+) [54], JAFFE [55], and BU-3 DFE [43]. In all experiments, person-independent FER scenarios were used [56]. Therefore, the subjects in the training set were completely different from the subjects within the test set (i.e., the subjects used for training were not used for testing). Following the recommendations in [38], each face image used in our experiments was cropped, based on the locations of the eye. The landmarks were obtained using Open-Face. The cropped face images were rescaled to \(256\times 256\) pixels. Figure 3 shows the various classification results.  
* CK+ data set: This includes 593 image sequences from 123 subjects. From the 593 sequences, we selected 325 sequences of 118 subjects, each of which met the criteria for one of the seven expressions [54]. The selected 325 sequences consisted of 45 angry, 18 contempt, 58 disgust, 25 fear, 69 happy, 28 sadness, and 82 surprise sequences [54]. In the neutral face scenario, we selected the first frame of the sequence of 33 randomly selected subjects.
* BU-3 DFE data set: This is known as the most challenging and difficult data set, mainly due to the presence of a variety of ethnic/racial ancestries and expression intensities [43].  
A total of 700 expressive face images (1 intensity \(\times\) 6 expressions \(\times\) 100 subjects) and 100 neutral face images (each of which was of one subject) [43] were used.
* JAFFE data set: This contains 10 female subjects and 213 facial expression images [55]. The number of images corresponding to each of the seven categories of expression (neutrality, happiness, sadness, surprise, anger, disgust, and fear) was almost always the same. Each actor repeated the same expression several times (i.e., two, three, or four times).
## 6 Efficiency Analysis of Intelligent Solid Backfilling Mining
### Backfilling Efficiency Analysis and Optimization Plan
#### 6.2.2 Recommended Actions for Optimizing the Backfilling Efficiency  
According to the performed analysis of Figure 11 and Table 2, reducing the number of tampings drastically improves the backfilling efficiency. The backfilling material used in working face #11233 is underground sorted gangue, which sources from various coal mining working faces. After sorting, its particle size becomes below 200 mm. The particle size gradation still needs to be optimized. We recommend the following measures to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Mechanized** & **Automated** & **Intelligent** \\ \hline Number of workers required for each shift (workers) & 20–30 & 15–20 & 7–10 \\ \hline Time of cyclic operation for a single support (min) & 20–35 & 10–20 & 5–10 \\ \hline Time of cyclic operation for the whole face (h) & 3.5–5.0 & 2.0–3.5 & 1.0–2.0 \\ \hline Backfilling efficiency & Moderate & Improve efficiency by 20\% & Improve efficiency by 40–80\% \\ \hline Interference and adjustment & Manual & Manual & Automated \\ \hline Electrohydraulic control system & No & Yes & Yes \\ \hline Intelligent system & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different parameters of the three technologies under study.  
Figure 12: Variation of backfilling efficiency in the three mining technologies under different numbers of tamping.  
improve the backfilling efficiency: change the mix ratio of the backfill material, improve the material's cohesiveness, increase the natural angle of repose, and reduce the number of tampings.  
The second direction is intelligent upgrade and transformation, which is underway in the case study site. Before the intelligence upgrade, workers were involved in many operation steps in the #11233 filling working face. The backfilling efficiency was greatly improved through intelligence upgrading and by increasing the number of supports in parallel while reducing the time gaps in the actions of cylinders and supports.
## 5 Experiments and Results
### Cyclical Learning Rate  
This method is based on the fundamental idea that the learning rate varies within a range of values, rather than assuming a fixed value. The learning rate varies cyclically between fixed limits [10]. A short run of only a few epochs in which the learning rate increases linearly is sufficient to estimate the boundary learning rates for the cyclic learning rate (CLR). In this section, we test several adapted synchronization cycles and training iterations (the results are in Table 3). We fit the strategies to our dataset and model. To this end, we experimented with different cyclic functions to test the performance of our model for classifying histopathological images. The features discussed in this work are the following, illustrated in Figure 9. For these experiments a range of LR has been taken between the base value \(10^{-4}\) and a maximum LR value of \(10^{-3}\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters and Hyperparameters** & **Value** \\ \hline Input shape & 224 \(\times\) 224 \\ \hline Bach size & 64 \\ \hline Number of layers & 27 \\ \hline Momentum & 0.9 \\ \hline Learning rate & \(10^{-5}\)–\(10^{-1}\) \\ \hline Epochs & 10 \\ \hline Loss function & Categorical cross entropy \\ \hline Optimizer & SGD \\ \hline Weights & Scratch- ImageNet \\ \hline Dropout & 0.5 \\ \hline L2 & \(10^{-3}\) \\ \hline Activation & Relu/Softmax \\ \hline Dense & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of parameters and hyperparameters for our framework. 1. Triangular: The LR adopts a triangular window linearly increasing and then linearly decreasing on a regular basis. Obtaining an accuracy of 96%. 2. Triangular drop: The difference with the previous method is that the upper and lower limits are halved after each cycle without affecting the predefined learning rates. In this case, the metrics are almost unchanged from the previous method. 3. Exponential: The learning rate varies between the minimum and maximum limits and each value of the limit decrease by an exponential factor gamma. This means the learning rate difference drops after each cycle. In this case, this factor is equal to unity and for Exponential 2, gamma is 0.9. This method achieves an accuracy of 95% and 94%, respectively. 4. Sine: The strategy based on a sine function decay is based on a cyclic triangular decay with a sinusoidal amplitude, where the learning rate of the iterations decreases by a fixed amount. With this strategy, the accuracy decreases by up to 92%.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 4 Displacement of Large Diameter Monopile under Scour
### Model Parameters  
The soil parameters in the construction site are shown in Table 4. The total length of the pile shaft is 40 m, and the embedment depth is 30 m. The outside diameter is 4 m and the wall thickness is 100 mm [25].  
[PERSON] et al. [5] have verified that the isotropic hardening model can accurately predict the stress-strain behavior of saturated soil, so the Mises yield criterion and isotropic hardening model are adopted in this study. The initial and limiting yield stress were obtained by tri-axial CU test, and 15 sets of subsequent yield stress and corresponding strain were imported to ABAQUS to describe the strain-hardening in the stage of plastic. The three-dimensional model is meshed by eight-node hexahedron element and reduced integration and hourglass control is used in the analysis. A surface to surface interaction is created to simulate the pile-soil interaction. To avoid the boundary condition effect, the soil is 160 m in diameter and 60 m in height. The soil and pile interaction surface was idealized as frictionless. The grid within 24 m of monopile foundation is densified. The finite element model is shown in Figure 12a.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Layer** & **Soil Name** & **Depth/m** & **Effective Unit** & **Friction** & **Cohesion/kPa** \\
**Number** & & & **Weight/(kN/m\({}^{2}\))** & **Angle/(\({}^{\circ}\))** & **Cohesion/kPa** \\ \hline
1 & Clay & 0–2.4 & 7.6 & 2.3 & 4.5 \\
2 & Medium sand & 2.4–6.1 & 8.1 & 23 & 0 \\
3 & Clay & 6.1–15.9 & 7.8 & 3.5 & 9.7 \\
4 & Medium coarse sand & 15.9–19.8 & 8.5 & 23 & 0 \\
5 & Sandy clay & 19.8–21.4 & 9.3 & 13 & 30 \\
6 & Silty clay & 21.4- & 10.5 & 26 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Soil parameters of the construction site.  
Figure 11: Deformation response calculated by different methods. (**a**) Shear force; (**b**) bending moment. (**c**) horizontal displacement.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Generation
#### 2.1.2 Magic Square Transform  
Magic square transform [14] uses the defined magic square matrix to block the watermark image, and determines the size of blocks according to the image complexity. Magic square matrix is an \(n\)-order matrix with natural numbers \(1,2,\cdots,n\) as elements, which can be shown as:  
\[\mathbf{A}=\left[\begin{array}{cccc}a_{11}&a_{12}&\cdots&a_{1n}\\ a_{21}&a_{22}&\cdots&a_{2n}\\ \vdots&\vdots&&\vdots\\ a_{n1}&a_{n2}&\cdots&a_{nn}\end{array}\right]. \tag{2}\]  
If the elements in \(\mathbf{A}\) meet:  
\[\sum_{i=1}^{n}a_{i,j}=\sum_{j=1}^{n}a_{i,j}=\sum_{i=1}^{n}a_{ii}=\sum_{j=1}^{n }a_{jj}=\frac{n(n^{2}+1)}{2}, \tag{3}\]  
then the matrix \(\mathbf{A}\) is called the standard magic square matrix. This transform also has periodicity. For an image with the size of \(N\times N\), the transform period is \(N^{2}\). This transform is fast, safe, and robust, but the scrambling effect is poor, and the computational complexity is high.
## 4 Detection of Air Gap Eccentricity Faults in PMa-SynRM from the ZSVC Spectrum  
To measure the ZSVC, the central point of the wye-connected stator windings must be accessible [21]. In addition, a balanced three-phase inexpensive resistor network is required to generate an artificial neutral point. This approach is compatible with fault tolerant drives adding an extra inverter branch to replace the failed phase, which is connected to the central point of the windings [32,33]. In this way, the ZSVC injected by the electronic drive can be removed from the neutral voltage of the PMa-SynRM [34].  
When dealing with a symmetric and healthy machine, according the layout shown in Figure 4, the fundamental harmonic of the \(v_{0,m}\) zero-sequence voltage component measured between the artificial neutral point and the central point of the stator windings can be expressed as [34]:  
\[v_{0,m}=-\frac{1}{5}\bigg{(}\frac{d\lambda_{a}}{dt}+\frac{d\lambda_{b}}{dt}+ \frac{d\lambda_{c}}{dt}+\frac{d\lambda_{d}}{dt}+\frac{d\lambda_{e}}{dt}\bigg{)} =-\frac{d\lambda_{PM,0}}{dt} \tag{4}\]  
where \(\lambda_{PM,0}\) is the zero-sequence flux component produced by the permanent magnets.  
In the case of a salient machine affected by an eccentricity fault, an additional term appears in (4) due to the changing air gap in the phase inductances.