esa-sceva/satcom-chunk-collection huggingface dataset:
# Special Issue on Applications of Artificial Neural Networks for Energy Systems
## 2 Artificial Neural Networks for Energy Systems
In reference [11], while aiming to achieve practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis was proposed. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. One paper in this special issue is about Biobutanol, which may be used as a fuel in an internal combustion engine. The most common method of producing biobutanol is the fermentation of simple sugars in biomass feedstock. In reference [12], a methodology for optimizing the biobutanol production process via the integrated acetone-butanol-ethanol (ABE) fermentation-membrane pervaporation process is proposed. In this study, artificial neural networks (ANNs) are used as metamodels in an attempt to reduce the time needed to circumscribe the Pareto domain and identify the best optimal operating conditions. Finally, the concept of the Internet of energy (EI) has been proposed as a new high-efficiency energy structure that allows the full benefits of multi-energy coupling to be exploited. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. In reference [13], a two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. In order to improve the efficiency and reduce operating cost and environmental cost of the proposed EI, an artificial neural network (ANN)-based reinforcement learning algorithm is proposed to manage the optimal energy routing path.
## 8 Implementation and Experiments
### Efficiency of Forward Computations in Convolutional Layers for Different Implementation Variants
As regards numba_cuda_viafft, one can benefit from its efficiency also for larger images and significantly large filters. In fact, one can note, in the last graph, that the execution times of numba_cuda_viafft become approximately _equal_ (see the overlapping red curves) for all large filter sizes from \(11\times 11\) onwards and suitably large images--a fact compliant with the algorithmic properties of FFT-based convolutions. In those cases, numba_cuda_viafft was superior to both numba_cuda_direct and numba_cuda_tiles. Last, but certainly not least, one should notice the very high efficiency of Keras--the pink curves in the first two graphs. In particular, for the \(7\times 7\) filter and \(64\times 64\) images, the execution time of keras was better by roughly one order of magnitude than that of numba_cuda_direct and numba_cuda_tiles. This demonstrates how highly optimized the underlying TensorFlow is in terms of exploiting GPU power and resources. All tests on convolutional layers can be re-executed (script: tester.py), and their current log files are available in the repository (folder: tests/). Figure 23: Execution times of convolutional layers (64 input and 64 output channels; batch size: 32) for different implementation variants, filter sizes, and image sizes. For details of the hardware and software environment see page 42.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 4 Architectures  
This section presents the design of four different architectures using the CMS to operate a satellite-based NTN. The architectures are classified depending on the interaction between NetOps and SatOps. Three different types of interactions are defined: (1) integrated operations refers to a control architecture where the NetOps are integrated into the SatOps; (2) coordinated operations refers to a control architecture where both NetOps and SatOps are coordinated by the same agent (the CMS), but executed through separate channels; and (3) adaptive operations refers to a control architecture where NetOps and SatOps are adapted to each other, respectively, and executed through separate channels as well. See Figure 3 for a schematic representation of the different operational interactions.  
In sporadic connectivity constellations, an integrated control architecture is the most appropriate choice due to its adaptability to traditional SatOps, which are well-suited for discontinuous operations and scheduled tasks. Nonetheless, an integrated architecture is also applied in a persistent connectivity constellation, demonstrating its versatility across both scenarios. In contrast, the coordinated and adaptive architectures are better suited for persistent connectivity constellations, as they leverage SDN and NFV, technologies more aligned with the demands of highly connected, terrestrial-like networks. Table 6 illustrates the mapping between the different operational architectures and their suitability for different connectivity scenarios. The difference between using the coordinated or the adaptive architectures in a persistent connectivity constellation will depend on the MANO capabilities, especially if the MANO has the autonomy and the intelligence to adapt NetOps to a set of SatOps.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Sporadic Connectivity** & **Persistent Connectivity** \\ \hline Integrated & X & X \\ \hline Coordinated & & X \\ \hline Adaptive & & X \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different architectures depending on: (1) the relation between SatOps and NetOps; and (2) the NTN connectivity. The X mark indicates the suitability of the architecture for a connectivity scenario.  
Figure 3: Operational architecture types for satellite-terrestrial network integration showing different NetOps-SatOps interaction models. **Integrated Operations:** NetOps are fully integrated into SatOps within the CMS framework, with unified control generating satellite plans for both ground and space segments. **Coordinated Operations:** NetOps and SatOps are coordinated by the CMS but executed through separate channels, with MANO handling network orchestration and SDN/VIM management while CMS manages satellite operations. **Adaptive Operations:** NetOps and SatOps adapt to each other through bidirectional interfaces, with CMS managing satellite operations and an intelligent NTN-MANO autonomously handling network orchestration through SDN/VIM, enabling mutual adaptation between operational domains.
# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Meta-Algorithms and Hybrid Methods
They trained a shape model on healthy brain MRIs which is relatively insensitive to lesions inside the brain. Adaptive thresholding has been employed to search for potential resection cavities at the brain boundary, and random walker (RW) [102] corrects for a leakage into ventricles. Another efficient algorithm formulated by [PERSON] et al. [103] is known as the \"simple paradigm for extra-cereb tissue removal (SPECTRE)\"; it is for skull stripping. It integrates different techniques, such as elastic registration, bran tissue segmentation, and morphological operations directed by the watershed principle. Primarily, SPECTRE was designed for T\({}_{1}\)W brain MRIs. However, the authors mentioned that it can be employed with other modalities (e.g., T\({}_{2}\)W and PDW) with a simple alteration. [PERSON] et al. [104] proposed a novel meta-algorithm, \"brain extraction and labelling (LABEL),\" for skull stripping, especially for pediatric brain MRIs. Other recent and new skull stripping methods are [105; 106]. One of the disadvantages of meta-algorithms and hybrid methods is that they often demand extensive training of the data to learn the particular brain features to accurately segment the brain MRIs. Some well-known conventional skull stripping algorithms have been listed in Table 3.
# Wave Orbital Velocity Effects on Radar Doppler
## 3 Simulation and Results
### 20 Hz Data--Wind Sea/Swell Interaction  
A common procedure in altimetric sea monitoring is to consider successive measurements at regular intervals, with 20 Hz being the usual standard. Since the satellite velocity is about 7 km/s, a 20 Hz sampling yields a 350 m interval, which is roughly equivalent to the nominal Doppler resolution. A computational algorithm with the following parameters (Table 3) was therefore set up to simulate this procedure.  
In order to take into account the interaction between a long swell and a shorter wind sea, a composite sea state was fed into the simulation, based on a JONSWAP spectrum (significant wave height Hs, spreading parameter Sp = 80, and peak wavelength Lp) and a sinusoidal long wave (H, T). Typical results are shown in Figure 8.  
The 20 single WFs are reported for both R and non-R-effect simulations. While of course each single waveform is different from the others, it appears that the dispersion of R-effect waveforms is  
Figure 8: Simulated waveforms on a 20 Hz sample for sinusoidal wave train (wave height H = 4 m, T = 16 s) superimposed to a JONSWAP sea (significant wave height Hs = 4 m; peak period Tp = 8 s): (**a**) without R-effect (red curve represents the average value); (**b**) with R-effect (green curve represents the average value).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters of the Simulations** & **Value** \\ \hline Size of test area in across-track (\(\chi\)) direction Lx & 6000 m \\ Size of test area in along-track (\(\gamma\)) direction Ly & 6000 m \\ Computational grid along the across-track (\(\chi\)) direction DX & 5 m \\ Computational grid along the track (\(\gamma\)) direction DY & 5 m \\ Resolution of distance between antenna and sea surface & 0.35 m \\ Number of measurements in a second & 20 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameters of the multi-look 20 Hz numerical simulations.  
much higher than the dispersion of non-R shapes; also, the comparison of single R and non-R WFs (Figure 9) yields considerable differences.  
When average 20 Hz WFs are compared, a consistent difference is evident, the R-WF being less steep (Figure 10) in the leading edge.  
The leading edge is actually of particular importance in the extraction algorithms for SWH, such as the ALES (Adaptive Leading Edge Subwaveform) retracker [29; 30]. Even if this does not imply an error in the estimation of SWH, since all the algorithms are amply tested and calibrated with experimental data, it could still make sense to verify whether taking the effect into consideration could affect the results.
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods
### QCP-Based Approaches
#### 2.3.2 ST-QCP  
The vocal tract filter obtained with QCP can still present a residual spectral tilt. In order to improve this aspect, a compensation procedure was proposed by [PERSON] et al. [29] using a first-order linear prediction filter to transfer the residual spectral tilt from the vocal tract to the glottal flow's estimated signal. In this paper, the spectral tilt compensation is performed as follows. First, the original VT frequency transfer function is computed, and from this, the least squares 1-order LPC fitting based on [34] is performed; finally, the QCP-based estimation of the glottal flow signal is filtered, with the obtained filter presenting the compensated glottal flow estimation.
## 3 Material and Methods
### Dimensionality Reduction
#### 3.2.2 Feature Extraction  
Feature extraction methods transform the existing features into lower dimensional space [36]. It is the process of deriving a reduced feature set from the original variables. A number of algorithms are available that can perform this transformation linearly and non-linearly. PCA is the one that can perform non-linear transformation.  
Principal Component Analysis: Principal Component Analysis is a widely used feature extraction technique that performs orthogonal transformation of correlated variables into uncorrelated features. These new features are called principal components. This technique preserves a dataset's original high-dimensional variance into low-dimensional principal components. The PCA technique efficiently removes correlated features, and the reduced feature set improves the learning algorithm's performance. If X is a (N \(\times\) D) dimensional-centered data matrix and its covariance matrix is as follows:  
\[\text{S}=\text{ N}^{-1}\text{X}^{\text{T}}\text{X} \tag{1}\]  
Eigenvectors of the covariance matrix are computed as:  
\[\frac{1}{\text{N}}\text{ X}^{\text{T}}\text{X}u_{i}=\lambda_{i}u_{i} \tag{2}\]where \(u_{i}\) is the unit vector and indicates the direction and \(\lambda_{i}\) is the vector of eigen values. Multiplying both sides by X, we obtain the following:  
\[\frac{1}{\text{N}}\ \text{XX}^{\text{T}}(\text{X}u_{i}) =\lambda_{i}\ (\text{X}u_{i})\] \[\text{putting}\ v_{i} =\text{X}u_{i}\] \[\frac{1}{\text{N}}\ \text{XX}^{\text{T}}v_{i} =\lambda_{i}\ v_{i} \tag{3}\]  
Equation (3) is an eigenvector equation.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 3 Classifier Models
### Support Vector Machine  
A Support Vector Machine (SVM) uses support vectors to classify and obtain comparisons between the support vectors and the class of the input to be predicted. It can perform binary and multiclass classification on a dataset [27].  
Suppose we have a dataset with \(N\) training examples \((\mathbf{x}_{i},y_{i})\), where \(\mathbf{x}_{i}\in\mathbb{R}^{n}\) is an input feature vector with \(n\) dimensions and \(y_{i}\in\{-1,1\}\) is the class label for each input (1 or \(-1\)).  
The goal of the SVM is to find a hyperplane that separates the data points such that the margin, that is, the distance between the hyperplane and the nearest data points from each class, is maximized. A hyperplane is defined as the set of points \(\mathbf{x}\) that satisfy the following equation:  
\[\mathbf{w}^{T}\mathbf{x}+b=0,\]  
where \(\mathbf{w}\in\mathbb{R}^{n}\) is a vector that is normal to the hyperplane, and \(b\in\mathbb{R}\) is the bias term.  
The decision function for predicting the class of a new input \(\mathbf{x}\) is  
\[f(\mathbf{x})=\text{sign}(\mathbf{w}^{T}\mathbf{x}+b).\]  
If \(f(\mathbf{x})>0\), class \(+1\) is predicted, and class \(-1\) is predicted otherwise.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design
### Data Processing
#### 3.3.2 Wavelet Filter  
Although the Hampel filter can remove outliers in the CSI amplitude sequence, high-frequency environmental noise still exists. Therefore, Wi-Senser needs to further filter out high-frequency environmental noise. Traditional low-pass filters such as Butterworth and Chebyshev filters suppress not only high-frequency noise but also the rising/falling edges in the CSI amplitude fluctuations, which are often caused by human motion events. Suppressing these edges may cause Wi-Senser to miss/detect false-positive head motion events. Compared with traditional low-pass filters, wavelet filters can better preserve transient changes in signals. Therefore, Wi-Senser utilizes a wavelet filter to suppress high-frequency environmental noise in the CSI amplitude sequence while preserving the rising/falling edges generated by head motion events. Specifically, Wi-Senser uses the Stein unbiased estimate as the threshold selection criterion and sets the threshold usage mode to soft thresholding in the wavelet filter. In each CSI subcarrier, Wi-Senser adopts 4-level db4 wavelet transform and retains the approximation coefficients (i.e., the useful low-frequency signal) to filter out high-frequency environmental noise. Figure 5 shows the CSI subcarriers filtered by the wavelet, where it can be observed that high-frequency environmental noise is effectively suppressed, and the rising/falling edges in the CSI amplitude changes that record head motion are fully preserved. The experimental results demonstrate the effectiveness of the low-pass filter (i.e., wavelet filter) selected by Wi-Senser to filter out high-frequency environmental noise.  
Figure 4: CSI subcarriers filtered by the Hampel Filter.  
Figure 3: CSI subcarriers before data processing.
# Test-Retest Intra-Session Reliability of Isokinetic Knee Strength Measurements in Obese Children
## 2 Materials and Methods
### Design and Procedures  
The study used a test-retest reliability design.  
Only the dominant leg was tested. To define this, participants were asked for their preferred kicking leg. After a 15 min warm-up, including articular mobilization and stretching, peak torque (PT) and total work (TW) during three maximal repetitions at 60\({}^{\circ}\)/s were recorded for knee extensors/flexors. Verbal encouragement was provided as stimulation to encourage the subjects to produce the maximal effort. This testing velocity has previously been used in reliability studies with children [12; 14] and was based on the ability of children to generate greater torque at this velocity compared with other faster velocities [22]. The weight of the limb was recorded using the dynamometer software, and gravity adjustments were made.  
The participants were asked to sit on the Biodex System 3 with the seatback at an angle of 85\({}^{\circ}\) to the seat. The hands grabbed the side supports of the seat that were intended for the test in the best conditions. A standardized protocol was followed, with the mechanical axis of the dynamometer aligned with the axis of movement of the knee [23].  
Three repetitions were performed in one single session. The first repetition was for familiarization purposes. The second repetition was then considered the \"test repetition\", and the third was the \"retest repetition\".  
The antagonist/agonist ratio (PT of knee flexor muscles/PT of knee extensor muscles) was also calculated. The ratio was not calculated with the variables divided by total mass or lean mass because these 2 mass variables did not change between test and retest, thus they did not affect the ratio. All tests were conducted by an expert researcher.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 2 Principle and Design of the Sharpness Evaluation Method
### Analysis of Edge Width  
The essence of an edge is a collection of pixel points with drastically changing grayscale values. To calculate the edge width of an edge point, it is necessary to firstly determine the edge direction corresponding to the edge point and then calculate the edge width along the edge direction according to appropriate rules.
###### Abstract  
A new quasi-three-dimensional (3D) shear deformation theory, called the spectral displacement formulation (SDF), is proposed for high-precision static bending analyses of functionally graded plates. The main idea is to expand unknown displacement fields into Chebyshev series of a unique form in the thickness direction; the truncation numbers are set to be adjustable to meet various application requirements. Specifically, 3D elasticity solutions and traction-free boundary conditions can be approached by increasing the number of Chebyshev bases. The SDF is also an extension of the classical plate theory and naturally avoids the shear locking problem, making it versatile for functionally graded material (FGM) plates of arbitrary thicknesses. The C1 continuity requirement for the discretization of the generalized displacements is conveniently fulfilled by the nonuniform rational B-splines (NURBS)-based isogeometric method. Numerical examples demonstrate the excellent performance of the proposed method for the displacement and stress analyses of functionally graded plates. The high precision and versatility of the present method have manifested its great potential applications in strain-based or stress-based reliability analysis, optimization design, fatigue analysis, and fracture analysis of FGM plates, and other related fields.  
keywords: functionally graded material plate; spectral displacement formulation; isogeometric analysis; Chebyshev series Article
## 3 Results and Discussion
### Region-Specific Climate Extremes and Threats to Environmental Stability
#### 3.2.3 Europe  
Climate extremes are increasingly affecting Europe, and as [PERSON] (2013) revealed, weather-related disasters could impact up to two-thirds of the European population annually by the end of the century. Consequently, there is a pressing need for adequate adaptation to safeguard critical infrastructure, as climate extremes are projected to increase six-fold by mid-century. Specifically, Austria faces a range of climate extremes, notably riverine flooding and agricultural drought, which have historically occurred in 2002, 2005, and 2013, placing significant financial stress on private and public resources. Riverine flooding, for instance, disrupts marine ecosystems, alters water flow, and induces biodiversity shifts, while agricultural drought threatens crop yields, degrades soil, triggers pest and disease outbreaks, and increases wildfire risks.  
Moreover, unprecedented warmth is another critical manifestation of climate extremes, posing substantial threats to environmental stability. [PERSON] et al. (2014) examined Spain's scorching summer in 2022, revealing that Spain has experienced significant increases in extreme heatwaves and severe droughts over the past two decades, particularly during the summer months. These extremes have the potential to disrupt natural and socio-economic systems alike. The effects of extreme warmth on environmental stability are extensive: accelerating glacial and ice sheet melt, raising sea levels, disrupting ecosystems and biodiversity, warming oceans and altering marine life, increasing ocean acidification, intensifying agricultural challenges, and encouraging pest and disease spread. Additionally, these changes heighten risks of heat-related illnesses, waterborne and foodborne diseases, and broader geopolitical and social instability.  
From the perspective of farmers and smallholders in the United Kingdom, the management of extreme weather and climate change in the United Kingdom was investigated by [PERSON] et al. (2013). Although it is common knowledge that there is a need for agriculture to adapt to climate change, how the risks from extreme weather and climate events were perceived at the farm level was documented in this research. Some of the extremes perceived at the farm level include the impact of heat and drought, heavy rainfall/flooding, extreme cold, extreme heat, stormy/windy weather, and more gradual changes to climatic averages. Heat and drought are a threat to agroecosystem stability, and they can affect the biodiversity in the ecosystem and impact yield, while heavy rainfall/flooding has the potential to wash off the soil and cause erosion.