esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison between Two Reduction Strategies for Shrouded Bladed Disks
## 5 Conclusions  
In this paper, two reduction strategies are described. They can be applied to generate ROMs of bladed disks in cyclic symmetry for nonlinear forced response analysis, such as shrouded bladed disks.  
Both rely on the cyclic symmetry properties of the system and make use of the Craig-Bampton reduction method to reduce the size of the final model to be used for nonlinear analysis.  
The first approach, here called the direct method, was already used in the literature to generate ROMs for linear forced response of mistuned bladed disk and it is extended here to the case of nonlinear forced response. The second approach, defined here as the two-step method, does not represent a classical approach although it is implemented in some commercial FE solver.  
The numerical results performed on different test cases showed that both methods ensure a high accuracy in terms of natural frequencies of the system (an error or less than 0.1% on the first ten natural frequencies of each harmonic index.  
In addition, test cases showed that ROMs of the same size, but obtained with the two methods, can guarantee the same accuracy.  
An interesting result was obtained when the numerical efficiency of the two methods was compared. The test cases showed that the numerical efficiency of the two approaches is affected by the ratio defined by the number of disk interface nodes over the number of inner nodes in the fundamental sector. In particular, when this ratio is lower than 10%, the two-step method is more efficient. It is worth noting that those values (less than 10%) are typical of industrial FE models, developed for high cycle fatigue analysis of the blades and characterized by a more highly refined mesh in the blades than in the disk.  
It is worth mentioning also here that the focus of this paper was to obtain efficiently CB-ROMs to be provided as inputs for nonlinear forced response analysis of shrouded bladed disks, but that the nonlinear simulations are not part of this work.  
Figure 9: Graph 2S/D calculation time vs. Interface/Inner nodes number.  
**Author Contributions:** Methodology, S.Z. and A.S.; Software, A.S. and S.Z.; Investigation, A.S.; Writing-Original Draft Preparation, A.S. and S.Z.; Supervision, S.Z.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### AI/ML Algorithms  
The CMS employs AI and ML algorithms in three primary roles: scheduling optimization through constraint satisfaction, resource allocation prediction using historical data patterns, and real-time adaptation through dynamic scenario analysis, playing a key role in addressing integrated management challenges. This is particularly critical in the context of NTNs, where resource constraints require efficient utilization of onboard resources, radio resources, and contact resources. Optimizing these components is essential for ensuring system performance and reliability. AI/ML techniques are particularly well-suited for tackling the mixed scheduling challenge through predictive task allocation algorithms and constraint optimization heuristics. Given the computational limitations of satellites, ML-based approaches enable efficient distributed operations by implementing a ground-train/space-inference architecture. A key advantage is the ability to train complex scheduling models on ground-based systems with abundant computational resources and deploy only lightweight inference engines onboard satellites, significantly reducing the computational burden on spaceborne systems while maintaining optimization effectiveness. Additionally, these algorithms help mitigate uncertainties in dynamic environments by training predictive models capable of smoothing fluctuations and deviations. This is crucial for addressing the uncertain scenario challenge of NTNs, as AI/ML techniques enhance the system's ability to anticipate and adapt to changing conditions. Regarding operational dynamics, AI/ML-based solutions improve space routing and traffic offloading by predicting future network states and optimizing resource distribution accordingly. Furthermore, AI-driven profiling employs machine-learning techniques such as time-series analysis and pattern recognition on historical operational data to enable more accurate forecasting of resource availability, both onboard satellites and within the broader NTNecosystem. The system can enhance network efficiency, adaptability, and resilience by leveraging these techniques.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 3 Prototype of 5G Integrated with TSN  
The 5G and TSN prototype development is divided into three parts: the wired communication using TSN, the wireless communication using a 5G URLLC pre-commercial test system, and the connection to end devices, including the mobile robotic platform and the factory cloud system.
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 5 Results and Discussion
### Approximate Model and Verification  
The design variables of 80 sample points generated by the OSF and the corresponding CFD simulation results are used as inputs and outputs to establish a Kriging approximate model. Specifically, the parameters \(\alpha\), \(\beta\), \(m\) and \(q\) of the jet pump are design parameters, and the corresponding efficiency \(\eta\) is the output variable. Table 2 lists the parameters of the Kriging model. The model parameters are composed of \(\beta_{0}\), \(\sigma\) and \(\theta\), where \(\beta\) is the regression model parameter and the others are the correlation model parameters. Figure 7 shows the degree of fitting between the predicted value of the objective function and the simulated value of CFD. On the diagonal, the predicted value is equal to the CFD value. It can be seen that all the points distribute about the diagonal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(q\) & \(m\) & \(\alpha\) (\({}^{\circ}\)) & \(\beta\) (\({}^{\circ}\)) \\ \hline
0.5835 & 4.256 & 20.22 & 9.164 \\
0.7259 & 1.789 & 30.54 & 7.646 \\
0.538 & 2.454 & 18.84 & 8.178 \\
0.4013 & 4.078 & 20.5 & 8.936 \\
0.3899 & 4.876 & 20.22 & 9.164 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
0.8 & 3.616 & 39.44 & 4.912 \\
0.7146 & 5.675 & 18 & 5.518 \\
0.4525 & 2.779 & 35.26 & 6.43 \\
0.6405 & 2.347 & 39.72 & 6.354 \\
0.4924 & 2.417 & 21.06 & 4.456 \\ \hline \end{tabular}
\end{table}
Table 1: Design parameters and efficiency of DOE sample points.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\beta_{0}\) & \(-0.0747\) \\ \(\sigma\) & 0.06742 \\ \(\theta\) & (1.2707 1.201 0.5417 0.2051) \\ \hline \end{tabular}
\end{table}
Table 2: The Kriging model parameters.  
Figure 6: Distribution of different sampling points. (**a**) \(\alpha\) and \(\beta\); (**b**) \(q\) and \(m\).  
For the sake of further verifying the Kriging modeling accuracy, a statistical measure of objective function, correlation coefficient is introduced for evaluating the approximate degree. The correlation coefficient \(R^{2}\) is defined as follows:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{i}-Y)}^{2}}{\sum\limits_{i=1}^{n}{(y_{i} -\overline{y})}^{2}} \tag{23}\]  
The closer \(R^{2}\) is to one, the better the fitting is. After calculation, \(R^{2}\) is 0.9459.
## 2 Materials and Methods
### Sources and Processing of Data
#### 2.2.3 Soil Type Data  
The World Soil Database (HWSD), also known as the Harmonized World Soil Database, is the source of the soil type data. This dataset is a high-resolution (1 km) soil database that includes information on soil types, soil properties, soil texture, soil depth, and soil moisture. HWSD covers soil information on a global scale, providing detailed soil data at both global and local scales. This is of enormous significance for land degradation assessment, environmental impact research, and sustainable land management interventions on a global scale. The data source within China comes from the 1:1,000,000 soil data provided by the Nanjing Soil Research Institute during the Second National Land Survey. Authoritative institutions have reviewed and verified these data to ensure their accuracy and reliability. This study used ArcGIS 10.8 software to mask and reclassify HWSD data, dividing all soils under the same soil type group into one category for subsequent calculations.
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 4 Image Stitching
### Accelerating Image Template Matching Using PSO
#### 4.3.1 Experiment 1  
The object in Figure 3 was used to evaluate the PSO search algorithm for the application of image stitching. The loss over the search space is shown in Figure 5. Lightness and HSV have distinct minimums and a funnel-like structure around the minimum making them ideal for a search algorithm. As lightness required one third of the computation, it was chosen as the matching method.  
The PSO was run 10 times.The average speed of the PSO search, 365 ms, is significantly faster than the previous method, which requires 2.85 s to achieve very similar performances. This means that the PSO approach does not deteriorate the performance with a 87% decrease in the search time--see Table 4.
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 3 Methods
### Segmentation Algorithms
#### 3.2.8 Gaussian Mixture Model  
Gaussian Mixture Models (GMMs) is a probabilistic model using a soft clustering approach for distributing the points in different clusters [37]. Hence, a Gaussian Mixture Model tends to group the data points belonging a probabilistic distribution of pixel levels \(p(\vec{x})\). This Gaussian distribution displays a bell-shaped curve, with the data points symmetrically distributed around the mean value [38] given by Equation (5).  
\[p(\vec{x})=\sum_{i=1}^{NG}\pi_{i}\cdot N(\vec{x}|\vec{\mu}_{i}\Sigma_{i}) \tag{5}\]  
where, \(NG\): number of Gaussians, \(\pi_{i}\): weights, \(\vec{\mu}_{i}\): centroids, and \(\Sigma_{i}\): covariance matrix. Finally, a Bayesian Information Criterion (BIC) is used to compare models from the perspective of decision theory by minimizing the balance between model error and model complexity, as measured by expected loss of parsimony [39]. Model parameters are estimated using the Expectation-Maximization algorithm, which is a maximum-likelihood estimation in the presence of latent variables (assignment of pixels to clusters is a latent, unknown variable). Based on this, an adaptation of the algorithm proposed by the work Finite Mixture Models is implemented [40].
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 2 Preliminaries
### Auditory Frequency Scales  
An important aspect of the auditory system to consider in auditory-motivated analysis is the frequency-to-place transformation that occurs in the cochlea. Briefly, when a sound reaches the ear it produces a vibration pattern on the basilar membrane. The position and width of this pattern along the membrane depend on the spectral content of the sound; high-frequency sounds produce maximum excitation at the base of the membrane, while low-frequency sounds produce maximum excitation at the apex of the membrane. This property of the auditory system can be modeled in  
Figure 3: General structure of a synthesis system. \(\mathcal{S}\) is a linear operator that maps the sub-band components \(y_{k}\) to an output signal \(\tilde{x}\).  
a first approximation as a bank of bandpass filters, named \"critical bands\" or \"auditory filters\", whose center frequencies and bandwidths respectively approximate the place and width of excitation on the basilar membrane. The frequency and bandwidth of the auditory filters are nonlinear functions of frequency. These functions, called auditory frequency scales, are derived from psychoacoustic experiments (see e.g., [50], Chapter 3 for a review). The Bark, the equivalent rectangular bandwidth (ERB), and Mel scales are commonly used in hearing science and audio signal processing [30]. To refer to the different frequency mappings we introduce the function \(F\colon f\to\text{Scale}\) where \(f\) is frequency in Hz and Scale is an auditory unit that depends on the scale. The ERB rate, for instance, is [30]  
\[F_{\text{ERB}}(f)=9.265\ln\left(1+\frac{f}{228.8455}\right) \tag{4}\]  
and its inverse is  
\[f=F_{\text{ERB}}^{-1}(F_{\text{ERB}})=228.8455\left(e^{F_{\text{ERB}}/9.265}-1 \right). \tag{5}\]  
The ERB (in Hz) of the auditory filter centered at frequency \(f\) is  
\[B_{\text{ERB}}(f)=24.7+\frac{f}{9.265}. \tag{6}\]  
Expressions for the Bark and Mel scales are respectively provided in [51; 52]. For scales that do not specify a bandwidth function, like the Mel scale, we propose the following function: \(B_{\text{scale}}(f)=\frac{\partial\left(F_{\text{scale}}^{-1}\right)}{ \partial f}(F_{\text{scale}}(f))\). This ensures a proper overlap between the filters' passband.
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 5 Results
### Performance of Proposed Classifier on Untrained Movements  
The leave-one-out error analysis (LEA) can be used to evaluate the reject ability of the classifier for untrained movements. If the classifier does not reject any untrained/unknown movements, it will result in an active decision, which is an error. So, LEA can be used to evaluate the ability of the classifier to decline untrained/unknown movement patterns. To assess the performance of the proposed classifier on unknown movements, each of the 12 hand movements is selected as an unknown movement sequentially, and the chosen unknown movement will not be used for training but used to test the trained classifiers. The classification result using the algorithms mentioned above is shown in Figure 8, obtained using the same parameters as Figure 5. From Figure 8, it can be observed that LDA-R and GMM-R classifiers have much higher error rates than GK-R in rejecting the untrained movements. The overall error rates of the LDA-R and GMM-R classifier are 78.3% \(\pm\) 5.9% and 51.2% \(\pm\) 6.7%, respectively, meaning they have unsatisfactory performances in rejecting the unknown movements. However, for GK-R, the error rate is reduced to 30.9% \(\pm\) 7.6%, which shows a significant improvement.
## 4 Results  
### Validation of the Proposed Methodology  
To evaluate our methodology, we compared the results of the FEA simulation and our methodology for six Schwarz primitive lattice domains. Three of the six domains (Figure 5a-c) were formed by eight unit cells of uniform density \(\rho=0.25,0.50,0.75\), respectively. The other three domains were formed by unit cells of graded density; that is, the isovalue \(t\) was not a constant but a function \(t:\mathbb{R}^{3}\rightarrow\mathbb{R}\). The resultant surfaces are the solutions to Equation \(F(x,y,z)=t(x,y,z)\) (see Equation (1)). The domain of 24 unit cells in Figure 5f was taken from [7] and corresponded to the result of mapping the results of topology onto Schwarz primitive cells [7]. The domains of eight cells displayed in Figure 5d,e were also taken from [7]. The isovalue functions associated with these two domains are:  
\[t(x,y,z) = \begin{cases}-\frac{5}{2}\left(\frac{3x}{2L}\right)^{2}+2&,0\leq x \leq\frac{2L}{3},y,z\in\mathbb{R}\\ -\frac{1}{2}&,\frac{2L}{3}<x\leq\frac{4L}{3},y,z\in\mathbb{R}\\ \frac{3}{2L}x-\frac{5}{2}&,\frac{4L}{3}<x\leq 2L,y,z\in\mathbb{R}\\ \end{cases} \tag{9}\] \[t(x,y,z) = 3\left(\frac{x}{L}-1\right)^{2}-\frac{1}{2},\quad 0\leq x\leq 2L,y,z \in\mathbb{R} \tag{10}\]  
The six domains were subjected to uniaxial compression (see Figure 5). The magnitude of the load was such that the resultant strains in the flat faces of the boundary of the cells lay in the range of analysis \(\varepsilon_{ij}\in 10^{-5}\times[-1.0,1.0]\). First, we compared the displacements field of the lattice and homogeneous domain (Section 4.1.1). Secondly, we applied our DOE-based methodology using the displacement results from the homogeneous domain to get the maximum von Mises stress in every cell. Finally, we compared the maximum von Mises stress obtained via (1) direct FEA of the lattice domain and (2) our proposed methodology (Section 4.1.2).
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 2 Background Review and Related Works
### LoRa Network Architecture  
A LoRa network is usually referred to as a \"star-like network\", as seen in Figure 1, because it comprises at least three different types of devices. The LoRaWAN network's basic architecture is as follows: (1) The terminal communicates with the gateway using LoRa and LoRaWAN [18]. The data from the terminal device are sent to the webserver through a high-throughput interface (often 4G, 3G, or Ethernet). (2) The gateway merely serves as a two-way relay or protocol converter, and (3) the network server is in charge of decoding packets received from the terminal and producing packets to be transmitted back to the terminal [19,20].  
Figure 1: LoRa Network Composed of Three Different Types of Equipment.
# An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
## 4 Single-Frequency PPP Processing Results and Analysis  
### Data Description  
Quad-constellation observation datasets collected from 12 globally-distributed MGEX stations, as well as the CODE GIM products and the DLR multi-GNSS DCB products on 4-10 September 2016 are used to assess the performance of the proposed single-frequency PPP approach. The distribution of stations is shown in Figure 3. All observations have a sampling rate of 30 s. In the PPP processing, the satellite elevation mask angle is set to 10\({}^{\circ}\). The quad-constellation mixed satellite orbit and clock products provided by CODE are used at sampling intervals of 15 min and 5 min, respectively. Since the precise coordinates of most stations are not available, their coordinate values are computed at the precise millimeter level through an Online Positioning User Service (OPUS) that is developed by  
Figure 1: Slant TEC derived from different agencies at station PFRR on 4 September 2016.  
Figure 2: RMS statistical values of slant TEC derived from different GIM products based on the CAS (white bar) and DLR (color bar) multi-GNSS DCB corrections.  
the United States' National Geodetic Survey [24]. The OPUS processing results also refer to \"IGS08\", which is consistent to the coordinate reference of the PPP solutions.  
The spectral density values for the tropospheric zenith wet delay, the receiver clock offset, the system time difference and the IHD parameters are empirically set to \(10^{-9}\), \(10^{5}\), \(10^{-7}\), and \(10^{2}\) m\({}^{2}\)/s, respectively. Both GPS and GLONASS code and phase observation precisions are set to 0.3 m and 0.002 m, respectively. Their IHD pseudo-observable variance is initially set to 0.025 m\({}^{2}\) and then gradually increases at a speed rate of 0.04 m\({}^{2}\)/min. Given that the BeiDou and Galileo constellations are still incomplete and their satellite orbit and clock corrections are at a relatively lower accuracy [25; 26], their observations are down-weighted by a factor of four.
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 1 Introduction
### Contribution and Paper Organization  
The results of this research work provide clear information to MFT designers to obtain more efficient (>98%), lower cost, and higher power density transformers, applied to DC-DC converters.  
This paper is organized as follows: Section 2 presents the description of magnetic materials, and the MFTs design. Section 3 presents the core dimensions, core losses, winding losses, and calculation of the dispersion inductance. Section 4 presents the simulations of the behavior of both transformers, with nanocrystalline alloys and with ferrites. Section 5 presents the experimental prototypes of the two transformers tested with the Full-Bridge converter. Finally, Section 6 presents a discussion of the results, and Section 7 has our conclusions.
## 3 System Architecture and Design of Intelligent Reflecting Surface (IRS)
### IRS Controller and Tunable Chips  
The individual elements of an IRS can be reconfigured electronically with the aid of devices such as positive-intrinsic-negative (PIN) diodes, field-effect transistors (FETs) or micro-electromechanical system (MEMS) switches [80; 83], which are commonly used due to their fast reflection response, low reflection loss, low energy and hardware cost [81]. Generally, in an IRS the equivalent PIN diode circuit is employed to control the biasing voltage with the help of a direct current (DC) feeding line. The PIN diode has two stages: ON or OFF. The stages can be generated with the help of a phase-shift difference of \(\pi\)[51]. The different phase shifts of the individual elements of an IRS can be obtained by varying the corresponding biasing voltage of the smart controller to adjust the reflection amplitude. A variable resistor load may be applied to each element design as mentioned in [84]. Practically, it is preferred to have an independent control of the amplitude and phase shift of each element followed by an efficient integration of the IRS circuit [84]. An IRS is able to reconfigure incident signals. This helps in reducing the intercell communication because of the tunable chips [24]. Communication between the IRS unit cells can be either wired or wireless, with the former being preferred, and the interconnect can easily cointegrate controllers on the same chip as implemented in low-power embedded systems. Wired intercell communication for the IRS can be limited when scaling the size. Multiple chips can result in a complex layout and routing. Considering energy, latency and robustness, it is ideal to implement wireless intercell communication for larger-scale or dense IRS [85].