esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of the Frequency Response Function of the Rotational Degree of Freedom
## 2 Theoretical Background
### Modal Peak Picking  
Modal analysis must be performed to conduct a compensation strategy. Modal analysis includes modal circle and modal peak picking. In this study, modal analysis was performed using modal peak picking. Modal peak picking is a method for obtaining modal parameters using real and imaginary numbers. Each number in Figure 4 represents the frequency value at the peak of the real and imaginary parts, and \(A\) and \(B\) represent the magnitude of the frequency in the imaginary part. The damping ratio, stiffness, mass, and damping coefficient are as denoted in Equations (19)-(22) [14; 15; 16].  
\[\zeta_{q1}=\frac{\omega_{4}-\omega_{3}}{2\omega_{n1}},\ \ \zeta_{q2}=\frac{ \omega_{6}-\omega_{5}}{\omega_{n2}} \tag{19}\]  
\[k_{q1}=\frac{-1}{2\zeta_{q1}A},\ \ k_{q2}=\frac{-1}{2\zeta_{q2}B} \tag{20}\]  
\[m_{q1}=\frac{k_{q1}}{\omega_{n1}^{2}},\ \ m_{q2}=\frac{k_{q1}}{\omega_{n2}^{2}} \tag{21}\]  
\[c_{q1}=2\zeta_{q1}\sqrt{k_{q1}m_{q1}},\ \ c_{q2}=2\zeta_{q2}\sqrt{k_{q2}m_{q2}} \tag{22}\]
## 6 Future Research Areas
### 5G and Beyond  
With the expected deployment of global 5G networks in the 2020s, it is time to raise the imperative question on the future of mobile network, that is, beyond 5G or 6G. One of the prominent areas of beyond 5G will be software-defined network (SDN) and network function virtualization (NFV) [71]. The SDN and NFV will play a vital role in enabling management and control systems for E2E structures. Software-based transformation (i.e., softwarization) [72] cannot be implemented within the defined 5G implementation time frame. Other areas of beyond 5G include security [73; 74], spectral and energy efficiency, resiliency (i.e., tolerance to interference and maintaining QoS), and MEC [58]. MEC and softwarization are among the promising candidates to provide a cost-effective, secure, manageable, and flexible architecture for 5G and beyond.
## 2 Experimental Methods
### Soil  
The soil sampled for this study came from a foundation pit in eastern Hangzhou, China as shown in Figure 2. The particle composition of soil determined by sieve analysis is summarized in Table 1. Silt contains 89.52% silt particles. Silty sand has a coarser particle size and contains 66.68% fine sand. Mucky silty clay is primarily composed of silt particles (67.76%), but also contains a significant amount of clay particles (30.08%).  
To maintain the uniformity of the reconstituted soil sample, the dry density and water content of silt, silty sand, and mucky silty clay soil samples were kept the same at 1.46 g/cm\({}^{3}\) and 22%, respectively. Water content, density, specific gravity, void ratio, liquid limit, plastic limit, and plastic index of the reconstituted soil are listed in Table 2. The void ratio and plastic index of mucky silty clay were greater, while silt and silty sand had similar physical properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Size** & **Silt** & **Silty Sand** & **Mucky Silty Clay** \\ \hline Fine sand (0.25–0.075 mm) (\%) & 2.48 & 66.68 & 2.16 \\ Silt (0.075–0.005 mm) (\%) & 89.52 & 27.32 & 67.76 \\ Clay (\textless{}0.005 mm) (\%) & 8.00 & 6.00 & 30.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size distribution of soil.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Physical Properties** & **Silt** & **Silty Sand** & **Mucky Silty Clay** \\ \hline Water content \(w\) (\%) & 22 & 22 & 22 \\ Density \(\rho\) (g/cm\({}^{3}\)) & 1.78 & 1.78 & 1.78 \\ Dry density \(\rho_{\text{d}}\) (g/cm\({}^{3}\)) & 1.46 & 1.46 & 1.46 \\ Specific gravity \(\
## 2 Related Work
### Deep-Learning-Based Approaches  
Deep learning techniques have made significant progress in the field of natural language processing, especially in named entity recognition tasks. Deep learning models perform end-to-end entity recognition without relying on lexicons and manual feature definitions, reducing reliance on manual and existing toolkits. These methods improve entity recognition accuracy by automatically learning features from data. [PERSON] et al. [16] proposed an end-to-end Chinese named entity recognition method based on BERT-BiLSTM-ATT-CRF, which combines BERT, BiLSTM, attention mechanism, and CRFs. [PERSON] et al. [17] based their method on recurrent neural networks (RNNs), combining word coding for the specialized features of the medical field and achieving better results than statistical machine learning. [PERSON] et al. [18] used an LSTM model based on the attention mechanism for named entity recognition. [PERSON] et al. [19] fused word and phrase information in training the BiLSTM model to efficiently deal with disease entities in Chinese electronic medical records. [PERSON] et al. [20] used a CNN-BiLSTM model to identify medically relevant entities. [PERSON] et al. [21] used a joint entity and relationship extraction method using dilated convolution and context fusion to achieve entity extraction from electronic medical records.  
Despite the superiority demonstrated by deep learning methods in named entity recognition, challenges remain, particularly in context understanding and context-dependent entity recognition. To address these challenges, we propose a Chinese medical named entity recognition model that integrates multiple state-of-the-art techniques. This model leverages the BERT pre-training model to capture rich contextual information and combines a fully connected layer with a novel memory unit to enhance performance in context-dependent awareness, complex named entity processing, and recognition of neologisms and domain-specific terms.
## 4 Experiments
### Ablation Study
#### 4.5.2 Contribution of Pixel-Wise Knowledge Distillation  
To evaluate the pixel-wise knowledge distillation module's efficacy, we excluded pixel-wise knowledge distillation from the full framework (self-training + noise augmentation) and assessed two variants:  
1. Full framework: we employed the combined task and KD losses defined in Equation (8).
2. KD ablation: we relied solely on the task loss specified in Equation (3), omitting KD components.  
Table 5 reveals notable performance deterioration when eliminating KD, with the RMSE rising from 0.090 to 0.163 and the REL increasing from 0.011 to 0.024. This demonstrates pixel-wise KD's critical role in transferring the teacher model's structural knowledge for dense pixel prediction, particularly preserving edge precision and spatial coherence through targeted supervision.
# Implementing Federated Governance in Data Mesh Architecture +
## 4 Introducing Blockchain-Powered Mesh Platform
### The Fybrik Data Platform  
Fybrik ([[https://github.com/fybrik/fybrik](https://github.com/fybrik/fybrik)]([https://github.com/fybrik/fybrik](https://github.com/fybrik/fybrik)) (accessed on 16 February 2024)) is an open-source project aimed at enabling a secure and efficient way of managing, accessing, and governing data across hybrid and multi-cloud environments. It focuses on data privacy, compliance, and optimized data usage across the enterprise, leveraging Kubernetes for workload orchestration.  
Fybrik's architecture is modular and utilizes custom resources for defining data assets, applications, and governance policies. Its design allows for pluggable components, making it possible to integrate various data stores, governance tools, and compute platforms [39]. The extension mechanism includes two modes: the definition of new modules--operational blocks for integrating with different data processing platforms, and plugins--control blocks for facilitating the platform behavior. The latter includes connectors for custom and metadata and policies engines.  
As a Kubernetes-based platform, Fybrik is designed to be interoperable with a wide range of cloud services and data sources. It supports integration with existing data management and analysis tools, making it easier for organizations to adopt and extend their current infrastructure.  
Fybrik integrates policy-driven data governance and compliance to ensure that data usage adheres to organizational and regulatory standards [40]. It allows organizations to define policies that control how and where data can be accessed and used, ensuring compliance with GDPR, CCPA, and other regulations. It provides a mechanism for secure and controlled access to data, regardless of where the data resides--on-premises, in the cloud, or across multiple clouds. It uses fine-grained access control and data masking techniques to protect sensitive information.  
Fybrik facilitates the movement and virtualization of data across different environments without the need to duplicate data. This capability supports efficient data management and reduces latency by bringing the computer closer to the data, thereby optimizing performance for data-intensive applications.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 4 Discussion
### Limitations  
First, the shape models used in this study were all based on linear spaces. This was mainly chosen to limit the computational complexity of this model, since non-linear high dimensional models would require many times more data and processing power/time. The shape model in this research was also limited due to the number of subjects in the model, and size constraints to keep the model usable. Since the accuracy of the measurements on both methods should be comparable for a similar subset, the regression method has an advantage in saving processing power, resulting in simple and easy to use measurements based on basic formulae.  
Next, within the data that were collected to run the predictions, all common data, such as age, weight, and stature, were based on the input from the subjects themselves, who know these parameters by heart. They were free to weigh or measure themselves, but this was not done per a standard. This way, the prediction was comparable to what customers would enter when using an online system. Notably, subjects disclaimed they might have understated their weight, since they preferred their target weight rather than their actual weight at the time of the experiments. When implementing an online shopping method, this could be a complication that needs to be considered.
## Appendix A  
Core function implemented in Matlab in order to obtain fast Fourier transform of X and Y oscillations. The symbol '%' and's' represent a Matlab comment and X or Y oscillations, respectively:  
L = length(s) % s vector of signal values  
Fs = 25; % Sampling frequency  
NFFT = 2'nextpow2(L); % Next power of 2 from length of s  
f = Fs/2*linspace(0,1,NFFT/2 + 1);  
S = fft(s,NFFT)/L; % FFT of a signal s  
modS = 2*abs(S(1:NFFT/2 + 1));  
ms = max(modS);  
S_norm = modS/ms;
## 1 Introduction
In the past, fatigue measurements in metallic components have been performed by scanning electron microscopy (SEM) [26], ultrasonic waves [27], acoustic emissions [28], X-rays [29], etc. Although these methods have been extensively used in the past, each method has its own pros and cons, such as expensive setup cost, requirement of physical contact, and need for experienced manpower. Therefore, improvement in existing methods and/or development of a new technique for a specified purpose has attracted several research groups. Recently, advanced signal processing techniques, e.g., blind feature extraction, sparse representation classification, artificial intelligence algorithms, and compressive sensing algorithms, have been successfully applied in structural health monitoring and damage detection, especially for rotating bodies such as bearings [30] and huge structures such as bridges [31]. Guided wave propagation-based NDT diagnostics are popular because they enable inspection of large areas in a single measurement, but the main limitation is the requirement of sensor installation at the target [32]. Conventional NDT methods for conductive materials include eddy current testing (ECT) to detect flaws [33]. Single-frequency sinusoidal excitation as a function of changes in voltage, impedance, or phase enables the detection of defects or flaws. Due to limited depths of penetration and complexity in signal analysis, ECT is limited to fewer applications. Studies have shown the possibility to apply wavelet transform for defect detection in steel on a laboratory scale [34]. In NDT, there is a powerful technique called photoacoustic imaging (PAI) [35], which combines the high-resolution advantages of optical imaging techniques [36] and good penetration capabilities of ultrasound [37]. PAI applications cover a broad spectrum, from biomedical imaging [38] to material composition problems, and even imaging deep-sea environments [39]. The working principle of PAI is based on the multi-physics phenomenon that occurs in the target material. First, the target is hit by an excitation laser, and thermal expansion occurs inside the material depending on the material properties and energy of the incident pulse laser beam. The phenomenon is described in Figure 1.  
In this study, simulations are performed on a steel pipe to study the wave signal that reaches the sensing point. The signal for a wave traveling through the pipe with no defect is saved for reference and comparison. The traveling wave may experience damage in the pipe at one of the several predetermined locations. The wave parameters, which reach the sensing point, are analyzed for an in-depth knowledge about the behavior of the wave in different crack conditions. These differences provide an indication for damage presence inside the target, which can be further worked out to localize the damage location in the future.
## 2 Isolation and Characterization of the Sub-6 GHz Multi-Port MIMO Antenna
### ECC Derivation  
In MIMO antennas, the ECC quantifies the extent of similarity in the radiation or signal paths of the different antenna elements. A low ECC indicates that the antenna patterns are uncorrelated. This is essential when a MIMO system is to exhibit a high diversity and a large capacity. The ECC is an essential metric when evaluating the performance diversity of MIMO antenna systems. Two methods are commonly used to estimate the ECC. One is based on the far-field radiation patterns and the other on the S-parameters. Each method offers distinct advantages and limitations depending on the design stage and the available data. In summary, the ECC may be computed as follows:  
1. Using S-parameters (approximations are in play under certain conditions);
2. Using 3D far-field radiation patterns.
# Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks
## 4 Proposed Trust-Aware Routing Mechanism
### Clustering through Modified GWO
Input: \(pop\), \(pmax\), \(P_{\alpha}\), \(P_{\beta}\), \(P_{\delta}\)  
Output:\(P_{\alpha}\)  
```
1: Initialize the gray wolf population \(P_{n}\) randomly
2: Initialize a, B & C
3: Determine the fitness of \(P_{n}\) using Equation (1)
4:while\(i\leq pmax\)do
5:for each wolf \(P_{n}\)do
6: Update the position using Equation (12)
7: Update \(P_{\alpha}\), \(P_{\beta}\), \(P_{\delta}\)
8:\(i=i+1\)
9:endfor
10:endwhile
11:return\(P_{\alpha}\)
```  
**Algorithm 1** Modified gray wolf optimizer based clustering  
Finally, the wolves attack, and the range of \(\vec{C}\) also reduces in \([-2a,2a]\), where \(a\) is reduced to \(0\) from \(2\). The two conditions that play an important role in selecting the optimal cluster head are as follows:  
1. When \(|C|>1\), the nodes diverge to search for the prey depending on the positions of \(\alpha\), \(\beta\), and \(\delta\). 2. When \(|C|<1\), the wolves converge to attack the prey. After the successful completion of clustering, the optimal CHs are chosen and the non-CHs join the CH in their proximity by sending a request for joining as a cluster member. After successful completion of clustering, the optimal CHs are chosen and the non-CHs join the CH in their proximity by sending a request for joining as a cluster member. The non-CHs are acknowledged by receiving an acceptance; this way, the clusters are formed. Figure 4 shows the positions of \(\alpha\), \(\beta\), \(\delta\) wolves and the candidate wolf with reference to the position of the prey. The nearest position is of \(\alpha\).
## 3 Results
### Analysis of MDS Trajectories
#### 3.7.4 Analysis of SASA, MolSA and PSA  
Solvent surface area affects their formation as well as the functionality underpinning biological macromolecules (SASA). Individual amino acid sequences on a protein's surface normally function as activity domains in the context of protein-ligand complex structures, and they may or may not combine with other molecules, such as receptors. This aids in comprehending the solvent-like properties (hydrophilic or hydrophobic) of a molecule.  
The concentration of SASA controls the structure confirmations together with the functioning of physiological biomolecules. The solvent-like qualities of a molecule are better understood when amino acid sequences on its surface operate as active sites and form bonds with other molecules and ligands. The SASA measurement for the peptide complexes, including the five ligands' substances, was consequently calculated and presented in Figure 5. The median SASA readings for the compounds CID-250006068, CID-5366020, CID-91696396, CID-7311, and CID-23725625 (control), ranged from 2 to 2202, suggesting a significant degree of engagement with the amino acid sequence to their chosen chemical in the resulting complex mechanisms.  
When using a 1.4 probe diameter, the MolSA is comparable with a van der Waals interface region. The conventional van der Waals region was present during our in silico investigation for CID-250006068, CID-5366020, CID-91696396, CID-7311, and CID-23725625 (control) (Figure 6). Furthermore, solely oxygen, as well as nitrogen atoms, give PSA to molecules. In this instance, a significant PSA result was seen correlated with the targeting proteins in CID-250006068, CID-5366020, CID-91696396, CID-7311, and CID-23725625 (control) (Figure 7).  
Figure 4: A simulation with a duration of 100 ns to calculate the radius of gyration (Rg). Blue, orange, gray, yellow, and sky-blue represent the selected compounds CID-250006068, CID-5366020, CID-91696396, CID-7311, and CID-23725625 (control) in association with the proteins, respectively.  
Figure 5: The 100 ns simulated interacting diagram applied to determine the solvent-accessible surface area (SASA) underlying the protein–ligand interacting complexes. The chosen substances CID-250006068, CID-5366020, CID-91696396, and CID-7311, together with CID-23725625 (control), appear as blue, orange, gray, yellow, and sky-blue in combination with the corresponding protein in them, correspondingly.  
Figure 6: The molecule surface area (MolSA) of the protein–ligand interacting complexes calculated using the 100 ns simulated interaction schematic. The chosen combinations CID-250006068, CID-5366020, CID-91696396, and CID-7311, followed by CID-23725625 (control), are shown as blue, orange, gray, yellow, and sky-blue in combination with the protein content, correspondingly.
# TCNformer Model for Photovoltaic Power Prediction
## 1 Introduction
Based on the above analysis, the current research mainly focuses on the prediction of data within a few hours. When applied to predict a longer time range [28] for photovoltaic power, these methods typically suffer from two major challenges: error accumulation and long-term time series feature extraction in order to simultaneously extract multiple time series features in the historical data of photovoltaic power and weather factors, and to avoid error accumulation. Inspired by the application of LSTM, LSTnet, and Transformer series models in the field of photovoltaic power prediction, this paper proposes a long and short temporary correction network (TCNformer), and we verified the model by using the real data of a photovoltaic station in Australia. According to the experimental results, the TCNformer model greatly optimizes various indicators compared with LSTM, SkipGRU, Transformer, and Informer, improving the accuracy of photovoltaic power prediction. The contributions of this paper include the following:  
1. According to the different impacts of various weather factors on photovoltaic power generation, a VS module was designed to screen and process data through correlation analysis and periodic analysis of data. 2. Aiming at the challenge to extract long-term time series features due to the limitation of the traditional Transformer, a LSTFE module was designed to extract multiple time series features through LSTM and a SkipGRU network. 3. In order to improve the temporal feature extraction and avoid error accumulation, one-step temporal convolutional network (TCN) decoding was used to realize the generative prediction.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 3 Problem Description and Mathematical Model
**Notations and Sets**  
\begin{tabular}{l l} \(n_{d}\) & = number of central warehouses \\ \(n_{s}\) & = number of satellites \\ \(n_{cs}\) & = number of charging stations \\ \(\psi\) & = number of copies of each charging stations \\ \(n_{c}\) & = number of customers \\ \(n_{v1}\) & = number of available vehicles in the first echelon \\ \(n_{v2}\) & = number of available electric vehicles \\ \(N_{D}\) & = set of central warehouses (node indices: \(1,\ldots,n_{d}\)) \\ \(N_{S}\) & = set of satellites (node indices: \(n_{d}+1,\ldots,n_{d}+n_{s}\)) \\ \(N_{R}\) & = set of charging stations and copies (node indices: \(n_{d}+n_{s}+1,\ldots,n_{d}+n_{s}+n_{cs}*\psi\)) \\ \(N_{C}\) & = set of customers (node indices: \(n_{d}+n_{s}+n_{cs}*\psi+1,\ldots,n_{d}+n_{s}+n_{cs}+n_{c}\)) \\ \(N_{DS}\) & = set of central warehouses and satellites (node indices: \(1,\ldots,n_{d}+n_{s}\)) \\ \(N_{RC}\) & = set of charging stations and customers (node indices: \\ \(n_{d}+n_{s}+1,\ldots,n_{d}+n_{s}+n_{cs}*\psi+n_{c}\)) \\ \(N_{SRC}\) & = set of satellites,charging stations and customers (node indices: \\ \(n_{d}+1,\ldots,n_{d}+n_{s}+n_{cs}*\psi+n_{c}\)) \\ \(N\) & = set of central warehouses, satellites, charging stations and customers (node indices: \\ \(1,\ldots,n_{d}+n_{s}+n_{cs}*\psi+n_{c}\)) \\ \(V1\) & = set of large vehicles serving in the first echelon (\(|V1|=nv_{1}\)) \\ \(V2\) & = set of electric vehicles \(|V2|=nv_{2}\) \\
**Problem data** \begin{tabular}{l l} \(d1_{ij}\) & = distance between node \(i\) and node \(j\), (\(i,j\in N_{DS}\)) \\ \(d2_{lm}\) & = distance between node \(l\) and node \(m\), (\(l,m\in N_{SRC}\)) \\ \(Q1_{k}\) & = loading capacity of large vehicle \(k\in V1\) \\ \(Q2_{e}\) & = loading capacity of electric vehicle \(e\in V2\) \\ \(M\) & = a big number \\ \(BC_{\epsilon}\) & = battery capacity of electric vehicle \(e\in V2\) \\ \(g_{\epsilon}\) & = charging rate of electric vehicle \(e\in V2\) \\ \(D2_{i}\) & = demand of customer \(i\), (\(\forall i\in N_{C}\)) \\ \(s_{i}\) & = service time of customer \(i\), (\(\forall i\in N_{C}\)) \\ \(twe_{i}\) & = earliest visiting time of customer \(i\), (\(\forall i\in N_{C}\)) \\ \(twl_{i}\) & = latest visiting time of customer \(i\), (\(\forall i\in N_{C}\)) \\ \end{tabular}   
**Decision variables**  
\[x_{kij} = \begin{cases}1&\text{if vehicle $k$ visits node $j$ after node $i$ in the first echelon}\\ 0&\text{otherwise}\end{cases}  \forall k\in V1,\forall i,j\in N_{DS}\] \[y_{elm} = \begin{cases}1&\text{if vehicle $e$ visits node $m$ after node $l$ in the second echelon}\\ 0&\text{otherwise}\end{cases}  \forall e\in V2,\forall l,m\in N_{SRC}\] \[z_{li} = \begin{cases}1&\text{if customer $l$ gets service from satellite $i$}\\ 0&\text{otherwise}\end{cases}