esa-sceva/satcom-chunk-collection huggingface dataset:
# Scanning of Historical Clothes Using 3D Scanners: Comparison of Goals, Tools, and Methods
## 3 Results
### Answer to Research Objective 1  
To answer Research Objective 1 (RO1: Identify whether studies on this topic exist and how many there are) we conducted a bibliometric analysis on the selected studies. In general, a bibliometric analysis assumes that each field of study can be described by using significant information collected from the metadata of the literature published in that field, such as authors, keywords, terms from titles and abstracts, citations, and references [32].  
We began by analyzing the frequency of published studies by year and by type (whether if it was published in a conference proceeding or a journal). Figures 2 and 3 show the results. In average one study per year was published starting from 2012. Only 1 of 12 studies was published before 2012--a technology (a laser scanner) showcase from 2001. Most studies (7 of 12) were published in journals, the rest in conference proceedings. Google Scholar was the source for seven works that were not indexed by the high-quality bibliographic databases.  
We then conducted an analysis on the keywords of each article to identify those used by the article's authors and also their frequency. We organized them into one of four groups:  
1. Technology: terms related to the technology that was used, such as scanning, resolution, virtual reality, visualization.
2. Clothing: terms related to the item being scanned, such as textile, fabric, costume.
3. Museum: terms related to the museum's aim in digitizing the object, such as archiving, documentation, preservation.
4. Cultural: terms related to the historical or cultural context of the scanned item, such as Alaska native or Middle Ages.  
To achieve this goal, some other procedures were carried out:  
Figure 3: Distribution of studies by (**a**) type and (**b**) database.  
Figure 2: Number of studies by year.  
* \"Compound\" keywords were split; for example, the keyword \"Gorenjska folk costume image analysis\" was divided into three: \"Gorenjska\" (cultural), \"folk costume\" (clothing), and \"image analysis\" (technical);
* Keywords were aggregated by their similarity under a common simplified keyword when we found it pertinent. This happened with the keyword \"National Museum of Slovenia\", which was simplified to the more general (and widely used) keyword \"museums\".  
Table 5 shows the keywords used, organized by type, and their frequency. The frequency of keywords is uniform--in average each keyword occurs once. The biggest group of keywords is technology-related (29 keywords), and the smallest one is culture-related (five keywords). Remaining groups are comparable in number, being 11 in the case of clothing, and 14 in the case of museum.
## 4 Results and Discussion
### Simulation Setup  
The ease of suitable data analyses and arithmetic operations are the major reasons for choosing MATLAB. In the detecting region of 200 m \(\times\) 200 m, there are between 100 and 150 sensor nodes distributed at random. The first-order radio model is used as an energy design to compare the proposed technique to different routing protocols. The simulation parameters used in the experiment are listed in Table 1. The goal of this research is to lower the entire energy usage of each network node. As a result, JILFO-based routing between the CHs and ICOA-based CH selection is used to provide cluster-based routing. For better CH selection, distance to BS, node centrality, residual energy, node degree, and distance to neighbors are the inputs given to ICOA. Distance, node degree, and residual energy are also inputs to the JILFO. Because these methods are commonly employed to enhance the WSNs energy efficiency, this proposed method is contrasted to several established approaches such as the Threshold sensitive Energy Efficient Sensor Network protocol (TEEN), Power-Efficient Gathering in Sensor Information Systems (PEGASIS).
## 3 Materials and Methods
### Models Overview
#### 3.7.5 XGBoost  
XGBoost (Extreme Gradient Boosting) [27] is an advanced gradient-boosting technique known for its computational speed, scalability, and effectiveness in handling large and complex datasets. Unlike traditional boosting algorithms, XGBoost incorporates second-order derivatives (i.e., the Hessian) in the objective function, allowing for more accurate gradient-based optimization. This feature makes XGBoost particularly suitable for applications involving intricate sensor data, such as human activity recognition.  
In each boosting iteration, XGBoost adds a new tree \(f_{m}(x)\) that minimizes an objective function, defined as:  
\[\text{Obj}^{(m)}=\sum_{i=1}^{n}L(y_{i},\hat{y}_{i}^{(m-1)}+f_{m}(x_{i}))+ \Omega(f_{m}), \tag{16}\]  
where \(L\) represents the loss function quantifying the difference between the true label \(y_{i}\) and the prediction \(\hat{y}_{i}^{(m-1)}\) from the previous iteration. The term \(\Omega(f_{m})\) serves as a regularization component to penalize the complexity of \(f_{m}\), thereby mitigating overfitting. This regularization, alongside the use of second-order gradients, is a distinctive feature that enhances XGBoost's robustness in complex classification tasks.  
The hyperparameters for XGBoost were optimized using a nested cross-validation approach, with five outer folds and three inner folds, along with Keras optimizers to facilitate hyperparameter tuning. The optimized parameters for each sensor configuration are detailed in Table 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Configuration** & **C** & **Class Weight** & **Gamma** & **Kernel** \\ \hline
2 Sensors—Non-overlapping & 10 & None & Auto & RBF \\
2 Sensors—50\% overlapping & 10 & None & Auto & RBF \\
4 Sensors—Non-overlapping & 10 & None & Scale & Linear \\
4 Sensors—50\% overlapping & 10 & None & Scale & RBF \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimal Hyperparameters for SVC in Different Sensor Configurations.  
Using nested cross-validation with Keras optimizers allowed for precise tuning of XGBoost, capturing complex interactions within multi-sensor data and yielding high accuracy across various sensor configurations. This approach ensured that the model was generalized effectively, enhancing its performance in real-world human activity recognition applications.
## 4 Discussion
### Limitations  
While refinements to the earlier PTF [1] have produced improved results, as described in this paper, there are still some fundamental limitations of this approach.  
1. The PTF is physically informed, with estimates bounded above and below by physically plausible limits (0%, 100%), and the estimates are enforced to be monotonic with increasing tension. However, the differences in response between soil types and other explanatory variables may not be explainable in a straightforward manner [36]. As for all PTF models, there is a compromise choice between the fidelity of predictions to measured data and the interpretability of the results.
2. The model indicates associative relationships between the various explanatory variables and the soil-water response, but those relationships may not be causal. For example, including sample depth does not cause a strong change in soil-water response by itself, but its beneficial inclusion is likely due to the strong association between sample depth with bulk density and carbon concentration, both of which are known to affect soil water retention [6; 37; 38; 39].
3. The training data used in development of the model were derived from historical data and do not represent random sampling over the landscape or over explanatory variables. It is difficult to determine the effect of this non-random sampling, but certain diagnostics were used to provide checks on potential problems. For example, the smooth effect of adding spatial coordinates was extracted to determine where spatial extrapolation of the estimated response (e.g., at \(-1500\) kPa) was notable. Although not shown here, the result did not suggest that there are extensive areas where the predicted response is likely to be biased.
# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
###### Abstract  
The utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples. To address this issue, this paper proposes a method for image augmentation using a CC-WGAN network. First, the generator incorporates the Convolutional Block Attention Module (CBAM) to enhance the assimilation of global information and local features in the input images. This integration also improves stability and avoids mode collapse problems associated with the original Generative Adversarial Network. Subsequently, the CBAM is incorporated into the discriminator to facilitate a better understanding of the relevance and significance of input data, thereby enhancing the model's generalization ability. Finally, based on this model, existing few-sample SSS images are augmented, and we utilize the augmented images for discrimination and detection with YOLOv5. The experimental results show that following training with the SSS dataset that is augmented by this network, the accuracy of target detection increased by 7.6%, validating the feasibility of our proposed method. This method presents a novel solution to the problem of low model accuracy in underwater target detection with side-scan sonar due to limited samples.  
s +
Footnote †: journal: Applied  
**Keywords:** side-scan sonar; WGAN; sample augmentation; target detection
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Types of C-RAN
#### 2.4.2 Partially Centralized  
In this architecture, as shown in Figure 3b, the RRH performs the radio functions as well as the functions related to layer 1. Meanwhile, the functions of the higher layers, layer 2 and layer 3, are still executed in the BBU. As the baseband processing is shifted to the RRH from the BBU, the bandwidth requirements between the BBU and RRH become lower in a partially centralized C-RAN. However, it also has certain drawbacks: as the baseband processing is integrated with the RRH, it is less flexible for upgrade and less convenient in terms of multi-cell collaborative signal processing [4].  
However, the authors in [29] extended this classification to three categories by adding another architecture for C-RAN: hybrid centralized architecture. In this architecture, a part of the layer 1 functions is performed in the RRH, whereas the others are performed in the BBU. The RRH performs the user- or cell-specific functions related to the signal processing. This architecture displays higher flexibility for resource sharing and the facility for reducing the energy consumption and communication overhead of a BBU
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 5 Instance Validation
### Multidimensional Superiority Assessment
#### 5.4.2 Comparison of the Effect of Different Manifold Learning Methods  
To verify the superiority of the L-ISOMAP manifold learning algorithm selected in this paper, the data are now processed sequentially using the ISOMAP, LLE, and t-SNE methods, and then input into the LightGBM model for prediction. Take the prediction of battery No. 5 as an example; the prediction results of its test set are shown in Figure 14.  
As can be seen from Figure 14, the prediction effect using the t-SNE method is the worst, while the prediction curve using the L-ISOMAP manifold learning algorithm has the highest overlap with the true capacity curve and the best fitting effect, and its prediction effect is significantly better than the prediction effect processed by the rest of the stream  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Predictive Models** & **Battery Number** & **MSE** & **MAE** & **AE** & \(\mathbf{R^{2}}\) & **Running Time (s)** \\ \hline \multirow{4}{*}{ELM} & 5 & 0.0025 & 0.0421 & 4 & 0.9489 & **28.6184** \\  & 6 & 0.1827 & 0.4158 & 5 & 0.9254 & **35.0837** \\  & 7 & 0.0023 & 0.0374 & 4 & 0.9521 & **29.7031** \\ \hline \multirow{4}{*}{LSTM} & 5 & 0.0033 & 0.0519 & 3 & 0.9675 & 42.9855 \\  & 6 & 0.0012 & 0.0258 & 5 & 0.9405 & 49.3947 \\  & 7 & 0.0016 & 0.0366 & 4 & 0.9677 & 36.0618 \\ \hline \multirow{4}{*}{Random forest} & 5 & \(4.5492\times 10^{-4}\) & 0.0169 & 2 & 0.9802 & 42.9855 \\  & 6 & \(8.0754\times 10^{-4}\) & 0.0209 & 3 & 0.9769 & 49.3947 \\  & 7 & \(3.0842\times 10^{-4}\) & 0.0112 & 2 & 0.9713 & 36.0618 \\ \hline \multirow{4}{*}{XGBoost} & 5 & \(1.3182\times 10^{-4}\) & 0.0081 & 2 & 0.9812 & 49.3246 \\  & 6 & \(6.8945\times 10^{-4}\) & 0.0225 & 1 & 0.9875 & 54.5812 \\  & 7 & \(1.4913\times 10^{-4}\) & 0.0072 & 1 & 0.9722 & 50.5719 \\ \hline \multirow{4}{*}{LightGBM} & 5 & \(\mathbf{1.2740\times 10^{-4}}\) & **0.0076** & **0** & **0.9889** & 46.9862 \\  & 6 & \(\mathbf{5.4299\times 10^{-4}}\) & **0.0175** & **1** & **0.9865** & 51.6523 \\ \cline{1-1}  & 7 & \(\mathbf{9.0437\times 10^{-5}}\) & **0.0069** & **0** & **0.9921** & 44.2364 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of multi-model prediction effects.  
Figure 14: Comparison of the prediction effects of different stream shape learning algorithms.  
shape algorithms, indicating that the L-ISOMAP manifold learning algorithm used in this paper has high superiority.
## 2 Materials and Methods
### Graph Construction  
Coherence matrices showed coefficients between 0.1 and 19.16. A threshold \(\sigma_{th}\) was set for graph construction, with its value varying between 0.1 and 1.25. The analysis was conducted to assess how graph characteristics change when including interactions above \(\sigma_{th}\). Various centrality measures were calculated such as degree centrality (number of edges [44]), betweenness centrality (fraction of shortest paths passing through a node [45]), and eigenvector centrality (importance of a node considering the importance of its neighbors [44]). Connected components of the graphs were explored [45]. The shortest path distances between all node pairs were calculated, and cycles in the graph were identified, and defined as connected graphs in which each vertex has degree 2 [46]. These metrics, with respect to \(\sigma_{th}\), are presented in Appendix (Figures A1-A7).  
To assess the connectivity of the graphs generated in this study, the 'conncomp' function in MATLAB was employed. This function facilitates the identification of connected components in an undirected graph, thus providing a measure of the level of connection within the network. This function assigns each node in the graph a connected component identifier, enabling the determination of the total number of connected components present in the graph. The graph's connectivity coefficient can be calculated as  
\[\kappa=\frac{1}{n}, \tag{2}\]  
where \(n\) is the number of connected components. This methodological approach allowed for the analysis of how the variation in the threshold \(\sigma_{th}\) affects the global connectivity of the graphs.  
A threshold value of \(\sigma_{th}=0.5\) was decided upon, as lower values show coherence interactions that may include noise, and values higher than this completely lose one of our frequency bands.  
With the specified threshold, we generated graphs explicitly depicting the mentioned measures using MATLAB. The calculation of the distances between node pairs was carried out without considering the weight of the edges, i.e., treating it as an unweighted graph. Betweenness centrality and eigenvector centrality measures can be found in Appendix A (Figures A6 and A7).
## 2 System Dynamic Description  
### System Model of Free-Form Surface Grinding Robot  
The dynamics of an FFSGR can be considered as the dynamics of an n-link robot manipulator, which are expressed as follows [41]:  
\[M(q)\ddot{q}+C\Big{(}q,\dot{q}\Big{)}\dot{q}+G(q)+F\Big{(}\dot{q}\Big{)}+\tau_ {d}=u_{D} \tag{1}\]  
where \(\Big{(}q,\dot{q},\ddot{q}\Big{)}\in R^{n\times 1}\) are the vectors of joint position, velocity, and acceleration, respectively, \(M(q)\in R^{n\times n}\) is the nominal symmetric inertial matrix. \(C\Big{(}q,\dot{q}\Big{)}\in R^{n\times n}\) are the vectors of the nominal Coriolis and centripetal forces, respectively. \(G(q)\in R^{n\times 1}\) expresses the nominal gravity vector. \(F\Big{(}\dot{q}\Big{)}\in R^{n\times 1}\) expresses the friction vector. \(\tau_{d}\in R^{n\times 1}\) is the disturbance. \(u_{D}\in R^{n\times 1}\) is the joint torque input vector with an actuator dead zone.  
In practice, it is hard to establish the exact mathematical model of the FFSGR. Considering the model uncertainty, one gets:  
\[\left\{\begin{aligned} M(q)&=M_{0}(q)+\Delta M(q)\\ C\Big{(}q,\dot{q}\Big{)}&=C_{0}\Big{(}q,\dot{q} \Big{)}+\Delta C\Big{(}q,\dot{q}\Big{)}\\ G(q)&=G_{0}(q)+\Delta G(q)\\ F\Big{(}\dot{q}\Big{)}&=F_{0}\Big{(}\dot{q}\Big{)}+ \Delta F\Big{(}\dot{q}\Big{)}\\ \tau_{d}&=\tau_{0}+\Delta\tau\end{aligned}\right.. \tag{2}\]  
The purpose is to design a torque controller \(u_{D}\) for the FFSGR with an actuator dead zone to make the real trajectory track the desired one. To achieve this objective, three properties of the dynamics of the robot model (1) must be satisfied [41]:  
**Property 1**.: \(M(q)\) _is a positive definite symmetric matrix and uniformly bounded:_  
\[m_{1}\|x\|^{2}\leq x^{T}M(q)x\leq m_{2}\|x\|^{2},\forall x\in R^{n\times 1}\]  
_where \(m_{1}\) and \(m_{2}\) are known positive constants and they depend on the mass of the robot manipulators._  
**Property 2**.: \(\dot{M}(q)-2C\Big{(}q,\dot{q}\Big{)}\) _is a skew symmetric matrix for any vector \(x\):_  
\[x^{T}\Big{(}\dot{M}(q)-2C\Big{(}q,\dot{q}\Big{)}\Big{)}x=0\]  
**Property 3**.: \(C\Big{(}q,\dot{q}\Big{)}\dot{q}\) _, \(G(q)\), \(F\Big{(}\dot{q}\Big{)}\), and \(\tau_{d}\) are bounded as follows:_  
\[\|C\Big{(}q,\dot{q}\Big{)}\dot{q}\|\leq C_{k}\|\dot{q}\|^{2},\|G(q)\|\leq G_{k },\|F\Big{(}\dot{q}\Big{)}\|\leq F_{k},\|\tau_{d}\|\leq\tau_{k},\]  
_where \(C_{k}\), \(F_{k}\), \(G_{k}\), and \(\tau_{k}\) are positive constants._  
**Remark 1**.: _The abovementioned properties are not very strict, and they are common assumptions in the literature about robot control [36]._
## 3 Results and Discussion
### Trend Analysis of Atmospheric Aerosols and Gaseous Species  
In Section 3.1, the changes in the concentrations of particulate and gaseous air pollutants over 8 years measured in Seoul and on Baengnyeong Island were shown. The Mann-Kendall test, which can examine long-term concentration change trends using measurement data, was performed in order to understand the degree of concentration changes more accurately. Table 2 summarizes the results of the Mann-Kendall test and Sen's slope estimation for ambient aerosols between 2012 and 2019. All the air pollutants except for OC showed a statistically significant decrease in Seoul. OC showed neither an increase nor a decrease. On Baengnyeong Island, all air pollutants showed a statistically significant decrease, except for NO3\({}^{-}\), which showed an increasing trend. Among the decreasing species, the Sen's slope for PM2.5 was the highest and the Sen's slope for EC was the lowest in both sites. Figure 2: The annual levels of gaseous species. The annual mean (red circle), minimum, interquartile range (Q1), median, interquartile range (Q3) and maximum values are shown. Table 3 shows the results of the Mann-Kendall test and Sen's slope estimation for ambient gaseous pollutants between 2012 and 2019. The degree of concentration changes was compared using Sen's slope. The change in SO\({}_{2}\) was smaller in Seoul than on Baengnyeong Island. However, in the case of SO\({}_{4}\)\({}^{2-}\), the decrease in concentration in Seoul was larger than on Baengnyeong Island during the same period. If other conditions were the same in both areas, this leads us to guess that there is another source of SO\({}_{2}\) emission around Seoul, or the conversion rate from SO\({}_{2}\) to SO\({}_{4}\)\({}^{2-}\) on Baengnyeong Island would be higher than in Seoul. Figure 3 shows the changes in SO\({}_{2}\), NOx and CO emissions from China, the metropolitan areas of South Korea (Seoul, Gyeonggi Incheon), and Chungcheongnam-do, Chungcheongbukdo and Jeollabuk-do, in the western part of Korea, from 2010 to 2017. The Chinese emission data are from [PERSON] et al. [3], and the Korean emission data are from the Clean Air Policy Support System (CAPSS) estimated by NIER, Korea [33].
## 3 Operating Principle and Performance of DCM-CRM Cuk PFC Converter Utilizing VI Control
### Input inductor Variation Range  
Based on the condition that the input inductor operates in DCM and the subsequent analysis, coupled with the circuit parameters detailed in Table 1, an initial value \(L_{\text{initial}}\) of 170 \(\upmu\)H was chosen for the variable inductor \(L_{\text{V}}\). According to (24) and considering the relationship between the intermediate capacitor voltage \(V_{\text{CI}}\) and the input voltage, the variation range of the inductor \(L_{\text{V}}\) over a half-line cycle can be depicted as illustrated in Figure 7.  
It can be seen from Figure 7 that when the input voltage varies from 90 Vac to 240 Vac, the variable inductor varies from about 170 \(\upmu\)H to 930 \(\upmu\)H. The \(L_{\text{V}}\) varies with the rectifier input voltage within the half-line cycle.  
To verify the relationship between the bias current and the inductance of the input variable inductor prototype, a DC bias current, which is created by connecting a DC voltage source in series with an electronic load operating in constant current mode, is applied to the bias winding of the variable inductor, then the real inductance of input variable inductor is measured by the Chroma 3302 component analyzer. According to measurement data, the relationship curve of the bias current and the variable inductance is shown in Figure 8. When the bias current increases from 0 A to 1 A, the variation range of the variable inductor is from 170 \(\upmu\)H to 1050 \(\upmu\)H. When the DC current injected into the bias winding is 0, the permeability of the left and right outer leg \(\mu_{\text{var}}\) is not affected, and the inductance value is about 1050 \(\upmu\)H. With the gradual increase in the bias current, the permeability of the left  
Figure 6: Key waveforms in half-line cycle.  
and right outer leg \(\mu_{\rm var}\) decreases, and the variable inductance also decreases. When the bias current reaches the designed maximum of 1 A. The minimum variable inductance is 170 \(\mu\)H. Therefore, the desired input inductance can be controlled by the bias current according to the control curve shown in Figure 8.  
Figure 8: The relationship of the bias current and the variable inductance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Parameter** & **Value** \\ \hline Input voltage \(V_{\rm in\_RMS}/\)V & 90–240 \\ Grid frequency\(f_{\rm L}/\)Hz & 50 \\ \(n_{3}\):\(n_{1}\):\(n_{2}\) (EI40 core) & 37:80:80 \\ Rated output current \(I_{\rm o}/\)A & 1.5 \\ Output voltage \(V_{\rm o}/\)V & 72 \\ Variable input inductor (proposed Cuk PFC) \(L_{\rm V}/\mu\)H & 170–930 \\ Input inductor (conventional) \(L_{1}/\mu\)H & 170 \\ Output inductor \(L_{2}/\mu\)H & 350 \\ Intermediate capacitor \(C_{1}/\mu\)F & 220 \\ Output capacitor \(C_{2}/\mu\)F & 200 \\ Diodes \(D_{0}\), \(D_{1}\) & STTH12R06 FP \\ Main switch \(S_{1}\) & FCP190N60 \\ Mosfet \(S_{2}\) & IRLR3410 TRBPF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key Circuit Parameters.  
Figure 7: The variation range of the input inductor \(L_{\rm V}\).