esa-sceva/satcom-chunk-collection huggingface dataset:
# Inner-Loop Controllers for Grid-Forming Converters
### Grid-Following Converters  
Most inverters used in photovoltaic (PV) systems and wind turbines currently operate in grid-following mode (Figure 2). This mode employs an inner current loop to ensure that the grid-side currents track their references and a synchronization algorithm to estimate the angular position \(\theta\) and frequency \(\omega\) of the voltage at the point of connection (PoC) [5]. Typically, the current references are generated to ensure the desired exchange of active and reactive power with the grid. For example, the active power reference \(p^{*}\) may originate from the DC bus voltage outer loop, while also incorporating frequency support actions. The reactive power reference \(q^{*}\), conversely, can be derived from grid-supporting functionalities, such as voltage-reactive power characteristics or from an external signal provided by the Microgrid Control System (MGCS) [6].  
While grid-following converters are currently prevalent in the integration of diverse inverter-based resources into the grid, a growing trend favors grid-forming converters due to their potential to enhance grid stability and resilience. The subsequent sections of this paper  
Figure 2: Block diagram of a grid-following inverter.  
will detail a systematic design methodology for the inner-loop controller, as well as provide a comparative analysis of virtual impedance and virtual admittance implementations.
# Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence
## 2 Materials and Methods
### Coherence Analysis  
EEG data were exported from Medicaid IV to calculate coherence with Neuroguide 2.6.5 software. Intrahemispheric coherences were obtained considering all the leads except the midline (Fp1, F3, C3, P3, O1, F7, T3, and T5 in the left hemisphere and Fp2, F4, C4, P4, O2, F8, T4 and T6 in the right hemisphere), i.e., Fp1-F3, Fp1-C3, Fp1-P3, Fp1-O1, Fp1-F7, Fp1-T3, Fp1-T5, F3-C3, F3-P3, F3-O1, F3-F7, F3-T3, F3-T5, C3-P3, C3-O1, C3-F7, C3-T3, C3-T5, P3-O1, P3-F7, P3-T3, P3-T5, O1-F7, O1-T3, O1-T5, F7-T3, F7-T5, and T3-T5 for the left hemisphere; and Fp2-F4, Fp2-C4, Fp2-P4, Fp2-O2, Fp2-F8, Fp2-T4, Fp2-T6, F4-C4, F4-P4, F4-O2, F4-F8, F4-T4, F4-T6, C4-P4, C4-O2, C4-F8, C4-T4, C4-T6, P4-O2, P4-F8, P4-T4, P4-T6, O2-T6, P8-T4, O2-T6, F8-T4, F8-T6, and T4-T6 for the right hemisphere). Interhemispheric coherences were obtained considering only homologous derivations (Fp1-Fp2, F3-F4, C3-C4, P3-P4, O1-O2, F7-F8, T3-T4, and T5-T6). The z values of EEG coherence were calculated before and after treatment.
# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 3 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objective** & **References** & **Cache Location** & **Methodology** \\ \hline \multirow{8}{*}{Cache/Contents Placement} & [9] & UE & Using SNA and CRP \\ \cline{2-4}  & [10; 11] & UE & Files popularity statistics \\ \cline{2-4}  & [23] & UE & Random content \\ \cline{2-4}  & [24] & UE & Probabilistic approach \\ \cline{2-4}  & [25] & UE & Optimization \\ \cline{2-4}  & [30] & Small Cell & Files instantaneous popularity learning \\ \cline{2-4}  & [31; 32] & Small Cell & Coded placement strategy \\ \hline \multirow{2}{*}{[33]} & \multirow{2}{*}{Small Cell} & Problem formulation using cost of \\  & & files retrieval and bandwidth \\ \cline{2-4}  & & & SNA and influential users content as \\ \cline{2-4}  & & file popularity index \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different caching techniques and their methodologies. Finding social hubs in social networks remains an active research area of Social Network Analysis (SNA). Various measures including Eigenvector Centrality measure, Betweenness measure, Closeness measure, Degree centrality measure and PageRank algorithm have been used extensively for finding the nodes importance in social graphs. Among these measures, Eigenvector centrality measure is the most widely used measure for detecting social hubs in social graphs. This measure has been used by three popular methods: PageRank [34], HITS (Hypertext Induced Topic Search) [35], and SALSA (Stochastic Approach for Link Structure Analysis) [36] for retrieving web information. It is worth mentioning that several prior studies exist that propose various design approaches for proactive caching schemes [9; 16; 37]. However, the novelty of the proposed work stems from the simple yet under-explored idea of considering the content of influential users as popular content. Furthermore, our work uses a new community detection algorithm for detecting communities in the network and then \(k\) influential users are found, one for each community. In the next section, we will explain our proposed methodology for proactive caching in socially aware D2D networks.
## 3 Envelope Detector-Aided, Machine Learning-Based Fault Type Detection Model
### Envelope Detector-Based Fault Region Detection Model
In this study, the threshold values are defined based on the relays and meters as defined in Table 3. After achieving the border values of the envelope signal, the border extraction process is applied to the envelope signal as  
\[\mathbf{s}_{\mathrm{inv,SE}}[n]=\left|\mathbf{s}_{\mathrm{inv}}[n]-\zeta_{ \mathrm{max}}\right|+\left|\mathbf{s}_{\mathrm{inv}}[n]-\zeta_{\mathrm{min}} \right|. \tag{6}\]  
Thus, the fault diagnosis operation in the algorithm is employed by considering the maximum changes between the extracted border values and the examined signal, which  
Figure 8: The block diagram of the envelope detector-based fault region detection algorithm. is defined as \(\Delta v=(\max(\mathbf{s}_{\text{NNME}})/\max(\mathbf{s}))\times 100\). Thus, the phase fault status is defined based on the type of signal, and the percentage of change (Table 3) is defined as  
\[\text{Voltage}= \begin{cases}\text{No Fault},&\Delta v=0\\ \text{Ground Error},&\Delta v\leq v_{voltage}\,,\\ \text{Fault},&\Delta v>v_{voltage}\end{cases}  \tag{7}\] \[\text{Current}= \begin{cases}\text{No Fault},&\Delta v=0\\ \text{Ground Error},&\Delta v\leq v_{current}\,,\\ \text{Fault},&\Delta v>v_{current}\end{cases} \,,\]  
Consequently, the envelope detector-based fault region detection algorithm obtains the region of anomaly based on the diagnosis results, which are given in (7).
## 4 Conclusions  
The performance of the (Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{1-y}\)Ni\({}_{y}\)TiO\({}_{3}\) at microwave frequency was investigated and analyzed. The dielectric ceramics with an ilmentie-type structure belonging to the trigonal space group R-3 could be founded in all samples. With the substitution of Ni\({}^{2+}\) for Mg\({}^{2+}\) and Zn\({}^{2+}\), the densification of (Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{1-y}\)Ni\({}_{y}\)TiO\({}_{3}\) was significantly enhanced. Compared to documented dielectric ceramics, excellent microwave performance can be demonstrated for (Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{0.95}\)Ni\({}_{0.05}\)TiO\({}_{3}\) sintered at 1200 \({}^{\circ}\)C/4h with \(\epsilon_{\gamma}\)-19.3, \(Qf\) value \(\sim\)165,000 (GHz) and \(\tau_{f}\sim-65.4\) ppm/\({}^{\circ}\)C. \(\tau_{f}\) of 0.94(Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{0.95}\)Ni\({}_{0.05}\)TiO\({}_{3}-0.06\)SrTiO\({}_{3}\) shows \(\sim+68.5\) ppm/\({}^{\circ}\)C and, thus, a near-zero mixture could be obtained in future work. The (Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{0.95}\)Ni\({}_{0.05}\)TiO\({}_{3}\) microwave dielectric material system shows ultrahigh potential for applications in devices and systems of satellite and wireless communications.  
Conceptualization, C.-H.S.; methodology, S.-H.L.; validation, C.-H.S. and C.-L.P.; formal analysis, C.-H.S.; investigation, S.-H.L.; data curation, C.-C.H. and C.-L.P.; writing--original draft preparation, C.-H.S. and S.-H.L.; writing--review and editing, S.-H.L.; visualization, S.-H.L.; supervision, S.-H.L.; project administration, S.-H.L.; funding acquisition, S.-H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology, Taiwan, under Grant No. MOST 108-2221-E-224-050, MOST 109-2622-E-224-013, and industrial cooperation with Live Strong Optoelectronics under contract no. Yuntech 109-3019-1.  
Not applicable.  
The data are contained within the article.  
The authors acknowledge the technical support from the Advanced Instrumentation Center of National Yunlin University of Science and Technology.  
The authors declare no conflict of interest.
# A New Method to Determine the Steel Fibre Content of Existing Structures--Evaluation and Validation
###### Abstract  
The in-situ measurement of the content and orientation of steel fibres in concrete structures is of great importance for the assessment of their specific mechanical properties, especially in the case of repair. For existing structures, the actual fibre content as well as the orientation of the fibres, which is based on many factors such as casting or compacting direction, is typically unknown. For structural maintenance or rehabilitation, those factors have to be determined in order to apply meaningful structural design calculations and plan necessary strengthening methods. For this reason, a new method based on the analysis of drilling cores of concrete structures has been established. The newly developed non-destructive test setup used in this research consists of a framework for cylindrical specimens in combination with an LCR meter to determine the electrical resistance of the fibre reinforced concrete. In combination with a suitable FEM model, concretes with fibre contents up 80 kg/m\({}^{3}\) were analysed to derive a first model to assess the actual fibre content of steel fibre reinforced concretes. After a calibration of the literature's equation by use of an adjusted aspect ratio for the analysis of drilling cores, the estimation of the fibre content is possible with high accuracy for the tested material combination. The results show that the newly developed test method is suitable for the rapid and non-destructive structural diagnosis of the fibre content of steel fibre reinforced concrete based on drilling cores using electrical resistivity measurements.  
steel fibre reinforced concrete; electrical resistivity; fibre content; non-destructive test method +
Footnote †: journal: Applied Sciences
## 2 Influential Factors on the Performance of GSP
### Admixtures
#### 2.3.1 Polymers  
Polymers are the most traditional admixtures used in GSP, including latex, resin, SBS, styrene-butadiene rubber (SBR), and waste rubber [74; 84]. Latex and resin are efficient to smooth the high rigidity of HCM and reinforce the tensile strength of the whole structure because of their hydrophilicity, which results in bonding at interfaces between materials [56; 76]. However, they have little or no influence on the high-temperature performance of GSP. That is because adding polymers into cement mortar will impair the compressive strength of HCM.  
In contrast, GSP with rubber as an admixture has little promotion to the low-temperature cracking resistance, due to the weak interface between rubber and cement [48; 75], although the fatigue life of rubber-modified GSP is prolonged with the increase of rubber content [85]. In this category, waste rubber is thought to be a cost-effective and environmental-friendly admixture as a substitute to replace raw materials with a high blending ratio up to 20% [75]. Its weakness of insufficient strength can be enhanced by adding an interface modifier, which will be introduced later in this paper [86].
## 5 Experiments
### Experimental Setup  
#### 5.1.1 Datasets  
Four available public datasets commonly used in the network analysis realm were used in these experiments. The critical characteristics of these datasets are summarized in Table 2 including the number of vertices, the number of edges and the generated concept number _!concepts_ of each dataset. The number of generated concepts by datasets is in different orders of magnitude.  
The statics description of the datasets is as follows: Karate ([[http://www-personal.umich.edu/](http://www-personal.umich.edu/)]([http://www-personal.umich.edu/](http://www-personal.umich.edu/))) [23] is a typical social network among the 34 members of the Karate club of a university in the USA. Terrorists ([[http://konect.uni-koblenz.de/](http://konect.uni-koblenz.de/)]([http://konect.uni-koblenz.de/](http://konect.uni-koblenz.de/))) [24] is an undirected network including contacts between suspected terrorists involved in the train bombing of Madrid on 11 March 2004 as reconstructed from newspapers. Football ([[http://www-personal.umich.edu/](http://www-personal.umich.edu/)]([http://www-personal.umich.edu/](http://www-personal.umich.edu/))) [25] is a network of American football games between Division IA colleges during regular season Fall 2000. Neural Network ([[http://www-personal.umich.edu/](http://www-personal.umich.edu/)]([http://www-personal.umich.edu/](http://www-personal.umich.edu/))) [26] is a network representing the neural network of C.Elegans.  
A network is represented as a graph \(G=(V,E)\) where the vertex set \(V\) represents individuals and the edge set \(E\) denotes the relationship between individuals. Here, a network can be formalized as the formal context \(K=(V,V,I)\) by the following modified adjacency matrix, in which \(I\) is the binary relationship between vertices.  
**Definition 4** ([2] Modified Adjacency Matrix): _Let \(G=(V,E)\) be a graph with vertices \(v_{1}\) to \(v_{n}\). The \(n\times n\) matrix \(M^{{}^{\prime}}\) is called a modified adjacency matrix, in which_  
\[M^{{}^{\prime}}=\begin{cases}a_{ij}=1&\text{if }(v_{i},v_{j})\in E\\ a_{ij}=1&\text{if }i=j\\ a_{ij}=0&\text{otherwise}\end{cases} \tag{6}\]
# Human Activity Recognition Using CSI Information with Nexmon
## 7 CSI Data Preprocessing and Feature Extraction
### Principal Component Analysis (PCA) as Dimension Reduction for Experiment 1.1  
The magnitude of CSI data is now preconditioned, free from the pilot, null, unchanged subcarriers, outliers, and noise. Further, we still have a matrix \(CSI_{denoised}\) [150 \(\times\) 104] for each file, where columns represent 104 dimensions. For each activity, the number of files is 1800, so the total data dimension would become 1800 \(\times\)\(CSI_{denoised}\). This data is quite significant to use for activity classification. PCA's main motive is to extract the essential data in a smaller dimension without losing vital information and characteristics. PCA contributes for transforming a large set of variables into a smaller one while the reduced dimension still contains most of the extensive dimension information.  
In our case, the first two components represent 75% to 81% of the variance of the data. So the whole dimension of [150 \(\times\) 104] was reduced to [150 \(\times\) 2] while preserving vital features. The columns correspond to the subcarriers, and the rows correspond to the values of these subcarriers in each packet.  
For each activity, the total dimension of the dataset transformed as [1800 \(\times\) 150 \(\times\) 2]. The PCA algorithm that is used for dimension reduction is depicted in Algorithm 4.  
```
0: extensive dimension matrix = \(\chi\) with (\(CSI_{ham+denoised}\) [150 \(\times\) 104])
0: Principal components as \(CSI_{pca}\)
1:\(CSI_{out}\) [150 \(\times\) 2] \(\leftarrow\) PCA on \(\chi\) with (\(CSI_{ham+denoised}\) [150 \(\times\) 104])
2:\(CSI_{pca}\)\(\leftarrow\)\(CSI_{out}\)
```  
**Algorithm 4** PCA
## 4 Discussion
### Method Characteristic Analysis
#### 4.1.3 Effect Analysis of Superpixel  
Speckle is a unique scattering phenomenon of PolSAR images. Speckles increase the difficulty of image interpretation and reduce the performance of image segmentation and classification. In the extraction of PolSAR buildings, due to the influence of the speckles, many non-buildings are mistakenly extracted into buildings. The superpixel algorithm aggregates pixels into area units of roughly uniform size. Reasonable superpixels are conducive to overcoming the interference of speckle noise in PolSAR images.  
Considering that the distribution of buildings on the earth's surface cannot be a scattered distribution of single pixels, we apply superpixel constraints to the building of PolSAR imagery. The core idea of the superpixel constraint is to filter the building extraction results by using each superpixel region and to filter out the scattered extraction results. From Figure 10b,d,f, the scattered misclassification results are corrected while maintaining the outline of the building after the introduction of superpixels. In Table 6, after using the superpixel constraint, the accuracy of the building extraction result decreases slightly, the false alarm rate decreases significantly, and the evaluation parameter F1 increases from 2% to 4%. Therefore, the introduction of superpixels can provide better adaptability and usability for the extraction of buildings under PolSAR.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**E-SAR**} & \multicolumn{3}{c}{**GF-3**} & \multicolumn{3}{c}{**RADASAT-2**} \\ \hline  & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** \\ \hline MLP & 75.36 & 57.67 & 54.21 & 81.37 & 40.94 & 0.6844 & 79.93 & 39.63 & 68.78 \\ \hline CNN & **88.05** & **25.01** & **80.99** & **95.56** & **15.45** & **0.8971** & **94.37** & **21.76** & **85.55** \\ \hline \end{tabular}
\end{table}
Table 5: Building extraction accuracy table using the MLP and CNN methods.  
Figure 10: Comparison of results before and after superpixel constraints under three data. (**a,b**) is the experimental result under ESAR data; (**c,d**) is the experimental result under GF-3 data; and (**e,f**) is the experimental result under RADASAT-2 data. (**a,c,e**) are experimental results without superpixel constraints and (**b,d,f**) are experimental results after using superpixel constraints.  
In this paper, the feature set of PauliRGB and G\({}^{0}\) statistical texture parameters is used as the input to CNN. It combines polarization information and the unique statistical texture information of the building under the PolSAR image, which improves the information utilization rate of the building and makes the building's extraction more accurate. The use of CNN to fully dig out building information in PolSAR images significantly improves building extraction. The introduction of superpixel segmentation optimizes the building extraction accuracy and makes the building contour boundary more accurate.
## 5 Conclusions  
In conclusion, our findings demonstrate that QCT with asynchronous calibration can provide vBMD values comparable to those obtained through DXA in osteoporoptic patients, thereby enabling opportunistic screening for osteoporosis. Furthermore, QCT demonstrated the ability to diagnose osteoporosis even in individuals with severe vertebral degenerative changes, where DXA performance is limited by such artifacts. We also demonstrated that vBMD effectively distinguishes between individuals with and without fragility fractures by providing a 3D measurement that excludes the posterior vertebral elements and accounts for bone size. TBS performed better than aBMD but did not reach the diagnostic accuracy of vBMD.  
The findings of this study confirm the potential advantages of QCT over DXA in assessing bone mineral density with greater accuracy. However, its translation into routine clinical practice may not be easy. While vBMD from QCT may offer a more precise volumetric measurement, its widespread implementation remains limited by factors such as accessibility, cost, and radiation exposure [12]. The use of QCT should be oriented to specific clinical scenarios where QCT could provide the greatest benefit, such as in patients with obesity, degenerative changes, or discordant DXA results, to refine its role in clinical guidelines and decision-making processes. The incorporation of TBS values may partially compensate for the limitations of DXA aBMD, particularly in subjects with degenerative changes, to refine osteoporosis risk stratification without the need for additional imaging modalities like QCT.  
**Author Contributions:** Conceptualization, D.A. and C.M.; methodology, S.F., E.G., C.G., L.M.S. and C.M.; software, D.A. and L.M.S.; validation, E.G. and C.M.; formal analysis, P.S., C.G. and C.M.; investigation, P.S.; data curation, S.F., P.S., E.G. and C.G.; writing--original draft, D.A. and C.M.; writing--review and editing, S.F., E.G., D.A., L.M.S. and C.M.; supervision, L.M.S.; project administration, C.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of San Raffaele Hospital (approval protocol code: RETRORAD, date of approval: 16/06/2023).  
**Informed Consent Statement:** Patient consent was waived in accordance with the ethics committee.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
## 2 Manual Measurement of Scoliosis  
The Cobb angle measurement is a commonly used method to assess the severity of scoliosis. It is determined by measuring the angle formed at the intersection of two perpendicular lines drawn at the end of vertebrae on the X-ray image of the spine [1]. Figure 1 shows the definition of Cobb angle. After the determined end vertebrae (thevertebrae with the greatest inclination to the concave side of scoliosis) are determined, draw parallel lines on the upper edge of the upper end vertebra and the lower edge of the lower end vertebra. The angle between these two lines is the Cobb angle [15].  
The size of Cobb angles plays an important role in the subsequent diagnosis and treatment. Depending on the age and remaining growth potential of the patient, it is determined whether to observe the treatment, use a brace or perform a surgical procedure [16; 17; 18]. The Cobb angle measurement results may vary due to the patient's body position during the X-ray and the skill level of the medical surveyors, with an error range of typically 3-5 degrees.  
The traditional manual measurement method is prone to differences in the professional level of the measurers and subjective factors, and is also inefficient, taking about ten minutes for a spine X-ray measurement. To address this, this paper proposes a deep learning network-based method to predict the vertebral center points, fit the spine curve, determine the end vertebra according to the curve slope variations, and finally obtain the Cobb angles. Compared to the traditional manual measurement method, this proposed method is more efficient, rigorous, and faster.
# Predicting the Onset of Freezing of Gait Using EEG Dynamics
## 2 Materials and Methods  
### Subjects  
Seventeen patients from the Parkinson's Disease Research Clinic at the Brain and Mind Centre, University of Sydney, participated in this study. The University of Sydney Ethics Committee provided ethics approval for this experiment (HREC approval number: 2014/255). All participants for the study were chosen based on the score for the third item on the self-reported FOG questionnaire and assessment of a clinical specialist. The mean age of the participants was 64 \(\pm\) 7.25 years, and none had any depression or dementia, as assessed by neurologist [PERSON] using the DSM-IV criteria. Furthermore, the participants had a Mini-Mental State Examination (MMSE) score \(\geq\) 24 and fulfilled the UK Parkinson's Disease Society Brain Bank (UKPDSBB) criteria [15; 17]. Furthermore, these participants had a varying severity and frequency of freezing and, when in their practically defined off period, having withdrawn PD medications overnight, had an MDS Unified Parkinson's Disease Rating Scale III stage of 40.10 \(\pm\) 12.21 and a Hoehn and Yahr stage of 2.34 \(\pm\) 0.73.