esa-sceva/satcom-chunk-collection huggingface dataset:
# Wide-Area Protection System for Radial Smart Distribution Networks
## 3 Proposed Protection Plan  
A protection system for power networks is responsible for fault detection, fault location, and fault isolation. In conventional distribution networks, this process is mainly performed by coordinated overcurrent relays. The coordination protocol is simply carried out due to the unilateralism of low dynamic fault currents and radial configuration of conventional distribution networks. However, in the presence of DERs, the coordination protocol is challenging. In addition to the coordination challenges, fault detection in DER-integrated distribution networks is also more demanding compared to conventional networks. This paper aims to propose a novel wide-area protection plan to cope with these challenges.  
Figure 1: Diagram illustrating the various components of a DFIG [31] and the associated crowbar resistances.  
The architecture of the proposed protection plan consists of two types of devices. The first type is a Central Processing Unit (CPU), and the second device is the network-installed relays. The CPU is responsible for fault location or fault section identification, and the network installed relays are responsible for fault detection. Upon a fault inception, relays detect the fault and send fault detection signals to the CPU, where the faulted section is identified, and a trip signal is sent to the appropriate Circuit Breakers (CBs) in order to isolate the faulted section. The fault detection and the fault location algorithms are discussed individually in the following subsections.
## Materials and Methods
### Welding Experiments
#### 3.4.3 Tele-Welding Experiment
The alignment time participants spent to position the torch tip significantly influences the overall tele-welding completion time compared to the welding itself. Thus, alignment times were measured to evaluate improvement in participant work efficiency with each condition as the VFs aid this process in particular. The number of accidental collisions between the torch tip and the metal was recorded as a performance metric. User effort and workload during teleoperation experiments were evaluated by the NASA task load index (NASA-TLX) score at the end of each task, assessing the qualitative mental demand, physical demand, time demand, performance, effort, and frustration (score range of 1-100, from the least to the most demanding) [45; 46]. User acceptance and system usability, including usefulness and ease-of-use, was assessed by a questionnaire based on the technology acceptance model (TAM) measuring acceptance and ease-of-use (score range of 0-7, from worst to best) [47; 48]. A one-way within-participants ANOVA with repeated measures analyzed the statistical differences among the means of all measurements [49]. Bonferroni correction indicated which mean values were significantly different and was used in this analysis when the ANOVA test showed a significant main effect of the experiment condition [50]. The Greenhouse-Geisser correction was applied to assess the difference in the welder reports of the baseline, MRnoVF, and MRVF modules as within-subject variables [51].
# Towards a Digital Transformation Hyper-Framework:
## 3 Materials and Methods
### The Foundation of Hypothesis 1 (H1)--Integrated Tailorable Life Cycle Models
#### 3.1.1 The Role of Systems Engineering (SysEng)  
The multidisciplinary nature of traditional Systems Engineering (SysEng) emerges from the Systems Engineering Body of Knowledge (SEBoK) [47], particularly the SysEng Foundation Knowledge Areas (KAs) (Systems Fundamentals, The Nature of Systems, Systems Science, Systems Thinking, Representing Systems with Models, and Systems Approach Applied to Engineered Systems), Enterprise Systems Engineering KAs (Creating Value, Resource Optimization, enabling Systems Engineering in the Organization, Kinds of Knowledge used by the Enterprise, Projects Programs and Business), Systems of Systems (SoS) KAs, Human Systems Integration KAs, and the variety of associated standards [48]. They specify SysEng as the overall range of knowledge, skills, competencies, and activities  
Figure 4: The quintuple helix model of a generic hyper-framework (adopted from [42]).  
appearing, either on the large-scale (socio-technical systems on a national, international, transnational, or global scale) or locally scaled systems with either technology-driven or scope-driven complexity [49]. In the context of digital transformation, SysEng role is to creatively link the problem and operation domains of the digitally transformed system (real twin) with the corresponding model (virtual twin) that reflects the current DgT context and stage.  
The emerging concept of integrated cyber-socio-technology-system-of-systems (CST-SoS) transforms traditional Systems Engineering from a multidisciplinary to a transdisciplinary research domain and thereby justifies our approach to DgT support through the hyper-framework. Additionally, with the proliferation of Artificial Intelligence (AI) and machine learning (ML), the autonomous system emerges as an engineered system that leverages AI algorithms to perform specific tasks with or without human involvement.  
Consequently, the role of Systems Engineering in the specification of integrated tailorable life cycle models reflects concepts, methods, and tools that enable the creation of problem and operation domains vision of a virtual twin, aka Systems Engineering Vision, that frames the global aspects of contemporary DgTs. According to the conceptual framework model proposed in [42] (Figure 4), Systems Engineering maps to the Problem and Operation helixes.
## 5 Evaluation of Wireless Power Harvesting Metrics
### Sensitivity  
The sensitivity is the measuring of the required minimum power to activate the system operation, which is expressed as follows:  
\[\text{Sensitivity }(\text{dBm})=10\log_{10}\biggl{(}\frac{P}{1 mW}\biggr{)} \tag{5}\]  
where \(P\) represents the smallest amount of power required to complete a task by the system. Impedance matching networks are one approach to increase the sensitivity of the RF-to-DC rectifier circuit [66]. Improving sensitivity with regulation functions using a dc/dc converter leads to better adjustment in various RF energy harvesting applications [67]. The sensitivity, which is affected by the threshold voltage of the metal-oxide-semiconductor (CMOS) technology, is higher at the lower CMOS threshold but also causes more leakage current, leading to reduced overall efficiency [68].
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 3 Theoretical Background
### Feature Extraction  
The captured traffic traces required preprocessing before being subjected to the prediction stage. There are many rules regarding preprocessing, but it remains an experiment-intensive art. In our case, the decent amount of data did not mean that the LSTM ANNs provided more accurate prediction. For example, large quantities of data can be relatively similar or correlated, which limits their contribution to ANN learning. As mentioned in Section 3.1, the statistical characteristics of our data changed over time, which is called concept drift. Hence, our task was to identify and group segments of the traces that had similar characteristics to build separate models for them. The individual trace segments could be labelled based on the location of the measurements to facilitate grouping according to additional domain knowledge. However, in reality, there are no such labels, so we propose an automatic mechanism based on feature extraction.  
A feature is a lower-dimensional representation mapping  
\[\phi:R^{w}\to R,\quad\phi(X_{i,w})=\phi([x_{i-w+1},\dots,x_{i}]),\]  
which captures a specific aspect of the time series \(X=\{x_{1},x_{2},\dots,x_{i},\dots\}\). In our work, we used the mean and variance of \(\phi(X_{i,w})\) because they are the primary measures applied in data analysis. The optimal feature selection and number is a complex topic. It depends on the dataset, clustering method, and predictive model. While there are some methods regarding feature selection for a few selected clustering algorithms [39], no such guidelines exist for an ANN ensemble. In other words, the optimal clustering produced by a set of features may not necessarily be the optimal training set for a predictive model. Other examples of features are maximum/minimum, the number of peaks with a certain steepness, their periodicity, or a global trend. For the feature extraction, we used _tsfresh_[40]. According to the technical documentation, _tsfresh_ provides 63 time series characterization methods and computes a total of 794 time series features.
# A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging
## 1 Introduction
Moreover, high accuracy superresolution results can be obtained by using the complex signal recovery deconvolution because no modulus or approximate operators are needed. Then, based on the model, the \(l_{p}\)-norm sparse constraint is considered as the prior information to reconstruct the target distribution. The forward-looking imaging is often applied to the important ground and sea surface target location and tracing, obstacle avoidance of helicopters, etc. The targets of interest we often focus on are sparse for the whole imaging region. The problem of reconstruction for sparse targets has been deeply discussed, and several sparse approaches have been developed in array signal processing, SAR and Inverse SAR (ISAR) imaging, etc. [26; 27; 28; 29]. Besides, the existing sparse methods demonstrate that the sparsity constraints also have denoising capability [30; 31]. With these motivations, we develop a sparse Bayesian method by adopting the \(l_{p}\)-norm (\(0<p\leq 1\)) sparse constraint. The advantage of the \(l_{p}\)-norm has been discussed in the related work on the sparse learning via iterative minimization (SLIM) method [23]. It shows that the \(l_{p}\)-norm could realize a high sparse result compared to the traditional \(l_{1}\)-norm [23; 32], which intends to provide higher resolution and reject false targets. With an accurate signal model and prior information, the proposed sparse Bayesian approach provides better superresolution performance than the conventional Bayesian approaches. This paper is organized as follows. In Section 2, the received signal model for the forward-looking scanning radar is constructed. Then, some techniques are taken to preprocess the received signal. We describe the forward-looking signal model of forward-looking scanning radar as a matrix operation. In Section 3, the likelihood function and prior knowledge are combined to make the objective function converge to a suitable solution. In addition, the derivation of the objective function is provided. In Section 4, the simulations and real measured data are given to show the superresolution performance of the proposed algorithms. Conclusions are provided in Section 5.
## 3 Results and Discussion  
### PSD Results  
#### 3.1.1 Dry PSD  
The materials' dry phase PSD results and analysis were conducted on the three different granular materials of LCMs (walnut, graphite, and marble). The five particle size distributions (Ds) and the span (the difference between D\({}_{90}\) and D\({}_{10}\) divided by D\({}_{50}\)) of all LCMs are shown in Table 4, and their cumulative distributions are plotted in Figure 1. Walnut showed much larger particle sizes than graphite and marble, with a median particle size of 2060 \(\upmu\)m, which is around 4 times larger than the median particle sizes of graphite and marble particles (445 \(\upmu\)m and 545 \(\upmu\)m, respectively). Furthermore, graphite showed the largest span of 1.31, indicating a much wider particle size distribution than walnut and marble (0.75 and 0.78, respectively).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **D\({}_{10}\) (\(\upmu\))** & **D\({}_{25}\) (\(\upmu\))** & **D\({}_{50}\) (\(\upmu\))** & **D\({}_{75}\) (\(\upmu\))** & **D\({}_{90}\) (\(\upmu\))** & **Span** \\ \hline Walnut & 1463 & 1665 & 2060 & 2578 & 3014 & 0.75 \\ Graphite & 189 & 306 & 445 & 593 & 774 & 1.31 \\ Marble & 352 & 442 & 535 & 662 & 770 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dry cumulative distributions values for all granular materials.
###### Abstract  
The fluctuating pressure acting on the radial gate in the high-head flood discharge outlet is the main excitation source of flow-induced vibration. Therefore, this paper delves into the distribution characteristics of fluctuating pressure on the panel of the high-head submerged radial gate based on hydraulic model tests. Hydraulic tests were first conducted to obtain the distribution patterns of time-averaged pressure and the root mean square (RMS) of fluctuating pressure on the radial gate panel. Secondly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and HHT method was employed to identify the causes of the fluctuating pressure on the gate panel. Finally, the ICEEMDAN-SSA (the sparrow search algorithm)-LSTM (long short-term memory) method was utilized to achieve accurate prediction of the fluctuating pressure on the gate panel. The results show that the time-averaged pressure in the middle of the gate panel is higher than that at the top and near the bottom edge, which differs significantly from the static pressure distribution. The RMS of the fluctuating pressure near the bottom edge is higher than that in the middle and at the top. The fluctuating pressure acting on the gate panel in the time domain can be regarded as a stationary process. The fluctuating pressure on the gate panel is caused by the combined diffusion and random mixing of multi-scale vortices in the turbulent eddy structure. The ICEEMDAN-SSA-LSTM combined method significantly improves the prediction accuracy of fluctuating pressure on the gate panel compared to the LSTM and ICEEMDAN-LSTM methods.  
flood discharge outlet; submerged radial gate; fluctuating load; time-averaged pressure; LSTM +
Footnote †: journal: Applied Sciences
## 4 Results
### Summary of Results  
Based on the analysis of the 32 experiments utilizing different topic modeling techniques and pre-processing steps, BERTopic emerged as the best-performing model in terms of topic coherence and diversity, despite its higher training time compared to NMF and LDA. Among the embeddings tested for BERTopic, the CAMeL-Lab/bert-base-arabic-camelbert-da embedding consistently achieved the highest coherence and diversity scores, particularly after applying cleaning to the data set. The qarib/bert-base-qarib embedding also performed well, especially in achieving high topic diversity. These results, as shown in Figure 1, underscore the critical role of pre-processing techniques in enhancing model performance. Further improvements could be achieved by exploring hyperparameter tuning.
## 2 Analytical Model
### External Tunnel Explosion Tests  
Four steel plates with a thickness of 2 cm were welded together to assemble a rectangular tunnel for blast tests. The tunnel's dimensions were \(30\times 30\times 120\) cm. In order to remain consistent with the tunnel's form, steel plates were added above and on both sides of the tunnel to ensure that the blast pressure generated by external explosions couldsufficiently enter the portal. The external tunnel explosion tests were conducted with a subscale tunnel model and 50-175 g of spherical C4 charges. The charges were placed 120 cm away from the portal, and the center of chargewas placed 15 cm above ground. The pressure sensor was set on the right side of the wall in the tunnel at 2, 30, 60 and 90 cm away from the portal. The explosion experiments were arranged as shown in Figure 2. The portal overpressure was measured for scaled distance \(Z\) between 2.15 and 3.26 m/kg\({}^{1/3}\). The scaled distance \(Z\) is expressed as follows:  
\[Z=\frac{R}{W^{1/3}} \tag{1}\]  
where R is the standoff distance (m), and W is the mass of charge (kg).  
Figure 1: Near-surface burst experiments.  
C4 charges from a US M5A1 pack can be formed intovarious shapes that are suitable for multiple applications. M6 detonators were used and were connected to C4 explosive charges. The 137A23 free-field blast pressure pencil probe made by the US PCB Piezotronic Corporation was used to obtain the free-field blast pressure. The 137A23 pressure probe has the capability to measure the blast pressure up to 345 kPa, which is about 3.4 times the atmospheric pressure. The 113A23 pressure sensor, also from PCB, was installed inside the tunnel and had a maximum pressure capability of 103,420 kPa. The 5054A (4 channels) oscilloscope made by US Agilent Technologies was used for signal acquisition, in which the maximum bandwidth was 100 MHz and the maximum sampling rate was 4 GHz.
# Automatic Era Identification in Classical Arabic Poetry
## 3 Dataset and Methods
### Dataset  
Our dataset includes 2978 poems authored by thirteen poets (original and attributed). Table 1 presents the distribution of poems per poet. Considering the distribution, it is clear that trying to identify individual poets with such a small and imbalanced dataset is a major challenge. Hence, we decided to focus on era identification. Table 2 presents the distribution of poems per era, which is more balanced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Poet** & **Period** & **\begin{tabular}{c} **Years [All Dates** \\ **Are CE]** \\ \end{tabular} & **Main Location** & \
\begin{tabular}{c} **Known** \\ **Poems** \\ **(Original)** \\ \end{tabular} & **Attributed** &
\begin{tabular}{c} **Poorly** \\ **Attributed** \\ \end{tabular} \\ \hline
**[PERSON]**[47] & Pre-Islamic & Died in 554 &
\begin{tabular}{c} Nejad and Yemen, \\ Arabian \\ Peninsula \\ \end{tabular} & 204 & 7 & \\ \hline
**[PERSON]**[48] & Pre-Islamic &
\begin{tabular}{c} Died ca. 580 or \\ 570 \\ \end{tabular} & Southern Iraq & 25 & \\ \hline
**[PERSON] b. [PERSON]**[42] & Pre-Islamic & \
\begin{tabular}{c} Born between 520 \\ and 535 and died \\ before 622 \\ \end{tabular} &
\begin{tabular}{c} Al-Hirah, \\ Southern Iraq \\ \end{tabular} & 64 & 37 & \\ \hline
**Al-Hutay’a**[49] &
\begin{tabular}{c} _Mubadrum_ (Lived in \\ the pre- and early \\ Islamic periods) \\ \end{tabular} & Possibly as late as & & & 265 & 11 \\ \hline
**Al-Hansa’**[50] & _Mubadrum_ & \
\begin{tabular}{c} Born around 575 \\ and died between \\ 634–644 \\ \end{tabular} &
\begin{tabular}{c} Nejad and \\ Medina, Arabian \\ Peninsula \\ \end{tabular} & 185 & & \\ \hline
**[PERSON]** & _Mubadrum_ & Died ca. 688–689 & Basra, Iraq & 140 & 19 & \\ \hline
**[PERSON]**[52] & Umayyad & Died in 715 & Basra, Iraq & 44 & 76 & 25 \\ \hline
**[PERSON]** & Umayyad & 680–743 & Kufa, Iraq & 681 & 30 & \\ \hline
**[PERSON]**[54] & \
\begin{tabular}{c} Lived during the \\ Umayyad and \\ ‘Abbasid eras \\ \end{tabular} & Died in 766 &
\begin{tabular}{c} Western Nejad, \\ Arabian \\ Peninsula \\ \end{tabular} & 94 & 13 & 14 \\ \hline
**[PERSON]**[55] &
\begin{tabular}{c} ‘Abbasid \\ \end{tabular} & Ca. 756–814 & Basra, Iraq & 173 & & \\ \hline
**[PERSON]**[56] & \
\begin{tabular}{c} ‘Abbasid \\ \end{tabular} & 804–846 &
\begin{tabular}{c} Syria, Egypt, \\ and Iraq \\ \end{tabular} & 903 & & \\ \hline
**[PERSON]**[57] & \
\begin{tabular}{c} ‘Abbasid \\ \end{tabular} & Died in 826 &
\begin{tabular}{c} Kufa and \\ Baghdad, Iraq \\ \end{tabular} & 200 & & \\ \hline
**[PERSON]** &
\begin{tabular}{c} ‘Abbasid \\ \end{tabular} & 932–968 & Aleppo, Syria & 408 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of poems in each period, grouped into two main categories: pre-‘Abbasid and ‘Abbasid.
# Fluorescence Correlation Spectroscopy Measurement Based on Fiber Optics for Biological Materials
## 3 Results and Discussion
### Molecular Size Dependence  
Figure 3 shows the molecular size dependence of the ACF. The samples were fluorescein, recombinant _Aequorea coerulescens_ GFP (AcGFP), and ATTO 488 labeled proteins, bovine serum albumin (BSA), human serum albumin (HSA), and immunoglobulin G (IgG). Furthermore, we estimated the hydrodynamic diameter of the sample molecules using the Einstein-Stokes relationship:  
\[D=\frac{k_{\text{B}}T}{3\pi\eta d}, \tag{6}\]  
where \(k_{\text{B}}\), \(T\), \(\eta\), and \(d\) are the Boltzmann constant, absolute temperature, the viscosity of the solvent, and diameter of the molecule, respectively. The estimated diameters and data from the literature are compared in Table 1. Figure 3a shows the normalized ACF of each sample. The larger the molecules, the longer the decay time of the ACFs. Figure 3b compares the diameters measured by FB-FCS and those reported in the literature. The values are also listed in Table 1. The diameters obtained by FB-FCS were in good agreement with those reported in the literature, indicating that FB-FCS could measure molecular size precisely even without any mechanical adjustment of the optical setup.  
Figure 3: FB-FCS measurements on fluorescently labeled proteins. (**a**) Normalized ACFs. (**b**) Comparison between diameters measured by FB-FCS and results from the literature. The dashed line shows the \(y\) = \(x\) line. The error bar represents the standard error (\(n\) = 5–8). FL denotes the data point of fluorescein.
## 3 Experiments and Results Analysis  
### Experimental Data and Evaluation Metrics  
The network was trained using the training set from the DIV2K dataset [19], which contains 800 images from different scenes. The corresponding low-resolution (LR) images were generated by downsampling high-resolution (HR) images using bicubic interpolation (with downsampling factors of 2, 3, and 4). To test the network's performance and generalization, the model was evaluated on five standard datasets: Set5 [20], Set14 [21], BSD100 [22], Urban100 [23], and Manga109 [24].  
For evaluation, the metrics PSNR [25] and SSIM [26] were computed on the Y channel in the YCbCr color space, which was derived from the RGB space. PSNR represents the ratio of the maximum possible signal power to the destructive noise power that affects the signal's representation accuracy, typically expressed in logarithmic decibels (dB). The SSIM index is in the range of [0,1], where a higher value indicates a smaller difference between the output image and the distortion-free image, meaning better image quality.