esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Results
### Measurement System
#### 3.1.5 Supervisory Application for PD Monitoring
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Actual Locations**} & \multicolumn{3}{c}{**LS Estimated Locations**} & \multicolumn{3}{c}{**R\&S Estimated Locations**} & \multicolumn{3}{c}{**Proposed Algorithm Estimated Locations**} \\ \cline{2-10} \multicolumn{1}{c}{**Source Position**} & \(\mathbf{X}\) **(m)** & \(\mathbf{Y}\) **(m)** & \(\mathbf{X}\) **(m)** & \(\mathbf{Y}\) **(m)** & \(\mathbf{X}\) **(m)** & \(\mathbf{Y}\) **(m)** & \(\mathbf{X}\) **(m)** & \(\mathbf{Y}\) **(m)** \\ \hline Position 1 & 13.50 & 4.50 & 12.04 & 4.69 & 12.21 & 4.77 & 12.44 & 4.45 \\ Position 2 & 4.50 & 4.50 & 5.41 & 5.27 & 5.38 & 5.52 & 5.73 & 5.22 \\ Position 3 & 4.50 & 13.50 & 4.91 & 14.26 & 4.33 & 14.63 & 4.12 & 14.72 \\ Position 4 & 13.50 & 13.50 & 11.89 & 14.11 & 11.68 & 14.50 & 11.35 & 15.24 \\ Position 5 & 10.00 & 6.00 & 11.81 & 8.67 & 12.55 & 9.25 & 12.58 & 7.20 \\ Position 6 & 6.00 & 8.00 & 5.15 & 9.26 & 5.45 & 9.86 & 5.63 & 9.68 \\ Position 7 & 8.00 & 12.00 & 8.80 & 15.21 & 7.89 & 14.60 & 8.95 & 13.86 \\ Position 8 & 12.00 & 10.00 & 13.32 & 11.86 & 12.53 & 12.37 & 13.18 & 10.83 \\ Position 9 & 4.50 & \(-\)4.50 & 3.91 & \(-\)7.91 & 3.91 & \(-\)5.60 & 3.97 & \(-\)5.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of estimated versus true locations with eight measurement sensors. Figure 11: Error comparison with seven measurement sensors used. The least squares algorithm performance has improved for positions 1, 2 and 3, however, the mean error has slightly increased although not to a great extent. This is mainly due to the fact that the least squares algorithm does not optimize the PLE. For the ratio and search and the proposed algorithm, the results have improved in terms of localization accuracy for the majority of the positions with less mean error for all nine positions. For ratio and search algorithm the mean error has slightly improved i.e., from 2.06 to 2.03 an improvement of 0.03 m. For the proposed algorithm, the mean error has improved from 1.80 m to 1.76 m i.e., an improvement of 0.04 m. This shows that by increasing the number of nodes, the overall location accuracy of the PD source estimation is improved. An error comparison between the algorithms can be seen in Figure 12, where eight measurement sensors were used. Results shown in Table 8 are much improved when compared with Table 7 for ratio and search and proposed algorithms. For individual locations, the accuracy has improved when compared with seven sensors used. For the proposed algorithm, the PLE values seem to be more realistic for most of the nine positions.
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 2 Classical FCM and Modifications on Its Objective Function
### An Entropy Term  
Another modification of the objective function involves an entropy term, which would stimulate more balanced membership degrees of data instances into the clusters:  
\[J(X,\ U,\ V;\ m,\ C,\ \lambda)=\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij}^{m}d^{2} \big{(}x_{i},\ c_{j}\big{)}-\lambda\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij}{\rm log }\mu_{ij} \tag{3}\]  
This term would penalize extreme membership values (close to 1 or close to 0), so influencing the data to be more distributed into the clusters. This is of particular usefulness in scenarios where data instances are expected to belong to multiple clusters, avoiding the tendency towards one or a small number of clusters in datasets varying in density and spread of the intrinsic clusters [17]. The idea has been used in several works, mostly in the form of a cluster validity measure, but also in the form of objective function modification being optimized via Lagrange multipliers rather than based on numerical methods [18].
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 5 Conclusions  
The S3 API is the de facto standard for accessing Cloud storage; this is why it is the component of choice when building cloud-agnostic applications. By amending IO500 to benchmark the S3 interface, we broaden the scope of the IO500 usage and enable the community to track the performance growth of S3 over the years and analyze changes in the S3 storage landscape, which will encourage the sharing of best practices for performance optimization. Unfortunately, the obtained results in Section 3 indicate that S3 implementations such as MinIO are not yet ready to serve HPC workloads because of the drastic performance loss and the lack of scalability.  
We believe that the remote access to S3 is mainly responsible for the performance loss and should be addressed. We conclude that S3 with any gateway mode is not yet a suitable alternative for HPC deployment as the additional data transfer without RDMA support is pricey. However, as the experimentation in Section 4 shows, an embedded library could be a viable way to allow existing S3 applications to use HPC storage efficiently. In practice, this can be achieved by linking to an S3 library provided by the data center.  
By introducing S3 Embedded, a new light-weight drop-in replacement for libs3, we investigate the cause of the performance loss while providing a road toward Cloud-HPC agnostic applications that can be seamlessly run in the public cloud or HPC.  
Figure 10: IO500 results of different runs using 5N-20 PPN.
## 4 Experiment, Results and Discussion
### Experiment Setup  
To ensure the successful execution of the experiment, an appropriate environment, including all necessary equipment and tools, was prepared. Table 2 shows the devices and tools that were utilized in the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NO.** & **Tools/Devices** & **Description** \\ \hline \multirow{4}{*}{1} & Samsung Galaxy A7 2016, Model no.: SM-A710 FD & \multirow{4}{*}{A smart mobile phone used in the experiment} \\  & Android Ver.: 7 0 Kernel3.10.61-14301096K & \\  & Issued No.: NRD90M.A710 FXXU2 CRL1 & \\ \hline \multirow{4}{*}{2} & Final Mobile Forensics & \multirow{4}{*}{Forensic tool: used for extraction and analysis} \\  & User version: & \\ \cline{1-1}  & File version is 2020.04.22. & \\ \cline{1-1}  & CDF version is 2020.04.22. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental tools.  
Figure 4: Converting.ab file to.tar file.  
Rooting the Smartphone  
Before proceeding with data retrieval, it was imperative to root the Android smartphone. Rooting grants the investigator \"superuser\" privileges, enabling physical acquisition. The rooting process was carried out as follows:  
1. Installing Odin 3.13.1 software on the workstation.
2. Enabling \"Developer Mode\" by tapping the build number seven times, activating USB debugging mode, and unlocking the Original Equipment Manufacturer (OEM) controls on the smartphone, as demonstrated in Appendix A Figure A6.
3. Powering off the smartphone, followed by simultaneously pressing and holding the Power, Volume Down, and Home buttons until a warning message appeared. Then, pressing the volume up button allowed the smartphone to enter \"Download Mode,\" facilitating communication with Odin3.
4. Connecting the smartphone to the workstation via a USB cable.
5. Running Odin3 software as an \"Administrator.\" The program verified the connection between the smartphone and the workstation when the message \"Added\" appeared, as depicted in Appendix A Figure A7.
6. Selecting the root file by clicking on \"AP,\" as illustrated in Appendix A Figure A7. This file typically carries an extension of.tar.md5 and must be compatible with the smartphone's make and Android version.
7. Initiating the rooting process by clicking the Start button.  
Upon successful completion of the rooting process, the BusyBox tool, which is a set of Linux commands, was installed to allow the performance of actions on Android with \"superuser\" privileges. The tool can only be installed on rooted devices and can be downloaded from the Play Store.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Titanium Dioxide Nanoparticles  
Titanium is a commonly used metal for the manufacturing of implants. In addition to this application, it can be used in the form of titanium dioxide nanoparticles (TiO\({}_{2}\)-NPs), which can be produced in various morphologies such as nanowires, nanorods, nanotubes, and nanoparticles [149].  
Titanium dioxide can be used in the preparation of silk fibroin (SF) based polymer scaffolds as a nanofiller. SF/TiO\({}_{2}\) scaffolds were prepared with different concentrations of TiO\({}_{2}\) (5, 10, 15, 20 wt%). Increasing the content of TiO\({}_{2}\)-NPs led to reduced porosity in scaffolds. This may be caused by the agglomeration of nanoparticles on the pore walls. The scaffold also showed improved mechanical and directional strength due to increased TiO\({}_{2}\)-NPs content. Testing biocompatibility with the SaOS-2 cell line showed high adhesion and proliferation. The viability of cells increased with growing SF content [150].  
Another way to use TiO\({}_{2}\)-NPs is the preparation of polymer-inorganic nanoparticle composite membranes. [PERSON] et al. prepared composite membranes of polysulfone-silica microfiber grafted with TiO\({}_{2}\)-NPs (PSf-SiO\({}_{2}\)-TiO\({}_{2}\)). Microspheres of TiO\({}_{2}\)-NPs 1-3 \(\mathrm{\SIUnitSymbolMicro m}\) in diameter were attached to silica microfiber using the sol-gel method. The final nanocomposite was prepared through the inversion phase method by evaporation. PSf-SiO\({}_{2}\)-TiO\({}_{2}\) nanocomposite membranes showed better flux and mechanical characteristics, retention,and chemical oxidation resistance than the membranes without TiO\({}_{2}\)-NPs. Moreover, scaffolds were tested for antimicrobial properties and proved to have an inhibitory effect on five Gram-negative microorganisms and stopped the proliferation of _Candida albicans_. Results of this study showed that PSF-SiO\({}_{2}\)-TiO\({}_{2}\) composite membranes could be applied as a functional barrier in guided bone regeneration [151].  
On the other hand, the degradation of titanium-based implants in the human body causes the release of ions and solid debris of TiO\({}_{2}\)-NPs, leading to peri-implant inflammatory reactions. In the in vitro study, the effect of titanium nanoparticles on MSCs and fibroblast was evaluated by the MTT test and production of ROS. It was found that TiO\({}_{2}\)-NPs caused a time-dependent decrease in cell viability and high production of ROS. Histological and molecular analysis of peri-implant tissue showed the occurrence of ROS, high levels of titanium particles, and enhanced bone turnover. There was also found to be a high expression of zinc finger protein (ZFP467) and the presence of adipose-like tissue. This suggests that the MSCs were dysregulated [152].
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 3 Parameters to Be Considered for the Design and Analysis of a Flexible Antenna
### Specific Absorption Rate (SAR)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Body Parts** & **Head** & **Chest** & **Abdomen** & **Upper Arm** & **Forearm** & **Thigh** & **Lower Leg** \\ \hline Skin (Thickness in mm) & 4 & 2 & 2 & 2 & 2 & 2 \\ \hline Fat (Thickness in mm) & - & 4 & 7 & 6.1 & 4.3 & 8.8 & 6.3 \\ \hline Muscle (Thickness in mm) & 9.5 & 38 & 25 & 20.3 & 14.9 & 28.7 & 20.8 \\ \hline Bone (Thickness in mm) & 20.5 & 46 & - & 9.1 & 6.3 & 13 & 9.4 \\ \hline Grey Matter (Thickness in mm) & 51 & - & - & - & - & - & - \\ \hline Heart (Thickness in mm) & - & - & 56 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different tissue layers and their thickness in different parts of the body (Reprinted with permission from ref. [43]. 2018 [PERSON] et al.). having different dielectric constant values, in turn, affects the antenna performance. Hence, these considerations should be considered while designing a wearable antenna.
## 3 Results
### Determination of the Rate Coefficient  
In the preliminary experiments performed in the absence of UV light and/or in the absence of Cl\({}_{2}\), different loss processes for T2M2B (\(k_{\text{loss}}\)) and the two reference compounds (\(k_{\text{ref,loss}}\)) were investigated and considered in the kinetic data analysis. The pseudo-first order rate coefficient in this case is given by  
\[k_{\text{loss}}=k_{\text{w}}+k_{\text{h}\text{v}}+k_{\text{Cl}_{2}}[\text{Cl} _{2}]. \tag{4}\]  
Wall losses (\(k_{\text{w}}\)) for T2M2B and the two reference compounds were observed, whereas UV photoinduced processes (\(k_{\text{h}\text{v}}\)) were perceptible only for T2M2B, and the dark reaction with Cl\({}_{2}\) (\(k_{\text{Cl}_{2}}\)) was detected only for isoprene. \(k_{\text{w}},k_{\text{h}\text{v}}\), and \(k_{\text{Cl}_{2}}\) were quantified in each case and are summarized for the three compounds in Table 1. These losses contributed around 1-10% to the total decay of T2M2B and the reference compounds.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & \(k_{\text{w}}\)**/10\({}^{-6}\) s\({}^{-1}\)** & \(k_{\text{h}\text{v}}\)**/10\({}^{-4}\) s\({}^{-1}\)** & \(k_{\text{Cl}_{2}}\)**/10\({}^{-20}\) cm\({}^{3}\) molecule\({}^{-1}\) s\({}^{-1}\)** \\ \hline _Trans_-2-methyl-2-butenal & \(8.40\pm 5.33\) & \(8.33\pm 5.60\) & - \\ Cyclohexane & \(0.400\pm 0.417\) & - & - \\ Isoprene & \(3.78\pm 1.65\) & - & \(2.70\pm 0.15\) \\ \hline \end{tabular}
\end{table}
Table 1: Rate coefficients for the different loss processes observed for the compounds used in the kinetic study. Errors are \(\pm 2\sigma\).  
In Figure 1, the loss of T2M2B with respect to that for the reference compound is plotted for all the experiment series carried out with each reference compound. Plots of \(ln([\text{T2M2B}]_{0}/[\text{T2M2B}]_{\text{t}})\)-\(k_{\text{loss}}t\) versus \(ln([\text{Ref}]_{0}/[\text{Ref}]_{\text{t}})\)-\(k_{\text{Ref,loss}}t\) show a good linearity, indicating that there were no kinetic complications. According to Equation (1), \(k/k_{\text{Ref}}\) was obtained from the slope of these plots. Then, the rate coefficient of reaction (R1), \(k\), was determined at room temperature considering the rate coefficients of the reference compounds previously reported for the Cl reaction of cyclohexane and isoprene [21; 22]. The individual rate coefficients \(k\) are summarized in Table 2 for the two reference compounds. The averaged value of \(k\) was determined to be \((2.45\pm 0.32)\times 10^{-10}\) cm\({}^{3}\) molecule\({}^{-1}\) s\({}^{-1}\). The \(\pm 2\sigma\) uncertainty in \(k\) includes the propagation of the reported errors in \(k_{\text{Ref}}\), the statistical errors from the slope of the plots shown in Figure 1, and the uncertainties in \(k_{\text{loss}}\).
## 1 Introduction  
The era of optical fibre sensors (OFSs) started 50 years ago with the granting of the Fotonic sensor (U.S.03327584; 27 June 1967) [1], almost together with the advent of fibre-optic communication technology.  
The many features offered by OFSs that make this technology surpass conventional ones have been widely addressed by several review papers, and include the immunity to electromagnetic interference, minimal invasiveness and lightweight, multi-parameters sensing, ease of multiplexing, and remote powering/interrogating capabilities. Furthermore, it is the only technology that enables the distributed monitoring of some physical fields (e.g., strain, temperature) along the fibre (i.e., with spatial continuity of the measurands). This particular type of OFS is called a distributed optical fibre sensor (DOFS) and exploits scattering processes occurring in the fibre. The features mentioned above along with this unique ability of DOFSs made them perfect candidates for sensing applications in harsh environments characterised by large geographical extension and requiring a high spatial density of sensing points like geo-hydroological monitoring. Early applications of DOFSs into geo-hydroological monitoring can be dated to more than 25 years ago, first with distributed temperature measurement campaigns in soil levees and embankments, but other examples regarding slope stability and landslide monitoring by distributed strain sensing soon followed.  
In the following sections, the application of DOFSs to some critical geo-hydroological processes will be presented and discussed. A great effort has been undertaken to include as many significant papers as possible, but at the same time, we are aware that many others have been published about these topics. Ultimately, the included references have been carefully chosen to support the understanding of key issues and potentials of DOFS technology in this field and with the aim of offering a comprehensive review of the use of DOFSs in practical geo-hydroological applications, both in-situ and in physical models.  
Firstly, the fundamental principles at the basis of distributed optical fibre sensing are introduced with a brief review of the acquisition techniques that are currently implemented in DOFSs. In particular, the three different processes employed in DOFSs--namely Raman, Brillouin, and Rayleigh scattering--are addressed separately, discussing their respective weaknesses and strengths.  
In the second part of the paper, the application of DOFSs to geo-hydroological monitoring is reviewed and discussed. The review is about the monitoring of levees, slope and subsidence processes causing the main common geographic hazards. Other minor applications are not considered in this review. Similarly, the paper includes neither approaches where distributed techniques are used only to interrogate concatenations of single point sensors [2,3] nor transducers inducing losses at discrete points along the fibre (e.g., employing bending) [4,5].
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 3 Scenarios for Variability and Uncertainty
### Scenario Generation and Sensitivity Analysis
The value of the uncertainty (i.e., the net load forecast error) is set as a particular percentage for the fixed case: i.e., S1, S3, and S5. For the system conditions, complementarity affects the variability of the system, e.g., if the outputs of two wind power systems with the same capacity are perfectly negatively correlated, i.e., the output of one system increases (decreases) while that of another system decreases (increases) at the same rate, the added variability becomes zero. On the other hand, if they are perfectly positively correlated, the added variability is doubled. The complementarity changes the variability, and thereby the flexibility. Related works were performed in [7]. The correlation between outputs of renewable energy resources may affect the complementarity. In this study, the penetration levels of renewable energy resources in each region are kept the same for every scenario; thus, the complementarity effect was fixed throughout the scenarios. The increased variability and uncertainty were varied within a given range, which may also differ according to the system conditions. Through the sensitivity analysis, the effects of variability and uncertainty on flexibility can be compared, and the most influential factor determined. The process of scenario generation is summarized in Figure 3.
## 3 Methodology
### Fusion Steps
When the maximum number of iterations is reached, the iteration is stopped and then a fused image \(FA\) can be obtained. More specific details about component fusion can be found in Section 3.3. (5) Image Reconstruction. The main purpose of this step is to substitute the final \(\alpha\) channel, \(F\) and \(B\), into an image matting model to obtain the final fused image. A fused image \(FB\) is reconstructed through a linear combination of \(SS\), \(LS\), and \(FA\), as shown in Formula (28). Then, \(FB\) is utilized as the last \(\alpha\) channel. According to Formula (1) in Section 2.1, the final fusion result \(HD\) is calculated through combining the final \(\alpha\) channel, \(F\), and \(B\), as shown in Formula (29). \[FB=SS+LS+FA \tag{28}\]  
\[HD=FB\times F+(1-FB)\times B \tag{29}\]  
Finally, the proposed method is summarized in the following Algorithm 1. ```
Input: low resolution MS image and high resolution PAN image Output: high resolution MS image
1: Calculation the Intensity Component A WLF-based fusion method is utilized to fuse all the bands of MS image to generate an intensity component \(IC\). 2: Spectral Estimation Setting \(IC\) is the original \(\alpha\) channel, a foreground image \(F\) and a background image \(B\) are obtained in accordance with Formula (2). 3: Multi-scale Decomposition Based on WMCF and GF, PAN is decomposed into three different scales by Formulas (30)-(34): large scale image \(LS\), small scale image \(SS\) and basic image. 4: Component Fusion A MSMDM-guided PAC-PCNN fusion strategy is used to fuse the basic image and \(IC\). Then, a fused image \(FA\) can be obtained. 5: Image Reconstruction A fused image \(FB\) is reconstructed through Formula (28). \(FB\) is utilized as the last \(\alpha\) channel. According to Formula (1), the final fusion \(HM\) result is calculated through combining the final \(\alpha\) channel, \(F\), and \(B\). 6: Return\(HM\)
```  
**Algorithm 1**A WMCF-based pan-sharpening method
# Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN
###### Abstract  
Universal circuit breakers are crucial devices in power systems, and the accuracy of their fault diagnosis is vital. However, existing diagnostic models suffer from low feature extraction rates and low diagnostic accuracy. In this paper, we propose a novel approach for fault diagnosis of universal circuit breakers based on analyzing vibration signals generated during the closing operation. Firstly, the vibration signal was decomposed into multiple modal components using Variable Mode Decomposition (VMD), and the modal components were subjected to time and frequency domain feature extraction. Then, the extracted features were fused and normalized to construct a training dataset for the proposed model. We propose a Deep Belief Network (DBN) diagnostic model based on the Whale Optimization Algorithm (WOA), where the WOA is employed to optimize the hyperparameters of the DBN. Experimental results demonstrate that the proposed VMD and WOA-DBN model achieved an average accuracy of 96.63%. This method enhanced the accuracy of feature extraction from vibration signals and outperformed traditional diagnostic models when using a single vibration signal for fault diagnosis of universal circuit breakers. It provides a novel solution for early fault diagnosis of universal circuit breakers.  
variational mode decomposition; whale optimization algorithm; deep belief network; universal circuit breaker fault diagnosis +
Footnote †: journal: Applied sciences  
0000-0001-9980-3399-2227]Xinyang Cheng  
1 School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China; [EMAIL_ADDRESS] (G.L.); [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (T.Z.); yangdaoxuan09158163.com (D.Y.)  
2
## 5 Results
### Object Location Estimation Evaluation  
In Section 3.6, we described two methods to estimate the GPS coordinates of the detected objects. To evaluate these models, we obtain the ground truth coordinates of ten  
Figure 6: Example of object tracking in consecutive frames with the ByteTrack method [41]. (**a**) First frame. (**b**) Second frame. (**c**) Third frame. (**d**) Fourth frame.  
Figure 7: F1 score of the tracking model at various emulated speeds.  
different urban objects and apply the two location estimation methods. Using the predicted and actual points, we calculate the error for each object and average them for each method.  
Table 3 shows the evaluation results for the two location estimation methods. As expected, the _depth_ is more accurate with only a 3.54 m average distance from the ground truth locations. On the other hand, the _fast_ method makes more coarse estimations with a 7.47 average error but requires no computation-intensive processing.