esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Index of Surrounding Rock during Deep Rock Excavation Considering Energy Release Speed
###### Abstract  
Rockburst is a kind of dynamic rock failure process that is easily induced by the excavation of a high-stress rock mass. However, from an energy perspective, the existing indexes for rockburst tendency have a limitation in that they do not consider the energy release speed. In this study, energy release effectiveness was proposed based on the local energy release rate while considering the influence of the energy release speed. The index can evaluate the stability of surrounding rock during the excavation of high-stress rock. The index can be obtained by recording the strain energy density of every element in the model during excavation and identifying the maximum strain energy density (\(E_{\text{imax}}\)), the minimum strain energy density (\(E_{\text{imim}}\)), the maximum time (\(t_{\text{imax}}\)), and the minimum time (\(t_{\text{imin}}\)) to calculate energy release speed and energy release effectiveness. A case study of the excavation of an experimental tunnel, namely, the URL of AECL, was adopted to validate the index. The results indicated that the proposed index can clearly identify the location and strength of the impact tendency area, and it can be effectively applied to the stability analysis during the excavation of deep tunnels.  
stability analysis; high in situ stress; rockburst; energy release speed; energy release effectiveness +
Footnote †: journal: Applied Sciences
###### Abstract  
The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett-Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.  
harmonic detection; DFT; DWT; Bartlett-Hann window +
Footnote †: journal: Applied Sciences
## 2 System Design and Method
### Gesture Recognition Procedure
testing accuracies obtained are reported in Figure 5. The prediction performance of the learning-based network was analyzed using the 20% test dataset, as shown in Figure 5. The matrix shows that more intra-axial and intra-radial misclassifications were obtained by the learning-based network. For instance, while 81.21% of 2960 push data samples were correctly classified, 7.25% were misclassified as pull motions, whereas none were misclassified as either of the radial motions. A similar result was obtained for the two radial motions. However, extreme misclassifications were observed at very low percentages; for example, 0.08% of the pull motions were predicted to be rotate-clockwise samples, and 0.09% of the rotate-clockwise motions were recognized to be pull samples by the learning-based network. Overall, the deep learning algorithm exhibited an acceptably high-test accuracy of 87.38%. Furthermore, the usability of data from the piezoresistive tactile sensor in different existing classification methods was observed to investigate the possibility of obtaining reliable hand motion recognitions with high accuracies. We compared the learning-based network with five commonly used data classification methods, namely, support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), neural network, and k-nearest neighbor. The recognition accuracies obtained using each method for the four hand motions are listed in Table 1. It can be seen from the table that the deep learning algorithm demonstrated values in the same range as the conventional learning algorithms, while the learning-based network arbitrarily introduced in this study showed the highest recognition rate. It should be noted that proper tuning of the parameters can improve the prediction performance of the models for the hand motion data. Figure 5: Prediction performance of the deep learning network with four typical hand motions used during intravascular catheterization. In summary, the contacting and releasing actions of the piezoresistive tactile sensor with an object can instantaneously convert finger tactile stimulus into notable real-time electrical signals, which can be used to train machine and deep learning networks for prediction tasks. These outputs can be varied according to the loading/holding contacting fingers and releasing fingers. We demonstrated how our sensory system combines four complex tasks, specifically, pushing, pulling, left rotating, and right rotating.
## 3 Results and Discussion
### SEM Scanning  
Figure 4(Aa,Ab) shows the SEM image of FeNP prepared. The FeNP appear to have a particle size within a range of around 200 nm. The observed SEM images in Figure 4(Aa,Ab) clearly reveal FeNP aggregation, which is caused by magnetic dipole-dipole interactions of the isolated iron nanoparticles.  
Figure 4(Ac,Ad) shows the TEM image of FeNP. It is clear that FeNP tends to aggregate into irregular morphology. However, Figure 4(Ad) shows some dispersions of the particles with an average particle size around 39 nm as obtained by TEM analysis via ImageJ j software--shown in Figure 4B. It is apparent from the figure that 54.8% of the particles have a size less 25 nm and 2.3% have a size greater than 100 nm. On the other hand, the particle size distribution of the prepared iron nanoparticles obtained by the dynamic light scattering method is shown in Figure 4C. The results indicate that the particle size distribution of the iron nanoparticles prepared is with an average size of 230 nm. It is known that the particle size distribution obtained by TEM is more realistic than that obtained by DLS. Since TEM measures the actual size, DLS measures the hydrodynamic size. [PERSON] et al. [21] reported an average iron particle diameter of 16-20 nm using leaf extract of this plant. The above results indicate that the iron nanoparticles are entirely within the nanoscale domain.  
Figure 4: \(Cont.\)  
Figure 3: (**A**) FTIR spectrum of FeNP and ANE; and (**B**) Energy Dispersive Spectroscopy (EDS) analysis of the synthesized FeNP.  
The XRD technique was used to identify the structure of the prepared iron nanoparticles. The observed XRD pattern shown in Figure 5 revealed the existance of diffraction peaks with 20 values of 24.14, 33.14, 40.86, and 39.28 corresponding to the hkl values of 012, 104, 113, and 006, respectively that denotes crystalline phase of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)-NP (JCPDS card No. 01-1030).  
Figure 4: **(A) Scanning electron microscopy (SEM) image at a magnification of 15 \(\times\) 10\({}^{4}\) (a), and 25 (b), transmission electron microscopy (TEM) image at a magnification of 4 \(\times\) 10\({}^{5}\) (c), and 2 \(\times\) 10\({}^{5}\) (d) for FeNP; (B) FeNP particles size distribution obtained by Image j analysis of TEM images, and (C) FeNP particles size distribution obtained by dynamic light scattering (DLS) analysis.**
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 6 Conclusions
### Limitations  
Despite the clear improvements achieved in the experiment used in this study, there are limitations to consider for implementing the proposed solution in practical applications.  
Sufficient training data need to be available or collected for every scenario that requires a separate model. By using first- or second-order polynomial models, a limited amount of training data can suffice to perform training.  
The different environment states need to reoccur in a stable way. This is the case with a rail vehicle in a station but can be more challenging with more flexible scenarios; for example, for different parked cars, a small car compared to a large truck could have a different impact on the attenuation. Evaluating the trained model on unseen data can provide insight into how well the model will behave in multiple scenarios.  
In the presented experimental setup, we mounted a multitude of sensors on the walls and ceiling of the subway station. This is possible in indoor environments, but outdoor environments often offer fewer options for mounting sensors. This will require more thought to find a suitable combination of nodes that minimizes the impact created by the dynamic environmental factor.
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Floating Photovoltaics  
Floating photovoltaics (FPV) consist of the deployment of PV modules on water surfaces [20]. The most common surfaces envisaged for this application are man-made water surfaces such as irrigation dams, industrial basins, water treatment plants or hydropowerreservoirs. Floating photovoltaics have also been deployed on natural waters like lakes and offshore sea locations (mostly at low wave categories). This PV application takes advantage of the cooling effect coming from the water beneath the PV modules [21; 22] and easy installation while contributing to water evaporation and algae growth reduction [23]. The current installed capacity in Europe is close to 0.5 GWp, while global installations have reached 2 GWp.  
In addition to the multiple benefits of FPV, they can exploit the already established grid connection when coupled with hydropower (or installed on dam surfaces) or wind energy [24]. In the case of earthen dams, the PV installation can protect the surface and minimize erosion caused by rain [25]. The coverage of only 10% of the EU's reservoir (i.e., man-made) area can generate approximately 140 TWh (160 GWp), which corresponds to approx. 7% of the EU's total electricity consumption in 2022 [26].  
Currently, most FPV system designs are optimized for low wave categories (TRL 6-8 for wave categories 1-2) [19]. The design and development of FPV systems for high wave categories (TRL 3-4 for wave category 3-4) is still a challenge and ongoing. The overall lifetime of FPV systems and reliability aspects are another issue, which are under investigation and need further development.
# Versatile Video Coding-Post Processing Feature Fusion:
###### Abstract  
Advanced video codecs such as High Efficiency Video Coding/H.265 (HEVC) and Versatile Video Coding/H.266 (VVC) are vital for streaming high-quality online video content, as they compress and transmit data efficiently. However, these codecs can occasionally degrade video quality by adding undesirable artifacts such as blockiness, blurriness, and ringing, which can detract from the viewer's experience. To ensure a seamless and engaging video experience, it is essential to remove these artifacts, which improves viewer comfort and engagement. In this paper, we propose a deep feature fusion based convolutional neural network (CNN) architecture (VVC-PPFF) for post-processing approach to further enhance the performance of VVC. The proposed network, VVC-PPFF, harnesses the power of CNNs to enhance decoded frames, significantly improving the coding efficiency of the state-of-the-art VVC video coding standard. By combining deep features from early and later convolution layers, the network learns to extract both low-level and high-level features, resulting in more generalized outputs that adapt to different quantization parameter (QP) values. The proposed VVC-PPFF network achieves outstanding performance, with Bjontegaard Delta Rate (BD-Rate) improvements of 5.81% and 6.98% for luma components in random access (RA) and low-delay (LD) configurations, respectively, while also boosting peak signal-to-noise ratio (PSNR).  
V 2024 14 8276. 10.3390/app1488276
## 9 Conclusions  
The following summarizes the impact of Stuxnet on cyber-attacks and cyber-defenses:  
* _Cyber-weapons_: Stuxnet will likely inspire, accelerate, and serve as a building block for the development of new cyber-weapons that target ICS devices. These weapons will also be influenced by the growing interest of the cyber-security research community in ICS security, which has led to recent releases of vulnerability information and exploit tools that can be used against ICS devices, but Stuxnet could push development towards more complex tools and effects. Some of these tools, like Stuxnet, could be precisely targeted.
* _Cyber-warfare_: Stuxnet could be a forbearer of the way nation-states use cyber-warfare, offering militaries a weapon that may be morally superior to a kinetic one, such as a bomb, when it incurs less harm and risk than the kinetic weapon while achieving the same objective. Stuxnet has also influenced academic and policy discussions about cyber-warfare, in part by offering a case study based on an actual incident.
* _Cyber-terrorism_: While terrorists have expressed some interest in Stuxnet-like attacks, they appear to lack the capabilities and resources to conduct them or other devastating cyber-attacks. At least in the near-term, Stuxnet has had little impact on cyber-terrorism, though it has been cited to draw attention to the threat.
* _Cyber-activism_: Some hacktivists might use tools derived from Stuxnet or other ICS weapons to conduct a cyber-attack against an ICS, but most are likely to prefer tools that are simpler and less likely to physically harm persons or the environment.
* _Cyber-crime_: Criminals might use Stuxnet-like weapons against an ICS in order to extort money from the owner or to sabotage it out of revenge, but most are likely to prefer simpler tools that offer a greater financial return on their efforts.
* _Cyber-pranks_: Some hackers like to fool around with cyber-weapons, in some cases deploying them without regard for or an understanding of potential consequences. Weapons derived from Stuxnet or other ICS exploits could attract some of these hackers, who might cause serious harm in the process of trying them out.
* _Cyber-defense_: At least in the US, the response to Stuxnet appears to be following general trends in this area, which include increased attention to ICS security, as well as cyber-security more generally. Stuxnet does not appear to have shifted the US cyber-defense strategy.  
Thus, Stuxnet's greatest impact may lie in the domains of cyber-weapons and cyber-warfare. It could be a prelude to a new class of cyber-weapons against ICS devices and to methods of achieving national security objectives that are less damaging and risky than kinetic strikes.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
###### Abstract  
The increase of renewable energy usage in the last two decades, in particular photovoltaic (PV) systems, has opened up different solar plant configurations that need to operate and properly perform in terms of efficient power transfer with respect to all of the involved components, such as inverters, grid interface, storage, and other electrical loads. In such applications, the power characteristics of the plant modules all together represent the main components that are responsible for power extraction, depending on both external and internal factors. Conventional maximum power point tracking techniques may not have a proper conversion efficiency under particular external dynamic conditions. This paper proposes an evolutionary-based maximum power point tracking algorithm suitable to operate under dynamic partial shading conditions and compares its performance with classical maximum power point tracking methods in order to evaluate their conversion efficiency in partial shading scenarios with relevant and dynamic changes in the environmental conditions. Simulations taking into account the different dynamic shading conditions were carried out to prove the effectiveness and limitations of the proposed approach with respect to classical algorithms.  
photovoltaics; MPPT algorithm; evolutionary algorithms; particle swarm optimization Article
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Rendering Device  
Table 7 shows the statistical results about the effects of rendering device on the four QoE aspects based on the Kruskal-Wallis test. It can be seen that there is no significant effect of the rendering device on the acceptability and the cybersickness (i.e., \(p>0.05\)). Meanwhile, the rendering device has significant effects with the \"small\" sizes on the presence and the perceptual quality (i.e., \(p<0.05\), \(0.06>\eta^{2}>0.01\)). This finding is contradictory to that presented in [17] that the impact of the rendering device is not significant. This contradiction can be explained by the difference of rendering device sets used in the two studies. In particular, two HMD devices of Oculus Rift and HTC Vive, which are both of high-end quality, are employed in [17]. Meanwhile, the comparative experiment in our study uses Samsung Gear VR and Google cardboard HMDs.  
Figure 11 compares the two device sets D1 and D2 in terms of presence and perceptual quality when the QP value is 22. It can be seen that the presence scores and the perceptual quality scores of device set D1 are always higher than those of device set D2 for all three contents. This result confirms that device set D1 is better than device set D2.  
From Figure 11, it is also observed that the difference between the two device sets can be clearly seen only if the quality is acceptable to the viewers. In particular, there are noticeable differences of the presence scores between the two device sets at the resolutions of 4 K, 2.5 K, and fHD (i.e., \(\geq\)0.39 MOS). Meanwhile, only small differences of the presence scores can be seen at the HD resolution (i.e., \(<\)0.25 MOS). For the perceptual quality scores, similar observ  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Aspect** & **Factor** & \(\mathbf{\chi^{2}}\) & \(\mathbf{p}\)**-Value** & \(\mathbf{\eta^{2}}\) \\ \hline _Acceptability_ & & 0.09 & 0.763 & \(<\)0.0001 \\ _Presence_ & & 24.64 & **\(<\)0.0001** & 0.011 \\ _Perceptual quality_ & & 58.1 & **\(<\)0.0001** & 0.027 \\ _Cybersickness_ & & 0.21 & 0.6429 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical results about the effects of rendering device on QoE aspects based on Kruskal-Wallis test. The bold numbers in column \(p\)-value represent to statistically significant effects of factors. The bold, italic, and underlined numbers in column \(\eta^{2}\) respectively correspond to “large”, “moderate”, and “small” effect sizes.  
Figure 10: Responses to Q5 for three contents.  
The impacts of wearing HMD (Q6) and FoV (Q7) on user experience are also investigated. The responses are showed in Figure 12. For device set D1, the scores of Q6 and Q7 are respectively 3.35 MOS and 3.46 MOS. For device set D2, the scores are 3.22 MOS for Q6 and 3.07 MOS for Q7. This result means wearing HMD and viewing through a narrow FoV are not very annoying to the viewers. However, these scores also imply that the HMDs still need to be improved.
## 4 Simulation Results
### Tree Topology WSNs  
Figure 7 shows the tree topology WSNs used in this simulation. Nodes 1 and 2 are route nodes. Networks 1 and 2 are composed of router node 1 and its end devices and router node 2 and its end devices, respectively. Each router node does not generate its own packet. Each router node generates a packet when relaying packets from the end devices. This study compares the proposed method, the DC control method considering buffer occupancy for 802.15.4-compliant homogeneous WSN, and the TMP method. We evaluate the proposed method under three scenarios. Table 3 shows the DC for each method. The proposed method uses three DC patterns since the proposed method tunes the DC according to the data rate of the subordinate nodes. Table 4 shows data rate of each node. In these tables, \(DC_{i}\) and \(DataRate_{i}\) are DC and data rate of router node \(i\), respectively.  
Figure 6: PDR of each node in the proposed method.  
Figure 7: Network topology in the simulation.  
Figure 10-12 show the PDRs of the IEEE 802.15.4, TMP, and proposed method in scenario 3. The IEEE 802.15.4 and proposed method achieve high PDR. On the other hand, as the data rate of the node increases, the PDR of the TMP decreases. The TMP method has a lower PDR, although it achieves low energy consumption.  
Figure 9: Number of packets in buffer without appropriate DC control.
## 3 Results
### Effect of Phytogenic Extracts on C. perfringens Toxin Gene Expression  
To understand the effect of phytogenic extracts on C. _perfringens_ toxins, we evaluated the expression of C. _perfringens_ (CP19 and CP240 strains) toxin encoding genes such as tpeL, Alpha toxin, and NetB. For the CP19 Alpha toxin gene, phytogenic extracts GRE, GTEC, and GTED reduced (\(p<0.05\)) the level of expression of Alpha toxin gene compared to control and OPEC; however, BMD reduced Alpha toxin gene expression compared to GRE, GTEC, and GTED. Furthermore, NetB toxin gene expression was reduced significantly (\(p<0.05\)) upon treatment with GRE, GTED, and OPEC, as well as BMD, compared to the control. However, NetB gene expression for GTEC was similar to the control. In addition, BMD, GRE, GTED, and OPEC reduced (\(p<0.05\)) the level of expression of tpeL compared to the control. However, the level of expression of tpeL for GTEC was similar to the control (Figure 5A-C). For the CP240 strain Alpha toxin encoding gene, all the selected phytogenic extracts reduced (\(p<0.05\)) the expression of NetB gene, with BMD having the lowest level of expression. Similarly, all the selected phytogenic extracts reduced (\(p<0.05\)) the expression of the NetB gene. However, only GRE was comparable to BMD in its potency to reduce the expression of NetB gene. Additionally, tpeL expression was reduced (\(p<0.05\)) by the selected phytogenic extracts, with GRE and OPEC showing similar values to BMD (Figure 6A-C).  
Figure 5: Effect of phytogenic extracts on CP19 toxin encoding genes (**A**) NetB, (**B**) tpeL, (**C**) \(\alpha\)-toxin. The data are expressed as means \(\pm\) SEM, with \(n>3\) per treatment; \(a\), \(b\), \(ab\), \(c\), \(d\), \(cd\) means bars not sharing a common superscript are significantly different among treatments (\(p<0.05\)).