esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 3 Results and Discussion  
### Destruction Mode of PVB LG  
Figure 7 illustrates the initial condition of the laminated glass and the morphology at the moment of the first failure. Throughout the loading process, two distinct failures are observed. Prior to the first failure, the glass plate uniformly deforms downward under the displacement load. Upon reaching the ultimate load, a sharp \"Bang\" is heard, signaling the first failure event. At this juncture, the middle and top glass layers remain intact, while the bottom layer shatters into numerous small fragments, creating a network-like pattern. Supported by the unbroken top and middle layers and the adhesive layer's bonding effect, the glass plate still maintains its flatness, with minimal debris falling off (0-3 pieces).  
After the initial failure, the displacement loading proceeds. When the second ultimate load is achieved, another \"Bang\" indicates the second failure. This time, the top and middle glass layers break almost simultaneously, exhibiting a spider web-like pattern of damage on the top layer. The center of the plate sags significantly, compromising its complete flatness, yet the overall integrity of the glass plate is preserved by the adhesive layer. Figure 8 compares the morphological differences between the two failures. Notably, both instantaneous failures occur during displacement loading--not during the pauses--hence, no crack development takes place while the displacement is maintained.
###### Abstract  
Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node's battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network's member nodes. As a result, we use clusters, which are produced by dividing the network's space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle's focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.  
UWSN; member nodes; routing protocol; cluster head; clustering and routing Article
## 4 Conclusions  
In this study, new glass samples of the 23 Li\({}_{2}\)O-7.5 Al\({}_{2}\)O\({}_{3}\)-(69.5 - x) B\({}_{2}\)O\({}_{3}\): xDy\({}_{2}\)O\({}_{3}\) system were prepared using the melt-quenching technique. The amorphous nature of glass samples was confirmed by the XRD analysis. Notably, the density and optical basicity increased with the presence of BO\({}_{4}\) tetrahedral units and, due to the structural changes, led to the decline in the direct and indirect energy gaps. From PL results, five emission bands were observed around at 458, 481, 575,661 and 689 nm, which are attributable to Dy\({}^{3+}\) transitions of \({}^{4}\)I\({}_{15/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{15/2}\), \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{15/2}\), \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{13/2}\), \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{11/2}\) and \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{9/2}\), respectively. The 576 nm (\({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{13/2}\)) band is the largest. Hence, optical properties and other physical parameters, such as refractive index, density, molar volume, molar refractive, electrical polarisation and optical basicity, show a strong connexion with the speciation of dysprosium ions.  
Conceptualization, M.K.A.K.; Data curation, O.B.A.; Formal analysis, O.B.A., M.K.A.K.; Funding acquisition, M.K.A.K. and I.N.C.I.; Investigation, M.K.A.K.; Project administration, I.N.C.I.; Resources, M.H.M.Z.; Supervision, M.K.A.K. and H.M.K.; Validation, N.M.N.; Visualization, M.H.A.M.; Writing--original draft, O.B.A.; Writing--review & editing, M.K.A.K. and M.H.A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Geran Putra IPM with the grant number [GP/IPM/9619800] and the APC was funded by Research Management Centre of Universiti Putra Malaysia.  
The authors wish to acknowledge support from to Biotechnology Research Center, Libya for scholarship and Universiti Putra Malaysia for providing research facilities.  
The authors declare no conflict of interest.  
Figure 11: Partial energy levels of the RE ion showing distinct transitions.
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 3 Technologies
### LoRa Technology  
LoRa [34; 35] uses a proprietary modulation based on chirp spread spectrum, which exploits chirps whose frequency increases or decreases linearly over a certain amount of time; information is inserted in chirps by introducing a frequency discontinuity at different time offsets [36]. These chirps occupy a bandwidth of 125, 250 or 500 kHz. One of the most important parameters of the physical layer is the Spreading Factor (\(SF\)), which is the ratio between the signal bandwidth and the symbol rate. Keeping the bandwidth constant, it is possible to improve the receiver sensitivity by increasing the airtime (duration of a packet transmission). More precisely, each increment by one unit of the SF, corresponds to a doubling of the airtime and a decrement of the receiver sensitivity of roughly 3 dB.  
At MAC layer LoRa uses the LoRaWAN protocol that describes three Classes: (i) Class A: end devices, after the transmission of a packet, open two receive windows to get an acknowledgement (ACK) or receive data from the gateway, then they stay in idle mode until the next transmission; (ii) Class B: end devices have more receive windows synchronized with a beacon provided by the gateway; (iii) Class C: end devices stay continuously in reception mode. Any device has to be compliant with, at least, Class A (considered in this paper), where ALOHA protocol is used in uplink.  
There are two types of transmission modes: confirmed, using ACKs, and unconfirmed, when no ACKs are used. When an ACK is expected but not received by the transmitter, a recovery algorithm, which consists in multiple retransmissions, is initiated. In case the gateway is able to receive simultaneous signals transmitted with different spreading factors, the standard suggests that every two failed retransmissions the data rate is decremented, resulting in an increase of \(SF\) and of the airtime, as well. Indeed, this algorithm implicitly assumes that the transmission failed due to poor connectivity, therefore a lower data rate should increment the success rate, as the receiver sensitivity will be better. However, if losses are due to collisions, such strategy does not mitigate transmission failures, as \(SF\) continues to be increased, resulting in longer airtimes and, ultimately, in a larger collision probability.  
As for network aspects, LoRaWANs are generally laid out in a star topology and the central node is usually called _gateway_.  
This is the technology adopted within the RIGERS project described in Section 5, which is actually based on a star network aimed at collecting data sensed by devices located inside and outside selected buildings in Bologna.
## 3 Results
### Structural Equations Model Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Associations between** & \multicolumn{4}{c}{**R.W.**} & \multicolumn{2}{c}{**S.R.W.**} \\ \cline{2-6}
**Variables** & **Estimations** & **S.E.** & **C.R.** & \(p\) & **Estimations** \\ \hline MDA \(\leftarrow\) TC & 0.019 & 0.015 & 1.293 & 0.196 & 0.128 \\ \hline MDA \(\leftarrow\) EC & 0.010 & 0.018 & 0.537 & 0.591 & 0.054 \\ \hline IR \(\leftarrow\) TC & 1.000 & & & & 0.911 \\ \hline EI \(\leftarrow\) TC & 0.672 & 0.048 & 14.057 & *** & 0.816 \\ \hline CL \(\leftarrow\) TC & 0.970 & 0.059 & 16.406 & *** & 0.899 \\ \hline PM \(\leftarrow\) EC & 1.000 & & & & 0.817 \\ \hline UR \(\leftarrow\) EC & 1.402 & 0.122 & 11.487 & *** & 0.918 \\ \hline MR \(\leftarrow\) EC & 0.858 & 0.097 & 8.873 & *** & 0.654 \\ \hline ANX \(\leftarrow\) TC & \(-0.114\) & 0.070 & \(-1.636\) & ** & \(-0.160\) \\ \hline ANX \(\leftarrow\) EC & 0.046 & 0.085 & 0.541 & 0.589 & 0.053 \\ \hline ANX \(\leftarrow\) MDA & 0.475 & 0.365 & 1.301 & ** & 0.099 \\ \hline EC \(\leftarrow\)\(\rightarrow\) TC & \(-0.296\) & 0.056 & \(-5.303\) & *** & \(-0.541\) \\ \hline \hline \end{tabular}  
* **Note 1:** EC = Ego climate; TC = Task climate; PM = Punishment for mistakes; UR = Unequal recognition; MR = Member rivalry; CL = Cooperative learning; EI = Effort/improvement; IR = Important role; ANX = Anxiety; MDA = Mediterranean diet adherence **Note 2: \({}^{**}\)\(p\leq 0.005\); \({}^{***}\)\(p\leq 0.001\).  
\end{table} 
Table 3: Structural model for participants showing a medium level of weekly PA.
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 4 Conclusions  
In this study, salinity-tolerant SMX-degrading bacteria A12 and L from milkfish pond sediments were isolated, identified, and characterized. SMX-degrading bacterial strains A2 and A12 are very close to _Vibrio_ sp., and bacterial strains L and M are very close to _Pseudomonas_ sp. and _Marinobacter_ sp., respectively. Aerobic and anaerobic batch and readdition experiments indicated that bacterial strains A12 and L exhibited high performance in SMX degradation in milkfish pond sediments. The results of this study suggest that bacterial strains A12 and L provide a potential solution for the treatment of wastewater and sediment from high-salinity aquaculture ponds with SMX contamination. These bacteria may also be applicable for the bioremediation of SMX-contaminated mangrove, estuarine, and coastal sediments.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/19/4000/s1](http://www.mdpi.com/2076-3417/9/19/4000/s1)]([http://www.mdpi.com/2076-3417/9/19/4000/s1](http://www.mdpi.com/2076-3417/9/19/4000/s1)), Figure S1: Biodegradation of sulfamethoxazole (SMX) by bacterial strains A12 and L (with fish pond sediment) in aerobic and anaerobic readdition experiments, respectively, Table S1: Aerobic SA degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) in Figure 1A, Table S2: Anaerobic SA degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) in Figure 1B., Table S3: SMX degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) in Figure 4, Table S4: 16S rRNA gene sequence analysed by the NCBI Blast in Figure 5, Table S5: SMX degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) in Figure 7, Table S6: The aerobic SMX degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) following the 3\({}^{\text{rd}}\) and 4\({}^{\text{th}}\) additions of SMX during 81 d of incubation in Figure 8A, Table S7: The anaerobic SMX degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) following the 3\({}^{\text{rd}}\) and 4\({}^{\text{th}}\) additions of SMX during 81 d of incubation in Figure 8D.  
**Author Contributions:** Investigation, B.-V.C., W.-L.C., D.L.K. and C.-W.Y.; Methodology, S.-L.Y. and D.L.K.; Project administration, W.-L.C.; Writing--original draft, B.-V.C. and C.-W.Y.; Writing--review and editing, B.-V.C. and C.-W.Y.  
**Funding:** This research was supported by the Ministry of Science and Technology, Taiwan (grant no. MOST 106-2632-B-031-001).  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 4 The Virtual Astronaut for Santa Maria
### Multi-Spectral Pancam Data Integration  
Rover based Pancam multi-spectral data are taken by 13 geology filters at wavelengths ranging from 400 to 1100 nm [35]. Six filters (L2 through L7), which are located on the left Pancam camera, have wavelengths in the visible range from blue to green to red. The other filters (R3 through R7) on the right camera cover in the near-infrared. Two additional filters on the right camera have redundant wavelengths in blue and red. The blue and red filters found on both cameras are used when acquiring stereo image pairs. These multispectral observations provide characterization of the mineralogy and color properties of the scene around the rover. The combination of images taken by the six visible  
Figure 4: Digital Elevation Model (DEM) integrated using rover-based and orbital data at Santa Maria Crater.  
wavelength filters can be used to construct an approximate true color picture of Mars [35]. Therefore, Pancam data provide the best source for the generation of a high-resolution texture mosaic.  
Because of the limitation of rover resources during the mission, not every image sequence had Pancam data taken with all six visible wavelength filters. Images taken through filters L2 (753 nm), L5 (535 nm), and L7 (432 nm) or through filters L4 (601 nm), L5 (535 nm), and L6 (482 nm) were often selected to generate false color images. The six filters L2 through L7 were only used to generate approximate true color when available. In other cases the available filters were combined and assigned to the red, green, and blue channels to create approximate true color images or false color images.  
A 360\({}^{\circ}\) Pancam panoramic view of the local terrain can consist of nearly 100 images in each filter. The images in these mosaics are often taken on different sols (Martian day) under slightly different lighting conditions. A MATLAB pipeline was built to create false color Pancam images and map them to the 3D terrain to generate textures models. The individual projected color images were mosaicked after contrast adjustment. Figure 5 shows one of the Pancam false color mosaics generated using data taken at the western rim of Santa Maria Crater. The color was manually adjusted between adjacent overlapping images in order to make a smooth transition of colors.  
Additionally, Pancam multi-spectral data acquired from multiple camera positions were also combined to generate a big mosaic covering the study area, as shown in the color part of Figure 6a.
## 5 Discussion and Conclusions  
### Discussion  
Empirical parameterized typhoon models demonstrate superior performance in simulating typhoon maximum wind speeds compared to the WRF model. This advantage arises from their direct use of critical typhoon information, such as maximum wind speed, during the computation process, enabling these models to closely replicate maximum wind speeds in CMA data. In contrast, the WRF model is constrained by the grid resolution of reanalysis datasets, such as FNL and ERA5. The maximum wind speed may occur between grid points, resulting in an attenuation of extreme wind speeds in the simulated data. Furthermore, the level of refinement and accuracy of the reanalysis of the driving fields significantly influences the WRF model's outputs. Therefore, employing finer and more realistic reanalysis datasets is pivotal for enhancing the WRF model's simulation performance.  
While increasing the resolution of WRF simulations can improve their accuracy, it also imposes significantly higher demands on computational resources. In comparison, empirical parameterized typhoon models require considerably fewer computational resources. However, the WRF model accounts for interactions between the typhoon and its surrounding environment, a feature absent in empirical parameterized typhoon models, which rely solely on empirical formulas to estimate typhoon parameters. Consequently,  
Figure 15: The location of the reference point near the coast, and the time series of significant wave height at this point for Typhoon Nepartak, the red point is the location of the reference point, the red line represents the FNL-driven WRF simulation, the blue line represents the ERA5-driven WRF simulation, the green line corresponds to the Holland case, and the yellow line corresponds to the Jelesnianski case.  
the WRF model outperforms empirical parameterized typhoon models in simulating wind fields in the outer regions of typhoons.  
The experiments revealed that the maximum wind radius simulated by empirical parameterized typhoon models tends to exceed the observations from SMAP data. This discrepancy can result in overestimated wind speeds and significant wave heights in regions slightly farther from the typhoon's central path. Therefore, careful calibration of the maximum wind radius parameter is crucial to improve the accuracy of these models.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Phase 1  
GANs, introduced in 2014, are a major advancement in deep learning. They consist of two competing neural networks: a generator that creates data mimicking a target distribution, and a discriminator that tells real from generated data. This adversarial process iteratively refines both networks, producing increasingly realistic synthetic data. GANs are versatile, used in areas like image and video synthesis, data augmentation, anomaly detection, and are particularly relevant for ransomware detection.  
In the context of adversarial attacks, GANs are employed to model the attacker's objective. Given a sample space X, where x represents a benign sample and \(g(x)>0\) denotes the classification function yielding a benign result, the attacker aims to generate a malware sample \(x^{*}\) such that \(g(x^{*})>0\). This objective can be formally expressed as  
\[x^{*}=arg\;max_{x}\;\hat{g}(x),\;s.t.d(x,x^{*})\leq d_{max} \tag{6}\]  
The GAN reduces the loss function value \(V\) during the training of both generator \(\mathcal{G}\) and discriminator \(\mathcal{D}\) by solving the following optimization function:  
\[\underset{\mathcal{D}}{min}\;\underset{\mathcal{G}}{max}\;V(\mathcal{G}, \mathcal{D}) \tag{7}\]  
where  
\[V(\mathcal{G},\mathcal{D})=E_{x}\;[log\;(D(x))]+E_{z}[log\;(1-D(G(z)))] \tag{8}\]  
Here, z denotes samples drawn from the latent noise distribution. While existing GAN-based malware detection methods show promise, their efficacy in early ransomware detection is limited by the inherent scarcity of ransomware samples. This scarcity results in a poorly defined empirical estimate of the authentic ransomware data distribution, leading to substantial divergence from the generator's output distribution. Consequently, the discriminator frequently rejects synthetic samples, hindering the generator's ability to learn and produce realistic ransomware examples. To address this, our algorithm is structured into two distinct phases: a GAN-based data augmentation phase, comprising a generator and discriminator, followed by an early detection phase that leverages the augmented dataset.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 8 Conclusions  
This paper presented a comprehensive survey of recent research on approaches that contribute to energy efficiency improvements of FiWi access networks. Emphasis was given to the extensive literature review of various power-saving techniques and energy-efficient models that are dedicated to the improvement of FiWi network energy efficiency. The presented review covered energy efficiency analyses of different types of FiWi networks which include the R&F FiWi networks, the RoF FiWi networks, the MEC-based FiWi networks and the FiWi networks based on the SDN concept. For the R&F networks, energy conservation techniques and research studies related to the optical and wireless domains were presented, as well as related works that deal with the improvement of FiWi networks' energy efficiency through the cooperation of techniques in wireless and optical domains. Furthermore, two basic RoF techniques, the D-RoF and A-RoF, were elaborated in the context of energy efficiency, and an overview of research studies in the field of improving the energy efficiency of D-RoF and A-RoF systems was given. Additionally, the C-RAN architecture was reviewed through the prism of energy consumption with the presentation of current research efforts related to the improvement of the C-RAN energy efficiency. The MEC-based FiWi networks, which introduce cloud computing at the edge of the mobile network, were further presented, and articles dedicated to the mechanisms and concepts for the optimization of the MEC FIWi network's energy consumption were highlighted. Finally, flexible SDN FiWi networks that offer high scalability and ease of management of the FiWI networks were presented, with an emphasis on research related to energy conservation techniques implemented in such networks. The literature suggests that there are many areas in which the energy efficiency of the FiWi networks can be enhanced. Overall, the overview presented in this work showed that EE is one of the major concerns in the FiWi networks and further intensive research attempts should be carried out in the endeavor of improving the energy efficiency of the FiWi networks.  
Conceptualization, J.L.; methodology, Z.K. and J.L.; validation, J.L. and D.B.; formal analysis, J.L. and Z.K.; investigation, Z.K. and J.L.; writing-original draft preparation, J.L. and Z.K.; writing-review and editing, J.L. and Z.K.; visualization, J.L. and Z.K.; supervision, J.L. and D.B. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
## 3 Real Application Case
### Model Establishment and Verification
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Data** & **Acquisition Time** & **Resolution** & **Source** \\ \hline Ground PM2.5 & PM2.5 (\(\upmu\)g/m\({}^{3}\)) & 8:00 a.m. & N/A & Tianqhiobuao website \\ \hline Remote sensing data & Terra MODISAOD products & 8:00 a.m. & 3 km & NASA, MODIS \\ \hline \multirow{4}{*}{Meteorological data} & Temperature/\({}^{\circ}\)C & & & \\  & Air pressure/hPa & & & \\ \cline{1-1}  & Relative humidity/\% & & & \\ \cline{1-1}  & Precipitation/mm & & & \\ \cline{1-1}  & visibility/km & & & \\ \cline{1-1}  & Wind speed/(m/s) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related parameters of data sourcesample points are roughly distributed on both sides of the diagonal line. The less sample points deviate from the diagonal line, which satisfies the law of error distribution. These results show that the verification results are good.