esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Setting
### Dynamic Response
#### 2.4.2 Linearization  
The linearization process starts with expressing the mechanical quantities of the dynamic problem as function of the mechanical quantities of the quasi-static problem with neglecting high order terms  
\[\begin{split}\mathbf{F_{2}}&=\mathbf{F_{1}}+ \boldsymbol{\
abla}\mathbf{w}\\ \mathbf{E_{2}}&=\mathbf{E_{1}}+\frac{1}{2}(\mathbf{F_ {1}}^{\intercal}\boldsymbol{\
abla}\mathbf{w}+\boldsymbol{\
abla}^{\intercal} \mathbf{w}\mathbf{F_{1}}+\underbrace{\boldsymbol{\
abla}^{\intercal}\mathbf{ w}\boldsymbol{\
abla}\mathbf{w}}_{\approx 0})\\ &\approx\mathbf{E_{1}}+\frac{1}{2}(\boldsymbol{\
abla}\mathbf{w}+ \boldsymbol{\
abla}^{\intercal}\mathbf{w}+\boldsymbol{\
abla}^{\intercal} \mathbf{u_{1}}\boldsymbol{\
abla}\mathbf{w}+\boldsymbol{\
abla}^{\intercal} \mathbf{w}\boldsymbol{\
abla}\mathbf{u_{1}})\\ &\approx\mathbf{E_{1}}+\mathbf{E_{w,u_{1}}}\end{split} \tag{32}\]  
The constitutive material law is linearized as follows  
\[\begin{split}\mathbf{S_{2}}&=\frac{\partial W}{ \partial\mathbf{E}}(\mathbf{E}_{2})=\frac{\partial W}{\partial\mathbf{E}}( \mathbf{E}_{1}+\mathbf{E_{w,u_{1}}})\\ &\approx\frac{\partial W}{\partial\mathbf{E}}(\mathbf{E}_{1})+ \frac{\partial^{2}W}{\partial\mathbf{E}\partial\mathbf{E}}(\mathbf{E}_{1}): \mathbf{E_{w,u_{1}}}\\ &\approx\mathbf{S_{1}}+\mathcal{C}_{1}:\mathbf{E_{w,u_{1}}}\end{split} \tag{33}\]  
The deformation integral term is linearized as follows  
\[\begin{split}\mathbf{P_{2}}:\boldsymbol{\
abla}\mathbf{v}& =\mathbf{S_{2}}:(\mathbf{F_{2}^{\intercal}\boldsymbol{\
abla} \mathbf{v}})\\ &=(\mathbf{S_{1}}+\mathcal{C}_{1}:\mathbf{E_{w,u_{1}}}):(\mathbf{ F_{1}^{\intercal}\boldsymbol{\
abla}\mathbf{v}}+\boldsymbol{\
abla}^{\intercal} \mathbf{w}\boldsymbol{\
abla}\mathbf{v})\\ &=\mathbf{P_{1}}:\boldsymbol{\
abla}\mathbf{v}+\mathbf{S_{1}}: \boldsymbol{\
abla}^{\intercal}\mathbf{w}\boldsymbol{\
abla}\mathbf{v}+( \mathcal{C}_{1}:\mathbf{E_{w,u_{1}}}):(\mathbf{F_{1}^{\intercal}\boldsymbol{ \
abla}\mathbf{v}})\end{split} \tag{34}\]  
The weak formulation governing the vibrations response is given by  
\[\int_{\Omega}\rho\ddot{\mathbf{w}}\cdot\mathbf{v}\ \mathrm{d}\Omega+\int_{\Omega} \mathbf{S_{1}}:\boldsymbol{\
abla}^{\intercal}\mathbf{w}\boldsymbol{\
abla} \mathbf{v}+(\mathcal{C}_{1}:\mathbf{E_{w,u_{1}}}):(\mathbf{F_{1}^{\intercal} \boldsymbol{\
abla}\mathbf{v}})\ \mathrm{d}\Omega=-\int_{\Omega}\rho\ddot{\mathbf{u}}_{ \intercal}\mathbf{v}\ \mathrm{d}\Omega\quad\forall\mathbf{v}\in\mathcal{V} \tag{35}\]  
The deformation integral term depends on the gradient of the quasi-static displacement. The quasi-steady acceleration \(\ddot{\mathbf{u}}_{1}\) appears as the excitation force. It is approximated using a finite difference scheme. The second derivative numerical approximation generates high frequency noise. It could be handled by using low-pass filter or by choosing an adequate discretization. Equation (35) is linear with respect to \(\mathbf{w}\) and could be solved using a time integration scheme.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 5 Discussion  
As a result of the research presented in this paper, a new approach for early detection and warning of wildfires has been developed. This research shows that wireless sensor networks (WSNs) are a viable environmentally friendly technology for spotting forest fires. Secondly, based on experimentation tests on simulators, we summarize the findings of Section 4. The computational study covered energy usage, cluster construction time, and alive node determination. While observing the findings, the superiority of the offered strategy was proven concerning the examined parameters. Energy consumption wise, EE-SS was the most efficient, followed by the PSO-HSA, SEED, and LEACH-C, whereas LEACH absorbed the most energy. Usually, PSO-HSA performance was better than others, but below our proposed algorithm with all evaluated parameters, but in alive node analysis, SEED works better than PSO-HSA. From the above figures and explanations for comparing different algorithms with our proposed algorithm, as seen in Figure 11, none of the related WSNs are considered unsuitable for disaster application. Moreover, these techniques can be beneficial as many people are killed each year in forest fires, and their property is destroyed. At the same time, the discharge of dangerous greenhouse gases and smoke particles contributes to air pollution and global warming through the removal of green cover.  
Figure 11: Overall performance comparison.  
Figure 10: Packet received comparison.
# Alluvial Fan Scree Deposits: Formation Characteristics and Erosion Mitigation Strategies
## 2 Theoretical Inquiry
### SH Polymer Material  
Soil chemical stabilization technology, a subdiscipline of geotechnical engineering, focuses on enhancing soil properties through additive treatments. Since its emergence in the 1950s, over 50 reinforcement materials have been developed globally [35; 36; 37; 38; 39; 40; 41; 42; 43]. However, few have achieved widespread field application, with most remaining experimental. SH polymer material, a liquid polymer derived from industrial by-products, is among the rare exceptions with practical implementations.  
SH polymer material is a non-toxic polymeric solution with a molecular weight of \(\sim\)20,000 Da. The key properties include the following: 1. Dry density: 1.68 g/cm\({}^{3}\). 2. Curing behavior: forms a 0.2-0.5 mm flexible hydrogel film upon ambient-temperature curing for 72 h. 3. Hydrophilicity: exhibits unlimited water miscibility. This cost-effective, room-temperature-curable material has demonstrated efficacy in aeolian erosion control [44], loess stabilization [45; 46; 47] and coastal saline-alkali soil remediation [48].  
When applied to AFSDs, SH polymer membranes enhance rainfall erosion resistance. However, the weak interparticle bonds characteristic of AFSDs raise concerns about treatment efficacy. Critical unresolved questions include the following: 1. Longevity: Can SH polymer membranes maintain protective effects under cyclic wet-dry conditions? 2. Structural integrity: Will treated AFSDs resist disaggregation during intense rainfall events? 3. Hydrological compatibility: Do the SH polymer membranes balance water retention and permeability to support vegetation?
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 4 Network Survivability Theory  
### Network Structure Entropy  
The satellite network can be built into a graph network model and there are often complex connection relationships between nodes. Essentially, the satellite network belongs to a special scale-free network, which has very few core nodes and a large number of end nodes in the network. Scale-free networks are extremely vulnerable to deliberate attacks. [PERSON] and [PERSON] believe that the scale-free characteristic of the network is the \"heterogeneity\" of the network and that the concept of \"entropy\" can be used to study this \"heterogeneity\" of the network [23; 24]. Therefore, the two scholars proposed the concept of \"network structure entropy\". They believe that the greater the entropy of the network structure, the more uniform the distribution of network nodes, and the weaker the heterogeneity of the network, that is, the fewer core nodes in the network, the stronger the network survivability when the network is attacked, and vice versa.
## 2 Materials and Methods
### IAQ Measurements  
Chemical and physical measurements at two locations per building (one indoor and one outdoor) were carried out continuously for five working days (Monday to Friday) in a representative office room. The indoor air pollutants measured in the buildings were chosen based on their potential association with health effects [15], and were: ozone (O\({}_{3}\)), nitrogen dioxide (NO\({}_{2}\)), sodium dioxide (SO\({}_{2}\)), aldehydes (formaldehyde, acetaldehyde, acetone, propionaldehyde, 2-butanone, benzaldehyde, and hexaldehyde), and VOCs (benzene, toluene, ethylbenzene, p,m-xylene, o-xylene, 1,2,4-TBM, a-pinene and d-limonene). The indoor sampling sites were selected according to ISO 16000-1 [38]. In all cases, the samplers and equipment did not disturb the normal use of the office rooms and were tampered with by occupants. The air diffusive samplers were placed in the center of each room, at least 1 m away from the wall, at the height of the breathing zone of seated occupants. Ventilation channels and heating sources, including the sun, were avoided. Any material that may have emitted any chemical pollutants was also strictly avoided during the placement of the samplers. Outdoor passive sampling units and monitors were mounted in a protective shelter in order to be protected by direct sunlight, precipitation, and human interactions.  
Before and after sampling, the passive samplers were stored in a refrigerator (<4 \({}^{\circ}\)C). For each pollutant sample, one non-exposed and non-transported on-site sampler per purchased package (i.e., a lot blank) was analyzed. In addition, one field blank per chemical parameter was used in each office room. The monitoring of VOCs, aldehydes, O\({}_{3}\), NO\({}_{2}\), was carried out with the use of Radiello passive samplers. VOCs were chemically desorbed by the passive samplers with CS2. Analysis of the VOCs follow the ISO 16017-2 [39] and was performed at GC-FID Agilent Technology 6890N using 2-fluorouracilene as the internal standard. Aldehydes were desorbed with HPLC grade acetonitrile by the passive samplers and analysis was performed at Agilent HLPC 1100 Series under the ISO 16000-4 [40]. For the chromatographic analysis methods of VOCs and aldehydes, the limits of detection (LODs) were defined as three times the signal-to-noise ratio (3:1). The limits of quantification (LOQs) were defined as 10 times the signal-to-noise ratio (10:1). The aqueous extracts of NO\({}_{2}\) and the 3-methyl-2-benzothiezolino hydrazone hydrochloride (MBTH) extracts of O\({}_{3}\) from the passive samplers were analyzed with UV-VIS spectrophotometry, using a RAYLEIGH UV-1601 spectrophotometer. The VOC, aldehyde, O\({}_{3}\), and NO\({}_{2}\) concentrations were all expressed in \(\mathrm{\mu g/m^{3}}\) after adjustment of the sampling rates on temperature and subtraction of the corresponding field blank value. An optical light scattering spectrometer (Grimm1.108) was used for measuring simultaneously PM\({}_{2.5}\) and PM\({}_{10}\) fractions.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 1 Introduction
At the same time, SAR systems are currently the most effective sensor to detect earth surface dynamics because they are independent from solar illumination and relatively insensitive to atmospheric weather conditions, owing to their near all-weather/day-night acquisition capabilities [17; 18; 19; 20; 21]. For the first step before data processing, the selection of suitable sensors that are both cost effective and the availability of free charge global dataset and open data source are critical issues in the development of disaster risk maps [22]. In order to provide more accurate disaster information such as flood extent in in unundated areas, multiple earth observation time series data could be a powerful tool in regular preparedness and emergency response to generate uniform information covering large areas. For the next data-processing step, development of advanced global flood mapping algorithms is a key process to create a flood indicator that produces essential risk information beyond the traditional issue of flood mapping for disaster risk reduction. The main purpose of this study is to detect and monitor the maximum extent of an annual nationwide flood in a transboundary river basin using multiple satellite data to minimize the limitations of optical and multispectral images. The proposed synchronized floodwater index (SfWi) was applied to a Bangladesh floodplain in a monsoon region. It was then tested to maximize the utilization of optical satellite sensors based on annual time series data with in-situ water level data. Imagery-derived products are able to support flood disaster management including forecasting and prediction of flood inundation through model calibration of optimized parameters and validation with a final product of hydrological flood simulation. As \"flood disaster big data\", based on the multi-dimension and multi-time-series data from multi-satellites, this approach will be able to contribute to the production of an accurate flood map at a national level in terms of an image fusion and synthesis, under the framework of nationwide monitoring for supporting emergency risk management and flood risk reduction.
## 2 Materials and Methods
### Membrane-Based Desorber
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Inlet temperature of the hot water, \(T_{htw}\) (°C) & 75 \\ Inlet temperature of the nanofluid, \(T_{nf}\) (°C) & 65 \\ Pore diameter of the membrane, \(dp\) (\(\mu\)m) & 1 \\ Nanofluid pressure, \(P_{nf}\) (kPa) & 5.5 \\ Vapour pressure, \(P_{v}\) (kPa) & 5.5 \\ Porosity of the membrane, \(\varepsilon\) & 0.8 \\ Height of the hot water channel, \(e_{htw}\) (mm) & 0.15 \\ Wall thickness, \(e_{w}\) (mm) & 2.7 \\ Height of the nanofluid channel, \(e_{nf}\) (mm) & 0.15 \\ Thickness of the membrane, \(e_{m}\) (\(\mu\)m) & 60 \\ Height of the vapour channel, \(e_{v}\) (mm) & 5 \\ Centre-to-centre distance between nanofluid channels (mm) & 1.6 \\ Centre-to-centre distance between hot water channels (mm) & 1.6 \\ Width of hot water channel, \(l_{htw}\) (mm) & 1.5 \\ Width of nanofluid channel, \(l_{htf}\) (mm) & 1.5 \\ Width of vapour channel, \(l_{v}\) (mm) & 20 \\ Channel length, \(L\) (mm) & 50 \\ Length of discretized element, \(dz\) (mm) & 0.27 \\ Total inlet flow rate of nanofluid, \(m_{nf,T}\) (g/s) & 0.5 \\ Total flow rate of hot water, \(m_{htw,T}\) (g/s) & 0.5 \\ Inlet vapour flow rate, \(m_{v}\) (g/s) & 0 \\ Inlet LiBr concentration, \(x\) & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometrical and operational data, corresponding to the scheme shown in Figure 2.  
To predict the global mass transfer coefficient (\(K\)), a model using resistances in series is used. It contains the mass transfer coefficients of the nanofluid boundary layer and the membrane active layer. The first one is calculated as in [32] and the mass transfer coefficient required, between the bulk nanofluid and the nanofluid-vapour boundary, is estimated bearing in mind the two regions defined for desorption. During direct diffusion, the correlation of Leveque [33] is used, while in the boiling mode, that of [34] is employed. The required diffusion coefficient is calculated using the equations of [35]. The coefficient characterizing the mass transfer across the microporous membrane is predicted as in [36], employing the dusty-gas model. The tortuosity of the membrane is estimated as in [37]. All the equations obtained are solved using Engineering Equation Solver, EES(tm), of F-Chart Software (Madison, USA) [38]. Properties of the H\({}_{2}\)O-LiBr solution and water are obtained using correlations included in EES(tm), except the surface tension of the H\({}_{2}\)O-LiBr solution, which is obtained using the correlation presented by [21] from data tabulated by [39]. A complete description of the model developed for the membrane-based desorber employing the H\({}_{2}\)O-LiBr pair, and the results obtained of a parametric study evaluating the effect of operating and design variables, can be found in [21; 22], respectively.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 5 Handover and Load Balancing in HWLNets  
The transfer of users to a different type of network in hybrid systems is called handover. There are two types of handovers: horizontal (HHO) and vertical (VHO). The main difference is in terms of the technology that would serve the users as they advance through the network. In HHO, users are being served by APs of the same technology; for example, if a user is moving in and out of different rooms covered with WiFi, APs thus reconnect the user to a network via different APs. In VHO, the user changes the type of technology used to access a network. In HWLNets, if the user is stationary in one room, they are served with LiFi AP, and when they decide to move, they will exit out of the FOV of the LiFi transmitter and will be reconnected to the network by the use of RF to the nearest WiFi AP.  
A lot of research has been done on this topic concerning heterogeneous networks (HetNets) [56] with different types of handover models used: received signal-strength (RSS), load balance, and energy-save. In HWLNets, the handover process is complex, and in this section, we will explain and review the handover models in LiFi and WiFi environments and also vertical transfer between the two.
## 5 Conclusions  
In this paper, a method combining transfer learning and DCGAN to optimize ShuffleNetv2-1.0-SE for tunnel lining crack detection is presented. In addition, we apply transfer learning on a publicly available small-scale concrete dataset and compare data augmentation using DCGAN and conventional techniques. At last, an LCNN named ShuffleNetV2-1.0-SE is proposed, incorporating the SE attention mechanism into ShuffleNetV2-1.0. Our main findings are given below:  
1. Transfer learning on a small-scale dataset enhances the image generation quality of DCGAN, the classification accuracy of LCNN for crack images and the detection performance of LCNN for true positive crack images.
2. The SE attention mechanism improves the crack feature extraction of ShuffleNetV2-1.0. An accuracy improvement from 94.23% to 98.14% was achieved with 1.45 million parameters and 017G floating-point computation using transfer learning and DCGAN data augmentation.
3. ShuffleNetV2-1.0-SE shows better generalization on the public dataset after being trained with the DCGAN-augmented dataset, demonstrating the effectiveness of the proposed data augmentation method, which combines transfer learning and DCGAN.  
Most LCNNs struggle to accurately classify crack images in a seepage background with or without data augmentation; therefore, we plan to further increase the number of images with a interference background in future work. In addition, quantization is required to detect cracks; therefore, the classification studied in this paper has limitations for practical applications. In the future, we will explore semantic segmentation models for crack images, which are simultaneously highly accurate and lightweight.  
Conceptualization, N.Z. and Y.S.; methodology, Y.S.; software, Y.S. and K.L.; validation, N.Z. and A.Y.; formal analysis, A.Y.; investigation, C.D.; resources, N.Z., K.L. and H.J.; data curation, C.D.; writing--original draft preparation, N.Z. and Y.S.; writing--review and editing, N.Z. and K.L.; visualization, A.Y. and H.J.; supervision, N.Z.; project administration, N.Z.; funding acquisition, N.Z. and H.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Project no. 51608081) and the Natural Science Foundation of Chongqing City of China (No. cstc2021 jcyj-msxmXl011).  
Not applicable.  
Figure 15: Comparison of the generalization performance of ShuffleNetV2-1.0-SE trained on different datasets: (**a**–**j**) represent crack image from [3].
## 5 Verifying Accuracy via Physical Structure Test
### Arrangement of the Stress Sensors  
Static strain gauges and a total of 29 three-dimensional stress sensors were utilized for the test, as depicted in Figure 12. The sensors numbered 1 to 20 were utilized for training and were strategically positioned at feature elements to provide input data for the ANN. Sensors numbered 21 to 26 were strategically placed in high-stress areas of the outer and intermediate bulkheads to fine-tune the ANN. Lastly, sensors numbered 27 to 29 were used for verification purposes and were placed in arbitrary positions within the high-stress area.  
\begin{table}
\begin{tabular}{c c c} \hline Principal dimension & Length & 3.95 m \\ \hline Principal dimension & Width & 1.70 m \\ \hline Principal dimension & Height & 3.20 m \\ \hline \multirow{2}{*}{Material} & Double-ear connector (the fixture) & \multirow{2}{*}{42 CrMo steel (yield strength 450 MPa)} \\  & & \\ \hline Material & Single-ear connector & ZG230-450H steel (yield strength 240 MPa) \\ \hline \end{tabular}
\end{table}
Table 2: Main parameters of the connector model.  
Figure 11: Uncertainty of generated global stress in S1 and S2 directions using simulation data.
## 2 Related Work
The paper shows that the multi-objective solver based on a simulated _GW_ society can find better non-dominated solutions in reduced time, compared to other _SI_ algorithms. Since a multi-objective solver can find several _PFs_ simultaneously, some forms of assessing the relative quality of _PFs_ is usually applied to output a final decision. The hypervolume [PERSON] et al. (2019) and the _R2_[PERSON] et al. (2019) indicators are two recommended approaches. The work in [PERSON] et al. (2019) follows this idea, and presents a hybrid multi-objective optimization algorithm which combines _PSO_ with _R2_. It turns out that this achieves a better performance than using the meta-heuristics alone. The good search capabilities of the _WOA_ led [PERSON] et al. [PERSON] et al. (2019) to use it as a multi-objective optimizer. The method complemented the _WOA_ with an external grid archive to store the best optimization values, found so far, for each objective. This heuristic achieves high convergence rates in short number of iterations. However, even though the method exploits the search capabilities of _WOA_, it does not propose any enhancements for the _WOA_ search capabilities. Specifically, the memory element in [PERSON] et al. (2019) was used to store non-dominated solutions, but no other solutions are further explored to add more diversity to the dynamics of the swarm members. Another version of _WOA_ was combined with the Simulated Annealing (_SA_) heuristic to be applied for feature selection [PERSON] et al. (2019). The method used the _SA_ for the intensification phase, which presents the need for _WOA_ for more enhancements. Another modification was proposed by [PERSON] et al. [PERSON] et al. (2019). They proposed combining a local search strategy with _WOA_ for the permutation flow shop scheduling problem. This clarifies the correctness of our analysis for the _WOA_ and its need for a local search method. To sum up, although _WOA_ exhibits a good performance in solving single objective and multi-objective problems, it still has some drawbacks. First, it is common that only a single best solution is recorded over the whole search space, thus increasing the probability to fall into convergence toward a local optimum. Secondly, despite adding an external archive to store multiple best solutions when solving _MOPs_, but these solutions are not always used to guide the future evolution of swarm members. Finally, the _WOA_ diversification technique is not efficient enough, so it was enhanced by adding a local search strategy as proposed by [PERSON] et al. [PERSON] et al. (2019). From previous work, the novelty of the proposed _MOWOATS_ algorithm is emphasized. The hybridization of _WOA_ and _TS_ for _MOPs_ is new and has not been proposed before. The next section contains a brief mathematical description of _MOPs_ and gives a presentation about both _TS_ and _WOA_ and how they can be combined for solving a _MOP_.