esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 2 Literature Review
In ([PERSON] et al., 2018), a hybrid energy-efficient clustering was proposed using a convolutional neural network (CNN) and a modified K-means (MKM) clustering algorithm. The CNN identifies the best partitioning or cluster formation, while MKM selects the CH. ML- and DL-based models are computationally complex, requiring significant processing power and memory, which are often limited in sensor nodes. Thisleads to increased energy consumption, a critical issue since WSN nodes typically rely on limited battery power. From the above literature, it can be surmised that hybrid metaheuristic algorithms perform better than single metaheuristic models for clustering and routing algorithms in terms of energy efficiency. The BOA is based on the social foraging behaviour of butterflies, using fragrance to share information among agents while incorporating information loss. Unlike the FOA and GA, the BOA's search mechanism includes a switch probability, balancing movements towards the best solution with random walks. The BOA gives all solutions an equal opportunity for improvement, avoiding the premature discarding of potential solutions. Moreover, the BOA's mechanisms for maintaining a balance between exploration and exploitation contribute to its robustness and stability in finding optimal clustering solutions. This helps achieve consistent results across different runs. The global search capability of PSO stands out among other metaheuristic algorithms. PSO excels at exploring the global search space to find optimal or near-optimal solutions. In routing problems, this capability is crucial for identifying the best routes among a multitude of possible paths. Additionally, PSO quickly converges to good solutions by moving particles towards the best-known positions, which is beneficial in routing problems where timely solutions are critical. No research has been conducted using a hybrid model of the BOA and PSO for clustering and routing algorithms. The performance of the BOA and PSO algorithm is highly dependent on the fitness function. In this study, we consider fitness parameters that are best suited for a disaster scenario. This study uses the hybrid model to measure the performance of the network.
## 2 Materials and Methods
### Validation Point Estimation  
The number of samples calculated with Equation (9) is shown in Table 5. The assumption of this expression claims that randomly generated validation points are proportional to the size of the selected class. In other words, the larger the class is, the more sample points are needed to verify the correct LULC assignation. For instance, as vegetation represented  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Value** & **Range** \\ \hline
1 & \textless{}0 & Poor \\
2 & 0.01–0.40 & Fair \\
3 & 0.41–0.60 & Acceptable \\
4 & 0.61–0.80 & Good \\
5 & 0.81–1.00 & Excellent \\ \hline \hline \end{tabular}
\end{table}
Table 4: Possible ranges of map comparison and level of agreement of Kappa coefficient (\(K\)).  
the larger area of the city and its surroundings, more points were needed to validate the land use classification. This behavior can be seen in Table 5 for the macro-classes in question. Similarly, the number of samples required to validate the classes presents the same behavior; the classes representing mixed forest (vegetation) and bare surfaces (bare soil) needed 79 and 88 samples, respectively, as they are the largest land uses in the study area.  
It is important to mention that pixels representing the sea area were not counted, as they did not change significantly during this period of time and we found no factor for any small increment of decrease that suggested any environmental effect on ties. However, it was taken into consideration to illustrate the applicability of the classification algorithm. On the other hand, rivers and water bodies might be subjected to changes due to water availability or variations in hydrologic processes.
## 4 Experimental Results and Analysis
### Experimental Result
#### 4.2.2 Sensitivity Analysis of Algorithm to Starting and Ending Points  
To prove that the proposed algorithm has the ability to plan routes, three pairs of starting and ending pairs (OD pairs) were selected on the road network datasets of Xi'an and Wuhan, and experiments conducted on three benchmark algorithms and improved genetic algorithms (\(urgency=0.2\), \(t_{0}=9:00\)). The average results are shown in Figure 8 (scenic score) and Figure 9 (running time). In Figure 8, for different starting and ending points, the four algorithms can plan routes with different scenic scores. The path scenic scores of the fastest algorithm and the improved GA are always the minimum and maximum. The path scenic score of the traditional GA is much lower than that of the improved GA, which shows that the improved genetic algorithm based on the gene replacement operator and the gene splicing operator has a good performance for this problem. The MA has a lower scenic score than the improved GA because when the MA encodes the chromosome, it randomly selects the edge close to the current origin, resulting in poor quality of the chromosome.  
Figure 9 shows the running time of several algorithms for planning different starting and ending routes. The fastest algorithm has the shortest running time, while the traditional GA has the longest. The running time of the improved GA is shorter than that of the MA and the traditional GA. The reason for this is that the improved GA adds a gene replacement operator. The implementation of this operator speeds up algorithm convergence to a certain extent. At the same time, the chromosome encoding method of the improved GA will  
Figure 8: Comparison of scenic score under different start end pairs. (**a**) Xi’an; (**b**) Wuhan.  
Figure 7: Convergence curves of the four algorithms.  
not produce too many chromosomes. Overall, on the road network datasets of Xi'an and Wuhan, the algorithms show identical performance, but the scenic score on the road network datasets of Wuhan is higher because Wuhan has more POIs to climb, which makes the density of scenic edges in the road network higher.
## 2 Materials and Methods
### Magnetic Field Data  
The local magnetic field intensity data was obtained using magnetometers (Zonge Engineering ANT-4) located at five sites, namely, Lithuania, Boulder Creek, CA, USA, the eastern province of Saudi Arabia and the north island of New Zealand. All of those devices are part of the Global Coherence Monitoring Network [28]. Two magnetometers are positioned in the north-south and east-west axes at each site in order to measure the time-varying magnetic field strengths (sensitivity 1 pT) over a wide frequency range (0.01-300 Hz) while maintaining a flat frequency response. The sampling rate of each magnetometer is 130 Hz. The magnetic field intensity data is captured by data acquisition infrastructure, stamped with the global positioning system time, and transmitted to a common server. As mentioned previously, the groups of participants are located not  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & \(\mathbf{N}\) & **Age Mean, y** & **Age SD, y** & **Gender, \% Female** \\ \hline United Kingdom & 14 & 39.2 & 12.3 & 57\% \\ Lithuania & 20 & 23.3 & 0.6 & 80\% \\ New Zealand & 22 & 42.7 & 10.1 & 50\% \\ Saudi Arabia & 20 & 20.7 & 2.6 & 50\% \\ California & 20 & 54.7 & 8.4 & 55\% \\ All participants & 96 & 36.0 & 15.1 & 58.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The age and gender breakdown for all participants by country. Note that columns 2–5 display the number of participants, mean and standard deviation of their age (in years) as well as the percentage of female participants, respectively.  
far (up to several hundreds of kilometers) from the sites where the magnetometers are installed. Such a proximity does help to ensure that local magnetic field data registered by the magnetometers does correspond to actual fluctuations of the magnetic fields at the sites of the experiment [28].
## 4 Conclusions  
This study evaluated the effect of underpinning piles' axial stiffness on the existing and underpinning pile's load sharing behavior during the construction of vertical extensions to existing buildings. A series of model tests was performed by placing underpinning micropiles at the center of a \(2\times 2\) array of existing piles. The results led to the following conclusions:  
(1) Single pile loading tests found the newly developed waveform micropile to show higher axial stiffness and load bearing capacity than a conventional micropile owing to the effect of shear keys along its shaft that increase the pile's shaft resistance;  
(2) Underpinning with micropiles reduced the total settlement of the foundations. The degree of settlement reduction increased linearly with the increment of the micropile's stiffness. The relationship between the settlement reduction and micropile's stiffness from the experiment agreed well with the theory of [PERSON] and [PERSON] [PERSON] and [PERSON] (2010). This result implies that additional data through future study can be used to propose the predictive equation for the design of underpinning pile foundations;  
(3) The load sharing capacity of a micropile increased with applied loads. Increasing the axial stiffness of a micropile increased its load sharing capacity under an applied load. The waveform micropile had a larger load sharing capacity than a conventional micropile due to the effect of its shear keys increasing the axial stiffness and load bearing capacity;  
(4) The normalized load sharing ratio increased linearly with the normalized stiffness ratio of the underpinning pile to the existing pile, with a gradient of about 1.8. This implies that increasing the underpinning pile's stiffness is an effective method to enhance its load sharing capacity. This could reduce the number of underpinning piles required and reduce construction costs.  
This work could be a reference for the practical design of underpinning guidelines. In addition, as small-scale tests are limited due to the stress level effect, the practical applicability of these model test results would be enhanced by further studies employing centrifuge tests and numerical analysis.  
Conceptualization, [PERSON]-T.H., C.W. and [PERSON]-[PERSON]; experimental methodology, C.W.; validation, J.-T.H. and Y.-E.J.; formal analysis, C.W., [PERSON]-[PERSON] and [PERSON]-E.J.; writing--original draft preparation, C.W.; writing--review and editing, Y.-E.J.; supervision, J.-T.H.  
This research was funded by (19 RERP-B099826-05) from Residential Environment Research Program (RERP) funded by Ministry of Land, Infrastructure and Transport of Korean government.  
The authors declare no conflict of interest.
## 5 Conclusions and Future Work
In particular, when the L-converted traffic volume was 20 or more, the ES \(\geq\) 750 value exceeded 11% of the total. In a comprehensive analysis, the ES \(>\) 750 was less than 10% when the fairway width was 6 km or more in the sea area with one crossing passage at 5 to 20 ships per hour, which is the experimental condition used in this study, and the ship operator's burden was determined to be insignificant. Based on these results, it is expected that when designing routes between offshore wind farms to be established in the future, it will be possible to design the minimum fairway width to ensure safety according to the traffic volume involved. However, additional research is needed due to the following limitations of this study:  
1. Review using various risk assessment models. In this study, a simulation evaluation was performed based on the ES model for risk evaluation. Although this is a model that expresses the degree of psychological risk of ship operators in a quantitative form, there are models that are similar to the ES model, so it will be necessary to derive comprehensive results from the diversification of evaluation models;
2. Generalization of characteristics in various waters. In this study, an evaluation was performed for the case of two cross-passages occurring in the waters in front of Gunsan Port. However, the degree of risk may differ according to the traffic volume or traffic type, and in order to apply the results of the study it is necessary to study various cases that can be generalized;
3. It is necessary to reflect the psychological influence of the fairway width. When the fairway width is greater than a certain amount, the change in risk will be small, but at a fairway width of 2 km or less the risk will significantly increase. This is considered to be a model characteristic that reflects the psychological influence of the ship operators. In order to prove this, it will be necessary to study the effect of psychological safety on risk through operator questionnaires. Conceptualization, Y.-S.P. and W.-S.K. ; methodology, W.-S.K. ; validation, Y.-S.P. and W.-S.K. ; writing--original draft preparation, W.-S.K. ; writing--review and editing, Y.-S.P. ; visualization, W.-S.K. ; supervision, Y.-S.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**  
**Table A1. Factor values indicating the effect in the Environmental Stress Model assessment.
## 4 Methodology
### Daf-FasterNet
#### 4.2.1 Funnel Activation-FReLU  
Research on actual image data has found that the distribution of flames exhibits regional characteristics and that the variation between regions is also one of the features of flame combustion states [29]. In response to this characteristic, this paper proposes improvements to the original network. The original FasterNet block, after convolution, uses ReLU [30] as the activation function. Although ReLU is the most widely used activation function, experiments have shown that its insensitivity to spatial information hinders further improvement in accuracy. Therefore, this paper improves ReLU by replacing it with an activation function specifically designed for visual tasks called FReLU [31] to address this limitation. The original ReLU uses \(\max(\cdot)\) to perform the nonlinear operation, where the condition is manually set to 0, so the form of ReLU is \(y=\max(x,0)\). [PERSON] et al. improved recognition accuracy by adding a 2D funnel-shaped spatial condition (\(T(x)\)) that depends on spatial context to the activation function, allowing it to extract the precise spatial layout of objects. To implement the spatial condition, the authors used parameter pooling windows to create spatial dependencies.The comparison between ReLU and FReLU is shown in Figure 7. Therefore, the new activation function takes the form shown in Equations (9) and (10).  
\[y(x_{c,j,j})=\max(x_{c,i,j},T(x_{c,j,j})), \tag{9}\] \[T(x_{c,j,j})=x_{c,j,j}^{w}\cdot p_{c}^{w}, \tag{10}\]  
Figure 7: (**a**) represents ReLu: \(y=\max(x,0)\); (**b**) represents FReLu: \(y=\max(x,T(x))\).  
In Equations (9) and (10), \(x_{c,i,j}\) represents the input pixel of the nonlinear activation function (\(y(\cdot)\)) in channel \(c\) of the 2D spatial position (\((i,j)\)). The function \(T(\cdot)\) represents the funnel condition. \(x_{c,i,j}^{w}\) represents the parameter pooling window (\(k_{h}\times k_{w}\)) centered at \(x_{c,i,j}\). \(p_{c}^{w}\) denotes the window coefficient shared within the same channel.  
Therefore, FReLU generates spatial dependencies while performing nonlinear activation, with activation dependent on spatial conditions. Utilizing the properties of the spatial condition (\(T(x)\)) and the function expressed as \(\max(\cdot)\), FReLU can provide spatial layout capabilities, naturally and easily extracting the spatial structure of objects, thereby enhancing the network's recognition accuracy.
## 3 Results
### Color Measurements  
Surface color was measured in the CIE L* a* b* system. When analyzing the results obtained (Figure 14), it can be seen that the increase in the amount of potassium oxide in the glaze composition resulted in an increase in the brightness of the analyzed surface. The exception is glaze 3, for which the highest value of the parameter L* = 86.8 was obtained. The color achromaticity, i.e., the values of the a* and b* parameters, which are closest to zero, were also obtained for glaze 3. In the parameter case of other glazes, a* is closer to zero for potassium glazes, whereas the value of the b* parameter for these glazes is at a similar level.  
Surface color was measured in the CIE L* a* b* system. From the results obtained, it can be seen that the increase in the amount of potassium oxide in the glaze composition resulted in an increase in the brightness of the analyzed surface. The exception is glaze 3, for which the highest value of the parameter L* = 86.8 was obtained. The color achromaticity, i.e., the values of the a* and b* parameters, which are closest to zero, were also obtained for glaze 3. In the parameter case of other glazes, the a* is closer to zero for potassium glazes, while the value of the b* parameter for these glazes is at a similar level.  
The surface color results for glaze 3 make it difficult to determine the effect of andesine on the obtained color. In the analysis of the cases of glazes 1, 2, and 4, it could be concluded that this is a negative effect. However, taking into account the obtained values for glaze 3, the analysis is not so obvious. It is possible that this is related to an anomaly shown in the analysis of characteristic temperatures, where a deviation from the trend was also shown for this glaze. It is interesting that the presence of zirconium oxide crystalline phases (in small amounts compared to the amount of zirconium silicate) increases the brightness of the obtained surfaces. That is, when a surface of higher brightness increases, zirconium crystalline phases increase the surface brightness parameters.  
Figure 13: Raman spectra of zirconium silicate (red curve) and zirconium oxide (pink curve) [14].
## 3 Results
### Grassland Cover Change Trend Analysis  
#### 3.2.1 Grassland Cover Change Trend Classification from 2011 to 2019  
Combining FVC with NPP, the northern part of IMAR reflected an obviously decreasing trend, while the western part of IMAR fluctuates more strongly (see Figure 3). Around 33.69% of grasslands in IMAR are above the average coefficient of variation for the whole area and also show a decreasing trend. These areas are mainly located in the meadow steppe area in the west of Hulunbuir, Hinggan and east of Xilin Gol. Under good climatic conditions, the persistent decreasing trend reveals a constant human pressure on these meadow and typical steppe areas. With a lower precipitation rate, the western area is more sensitive to climate factors and reflects both increasing (19.21%) and decreasing trends (25.69%). Areas with an increasing trend are concentrated in the west and southeast area, covering the main part of Ordos, Hohhot, Bayan Nur, Baotou and Ulanqab. With mainly semi-desert grassland in these areas, the fluctuating increasing trend of 19.21% and persistent increasing trend of 21.42% reflects an overall improvement of grassland condition both in vegetation coverage and production.
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 3 SOC Estimation Based on the HIF Algorithm
### SOC Estimation Accuracy Verification
#### 3.3.3 Uncertainty of Application Conditions  
The applicability of the HIF algorithm under different operating conditions was evaluated using DST, FUDS, and HPPC conditions. It can be seen from Figures 11 and 12 that under DST and FUDS conditions, the current and voltage fluctuations are relatively severe. The noise interference of the battery model in this case is obvious, and it no longer exhibits the characteristics of zero mean. In addition, the model parameters change to a large extent with the current mutation. Figures 13 and 14 show that the EKF algorithm is used to estimate the battery SOC under the DST and FUDS conditions. The simulated output voltage of the battery model deviates greatly from the real voltage value, so that when the current pulse is large, the SOC estimation accuracy is not high, and fluctuates within \(\pm\)0.04.  
Figure 11: Current and voltage diagram under DST conditions.  
Figure 10: Comparison of SOC estimation results and errors under inaccurate SOC initial value based on DST conditions.  
on the HIF can be controlled within \(\pm\)0.01, which further verifies the applicability of the HIF algorithm under different working conditions.
# Risk and Resilience Assessment of Lisbon's School Buildings Based on Seismic Scenarios
## 3 Characterization of 32 School Buildings in Lisbon
This system consists of regular frames with spans around 4.5 m. The number of frames in the two directions form different building configurations (e.g., three spans of 4.5 m in each direction of the 3 \(\times\) 3 typology) and justify the definition of these three different typologies, thus facilitating the analysis and interpretation of results. Otherwise, typology \"reinforced concrete structure\" (RC) includes structures that do not fit into the aforementioned typified categories and, consequently, require an individualized analysis. Although these RC buildings are mostly frame structures, they are not based on modular, or typified, building structures. Among these typologies, two main building construction techniques can be identified. First, buildings typically from before 1960, mainly unreinforced masonry buildings with timber floors or reinforced concrete slabs, have rubble stone or brick masonry walls made with lime or cement mortar and two to three unconnected layers across the thickness. In the most recent buildings, it is also possible to find some vertical elements (columns) and  
Figure 2: Examples of the identified structural typologies of the school buildings. horizontal elements (beams) made of reinforced concrete. The poor connections between orthogonal walls and the presence of floors providing a weak diaphragm restraining effect may contribute to the poor seismic response of URM buildings [54]. Second, a large percentage of school buildings are made of reinforced concrete, which is one of the most representative building typologies in Portugal. A common feature in these buildings, namely in those constructed under non-existent or low-seismic design codes, is the lack of adequate seismic detailing and design philosophies now included in modern design standards around the world. The columns were generally designed only for gravity loads with low shear and flexural capacity. The lack of shear reinforcement in the joints, combined with the increase in forces due to the interaction between the RC frame and masonry infills, often caused the shear failure of beam-to-column joints in similar buildings around Europe [55]. Furthermore, in RC buildings, poor connection detailing is also associated with the collapse of infill wall panels. Table 2 presents the list of schools and corresponding construction dates, typologies, built-up areas, approximate number of students, as well as individual IDs that are represented in Figure 1. The detailed characterization of the typologies and their respective application to schools can be checked in the full report of this research study [22]. Among the 32 schools, there are four schools (greyed out in Table 2) that were excluded because they did not respect the basic assumptions of the study requested by the CML or because there was no information available to allow their analysis.