esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### The Basic Description of the Virtual Community Case  
As discussed in previous studies [8; 29; 36], the use of virtual community resilience testbeds is increasingly prevalent in natural hazard engineering research. These testbeds allow researchers to evaluate and validate resilience algorithms at various scales and spatial resolutions, departing from traditional component- and building-level simulations. This section introduces a simplified virtual community used to demonstrate the application of fragility curves and an interaction matrix for quantifying flood resilience at the community level. To ensure the virtual community represents a realistic yet manageable testbed for flood resilience analysis, the data selection process involved specific inclusion and exclusion criteria. The inclusion criteria encompassed the following: (1) building archetypes commonly found in flood-prone U.S. communities; (2) availability of fragility curve parameters (lognormal median \(\lambda\) and standard deviation \(\xi\)) from the established literature to ensure consistency with performance-based engineering principles; and (3) relevance to the four critical sectors (residential, business, educational, and public service) that collectively support community functionality. The exclusion criteria included the following: (1) building types with insufficient flood vulnerability data, such as specialized industrial facilities or high-rise structures not typically represented in small-scale communities; (2) archetypes lacking empirical or simulated flood damage records, which would preclude reliable fragility curve development; and (3) structures irrelevant to the study's focus on population dislocation as a resilience metric. This selection process resulted in a consolidated set of 11 building archetypes (as detailed in Table 3), balancing representativeness and analytical feasibility. The hypothetical community comprises approximately 1000 buildings distributed across four zones--residential, business, educational, and public service--as depicted in Figure 4. Building types in the residential zone include one-story and two-story single-family and multi-family residential buildings, while the business zone features multi-unit retail buildings, small commercial facilities, and large retail centers located along major roads. The public service zone consists of essential facilities such as hospitals, clinics, and gas stations, while the education sector includes one-story and two-story school buildings. To simplify the analysis, the building types within each zone are consolidated into archetypal categories that represent the minimum components necessary for a resilient built environment. This approach aligns with methodologies employed in previous resilience studies [8; 36], ensuring that the results remain generalizable. The virtual community serves as a case study to demonstrate the integration of fragility curves and interaction matrices in assessing flood resilience, providing insights into how these tools can guide planning and decision-making processes.
## 4 Results and Discussion
### Characterization of the Adsorbents  
#### 4.2.1 FTIR Analysis  
The FTIR spectra in Figure 1 show the presence of various functional groups on the surface of the paper waste-based adsorbents. A -CaO peak is observed at 625 cm\({}^{-1}\) for all adsorbents. This result is supported by the previous elemental analysis data and may be due to the manufacturing process of the paper [24]. The -CH functional groups are present in all adsorbents in the 2816 and 832 cm\({}^{-1}\) regions. However, the peak was more pronounced in the 832 cm\({}^{-1}\) region, which can be attributed to the cellulosic nature of the paper. A C-O peak was observed in the 1017 cm\({}^{-1}\) region and another peak, the -C=C-group, was visualized in the 1172 cm\({}^{-1}\) region [27]. The spectra of the samples were similar; the main difference as the intensity of the peak for -C=C-, which varied. This indicates that APWN and APWP had a higher content of the -C=C- group than the other samples.
## 3 Performance Analysis of System
### Harvest Energy Estimation for GCI-DCSK SWIPT  
It is assumed that the frame duration is 1s, the total direct-current (DC) energy collected in a frame is given by  
\[E_{h} =\eta(n-p_{2})\sum_{k=\beta_{1}+1}^{\beta}\left(\sqrt{\frac{\mu \mathbf{P}_{S}}{\mathrm{d}_{\mathrm{tr}}^{m}}}\sum_{l=1}^{L}\alpha_{v_{l}}a_{v _{k}-\eta_{l}}+n_{a,k}^{j}\right)^{2}\] \[=\eta(n-p_{2})(\beta-\beta_{1})\left(\mu P_{s}\;\mathrm{d}_{ \mathrm{tr}}^{m}\sum_{l=1}^{L}\alpha_{v_{l}}^{2}\frac{\mu E_{c}^{v}}{\beta}+ \frac{N_{0,a}}{2}\right), \tag{30}\]  
where \(\eta\) (\(0\leq\eta\leq 1\)) is the energy conversion efficiency factor when converting RF to DC, which is set to 0.5 in general [17]. As the energy sent by the transmitter is \(E_{S}=P_{s}\big{[}p_{2}+1+\mu^{2}(n-p_{2})\big{]}E_{c}^{v}\), the ratio of the harvested energy to the transmitted energy in a frame is given by  
\[\xi =\frac{E_{h}}{E_{s}}\] \[\approx\underbrace{\frac{\mu^{2}(n-p_{2})(\beta-\beta_{1})}{2 \beta[p_{2}+1+\mu^{2}(n-p_{2})]d_{tr}^{m}}}_{F}+\underbrace{\frac{(n-p_{2})( \beta-\beta_{1})}{4p\gamma_{b}d_{tr}^{m}}}_{C}, \tag{31}\]  
where \(F\) presents the harvested energy by the signals on inactive carriers, and \(C\) presents the collected energy by the overall noise, which can be ignored compared to \(F\). As mentioned in [17], if \(\xi\geq 0.01\), the system is energy self-sustainable. Then, the optimal value of \(\mu\) can be given by  
\[\mu\geq\frac{0.02\beta d_{tr}^{m}(p_{2}+1)}{(\beta-\beta_{1}-0.02\beta d_{tr}^ {m})(n-p_{2})}. \tag{32}\]
## 4 Experiment Environment
The reference point can be set to the point that the value is the upper bound of the corresponding objective function in every dimension. Thus, the calculation of MHVD for a solved _PF_ is formulated as Equations (18) and (19), where _HV_(_S_) represents the hypervolume of a point set \(S\). \[HVD_{t} =HV(PF_{t}^{*})-HV(PF_{t}) \tag{20}\] \[MHVD =\frac{1}{T}\cdot\sum_{t\leq T}HVD_{t} \tag{21}\]  
MACD is the time-average of the Average Crowding Distance (ACD) of PF, where the calculation of ACD can refer to [19]. For each of these three above performance metrics, a smaller value indicates better overall algorithm performance in both convergence and distribution characteristics. In this paper, all experiments were implemented by Python 3.12.2 and Numpy 1.26.4, and run on a personal computer with the Windows 11 HOME operating system, a 14 th Gen Intel Core i7-14700(F) CPU, and 16 GB of DDR5 memory. Each experiment was repeated 20 times, and the average result is reported below. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Problem** & **Objectives** & **PS** & **PF** \\ \hline DF1 & \(\min\begin{cases}f_{1}(x)=x_{1},\\ f_{2}(x)=g(x)(1-(\dfrac{x_{1}}{g(x)})^{H(t)})\\ g(x)=1+\sum\limits_{i=2}^{n}(x_{i}-G(t))^{2}\\ H(t)=0.75\sin(0.5\pi t)+1.25\end{cases}\) & \(0\leq x_{1}\leq 1\) & \(0\leq f_{1}\leq 1\) \\ DF2 & \(g(x)=1+\sum\limits_{i=1}^{n}(x_{i}-G(t))^{2}\) & \(x_{i}=G(t),i=2,\ldots,n\) & \(f_{2}=1-f_{1}^{H(t)}\) \\  & \(H(t)=0.75\sin(0.5\pi t)+1.25\) & \\  & \(G(t)=|\sin(0.5\pi t)|\) & \\ \hline DF3 & \(\min\begin{cases}f_{1}(x)=x_{1},\\ f_{2}(x)=g(x)(1-(\dfrac{x_{1}}{g(x)})^{H(t)})\\ g(x)=1+\sum\limits_{i=2}^{n}(x_{i}-G(t)-x_{1}^{H(t)})^{2}\\ G(t)=\sin(0.5\pi t)\\ H(t)=1.5+G(t)\end{cases}\) & \(0\leq x_{1}\leq 1\) & \(0\leq f_{1}\leq 1\) \\ DF4 & \(\min\begin{cases}f_{1}(x)=g(x)|x_{1}-a|^{H(t)},\\ f_{2}(x)=g(x)|x_{1}-a-b|^{H(t)}\end{cases}\) & \(0\leq x_{1}\leq a+b\) & \(0\leq f_{1}\leq b^{H(t)}\) \\ DF5 & \(g(x)=1+\sum\limits_{i=2}^{n}(x_{i}-G(t))^{2}\) & \(x_{i}=\dfrac{ax_{1}^{2}}{ic^{2}},i=2,\ldots,n\) & \(f_{2}=(b-f_{1}^{\frac{1}{H(t)}})^{H(t)}\) \\  & \(b=1+|\cos(0.5\pi t)|\) & \\  & \(c=\max\left(|a|\right|\), \(a+b\)) & \\  & \(H(t)=1.5+a\) & \\ \hline DF5 & \(\min\begin{cases}f_{1}(x)=g(x)(x_{1}+0.02 sin(w_{t}\pi x_{1})),\\ f_{2}(x)=g(x)(1-x_{1}+0.02 sin(w_{t}\pi x_{1}))\\ \end{cases}\) & \(0\leq x_{1}\leq 1\) & \(0\leq f_{1}\leq 1\) \\ DF6 & \(g(x)=1+\sum\limits_{i=2}^{n}(|G(t)|y_{i}^{2}-10\cos(2\pi y_{i})+10)\) & \(0\leq x_{1}\leq 1\) & \(f_{1}^{\frac{1}{n}}+f_{2}^{\frac{1}{n}}=1\) \\  & \(G(t)=\sin(0.5\pi t)\) & \(x_{i}=G(t),i=2,\ldots,n\) & \(+0.2\sin\left(3\pi\dfrac{f_{1}^{\frac{1}{n}}-f_{2}^{\frac{1}{n}}+1}{2}\right)\) \\  & \(y_{i}=x_{i}-G(t)\) & & \\  & \(a_{t}=0.2+2.8|G(t)|\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of DF1-DF6 in the CEC 2018 benchmark suite [9].
## 3 Experimental Results and Discussion
### NMR Results  
Based on Equation (1), the pore proportion with different sizes could be calculated for concrete specimens. However, there is no unified standard to accurately distinguish the pore size scale of cement-based materials (i.e., concrete, mortar, and cement soil). In the present study, the pore size division method of concrete proposed by [PERSON] et al. [43] was adopted to quantitatively analyze the degree of pore structure-repair of deteriorated concrete specimens by different surface reinforcer types. [PERSON] et al. [43] divided the pore size into four intervals according to the pore radius R: Harmless pores (R \(\leq\) 10 nm), less-harmful pores (10 nm \(<\) R \(\leq\) 25 nm), harmful pores (25 nm \(<\) R \(\leq\) 100 nm), and multi-harmful-pores  
Figure 4: \(E_{50}\) under different test conditions of surface reinforcer. (**a**) \(E_{50}\) changes with different surface reinforcer types. (**b**) \(E_{50}\) changes under different action durations. (**c**) \(E_{50}\) changes after different numbers of applications.  
(R \(>\) 100 nm), as shown in Figure 5. Table 3 illustrates the percentages of four kinds of pores under different conditions.  
Figure 5 and Table 4 clearly demonstrate that, for the standard concrete specimen, the proportions of harmless pores, less-harmful pores, harmful pores, and multi-harmful pores were 70%, 16%, 2%, and 12%, respectively, while the values changed to 67%, 17%, 5%, and 11% for freeze-thaw-treated specimens, respectively. The overall proportion  
Figure 5: Pore size distribution of repaired specimens. (**a**) Pore size distribution with different surface reinforcer types. (**b**) Pore size distribution under different action durations. (**c**) Pore size distribution under different numbers of applications.  
of multi-harmful pores and harmful pores was 14% for S-1, and this increased to 16% after 80 freeze-thaw cycles. In addition, the ratio of different pore sizes changed after the application of the surface reinforcer, with a decrease in the total proportion of multi-harmful pores and harmful pores, and an increase in the total proportion of harmless pores and less-harmful pores. The above phenomenon revealed that the surface reinforcer penetrated the interior of the deteriorated concrete specimen and was able to fill the harmful pores, thus decreasing the porousness and enhancing the compactness of the concrete specimens. Moreover, the total proportion of multi-harmful pores and harmful pores decreased with the increase in the action duration and number of applications, as shown in Figure 5b,c. Under the same action duration and number of applications, the repairing effect of surface reinforcer type C on the pore structure of deteriorated concrete was weaker than that of surface reinforcer types A and B.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 5 Natural Resource Extraction  
Australia's prevalent below- and above-ground natural resource extraction industries (e.g., the largest global producer of iron ore, bauxite, rutile, and zircon: [29]) present subsidence and land-stability monitoring needs to reduce potential risks to people, infrastructure, equipment, and productivity (e.g., [50]). Consequently, ground-based geotechnical monitoring is routinely performed, but the measurements are restricted to localised areas (e.g., using slope-scanning radar) and/or sparsely distributed points (e.g., surveying), failing to cover the entire operation. Conversely, InSAR facilitates measurements of displacements over a whole extraction site, providing spatially and temporally dense constraints upon the extent and advancement of ground displacements, which can be applied to validate and improve predictive models of subsidence or slope instabilities (e.g., [51]).  
Over the last 5-10 years, SAR and InSAR have begun to be incorporated into mine-site safety monitoring operations globally (e.g., [52]), facilitating complete monitoring of mine sites, including the stability of open pits walls/roads, waste piles [51], and tailings ponds and dams. The case studies presented in Sections 5.1 and 5.2 are the result of projects undertaken to demonstrate the value of InSAR as a tool for monitoring mining operations. Section 5.3 then summarises the work that has been undertaken to assess the impacts of coal seam gas extraction in the Surat Basin. In addition to these examples, commercial providers deliver operational geotechnical monitoring products utilising SAR data at other resource extraction operations in Australia (e.g., [51]).  
Figure 5: Observations of the Torrington fire, New South Wales, from Sentinel-1 imagery during November 2019. (**A**) RGB composite image utilising Sentinel-1 backscatter intensity imagery. The red line shows the extent of the burned area obtained from [48] with map product dated 20191113 and based on Landsat-8 data. (**B**) Sentinel-2 natural colour (RGB bands 4-3-2) optical images of the same region.
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 6 Conclusions  
In terms of shield tunnel crossed through the existing bridge piles foundation area in this project a feasible and safety foundation underpinning scheme is carried out, according to the field monitoring data and numerical simulation results, the following conclusions are drawn:  
1. A numerical simulation model is established which took all the influencing factors into consideration, according to the site monitoring data from underpinning construction process that proves the feasibility and reasonability of the pile foundation underpinning construction scheme.
2. To ensure the quality and safety of pile foundation underpinning construction work, a key step is to reducing the disturbance on the existing bridge structure during transfer the superstructure load of the existing pile foundation to the new underpinning foundation. Thus, choose a reasonable pre-jacking method is necessary. In this project, the load increment is added in stages which gives project construction engineers enough time to check the structural state of existing bridge and ensure the implementation of pre-jacking process.
3. According to the site monitoring data, the settlements of the bridge structure are dominated by the load transfer process while the prestress system is established and shield machine disturbance during shield tunneling. The settlement caused by the pile foundation underpinning construction accounts for nearly half of the total settlement that indicates the importance of taking reliable pile foundation underpinning design plan.  
As a conclusion, the maximal settlement in site monitoring of bridge structure during shield tunneling is 6.55 mm; the maximal concrete principal compressive stress and maximum principal tension stress of the underpinning bearing platform are 6.1 MPa and 0.36 MPa, respectively. Those results meet the settlement control target and concrete strength design index. Such methods could provide good reference and guidance for the construction of similar engineering in the future.
# HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing
## 1 Introduction
Another type of multi/hyperspectral sensor is a wavelength-scanning imager using a filter wheel or a tunable filter. This type of sensor also employs a 2D detector array, but it captures a spatial 2D image filtered at a certain spectral band that is switched by mechanically rotating the filter wheel or by electrically tuning the filtered wavelength. While a finite response time is required to switch from one spectral band to another for wavelength scanning, the resolution and the quality of the data do not depend directly on the  
Figure 1: Diagram of the high and moderate spatial resolution multispectral Earth observation satellites/sensors launched in the past. satellite speed. A filter wheel mounted with 15 spectral filters has already been applied to spaceborne low spatial resolution (>100 m) multispectral sensors, such as the Polarization and Directionality of the Earth's Reflectances instrument on the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a light detection and ranging (LiDAR) (PARASOL) microsatellite [7]. The disadvantage of a filter wheel in relation to small satellites is that the diameter, weight, and power consumption of the filter wheel all increase with the number of spectral bands. Recently, we have developed a high spatial resolution multispectral sensor using a tunable filter to overcome the disadvantage of a filter wheel, and installed it on the RISING-2 microsatellite. This sensor, which is equipped with a liquid crystal tunable filter (LCTF) for wavelength scanning, is called the High-Precision Telescope (HPT). To the best of our knowledge, the HPT is the first spaceborne sensor using LCTF technology. The advantages for small satellites in using the LCTF are related not only to the reductions in the size, weight, and power consumption of the sensor, but also to the increased flexibility of the spectral bands and enhanced data volumes. The central wavelength of the spectral bands is electrically tunable for every image acquisition; hence, the data volume could be reduced by choosing appropriate spectral bands for specific purposes. This flexibility allows a single multispectral sensor on a nano/microsatellite to be applied to various remote sensing fields, making it comparable with hyperspectral sensors. As shown in Figure 1, the HPT holds a unique position amongst the current multispectral Earth observation sensors. This paper presents descriptions of both the design and development of the HPT deployed on the RISING-2 microsatellite, and the data analysis procedure adopted for the acquired images.
and test case prioritization problems [9; 10; 11; 12]. [PERSON] and [PERSON] introduced and showed the benefits of agent sensitivity, including sensitive robots, in security-related problems [13; 14], such as a denial jamming attack on sensor networks. As a reference, the current work makes use of the state-of-the-art Canny [15] edge detection technique. Recently, the Canny operator was used in [16] during a symmetrical difference kernel SAR image edge detection process.  
The current paper introduces a version of ant colony method with a specific feature called pheromone sensitivity level, \(PSL\), for solving the medical edge detection problem. The artificial ants are endowed with different levels of artificial pheromone sensitivity; thus, the agents have different reactions in a dynamic environment. Here, the new algorithm is applied to the image edge problem and requires a heuristic value computed with two admissible perturbation operators applied to a demicontractive mapping.  
[PERSON] and [PERSON] proposed the first related theoretical approach in [17]; a step forward was made in [18]. It includes more tests for both ant colony versions of medical image edge detection and a comparison of these techniques; details, including the efficiency of the new parameters and the use of some demicontractive operators, are presented.  
The current work's content is as follows:  
* Sensitive ant colony optimization (SACO) for medical image edge detection is introduced to improve the analysis of CT and X-ray images.
* Image pre-processing is done.
* The use of several demi-contractive operators is employed to check their behavior for both ACO and SACO.
* Postprocessing, including the use of the DeNoise convolutional neural network (DnCNN), is done.
* A comparison between ACO, SACO and several state-of-art methods to ensure the validity of the sensitive approach on a CT and X-ray image dataset is made.  
The next section includes the present work's prerequisites with mathematical support, the edge detecting problem and the sensitive ant colony optimization (SACO) method. The numerical tests and bio-inspired methods results follows in Section 3. The comparison of methods, the operators' behavior and the representation of medical image results are discussed in Section 4. Future work and arguments regarding the benefits of ACO and SACO for medical images conclude the present study.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 3 System Description
### Hardware of the Transmitter
#### 3.1.1 Placement  
The transmitter hardware is placed near the antenna at the top of an elevation near the ASJI in a water-sealed box that protects the electronics from weather conditions and provides strong electromagnetic shielding (see Figure 2a). All the inputs and outputs from the box are properly filtered and buccaneer connectors are used for both data and power supply. It has also an air extraction system capable of working under strong winds and snow. The sensors are located near the ASJI so the data has to be sent to the transmitter, which is approximately 150 m away from the sensors. Initially, the communication between the sensors and the transmitter was made via a shielded twisted pair, but it broke several times because of the extreme weather conditions. Then, we decided to transmit the data and the control signals via WiFi from the ASJI, which proved to be a more reliable solution.
## 2 Construction Method of EMR-SF along the Road Collection Data
### Construction of EMR-SF by the LRBF  
The EMR data collected along the road are taken as the sample point set: \(S_{0}=\{s_{1}\) (\(x_{1}\),\(y_{1}\),\(e_{1}\)), \(s_{2}\) (\(x_{2}\),\(y_{2}\),\(e_{2}\)),..., \(s_{n}\) (\(x_{n}\),\(y_{n}\),\(e_{n}\))]. The set of interpolated sample points is S. The set of grid points after stratification is G = {G\({}_{1}\), G\({}_{2}\),..., \(G_{l}\)}, where G\({}_{l}\)= {\(g_{1}\) (\(x_{1}\),\(y_{1}\),\(g_{2}\)), \((x_{2}\),\(y_{2}\),\(g_{2}\)),..., \(g_{k}\) (\(x_{k}\),\(y_{k}\),\(g_{k}\))}, representing the grid point at the layer \(l\) and so on. The process of constructing the EMR-SF using the LRBF spatial interpolation method is as follows:  
1. The interpolation sample point set \(S=S_{0}\) is initialized, and the index number \(l=1\) is set.
2. Point set G\({}_{l}\) is taken from the grid point set.
3. The EMR value of each point in point set G\({}_{l}\) is generated by sample point set S according to Equation (1).
4. Point set G\({}_{l}\) is added to the interpolation sample point set S to form a new interpolation sample point set S = S \(\cup\) G\({}_{l}\).
5. \(l=l+1\).
6. Steps (2)-(5) are repeated until the EMR value of each grid point is calculated.
7. Finally, the EMR-SF is constructed according to the updated interpolation dataset S = {S\({}_{0}\), G\({}_{1}\), G\({}_{2}\),..., G\({}_{l}\)}.  
The LRBF spatial interpolation method flow is shown in Figure 6:  
Figure 5: Grid point layered. (**a**) The grid points. (**b**) The first layer. (**c**) The second layer. (**d**) The third layer.  
Figure 6: Procedure of the EMR-SF construction by LRBF.