esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study presents part of a pilot work for the structural health monitoring of a large tied-arch reinforced concrete aqueduct in eastern China. Based on field-monitored data for over a year, it mainly focuses on the effect of air temperature and water load variations on the static behavior of a typical span of the aqueduct through field monitoring and 3D FE model analysis. It is found that the longitudinal deformation of the composite tied-arch shows a good linear relationship with the air temperature during the non-operation period and also has a good bilinear correlation with the air temperature and water level during operation. However, isolation of the air temperature effect from the second bilinear correlation using the first linear relationship results in a poor correlation between the longitudinal deformation and water level due to the dominance of the temperature effect. Therefore, it is recommended to use the bilinear regression to predict the longitudinal deformation of the tied-arch during operation. The vertical deformation of the tied-arch is insignificantly affected by air temperature, whereas it shows a fair bilinear correlation with the air temperature and water level during operation, which can be used to provide a reasonable estimation of the vertical deformation of the tied-arch. The strain measurements of the tied-arch using vibrating-string gauges are more complicated due to the notable influence of the ambient temperature and solar radiation, but the relatively consistent bilinear regression of the strains versus the air temperature and water level can still give fair predictions for the strains of the bottom tension rods during operation. The 3D FE model can provide a fair estimation for the vertical deformation of the tied-arch under water load, but its predictions for longitudinal deformation and strains are less satisfactory when compared to monitored data excluding temperature effects.  
a +
Applied Sciences
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 2 MFT Design and Magnetic Materials  
### MFTs Design  
Currently, there are several MFT design methods [9; 10; 16; 17; 18]. Each method has a specific contribution and main emphasis on specific characteristics of the MFT, such as temperature, core losses, core cost, and power density, among others. Each method is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Frequency** & **\(B_{ac}\)** & **Core** & **Power** & **Efficiency** & **Power Density** \\  & **(kHz)** & **(T)** & **Material** & **(kVA)** & **(\%)** & **(kW/L)** \\ \hline
[19] & 1 & 0.9 & Nanocrystalline & 1 & 80.2 & 2.50 \\
[21] & 10 & 0.46 & Nanocrystalline & 200 & 99.4 & 8.00 \\
[17] & 5 & 0.9 & Nanocrystalline & 50 & 99.5 & 11.50 \\
[16] & 5 & 0.9 & Nanocrystalline & 1 & 99.4 & 15.01 \\
[15] & 10 & 0.3 & Nanocrystalline & 5 & 96 & 19.90 \\
[22] & 10 & 0.3 & Nanocrystalline & 35 & 99.2 & 23.30 \\
[14] & 10 & 0.3 & Nanocrystalline & 35 & 98 & 23.30 \\ This proposal & 20 & 0.8 & Nanocrystalline & 1 & 99.7 & 36.91 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of medium frequency transformers with nanocrystalline core.  
commonly verified, implementing and testing an experimental prototype designed with a specific material (nanocrystalline alloys, ferrites, or silicon steel).  
For this research work, the design method is based on [16], in order to obtain a high power density, and therefore increase the power density of DC-DC converters that use MFTs, in addition to increasing the power density of systems that use this type of converter, such as photovoltaic and wind systems, electric vehicles, and solid state transformers. Figure 2 presents the flow diagram of the design procedure used in this work.  
Two MFTs are designed, one with nanocrystalline alloy core, and another with a ferrite core both at to 20 kHz, 1 kVA, and 120/240 V.
## 2 Image Processing in Remote Sensing
### Image Feature Detection
On comparing the different image processing techniques, it can be assessed that the efficiency of the techniques depends on the application, type of noise present in the image and the type of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Test Images** & **Application** & **Performance Metrics** \\ \hline
1. Fuzzy logic based detection & Hyperspectral image [78] & Edge detection & _PSNR_, _MSE_ \\ \hline
2. Deep Convolutional Neural Network [79] & QuickBird VHR image & Informal settlement detection & Overall accuracy \\ \hline
3. Invariant pixel detection, PCA [80] & Landsat 7 ETM+ images & Cloud detection & Accuracy and F-measure \\ \hline
4. Active contour model [81] & Google Earth images & Shadow detection and height estimation & Precision and recall \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Feature Detection methods and their performance metrics. image used. The accuracy of feature extraction, detection, fusion and change detection affect the extend of segmentation accuracy and hence the result. The presence of varying noise types decide the enhancement technique which affect the final outcome. The complexity of the satellite images in terms of edges, contours and textures paves way towards importance of general feature extraction. By adopting techniques suitable for application, the final image processing outcome can be modified to get the required result.
###### Abstract  
This study aimed to investigate the chloride-binding capacity of alkali-activated fly ash (denoted as FSW) and slag (denoted as SSW) samples and their synthesized Cl-bearing phases, which are capable of binding and immobilizing chloride when seawater is used as the mixing water. This study also examined the progressive changes in the pore structures of the FSW and SSW samples over time. The results show that the SSW sample is significantly more effective in the uptake of chloride ions compared to the FSW sample at 28 days of curing. While the FSW sample forms Cl-bearing zeolites (Cl-chabazite and Cl-sodalite) (possibly with similar types of geopolymeric gels), the SSW sample synthesizes Cl-bearing, layered double hydroxides (LDH) (Cl-hydrocalumite and Cl-hydrotalcite). Although both samples involve Cl-binding phases, the FSW sample is likely to be less efficient because it largely produces zeolites (or similar geopolymeric gels) with no Cl-binding capability (i.e., zeolites X and Y). Meanwhile, the SSW sample produces Cl-bearing LDH phases as well as C-S-H(I), which can physically adsorb chloride. The SSW sample exhibits both pore-size refinement and porosity reduction over time, while the FSW sample only exhibits pore-size refinement. Therefore, the SSW system is more advantageous in the use of seawater because it more effectively prevents Cl ingression due to greater impermeability.  
alkali-activated binders; fly ash; ground-granulated blast furnace slag (GGBFS); seawater; chloride; zeolite; layered double hydroxide
# WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds
## 2 Methods
### Orientation and Dense Model Generation  
#### 2.2.1 Images' Orientation and Self-Calibration  
The cameras' orientation and self-calibration were performed hierarchically in three steps following the pipeline of Figure 1:  
* First, the initialisation of the first image pair was carried out, selecting the best pair of images. To this end, a trifold criterion was established for selecting the initial image pair: (i) guarantee a good ray intersection; (ii) contain a considerable number of matching points; (iii) present a good matching points distribution along the image format. Note that initialising with a good image pair usually results in a more reliable and accurate reconstruction.
* Second, once the image pair was initialised, image triangulation was performed through the direct linear transformation (DLT) [45], taking the matching points and the camera pose provided by the fundamental matrix as input data. Afterwards, and considering this initial image pair as a reference, new images were firstly registered and then triangulated using again the DLT. The DLT allows us to estimate first the camera pose and then to triangulate matching points in a direct way, that is, without initial approximations and without calibration camera parameters. As a result, 2D-3D correspondences are provided together with the registration of the images.
* Third, although all the images were registered and triangulated based on DLT, this method suffers from limited accuracy and reliability, which could drift quickly to a non-convergent state. To cope with this problem, a bundle adjustment (BA) based on a collinearity condition [24] was applied with a threefold purpose: (i) compute registration and triangulation together and in a global way; (ii) consider the estimation of the inner parameters of the camera, self-calibration; (iii) obtain more accuracy and precision in the image orientation and self-calibration, using a non-linear iterative procedure supported by the collinearity condition that minimises the reprojection error.  
As a result of this orientation and self-calibration stage, an integrated registration and triangulation are obtained which entails that all images are connected and related according to the camera pose (rotation matrix and translation vector), without forgetting the inner parameters of the camera (self-calibration).
## 3 Results and Discussion
### Kinetics of Release during In Vitro Digestion  
To investigate the potential application of microcapsules containing 10-HDA in practical delivery [38], in vitro release was carried out, and the results are shown in Figure 9. During SGF, a rapid release was observed during the first 30 min, possibly due to unembedded 10-HDA. However, over the 2-h exposure period, the release rate eventually stabilized to \(32.95\pm 2.34\%\). In the stomach environment, GE and AG had opposite charges, and electrostatic attraction prevented the microcapsules from disintegrating. However, this force disappeared in the intestinal environment because both GE and AG were negatively charged [12]. Due to the hydrolysis of pepsin, the release rate of pepsin began to rise after 90 min. Under exposure to SIF, the release rate increased rapidly. The electrostatic interaction between GE and AG changed due to the intestinal pH, leading to disintegration of the microcapsules. Trypsin specifically hydrolyzed hydrophilic amino acid residues, which accelerated the disintegration of the microcapsules [39]. The microencapsulated 10-HDA was released almost completely (\(98.77\pm 1.91\%\)) in 240 min, which would be conducive to absorption in the intestine.  
Figure 8: LA and PCA results for the electronic tongue: **(a)** LA and **(b)** PCA.  
Figure 9: Release rate of microcapsules under in vitro digestion.  
The in vitro digestion experiments showed that the final release rate in the gastric phase was only about 30%, with a more rapid release observed in the intestinal phase, suggesting that the microencapsulation of RJSR provided better enteric-coated properties [40].
## 1 Introduction
This requires the development of robust control strategies that can process AE signals, extract relevant information, and adjust machining parameters accordingly. Moreover, the adaptive control system must be capable of responding to real-time changes in machining conditions without compromising the stability and performance of the CNC machine. To address these gaps, the main contributions of this study are as follows:  
* The investigation of AE signal characteristics during the CNC milling of wood-based materials under varying machining conditions;
* The evaluation of the feasibility of using AE signals for adaptive control by linking AE features to critical machining variables;
* The identification of challenges and limitations associated with AE-based monitoring, including signal variability due to material heterogeneity and environmental influences. The aim of this study is to investigate the use of acoustic emission (AE) signals for adaptive control in the CNC wood-based-material milling process. By analyzing the relationship between AE signal characteristics and machining variables, this research seeks to enhance the ability of CNC systems to respond to changing conditions in real time, improving overall machining performance and product quality. Additionally, the study will address the challenges associated with signal variability, including material differencesand the influence of environmental factors, which can affect the accuracy of AE-based monitoring systems. The novelty of this study lies in the application of acoustic emission (AE) signals for real-time adaptive control in CNC wood-based-material milling--an area where limited research exists and where AE integration remains controversial. Unlike previous studies, this work systematically investigates the correlation between AE signal features and machining parameters in wood materials.
# The Effect of Exposure to an Electromagnetic Field on Entomopathogenic Fungi
## 1 Introduction
Species like _Beauveria bassiana_, _Cordyceps fumosorosea_ (formerly _Isaria funosorosea_), or _Metarhizium anisopliae_ are used for controlling many pests which affect humans, such as mosquitros or ticks, or plants, such as bark beetles, aphids, whiteflies, thrips, or mites [25]. Entomopathogenic fungi-based biopesticides play an important role in the integrated pest management (IPM) programs of many crops [26; 27; 28]. _Beauveria bassiana_, _Cordyceps fumosorosea_, and _Metarhizium anisopliae_ are the main species of EPF used in plant protection products. EPFs colonize the soil environment or plants with hyphae that compose the mycelium [29]. They infect hosts mainly by penetrating their bodies through the exoskeleton. The hyphae and mycelium growth of these fungi depends on many abiotic and biotic factors, such as temperature, moisture, soil pH, nutrient availability, or pollutants [30]. It is possible to use some factors to stimulate or inhibit the growth and sporulation of fungi, bacteria, and other microorganisms on artificial media [30], which may be important, among other things, for the mass production of EPFs. One of the directions of research on entomopathogenic organisms is the search for factors that may increase their pathogenic abilities. Current research results indicate the possibility of using magnesium ions [31] or static electromagnetic fields [32], among other factors. The aim of the study was to determine the effect of exposure to 900 and 1800 MHz electromagnetic fields on the colony growth, sporulation, and pathogenicity of the entomopathogenic fungi _B. bassiana_, _C. fumosorosea_, and _M. anisopliae_.
## 2 Related Work
### Lightweight and Real-Time Models  
In response to growing demand for deployable solutions, researchers have introduced several lightweight architectures for low-light image enhancement. EnlightenGAN [12] proposed an unpaired learning framework with a relatively shallow architecture. [PERSON] et al. [13] presented a fast and lightweight network that balances inference time and image quality for real-time applications.  
Several models employ encoder-decoder frameworks optimized with depthwise separable convolutions, channel attention mechanisms, or mobile-friendly backbones such as MobileNet and GhostNet [22; 23]. These designs reduce FLOPs and parameter count without drastically compromising visual quality.  
There is also growing interest in hardware-aware neural architecture search (NAS) techniques to automatically discover efficient models tailored to specific deployment platforms [24].  
To better contextualize our approach, Table 1 provides a comparative overview of recent low-light image enhancement methods, highlighting their supervision strategies,computational characteristics, and applicability to real-time and edge scenarios. This comparison also outlines how our model addresses key limitations in prior work.
## 3 Results
### The Regional and Seasonal Contribution to the Global NEP IAV
#### 3.2.2 Seasonal Contributions to IAV of NEP  
The contribution to NEP IAV varied between seasons (Figure 7). In MAM, some regions around the equator contributed the most to NEP IAV, with contributions larger than 0.006% to global NEP IAV at grid-cell scale. There were negative contributions to global NEP IAV in some southwestern North America, Asia, and Oceania regions. In JJA, the contribution in the Northern Hemisphere was larger than in the Southern Hemisphere. In some areas of North America and Europe, the contributions to IAV NEP were larger than 0.01%. However, in most areas of the Southern Hemisphere, the contributions were less than 0.001%. In SON, the contributions were the smallest, and the contribution was smaller than 0.001% in most regions over the globe. In DJF, the contribution in the Southern Hemisphere was larger than in the Northern Hemisphere, especially in most areas of South America and Africa, where the contributions were larger than zero. However, in the Northern Hemisphere, most contributions were less than zero.  
Figure 5: The spatial patterns of contribution to NEP IAV.  
We counted the seasons' contributions to the IAV of NEP. The results showed that JJA contributed most (43.1%) to global NEP IAV, followed by MAM (25.33%) and DJF (22.89%). Furthermore, SON contributed the least to the global NEP IAV compared to other seasons (Figure 8). These results indicated that JJA played a vital role in regulating the global NEP IAV.
## 5 Energy Consumption and System Optimization in WBSNs
### Constraints  
Based on a patient's condition and the level of monitoring required, the physician can specify a monitoring interval of \(T_{idr}\) data for each sensor update, _i.e._, the time interval for sensor \(i\), will satisfy \(0\leq T_{i}\leq T_{idr}\) for \(i=1,2\...,N\). There is a restriction regarding the total amount of available time partitions. Each partition or \"slot\" has a \(T\) size, which gives \(1/T\) partitions in a time interval unit. Since each sensor is \(1/T\) partitions in this interval, and each partition can be assigned at most to one sensor, we get the restriction \(\sum_{i=1}^{N}\frac{1}{T_{i}}\leq\frac{1}{T}\)[49]. Sample size measurement between two updates will be constrained to only a fraction \(X_{i}\) of the size of buffer \(B_{i}\), without exceeding \(X_{i}B_{i}/(SF_{i}M_{i})\). Another constraint is referring to the total bits to transmit by each update, which is limited to the Time Slot length \(DT\), Data Rate and the Overhead \(\left(\frac{DT\cdot DR-\partial v}{SFiMi}\right)\).
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 5 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Average [Maximum] Position RSSE** & **Average [Maximum] Velocity RSSE** \\ \hline EKF (all) & 16.51 [31.83] km & \(2.07\times 10^{-2}\) [\(3.40\times 10^{-2}\)] km/s \\ SLFN (training) & 5.50 [13.20] km & \(6.32\times 10^{-3}\) [\(1.33\times 10^{-2}\)] km/s \\ SLFN (prediction) & 6.90 [19.70] km & \(8.76\times 10^{-3}\) [\(2.13\times 10^{-2}\)] km/s \\ \hline \multicolumn{3}{c}{Standard Deviation (Position)} & Standard Deviation (Velocity) \\ \hline EKF (all) & 6.27 km & \(6.36\times 10^{-3}\) km/s \\ SLFN (training) & 2.49 km & \(2.62\times 10^{-3}\) km/s \\ SLFN (prediction) & 3.04 km & \(3.72\times 10^{-3}\) km/s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ELM-trained SLFN and EKF performance comparison. Figure 8: The daily EKF (red) and SLFN (blue) position (**top**) and velocity (**bottom**) mean RSSE over time, obtained from an extended 120-day simulation. Daily standard deviations are represented as error bars. Figure 7: The EKF (red) and SLFN (blue) position (**top**) and velocity (**bottom**) RSSE over time. The yellow and green backgrounds distinguish between the training and prediction phases of the SLFN, respectively. The horizontal dashed lines correspond to the mean RSSE values over the whole run.
## 4 Proposed DETD Algorithm and Simulation Results
### Short-Comings of Existing Schemes and Novel Features of DETD  
Every day with variation of probable theft/non-theft durations requires the generation of new theft cases based on the concept of \"theft window\". Existing schemes based on our literature review have shown to have really high theft detection performance for traditional standard theft cases. However, for ToU pricing-based theft cases, we can see via simulation that existing schemes have deteriorated performance.  
For example, in our average simulation results for 1000 consumers, Table 2 shows the existing SVM-based CPBETD algorithm [13] has a detection rate of 67% for d-ToU cases and 72% for f-ToU cases, while the XGBoost-based GBTD algorithm [15] has a detection rate of 87% for d-ToU cases and 89% for f-ToU cases. Additionally, the CPBETD algorithm exhibits 34% FPR in d-ToU cases and 29% in f-ToU cases, while the GBTD algorithm shows 11% FPR in d-ToU cases and 8% in f-ToU cases. As a result, existing schemes do not give satisfactory results for those ToU cases. Hence, in order to protect the SG from potentially such \"smart\" theft, this paper presents another XGBoost-based algorithm called \"DETD\", which will be explained in the next subsection in detail.
## 3 Results and Discussion
### Effect of Spacer Structure on the Size of Silver Nanoparticles  
The size of AgNPs capped with gemini and their particle size distributions were obtained using DLS measurements. The Figure 7 shows hydrodynamic diameter of nanoparticles stabilized with gemini surfactants16-6-16 and 16-O-16, which is plotted as a function of the nAg/nGemini molar ratio. The nanoparticle diameter values illustrate an average value of the particle size distribution.  
For experiments 4-6 we can observe, that with decreasing of 16-O-16 concentration the silver nanoparticles diameter size increase. However, in the case of 16-6-16 (experiments 1-3) the opposite trend is noticed--we monitor the almost constant diameter size in the range of all molar ratios. Construction of the spacer of gemini surfactants have a considerable impact on nanoparticles size. Compounds with more hydrophobic linker stabilizes more efficiently and gives smaller AgNPs than analog with spacer functionalized by ether group.  
Hydrodynamic diameter of silver nanoparticles, stabilized with 16-6-16 from synthetic procedure with excess of reductor, are 110 and 220 nm for experiments 7 and 8, respectively. These results clearly show that synthetic procedure applied in experiment 7 gives the smallest AgNPs with the best stabilizing effect of gemini bilayer. Figure 8 represents the particle size distribution histograms of AgNPs synthesized in the experiments 7 and 8.  
In fact, the particle sizes of AgNPs measured by DLS is the whole conjugate size, with countslayer of capping agents too [88]. This explains the relatively big diameters. [PERSON] et al. explained that the gemini cationic surfactants can be adsorbed on the particle surface due to electrostatic interactions between surfactant positive charge head group and counterions attached onto the nanoparticle surface. This causes the formation of coating layers, which lowers the aggregation of particles [90]. [PERSON] et al. demonstrates that gemini surfactants with dodecyl substituent and different spacer length from two to twelve carbon atoms are able to efficiently stabilize silver nanoparticles [87]. Our results indicate that gemini surfactants with longer substituent can also be applied as efficient stabilizer for AgNPs.  
Figure 8: Particle size distribution histograms of AgNPs obtained by DLS measurements.  
Figure 7: Size of silver nanoparticles capped with 16-O-16 and 16-6-16 as a function of nAg/nGemini molar ratio.  
Introduction of oxygen atom to spacer results in an increase of hydrophilicity of surfactant molecule. This caused increasing of formed silver nanoparticles size, indicating that the stabilizing effect of gemini bilayer is less efficient. We also demonstrate that, not only the kind of stabilizing agent is crucial, but also synthetic procedures and silver to gemini surfactant molar ratios are very important factors in AgNPs synthesis. These results are in good correlation with what was described previously [87].