esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Atmospheric Water Capturing: The Integrated Approach
Even if the energy required for condensation could be minimised, a meaningful amount of energy is still needed to move large quantities of air through the AWG, bearing in mind that the vapour content in environmental air is typically on the order of a few tens of grams per kilogram of dry air [21]. Nevertheless, in a traditional VCRC AWG, all the energy spent in the process gives only one useful effect, namely, water extraction from the air; consequentially this technique rises some concerns about its sustainability [29]. Figure 1: VCRC AWG working cycle scheme, highlighting the major components. The integrated AWG represents an improvement over such technology and could be a game changer in atmospheric water capturing [30] because it allows users to recover the thermal energy fluxes generated during the process, thereby enhancing the global efficiency of the system. The working scheme of the machine is almost the same as that of a common VCRC AWGs; however, the key difference lays in the specific design of these systems, which has been optimised to make the dehumidified, cooled air and the heating flux removed from the treated air available for reuse. In particular, these machines are equipped with an additional plate condenser, suitable for coolant-to-water heat exchange, allowing, for example, domestic water heating. Moreover, the air outlet is designed to facilitate the delivery of the treated air--cooled and dehumidified--via ducts to an Air Treatment Unit (ATU) or directly to rooms as primary fresh air. In such a way, the integrated AWG can simultaneously provide three useful effects, namely, water, a flux of cold and dry air, and thermal energy, all with the same energy consumption (_en_, as shown in Figure 2). In Figure 3, the three useful effects are highlighted. In particular, it is possible to identify three outputs, labelled, \(Q_{c}\), \(Q_{w}\), and \(Q_{ht}\), representing, respectively, the cooling energy, the water condensation energy, and the heating energy, all simultaneously produced by the machine. The integrated AWG can be used in new plant designs, as a component of the HVAC system following the integration perspective, as well as in plant retrofitting, either complementing or replacing the functionalities of other pieces of equipment. Such a configuration can achieve meaningful energy savings, as demonstrated in [18], while also providing the required drinking water. The solution can be highly effective and should be considered among the different design/retrofitting options. Nevertheless, to make a fair decision among the different proposals, appropriate evaluation tools are necessary. In the next section, there is a short summary of the main indicators and indices concerning AWGs with a particular focus on WET and GEI, the latter specifically developed to assess the effectiveness of integrated HVAC plants, including those encompassing integrated AWGs. Figure 2: VCRC Advanced AWG working cycle scheme, figure partially modified by authors from a previous version in [22].
## 4 Discussion
### Comparison with CLIP  
When comparing the results obtained by the CLIP and BiomedCLIP models, a key question arises: why did an untrained model like CLIP achieve 100% accuracy in detecting severe single-curve scoliosis, while BiomedCLIP, specifically trained on medical data, performed less effectively in similar cases? Notably, CLIP was tested on only 23 images of severe single-curve scoliosis, whereas BiomedCLIP analyzed a larger and more diverse dataset of 262 images, covering various severities and scoliosis types.  
One explanation for this paradox is the simplicity of the task given to CLIP. Severe single-curve scoliosis presents clear and easily detectable physiological changes, enabling even an untrained model to identify these cases. The prominence of severe scoliosis in X-ray images likely made it easier for CLIP to succeed without requiring specialized medical data training. In contrast, BiomedCLIP faced a more complex task, having to differentiate not only severe scoliosis but also mild and moderate cases, as well as classify scoliosis types. This broader scope introduced additional challenges for the model.  
Trained models like BiomedCLIP are also more prone to errors due to the diversity of the datasets they are trained on. Learning to recognize subtle and inconsistent nuances across a wide range of cases may dilute the model's predictive accuracy, making it less effective in straightforward tasks such as detecting severe scoliosis. This diversity, while necessary for generalization, can reduce performance when handling highly specific or obvious changes.
# Deepwater 3D Measurements with a Novel Sensor System
## 2 Materials and Methods
### Data Recording  
#### 2.2.1 Generation of Measurement Data  
The sensor system produces two kinds of three-dimensional measurement data. The first kind is a rough 3D data stream of the detected seabed environment below the realized path of the sensor system. This kind of data generation is also known as Simultaneous Localization and Mapping (SLAM), which involves the generation of a 3D map of the observed environment [14]. The data stream for the production of a 3D map is obtained via visual odometry [29] using the image sequences of the color camera and the IMU. A detailed description of the process using the introduced sensor system is given by [PERSON] et al. [30].  
The highly accurate capture of the 3D surface geometry is achieved through the classical triangulation of corresponding pairs of image points [24] from the two measuring cameras. The aperiodic stripe pattern generated by the GoBo projector is used to find point correspondences. Considering beam refraction at the media transitions, we used an extended pinhole camera model (see [32]). In the next section, a brief description of the camera modeling, calibration, and 3D point calculation is given.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 2 Interference Management
### Related Work
#### 2.1.2 Digital System
\right] \tag{9}\]  
After PCA and ICA, the inverse of the mixing matrix is solved, and the SOI is separated from the interference.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 3 Proposed Methodology
### Pre-Processing of Real Trajectories
The complexity of the shapelet discovery algorithms, is upper bounded by the number of candidates in a dataset (the authors of Reference [23] say that it is \(O(NM^{2})\), where N is the number of training series and M is their length). This complexity is evident at training time, when many synthetic trajectories and hundreds of interesting points are employed. However, at test time the shapelet feature extraction can be easily parallelized for the trajectories of different vessels and is quite fast when only the important points of the trajectory are processed (after scale-down is applied). As far as it concerns the training of the classifiers on synthetic data and their application on real trajectories at test time, the classifier complexity (in our case the logistic regression) is not a bottleneck.
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 4 Results  
### Correction through 3D Reconstruction  
The six-degrees-of-freedom (6 DOF) UAV motion, including three DOF rotations and three DOF translations, can be obtained after the camera calibration (Figure 10). It represents the movement of the UAV camera around the three axes of the world coordinate system. During the experiment, the bridge model is excited five times; hence, there will be five peaks in the displacement curve.  
Figure 8: World coordinate system.  
Figure 9: Positions of the fixed object: (a) \(Z_{w}\) = 0 m; (b) \(Z_{w}\) = 1 m; (c) \(Z_{w}\) = \(-\)1 m.  
The displacement time-history curves (\(y\)-direction) of a reference point (the top left corner point) before and after correction are shown in Figure 11. Since it is a fixed point, its corrected displacement curve is almost a straight line along the \(x\)-axis. The uncorrected displacement of the target point collected by the UAV is presented in Figure 12, which contains the false displacement induced by UAV motion. The real displacement of the target point (\(y\)-direction) obtained by the 3D reconstruction method is also shown in Figure 12.  
The OMA is used to analyze the modal parameters of the corrected displacement curve of the UAV, and the result is shown in Figure 13. It can be seen from Figure 13 that the first natural frequencies obtained by the two methods are 3.940 Hz, which demonstrates that the OMA successfully extracts the modal parameters from the 3D reconstruction-based displacement data.  
Figure 11: Displacement of the reference point before and after correction.  
Figure 10: Six degrees of freedom motion of the UAV (\(Z_{w}=0\)).
# Luminescence Materials for Dye-Sensitized Solar Cells: Advances and Directions
## 4 Luminescent Materials in DSSCs
### Down-Conversion  
Down-conversion materials absorb high-energy photons and emit two or more lower-energy photons. Figure 4b shows the research interest of down-conversion materials in the past decade, with 2022 having the highest research interest, followed by 2021 and 2020. The total research interest for the material produced 14,098 publications in the past decade. The process of down-conversion luminescent materials allows the conversion of ultraviolet (UV) light, which can degrade organic components in DSSCs, into visible light that is more suitable for energy conversion. Common down-conversion materials include phosphors doped with rare-earth elements like Eu\({}^{3+}\) and Tb\({}^{3+}\), due to their properties like a wide absorption spectrum in the UV region and the conversion of high energy photons into photons of lower energy to foster sufficient photon absorption and charge carrier generation. Synthesis methods such as solid-state reactions and combustion synthesis are often used to produce these materials. The incorporation of down-conversion phosphors into DSSCs can be achieved by coating the surface of the photo-anode or mixing them with the dye. For example, integrating Gd\({}_{2}\)O\({}_{3}\): Eu\({}^{3+}\) down-conversion phosphors with TiO\({}_{2}\) photo-anodes has demonstrated improved light harvesting and reduced UV-induced degradation, thereby enhancing both the efficiency and stability of the DSSCs [50].
## 3 Results and Discussion
### Electrochemical Measurements
#### 3.4.3 Salt Spray Tests  
For the salt spray tests, each sample was coated with two thicknesses of 100 \(\upmu\)m and 200 \(\upmu\)m, and placed in a salt spray test box after edge sealing. Figure 10 showed the optical images of the panels coated with EP/GP, EP/PDA, EP/GPP14, EP/GPP12, EP/GPP11, EP/GPP21 and EP/GPP41 over 7 days after salt spray. We saw the occurrence of corrosion in all samples, especially for EP/GP, EP/PDA, EP/GPP12, EP/GPP14 coatings shown in A, B, F, and G of Figure 10. Their distribution of corrosion products is greater than other coatings having obvious corrosion and rust. However, in the case of EP/GPP41, EP/GPP21, and EP/GPP11 coatings shown in C, D, and E in Figure 10, the surface was relatively clean with less corrosion lines and reduced corrosion degree. Among them, the surface of the EP/GPP21 coated steel plate (Figure 10D) exhibited the smoothest surface without corrosion product stripes detected, indicating that EP/GPP21 coating has the best corrosion resistance.  
Figure 9: Potentiodynamic polarization curves for the different coatings.  
Further salt spray experiments were performed on the EP/GPP21 coatings with different thickness (100 \(\upmu\)m and 200 \(\upmu\)m). The results over the salt spray test duration are as shown in Figure 11. It should be noted that we did not observe any significant thickness changes in coatings during the salt spray experiments. The earliest corrosion occurred in the left part of the plate in Figure 11 (D7), and the corresponding corrosion time was 35 days with 100 \(\upmu\)m thickness of EP/GPP21 coatings. Surprisingly, the surface with 200 \(\upmu\)m thickness of EP/GPP21 coatings (Figure 11, D8) did not exhibit such corrosion, indicating that the 200 \(\upmu\)m thickness of EP/GPP21 coatings has better anti-corrosive performance under the same salt spray testing conditions. The salt spray experiment was continued until 45 days. As it can be seen in the Figure 11, the corrosion area of the 100 \(\upmu\)m plate expanded, and the right side of 200 \(\upmu\)m gradually corroded and rusted. This is because the corrosive medium contacts the substrate after the coating is damaged, which further accelerates the corrosion of the metal, but the corrosion part of the two plates is less than 30%. The sample continued to corrode in the salt spray environment for 35 days. We found that EP/GPP21 composite coating has the best anti-corrosion properties, indicating that the well-dispersed GO-PANI-PDA nanosheets are beneficial to improve the anti-corrosion performance of epoxy coatings. This corrosion resistance performance is attributed to the strong adhesion performance of PDA between the coating and the steel, and fully exerts the dense barrier performance and electrochemical redox performance of the EP/GP coating structure.  
Figure 10: Photographs of the panels coated with (**A**) EP/GP, (**B**) EP/PDA, (**C**) EP/GPP41, (**D**) EP/GPP21, (**E**) EP/GPP11, (**F**) EP/GPP12 and (**G**) EP/GPP14 over 7 days after salt spray.
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
###### Abstract  
In this paper, the method for compensating the temperature drift of high-G MEMS accelerometer (HGMA) is proposed, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), RBF NN based on GA with Kalman filter (KF), and the RBF NN + GA + KF method compensated by the temperature drift model. First, this paper introduces an HGMA structure working principle, conducts a finite element analysis, and produces the results. The simulation results show that the HGMA working mode is the 1 st order mode, and its resonant frequency is 408 kHz. The 2 nd order mode resonant frequency is 667 kHz, and the gap with the first mode is 260 kHz, indicating that the coupling movement between the two modes is tiny, so the HGMA has good linearity. Then, a temperature experiment is performed to obtain the output value of HGMA. The output values of HGMA are analyzed and optimized by using the algorithms proposed in this paper. The processing results show that the RBF NN + GA + KF method compensated by the temperature drift model achieves the best denoing consequent. The processing results show that the temperature drift of the HGMA is effectively compensated. The final results show that acceleration random walking improved from 17130 g/h/Hz\({}^{0.5}\) to 765.3 g/h/Hz\({}^{0.5}\), and bias stability improved from 4720 g/h to 57.27 g/h, respectively. The results show that after using the RBF NN + GA + KF method, combined with the temperature drift model, the temperature drift trend and noise characteristics of HGMA are well optimized.  
high-G MEMS accelerometer; theoretical simulation; temperature drift compensation; radial basis function neural network; genetic algorithm; Kalman Filter +
Footnote †: journal: Applied Sciences
# A New Method of Ski Tracks Extraction Based on Laser Intensity Information
## 3 Methodology
### The First Segmentation Based on Intensity Information  
The first segmentation and first classification of targets using LiDAR intensity information need to analyze the influence of each factor on intensity. The intensity returns a value of 3D LiDAR and can be regarded as the discrete integer value of the received power of LiDAR after certain signal processing. The echo intensity of LiDAR is mainly characterized by the LiDAR equation [20; 21; 22]. According to the LiDAR equation, the laser echo light power is determined by the laser transmitting power, the transmitting optical system parameters, the atmospheric laser transmission characteristics, the receiving optical system parameters, and the target reflection characteristics [23; 24]. The expression is as follows:  
\[P_{r}=\frac{4P_{t}}{\pi\theta_{t}^{2}R_{t}^{2}}\cdot\tau_{t}\eta_{t}\tau_{t} \eta_{r}\cdot\rho cos\theta_{i}cos\theta_{r}A_{t}\frac{D^{2}}{4R_{r}^{2}} \tag{1}\]  
where \(P_{r}\) represents the received echo power of the target, \(P_{t}\) represents the power of the laser emission, \(\theta_{t}\) represents the laser reflection angle, \(R_{t}\) is the distance from the laser light source to the target, \(\tau_{t}\) represents the atmospheric transmittance between the laser light source and the target, \(\eta_{t}\) represents the optical efficiency of the transmitting optical system, \(\tau_{r}\) s the atmospheric transmittance between the target and the receiver, \(\eta_{r}\) represents the optical efficiency of the receiving optical system, \(\rho\) represents the reflectivity of the target, \(\theta_{i}\) and \(\theta_{r}\) are the incident and reflection angles of the target relative to the LiDAR, \(A_{t}\) represents the effective area of the laser beam on the target, \(R_{r}\) represents the distance from the receiver to the target, and \(D\) represents the aperture of the receiving detector.  
The common three-dimensional scanning LiDAR system is generally a single station structure integrating transmitting and receiving [25; 26; 27; 28; 29]. In this case, there are the following equation relations: \(R_{t}=R_{r}=R\), \(\theta_{i}=\theta_{r}=\theta\), and \(\tau_{t}=\tau_{r}=e^{-\alpha R}\), where \(\alpha\) is the atmospheric extinction coefficient. In summary, the LiDAR equation can be derived in the following form:  
\[P_{r}=P_{t}\eta_{t}\eta_{r}\frac{D^{2}}{4}\cdot\frac{e^{-2\alpha R}}{R^{2}} \cdot\rho cos^{2}\theta \tag{2}\]  
Then, the downsampled point cloud datasets were attached with different colors according to different intensities. On this basis, according to the intensity distribution of point cloud, the statistical histogram of the intensity information of laser point cloud was drawn, the suitable segmentation range was selected, the starting and ending points of intensity segmentation value were set in the program, and the initial segmented point cloud was obtained.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Quercetin
#### 3.2.2 Biological Activity  
Despite its high polarity, the aglycone form of quercetin has low water solubility--approximately 0.92 g/L at 25 \({}^{\circ}\)C [115]. However, glycosylated derivatives such as quercetin-3-O-glucose show significantly higher solubility and bioavailability [115]. Encapsulation of quercetin in \(\beta\)-cyclodextrinrins enhances its solubility and stability by forming hydrophilic-lipophilic inclusion complexes, thereby improving bioavailability and protecting the compound from environmental and physiological degradation [113; 122]. Quercetin is a potent antioxidant; its activity derives from the number and position of hydroxyl groups that enable neutralization of hydroxyl and peroxyl radicals [115; 119; 122; 123]. In addition to its antioxidant properties, it inhibits inflammation-related enzymes such as COX and LOX (lipoxygenase), making it a promising agent for the prevention of metabolic and chronic inflammatory disorders [7; 118; 123]. Moreover, in vitro studies have shown that quercetin can suppress epidermal growth factor receptor (EGFR) activity in gastric cancer cells, indicating its potential as a targeted anticancer agent [124].  
Quercetin acts as a potent antioxidant and anti-inflammatory agent, inhibits MMP activity, protects collagen, and improves skin elasticity and hydration. In vivo application of 1% quercetin after UV exposure increased glutathione levels and skin hydration, while reducing oxidative stress markers. It also inhibits tyrosinase activity, contributing to skin lightening and anti-pigmentation effects [125].
## 2 Materials and Methods  
### Suspension Characterization  
In the LCM process, the layer thickness is one of the crucial parameters for the shaping process. For this reason, light penetration tests were conducted to quantify how far light can penetrate into a given ceramic suspension. The ceramic suspension was exposed to the same level of maximum light intensity (47.1 mW cm\({}^{-2}\)) for five different time periods, resulting in distinct energy doses through a cylinder with a diameter of 10 mm. The number of tested suspension samples was 3 for each of the energy doses. The thickness of the polymerized layer was then measured using a micrometer screw. Once the thickness of the polymerized layer was determined, the lateral over-polymerization was also measured for all polymerized layer with an aid of a light microscope. The difference between target value and actual value was noted as over-polymerization. [PERSON] et al. have shown that the  
Figure 2: Steps of the thermal treatment after green body fabrication: drying, debinding and sintering.  
scattering efficiency term (Q) is proportional to the difference of refractive indices (n) between the ceramic particles and photocurable matrix.  
\[\text{Q}=\beta\ \Delta\text{n}^{2} \tag{1}\]  
\[\Delta\text{n}^{2}=(\text{n}_{\text{ceramic}}-\text{n}_{\text{solution}})^{2} \tag{2}\]  
The penetration depth is inversely proportional to the refractive index, and the term \(\beta\) is relevant to the particle sizes and wavelengths [26]. The refractive index of silicon nitride powder is 2.0167 [37], whereas the refractive index of organic binder is 1.4630 (\(\pm\) 0.0005).  
The rheological measurements of photocurable suspension were performed on a rheometer (MCR 92, [PERSON], Graz, Austria) at a temperature of 20 \({}^{\circ}\)C with a plate-plate arrangement. The plate-plate measuring system has a diameter of 25 mm and the gap between plates was 0.5 mm. The viscosity was measured three times for each linearly at shear rates between 5 and 50 s\({}^{-1}\).  
Finally, an evaluation of the typical materials efficiency was performed for the LCM process in combination with the LithaNit 720 suspension that was employed in this work. For this reason, the weight of the suspension was recorded both before and after printing. Furthermore, the exact mass of printed samples in the green state was measured once the parts had been cleaned. The factor of suspension was calculated by dividing the mass of the suspension for printing by the mass of the printed parts after cleaning is a measure for the materials efficiency of the used LCM process. This process was repeated three times in order to find a mean value and standard deviation.