esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 4 Remote Sensing Image Data Processing  
### Construct Models  
#### 4.1.1 Aerial Triangulation  
The photos containing POS data are imported into DJI Terra Mapping & Surveying Edition software, where feature points are automatically extracted and matched using a feature extraction and matching algorithm. Representative feature points are identified by analyzing grayscale, texture, and other image attributes. Corresponding feature points are then found between different images. The area network is computed using the air-triangulation method, with each image beam serving as a basic unit. The beams intersect in space to perform a parity calculation of encrypted points, with the goal of minimizing reprojection error [32].  
\[\underset{X,R,t}{\min}\sum_{i=1}^{n}\sum_{j=1}^{m}\left\|p_{ij}-\pi(R_{i}X_{j}+ t_{i})\right\|^{2}, \tag{1}\]  
where \(X_{j}\) represents the coordinates of the 3D point, \(R_{i}\) and \(t_{i}\) represent the rotation matrix and translation vector of the ith image, and \(\pi\) denotes the camera projection model (e.g., pinhole model). In this study, a high feature point density is used to ensure that enough feature points are obtained for each impact, improving the accuracy of the results. The WGS 84/UTM Zone 50N projection coordinate system is used.  
The Air-3 calculation report is shown in Table 2. In the RTK solution of the Air-3 calculation, the positioning accuracy of the floating-point solution is at the decimeter level, and the positioning accuracy of the single-point solution is at the meter level. \"No solution\" means there is no RTK positioning solution. If all the solutions are fixed, it guarantees that image-control-free accuracy can reach the centimeter level under the POS coordinate system. The unmanned aerial camera system used in this study can achieve image-control-free flight photogrammetry, and the results of the Air-3 calculation indicate that all solutions are fixed. Therefore, it is reasonable to omit the image-control point setting in this study. As all the solutions from the Air-3 calculation are fixed, omitting the image-control point setting in this study is reasonable. The two aerial photography images in this study were calibrated, confirming that the route planning and parameter settings are appropriate. The root-mean-square error (RMSE) of geographic alignment is 0.957 m for the first shot and 1.364 m for the second shot. These values represent the root-mean-square error (i.e., the POS relative accuracy) between the solved image position and the initial image record. Both values are under 2 m, meeting the accuracy requirements for the actual work. An unsimplified model processing enhances the accuracy of the model, making it less prone to sharp corners, damage, and other issues. The processing time for the two aerial photography images was 13 min and 9 s, and 14 min and 42 s, respectively. With the two aerial photography sessions lasting 33 min, the entire process was completed within 50 min, significantly improving efficiency compared to manual inspection and other traditional methods.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### End-to-End Assessment
#### 4.3.1 Analysis per Network Segment  
The most time-consuming E2E communication flows are seen in the hybrid network communication flows, where messages are generated in the OBU and propagated until the smartphone and vice versa. In particular, the greatest median E2E time is observed in the flow Smartphone \(\rightarrow\) OBU using the MFSPV protocol, 46.75 ms, which was anticipated. The hybrid network's communications flow shows an average E2E time of 41.26 ms.  
Conversely, the E2E times achieved by G5 exclusive communication flows are the lowest, namely in the OBU \(\rightarrow\) RSU flow, where the E2E time is just 11.63 ms without the use of security. The G5 network shows an average E2E time of 12.97 ms.  
From these E2E results (Table 11), it can be concluded that hybrid communication flows impose (on average) an extra 28.29 ms of E2E time, which translates into an increase of 218% compared to G5-only communication flows. Table 12 illustrates the average E2E results obtained in each network segment.
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 2 Non-Mechanical Beam Steering with PGs
### Optical Properties of PG-Based Non-Mechanical Steering Systems
#### 2.2.5 Polarization Considerations  
The polarization-sensitivity of PG steering provides an additional constraint on potential applications. For active sensing applications that might not otherwise be polarization-sensitive, such as time-of-flight and flash lidar imaging, the varying degrees of depolarization of backscattered light from the target scene present an added loss factor [54; 55]. As we show in Section 3.1, this loss factor is often significantly outweighed by the SNR benefits achieved from narrowing the instantaneous field of view (FOV) of such systems and using PGs to stitch the narrow FOVs to recapture the larger scene. Additionally, some applications benefit from increasing polarization sensitivity as a means to differentiate artificial objects or reject background radiation [56; 57].  
For applications, requiring bidirectional operation through a single PG steering assembly, special attention must be paid to polarization in both directions to ensure light follows a common path. From Equation (2), we note that the \(\pm 1\) diffraction orders (i.e., steering angles) correspond to orthogonal polarization states. It is also worth briefly noting here that the halfwave retardance of the PG itself also serves to flip the handedness of the incident polarization. The result is that the various steering angles that emerge from a PG steering assembly are not uniform in polarization, but rather output polarization varies with steering angle. For bidirectional operation, the polarization must be identical in both directions for the light to follow a common path through the PG assembly (Figure 7a,b). As a result, where transmit and receive paths must be split off inside a system, they must do so without using polarization-based beam splitting.  
For applications that rely on transmitting through a PG steering assembly and collecting the backscattered light through the same aperture, such as monostatic lidar, the handedness flip that occurs upon specular backscatter causes the polarization of the returning light to be in the incorrect polarization (Figure 7a). In such scenarios, it becomes important to convert the transmitted light into a linearly polarized state upon transmitting out of the PG assembly so that the polarization handedness flip on backscatter can be avoided (Figure 7b). This is especially true in polarization-sensitive applications, such as the coherent lidar shown in Section 3.2
## 1 Introduction
### Contribution  
In this paper, we propose using metric space indices for control optimization in a P2P-based crowdsourcing platform which was designed to classify images taken after a natural disaster occurs. Our proposal operates using thresholds, comparing current inputs with historical data to optimize configurable parameters. It enables continuous evaluation of various scenarios, simultaneously addressing multiple objectives such as maximizing consensus while minimizing communication, computation costs, and keeping resource utilization levels between a given range. Importantly, it does not require a training phase.  
The platform is based on the collaboration of volunteers who can classify blurred photos taken in places where the natural disaster occurred to identify hurt people, blocked routes, etc. The platform is formed by (1) a centralized server which distributes the tasks among the volunteers and also computes the consensus of the tags selected for each image, (2) the P2P network that the volunteers connect to though a TV which has an STB device, and (3) an internet server provider that connects the P2P network and the server. In [13], we presented different routing algorithms for this platform and we identified the most critical parameters.  
This work focuses on how to effectively control the state of the platform as the burst of incoming tasks varies using a metric index. The index is built off-line and contains vectors \(v=<[nc],[c],[m]>\) with a set of parameters that can be controlled (\([c]\)) and have a major impact on the platform's performance; parameters that cannot be controlled (\([nc]\)), like the number of volunteers and the incoming rate of tasks; and, finally, the expected values of metrics (\([m]\)), like communication latency and CPU utilization.  
Then, at running time, we use the index to search for the optimal values of the controlled parameters in such a way that the platform is able to process the tasks as soon as they arrive. The platform monitors the workload of the computational and communication resources involved in the image classification processing every \(\Delta_{t}\) units of time. If the workload is above a given threshold, new values are obtained from the index. We evaluate our proposal and compare the results with the ones obtained with traditional dynamic optimization techniques. The experimental results show that the proposed solution reduces the working time of the system by 40% on average, without saturating the available resources.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 3 The Principle of Vector Observation Based on Position Loci
### Discrete Recursive Formulae
\begin{array}{l}\int_{t_{m}}^{t_{m+1}}C_{n(\sigma)}^{n(t_{m})}\mathsf{g}^{n}d \sigma&\approx\int_{t_{m}}^{t_{m+1}}\left[\mathrm{I}+(\varphi_{n}\times) \right]\mathsf{g}^{n}d\sigma=\int_{t_{m}}^{t_{m+1}}\left[\mathrm{I}+(\sigma-t_{m })(\omega_{in}^{n}\times)\right]\mathsf{g}^{n}d\sigma\\ &\approx\left(\Delta t_{G}\mathrm{I}+\frac{\Delta t_{G}^{2}}{2}(\omega_{in}^{ n}\times)\right)\mathsf{g}^{n}\\ \int_{t_{k}}^{t_{k+1}}\int_{t_{k}}^{\tau}C_{n(\sigma)}^{n(t_{k})}\mathsf{g}^{n}d \sigma d\tau&\approx\int_{t_{k}}^{t_{k+1}}\int_{t_{k}}^{\tau}\left[\mathrm{I}+( \varphi_{n}\times)\right]\mathsf{g}^{n}d\sigma d\tau=\int_{t_{k}}^{t_{k+1}}\int_ {t_{k}}^{\tau}\left[\mathrm{I}+(\sigma-t_{m})(\omega_{in}^{n}\times)\right] \mathsf{g}^{n}d\sigma d\tau\\ &\approx\left(\frac{\Delta t_{G}^{2}}{2}\mathrm{I}+\frac{\Delta t_{G}^{3}}{6}( \omega_{in}^{n}\times)\right)\mathsf{g}^{n}\end{array}  \tag{26}\]  
Finally, we acquire the recursive calculation process for the observation vector based on the formulae described:  
\[ \begin{array}{l}\beta_{p}\approx C_{n(t_{M})}^{n0}\mathsf{r}^{n}-\mathsf{t} \mathsf{v}^{n}(0)-\sum\limits_{k=0}^{M-1}\Delta t_{G}\sum\limits_{m=0}^{k-1}C_{n(t_ {m})}^{n0}\left(\Delta t_{G}\mathrm{I}+\frac{\Delta t_{G}^{2}}{2}(\omega_{in}^{n }\times)\right)\mathsf{g}^{n}-\sum\limits_{k=0}^{M-1}C_{n(t_{k})}^{n0}\left( \frac{\Delta t_{G}^{2}}{2}\mathrm{I}+\frac{\Delta t_{G}^{3}}{6}(\omega_{in}^{n }\times)\right)\mathsf{g}^{n}\\ \mathsf{r}^{n}\approx\sum\limits_{k=1}^{M}R_{c}^{-1}(k)(\mathsf{p}_{k}^{n}- \mathsf{p}_{k-1}^{n})\end{array}  \tag{27}\]where \(\mathrm{p}_{k}^{n}\) represents the position information of the vehicle at time instant \(k\), and \(\mathrm{R}_{c}^{-1}(k)\) is the inverse matrix of Equation (10). It is worth noting that the position information can be acquired through GPS directly.
## 5 Discussion
### Impact of Angle Effect Correction on Vegetation Parameters Retrieval  
Given the high spatial heterogeneity of agricultural fields and the rapid fluctuations in crop growth, crop parameters vary considerably across different growth phases, leading to a differential impact of the angular effect on various crop parameters. During the middle and late stages of crop growth, when plant density is elevated, the inversion accuracy for the LAI is markedly lower compared to that for the FVC and CHL. This is partly due to the differential response of the LAI to red band saturation as opposed to the FVC and CHL, a phenomenon also observed by [PERSON] et al. [47]. Furthermore, the angular effect differentially influences the reflectance of distinct spectral bands [50], resulting in a pronounced variation in the impact of the angular effect on various vegetation indices. Consequently, the inversion algorithms for crop parameters based on these indices will also exhibit variations.  
The observational data from the Jilin-1 GP01/02 satellites, which were synchronized with the measured data in this study, are predominantly backscattered, leading to a lower reflectivity post-correction compared to pre-correction. The binomial reflection from vegetation is a complex process involving the transmission of canopy and leaf radiation, which is intricately linked to the type of vegetation, growth phase, and incident light conditions. The data collected in this study pertain to the middle and late stages of crop growth, thus not delving deeply into the more complex, mixed radiation characteristics of the vegetation-soil interface observed during the early stages of crop development.  
While the angular effect correction outcomes for the Jilin-1 GP01/02 PMS presented herein are promising, there are inherent limitations. The correction method introduced in this study has been tested solely within the mid-latitudes of the Northern Hemisphere, indicating a need for future research to evaluate the method's efficacy across a broader range of geographical and environmental contexts. Furthermore, the Jilin-1 GP01/02 PMS encompasses a multitude of narrowband visible light, red edge, and near-infrared bands in addition to the green, red, and near-infrared bands. Future studies will also explore BRDF correction methodologies for these bands to fully leverage the high-resolution and multispectral capabilities of this satellite imagery.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 2 Experimental
### Photodegradation Studies of Calcipotriol  
Calcipotriol was irradiated in methanolic solutions at a concentration of 0.1 mg mL\({}^{-1}\). For the photodegradation studies, the solutions were placed in quartz Petri dishes (50 mm diameter, Hornik, Poland), which were covered with quartz lids to minimize ethanol evaporation and sealed with parafilm for additional protection.  
Photostability testing of calcipotriol under UVA conditions was conducted using a KBF-ICH 240 APT.line(tm) climate chamber (Binder GmbH, Tuttlingen, Germany). During irradiation, samples were positioned 13 cm from the light source. For UVA exposure (maximum wavelength 365 nm), the chamber was maintained at a constant temperature of 25 \({}^{\circ}\)C and relative humidity of 60%. The radiation dose was measured with a VLX-3 radiometer (Vilber Lourmat) equipped with a CX-365 sensor, yielding a consistent dose rate of 1.9434 \(\cdot\) 10\({}^{-2}\) J cm\({}^{-2}\) min\({}^{-1}\). Samples were subjected to UVA irradiation for 60 min.  
Photodegradation studies of calcipotriol in methanolic solution were carried out in the presence of organic UV filters. In summary, each 2 mL sample was prepared by mixing 1 mL of calcipotriol stock solution, 0.8 mL of methanol, and 0.2 mL of a selected UV filter solution. Control samples, prepared in the same way but wrapped in aluminum foil to exclude light, served as dark controls. All samples were analyzed using UPLC-MS, and each experimental condition was set up in triplicate.  
The photostability of calcipotriol from a foam formulation was evaluated using Enstilar foam (LEO Pharma A/S, Denmark), which contains calcipotriol (50 \(\upmu\)g/g) and betamethasone dipropionate (0.5 mg/g). The samples were prepared by weighing 2 g of foam, adding 10 \(\upmu\)L of suisobenzene solution in propylene glycol, and thoroughly homogenizing the preparation in a mortar. The suisobenzone solution was prepared by dissolving 10 mg of sulisobenzene in 1 g of propylene glycol. Then, 0.5 g of the mixture was spread evenlyon the PMMA plates (poly(methyl methacrylate) plates). The dark control samples were prepared in the same manner and wrapped in aluminum foil. Following irradiation, the foam was collected from the plates using a spatula, transferred into Eppendorf tubes, and weighed. Subsequently, 1 mL of methanol was added to each tube. Extraction was carried out using a tube shaker (Lab Dancer Vario, IKA) for 20 s, followed by 10 min of ultrasonication in a water bath at 40 \({}^{\circ}\)C (Elmasonic S40H, Elma, Wetzikon, Switzerland). After extraction, the solution was filtered through a 0.45 \(\upmu\)m pore-size filter before the UPLC-MS\({}^{\mathrm{E}}\) analysis.
## 5 Critical Aspects of FSS Technology
### Dissemination of FSS Technology  
Although FSS technology has demonstrated significant potential, its dissemination has been somewhat limited to niche applications and high-end industries. Currently, FSSs are predominantly used in aerospace, defense, telecommunications, and certain industrial sectors where precise control over electromagnetic wave propagation is critical. Thetechnology is also used in advanced sensing and imaging systems, including radar and terahertz imaging, for applications ranging from security scanning to medical diagnostics. However, broader adoption in consumer markets or everyday building environments is still in its early stages, partly due to cost considerations and the complexity of design requirements for mass-produced structures.
## 5 Conclusions  
This paper presents the conversion fraction calculation method for pozzolan blended concrete using \({}^{29}\)Si MAS NMR analysis and the relationship between the carbonation resistance of the concrete and the conversion fraction of the pozzolanic materials. Based on the results of this study, the following conclusions were reached.  
Figure 11: Relationship between conversion fraction and carbonation depth.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & \(\mathbf{Q^{0}}\) & \(\mathbf{Q^{1}}\) & \(\mathbf{Q^{2}}\) & \(\mathbf{Q^{3}}\) & \(\mathbf{Q^{4}}\) \\ \hline OPC & \(-0.009\) & \(-0.028\) & \(-0.01\) & \(0.029\) & \(0.018\) \\ FA15 & \(-0.066\) & \(-0.035\) & \(-0.019\) & \(0.046\) & \(0.073\) \\ SF35 & \(-0.021\) & \(-0.018\) & \(0.014\) & \(0.057\) & \(-0.032\) \\ NS20 & \(-0.036\) & \(-0.009\) & \(0.014\) & \(0.021\) & \(0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Difference in Q\({}^{n}\) after Carbonation.  
The 15%, 3.5%, and 2.0% fly ash, silica fume, and nanosilica blended concrete specimens, respectively, were constructed. All tested mixtures were intentionally designed to have similar mechanical properties to remove the strength effect on the carbonation resistance. The 28-day compressive strengths of the concrete specimens were similar to each other and were all approximately 50 MPa. The differences in the other mechanical properties between the specimens were also not significant.
2. Accelerated carbonation tests were conducted. The concrete specimen with fly ash showed the poorest carbonation resistance among the specimens. The average carbonation depths of the silica fume and nanosilica blended concrete specimens were 1.55 and 1.34 mm, respectively. These values were approximately 70.2% and 74.3% lower than that of the OPC specimen.
3. The conversion fraction of pozzolans was calculated using \({}^{29}\)Si MAS NMR analysis. For this, the amount of sand included in the concrete NMR sample was first evaluated using the results of the analysis of the OPC sample. Then, the conversion fractions were adjusted by removing the effect of the sand. The detailed methodology and procedure are shown in Section 4.2. From the results, the conversion fractions of the fly ash, silica fume, and nanosilica were 36.3%, 83.1%, and 97.4%, respectively. Thus, the nanosilica was almost completely converted.
4. The \({}^{29}\)Si MAS NMR analysis results before and after carbonation show that the low-order Q\({}^{n}\) is decreased with increasing high-order Q\({}^{n}\) after carbonation. This could be attributed to the calcium in the C\({}_{3}\)S, C\({}_{2}\)S, and C-S-H being used in the carbonation reaction, and causing the separated silicate to be detected as high-order Q\({}^{n}\).  
Methodology, J.M., J.J.K., and K.-S.Y.; investigation, J.J.K., and K.-S.Y.; writing--original draft preparation, J.J.K.; writing--review and editing, J.M., J.J.K., and K.-S.Y.; project administration, J.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Research Grant from Kangwon National University.  
The authors declare no conflict of interest.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Datasets and Metrics
#### 4.1.3 Metrics  
For the DAVIS dataset [13], this paper employs the region similarity \(\mathcal{J}\). To assess region-based segmentation similarity, specifically the count of mislabeled pixels, the Jaccard index \(\mathcal{J}\) is utilized. Given the output segmentation \(M\) and the corresponding ground truth mask \(G\), it is formally defined as \(\mathcal{J}=\frac{|M\cap G|}{|M\cup G|}\). Contour accuracy, denoted as \(\mathcal{F}\), is evaluated through contour-based precision and recall, \(P_{c}\) and \(R_{c}\). This is formally defined as \(\mathcal{F}=\frac{2.\mathcal{P}_{c}^{\prime}\cdot R_{c}}{R_{c}+R_{c}}\). The \(\mathcal{J}\)&\(\mathcal{F}\), denoting the average of the region similarity metric (\(\mathcal{J}\)) and contour accuracy (\(\mathcal{F}\)) for the DAVIS dataset, is formally defined as \(\mathcal{J}\)&\(\mathcal{F}=\frac{\mathcal{J}+\mathcal{F}}{2}\). Unlike the DAVIS [13] dataset, in YouTube-VOS 2019 [14] classes are categorized into seen and unseen categories. The final result (\(\mathcal{G}\)) is the average of four metrics: \(\mathcal{J}\) for seen categories (\(\mathcal{J}_{S}\)), \(\mathcal{F}\) for seen categories (\(\mathcal{F}_{S}\)), \(\mathcal{J}\) for unseen categories (\(\mathcal{J}_{U}\)), and \(\mathcal{F}\) for unseen categories (\(\mathcal{F}_{U}\)).