esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### High-Resolution Remote Sensing Data
#### 3.2.1 Change Detection
The debris material covered the middle part of the coast as well as a segment of the road (black line). The width of this material was 200 m while its length was almost 400 m (Figure 16). The whole landslide extent (i.e., from the beach to the crown) at this specific site overpassed 550 m. Meanwhile, areas with intermediate slope values (orange color) have been increased, while the areas marked with the red color (higher inclination) have been diminished (Figure 17). Figure 7:2 On the (**left**), the slope map of the Okeanos coast in 2016 is displayed, while in the (**middle**), there is the respective map of the year 2015. Color changes indicate the changes to the cliff inclination before and after the earthquake occurrence. The slope difference values are presented on the (**right**) image. Figure 7:3 On the (**left**), a Pleiades orthophoto of the Gialos coast in 2016 is displayed. On the (**right**), the same area has been presented in a 2015 Pleiades orthophoto. The yellow line indicates the landslide extent. The backwards movement of the cliff is evident on the left image. The green dashed line indicates areas with deep erosion. Figure 7:4 On the (**left**), the slope map of the Gialos coast in 2016 is displayed. In the (**middle**), the same area is displayed in the year 2015. Color changes indicate the changes to the cliff inclination before and after the 2015 earthquake. On the (**right**) image, the slope difference values are presented.
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 7 Conclusions
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Method** & **MPJPE (mm)** & **Complexity** \\ \hline VIBE [75] & 65.6 & Combines deep learning and optimization techniques. \\ \hline IPNET [73] & 43.8 & Computationally complex due to high-density reconstruction. \\ \hline PTF [74] & 60.2 & Designed for high-fidelity 3D human pose estimation with dense geometry. \\ \hline GCMR [76] & 71.9 & Utilizes graph convolutional networks for mesh regression; computationally intensive but effective for detailed reconstructions. \\ \hline HMR [77] & 87.9 & Balances complexity and efficiency; suitable for real-time applications. \\ \hline UP [78] & 80.7 & Complexity varies depending on implementation; flexible but computationally expensive. \\ \hline SMPLify [14] & 82.3 & Optimization-based approach to fitting the SMPL body model, requiring iterative refinement. \\ \hline VIBE with Vit [70] & 42.0 & Integrates ViT and VIBE methodologies for improved accuracy and robustness in low-resolution scenarios. \\ \hline
**Our method** & **39.0** & **Enhancing ViT and VIBE by integrating a probabilistic human model and depth information for improved 3D pose estimation.** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of 3D human pose estimation methods based on MPJPE (Mean Per Joint Position Error), complexity, and performance. The evaluated methods include VIBE (Video Inference for Body Pose and Shape Estimation), IPNET (Implicit Part Network), PTF (Piecewise Trans-Formation Fields), GCMR (Graph Convolutional Mesh Regression), HMR (Human Mesh Recovery), UP (Unite the People), and SMPLify, VIBE with Vit and our proposed method strategies to enhance 3D reconstructions from low-resolution images. \begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Performance** \\ \hline VIBE & Delivers improved accuracy in dynamic scenarios. \\ \hline IPNET & Computational complexity and precision in shape reconstruction. \\ \hline PTF & High-fidelity 3D human pose estimation, particularly excelling in scenarios with dense geometry. \\ \hline GCMR & Performance can be computationally demanding. \\ \hline HMR & Achieves a good balance between accuracy and speed. \\ \hline UP & Ensures reliable performance. \\ \hline SMPLify & Deliver reasonable accuracy in controlled environments. \\ \hline VIBE with Vit & Capable of generating 3D reconstructions from low-resolution images. \\ \hline
**Our method** & **Enhancing 3D human pose reconstruction through NIR-SPI in scenarios with missing information and low-resolution inputs.** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of 3D human pose reconstruction methods, highlighting accuracy, computational demands, and suitability for dynamic and low-resolution scenarios.
## 2 Dynamic Modeling of Free-Floating Dual-Arm Space Robot  
This section mainly introduces and analyzes the dynamic model of a free-floating dual-arm space robot based on the explicit multi-rigid-body dynamic modeling method and combined with the concept of DEM, which is used for subsequent control research. Because kinematics is the foundation of dynamics [17], the kinematic model is introduced first before the dynamic modeling.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 6 Conclusions  
The SQRT scheme proposed in the paper can effectively ensure that both user equipment and the directory server faithfully follow the routing querying protocol and protect the privacy information of both parties to the greatest extent. Compared with the existing schemes, the SQRT scheme proposed in the paper has obvious performance advantages in the degree of anonymity, running time, and communication traffic. Specifically, the SQRT scheme has the following four advantages:  
1. The first is correctness. If the directory server and user equipment abide by the protocol, the user equipment will obtain the routing node information it needs after the implementation of the scheme.
2. The second is the confidentiality of the information of other routing nodes in the directory server, that is, after the policy has completed execution, the user equipment  
Figure 7: Comparison of the degree of anonymity between SQRT scheme and other existing schemes.  
cannot obtain the information of any other routing nodes except the information of the routing nodes it needs.
3. The third is the confidentiality of the routing node information obtained by the user equipment. After the policy has completed execution, the directory server knows neither the specific requirements of the routing node submitted by the user equipment, nor which part of the routing node information is obtained from the user equipment.
4. The fourth is the efficiency of the network performance of the SQRT scheme in the operation process. For the semi-honest model, the SQRT scheme not only ensures the correctness and confidentiality, but also reduces the running time and traffic compared with the other existing schemes. It is expected to provide support and reference for the design and planning of the future secure communication system.  
At the same time, this paper also has some limitations, mainly in the security analysis. This paper only carried out a theoretical analysis of the main possible threats; there was no strict mathematical basis to support this security analysis, and we did not use a provable security method for a more reliable security analysis. Therefore, the safety analysis part can be improved in future research.  
Conceptualization, Y.Z. and B.T.; methodology, Y.Z., N.H. and Y.Y.; software, Y.N. and L.L.; validation, B.T., Q.Z. and L.L.; formal analysis, Y.Z. and Y.Y.; investigation, N.H. and Y.Y.; resources, Y.N. and L.L.; data curation, Q.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Y.; visualization, B.T.; supervision, Y.Y.; project administration, Q.Z.; funding acquisition, B.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant U20B2049, U20B2046, 61976064, and in part by the Key Research and Development Project of Sichuan Province of China under Grant 2022 YFG0172.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Discussion
### Cross-Calibration of Satellite Indices  
The crop yield forecasting system presented here employs the long-term time series of crop growth conditions and corresponding crop yield statistics used to train machine learning models. While agro-meteorological indicators are seamlessly derived from climate reanalysis, satellite-based indicators have been combined from two satellite sensors. First, sensors onboard the Sentinel-3 platforms provide data used for operational near-real time use, however these data have only been available since 2018. Second, data for previous years (since 2000) have been extracted from the MODIS archive. Data acquired by sensors with different spectral channels characteristics require cross-calibration and homogenization. In this study, the calibration was rendered on the product level, i.e., for NDVI and LST. An alternative approach would be to cross-calibrate radiances measured by the MODIS and OLCI/SLSTR instruments (at the Level-1 product level), then to perform atmospheric and Rayleigh scattering corrections in order to derive a homogeneous, cross-calibrated time series of the NDVI and LST products. This approach would significantly increase computational demand and complexity of the forecasting system, which is not advisable for an operational system. Furthermore, within this study, the cross-calibration at the Level-2 product level was found to significantly improve the homogeneity of data records (Table 2) and not to introduce significant variability, which would influence the crop yield forecasting system (see Section 4.1).
###### Abstract  
A COSMIC-1/FORMOSAT-3 (Constellation Observing System for Meteorology, Ionosphere, and Climate-1 and Formosa Satellite Mission 3) follow-on mission, COSMIC-2/FORMOSAT-7, had been successfully launched into low-inclination orbits on 25 June 2019. COSMIC-2 has a significantly increased Signal-to-Noise ratio (SNR) compared to other Radio Occultation (RO) missions. This study summarized the initial assessment of COSMIC-2 data quality conducted by the NOAA (National Oceanic and Atmospheric Administration) Center for Satellite Applications and Research (STAR). We use validated data from other RO missions to quantify the stability of COSMIC-2. In addition, we use the Vaisala RS41 radiosonde observations to assess the accuracy and uncertainty of the COSMIC-2 neutral atmospheric profiles. RS41 is currently the most accurate radiosonde observation system. The COSMIC-2 SNR ranges from 200 \(v\)/\(b\) to about 2800 \(v\)/\(c\). To see if the high SNR COSMIC-2 signals lead to better retrieval results, we separate the COSMIC-2-RS41 comparisons into different SNR groups (i.e., 0-500 \(v\)/\(v\) group, 500-1000 \(v\)/\(v\) group, 1000-1500 \(v\)/\(v\) group, 1500-2000 \(v\)/\(v\) group, and \(>\)2000 \(v\)/\(v\) group). In general, the COSMIC-2 data quality in terms of stability, precision, accuracy, and uncertainty of the accuracy is very compatible with those from COSMIC-1. Results show that the mean COSMIC-2-RS41 water vapor difference from surface to 5 km altitude for each SNR groups are equal to -1.34 g/kg (0-500 \(v\)/\(b\)), -1.17 g/kg (500-1000 \(v\)/\(b\)), -1.33 g/kg (1000-1500 \(v\)/\(v\)), -0.93 g/kg (1500-2000 \(v\)/\(v\)), and -1.52 g/kg (\(>\)2000 \(v\)/\(v\)). Except for the \(>\)2000 \(v\)/\(v\) group, the high SNR measurements from COSMIC-2 seem to improve the mean water vapor difference for the higher SNR group slightly (especially for the 1500-2000 \(v\)/\(v\) group) comparing with those from lower SNR groups.  
2020
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 2 Fundamentals of Array Processing
### The Beamforming Principle  
The term beamforming describes the process of linearly combining the samples received from each of the antennas in the receiver to obtain, at the output of the array, a single sample. How said linear combination is performed will define the purpose of the beamformer, which is normally, as in the case of this work, to filter out any type of interference while maintaining the desired contribution. This is achieved through the application of a set of spatial coefficients or weights, \(\mathbf{w}\in\mathbb{C}^{L\times 1}\), to the spatial samples presented in Equation (1). The result is a single sample at the output of the beamformer, \(y[n]\), as it is expressed in Equation (4).  
\[y[n]=\mathbf{w}^{H}\mathbf{x}[n] \tag{4}\]  
Hence, the essence of the problem is determining the weights in \(\mathbf{w}\) that should be applied according to a certain optimization criterion, and this is where the beamforming techniques come into play, as they will be the ones dictating the values of the coefficients.
## 5 Scattering of Surface Waves by a Cavity of Arbitrary Shape
### Concentrated Loading Approximation  
As a simple approximation, the tractions on the surface of the cavity are replaced by three equivalent resultant forces applied at the origin of the coordinate system in the same directions. These forces then generate a scattered field of surface waves in the half-space. This approximation is supposed to be valid only when the depth and the width of the cavity are very small in comparison with the wavelength.  
For an incident surface wave propagating in the positive \(x\)-direction with amplitude \(A_{in}\), the resultant forces \(\mathbf{F}\big{(}F_{x},F_{y},F_{z}\big{)}\) are obtained via integration of the tractions over the surface \(S\). These forces generate a field that approximates the scattered field. The total vertical displacement of the scattered field in the \(z\)-direction is the summation of the displacements generated by the resultant forces \(F_{x},F_{y},F_{z}\) and is written as:  
\[u_{z}=\frac{kW(z)}{4i\mu E}\Big{[}U(0)\big{(}F_{x}\cos\theta+F_{y}\sin\theta \big{)}H_{1}^{(1)}(kr)+W(0)F_{z}H_{0}^{(1)}(kr)\Big{]}. \tag{6}\]
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement
### Vessel Portrait Construction
#### 4.2.2 Vessel Portrait Construction  
A vessel portrait consists of Shallow-level Attributes and Deep-level Features. Regarding the processing of Shallow-level Attributes, we employ the following approach: firstly, select AIS data with non-empty attributes (such as length, width, draft, and type). And then we randomly select 100 data points based on the Maritime Mobile Service Identity (MMSI). For each attribute, consider the value with the highest frequency as the current vessel's attribute to construct the vessel's shallow profile. This process can be expressed in the formula as Equation (2)  
\[Y=\{y_{i}|y_{i}=\{x_{1}^{*},x_{2}^{*},\dots,x_{m}^{*}\},D_{k}\subset X,\forall x _{j}^{*},P(x_{j}^{*}|D_{k})\rightarrow\max\} \tag{2}\]  
where \(Y\) represents the Shallow-level Attributes of all vessels, and each element \(y_{i}\) denotes those of the \(i\)-th vessel. \(D_{k}\) represents the AIS data collection with complete attributes, \(X\) denotes the attribute set of all AIS data, and each element \(x_{j}^{*}\) represents the highest frequency value for \(D_{k}\).  
After obtaining Shallow-level Attributes, we focused on the process of acquiring Deep-level Features, which include the Sailing Location Preference, the Voyage Time Preference, and the Anchoring Time Preference. For the Sailing Location Preference, due to the difference in the quantity and distribution of ship trajectory points, we employed HDBSCAN (hierarchical density-based spatial clustering of applications with noise) [54] for cluster analysis. The clustering results are shown in Figure 4. Different colors represent different clusters, and the black labels denote the clustering centers. Meanwhile, to capture the Voyage Time Preference and the Anchoring Time Preference, we divided a day into 24 segments and assigned each vessel's trajectory points to the corresponding periods. The distribution of trajectory points in each period reflects the temporal preferences of the vessels. After processing, each vessel's profile can be expressed as Equation (3)  
\[S_{nmsi}=\{l_{nmsi},w_{nmsi},d_{nmsi},t_{nmsi},\alpha_{nmsi},\beta_{nmsi}, \gamma_{nmsi}\} \tag{3}\]  
Figure 3: Label-level modeling and analysis process.  
where \(l_{mmsi}\), \(w_{mmsi}\), \(d_{mmsi}\), and \(t_{mmsi}\), respectively, represent the Shallow-level Attributes of the length, width, draft, and type, \(\alpha_{mmsi}\) represents the Voyage Time Preference, \(\beta_{mmsi}\) stands for the Anchoring Time Preference, and \(\gamma_{mmsi}\) is the Sailing Location Preference. Whereas \(\alpha_{mmsi}\) and \(\beta_{mmsi}\) are transformed into two 24-dimensional features, \(\gamma_{mmsi}\) is converted into a 114-dimensional feature. The utilization of the vessel portrait is elaborated in Section 4.3.1.
## 3 Results and Discussion  
### Total Se Content and Its Species in Beetroot Juices  
The concentration of total selenium and its individual species in the studied beetroot juices are presented in Table 1.  
Homemade freshly squeezed beetroot juice contains much more selenium compared to the sold organic juice. Inorganic Se(IV), followed by selenomethionine oxide, are the main Se species in this homemade juice from raw beets. Additionally, SeMetO also dominates, after Se(IV), in purchased beetroot juice, while the concentrations of SeMet and MeSeCys did not differ significantly from each other in the studied juices. The root vegetable non-accumulators (which accumulate less than 100 mg Se/ kg of their dry weight) are capable of absorbing inorganic selenium species present in the soil, converting at least a portion of Se into other compounds and storing selenium in their tissues both as inorganic and organic forms [31,32]. For organic juice, the sum of determined selenium species obtained by chromatographic analysis amounted to 97% and 98% of total selenium for organic and homemade beetroot juices, respectively.  
The literature data proposed different mechanisms as well as various final products, which may be involved in the SeMet transformation cycle [33, 34, 35, 36, 37, 38]. Selenomethine oxide and methyloselenone are the possible reaction products according to [PERSON] et al. [33]. In another work, the first oxidation product of SeMet in the initial phase of the reaction was described as selenomethine selenoxide, which was then converted via the deaminated selenoxide and further oxidized to Se(IV) and methaneseleninic acid [36]. Other studies showed the presence of selenium heterocycles, called selenuranes, [37] as well as the formation of SeMetO and then S-(methylseleno) cysteine as the next product of SeMet degradation [23]. The formed selenomethionine selenoxide can be reduced back by the addition of thiols and ascorbic acid [38] or glutathione [39], while [PERSON] et al. reported that the SeMetO, originally present in a sample, cannot be converted back to SeMet [40]. The wide range of SeMet to SeMetO ratios observed during selenium speciation analysis in different samples probably depends on storage time and conditions, as well as on their matrices.
## 6 Performance Analysis
### Communication Overhead
In addition, in order to help the cloud to complete the query in the ciphertexts, the LBS provider also sends the evaluation data set \(EDS=\{ED_{0},ED_{1},\ldots,ED_{i},\ldots,ED_{10,000,000,000}\}\) to the cloud, where \(ED_{i}=H(PB^{i})\). If the bit length of hash \(H()\) is set as \(|H()|=128\) bit, then the size of the set \(EDS\) is about \(128\times 10^{10}\) bits. Hence, the communication overheads for the LBS provider are about \((4384\cdot k+128\times 10^{10})\) bits. After finding \(f\) matched LBS items, the cloud returns the query result \((E_{\pi q_{1}}(TRL)||ID_{CS}||TS||Sig_{CS})\) to the user, which requires \((128\cdot f+1184)\) bits to transmit. Hence, the total communication overheads between a mobile user and the cloud are about \((128\cdot f+6464)\) bits. We present the communication overhead comparison of our FGPQ scheme, the EPLQ scheme [18] and the EPQ scheme [19] in Table 2, where \"LBS provider-to-cloud (fog nodes)\" and \" User-to-cloud (a fog node) \" denote the communication between the LBS provider and the cloud (or fog nodes) and the communication between a mobile user and the cloud (or a fog node), respectively. As described in the EPLQ scheme [18], the codeword length \(N\) satisfies \(N\geq 7\), since the original dimension of the location vector is 7. Therefore, from Table 2, we can know that the communication overheads between the LBS provider and the cloud (or fog nodes) in these three schemes are about the same. However, in the user-to-cloud (a fog node) communication, the communication overhead of our FGPQ scheme is much less than that of other two schemes, as the number of LBS items matched the given spatial range is usually more than a few dozen, i.e., \(f\) is more than a few dozen, and \(f\) is likely to increase with \(k\) or the searching spatial range increasing.
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
## 3 Proof-of-Concept Experimental Investigation
### Experimental Results  
A total of eight earthquake simulations were conducted, and the test setup proved to be robust and repeatable. The re-centering mechanism was performed successfully after each ground motion had ceased. The acceleration responses of the structure, with and without the smart scissor lift brace, are presented in Figure 12. Peak values of the structural responses (absolute accelerations) are compared in Figure 13. It is evident that the roof accelerations generally increased with the exception of the Kobe Earthquake. When the scissor lift brace was deployed, additional stiffness was added to the structure, causing an increase in vibration. An increase in the roof vibrational level is an adverse effect; however, it is an inevitable result to control inter-story displacement as discussed below.  
Figure 11: Circuit diagram of the smart deployable scissor lift brace system.  
On the other hand, the inter-story drifts were measured using the difference in displacement between the roof and the base of the open floor. The time-history response of the inter-story drifts are shown in Figure 14. The reduction in the inter-story drift is summarized in Table 1. There was a significant reduction in the inter-story drift when the open first floor of the building was equipped with the smart scissor lift system for all four earthquake events. This is a more favourable condition for the soft-story building, as the  
Figure 12: Roof level vibrations in the experiments. (**a**) 1979 Imperial Valley; (**b**) 1992 Mendocino; (**c**) 1994 Northridge; (**d**) 1995 Kobe.  
Figure 13: Peak roof vibrations.  
decrease in the inter-story drift indicates less deformation and improved building resilience during an earthquake.