esa-sceva/satcom-chunk-collection huggingface dataset:
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 2 Instrumentation and Methods
### RFID Instrumentation and Localization  
#### 2.2.1 RFID Instrumentation  
In February 2020, a section of the landslide was equipped with an RFID system consisting of 32 battery-assisted passive tags and an acquisition station located near the landslide scar (see Figure 1). These tags can last about a decade without maintenance or replacement in the present real-time monitoring scenario. The station includes four antennas, an interrogator (Impijn SR420), a micro-computer (RPI-3B), and a modem to send the data automatically to a remote server, as described by (patent pending FR-17/53739). It is powered by a photovoltaic module and a wind turbine. The station collects RFID data for 3 min every 20 min from every tag and every antenna. The data includes the phase of arrival (termed here \"phase\") measured at 865.7 Hz, the received signal strength indication and the tag temperature. The tags were placed in pairs on fiber glass stakes 50 cm and 1 m above ground. They were spread out within the antennas reading range in a zone approximately 30 m \(\times\) 30 m wide (see Figure 1), in such a way as to maximize the line-of-sight readability of each tag by multiple antennas. To validate the RFID localization calculations, the position of the tags was measured with a LEICA TCR805 tacheometer and a handheld target (estimated precision 4 cm), approximately once every month.
## 5 Results and Discussion
### The Discussion of the Results  
Through the processing of the compensation and detection survey flight data, the effectiveness of the algorithm proposed in this paper was well confirmed. Through the analysis of the results of the experiment, the conclusions can be summarized as follows:  
1. The traditional T-L aeromagnetic compensation model can only eliminate the interference magnetic field associated with the attitude of the manned helicopter, and its effective working frequency band is generally lower than 2 Hz.
2. The ICNS-based method proposed in this paper can eliminate the interference field caused by the probe vibration and has little influence on the target signal, so it can effectively improve the detection capability of the helicopter system for low-frequency target signals.
3. The ICNS-based method can be used for the vibration magnetic field interference during the movement of the aircraft if the interference field has obvious correlation characteristics with a pair of magnetometers.
## Materials and Methods
### Reconstruction Procedure
#### 2.2.2 Challenges and Automation of Calibration Process  
The quality of the laser calibration images is fairly homogeneous within one calibration series but highly variable between different experimental settings depending on the camera, the laser and the surrounding lighting. In order to apply the calibration algorithm in the clinical routine, the procedure must be highly flexible and robust.  
Building on existing work, the corner detection algorithm as described in [20] is extended by several pre- and post-processing steps. A simplified flowchart of the presented algorithm is shown in Figure 9. An adaptive pre-processing algorithm allows flexible compensation of fluctuations in the initial quality of the calibration images and therefore provides constant conditions for the following detection. Previous implementations already employed a homomorphic filter to equalize the brightness distribution throughout the image and increase the contrast. In addition to that, the calibration images are now sharpened by the use of \"unsharp masking\" [27] and the naturally resulting increase of noise is compensated for by a bilateral filter [28], which preserves the relevant edges and corners of the checkerboard pattern. As before, a dynamic threshold for binarization is then determined using [PERSON]'s method [29] and the detected corner points are checked for symmetry by the method of [PERSON] et al. [30].  
In our work, only the inner corners of the checkerboard are used, since they are more reliable for the determination of the vanishing lines, which are essential for the 3D reconstruction. In the previous  
Figure 8: Calibration targets: white crosses on black background for camera calibration and a checkerboard frame for laser calibration.  
Figure 9: Flowchart of the calibration process. White corners indicate new processing steps.  
algorithm, the outer edges of the checkerboard had to be removed manually. Now, a post-selection step that only allows corner points with a surrounding black-white-ratio between 38-62% replaces this dispensable interaction step. Additionally, we apply an iterative procedure interpolating and extrapolating along the axes of the previously established grid based on the average grid distance.
## 1 Introduction
The traditional governing equation of a discrete vibration system is an Ordinary Differential Equation (ODE) using factors related to mass, \(m\), stiffness, \(k\), and damping ratio, \(d\), which should be individually measured to build the governing equation. Nonlinear vibration systems can also be interpreted with similar concepts. For example, the Duffing oscillator has cubic stiffness in relation to displacement [38]. Thus, a SINDy model of a vibration system not only predicts the dynamic behavior but also is interpretable with fundamental concepts. Furthermore, the results of identification can contribute to a deeper theoretical understanding of an unknown system. In summary, SINDy shows potential for identifying the governing equation of discrete vibration systems in a \"top-down\" and interpretable manner without individually acquiring the parameters \(m\), \(k\), or \(d\) related to the domain knowledge of structural vibrations. In this research, we explore the possibility of identifying the governing equation of an oscillator using the two sparse regression methods LSST and LSPL. We conduct numerical uncertainty analysis and experimental validation. The methods LSST and LSPL are introduced in detail in Section 2. In Section 3, we test SINDy on a numerical model of a single-mass oscillator. In order to simulate the true use case, noise signals at different levels are artificially added to the training data. At the same time, the highest polynomial order of the library varies from 1 to 5. The uncertainties of the results for both regression methods are obtained through Monte Carlo Simulation (MCS) with randomly generated parameters of the oscillator. LSST, the original sparse regression algorithm of SINDy, delivers less accurate sparsity and worse overfitting at increased noise levels. A similar statement was mentioned in a previous work, where the approximately noise-free measurement data is the requirement of SINDy [37]. To improve the robustness of SINDy against noise, we suggest the method LSPL to replace the original LSST method. After the numerical uncertainty analysis, the modified SINDy method, SINDy-LSPL, is tested on a real test bench in Section 4. In Sections 4 and 5, we define a complete workflow from acquisition of the time series to identification and evaluation. Overall, in both numerical and experimental tests, we review how SINDy-LSPL performs at discovering the governing equation of a simple vibration system, which is fundamental for further research dealing with complex systems with more than one Degree of Freedom (DoF). Detailed conclusions and outlooks for this research are given in Section 6.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 2 IoT Tide Gauge Network Design
### Converting in-Water Pressure Measurements to Water Depth  
Using measurements of the water pressure and atmospheric pressure, a water depth can be calculated using the equation:  
\[\text{water depth}=(P-P_{\text{atm}})/(\rho\cdot g) \tag{1}\]  
where water depth is in cm, \(P\) is the total pressure at the sensor in millibars, \(P_{\text{atm}}\) is the atmospheric pressure in millibars, \(\rho\) is the density of the seawater in kg m\({}^{-3}\), and \(g\) is the gravitational acceleration 9.81 m s\({}^{-2}\).  
The main errors in this calculation are associated with determining the seawater density and water temperature. Another uncertainty is introduced because the atmospheric pressure sensor is not located at the same height as the in-water pressure sensor.
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 5 Experimental Results
### Space Maintenance Point Clustering  
Due to the different degrees of damage to the solar panels in the space solar-power station, their maintenance priorities are also different. For the degree of each solar panel damage Class I, Class II and Class III, the benefits increase as the degree of damage increases. This paper takes the maintenance of solar array as an example, and performs task clustering on 800 maintenance point targets, so as to test the performance of the algorithm. The target coordinates of maintenance points are input into the clustering algorithm, and the maintenance points are clustered into regional target clusters and single maintenance point clusters by the clustering algorithm. The results of the clustering algorithm are shown in Table 1, (x, y) represents the position coordinates of each cluster center, Number of points in the cluster (NPC) is the number of points that need to be repaired in each cluster area, and the Income within cluster (IC) represents the sum of the income of all maintenance points within the cluster. As shown in Equation (17), this paper sets the profit coefficient _gain_ to 10. According to Equation (9), we set \(eps=0.5\) (half of the robotic arm length), \(k=2\).  
When the maintenance point in the cluster is greater than or equal to 3, it is the regional target clustering--and when it is less than 3--it is the noise point in the clustering algorithm, namely, the single target clustering. As can be seen from Table 1, the clustering results include 92 regional target clustering and 8 single target clustering, and the total income of 800 maintenance points is 15,870. The clustering algorithm is used to reduce 800 maintenance nodes to 100 clustering centers, which effectively reduces the scale of planning problems and reduces the difficulty of solving the planning problems.
## 3 Results and Discussion
### Comparison of the FEA Results with the Experiment  
Figure 14 shows that compression and tension zones of the endplate behaved differently during the experiments as well as in the FEA. When the middle of the endplate was in firm contact with the column, it increased the stiffness of the compression zone and lowered the stiffness of the tension zone. Such contact significantly reduces the warping of the endplate in the compression zone and does not allow the prying action to occur (Figure 15a). Furthermore, in the tension zone, connections with the gaps in the middle have a higher initial stiffness than firm contact. It is plausibly due to the prying action, which causes favourable deformations, increasing the clamping (Figure 15b).  
Figure 14: Horizontal displacements of the tension and compression zones for the joints with the bolts which were preloaded to \(0.7f_{\mathrm{ub}}\) (L—left side; R—right side).  
Figure 13: Failure mode of numerical models in which bolts were preloaded to \(0.7\) f\({}_{\mathrm{ub}}\): (**a**) Punching shear of the column wall in tension; (**b**) von Mises stress of the bolt from the tension zone.  
The shape of the graphs also suggests that variation of imperfections took place, i.e., diminishment of the gaps in the corners was led by the closing of the gap in the middle.  
Furthermore, higher experimental strength of the tension zone (Figure 14) in comparison to the FEA was probably caused by the adopted engineering stress-strain curve (Figure 7). Nevertheless, even with the engineering stress-strain curve, the graphs of numerical simulations are a bit higher than the experimentally obtained ones (Figure 16). If the true stress-strain curve was adopted, it would increase the moment resistance of the connection even more, so one would need to decrease the preloading force in the FEA. Therefore, as long as the experimental bolt force is unknown, the FEA becomes a curve-fitting problem--picking the proper combination of material model, imperfection, contact definition, and preloading force.
# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 3 Criteria for Optimizing Container Storage at Inland Terminals  
To develop the model in the present study, criteria for container storage were identified after an analysis of 11 inland terminals in Europe whose total turnover is up to 50,000 TEU annually and whose storage area is up to 100,000 m\({}^{2}\), with storage capacity of 1000-5000 containers. All 11 terminals receive inbound containers of 20, 30, 40, and 45 feet, which are stored on three tiers using reach stackers and on four tiers using rail-mounted gantries. In all cases, containers are stored on a \"first-in, first-out\" basis, and containers are stored for 5-10 business days. Logistic processes and data for train arrivals and departures to seaports were analyzed during model validation.  
At the observed terminals, 45-60% of all manipulations are unproductive, because container retention times are not known in advance. Terminal operators position the containers based on the likely retention time predicted by experienced staff, without any predefined model.  
Interviews with terminal operators indicated that containers are positioned within the inbound zone according to the criteria defined in Table 1. (Container storage in the outbound zone was not considered in the present study.) During the 12-month observation period, the average storage capacity of the terminals was 2400 TEU, and an average of 75% of storage space was occupied by containers. Inbound trains spent an average of 312 min at the terminals, and unloading lasted an average of 174 min.  
Table 2 shows the observed daily average number and retention time of containers, categorized by dimension, for the entire 12-month observation period.  
The average retention time \(\overline{t}\) was calculated to be the weighted harmonious mean of the individual retention times \(t_{1},\ t_{2},\ t_{3},\ t_{4}\)  
\[\overline{t}=\frac{\frac{w_{1}+w_{2}+w_{3}+w_{4}}{\overline{t_{1}}+\frac{w_{2} }{t_{2}}+\frac{w_{3}}{t_{3}}+\frac{w_{4}}{t_{4}}}}{\overline{t_{1}}}. \tag{1}\]  
Unknown retention time increases the number of unproductive manipulations. The new mathematical container storage model system proposed here is based on the expected retention time, estimated after ranking the criteria according to the preferences expressed by terminal operators.
## 4 Evaluation
### Implementation and Setup
#### 4.1.3 Metrics  
We use the following metrics to measure the performance of BlockMatch and baselines:  
* **ROC**, short for Receiver Operating Characteristic, is a commonly used evaluation measure in various fields. It provides a comprehensive analysis of the trade-off between the true positive rate (TPR) and the false positive rate (FPR) at various decision thresholds. An ROC curve is a convenient visual metric for evaluating classification models. Better performance is indicated when the ROC curve is closer to the left-hand and top border.
* **AUC** stands for Area Under the ROC Curve. It represents the probability that a randomly chosen positive sample will have a higher predicted probability than a randomly chosen negative sample. A high AUC suggests the evaluated model achieves a high TPR with a low FPR. A perfect classifier will have an AUC value of 1, indicating a clear separation between the similar samples and dissimilar samples.
* **Recall@K** is a ratio of the number of correctly retrieved similar basic blocks in the top-K candidate basic blocks to the total number of similar basic blocks in the dataset. A higher recall@K value indicates that the model has successfully retrieved a larger proportion of similar basic blocks within the top K candidates, indicating better performance.  
* **MRR** stands for Mean Reciprocal Rank, which measures the average reciprocal rank of the first similar basic block for the given candidates. It is calculated as the inverse of the rank of the first similar basic block, with a value of 1 indicating that the first result is correct.  
In our experiments, given a basic block, we use models to find basic blocks that are similar to the given one from a function composed of a large number of basic blocks. Here, we place greater emphasis on the relevance and recalls of the top-k candidates to the given basic block, so we use recall@K and precision@K instead of recall or precision.
## 2 Materials and Methods  
### Coating Material  
Using the Electron Beam Physical Vapor Deposition (EB-PVD) technique, NiCrAJY layers with a thickness of approximately 1.5 \(\upmu\)m were applied to the surface of 310H stainless steel samples that were bought from Outokumpu Stainless AB Company (Degerfors, Sweden) and cut to dimensions of 25 mm \(\times\) 15 mm \(\times\) 2 mm. After being hot rolled and heated to 1100 degrees Celsius, the plate was quenched with water. Table 1 lists this material's chemical makeup [29] in weight percentage (wt. %).  
The samples were provided with a hole 3 mm in diameter at one end for mounting on the autoclave holder. The samples were mechanically sanded with abrasive paper of various granulations (#120, #240, #400, and #600 \(\upmu\)m) before deposition, after which they were exposed to the acetone ultrasound for 30 min. The samples were ultrasonically cleaned, dried, and then weighed with an analytical balance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Alloying Elements, [wt. \%]**} \\ \hline
**C** & **Si** & **Mn** & **P** & **S** & **Cr** & **Ni** & **N** & **Fe** \\ \hline
0.063 & 0.71 & 1.61 & 0.016 & 0.001 & 24.13 & 19.03 & 0.04 & 54.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition of 310 H SS.  
The present coating selection is based on the investigation from a previous paper [30], which established such coatings as viable candidates for protective coatings for high-temperature and high-pressure work, both of which were the basis for this coating's selection. Table 2[31] shows the chemical composition of NiCrAlY.
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Spatiotemporal Beamforming  
LCMV-beamforming was initially introduced to EEG analysis as a filter for source localization [26] to enhance the signal-to-noise ratio (SNR). [PERSON] et al. [20] first applied the spatiotemporal LCMV-beamformer as a method for the analysis of ERPs. The extension of this method to the combined spatiotemporal domain [20] and the data-driven approaches proposed by [PERSON] et al. [27] and [PERSON] et al. [21] allow for its application to classification problems.  
For the following analyses, we assume that all EEG channels are normalized with zero mean and unit variance without loss of generality. Solving Equation (1) under the linear constraint given by Equation (2) returns the filter weights \(\mathbf{w}\) defining the spatiotemporal LCMV-beamformer.  
\[\underset{\mathbf{w}}{\arg\min}\mathbf{w}^{\mathsf{T}}\mathbf{C}\mathbf{w}^{ \mathsf{T}} \tag{1}\] \[\mathbf{a}^{\mathsf{T}}\mathbf{w}=1 \tag{2}\]  
These weights minimize the variance of the output of the filter while enhancing the signal characterized by the constraint. \(\mathbf{a}=\mathrm{vec}(A)\) is the data-driven activation pattern,a template of the signal of interest maximizing the difference between two classes of epochs, determined as follows:  
\[\mathbf{a}=\frac{1}{\left|\text{class 1}\right|}\sum_{\text{class 1}}\mathbf{x_{i}}- \frac{1}{\left|\text{class 2}\right|}\sum_{\text{class 2}}\mathbf{x_{i}} \tag{3}\]  
The method of Lagrange multipliers then gives the closed-form solution to the minimization problem posed by Equations (1) and (2) as:  
\[\mathbf{w}=\frac{C^{-1}\mathbf{a}^{\intercal}}{\mathbf{a}C^{-1}\mathbf{a}^{ \intercal}} \tag{4}\]  
The beamformer can be applied to epochs (unseen or not) as:  
\[y_{i}=\mathbf{w}\mathbf{x}_{i} \tag{5}\]  
resulting in a scalar output \(y_{i}\) per epoch. The linear constraint in Equation (2) ensures that the beamformer maps epochs containing a target response to a score close to one and, conversely, epochs not containing a target response to a score close to zero.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 2 Preliminaries
### B+ Tree Structure  
B+ tree is a balanced multiway search tree, which is widely used in file systems. Its characteristics are that it can keep the data stable and orderly and the insertion operation has a relatively stable logarithmic time complexity. An M-order B+ tree node has the following characteristics:  
1. Each node has at most m subtrees.
2. A node with n subtrees contains n keywords.
3. All leaf nodes contain the information of all keywords and pointers to records containing these keywords, and the leaf nodes themselves are linked in ascending order according to the size of the keywords.
4. All non-terminal nodes can be regarded as the index part, and the node only contains the largest (or smallest) keyword in its subtree.  
In data retrieval, because B+ tree has the characteristics of strong range query and sorting ability, as well as stable efficiency, this scheme adopts 3-order B+ tree as the index structure of the data buffer. The leaf node information structure is shown in Figure 1. Each keyword index corresponds to the quantum key ID and the ciphertext information of the meeting minutes. The B+ tree structure is shown in Figure 2. In order to solve the conflict of the same keyword index value, this scheme stores the node information of the same keyword index value in an adaptive space, where it is required.  
To query the node information with the keyword index value \(key=25\), the following retrieval rules are required:  
1. \(8\leq key\leq 46\), select the P1 pointer.
2. \(21\leq key\leq 35\), select the P2 pointer.
3. In the leaf node set pointed to by pointer P2, find the node with \(key=25\) through traversal.
## 4 Experiments
### Ablation Study
#### 4.4.2 Cascaded U-Net Network (CU-Net)  
The enhanced cascaded U-Net feature extraction module (CU-Net) excels in extracting more precise and comprehensive multi-scale 2D features. It not only emphasizes overall features but also focuses on effectively capturing local details, particularly in handling low-texture areas. This addition enhances sensitivity to local details, leading to superior feature information acquisition. As demonstrated in the experiments in Table 4, this module significantly improves the model's performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Acc. (mm)** & **Comp. (mm)** & **Overall (mm)** \\ \hline None & 0.405 & 0.326 & 0.366 \\ CU-Net & 0.351 & 0.279 & 0.315 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of CU-Net module on experimental results.  
Figure 7: Comparesthe GPU memory usage and runtime of different methods. (**a**) Contrasts in GPU memory usage. (**b**) Comparisons of runtime.