esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we presented an automatic cube (die)-based registration method which can deliver registration accuracy comparable to the conventional sphere-based method for point clouds collected by laser scanning at close range (up to 12 m). The proposed method is advantageous in terms of flexibility and equipment cost as only a single low-cost die-like object is required as a target to perform the registration. The quality of the die's manufacture is not a factor since the deviations from the perfect cube shape can be modeled and compensated by the proposed algorithms. The method first constructs coordinate systems by using lines between the intersections of the scanned facets to form the axes. Then, the non-orthogonality between axes is corrected based on the projection geometry. This is followed by simulating three pairs of conjugate points along the axes as the correspondences for the registration. The distances between the conjugated is constrained to be unity so the main problem is to determine the relative position and orientation between coordinate systems for the die space obtained from two scans. The registration accuracy can be improved by a simple calibration procedure for the die's dimensions.  
Based on the proposed method, two different low-cost dice (with a volume of 0.125 m\({}^{3}\) and 0.027 m\({}^{3}\)) were used for registering four real datasets collected by a Trimble scanner. The results from the proposed method match well (with check plane RMSE discrepancy less than 0.5 mm) with those obtained from the traditional sphere-based method which required four to six spherical targets to be distributed evenly in the scene. There is no need to have overlapping facets scanned so the placement of the target becomes very flexible. The proposed method not only provides scanner users with high flexibility for target placement but also with low instrument cost to enhance project management. Theoretically, the proposed method can be modified readily to a multi-dice version for registering very large scenes consisting of hundreds or thousands of scans.  
Conceptualization, T.O.C.; methodology, T.O.C., L.X. and D.D.L.; software, X.P., Y.C. and M.H.L.; validation, D.D.L. and X.W.; formal analysis, T.O.C., X.P., Y.C. and M.H.L.; investigation, T.O.C., D.D.L., X.W., X.P. and Y.C.; resources, T.O.C., L.X., D.D.L. and X.W.; data curation, X.P., Y.C. and M.H.L.; writing--original draft preparation, T.O.C.; writing--review and editing, T.O.C., L.X., D.D.L., X.W., Y.C. and M.H.L.; visualization, X.P., Y.C. and M.H.L.; supervision, T.O.C. and L.X.; project administration, T.O.C., D.D.L. and X.W.; funding acquisition, T.O.C. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Program of Guangzhou, China (202201011686).  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical.  
The authors declare no conflict of interest.
## 3 Results  
For binary classification, i.e., pairwise comparison between two velocities, we evaluated the performance of the classifier in three cases: 10 vs. 25, 10 vs. 45, and 25 vs. 45 cm/s. As commonly expected for bi-classifiers, and as shown in Figure 3a, they perform almost perfectly (Accuracy \(>\) 99%) in both active and passive tasks. However, when given the task of distinguishing the correct velocity out of the possible velocities, i.e., a tri-classifier with chance level at 33%, as shown in Figure 3b, the average performance drops to 51.7% and 55.6% accuracy for passive and active tasks, respectively, ranging from 40.6% accuracy at worst and 72.6% at best.  
Figure 3 demonstrates that the average performance of the classifiers greatly exceeds the chance level. In Figure 4, the detailed classification performance of tri-classifiers is shown for each of the 11 participants, to confirm the consistency of this performance for every participant.  
Figure 4: Average classification performance of tri-classifiers in the passive (green) and active (yellow) tasks for individual participants.  
Figure 3: Average classification performance in bi- and tri-classifiers in the passive (green) and active (yellow) tasks, across 11 participants. (**a**) three bi-classifiers performing almost perfectly (accuracy \(>\) 99%). (**b**) three tri-classifiers and their average result.  
We show in Figure 5 the performance of the tri-classifiers in both active and passive tasks for every velocity and participant to further investigate the performance of the classifiers across participants and tasks.  
Despite the consistent aggregated performance of the tri-classifiers above the chance level shown in Figures 3b and 4, Figure 5 shows that, of the 33 individual classification tasks, five and nine instances fail to perform above the chance level in active and passive experiments, respectively.  
Figure 6 shows the confusion matrices for the classification performance.  
For the active experiment, the tri-classifier performs the best in detecting the highest velocity (64% accuracy), and the worst for the lowest velocity (48% accuracy), as demonstrated in Figure 6.  
For the passive experiment, the tri-classifier performs reliably well in detecting the middle velocity of 25 cm/s with 73% accuracy, while only being marginally acceptable for the lowest and highest velocities (41% and 42% accuracies, respectively).  
Figure 5: Average classification performance of tri-classifiers in distinguishing the three velocities of 10 cm/s (Green), 25 cm/s (Yellow), and 45 cm/s (Red) in Active (left panel) and Passive (right panel) tasks.  
Figure 6: Confusion matrices of the tri-classifiers for the Active (left panel) and Passive (right panel) experiments.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 3 Rotational Stiffness of Corrugated Steel Flange Joint
### Rotational Stiffness of Joint  
#### 3.3.1 Rotational Angle of CSFJ  
To derive the calculation equation of the rotational stiffness of the CSFJ, this paper refers to the calculation method of the joint stiffness of the traditional shield segment [27], that is,  
\[k_{\theta}=\frac{M}{\theta} \tag{6}\]  
where \(M\) and \(\theta\) refer to the bending moment and rotation of the joint, respectively.  
For the convenience of calculation, the rotation of the CSFJ (\(\theta\)) is defined as the included angle after deformation between the tangent lines of the wave crest (or wave trough) of the CSP on both sides of the CSFJ, as shown in Figure 11.  
According to the geometric relationship in Figure 11, the following angle relationship (where the rotation is in the radian system) can be deduced as follows by reading the deflection value and horizontal distance from Point 1 to Point 4 near the joint:  
\[\theta=arctan\bigg{(}\frac{\omega_{2}-\omega_{1}}{L_{12}}\bigg{)}-arctan \bigg{(}\frac{\omega_{4}-\omega_{3}}{L_{34}}\bigg{)} \tag{7}\]  
where \(\theta\) refers to the rotation (rad); \(L_{12}\) and \(L_{34}\) refer to the horizontal distance (m) between Point 1 and Point 2 and between Point 3 and Point 4 in the figure, respectively; and \(\omega_{1}\), \(\omega_{2}\), \(\omega_{3}\), and \(\omega_{4}\) refer to the vertical deflection values (m) of Point 1, Point 2, Point 3, and Point 4, respectively.
## 3 Mathematical Models for the RT POD of LEO Satellites
### Observation Model  
The observation model considering the LEO dynamic parameters is presented in the following. An LEO satellite flies above the troposphere, and the tropospheric delay is thus ignored. The linearization ionosphere-free observation model for the RT POD of LEO satellites without tropospheric delay is expressed as [21]  
\[P_{lo,IF,i}^{G}=-u_{loo}^{G}\Phi(t_{s},t_{0})^{loo}o^{loo}+c\delta t_{lov,i}^{G} +\varepsilon p_{leo,IF,i}^{G} \tag{6}\]  
\[L_{leo,IF,i}^{G}=-u_{loo}^{G}\Phi(t_{s},t_{0})^{loo}o^{loo}+c\delta t_{leo}^{G} +\lambda_{IF}^{G}B_{lo,IF}^{G}+\varepsilon L_{leo,IF,i}^{G} \tag{7}\]  
\[P_{leo,IF,i}^{C}=-u_{loo}^{C}\Phi(t_{s},t_{0})^{loo}o^{loo}+c\delta t_{loo}^{ C}+ISB^{G/C}+\varepsilon p_{leo,IF,i}^{C} \tag{8}\]  
\[L_{leo,IF,i}^{C}=-u_{loo}^{C}\Phi(t_{s},t_{0})^{loo}o^{loo}+c\delta t_{loo}^{ C}+ISB^{G/C}+\lambda_{IF}^{C}B_{lo,IF}^{C}+\varepsilon L_{leo,IF,i}^{C} \tag{9}\]  
\[o_{0}^{loo}=\left[x_{0}^{loo},y_{0}^{loo},z_{0}^{loo},x_{0}^{loo},y_{0}^{loo },z_{0}^{loo},p_{1}^{loo},p_{2}^{loo},\cdots p_{n}^{loo}\right]^{T} \tag{10}\]  
A superscript \(G\) indicates the GPS whereas a superscript C indicates the BDS. A subscript \(IF\) indicates ionosphere-free combination observations and a subscript \(leo\) indicates an LEO satellite. \(P\) indicates the observed-minus-computed code measurements at the corresponding epoch. Similarly, \(L\) indicates the observed-minus-computed carrier measurements at the corresponding epoch. \(u\) is the unit vector in the direction from the satellite-borne receiver to the navigation satellite. \(\Phi(t_{s},t_{0})^{loo}\) is the matrix for the transition between epoch \(t_{0}\) and epoch \(t_{s}\). \(o_{0}^{loo}\) indicates the unknown initial receiver state.  
\(\left[x_{0}^{leo},y_{0}^{leo},z_{0}^{leo}\right]^{T}\) is the 3D position vector of the LEO satellite. \(\left[x_{0}^{leo},y_{0}^{leo},z_{0}^{leo}\right]^{T}\) is the 3D velocity vector of the LEO satellite. \(\left[p_{1}^{leo},p_{2}^{leo},\cdots p_{n}^{leo}\right]^{T}\) are dynamic parameters including the coefficient of solar pressure \(C_{R}\), atmospheric drag coefficient \(C_{D}\), and empirical acceleration vectors \(a=\left[a_{R},a_{a},a_{c}\right]\) in the three axial directions of the centroid orbit coordinate system. \(c\) is a constant representing the speed of light. \(\delta t\) is the spaceborne receiver clock offset. \(ISB^{G/C}\) is the inter-system bias (ISB) parameter between the GPS receiver clock offset and BDS receiver clock offset. \(B\) represents the floating-point solution of ambiguity. \(\lambda\) is the wavelength, \(\varepsilon P\) is the code measurements noise, and \(\varepsilon L\) is the phase measurements noise. This model is applicable to not only the broadcast ephemeris but also PPP-B2b.
# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 3 Data Analysis of Lithium Battery Aging and Extraction of Health Factors  
### Introduction to the Dataset  
The dataset used in this study comes from the Predictive Center of Excellence (PCoE) of the National Aeronautics and Space Administration (NASA) [30]. The NASA PCoE laboratory conducted multiple charge-discharge cycle experiments on 2 Ah 18,650 commercial lithium batteries under different ambient temperatures and charge-discharge modes. Key parameters characterizing the aging of lithium batteries, including capacity, voltage, resistance, and current, were obtained through charging, discharging, and impedance measurements. The charge-discharge cycle experiment process is divided into two stages: constant current-constant voltage (CC-CV) charging and constant current discharging [31]. First, the battery is charged at a constant current of 1.5 A until the voltage reaches 4.2 V and then continues to be charged at a constant voltage of 4.2 V until the current drops to 20 mA, at which point charging stops [32]. Finally, the battery is continuously discharged at a current of 2 A until the voltage drops to the set discharge cut-off voltage and discharging stops [33].  
Considering the working conditions of electric vehicles during actual driving, this study selected the aging data of three batteries that were tested at a normal temperature of 24 \({}^{\circ}\)C for further research. The batteries were designated B0005, B0006, and B0007, respectively, and battery capacity was used as the main parameter to characterize the SOH of the battery. The specific charging and discharging experimental conditions are shown in Table 1.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 4 Performance Tradeoffs Analysis
### Array 2 Gain vs. Spatial Multiplexing Gain Tradeoff
**Corollary 2:**_Maximize spectral efficiency (\(\eta_{s,\ell}\)) with downlink power constraint \(P_{d}\) if:_  
\[\max\limits_{P_{d}}R_{rate} \tag{66}\] \[f_{1}:\;R_{rate}=\min\{M,K,\frac{T_{coh}}{2}\}\left(1-\frac{ \tau}{T_{coh}}\right)\sum\limits_{k=1}^{K}\log_{2}(SINR)\ ^{\prime}\] (67) \[f_{2}:\;p_{d}\geq\sum\limits_{k=1}^{K}p_{dk},\]  
_then, we have_  
\[\max\limits_{R_{rate}}\eta_{s,\ell}\propto\min\{M,K,\frac{T_{coh}}{2}\}\simeq K, \tag{68}\]  
_where \(T_{coh}\) is the channel coherence block in symbols._  
At high SNR regime, according to Theorem 2 and Corollaries 1 and 2, expression (60) will be  
\[\frac{d\eta_{e,\ell}}{d\eta_{s,\ell}}=\frac{\sum\limits_{k=1}^{K}\frac{\partial \eta_{e,\ell}}{\partial p_{d\ell}}dp_{d\ell k}}{\sum\limits_{k=1}^{K}\frac{ \partial\eta_{e,\ell}}{\partial p_{d\ell}}dp_{d\ell k}}. \tag{61}\]0  
At spectral efficiency \(\eta_{s,\ell}\)\(\rightarrow\)\(\infty\), we have (See Appendix B)  
\[\frac{d\eta_{e,\ell}}{d\eta_{s,\ell}}=\frac{\sum\limits_{k=1}^{K}\frac{\partial \eta_{e,\ell}}{\partial p_{d\ell}}dp_{d\ell k}}{\sum\limits_{k=1}^{K}\frac{ \partial\eta_{e,\ell}}{\partial p_{d\ell}}dp_{d\ell k}}. \tag{61}\]1  
As a result at high spectral efficiency zone, the slope between the spectral efficiency and energy efficiency relies on the number of standalone data streams under the cost of pilot overhead, i.e., \(\left(1-\frac{\tau}{T_{coh}}\right)\).
## 4 Case Studies
### A Numerical Case Study on a Synthetic Dataset of Cylinders
#### 4.2.1 Reference MD Space Based on Healthy Samples  
Figure 11 presents the classification results of **IMCS\({}_{\text{PSO}}^{\text{BPSO}}\)** for the training pool dataset and testing dataset, with an accuracy of 99.06% and 98.68%, respectively.  
It is worth remembering that the feature dimension has been significantly reduced from 44 to only 9, which is conducive to the processing of big data. The screened features are listed in Table 5.  
Figure 11: (**a**) Classification on the training dataset; (**b**) classification on the testing dataset.
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 2 Experimental Setup
### Restoration Algorithm-Synthetic Aperture Focusing Technique (SAFT)  
In US, data are acquired simultaneously from all directions over a number of emissions. Echo signals are recorded independently by individual elements of the receive sub-aperture. However, signals shown in the ultrasound image may not indicate the real position of where the acquired echoes originated. To access reliable inside information of a medium from an ultrasound image, a restoration operation has often been applied to the acquired data to reconstruct the interior structure. Among all kinds of existing restoration algorithms, SAFT is the one that has been around for a long time and is extensively used to achieve echo energy refocusing and image resolution enhancement. In the process of SAFT, the relationship between the echo signals of N sensors disposed of inline (multi-channel scans) is calculated, and thereafter a defocused image is reconstructed by summing all demodulated input echo signals [21; 22; 23; 24; 25]. SAFT can be performed either in the image domain or the frequency domain to accommodate image reconstruction. In the subsequent stage of image processing in this work, SAFT is performed in the time domain using delay-and-sum (DAS) processing to restore ultrasonic images obtained from B- and pitch/catch scans under acoustic shadowing.  
A brief description of applying SAFT to facilitate the reveal of a shadowed image is shown in Figure 2. It is assumed that a point diffractor (T) is immersed in water, and a sensor emitting a spherical wave is used to obtain a B-scan image of the diffractor (T) (Figure 2). By summing the delayed time traces of the B-scan image [\(SPi(x\pm I\times dx,t+dt_{i})\)], the reconstructed image [\(SP(x,t)\)] processed by the SAFT is given by  
\[\mathrm{T}=SP(x,\ t)=\sum_{i=-n}^{n}SP_{i}(x\pm i\times dx,\ t+dt_{i})\, \tag{1}\]  
\[dt_{i}=\sqrt{\left(\frac{2i\times dx}{V}\right)^{2}+t^{2}}-t\, \tag{2}\]  
where \(dx\) is the successive lateral scanning interval; \(t\) is the vertical two-way propagation time of the pulse from the probe to the flaw and back; and \(V\) is the speed of sound in water. For scanning position \(x\), \(dt_{i}\) in (1) is the time delay for the successive scanline, \(i\times dx\), with respect to the point diffractor (T); \(i=\pm n\); the maximum lateral distance at which the echo signals diffracted from the diffractor (T) can be detected by the sensor.
# High-Dimensional and Multi-Intensity One-Photon-Interference Quantum Secure Direct Communication
## 3 Security Analysis
Although our protocol is not fully DI and may suffer from certain side-channel attacks, such as Trojan-horse attacks on light sources [54], its architecture, which includes placing the measurement in an untrusted location, decouples the security from the trustworthiness of the detectors, making it immune to many common attacks targeting measurement devices. A final security concern is the assumption of an infinite block length in our analysis. In practical communication systems, [PERSON] and [PERSON] can only send finite numbers of WCS pluses rather than an idealized infinite number. This finite block length introduces statistical fluctuations in the estimation of channel parameters, which further tightens the upper bound of the secrecy channel capacity. Notably, unlike investigations in QKD systems [55; 56; 57], where random keys are negotiated, QSDC involves the direct transmission of deterministic information, and its performance under these conditions requires specific handling and analysis. While a comprehensive study of finite block length effects and their impact on practical QSDC systems is beyond the scope of this paper, valuable insightson this topic can be found in Refs. [58; 59]. In the following discussion, we adhere to the asymptotic limit, assuming an infinite block length.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 3 Air Resistance Approximation and Motion Primitive Generation  
This section will linearize and approximate the air resistance, with the aim of minimizing the error between the quadcopter dynamics model and actual conditions under high-speed flight, while utilizing the Pontryagin principle to solve the motion primitives of the optimal trajectory in a closed form.
## 1 Introduction
While empirical-statistical approaches have the disadvantage of difficult transferability to other sensors, sites, or dates, as the model may be overfit, RTM inversions' principal drawback is their ill-posedness: An RTM simulates top-of-canopy reflectance using a modeled scene and physical principles to compute the radiative budget, and various sets of inputs may lead to similar outputs as the problem faces too many unknown variables. However, various options have been successfully explored to mitigate these issues and obtain reliable solutions, such as using prior information to lower the variability of the estimations' distribution or introducing spatial constraints during the inversion process (see an overview of possible options in Baret and Buis [21]). In the present study, a look-up table (LUT)-based RTM inversion was performed using the DART 3D canopy model [22], based on the work done in [PERSON] et al. [23]. The aim of this study was to find an inversion strategy suited for a heterogeneous tree-grass ecosystem's health (e.g., resiliency to change, continuity in GPP, and nutrient capital [24]) monitoring, i.e., capable of estimating LAI and leaf C\({}_{ab}\) and Car over time using multitemporal hyperspectral airborne acquisition with medium ground sampling distance. Canopy LAI and biochemistry estimations using hyperspectral imagery have mostly been done over canopies with high LAI (for instance, see [PERSON] et al. [16], [PERSON] et al. [25], [PERSON] et al. [26], [PERSON] et al. [27], [PERSON] et al. [28]). While various estimations have also specifically been done over open-canopy ecosystems (e.g., the works of [PERSON] et al. [29], [PERSON] et al. [30], [PERSON] et al. [31]), research concerning acceptable modeling methods within RTM is still ongoing [32; 33]. The validity of a simplified representation of trees (simple forest representation, SFR) within the DART scene (such as the one done by [PERSON] et al. [14]) when working with medium-resolution hyperspectral images of very sparse forests was tested in this study, based on the work presented in [PERSON] et al. [32]. As acceptable representation of coniferous trees within RTM is still a difficult topic [34]; this study only focused on broadleaved trees as a preliminary test case to study Mediterranean forests. Validation data were available at multiple dates, allowing for a more robust calibration of the inversion method. The validity of the results presented in this paper was assessed using: (i) comparison between estimations and field measurements when and where available, and (ii) comparison between the site's expected and estimated LAI and foliar pigment contents variations over two years.
## 4 Discussion
### Guidance for Deep Underground Engineering  
In this study, the presence of fissures caused the intensity of rock spalling failure (rockburst) and the size of large fragments to initially decrease and then increase, offering a new approach for controlling deep surrounding rock slab cracking (rockburst). [PERSON] et al. [42] conducted true triaxial compression tests on sandstone specimens with and without concrete support, showing that concrete helps restrain tunnel failure. [PERSON] et al. [43] performed uniaxial compression experiments on high-strength gypsum models using prestressed anchors made of aluminum rods. The results indicated that prestressed anchors improved the specimen's internal stress, inhibited fissure propagation, and bonded split rock slabs, enhancing their bending stiffness and preventing buckling deformation. Similarly, [PERSON] et al. [47] investigated the effect of bolt diameter on the failure behavior of intact brittle rock under uniaxial compression using laboratory model tests. Their findings showed that bolts changed the failure mode of brittle rock from splitting to shear by inhibiting internal crack propagation. Numerous engineering practices have also demonstrated that anchor support technologies--such as anchor spray, anchor net support, and anchor net spray--are effective in controlling deep rock mass instability and failure.  
These findings suggest that excavating energy-release grooves on tunnel sides could reduce rock slab cracking failure (rockburst) intensity and improve the safety and stability of deep underground tunneling projects. Additionally, as fissure penetration increases, the  
Figure 21: Fractal fitting curves for different specimens.  
failure strength of the surrounding rock in rectangular tunnels decreases. Fissures enlarge and deepen V-shaped notches on adjacent tunnel sidewalls, reducing plate fissure failure but significantly weakening the surrounding rock's load-bearing capacity. Controlling the excavation boundary to mitigate stress concentration may be another effective strategy for ensuring the safety of deep mining operations.