esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Systematic Verification of the ADCS
#### 4.2.2 Model-Based Functionality Performance  
Before presenting the results, the test conducted for on-orbit calibration is shown in Figure 19. The data were gathered during the detumbling maneuver. Note that the calibrated values were used for the HIL test. It is important to highlight that, ideally, the best data acquisition for magnetometer calibration should encompass all possible 3D rotations. However, the conducted test is intended to be representative, and further studies could be conducted using the proposed framework for simulating on-orbit calibration.  
Figure 14: Standard deviation of pointing accuracy with Monte Carlo simulation. Angular velocity, initial orbit condition, Euler initial rotation, \(I_{\text{B}}\).  
Figure 16: Mean of pointing accuracy performing variations in IB.  
Figure 17: Mean of pointing accuracy performing variations in Euler angles.  
Figure 15: Mean of pointing accuracy performing variations in angular velocity.  
Subsequently, a comparison of the complete attitude determination was conducted for Bdot and nadir, as depicted in Tables 6 and 7, in order to estimate their respective performance in evaluating the mean pointing accuracy, which should tend towards zero, and minimize the standard deviation. In Figure 20, a comparison of the Bdot controller's initial angular velocities with \(\left[-0.01\quad 0.03\quad 0.02\right]\) [rads/s] and in Figure 21 the nadir controller with \(K_{p}=0.00013555\), \(K_{d}=0.0743\), and \(\left[-0.001\quad 0.003\quad 0.002\right]\) [rads/s] radians is presented. The quality control for both controllers spans two orbits.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Approach** & **Pointing Accuracy, Mean Value** &
\begin{tabular}{c} **Pointing Accuracy,** \\ **Standard Deviation** \\ \end{tabular} \\ \hline SIL & 21.4017 & 7.2141 \\ MIL & 11.6914 & 3.5952 \\ PIL & 18.2179 & 6.1331 \\ HIL & 9.9649 & 2.8167 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model-based performance results for nadir (calculated after 2 orbits).  
Figure 19: ADCS magnetometer data, on-orbit calibration during detumbling (HIL).  
Figure 18: Mean of pointing accuracy performing variations in initial condition orbit.  
For HIL simulation, latencies of 5.2 ms and 15 ms are observed for Bdot and nadir, respectively. This contrasts with PIL, where latencies of 4 ms and 12 ms are recorded for the same controllers. The reported computational resources post-code generation for the STM32F446 RE, used for PIL validation, show a RAM usage of 32.2 KB out of 128 KB and a flash memory usage of 141.99 KB out of 512 KB. Conversely, the HIL test shows a RAM usage of 34.27 KB out of 128 KB and a flash memory usage of 142.57 KB out of 512 KB.
# A Low-Profile Dielectric Resonator Antenna Array for OAM Waves Generation at 5G NR Bands
## 3 Results and Discussion
### Radiation Pattern  
The experimental setup to measure the 2D radiation patterns in the \(E\)-plane of the DRA is shown in Figure 8a. The proposed antenna acted as the transmitting antenna or antenna under test (AUT), while the horn antenna acted as the receiving antenna. This measurement was conducted inside a fully equipped anechoic chamber with the assistance of a vector network analyzer and Diamond Engineering Automated Measurement Systems (DAMS). The radiation pattern setup for the proposed DRA is shown in Figure 8b.  
The simulated and measured 2D polar radiation pattern at phi = 0 (\(E\)-plane or \(xz\) plane) is shown in Figure 9a. Both the simulated and measured radiation patterns show very similar performance for the proposed DRA. A null at the center of the radiation pattern indicates a characteristic of OAM waves, which is apparent in the plot. However, there are some differences between the simulation and measurement results due to discrepancies in the fabrication process. Moreover, it can be observed that the measured beamwidth is narrower than the simulated beamwidth. The simulated 3D radiation pattern is shown in Figure 9b, where a null at the center of the beam can be observed clearly. Gains of between 3.6 dBi and 7.3 dBi are observed across these frequencies. The slight differences between the simulated and measured results could have occurred due to inaccuracies during fabrication and experimental tolerances.  
Figure 8: 2D radiation pattern measurement setup. (**a**) Overall graphical presentation of the measurement setup; (**b**) DRA setup inside the anechoic chamber.
## 2 Numerical Method and Its Validation
### Validation of Numerical Model  
To verify the reliability of the above numerical method, simulations on the free field test and SSI test in a scaled model experiment [28] were conducted using the introduced numerical model. The scaled model experiment was conducted in the Structural Engineering Laboratory of Southeast University, as shown in Figure 3. In the scaled model experiment, the scale factors of the density, elastic modulus, and length are 1:1.5, 1:6, and 1:20, respectively; the size, density, and shear wave velocity of soil in the hypothetical  
Figure 1: Schematic diagram illustrating the finite element model of the soil–structure system.  
Figure 2: Stress–strain relationships of the Concrete02 material and Steel02 material.  
prototype soil are 58 m \(\times\) 40 m \(\times\) 25 m, 1.65 ton/m\({}^{3}\), and 120 m/s, respectively, and those of scaled soil are 2.9 m \(\times\) 2.0 m \(\times\) 1.25 m, 1.1 ton/m\({}^{3}\), and 60 m/s, respectively. The height and period of the hypothetical prototype SDOF structure are 12.1 m and 0.5 s, respectively, and those of the scaled SDOF structure are 6.05 m and 0.05 s, respectively. The specimen of the scaled SDOF structure with pile foundations is shown in Figure 4. The scaled soil with a density, height, and shear wave velocity of 1.1 ton/m\({}^{3}\), 1.25 m, and 120 m/s was placed in a laminated shear soil box [29; 30; 31] with a clear size of 2.9 m \(\times\) 2.0 m \(\times\) 1.54 m. The time-history curves of the seismic records used in the free field test and SSI test are shown in Figure 5. The acceleration in the center of the surface soil was measured in the free field test, and the acceleration at the top of structure was measured in the SSI test.  
Based on the elevation dimension of the scaled model, the plane finite element models of the free field and SSI system were established and are shown in Figure 6, where the element size was set as 0.05 m because this size can ensure the influence of the element size is negligible [32]. Dynamic time-history analyses were conducted on the free field model and SSI system model using the seismic records in Figure 5. The comparisons of the experiment and simulation results of the acceleration in the center of the surface soil and the acceleration at the top of the superstructure are shown in Figure 7a,b, respectively. It is found that the experiment results are in good agreement with the simulation results in the time domain, indicating that the above numerical method is reliable and can be applied to the subsequent numerical study.  
Figure 4: Specimen of the scaled SDOF structure.  
Figure 3: Field photos of the shaking table tests.  
Influence of the Structure Frequency, Soil Shear Wave Velocity, and Spectral Characteristic of the Seismic Record
## 6 Discussion  
Table 6 below, illustrates the strengths and weaknesses of different security protocols while highlighting the properties of the proposed blockchain-based scheme.  
In addition to better performances at device level (i.e., low computational cost, low communication and storage overheads using relatively limited number of exchanged short messages), the proposal uses longer cryptographic keys of 160 bits, comparatively to other protocols using 128 bits hash values for cryptographic functions [8]. This leads to a more robust cryptographic solution (160 bit ECC public keys is equivalent to 1024-bit RSA cryptosystem [17]). Compared to our scheme, the weak point of the scheme presented in [24] is its higher energy consumption, as shown in Figure 13.  
The proposed scheme benefits from advantages offered by the blockchain technology, which leads to more powerful authentication process and more secure data sharing and storage while preserving their integrity, availability and traceability (or transaction history).  
Most of proposed authentication protocols for securing constrained wireless devices ensure key agreement and mutual authentication, but present the drawback of SPF and centralized architecture (organized around an AS: Authentication Server as in [8], RA: Registration Authority in [16], GN: Gateway Node in [17], or Broker in [18]).  
Contrarily, rather than using the authority of a centralized organization, the proposed scheme, taking advantage of blockchain technologies and comparable to the proposed system in [24], is decentralized and free of SPF. The security features of the blockchain evidently ensure a high level of trust without the need for managing trust amongst devices and base stations.  
Obviously, the blockchain-based scheme consumes more processing time, related to the additional crypto mining and hashing operations, which consequently increases the power consumption at the base stations. The latter are considered high-resource devices, responsible for mining and storing the distributed ledger as a private and secure blockchain with relatively low additional computational and communication costs at the device network level.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 1 Introduction
The application scenarios of power industry optical wireless technologies are shown in Figure 1. Figure 1a gives the indoor scenario that PLC and IWOC combine for smart home. The tunnel inspection is as shown in Figure 1b. The inspector communicates with the LED through a handheld device and transmits detection signals to the data center through the PLC link. The outdoor street lamp monitoring is as shown in Figure 1c, which is used to prevent traffic accidents. In addition, Figure 1d shows the hospital monitoring system, which could monitor the patient's physical health in real time. Furthermore, Figure 2 lists the block diagram of the power industry optical wireless technologies, including technical classifications, modulation methods, and application scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Types** & **Transmitter** & **Receiver** & **Distance** & **Data Rate** & **Spectrum** & **Main Limits** & **Ref** \\ \hline \multirow{4}{*}{Indoor (VLC)} & \multirow{4}{*}{LED/LD 1} & \multirow{4}{*}{PD 2/camera} & \multirow{4}{*}{10 m} & \multirow{4}{*}{40 Gbps} & 1. Short distance communication; 2. Not suitable outdoor; 3. No guaranteed of mobility support \\  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & 10 Gbps & 2. Not suitable outdoor; \\ \cline{1-1} \cline{5-6}  & & & & & (LD) & 3. No guaranteed of mobility support \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 1: Classification parameters of optical wireless communications [7; 8; 9].
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 4 Evaluation
### Intrusion Detection
#### 4.3.2 Detection Accuracy  
Table 2 shows the confusion matrix for the classification of 1-3 visitors in the experiment. Each row and each column in the table represent the real and estimated number of the moving visitors, respectively. Each element in the matrix represents the percentage  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Visitor Quantity** & **Legal Visitor Quantity** & **Illegal Visitor Quantity** \\ \hline
1 & 1 & 1 & 0 \\ \hline
2 & 2 & 2 & 0 \\ \hline
3 & 2 & 1 & 1 \\ \hline
4 & 3 & 3 & 0 \\ \hline
5 & 3 & 2 & 1 \\ \hline
6 & 3 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup of tailgating intrusion detection.  
Figure 17: Detection accuracy for the visitors with different shapes and behaviors.  
correctly estimated. As shown in Table 2, the average recall rate of the classification is 96.10%, and its standard deviation is 0.016. The cases of 1-3 visitors are not wrongly judged as zero, and the recall rates of three categories are higher than 95% for people counting. The overall accuracy is 95.67%. Specifically, if the estimated number is greater than the real one, it is considered that illegal visitors exist in the access control system. The false alarm rate in the experiment is 1.33%. On the contrary, if the estimated number is less than the real value, there exists a possibility of missing the illegal intrusion, and the rate in the experiments is 0.03%. Both the false alarm rate and the omission rate are low. In a single intrusion detection, the accuracy rate is as high as 99.9%. Based on the above experiments of single intrusion detection and tailgating intrusion detection, we can obtain that the accuracy of RF-Access for illegal intrusion detection is 96.67%.
## 3 Results and Discussion
### Re-Cr-Co (\(\sigma\) Phase)  
We have already discussed the peculiar features of the \(\sigma\) phase in the Re-Cr system. A \(\sigma\) phase is also present in the Cr-Co system, in which Cr occupies preferentially high CN sites according to both a larger atomic radius and the \(bcc\) character. Site occupancies have been studied in [26]. The ternary Re-Cr-Co system has been studied experimentally by [PERSON] _et al._[27]. These authors have demonstrated a complete homogeneity domain between the \(\sigma\) phase present in the two binary systems. One could therefore expect the Cr reversal across the homogeneity domain of the \(\sigma\) phase. This system is particularly interesting because the stable homogeneity domain is just on the reversal line.  
Two ternary samples have been synthesized and were annealed at 1150 \({}^{\circ}\)C. Their composition, homogeneity, and single phase character were checked by EPMA (see Table 2). They were studied by both XRD and ND. The compositions are shown on the phase diagram in Figure 14. The site occupancy results obtained by joint Rietveld refinement are shown together with binary data obtained by XRD or ND only [20,26] in Figure 15. On the same figure are presented the results of calculations from the DFT, as explained above. Both spin polarized and non-spin polarized calculations have been used. The difference is slight between both calculations but the non-spin polarized calculation seems to slightly better reproduce the experimental data. This phenomenon was also observed for the \(\sigma\) phase of another magnetic system (Re-Fe [28]). Though the uncertainty of the experimental results is larger than in the case of Mo-Re-Ni system since the relative XRD and ND contrasts are much less favorable, the reversal of Cr site occupancies is evident from both experiments and calculation.
###### Abstract  
Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management.  
excavator; activity recognition; deep learning; multi-modal; two-stream CNN-LSTM; point cloud; data fusion 2025  
5
## 4 Discussion
### Genesis of Amphibole Crystal Population
#### 4.1.2 Crystallization Conditions for Amphibole
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**T \({}^{\prime}\)C**} & \multicolumn{3}{c}{**P (MPa)**} & \multicolumn{3}{c}{**Continental Depth (nm)**} & \multicolumn{3}{c}{**\(\Delta\)NNO**} & \multicolumn{3}{c}{**logOf\({}_{2}\)**} & \multicolumn{3}{c}{**H\({}_{\text{G}}\)**(**mst.w.x.**)**} \\ \cline{2-13}  & & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Avg**} \\ \hline Type 1 & DTF-B1-02 & 847 & 881 & 868 & 09 & 176 & 127 & 3.5 & 6.7 & 4.8 & 1.0 & 1.3 & 1.1 & -11.5 & -11.2 & -11.4 & 5.1 & 5.6 & 5.3 \\ Type 2 & DTF-B1-01 & 919 & 1018 & 978 & 139 & 544 & 31 & 5.3 & 19.4 & 12.9 & 0.3 & 1.0 & 0.7 & -10.8 & -9.3 & -9.8 & 4.4 & 5.9 & 4.9 \\ Type 3 & DTF-B1-03 & 912 & 1018 & 1011 & 200 & 767 & 632 & 11.0 & 2.0 & 23.9 & 2.0 & 0.5 & -10.8 & -8.8 & -9.5 & 5.7 & 2.6 & 6.5 \\ Type 3C & DTF-B2-01 & 984 & 1051 & 1030 & 457 & 804 & 604 & 17.3 & 30.4 & 24.9 & 0.5 & 0.9 & 0.6 & -9.7 & -8.7 & -9.0 & 5.6 & 6.9 & 6.3 \\ Type 4D & DTF-B2-02 & 696 & 1034 & 1010 & 203 & 804 & 622 & 11.1 & 30.4 & 23.5 & 0.6 & 1.0 & 0.8 & -9.9 & -8.8 & -9.1 & 4.9 & 6.8 & 5.8 \\ Type 4D & DTF-B1-01 & 984 & 1062 & 1035 & 123 & 123 & 70 & 63.4 & 4.6 & 25.5 & 23.9 & 0.4 & 0.9 & 0.5 & -10.3 & -8.5 & -9.0 & 5.3 & 7.3 & 6.7 \\ Type 5D & DTF-B1-05 & 962 & 1050 & 1000 & 289 & 751 & 507 & 109 & 28.4 & 21.8 & 0.1 & 1.3 & 0.4 & -10.1 & -8.9 & -9.2 & 5.1 & 7.4 & 6.5 \\ Type 6C & DTF-B2-03 & 965 & 1000 & 1018 & 407 & 82 & 625 & 17.8 & 33.0 & 23.7 & 0.4 & 0.8 & 0.6 & -100.0 & -8.9 & -9.2 & 5.2 & 6.6 & 5.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Crystallization conditions of amphibble crystal population. The Type 2 amphibole (D17-B1-01) shows higher T (919 \(\pm\) 22 \({}^{\circ}\)C-1018 \(\pm\) 22 \({}^{\circ}\)C) and P (139 MPa-514 MPa) compared to the Type 1 amphibole, but lower than the other types. The calculated crystallization depth ranges from 5.3 to 19.4 km, with an average of 12.9 km. The logfO\({}_{2}\) ranges from -10.8 to -9.3 and the ANNO from +0.3 to +1.0, with H\({}_{2}\)O\({}_{\rm melt}\) values ranging from 4.4 to 5.9 wt.% (Table 2). The wide range of temperatures (912-1051 \({}^{\circ}\)C, 954-1062 \({}^{\circ}\)C) and pressures (290-804 MPa, 123-872 MPa) (Figure 10, Table 2) for the Type 3 and Type 4 amphiboles indicates varying conditions during crystallization, which corresponds well with the crystal's zoning texture. The calculated crystallization depth ranges from 4.6 to 33.0 km, with an average depth of 21.8-24.9 km.
# Variable Multiple Interleaved Bi-Directional DC/DC Converter with Current Ripple Optimization
## 1 Introduction
In reference [25], increasing the switching frequency can reduce the current ripple, but this will inevitably increase the loss and reduce the efficiency. The biggest feature of the method in this paper is that the actual operating multiplicity of the multiplexed DC/DC converter will be dynamically changed according to the duty cycle, as a result, the current ripple is optimized without changing the switching frequency. The improvements are shown as follows: Firstly, the relationship between the current ripple and parallel multiplicity and duty cycle is studied. Secondly, a control method that can change the parallel multiplicity of the DC/DC converter to optimize the current ripple is proposed. Thirdly, considering there are many similar circuits in the multiplex DC/DC converter, there will be the problem of uneven current distribution due to the differences in devices. Although automatic master control overcomes the vulnerability of the master-slave method, where the master is auto-selected by the maximum, it can trigger larger fluctuations in the output current [26]. In this paper, automatic master control and current compensation control are combined, compared with the single automatic master control, this method can better realize the current balance distribution and optimize fluctuations on the output current when the parallel multiple is switching. This paper is organized as follows: In Section 2, the relationship between the current ripple and parallel multiplicity and duty cycle is deduced, and the basic topology of variable multiplicity bidirectional DC/DC power converter is determined. In Section 3, the average value model and AC small signal model of the system are established. In Section 4, the current compensation control is designed. In Section 5, the simulation model and the experimental platform of variable multiplicity interleaving bidirectional DC/DC power converter is built. The simulation and experiment results show that the optimization of the current ripple of the variable multiplicity bidirectional DC/DC converter is obvious.