esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Attribute Machine Learning Model Selection
### Support Vector Regression (SVR)  
Support vector regression is a popular machine learning model used for both data classification of continuous datasets and regression. SVR is a supervised learning algorithm that uses the same principles as Support Vector Machine and is suitable for prediction of discrete values [29]. The algorithm searches the best line that can fit the data. SVR allows the introduction of the error limit, or the tolerance and will fit the data through a line or a hyperplane (in multiple dimensions). The SVR objective is to minimize the l2 norm, \(min(\frac{1}{2}||w||^{2})\), where the maximum error is set to have limited variation.  
When using SVR algorithm, one of the most important aspects to take care of is the dataset size. SVR is not suitable for training large datasets because the training time increases with more than the square of number of samples [30] and becomes too computational expensive. For large datasets, defined by more than 10,000 samples, it becomes infeasible to use SVR due to the increased training time. For large datasets, Linear SVR or SDR regressor can be used as substitutes for classical SVR algorithm [31]. However, as the maximum size of the dataset used for this work is 1000, the classical SVR algorithm will be applied. Aside from this, SVR was chosen for this work due to its advantages for small datasets: it is robust to outliers, it has excellent generalization capability, a high prediction accuracy and it is easy to implement.  
Nevertheless, another aspect that must be considered when applying SVR on datasets is the ratio between the number of features and the number of training samples. When the ratio is greater than one, meaning that the number of features for each data point exceeds the number of training samples, the SVR algorithm will underperform. To avoid this, the dimension of the features can be reduced by applying Principal Component Analysis (PCA) [32] to extract the first \(n\) principal components with highest corresponding eigen-values. However, PCA-based feature extraction can lead to errors, due to the loss of information, especially if the relationship between the features and the data is highly nonlinear. Another method to reduce the dimension of the features is to apply a feature selection algorithm [33] that allows the selection of the features that are most relevant to build the model. However, the dataset under investigation does not require any feature dimensionality reduction, because the number of features is much smaller than the number of samples. For the tested datasets, the number of features is always constant and equal to nine for the electromagnetic datasets (i.e., torque harmonics amplitudes and phases and back emfs harmonics amplitudes), equal to six for the structural dataset, and it is seven for the motor losses datasets, while the number of samples varies from 250 to 1000.  
Regarding the hyper-parameters chosen to train the models, they were kept constant and equal to C = 3.2 and the default kernel, 'rbf' (radial basis function), for all cases.
## 3 The Dynamic-Static Coupling Compaction Method and Control Parameters
### The Impact of Different Compaction Methods on Compaction Density  
The comparative results of the compaction density variations using different compaction methods are shown in Figure 4 and Table 1. It can be observed that, with an increase in compaction passes, the compaction density gradually increases for various compaction methods and eventually stabilizes. Among them, static compaction exhibits more stable variations, resulting in the highest final compaction density.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compaction Passes**} & \multicolumn{3}{c}{**Compaction Density/\%**} \\ \cline{2-4}  & **High-Frequency** & \multirow{2}{*}{**Static Pressure**} & **Low-Frequency** \\  & **Vibration** & & **Vibration** \\ \hline
2 & 83.38 & 90.78 & 78.17 \\
4 & 93.87 & 90.34 & 88.81 \\
6 & 100.94 & 93.19 & 95.72 \\
8 & 100.28 & 93.37 & 99.14 \\
10 & 101.23 & 95.3 & 102.53 \\
12 & 97.3 & 97.44 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variations in subgrade compaction for different rolling methods.  
Figure 4: Curve of variations in roadbed compaction for different compaction methods.  
Furthermore, in comparison to vibratory compaction, static compaction generates a higher initial compaction density during the initial compaction phase. The compaction of loose roadbed fill materials is a crucial aspect of roadbed engineering, effectively improving the bearing capacity and stability of the roadbed. When applied to roadbed fill materials, static compaction, in particular, holds significant advantages, producing a smoother road surface and effectively filling the gaps between coarse and fine particles.  
After multiple compaction passes, vibratory compaction proves more effective in balancing the porosity of the soil-rock mixture. Therefore, in practical construction, a combination of compaction methods should be selected based on specific conditions. To achieve better compaction density, a combination of static and vibratory compaction, along with quantified high-energy impact compaction, can be employed. These methods ensure the uniformity of roadbed fill materials, achieve excellent compaction, enhance bearing capacity, and improve stability.
## 2 Method and Experiments
### Characterization  
Avalanche angle testing was performed using the revolution method (revolution powder analyzer, Mercury Scientific Inc., Newtown, CT, USA). In this test, a tap volume of 100 cm\({}^{3}\) was measured using a cup, which was filled and tapped until no more powder could be filled in it, and a sharp edge was used to remove the excess powder. The powder was then transferred to a cylindrical drum surrounded by a see-through glass window. The drum was configured to rotate at 0.3 rpm to minimize the effect of surface friction, and the flow behavior of the powder was analyzed using a digital camera. Avalanche angle measurements were averaged on the basis of 125 snapshots, for a total time of 120 s, for reliability. The uniaxial compressive strength was measured using a universal tester (MTS810, Material Test System Corp, Eden Prairie, MN, USA) with a cylindrical sample, which had a diameter, height, and fixed speed of 10 mm, 10 mm, and 0.2 mm/min, respectively. Thermogravimetric (TG) and differential scanning calorimetry (DSC) analyses were performed by heating the samples at 10 \({}^{\circ}\)C/min in an atmospheric environment using a thermal analyzer (STA449C/3/MFC/G, NETZSCH, Germany). The crystal phase of the 3D-printed template was profiled by X-ray diffraction (XRD, D/max Rigaku 2200V/PC, Japan).
# Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
## 6 Evaluation
### Testing BER under Different Angle and Distance  
The bit error rate is an important fact to evaluate performance of transmission system. We conduct a series of experiments about the influence of angle and distance on BER.  
Different distances between the flashlight to the ambient light sensor result in different light intensities captured by the sensor in the downlink. Figure 9(a),b demonstrates the BER of ODH-PIM with different angles and distances. From Figure 9(a), it is obvious that BER is very high when the transmission distance exceeds 20 cm. The reason is that the sensor is difficult to detect the flashlight half-on state once exceeding 20 cm and BER boost due to lack of the state.  
Since VLC owns the directionality character, the transmission angle is also a factor to impact the transmission performance. As shown in Figure 9(b), when the angle between both devices exceeds 45\({}^{\circ}\), VLC is unavailable using a smartphone and TelosB sensor. The angle also affects the light intensity collected by the sensor. When the angle is less than 25\({}^{\circ}\), the sensor can capture enough light intensity from the flashlight so that the VLC can work with a low BER. When the angle is between 25\({}^{\circ}\) and 45\({}^{\circ}\), the flashlight cannot provide enough light intensity for communication, leading to boost BER.  
We also use BER to measure uplink performance under different communication angles and distances, as shown in Figure 10. The distance between the camera and the sensor node determines the size of ERA in the image. From Figure 10(a), modified DH-PIM obviously reduces BER when distance less than 4 cm. Once the distance is over 4 cm, uplink communication is disabled with high BER because the ERA is too small to be recognized.  
Figure 10: (a) BER under different distances in downlink. (b) BER under different angles in downlink.  
Figure 9: (a) Completion time in indoor environment (650 lux); (b) completion time in indoor environment (2100 lux).  
However, most smartphone's cameras offer optical zoom, which can amplify the ERA and further increase communication distance.  
Figure 11b shows that when the angle is smaller than \(6^{\circ}\), the BER of modified DH-PIM can keep at a low level. When the angle is greater than \(6^{\circ}\), the transmission cannot be performed normally. The camera cannot capture enough light to form ERA with exceeding \(6^{\circ}\) communication angle.
###### Abstract  
This paper presents a wide-tuning-range, low-power tunable active inductor (AI) designed and fabricated using 130 nm CMOS technology with six metal layers. To achieve high performance with a relatively small silicon area and low power consumption, the AI structure is carefully designed and optimized using a cascode stage, a feedback resistor, and multi-gate finger transistors. In the proposed circuit topology, inductance tuning is realized by adjusting both the bias current and the feedback resistor. The performance of the circuit is evaluated in terms of tuning range, quality factor, power consumption, and chip area. The functionality of the fabricated device is experimentally validated, and the fundamental characteristics of the active inductor are measured over a wide frequency range using a Cascade GSG probe, with results compared to simulations. Experimental measurements show that, under a 1 V supply, the AI achieves a self-resonant frequency (SRF) of 3.961 GHz and a quality factor (Q) exceeding 1586 at 2.383 GHz. The inductance is tunable between 6.7 nH and 84.4 nH, with a total power consumption of approximately 2 mW. The total active area, including pads, is 345 \(\times\) 400 \(\upmu\)m\({}^{2}\).  
gravonded active inductor; wide tuning range; radio frequency; CMOS 2025253070
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 5 Challenges and Potential Solutions for Automating Vulnerability Detection
### Cyber Risks Challenges  
New technologies and high complexity emerge as new vulnerabilities, making systems outdated quickly. Moreover, new sophisticated cyberattacks using advanced strategies, such as advanced persistent threats (APT), outline real challenges for cyber teams in handling and reducing the nature of these cyber risks. Additionally, zero-day exploits harness organizations before they are found or fixed [144; 145].  
Potential Solutions. To address these issues, adopting automated platforms for continuous learning models helps us stay up to date with the latest vulnerabilities. Moreover, ongoing monitoring of assets for behavior-based analysis and immediate software patching are required to reduce the impact of zero-day vulnerabilities [146].
# Multizone Leak Detection Method for Metal Hose Based on YOLOv5 and OMD-ViBe Algorithm
## 3 Experiment and Results
### Experimental Results  
#### 3.2.1 YOLOv5 Results  
This section analyzes and discusses the training results based on YOLOv5 under the self-made metal hose dataset. Figure 6 shows that the training accuracy and recall rate of YOLOv5 was 100 %, the mAP50 result was 99.5%, the detection speed with the YOLOv5 model was 30 FPS, and the detection with the YOLOv5 model could meet the detection requirements.
## 4 Discussion
### Patch Selection  
In the case of the U-Net model, randomness in patch selection and the proportion of non-red tide pixels affect the performance of the predicted red tide map. For non-random sampling, we selected a continuous patch around the region where the red tide occurred on each image. In the four U-Net models, the red tide area of the non-random case was less overestimated than that of the random case. When we constructed training datasets with spatially-adjacent patches, it resulted in a higher performance than when patches were randomly selected. We confirmed that non-random sampling can guarantee to the selection of informative red tide patches for good training. In relation to the ratio of non-red tide and red tide pixels, we confirmed that, when using only red tide patches, the edge and non-red tide pixels were recognized as red tide pixels. This is most likely due to the lack of learned knowledge of non-red tide patches, and because the U-Net model is unable to recognize non-red tide pixels well. As a result, the F-measure level increased as the proportion of non-red tide pixels increased. We confirmed the importance of constructing training datasets for the U-net model to reduce the misdetection rate of red tide pixels. These results show that the U-Net model exploits both spatial and spectral features for red tide detection. Furthermore, the adjustment of patch selection allows for overcoming edge misdetection and potential errors resulting from the limitations of the terrestrial sensor.
## 4 Results and Discussions
### Identification of Slope Hazards and Field Validations
Since the begin date of the InSAR calculation is just near the completion date of the new slope stabilization structure, it is speculated that the slope deformation may be postconstruction settlement caused by construction disturbance.
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 4 The a Priori SRP Models for BeiDou-3 I2-S Satellite
### Analytical Cuboid Box Model  
The cuboid box model, as expressed in Equation (10), was employed in a specific parameterization, which separates the contribution of an ideal cube from that of a cuboid and the \(\pm\)z asymmetry. By analyzing the correlation between the parameters and their contributions to the SRP perturbation, only two independent parameters have been selected to simplify the model as  
\[\begin{array}{l}a_{D}=-a_{C}^{\alpha\delta}(|\cos\varepsilon|+\sin \varepsilon+\frac{2}{3})-a_{S}^{\alpha\delta}(|\cos\varepsilon|-\sin \varepsilon-\frac{4}{3}\sin^{2}\varepsilon+\frac{2}{3})\\ a_{B}=-\frac{1}{3}a_{S}^{\alpha\delta}(\cos\varepsilon\sin\varepsilon)\end{array} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Along-Track** & **Cross-Track** & **Radial** & **3D** \\ \hline ECOM1 & 44.1 (44.7) & 23.8 (22.4) & 11.5 (12.6) & 51.4 (51.6) \\ ECOM2 & 42.1 (42.6) & 25.2 (26.1) & 12.4 (18.6) & 50.6 (53.4) \\ ABW & 41.8 (54.1) & 26.2 (20.5) & 10.6 (11.0) & 50.5 (58.9) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean value of OBDs in Along-track, Cross-track, radial and 3D for BeiDou-3 I2-S ECOM1, ECOM2 and ABW solutions. Values in brackets was for eclipse season (unit: cm).  
Figure 3: Time series of orbit boundary discontinuities (OBDs) for **(a)** ECOM1, **(b)** ECOM2 and **(c)** ABW solutions of BeiDou-3 I2-S. The red, green and blue curves represent OBDs in along-track, cross-track and radial direction, respectively.  
As the first one cannot be separated from the contribution of SPs. Hence, the value is calculated from the optical and geometrical parameter listed in Table 1. The second one was adjusted from a least-squares adjustment based on the Equation (13). Table 3 lists the estimated values for these two parameters.
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 2 Principle of the Dual-Channel Round Inductsyn  
The round inductsyn is a multi-pole induction device. When an excitation signal is input from the outside, the electromagnetic induced signals between the stator and rotor plane windings change with displacement, producing alternating electromotive forces that vary sinusoidally and cosinusoidally. When one phase winding is at the zero position, the other phase winding outputs the maximum electromotive force. Through the output of the two-phase electromotive forces, the angular position can be clearly reflected at any position within one cycle. With the appropriate decoding circuit, the current angular position can be detected. This paper describes the JGX120/360 type dual-channel round inductsyn developed by Changzhou Weinengda Precision Machinery Co., Ltd., Changzhou, China. It has a speed ratio of 1:180 and a structure as shown in Figure 1, consisting of an outer cover, rotor, and stator from top to bottom. The two wirings above the outer cover are the differential inputs for the excitation signal, while the eight terminals on the stator correspond to the differential outputs of the sine- and cosine-induced signals for the coarse channel and fine channel, respectively.  
The dual-channel round inductsyn operates based on the principle of mutual inductance between the alternating magnetic field and the coil windings. The electrical schematic diagram is shown in Figure 2. \(EXC^{+}\) and \(EXC^{-}\) are the input terminals for the excitation signal of the round inductsyn, \(C\_SIN^{+}\) and \(C\_SIN^{-}\) are the terminals for the coarse-channel sine winding on the stator, \(C\_COS^{+}\) and \(C\_COS^{-}\) are the terminals for the coarse-channel cosine winding on the stator, \(F\_SIN^{+}\) and \(F\_SIN^{-}\) are the terminals for the fine-channel sine winding on the stator, and \(F\_COS^{+}\) and \(F\_COS^{-}\) are the terminals for the fine-channel cosine winding on the stator.  
When the input is a sine excitation signal \(u_{E}=U\sin(wt)\), the electrical signals induced by the coarse- and fine-channel windings are shown in Equation (1), where \(U\) and \(w\) are the amplitude and angular frequency of the excitation signal; \(k_{C}\) and \(k_{F}\) are the signal transformation ratios for the coarse and fine channels, respectively; \(u_{C\_SIN}\) and \(u_{C\_COS}\) are the sine- and cosine-induced signals at the stator coarse-channel winding, and \(u_{F\_SIN}\) and \(u_{F\_COS}\) are the sine- and cosine-induced signals at the stator fine-channel winding; \(\theta_{C}\) and \(\theta_{F}\) are the angles of the stator and rotor, respectively, and \(w\) is the angular frequency of the excitation signal.  
\[\left\{\begin{array}{l}u_{C\_SIN}=k_{C}U\sin(wt)\sin\theta_{C}\\ u_{C\_COS}=k_{C}U\sin(wt)\cos\theta_{C}\\ u_{F\_SIN}=k_{F}U\sin(wt)\sin\theta_{F}\\ u_{F\_COS}=k_{F}U\sin(wt)\cos\theta_{F}\\ \end{array}\right. \tag{1}\]
# Computational Characterization of Turbulent Flow in a Microfluidic Actuator
## 3 Validation of Simulations  
As previously mentioned, the numerical simulation of the flow in the fluidic oscillator was carried out with the commercial software ANSYS-Fluent v. 19. Computations are validated versus experimental data [14] considering the resulting oscillation frequency at four Reynolds numbers; the comparison is shown in Figure 5. In this figure, the single result in [15] for \(Re\ \approx\ 16,000\), using the SST k-\(\omega\) turbulence model, is also included, as well as the computed frequencies obtained in [16] using 3D simulations and a hybrid DES turbulence model, which were performed with the software OpenFoam.  
In Figure 5, the error bars, which are superimposed to the experimental points, indicate a 5% deviation for each particular value. As can be seen, the present computations lie within the error bars, although the frequency is always slightly overpredicted. The 3D simulations in [16] show the same trend as the actual two-dimensional simulations and, in fact, the values are very close to each other, with maximum differences between them of around 1%. This result suggests that the 2D simulations suffice to reproduce the global flow features in the oscillator and, in particular, its operation frequency. Finally, in Figure 5, the frequency obtained in [PERSON] et al. [15] at \(Re\approx 16,000\) is also displayed, which in this case is below the experimental measurement but within the 5% error bars. It is necessary to  
Figure 4: Time-step sensitivity study: normalized mass flow versus time at outlet 1 for two time steps.  
Figure 3: Grid refinement study: normalized mass flow versus time at outlet 1 for the three considered meshes.  
stress that the present simulations and those of [15] are two-dimensional and use the same turbulence model, but were performed with different software (Fluent in this work and CFX in [15]).  
An interesting exercise is to plot the normalized mass flow at outlet 1 versus a non-dimensional time obtained by dividing the real time over the period of oscillation for the four Reynolds numbers simulated. As can be seen in Figure 6, the four curves overlap each other approximately, which means that the non-dimensional frequency of the fluidic actuator is virtually independent of \(Re\) for the studied range of Reynold numbers, between 8000 and 16,000.
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## 3 Results and Discussions of Thrombysis Experiment In Vitro  
### Drug Release Experiment  
A triggerable drug delivery system has been developed to control the release of urokinase as required, which can enhance therapeutic effect. According to the same approach as Section 2.4, the microplate reader FlexStation3 was used to evaluate the effect of alternating magnetic fields on urokinase release from the drug-loaded NPs. The recent kit containing the solution of urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs (756 \(\upmu\) urokinase coated on 1 mg PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs) was placed in the working area of the magnetic control system, and the alternating magnetic fields of different frequencies generated by the cores on the x-axis and z-axis were applied to the regent kit for several minutes. The drug concentration released in the solution can be determined according to the calibration line shown in Figure 4a and the absorbance of the solution measured by FlexStation3. The release amount of urokinase can be obtained by \(CV\), where \(C\) is the measured concentration of urokinase in the supernatant after centrifugation of the NPs solution and \(V\) is the volume of the supernatant.  
The experiment was performed with 1 mg PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs. Figure 7 shows the drug release of urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs over time under the action of AC magnetic field with frequencies of 50 Hz and 100 Hz and in the absence of AC magnetic field. As shown in the figure, in the absence of AC magnetic field, the release of urokinase is only about 56 \(\upmu\)g after 60 min. After this time, 92.6% of the initial amount of urokinase remained bound to NPs. This indicates that the prepared urokinase coated magnetic NPs are very stable and are expected to reduce the influence on non-targeted areas in long-distance transmission. While under 50 Hz and 100 Hz AC magnetic fields, the total release of urokinase includes magnetic triggering and non-magnetic triggering, which increase with time and reach 310 \(\upmu\)g and 391 \(\upmu\)g, respectively, within 60 min. Therefore, urokinase released in the presence of an alternating magnetic field is much larger than urokinase released in the absence of an alternating magnetic field. This indicates that the AC magnetic field can not only trigger and accelerate drug release, but also act as a switch for the release of the protein drug. The tentative explanation for the mechanism of drug magnetic release may be that the oscillation of magnetic NPs under alternating magnetic field would induce the breakage of the amide bond which acts as a linkage of urokinase and NPs [32].