esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Verification with Finite Element Model  
### Test Introduction  
In order to verify the effectiveness of the established finite element analysis model, the axial compression test for a concrete-filled steel tubular column is carried out, and the finite element model of the test concrete-filled steel tubular column is established. The numerical analysis results are compared with the test results to verify the feasibility of the finite element model.  
The cross-sectional dimension of the test piece is 230 \(\times\) 230 mm, height 700 mm. Q235 steel is used for the steel pipe and the concrete is C40. The connecting part between the short column and the foundation is directly anchored, and the loading mode adopts displacement control. The dimensions of the test piece are shown in Figure 1.  
Tensile tests on steel coupons were conducted based on the Chinese Standard GB/T 228-2010 [15]. Each set of tests consisted of three tensile specimens and the results were averaged. The measured yield strength and elastic modulus are listed in Table 1, where \(f_{y},f_{u},E_{s}\) represents the yield strength, ultimate tensile stress and elastic modulus of steel. Three concrete blocks (150 mm cube) were tested under axial loading in each group to obtain the compressive strength, and the results were averaged. According to the conversion relationship between the prism compressive strength (\(f_{c}\)) and the cubic compressive strength (\(f_{cu}\)) suggested in Chinese Standard GB 50010-2010 [16], the compressive strength of the concrete prism was calculated and is listed in Table 2, where \(E_{c}\) represents the elastic modulus of concrete.  
Figure 1: Specim size of concrete-filled steel tubular column (mm).  
Figure 2a shows the steel pipe concrete column specimen before loading. With the larger load on the concrete-filled steel tubular column, the test specimen experiences the elastic stage, peak bearing capacity stage, and failure stage. When the bearing capacity of the concrete-filled steel tubular short column reaches the peak, the part circled by the red line in Figure 2b shows no obvious drum. As the load increases, the damage in the concrete-filled steel tubular column becomes more and more obvious, and the drum of the steel tube becomes more and more obvious.  
With the increase in load, the bearing capacity of the steel pipe is lost. It can be seen that the bulge of the steel pipe stopped after the vertical loading (as shown in Figure 2c).
# Analysis of the Interaction Effects of Shield Structure Oblique Passing under an Existing Tunnel
## 3 Three-Dimensional Numerical Model
### Analysis of Numerical Simulation Results  
Figure 7 shows the numerical simulation values calculated for the settlement after excavation of Metro Line 3 for the left line tunnel (LT) and right line tunnel (RT), which were compared and analyzed with the actual measured values in the field. As can be seen from Figure 7a, after the RT excavation was completed, a settlement trough with a width of about 45 m was formed on the ground surface above the existing tunnel axis. After the LT excavation was completed, the settlement trough became wider and deeper, and the maximum settlement value was located at the center line of the new two-lane tunnel. As can be seen in the figure, the settlement data measured on site were significantly larger than the calculated values, which is due to the fact that the existing tunnel was not only affected by the new tunnel excavation during the shield excavation but also by complex ground loads, such as construction loads and vehicle loads. It is obvious from Figure 7b that the settlement fitting curve fits the measured values more closely because the hardening process of the grouting layer was considered and the actual field construction conditions were simulated, making the calculated values more accurate. In addition, to further verify the accuracy of the numerical simulation, the measured and calculated values of the existing tunnel vault settlements during the excavation of the left and right lines were compared (as shown in Figure 8a,b). The graph shows that the excavation process of the new tunnel causes the measured values of the existing tunnel vault settlement to fluctuate widely, which is due to the complexity of the ground load and the unstable parameters used for the shield machine. Comparatively, the settlement of the existing tunnel vault during the left line shield tunneling after the completion of LT excavation is significantly larger than that of RT, which is due to the secondary disturbance of the surrounding rock during the LT excavation. Figure 8c,d shows the settlement of the vault considering the slurry hardening process, from which it is obvious that it was closer to the measured values of field data than Figure 8a,b, which further illustrates the importance of the slurry hardening process to the accuracy of the numerical simulation. In a comprehensive comparison, the calculated and measured values of the vault settlement of the existing tunnel were in good agreement and were within the settlement control requirements (<10 mm); thus, the numerical simulation results in this paper are reliable.  
Figure 7: Comparison of calculated and measured values of surface settlement after RT (right line, tunnel) and LT (left line tunnel) excavation: (**a**) no consideration of slurry hardening; (**b**) consideration of slurry hardening.
## 4 European Standards, Norms and Guidelines  
Most of the current design standards and guidelines are based on reliability-based design, as specified in ISO 2394 [11] or JCSS [12] documents. The majority of them regulate the design of new structures, such as the Eurocodes. Nevertheless, the intention is to make the Eurocodes applicable also for existing structures. Work on new technical rules for the assessment and retrofitting of existing structures is currently very intense, with special attention being given to heritage structures, aiming at elaborating the new Eurocode part for existing structures.  
One of the few international guidelines for the assessment of existing structures is the ISO 13822 document [13]. Although different guidelines for the maintenance of existing structures exist in other European countries, only a few of them have issued standards for the assessment of existing structures (e.g., Switzerland [14] and the Netherlands [15]). More in detail, SIA 462 [14] standard, in combination with the SIA 469 [16] document, specify the general fundamentals for the assessment of the load-carrying capacity of existing buildings, and regulate the professional and economical maintenance of buildings according to their cultural value, respectively.  
Nonetheless, the daily basis for the assessment and rehabilitation of existing structures is still based on a rather rudimentary scheme, mainly replacing and reinforcing defective members. More sophisticated procedures are needed, in addition to those available in seismic codes (such as Eurocodes). Eurocode EN 1998-3 [17] provides some suggestions for the assessment of masonry structures, but it is only informative and lacks of detailed practical suggestions. ISO 13822:2010 [13] gives general recommendations for a rough assessment of existing structures, but does not take materials into account. Similar to EN 1998-3 [17], it is mainly informative and lacks depth. Neither of these standards are very practical. The American documents ASCE 41-13 [18,19] and ASTM [20, 21, 22, 23, 24, 25], in this regard, constitute a more comprehensive guide, including suggested equipment, procedures of assessment, number of tests, as well as analysis and strength calculation methods. From the perspective of an engineer, this standard is hence more practical and easily applicable.
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 2 Mathematical Modeling
It can be seen from Equation (2) that, in the multi-extended target tracking framework, the measurement set needs to be divided, and the accuracy of the division result directly affects the subsequent filtering result. The DP algorithm proposed in reference [14] is a widely used measurement set partitioning method. This method uses the distance between the measurements to cluster, but when two or more extended targets are close to each other, the measurements belonging to different targets will fall into the same cluster unit after partitioning, resulting in the reduction in the number of measurement units. However, the number of measurement values in the existing measurement units increases. To solve this problem, [PERSON] proposed a second partition method [14], which divided the divided measurement set again, but this method had a large amount of calculation and poor real-time tracking. In 2020, a research paper [18] used the method based on shared nearest neighbors (SNN) of directed graph to divide the measurement values. These two methods do not consider the correlation between adjacent extended target measurements. Therefore, this paper proposes a partition method of an extended target measurement set, based on spatio-temporal correlation.
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 3 Results
### Comparison of Coaxial Transmission Line Method and Free-Space Measurement  
To further understand the relationship between the EMI shielding performance and carbon fiber orientation, the shielding result obtained by the transmission line method was compared to that by free-space measurement. The EMI shielding performance of unidirectional CFRP with a carbon fiber orientation angle of 0, 30, 45, 60, and 90\({}^{\circ}\) at the frequencies from 5 to 15 GHz are shown in Figure 8. CFRP showed an excellent electromagnetic shielding value when the carbon fiber orientation angle is 0\({}^{\circ}\), where the electric field polarization direction of EM waves is parallel to the fiber direction. The SE of CFRP decreased as the fiber orientation angle increased due to the decrease in electrical conductivity. When the orientation angle was 90\({}^{\circ}\), the electric field polarization direction was perpendicular to the carbon fiber, and the CFRP obtained the lowest electrical conductivity in this orientation angle. Thus, CFRP showed the lowest SE value of about 10 dB.  
The red curve in Figure 8 represents the EMI shielding result measured by using the coaxial transmission line method. The SE value obtained by this method was about 19 dB. This is due to the fact that the electric field of EM waves was quasi-radially distributed inside the coaxial tube (Figure 2), which has been discussed in our previous work [46]. When the coaxial transmission line method was adopted to evaluate the EMI shielding performance of unidirectional CFRP, the EMI shielding result did not change if the position of the composite material was rotated along the inner conductor. However, under the free-space measurement system, the EMI shielding performance greatly depended on the test angle. Unidirectional CFRP exhibits remarkable EMI shielding anisotropy. This characteristic of the one-side composite material can be used for the bias material of EM wave radiation, meaning that the EM waves polarized in a specific direction can be selectively shielded by adjusting the angle of the CFRP composites.  
Figure 7: EMI SE of CFRP for experimental and predicted results at the frequency of 10 GHz.  
The results show that when different test methods are used to evaluate the shielding performance of unidirectional CFRP composites, the results will differ. Therefore, when assessing anisotropic materials, especially materials prepared by CFRP prepregs, special attention should be paid to the influence of electromagnetic shielding anisotropy.
## 3 Field Test of Water Pressure around the Tunnel  
Influenced by reservoirs, gully topography, and fault fracture zones, the Gudou Mountain Tunnel experiences increased groundwater levels and water pressure behind the lining during rainy seasons, leading to potential seepage and leakage during operation. To determine the water pressure distribution around the tunnel, a field test was conducted. Feedback from monitoring and measuring water pressure near the tunnel lining provides reliable data for subsequent numerical simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical–Mechanical Parameter** & **Surrounding Rock** & **Geologic Fault** \\ \hline Hydraulic conductivity (\(10^{-5}\) cm/s) & 0.612 & 8.06 \\ Porosity (\%) & 0.5 & 0.73 \\ Modulus of elasticity (GPa) & 25 & 11 \\ Poisson’s ratio & 0.35 & 0.45 \\ Internal friction angle (\({}^{\circ}\)) & 30 & 29 \\ Cohesion (MPa) & 0.25 & 0.01 \\ Volumetric weight (kN/m\({}^{3}\)) & 22 & 17.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of the surrounding and faulted rock masses.  
Figure 3: Water seepage and leakage in the Gudou Mountain tunnel. (**a**) Vault surge. (**b**) Water seepage at the arch waist. (**c**) Waterlogging in the inverted arch.
## 4 Experiment  
### Dataset and Evaluation Metrics  
Experiments were first conducted on the ReClor [6] dataset, which consists of 6138 multiple-choice questions, each with a context and its corresponding four answer options, only one of which is the correct answer. The majority of the questions in the dataset are logical reasoning questions from the Graduate Management Admission Test (GMAT) and the Law School Admission Test (LSAT), accounting for 91.2% of the questions,  
Figure 5: Illustration of answer prediction module.  
with the remainder being supplemented by high-quality practice exams. Answering these questions requires complex logical reasoning. The data set was divided into three parts: training, validation, and testing, with the training set having 4638 questions, the validation set having 500 questions, and the testing set having 1000 questions. The questions in the test set are classified into EASY and HARD according to their difficulty, in which the questions that can predict the correct answer more consistently by eliminating the context and questions only through the options are classified as the EASY set with 440 questions, and the rest are classified as the HARD set. The training and validation sets have clearly labeled answers, while the testing set does not include answers, and the test results are obtained by submitting the predictions to the leaderboard.  
Then we also conducted experiments on the LogiQA [5] dataset. Different to ReClor, the problems in LogiQA are generated based on the Nation Civil Servants Examination of China. A total of 8678 samples are divided into three sets at 8:1:1. The training set has 7376 questions, and both the validation and test set have 651 questions. Unlike ReClor, LogiQA has no need to submit predictions; the answer of each question is open. Also, the dataset does not differentiate between the easy set and the hard set.  
ReClor is more diverse in the number of logical reasoning types, while LogiQA contains more examples. Both of them are challenging for the task of logical reasoning.  
The evaluation metrics for this experiment use accuracy, i.e., the percentage of questions with correct answer predictions.
## 2 Proposed LED Driver and Analysis of Steady-State Operation  
### Circuit Topology  
Figure 1 shows the circuit topology of the high-power factor ac/dc LED driver proposed in this paper. The circuit consists of a low-pass filter (\(L_{f}\), \(C_{f}\)), a bridge rectifier (\(D_{r1}\)-\(D_{r4}\)), and two modified SEPIC circuits. One SEPIC circuit includes inductor \(L_{p1}\), diode \(D_{1}\), active switch \(S_{1}\), dc-link capacitor \(C_{B1}\), flywheel diode \(D_{3}\) and the primary winding of the coupled inductor \(T_{1}\), and the other includes inductor \(L_{p2}\), diode \(D_{2}\), active switch S\({}_{2}\), dc-link capacitor \(C_{B2}\), flywheel diode \(D_{4}\) and secondary winding of \(T_{1}\). Diodes \(D_{S1}\) and \(D_{S2}\) are the intrinsic body diodes of \(S_{1}\) and \(S_{2}\), respectively. Figure 2 shows the control voltages (\(v_{CS1}\), \(v_{CS2}\)) of \(S_{1}\) and \(S_{2}\). They are both square wave voltages with a phase difference of 180 degrees and partially overlap, i.e., both \(S_{1}\) and \(S_{2}\) are in conduction for a certain period of time.  
Typically, a SEPIC circuit is a single-stage topology using only one active switch. The first half stage of the SEPIC circuit operates like a boost converter and the second half operates like a buck-boost converter. Compared with the traditional two parallel SEPIC circuits, the circuit proposed in this paper has two differences. The first is the addition of two diodes (\(D_{1}\), \(D_{2}\)) to avoid the currents in \(L_{p1}\) and \(L_{p2}\) flowing in the opposite direction. If \(i_{p1}\) and \(i_{p2}\) have reverse currents, the power factor correction performance will be impaired, and some excess conduction loss will occur. The second point is to use a coupled inductor \(T_{1}\) instead of two separate inductors for the second half stage of the two SEPIC circuits. \(T_{1}\) consists of an iron core and two windings with the same number of turns. Unlike traditional SEPIC circuits, which require additional components to enable ZVS operation for active switches. The winding current of the coupled inductor is designed to operate close to the boundary conduction mode (BCM). Based on the principle of magnetomotive force balance, the mutual inductance current will be converted between the two windings and the winding current is used to discharge the store charges in the parasitic capacitance of the active switch to fulfill ZVS operation.
## 4 Evaluation Performance
### Evaluation in Captured Data  
#### 4.2.1 Data Acquisition  
The precise monitoring and visualization of micro-deformation are important for understanding the structure of the defects [41]. To show the denoising effect to help the structural diagnosis, the captured data from the portable DHSPI system [13] are used to verify the performance. Figure 7 shows the schematic diagram of the measuring device [42], where IR lamps are used to provide the thermal excitation for the object. The object response after the thermal excitation is measured and recorded by the DHSPI system, and the personal computer (PC) can visualize results from the recorded data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Figure** & **Noisy Data (MSE)** & **Denoising Data (MSE)** \\ \hline Figure 5a & 3.9881 & 0.5823 \\ Figure 6a & 4.3608 & 0.8654 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the proposed method.  
Figure 6: Simulated data (the speckle radius of 2 pixels): (**a**) Simulated noisy wrapped phase; (**b**) denoising result of (**a**) by the proposed method; (**c**) unwrapped phase of (**b**); (**d**) error distribution of (**c**).  
The object to be measured is marquetry as shown in Figure 8a. Figure 8b-f shows the estimated wrapped phase from the portable DHSPI system, and every wrapped phase represents a different state of the sample while the sample is cooling down after thermal excitation with IR lamps. The image in Figure 8b is the wrapped phase with a time interval of 12 s after the excitation. Subsequently, there are the wrapped phase maps with defect reaction at 53 s, 103 s, 151 s, and 218 s in a cooling down process after thermal excitation, respectively. However, we can observe obvious noise from these wrapped phase maps. In the high-frequency regions of the phase denoted by the red arrow, noise causes phase distortion. In such cases, it is difficult to directly estimate the depth information of the defect.
## 3 Results
### Extension  
Compared with the INT model, the DEF and NIPD models exhibited a 23% higher and 23% lower ROM, respectively, for the L3-L4 motion segment during extension. The NIPD increased spinal stability during extension (Table 3).  
Figure 4: Different loading conditions on the lumbar spine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **INT** & **DEF** & **NIPD** \\  & **Moment: 4.8 Nm** & **Moment: 4.5 Nm** & **Moment: 5.4 Nm** \\ \hline \multicolumn{4}{c}{**ROM (degree)**} \\ \hline L1–L2 & 3.25 & 3.11 & 3.50 \\ L2–L3 & 3.30 (100\%) & 3.13 (95\%) & 3.45 (105\%) \\ L3–L4 & 3.30 (100\%) & 3.64 (110\%) & 2.72 (82\%) \\ L4–L5 & 4.73 & 4.71 & 5.10 \\ Total & 14.58 & 14.59 & 14.77 \\ \hline \multicolumn{4}{c}{**Stiffness (Nm/degree)**} \\ \hline L3–L4 & 1.45 (100\%) & 1.24 (86\%) & 1.99 (137\%) \\ \hline \multicolumn{4}{c}{**Disc max. stress (KPa)**} \\ \hline L2–L3 & 631 (100\%) & 604 (96\%) & 664 (105\%) \\ L3–L4 & 535 (100\%) & 540 (101\%) & 500 (93\%) \\ \hline \multicolumn{4}{c}{**Facet joint force (N)**} \\ \hline Left & Right & Left & Right & Left \\ L2–L3 & 0 & 0 & 0 & 0 \\ L3–L4 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular} Note: values in parentheses represent the normalized results ([DEF or NIPD]/INT] \(\times\) 100%).  
\end{table}
Table 2: Comparison of the biomechanical characteristics among the three FE models in flexion.  
Figure 5: Disc stress distributions in the three FE models during flexion: (**A**) implanted level L3–L4 and (**B**) adjacent level L2–L3. Note: the arrow indicates the region of high stress.  
Compared with the DEF model, the NIPD also guided stress flow into the posterior element and reduced the stress at the anterior vertebral body, as depicted in Figure 6. In the L3-L4 bridged level disc, the stress increased by 80% in the DEF model and focused on the posterior region (Figure 7A). In the adjacent level (L2-L3), the disc stress in the NIPD model was the same as in the INT model (Figure 7B). Moreover, at the L3-L4 bridged level, the facet joint contact force decreased by approximately 58% in the NIPD model compared with the INT and DEF models.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 4 The Proposed Detection Scheme
### Step 1: Real Time Traffic Monitoring  
Our proposed approach is based on the idea that malicious activities have performance characteristics that are significantly different from the normal ones. Thus, the first step of  
Figure 2: Our proposed MVSDS detection scheme.  
our approach consists of constructing the input data by monitoring the vehicular network traffic over time. This monitoring system is deployed in every receiving node and consists of the measurements of three key traffic metrics, namely throughput, dropped packets ratio, and overhead traffic ratio.