esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 4 Results and Discussion
### Detecting Small Damage Ratios in Laboratory Models  
So far, it is explained how 100% damage to a cable, such as losing the connection between a cable and the bridge, could be located in the RDSs using AI as damage indicator. There are, however, some conditions in which a cable partially loses its stiffness due to some reasons such as corrosion. As shown in Figure 9, each cable is composed of 7 twisted thin cables. Therefore, it is possible to model the damage from 14% to 100% in one cable. In this section, the detection of small damage is addressed and the capability of the proposed method is assessed through laboratory results.  
A case with 100% damage in the beam is equivalent to a cut through the whole beam, which does not seem rational. Hence, the 100-percent damage scenario is not described. Instead, other damage ratios are targeted as shown in Figure 6 and illustrated in Table 2.  
Figure 17 shows the AI in the damage location, while the other values are zero along the beam. It should be noticed that the aim of the proposed method is to find the maximum normalized AI along the beam. Due to normalizing the AIs, subtracting 1000 from AIs could yield much better results (Figure 16b). In the beam, subtraction of 1000 from the AIs leads to positive values only in the nodes close to damage location.  
Figure 18 shows how small damage can be found in a cable using RDSs and normalized AI. In this figure, the damage occurs in node 2 (second cable from left) and node 4 (middle of the bridge) with different damage ratios. As observed in all these scenarios, the maximum amount of normalized AI appears close to damage location.  
According to Figure 18, it is proved that the proposed method can locate the damage accurately. The small positive amount of normalized AI in other places (nodes 1, 3, and 7) shows that the damage can affect those nodes as well. The fact that the proposed method can provide acceptable results in a noisy environment is illustrated in Figures 17 and 18. This result is relevant for velocities between 0.3 and 0.5 m/s. Using the proposed model in a noisy environment at a speed more than 0.5 m/s is accompanied by a margin of error.  
It should be mentioned that the proposed model could be used as a real-time damage detection method if the optimized values of truck velocity, triggering level, number and location of accelerometers, and length of signal are calculated in advance.  
Figure 17: Distribution of AIs along the beams for all accelerometers assuming (**a**) Case N5D40 from beam model: delta = 40 at Node 5 and (**b**) Case N7D30 from beam model: delta = 30 at Node 7.
## 5 Establishment of 3D Geological Model
### Analysis of Variation Function
#### 5.2.2 Correlation of Lithofacies Profile  
The fidelity of modeling results is assessed not only by their alignment with the modeling log data but also by their accordance with established geological comprehension. The simulation's efficacy in replicating sedimentary characteristics is gauged by the consistency between the lithofacies of a single well and the surrounding modeling outcomes. As depicted in Figure 11a, the lithofacies section of each minor layer, after the probability plane constraint, exhibits a notably higher proportion of sandstone relative to mudstone. This indicates superior continuity of the sand body, manifesting in extensive, continuous distributions. The gamma ray (GR) curve of the Ning 6 well demonstrates an upward-thinning sequence, with values increasing from low to high, characteristic of a positive rhythm indicative of lake basin sedimentation. A thinner, less continuous mudstone caps this sequence. As can be seen in Figure 11b, sandstone and mudstone alternately develop in the lithofacies profiles of each small layer after the probability plane constraint, and the continuity is better than that of the lithofacies profiles without constraint. The sandstones on the west and east sides of the Ning 7 well have good continuity, and mudstone rarely appears. The GR curve in the lower part of the Ning 7 well has a positive rhythm from low to high values from bottom to top, while the GR curve in the upper part has a negative rhythm from high to low values from bottom to top, indicating upward coarsening. The poor continuity line of the Ning 20 well sand body is caused by the obstruction caused by more mudstone. As shown in Figure 11c, the probability-plane-constrained sections have a greater prevalence of sandstone over mudstone, with enhanced continuity and an absence of abrupt changes. The Ning 26 well, centrally located within the lithofacies section, displays a gradation from coarse to fine lithology, with a thin underlying mudstone. The GR curve of this well traces a positive rhythm, with values escalating from bottom to top.  
The application of geological probability surface constraints more vividly elucidates the continuity and physical property variations at the center and edges of the sand bodies. It offers a more direct representation of the distribution characteristics of channel sediments and enhances the precision of the geological model. This methodological advancement ensures that the model more closely mirrors the actual geological conditions, providing a robust framework for subsequent analysis and interpretation.
## 3 Findings and Discussion
### Future Directions  
Future research should highlight the development of large-scale longitudinal studies involving different groups to improve our understanding of the possible effects of RF-EMF exposure on cognitive performance. Additionally, it is essential to look into the underlying causes of the apparent inverse relationship between RF-EMF exposure and memory function. It is fundamental to carefully investigate how long-term exposure to RF-EMFs affects cognitive functions. Expanding the duration of research to cover longer time periods will make it possible to assess whether any cognitive impairments linked with RF-EMF exposures persist or alter over time. To determine the generalizability of the reported effects, investigations in multiple populations and across different countries are essential. The relation between exposure to RF-EMF and cognitive performance is complex, and these types of studies can shed light on the influence of societal, environmental, and genetic factors [72]. Furthermore, investigating the prospective effects of RF-EMF exposure on cognitive domains other than memory, such as speech, executive function, and attention, can provide an in-depth understanding of the cognitive effects of RF-EMF exposure. Additionally, more research is required to examine how exposure to RF-EMFs affects people of different ages, including kids and adults. This will aid in the identification of possible age-related vulnerabilities or variations in cognitive reactions after RF-EMF exposure. These research efforts will considerably contribute to a more thorough knowledge of the potential consequences of RF-EMF exposure on cognitive functions, as well as provide direction for the creation of evidence-based guidelines and legislation.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 3 System Modeling
### Problem Formulation
 \tag{11}\]  
where \(r_{S,p}\) is the total resource of satellite, \(r_{B,i,\max}\) represents the total satellite resources, \(\gamma_{B,i,\min}\) refers to the signal to interference noise ratio threshold of cellular user, and \(\gamma_{S,l,\min}\) is the signal to interference noise ratio threshold of satellite user. Constraints C1 and C2 denote the total power constraint of the satellite and BS, respectively. Constraint C3 denotes the user power control factor in the group, Constraint C4 ensures the minimum resources of each subchannel, Constraint C5 is the SINR threshold value for satellite users and BS users, and Constraint C6 is the maximum transmit power of the satellite and BS. As the optimization problem in (11) is non-convex and NP-hard that has NP complexity, which cannot be solved directly. Moreover, as the satellite interferes all the BS users within its coverage, it will lead to a relatively small power allocation for the satellite user if we simply maximize the total capacity. Considering both of the above problems, we decompose the optimization problem into three parts: the dynamic user paring scheme, the terrestrial resource allocation scheme, and the satellite resource allocation scheme. Then, an irrelative algorithm is proposed to optimize the capacity performance of the whole system.
## 2 MMC Modeling and Operating Principle  
Figure 1a shows the schematic diagram of the MMC. It consists of three identical phases connected in parallel and powered by a DC voltage source. Each phase consists of an upper and a lower arm, each arm combines N SMs in series with an inductor \(L_{arm}\).  
The most commonly used SMs are half-bridge SM and full-bridge SM [24,25]. The half-bridge SM uses two IGBT switches with two antiparallel diodes and a DC storage capacitor, and the full-bridge SM uses four IGBT switches with four antiparallel diodes and a capacitor. The half-bridge SM quickly became the dominant choice of converter topology. This topology offers low switching losses, cost-effectiveness, and minimal AC distortion, all of which contribute to better energy efficiency. This is what motivated the choice of the half-bridge MMC in our work. The existing SMs in the arm are controlled so that the capacitor is inserted into the circuit or bypassed. The schematic diagrams of the half-bridge SM and full-bridge SM are shown in Figure 1b.  
The basic control principle of MMCs is analyzed based on the dynamics of each converter phase. Applying [PERSON]'s voltage law on an arbitrary phase of Figure 1a gives:  
\[i_{p}=\frac{i_{s}}{2}+\frac{i_{dc}}{3}+i_{c} \tag{1}\]  
\[i_{n}=-\frac{i_{s}}{2}+\frac{i_{dc}}{3}+i_{c} \tag{2}\]  
\[-\frac{v_{dc}}{2}+v_{p}+Ri_{p}+L_{arm}\frac{di_{p}}{dt}+L_{T}\frac{di_{s}}{dt}+ v_{g}=0 \tag{3}\]  
\[\frac{v_{dc}}{2}-v_{n}-Ri_{n}-L_{arm}\frac{di_{n}}{dt}+L_{T}\frac{di_{s}}{dt}+ v_{g}=0 \tag{4}\]  
where \(R\) and \(L_{T}\) are the equivalent resistance in the loop and transformer leakage reactance, respectively. \(i_{p}\) and \(i_{n}\) are the upper and lower arm current, \(i_{s}\) is the phase current, \(i_{dc}\) is the current of the continuous bus, \(i_{c}\) is the circulation current, \(v_{dc}\) is the bus voltage, \(v_{p}\) and \(v_{n}\) are the upper and lower arm voltage, and \(v_{g}\) is the grid voltage.  
In an MMC, \(i_{c}\) refers to the unwanted current that flows within the MMC's phase legs, between the upper and lower arms, without contributing to the output power delivered to the load or grid. It arises mainly due to voltage differences between the SM capacitors in the arms and the DC-link voltage. These differences occur naturally because of the switching  
Figure 1: Multilevel converter topology: (**a**) MMC; (**b**) half-bridge and full-bridge SM.  
actions and the charging/discharging of capacitors as the MMC generates the desired AC output waveform.  
Adding (3) and (4) can obtain a dynamic equation for the output current:  
\[L_{eq}\frac{di_{s}}{dt}=v_{s}-\frac{R}{2}i_{s}-v_{g} \tag{5}\]  
where:  
\[\begin{array}{c}v_{s}\\ i_{s}\\ L_{eq}\\ \end{array}=\frac{v_{s}-v_{g}}{2}\]  
with \(v_{s}\) is the inner EMF, which drives the converter phase current \(i_{s}\), and \(L_{eq}\) is the equivalent reactance in the loop. From (5) we obtain:  
\[\frac{di_{s}}{dt}=\frac{1}{L_{eq}}v_{s}-\frac{R}{2L_{eq}}i_{s}-\frac{1}{L_{eq} }v_{g} \tag{6}\]
## 2 Materials and Methods
### Performance Metrics  
The evaluation of the trained model's performance was conducted utilizing precision, recall, and mAP (mean average precision) metrics. Precision is the fraction of correctly detected objects in all samples, while recall is the fraction of correctly detected objects in all positive samples. The formulation of precision and recall metrics are expressed in Equations (1) and (2), where TP is true positive objects, FP is false positive objects, and FN is false negative objects. The mAP is computed by taking the average of the Average Precisions (APs) obtained for each class, as specified by Equation (3). Here, the variable i denotes the total number of classes. AP is determined by computing the area under the precision-recall curve which represents the accuracy and recall values at various Intersection over Union (IoU) thresholds. IoU quantifies the degree of overlap between the detection and the ground truth. In this study, we used two types of mAP; [EMAIL_ADDRESS] and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Batch size & 16 \\ Epoch size & 1000 \\ Image size & 640 \(\times\) 640 \\ Learning rate & 0.001 \\ Number of workers & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The training configuration and setup parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Number** & **Duration** & **Environment** \\ \hline Video 1-1 & 00.00–00.05 & Night, windy, congestion \\ Video 1-2 & 06.00–06.05 & Night, windy, low traffic \\ Video 1-3 & 12.00–12.05 & Day, windy, normal \\ Video 1-4 & 18.00–18.05 & Night, calm, normal \\ Video 2-1 & 00.00–00.05 & Night, windy, normal \\ Video 2-2 & 06.00–06.05 & Night, rainy, normal \\ Video 2-3 & 12.00–12.05 & Day, overcast, normal \\ Video 2-4 & 18.00–18.05 & Night, calm, normal \\ \hline \hline \end{tabular}
\end{table}
Table 3: The duration and environmental conditions of the recorded video streams.  
[EMAIL_ADDRESS]:0.95. In [EMAIL_ADDRESS], the IoU overlap value is fixed; however, in [EMAIL_ADDRESS]:0.95, the value is calculated as the average of the IoU overlap values in increments of 0.05.  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[mAP=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}AP_{i} \tag{3}\]  
To assess the accuracy of each class, we conducted a comparative analysis between the results of the model and the manual count, which is referred to as the ground truth, and utilized Equation (4) on the traffic surveillance video streams.  
\[Accuracy(\%)=100-1008*\frac{|\text{Ground Truth}-\text{Detected Vehicles}|}{\text{Ground Truth}} \tag{4}\]
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 4 Capacity Bounds Analysis
### Multiservice Capacity Bounds
#### 4.2.2 Capacity Bounds Due to Limited Uplink Power  
The capacity bounds are limited by the maximum transmission power, \(PS_{\max}\). Therefore, the maximum distance between the UE and Node B for a given service class, \(k\), is expressed using (15) and (38), as follows:  
\[d_{\max}(n_{k},\mathbf{n})=\frac{\lambda}{4\cdot\pi}\cdot\sqrt{\frac{P_{\max} \cdot PL_{E}(d)}{N_{t}}\left(S_{k}\ -\frac{1}{F}\cdot(n_{k}-1)V_{k}\cdot\varepsilon_{k}-\frac{1}{F}\sum_
## 4 Conclusions  
A locally resonant (LR) plate with attached quasi-zero-stiffness (QZS) resonators is investigated in this study. The design of the QZS resonator is a unique blend of a mass-in-mass resonator and a QZS system tailored for the attenuation of ultralow-frequency vibrations. The QZS resonator is designed by combining a mass-in-mass resonator and a QZS system to meet the demand for ultralow-frequency vibration attenuation. The plane wave expansion method is employed to derive the band structure of the LR plate. The result shows that the LR plate with QZS resonators exhibits two bandgaps, and the ranges of the bandgaps agree well with the vibration attenuation bands calculated by the finite element method.  
The stiffness ratio and initial compression of the negative stiffness spring are taken into consideration to evaluate the effects on the bandgaps by parameter analyses. The parameter variable trends show that increasing the stiffness ratio and the initial compression  
Figure 11: Vibration transmittance comparison of various resonator arrays attached to the plate.  
Figure 10: Schematic diagram of a finite LR plate with multiple periodic arrays of attached QZS resonators.  
of negative stiffness spring can lower the frequencies of the bandgaps, thereby obtaining low-frequency or even ultralow-frequency bandgaps. The effect of damping is also studied, and the result shows that the damping from the resonator spring can broaden the vibration attenuation band.  
Inspired by the discoveries of varying bandgap trends with changes in the negative stiffness spring's initial compression, a distance-adjusting device is introduced. This device can provide a versatile solution for tuning bandgap frequencies on demand. This innovation eliminates the need for additional mass loads and can significantly enhance the QZS resonator's engineering practicality.  
The QZS resonators designed in this paper have the application prospect of suppressing broadband low-frequency transverse waves of plate-type structures.  
Conceptualization, Z.G. and B.X.; methodology, Z.G. and B.X.; software, B.X.; validation, Z.G.; formal analysis, B.X.; investigation, Z.G.; resources, M.S.; data curation, H.Z.; writing--original draft preparation, B.X. and Z.G.; writing--review and editing, M.S. and H.Z.; visualization, B.X. and Z.G.; supervision, M.S.; project administration, M.S.; funding acquisition, Z.G. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52301388), the Natural Science Foundation of Ningbo City, China (grant number 2021J057), and the Natural Science Foundation of Shaanxi Province, China (grant number 2021 JLM-39).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
## 2 Analysis Methods
### Code Validation  
#### 2.2.1 Computational Domain and Flow Conditions  
The proposed numerical approach was validated to confirm that it can generate a reasonable solution for the energy deposition, propagation, and interaction between the shocks. It is desirable to compare the results with an experimental case of shock wave reflections in the dual solution domain by energy deposition. However, few experiments have been reported. Furthermore, wind tunnel test results contain flow perturbations, and the shock reflection can be sensitive to small disturbances in the dual solution region. Thus, it is difficult to examine the effects of energy deposition under conditions that are independent of the uncertainty of wind tunnel testing.  
Experimental results using laser energy deposition around a sphere model were used to validate the numerical method. [PERSON] et al. [PERSON] _et al._ (2018) investigated the propagation of a blast wave due to laser energy deposition and the interaction with the bow shock in front of a sphere model. The Mach number of the freestream is 3.45, the diameter (D) of the sphere is 25.4 mm, and the energy is deposited at 1.0D away from the sphere body. Further details of the flow conditions are shown in Table 1.  
Figure 1 shows the grid and boundary conditions that were used in the validation study. The computational domain is extended to 1.3D and only a section of the domain is modeled. Axisymmetric conditions are applied at the centerline and freestream conditions are assigned at the inlet. The properties of the outlet were determined from extrapolation.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 3 Mechanisms of Abrasion
The inorganic abrasion test (ABNT NBR 12042: test equipment shown in Figure 3) is one of the accelerated methods for abrasion tests to understand the wear from sand and inorganic particles. Standard dimension (100 mm \(\times\) 100 mm \(\times\) 50 mm) concrete specimens are prepared and dried. The test simulates abrasion under regulated pressure for a certain  
Figure 1: Failure mechanisms associated with exposed aggregates (Stage III) [14]. Figure 2: ASTM C 779 apparatus setup. amount of time, with the number of revolutions or time being predetermined, using a revolving steel disk or grinding wheel with a defined weight and speed. In addition to recording weight loss before and after testing, the depth of abrasion or material loss (in millimeters) is measured. [PERSON] et al. [22] investigated the characteristics of different accelerated tests and the results indicated that the inorganic abrasion test created smoother surfaces compared to the other. Different regions worldwide employ various testing methods to determine the abrasion resistance of concrete. The Indian Standard IS 9284:1979 outlines the assessment of abrasion resistance of concrete surfaces. The test setup includes an abrasive disc or wheel, a specimen holder, and a load application mechanism. The concrete sample undergoes controlled abrasion under specified pressure and motion, with wear measured by volume loss or depth [23]. According to BS EN 1338, the \"wide wheel abrasion test\" is used for measuring abrasion in concrete paving blocks. In this test, the test specimens are placed under a regulated vertical load while they rotate on a steel wheel coated in rubber. After a predetermined number of spins, the groove length provides the volume loss of material, where a higher value of groove length represents lower abrasion resistance [21]. In the case of the aggregate, the Micro-Deval abrasion test is used to assess abrasion loss in coarse aggregate in the presence of water and an abrasive charge. The inclusion of water in this test accounts for the fact that wet aggregates are more prone to abrasion [24]. Similarly, the Los Angeles Test is the most widely used abrasion test for coarse aggregate, which simulates impact and abrasion on coarse aggregate using a rotating steel cylinder with steel spheres. To determine the percentage of wear, the weight loss of the aggregate after a predetermined number of revolutions is measured [25].