esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Postulates  
The earthquake catalog we used here was documented by the regional seismic network and then verified by the China Earthquake Networks Center (CENC) along the Longmenshan fault during the period from 1 January 2000 to 1 January 2019. It is a relatively complete catalog containing the Wenchuan-Lushan earthquake sequence.  
Figure 2 shows the magnitude-time distribution of earthquakes in the Wenchuan source region and Lushan source region. The locations of the earthquakes in this catalog were corrected for accuracy. Each event includes the time, location, magnitude, and depth. Homogeneity of the catalog was iterated and optimized for subsequent research and analysis.  
Stress change has been widely used to interpret the triggering of the mainshock-mainshock and mainshock-aftershock events [7; 32; 33; 34; 35; 36]. However, the exact measurement of stress states is difficult; thus, a relationship between the stress state and \(b\) value has  
Figure 2: **Magnitude versus time for events in the Longmenshan fault zone from 2000 to 2019.** (**a**) Magnitude versus time for events in the \(M_{\rm S}\) 8.0 Wenchuan source region; (**b**) magnitude versus time for events in the \(M_{\rm S}\) 7.0 Lushan source region.  
been proposed [16; 24; 37; 38]. [PERSON] et al. [24] demonstrated that the \(b\) value could be regarded as a stress indicator that depends inversely on differential stress. Therefore, changes in the stress state on faults lead to the variation in \(b\) values, which is followed by time-dependent recovery. In general, the larger the magnitude of the earthquake, the greater the stress changes and the larger the \(b\) value fluctuations. No event of magnitude larger than \(M_{\rm S}\) 7.0 had been reported in the historical record of the Longmenshan fault zone and the catalog we used in this paper contains more than 80,000 events and only two large earthquakes greater than magnitude 6.5, that is, the Wenchuan \(M_{\rm S}\) 8.0 and Lushan \(M_{\rm S}\) 7.0 earthquakes. Thus, these two events are mainly responsible for the apparent changes in stress state along the Longmenshan fault zone in the past 20 years.  
Therefore, there are two postulates: first, without a large earthquake perturbation, the \(b\) value will remain in a stable state with a small fluctuation range for a long time; second, after the perturbation of a large earthquake, the \(b\) value may recover to another stable state. We evaluated the levels of the \(b\) value for the stable state before and after these two large earthquakes in the study region.
## 1 Introduction
Cosine and sinusoidal offsets are utilized to generate jump paths with spiral perturbations to enhance the diversity of local development. In the second exploitation phase, a dynamic balance between elite guidance and population diversity is achieved by combining adaptive weighting coefficients with enhanced Levy flight, thereby ensuring both individual stability and effective information transfer within the population. SAVOA is applied to the three-dimensional path-planning problem of UAVs, simulation experiments are conducted in two simulated environments, a mountainous area and urban area, and compared with various algorithms including the standard AVOA, and the results show that SAVOA exhibits better optimization performance. The rest of the paper is structured as follows: Section 2 presents the problem description of UAV path planning, including curve fitting, environment modeling, fitness function, and constraints; Section 3 describes the original AVOA; Section 4 illustrates the improvements to the AVOA; Section 5 shows the results of the experiments in different environments and a specific analysis of these results; and Section 6 briefly describes the improvements in future research directions.
## 2 Calculation Model
### Simulation Model  
After the vortex pump model is established by UG software, the fluid is selected to flow at the inlet and outlet of the vortex pump and the inner surface of the flow channel for fluid domain volume extraction. The fluid domain of the vortex pump is the area between the pump casing and the pump cover other than the impeller. The numerical simulation of the vortex pump is carried out with the FLUENT 2022 R1 software, which is widely used in the field of fluid flow simulation and is based on the finite volume method with completely unstructured meshes. The internal fluid domain and mesh division are shown in Figure 3.  
In the process of analysis, either structured or unstructured meshes are commonly used, and unstructured meshes can make up for the lack of structured meshes that cannot solve any shapes and any connected areas. Due to the complex volume of irregular connections, such as the tongue area, the narrow area between the impeller and wall, and the variable sectional area at blade junctions in vortex pumps, the fluid domain is divided by the unstructured mesh method, and a tetrahedral computational mesh is generated. In general, the number of mesh divisions in the mesh model directly affects the calculation results. A larger number of mesh elements can improve the calculation accuracy but significantly increase the calculation cost, while a smaller number of mesh elements can save calculation time, but there are errors in the calculation accuracy. Therefore, it is necessary to verify the mesh independence of the mesh model by considering both computational efficiency and calculation result errors. The fluid domain model of the vortex pump is divided into five different mesh numbers to verify the mesh independence. When the number of meshes is increased from 7.62 \(\times\) 10\({}^{6}\) to 8.22 \(\times\) 10\({}^{6}\), the maximum error in the head and efficiency of the vortex pump is about 6.87%. When the number of meshes is further increased to 1.068 \(\times\) 10\({}^{7}\), the maximum error in the head and efficiency of the vortex pump is about 2.87%. Therefore, the overall number of meshes is finally selected to calculate the mesh model with 8.22 \(\times\) 10\({}^{6}\) mesh elements for the mesh model calculation to ensure high-precision simulation results within reasonable computational cost.
# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
## 4 Challenges and Recommendations for Future Work
### Directions of Future Studies
#### 4.2.3 Possible Integrations with Other Digital Technologies  
Future research can integrate DL with other novel technologies, such as AR and virtual reality (VR) [135; 136], building information modeling (BIM) [137; 138; 139; 140], 5G technology and the Internet of things (IoT) [64], into real-time SHM and JSM to extract information from the massive amounts of data continuously received from wireless sensor networks. Such research can further expand the scope of SHM and JSM through various advanced sensors to provide decision-making assistance accurately. Being capable of interpreting unstructured data in large volumes, DL technology can be employed to facilitate the entire integrated system cost-effectively and intuitively. Future research can propose such a new paradigm in relation to IoT-based sensor data collection, transformation and visualization as part of the DL-based SHM and JSM applications. Nowadays, unmanned aerial vehicles(UAVs), mobile phones and AR devices (e.g., HTC VIVE Focus Plus, Microsoft Hololens and Google Glass) have become an integral part of human activities. These compact devices can normally collect digital data and have good computing and network technology. Moreover, most of these devices are programmable, having the potential to achieve real-time and cloud-based assessments. Currently, some popular CNNs (e.g., MobileNet-SSD and YOLOv3-Lite) have been proposed. These networks do not require a high computing power and can maintain relatively stable performance. Therefore, it is possible to conduct on-site safety inspection through these mobile devices. With the advent of the 5G era, low-latency collaboration and intelligent optimization become possible. For example, UAVs can be paired with mobile phones to obtain and process image data while embedding light-weight DL algorithms. In addition, AR technologies have been broadly exploited in construction through superimposing virtual elements onto the construction site [84; 141; 142]. By integrating these advanced digital devices and technologies with DL, it is possible to establish a real-time hazard alerting system. First, site images or videos are transmitted to the mobile phone continuously by using UAVs. Then, a mobile phone with DL algorithms installed can be used to detect hazardous activities based on these image-based data. When a worker enters the hazardous area (e.g., working too close to the construction vehicles), the AR device worn by the worker can provide timely alerts through popping up a warning in the worker's point of view. However, current lightweight DL algorithms are only qualified for detecting and localizing objects with apparent features in the images; things such as cracks and minor defects of structures with subtle features may not be applicable in this case. Therefore, an optimized version of the DL structure is still needed in the future.
# Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater
## 4 Results  
### Evaluation of Qualitative Changes  
Table 1 provides a complete overview of the occurrence of PPCP substances at the monitoring points in the period before and during the infiltration experiment. For substances that were analyzed in more detail, the mean and median content values are also given. A comparison of both recipients of PPCPs (i.e., treated wastewater from the WWTP and water in the Morava River) reveals their similarity in terms of the range of detected components. Upstream from the Kojetin site, many other wastewater treatment plants are connected to Morava River. A total of 80 PPCP substances with values above the detection limit appeared in at least one sample on the monitored objects. The permanent occurrence (that is, they were detected in all samples) exhibited 45 PPCP substances concerning treated wastewater and 32 substances concerning water in the Morava River. At the beginning of the VSK infiltration experiment (7 November 2022), well PZ5 already contained 29 PPCP substances, whilst wells CP1 and PZ1 contained 13 resp. 12 substances. The PZ5 well is located in the proximity of the Morava River (Figure 2). It follows that the river water negatively affects the adjacent fluvial aquifer and a non-negligible set of PPCP substances is \"naturally\" present in the groundwater unaffected by the infiltration experiment. During floods and due to the dam cascade, influent conditions exist occasionally or permanently in some of areas between the aquifer and the Morava River. This causes the PPCP to transport from the river into fluvial sediments. The presence of more or less similar PPCP substances both in the river and in the outlet from the WWTP is not surprising; especially in times of low flows, inflows from treatment plants represent a non-negligible part of the flow of watercourses in Europe. This fact is documented by comparing the contents of PPCP substances in some Central European (CE) rivers (see Table 2). The water quality in the Morava River in terms of the range of detected PPCP substances fits very well into this regional context; in absolute values the contents of some substances are even significantly higher (e.g., Oxypurinol, Iopromide, Telmisartan). Despite the presence of more or less identical substances, large quantitative differences occur between the outlet of WWTP and the Morava River. The highest average
# Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures
## 5 Conclusions  
In this paper, for stochastic modeling of uncertainties for fatigue strength parameter, MLM as a common methodology is utilized to fit the statistical parameters in a regression model based on available test data. The Bootstrapping method is used to generate synthetic data. Example investigations in this paper indicate that Bootstrapping cannot be used if run-out data are to be accounted for. Thus, further steps are not proceeded to estimate statistical parameters. It should be mentioned that if the Bootstrapping method was fulfilled the requirement (random pattern), another methodology such as least square method or even Bootstrapping could be used for parameter estimation in the next step. Subsequently, the use of Bayesian inference with the Markov Chain Monto Carlo approach is studied.  
Reliability analysis of a selected detail in the Cret De l'Anneau Viaduct is used to illustrate and compare different stochastic models obtained by the statistical methods. The results obtained by MLM is used in reliability analyses and is assumed as a prior for Bayesian. The results show difference in the reliability indices, indicating the importance of accurate estimation of the model uncertainty of the SN-curve. The results emphasize the choice of statistical method as it influences the reliability analyses. In this case study, Bayesian provided better statistical uncertainty, hence better fatigue reliability assessment.  
The main idea for the paper was proposed by [PERSON] [PERSON] wrote the first draft of the paper, except Section 4 which was drafted by [PERSON] [PERSON], [PERSON] and [PERSON] provided literature review. [PERSON] developed the methodology wrote relevant codes for Maximum likelihood method, Markov Chain Monte Carlo, Bayesian inference and Bootstrap and reviewed by [PERSON] [PERSON] developed the reliability framework with relevant codes with input for stochastic model from [PERSON]. [PERSON] and [PERSON] post-processed the results. [PERSON] supervised the findings of this work and reviewed methodology. [PERSON], [PERSON], [PERSON] and [PERSON] contributed for articulate the research work in its current form as full research manuscript. All authors discussed the results and contributed to the final results. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Reliability index as function of time.  
**Funding:** Current work is carried out under the project INFRASTAR (infrastar.eu), which has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676139. The grant is gratefully acknowledged.  
These current codes were further developed based on codes provided in DTU Summer School on Uncertainty and sensitivity analysis of numerical models--7-11 August 2017, lecturer: [PERSON], to suit current application.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Results
### Ablation Experiments of the Proposed ReSTiNet  
We have conducted ablation experiments in the ReSTiNet network by sequentially adding fire modules, residual connections, and dropout layers to demonstrate the impact of these methods on ReSTiNet's performance. Table 3 shows the results (mAP) of the proposed model, ReSTiNet, and the original Tiny-YOLO on the \"Pascal VOC\" and the \"MS COCO\" dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameter** & **Range** \\ \hline input size & 416 \(\times\) 416 \\ learning rate & 0.001 \\ activation & Leaky ReLU (\(\alpha=0.1\)), ReLU \\ batchsize & 16 \\ no. of epoch & 50 \\ optimizer & adam (\(\beta_{1}=0.9\), \(\beta_{2}=0.999\), \(\epsilon=1\times 10^{-8}\)) \\ loss function & custom loss \\ dropout & 0.2, 0.5 \\ iou\_threshold & 0.5 \\ score\_threshold & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used in the ReSTiNet.  
Detection accuracy is commonly evaluated using the mAP. The new proposed model achieved 27.31% mAP on the MS COCO dataset, whereas Tiny-YOLO obtained 19% mAP. The dropout layer and residual connections helps the ReSTiNet model in achieving higher accuracies than Tiny-YOLO. ResTiNet achieves 63.79% mAP on the Pascal VOC dataset; on the other hand, Tiny-YOLO reaches 42.21% mAP. The use of residual connections between the fire modules and the dropout layer significantly contributes to the increase in mAP without requiring an excessive number of parameters. Utilizing residual connections and dropout improves mAP by 12.06% on \"MS COCO\" and 14.59% on \"Pascal VOC\" based on adding fire modules. In addition, employing the dropout layer helps reduce the training time and helps the model from the over-fitting problem. ReSTiNet outperforms Tiny-YOLO, showing 43.74% and 51.09% improvements on the \"MS COCO\" and \"Pascal VOC\" datasets, respectively.  
Detection Time, Parameter, and FLOPs Comparison between Tiny-YOLO and ReSTiNet  
The entire testing time is calculated for 1208 images from the \"INRIA Person\" dataset using the Tesla K80. Table 4 shows the average test time, total parameter, and FLOPs for both Tiny-YOLO and ReSTiNet models. As observed, the overall time needed to detect 1208 images using the ReSTiNet is less than 40 s. ReSTiNet outperforms Tiny-YOLO in terms of detection speeds. Tiny-YOLO completes the detection in more than 74 s. When compared to Tiny-YOLO, the detection speed of ReSTiNet is improved by 49.2%. On the other hand, it has been observed that ReSTiNet has 80.90% less parameters than Tiny-YOLO, and the FLOP's amount is also reduced by 34.47%.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 3 Design of the Separation Mechanism
### Mechanism Design
#### Olmdc  
If the door is opened unconstrained, it will collide with the separation mechanism's side panel and bounce, obstructing the MF satellite's ejection process and jeopardizing its safety and normal separation. As illustrated in Figure 5, a torsion spring is added to the rotating shaft of the door cover to increase the opening velocity and prevent the door from rebounding. The OLMDC employs a spring positioning pin structure, as illustrated in Figure 6. After the door cover has been extended to a specified angle, the spring positioning pin structure is used to lock it.  
Figure 5: OLMDC model.  
Set the matching gap between the pin and the pinhole to \(\varepsilon_{0}\), the door cover's rotating angular velocity when it rotates to the locking position to \(w_{0}\), the distance between the locking hole and the door cover's rotating shaft to \(h_{0}\), the weight of the positioning pin to \(m_{0}\), the compression spring's stiffness to \(k_{0}\), the spring's deformation to \(d_{0}\), and the pin's locking stroke to \(\delta\). When the time required to rotate the door cover \(\varepsilon_{0}/h_{0}\) degrees exceeds the time required to lock the compression spring, the door cover can be successfully locked. The following is advisable according to the dynamic principle:  
\[\frac{\varepsilon_{0}}{h_{0}\omega_{0}}>\sqrt{\frac{m_{0}}{k_{0}}}\text{arccos }\frac{d_{0}-\delta}{d_{0}} \tag{4}\]  
Formula (4) can be used to determine the elastic coefficient selection range of a compression spring. Appropriate positioning pin spring parameters can be determined by combining the shape and size of the positioning pin and pinhole.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 2 Materials and Methods
### HPNet Architecture  
This research proposes a hierarchical pooling graph neural network method based on a CNN [38] and DiffPool [39]. The architecture is displayed in Figure 1. Initially, the original RNA sequences in the dataset were represented using one-hot encoding and RNA secondary structure graph representations, respectively. Then, both types of data were processed independently. A two-layer CNN was used to discover sequence features within the one-hot encoding matrix, while DiffPool was employed to extract graph representations of the RNA secondary structures. In the initial DiffPool layer, global information was learned using a GCN. Since the nodes in the graph structures corresponded to the nucleotides in the sequences, the features extracted by the same nucleotide in different models could be shared. The results of the first layer of the CNN could be expressed as weight features, representing each nucleotide's importance in the RNA sequences. Combining the weight features with the features of each node in the graph structures highlighted the nucleotide nodes with high importance in the secondary structures, facilitating better feature extraction of the RNA secondary structures. Finally, the DiffPool and CNN results were connected and fed to the fully connected layers for prediction. The model outputs predicted labels representing the presence or absence of a binding site in the RNA sequences.  
\begin{table}
\begin{tabular}{c c c} \hline
**Structure** & & **Description** & **Graphical Representation** \\ (**Abbreviation**) & & & \\ \hline Stem (S) & Consecutive nucleotide-paired regions & & \\ Hairpin loop (H) & An unpaired loop structure formed by the nucleotide strings & & \\  & strings vacated between the two nucleotide strings & & \\  & forming the stem region & & \\ Interior loop (I) & The internal loop can contain unpaired nucleotides on & & \\  & either or both strands, flanked by stems & & \\ Multiloop (M) & The single-stranded region between two stems & & \\ Fiveprime (F) & The unpaired nucleotides at the 5\({}^{\prime}\) end of a & & \\  & molecule/chain & & \\ Threeprime (T) & The unpaired nucleotides at the 3\({}^{\prime}\) end of a & & \\  & molecule/chain & & \\ \hline \end{tabular}
\end{table}
Table 1: Abbreviations, descriptions, and common graphic representations of the six RNA secondary structure types.
## 2 Mathematical Model of VTOL Platform with PI Control
### Actuator Model of VTOL System  
The actuator of the VTOL system is based on a DC motor used to operate the fan, which provides the uplift to the system. The following differential Equation (6) describes the actuator's mathematical model of the system.  
\[L_{m}\frac{di_{m}}{dt}+R_{m}i_{m}=V_{m} \tag{6}\]  
\(L_{m}\) is the motor inductance, \(R_{m}\) is the resistance of the motor, \(V_{m}\) is the applied voltage to the motor, and \(i_{m}\) is the motor current. Taking the Laplace transform \(\mathcal{L}\) of Equation (6) in Equation (7),  
\[L_{m}sI_{m}(s)+R_{m}I_{m}(s)=V_{m}(s)\text{ or }\frac{I_{m}(s)}{V_{m}(s)}=\frac{1} {L_{m}s+R_{m}} \tag{7}\]
## 2 Dual-Port MIMO Antenna
### Group Delay  
Group delay serves as a key parameter in evaluating the performance of wideband antennas, as it reflects the extent to which a signal waveform is altered during transmission. Figure 9a,b depict the experimental setup for the transmit-receive antenna arrangement and group delay performance. To guarantee far-field measurement conditions, the receiving antenna is positioned 50 cm away, with the far-field distance determined using Equation (1), where 'B' represents the separation between the transmit and receive antennas, and 'S' denotes the largest dimension of the presented antenna. Figure 9b illustrates the group delay remains uniform throughout the full UWB spectrum.  
Figure 8: (**a**) \(S_{XX}\) vs. frequency (**b**) \(S_{XY}\) vs. frequency.  
\[B=\frac{2S^{2}}{\lambda} \tag{1}\]  
The delay variation stays within 0.25 ns, indicating that the transmitted signal experiences very little distortion across the operating range.