esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, none the proposed methods addresses the fact that they cannot directly handle delay samples because communication delay is considered as additional information related to physical parameters. In contrast, this study can be used for the detection of communication delay, and it is up to the filter designer's discretion how to treat the physical parameters associated with it. There has been great discussion about performance evaluation regarding teleoperation. For instance, bilateral teleoperation systems, at their core, are inherently feedback-driven systems, and they lead to a critical factor in the equation of transmission delay. Without any proper considerations, the communication delay can, at best, compromise the effectiveness of the closed-loop control and, at worst, jeopardize the stability of the entire bilaterally controlled teleoperation system. In order to maintain the robustness of the system, the predictive approach for bilateral teleoperation was designed, assuming that the communication delay is measured and bonded with constraints [28]. However, it is essential to focus on whether the communication outlier exists and, moreover, whether the robustness of the system can be guaranteed if a communication delay coexists. For this reason, there is a possibility that the robustness of many reported evaluation methods may not be preserved if outliers of communication delay are not considered in common scenarios, such as teleoperation via shared impedance control [29], delayed bilateral control [30], evaluation of display methods [18], and so on. In addition, several studies have proved that compensation strategies are effective in reducing the effect of large communication delays on vehicle mobility [6; 31; 32; 33]. Again, what seems to be lacking is the considerations regarding communication outlier detection and the implementation of countermeasures. Therefore this research was undertaken regarding communication delay estimation and evaluations and further detecting communication delay outliers.
## 3 Materials and Methods
### Model Design
#### 3.6.6 Evaluation Performance Model  
The evaluation process is carried out to determine the extent to which the model matches the data used and how well the model produces predictions. This research used four evaluation techniques for the model evaluation process, each with its function. The methods used are the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) [42].  
The RMSE technique, as shown in Equation (13), measures the error between predicted and actual values. Meanwhile, MAPE, as shown in Equation (14), is used to calculate prediction errors as a percentage. As Equation (15) indicates, MAE measures the error between predicted and actual values, considering tolerance for outliers. Additionally, MSE is used to measure the difference between predicted values and actual values. The MSE equation is presented in Equation (16) [51; 52].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Properties** \\ \hline
1 st Convolutional & filter = 50, kernel size = 3, activation = ReLU \\
2 nd Convolutional & filter = 50, kernel size = 2, activation = ReLU \\
1 st LSTM & unit = 50, activation = ReLU \\
2 nd LSTM & unit = 50, activation = ReLU \\ Flattening & - \\ Dense & unit = 10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CNN-LSTM properties of the proposed model.  
Figure 9: The architecture of CNN-LSTM.  
In this research, the symbols in the evaluation equation can be explained as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\widetilde{y_{i}})^{2}}{n}} \tag{13}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\widetilde{y_{i}}|}{y_{i}}\times 1 00\% \tag{14}\]  
\[MAE=\frac{\sum_{i=1}^{n}|y_{i}-\widetilde{y_{i}}|}{n} \tag{15}\]  
\[MSE=\frac{\sum_{i=1}^{n}|y(i)-\widetilde{y_{i}}\mid^{2}}{n} \tag{16}\]  
\(n\) = Number of data points;  
\(y_{i}\) = Actual values;  
\(\widetilde{y_{i}}\) = Predicted values;  
\(\Sigma\) = Summation of all data.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 2 Monitoring Technology
### Sensor Monitoring Technology in BHM  
During the service of bridges and other building structures, the important components of the structure are prone to failure due to the impact of severe working conditions. Given the problems of inaccurate measurement accuracy, poor stability, short service life, and the high energy consumption of traditional sensors, researchers have been seeking improvement methods over the past two decades. Fiber optical sensors (FOS) have emerged as an excellent sensing technology due to their inherent advantages such as small size, light weight, strong anti-electromagnetic interference capability, corrosion resistance, and embeddability [15, 16, 17].
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 6 Conclusions  
In this paper, we discuss the SSA problem in the context of ISTN. In this scenario, LEO satellites provide services to UEs with diverse resource requirements, allocating resources based on service demands. Subsequently, we have formulated the optimal service allocation problem under the constraint of limited satellite resources, considering user allocation rate and satellite resource utilization rate. We model this problem as a variable-length MDP. To address this problem effectively, we propose an optimization algorithm based on reinforcement learning. Following the development of the algorithm, we conducted simulation experiments, using random allocation and the MCF algorithm as experimental benchmarks. According to the results, our proposed algorithm demonstrates rapid convergence starting from 50 epochs with outstanding performance. Moreover, compared to the baseline algorithm, our method exhibits significant advantages in several aspects including service allocation rate, occupied server number, and resource utilization, highlighting the superiority and effectiveness of the proposed approach.  
Conceptualization, Y.Z. and J.Z.; methodology, Y.Z. and J.Z.; software, Y.Z. and J.Z.; validation, Y.Z., J.Z. and Z.C.; formal analysis, Z.C. and X.W.; investigation, Z.C. and X.W.; resources, Z.C. and X.W.; data curation, Z.C. and X.W.; writing--original draft preparation, Y.Z. and J.Z.; writing--review and editing, Z.C. and X.W.; visualization, Y.Z. and J.Z.; supervision, Y.Z. and J.Z.; project administration, Y.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the BUPT Excellent Ph.D. Students Foundation under grant no. CX2023152 and BUPT innovation and entrepreneurship support program under grant no. 2024-YC-T001.  
The data presented in this study are contained within the article.  
The authors acknowledge the contributions of School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, for supporting this work with the research facilities and resources.  
Figure 9: Experimental results for each algorithm.
## 1 Introduction  
The outcome of patients with an acute ischemic stroke (AIS) caused by a large vessel occlusion (LVO) of the anterior circulation has been significantly improved since the introduction of endovasculartreatment (EVT) [1; 2]. However, the number of patients that reach a favorable functional outcome after treatment is still low [2]. Reducing the time to treatment is currently the main goal in optimizing treatment of AIS [3; 4; 5].  
Fast recognition of the occluding thrombus is key in diagnosis and treatment selection. Patients with symptoms of AIS are most commonly brought to the nearest primary stroke center [6; 7]. In most hospitals, computed tomography (CT) is the modality of choice for AIS diagnosis because of its speed and broad availability at emergency rooms [8]. A non-contrast CT (NCCT) is initially acquired to exclude hemorrhagic stroke. An intra-arterial thrombus might already be recognized on NCCT by a hyperdense artery sign (HAS) [9; 10; 11]. An additional CT angiography (CTA) is normally used to determine the location of the occlusion and to evaluate the accessibility to the thrombus with EVT. If the stroke is caused by an LVO, patients might need to be transferred to a comprehensive stroke center to receive EVT. Consequently, if an LVO is missed by the radiologist, a patient might be withheld from EVT and not receive appropriate treatment.  
Furthermore, delays in diagnosis and assessment of treatment eligibility reduces the chance on favorable outcome [12; 13; 14]. In many primary stroke centers, radiologists are less familiar in the detection of thrombus. Moreover, specialized neuroradiologists are often limitedly available during non-office hours. The automated detection and segmentation of thrombus on baseline NCCT images has the potential to reduce time to treatment and improve appropriate treatment selection.  
Convolutional neural networks (CNN) have shown high potential for the automated analysis of medical images. They have been applied with high performance for multiple segmentation tasks, including brain lesion and vessel segmentation [15; 16; 17; 18]. The aim of this study was to assess the accuracy of a CNN for (1) identification of intra-arterial thrombus in NCCT of the brain, (2) thrombus location assessment, and (3) thrombus segmentation on baseline NCCT images.
# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 3 Hazard Assessment Results for the Study Area
### Evaluation of Ground Subsidence Hazard in the Study Area
#### 3.2.1 Weight Calculation for Evaluation Indicators
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Influencing Factor** & **Road Load** & **Subsidence** & **Goaf Area** & **Road Network** & **Eigenvector** & **Weight** \\ \hline Road load & 1 & 1/2 & 1 & 3 & 0.4159 & 0.234 \\ Subsidence area & 2 & 1 & 2 & 5 & 0.7956 & 0.448 \\ Goaf area & 1 & 1/2 & 1 & 3 & 0.4159 & 0.234 \\ Road network density & 1/3 & 1/5 & 1/3 & 1 & 0.1453 & 0.084 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Judgment matrix of index layer–engineering factor criterion layer. The consistency requirement was satisfied for all the judgment matrices, as indicated by the matrix calculation results. We know from the matrix calculation results that \(\lambda_{\text{max}}=4.0042\), \(RI=0.90\). \[CI=(\lambda_{\text{max}}-n)/(n-1)=(4.0042-4)/3=0.0013\] \[CR=CI/RI=0.0013/0.90=0.0144<0.1\]  
The consistency requirement was satisfied for all the judgment matrices, as indicated by the matrix calculation results. We know from the matrix calculation results that \(\lambda_{\text{max}}=4.0145\), \(RI=0.90\). \[CI=(\lambda_{\text{max}}-n)/(n-1)=(4.0145-4)/3=0.046\;CR=CI/RI=0.046/0.90=0.051<0.1\]  
A visual inspection of these tables reveals that factors such as the underground pipeline leakage, distribution of karst collapse areas, rainfall impact, groundwater level, distribution of mined-out areas, and road load exert substantial influences on the urban ground subsidence hazard within the evaluation indicator system for ground subsidence. Moreover, the influences of the geological structure, seismic liquefaction, seismic acceleration, lithology, road network density, and distance-to-water systems progressively diminish, in that order.
## 3 Results and Discussion
### The Statistical Analysis
The association between radionuclides and ash is explained by their main influx with the atmospheric fallout. The detected influence of the Eh (rH) parameter on the radionuclide distribution is due to the following: redox and acid-base conditions are the determining factors for the existence of different forms of U and \({}^{210}\)Pb in the deposit, whose solubility is unequal, and thus affect their mobility. You can also identify the indirect influence of Eh on the distribution and mobility of \({}^{137}\)Cs, \({}^{210}\)Pb and U through the influence of redox conditions on the state of the organic and mineral component of peat, involved in the procedures of physical and chemical adsorption of these pollutants [70; 71]. In particular, [PERSON] [15] found that Pb binding increases with an increasing pH and decreasing Eh, which is explained by its interaction with organic matter, as well as Mn compounds and sulphur sulphides. The third factor accounts for 11.58% of the dispersion with high factor loadings on \({}^{137}\)Cs, \({}^{210}\)Pb and CO\({}_{3}\)\({}^{2-}\) carbonate content, water soluble salts, salt extract pH and N. The isolation of \({}^{137}\)Cs and \({}^{210}\)Pb in this factor indicates the influence of additional mechanisms explaining the migration of these radionuclides in the peat core of the dried section of ISO-1. Apparently, most of the \({}^{137}\)Cs in peat and soils [72] is bound reversibly by the ion-exchange mechanism, as evidenced by the presence of a strong relationship with the exchangeable acidity index pH\({}_{\rm salt}\). In addition, a small part of \({}^{137}\)Cs is in a dissolved state in the form of compounds with humus and low molecular weight organic acids. In this case, there is a dynamic equilibrium in its distribution among the solid and liquid phases of the peat. \({}^{210}\)Pb is present in peat mostly in the associated state with mineral and organic parts, and the share of mobile lead due to the presence of its compounds with fulvic acids and low molecular weight organic acids is small, which determines its low mobility and low rate of migration in the peat profile. The fourth factor with a variance of 7.19% by positive loading isolates the carbonates CO3\({}^{-}\) and elemental parameters N and H.  
The fifth factor accounts for 5.26% of the dispersion with a positive load on uranium isotope ratio \({}^{234}\)U/\({}^{238}\)U. At the same time, several parameters, such as the pH of the aqueous extract, organic matter, elemental components of organic matter C and C/N, but also the natural moisture content and bulk density, are not included in any factor.
###### Abstract
As examples there exists the anaerobic filter (AF) with a packing bed along the column that supports a suspended biomass, and the up-flow anaerobic sludge blanket (UASB) that keep dense granules settled in the reactor. According to [PERSON] et al. [21], AF and UASB are the most studied units regarding the treatment of swine manure by anaerobic processes, and individually present some functional disadvantages during long periods of operation. In the case of AF, the drawbacks correspond to the formation of preferential paths and/or the creation of clogging in the packing bed over time, whose main consequence is the decrease in the useful volume of the unit. Regarding the UASB reactor, it can lose biomass (granules) as is devoid of any filling medium and, in an extreme situation, it can be subjected to system washout, especially when an overload occurs. The combination of these two types of reactors resulted in the hybrid anaerobic reactor, which, by having the packing medium only in the upper area of the unit, gathers the advantageous characteristics and avoids the operational inconveniences of each one. Different configurations of the hybrid anaerobic reactor have been reported in the treatment of effluents. The integral hybrid system, configured in two-sections--the UASB section under anaerobic conditions (initial volume of 2 L) and the UASB section + aerobic packing bed with polyethylene rings (total volume of 3 L)--was used to degrade the swine wastewater [22] where the novel hybrid anaerobic-aerobic baffled reactor was applied to the treatment of the manure-free piggery wastewater, without any supplementation of an external carbon source [23]. The hybrid anaerobic baffled reactor was also reported to digest (synthetic) brewery wastewater [24]. In a previous study, the authors used the Next-Generation Sequencing (NGS) technique to find that changes in the organic composition of the substrate had a clear relationship with changes in the structure of microbial communities during the anaerobic digestion of olive mill wastewater (OMW) and piggery (PE) effluent, by exploiting the complementary effluents concept [20]. The reduction in the initial population diversity occurred towards an acclimation of the operational conditions in the anaerobic process, and the predominant microbial sequences allowed the finding of the best substrate proportion, 30% OMW plus 70% PE (\(v/v\)). The present work intends to continue the previous work, considering the concept of the complementarity of substrates, using brewery wastewater (BWW) to decrease the toxicity and complement the unbalanced substrate, such as the piggery effluent. The experimental conditions were scaled-up to a hybrid anaerobic reactor, as it is important to assess the toxicity of an unbalanced substrate, such as the provided piggery effluent, by exploiting the complementary effluents concept. It aims to assess hybrid anaerobic reactor behavior under high organic load conditions, through the efficiency of organic matter conversion and biogas/methane production as well as the molecular characterization and dynamic changes of microbial communities.
## 3 5G and Beyond  
5G allows for several multi-partnership models where traditional services are enriched by adding external third-party functionalities. One example of these business cases is the \"operator offer enriched by partner\" defined by NGMN [25], where connectivity offered by the operator is enriched through a set of third-party applications. This example manifests the high complexity in such multi-partner services because the responsibility is blurred among several parties. Indeed, each partner must trust each other regarding the decisions made by the rest of the partners to contribute to meet the required overall service quality level.  
Moving to 6G, use cases and business models, as pointed out by [PERSON] et al. in [26], are rather more focused on AI-empowered slice and resource management, haptic communications, internet or robots, tele-robotics and ultra high data density communications, pushing even more forward the technological boundaries. The Finnish 6G Flagship program led by the University of Oulu already published a series of twelve 6G white papers (e.g., [8]) that shed light on main radio opportunities and a vision on emerging use cases, such as holographic teleportation, and unmanned aerial vehicle pervasive connectivity, among others. Table 1 summarizes the requirements for both 5G and 6G.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Requirement** & **5G** & **6G** \\ \hline Energy/bit & NS & 1 pl/bit \\ Jitter & NS & 1 us \\ Latency & 1 ms & 0.1 ms \\ Traffic Capacity & 10 Mbps/m\({}^{2}\) & 10 Gbps/m\({}^{3}\) \\ Localization Precision & 10 cm on 2D & 1 cm on 3D \\ User Experience & 50 Mbps 2D & 10 Gbps 3D \\ DL Peal Rate & 20 Gbps & 1 Tbps \\ UL Peal Rate & 10 Gbps & 1 Tbps \\ Reliability & FER 10\({}^{-5}\) & FER 10\({}^{-9}\) \\ Mobility Support & up to 500 km/h & up to 1000 km/h \\ Satellite integration & No & Fully \\ AI & Partial & Fully \\ Autonomous vehicle & Partial & Fully \\ Service level & Augmented/Virtual reality & Tactile \\ Architecture & Massive MIMO & Intelligent surface \\ Positioning precision & m level & cm level \\ \hline \hline \end{tabular}
\end{table}
Table 1: 5G and 6G requirements [27, 28, 29].
## 3 Results and Discussion
### Analysis of Surface Microtexture and Roughness
#### 3.1.2 Analysis of Cu-SiC Surfaces Processed by Laser Radiation  
The SEM images of laser-processed samples No. 2-8 show that a multimodal texture with developed roughness was formed on the surfaces due to melting, ablation, and crystallization of the material. The roughness parameters of the surfaces of samples No. 2-8 are Sa = 4.1-4.3 \(\mu\)m, Sz = 26.4-26.8 \(\mu\)m, Sdr = 19.0-20.0%. As a result of laser processing, the elemental composition of the near-surface layer of all studied samples became homogeneous (Table 2). It should be added that no obvious changes were recorded in the content of Si and C on the surfaces containing SiC precursors from 5 to 20 wt.% in the initial composition of the Cu-SiC bulk product. Therefore, laser modification of Cu-SiC composite materials makes it possible to form both a developed and multilevel roughness on the surface, as well as a near-surface layer with a uniform elemental composition.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 3 Test Results and Analysis
### Load-Separation Behavior  
The separation between the steel beam and the concrete slab occurred as the applied pull-out load increased. The average separation from two LVDTs versus the pull-out load is shown in  
Figure 4: Test setup and instrumentation.  
Figure 5. The envelopes of load-separation curves under uniaxial cyclic loading resembled those under monotonic loading. Three stages were observed in the typical load-separation curves of both conventional and notched perfobond shear connectors. At the first stage, these curves were steep without obvious separations, showing elastic behavior and great stiffness. The following stage was a parabolic curve where the load increased and the stiffness reduced slowly with the separation. Beyond the ultimate load, the separation continued to increase as the pull-out load decreased.  
The pull-out test results are summarized in Table 2, including the pull-out resistance, stiffness, and separation deformation of the perfobond shear connectors. The pull-out resistance (\(T_{u}\)) is defined as the maximum load during the tests. The pull-out stiffness (\(k_{t}\)) represents the secant slope at the separation of 0.2 mm [9]. The separation deformation is evaluated by the peak separation (\(s_{p}\)), which is the separation corresponding to the pull-out resistance (\(T_{u}\)).
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 4 Experiments and Analysis
### Spectral Feature  
#### 4.4.1 Parameter Analysis in Spectral Features  
The following content introduces the determination of parameters \(F\) and \(\beta\). \(Acc\) is used as evaluation metrics. The higher \(Acc\), the better the performance of the parameter.  
In four types of beats (N, S, V and F), we can randomly select 500 samples from each type. The total number of the samples is 2000.  
Figure 7: Top view of 4 types of ECG segments after 2D-GFT (Z axis ranges from -5000 to 20000). (**a**) N beat (No. 100); (**b**) S beat (No. 232); (**c**) V beat (No. 208); (**d**) F beat (No. 213).  
Since the eigenvalue matrix has symmetry and the values in first columns of the matrix are too small, so the boundary is set from 17, second half of matrix.  
To determine the optimal boundary \(F\) in spectral brightness, 15 experiments have been performed on different boundary values. Figure 8a shows the \(Acc\) results under varied boundary from 17 to 31. The recognition rate reaches up to the highest when \(F\) is 25, which is 94.7%. There's a slight decline around 25. And \(Acc\) is the lowest value when \(F\) is 31, that's because most power are concentrated before the boundary, leaving little room for fluctuation.  
Similar to the boundary in spectral brightness, with same number of samples, coefficient \(\beta\) in spectral roll-off is also determined by several experiments. As shows in Figure 8b, \(Acc\) results change with increasing parameters (0.1 to 0.9). Accuracy reaches up to the peak when the coefficient \(\beta\) is 0.3, which is 95.4%. Higher or lower coefficients both lead to the decrease of recognition rate.