esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Design of Expandable Spinal Cage for Efficient Lumbar Spine Fusion Operation
## 4 Compression and Compression-Shear Test
### Compression and Compression-Shear Tests and Results  
The bench test was conducted based on ASTM F2077, a standard for measuring mechanical performance of spinal cages, as previously mentioned [30]. The test setup is depicted in Figure 11. First, a universal joint is used to connect the pushrod to the testing machine, and a spherical joint connects the pushrod to the upper fixture. The lower fixture is fixed to the base. Metal blocks are assembled to the upper and lower fixtures, and the cage is secured to these metal blocks, which are designed to match geometric features of the cage.  
In the compression test, the cage is mounted perpendicular to the pushrod axis, while in the compression-shear test, it is mounted at a 45\({}^{\circ}\) angle to the pushrod axis. In both cases, the center of the cage must align with the axis of the pushrod. To minimize offset between the pushrod axis and the load cell axis, the pushrod was designed to be 400 mm in length. All components of the setup were fabricated from stainless steel with an ultimate tensile strength of 1310 MPa. The load was applied at a rate of 1 mm/min up to 6 mm or until failure or loss of function occurred. This test generated the load-displacement curve of the cage. Stiffness was determined from the initial slope of the curve, and yield load was defined as a load causing 2% permanent deformation.  
In compression and compression-shear tests, failure occurred before yielding load could be determined, making it impossible to derive the yielding load. As an alternative, the ultimate load was compared to the fifth percentile ultimate load of spinal cages approved by the U.S. Food and Drug Administration (FDA) to evaluate mechanical stability.  
First, in compression test results, as shown in Figure 12, the ultimate load was 9888 N. This exceeds the fifth percentile ultimate load of 6989 N for FDA-approved spinal cages,  
Figure 10: 3D-printed expandable cage comparison with 3D modeling.  
indicating that current model meets performance targets in compression test. As shown in Figure 13, failure occurred at the anterior axis of the upper plate.  
Next, in compression-shear test results, as shown in Figure 14, ultimate load was 3315 N, which surpasses FDA-approved fifth percentile ultimate load of 1996 N. Thus, the current model also meets performance targets in the compression-shear test. As shown in Figure 15, failure occurred at edge of the column where the axis of the lower plate is located.  
Figure 11: Experimental setup for mechanical test with ASTM F2077 condition. (**a**) Compression test. (**b**) Compression–shear test.  
Figure 12: Load–displacement curve from compression test.  
Figure 14: Load–displacement curve from compression–shear test.  
Figure 13: Failure in compression test. (**a**) Part where failure occurred. (**b**) Area where failure occurred.  
Figure 15: Failure in compression–shear test. (**a**) Part where failure occurred. (**b**) Area where failure occurred.
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 3 The Improved Multipath Routing Algorithm
### Adaptive Multipath Selection Algorithm Based on Service Requirements
\[P_{loss\_rate}(j)=1-\frac{receive\_nums(j)}{send\_nums} \tag{9}\]  
\[P_{success}=P(P_{t}-Los-P_{N}>\theta)=\frac{1}{2}\bigg{\{}1+erf(\frac{\Psi(d)}{ \sqrt{2}\sigma})\bigg{\}} \tag{10}\]  
\[\tau_{q}>\alpha\sum_{k=1}^{\max\{hc_{i}\}}\left(T_{trans,k}+T_{queue,k}\right) \tag{2}\]0  
\[\tau_{q}>\alpha\sum_{k=1}^{\max\{hc_{i}\}}\left(T_{trans,k}+T_{queue,k}\right) \tag{2}\]1  
where \(P_{loss\_rate}(j)\) represents the packet loss probability of the \(j\)-th node (\(j_{\max}\) means the destination node), \(send\_nums\) is the number of packets sent from the source node, \(receive\_nums(j)\) represents the number of successfully received packets at the \(j\)-th node, \(P_{L}\) represents the path loss, and \(\xi\) represents the shadow fading coefficient, which follows a normal distribution with a standard deviation \(\sigma\). The successful reception of packets by a node mainly depends on whether the received power can be higher than the received threshold after the influence of large-scale fading and noise power, and if it is higher than the received threshold, the reception is considered successful. The packet loss rate from the source node to the destination node can be estimated by combining Equation (9), as shown in Equation (13). \[\tau_{q}>\alpha\sum_{k=1}^{\max\{hc_{i}\}}\left(T_{trans,k}+T_{queue,k}\right) \tag{2}\]2  
It can be seen that increasing the number of alternate paths \(B\) will result in more paths retransmitting packets, thereby improving the packet loss rate and increasing the reliability of the system. In addition, by combining the above equations, the throughput of the path can be calculated as shown in Equation (14). \[\tau_{q}>\alpha\sum_{k=1}^{\max\{hc_{i}\}}\left(T_{trans,k}+T_{queue,k}\right) \tag{2}\]3  
where \(TH(i)\) represents the throughput of the \(i\)-th path, and \(L*(1-P_{loss\_rate}(i))\) represents the number of bits received by the destination node. The throughput represents the average rate at which a data packet can successfully reach the destination node from the source node.
###### Abstract  
An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%).  
ECG beats; classification; feature extraction; convolutional neural networks; spatial pyramid pooling +
Footnote †: journal: Applied Sciences
# Event-Based Emergency Detection for Safe Drone
## 3 Proposed Methodology
### System Architecture  
Firstly, in terms of implementing our time-window (temporal scope) to a drone, we designed an event-based controller that cooperates with other controllers, as shown in Figure 2. We assumed that the overall drone system includes a group of input sensors, a group of actuators, a sensor-based low-level control loop, and a mission-level controller. By adding our event-based controller (decision-maker) to the drone system mentioned above, we were able to define a safe drone.  
The expected time-window data for each waypoint interval should be pre-defined before flight operations and stored in the knowledge base, inside the event-based controller. We attempted to illustrate a safe drone system with our event-based controller mounted on top of a typical drone system and combining both into a decision-making controller.  
The event-based controller will receive processed sensor data such as time and location, refined by the existing controller. It will then process the emergency-detection algorithm using both the delivered sensor data and its pre-defined time-window data, stored inside its knowledge base. If needed, the event-based controller can command the PID-based controller to proceed with an emergency landing.
# A Review of Commercially Available 3D Surface Imaging Systems for Body Composition Estimation
## 4 Discussion
### Limitations of Body Composition through 3D Surface Imaging  
It is essential we remain realistic regarding the capabilities of 3D surfacing imaging in estimating body composition. Using 3D surface imaging to estimate body composition is a doubly indirect method, predicting body composition through the use of a predictive regressions algorithm of the relationship between anthropometrics and direct or indirect methods. Consequently, actions such as using the term'measurement' or suggesting differential abilities between internal and visceral fat are fundamentally incorrect. By overselling the capabilities of 3D surfacing imaging to estimate body composition we risk the inappropriate selection and interpretation of outputs, alongside damaging the reputation and potential usefulness of this technology. In addition, at present, the differing hardware, software, calibration techniques, anthropometric definitions, and data collection procedures used by each system, makes the comparison of body composition estimates unsuitable. Thereby limiting its suitability for use in research and practice. A further critical review of 3D surfacing imaging as a method of estimating body composition is presented by [PERSON] et al. [9].
# Prediction of Compressive Strength Loss of Normal Concrete after Exposure to High Temperature
## 5 Conclusions  
Fires in buildings cause serious damage to structural concrete. Post-fire assessments currently used are usually subjective and destructive. Therefore, BP, PSO-BP and RF models \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BP** & **PSO-BP** & **RF** \\ \hline Range (\%) & [0.00–43.37] & [0.23–31.57] & [0.20–36.98] \\ Average (\%) & 8.88 & 6.37 & 5.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistic of the error between the results measured and predicted. Figure 11: Error between the values predicted and measured. (**a**) BP model. (**b**) PSO-BP model. (**c**) RF model. Figure 12: Importance of input parameters based on the RF model. Figure 10: Appearance of concrete specimens before and after high-temperature exposures. were established in this paper to predict the compressive strength loss of normal concrete after high temperatures. 1. To establish the models, 1803 sets of data from the publicly published literature were used, W/B, T, V, MD, C and RD were determined as input parameters and P was applied as an output parameter. Based on RMSE and MAE error evaluation, the accuracy of all three models was sufficiently high. Compared to the BP model, both the PSO-BP and the RF models were more accurate. 2. Parallel experimental work was carried out with modeling prediction using the same parameters. An error value between the results measured and predicted of less than 10% proved that all three models had sufficient feasibility to complement the prediction. Compared to the other two models, RF one was much more feasible. 3. Based on the RF model, the importance of the input parameters was ranked in the sequence of T \(>\) W/B \(>\) V \(>\) MD \(>\) RD \(>\) C.
4. With the continuous expansion of data size, the accuracy of the models could be improved further. Such prediction work has provided a new perspective to assess the post-fire properties of concrete non-destructively and objectively. Additionally, it could be used to guide performance-based design for fire-resistant concrete. Conceptualization, X.Q. and Q.M. ; methodology, Q.M. ; software, X.Q. ; validation, R.G. and S.T. ; formal analysis, X.Q. ; investigation, Q.M. ; resources, R.G. ; data collection, S.T. ; writing original draft preparation, X.Q. ; writing--review and editing, Q.M. ; visualization, S.T. ; supervision, R.G. ; project administration, Q.M. ; funding acquisition, Q.M. 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 [52068038] and the Yunnan Provincial Department of Science and Technology grant number [202101 AT070089]. Not applicable. All the data used to support the finding of this study have been
# Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem
## 2 Mathematical Model of EDRCFJSP
### Problem Modelling  
In our previous work, an energy-aware flexible job shop scheduling problem is studied without the consideration of the dual-resource constraints [12]. Based on the existing model, a new mathematical model of the EDRCFJSP problem is modeled as below.  
\[\min F=\sum_{i=1}^{n}\sum_{j=1}^{J_{i}}\sum_{k=1}^{m}\sum_{l=1}^{w}PE_{ijkl}y_{ ijkl}p_{ijkl}+\sum_{k=1}^{m}SE_{k}(C_{k}-S_{k}-W_{k})+\sum_{k=1}^{m}TUl_{k}TST_{k}+CE \times C_{\max} \tag{6}\]  
\[\text{s.t. }C_{ij}-S_{ij}=\sum_{k=1}^{m}\sum_{l=1}^{w}y_{ijkl}p_{ijkl},\ i=1,2, \ldots,n;\ j=1,2,\ldots,J_{i} \tag{7}\]  
\[S_{i(j+1)}-C_{ij}\geq 0,\ i=1,2,\ldots,n;j=1,2,\ldots,J_{i}-1 \tag{8}\]  
\[S_{j^{\prime}j^{\prime}}+\text{M}(1-z_{ij^{\prime}j^{\prime}k})\geq C_{ij}+SU _{ij^{\prime}j^{\prime}k},\ i,i^{\prime}=1,2,\ldots,n;j,j^{\prime}=1,2,\ldots, J_{i};k=1,2,\ldots,m \tag{9}\]  
\[S_{ij}+M\text{z}_{ij^{\prime}j^{\prime}k}\geq C_{i^{\prime}j^{\prime}}+SU_{i^{ \prime}j^{\prime}ijk},\ i,i^{\prime}=1,2,\ldots,n;j,j^{\prime}=1,2,\ldots,J_{i} ;k=1,2,\ldots,m \tag{10}\]  
\[\sum_{k=1}^{m}\sum_{l=1}^{w}y_{ijkl}= 1,\ i=1,2,\ldots,n;\ j=1,2,\ldots,J_{i} \tag{11}\]  
\[W_{k}=\sum_{i=1}^{n}\sum_{j=1}^{J_{i}}\sum_{l=1}^{w}p_{ijkl}y_{ijkl},\ k=1,2, \ldots,m \tag{12}\]  
\[TST_{k}=\sum_{i=1}^{n}\sum_{j=1}^{J_{i}}\sum_{l^{\prime}=1}^{n}\sum_{j^{ \prime}=1}^{J_{f}}SU_{l^{\prime}j^{\prime}ijk}\text{z}_{l^{\prime}j^{\prime}ik },\ k=1,2,\ldots,m \tag{13}\]  
\[C^{\prime}_{k}=\max\{C_{ij}y_{ijkl}\},\ i=1,2,\ldots,n;j=1,2,\ldots,J_{i};\ k=1,2, \ldots,m;l=1,2,\ldots,w \tag{14}\]  
\[S^{\prime}_{k}=\min\{S_{ij}y_{ijkl}\},\ i=1,2,\ldots,n;j=1,2,\ldots,J_{i};\ k=1,2,\ldots,m;l=1,2,\ldots,w \tag{15}\]  
\[y_{ijkl}\in\{0,1\},\ i=1,2,\ldots,n;j=1,2,\ldots,J_{i};k=1,2,\ldots,m;\ l=1,2, \ldots,w \tag{16}\]\[z_{ij;f^{\prime}k}\in\{0,1\},\ i,i^{\prime}=1,2,\ldots,n;j,j^{\prime}=1,2,\ldots,J_{i};k=1,2,\ldots,m \tag{17}\]  
Equation (6) is the objective function; Constraint (7) indicates that the processing of each operation must not be interrupted; Constraint (8) defines the precedence order between operations in a job; Constraints (9) and (10) ensure that each machine cannot process more than one operation at a time. For any two operations on the same machine, the processing of the operation cannot be started until its preceding operation is completed and the setup of the machine is finished; Constraint (11) represents that each operation is processed by one machine and the assigned machine is operated by one worker. Constraint (12) shows the workload of each machine; Constraint (13) gives the total setup time of each machine; Constraint (14) denotes the completion time of each machine; Constraint (15) defines the start time of each machine; Constraints (16) and (17) show the binary variables.
## 3 Preliminaries of IMU and Extended Kalman Filter Used in UWB Positioning
### Extended Kalman Filter
#### 3.2.1 Definition of State Space  
The general form of the mathematical model of a nonlinear discrete system can be expressed in Equation (8).  
\[\left\{\begin{array}{l}x_{k}=f(x_{k-1},u_{k-1},w_{k-1})\\ y_{k}=h(x_{k},v_{k})\end{array}\right. \tag{8}\]  
where \(x_{k}\) is the state vector of the system at moment \(k\), \(u_{k-1}\) is the input vector of the system at moment \(k-1\), \(w_{k-1}\) is the stationary noise sequence of the process of the system at moment \(k-1\), \(f()\) is the state transition function of the system and nonlinear (or linear) function of the state \(x_{k-1}\), \(y_{k}\) is the measurement vector of the system at moment \(k\), \(v_{k}\) is the noise sequence of the system at moment \(k\), and \(w\) and \(v\) satisfy normal distribution.
# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 4 Experiments and Results
### Backbone Network Selection  
The segmentation performance of the semantic segmentation model is closely related to the feature extraction ability of the backbone network. With the rapid development of deep learning, more and more networks have been designed and applied to different tasks. In this experiment, six networks are selected for analysis and comparison, namely MobileNet, Vgg16, Xception, ResNet18, ResNet50, and ResNet101. They are used as the backbone network in turn to train the Bijie data images, and the model is evaluated on the validation set. Figure 7 shows the accuracy on the validation set using different backbone network models  
As shown in Figure 7, except for the DeepLabv3+-MobileNet network model, the accuracy of the other network models on the validation set is more than 96%. DeepLabv3+-ResNet18, DeepLabv3+-ResNet50 and DeepLabv3+-ResNet101 have high accuracy, and DeepLabv3+-ResNet101 has the best performance, with higher accuracy than the other network models.  
Figure 8 shows the prediction results of the different backbone networks for landslide images. The figure contains five landslide image samples from different locations, which are, respectively, labeled as landslide I, II, III, IV and V. Among them, landslide I, III, IV and V have obvious landslide characteristics and forms, while landslide II has less obvious characteristic forms. The label of the landslide in the experiment is the mask image, which is used to compare with the predicted map, and the white area is the location where the landslide occurred. Different backbone network models are used to obtain the predicted mask images. For landslides with the obvious morphology and characteristics I, III, IV, and V, the six backbone networks have certain recognition effects. Only DeepLabv3+-ResNet101 has the most prominent performance on landslide II. This is because the characteristics of the landslide II landslides are not obvious and there is interference information such as roads in the image. The other models found it easy to misjudge these non-landslide information as landslides when processing, resulting in a large difference from the real landslide cover. Through its deep architecture and residual connection design, ResNet101 enables the model to learn complex patterns and capture multi-scale information, so that landslide II can be better identified and achieve better results. Through the above analysis of various experimental data, it is verified that the DeepLabv3+-ResNet101 model has the best performance and can deal with the landslide detection task more accurately. The subsequent experiments are carried out on the basis of this model.  
Figure 7: Accuracy of different backbones on the validation set.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 1 Introduction
Experimental results demonstrate that the deployment scheme optimized by the QSA algorithm can reduce the average number of hops by 0.1-0.6 compared to traditional heuristic algorithms. This study provides a new theoretical foundation and methodological guidance for reducing average user latency, offering significant practical value for enhancing network service quality.
## 2 Materials and Methods
### Datasets and Preprocessing  
#### 2.2.1 Meteorological Data  
In this study, we used daily precipitation, temperature, and relative humidity data from monitoring stations provided by the China Meteorological Data Network (CMDN, [[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 20 April 2022)). Observed near-surface meteorological data at weather stations in China are routinely publicized, with a lag of about three months, a period used for data compilation and quality control. Therefore, this dataset has high accuracy and timeliness, and has been widely used in studies related to climate change [34; 35; 36; 37]. To ensure continuous and complete data records, 786 meteorological stations were chosen for our study. The data from the meteorological stations was interpolated to 0.1\({}^{\circ}\) grid points using ANUSPLIN software, which was generated using the thin-plate spline algorithm [38]. In order to improve the accuracy of interpolation in complex terrain areas, it is necessary to input elevation elements in the interpolation process. In China, interpolation of temperature and relative humidity based on this method has high confidence, and error estimates of precipitation for southern China were quite low [39]. This data was used to calculate SPEI and VPD.
## 1 Introduction
Although previous articles have proposed methods to characterize mechanical defects or PDs, it is important to investigate whether PDs and mechanical defects in switchgear can be identified directly and simultaneously from audio signals captured by smartphones. Therefore, this paper performed a study and provides a convenient method for defect detection and audio recognition. When a switchgear is defective and emits abnormal audible sound waves [17], this can assist technicians, even those who do not have a basic knowledge of acoustics, to use their smartphones to make a preliminary diagnosis of the type of defect in the switchgear and effectively identify mechanical vibration or PD for a subsequent evaluation with more specialized testing equipment. To solve the appeal problem and to verify the feasibility of using the recording function of smart phones for the simultaneous detection and identification of mechanical defects and PDs in switchgear, the relevant work was carried out as follows: First, in view of the popularity of smartphones and the increasing power of audio functions, the data source was set to be the audio files captured by the microphone of smartphones. The 12 sets of audio files in this paper were provided by the technicians of substations or distribution stations, and according to the type of audio signal, they can be classified into three categories: namely, background noise, mechanical vibration, and PD. The three types of audio signals were analyzed in terms of time-domain features, such as waveform autocorrelation, denseness, and steepness, and frequency-domain features, such as band energy and harmonic components, and a new combination of features suitable for identifying these three types of audio is proposed. Second, recognition tests were conducted using an SVM multiclassification support vector machine for time-domain features, frequency band energy features, and MFCC features from the literature [15], as well as the present method. The best features were obtained by comparing the accuracy of different features for recognizing mechanical vibration, PD, and background noise. Third, based on the above work, this paper proposes an audio generalized recognition method for PD and mechanical defects of a switchgear using smartphones.A set of mechanical defects and PD defects were set for the switchgear, to practically verify the generalization and effectiveness of the method. This paper is organized as follows: Section 2 sequentially describes the acoustic theory of defects, the sources and types of audio file, the data preprocessing, and the methods of analysis and identification. Section 3 analyzes the characteristics of the audio data from the time and frequency domains and summarizes the features. The recognition accuracy of time domain, frequency band energy, MFCC features, and the present method on audio signals is compared using an SVM classifier. Section 4 gives the experimental validation of the method. Section 5 summarizes the research content and significance of this paper and proposes future work.