esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation
### Throughput  
The maximum BLE application layer throughput for a connection between two devices has been obtained in prior work by simulation and mathematical analysis, as a function of _connInterval_ and BER [13]. Whereas the physical layer data rate is 1 Mbps, the maximum application layer throughput is equal to 236.7 kbps. On the other hand, recall that if any of the two connected devices receives two consecutive packets with an invalid CRC check, the connection event ends. Thus, the transmission of data cannot be resumed until the beginning of the next connection event, whereby a new data channel (_i.e._, a new frequency) is used. This behavior prevents any unnecessary waste of energy while bit  
Figure 8: Theoretical maximum number of slaves per piconet for various types of interactions between devices and scheduling schemes.  
errors are being found in one data channel. However, this scheme degrades the effective throughput of BLE in the presence of bit errors. In such conditions, moderate to high throughput can only be achieved for very low _connInterval_ values.  
Despite the theoretical or simulation-based findings, the maximum BLE throughput that can be achieved in a real scenario may be limited due to a variety of factors. These include implementation constraints such as the number of application layer messages a device can send per connection event (due to memory limitations), as well as processing delays.  
We have conducted experiments in order to measure the maximum achievable throughput in a BLE link composed of two connected CC2540 devices. The distance between both devices is 0.5 m and their transmit power is 0 dBm. According to our measurements, in these conditions, the BER of the BLE link is lower than \(10^{-5}\). In each experiment, the slave has been programmed to send the maximum number of notifications allowed by the BLE stack currently used in the CC2540 (_i.e._, four notifications per connection interval). Accordingly, we have set _connInterval_ and _connSlaveLatency_ to the smallest possible values (_i.e._, 7.5 ms and 0, respectively). Each notification has the maximum size (_i.e._, 20 bytes of application layer payload). The experiment is carried out for 1000 connection events. In the described conditions, the maximum application layer throughput we have measured is 58.48 kbps. This low result can be explained by the following two facts: (i) whereas, in theory, up to eleven such notifications can be transmitted within a connection event of 7.5 ms, only four notifications are allowed per connection event, as aforementioned; and (ii) we have observed that less than four notifications are actually transmitted in most connection events during the experiment (however, the same phenomenon occurs less frequently for connection intervals greater than 7.5 ms). These observations show that high throughput has not been a primary goal in the design of the BLE implementation used in the evaluation.
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 2 Data and Methods  
### Data  
#### 2.1.1 Seismotectonic Setting  
The Xichang area is located in the middle of the Kangdian earth's axis, at the junction of the east and west tectonic units in China, and the rhomboid block region's core Xianshuihe and Xiaojiang fault system [32; 33]. Due to the complex seismogenic structure and dynamic mechanism, this region has become important for studying intracontinental deformation. Large fault zones such as the Anninghe fault zone, Xianshuihe fault zone, and Daliangshan fault zone are mainly developed in this region with strong historical seismic activity [34]. Multi-stage magmatic rocks, metamorphic rocks, and sedimentary rocks are in contact, resulting in extremely complex tectonic and geological conditions [35]. The density and quality of the regional seismic network have been greatly improved in recent decades. Many teams have deployed dense arrays with different scales and station spacing. With the constant improvement of the precision and quality of observation data, more and more fundamnetal investigations are focused on the fault block and seismicity in this area [36; 37], sliding rate of active faults [38; 39], motion mode [40], structural characteristics [40; 41], focal mechanism [7]. Therefore, we have now a great variety of geophysical and geological results thus providing an accurate constraint for the modeling of crustal dynamics in the Xichang area.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### The Process of Route Discovery
#### 2.4.3 Generate RREP Message  
When the destination node generates the RREP message, the node fills the destination node IP address, destination node serial number, next hop node and other relevant information into the corresponding fields of the RREP message, and signs and hashes the message. The message has a signature for the invariant part and a hash chain for the variable part.  
The RREP message generated by the intermediate node is in a double-signature extended format. As described in Section C, it has two more lifetimes and signature fields corresponding to the lifetimes than the RREP message in the signature extension format generated by the destination node.
## 3 Materials and Methods
### Evaluation Techniques  
Accuracy, commonly used as the metric of evaluation for a multiclass classification model, is the ratio of correct predictions to the total number of predictions made. However, accuracy has limitations in the context of classification on imbalanced datasets [86].  
With safety-related systems, the cost of false negatives far outweighs that of false positives. For this reason, we prioritize recall. The recall metric, also known as sensitivity, represents the true positive rate [86]. As proposed in [87; 88], we use the measures Receiver Operating Curve (ROC) and ROC Area Under Curve (ROC-AUC) in place of accuracy as metrics of classifier performance when training on imbalanced datasets. ROC [86; 89] is a two-dimensional representation of the classifier performance, plotting the true positive rate against the false positive rate for all possible prediction thresholds. Interpretation of ROC is such that the closer the curve is to the top left corner, the better the classifier, effectively maximizing the true positive rate while minimizing the false positive rate.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Fault** & **Entropy** & **Uncertainty (\(\%\))** \\ \hline Dirtiness & \(5.903\times 10^{-1}\) & 59.025 \\ Dirtiness & \(5.280\times 10^{-1}\) & 52.802 \\ Stains & \(5.096\times 10^{-1}\) & 50.961 \\ \hline Dirtiness & \(4.610\times 10^{-1}\) & 46.096 \\ Dirtiness & \(4.497\times 10^{-1}\) & 44.968 \\ Stains & \(4.461\times 10^{-1}\) & 44.606 \\ Stains & \(3.894\times 10^{-1}\) & 38.942 \\ Stains & \(2.716\times 10^{-1}\) & 27.163 \\ Dirtiness & \(2.335\times 10^{-1}\) & 23.354 \\ Dirtiness & \(1.893\times 10^{-1}\) & 18.926 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A final report showcasing inference results from the Steel Plates Faults dataset. A threshold set at 50% implies the human expert has to cross-check only the first three records.  
ROC-AUC [86] on the other hand, provides a single scalar quantity that summarizes the classifier's expected performance. It represents a measure of the degree of separability between classes by the classifier. Actual values of ROC-AUC range from 0 to 1 and interpretation is such that the higher the quantity, the better the classifier performance.  
For additional analysis of the classifier performance, we generate a Confusion Matrix (CM) [90]. Interpretation of the CM is such that the numbers along the leading diagonal represent the correct decisions made, while off-diagonal elements represent the errors or confusion between classes. The authors of [91] describe the entries on the CM as one that is made up of the predicted class values as its columns and the actual class values as its rows.  
We use Scikit-learn (ver. 0.23.2), a machine learning library written in Python [92], to generate the metrics ROC, ROC-AUC, and CM from the classifier results.
# Subjective Distance Estimates and Sense of Agency in Robotic Wheelchair Control
## Experiment 1
### Results  
#### 2.2.1 Distance Estimates and the Degree of Control  
On average, participants significantly underestimated the distances covered by the wheelchair before the sound for all but 50 cm distances. It should be kept in mind that after the sound, the wheelchair took a considerable braking distance. We expected that, in view of this, participants would, on the contrary, overestimate the distances. Below, we present a graph of the dependence of estimates on real distance (Figure 3). Relatively large standard deviations resulted from the fact that different participants tended to estimate the distances with individual patterns, i.e., they consistently overestimated or underestimated the distances.  
In order to test the possible influence of the degree of control on the subjective distance estimates, we used repeated measures ANOVA. The analysis was carried out in Statistica 10 (Statsoft, Tulsa, OK, USA).  
Having obtained a specific dependence of the estimates on real distances, we abandoned the usual comparison of differences between estimates and real distances in different conditions. Absolute differences are functions of real distances, and the accuracy of the estimates decreases with distance in a non-trivial way (if we consider distances of 0.5 m).  
Figure 3: Group means of median subjective estimates in three experimental conditions. Vertical lines denote 95% confidence intervals.  
Therefore, instead of differences, we compared the estimates. Such an analysis is justified if the estimates of the different distances are separated. Moreover, in different conditions, the wheelchair traveled, on average, almost the same distance (1.95 \(\pm\) 0.14 in Act, 1.97 \(\pm\) 0.14 in Exp, 1.92 \(\pm\) 0.16 in Rem). Direct comparison of estimates instead of differences has already been used by other authors who studied IB (e.g., [31; 32]). For each participant, we determined the median of the estimates in every experimental condition for all five distance values. In contrast to averages, medians reduce the impact of statistical outliers.  
According to two-way repeated measures ANOVA, the effect of the \"Activity\" factor was statistically significant (F(2, 44) = 4.3, \(p\) = 0.02). As expected, the effect of the \"Distance\" factor was also significant (F(4, 88) = 77.67, \(p\) < 0.00001), while the interaction of factors was not significant F(8, 176) = 0.08, \(p\) = 0.65). Further comparisons were made using the Fisher LSD post-hoc criterion. Post-hoc analysis revealed differences between the Act condition and Exp condition (\(p\) = 0.02), and between the Act condition and Rem condition (\(p\) = 0.01), but not between Exp and Rem (\(p\) = 0.85). To our surprise, the estimates turned out to be greater in the Act condition, refuting our initial hypothesis.
# Study of Orientation Error on Robot End Effector and Volumetric Error of Articulated Robot
## 1 Introduction
Geometric errors in robots, i.e., errors in joint angles and datum location error, are considered to be the main sources of position error [8; 21]. The repeatability errors are non-geometric; these include gear transmission error, joint drive compliance, backlash, bearing, and encoder inaccuracy. Other sources of robot inaccuracy include link geometry errors, elastic deflection, scale and control errors, and environmental effects. Consequently, a new system using a six-point kinematic design was developed by the author for measuring the orientation error and tested on the industrial robot because a six-point kinematic design can attain good repeatability and has good rigidity. The aim of this research is to develop a strategy for the robot pose evaluation of industrial robots by using low-cost hardware in the form of ball bars. A new design for the kinematic system will be investigated in order to overcome orientation measurement limitation with existing systems. The problem of interpretation of the data obtained from the new hardware will be investigated by simulating the kinematic model of the robot structure for six degrees of freedom and comparing these with the experimental data obtained using this new hardware. The experimental verification of the strategy will be undertaken on a standard six axes industrial robot and the effect of orientation errors on the kinematics is theoretically simulated and analyzed.
## 4 Results and Discussion
### Scenario 1: Chaotic Situation  
The configurations in Tables 1 and 2 were used in the simulation of cases 1 and 2, respectively. Case 1 examined the evacuation time while increasing the probability of errors. Conversely, case 2 explored the impact of pre-evacuation delays and delays at intersections, alongside an increasing error probability.  
The simulation of case 1 assessed the average evacuation time when errors were introduced during an emergency. The results indicated that the average evacuation time per person increased considerably for the first few configurations and then stabilized irrespective of further errors. These findings suggested that a mistake by even one miner during the evacuation could have a significant effect on the overall evacuation performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Simulation Data** & **Configuration 1** & **Configuration 2** & **Configuration 3** & **Configuration 4** & **Configuration 5** & **Configuration 6** \\ \hline Pre-evacuation delay (minutes) & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Delay at intersection & 0 & 0 & 0 & 0 & 0 & 0 \\ (minutes) & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Probability of error (\%) & 0 & 20 & 40 & 60 & 80 & 100 \\ \hline Speed (constant value) & 1.48 & 1.48 & 1.48 & 1.48 & 1.48 & 1.48 \\ \hline Speed (m/s) & (0.9, 1.48) & (0.9, 1.49) & (0.9, 1.50) & (0.9, 1.51) & (0.9, 1.52) & (0.9, 1.48) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for case 1 of a chaotic scenario.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Simulation Data** & **Configuration 1** & **Configuration 2** & **Configuration 3** & **Configuration 4** & **Configuration 5** & **Configuration 6** \\ \hline Pre-evacuation delay (minutes) & (0, 0, 0) & (0, 1, 1) & (0, 1, 2) & (0, 1, 3) & (0, 1, 4) & (0, 1, 5) \\ (triangular distribution) & (0, 0, 0) & (0, 1, 1) & (0, 1, 2) & (0, 1, 3) & (0, 1, 4) & (0, 1, 5) \\ \hline Probability of error (\%) & 0 & 20 & 40 & 60 & 80 & 100 \\ \hline Speed (constant value) & 1.48 & 1.48 & 1.48 & 1.48 & 1.48 & 1.48 \\ (m/s) & (0.9, 1.48) & (0.9, 1.49) & (0.9, 1.50) & (0.9, 1.51) & (0.9, 1.52) & (0.9, 1.48) \\Additionally, there was a significant increase in evacuation time when miners had varying stamina levels (Figure 7).  
The simulation of case 2 aimed to examine the effects of pre-evacuation delay, intersection delay, and increased error probability on the evacuation time. The results, presented in Figure 8, demonstrated a linear increase in the average evacuation time as the pre-evacuation delay, intersection delay, and error percentage increased. This trend was observed in simulations with both uniform and varied stamina levels. However, different stamina levels led to a significant spike in the average evacuation time for each configuration. Therefore, delays in evacuation decisions, increased intersection delays, and the introduction of errors during evacuation could significantly impact the evacuation performance. Additionally, the stamina of the miners also influenced the process.
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 3 Implementation of Modeling Methods
### Adaptive Second-Order Learning Algorithm  
Currently, first-order optimization algorithms such as the error back-propagation (EBP) algorithm in [33] are widely used for parameter learning, but EBP tends to fall into the local minimum when dealing with large-scale computing problems. In order to improve the system performance, in this paper, we use the improved second-order LM algorithm proposed in [34] to implement parameter updates, combining it with the adaptive learning rate in [20], which can further speed up the convergence rate.  
Figure 5: Schematic diagram of neuronal merger(A and B are overlapping neurons, and C is the merged neuron).  
```
Input: The input data in the training set are generally four inputs. Output: Single output datum in training set.
1. Set a parameter for the number of clusters: ma_addth, e_min, den_th, and S_th
2. Using k-means clustering Formula (2) to initialize the network center
3. for p = 1 to \(n\) do
4. Calculate the error of the sample according to (13)
5. Update the parameters of the current network according to (38)
6. for k = 1 to \(n\) do
7. Calculate the error of the sample according to (13)
8. Calculate the RMSE according to (14)
9. end
10. if mod(_p_5) = 0
11. if RMSE(_k_) > RMSE(_k_-1) and md_ind < md_addth
12. Add a new neuron according to (16)
13. end
14. Calculate neuron impact intensity and density potential energy according to (18), (21)
15. if AS < e_min
16. Update width according to (20)
17. end if
18. if den_pot > den_th
19. Update width according to (23)
20. end if
21. Calculate the Similarity index according to (27)-(31)
22. if S > S_th
23. Merge related neurons according to (34)-(37)
24. end if
25. end if
```  
**Algorithm 1**Pseudocodes for constructing SOFNN-CA network  
Figure 6: The flowchart of SOFNN-CA.
###### Abstract  
As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering \"temporal-spatial-periodic\" characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.  
urban big data; traffic flow prediction; tensor CP decomposition +
Footnote †: journal: Applied Sciences
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 2 Reliability Model of a Linear Transformer Driver (LTD) System
### System Reliability Model
Thus, Equation (3) should be modified as:  
\[R\Big{(}m_{ijs}\big{)}=R\Big{(}T\Big{(}m_{ijs}\big{)}\Big{)}\prod_{1\leq t \leq 20}R\Big{(}u_{ijst}\big{)} \tag{8}\]  
In summary, the system reliability can be calculated by Equations (1) to (8) except (3). The remaining modules reliability \(RS^{k}\big{(}m_{ijs}\big{)}\) is the core to solve the problem, which is directly related to the load-sharing rule that we will discuss in the subsequent section.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 5 Numerical Results
### Physical Layer Cooperative Communication Performance
This is due to the fact that the relay node amplifies not only the useful part of the signal, but also the noise component related to the first hop link. So, even though proper filtering may be applied, a residual noise signal is transmitted over the second hop path, lowering the quality of the signal received by the destination node and thus reducing the detection reliability. Regarding DF, it is interesting to note that best performance are achieved in the case B where the relay is placed at half way from source and destination. So, the quality of both hops is good and balanced, leading the detection to be reliably performed by both relay and destination nodes. Figure 5 describes the performance of collaborative communication that considers the detection performed over a unique signal copy received from the relay. This is sufficient to achieve higher reliability than in the case where direct communication between source and destination nodes is performed. In Figure 6, we instead report the BER performance achieved by performing MRC at the destination, exploiting the signal coming from the source and that one coming from the relay node. Typically, MRC-like mechanisms are implemented in the spatial domain considering a RAKE receiver equipped with multiple antennas, each one collecting a different copy of the received signal. In our case, we reasonably assume that, since the direct and secondary paths have different length, the signal received from the source and the relay arrive separated in time. So, we realize a MRC in the time domain where the different copies of the signal are combined after phase synchronization realized via software. In order to realize MRC, we assume channel state information (CSI) as available at the destination, with combining coefficients being  
Figure 5: BER performance of cooperative communication with single detection. calculated as in [61]. As expected, MRC leads to a more reliable detection, allowing AF to reduce the performance gap with DF.
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology  
### UBCSAND2 Constitutive Model  
[PERSON] and co-workers [3; 18; 19] developed a simple constitutive model called UBCSAND to analyze soil liquefaction problems. The UBCSAND stress-strain model modifies the Mohr-Coulomb model to capture the plastic strains that occur at all stages of loading. Based on that, the UBCSAND2 model has been continuously developed by [PERSON] [8; 20] to better model the observed behavior of sand and to account for the effects of principal stress rotation (PSR) or initial horizontal stress states \(K_{0}\) (\(=\sigma^{\prime}{}_{\rm x0}/\sigma^{\prime}{}_{\rm yy}\)), and plastic unloading. The formulation of the model is briefly summarized below.  
In the UBCSAND2 model, the fluid (including air and water) in the pores of the sand is the constraint in the skeleton if drainage is restricted. This leads to the generation of pore pressure or liquefaction. Upon providing input parameters of the soil skeleton, drainage conditions, and bulk modulus of the pore fluid (\(B_{\rm f}\)), the seismic behavior of sand can be predicted [3; 8]. Thus, UBCSAND2 is considered to be a fully coupled effective stress analysis.