esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 4 Fast Time Simulation Results
### LECMDGU Sector
#### 4.3.2 Environment and Airspace User Cost-Efficiency  
Figure 10 presents the results obtained for the LECMDGU sector. There are specific benefits in this sector, albeit not as substantial as those seen in the LECMZMU and LECMTLU sectors. In this sector, conflict resolution primarily involves level change maneuvers. Consequently, the most significant benefits manifest in the metrics of fuel consumption and \(CO_{2}\) emissions, while the improvements in flown distance and delay are practically negligible. Despite being small, these positive values contribute to the overall benefit of implementing the Ad Hoc separation concept.  
These values are contextualized in relation to the characteristics of the sector. In LECMDGU, the average flown distance is 94.2 NM and the average flight time in the sector is 12.3 min (obtained from NEST data [46]). Therefore, if the total \(LOS\) results (lateral and longitudinal) are analyzed considering both maneuvers together, the following results are obtained:  
* Distance: reduction, on average, of 0.03 NM per \(LOS\) saved, or 0.03% of the total distance travelled in the sector.
* Delay: reduction, on average, of 0.03 min per \(LOS\) saved, or 0.24% of the total flight time in the sector.
* Fuel and \(CO_{2}\): reduction, on average, of 20.11 kg of fuel and 63.35 kg of \(CO_{2}\) emissions.
# Computing Idle Times in Fuzzy Flexible Job Shop Scheduling
## 2 The Fuzzy Flexible Job Shop Problem  
The flexible job shop scheduling problem consists in scheduling a set \(\mathbf{O}\) of operations (also called tasks) in a set \(\mathbf{R}\) of \(m\) resources (also called machines) subject to a set of constraints. Operations are organised in a set, \(\mathbf{J}\), of \(n\) jobs, so operations within a job must be sequentially scheduled. Given an operation, \(o\in\mathbf{O},\chi(o)\) will denote the job in \(\mathbf{J}\) that this operation belongs to, and \(\eta(o)\) will denote the relative position of \(o\) in the job, \(\chi(o)\). Eachoperation, \(o\in\mathbf{O}\), can only be processed in resources from a given subset, \(\mathbf{R}(o)\subseteq\mathbf{R}\), with a processing time, \(p(o,r)\), which is dependent on the resource, \(r\in\mathbf{R}(o)\), where it is executed. Furthermore, the following statements are assumed:  
1. All jobs and resources are available at the first instant of time. 2. Preemption is not allowed; i.e., each operation must be completed without interruption once started. 3. Machines cannot perform more than one operation at a time. 4. Transportation times between resources and setup times are negligible. 5. Operations are assumed to be successfully completed after the first attempt; the scrap rate is zero. 6. The available capacity of resources is known; there are no stoppages, breakdowns, etc. 7. Buffers with infinite capacity are available for all resources. Under this problem formulation, the idle time is the time spent by a resource waiting for an operation to process it. The total idle time in a resource can usually be decomposed into front, back, and core idle times. The front idle time is the time a resource waits before starting the first operation (while other resources are already working), the back idle time is the time between the instant when a resource completes its last operation and the instant the last resource finishes working, and the core idle time refers to the time a resource is idle between operations, that is, once it has finished processing one operation and is waiting for the next operation to start being processed. The objective of this paper is to minimise the total core idle time (that is, the sum of core idle times for all resources) in the fuzzy flexible job shop. In the following subsections, we will describe how to work with fuzzy processing times and how they affect idle time calculations. The notation used throughout the paper is summarised in Table 1.
## 3 Results and Discussion
### Percolation Column Test Results
#### 3.1.2 Release of Arsenic (As) from the Soil: An Up-Flow Percolation Column Test  
The up-flow column percolation test was used to assess the As-contaminated soil (control soil) and stabilized soils created by mixing various stabilizers (AMDS, steel slag, CMDS, and cement) (see Figure 5 for a graphical representation of the results). The initial release of As from the contaminated and stabilized soils occurred early, at a low L/S ratio (0.2 L/S, representing 0.2 years of exposure); this leaching continued to decrease until 5 L/S (representing weathering over 5 year or more). Leaching remained almost constant as time progressed (the cumulative L/S). Similar results were obtained in the eluate concentrations for each of the alkali metals (Na, Mg, K, Ca) and Fe (see Figure S3), as described in Supplementary Text S4. The initial eluate collected by the up-flow percolation column test provided insight into the pore solution composition in the bed of the soil column. A noticeable volume of As was initially released from all of the stabilized soils; this result implies that, after stabilization, the soluble As constituents were flushed from the pores and collected in the eluate (0.2 L/S). The decrease in As release with L/S ratio (or time) supports the findings of previous research.  
This study demonstrates that stabilization can be achieved with an additional 5% of a stabilizer to greatly reduce As release from contaminated soil (see Figure 5 for details). The stabilized soils reduced the initial As leaching from 137.6 ug/L (control) to 43.5 ug/L (AMDS), 87.6 ug/L (steel slag), 42.7 ug/L (CMDS), and 56.1 ug/L (cement) in the initial stage of the percolation test (0.2 L/S), corresponding to approximately 0.2 of a year of exposure. After 5 L/S (corresponding to the first five years of weathering), the As releases from all of the stabilized soils reduced slightly until 10 L/S (10-11 years) and remained almost constant until 50 L/S (51.1-56.8 years) with approximately 10 ug/L or less. This result shows that As leaching into groundwater can be substantially reduced by mixing stabilizers into the soil. However, the results of this study suggest that groundwater near the stabilized soil should be monitored and initially prohibited for use for approximately five years.  
The efficiency at mitigating As release by the different stabilizing agents can be compared using the column effluent. The order of effectiveness of the stabilizers was:  
Figure 5: Eluate concentration curves of the released arsenic from contaminated (control) and stabilized soils as a function of L/S ratio using the column-percolation test.  
steel slag (55.0%) < AMDS (74.3%) < cement (78.1%) < CMDS (81.5%) (see Figure 5). The reduction in As leaching from stabilized soil increased with the calcium content in the stabilizers (i.e., AMDS, CMDS, and cement) (Figure S4).
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 3 POD Estimation
### Calculating Probability of Making Type-I Error  
One of the significant disadvantages of AE testing is the presence of external noise associated with technological processes of the testing structure or with the loading process. The most common sources of noise include friction, fluid leakage, bubbling and cavitation, and various types of electrical and electromagnetic noise. This section provides a methodology for calculating the type-I error probability associated with false defect detection. To calculate the probability of false detection, it is necessary to calculate the probability of the location cluster formation in the absence of a defect. We assume that the noise parameters are constant throughout the entire testing procedure. The initial data for calculation are the probability distribution density of the noise process, the value of the amplitude discrimination threshold _thr_, location parameters, distance between the AE sensors \(L\) and the sound velocity \(c\). The probability \(p\) is estimated from the condition that the samples of the time realization of _noise_(\(t\)) exceed a certain threshold value _thr_:  
\[p_{thr}=P(n(t)>thr). \tag{3}\]  
For the AE event to be located and displayed on the location diagram as an indication, the difference \(\Delta t\) in the recording times of signals must satisfy the inequality \(\Delta t<\frac{L}{c}\), where c is the velocity of AE waves propagation. The probability of locating the noise signal \(p_{loc}\) can be calculated as the probability that the difference in the arrival times of signal recorded by the sensor array channels will be less than \(\Delta t\). For this, [PERSON]'s formula is appropriate. It allows calculating the probability of a false location as:  
\[p_{\Delta N}=(p_{thr}.\Delta t\cdot f_{d})e^{-(p_{thr}.\Delta t\cdot f_{d})}, \tag{4}\]  
where \(f_{d}\) is the sampling rate of the AE signal.  
For uncorrelated noise, the probability of a false indication \(p_{loc}\) can be calculated as \(p_{loc}=(p_{thr})^{2}\). For correlated noise, it is \(p_{loc}=p_{thr}\). According to the selected criterion for defect detection, the probability of making a type-I error is estimated as the probability of the appearance of \(N_{clust}\) indications in the interval \(\Delta L\). To estimate the probability of such an event, it is necessary to set the total measurement time \(T\), since the type-I error increases with the increase of the number of recorded noise samples. The probability of an error \(\alpha\) can be estimated using the Poisson formula as the probability of occurrence of \(N^{clust}\) events with the probability \(p_{loc}\) when the number of trials is \(T/\Delta t\):  
\[\alpha=\frac{\left(\lambda\right)^{N^{clust}}}{\left(N^{clust}\right)!}e^{- \lambda},\lambda=p_{loc}\frac{T}{\Delta t}. \tag{5}\]
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis  
For performance evaluation, GloMoSim (Global Mobile System Simulator) (2.03, UCLA (University of California Los Angeles), Los Angeles, CA, USA, 2008) [3; 42; 35] is used to carry out simulations.
## 5 Performance Evaluation
### Results
#### 5.3.5 The Impact of Decomposition Selection Cost Parameters  
The performance of DcPSM has been experimentally tested with three different sets of values for weighting parameters: (1) \(w_{e}=1.0,w_{p}=0.0,w_{n}=0.0\), (2) \(w_{e}=0.0,w_{p}=1.0,w_{n}=0.0\), and (3) \(w_{e}=0.0,w_{p}=0.0,w_{n}=1.0\). It has been found that the worst case is the third set of values  
Figure 11: Average cost to average revenue for: (**a**) Simple-Small, (**b**) Simple-Large, (**c**) Multiple-Small, and (**d**) Multiple-Large scenarios. The shaded background behind each curve represents the 95% confidence interval on the reported average values.  
Figure 12: Average embedding cost to average revenue for service requests vs. (**a**) the number of nodes and edges; (**b**) the number of paths and edges.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scheme** & **Total Number of Solved Requests** & **AR** & \(\textbf{R}_{\textit{eff}}\) \\ \hline DcPSM & 128,528 & 47.46\% & 0.3629 \\ DSBM & 128,528 & 56.21\% & 1.0775 \\ ILP-A & 59,639 & 100\% & 0.5461 \\ ILP-P & 122,417 & 100\% & 0.5299 \\ \hline \hline \end{tabular}
**AR** is the acceptance ratio, and \(\textbf{R}_{c/r}\) is the ratio of average cost to revenue.  
\end{table}
Table 5: Total average cost to revenue for all solved requests in all long simulation run scenarios.  
when \(w_{n}=1.0\), while the first and the second sets perform well with a small advantage for the first where \(w_{e}=1.0\). To conclude, less number of edges might lead to less number of paths.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 7 Conclusions and Future Scope  
While existing optimistic asynchronous BFT protocols can rapidly operate and possess the robustness of asynchronous consensus in favorable network conditions, they introduce significant latency in adverse network environments due to their fixed path order. To address this issue, we introduce FlexBFT, which primarily focuses on the persistence of back-up mode. This approach allows the protocol to refrain from hastily returning to the fast lane under adverse network conditions, thereby reducing the overhead of constantly switching from the fast to the slow lane in asynchronous network environments. In order to further enhance system efficiency, we adopt a strategy of running bandwidth-intensive and communication-intensive components in parallel, decoupling consensus and downloading in fast mode, and incorporating optimistic output in back-up mode. Our experimental results demonstrate that FlexBFT is a qualified optimistic asynchronous BFT, achieving  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Normal Network**} & \multicolumn{2}{c}{**Challenging Network**} \\ \cline{2-5}  & **FlexBFT** & **BDT** & **FlexBFT** & **BDT** \\ \hline Latency(s) & 0.309 & 0.320 & 0.980 & 1.425 \\ \hline Throughput & 10232 & 9731 & 6819 & 5811 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance of FlexBFT and BDT in the same parameter.  
performance with 31.6% less latency than BDT under network fluctuations. In the future, with the excellent asynchronous consensus component framework of FlexBFT already in place, it becomes even more necessary to open the black box of consensus components for more integrated combinations.  
Conceptualization, A.S. and C.Z.; methodology, A.S. and C.Z.; software, C.Z.; validation, A.S. and C.Z.; formal analysis, A.S.; investigation, C.Z.; resources, A.S.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, A.S. and C.Z.; visualization, C.Z.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript..  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Improving Software Defect Prediction in Noisy Imbalanced Datasets
###### Abstract  
Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by generating the minority class samples is one of the most well-known methods to improving the quality of datasets; however, it often introduces overfitting noise to datasets. To better improve the quality of these datasets, this paper proposes a method called US-PONR, which uses undersampling to remove duplicate samples from version iterations and then uses oversampling through propensity score matching to reduce class imbalance and noise samples in datasets. The effectiveness of this method was validated in a software prediction experiment that involved 24 versions of software data in 11 projects from PROMISE in noisy environments that varied from 0% to 30% noise level. The experiments showed a significant improvement in the quality of datasets pre-processed by US-PONR in noisy imbalanced datasets, especially the noisiest ones, compared with 12 other advanced dataset processing methods. The experiments also demonstrated that the US-PONR method can effectively identify the label noise samples and remove them.  
software defect prediction; class imbalance; undersampling; propensity score matching; oversampling; noise reduction
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 2 Materials and Methods
### Moment Magnitude (Mw) Estimation  
Moment is related to the total energy released during an earthquake event. Moment is proportional to the slip on the fault multiplied by the fault area that slips. The formula is shown below [36]:  
\[M_{o}=\mu\cdot area\cdot slip=\mu\cdot(L\cdot W)\cdot l_{s}, \tag{7}\]  
where \(M_{o}\) is moment seismicity, \(\mu\) is the rigidity parameter or shear modulus of the crust (approximately \(3\times 10^{10}\) N/m), \(L\) is the fault length (m), \(W\) is the fault width (m), and \(l_{s}\) is the slip length (m).  
Moment seismicity can be converted into an earthquake's magnitude standard called moment magnitude using the following formula [36]:  
\[Mw=\frac{2}{3}\cdot\log(M_{o})-6.06. \tag{8}\]
## 3 Results
### Scoping Review
#### 3.2.5 Applicability of Different Technologies  
Various researchers have used technologies to monitor fall risks in construction, focusing on aspects such as the correct use of PPE, fall risk assessment, workers' exposure to hazards, and their awareness of task-related hazards, as shown in Table 2. However, it is important to note that some of these studies have not been applied to real construction sites.  
Several authors have focused on monitoring workers to prevent the risk of falls from height, as this has great potential for practical applications that can assist in monitoring construction sites and potentially improve site safety.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology Used** & **Type of Monitoring** &
\begin{tabular}{c} **Use in a Real** \\ **Work Context** \\ \end{tabular} & **Reference/Literature** \\ \hline \hline \multirow{9}{*}{BIM software} & Yes & [37] \\  & No & [39] \\  & Yes & [40] \\  & Yes & [41] \\  & Identification of construction site risks, & Yes & [42] \\  & not just falls from height. & Yes & [43] \\  & Yes & [44] \\  & Yes & [19] \\  & Yes & [47] \\ \hline \multirow{2}{*}{Assessment of the correct use of safety harnesses.} & No & [38] \\  & No & [45] \\  & No & [46] \\ \hline \multirow{2}{*}{Assessment of the risk of falling from a height when working on a ladder.} & No & [7] \\  & No & [48] \\  & No & [48] \\ \hline \multirow{2}{*}{Assessment of the correct use of safety harnesses when workers are on scaffolding.} & Yes & [55] \\  & Yes & [53] \\  & Yes & [59] \\  & No & [63] \\  & Yes & [65] \\  & Yes & [66] \\  & Yes & [67] \\  & Yes & [68] \\  & Yes & [70] \\ \hline \multirow{2}{*}{Assessment of the correct use of PPE.} & No & [51] \\  & No & [52] \\  & Yes & [58] \\  & No & [61] \\  & Yes & [62] \\  & Yes & [69] \\ \hline \multirow{2}{*}{Assessment of the absence of guardrails.} & No & [54] \\  & No & [56] \\ \hline \multirow{2}{*}{Identification of the risks to which workers may be exposed, based on the construction conditions} & No & [71] \\  & No & [71] \\  & Yes & [72] \\ \hline \multirow{2}{*}{BIM software} & Use of RFID and BIM technology to raise awareness of hazards. & Yes & [37] \\ \hline \multirow{2}{*}{BIM software} & Use of RTLS/GPS and BIM technology to address construction site risks, not just falls from height. & No & [39] \\  & No & Yes & [42] \\ \hline \multirow{2}{*}{Use of BLE and BIM technology to address construction site risks, not just falls from height.} & No & [46] \\  & No & Yes & [19] \\ \hline \multirow{2}{*}{Use of computer vision and BIM technology to address address construction site risks, not just falls from height.} & Yes & [50] \\  & No & Yes & [53] \\ \hline \multirow{2}{*}{Hazard identification using VCS and virtual reality.} & No & [68] \\  & No & [71] \\ \hline \end{tabular}
\end{table}
Table 2: Use of different technologies found in the relevant literature.
## 4 Performance Evaluation
### Supervised Evaluation  
This evaluation is dependent on the corresponding references for the segmented images such that it reflects the matching approach between the segmentation result and the required ground truth [33; 34; 35]. This evaluation uses specific measurements for pixels classified based on their regions, _TN_, _TP_, _FP_, and _FN_, as shown in Figure 4.  
A supervised evaluation uses the following metrics.  
\[Jaccard_{index}=\frac{TP}{TP+FP+FN} \tag{15}\]  
\[F_{Score}=2*\frac{Precision*Recall}{Precision+Recall} \tag{16}\]  
\[Accuracy=\frac{TP+TN}{FN+FP+TP+TN} \tag{17}\]  
\(Jaccard_{index}\) is the measurement of the intersection ratio. \(F_{scores}\) represents the measurement of the probability of the true positivity such that precision uses the computation of \(TP\)/\(TP\) + _FP_ and recall uses the computation _TP_/_TP_ + _FN_. Accuracy is the measurement of the comparison and matching ratio of the pixels from the segmented results and the corresponding references.  
Figure 4: \(TN\) are pixels in True Negative or the background and segmented as the background, _TP_ are pixels in True Positive or the object and segmented as belonging to the object, _FP_ are pixels in False Positive or the background and segmented as belonging to the object, and _FN_ are pixels in False Positive or object and segmented as belonging to the background.
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 5 Experimental Results
### Verifying the Deep Learning-Based Localization Switching Method to Solve Corridor Effect
#### 5.3.1 Evaluation of the Deep Learning-Based Corridor Recognition Method  
Because we consider the corridor recognition problem as the binary classification problem, we collect two types of point-cloud data: the data of corridor area and the data of non-corridor data. Then we preprocess the collected 2D LiDAR images as mentioned in Section 4.2 and split the training/test dataset in a ratio of 9:1. Furthermore, due to small amount of 2D LiDAR data, we apply some data augmentation operations, such as flip and rotation, to enrich training data. As shown in Table 4, compared to the traditional Support Vector Machine (SVM) [24] classifier, the deep learning-based.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Accuracy (\%)** & **Number of Parameters** \\ \hline SVM [25] & 80\% & - \\ InceptionV3-based model & **100\%** & -22 million \\ LeNet-based model & **100\%** & **-1.1 million** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of corridor recognition models.  
Figure 16: The practical corridor environment for experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Trajectory-Tracking** & **Average Speed (m/s)** & **Task Time (s)** &
\begin{tabular}{c} **Standard Deviation** \\ **of Speed (s)** \\ \end{tabular} \\ \hline \hline \multirow{3}{*}{MPC} & **Double-L-shaped path (14.9 m)** & \\  & 0.396 & 40.31 & \(\pm\)0.070 \\ \cline{1-1} \cline{2-3} \cline  
Models have better accuracy results in the test dataset. The LeNet-based model has only about 1.2 million parameters for the model size comparison, while its accuracy is similar to the bigger InceptionV3-based model. This guarantees that our proposed deep-learning-based model can be deployed on AGV and give a reliable and effective performance.