esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Rqi  
Table 1 shows the precision, recall, and F1-score when the bug reports in the SDS are summarized, and Table 2 shows the precision, recall, and F1-score when the bug reports in the ADS are summarized.  
Our method outperforms BugSum [5] by approximately 2% in terms of the precision, recall, and F1 score on the SDS. Our method also outperforms the ADS by approximately  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** \\ \hline BugSum & 0.501 & 0.429 & 0.442 \\ Proposed Method & 0.525 & 0.449 & 0.463 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quality comparison of summarizing bug reports in the SDS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** \\ \hline BugSum & 0.506 & 0.507 & 0.475 \\ Proposed Method & 0.534 & 0.535 & 0.502 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quality comparison of summarizing bug reports in the ADS.  
3%. When we calculate \(p\)-value from a \(t\)-test on the SDS, the proposed method is not statistically significant compared to BugSum (\(p>0.05\)). When we calculate \(p\)-value from a \(t\)-test on the ADS, the proposed method is statistically significant compared to BugSum (\(p<0.05\)). This means that, if we consider two datasets together, our proposed method will show its statistical significance, because of the increased size of samples; therefore, we interpret these results as indicating that additional factors, sentence-to-sentence cohesion, and topic association are useful.  
Since the scores in the tables are around 0.5, one might think that a purely random coin-flipping approach would yield similar results; however, we remind that approximately 25% of the sentences in a bug report are included in GSS (see Section 4.2) and 30% of the sentences are selected automatically by both BugSum and our proposed method (see Section 3.2.6). We simulated the random cases. Our simulator randomly selected 25 out of 100 numbers as GSS, and randomly selected 30 out of 100 numbers again. We calculated precision, recall, and F1 scores from the result. We repeated the simulation 1000 times and averaged precision, recall, and F1 scores. The averaged precision score is 0.245, the averaged recall score is 0.294, and the averaged F1 score is 0.267. Both BugSum and our proposed method yielded much higher scores than the random selection.  
Furthermore, we measured the performance improvement of the proposed method by using ROUGE-1. Table 3 shows the ROUGE-1 scores of BugSum and our method.  
Our method yields 3% higher ROUGE-1 score than that of BugSum for the SDS and 17% higher score for the ADS, which clearly shows that our method outperforms BugSum in terms of the ROUGE-1 score.  
**RQ1.** _Our proposed method of using sentence significance factors yields higher F1-score than that of BugSum by 2.1% on the SDS and 2.7% on the ADS. Our method also yields higher ROUGE-1 score than that of BugSum by 3% on the SDS and 17% on the ADS._
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 7 Conclusions and Future Researches
Further comparison with the detour scheme proposed in reference [33] shows a 10.1% reduction in delivery costs, highlighting the value of the detour scheme in the research of TDVRPD-VRZ-NFZ in this study. The work of this paper provides a solid foundation for future research. Future studies will focus on the following aspects: (1) considering simultaneous collection, customer satisfaction, and multi-drone systems on vehicles; and (2) designing algorithms with better performance. Conceptualization, S.W. ; methodology, S.W. software, S.W. ; formal analysis, S.W. and X.R. ; resources, H.F.; writing--original draft preparation, S.W. and X.D. ; writing--review and editing, H.F. and X.R. ; visualization, S.W. ; project administration, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript. Funding Funding was provided by the Key Projects for Later-stage Funding of National Social Science Fund of China (23 FGLA010) and the Special Project of National Emergency Management System Construction of the National Social Science Fund of China (20 VYJ024). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns. Acknowledgments: The authors gratefully acknowledge the reviewers for their comments to improve the quality of this paper and would also like to thank the editors for their help with this paper. Conflicts of Interest: The authors declare no conflicts of interest.
## 4 Results and Analysis
### Verification of the Numerical Simulation Approach
#### 4.2.2 The Distribution and Evolution Characteristics of Acoustic Pressure  
The distribution and evolution characteristics of acoustic pressure are presented in Figure 9. Similarly, the acoustic pressure is also less than 0 basically. The acoustic pressure shows a decreasing trend from inlet to outlet of impeller, the minimum value of acoustic pressure is distributed near the volute tongue, the main noise region corresponding to the main acoustic source region. Moreover, the evolution trend of acoustic pressure near the tongue is similar to that of acoustic source. When the blade is located at the tongue, the minimum acoustic pressure region focuses around the tongue (Time step = 10). The acoustic pressure near the tongue increases significantly with blade away from the tongue (Time step = 20). Then the minimum acoustic pressure region refocuses near the tongue due to another blade closing to the tongue (Time step = 30).  
Figure 8: The STD distribution characteristics of different acoustic source components.  
Figure 7: The standard deviation (STD) distribution characteristics of overall acoustic source.  
Figure 9: The distribution and evolution characteristics of acoustic pressure.
## 4 Results and Discussion
### Discussion
#### 4.4.1 Empirical Findings and Behavioral Insights

Table 9: Cross-stock generalization performance: training on AAPL, testing on MSFT. and inventory interventions, simulating real-world market uncertainty such as competitor price cuts or temporary policy changes. Without this module, the model's adaptability diminished, leading to increased policy volatility and overreaction to minor behavioral disturbances. The profit-backtracking module was especially effective in desynchronized sequences, in which platform actions such as pre-event advertisements precede actual revenue realization by several days. By leveraging multi-step discounted returns, the module allowed the early incorporation of anticipated profits, thus enhancing foresight and buffering capacity in policy generation. The strategy stabilizer played a critical role under high-frequency policy update constraints, as operational systems often limit the number of adjustments per day. This module applied weighted reinforcement from historical behavior to smooth the current policy output, effectively controlling fluctuations and enhancing the operational feasibility and responsiveness of platform strategies.
# Pressure Study on Pipe Transportation Associated with Cement Coal Gangue Fly-Ash Backfill Slurry
## 3 Computational Models  
The mathematical model mainly consists of mass, momentum and energy conservation equations in partial differential forms with associated boundary conditions. The CFD code FLUENT was used  
Figure 4: Geometry of the simulated pipe modes: (**a**) straight pipe; (**b**) 90\({}^{\circ}\) elbow pipe.  
to estimate the pressure drop phenomenon in the slurry pipeline. In FLUENT, the Herschel-Bulkley model [25; 26] was modified to represent the Bingham plastic model.  
The CGFB slurry is mixed with multiple components including coal gangue, fly ash, cement and water as a Bingham plastic body under a high solid concentration of 78.05% as a single substance [13; 25; 27]. The slurry density is considered as the constant of 2000 kg/m\({}^{3}\). The slurry viscosity was defined with the Herschel-Bulkley model. In the pipe transportation, the slurry move forwards with the plunger shape with higher yield stress and larger slurry relative viscosity like the entire [28]. We used the figure of 1.71 Pa-s as the consistency index and Figure 1 as the power-law index while setting the yield stress threshold as 78.91 Pa. The viscous-laminar model was applied to the high solid consideration of CGFB slurry to determine the flow layer characteristics of the mixture in the pipe.  
The boundary conditions, namely velocity inlet, pressure outflow and no-slip conditions, were applied to the fluid flow domain. The inlet boundary condition was applied to the fluid flow at the velocities of 1.4 m/s, 1.6 m/s, 1.8 m/s, 2.0 m/s and 2.2 m/s, respectively and the volume fraction tended to generate a stabilized flow along the length of the pipe. The outlet boundary condition was applied at te outlet section. In order to simplify the analysis, the export pressure was provided with 0 Pa at the outlet section. The wall was treated as stationary with no slip. The average roughness height was calculated using water flow data of 0.5 that was mentioned in FLUENT for a uniform pipe surface roughness [21; 29].  
According to the actual delivery of the slurry, the accuracy of the simulation is set as 1 \(\times\) 10\({}^{-4}\); the number of iterations is set as 600. The data processing results mainly include the pressure loss contour of both the straight pipe and elbow pipe, the velocity distribution contour and velocity distribution profile.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 2 Methodology
### Problem Formulation
#### 2.1.1 The Principle of Channelization  
Wideband digital receivers need to consider a high sampling rate and a high processing rate that matches the sampling rate. However, digital signal processing devices' processing rate always lags behind the analog-to-digital converter's sampling rate. The channelization method uses a digital filter bank to convert the sampled high-speed data into a baseband signal and decimate it simultaneously, reducing the data rate so that the signal can be real-time analyzed and processed by the signal processor [10].  
Under ideal conditions, the monitoring frequency band can be divided into \(K\) channels. The filter banks for channelization is shown in Figure 1, where \(h_{k}(n)(k=0,1,\cdots,K-1)\) is the band pass filter.  
When the monitoring frequency band is evenly divided into \(K\) channels, the bandwidth of the filter bank output signal \(y_{k}(m)\) is \(2\pi/K\). According to the Nyquist sampling theorem, the spectral structure of \(y_{k}(m)\) will not change even though \(y_{k}(m)\) is decimated by \(K\) times. A simple implementation of uniform channelization for the complex signal is shown in Figure 2[9], where the number of channels is \(K\), the center frequency of each channel is \(\omega_{k}(k=0,1,\cdots,K-1)\), and the decimation factor is \(D,D\leq K\).  
Ideally, the channel bandwidth is \(2\pi/K\), and the frequency estimation accuracy is \(\pm\pi/K\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Meaning** & **Symbol** & **Meaning** \\ \hline \(x(n)\) & Input sampling sequence & \(f_{s}\) & System sampling rate \\ \(h(n)\) & Prototype low-pass filter system function & \(f_{ch}\) & Channel sampling rate after downsampling \\ \(h_{k}(n)\) & The \(k_{th}\) channel band-pass filter & \(K\) & Channel number \\ \(y_{k}(m)\) & The output sampling sequence of channel \(k\) & \(D\) & Downsampling decimation factor \\ \(\omega_{k}\) & The center frequency of the \(k_{th}\) channel & \(\delta\) & Channel overlap factor for channel division \\ \(\Delta\phi_{k}[m]\) & The phase difference between the \(m\) and \(m-1\) & \(\hat{\alpha}_{k}^{i}\) & The estimated angular frequency of the \(m\) \\  & sampling point in channel \(k\) & \(\hat{\alpha}_{k}^{i}\) & sampling point in channel \(k\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison table of symbols and their meanings.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
###### Abstract  
This research presents a novel method to assess inter-chain message relay time in the Cosmos blockchain network, which employs the Inter-Blockchain Communication (IBC) protocol for blockchain interoperability. While inter-chain transactions in Cosmos lag behind intra-chain transactions, this research conducts a thorough performance evaluation, emphasizing message relay time across diverse Cosmos chains. The results show a strong association between inter-chain transaction frequency and overall transaction processing speed (TPS), underscoring the inherent trade-off between scalability and compatibility in inter-chain communication protocols. A thorough understanding of inter-chain transaction performance is essential for advancing interoperability and improving cross-blockchain network designs. As a result, this research improves our understanding of Cosmos' functionality and provides insightful recommendations for boosting the effectiveness and scalability of inter-chain communication. The study also sheds light on message delivery patterns in the Cosmos ecosystem, showing that IBC message relay time has an average duration of 55.448 s and is distributed lognormally. Notably, the time between the commitment of the IBC RecvPacket transaction and the commitment of the IBC Acknowledgement transaction (R to A time) considerably impacts the effectiveness of message transmission, which causes delays in the IBC message relay process.  
blockchain; Cosmos; Inter-Blockchain Communication (IBC) protocol 2023
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 3 Methodology and Proposed Approach  
This work presents a reference model for shared mobility that enables the data collection, processing, management, and monitoring of individuals to ensure the safety of the riders. The system must monitor and manage vehicles (cars, bikes, motorbikes, scooters, etc.). The proposed framework transforms the physical shared mobility environment into a digital form to enable the system's processing, monitoring, and management features. The focus is to automate the safety monitoring features, where the system identifies vehicle accidents, violations, and misuse.  
An adequate design of the shared mobility framework must achieve the following objectives:  
1. Collect the required data representing the physical shared mobility environment, including readings from the accelerometer, gyroscope, GPS, ultrasonic, and video of the surrounding environment. The design shall accommodate the addition of new sensors within the environment.
2. Transform the collected data to make meaningful insights related to the safety of the riders.
3. Expose functionalities for the riders and administrators to view the driving and safety behavior of the shared mobility. Those functionalities must have a modular design for future extensions to the framework.
4. Automate the system's decision component to maximize the riders' safety to avoid and identify accidents and misuse of the vehicles.
5. Locate and track vehicles.  
The shared mobility layered reference model is presented in Figure 1, where the top layer depends on the layer below it to meet the design objectives. A detailed description of each layer and its deployment is presented in the subsections.
## 2 Materials and Methods
### Overview of Selected ML Algorithms  
Over the last three decades, several ML algorithms have been developed, and it is confusing which algorithm to adopt. Each has benefits and drawbacks and was designed for a specific application. Recently, the CatBoost algorithm has caught widespread attention due to its proven excellent performance across various regression and classification tasks. Nonetheless, its application to RCDBs is quite limited in comparison to other tree-based algorithms, as evidenced by the literature. Thus, CatBoost was adopted as the primary algorithm in this research to build a data-driven predictive model for the SS of RCDBs. Three well-known ML algorithms, such as Random Forest, Extra Trees, and AdaBoost, were also adopted for comparison with the CatBoost model. The predictive models were built and coded using Colab Notebooks environment with Python 3 and _scikit-learn_ library. The selected algorithms are briefly described in following subsections.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & **Feature** & **Type** & **Unit** & **Min** & **Max** & **Mean** & **SD** & **Skew** & **Kurtosis** \\ \hline Concrete property & \(f_{c}^{\prime}\) & Input & MPa & 11.30 & 120.10 & 41.67 & 21.40 & 0.88 & \(-\)0.14 \\ \hline  & \(b\) & Input & mm & 51.00 & 600.00 & 172.89 & 85.24 & 1.45 & 2.58 \\  & \(l_{e}\) & Input & mm & 300.00 & 6400.00 & 1623.63 & 855.16 & 1.55 & 3.91 \\  & \(h\) & Input & mm & 152.00 & 1829.00 & 522.30 & 252.32 & 1.97 & 5.32 \\ Geometric dimension & \(a\) & Input & mm & 102.00 & 2625.00 & 577.50 & 402.52 & 2.36 & 7.04 \\  & \(d\) & Input & mm & 132.00 & 1778.00 & 463.67 & 233.02 & 1.97 & 5.35 \\  & \(l_{0}/h\) & Input & - & 0.91 & 5.85 & 3.24 & 1.17 & 0.20 & \(-\)0.49 \\  & \(a/d\) & Input & - & 0.22 & 2.74 & 1.29 & 0.58 & 0.49 & \(-\)0.47 \\ \hline Longitudinal & \(\rho_{l}\) & Input & \% & 0.00 & 11.27 & 1.89 & 1.09 & 1.59 & 7.27 \\ reinforcement & \(f_{yl}\) & Input & MPa & 0.00 & 1330.00 & 455.25 & 150.00 & 1.54 & 7.53 \\ \hline Horizontal web & \(\rho_{h}\) & Input & \% & 0.00 & 3.17 & 0.17 & 0.42 & 3.86 & 17.60 \\ reinforcement & \(s_{h}\) & Input & mm & 0.00 & 801.00 & 46.57 & 99.87 & 3.11 & 11.62 \\  & \(f_{yh}\) & Input & MPa & 0.00 & 860.00 & 127.33 & 211.46 & 1.28 & 0.26 \\ \hline Vertical web & \(\rho_{v}\) & Input & \% & 0.00 & 2.86 & 0.27 & 0.41 & 2.51 & 8.65 \\ reinforcement & \(s_{v}\) & Input & mm & 0.00 & 457.00 & 87.97 & 108.11 & 1.12 & 0.33 \\  & \(f_{yv}\) & Input & MPa & 0.00 & 1051.00 & 224.69 & 232.69 & 0.47 & \(-\)0.65 \\ \hline Shear strength & \(V_{u},Exp.\) & Output & kN & 21.00 & 1984.50 & 411.15 & 332.60 & 1.98 & 4.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the selected features incorporated in the database.  
Figure 3: Statistical distributions for all features included in the database. The inputs are indicated by a blue color, while the output is indicated by a green color.
## Materials and Methods
### Methods
#### 2.2.1 Outline of the Proposed Automatic Classification  
To build the multistage classification described in the step 3 of Figure 3, five phases were conceived. Phases 1 to 4 are related to the construction of the classification model, while phase 5 is related to the development of a GUI that integrates the steps presented in Figure 3. Figure 4 shows the required phases to generate a classification model and build a karyogram. Phase 1. Classifiers Training  
During this phase, a set \(M\) is generated, whose elements are the _classification models_, \(m\), that were constructed to classify the chromosome images. \(m_{n}\) is an element of the set \(M\), that is \(m\in M\). In this sense, \(m\) is composed of features, algorithms and architectures. \(m_{n}\) is defined as \(m=(c_{i},a_{j},q_{k})\), where \(c_{i}\) represents the features, \(a_{j}\) the algorithms, and \(q_{k}\) the architectures. _Features_. The elements of the set \(C\) are the extracted features from the segmented chromosomes images. In this way, \(c_{i}\) is a subset of the set \(C\), where \(i\) is the index of the current subset. In Table 1, the features used in each element of the \(C\) set are described. Figure 4: Schematic representation of the required phases to generate a model to classify G-band chromosome images and build a karyogram. _Algorithms_. The set of classification algorithms \(A\), is composed of 51 algorithms found in the _Weka_ (Waikato Environment for Knowledge Analysis) platform. Hence, \(a_{j}\) represents the subset of algorithms, where \(a_{j}\subset A\), and \(j\) is the current algorithm. Table 2 summarizes the algorithms from set \(a_{1}\). Table 3 summarizes the top rated algorithms \(a_{2}\) obtained at the end of phase 1. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Set** & **Description** & **Normalized** \\ \hline \(c_{1}\) & 32 intensity values along medial axis & NO \\ \hline \(c_{2}\) & 32 intensity values along medial axis & YES \\ \hline \(c_{3}\) & \(\begin{array}{l}\text{Perimeter}\\ \text{Area}\\ \text{Medial axis length}\\ \text{Intensity levels along 64 traversal lines touching the medial}\\ \text{axis}\\ \text{Length of 64 traversal lines touching the medial axis}\\ \end{array}\) & YES \\ \hline \(c_{4}\) & \(\begin{array}{l}\text{Perimeter}\\ \text{Area}\\ \text{Medial axis length}\\ \text{Medial axis length}\\ \end{array}\) & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Subset of features \(\{c_{1},\ c_{2},\ c_{3},\ c_{4}\}\subseteq C\).
# UAV Communication Recovery under Meteorological Conditions
## 3 Methods
### UAV Coverage Radius with Elevation Angle under Different Weather Conditions
3.2 Effects of \(R_{\rm rain}\), \(R_{\rm fog}\), and \(R_{\rm snow}\) on the Optimal Height of the UAV  
The relationships between the UAV's optimal height and the rainfall rate, liquid water density, and snowfall rate in the four regions are illustrated in Figure 12, Figure 13, and Figure 14, respectively. In these figures, each discrete marker point represents the optimal height of the UAV calculated based on the respective rainfall rate, liquid water density, or snowfall rate. The solid and dashed lines indicate the fitting curves for the frequencies of 28 GHz and 71 GHz, respectively. The extents of the decrease in the optimal height of the UAV due to increases in rainfall rate, liquid water density, and snowfall rate are summarized in Table 9. It can be seen from the three figures (Figures 12-14) and Table 9 that when the frequency is 28 GHz, the decreases in the optimal heights of the UAV in the four regions due to rain, fog, and snow are over 15 m, 0.6 m, and 0.1 m, respectively. When the frequency is 71 GHz, the decreases in the optimal height of the UAV in the four regions due to rain, fog,