esa-sceva/satcom-chunk-collection huggingface dataset:
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### Performance Metrics
#### 3.4.7 Expected Transmission Count (ETX)  
In the context of wireless networks, the Expected Transmission Count (ETX) is a metric that refers to the number of transmissions required to successfully deliver a packet from a source node (i.e., sensor node) to a destination node (i.e., sink node). It is commonly used to evaluate and optimize the reliability and efficiency of communication links in WBANs. The ETX metric can be calculated based on the packet delivery ratio (PDR) [68], which provides information on the reliability of a link. The intuition behind ETX is that a higher PDR indicates a more reliable link, while a lower PDR suggests a less reliable link. The formula for calculating ETX is as follows:  
\[ETX=\frac{1}{PDR}. \tag{6}\]  
In WBANs, where energy efficiency is crucial due to the limited battery resources of sensor nodes, ETX serves as an important metric to evaluate link quality and optimize communication performance. It helps to select more reliable links to transmit critical data, minimize the number of dropped packets and retransmissions, and conserve energy. By monitoring and evaluating ETX values for different links in a WBAN, network administrators or routing protocols can make informed decisions about selecting routes or next-hop nodes with lower ETX values, indicating more reliable and efficient communication links. This ultimately leads to improved packet delivery, reduced latency, and better overall network performance.
# Development of a Deep Neural Network Model for the
### Engineering Context  
The relocation of deep mining-induced earthquakes, particularly those associated with coal mining operations, represent a critical area of study within the field of geophysics, and investigations into such a topic are currently in urgent demand due to the frequency of mining-related seismic disasters [17; 18; 19; 20]. Also known as rock bursts [21], mining-induced seismicity is a phenomenon frequently observed in deep coal-mining activities, where the sudden movement of rock masses that resembles earthquake events occur when the accumulated stress within the rock layers is suddenly released, often triggered by the excavation processes inherent to mining operations [22]. Unlike the relatively predictable microseismicity induced by hydraulic fracturing, rock bursts are characterized by their sudden onset and potentially catastrophic impacts on mine safety and operations [23].  
Hydraulic fracturing deliberately induces microseismicity to fracture rock formations and facilitate the extraction of oil or gas. In this context, the induced seismicity is a direct consequence of fluid injection under high pressure, leading to predictable and manageable microseismic events. These microseismic events are generally of low magnitude, and their monitoring helps in optimizing the fracturing process and assessing its effectiveness [24].  
In contrast to hydraulic fracturing, where induced microseismic events are often a controlled and monitored aspect of the extraction process, the seismicity associated with coal mining, especially in the form of rock bursts, poses significant challenges for the safety of mining operations [22], particularly when the mining depth goes into the deep subsurface. The seismicity induced by deep coal-mining operations, particularly through the mechanism of rock bursts, is inherently linked to the removal of material from the Earth, leading to an unbalancing of the stress distribution in the surrounding rock [23]. The abrupt release of this stress can result in violent and unpredictable seismic events, posing significant risks to mine infrastructure and personnel. The complex interplay among geological conditions, mining depth, and the method of coal extraction adds layers of complexity to predicting and mitigating rock bursts [21]. This unpredictability and potential for substantial impact differentiate rock burst phenomena from the more controlled seismic events associated with hydraulic fracturing.  
The relocation and detailed analysis of seismic events caused by deep mining activities, especially rock bursts, are thus crucial for developing strategies to mitigate these risks [25; 26]. By developing seismic inversion techniques that is applicable for deep subsurface mining, researchers can gain a better understanding of the stress conditions and geological factors contributing to these events [27; 28]. This knowledge not only aids in improving the safety and efficiency of mining operations but also contributes to our broader understanding of induced seismicity and its various manifestations across different industrial activities.
## Appendix B Pseudocodes
### Pseudocode for Training the Other Models  
Algorithm 2 shows the steps for training the other models for selected offer or outcome predictions using 10-fold cross-validation to optimize the models' parameters.  
Both Algorithms A1 and A2 work the same way for selected offer and outcome predictions. To train the model for a specific task and scenario, it is important to leave only the features we want to use from the original dataset and define the output class column correctly.  
```
Input: Dataset \(D\), model \(M\) Output: Trained model \(M^{*}\)
1 Drop the redundant columns from \(D\), depending on the scenario;
2 Define \(y\), depending on the scenario (selected number of chasers or outcome);
3 Split \(D\) into \(D_{train}\) and \(D_{test}\) in a 80:20 ratio;
4 Optional: Oversample the \(D_{train}\) using SMOTE;
5 Run 10-fold cross-validation on the \(D_{train}\) dataset to obtain the optimal \(M\) parameters \(\theta\);
6 Train \(M\) on \(D_{train}\) using \(\theta\);
7 Evaluate \(M^{*}\) on \(D_{test}\);
8 return\(M^{*}\)
```  
**Algorithm A2** Training the other models for the selected offer or output prediction.
## 3 Data Collection, Preprocessing and Methodology
### Used Hardware and Software  
Experiments, calculations, and visualization were performed on a machine with an Intel Core i7 at 2.6 GHz and 16 GB of RAM, which represents acceptable computational requirements for the used data. The only exception is deep learning. Its performance can be improved by using a GPU (Nvidia GTX 960) for computation. Using a GPU provides much more effective processing of data. The GPU calculation of the deep learning method provides faster processing, from 6 h for only a CPU-based calculation to 30 min using both CPU and GPU.  
DJI GS PRO, ENVI OneButton, and ArcGIS software were used for the following: DJI GS PRO (on iOS, version 2.0.13) was used to plan and control flights, and ENVI OneButton (version 5.0.0.181) was used to create mosaics from collected images. ArcGIS for Desktop (version 10.6) and ArcGIS PRO (version 2.7) are GIS software used for image classification by unsupervised and supervised methods and for accuracy assessment.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 4 Quantum Key Distribution Categories
### QKD Based on Multiphoton
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Name of Protocol** & **Citation** \\ \hline
1984 & BB84 & [132; 133; 134] \\
1991 & E91 & [135; 136] \\
1992 & BBM92 & [137; 138; 139] \\
1992 & B92 & [140; 141; 142] \\
1999 & SSP & [143] \\
2002 & GG02 & [144; 145] \\
2002 & Ping-Pong & [146] \\
2003 & DPS & [147; 148; 149] \\
2004 & COW & [150] \\
2004 & SARGO4 & [151; 152; 153] \\
2004 & Coherent State Heterodyne & [154; 155] \\
2005 & LM05 & [156; 157] \\
2013 & S13 & [105] \\
2014 & RRDPS & [158] \\
2015 & Multiphoton & [159; 160] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: QKD Protocol based on Heisenberg’s Uncertainty Principles.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 4 Results  
In this section the performance of the quality controlled CYGNSS wind speed data set is assessed. Two identical Neural Networks, one for each wind speed region discussed in Section 3 are trained. The first NN is applied to CYGNSS winds between 0-8 m/s and the second NN is applied to CYGNSS winds \(>\)8 m/s. This slight shift between the training and testing wind speed regimes is to improve the net performance of the filter, as the first NN will be biased towards the lower winds where the highest density of samples are present and the second NN will again be biased towards the lower winds in its range (10-35 m/s). The resulting quality controlled CYGNSS wind speed dataset is shown in Figure 6.  
Comparing Figures 1 and 6 demonstrates the effectiveness of the filter. The large cluster of high CYGNSS winds at low MERRA-2 winds has been removed by this filter (compare the red box region  
Figure 5: Family of PD and FAR curves for different definitions of good and outlier samples. The dark-light blue curves represent PD and the orange-red curves represent FAR.  
Figure 6: CYGNSS retrieved wind dataset after quality control. The outliers in the red box have been mostly eliminated here relative to Figure 1.  
between the two figures). Also, the CYGNSS samples are now evenly distributed along the GMF line (solid black line) unlike in the original dataset. Finally, a significant reduction of scatter in the dataset can be observed. The performance of this proposed QC filter is assessed in the following subsections based on the error statistics Mean Difference (MD), Root Mean Squared Difference (RMSD) and variance of data. The test dataset consists of all the sample points (\(\sim\)153 million).
## 3 Results  
### Model Validation  
The observed NPP and the NPP simulated by the CASA model were used to evaluate the performance of the NPP simulated by the CLM model. The comparison of the measured NPP and the corresponding NPP simulated by CLM 5.0 is shown in Figure 3. The coefficient correlation R\({}^{2}\) is 0.70, which has passed the 95% level with the F test. Figure 4 shows the spatial patterns of the NPP simulated by the CASA model and by CLM 5.0. It has been found that the NPP simulated by CLM 5.0 agrees with the NPP simulated by the CASA model in most areas of the Tibetan Plateau. Compared to the CASA model, CLM 5.0 simulated the spatial patterns of NPP in detail (Figure 4), although there are some regional differences. The NPP of the TP decreases from southeast to northwest, and the highest NPP areas are located on the southern flank of the Eastern Himalayas and in the high mountains of Sichuan and Tibet. Low-NPP areas are in the highland deserts, including the desert areas of the Qaidam Basin, the desert areas of the Kunlun Mountains, and the desert areas of the Ngai Prefecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental**} & \multicolumn{4}{c}{**Climate Factors**} \\ \cline{2-4}  & **Temperature** & **Precipitation** & **Radiation** \\ \hline Scenario One & T & T & T \\ Scenario Two & C & T & T \\ Scenario Three & T & C & T \\ Scenario Four & T & T & C \\ \hline \hline \end{tabular} Note: T represents parameters that change over time, while C represents parameters that are calculated as multi-year averages.  
\end{table}
Table 1: Experimental design.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network
### Underwater WSNs  
Underwater WSNs are extended terrestrial WSNs that have attracted considerable attention because of their value in obtaining resources that are difficult to transport. Underwater WSNs are deployed in the sea. However, its hostile environment due to the time taken for exploration, expense, and people is difficult to measure [47]. UWSNs and terrestrial WSNs can be compared based on their different environment variable quantities. However, a UWSN has high signal attenuation because underwater communication is acoustic, with frequent synchronization, propagation delays, limited memory, and less bandwidth. These sensor nodes cost more than terrestrial sensors [48; 49]. Some UWSN applications include seismic and tsunami sea life exploration, environmental condition of underwater quality, oil/spills, gas monitoring, agriculture, minefields, pipeline and cables, submarines, natural resources fish farms, flood, volcano, earthquake, coalmine tunnels, and tsunamis [50]. The most critical problem of UWSNs is the energy consumption of sensor nodes, because it is more difficult to recharge or replace battery power. Thus, energy saving to prolong the network's lifetime becomes a crucial issue in many UWSN applications [41]. Efficient routing protocols have been developed for UWSN. Such protocols are constraint depth-based routing (CDBR), which is an extension of (DBR) [51], and constraint energy-efficient depth-based routing (CEEDBR) extended from (EEDBR) [52]. There are four types of UWSN applications: scientific, industrial, military, and security. Monitoring, controlling, and ocean sampling in specific sectors have great barrier reef operations and are among the many uses for UWSNs in the scientific sector. Furthermore, time-driven data are collected by robotic fish to monitor temperature and pressure as well as detect oxygen levels in water [53]. UWSNs' industrial applications have a major influence on commercial activity facilitation. Underwater monitoring applications of oil and gas pipeline are possible with UWSNs. In the study [54], a prototype is designed for monitoring underwater oil and gas pipelines. The technology is created to give an event data-driven model of the health of pipelines that operate across huge territories. Similarly, [PERSON] et al. [55] also built an underwater oil and gas pipeline monitoring system, which is required for the monitoring of an actuator mechanism. The various military and security applications utilize event-driven data models such as performing port and harbor control and monitoring, sea mine identification, border security from unauthorized battleships or submarines, and decentralized situational surveillance, military, and defense applications, which employ a mix of underwater sensors to look for possible threats early [56; 57].
# Diverse Decoding for Abstractive Document Summarization
## 4 Experiment
### Experiment Results
#### 4.2.3 Overall Summarization Performance  
The results in Table 2 show the overall performance evaluation of baseline models and our proposed NASDD model. Firstly, our proposed method exceeded the state-of-the-art neural summarization model (PGN) by over one ROUGE point (+1.82 ROUGE-1, +1.12 ROUGE-2, + 2.54 ROUGE-L). Then, the extractive methods were still tough to beat, but our method achieved competitive performance. The sixth row of Table 2 is NASDD without the proposed selection method, which only uses the DPBS algorithm in decoding and adopts the conventional maximal possibility selection method for the final candidates of DPBS. The results were 39.53 (ROUGE-1), 16.02 (ROUGE-2), and 35.71 (ROUGE-L). This shows that without the selection method, only using the original maximal possibility selection method cannot achieve better, sometimes even worse, results than the baseline.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Rouge**} \\ \cline{2-4}  & **1** & **2** & **L** \\ \hline lead-3 & 40.34 & 17.70 & 36.57 \\ REFRESH & 40.0 & 18.2 & 36.6 \\ SummaRuNNer & 39.6 & 16.2 & 35.3 \\ \hline Graph-Abs & 38.1 & 13.9 & 34.0 \\ PGN & 38.15 & 16.46 & 35.37 \\ NASDD (w/o selection) & 39.03 & 16.02 & 35.71 \\ NASDD & 39.97 & 17.58 & 37.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ROUGE F1 scores on the test set. All of these ROUGE scores have a 95% confidence interval of at most \(\pm\) 0.25 as reported by the official ROUGE script. PGN, Pointer Generator Network; NASDD, Neural Abstractive Summarization with Diverse Decoding.  
Figure 5: ROUGEscores achieved on the validation set with various value combinations of \(\lambda\) and \(\eta\). The two parameters affected the result from different aspects.  
As the DPBS method is to improve the diversity of the beam search method to increase the possibility of generating better sequences, at the same time, worse sequences may also be generated, so the selection method capable of filtering bad candidate sequences becomes necessary. Moreover, the ROUGE evaluation has its own limitation: the evaluation is only performed based on the n-gram overlap between the generated sequences and the ground truth summaries written by a human. This means sequences containing insufficient words appeared in the ground truth summaries and will get very low ROUGE scores even when they are expressing the same idea. The selection method augmented the important words' influence in the selection process, and these words were also highly likely to appear in the ground truth summaries. In summary, the DPBS method can generate better sequences as summaries with the increase of diversity, and the selection method can effectively choose these sequences.
## 7 Conclusions  
Many scholars are intrigued by the development of 360-degree video technology. It is widely used in various sectors such as education, entertainment and economics. Many good attempts have been made over the years to improve 360-degree video streaming. However, due to their high video quality (\(\geq\)6K), such videos have always required a higher bandwidth than typical videos.  
This paper described the streaming architecture of 360-degree video, MPEG-DASH and DASH-OMAF compliant. Modern streaming technologies, such as tile-based streaming, viewport-based streaming and machine learning, are given and discussed in an effort to reduce bandwidth-delay needs and increase the quality of high-resolution content. Following that, this article explores network techniques such as network caching, 5G and 6G networks, and MEC to optimize 360-degree video streaming. Next, the challenges faced when modeling QoE assessment of 360-degree video and the existing QoE assessment methods which include various objective and subjective assessment methods have been discussed to evaluate the QoE of 360-degree video.  
Despite the topic's popularity and extensive research efforts, there are significant research challenges, most notably in the fields of bitrate optimization, encoding schemes, tiling weighting schemes, projection formats, viewport prediction approach, and so on. In terms of delivering relevant knowledge for 360-degree video streaming, standardized projects are already showing a lot of potential. Such problems should be addressed before deployment to enable the utmost user experience.  
Conceptualization, E.S.W. and N.H.A.W.; Data Curation, E.S.W.; Formal Analysis, E.S.W. and N.H.A.W.; Investigation, E.S.W. and N.H.A.W.; Methodology, E.S.W. and N.H.A.W.; Project administration, N.H.A.W.; Resources, E.S.W. and N.H.A.W.; Software, E.S.W.; Supervision, N.H.A.W., F.S. and N.A.; Validation, E.S.W., N.H.A.W., F.S. and N.A.; Visualization, E.S.W., N.H.A.W., F.S. and N.A.; Writing--Original draft preparation, E.S.W.; Writing--review and editing, N.H.A.W., F.S. and N.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Ministry of Education (MOE) through Fundamental Research Grant Scheme (FRGS/1/2021/ICT10/UTM/02/3). We also want to thank the Government of Malaysia which provide MyBrain15 program for sponsoring this work under the self-fund research grant and L0022 from Ministry of Science, Technology and Innovation (MOSTI).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Evaluations
### Experimental Identification of Actuator Parameters  
Before conducting a comparison study of the actuators, their parameters need to be identified to verify the design and to ensure that they are comparable systems. Thus, this study conducted static and dynamic testing to experimentally determine key parameters, such as the natural frequency, damping ratio, and spring constant. In the static testing, the actuator remained unpowered, and a dynamic mechanical analyzer (DMA) was used to measure the stiffness of the actuator's radial springs. In the dynamic testing, the actuator was excited to measure the natural frequency and damping ratio. To find the natural frequency of the system, a sine chirp signal was run through each actuator from 50 to 250 Hz over 10 s and the acceleration response was recorded using an accelerometer, seen in Figure 5a. By taking the Fourier transform of the experimental response data, the natural frequency was determined to be around 129 Hz for the single-electrode actuator and around 134 Hz for the dual-electrode actuator. The half-power method was used on the frequency response plots to estimate the damping ratio. The damping ratios for the single-electrode and dual-electrode ERAs were estimated to be 0.0143 and 0.0141, respectively.  
Figure 5b shows the dynamic mechanical analysis testing setup. The actuators were mounted onto the DMA machine's baseplate to secure them during testing. The DMA machine's driveshaft applied a sinusoidal force input onto the movable mass while measuring its displacement. Using Hooke's Law, the spring constant of the radial beam springs was calculated to be 16.62 kN/m. Although the actuators were fabricated with identical parameter values, they have slightly different natural frequencies and damping ratios. Small discrepancies in the actual and the theoretical design values were attributed to variations in the thickness of the raw materials and material property changes caused by high precision  
Figure 5: (a) Acceleration testing setup used to measure the actuator’s damping ratio; (b) dynamic mechanical analyzer (DMA) testing setup used to measure the actuator’s spring stiffness.  
manufacturing processes, such as the wire-cutting of the slots for the beam springs. Nonetheless, the discrepancies were deemed to be negligible, and the two actuators were considered comparable.