esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### End-to-End Delay Optimization
#### 3.3.1 Data Forwarding Model and Node Transfer Latency  
The limited capacity of the cache is a vital factor that influences network performance. The efficiency of the cache directly affects the reliability and stability of the entire network, and also indirectly affects its survival time. The unreasonable routing protocol always leads to data gathering together at some nodes, which makes their cache overflow. More seriously, they can only receive data from descendant nodes, and can not be forwarded further. This \"invalid operation\" node greatly increases the end-to-end transmission time. On the other hand, if some nodes' cache utilization rates are too high, that is, there are too many data frames in the node, and then its CPU (Central Processing Unit) will spend too much energy on receiving, handling and sending data. If these conditions continue, a certain number of nodes will soon fail, resulting in network vacancies, which seriously undermine the network connectivity, extending the end-to-end delay.  
In Figure 6, S1 to S4 represent the data sources, and R1 to R3 represent the relay nodes. If all the data sources select R2 to do data transmission, then that node will gradually become a bottleneck node. Due to the restrictions of the cache, it must lose some data if the cache utilization rate achieves a certain level.  
This paper calculates the nodes' delay through cache utilization. Let the size of the nodes' cache be S. Assume the data arriving obeys the Markov procedure and that the transmission of relay nodes also obeys the Markov procedure, then we can abstract the transmission model to a M/M/1/S/FCFS model. If the data packet arriving at the nodes can not be placed into the cache pool, these packets will be lost. According to the queuing theory, the nodes' cache can have the desired value, such as:  
\[L=\left\{\begin{array}{ll}S/2&\delta=1\\ \frac{\delta\left[1-(S+1)\delta^{S}+S\delta^{+1}\right]}{(1-\delta^{S+1})(1- \delta)}&\delta\
eq 1\end{array}\right. \tag{12}\]  
Here, \(\delta=S_{r}/D_{p}\cdot T_{p}\cdot N_{task}\).  
The remaining cache \(R\) is \(R=S-L\). The remaining cache has a significant impact on the loss of packets. A node's packet loss ratio is:  
\[p=\left\{\begin{array}{ll}1/(R+1)&\delta=1\\ \frac{\delta^{R}(1-\delta)}{1-\delta^{R+1}}&\delta\
eq 1\end{array}\right. \tag{13}\]  
Combining \(L\) with \(P\), the resulting data packet's average waiting time is:  
\[T=\frac{L}{S_{r}(1-p)}-\frac{1}{D_{p}\cdot T_{p}\cdot N_{task}} \tag{14}\]  
The waiting time of a data packet is determined by \(L\), \(P\), \(S_{r}\), \(D_{p}\), \(T_{p}\), \(N_{task}\). \(L\) and \(P\) can be solved by the last four parameters. The last three parameters are determined by sensors, that is, they are constants. The average waiting time is mainly caused by the amount of receiving data. This means that a reasonable allocation of traffic can reduce the end-to-end transmission time.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Attention-Based Model
#### 5.10.5 Long Short-Term Memory Networks with Attention (LSTM-Att)  
Long Short-Term Memory Networks with Attention (LSTM-Att): By fusing the capacity of LSTMs to capture long-term dependencies in sequential data with the interpretability of attention mechanisms, the model has demonstrated promising results in the detection of cyberbullying. Recurrent neural networks of LSTM variants can deal with sequential data of different lengths, making them suitable for modeling text data. By incorporating attention mechanisms with LSTMs, the model can focus on the most crucial portions of the input text, improving its ability to understand and categorize social media bullies. Therefore, LSTM-Att might be a powerful tool for enhancing the precision of automated systems to detect cyberbullies [120]. Attention-Based Bi-LSTM (AB-LSTM) is also an effective neural network model for detecting cyberbullying on social media sites such as Twitter [115; 121; 122].
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 7 Conclusions and Further Work  
This paper proposes an application-level optimization methodology by adaptively leveraging switchless calls to reduce SGX overhead. Based on a systematic analysis, we define a metric to measure the efficiency of leveraging switchless calls for each wrapper function that raises enclave transitions. Compared with the previous optimization schemes, our approach reflects the characteristics of legacy SGX applications without introducing a significant engineering effort. Our evaluation shows that the adoption of our optimization methodology improves the pattern matching throughput of SGX-enabled middleboxes, one of the performance-critical cloud applications, while a naive adoption dramatically degrades the performance.  
Our scheme uses a heuristic to estimate the efficiency of utilizing switchless calls based on a systematic study. To prove its validity or improve optimization efficiency, leveraging machine learning techniques would be effective to precisely infer the threshold of efficiency. The data acquisition for applying such schemes and accurate performance profiling for cloud-native applications are also important and challenging issues to be addressed. In future work, we will elaborate our methodology to find optimal or near-optimal parameters with machine learning techniques based on the practical implications that we have identified from this study.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Operation Type** & **Methodology** & **ECALL** & **OCALL** \\ \hline \multirow{2}{*}{Empty Call} & Assigns CPU core for each thread group & 0.902 μs (13.8\%\(\dagger\)) & 0.602 μs (27.7\%\(\dagger\)) \\  & Pairs worker and application thread & 1.05 μs & 0.833 μs \\ \hline OCALL with I/O & Assigns CPU core for each thread group & - & 8.90 μs \\ (I KB Read) & Pairs worker and application thread & - & 4.91 μs (44.9\%\(\dagger\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between two core affinity strategies for switchless ECALL and OCALL.  
This work was supported by the Sungshin Women's University Research Grant of H20200128.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 2 Literature Review
Other researchers ([PERSON], [PERSON]; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], 2020) [61] extracted decision rules in the form of IF-THEN rules from CART results, where \"IF\" refers to the statuses of various independent variables and \"THEN\" is the status of the corresponding crash severity class variable. They contrasted the rules produced through association rule learning with the IF-THEN rules. The outcomes demonstrate the consistency of the two sets of regulations. Discovering relationships between independent factors and crash severity outcomes can be conducted by applying association rule learning, which is a straightforward process. The influence of independent factors on the seriousness of crashes in each rule cannot be measured, and its forecast accuracy is restricted in comparison to other ML techniques. The breadth of traffic crash severity prediction is the subject of this research. The goal of traffic crash data mining research can be separated into two categories: (1) predicting the severity of traffic crashes and (2) identifying relevant elements determining the severity of crashes. The severity of road crashes is reliably predicted using ensemble learning algorithms in this article. Gradient boosting machine and RF are two tree-based ensemble learning models employed in this investigation. Additionally, this study not only considers machine learning methods but also makes an effort to assess the spatial effects of crashes in order to highlight and pinpoint possible hotspots using various variables. Thus, the research contributes to the field of road safety by offering a model that would identify the important features for estimating, preventing, and forecasting crashes, particularly in the study region.
## 6 AOG Downtime Reduction and Implementation Results
### The trend of AOG Incidents Over Time  
It is apparent from the AOG incidents analyzed over time that there has been a consistently fluctuating number of incidents, with occasional sharp increases in frequency.  
The plot (Figure 12) illustrates the daily Aircraft On-Ground (AOG) incidents from 2019 to 2024, clearly showing their frequency and distribution over time. Key insights from the visualization include the following:  
Rare AOG Occurrences: Most data points are clustered at 0, showing that AOG incidents are infrequent on most days. This aligns with the dataset's mean AOG value of 0.199, meaning only about 20% of days recorded any AOG events. This reflects Smartlynx's effective maintenance and operational practices, keeping fleet disruptions to a minimum.  
Figure 11: Correlation heatmap of continuous variables.  
Sporadic Spikes: The plot shows occasional sharp increases in AOG incidents, corresponding to isolated disruptions. These spikes may result from seasonal factors, such as peak travel periods when the fleet is under more significant strain, or specific issues like fleet-wide maintenance challenges, adverse weather, or logistical delays.  
Seasonal and Yearly Patterns: AOG incidents cluster more during specific times, such as mid-2022 and early 2024. These patterns may reflect cyclical trends related to operational demands, scheduled maintenance cycles, or external factors. Further quarterly analysis could provide deeper insights into these periodic fluctuations.  
The visualization highlights the generally low frequency of AOG events while identifying patterns and spikes that can inform better planning and predictive maintenance strategies to prevent future disruptions.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 3 Stay Cable Experiments
### Error Analysis  
From the above results, it can be seen that the proposed method identified the time point and trend in the sudden increase and decrease in the cable force, which coincides with the true value of the cable force in the interval stability stage. It can been seen from Figures 6 and 12 that there was a time delay of estimated force for condition 3 and 4 in scaled model experiment, and there was little delay in full-scale model experiment. This may be caused by the mass of sensor, the force of steel wire in scaled model can be influenced by the accelerator while there may be little influence considering the large size and weight of full-scale model experiment which selected the real cable. Although the sensitivity of force change is different, the cable force value and sudden change identified by this two experiments were consistent with the experimental design. Errors were calculated according to the stationary stage and the mutation, and the relative error was used for calculation. The results are shown in Tables 3 and 4. It can be seen from the table that the proposed method had a better recognition effect in the middle part of the signal. The errors of the scale model and the full-scale model in the stationary stage were both below 6%, and the average error was also below 5%. In the scale experiment, the error of mutation points was relatively large. The main reason is that when a sudden force is applied, it is adjusted with a manual wrench, which interferes with the vibration of the cable, but all of them identified the mutation trend very well. The error at the mutation points is relatively small because the abrupt force applied by the hydraulic device is actually smoother and automatic.  
Figure 12: Time-varying cable forces of the full-scale model experiment: (**a**) Condition 1, (**b**) Condition 2, (**c**) Condition 3.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Conditions** & **Maximum Stationary Phase Error (\%)** & **Maximum Mutation Point Error (\%)** & **Average Error (\%)** \\ \hline
1 & 8.941 & / & 4.243 \\
2 & 3.189 & / & 0.825 \\
3 & 3.542 & 13.939 & 2.022 \\
4 & 2.563 & 29.465 & 1.504 \\ mean & 4.753 & 21.702 & 2.067 \\ \hline \end{tabular}
\end{table}
Table 3: Scale experiment error statistical table.
## 3 Materials and Methods
### RGB-D and IRT Images Fusion Module
#### 3.4.2 Calibration Target Used to Calibrate the IRT Camera  
Standard calibration patterns, such as a printed chessboard, are not suitable for IRT calibration due to their near-uniform temperature. To solve this problem, the calibration target boards must be made of materials with two different emissivities. Several techniques and pattern designs have been proposed to be able to calibrate thermal cameras. In [57], a very clear calibration target classification was made; they can be classified based on working principle (active or passive), on the markers employed (e.g., corners, circles, Aruco, and so on), and the kind of pattern that the markers follow (structured or unstructured). The most interesting target classification for IRT is that based on the working principle. Passive targets do not use any external energy sources to be detectable by the sensor, while active ones need external energy sources to be detectable. The target board designed in this work was an active evolution of the passive asymmetric circle board constructed in [57]. Specifically, circles in a 9 \(\times\) 3 asymmetric pattern were cut out of white stainless steel plate with a CNC laser cutter machine. It had the same size as an A4 paper sheet and a thickness of 1 mm. To be able to heat the target board, several nichrome wires (with 0.2 mm diameter) were attached across the back of the plate, surrounding the circles of the board to ensure that there was enough temperature contrast to detect the circle contours accurately. Then, the target board was screwed into a black-painted wood plank, leaving a 2 cm gap between the board and the wood base, to isolate the nichrome wires from the  
Figure 7: Used setup.  
wood base. For convenience and portability, a 5 V/2.1 A powerbank powered the nichrome circuit. The calibration pattern reached a temperature which was safe to be handled but high enough to obtain high-contrast IRT images (see Figure 8).  
The reprojection error obtained with the geometric calibration of the IRT camera, using 50 images of varying orientation and distance to the camera, gave a result of \(0.332\pm 0.09\) pixels. This error was similar to that obtained in [58] with custom-made calibration algorithms. A reprojection error greater than 0.8 pixel would indicate to a MANTRA user that the calibration process is not good enough and it has to be repeated.  
The developed calibration pattern is effective for long periods of time (depending on the powerbank capacity), accurate for RGB-D and IRT cameras, can be employed with optimized conventional algorithms, is portable, and is not difficult to manufacture.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 2 Study Area and Data
### Data Sources
#### 2.2.3 FLUXCOM  
The FLUXCOM dataset is acquired via machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat, and their uncertainties [22; 32; 33]. The FLUXCOM dataset includes two types of forcing data, i.e., 0.0833\({}^{\circ}\) resolution using a MODIS remote sensing dataset (RS), and 0.5\({}^{\circ}\) resolution using remote sensing and meteorological data (RS + METEO). Nine machine learning methods and three energy balance correction methods were utilized for RS forcing data. Three machine learning methods, three energy balance correction methods, and four global climate datasets were adopted for RS + METEO forcing data [22; 32]. In this study, daily RS + METEO-based ensemble latent heat energy with 0.5\({}^{\circ}\) resolution from 2004 to 2010 that includes all fluxes produced by all energy balance correction methods, all machine learning methods, and all global climate datasets is used and can be freely downloaded ([[http://www.fluxcom.org](http://www.fluxcom.org)]([http://www.fluxcom.org](http://www.fluxcom.org)), accessed on 21 December 2020). Latent heat flux divided by latent heat of vaporization is ET.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Supply-and-Demand Balancing
#### 2.1.6 A List of Research Challenges  
In terms of the sub-areas discussed above, the following challenges, listed in Table 1, are apparent when seeking to improve grid operational flexibility and thus reliable and cost-effective balancing of supply and demand.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Challenges**} & \multicolumn{2}{c}{**Research Questions and Requirements**} \\ \hline Timeframes & \(\bullet\) & Services that ensure a stable supply-and-demand balance and flexible power delivery should be expanded and _incorporated at different timeframes_. \\ \hline Forecasting & \(\bullet\) & _Long-term datasets with high spatiotemporal resolution_ should be collected, processed, and utilized to ensure accurate forecasting of VRE generation patterns. _High-quality assessment of forecast uncertainty_ is required to integrate the weather-dependent characteristics of VRE sources into the operational algorithms and analytical tools of grids. \\ \hline Algorithms and tools & \(\bullet\) & VRE curtailment caused by network congestion can be avoided by _improving the accuracy of power flow analyses by enhancing network modeling and nonlinear solvers_. \\ \hline  & \(\bullet\) & It is necessary to capture more details for each service to _aggregate the technical details of constraints on individual resources into a single model or algorithm_. \\ \hline  & \(\bullet\) & _Stability constraints_ should also be represented better, including the limits of stored rotational energy, the frequency regulation reserve, and locational voltage deviations. \\ Modeling and analysis & \(\bullet\) & To establish risk-aware power balancing, new optimization methods and new computations should be used _to update the deterministic models and tools to the stochastic status_, and to further develop existing probabilistic models and tools. \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of research challenges in balancing supply and demand.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 1 Introduction
### State-of-the-Art Issues  
We address two specific issues dealt with the locking phenomena. The first one concerns some basic research on the nature of this phenomenon, while the second issue is the existing methods of removal of the numerical consequences of the phenomenon.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 4 Conclusions  
In this paper, a radiation hardened read-stability and speed enhanced 20T (RHMSE-20T) SRAM is proposed for environments prone to radiation exposure. The RHMSE-20T SRAM demonstrates a read static noise margin (RSNM) that is improved by 255% compared to the average of conventional cells, along with a word line trip voltage (WWTV) that shows a 19% improvement. Additionally, read delays are reduced by 30% compared to conventional SRAM cells, significantly enhancing overall performance. Furthermore, the design shows immunity to potential soft errors, achieved for SEU, DNU, and MNU, thus providing superior radiation resistance. These advancements make the proposed RHMSE-20T SRAM particularly well-suited for terrestrial, atmospheric, and space applications, where reliability in harsh conditions is essential.  
Conceptualization, W.C.C. and S.-H.J.; methodology, W.C.C.; validation, W.C.C.; data curation, W.C.C.; writing--original draft preparation, W.C.C.; writing--review and editing, S.-H.J.; visualization, W.C.C.; supervision, S.-H.J.; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data are contained within the article.  
The EDA Tool was supported by the IC Design Education Center, Korea.  
The authors declare no conflicts of interest.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 5 Experiment and Evaluation
### RTT Fairness
#### 5.3.2 Dynamic Network Experiment  
Using NS3, this experiment evaluates the dynamic RTT fairness of the Ad-BBR algorithm. A 20 Mbps bottleneck link is established with an initial 50 ms flow at 0 s. At 30 s, a 10 ms flow is added, followed by a 100 ms flow at 60 s. The 50 ms and 10 ms flows are withdrawn at 90 s and 120 s, respectively. The experiment monitors the change in Jain's fairness index over a 150 s period to assess dynamic RTT fairness, and the results are presented in Figure 14. When only 50 ms streams exist in the link, no competition occurs, so the fairness index for all algorithms remains at 1. At 30 s, when 10 ms streams are introduced, the bandwidth allocation fluctuates and resource contention intensifies, causing the fairness indices of all algorithms to drop steeply. In particular, the fairness indices of BBRv1 and BBQ decline most significantly, while BBRv3 and Ad-BBR perform better. Notably, the fairness index of Ad-BBR quickly recovers to a higher level, indicating that its adaptive sending rate adjustment mechanism effectively mitigates the impact of the new streams. At 60 s, with the addition of a 100 ms stream, the fairness indices of all algorithms begin to fluctuate dramatically. Due to their distinct congestion control principles, the Cubic and Reno algorithms consistently maintain higher fairness indices. Although Ad-BBR initially struggles to adapt to the drastic changes, its fairness index remains relatively stable and quickly recovers after adjustment. Overall, Ad-BBR performs well throughout the dynamic experiment, demonstrating more stable dynamic RTT fairness than other BBR-based algorithms. This makes it a promising solution for maintaining long-term link fairness.  
Figure 14: Dynamic fairness variation.
## 4 Test Results and Discussion
### Relationship between Shear-Compression Ratio and Pressure Features
#### 4.4.1 Initial Friction Load of FHSB T-Stub Connection  
The initial friction load depends on the friction induced by bolt clamping force and its compressive component. Any change to the bolt clamping force will cause the friction between connecting plates to change. The designed shear capacity that a HSB provides to the connection can be expressed as [22]:  
\[N_{\mathrm{v}}^{\mathrm{b}}=0.9n\mu n_{f}P, \tag{1}\]  
where, \(n\) is the number of HSBs; \(n_{f}\) is the number of friction surfaces; \(P\) is the designed clamping force of a HSB and \(\mu\) is the anti-slip coefficient of contact surfaces and the usual value is 0.3-0.5.  
The tangential component of initial friction load of the contact surfaces can be described as:  
\[F_{\mathrm{v}}=F_{\mathrm{I}}\sin\theta, \tag{2}\]  
Then, the improved coefficient of anti-slip bearing capacity (referred to as \"improved coefficient\") can be defined as [29]:  
\[v=\frac{F_{\mathrm{v}}}{N_{v}^{b}}=\frac{10F_{\mathrm{I}}\sin\theta}{9n\mu n_ {f}P}, \tag{3}\]  
As the shear-compression ratio dropped from 0.70 through 1.00-1.73, the mean improved coefficient decreased from 7.21 through 5.55-2.12.