esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Signal Model  
### Space Environment  
As illustrated in Figure 1, we consider a multiple UAV-based hybrid FSO/RF aeronautical communication network consisting of an AWACS as the source node \(S\), the mobile GS as the destination node \(D\), and \(N\) UAVs as relay nodes operating in the decode-and-forward (DF) mode terminals, denoted by \(R_{i},i=1,2,\cdots N\). In this setup, the direct connection between \(S\) and \(D\) is considered unavailable due to the unstable weather conditions and heavy shadowing. To ensure reliable information transmission from air to ground, the UAVs divide the link into two hops. The first hop is the AWACS-UAV link, denoted by \(SR_{i}\), and the second hop is the UAV-to-GS link, denoted by \(R_{i}D\). Considering the intricate atmospheric conditions in the troposphere, the hybrid FSO/RF transmission scheme is implemented for both hops, in which the FSO link serves as the primary link, and the RF backup link is activated when the SNR requirement cannot be met by the FSO link. The AWACS is assumed to maintain a constant cruising altitude \(H_{AWACS}\). Additionally, for simplicity, we assume that all UAVs have an identical altitude \(H_{UAAV}\) and zenith angle \(\xi_{UAV}\), while the zenith angle of the ground station is denoted as \(\xi_{GS}\). Moreover, we assume that the channel states are slowly fading in a time-varying manner, and that the channel state information (CSI) is still up-to-date information when arriving at the AWACS and UAVs.
## 4 Results and Discussion
### Survey
#### 4.3.1 Cool Roofs
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Cool Roof**} & \multicolumn{2}{c}{**Percentage of Respondents (\%)**} & \multirow{2}{*}{**Likely (4)**} & \multirow{2}{*}{**Very Likely (5)**} \\ \cline{3-3} \cline{5-6}  & & **Very Unlikely (t)** & & & **Unlikely (2)** \\ \hline \multirow{4}{*}{Opinions} & - Need for climate change mitigation & 0.0 & 1.5 & 6.3 (do not know) & 60.9 & 31.3 \\ \cline{2-6}  & - Awareness of facility & - & 34.4 & - & 50.0 & 15.6 \\ \cline{2-6}  & - Interest in cool roof installation & 0.0 & 7.7 & 64.1 & 18.8 & 9.4 \\ \hline \multirow{4}{*}{Perception of effects} & - Perceived room temperature change & 0.0 & 12.5 & 48.4 & 34.4 & 4.7 \\ \cline{2-6}  & - Perceived reduction in air & 0.0 & 20.3 & 46.9 & 29.7 & 3.1 \\ \cline{1-1} \cline{2-6}  & - Perception of sleep disorder & 3.1 & 17.2 & 54.7 & 21.9 & 3.1 \\ \cline{1-1} \cline{2-6}  & - Perceived reduction in & 0.0 & 18.8 & 57.8 & 20.3 & 3.1 \\ \hline \multirow{4}{*}{Satisfaction} & - Satisfaction with temperature & \multirow{2}{*}{1.6} & \multirow{2}{*}{15.6} & \multirow{2}{*}{51.6} & \multirow{2}{*}{28.1} & \multirow{2}{*}{3.1} \\ \cline{1-1} \cline{2-6}  & reduction & & & & & \\ \cline{1-1} \cline{2-6}  & - Overall satisfaction & 0.0 & 10.9 & 57.8 & 29.7 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: User satisfaction survey results after installation of cool roofs. respondents gave the new technology 5 points, 29.7% gave it 4 points, 57.8% gave it 3 points, 10.9% gave it 2 points, and no respondents gave it 1 point; thus, the average score was 3.2 points, indicating a moderate level of satisfaction. Other comments revealed that some respondents found the new surfaces to be slippery, easily soiled (i.e., caused management difficulty), and unsightly because of glare or dirt. Thus, residents were generally satisfied with the temperature reduction effects of cool roofs, and they had high levels of interest in the technology; however, installation methods should be improved to resolve the reported issues during the future expansion and maintenance of cool roof systems.
## 3 Graph Neural Networks for Next-Generation Networks
### GNN in Access Networks  
In this section, we examine the current body of literature that explores the application of graph-based approaches in the management of access networks. Our review specifically centers around three key areas: _resource allocation_, _traffic prediction_, and _network security_, particularly _intrusion detection_.
## 2 Materials and Methods
### Amplicon Sequencing  
Total DNA was extracted from the fermented grains using the Soil DNA Isolation Kit (FOREGENE, Chengdu, China), following the manufacturer's instructions. For bacterial analysis, the V3-V4 region of the 16S rRNA bacterial gene was amplified using the universal primers 338F (5=ACTCCTACGAGGGCAGCAG-3=) and 806R (5=-GACTACHVGGGTWTCTAAT-3=). For fungi, the ITS2 region was amplified using the primers ITS2 (5=-GCTGCTTCTCATCGATGC-3=) and ITS3 (5=-GCATCGATGAAGAACCGC-3=). Purified PCR products were sequenced on an Illumina Hiseq 2500 platform at a biotechnology company. Sequencing adhered to standard protocols provided by Novogene Technology Co., Ltd. (Tianjin, China). Raw sequencing reads were demultiplexed, quality-filtered by fastp version 0.23.1 [16], and merged by FLASH version 1.2.11 [17]. Clean data were classified into operational taxonomic units (OTUs) with at least 97% similarity using UPARSE version 7.1. The Vsearch cluster OTU function was employed to generate the representative sequences [18]. Taxonomy annotation of each OTU was performed using RDP Classifier version 2.2 against the 16S rRNA and ITS databases [19].
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 3 Results and Discussion
### Effect of HRT in Reactor on Phosphorus Removal  
The results of the effect of hydraulic retention time in reactor on phosphorus removal are presented in Figure 10. In cases where the HRT increases, the phosphorus removal rate increases significantly at a low adsorbent dosage, but with an increasing adsorbent dosage, the increase in the phosphorus removal rate of the adsorption reactor slows down [26; 37]. The trend is similar to that of agitation experiments. [PERSON] et al. achieved the same result. They explained that the rapid initial removal was mainly attributed to the precipitation of phosphate with exchangeable and dissolved Ca\({}^{2+}\) rather than the adsorption. The liberated Ca\({}^{2+}\) from the exchange site or from the dissolution of CaCO\({}_{3}\), CaO and Ca(OH)\({}_{2}\) were preferably precipitated by phosphate, which gave a high initial rate of adsorption. The exact contribution of the adsorption and precipitation phases to the removal of phosphate remains unclear [38]. Another possible explanation might be that when the HRT is short, the dosage of the adsorbent selected in the experiment cannot balance the adsorption reaction in the wastewater. In other words, the P concentration differs greatly from the equilibrium state. Although the reaction effect of the adsorbent has declined, the removal efficiency has not been excessively inhibited. An earlier study revealed that when the HRT is long, the instantaneous P concentration of the wastewater approaches the equilibrium state of the reaction, where the function of the adsorbent is obviously suppressed [39; 40]. In cases where the dosage increases, the removal rate of phosphorus in the wastewater is still extremely slow. This is reflected in the fact that the longer the hydraulic retention time is, the smaller the influence of the adsorbent dosage on the P concentration of the effluent is.  
Figure 9: Effect of RSPRC dosage in the reactor on the effluent phosphorus concentration.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 4 Simulation Assessment  
This section presents a simulation assessment of the proposed enhanced association mechanism. The main objective is to compare the network behavior when applying the proposed mechanisms vs. the IEEE 802.15.4 standard mechanisms. In order to assess this proposal, the communication infrastructure was implemented in the OMNeT++ 4.6 (the OMNeT++ Simulator: [[https://omnetpp.org](https://omnetpp.org)]([https://omnetpp.org](https://omnetpp.org))) network simulator with Castalia 3.3 framework. For this simulation, we have used the CT-SIM simulation model [34], which provides the required set of features to support a cluster-tree multi-hop topology.  
Figure 5: Message sequence chart for enhanced association mechanism.
# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 3 Analyzing the Relationship Between OSS Survivability and Features
### Measuring Issue Retention Rate of Oss
#### 3.2.2 Survival Function of OSS  
To quantitatively evaluate the issue retention rate of OSS projects, this study employed the Kaplan-Meier estimation method. Traditionally used in fields such as medicine to measure survival rates by setting death as an event or in business to assess retention rates with customer churn as the event, this method was applied here to measure issue retention rates over time. By generating survival functions for entities, the Kaplan-Meier method provided insights into the occurrence rates of events over time, enabling the measurement of issue retention rates and durations for each OSS project. For this study, issue resolution was treated as the event, and survival functions were generated for each project.  
The Kaplan-Meier method was implemented using Python's scikit-learn library, and the survival curves for each OSS project were estimated based on the following data:  
* issue creation time.
* **Event observed (resolution status):** This indicates whether the issue has been resolved. Resolved issues were marked as 1, representing an event occurrence, while unresolved issues were marked as 0, treated as censored data.  
When applying the Kaplan-Meier method, it is crucial to consider the data collection period, as the survival function results may vary depending on the duration of issue data used. Shorter data periods reflect recent patterns, while longer periods provide a clearer understanding of long-term issue resolution trends in projects. To determine the effect of different data durations on the accuracy of the analysis, this study tested various data collection periods, such as one month and one year.  
The survival function generated with a 90-day data duration is shown in Figure 2. Using this function, the issue retention rate at \(t=15\) was extracted. The choice of \(t=15\) was motivated by the focus on identifying the likelihood of issues being resolved quickly. Specifically, \(t=15\) corresponds to the typical duration of a single sprint in the Scrum methodology, providing a practical benchmark for early issue resolution. The distribution of the measured issue retention rates is shown in Figure 3. This figure reveals that the distribution of issue retention rates was imbalanced, highlighting the need for analytical models suitable for imbalanced data.
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 5 Discussions
### Compared with Other Methods  
To further show the performance of the proposed collaborative recommendation algorithm (ICFTU), it is compared with two other state-of-the-art recommendation approaches. The first one is a new collaborative filtering framework based on a gauss core and an extension classification method (known as GCEDA) [26]. The second one is an advanced approach, which is based on Deep Feed-Forward Neural Networks (known as DFFN) [35]. This comparison experiment is carried out on ML-100k data set, and the results are shown in Table 5 and Figure 10.  
The results show that the proposed ICFTU in this paper has a better recommendation effect compared to GCEDA and DFFN. The results in Table 5 show that the performance of the GCEDA and DFFN methods are close to the K-MCF method (see Tables 2 and 5), the main reason is that all the three methods use the information of the input data by different strategy. However, the comprehensive performance of the proposed model is the best. The MAE value of the proposed model is 3.50% and 1.03% lower than that of GCEDA and DFFN. Meanwhile, the RMSE value of our model is 3.88% and 1.88% lower than that of GCEDA and DFFN.  
Figure 10: The results of the compared experiment with other two recommendation approaches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **GCEDA** & **DFFN** & **ICFTU** \\ \hline MAE & 0.799 & 0.779 & 0.771 \\ Improvements & 3.50\% & 1.03\% & - \\ RMSE & 0.979 & 0.959 & 0.941 \\ Improvements & 3.88\% & 1.88\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the compared experiment with other two recommendation approaches.  
Figure 9: The results of the ablation experiment: (**a**) of MAE; (**b**) of RMSE.
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 2 Vehicle Performance Modelling
### Battery Performance and Electro-Thermal Model  
The essence of incorporating a battery electro-thermal model in the vehicle model (Figure 2) is to provide information to the ECMS controller for efficient fuel consumption minimization while preventing thermal runaway. At each time instant \(k\), the adopted ECMS controller requires information of the state of charge \(SOC(k)\), the temperature \(\theta\), and the rate of the change of the temperature \(\dot{\theta}\). This information is the output of the battery pack electro-thermal model that takes the power request \(P_{bat}\) as input (from Equations (8) and (9). Considering the 14s6p battery configuration, the battery consists of a series module pack (SMP) composed of a series of fourteen (14) modules. Each of the modules consists of six (6) parallel cells, otherwise known as parallel cell modules (PCM).  
The pack model is developed from the electro-thermal model of a single cell according to the work of [37]. First, an enhanced self-correcting (ESC) single-cell model is developed to form the building block for the PCM that is scaled to obtain the SMP. The resulting model is validated with an experimental dataset at an ambient environmental temperature of 20 \({}^{\circ}\)C.  
The cell model takes into consideration both the static and the dynamic voltage characteristics as shown in Figure 6. A detailed description of each subsystem of the battery pack model is given below.  
The static voltage computed from the OCV block is integrated with the dynamic voltage from the dynamic process to obtain an ESC cell model.  
Figure 5: Static efficiency map of the electric motor and maximum torque characteristic lines.  
Figure 6: Schematic component of the battery pack electro-thermal model. The variables in dotted blocks are the inputs and outputs to the battery model.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 2 Background
### HAWK Signature Scheme [11]
#### 2.1.2 HawkSign (Algorithm 2)  
A hash M is generated through the message (m) and the private key hash (hpub), and a hash value h is generated by combining M and salt. Sample vector x from the expanded lattice using the generated h. Compute the vector w using x, and generate the signature vector s using w and h. The generated s is compressed to create a signature value s1, and the final signature is created by combining Salt and s1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Private Key** & **Public Key** & **Signature** \\ \hline HAWK-512 & 184 & 1024 & 555 \\ HAWK-1024 & 360 & 2440 & 1221 \\ \hline FALCON-512 & 1281 & 897 & 666 \\ FALCON-1024 & 2305 & 1793 & 1280 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key and signature size for HAWK in bytes.  
```
0: A message \(\mathbf{m}\) and secret key \(sk=(B,h_{\text{pub}})\)
0: A signature \(\mathbf{sig}\) formed of a uniform salt \(s\in\{0,1\}^{\text{salten}}\) and \(s_{1}\in R_{n}\)
1:\(\mathbf{M}\gets H(m\|h_{\text{pub}})\)
2:\(\mathbf{salt}\leftarrow\text{Rnd}(\text{salten}_{\text{bits}})\)
3:\(\mathbf{h}\gets H(M\|salt)\)
4:\(\mathbf{t}\gets B\cdot h\mod 2\)
5:\(\mathbf{x}\gets D_{222^{n}}+t,2\sigma_{\text{sign}}\)
6:if\(||x||^{2}>4\cdot\sigma_{\text{verify}}^{2}\cdot 2n\)then restart
7:\(\mathbf{w}\leftarrow B^{-1}x\)
8:if sym-break(\(w\)) is false then \(\mathbf{w}\) := \(-\mathbf{w}\)
9:\(\mathbf{s}\leftarrow\frac{1}{2}(\mathbf{h}-\mathbf{w})\)
10:\(s_{1}\leftarrow\text{Compress}(s)\)
11:if sig-encoding(\(salt,s_{1}\)) is false then restart
12:return\(sig\leftarrow(salt,s_{1})\)
```  
**Algorithm 2** HawkSign.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 4 Deep Learning-Based Fault Prediction
### Data Preparation and Simulation  
This subsection introduces the dataset used to train and evaluate the fault prediction model. The data simulate the operational states of three machines, denoted A, B, and C, over a 24-h period, capturing key factors influencing machine faults. The goal is to replicate realistic machine behavior, in order to enable robust model training and evaluation. The dataset comprises several key operational features recorded at one-min intervals:  
* Temperature: Reflects heat generated during operations.
* Vibration: Indicates the intensity of mechanical oscillations.
* Operating Hours: Tracks cumulative runtime as a wear indicator.
* Tool Wear: represents the wear progression of the machine tool.
* Fault Probability: The likelihood of a fault occurring based on wear and other states. The tool wear was designed to follow realistic wear patterns, divided into three phases:
* Initial Wear Phase: A gradual increase in wear during the early stages of operation.
* Stable Wear Phase: The tool wear increases steadily at a lower rate during normal operations.
* Rapid Wear Phase: The wear rate accelerates significantly, leading to a higher fault probability as the tool nears the end of its life cycle.  
The fault probability is modeled to increase as the tool wear approaches its threshold. When a tool reaches its maximum wear level, the machine is marked as faulty and requires maintenance or tool replacement, at which point the tool wear and fault probability are reset. These phases reflect the typical lifecycle of industrial tools, as illustrated in Figure 2. For Machines A, B, and C, the data reflects their distinct operational characteristics. Machine A experiences high vibration levels due to its drilling tasks, Machine B exhibits greater tool wear with minimal vibration from threading operations, and Machine C shows longer stable wear phases and higher temperature increases, consistent with milling operations.  
The synthetic dataset contains:  
* Timestamp: One-min intervals over 24 h.
* State Features: Temperature, vibration, tool wear, and operating hours.
* Fault Probability: Modeled based on cumulative tool wear and operational state.  
Table 2 provides a summary of the data for Machine A, while Figure 3 visualizes its temporal patterns across the 24-h simulation period.  
Figure 2: Tool wear graph for synthetic data generation.
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 3 Optimization of Real-Time Performance for RISC-V Processors
### Optimization of the Bran_Pre Module (Branch Prediction Module)
#### 3.2.1 Introduction to Branch Jump Instructions
The Rev-riscv processor supports six conditional branch instructions: beq, bne, blt, bltu, bge, and bgeu. The detailed explanations of each instruction are provided in Table 2.  
The proportions of the six conditional branch instructions among all conditional branch instructions are shown in Figure 5.  
Therefore, how to improve the prediction success rate of conditional branch instructions and reduce the processor's runtime has become a focus of this paper.
## 4 Applications of AR and VR in Dentistry
### AR and VR in Dental Education/Training  
Traditionally, the training of dental students has been conducted on phantom heads and teeth to practice and improve their clinical skills before performing treatments on patients. These simulators allow teachers to demonstrate the treatment techniques and aim to enhance the manual dexterity of the students. The use of such simulators makes it mandatory for the students to give their teachers constant feedback about their progress before moving to different treatment techniques.  
In the delivery of dental education and training, instructors can help students and PG residents to achieve precision in pre-clinical/clinical skills, respectively, with 3D real-time digital simulations as well [1,9]. Learning and retaining the anatomy of the head and neck is a crucial part of dental education. Lectures and 2D images from textbooks of anatomy have been used for teaching this subject to students [10]. In this traditional mode of teaching, cadaveric skulls are commonly employed, and students are instructed through these. However, the visualization of all associated muscular, neural, vascular, and other structures is quite challenging for students and it is not fully effective either [22].  
In the midst of the COVID-19 pandemic, students suffered due to limited learning opportunities and the ability to enhance their pre-clinical skills as students could not train at their universities without the instructor's direct supervision. Therefore, helping these students to learn while controlling the spread of COVID-19 has been the most challenging aspect for the universities. Amidst the quest for better teaching methodologies, one group described an innovative technique to teach clinically relevant anatomy to students of dentistry that replaces cadavers with dissected and sliced plastinated specimens [8]. However, one recent study incorporated digital tools and a 3D augmented curriculum, which is an interactive 3D experience that combines the view of the actual world with elements generated by computers along with traditional teaching methods (lectures and cadavers). The study demonstrated improved understanding and a positive influence on  
Figure 1: Use of virtual /augmented reality in dentistry.  
the retention of anatomical knowledge in students as compared to the control group (taught via textbooks/2D images) [23].  
To further increase the knowledge of dental students, virtual reality allows the students to watch oral treatments as a direct participant. Moreover, students can also try to reproduce the oral treatment scenario under the guidance of professionals.