esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Training Set (\%)**} \\ \cline{2-7}  & & _Accuracy_ & _Precision_ & _Recall_ & **F1** & **FNR** \\ \hline \multirow{4}{*}{_DoS_} & _SOMK-C_ & _97.78_ & _90.88_ & _100.00_ & 95.22 & _0.00_ \\  & _CI_ (95\%) & _97.45–98.10_ & _90.25–91.51_ & _100.00–100.00_ & 94.75–95.69 & _0.00–0.00_ \\ \cline{2-7}  & SOMK-D & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 0.00 \\  & CI (95\%) & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 0.00–0.00 \\ \hline \multirow{4}{*}{_Spoofing_} & _SOMK-C_ & _95.60_ & _79.70_ & _100.00_ & _88.70_ & _0.00_ \\  & _CI_ (95\%) & _95.15–96.05_ & _78.82–80.58_ & _100.00–100.00_ & _88.01–89.40_ & _0.00–0.00_ \\ \cline{2-7}  & SOMK-D & 100.00 & 100.00 & 100.00 & 100.00 & 0.00 \\  & CI (95\%) & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 0.00–0.00 \\ \hline \multirow{4}{*}{_Spoofing_} & _SOMK-C_ & _95.49_ & _79.04_ & _100.00_ & _88.29_ & _0.00_ \\  & CI (95\%) & 95.03–95.94 & _78.14–79.93_ & _100.00–100.00_ & _87.59–89.00_ & _0.00–0.00_ \\ \cline{2-7}  & SOMK-D & 100.00 & 100.00 & 100.00 & 100.00 & 0.00 \\  & CI (95\%) & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 0.00–0.00.00 \\ \hline \multirow{4}{*}{_Fuzzy_} & _SOMK-C_ & _73.20_ & **0.46** & _0.20_ & _0.28_ & _99.80_ \\  & CI (95\%) & 72.23–74.17 & 0.32–0.61 & _0.10–0.30_ & _0.16–0.39_ & 99.70–99.80 \\ \cline{2-7}  & SOMK-D & **99.58** & **99.66** & **98.07** & **98.86** & **1.93** \\  & CI (95\%) & 99.43–99.72 & 99.54–99.79 & 97.77–98.37 & 98.63–99.09 & 1.63–2.23 \\ \hline \multirow{4}{*}{_Mixed_} & _SOMK-C_ & _82.55_ & _61.76_ & _27.91_ & _38.45_ & _72.09_ \\  & CI (95\%) & 81.72–83.38 & 60.69–62.82 & _26.93–28.90_ & _37.38–39.51_ & 71.10–73.07 \\ \cline{2-7}  & SOMK-D & 99.86 & 99.68 & 99.62 & 99.65 & 0.38 \\ \cline{1-1}  & CI (95\%) & 99.78–99.94 & 99.56–99.80 & 99.48–99.75 & 99.52–99.78 & 0.25–0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for the training sets. Figure 8: SOMK-D trained on the test set.
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 2 System Model  
### Network Model  
As shown in Figure 1, we consider a cloud-assisted multi-park distributed PV system, where an internal trading market in the cloud facilitates the communication and coordination among the parks. The electric energy can be transferred between different parks via an energy bus. This market enables the energy-rich parks to sell their surplus power to other parks at a favorable price, rather than to the external grid at a low price, and the energy-deficient parks to buy the power at a low price. The internal market settles the total cost of each park based on their energy transactions. Therefore, the multi-park cooperative operation based on the internal market can exploit the energy complementarity and increase the self-supply and self-consumption ratio of the distributed PV system.  
In the proposed cloud-assisted multi-park distributed PV system, each park communicates its planned energy purchase or sale information to an internal trading market. The internal trading market then allocates the electric power among the parks to achieve the balance of supply and demand in the system.  
To enable efficient and robust energy exchange and optimization, we employ a distributed coordination mechanism based on an intelligent agent. Each park has a local intelligent agent, which optimizes its charging and discharging amount of the energy storage device according to the current electricity supply and demand, electricity price, and other information of all parks. Instead of using a fixed priority rule, we schedule the priority of the energy-demanding and supply-given parks based on an intelligent mechanism, which allows each agent to adapt its strategy and behavior dynamically according to its objective function (i.e., minimizing its electricity cost). Our system can automatically balance the energy flow among the parks according to the real-time market situation, so as to minimize the total electricity cost, while ensuring that each park can meet its own demand.  
Figure 1: Cloud-assisted multi-park distributed PV system.
# Wearable Plasma Pads for Biomedical Applications
## 2 Experimental Methods
### Evaluation of Antibacterial Activity Using Plasma Treatment  
To confirm the antibacterial activity of the plasma pads, _Staphylococcus aureus_ (_S. aureus_ ATCC 14458) and methicillin-resistant _S. aureus_ (MRSA), isolated from patients at the Yonsei Medical Center in Seoul [26], were used. _S. aureus_ was grown on plate count agar (PCA, Becton, Dickinson and Company, Sparks, MD, USA) at 35 \({}^{\circ}\)C for 24 h and MRSA was grown on Brain Heart Infusion agar (BHIA, Becton, Dickinson and Company, Sparks, MD, USA) at 35 \({}^{\circ}\)C for 24 h. Pre-cultured bacterial cells were suspended at 1 \(\times\) 10\({}^{6}\)-1 \(\times\) 10\({}^{7}\) colony forming units (CFU)/mL in nutrient broth for (_S. aureus_) and BHI broth for MRSA. The bacterial cells were inoculated into 24-well plates after 10-fold dilution to yield 1 \(\times\) 10\({}^{5}\)-1 \(\times\) 10\({}^{6}\) CFU/mL and were incubated at 35 \({}^{\circ}\)C after plasma treatment for 0 min, 1 min, 3 min, 5 min, and 10 min. After incubation for 24 h, the bacterial cells were serially diluted by 10-foldincrements (1 \(\times\) 10\({}^{1}\) to 1 \(\times\) 10\({}^{7}\)) and inoculated into PCA or BHIA to confirm cell viability. Bacterial cell viability was determined by counting the CFU of each bacterial type, and the antibacterial activity was defined as a \(>\)3-log decrease in CFU/mL.
## 6 Conclusions  
In this paper, considering the requirements of improving the data rate and spectral efficiency of the satellite communication system and the non-linear characteristic of the HPA, we develop a new 7-point constellation and adapt it to NB-LDPC codes over GF(7). First, we give the design and performance theoretical analysis of the 7-QAM constellation. The analysis demonstrates its PAPR, the derivation of its exact expression for the SEP over the AWGN channel, and its insensitivity to the AM/AM conversion of non-linear distortions introduced by the HPA. Then, NB-LDPC codes over  
Figure 10: BER of different modulations combined with NB-LDPC codes over different channels.  
Figure 9: SER of 7-QAM combined with different NB-LDPC codes over different channels.  
GF(7) are combined with 7-QAM to guarantee the reliability of the data transmission. The simulation results illustrate that 7-QAM has a much lower demodulation threshold than 8-QAM and 8-PSK, and the proposed NB-LDPC coded 7-QAM scheme is suitable for the satellite communication system because of its better error performance than NB-LDPC coded 8-PSK scheme over the non-linear channel model.  
Conceptualization, Y.L. and W.C.; methodology, W.C., A.W. and C.H.; software, Y.L.; formal analysis, Y.L. and W.C.; investigation, Y.L. and W.C.; writing--original draft preparation, Y.L.; writing--review and editing, W.C., A.W., and C.H.  
This research was funded in part by the National Natural Science Foundation of China Grant No. 61671324 and the Director's Funding from the Pilot National Laboratory for Marine Science and Technology (Qingdao).  
The authors declare no conflict of interest.
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions
#### 3.1.3. ZKP Approach for Privacy Protection in IoE-Based Systems
In healthcare systems, ZKP protocols are particularly critical for ensuring privacy-preserving verification and sharing of patient data, enabling the secure management of sensitive health records [49]. In conclusion, ZKP mechanisms are one of the most robust methods for ensuring secure access and transaction verification in IoE environments while preserving privacy. The advantages of different protocol types should be evaluated contextually, considering hardware limitations, security requirements, and integration ease. In distributed and high-volume IoE systems, factors such as quantum resistance, verification time, and trusted setup requirements play a critical role [9; 79; 80].
# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 4 Results
### Sentiment and Emotion Analysis of Each Topic  
The last step of this work involves the detailed analysis of the sentiments and emotions for each of the different topics described. To perform this task, we have combined both topic modeling techniques (Section 4.2) and the hybridized Genetic Algorithm (Section 4.3) to group each tweet that contains sentiments and emotions into the identified topics.  
The volume of sentiments and emotions associated with the different topics is shown in Figure 9. This figure plots two vertical stacked bars for each topic where the left bar defines the sentiments of the topic (red for negative sentiments and green for positive ones), and the right bar describes the emotion volume for each topic.  
Based on the sentiment values, it is observed that all the discussed topics in the dataset show more negative tweets than positive ones. This result means that Uber users address the customer service platform (@Uber_Support) to tweet about complaints and problems with a negative expression on their posts with more frequency than using positive expressions, independently of the topic they are referring to. Specifically, the tweets related to the _\"Time and cancellation\"_, _\"Uber account / Uber App\"_, and _\"Uber Eats\"_ topics seem to have the highest negative sentiment values since these topics show the highest difference between negative tweets and positive ones.  
With respect to the emotions, two relevant conclusions can be extracted. Firstly, we can observe that the topic related to _\"Opinions about Customer Service\"_ shows the highest volume of emotions. Furthermore, it is observed that the predominant emotions of this topic are related to _disgust_ and _anger_ expressions. In light of this result, it is safe to assume that most of the negative tweets from this topic could be related to customers who are angry or disgusted about the support provided by the customer service. This emotion result justifies the high values of _anger_ and _disgust_ described in Figure 4. The second relevant feature extracted from this analysis emphasizes that the topic related to _\"Uber Eats\"_ shows that the _anticipation_ emotion is the most repeated one among these tweets. This result could be intimately related to the user's expectancy of awaiting food that did not arrive on time (e.g., \"@Uber_Support my food never arrived\"), which can also be related to the high volume of negative tweets from this topic.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 3 Materials and Methods  
The multi-parametric dataset obtained in [35] is used in the present work as the data source for SQI analysis. The dataset comprises 21 records from 11 healthy participants (8 males and 3 females), averaging 32.1 years old (\(\pm\)8.2 years). The recordings include five raw MW-PPG wavelengths (470, 525, 590, 631, and 940 nm) acquired simultaneously from the index fingertips of each subject's hands, along with CF and temperature signals at the measurement point. Subjects were in a supine position during measurements. The average data record duration is 21.84 min (\(\pm\)6.09) for the left hand and 21.10 min (\(\pm\)6.36) for the right hand, totalling up to 7.5 h of recordings. The acquisition system was configured for 16-bit ADC resolution and 100 Hz sampling rate, except for temperature, which was recorded at 1 Hz. Skin tone was estimated visually using the Fitzpatrick scale. As shown in Figure 2, the signals were acquired continuously but divided into two consecutive phases. During the first phase (increasing phase), the CF was manually increased in discrete steps of approximately 0.1 N. In the second (decreasing phase) phase, the CF was similarly reduced in steps of 0.1 N. The inflexion point between phases (maximum CF) was determined based on the 940 nm PPG signal, where the presence of pulses of indistinguishable amplitude or absence of pulses for periods of no less than 10 s was used to indicate the complete occlusion of blood vessels. From this point, the pressure applied to the skin was progressively decreased to its minimum value. Figure 3 illustrates the variability in the maximum CF achieved by the index fingers of each subject.
## 3 Results and Discussion
### GC-MS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.1.1. Alcohols  
As shown in Table 1, the alcohols identified in dried citrus peel were mainly terpenic alcohols, with 2-methyl-3-buten-2-ol only identified in GFD and \(\beta\)-copaen-4\(\alpha\)-ol only identified in GAD/GSD. Moreover, linalool and \(\alpha\)-terpineol were the main alcohols in both cultivars, with the highest content of linalool and \(\alpha\)-terpineol observed in FSD and GSD, respectively. In addition, for both cultivars, SD and AD showed much higher content of linalool and \(\alpha\)-terpineol than FD, demonstrating the unfavourability of FD to preserve alcohols in dried citrus peel. Since FD was also unfavorable to keep other categories of volatile compounds, the percentage of alcohols in volatiles of freeze-drying citrus peel was no less than citrus peel made from SD and AD, as shown in Figure 1b. Thus, the combination of content and relative percentage was necessary to investigate the effect of drying methods on the volatile composition.  
Since much isomers were detected in volatiles of dried citrus peel, the extracted ion chromatogram (EIC) of different isomers is shown in Figure 2, to more clearly demonstrate the chemical composition of dried citrus peel as influenced by drying methods for the two cultivars. Figure 2a shows the chromatograms of three terpenic alcohol isomers (C\({}_{10}\)H\({}_{18}\)O: linalool, terpinen-4-ol, \(\alpha\)-terpineol) through EIC of \(m/z\) 136. It can be seen from Figure 2a that dried Chachi peel showed much higher content of \(\alpha\)-terpineol and terpinene-4-ol (both cyclic terpenic alcohol), especially for SD; and smaller content of linalool (acyclic terpenic alcohol). Thus, the ratio of cyclic to acyclic terpenic alcohol might be a potential indictor to discriminate dried Chachi peel and dried Ponkan peel.
## 4 Conclusions  
Three-dimensional printing technology offers notable advantages over traditional casting methods in construction applications. However, advancing this technology requires interdisciplinary researchers to develop efficient printing systems, innovative design approaches, and materials tailored for 3D printing. A key focus should be on creating materials with suitable rheological properties to ensure satisfactory performance under both fresh and hardened conditions. In this study, we investigated the effects of partial cement replacement with FA and GGBFS on the fresh and hardened properties of 3 DPC. The key findings are as follows:  
1. Flowability and printability: The workability values of 3 DPC, assessed based on flowability and slump, follow the order S0F5 > S25F25 > S5F0, indicating that higher  
Figure 20: Drying shrinkage of 3 DPC.  
FA content enhances flowability. Despite these variations, all mix designs demonstrated sufficient extrudability and buildability, meeting the essential printability criteria for 3 DPC.
2. Mechanical and durability performance: The specimens with a higher proportion of GGBFS exhibited superior mechanical properties, including higher unit weight, compressive strength, and flexural strength, while demonstrating lower water absorption and reduced drying shrinkage. This suggests that GGBFS contributes to enhanced material densification and long-term durability in 3 DPC.
3. Anisotropic strength behavior: The compressive strength of 3 DPC varied with the printing direction, following the trend X direction > Z direction > Y direction. Notably, the compressive and flexural strength in the X direction exceeded those of the conventionally cast specimens at 28 days, highlighting the structural integrity and potential advantages of the layered deposition process in 3 DPC. These findings emphasize the viability of replacing up to 50% of PC with FA and GGBFS in 3 DPC while maintaining high printability and mechanical performance. This study provides novel insights into the anisotropic behavior of 3 DPC and the role of SCMs in optimizing fresh and hardened properties, paving the way for more sustainable and structurally efficient 3D-printed construction materials.  
Conceptualization, M.C.; Methodology, K.-C.T., M.C. and W.Y.; Formal analysis, W.Y.; Investigation, K.-C.T.; Writing--original draft, K.-C.T.; Writing--review & editing, M.C., W.Y. and R.H.; Supervision, R.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology grant number MOST 110-2221-E-507-010-MY3.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Deterministic Discrete Fracture Network Model and Its Application in Rock Mass Engineering
## 2 Acquisition of Structural Surface Parameters
### Structural 3D Modeling of Rock Mass
(2) Flat plate model for layers and faults:  
Due to their extensive nature and the relatively small size of the experimental rock mass area, layers and faults are represented by a flat plate model that extends through the rock mass area. The data required for the flat plate model in 3D space include the normal vector of the plane and the starting point of the plane's extension. The starting point of the plane's extension is the midpoint of the trace line of the layer or fault structural surface, and the normal vector of the plane can be obtained from the dip direction and dip angle of the plane. The distribution of the layer and fault models in 3D space are shown in Figure 4.  
(3) The Size of the Rock Mass Model  
In order to avoid the occurrence of edge effects, it is necessary to expand the rock mass model until it includes all structural surfaces. The REV can be determined by setting up multiple observation windows to monitor the extension of structural planes within the rock mass. (4) Surrounding Rock Classification  
In the field of geotechnical engineering, blockiness serves as an efficient and effective indicator for evaluating rock mass quality and is widely used. In the 1998 Engineering Rock Mass Classification Standard, blockiness was correlated with rock mass quality grades, as shown in Table 2 [31; 32]. The influence of blocks with varying shapes and volumes on the calculation of blockiness was analyzed in detail in [33; 34; 35]. To account for these variations, weight factors were introduced for blocks of different volumes. The modified blockiness calculation method, incorporating these weighted parameters, is expressed as follows:  
\[B_{z}=B_{1}+\frac{1}{2}B_{2}+\frac{1}{3}B_{3}+\frac{1}{4}B_{4}+\frac{1}{5}B_{5} \tag{7}\]  
where \(B_{1,2,3,4}\) variables represent the proportions of blocks with volumes in the ranges of 0-0.008, 0.008-0.030, 0.030-0.200, 0.200-1, and \(>\)1 m\({}^{3}\), respectively, relative to the total volume of the rock mass. A specific table showing the correspondence between the two is presented in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification of Blocking Degree** & **Rock Mass Grade** & **Percentage of Block** \\ \hline Non-blocky rock & V & 0–10 \\ Slightly blocky rock & IV & 10–30 \\ Moderately blocky rock & III & 30–60 \\ Blocky rock & I/II & More than 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The relationship between the grade of rock mass quality and the degree of blockiness. Figure 4: The distribution of the Layer and Fault 3D Models.
Additionally, we have implemented two repair distribution models: a general distribution and a Gumbel-Hougaard copula distribution. By utilizing transition diagrams and a system of partial differential equations, we have conducted an analysis to determine various reliability metrics such as reliability, availability, mean time to failure (MTTF), sensitivity analysis, and profit function. These insights can be valuable for managerial staff in the industry when making decisions. The paper is structured into several sections. Section 1 examines the existing literature and research findings. Section 2 provides an overview of the system, including its design assumptions and notation. Section 3 illustrates the system's behavior through transition diagrams under varying failure and repair scenarios along with mathematical modeling using differential equations. Section 4 presents analytical findings on system performance metrics such as availability, reliability, mean time to failure (MTTF), sensitivity analysis, and expected profit margins demonstrated through specific cases. Finally, Section 5 offers concluding remarks on the study.
## 2 Data Analysis and Preprocessing
### Data Preprocessing  
To ensure the data quality and the efficiency of model calculation, it is necessary to process all data sources. This paper adopts three methods: data standardization, missing data filling, and abnormal data repair [31; 32].
## 3 Case Study
### Background  
The PGM mine is an underground hard rock mine in Southern Africa with mining operations to extract precious metals through bord and pillar mining. The mine uses trackless mobile machinery for its operations, except for conveyance of ore from the underground mining sections to the processing plant, which uses fixed conveyor belts. There are three main categories for this production trackless mobile machinery--face drilling machines, roof supporting machines, and hoisting machines, which are commonly known as Load, Haul, and Dump (LHD) machines. The incident reviewed in this case study occurred on one of the LHD machines. The mine utilizes three models for these machines, which will be referred to as model A, 500, and model C. The LHD machines are used to hoist broken ore from the blasted faces to tipping points for sizing by hydraulic breakers before conveyance to the processing plant. These machines are hydraulic-operated machines powered by diesel engines.
## 4 Range-Based Algorithms
### Bayesian Inference Method  
The Bayesian approach gives a distribution, instead of an estimated value, as an outcome which is more informative than LS. Compared to ML, where parameters are considered fixed, in the Bayesian method, they are treated as a random variable with known prior distribution [41]. Based on [PERSON]'s theorem, the position and the parameters act as random variables where the prior information and observations are leveraged to infer and update the posterior distribution of unknown random variables. For example, in [42], PLE and position are treated as mutually independent random variables from which the posterior distributions are derived. To do so, a message passing algorithm, called belief propagation [43], is used on the factor graph, which allows for efficient computation of marginal distributions and dealing with the problem's intractability. The cooperative localisation scenario is also dealt with using the Bayesian method in [42]. In this paper, in addition to PLE, transmission power is estimated.