esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions of Wave Velocities, Dispersion and Attenuation Characteristics
### Comparison of Attenuation Coefficients in Different Saturated Soils  
Figure 2 shows the relations of permeability, loading frequency, and the attenuation coefficients of the P1, P2, and S waves in the case of finite permeability. For P1 and S waves, as the permeability reaches \(k<1\times 10^{-4}\) m/s the attenuation coefficients always remain at zero, which illustrates that the two waves do not attenuate in the propagation procedure. With the increase in the permeability, a wave crest appears in the range of \(k>1\times 10^{-4}-1\) m/s and the maximum values of the attenuation coefficients appear as permeability is about \(1\times 10^{-2}\) m/s for the P1 and S waves. This indicates that two waves show slight attenuation. For the P2 wave, as permeability \(k>1\times 10^{-4}\) m/s, the attenuation coefficients change from 1.0 to 0.0 with the increase of the permeability. The P2 wave gradually attenuates to zero. Contrasted with the loading frequency, the permeability has more influence on the attenuation coefficient.  
Figure 2: _Cont._  
Comparing the relations of permeability, loading frequency, and the attenuation coefficient in the four saturated soils, we can find that different saturated soils do not change the shapes of the attenuation coefficient curves, but do change the amplitudes of these curves. Especially when the saturated soils changed from the gravel to the silt, the amplitudes of the attenuation coefficients of the S wave obviously shows an increased tendency. The S wave attenuates more significantly in soft soils.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.5 Performance Metrics  
The following metrics were used to evaluate classifier performance:  
* True Positive Rates (TPR) where both the Transformer's current state of health and its anticipated state of health are positive;
* Inaccurate Rates Negative (FNR) where the Transformer is in good condition (positive), a fault is anticipated (negative);  
Figure 2: Diagram showing the defect diagnostic process using KNN.  
* False Discovery Rates (FDR), It is expected that the Transformer will be defective (negative), even though it is faulty (negative);
* Positive Predictive Values (PPV), in this case both the Transformer's current state of health and its anticipated state of health are positive.  
This ensures that the system not only identifies faults effectively but also minimizes false alarms and missed detections. Such a comprehensive evaluation is crucial for optimizing transformer maintenance strategies, reducing downtime, and enhancing the overall reliability of power systems. Ultimately, the refined predictive performance contributes to proactive maintenance practices, extending the lifespan of transformers and ensuring uninterrupted power supply.
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 2 Background and Related Work
### Related Work
Then, the consensus process is composed of two steps: (1) the consensus within the primary group; and (2) the consensus between the remaining nodes and the primary group. It can improve scalability by reducing change the proportion of the single primary node. However, its number of communications is the same as the PBFT, so it is hard to expect a distinct improvement of scalability. A new hardware-based execution environment is also introduced for improving performance of the BFT-based consensus algorithm. [PERSON] et al. proposed a hardware-based BFT algorithm execution environment, which is named Fast BFT [23]. All nodes use a hardware chip (e.g., _Intel SGX_) for using TEE (Trusted Execution Environment) to execute the consensus algorithm and the TEE supports public key operation (e.g., _multi signatures_) during the consensus process. They also suggested the new Fast BFT algorithm that reduces verification steps by collaborating with TEE. While they improved the BFT algorithm's scalability, their assumptions that all nodes should be executed upon TEE must be their limitations.
## 6 Experimental Results
### ToN-IoT Dataset
The best accuracy we obtained using DenseNet was 99.9% for  
Figure 8: (**a–c**) The training and validation accuracy, F1-score, loss, recall, and precision on the ToN-IoT dataset after training with the Inception Time model. Figure 9: The results of the IoT network in the inception time model of the ToN-IoT dataset. Win10-Network, while the lowest was 97.7% for Win10. However, when using the Inception Time model, the maximum accuracy was 100% in the Win10-network model, while the lowest accuracy was 98.3% in the Win10 model, as shown in Figure 10.  
Compared to the previous trials in Table 7, the Inception Time model outperformed each (i.e., DT, NB, and XGBoost) in categorizing multicategory attacks. Table 8 further indicates our model's superiority in classifying multi-class assaults in IoT devices (fridge, garage, GPS, Modbus, motion lighting, thermostat, weather) and utilizing the ToN-IoT dataset. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Research** & **Method** & **Type** & **Acc** & **Pre** & **Rec** & **F1** & **Class** \\ \hline \multirow{3}{*}{[PERSON], et al. [20]} & DT & \multirow{3}{*}{Network} & 97.29 & \multirow{3}{*}{-} & 99 & \multirow{3}{*}{Multi} \\  & NB & & 96.78 & & & & 98 \\ \hline [PERSON], et al. [12] & XGBoost & Network & 98.3 & 98.3 & 98.3 & 98.3 & Multi \\ \hline \multirow{6}{*}{**Our proposed method**} & \multirow{3}{*}{DenseNet} & Win7 & 98.36 & 95.97 & 95.91 & 95.91 & \multirow{3}{*}{Multi} \\  & Win10 & 97.87 & 97.87 & 97.87 & 97.87 & 97.87 & \multirow{3}{*}{Multi} \\  & Network & 98.57 & 98.59 & 98.57 & 98.57 & 98.57 & \\  & Win10–N & 99.95 & 99.95 & 99.95 & 99.95 & \\ \cline{2-8}  & \multirow{3}{*}{Inception Time} & Win7 & 99.21 & 99.21 & 99.21 & 99.21 & \multirow{3}{*}{Multi} \\  & Inception & Win10 & 98.30 & 98.30 & 98.30 & 98.30 & \\  & Time & Network & 99.65 & 99.68 & 99.64 & - & \\  & Win10–N & 100 & 100 & 100 & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the results of previous studies using the ToN-IoT dataset. Figure 10: The comparison of results between DenseNet and Inception Time using the ToN-IoT dataset.
# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 4 Experimental Results and Analysis
### Quantitative Contrast Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **ME** & **RAE** & **TPR** & **FAR** \\ \hline ISSD & **0.2036** & **0.1768** & 0.9551 & 0.1957 \\ ITDBE & 0.4617 & 0.6978 & 0.6137 & 0.7526 \\ SRISTAS & 0.2211 & 0.6346 & 0.9332 & 0.6542 \\ Proposed & 0.2042 & 0.2868 & **0.9705** & **0.0692** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The average value of each algorithm index in 6 sequences. Figure 14: Line charts of the four metrics in each sequence. method combined with multi-feature discrimination, which effectively eliminates interference regions resembling the target within the scene. Furthermore, Figure 14d indicates that the proposed algorithm outperforms the comparison algorithm in terms of detection rate across most scenarios. Although using the convolutional template to extract the low gray area may result in some loss of edge pixels for the target, the RAE and ME values of the proposed algorithm are comparable to those of the best performing ISSD algorithm. Notably, the RAE values of the proposed algorithm are significantly smaller than those of the other two algorithms. This suggests that the targets detected by the proposed algorithm closely resemble the real targets, as visualized in Figure 14a,b. Considering the average results of the four metrics in the given sequences, the proposed algorithm outperforms the comparison methods in terms of TPR and FAR, with only a slight difference in ME and RAE compared to the best results. Therefore, overall, the detection performance of the proposed algorithm in this paper is superior to the ones used for comparison.
## 2 Related Work
The topology control algorithm showed good performance in anti-jamming and could guarantee end-to-end delay; however, the algorithm was insufficient in improving network coverage and extending the network life cycle. Our contributions are as follows:  
* In this paper, a cube is used as the partition unit, and a rule 3D space segmentation scheme is used for network partition. Based on the establishment of a 3D topological control model for UWSNs, the concepts of a UWSN unit and underwater cluster are proposed. The size of the partition unit and cluster scale are calculated according to the constraints of energy efficiency and quality-of-service awareness. * A sleep-wake scheduling algorithm for UWSN clusters is constructed, which can build initial randomly placed sensors into a 3D network and form a UWSN with higher coverage, higher connectivity, and lower energy consumption through the sleep-wake scheduling algorithm.
## 4 Case Studies
### Port of Immingham  
The Port of Immingham (www.abports.co.uk/locations/immingham, accessed on 18 November 2023) is the second largest port in the UK in terms of tonnage, owned and operated by Associated British Ports (ABP). The Port of Immingham moved over 50.17 million tonnes in 2022 covering various types of goods, including dry bulks, containers, bulk energy, liquid bulk, project cargo, rail freight, and ferries, in addition to servicing offshore wind business. The port is a critical part of the supply chain for sustainable electricity generation and other energy production in the UK.  
The Port of Immingham set 2040 as the target year to achieve net zero emissions from its own operations. The port is targeting emission reduction across its major plants and equipment types (equipment, vessels, dredgers, cranes, vehicles, etc.) to meet the scope 1 and scope 2 net zero emission target. ABP has reduced scope 1 and 2 emissions by 38% between 2014 and 2021. Decarbonisation measures at the Port of Immingham include the following:  
* **Electric cargo handling equipment**: In 2020, ABP deployed GBP 7 million electric RTG cranes as part of a GBP 33 million investment; in 2022, 14 electric forklifts were deployed at the Port of Immingham.
* **Hydrotreated Vegetable Oil (HVO)**: In 2022, four HVO reach stackers were used to replace older diesel equipment which had reached the end of its usable life.
* **Hydrogen truck**: In 2023, a hydrogen-fuelled truck was used in the port to cut GHG emissions.
* **Green hydrogen**: In 2021, a feasibility project was conducted to investigate the production of 20 MW of green hydrogen for use at the Port of Immingham by 2025.
* **Renewable energy**: In 2020, ABP completed the UK's largest rooftop solar array at the Port of Hull near Immingham. In 2023, the Ports of Grimsby and Immingham initiated the onshore wind projects for a generation capacity potentially up to 36 MW. Plans are being developed for up to four turbines at Grimsby (up to 24 MW) and at least two at Immingham (up to 12 MW).
* **Infrastructure**: In 2022, electric charging for vehicles was rolled out for colleagues and guests. In 2023, ABP installed a mobile hydrogen filling station to fuel the vehicle.
* **Carbon capture and storage**: In 2022, plans were announced to develop a CO\({}_{2}\) terminal to serve as a hub for the collection of CO\({}_{2}\) emissions from industrial businesses around the country. Its first CO\({}_{2}\) capture is expected in early 2027. It would not only decarbonise the maritime sector but also the Immingham landslide industries, which is the most carbon-intensive industrial cluster in the UK.
* **Green certificate**: In 2021, ABP gained ISO 14,001 Environmental Management Certification.
_Article_  
**Thermodynamic Performance Analyses and Optimization of Dual-Loop Organic Rankine Cycles for Internal Combustion Engine Waste Heat Recovery**  
**[PERSON] 1,2, [PERSON] 2, [PERSON] 2,*, [PERSON] 2 and [PERSON] 1**  
1 School of Architecture and Urban Planning, Yunnan University, Kunming 650500, China; [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (Z.X.)  
2 Key Laboratory for Thermal Science and Power Engineering of MOE, Beijing Key Laboratory for CO2 Utilization and Reduction Technology, Tsinghua University, Beijing 100084, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (Z.Y.)  
* Correspondence: [EMAIL_ADDRESS]  
Received: 3 January 2019; Accepted: 12 February 2019; Published: 16 February 2019  
**Featured Application: This work examined the effects of an internal combustion engine (ICE) exhaust gas temperature on dual-loop organic Rankine cycles (DORC) for ICE waste heat recovery. High-temperature loop evaporation, condensation temperatures, and superheat degrees were optimized. Results could provide theoretical and data support for determining suitable operating parameters and working fluid for DORC with various engine exhaust gas temperatures.**  
**Abstract: Waste heats of an internal combustion engine (ICE) are recovered by a dual-loop organic Rankine cycle (DORC). Thermodynamic performance analyses and optimizations are conducted with 523.15-623.15 K exhaust gas temperature (\(T_{\text{g1}}\)). Cyclopentane, cyclohexane, benzene, and toluene are selected as working fluids for high-temperature loop (HTL), whereas R1234 ze(E), R600a, R245 fa, and R601a are selected as working fluids for low-temperature loop (LTL). The HTL evaporation temperature, condensation temperature, and superheat degree are optimized through a genetic algorithm, and net power output is selected as the objective function. Influences of \(T_{\text{g1}}\) on system net power output, thermal efficiency, exergy efficiency, HTL evaporation temperature, HTL condensation temperature, HTL superheat degree, exhaust gas temperature at the exit of the HTL evaporator, heat utilization ratio, and exergy destruction rate of the components are analyzed. Results are presented as follows: the net power output is mainly influenced by HTL working fluid. The optimal LTL working fluid is R1234 ze(E). The optimal HTL evaporator temperature increases with \(T_{\text{g1}}\) until it reaches the upper limit. The optimal HTL condensation temperature increases initially and later remains unchanged for a cyclopentane system, thus keeping constant for other systems. Saturated cycle is suitable for cyclohexane, benzene, and toluene systems. Superheat cycle improves the net power output for a cyclopentane system when \(T_{\text{g1}}\) is 568.15-623.15 K.**  
**Keywords: dual-loop organic Rankine cycle; waste heat recovery; performance analysis; parametric optimization; exhaust gas temperature**
# A Power Monitor System Cybersecurity Alarm-Tracing Method Based on Knowledge Graph and GCNN
## 3 Results and Discussion
### Case Settings  
To validate the proposed method's effectiveness and accuracy, a case study analysis was conducted using 70,107 alert messages provided by an actual power company. Table 2 shows the relevant parameters used in the intelligent tracing method.  
In this section, the PowerCyberSec alert traceability sample dataset is used to train and test the effectiveness of the proposed intelligent traceability method. Validation methods M0-M6 are set up, and a comparison table of the precision of seven traceability methods is obtained, as shown in Table 3. By comparing the accuracy of various methods in tracing different types of alert causes and their ROC effect plots, it is verified that the proposed intelligent traceability method M0 can achieve precise and intelligent traceability for various types of alert causes.  
M0: The intelligent alert traceability method proposed in this paper is based on KG and graph attention networks, and it addresses the issue of sample imbalance.  
M1: Traceability Method Based on KG and Graph Attention Networks;  
M2: Traceability Method Based on KG and Graph Convolutional Networks;  
M3: The Traceability Method Based on Tf-idf Text Vectorization and SVM Proposed in ref. [7];  
M4: An Improved Traceability Method Based on KG and SVM, Considering Sample Imbalance, Derived from ref. [7]'s Approach;  
M5: The Traceability Method Based on Tf-idf Text Vectorization and Convolutional Neural Networks Proposed in ref. [14];  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Numerical Value** \\ \hline \(m_{GAT}\) & 2 \\ \(n_{\text{dim}}\) & 50 \\ \(m_{base}\) & 10 \\ \(s\) & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Model Parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **R1/\%** & **R2/\%** & **R3/\%** & **R4/\%** & **R5/\%** & **Overall Accuracy** \\ \hline M0 & 93.41 & 100 & 99.19 & 96.35 & 91.57 & 96.59 \\ M1 & 71.33 & 50.0 & 84.91 & 93.34 & 73.16 & 84.93 \\ M2 & 68.66 & 25.0 & 74.51 & 99.10 & 56.14 & 80.97 \\ M3 & 0.0 & 0.0 & 0.0 & 100.0 & 0.0 & 35.27 \\ M4 & 24.66 & 0.0 & 35.74 & 99.54 & 59.35 & 60.21 \\ M5 & 0.0 & 0.0 & 0.0 & 100.0 & 0.0 & 35.27 \\ M6 & 52.67 & 0.0 & 50.60 & 98.94 & 55.08 & 68.58 \\ \hline \end{tabular}
\end{table}
Table 3: Influence of Knowledge Graph and Knowledge Representation Techniques on Traceability Accuracy.  
M6: An Improved Traceability Method Based on KG and CNN, Derived from ref. [14]'s Approach.  
The PowerCyberSec alert tracing dataset consists of 70,107 alert messages in sentence form, each containing details such as the alert time, alert level, alert type, alert description, alert site, source site, destination site, alert device, and alert cause. The alert causes are categorized into \"Routine Device Operation\" (R1) with 737 entries, \"Zone 4 and Zone 5 Alerts\" (R2) with 32 entries, \"User Attempted Access (R3)\" with 632 entries, \"Excessive Service\" (R4) with 66,347 entries, and \"Other\" (R5) with 2359 entries.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Flora and Fauna Applications
#### 2.4.3 Livestock Farming  
Livestock farming is a main sector of the fauna domain. WSNs can be applied in various tasks such as livestock monitoring. Below we have collected some examples of these applications.  
In [33], a sensor-based system developed to analyze the behavior of livestock animals and support their farming was proposed. Green pastures are used by cattle for grazing, so grass growth was analyzed through photographic sensors, so that animals can be moved towards them. Their major objective of work was to design rugged hardware that could be used outdoors for modeling both individual and herd behavior of animals. Although, specially designed sensors to monitor animal behavior, such as sleeping, grazing, and ruminating, are used, cattle monitoring still poses several challenges like radio attenuation caused due to factors such as animal body, mobility etc.  
In [34], an inexpensive low power collar designed to assist livestock farming is proposed. The system developed uses a solar power relay router and two antennas placed so that the collar radio coverage is optimized. Also, a novel protocol, named Implicit Routing Protocol (IRP), was proposed in order to cope with the packet losses, which are caused due to the mobility of the animals.  
In [35], the authors describe a system based on RFID and WSN technologies, which can be applied in modern farm large-scale management systems. The proposed system can identify and track all animals (i.e., pigs) that are present within the farm, and monitor their health as well as the environmental conditions. According to the authors this system, by the aforementioned parameters, can be used to prevent diseases, determine environment pollution, and inform on food safety issues.
###### Abstract  
This study focuses on the use of oligomeric hydrocarbon resins in order to benefit from their effect on improving the performance of aircraft tire retreads. The aim was to enhance the tackiness for the retreading process and their final performance in terms of superior stress-strain properties and low heat generation in order to decrease readwear; thus, increasing the tire's service life, and in terms of traction or skid resistance to improve safety during landing of an aircraft. Two types of resins are investigated: a terpene phenol and an aromatic hydrocarbon C9 resin, added to compounds with different filler systems: Carbon Black (CB), hybrid Carbon Black/Silica (CB/SI), and pure Silica (SI). The rubber compounds and vulcanizates are compared to their controls for each filler system. The use of resins improves processing independent of the filler system, with a slight improvement of tensile strength, Modulus at 300% (M300%) and Elongation at Break (EAB). The incorporation of resins improves the tackiness for the compounds with all filler systems, which is beneficial for the retreading process. A significant improvement in Ice Traction (ICT) and Wet Skid Resistance (WSR) with a trade-off in Heat Build-Up (HBU) is observed in CB- and CB/SI-reinforced compounds when resins are added. Terpene phenol and aromatic hydrocarbon C9 resin show comparable ICT, while the aromatic hydrocarbon C9 resin gives a better WSR performance than the terpene phenol in all compounds. However, a slight improvement in HBU with the use of both resins is only observed in the SI-filled system. The present exploratory study into the addition of resins demonstrates the potential to significantly improve the overall performance of aircraft tire retreads, justifying more in-depth investigations into this possibility in real tires.  
resins; rubber formulation; aircraft tire retreads +
Footnote †: journal: Applied Sciences
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion
### Antenna Current Distribution and Analysis  
Figure 19 shows the surface current distribution of the radiating patch at the top and bottom of the antenna at the 2.45 GHz frequency point. From the figure, it can be seen that the current distribution of the antenna is relatively uniform. We increase the bandwidth of  
Figure 17: Measurement device for wireless data transmission.  
Figure 18: (a) Transmission rate, (b) transmission quality.  
the antenna by adjusting the geometry and size of the antenna to create a uniform current distribution in the operating frequency band. Uniform current distribution results in better impedance matching of the antenna, more stable radiation efficiency, and lower SAR.