esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Apparatus and Methods  
The machine tool used for the Inconel 601 drilling experiment in this study was a SIRIUS-2 3-axis machining center (Hwachen, Korea), and the drill used was a tungsten carbide drill (DH 423050, YG1) coated with TiAlN. The wear of the drill tip was measured using a tool microscope (SOMETECH, Korea). The oil-mist supply device (MQL device) was a Vario UFV 10-003 (Vogel, Germany), and Lubri Fluid F100 was used as the MQL-specific oil. In the adhesion experiment of the POSS-containing MQL oil, the POSS ester compounds with varying concentrations (P) and flow rates (Q) were added into the MQL-specific oil; the mixtures were used to perform drilling for 5 min using the MQL device; the weight of the oils in relation to the drill were measured. The drilling-evaluation experiment was performed using a 5\(\Phi\) drill coated with TiAlN, with the following conditions: spindle speed (V\({}_{\rm s}\)) = 500 rpm, feed (f) = 0.05 mm/rev, oil-mist flow rate (Q) = 60, 120, 180, and 240 mL/h, and step feed (mm \(\times\) times) = 2 \(\times\) 3, applying a sealed cover.  
Figure 3: \({}^{1}\)H-NMR spectrum of POSS-compounds in CDCl\({}_{3}\). (**a**) Octa-chloro-POSS: \(\delta\) 0.78 (t, 16H, SiCH\({}_{2}\)), 1.84 (qui, 16H, CH\({}_{2}\)), 3.53 (t, 16H, CH\({}_{2}\)Cl) **(b)** POSS-C8 **(1): \(\delta\) 4.09–3.97 (m, 16H), 2.30–2 (t, J = 7.32 Hz, 16H), 1.67–1.56 (m, 32H), 1.37–1.19 (m, 64H), 0.88 (t, J = 7.32 Hz, 24H), 0.72–0.59 (m, 16H) (c) POSS-C2 **(2): \(\delta\) 4.05–4.02 (t, J = 6.4 Hz, 16H), \(\delta\) 2.06 (s, 24H), \(\delta\) 1.71–1.70 (m, 16H), \(\delta\) 0.68–0.64 (t, J = 8.4 Hz, 16H)**(d)** POSS-C1 CF\({}_{3}\) **(3): \(\delta\) 4.35–4.13 (m, 16H), \(\delta\) 1.85–1.81 (m, 16H), \(\delta\) 0.73–0.69 (t, J = 8.4 Hz, 16H).
## 3 Results and Discussion  
### Pretreatment of Petrochemical Wastewater by Fenton Oxidation  
#### 3.1.1 Effect of Fe\({}^{2+}\) Concentration  
Figure 1 shows the effect of Fe\({}^{2+}\) concentration on COD removal (Figure 1a), B/C values (Figure 1b), and BOD\({}_{5}\) variation (Figure 1c) during Fenton oxidation. When the wastewater is acidified to pH = 3 that is suitable for Fenton reaction, the COD is significantly reduced, but the B/C value is 0.26, which is still less than 0.3, indicating that the biochemical properties are still low and needs to be pretreated by Fenton oxidation. The COD decreased significantly from 560.60 mg/L to 376.19 mg/L when the Fe\({}^{2+}\) concentration increased from 20 mg/L to 120 mg/L at 300 mg/L H\({}_{2}\)O\({}_{2}\) after 60 min. This was due to the reaction between Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\) in the wastewater to produce the strongly oxidizing \({}^{\bullet}\)OH (Equation (1)) [22], which can mineralize most organic pollutants. However, the COD increased to 504.04 mg/L when excess Fe\({}^{2+}\) increased to 200 mg/L, because of the consumption of the generated \({}^{\bullet}\)OH (Equation (2)) (Figure 1a) [23]. The B/C value reached 0.495 when Fe\({}^{2+}\) was 120 mg/L, greater than 0.3, and higher than other experimental groups (Figure 1b), which also has the lowest BOD\({}_{5}\) of 186.21 mg/L (Figure 1c). It indicates that the pretreatment  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wastewater** & **pH** & **COD (mg/L)** & **BOD\({}_{5}\) (mg/L)** & **B/C** \\ \hline Petrochemical wastewater & 6.26 & 2458.75 & 127.86 & 0.052 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Petrochemical wastewater quality indicators.  
effect was achieved under the selected condition of 120 mg/L Fe\({}^{2+}\) with higher biochemical properties.  
\[\text{Fe}^{2+}+\text{H}_{2}\text{O}_{2}\rightarrow\text{Fe}^{3+}+\text{OH}^{-}+ \text{{}^{\bullet}OH} \tag{1}\]  
\[\text{Fe}^{2+}+\text{{}^{\bullet}OH}\rightarrow\text{Fe}^{3+}+\text{OH}^{-} \tag{2}\]
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 4 Findings: The Accelerating Strategy
### Information Acceleration Transmission Strategy  
In this section, we will introduce the information acceleration strategy in detail. The core of the information accelerated transmission strategy is to calculate the importance of each node, select an appropriate number of nodes as secondary nodes according to the network situation, and give a larger bandwidth and a higher cache to help the network distribute data and reduce redundancy. Equations (2)-(7) give the calculation method of the above parameters. The specific flowchart is shown in Figure 3. The detailed process is as follows:  
1. Generate a topology according to the real network data. After the topology is successfully generated, the FIB table of each node is generated according to the best route routing strategy, and then run the blockchain system on each node;
2. Traverse the entire network according to the FIB table, and record the data interaction path between any node \(i\) and node \(j\).
3. According to the traversal result, delete nodes and data links which do not interact with any other nodes in the network, and established a new topology network.
4. Calculate the importance of each node according to the new network topology. The calculation equation refers to Equation (2) and Equation (3).
5. After calculating the degree of each node, obtain a \(p\) according to the network state, and substitute the \(p\) value and the degree of each node into Equations (2) and (3) to calculate the value of importance for each node. Substitute the \(p\) value into Equations (4) and (7) to calculate the value of \(L_{j}\).
6. According to the \(L_{j}\), an appropriate number of nodes are selected as secondary nodes to assist the blockchain network to accelerate information transmission.  
Based on the execution process and operation logic of the information acceleration strategy, we present the execution pseudocode of the strategy, which is used to simulate the information acceleration strategy on the physical device. The specific pseudocode is shown in Algorithms 1.  
```
INPUT: network topology information OUTPUT: secondary nodes
1:functionInformation acceleration transmission strategy
2: Generate FIB table according to the BestRoute policy
3: Traversing the topology path according to the FIB table
4:For path \(r\)
5:If (Path \(r\) is an unrelated path)
6:Delete path \(r\)
7:end
8: Generate the new topology network
9:Calculate the importance of every node in the new topology network according to Equation (2) and Equation (3)
10: Set the value of \(P\)
11:Calculate the \(P_{j}\) and \(L_{j}\) according to Equation (4) and Equation (7)
12: Select the appropriate number of nodes as secondary nodes
13: Set these nodes as the secondary nodes
```  
**Algorithm 1** Pseudocode of the specific transmission strategy.  
Figure 3: The flow chart of information acceleration transmission strategy.
## 5 Results and Discussion
### RO-PUF Metric
This work avoids temperatures higher than 50 \({}^{\circ}\)C to prevent the chip's performance degradation since the FPGA board is tested repeatedly over various patterns using various runtimes. This study found that uniqueness and reliability are varied among patterns, as listed in Table 3. The realization of using Pattern-2 resulted in the best uniqueness of 52.71%. It agrees with the narrow frequency range and low standard deviation of Pattern-2, as shown in Figure 5. While the RO arrangement, according to Pattern-1, produces the lowest uniqueness. Regarding reliability, Pattern-1 resulted in the highest score, while Pattern-2 produced the lowest score. However, the difference in reliability of all patterns is less than 1%. The uniqueness and reliability are subject to change upon scenarios, as shown in Figure 6. The uniqueness of all patterns and their shift upon scenarios are shown in Figure 6a. While Figure 6b viewed the reliability of all patterns and their shift when the scenarios are applied. In most scenarios, the uniqueness increase, and the reliability decrease compared to when all ROs are involved. The study identified unusual issues among scenarios where the uniqueness decreases and the reliability increase. The first issue, uniqueness, is decreased when ROs implemented using Pattern-1, Pattern-4, and Pattern-5 using WR1, RH2, and RH1 scenarios, respectively. The second issue was the increased reliability when the ROs realized using Pattern-1, Pattern-4, and Pattern-5 using WR2, WR2, and WR1. The issues occurred because the presented data in Figure 6 show the average of all runtimes; second, some excluded ROs may be unstable [5; 42; 43]. Unlike uniqueness and reliability, the uniformity trend is generally varied over patterns and scenarios, as shown in Figure 7. The uniformity of Pattern-2 and Pattern-3 is close to the ideal value of 50%. The realization using Pattern-1 resulted in the least random. The uniformity of RO-PUF when Pattern-4 and Pattern-5 are applied is better than Pattern-1 but not as random as Pattern-2 and Pattern-3. Most of the patterns produced more \"1\" in a series of responses, but Pattern-5 resulted in more \"0\". Upon the scenario implementations, most of them resulted in less random outcomes. Table 3 shows the performance and statistical properties for different patterns. Varying performance among patterns is due to different LUT input configurations, which in turn affect the delay of RO among patterns. Figure 6: The shifts upon four scenarios of (**a**) uniqueness (**b**) reliability.
## 2 Background: Electric Vehicle Charging Systems and Standards
### IEC 61850 and IEC 61850-90-8: Communication with the Operator  
For the EV charging to be practical, the operator, a representative name of secondary actors, needs to get involved in user identification and authorization, billing and power control. Although there are protocols for management purposes between the operator and EVSE, such as CIM (Common Information Model), OPC UA (Open Platform Communications Unified Architecture) and DMTF (Distributed Management Task Force), we are mainly interested in the exchange of electric power-related values between them. In this regard, we assumed that all of the communication between EVSE and the operator is based on the IEC 61850 standard, as the EV and EV charger become part of the power grid from the operator's point of view.  
When EV and EVSE communicate by ISO/IEC 15118 and EVSE and the operator communicate by IEC 61850, there should be a bridging mechanism to transform 15118 messages to 61850 and pice versa_. The transform work is done by converting the 15118 schema-based XML message to the 61850 schema-based XML message in the XSL (EXtensible Stylesheet Language) processor [24]. Although not complete, a technical report IEC 61850-90-8 defines such a transformation. We implemented this bridging mechanism in our reference EV charger, as shown in Figure 3. The bridging mechanism dynamically converts ISO/IEC 15118 messages to and from the IEC 61850 messages based on the corresponding XML schemas. Our test system assumes that the target EV charger implemented this feature if it claims to communicate with the operator.
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 3 Results
### Characterization of Drained Graywater
#### 3.1.3 Variation in the Biological and Microbiological Properties of Graywater  
The results for the biological parameters (five-day biochemical oxygen demand (BOD) and chemical oxygen demand (COD)) and the four microorganisms (fecal coliforms (FC), total coliforms (TC), _Escherichia coli_ (_E. coli_), and fecal streptococci (FS)) are presented in Figure 3. The average BOD values are 31.33, 24.82, 20.63, and 15.74 mg O\({}_{2}\)/L for RGW, GWT1, GWT2, and GWT3, respectively, complying with Tunisian regulation NT 106.03 and the WHO's guidelines (30 mg O\({}_{2}\)/L). A significant decrease in BOD\({}_{5}\) was observed with each treatment stage. For COD, the average values were 102.64, 88.85, 74.99, and 54.19 mg O\({}_{2}\)/L for RGW, GWT1, GWT2, and GWT3, respectively, also meeting the 90 mg O\({}_{2}\)/L limit set by both regulations. The microbial load of graywater, including FC, TC, _E. coli_, and FS, was higher in raw graywater compared to treated graywater. Cyclic treatment significantly reduced these microorganisms (Figure 3). For FC, the average decreased from 2.41 log10/100 mL in RGW to 0.57 log10/100 mL in GWT3. Similarly, TC dropped from 1.92 to 0.41 log10/100 mL, _E. coli_ from 0.87 to 0.58 log10/100 mL, and FS from 1.96 to 0.
## 6 Application Cases  
Taking the key part of the EMU bogie as an example, the feasibility of a complex product's design, manufacturing and O&M integration framework is verified based on the digital twin model in this paper. The fault times of EMU bogie at a certain time are collected in this case, the information of accessories and equipment needing repairing is fed back to the O&M Center based on fault prediction data, and the O&M plan is formulated promptly. The data are fed back to the manufacturing department for prompt production and improvement as per the demands. The data are fed back to the design department for the prompt adjustment of the structural design of each part as well as structure simulation and verification to continuously finish the product improvement and optimization design.
# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
## 4 Experiment  
A helicopter outfield flight test was conducted at an airport in Beichuan to verify the effectiveness of the proposed method for rotor aerodynamic noise signal enhancement. The flight data of the Robinson R44 helicopter was obtained. Robinson R44 helicopter is displayed in Figure 3.  
There are many devices in the whole helicopter acoustic target experiment. Table 1 gives the names, quantities, and uses of most of these devices. The sensitivity of the microphone is about 4 mV/Pa; the RTK (real-time kinematic) of the handheld GPS is 2.5 cm + 1 ppm, and its tracking sensitivity is 158 dBm; the RTK of the differential positioning device is 1.5 cm + 1 ppm; the heading accuracy is less than 0.2\({}^{\circ}\). As we all know, the  
Figure 3: Robinson R44 helicopter.  
Figure 2: (**a**) Spectrum diagram of the Robinson R44 helicopter at 0.89 km; (**b**) envelope spectrum diagram of the Robinson R44 helicopter at 0.89 km.  
most important device for collecting acoustic signals is the microphone, which is a sensor that converts acoustic signals into electrical signals. Therefore, it is necessary to analyze the uncertainty of the microphone, which is \(\pm\)0.06 dB at a 250 Hz acoustic input signal. The experimental process is presented in Figure 4.  
The SNR is high when the helicopter is close to the microphone. However, the SNR gradually decreases with the increase in the distance between the helicopter and the microphone, due to the attenuation of the helicopter acoustic signal when it propagates in the air. During the field flight test, the helicopter first started from a short distance, gradually flew farther, and then flew back. Additionally, the distance between the Robinson R44 helicopter and the microphone is provided in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement Equipment** & **Amount** & **Purpose** \\ \hline \hline Data acquisition host computer & 1 & Running the helicopter acoustic detection data acquisition and processing software. \\ Controller/power box & 1 & Data acquisition equipment control and power supply. \\ Data acquisition equipment & 9 & Controlling microphones to capture noise/data relay. \\ Microphone array frame & 9 & Supporting and mounting the microphones. \\ Array transmission cable & 9 & Equipment power supply and signal transmission. \\ Microphone & 9 \(\times\) 15 & Acquiring noise signals. \\ Dedicated mobile hard disk & 1 & Data storage. \\ Handheld GPS device & 1 & Recording helicopter position. \\ Differential positioning device & 1 & Recording the position of the microphone array. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental devices.  
Figure 4: Helicopter acoustic target detection experiment. (**a**) An array frame; (**b**) Robinson R44 helicopter flight experiment site.  
Figure 5: Robinson R44 helicopter distance from the microphone.
# Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
###### Abstract  
**Background:** Accurate estrus identification in dairy cows is essential for enhancing reproductive efficiency and economic performance. The dispersed nature of estrus data and individual cow differences pose significant challenges for accurate identification.  
**Methods:** This study gathered cow estrus data from 812 literature sources using Python 3.9 crawler technology. The data were then preprocessed using CiteSpace 6.4. We constructed a knowledge graph depicting physiological, behavioral, and appearance changes during estrus through entity and relationship extraction. To uncover potential relationships within the graph, we applied and compared two association rule algorithms: FP-Growth and Apriori. We utilized Boolean functions derived from association rule learning to validate the ability of the rules to identify normal estrus. Additionally, we employed an enhanced Iforest-CCSVM anomaly detection model to assess the performance of the association rules in detecting abnormal estrus. Furthermore, we optimized the Incremental FP-Growth Algorithm for Dynamic Knowledge Expansion. **Results:** Based on the initial knowledge graph with 86 entities and 9 relationships, mining added 17 new strong association relationships marked by 'with', enhancing its completeness and providing deeper insights into estrus behaviors and physiological changes. Furthermore, these strong association rules exhibited notable effectiveness in both normal and abnormal estrus detection, validating their robustness in practical applications. The algorithm's optimization bolstered its scalability, making it more adaptable to future data expansions and complex knowledge integrations.  
**Conclusions:** By constructing a knowledge graph that integrates physiological, behavioral, and appearance changes during estrus, we established a comprehensive framework for understanding cow estrus. Association rule mining, particularly with the FP-Growth algorithm, added 17 new strong association relationships to the graph, enriching its content and offering deeper insights into estrus behaviors and physiological changes. The strong association rules derived from FP-Growth demonstrated notable effectiveness in both normal and abnormal estrus detection, validating their robustness and practical utility in enhancing estrus identification accuracy, and providing a robust foundation for future multi-dimensional estrus research.  
**Keywords:** cow estrus; knowledge graph; knowledge complementation; association rule algorithm
## 9 Conclusions  
This work investigated the major challenges presented by VANETs' demanding needs, including extremely low latency, exceptional reliability, and seamless communications at high mobilities. It introduced a novel structure for VANETs based on fog computing, MEC, and microservices architecture. Utilizing this two-layer edge computing approach improves the network's capacity to handle computational activities and maintain minimal latency effectively. Introducing the microservice architecture with prioritizing tasks provided improved flexibility, modularity, and scalability in the design and implementation of automotive applications. An efficient offloading scheme was implemented to distribute the computing workload efficiently between vehicles and the two-level edge servers, resulting in a significant decrease in system delay, improved resource utilization efficiency, and higher network availability. In addition, a seamless migration scheme was developed and implemented with an efficient HO strategy to provide seamless communications with the intended QoS at high vehicle mobility. The proposed microservice-based VANET was simulated for heterogeneous real-based scenarios considering different TDs, different numbers of deployed vehicles, and different vehicle mobilities. The results indicated that the proposed system outperformed existing VANET systems regarding latency, availability, and reliability.  
Figure 24: Latency performance improvement at different numbers of vehicles for Category (III) tasks.  
Figure 25: Latency performance improvement at different numbers of vehicles for Category (IV) tasks.  
**Author Contributions:** Conceptualization, M.M.Z., A.A.A., M.M.M.A. and M.S.S.; methodology, M.M.Z., A.A.A., M.M.M.A., M.E. and M.S.S.; software, M.M.Z., A.A.A., M.M.M.A. and M.S.S.; formal analysis, M.M.Z., A.A.A., M.M.A. and M.S.S.; investigation, A.A.A., M.M.A. and M.S.S.; resources, A.A.A., M.M.A. and M.S.S.; writing--original draft preparation, M.M.Z., A.A.A., M.M.A. and M.S.S.; writing--review and editing, A.A.A., M.M.A., M.E. and M.S.S.; visualization, M.M.Z.; supervision M.S.S.; project administration, A.A.A.; funding, M.E. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Prince Sultan University.  
**Data Availability Statement:** The data are contained within the article and/or available from the corresponding author upon reasonable request.  
**Acknowledgments:** The authors would like to acknowledge the support of Prince Sultan University for paying the article processing charges (APC) of this publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.