esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Joint Push-Pull Communication Mode
To reduce the delay, the last-hop vehicle selects the vehicle node that is as far as possible within its communication range as the next-hop forwarding node. \[D_{kh}=\frac{\sqrt{\left(x_{k}-x_{h}\right)^{2}+\left(y_{k}-y_{h}\right)^{2}} }{R}. \tag{3}\]  
Reputation values evaluate the ability of vehicle nodes to forward data packets, which is assessed by the difference between the actual packet loss monitored and the estimated normal loss. Every vehicle node monitors neighboring vehicle nodes to evaluate reputation values. The total packets lost in the transmission from vehicle node \(k\) to vehicle node \(h\) can be calculated as [39]:  
\[M_{kh}=S_{kh}-F_{kh}, \tag{4}\]  
where \(M_{kh}\) is the total number of packets lost because of normal and malicious activity, \(S_{kh}\) is the total number of data packets sent from vehicle node \(k\) to vehicle node \(h\), and \(F_{kh}\) is the total number of data packets forwarded by vehicle node \(k\). The relay-forwarding vehicle selection in a multi-hop communication routing is shown in Algorithm 1.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Materials** & \(f_{\text{MR}}\) (**GHz**) & \(d_{\text{RLmin}}\) (**mm**) & \(\text{RL}_{\text{min}}\) (**dB**) & \(f_{\text{RLmin}}\) (**dB**) & \(f_{\text{RLmin}}\) (**RL \textless{}-10 dB**) & \(f\)**-Range (**GHz**) & **\(\Delta f\)**-** \\ \hline \multirow{3}{*}{Ni-ZnFe\({}_{2}\)O\({}_{4}\)} & 1.45 & 5.6 & \(-63\) & 2.9 & 2.5–3.5 & 1.0 & [26] \\  & 1.45 & 3.1 & \(-37\) & 12.6 & 4.0–13.0 & 9.0 & [26] \\  & - & 3.7 & \(-42\) & - & 2.9–11.9 & 9.0 & [27] \\ \hline \multirow{3}{*}{Mn-ZnFe\({}_{2}\)O\({}_{4}\)} & - & 4.3 & \(-44\) & 6.2 & 4.6–7.5 & 2.9 & [28] \\  & - & 7.6 & \(-21\) & 3.8 & 2.3–5.3 & 3.0 & [28] \\ \hline \multirow{3}{*}{Co-Ti SrM} & 6.4 & 3.1 & \(-42\) & 6.4 & 5.1–9.1 & 4.0 & [29] \\  & 8.2 & 2.7 & \(-40\) & 8.5 & 6.7–13.3 & 6.6 & [29] \\  & 15.0 & 1.5 & \(-30\) & 13.5 & 11.8–18.0(+) & 6.2+ & [29] \\ \hline \multirow{3}{*}{Zn-Zr SrM} & 8.3 & 2.7 & \(-45\) & 8.8 & 7.5–14.7 & 7.2 & [30] \\  & 10.8 & 2.0 & \(-39\) & 11.8 & 10.2–18.0(+) & 7.8+ & [30] \\ \hline \multirow{3}{*}{Ni-Ti BaM} & - & 0.9 & \(-52\) & 29.5 & 34.8–44.9 & 10.1 & [31] \\ \cline{2-6}  & - & 1.9 & \(-66\) & 11.3 & 9.7–13.4 & 3.7 & [32] \\ \hline \multirow{3}{*}{Sr\({}_{0.75}\)Ca\({}_{0.25}\)Zn\({}_{2-x}\)Co\({}_{y}\)W} & 8.2 & 2.6 & \(-38\) & 7.6 & 7.1–18.0(+) & 10.9 & [12] \\ \cline{2-6}  & 2.9 & 4.7 & \(-51\) & 3.1 & 2.2–7.8 & 8.7 & [33] \\ \hline \multirow{3}{*}{Ba\({}_{3-1}\)La\({}_{x}\)Co\({}_{2}\)Z} & - & 3.5 & \(-47\) & 3.5 & 3.7–13.7 & 10.0 & [34] \\ \cline{2-6}  & 10.1 & 1.9 & \(-39\) & 11.0 & - & 6.3 & [35] \\ \cline{1-1}  & 13.2 & 1.9 & \(-44\) & 11.3 & - & 8.6 & [35] \\ \hline \multirow{3}{*}{Co2Y} & - & 3.0 & \(-15\) & 11.3 & 10.2–17.3 & 7.1 & [36] \\ \cline{2-6}  & - & 1.3 & \(-22\) & 17.5 & 9.8–18(+) & 8.2+ & [36] \\ \hline \multirow{3}{*}{Fe\({}_{3}\)O\({}_{4}\)@PANI} & - & 1.6 & \(-56\) & 17.3 & 12.6–18 & 5.4 & [37] \\ \cline{1-1} \cline{2-6}  & 2.7 & \(-21\) & - & 9.0–14.2 & 5.2 & [38] \\ \hline \multirow{3}{*}{BaZn\({}_{0.75}\)Co\({}_{1.25}\)W} & 8.1 & 2.6 & \(-69\) & 7.89 & 6.32–18(+) & 11.7 & this work \\ \cline{1-1} \cline{2-6}  & 2.2n\({}_{1.0}\)Co\({}_{1.0}\)W & 4.89 & 4.89 & \(-58\) & 5.29 & 5.12–17.64 & 12.5 & this work \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Electromagnetic wave absorption properties of various magnetic powder–polymer composite materials.
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 4 Experimental Study
### A Real-World Application: IoV Deployment  
Managing communication networks in the Internet of Vehicles (IoV) is a real-world application of clustering [50], where vehicles communicate while sharing the same route or traffic area [51]. Clustering can improve ad hoc networks in the IoV, as demonstrated by [PERSON] et al. [52], who proposed a method based on swarm optimization that improves communication by considering vehicle mobility. Various IoV clustering algorithms have focused on different performance metrics [53]. Beyond improving communication, clustering offers broader benefits, such as optimizing vehicle routing, reducing congestion, and promoting environmentally friendly driving habits. This enhances traffic management and helps mitigate environmental impacts, driving us toward a more sustainable future. Figure 12 presents a model of this application, illustrating a smart city with areas such as downtown, surrounding suburbs, and a nearby highway with multiple exits. The simulation included several vehicles grouped into four clusters, as shown in Figure 12. The proposed algorithm successfully identified these clusters, demonstrating the necessity for a non-spherical clustering approach--such as the proposed ellipsoidal clustering--to handle the significant variance in cluster shapes.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Dataset/Algorithm** & **Mixed\_3.2** & **Ellip\_2.2** & **Square1** & **Square4** & **Sizes5** \\ \hline
**EM** & **0.08** & 0.87 & 0.25 & 0.51 & 0.16 \\
**SAELLC** & 0.15 & **0.00** & **0.14** & **0.35** & **0.12** \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of SAELLC with EM [49] as a stochastic-based approach for normally distributed data.  
Figure 12: IoV deployment is a real-life example, which illustrates how traditional spherical clusters would fail to accommodate such diverse distribution patterns of data points.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Prohibited Zone with Beamforming Antenna  
Herein, we focus on a beamforming antenna. A beamforming antenna has a main lobe and several side lobes. The antenna gain is significantly different in at different angles. In order to better consider a real-world interference scenario, the following assumptions are made for the beamforming antenna:  
1. A commonly used 16 \(\times\) 16 beamforming antenna is selected.
2. The main lobe of the beamforming antenna can sweep in a 360\({}^{\circ}\) range horizontally using electronic steering technology.
3. The main lobe of the beamforming antenna can sweep in range of 0-10\({}^{\circ}\) vertically. The angle between the main lobe and the horizontal plane is the vertical scan angle (\(\theta\)) (\(\theta\in(0,10^{\circ})\)), as shown in Figure 10.  
Furthermore, based on the analysis results of the omnidirectional antenna, the RMa base station has the largest prohibited zone. In order to simplify the analysis process, only the RMa base station is discussed in detail in the analysis.
# An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks
## 3 Intersection Signal Control Mechanism
### Dynamic Phase and Period Signal Control Scheme
#### 3.4.2 Decision-Making for the Combination Phase and Green Light Duration  
As described in Section 3.1, the 8 traffic flows at the intersection can be combined into 12 signal phases (\(P_{i},\,\in[1,\,\,12]\)) according to the principle of no conflict. Furthermore, we can utilize the equations in the previous section to obtain the eliminable waiting time \(ET_{i}^{T_{g}}\) of each combination phase.  
In addition, if there are few vehicles in a traffic flow, they generally need to wait a longer time for the decision-making system to open their signal phase, which may cause an anxious feeling and a bad experience for the driver. For this point, we adjust the value of \(T_{d}\) to increase the weight gradually with time according to:  
\[T_{d}^{\prime}=e^{nT_{d}}-1, \tag{16}\]  
where \(T_{d}^{\prime}\) is the adjusted waiting time and \(\alpha\) is the adjustment factor.  
Figure 4 shows the decision-making process of our proposed dynamic phase and period signal control scheme. From \(t_{e}+T_{g}\) to \(t_{e}+\overline{T_{g}}\), \(t_{f}\) is sequentially set per unit time to calculate the eliminable waiting time (\(ET_{p_{i}}^{t_{f}-t_{e}}\), \(t_{f}\in\left[t_{e}+T_{g},\,t_{e}+\overline{T_{g}}\right]\)) for all 12 combination phases, and the ratio of the eliminable time of each phase to the total waiting time of all phases (\(t^{f}\)) simultaneously according to:  
\[r^{t_{e}}=\frac{ET_{p_{i}}^{t_{f}-t_{e}}}{WT_{g}^{t_{f}}},\,\,t_{f}\in\left[t _{e}+T_{g},\,\,t_{e}+\overline{T_{g}}\right]. \tag{17}\]The value of \(t_{f}\) with the maximum value of \(r^{J_{f}}\) is selected as the end time of the next green light, and the corresponding combination phase is the next opening signal phase.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 3 Logic of Events
### Basic Definition  
3.2.1. Sequence of Events  
In the context of identifying points where information is transmitted during execution, such points are referred to as events, denoted as \(e\). The definition of \(info(e)\) is the raw information associated with the event \(e\). The structure of an event sequence is defined as \(<E,loc,<,info{>}\) where \(<\) represents a transmission relation on \(E\). In Logic of Events Theory, the address \(loc(e)\) of an event \(e\) is the entity where it occurs. The causal sequence of events on the same entity is denoted as \((e^{{}^{\prime}}<e\wedge loc(e^{{}^{\prime}})=loc(e))\), abbreviated as \(e^{{}^{\prime}}<_{loc}e\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Semantics** \\ \hline ID & Participants in the agreement \\ Atom & Unpredictable information \\ Data & Plaintext \\ e & Event \\ E & Event set \\ nonce & Random number \\ loc(e) & The location of event e \\ has & Logic contains \\ \(<\) & Cause and effect sequence of events \\ \(||\) & Logically independent \\ Key(e) & The secret key of the principal of event e \\ bs & Basic sequence \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic notations and semantics.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 3 Power Transfer Capability Evaluation
### Power Transfer Capability in Particular Corridor  
Transmission capacity can be observed by changing (i) load demand and (ii) power generation. In this paper, system load is kept constant and generation in importer area is decreased while generation in an exporter area is increased with the same amount, once the transmission link is established. If the exchange variation is (\(\Delta E\)), Equation (1) gives the expression to determine the power change in generator \(i\) (\(\Delta P_{i}\)):  
\[\Delta P_{i}=\Delta E\times\frac{P_{i}}{\sum\limits_{i=1}^{N_{A}}P_{i}} \tag{1}\]  
where, \(P_{i}\) denotes the prior output power of \(i\) generator, while \(N_{A}\) is the number of synchronous generators in specific area. In importer area, \(\Delta P_{i}\) is negative while it would be positive in exporter area. Further, \(\Delta P_{i}\) should be selected based on the maximum rating of each generator.  
Power dispatch corresponds to the exchange value and, for every dispatched power, static and dynamic security assessments are accomplished based on pre-determined criteria. System will be considered secured only if no security violation is observed and thus additional re-dispatch will be performed according to Equation (1). However, if any security criterion is violated, a step back in re-dispatch is performed via binary hunt until a safe region is reached.
## 2 Monitoring Protocols and Methods
### Soil-Gas Flux  
Soil-gas flux was measured using a direct method: the accumulation-chamber method with external recirculation [47], a protocol that is intermediate between the static and the dynamic methods. A 0.25 m\({}^{2}\) chamber hermetically encloses the soil surface after which gases emitted by the soil surface accumulate in the chamber. During the measurement period (typically 3 minutes), gas accumulation is characterizedby a roughly linear increase of the gas concentration inside the chamber [48]. Afterwards, the flux is calculated using the slope of the concentration versus the time regression line. The gas fluxes are expressed in cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\) STP (standard conditions for temperature and pressure). Conversion factors are for CO\({}_{2}\): 1 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\) STP = 0.033 mg\(\cdot\)s\({}^{-1}\cdot\)m\({}^{-2}\); for CH\({}_{4}\): 1 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\) STP = 0.012 mg\(\cdot\)s\({}^{-1}\cdot\)m\({}^{-2}\).  
CO\({}_{2}\) is detected with a Licor Li800 infrared device (detection limit of 1 ppm), which can be used for the monitoring of very low fluxes around 0.1 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\) to very high gas emissions around 4000 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\) with a detection limit of 0.03 to 0.5 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\)[40].  
CH\({}_{4}\) flux was measured with the same accumulation chamber, associated with a Gazomat Inspectra infrared laser detector (detection limit of 0.5 ppm). This system permits detecting an extremely low CH\({}_{4}\) emission level of around 0.01 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\).
## 4 Ventilation Cooling Optimization Schemes with Non-Uniform Core Segments
### Optimized Design of Non-Uniform Core Segments  
In the paper, CFD is used to simulate the thermal field of the generator with a radial ventilation ducts width of 6 mm, as shown in Figure 13. It indicates that the maximum temperature difference of the generator stator is 13.97 \({}^{\circ}\)C. The reason is there are differences in air velocity and temperature distribution along the cooling path. When the generator is operating, heat generation is concentrated in the stator winding area, causing its temperature to be higher than other parts, reaching the maximum temperature of 54.97 \({}^{\circ}\)C.  
The areas with the highest temperatures inside the generator are mainly on the stator, and the temperature distribution within the stator is uneven. This paper optimizes the temperature distribution of the stator. The specific method is to perform non-uniformly treatment on the core, change the thickness of the core segments, and arrange and combine core segments of different thicknesses to explore the impact on the internal heating of the generator.  
The total length of the stator core is 1382 mm, consisting of 20 core segments and 19 ventilation ducts. While keeping the ventilation ducts width of 6 mm constant, the 20 core segments are divided into three groups: Group A has six core segments with a width of 67.4 mm, Group B has six core segments with a width of 63.4 mm, and Group C has eight core segments with a width of 60.4 mm. The six thickest core segments in Group A are fixed on both sides, while the positions of the eight thinnest core segments in Group C are changed, placing them on the left side, right side, and in the middle, respectively. Three optimized schemes are proposed and compared with the original scheme where the core segments have a uniform ventilation ducts width of 6 mm. The distribution schemes of the non-uniform core segments are shown in Table 3.  
Figure 13: Thermal field distribution of generator with radial ventilation ducts width of 6 mm: (**a**) overall thermal distribution; (**b**) thermal distribution of stator core and windings.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 2 Analysis of Models and Limitations in VLC Systems
### \(\mu\)-LED Model  
The LED device utilized to generate light signals in this VLC system was a red-green-blue (RGB) \(\mu\)-LED with a pixel size of 200 \(\upmu\)m \(\times\) 100 \(\upmu\)m and whole-package dimensions of 0.79 mm \(\times\) 0.79 mm. As shown in Figure 2a, the compact equivalent circuit model was composed of a PCB test fixture model, a bonding wire model, intrinsic device model I, and a Verilog-A code model [22; 23; 24]. L\({}_{1}\), R\({}_{1}\), C\({}_{1}\), L\({}_{2}\), R\({}_{2}\), and R\({}_{3}\) were the passive components representing the PCB fixture model, while L\({}_{\text{B}}\), R\({}_{\text{B}}\), and C\({}_{\text{B}}\) were the bonding wires. The intrinsic device model of the LED consisted of contact resistor R\({}_{\text{S}}\), active region capacitor C\({}_{\text{J}}\), and junction resistor R\({}_{\text{J}}\), which were all current-dependent. Since the parasitic components were negatable compared with the components of the intrinsic device in the low-frequency domain, the parasitic model mainly affected signals operating in the frequency domain beyond 500 MHz with second-order distortion. Therefore, when considering the lower-frequency parts of the LED frequency response curve, the dominant part was determined by the intrinsic circuit model, which was a first-order RC filter. The code model represented opto-electric conversion, and the output optical power with a current through R\({}_{\text{J}}\) followed the relationship of the first-order low-pass filter function. The values of these components in the compact model were extracted from the measured S-parameters of a fabricated \(\upmu\)-LED with the fitting process in ADS.  
The simulated results of bandwidth with this proposed compact equivalent model of \(\upmu\)-LED under different bias currents corresponded with the measured bandwidth curves and are presented in Figure 2b. It can be noticed that the \(-3\) dB bandwidth of this \(\upmu\)-LED was 50 MHz when the bias current was 70 mA. This limited bandwidth was the main obstruction to increasing the communication data rate.  
Figure 2: (a) Compact equivalent circuit model of \(\upmu\)-LED; (b) simulated and measured bandwidth curves of \(\upmu\)-LED.
## 4 Formal Description of Timed Colored PNs  
We use a formal description of colored Petri nets as nonuples [16]:  
\[\text{CPN}=(\text{P, T, A, C, V, S, G, E, I}), \tag{4}\]  
where:  
1. P is a finite set of places.
2. T is a finite set of transitions such that P\(\cap\)T = /0.
3. A \(\subseteq\) P \(\times\) T \(\cup\)T \(\times\) P is a set of directed arcs.
4. C is a finite set of non-empty color sets.
5. V is a finite set of typed variables such as Type [v] \(\in\) C for all variables v \(\in\) V.
6. S: P\(\rightarrow\)C is a color set function that assigns a color set to each place.
7. G: T\(\rightarrow\)EXPR is a guard function that assigns a guard to each transition such that Type [G (t)] = Bool.
8. E: A\(\rightarrow\)EXPR is a function that assigns an arc expression to each arc such that Type [E (a)] = C (p)\({}_{\text{MS}}\), where p is the place connected to the arc a.
9. I: P\(\rightarrow\)EXPR is an initialization function that assigns an initialization expression to each place p such that Type [I (p)] = C (p)\({}_{\text{MS}}\). Timed colored Petri nets are:  
\[\text{TCPN}=(\text{CPN},\Theta_{1},\Theta_{2},\Theta_{3}), \tag{5}\]where CPN stands for colored Petri net, and \(\Theta_{1}\), \(\Theta_{2}\), and \(\Theta_{3}\) are time delays assigned to net places, arcs, and transitions, respectively.  
We used CPN Tools 4.0.0 [51] to implement a hierarchical model of technological processes and equipment maintenance and repair. This software package's capabilities allow the multiset processing equipment to consider the timing relationship Gantt chart in order to analyze process control features.  
Hierarchical Petri Net Model Used to Simulate the Repair and Maintenance Process 5.1. General Scheme  
The design of the hierarchical HPN model was carried out using a top-down development. The top-level page uses subpages for which only a set of inputs and outputs are still defined. The structure of the hierarchical HPN model is depicted in Figure 3.  
Hierarchical PNs include four levels of modules. The first level consists of the following modules:  
1. ZSch module handles task scheduling and unit assignment;
2. ZO module handles task operations;
3. MR module handles maintenance and repair performance;
4. RS module is the resource for maintenance and repair.  
The first level is formed by the modules described in the previous paragraph. The main ZO module contains K basic ZExec modules of the task's operation, where K is the number of tasks in the technological process schedule. The ZExec module provides a simulation of unit assignment and basic task operations. Each ZExec module is associated with its own RTA (runtime analyzer) module, which evaluates the time parameters of running units and determines failures or predictive maintenance requests. The RTA module simulates an ensemble of m units for a specific task \(z_{k}\). The Unit ID sub-module determines the accumulated operating time, remaining useful life, and time before preventive maintenance, and simulates stochastic processes of failures and prediction maintenance. A detailed description of the implementation of HPN modules is given below.
# A Review on Bioactive Compounds and Pharmacological Activities of _Citrus unshiu_
## 4 Pharmacological Activities of Satsuma Mandarin
### Antioxidant Activity
unshiu_ flowers using methanol, the pure components were separated using column chromatography over silica gel, ODS, and Sephadex LH20. Among the isolated compounds, quercetin and rutin demonstrated the highest radical scavenging activities against DPPH, with IC\({}_{50}\) values of 11.5 and 19.6 \(\upmu\)M, respectively [47]. Several techniques were used to evaluate the antioxidant activity of quercetin from the peel of _C. unshiu_. The dried peel was first powdered and extracted in 800 mL L\({}^{-1}\) methanol for 12 h. The extracts were then evaporated and concentrated with chloroform (10.06 g), n-butanol (25.58 g), and ethyl acetate (14.55 g). The ethyl acetate fraction (6.5 g) was purified by elution with ethyl acetate/methanol (10:1-1:1 \(v/v\)) and n-hexane/ethyl acetate (10:1-1:1 \(v/v\)) to produce 15 fractions. High-performance liquid chromatography was performed to separate fraction 11.5.5 (2.3 mg) with 400 mL L\({}^{-1}\) methanol to produce the final compound (0.8 mg). Quercetagetin showed high DPPH activity (IC\({}_{50}\) = 7.89 \(\upmu\)mol L\({}^{-1}\)) and reduced ROS formation in Vero cells, as well as H\({}_{2}\)O\({}_{2}\)-induced DNA damage [103]. Using the Trolox equivalent antioxidant capacity (TEAC), the antioxidant activity of the _C. unshiu_ peel extracts produced by ultrasound treatment has been assessed. After extracting the dried _C. unshiu_ peel using 20 mL of diethyl ether/ethyl acetate (1:1, \(v/v\)), 80% methanol was added to bring the final amount down to 10 mL. The methanolic solution was filtered using a 0.45 \(\upmu\)m microporous membrane and the filtrate analyzed by high-performance liquid chromatography. Ultrasound treatment at 15 or 30 \({}^{\circ}\)C for 20 min resulted in enhanced extraction of hesperidin and narirutin. Following treatment, TEAC and total phenolic acid levels showed a positive association (R2 = 0.8288 at 15 \({}^{\circ}\)C,R2 = 0.7706 at 30 \({}^{\circ}\)C), indicating that these phenolic acids may have a part in boosting the antioxidant activity of _C. unshiu_ peel [104].