esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Future wireless communications have been envisaged to benefit from integrating drones and free space optical (FSO) communications, which would provide links with line-of-sight propagation and large communication capacity. The theoretical performance analysis for a drone-assisted downlink FSO system is investigated. Furthermore, this paper utilizes the Malaga distribution to characterize the effect of atmospheric turbulence on the optical signal for the drone-terrestrial user link, taking into account atmospheric attenuation, pointing errors, and angle-of-arrival fluctuations. The probability density function and cumulative distribution function are then expressed in closed-form using the heterodyne detection and indirect modulation/direct detection techniques, respectively. Thereafter, the analytical expressions including the average bit error rate (BER) and the ergodic capacity are given. Particularly, the asymptotic behavior of the average BER of the considered system is presented using heterodyne detection at high optical power. The Monte Carlo simulation results certify the theoretical analytical results. Correspondingly, the field-of-view of the receiver is analyzed for optimal communication performance.  
angle-of-arrival fluctuations; average bit error rate; ergodic capacity; optical communication; Malaga distribution +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 3 Multi-Objective Path Planning
### Optimization Method
```
Inputs:\(global\_path\): The global path to be optimized \((traffic\ lights,road\ conditions)\): Traffic light status and road conditions \((avoid\_points,avoid\_zones)\): Points and areas to avoid during path optimization \(\epsilon\): Exploration factor for the DQN policy \(\alpha,\beta,\dots,\theta\): Weights for multi-objective reward computation Output:\(optimized\_path\): The optimized path from start to goal after local adjustments 1 Initialize Q-network and experience replay buffer 2 optimized_path \(\leftarrow\) [global_path[0]] 3 current_node \(\leftarrow\) global_path[0] for\(i=1\)to len(global_path)do 5 current_state \(\leftarrow\) get_state(\(current\_node,traffic\ lights,road\_conditions\), avoid_points, avoid_zones) 5 current_state \(\leftarrow\) get_state(\(current\_node,traffic\ lights,road\_conditions\), avoid_points, avoid_zones) 6 current_state \(\leftarrow\) get_state(\(current\_node,traffic\ lights,road\_conditions\), avoid_points, avoid_zones) 7 current_state \(\leftarrow\) get_state(\(current\_node,traffic\ lights,road\_conditions\), avoid_points, avoid_zones) 8 current_state \(\leftarrow\) get_state(\(current\_node,traffic\ lights,road\_conditions\), avoid_points, avoid_zones) reward \(\leftarrow\) compute_reward(next_node, \(\alpha,\beta,\dots,\theta\)) 9 store_transition(current_state, action, reward, next_state) 10 ifreplay_buffer is full then 11 batch \(\leftarrow\) sample_batch() for each transition in batch 21 update_Q_network(transition) endfor 23 current_node \(\leftarrow\) next_node endfor return optimized_path
```  
**Algorithm 4**Local path optimization using DQN.
# Improving Stability Prediction in Peripheral
## 2 Materials and Methods
### Milling Stability Prediction with the EMHP Method
Equation (7) was then introduced into Equation (6), to get:  
\[\dot{\mathbf{x}}_{i}(T)=\mathbf{A}_{i}\mathbf{x}_{i}(T)+\mathbf{B}_{i} \mathbf{x}_{i-N}-\frac{N-1}{\tau}\mathbf{B}_{i}\mathbf{x}_{i-N}T+\frac{N-1}{ \tau}\mathbf{B}_{i}\mathbf{x}_{i-N+1}T \tag{8}\]  
where:  
\[\mathbf{A}_{t}=By following our homotopic procedure, we assumed the homotopic representation of Equation (8) was of the form:  
\[H(\mathbf{X}_{i},p)=L(\mathbf{X}_{i})-L(\mathbf{x}_{i0})+pL(\mathbf{x}_{i0})=p \bigg{(}\mathbf{A}\mathbf{X}_{i}+\mathbf{B}\mathbf{x}_{i-N}-\frac{N-1}{\tau} \mathbf{B}\mathbf{x}_{i-N}T+\frac{N-1}{\tau}\mathbf{B}\mathbf{x}_{i-N+1}T \bigg{)} \tag{10}\]  
To obtain the stability lobes of Equation (1), the solution of Equation (10) was rewritten in the form:  
\[\mathbf{x}_{i}(T)\approx\mathbf{P}_{i}(T)\mathbf{x}_{i-1}+\mathbf{Q}_{i}(T) \mathbf{x}_{i-N}+\mathbf{R}_{i}(T)\mathbf{x}_{i-N+1} \tag{11}\]  
where:  
\[ \begin{array}{ll}\mathbf{P}_{i}(T)=\sum\limits_{k=0}^{m}\frac{1}{k!}\mathbf{ A}_{t}^{k}T^{k},\\ \mathbf{Q}_{i}(T)=\left\{\begin{array}{cc}\sum\limits_{k=1}^{m}\frac{N-1}{(k +1)!(\tau)}\mathbf{A}_{t}^{k-1}\mathbf{B}_{t}T^{k+1}&m\geq 1\\ \mathbf{0}&m=0\end{array}\right.\\ \mathbf{R}_{i}(T)=\left\{\begin{array}{cc}\sum\limits_{k=1}^{m}\frac{1}{k!} \mathbf{A}_{t}^{k-1}\mathbf{B}_{t}T^{k}-\mathbf{Q}_{m}&m\geq 1\\ \mathbf{0}&m=0\end{array}\right.\end{array}  \tag{12}\]  
The approximate solution given by Equation (12) can be written as a discrete map by using the following identity:  
\[\mathbf{w}_{i}=\mathbf{D}_{i}\mathbf{w}_{i-1} \tag{13}\]  
where \(\mathbf{D}_{i}\) is a coefficient matrix, \(\mathbf{w}_{i-1}\) is a vector of the form:  
\[\mathbf{w}_{i-1}=\big{[}x_{i-1},\dot{x}_{i-1},x_{i-2},\ldots,x_{i-N}\big{]}^{T} \tag{14}\]  
By using Equations (11) and (12), we could easily construct the coefficient matrix \(\mathbf{D}_{i}\)[8]. We then determined the transition matrix \(\mathbf{\Phi}\) over the period \(\tau\) = \((N-1)\Delta t\) by coupling each approximate solution through the discrete map \(\mathbf{D}_{i}\), \(i\) = 1, 2,..., \((N-1)\), to get:  
\[\mathbf{\Phi}=\mathbf{D}_{N-1}\mathbf{D}_{N-2}\ldots\mathbf{D}_{2}\mathbf{D}_ {1} \tag{15}\]  
Then, the stability lobes of Equation (1) were determined by computing the eigenvalues of the transition matrix given by Equation (15).
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 2 Evolution of Single Antenna Element  
The proposed circular antenna is designed using CST Microwave Studio software 2018, and fabricated based on RT5880. In this section, design simulation, fabrication results and comparison with previous works, achieving optimal signal integrity, high gain, and outstanding performance.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Link Expiration
#### 4.3.3 \(Ret_{max}\)  
\(RET_{max}\) is the highest value of \(RET\) across all possible routes at the destination (which can be calculated using Equation (23)). As explained earlier, the \(LET\) between any two nodes can be acquired using Equation (21) and the minimum \(LET\) in the possible route tagged as the \(RET\) of that route. As depicted in Figure 6, multiple possible routes are available if node A aims to send data to node \(H\). The connection between any two nodes signifies the \(LET\) of both nodes, for example, the \(LET\) between node \(A\) and node \(B\) is equal to 50.  
\[RET_{max}=max(RET_{Route1},RET_{Route2},\ldots,RET_{RouteN}) \tag{23}\]  
Since \(RET_{Route1}=30,RET_{Route2}=20\) & \(RET_{Route3}=25\)  
Therefore  
\(RET_{max}=max(RET_{Route1},RET_{Route2},RET_{Route3})\)  
\(RET_{max}=max(30,20,25)\)  
\(RET_{max}=30\)  
Figure 7 summarizes Figure 6 for a better understanding of \(RET_{max}\).
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Horizontal Take-Off and Landing (HTOL)
#### 2.1.1 Fixed-Wing
As such, for most fixed-wing aircraft, standards have been developed to ensure such margins (as an example, FAR 23 requires commercial airliners to be designed in such a way to still have a certain minimum flight performance quality in the event of an engine failure) [25]. Naturally, the stringency of these standards and regulations varies depending on the type of fixed-wing configuration in question and as a result, fault tolerance also somewhat varies based on this.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods  
### Data Collection  
This study employs a real-world dataset collected through the TestYourself platform, a web-based adaptive testing system developed by the authors to provide personalized and scalable educational assessments. The participants of this study consisted of 150 undergraduate students enrolled in the 2 nd, 3 rd, and 4 th years of the Faculty of Engineering at Gaziantep Islam, Science and Technology University. Students volunteered to participate in the study without any academic or financial compensation. While age data were not collected, all participants were actively registered undergraduate students at the time of the assessment.  
The dataset includes responses from these 150 students, each of whom completed 30 multiple-choice mathematics test items in a web-based, self-paced, and untimed testing environment. The TestYourself system was developed not only to administer adaptive tests but also to record accuracy (correct/incorrect responses) and response time (measured in seconds) for each item.  
The test content was carefully constructed to include a diverse range of difficulty levels, ensuring that the assessment effectively discriminates between students of varying proficiency. The adaptive functionality of TestYourself follows item selection algorithms that dynamically adjust question difficulty based on real-time performance, closely mimicking the behavior of large-scale adaptive testing environments such as the Graduate Record Examination (GRE) and the Computerized Adaptive Testing (CAT) framework.  
No time constraints were imposed on participants, allowing students to complete the assessment at their own pace and ensuring natural variation in response times.  
To ensure compliance with ethical research standards, all participants provided informed consent before taking part in the study. The data collection process was approved by the institution's Ethics Committee for Educational Research, adhering to guidelines on data privacy and student anonymity. To enhance data quality, extreme outliers in response times (e.g., significantly low values due to accidental clicks or high values due to interruptions) were identified and removed using statistical anomaly detection techniques.
# On the Alkalinity of Solid Catalysts for Transesterification of Dimethyl Carbonate and Ethanol
## 3 Results
### Alkainity of Different Catalysts
Combining the experimental results in Tables 1 and 3, it can be seen that the alkali strength (H\({}_{0}\)) of the solid catalyst required for the transesterification reaction system between DMC and ethanol must be greater than 9.3. Otherwise, the catalytic performance will be poor, as seen with ZnO and Al\({}_{2}\)O\({}_{3}\). The conclusion drawn from this part of the experiment is consistent with the relationship between the activity of the catalyst and the alkalinity when CaO catalyzes the transesterification reaction [22]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Catalyst** & **Brb** & **Phe** & **AyR** & **Din** & **Alkali Strength (H\({}_{0}\))** \\ \hline MgO & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 11.0–15.0 \\ Al\({}_{2}\)O\({}_{3}\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 7.2–9.3 \\ ZnO & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 7.2–9.3 \\ Mg-Al-Zn & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 9.3–11.0 \\ \hline \end{tabular} The indicators were prepared as 0.5\% ethanol solutions.  
\end{table} 
Table 3: Alkali strength of different catalysts. Figure 6: DRIFTS of Mg-Al-Zn catalyst of CO\({}_{2}\) adsorption followed by Ar sweeping. (Approximately 20–30 mg of catalyst is loaded into the in situ cell). The samples are first pretreated in an Ar atmosphere at 400 \({}^{\circ}\)C for 1 h. Then, the samples are slowly cooled to 20 \({}^{\circ}\)C while sweeping with Ar gas, and the background spectra are recorded. After a 10 min purge with Ar gas, CO\({}_{2}\) gas is introduced. After 20 min of CO\({}_{2}\) adsorption, the Ar is purged from the cell, and after 5 min of blowing, the cell is heated to 400 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. From Figure 7, it can be observed that the surface alkalinity of the Mg-Al-Zn solid is 3.267 mmol/g, while the MgO solid is 0.53 mmol/g. This difference in alkalinity may explain why the catalytic activity of Mg-Al-Zn is superior to MgO. With the increase in the number of uses, the conversion rate of MgO gradually decreases, and the number of alkali sites on the solid surface of MgO also progressively diminishes. This indicates that the decrease in the activity of the MgO catalyst is directly related to the reduction of surface alkaline sites. For the Mg-Al-Zn catalyst, the number of alkali sites decreased from 3.27 mmol/g to approximately 2.70 mmol/g after the first reaction and then tended to stabilize. The catalytic activity of the Mg-Al-Zn catalyst also slightly decreased after the first use and then remained basically unchanged. This indicates that the stability of the Mg-Al-Zn catalyst is directly related to the alkalinity of the solid surface.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 5 Results
### Effect of Population Size  
The population size used in a genetic algorithm can affect its performance and the quality of solutions. To observe the effects of population size, we ran GA and GA-F for three different population sizes, 10, 20 and 40. These sizes were chosen with the execution time constraints in mind. For a fair comparison, we limited the number of generations for each compiler to 800 generations, and we repeated the experiments for five trials, excluding results from any trial which was unable to complete the 800 generations in the given time limit. Figure 15 shows the results for 2 and 3 qubits, respectively, with the median values of metrics plotted.  
Figure 15: Compilation performance for GA and GA-F across different population sizes. (**a**) 2 qubits. (**b**) 3 qubits.  
We can observe that increasing population sizes can sometimes produce slightly better results in terms of approximation error, although there are anomalies and the differences are not significant. There appears to be no noticeable improvements in depth and CNOT across the different population sizes. GA and GA-F perform similarly over the different population sizes. These results, combined with analysis from the previous section, indicate that number of generations has a bigger effect on convergence than the population size, and requires further investigation.
# New Insights into Fuzzy Genetic Algorithms for Optimization Problems
## 2 Fuzzy Genetic Algorithms: Dealing with Individuals and Fitness
### Preliminaries
#### 2.1.2 Genetic Algorithms  
Natural evolution is the source of inspiration for GAs, and typical evolutionary terminology can be found through the algorithm. One might think of the optimization problem as the environment and the proposed solution as a single entity inside it. Individual fitness is correlated with the quality of the candidate solution as determined by the objective function. The population is first randomly initialized by the algorithm, which then iterates through generations. Every individual in a generation is assessed for fitness, and new individuals (offspring) are produced depending on that evaluation. By applying mutations or merging parents (crossover), a new population is created.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 4 Analysis and Discussion of Data Results
### Channel Impulse Response  
From Figures 12 and 13, we can observe that the CIR intensity for \(t_{1}(t_{2})-\)the other receivers is very slow, whereas the CIR intensity for \(t_{1}(t_{2})-r_{1}(r_{2})\) is very high. The data show that the photons emitted by \(t_{1}(t_{2})\) in the planar circle array MIMO communication system are almost received by \(r_{1}(r_{2})\) with only a small portion captured by other receivers. This is because, in the transceiver structure of the planar circle array MIMO communication system, \(t_{1}(t_{2})\) and \(r_{1}(r_{2})\) are perfectly aligned. Moreover, as seen in Figure 12b, the CIR curve for \(t_{1}\) to the other receivers shows consistent temporal broadening and intensity. This is because the other receivers are symmetrically distributed with respect to \(r_{1}\). In Figure 13b, we found that the CIR curve for \(t_{2}-r_{4},r_{5},r_{6}\) disappears. This is because \(r_{4}\), \(r_{5}\), and \(r_{6}\) are located on the negative \(x\)-axis, far from \(t_{2}\) and basically do not receive any photons, hence the CIR cannot be obtained through statistical methods.  
Figure 11: Path loss under different communication system settings for transmitter deflection: (**a**) planar circle array and (**b**) angular circle array.  
Figure 12: CIR curves of transceiver 1 in the planar circle array MIMO communication system when \(L=2m\): (**a**) \(t_{1}-r_{1}\) and (**b**) \(t_{1}-\)the other receivers.  
Figure 13: CIR curves of transceiver 2 in the planar circle array MIMO communication system when \(L=2m\): (**a**) \(t_{2}-r_{2}\) and (**b**) \(t_{2}-\)the other receivers.  
Comparing Figures 12 and 14, it can be seen that the CIR curves for transceiver 1 are almost identical in both system configurations. This is because the central transceiver pair in the angular circle array MIMO communication system is aligned. Although the side receivers of the angular circle array MIMO communication system are inclined outward at a certain angle, the short communication distance results in little change in the number of received photons, hence the CIR intensity for \(T_{1}-\)the other receivers do not change significantly. In the case of longer distance communication, the special structure of the angular circle array MIMO communication system makes the CIR intensity for \(T_{2}-\)the other receivers decrease. Comparing Figures 13 and 15, it can be seen that transceiver 2 has less CIR intensity in the angular circle array MIMO communication system setup. This is because transmitter \(T_{2}\) and the receivers are inclined outward at a certain angle, breaking the LOS link and resulting in fewer received photons.
# Design and Implementation of a Tether-Powered Hexacopter for Long Endurance Missions
## Appendix B Details of DC-DC Convertor  
The DC-DC convertor equipped on UAVs should be with light weight and high efficiency. We tried to find off-the-shelf ones, but their specifications couldn't satisfy our demand. Therefore, we built our own DC-DC convertor to step-down the high-side tether voltage (\(\sim\)100 V) to a constant low-side voltage 24 V. The most challenging point is that the high-side voltage is time-varying because of the long tether with significant resistance. A bidirectional synchronous buck or boost controller LTC3871 (Analog Devices, USA) is selected because of its high efficiency and unique architecture allows dynamic regulation of input voltage.  
The schematic drawing and the photograph of the DC-DC converter's printed circuit board are shown in Figures 12 and 13. The low-side output voltage (LV) is programmed at 24 V by two external feedback resistive dividers, R15 and R16. The divided voltage is fed back via the pin #2 (VFBLow) into the internal control loop, which keep the output voltage at constant 24 V. The switching frequency, maximum input voltage, minimum input voltage, nominal output current, and maximum output current are 200 kHz, 96 Vdc, 48 Vdc, 24 Vdc, 25 A, and 30 A, respectively.  
Several surface mount MOSFETs are selected because of their small footprint and light weight. They are placed enough separately to avoid heat concentration. Their heat can be conducted to the backside of PCB with vias and dissipates to the air through the thermal conductive pad and the aluminum fin. Two fans are installed on both sides of PCB in order to improve the convection.  
Figure 11: The schematic drawing of the DC-DC converter’s printed circuit board. “HV” labels the high voltage input; “LV” labels the low voltage output.  
Figure 12: The photograph of the DC-DC converter’s printed circuit board.
## 4 Discussion
The proposed CWGO algorithm demonstrates high efficiency in energy consumption, low latency, and robust performance in both static and dynamic WSN environments, as well as across different wireless communication technologies. This flexibility enables its application in diverse communication networks and scenarios, such as smart city infrastructures and large industrial sensor networks where scalability and robustness are crucial. However, one of the disadvantages of CWGO is its computational complexity, which can become an issue in larger networks, especially in resource-constrained environments where computational resources are limited. The proposed unified metric's main advantage lies in its balanced and straightforward approach, offering a comprehensive view of WSN performance by combining multiple critical metrics. This unified evaluation approach is beneficial in assessing overall network efficiency, particularly for large-scale comparisons. However, there may be application-specific scenarios where the weight of each component metric within the unified evaluation would need to be adjusted to reflect specific priorities or performance requirements.
## 2 Materials and Methods
### Model Validation
#### 2.7.1 Msd  
A 200 ps simulation in the NPT ensemble was conducted to obtain the reasonable density at different temperatures. Subsequently, a 200 ps simulation in the NVT ensemble was performed on the interface system at this density, yielding the MSD of the PU system. The temperature range for the simulation was set from 200 K to 600 K, with an interval of 50 K. The results of the MSD calculations are shown in Figure 12. The slope of the MSD curve represents the motility of the molecules. The larger the slope, the stronger the motility of the chain segments [49]. At lower temperatures (200 K-400 K), the slope of the MSD curve of PU is smaller, and the slope change is more stable. This indicates that, at low temperatures, the molecular structure of PU is in a more stable state, the intermolecular movement is relatively fixed, and the low-temperature performance is better, which is consistent with the actual performance of PU. When the temperature increases to 450 K, the slope of the MSD curve of PU gradually becomes larger. When the temperature increases to 550 K, the slope of the MSD curve of PU increases sharply, which may be due to the gradual separation of PU molecules at high temperatures. The trend of the MSD of the PU system with the temperature is consistent with the thermal properties of PU, which suggests that the constructed PU model is reliable.  
Figure 11: Principle of calculation of the interfacial interaction energy.