esa-sceva/satcom-chunk-collection huggingface dataset:
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 3 Model Development
### Mathematical Model
The constraint in Equation (6) guarantees that the recharging percentage can be determined when the mission of the drone is to recharge its battery. However, the constraint in Equation (7) also guarantees that the drone recharges its battery to an amount greater than the minimum recharging ratio to prevent full discharge. In this study, we set this minimum recharging ratio to 0.1. Thus, the battery needs to be recharged to at least 10% of the battery capacity once recharging is selected. The constraint in Equation (8) is a flow conservation equation for the battery charge. This explains that the current battery charge is determined by either subtracting the consumed charge for delivery from the battery charge remaining after the previous task or by adding the recharged amount to the battery charge remaining after the previous task. The constraint in Equation (9) limits the battery recharging amount. For example, if the battery charge after the previous task (\(f_{ik-1}\)) is 0.3 \(B\), the upper bound of \(z_{ik}\) is set to 0.7. In other words, when \(f_{ik-1}\) is 0.3 \(B\) (i.e., the current battery charge is 30% of the full capacity), the battery can be recharged by up to 70% of its full capacity. The constraint in Equation (10) sums the operating times of either delivery or recharging to check the duration of each operation. The constraint in Equation (11) guarantees that the accumulated operation time after the final task is less than the available hours. The constraint in Equation (12) requires that the decision variables for the battery charge, time, and battery recharging ratio are nonnegative. Finally, the constraint in Equations (13) and (14) show the binary decision variables that are relevant to delivery and recharging. In the above DROP-Model, we can replace the constraint in Equation (9) with Equation (15) below to make the drone fully recharge its battery when it is assigned to recharge. In other words, the battery recharging ratio (\(z_{ik}\)) is set to make the battery capacity full. \[k=1,\ldots,K\]2  
After preparing all the required data, we can obtain the optimized drone operation plan by running the proposed DROP-Model.
## 4 Experiments and Analysis of Results
### Reliability Verification of Motion Blur Restoration Tasks
The PSNR and SSIM were used to quantitatively evaluate the image quality and similarity to the original image after restoration with the state-of-the-art method and the method used in this paper, as shown in Table 4. It can be found that the method used in this paper has  
Figure 8: The intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of various deblurring methods on the MMCBNU and SDUMIA-FV datasets. Figure 9: The intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of various deblurring methods on the UTFVP and FV-USM datasets.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 4 Time Management and Synchronization
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Standard/Recommendation** & **Description** & **Application** \\ \hline IEEE 1588 [27] & Standard for a precision clock synchronization protocol for networked measurement and control systems & Defines the Precision Time Protocol (PTP) for clock synchronization in networked measurement and control systems. \\ \hline IEC 61850-9-3:2016 [59] & Communication networks and systems for power utility automation & Specifies time accuracy and synchronization requirements in substation automation networks. \\ \hline IEC 61588 [70] & Precision clock synchronization protocol for networked measurement and control systems & Equivalent to IEEE 1588, applicable in industrial automation and control networks. \\ \hline NIST SP 800-82 [71] & Guide to Industrial Control System (ICS) Security & Provides guidelines for the security of industrial control systems, including aspects of time synchronization. \\ \hline IEEE C37.238 [72] & Standard Profile for Use of IEEE 1588 Precision Time in Power System Applications & Defines the profile for using IEEE 1588 in power system applications, ensuring precise synchronization in electrical environments. \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Standards related to time management and synchronization.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Time-Domain Distance Protection Based on the R-L Model  
The method used to solve the differential equation based on the _R-L_ time domain model is not involved in the signal frequency domain information. The method establishes the differential equations for the transmission line by using the instantaneous current and voltage values in the relay location. It is not affected by the DC component, the low-frequency component, or the fluctuation in the power grid frequency. Therefore, the differential equation algorithm can overcome the influence of the frequency deviation of the wind power system. If the distribution capacitance of the protected line can be ignored, then the line segment from the fault point to the relay location can be approximated by a resistor and an inductor series circuit. When a fault occurs in a certain point on the line, the differential equation in the relay location is:  
\[u=Ri+L\frac{di}{dt}, \tag{12}\]  
where \(R\) and \(L\) are the positive sequence resistance and inductance of the fault point to the relay location, respectively, and \(u\) and \(i\) are the instantaneous voltage and current values of the relay location, respectively. When using a microcomputer, the derivative of the current can be calculated by the difference equation. Then, the unknown part of the differential equation is only \(R\) and \(L\). In theory, the equations can be solved with only three sampling points. To ensure the accuracy of the solution, the voltage and current are sampled multiple times. These values are substituted into Equation (12) to obtain a series of difference equations. Then, the equations are solved with the least-squares method [35]. Set the sampling period to be _T\({}_{s}\)_. _t\({}_{k}\)_ and _t\({}_{k+1}\)_ are two sampling times, and the corresponding measured voltage and current sampling value are, respectively, _u\({}_{k}\)_, _u\({}_{k+1}\)_, _i\({}_{k}\)_, and _i\({}_{k+1}\)_. For simplicity, set _y\({}_{k}
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 4 Parameter Estimation and Controller Design Based on EDM System  
### Parameter Estimation  
After the model order of the EDM system was determined, the parameters of the model needed to be estimated. The least-squares recursive algorithm is used to estimate the parameters in actual projects. With the increase of data collection, in order to prevent \"data saturation\", this paper used the forgetting factor recursive least-squares method to estimate the parameters of the system. Since EDM is a two-order system, for the SISO system model described in Equation (13), the least-squares expression is  
\[y(k)=-a_{1}y(k-1)-a_{2}y(k-2)+b_{0}u(k-1)+b_{1}u(k-2)+\xi(k) \tag{18}\]  
where \(a_{1}\), \(a_{2}\), \(b_{0}\), and \(b_{1}\) are model parameters, \(u(k)\) is the moving position of the electrode, and \(y(k)\) is the interelectrode gap \(d\).  
Equation (18) can be written as  
\[y(k)=\mathbf{h}^{\mathrm{T}}(k)\mathbf{\theta}+\xi(k) \tag{19}\]  
where  
\[\left\{\begin{array}{l}\mathbf{h}(k)=\left[-y(k-1)\quad-y(k-2)\quad u(k-1) \quad u(k-2)\right]^{\mathrm{T}}\\ \mathbf{\theta}=\left[a_{1}\quad a_{2}\quad b_{0}\quad b_{1}\right]^{\mathrm{ T}}\end{array}\right. \tag{20}\]  
The parameter estimation formula for the forgetting factor recursive least-squares method can be derived by using the performance indicators shown in Equation (21):  
\[\left\{\begin{array}{l}\hat{\mathbf{\theta}}(k)=\hat{\mathbf{\theta}}(k-1)+ \mathbf{K}(k)\big{[}y(k)-\mathbf{h}^{\mathrm{T}}(k)\hat{\mathbf{\theta}}(k-1) \big{]}\\ \mathbf{K}(k)=\frac{\mathbf{p}(k-1)\mathbf{h}(k)}{\lambda+\mathbf{h}^{\mathrm{ T}}(k)\mathbf{p}(k-1)\mathbf{h}(k)}\\ \mathbf{P}(k)=\frac{1}{\lambda}[\mathbf{h}-\mathbf{K}(k)\mathbf{h}^{\mathrm{T} }(k)]\mathbf{P}(k-1)\end{array}\right. \tag{21}\]  
where \(\lambda\) (\(0<\lambda\leq 1\)) is the forgetting factor and \(\mathbf{P}\) is the covariance matrix.
## 3 Proposed SPMIPv6 Protocol
### Application Scenarios of IP-WSN based on SPMIPv6
#### 3.3.4 Connected Home  
The connected home or smart home is with no doubt an area where SPMIPv6 based IP-WSN can be used to support an increasing number of services: home safety/security, home automation and control, personal healthcare, smart appliances and home entertainment systems. In home environments SPMIPv6 based IP-WSN networks typically comprise a few dozen units and probably in the near future a few hundreds of nodes of various natures: IP sensors, actuators and connected objects.
## 4 Experimental Results and Discussions
### Wet-Dry Cycle Test  
The effect of various wetting-drying cycles on the unconfined compressive strength of soil brick specimens was evaluated through a combined test that included wet-dry cycle and uniaxial compressive tests. The specimens were first cured at room temperature for 28 days, then subjected to 0, 3, 6, or 12 wetting-drying cycles, and their unconfined compressive strength was measured at each cycle level. The goal was to determine the patterns of change in the unconfined compressive strength of the specimens with respect  
Figure 6: Unconfined compressive strength with various woodchip contents containing 15% stabilizer.  
Figure 7: Bending strength with various stabilizer contents containing 0.5% wood chip.  
to the different wetting-drying cycles. The experimental results, presented in Figure 8, demonstrate that the unconfined compressive strength of soil brick specimens decreased with an increasing number of wetting-drying cycles. Specifically, the strength showed a gradual decrease over the first three cycles, followed by a steep decline of 63% between cycles three and six. However, as the number of cycles came closer to the termination point of 12 cycles, the decrease became smoother, averaging less than 15%.  
The experimental results, as illustrated in Figure 8, depict the ratio of unconfined compressive strength at each cycle to the initial strength. The test-to-baseline strength ratios indicate a gradual decline up to three cycles, followed by a steep drop between cycles three and six. Notably, at a stabilizer content of 5%, the strength decreased by up to 43% during this period. The experiment revealed a strength change pattern that varied with the stabilizer content of the soil brick specimens. As the stabilizer content increased, the test-to-baseline strength ratio decreased. Notably, at a stabilizer content of 5%, the strength decreased by as much as 32%. However, at stabilizer contents of 10% and 15%, the strength decrease was 25% or less, which was significantly lower than the decrease observed with cementitious stabilizer (60%).  
The study reveals that stabilizer content significantly affects the performance of soil brick structures under wet-dry conditions. While eco-friendly stabilized soil bricks exhibit reduced sensitivity to climate-induced effects when compared to conventional stabilizers, they are sensitive to the number of wetting-drying cycles. Thus, careful stabilizer selection is crucial for optimal performance. Overall, the study indicates that the soil bricks used in this research are considerably less susceptible to climate-induced effects than conventional stabilizers.
## 2 Literature Review
### Solar Cells
A combination of a quasi-Z-source converter, DC link capacitor, three-phase voltage source inverter, MPPT controller, and a brushless DC drive was used in [46] to enhance the performance of a Solar PV Driven System. Some studies have focused on maximizing power quality in systems with renewable energy sources. For example, [PERSON] et al. [47] used a genetic algorithm to minimize harmonics in a microgrid with renewable energy sources and maximize power quality. In another study, [PERSON] et al. [48] proposed a method for maximizing the efficiency of renewable energy sources through energy management from the consumer side. Since the goal was to maximize the output of renewable sources and minimize fluctuations during changes in renewable source inputs, energy management algorithms with demand-side management approaches were not considered. Based on the review conducted, it is evident that the PV systems proposed thus far have either focused solely on MPPT for individual PV cells or have not considered solar farms. A comprehensive analysis of various conditions under different scenarios has not been performed for systems involving many PV cells across a wide area. Therefore, the present study aims to enhance existing solar farm-wind turbine-battery systems by considering predictable factors. The main contributions of this study are as follows:  
* We improve existing solar farm systems by considering the shadow conditions and MPPT;
* We use motion detection to predict the shadow pattern in a solar farm;
* We propose an effective hybrid system, including PV and wind sources, using a combination of well-known DFIG, ANFIS, and Z-source converter components.
## 3 Results and Discussion
### Physicochemical and Chemical Characterization of Juices
Among HPP juices, the statistically lowest amount of sulcatoe was observed in the RD cultivar (0.03 \(\pm\) 0.0 ppm). While [PERSON] et al. (2017) reported that the presence of sulcatoe was not observed in HPP-treated juices pertaining to Pink Lady, Granny Smith, and Jonagold cultivars, in the present study, HPP treatment affected the concentration of sulcatoe, depending on the cultivar considered [12]. To stress and confirm diversities and analogies among the differently treated juices and the three cultivars, obtained data were statistically elaborated using an unsupervised approach. Principal component analysis (PCA) was performed using the concentrations of common volatile compounds as independent variables. To this aim, two components were extracted and covariance matrix method was applied. The first two components accounted for 87% of the total variance, as shown in Figure 4. More in detail, PIN and RD juices were separated from the GD ones on the basis of component 2, confirming the peculiarities of each variety. GD samples were characterized by a higher content of aldehydes, such as heptanal and benzaldehydes, in comparison with PIN and RD. Among GD cultivar, while NT and TT juices present a similar volatile content, characterized by the scarce amount of 3-hexanol, HPP samples were differentiated from the firsts on the basis of component 1, and in particular by hexanal, 2-nonenal, isoamyl alcohol, and benzaldehyde concentrations that were higher after HPP processing. Concerning PIN and RD samples, NT juices were well separated from treated samples, on the basis of a more elevated number of aldehydes, mainly hexanal. In addition, TT and HPP samples were distinguishable from each other; this distinction was based on the different quantity of hexyl acetate, 2-ethyl-1-hexanol, isoamyl acetate, (E)-2-methyl-2-butenyl acetate, and isobutyl acetate, which were higher in TT juices in respect to the HPP ones. Moreover, while for GD, NT and TT samples were differentiated from HPP, for PIN and RD cultivars, NT and HPP samples were similar for the second component. In particular, they presented comparable amounts of octanal, hexyl-n-valerate, and 1-octen-3-ol. On the basis of statistical data elaborations, it is possible to conclude that thermal and high-pressure treatments have a different influence on the volatile profile of considered juices, and changes given by processing seemed to be cultivar-dependent, as also stated by [PERSON] et al. in 2017 [12]. In our case, HPP preserved better the aromatic characteristics of PIN and RD cultivars in respect to GD, for which an increase in volatile profile was observed.
## 4 Results
### Network Simulation  
The results from the network latency measurements were used to perform curve fitting, as detailed in the Methodology section of this paper. Using the Maximum Likelihood  
Figure 6: Sample Scada network with OT protocol support.  
Figure 7: Packet from network emulation.  
Estimation (MLE) method, the parameters of the log-normal distribution were estimated, yielding values of \(\hat{\mu}=3.507\,\mathrm{ms}\) and \(\hat{\sigma}=0.384\,\mathrm{ms}\). These parameters characterize the underlying distribution of the measured latency data, and the resulting curve fit is shown in Figure 8.  
While the fitted log-normal distribution does not perfectly mirror the measured response times, it provides a closer overall approximation of the device's behavior than would be possible with simulations based on random values. This approach offers a more accurate representation of the latency characteristics observed in real-world conditions and serves as a valuable tool for network emulation, particularly in cases where it is important to model latency with a degree of statistical rigor.  
Following the curve fitting analysis, the next step in the study involved the implementation of OT protocol support within the INET framework, a crucial step for simulating industrial network environments. This was achieved through C++ programming, which enabled the development of an application layer positioned above the physical, link, and transport layers defined in the TCP. Support for key industrial communication protocols, specifically IEC-61850 GOOSE and Modbus/TCP, was incorporated into the device models as shown in Figure 9. The addition of these protocols allowed for realistic simulation of industrial control and automation systems within OMNeT++/INET. Furthermore, the modular nature of the INET and OMNeT++ frameworks ensures that expansion to additional protocols is straightforward, allowing the inclusion of other industrial communication standards in future work.  
Once the framework for an Intelligent Electronic Device (IED) was established within the simulation software, latency response times were generated for each packet that closely aligned with the log-normal distribution and our observed values. Additionally, packets were randomly selected for drop events to replicate the link reliability characteristics observed in Figure 4. The results from the simulated IED are presented in Figure 10, demonstrating the simulation's ability to closely replicate the physical device's operational characteristics. These findings highlight the simulation framework's effectiveness in producing results consistent with the device's real-world behavior. This capability can also be  
Figure 8: Curve fitting results for device response.  
expanded to verify the presence of any vulnerabilities predicted by formal modeling, such as for incorrect responses to packets.
## 2 Related Work
### Comparison with Related Work  
In literature, most research has been focused on individual RFID system challenges such as antenna deployment, reader collisions, or tag collisions. All these challenges have been addressed and resolved in our research work. The proposed two-ray solution provides the optimization of antenna height and signal strength accounting the reflection coefficient of the ground path. In outdoor environment, concrete road is considered to determine the received power. Distributed algorithm, Pulse protocol is modified further to reduce reader collision probability and provide equal opportunity to all readers. Query tree is suggested with M-elements and trimming method is also applied to omit the idle nodes for enhancing the identification time of tag.  
Antenna propagation model: Comparing the received signal strength, refs. [2; 3; 4] investigated on general wireless communication model, where, our proposal is for real application of RFID toll system. Instead of approaching multipath propagation like [4] our approach is simple ground reflection phenomenon considering real concrete road having fundamental computational process. Our approach is cost effective on a larger scale as it has not included frequency diversity like [3]. Furthermore, we have estimated the antenna installation specification such as height and downtilt angle.  
Reader Collision: Unlike [13], our approach does not need a time slot allocation and can avoid delay in identification speed. There are no messaging scheme between readers in our protocol like [15] which leads to add extra communication system. Sharing information of neighboring reader's signal strength may cause congestion in [16]. Waiting time interval and probability distribution function of contention window are improved than [20] to reduce collision and fair chance to each reader to communicate with tag.  
Tag collision: The prime challenge of [21; 22] is to handle large number of tags as the identification method is slot based. In [21], tags are pre-defined correlated ID's therefore, if tag ID's are assigned randomly such as vehicle management deployment, efficiency of the algorithm is reduced. In the same manner if ID distribution is not uniform collision may still occur in [10]. Our algorithm is scalable for large number of tags. In [23], multiple tag respond in same slot, increases complexity. In our approach each tag respond when query occurs, reducing interferences. In a real application such as ETC, where large number of random vehicle needed to manage, our approach is more reliable and efficient.
## 5 Experimentation  
### Prototype and Experimental Scheme  
A 3-phase 2-pole BLDC machine was designed and manufactured by the aviation industry corporation who offered this research subject. A simulation model for the control system was established in Simulink. The specifications of the machine are tabulated in Table 2. The experimental prototype and the required equipment are shown in Figure 10.  
The controller contained two control boards: an interface board, a boost converter and other power devices. The intelligent power module (IPM) _PM150 RL1A060_ was used, and other power switches adopted IGBT module _CM15 ODY-24A_. All of them were products of Mitsubishi. A floating-point DSP _TMS320F28335_ and a _MAX-II_ series CPLD _EPM1270_ were employed to build a central controller unit  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Quantity** \\ \hline Voltage, \(U\) & 270 V \\ Speed, \(n\) & 12,500 r/min \\ Torque, \(T_{e}\) & 7 N.m \\ Output power, \(P\) & 10 kW \\ Terminal resistance, \(R\) & 0.8 \(\Omega\) \\ Armature inductance, \(L\) & 0.1 mH \\ Torque factor, \(k_{\mathrm{t}}\) & 0.147 N.m/A \\ \hline \hline \end{tabular}
\end{table}
Table 2: The BLDC machine specifications.  
Figure 10: Prototype and the required equipment.  
Figure 9: Phase over-current protection logic.  
using the proposed control and protection methods. The A/D sampling process was accomplished by an _AD7329_ chip and the sampling frequency was chosen to be 20 kHz. The driving and protection logics of the system which belonged to sequential statements was implemented in CPLD, and the dual closed-loops control method was carried out in DSP. In the testing, the BLDC machine was coupled to a load simulator. A power analyzer and an oscilloscope with a current probe were utilized to acquire experimental data. The data transmission between the controller and the PC through SCI was realized by a RS-485 transceiver. A thermo-detector was used to monitor and verify the sample temperatures of the controller and the machine.