esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, this work shows the analysis of avocado oil extracted from the Zutano variety by two (SE and UAE) techniques. In this context, petroleum ether volatile fractions were estimated over three ripening stages (breaking, ripe, and overripe) using HS-SPME-GC-MS. The Zutano variety, which is cultivated in the Crete region, gave a fragrant oil which has not been previously studied. This cultivar is characterized from seven main volatile compounds, including 2,6,6-trimethylbicyclo[3.1.1]hept-2-ene (\(\alpha\)-pinene), 1-methyl-4-(prop-1-en-2-yl)cyclohex-1-ene (D-limonene), 4,10-dimethyl-7-propan-2-yltricyclo[4.4.0.01.5]dec-3-ene (\(\alpha\)-cubebene), (15,65,75,85)-1,3-dimethyl-8-propan-2-yltricyclo[4.4.0.02.7]dec-3-ene (a-copaene), (18,95,2)-4,11,11-trimethyl-8-methylenebicyclo[7.2.0]undec-4-ene (\(\beta\)-caryophyllene), 2,6-dimethyl-6-(4-methylpent-3-enyl)bicyclo[3.1.1]hept-2-ene (\(\alpha\)-bergamotene), and (1E,4E,8E)-2,6,6,9-tetramethylcyloundeca-1,4,8-triene (humulene). The analyzed fractions consisted of a high content of terpenoids with an average relative abundance of over 61.7%. ANOVA revealed that the extraction methods did not have statistically significant differences between their qualification and semi-quantification results. In contrast, the application of MANOVA between the ripening stages and some volatiles indicated that the \(p\)-values were lower than 0.05. Even though ripening is one significant factor that affects volatiles, additional research is required to approve the above results. This study could form the foundation for additional research on the impact of ethylene and the metabolism of avocado oil volatiles.  
Conceptualization, M.X. and C.S.P.; methodology, M.X., L.V., E.H.K., P.-K.R. and C.S.P.; software, M.X., L.V., E.H.K. and P.-K.R.; validation, M.X. and E.G.; formal analysis, E.G.; investigation, M.X., E.G., L.V. and E.H.K.; resources, E.G.; data curation, M.X. and E.G.; writing--original draft preparation, M.X.; writing--review and editing, L.V., E.H.K., P.-K.R., C.S.P. and P.A.T.; visualization, C.S.P. and P.A.T.; supervision, C.S.P.; project administration, C.S.P. and P.A.T.; funding acquisition, E.G. and P.-K.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] for the sponsorship of _Persea americana_ Mill. \"Zutano\" variety avocados from the Greek island of Crete, Chania.  
The authors declare no conflict of interest.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Bridging AI and IoT: Innovations in PV Energy Management
Overall, the study emphasizes how AI and IoT technologies can enhance the reliability, efficiency, and scalability of solar energy systems across national infrastructure. In a similar vein, [PERSON] et al. [198] focus their article on the use of AI and IoT technologies to improve the efficiency of solar energy systems. It emphasizes the importance of smart solar panel cleaning systems that monitor and optimize panel performance by detecting dirt and debris, which reduce energy production. The study compares traditional cleaning methods to autonomous smart systems, which provide real-time purification and remote monitoring. A pilot project demonstrated the effectiveness of these smart systems in increasing solar power generation and reducing operational costs. The article also explores the role of AI (including machine learning and deep learning) and IoT in enhancing energy distribution, renewable energy generation, and grid management. The integration of these technologies is shown to improve energy efficiency, reduce costs, and increase the resilience of electrical systems. In the field of solar powered electric vehicles, [PERSON] et al. [199] discuss solutions for charging systems using hybrid sources, particularly solar energy, alongside plug-in hybrid electric vehicles (PHEVs) and all-electric vehicles (EVs). It emphasizes the role of solar power in hybrid charging systems, where solar panels directly charge the vehicle's battery, which is then used to power the vehicle. As the vehicle operates, the battery is charged through multiple sources, including solar energy. They explore how the IoT and AI can enhance the monitoring of charging systems and enable fully autonomous driving in EVs through connected sensors. AI automates decision-making and tasks, advancing the move toward full automation for electric vehicles. In the future, solar-powered, IoT, and AI-based autonomous EVs could help reduce battery charging times, parking issues, and traffic problems, while promoting the development of smart cities. The integration of Artificial Intelligence (AI) and the IoT in photovoltaic PV energy systems marks a significant step toward smarter and more efficient energy management. By enabling real-time monitoring, fault detection, and predictive analytics, AIoT enhances the performance and reliability of PV installations. This synergy supports data-driven decisions, optimizes energy output, and contributes to sustainability goals. However, challenges remain--such as high setup costs, data security risks, device interoperability, and the demand for advanced computational resources. These barriers highlight the need for scalable, secure, and user-friendly AIoT solutions to fully unlock their potential in PV energy applications.
# Estimation of SMPS' Conducted Emission According to CISPR Standards
## 3 Results  
The results obtained for the HFSS approach are shown in Figure 20, while those obtained using the Q3D approach are shown in Figure 21.  
In order to verify the results, the real measurement was conducted. The test setup according to the CISPR 16-2-1 shown in Figure 22 was reproduced in the laboratory. Instruments that were used for measurement are shown in Table 1.  
Figure 23 shows the result of the measurement in the laboratory conditions of the real sample using devices listed in Table 1; the software that was used was the EMC32. The parameters of the measurement are presented in Table 2. It should be noted that chosen dwell time is longer than the minimum single frequency measurement time specified by CISPR 16-2-1. Single sweep in this case is sufficient to capture continuous, narrowband signals that are present in the EUT emission spectrum. In Figures 24 and 25, the comparison of estimation and measurement is shown.  
Figure 21: Conducted emission estimation using the Q3D approach. Frequency sweep 9 kHz to 30 MHz.  
Figure 20: Conducted emission estimation using the HFSS approach. Frequency sweep 9 kHz to 30 MHz.  
Figure 23: Conducted emission measurement result. EMC32 (Rohde & Schwarz, Germany, Munich) software view.  
\begin{table}
\begin{tabular}{c c} \hline
**Device Type** & **Device Part Number** \\ \hline LISN & Schwarzbeck NNLK 8121 RC \\ EMI Receiver & Rohde \& Schwarz ESR7 \\ Power Supply & LAB/HP51500 0–1500 V/0–4, 7 A \\ Load & 20 Ohm Resistor \\ \hline \end{tabular}
\end{table}
Table 1: Devices used for conducted emission measurement.  
Figure 22: Test setup for conducted emission measurement.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 4 FoMs Analysis
### Gabor Bandwidth Analysis Results  
The Gabor bandwidths of each candidate are shown in Figure 4. The Gabor bandwidth increased as the front-end bandwidth increased. For BOC signals, the front-end bandwidth increased steeply at the point where it contained two main lobes of the PSD.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Candidates**} & \multicolumn{2}{c}{**DLL Error (m)**} & \multicolumn{2}{c}{**Minimum Required**} \\ \cline{2-4}  & \(\textit{C/N}_{0}\) **= 20 dB-Hz** & \(\textit{C/N}_{0}\) **= 40 dB-Hz** & \(\textit{(dB-Hz)}\) \\ \hline BPSK(1) & 2.13 & 0.17 & 15.12 \\ BPSK(4) & 1.01 & 0.08 & 18.81 \\ BPSK(5) & 0.87 & 0.07 & 19.30 \\ BPSK(8) & 0.66 & 0.05 & 21.06 \\ BOC\({}_{\text{s}}\)(1,1) & 1.24 & 0.10 & 12.43 \\ BOC\({}_{\text{s}}\)(2,1) & 0.83 & 0.06 & 10.65 \\ BOC\({}_{\text{s}}\)(2,2) & 0.83 & 0.07 & 13.86 \\ BOC\({}_{\text{s}}\)(3,1) & 0.72 & 0.06 & 9.94 \\ BOC\({}_{\text{s}}\)(3,2) & 0.72 & 0.06 & 13.25 \\ BOC\({}_{\text{s}}\)(3,3) & 0.74 & 0.06 & 15.40 \\ BOC\({}_{\text{s}}\)(4,4) & 0.59 & 0.05 & 15.77 \\ BOC\({}_{\text{s}}\)(5,2) & 0.43 & 0.03 & 10.79 \\ BOC\({}_{\text{c}}\)(1,1) & 0.97 & 0.08 & 11.31 \\ BOC\({}_{\text{c}}\)(2,1) & 0.73 & 0.06 & 10.16 \\ BOC\({}_{\text{c}}\)(2,2) & 0.65 & 0.05 & 12.66 \\ BOC\({}_{\text{c}}\)(3,1) & 0.66 & 0.05 & 9.73 \\ BOC\({}_{\text{c}}\)(3,2) & 0.56 & 0.04 & 12.06 \\ BOC\({}_{\text{c}}\)(3,3) & 0.50 & 0.04 & 13.50 \\ BOC\({}_{\text{c}}\)(4,4) & 0.47 & 0.04 & 14.65 \\ MBOC & 1.24 & 0.10 & 11.09 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The DLL error and minimum required carrier-to-noise density ratio (\(C/N_{0}\)) for stable DLL tracking of the candidates.  
Figure 4: Gabor bandwidth of the candidates in the S-band.  
The front-end bandwidth differs depending on the uses of the receiver. Therefore, we analyzed the Gabor bandwidth when the front-end bandwidth was 4 MHz, 16.5 MHz, or corresponded to the main lobe bandwidth. The Gabor bandwidths for each case are listed in Table 8.  
When the front-end bandwidth was 4 MHz, BOC\({}_{\text{c}}\)(3,1) only had small side lobes within the bandwidth. Therefore, it had the smallest Gabor bandwidth of 0.15 MHz. Among the candidates, BOC\({}_{\text{s}}\)(2,1) had the largest Gabor bandwidth of 1.16 MHz. The PSD of BOC\({}_{\text{s}}\)(2,1) was maximized at \(\pm\)2 MHz and had a large side lobe at the center frequency.  
When the front-end bandwidth was 16.5 MHz or corresponded to the main lobe width, BPSK(1) and BOC\({}_{\text{s}}\)(5,2) had the minimum and maximum Gabor bandwidths, respectively. Similar to the code tracking performance, with the same chip rate and subcarrier frequency, the cosine-BOC signal had a larger Gabor bandwidth than the sine-BOC signal.
# Design and Development of an Embedded Controller for a Hydraulic Walking Robot WLBOT
## 3 Embedded Controller Hardware Design  
As is displayed in Figure 3, the embedded controller meets both functional requirements and nonfunctional requirements.  
* Each embedded controller needs to sample each joint angle and angular velocity, two chambers' pressure, system pressure, and foot force, a total of 14 sensor signals. After that, A/D converting and signal processing will be implemented.
* The embedded controller can receive joint angle command from PCM-3365 and give 14 processed sensor signals back to it via CAN bus.
* The control algorithm and D/A converting will be calculated to realize the position servo control of each joint.
* The embedded controller can run a 2 kHz high control frequency.
* Each controller can communicate with PCM-3365 in 200 Hz via CAN bus, while this frequency is determined by PCM-3365.
* The sampling voltage range is 0 V\(\rightarrow\)+5 V, while the sampling resolution reaches 0.1%, and the sampling rate reaches 300 kHz. The output voltage range is \(-\)10 V\(\rightarrow\)+10 V.  
In order to meet all these requirements above, we develop an embedded controller based on STM32F407 ZGT6 ARM chip as Figure 4 depicts. The hardware includes a CAN bus module, a sensors module, a DAC output module, a data storage module, a DC-DC power module, and other peripheral circuits.  
Figure 3: Configuration of the hierarchical control system. (PC: personal computer; GPS: global positioning system; IMU: inertial measurement unit; LF leg: left front leg; RF leg: right front leg.).  
The electrical connections of the embedded controller are shown in Figure 5. Three encoders are mounted in the joints to measure the joint angles. A force sensor is mounted on the foot of the robot foot to measure the ground contact force. Seven pressure gauges are mounted on the manifold to measure the pressure of the cylinder chamber as well as the system pressure. When the robot is walking, the control inputs are calculated and the voltage outputs are sent to the servo valves through a D/A converter which is used to drive the joints of the hydraulic cylinders. The angular velocity adopts a secondary amplifier differential circuit and both cut-off frequencies are 72.3 Hz, while other sensor signals adopt the same first-order filtering circuit whose cut-off frequency is 241 Hz.  
Figure 4: The hardware structure of the embedded controller.  
Figure 5: Electrical connections of the embedded controller.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 3 Cotton Boll Disease Detection Algorithm and Improvement
### YOLOv11 Convolutional Neural Network Model  
YOLOv11 is a next-generation real-time object detection model developed by Ultralytics. Building upon the YOLO series, it achieves significant improvements in speed, accuracy, and efficiency through multiple architectural innovations. The YOLOv11 network architecture consists of four main components: the Input, Backbone, Neck, and Head. The Input module preprocesses the image and resizes it to a predefined dimension. The Backbone is constructed based on an improved version of CSPDarknet53, incorporating C3K2 blocks [9] and the C2 PSA spatial attention module to enhance feature extraction efficiency. The Neck utilizes PANet to achieve multi-scale feature fusion and integrates a lightweight C2f module to further strengthen feature representation. Finally, the Head predicts the object's location, class, and confidence score based on the feature maps provided by the Neck.  
YOLOv11 achieves a dual breakthrough in both speed and accuracy by introducing the C3K2 feature extraction component, the C2 PSA attention enhancement module, and the SPPF [10] design. The C3K2 module improves the efficiency of feature extraction, enabling the model to perform better in small object detection and complex scenes while maintaining high inference speed. The C2 PSA module, with its enhanced spatial attention mechanism, automatically focuses on key information in the image, thereby improving the model's robustness in challenging scenarios such as occlusion and varying lighting conditions. The SPPF design optimizes multi-scale feature extraction through a fast pooling strategy, reducing computational resource consumption while increasing the model's response speed. Thanks to its modular architecture and multitask support, YOLOv11 offers significant advantages for efficient deployment on embedded devices and in complex application environments.
## 4 Discussion
### Optimization of Extracting Parameters  
The experimental results of trans-resveratrol content at different solid-liquid ratios (1:20, 1:35 and 1:50 g/mL), time (2, 4 and 6 days) and granulosity (<125, 500-350 \(\upmu\)m and >1000 \(\upmu\)m) were placed to the quadratic equation using the response surface analysis. The equation for trans-resveratrol content is displayed in Equation (1):  
\[\begin{array}{l}\text{Trans -- resveratrol}\\ =205.16+41.27\times\text{A}+1.15\times\text{B}+17.68\times\text{C}+24.08\times \text{AB}+29.56\times\text{AC}-7.85\times\text{BC}\\ -67.75\times\text{A}^{2}-27.41\times\text{B}^{2}-41.30\times\text{C}^{2}\\ \end{array} \tag{1}\]  
where A--solid/liquid ratio g/mL, B--Time, C--particle size interval.  
The analysis of variance indicated that the quadratic regression model is statistically significant (\(p<0.05\)) with a coefficient of regression R\({}^{2}\) = 0.90. Figure 7 displays the 3D evolution of trans-resveratrol content in the function of the parameters employed (Table 2).  
According to Figure 7, the highest trans-resveratrol content is recorded at the correlation of the following parameter values 1:35 g/mL solid:liquid ratio, 4 days with 500-350 \(\upmu\)m (148.55 mg/kg D.W.), having as extraction solvent ethanol:diethyl ether in a ratio of 4:1. At these conditions, the predicted response for the optimized value is the same as the measured response, namely a resveratrol content of 205.16 mg/kg dry weight. The lowest value was observed at 1:20 g/mL solid-liquid ratio, 6 days with 500-350 \(\upmu\)m (43.70 mg/kg D.W.). These results are in compliance with those reported by [PERSON] et al. (2015) on the extraction of resveratrol from vine shoots [39] and [PERSON] et al. (2009) on the extraction of resveratrol from skin and seeds of grape [48], regarding obtaining a maximum yield on day 4 of extraction. Additionally, [PERSON] et al. (2014) reported an optimum solid-liquid of sample/solvent ratio of 1:40 for extracting resveratrol from peanut grass _Aracis repens_[47], results that are near the results obtained, namely an optimal ratio of 1:35 solid/liquid.  
The predicted optimum conditions for trans resveratrol extraction (Table 3) were very similar to the experimental concentration achieved under these conditions. Any increase or decrease in the value of the extraction parameters negatively affected the extraction yield.
## 5 Handling Stability Simulation and Comparison
### Simulation and Comparison of the Step Steer Tests  
The purpose of the step steer test is to characterize the transient response behaviors of vehicles [44]. The steering input ramps up from an initial steer value to the maximum steer value, as shown in Figure 13. During the step steer test, the steady state of the truck changes from the straight driving to the circular motion and the transition between the two steady-state movements is the transient response. The simulations of the step steer tests were conducted in Adams based on the two truck models, respectively. According to Chinese GB/T6323.2-94, the simulation settings are as follows: the initial steer value = 0\({}^{\circ}\), the step start time 2.0 s, the duration time 6.0 s, the end time 30.0 s, and the final steer value 100.0\({}^{\circ}\). A comparison of the simulated dynamic responses is shown in Figure 14.  
Figure 14: _Cont._  
Figure 13: The curve of the input versus the time for the step steer test.  
From Figure 14a, it can be seen that the steering wheel torques from the two truck models are basically the same from 0.0 s to about 6.5 s. The steering wheel torque of the truck with the taper-leaf spring suspension is slightly smaller than that of the truck with the multi-leaf spring suspension from 20.0 s to 30.0 s. Moreover, the steering wheel torque of the truck with the taper-leaf spring suspension is obviously smaller than that of the truck with the multi-leaf spring suspension from 6.5 s to 20.0 s. The results prove that the truck with the taper-leaf spring suspension has better steering portability during the step steer test. From Figure 14b, it can be seen that the two truck models show almost the same yaw rate during the step steer test. The result further shows that replacing the multi-leaf spring with the taper-leaf spring has almost no effect on the yaw rate. From Figure 14c, it can be seen that the  
Figure 14: A comparison of the simulated dynamic responses obtained from the step steer tests: (**a**) The steering wheel torque, (**b**) the yaw rate, (**c**) the truck roll angle, and (**d**) the truck sideslip angle.  
truck roll angles on the basis of the two truck models are almost the same from 0.0 s to 3.0 s during the step steer test. With the increase of the steer value, the roll angle of the truck with the taper-leaf spring increases more obviously. The results show that the truck with the taper-leaf spring suspension has the poorer stability. Figure 14d illustrates that the sideslip angle of the truck with the taper-leaf spring suspension is obviously larger than that of the truck with the multi-leaf spring suspension. The result further shows that the truck handing performs worse after replacing the multi-leaf spring with the taper-leaf spring.
## 4 Summary and Conclusions  
In this paper, an interaction integral method combined with the ABAQUS postprocessing routine technology is proposed for effectively calculating the crack fracture parameters for heterogeneous materials. Some conclusions are presented below:  
1. The present method can be directly implemented within ABAQUS, and the computational results exhibit good accuracy, mesh convergence, and domain-independence.
2. For two-dimensional and three-dimensional models with inclusions, the results show that when the integration domain intersects the inclusions, the stress intensity factors calculated by ABAQUS fail to accurately reflect the crack-tip state, while the present method demonstrates good domain-independence, thereby expanding the practical application scope of ABAQUS.
3. The influence of the inclusion's properties on the stress intensity factor was analyzed. The results show that high-modulus inclusions near the crack tip reduce the stress intensity factor. Conversely, low-modulus inclusions in the same area increase it.  
Figure 14: (**a**) A three-dimensional heterogeneous material with multiple inclusions; (**b**) model meshes.  
Figure 15: Comparison of computational results obtained by the present method and ABAQUS for a three-dimensional example with multiple inclusions.  
* The proposed method in this paper can be applied to heterogeneous materials with complex interfaces, such as particulate-reinforced composites and solid rocket motor grains. It can guide the crack conditions and crack propagation of practical components. In the future, it can also be combined with other software to provide guidance for engineering applications.  
Conceptualization, F.G.; methodology, F.G.; software, Y.L.; validation, Y.L.; formal analysis, P.L.; investigation, P.L.; resources, Y.C.; data curation, X.W.; writing--original draft preparation, Y.L.; writing--review and editing, F.G.; visualization, Y.C.; supervision, X.W.; project administration, F.G.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Natural Science Foundation of China (Grant No. 12372075).  
Not applicable.  
The data presented in this study are available on reasonable request.from the corresponding author.  
The authors declare no conflicts of interest.