esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Experimental Results and Discussion  
### Field Experimental Scheme  
The field experiment was conducted at the Shangzhuang Experimental Base of China Agricultural University. As shown in Figure 12, it mainly includes tractor, pressure control system and communication module. The original tractor electro-hydraulic hitch was changed into pressure control experimental platform. The pressure sensor is ak-4, the range of pressure sensor is 0-10 MPa; the output voltage is 0-5 V, the relative error is \(\pm\) 0.5%. The displacement sensor is Miran, the range of displacement sensor is 0-250 mm, the output voltage is 0-5 V, the relative error is \(\pm\) 0.1%.  
Figure 11: Position coordinate curves of each point.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Input Signal**} & **Compensation** & **Controller** & **Overshoot** & **Response Time** & **Stable Time** \\ \cline{2-6}  & **Correction** & **No** & **Traditional PID** & **instability** & 1 s & **instability** \\ \cline{2-6}
1.5 MPa & **Yes** & **Traditional PID** & **0** & **3.5 s** & **3.5 s** \\ \cline{2-6}  & **Yes** & **Adaptive** & **0** & **1 s** & **1 s** \\ \cline{2-6}  & **Fuzzy PID** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of control algorithms.  
The effectiveness of the pressure control algorithm is verified by simulation. The control program is written in codesys and downloaded to TTC60 through CAN0. The parameters of the controller were adjusted on the field tractor until a satisfactory result occurred. CAN0 and CAN1 were connected to the computer, respectively. When CAN0 is connected to PC, the parameters of the controller can be observed in codesys in real-time. During the experiment, and according to the requirement of ploughing depth, the land wheel was adjusted to a reasonable position; the upper pull rod was adjusted to maintain a certain angle of penetration; the left and right pull rods were adjusted to make the plough in a horizontal position; then the lowering valve was opened to make the plough in a floating state. The tractor is adjusted to B1 gear, and the speed is stable at 6 km / h. When the plough is completely in the soil, the control signal is sent to the controller through CAN0. The experimental data, such as the hydraulic cylinder pressure and control input signal, were saved in real-time through CAN1.
### System Configuration Optimization
#### 2.3.3 Configuration Optimization Constraints  
The PEDF system's configuration optimization must consider the dynamic energy balance involving building photovoltaic power generation, building-grid interaction power, energy storage charging and discharging power, and the building's electricity load. The constraints for building PEDF systems include:  
* Power balance constraint \[P_{\text{pv}}+P_{\text{c}}+P_{\text{d}}+P_{\text{load}}+P_{\text{grid}}=0\] (10) where \(P_{\text{pv}}\) is the photovoltaic power generation (kW); \(P_{\text{c}}\), \(P_{\text{d}}\) are the energy storage charging and discharging power (kW); \(P_{\text{load}}\) indicates the power load of building electricity (kW); \(P_{\text{grid}}\) represents the interaction power between the system and the power grid (kW).
* Photovoltaic output constraint \[P_{\text{pv}}^{\text{min}}\leq P_{\text{pv}}\leq P_{\text{pv}}^{\text{max}}\] (11) where \(P_{\text{pv}}^{\text{min}}\) and \(P_{\text{pv}}^{\text{max}}\) represent the minimum and maximum photovoltaic output (kW).
* Energy storage charging and discharging constraint \[0\leq S_{\text{oc}}^{\text{min}} \leq S_{\text{oc}}\leq S_{\text{oc}}^{\text{max}}\leq 1\] (12) \[P_{\text{c}}^{\text{min}} \leq|P_{\text{c}}|\leq P_{\text{c}}^{\text{max}}\] (13) \[P_{\text{d}}^{\text{min}} \leq|P_{\text{d}}|\leq P_{\text{d}}^{\text{max}}\] (14) \[S_{\text{oc}}^{\text{start}}=S_{\text{oc}}^{\text{end}}=0.5\] (15) where \(S_{\text{oc}}\) represents the values of the state of charge; \(S_{\text{oc}}^{\text{min}}\) and \(S_{\text{oc}}^{\text{max}}\) denote the minimum and maximum values of the state of charge; \(P_{\text{c}}^{\text{min}}\) and \(P_{\text{c}}^{\text{max}}\) indicate the minimum and maximum values of energy storage charging power (kW); \(P_{\text{d}}^{\text{min}}\) and \(P_{\text{d}}^{\text{max}}\) are the minimum and maximum values of energy storage discharging power (kW); \(S_{\text{oc}}^{\text{start}}\) and \(S_{\text{oc}}^{\text{end}}\) denote the initial and final state of charge for energy storage.
* Interconnection line constraint \[P_{\text{grid}}^{\text{min}}\leq P_{\text{grid}}\leq P_{\text{grid}}^{\text{ max}}\] (16)where \(p_{\text{grid}}^{\text{min}}\) and \(P_{\text{grid}}^{\text{max}}\) represent the minimum and maximum interaction power of the interconnection line between the PEDF system and the power grid (kW).
* Demand Response Constraint \[P_{\text{load}}(t)=P_{\text{LL}}(t)+P_{\text{LL}}(t)+P_{\text{TL}}(t)+P_{\text{ CL}}(t)\] (17) where \(P_{\text{load}}(t)\) is the total load at time \(t\) considering the demand response model, indicating that all loads consist of uncontrollable load \(P_{\text{UL}}(t)\), interruptible load \(P_{\text{LL}}(t)\), transferable load \(P_{\text{TL}}(t)\), and curtailable load \(P_{\text{CL}}(t)\) (kW).
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 2 Description of the Multi-UAV Cooperative Attack Problem  
The problem considered in this study consists of assigning a fleet of heterogeneous UAVs to a set of targets. At the initial time, there are some targets in the environment, it is assumed the information of these targets are known at the initial time. The UAVs are required to attack the targets.  
In this paper, the UAVs and targets are modeled based on their resources. Each target may need a coalition of UAVs. According to the information of the targets and UAVs, the appropriate UAVs are allocated to each target. In order to maximize the system performance, the UAVs performing the same target are required to arrive to the target simultaneously. Then, the path planning process is used to generate simultaneous arrival paths for the UAVs in the same coalition. The resource requirement and simultaneous arrival of targets, the UAV's kinematic constraints and collision avoidance with obstacles are taken into account.  
In this study, some assumptions were made to simplify the problem:  
1. For simplicity, the take-off and landing process of UAVs were not taken into account, the collision avoidance among UAVs were achieved by altitude layering.
2. In the task allocation process, the communication topology remained unchanged.
3. The aerodynamic and attitude of UAVs were not considered.
4. The obstacles and targets considered in this paper were static.
## 2 Reconfigurable RFID Sensor for Detecting Solvent Vapors/Gas
### Varactor Diode Reverse Biasing  
The process of RF energy transfer involves converting RF energy into DC energy for direct use to supply a low-power device [34]. The key element of the RF power transfer system is the rectifying antenna, also called a rectenna. It consists of a receiving antenna and an RF-to-DC rectifier. The performance of the rectenna is defined by the value of its output voltage and its RF-to-DC conversion efficiency. A variety of topologies can be used to convert RF power to DC power, such as a single diode in series or shunt, a voltage doubler circuit [35], or a bridge circuit [36]. For equivalent RF input power, the advantage of the voltage doubler circuit is that it can achieve a higher output voltage than a single diode configuration.  
In the present paper, the aim was to rectify the signal obtained from the received wave of a frequency much lower than the sensor resonant frequency range and convert it into useful DC voltage to control the capacitance of the varactor. Accordingly, the suggested rectifier circuit presented in Figure 4 was a voltage doubler configuration using shunt-mounted (packaged SOT143) BAS3007A-RPP Schottky diodes [37]. The rectifier received the energy that came from the receiving antenna and was filtered by the capacitance \(C_{1}\), then it converted the energy to DC voltage in order to power the varactor. Another capacitance, \(C_{2}\), was placed across the output of the rectifier in parallel with the varactor diode and was used to smooth the output of the rectifier into a pure DC output signal. Coming out of \(C_{2}\), the signal passed through the Zener diode \(D_{\text{Z}}\), which was used as a voltage regulator; i.e., to limit the voltage reaching the varactor and protect it from damage. The signal then passed through a resistor in order to prevent the high-frequency signal of 3.2 GHz, at which the resonator worked, from leakage into the rectifying circuit; finally, the signal arrived at the \(D_{\text{V}}\) varactor diode to power it.  
The above-described circuit was printed on the backside of the sensor tag (see Figure 5). The rectangular conducting loop, originally present to set up the appropriate level of the sensor RCS, was used as an antenna. This antenna received the low-frequency controlling signal of 7.2 MHz, which was then rectified and fed to the varactor to control its capacitance. The loop was cut by an interdigital capacitor to prevent DC short circuits. To incorporate the rectifying circuit, it was necessary to modify the geometry using both sides of the extremely thin substrate and appropriate placement of the conductive paths so that the high- and low-frequency parts did not negatively interfere with each other. Part of the modification also included the design of the capacitive loop and the U-dipole arm breaking for DC decoupling, again with a view toward avoiding negative interference (see Figure 5).  
Figure 4: Rectifying circuit.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 2 Study Area and Materials
### Materials
#### 2.2.2 VIIRS/DNB Night-Light Images  
Due to having higher image quality and spatial resolution than other systems, VIIRS/DNB nighttime light data are more accurate for use in electricity consumption simulations. A total of 92 monthly nighttime light images were used in this work, which were published by the Payne Institute for Public Policy from April 2012 to December 2019. The National Polar-orbiting Partnership satellite was launched in October 2011, with the DNB band used to detect nighttime lights in the wavelength range of 0.5-0.9 pm at a spatial resolution of 750 m and 14-bit spectral resolution with in-orbit radiometric calibration. The Group on Earth Observations (GEO) uses nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) to produce monthly average synthetic data. Prior to averaging, the cloud coverage was determined using the VIIRS Cloud Mask product (VCM), while the DNB data were filtered to exclude data affected by stray light, lightning, lunar illumination, and cloud cover. Moreover, the data near the edges of the strips were excluded from the averaging calculations. The product is offered in GeoTIFF format in sets of six tiles. More specifically, the tiles are cut at the equator and each spans 120 degrees of latitude. The current monthly composite image is filtered to screen out light from auroras, fires, boats, and other temporary sources, and has not been background denoised. However, the incorporation of layers of annual composites can eliminate the temporary light and background (non-light) values, and the first version of annual images includes only two years: 2015 and 2016.
# Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant
## 2 Materials and Methods
### Semi-Industrial-Scale Subcritical Water Extraction (SI-SWE)  
Semi-industrial-scale subcritical water extraction (SI-SWE) was carried out on a pilot-scale customized prototype (Tropical Food Machinery SRL, Busseto, Italy).  
This system provided is made up of a heating system consisting of a boiler (which generates superheated steam, up to 9 bar and 170-190 \({}^{\circ}\)C). Water at 90 \({}^{\circ}\)C is maintained in a 40 L hot recirculation tank, then it passes through a heat-exchanger (steam-fed), and it is heated to the desired temperature, in a closed loop. The reactor is composed of two 100 L stainless steel cylindrical extractors (usable separately), with respective loading and unloading connections, equipped with five perforated baskets (0.5 mm meshes), inside  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ABTS** & 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) & **PNF** & \multicolumn{1}{c}{NF permeate} \\
**CTs** & Condensed tannins & **PUF** & \multicolumn{1}{c}{UF permeate} \\
**CW** & Chestnut wood & **RD** & \multicolumn{1}{c}{Diafiltration retentate} \\
**CWC** & Chestnut wood chips & **RNF** & \multicolumn{1}{c}{NF retentate} \\
**CWS** & Chestnut wood sawdust & **ROS** & \multicolumn{1}{c}{Reactive oxygen species} \\
**DAD** & Diode array detector & **RT** & \multicolumn{1}{c}{Room temperature} \\
**DM** & Dry matrix & **RUF** & \multicolumn{1}{c}{UF retentate} \\
**DPPH** & 2,2-diphenyl-1-pcriildrazijl & S/L & Solid/Liquid ratio \\
**GAE** & Gallic acid equivalents & **SI-SWE** & Semi-industrial SWE \\
**HTs** & Hydrolysable tannins & **SWE** & \multicolumn{1}{c}{Subcritical water extraction} \\
**IC\({}_{\text{50}}\)** & Half-maximal inhibitory concentration & **TPC** & \multicolumn{1}{c}{Total polyphenol content} \\
**MASWE** & Microwave-assisted subcritical water extraction & **UF** & \multicolumn{1}{c}{Ultrafiltration} \\
**NF** & Nanofliltration & **US** & \multicolumn{1}{c}{Ultrasound} \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations.  
which the matrix to be extracted is placed. Once the baskets have been loaded with CWC and the system is sealed, the water is pumped inside the chambers. The extractions were carried out, loading only one chamber with CWC (13,5 kg), using a volume of 160 L of water (S/L ratio 1:10, _approx._), for 30 and 60 min, respectively. The extract was sampled and freeze-dried to evaluate extraction yield and polyphenol content.  
To concentrate and reduce the extract volume, an expansion bubble was applied, performing a flash evaporation reducing the temperature to 50-60 \({}^{\circ}\)C in a few minutes and then heated with a steam coil to concentrate it by means of a vacuum pump. The concentrated product can be dried with the spray-drying technology.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
###### Abstract  
In vehicle-to-everything (V2X) networks, security and safety are inherently difficult tasks due to the distinct characteristics of such networks, such as their highly dynamic topology and frequent connectivity disruptions. Jellyfish attacks are a sort of denial of service attack that are challenging to deal with, since they conform to protocol norms while impairing network performance, particularly in terms of communication overhead and reliability. Numerous existing approaches have developed new techniques with which to identify and prevent these attacks; however, no approach has been capable of facing all three types of Jellyfish attacks, which include reordering attacks, delay variance attacks, and periodic drop attacks. In this work, we design a new protocol that analyzes the behavior of every node in a network and selects the trusted routes for data transmission to their intended destination by calculating different Honesty metrics. The OMNET++ simulator was used to evaluate the overall performance of the proposed protocol. Various evaluation metrics, such as the packet delivery ratio, end-to-end delay, and throughput, are considered and compared with other existing approaches.  
V2X networks; security and safety; Jellyfish attacks; secure routing protocol Article  
1  
Migating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks. _Appl. Sci._**2023**, 13, 4591. [[https://doi.org/10.3390/app13074591](https://doi.org/10.3390/app13074591)]([https://doi.org/10.3390/app13074591](https://doi.org/10.3390/app13074591))
# The Impact of Thermal Treatment on Structural Changes of Teak and Iroko Wood Lignins
## 2 Materials and Methods  
### Wood Thermal Modification  
Teak (_Tectona grandis_ L.f) (density--711 kg\(\cdot\)m\({}^{-3}\)) and iroko (_Milicia excelsa_ (Welw.) [PERSON]) (density--615 kg\(\cdot\)m\({}^{-3}\)) were harvested in Myanmar and Gabon, respectively [14; 16]. Wood samples with the dimensions 20 \(\times\) 20 \(\times\) 300 mm (T \(\times\) R \(\times\) L) were modified at the temperatures of 160 \({}^{\circ}\)C, 180 \({}^{\circ}\)C and 210 \({}^{\circ}\)C in an open system thermal modification chamber (Type S400/03, LAC Ltd., Rajhrad, Czech Republic) following the ThermoWood process [21]. Untreated (reference, denoted as 20 \({}^{\circ}\)C) and thermally modified samples were ground to sawdust and extracted in the Soxhet apparatus with a mixture of ethanol and toluene according to the ASTM D1107-96 standard [22]. Acid-insoluble lignin (Klason lignin) was determined according to [PERSON] et al. [23].
## 7 Relative Leakage Ratio  
The relative leakage ratio of one private key refers to the ratio of the leakage amount of one private key to the length of the private key.  
In our proposed scheme, \(w_{1},w_{2}\) and \(w_{3}\) are primes with length of \(\theta\) bits. This private key has \(2\times 2\times 3\theta\) bits. The leakage amount of the private key amounts to \(2\times 2(1-2\Lambda)\theta\) bits, where \(\Lambda\) is a very small constant value. So, the relative leakage ratio about the private key is \(\frac{4(1-2\Lambda)\theta}{12\theta}=\frac{4(1-2\Lambda)}{12}\approx\frac{1}{3}\).  
Table 4 shows some comparisons about the proposed scheme and some related schemes are given in [51; 53]. We will consider the private key size, leakage amount, storage requirement and leakage rate. Ref. [53] gives an anonymous IBBE scheme but does not consider leakage. Ref. [51] proposes a continuous-leakage-resilient (CLR) IBBE scheme (CLR-IBBE), which essentially uses the private key extension technology, but does not consider the anonymity. The scheme given in this paper takes account of both key leakage and anonymity.  
From Table 4, we see that the leakage resilience of the given scheme is better than that of [51]. In addition, because the scheme of [51] requires \(n\geq 2\), the storage requirement of our scheme is better than that of [51]. In fact, for the scheme of [51], when \(n\) is a large value, a high leakage rate can be obtained. For example, when \(n=2\), the private key leakage ratio in [51] is \(\frac{1}{12}\). When \(n=4\), the private key leakage ratio of the scheme [51] is \(\frac{1}{6}\). The leakage rate of the scheme in [51] increases with the increase in \(n\), but the maximum leakage rate is \(\frac{1}{3}\). The essence is that the scheme of [51] obtains certain leakage resilience at the expense of storage space and calculation cost. The scheme of this paper divides the private key into two different states through state partition technology, so that the private key can be properly separated to obtain leakage resilience.
# Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements
## 3 Spatial and Temporal Distribution of Sea Level Anomalies  
Since the sea level has a significant one-year cycle and the carrier generally does not operate underwater for more than one year, this study chooses 2020 as the representative year. The daily mean sea level anomaly data of 366 days from 1 January 2020 to 31 December 2020 are used to calculate and analyze the impact of sea level height anomaly on the underwater gravity gradient measurements. The spatial distribution of the annual mean sea level anomaly in 2020 (Figure 2) was obtained by time-averaging the 366-day data. The results show that most of the world's oceans were above the long-term mean sea level in 2020, which is consistent with the current trend of rising sea levels due to global warming. Significant regional differences in the sea level are also observed. Furthermore, the amplitude of the sea level anomaly in the middle of the ocean is small, and positive and negative anomalies with large amplitudes are present in the west boundary current area of the mid latitude ocean and the Antarctic circumpolar current area of the Southern Ocean. Among them, the Kuroshio extension area of the western boundary current in the Pacific Ocean is the most significant, with the highest value of 0.95 m (35.9\({}^{\circ}\) N, 145.9\({}^{\circ}\) E) and the lowest value of \(-\)0.83 m (31.9\({}^{\circ}\) N, 136.4\({}^{\circ}\) E).  
The difference between the maximum and minimum values in the 366 days at each grid point is used to obtain the annual range of global sea level anomalies in 2020 (Figure 3), representing the variation range of the sea level height in 2020. Figure 3 shows that the high-value areas of the annual range are mainly concentrated in the western boundary flow area of the ocean and the Southern Ocean, with amplitudes exceeding 1 m, which indicates that the sea level significantly changes in these areas during the year. The sea area near the southern Cape of Good Hope in Africa (39.4\({}^{\circ}\) S, 19.6\({}^{\circ}\) E) has an amplitude of 2.11 m, which exhibits the maximum annual range of global sea level anomalies in 2020.  
The above analysis shows that the sea level anomaly varies across different regions of the world with a maximum difference of more than 1 m, and the amplitude of changes within a year also differs, with a maximum change of 2 m. Therefore, this study aims to determine whether the existence of sea level anomalies and their variation with the spatiotemporal distribution affect the underwater gravity gradient measurements.  
Figure 2: Global annual mean sea level anomaly in 2020 (unit: m).
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 3 Results
### Prostate Cancer Detection  
In the domain of prostate cancer detection, an advanced analytical approach has been developed that combines fractal analysis with discrete Fourier analysis of ultrasound radiofrequency (RF) [28]. This innovative technique utilizes a set of features derived from the frequency spectrum of RF signals, significantly improving the accuracy of prostate cancer diagnostics. By leveraging the discrete Fourier transform (DFT) of ultrasound RF time series, six features representing the frequency spectrum were extracted and used in conjunction with neural network classifiers to detect cancerous regions as small as 0.03 cm\({}^{2}\)[28].  
Previous studies have established that the fractal dimension of these RF signals correlates strongly with tissue microstructure. Building on this, the new approach incorporates additional spectral features to enhance diagnostic performance. In practical applications, this method has achieved remarkable results, demonstrating a mean sensitivity of 92% and a specificity of 90% in detecting prostate cancer, as validated by detailed pathology results from human prostate specimens [29]. This technique's efficacy is underscored by its ability to identify malignancies in very small tissue regions, which is a critical factor in early and accurate cancer detection. The robustness of this method offers promising advancements in the field of medical imaging and prostate cancer diagnostics, providing a noninvasive, reliable and highly sensitive tool for identifying cancerous tissues [29]. The combination of discrete Fourier and fractal analyses of ultrasound RF time series represents a substantial advancement in prostate cancer diagnostics. Its high accuracy and noninvasive nature make it a valuable tool for clinicians, offering the potential to improve early detection and treatment outcomes for prostate cancer patients [30].