esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Analysis of CSPSWs
### Influence of Different Wave Height on Energy Dissipation Capacity of CSPSWs  
Figure 25 shows the results of the finite element simulation calculations carried out using the four models from Table 3, TSCPSW-C, TCSPSW-Ph-50, TCSPSW-Ph-100, and TCSPSW-Ph-150, with corrugation heights of 50 mm, 75 mm, 100 mm, and 150 mm, respectively. As the corrugation heights of the models TSCPSW-C, TCSPSW-Ph-50, TCSPSW-Ph-100, and TCSPSW-Ph-150 increase progressively, the energy dissipation of the models  
Figure 23: Comparison chart of different board widths: (**a**) Hysteresis curve, (**b**) Skeleton curves, (**c**) Energy dissipation coefficient, (**d**) Energy consumption per loading stage.  
Figure 24: Stress comparison diagram of different board width models: (**a**) TCSPSW—w/H—0.5, (**b**) TCSPSW—C, (**c**) TCSPSW—w/H—1.0, (**d**) TCSPSW—w/H—1.5.  
decreases. This is because increasing the corrugation height actually reduces the lateral load-bearing capacity of the corrugated steel plates, thereby reducing the energy dissipation of the models. As the corrugation height increases, the loading stage at which the models enter the elastoplastic stage is also delayed. It can be seen that before the stage of \(\Delta/\mathrm{H}=1.5\%\), the energy dissipation coefficient (E\({}_{0}\)) of the four models is negatively correlated with the corrugation height. At the stage of \(\Delta/\mathrm{H}=2.0\%\), the E\({}_{0}\) of the steel plate model with a corrugation height of 150 mm (TCSPSW-Ph-150) exceeded that of the steel plate model with a corrugation height of 50 mm (TCSPSW-Ph-50), with a value of 1.25, which was 43.61% higher than that of the model with the lowest E\({}_{0}\) (TSCPSW-C). In terms of energy dissipation, the steel plate model with a corrugation height of 50 mm (TCSPSW-Ph-50) had the highest energy dissipation at the stage of \(\Delta/\mathrm{H}=2.0\%\), reaching 69.44 kJ, which was 150.6% more than that of the model with the lowest energy dissipation (TCSPSW-Ph-150). It can be seen that the corrugation height has a significant impact on the energy dissipation capacity of the corrugated steel plate shear walls.  
Figure 26 shows the stress cloud diagrams of the four models with different corrugation heights. As the corrugation height increases, the stress distribution of the models becomes more concentrated at the valleys near the free ends on both sides. This excessive stress concentration is not conducive to the structural loading, which may lead to the premature appearance of cracks or fractures in this area. Such situations should be avoided in the design phase.  
Figure 25: Comparison chart of different wave heights: (**a**) Hysteresis curve, (**b**) Skeleton curves, (**c**) Energy dissipation coefficient, (**d**) Energy consumption per loading stage.
## 3 The Impact of the Electromagnetic Interference on Drones
### Influence of Electromagnetic Interference of Converter Station on UAV Communication
#### 3.1.2 Impact on UAV Navigation and Positioning System  
Corona discharge, insulator pollution flashover, and electromagnetic scattering in the environment of HVDC converter stations will all affect the GPS positioning accuracy of line-following drones.  
Corona discharge is a plasma discharge that occurs on the surface of an insulator. When some insulators are damaged and their performance decreases, other normal insulators may withstand higher voltages and generate very strong electromagnetic fields [29]. In addition, when the potential gradient on the surface of the transmission line reaches a certain value, the air close to the wire will also be ionized, and then corona discharge will occur. Insulator pollution flashover is a flashover discharge caused by serious contamination on the surface of the insulator, which also generates a strong electromagnetic field [30]. In addition, when the GPS signal transmitted by the satellite is transmitted to the line tower, the scattering phenomenon will also adversely affect the GPS positioning performance.  
The partial discharge frequency range of polluted insulators is 1 MHz-10,000 MHz, which overlaps with the L1 and L2 frequency bands of GPS [31]. The power of partial discharge of contaminated insulators is distributed from \(-\)70 dBm to \(-\)10 dBm. GPS positioning accuracy is under the L1 and L2 frequency bands, as long as the power exceeds \(-\)40 dBm, it will suffer obvious interference.  
When the abnormal discharge occurs in power equipment (such as corona discharge [32] and flashover discharge), if the generated electromagnetic wave signal frequency is the same as the GPS signal frequency and reaches a certain signal strength, the co-channel interference [33] will degrade GPS positioning accuracy. This is extremely dangerous for power line patrol drones that use GPS for integrated navigation.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 2 Modelling Approach
### Economic Results Calculation  
The NPC of each combination of components and the evaluated control strategy is computed considering all costs during the system lifetime: the CAPEX and OPEX of all components of the system, their replacement cost during the system lifetime, and cost of electricity purchased from the AC grid. All cash flows are converted to the present moment using a discount rate. In addition, the income from selling the electricity to the grid during the lifetime of the system was considered (negative values as income) [34].  
\[NPC_{i,k}=\sum_{j}\left(CAPEX_{j}+NPC_{rep,j}+\sum_{y=1}^{Life_{g}}\left(OPEX_{j} \frac{(1+lnf)^{y}}{(1+1)^{y}}\right)+\frac{TAX_{y}}{(1+1)^{y}}\right)-\sum_{y=1 }^{Life_{g}}\left(\frac{Inc_{all,g,y}-Cost_{purch,k,g}}{(1+1)^{y}}\right), \tag{36}\]  
where \(I\) and \(Inf\) represent the annual nominal interest or discount rate and annual inflation rate, respectively; \(OPEX_{j}\) represents the annual OPEX of component \(j\) at the beginning of the system lifetime (year 0). For the pump turbine, we consider the start-up cost (a cost is considered each time the pump turbine starts to work), which will be added to the annual OPEX. \(NPC_{rep,j}\) represents the sum of the present costs because of the replacement of component \(j\) (PV, wind turbine group, pump/ turbine, and reservoir) during the system lifetime minus the incomes attributed to the residual life of all components at the end of the system lifetime. \(Inc_{\text{sell,E\_y}}\) represents the income from the electricity sold to the AC grid during year \(y\). \(Cost_{\text{purch\_g}}\) represents the cost of electricity purchased from the AC grid during year \(y\) (\(1\ldots Life_{g}\)). \(TAX_{y}\) represents the cost of payable taxes related to project benefits: revenues minus operational expenses, interest, depreciation, and amortisation multiplied by the applicable tax rate \(TR\) (%), calculated as shown in ref. [41].  
In the LSS, the levelised cost of energy (LCOE) is the cost per kWh consumed and sold to the grid.  
\[LCOE_{i,k}=\frac{NPC_{i,k}}{\sum_{y=1}^{Lif_{e}}\left[\sum_{t}\left(P_{L}(t)+P_{ sell}(t)\right)\frac{15}{60}(1+Inf)^{y}/(1+I)^{y}\right]}, \tag{37}\]  
where \(P_{L}(t)\) and \(P_{sell}(t)\) represent the 15 min load and energy sold to the grid during year \(y\), respectively.  
In the PGS, the net present value (NPV) of each combination of components and the control strategy evaluated is the opposite of the NPC.  
\[NPV_{i,k}=-NPC_{i,k}. \tag{38}\]  
In PGS systems, the LCOE is the generation cost of kWh injected into the grid, which is calculated as  
\[LCOE_{i,k}=\frac{Total\_Present\_Cost}{\sum_{y=1}^{Lif_{e}}\left[\sum_{t}P_{sell} (t)\frac{15}{60}(1+Inf)^{y}/(1+I)^{y}\right]}, \tag{39}\]  
where \(Total\_Present\_Cost\) represents the sum of the total present costs of the system over its lifetime (NPV minus income).
## 2 Materials and Methods
### Performance Evaluation  
The main question is whether the connections in the ground truths could be detected by the evaluated networks and by TRGC (\"True Positives\", TP) without detecting too many false connections (\"False Positives\", FP), thus connections that are not present in the ground truths. Measures based upon these are Precision, Sensitivity/Recall, and F1-score (Figure 6), which we used for comparing TCDF, LSTM-NUE, Conv2D and TRGC.  
The results on connectivity strength are not directly compared between models as they differ substantially. These strength estimates, based on the mean over five runs on the same data set, are calculated and ranked. It must be emphasized that these strength estimates are relative per model and target training as, for each target time series, the network is trained differently. The latter implies that connection strengths obtained in the prediction of a particular Target time series X1 cannot be readily compared with connection strengths obtained in the prediction of another target time series X2. If F1 \(<\) 50%, only rankings are presented. Self-connectivity is not taken into account to avoid an overly positive perception of the results.
## 1 Introduction
According to the following assumptions, this paper takes into account a solar power forecasting model that utilizes the WT as a denoising technique, the ANFIS model as a forecasting tool and the hybrid firefly particle swarm optimization (HFPSO) as a hybrid optimization algorithm. The accuracy of the proposed model (WT-ANFIS-HFPSO) is compared with several forecasting strategies, namely, MLP, RBFNN, ANFIS, WT combined with MLP (WT-MLP), WT combined with RBFNN (WT-RBFNN), WT combined with ANFIS (WT-ANFIS), WT-ANFIS combined with PSO algorithm (WT-ANFIS-PSO) and WT-ANFIS combined with the firefly (FF) algorithm (WT-ANFIS-FF). The outcomes of the proposed model of the WT-ANFIS-HFPSO have shown excellent and accurate results for solar power forecasting. The paper is organized as follows: Section 2 presents the procedures of forecasting strategy that elaborates the data analysis of this work and describes the detailed methodology of the proposed WT-ANFIS-HFPSO. The five performance metrics that were used to evaluate the accuracy of the forecasting models are discussed in Section 3. The simulation results of the developed work and the accuracy comparison of the proposed work with other eight forecasting strategies are explained in Section 4. The concluding remark of this study is shown in Section 5.
## 3 Material and Methods
### RNA Isolation and qPCR  
Total RNA was isolated from cucumber leaves of seedlings using the Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. Concentration and purity of RNA were determined with a NanoDrop ND-1000 photospectrometer. cDNA was synthesized with 5 \(\upmu\)g of total RNA using the M-MLV reverse transcriptase (Promega, Madison, WI, USA). The specific primers of 23 _CsLOX_ genes were synthesized by previous study [11]. Three cucumber _HPL_ gene sequences were downloaded from Cucumber Genome DataBase [27], the specific primers were designed using DNAMAN 6.0 (Lynnon Biosoft, San Ramon, CA, USA) for RT-PCR and qPCR (_CsHPL1_-F: TACCTTCATCATTTTTCCCCCC, _CsHPL1_-R: GAAGAACTTATCCGGTCCTT; _CsHPL2_-F: CCCGAATTACCAAATACAC, _CsHPL2_-R: TAAGTTCCGTCAGAAATGTT; _CsHPL3_-F: TCACTTTCTCTCAGAACACCC, _CsHPL3_-R: GATCGAAAGAAGGTTTCTCT). The amplified PCR products were quantified by an Applied BioSystems 7500 Real Time PCR System (Bio-Rad Laboratories,Hercules, CA, USA), with a SYBR Premix Ex Taq kit (Takara, Tianjin, China). All qPCR analyses were performed in three biological replicates with three technical replicates and the results were generated using 7500 software (Bio-Rad Laboratories, Hercules, CA, USA). For relative quantification, _CsACT_ (Csa2M301530) (for the quantitation of gene expression during N-deficient treatments) gene was detected as an internal reference, and the 2\({}^{-\Delta\Delta\text{Ct}}\) method was used. PCR conditions were as follows: 5 min initial denaturation at 95 \({}^{\circ}\)C followed by 30 cycles of denaturation at 94 \({}^{\circ}\)C for 30 s, annealing at 50 \({}^{\circ}\)C for 30 s and an extension at 72 \({}^{\circ}\)C for 2 min. qPCR analysis was performed according to [PERSON] et al. [3].
## 2 Materials and Methods
### Data Collection  
At five months after inoculation, image acquisition was carried out inside the greenhouse using FirefLEYE S185 (Cubert GmbH, Ulm, BW, Germany) hyperspectral snapshot camera. The camera captured 125 bands ranging from 450 nm to 950 nm, including the blue, green, red and near-infrared (NIR) spectrum with a sampling interval of 4 nm. The camera was mounted on a custom tripod and positioned 2.6 m vertically from the ground to obtain the nadir view of the seedlings (Figure 1). The camera was calibrated before each image acquisition with white and dark reference to maintain a similar integration time to minimize the effects of illumination and detector sensitivity. The white calibration was performed by placing the provided white rectangular board (99% light reflection) flat  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Scale** & **Applied Sensor** & **Machine Learning Model** & **Accuracy (\%)** & **Reference** \\ \hline \multirow{3}{*}{Nursery} & APOGEE spectroradiometer & Maximum likelihood & 82 & [34] \\  & FirefLEYE S185 hyperspectral camera & SVM kernel functions & 100 & [35] \\  & FirefLEYE S185 hyperspectral camera & linear SVM & 94.8 & [36] \\ \hline \multirow{6}{*}{Plantation} & Intelligent electronic nose (e-nose) & ANN & 85 & [37] \\  & Unispec spectroradiometer & PLS-DA & 92 & [38] \\  & Unispec spectroradiometer & PLS-DA & 94 & [28] \\  & ASD field spectroradiometer & kNN & 97 & [29] \\  & FT-IR spectrometer & LDA & 92 & [30] \\  & GER 1500 handheld spectrometer & ANN & 100 & [31] \\  & Dielectric spectroscopy & QDA & 81 & [32] \\  & FARO laser scanner & kernel naïve Bayes & 85 & [33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of machine learning techniques used for _G. boninense_ detection in oil palm nurseries and plantations.  
and close to the lens, while the dark calibration was performed by completely closing the camera lens. Thus, each collected image was calibrated as:  
\[Reflectance=\frac{(Image-Dark)}{(White-Dark)} \tag{1}\]  
The calibrations were tested before image acquisition to ensure a good quality of the output image. One seedling was imaged at a time against a black background board (2 m \(\times\) 2 m) on a sunny clear day from 11:00 a.m. to 2:00 p.m. local time to maintain a natural illumination. The image acquisition system was controlled by Cube-Pilot software supplemented by the manufacturer.
## 6 Installation Error Compensation for the Shearer's SINS
### Simulation Analysis of Coning Error Compensation  
The error results before and after coning error compensation are compared through simulation. The simulation parameters are as follows: the shearer body mass 36.5 t, the rotational angular velocity of the earth \(\omega_{\rm ie}\) = 7.29 \(\times\) 10\({}^{-5}\) rad/s, the earth radius \(R_{M}\) = 6.36 \(\times\) 10\({}^{6}\) km, \(R_{N}\) = 6.39 \(\times\) 10\({}^{6}\) km, the simulation time 40 s, \(\alpha\) = 1\({}^{\circ}\), \(\omega\) = 2\(\pi\) rad/s, \(h\) = 10 ms, and \(n\) = 3. The coming error curve of the shearer SINS is shown in Figure 15. The coning error exhibits evident periodic fluctuation in the X- and Y-axis directions, with a small wave with a different period superposed on the periodic wave of the curve. The coning error exhibits a linear relationship in the Z-axis direction. On the whole, the SINS exhibits coning drift error on the Z axis and tiny oscillating errors on the X and Y axes. The comparison of results before and after coning error compensation is shown in Figure 16. The coning error has similar change trends before and after compensation, but the values are different. The maximum errors in the three axis directions are 4.2 \(\times\) 10\({}^{-6}\), 7.7 \(\times\) 10\({}^{-6}\), and 1.95 \(\times\) 10\({}^{-8}\), respectively, without coning error being compensated, and they are 1.5 \(\times\) 10\({}^{-6}\), 2.8 \(\times\) 10\({}^{-6}\), and 0.98 \(\times\) 10\({}^{-8}\), respectively, with coning error being compensated, decreasing by 64.3%, 63.6%, and 49.7%, respectively. The coning error effect is reduced significantly and the monitoring accuracy is improved effectively.
## 4 Experimental Validation
### Validation of the Proposed Power Loss Model
#### 4.2.2 Comparison of Two Losses Models  
Two models are put into the same inverter application and these two losses models are realized simultaneously to verify the theoretical feasibility. During the inverter operation, not every switching tube produces losses at any given moment, but for IGBT modules, losses are generated all the time. Therefore, the accuracy of the proposed methods can be verified by comparing the total loss of IGBT modules.  
Two situations of the torque rise and the torque drop are shown in Figure 13. The motor is set to be operated in the torque loop mode. As shown in Figure 13a, the command torque increases from 0 Nm to 10 Nm, 150 Nm, and then to 200 Nm; the load torque also increases with the lag of 0.5 s. According to the three-phase current waveform in Figure 13a, the current loop is stable and controllable.  
Figure 11: The principle of the electro–thermal interaction for the losses calculation.  
Figure 12: The waveform of losses and IGBT junction temperature under different currents.  
As can be seen from the losses curves of the IGBT modules, the yellow curve represents the total losses of IGBT modules based on PWM period, while the blue curve is the losses based on the current period. The losses calculation based on the PWM period is a transient calculation method with a certain periodicity, while the method based on the phase-current period is an average calculation scheme with a dc losses waveform. When the torque changes, the dynamic response shows that the losses models can follow the torque changes with good tracking speed. Under the different command load, the steady difference between these two models is satisfactory. At 200 Nm, the losses based on current cycle are about 1460 W, while the average loss based on PWM cycle is about 1500 W with an error of less than 3%. Figure 13b shows the situation of the torque drop and that it can also obtain similar experimental results.  
It can be concluded that the proposed losses model can be regarded as an equivalent losses model of the traditional losses model, but with the benefits of lower computation requirements and the simple parameter measurement process.
## 2 Experimental Design
### Experimental Platform  
The structure of the prototype is shown in Figure 3a, including system power module, analog signal modulation, encoder module, GMR sensor array, data storage control unit, and corresponding bypass.  
Figure 1: The principle of the unsaturated magnetic excitation (UME) testing method. The solid line represents the magnetic loop of the excitation source; the dotted line represents the wire rope inner magnetic field.  
Figure 2: Discontinuity recognition flow chart.  
The two analog signal modulations consisted of eight times preamplifier on GMR differential signal, then an additional circuit was used for impedance matching and lifting signal baseline. The voltage of the additive circuit was about 2 V after preamplifier. The system used equal-space sampling to acquire the weak MFL, and the pulse signal was produced by an encoder whose rotation perimeter was 0.31 m, sending 1024 pulses. With every pulse, the controller unit operated two analog-digital convertors(ADCs) on the 18 sensors' output, then the 18-length array data was stored in an secure digital memory (SD) card. All the data was processed offline or transmitted to PC for further processing. System working frequency was 120 MHz, 18 GMR sensors were installed with switch devices, then transferred signal to ADC. The prototype object is shown in Figure 3b; the diameter of the detection array was 30 mm, the excitation source was about 15 cm away from the detection board, and magnetization spacing was 1.5 cm [22].  
It is necessary to point out that the used GMR sensors had linear output when magnetic field varied between 0.5 and 3.0 Oe, and sensor sensitivity varied between 11 and 18 mV/V-Oe (the magnetic field strength corresponds to 11 and 18 mV multiplied by supply voltage). This sensor's output is unipolar differential voltage; thus, the output is not affected by magnetic direction. Besides, because of the hysteresis effect, the sensor output would hysteretically export minimum voltage when the magnetic field direction changed from zero Oe. Thus, this sensor could export normal curve voltage to small defects, but big defects exported folding voltage curve. It can be a good feature to distinguish small and big defects. The GMR output curve when the temperature was 26 \({}^{\circ}\)C and supply voltage was 5 V is showed in Figure 4.  
Figure 4: Giant magneto resistance (GMR) sensor output under 25 \({}^{\circ}\)C.  
Figure 3: (**a**) The structure of the prototype; (**b**) the object of experimental application.  
The traditional detection system in [9; 10] needed to magnetize wire rope first, and the step may be repeatedly implemented until it is magnetized to form stable remanence. However, the remanence of wire rope would drop away after one week, and the rope need be magnetized again. The repetitive step increases maintenance time and cost contrasting with the UME detection method. More technology comparison details are showed in Table 1. It can be seen that the UME testing system has smaller size, lighter weight and simpler operation.
## 2 Materials and Methods
### Analysis of Physicochemical Characteristics  
Physicochemical characteristics, including pH, volatile basic nitrogen (VBN), and thiobarbituric acid-reactive substances (TBARS), were measured following the methods as described in previous study [6] to ensure the quality of the test HMR product. Briefly, each sample was homogenized with 3 rd-distilled water in a ratio of 1:9 (\(w/v\)) using SHG-15D homogenizer (SciLab Co. Ltd., Seoul, Korea). Thus, An ST 3100 pH meter (Ohaus Co., Parsippany, NJ, USA) was used to measure the pH of the samples. VBN was analyzed using a two-way microdiffusion method. A WiseTis SHG-15D homogenizer (SciLab Co. Ltd., Seoul, Korea) was used to homogenize 5 g of the sample and 25 mL of distilled water. The supernatant of the homogenized sample was separated by centrifugation and filtration, then analyzed using Conway microdiffusion. For TBARS analysis, 5 g of each sample was homogenized with 12.5 mL of trichloroacetic acid (20%) in phosphoric acid (2 M), and distilled water was added to bring the total volume to 25 mL. The supernatant was collected by centrifugation of the homogenized sample for 15 min at 4 \({}^{\circ}\)C, then mixed with 0.005 M thiobarbituric acid in a ratio of 1:1 (\(v/v\)) and incubated for 30 min at 95 \({}^{\circ}\)C. Spectrostar Nano (BMG Labtech Ltd., Ortenberg, Germany) was used to measure TBARS at 530 nm. All physicochemical characteristics were measured in triplicate.