esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Characteristics of an Orthopter-Type Vertical Axis Wind Turbine in Shear Flows
###### Abstract  
To properly conduct a micro-siting of an orthopter-type vertical axis wind turbine (O-VAWT) in the built environment, this study investigated the effects of horizontal shear flow on the power performance characteristics of an O-VAWT by performing wind tunnel experiments and computational fluid dynamics (CFD) simulations. A uniform flow and two types of shear flow (advancing side faster shear flow (ASF-SF) and retreating side faster shear flow (RSF-SF)) were employed as the approaching flow to the O-VAWT. The ASF-SF had a higher velocity on the advancing side of the rotor. The RSF-SF had a higher velocity on the retreating side of the rotor. For each type of shear flow, three shear strengths (\(\Gamma=0.28\), \(0.40\) and \(0.51\)) were set. In the ASF-SF cases, the power coefficients (\(C_{p}\)) were significantly higher than the uniform flow case at all tip speed ratios (\(\lambda\)) and increased with \(\Gamma\). In the RSF-SF cases, \(C_{P}\) increased with \(\Gamma\). However, when \(\Gamma=0.28\), the \(C_{P}\) was lower than the uniform flow case at all \(\lambda\). When \(\Gamma=0.51\), the \(C_{P}\) was higher than the uniform flow case except at low \(\lambda\); however, it was lower than the ASF-SF case with \(\Gamma=0.28\). The causes of the features of \(C_{P}\) were discussed through the analysis of the variation of blade torque coefficient, its rotor-revolution component and its blade-rotation component with azimuthal angle by using the CFD results for flow fields (i.e., horizontal velocity vectors, pressure and vorticity). These results indicate that a location where ASF-SFs with high \(\Gamma\) values dominantly occur is ideal for installing the O-VAWT.  
orthopter; vertical axis wind turbine; power coefficient; torque coefficient; shear flow; wind tunnel; CFD; delayed detached-eddy simulation Article
## 4 Discussions  
### Ablation Test and Calibration Performance Analysis  
To demonstrate the outstanding calibration performance of PartsNet in addressing the issue of parameter unidentifiability, we designed the following four sets of ablation experiments: pure RNN and GRU, RNN and GRU with dot-product attention mechanism, RNN and GRU with Transformer encoder (TransE), and RNN and GRU with our proposed sparse mask-based sparse attention mechanism Transformer encoder (STE). The comparison between the first set and the other sets highlights the necessity of introducing the attention mechanism, while the comparisons within the latter three sets reveal the differences among the different types of attention mechanisms in the DEM parameter calibration task. PartsNet demonstrates state-of-the-art performance in these experiments based on multiple indexes.  
Table 4 presents the ablation experiments conducted on PartsNet and other neural networks. In these experiments, the eight networks shared the hyperparameters specified in Section 2.4. Across all evaluation metrics, RNNs without attention mechanisms performed the worst, with a nearly 40% lower calibration accuracy compared to PartsNet. The introduction of dot-product attention led to some improvements. It is crucial to emphasize that PartsNet achieves the highest training accuracy (0.913) while simultaneously attaining a decent convergence speed. Specifically, PartsNet exhibits an improvement with a speed advantage of 21.25% over the Transformer encoder-GRU-based network. Additionally, the GRU performs significantly better than the RNN both with and without the addition of the attention mechanism. Overall, PartsNet demonstrated the highest calibration precision in these tasks and exhibited a significant advantage in terms of time compared to the multi-head attention mechanism used in Transformer.
## 3 Results and Discussions
### Performance Characterization of Microcapsules
#### 3.2.3 Efficiency Encapsulation Performance of the Shell  
By comparing the FT-IR absorption peaks of the core material, shell material, and microcapsules, it can be observed that the shell material encapsulates the core material effectively. As shown in Figure 8, the main characteristic absorption peaks of the shell material appear at 2921.3 cm\({}^{-1}\), 2089.1 cm\({}^{-1}\), and 1733 cm\({}^{-1}\), corresponding to C-H stretching vibrations, C\(\equiv\)N or C=C bond vibrations, and C=O carbonyl stretching vibrations, respectively. In the microcapsule spectrum, these absorption peaks of the shell material are clearly visible, particularly the peak at 1733 cm\({}^{-1}\), which shows high intensity, indicating that the shell material is well-preserved within the microcapsules and has not undergone significant chemical structural changes, demonstrating high stability. At the same time, the characteristic absorption peaks of the core material (e.g., 3440.41 cm\({}^{-1}\) and 3037.9 cm\({}^{-1}\)) are also retained in the microcapsules. This suggests that the core material maintains its basic chemical structure under the shell material's encapsulation and is not completely shielded, indicating the relative independence of the core material within the microcapsules. Furthermore, the coexistence of the characteristic absorption peaks of both the shell and core materials in the microcapsules further confirms their interaction within the microcapsule structure.
## 2 Materials and Methods
### Nutrient Extraction Efficiency  
The efficiency of _H. portulacoides_ in extracting N and P was determined using a retention time (the amount of time the solution remains in the hydroponic unit) of one week (7 days). The time frame allowed for nutrients to be taken up by the plants was crucial to the performance of a hydroponic unit and the suitable retention time could be highly variable. Previous nutrient extraction studies with halophytes used a wide range of retention times, from 12 h to 5 weeks [20, 41-43]. Following [PERSON] et al. [16], a one-week retention time was considered appropriate for the present study.  
By the end of each extraction period, water samples from each hydroponic unit were collected and filtered (Whatman GF/C filters) into flasks, and final concentrations of ammonium (NH4-N), oxidized forms of inorganic nitrogen (NO\({}_{\infty}\)-N) and orthophosphate (PO\({}_{4}\)-P) in the hydroponic media were determined using a San++ Continuous Flow Analyzer (Skalar Analytical, Breda, The Netherlands) following Skalar's standard automated method (NH4-N: modified Berthelot reaction for ammonia determination; NO\({}_{\infty}\)-N: Total UV digestible nitrogen/nitrate + nitrite/nitrite; PO\({}_{4}\)-P: Total UV digestible phosphate/orthophosphate). After sampling, the medium in each unit was renewed with a new batch of its corresponding treatment solution. The real initial concentrations of NH4-N, NO\({}_{\infty}\)-N, and PO\({}_{4}\)-P in every new batch were determined the same way as described above. Nutrient extraction efficiency was estimated (both weekly and in total) for dissolved inorganic nitrogen (DIN-N), calculated as the sum of NH4-N and NO\({}_{\infty}\)-N, and dissolved inorganic phosphorus (DIP-P), equivalent to PO\({}_{4}\)-P.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Fluorescent Lamp** & **LED Tile** \\ \hline Reference name & Philips 54W/830 Min Bipin T5 & AquaBeam 1500 Ultima NP \\  & HO ALTO UNP & Ocean Blue Light \\ Power (W) & 54 & 30 \\ Luminous flux (lm) & 5000 & 1965 \\ Luminous efficacy (lm/W) & 93 & 66 \\ Correlated color & 3000 & 20,000 \\ temperature (K) & & \\ Chromaticity coordinates & X: 0.436; Y: 0.404 & X: 0.250; Y: 0.253 \\ Photosynthetic photon flux & & 371.0 \(\pm\) 12.0 \\ density (umol m\({}^{-2}\) s\({}^{-1}\)) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical information of lighting systems.
# On the Need for a General REST-Security Framework
## 6 Towards a General REST-Security Framework
### REST Message Authentication (REMA)
#### 6.2.3 REST Message Signature Verification  
Algorithm 2 specifies the signature verification procedure for REST messages signed by Algorithm 1. The signature verification algorithm requires a signed REST message \(r\) as input and it returns a boolean value expressing the signature validation result. From the signed message \(r\) the required parts are extracted, including the message body \(b\) and the message header \(h\). From \(h\) the authentication control data header \(m_{cpa}\) is obtained next containing the concatenated values \(sig\), \(hash\), \(kid\), \(tvp\), \(sv\) and \(desc\). After building \(\tilde{h}\) in line 5, the next statement appends the additional header entries defined in _desc_ to \(\tilde{h}\) in order of appearance. Then the headers in \(\tilde{h}\) are iterated in the same manner--and especially the same order--as during the signature generation process to build _tbs_. With _tbs_ and the signature verification key identifier _kid_, the verification of the signature value _sv_ can be performed. The boolean verification result is assigned to the variable _valid_, which represents the output of the signature verification procedure.  
```
0:Signed REST message \(r\)
0:Boolean signature verification result _valid_
1:\(b\gets getBody(r)\)
2:\(h\gets getHeader(r)\)
3:\(m_{cpa}\gets getAuthenticationControlData(h)\)
4:\((sig,hash,kid,tvp,sv,desc)\gets split(m_{cpa})\)
5:\(\tilde{h}\gets getTbsHeaders(h)\)
6:\(\tilde{h}\leftarrow\tilde{h}||getTbsHeaders(h,desc)\)
7:\(tbs\gets tvp\)
8:\(i\gets 0\)
9:while\(i<|\tilde{h}|\)do
10:\(tbs\gets tbs||delimiter\|normalize(\tilde{h}_{i})\)
11:\(i\gets i+1\)
12:endwhile
13:\(tbs\gets tbs||delimiter\|hash(b)\)
14:\(verify\gets getVerificationAlgorithm(sig)\)
15:\(valid\gets verify(kid,tbs,sv)\)
```  
**Algorithm 2** REST message signature verification [28].
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
###### Abstract
A simplified rigid-rod model of a two-body tethered satellite is described in Section 2. Two simplified models of three-DOF equations are discussed in Section 3. Two control laws of tether release rate and tether tension are proposed in Section 4. The periodic stability of time-varying control systems is analyzed by using Floquet theory in Section 5. Conclusions are discussed in Section 6.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
## 2 Lumped-Parameter Modeling of Piezoelectric Metamaterial
### Configuration of Conventional Piezoelectric Metamaterial  
The configuration of the unit-cell of piezoelectric metamaterial is shown in Figure 1. The unit-cell consists of an aluminum substrate and two piezoelectric transducers bonded onto the top and bottom surfaces of the substrate. Inductance element is connected to the top and bottom surfaces of the piezoelectric transducer as the shunt circuit. Note that a piezoelectric transducer acts electrically as a capacitor. The combination of the piezoelectric capacitance and the inductive shunt, creates an LC resonant unit. Moreover, since the piezoelectric transducer possesses two-way electro-mechanical coupling, the local resonance in the electrical domain can affect the system dynamics in the mechanical domain. As such, the LC resonance of the shunt circuit creates bandgap for the integrated system.
## 3 Three-Dimensional Freezing Temperature-Field Calculation Model
### Establishment of a 3D Finite Element Numerical Model  
In the model developed using the finite element software COMSOL Multiphysics 5.6, the basic assumptions of the numerical calculation model are:  
1. Soil mass is a saturated porous medium composed of soil particles, water and ice.
2. All parts of soil mass are homogeneous and isotropic.
3. In the freezing process, convective heat transfer caused by groundwater seepage is not considered.
4. In the freezing process, the influence on the temperature field of frost having is not considered [23].  
With this model, the primary aim was to investigate the evolution patterns of the freezing temperature field before and after the excavation of the open excavation section of the shaft. Accounting for the impact radius of the temperature field, the overall model was constructed in the form of a hexahedron with dimensions of 30 m \(\times\) 40 m \(\times\) 50 m. Basedon the actual construction site conditions, plugging holes, side-row holes, and middle-row holes were established. The inclination angle of the inclined shaft was set to 14\({}^{\circ}\). To ensure the calculation accuracy while conserving computational resources, four-node tetrahedral solid heat transfer units were employed in the model. The soil near the freezing holes was divided into denser grids, while sparser grids characterized areas farther from the freezing holes with minimal temperature gradients [24], totaling 3,742,752 units. Figure 3 shows the 3D finite element computational model.
## 3 Results and Discussion  
### Validation of the Numerical Model  
The reliability of the ENVI-met model in reproducing the measured concentrations of PM\({}_{2.5}\) and PM\({}_{10}\) was assessed using the statistical indicators described in Section 2.5, based on the data collected by portable sensors during the monitoring campaign. In particular, continuous measurements carried out at Traffic Monitoring Station TMS1 provided a high-resolution dataset suitable for direct comparison with simulation results. Figure 8 illustrates the comparison between the measured and simulated hourly concentrations of PM\({}_{10}\) and PM\({}_{2.5}\).  
The observed hourly concentration profile ranged from 0.95 \(\upmu\)g/m\({}^{3}\) to 1.3 \(\upmu\)g/m\({}^{3}\) for PM\({}_{2.5}\), and from 0.75 \(\upmu\)g/m\({}^{3}\) to 1.17 \(\upmu\)g/m\({}^{3}\) for PM\({}_{10}\). These values are consistent with the prevailing traffic conditions, characterized by a low-emission vehicle fleet and the absence of congestion. Under such conditions, the contribution of particulate matter from exhaust, brakes, and tires is limited [84]. Moreover, the open campus morphology, low-rise buildings, and good natural ventilation support pollutant dispersion and help maintain low concentration levels. Similar outcomes have been documented in other studies conducted in comparable urban environments [85]. The key statistical indicators used to evaluate the model's performance are presented in Table 11.  
The results of the statistical analysis provide robust evidence of the ENVI-met model's reliability in simulating the dispersion of PM\({}_{10}\) and PM\({}_{2.5}\). The coefficients of determination (R\({}^{2}\)), which approach 0.80 for both pollutants, indicate a strong linear relationship between measured and simulated values and confirm the model's ability to reproduce a substantial portion of the variability observed in the experimental data. The Mean Absolute Error (MAE) values are remarkably low, highlighting the model's accuracy in replicating the temporal dynamics of the observed concentrations. These results are further supported by the Bland-Altman analysis, which shows minimal mean differences and narrow acceptance limits, all within the ranges considered acceptable by current standards. Taken together, these indicators confirm the solid performance of the ENVI-met model in representing  
Figure 8: Comparison between measured and simulated hourly concentrations of PM\({}_{10}\) and PM\({}_{2.5}\).  
particulate matter distribution under the specific environmental and traffic conditions analyzed in this study.  
Although the recorded PM\({}_{10}\) and PM\({}_{2.5}\) concentrations were well below regulatory thresholds, the site was not selected due to high pollution levels but for its representativeness. The university campus combines several microclimatic vulnerability factors--high pedestrian density, intense solar radiation, limited vegetation, and internal vehicular circulation--which make it an ideal testbed for assessing integrated mitigation strategies under typical Mediterranean urban conditions.
## 3 Results and Discussion
### Oxidative Stability of BCSO
#### 3.1.2 FTIR Spectra  
FTIR spectroscopy is an effective tool for analysis, as band intensities correlate with compound concentrations. It is widely used to characterize edible oils and fats, as the FTIR spectra vary in intensity and frequency based on the sample's composition and nature. In this study, the FTIR spectra of oil samples were used as an indicator for lipid oxidation and were simultaneously used to track changes in the TQ content (see Section 3.2).  
The FTIR spectra of the thermal treatment BCSO \"open\" probes, scanned at mid-infrared regions, were shown on Figure 2. They exhibit the characteristic peaks and shoulders commonly present in fatty acids and triacylglycerglycerols, of which the assignment to various functional groups can be found in the literature [20; 29]. Some of them, when the oil is exposed to excessive heat or oxidation, change in intensity and thereby can be used to monitor aging processes of oils [30]. A peak at around 3009 cm\({}^{-1}\) arises from the stretching vibration of the cis double bond (=C-H), which, during oxidation, was converted in a single methylene bond confirmed by a peak at 2854 cm\({}^{-1}\). Another peak at 987 cm\({}^{-1}\) associated with the bending vibration of the C-H trans and cis conjugated diene groups of hydroperoxides can be connected with the first step of the oxidation of the tested oil. Hence, the peak ratio 3009/2854 or intensities at 987 cm\({}^{-1}\) could be used as an indicator for the oxidation level of lipids [31].  
All samples were shown to have no significant modifications in the intensity of the absorption bands' characteristic for the oxidation process. Presented in Figure 3, the value of the peak ratio 3009/2854 for both the \"open\" and \"closed\" BCSO samples oscillated about 0.226-0.228 over 24 days of thermal treatment with any characteristic tendency, whereas a change in intensities at 987 cm\({}^{-1}\) was exhibited, with a slight increase after 10 and 14 days of thermal treatments, for the \"closed\" and \"open\" samples, respectively. This agreed with the trend of the specific absorptivity value at 232 nm indicating only a small amount of increase in hyperoxides as the primary oxidation products in BCSO undergoing thermal stress. The high oxidative stability of BCSO compared to other cold-press oils was also reported in previous studies [26; 32].  
Figure 2: Infrared spectrum of “open” BCSO samples at room temperature obtained at 0, 3, 6, 10, 14, 18, 21, and 24 days of heating with the main peaks’ assignment; inset: changes in the absorption intensity at 1659 cm\({}^{-1}\).
## 5 Improved Performance of Harmonic Injection by the Phase Perturbation Method
### Impedance Calculation (Section B)  
In [29], the island detection method is based on variations of the quadrature synchronous reference voltage considering a symmetrical and balanced system. The methodology presented in this paper is able to detect the island in a generic unbalanced system, as in [38; 39], where the detection is based on impedance measurement.  
In the case of a four-wire microgrid inverter intended to operate with different power references per phase, an individual voltage measurement of each phase is needed, i.e., three degrees of freedom are being considered. Consequently, the voltage and current 100-Hz amplitude per phase must be obtained. As shown in Figure 14, the discrete Fourier transform (DTF) is used. In this case, a DFT is a mathematics calculation that requires high computational burden. However, this is implicit in any IM-based AIDM. As AIDM, the IM does not require being accurate in value, but it should be precise in value change. Thus, it is proposed in Section 5.5 to calculate the DFT at a lower frequency than that used for control purposes.  
Figure 12: Impedance measurement operation diagram.  
Figure 13: Magnified effect of phase perturbation distortion with \(k=0.5\).
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
## 2 Working Principle of the Sensor
### Magnetic Circuit Sensing System
#### 2.1.3 Permanent Magnet  
Magnetostrictive materials have unipolar characteristics, i.e., the expansion and contraction of magnetostrictive materials is only related to the magnitude of the applied magnetic field, but has nothing to do with its direction. When the applied magnetic field is an alternating magnetic field, although the directions of the positive and negative half-cycles are opposite, the expansion and contraction directions of magnetostrictive materials excited by the two half-cycles are the same. The unipolar characteristics of magnetostrictive materials make a frequency-doubling effect appear in the working process, as shown in Figure 4.  
Permanent magnets are added to the magnetic circuit to provide a constant magnetic field. Under the action of a constant magnetic field, the total magnetic field applied to magnetostrictive materials is all positive or all negative. Therefore, the permanent magnet avoids the influence of the unipolar characteristics of magnetostrictive materials,  
Figure 4: Influence of frequency doubling on magnetostrictive materials. (**a**) “frequency doubling” phenomenon. (**b**) no “frequency doubling” phenomenon.  
Figure 3: Schematic diagram of fiber grating.  
and enables the sensor to obtain a complete alternating current (AC) cycle, as shown in Figure 5.
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Experiment I: Classical Benchmark Function
#### 4.1.4 Scalability Test  
Currently, most intelligent algorithms are susceptible to \"dimensional catastrophe\" and are extremely prone to failure when the dimensionality of the optimization problem is increased. Scalability is often used to describe the execution efficiency of the same algorithm in different spaces. To investigate the scalability of the IHSSAO algorithm proposed in this paper, we used the IHSSAO algorithm to optimize functions \(F_{1}\)-\(F_{13}\) in higher dimensions. The dimensionality of the optimization problem was extended to 50, 100, and 500, and the Avg obtained by using the original SSA, AO, and proposed IHSSAO to do operations on functions \(F_{1}\)-\(F_{13}\) are simultaneously shown in Table 7. The results show that for the same algorithm to calculate the identical function, convergence accuracy of this algorithm become poorer, as the number of dimensions increase. The reason for this phenomenon is that as the dimensionality increases, the algorithm needs to optimize more elements. For functions \(F_{1}\)-\(F_{8}\) and \(F_{12}\)-\(F_{13}\), the experimental results of IHSSAO are significantly better than the original SSA and AO, and the optimization performance gap between them becomes more and more obvious as the number of dimensions increases. In addition, for functions \(F_{9}\)-\(F_{11}\), the IHSSAO and AO algorithms obtain the same results and both are stronger than SSA. The above results fully demonstrate that the performance of the proposed IHSSAO is not significantly degraded compared with the basic SSA and AO in dealing with high-dimensional problems, and has good exploitation and exploration capabilities.  
Figure 6: _Cont._  
Figure 6: Boxplot of IHBSAO and other different algorithms on 23 benchmark functions.  
Figure 7: _Cont._  
Figure 7: Convergence curve of IHSAO and other different algorithms on 23 benchmark functions.