esa-sceva/satcom-chunk-collection huggingface dataset:
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 2 Modeling
### Propeller Inverse Design Method for the Cruising Stage  
The level flight mode, which includes climbing and cruising, is the main energy consumption mode for VTOL aircraft [23]. To rapidly achieve maximum energy efficiency in level flight, the inverse design method based on the Betz optimal theory was innovatively applied to the cruising stage.  
To reach the optimal propellers with a minimum energy loss, the axial displacement velocity ratio \((V_{0}+v^{\prime})/2\pi n_{s}r=\xi\) must be constant along the radius, but the value is unknown [25]. When this optimal theory and BEMT are combined, an optimal propeller can be acquired by 3-4 numerical iterations, according to [26]. This procedure is named the regular design method. Usually, a lift coefficient distribution corresponding with the maximum lift-drag ratio along the radius is prescribed as the input for this regular method. In fact, as far as the aerodynamic coefficients, chord-length and twist angle distribution are concerned, once any one of them is prescribed, the rest of the two distribution rules are easily calculated by the numerical iterative method. Therefore, [24] proposed an inverse design method for a propeller with a prescribed chord-length distribution. The regular and inverse design methods are shown in Figure 11. Applying this inverse method to the VTOL propeller optimization framework on the cruising stage can eliminate the work of parameterizing the twist angle distribution and provide a minimum energy loss propeller for cruising flight rapidly and automatically. Parameterizing the chord-length distribution makes it intuitive to set the structural constraints by limiting the minimum chord-length, generating fewer infeasible designs at the beginning than parameterizing the lift coefficients.  
Figure 11: Regular and inverse design methods based on [PERSON] optimal theory.  
Figure 10: Thrust and efficiency comparisons between the BEMT, CFD and wind tunnel experiments for the 3-blade NACA 5868-9 propeller. (**a**) Thrust coefficients; (**b**) efficiency.
## 2 Past Research and the Innovations in This Work
### Past Research
Similar to this, the study in [41] uses techniques like LIME and Counterfactual XAI while emphasizing explainability in IoT intrusion detection, utilizing recent datasets like CICIoT2023 and IoTID20. However, it fails to adequately address dataset balancing and feature elimination, compromising both the accuracy and fairness of predictions. Finally, a common limitation across many studies deploying XAI is the reliance on a single explanation method, which restricts the depth and reliability of insights. To balance the dataset and attain a 96.25% detection accuracy, the authors in [42], for example, used the SMOTE technique in conjunction with decision trees, random forests, and SVM algorithms in their intrusion detection system. However, they solely depend on LIME for explainability, omitting feature elimination techniques that could further improve model performance. In a similar vein, the authors of the paper [43] employed XGBoost for network intrusion detection, attaining 93% accuracy with the NSL-KDD dataset, yet did not implement feature refinement techniques while relying exclusively on the SHAP explanation framework. In [44], the CICIDS2017 dataset is used achieving 90% accuracy, but the authors rely solely on SHAP for explaining network anomalies, which hinders adaptability to contemporary and evolving threats.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### Abnormal KPI Screening Based on 1D-CNN-TCN Model
#### 3.3.2 Acquisition of Residual Sequence  
In this paper, the 1D-CNN-TCN time series prediction model in Section 3.2 is used to predict KPIs. The predicted value is compared with the actual observation data of the same period at the corresponding time point, and residual sequences are obtained. The formula is shown as Equation (2). When an original sequence is found to be abnormal at a given time, the corresponding position in the residual sequence will produce large fluctuations, different from the normal trend of the time series data. The obtained residual sequence can increase the fluctuation trend of the anomaly, reduce the influence of the original trend in the KPI itself, facilitate correlation calculation and anomaly detection, and make the results more accurate.  
Suppose sequence \(X=(x_{1},x_{2},\ldots,x_{l})\) is the predicted time series, and \(Y=(y_{1},y_{2},\ldots,y_{l})\) is the real time series. The calculation formula for each element \(z_{i}\) in the residual sequence \(Z=(z_{1},z_{2},\ldots,z_{l})\) is as follows:  
\[z_{i}=y_{i}-x_{i},i\in(1,l) \tag{2}\]
# An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic
## 4 The Quasi-Random P-S Multirate Loss Model with Restricted Accessibility
### The Case of the CS Policy (qr-P-S Model)  
In the qr-P-S multirate loss model, a new service-class (\(k\),\(l\)) call requires \(b_{l}\) subcarriers. Assuming that the occupied subcarriers and power in the cell are \(j_{1}\) and \(j_{2}\), respectively, then, the new call: (a) is accepted for a generally distributed service-time with mean \(\mu^{-1}\), if \((M-j_{1}\geq b_{l})\cap(j_{2}+p_{k}b_{l}\leq P)\) and (b) is blocked and lost, if \((M-j_{1}<b_{l})\cup(j_{2}+p_{k}b_{l}>P)\).  
Following Section 4, it can be proved that the (unnormalized) values of \(q_{\rm fin}(j_{1},j_{2})\) in the qr-P-S model are given by:  
\[q_{\rm fin}(j_{1},j_{2})=\left\{\begin{array}{l}1,\quad{\rm for}j_{1}=j_{2 }=0\\ \frac{1}{j_{1}}\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}(N_{kl}-n_{kl}+1) \alpha_{kl,{\rm idle}}b_{l}q_{\rm fin}(j_{1}-b_{l},j_{2}-p_{k}b_{l})\\ {\rm for}j_{1}=1,\ldots,M{\rmand}\,j_{2}=1,\ldots,P\end{array}\right.. \tag{32}\]Note that if \(N_{kl}\rightarrow\infty\) for all service-classes and the total offered traffic-load is constant, then we have (4) of the P-S model.  
In order to overcome the equivalent stochastic system determination required for the calculation of \(q_{\rm fin}(j_{1},j_{2})\) via (32), we present the following algorithm for the calculation of the various performance measures in the qr-P-S model:  
(a) Determine the values of \(q(j_{1},j_{2})\) according to (4) (i.e., via the P-S model).  
(b) Determine the values of \(y_{kl}(j_{1},j_{2})\) via the formula:  
\[y_{kl}(j_{1},j_{2})=\frac{\alpha_{kl}q(j_{1}-b_{l},j_{2}-p_{k}b_{l})}{q(j_{1}, j_{2})}. \tag{33}\]  
(c) Modify (32) to the following formula, where \(y_{kl}(j_{1},j_{2})\) has been determined via (33):  
\[q_{\rm fin}(j_{1},j_{2})=\left\{\begin{array}{l}1,\quad{\rm for}j_{1}=j_{2}= 0\\ \frac{1}{\lambda}\sum\limits_{k=1}^{K}\sum\limits_{l=1}^{L}(N_{kl}-y_{kl}(j_{ 1}-b_{l},j_{2}-p_{k}b_{l}))\alpha_{kl,i{\rm dle}}b_{l}q_{\rm fin}(j_{1}-b_{l},j _{2}-p_{k}b_{l})\\ {\rm for}j_{1}=1,\ldots,M{\rm and}j_{2}=1,\ldots,P\end{array}\right.. \tag{34}\]  
(d) Determine \(E_{\rm fin}(k,l)\) via (20), where \(y_{kl,\rm fin}(j_{1},j_{2})\) is given by:  
\[y_{kl,\rm fin}(j_{1},j_{2})=\frac{(N_{kl}-y_{kl}(j_{1}-b_{l},j_{2}-p_{k}b_{l}) )\alpha_{kl,i{\rm dle}}q_{\rm fin}(j_{1}-b_{l},j_{2}-p_{k}b_{l})}{q_{\rm fin}( j_{1},j_{2})}. \tag{35}\]  
(e) Determine the TC probabilities of service-class (\(k\),\(l\)) calls, \(B_{\rm TC}(k,l)\), via:  
\[B_{\rm TC}(k,l)=\sum\limits_{\{(j_{1}+b_{l}>M)\cup(j_{2}+p_{k}b_{l}>P)\}}G^{-1 }q_{\rm fin}(j_{1},j_{2}), \tag{36}\]  
and the BP\({}_{\rm TC}\), the SU\({}_{\rm fin}\), and the PU\({}_{\rm fin}\), via (22)-(24), respectively.
## 3 Optically Pumped Probe Design
### Electronics Unit Design
#### 3.2.1 Signal Conditioning Circuit  
The signal conditioning circuit filters, amplifies, and conditions the voltage signal output by the photodetector. As shown in Figure 7, the circuit can be divided into five main functional modules: the pre-amplifier circuit, automatic gain control circuit, intermediate amplification stage, phase-shift circuit, and waveform shaping circuit.  
The Larmor signal output by the photodetector of the cesium optically pumped probe is first processed by a pre-amplifier, then stabilized by automatic gain control, followed by intermediate amplification. The signal is then sent to the internal feedback coil of the probe through a phase-shift circuit, achieving self-oscillation through positive feedback. Simultaneously, the intermediate output signal is shaped into a square wave by a comparator and output to the frequency measurement circuit, yielding the corresponding external magnetic field strength. Given that the geomagnetic field ranges from 20,000 to 70,000 nT,  
Figure 6: A cesium absorption cell model.  
in this study, the bandwidth of the signal conditioning circuit was set to 70-245 kHz. The signal conditioning circuit sequentially performs pre-amplification, automatic gain control, intermediate amplification, phase shifting, and comparison within this frequency range.  
The pre-amplifier circuit converts the photodiode current output by the probe into a voltage signal, constituting a transconductance circuit based on an ultra-low noise amplifier. The signal conditioning circuit diagram is shown in Figure 8. The voltage signal from the pre-amplifier circuit is processed by a controllable gain amplification circuit and an intermediate amplification circuit to output a stable-amplitude sinusoidal signal, the frequency of which is the Larmor frequency. Automatic gain control of the circuit is achieved by performing peak detection on the output signal and feeding it back to the controllable gain amplification circuit. The intermediate amplification circuit adjusts the gain and bandwidth of the circuit. To be more specific, in Figure 8, D3 and D4 (BAS321) are used to protect the circuit from overvoltage. The AD8338 serves as the controllable gain amplifier, with its gain dynamically adjustable by directing the output voltage to the gain control input pin (pin 14).  
Figure 8: A circuit diagram of the signal conditioning circuit.  
Figure 7: A schematic diagram of the signal conditioning process.
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
###### Abstract  
On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products.  
GF3-02; SAR; sea surface wind; CMOD +
Footnote †: journal: remote sensing
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Baseline** & **12 Weeks** & **\(p\)-Value** \\ \hline Go accuracy (\%) & 85.58 \(\pm\) 2.46 & 97.92 \(\pm\) 0.97 & 0.001 ** \\ Go error (\%) & 14.42 \(\pm\) 2.46 & 2.08 \(\pm\) 0.97 & 0.006 * \\ No-go accuracy (\%) & 88.25 \(\pm\) 3.36 & 90.75 \(\pm\) 3.23 & 0.389 * \\ No-go error (\%) & 11.75 \(\pm\) 3.38 & 9.25 \(\pm\) 3.23 & 0.324 * \\ Go response time (ms) & 599.19 \(\pm\) 32.57 & 610.61 \(\pm\) 32.99 & 0.649 * \\ \hline \end{tabular}  
* Statistically significant change (\(p\) < 0.05). a calculated by paired \(t\)-test. b calculated by [PERSON] matched-pairs signed-rank test.  
\end{table} 
Table 3: Attention test (go/no-go task) responses (\(n\) = 10, mean \(\pm\) SE). Figure 3: Change in brain oscillation and topography localization after the 12 th week of _L. paracasi_ HII01 administration. **(a)** Modulation of theta oscillation; \(p=0.002\). **(b)** Theta wave activity at baseline (Brodmann area 18; X = 15, Y = \(-100\), Z = 15; MNI coords; 2.95 \(\upmu\)V) in occipital gyrus of right-hemisphere. **(c)** Theta wave activity at the 12 th week (Brodmann area 37; X = 55, Y = \(-70\), Z = 0; MNI coords; 5.66 \(\upmu\)V) in middle temporal gyrus of right-hemisphere. **(d)** Modulation of alpha oscillation; \(p=0.005\). **(e)** Alpha wave activity at baseline (Brodmann area 19; X = \(-20\), Y = \(-100\), Z = 10; MNI coords; 2.33 \(\upmu\)V) in middle occipital gyrus of right-hemisphere. **(f)** Alpha wave activity at the 12 th week (Brodmann area 37; X = 65, Y = \(-55\), Z = 0; MNI coords; 3.63 \(\upmu\)V) in middle temporal gyrus of right-hemisphere. **(g)** Modulation of beta oscillation; \(p=0.005\). **(h)** Beta wave activity at baseline (Brodmann area 19; X = \(-20\), Y = \(-100\), Z = 10; MNI coords; 4.73 \(\upmu\)V) in middle occipital gyrus of right-hemisphere. **(i)** Beta wave activity at the 12 th week (Brodmann area 37; X = 70, Y = \(-25\), Z = \(-10\); MNI coords; 2.85 \(\upmu\)V) in middle temporal gyrus of right-hemisphere. Yellow color indicates local maxima of increased electrical activity in right hemisphere. Blue dot marks center of significantly increased electrical activities. ** significant difference \(p<0.005\).
# An Overview of Key Technologies in Physical Layer Security
## 2 Background
### Performance Metrics in PLS
#### 2.4.3 Quality of Service (QoS)-Related Metrics  
Signal-to-interference-plus-noise ratio (SINR): This metric may be described as the quantitative relationship between the power of the received signal and power of the interference plus noise. The performance of a communication link is characterized by the QoS. SINR is directly related to the QoS, and this can aid with the design of secrecy algorithms. A minimum value of SINR for transmission from [PERSON] to [PERSON] and a maximum value of SINR for [PERSON] means a good receiving performance in terms of security and reliability. This paves the way for a robust transmission capable of achieving the desired minimum and maximum error levels for [PERSON] and [PERSON], respectively. Improvement of the SINR can facilitate the use of PLS techniques such as beamforming. A detailed discussion of beamforming is given in Section 3.  
Bit error rate (BER): This metric is defined as the ratio of the number of information bits received in error to the total number of bits transmitted. Different modulation and coding techniques have varyingBER performances at the same SINR level. For the establishment of a communication link, the BER of a system must be above the minimum required level. Therefore, PLS techniques utilize optimization methods to improve the security of a communication system by degrading the BER of illegitimate users. Thus, the BER can be employed to measure the QoS and the security of a communication system.
# Durability of Superamphiphobic Polyester Fabrics in Simulated Aerodynamic Icing Conditions
###### Abstract  
Fabrics treated to repel water, superhydrophobic, and water and oil, superamphiphobic, have numerous industrial and consumer-level benefits. However, the liquid repellency decreases in the course of time. This is largely due to chemical or physical changes of the coating due to prolonged exposure to relatively harsh environments. To develop more durable fabric treatments for specific applications, it is necessary to measure the extent to which the treated fabrics retain their low-wettability after being subjected to controlled aggressive environmental conditions. In this study, plain weave fabrics made from polyester filaments and coated with silicone nanofilaments in-solution were exposed to aerodynamic icing conditions. The coated fabrics showed superhydrophobic behavior, or superamphiphobic for those that were fluorinated. The wettability of the fabrics was progressively evaluated by contact angle and roll-off-angle measurements. The coated fabrics were able to maintain their low-wettability characteristics after exposure to water droplet clouds at airspeeds up to 120 m/s, despite damage to the silicone nanofilaments, visible through scanning electron microscopy.  
superhydrobicity; superamphiphobicity; aerospace; atmospheric icing; durability; ultra-light aircraft +
Footnote †: journal: _coatings_
###### Abstract  
Theoretical and experimental studies are presented to characterize the anisotropic plastic response under torsion loading of two nominally identical aluminum Al6061-T6 extruded round bars. Theoretical models are developed using isotropic ([PERSON] 1913) and anisotropic ([PERSON] 1991) yield criteria, along with isotropic strain hardening formulae, to model post-yield behavior under simple torsion loading. For the case of simple shear loading, incremental plasticity theory is used to determine the theoretical elastic, plastic, and total shear strains. A set of experiments are performed to calibrate Barlat's 1991 yield function. Several specimens are extracted at different orientations to the longitudinal direction of each round Al6061-T6 bar and tested under uniaxial tension and simple torsion to optimally determine all anisotropic ([PERSON] 1991) yield function parameters. During loading, Stereo Digital Image Correlation (DIC) is used to quantify surface deformations for the torsion experiments and a baseline tension specimen to identify and correct measurement anomalies. Results show the isotropic yield model either underestimates or overestimates the experimental shear strains for both extrusions. Conversely, results using the [PERSON] 1991 anisotropic yield criteria are in excellent agreement with experimental measurements for both extrusions. The presence of significant differences in the anisotropic parameters for nominally similar extrusions confirms that plastic anisotropy is essential for the accurate prediction of mechanical behavior in longitudinally extruded Al6061-T6 bars.  
Keywords:incremental plastic theory; stereo digital image correlation; plastic anisotropy; isotropic strain hardening; anisotropic yield criteria +
Footnote †: journal: Applied Sci.
## 2 Materials and Methods
### HPLC Determination of Phenolic Acids and Flavonoids  
Phenolic acids and flavonoids were extracted following the method of [PERSON] et al. [13] and were analyzed using the HPLC method [14]. The condition of HPLC (series 20, Shimadzu, Kyoto, Japan) was as follows: C18 column (250 mm \(\times\) 4.6 mm, 5 \(\upmu\)m; InertSustain, GL Sciences, Tokyo, Japan); the mobile phase consisted of purified water with 1% (_v_/_v_) acetic acid (solvent A) and acetonitrile (solvent B) at a flow rate of 0.8 mL/min. A linear gradient elution was performed as follows: 0-5 min, 5-9% B; 5-15 min, 9% B; 15-22 min, 9-11% B; 22-38 min, 11-18% B; 38-43 min, 18-23% B; 43-44 min, 23-90% B; 44-45 min, 90-80% B; 45-55 min, 80% B; 55-60 min, 80-5% B. A re-equilibration period of 5 min with 5% B was used between individual runs. Operating conditions were as follows: column temperature, 38 \({}^{\circ}\)C; injection volume, 20 \(\upmu\)L; detection wavelength including 280 nm (hydroxybenzoic acids), 320 nm (hydroxycinamic acids), and 370 nm (flavonoids). The phenolic compounds in the extracts were identified using the method of external standards. Linear equations for the quantitative calculation of concentration are shown in Table S1.  
Extraction and Determination of \(\gamma\)-Oryzanol and \(\alpha\)-, \(\delta\)-, and \(\gamma\)-Tocopherols  
The extraction of \(\gamma\)-oryzanol and tocopherols followed the method described by [PERSON] and [PERSON] [15] with slight modifications. A stink bug sample weighing one gram was vortexed with a mixture of acetone and water (1:10, _v_/_v_), followed by centrifugation (Sigma 6-16K benchtop centrifuge, Sigma-Aldrich, Castle Hill, NSW, Australia) at 2500 rpm for 20 min. The process of extraction was repeated twice using the same procedure. The resulting supernatants were combined, and the solvent was evaporated under a stream of nitrogen gas until dryness was achieved. Subsequently, the extracts were subjected to separation of \(\gamma\)-oryzanol and tocopherols using a reverse-phase high-performance liquid chromatography (RP-HPLC) system (series 20, Shimadzu, Kyoto, Japan) with C18 column (250 mm \(\times\) 4.6 mm, 5 \(\upmu\)m; InertSustain, GL Sciences, Tokyo, Japan). The mobile phase employed was acetonitrile/methanol (25:75, _v_/_v_), and isocratic elution was carried out at a flow rate of 1.5 mL/min. The wavelengths of 292 nm and 325 nm were set for the analysis of tocopherols and \(\gamma\)-oryzanol, respectively. Calibration curves were constructed with external standards. Linear equations for the quantitative calculation of concentration are shown in Table S1.
## 2 Materials and Methods
### Stability of Periodic Orbits  
A detailed analysis of the stability of \(1T\)-periodic orbits is made using the characteristic multipliers. The idea is to find the Jacobian matrix of the Poincare map and evaluate it at the fixed points of that map. It is known that, if the eigenvalues of the Jacobian evaluated at the equilibrium points are within the unit circle (stability frontier), the \(1T\)-periodic orbit is stable; if there is an eigenvalue outside the unit circle, then the \(1T\)-periodic orbit is unstable [24].  
The above can be seen in terms of the spectral radius of the Jacobian matrix [25].  
Recall this: Let \(H\) a Hilbert space, \(\mathcal{L}(H)\) Banach algebra of bounded operators on \(H\), \(T\)\(in\mathcal{L}(H)\). _If_\(r(T)<1\) (the spectral radius of \(T\)), _then_\(I-T\) is invertible and also  
\[(I-T)^{-1}=I+T+\cdots+T^{n}+\cdots(\text{\emph{Neumann series}}).\]  
Now, the following relationship between the spectral radius of the operator \(T\) and the spectrum \(\sigma(T)\) of \(T\) is obtained [25; 26]:  
\[r(T)=\max\{|\lambda|:\lambda\in\sigma(T)\}\ \ (\text{\emph{Spectral radius formulation}}) \tag{46}\]  
Consequently, if the spectral radius of a linear operator \(T\) (or the spectral radius of its associated matrix \(A_{T}\)) is less than \(1\), then all its eigenvalues will lie within the unit circle, according to Equation (46). Therefore, to analyze the stability of \(1T\)-periodic orbits, it is enough to study the spectral radius of the Jacobian matrix associated with the orbit.