esa-sceva/satcom-chunk-collection huggingface dataset:
# WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy
## 2 Related Works  
As we know, the widely-used location fingerprinting based Wi-Fi indoor passive positioning systems generally involve two phases: offline and online phases. In the offline phase, the RSSI measurements are recorded with the corresponding coordinates as the location fingerprints to construct the fingerprint database. Then, in the online phase, the newly-recorded RSSI measurements are matched against the pre-constructed fingerprint database to conduct the positioning of the target. Most of the location fingerprinting based Wi-Fi indoor passive positioning systems such as the RADAR [4] are able to achieve the meter level accuracy. However, their performance significantly depends on the reliability of fingerprint database. In addition, the fingerprint database construction is time consuming and labor intensive, which also requires the database updating as the environmental layout changes. To reduce the deployment and management burden, the organic indoor location system [5] merges the offline and online phases that were distinct in earlier work into a single state. Besides, the automatic Wi-Fi based room fingerprinting for indoor localization system (ARIEL) [6] proposed a zone-based clustering algorithm to accurately and automatically identify the in-room occupancy hotspot(s) by using the Wi-Fi signature, and, meanwhile, a motion-based clustering algorithm to identify the inter-zone correlation.  
More recently, with the rapid development of smart antenna technique, indoor passive positioning systems depending on AOA measurement have become popular. [PERSON] proposed an AOA based positioning system by using the base stations of the Long Term Evolution (LTE) system [28]. [PERSON] relied on the Synthetic Aperture Radar (SAR) technique to design the Ubicarse positioning system [29], which achieves the median error 0.39 m. [PERSON] constructed the prototype of the ArrayTrack positioning system [30], in which three APs are integrated for the sake of constructing a large antenna array. [PERSON] estimated the AOA measurement based on the principle of interferometer direction finder [31]. However, the systems mentioned above have many limitations. The Ubicarse positioning system requires the modification of the target with the purpose of supporting the rotation of antenna. The ArrayTrack positioning system cannot be easily implemented by using the commercial Wi-Fi network due to the requirement of AP integration. It is not an easy task to accurately estimate the AOA measurement based on the principle of interferometer direction finder under the multi-path fading.
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
## 4 Model of SCIG Connected to the Grid
### Sclg Model  
The algebraic expressions that detail the voltage of the SCIG's stator and rotor in the rotating \(d-q\) reference frame are as follows [30; 31; 32; 33; 34; 35]:  
\[\left\{\begin{array}{l}v_{sd}=R_{s}I_{sd}+\frac{d\phi_{sq}}{dt}-\omega_{sy} \phi_{sq}\\ v_{sq}=R_{s}I_{sq}+\frac{d\phi_{sq}}{dt}+\omega_{sy}\phi_{sd}\\ 0=R_{r}I_{nl}+\frac{d\phi_{sq}}{dt}-\big{(}\omega_{sy}-\omega_{r}\big{)}\phi_{ rq}\\ 0=R_{r}I_{rq}+\frac{d\phi_{sq}}{dt}+\big{(}\omega_{sy}-\omega_{r}\big{)}\phi_{ rq}\end{array}\right. \tag{14}\]  
where on the \(d\) and \(q\) axes, the stator voltages are \(v_{sd}\) and \(v_{sq}\), respectively; the currents flowing through the stator are \(I_{sd}\) and \(I_{sq}\), respectively; the currents flowing through the rotor are \(I_{rd}\) and \(I_{rq}\), respectively; the flux elements present in the stator are \(\phi_{sd}\) and \(\phi_{sq}\), respectively; and the flux elements present in the rotor are \(\phi_{rd}\) and \(\phi_{rq}\), respectively. In addition, \(R_{s}\) and \(R_{r}\) stand for the stator and rotor resistances, respectively, while \(\omega_{sy}\) represents the synchronous speed and \(\omega_{r}\) represents the rotor's spin velocity.  
Figure 11: Curves of \(C_{power}-\gamma\) for various pitch angles.  
Figure 12: PI-controlled pitch angle.  
Flux and current relationships are expressed by the equations below:  
\[\begin{cases}\phi_{sd}=l_{s}I_{sd}+l_{m}I_{rd}\\ \phi_{sq}=l_{s}I_{sq}+l_{m}I_{rq}\\ \phi_{rd}=l_{r}I_{rd}+l_{m}I_{sd}\\ \phi_{rq}=l_{r}I_{rq}+l_{m}I_{sq}\end{cases} \tag{15}\]  
where \(l_{s}\), \(l_{r}\), and \(l_{m}\) stand for the inductances of the stator, rotor, and magnet respectively.  
The SCIG's electromagnetic torque equation is as follows:  
\[T_{em}=\frac{3}{2}p\big{(}\phi_{sd}I_{sq}-\phi_{sq}I_{sd}\big{)} \tag{16}\]  
where \(p\) stands for how many machine pole pairs there are.  
Additionally, the subsequent formula employed to calculate the active and reactive power emitted by the stator:  
\[\begin{cases}P_{s}=\frac{3}{2}\big{(}v_{sd}I_{sd}+v_{sq}I_{sq}\big{)}\\ Q_{s}=\frac{3}{2}\big{(}v_{sd}I_{sq}-v_{sq}I_{sd}\big{)}\end{cases} \tag{17}\]
## 3 Results and Discussion
### The HPLC Profile of R. typhina Fruit Extract  
Identification and quantification of phenolic compounds in _R. typhina_ extract obtained from dried autumn fruits (BBE, 61.51% ethanol, extraction time of 5 min, solvent/solid ratio of 20/1, 2.8 mm ceramic beads) was conducted using the HPLC-PDA analysis. A total of fifteen standard phenolic acids and flavonoids (catechin, epicatechin, kaempfer, myricetin, apigenin, quercetin, isoquerctirin, naringenin, \(p\)-coumaric, protocatechuic, chlorogenic, ferulic, caffeic, ellagic, and gallic acids) was investigated.  
The results and the HPLC chromatogram are shown in Table 11 and Figure 9.  
Among phenolics, \(p\)-coumaric acid was the predominant compound. Ferulic acid and myricetin were found in similar amounts. \(p\)-coumaric and ferulic acids were reported in fruits of other species of _Rhus_, e.g., _R. coriaria_[89] and _Rhus tripartita_[90]. Myricetin was previously identified in _R. coriaria_ fruits [91]; being also a leaf flavone defining the genus _Rhus_, it was also found in _R. typhina_[92].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Phenolic Compound** & **Concentration** \\
**(\(\upmu\)/mL)** \\ \hline Coumaric acid & 17.62 \(\pm\) 0.59 \\ Ferulic acid & 8.91 \(\pm\) 0.74 \\ Myricetin & 8.89 \(\pm\) 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Phenolic compound concentrations as determined by HPLC of ethanolic extract of _R. typhina_ fruits.  
Other phenolics such as gallic acid, ellagic acid, and quercetin were previously reported in fruits of _R. typhimurium_[13; 28; 32; 86]. The differences may be attributed to diverse geographical areas, harvesting time, fruit pre-processing (freezing, drying), and conditions for the preparation of fruit extracts.  
_p_-coumaric acid, one of the hydroxycinnamic acids abundantly represented in plants, in particular in fruits, was found to be a major phenolic here and was previously reported as having strong in vitro and in vivo antioxidant activity; antimicrobial activity; anti-inflammatory activity; anticancer properties in colon cancer, adenocarcinoma, hepatocarcinoma, and breast cancer; anti-diabetic; anti-melanogenic; and antidepressant effects [93; 94].
## 2 Methodology  
### Overview  
The DEM is suitable for the deformations and failures analysis of jointed rock slopes inherently. The DEM calculations involve an iterative process that needs to be terminated appropriately according to specific conditions. However, when to appropriately terminate the DEM iterative process is still a difficult problem. That is, in the applications of discrete element modeling, there are often difficulties determining when to terminate the iterative process. When the number of iterations is too few, the calculation results would be inaccurate, while if the number of iterations is too many, it would be redundant.  
In this paper, an energy-based discrete element modeling method combined with time-series analysis is proposed for analyzing the deformations and failures of jointed rock slopes. In the proposed method, the convergence of the SD/mean of energy is used to determine when the iteration terminates. The proposed energy-based method is a generalized form of the displacement-based discrete element modeling method [22], and the proposed energy-based method considers not only the displacement of each block but also the weight of each block.  
In short, the most essential idea behind the proposed energy-based discrete element modeling method is that, an energy-based criterion is proposed to terminate the DEM iterative process to analyze the deformations and failures of jointed rock slopes. As illustrated in Figure 1, the proposed energy-based discrete element modeling method is roughly composed of the following three steps.  
(1) The geological model and computational model are established according to the details of the study area, and the appropriate parameters for the computational model are determined;  
(2) The DEM modeling is conducted to calculate the deformation of the jointed rock slope under gravity, and the positions and volumes of all rock blocks are achieved;  
(3) Time-series analysis is carried out to appropriately terminate the iterative process of DEM modeling. Specifically, on the basis of the achieved positions and volumes of all rock blocks, the energy of each block under gravity is first calculated; then, the sequence of the mean and SD of the energy is calculated. Finally, the convergence of the sequence of the SD/mean values is analyzed. If the sequence of the SD/mean values converges, the DEM calculation would be terminated.  
Figure 1: Workflow of the proposed method.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 4 Linearized Cadzow Iterations in the Signal-Estimation Problem
### Limit Property of the Composition of Projections
#### 4.4.2 Numerical Example for the Dependence of Convergence on \(\alpha\)  
Figure 1 shows the \((2r+1)\)-th eigenvalue of the \(\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L},\mathbf{R})}\) (the squared cosine of the \((2r+1)\)-th principal angle between the corresponding subspaces) for the series \(\mathsf{S}_{0}=(s_{1},\ldots,s_{N})^{\mathrm{T}}\) with the common term \(s_{k}=\sin\Bigl{(}\frac{2\pi k}{6}\Bigr{)}\) and \(N=40\); the window length is \(L=20\) and the signal rank is \(r=2\). The values \(\alpha\) are taken from \([10^{-4},1]\) on the logarithmic scale, and the matrix \(\mathbf{R}\) is calculated using the weight search method from [14]. Figure 1 demonstrates that the eigenvalues are close to \(1\) for small \(\alpha\); however, they do not equal one; that is, the Cadzow iterations converge. This result explains the decrease in the convergence rate of the Cadzow method with decreasing values of the parameter \(\alpha\), which was numerically demonstrated in [13; 14].
## 2 Structural Model of the Climbing Robot
### Overall Scheme for the Climbing Robot  
The proposed robot involved the clamping of a cable clamped with three wheels. According to the principle illustrated in Figure 1a, wheels A and C were set on the left and right support plates which are opposite to each other, while wheel B was set on the left and right swing plates in an opposed arrangement. Swing plates and support plates were hinged and rotated freely around the hinge, while the spring mechanism pushed the upper and lower support bars and thus the robot clamping cables. The shapes of support plates and swing plates are shown in Figure 1b. Motors 1 and 2 directly drove wheels A and B through the synchronous belt drive and wheel C was driven by motor 1 through another synchronous belt, thus forming the all-wheel-drive climbing robot (Figure 2).  
Figure 1: The model of climbing robot. 1—Spring mechanism; 2—Wheel B; 3—Swing plate; 4—Wheel A; 5—Support plate; 6—Wheel C. (**a**) The principle of the climbing robot; (**b**) Left view of the robot.  
Figure 2: The structure of the climbing robot. 1—Left swing plate; 2—Left half-wheel B; 3—Motor 2; 4—Right half-wheel B; 5—Right swing plate; 6—Right hinge; 7—Right half-wheel A; 8—Right support plate; 9—synchronous belts; 10—Synchronous pulley; 11—Right half-wheel C; 12—Left half-wheel C; 13—Lower support bars; 14—stop lever; 15—Left support plate; 16—Left half-wheel A; 17—Left hinge; 18—Spring mechanism; 19—Upper support bars.  
Each of the three rolling wheels were comprised of two half-wheels on the left and right (Figure 2), which allowed easy adjustment of the distance between the two half-wheels and the clamping force. All parts of the robot were set between the left and right support plates.  
Based on the joint effects of the relative rotation of swing plates and support plates, scaling of the spring and adjustment of spacing of rolling wheels, the robot clamped the cable. In practical applications, it is more convenient to adjust clamping forces by adjusting the spacing of the rolling wheels, so the design allowed for a mass of 11 kg and a length ranging from 370 mm to 400 mm. Moreover, the width was 236 mm and the height was able to be adjusted within the range from 410 to 440 mm.
# FEAROL: Aging Flow Entries Based on Local Staircase Randomized Response for Secure SDN Flow Tables
## 7 Discussion  
Flow table overflow LDoS attacks are difficult to detect with the existing detection mechanisms for the LDoS attacks due to the low packet rates. This work chooses to change the attack surface by randomizing the real lifespans of flow entries at switches and breaking the synchronization between attack flows and the flow-entry timeouts to proactively protect from the flow table overflow LDoS attacks instead of designing reactive attack detection and mitigation mechanisms. This work is actually a moving target defense (MTD) approach that increases the security of SDN systems [45]. It provides a local differential privacy approach that keeps the true lifespan of each flow entry private without adding extra mechanisms on the controllers or the third parties.  
It is lightweight and works on the aging process of flow entries that does not directly add overhead to the flow-packet-forwarding process. Since our proposed approach can proactively protect networks from LDoS attacks, while the state of the art of LDoS detection and mitigation techniques are typically reactive, it is difficult to compare the performance of these two types of approaches. Since we randomized the seeds to generate \(p\) for each  
Figure 9: The average time cost of aging a flow entry. The items of no randomized response, strategy 1, strategy 2 and strategy 2 without randomization are the time cost without applying staircase randomized response, applying staircase randomized response strategy 1 and applying staircase randomized response strategy 2 but the probability \(p\) is not randomly generated.  
time a flow entry was aged, we maximized the generality in the evaluation and avoided outperformance arising by chance, and curves with confidence intervals were not needed.  
More advanced MTD mechanisms that change the attack surface, such as dynamically changing the network topology and IP addresses [46], can be investigated in our future work to increase the difficulty of probing network settings and, thus, launching flow table overflow LDoS attacks. Other LDoS attack detection and mitigation approaches using flow classification based on machine-learning mechanisms [23] can be added to further reduce the impact of LDoS attacks. As the proposed approach requires a minor update of the software of switches, the current prototype was implemented in OVS.  
Although real traffic traces increase the variety of the legitimate flows, they create trouble in simulating the network topology and addressing the space of the traces and, hence, the complex test scenarios due to the lack of related information. However, further evaluations that estimate the performance of the current prototype in more complex network scenarios should be performed, and the prototype should be extended to programmable datapaths, such as P4-based [47] or netFPGA-based [48] SDN switches.
## 5 Policy Control
### Legal Aspects and Potential of Remote Sensing  
The use of RS imagery as a source of information to support non-compliance or fraud ultimately requires admission of the remotely sensed information as evidence in court [57]. RS data's admissibility as evidence depends, amongst others factors, on the possibility of accurately detecting fraud and non-compliance in the imagery. The advent of sub-meter resolution satellite imagery greatly increased the potential of EO in policy control, as this allows for the accurate detection of indicators that are either not, or only poorly, discernable with lower resolution imagery. [PERSON] [57] further discusses the acceptance of EO as a source of evidence and privacy issues as barriers to the use of satellite imagery in policy control.  
The potential of EO in policy control has long been recognized [58] and [PERSON] [57] concludes that the greatest use of remote sensing may lie in the field of compliance and enforcement. It is also our impression that remote sensing has a much wider potential for application in policy control than has been realized so far. It remains difficult, however, to assess the actual use and potential of this market. While the use of satellite imagery has been prescribed as one of the ways to demonstrate compliance in case of EU agricultural policy, and the ESA developed RS techniques which could be employed by individual member states, it remains unclear to what extent remote sensing is being used for this purpose in actual practice. One reason for this could be the aforementioned lack of publications reporting on the actual use of earth observation, but it might also be a deliberate choice, as detailed explanations on how RS is used in policy control might trigger control avoidance strategies.  
The market of EO in control would appear very attractive when imagining that this would require continuous monitoring. It is questionable, however, whether continuous monitoring would be required; the rumour that remote sensing is used might affect behaviour such that incidental control would suffice. No studies so far have been undertaken to assess how detection of fraud and non-compliance by EO influences behaviour, and we suggest it would be interesting to study this.
## 2 Principles  
### Generalized S-transform (GST) with Four Parameters  
Standard S-transform (ST) is proposed by [PERSON] et al. [2]. This transform is defined as,  
\[ST_{x}(\tau,f)=\int_{-\infty}^{+\infty}x(t)\frac{|f|}{\sqrt{2\pi}}\exp\Bigl{\{} -(t-\tau)^{2}f^{2}/2-i2\pi ft\Bigr{\}}dt \tag{1}\]The basic wavelet function in standard ST is,  
\[w_{f}(t)=\frac{|f|}{\sqrt{2\pi}}\exp\Bigl{(}-t^{2}f^{2}/2-i2\pi ft\Bigr{)}, \tag{2}\]  
where \(x(t)\) represents a signal, \(ST_{x}\) denotes the ST of \(x(t)\). \(f\) is frequency, \(t\) is time and \(\tau\) denotes the time shift.  
The inverse transform of the standard ST is obtained by,  
\[x(t)=\int_{-\infty}^{+\infty}\left\{\int_{-\infty}^{+\infty}ST_{x}(\tau,f)d \tau\right\}\exp(i2\pi ft)df \tag{3}\]  
Although ST inherits the advantages of STFT and WT, its application is limited because of its fixed basic wavelet function. [PERSON] et al. use a modulated harmonic wave with four undetermined parameters to replace the basic wavelet in ST to overcome the disadvantage caused by the fixed basic wavelet function. The modulated harmonic wave is written as,  
\[w_{f}(t)=A|f|\exp\Bigl{[}-\alpha(ft-\beta)^{2}-i2\pi f_{0}ft\Bigr{]}, \tag{4}\]  
where \(A\) is amplitude of the basic wavelet, \(\alpha\) is energy attenuation ratio \((\alpha>0)\), \(\beta\) is energy delay time and \(f_{0}\) is video frequency of the basic wavelet.  
So the generalized S-transform (GST) with four parameters of a signal \(x(t)\) is,  
\[GST_{x}(\tau,f)=A|f|\int_{-\infty}^{\infty}x(t)\exp\Bigl{\{}-\alpha[f(t-\tau)- \beta]^{2}\Bigr{\}}\exp(-i2\pi f_{0}ft)dt, \tag{5}\]  
where \(GST_{x}\) denotes the result of the GST with four parameters. Its inverse transform is,  
\[x\biggl{(}\frac{t}{f_{0}}\biggr{)}=f_{0}IFT\biggl{[}\frac{1}{A}\sqrt{\frac{ \alpha}{\pi}}\int_{-\infty}^{\infty}GST_{x}(\tau,f)d\tau\biggr{]}(t), \tag{6}\]  
where \(IFT\) represents the inverse transform of Fourier transform.  
It is obvious that the four-parameter GST is degenerated into a standard ST when \(A=1\), \(\alpha=\frac{1}{2}\), \(\beta=0\) and \(f_{0}=1\).
## 2 Performance Assessment
### Maximum Permissible Exposure for Different Wavelengths  
Wavelength is one of the key parameters in the design of any lidar system. Dependant on this parameter are the laser emitters and photodetectors that can be used, the mechanisms of interaction with the atmosphere and the eye safety level that will be required. As the design starting-point, a comparative analysis is conducted in this section of the maximum permissible exposure (MPE) to the following wavelengths:  
* \(\lambda=355\) nm. Typical of UV lidars [12], corresponding to the third harmonic of the Nd:YAG solid state laser.
* \(\lambda=523\) nm. Visible radiation used by the Micro Pulse Lidar [13].
* \(\lambda=905\) nm. Commonly applied in lidar ceilometry [14], corresponding to the InGaAs laser diode.
* \(\lambda=1064\) nm. IR radiation [15] generated by the Nd:YAG laser (fundamental frequency).
* \(\lambda=1.5\) um. Commonly used in eye-safe systems [16].  
Figure 1 shows the maximum permissible exposure for a single pulse as a function of the pulse repetition frequency. This graph was generated considering for each wavelength and emission frequency the most restrictive of the criteria defined by IEC/EN 60825.  
It can be seen that at 355 nm the safety level varies substantially with the exposure time. So, for exposures of 10 s this is the safest wavelength, whereas for more prolonged exposures (and high PRFs) it is one of the most dangerous. This behaviour is due to the UV-A radiation doses being additive. At 532 and 905 nm the MPEs are very similar, while for 1064 nm the safety threshold is approximately twice as high. The 1.5 um wavelength is known to be eye-safe, though this condition depends on the emitted radiant exposure. It can be seen that at 1.5 um, permissible exposure is appreciably higher than in the visible or near infrared. In addition, unlike in the ultraviolet, there are no additive photochemical effects in the IR-B.
# S-Band Class-C-F Power Amplifier with 2 nd Harmonic Control at the Input
## 5 Conclusions  
In this paper, the concept of gate harmonic control is discussed for the design of a class-C-F PA, validating the proposed approach though the design of a PA with CREE device CGHV1F006s. In particular, in order to avoid issues with a possible device breakdown, the performance of the PA was limited to the maximum power of 5 W. The experimental results demonstrate the possibility to design a class-F PA biased in class-C condition by properly manipulating the harmonics at the input node. Measurements confirmed an output power close to 5 W with a DE higher than 60% (being the overall gain higher than 16 dB) and working bandwidth up to 11% from 2.2 GHz to 2.43 GHz.  
The reported theory and experimental results extend the theory of harmonic tuning PA to class-C bias range and proved that harmonic manipulation at gate node is an available method to realize high efficiency PAs. By properly manipulating the 2 nd harmonic at gate node, the proper phase relationship between 3 rd harmonic and fundamental one at the drain node is obtained, thus realizing a class-C-F PA.  
Taking the advantage of the input non-linearity provides more scope for PA designers to balance the performance to satisfy various application scenarios. It is important to set proper harmonic impedance at both input and output in harmonic tuned PA design, especially when the non-linearity is not negligible.  
Writing--original draft and Validation, X.L. ;Writing--review, editing and supervision, P.C.; Supervision, F.G.; Supervision, H.Y.; Writing--review, C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.