esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
## 3 Synchronized Swept-Sine Technique  
The synchronized swept-sine is generated using [37]  
\[x(t)=\sin\left[2\pi f_{1}L\exp\left(\frac{t}{L}\right)\right], \tag{2}\]  
where  
\[L=\frac{T}{\ln\left(\frac{f_{2}}{f_{1}}\right)}, \tag{3}\]  
and where \(f_{1}\) and \(f_{2}\) are initial and final frequency, respectively, and \(T\) is duration of the swept-sine. Note that the definition of the exponential swept-sine (Equation (2)) does not contain the \"\(-1\)\" term contrary to the usual definition [16; 38]. The original definition [38] led to a good estimation of amplitudes of Higher Harmonic Frequency Responses (HHFRs), but the phases of HHFRs have been estimated poorly. A restriction of the parameters \(f_{1}\), \(f_{2}\), and \(T\) in order to synchronize the swept-sine signal has been proposed in [16], avoiding the problem of poor phase estimation and allowing an estimation of the filters \(G_{n}(f)\) of the Generalized Hammerstein model. A similar approach has been proposed in [17]. It has been shown, in [37], that in removing the \"\(-1\)\" term from the original definition, the synchronized swept-sine becomes suitable for estimation of the HHFRs without any need of correction or restriction of the parameters \(f_{1}\), \(f_{2}\), and \(T\).  
The method, originally developed by [PERSON] [38], consists of de-convolving the measured signals with a so-called inverse filter as  
\[h(t)=\mathcal{F}^{-1}\left\{\mathcal{F}[y(t)]\widehat{X}(f)\right\}, \tag{4}\]  
where \(y(t)\) is the acquired response of the nonlinear system (displacement or voltage signal) to the synchronized swept-sine. The Fourier transform of inverse filter \(\widehat{X}(f)\) can be either calculated numerically from the generated signal \(x(t)\), or an analytical formula derived in [37],  
\[\widehat{X}(f)=2\sqrt{\frac{f}{L}}\exp\left\{-j2\pi fL\left[1-\ln\left(\frac{ f}{f_{1}}\right)\right]+j\frac{\pi}{4}\right\}, \tag{5}\]  
can be used. The impulse response \(h(t)\) then consists of time-delayed higher harmonic impulse responses, separated by time delays  
\[\Delta t_{m}=L\ln(m), \tag{6}\]  
\(m\) being the order of the higher harmonic.  
Finally, to get the Higher Harmonic Frequency Responses (HHFRs), the time-delayed higher harmonic impulse responses \(h_{m}(t)\) are windowed from \(h(t)\) and Fourier Transformed to obtain  
\[h_{m}(t)\xleftrightarrow{\mathcal{F}}\mathcal{H}_{m}^{(x,u)}(f), \tag{7}\]  
\(x\) and \(u\) denoting input and output.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 3 Proposed Dereverberation Method
### Initialization of Kalman Filtering
We can redefine the covariance matrices presented in (11) and (12) at each time-frequency bin as follows:  
\[\mathbf{C}^{(\mathrm{x+r+n})}(l)=\mathbf{y}(l)\mathbf{y}(l)^{H} \tag{60}\]  
\[\mathbf{C}^{(\mathrm{n})}(l)=\lambda(l)^{(\mathrm{n})}\mathbf{y}(l)\mathbf{y}( l)^{H} \tag{61}\]  
where \(\lambda\) (\(l\))(\({}^{\mathrm{n}}\)) denotes the mask or probability of the scenarios that only contain noise. Thus, the covariance matrix of the noiseless speech at each time-frequency bin can be given as follows:  
\[\mathbf{C}^{(\mathrm{x+r})}(l)=\mathbf{C}^{(\mathrm{x+r+n})}(l)-\mathbf{C}^{( \mathrm{n})}(l) \tag{62}\]  
Obviously, the diagonal elements of the covariance matrix \(\mathbf{C}^{(\mathrm{x+r})}(l)\) represent the PSD of the speech signal. We use vector \(\Theta(l)\) to express its diagonal elements as follows:  
\[\Theta(l)=\mathrm{Diag}\Big{[}\mathbf{C}^{(\mathrm{x+r})}(l)\Big{]}=\big{[} \phi_{x_{\mathrm{R1}}}(l),\phi_{x_{\mathrm{R2}}}(l),\cdots,\phi_{x_{\mathrm{ Rm}}}(l),\cdots,\phi_{x_{\mathrm{Rd}}}(l)\big{]}^{T} \tag{63}\]  
where \(\phi_{x_{\mathrm{Rm}}}(l)\) represents the power spectral density of the \(m\)th microphone signal that is processed by the beamformer. The diagonal elements are summed and averaged as the approximation of the PSD of the desired signal required in the [PERSON] filtering process, i.e.,  
\[\phi_{x_{\mathrm{R}}}(l)=\frac{1}{M}\sum_{m=1}^{M}\phi_{x_{\mathrm{Rm}}}(l) \tag{64}\]  
where \(\phi_{x_{\mathrm{R}}}(l)\) is an approximation of the power spectral density \(\phi_{x_{\mathrm{e}}}(l)\) of the desired signal, and the low-complexity version also utilize \(\phi_{x_{\mathrm{R}}}(l)\) as the initialization of \(\phi_{x_{\mathrm{e}}}(l)\). Under ideal circumstances, the closer the initialized PSD is to the PSD of the desired speech, the better the algorithm will achieve, but this is obviously impractical. Blind estimation of the PSD of the desired signal is very difficult only from the signal received by the microphones. Compared with the PSD estimation method in ISCLP [12], the proposed method does not need the incident angle of the desired speech and other prior information. Applying such crude approximation does not mean a wrong result, because the linear prediction coefficients are used instead of the desired signal itself in the Kalman filtering, the effect of the approximation error can be mitigated during the Kalman filtering. Meanwhile, the computational complexity of this kind of PSD estimation is lower than that used in ISCLP. Therefore, it is feasible to adopt this approximation of the PSD, which is also confirmed by the following experiments.
## 2 Experimental Overview
### Experimental Apparatus and Equipment
#### 3.1.4 Poisson's Ratio  
The Poisson's ratio \(\mathrm{\gamma_{c,T}}\) of PPF concrete is defined as the ratio of transverse strain to longitudinal strain. The curve of the Poisson's ratio \(\mathrm{\gamma_{c,T}}\) with respect to temperature T is shown in Figure 11. At room temperature, the bridging effect of fibers slows down the propagation of cracks, and increases the deformation capacity of concrete, and, therefore, the Poisson's ratio of PPF concrete is higher than that of ordinary concrete. The Poisson's ratio of the ordinary concrete group was 0.206, and the highest Poisson's ratio was in the PL3C0.5 group, which was 11.31% higher than that of ordinary concrete. At 200 \({}^{\circ}\)C, the Poisson's ratio of all groups showed a decreasing trend, and the decrease in Poisson's ratio in fiber concrete was less than in ordinary concrete. This is because the continuous evaporation of free water in PPF concrete promotes reaction between water and cement particles, fills the micro-cracks inside, strengthens the aggregation between concrete components, and reduces the transverse strain. When the temperature was below 300 \({}^{\circ}\)C, the fluctuation range of the Poisson's ratio of PPF concrete and ordinary concrete was within 12.73%. After the temperature reached 400 \({}^{\circ}\)C, the Poisson's ratio increased rapidly with temperature, and the dispersion of the Poisson's ratio of different PPF contents and lengths increased with temperature. The reason for this is that higher temperatures have a greater impact on the Poisson's ratio, and different types of PPF have different degrees of weakening effect on the concrete section. In addition, PPF melts and gasifies at high temperatures, releasing some of the internal thermal stress, which makes the change in Poisson's ratio more complex as the temperature increases, although, overall, it tends to increase. From the graph it can be observed that, after the high temperature of 400 \({}^{\circ}\)C was reached, relatively stable maximum and minimum Poisson's Ratio appeared in the PL15C1.0 and PL15C0.5 groups, respectively, which were 5.7% higher and 7.37% lower than the Poisson's ratio of ordinary concrete, respectively.  
Figure 10: The relation between elastic modulus and temperature.
## 4 Structure of SFrame Video Conferencing System  
Here in Section 4, the group communication protocol SFrame and the key management system MLS, which are in the process of standardization as video conferencing systems, are analyzed. Subsequently, we describe how the E2 EE-related Zoom and SFrame protocols were investigated to compare and analyze the two systems. SFrame applies the E2 EE mechanism to the WebRTC protocol, enabling real-time communication [3].  
The SFrame protocol employs double encryption to media, such as video or audio, ensuring secure transmission with E2 EE. As a result, the SFU central server, which relays end-to-end media traffic, cannot access the plaintext message information, but only the necessary metadata for communication routing. Moreover, SFrame encrypts the entire media frame during transmission rather than encrypting individual media packets separately. In other words, SFrame uses an initialization vector (\(IV\)) and an authentication tag for each frame for encryption. This method has gained significant attention, as it reduces communication overhead, which is a well-known challenge in E2 EE [3].
## 6 Side-Channel Timing Attack on Streaming Applications over NDN
### Possible Scope of Attack  
In the NDN side-channel attack, the scope of the attack may vary by the intention of an adversary and be targeted to a specific streaming application (e.g., NDNtube [22], NDNlive [22], NDNvideo [20], and NDN-RTC [21]). Based on the CRT values, information privacy can be threatened by the adversary, as described by [1,2,6]. For instance, an adversary defines the targets by their content name (e.g., /ndn-content) or content segment (e.g., /ndn-video/%00%12%34) by side-channel timing attack.  
The name of streamed content is defined by its producer and content itself. In streaming attack scope, an adversary may also target the name to obtain the popularity of content. Additionally, when an adversary distinguishes between cached and uncached targets, this information may be used to distinguish where the popular content has been recently cached and how long it stayed popular. This type of attack is used for content monitoring.  
In a traditional attack, the sequence number is required. However, our simulation experiments showed that the attack success probability can be higher if prefix matching is allowed from the application (e.g., NDNtube or other streaming apps). For instance, instead of requesting a certain sequence number (e.g., /ndntube/videos/video-1/%00%12%34), the adversary can request a prefix (e.g., /ndntube/videos/video-1), and the router replies to the request with its segment (e.g., /ndntube/videos/video-1/%00%12%34).  
Figure 8 illustrates two possible adversaries, based on CRT calculations. For instance, _i._ an adversary node can determine the target at the edge node (router) (Figure 7(a)), and _ii._ an adversary can determine the cached targets from away nodes (routers) (Figure 7(b)). Whenever the attack is completed to determine the locations, the attacker compares (or examines) each collected CRT metric (value), which may be used in three different main decisions for locations where the consumer's target has been cached.  
For instance, _i._ a maximum CRT shows that the target has not been cached yet. _ii._ A minimum CRT shows that a target was cached by the edge node (router) recently. _iii._ A CRT between minimum and maximum shows that the target was cached by an away NDN component (e.g., router).
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating the Water Environment Impact on Energy Production
#### 3.1.4 Effects of Soiling on FPV  
In the case of floating photovoltaic systems, the effect of the basin water quality on the cleanliness of the PV modules remains almost completely underestimated. The soiling phenomenon affecting photovoltaic systems performances is well known and several reviews have been published on the topic [44]. It is, moreover, well known that soiling-related economic losses can be very relevant, especially in those cases where the photovoltaic energy production system coexists with other production activities, such as those connected to agricultural crops [45].  
As regards FPV in particular, it should also be considered that in the soiling mechanism, the role of water, mainly intended in terms of a humidity rate, is of the utmost relevance; in environments with a high humidity, consolidation phenomena of material residues affecting the electrical conversion efficiency might occur more frequently [46]. Extrema in these cases can be observed in the case of marine waters where, power limitations higher than 10% have actually been observed, suggesting that FPV systems could be subject to severe reductions in the power generated [47]. Finally, in environments characterized by poor water quality, it has been observed that the cleaning efficiency due to the mechanical action of the water itself may be quite limited and that, therefore, water quality could also play a role in energy production [48].
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
###### Abstract  
The Athenet network is the network of the Seismological Laboratory of the National and Kapodistrian University of Athens. We present the geophysical investigation that has been carried out at six seismological stations of the Athenet network for their site characterization. More specifically, at the location of each seismological station, four geophysical methods have been carried out: Seismic Refraction Tomography (SRT), Multichannel Analysis of Surface Waves (MASW), the Horizontal to Vertical Spectral Ratio (HVSR) technique, and Electrical Resistivity Tomography (ERT). The applied geophysical survey provided important information regarding the site characterization at the selected seismological stations, including key parameters such as the fundamental frequency \(f_{o}\), the shear-wave velocity \(V_{S}\), the average shear-wave velocity for the upper 30 m depth (\(V_{SS0}\)), the seismic bedrock depth, the soil type, and the subsurface geology. Moreover, selected elastic moduli (Poisson's ratio, shear, bulk, and Young moduli) have been calculated. The site characterization information contributes to the determination of the amplification factors for each site that can lead to more accurate calculation of Peak Ground Acceleration (PGA) or Peak Ground Velocity (PGV) and, therefore, trustworthy Probabilistic and Stochastic Seismic Hazard Assessments. The derived fundamental frequency for the seismological stations of _VILL_, _LOUIT_, _THAL_, and _EPID_ have been determined to be equal to 10.4, 2.7, 1.4, and 7.1 Hz and their amplification factors to be 1.9, 3.1, 1.7, and 2.6, respectively. For stations _MDRA_ and _ATAL_, these parameters could not be determined.  
SRT; MASW; ERT; HVSR; elastic moduli; Vgk0; EC8 2023, 13, 4932. [[https://doi.org/10.3300/](https://doi.org/10.3300/)]([https://doi.org/10.3300/](https://doi.org/10.3300/))
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method
### Propagation Time  
The propagation time from the sound source to the shear layer does not change, regardless of the flow velocity. This is because the \(Y_{n}\) and \(Z_{n}\) components of the flow velocity are zero, and the sound speed towards the shear layer does not change. This is based on the assumption that sound wave propagates through air as a medium and, therefore, the effective sound speed changes according to the speed of the medium. In other words, the propagated point in the shear layer is changed by the convection due to the freestream velocity under the same propagation time as that without convection. Therefore, the propagation time inside the wind tunnel \(T_{\text{inside}}\) is derived, as follows:  
\[T_{\text{inside}}=\frac{\sqrt{(x_{s}^{\prime}-x_{0})^{2}+(y_{s}-y_{0})^{2}+( z_{s}-z_{0})^{2}}}{c}, \tag{4}\]  
where \((x_{s}^{\prime},y_{s},z_{s})\), \((x_{0},y_{0},z_{0})\), and \(c\) are the positions that the sound wave reaches the shear layer without the flow velocity, the sound source position, and the sound speed, respectively. Because the sound wave is only advected in the flow direction by \(McT_{\text{inside}}\) before reaching the shear layer, \(x_{s}\) is given by Equation (5).  
\[x_{s}=x_{s}^{\prime}+McT_{\text{inside}} \tag{5}\]where \((x_{s},y_{s},z_{s})\) is the position of the refraction in the local coordinate system. subsequently, the propagation time inside the wind tunnel is derived from Equations (4) and (5), as follows.  
\[T_{\text{inside}}=\frac{Mc(x_{s}-x_{0})-\sqrt{(M^{2}c^{2}(x_{s}-x_{0})^{2}-c^{2 }(M^{2}-1)\{(x_{s}-x_{0})^{2}+(y_{s}-y_{0})^{2}+(z_{s}-z_{0})^{2}\}}}{(M^{2}-1) c^{2}} \tag{6}\]  
The propagation time outside the wind tunnel \(T_{\text{outside}}\) is given by the following equation:  
\[T_{\text{outside}}=\frac{\sqrt{(x-x_{s})^{2}+(y-y_{s})^{2}+(z-z_{s})^{2}}}{c}, \tag{7}\]  
where \((x,y,z)\) is the position of the microphone in the local coordinate system. Therefore, the propagation time from the sound source to the microphone is given by  
\[T=T_{\text{inside}}+T_{\text{outside}}. \tag{8}\]
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 7 Results of All-Bands BadNets Attack and Defense
### Fine-Pruning and STRIP  
We applied the fine-pruning method to remove the all-bands BadNets backdoor in the DCNN architecture with the EruoSat dataset. We embedded a white square trigger at the bottom of the image in all image bands. Fine-pruning failed to remove the backdoor for all trigger sizes from \(2\times 2\) to \(10\times 10\) as shown in Table 5. STRIP-based defense identified the backdoor samples six times out of ten scenarios for the BadNets attack on the EuroSat dataset in Table 5, where all-bands refer to BadNets backdoor attack setting. However, STRIP requires training datasets to detect a backdoor, and users typically do not have training datasets.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 5 RTT Measurements for Each Modulation at Several Temperatures
### Average RTT for 18 \({}^{\circ}\)C  
Figure 16 presents the average values of RTT (in ms) depending on the working frequency at 18\({}^{\circ}\)C for maximum distances registered at all data transfer rates. It also presents their typical errors in ms. Figures 13 and 14 showed that 11 Mbps, 12 Mbps and 18 Mbps reach the maximum distances. As Figure 16 shows, the data rate that provides lowest RTT is 18 Mbps with a value of 5.61 ms. To work at 18 \({}^{\circ}\)C, the best combination is to use 18 Mbps as data transfer rate, working at 2437 MHz.
## 4 Conclusions  
In this paper, we have proposed the intensity-dependent and wavelength-dependent angle measurement methods by THG in centrosymmetric crystals. Three prospective crystals, including the \(\alpha\)-BBO, calcite, and rutile, have been considered in this research for the two angle measurement methods. The effects of the focusing parameters on the angle dependence have also been considered. The optimized focusing parameters have been found for future experimental work.  
For the intensity-dependent method, the type I phase-matching angle of rutile and the type II phase-matching angle of \(\alpha\)-BBO have been calculated in the wavelength range from 900 nm to 2500 nm. The results show that the type II phase-matching angle has a good performance due to its dispersion-less property in the wavelength range of 1700-2000 nm. The sensitivities of angle measurement have been evaluated for calcite and \(\alpha\)-BBO, which are \(-0.108\) a.u./arcsec and \(-0.087\) a.u./arcsec, respectively. They are much higher than the sensitivity of 0.002 a.u./arcsec for rutile. The sensitivity of the wavelength-dependent method has been evaluated in the dispersive range of around 1560 nm. The peak shift in the frequency domain has been shown for the three investigated crystals. The weighted wavelengths \(\lambda_{\mathrm{w}}\) have been used to characterize the peak shift speeds, which were  
Figure 10: The figure for showing the effect of different focused positions on angle dependence. The lines/circles in different colors correspond to the positive/negative beam waist movements relative to the crystal center.  
\(-0.0054\) nm/arcsec, \(-0.0063\) nm/arcsec, and \(-0.0002\) a.u./arcsec for \(\alpha\)-BBO, calcite, and rutile, respectively. \(\alpha\)-BBO and calcite also have fast peak shifts around 1560 nm.  
In addition, the focal length \(f\) has been changed from 10 mm to 150 mm for investigating the effect of \(f\) on the angle measurement. The optimized focal length has been obtained as \(f=100\) mm. It has been demonstrated that the maximum sensitivity is achieved when the focus position is in the center of the crystal.  
Methodology, K.L., R.S. and H.M.; Formal analysis, K.L.; Investigation, J.L., R.S. and H.S.; Data curation, J.L. and Z.Z.; Writing--original draft, K.L.; Writing--review & editing, H.M.; Supervision, W.G. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Japan Society for the Promotion of Science (JSPS) 20H00211.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
[PERSON] would like to thank the Chinese Scholarship Council (CSC) for living cost support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 6 Results
### Baseline Rule Results  
The following graphs contain the results of the application of the proposed baseline rules over the five power variables (see Figure 6). On the \(x\)-axis, the number of days back expresses the value of the hyperparameter (\(n\)), while the \(y\)-axis shows the absolute value of the \(CV(RMSE)\). The colored circular points (connected by continuous lines) correspond to the strict application of the rules, while the triangles (connected by dotted lines) are obtained from the non-strict application.  
In the cases of \(ge1\), due to its negative average value, the values of \(CV(RMSE)\) are negative. For this reason, the axes of the graphs show the absolute value of this indicator. It must be remembered that the best forecast corresponds to the lowest absolute values (the nearer to zero, the better).  
It can be noted that the \(ge2\) and \(lc\) variables show behaviors that are strongly dependent on the type of day (weekday and weekend), with the rules of type _baseline_cnb_ (\"const_num_back\") and _baseline_bw_ (\"basic_weekend\") being the best.  
For \(ge1\) and \(ut\), the best rule is _baseline_s_ (\"simple\"), as they show behaviors that are less related to the type of day. This is probably because the need for hot water (partially produced by microturbines) does not change substantially from weekdays to weekends (as many students live on campus).  
Finally, \(pv\) is best forecasted using the _baseline_s_ rule, which was expected because solar irradiation is independent of the type of day.  
These results show how testing multiple types of baselines provides information about the behaviors of the variables, while addressing which type of days are the best indicators of each of these variables. Particularly, in the case of the variable \(ge1\), whose behavior could at first be difficult to guess without a previous analysis, it was found that this variable is strongly dependent on the most recent previous days.