esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Test Materials  
#### 2.3.1 Test Model  
The test model was designed based on the actual engineering structure, including the railway embankment and a snow fence model. The subgrade model was constructed of polished wood board, and the fence was made of single-line railway section model (Figure 4). The prototype dimensions were a depth of 3 m, a top width of 8 m, and a slope of 1:1.5. The fence was a snow grid with adjustable height and porosity; a single prototype panel featured a porosity of 50% and a height of 0.5 m. As illustrated in Figure 3, the wind tunnel test utilized a fence comprising six panels, resulting in a total height of 10 cm and an overall porosity of 75%. The model reduction ratio was 1/30, yielding a blockage ratio of 3.9% which meets the requirements outlined in JGJ/T 338-2014 \"Wind tunnel test method Standard\" [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Similarity** & **Definition** & **Scale and Relationships** & **Purpose** \\ \hline Size & The geometric scale ratio of the model is equal to the prototype: & Geometric scale (1:30) & We ensure that the model is consistent with the geometry of the real operating conditions, and the spatial distribution of the flow field after the scale is similar. \\ \hline Kinematics & Equal speed ratio: & & \\  & Particle starting conditions are & & \\  & similar: & & \\  & The settlement velocity is similar: & the prototype. & \\  & The \(Fr\) and \(Re\) is equal: & & \\  & \(Fr_{m}=Fr_{p}\), & & \\  & \(Re_{m}=Re_{p}\) & & \\ \hline Dynamics & Pneumatic resistance is similar: & The resistance of the particle is in proportion to the inertial force. & Control the force balance of particles in the flow field, reflecting the interaction mechanism of air flow and particles. \\ \hline Accumulation pattern & The rest Angle is equal: & Simulation material (silica sand) and snow The particle resting angle is consistent with the actual snow cover. \\ \hline Time scale & Sedimentation time ratio: & The deposition process time is scaled (shortened snow cover evolution process in the prototype. \\ \hline \hline \end{tabular} Note: The subscripts \(m\) and \(p\) represent the model and the prototype, respectively.  
\end{table}
Table 1: Similarity parameters and criteria for the wind tunnel test.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 4 mmWave and MIMO-Specific Attacks
### MIMO and mmWave Attacks Mitigation Techniques  
In order to mitigate or detect jamming and spoofing attacks in NR FR2, several methods have been published. These methods typically use virtual channel [89] representation, which effectively describes the spatial properties of the channel.  
In [80; 90], the authors proposed a randomized probing technique, which involves transmitting a corrupted probing sequence to enable the user equipment to reject the jamming signal using subspace-based orthogonal projections and jamming cancellation. This method shows promising results in maintaining the accuracy of beam selection and ensuring the quality of service despite the presence of smart jammers.  
In [91], the authors applied a Principal Components of Channel Virtual Representation (PC-CVR)-based method to detect spoofing attacks. The method uses a statistical test for static environments and machine learning for dynamic environments--achieving high accuracy in both scenarios. The non-machine-learning is introduced in [92], where the authors applied a more simplified method based on leveraging the sparseness and statistical features of virtual channels.  
In [93], the authors proposed an active-passive cascaded RIS-aided receiver for jamming nulling and signal enhancing based on a low-complexity optimization framework using Alternating Majorization-Minimization (AMM) and Conventional/Modified Cyclic Coordinate Descent (CCD) (C/M-CCD) methods to obtain the coefficients of the active RIS. Another method is proposed in [94], where the algorithm forks in two phases. Firstly, uncertain jamming information is robustly processed using CCD and Successive Convex Approximation (SCA) to optimize the RISs' phase shift and amplitude matrices. Additionally, closed-form solutions for the transmitting and receiving beams are derived. Finally, a low-complexity Block Coordinate Descent (BCD) algorithm alternately optimizes these variables and a greedy algorithm manages and adjusts the multiple RISs.
## 3 Results  
### Simulations of \(\text{SIF}_{760}\), GPP, and ET at Different Time Scales  
The results (Figure 3) showed that, when employing the calibrated fluorescence parameterization based on all the measured leaf gas exchange and fluorescence data, the R\({}^{2}\) between the simulated and observed \(\text{SIF}_{760}\) increased by 1.76% and 1.70% at the half-hourly and daily scales, respectively. Furthermore, when using the fluorescence parameterization calibrated by the measured leaf gas exchange and fluorescence data grouped by time period, the R\({}^{2}\) between the simulated and observed \(\text{SIF}_{760}\) improved by 1.41% and 1.00% at the half-hourly and daily scales.  
Overall, the simulated \(\text{SIF}_{760}\), GPP, and ET exhibited similar seasonal variations to the observations in 2019 (Figure 4). During the rapid growth stage, both GPP and ET showed rapid increases, although the simulated GPP was lower than the observations, while the simulated ET was higher. In summer, there were small biases for GPP and ET but a significant overestimation of \(\text{SIF}_{760}\). During the flowering and ripening growth vegetation stage, the vegetation wilted and entered a dormant state, and the simulations of \(\text{SIF}_{760}\), GPP, and ET exhibited similar decreasing patterns to the observations, although they were generally underestimated.  
Figure 3: Correlation between the observed canopy solar-induced chlorophyll fluorescence at 760 nm (\(\text{SIF}_{760\text{ obs}}\)) and the simulated canopy solar-induced chlorophyll fluorescence at 760 nm (\(\text{SIF}_{760\text{ sim}}\)) (**a1,b1**) using the fluorescence parameterization with a default setting, (**a2,b2**) calibrated with all the measured leaf gas exchange and chlorophyll fluorescence data, and (**a3,b3**) calibrated with data from June–August and August–November at (**a1–a3**) half-hourly and (**b1–b3**) daily time scales.  
Under tidal inundation, the simulations of SIF\({}_{760}\) and GPP were relatively accurate (R\({}^{2}\) = 0.53 and 0.87, respectively), though SIF\({}_{760}\), GPP, and ET were overestimated (Figure 5). During the early and rapid vegetation stage, the accuracy of the SIF\({}_{760}\) and GPP simulations decreased, with the accuracy of SIF\({}_{760}\) simulations further deteriorating during the peak vegetation stage. ET simulation was more accurate during these stages under tidal inundation. Notably, during the wilt vegetation stage, the simulations for GPP and ET both performed poorly.
## 4 Overcoming the Nyquist Limit for a Lens with Low F-Number
A second experiment was carried out to show the importance of using the optimized characterization curve in each case and to test the effect of the quantization error on the implementation of the lenses. The three different lenses were now displayed on the LCoS SLM by using a single calibration curve: the one corresponding to a linear \(6\pi\) phase variation (Figure 6e). In this situation, only the full 256 grey level range was available to encode the \(6\pi\) phase modulation of the third-order diffraction lens, whereas only parts of it were effectively used to encode the \(4\pi\) phase modulation of the second-order diffraction lens (166/256 grey levels) or the \(2\pi\) phase modulation of the first-order diffraction lens (just 66/256 grey levels). Figure 9 plots the profiles of the corresponding diffraction orders. They are all normalized to the maximum intensity peak shown in Figure 8a for the sake of comparison. In the analyzed situation, the most intense focalization peak was obtained for the third-order lens because its profile--with greater rings corresponding to a longer focal length and bigger facets at the periphery--could be encoded with the full grey level range. A slightly less intense peak (less than 5% of intensity reduction) was obtained for the second-order lens, mainly because it had a reduced number of phase levels (166 grey levels) to encode the \(4\pi\) phase modulation. The focalization peak obtained for the conventional \(2\pi\) lens had the lowest intensity among the profiles depicted in Figure 9a. The intensity reduction was around 15% compared to the third-order diffractive lens with full grey level range. The poor performance of the first-order lens can mainly be due to the combination of aliasing at the boundaries of the lens and the quantization error produced for the even more limited number of grey levels available. Figure 9: **(a)** Coincident focalization planes at \(f_{0}=80\) mm for first-, second-, and third-order diffraction lenses when the \(6\pi\) calibration curve (Figure 6e) is used in all the cases. Different diffraction orders, at different locations, for (**b**) the second-order diffraction lens with \(f=160\) mm and (**c**) the third-order diffraction lens with \(f=240\) mm. Figure 9b,c show the different diffraction orders obtained at different planes for both multiorder lenses. In this case, when both lenses were displayed on the LCoS screen with the same calibration curve, which corresponds to 6\(\pi\) phase modulation, they obtained very similar results. The results of Figures 8a and 9a lead us to remark the importance of using an optimized SLM calibration curve to display DOEs. For instance, the performance of both the modulo 4\(\pi\) lens with only 166 grey levels and the modulo 2\(\pi\) lens displayed with only 66 grey levels (Figure 9a) experienced an intensity reduction of about 25% with respect to the same lenses implemented with the full grey level range (Figure 8a).
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 2 Preliminaries
### Convergence Analysis
We can draw the conclusion that Zeno-behaviors are excluded for the multi-robot system before consensus is reached.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Indicator-Based Approach
#### 4.1.5 All Hazards Knowledge Framework (AHA)  
The primary motivation behind developing the AHA [41] was the need for a resilience tool capable of assessing the resilience of interdependent CI systems in an all-hazards context. The AHA is a framework that combines data and expert measures to calculate overall resilience. The AHA structure consists of three components: (1) facility-level dependency profiles; (2) dependency models; and (3) a text analysis system (TAS). Employing the  
Figure 4: Dashboard displaying values of Resilience Indicator components for a sample facility [40].  
Figure 3: The six levels in the hierarchical model [38].  
AHA tool enhances the decision-making experience by providing decision makers with the ability to comprehend critical dependencies, resilience, and the impacts of hazards on smart ICI.  
Figure 5 illustrates the high-level deployment of the AHA tool, which employs a top-down approach. The process begins with identifying the region to be assessed and the aid sector service providers for that region. In the figure, the aid sector is represented by the blue rectangles in the middle row, followed by their critical infrastructures, depicted as orange squares. A significant electric generation station could be an example of such a case. Dark blue rectangles display the most critical dependencies between them. They propose a holistic approach to resilience that incorporates all resilience components. The Equation (2) presents a resilience measurement approach based on the AHA:  
\[Res=f(aIR,bCR,cOR,dSR,ePR)|_{r}, \tag{2}\]  
where \(Res\) represents resilience; \(f\) denotes the function of; \(a,b,c,d,e\), are scaling constants ranging from 0 to 1, based on the risk type being analyzed; \(r\) signifies risk, concerning interdependencies that may influence all risk elements; and \(|_{r}\) is assessed at different levels of hazard. This equation asserts that resilience is a broad concept, formulated as a function of infrastructure resilience \((IR)\), community resilience \((CR)\), organizational resilience \((OR)\), social resilience \((SR)\), and personal resilience \((PR)\). The weighting of each of these elements varies based on factors that change (geography, sector, scope, incident type, and time). Risk is a function of threat, vulnerability, and consequence that must be examined as a part of resilience assessment.  
_Limitations:_ The primary drawback of this approach is that most models rely on judgmental data, which lacks accurate empirical information for determining resilience. Judgmental indicators result from expert questionnaires and the completion of indicators for each infrastructure. Indicators can only provide indications, not scientific proof or detailed explanations of change, partly because they are based on assumptions about system functionality, albeit informed assumptions.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Running Time** \\ \end{tabular} } & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{\
\begin{tabular}{c} **Positive** \\ **Predictive** \\ **Value (PPV)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Negative** \\ **Predictive** \\ **Value (NPV)** \\ \end{tabular} } \\  & & & & & **Value (PPV)** \\ \hline Template matching & 96.0\% & 3.20 s & 96.6\% & 94.8\% & 97.8\% & 91.9\% \\ BP neural network & 84.8\% & 5340 s & 74.9\% & 52\% & 89.7\% & 27.1\% \\ SVM & 78.2\% & 2.1 s & 89.5\% & 95.8\% & 98.7\% & 7.2\% \\ DBN & 77.4\% & 112.06 s & 100\% & 0 & 70.8\% & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of four classifiers. by template matching algorithm. Results were shown in the supplementary Figure S1. Furthermore, systemic study for identifying and classification of VPs is our future work.
## 3 Results and Discussion
### Study Goal
#### 3.2.2 Bombus
psucorum_ was investigated, and a system based on the time it takes for the maximum peak amplitude of a buzz to be achieved was proposed to classify arrival and departure. Peaks closer to the beginning of a buzz were indicative of an arrival, otherwise a departure was inferred; an overall accuracy around 96% was achieved. In addition to foraging activity detection, the authors of [87] also proposed the use of spectral contents of acoustics to predict pollination efficacy of the bumblebee (_B. Terrestris_) and wild bees in the field; similar results were found in [88] for _B. balteatus_ and _B. sylvicola_. The work in [89], in turn, relied on audio to identify the colony's circadian rhythm. One-second-length audio were recorded every 15 minutes for 9 days to investigate the existence of circadian rhythm. Using the root mean square (RMS) signal level as bee buzz or loudness, RMS growth in the early hours of the morning and repetitive cycles were noticed. Time range between 11 p.m. and 4 a.m. was concluded as the bees' deep night, as bees have repetitive behaviour during which colony activity decreases the most. As an attempt to complement this study, the authors in [96] investigated sound indices features to identify the bee day as a convenient time for bee colony characterization. They showed that by using temperature, time, and spectral entropy it is possible to find colony characteristics which are the hive-specific hours when the sound of a particular colony is most valuable. Using a temperature filtering step, they rejected recordings collected during weather conditionsthat are not favourable to bees, and by comparing each hive's spectral entropy daily trends, 11:00 a.m. to 4:00 p.m. for the fall and winter seasons were considered as a bee day. Lastly, in [90], the authors investigated the effect of the 2017 total solar eclipse, which caused a drop in sunlight by three orders of magnitude and temperature by 10-15 \({}^{\circ}\)C (21 August 2017, which traversed over 5000 km from coast to coast of North America). Hive acoustics were used to monitor flight patterns of _Apis mellifera, bombus, Megachile_, and _Halictus_ across the Pacific Coast, Rocky Mountain, and Midwest regions of the United States. It was found that buzzing rates changed as a function of light intensity (and not temperature drop), with the bees ceasing flying during periods of complete darkness.
## 3 Results and Discussion
### Future Research Perspectives and Hallenges  
Although a lot of studies have successfully examined the treatment efficiency of MWCNTs and CNT-based adsorbents, more studies should be focused on the following:  
1. Improving CNT filters, films, and sheets for real industrial wastewater purification on an experimental scale.
2. Creating purified and functionalized compounds of CNT in commercial amounts with little environmental impact at a reasonable price.
3. Producing CNTs with comparable adsorption capabilities through various techniques such as laser ablation, chemical vapor deposition, and arc discharge processes.
4. Predicting the adsorption mechanism and dye elimination capability from real industrial wastewater under a range of operating conditions in batch and column processes, as well as on a larger scale.
5. Examining the toxicity of CNTs to the environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Before** & **After** & **Standards for Cotton Textile** \\  & **Treatment** & **Treatment** & **Industries [66]** \\ \hline pH & 9.5 & 2–6 & 5.5–9.0 \\ \hline TSS (mg L\({}^{-1}\)) & 2050 & 110 & 100 \\ \hline TDS (mg L\({}^{-1}\)) & 3240 & 652 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of the actual wastewater before and after treatment, and standards for the safe disposal of prescribed dye effluents.
## 3 Results and Discussion
### Electrochemical Characterization of Catalytic Surface: DA-Sensor
After the DA immobilization, a comparison of the response obtained with the redox probe on a bare non-grafted SPCE (SPCE-GLU-DA) with that obtained on a grafted SPCE (SPCE-GRAFT-GLU-DA) revealed that the catalytic effect is lower, with a decrease in the anodic and cathodic peak currents of 68% and 27%, respectively. In addition, the signal of redox probe on SPCE-GLU-DA was lost in two days, while the SPCE-GRAFT-GLU-DA showed stable measurements up to three months, as the response only decreased a 7% after that time. These results suggest that the grafting step enhances electron transfer while providing long-term stability to the sensor, making the device robust for many future applications.
## 2 Numerical Methods
### Average (Body-Force Propeller) Model  
In the average model [37], the flow total velocities (\(u/U\), \(v/U\), and \(w/U\)) provided by the URANS solver were directly input into the inflow to calculate the lift and drag on each blade element as shown in Figure 1. The lift coefficient \(C_{L}\) was obtained by  
\[C_{L}=2\pi k_{1}\sin\alpha, \tag{5}\]  
where \(k_{1}\) is an empirical correction for finite blade width and \(\alpha\) is the AOA (angle of attack) against the zero-lift line. Drag coefficient is assumed to be constant:  
\[C_{D}=0.01. \tag{6}\]  
The local thrust \(dT\) and torque \(dQ\), at each grid point inside the propeller disk (cylindrical block) are calculated from the \(C_{L}\) and \(C_{D}\) on the corresponding blade element. Propeller pitch is considered to calculate \(\alpha\). Chord length is needed when computing \(dT\) and \(dQ\) from \(C_{L}\) and \(C_{D}\). Finally, the axial and tangential body forces (\(\psi_{x}\),\(fb_{\theta}\)), i.e., force per volume, are obtained as:  
\[fb_{x}=\frac{dT\cdot N}{\Delta x\cdot 2\pi r}, \tag{7}\]  
\[fb_{\theta}=\frac{dQ\cdot N}{\Delta x\cdot 2\pi r\cdot r}. \tag{8}\]  
As implied in Equation (7) of [37] and Equation (8), the model is based on the average effect of \(dT\) and \(dQ\) among \(N\) blades, propeller rotation perimeter \(2\pi r\) at certain radius \(r\), and thickness of the propeller actuator disk \(\Delta x\).  
The average effect, i.e., the quasi-steady assumption, is useful for ship applications because propellers rotate at high speeds in many scenarios such as self-propulsion in calm  
Figure 1: Blade element theory [37].  
water or waves and seakeeping and manoeuvring problems. The momentum equations in the URANS solver are solved along with those body forces as the source terms. The six components of total propeller forces and components are considered in the equations of motions in a 6 DOF solver. The flow field is solved in an earth-fixed coordinate, 6 DOF motions are solved in ship-fixed coordinate, and the propeller model computation is in the propeller-shaft coordinate. Thus, several coordinate transformations are required in the code.
# A Millimeter-Wave 4 th-Harmonic Schottky Diodes Mixer with Integrated Local Oscillator
## 2 Mixer Design
### Harmonic Balance  
Once the passive circuits had been optimized separately, the whole structure was simulated in Ansys HFSS for obtaining the linear electromagnetic behavior of the whole passive components (transitions and filters). Then the S-Parameters of the structure were imported to Keysight ADS software. In this software the non-linear behavior of the Schottky diodes is taken into account using the harmonic balance analysis, where the fundamental frequencies are RF and LO and we consider a maximum mixing order of 11. In this analysis the LO power has been swept from 3 to 12 dBm, in order to take into account the fact that the power generated by the integrated LO can be lower than the nominal value and also suffer losses. The RF signal input frequency has been swept from 298 GHz to 310 GHz, while keeping the IF fixed to 2 GHz. This implies that the LO frequency had to be adjusted accordingly, since \(f_{RF}=4\times f_{LO}+f_{IF}\).  
The minimum conversion loss, 10.5 dB, is achieved at 302 GHz when the LO power is 12 dBm. The 3-dB bandwidth for this power level is 9 GHz (between 298 and 307 GHz), which represents 3 %. The mean CL for the whole band is 13.3 dB. For lower power levels the performance worsens: both the CL and the DSB ENT (ENT) increase. In addition, the bandwidth where the ENT is low narrows. For 6 dBm LO power the mean conversion loss increases up to 16.7 dB. Similarly, the ENT increases from 3000 K for 12 dBm to 10,000 K for 6 dBm. If the local oscillator power is 3 dBm the diodes are under-pumped and the performance degrades. In this case the conversion losses and ENT are higher than 20 dB and 30,000 K, respectively. These results are shown in Figure 3, where they are compared with the experimental results.  
Figure 3: Comparison between the experimental performance of the fourth-harmonic mixer for an intermediate frequency, \(IF=2\) GHz, and its simulated performance for various LO powers. (**a**) DSB equivalent noise temperature and (**b**) conversion losses.