esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Program
### Test Setup  
To investigate the electrical characteristics and sensitivity of the NCSS, the experimental apparatus shown in Figure 2 was constructed. The NCSS was tightly bonded with an adhesive epoxy on top of a thick steel cantilever (300 mm \(\times\) 25 mm \(\times\) 2 mm). The center of all sensor samples was located 50 mm from the fixed end of the cantilever. We installed a pair of NCSSs on the cantilever to double-check the outputs. The sensor-installed cantilever was mounted on an optical table by fixtures. We manually bent the free end of cantilever at steps of 20 mm from \(-\)140 mm to 140 mm, and the deflection was measured with a laser sensor (KEYENCE Co., Seongnam-si, Korea, IL-300). When a displacement is applied to the free end of the cantilever, strain is generated on the sensor attached to the upper part of the cantilever. The induced strain changes into sensor resistance change due to its piezoresistive characteristics. The sensor resistance was measured by a multi-meter (KEYSIGHT technologies Co., Santa Rosa, CA, USA, 34465A) with the two-probes method. The induced strain was later calculated in terms of the deflection by strain and bending relation equation.  
Figure 1: Schematic diagram of the nanocarbon composite fabrication process. MWCNT: multi-walled carbon nanotube.
## Materials and Methods
### Meteorological and Eddy Covariance Measurements  
Eddy covariance measurements were performed with an eddy tower installed at the study site in July 2019. The tower is equipped with an open path CO\({}_{2}\)/H\({}_{2}\)O IRGA analyser with an integrated sonic anemometer (Irgason, Campbell Scientific Inc., Logan, UT, USA), enhanced barometer (PTB 110, Vaisala Inc, Vantaa, Finland) and autonomous air temperature probe. The measurement height is 17.5 m above ground, thus 4 m above mean tree height. The tower is also equipped with a full meteorological station, which includes a photosynthetically active radiation (PAR) sensor (SKP-215, Skye Instruments Ltd., Llandrinod Wells, UK), a pyranometer (SKS 1110, Skye Instruments Ltd., Llandrinod Wells, UK) for monitoring solar radiation (Rg), a temperature/humidity probe (HygroClip2 Advanced, Rotronic Inc., Bassersdorf, Switzerland) for recording air temperature and air relative humidity, a rain gauge (Rain-O-Matic Professional, Pronamic Inc., Skjern, Denmark) and a sensor for SWC measurements at 12 cm depth (CS655; Campbell Scientific Inc., Logan, UT, USA). Eddy data were sampled with a 10 Hz rate and meteorological data were stored with a 30 min time step. VPD was calculated by air temperature and air relative humidity, according to [PERSON] and [PERSON] [45].  
Raw eddy data were processed with the EddyPro 7 software (Licor Biosciences, Lincoln, NE, USA) with default settings, and half-hour fluxes were estimated. Outliers and spikes were detected and removed with the use of the double-differenced time series, using the median of absolute deviation about the median [46]. Gap filling was performed with the marginal distribution sampling (MDS) methodology after u* filtering, and flux partitioning was based on the night-time fluxes method [47]. These post-processing analyses were performed with the use of the REddyProc package [48] available with Rstudio software in R programming language. Full measurements were performed during the period August 2019-December 2021, and were used to assess the annual GPP and ET, as well as their seasonal variation at the study site. In order to determine the effect of environmental parameters on GPP and ET, we focused on the period from June to August (see below \"Ecophysiological and statistical analysis\").
## 1 Introduction  
Cryptographic functions [1; 2] are an integral part of cryptographic algorithms. The applications of these functions are observed in the round functions of the bock cipher, key generation algorithms, stream ciphers, message digest algorithms and many more [1]. The cryptographic functions must possess some of the important characteristics such as non linearity, symmetricity, resiliency, propagation criterion, immune to correlation etc. Moreover, the introduction to the pseudo random number generator (PRNG) helps to provide randomness in the cipher outputs to increase confusion-diffusion property in the algorithm. Such randomness of the function provides robustness to withstand with correlation attacks [3]. However, PRNGs have also faced different attacks in recent times [4]. Therefore, there is an urge of generating a new random function which can provide the robustness against the attacks of evaluating differential, linear or non-linear cryptanalysis process for understanding the plaintext bits or key bits. In this paper, an analysis is executed on the properties of Symmetric Random Function Generator(SRFG).  
Symmetric Boolean functions are very important for cryptography [5]. Their outputs posses special characteristics in cryptographic developments. Hamming distances of these outputs make use of proper correlation features as required. As the desirable properties of a cryptographic function is in objective, we have evaluated the propagation characteristics of the differentiations of SRFG outputs. Moreover, we have also analysed the immunity strength of SRFG: correlation immunity and algebraic immunity.  
The organization of the paper is coherences in several sections. Introductory problem statement has been already shown in Section 1. In the upcoming sections, Section 2 reviewsthe works in the direction of cryptographic functional properties. Section 3 shows the mathematical model of SRFG. Section 4 describes explains the propagation criterion of SRFG. Section 5 discusses about immunity of the functions. Section 6 analyses the results, shows the statistical interpretaion and validation and, Section 7 provides a logical conclusion.
## 5 Plume Navigation Model
### Female-Generated Pheromone Plume Dynamics  
The dynamics of the female-generated pheromone plume dispersing in turbulent wind is evaluated in MothPy [75] via the open source package PomPy ([PERSON] [76]), based on the model of [PERSON] et al. [46]. We implement a simplified version of its dynamics in our Plume Navigation Model. Here, a stationary female moth, located at the origin, emits a series of \(i\) pheromone puffs at time \(t_{i}\), where \(i=1,\ldots,n\). Each puff contains \(m_{i}\) pheromone, contributes to the overall plume as a spreading Gaussian centered at \(\mathbf{x}_{p}(t)=(x_{p}(t),y_{p}(t))\), and is characterized by a time-dependent diffusivity \(\sigma_{i}(t)=\sigma_{0}(t-t_{i})\) relative to the turbulent diffusivity. The \(\mathbf{x}_{p}(t)\) center of each Gaussian may change in time, as advected by the wind velocity field \(\mathbf{U}=(U,0)\). The overall plume concentration \(c(\mathbf{x},t)\) at position \(\mathbf{x}=(x,y)\) and at time \(t\) may thus be written as a superposition of diffusing terms originating from each of the \(i\) puffs as follows:  
\[c(\mathbf{x},t)=\sum_{i=1}^{n}H(t-t_{i})\frac{m_{i}}{2\pi\sigma_{i}(t)^{2}} \exp\left(-\frac{|\mathbf{x}-\mathbf{x}_{p}(t-t_{i})|^{2}}{2\sigma_{i}(t)^{2} }\right), \tag{21}\]  
where \(H\) is the heaviside function and \(\mathbf{x}_{p}(t)\) is advected by the wind velocity field so that, at each time step,  
\[\mathbf{x}_{p}(t+\Delta t)=\mathbf{x}_{p}(t)+(U,0)\Delta t. \tag{22}\]  
The various female calling strategies can be incorporated by selecting the proper \(t_{i}\) sequences as well as the amount of pheromone released per puff \(m_{i}\).
## 4 Results and Discussion
### Impact of Dataset Balancing on Results  
The three types of traffic attacks, Probe, R2L, and U2R, in the NSL-KDD dataset accounted for less than 10% of the samples, while the Normal class made up over 50%, as shown in Table 4. To address the data imbalance, this paper applied the ADASYN upsampling algorithm to balance the dataset, generating 384,804 synthetic samples. After the balancing operation, the number of minority classes R2L and U2R was increased to match the majority class, as shown in Table 7.  
After balancing the data using ADASYN, the model's precision, recall, and F1-Score improved by 14.03%, 5.28%, and 10.79%, respectively, while accuracy decreased by 1.67%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Testing Time(s)** \\ \hline Original & 0.94195 & 0.85528 & 0.92502 & 0.88007 & 11.6 \\ \hline PCA & 0.93248 & 0.90731 & 0.93431 & 0.91545 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of indicators before and after the use of PCA in the model.  
Figure 8: PCA feature selection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Quantity Before Generation** & **Quantity After Generation** \\ \hline Normal & 76,967 & 77,079 \\ Intrusion & 70,921 & 307,725 \\ - Dos & 52,987 & 76,984 \\ - Probe & 13,935 & 76,969 \\ - R2L & 3880 & 76,967 \\ - U2R & 119 & 76,805 \\ Total & 147,888 & 384,804 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of counts before and after the use of ADASYN.  
This decrease was due to the increase in the number of minority classes after balancing the dataset, which led to a reduction in model accuracy and an increase in test time. The experimental results are shown in Table 8. Using the original NSL-KDD dataset, the proposed model achieved higher accuracy in recognizing DoS attacks and Normal traffic compared with after balancing with ADASYN. This was because, before balancing the dataset, the weights for DoS and Normal classes were higher, making the model's classification easier. After balancing the dataset with the ADASYN algorithm, the number of minority classes U2R and U2L increased. The precision, recall, and F1-Score of U2R improved by 235.89%, 41.06%, and 138.62%, respectively, while the precision, recall, and F1-Score of U2L increased by 13.62%, 1.96%, and 7.25%, respectively. The radar chart is shown in Figure 9.
## 1 Introduction
These studies, however, use a customer outage cost model that was prepared for HL-I, which is not appropriate for outage cost estimation associated with transmission-related outages. Customer interruptions due to transmission-related outages are not confined to the peak load period as in HL-I but can occur at other times with a relatively high probability depending on weather patterns and loading conditions of individual lines at different times of the day and season. It should further be noted that the cost of a power outage can vary widely at different times of the day and season. For example, the power interruption cost to a commercial customer during business hours is much higher than that outside of business hours. Also, the cost of a power interruption to a residential customer is generally low during a mild spring evening when compared to a hot summer afternoon. The outage cost model for HL-II can therefore be very different from the model obtained for HL-I. Ref. [37] provides estimates of customer damage functions that can be applied to calculate interruption costs per event by season, time of day, day of week for various electricity customers in the United States. However, in [37], the outage cost was normalized by the average demand and unable to obtain a demand-normalized cost of power interruptions for a range of possible load demands in the study period. Ref. [38] proposes a VBRI methodology for transmission expansion planning by using customer-surveyed outage cost data and historical annual load profiles divided into the summer period, winter period, and off-peak period. This study lacks the consideration of diurnal load variation, as power outage costs at different times of the day can vary considerably as mentioned above. It is therefore important to recognize the impact of transmission system-originated failures on customer interruptions, to estimate the frequency and duration of their occurrence at different times of the day, season, and year, and to develop a VBRI methodology to incorporate them in a bulk system reliability model. This paper presents a methodology to develop a composite customer damage function (CCDF) for VBRI assessment of a bulk electric power system that considers customer interruptions arising from transmission system-originated failures. The proposed method incorporates the probability of a power outage occurrence at different times of the day and year and the impact of the time of occurrence and duration of the outage on the customer interruption cost.
## 5 Results and Discussion
The simulation results reveal that the BER's WDM/PAPM-DPPM is better than that of OOK-NRZ by approximately 10 dB-11 dB of \(10^{-12}\). Table 3 provides the performance comparison of the proposed hybrid M-ary-PAPM M-DPPM-based DWDM FSO optical fiber network link with contemporary literature for the references [2; 5; 5; 9; 15; 42]. The numerical results show a highly efficient M-ary-PAPM M-DPPM-based DWDM FSO has been designed by incorporating a DWDM-PON optical fiber network. The results of the numerical investigation demonstrate that the proposed link performance improves on increasing the increased receiver sensitivity, capacity, and efficiency under the atmospheric turbulence effects. We illustrate a comparative investigation of the proposed M-ary DPPM-M-PAPM-based DWDM-FSO optical fiber communication systems performance with contemporary literature and show that the proposed link performs better for maximum range, efficiency, and channel capacity. We reduce the ASE noise, AT channels, and (ICC for the DWDM-FSO optical fiber communication systems. Table 4 shows the performance comparison of the proposed M-ary DPPM-M-PAPM based-DWDM-FSO optical fiber link with reference [50]. The proposed hybrid M-ary-PAPM M-DPPM provides an improvement of 4-8 dB at a BER of \(10^{-12}\) (OSNR) [proposed work] while in Ref. [50] it is 1 and 2 dB at a BER of \(10^{-6}\). \begin{table}
\begin{tabular}{c c c c} \hline
**Modulation Scheme** & **Bits per Symbol** & **Bandwidth Requirement** & **Spectral Efficiency** \\ \hline
**MPPM** & \(\left|\log_{2}\left(\begin{array}{c}M\\ n\end{array}\right)\right|\) & \(\frac{MR_{k}}{\left|\log_{2}\left(\begin{array}{c}M\\ n\end{array}\right)\right|}\) & \(\frac{\left|\log_{2}\left(\begin{array}{c}M\\ n\end{array}\right)\right|}{M}\) \\ \hline
**M-ary-PAPM** & \(\left|\log_{2}k^{n}\left(\begin{array}{c}M\\ n\end{array}\right)\right|\) & \(\frac{\left|\log_{2}k^{n}\left(\begin{array}{c}M\\ n\end{array}\right)\right|}{M}\) \\ \hline
**Hybrid M-ary PAPM-** & \(\left|\log_{2}k^{n}\sum_{i=1}^{n}\left(\begin{array}{c}M\\ i\end{array}\right)\right|\) & \(\frac{\left|\log_{2}k^{n}\sum_{i=1}^{n}\left(\begin{array}{c}M\\ i\end{array}\right)\right|}{M}\) & \(\frac{\left|\log_{2}k^{n}\sum_{i=1}^{n}\left(\begin{array}{c}M\\ i\end{array}\right)\right|}{M}\) \\ \hline \end{tabular}
\end{table} 
Table 2: The proposed M-ary DPPM-PAPM based modulation schemes DWDM-FSO optical fiber communication network system [2; 50; 9; 17; 42; 5]. Figure 9: BER vs. AOP at RCL input (dBm) for M-ary DPPM and OOK using D\({}_{\text{RX}}\) = 25 mm, G = 30 dB, M = 5, and \(l_{f_{50}}\) = 4000 m for NT with **(a)** WT, **(b)** MT, and **(c)** ST.
# Design of Power Splitters Based on Hybrid Plasmonic Waveguides
## 2 Materials and Methods  
The hybrid mode in the single HPW was discussed in detail in ref. [3]. Here, we considered a coupled system composed of five HPWs, shown in Figure 1. The HPW consists of a high-permittivity semiconductor nanowire embedded in a low-permittivity dielectric near a metal surface [3]. The semiconductor nanowire was GaAs, of which the relative permittivity is 12.25. The low-permittivity dielectric was SiO\({}_{2}\), with relative permittivity 2.25. The relative permittivity of Ag was taken to be the measured value of \(\varepsilon=-129+3.3j\) at the telecommunications wavelength \(\lambda=1550\) nm [16]. The diameter of the GaAs nanowire was d = 200 nm and the dielectric gap was h = 50 nm. The distance between adjacent waveguides was S = 500 nm. The plasmonic mode supported by a single HPW is shown in Figure 1c, which was consistent with the results of ref. [3].  
We numerically simulated the propagation behavior of SPPs in five HPWs by the 3D FDTD method. The grid size was \(\mathrm{dx}=\mathrm{dz}=10\) nm and the time increment was \(\Delta\mathrm{t}=\Delta\mathrm{s}/2\mathrm{c}\). A perfectly matching layer was adopted to absorb the outgoing electromagnetic wave around the computational domain.
## 2 Machine Structure and Force Operation Principle
### Operation Principle  
To take advantage of the flux-modulation effect of the Vernier machine, the number of winding pole pairs \(P_{w}\), the rotor pole pairs \(P_{r}\), and the stator slot \(Z_{s}\) should satisfy  
\[P_{w}=|Z_{s}-P_{r}| \tag{1}\]  
The MMF produced by the PMs can be expressed as:  
\[F_{PMs}(\theta,t)=\sum_{\alpha=1,2,3\cdots}F_{am}cos(\alpha P_{r}(\theta+\phi_{ r}-\Omega_{r}t)) \tag{2}\]  
where \(F_{am}\) is the magnitude of MMF produced by the rotor-PMs; \(\theta\) is the mechanical angular position; \(\phi_{r}\) is the initial phase of the rotor.  
The rotational angular velocity of the rotor \(\Omega_{r}\) can be expressed as:  
\[\Omega_{r}=\frac{2\pi f_{E}}{P_{r}} \tag{3}\]  
Figure 2: Connection of AC armature winding. (**a**) FP winding (**b**) SP winding.  
Using Fourier decomposition, the average and fundamental components of the permeance can be expressed as:  
\[\Lambda(\theta)=\frac{\Lambda_{0m}}{2}+\sum_{\beta=1,2,3\cdots}\Lambda_{\beta m} cos(\beta Z_{s}(\theta+\phi_{s})) \tag{4}\]  
\(\phi_{s}\) is the initial phase of the stator. From (2) and (4), the air gap flux density can be expressed as:  
\[B=F_{PMs}(\theta,t)\Lambda(\theta)\] \[= \sum_{\alpha=1}^{+\infty}\sum_{\beta=-\infty}^{+\infty}\frac{F_{ em}\Lambda_{|\beta|m}}{2}cos(\alpha P_{r}(\theta+\phi_{r}-\Omega_{r}t)+\beta Z_{s}(\theta\] \[+ \phi_{s})\]  
\[\left\{\begin{matrix}&B_{(\alpha,\beta)}=\frac{F_{em}\Lambda_{|\beta|m}}{2}\\ P_{(\alpha,\beta)}=|\alpha P_{r}+\beta Z_{s}|,\alpha=1,2,3\cdots,\beta=0,\pm 1,\pm 2,\pm 3\cdots\\ &\Omega_{(\alpha,\beta)}=\frac{\alpha P_{r}\Omega_{r}}{(\alpha P_{r}+\beta Z _{s})}\end{matrix}\right.\]  
where \(B_{(\alpha,\beta)}\) is the amplitude of the modulated flux, \(P_{(\alpha,\beta)}\) is the harmonic order, and \(\Omega_{(\alpha,\beta)}\) is the rotation speed.  
The working harmonic corresponds to the case when \(\alpha=1\). Therefore if the higher-order harmonics are ignored, (5) can be rewritten as:  
\[B =F_{PMs}(\theta,t)\Lambda(\theta)\] \[\approx B_{(1,0)}cos(P_{r}(\theta+\phi_{r}-\Omega_{r}t))+B_{(1,-1)}cos(P_{ r}(\theta+\phi_{r}-\Omega_{r}t)-Z_{s}(\theta\] \[+ \phi_{s}))+B_{(1,-1)}cos(P_{r}(\theta+\phi_{r}-\Omega_{r}t)+Z_{s}( \theta+\phi_{s}))\]  
Due to \(\frac{P_{r}}{Z_{s}+P_{r}}\) is very small, so in the case of \((\alpha=1\), \(\beta=1)\) often can be ignored.
## 1 Introduction
That is why mid or ex situ doping was proposed by some researchers [6; 16]. For example, [PERSON] et al. [16] doped Cr into the surface of as-prepared \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorods through a spin coating method so that the original nanorod structure was not damaged. Considering Co and Ni are in the same group and the same period as Fe (i.e., VIII group and the 4 th period) in the periodic table of elements, and the three elements have similar chemistry, we hypothesize that the in situ doping of Co or Ni into \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) may have little influence on the structure of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). The Co or Ni doping into \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) has been reported in the literature [25; 36; 37; 38]. [PERSON] et al. [36] synthesized a three-dimensional branched Co-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod/MgFe\({}_{2}\)O\({}_{4}\) heterojunction array by hydrothermal deposition. Their results suggested that the Co doping can effectively enhanced the PEC activity of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) via extending the light-absorbing region and accelerating the charge carrier separation efficiency. [PERSON] et al. [37] prepared Co-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanocrystalline via a hydrothermal method and characterized the sample by various techniques. The Co-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) showed a pure \(\alpha\)-Fe\({}_{2}\)O3 crystal phase without any impurity phases. The increase in the Co concentration could effectively increase the agglomeration of the particles. But the PEC performance of the Co-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) was not investigated by them. [PERSON] et al. [38] improved both the photocurrent and the onset potential of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod photoanodes by in situ Co and ex situ Sn co-doping. The Sn doping mainly contributed to the increased carrier density, while Co doping mostly improved the surface kinetics of oxygen evolution reaction on the Fe\({}_{2}\)O\({}_{3}\) nanorods. [PERSON] et al. [25] prepared Ni-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles by the chemical co-precipitation method and examined the photocatalytic activity for the degradation of model organic pollutants. Their results suggested that the adding of Ni didn't change the crystal phase but decreased the size of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles. The incorporation of 8 mol\(\%\) Ni into the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) decreased the band gap from 2.02 eV to 1.81 eV. In this study, in situ Co or Ni doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod arrays will be prepared on a conductive substrate through aqueous chemical growth followed by annealing under air. The impacts of the Co or Ni doping on the crystalline phase, the morphology, the light absorption and the PEC activity of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod arrays will be investigated.