esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 3 PMF-AiP Coupling  
RF-over polymer applications require coupling between the transceiver mm-wave IC (MMIC) and the PMF. Multiple solutions have been proposed in [6; 7; 8; 9; 10], such as MMIC couplers and on-chip antennas. A promising solution appears to be the antenna-in-package (AiP) [16], which is further investigated in this work.  
Our proposed design provides an in-package solution for the IC-to-PMF coupling problem that does not suffer from electrical/mechanical performance issues, such as PMF displacement, while it is simple and cost-effective. For the implementation of this solution, the embedded wafer level ball grid array (eWLB) advanced packaging technology [28] is employed: a variation of a two-dimensional Vivaldi antenna is designed in the fan-out area of the eWLB, and the antenna is used to wirelessly couple the (packaged) IC to the PMF. The antenna was selected and designed to provide lateral radiation, so, instead of using a perpendicular configuration of the IC-PMF pair, a neat coplanar solution is provided. The solution is very simple and highly integrated: the IC readily connects to the antenna, taking advantage of the eWLB construction, so the coupling is merely achieved by the packaged IC, without the need for any extra interface. The implementation of the IC-to-PMF coupling is achieved without any extra cost and without compromising the performance.  
The proposed structure implies the use of an antenna with high lateral directivity to ensure minimal coupling loss between the IC and the PMF. Although the term \"antenna\" implies a far-field analysis, it is important to mention that the proposed structure utilizes an antenna in near-field operation. As a result, optimization with the presence of the PMF is needed. The lateral antenna coupling offers low coupling losses and higher antenna-PMF  
Figure 7: Simulation results in terms of the attenuation of the rectangular cross-section PMF with and without a metal coating.  
alignment tolerance in comparison to perpendicular coupling. In addition, by utilizing a lateral coupling PMF, bending losses are fully avoided as no bending is required to turn the PMF to the correct direction for the application.
# Algorithms for Calculating Generalized Trigonometric Functions
## 7 Generalization of Hyperbolic \(Ateb\)-Functions to the Quantum Calculus  
At the beginning, we present all definitions from \(q\)-analysis, which we need for future constructions. There, we introduced \(q\)-analysis, where the \(q\)-derivative is defined by the following formula [12]  
\[D_{q}f(x)=(f(qx)-f(x))/(qx-x). \tag{44}\]  
The \(q\)-analog for a real number, also called the \(q\)-bracket or \(q\)-number of \(b\), is defined by the following formula [28]  
\[[b]=(q^{b}-1)/(q-1). \tag{45}\]  
The \(q\)-analog of the definite integral on a closed interval \([0;a]\) is defined by the following formula [29]  
\[\int_{0}^{a}f(x)d_{q}x=(q-1)a\sum_{i=0}^{\infty}q^{i}f(q^{i}a). \tag{46}\]  
The analog for the Gamma-function \(\Gamma_{q}\)-function is constructed as  
\[\Gamma_{q}(t)=\int_{0}^{[\infty]}x^{t-1}E_{q}^{-qx}d_{q}x, \tag{47}\]  
where \(E_{q}^{z}=\prod_{i=0}^{\infty}(1+(1-q)q^{i}z)\). An incomplete \(B_{q}-\)function is presented by the following formula  
\[B_{q}(p,s,\omega)=\int_{0}^{\omega}x^{p-1}(1-qx)_{q}^{s-1}d_{q}x. \tag{48}\]  
Let us construct the \(q-\)generalization for hyperbolic \(q\)-_Ateb_-functions. The generalization for the quantum calculus in a periodic case is presented in [30]. In conditions (6), we will have an aperiodic (hyperbolic) case. Let us introduce the expression  
\[\omega=\frac{n+1}{2}\int_{0}^{0\leq V\leq\infty}(1+\overline{V}^{n+1})_{q}^{- \frac{m}{m+1}}d_{q}\overline{V}. \tag{49}\]  
If we consider the inverse dependency \(V\) from \(\omega\), where conditions (6) are satisfied, we obtain the \(q\)-analog of hyperbolic \(Ateb\)-sine and we propose the following notation  
\[V=sha_{q}(n,m,\omega). \tag{50}\]Let us introduce the following expression  
\[\omega\prime=-\frac{m+1}{2}\int_{1}^{1\leq U\leq\infty}(\overline{U}^{m+1}-1)_{q }^{-\frac{n}{n+1}}d_{q}\overline{U}. \tag{51}\]  
In conditions (6), Formula (51) presents the inverse dependency \(u\) from \(\omega\prime\); it is named \(q\)-analog hyperbolic _Ateb_-cosine, and it is denoted as  
\[U=cha_{q}(m,n,\omega\prime). \tag{52}\]  
It is clear that if \(q\to 1,m=1\), and \(n=1\) in expressions (49)-(52), we obtain in the limit the usual hyperbolic functions. They are clearly the properties below, which follow from the definition of the \(q\)-analog of _Ateb_-functions.  
\[sha_{q}(n,m,0)=0;cha_{q}(m,n,0)=1. \tag{53}\]  
Extending the functions to the case of quantum calculus will provide new applications of these functions for solving future mathematical modeling problems.
## 2 Materials and Methods
### Nonlinear Parameters Calculation  
The study used the linear parameter of CoP path length and three nonlinear measures, sample entropy (SampEn), fractal dimension (FD), and Lyapunov exponent (LyE), to assess CoP dynamics. CoP time series data were exported from the AMTI plate, and nonlinear coefficients were counted, using MATLAB software (MathWorks, Natick, MA, USA), separately for mediolateral (ML) and anterior-posterior (AP) directions, according to the rules described below. The data for the 30 s had 3000 points in each direction.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 6 Performance Analysis
### Packet Loss Ratio  
Packet loss ratio (\(PR\)) is defined as the percentage of dropped packets in total sent packets. The equation to compute \(PR\) is depicted as follows:  
\[PR=\frac{1}{N}\sum_{i=1}^{N}\frac{D_{i}}{R_{i}} \tag{2}\]  
where \(N\) refers to the number of vehicles within the communication range of RSU, \(D_{i}\) represents the number of data packets dropped, and \(R_{i}\) is the total number of packets sent.  
Figure 10 shows the relationship between \(PR\) and the number of vehicles within the RSU. In V2I authentication, due to the limitation of network bandwidth, with the increase in vehicles, the signal-to-noise ratio (SNR) is decreasing. At the same time, when a large number of vehicles send messages to the RSU, a channel congestion issue has to be faced, which results in \(PR\) increasing. EAAP, TAAP, and LIAP require vehicles and RSUs to send request/response packets, which causes a longer transmission delay and a higher \(PR\) compared with PAS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Hardware platform & CPU: 2.6 GHz Intel(R) Core(TM) i7-6700 HQ, 2 GB RAM \\ Operating system & Debian 9.4 \\ Traffic generator & SUMO \\ Network simulator & OMNET++ \\ Simulator & veins \\ Simulation area & 2000 \(\times\) 2000 (m\({}^{2}\)) \\ Data Transmission Rate & 6 Mbps \\ Transmission Power & 20 mW \\ Simulation time & 500 s \\ Number of cars & 20–200 \\ Cryptography library & The Pairing-Based Cryptography (PBC) Library, Crypto++ Library \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.
## 2 Preliminaries and Background  
### SWaT--Architecture  
SWaT (Secure Water Treatment) is a testbed used for education, training, and research. It is one of the testbeds used during CISS2021-OL. Details of SWaT architecture and the treatment processes used are in [4]; a summary follows. SWaT consists of six interconnected stages labeled stage 1 through stage 6. Each stage consists of a set of sensors and actuators controlled by a Programmable Logic Controller (PLC). In stage 1, the incoming water is stored in a raw water tank T101. Water from T101 is transferred via a chemical dosing station in stage 2 to an Ultra-filtration (UF) unit in stage 3 for the removal of undesirable materials. Using UF feed pumps, the water is passed from stage 3 to stage 4 for the removal of excess chlorine through an Ultraviolet (UV) de-chlorination process. In the next filtration step, the inorganic impurities are removed from the decchlorinated water using a two-stage Reverse Osmosis (RO) process in stage 5. The treated water is stored in stage 6 for distribution, or it can be recycled.
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model
### Stochastic Model  
The most traditional and frequently used elevation-dependent weighting model is adopted, which means that the observation of higher elevation satellite tends to have a larger weight and the highest satellite is viewed as the pivot satellite. In this case, the weight of non-differenced observation is defined by  
\[q=1/\sin^{2}\theta \tag{10}\]  
Assuming that \(q_{r}=\left[q_{1r}\;q_{2r}\;\cdots\;q_{mr}\right]^{T}\) and \(q_{b}=\left[q_{1b}\;q_{2b}\;\cdots\;q_{mb}\right]^{T}\) are the \(m\times 1\) vectors consisting of all satellites' weights of the rover and base stations excluding the pivot satellite; \(q_{rp}\) and \(q_{bp}\) are the weights of the pivot satellite with respect to the rover and base stations. Subsequently, the cofactor matrix of single-system DD observation can be expressed as  
\[Q=\left(q_{r}+q_{b}\right)*\left[1\right]_{m}+\left(q_{rp}+q_{bp}\right)*I \tag{11}\]  
where \(\left[1\right]_{m}\) denotes a \(m\times m\) matrix full of 1 and \(I\) denotes a unit matrix. Afterwards, for a single GPS, the variance-covariance matrix can be derived as:  
\[D^{G}=\begin{bmatrix}\lambda_{pj}^{2}Q^{G}&I_{0}\\ I_{0}&\lambda_{\phi j}^{2}Q^{G}\end{bmatrix} \tag{12}\]  
where \(\lambda_{pj}\) and \(\lambda_{\phi j}\) denote the code and phase standard deviation with respect to GPS frequency j, respectively; \(I_{0}\) denotes a 0 square matrix and has the same dimension as \(Q^{G}\). Subsequently, for GPS/Galileo LCM, the variance-covariance matrix can be transformed into [31]:  
\[D^{G/E}=\left[\begin{array}{cccc}\lambda_{pj}^{2}Q^{G}&I_{0}&I_{0}&I_{0}\\ I_{0}&\lambda_{pj}^{2}Q^{E}&I_{0}&I_{0}\\ I_{0}&I_{0}&\lambda_{\phi j}^{2}Q^{G}&I_{0}\\ I_{0}&I_{0}&I_{0}&\lambda_{\phi j}^{2}Q^{E}\end{array}\right] \tag{13}\]For GPS/Galileo TCM with/without ISBs, the variance-covariance can be unified to  
\[D^{GE}=\begin{bmatrix}\lambda_{P}^{2}Q^{GE}&I_{0}\\ I_{0}&\lambda_{\phi j}^{2}Q^{GE}\end{bmatrix} \tag{14}\]  
where the dimension of \(D^{GE}\) is two less than that of \(D^{G/E}\).
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 2 Dynamic Energy-Reliability Pricing-Based Transaction Mechanism
### ERT Operation Procedure  
Figure 2 shows the operation sequence of the proposed ERT mechanism.  
**(Step 1. ERT announcement)**: First, a utility or retailer announces the ERT, \(P_{ERT}\), which consists of energy prices and corresponding reliability bands. Basically, the energy price would similarly change over time but should be differently rewarded depending on the quality of a prosumer's resources.
**(Step 2. Reliability forecasting and band selection)**: Under the ERT, prosumers should forecast their energy consumption or production, \(\hat{E}=\{\hat{e}_{1},\cdots,\hat{e}_{t},\cdots\hat{e}_{T}\}\); this is an essential contribution from the prosumer. In addition, they need to forecast the uncertainty of their energy forecast \(\hat{\gamma}=\{\hat{\gamma}_{1},\cdots,\hat{\gamma}_{t},\cdots\hat{\gamma}_{T}\}\). When a prosumer understands their own resource characteristics, they can select the most appropriate reliability band or strategically change their operation plans to maximize their expected benefit by deducing potential risks, as follows:  
\[\beta_{i^{\prime},t}=\underset{i\in\text{B}}{\text{argmax}}_{i\in\text{B}}( \beta_{i,t},\hat{\gamma}_{t})p_{i}\hat{e}_{t},\quad\forall t\in T. \tag{4}\]  
Prosumers can even decide to invest in additional infrastructures, such as EES, to increase their forecast accuracy and to reduce their uncertainty. Once a prosumer forecasts their energy usage and uncertainty considering their resource characteristics and operation plan, the forecast and selected uncertainty level-based band are sent to the utility.
**(Step 3. Settlement and billing)**: The utility aggregates the individual prosumer forecasts and commitments, incorporating this information into its own forecasts and grid operation plans. The basic assumption here is that, by obtaining prosumers' forecast and band selection information, a utility can increase its own forecasting accuracy regarding both amount and uncertainty. Even if the utility's forecast based on this information includes errors, it can  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Communication** & **Pricing** \\ \hline Conventional pricing & One-way & \
\begin{tabular}{c} One-dimensional \\ (Time) \\ \end{tabular} \\ \hline Proposed ERT & Two-way &
\begin{tabular}{c} Two-dimensional \\ (Time and reliability) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the conventional pricing mechanism and the proposed dynamic energy-reliability pricing-based transaction mechanism (ERT).  
Figure 2: The ERT operations sequence.  
minimize its financial risk by penalizing prosumers for inaccurate forecasting. Based on a prosumers' band price selection, the utility or prosumer can perform settlement based on the prosumer's energy usage. Then, billing occurs as usual,  
\[\text{Energy bill}=\sum_{t\in T}\alpha_{t}(\beta_{t^{*},t},\gamma_{t})p_{t}e_{t}. \tag{5}\]
## 2 Description of the Numerical Tools
### The Hybrid Solver
#### 2.4.2. Communicating the Presence of \(S_{b}\) to the Lagrangian Solver  
The PM grid that the Lagrangian solver is using, does not respect the solid boundaries and therefore the presence of \(S_{B}\) is missing. For a Lagrangian flow description based on the \(\theta-\bar{\omega}\) formulation, flow perturbations are represented by modifications in the dilatation and vorticity fields. Assuming that the Eulerian solution in \(D_{E}\) is correct if the solver is given appropriate conditions at \(S_{E}\), then the modifications in vorticity and dilatation due to \(S_{B}\) are already contained in the Eulerian solution. Therefore, two steps are taken that define the coupling procedure between the Eulerian and Lagrangian parts of HoPFlow: (a) transform the Eulerian solution into compressible particles and (b) replace the Lagrangian particles contained in \(D_{E}\) by those produced in step (a) (Figure 3). As the Eulerian solver is cell centered and does not use as primary quantities dilation and vorticity, step (a) includes: (i) use of Green-Gauss formula in order to obtain \(\theta\) and \(\bar{\omega}\) at the grid nodes, (ii) interpolation of \(\rho\) and \(p\) from cell centers at the Eulerian grid notes, and (iii) interpolation of the flow information from the Eulerian grid nodes at regular positions within every cell (in the schematic, there are four particles per cell). It is important to have good spatial density of particles, and so more than one Eulerian particles are defined per cell. Along with space particles, surfaces ones are also generated in order to obtain the correct solution near solid boundaries(Figure 3 right). The key difference is that surface intensities are multiplied by the corresponding surface in order to obtain the particle intensities. Next, space and surface particles are both projected to the PM grid in order to obtain the Lagrangian solution.  
Figure 2: The Eulerian domain \(D_{E}\) contains the solid boundary \(S_{B}\). The Lagrangian domain \(D_{\infty}\) includes \(D_{PM}\) and and \(D_{E}\).
## 4 Characterization of Grassland Ecosystem Vulnerability Studies
### Watershed Scale  
At the watershed scale, [PERSON] et al. used remote sensing and GIS techniques with a hierarchical analysis-principal component entropy method model to select indicators such as elevation, slope, and land use type to evaluate the ecological and environmental vulnerability of the middle and upper reaches of the Yalong River during 2000-2018. Additionally, they invoked the CA-Markov model to predict the ecological and environmental vulnerability of the region, thus revealing the dynamic change pattern and future development of ecological and environmental vulnerability in the region, and provided a theoretical basis for the formulation of ecological and environmental protection measures in the region [92]. [PERSON] et al. analyzed the changes in land use and cover in the upper reaches of Minjiang River from 2000 to 2010 and the changes in ecological sensitivity and fragility using NPP characterization, showing that the overall vulnerability/sensitivity of the upper watershed area is low, and the ecological vulnerability/ecological sensitivity is correlated with environmental factors (precipitation, drought index, etc.) [93]. This indicates that future studies of ecosystem vulnerability/sensitivity should pay more attention to the effects of climate change and human activities. For example, [PERSON] et al. selected indicators reflecting regional characteristics (vegetation, hydrology, climate, topography, soil, human activities, etc.), used co-linear diagnostic analysis to construct an ecological vulnerability evaluation system for the Amu Darya River basin, and used an improved entropy weighting method integrating AHP and entropy weighting to determine indicator weights, which in turn quantified the ecological vulnerability and analyzed the spatial and temporal characteristics, with a deteriorating trend of the watershed ecological environment [94].  
In summary, grassland ecosystem vulnerability studies at different spatial scales are based on different technical means and methods to evaluate the current state of fragility of grassland ecosystems, to predict ecological risks and strategies to cope with them, and to reduce ecological vulnerability. Meanwhile, climate change and human activities have been the inevitable background and causal factors considered in ecosystem vulnerability studies to enhance ecosystem stability and enhance ecosystem services for the purpose of enhancing human well-being.
## 4 Experimental Results  
The experimental framework of this study was inspired by the research of [PERSON] et al. [8]. In their study, [PERSON] et al. [8] employed the ResNet18 model and log mel-spectrogram to classify four emotions--happiness, neutrality, anger, and sadness--using a self-constructed Korean speech emotion database containing 1200 samples. In contrast, this study utilized a public dataset, AIHub DB, comprising 11,392 samples, to classify the same four emotions under similar conditions. While [PERSON] et al. [8] reported an emotion classification accuracy of 96.06%, applying the same ResNet18 model and log mel-spectrogram to the AIHub DB in this study resulted in an accuracy of 56.96%. This significant performance gap of 39.1% is attributed to differences in data scale, quality, and database characteristics. To address these issues, this study employed data augmentation techniques to mitigate the overfitting problem caused by insufficient data and to improve model performance. The primary objective was to enhance the emotion classification accuracy from 56.96% to over 90%.  
To achieve this objective, experiments were conducted to classify emotions into four categories using CNN models. The experiments employed five-fold cross-validation, and the results were reported in terms of both means and standard deviations to ensure statistical reliability. A total of 11,392 samples were utilized, with 2798 samples allocated to each of the four emotions.
## 4 Experiments and Results
### Imperceptibility Analysis
#### 4.1.1 Qualitative Analysis  
We present a comparison of the details of the BIM experimental data before and after watermark embedding in Table 2.  
Subjective visual observation shows no significant difference between the architecture and structure before and after watermark embedding, indicating that the embedding of the digital watermark does not affect the visual quality of the original data. Additionally, the watermarking algorithm presented in this paper has good imperceptibility in terms of invisible character embedding, as they cannot be observed in the graphical element attribute fields after opening the software interface.  
\begin{table}
\begin{tabular}{c c c} \hline
**BIM Data** & **Size (MB)** & **Number of Elements** \\ \hline Office building & 13.65 & 5841 \\ Gymnasium & 17.36 & 848 \\ Structural model & 5.54 & 844 \\ \hline \end{tabular}
\end{table}
Table 1: The basic information of BIM experimental data.  
Figure 6: Experimental models. (**a**) Office building. (**b**) Gymnasium. (**c**) Structural model.
## 4 Experiment and Results
### Q-Network Algorithm
#### 4.2.1 Programming Environment  
The whole program is written in Python 2.7 with a Python Application Programming Interface (API) developed by Aldebaran robotics to communicate with the NAOqi framework.  
We designed and trained the artificial neural networks with the Python library Keras; this is a high-level neural network API, written in Python, and is capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation (retrieved from [[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/))).  
The library contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers, and a lot of tools to make working with images and text data easier. From these, we selected and loaded only the required modules in order to have an efficient use of memory.  
Each time a new action is taken, the reward that this action yields must be evaluated and the model is trained again based on this information. Because of this, Graphical Processing Unit (GPU)-optimization is needed. We use the Keras backend with Theano to take advantage of the GPU of the NVIDIA Geforce GTX 580; otherwise, we would not have been able to compute the results fast enough. The motors of the NAO take 0.1 seconds to complete a movement instruction, while computing the update of a Q-network using the Central Processing Unit (CPU) took approximately 2 seconds (this was implemented on an Intel Core i5 computer with 8 GB of RAM; see Figure 10). Evidently, this is not fast enough. Fortunately, using the GPU, we could compute the network update in around 0.002 seconds (see Figure 11).  
Figure 11: Training Time using a Graphical Processing Unit (GPU).  
Figure 10: Training Time using a Central Processing Unit (CPU).