esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Vehicular Networks  
Vehicular networks, or Vehicular Adhoc NETworks (VANETs), are a particular kind of Mobile Adhoc NETworks, or MANETs [1]. The main difference is that MANETs are mostly composed of mobile nodes connected by wireless links that move on random trajectories ([15] in [16]), while in VANETs, fixed entities or roadside units (RSUs) might be present in addition to mobile entities or onboard units (OBUs) [1] that move along predefined roads [3]. A recent study also described a remote controller called a Trusted Authority (TA) [17] with functions such as registering nodes and network security. However, we aim to communicate with nodes faced with the task of communicating EMs only under space-time constraints; thus, security and related matters are outside of the scope of our research, and therefore this term will be intentionally omitted from the rest of this paper.  
A major objective of VANETs is to provide physical welfare to the vehicle occupants through the opportune communication of data; therefore, several safety applications have been proposed and studied [1]. Other non-safety applications include route efficiency and fuel economy (see Table 1); for these applications, communication among entities in VANETs is mandatory.  
In general, authors in the literature agree that there are three kinds of communication depending on the entities involved [1,3]: infrastructure-to-infrastructure (I2I), i.e., RSU to RSU communication; vehicle-to-infrastructure (V2I), i.e., OBU to RSU communication; and vehicle-to-vehicle (V2V), i.e., OBU to OBU communication. See Figure 1. Mobility issues, however, represent problems in the desired communication, namely packet routing and packet delivery [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Delay Tolerance** & **Purpose** \\ \hline \multirow{2}{*}{Safety} & Most strict, latency should & Main purpose of these \\  & be less than 100 ms & applications is to avoid \\  & & collisions and accidents. \\ \hline \multirow{2}{*}{Non-Safety: Efficiency} & On-time information & These are not as important \\  & needed, latency can be a & but they can save time and \\  & few seconds & money if they are in place. \\ \hline \multirow{2}{*}{Non-Safety: Comfort} & No latency constraints as & These applications provide \\  & long as the information is & information about hotels, \\  & received in a time bound & restaurants, etc. \\ \hline \multirow{2}{*}{Non-Safety: Entertainment} & Generally, these do not & These applications are \\  & have any real-time & related to multi-media \\ \multicolumn{1}{c}{} & constraints & sharing, the Internet, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: VANET applications classification and time requirements (taken from [1]).
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 2 The Model of Correlated Wind Speeds
### The Solution for the Correlation Coefficient of the Wind Speed  
For the complexity of calculating Equation (2), [PERSON] [15] employed the integral space transformation method to transform Equation (2) into Formula (5), as follows:  
\[\begin{array}{ll}\rho_{ij}&=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty} \left(\frac{v_{j}-\mu_{j}}{\sigma_{i}}\right)\cdot\left(\frac{v_{j}-\mu_{j}}{ \sigma_{j}}\right)f_{v_{i}v_{j}}(v_{i},v_{j})dv_{i}dv_{j}\\ &=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}\left(\frac{v_{j}-\mu_{j}}{ \sigma_{i}}\right)\cdot\left(\frac{v_{j}-\mu_{j}}{\sigma_{j}}\right)\phi(u_{i })\phi(u_{j})du_{i}du_{j}\end{array}, \tag{5}\]  
where \(u_{i}\), \(u_{j}\) is the \(i\)th and \(j\)th element of the vector \(\mathbf{U}\), respectively, and \(\phi(\cdot)\) is the PDF.  
Then, the bi-dimensional Nataf transform and Gauss-Hermite integral method are applied to calculate the integral in Formula (5), \(\rho_{0,ij}\) can be achieved by solving the nonlinear equation as follows:  
\[\rho_{0,ij}-\sum_{l=1}^{m}\sum_{k=1}^{m}w_{l}w_{k}\left(\frac{v_{il}-\mu_{i}}{ \sigma_{i}}\right)\left(\frac{v_{jk}-\mu_{j}}{\sigma_{j}}\right)=0, \tag{6}\]  
where \(m\) is the number of integral nodes; \(w_{l}\) and \(w_{k}\) are the weight values of the integral nodes \(l\) and \(k\), respectively; and \(\left(v_{il},v_{jk}\right)^{T}\) is derived from Equations (7) and (8), as follows:  
\[(x_{il},x_{jk})^{T}=\mathbf{L^{\prime}}_{0}\times(z_{il},z_{jk})^{T}, \tag{7}\]  
\[(v_{il},v_{jk})^{T}=(F_{i}^{-1}(\phi(x_{il})),F_{j}^{-1}(\phi(x_{jk}))), \tag{8}\]  
where \(\mathbf{L^{\prime}}_{0}\) is a bi-dimensional inferior triangular decomposition matrix, which is derived from Nataf transformation; \((z_{il},z_{jk})^{T}=\sqrt{2}(u_{il},u_{jk})^{T}\); and the typical weight values \(w_{l}\) and \(w_{k}\) and the node values \(z_{il}\) and \(z_{jk}\) can be obtained from the literature [16].
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 2 Related Works
There is a wide variety of research on hierarchical Bayesian (HB) models applied to spatiotemporal data, such as environmental data, including weather conditions and ozone (\(O^{3}\)) level concentrations [35]. These studies have shown that hierarchical Bayesian is more effective, accurate, and resilient, compared to other models. [PERSON] et al. [18] propose a spatiotemporal model to predict the ozone concentration level in New York City. The model is implemented in their spTimer R package to use one of the three models: GP, AR, or GPP. In their framework, they use a Gibbs sampler to estimate the likelihood functions. The structural framework and the three models have achieved precise results in analyzing and predicting spatiotemporal data. In order to leverage this robust approach in the spatiotemporal traffic domain, we apply all three different models with different traffic data, and different covariance matrices in the GP model. The findings of the performance of the covariance matrices conclude that covariance matrices perform differently based on different characteristics of the dataset. Before discussing the methodology, we look into the existing literature on the GP, AR, and GPP models to explore the key theoretical concepts for better understanding.
## 3 Results and Discussion  
### Nutrient Compositions  
We prepared Italian ryegrass and whole crop corn silages and fermented feed using lactic acid bacteria (LAB) as a biological additive in the current study. We then determined its nutrient profiles, such as Dry matter content (DM), crude protein (CP), Acid detergent fiber (ADF), Neutral detergent fiber (NDF), and Total digestible nutrient (TDN) in silages and fermented feed. The impact of silages and fermented feed on growth performance, carcass characteristics, and meat quality investigations during fattening periods in Korean cattle Hanwoo steers was studied and compared to commercial concentrate. Nutrient profiles, such as CP content (8.40 \(\pm\) 0.13; 8.69 \(\pm\) 0.50) and TDN (57.77 \(\pm\) 0.34; 68.06 \(\pm\) 0.97), slightly varied between Italian ryegrass (IRG) silage and whole crop corn (WCC) silage. However, rice straw had a very low CP content (C4.40 \(\pm\) 0.05) and a moderate level of TDN (55.65 \(\pm\) 0.15) compared to the silages. Dry matter content (DM) (65.04 \(\pm\) 0.84; 68.04 \(\pm\) 0.34), ADF (39.41 \(\pm\) 0.43; 31.20 \(\pm\) 2.29), and NDF level of (66.85 \(\pm\) 0.43; 46.82 \(\pm\) 2.12) of IRG and corn silage were lower compared to rice straw (DM: 88.50 \(\pm\) 0.40; ADF: 42.10 \(\pm\) 0.19; NDF: 69.09 \(\pm\) 0.12) (Table 1). The nutrient profiles of IRG silage in this study were close to that of corn silage reported previously [28; 29]. LABs are considered essential biological additives for silage production by rapid induction of fermentation process at ensiled condition. They can produce various organic acids, particularly lactic acid concentrations, which increase the acidification of silages. Rapid acidification prevented the yeast and mold growth in silages and preserved with high nutrients. Therefore, we used microbiological additives for animal feed production. It is a cost-effective strategy with high/similar effects compared to commercial feed. Heterofermentative lactic acid bacteria can promote acetic acid biosynthesis, thus improving the aeration of silage and its quality. On the other hand, water-soluble carbohydrates are fermented by homofermentative bacterial inoculants into organic acids, especially lactic acid, which can quickly acidify silage and inhibit the growth of undesirable bacteria. To regulate the growth of yeast, homolactic inoculants, such as lactobacilli, known to produce acids, such as acetic and propionic acids, that inhibit the growth of microorganisms that trigger spoilage [30] were added. Inoculants include one or two groups of acidophilic bacteria. During co-fermentation, the synthesis of acetic acid, 1,2-propanediol, and propionic acid was stimulated by bacterial strains of these species. They make sustainable feed silage more aerobically stable. The findings of the study could be used to establish bacterial preparations that can facilitate the ensilage of renewable raw materials.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.1.5 Transmission Loss  
This is the average signal loss between intermediate nodes during data forwarding. It is measured in decibels (dBs).
# Efficient Design of Reversible Adder and Multiplier Using Peres Gates
###### Abstract  
This paper details the approach to the efficient design and optimization of a reversible adder and multiplier utilizing Peres gates, which is a three-input, three-output gate. Peres gates are recognized for their universality and energy-efficient properties and present an intriguing option for constructing reversible circuits. Reversible logic is characterized by its ability to uniquely determine input states from output states. The design methodology involves a cascading arrangement of Peres gates, each performing a reversible XOR operation on corresponding bits of the input numbers and the carry-out from the preceding stage. This paper presents a detailed schematic representation, simulation results, and analysis of the proposed designs of different adders and multipliers, showcasing their potential for reversible applications. The integration of Peres gates in the adder and multiplier design signifies a step forward in the exploration of reversible logic circuits and their applications in contemporary computing paradigms. The overall hardware reduction is the main achievement of this research in terms of quantum cost.  
c 2024 14 9385 1
## 5 Failure Case Analysis and Limitations  
In our experiments, we observed that the introduction of the multi-head attention mechanism had a significant positive impact on the model's performance, making it more adaptable to complex task scenarios and improving performance effectively. However, the effect of the multi-head attention mechanism may vary across different tasks and datasets. For example, in the experiment on the FBS dataset, the combination of the multi-head attention mechanism and the inconsistency loss function resulted in a decline in performance, while no such issue occurred with our dataset. This suggests that in some cases, the inconsistency loss function may introduce information that is irrelevant to the task, negatively impacting the model's performance on the target task.  
Additionally, this study has several limitations. The datasets used in the experiments, although they contain tens of thousands of items, do not fully cover the entire spectrum of telecom fraud text categories, limiting the model's generalizability. Moreover, while the multi-head attention mechanism significantly improves the model's performance, it also increases the model's complexity and training time, which can pose challenges in resource-constrained environments. Finally, our research primarily focused on the effects of the multi-head attention mechanism, residual connections, and the inconsistency loss function without investigating other potential improvements or alternative architectures.
# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 2 System Model and Problem Formulation  
As illustrated in Figure 1, we explore a UAV-assisted ISAC system comprising a UAV that serves as a dual-function base station, legitimate users, and potential eavesdropping targets. The UAV is equipped with an \(N\)-antenna uniform linear array (ULA), enabling simultaneous communication with \(K\geq 1\) single-antenna users on the ground while detecting \(J\) single-antenna eavesdroppers also located on the ground.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definitions** & **Symbol** & **Definitions** \\ \hline \(N\) & Number of antennas & \(\mathbf{x}\) & Transmitted signal of the UAV \\ \(K\),\(J\) & Number of users and eavesdroppers & \(P_{\text{max}}\) & Maximum transmit power \\  & Communication & & \\ \(\varsigma_{k^{\prime}}^{c}\), \(s_{n}^{r}\) & signal & \(\mathbf{q}\), \(\mathbf{u}_{k}\), \(\mathbf{e}_{j}\) & Location of UAV, user k \\  & and sensing signal & & and eavesdropper \(j\) \\ \(\mathbf{\omega}_{k^{\prime}}^{c}\), \(\mathbf{\omega}_{n}^{r}\) & Beamforming vector & \(\mathbf{h}_{k}(\mathbf{q},\mathbf{u}_{k})\) & Communication channel from the UAV to the user \(k\) \\ \(\mathbf{a}(\mathbf{q},\mathbf{u}_{k})\) & Beamforming & \(\mathbf{g}_{j}\big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\) & Eavesdropping channel \\ \(n_{k^{\prime}}\), \(z_{e}\) & Additive white Gaussian noise & \(\sigma_{k}^{2},\sigma_{e}^{2}\) & Noise power spectral density \\ \(\varsigma\Big{(}\theta\big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\Big{)}\) & Beam pattern gain & \(\Gamma_{k}^{\text{com}},\Gamma_{e}^{\text{sen}}\) & Communication and sensing \\ \(A\), \(B\), \(c_{ijk}\) & Slack variables & \(R\) & Trust region radius \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key notations.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 2 S-Band and Adjacent Bands Systems  
When designing a new navigation signal, investigations on the existing systems in the same and adjacent bands should be undertaken to analyze RF compatibility. Services using the S-band and adjacent bands via ITU frequency allocation are listed in Table 1. The S-band is shared with several primary services, including RDSS, and various terrestrial wireless communication services are being operated in the adjacent bands [26].
# Improved Cryptanalysis of Some RSA Variants
## 2 Preliminaries
### Lattice Reduction and Coppersmith's Technique
: _Let \(P(z_{1},z_{2},\ldots,z_{n})\) be a multivariate polynomial in \(\mathbb{Z}[z_{1},z_{2},\ldots,z_{n}]\) with no more than \(\omega\) monomials. Let \(e\) and \(\mu\) be positive integers. Then, the following statements_  
1. \(P\big{(}z_{1}^{(0)},z_{2}^{(0)},\ldots,z_{n}^{(0)}\big{)}\equiv 0\pmod{e^{\mu}}\)2. \(\|P(z_{1}Z_{1},z_{2}Z_{2},\ldots,z_{n}Z_{n})\|<\frac{e^{n}}{\sqrt{\omega}^{\prime}}\),
3. For all \(i=1,\ldots,n\), \(|z_{i}^{(0)}|<Z_{i}\),  
_imply that \(P\Big{(}z_{1}^{(0)},z_{2}^{(0)},\ldots,z_{n}^{(0)}\Big{)}=0\)._  
In systems involving more than two variables, extensions of [PERSON]'s method typically depend on heuristic approaches. For the purposes of this study, we make the following assumption [4; 12; 20; 21]:  
**Assumption 1**. : _The polynomials \(r_{1},r_{2},\ldots,r_{\omega}\) generated by the LLL algorithm are algebraically independent._  
Based on this assumption, the unique solution \(\Big{(}z_{1}^{(0)},z_{2}^{(0)},\ldots,z_{n}^{(0)}\Big{)}\) to the system of polynomial equations \(r_{i}(z_{1},z_{2},\ldots,z_{n})=0\) for \(i\leq n\), can be computed using methods such as the Grobner basis or resultant computations.
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Explanation** \\ \hline DC-IM & DC insulation monitoring \\ GC & Ground capacitance \\ \(R_{\text{f}1}\) & The negative insulation resistance \\ \(R_{\text{f}2}\) & The positive insulation resistance \\ \(R_{\text{a}}\) & The larger bridge resistance \\ \(R_{b}\) & The smaller bridge resistance \\ \(C_{1}\) & The capacitance value of the DC negative pole to the earth \\ \(C_{2}\) & The capacitance value of the DC positive pole to the earth \\ \(v_{1}\) & The voltage value of the DC negative pole to the earth \\ \(v_{2}\) & The voltage value of the DC positive pole to the earth \\ \(v_{11}\) and \(v_{12}\) & The sample value of \(v_{1}\) in \(M_{1}\) and \(M_{2}\) phase respectively \\ \(v_{21}\) and \(v_{22}\) & The sample value of \(v_{2}\) in \(M_{1}\) and \(M_{2}\) phase respectively \\ \(\Delta t\) & The time interval of sampling \\ \(E_{1}\) & \(e^{-\frac{\Delta t}{1}}\) of \(M_{1}\) phase \\ \(E_{2}\) & \(e^{-\frac{\Delta t}{2}}\) of \(M_{2}\) phase \\ \(\hat{E}_{1}(k)\) & The estimated value of the \(k\)th \(E_{1}\) \\ \(\hat{E}_{2}(k)\) & The estimated value of the \(k\)th \(E_{2}\) \\ \(\overline{v}\) & Estimated voltage value of the half-bridge voltage \\ \(\hat{v}\) & Correction voltage value of the half-bridge voltage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of some symbols used in the operation optimization.
## 3 Proposed Methodology
### Final Alignment  
For the final step, we repeat the process up to the point of fitting the GAM curve. In this iteration, the number of splines used in the GAM is different, but once again, it is selected by the user. This flexibility allows for adjusting the model to better fit the characteristics of the semi-processed text image. Assuming that the semi-processed text image is now nearly aligned due to the preliminary processing steps, we can proceed with fine-tuning the alignment. This is achieved by manipulating the pixel columns based on the second curve estimation obtained from the updated GAM. Specifically, the pixel columns are rolled up or down according to the deviations identified in the second curve estimation. This adjustment corrects any remaining misalignments in the text, ensuring that it conforms more closely to the desired curvature. By leveraging the refined curve, we align the text with greater precision, resulting in a final image that accurately represents  
Figure 5: The sample points on the curve are represented by red dots, while the control points are indicated by green dots. The lines connecting these control points are shown in blue.  
Figure 6: The semi-processed image, prior to the final alignment step.  
the intended curved text layout. It is important to note that this final step can be effectively utilized independently for scanned images of text lines where the curvature of the text is relatively mild. In such cases, the initial complex preprocessing steps may not be necessary. Instead, directly applying the final alignment process can suffice to achieve the desired text alignment. Figure 7 presents the final image, fully processed and nearly perfectly aligned. The number of splines used in this final alignment is 9. Figure 8 presents a detailed schematic diagram showcasing the workflow of the proposed method.
# Water Temperature and Salinity Measurement Using Frequency Comb
## 2 Measurement Set-Up
### Salinity Measurement Setup  
The salinity measurement setup is arranged as in Figure 1, which is the same with the temperature system. Please note that the salinity sensor (Mellter Toledo, Seven2 go Pro) is used here to measure salinity as a reference, which will be compared with the measurement results. Meanwhile, the temperature sensor serves as a temperature monitor. The water used here is also distilled water in order to get a larger salinity measurement range. Considering the temperature also contributes to the refractive index, the environment should be constant. The experiment photograph is shown in Figure 2.
## 3 Results and Discussion
### Cu\({}^{2+}\) Removal Performance of the Biochar Sample
In consequence, Cu\({}^{2+}\) adsorption on BC450 and BC600 was faster than on BC300, which agrees with the pseudo-first order model which better describes the initial stage of the sorption process when rapid sorption occurs [54; 55]. This is in accordance with the pseudo-first model in which the adsorption rate is proportional to the  
Figure 5: Fitted experimental data with the (**A**) pseudo-first-order model, (**B**) pseudo-second-order model, (**C**) liquid layer diffusion model, and (**D**) intra-particle diffusion for the biochar samples under study (BC300, BC450, and BC600). number of unoccupied sites [47; 54]. On the contrary, in the case of the pseudo-second order model, the adsorption rate is proportional to the squared number of unoccupied sites [56]. Diffusion-based models were applied to assess the mechanisms of the adsorption process. The models applied were liquid film diffusion (Equation (10)) and, intra-particle diffusion (Equation (11)), as proposed by [PERSON] et al. (1947) [57] and [PERSON] [53], respectively (Figure 5C,D; Table 5). The models are expressed as follows:  
\[-\ln(1-F)\ =\ k_{fd}\ t \tag{10}\]  
\[q_{t}\ =\ k_{\text{f}}t^{0.5}+C \tag{11}\]  
where \(k_{fd}\) (min\({}^{-1}\)) is the film diffusion rate constant (calculated from the graph of \(-\ln(1-\text{F})\) vs. \(t\)), \(F\) is the fractional attainment of equilibrium (\(F=q_{t}\ q_{e}^{-1}\)) at time \(t\), \(k_{i}\) (mg g\({}^{-1}\) min \({}^{0.5}\)) is the intra-particle diffusion rate constant (obtained graphing \(q_{t}\) vs. \(t^{0.5}\)), and \(C\) is a constant (mg g\({}^{-1}\)) that provides an idea regarding the thickness of the boundary layer on the adsorbent surface. Adsorption kinetics typically involve three steps [9]: (1) liquid-film transport, (2) intra-particle diffusion, and (3) adsorption on the interior sites of adsorbent to attain the equilibrium. For the liquid film diffusion model (Figure 5C), all biochar samples presented a straight line with regression coefficients between 0.854 and 0.979, being better for BC300 and BC600, showing that film diffusion was involved in the Cu adsorption onto biochar samples. However, the lines did not pass through the origin; therefore, the liquid film diffusion was not the sole rate-limiting step that controlled the adsorption process [58]. This finding is corroborated by the intra-particle diffusion model, where different phases were observed using different adjusted linear regressions (Figure 5D, Table 5). Intra-particle diffusion is involved if the plot \(q_{t}\) vs. \(t^{1/2}\) produces one straight line, and is the rate-limiting step if the line passes through the origin [55]. This was not observed, suggesting that both film diffusion and intra-particle diffusion are the rate-limiting steps.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 4 Controlled Reception of LEO Satellites Using the Proposed Phased Antenna Array System
The signals from the phase shifters were summed using a \"Minicircuits\" splitter/combiner. The phase shifters utilized were integrated vector modulator-based, and their basic operation mode will be described in Section 5. Finally, the antenna array itself consists of four vertical monopole antenna elements configured at three-quarter wavelength, arranged in a square topology (with each element positioned at the corners of the square). This will also be discussed in the following section.