esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Scheme
### Input Preprocessing Based on Volterra Feature  
The composition and structure of the virgin input data can directly affect the performance of deep learning. Due to the complexity of the VLC channel, it is very necessary to transform or encode \(y(n)\) so that it may be easily parsed by the machine. The main agenda for the proposed model to be accurate and precise in its predictions is that the algorithm should be able to easily interpret the data's features. As demonstrated in (5), for \(p\geq 2\), \(\hat{x}(n)\) can be considered as the sum of the response for each \(h_{p}\big{(}k_{1},\cdots,k_{p}\big{)}\) and \(\mathbf{y}_{p}(n)\), shown as  
\[\hat{x}(n)=\sum_{p=0}^{p}\mathbf{y}_{p}^{T}(n)\mathbf{h}_{p}+v(n), \tag{7}\]  
where \(\mathbf{y}_{p}(n)=\mathbf{y}_{p-1}(n)\otimes\mathbf{y}_{1}(n)\), and \(\mathbf{h}_{p}\) denotes the corresponding kernel coefficients for \(h_{p}\big{(}k_{1},\cdots,k_{p}\big{)}\). Let \(\mathbf{y}(n)=\big{[}1,\mathbf{y}_{1}^{T}(n),\cdots,\mathbf{y}_{p}^{T}(n) \big{]}^{T}\). (7) can be further formed as  
\[\hat{x}(n)=\mathbf{y}^{T}(n)\mathbf{h}+v(n), \tag{8}\]  
where \(\mathbf{h}=\big{[}\mathbf{h}_{0}^{T},\mathbf{h}_{1}^{T},\cdots,\mathbf{h}_{p} ^{T}\big{]}^{T}\) is the Volterra kernel vector and \(\mathbf{h}_{p}\) contains the corresponding kernel coefficients \(h_{p}\big{(}k_{1},\cdots,k_{p}\big{)}\), which are arranged sequentially for the index \(\{k_{1},\cdots,k_{p}\}\).  
Therefore, \(y(n)\) should be firstly stacked for the last \(L\) points shown in (6) and then transformed into the sequence \(\mathbf{y}(n)\) by using the above approach based on the Volterra structure feature. In order to shrink the computational complexity and speed up the learning progress, the sequence \(\mathbf{y}(n)\) is truncated with a \(2m\) length. Then, the first appropriate pattern can be formed by the following way, shown as  
\[\mathcal{Y}_{1} =\left[\begin{array}{cccc}\mathbf{y}(1)&\mathbf{y}(2)&\cdots& \mathbf{y}(m)\\ \mathbf{y}(2)&\mathbf{y}(3)&\cdots&\mathbf{y}(m+1)\\ \vdots&\vdots&\cdots&\vdots\\ \mathbf{y}(m)&\mathbf{y}(m+1)&\cdots&\mathbf{y}(2m-1)\end{array}\right]. \tag{9}\]  
With the time sliding window moving forward one step, the second pattern can be generated by  
\[\mathcal{Y}_{2} =\left[\begin{array}{cccc}\mathbf{y}(2)&\mathbf{y}(3)&\cdots& \mathbf{y}(m+1)\\ \mathbf{y}(3)&\mathbf{y}(4)&\cdots&\mathbf{y}(m+2)\\ \vdots&\vdots&\cdots&\vdots\\ \mathbf{y}(m+1)&\mathbf{y}(m+2)&\cdots&\mathbf{y}(2m)\end{array}\right]. \tag{10}\]Until the last \(N\)-th points, multiple patterns \(\overline{\mathcal{Y}}\) can be obtained subsequently, which will enter the neural network as the features for the input layer. Note that the step of the sliding window was set as \(1\) in this paper, and \(m\) also denotes the time step used in the following DL model.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 4 Discussion
### Interpretation of Results  
The success of the PWCL and HYBRID-MAPPED algorithms can be attributed to several factors. Firstly, the use of PWCL allowed for a stronger emphasis on signals closer to the transmitters, effectively minimizing the influence of weaker signals and thus reducing error. Secondly, the incorporation of advanced filtering techniques, such as outlier detection and mapping filters, proved crucial in mitigating the impact of environmental noise and ensuring more reliable signal processing.  
Our results also highlight the importance of the physical environment in which the localization takes place. The experimental setup, which included glass corridors and rooms, provided a challenging yet realistic scenario for testing our methods. The positive outcome in such an environment underscores the robustness of our approach in real-world applications.
## 4 Simulation Results
### Evaluation of the Wind Turbine, DFIG and ANFIS  
Due to the flexibility of the ANFIS and its applications in various control solutions, it allows for adjustment of the pitch angle of the wind turbine and control of the power injected into the grid under all working conditions. The fuzzy membership functions of the controller were defined based on the stator, rotor, and grid parameters, including the voltage and current of the stator, rotor, and grid, rotor angle, and speed of the stator induction generator (Figure 1).  
Figures 14-16 show the wind turbine, DFIG, and ANFIS block output in the input of 25 kV/575 V transformer. The DFIG, transmission line, wind turbine, and load model were selected based on the 9 MW wind farm model of the Matlab R2024a software. The wind speed was taken as 10 m/s and 15 m/s for Figures 14 and 15 and changed from 10 to 15 m/s (Figure 16). The proposed block accurately controlled and stabilized the output voltage in all conditions. The voltage drop between 0.035 to 0.08 s happened because of the initial assumption of load and transmission line events (see Wind Farm DFIG Detailed Model ([[https://www.mathworks.com/help/sps/ug/wind-farm-dfig-detailed-model.html](https://www.mathworks.com/help/sps/ug/wind-farm-dfig-detailed-model.html)]([https://www.mathworks.com/help/sps/ug/wind-farm-dfig-detailed-model.html](https://www.mathworks.com/help/sps/ug/wind-farm-dfig-detailed-model.html))) (accessed on 20 September 2024)), which is another confirmation of the stability, speed, and robustness of the proposed controller.  
Figure 14: Output voltage (Pu) of the studied wind turbine block with a wind speed of 10 m/s.
## 4 Research Results
### Analysis of Survey Data
One of the questions that was asked in the survey concerned the number of consumed \"standard\" alcoholic beverages, i.e., 4.5% beers with a capacity of 500 mL--one \"large beer\". The obtained answers showed that the largest number of \"large beers\" consumed during a day (500 mL) was equal to 10 and was declared by nine people. The most common answer was one beer, and such information was given by 144 respondents, i.e., 25.1% of all the respondents. The remaining people declared the quantities that are shown in Figure 12. The average value for the studied population was 1000 mL \(\pm\) 1000 mL of 4.5% beer (2 \(\pm\) 2 beers). The analysis of the place of residence showed that most often people declared living in the countryside--99 people, and in small towns (up to 100,000 inhabitants)--95 people. A total of 80 people declared that they permanently lived in cities (over 100,000 inhabitants). The age of people who consumed alcohol, as well as the age of abstainers, was also analyzed. Table 4 presents the number of people with regards to their age. Figure 11: The consumption of alcohol by construction workers (own elaboration). Figure 10: The age structure of the respondents (own elaboration). The following conclusions can be drawn as a result of the obtained data (Table 4):  
* The most numerous group who consumed alcohol during the day were employees aged 30-39 (100 people) and 20-29 (85 people);
* The highest percentage of people consuming alcohol occurred among employees aged over 60 (55.6%) and 50-59 (54.7%);
* The number of people consuming alcohol decreased with age (decreasing tendency) and the ratio of people consuming alcohol to the abstainers changed (the number of people consuming alcohol increased);
* Among younger employees (18-19, 20-29, 30-39, 30-39), the percentage of people consuming alcohol was smaller than the percentage of abstainers. During the tests, photographic documentation of the construction site was also carried out. It evidences that alcohol was consumed at the examined sites, e.g., Figure 13 shows an abandoned beer can at a litter site at the examined construction site, and Figure 14 shows a beer can in the assembly yard at the examined construction site. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Age Range** & **Number of People** & **Answer** & **Number of People** & **Percentage** \\ \hline
18–19 & 13 & Yes & 3 & 23.1\% \\  & & No & 10 & 76.9\% \\ \hline
20–29 & 178 & Yes & 85 & 47.8\% \\  & & No & 93 & 52.2\% \\ \hline
30–39 & 209 & Yes & 100 & 47.8\% \\  & & No & 109 & 52.2\% \\ \hline
40–49 & 102 & Yes & 47 & 46.1\% \\  & & No & 55 & 53.9\% \\ \hline
50–59 & 53 & Yes & 29 & 54.7\% \\  & & No & 24 & 45.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of people consuming alcohol (“Yes”), and the number of abstainers (“No”), with regards to their age (own elaboration). Figure 12: Number of consumed alcoholic beverages (“large beers”) declared by construction workers during a day (own elaboration).
# Analysis of Mobile Edge Computing for Vehicular Networks +
###### Abstract  
Vehicular ad-hoc Networks (VANETs) are an integral part of intelligent transportation systems (ITS) that facilitate communications between vehicles and the internet. More recently, VANET communications research has strayed from the antiquated DSRC standard and favored more modern cellular technologies, such as fifth generation (5G). The ability of cellular networks to serve highly mobile devices combined with the drastically increased capacity of 5G, would enable VANETs to accommodate large numbers of vehicles and support range of applications. The addition of thousands of new connected devices not only stresses the cellular networks, but also the computational and storage requirements supporting the applications and software of these devices. Autonomous vehicles, with numerous on-board sensors, are expected to generate large amounts of data that must be transmitted and processed. Realistically, on-board computing and storage resources of the vehicle cannot be expected to handle all data that will be generated over the vehicles lifetime. Cloud computing will be an essential technology in VANETs and will support the majority of computation and long-term data storage. However, the networking overhead and latency associated with remote cloud resources could prove detrimental to overall network performance. Edge computing seeks to reduce the overhead by placing computational resources nearer to the end users of the network. The geographical diversity and varied hardware configurations of resource in a edge-enabled network would require careful management to ensure efficient resource utilization. In this paper, we introduce an architecture which evaluates available resources in real-time and makes allocations to the most logical and feasible resource. We evaluate our approach mathematically with the use of a multi-criteria decision analysis algorithm and validate our results with experiments using a test-bed of cloud resources. Results demonstrate that an algorithmic ranking of physical resources matches very closely with experimental results and provides a means of delegating tasks to the best available resource.  
cloud computing; distributed computing; mobile computing; VANET; wireless networks +
Footnote †: This paper is an extended version of “Context-Aware Mobile Edge Computing in Vehicular Ad-Hoc Networks” published in the Proceedings of the 2018 28 th International Telecommunication Networks and Applications Conference (ITNAC), Sydney, Australia, 21-23 November 2018.
## 2 Results and Discussion
### Profiling of Onion and Lemon Secondary Metabolites via UPLC-QTOFMS and Multivariate Data Analysis  
To assess changes in secondary metabolite compositions of brine-preserved lemon and onion, non-targeted profiling was conducted using ultra-performance liquid chromatography (UPLC) coupled to a photodiode array and high resolution QTOF-MS operated in the negative- and positive-ionization modes (Figure S2). It should be noted that extracts were analyzed in both positive and negative-ion electrospray ionization (ESI) MS modes (Supplementary Figure S2) as changes in ESI polarity can often circumvent or significantly alter competitive ionization and suppression effects, revealing otherwise suppressed metabolite signals. We have previously reported on the UPLC-MS characterization of onion bulb, as published elsewhere [28], and we now present an annotation of metabolites for lemon fruit. Compared to the positive-ion ESI mode, the negative-ion MS spectra revealed better sensitivity and more observable peaks in the case of lemon. Nevertheless, it did not yield as much fragmentation information as in the positive-ion mode. Thus, the positive-ion mode provided more structural context and the negative-ion mode greater sensitivity. In total, 14 peaks from lemon fruit were annotated based on their negative-ionization mass spectral data versus 32 in the positive-ion mode, see Table 3. This is the first profile of _C. limon_ fruit methanol extract from Egypt obtained via a UPLC-QTOFMS platform and it is presented herein as part of this study towards the composition of fresh _C. limon_. LC-MS and NMR were reported for the metabolite profiling of citrus oil derived from Italy [42]. A representative UPLC-MS base peak chromatogram (BPC) of _C. limon_ is shown in (Figure S2A). The identities, retention times (rt), UV characteristics, and observed molecular and fragment ions for individual secondary metabolites are presented in Table 3. A total of 49 metabolites were detected, of which 36 were annotated. Metabolites belonged to several natural product classes including flavonoids, limonoids, coumarins, and phenolic/organic acids. Figure 2: OPLS-DA score plot derived from modeling volatile metabolites. The metabolites were analyzed via SPME/GC-MS, and were derived fresh and pickled lemon fruit (**A**) and onion bulb (**C**), each modeled one at a time (\(n=3\)). The respective _S_-plots, (**B**) and (**D**), show the covariance p[1] against the correlation p(cor)[1] of the variables of the discriminating component of the OPLS-DA model. Cut-off values of \(p<0.05\) were used; selected variables are highlighted in the _S_-plot and identifications are discussed in the text.
## 5 Experiment and Results  
### Experimental Setup and Procedure  
The experimental setup is shown in Figure 5. The robot is a self-developed cable-driven six-joint serial manipulator, and the positioning accuracy is 1.5 mm. In the physical human-robot interaction scenarios, the flexibility of operation is an important factor that needs to be considered. Compared with traditional robot, our robot used cable-pulley transmission to replace gear reducer, which will significantly reduce the inertia of the manipulator arm, thus improving the flexibility. Therefore, this robot adopts a cable-drive mode. A six-DOF force sensor (SRI-M3552B, Sunrise Instruments, Shanghai, China) is installed at the end of the robot to measure the interaction force between the operator and robot, with a range of 150 N in the \(X\) and \(Y\) directions and a range of 250 N in the \(Z\) direction. The nonlinearity and hysteresis are less than 1.5% F.S., and the refresh rate in the experiment is 200 Hz. An indicator pen is installed on the robot's sixth joint (end joint), which is used to represent grinding tools, while auxiliary testers move robots by task path. The simulation experiment is to operate the robot to grind the prosthesis implantation plane, and its task path is an N-shaped path covering the plane. To simplify the analysis of experiment data, the grinding plane is limited to \(XY\) plane.  
The experiment is divided into four groups, and the four different trajectory predictors in the above section are adopted, respectively. The tester holds the handle and drags the robot according to the designated task path. The moving speed and acceleration of the robot end tool on the path are adjusted by the tester according to his own habits. In particular, the overall time for each group experiment is roughly the same.
# Improved Cryptanalysis of Some RSA Variants
## 6 Conclusions  
In this paper, we presented an enhanced cryptanalytic approach that builds upon [PERSON]'s method and incorporates lattice basis reduction. We applied this technique to analyze several variants of RSA where the modulus is \(N=pq\), the modified totient function is \(\psi(N)=(p^{2}-1)(q^{2}-1)\), and the generalized key equation is \(eu\equiv z\pmod{\psi(N)}\). Our method successfully enables the factorization of the RSA modulus \(N\) in polynomial time, even for real-world examples on a large scale. Our method works in the scenario where an amount of the most significant bits of \(p\) is known, and the unknown parameters \(u,z\) in the equation \(eu\equiv z\pmod{\psi(N)}\) are suitably small. Our method extends all methods  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**bl(N)** & **bl(_e_)** & \(\delta\) & \(\delta_{0}\) & \(\alpha\) & \(\gamma\) & \(\sigma\) & \(\mathbf{Ns(\emph{p})}\) & \(\mu\) & \(\zeta\) & \(\omega\) & \(\mathcal{T}(s)\) \\ \hline
1024 & 2047 & 0.390 & 1.994 & 2.00 & 0.488 & 0.293 & 212 & 3 & 3 & 52 & 8.00 \\
1100 & 2198 & 0.454 & 1.999 & 2.00 & 0.490 & 0.266 & 250 & 3 & 3 & 52 & 10.09 \\
1200 & 2398 & 0.426 & 1.999 & 2.00 & 0.500 & 0.325 & 210 & 3 & 3 & 52 & 9.98 \\
1300 & 2598 & 0.477 & 2.000 & 2.00 & 0.500 & 0.317 & 237 & 3 & 3 & 52 & 10.48 \\
1400 & 2797 & 0.486 & 1.999 & 2.00 & 0.500 & 0.279 & 310 & 3 & 3 & 52 & 14.81 \\
1500 & 2997 & 0.487 & 1.999 & 2.00 & 0.500 & 0.328 & 255 & 3 & 3 & 52 & 13.15 \\
1600 & 3199 & 0.469 & 1.999 & 2.00 & 0.500 & 0.355 & 231 & 3 & 2 & 40 & 5.34 \\
1700 & 3399 & 0.494 & 2.000 & 2.00 & 0.500 & 0.347 & 260 & 3 & 2 & 40 & 5.69 \\
1800 & 3595 & 0.497 & 2.000 & 2.00 & 0.506 & 0.366 & 241 & 3 & 1 & 22 & 1.38 \\
1900 & 3794 & 0.495 & 2.000 & 2.00 & 0.500 & 0.316 & 350 & 3 & 1 & 22 & 1.98 \\
2048 & 4096 & 0.487 & 2.000 & 2.00 & 0.488 & 0.369 & 269 & 2 & 2 & 21 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiments for various values of \((N,e)\).  
where the equation is \(ed\equiv 1\pmod{\psi(N)}\) with small \(d\). In addition, the results of this paper highlight the superiority of our attack over existing techniques that are not based on quantum computation, especially in scenarios involving small private exponents and partial prime information.  
Conceptualization, M.R. and A.N.; methodology, M.R. and A.N.; software, M.R.; validation, M.R., A.N. and M.Z.; formal analysis, M.R. and A.N.; investigation, M.R. and A.N.; resources, M.R. and A.N.; data curation, M.R. and A.N.; writing--original draft preparation, M.R.; writing--review and editing, M.R. and A.N.; visualization, M.R. and A.N.; supervision, M.R., A.N. and M.Z.; project administration, M.R., A.N. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers
## 4 Non-Enzymatic HbA1c Sensors Based on FV Detection Principle
MIP sensors have been explored for measurement of FV as a non-enzymatic option for HbA1c detection. A potentiometric sensor with the molecular imprints of FV in poly-aminophenyl boronic acid (PAPBA) was deposited over indium-doped tin oxide (ITO) electrodes. The molecularly imprinted PAPBA sensor demonstrated good selectivity for the fructose moieties of the Amadori compound [66]. Further studies using this sensor on human samples is warranted to understand its potential as a reliable HbA1c sensor. Recently, a novel dual-imprinted polymer-based flexible electrochemical sensor for diabetic markers was fabricated from flexible carbon paste aluminum foil modified by electro-polymerization of dual molecularly imprinted poly(rhodamine B) along with PAPBA nano-cubes. While poly(rhodamine B) was used for non-covalent binding of hemoglobin, PAPBA was employed for cis-diol interaction-based specific detection of HbA1c [67]. Despite the molecular imprints produced on PAPBA being relatively cheap, subsequent studies by other groups have revealed that these sensors are best for single-use devices, as their protein recognition ability deteriorates after the first use [68].
## 3 Results and Discussion
### Effects of BGLSP on Sensory Characteristics of CSB  
Sensory evaluation serves as a crucial metric for assessing food quality, primarily relying on human senses such as sight, smell, and taste to appraise the sensory attributes of food, thereby elucidating consumer preferences for various products [21]. The sensory evaluation outcomes for CSB are presented in Figure 6. As depicted in Figure 6, the inclusion of BGLSP at concentrations ranging from 0.5% to 5% resulted in relatively high flavor scores, indicating that the aroma of BGLSP may enhance the quality of CSB. Conversely, increases in BGLSP concentration from 0.5% to 10% were associated with declines in specific volume, appearance, color, internal structure, and texture scores of CSB. These findings align with the previously determined physicochemical properties of flour blends and the quality characteristics of CSB. Regarding overall acceptability, the sensory evaluation scores for CSB with 0.5% and 1% BGLSP were significantly higher than those of other samples, with scores of 95.37 and 94.30, respectively. Prior research has similarly indicated that the excessive incorporation of flaxseed flour can adversely affect the sensory scores of CSB [1; 29].