esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 5 Security and Performance of Proposed E-Commerce Applications
### Performance of Proposed E-Commerce Applications
#### 5.3.2 Results, Comparison and Analysis  
In practice, our protocol performs lighter security measures for e-commerce applications than existing protocols. Although the environments, parameters and algorithms used in our protocol are not very identical to existing protocols, we investigated finding some comparable aspects between our protocol and existing protocols. Table 6 provides a comparison of performance parameters (key lengths, encryption algorithm, key generation algorithm, key generation algorithm, key generation support techniques, and encryption type) between our protocol and existing protocols.  
[PERSON] et al. [10] relied on RSA to encrypt e-commerce requests. However, we indicated in Table 1 that ElGamal is more efficient than RSA. In addition, our protocol uses ElGamal only for generating public and private keys and not for encryption and decryption procedures. [PERSON] et al. [11] used AES to encrypt the data with the shared key of all users. However, their scheme does not have the flexibility offered in our protocol to generate separate keys for each user. [PERSON] et al. [30] used ElGamal for encryption and decryption in their scheme. This means that encryption of any data request will require a lot of execution time, especially in large networks such as e-commerce. In comparison with our protocol, it only uses ElGamal to generate public and private keys, and the encryption and decryption process is only performed by the AES-256 algorithm, which is fast and efficient. [PERSON] et al. [13] also used the ElGamal algorithm with a scanning technique to encrypt the data. In terms of performance, their proposal does not concern memory overheadsand key sizes since traditional ElGamal uses 1024, 2048 keys, etc. When the number of data encryption requests is large, it will affect the memory size. Fortunately, our protocol generates ElGamal random keys and converts them into small sizes of length 256, which are suitable for encryption in AES and e-commerce environments. [PERSON] et al. [15] utilized several built-in methods such as PBKDF2, RNG and slave key to generate shared keys. However, their protocol is complex and increases implementation running time costs. In addition, the key remains shared between the source and the target, while our protocol uses a CRT-only approach to improve private key generation performance. Although [PERSON] and [PERSON] [16] indicated that they improved the performance of AES by reducing the number of rounds, the execution time for encryption and decryption is 15 s, which is very large compared to our protocol, as the execution time for encryption is 0.369143 ms, and for decryption, it is 0.274815 ms (taking into account the different environments, such as the different processor speeds).
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion
### Results Using Ideal Signals  
For this analysis, the following functions \(P_{E}(\mathrm{t})\) and \(P_{C}(\mathrm{t})\) were considered over the interval from 0 to 10 s, with a sampling time of 1 ms:  
\[P_{E}(\mathrm{t})=78309+4421.3\cdot\cos(145.76\cdot\mathrm{t}), \tag{29}\]  
and  
\[P_{C}(\mathrm{t})=78359+2218.7\cdot\cos(145.76\cdot\mathrm{t}-1.0451), \tag{30}\]Figure 10 shows the convergence of the DC component estimation for both \(P_{E}(t)\) and \(P_{C}(t)\). The DC component estimation algorithm shows good convergence, stabilizing after approximately three cycles.  
Figure 11 compares the AC component of \(P_{E}(t)\) and \(P_{C}(t)\), both filtered and non-filtered. The moving window size for the filtering is W = 3 in all the study cases.  
Figure 12 shows the instantaneous power obtained using method 1.  
Figure 10: Estimation of the direct current (DC) component of the signal of \(P_{E}(k)\) and \(P_{C}(k)\) (ideal signals).  
Table 2 shows the average power obtained with the proposed methods and compares them to the average power obtained with the reference procedure [17]. The error is defined as follows:  
\[Error(\%)=\frac{\dot{E}_{load\_ref}-\dot{E}_{load\_method}}{\dot{E}_{load\_ ref}}\cdot 100, \tag{31}\]  
where \(\dot{E}_{load\_ref}\) is the average power obtained with the reference procedure and \(\dot{E}_{load\_method}\) refers to the average power obtained with the proposed methods (1 or 2).  
The first three cycles (needed to stabilize the DC components) have not been considered in calculating the averages.  
These results show that good correspondence is obtained when using the three methods with an ideal synthesis signal sampled at approximately 40 measurements per period: the difference is lower than 1.8%.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Possible Sources of Measurement Error
#### 2.6.2 Imperfect Modulation Source  
As the sensor encodes the measured field intensity onto a particular harmonic of the modulation frequency, spurious harmonics of the modulation source can affect the measurement accuracy. Figure 18 shows the power spectral density of the modulation signal applied to the bias crystal, which is assumed to be a pure high-amplitude 600 Hz sine wave.  
Figure 17: Absorption coefficient of \(LiNbO_{3}\) doped with manganese [21].  
The second harmonic at \(f=1200\) Hz is the prevalent spurious component, with its amplitude lower by 53 dB, or a factor of 450, relative to the fundamental component.  
Numerical simulations were used to evaluate the impact of various spurious harmonics on the measurement accuracy. The simulated modulation signal was a superposition of a pure sine wave at the fundamental frequency and a 100 times smaller sine wave at one of the higher harmonics.  
Figure 19 shows that only the second harmonic has a strong impact on the measurement accuracy which becomes evident for measured field intensities below 1 kV/m. Therefore, further analysis focuses on this harmonic only. In particular, the impact of its amplitude and the phase difference with respect to the fundamental harmonic are evaluated.  
Figures 20 and 21 show, respectively, the effects of the second harmonic amplitude and phase offset with respect to the fundamental frequency on the measurement accuracy. The results are benchmarked against modulation with a pure sinusoidal signal, i.e., \(H2=0\).  
Figure 19: Impact of the first 5 spurious harmonics on the measurement accuracy compared to the fundamental harmonic. The amplitude of each harmonics was 1% of the fundamental frequency amplitude.  
Figure 18: Power spectral density of the modulation voltage.  
The effects are not negligible, especially for low values of the electric field to measure, making the spurious harmonics on the applied modulation signal a very plausible explanation for the error observed in experimental measurements of low electric fields.  
However, since the output remains a unique function of the electric field, both of these errors can be corrected by data postprocessing, once the system is calibrated prior to the measurement.
## 3 Results  
### Cross-Validation Accuracies  
The class-wise accuracies in terms of Kappa coefficient and F1-score obtained from the 10-fold cross-validation method for each prefectural region are provided in Supplementary Material S4. For instance, the class-wise cross-validation accuracy obtained in one of the prefectural regions (Yamagata prefecture) is shown in Table 3.  
\begin{table}
\begin{tabular}{l c c l c c} \hline \hline
**Legend** & **Kappa** & **F1-Score** & **Legend** & **Kappa** & **F1-Score** \\ \hline Abandoned land & 0.785 & 0.786 & Open space Herb & 0.854 & 0.855 \\ Abies ECF & 0.845 & 0.846 & Orchard & 0.708 & 0.709 \\ Acer DBF & 0.924 & 0.929 & Paddy field & 0.818 & 0.819 \\ Acer Shrub & 0.907 & 0.910 & Pasture & 0.904 & 0.906 \\ Alnus DBF & 0.839 & 0.843 & Pinus ECF & 0.849 & 0.850 \\ Alnus Shrub & 0.905 & 0.908 & Pinus Shrub & 0.981 & 0.982 \\ Alpine Herb & 0.925 & 0.926 & Populus DBF & 1.000 & 1.000 \\ Alpine Shrub & 0.944 & 0.944 & Pterocarya DBF & 0.815 & 0.817 \\ Bamboo EBF & 0.943 & 0.945 & Quercus DBF & 0.792 & 0.794 \\ Barren & 0.812 & 0.814 & Quercus Shrub & 0.886 & 0.887 \\ Betula DBF & 0.920 & 0.924 & Rhododendn Shrub & 0.980 & 0.980 \\ Built-up & 0.984 & 0.990 & Robinia DBF & 0.852 & 0.859 \\ Carpinus DBF & 0.863 & 0.863 & Salix DBF & 0.773 & 0.775 \\ Coastal Herb & 0.921 & 0.921 & Salix Shrub & 0.837 & 0.838 \\ Coastal Shrub & 0.961 & 0.961 & Sasa Shrub & 0.880 & 0.881 \\ Cryptomeria ECF & 0.789 & 0.791 & Solar panels & 0.985 & 0.986 \\ Deciduous Shrub & 0.953 & 0.953 & Thujopsis ECF & 0.966 & 0.967 \\ Fagus DBF & 0.867 & 0.868 & Tilia DBF & 0.966 & 0.966 \\ Fraxinus DBF & 0.950 & 0.951 & Tsuga ECF & 0.806 & 0.807 \\ Hydrangea Shrub & 0.793 & 0.795 & Ulmus DBF & 0.750 & 0.750 \\ Juglans DBF & 0.729 & 0.732 & Upland field & 0.793 & 0.794 \\ Lark DCF & 0.879 & 0.880 & Water & 0.843 & 0.844 \\ Machilus EBF & 0.932 & 0.932 & Weigela Shrub & 0.883 & 0.884 \\ Mallotus DBF & 0.935 & 0.935 & Weitand Herb & 0.859 & 0.860 \\ Miscanthus Herb & 0.759 & 0.761 & Zelkova DBF & 0.852 & 0.853 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class-wise cross-validation accuracy obtained for Yamagata prefecture.  
A summary of average accuracies across all classes for each prefectural region is shown in Table 4.
## 3 Results
### Multi-source data fusion.
When the three single-information early-warning results are different, traditional D-S theory often gives fusion results that are the opposite of reality. However, the method proposed in this paper makes full use of the available information, including conflict information. It takes into account the data of three risk sources, namely surface settlement, groundwater level, and pile horizontal displacement, which makes the early-warning model closer to the actual situation and improves the accuracy of the early-warning results.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 3 Methodology
### Aspect Term Extraction Structure
#### 3.1.3 Global-Local Context Modeling  
Aspect terms consist of consecutive words in the text. Each word has the problem of having multiple semantics. To solve the problem, the BERT model is used to generate contextual word vectors. We construct global word vectors and local contextual word vector representations and design the integration of word vector information by combining both types of word vector information using a gate cell structure.  
The representation of words in the global sequence refers mainly to the dependencies between words at the sentence level. We use the BERT model to mine words for global sequence features in sentences. First, the input text \(S=\{w_{1},w_{2},\ldots,w_{n}\}\), where \(n\) is the length of the text, is transformed into the input format of the BERT model, \(S=([CLS],w_{1},w_{2},\ldots,w_{n},[SEP])\).  
\[g_{i}=BERT(w_{i}) \tag{1}\]  
where \(g_{i}\) is the global sequence representation of the word \(w_{i}\) and \(i\) indicates the position of the word in the sentence.  
In the ATE task, the neighboring words of each word will have a significant impact on predicting its label, and the more distant words will have less impact, requiring a focus on local contextual information. We propose to use equal-width convolutional neural networks to mine the contextual information around words and extract local features.  
In this study, the local contextual features of each word are obtained using an equal-width convolutional layer with a convolutional kernel size of \(K\) and a step size \(S=1\), with \(P\) zeros complemented at both ends of the input.  
\[P=\frac{K-1}{2} \tag{2}\]  
\[l_{i}=Conv(g_{i}) \tag{3}\]  
where \(l_{i}\) is the local contextual feature of the word \(w_{i}\).  
To enhance the word vector information, the global sequence features and local sequence features are combined through the gating unit \(f_{i}\).  
\[f_{i}=\sigma(W_{f}g_{i}+U_{f}l_{i}+b_{f}) \tag{4}\]  
where \(W_{f}\), \(U_{f}\), and \(b_{f}\) are the training parameters of the gating unit, \(g_{i}\) is the global sequence feature, and \(l_{i}\) is the local sequence feature.  
The gating unit can combine global and local information, keep the important information, and remove the redundant information. Finally, we obtain the global-local \(gl_{i}\).  
\[gl_{i}=f_{i}g_{i}+(1-f_{i})l_{i} \tag{5}\]  
The parameter \(f_{i}\) controls the filtering of information. When \(f_{i}>0.5\), it means the global information is more important; when \(f_{i}<0.5\), it means the local information is more important; when \(f_{i}=0.5\), it means both parts of information are equally important.
## 3 Related Work and Methodology
### Average Nearest Neighbor (ANN)  
The Average Nearest Neighbor (ANN) method is a widely used technique in spatial statistics to analyze the pattern of a set of earthquakes within a given space. It provides a measure to determine whether the earthquake epicenter pattern exhibits clustering, randomness, or uniformity [14; 18; 34; 35]. To define the ANN method, consider a set of \(n\) earthquake epicenters in the Red Sea region. The method computes the distance from each earthquake to its nearest neighbor, resulting in \(n\) distances. The average of these distances, denoted as \(\overline{D}\), is compared to the expected average distance for a random pattern, \(E[D]\), in the Red Sea region of the same size and shape. The ratio of these two distances is used to quantify the earthquake pattern [36]:  
\[R=\frac{\overline{D}}{E[D]} \tag{1}\]  
The expected average distance \(E[D]\) in a random distribution can be estimated for a two-dimensional space with uniform point distribution using the formula [18]:  
\[E[D]=\frac{1}{2\sqrt{\lambda}} \tag{2}\]  
where \(\lambda\) is the density of earthquakes (number of earthquakes per unit area) in the Red Sea region. The value of \(R\) indicates the nature of the earthquake pattern: If \(R=1\), the pattern is considered random. If \(R<1\), the earthquakes are more clustered than expected in a random pattern. If \(R>1\), the earthquakes are more regularly spaced (uniformly distributed) than expected in a random pattern. This method is simplistic yet powerful in assessing spatial earthquake patterns.
## 4 Conclusions
\begin{tabular}{l l} \(q\) & heat transfer rate (W) \\ \(q^{\prime\prime}\) & local heat flux (W/m\({}^{2}\)) \\ \(q^{\prime\prime\prime}\) & local dimensionless heat flux (=\(q^{\prime\prime}/q^{\prime\prime}_{o}\)) \\ \(q^{*}\) & dimensionless heat transfer rate (=\(q/q_{o}\)) \\ \(r^{+}\) & radial coordinate \\ \(r^{+}_{p}\) & radius of a PCM particle (m) \\ \(r\) & dimensionless coordinate (=\(r^{+}/r^{+}_{i}\)) \\ \(Ste\) & modified Stefan number (=\(c_{p,b}(T_{m}-T_{\infty})/h_{\rm{ls}}\)). \\ \(t\) & time (sec) \\ \(t_{w}\) & dimensionless tube wall thickness (=\(t^{+}_{w}/r^{+}_{i}\)) \\ \(t^{+}_{w}\) & tube wall thickness (=\(r^{+}_{o}\)-\(r^{+}_{i}\)) (m) \\ \(T\) & temperature (K) \\ \(u\) & dimensionless axial velocity (=\(u^{+}/u^{+}_{m}\)) \\ \(u^{+}\) & axial velocity (m/s) \\ \(U\) & overall heat transfer coefficient (W/m\({}^{2}\) K) \\ \(x\) & dimensionless axial coordinate (=\(x^{+}/\left(r^{+}_{i}Pe\right)\)) \\ \(x^{+}\) & axial coordinate \\ \(G\) _exp symbols_ & \\ \(\alpha\) & thermal diffusivity (m/s\({}^{2}\)) \\ \(\theta\) & dimensionless temperature (=\((T-T_{m})/(T_{m}-T_{\infty})\)) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\rho^{*}_{bf}\) & density ratio (=\(\rho_{b}/\rho_{f}\)) \\ \(\rho^{*}_{pf}\) & density ratio (=\(\rho_{p}/\rho_{f}\)) \\ \(\rho^{*}_{wf}\) & density ratio (=\(\rho_{w}/\rho_{f}\)) \\ \(\xi\) & volumetric liquid fraction in a PCM particle \\ _Subscripts_ & \\ \(b\) & bulk fluid \\ _bf_ & bulk-to-base fluid ratio \\ cond & heat conduction \\ conv & convection \\ \(d\) & downstream \\ \(f\) & suspending fluid \\ \(h\) & heated section \\ \(i\) & inner surface of the tube \\ _in_ & inlet \\ \(l\) & liquid phase of PCM \\ _lat_ & latent heat \\ \(L_{h}\) & heated section \\ \(m\) & melting point \\ \(o\) & outer surface of the tube \\ \(p\) & particle \\ _pf_ & particle-to-fluid ratio \\ _sen_ & sensible heat \\ \(u\) & upstream \\ \(w\) & tube wall \\ _wf_ & wall-to-fluid ratio \\ \(\infty\) & ambient \\ _Superscripts_ & \\ - & surface-averaged quantity \\ \({}^{*}\) & ratio of quantities \\ + & dimensional quantity \\ \(\xi\) & \(\xi\) \\ \end{tabular}
# Pyrometallurgical Technology for Extracting Iron and Zinc from Electric Arc Furnace Dust
## 2 Research Objective  
In Russia, more than 70 million tons of steel are produced annually, of which 22 million tons are in the electrothermal process. The main enterprises manufacturing steel electrothermally are vertically integrated companies with a total share of about 90% in Russian steel production: EvrazHolding LLC, Several PJSC, Magnitogorsk Iron and Steel Works PJSC, Novolipetsk Iron and Steel Works PJSC, Mechel PJSC, MC Metalloinvest LLC, United Metallurgical Company JSC, and Pipe Metallurgical Company PJSC [26].  
Arc furnace enterprises generate about 650 thousand tons of technogenic dust per year, which is not currently processed but almost completely dumped. Such dust contains zinc within 2-20%; in some types of dust, the zinc content is up to 30-35%. This technogenic raw material also contains a significant amount of iron oxides (30-60%) and up to 20% of oxides of other elements (silicon, calcium, magnesium).  
The study object was the iron-zinc-containing dust from the EAF of PJSC Magnitogorsk Iron and Steel Works (Magnitogorsk, Chelyabinsk region). It was studied using laser diffraction, electron probe microanalysis (EPMA), X-ray phase and fluorescence (XRF) analysis techniques, and scanning electron microscopy (SEM).  
According to the study, EAFD is a red-brown powder with a particle size of \(<\)0.06 mm, determined on a laser particle size analyzer Analysette 22 NanoTecPlus (Fritsch, Idar-Oberstein, Germany); see Figure 1, Table 1.  
Chemical analysis of the samples was performed on an X-ray wavelength-dispersive spectrometer S8 TIGER (Bruker, Billerica, MA, USA); the results are given in Table 2. Dust samples were also studied by scanning electron microscopy (SEM) on an X-ray diffractometer XRD-7000 (Shimadzu, Kyoto, Japan), Figure 2.  
The iron distribution throughout iron-containing phases in EAFD samples was studied by X-ray phase analysis on a DRON-3.0 diffractometer (JSC Scientific production enterprise \"Burevestnik\", Saint Petersburg, Russia). It has been established that the dust contains valuable components in the form of ZnFe\({}_{2}\)O\({}_{4}\) and ZnO (Figure 3) [27].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & Components & & & & & & \\ \hline ZnO & Fe\({}_{2}\)O\({}_{3}\) & Na\({}_{2}\)O & K\({}_{2}\)O & CaO & Cl & P\({}_{2}\)O\({}_{5}\) & SO\({}_{3}\) & SiO\({}_{2}\) & Al\({}_{2}\)O\({}_{3}\) & MgO & MnO & PbO \\ \hline \multicolumn{10}{c}{Content, wt\%} \\ \hline
26.02 & 37.7 & 5.975 & 2.98 & 10.43 & 3.755 & 0.34 & 1.04 & 4.79 & 0.66 & 1.31 & 2.885 & 2.11 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition of an EAFD sample.  
Figure 3: X-ray diffraction diagram of the EAFD sample.  
Figure 2: Electronic image of an EAFD sample: (**a**) magnification \(\times\)500; (**b**) magnification \(\times\)2000.
## 4 Case Study
### Carbon Footprint Influence Factor Analysis Based on DEMATEL-ISM-MICMAC  
This study classifies the influential factors and assigns an assessment to each one. Five levels are identified for \(S_{i}(i=1,2,\ldots,4)\), and the values 0 to 4 assigned to each level quantitatively represent the degree of influence between the factors: 0 represents a very small influence, 1 represents a small influence, 2 represents a moderate influence, 3 represents a large influence, and 4 represents a very large influence. We invited 20 scholars and carbon footprint researchers in the field of assembled buildings to participate in scoring the influencing factors, comparing the influence of one factor on another, and evaluating  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Point** & **Carbon Footprint/kg CO\({}_{2}\)** & **Percentage** \\ \hline The building material production stage & 4,005,935.99 & 88.24\% \\ Conveyance of building materials phase & 60,011.38 & 1.28\% \\ Component manufacturing phase & 18,451.3 & 0.39\% \\ Components’ phase of transportation & 30,118.57 & 0.64\% \\ The building phase & 442,508.31 & 9.45\% \\ Overall amount & 4,683,025.55 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Carbon footprint.  
Figure 4: Geographic location of Cixi City.  
the correlation between the carbon footprint factors of assembled homes. The arithmetic mean method was used to average expert scores and create an initial impact matrix for carbon footprint factors in assembled homes.
## 3 Results
### Valorization via Liquid Hot Water (Temperatures from 120 to 180 \({}^{\circ}\)C)
#### 3.2.1 Quantification of Liquid Fractions  
The liquid phase analysis and quantification of the corresponding components were carried out with an HPLC-RI. As shown in Table 7 and Figure 5, the sugar yield varied between 36.9% and 70.0%w when reactions of 1 h at 120 \({}^{\circ}\)C and 180 \({}^{\circ}\)C were performed, respectively, showing that the more energy provided via heat to the system, the higher the hydrolysis of the lignocellulosic structures. Moreover, levulinic acid production was achieved only at the higher reaction temperatures, 170 \({}^{\circ}\)C and 180 \({}^{\circ}\)C. There was not enough energy in the system to achieve the transformation of sugars into other molecules, which, as will be seen later, can be achieved by longer reaction times, higher temperatures, or the use of catalysts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Time (h)**} & \multicolumn{2}{c}{**Concentration PC (g/L)**} & \multirow{2}{*}{**Yield * (\%w)**} \\ \cline{3-3} \cline{5-5}  & & **Sugars** & & **Levulinic Acid** \\ \hline
120 & 1 & 18.098 & & 36.903 \\
130 & 1 & 18.258 & & 37.228 \\
140 & 1 & 18.445 & & 37.610 \\
150 & 1 & 23.594 & & 56.265 \\
160 & 1 & 26.004 & & 53.023 \\
170 & 1 & 30.450 & 0.336 & 62.089 \\
180 & 1 & 34.818 & 0.577 & 70.001 \\ \hline \hline \end{tabular}  
* Based on lignocellulosic structure.  
\end{table}
Table 7: Quantification of platform chemicals via LHW at 1 h.  
Figure 4: Preliminary follow-up of content of platform chemicals at 180 \({}^{\circ}\)C for 1–4 h. (**a**) pH vs time (**b**) Conductivity vs time.  
Figure 5: Obtention of platform chemicals via LHW for 1 h.  
Figure 6 and Table 8 show that PC are obtained by the reaction of sugars from the lignocellulosic biomass, previously broken, and subsequent dehydration reactions of the sugars. The dehydration reactions, in the first place, can lead to products such as HMF or furfural, depending on whether the sugar that underwent dehydration was glucose or xylose, respectively. The subsequent dehydration reactions lead to products such as levulinic acid and formic acid.