esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation with Data from International GNSS Service  
### Data Description  
In order to verify the performance of PPP and POP ambiguity fixing, 136 global multi-GNSS reference stations are selected among which seven (CAS1, DAV1, MAW1, MCM4, OHI3, OHI2 and SYOG) are located in Antarctica. One reason why we use a global network is that the Galileo and BDS satellite clocks cannot be estimated well with a sparse distribution of IGS network around Antarctica. The another is that such a regional network is not sufficient for GLONASS ambiguity fixing since there are not enough exactly the same type of receivers as those in Antarctica. The distribution of these multi-GNSS stations are shown in Figure 2.  
The coordinates of these reference stations are fixed to the IGS weekly solutions. The GFZ (German Research Centre for Geosciences) analysis center final orbit and clock products are used for PPP, whereas the satellite clock offsets are estimated with the network in Figure 2 for POP.  
The PPP and POP AR performance in terms of fixing rate and positioning accuracy of solutions of GPS, GPS + GLONASS (GR), GPS + Galileo (GE), GPS + BDS (GC) and all four systems GPS + GLONASS + Galileo + BDS (GREC) are analyzed.  
During the data processing, we discovered that observations of the Antarctic stations MAW1 and SYOG cannot be processed properly with significant outliers in the positional estimates, and OHI2 and OHI3 are collocated stations. Therefore, only stations CAS1, DAV1, OHI3 and MCM4 are processed in the kinematic mode, since they observe the most GNSS systems. Observation data from the days of years 1 to 14, 2018 are processed.  
In the ambiguity fixing, all possible DD ambiguities between a kinematic station and all the static stations are defined as candidates and are checked for fixing according to the probability test. For the GLONASS fixing, there are quite a few receivers of the same type and with exactly the same firmware and antennas as the receivers in Antarctica used as rovers in the experiment. Therefore, we ignore the differences in the firmware versions of receivers. Four types of the most commonly used receivers for GLONASS ambiguity fixing, namely SEPT POLARX5, TRIMBLE NET8, JAVAD TRE_G3 TH DELTA and LEICA GR25, are selected each for one rover station, respectively. Their distributions are shown in Figure 3.  
Figure 2: The distribution of global multi-GNSS network used in this study. The colors represent the tracked GNSS systems on a station, GPS in blue, GLONASS in green, Galileo in red and Beidou in black.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 6 Results and Discussion
### Spoken Language Identification Results
In addition, six Spanish test files were identified as Chinese, eight as English, seven as French, twenty-six as German, and six as Russian. This experiment confuses Chinese and Spanish, English and Spanish, and Spanish and French. As mentioned previously, these are similar languages. Additionally, based on the results of the confusion matrices in Table 7, we can conclude that the German language is confused with Arabic, Chinese, English, French, and Spanish, as in the predicted German column. This implies a considerable similarity between German and these languages. We can also conclude that German is the only language that serves as a source of confusion for Arabic, as in the actual Arabic row. However, Arabic causes minimal confusion for all other languages, as in the predicted Arabic column. From the Experiment 11 results in Table 8, seven Arabic test files were identified as German. Ten Chinese test files were identified as English, eight as French, and fourteen as German. Sixteen English test files were identified as German and five as Spanish. Twenty-one French test files were identified as German. Seven Spanish test files were identified as English, eight as French, and twenty-four as German. As the results indicate, this experiment still confuses English and Spanish. In contrast, the system was effective at distinguishing between Spanish and Chinese, as well as between French and Spanish. We can draw the same conclusions from Experiment 11 as from Experiment 1. In particular, German was the main source of confusion for all six other languages, as seen in Table 8. In addition, Arabic was not a source of confusion for any of the other six languages. From Tables 7 and 8, German achieved the lowest P values (80.95% and 81.87% in Experiments 1 and 11, respectively) and the highest R values (96.45% and 97.55%, respectively), as well as the lowest F1 values: (88.02% and 89.02%, respectively). The impact of the German language is clear from the confusion matrices in these tables, where most spoken LID error files of any language, and Spanish in particular, were linked to German. Interpreting the relationships between these languages requires further linguistic research. Nevertheless, Tables 7 and 8 indicate that the proposed system is ideal for identifying Arabic spoken language and distinguishing it from the other six languages. Moreover, the system can discriminate among similar languages with the best accuracy. Table 9 presents the studies that used CRNN, CNN, LSTM architectures, or Mozilla corpus to compare with our proposed system.
# Machine Learning Based Recommendation System for Web-Search Learning
## 4 Results and Discussion
### Eye Tracker Efficiency Analysis  
* The technology implemented in the eye tracker software measures the learners' characteristics--pupil dilation, visual blinking, eye movements, gazing point, and visual attention of engaging and ignoring.  
Figure 8: Size of the recommendation list versus precision.  
Figure 9: Precision and recall for different learner group sizes.  
* The accuracy of the eye tracker is less than 0.5\({}^{\circ}\) in the controlled environments, with the actual gaze point offset frequently by at least 1\({}^{\circ}\). The gazing point is sampled at different rates.
* The standard frame rate lies (60 Hz, 500 Hz) images per second. The frame rate of the web camera lies (5 Hz, 30 Hz).
* The learner's performance in answering the questions is also analyzed. The repetition of gazing in choosing some options is a wavering characteristic of the learners when they are confused about choosing the option. The system predicted that 12% of the learners had the wavering characteristic, in which 7% of the learners failed to choose the correct option, and 5% chose the right option.
* The learner's knowledge acquisition is evaluated as 88% ground truth responses and 12% underperformance.
* The learner's engagement and findability characteristics in solving quizzes are analyzed as follows: 84% of the quick response learners, 16% of the learners are gazed or uninterested.
* The average response times of the simulation are observed as follows: choosing correct options (selectivity) 2.5 min, and choosing incorrect options lies 3 to 5 min. Sensitivity: 87% of the learners sensed the suitable options, 8% showed inattentional blindness, and 5% of the learners with had wavering gaze characteristics.
* The proposed model's overall expected eye tracking or eye movement percentage in web-search learning is 88%.
## 2 Numerical Model
### Mesh Convergence Test  
The modification of our previous FD code on the solution of the fluid field is substantial, but there are no data on DLD in literature available for rigorously validating our code. Hence, we perform a mesh convergence test for the solution of the flow in the absence of the particle, which also helps to choose a suitable mesh resolution. The computational domain size is \(2D\times 2D\times D\), as mentioned earlier. The profiles of the streamwise velocity normalized with its maximum between vertical neighboring posts for the mesh numbers of \(256\times 256\times 128\), \(128\times 128\times 64\), \(64\times 64\times 32\), and \(32\times 32\times 16\) are compared in Figure 3. One can see that the solution with the mesh \(128\times 128\times 64\) is in excellent agreement with the one with \(256\times 256\times 128\).  
The mesh \(128\times 128\times 64\) is chosen for the simulation of the particle motion in DLD. The smallest particle diameter we consider is \(0.24D\), which covers more than 15 meshes. Our previous works showed that this mesh resolution is sufficient for the prediction of the  
Figure 3: Profiles of the streamwise velocity normalized with its maximum between vertical neighboring posts for the mesh numbers of \(256\times 256\times 128\), \(128\times 128\times 64\), \(64\times 64\times 32\), and \(32\times 32\times 16\).  
particle motion with reasonable accuracy for various situations [22; 29; 31; 32; 33; 34]. Throughout this study, the Reynolds number based on the cylinder diameter and the fluid mean velocity is \(Re=0.2\), as an approximation to the creep flow. Our test showed that further decrease in \(Re\) does not change the result. The time step is \(0.001D/U_{m}\), which was found to be small enough for the time-step-independent particle trajectory. The particle is assumed to be non-colloidal, and thus the Brownian motion does not need to be considered. The density ratio is unity.
## 5 Scientific Novelty and Scientific and Practical Significance of the Research Results
### The Main Areas of Practical Application of the Results
#### 5.2.2 Development of Microwave (MW) Technology and Electronic Control Systems
The methods of nonlinear polarization theory acquire even greater scientific and practical significance when moving to the problems of using high-energy microwave systems for a wider range, up to the terahertz frequency range (0.3-30 THz). For the MW technology, the terahertz range has a number of specific features that make it very attractive for a wide range of fundamental and applied research in the field of physics and technology. Radio waves with frequencies from 0.3 to 30 THz are promising for diagnostics and spectroscopy of various media in different states of aggregation, including the development of high-resolution electron paramagnetic resonance (EPR) and nuclear magnetic resonance (NMR) methods [146]. Powerful terahertz radiation can be used to create dense plasma and control its parameters (controlled thermonuclear fusion; \"point\" plasma sources of X-ray radiation; remote detection of ionizing radiation sources) [147; 148]. The development of terahertz gyrotrons is actively being carried out in a number of scientific centers and industrial companies [149; 150; 146; 151; 152; 153; 154]. Over the past 10-15 years, a frequency of 1 THz has been achieved at a power level of 10-100 W in the mode of single pulses up to 1 ms in duration (FIR FU, Japan). A gyrotron with a pulsed magnetic field intended for operation in modes with a pulse repetition frequency has been developed (CCR, USA). A series of continuous gyrotrons with a power of about 100 W in the frequency range of 0.26-0.52 THz has been created (MIT, CPI-USA; FIR FU, Japan; EPFL, Switzerland) [155; 156; 157].
# Design Techniques for Low-Voltage RF/mm-Wave Circuits in Nanometer CMOS Technologies
## 4 Circuit Topologies for Low-Voltage Operation of RF/mm-Wave Blocks
From Equation (3), it is evident that the current-combining technique is more flexible in the selection of the optimum resistance, since the turn ratio, \(N\), can be properly used to tune the optimum impedance, \(R_{\text{OPT}}\). In particular, a larger turn ratio is required to lower the \(R_{\text{OPT}}\) when a higher \(M_{\text{P}}\) is used. Unfortunately, increasing \(N\) often produces lossy and area-consuming integrated transformers. Moreover, only integer number turn ratios can be practically implemented. Figure 8: Example of an SCT for PA voltage combining. Figure 7: Power-combining techniques (**a**): voltage-combining (SCT), (**b**) current-combining (PCT). These drawbacks can be overcome by using a hybrid-combining technique, based on both series and parallel transformers. Figure 9 shows two types of hybrid-combining topologies, namely, voltage-current combining (SPCT) and current-voltage combining (PSCT), both implemented for \(M_{\text{S}}=M_{\text{P}}=2\). Generally, the output powers of \(M_{\text{S}}\times M_{\text{P}}\) PA units can be increased up to \(M_{\text{S}}\times M_{\text{P}}\) times. This hybrid approach gives more freedom to designers, with respect to pure voltage or current technique, since the optimum resistance is given by the following:  
\[R_{\text{OPT}}=\frac{M_{\text{P}}\cdot M_{\text{S}}}{2\cdot N^{2}}R_{\text{LOAD}} \tag{4}\]  
Figure 10 depicts an example of a series-parallel-combining transformer for the circuit topology in Figure 9a. The transformer combines four PA units by means of interleaved coupling, thus requiring only two metal layers. Moreover, the structure maximizes the use of the top metal layer (i.e., the thicker layer of a standard CMOS BCOL), while reducing the need for the second metal layer, which is exclusively used for the underpass connections. It is worth noting that stacked coupling could be used to increase the magnetic coupling and save silicon area, but stacked transformers require at least two thick metal layers to have an advantage [18]. Figure 9: Power-combining techniques (**a**): voltage–current combining (SPCT); (**b**) current–voltage combining (PSCT).
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 3 Methodology  
### Problem Description  
Fuel consumption prediction involves using a multi-dimensional time series (MTS) where each dimension represents a variable and each time step represents a node, with trucks operating over a distance of two kilometers. The MTS can be denoted as \(\mathbf{x}_{t}\):  
\[\mathbf{x_{t}}=[x_{1,t},x_{2,t},\ldots,x_{D,t}] \tag{1}\]  
where \(x_{i,t}\) represents the value of the \(i\)-th variable related to fuel consumption at time \(t\).  
Fuel consumption prediction is a typical time series forecasting problem. The goal is to predict the fuel consumption at the next time step based on the previous \(n\) time steps of the multi-dimensional time series \(x_{t}\), using the CLMFD model represented by \(F\). This prediction process can be expressed by the following formula:  
\[\hat{y}_{t+1}=F(\mathbf{x_{t-n+1}},\mathbf{x_{t-n+2}},\ldots,\mathbf{x_{t}}) \tag{2}\]  
where \(\hat{y}_{t+1}\) represents the predicted fuel consumption at the next time step.
###### Abstract
Generally, Ag shows a stronger LSPR effect than Au. In addition, the absorption range of TiO\({}_{2}\) extended to the visible light region due to the LSPR effect of the Ag and Au NPs. Since the LSPR sensitization effect of plasmonic NPs depends mainly on their size and shape, the LSPR effect is strengthened when the size of the Ag and Au NPs increases [31; 32]. Hence, the S\({}_{\text{Au}}\) and S\({}_{\text{Ag}}\) samples prepared by photoreduction showed higher efficiency in the photodegradation of RhB. During this process, RhB was first broken down into benzoic acid and phthal acid and then into ethanedioic acid, while the organic carbon content remain unchanged [33]. Finally, the RhB was degraded into small molecules, such as CO\({}_{2}\), NOx and H\({}_{2}\)O, and finally, oxidized completely, with the organic carbon content decreased significantly [34; 35; 36]. The degradation of RhB by photocatalysts has been reported by many groups. TiO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) co-doped graphene aerogel can remove 97.7% RhB within 60 min under visible light irradiation [37]. SiO\({}_{2}\)@C-doped TiO\({}_{2}\) hollow spheres can degrade 61.7% RhB after 100 min under UV irradiation [38]. Ag/TiO\({}_{2}\) composites can degrade 80% RhB after 120 min under UV irradiation [39]. Compared with the above photocatalysts, the Ag decorated TiO\({}_{2}\) membrane can almost degrade 90% RhB within 30 min under visible light irradiation. Therefore, Ag/Au-decorated TiO\({}_{2}\) membranes showed excellent photodegradation efficiency. The prepared TiO\({}_{2}\) membrane was characterized as being twos atoms thick [26]. The specific surface area was greatly increased compared with P25 TiO\({}_{2}\). The target molecules were more easily  
Figure 2: Photocatalytic degradation of RhB by TiO\({}_{2}\) membrane decorated with Ag and Au NPs. absorbed on TiO\({}_{2}\) membrane. The degradation of the target molecules experienced a dynamic adsorption-photocatalysis-desorption process. The electron and hole produced under irradiation reacted with the target molecules on the enlarged surface; thus, the recombination of the electron and hole was also inhibited. The process is presented in Figure 3. As a result, the photocatalytic efficiency of the TiO\({}_{2}\) membrane was enhanced significantly compared to P25 TiO\({}_{2}\).
## 2 Materials and Methods
### Acid Activation of Raw Material  
Clay mineral was treated with sulfuric acid. According to previous works [12] and preliminary tests, it was shown that the particle size of the solid had no significant effect on the kinetics of the activation or the pore properties of clay particles when the dimensions were equal to or less than 40 \(\upmu\)m. Moreover, it was found that the agitation does not have much effect on the kinetics of the acid attack from certain values. Hence, the stirring has been adjusted to the maximum value of a laboratory stirrer in order to ensure the homogenization of the reaction mixture. Consequently, this study was conducted using only four major factors: the strength of acid (wt%), the reaction temperature (T), the reaction time (t) and the liquid to solid mass ratio (R).  
Based on preliminary tests and from the literature review [12], samples were prepared according to the following ranges: Temperature (60-90 \({}^{\circ}\)C), contact time (1.5-6 h), liquid/solid mass ratio (4-7) and mass percentage of acid (15-50%). It was shown that the selected ranges of these factors, are appropriate to investigate the effect of acid activation on raw material and efficient to make difference in activation degree between the samples. Three activated clay samples were prepared: the first sample (S\({}_{1}\)) was prepared under low conditions (wt = 15%, T = 60 \({}^{\circ}\)C, t = 1.5 h, R = 4), the second sample (S\({}_{2}\)) was activated at average conditions (wt = 32.5%, T = 75 \({}^{\circ}\)C, t = 3.75 h, R = 5.5), while the last sample (S\({}_{3}\)) was prepared under strong conditions (wt = 50%, T = 90 \({}^{\circ}\)C, t = 6 h, R = 7).  
A series of treatment steps were used for the acid-treatment of clay. Experiments were carried out with sulfuric acid in a jacketed glass reactor equipped with a thermometer, a reflux condenser and a stirrer. Each sample was prepared according to the following procedure: 100 g of raw clay, with a particle size less than 40 \(\upmu\)m, was mixed with an appropriate aqueous solution of H\({}_{2}\)SO\({}_{4}\). The slurry was stirred at maximum speed by a laboratory agitator of type Kirk 210 (Bicasa, Monza Brianza, Italy). Then, slurry was air-cooled and filtered with a glass fibber. The filter cake was washed with distilled water several times until the wash water was neutral with no trace of sulphate. The pH of the filtrate is monitored by universal pH test strips.  
Finally, the clay product was dried in an oven at 70 \({}^{\circ}\)C for 48 h until the weight remains constant. The freshly prepared dry sample was crushed into powder by a mortar. Then the activated clay was sieved before use according to U.S. standard sieve designations (Afnor nF-11504). All the activated clay samples (S\({}_{1}\), S\({}_{2}\) and S\({}_{3}\)) were prepared with the same described procedure.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 5 Performance Evaluation
### Theoretical Analysis  
To reveal the performance of the proposed scheme, we compared it with the existing SPE-CKS schemes. For simplicity, we denote these schemes introduced in References [7; 8; 10] by ZZ11, OT15 and ZLW19, respectively. Concretely, ZZ11 is a standard SPE-CKS scheme; OT15 is an efficient IPE scheme, which can be changed to a SPE-CKS scheme by using a method mentioned in Reference [21]; ZLW19 is a SPE scheme that supports disjunctive and conjunctive keywords search simultaneously. Moreover, for simplicity, we combine the \(KeyGen_{R}\) algorithm to the \(KeyGen_{S}\) algorithm and denote these two algorithms by \(KeyGen\). Tables 1 and 2 show the comparison between our scheme and the previous schemes in terms of the storage and time overhead.  
Because the time cost of exponentiation computation and pairing is much higher than that of other operations, such as addition and multiplication operations, the comparison only considers these two operations. Table 1 shows that the time cost of index building, trapdoor generation and test in our scheme are all less than that in other three schemes. Although the time cost of \"KeyGen\" algorithm in our scheme is not as good as that in ZZ11, our scheme is also practical since this algorithm only runs when system initialization and key pair replacement. In addition, because the pairing operation and exponentiation computation are big computation burden in the test process, we can argue that the test efficiency is improved a lot in our scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **ZZ11 [7]** & **OT15 [8]** & **ZLW19 [10]** & **Our Scheme** \\ \hline KeyGen & \(P_{1}\) & \(O(n^{2})P_{1}\) & \((2n+3)P_{1}+P_{2}\) & \((n+2)P_{1}+P_{2}+2P\) \\ IndexBuild & \((n+1)P_{1}+(n+2)P_{2}+e\) & \((12n+10)P_{1}\) & \((3n^{2}+4n)P_{1}+P_{2}+P\) & \((n+2)P_{1}+P_{2}\) \\ Trapdoor & \(3(n+2)P_{1}+P_{2}\) & \((12n+10)P_{1}\) & \((2m+2)P_{1}\) & \((n+2)P_{1}+P\) \\ Test & \((2n+3)e\) & \(11e+5(n-1)P_{1}\) & \(2n(m+1)e\) & \((n+2)e\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with previous searchable public key encryption supporting conjunctive keywords search (SPE-CKS) schemes on time complexity.  
For simplicity, we denote \(pk_{S}\) and \(pk_{R}\) by \(pk\) and \(sk_{S}\) and \(sk_{R}\) by \(sk\). From Table 2, we can find that the storage cost of index in our scheme is much less than that in the other schemes. Moreover, the storage cost of \(pk\) is also the smallest of all. Since \(n\) is not large and the storage cost of \(|Z_{q}|\) is small, the storage cost of \(sk\) and trapdoor in our scheme is still as practical as the other schemes.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 4 Scheme Analysis
### Performance Analysis  
As previously described in Section 3.1, our scheme integrates various cryptographic techniques. To assess the overhead of our approach, we use processing time as a performance metric. \(t_{bg\_kgen}\) represents the key generation time for the BG algorithm, \(t_{bg\_dec}\) represents the encryption time for the BG algorithm, and \(t_{bg\_dec}\) represents the decryption time for the BG algorithm. Similarly, \(t_{smg\_kgen}\) represents the key generation time for the SM9 algorithm, \(t_{smg\_enc}\) denotes the encryption time for the SM9 algorithm, and \(t_{smg\_sign}\) represents the signing time for the SM9 algorithm. Additionally, \(t_{ped}\) represents the time for Pedersen commitments.  
Our scheme includes the implementation of anonymous identities, transaction amount privacy protection, transaction legitimacy verification, and regulatory function implementation. For its corresponding computational overhead, refer to Table 1.  
Our experiments were performed on a multi-cluster configuration with CPU 3.8 GHz, GPU RTX 4090, and 32 GB of RAM per machine, completing the joint experiments on graph neural network detection and blockchain-based storage. Detailed information is provided in Table 2.  
The BG and SM9 algorithms used in our scheme are mature and have been widely applied. As can be seen in Table 1, the transaction amount privacy protection and transaction legitimacy verification involve numerous Pedersen commitments, which incur some overheads. However, Pedersen commitments are currently key technologies and common methods in blockchain privacy protection. Therefore, the performance overhead of our scheme falls within a normal and acceptable range. We have analyzed the performance of this scheme; refer to Table 3 for details.