esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 8 Performance Analysis
### Computation Cost  
We compared the computation costs of our protocol to those of existing protocols [6,8,15,16]. Referring to [32,33], we estimated the approximate execution time of each cryptographic operation on the following configurations of the computer system. Windows 7 OS and Android phones were used and the system structure of the mobile phone ws Android 4.4.4 KTU84P along with a 2 GB RAM and 1.8 GHz processor. Furthermore, the configurations of the computer system were Windows 7, Professional with an Intel(R) Core(TM) 2 Quad CPU Q8300, 2 GB RAM, @2.50 Hz. The XOR function was not included as it was negligible compared to other functions. The following shows the time complexity for the computational analysis.  
* \(T_{h}:\) The time complexity of a one-way hash function operation \(\approx\) 0.0005 s.
* \(T_{m}:\) The time complexity of a modular multiplication operation \(\approx\) 0.00125 s.
* \(T_{mm}:\) The time complexity of a modular exponentiation operation \(\approx\) 0.522 s.
* \(T_{pm}:\) The time complexity of a elliptic curve point multiplication operation \(\approx\) 0.0503 s.
* \(T_{sym}:\) The time complexity of a symmetric encryption/decryption operation \(\approx\) 0.0087 s.
* \(T_{ecc}:\) The time complexity of a asymmetric encryption/decryption operation \(\approx\) 0.3057 s.  
The total computation costs for our protocol and for [PERSON] et al.'s scheme were \(27T_{h}\) (\(\approx\)0.0135 s) and \(10T_{h}+3T_{mm}+4T_{sym}\) (\(\approx\)1.6058 s), respectively. Table 5 presents the result for  
Figure 9: Analysis of the simulation results using CL-AtSe and OFMC.  
computation costs. Consequently, we provided better efficient computation costs compared with related schemes because it only uses one-way hash functions. Therefore, the proposed scheme is considered efficient in the application for practical mobile environments.
# A Reinforcement Learning Approach to Speech Coding
## Appendix A DPCM and Tree Coding  
The reinforcement learning approach introduced in this paper for speech coding builds upon two classic speech coding principles, predictive coding, and delayed encoding. Although much more extensive treatments of these concepts for speech coding are available elsewhere, we provide concise developments here to put the reinforcement learning approach into context. First, we introduce differential pulse code modulation (DPCM) and then add the extension to tree coding.
applied sciences Article  
Analysis of the Seismic Properties for Engineering Purposes of the Shallow Subsurface: Two Case Studies from Italy  
and Croatia  
[PERSON]=true,font=\"Federico.DaColabelabelabelabel=true,font=\"Federico.DaColabelabelabelabelabel=true,font=\"Federico.DaColabel  
the Multichannel Analysis of Surface Waves (MASW) [10]. Technically, MASW relies on the dispersive behavior of the surface waves and consists in extracting a dispersion curve from a recorded seismogram and inverting it, obtaining a 1D shear-wave velocity profile. [PERSON] et al. (2018) [11] show that the method is able to estimate accurately V\({}_{\text{s}}\) profiles and therefore compute the V\({}_{\text{S30}}\) with a relatively small error. Another method that relies on the surface waves is the Horizontal to Vertical Spectral Ratio (HVSR, [12]). The method consists in processing the data from 3C single-station data by computing the ratio between the horizontal and vertical spectra and thereby extracting a 1D shear-wave velocity profile together with the resonant frequency of the terrain.  
A method that is gaining popularity is the travel-time tomography of first breaks on an SH active seismic survey [13; 14]. The main advantage of this method is that it provides a 2D (potentially even 3D) S-wave velocity profile and therefore a computation of the V\({}_{\text{S30}}\) along the entire seismic line.  
A more accurate characterization of the shallow subsurface, beyond the computation of the V\({}_{\text{S30}}\), can be of high interest for engineers. For instance, from the computation of [PERSON]'s ratio or V\({}_{\text{P}}\)/V\({}_{\text{S}}\), information regarding the fracturing of the rocks [1] and its fluid saturation [15] can be inferred.  
In this work, we present the application of an innovative survey technique in two case studies from two very different sites. The first was recorded along the roads of a small town lying on hard rock in the area of Split in Croatia. The second case study consists of a dataset recorded in an alluvial plain in an intensively cultivated countryside close to the city of Ferrara. The novelty of the method proposed lies in the fact that we recorded separately compressional- and shear-wavefields, distinguishing in the latter between the components orthogonal (SH) and parallel (SV) to the seismic line. In fact, while integrating P- and SH-waves is increasingly popular in the near-surface geophysics community [16; 17], the distinction between SH- and SV-wavefields is much rarer. Seismologists performed several numerical simulations [18] and analyzed field data [19] aiming at the study of shear-wave splitting and anisotropy, but hardly any field example using controlled sources can be found in the literature.  
In the same surveys, data specifically for surface-wave analysis was also recorded. The final aim of our analyses is to provide detailed engineering information of the shallow subsurface without invasive tests at a relatively large scale. Successful testing of the method in two such different locations should allow us to confirm whether the method is replicable in most situations.
# Research Status of and Trends in 3D Geological Property Modeling Methods: A Review
## 3 Interpolation Methods for 3D Property Modeling
### Multiple-Point Geostatistics Methods
#### 3.2.1 Probability-Based Methods  
Single Normal Extended Simulation (SNESIM)  
Proposed by [PERSON] [73; 74], SNESIM is a probabilistic method similar to, and an improvement of, the one proposed by [PERSON] and is used for simulating discrete variables. To reduce the computational complexity of conditional probability, SNESIM no longer scans the training image for each conditional data template; instead, SNESIM scans the training image once and stores all probabilities in a search tree. This search tree structure makes it possible to quickly obtain the required conditional probabilities during simulations. SNESIM is a pixel-based sequential simulation method and is more likely than the object-based method to be faithful to well seismic data. One of SNESIM's drawbacks is that the training image must be unconditional and cannot be used for continuous variables. Moreover, the SNESIM algorithm can capture only the static features of the training image. In addition, any trend, such as vertical or area ratio change, needs to be defined explicitly.  
Indicator-Based MethodSome researchers have attempted to combine multiple-point probabilities with indicator techniques. For example, [PERSON] [75] incorporated multiple-point geostatistics by using an indicator technique under the assumption of conditional independence in the Bayesian framework and between information sources. In this technique, multiple-point statistics are obtained directly from valid data. At each location of the grid, the conditional probability derived from the multiple-point information is integrated with the conditional probability obtained by the indicator kriging technique as the property value of this location. Later, [PERSON] [76] proposed another method that can integrate multiple-point geostatistics into all traditional sequential methods.  
ImpalaImpalaImpala is a probability-based multiple-point simulation method that uses lists. Impala follows the same principle as SNESIM in that Impala stores conditional probabilities obtained from the training image and then uses them in the simulation. The difference is that Impala uses a list structure instead of the SNESIM search tree to store the multiple-point statistics inferred from the training image [77], thereby reducing the storage capacity  
Figure 5: Construction of multi-point geostatistical data template.  
of the multiple-point simulation (MPS) algorithm. In addition, Impala can obtain conditional probabilities in parallel. Despite the storage and central processing unit (CPU) advantages of using lists, the implementation of this method is very similar to that of the SNESIM algorithm.
# Evaluating Deep Learning Techniques for Natural Language Inference
## 6 Testing Generalization
### Breaking NLI Test  
We attempted to \"break\" our models by testing them on the breaking NLI dataset. This dataset was created for testing trained NLI models regarding their generalization. It consists of examples that differ by, at most, one word from sentences in the training set, but the performance of well-known trained models is substantially reduced on that set [42]. In our case, for training data, we used SNLI and MNLI. The results of the Breaking NLI set are shown in Table 4. We see that DAM and ESIM cannot handle the test set sufficiently. In the case of DAM, the accuracy is at the level of random chance. The BERT models, on the other hand, perform exceptionally well, almost all above 90%, whether they are trained on  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **Params** & **SNLI** & **SICK** & **SciTail** & **DNLI** & **MNLI** \({}^{*}\) & **RTE** \({}^{*}\) & **QNLI** \({}^{*}\) & **WNLI** \({}^{*}\) \\ \hline DAM & 382 K & 85.13 & 83.37 & 69.50 & 83.92 & 72.52 & 68.20 & 75.01 & 76.00 \\ ESIM & 4.3 M & 87.07 & 80.81 & 75.17 & 87.07 & 77.59 & 61.49 & 81.16 & 67.55 \\ BERT-Base & 110 M & 90.34 & 85.71 & 93.65 & 90.68 & 84.64 & 83.75 & 89.36 & 60.56 \\ BERT-Large & 340 M & 90.63 & 89.85 & 93.79 & 91.09 & 86.56 & 84.48 & 90.54 & 60.56 \\ RoBERTa-Base & 125 M & 91.41 & 89.59 & 93.51 & 91.42 & 87.94 & 87.36 & 90.77 & 56.34 \\ RoBERTa-Large & 355 M & **91.92** & **91.50** & **96.52** & 90.87 & **90.52** & **90.97** & 93.21 & 56.34 \\ ALBERT-Base & 11 M & 87.71 & 89.06 & 94.21 & 90.90 & 85.80 & 84.48 & **93.80** & 59.15 \\ ALBERT-Large & 17 M & 89.96 & 89.85 & 94.64 & **91.50** & 88.60 & 84.47 & **93.80** & 61.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracies (%) of the best implementations on test data. We use validation data (\({}^{*}\)) when test data are not publicly available. For MNLI, we present the results for the matched dataset. The best results are in bold.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **MNLI** & **RTE** & **QNLI** & **SNLI** & **SciTail** \\ \hline BERT-Large [10] & 86.7 & 70.4 & 92.3 & & \\ ALBERT-Large [25] & **90.8** & 89.2 & **95.3** & & \\ SMART-BERT [38] & 85.6 & 71.2 & 91.6 & 91.1 & 93.2 \\ HyPe-RoBERTa [39] & 90.32 & & 94.19 & & \\ PowerQuant-BERT [40] & 80.54 & 61.82 & 91.47 & & \\ RoBERTa-Large [24, 41] & 90.2 & 86.6 & 94.7 & 91.2 & 96.0 \\ Our-RoBERTa-Large & 90.52 & **90.97** & 93.21 & **91.92** & **96.52** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the state-of-the-art models on well-known datasets with our best model (RoBERTa-Large) in terms of accuracy (%). The best results are in bold, the second best underlined.  
SNLI or MNLI. The best performance comes from RoBERTa-large, trained on MNLI, and ALBERT-large, trained on SNLI.
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
## 5 Discussion  
The research presented in this study significantly contributes to the field of robotic dynamics, particularly for optical machining robots with prismatic joint clearances. By modeling the dynamics of a space-parallel robot with joint clearances, the simulation results demonstrate that the effects of clearances on the moving platform's dynamics align with previous literature: joint clearances significantly impact the robot's dynamics and must be compensated for and controlled through specific measures. Additionally, the current model incorporates certain simplifications in joint contact mechanics and material properties. Future work could explore more complex contact modeling and experimental validation to enhance the accuracy of dynamic predictions.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(N_{\text{T}}\) & The number of antennas of the source node \\ \(N_{\text{R}}\) & The number of antennas of the relay node \\ \(N_{\text{D}}\) & The number of antennas of the destination node \\ \(N_{\text{RF}}\) & The number of antennas of RF chains at each node \\ \(N_{\text{S}}\) & The number of data streams \\ \(\mathbf{s}\) & The original signal of the source node \\ \(\mathbf{F}\) & The HBF matrix of the source node \\ \(\mathbf{F}_{\text{B}}\) & The DBF matrix of the source node \\ \(\mathbf{F}_{\text{RF}}\) & The ABF matrix of the source node \\ \(P_{\text{B}}\) & The power threshold of the source node \\ \(\mathbf{H}_{1}\) & The mmWave channel matrix of the source-to-relay link \\ \(\mathbf{G}\) & The HBF matrix of the HD relay node \\ \(\mathbf{G}_{\text{RF}_{1}}\) & The receive ABF matrix of the HD relay node \\ \(\mathbf{G}_{\text{RF}_{2}}\) & The transmit ABF matrix of the HD relay node \\ \(\mathbf{G}_{\text{B}}\) & The DBF matrix of the HD relay node \\ \(\sigma_{\text{R}}^{2}\) & The noise variance of the relay node \\ \(P_{\text{R}}\) & The power threshold of the real node \\ \(\mathbf{H}_{2}\) & The mmWave channel matrix of the relay-to-destination link \\ \(\mathbf{W}\) & The HBF matrix of the destination node \\ \(\mathbf{W}_{\text{RF}}\) & The ABF matrix of the destination node \\ \(\mathbf{W}_{\text{B}}\) & The DBF matrix of the destination node \\ \(\sigma_{\text{D}}^{2}\) & The noise variance of the destination node \\ \(\gamma_{\text{T}}\gamma_{\text{T}}\gamma_{\text{2}}\) & The scaling factors \\ \(\mathbf{F}_{\text{U}}\) & The unnormalized DBF matrix of the source node \\ \(\mathbf{G}_{\text{U}}\) & The unnormalized DBF matrix of the real node \\ \(\mathbf{F}\) & The unnormalized HBF matrix of the source node \\ \(\mathbf{\overline{G}}\) & The unnormalized HBF matrix of the real node \\ \(\mathbf{G}_{\text{B}_{1}}\) & The receive DBF matrix of the FD relay node \\ \(\mathbf{G}_{\text{B}_{2}}\) & The transmit DBF matrix of the FD relay node \\ \(\mathbf{G}_{1}\) & The receive HBF matrix of the FD relay node \\ \(\mathbf{G}_{2}\) & The transmit HBF matrix of the FD relay node \\ \(\mathbf{H}_{SJ}\) & The SI channel matrix of the FD relay node \\ \(\mathbf{\overline{H}_{1}}\mathbf{\overline{H}_{2}}\mathbf{\overline{H}_{S\text{I }}}\) & The estimated channel matrixes \\ \(\mathbf{\Theta}\) & The covariance matrix of estimation error at the receiver side \\ \(\mathbf{\Theta}\) & The covariance matrix of estimation error at the transmit side \\ \(\mathbf{\Delta}\) & The unknown part of CSI \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results
### CorvisST Airflow Pressure
#### 3.2.4 Comparison to Piston-Chamber Pressure
(**d**) The time point when the maximum pressure was detected was consistent within the area of the pressure (within the black contour lines which represent the ten pressure levels). The maximum airflow pressure (at the pivotal point of the airflow cone) and the piston-chamber pressure were significantly different between frame 7 and 126 (\(p<0.001\)), Figure 8b. The maximum airflow pressure at an 11 mm distance was detected at frame 65 (15.011 ms) by 96.4 \(\pm\) 1.42 mmHg; the recorded piston chamber pressure had its maximal value at frame 70 measuring 182.86 mmHg. The FWHM for the piston chamber pressure was found to be 10.9 ms, and for the airflow pressure at an 11 mm distance, 15.2 ms.  
Figure 8: Graphical analysis of the piston chamber pressure and the airflow pressure of the CorvisST. (**a**) Comparison between the piston chamber pressure detected by the CorvisST (orange) and piston chamber pressure recorded by the myDAQ (black). A maximum difference of 2.3% between the two pressure readings (dotted line below) was observed at frame 99 (22.869 ms). (**b**) Comparison between the piston chamber pressure recorded by CorvisST (orange) and the detected airflow pressure at the central measurement point (black). Figure 7: Relative airflow pressure at the central horizontal plane (**a**) 2D profile of the spatial and temporal distribution, which was separated (**b**) across the central horizontal plane (spatial distribution) and (**c**) over time (temporal distribution).
## 3 Results and Discussion
### Process water pH and TOC  
Process water obtained from HTC runs was characterized in terms of pH and TOC (Table 1). For all HTC tests, the pH of PW was in the acidic pH range and slightly increased after recirculation steps. This is in good agreement with observations done by previous researchers [41; 45].  
Figure 4: Effect of process water (PW) recirculation on hydrochars energy yield at the three temperatures investigated.  
Figure 3: Contribute of ash content in hydrochar yield increase after recirculation steps.  
Particularly, the recirculation of PW led to an increase of pH from 3.87 to 3.97 at 180 \({}^{\circ}\)C, from 4.40 to 4.50 at 220 \({}^{\circ}\)C and from 4.66 to 4.70 at 250 \({}^{\circ}\)C.  
It can be seen that the higher the temperature, the higher the pH, while it also appears that the pH became more or less stable after step R1.  
As expected, TOC results showed a considerable increase in values after PW recirculation due to the increase of organic soluble compounds at each step, in addition to those produced in the previous cycle. Not surprisingly, this trend was more pronounced at higher HTC temperatures.  
Trends of pH and TOC are consistent with the interpretation reported in the Section 3.1. At higher temperature, pH increased while TOC decreased, thus the concentration of organic acids and reactive compounds also reduced, leading to lower hydrochar yield increase during step R1. This explanation seems to be supported by the fact that, at a higher acid concentration, the degradation of biomass macro-components is promoted and newly formed compounds are added to the surface of hydrochar [44]. During step R2, the increment in TOC decreased probably because some of the dissolved organic matter took part in polymerization reactions (condensation). Organic acids were subjected to decarboxylation to form further gaseous products at temperatures of 180 and 220 \({}^{\circ}\)C. At 250 \({}^{\circ}\)C, TOC increased almost constantly during recirculation steps, probably due the lower initial concentration of soluble compounds, which led to a delay in reaching a dynamic equilibrium of organic species concentration in aqueous phase [42]. Thus, an increment of the concentration of reactive substances in the liquid phase at step R2 for HTC at 250 \({}^{\circ}\)C may have accelerated the rate of polymerization and led to a greater solid mass yield increase.  
Recent literature [42; 46; 49] has reported that organic acids concentration in PW initially increases to reach a nearly constant value of a dynamic equilibrium thus the pH slight increase during recirculation can be also attributed to an inorganic ions (i.e., alkali metals) up-take [44; 45].
# Development of Screen-Printable Nafion Dispersion for Electrochemical Sensor
## 2 Materials and Methods  
### Materials and Chemicals  
Commercial Nafion(tm) ink (D2021, 20% PFSA dispersion) was purchased from Fuel Cell Store (College Station, TX, USA). The starting polymer had an available acid capacity of >0.92 meq/g on a H\({}^{+}\) polymer basis. Lead nitrate, potassium nitrate, and the solvents used in rotor evaporation, such as 2-methyl-1,3-propanediol, were of ACS grade and purchasedfrom Sigma Aldrich (St. Louis, MO, USA). The buffer solution consists of 10 mM potassium nitrate in deionized water (18.2 M\(\Omega\)/cm) and the pH was adjusted to 5 by adding 5 mM acetic/acetate buffer.  
Standard stock solution (1000 mg/L) of lead nitrate was prepared by dissolving the associated salts in a buffer solution. The prepared stock solution was further diluted to obtain working standard solutions in the range of 1 to 500 \(\upmu\)g/L of Pb(II) using a buffer solution. Commercial screen-printed sensors (SP-1302, silver working electrode) for the detection of Pb(II) were purchased from BASi Research Products (West Lafayette, IN, USA). The sensors are in 3-electrode configuration including a 2-mm silver working electrode, Ag/AgCl reference electrode, and platinum auxiliary electrode. All the electrodes were screen printed on corundum ceramic substrates.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 6 Experimental Results and Analysis
### Analysis of Primer Sequence Identification and Index Check  
To evaluate the effectiveness of the proposed primer sequence identification and index check method, multiple tests were conducted under varying base error rates. Index sequences were verified using parity check relations (by computing the syndrome), and reads that failed the verification were discarded (Figure 11).  
An index was considered successfully verified if it passed the parity check, enabling identification of the corresponding original index and the subsequent clustering of associated sequencing reads. Approximately 1% of reads were assigned to the wrong index cluster despite passing the parity check.  
Although the proposed index check strategy excluded a relatively large number of sequencing reads, it significantly reduced the probability of index crosstalk. This trade-off enhanced the overall reliability of read clustering, particularly in instances where there were sequencing errors.