esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 3 Proposed Scheme
### Key Agreement Phase
#### 3.4.1 Session Key Creation  
In this sub-phase, \(CS\) computes the session key between \(U_{i}\) and \(S_{j}\) as follows:  
Step 1: \(CS\) generates a random nonce \(n_{CS}^{new}\) and computes the session key \(SK_{U-S}\) as follows:  
\[SK_{U-S}=h\left(h\left(n_{U}^{new}\right)\oplus h\left(n_{S}^{new}\right) \oplus h\left(n_{CS}^{new}\parallel T_{CS}^{new}\right)\right) \tag{24}\]  
Figure 5: User authentication, server authentication and evidence of connection attempt sub-phases.  
Figure 6: Key agreement and mutual authentication phases.  
Equation (24) is used to compute the session key. The session key contains security parameters generated by \(U_{i}\), \(S_{j}\) and \(CS\) which represents the relationship among all the participants.  
Step 2: \(CS\) computes the verification parameters for \(S_{j}\) and \(U_{i}\) as follows:  
\[D_{6}=B_{2}\oplus h\Big{(}T_{S}^{new}\parallel SID_{j}\Big{)}\oplus T_{CS}^{new} \tag{25}\]  
\[D_{7}=h\Big{(}n_{CS}^{new}\parallel T_{CS}^{new}\Big{)}\oplus h\Big{(} SID_{j}\parallel T_{CS}^{new}\Big{)}\oplus h\Big{(}n_{U}^{new}\Big{)} \tag{26}\]  
\[D_{8}=C_{2}\oplus h\Big{(}T_{U}^{new}\parallel ID_{i}\Big{)}\oplus T_{CS}^{new} \tag{27}\]  
\[D_{9}=h\Big{(}n_{CS}^{new}\parallel T_{CS}^{new}\Big{)}\oplus h\Big{(}ID_{i} \parallel T_{CS}^{new}\Big{)}\oplus h\Big{(}n_{S}^{new}\Big{)} \tag{28}\]  
where \(D_{6}\) and \(D_{7}\) are for \(S_{j}\), while \(D_{8}\) and \(D_{9}\) are for \(U_{i}\). Equations (25) to (28) contain information generated by \(CS\) for computing the session key.  
Step 3: \(CS\) computes the challenge-response message for \(S_{j}\) and \(U_{i}\) as follows:  
\[D_{10}=E_{SK}\Big{(}h\Big{(}n_{CS}^{new}\Big{)}\oplus h\Big{(} SID_{j}\parallel PSID_{j}\parallel B_{2}\Big{)}\Big{)} \tag{29}\]  
\[D_{11}=E_{SK}\Big{(}h\Big{(}n_{CS}^{new}\Big{)}\oplus h(ID_{i} \parallel PID_{i}\parallel C_{2}\Big{)}\Big{)} \tag{30}\]  
\(CS\) computed the challenge-response message for each entity using the session key; this means that, a legitimate participant can recover the security parameters to construct the session key. Finally, \(CS\) sends the authentication response message \(M_{7}\) to \(S_{j}\) through an open communication channel.
## 3 Application
### Labeling the Initial CVs from 1 to 0 as Linearly-Spaced Values for GRNN  
The four high-score features, i.e., the RMS error, mean, maximum, and minimum of signals, are treated as the variables of the input layer \(\mathbf{X_{1}}\ \sim\ \mathbf{X_{4}}\) in Figure 1, and the initial CVs for \(\mathbf{P_{1}}\)-\(\mathbf{P_{315}}\) from 1 to 0 as linearly-spaced values in the pattern layer in Figure 1. An artificial neural network (ANN) is a mathematical model based on biological neural networks, and is trained using previously recorded data. An ANN determines the relationship between the input and output variables through nonlinear regression analysis. For three-fold cross-validation of the GA-GRNN, \(315\times 4\) data points were divided into three. Two-thirds of the 315 data points were trained, and one-third were tested. Two parameters, \(\sigma\) and b in Equation (2), were tuned by the GA within the GRNN structure. Convergence of the GA based on the other one-third for validation was achieved, and a very low RMS error was obtained. \"Best\" and \"Mean\" in Figure 10 indicate the fitness value (convergence) of the optimized particle and the average fitness value of particles, respectively. Both converge at a fitness value of approximately 0.0125 after approximately 30 iterations.  
Figure 10: Convergence plot obtained by the GA for the GA-GRNN model through three-fold cross-validation.  
Figure 9: Fisher scores for nine features.  
After three GA-GRNN models were built, the data of the original 331 days plus the 38 unexpected power shutdown days were packaged, and the average outputs \(y\) are plotted in Figure 11 as black dots. As mentioned before, the initial CVs of the WHRA start from 1 and descend to 0 (denoted by the blue straight line covering 369 days in Figure 11) when the curve of the RUL of the WHRA is unknown in a prior iteration. The entire history of a working WHRA until its failure or call for maintainence would restrict the accuracy of the RUL model, as previous partial maintenance procedures result in the asset exhibiting an improved health condition. Users are keen to determine unclear working lifetimes and health statuses when the WHRA is reused, repaired, and a frequent occurrence in practice.
## 5 Conclusions  
At present, one of the hindrances for the application development of the LOM is the lack of load testing system to have in depth research on the dynamic performance of the LOM with enough external force loads, in order to exhibit the ability of force load tolerance to achieve accurate output position of the LOM. In this paper, a linear oscillating load system is proposed and designed to analyze the dynamic performance of the LOM with external load forces at short stroke and high-frequency working condition. Both physical and mathematical models are built, and both experiment and simulation results indicate the same effects on the dynamic performance of the LOM as the spring loads influence both amplitude responses and phase responses to lead more amplitude attenuation and less phase lag, and damping loads and rectangular loads mainly reflect more amplitude attenuation. Furthermore, when proper external force compensation control is applied, the tolerance force can be of 800 N amplitude rectangular force load, indicating that the LOM has the ability to maintain an accurate output position with large force load at the resonant frequency of the LOM, which provides the basis for research into the linear driven electrical hydraulic actuator.  
Conceptualization: Z.J.; Data curation: Y.C.; Investigation: L.Y., X.L. and L.Z.; Methodology: Y.C. and X.L.; Supervision: Z.J.; Visualization: Y.C.  
This work was supported by the National Natural Science Foundation of China under grant 51890822, 51875013 and 51575026, National Key Basic Research Program of China under grant 2014 CB046400, the National Key R&D Program of China under grant 2017 YFB1300400, the Fundamental Research Funds for the Central Universities, Science and Technology on Aircraft Control Laboratory, and Ningbo Institute of Technology, Beihang University.  
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 5 Discussion
### Dissemination  
The other insensitive study routing application is dissemination. The goal of data-dissemination algorithms in VANETs is to deliver information to drivers, passengers, and vehicles, typically in emergency situations. So, it is important to consider that information has to be distributed to all the vehicles in the interest area [87; 88]. The main challenge when dissemination is used is how information should be distributed, taking into account characteristics in a VANET like mobility. In VANET, communication V2V, V2I, and Vehicle to Roadside (V2R) are allowed, and information dissemination is important. Flooding is the easiest mechanism but generates the known storm problem in dense scenarios. To reduce this problem, protocols were designed based on smart dissemination. The objective in [89] was to define a distributed dissemination protocol that supports high-rate message flows and does not use beacon messages. This proposal took into account metrics like distance, density, and the number of hops, and it is called timer-based distributed dissemination protocol for VANETs. The authors in [90] proposed a warning service to prevent accidents by alerting drivers about accidents and dangerous road conditions using a dissemination mechanism. The proposal used several metrics in the broadcast-dissemination mechanism, such as density, weighted moving average, and vehicle direction. The investigation in [91] presented a decentralized stochastic proposal called Adaptive Distributed Dissemination (ADD), for the data-dissemination problem using two game-theoreticalmechanisms. The goal was to disseminate warning messages to all vehicles inside the region of interest (ROI). Metrics like vehicle location, direction, and velocity were also included. The mechanism to carry the stored message was also used in cases of sparse traffic zones.  
The dissemination approach can also function as a technique of information distribution in a vehicular network. This method deals with the avoidance of the storm problem in order to accurately send information. Even though it is a novel and challenging topic in VANETs, we did not consider it with deeper analysis because it is out of the focus of our topic, which is routing protocols that select a path hop-by-hop by using several metrics. As future work, it would be interesting to compare between the two approaches, routing and dissemination.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Oxidative Stability  
In addition to the bioactive expulsion from the lipid matrix, the oxidation of lipids and oxidative degradation of encapsulated bioactives are another major stability problem for SLNs and NLCs in use as nanovhelics for bioactives. The selection of the lipid matrix is of crucial importance for the development of SLN- or NLC-based nanovhelics for heat- or UV-labile bioactives. In general, TAGs with long-chain fatty acids display better oxidative stability than those with short-chain fatty acids, and more unsaturated TAGs are less stable against oxidation than less unsaturated TAGs [19]. Using the hot HPH technique, the chemical stability of the lipid matrix for SLNs is unaffected by the production process, and some SLNs with TAGs as the solid lipid show excellent chemical stability at room temperature [48]. In an aqueous environment, the oxidation of lipids in SLNs or NLCs generally occurs at the oil-water interface, which may be initiated by metal ions, light, and oxygen in the system. For example, a previous work indicated that the dilution of the NLCs dispersion with water decreased the oxidative stability of their encapsulated \(\beta\)-carotene, largely as a result of increased dissolved oxygen [49]. Thus, the mobility of lipids in the lipid matrix (or physical state of the lipid matrix core) and the interface-stabilizing structure are two important parameters determining the lipid oxidation and degradation of encapsulated bioactives of SLNs and NLCs. For example, [PERSON] et al. [50] reported that the use of high-melting (HM) lecithin as the emulsifier remarkably inhibited the oxidation degradation of \(\beta\)-carotene encapsulated in the SLNs with tripalmitin as the solid lipid matrix. Meanwhile, in the case of liquid carrier lipids, no inhibition was observed. They attributed this high oxidative stability of SLNs to the role of the emulsifier (HM-lecithin) tails in the maintenance of the less polymorphic transition of lipid crystals. The same group further confirmed that the encapsulation of \(\omega\)-3 fatty acids in the NLCs stabilized with HM-lecithin remarkably inhibited the oxidation of these fatty acids, as a result of the formation of a solid surfactant interfacial layer [30].  
In the case of conventional SLNs stabilized by Tween 80, the loaded \(\beta\)-carotene degraded faster than that in the liquid lipid nanoparticles after 8 days of storage [51]. The unexpected result was attributed to the occurrence of \(\beta\)-carotene expulsion in the lipid matrix due to the partial coalescence of lipid nanoparticles caused by the formation of fat crystals. [PERSON] et al. [46] further indicated that \(\beta\)-carotene could be excluded from the lipid domain in both SLNs and NLCs, but the extent of exclusion decreased with an increase of liquid lipid content of NLCs. However, when the liquid lipid content was 50% or above, the oxidative stability of the encapsulated \(\beta\)-carotene in NLCs was significantly better than that in emulsion, or the results were reversed [46].
## 2 Materials and Methods  
This study evaluates the accuracy of the radial surface current retrievals by comparing them with observations from drifting buoys and the SMOC product, both spatially and temporally collocated with the Sentinel-1 SAR Doppler shift measurements. The DPDop model, used to estimate the wave-induced Doppler contributions, requires several input variables, including the wind speed and direction, SWH, wave direction, wave number, and radar configuration parameters. The wind speed was retrieved using geophysical model functions (GMFs), such as CMOD-IFR2 [30] or CMOD5.N [31], while the wind direction data were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The Wind speed and direction were also available from the Sentinel-1 Level-2 Ocean (OCN) products. The wave characteristic parameters, including the SWH, wave number, and wave direction, were simulated using the WAVEWATCH III (WW3) model. The spatial resolutions of the ECMWF and WW3 datasets are 0.25\({}^{\circ}\) and 0.5\({}^{\circ}\), respectively, with a temporal resolution of 1 h.
## 3 Experimental Setup
### Photon Counting with PCM
#### 3.3.3 Distribution of Photocounts  
According to Formulas (15) and (16), we measured the mean value \(\mu_{k}\) and the variance \(\sigma_{\text{2K}}\) of output photocounts for the PCM as functions of average photon arrival rate \(\uplambda\)s when the photocount time was 3 \(\uplambda\)s. As shown in Figure 11a, it is obvious that the photo count distribution of a PCM cannot be modeled as an ideal Poisson process whose mean and variance are always equal, which is mainly due to the influence of dead time. The mean value of photocounts gradually converges to 100 as \(\uplambda_{s}\) increases, that is, the total number of counted photons during a counted interval [0, \(T_{\text{s}}\)] cannot exceed \(\text{kmax}=[T_{\text{s}}/\tau]+1\). The measured and simulated \(\mu_{k},\sigma_{\text{2K}}\) have an obvious difference, and the difference becomes more significant as \(\uplambda_{s}\) increases. At low photon arrival rate \(\uplambda_{s}\), the measured and simulated \(\mu_{k}\) and \(\sigma_{\text{K}}^{2}\) are approximately equal, but after the \(\uplambda_{s}\) reaches approximately 2 \(\times\) 10\({}^{7}\) photocounts/s, the difference between the measured value and simulated value is obvious. The probability density function (PDF) obtained in Equation (13) is plotted in Figure 11b and compared with experimental results for different values of the photocount time interval. In this figure, time intervals \(T_{\text{s}}\) = 3 \(\uplambda\)s, 1.5 \(\uplambda\)s, 0.3 \(\uplambda\)s are set, and \(\uplambda_{s}\) = 2 \(\times\) 10\({}^{7}\) photoelectrons/s. As shown in Figure 11b, the experimental and simulation results have a good agreement.  
Figure 11: PCM photocounts characteristics: (**a**) Mean and variance of photocounts with \(t\) = 3 \(\uplambda\)s, (**b**) probability density distribution of PCM photocounts for \(T_{\text{s}}\) = 3 \(\uplambda\)s,1.5 \(\uplambda\)s, 0.3 \(\uplambda\)s, \(\uplambda_{\text{s}}\) = 2 \(\times\) 10\({}^{7}\) photocounts/s.  
Figure 10: The measured background and dark count: (**a**) the histogram of background and dark counts per plot time (80 ns), (**b**) the histogram of background and dark counts per symbol time (2.56 \(\uplambda\)s).
## 3 Materials and Methods
### Sensory Analyses  
The sensory quantitative descriptive analysis method (ISO 13299:2016-05) [55] was used with an unstructured, linear graphical scale: 100 mm was converted to numerical values (0-10 conventional units, where 0 means absence or very low intensity of the descriptor and 10 means very high intensity of the descriptor). Descriptors were chosen and defined during a panel discussion and then verified in a preliminary session. Finally, a list of 17 sensory attributes with definitions was used by the panel: four for odour, two for colour, one for appearance, one for texture by mouth, and eight for flavour and overall sensory quality. Sensory analyses were performed by a panel of 8 trained persons (5 women and 3 men, aged 30-45 years) according to the ISO 8586:2014-03 procedure [56]. The panelists were selected for their sensory ability and experience with the sensory evaluation process of meat products. They possessed the necessary skills to describe the flavour, taste, and odour attributes of different samples. They underwent descriptive tests with the use of a series of food products that included meat fermented products in which they described the sensory characteristics of the samples. During preliminary evaluation sessions, the proper understanding of the given set of attributes by the panelists was confirmed. The dry fermented sausages were sliced to an approximately 2 mm thickness with an electric slicing machine and placed in plastic odourless transparent boxes covered with lids. All the samples for evaluation were individually coded with three-digit codes and delivered at random sequence to limit the impact of the carryover effect (i.e., the impactof a previous sample on a subsequent sample). The evaluation was repeated twice so that each mean result was based on a minimum of 16 unitary results while carrying out sensory assessments, constant temperature, lighting, and elimination of distracting factors, such as noise and off odours, were applied. A trained panel was used to evaluate the sensory quality of sausages after fermentation and 6 and 12 weeks of storage under refrigeration (4 \({}^{\circ}\)C).
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Comprehensive Comparative Analysis of Models
#### 3.1.1 Comparison Criteria  
Coefficient of determination (R\({}^{2}\)) measures the goodness-of-fit of the regression model by the statistic R\({}^{2}\), where the best value is 1. The closer the value of R\({}^{2}\) is to 1, the better the model fits the data, and the higher the prediction accuracy:  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2} }{\sum_{i=1}^{n}(y_{i}-\overline{y_{i}})^{2}} \tag{1}\]  
Root mean squared error (RMSE) is more sensitive to isolated outliers; it measures the mean absolute error between the predicted value and the true value. The smaller the value, the higher the prediction accuracy:  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}}=\sqrt{MSE} \tag{2}\]Mean absolute error (MAE) more comprehensively reflects the overall dispersion of prediction error; again, the smaller the value, the better the prediction effect of the model:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{3}\]
## 5 Experiment and Analysis
### Evaluation of SAPRM
#### 5.2.4 Generalization Capability  
To verify that our model has a certain degree of generalization performance, experiments are conducted on three general datasets: ADE, WebNLG, and SciERC. The symbol \(\diamondsuit\) represents dataset using macro-F1 while \(\blacklozenge\) means usage of micro-F1. The symbol \(\dagger\) represents usage BERT-BASE-CASED as BERT encoder, while \(\ddagger\) means SciBERT [30] as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\lambda\)} & \multicolumn{2}{c}{**NER**} & \multicolumn{2}{c}{**RE**} \\ \cline{2-5}  & **P** & **F** & **P** & **F** \\ \hline
1 & 86.70 & 85.35 & 79.48 & 74.62 \\
0.67 & 83.86 & 84.20 & 78.40 & 73.92 \\
0.5 & 82.84 & 83.58 & 76.36 & 71.09 \\
1.5 & 78.84 & 81.59 & 76.68 & 72.85 \\
2 & 79.84 & 81.26 & 77.08 & 72.45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Influence of different \(\lambda\) setting.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ablation**} & \multirow{2}{*}{**Settings**} & \multicolumn{3}{c}{**NER**} & \multicolumn{3}{c}{**RE**} \\ \cline{3-8}  & & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Layers & N = 1 & 86.70 & 84.04 & 85.35 & 79.48 & 70.32 & 74.62 \\ attention & w/o & 80.86 & 79.32 & 80.17 & 76.30 & 60.69 & 67.61 \\ partition & w/o & 79.39 & 82.04 & 80.71 & 74.74 & 63.22 & 68.44 \\ global & w/o & 82.85 & 79.76 & 81.27 & 75.62 & 61.65 & 67.94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results of SAPRM.  
BERT encoder. The results are as shown in Table 7. The results indicate that our model outperforms other methods in NER task on the WebNLG dataset and achieves second-best performance in RE task. On the ADE and SciERC datasets, our model outperforms other methods in both NER task and RE task.  
According to the generalization experiment, although our model does not rely heavily on any particular dataset structure, it achieves better performance in domains characterized by a relatively small number of entity types with high occurrence, such as the ADE dataset, which contains short biomedical sentences rich in fine-grained entities. The SAPRM architecture, through its partition-and-recombination mechanism, can dynamically capture task-specific features and preserve contextual information, which contributes to its strong performance even on complex scientific texts in SciERC and redundant encyclopedic data in WebNLG with long sentences, overlapping relations, or diverse entity types.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 2 Compressive Sensing
### Reconstruction Algorithms
#### 2.3.1 Convex Optimization  
To reconstruct the original signal \(\mathbf{x}\), the trivial approach is to solve the \(l_{0}\) minimization problem:  
\[\hat{x}=\underset{x}{\text{argmin}}\left|\left|x\right|\right|_{0}s.t.\ y=\Phi x \tag{5}\]Since, \(l_{0}\) minimization is an NP-hard problem fro large scale matrices, in our case \(\Phi\) is computationally complex, \(l1\) minimization process is proposed to overcome the limitations of \(l0\). In this case, the minimization problem, known as basis pursuit (BP) [16], becomes:  
\[\hat{x}=\underset{x}{\text{argmin}}\;||x||_{1}\;\text{s.t.}\;y=\Phi x \tag{6}\]  
Another approach called basis pursuit denoising (BPDN) [17] is adapted when dealing with noisy systems. In addition, Least Absolute Shrinkage and Selection Operator (LASSO) [18] can be used when we have no prior knowledge about the noise level. The minimization process of some variational problems can also practically be solved using fast iterative thresholding algorithm (FISTA) [19], forward-backward splitting (FBS) [20] or approximation message passing (AMP) [21].