esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Based Framework to Enhance Anonymous Services with Accountability Guarantees
## 5 The Proposed Approach
### Interaction between \(U\) and \(Sp\)  
Through the third interaction of our protocol, \(SP\) associates \(Add_{2}^{U}\) with a pseudonym \(user_{U}\) chosen by \(U\). For simplicity, we assume that the access credentials of \(U\) are represented by a username-password pair. However, the password can be replaced by the device ID of the user's phone, as with Whisper [5].  
Again, we exploit the challenge-response mechanism of Section 4.  
\(U\) provides \(SP\) with \((Add_{2}^{U},user_{U},pass_{U})\), where \(pass_{U}\) is the password chosen by \(U\). \(SP\) verifies that \(Add_{2}^{U}\) is in state \"Confirmed\" on the instance \(C^{LA}\). Then, it picks a random \(\hat{R}\) and generates the challenge \(ch=Reccak256(Keccak256(\hat{R}\|user_{U}))\). Finally, \(SP\) generates a transaction \(T_{5}=\langle Add^{SP},Add^{Cl^{SP}},setChallenge(Add_{2}^{U},ch)\rangle\). This transaction invokes the function setChallenge, which associates the challenge \(ch\) with the address \(Add_{2}^{U}\) and sets \(Add_{2}^{U}\) to state \"To Confirm\". The random \(\hat{R}\) is provided to \(U\).  
\(U\) computes the solution to the challenge as \(sol=Keccak256(\hat{R}\|user_{U})\) and generates the transaction \(T_{6}=\langle Add_{2}^{U},Add_{1}^{Cl^{SP}},solveChallenge(sol)\rangle\). This proves to \(SP\) that \(U\) is the owner of \(Add_{2}^{U}\), which will be set to state \"Confirmed\" by the function solveChallenge.  
Finally, \(SP\) verifies the state \"Confirmed\" of the address \(Add_{2}^{U}\) on the instance \(C_{1}^{SP}\) and locally stores the tuple \(\langle Add_{2}^{U},user_{U},\hat{R},pass_{U}\rangle\). Observe that the password \(pass_{U}\) is not included in the notarized information since it is used by \(SP\) only to authenticate \(U\).  
The above steps are summarized in the sequence diagram of Figure 3.  
Figure 2: Sequence diagram of the interaction between \(U\) and \(LA\).  
As a final remark, we want to observe that, if the user is not registered with IP, SP does not provide the anonymous service. Indeed, it checks that the address \(Add_{2}^{U}\) is in the state \"Confirmed\". Moreover, this state is set by \(LA\), which, in turn, verifies that \(Add_{1}^{U}\) is in the state \"Confirmed\". The state \"Confirmed\" of \(Add_{1}^{U}\) is set by \(IG\) only after the identity of \(U\) is verified by \(IP\).  
We conclude this section by observing that, as we will see in Section 6.2, in order to develop a user-friendly application, almost allof the client-side operations are performed automatically by some scripts without requiring the interaction of the users. They just have to confirm some operations (the generation of the transactions and signature of messages) for security reasons. Furthermore, to simplify the client-side operations, the interactions of \(U\) with \(SP\) and \(LA\) are merged into a single interaction with \(SP\) in which the user is redirected to \(LA\).
## 2 The Proposed Model
### FLR-FSWGARCH Model
#### 2.1.3 Fuzzy Window Variance Computation  
The fuzzy window variance is then calculated in the third step of the proposed model. The fuzzy window variance is a summation of the multiplication of each weight with the returns of the observation. It is employed in the FLR-FSWGARCH model as follows:  
\[F_{w}=u_{t}^{2}\times W_{1}+u_{t-1}^{2}\times W_{2}+\ldots+u_{n}^{2}\times W_{n}, \tag{15}\]  
where \(W_{i}\) is the fuzzy window weight that is subject to \(W_{i}+W_{i-1}+W_{i-2}+\ldots+W_{i-n}=1\). The fuzzy window weight is determined by selecting the window that produced the minimum mean Euclidean distance in the previous step. The time series gives more weight to recent events as opposed to older observations. The total of the window size weights is calculated using Equation (16) and then the weights are normalized using Equation (17):  
\[\begin{array}{rl}\mathit{TL}=&w_{n}+w_{n-1}+w_{n-2}+\cdots+w_{1}\\ &\qquad\qquad=1+2+3+\cdots n,\end{array} \tag{16}\]  
\[W_{i}=\frac{w_{i}}{TL}, \tag{17}\]  
where the \(w_{i}\) are the weights that are dependent on the selection of the sliding window. Then, the fuzzy window variance is utilized in the computation of the variance \(\sigma^{2}\) based on the most recent return and the most recent variance using a recursive procedure:  
\[\sigma_{n}^{2}=\gamma F_{w}+\alpha u_{n-1}^{2}+\beta\sigma_{n-1}^{2}, \tag{18}\]  
where \(u_{n-1}^{2}\) is a recent return and \(\sigma_{n-1}^{2}\) is a recent variance.
## 4 Models and Algorithm Design
### Algorithm with Multiple Uncertainties
#### 4.5.2 Noisy Gradient Calculation  
The noisy gradient calculation means that the generator does not require the exact gradient value but a noisy version. This noise can arise from measurement errors or fluctuations in the energy network. A label for the observation noise \(v_{i}(k)\) will be introduced for the \(i\)-th generator in the network at step \(k\). The noise affects the algorithm solely in the equation for \(\lambda_{i}(k+1)\) to calculate the gradient:  
\[\lambda_{i}(k+1)=\sum_{j=1}^{N}w_{ij}\lambda_{j}(k-\tau_{ji})-\gamma_{i}(k) \frac{\
abla\Phi_{i}(\lambda_{i}(k))+v_{i}(k)}{y_{ii}(k)}.\]  
Next, the \(\delta\)-algebra \(F_{k}\) will be introduced. (A delta-algebra includes sets that remain closed under countable intersections. In simpler terms, if you have many sets in the delta-algebra, their intersection will also belong to it. Delta-algebras are a broader version of \(\sigma\)-algebras, encompassing sets closed under countable unions and intersections. The key difference is that a delta-algebra does not have to include sets necessarily closed under countable unions.) This will be generated by the entire running history of the algorithm up to the \(k-\)th iteration. Then the noise \(v_{i}(k)\) is bounded by the zero-mean noise variance:  
\[E[v_{i}(k)|F_{k-1}]=0,\quad E[v_{i}^{2}(k)|F_{k-1}]\leq\vartheta^{2},\]  
where \(\vartheta>0\) for all agents in the network and \(k\geq 0\).
## 7 Results and Discussion
### Results  
In this research problem, the rates of flow \(f_{ij}\) and \(f_{IB}\); time periods \(\tau\), \(\tau_{i}\), and \(\tau_{vac}\); and energy utilization \(p_{i}\) are the variables used to denote the optimization, and \(R_{i},\rho,C_{ij},C_{iB},U,E_{max}\), \(E_{min}\), and \(\tau_{TSP}\) are constants. This research issue has both a non-linear term \(\dfrac{\tau_{vac}}{\tau}\) and non-linear terms \((p_{i}\) and \(\tau_{i}p_{i})\). Of note, there are two possible results for the optimization issue OPT: either the result is optimal or infeasible, denoted by OPTS. There are various scenarios where the outcomes may occur later, e.g., (i) the UAV energy charge rate is varied, and it may be very low or small or may be large; (ii) the interval between UAVs is high. Due to the problem constraints, OPT could not be held. These are some of the restrictions for a UAV to regain some energy in a WSN network [22; 25; 29; 40; 41].  
The good results estimated by the energy utility model proposed with UAV are shown in Figures 8-11.  
Figure 11: Node energy level using the energy utility and re-usability model with energy transfer from compromised nodes.  
Figure 9: Maximizing the energy using the UAV utility and re-usability model from the detected compromised nodes.
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 2 Mechanical Simulation Analysis of Hybrid Laser INS Structure  
### Working Principle of the Hybrid Laser INS  
The hybrid laser inertial navigation system (referred to as \"hybrid INS\") serves as a primary sensor device in the control system of high-performance, maneuverable missile projects. During the flight phase, the inertial assembly operates in a rotational modulation mode, isolated from angular movement. Its output, processed through the flight control computer, provides the missile's position, velocity, and attitude while simultaneously outputting angular rate signals for attitude control.  
A schematic of the working principle is shown in Figure 1.
## 5 Validation of the FE Model  
The partial surface saw-cuts in CRCP are originally intended to induce cracking from pre-defined positions (saw-cut tips), which ultimately results in the formation of a more regular spaced crack pattern, as demonstrated in Figure 5[12; 13; 26; 27]. The same  
Figure 9: Boundary conditions of the FE model: (**a**) thermal boundary and (**b**) structural boundary.  
configuration of saw-cuts applied in the field test sections on Motorway E313, as discussed in Section 3, was used in the reference FE model to simulate the crack induction. As expected, the saw-cut tips exhibit the maximum tensile stress compared with the rest of the concrete slab. The development of tensile stress is plotted against tensile strength over the saw-cut tips along the length of the concrete slab, as shown in Figure 10. The tensile stress exceeds tensile strength exactly over the saw-cut tips. Hence, crack induction occurs from the saw-cut tips, as illustrated in Figure 11. The crack propagation along the width of the concrete slab is demonstrated in Figure 12. \"Eknn\", shown in Figures 11 and 12, represents the crack strain. It can clearly be observed that cracks initiate exactly from the tips of the saw-cut and propagate along the pavement width  
The third longitudinal steel bar just ahead of the saw-cut tips is selected for evaluating the stress distribution in the steel bar. The development of maximum steel stress distribution along the length of the steel bar is depicted in Figure 13. It can clearly be noticed that the steel bar exhibits the maximum stress exactly below the position of saw-cuts. These findings indicate that the proposed methodology and assumptions in the development of the reference FE model are appropriate for evaluating early-age crack induction in CRCP under environmental loading.  
Furthermore, the early-age crack induction in the CRCP segment without partial surface saw-cuts is also evaluated and a comparison is made with the field observations of cracking development on the Motorway E17 test section. The observed crack pattern was characterized as the cluster of closely spaced cracks, as discussed in Section 3. Figure 14 illustrates the crack induction in CRCP predicted by the FE model without saw-cuts. The same crack pattern with cluster crack formations is induced as indicated by field observations as shown in Figure 3. The FE model with and without saw-cuts predicts the same crack induction as observed on the CRCP test sections of Motorway E313 and E17. Based on these findings, it may be assumed that the reference FE model developed with appropriate considerations could be further used to advance the structural design concept of CRCP.  
Figure 12: The predicted transverse crack pattern of CRCP.  
Figure 13: Stress variation in the longitudinal steel bar.
## 2 Materials and Methods
### Physical Characterization of LIP and Plasma Longitudinal Expansion
#### 2.2.2 Plasma Expansion  
The laser-induced plasma behavior is strongly influenced by several parameters--the characteristics of the ambient medium (pressure, chemical composition), the physical and chemical properties of the target, and the laser source features (energy density, wavelength, pulse duration). For the plasma formation, a breakdown threshold must be reached by focusing the laser beam with a lens into gas or onto the surface of a solid or liquid.  
The absorption of the laser pulse energy and the resulting ablation of solid samples are achieved through several interaction processes. These initial processes differ depending on the pulse duration and the material properties, as described, for example, in [71]. At the focus spot, rapid material heating and the release of electrons, ions, atoms, molecules, and dust particles from the sample surface is induced. The plasma plume created above the sample surface and containing the ablated matter expands with supersonic velocity, which results in the propagation of a shock wave compressing, heating, and further ionizing the ambient gas in all directions. After the termination of the laser pulse, the plasma gradually dies away through recombination and the diffusion of electrons and ions.  
The expansion velocity of plasma is a rapidly changing parameter over time. It depends on several experimental parameters, such as the laser characteristics (energy, pulse duration), focus spot size, ambient pressure, and target material. Experimental observations ([72; 73], and references therein) have shown that for ultra-short laser pulses (fs-ps), an early-stage plasma with longitudinal extent is formed ahead of the material vapor plume and above the sample surface. This plasma is characterized by a high electron number density, which originates from the ambient air breakdown assisted by electrons emitted from the target. During the duration of the laser pulse, the propagation of the plasma ionization front can reach velocities of up to \(10^{9}\) cm/s, but decreases rapidly after the pulse termination. The later-appearing hemispherical vapor plume consisting of the ablated matter expands with velocities of lower than several orders.  
The presence of an ambient gas plays a significant role in the expansion process of the laser-induced plasma. Compared to plasma in vacuum, plasma in an ambient gas isspatially contracted, thanks to the confinement effects, which results in a smaller size of the plume and a higher density and collision frequency between ablated and ambient gas species, and the expansion is slowed down [71].
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 2 Materials and Methods
### Model Evaluation Criteria  
In order to comprehensively assess the performance of the various classical models and their improved models, the following statistics were used to evaluate the prediction results: Mean Square Error (MSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R\({}^{2}\)) were used as performance indicators to validate the accuracy and adaptability of the models.  
Mean Squared Error (MSE) is a commonly used metric to gauge the performance of the model in predicting corn yields. This metric calculates the average squared deviation between the model's predicted values and the actual yields. By squaring the prediction errors for each data point and then averaging them, MSE provides an overall measure of the model's prediction errors across all data points. A smaller MSE value indicates that the model's predictions are closer to the actual values, signifying better predictive performance.  
Mean Absolute Error (MAE) is another key metric used to measure the predictive accuracy of the model. In this study, MAE calculates the average absolute error between the actual and predicted corn yields for each data point, offering an overall assessment of the model's accuracy in yield prediction.  
Coefficient of Determination (R\({}^{2}\)) is an indicator of model explanatory power, representing the proportion of the target variable's variance that the model can explain. A higher R\({}^{2}\) value indicates that the model can better fit the predicted values of corn yield to the actual yields.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & **Total** & **Total** & **Total** & **Total** & **Effective** & **Rapid** & **Rapidly** & & **Moisture** & **Volume** & \\  & **Nitrogen** & **Carbon** & **Phospho-** & **Fotas-** & **Nitrogen** & **Available** & **Available** & **pH** & **Content** & **Weight of** & **Output** \\  & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & & **(g/cm)** & & **(g/cm)** & \\ \hline
1 & 0.198 & 2.057 & 0.749 & 22.508 & 135.912 & 8.264 & 228.981 & 6.347 & 0.268 & 1.088 & 951.241 \\
2 & 0.182 & 1.911 & 0.649 & 22.940 & 113.677 & 7.325 & 144.621 & 6.117 & 0.239 & 0.992 & 824.873 \\
3 & 0.197 & 1.971 & 0.686 & 23.131 & 140.839 & 9.131 & 188.493 & 6.187 & 0.289 & 1.098 & 975.272 \\
4 & 0.197 & 1.923 & 0.697 & 25.238 & 120.430 & 12.263 & 219.960 & 6.023 & 0.272 & 1.090 & 953.982 \\
5 & 0.216 & 2.233 & 0.831 & 24.603 & 154.821 & 16.497 & 327.576 & 6.193 & 0.282 & 1.107 & 669.798 \\
6 & 0.212 & 1.979 & 0.655 & 26.200 & 129.953 & 8.645 & 276.984 & 5.920 & 2.044 & 1.249 & 971.868 \\
7 & 0.192 & 1.832 & 0.666 & 22.626 & 114.735 & 11.070 & 298.185 & 6.073 & 0.313 & 1.038 & 940.098 \\
8 & 0.200 & 1.943 & 0.714 & 24.635 & 126.036 & 10.680 & 249.857 & 6.157 & 0.294 & 0.938 & 1016.751 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Raw data section table.
## 7 Conclusion  
There will almost always be estimation error in \(\hat{\Sigma}\), and often the measurement errors are non-normal. Therefore, we considered the following three topics: (1) alternatives to GLS when there is estimation error in \(\hat{\Sigma}\), (2) approximate Bayesian computation [22] to provide estimators other than GLS that use the estimated likelihood in the case of non-normal error models that make ML estimation difficult, and (3) completion of an incompletely specified ad hoc approach to deal with non-normal likelihoods [8].  
Regarding (1), it was illustrated in the PPP case that weighted estimates do not always outperform equally-weighted estimates when there is estimation error in the weights. We have already noted that estimation of \(\Sigma\) in Equation 1 introduces apparent PPP when PPP does not actually occur.  
Regarding (2), ABC based on density estimation to estimate the required likelihood was introduced. We showed that for some likelihoods, Bayesian estimation can outperform GLS, and that for some data realizations, the ABC estimator fell within the range of the simulated \(x_{1},x_{2}\) data pairs. Of course GLS provides a good estimate \(\hat{\mu}\) in general because of its well-known BLUE property (and UMVUE if the data is normal) and in particular for the PPP problem if the covariance \(\Sigma\) is well known.  
Regarding (3), ad hoc estimators based on incomplete statistical reasoning did produce values that lie within the data range [8]. We modified the incomplete approach by another use of Bayesian reasoning that involved a two-component prior. We concur that for some data realizations, the ad hoc estimator can lie within the data range, but in the examples considered, the RMSE of this estimator was slightly higher than the RMSE of the GLS or ABC estimators.  
We acknowledge the U.S. NNSA, and the Next Generation Safeguards Initiative (NGSI) within the U.S. Department of Energy.
# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 4 Algorithm Description
### Initial Messages  
In our proposed routing protocol, the initial messages are of four types as follows:  
**RSU location broadcast:** These are RSUs that transmit broadcast identification messages over the RF containing their zone assignments and schedules. These messages allow the vehicles to associate with the strongest signal RSU.  
**Vehicle association messaging:** Upon receiving an RSU broadcast, vehicles respond with association messages to the corresponding RSU over both RF and VLC to establish the connection.  
**Beacon messaging:** Once associated, the vehicles periodically send beacon updates to their RSU containing location, direction, speed, and other required data.  
**RSU scheduling:** The RSU schedules alternate the RF and VLC transmission periods to maximize the reliable delivery of these status messages.
## 3 Materials and Methods
### Pre-processing: Normalization and Segmentation  
The image pre-processing process is a fundamental stage to homogenize, normalize and segment the MRIs of the different patients. This process has been carried out with the statistical tool Statistical Parametric Mapping (SPM) [39,40], in the SPM12 version. Therefore, bias correction, spatial normalization and segmentation are performed. The new version is based on the method described by [PERSON] et al. [41] known as proposed unified segmentation algorithm, in which a probabilistic framework was defined, with which image registration, bias adjustment and tissue categorization can be merged in a unified model. The model obtained is based on a mixture of Gaussian values with the possibility to incorporate a smooth intensity adaptation and a non-linear protocol with tissue probability maps.  
All the MRIs from patients and volunteers in this contribution were spatially normalized. The bounding box of 157 \(\times\) 189 \(\times\) 130 voxels in the X, Y and Z directions, respectively, were used. It should be kept in mind that the MRI are volumetric (three-dimensional), and in this contribution, a three-dimensional wavelet transform will also be performed, with which each voxel is adjusted to the measure of 1 mm \(\times\) 1 mm \(\times\) 1 mm. In the study of brain images, it is relevant to separate or segment what is called gray matter (C1 images), white matter (C2 images) and whole matter (W images). These images were available in different files.  
It is relevant to note that MRI images are often corrupted by an artifact or noise that varies spatially and changes the intensity level of the brain image (bias). Although the noise that can be present in an image may not excessively affect the visual inspection by an expert neurologist, it can adversely influence the performance of an automatic image pre-processing methodology, and therefore, it requires removal.
## 3 History of the Methods to Produce Transgenic Rabbits  
### Traditional Pronuclear Microinjection to Generate Transgenic Rabbits  
After the first production of transgenic rabbits using the pronuclear microinjection method by [PERSON] et al. [6], almost all transgenic rabbits have been produced by this method. Pronuclear microinjection remains as the major method to produce transgenic animals, but transgenic efficiency is generally low in rabbits [9; 11]. The production efficiency of a transgenic rabbit is thought to be influenced by several factors such as DNA quality (concentration, purity, and size), the skill of microinjection technician, and the colony of rabbit breed. In our laboratory, we have produced more than eight types of transgenic rabbits to study human diseases. The mean efficiency of the transgenic rate was lower than 5% of total born rabbits and 0.5% of the total injected and transplanted embryos [11]. Moreover, using the pronuclear microinjection method, we cannot control the gene copy number or the insertion site in a chromosome. Therefore, we sometimes encountered the \"position effect\". In this case, we may not be successful in demonstrating protein expression, even though we have successfully generated transgenic founders. The expression levels of the transgene may depend on both the insertion site and copy number. Therefore, several alternative transgenic methods to pronuclear microinjection for improvement of the transgenic efficiency and stability have been explored in rabbits. For example, the sperm vector [12; 33; 34], ICSI-mediated transgenesis [35; 36], transgenesis by somatic cell nuclear transfer (SCNT) [37] or chimeric SCNT [13; 38], lentiviral vectors [14], transposon-mediated transgenesis [15; 39], and novel genome editing technology [16; 40; 41] have been examined to produce transgenic rabbits. The characteristics of these major historical techniques are summarized in Table 1. Currently, the most expected alternative candidate that is better than traditional pronuclear microinjection should be novel genome editing technology. In particular, the CRISPR/Cas9 mediated genome editing technique is a powerful tool to generate genetically modified animals. Recently, the detail of genome engineering technologies in rabbits was well reviewed by [PERSON] et al. [42].  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Reported Year/Technique**} & \multicolumn{1}{c}{**Remarks**} & \multicolumn{1}{c}{**Ref.**} \\ \hline \multirow{5}{*}{1985} & \multicolumn{3}{c}{The gene copy number and insertion site in a chromosome are} \\  & \multicolumn{1}{c}{uncontrollable (the position effect will sometimes occur).} & \multirow{5}{*}{[6]} \\ \cline{3-3} \cline