esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Rehydration Ratio and Hygroscopic Changes after Pretreatments and Drying  
Rehydration determines the material's ability to absorb water [62]. Table 2 contains the rehydration ratio (RR) values obtained after 3 h of analysis by the reference sample (untreated) and beetroots subjected to blanching, soaking, and ultrasound treatment prior to infrared-hot air drying. Among all analyzed samples, only the US 35 kHz 20' sample exhibited a significantly higher RR (by 20.4%) than the untreated sample. The sorption mechanism depends not only on the physical properties of a given material. Its chemical composition also has a significant impact on this parameter [63]. Perhaps ultrasonic treatment with the following process parameters, a frequency equal to 35 kHz and treatment time equal to 20 min, led to significant changes in the treated tissue at the chemical level, beneficial from the perspective of rebinding water. Despite the lack of statistically significant differences, samples that were blanched and soaked before drying had slightly higher RR values than the untreated sample (RR values rose in line with extending the soaking time). A similar tendency--an increase in RR with the extension of immersion duration--was observed after treating beetroots with ultrasound with a frequency of 21 kHz. The improvement in the hydration properties of these samples could result from the leaching of some soluble solids [64] and from opening the structure of the material [65].  
Using hygroscopic properties, it is possible to assess the ability of a given material to adsorb water vapor [36]. Most of the obtained dried beetroots did not differ from each other in terms of hygroscopicity (Table 2). In relation to the reference sample (untreated), only the soaked 10' and US 35 kHz 10' samples exhibited statistically significant differences in water content after spending 72 h over the NaCl solution. Their values were 26.1% lower and 21.7% higher than the untreated sample, respectively. As mentioned above, sorption processes (adsorption, absorption, desorption) depend on various factors--those related to the material are both physical (the characteristics of the material surface, its porosity, occurrence of drying shrinkage, etc.) and chemical (chemical composition) [63; 66; 67]. As shown in Figure 1, the US 35 kHz 10' sample was one of the samples whose drying time was the shortest, which could prevent negative material changes from the stability point of view.
## 4 Experimental Evaluation of the VLC System and Experimental Results
### Experimental Setup No. 4--Long Communication Distance in Outdoor Snowfall Conditions
Additionally, as these tests were performed in outdoor uncontrolled conditions, the lower BER results illustrated for the higher data rates are due to a lower intensity of the snowfall. From these reasons, the DSSS modulation and the Miller code have not been evaluated in this experimental setup in order to prevent any misleading of the reader, as the results could have been ambiguous.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 5 Experimental Study
### Impact of Factors
#### 5.4.2 Size of Cache  
The vehicular nodes adopt a \"store-carry-forward\" strategy [40] for the message transmissions, so the cache size of nodes would have an impact on the performance. Figures 7 and 8 show that the recall rate and message transmissions grow as the cache size increases. This is true especially for PROPHET and ESSMD, because within these schemes, the nodes work in the high cost sensing mode as the default, and they would diffuse the sensed data to neighboring nodes, which requires a larger cache size for the data storage, and the message transmissions would increase. When the cache size is small, e.g., 50 Megabytes, some data are dropped, and the recall of events is lower than 0.3 and the message transmissions relatively low. For the TPEG scheme, the cache size has less impact on the recall rate and the message transmissions. When the cache size grows from 50 M-300 M, the recall rate increases from 0.8926-0.9245 and the message transmissions grow from \(302\times 10^{4}\)-\(493\times 10^{4}\). This is because the nodes generate data in LCS mode by default, which requires less cache space. Moreover, the two-level thresholds suppress the message diffusion and upload and further reduce the message transmissions.  
Figure 9 shows the impact of cache size on the detection time. For the PROPHET and ESSMD scheme, the detection time decreases as the cache size grows. The length of time drops from 51(41)-28(30) for PROPHET(ESSMD) when the cache size grows from 50 M-300 M. This is because when there are larger cache sizes, a larger number of messages is transmitted, and more data are uploaded to RSU to speed up the detection of events. For the NAIVE and TPEG scheme, the cache size has little impact on the event detection time.  
Figure 6: Impact of the number of nodes on the average detection time.  
Figure 8: Impact of cache size on the number of message transmissions.  
Figure 7: Impact of cache size on the recall rate.  
Figure 9: Impact of cache size on the average detection time.
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 3 State Space Iterating (SSI) Scheme
### LCO Analysis Using the SSI Scheme
cases, \(\mathbf{x}_{0}\in\mathbf{X}_{0}^{\{n\}}\). Carry out time integrations so that \(\mathbf{x}_{0}^{*}=F^{N_{T}}(\mathbf{x}_{0})\) can be obtained. Here, we need to check if all \(\mathbf{x}_{0}^{*}\) values still remain in \(\mathbf{X}_{0}^{\{n\}}\): \(\mathbf{x}_{0}^{*}\in\mathbf{X}_{0}^{\{n\}}\) because we need to make sure that \(\mathbf{X}_{0}^{\{n\}}\) is a subset of basins of attraction \(\overline{U}_{L}\), as explained in Sections 3.1.2 and 3.2--if not, expand the range of \(\mathbf{X}_{0}^{\{n\}}\) until any \(\mathbf{x}_{0}\in\mathbf{X}_{0}^{\{n\}}\) leads to \(\mathbf{x}_{0}^{*}\)\(=F^{N_{T}}(\mathbf{x}_{0})\in\mathbf{X}_{0}^{\{n\}}\). Second, construct iteration plots using \(\mathbf{x}_{0}\) and \(\mathbf{x}_{0}^{*}\) as explained in Section 3.4, where \(N_{S}\) points form a region \(R_{0}\) and intersect with \(l_{\text{equ}}\) at \(L_{0}\), as shown in Figure 8. The key point is that \(L_{0}\) normally occupies a smaller range \(X_{0,j}^{\{n+1\}}\) on \(x_{0,j}\) than \(X_{0,j}^{\{n\}}\)--that is, \(X_{0,j}^{\{n+1\}}\subseteq X_{0,j}^{\{n\}}\). Thus, the range of \(X_{0,j}^{\{n\}}\) can be contracted to \(X_{0,j}^{\{n+1\}}\). By combining all \(X_{0,j}^{\{n+1\}}\) on the iteration plots, a new set \(\mathbf{X}_{0}^{\{n+1\}}\subseteq\mathbf{X}_{0}^{\{n\}}\subseteq\overline{U} _{L}\) is found. Repeat the two procedures above, and a series set \(\mathbf{X}_{0}^{\{1\}},\mathbf{X}_{0}^{\{2\}},\ldots,\mathbf{X}_{0}^{\{n\}}\) can be obtained. The iteration will stop once \(\mathbf{X}_{0}^{\{n\}}\) can no longer been contracted--that is, \(\mathbf{X}_{0}^{\{n+1\}}\equiv\mathbf{X}_{0}^{\{n\}}\). After that, if \(R_{0}\) no longer has a \"thickness\" to the graph, as shown in Figures 4 and 7, the \(L_{0}\) in Figure 8 will degenerate to a fixed point \(\mathbf{x}_{L}\), where an LCO solution is found; otherwise, quasi-periodic motion or chaotic motion should be suspected in the set \(\{\mathbf{x}|\mathbf{x}\in L_{0}=R_{0}\cap l_{\text{equ}}\}\). The present paper only focuses on LCOs, so any other kind of motion will be estimated as an LCO with a perturbation on the orbit. Studies on LCOs together with all other kinds of nonlinear responses are expected in our future works. Note that when \(N_{T}=1\), the proposed mapping is a Poincare map. However, we found that using \(N_{T}>1\) could accelerate the determination of clear patterns on the iteration plots and make the SSI scheme more robust. We tried using \(N_{T}=3\), 5, 9, and 11, all of which yielded the same LCO results but required different numbers of SSI iterations to contract the set \(\mathbf{X}_{0}^{\{n\}}\).
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Communication Model  
The achievable up-link data rate for the transmission of task \(k\) can be obtained based on the Shannon theorem as  
\[r_{k}=B\log_{2}\!\left(1+\frac{P_{k}^{tr}H_{k}}{\sigma^{2}}\right), \tag{3}\]  
whereby \(B\) is the channel bandwidth between the sender and receiver. The sender options include the UE and the UHs, and the receivers can be the UHs and the MEC servers. We denote \(P_{k}^{tr}\) for the transmission power for task \(k\), denote \(H_{k}\) for the channel gain between the sender and receiver while transmitting task \(k\), and denote \(\sigma^{2}\) for the Gaussian channel noise variance (with default value \(\sigma^{2}=10^{-9}\) W). We note that more complex channel models, e.g., models that include fading and shadowing, can be substituted into our overall task offloading model in a straightforward manner and are left as a future research direction.  
In our scenario, the computed results of a task are of negligible size, and their downlink transmission is not explicitly modeled. We note that in order to mitigate interference, the UE should be actively uploading (transmitting) only one task at a time to a server via the cellular channel and one task at a time to a helper or relay node via the D2D channel, whereby both cellular and D2D transmission can occur simultaneously as they do not interfere with each other. We model both the cellular channel and the D2D channel to have bandwidth \(B\). We do not consider the transmission sequencing to at most one cellular and one D2D transmission at a time in the current model and leave this transmission sequencing as a future model refinement.
# Logic Synthesis Strategy Oriented to Low Power Optimization
## 5 Experimental Results
Similarly, in ABC, similar descriptions were generated for the PowerDC script (PowerDC was selected due to the fact that the total value of \"Power\" was the lowest in Table 1). It was decided to perform two series of experiments for PowerDC. In the first \"PowerDC SF,\" HDL descriptions of single functions (without sharing logical resources) obtained from ABC were further synthesized in Vivado. In the second \"PowerDC MF,\" descriptions of sets of functions were further synthesized. PowerDekBDD decomposes individual functions, but providing results for competing systems, including a set of functions, will, according to the authors, better define how the obtained solutions fit into the spectrum of other results. The obtained results are summarized in Table 2. The individual columns in Table 2, for each of the systems, describe: the number of \"LUTs\" blocks, the dynamic power consumed by the \"Dynamic\" implementation, and the dynamic power consumed only by the \"LUT Power\" LUTs. Figure 6: Comparison of academic systems in terms of: (**a**) the number of LUTs; (**b**) the number of logic levels; (**c**) switching activity (Power). The last rows of Table 2 contain the sum value and the geometric sum. The values of the respective sums are summarized in the graphs in Figure 7.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|} \hline  & & & & \multicolumn{3}{c|}{**PowerdckBDD SF**} & \multicolumn{3}{c|}{**PowerDC SF**} & \multicolumn{3}{c|}{**PowerDC MF**} \\ \cline{4-14}  & & & & & & & & & & & & & \\ \cline{2-14}  & & & & &  
When analyzing the diagram in Figure 7a, it can be observed that the solutions obtained for PowerDekBDD are very effective in terms of the number of blocks. The obtained results are much better than the results for PowerDC for single functions and practically the same as for the group of functions (thus taking into account resource sharing). Taking into account the overall dynamic power (graph in Figure 7b), it can be observed that the obtained results are slightly worse than for PowerDC MF (which should be expected) and slightly better than for PowerDC (a reduction of 8% was achieved). A very similar situation occurs in the case of the diagram in Figure 7c, showing the dynamic power consumed by the LUTs. In this case, it was possible to achieve a reduction of almost 24% compared with PowerDC SF. It should be emphasized, however, that in both cases, the values of the geometric mean were the same.
## 3 Results
#### 3.2.4 Prediction Evaluation of Single-Exponential T\({}_{\text{JS}}\) Index  
The prediction and evaluation commenced at the slope change point located 90 m into the excavation face of heading ZF1212 on 10 January and concluded at the 117 m mark where the incident occurred on 26 January, encompassing a total advance of 27 m. Throughout this excavation segment, no geophysical exploration was conducted. Based on the drilling design and its implementation, calculations were performed using a long drilling advance distance of 30 m and a dip angle of 5.74\({}^{\circ}\). The results of these calculations are presented graphically as a curve of the excavation progress versus \(T_{JS}\), as illustrated in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decoupling** & \(R_{j1}\) **(m)** & \(R_{j2}\) **(m)** & \(R_{j3}\) **(m)** & \(\sum R_{ji}\) **(m)** \\
1 & 3.819 & 2.919 & 195.334 & 1 \\
1.1 & 2.694 & 2.059 & 37.484 & 1.1 \\
1.2 & 1.958 & 1.497 & 8.242 & 1.2 \\
1.3 & 1.463 & 1.119 & 2.063 & 1.3 \\
1.4 & 1.11 Throughout the blast excavation from the 90 m to 117 m mark in the ZF1212 heading face, \(T_{JS}\) consistently remained above the discriminant threshold line, indicating persistently high potential for water inrush. As calculated, conditions \(z_{g}<0\) and \(y_{g}>0\) were observed during excavation between 90 m and 108 m, signifying that hydraulic connectivity between the water-induced fractures and excavation-induced fractures had been established in the vertical direction. Furthermore, the extent of fracture propagation in the vertical dimension critically compromised the integrity of the water-resisting zone. Beyond the 108 m advance point, conditions \(z_{g}<0\) and \(y_{g}<0\) were calculated, accompanied by an abrupt change in the \(T_{JS}\) curve. This demonstrates that effective hydraulic pathways had become fully developed, suggesting imminent instantaneous water inrush. The predictive results indicate that the ZF1212 heading face advanced under continuously hazardous conditions with imminent water inrush risk from the 90 m slope-change point onward.
## EUV and SXR Full Field Microscopes Based on a Double Stream Gas-Puff Target Source
### Full Field EUV Microscope
#### 3.1.3 Fresnel Zone Plate for EUV Microscopy  
The employed FZP by Zone Plates Ltd. (London, UK), having a diameter D\({}_{\text{FZP}}\) = 200 \(\upmu\)m, number of zones N\({}_{\text{FZP}}\) = 1000 and outer zone width \(\Delta\)r = 50 nm, made by electron beam lithography in a 200 nm thick polymethyl methacrylate (PMMA) layer spin-coated on top of a 50 nm thick Si\({}_{3}\)N\({}_{4}\) membrane, resulting in first order diffraction efficiency of \(\sim\)20%. The theoretical spatial resolution is 61 nm and the depth of focus (DOF) \(\pm\) 369 nm. The match of the numerical aperture of the condenser (NA\({}_{\text{-CH}}\) = 0.11 in horizontal and NA\({}_{\text{-CV}}\) = 0.15 in the vertical direction) with the numerical aperture of the FZP objective (NA\({}_{\text{-FZP}}\) = 0.137) guarantees the spatial incoherent object illumination [74]. The geometrical magnification (at a distance FZP to CCD of 28.1 cm) was 410\(\times\). The radiation emitted from the Ar plasma was collected and spectrally narrowed down by the ellipsoidal multilayer mirror, located at a distance of 254 mm from the plasma. The condenser reflects radiation at the wavelength of 13.5 \(\pm\) 0.5 nm (FWHM), with an optimized curvature to minimize the spherical aberrations at the incidence angle of 45\({}^{\circ}\). An ellipsoidal silica mirror (Reflex s.r.o., Prague, Czech Republic), 80 mm in diameter (radii of 179.6 mm and 254 mm in two orthogonal directions), was used as a substrate for the deposition of the Mo/Si multilayers (made at the Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Jena, Germany). Thus, the plasma was imaged by the condenser with a magnification of M = 1. The quasi-monochromatic EUV radiation was produced and focused at the sample plane, corresponding to the second focal plane of the condenser, ensuring the maximum energy and uniform spatial distribution of the illumination on the sample surface, within the field of view (FOV) of the microscope. To absorb the stray EUV radiation through the FZP, a circular beam stop made from a 0.3 mm thick Al sheet (diameter of 3.2 mm) was placed 20 cm downstream of the condenser. The smallest achievable spot was ellipsoidal, horizontally elongated, having an FWHM of 1.5 \(\times\) 0.8 mm\({}^{2}\).
## 2 Model Constructions and Hypothesis
### Critical Analysis of Theoretical Constructs
#### 2.1.2 Effort Expectancy  
Effort expectancy (EE) is an element of the UTAUT framework. It is the degree of ease related to the use of the system [31]. Effort expectancy (EE) is both positively and negatively associated with innovation [43].  
The construct of effort expectancy is presented in [31], where the items EE1, EE2, and EE3 originated from the \"Perceived ease of use\" variable, which was originally [37; 38], and have been used in Technology Acceptance Models (TAMs). The item EE4 originated from the \"Ease of use\" variable, which was originally introduced by [39], and has been used in Innovation Diffusion Theory (IDT).  
In some studies on robot acceptance, perceived ease of use had a relationship with perceived usefulness [26; 32], and effort expectancy had a significant relationship with performance expectancy [8; 9; 18].  
Some research has shown that using the UTAUT framework in a robot acceptance model mostly leads to finding a significant relationship between effort expectancy and intention to use [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19].  
Therefore, the following hypotheses were proposed:  
**H2a**: _There is a significant relationship between effort expectancy (EE) and performance expectancy (PE) while working with robots._
**H2b**: _There is a significant relationship between effort expectancy (EE) and behavioral intention (BI) to use the robot as a co-worker._
## 2 Methodology
### Generation of Climb Paths  
To exploit the fact that the general form of optimal trajectories in the h-M plane can be estimated using E-M theory, in the present study, Bezier splines [34] were selected for the generation of climb paths in the form of two-dimensional curves in this plane. These are parametric curves built around polynomial expressions, known as the Bernstein polynomials. A Bezier curve of order n is defined by a set of control points, \(P_{0}\) through \(P_{n}\), under the formula:  
\[B(t)=\sum_{i=0}^{n}\left(\begin{array}{c}n\\ i\end{array}\right)(1-t)^{n-i}iP_{i}\ 0\leq t\leq 1 \tag{7}\]  
The selection of Bezier splines to construct flight paths is justified by a number of advantages over other curve-fitting approaches:  
1. Complex curve geometries may be generated using a small number of control variables.
2. Boundary conditions may be easily applied.
3. Bezier splines allow for the representation of non-functional relations between h and M, which may be generated by combinations of accelerated climbs/descents with zoom climb-type maneuvers.
4. The curves produced are directional, a feature that can be exploited by the aircraft's path-tracking guidance logic.  
An example of a Bezier-spline-generated climb path is shown in Figure 2, plotted over contours of Specific Excess Power.  
An acceptable climb path should have positive values of altitude along its entire length; depending on the splines' degrees of freedom, this may lead to an excessive number of rejected (or penalized)  
Figure 2: Climb path (red) generated using a Bezier spline, plotted over Specific Excess Power contours. The spline’s control points are shown in blue.  
solutions during the optimization run. With a view to reducing the amount of unacceptable solutions without affecting the optimizer's performance, in place of an inequality path constraint, the resulting negative values were simply forced to zero. Hence,  
\[h(t)=\max\{h[B(t)],\ 0\} \tag{8}\]
## 7 Results and Analysis
### Numerical Results
As shown in Figure 12, the confusion matrix for the SVM classifier shows the prediction results, as follows;  
* **True Positive (TP)**: Successful calls rightly predicted as a success (bottom right). * **True Negative (TN)**: Failed calls rightly predicted as failure (top left). * **False Negative (FN)**: Successful calls wrongly predicted as failure (bottom left). * **False Positive (FP)**: Failed calls wrongly predicted as successful calls (top right). Figure 12: Confusion matrix for SVM classifier. It is shown that the FP is relatively low compared to the TN, indicating that the proposed model can predicate a high percentage of the failures. Moreover, we can calculate the True Positive Rate (TPR) and the False Positive Rate (FPR), as follows;  
\[TPR=\frac{TP}{TP+FN}=\frac{916,010}{916,010+23,546}=0.975 \tag{2}\]  
\[FPR=\frac{FP}{FP+TN}=\frac{20,524}{20,524+36,816}=0.43 \tag{3}\]  
As shown in Figure 13, the ROC curve shows the confusion matrix result on all classification thresholds, where the x-axis is the FPR and the y-axis is the TPR. We compare between different classifiers with measuring the Area Under the Curve (AUC); the classifier with the highest AUC shows the best performance in classification, as it has the highest variance between FPR and TPR over all classification thresholds. Although the naive Bayes has a better prediction accuracy than the decision tree, the later classifier demonstrates better performance in distinguishing between the failure and success calls. The decision tree has a higher AUC than naive Bayes, where the AUC of naive Bayes is 0.74 while the AUC of the decision tree is 0.79. In order to implement the proposed technique in a real network, first, the data is collected by identifying the SIP interfaces that should be monitored in IMS, as shown in Figure 4 (red highlighted parts). Then, we tapped on these interfaces using probes to collect and assemble SIP messages generated. Second, we ran the model on the IMS virtual machine. Third, an analysis was conducted to obtain and conclude the root causes of call failures, as shown in the following section.