esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
It is very hygroscopic and even deliquescent, so it might have been dissolved in small amount of water adsorbed to the reaction medium. This aqueous solution does not penetrate to the hydrophobic PDVB microspheres, and therefore, most of the ZnCl\({}_{2}\) was concentrated outside the PDVB microspheres. We estimate that this small amount of water also interact with the dissolved Zn\({}^{2+}\) ions to form Zn(OH)\({}_{2}\), which turn into ZnO by heating [20]:  
\[\mathrm{Zn}^{2+}+2\mathrm{H}_{2}\mathrm{O} \rightarrow\mathrm{Zn(OH)}_{2}+2\mathrm{H}^{+} \tag{1}\] \[\mathrm{Zn(OH)}_{2} \rightarrow\mathrm{ZnO}+\mathrm{H}_{2}\mathrm{O} \tag{2}\]  
This estimation is supported by the thermogravimetric analysis of the ZnCl\({}_{2}\)-PDVB microspheres (Figure 10A), demonstrate one slope between 30 and 120 \(\,\mathrm{\SIUnitSymbolCelsius}\), corresponding to the reaction of ZnCl\({}_{2}\) with water to produce ZnO, and another steep slope between 380 and 540 \(\,\mathrm{\SIUnitSymbolCelsius}\), corresponding to the PDVB template microspheres decomposition, shown also in previous studies [16]. These observations are in good agreement with the DSC results, poses two endothermic peaks: around 60 and around 460 \(\,\mathrm{\SIUnitSymbolCelsius}\) (Figure 10B). Figure 10: (**A**) Thermogravimetric analysis (TGA); and (**B**) differential scanning calorimetry (DSC) thermograms of the ZnCl\({}_{2}\)-PDVB microspheres. In contrast to the ZnCl\({}_{2}\) precursor, our previous study [16] illustrates that the zinc acetate mostly penetrated into the PDVB microspheres, rather than coating their surface, and therefore decomposed during the calcination to form porous spherical ZnO microparticles, preserving the PDVB template structure shape (Figure 6B). This difference between the two precursors may be explained, at least partially, by the more hydrophobic nature of the zinc acetate relative to ZnCl\({}_{2}\). The diverted synthesis mechanisms of the two different structures of ZnO microparticles obtained using zinc acetate and ZnCl\({}_{2}\), are summarized in Figure 1:0
## 3 Systematic Error Correction
### Passive Correction by Post-Processing  
Figure 4 shows the passive correction results of the systematic error, of which the mean line before correction is shown in Figure 3a. The residual error after correction is found to be \(\pm\)7.8 \(\upmu\)m (1 \(\sigma\)) over a 2 m range. The accuracy of the phasemeter used in the calibration is 0.02\({}^{\circ}\), which corresponds to 8.4 \(\upmu\)m. This implies that our passive correction almost reached the ultimate limitation imposed by the phasemeter. For more comprehensive analysis, the residual error after correction was Fourier transformed as shown in Figure 4b. The result revealed that the periodic error of an amplitude of 22.6 \(\upmu\)m is completely suppressed, whereas the residual error after correction is mainly dominated by the non-periodic error.
## 3 Self-Configuration Management (SCM)
### Data and Radio Model
The first is a request from the suspicious sensor to resend the data within a preset time (t = 300 us.) (head request query, HRQ). The second procedure \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Deploy Area & 220 \(\times\) 220 m\({}^{2}\) \\ Initial Energy & 2.0 J \\ Broadcast Time (Between Succession Packets) & 300 us. \\ Packet Size & 256 byte \\ Size of Tiny OS replica & 400 bytes \\ The Ratio of Dispatch Energy Cost E\({}_{\text{T}}\) & \(\cong\)60\% of the battery size \\ The Ratio of Receipt Energy Cost E\({}_{\text{E}}\) & \(\cong\)30\% of battery size \\ The Ratio of Dissipated Energy Cost E\({}_{j}\) & \(\cong\)10\% of battery size \\ Heartbeat Message & ”00000000” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results were revealed by applying SCM deployments. involves comparing some nearby pieces of data to the original data from the sensor's transmitter. Comparing bits of data will detect the functional condition of the suspected sensor, provided that the distance parameter was obtained at short distances (D) < 50 m, and assuming that it is more effective in the reorganization of system malfunction. Throughout the communication mode, we computed relative connectivity (RC) via each operational round to ensure the network's communication strength; RC was determined using the following Equation:

\[RC=\frac{number\ of\ suspected\ nodes\ in\ the\ round\ (Offbeat)}{total\ number\ of\ lived\ nodes\ in\ the\ round}-1(\%). \tag{15}\]

The details of fault-detect confirmation will be taken in the next system fault tolerance section. ### System Fault Correction-Management

System fault correction management (SFCM) is a self-alert algorithm that is divided into two parts: autonomous detection and configuration. The SFCM operated well in asynchronous systems (where there is no upper limit on when a response to a request will be established). We optimized it to cut down on overhead. Its goal was to fix several difficulties linked with system malfunctions and to correct existing solutions. #### 4.2.1 Fault Detection Method

We used the defect detection method based on alert theories during this period. Initially, each head node Si essentially discovers the partial defect (responding with an inaccurate result was rank1) by comparing the received data from the clustering nodes Ci with the data from its neighbor sensors \(\text{S}_{\text{Negi}}\). The detection of errors is based on binary hypothesis testing. Let us consider the binary decision (0 or 1) made by the sensor node Si
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 5 NRA Crossing in Parametric Paths
### Multi-Ring Polygons and Multi-Polygons  
There is a category of planar domains commonly used in Geographical Information Systems (GIS), namely to represent contours of countries and other geographical entities, that accounts both for multiple separate polygons (multi-polygons) or for polygons that include \"holes\" defined by one (or more) separate closed lines fully included in the outer polygon; each of these separate contours is named a ring. A simple polygon has one ring, and a polygon with two \"holes\" has three rings [28]. There are also strict cases of multi-ringmulti-polygons where rings nest inside each other. All these situations can be handled with the same concepts described earlier using the sense or circulation of a contour. Figure 10 illustrates three situations of multi-ring polygons.  
The figure also illustrates the need for defining correctly the sequence of the polygon vertices in order to apply the same concepts of winding sense and, therefore, obtain the number of NRA intersections. Extension of the algorithm to this type of region is achieved by simply applying the procedure on each of the 2, 3, or more contours (rings) and sum up the separate results of NRA intersections.
## 3 Proposed Work
### Deep Contextual Bandit Network Slicing Scheme
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(s_{i}\)\(w_{i}\) & \(w_{1}\) & \(w_{2}\) & \(w_{3}\) & \(w_{4}\) \\ \hline \(s_{1}\) & 0.50 & 0.75 & 0.75 & 0.50 \\ \hline \(s_{2}\) & 0.50 & 1 & 1 & 0.75 \\ \hline \(s_{3}\) & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weight value assignment for RIFs based on the network state \(s_{t}\). where \(Y_{t}(a)\) is the number of instances when action \(a\) is chosen before time \(t\), and \(c\) is a predefined confidence value controlling the degree of exploration. The UCB strategy implies that by checking each action's \(Q_{t}(a)\) and the number of instances it was executed, it can exploit the action with the highest \(UCB\). However, other possible actions are explored and re-evaluated. The calculated rewards are then evaluated for every action the agent performs to assess its quality. At the end of the training period, the cumulative mean rewards \(q_{*}(a)_{\mu}\) for each action are taken as,  
\[q_{*}(a)_{\mu}=\frac{\sum_{a=0}^{A}Q_{t(a)}}{Y_{a}} \tag{19}\]  
where \(Y\) denotes the number of instances when the agent chose action \(a_{i}\). These values are then ranked in descending order, and the action with the highest \(q_{*}(a)s_{\mu}\) is implemented as a network slice \(NS_{NS_{k,t}}\). Finally, the proposed DCB-NS scheme details are presented in Algorithm 3. ```
1:initialize \(NSR_{i,t}=(C_{NSR},B_{NSR},N_{NSR},D_{NSR},L_{NSR},\ T_{NSR})\), \(x,y,c\)
2: perform Node Selection (Algorithm 1)
3: for x in NVA:
4: perform Node Mapping (Algorithm 2)
5: add mapped nodes to candidate slices array \(Slices[]\)
6: check for network state \(s_{t}\)
7: while \(y>0\):
8: select action a candidate slice from Slices[] as action \(a_{i}\)
9: calculate action value \(q_{*}(a)\) (Equation (10))
10: calculate reward \(R_{i}\) (Equation (16))
11: calculate total estimated value \(Q_{t}(a)\) (Equation (17))
12: calculate Upper Confidence Bound \(UCB\) (Equation (18))
13: if \(length(A)=1\):
14: set \(UCB\) of \(a_{i}\) as the max UCB
15: select new action \(a_{i+1}\), repeat steps 8-12
16: else: Compare \(UCB\) values for all actions performed
17: if \(UCB\) of current action is highest:
18: set \(UCB\) of that action as max UCB (exploit)
19: else: select new action and repeat from step 8 (exploit)
20: select action with the highest \(q_{*}(a)_{\mu}\) from all actions performed in \(a\in A\)
21: implement selected action as network slice \(NS_{NS_{k,t}}\) for \(NSR_{i,t}\)
22: return \(NS_{NS_{k,t}}\)
```  
**Algorithm 3** Proposed Deep Contextual Bandit Network Slicing Scheme.
## 5 Experiment
### Comparison Experiment of Intelligent Path Planning Algorithms
#### 5.4.1 Map Size  
In the experiment, seven sets of grid maps were randomly generated, and the side length N of the map was increased from 100 to 700 with an increment of 100 each time. The obstacle grid and starting point were randomly generated on each map (ensuring feasible solutions), and the obstacle rate \(\rho\) was set to 0.1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resolution** & **SGPVEFA** & **TSP** \\ \hline
256 \(\times\) 256 & 363.46 & 363.46 \\
512 \(\times\) 512 & 755.91 & 755.91 \\
1024 \(\times\) 1024 & 1534.81 & 1534.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average path length searched by SGPVEFA is compared with the TSP.  
the individual learning factor \(c_{1}\) is 1.2, and the group learning factor \(c_{2}\) is 1.2. The SSA algorithm has an explorer ratio \(P_{percent}\) of 0.2.  
The path length of each algorithm changes with the map edge length N as shown in Figure 18, and the search time changes with the map edge length N as shown in Figure 19. Since the search time approaches 0 when the map edge length is too small, only part of the search time comparison is given in Figure 19.  
The experiments show that SGPVEFA has a clear advantage in terms of path length, and this advantage becomes more pronounced as N increases. In terms of search time, when N is small, SGPVEFA's search time is slightly shorter than that of the intelligent path planning algorithm. As N increases, SGPVEFA's search time aligns with that of the intelligent path planning algorithm (excluding GA).
## 5 Empirical Case Study: Courier Company in Bangkok
### From a Fixed Time Instance to Time-Dependent Traffic Information Urban Logistics Problems  
In light of still very limited implementation and with the goal of filling in some of the research gap, we examine the feasibility/infeasibility of transferring the route solution obtained with one time instance to actual time routing case. Results in this section enable better understanding of the effect of using a pre-determined route, that was built based on a static pre-trip travel time, with the en-route real-travel-time scenario.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} \\  & **Instance** & **9:00** & **10:00** & **11:00** & **12:00** & **13:00** & **14:00** & **15:00** & **16:00** & **17:00** & **18:00** & **19:00** \\ \hline \multirow{3}{*}{Scenario A} & Speed & 22.68 & 19.64 & 16.21 & 17.96 & 22.21 & 25.42 & 29.53 & 33.46 & 36.35 & 37.33 & 36.06 \\ \cline{2-11}  & \(\tau^{*}\) & 414 & 423 & 449 & 440 & 421 & 400 & 381 & 371 & 365 & 362 & 360 \\ \cline{2-11}  & Change & 28\% & 26\% & 25\% & 27\% & 26\% & 26\% & 28\% & 27\% & 28\% & 28\% & 28\% \\ \hline \multirow{3}{*}{Scenario B} & Speed & 28.70 & 24.44 & 20.93 & 22.61 & 27.59 & 31.19 & 34.78 & 39.01 & 40.87 & 41.87 & 41.22 \\ \cline{2-11}  & \(\tau^{*}\) & 455 & 473 & 500 & 494 & 467 & 450 & 430 & 420 & 414 & 407 & 409 \\ \cline{2-11}  & Change & 16\% & 17\% & 16\% & 18\% & 17\% & 17\% & 17\% & 17\% & 17\% & 17\% & 18\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average travel speed (kilometers per hour), optimal working time \(\tau^{*}\) (minutes) and the percentage change in customer assignment from the original coverage area at each time instance for problems in Scenarios A and B.  
Figure 9: Optimal working time \(\tau^{*}\) as a function of road average speed.
## 3 Results
### Selected DAMP Sensors mRNA Detection in HGT, HGK, or HGF
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**HGK**} & \multicolumn{4}{c}{**HGF**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Oxygen Conc.**} & \multicolumn{4}{c}{**LPS**} & \multicolumn{2}{c}{**Oxyygen Conc.**} & \multicolumn{4}{c}{**LPS**} \\ \cline{2-13}  & **18\%** & **1\%** & **\(p\)-Value** & & **Nil** & **2 ug/mL** & **18\%** & **1\%** & **2** ug/mL** & **Nil** & **2** ug/mL** & _p-Value** \\ \hline TLR1 & 1 & 0.140 & 0.002 & 1 & 1.416 & 0.658 & 1 & 1.481 & 0.005 & 1 & 2.390 & 0.030 \\ TLR2 & 1 & 2.049 & 0.002 & 1 & 2.281 & 0.002 & 1 & 5.059 & 0.002 & 1 & 3.083 & 0.002 \\ TLR3 & 1 & 0.286 & 0.003 & 1 & 0.575 & 0.780 & 1 & 0.739 & 0.002 & 1 & 0.723 & 0.070 \\ TLR4 2 & 0.000 & 0.000 & 1.000 & 1 & 1.316 & 0.0655 & 1 & 1.955 & 0.010 & 1 & 0.776 & 0.113 \\ TLR5 & 1 & 0.881 & 0.060 & 1 & 0.833 & 0.980 & 1 & 0.759 & 0.010 & 1 & 0.760 & 0.230 \\ TLR6 & 1 & 0.109 & 0.002 & 1 & 1.046 & 0.990 & 1 & 0.751 & 0.239 & 1 & 3.281 & 0.0002 \\ NOD1 & 1 & 0.758 & 0.006 & 1 & 0.885 & 0.065 & 1 & 1.325 & 0.008 & 1 & 0.858 & 0.200 \\ NOD2 & 1 & 4.596 & 0.002 & 1 & 3.978 & 0.003 & 1 & 0.816 & 0.063 & 1 & 6.336 & 0.002 \\ NLRP1 & 1 & 1.819 & 0.002 & 1 & 1.115 & 0.650 & 1 & 1.729 & 0.002 & 1 & 0.947 & 0.380 \\ NLRP2 3 & 1 & 0.309 & 0.002 & 1 & 1.231 & 0.082 & 0.000 & 0.000 & 0.593 & 1 & 0.953 & 0.080 \\ \hline \end{tabular}  
* Results normalization were achieved using corresponding \(\beta\)-actin expression of untreated cells at normoxia as internal reference when the relative standard curve reflecting target mRNA level without stimulation was normalized. Transcript expressions of TLR1-6, NOD1/2 and NLRP1/2 were detected under 1% O\({}_{2}\) or 2 ug/mL _E. coli_ LPS for 24 h (\(n=13\)). 2 TLR4 was only detectable from six (out of 13) normoxic or five (out of 13) hypoxic samples, hence only these results were compared and shown. 3 NLRP2 was only detectable from five (out of 13) normoxic or three (out of 13) hypoxic samples, hence only these results were compared and shown.  
\end{table} 
Table 4: Expression level of damage-associated molecular patterns of interest upon hypoxia or 2 ug/mL _E. coli_ lipopolysaccharide-charide (LPS), 24 h stimulation 1.
## 5 Atomic Clock, Time Dilation, and Absolute Velocity Determination  
As the atomic clocks are subject to time dilation [7; 12], it results from the fact that they realize the signal clock. It should be suspected that atomic clocks use a signal that propagates in a distinguished medium (e.g., an electromagnetic signal propagating in the ether). It is possible that this mechanism is associated with atoms used as oscillators, but it is also possible with other elements of the clock, such as microwave cavities.  
There is a large number of atomic clocks, which is why there is no place in this paper to analyze their structure and to search for relations with signal clocks.  
Longitudinal and transverse contractions in STE are shown in Figure 5. In the paper [1], it was shown that these contractions must be exactly such if the average speed of light in a vacuum is to have in every inertial system a value \(c\).  
It is very likely that the actual contractions of the signal clock arms in the atomic clocks are not as ideal as those shown in Figure 5. It is enough for longitudinal contraction to be slightly different or transverse contraction to be slightly different from that shown in Figure 5. Then the timing of such clocks will depend slightly on the way they are inclined in relation to the speed of motion relative to the universal frame of reference. If this effect is very insignificant in our frame of reference, it can be immeasurable using the Michelson-Morley experiment, but it can be measured using atomic clocks.  
In order to determine the velocity in relation to the universal frame of reference, an atomic clock should be used, in which the arm of the signal clock contained in it has a strictly defined direction if such a clock can be constructed. For this purpose, several atomic clocks must be placed at different angles to one another on a rotating platform. The rotating platform must be capable of maintaining a constant position of the clocks in relation to space. In order to determine the absolute velocity, it is necessary to look for correlations between the speed at which the clocks operate and the directions in which their arms are positioned in space.
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 4 RQ2: Authentication Standards
From the results, we have that:  
* 72% of notified eID means use at least one of the SSO protocols;
* 62% of notified eID means use SAML standard within their solutions;
* German eID means is the only eID means that provides a hybrid solution to support both federated and direct authentication;
* Latvian and Lithuanian eID means are the only solutions under analysis that adopt OAuth 2.0 protocol instead of OIDC for authentication. To the best of our knowledge, the usage of OAuth for authentication can lead to several vulnerabilities [51; 52; 53];
* The solution of Baltic countries (SMART-ID) and the Italian CIE/SPID eID schemes published their national OIDC specification [25; 54] and they are in the process of implementing the OIDC within their eID schemes;
* The Swedish OIDC working group, in collaboration with Swedish eID means, is working on the definition of the Swedish OIDC profile. This profile is going to be adopted by the notified Swedish eID means [55];
* None of the notified eID means use Mobile Connect as an authentication standard. Mobile Connect [56] is an authentication mechanism based on OIDC, where the federated telco operators perform the user authentication;
* The eID means of Czech Republic (mojeID) and Sweden (EFOS) are the only available solutions that support FIDO [6]. Our updated analysis in September 2022 highlights a new trend related to FIDO; however, in our preliminary analysis in November 2021 [4], none of the considered eID means supports FIDO, in our latest analysis, two eID means (Czech Republic (mojeID) and Sweden (EFOS)) are supporting it, probably because this is an emerging technology [6].
## 2 Methods
### Data Preprocessing  
The HS signals were filtered with a zero-delay third order butter-worth bandpass filter in the range of 20-200 Hz to remove noise outside the frequency band of heart sounds. The R-waves of ECG signals were recognized by the Pan Tomkins algorithm [24]. In this paper, the ECG signal was used as a reference for HS signal segmentation. It was essential for this research. Heart sounds recording with ECG provide insights into the electrical-mechanical activity of the heart in an unsupervised, non-invasive and inexpensive manner. The first heart sound (S1) occurs immediately after the R-peak (ventricular depolarization) of the ECG. The interval between the current R-peak and next R-peak was called the cardiac cycle. The R-wave of each cardiac cycle indicated the fiducial time of each cardiac cycle of the simultaneous HS signal. The heart sound signals were segmented into consecutive cardiac cycles based on the R-wave positions. Each cardiac cycle of ECG signal corresponded the HS signal which included S1 and the second heart sound (S2), sometimes included the third heart sounds (S3) and the fourth heart sound (S4). It normalized the heart sound signals of all cycles to the same time-scaling of one second. The RVSBP values were detected by the maximum of the RVBP signal in each cardiac cycle. The stretched cyclic heart sound signals were used as inputs to the model and the corresponding RVSBP values were considered as the outputs. The process was shown in Figure 3. Outliers detection and replacement for RVSBP values were sometimes needed because of the motion artifacts or man-made interference [25].
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Mobile App Development  
The mobile application is developed based on the Android system, and the reason for choosing the Android system is its good open-source nature, large user base, and coverage of various phone models. An application based on the Android platform can reach a wider audience and contribute to the growth of the number of crack inspection users.
## 7 Attack and Countermeasure Designs
### Attack Design  
An adversary can improve the success of an attack by engaging multiple targets with rapid time by the brute-force attack design. Additionally, the targets can be attacked repeatedly at random intervals to increase the success of the attack [37].  
Figure 8: Attack designs and scopes: (**a**) Identifying closest hop cached contents. (**b**) Identifying distant hop cached contents.  
Figure 9 illustrates this work's attack design which is supported by a brute-force attack process. In this attack scheme, the adversary outlines multiple predefined targets \((T_{n})\) which are the name of contents (data packets) to start the procedure of an attack. Moreover, assuming that if any of a predefined target (e.g., NDN streamed data contents) has not yet been produced (or nonexistent) by the streaming producer, the NACK packet may occur (e.g., \"streamed data unavailable\").  
In this attack design, an adversary can repeat several attacks (depending on attack setup) to distinguish between uncached and cached targets. Each repetition presents various or matching CRT metrics to accomplish the attack decisions (e.g., the target is located or cached from the edge, neighbor, or away from locations/routers).  
Attack algorithm (Figure 9) illustrates that an adversary randomly picks (or selects) the target ((\(T_{x}\))) from all predefined targets ((\(T_{n}\))). In this design, an adversary repeats the attack for each target for a minimum of two periods. In addition, the attack replication/repetition can be different through the design of the attack or purpose. The attack repetition is identified as \(n-1\). After that, \((CRT_{i})\) values are collected for repetition of the target (\(T_{x}\)).  
Whenever the attack is completed, an adversary determines (or identifies) the target locations by relying and making comparisons on CRT values. For example, if the variance accompanied by the first CRT and other CRTs is slightly small (smaller than \(\epsilon\)), the \(CRT_{i}\) shows that a target \((T_{x})\) has just been (or recently) cached by the edge NDN node (router). Otherwise, it is cached by away NDN router(s). Furthermore, the work [39] showed that the adversaries identified geographic locations with about 90% attack success in different scenarios (including content fetch time (CFT)).  
Figure 9: Attack process algorithm with the brute-force design (Adapted from [38]).