esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Scheme Model
### Design Goals  
Based on the considered threat assumptions and to ensure security and privacy against internal and external attackers, our design goals for our scheme are laid out as follows:  
* The proposed e-voting scheme should neither rely on a central entity to run the election nor require certain infrastructure to set up a private blockchain network. Central entities are vulnerable to a single-point-of-failure or attack. Our goal is to develop and implement the scheme using an open source blockchain platform and cloud-based infrastructure to avoid hardware costs required to host a private blockchain.
* The proposed scheme should protect voters' privacy by ensuring the following: (i) Only authenticated voters should be able to cast their votes to the ballot while protecting their real identities from being revealed or tracked. (ii) Since anonymity alone is not sufficient to ensure voters' privacy against internal attackers who can link voters' pseudonyms to their real identities by knowing their IP addresses, the proposed scheme should withstand such eavesdropping attacks by making it difficult to determine the exact selection of a specific voter.
* The scheme should also resist any fraud or manipulation attacks that would give preference to specific candidates over others. For example, blockchain nodes or internal attackers may try to replay legitimate voters' votes or recorded transactions on the blockchain to give a candidate more votes than their actual vote count. In other words, authenticated voters can cast their encrypted votes, and the blockchain nodes can only compute the number of votes per candidate _correctly_.  
Finally, one primary goal of this work is to _evaluate the efficacy of developing the blockchain-based e-voting scheme on top of IBM cloud-based infrastructure_ in terms of throughput and scalability.
## 2 The Peak Shrinking and Interpolation (PSI) Technique
### Matching/Smoothing Algorithm in PSI Technique  
Figure 7 presents two cases: (1) the case that the shrunk peak does not need smoothing process is presented, where it is seen that \(D_{R}\) is 0.001 which is smaller than \(D_{max}\). It should be noted that by checking the EVM performance, the suitable value for \(D_{max}\) is considered to be 0.005.  
```
\(D\leftarrow\) Absolute distance between power of borders; \(D_{L}\leftarrow\) Left side border point distance between peak surrounding and the rest of the signal; \(D_{R}\leftarrow\) Right side border point distance between peak surrounding and the rest of the signal; \(D_{max}\leftarrow\) Maximum value that \(D\) can have \(=0.005\) (normalised power); if\(D_{L}\geq D_{max}\)then  Border point is shrunk with \(SR_{rev.}\) instead of SR; endif if\(D_{R}\geq D_{max}\)then  Border point is shrunk with \(SR_{rev.}\) instead of \(SR\); endif
```  
**Algorithm 2** Matching/smoothing algorithm (performed after the shrinking process)  
For the next case (2) the peak definitely requires smoothing process, as \(D_{R}\) is grater than \(D_{max}\), and the peak after the smoothing process is seen in Figure 7c.  
It should be noted that in some cases, the peak can be found at the very beginning of the slice, or at the very end of signal (\(L_{index}=0\) or \(R_{index}=N\)). It can be understood that smoothing algorithm does not have access to the adjacent points to perform smoothing correctly, in order to avoid the scenario which happens 5% of the times when a signal with length of \(30\times 10^{3}\) is tested, overlapped slicing, as seen in Figure 5 is recommended, which removes the scenario completely.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 7 Comparisons
### Practical Comparisons  
Among all the ORAMs that are considered in the above asymptotical comparisons, CNE-ORAM is the one most comparable to TSKT-ORAM. Thus, we make a more detailed comparison between TSKT-ORAM and CNE-ORAM in practical scenarios. The comparisons are conducted in terms of communication cost, storage cost and access delay. The access delay comparison includes all non-implementation factors that result in the access delay. In the comparison, we consider the following parameter settings: (1) \(N\) ranges from \(2^{16}\)-\(2^{34}\); (2) \(B\) is set to 1 MB for both schemes; (3) security parameter \(\lambda\) is set to 80; (4) system parameter \(k\) in TSKT-ORAM is set to 128. Table 2 shows the communication and computation costs of these schemes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ORAM** & **C-SComm.Cost** & **S-SComm. Cost** & **Client Star.Cost** & **Server Stor. Cost** & **\# of Servers** \\ \hline T-ORAM [22] & \(O(\log^{2}N\cdot B)\) & N.A. & \(O(B)\) & \(O(N\log N\cdot B)\) & 1 \\ Path ORAM [23] & \(O(\log N\cdot B)\) & N.A. & \(O(\log N\cdot B)\cdot\omega(1)\) & \(O(N\cdot B)\) & 1 \\ \(*\) P-PIR [32] & \(O(\log N\cdot B)\) & N.A. & \(O(B)\) & \(O(N\log N\cdot B)\) & 1 \\ \(*\) C-ORAM [29] & \(O(B)\) & N.A. & \(O(B)\) & \(O(N\cdot B)\) & 1 \\ \hline MS-ORAM [33] & \(O(\log N\cdot B)\) & \(O(\log^{3}N\cdot B)\) & \(O(B)\) & \(O(N\log N\cdot B)\) & 2 \\ MSS-ORAM [27] & \(O(B)\) & \(O(\log N\cdot B)\) & \(O(\sqrt{N}\cdot B)\) & \(O(N\cdot B)\) & 2 \\ CNE-ORAM [30] & \(O(B)\) & N.A. & \(O(B)\) & \(O(N\cdot B)\) & 4 \\ \hline TSKT-ORAM & \(O(B)\) & N.A. & \(O(B)\) & \(O(N\cdot B)\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Asymptotic comparisons in terms of client-server communication cost, server-server communication cost, client storage cost, server storage cost and the minimum number of non-colluding servers required. \(N\) is the total number of data blocks and \(B\) is the size of each block in the unit of bits. \(B=O(N^{\epsilon})\) for some \(0<\epsilon<1\). For TSKT-ORAM, \(k=O(N^{\epsilon})\) where \(0<\epsilon<1\) and \(c=4\). For all tree structure ORAMs, the index table is outsourced to the server with \(O(1)\) recursion depth. A scheme marked with the asterisk symbol requires homomorphic encryption.
## 1 Introduction
* We propose a UAV-aided CCS by using NOMA, where the UAV devices act as a transmitter to transmit covert messages under the monitor of malicious users, one accessed terminal with NOMA receives covert messages, and the other accessed terminal jams the communication of the eavesdropper. * To maximize the covert rate, we formulate the optimization problem to optimize the transmit power and the altitude of the UAV under a finite transmission block and the covertness. * To solve the formulated nonconvex optimization problem, we first convert the signal programming (SP) into a geometric planning (GP) problem and then adopt a symbolic geometric planning approximation (SGPA) algorithm to solve the optimization problem. Furthermore, the convergence is proved by verifying the given conditions. The remainder of the paper is organized as follows. In Section 2, we present the UAV-assisted CCS with NOMA and introduce covertness as the measurement metric of the CCS. In Section 3, the optimization problem of the CCS is formulated by optimizing the finite block length under the covertness constraint. Then, the joint optimization problem is transformed into a SP problem. The solution algorithm with the approximation of SGPA is developed, and the convergence of the algorithm is analyzed. Section 4 presents the numerical results, and the conclusion is given in Section 5.
## 3 Numerical Examples
### Results and Discussion
As we can see from the figure, for all the cases of radius \(r\), the larger absolute values of vorticity (\(|\omega|\geq 1\)) appear in the thin shear layer region near the walls of the cavity and walls of the obstacle, the absolute value of the maximum vorticity appears in the top corner region of the upper left and right corners of the cavity, and the vorticity on the upper side in the top corner region is negative, whilv the vorticity on the underside at the top corner region is positive. For the smaller Reynolds numbers, the radius \(r\) has no substantial effect on the vorticity distribution. The distribution of vorticity inside the square cavity is similar at different radii; however, the radius of the embedded circular obstacle has an important effect on the vorticity inside the cavity. The variation of the \(Re\) has a great influence on the square cavity flow with an embedded circular obstacle. At a higher Reynolds number (\(Re=5000\)), the absolute value of vorticity (\(|\omega|\geq 1\)) is larger in the near wall area and the area surrounding the circular obstacle, as shown in Figure 10.  
The figures above (Figures 2-10) are plotted by visualizing those data files obtained from the spectral/hp element methods. Figure 2:0 Streamlines of the lid-driven square cavity flow with an internal circular obstacle at different Reynolds numbers at \(r=2/5\). Figure 2:1 Vorticity contours for various \(r\) at \(Re=5000\). Figure 2:2 Vorticity contours for various \(r\) at \(Re=100\).
## 3 Experimental Results
### Static Test Results
#### 3.5.2 Load-Deflection Curve under Static Load  
During the static loading process, the specimen structure remains in the elastic phase. The deflection changes linearly with respect to the thermal load variation. The slope of the load-deflection plot can be used to characterize the structural stiffness, as shown in Figure 23, from which it can be seen that, under the same static load, the deflection of the beam after the repeated thermal loading is slightly larger than that before the repeated thermal loading. This indicates that the stiffness of the structural system degrades after the repeated thermal loading, with a relatively small degradation range.
## 3 Results and Discussion
### Optimization of the HS-SPME Methods
#### 3.1.3 Effect of Extraction Temperature and Time  
The extraction temperature was evaluated in univariate mode at 35, 40, 45, 50, 55, and 60 \({}^{\circ}\)C while keeping the other variables at their optimum value. Figure 3C indicates that the extraction efficiency of most types of trace compounds increased first and then decreased with the change of extraction temperature, but the trend of individual compounds may not be obvious. Among them, sulfur compounds and pyrazines decreased significantly above 45 \({}^{\circ}\)C, and alcohols decreased significantly above 50 \({}^{\circ}\)C. This indicated that volatile compounds that were entirely in the gaseous phase at a specific temperature will adsorb less on the fiber at a higher temperature. Moreover, at a temperature above 50 \({}^{\circ}\)C, the properties of some compounds will change [32]. The results of the analysis performed in triplicate indicated that the extraction temperature of 45 \({}^{\circ}\)C was a compromise temperature for all compounds and was used for further analysis.  
As the last parameter, the extraction time was assessed using variation between 15 and 75 min. Figure 3D shows that the extraction efficiency of almost all compounds increased first and then leveled off with the change of extraction time; the same trends for HS-SPME-GC-MS were seen in previous studies [33]. However, the peak area of sulfur compounds and phenols decreased slightly after the extraction time exceeded 45 min, which could be explained by competition effects during adsorption to the fiber. Because our targets are trace compounds, the concentration of the other high content components in the headspace increased with increasing extraction time, and due to their higher affinity for the fiber,  
Figure 3: Effects of parameters on the 10 different chemical classes of compounds’ peak area: (**A**) sample dilution, (**B**) liquid volume, (**C**) extraction temperature, and (**D**) extraction time.  
some of the target compounds may desorb from the fiber due to competition. Therefore, the procedure of 45 min, according to the optimal accuracy with time-efficient extraction, was used for further analysis.
## 3 Determination Method of a Reasonable Completion State
### Finite Element Model  
The spatial FE mode of Hunan Road Bridge established using ANSYS software is shown in Figure 7. The Timoshenko beam element (BEAM188) in ANSYS was adopted to simulate the tower, crossbeam, and girder. The tension-only link element (LINK10) was used to simulate the cable, hanger, and prestressed tendons. The internal forces of the cable, hanger, and prestressed tendons were applied by defining the initial strains. The additional quality element (MASS21) was adopted to simulate the weights of deck pavement, saddle, clip, and approach bridge. The nodes of tower bottoms were consolidated completely. The DOFs between the girder, tower, and approach bridge were constrained, according to the actual bearing types [31]. As shown in Figure 8, the arrows represent the sliding directions of bearing. The bearings between the girder and the crossbeams at west and east towers are transverse unidirectional sliding bearing and bi-directional sliding bearing, respectively. The static equilibrium analyses under self-weight were conducted in the follow sections. Moreover, the massless rigid arms (BEAM188) were introduced to connect the cables with the girder ends and tower tops. The compressions of girder and towers caused by cable force and the concrete shrinkage and creep during the construction period were simulated by changing the temperatures of rigid arms. The structural sectional properties and material parameters are shown in Table 1.  
Figure 8: Bearing types of the Hunan Road Bridge (The dashed boxes represent the locations of towers).  
Figure 7: Spatial FE mode of the Hunan Road Bridge.  
In addition, the refined simulation method of cable saddles was proposed as shown in Figure 9. The deformation of the saddle was ignored. The middle part of the saddle top was assumed to be in constant contact with cable, and there was no relative displacement between the splay saddle base and girder [32]. The changes in contact relationships between cable and saddles during system transformation were simulated precisely and conveniently.  
As can be seen in Figure 9a, the main saddle body was simulated by the rigid beam elements distributed radially along the top surface of the saddle. The main saddle base was simulated by the rigid beam element co-locating with the tower. The rigid connections relaxing the longitudinal constraints were used to connect the saddle base and the bottom surface of the main saddle. Thus, the displacement of the main saddle during system transformation can be simulated. Moreover, the rigid connections were used to simulate the contact between the cable and the middle part of the saddle top. The encryption areas of compression-only connections were set in both sides of the saddle. Thus, the separation of cable from the main saddle and the changes in cutting point positions can be simulated. In addition, the splay saddle is also an important steering member for the cable. As can be seen in Figure 9b, the rigid connections were used to simulate the connections between the splay saddle base and the main girder.
## 6 Discussion on Blast Overpressure Reduction
### Effect of the External Entrance Slope  
Figure 19a shows the relationship between the maximum overpressures, \(P_{M}\), and slope angle, \(\alpha_{s}\), for the considered blast-pocket ratios, \(L_{b}/L_{w}\). The maximum overpressures, \(P_{M}\), tend to diminish with the decrease in the angle, \(\alpha_{s}\), because the inclined entrance, compared to the vertical entrance, relieves the overpressures as much as the sloping portion. The maximum overpressures, PM, were the smallest at an angle, \(\alpha_{s}\), of \(60^{\circ}\) for the ratios, \(L_{b}/L_{w}\), of 0.5, 1.0, and 2.0, and \(\alpha_{s}\) of \(45^{\circ}\) for \(L_{b}/L_{w}\) of 1.5. The angles, \(\alpha_{s}\), for the lowest points of the maximum overpressures, PM, differ slightly according to the ratios, \(L_{b}/L_{w}\), because of the complex propagation of shock waves inside the tunnel. The overpressure mitigations with angle, \(\alpha_{s}\), are shown in Figure 19b. The maximum overpressure, \(P_{M}\), was reduced by up to 64.5% for \(\alpha_{s}=60^{\circ}\) and \(L_{b}/L_{w}=0.5\), compared to the original model.  
Figure 19: Maximum overpressures and reduction varying slope angle of external tunnel entrance for \(L_{b}/L_{w}=0.5\), 1.0, 1.5, and 2.0. (**a**) Maximum overpressure; (**b**) Overpressure reduction.  
Figure 20a,b show the overpressure distributions for Model Arc-90-0 (original) at \(t\) = 73 ms and Model Arc-60-05 at \(t\) = 77 ms, respectively. Evidently, the overpressures for Model Arc-60-05 at the shock front and Mach stem were slightly smaller than those for the original because some of the shock waves propagated inward via the first blast pocket. This is similarly observed for the second blast pocket, as shown in Figure 20c,d. The blast overpressure reductions for all the numerical models are listed in Table 5.
## 4 Conclusions  
The results presented in this paper link the angular and energy distributions of electrons, protons, and photons calculated within 0-360\({}^{\circ}\), with an angular step of five degrees, to the radiation dose map. This is achieved through integration into the simulation model and the use of an experimental set up mesh. The geometry of the experimental set-up was designed in the Geant4 General Particle Source code and was considered for the calculation of the radiation dose around the high-power laser-solid thin foil interaction point. When comparing the calculated dose map with the experimental one, certain discrepancies between the values of dose obtained at different angles were observed. This is most probably because the scattering of radiation into the vacuum chamber was not considered in the simulation model.  
Figure 7: **(a)** Map of the measured dose (blue dots) inside the target chamber and of the simulated radiation dose (red dots); **(b)** The fluctuations in the angular distribution of measured and simulated dose.  
Integrated dose per laser shot of about 15 mGy (due to electrons and photons) in the normal direction to the target, at 30 cm distance from the laser-target interaction point was experimentally measured.  
Excepting the geometry of our experimental set-up, the presented simulation model allows for the calculation of the fluencies of electrons, protons, and photons based on their energy and angular spectral distributions. Our results show that electrons contribute almost \(\sim\)99% to the value of total fluencies generated around the interaction point, while photons have a 1% share.  
The numerical and experimental investigations presented in this paper suggest that high power laser-thin solid target experiments can be used as a test environment for electronic devices placed onboard spacecraft and space stations, the main advantage being the simultaneous generation of electrons, protons, and photons.  
Conceptualization, A.G., A.C., B.B., and M.S.; methodology, A.G, B.B., M.S., E.S., and D.B.D.; investigation, B.B., M.S., E.S., and D.B.D.; software, A.C. and D.B.D.; validation, A.G., A.C., M.G., B.B., M.S., and E.S., D.B.D.; formal analysis, A.G. and A.C.; writing--original draft preparation A.G., A.C., E.S., B.B., E.S., M.S., D.B.D., and M.G.; writing--review and editing A.G., A.C., E.S., and D.B.D. All authors have read and agreed to the published version of the manuscript.  
Work has been funded by European Space Agency within the ESA contract No. 4000121912/17/NL/CBi/2017.  
We acknowledge support from the CETAL-PW facility at the National Institute for Laser, Plasma and Radiation Physics, Magurele, Romania. We also thank to [PERSON] from GSI-Darmstadt for fruitful discussions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# The Mobility Analysis of a 3-CCR Parallel Manipulator with Three Screw-Type Terminal Constraints
## 3 Terminal Constraint and DOFs Determination Based on Principal Screws Analysis
### Mobility Identifying Approach
Based on the above analysis, the steps for the mobility analysis of PMs with three screw-type constraints are as follows:  
1. Express the joint vectors. Establish the expression of joint vectors in terms of the independent pose parameters of the end-effector. This step can be achieved through a kinematic inverse solution. 2. Compute workspace and feasible joint vectors. Calculate the workspace of the PM and solve for the expressions of joint vectors under feasible poses within the workspace. 3. Solve pitches of principal screws for discrete poses. Substitute the expressions of the joint vectors sequentially into Equations (4), (5), and (16)-(18) to solve for the principal screws and their pitches of the constraint screw system under feasible poses. 4. Determine the type of DOFs. Utilize the one-to-one correspondence between the principal screws of the constraint screw system and the types of DOFs (as shown in Table 1) to determine the DOFs at each feasible pose, thereby inferring the full-cycle mobility of the PM. Figure 2 shows the above steps.
# Effect of Venturi Structures on the Cleaning Performance of a Pulse Jet Baghouse
Previous research has reported that the cleaning performance of filter bags was affected by the presence of the venturi tubes as well as by their shapes. This, in turn, affected the cleaning baghouse performance. However, studies on the effect of venturi structural parameters on the cleaning performance of the filter bag, and of the venturi tube type on dust cleaning performance and the dust emission concentration of the baghouse, are limited. This paper reports on the effect of different lengths and throat diameters of venturi tubes on the cleaning performance of long filter bags (8000 mm). Given that filter bags with typical venturi tubes display poor cleaning efficiency in the bag mouth area, a new venturi type is designed. This new tube is tested in order to determine the cleaning performance and dust concentration in the baghouse emissions.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 2 Materials and Methods
### Audio Conversion
#### 2.2.2 WAVE Audio Conversion  
The WAVE prototype simplifies the process of changing the language and voice for message transmission, primarily achieved by substituting the SD (secure digital) card with another containing the desired audio files.  
For optimal and faithful message transmission, it is advisable to employ high-performance recording devices. The WAVE files to be exported should conform to the following format:  
* Bit resolution: 8 bits.
* Audio frequency: 16 kHz.
* Audio channel: Mono.
* PCM format: U8.  
The file names must follow a specific convention; otherwise, the program will not recognize them:  
* The **STOP** message corresponds to the file named 0.
* The **DANGER** message corresponds to the file named 70.
* The **CAUTION** message corresponds to the file named 140.
* The **CLEAR** message corresponds to the file named 210.  
Furthermore, it is crucial to copy the files to the root of the card; placing them in different directories will result in them not being detected.  
Through the utilization of the SD card reader, this prototype offers users considerable flexibility in selecting the language and vocal tone of the messages. The change essentially entails swapping the card with another one.