esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 2 Related Works of E-Commerce Security
[20] proposed the use of double encryption by utilizing the ElGamal encryption and the AES encryption approaches to distribute the symmetric key and to secure message data or confidential information. They claimed that their method providessufficient security and performance when users' requests are transmitted over the network. Notwithstanding, their method did not provide appropriate performance because the method uses double encryption, the security analysis of the system was not performed in a deep or clear way, and the type of messages was not specified as to whether they were transmitted or stored, such that they were incomprehensible. [PERSON] et al. [21] proposed a solution to preserve the integrity of the file and to provide the receiver assurance that the data are unaltered by outside parties. The receiver will be aware that the file is no longer validated if it has been modified, helping them to avoid utilizing the incorrect information. The message digest 5 (MD5) algorithm is used as a hash function method to construct a message digest, and the ElGamal algorithm is used as a public key cryptography to form the digital signature of the message digest in order to guarantee the data validity. However, their proposal is not secure. For example, the MD5 function has the same message digest length for each processed file, which is 32 or 128 bits of message digest. Therefore, if the attacker tries to modify the file, it takes a lot of time to find a pair of data. In the ElGamal algorithm, the public key is on a pair, only one of the private keys, and thus, the compiler cannot encrypt it utilizing any private key. It takes a private key with a pair matching the public key so that it is modified to an unknown. The performance is not balanced with security procedures. An analysis of the relationship between privacy, security, confidence in the determination to reuse e-commerce apps, and perceived value was proposed by [PERSON] et al. [22]. Employing the purposive sample method, they applied this strategy to a set of customers who had at least once transacted using the Tokopedia application. The data-collection approach uses Google Forms to distribute surveys online, and the SmartPLS 3.3 program is used to analyze the data using the partial least square (PLS) approach. A total of 242 respondents participated in the survey. Nevertheless, their scheme encountered many problems including that the technique of collecting data was not more in-depth, utilizing random interview techniques with many of the participants. The application of e-commerce suffers from a set of variables such as price and method of promoting goods. This affects the security of e-commerce due to the development of technology exposing types of attacks that lead to the penetration of sensitive user information.
## 4 Results and Discussion  
### Downhole Results  
We have used the information provided from two boreholes drilled in the area (Figure 3) to identify the main characteristics of the soil columns. In Figure 4, the stratigraphic information from the boreholes and the Vs profiles deduced from the downhole test are depicted. The soil columns observed at both boreholes show a series of reddish silty clay and clayey silt levels with thin sand or conglomerate levels. At depths of 43 m (at borehole S2) and 48 m (S1), the soil samples showed polished surfaces, which are interpreted as slicensides formed by the slip along a failure surface of the landslide. The water table is found at less than 10 m in both boreholes.  
The first borehole (S1) was drilled to a depth of 70 m (Figure 4). From the downhole study, we deduced a three-layer Vs profile. The first layer has a thickness of 5-10 m and a Vs value less than 500 m/s. The second layer reaches a depth of 40-43 m with a Vs value of approximately 800-900 m/s. Finally, the depth of the third layer is between 43 and 50 m with a Vs of 600-700 m/s. Below 43-50 m, hard materials are found with a Vs value of approximately 1000 m/s.  
The second borehole (S2) was drilled to a depth of 60 m (Figure 4). From the downhole study, we deduced a four-layer Vs profile. The first layer of fills and clayey silts presents a maximum thickness of 5 m and a Vs value less than 500 m/s, which is similar to that observed at S1. The depth of the second layer lies between 25 and 30 m with an average Vs value of approximately 700 m/s. The third layer reaches a depth of 35-40 m and presents a Vs value of 800-900 m/s. Finally, the depth of the fourth layer is found at approximately 45-50 m, with a Vs value of approximately 700 m/s. Below 45-50 m, hard materials are found, presenting a Vs value close to 1000 m/s.  
The deeper layers on both profiles are located below the surface failures identified in the soil columns, and represent the in situ materials. They show a certain contrast in themeasured velocities with respect to shallower layers (Figure 4). Sudden increases in Vs velocities in S1 just below the failure surface are evident. In S2, this contrast is not as clear, but even in this case, it is recognizable that the Vs is lower at the zone of failure than the values observed in materials above and below this zone.
## 4 Results
### Calculation and Analysis Model of Frost-Heave Force
#### 4.2.2 Frost-Heave Force of Circular Wedge-Shaped Voids without Considering Drainage Conditions  
Assuming that the surrounding rock has the same properties and the gravity of the lining and the surrounding rock are not considered, the water storage space is formulated as a circular wedge shape. Referring to the calculation model in Figure 12, we must first calculate the frost-heave force caused by the void water without considering the drainage conditions. According to the local deformation theory, under the action of frost-heave force, the deformation displacement value of the surrounding rock is  
\[\delta=\frac{1}{K_{r}+K_{i}} \tag{3}\]  
where \(K_{r}\) is the elastic resistance coefficient of the surrounding rock (MPa/m), while \(K_{i}\) is the elastic resistance coefficient of the ice (MPa/m). The deformation displacement value of the lining is  
\[\Delta=\frac{1}{K_{i}+K_{l}} \tag{4}\]  
where \(K_{l}\) is the elastic resistance coefficient of the lining (MPa/m). Since the frost-heave forces acting on the lining structure and the surrounding rock are the same,  
\[\delta(K_{r}+K_{i})=\Delta(K_{i}+K_{l}) \tag{5}\]  
Figure 11: Schematic diagram of void position and shape. Figure 12: Cross-sectional view of the void position. Let  
\[\frac{\delta}{\Delta}=\frac{K_{r}+K_{i}}{K_{i}+K_{l}} \tag{6}\]  
The calculation formula for the volume expansion of water in the cavity is  
\[V_{i}=\alpha\cdot V_{v} \tag{7}\]  
where \(V_{i}\) is the volume expansion of water in the cavity, \(\alpha\) is the frost heave rate of ice, and \(V_{v}\) is the cavity volume or the volume of water in the cavity. Now, assume that the outer radius of the lining is \(r_{0}\), the maximum height of the lining void is \(\zeta_{0}\), the plane angle at the corresponding position is the void length \(z_{0}\), the center of the arc of the tunnel arch is the origin, and the longitudinal direction of the tunnel is the z-axis to establish a coordinate system. Its spatial model is as shown in Figure 13.  
The geometric equation of the cavity boundary curve is  
\[\left\{ \begin{matrix}y=\frac{\zeta_{0}}{z_{0}}z+r_{0}-\zeta_{0}\\ x^{2}+y^{2}=r_{0}^{2}\end{matrix} \right. \tag{8}\]  
Then, the volume of the cavity is  
\[ \begin{matrix}V_{v}=2\int_{0}^{z_{0}}\int_{0}^{\sqrt{2\,\zeta_{0}\zeta_{0}- \zeta_{0}{}^{2}}}\sqrt{r_{0}^{2}-x^{2}}-\Big{(}\frac{\zeta_{0}}{z_{0}}z+r_{0}- \zeta_{0}\Big{)}dxdz\\ =z_{0}r_{0}{}^{2}\theta_{0}-z_{0}r_{0}\sqrt{2r_{0}\zeta_{0}-\zeta_{0}{}^{2}} \end{matrix}  \tag{9}\]  
where  
\[\theta_{0}=\arcsin\frac{\sqrt{2r_{0}\zeta_{0}-\zeta_{0}{}^{2}}}{r_{0}} \tag{10}\]  
The cavity bottom area is  
\[S_{bot}=2(z_{0}^{2}+{\zeta_{0}}^{2})-\frac{2}{3}\frac{\big{(}z_{0}^{2}+\zeta_ {0}{}^{2}\big{)}^{2}}{2r_{0}\zeta_{0}-\zeta_{0}{}^{2}} \tag{11}\]  
The cavity top surface area is  
\[S_{top}=\frac{4}{3}z_{0}r_{0}\theta_{0} \tag{12}\]  
Figure 13: Calculation model of frost-heave force in a circular wedge-shaped void.
# Calibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy
## 3 Test Results  
### Presentation of Tests and Results  
Calibration of the prototype O-X glide machine tool was realized using measuring equipment for laser interferometry manufactured by HP (laser head 5500 C, display 5505 A, interferometer 10565 B retroreflector 10550 B, and compensator of external conditions 2044 A). Figure 5 shows the setup of the measuring equipment on the machine prototype during measurement, where (1) denotes the laser measuring head, (2) indicates the interferometer, and (3) signifies the retroreflector.  
Data acquisition and processing were implemented using the Matlab r2024a programming environment, which included an application for compensating environmental conditions, calculating errors, and generating graphics created for research purposes.  
Testing and calibration of the hybrid machine tool was carried out in three stages:  
* Testing the machine in an uncompensated state for all three external axes. After that, the characteristic error sizes were calculated, and graphs were created for all three axes;
* Defining compensation values for the unidirectional compensation method, creating compensation files for all three internal axes. After compensating for the errors, the machine was tested after calibration;
* Defining compensation parameters for movement in the positive and negative direction for all three axes (bidirectional compensations).  
For the Y axis (serial axis), compensation values were expressed directly from the measurement results, while for X and Z (plane parallel mechanism segments), compensation parameters were calculated for each point according to Expression (11).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Position** & **Compensation in + Direction** & **Compensation in – Direction** \\ \hline
14.98487 & 0.14448 & 0.12976 \\
33.98487 & 0.11437 & 0.10103 \\
52.98487 & 0.10706 & 0.09394 \\
71.98487 & 0.07692 & 0.06408 \\
90.98487 & 0.06960 & 0.05900 \\
109.98487 & 0.05155 & 0.03921 \\
128.98487 & 0.05041 & 0.03854 \\
147.98487 & 0.02685 & 0.01830 \\
166.98487 & 0.02138 & –0.00761 \\
185.98487 & –0.00636 & –0.01888 \\
204.98487 & 0.00225 & –0.00121 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of bidirectional error compensation.  
Figure 5: Realization of measurement.  
Figure 6a,b show graphs for the initial state and bidirectional error compensation for the Y axis.  
Figure 7a,b show the graphs for the initial state and bidirectional error compensation for the X axis.  
Figure 6: Initial condition (**a**) and test results after error compensation (**b**) for Y axis.  
Figure 7: _Cont._  
Figure 8a,b show graphs for the initial state and bidirectional error compensation for the \(Z\) axis.  
The presentation of numerical values of characteristic quantities before the compensation process as well as after unidirectional and bidirectional calibration is shown in Table 2.  
Figure 8: Initial state (**a**) and test results after error compensation (**b**) for the \(Z\) axis.  
Figure 7: Initial state (**a**) and test results after error compensation (**b**) for the X axis.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Multistatic Deployment  
A multistatic configuration consists of spatially distributed transmitting and receiving devices, which can include both BSs and UEs, where each transmitter and receiver forms a bistatic pair. The observations made by receivers are shared with a FC, enhancing sensing performance compared to previous setups. Indeed, the radar cross-section (RCS) is typically highly dependent on the incidence and scattering angles and can exhibit low values or even notches in certain directions. Therefore, collecting echoes from the same object at different angles ensures a diversity gain. In addition to the synchronization challenges inherent in bistatic configurations, a well-designed spatial layout, effective data fusion methods, and strategic scheduling are essential in a distributed configuration. However, unlike bistatic deployments that are affected by the blind zone problem, the multistatic configuration allows the FC to leverage information from multiple bistatic pairs, effectively mitigating this issue. If a target is within the blind zone of one bistatic pair, it can still be detected and located by other pairs.
## 2 Related Works
### 360-Degree Video Control  
A method to track an object included in a 360-degree video taken with an omnidirectional camera and provide users with an NFoV has been previously developed [15; 16]. Objects set by the user are extracted from the captured 360-degree video. The object is selected by setting priorities among the objects and NFoV is provided to the user based on the 360-degree video position of the object having the highest priority. When tracking two or more objects, a method of providing an NFoV that considers the relationship between objects is needed.  
A saliency map is constructed based on the 360-degree video to determine the NFoV to be provided to the user [17]. Users are provided with the highest score in the saliency map as the NFoV. To calculate the saliency map, the score of each object must be set. If there is a similar score, the change in the NFoV provided to the user may be significant. It is difficult to specify the objects that the user should intuitively observe. When photographing one or more objects, calculating the situation of the surrounding objects and providing them to the user is necessary.  
A method has been developed to provide NFoVs by controlling 360-degree videos using end-to-end controls. This provides the user with an NFoV as a video control signal inferred by inputting a 360-degree video. However, if the video control signals for the cube are specified, the boundaries of the NFoVs may not be natural; if the video control signals are specified in detail, unused video control signals may be included; if insufficient video control signals are specified, there may be NFoVs that user cannot access. Thus, there is a need for a method that automatically calculates video control signals and controls 360-degree video based on end-to-end controls.  
End-To-End Control-Based 360-Degree Video Control Method Using Video Control Signal Generation Based on K-Means Algorithms  
This section describes the process of calculating the NFoVs provided in captured 360-degree videos using end-to-end controls. A method of training a convolution neural network (CNN) model using 360-degree video and generated video control signals is introduced, and a method of providing NFoVs to a user using video control signals inferred using the trained CNN model is described.
## 3 Results and Discussion  
### Compressive Strength  
The compressive strength of each group of cementitious materials is shown in Figure 4a-c. The incorporation of only EMR or CS hindered the strength development of the cementitious materials because EMR or CS are not ideal as a single activator. With the addition of EMR and CS, the compressive strength of each multicomponent solid waste  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment Number** & **Cement/\%** & **EMR/\%** & **GBFS/\%** & **CS/\%** & **W/C** & **Sand/g** \\ \hline PO425 & 100 & - & - & - & & \\ EGC0 & 70 & - & 30 & - & & \\ EGC1 & 70 & 0 & 25 & 5 & & \\ EGC2 & 70 & 1 & 25 & 4 & & \\ EGC3 & 70 & 2 & 25 & 3 & & \\ EGC4 & 70 & 3 & 25 & 2 & & \\ EGC5 & 70 & 4 & 25 & 1 & & \\ EGC6 & 70 & 5 & 25 & 0 & & \\ EGC7 & 70 & 0 & 20 & 10 & & \\ EGC8 & 70 & 2 & 20 & 8 & & \\ EGC9 & 70 & 4 & 20 & 6 & & \\ EGC10 & 70 & 6 & 20 & 4 & & \\ EGC11 & 70 & 8 & 20 & 2 & & \\ EGC12 & 70 & 10 & 20 & 0 & & \\ EGC13 & 70 & 0 & 15 & 15 & & \\ EGC14 & 70 & 3 & 15 & 12 & & \\ EGC15 & 70 & 6 & 15 & 9 & & \\ EGC16 & 70 & 9 & 15 & 6 & & \\ EGC17 & 70 & 12 & 15 & 3 & & \\ EGC18 & 70 & 15 & 15 & 0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proportioning design of multicomponent solid waste cementitious materials.  
cementitious materials first increased and then decreased, indicating that the addition of appropriate amounts of EMR and CS effectively stimulated the hydration activity of GBFS and improved the strength. Sample EGC11 (GBFS content of 20%, EMR content of 8%, and CS content of 2%) exhibited the optimal strength, and the 28 d compressive strength was 45.5 MPa. The strength of the specimen improved, owing to the ability of EMR and CS to fill the pores and enhance the density of the mortar [28]. Additionally, the sulfate phase in EMR and the free alkali substances in CS effectively stimulated the hydration activity of GBPS, leading to the generation of more hydration products [29; 30].  
Figure 4: Mechanical properties of EGC cementitious material system: (**a**–**c**) compressive strength; (**d**–**f**) compressive strength distribution map.  
Figure 4d-f shows the distribution of the compressive strength of the multicomponent solid waste cementitious materials system. With the increases in the EMR content and curing time, the compressive strength increased; thus, the extension of curing time was beneficial to the development of 3 d, 7 d, and 28 d compressive strengths, especially for the 28 d compressive strength, mainly because SO42- in the EMR could continue to react with GBFS to generate more AFt in the later stage and AFt was the main hydration product. However, under excessive EMR, the strength significantly decreased, which was related to the large amount of inert material SiO2 contained in the EMR. Moreover, the compressive strength of each age was optimal at the GBFS content of 20%; therefore, considering the utilization rate of solid waste and the mechanical properties of mortar, this study mainly explored the multicomponent solid waste cementitious materials system with a GBFS content of 20% (group EGC7-12).
## 2 Linear and Nonlinear Methods
### Artificial Neural Network Approach  
The artificial neural network-based approach is a typical nonlinear method for wind speed forecasting. Nonlinear artificial neural networks consist of an input layer, one or more hidden layers, and an output layer. Each layer has a number of artificial neurons, and the activation functions between neurons of different layers make the network capable of non-linear mapping. This approach offers a black-box model to simulate the complicated nonlinear relationship between input and output layers and requires no explicit mathematical expressions as used in the physical and time-series approaches. The networks of BPNN, LSTM, and GRU are summarized as follows.
## 5 Identification of Damage on an Experimental Beam  
### Experimental Setup  
An experimental cantilever beam is studied in order to investigate the accuracy of the proposed strategy using FE based FSC approach. As with the numerical studies, the study is done for a rectangular damage. A steel beam (length \(L=1\) m, width \(W=25\) mm and thickness \(T=5.3\) mm) is instrumented with 2 accelerometers, as displayed in Figure 16. Material properties (\(E\), \(\rho\), \(v\)) of beams for the experiment are similar to those depicted in Section 4.1. Two experimental beams were measured and tested; the first is considered as healthy, and the second is a damaged beam. On damaged beam, a rectangular damage is done (saw cut) and measured with a depth of 1.55 mm and a width of 1.3 mm. It is placed at 710 mm from the clamped end. In order to extract the natural frequencies from the healthy and damaged beam, impact testing is chosen, and signals are recorded with a Siemens LMS multianalyser. The input excitation is produced through an impact hammer, while monoaxial accelerometers (Bruel et Kjaer) record the output data. Two accelerometers are placed at 0.35 m and 0.75 m from the clamped end. Frequencies of a healthy and damaged structure are obtained from frequency response function by applying circle fit method using EasyMod package [27]. The first seven experimentally identified frequencies for each beam are listed in Table 6. Slight differences in natural frequencies are noticeable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Beam Case**} & \multicolumn{8}{c}{**Natural Frequencies (Hz)**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \(f_{healthy}\) & 4.23 & 26.33 & 73.26 & 142.34 & 240.12 & 355.67 & 499.66 \\ \(f_{damaged}\) (rectangular) & 4.24 & 26.21 & 72.82 & 141.73 & 239.38 & 354.36 & 498.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimentally identified natural frequencies of healthy and rectangular damaged beam.  
Figure 15: (**a**) FSC as a function of damage position and severity for damage described in Figure 13b; the white circle indicates that the damage position and severity are 0.70 m and 25.35%, respectively; (**b**) width and depth are extracted for 25.35% severity from the damage library (blue dots: raw extraction, red line: cubic fit, black cross: recovered geometry).
## 3 Experiments and Results
### Experiments  
A noisy Barbara image, as shown in Figure 1, was used to demonstrate the above assumption. Gaussian white noise was padded to this image first (\(\mu=100\), \(\sigma=10\), on 20% of the entire image padded randomly) and it was then filtered with a 3 \(\times\) 3 and a 5 \(\times\) 5 average filter to generate RE\({}_{3}\) and RE\({}_{5}\), respectively. Following this, we measured [PERSON]'s Z in both REs.  
[PERSON]'s Z showed a spatial correlation, which corresponded well with the variation in image spatial properties. A higher Z value means less noise and more structures in an area and vice versa. The spatial properties shown using [PERSON]'s Z histogram for RE\({}_{3}\) and RE\({}_{5}\) are different, as shown in Figure 2. [PERSON]'s Z was measured using a 5 \(\times\) 5 sliding window. It is obvious that the curve of RE\({}_{5}\) shifts to a higher Z area than RE\({}_{3}\). The result shows that there are more structure areas in RE\({}_{5}\) than in RE\({}_{3}\). The spatial autocorrelation scheme clarified the spatial information in the images.  
These [PERSON]'s Z measurements were then used as feature data in the FCM algorithm. Three clusters were pre-assumed for FCM in this work: they were heavy noise (lowest Z area), medium noise, and less noisy areas (highest Z area), as shown in Figure 3. This figure also shows three cluster center points in the feature domain, where the [PERSON]'s Z values for RE\({}_{3}\) and RE\({}_{5}\) are coordinates. Each point in the feature data space has three membership rates. A brief flow chart for the proposed image de-noise scheme is shown in Figure 4.  
Figure 3: The calculated cluster centroids in the feature domain (where [PERSON]’s Z values of RE\({}_{3}\) and RE\({}_{5}\) are coordinates). \(\bigcirc\) = less noise or edge, \(\chi\) = medium noise, + = heavy noise.  
Figure 2: The minus sign was given by Matlab software automatically histogram of [PERSON]’s Z for RE\({}_{3}\) and RE\({}_{5}\) for noisy Barbara image.