esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Stress State of Inclined Pillar
### Stress State Equation of Inclined Pillar  
In the mining of an inclined ore-body, the pillar is under a combination of compression and shear loading, i.e., the pillar is subjected to compressive stress \(\sigma_{\theta}\) and shear stress \(\tau_{\theta}\), as shown in Figure 3a,b.  
The force of a single pillar per unit thickness is analyzed. If the pillar is in equilibrium under the combined compression-shear load, the equilibrium equation is listed according to the stress state of Figure 3c, then  
\[\sigma_{\theta}dx\cos\alpha=\sigma ds+\tau_{\theta}dx\sin\alpha \tag{3}\] \[\tau ds=\sigma_{\theta}dx\sin\alpha+\tau_{\theta}dx\cos\alpha\]  
where \(\sigma\) and \(\tau\) are the average compressive stress and shear stress on any slope in the pillar, and \(dx=ds\cdot\)cos\(\theta\).  
Trigonometric identities need to be satisfied:  
\[\cos 2\alpha=2\cos^{2}\alpha-1=1-2\sin^{2}\alpha \tag{4}\] \[\sin 2\alpha=2\sin\alpha\cos\alpha\]  
Figure 3: Sketches of inclined pillar stresses. (**a**) Loading condition of the inclined pillar; (**b**) stress state of a single pillar; (**c**) stress state of the arbitrary inclined plane.  
Figure 2: Sketch of force acting on the inclined layered ore-body and pillar.  
By substituting Equation (4) into (3), the average compressive stress \(\sigma\) and shear stress \(\tau\) are obtained as follows:  
\[\sigma =\frac{\sigma_{\theta}}{2}(1+\cos 2\alpha)-\frac{\tau_{\theta}}{2}\sin 2 \alpha \tag{5}\] \[\tau =\frac{\sigma_{\theta}}{2}\sin 2\alpha+\frac{\tau_{\theta}}{2}(1+ \cos 2\alpha)\]  
The elimination of \(\alpha\) in Equation (5) was arranged.  
\[\left(\sigma-\frac{\sigma_{\theta}}{2}\right)^{2}+\left(\tau-\frac{\tau_{ \theta}}{2}\right)^{2}=\left(\frac{\sigma_{\theta}}{2}\right)^{2}+\left(\frac {\tau_{\theta}}{2}\right)^{2} \tag{6}\]  
Similarly, when the shear stress is negative, the stress \(\sigma\) and shear stress \(\tau\) on an inclined plane in a pillar is also obtained, as follows:  
\[\left(\sigma-\frac{\sigma_{\theta}}{2}\right)^{2}+\left(\tau+\frac{\tau_{ \theta}}{2}\right)^{2}=\left(\frac{\sigma_{\theta}}{2}\right)^{2}+\left(- \frac{\tau_{\theta}}{2}\right)^{2} \tag{7}\]  
Equations (6) and (7) can be expressed as a circle on the \(\sigma\)-\(\tau\) coordinate plane through the origin of the coordinates; the center of the circle is (\(\sigma_{\theta}/2\), \(\pm\tau_{\theta}/2\)), and the radius is equal to \(0.5\sqrt{\sigma_{\theta}^{2}+\tau_{\theta}^{2}}\). The circle equations are expressed by stress parameters; they are called stress circles. Equations (6) and (7) are stress circle equations characterizing the stress state of the inclined pillar, and their loci are symmetrical to the stress axis, as shown in Figure 4. Compared with the traditional stress circle, the center of the generalized stress circle is not on the stress axis. The stress circle is called a generalized Mohr's circle or generalized stress circle [11].  
Figure 4: Generalized Mohr’s circle characterization of the stress state of an inclined pillar.
## 5 Results and Discussion
### Turbine Map Generation  
#### 5.3.1 Axial Turbine Map Generation  
Generating the map in WAVE requires a minimum of 5 constant speed lines with at least 7 mass flow rates for each speed line. As WAVE's fitting algorithm performs better with more lines, a total of 7 speeds lines was chosen. With the design point being one of the constant speed lines, additional speeds need to be selected. The speeds were chosen to correspond with the current operating conditions of the engine range with enough spacing between each line to achieve a complete profile of the corrected mass flow rate against the total to static pressure ratio. The selected points are shown in Table 10.  
The performance map was generated by utilising ANSYS \"Performance Map\" module, in which the design point operating conditions for turbocharger speed and mass flow rate are input and then several speed lines can be generated based on their percentage of the design point condition. The module can then determine the mass flow rates for each speed line to be run. These are then linked to the optimisation CFX setup that is already created to test the new blade under these conditions. The mesh specification and numerical methodology employed for the CFD (Computational Fluid Dynamics) analysis are reported in an earlier publication related to this project [7]. Figures 25 and 26 show the mass flow rates and efficiencies for each map points. It is important to note that the stage efficiencies and pressure ratios but not the full system efficiencies and pressure ratios of the final design have been obtained. However, the data is satisfactory enough to allow the implementation of the axial turbine and VGT. With the data obtained, the shaft speeds and mass flow rates are normalised to ambient conditions to allow a comparison with the radial turbine. This is done by following the process outlined in Section 4.2.  
Overall, the result shows that the majority of the corrected mass flow rate profile was captured and the results have allowed for the generation of the maps in WAVE. While the design point yielded an efficiency of 75.87%, the maximum efficiency of the blades is actually achieved at a lower mass  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Engine Speed (rpm)** & **Load (\%)** & **Turbocharger Speed (rpm)** \\ \hline
5000 & 100 & 158,850 \\
3000 & 100 & 143,360 \\
5000 & 80 & 121,476 \\
2000 & 100 & 106,500 \\
2000 & 80 & 69,536 \\
1000 & 100 & 46,700 \\
1000 & 80 & 26,978 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Turbocharger shaft speed based on engine model operating conditions for map generation.  
Figure 25: Mass flow rate vs. pressure ratio plots at constant shaft speeds for axial turbine.  
flow rate (0.1143 kg/s) along 158,850 rpm speed line, and it is 77.3%. However, this is to be expected, as the optimisation was set up to find the best compromise between power generation and efficiency. Therefore, while this efficiency is higher, it is likely at this test point that the power is lower than at design point.
## 4 Ankle Neurorehabilitation System (ANS)
### Experimental Protocol  
Using the system explained above, we first verified that the signal of motor imagery of ankle uplift movement was indeed detected, which in turn operated the machinery. The experiment was conducted in three stages: (1) develop and practice motor imagery for the participant, (2) tune up the system to determine the parameters of motor imagery EEG, and (3) test motor imagery EEG to use the device to activate machinery.  
For motor imagery development, voluntary upward movement of the right ankle was done first (voluntary task: 30 trials). (I) Motor imagery of the voluntary movement immediately followed (imagery task: 30 trials). The machine produced autonomous ankle movements simulating human rehabilitation behavior (autonomous movement task: 30 trials). Each trial was initiated by a trigger LED and each consisted of 4 s periods. EEG was done during this session. (II) The second process of system tune-up consisted of two substeps during which (a) EEG signals of the motor imagery were detected (2 min) (for comparison, a baseline EEG signal was measured during the resting period (2 min)), and (b) the heuristic BCI system using the L-FTM algorithm was applied to ascertain parameters to discriminate the two states of motor imagery and resting EEG. The FTM calculation took 60 s. (III) Finally, as the third step, the heuristic BCI system using L-FTM logic was tested by detecting the real motor imagery EEG. A trigger signal was generated as the output FTM signal. As soon as the system detected the motor imagery EEG, the triggering signal was presumed to be generated, which was to be transferred to the robot system. To verify this point, two tasks took place: participants were asked to (1) employ motor imagery when the LED light was illuminated, and (2) rest in order to test the resting EEG signal.  
To analyze the brain activity tendency, independent component analysis was carried out using EEGLAB [20]. The EEG signal was first notch-filtered (60 \(\pm\) 0.5 Hz and 120 \(\pm\) 0.5 Hz bandwidth) to exclude artifacts from the electrical current, followed by band-pass filtering with frequencies of 0.04-200 Hz (finite impulse response (FIR) filtering). Special care was taken to minimize the notch filter artifacts when determining optimal bandwidth.
# Efficacy of Instruments for Professional Oral Hygiene on Dental Implants: A Systematic Review
## 2 Materials and Methods
### Quality Assessment  
The in vitro studies included were evaluated and classified as per the method design and risk of bias through the Grading of Recommendations Assessment, Development and Evaluation (GRADE) classification system [15].  
The selected articles were graded for the evidence level as per the Oxford Center for Evidence-Based Medicine [16]. The studies were categorized into levels 1a, 1b, 1c, 2a, 2b, 2c, 3a, 3b, 4, and 5. The level of evidence 1 document Centre for Evidence-Based Medicine (CEBM) establishes some questions to systematize this process: therapy or prevention, etiology or harm, prognosis, diagnosis, differential diagnosis or study of the symptom's prevalence, and the economic and decision analysis.  
The risk of bias of included clinical studies was assessed using the Newcastle Ottawa scale (NOS) [17].  
Two reviewers ([PERSON], [PERSON]) independently evaluated the quality of studies based on the following parameters: Selection, Comparability and Outcome/Exposure. A maximum of 4 stars in selection domain, 2 stars in comparability domain and 4 stars in outcome/exposure domain were given. The included studies were qualified as \"Good\", \"Fair\" and \"Poor\" quality based on the total NOS score they achieved. Studies with a NOS score \(\geq\)7 were considered good-quality studies.  
Cochrane Handbook for Systematic Reviews of Interventions was employed for randomized clinical trials and randomized controlled trials [18].  
The following quality criteria were assessed: sequence generation, allocation concealment, systematic differences in care provided to members of different study groups other than intervention under investigation (performance bias), systematic differences between groups in how outcomes were determined (detection bias), unequal loss of participants from study groups (attrtition bias), within study selective outcome reporting (selective reporting bias), and other potential risks of bias.  
A meta-analysis was not appropriate because of the methodological heterogeneity of the included studies.
# Long-Period Grating with Asymmetrical Modulation for Curvature Sensing
###### Abstract  
We propose and demonstrate a curvature sensor based on long-period fiber grating (LPFG) with asymmetric index modulation. The LPFG is fabricated in single-mode fiber with femtosecond laser micromachining. The grating structure is not introduced in the central fiber core, but is located off-axis with a distance of a few micrometers. Experimental results indicate that the offset distance has direct influence on the grating spectra. By utilizing such an asymmetric structure, two-dimensional vector curvature sensing can be realized. For an LPFG with an offset distance of 6 \(\mu\)m, the curvature sensitivity is around 29 nm/m\({}^{-1}\) in the 0\({}^{\circ}\) and 180\({}^{\circ}\) direction and about 20 nm/m\({}^{-1}\) in the 90\({}^{\circ}\) and 270\({}^{\circ}\) direction. The difference in curvature sensitivity in different bending directions makes the sensor capable of distinguishing the curvature orientation. The temperature response of the sensor is also experimentally investigated, and results indicate that the sensor has a very low temperature cross-sensitivity of 0.003 m\({}^{-1}\)/\({}^{\circ}\)C. The characteristics of high curvature sensitivity, two-dimensional bending direction identification, and compact structure make the device an ideal candidate to be applied in the field of power grid health monitoring and intelligent robotics.  
long-period grating; curvature sensing; femtosecond laser micromachining +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods: Transducers Design and Fabrication
### Methods for Transducer Characterization
#### 2.3.2 Transducer Characterization: Pulse-Echo Measurements in Water Immersion  
The experimental set-up for pulse-echo measurements is shown in Figure 3; transducers and a flat steel block (53 mm thick) used as reflector were immersed in a water tank. The transducer is driven by the an Olympus 5058 pulser/receiver, the received echo is received through the receiver stage of the Olympus 5058 PR, filtered, amplified (40 dB) and then sent to an oscilloscope where the signal is digitized and stored. For the focused transducers, the reflector is located at the transducer focal distance, while for the flat transducer, the separation was 40 mm (which is close to the transition between near and far field).
## 3 Forecasting Approach
### The Proposed Hybrid Methodology
#### 3.1.3 Step III: SVR
As a consequence, \(NL_{t}\) is a predicted error and it is a function of the residuals obtained from the linear model:  
\[NL_{t+ph}=f(e_{t},e_{t-ph},\ldots,e_{t-n-ph})+\varepsilon_{t}=\hat{e}_{t+ph}, \tag{8}\]  
where \(\hat{e}_{t}\) is the predicted residual, \(f\) is the nonlinear function obtained by the SVR model, \(n\) is the size of the autoregressive window, \(ph\) is the prediction horizon, and \(\varepsilon_{t}\) is the error term. Finally, the prediction is obtained by adding the two single components:  
\[Y_{t+ph}=\hat{L}_{t+ph}+NL_{t+ph}. \tag{9}\]  
Other proposals in the framework of these kinds of hybrid models have emerged in recent years. Particularly, incorporating the predicted values from a previous step and the original data with the residuals as inputs of the non-linear forecasting model has gained special attention. The time series is considered as a function of several variables, such as the original data, the residuals, and the predicted values from the linear model. Some examples of this two-step methodology are the works of [PERSON] and [PERSON] [15; 21]. Based on these last works, the following hybrid approach 2 was developed (after applying the SOM method) considering two or three types of variables as inputs. _SOM-SARIMA-SVR-2 model (hybrid approach 2)._ The time series is here considered a nonlinear function of the original data and the residuals and the predicted values from the first step:  
\[\hat{Y}_{t+ph}=f(y,e,\hat{p})+\varepsilon_{t+ph}, \tag{10}\]  
where \(f\) is the nonlinear function obtained with the SVR model, \(y_{t}\) is the original data, \(e_{t}\) is the residual obtained in the SARIMA model, and \(\hat{p}\) is the predicted value from the SARIMA model at time \(t\). These variables are presented in an autoregressive form, as it is expressed in the following equation:  
\[\hat{Y}_{t+ph}=f(y_{t},y_{t-1+ph},y_{t-2 ph}\ldots,y_{t-n-pph},e_{t+ph},e_{t},e_{ t-1+ph},\ldots,e_{t-n-pph},\hat{p}_{t+ph},\hat{p}_{t},\hat{p}_{t-1+ph}, \ldots,\hat{p}_{t-n-pph})+\varepsilon_{t+ph} \tag{11}\]  
where \(ne\), \(ny\) and \(np\) represent autoregressive window sizes for \(e\), \(y\) and \(\hat{p}\) variables, respectively. Considering two or three variables, and their autoregressive window sizes, a large number of possible functions can be tested. These two hybrid approaches are used in each clusters formed in the second step. Considering two clusters, the parameter ranges used are also presented in Table 1. Note that in cases which variables \(e\) and \(p\) are involved, their first value used as input is the predicted value at \(t+ph\) time. The same cannot be said for the original data variable \(y\), due to the \(y\) value at time \(t+ph\) is the final prediction pursued. In all instances, the SARIMA model is necessary to obtain the required data used as inputs in the third step, together with the original patterns of the container series.
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 2 System Model  
### Graph Coloring Model Based on CRN  
A topological structure of the CRNs can be abstracted into a simple undirected graph that is represented as \(G(V,E,L)\), where \(V\) is a finite set of vertices, representing SUs; \(E\) is a set of edges that are subject to interference matrix; \(L\) is a collection of available colors which represent the idle spectrum. Allocating available licensed spectrum resources to SUs is equivalent to vertex coloring in the graph coloring problem.  
Assuming that there is \(N\) SUs who compete with each other to access \(M\) channels. Each SU occupies an available channel at a certain time.  
**Definition 1**.: _Channel Availability Matrix \(L=\left\{l_{n,m}|l_{n,m}\in\left\{0,1\right\}\right\}_{N\times M}\) is a \(N\times M\) binary matrix, expressing availability of these licensed channels for SUs. If \(l_{n,m}=1\), channel \(m\) is available for the \(n\)-th SU; otherwise, \(l_{n,m}=0\)._  
**Definition 2**.: _Channel Utility Matrix \(B=\left\{b_{n,m}|b_{n,m}>0\right\}_{N\times M}\) indicates the benefits that each SU can obtain from different channels. \(b_{n,m}\) is the benefits obtained by the \(n\)-th SU when it uses the channel \(m\)._  
**Definition 3**.: _Interference Matrix \(C=\left\{c_{n,k,m}|c_{n,k,m}\in\left\{0,1\right\}\right\}_{N\times N\times M}\) is a binary matrix of \(N\times N\times M\), expressing the interference among SUs of each channel. \(c_{n,k,m}=1\) means that the \(n\)-th SU and the \(k\)-th SU cannot access into channel \(n\) simultaneously, because that would cause transmission interference._  
**Definition 4**.: _Interference-Free Allocation Matrix \(A=\left\{a_{n,m}|a_{n,m}\in\left\{0,1\right\}\right\}_{N\times M}\) is a \(N\times M\) binary matrix, it indicates a feasible channel allocation scheme. \(a_{n,m}=1\) denotes that channel \(m\) is allocated to the \(n\)-th SU. On the contrary, \(a_{n,m}=0\)._
## 2 Materials and Methods
### Test System and Experimental Procedure  
Before starting the experiment, a layer of white matte paint was evenly sprayed on the front of each specimen to cover the original rock particles and structural surfaces, avoiding their impact on speckle analysis. After the white paint dried, black matte paint was sprayed diagonally upward into the air to allow it to fall evenly on the white painted surface, and then let to dry naturally. The camera used in the experiments was a GS3-U3-123S6M-C monochrome industrial camera (Point Gray Research Inc., Richmond, BC, Canada), with a resolution of 2592 \(\times\) 2048 px, a full frame rate of 73 fps, a pixel size of 4.8 \(\times\) 4.8 \(\upmu\)m, and a shooting frame rate of 10 frames per second.  
Acoustic emission monitoring is an important analysis method in this study. In the designed experiment, to ensure that the acoustic emission sensors can envelop the specimen to the maximum extent, 30 acoustic emission sensors need to be arranged on the surface of a specimen. Considering the influence of the loading surface and the arrangement of the acoustic emission sensors, the experiment in this work arranges 30 acoustic emission sensors on the four sides of the specimen. Since the front of the specimen is sprayed with speckles and digital speckle photography will be conducted, only 4 sensors are placed at the four corners of the front of the specimen, while the back, left, and right sides of the specimen have a denser arrangements of sensors. The Vallen acoustic emission (AE) monitoring system AMSY-6 from Germany was used to monitor the rupture signal during the loading process. The sensor type is VS45-H, with a natural frequency of 280 kHz and a response frequency range of 20-450 kHz. The sampling frequency of the acoustic emission system is set to 10 MHz. Before the experiment, 30 acoustic emission sensors were arranged on the 4 sides of the specimen, and the sensor coordinates were accurately measured. The sensor coordinates were shown in Table 1.  
The experiment was conducted on the INSTRON 1346 testing machine designed by INSTRON company, Norwood, MA, USA, which can perform mechanical experiments  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**No.** & **X mm** & **Y mm** & **Z mm** & **No.** & **X mm** & **Y mm** & **Z mm** & **No.** & **X mm** & **Y mm** & **Z mm** \\ \hline S1 & 10 & 0 & 10 & S11 & 150 & 80 & 10 & S21 & 100  
such as uniaxial compression, biaxial compression, direct shear, and variable angle shear tests. The vertical and horizontal loads can reach 2000 kN and 250 kN, respectively. The experiments adopt \(Z\) direction loading, with a constant loading rate of 500 N/s. It should be noted that although the uniaxial compression condition cannot fully meet the deep three-dimensional stress conditions, it still meets the one-dimensional stress conditions of isolated pillars and some other structures. The loading process, AE monitoring, and speckle image capture begin simultaneously. The technical roadmap of the research is shown in Figure 1.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Opinions about Encrypted Emails
\begin{table}
\begin{tabular}{l l} \hline \hline
**Encryption Meaning** & **No. of Participants** \\ \hline Information Protection & 12/19 \\ Scrambling Messages & 7/19 \\ Data Encoding and Decoding & 4/19 \\ Communication Safety & 2/19 \\ Prevent Unauthorized Access & 2/19 \\ Uses Codes & 2/19 \\ Uses Keys & 3/19 \\ Security System & 1/19 \\ Uses Numbers and Blockchain & 1/19 \\ Web Security and Privacy & 1/19 \\ Uses Password & 1/19 \\ Data Encapsulation & 1/19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Participant opinions about what encryption means. \begin{table}
\begin{tabular}{l l} \hline \hline
**Entities with Access to the Email** & **No. of Participants** \\ \hline None & 7/19 \\ Hackers & 3/19 \\ Gmail employees with access & 3/19 \\ Google employees with access & 3/19 \\ University employees with access & 2/19 \\ Third parties & 2/19 \\ Anyone with CC & 1/19 \\ Government & 1/19 \\ Police & 1/19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Participant opinions about who can access their emails aside from the recipients. government direct access to their emails. We found that 11 out of 19 participants mentioned that Google needs valid reasons to give the government direct access to their emails. Many participants shared that investigating crime-related cases (8/19) and having a warrant (5/19) can allow the government access to case-related emails. For example, one of the participants (P7) commented, _\"If they have a warrant, and they do have like evidence that if there were a crime being committed, then I believe they would be able to give the access.\"_ The other circumstances mentioned by the participants for Google to give the government direct access to their emails were being required by law enforcement (2/19), depends on the requester (1/19), and needs user permission (1/19). The majority of our participants demonstrated awareness of encryption technology but did not understand how it works. Some participants thought that no one besides the recipient could read or access the emails. However, the majority of the participants believed that Google gives the government direct access to their emails when requested, especially if the email is related to a criminal case or law enforcement has a warrant to investigate it.