esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Fault Severity Recognition by the Proposed Method  
In order to verify the effectiveness of the proposed method in fault severity recognition, the proposed method was used to calculate the complexity of the above bearing datasets. The results of LZ and SAX-LZ were also used to compare with the proposed method. The calculated results of the two datasets are shown in Figures 6 and 7, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nominal Load (N)** & \multicolumn{3}{c}{**Nominal Speed (Hz)**} \\ \hline
1000 & 100 & 200 & 300 & 400 \\
1400 & - & 200 & 300 & 400 \\
1800 & 100 & 200 & 300 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: The list of the used load and speed cases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Rotational Speed (rpm)** & **Load Torque (Nm)** & **Radial Force (N)** & **Name of Setting** \\ \hline
1 & 900 & 0.7 & 1000 & N09-M07-F10 \\
2 & 1500 & 0.7 & 400 & N15-M07-F04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The operating parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Severity of Damage** & **Location of Fault** & **Characteristic of Damage** & **Arrangement** & **Damage** \\ \hline
1 & Inner race & Single point & No repetition & Fatigue: pitting \\
2 & Inner race & Single point & No repetition & Fatigue: pitting \\
3 & Inner race & Single point & No repetition & Fatigue: pitting \\ \hline \hline \end{tabular}
\end{table}
Table 3: The test bearings with real damages caused by accelerated lifetime test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pitch Diameter D** & **Rollers Diameter** & **Contact Angle \(\Phi\)** & **Rolling Elements Z** \\  & **(mm)** & **D (mm)** & **(\({}^{\circ}\))** & **Rolling Elements Z** \\ \hline Size & 40.5 & 9.0 & 0 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The main properties of the rolling bearings.  
the monotonicity of SAX-LZ was better than that of LZ, that of SAX-LZ was also affected by noise and interference. However, the SAX-LZ could effectively recognize the fault severity of bearing after the signal was processed by ACLHC.  
Therefore, the ACLHC can effectively reduce the influence of noise and interference and highlight the fault information. The proposed method can be effectively used to recognize the fault severity of bearings.
###### Abstract  
Internet-of-Things (IoT) include a large number of devices that can communicate across different networks. Cyber-Physical Systems (CPS) also includes a number of devices connected to the internet where wearable devices are also included. Both systems enable researchers to develop healthcare systems with additional intelligence as well as prediction capabilities both for lifestyle and in hospitals. It offers as much persistence as a platform to ubiquitous healthcare by using wearable sensors to transfer the information over servers, smartphones, and other smart devices in the Telecare Medical Information System (TMIS). Security is a challenging issue in TMIS, and resourceful access to health care services requires user verification and confidentiality. Existing schemes lack in ensuring reliable prescription safety along with authentication. This research presents a Secure Authentication and Prescription Safety (SAPS) protocol to ensure secure communication between the patient, doctor/nurse, and the trusted server. The proposed procedure relies upon the efficient elliptic curve cryptosystem which can generate a symmetric secure key to ensure secure data exchange between patients and physicians after successful authentication of participants individually. A trusted server is involved for mutual authentication between parties and then generates a common key after completing the validation process. Moreover, the scheme is verified by doing formal modeling using Rubin Logic and validated using simulations in NS-2.35. We have analyzed the SAPS against security attacks, and then performance analysis is elucidated. Results prove the dominance of SAPS over preliminaries regarding mutual authentication, message integrity, freshness, and session key management and attack prevention.  
authentication; key agreement; telecare medical information system; anonymity; un-traceability +
Footnote †: journal: Accepted: 12 October 2017; Published: 16 October 2017
## 2 Experiments
### Experimental Phenomena  
During the whole process of the test, the cracks of the interface and the infill wall were observed by a magnifying glass and a crack width card. At the start of loading, the lateral displacement of specimen W1 was small under a horizontal load and the load-displacement curve showed a linear change. When the load increased to 404 kN and the corresponding drift angle was 1/2537, the interface between the BLC-C composite wall and the infill wall initially cracked, resulting in microcracks. Oblique cracks appeared in a diagonal direction (from the bottom left to the top right) in the center of the infill wall when the load increased to 800 kN and the corresponding drift angle was 1/1015. As the load rose to 1200 kN and the corresponding drift angle was 1/557, the plaster layer between the top of the infill wall and the loaded beam exhibited slight spalling. In the center of the infill wall, penetrating diagonal cracks formed when the load grew to 1300 kN and the corresponding drift angle was 1/511. Specimen W1 was loaded until the drift angle reached 1/400.  
The overall structure of specimen W2 was elastic in the early stage of loading. When the load climbed to 700 kN and the corresponding drift angle was 1/2537, interface cracks began to appear between the BLC-C composite wall and the infill wall. Diagonal cracks appeared below the center of the infill wall when the load grew to 800 kN and the corresponding drift angle was 1/1982. With an increase in the load, oblique cracks rapidly developed above and below the syncline. As the load increased to 1500 kN and the corresponding drift angle was 1/813, penetrating oblique cracks formed below the center of the infill wall. The lower right corner of the infill wall slightly separated from the foundation beam and cracks appeared when the load rose to 1600 kN and the corresponding drift angle was 1/736. Specimen W2 was also loaded until the drift angle reached 1/400. Figure 5 shows the crack distribution when the drift angle reached 1/400.  
The process of specimens resisting the horizontal load could be divided into two stages. In the first stage, the elastic stage, the interface initially cracked. With an increase in the load, the number of interface cracks increased, but there was no penetrating interface crack. The BLC-C composite wall and the infill wall together constituted the lateral force-resistant components. The second stage was the elastoplastic stage. At this stage, oblique cracks appeared in the middle of the infill wall and continued to expand. The connection between the BLC-C composite walls, the steel beams, and the corner of the infill wall gradually  
Figure 4: Field test diagram of specimen W2 (units in mm).  
disengaged. Furthermore, the infill wall gradually withdrew from work and the BLC-C composite wall became the main lateral force-resistant member.  
Figure 5: Crack distribution diagram obtained when the drift angle reached \(1/400\): (**a**) specimen W1 and (**b**) specimen W2.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 3 Methods  
Our work implements optimization in two distinct phases: tracking and training, as illustrated in Figure 1. In the tracking phase, the tracking result scoring module (TRSM) is utilized to assess the reliability of tracking outcomes. Results with reliability values surpassing a predefined threshold \(\tau\) are subsequently selected as high-quality target templates. During training, a loss function incorporating directional guidance is employed to instruct the tracking model in learning target motion direction information.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 6 Clinical Applications of Commercially Available TD-NIRS Systems by Japanese Researchers
### Group from Nihon University School of Medicine  
[PERSON] et al. collaborated with Hamamatsu Photonics in using the 16-channel TCSPC system (exceptionally in this section) to measure the changes in the mean optical pathlengths and cerebral blood oxygenation at the adult prefrontal cortex during verbal fluency and driving simulation tasks [50; 231]. During the verbal fluency task, \(C_{oxy-Hb}\) and \(C_{total-Hb}\) increased while \(C_{deoxy-Hb}\) decreased. On the other hand, \(C_{oxy-Hb}\) and \(C_{total-Hb}\) decreased while \(C_{deoxy-Hb}\) increased during the driving simulation task. The mean optical pathlengths did not change by the tasks. [PERSON] et al used the TRS-10 to detect vasospasm of the patients with subarachnoid hemorrhage by measuring the cortical oxygen saturation [232]. [PERSON] et al. used the TRS-10 to conclude that the change in \(C_{oxy-Hb}\) at the lateral prefrontal cortex during a working memory task correlated with working memory performance [233]. [PERSON] et al. examined the effect of the extract of _Ginko biloba_ leaves on the performance of working memory with TRS-10 probes attached on the prefrontal cortex and found that the right laterality score of \(C_{oxy-Hb}\) increased by administration of _Ginko biloba_ leaves [234]. [PERSON] et al. showed that cosmetic therapy to elderly women with mild cognitive impairment was effective to improve the activities of the prefrontal cortex with increased \(C_{oxy-Hb}\) and \(C_{total-Hb}\) measured by the TRS-20 [235]. [PERSON] et al. also used TRS-20 to evaluate the difference in the women's emotion between pleasure and displeasure induced by the difference in lipsticks and found that the difference in the lipsticks resulted in different activities in the left and right prefrontal cortex [236]. [PERSON] et al. studied the relationship between cognitive function and cerebral blood oxygenation measured with the TRS-20 in the prefrontal cortexes of 113 elderly people and found strong correlations between working memory function and \(C_{oxy-Hb}\), \(C_{total-Hb}\), and \(S_{02}\)[237].
## 1 Introduction
There is limited information available regarding the influence of manufacturing techniques on the marginal adaptation of Co-Cr frameworks; thus, it is important to investigate the precision of fit of these restorations. Therefore, the present in vitro study's purpose was to evaluate and compare the vertical marginal discrepancy of posterior Co-Cr fixed partial dentures (FPDs) fabricated with different technologies and with two types of framework design (intermediate or cantilever pontic). The null hypotheses to be tested were that no differences in marginal fit would be found among the Co-Cr frameworks fabricated by the different technologies and framework designs.
## 3 Simulation  
In order to demonstrate the generality and validity of the proposed method, a series of volume grating measurements are simulated, with different dielectric constant distribution functions. Without losing generality, three typical distribution functions such as periodic binary distribution, periodic linear distribution, as well as continuous sinusoidal distribution are selected. These functions are selected because most of the complex functions can be easily assembled by the linear combinations of these simple functions. For the convenience of comparing the effects of function type on the Mueller matrix spectra, the nominal parameters of sample setting and measurement configuration are shared. The angle of incidence \(\theta\) is fixed as \(25^{\circ}\), and the angle of azimuth \(\varphi\) is set as \(20^{\circ}\). The substrates are glasses. The pitch of the dielectric distribution \(\Lambda\) is fix as 800 nm. For each test case of different function type, two thickness settings \(H\) are examined--3 \(\upmu\)m and 5 \(\upmu\)m, respectively. Considering the pitch \(\Lambda\) can be usually well controlled by the process of grating fabrication, we fixed it as a constant. Since the thickness \(H\), angle of incidence \(\theta\), and the angle of azimuth \(\varphi\) are easily varied parameters, we need reconstruct these parameters in the fitting process.  
The general procedure of the simulation can be divided into three steps. At first, according to the trues values of parameters, the theoretical Mueller matrices will be calculated. Then, random errors with signal-noise-ratio \(SNR=10000\) will be injected into the theoretical Mueller matrices to generate a set of measured Mueller matrices. At last, by a regression started with the initial values of parameters will be carried with the objective to best fit the measured Mueller matrices. When the optimal solution is achieved, we achieved the measured values of these parameters as well as corresponding fitted Mueller matrices.  
To avoid the unphysical refractive index spectra, we quoted the correlations between the refractive index and nanoparticles weight fractions \(\omega\) reported in Ref. [31], so that the appropriate refractive index curve can be selected according to the value of nanoparticles weight fraction. Then, the dielectric constant distribution function can be represented by the distribution function of the nanoparticle concentration.
## 3 Results and Discussion
### Catalyzed Oxidation of \(\beta\)-Myrcene
#### 3.4.1 General Experimental Considerations  
The oxidation of \(\beta\)-myrcene was conducted for 5 h with a POM/myrcene/oxidant molar ratio (the POM being an organic salt of the POM described later) of 0.25/100/200, using two different temperatures due to the nature of the oxidant, i.e., at 80 \({}^{\circ}\)C with TBHP (in water or decane) and 70 \({}^{\circ}\)C with H\({}_{2}\)O\({}_{2}\). In some cases, solvent (toluene or ethyl acetate) was added with H\({}_{2}\)O\({}_{2}\). With **MROrg@POMs**, 0.1 g was employed, corresponding to a POM/myrcene/oxidant molar ratio of 0.25 \(\times\) 10\({}^{-3}\)/100/200 under similar temperature and time conditions with TBHP\({}_{\text{aq}}\) and H\({}_{2}\)O\({}_{2}\). The reaction was followed using GC and using naphthalene as an internal standard. Reactions were repeated three times and were reproducible with a 5% error range. All results have been summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Catalysts** & **Entry** & **T (\({}^{\circ}\)C)** & **Oxidant** & \(\beta\)**-Myrcene** & **Myrcene** \\  & & & & **Conv (\%)** & **Oxide Yield (\%)** \\ \hline None & M1 & 80 & None & 47 \({}^{2}\) & DA * \\ \hline \multirow{4}{*}{**(BmIm)\({}_{3}\)PMo\({}_{12}\)O\({}_{40}\)**} & M2 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) & 75 & 1 \\  & M3 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) + Toluene & 82 & 29 \\  & M4 & 80 & TBHP\({}_{\text{aq}}\) & 40 & 7 \\  & M5 & 80 & TBHP\({}_{\text{dec}}\) & 89 & 39 \\ \hline \multirow{4}{*}{**(BuPyr)\({}_{3}\)PMo\({}_{12}\)O\({}_{40}\)**} & M6 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) & 79 & 2 \\  & M7 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) + Toluene & 29 & 2 \\  & M8 & 80 & TBHP\({}_{\text{aq}}\) & 60 & 10 \\  & M9 & 80 & TBHP\({}_{\text{dec}}\) & 80 & 15 \\ \hline \multirow{4}{*}{**(BmIm)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)**} & M10 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) & 87 & 7 \\  & M11 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) + Toluene & 87 & 28 \\  & M12 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) + EtOAc + CH\({}_{3}\)CN \({}^{1}\) & 94 & 2 \\  & M14 & 80 & TBHP\({}_{\text{aq}}\) & 81 & 53 \\  & M15 & 80 & TBHP\({}_{\text{dec}}\) & 88 & 41 \\ \hline \multirow{4}{*}{**(BmIm)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)**} & M16 & 80 & NNone & 37 \({}^{2}\) & \\  & M17 & 40 & H\({}_{2}\)O\({}_{\text{2 aq}}\) & 20 & 5 \\  & M18 & 40 & TBHP\({}_{\text{aq}}\) & 24 & 4 \\ \hline \multirow{4}{*}{**(BuPyr)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)**} & M19 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) & 75 & 1 \\  & M20 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) + Toluene & 26 & 2 \\ \cline{1-1}  & M21 & 80 & TBHP\({}_{\text{aq}}\) & 70 & 41 \\ \cline{1-1}  & M22 & 80 & TBHP\({}_{\text{dec}}\) & 85 & 9 \\ \hline \multirow{4}{*}{Naked MR} & M23 & 70 & H\({}_{2}\)O\({}_{\text{2 aq}}\) & 47 & \\ \cline{1-1}  & M24 & 80 & TBHP\({}_{\text{aq}}\) & 42 & \\ \cline{1-1}  & M25 & 80 & TBHP\({}_{\text{dec}}\) & 39 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(\beta\)-myrcene oxidation catalytic results.
# Electric Drive with an Adaptive Controller and Wireless Communication System
## 5 Simulation Tests  
After the optimization of the gains of the input layer of the neural controller, tests of the optimized control structure were performed. Firstly, the work of the predictor was verified. The optimized control structure with a predictor applied was compared to the regular MRAC structure (with no predictor). All the tests were conducted for 20 seconds. The load torque was applied in the 9 th second of the drive operation, and it was switched off in the 11 th. Selected time frames from the simulation were chosen in the comparison. In  
Figure 8: Transients of the load speed of the drive during the optimization process.  
Figure 7: Fitness function values during the optimization process.  
all parts of Figure 9, the load speed of the drive, after the predictor application, has a lower value of overshoot than the MRAC one. In Figure 9a, the startup process of the drive is presented; at first, the speeds match, and after some adaptation of the neural controller, the settling time is lower. After the adaptation of both networks (the Elman controller and the ADALINE predictor), it can be clearly seen that the latter one works better just before the load is switched on (Figure 9b) and after performing a reversion with a load torque applied (Figure 9c).  
The prediction element is applied to the learning algorithm. For the test purposes, it was deactivated to check the difference. The fitness functions of the both structures are presented in Table 1.  
Next tests, after the comparison, were performed for a different moment of inertia added to the different parts of the drive. Firstly, the nominal parameters were tested (transients of speeds and torques are presented in Figure 10). There is a small overshoot observed after the start of the drive. Due to the update operation of the internal weights of both neural networks, it is minimized after one reversion. The time constants of the motors in industrial applications (e.g., robotic arms) are changing under the operation. Figure 11 presents the transients gathered in the second test, where the load time constant \(T_{2}\) was increased. No apparent changes are visible in the speeds other than a lower error of speed after applying the load torque, which was presumed. Higher values of torques are observable due to the fact that the controller tries to achieve the same speed as in the nominal parameter case, but higher values of inertia imply higher amplitude of torques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure** & **Fitness Function Values** \\ \hline MRAC & 0.0525 \\ MRAC with predictor & 0.0499 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fitness function comparison of analyzed control structures.  
Figure 9: Comparison of a control structure with and without a predictor: (**a**) starting operation of the drive, (**b**) and (**c**) selected parts of the operation with reversions.
## 2 Materials and Methods
### Assessment according to the Inelastic Criteria  
The SDC-IC defines load factors and strain factors with which the primary and secondary loads should be multiplied for the assessment of the IPFL and ILFED failure modes. For the Level-D criteria, these factors are \(\Gamma_{L}=1.35\) (_mechanical \(-\) primary loads_) and \(\Gamma_{S}=1.0\) (_thermal \(-\) secondary loads_) for both IPFL and ILFED. Further, the SDC-IC defines the below criteria [24].  
\[\left(\widetilde{\epsilon}_{m}\right)_{pl}\leq\frac{\epsilon_{u}(T_{m},\Phi t_ {m})}{2}\ (\text{IPFL})\ \text{and}\ (\widetilde{\epsilon})_{pl}\leq\frac{\epsilon_{tr}(T,\Phi t)}{TF}\ (\text{ILFED})\]  
where \(\left(\widetilde{\epsilon}_{m}\right)_{pl}\) and \(\left(\widetilde{\epsilon}\right)_{pl}\) are the significant mean plastic strain and significant local plastic strain, respectively, which represent the greatest maximum principal strain values, evaluated from the corresponding strain tensors. \(\epsilon_{u}(T_{m},\Phi t_{m})\) is the minimum uniform elongation dependent on the thickness-averaged temperature, \(T_{m}\) and the thickness-averaged neutron fluence, \(\Phi t_{m}\). Similarly, \(\epsilon_{tr}(T,\Phi t)\) is the minimum true strain at rupture dependent on the local temperature \(T\) and the local neutron fluence \(\Phi t\). The parameter \(TF\) is the triaxiality factor defined as the ratio of Hydrostatic stress and von Mises stress normalized to unity for uniaxial loading, i.e., \(TF=3\sigma_{H}/\sigma_{vM}\). In Figure 5, the strain limits, \(\epsilon_{u}\) and \(\epsilon_{tr}\) for Eurofer97 material is estimated and plotted. Minimum saturation values of the irradiated properties are used in the temperature range of 20 \({}^{\circ}\)C to 350 \({}^{\circ}\)C. Unirradiated properties are used above 500 \({}^{\circ}\)C since the effect of irradiation is considered negligible above that temperature [25]. Values are linearly interpolated in the range between 350 \({}^{\circ}\)C to 500 \({}^{\circ}\)C.  
The ILFED strain limits are further dependent on the stress triaxiality according to the SDC-IC criteria as mentioned above. This dependence is plotted in Figure 6. According to the equation, the limit strain values increase for \(TF\) values less than 1. However, it is thought that the void nucleation-dominated material failure at low triaxial values may not be appropriately represented by the equation. Therefore, a constant limit value is used for \(TF\) values less than \(1\), which is assumed to be conservative in the range.  
Figure 5: Strain limits with respect to temperature for Eurofer97 material.  
Figure 6: ILFED strain limits with respect to the triaxiality factor.