esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### In-Line Dimensional Inspection
#### 3.2.5 Dimensional Measurements  
After the workpiece revolution axis \(A\) has been calculated, the circular runout can be computed on \(M\). Given a circular feature \(P\subset M\), perpendicular to the revolution axis \(A\) (i.e., \(P\perp A\)), the circular runout of \(P\) with respect to \(A\) measures how much the feature \(P\) oscillates when the workpiece is rotated around the axis \(A\)[8].  
The following steps describe the system's approach to compute the circular runout of the workpiece:  
1. Compute the plane \(T_{h}\) with normal vector \(\vec{v}\) and pivot point \(a_{0}+h\vec{v}\) (the parameter \(h\) has been defined already by the metrologist in the planning step, Section 3.1.2). See Figure 9a,b.
2. Compute the circular feature \(P\) defined as: \[P=M\cap T_{h}\] (7)
3. Filter outliers by removing all points in \(P\) whose distance to the theoretical section is greater than a given threshold.
4. Compute the inscribed circle \(B_{small}\) and circumscribed circle \(B_{large}\) of \(P\) with center \(A\) and respective radii \(r_{small}\), \(r_{large}\) (Figure 9c). \(0<r_{small}\leq r_{large}\).
5. Compute the circular runout \(\Delta\Phi\) defined as [8]: \[\Delta\Phi=r_{large}-r_{small}\] (8)  
Figure 8: Computation of the reference point \(a_{0}\) on \(\mathcal{C}_{cone}\). (**a**) Mesh-cylinder intersection; (**b**) Plane-axis intersection.  
Before the computation of the circular runout, our system performs circle fitting on the feature \(P\) using RANSAC. Such a fitting improves the robustness of the runout estimation by filtering noise and outliers from the scanned mesh.  
It is worth noting that the runout deviation includes the eccentricity of both axes and roundness deviations of the measured circle. This is an expected behavior following the standards for geometrical dimensioning and tolerancing [8].
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
## 1 Introduction
consisted of a front cap, an end cap, and piezoelectric ceramic sets sandwiched between the two caps. The piezoelectric ceramic was quadratically polarized. An elliptical trajectory was generated when orthogonal signals were simultaneously applied to the piezoelectric ceramic set. The cone of the motor was used to amplify the motion trajectory, which greatly increased the motor's size [32]. Most ceramic-chip cylindrical multi-DOF motors have PZTs affixed directly to the stator, making it easier to miniaturize the size. [PERSON] et al., developed a motor consisting of a 10 \(\times\) 10 \(\times\) 18 mm stator with four d31 PZTs. The top of the stator had four bumps to contact the rotor [33]. In ref. [34], the designed actuator comprised four \(d_{31}\) PZTs polarized along the thickness direction with a base. Applying a positive/negative excitation voltage to the PZTs, the PZTs shortened/stretched in different directions, thus driving the rotor in multiple degrees of freedom. Application scenarios for multi-DOF cylindrical ultrasonic motors have gradually started to favor miniaturization, which places higher demands on the motor size. For cylindrical single-stator multi-DOF motors, the large size, mounting of the stator and rotor, and the design of the support mechanism are among the key issues affecting their application. The majority of ceramic-chip motors can be miniaturized, but the PZT pasting process has a significant impact on motor performance. Bolted motors using piezoelectric ceramics do not require pasting and are far simpler to install and manufacture [35]. Compactness has always been an important design goal for bolted cylindrical multi-DOF motors. With a certain cross-section, when the stator electromechanical coupling coefficient is lower as the motor is lowered using standard first-order longitudinal and third-order bending as the energy transfer wave. In this case, the energy transfer wave is not able to effectively and continuously drive the rotor ball, which affects regular operation. Therefore, this paper focuses on designing a simple and reliable bracket and frame, determining a suitable range of modal drive frequencies, and providing a suitable aspect ratio for the entire motor structure to suit the application. This paper develops a 3-DOF cylindrical ultrasonic motor based on hybrid vibration modes with good output characteristics. Hybrid modes encompass non-standard longitudinal and non-standard bending modes, i.e., vibration modes where the vibration pattern differs from the standard vibration pattern. The motor consists of a cylindrical stator and rotor, with a simple structure and high output torque. The motor structure, the piezoelectric ceramic distribution, and the operating principle are described in detail. Simulation studiesof the stator characteristics and motion are then carried out, and a prototype is built and experimentally tested.
# Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel
## 5 Conclusions  
In this paper, we proposed a method to decompose the 2D ISI GPR target into two serial 1D vertical and horizontal targets. Firstly, we used the general 2D GPR target to estimate the 2D interference from the HDS channel. Then, based on these interferences, we approximated the general 2D GPR target to two serial 1D targets, which includes horizontal and vertical interferences. Therefore, we can exploit the serial detection with two 1D Viterbi detectors for the HDS systems. This makes the detector very simple compared with  
Figure 8: BER of the proposed GPR target varying the blur from 1.8 to 3 at 10% misalignment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Mul/Div** & **Add/Sub** \\ \hline Our model & 252 & 700 \\
2D SOVA [13] & 123 & 94 \\
1D GPR [16] & 33 & 48 \\ Model in [22] & 571 & 1229 \\ DFE [23] & \textgreater{}36 & \textgreater{}34 \\ Complicated 2D Viterbi & \textgreater{}537 & \textgreater{}1560 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The complexity of the proposed and other methods.  
the complicated 2D Viterbi detector and significantly improves the performance of the HDS system.  
The proposed 2D GPR target achieves similar BER performance at 0% and 10% misalignment, which means that the proposed 2D GPR target can resist the misalignment effect. The proposed 2D GPR target can provide gains of approximately 2.5 dB at \(10^{-3}\) compared to the 1D GPR target [16] with 1.8 blur and 0% and 10% misalignments. In addition, our proposed method can still achieve the best performance when the blur increases.  
Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government. (MSIT) (2021R1A2C1011154).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
In recent years, flexible and wearable strain sensors, consisting of a polymer matrix and a conducting filler, have received extensive attention owing to their physical advantages, such as being lightweight, stretchable, and having the potential for application to complex forms. However, achieving a low hysteresis of the relative change in resistance, wide sensing range, and reduced plastic deformation is still challenging. To address these issues, in this study, we developed hybrid conducting composites with a wide range of sensing abilities and low hysteresis. The bi-layer composites, comprising a carbon nanotube (CNT) composite layer with reinforced/conducting properties, and a natural rubber-based layer with extreme strain properties, could effectively circumvent their limitations. Compared to single-layer CNT composites, the bi-layer structure could increase the tensile strain with reduced plastic deformation, resulting in the prevention of surface cracks on the CNT composite. In addition, it has the benefit of measuring a wider sensing range, which cannot be measured in a single-CNT composite system. A cyclic stretching/releasing test was performed to demonstrate that the strain sensor exhibited excellent reproducibility. Our results can function as a useful design guide for stretchable sensor applications.  
polymer composite; carbon nanotube; strain sensor; hysteresis; plastic deformation +
Footnote †: journal:  
of hybrid-structured composites covering the elastomeric polymer layer above and below the conducting layer [2; 3; 4; 5; 29]. However, in practical terms, the analysis of the effect of such a hybrid-structured system compared with the existing single composite is insufficient.  
Therefore, we analyzed the advantages of a hybrid-structured composite compared to a single composite from the perspective of hysteresis and strain range. The bi-layered composite was prepared by sealing natural rubber (NR) with excellent tensile strain (<500%) onto MWNT/NR with excellent electrical conductivity in a single composite. NR was selected as the base polymer matrix and hybrid-structure layer owing to its ultra-stretchability and biocompatibility. The effect of the pure NR layer was analyzed with MWNT contents of 1 and 5 wt.%. In addition, the adhesion between homogeneous polymers was excellent, according to the cross-sectional image after tensile strain. This bilayer composite could lower the hysteresis of the change in the initial resistance, compared to the single composite system, because the NR layers prevent surface cracks from forming on the conducting layer due to strain. In addition, this hybrid structure can reduce plastic deformation and widen the sensing range. Furthermore, this system showed excellent repeatability and durability in a cyclic test (130% strain, 100 cycles) in the 5 wt.% sample.
## 4 System Implementation and Experimental Results
### Wrist Joint Flexion-Extension Exercise  
In this case, the subject performs a few repetitions of wrist joint flexion-extension exercise of his right upper limb. The extracted sensor orientation angles \(\phi\), \(\theta\) and \(\psi\) contribute to the wrist DoFs angles estimation. During this exercise the subject's elbow is steadily flexed at \(\theta_{4}=90^{\circ}\) being perpendicular to the coronal plane, and the hand is repeatably flexed with the palm towards the chest at almost \(\theta_{6}=-55^{\circ}\) and extended until almost \(\theta_{6}=25^{\circ}\), as shown in Figure 12. In Figure 13, a deviation around a mean value is presented that corresponds to the second DoF of the wrist, this of radial-ulnar deviation. Actually, a small deviation is present during the subject's wrist motion, thus the deviation in this graph is reasonable.  
The concluded joints' trajectories, as extracted by the upper limb model for the wrist DoF angles \(\theta_{6}\) and \(\theta_{7}\) is illustrated in Figure 14. It represents the exercise that the subject has performed, along with this small deviation. The corresponding trajectories of the shoulder, the elbow, the wrist and the tip of the hand in \(x\), \(y\), and \(z\) axes are presented in Figure 15.  
In this case, a comparison among the custom-made and the Shimmer3 sensor node was not realized, because it was not possible to attach the array of the aligned sensors on the subject's hand, due to the hardware dimensions.  
Such graphs of upper limb trajectories and joint DoFs angles during exercise sessions are useful for a subject's upper limb status evaluation by the physical therapists. Especially, the analysis over the range of motion for each joint angle or even the estimation of the  
Figure 14: Joints trajectories coordinates in \(x\), \(y\), \(z\) axes for wrist flexion-extension exercise—Custom-made sensor node.  
Figure 15: Wrist and hand trajectories during wrist joint flexion-extension exercise.  
speed during the execution of an exercise indicate the status and progress of a patient with upper limb movement disorders. Hence, the therapists could tune the sequel rehabilitation sessions accordingly.
## 2 Materials and Methods
### Laboratory Testing Program
#### 2.2.4 DSR Test  
The high and intermediate-temperature properties of the developed compound-modified asphalt binders were measured using a dynamic shear rheometer (DSR) by following AASHTO T 315. The geometry, with a 2 mm gap and an 8 mm diameter, was applied. The intermediate performance grading (PG) test for asphalt binders was conducted at various temperatures (i.e., 19, 22 and 25 \({}^{\circ}\)C) to assess the fatigue cracking resistance of the proposed compound-modified asphalt binders. The fatigue factor (\(G^{*}\)sin\(\delta\)) was obtained at each temperature, and the intermediate failure temperature was determined based on ASTM 7643. A temperature sweep test was also conducted using the DSR. The temperature varied from 30 to 90 \({}^{\circ}\)C. The strain control mode (1%) was applied, and the loading frequency was 10 rad/s. In addition, four various temperatures (i.e., 10 \({}^{\circ}\)C, 22 \({}^{\circ}\)C, 34 \({}^{\circ}\)C, and 46 \({}^{\circ}\)C) with different loading frequencies ranging from 100 to 0.1 rad/s were chosen in the frequency sweep test to evaluate the aged samples.  
The 2S2P1D model was applied to analyze and model the frequency sweep testing data. The 2S2P1D model has five components: two springs, two parabolic elements and one dashpot. Equation (1) shows the 2S2P1D model for the complex modulus (\(G^{*}\)). The shift factor (\(\alpha(T)\)) was calculated based on the Williams-Landel-Ferry theory (Equation (2)).  
\[G^{*}(\omega)=G_{0}+\frac{G_{g}-G_{0}}{1+\alpha(i\omega\tau)^{-k}+(i\omega\tau )^{-h}+(i\omega\beta\tau)^{-1}} \tag{1}\]  
\[\log\alpha_{T}=-\frac{C_{1}(T-T_{0})}{C_{2}+T-T_{0}} \tag{2}\]  
\[\tau=\tau_{0}\times\alpha_{T}(T) \tag{3}\]  
where the variables are as follows:  
\(\omega=\) angular frequency, rad/s;  
\(i=\) complex number (\(i^{2}=-1\));  
\(k\), \(h=\) dimensionless constants, with \(0<k<h<1\);  
\(G_{0}=\) static modulus, when \(\omega\)\(\rightarrow\)0, Pa;  
\(G_{g}=\) glassy modulus, when \(\omega\)\(\rightarrow\)\(\infty\), Pa;  
\(\tau=\) characteristic time as a function of temperature; \(\tau_{0}\) is the characteristic time at the reference temperature (\(T_{0}\));  
\(\beta=\) dimensionless constants as functions of \(\eta\) and \(\tau\);  
\(\alpha(T)=\) shift factor as a function of temperature;  
\(C_{1}=\) dimensionless constants;  
\(C_{2}=\) constants/\({}^{\circ}\)C.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Conventional RO Processing  
In this section, we present the processing and analysis of the data using two conventional RO techniques. The first one is the closed loop processing, usually employed in standard GPS receivers or early GPS RO receivers. The second one corresponds to the open loop processing used in current RO receivers. These results will be used later in our work as a reference to validate the performance of the new interferometric RO technique.
## 4 Computational Experiments and Results
### Tuning of Production Systems' Parameters
The values of NP and values of \(S/N\) of NP obtained are averaged against each level of the buffer size. The mean value of NP and mean value of \(S/N\) are obtained for each scenario, and these mean values are considered to obtain the mean value of the mean of NP for all ten scenarios of each category of the problem. Similarly, the mean values of the mean of \(S/N\) of all 10 scenarios are obtained for each category of problem. The graphical method is used here to obtain the optimal level of buffer size of each product model for each category of problem. Figure 7 shows the mean of mean NP and mean of mean \(S/N\) of scenarios against the level of buffers for product models in TS, TM, TL, LS, LM, and LL problems. The optimum levels of buffer size of different product models are obtained by analyzing both mean value of mean NP and mean of \(S/N\) values of objectives for each category of problem as given in Table 4.  
buffer sizes and their NP values are computed. The comparison results of NP values of each problem category using the proposed heuristic before and after using optimal buffer size are illustrated in Figure 8.  
It can be seen from Figure 8 that, for different categories of problems, the value of NP is greater when optimal buffer size is used. Moreover, variation in the values of NP in different scenarios is less. These results also indicate the consistency of the results obtained from the proposed heuristic method when optimal buffer size is used, which shows the significance of the proposed heuristic method. The mean and median value of NP for each problem category before and after tuning of buffer size are calculated and compared as given in Table 5. It can be seen from Table 5 that the mean value of the NP for all categories of problems obtained after buffer size tuning is higher than the NP values obtained before buffer size tuning except for problem LS. Furthermore, Table 5 also indicates the median value obtained from the proposed heuristic method after buffer size tuning is better as compared to the median value of NP obtained before buffer size tuning. These results indicate that the tuning of buffer size for each category of problem can improve the NP value by using the proposed heuristic method.
## 3 Results and Discussion
### SEM and XPS Results
Figure 3 shows the surface morphologies of Ti-Zr-V-Hf and Ti film coatings with cleaned OFHC substrates. It was observed that Ti-Zr-V-Hf film with cleaned OFHC substrate in Figure 3c was denser than the one with cleaned laser processed OFHC substrate in Figure 2c. The same principle appeared to Ti films with cleaned, untreated, and laser processed OFHC substrates. This may be attributed to the SEY decrease of Ti films with cleaned laser processed substrate. Several researchers reported that a rough surface could suppress secondary electron emission (SEE) dramatically [21; 22; 23; 24; 25; 26]. Despite the increased emitting surface area induced by laser processing which favors SEE, a portion of the SEE can be absorbed by the surface protrusions. The balancing effect is the partial mitigation of SEE by absorption, to some extent resting with the surface morphology formed by laser processing, thus synthesizing micro-structured surfaces with a high possibility to acquire low SEY surface. As shown in Figure 4, the XPS survey spectra indicate that the ratio of Ti, Zr, V, and Hf elements is around 2.4:2.6:1.0:1.2. It is worth noting that O and C are the two main contaminants for Ti film and Ti-Zr-V-Hf film coatings. This can be explained because C and O elements were introduced during air exposure and sample transfer. Figure 4: The X-ray photoelectron spectroscopy (XPS) survey spectra for the surfaces of (**a**) Ti film and (**b**) Ti–Zr–V–Hf quaternary alloy film coatings. Figure 3: The SEM (Scanning electron microscope) micrographs of the Ti and Ti–Zr–V–Hf film coatings with cleaned OFHC substrates at different scales. Here, (**a–c**) are the SEM micrographs of Ti–Zr–V–Hf film coatings. (**d–f**) are the SEM micrographs of Ti film coatings.
## 4 Experiments
### Main Results and Discussion  
#### 4.2.1 Main Forecasting Results  
As shown in Table 1, the PCC module can effectively improve the performance of all backbone models on weather time series forecasting tasks across different prediction horizons. The models augmented with the PCC module all achieve significantly lower MAE and MSE than the original models. Notably, our PCC respectively reduces the MSE of the DLinear and SegRNN models by 17.34% and 11.02% on average. The improvement is particularly pronounced for shorter prediction horizons, with a 26.57% reduction in MSE for DLinear and 11.11% reduction for SegRNN with a prediction horizon of 96. The detailed error bars, which include the standard deviation and the confidence interval, are listed in Appendix B.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Design**} & \multicolumn{2}{c}{**96**} & \multicolumn{2}{c}{**192**} & \multicolumn{2}{c}{**336**} & \multicolumn{2}{c}{**720**} & \multicolumn{2}{c}{**Average**} \\ \cline{3-11}  & & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline \multirow{3}{*}{stackLSTM} & Original & 0.298 & 0.318 & 0.387 & 0.373 & 0.501 & 0.459 & 0.548 & 0.495 & 0.434 & 0.411 \\  & +PCC & **0.221** & **0.266** & **0.242** & **0.289** & **0.285** & **0.314** & **0.369** & **0.376** & **0.279** & **0.311** \\ \hline \multirow{3}{*}{DLinear} & Original & 0.207 & 0.233 & 0.244 & 0.269 & 0.286 & 0.306 & 0.345 & 0.354 & 0.271 & 0.290 \\  & +PCC & **0.152** & **0.197** & **0.196** & **0.239** & **0.243** & **0.276** & **0.304** & **0.318** & **0.224** & **0.258** \\ \hline \hline \end{tabular}
\end{table}
Table 1: MSE and MAE of forecasting results for different backbones with and without PCC across different forecasting horizons (\(\{96,192,336,720\}\)) with a consistent look-back length of 96. Lower value of MAE and MSE indicate better forecasting performance, and are indicated in **bold**.