esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
It was found that the path-weighting functions (PWFs) for covariance depend on four variables: \(\gamma\), \(d/D\), \(F_{N}\), and \(D_{S}L/DL_{S}\). In other words, the optical effects of turbulence are influenced by not only the turbulence strength but also these four variables. Numerical results show that the effect of diffraction on the variance of the tilt phase or piston amplitude ismore significant than it is on the covariance, e.g., the two curves of \(W_{T}(u)\) for \(d=0D\), \(F_{N}=5\) and \(d=2D\), \(F_{N}=1\) almost coincide with each other, and it is possible to measure the tilt isoplanatic angle through the covariance of intensity rather than scintillation. More specifically, the curve of the PWFs for the tilt phase shifts upwards as the value of \(d/D\) or \(F_{N}\) increases, or the value of \(R\) (\(R=D_{S}L/DL_{S}\)) decreases (see Figures 2 and 4). The peak position of \(W_{p}(u)\) shifts to the start of the path as the value of \(d/D\) or \(F_{N}\) decreases, or the value of \(R\) increases (see Figures 3 and 5).  
In the future, work will be conducted to prove the validity of measuring the tilt isoplanatic angle through the covariance of intensity and to investigate more suitable PWFs for practical applications. The linear combination method is an effective and powerful method for measuring the turbulence integral parameters of interest as there are a potentially large number of various PWFs that we can obtain.  
Conceptualization, H.S. and L.Y.; methodology, H.S. and L.Y.; formal analysis, H.S., L.Y., and X.J.; investigation, F.T.; resources, X.J.; data curation, F.T.; writing--original draft preparation, H.S.; writing--review and editing, L.Y.; funding acquisition, H.S. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 61665004 and 41605007.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Wing Component Structure Design Optimization Process
### Rib Structure Topology Optimization  
The baseline TO proposes seven ribs for both wing segments, as shown in Figure 8, with approximate distances between ribs inside the wings [29]. These ribs are used to minimize the wing deformation by maintaining its segment's geometrical structure. Thus, the external loads are evenly distributed on the wing skin. Fixed wing ribs should be designed with a cavity, as they house their moving segment, as illustrated in Figure 8. Therefore, the TO was implemented on the inner six ribs for both segments. The external ribs were not considered for the fixed and moving wing segments within the design optimization region, as the fixed wing's last rib should only contain the cavity for the moving wing segment. Additionally, the ribs of the moving wing segment should have a closed geometrical shape.  
The optimum material distribution inside the ribs was determined by the original design space configuration and the boundary conditions. A uniform tetrahedron mesh with elements of 3 mm in size was generated for the wing ribs for the two segments in order to implement TO. Both wing ribs were discretized into various elements, and their node numbers were based on their rib's sizes.  
Figure 7: An example of the meshing and boundary conditions for the front spar—fixed wing. (**A**) Front spar for a fixed wing, (**B**) The meshing and the designable and non-designable areas of the front spar and (**C**) The boundary conditions of the front spar.  
Figure 9 shows the meshing and its boundary conditions of the ribs for the fixed wing. A volume constraint was applied to all wing ribs in the optimization problem by its consideration as an opposite constraint. The aerodynamic analysis (see Table 3) can define the loads for each rib. The objective function was extended to the designable region and the rib edge. The link spots connecting the ribs inside the spars were considered as a non-designable region. The non-designable regions at the link spots with spars were stabilized during the optimization process, as they were excluded from the design domain.  
Figure 8: Baseline geometrical shape for morphing wing spars for fixed and moving segments at the original position.  
Figure 9: Geometry, meshing and boundary conditions for the ribs 1 and 2—fixed wing as an example. (**A-1,B-1**) Geometry for ribs 1 and 2 and the designable and non-designable areas for a fixed wing; (**A-2,B-2**) ribs 1–2 meshing; and (**A-3,B-3**) Boundary conditions of the ribs 1–2).
## 2 Materials and Methods
### Optimization Methodology and Meteorological Data Collection  
As the number of optimized parameters is extraordinarily high and includes a different number of layers and the number of nodes inside the layers, only an automatic approach is feasible. In this study, a genetic algorithm (GA) was applied. This technique allows an efficient heuristic search for the global optimum (in this case the minimum) for a specific objective function. The exact form of the objective function, mean relative error (MRE), is presented below:  
\[MRE=\frac{1}{n}\cdot\left(\sum_{i=1}^{n}\frac{|P_{T}-P_{ANN}|}{P_{T}}\right) \cdot 100\% \tag{1}\]  
where P\({}_{T}\) denotes the target power output, measured on PV\({}_{S}\), and P\({}_{ANN}\) refers to the output forecast by the network. The n value represents the number of measurements included in the sum. The maximal number of hidden layers was limited to 2, where the number of nodes could not exceed 10. Also, the proportions between the size of training, validation, and the test sample were constrained. The training set was assumed to have 80-95% of available measurements, the validation--5-20%, and the test--0-15%. The genetic algorithm was implemented in the MATLAB 2022b environment using the Global Optimization Toolbox package with a function tolerance of \(1\times 10^{-2}\) and a maximum number of generations limited to slightly over 50, based on previous screening. The GA was also constrained to ensure that the percentage of measurements selected for all three learning sets summed up to 100%.  
Daily measurements of weather and the energy produced in 457 consecutive days were used as data for ANN learning. Days with zero production (maintenance or other disruptions) were excluded from the analysis. The input was meteorological information: maximal daily temperature (related to overheating of the PVs), sunshine duration (the most direct factor that defines the amount of energy produced), average cloud cover (common limiting factor), and time of precipitation (water on panels can decrease the efficiency in significant range). Additionally, to indirectly include information about the sun angle, the number of the day of the year was also included. This is an important aspect of novelty in this study, as this modification could not be applied to more conventional approaches. In typical modeling studies, only temporary factors are considered, while here, the most complex possible approach is applied.
## 4 Basic PUMA Architecture
### Workflow Instantiation and Monitoring
#### 4.3.2 Monitoring of Services  
The special role of the Monitoring Service stems from the dynamic properties of streaming over unreliable media, like the Internet or wireless networks. Monitoring Services includes the checking of availability of a service and also the just-in-time replacement of failing services. Other types of adaptation include the replacement of services when the network parameters, e.g., bandwidth or latency, change so that a certain service or workflow is not suitable for adaptation any more. For the implementation of the monitoring, the E\({}^{2}\)Mon algorithm [10] is used in the PUMA framework.
# Integrated Systems of a Solar Thermal Energy Driven Power Plant
## 4 Experimental Setup
### Analysis and Interpretation  
All models will be simulated and analyzed on a 64-bit Windows 11 system with an Intel I5 CPU and 8 GB of RAM. We will test and evaluate the proposed and existing models in various WSN network configurations.  
_Average Delay_: Average time a packet takes to travel from origin to destination. The formula is as follows:\[D=\frac{\sum_{i=1}^{N}d_{t}^{i}+d_{p}^{i}+d_{pc}^{i}+d_{q}^{i}}{N} \tag{1}\]  
where \(N\) is the total number of transmission links, \(d_{t}^{i}\) is the transmission delay of \(i^{th}\) link, \(d_{p}^{i}\) is the propagation delay, \(d_{pc}^{i}\) is the processing delay, and \(d_{q}^{i}\) is the transmission delay.  
_Average Throughput_: This metric counts packets per second. The usual Kbps transfer rate is as follows:  
\[T=\bigg{(}\frac{R}{T^{2}-T^{1}}\bigg{)}\times\bigg{(}\frac{8}{1000}\bigg{)} \tag{2}\]  
where \(R\) is the complete received packets at all destination nodes, \(T^{2}\) is the simulation stop time, and \(T^{1}\) is the simulation start time.  
_Average Energy Consumption:_ The network average is calculated by adding together each node's energy usage after the simulation. The total energy consumption formula, \(E^{tot}\), is as follows:  
\[E^{tot}=\sum_{i=1}^{N}E_{i}^{initial}-E_{i}^{consumed} \tag{3}\]  
where \(E_{i}^{initial}\) and \(E_{i}^{consumed}\) are the initial and consumed energy of \(i^{th}\) node, respectively. \(N\) is the total number of nodes in the network. The average consumed energy is computed as follows:  
\[E^{avg}=\frac{E^{tot}}{N} \tag{4}\]  
E: Network Lifetime Lifetime (Rounds) = Total Remaining Energy/10  
_PDR_: The ratio of packets transmitted and received is calculated. The formula is as follows:  
\[P=\bigg{(}\frac{P_{r}}{P_{S}}\bigg{)}\times 100 \tag{5}\]  
where, \(P_{r}\) is the number of received packets, and \(P_{S}\) is the number of generated packets.  
_Communication Overhead_: Number of routing packets compared to network data packets. The formula is as follows:  
\[O=\sum_{t}\bigg{(}\frac{RT^{t}}{DT^{t}}\bigg{)} \tag{6}\]  
where, \(RT^{t}\) is the total number of routing packets, and \(DT^{t}\) is the total number of data packets at time \(t\).
###### Abstract  
The purpose of this study was to investigate the feasibility of using neck-surface acceleration signals to discriminate between modal, breathy and pressed voice. Voice data for five English single vowels were collected from 31 female native Canadian English speakers using a portable Neck Surface Accelerometer (NSA) and a condenser microphone. Firstly, auditory-perceptual ratings were conducted by five clinically-certificated Speech Language Pathologists (SLPs) to categorize voice type using the audio recordings. Intra- and inter-rater analyses were used to determine the SLPs' reliability for the perceptual categorization task. Mixed-type samples were screened out, and congruent samples were kept for the subsequent classification task. Secondly, features such as spectral harmonics, jitter, shimmer and spectral entropy were extracted from the NSA data. Supervised learning algorithms were used to map feature vectors to voice type categories. A feature wrapper strategy was used to evaluate the contribution of each feature or feature combinations to the classification between different voice types. The results showed that the highest classification accuracy on a full set was 82.5%. The breathy voice classification accuracy was notably greater (approximately 12%) than those of the other two voice types. Shimmer and spectral entropy were the best correlated metrics for the classification accuracy.  
neck-surface vibration; machine learning; voice type discrimination 1
# Design of a Cell Phone Lens-Based Miniature Microscope with Configurable Magnification Ratio
## 2 Materials and Methods  
### Principle of Miniature Cell Phone Microscope  
The function of the cell phone camera lens is to image a meter-scale scene onto a CMOS image sensor with a size of several millimeters (Figure 1A). At present, a cell phone is normally equipped with multiple cameras with diverse values of FOV, and the focal length of camera lenses varies within the range of 1-8 mm. Such camera lenses are usually composed of 5-9 highly complex aspheric lenses. Therefore it is capable of high-quality imaging with minute aberration and field curvature and with a resolution power of up to 400-600 lp/mm. Benefiting from the mass production of the cell phone industry, the cost of these precise camera lenses is fairly low compared with similar quality industrial camera lenses, making them attractive in the microscope domain.  
The principle of the proposed miniature microscope is as follows (Figure 1B). The microscopic imaging system is usually a combination of the object lens, tube lens and camera sensor. Here, the cell phone camera serves as an accurate miniature component of the tube lens and CMOS sensor. The object lens of a microscope is realized with another cell phone camera lens, the direction of which is reversed. Then, the sample to be imaged is placed on the original CMOS sensor position, and the low field curvature feature of the cell phone camera lens makes it possible to image with a large FOV and high resolution. By combining the two cell phone lenses in this way, a microscope can be constructed with the following magnification ratio,  
\[M=f_{tube}/f_{object} \tag{1}\]  
where \(f_{tube}\) and \(f_{object}\) are focal length of tube lens and objective lens, respectively.  
On this basis, we propose that the object lens can be a switchable camera lens module with a variable focal length so as to adjust the magnification of the microscope, which is a very desirable feature of the microscope. We can find a large pool of commercial cell phone camera lenses with different focal lengths, making this approach quite feasible. In addition, we suggest moving the miniature camera module (including tube lens and CMOS sensor) out of the cell phone, as shown in Figure 1B. The size of a miniature camera module is typically 10 mm \(\times\) 10 mm \(\times\) 8 mm, while the cell phone size is typically about 120 mm \(\times\) 80 mm \(\times\) 10 mm by comparison. Therefore, compared with the previously reported cell phone microscope, the final size of the microscope module can be greatly reduced. The imaging operation can be greatly simplified, because the users only need to deal with a cube with a side length of 10 mm.  
Other advantages of the cell phone microscopes still exist in the proposed structure, including low costs and direct access to the Internet.
## 6 Simulations and Testbed Experiments
### Simulation Results
#### 6.2.1 Overview  
To obtain a panoramic view of the performances of our CDSAs, firstly we present the performance results of each scheme in Table 3. Each numeric result is the statistic value of 20 simulation experiments in each group. In our simulations, the SP parameters are exchanged through packets at the SU stage instead of in the payload of a routing protocol or other protocol. All nodes use 92.337 W for O-CDSA and 97.628 W for T-CDSA in total at SU stages. The total energy consumption in Table 3 contains the energy consumption used at the SU stages, which is also applied to the Section 6.2.2. As is shown in Table 3, the results show that the two CDSAs can save energy significantly compared with the non-scheduling approach. It can save approximately 38% energy for the O-CDSA and almost 61% energy for the T-CDSA, according to the experimental results. In terms of maximum queue length, it occupies the majority of buffer for O-CDSA, while buffer occupation of T-CDSA is close to that of non-scheduling approach, and in terms of the network throughput, the O-CDSA is slightly slower than other approaches, and the T-CDSA reaches very close to that of non-scheduling. In addition, the packet delay of the O-CDSA is much longer than that of the non-scheduling approach, while the packet delay of T-CDSA is very close to that of non-scheduling approach. In conclusion, the results of T-CDSA are very close to that of non-scheduling in terms of queue length, network throughput and packet delay. With regard to the stability of scheduling approach, most of the standard deviation of performance metrics of T-CDSA is less than that of O-CDSA, i.e., T-CDSA is much more stable than O-CDSA. It is attributed to the sleep scheduling and collision avoidance mechanisms for energy saving and decline in other performances of the CDSAs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Performance Metrics** & **Non-scheduling** & **O-CDSA** & **T-CDSA** \\ \hline Total energy consumption (W) & 27,255.492 (232.407) & 16,899.257 (152.045) & 10,688.178 (158.359) \\ Maximum Queue length & & & \\ (Number of Packets) & 71.650 (16.989) & 946.600 (671.109) & 78.700 (27.902) \\ Network throughput (Bps) & 1,194.000 (0.000) & 1,051.950 (83.583) & 1,194.333 (1.330) \\ Packet delay (S) & 3.801 (0.412) & 180.018 (41.854) & 5.685 (1.591) \\ \hline \end{tabular} where the statistic value out of parenthesis denotes the mean value of 20 experiments in each group, and the statistic value in parenthesis represents the standard deviation of the 20 experiments.  
\end{table}
Table 3: Performance comparison of the three schemes.
###### Abstract  
Since 1970, continuously reinforced concrete pavements have been used in Belgium. The standard design concept for CRCP has been modified through several changes made in the design parameters to eliminate the cluster of closely spaced crack patterns, since these crack patterns lead to the development of spalling and punch-out distresses in CRCPs. Despite adjusting the longitudinal reinforcement ratio, slab thickness, and addition of asphalt interlayer, the narrowly spaced cracks could not be effectively removed. The application of transverse partial surface saw-cuts significantly reduced the probability of randomly occurring cracks in the reconstruction project of the Motowway E313 in Herentals, Belgium. The field investigation has also indicated that the early-age crack induction in CRCP is quite susceptible to the saw-cut depth. Therefore, the present study aims to evaluate the effect of different depths and lengths of the partial surface saw-cut on the effectiveness of crack induction in CRCP under external varying temperature field. For this purpose, the FE software program DIANA 10.3 is used to develop the three dimensional finite element model of the active crack control CRCP segment. The characteristics of early-age crack induction in terms of crack initiation and crack propagation obtained from the FE model are compared and discussed concerning the field observations of the crack development on the active crack control E313 test sections. Findings indicate that the deeper saw-cut with longer cut-lengths could be a more effective attempt to induce the cracks in CRCP in desirable distributions to decrease the risk of spalling and punch-out distresses in the long-term performance of CRCP. These findings could be used as guidance to select the appropriate depth and length of saw-cut for active crack control sections of CRCP in Belgium.  
early-age crack induction; saw-cut depth; saw-cut length; continuously reinforced concrete pavement; finite element analysis +
Footnote †: journal: Applied Sciences
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 5 Discussions and Perspectives
### Future Trends of Silicon Photonic Edge Couplers
#### 5.4.2 Multilayer Silicon Nitride-on-Silicon Photonic Integrated Platform  
To improve the integration density of silicon photonic integrated circuits, recent developments have shown increasing interest in the gradual emergence of multilayer integrated photonic platforms and multilayer silicon nitride-on-silicon integrated platforms in particular. Silicon nitride is a promising candidate in silicon photonic platforms due to its many advantages. Firstly, SiN is well compatible with the fabrication process of silicon photonics, which can be grown via low-pressure chemical vapor deposition (LPCVD) at high temperature (>700 \({}^{\circ}\)C) or plasma-enhanced chemical vapor deposition (PECVD) at low temperature (<400 \({}^{\circ}\)C) [133]. Secondly, silicon nitride waveguides have a lower refractive index (around 2.0 at 1550 nm) than that of silicon [134]. The relatively low index contrast with silica brings less light confinement and thus a larger waveguide dimension, making it less sensitive to sidewall roughness. Thus, the SiN waveguide has a lower propagation loss in the O-band than Si waveguide [133]. Although N-H and Si-H bonds absorption exists in the C-band, it can be avoided by choosing LPCVD instead of PECVD. Thirdly, SiN can support high optical power and is less sensitive to thermal change compared with Si, which means that it is possible to integrate active devices into a silicon photonic platform with the aid of SiN [108].  
In cases of fiber-to-chip edge couplers, SiN waveguides have two major advantages over Si waveguides. Firstly, a SiN waveguide has a lower refractive index mismatch with fiber core than silicon, which can be designed properly to reduce the modal discrepancy with the fiber core and thus improve the modal overlap for fiber-to-chip coupling [135]. Secondly, the insertion loss can be reduced when assisted with multiple upper SiN waveguides, as described previously in Section 4.1. In addition, for vertical coupling, SiN allows a tri-level polarization independent grating coupler [134] and increases the coupling efficiency when silicon is served as a bottom mirror [136]. There are several leading research groups focusing on the multilayer silicon nitride-on-silicon platform, including [PERSON] et al. from the University of Toronto [134; 137; 138], [PERSON] et al. from the Institute of Microelectronics, Singapore [139; 140], [PERSON] et al. from the University of California, Santa Barbara [141; 142], etc. Some related research works of SiN couplers are demonstrated in Table 3, with comparison to photoniccouplers in the SOI platform. Apart from the SiN-on-Si platform, there are also some research works on multilayer integrated photonic circuits with a combination of crystalline silicon and amorphous silicon [62; 94]. The multilayer integrated photonic platform is a prospective trend for high link capacity and data transmission.
# Development and Assist-As-Needed Control of an End-Effector Upper Limb Rehabilitation Robot
###### Abstract  
Robot-assisted rehabilitation therapy has been proven to effectively improve upper limb motor function and daily behavior of patients with motor dysfunction, and the demand has increased at every stage of the rehabilitation recovery. According to the motor relearning program theory, upper limb motor dysfunction can be restored by a certain amount of repetitive training. Robotics devices can be an approach to accelerate the rehabilitation process by maximizing the patients' training intensity. This paper develops a new end-effector upper limb rehabilitation robot (EULRR) first and then presents a controller that is suitable for the assist-as-needed (AAN) training of the patients when performing the rehabilitation training. The AAN controller is a strategy that helps the patient's arm to stay close to the given trajectory while allowing for spatial freedom. This controller enables the patient's arm to have spatial freedom by constructing a virtual channel around the predetermined training trajectory. Patients could move their arm freely in the allowed virtual channel during rehabilitation training while the robot provides assistance when deviating from the virtual channel. The AAN controller is preliminarily tested with a healthy male subject in different conditions based on the EULRR. The experimental results demonstrate that the proposed AAN controller could provide assistance when moving out of the virtual channel and provide no assistance when moving along the trajectory within the virtual channel. In the close future, the controller is planned to be used in elderly volunteers and help to increase the intensity of the rehabilitation therapy by assisting the arm movement and by provoking active participation.  
robot-aided training; end-effector rehabilitation robot; assist-as-needed; virtual channel