esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of the Mechanical Power Circulation Test Rig  
### Conceptual Design  
The mechanical power circulation (or mechanical back-to-back type) rig has several gearboxes connected in a closed-loop to circulate the torque loads. It is composed of an electric motor, two auxiliary gearboxes (parallel gearboxes for torque circulation), a torque-applying device, lubrication systems, cooling systems, and control systems (Figure 5). The torque-applying device consists of a planetary gearbox and a hydraulic control system. With this device, the desired torque can be applied to the test gearbox. The torque is applied to the ring gear of the planetary gear train by a hydraulic control system that can be automatically controlled during the test. The applied torque results from the difference of the rotation direction and the rate of the sun and ring gears. The number of teeth of the gears is chosen such that, for a given rotation angle of the ring gear, the sun gears rotate in a different direction and at different angles, resulting in torque. Load is applied in the same manner as the planet carrier. The speed of the test gearbox is controlled by an electric motor, and the power supplied from the motor covers the frictional loss of the test rig. Figure 6 shows the scheme of power flow with circulation in a closed loop. In this figure, PL_SG, PL_MG, PL_AG1, PL_AG2, and PL_TAG represent the power losses due to friction between the gears and the bearings inside the three gearboxes and two test gearboxes. From this scheme, it is evident that the slave gearbox is more loaded than the master gearbox.  
Figure 5: Layout of the developed test rig. (**a**) Concept of the developed test rig (**b**) Configuration of the test developed rig.  
The test rig was designed based on the size, capacity, and test requirements of the WTGBs. Figure 5 shows the conceptual design and layout of the apparatus, and Table 1 lists the specifications of the WTGBs that can be tested in the rig. In the case of a WTGB, the rotational speed of the high-speed shaft is usually 1100-2200 rpm and the rated rotational speed of an electric motor is 1180 rpm. Thus, gear ratios of the auxiliary gearboxes were selected such that all WTGBs of 5.8 MW or less could be tested. In the design of the mechanical power circulation test rig, if the slave and master gearboxes do not rotate at the same speed, additional torques will accumulate in the closed-loop, and the test rig could be damaged. Thus, this was considered for the gear ratio selection.  
A 3D model view and a photograph of the developed test rig are given in Appendix A.
## 4 Experiment and Results
### Results
#### 4.2.2 Memory Consumption  
Table 3 shows the results of the memory consumption of different deep-learning-based MVS networks. As can be seen from the table, the memory consumption of CasMVSNet was the lowest. When the resolution of the input image was 1184 \(\times\) 800, the memory consumption of CasMVSNet was 9232 MiB and 500 MiB lower than that of AA-RMVSNet and \(D^{2}\)HC-RMVSNet, respectively. When the resolution of the input image increased to 2160 \(\times\) 1440, the memory consumption between different models was more distinct. Compared with the memory of 9955 MiB of CasMVSNet, the memory consumption of \(D^{2}\)HC-RMVSNet was 4400 MiB higher. The memory consumption of AA-RMVSNet was the largest, which was about 2.3 times that of CasMVSNet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Calculating Camera Parameters**} & \multicolumn{2}{c}{**3D Reconstruction (min)**} \\  & & **1184 \(\times\) 800 Pixel** & **2160 \(\times\) 1440 Pixel** \\ \hline AA-RMVSNet & 61 & 163 & 499 \\ \(D^{2}\)HC-MVSNet & 61 & 124 & 447 \\ CasMVSNet & 61 & 16 & 52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumption of various methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **1184 \(\times\) 800 (MiB)** & **2160 \(\times\) 1440 (MiB)** \\ \hline AA-RMVSNet & 13659 & 22933 \\ \(D^{2}\)HC-MVSNet & 4907 & 14395 \\ CasMVSNet & 4407 & 9995 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Memory consumption of various deep learning methods in different sizes of images.  
The reason for this phenomenon is that CasMVSNet narrows the depth range as the resolution of the feature map increases, building a smaller cost volume at each stage to lower the memory consumption compared to the other two deep learning methods. The \(D^{2}\)HC-MVSNet network using a recurrent neural network instead of 3D CNN for cost aggregation decomposes the whole cost volume into a cost map at each depth. The memory consumption is slightly higher than that of CasMVSNet in low-resolution performance, but with the increase of input image resolution, its consumption will be significantly higher than that of the CasMVSNet model. The AA-RMVSNet model is improved on the basis of the \(D^{2}\)HC-MVSNet model. The Inter-view AA module added to the model performs pixel-level weighted aggregation on the cost volume constructed from multiple perspectives, so the model is higher than the other two models in memory consumption.
# Optimal Collision-Free Grip Planning for Biped Climbing Robots in Complex Truss Environment
## 3 Computation of Operational Regions for Negotiating Obstacles
### Objective: Minimum Climbing Steps and Good Manipulability  
#### 4.1.1 Minimum Climbing Steps  
To form a firm grip, BiCRs must accomplish several procedures, i.e., detecting the target member, adjusting the gripper's orientation accordingly, and then approaching the member and gripping it. According to our experiments, the grip operation normally consumes two-thirds of the time in each climbing cycle [28; 29]. Therefore, minimizing the total number of climbing steps is a promising solution to reduce the time consumption. Suppose \(n\) members are involved in the feasible route, where \((n-1)\) periods of transitions are required. The objective function to minimize the total number of climbing steps can be written as:  
\[\underset{j_{x_{k}},\,y_{k}}{\text{minimize}}\ \sum_{j=1}^{n}\sum_{k=1}^{n_{j}+1} \left(g({}^{j}x_{k},\,{}^{j}y_{k})+1\right), \tag{6}\]  
where \({}^{j}y_{k}\) is generally a grip within the landing segment of an operational region, and \({}^{j}x_{k}\) is another one within the takeoff segment, while \(g({}^{j}x_{k},\,{}^{j}y_{k})\) is the function calculating the minimum number of steps required to move from \({}^{j}y_{k}\) to \({}^{j}x_{k}\). This function will be discussed in detail in Section 4.2.1. It should be noted that the landing grip \({}^{j}y_{k+1}\) is corresponding to the takeoff grip \({}^{j}x_{k}\) in a transiting gait.  
In Equation (6), one step should be counted for performing transitions or negotiating obstacles. For the last member, a virtual step after reaching the destination (as shown in Figure 2) is added to maintain the consistency of the form.
# Improved Durability of Wood Strand-Based Panels Using Guayule
## 2 Materials and Methodology  
### Materials  
The commercial southern yellow pine (SYP; _Pinus_ spp. L.) wood strands used in this study were obtained from West Fraser (Guntown, MS, USA). We chose SYP based on many factors; for example, it is the most abundant, rapidly renewable softwood species resource in the southeastern United States and throughout the entirety of North America, and it has been used as a major construction material due to its high strength per unit weight, anatomical structure, workability, relatively low cost, high-energy content, aesthetic value, and sustainability overall [5]. The pine wood strands were 10.16 cm long and 2.54 to 5.08 cm wide with an average thickness of 0.0715 cm. Previously reported works stated that the content of this type of wood was rich in lignin (27.50% to 32.39% in mature pine) in comparison to other wood species [26]. The industrial wood composite adhesive used in this study was phenol formaldehyde (PF) resin, from Hexion Inc. (Columbus, OH, USA). The pine wood strands were dried in a high-temperature oven at 88 \({}^{\circ}\)C for at least 24 h to reach an average moisture content (MC) of 3.00% and then mixed with PF resin in a drum blender to a target resin content of 5.00% of the oven-dry weight of the pine wood strands. The wood strands' MC was determined to be at 3.00% by using a scientific halogen moisture biomass analyzer and balance equipment.  
The guayule resin was provided by the United States Department of Agriculture (USDA) Agriculture Research Service (ARS) for research purposes, and we discovered that this product is extremely difficult to work with as it is a hard solid at room temperature (see Figure 1a in [PERSON] et al. [19]). Therefore, to reduce the viscosity of the guayule, it was kept in a laboratory conventional oven above 70 \({}^{\circ}\)C as stated by [PERSON] et al. [27] and [PERSON] [28]. To further decrease its viscosity and improve the workability, a guayule-acetone solution was prepared by mixing guayule resin with pure acetone ((CH\({}_{3}\))\({}_{2}\)CO) at a 1:1 weight ratio [19; 29; 30]. The solid content of guayule resin was 100.00%, whereas the guayule-acetone solution had a solid content of \(\sim\)57.00%.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis
### Fault Diagnosis Based on Structural Analysis  
As a model-based fault detection approach, the fundamental aim of structural analysis is to investigate the structural model to identify the subsystems that comprise ARRs (consistency relations, parity relations, and residuals) and to identify the ARRs included in them. The two generally used techniques for structural analysis are the matching of bipartite graphs and structural decomposition. Matching-based structural analysis is mainly used to obtain the ARR. It establishes the matching relationship of the unknown variables, utilizes the matched constraints, solves the unknown variables' analytical expressions, and then brings them into the non-matched constraints to obtain the ARR [24]. Structural decomposition is primarily used to break down the structural model into structurally underdetermined, structurally just-determined, and structurally overdetermined sections by Dulmage-Mendelsohn (DM) decomposition [25]. And the ARR is incorporated into the structurally overdetermined element. The structural detectability and isolation are qualitatively analyzed by analyzing the sensitive relationship between the ARR and the faults and by building a fault characterization matrix [26].
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 2 Lamb Wave Communication Method Based on FDM-PPM
### The FDM-PPM Decoding Method  
Information is transmitted as data packets in the inter-node communication protocol based on the Lamb wave communication sensor network described above. After receiving the FDM-PPM signal, the sensor node first performs equalization on the Lamb wave echo signal to compensate for distortion and attenuation during signal transmission. Subsequently, the node conducts demodulation operations to recover the initially transmitted information from the modulated signal. The decoding workflow of the FDM-PPM signal is illustrated in Figure 6, and its detailed process can be summarized as follows:  
Figure 4: The structure of packets used in the Lamb wave communication protocol.  
Figure 5: Data packet-encoding book: (**a**) header-packet-encoding book and (**b**) data-packet-encoding book.  
Figure 6: Schematic of subcarrier signal modulation.  
Step 1: Pre-capture signals of different combinations of header packets and data packets under single excitation to construct a demodulation atom library.  
Step 2: Demodulate the signal of the header packet and identify the key position within the signal, which serves as the reference for the demodulation of subsequent data packet signals.  
Step 3: Along the time axis, extract the next data packet signal, perform equalization on it, and determine its central position. This position serves as the reference for subsequent data packet echo signals.  
Step 4: Demodulate the signal pulse positions within the data packet to extract the pulse signals' location information.  
Step 5: Carry out a Fourier transform on the signal pulses within the data packet to obtain their amplitude-frequency and phase-frequency characteristics.  
Step 6: Complete the demodulation of the transmitted information based on the location, amplitude-frequency, and phase-frequency information of the FDM-PPM signals in conjunction with the corresponding code table.  
Step 7: Repeat Steps 3 to 6 until all information transmitted by the FDM-PPM communication system is demodulated.
## 3 Federated Kalman Filter Design
### Inertial/Odometer Dead-Reckoning Error Model  
The inertial/odometer navigation calculation algorithm primarily consisted of an attitude updating algorithm and a position updating algorithm. Using the strapdown inertial navigation system error equation, the attitude error equation and position error equation based on the inertial/odometer navigation calculation can be obtained. The main error sources of the navigation calculation are attitude, gyroscope data measurement, and odometer measurement errors.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 5 Experimental Validation
### Crack Identification Using Freequncy Contour-Strip Method
#### 5.2.2 Case 2 and Case 3  
The above procedure is implemented to identify cracks for Case 2 and Case 3. The eventual intersection regions are presented in Figures 14 and 15, respectively. The identified locations and depths for Case 2 and Case 3 are shown in Table 3. As seen from Table 3, the error of identification for crack location is less than 3.4% and that for crack depth is less than 2.5%.  
Figure 13: Frequency spectra of cantilever beam with a crack of the depth, y, increasing from 10% to 40% at the step of 10% at the location of \(x\) = 3.3%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Crack Location**} & \multicolumn{3}{c}{**Crack Depth**} \\ \cline{2-7}  & **Actual** & **Identified** & **Error** & **Actual** & **Identified** & **Error** \\ \hline Case 1 & 3.30\% & 2.95\% & 3.39\% & 10\% & 9.75\% & 2.5\% \\ Case 2 & 46.70\% & 46.00\% & 1.50\% & 20\% & 20.30\% & 1.5\% \\ Case 3 & 86.70\% & 83.85\% & 3.10\% & 40\% & 39.00\% & 2.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three crack cases with identified crack location and depth.  
These three cases verified the frequency contour-strip method to detect the cracks effectively, which were located near the fixed end, in the central part and near the free end of the beams, respectively. These cases illustrate the availability of the proposed method to detect the crack with about 3.7% errors of the testing frequencies. According to the research of [PERSON] et al., the error of the damage correction factor in this research shown as Equation (7b) is less than 0.5% for all crack depths. That is to say, the frequency contour-strip method is applicable, when the vibration equation can characterize the cracked beam.  
Figure 14: Intersection region R for Case 2.  
Figure 15: Intersection region R for Case 3.  
In fact, when the crack is very small, the frequency contour-strip will be wide, even though the error of the testing frequency is small. The main reason is that the frequency is not sensitive to the little small crack of the beam. Therefore, the relative error of the proposed method may become bigger when the crack is much smaller.
## 2 Circuit Design
Balun-2 attains the best AI of 0 dB at 27.5 GHz and AI within \(\pm 1\) dB for 23.5-30.6 GHz, as well as the best PD of \(0^{\circ}\) at 26.4 GHz and PD within \(\pm 5^{\circ}\) for 24.1-32.6 GHz. Figure 6b shows the simulated RF-port reflection coefficient (\(\rm S_{11}\)), LO-RF isolation, CG, and NF of the mixer. The mixer achieves a minimum \(\rm S_{11}\) of \(-32.9\) dB at 39 GHz and \(\rm S_{11}\) better than \(-10\) dB for 21.7-44.5 GHz (i.e., \(\rm f_{10 dB}\) of 22.8 GHz). The good \(\rm S_{11}\) is attributed to the decent input matching of the balun (see Figure 5a) and good matching between the balun outputs and the differential RF inputs. Due to the symmetrical layout and \(\rm C_{by}\) of the mixer core, the mixer achieves decent LO-RF isolation of 44.9 dB at 28 GHz and 40.3-62.4 dB for 0-50 GHz. Moreover, the mixer achieves a maximum CG of 15.8 dB at 26 GHz and CG of 14.3 \(\pm\) 1.5 dB for 14.3-32.5 GHz (i.e., \(\rm f_{3 dB}\) of 18.2 GHz). The decent CG and \(\rm f_{3 dB}\) are attributed to the CCPT-RL-based core IF load and the gain-enhanced gm stage. The mixer achieves a minimum NF of 6.2 dB at 24 GHz and NF of 6.2-9.3 dB for 14-33 GHz. The good NF of the mixer is attributed to its high CG and simultaneous noise and \(\rm Z_{in}\) matching of the gm stage.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **System Model** & **Usering Pairing** & **Optimization Method** & **Limitations** \\ \hline
[15,16] & Multi BS cell + K users & Mean clustering & Branch-and-bound method & Fixed power allocation lead to suboptimal solution \\
[18] & Multi BS cell + K users & Greedy algorithms & Pareto-boundary & Fixed power allocation lead to suboptimal solution \\  & & & & \\
[20] & Multi BS cell + K vehicles & Random & Analytic techniques & Channel difference of user pairing cannot be guaranteed \\
[21] & Multi BS cell + K users & Random & Successive convex approximation & Random user pairing will affect the transmission rate of users \\
[24] & Cognitive radio networks & Random & Analytic techniques & No user pairing method and CR introduces signal interference between networks \\
[26] & A fixed satellite + BS + k & No & Slotted Aloha method & Users number is limited by OMA method \\
[27] & Satellite + k UAVs & Random & Heuristic algorithm & Random user pairing will affect the transmission rate of UAV \\
[28] & Satellite + k users & Random & Iterative optimization & Channel difference of user pairing cannot be guaranteed \\  & & & & \\
[29] & Satellite + k users & Channel correlation coefficient & A suboptimal algorithm based on alternate direction & User pairing method will not guarantee stable pairing \\
[30] & Fixed satellite + k earth & Stagger and fold & Microtion & relationship \\  & stations & method & & The impact of satellite \\
[34] & Satellite + BS cell + K & Channel correlation & Iterative scheme based on Karush-Kuhn-Tucker approaches & User pairing method will not guarantee stable pairing \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: NOMA for integrated terrestrial–satellite networks.
## 6 Data Quality Analyze 5 (DQA5): Applicable Almanac Period
The middle figure presents the elevation angle error which relates to using the almanac to select the satellites in view. The bottom plot of Figure 19 is the original analysis, which shows the almanac positioning error. The velocity and elevation angle still have enough accuracy after one week. The receiver can use the almanac of an age less than one week to shorten the TTFF. For the other constellation satellites, the user cannot continuously receive the ephemeris data due to the fact that the satellite will move outside the line of sight. In the following analysis, the maximum positioning error when the satellite ephemeris can be used during one week is calculated. As shown in Figure 19, first, one week of ephemeris and almanac data is used to calculate the satellite positioning difference for each satellite. After that, each positioning difference is compared to select the maximum satellite PRN number and record the occurrence time for that satellite. This selected satellite represents the maximum almanac error during this week. Finally, the maximum satellite information _i.e_., the velocity error, elevation angle error and the positioning error for two hours is calculated. These two hour analysis results are composed of one hour of data before the maximum positioning error and one hour of data after the maximum positioning error. After generating all of the results, it is necessary to go back to the first step and calculate the data for the following week. Figure 19: BDS DQA1: The flow chart for generating the applicable almanac period. Figure 18: BDS GEO almanac using period test for one week. The following results presents the almanac use period for a five-week estimation. Figure 20 shows the color information for the following results: Figure 21 is the GPS five week estimation, and the statistical results are shown in Table 7. The velocity error for the maximum satellite at the fifth week is under 20 m/s. It is still far away from the threshold, which is 1427.203 m/s according to the calculation discussed in the previous paragraph. The elevation angle error for the fifth week is not more than 0.5 degrees, which is also accurate enough for satellite selection. Finally, the positioning error for the fifth week is more than 150,00  
improved. Until this problem is solved, this research suggested that users update the BDS almanac information every time the user receives the BDS signal. Figure 23: BDS DQA1: BDS IGSO almanac using period test for five weeks. Figure 16:0 BDS MEO almanac using period test for five weeks. Figure 16:1 BDS DQA1: BDS GEO almanac using period test for five weeks.
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 5 Results and Discussion
By obtaining a better understanding of the model, it  
The attributes that most influence the model's prediction are the appointment's month (especially September, July and August) and whether or not the patient was contacted before the appointment. This can reinforce the previous data analysis that indicated that in rainy or cooler days patients tend to miss their medical appointments. On the other hand, when the patient is encouraged to confirm their scheduled appointment few days before by a contact from the health centre, their chance to miss their appointment falls drastically. However, despite the importance of these features, even after removing the other features from the dataset and generating a new model with the same algorithm with the most relevant attributes, the models had the same performance. A key strength of this study is to elucidate a new way to reduce costs and improve patient care in Brazilian health care system. Without the SUS, the 78% of the population that does not have private insurance would not have had proper access to health services. In a large health system as in Brazil, the impact of solutions that use machine learning to improve the access to the services is huge. While most studies focus on the private health system, this study has included records from the public health care system in Brazil. Besides that, the dataset analyzed was restricted mainly to patients with motor disabilities and underage patients. Solutions involving treatment of patient data privacy should also be considered, as described in [45; 46]. This can be challenging because is a sample with very specific characteristics and maybe could not be properly generalized to other health  
Figure 19: Feature importance in Random Forest model. Figure 18: AUC-ROC curve performance. specialities. Although, as a starting point for validation of predictive models for medical no-show problem, this work can be valuable.