esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 4 Results
### Performances of Different Detection Algorithms  
To evaluate the performance of the proposed method, we tested several detection algorithm methods, including Faster R-CNN, RetinaNet-Res50, YOLOv3-tiny, and YOLOv5s.  
As shown in Table 2, the mAP of the proposed method was 89.8%, which was 8.2% and 6.9% higher than those of Faster-RCNN and RetinaNet-Res50, respectively. Moreover, Faster-RCNN and RetinaNet-Res50 were not suitable for embedded devices. YOLOv3-tiny was similar to YOLOv7-tiny in model size, but the mAP of the latter was 12.5% higher. The main reason was that YOLOv3-tiny had only two outputs of different sizes. YOLOv7-tiny had three outputs, which resulted in better performance in detecting small objects. Therefore, the proposed method had better performance in detection speed and accuracy.  
Figure 12 shows the detection results of the different models. When the YOLOv3-tiny model detected the small object \"orange\", the detection would be missed, resulting in an error, which was consistent with the results in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AP\%**} & \multirow{2}{*}{**mAP\%**} & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**Speed** **(ms/img)**} \\ \cline{2-2} \cline{6-6}  & **Basket** & & & & & \\ \hline YOLOv7-tiny & 97.5 & 76.5 & 92.7 & 88.9 & 13.2 & 119 \\ YOLOv7 + FL & 97.4 & 77.1 & 95.5 & 90.2 & 13.2 & 119 \\ YOLOv7 + FL + Prune & 97.2 & 77.1 & 95.1 & 89.8 & 6.7 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of ablation experiment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AP\%**} & \multirow{2}{*}{**Size** **(MB)**} & \multirow{2}{*}{**GFLOPs**} \\ \cline{2-2} \cline{6-6}  & **Basket** & & & & & \\ \hline Faster-RCNN & 88.9 & 72.4 & 83.7 & 81.6 & 165.8 & - \\ RetinaNet-Res50 & 90.7 & 72.8 & 84.3 & 82.9 & 145.8 & 156 \\ YOLOv3-tiny & 95.0 & 44.6 & 89.6 & 76.4 & 17.4 & 12.9 \\ YOLOv5s & 96.4 & 77.1 & 91.3 & 88.2 & 14.4 & 16.4 \\ YOLOv7-tiny & 97.5 & 76.5 & 92.7 & 88.9 & 12.3 & 13.2 \\ Proposed Method & 97.2 & 77.1 & 95.1 & 89.8 & 6.3 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different algorithms.
As soon as an athlete has returned to the baseline values, it is assumed that he/she has physically recovered from the exercise stimulus [10]. Recovery kinetics and recovery time primarily depend on the intensity, duration, extent, and type of the preceding exertion as well as the athlete's performance capability and individual predisposition matrix [13]. Research that has addressed the recovery kinetics of various performance parameters after acute training loads has mainly compared groups of young (ages of mostly \(<\) 30 years) and older (ages of mostly \(>\) 50 years) athletes (for a review, see [7]). However, previous research findings on the recovery of young and older trained athletes do not apply to multidirectional team sports directly, partly because most research studies include older people over the age of 50, which does not represent the age span typical for team sport athletes. Therefore, it is still unclear how the age composition of a main team may influence player recovery following training sessions. Findings of this study may assist coaches with planning training sessions to ensure optimal physiological states for players and promote training adaptation with reduced risk of injury. The current study explored whether physical performance and recovery are affected by age, within an age span typical for team sport athletes, after a high-intensity interval exercise. Recovery processes from repeated sprint interval exercise, both on a physiological and on a psychological level, were investigated as a function of age in semi-professional handball players. More specifically, the assumption was that older players would regain their maximum jump height at a later point in time (due to impaired muscle recovery) and that their creatine kinase (CK) value would decrease more slowly after repeated sprint intervals and return to baseline later, compared to young players. Furthermore, it was expected that the older players' vagal HRV as indicated by the root mean square of successive differences (rMSSD), would return to its baseline value later in time and they were expected to feel muscle soreness for a longer period of time compared to the young players. It was also assumed that, due to the delayed recovery as a consequence of the repeated sprint interval exercise, increased skeletal muscle damage, measured by the free CK in the blood, would be observable in the older players compared to the young players.
## 6 Key Outcomes
### Nomenclature  
\begin{tabular}{l l} \(d/dt\) & Material time derivative \\ \(d\) & Body force \\ \(\overset{\leftrightarrow}{T}\) & Cauchy stress tensor \\ \(p\) & Pressure \\ \(\overset{\leftrightarrow}{I}\) & Identity tensor \\ \(\overset{\leftrightarrow}{\tau}\) & Extra stress tensor for [PERSON] fluid model \\ \(\mu_{\infty}\) & Infinite shear rate-limiting viscosity \\ \(\mu_{0}\) & Zero shear rate-limiting viscosity \\ \(\Gamma\) & Material time constant \\ \(\Lambda_{1}\) & First Rivilin-Erickson tensor \\ \((X_{1},X_{2})\) & Space variables \\ \(\widetilde{V}=(U_{1},U_{2})\) & Velocity field \\ \(\
u_{1}\) & Kinematic viscosity \\ \(B\) & Applied magnetic field strength \\ \(\sigma\) & Fluid electrical conductivity \\ \(\rho\) & Fluid density \\ \(\mu\) & Dynamic viscosity \\ \(K_{p}\) & Permeability of the porous medium \\ \(\widetilde{T}_{\infty}\) & Ambient temperature \\ \(\widetilde{T}\) & Fluid temperature \\ \(Q_{1}\) & Heat generation/absorption coefficient \\ \(c_{p}\) & Specific heat at constant pressure \\ \(k\) & Thermal conductivity \\ \(\widetilde{C}_{\infty}\) & Ambient concentration \\ \(\widetilde{C}\) & Fluid concentration \\ \(D_{c}\) & Mass diffusivity \\ \(k_{1}\) & Chemical reaction rate \\ \(D_{1}\) & Thermal slip factor \\ \(L_{1}\) & Velocity slip factor \\ \(b\) & Stretching rate \\ \(V=(U,V)\) & Velocity field (dimensionless) \\ \((X,Y)\) & Space variables (dimensionless) \\ \(\Psi\) & Stream function \\ \(\epsilon\) & Small parameter \\ \(\lambda_{i=1...6}\) & Real numbers \\ \(F_{m}{}^{\prime}(\lambda)\) & Dimensionless velocity \\ \(T_{w}(\lambda)\) & Dimensionless temperature \\ \(C_{w}(\lambda)\) & Dimensionless concentration \\ \(Pm\) & Porosity parameter \\ \(Mg\) & Magnetic field parameter \\ \(Wb\) & Weissenberg number \\ \(Hr\) & Heat generation parameter \\ Sc & Schmidt number \\ Pr & Prandtl number \\ \(Rs\) & Chemical reaction parameter \\ \(Ts\) & Thermal slip parameter \\ \(Vp\) & Velocity slip parameter \\ \(Hs\) & Heat absorption parameter \\ \(\text{ShD}=-C_{w}{}^{\prime}(0)\) & Sherwood number \\ \(\text{Nm}=-T_{w}{}^{\prime}(0)\) & Nusselt number \\ \(\text{Skin-FC}=\sqrt{\text{Re}}C_{F}\) & Skin friction coefficient \\ \end{tabular}
# Low Delay Inter-Packet Coding in Vehicular Networks
## 1 Introduction
### Convolutional Codes for Inter-Packet Coding  
A specific feature of packets transmission over wireless is that both burst and isolated packet losses (erasures) happen. Many papers address this problem (see, for example, [9; 10] and references therein). However, low delay and low complexity requirements narrow down the list of coding techniques suitable for this application [11]. Moreover, the important feature of drive-thru scenario is a short residence time of a vehicle in the coverage area of the RSU. This restriction makes convolutional coding practically the only suitable solution to the problem.  
Thorough analysis of state-of-the-art solutions for reliable data transmission in V2V and V2R communication scenarios is presented in [12]. Moreover, the batched sparse code (BATS code) [13] based protocol is suggested. BATS consists of an outer code and an inner code. The outer code represents matrix generalization of the fountain code (see [14]) and the inner code is random linear network code. The two classes of convolutional codes studied below can be suggested as low-delay alternatives to fountain codes in BATS code construction.  
Another specific feature of drive-thru scenario is the existence of entrance and exit zones with extremely poor channel conditions which can be characterized by a high probability of communication failure. Since error correction coding at physical level cannot improve quality of communication in these areas, there is a need for packet level error correction. By recovering lost packets at this coding level, we increase a zone and/or an interval of reliable communication [6; 7; 8]. To take into account nonstationary channel conditions, we introduce a new performance measure called successful delivery function (SDF). In what follows, the studied codes are compared with respect to both conventional criteria, such as frame error rate (FER) and bit error rate (BER), and the SDF criterion.  
In this paper, we study two classes of high-rate convolutional codes with sliding-window (SW)-decoding. The idea to apply convolutional codes to correct packet losses in different networks was previously considered in [9; 11; 15]. We compare the SW-decoding performance of the binary Wyner-Ash (WA) convolutional codes [16] and the Reed-Solomon (RS)-convolutional codes [17]. In particular, we analyze performances of SW-decoding based on maximum-likelihood (ML) decoding (SWML) and belief propagation (BP) decoding (SWBP) of window zero-tail terminated (ZT) convolutional codes [18]. A modified low-complexity iterative decoding procedure is suggested. It is shown that extending the parity-check matrix of the ZT code by a very limited number of redundant parity-checks (without changing the code) leads to the coincidence of the ML and BP decoding performance for this code.
## 4 Conclusions  
The reactions taking place on the surfaces of nanostructured Cu/CuO and Cu/CuO/Pd electrodes at different potential, \(E\), values in the solutions of 0.1 M KOH in normal and heavy water were studied using the surface enhanced Raman spectroscopy technique. The following conclusions can be drawn:  
1. A clear correlation between the SERS spectra and potential governed surface state of the CuO/Pd electrodes was determined. However, due to the dipole nature of the electron-ion pair, the Raman signatures were expected to be weak.
2. The SERS spectra measured at the controlled electrode potential of the black-CuO/Pd electrode were consistent with the existence of weakly adsorbed (H\({}_{3}\)O\({}^{+}\))\({}_{\text{ad}}\) and (H\({}_{2}^{+}\))\({}_{\text{ad}}\) ions as intermediate in the reversible HER/HOR processes in the region of \(E\) potentials from +0.3 V to 0 V (SHE). Raman signatures at the wavenumber region from 1320 to 1570 cm\({}^{-1}\) were caused by inelastic light scattering from weakly bound ionic pairs, which have interaction energy of 15-21 kJ mol\({}^{-1}\). These adsorption compounds cannot be treated as Pd hydrides (see Appendix A for different HER scenarios).
3. In accordance with the electrochemical redox behaviour, SERS bands observed at wavenumbers 125-146 cm\({}^{-1}\) and 520-565 cm\({}^{-1}\) in 0.1 M KOH solution in the range of the potentials between 0.7 and \(-\)0.5 V (RHE) corresponded to Cu(I) and Cu(II) oxygen species.  
Conceptualization, [PERSON] and K.J.; methodology, G.N. and I.M.; SERS experiments, G.N. and I.M.; electrochemical experiments, B.S. and I.S.; preparation of electrodes, A.B.; writing, original draft, J.J.; visualization, S.J.; discussion and data analysis [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; all authors contributed to the final editing of the manuscript.  
This research was partially funded by ARC Grant Number DP190103284.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Acquisition, 3D Landmark Determination, and Image Reorientation  
CBCT data were acquired using the Alphard3030 (Alphard Roentgen Ind., Ltd., Kyoto, Japan) at 80 kVp and 10 mA and with a \(200\times 200\) mm field of view at two different times, namely before and 1 year after surgery. The voxel size was 0.39 mm. The CBCT images were converted to DICOM 3.0 files and stored on a Windows-10 based workstation (Intel Core i7-4770, 32 GB). Using the medical studio of Invivo 6 (Anatomage, San Jose, CA, USA), eight 3D anatomical landmarks were selected in accordance with the procedure used in previous studies (Table 1) [12; 13; 17].  
The images were reoriented using two reference planes: the MSP, passing through the nasion (N) and the sella (S), and perpendicular to the Frankfort horizontal plane (FHP). FHP passed through the center of the bilateral portions (Po) and the left orbitale (Or), according to the Frankfort Cranitic Agreement of August 1882, among a variety of options in this study.  
After configuration of the two reference planes and hard tissue landmarks via the Invivo 6 software as described above, the coordinates of 3D landmarks and the binarization thresholds of bone (320-520 HU) were saved in an Excel file format. A custom program using MATLAB 2019b (MathWorks, Natick, MA, USA) was used for successive image processing for all methods in this study [17; 19].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Landmark** & **Definition** \\ \hline N (nasion) & Midpoint of nasofrontal suture \\ S (sella) & Center of sella turcica \\ Or (orbitale) & Most inferior point of the lower margin of the orbit \\ Po (porion) & Most superior point of the external auditory meatus \\ Me (menton) & Most inferior midpoint on the symphysis \\ B (supramental) & Midpoint of greatest concavity on the anterior border of the symphysis \\ G (genial tubercle) & Midpoint on genial tubercle \\ Mf (mental foramen) & Midpoint of mental foramen \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anatomical landmarks.
###### Abstract  
A single-shot three-dimensional measuring method based on quadrature phase-shifting color composite grating projection is proposed. Firstly, three quadrature phase-shifting sinusoidal gratings are encoded in red (R), green (G), and blue (B) channels respectively, composed single- frame color composite grating. This color composite grating is projecting obliquely on the object by DLP. After that, the color camera which is placed in a specific location is used to capture the corresponding color deformed pattern and send it to the PC. Then, by color separation, the color deformed pattern is demodulated as the corresponding three-frame monochromatic deformed patterns with a shifted quadrature phase. Due to the existences of sensitivity differences and color crosstalk among the tricolor channels, we propose a gray imbalance correction method based on the DC component's consistency approximation. By the established 3D reconstruction physical model, the measurement of 3D shape can be achieved. Many experimental results for static and moving objects prove the proposed method's feasibility and practicability. Owing to the single-shot feature of the proposed method, it has a good application prospect in real-time and high-speed 3D measurement.  
three-dimensional measurement; real-time three-dimensional measurement; color encoding grating; color crosstalk +
Footnote †: journal: Applied Sciences  
Article  
A Single-Shot 3D Measuring Method Based on Quadrature Phase-Shifting Color Composite Grating Projection  
2021  
112522[[https://doi.org10.3390/app11062522](https://doi.org10.3390/app11062522)]([https://doi.org10.3390/app11062522](https://doi.org10.3390/app11062522))
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## 3 Results and Discussion  
All datasets used in the comparison are produced by following the procedure provided in Figure 6 (Section 2.3.1). The complete properties of the AIS dataset collected under nadir and maneuvered conditions are shown in Table 1.  
As shown in Table 1, for each maneuver, a total 33 h of continuous acquisition has been performed. The 33 h of duration was allocated to provide 11 h of acquisition before the maneuvering, during the maneuvering, and after the maneuvering, respectively. It is also shown that each maneuver was separated by different time distances, e.g., 2 days between Run-1 and Run-2 and 1 day between Run-3 and Run-4. This time separation has been chosen by considering other missions that have to be accomplished by LAPAN-A2 satellite. However, the existence of this separation is not an issue since the observation is not intended to be performed on a similar ship at all but rather to the ship on a similar location. Therefore, as long as the area covered by the satellite between the maneuvers is similar, the start and end time of the observation can be chosen freely. In addition, the problem might be faced if one uses overlapped datasets in which the message recorded in a dataset is re-used by another dataset.  
Figure 8: The re-detection probability of LAPAN-A2 AIS generated using dataset acquired on 1 October 2018, 00:00:00 UTC to 2 October 2018, 23:59:56 UTC.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 4 Health Monitoring Techniques for Wind Farms
### Health Monitoring System of Blades  
The most important part of the wind energy generation and also the ones that can be most easily affected by damages are the blades of a wind turbine. They are made up of a wide range of elements that are combined together. They also need to maintain high flexibility, and thus, any damage that they face gets easily transmitted to the whole blade, causing them to wear easily. The damage spreads as cracks and delamination. The damage can be harmful because it can cause imbalance, which will not only disrupt the readings in general but also cause major catastrophic events leading to destruction of the wind farm.  
These are the reasons that make the monitoring of the damage of the blades so important. The first stage is to find out the way for perfect monitoring, which would include testing and analysis. [PERSON] in 1994 analyzed the blade with non-destructive techniques: acoustic emission and the coherent optical technique. The technique for blade analysis continued including neural network systems and wireless sensor networks [171; 172; 173].  
One of the major problems in creating monitoring systems for a blade is the dynamic conditions faced by blades. Static systems can in no way emulate the ever-changing conditions a blade has to face. It almost makes it necessary to have a monitoring system for the monitoring system itself. The wind harvester is an acoustic emission-based telemetric system, e.g., NEG-MICON NM 48/750, which can collect data for about half a year [174; 175; 176].  
Furthermore, the wind turbine changes a lot of its properties when it faces damage, and thus, the damage receptor and interpreter will also have to be very adaptive while looking at the damage and thus inherently change the parameters for reading the damage as the damage increases. Based on this observation, a system to adapt to changes was introduced known as the SMART Wind Turbine Rotor project, which integrated a number of condition-analyzing tools that could provide real-time analysis on the blade conditions; it was developed by the Sandia National Laboratory [177]. An even better version known as the fibrous Bragg grating sensor attracted attention due to being unaffected by the electromagnetic radiations and thunder; it also was more durable and required lesser maintenance [178]. Modified forms of the above ideas integrated into different materials and locations were utilized for better and more accurate results.  
Thermal and electromagnetic technologies are also not lagging behind when it comes to monitoring. Apart from the mechanical methods, [PERSON] has made use of strain gauges, photogrammetry, and laser interferometry to identify the blades according to several conditions tested at different speeds [179]. A laser thermographic system also became popular in being able to analyze the damage and simulate it in a different place [180; 181].
# Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
## 5 Discussion  
### The Calculation Method for'm' in Multilayered Foundations  
The quality of the soil within a specific depth range \(h_{m}\), close to the ground or the lowest erosion line, significantly influences the lateral soil resistance of the pile. Therefore, an average value '\(m\)' for the foundation coefficients of various soil layers within the \(h_{m}\) depth can be determined. This can be achieved either as a weighted average based on layer thickness or by ensuring equal areas before and after conversion to represent the'm' value throughout the entire buried depth of the pile. As illustrated in Figure 18, in cases where there are three soil layers surrounding the pile, the areas under the original and converted foundation coefficient curves are equal within the \(h_{m}\) depth, allowing '\(m\)' to be determined as follows:  
\[m=\frac{m_{1}{h_{1}}^{2}+m_{2}(2h_{1}+h_{2})h_{2}+m_{3}(2h_{1}+h_{2}+h_{3})h_ {3}}{h_{m}^{2}} \tag{32}\]  
If there are only two different foundation layers, it can be expressed as:  
\[m=\frac{m_{1}h_{1}+m_{2}(2h_{1}+h_{2})h_{2}}{h_{m}^{2}} \tag{33}\]  
In the equation:  
\[h_{m}=\left\{\begin{array}{ll}2(d+1)(H>2.5/\alpha),\\ H(H\leq 2.5/\alpha),\end{array}\right. \tag{34}\]  
'\(\alpha\)' is the pile's deformation coefficient (flexibility factor).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Load (kpa)** & & \multicolumn{2}{c}{**Numerical**} & \multirow{2}{*}{**Error**} \\ \hline  & Calculated Value & Positive Shear & \(5.973\) & \\
20 & Experimental & Positive Shear & \(-2.085\) & \\  & Value & Positive Shear & \(6.545\) & \\  & Calculated Value & Negative Shear & \(-2.258\) & \\
40 & Experimental & Positive Shear & \(7.366\) & \\  & Value & Negative Shear & \(-2.535\) & \\  & Experimental & Positive Shear & \(8.024\) & \\  & Value & Negative Shear & \(-2.947\) & \\  & Calculated Value & Positive Shear & \(8.188\) & \\
60 & Experimental & Negative Shear & \(-2.986\) & \\  & Experimental & Positive Shear & \(8.792\) & \\  & Value & Negative Shear & \(-3.358\) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum shearing force values under different loads.
## 2 Structure and Working Principles of the SC-EPS System  
The structure of the SC-EPS system is shown in Figure 1, including the torque/angle sensor, recirculating ball steering gear, assist motor, controller, DC-DC converter and supercapacitor. The supercapacitor was connected in parallel with the vehicle power system through the DC-DC converter to form the hybrid power system.  
Figure 1: Structure of the SC-EPS system.  
The SC-EPS system has three power supply modes: Hybrid power supply mode, vehicle power supply mode and supercapacitor power supply mode. When the vehicle speed is low, the SC-EPS system is in hybrid power supply mode. In this case, the vehicle power system and supercapacitor jointly provided electric power to the assist motor, the controller determines the mode and proportion of power distribution according to the signals, such as the vehicle speed and steering wheel angle. When the vehicle speed is high, the SC-EPS system is in vehicle power supply mode, which means the vehicle power system solely provides electric power to the assist motor. Meanwhile, the supercapacitor is in charging state to ensure that the stored energy can be used during low-speed steering. When the vehicle power system fails, the SC-EPS system is in supercapacitor power supply mode, under which the supercapacitor emergently provides electric power to the assist motor to maintain the steering assist for a short time.