esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU Accelerated PIC and SIC for OFDM-NOMA
###### Abstract  
Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme for the fifth-generation (5G) cellular networks. In NOMA systems, all users operate at the same frequency and time, which poses a challenge in the decoding process at the receiver side. In this work, the two most popular receiver structures, successive interference cancellation (SIC) and parallel interference cancellation (PIC) receivers, for NOMA reverse channel are implemented on a graphics processing unit (GPU) and compared. Orthogonal frequency division multiplexing (OFDM) is considered. The high computational complexity of interference cancellation receivers undermines the potential deployment of NOMA systems. GPU acceleration, however, challenges this weakness, and our numerical results show speedups of about from 75-220-times as compared to a multi-thread implementation on a central processing unit (CPU). SIC and PIC multi-thread execution time on different platforms reveals the potential of GPU in wireless communications. Furthermore, the successful decoding rates of the SIC and PIC are evaluated and compared in terms of bit error rate.  
Keywords:non-orthogonal multiple access; successive interference cancellation; parallel interference cancellation; orthogonal frequency division multiplexing; graphics processing unit; CUDA +
Footnote †: journal:  
_Article_  
GPU Accelerated PIC and SIC for OFDM-NOMA
## 3 Test Data Analysis
### Frequency-Division Vibration Level Analysis
#### 3.3.2 Track Parts Comparison  
According to the analysis in Section 3.3.1, the frequency-division vibration levels of rail, track bed, and tunnel walls will be different under different working conditions. Therefore, the frequency-division vibration levels of the rail components under each working condition are compared, as shown in Figure 18. Under the working conditions of the same track type, the distance between the frequency-division vibration level curves of the straight line and the curved segment is relatively close. Each frequency band of the cross-frequency vibration-level curve in the curved segment is slightly larger than the straight line segment, and the difference is significant in the high-frequency band.  
According to Figure 18a, although the restraint conditions at the bottom of the track bed are different, the vibration level curves of the rail under the four working conditions are very close, and the vibration level curve basically shows the phenomenon that the vibration level of high frequency is larger. The peak point of ordinary monolithic track appears at 1600 Hz, while the peak point of the steel-spring-floating slab track is located at 2500 Hz, and the shape of the rail frequency-division vibration level curve of the same track structure is basically the same.  
According to Figure 18b, it can be seen that in most of the segments (especially above 6.3 Hz), the frequency-division vibration level curve of the steel-spring-floating slab track is larger than that of the ordinary monolithic track, and the vibration level of the high-frequency band in the curved segment is significantly larger than that in the straight line segment. Under the influence of line conditions, the frequency-division vibration-level curve of the ordinary monolithic track for frequencies above 315 Hz has multiple peak points, which are significantly larger than those of the straight line segment ordinary monolithic track. It even exceeds the steel-spring-floating slab track at 1250 Hz, indicating that the vibration of the ordinary monolithic track is more susceptible to line conditions.  
Figure 17: Cont.  
According to Figure 18c, it can be seen that the sub-frequency vibration level of the tunnel wall at the steel-spring-floating slab track acceleration is significantly smaller than that of the ordinary monolithic track in most frequency bands. However, in the low-frequency range below 16 Hz, the frequency-division vibration level of the steel-spring-floating slab track is larger than that of ordinary monolithic track, and the frequency-division vibration level of the ordinary monolithic track above 2000 Hz is slightly larger than that of the steel-spring-floating slab track. It can be seen that the vibration damping effect of the steel-spring-floating slab track is mainly reflected in the range of 16-2000 Hz.
## 7 Conclusions  
In this paper, a direct georeferencing system has been presented, which is intended to provide highly accurate positions and attitudes (better than 5 cm and 0.5\({}^{\circ}\)) of a micro- and mini-sized UAV in real time. The paper has mainly been focused on the attitude determination with this system. For the attitude determination MEMS IMU readings, magnetic field observations, a GPS baseline and a GPS position measurement are integrated in a sixteen-state Kalman filter. To be able to resolve the ambiguities of the single-frequency GPS baseline reliably and instantaneously, the float solution has been improved. For the integer ambiguity estimation, the MLAMBDA method and the AFM are combined in a suitable way.  
With a test measurement, where a navigation-grade INS served as a reference, it could be shown that the implemented algorithms allow for attitude accuracies of about 0.05\({}^{\circ}\) for the roll and the pitch and 0.2\({}^{\circ}\) for the yaw angle during kinematic applications. With the developed ambiguity resolution algorithms, the ambiguities of the single-frequency GPS attitude baseline could be resolved instantaneously in more than 90% of the epochs. It could also be shown that short-term losses of the GPS baseline can be bridged successfully by the inertial sensors without a deterioration of the attitude accuracy.  
As a final conclusion, the use of a low-cost single-frequency GPS baseline can significantly improve the attitude determination of micro- and mini-sized UAVs, even in non-ideal GPS measurement environments.
## 3 Results
### Intrasubject Models
#### 3.1.1 sEMG Models
However, this model could estimate the plantar flexor peak moment value and had better time synchronization with the ID results than the other two models. RCNN (TA+GM+PB MAV+WL) and RCNN (TA+SOL+GM+PB WL) were the best intrasubject models trained on sEMG features. Therefore, these two models were used for training intersubject models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Volunteer**} & \multicolumn{2}{c}{**MLP (TA+SOL+GM+PB MAV+WL)**} & \multicolumn{2}{c}{**RCNN (TA+GM+PB MAV+WL)**} & \multicolumn{2}{c}{**RCNN (TA+SOL+GM+PB WL)**} \\  & \(R^{2}\) & **RMSE (Nm/kg)** & **NRMSE** & \(R^{2}\) & **RMSE (Nm/kg)** & **NRMSE** & \(R^{2}\) & **RMSE (Nm/kg)** & **NRMSE** \\ \hline S1 & 0.9514 & 0.103 & 0.056 & 0.9605 & 0.092 & 0.051 & 0.9761 & 0.072 & 0.039 \\ S2 & 0.9747 & 0.076 & 0.041 & 0.9791 & 0.069 & 0.038 & 0.9834 & 0.062 & 0.034 \\ S3 & 0.9754 & 0.067 & 0.039 & 0.9619 & 0.084 & 0.048 & 0.973 & 0.07 & 0.041 \\ S4 & 0.9495 & 0.099 & 0.061 & 0.9621 & 0.086 & 0.052 & 0.9538 & 0.094 & 0.058 \\ S5 & 0.9655 & 0.076 & 0.045 & 0.9992 & 0.082 & 0.049 & 0.9752 & 0.064 & 0.038 \\ S6 & 0.9634 & 0.075 & 0.049 & 0.962 & 0.076 & 0.05 & 0.973 & 0.064 & 0.022 \\ S7 & 0.9499 & 0.073 & 0.051 & 0.9455 & 0.076 & 0.053 & 0.9479 & 0.074 & 0.052 \\ \hline \hline Summary & \(96.14\%\pm 1.13\%\) & \(0.081\pm 0.014\) & \(0.0489\pm 0.0079\) & \(96.15\%\pm 0.98\%\) & \(0.081\pm 0.008\) & \(0.0487\pm 0.005\) & \(96.89\%\pm 1.29\%\) & \(0.071\pm 0.011\) & \(0.0434\pm 0.0085\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: sEMG Intrasubject top three models’ results. The first model was a multi-layer perception (MLP) model trained by extracting the mean absolute value (MAV) and waveform length (WL) features from the tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) muscles. The second model was a recurrent convolutional neural network (RCNN) model trained by extracting the MAV and WL features from TA, GM, and PB muscles. The third model was an RCNN trained by extracting the WL feature only from TA, SOL, GM, and PB muscles. Figure 4: This figure compares the estimated S1 (first volunteer) ankle moments from the inverse dynamic (ID) Equation (7) against intrasubject models (MLP, RCNN, LSTM) using the mean absolute value (MAV) and waveform length (WL) features extracted from tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) sEMG signals. The x-axis represents the normalized gait cycle, starting from the swing phase and ending with the subsequent swing phase. The y-axis shows the normalized ankle moment. Positive values indicate plantar flexion, while negative values indicate dorsiflexion. The shaded areas represent the standard deviation of the estimations across subjects.
## 4 Proposed System Design  
The architecture of the proposed OAM/OCDMA-FSO system is given in this section. It consists of transmitter, channel, and receiver.
# Smart Cities in Russia: Current Situation and Insights for Future Development
## 4 Smart City Components
### Smart Environment  
The environment is everything that surrounds the citizens daily. In a smart city, the environment should be interactive (in terms of information technology). Furthermore, it should increase the citizen's quality of life. A smart environment is the composition of the following factors.  
1. **Smart buildings:** Buildings are a key component of any city. In a smart city, buildings should be energy efficient and secure. Security includes monitoring the building's energy state and fire control system, as well as personality identification at the entrance.  
Energy efficiency can be achieved via the energy-saving design of the building and the use of renewable sources of energy, such as solar panels [30].
2. **Smart waste sorting:** Waste management is an essential part of a city's infrastructure. The growing human impact on the environment has made governments take measures to reduce harmful effects. One of the problems with waste sorting is inattentive or uninformed citizens who do not practice waste sorting. This makes the process harder, as the waste must be sorted by professionals as well. [PERSON] and [PERSON] [31] discussed the problem of waste management in China. They investigated the impact of smart bins on people's behavior and came to the conclusion that smart trash bins can assist in imposing some external pressure on residents who do not follow waste sorting regulations while also offering some financial benefits for those who do.
3. **Charging stations:** The growing role of electric devices such as smartphones imposes on the city environment certain obligations. Mobile charging stations can be quite convenient for citizens and also affect the city's economy. The lack of charging stations for electric cars is a stopping factor in buying one for some people. Therefore, it is important to have a system of charging stations for electric cars.
4. **Interactivity:** The environment of the city should be interactive in terms of information technologies. Informational QR-codes, interactive public transport schedules on the bus stops, free internet access points, and audio guides through the landmarks of the city are examples of such interactivity. These features could increase the tourist potential and convenience for the city's residents.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 1 Introduction
### Global Positioning  
Global navigation satellite systems (GNSSs) have played a significant role in forest resource management. These systems have been employed for inventory plot localization [5], forest traverse surveys [6], mapping forest disturbances [7], machine tracking [8; 9], automated time studies [10; 11], and general operational monitoring [12]. Recently, GNSS technology has been applied to enhance occupational safety in logging operations by providing virtual geofences around workers on the job site [13; 14; 15]. Most modern forest machines come equipped with a GNSS receiver from the factory, enabling real-time positioning of the machine during operation.  
While highly accurate GNSS positioning may not be essential for many forestry applications, it becomes crucial when constraining machines with a virtual boundary, particularly when boundaries coincide with ownership demarcations or delineate high-value, sensitive, or hazardous areas. High-precision localization is also necessary for mapping individual trees. Challenges with GNSS accuracy have impeded the widespread adoption of GNSS technology in the virtual boundary and mapping domains.  
GNSS accuracy depends on various factors, one of which is the number and geometry of visible satellites. This is characterized by an index called position dilution of precision (PDOP), which is a multiplicative term that scales the expected accuracy of the receiver. PDOP values less than 1 provide the highest possible accuracy, while PDOP values greater than 20 generally render coordinate readings futile, e.g., a PDOP value of 20 with a GNSS receiver capable of 3 m accuracy results in an actual accuracy of 60 m. The forest canopy can block signals from reaching the receiver, an effect known as the canopy effect [6]. Another important factor--primarily responsible for degraded GNSS accuracy under forest canopy--is signal diffraction and reflection, known as multipath errors [16]. Lastly, GNSSaccuracy also depends on the class of receiver, such as survey-grade or consumer-grade receivers. Research on GNSS accuracy in forested environments has shown consumer-grade receivers to have an accuracy range between 4 and 12 m [17; 18; 19; 20; 21]. In this work, we utilize a specific GNSS called the global positioning system (GPS), operated by the United States Air Force. The GPS satellite constellation comprises 33 satellites, 31 of which are currently operational.
# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 4 Comparative Static Network Performance Evaluation  
To evaluate the performance of MTNs, it is beneficial to analyze the network's static parameters. MPCs require an implementation with a low constant degree, low cost, good connectivity, high fault tolerance, and high scalability [39]. Such features could be derived from the static performance evaluation, as shown in this section. Our MTN was compared with two conventional networks, mesh and torus, since such networks are still widely used in supercomputer applications [34]. However, several proposed HINs were included in the comparison to evaluate their capabilities in connecting nodes while maintaining the required characteristics.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-EM Model
#### 2.1.2 Comparison between the Adapted Solution and FEM  
In order to compare the adapted solutions to FEM, Maxwell Ansoft, which is a FE solver, is utilised. The FEM model has a rectangular cross-sectional coil located above the cross-sectional aluminium plate, and is surrounded by a vacuum region that is four times larger than the sample. The FEM subdivides the large model to smaller elements, and this FEM solves the calculation by minimising the energy error. In this work, when the elements number is beyond 20,000, the energy error is as low as 0.05%, which is sufficiently accurate for the FEM computation. In this work, the mesh number used is 20,395, and the boundary used is a balloon boundary to simulate an infinite space. The distribution of **A** at the sample's surface (\(z=0\)) is presented in Figure 3. The analytical solution and FEM present a good agreement at an operational frequency of 1 kHz. However, at a working frequency of 1 MHz, the distribution of **A** from the FEM is not smooth compared to that from the analytical solution; the reason is that the FEM is affected by the elements density and numerical approximation is unavoidable, etc. Therefore, the adapted analytical method presents a more accurate result compared to FEM, especially for a high working frequency.  
Figure 3: The distribution of A from the adapted analytical solution and the finite element method (FEM). The left curves are the results at 1 kHz, while the right curves are the results at 1 MHz. The red curve is the result from the FEM, and the blue curve is the result from the analytical solutions. Reproduced with permission from [15], IEEE, 2016.
## 4 Conclusions  
In this study, we developed a flexible sensor array system capable of perceiving and identifying objects of various shapes and sizes. Utilizing a custom-designed multi-channel acquisition and signal transmission circuit, the system enables real-time signal transmission and has successfully collected data on two different-sized spheres, three different-sized cylinders, a cone, and a rectangular block. Leveraging these datasets, a convolutional neural network (CNN) was employed to train a deep learning model, achieving an impressive overall recognition rate of 99.60% on the test set. When testing groups composed of different genders, the model achieved a classification accuracy rate of 93.75% on the test set. Furthermore, during dynamic tests--such as the ball-tossing motion--the system accurately distinguished different action phases, which also applied to others repeating the same action. This research significantly contributes to the field of artificial tactile technology, providing valuable insights for the future development of more intelligent robotics.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15063302/s1](https://www.mdpi.com/article/10.3390/app15063302/s1)]([https://www.mdpi.com/article/10.3390/app15063302/s1](https://www.mdpi.com/article/10.3390/app15063302/s1)), Figure S1: CNN Training Results Obtained from Different Population Data; Figure S2: Participant F: repeated throwing results and continuous throwing results; Table S1: Comparison with other sensors; Table S2: Comparison with the Process Steps in Reference [54]; Table S3: S3 Participant Information.  
**Author Contributions:** Investigation, H.Z.; Methodology, H.Z.; Validation, H.Z.; Project administration, Y.T.; Formal analysis, K.S.; Resources, J.L.; Writing--review & editing, J.S., S.X. and Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Natural Science Foundation of China (Nos. 12175036 and 12205041). This work was also supported by AI-Enhanced Research Program of Shanghai Municipal Education Commission (SMEC-AI-DHUY-07).  
**Institutional Review Board Statement:** The study was approved by the Institutional Review Board of Science and Technology Ethics Committee of Donghua University (protocol code SRSY202503140032 and date of approval: 14 March 2025).  
Figure 6: The data collection process for the throwing motion. **(a)** The four key stages of the throwing motion using the sensor array glove: (i) the initial stage, empty-handed; (ii) the preparation stage, holding the ball; (iii) the execution stage, throwing the ball; (iv) the completion stage, catching the ball. **(b)** Collected data for each stage of the throwing motion.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussions
### Effect of Cylinder Diameter on the Energy Harvesting Performance
The resulting additional force on the trailing edge is minimal, roughly around the zero point. Nonetheless, in the event that the trailing edge incorporates a cylinder, the pressure coefficient curves pertaining to the upper and lower surfaces shift in an upward direction, while also assuming a more distinct form. Moreover, the pressure coefficient curves that exist between these two surfaces exhibit increasingly greater spacing as the diameter of the cylinder grows larger. This indicates that the difference between the upper surface pressure and the lower surface pressure is increasing, leading to a greater extra force on the trailing edge of the foil. This finding confirms the foil's increased lift and energy collection efficiency, in accordance with the law of energy conservation.
## 2 Materials and Methods
### Methods
#### 2.2.4 Onboard Autonomous Planning Algorithm
Insertion neighborhood means inserting the pending tasks that satisfy operational constraints into the current task sequence, and the basic steps are as follows: Step 1: Sort the pending tasks in descending order of priority. Select the first one, and calculate of the position of the observation window of the pending task in current task sequence of the satellite; Step 2: Build a task tabu list to conserve the planned tasks, which means the planned task will not be considered again; Step 3: Judge whether the operational constraints are satisfied between the pending task and the adjacent tasks before and after, and, if so, insert the task into the current task sequence and write it down in the tabu list. In addition, if the observation window of the task is located before the first task or after the last task, only the constraint satisfaction relationship with the first and last task needs to be judged; Step 4: Update the planning rewards and consumed storage and power; and Step 5: Judge whether there are tasks in pending task set, and if so, repeat Steps 1-4. Otherwise, exit the algorithm. Based on the above design, the flowchart of the onboard autonomous planning algorithm is shown in Figure 4.
###### Abstract  
The role of the elemental carbon (EC), in synergy with hygroscopic ionic species, was investigated to study the formation of electrical bridging phenomena once the aerosol deliquescence is achieved. Ambient aerosol samples were collected on hydrophobic surfaces in urban and rural sites in Northern Italy; their conductance was measured in an Aerosol Exposure Chamber (AEC) while varying the relative humidity. An electric signal was detected on 64% of the collected samples with conductance values (\(11.20\pm 7.43\) uS) above the failure threshold (1 uS) of printed circuit boards. The ionic content was higher for non-electrically conductive samples (\(43.7\pm 5.6\%\)) than for electrically conductive ones (\(37.1\pm 5.6\%\)). Conversely, EC was two times higher for electrically conductive samples (\(26.4\pm 4.1\) uS cm\({}^{-2}\); \(8.4\pm 1.7\%\)) than for non-electrical ones (\(12.0\pm 4.1\) uS cm\({}^{-2}\); \(5.2\pm 1.9\%\)) suggesting that the synergy between the ionic and carbonaceous fractions is necessary to promote a bridging phenomenon. Synthetic aerosols (EC only, saline only, mixed saline and EC) were generated in laboratory and their conductance was measured in the AEC to verify the ambient results. Only in case of a contemporary presence of both EC and ionic components the bridging phenomenon occurred in keeping with the theoretical deliquescence values of each salt (R\({}^{2}\) = 0.996).  
Keywords:elemental carbon; aerosol; bridging; failure; energy; Po Valley +
Footnote †: journal: Applied Soft. 2020, 10, 5559; doi:10.3390/app10165559