esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architecture and System Evaluation
### Evaluation Scenario  
For the evaluation of the system's performance, we used the COCO dataset [34] which consists of a big collection of images and objects with corresponding ground truth annotations. For the evaluation of the models performance, we used the precision, recall, and mean average precision metrics for a range of intersection over unit values (IoU). The detection was considered successful as a true positive if the intersection of the unit value was bigger than 0.5. The edge server included all of the COCO images data, and using a Python script, the server sent subsets of those data to the end devices. We used 1500 images from this validation set; images were divided into subsets of images and sent for processing to different end devices, with different hardware and/or inference machines, and different network conditions. This allows us to have a better understanding of the system's operation in diverse scenarios. Apart from revealing the trade-offs and challenges of this problem, our results can also be us to create look-up tables with the best configurations for different systems and cases. Furthermore, for the evaluation of the system's performance, we used metrics that measure the time needed to process every subset; in other words inference  
Figure 8: Transmission delay for different batch sizes with bad channel conditions.  
Figure 7: Transmission delay for different batch sizes with medium channel conditions.  
processing. We also used metrics that measure the CPU utilization for the processing of every image subset [33] as shown in Section 3.6.
## 4 State Estimation Based on Nonlinear/Non-Gaussian Systems  
### CPF Algorithm  
The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in a non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. Therefore, it is difficult to meet the actual system's requirements, and the Kalman filter fails to achieve optimal estimation performance.  
In nonlinear and non-Gaussian systems, the particle filtering algorithm can approximate the posterior probability density of random variables satisfying any distribution (Gaussian or non-Gaussian) and state (linear or nonlinear) as the number of particles increases. However, the importance density function of traditional particle filtering is the state transfer probability function, which does not consider the latest quantitative information.  
As a result, the particles are prone to degeneracy. To alleviate this problem, the cubature Kalman filter is applied to the particle filtering framework.  
At time step \(k\), the CKF algorithm is employed to compute the mean and variance of the importance density function based on the observed data. A new posterior probability density distribution is then generated, which can generate new particles. The weights of the particles are calculated, normalized, and resampled to complete the state estimation process.  
The importance density distribution used in the PF is designed by the CKF algorithm, which is obtained by the calculations described in Section 3.2.  
\[q(\hat{x}_{k|k}^{i}|\hat{x}_{k|k-1}^{i},z_{k})=N(\hat{x}_{k|k}^{i},P_{k|k}^{i}), \tag{30}\]  
where \(N\) is the total number of particles, which is sampled from the importance function at moment \(k\):  
\[x_{k}^{i}\sim N(\hat{x}_{k|k}^{i},P_{k|k}^{i}), \tag{31}\]  
When \(i=1,2,...N\), calculate the importance weights of the particles again and normalize:  
\[\begin{split} w_{k}^{i}&=\frac{p(x_{k}|X_{k}^{i})p( x_{k}^{i}|x_{k-1}^{i})}{q(\hat{x}_{k|k}^{i}|\hat{x}_{k|k-1}^{i},z_{k})},\\ \overline{w_{k}^{i}}&=w_{k}^{i}[\sum_{i=1}^{N}w_{k} ^{i}]^{-1},\end{split} \tag{32}\]  
When the weights of the particles fall below the designated threshold, the particles are resampled, resulting in \(N\) particles with equal weights \((X_{k}^{i},\frac{1}{N})\). Calculate state estimates as follows:  
\[\begin{split}\hat{x}_{k|k}&=\frac{1}{N}\sum_{i=1}^{N }x_{k}^{i},\\ \hat{P}_{k|k}&=\frac{1}{N}\sum_{i=1}^{N}(x_{k}^{i}- \hat{x}_{k|k})(x_{k}^{i}-x_{k|k}^{*})^{\text{T}},\end{split} \tag{33}\]
## 2 Mission Description  
### Concept of the Mission  
For the CANYVAL-C mission, the two CubeSats were in operation in a sun-synchronous orbit at an altitude of 525-550 km, an inclination of 97.6\({}^{\circ}\), and a local time of ascending node (LTAN) of 11:00. The relative motion is described in the local-vertical and local-horizontal coordinate frame, which is a chief-centered rotating frame (Figure 1a). Because the relative displacement is less than tens of kilometers, it is represented by the Hill-Clohessy-Wiltshire equations (HCW), which are linearized equations of relative motion. The 1U CubeSat, the chief satellite, performs an attitude pointing toward the observation target, carrying an optical camera. The four nozzles of the 2U propulsion system can fire with two degrees-of-freedom perpendicular to the occulter (Figure 1b). The deployable occulter that is contained by the deputy satellite is 0.5U in size and has a cross-sectional area of \(0.75\times 0.75\) m\({}^{2}\) when deployed.
## 2 Related Work  
Most of the research done on cloud detection is ground-based, where clouds are captured with instruments that obtain continuous all-sky images at pre-defined time intervals [12; 13]. For a UAV, it is impossible to keep these conditions since the update intervals of information need to be shorter. Moreover, algorithms should not have a high computational cost, because onboard computers may not have the same processing power and memory capabilities as an off-board station. Also, a computer with high processing power in a UAV would require a higher demand for energy, which would require batteries with higher capabilities increasing the UAV weight, affecting the fuel consumption of the aircraft negatively.  
Other works solve the problem of object identification using an undirected graph [14]. Computing the graph association matrix could be computationally expensive; in the worst-case scenario, it is a problem of \(O(n^{2})\) complexity [7; 14]. These approaches are not suitable for real-time applications  
Figure 1: Captured image of a video stream from a camera mounted on an unmanned aerial vehicle (UAV). The image resolution is 720 width and 480 height at a 30 frames per second.  
working with high definition images [9]. In Reference [13], an automatic cloud detection for all-sky images using SPS is presented; the result and implementation of this algorithm shown in Figure 2. It can be seen that even if it is a good approximation, some information is lost in the final result. Considering these results and the computational complexity of the algorithm, it may not be suitable for these kinds of applications.  
On the other hand, algorithms based on image matting [6; 8; 15] try to reduce computational complexity. These algorithms extract foreground objects in images, but they are not easy to implement and take long processing time [9]. In these approaches, the algorithm distinguishes only between two classes (sky or ground), and it is difficult to add more classes.  
Recently, deep learning techniques have been used to solve many computer vision tasks [14; 16; 17; 18; 19; 20]. In particular, CNNs are good image classifiers [21; 22; 23; 24; 25; 26]. Approaches like the ones presented in References [27; 28] use CNNs that are trained to predict a class for each pixel. In contrast, this paper employs a segmentation on top of a CNN to label these clusters of pixels as the clean sky, clouds and ground.
## 5 Implementation and Evaluation
### HGR Performance Evaluation
#### 5.4.2 SVM Classifier Execution Time  
The computational complexity is a key index for evaluating a given algorithm. Figure 13 shows the average time per sample for training and testing on each dataset, using MIFS with \(k=80\). The time taken for testing phase is higher than the training phase for all datasets. Lab dataset with 150 gestures reports least execution time per sample (Training: 2.76 ms; Testing: 3.13 ms), whereas _lab + home_ dataset with 276 gestures reports highest execution time (Training: 4.05 ms; Testing: 8.94 ms). The simulations are done on an Intel(R) Core(TM) i5-7200U CPU @2.50 GHz 2.71 GHz RAM 8.00 GB.  
Figure 13: SVM classifier execution time.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference/** & **Signal Processing (SP)** & **Learning Algorithm** & **Application** & **Number of Gestures** & **Recognition** & **Accuracy** \\ \hline \hline \multirow{2}{*}{Support [34]} & Wavelet Filter, FT, DWF, & Pattern & Hand Gesture Recognition & 7 hand & 87.5\%,80\%, \\  & Threshold based & Matching & Hand movements with & gestures & (1 A73 A75) \\  & & significant extraction; & Segmentation: multi-level & & & 97.28\%, 91.8\%, \\  & & & wavelet decomposition & Hand gesture & 7 hand & 95.5\%, 94.4\%, and \\  & & pass filter & algorithm and the short-time & recognition & gestures & 91.5\% (Counts 1 to 5) \\  & & energy algorithm, DTW & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of recognition accuracy of systems using COTS Wi-Fi devices.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Number of** & **Number of** & **Number of Samples per** & **Total Number of** & **Number of Samples** & \multicolumn{3}{c}{**Overall Recognition Accuracy**} \\  & **Gestures (N\({}_{p}\))** & **Users (N\({}_{p}\))** & **Gesture per User (N\({}_{m}\))** & **Gesture Samples (N\({}_{p}\))** & **Per User** & **SignHi + CNN** & **SignHi + CNN** & **Present Work without** \\ \hline \(Lab\) 276 & 276 & 1 & 20 & 5530 & 5530 & **95.27** & 96.01 & **97.84** \\ \(Home\) 276 & 276 & 1 & 10 & 2700 & 2760 & **93.98** & 96.91 & **98.26** \\ \(Lab\) + _Home 276_ & 276 & 1 & - & 820 & 8200 & **92.21** & 94.81 & 96.34 \\ \(Lab\) 150 & 150 & 5 & 10 & 7500 & 1500 & - & 66.66 & **96.23** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of overall recognition accuracy.
## 3 System Overview  
### System Configuration  
In this study, we analyzed the diffusion of CO\({}_{2}\) generated by human breath by simultaneously measuring CO\({}_{2}\) concentrations at multiple locations in a studio. A single measurement device can only measure locally because it measures the atmospheric CO\({}_{2}\) concentration that enters the measurement section in the sensor through the nonwoven fabric. Therefore, to accurately measure the distribution of CO\({}_{2}\) concentration in space, it is necessary to use multiple measurement devices to simultaneously sense CO\({}_{2}\) diffusion at multiple points. Each measurement device is connected to the RaspberryPi3 via an on-board Bluetooth module and acquires data every minute. We used the Universal Asynchronous Receiver/Transmitter (UART) for serial communication between the RaspberryPi3 and the measurement device. Six measurement devices were able to be stably connected to RaspberryPI3 per one. In this study, two RaspberryPi3 were used to verify the CO\({}_{2}\) diffusion. Figure 1 shows a concept diagram of the system created.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 4 Discussion  
Despite HPNet's significant performance gains, it can still be improved in many ways. First, HPNet uses a CNN to study sequence features. However, all sequences must be the same length, and the padding values are replaced by mean values, which can introduce bias in the learning results and require additional storage space. The CCNN approach [47] described by [PERSON] et al. can be utilized to address this issue. It employs a universal CNN that works with inputs of arbitrary length. Second, HPNet uses DiffPool to learn RNA secondary structure hierarchical information. However, dense graphs can be generated after pooling, which can increase the complexity of the model and reduce the computational efficiency. To overcome this problem, the self-attention approach can be used to simplify the network and speed up computation by eliminating unneeded nodes. Finally, while HPNet uses a deep-learning network for prediction, the details of how it arrives at its conclusions are hidden from view. Therefore, it is crucial to investigate the interpretability of the deep learning, as it could shed light on the RBP-RNA binding process.
# Gamification and Immersive Experiences: A Gamified Approach for Promoting Active Aging
## 4 Pilot Study
### Instruments  
A mixed-method approach was used to collect data using the following instruments:  
1. Player Experience (PX) questionnaire: This instrument assessed participants' engagement, enjoyment, and satisfaction with the gamified activities. The questions covered various aspects of gameplay, such as ease of use, challenge level, and the perceived value of rewards.
2. Technology Acceptance Model (TAM) questionnaire: This tool measured participants' attitudes toward using digital technologies and their willingness to continue engaging with these tools in the future. It also evaluated their confidence and comfort in adopting new technologies like virtual reality.
3. Field Observations and Video Documentation: The researchers maintained a field diary to capture qualitative data based on their session observations. Additionally, video recordings provided deeper insights into participants' behavior and interactions with the platform.  
The Player Experience (PX) questionnaire is a validated tool widely used to assess user engagement, enjoyment, and perceived challenges in gaming contexts. It evaluates key elements such as immersion, enjoyment, and ease of learning. Sample items include statements such as \"I felt a sense of accomplishment while playing\" (engagement) and \"The game was easy to understand\" (ease of use). This tool has demonstrated strong internal consistency in previous studies [24].  
The Technology Acceptance Model (TAM) questionnaire, developed by [PERSON] et al. (1989) (cited in [PERSON] et al., 2021), measures perceived usefulness, ease of use, and behavioral intention to use technology. Its validity and reliability are well-established across diverse demographics. Sample items include \"I believe using the platform would enhance my cognitive abilities\" (perceived usefulness) and \"I find the platform easy to use\" (ease of use) [25; 26]. Insights from PX and TAM results directly informed platform improvements, such as adjusting game difficulty progression and enhancing user engagement.
Sensors_**2012**, _12_, 92-114; doi:10.3390/s120100092  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks**  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2,\({}^{\star}\)**  
1 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H-12, Islamabad, Pakistan; E-Mails: [EMAIL_ADDRESS] (M.N.); [EMAIL_ADDRESS] (S.Q.)  
2 Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, Yongin-Si 446-701, Korea  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 28 October 2011; in revised form: 30 November 2011 / Accepted: 20 December 2011 / Published: 22 December 2011_  
**Abstract:** In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.  
**Keywords:** wireless sensor networks; hierarchical clustering; cooperative communication; energy conservation
###### Abstract  
Reduction of flammability and improvement of thermal stability of polymers during heating can be achieved by the introduction of fillers. Epoxy composites filled with different loadings of multi-walled carbon nanotubes (MWCNTs) and expanded graphite (EG) were prepared. The thermal oxidation stability of the prepared samples was investigated under heating in an oxidizing atmosphere using thermal analysis. The hardness was measured using the Shore D hardness test. The flammability of the prepared composites was evaluated by the ignition temperature and time-to-ignition. It was found that there was a rise in temperature corresponding to a 5% weight loss during heating for both epoxy/MWCNT and epoxy/EG composites compared to neat epoxy resin. The Shore D hardness of epoxy/MWCNT composites increased with content growth up to 0.1 wt.% and decreased with further concentration rise. The addition of MWCNTs and EG leads to an increase in the ignition temperature. It has been shown that MWCNTs improve the thermal behavior of epoxy resin in a low temperature region (below \(\sim\)300 \({}^{\circ}\)C) whereas EG shows almost the same thermal behavior above 300 \({}^{\circ}\)C. The improvement of thermal properties can be achieved using MWCNTs and EG as fillers.  
epoxy composites; thermal oxidative degradation; flammability; multi-walled carbon nanotubes; expanded graphite; exfoliate graphite +
Footnote †: journal: Applied Physics Letters
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Number of Vehicles
#### 4.3.4 Simulation Results of Rician Propagation Model regarding Number of Vehicles  
Due to its association with a multi-path propagation channel, the mRSU demonstrates superior performance in terms of throughput in the Rician scenario. Furthermore, when compared to the sRSU, the average latency is decreased in the Rician propagation model for the mRSU. This reduction in latency is a result of the mRSU being in motion alongside the nodes, leading to a decrease in the distance between them. Consequently, the latency is reduced. Additionally, the multi-path process enhances the throughput by enabling the acceptance of messages from multiple hops.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 3 Materials and Methods
### Proposed ANN-Based Rotational Speed Control
#### 3.1.2 Rotor-Side ANN Rotational Speed Control  
For the rotor-side converter the doubly fed induction generator is controlled by the Stator-Flux Orientation to align the \(d\)-axis with the stator-flux vector [27]. Hence \(\psi_{ds}=\psi_{s}\) and \(\psi_{qs}=0\). Thus, the stator currents and stator voltages may be written as:  
\[\left\{\begin{array}{l}i_{ds}=\frac{1}{L_{s}}(\psi_{s}-L_{m}i_{dr})\\ i_{qs}=-\frac{L_{m}}{L_{s}}i_{qr}\end{array}\right.\quad\left\{\begin{array}{l}v _{ds}=\frac{\psi_{s}}{L_{s}}R_{s}-\frac{L_{m}}{L_{s}}R_{s}i_{dr}\\ v_{qs}=\omega_{s}\psi_{s}-\frac{L_{m}}{L_{s}}R_{s}i_{qr}\end{array}\right. \tag{30}\]  
Using (30), the stator active and reactive powers also may be written as:  
\[\left\{\begin{array}{l}P_{s}=\frac{3}{L_{s}}\frac{\psi_{s}}{L_{s}}\left( \frac{R_{s}}{L_{s}}\psi_{s}+L_{m}\omega_{s}i_{qr}\right)\\ Q_{s}=\frac{3}{L_{s}}\frac{\psi_{s}}{L_{s}}\left(\omega_{s}\psi_{s}+L_{m} \omega_{s}i_{dr}\right)\end{array}\right. \tag{31}\]  
Looking into (31), it indicates that the direct rotor-current \(i_{dr}\) is controlled to regulate the reactive power whereas the quadrature rotor-current \(i_{qr}\) is controlled to regulate the active power.  
Thanks to the Stator-Flux Orientation the rotor voltages can be written as:  
\[\left\{\begin{array}{l}v_{dr}=R_{r}i_{dr}+L_{r}\frac{dif_{dr}}{dt}\frac{ \triangle v_{dr}}{-\omega_{slip}\sigma L_{riqr}}\\ \frac{\triangle v_{qr}}{+\omega_{slip}\sigma L_{ridr}+\omega_{r}L_{0}ims} \end{array}\right. \tag{32}\]  
where \(\Delta v_{dr}\) and \(\Delta v_{qr}\) are feed-forward compensation terms which are added back to the control signals.  
The control scheme of rotor-side converter is illustrated in Figure 11 which uses two PI controllers to regulate the rotor currents \(i_{dr}\) and \(i_{qr}\). Another two PI controllers, in cascade to the outer loops PI controllers, are used to regulate the desired rotor voltages.  
As with the grid-side converter control, the outer loops produce \(i_{dr}^{*}\) and \(i_{qr}^{*}\) by using as reference inputs the stator reactive power \(Q_{s}^{*}\) and the rotational speed \(\omega_{r}^{*}\). \(Q_{s}^{*}\) is set to zero whereas \(\omega_{r}^{*}\) is provided by an ANN-based MPPT for OWC. The reference voltages \(v_{dr}^{\prime}\) and \(v_{qr}^{\prime}\) are generated by the inner loops which are compensated to obtain \(v_{dr}^{*}\) and \(v_{qr}^{*}\).  
Figure 11: ANN-based rotor-side converter control scheme.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 2 Related Works
### Limitations  
For a fair comparison, we present the strengths and weaknesses of previous works along with the proposed method in Table 1. Most of the previous works have at least one of the following limitations:  
* The drones are controlled remotely in most previous approaches [9; 10; 11], which puts a burden on the technicians. Remotely controlling a drone in large PV stations can take several minutes to completely monitor the whole area.
* Previous works [12; 13; 14] are unable to localize the exact location of the faulty PV module. The location of the defective module is crucial for quick repairing process in case of large-scale PV systems.
* Most researchers [15; 16; 17] have tested their approaches on small datasets or in artificial environments. This can limit the practical applicability of their works.