esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Importance of URLLC
Such as that the automated car assembly line must have minimum latency to keep up with the moving ray and high reliability to avoid any damage to the car parts during assembly. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Industry** & **Application** & **Importance of Reliability and Low Latency** \\ \hline Medical and Health Care & Remote surgery/patient diagnosis. & Remote surgery or remote patient’s diagnosis might be carried out with the help of a robot. In such cases, the reliability of data transmitted as instruction for robot needs to be ultra-reliable because even a slight latency or delay could be very harmful to the patient. \\  & Live reporting of an event, live sports events, online gaming, cloud-based entertainment (VR/AR). & With the help of technology, the entire world is shrinking in terms of communications. Users desire to be up to date on world events and entertainment in real-time. Even in terms of business, the delay could make a huge impact on trades carried out in the world. In online gaming, the lag could be very frustrating for gamers. \\  & Drone-based delivery, remote driving, self-driven cars, traffic management, sub-station management (system synchronization, traffic management) & Through new features and attractions for users such as Amazon Prime Air [8] to deliver orders, it is very important for drones to respond in real-time. Similar to Amazon Prime Air, Google’s self-driven car (WAYMO) [9] is quite important for the future automobile industry. The importance of reliability and latency is self-explanatory in such projects. \\  & Control systems, automated assembly lines with robots, machine status reports, surveillance, power grid management. & In order to maximize productivity, industries have moved toward automation. Higher reliability and productivity can be obtained by replacing humans with robots in the manufacturing process. Apart from the manufacturing industry, the agriculture, journalism, and education sectors have also moved towards automation [10]. In the mentioned industrial areas, reliability will be a key factor. Such as that the automated car assembly line must have minimum latency to keep up with the moving ray and high reliability to avoid any damage to the car parts during assembly.
## 3 Methodology
### Mesh Independence Study  
Figure 3 indicates the mesh independence study performed using three different mesh sizes of 5 million, 4.25 million, and 3.75 million cells, and the plot shows the tool tip temperature variations. It is noticed that the tool tip temperature decreases with an increase in the distance from the tool tip. The deviations in the temperature distribution between the three different mesh sizes are insignificant, and hence a mesh size of 4.25 million cells is considered for all the numerical simulations in the present study.  
Figure 3: Grid independency test showing the temperature distribution from the tool tip for three different grid sizes.  
Figure 2: Meshed computational domain with a mesh count of 4.25 million cells.
## 4 Modelling and Simulation
### Connections and Contacts  
The connection of different components in the assembly is defined using bolts, related to suspension and suspension link, or contact functions for spacers and bearings. Figure 7a presents bolts connections placement and Figure 7b depicts the contact connections as modelled on the assembly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Aluminum 7075-T6** & **Stainless Steel AISI 304** & **Steel AISI 4130** \\ \hline
**Elastic modulus, E(GPa)** & 71.7 & 200 & 205 \\
**Poisson ratio, v** & 0.33 & 0.29 & 0.29 \\
**Shear modulus, G(GPa)** & 26.9 & 86 & 80 \\
**Density, \(\rho\) (x10\({}^{3}\) kg/m\({}^{3}\))** & 2.81 & 8 & 7.85 \\
**Yield strength (MPa)** & 503 & 215 & 435 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Materials properties used in preliminary simulations.  
Figure 6: Mesh of components assembly for the swingarm design.  
Figure 7: Connections modeled: (**a**) with bolts; (**b**) with contacts.
## 7 Conclusions and Future Work  
In this paper, the examination timetabling problem is studied and solved on a GPU platform. The proposed solution is the use of a hybrid evolutionary algorithm in conjunction with a GPU to accelerate the performance of the algorithm and the ability that GPUs give in order to explore bigger  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **This Work** & **IGA [38]** & **hMOEA [35]** & **MMAS [34]** & **Ersoy [39]** & **Best** \\ \hline car-f-92 & 4.47 & **4.2** & **4.2** & 4.8 & - & 3.74 [61] \\ car-s-91 & 5.24 & **4.9** & 5.4 & 5.7 & - & 4.42 [61] \\ ear-f-83 & 34.41 & 35.9 & **34.2** & 36.8 & - & 29.3 [30] \\ hec-s-92 & **10.39** & 11.5 & 10.4 & 11.3 & 11.6 & 9.2 [30] \\ kfu-s-93 & **13.77** & 14.4 & 14.3 & 15.0 & 15.8 & 12.81 [62] \\ lse-f-91 & 11.06 & **10.9** & 11.3 & 12.1 & 13.2 & 9.6 [30] \\ pur-s-93 & 5.25 & **4.7** & - & 5.4 & - & 3.7 [30] \\ rye-s-93 & **8.61** & 9.3 & 8.8 & 10.2 & - & 6.8 [30] \\ sta-f-83 & 157.05 & 157.8 & **157.0** & 157.2 & 157.7 & 134.70 [28] \\ tre-s-92 & 8.51 & **8.4** & 8.6 & 8.8 & - & 7.72 [62] \\ uta-s-92 & 3.63 & **3.4** & 3.5 & 3.8 & - & 3.06 [61] \\ ute-s-92 & **24.87** & 27.2 & 25.3 & 27.7 & 26.3 & 24.21 [63] \\ yor-f-83 & 37.15 & 39.3 & **36.4** & 39.6 & 40.7 & 34.78 [62] \\ \hline normalized average distance & **16.51\%** & 17.14\% & 21.11\% & 25.23\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 10: A comparison with other evolutionary approaches and the best solution reported.  
parts of the solution space faster. The algorithm was evaluated by using the well-known Carter/Toronto benchmarks. The results obtained were competitive with the best available in terms of quality, and the speedup achieved was up to 26\(\times\). Furthermore, the tournament selection size is analyzed, and it was shown that it should be dependent on the population size. The steepest descent algorithm helps the genetic algorithm to further improve the quality of solutions, while the use of the compressed sparse row format is also beneficial in terms of speed, as the conflict matrix in most datasets is extremely sparse.  
The results of this paper give a clear motivation and direction for future work. The solution of the examination timetabling problem with methods based on populations can be significantly improved by incorporating the use of GPUs and the proposed encoding.
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 4 Discussion  
We have applied af RCNN technique for the localization and recognition of the diabetes-based eye diseases. Our method is based on fast RCNN and Fuzzy k-means clustering. Our main contribution is to present a consolidated model to target three eye diseases i.e., diabetic retinopathy, diabetic macular edema and glaucoma. In localization, the proposed model achieved mAP of 0.945, 0.943 and 0.941 for DR, DME and glaucoma, respectively. FRCNN utilizes a backpropagation estimation, also added the bbox regression and classification head, and trained the model with a multi-task loss. The proposed method detects the abnormalities of DR, DME, and glaucoma regions simultaneously using FRCNN and lastly, fKM clustering precisely extracts the regions from localized regions. For segmentation we have achieved the accuracy of 0.952, 0.958 and 0.9526 for DR, DME and glaucoma regions, respectively.  
The existing literature emphasized on the recognition of these diseases individually and there is very limited amount of work available which expands the scope of the modern machine/deep learning models to simultaneously detect multiple eye diseases. The fundamental reason is that each disease is based on different abnormality signs which are specific to that particular eye disease. Moreover, the model which is optimized for one disease may give bad performance for other diseases. However, with this work we have addressed this misconception and came-up with a robust approach in the form of a model which can detect and recognize three different eye diseases with very high accuracy. Therefore, with this work we have proved that the deep learning models have the potential to detect multiple eye diseases just like an ophthalmologist. Moreover, this work is more towards the application  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Year** & **Dataset** & **SE** & **SP** & **AUC** & **D\({}_{\text{c}}\)** & **Time** \\ \hline [PERSON] et al. [70] & 2019 & ORIGA & - & - & 0.88 & 0.9 & - \\ [PERSON] et al. [71] & 2015 & ORIGA & - & - & 0.838 & - & - \\ [PERSON] et al. [72] & 2013 & ORIGA & 0.58 & - & 0.823 & - & - \\ [PERSON] et al. [73] & 2016 & ORIGA & - & - & 0.8384 & - & - \\ [PERSON] et al. [37] & 2019 & ORIGA & 0.71 & - & 0.868 & - & - \\ [PERSON] et al. [39] & 2020 & HRF & 0.849 & 0.999 & - & - & 1.49 s \\ [PERSON] et al. [74] & 2017 & HRF & 0.7025 & 0.997 & - & - & - \\ [PERSON] et al. [12] & 2019 & DR HAGIS & 0.94 & - & - & - & - \\ \hline \multirow{2}{*}{**Proposed**} & \multicolumn{3}{c}{ORIGA} & 0.941 & 0.945 & 0.94 & 0.943 & 0.9 s \\  & \multicolumn{3}{c}{HRF} & 0.95 & 0.96 & 0.963 & 0.952 & 0.9 s \\ \cline{1-1}  & \multicolumn{3}{c}{DR HAGIS} & 0.945 & 0.941 & 0.94 & 0.89 & 0.9 s \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison of the Proposed Method with latest techniques.  
end of the computer vision and our target audience is the engineers and manufacturers particularly those developing intelligent CAD systems.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 4 Applying Corrections to AD/NS Simulation
### Application of Glauert Tip Loss Factor \(F\)
#### 4.1.1 Glauert-A Correction  
[PERSON] et al. [41] performed a tip loss correction by applying factor \(F\) to modify the aerodynamic force that determines the external body force in the NS equations. They replaced the lift coefficient \(C_{l}\) by \(C_{l}/F\) (there was no need to modify the drag in their study as the drag was assumed not to produce the external body force). In the present Glauert-A correction, \(C_{n}\) and \(C_{t}\), in Equations (18) and (19), are replaced by:  
\[\tilde{C}_{n} =C_{n}/F, \tag{44}\] \[\tilde{C}_{t} =C_{t}/F. \tag{45}\]  
That is equivalent to replacing \(C_{l}\) by \(C_{l}/F\) and \(C_{d}\) by \(C_{d}/F\), according to Equations (16) and (17). [PERSON] et al. [41] did not explain the reason why factor \(F\) could be directly used to modify the force. They might be inspired by Equations (32) and (33) in which the existence of \(F\) can be looked as corrections to \(C_{n}\) and \(C_{t}\), although from a physical point of view it is a correction to the interference factors. It is important to note that the corrected force is only used for determining the external body force, so as the flowfield, while the force acting on the blade should be regarded as the original one. In addition, the interference factors in Equations (9) and (10) should no longer be corrected, leading to \(\bar{a}=a\) and \(\bar{a}^{\prime}=a^{\prime}\).  
This implementation involves a division operation with denominator \(F\), which causes unreasonable big values of \(\tilde{C}_{n}\) and \(\tilde{C}_{t}\) at the extreme tip where \(F\to 0\). However, there is no exact criterion for defining what a big value is unreasonable because the \(\tilde{C}_{n}\) and \(\tilde{C}_{t}\) themselves are introduced as an engineering correction rather than a physical concept. [PERSON] et al. [41] did not mention this problem, and no obvious numerical fluctuation is observed in his result. That is possible because the problem is limited to a very small area at the tip, and thus, the integral effect of the resulted unreasonable body force is also small.
## 3 Transferring a Fraction \(\alpha\) of Jobs to the Remote Cluster  
When the user decides to transfer some fraction \(\alpha\) of its jobs to the remote cluster, and assuming that the RC has another load of jobs arriving at rate \(\lambda\), we obtain that the net average response time perceived by the users who emanate from the LC is:  
\[W_{U}=\alpha W_{R}(F_{R},\lambda+\alpha\lambda_{L})+(1-\alpha)W_{L}(F_{L},(1- \alpha)\lambda_{L}) \tag{12}\]  
where we have assumed that additional network delays between the users and the two clusters are equivalent since the users need not be \"resident\" in the facility that hosts the LC.  
Under the assumption that each of the two clusters shares its load equally among its processors, that the RC processors are \(f\) times faster than the LC processors, and that all job arrival traffic is Poisson, we can use the well known Pollaczek-Khintchine formula [4] to estimate the average response time per job as a function of the load dispatching policy characterised by the fraction \(\alpha\) of jobs that are sent to the RC. We have:  
\[W_{R} = \frac{E[S]}{f}[1+\frac{\rho_{R}(1+C_{S}^{2})}{2(1-\rho_{R})}] \tag{13}\] \[\rho_{R} = \frac{[\lambda+\alpha\lambda_{L}]E[S]}{fR}\]  
and  
\[W_{L} = \frac{E[S]}{f}[1+\frac{\rho_{R}(1+C_{S}^{2})}{2(1-\rho_{L})}] \tag{14}\] \[\rho_{L} = \frac{(1-\alpha)\lambda_{L}E[S]}{L}\]The composite cost function that includes both the energy consumption per job and the average response time then becomes:  
\[C=b[\alpha E_{R}+(1-\alpha)E_{L}]+aW_{U} \tag{15}\]  
where \(a\) and \(b\) are the relative importance of response time versus energy.
## 3 Energy Harvesting
### Energy Harvesting Material: Piezolectric  
A piezoelectric diaphragm (PZT) can convert mechanical pressure, such as footsteps, into electrical energy. Figure 3 shows how piezoelectric power generation works: when mechanical pressure deforms the piezoelectric material, it produces an electric charge because of the material's internal structure. This charge can then be harvested as usable electrical power. PZT is often chosen because of its durability and cost-effectiveness in delivering a consistent energy output [45].  
Figure 2: The basic working principle of triboelectric nanogenerators (t-TENGs) for energy generation, from contact-induced charge transfer to energy storage.  
[PERSON] et al. [5] used Lead zirconate titanate (PZT) piezoelectric transducers to convert footstep pressure into electrical energy. A related system in [15] combined 16 piezoelectric discs (arranged in series, parallel, or series-parallel) mounted on a footboard to capture energy from human footsteps, along with a 12 V solar panel to gather solar power. This setup included an AC-DC converter with different voltage doubler circuits, ensuring that the alternating current generated by the piezoelectric discs could be converted into direct current.  
To enhance the energy-harvesting effectiveness of piezoelectric gyroscopes [4], researchers investigated various electric field models across multiple load resistances. However, a challenge remains: many existing models assume a constant electric field, which may not accurately capture real-world conditions for energy harvesting.  
Similarly, the study in [3] explores how human footsteps can generate electrical power, focusing on piezoelectric tiles (ceramic diaphragms with specific dimensions: 20 mm diameter for the metal plate and 14 mm for the piezoelectric element) embedded in floors. After undergoing a unique process, certain ceramics have shown excellent performance in energy-related tasks, such as thermal management and absorption, and can even be tuned to match particular application needs by adjusting porosity and density [41; 46]. Such a surface tension-based approach also has the advantages of being both scalable and environmentally friendly, which makes it a good candidate for combining with PZT.  
Traditional ways to boost a material's piezoelectric coefficient often require adding large amounts of ceramic, which then reduces the lightweight and flexible nature of the polymers [6; 47; 48]. This creates a pressing need for more sustainable, efficient, and flexible methods to create piezoelectric polymer composites [47]. [PERSON] et al. [49] studied piezoelectricity in chalcogenide perovskites and found high sensitivity, strong energy conversion, and solid stability under changing environmental conditions, with coefficients that match or even exceed those of PZT. However, ensuring precise production control and testing long-term stability under repeated stress remains a significant hurdle for scaling up these materials in real devices.
## 7 Performance Evaluation and Simulation Results
#### 7.4.1 Very High Social Media Interaction.  
Node's Density = 100 Nodes/km\({}^{2}\)  
Figure 16 shows the average percentage of packet losses. As we can see, we get the lowest value (around 20%) for \(w_{TS}=0\) (i.e., we select the path with best QoS performance), and the highest value (around 23%) for \(w_{TS}=1\) (i.e., when we just consider the TS values of the paths). Again, there is no much variation in the values when we vary the weight \(w_{TS}\), since this density of nodes all the paths have more or less the same QoS performance. Also, for this low \(\sigma=1\) (compared to the average \(\mu=4\)) nodes show very similar high social media interaction with the other nodes. Therefore, the best \(w_{TS}\) is 0, since the \(\overline{TS}\) measured is very near to 4 in all the cases.
## 1 Introduction
In contrast to VSIs, their modulation and control are still less studied, in part because of the complex nature of CSI space vector modulation and the lack of available simulation models. This work fills this important gap by offering the first comprehensive, modular simulation models for the three main CSI modulation techniques:  
1. VSI-derived CSI SVM modulator;
2. Direct CSI SVM modulator;
3. DDPWM (direct duty ratio CSI PWM modulator). Since the CSI modulator is not as straightforward as the VSI modulator, the provided simulation models are designed to enable researchers and practicing engineers to better understand the CSI, connect it to existing theory, and explore its suitability for their specific applications. Unlike prior studies that focus on theoretical analysis [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]; this study offers ready-to-use MATLAB/Simulink and PLECS models, empowering researchers and engineers to evaluate, modify, and optimize CSI performance. By integrating these models with comprehensive fundamentals and comparative performance metrics (e.g., THD below 5% for all methods), this work accelerates the adoption of CSIs in smart grids, industrial drives, and renewable energy systems. This work is divided into a few parts. First, a quick theory of the CSI operation and basic relations are provided. Then, the theory of the CSI modulators needed for the simulation models is explained in detail with the building blocks. The CSI modulators are then shown by joining the building blocks of each model to form the final simulation model so that the reader can use them. Finally, the application examples are shown to ensure that proper and allowed limits are applied. This enables the comparison between actual performance and theoretical analysis and design. To improve understanding of the operation of the CSI and simplify simulations, the suitable ranges for variables such as \(V_{DC}\), \(V_{AC}\), modulation index (\(m\)), switching frequency (\(f_{SW}\)), and filter parameters are presented.
## 3 System Model
### Waiting Time of the Nodes  
The waiting time at each node in the cluster is computed to find out whether a node in the cluster can be selected as CH or not.  
\[WT=WT_{max}\times\omega\bigg{(}1-\frac{N_{RE}}{N_{IE}}\bigg{)}\times\sigma \bigg{(}|\text{Avg}(S_{i}^{k})-S_{n}|\bigg{)} \tag{12}\]  
where \(WT_{max}\) represents the maximum pre-defined time to wait, \(\text{Avg}(S_{i}^{k})\) denotes the average speed of adjacent nodes within the range of transmission, and \(S_{n}\) signifies the speed of the node.
## 4 Performance Optimization and Testing
### Memory Access Optimization  
To optimize the performance of OpenCL programs, reducing access to global memory is an important optimization technique. In parallel algorithms, utilizing shared memory, a faster-access memory, can improve program performance. During the flow process of particles, nine distribution functions of particles need to be obtained from the neighboring eight lattice distribution functions, as shown in Figure 11. Shared memory allows all work items within the same work group to access the same memory. In this case, each work item can load the distribution functions of the surrounding eight lattices it requires into shared memory, enabling all work items in the same work group to access these data. The grid data of size 3 \(\times\) (work group size + 2) were transmitted to shared memory, where the work group size is 1 \(\times\) (work group size), and each lattice stores nine distribution functions. This allows each work item in the work group to access the distribution functions of the surrounding eight lattices, as illustrated in Figure 12.  
When using shared memory, it is important to consider the size limitation, data synchronization, and memory access pattern optimization. In this experimental platform, the size of shared memory in a single compute unit is 64 KB, so attention should be paid to the size of data transmitted to shared memory. When different work items within a work group collaborate, proper data synchronization is required. In this study, the barrier (CLK_LOCAL_MEM_FENCE) is used for synchronization. Since each work item only accesses data from the surrounding eight lattices, memory conflicts caused by irregular access patterns can be avoided.  
The experimental platform used in this study is based on the domestic DCU architecture. The wavefront in the domestic DCU architecture is similar to NVIDIA's warp, but a warp consists of 32 threads, while a wavefront consists of 64 threads. The 64 threads in a wavefront execute the same instruction in a SIMD manner but operate on different data. This enables parallel execution of 64 threads at the same time, thus achieving parallel execution of 64 work items. Starting from 64 work items, the performance of the program is tested on a single node with a grid size of 1024 \(\times\) 1024 under different work group sizes and different sizes of shared memory. The results are shown in Table 2.  
Figure 11: Particle distribution function flow diagram.  
Figure 12: Shared memory storage method.  
From the table, it can be seen that when the work group size is 320, the shared memory requested by a single work group exceeds 64 KB, and an error occurs. The other results are not significantly different. In this study, a work group size of 1 \(\times\) 128 was used, and the grid size in shared memory was 3 \(\times\) (128 + 2). The test results before and after memory access optimization are shown in Figure 13.
# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 2 Algorithms and Methods
### Transformers
MOVAD operates through end-to-end training using only RGB frames, eliminating the need for optical flow or bounding boxes. The architecture includes two main modules: the short-term memory module (STMM) and the long-term memory module (LTMM), as illustrated in Figure 17. The STMM, implemented with a video swin Transformer (VST), captures spatio-temporal correlations from recent frames, while the LTMM, based on an LSTM network, incorporates past context to enrich anomaly detection. MOVAD was evaluated on the DoTA [53] dataset of accident videos, achieving an AUC of 82.17%. Ablation studies identified the optimal configuration as a four-frame temporal window for the STMM and a three-cell LSTM for the LTMM. The model demonstrated the  
Figure 16: The AVACA architecture. From [48]. ability to detect anomalies even in scenarios where participants are not visible, highlighting its robustness and versatility.