esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Preparation and Augmentation  
The original data can be directly downloaded from Kaggle's official website [45]. Each sample in the dataset was resized to 224 \(\times\) 224 pixels to facilitate its use with many pre-trained deep learning models. Aravind Eye Hospital in India hopes to detect and prevent this disease among people living in rural areas where medical screening is difficult. Aravind technicians travel to these areas to capture images and then rely on highly trained doctors to review the images and provide a diagnosis. Tags have sorted the dataset. Five classes, respectively, represent no DR, mild NPDR, moderate NPDR, severe NPDR, and PDR [46]. The sample sizes are 1805, 370, 999, 193, and 295.  
The number of samples in each of the five categories is highly imbalanced. With this imbalanced dataset, an optimal real-time result could not be obtained because the model never adequately examines the implicit categories. Data augmentation is framed by aligning one category to the category with most samples to balance the data among the diabetic retinopathy severity categories. We employed data augmentation techniques, specifically the rotation by multiples of 90 degrees, color jittering, and contrast adjustment. Rotating by multiples of 90 degrees was implemented to preserve the integrity of the image content. In this case, the image dimensions remained unchanged, and the rotated images did not experience stretching or compression. This approach helped maintain the consistency of the object shapes and proportions in the images, thus avoiding information loss. Color jittering and contrast adjustment were employed to address the significant color differences between the data and improve the model's generalization, for example, in Figure 1c, where elderly patients may exhibit more signs of age-related macular degeneration. Moreover, for some data instances in the dataset that were already blurry or had low contrast, introducing additional noise was not considered beneficial as it could lead to underfitting during model training.  
First, we randomly selected 200 images from the no DR samples and applied a 180-degree rotation. For the moderate NPDR samples, all images were rotated 180 degrees, and two images were randomly selected for a 90-degree rotation. The mild NPDR, severe NPDR, and PDR samples were subjected to 90-degree, 180-degree, and 270-degree rotations. Next, we randomly selected 260 images from the mild non-proliferative diabetic retinopathy (NPDR) class, applied a 5% colour dither, and performed a 1.3 contrast adjustment once. For the severe NPDR class, we randomly selected 614 images and performed 5% color dithering once. In the case of proliferative diabetic retinopathy (PDR), we randomly selected 410 images and applied both a 5% color dither and a 1.3 contrast adjustment once. Generally speaking, we augmented the number of samples for each category to 2000, resulting in 10,000 images for the five categories.  
Figure 2: The proposed WSN architecture for DR grading.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### Model Complexity Analysis  
To further substantiate the efficiency of DeepST-Net, we provide a rigorous analysis of its computational complexity, which complements the hyper-parameter details described earlier. In general, for a fully connected dense layer with \(n_{\text{in}}\) input neurons and \(n_{\text{out}}\) output neurons, the number of trainable parameters is given by  
\[P=n_{\text{in}}\times n_{\text{out}}+n_{\text{out}},\]  
and the approximate number of floating-point operations (FLOPs) per layer is estimated as  
\[F\approx 2\times(n_{\text{in}}\times n_{\text{out}})+n_{\text{out}}.\]  
Figure 8: Proposed artificial neural network model.  
A more refined formulation that incorporates the computational overhead of the activation function is expressed as  
\[F\approx n_{\text{out}}\Big{(}2\,n_{\text{in}}+\phi(\text{activation})\Big{)},\]  
where \(\phi(\text{activation})\) represents the additional cost incurred by the activation function (with \(\phi(\text{ReLU})\approx 1\) for ReLU).  
DeepST-Net, which comprises an input layer, four hidden layers, and an output layer, achieves the following:  
\[P_{\text{total}}\approx 67,\!841\quad\text{trainable parameters},\]  
\[F_{\text{total}}\approx 135,\!041\quad\text{FLOPs per forward pass}.\]  
with the understanding that these values are approximate and may vary slightly based on the specific state of the dataset as investigated in our ablation study (see Section 5.2).  
DeepST-Net is intentionally designed with a moderate level of complexity, striking an optimal balance between expressive capacity and computational efficiency. With approximately 68 K parameters and 135 K FLOPs, the compact architecture supports rapid inference averaging about 0.108 s per sample on standard hardware while still capturing the intricate, non-linear dependencies inherent in urban traffic patterns. Under our experimental conditions (see Section 5), this efficiency extends to the training process as well, with the network typically converging in roughly 2 h over 120 epochs (employing early stopping to prevent overfitting). This favorable combination of low computational demands, swift inference, and efficient training underscores the scalability and real-time applicability of DeepST-Net, making it a compelling solution for urban travel time prediction compared to more computationally intensive architectures.
## 2 Sample Environment Components
### SANS Measurements  
To validate the sample environment design for SANS measurements, tests were performed at the MLZ. A sample was measured in the newly constructed sample environment and compared to neutron data previously recorded using the D11 SANS machine at the Institute Laue Langevin (ILL). Figure 6 shows the results of a measurement using the sample holder introduced in this work at the KWS-1 [7] SANS instrument at the MLZ. Figure 6, top, shows a comparison of measurements of the same NIPMAM microgel as investigated in Figure 5, performed as the reference at the ILL using the instrument D11 and with the new sample environment at MLZ. The scattering curves show a satisfactory agreement. The slight deviation at low \(q\) can be attributed to resolution effects.  
Figure 5: **(left)**: Swelling behavior of a NIPMAM microgel measured with the SANS/DLS setup at both scattering angles. **(right)**: comparison of autocorrelation functions of the NIPMAM microgel at 56 \({}^{\circ}\)C for both angles (red) compared to ACFs measured with a commercial ALV setup (black curves, measured with a different laser wavelength and therefore at different angles to achieve the same \(q\) value) at the same \(q\)-values of 0.23 nm\({}^{-1}\) and 0.15 nm\({}^{-1}\).  
Figure 6, bottom, shows detector images of empty-cell measurements at 2 m, 5 m and 20 m sample-detector distances. Especially for the detector image of the 5 m measurement, an inhomogeneous scattering can be observed. This behavior is attributed to reflections on the sample holder and the walls of the cuvettes which is caused by the rather large distance between the final window of the collimation system and the sample cells and the not fully adapted collimation. This large distance is due to spatial constraints of the sample environment which was not optimized for the KWS-1 machine.  
Furthermore, a rather strong background scattering can be observed. This is also due to the relatively long air path which we had to take in the test measurements. This causes the increased background scattering. However, this will not be a problem for the instruments SKADI and LOKI at ESS, due to the optimization of the sample environment for these SANS instruments.  
Figure 6: (**top**): SANS data measured employing the new sample environment from this work using the KWS1 machine at MLZ compared to SANS measurements made with the ILL standard setup at the D11 instrument. For both measurements, the same NIPMAM microgel sample as for the DLS measurements was used. (**bottom**): detector images for an empty cell measurement at 2 m (**a**), 5 m (**b**) and 20 m (**c**). The reflections seen here, are caused by the large distance between the nose of the collimation system and the sample cells.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 5 Experiment and Result
### Detection Performance
JaSt extracts syntax units from JavaScript programs, which is a syntactic level of program features, and then uses them to train the model. This feature extraction method also captures the syntax changes caused by obfuscation. In other words, JaSt considers that obfuscation is one of the characteristics of malicious code. The author of JaSt also confirmed this in the paper. However, this features extracting method has obvious limitations. On the one hand, benign obfuscation is widely used to protect the intellectual property rights of program developers. Using obfuscation as a feature of malicious code leads to false positives. On the other hand, the syntactic level program features have limited expression power to malicious code. Experienced attackers can still evade the detection of JaSt by adjusting the syntax structure, which makes it difficult to improve JaSt's performance. In summary, the assumptions about the effects of obfuscation in existing studies are not entirely correct. The impact of obfuscation is related to the abstract representation of the program and the choice of model. Semantic level program features are more sensitive to obfuscation than syntactic level program features. Deep learning models are more sensitive to obfuscation than traditional machine learning models.
## 4 Results and Discussion
### Electrical Resistivity  
Concrete is considered a relatively non-conductive material, and the electrical conductivity in specimens is due to the presence of water in pores and cavities. Therefore, the lower the number of pores, the lower the electrical conductivity, so that electrical resistivity is higher. As shown in Figures 14 and 15, the electrical resistivity of specimens containing 5% waste was similar to that of the control mix at 7 days, while at 28 and 42 days, it increased by 4%. At 7 days of age, the electrical resistivity of specimens with 10, 15, 20, 30, 40, and 50% waste powders decreased by 12, 23, 25, 46, 58, and 62%, respectively. However, after 28 days of curing, these values, which show reduction in electrical resistivity compared to the control mix, decreased to 7, 12, 18, 28, 32, and 33%, respectively. Based on these results, cement replacement at more than 5% led to a decrease in electrical resistivity at different ages. In this study, an increase in the replacement ratio resulting in electrical resistivity degradation. Thus, in these mortar mixes, electrical conductivity and permeability are not the determinant factors for electrical resistivity. In fact, higher EAFD content in mortar mixes means higher heavy metal content so that higher conductivity can be justified, which causes less electrical resistivity; this hypothesis was confirmed by [PERSON] et al. [70].  
Figure 13: Water absorption at 28 and 120 days (after 30 min and 24 h).
## 4 Simulation and Results  
To validate the effectiveness of the proposed scheduling method, a series of simulation experiments are conducted under various task scenarios. These experiments are designed to evaluate the proposed method's performance in terms of the scheduling success rate, execution efficiency, priority assurance, and time shifting control. Both the traditional time-pointer-based method and the improved approach are tested for comparison.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 2 Trends of the _Smart Metering_ Systems
### Considerations for a Smart Metering System  
A _Smart Metering_ system can be seen as three differentiated phases: planning, rollout, and operation. The widely ranging requirements and characteristics of each specific application, technology, and deployment scenario make the planning of a _Smart Metering_ system a nontrivial task [15]. Table 2 summarizes some of the most important consideration to take into account when designing a _Smart Metering_ system. The rollout phase refers to the actions involved in replacing all the meters included in the initial scope of the project. This phase also includes deploying the communication infrastructure, including all components and devices. Finally, the operation phase refers to actions carried out on the infrastructure after the rollout, that is, after the scheduled replacement, initial configuration of devices, and initial setup of the central system [16].  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Planning Aspects**} \\ \hline
**Technological** & Election of the most suitable technology according the final end \\  & Implementation of software \\ \hline
**Physical aspects** & Resilience and strength \\ \hline \multirow{4}{*}{**Communication**} & Type of network (wired, wireless, hybrid) \\  & Range of network \\  & Bandwidth \\  & Quality of signal \\  & Security \& privacy \\ \hline \multirow{3}{*}{**Costs**} & Costs of devices \\  & Costs of communication network infrastructure \\ \cline{1-1}  & Maintenance \\ \hline \multirow{2}{*}{**Customers**} & Service providing \\ \cline{1-1}  & Access to personal data \\ \hline \hline \end{tabular}
\end{table}
Table 2: Planning considerations for a _Smart Metering_ system.
## 7 Conclusions  
CRNs are designed to manage radio resources efficiently by utilizing the spectral holes in licensed frequency bands. The in-band FD technology, a new paradigm for CRNs, allows SUs to simultaneously sense and access the spectral holes. However, the efficient sharing of spectrum between the dynamic PU and FD-enabled SU transmitters (FD-SU TXs), in dense small-cell IoT scenarios, presents challenges that must be addressed. The random and dense distribution of FD-SU TXs with sensing capabilities creates heterogeneous environments with temporal and spatial spectral opportunities. In this regard, we considered the traffic variations of the PU both in time and space domains, and proposed a two-dimensional spatial and temporal spectral hole-sensing model for the FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, an analytical formulation and evaluation of the UoS scheme was proposed for different FD-SU TXs. The performance of the UoS scheme was investigated in terms of average number of sensing slots used for the successful secondary communication in each time-slotted frame. The numerical results validated the influence of different network and sensing parameters over the proposed spatial-temporal spectral hole-sensing model and UoS scheme. In the future work, we can extend the proposed approach by considering the temporal and spatial variations of idle channels in more complicated IoT-CRN scenarios such as full duplex (cooperative) or ARQ/HARQ enabled SU nodes co-exist with the multiple PU-TXs.  
Conceptualization & Methodology, W.K. and H.Y.; Software & Formal Analysis, W.K.; Validation & Investigation, H.Y.; Resources, H.Y.; Data curation & Writing-Original Draft Preparation, W.K.; Writing-Review & Editing, W.K. and H.Y.; Supervision & Project Administration, H.Y.; Funding acquisition, H.Y. This research was supported by the Brain Korea 21 Plus Program (22A20130012814) funded by the National Research Foundation of Korea (NRF) and Basic Science Research Program (2017R1D1A1B03030757) through the NRF funded by the Ministry of Education and by the Ministry of Science and ICT (MSIT), Korea,  
Figure 10: Average number of secondary communicating sensing slots with different PU mean OFF durations and SIS factors.  
under the Information Technology Research Center (ITRC) support program(IITP-2019-2016-0-00313) supervised by the Institute for Information & communications Technology Promotion (IITP).  
The authors declare no conflict of interest.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 4 Experiments
### Environment Reconstruction  
After capturing the original point cloud data, we processed the terrain information based on the point cloud. The process began with preliminary steps, such as downsampling, normal estimation, and FPFH feature extraction. This was followed by initial registration and then precise alignment using the ICP method for the point clouds collected by the left and right radars of the bucket. Next, we reconstructed the point cloud by first removing outliers and any unreasonable points. We then defined the ROI for the actual excavation process. After selecting the ROI, we used Poisson surface reconstruction techniques to rebuild the point cloud, filling in gaps and preparing for the conversion of the point cloud to a grid and feature extraction.  
Our methodology employed the OpenCV library for normal estimation and point cloud registration. This approach facilitated rapid neighborhood searches and maintained a balance between accuracy and computational efficiency.  
The preliminary registration aimed to align the left and right radar point clouds roughly, setting the stage for more precise alignment. We utilized OpenCV's feature-based method to quickly estimate an initial transformation matrix.  
For fine-tuning the alignment, we applied the ICP algorithm, optimizing the point clouds' alignment accuracy. The processed point clouds, an initial transformation from the preliminary step, and a set maximum distance for corresponding points were fed  
Figure 2: Excavation operation: Real to simulated.  
into OpenCV's ICP implementation. This step refined the point clouds' alignment by minimizing the direct Euclidean distance between corresponding points.  
Figure 3a shows the unregistered point clouds from the left and right radars, where the misalignment is evident. Figure 3b illustrates the point clouds after preliminary registration, showing basic alignment. After ICP registration, as shown in Figure 3c, the left and right point clouds are well aligned, resulting in a fused point cloud.  
In our research, we focused on the terrain changes in front of the excavator bucket before and after each scoop. In Open3D, we created an axis-aligned bounding box as the ROI to study the terrain changes in detail. As shown in Figure 3e, we defined the ROI boundaries as (3, 2, \(-\)4) and (6, 5, 1), with 'x+' pointing down and 'z\(-\)' to the right.  
Surface reconstruction organized the points in the point cloud into a smooth surface, effectively representing the continuity and smoothness of the surface. The reconstructed model usually has uniform data density, making the surface features more consistent and continuous. The pre-reconstruction point cloud, as seen in Figure 3d, shows an uneven distribution of points within the ROI and some gaps. After reconstruction, as shown in Figure 3f, the holes and missing parts in the point cloud were filled in, adding points to the area (marked in black), transforming the sparse and incomplete point cloud into a continuous surface model, ready for further modeling operations.
## 5 Discussion
### Technical Analysis and Comparative Effectiveness of ML and DL Models
#### 5.2.3 Support Vector Machine (SVM)  
Technical Details: Support vector machines (SVMs) are supervised learning models used for classification and regression analysis. They work by finding the optimal hyperplane that maximizes the margin between different classes. SVMs are particularly effective at handling high-dimensional data and can manage nonlinear relationships using kernel functions.  
Performance: As highlighted by [PERSON] et al. [35], support vector machines (SVMs) perform well in demand prediction by finding the optimal hyperplane that maximizes the separation between groups. In electricity demand forecasting, the SVM model achieved approximately a 12% reduction in the forecast error rate compared to conventional linear models.
## 2 Literature Review
Their results showed that the GA, despite being more high performing with a lower makespan, requires more changeover than the heuristic approach, thus increasing the setup time, with an average increase in the setup time of 43.2%. GA have also been adopted to solve the TSP problem for mobile robots[40]. In [37], such a GA was proposed to reduce the travel path of a mobile robot that performs logistics-related tasks (picking and delivery). The algorithm was compared to a traditional greedy algorithm, i.e., a planner that picks nearest tasks, showing a reduction in the travel path of at least 40%. A GA was also adopted in [41], where it minimizes the energy usage by minimizing the traveling time. The system was also applied to an industrial application, where the robot was successfully capable of continuously picking and placing SLCs from the warehouse. Lastly, [42] compared the performance of a GA-based heuristic with a mixed-integer programming (MIP) model. Indeed, the MIP was capable of finding optimal solutions, but proved to be too time consuming. On the other hand, the approximated GA approach was significantly faster, with differences in the results of only 3%. Considering our review, the majority of works have focused on the search algorithm, e.g., tabu search [15]. [PERSON] et al. [13] proposed a custom search algorithm, CFIM2, which improves the traditional tabu search. Whereas the two algorithms achieve similar performance, tabu search generates a larger number of solutions, thus requiring a higher computational time. [PERSON] et al. [17] combined a variable neighborhood search with a clustering approach. The authors introduced a clustering analysis to improve the efficiency of the algorithm by grouping a set of jobs on the basis of the processing time. This approach leads to better solutions, increasing the improvement percentage as the mean time job arrivals increases, thereby improving the schedule as the problem complexity increases. As a result of our literature review, we can identify that previous studies agree that real industrial scheduling problems are complex, and meta-heuristics or approaches that decompose the problem into subproblems should be considered. Despite previous works having presented algorithms to reduce the setup times, little focus has been directed toward the reduction in setup time in sales kitting. Moreover, the literature regarding automatic kitting processes focuses, to the authors' knowledge, on technical solutions and performance evaluation models. Approaches to minimize kitting process times have been developed regarding mobile robots, where heuristics have been adopted to solve the TSP. However, these works do not focus on the minimization of the retooling of feeders, which is another important problem in automatic kitting processes. Therefore, this work aims to reduce the setup time in the sales kitting industry, adopting a multi-clustering approach to reduce the complexity of the problem due to the high number of products and components characteristic of this process.