esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 2 Feature Point-Based Change Detection Method
### Feature Extraction  
Feature points used in this paper are the points that have distinctive texture patterns, such as abrupt brightness changes in their vicinity. Feature points are regarded as a means to describe an arbitrary object, and they have been used to find corresponding points of the same object through matching [37; 38; 39]. In this paper, we wish to check whether they can be a useful unit of change for small objects. We used Scale-Invariant Feature Transform (SIFT) [35] and the Accelerated-KAZE (AKAZE) algorithm for extracting feature points [36]. SIFT can extract features robust to image rotation, affine transformation, and scale [46]. It constructs a scale space using a Gaussian kernel, generates a Difference of Gaussian (DoG), and localizes feature points by eliminating low-contrast points. AKAZE uses a nonlinear diffusion filter to remove noise and highlight edge areas and detect feature points as points with the maximum response of a Hessian-based filter [36]. The brightness values of ortho-rectified panchromatic bands were re-scaled from 16 bits to 8 bits, and feature extractors were applied. Through this processing, a lot of features were generated from previous and recent images. In many cases, multiple features at very close distances were extracted from a single object. For some very small objects, no features were extracted. However, to check the capability of feature-based change detection, we did not apply additional post-processing to filter out these multiple features or to amend objects without features.
###### Abstract
Tools have been developed for automatic labelling of the generated images for traffic lights and signs, vehicles, and pedestrians [14] using the CARLA platform. Dataset generation using simulators is common as well [15], as it provides data collection in a faster and cheaper way. For autonomous driving, there are open-access datasets such as Waymo [16], the Leddar PixSet [17] that includes both camera images and Lidar data, and the Udacity dataset [18], among others, none of which are adapted to the specifications of the present project. The necessary specifications are related to the Portuguese Open competition, and due to its specificities (size and track shape, special traffic signs, obstacles, etc.) these open-access datasets do not provide the needed data for training. Therefore, it was necessary to use a simulator to generate these specific details for the intended dataset along with automatic labelling for steering. A 3D graphical simulation (CoppeliaSim) was used to generate the intended images of a road track and obstacles. This simulation framework allowed for automatic generation of images, simulating the vehicle on the road in uncommon and different situations, and generating as many images as necessary for training the model. The supervised learning model that we used was developed in Python with the OpenCV library for Computer Vision algorithms, and Robot Operating System (ROS) was used as middleware to interact with the simulator. The neural network implementation and training were carried out using the NVIDIA Jetson NanoTM developer kit. This work demonstrates that a neural network model trained in a simulation environment can drive a robotic vehicle better than a conventionally programmed counterpart, with similar performance in a real scenario. This document is structured in eight parts, with this introduction first describing the problem and main objectives and providing an analysis of the existing related research. The second part covers the theoretical foundations required for a better understanding of this work. Sections 3-5 describe the practical work that was carried out and respectively consider the simulation framework, computer vision and control algorithms, and neural network development. The results are presented in Section 6, and the following Section 7 presents a discussion of the tests carried out both in the simulator and with a real car. Finally, Section 8 presents the main conclusions of this project and suggests further improvements.
## 5 Discussion
### What Are the Most Used Techniques in ML and DL to Predict Judicial Decisions?  
Even though ML and DL fall under the AI umbrella, some differences exist. Supervised ML extracts patterns from vectorized textual data to classify and improve the experience. DL is a subset of ML that uses complex, multi-layer neural networks. Even though ML and DL fall under the AI umbrella, DL networks have a significant benefit in that they frequently improve as the quantity of data grows [77].  
For the classification of legal documents, several traditional ML techniques, such as NB, Logistic Regression, SVM, RF, and variations MLP, and XGBoost, were used. Most of them were conducted well in terms of the classification ratio or accuracy. The DL methods have also been used for legal textual data classification. Algorithms such as LTSM, CNN, BERT, and other variations were successfully used in the literature. The percentage of ML (70.6%) and DL (29.4%) techniques used in the works in the literature is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nro.** & **Reference** & **Corpus Size** & **Lang.** & **Model** & **Repr.** & **Metric** & **Min(\%)** & **Max(\%)** & **Count** \\ \hline
1 & [65] & 5990 & G & Naive Bayes classifier & norm-sized thesaurus & F-measure & 53 & 58 & 2017 \\ \hline
2 & [71] & 500,000 & E & RF & feature matrix & Accuracy & 75 & 82 & 2017 \\ \hline
3 & [73] & 545,760 & E & CNN, RNN & LSTM, RNN & CBoW & F-measure & 99.74 & 99.89 & 2018 \\ \hline
4 & [69] & 4762 & P & SVM, DT, XGBoost, BERT, GRU, LSTM, CNN & TF-IDF & F-measure & 73.42 & 80.22 & 2019 \\ \hline
5 & [66] & 53,397 & G & Transformers, BERT RobBERT & PQR & Accuracy & 61 & 87 & 2021 \\ \hline
6 & [67] & 16,040 & E & SVM & Embeddings & F-measure & 79.5 & 92.2 & 2021 \\ \hline
7 & [68] & 30,311 & E & ooBERT, ftBERT, and LEGAL-BERT, biLSTM, GRU, LR, SVM & weighted TF-IDF & F-measure & 75.5 & 81.3 & 2021 \\ \hline
8 & [70] & 7904 & E & adaBoost, decision trees, gradient boosting, random forests, SVM, XGBoost & TF-IDF, doc2 vec, law2 vec & Accuracy & 56.61 & 62.74 & 2021 \\ \hline
9 & [72] & 34,816 & I & Logistic Regression, SVM, Random Forest, BiGRU, GloVe, BERT, RoBERTa, XL-Net, LEGAL-BERT & Sent2 Vec, Doc2 Vec, Hierarchical Attention Network, BERT, DistiBERT, RoBERTa, XL-Net, LEGAL-BERT & Accuracy & 78 & 94 & 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of ruling decision prediction in Tax Law, Intellectual Property, and Administrative Law, where E = English, F = French, G = German, P = Portuguese and I = Iranian.  
The best result was obtained using the ML algorithm (Multi-Layer perceptron) with 98.7%.
## 5 Discussion
### Impact of Learning Rate Schedulers  
The choice of learning rate scheduler significantly influences the performance of deep learning models by controlling how the learning rate adapts during training, thereby affecting convergence and generalization. Each scheduler offers distinct strategies for adjusting the learning rate, impacting performance metrics. For instance, CosineAnnealingWarm-Restarts often facilitates better exploration of the loss landscape through periodic learning rate restarts, potentially leading to improved performance, as evidenced by the superior accuracy of ResNet50 under this scheduler. Conversely, StepLR's fixed step reductions may result in suboptimal convergence if not carefully tuned. The radar graphs (Figure 8a-d) generated for each scheduler visually demonstrate these effects, highlighting the comparative performance of the best models across the evaluated metrics, with each graph showcasing the strengths and trade-offs induced by the respective scheduling strategy.  
Figure 7: Radar graph comparing model performance across different optimizers for the car collision detection system while keeping LR, loss, and schedulers the same.
# Effect of Audio-Visual Factors in the Evaluation of Crowd Noise
## 3 Results
### Effect of Audio-visual Stimuli in Different Environments  
Figure 6 shows the sound preferences for conversational sounds in different environments under simultaneous audio-visual stimuli (The error bars represent the S.D. of the average values). For all three environmental groups, there was no significant difference between the overall preference and visual environment preference scores for the same background sound. However, there was a significant difference in the sound preference and acoustic comfort scores for the three groups of sounds in the same set of visual environments. The change in volume also significantly affected visual preference and comfort scores. For the original three groups of voices, the lower the sound, the higher the sound preference score (\(p<0.01\)). The overall preference and comfort scores changed after adding visual factors. The environmental evaluation of the videos one and two changed from significant to non-significant, with a difference in scores between the low and medium groups. The difference between the scores of the medium and high groups was still significant (\(p<0.01\)). The louder the sound, the lower the overall preference and comfort scores. Therefore, after adding the visual factor, evaluations of the overall environment were not affected when the sound pressure level of the human voice was at 45.6 to 55.6 db. However, when the sound pressure level was 55.6 to 66.6 dB, overall environment scores were lower as the sound got louder.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Subjective** \\ **Evaluation** \\ \end{tabular} } & \multirow{2}{*}{**Crowd Density**} & \multicolumn{3}{c}{**Sound Volume**} \\ \cline{3-5}  & & **Low** & **Medium** & **High** \\ \hline \multirow{3}{*}{Acoustic comfort} & Low & 3.45 & 2.87 & 1.77 \\  & Medium & 3.16 & 2.80 & 2.16 \\  & High & 2.97 & 2.71 & 1.97 \\  & Low & 3.39 & 2.74 & 1.51 \\ Sound preference & Medium & 3.06 & 2.61 & 2.00 \\  & High & 3.06 & 2.52 & 1.87 \\  & Low & 2.55 & 3.48 & 4.10 \\ Sound loudness & Medium & 2.84 & 2.97 & 3.90 \\  & High & 2.68 & 3.13 & 4.13 \\  & Low & 3.45 & 3.52 & 3.00 \\ Visual comfort & Medium & 3.26 & 3.13 & 2.81 \\  & High & 3.03 & 2.84 & 2.48 \\  & Low & 3.52 & 3.45 & 2.94 \\ Visual preference & Medium & 3.06 & 3.03 & 2.55 \\  & High & 3.00 & 2.58 & 2.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The evaluation under the effect of sound and visual stimuli.
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
## 1 Introduction
The third section provides a detailed analysis of simulated time-domain analysis in two different orientations, which are face to face (FTF) and side by side (STS). The fourth section covers the conclusion. In this study, all the simulation results are obtained from CST studio suite 2021. Computational work was conducted by MATLAB R2015a and the analysis and plotting by Origin 8.5 Pro. In all operating bands, the measured values provide good agreement with respect to the simulations.
## 6 Conclusions  
In this work, we proposed an Intelligent Tactile Edge framework to predict the congestion level at the network layer by utilizing the ECN mechanism and adjusting the packet size and packet rate of the haptic transmission at the application layer by employing different haptic codec schemes with a focus ensure QoS and QoE. The proposed ITE utilizes the LSTM model to estimate the network congestion and then the Q-learning algorithm applies to take the action and select optimal haptic codecs to solve the communication network issues (transmission delay, jitter, and packet loss). We employ the power function of the connection as a reward function to optimize the Q-learning algorithm and find the pattern. The proposed ITE was investigated on the Tactile Internet standard 3 DoF teleoperation traffic dataset. The simulation results suggest that the ITE is able to ensure the QoS and QoE requirements of the haptic-enabled bilateral teleoperation application. The proposed framework was simulated on different parametric settings to tune the algorithm and show the algorithm convergence to select the optimal parametric configuration. In the recent future, we plan to implement the proposed system in real-work application with a haptic device and extended it with advanced machine learning techniques such as federated learning-aware approaches at the ITE to support emerging technologies.  
Methodology, R.A., A.H. and M.Z.L; Software and coding, M.Z.I. and S.; Experimentation and formal analysis, M.Z.L, R.A. and A.H.; Writing--original draft preparation, M.Z.I. and A.H.; Writing--review and editing, M.Z.I., S., A.H. and R.A.; Supervision, H.S.K.; Funding acquisition, M.Z.I. and H.S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant by the Korean Government through the Ministry of Science and ICT (MSIT) under Grant 2022-0-00331, in part by the National Research Foundation of Korea (NRF) Grant by the Korean Government through MSIT under Grant 2022R1F1A1063662, and in part by the Strengthening Research and Development Capability Program of Sejong University.  
Not applicable.  
The 3 DoF haptic datasets used in this work are available online with open access for academic research use. The static and dynamic interaction haptic dataset is available online at [[https://cloud.lmt.ei.tum.de/s/4](https://cloud.lmt.ei.tum.de/s/4) FmHUCsoUvwRle3]([https://cloud.lmt.ei.tum.de/s/4](https://cloud.lmt.ei.tum.de/s/4) FmHUCsoUvwRle3) (accessed on 15 September 2021), and the simulation experiment results files are availabe at [[https://github.com/zubair1811/IntelligentTactileEdge2022](https://github.com/zubair1811/IntelligentTactileEdge2022)]([https://github.com/zubair1811/IntelligentTactileEdge2022](https://github.com/zubair1811/IntelligentTactileEdge2022)), accessed on 15 September 2022.  
The authors declare no conflict of interest.
## 4 Model (SIRPM)
### Characteristics and Benefits of the Model (SIRPM)  
The SIRPM model presents characteristics (Table 10) and benefits (Table 11) that make it a robust and well-suited solution for predictive maintenance systems in industrial environments. This section highlights the main technical features and the advantages associated with its implementation. The SIRPM model offers an ideal balance between automation, intelligence, and flexibility, enabling companies to maximize productivity while minimizing costs and environmental impacts. The modular structure and continuous learning make the model a long-term solution for companies seeking innovation and sustainability in managing their industrial assets. Below, the comparative Table 12 is presented between the existing studies and the proposed model (SIRPM). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Model (SIRPM) Characteristics**} \\ \hline \multicolumn{2}{c}{**Characteristics**} & \multicolumn{2}{c}{**Description**} \\ \hline \multirow{3}{*}{1.} & Integration of Advanced Technologies & \(\bullet\) & Combines robotic process automation (RPA), machine learning (ML), and the IoT. \\ \cline{3-4}  & & \(\bullet\) & Enables continuous data flow between sensors, analysis platforms, and operational teams. \\ \hline \multirow{3}{*}{2.} & Intelligent Automation & \(\bullet\) & Automates data collection, processing, analysis, and execution of maintenance-related actions. \\ \cline{3-4}  & & \(\bullet\) & Reduces manual intervention, minimizing human errors. \\ \hline \multirow{3}{*}{3.} & Continuous Learning & \(\bullet\) & The machine learning model is continuously updated with maintenance feedback, becoming more efficient and accurate over time. \\ \hline \multirow{3}{*}{4.} & Modular Configuration & \(\bullet\) & Clearly divided into phases that can be implemented gradually, depending on the company’s needs. \\ \cline{3-4}  & & \(\bullet\) & Supports different algorithms and sensors, ensuring flexibility for integration with existing systems. \\ \hline \multirow{3}{*}{5.} & Real-Time Monitoring & \(\bullet\) & Allows real-time data analysis for anomaly detection and immediate alert generation. \\ \hline \multirow{3}{*}{6.} & Sustainability and Energy & \(\bullet\) & Promotes waste reduction and resource optimization through detailed predictive analyses. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Model (SIRPM) characteristics.
## 4 Results and Validation
### Orbital Configurations  
The year 2008 is characterized by an AVHRR-carrying constellation of five satellites (Figure 2a), each with a specific semi-constant local equatorial crossing time: NOAA-15 (-5 h,-17 h), MetOp-A (-9 h30), NOAA-17 (-10 h), NOAA-18 (-13 h30), and NOAA-16 (-4 h30,-16 h30). Most important are the (mid-)morning (MetOp,NOAA-17) and early afternoon (NOAA-18) orbits, since these observations have superior illumination conditions and are generally better suited for temporally interpolating the diurnal cycle, compared to early morning or late afternoon observations. A big challenge of the Level-3 TOA flux retrieval is to capture the diurnal variability, which depends on the dominant regional climate features. Features that exhibit systematic asymmetry around solar noon especially require observations during both morning and afternoon (e.g., marine stratocumulus clouds that \"burn off\" during the afternoon).  
On time scales longer than the particular year 2008, this orbital configuration is subject to orbital drift and eventually phasing out of some satellites, as well as their replacement with new satellites which keep the orbital configuration more or less similar until about 2016. However, during its drift towards an evening orbit from 2016 onwards (Figure 2a), the most recent early afternoon AVHRR instrument has not been replaced because it was the last produced piece of its series (NOAA-19); the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been mounted on the new satellites instead. For data records exclusively relying on AVHRR observations, this has a considerable impact, which is estimated here by performing the validation for different orbital configurations. Besides the default configuration, using all available satellites, an alternative configuration was validated, which excludes the early afternoon satellite NOAA-18 and thus mimics the post-drift NOAA-19 situation.  
Furthermore, the added value of mid-morning orbits can be estimated by excluding NOAA-17 and MetOp. The mid-morning orbits were considered together, as they are almost simultaneous in local equatorial crossing time and hence do not provide a lot of added value with respect to each other. Their removal anticipates the future situation when the MetOp satellites will be decommissioned.  
Finally, five additional configurations consist of each satellite separately, to estimate their relative importance. Early morning or late evening satellites do not provide global coverage, as the day length determines whether these observations can be used that early (or late) on a given UTC day, which in turn depends on the latitude and season. An overview of all the tested orbital configurations is given in Table 5.  
Two daily means from 15 th January and 15 th June, retrieved using all available satellites, are shown in Figure 10 to illustrate the level of detail and realism of the obtained TOA flux.  
Figure 10: Maps depicting daily mean RSF, retrieved using all available satellites (N15,N16,N17,Met,N18), **(a)** for 15 January 2008 and **(b)** for 15 June 2008.
# Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging
## 3 Fault Detection Rate-Dependent SRGM with Imperfect Debugging  
### Basic Assumptions  
Based on the recognition of the test environment, the assumptions of the imperfect debugging model considering the correlation of the fault detection rate are as follows [29; 30; 31; 32; 33; 34]:  
* The fault detection and repair process obeys the nonhomogeneous Poisson distribution process NHPP (nonhomogeneous Poisson process);
* Software failure is caused by the remaining faults in the software;
* In the time interval \((t,t+\Delta t)\), at most one fault occurs, detected faults are proportional to remaining faults, with a proportional coefficient of \(p(t)\);
* In the time interval \((t,t+\Delta t)\), the repaired faults are proportional to the number of faults detected;
* In the process of fault repair, new faults are introduced. The number of introduced faults is proportional to the number of accumulated repaired faults, and the proportional coefficient is \(R(t)\).  
Based on the above assumptions, two types of imperfect debugging models can be established.
## 2 General Features of ClyA
### Secretion Pathway
For example, heat-labile toxin, one of the main enterotoxins produced by enterotoxigenic _E. coli_, is secreted via OMVs [49]. OMVs released by enterohaemorrhagic _E. coli_ O157 cause cell death by delivering a cocktail of virulence factors, such as Shiga toxin 2a, cytolethal distending toxin V, and flagellin, into host cells [50]. ClyA has been identified as being an _E. coli_ OMV cargo protein that is exported in a manner independent of canonical secretion systems [20]. [PERSON] et al. demonstrated that ClyA is exposed on the surface of bacterial cells and accumulates in OMVs using immunofluorescence, electron microscopy, and atomic force microscopy. They concluded that the ClyA protein is translocated to the periplasmic space in its monomeric (inactive) form, and then oligomerizes to form active pore assemblies within the OMVs (Figure 1). This OMV-ClyA has been recently applied as a powerful tool in biomedical and bioengineering fields, as described in further detail below.