esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 3 Methodology
### Quality Metrics  
Metrics are needed to evaluate (1) the quality of harvest dates determining, (2) the sugarcane area harvested in a month.  
Each complete or partial field harvest must be determined. Since the reference harvest dates are determined by visual interpretation of the optical data being the same with the data applied in the algorithm, it is incorrect to assess the error of harvest dates determining with the help of traditional error metrics, such as mean absolute error or root mean square error. To assess the quality of harvest dates determining, the Table 2 is used. The table columns correspond to the harvest dates for the reference data; the rows correspond to the harvest dates determined with the help of the algorithm.  
True Match is the number of coincidences of harvest dates. The algorithm error is the total of false responses (False Match) and omissions (False Not-match) of the harvest dates. Introduce also the following  
\[\text{True match rate}=\frac{\text{True Match}}{\text{True Match}+\text{ False Not-match}}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Match** & **Not-Match** \\ \hline
**Match** & True Match & False Match \\
**Not-match** & False Not-match & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of the quality assessment for harvest dates determining.  
and  
\[\text{Match predictive value}=\frac{\text{True Match}}{\text{True Match}+\text{ False Match}}.\]  
True match rate shows the proportion of matches for harvest dates determined by the algorithm from the total number of harvest dates of the reference sample. Match predictive value shows the proportion of matching harvest dates among all dates found by the algorithm.  
Estimation of the sugarcane area harvested per month is rather useful for practical applications. The harvested area is determined by harvest end dates for cells. The harvest end date is the last date in the sequence of harvest dates for a cell, separated by a time interval less than one month.
## 2 Literature Review  
A literature survey of existing computer methods in biomedical research found that deep learning, specifically convolutional neural networks (CNNs), is one of the most promising techniques used to solve problems in neurology and cardiology. One such case can be found in [36], which presented a model for automated EEG-based (brain's electrical activity) screening of depression using a 13-layer deep neural network. The proposed approach was designed to automatically learn differentiating features from the input EEG signals from depressive and normal subjects, achieving accuracy rates of 93.5% and 96.0% for the left and right hemispheres, respectively. The results of the study were found to be consistent with the research theory that depression is primarily associated with the right hemisphere.  
Ref. [37] discussed ECG signal processing and heartbeat segmentation, feature description methods, and learning algorithms used in typical arrhythmia detection situations, which influenced the approach undertaken in this work, in addition to the use of a convolutional neural network.  
The presented works are complementary to our approach, as our goal is to study the effects of transferring information learned from ECG to PPG waveforms due to the scarcity of PPG data in the public domain. We summarize the most relevant work in Table 1.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper** & **Task** & **Model** & **Data** & **Results** \\ \hline
[36] & Depression screening from EEG data & CNN & EEG data from Psychiatry Department, Medical College, Calicut, Kerala, India & 93.5\% and 96.0\% accuracy (left and right hemispheres) \\ \hline
[38] & Arrhythmia and myocardial infarction classification from ECG data & CNN & ECG data from MIT-BIH and PTB diagnostic databases & 93.4\% and 95.9\% accuracy for arrhythmia and MI \\ \hline
[12] & Arrhythmia classification from ECG data & CNN & ECG data from MIT-BIH database & 91.33\% accuracy \\ \hline
[14] & Arrhythmia detection from ECG data & CNN & ECG data from MIT-BIH database & 99.4\% accuracy \\ \hline
[39] & Atrial fibrillation detection from PPG data & CNN & PPG data from wrist-wearable devices & 95\% AUC \\ \hline
[40] & Classification of rhythm classes from ECG data & CNN & ECG data from Zio monitor & 0.837 F1 \\ \hline
[41] & Atrial fibrillation detection from PPG data & CNN-LSTM (CLSTM) & PPG data from MIMIC-III, IEEE TBME Respiratory Rate Benchmark, and synthetic PPG data & 98.21\% accuracy \\ \hline
[42] & Atrial fibrillation detection from PPG data & BNN & Stanford wearable PPG dataset & 0.671 F1 \\ \hline
[43] & Paroxysmal atrial fibrillation detection from ECG and PPG data (transfer learning) & CNN, BRNN with attention & ECG data from University of Virginia Heart Station, PPG data from smartwatch & 0.97 AUC for both ECG and PPG \\ \hline
[44] & Atrial fibrillation detection from PPG data & CNN, CNN-RNN (CRNN) (CRNN) & PPG data from wearable devices & 99\% AUC \\ \hline \end{tabular}
\end{table}
Table 1: Summary of papers on ECG and PPG classification.
## 5 Results and Discussion
### Results and Analysis
Our model performed better than the other four models, with accuracy measured as 99.99% on both datasets and low FPR values of 0.0003 on the BoT-IoT dataset and 0.001 on the TON_IoT dataset. The LSTM model showed good performance in terms of accuracy and FPR, while the GRU showed a high TPR compared to the LSTM on the BoT-IoT dataset. On the TON_IoT dataset, the GRU performed poorly compared to the other models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Avg. Training Time per Epoch** & **Classification Time** \\ \hline With LSTM & 99.99\% & 100\% & 100\% & 100\% & 86.3 s & 16 s \\ \hline With QRNN & 99.99\% & 100\% & 100\% & 100\% & 66.5 s & 13 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of our proposed model while using LSTM and the QRNN based on TON_IoT dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy\%** & **Precision\%** & **Recall\%** & **F-Score\%** \\ \hline K-NN [19] & 99.00 & 99.00 & 99.00 & 99.00 \\ \hline Hybrid IDS [27] & 99.97 & - & - & 95.7 \\ \hline RF [28] & 99.80 & 99.00 & 99.00 & 98.80 \\ \hline RF [37] & 99.99 & 79.76 & 62.98 & 65.08 \\ \hline TP2 SF [37] & 99.99 & 99.97 & 94.92 & 97.08 \\ \hline Our model & 99.99 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of our proposed model with state-of-the-art models based on the BoT-IoT dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy\%** & **Precision\%** & **Recall\%** & **F-Score\%** \\ \hline RF [37] & 97.81 & 87.55 & 85.43 & 86.41 \\ \hline TP2 SF [37] & 98.84 & 97.23 & 94.03 & 95.28 \\ \hline Our model & 99.99 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of our proposed model with state-of-the-art models based on the TON_IoT dataset.
## 5 Academic Efforts to Blockchain-Based Supply Chain Management:
### Efforts against Communication Attacks
#### 5.1.1 Preserving-Privacy  
Some of the academic efforts focused on the assurance of privacy of the shared-data among different coordinating parties. These research efforts are made in the supply chain processes of finance and logistics. These efforts attempted to ensure traceability and privacy of the information exchanged through blockchain, by allowing traceable proof of ownership of the products, authenticity of products, and audit of the products at anytime. For instance, Ref. [27] enabled supply chain management with IoT-based solutions, integrating special tags (e.g., RFID, NFC, and QR-codes) with products to create Smart Tags (ST). These tags eventually helps to track products during their supply chain lifecycle. Being based on distributed ledger technology (DLT), the solution offers a decentralized, privacy-preserving, and verifiable management of Smart Tags during a product's supply chain lifecycle. Furthermore, Ethereum blockchain is used for interaction among various stakeholders while product exchange process. Since the consensus requires agreement on the product's information on the blockchain, all involved stakeholders and consumers can verify the authenticity of the product's information without revealing their identity. Although, the solution provides traceability of the product-the origin of the product and its journey across the supply chain-without manipulation, this works under the assumption that ST generator and other stakeholders within the supply chain are providing authentic data related to the products, which is not true in practice. Furthermore, in a supply chain system by [PERSON] et al., [21], a homomorphic encryption is used to ensure the information-privacy among different stakeholders for a transaction. However, this system assumed that a certain percentage of the participants in decentralized-mixing protocols are honest. Again, this assumption may not be practical, thus such systems cannot avoid some attacks like Sybil. Besides, in another effort [28], a real-world implementation of blockchain was demonstrated to ensure traceable proof of ownership while transferring goods in freight carriers. To preserve the privacy, a blockchain-based decentralized system lead to business networks, and company information was being disclosed through data triangulation. However, such approaches works, if the information is only exchanged among trusted stakeholders, which is not typical in the case of real-time transfer of goods, particularly when the transfer is across the border. Similarly, there are other systems [38; 39] that mainly addressed the privacy concern in a supply chain system using the blockchain.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Image Analysis
Comprehensive image analysis enables novel search functionalities, e.g., if users want to find all images of themselves (or other users) that are available in a social network. Image analysis is also a key factor in law enforcement and security today, as it can be used to identify suspects rapidly in video recordings. Privacy Threats:  
When an image is analyzed, locations or activities can be identified in addition to the people depicted in it. By linking this information, a lot of knowledge about the data subject can be derived. Furthermore, by combining all available images, a comprehensive insight into the lives of the depicted persons can be gained. The algorithms are subject to certain probabilities of error. If people are incorrectly identified, they may be assigned to the wrong circles of acquaintances or interests, unnoticed by the data subject, which in the worst case can have damaging consequences for their reputation. Third parties can also be tagged on images without their knowledge, allowing the algorithm to learn their faces. As a result, they can also be identified in pictures, which means that parts of their private lives are revealed completely without their knowledge.
###### Abstract  
Background: Ultrasound plays a crucial role in early diagnosis of breast cancer. The aim of this research is to evaluate the diagnostic performance of BI-RADS classification in comparison with new semi-automatic software Resona R9, Mindray, \"SmartBreast\" and strain elastography (SE), point shear wave (pSWE), and 2D shear wave (2D SWE) Elastography for breast lesion differentiation. Methods: Ninety-two breast nodules classified according to BI-RADS lexicon by an expert radiologist were evaluated by a second investigator with B-mode ultrasound, color Doppler, \"SmartBreast\", and elastography. Histopathology was considered the gold standard. Results: The agreement between software and investigator was excellent in the identification of the posterior features of breast masses ([PERSON]'s k = 0.94), good for shape and vascular signal ([PERSON]'s k, respectively, of 0.6 and 0.65), poor for orientation, margins, and echo pattern ([PERSON]'s k, respectively, of 0.28, 0.33 and 0.48), moderate for dimensions ([PERSON]'s correlation coefficient of 0.90, \(p\) = 0.07). SE showed a greater area under curve (AUC) than pSWE and 2D SWE (0.84, 0.64, and 0.61, respectively), with a greater specificity and a comparable sensitivity to pSWE (respectively, of 0.86 and 0.55, 0.81 and 0.84). Conclusions: SE improved the diagnostic performance of BI-RADS classification more than pSWE and 2D SWE, \"SmartBreast\" showed good agreement only for shape and vascularization but not for the other ultrasound features of breast lesions.  
ultrasound; computer-aided diagnosis; \"SmartBreast\"; strain elastography; shear wave elastography; pSWE; 2D SWE
## 4 Discussion
### Comparative Analysis of Hidden Layer Width N  
The width of the hidden layer is crucial to the performance of the model. In this study, we conducted a detailed comparative analysis of different hidden layer widths \(N\), as shown in Figure 10. By adjusting the hidden layer width, we evaluated its impact on model performance, computational complexity (FLOPs), and the number of parameters. The experimental results indicate that as the hidden layer width increases, the model's loss value exhibits a nonlinear pattern, suggesting that the model may face risks of overfitting or underfitting under certain configurations.  
From the perspective of computational complexity, a larger hidden layer width significantly increases FLOPs and the number of parameters. While a wider hidden layer may enhance the model's representational capacity, it also comes with higher computational costs. Notably, when the hidden layer width was expanded to 1.2 times that of the original  
Figure 10: This chart compares the training loss, computational complexity (FLOPs, Floating Point Operations per Second), and model parameter count (in millions) across different hidden layer width configurations. The left y-axis corresponds to the relative loss values (based on the original model), while the right y-axis corresponds to the FLOPs and model parameter count. The model is based on the one used in Section 3.2 “Results of Simulating Three Types of Fibers Using a Single Model”, with only the hidden layer width adjusted and all other parameters kept unchanged. FLOPs (G): Computational complexity in Giga Floating Point Operations per Second. Params (M): Number of model parameters in millions.  
model, the FLOPs increased to 6.98G, and the number of parameters rose to 13.11 M. However, this expansion did not result in a significant reduction in the loss value and, in some cases, even led to a decrease in model performance.  
Conversely, reducing the hidden layer width (e.g., to 0.2 times that of the original model) significantly reduced both computational complexity and the number of parameters, but also resulted in a higher loss value (0.030561). This suggests that balancing complexity and performance is crucial in model design to avoid performance degradation due to insufficient model capacity.  
Overall, this study provides important insights into model optimization through a systematic analysis of the hidden layer width, particularly in scenarios where computational resources are limited. We recommend that in practical applications, the hidden layer width should be carefully selected based on specific task requirements to achieve the best balance between performance and computational cost.
# Digital Signal Processing of the Inharmonic Complex Tone
## 4 Experimental Results
### Analysis of the Irregularities in the Signal Spectrum  
The same signal is used for the demonstration of the detection of the phantom partial, i.e., partial with a frequency that is not part of the expected array of frequencies for the estimated value of \(B\). In Figure 13, the part of the spectrum corresponding to the 6 th and 7 th partials of the recorded harp As4 signal is presented for the set values \(f_{0}\) = 416.9 Hz and \(B\) = 0.00022. The red lines correspond to the expected positions of the partials, and the blue and green lines correspond to the expected positions of the phantom components. The frequencies of the phantom components are calculated based on the spectrum content and positions of the lower-order signal partials. The spectrum is calculated as the whole-length DFT of a signal with a rectangular window. Therefore, further analysis is necessary to determine the time-frequency content of the signal. The STFT cannot provide a good enough resolution in frequency. The time envelope of the 6 th channel is already plotted in Figure 11, and simultaneous analysis of time and frequency can be used as a verification of the nature of the additional spectral component. The shape of the envelope of the 6 th partial indicates that two spectral components of the signal are overlapping in time, i.e., can be used to confirm that the additional spectral component is a phantom partial. The frequency of the quasi-sinusoidal part of the envelope is approximately 3.2 Hz and matches half of the difference between the two spectral peaks. The extracted content of the 6 th channel is available as Harp_As4_channel_6.wav.  
Figure 12: The spectrum of the difference between two signals \(x\) and \(y\). The signal x is obtained by summing all channels of the decomposed input signal. The signal y is obtained by replacing channels 6, 7, 8, and 9 with the synthesized signals. Those channels correspond to the frequency band from 2.2 to 4 kHz.  
Figure 13: The spectrum \(X\) of the selected partial, showing the case when the prominent phantom component is present close to the actual partial of the signal. Signal partials marked with red dashed lines. Cyan and green dashed lines indicate phantom partials originating from nonlinearities.
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
## 2 Related Work  
### Cooperative Coevolution  
CC is an effective framework for solving many hard optimization problems. When solving LSGO problems, CC is used for the decomposition of the high-dimensional problem into subcomponents that are smaller and easier for applying an EA. The original version of CC has been proposed by [PERSON] and [PERSON] [18]. They have proposed two variants of CC: CCGA-1 and CCGA-2. Both algorithms decompose \(N\)-dimensional optimization problems into \(N\) 1-dimensional subcomponents. In CCGA-1, each subcomponent is optimized by its own EA using the best-found individuals from other subcomponents. CCGA-2 evolves individuals using a random collaboration of individuals from other subcomponents. The performance of CCGA-1 and CCGA-2 have been estimated using five optimization problems: Rastrigin, Schwefel, Griewangk, Ackley, and Rosenbrock. The maximum number of variables was 30. The numerical experiments have shown that CCGA-1 and CCGA-2 outperform the standard GA. GA and CCGA-2 have demonstrated equal performance and they have outperformed CCGA-1 only on the Rosenbrock problem. It can be explained by the fact that Rosenbrock has overlapped variables, and the strategy of choosing the best representative individuals in CCGA-1 leads to a stagnation of the searching process. In [18], the standard GA has been used as a subcomponent optimizer, but authors have noted that we can use any EA.  
A pseudo-code of the CC framework is presented in Algorithm 1. The termination criterion is the predefined number of FEVs.  
```
Set \(m\) (the number of subcomponents), and a subcomponent optimizer (an EA)
1: Generate an initial population;
2: Decompose a large optimization vector into \(m\) independent subcomponents;
3:while (termination criteria is not satisfied) do
4:for\(i=1\) to \(m\)
5: Evaluate the \(i\)-th subcomponent using the _optimizer_ in a round-robin strategy;
6: Construct an \(N\)-dimensional solution by merging the best-found solutions from all subcomponents;
7:endfor
8:endwhile
9:return the complete best-found solution;
```  
**Algorithm 1** The Cooperative Coevolution framework
In this context, computational evolution has given rise to various other tools, and among them are artificial neural networks (ANNs). The fundamental distinction between machine learning (ML) and ANNs is that the former refers to a broad field that encompasses algorithms, regressions, statistical methods, and computational techniques used to analyze large databases, detect patterns, and make predictions or decisions as rapidly as possible. ML focuses on using data to train models that learn from experience without explicit programming for specific tasks. ANNs, on the other hand, are subset computational models of ML techniques, inspired by the human nervous system, that use layers of interconnected nodes (or neurons) to mimic biological neural networks. ANNs specialize in capturing complex, nonlinear relationships between input and output data, making them particularly useful for tasks where traditional methods struggle. Another striking characteristic of these systems is that although ANNs rely on mathematical optimization techniques during the training process (e.g., backpropagation and gradient descent), they do not require the user to manually formulate the complex mathematical relationships that govern the studied system. Instead, ANNs learn these relationships directly from data, making them highly effective for applications where such relationships are unknown or too complex to model explicitly [6]. At the highest level, AI refers to the overarching field focused on creating systems capable of performing tasks that would typically require human intelligence, such as problem-solving, reasoning, and perception. Both ML and ANNs are key tools within AI, contributing to its goal of building intelligent systems capable of learning from data and adapting to new situations. This paper review systematically compiles the most current and relevant research related to the application of ANNs in the treatment of dyed effluents through adsorptive processes, employing the Methodi Ordinatio 2.0 methodology [20]. In adsorption-based environmental remediation, ANNs offer significant advantages by modeling complex, nonlinear relationships between factors like adsorbent properties and environmental conditions, aiding in the selection and optimization of effective adsorbents. They also enhance the development of new materials and improve real-time system monitoring, leading to more efficient and cost-effective environmental cleanup. The application of data collection and analysis tools allows the identification of existing gaps in a specific research area. Therefore, coupled with portfolio construction, the general objective is to obtain the most used predictive models and their respective statistical validation criteria, the detailed view of recent materials employed in the adsorption of contaminants from industrial processes, and the applied treatment methodologies. Lastly, a brief discussion is carried out regarding the use of AI tools through hybrid models and their potential in the field of environmental pollution control.
## 4 Simulation and Dataset Generation  
This section provides a detailed explanation of the testing setup, starting from the simulation environment and the characteristics of the satellite taken into consideration. Details are provided also on the description of the spacecraft and its specific subsystems, as well as the tunable parameters of the simulations, to produce a wide and diverse dataset.