esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 4 Experiment
### Comparison with Different Traditional Methods  
The separation performance of MConv-TasNet is compared with the PASA [10] and TDBSS [9] algorithms, which have better performance among the existing single-antenna based ADS-B signal separation algorithms. They are tested in the SR-ADSB dataset. Moreover, the decoding accuracy and BER are also used to measure the impact of SNR, power difference, carrier frequency difference and relative time delay on the performance of the algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Decoding Accuracy** & **BER** \\ \hline Conv-TasNet [18] & 88.48\% & 0.45\% \\ Bidirectional LSTM [21] & 88.39\% & 0.45\% \\ Transformer [22] & 88.80\% & 0.57\% \\ \hline MConv-TasNet & 90.34\% & 0.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different deep learning methods on the SR-ADSB dataset.  
The average decoding accuracy of PASA, TDBSS and MConv-TasNet algorithms in Figure 5 is 14.99%, 13.17% and 99.97%, respectively. It can be observed that the decoding accuracy of the MConv-TasNet algorithm is less affected by the signal power difference. However, the decoding accuracy of PASA is affected, and the performance of algorithm is better when the power difference is smaller. The TDBSS algorithm is very sensitive to the power difference between overlapped signals, and the decoding accuracy of TDBSS algorithm gradually increases as the power difference and SNR increase. Overall, the decoding accuracy of PASA and TDBSS algorithm are lower than that of the MConv-TasNet algorithm.  
The BERs of PASA, TDBSS and MConv-TasNet algorithms with different SNR and power difference are shown in Figure 6, and the average BERs of the three algorithms are 26.22%, 14.03% and 0.01%, respectively. The MConv-TasNet algorithm only has a 0.1% BER when the SNR is 5 dB and the power difference is 2 dB. At the same time, the signal power difference has less influence on the BER of PASA. However, the TDBSS algorithm is very sensitive to the signal power difference and SNR variation, and its BER decreases gradually with the increase in signal power difference and SNR. The overall BER of the MConv-TasNet algorithm is significantly lower than that of the PASA and TDBSS algorithm.
## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.2 MYO and LVC Segmentation Stage
On the other hand, the test set is composed of ROIs extracted following the proposed method's workflow, i.e., the mask aggregation process uses the coarse segmentation generated via EAIS-Net as reference segmentation since this network presented the best performance in the previous experiment. The same hyperparameters (epochs, optimizer, and learning rate) of those networks tested in the coarse segmentation substage were used for the training process. The outcomes can be seen in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Methods** & **Dice** & **IoU** \\ \hline \multirow{4}{*}{LVC} & U-Net & 0.8903 & 0.8400 \\  & U-Net + AIS Blocks & 0.8876 & 0.8432 \\  & FCN (EfficientNetB5) & 0.8995 & 0.8558 \\  & FCN (EfficientNetB7) & 0.8998 & 0.8564 \\  & **EAIS-Net** & **0.9154** & **0.8742** \\ \hline \multirow{4}{*}{MYO} & U-Net & 0.8016 & 0.7002 \\  & U-Net + AIS Blocks & 0.8141 & 0.7183 \\ \cline{1-1}  & FCN (EfficientNetB5) & 0.8262 & 0.7299 \\ \cline{1-1}  & FCN (EfficientNetB7) & 0.8332 & 0.7435 \\ \cline{1-1}  & **EAIS-Net** & **0.8454** & **0.7578** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results obtained in experiments for the coarse segmentation substage. As a result of the mask aggregation process, the new ROIs contain fewer background pixels. Thus, the structures of interest are more prominent, and class imbalance is further reduced. As the EAIS-Net is deeper and has more complex convolutional blocks than the conventional U-Net, its performance is better for segmentation in a context with more information. The U-Net with AIS blocks, in turn, is a less robust network, and its results surpassed EAIS-Net. Metrics show that U-Net outperforms the other tested networks. It is a network with a simpler architecture, and because of that, it presented better performance when applied to the new ROIs extracted since they have a reduced scope. Finally, the hypothesis that inspired the development of the refined segmentation substage was validated. Therefore, the results justify the choice of the EAIS-Net and U-Net networks to compose the coarse and refined substages of the MYO and LVC segmentation process.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
###### Abstract  
In order to achieve accurate identification of a shearer cutting state, infrared thermal images were creatively adopted in this paper. As the position of a shearer cutting unit is constantly changing, and the temperature in the vicinity is obviously distinct, mathematical morphology theory was used to detect the cutting unit in an infrared thermal image. Furthermore, a target tracking method is put forward to achieve cutting unit tracking based on the combination of morphology and a spatio-temporal context (STC) algorithm. Then, the temperature field features of this tracking area were extracted, and an intelligent classifier based on a support vector machine (SVM) was constructed to identify the cutting state of the shearer. Some experiments are presented, and the results indicate the feasibility and superiority of the proposed method.  
s achearer; cutting state recognition; cutting unit tracking; infrared thermal images; morphology; spatio-temporal context 2018 8 1772 10.3390/app810772
# Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360
## 4 Related Work  
ML fairness has become one of the most pivotal challenges of the decade [19]. Intended to intelligently prevent errors and biases in decision-making, ML models sometimes unintentionally become sources of bias and discrimination in society. Concerns have been raised about various forms of unfairness in ML, including racial biases in criminal justice systems, disparities in employment, and biases in loan approval processes [20]. The entire life cycle of an ML model, covering input data, modeling, evaluation, and feedback, is susceptible to both external and inherent biases, potentially resulting in unjust outcomes.  
Techniques for mitigating bias in ML models can be categorized into pre-processing, in-processing, and post-processing methods [21]. Pre-processing acknowledges that data itself often introduces bias, with distributions of sensitive or protected variables being discriminatory or imbalanced. This approach modifies sample distributions or transforms data to eliminate discrimination during training [5]. It is considered the most flexible part of the data-science pipeline, making no assumptions about subsequent modeling techniques [22]. In-processing adjusts modeling techniques to counter biases and incorporates fairness metrics into model optimization [23]. Post-processing addresses unfair model outputs, applying transformations to enhance prediction fairness [24].  
Figure 8: Performance comparison via BA vs. DI before and after reweighting samples on COMPAS dataset with respect to the protected attribute _Sex_.  
Pre-processing assumes that the disparate impact of the trained classifier mirrors that of the training data. Techniques include massaging the dataset by adjusting mislabeled class labels due to bias [15; 25] and reweighting training samples to assign greater importance to sensitive ones [5; 26].
## 1 Introduction  
Bread and biscuits are among the most popular foodstuffs worldwide [1]. Gluten is the protein complex involved in the development of bread's volume and springiness. Structurally, gluten provide the network in which starch will gelatinize during baking, leading to bread's most common physical characteristics [2]. However, a significant amount of the worldwide population is not able to consume products containing this protein complex.  
Celiac disease is an autoimmune disorder which shows gluten intolerance. Characteristic symptoms are chronic inflammation and atrophy of intestinal villi, impairing digestion and absorption processes [3]. Therefore, there is an increasing demand for gluten free (GF) products due to the attention celiac disease is receiving worldwide. There are several approaches researchers have taken to develop GF products, they vary from complex ones like using proteases to lower gluten content [4] to simpler ones such as using GF flours from alternative sources such as rice, chestnut and cassava [5; 6; 7].  
Cassava (_Manilot esculenta_), which has been used in several foodstuffs including bread, is one of the most economical sources of carbohydrates and energy and its production is increasing [8].  
Due to its high carbohydrate content, it could be used potentially as a sourdough substrate to improve physical and nutritional characteristics of products made from cassava.  
Sourdough fermentation with lactic acid bacteria (LAB) has been reported to provide several health enhancing components such as \(\gamma\)-aminobutyric acid [9] and to modify rheological and physical characteristics of GF products [10]. However, one of sourdough's downsize presently is the high amount of time consumed during fermentation.  
Several LAB have been employed in the development of sourdough, most commonly _Lactobacillus plantarum_. However, _L. paracasei_ has seen increasing interest due to its health benefits on colon cancer [11] and for its use as sourdough starter [12].  
The objective of this work is to evaluate the effect, in rheology, physical and chemical properties, on dough and biscuits fermented with _L. paracasei_ for short fermentation times, in order to assess a faster method of enhancing biscuit's physical characteristics.
## 4 Case Study
### Energy Consumption Analysis of the Two Presses
The experimental data from Tables 5 and 6 compared with the traditional hydraulic press are summarized in Figure 10.  
As shown above, since the installed power of the hydraulic press equipped with PFS was lower than that of the traditional press by 22.86%, the throttling loss and the no-load loss of the press were significantly reduced, and the average electrical energy consumption was minimized in each stage, as shown in Figure 10a. Total electrical energy consumption was reduced from 3227.66 KJ to 2382.76 KJ, so 804.90 KJ energy was saved per working cycle. During the PS stage, as the hammer contacted the forming workpiece, the load power rose and exceeded the nominal power supplied by the TPU, the servo valve outside the PFS opened, and the output power and flow of the PFS were \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Operations (i)** & **FF** & **PS** & **UL** & **FR** & **SR** & **WT** & **Total** \\ \hline Energy (KJ) & Time (s) & 0–0.5 & 0.5–1.4 & 1.2–1.3 & 1.3–1.9 & 1.9–2.2 & 2.2–3 & 3 \\ \hline Input energy (\(\text{E}_{i-Input}\)) & 492.28 & 901.89 & 99.38 & 604.22 & 297.09 & 832.80 & 3227.66 \\ Energy efficiency & 33.60\% & 72.82\% & 9.05\% & 74.00\% & 22.95\% & 0 & 37.04\% \\ \(\text{\boldmath$\Delta$E}_{i-Input}\) & +102.37 & +198.61 & +31.17 & +121.85 & +107.98 & +242.92 & +804.90 \\ \(\text{\boldmath$\eta$}\) & & & & & 17.35\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Energy consumption of each operation of the traditional hydraulic press (Constant Forging). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Operations (i)** & **FF** & **PS** & **UL** & **FR** & **SR** & **WT** & **Total** \\ \hline Energy (KJ) & Time (s) & 0–0.5 & 0.5–1.4 & 1.2–1.3 & 1.3–1.9 & 1.9–2.2 & 2.2–3 & 3 \\ \hline Input energy (\(\text{E}_{i-Input}\)) & 389.91 & 703.28 & 68.21 & 472.37 & 189.11 & 559.88 & 2382.76 \\ Useful energy & 165.41 & 692.87 & 9.28 & 439.78 & 68.19 & 0 & 1375.53 \\ Energy efficiency & 42.41\% & 98.42\% & 13.23\% & 93.00\% & 35.98\% & 0 & 57.72\% \\ \(\text{\boldmath$\eta$}\) & & & & & 29.05\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Energy dissipation of each operation in the hydraulic press with PFS (Constant Forging). used in conjunction with those of the TPU as the load pressure was reaching its maximum pressure. The difference between the input energy of the two presses is the energy provided by the PFS, which is about 198.61 KJ. After several experimental tests, compared with the traditional hydraulic press, the overall energy efficiency and the forming efficiency of the hydraulic press with PFS were improved by 26.71% and 11.70%, respectively.
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 4 Results and Discussion
### Comparative Analysis of Experiments
#### 4.4.1 Example 1
Although the average error of the results smoothed using Laplacian is small, the error in the x-direction is relatively large. This section verifies the lifting effect of the proposed method for finite element calculations using two examples. The effect of improving the accuracy of finite element calculation is achieved by optimizing the minimum angle and improving the minimum element quality. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mesh** & **Method** & **Time/(s)** & **Nodes** & **Min. 0** & **Max. 0** & **(a)** & **(b)** & **Min. q** & **Avg. q** \\ \hline \multirow{8}{*}{2} & Initial & / & 8.32 & 153.96 & 1562 & 1095 & 0.2190 & 0.7312 \\  & Laplacian & 0.6207 & & 26.52 & 111.37 & 12 & 24 & 0.6683 & 0.9507 \\  & Angle-based & 0.6969 & & 26.52 & 111.32 & 11 & 21 & 0.6679 & 0.9411 \\  & ODT & 0.7397 & 1665 & 14.30 & 148.60 & 27 & 40 & 0.3159 & 0.9336 \\  & CVT & 0.8148 & & 16.93 & 135.92 & 25 & 32 & 0.6355 & 0.9437 \\  & Unconstrained DQN & 1.2549 & & 27.09 & 127.17 & 33 & 30 & 0.5264 & 0.9188 \\  & Constrained DQN & 1.3166 & & 31.01 & 108.64 & 0 & 2 & 0.6814 & 0.9372 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison table of the experimental results. Figure 31: Finite element calculation results of the initial mesh. (**a**) Global; (**b**) local. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Nodes** & **Element** & **Stress (x)** & **Error (x)** & **Stress (x)** & **Error (y)** &
\begin{tabular}{c} **Mean** \\ **Error** \\ \end{tabular} \\ \hline Initial & & & 9.6259 & 30.25 & 10.2441 & 30.03 & 30.14 \\ Laplacian & & & 9.9706 & 27.75 & 10.9232 & 22.34 & 25.04 \\ Angle-based & & & 9.8940 & 28.30 & 10.9515 & 25.19 & 26.75 \\ ODT & 1665 & 3178 & 9.7165 & 29.59 & 10.6509 & 27.25 & 28.42 \\ CVT & & & 9.7404 & 29.42 & 10.8809 & 25.68 & 27.55 \\ Unconstrained DQN & & & 10.0845 & 26.92 & 11.2019 & 23.48 & 25.20 \\ Constrained DQN & & & 10.6940 & 22.51 & 11.8218 & 19.25 & 20.88 \\ Reference [30] & 8263 & 10,270 & 13.8000 & / & 14.6400 & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison table of finite element calculation results.
###### Abstract  
Urbanization nowadays results in the most dynamic and drastic changes in land use/land cover, with a significant impact on the environment. A detailed analysis and assessment of this process is necessary to take informed actions to reduce its impact on the environment and human well-being. In most parts of the world, detailed information on the composition, structure, extent, and temporal changes of urban areas is lacking. The purpose of this study is to present a methodology to produce high-resolution land use/land cover maps by the use of free software and satellite imagery. These maps can help to understand dynamic urbanization processes to plan, design, and coordinate sustainable urban development plans, especially in areas with limited resources and advancing environmental degradation. A series of high-resolution true color images provided by Google Earth Pro were used to do initial classifications with the Semi-Automatic Classification Plug-in in QGIS. Afterwards, a new methodology to improve the classification by the elimination of shadows and clouds, and a reduction of misclassifications through superimposition was applied. The classification was carried out for three urban areas in Leon, Nicaragua, with different degrees of urbanization for the years 2009, 2015, and 2018. Finally, the accuracy of the classification was analyzed using randomly defined validation polygons. The results are three sets of high-resolution land use/land cover maps of the initial and the improved classification, showing the detailed structures and temporal dynamics of urbanization. The average accuracy of classification reaches 74%, but up to 85% for the best classification. The results clearly identify advancing urbanization, the loss of vegetation and riparian zones, and threats to urban ecosystems. In general, the level of detail and simplicity of our methodology is a valuable tool to support sustainable urban management, although its application is not limited to these areas and can also be employed to track changes over time, providing therefore, relevant information to a wide range of decision-makers.  
land use/land cover classification; high resolution; urban ecology; urban planning; Google Earth; true color satellite image; green infrastructure; Nicaragua; Leon 2019 115266 doi:10.3390/s11195266
###### Abstract  
This article provides an in-depth review of selected technical chains, with particular emphasis on link chains and their load transmission mechanisms. It explores structural and functional characteristics, highlighting how chain geometry affects stress distribution, fatigue life, and performance under various loading conditions. The study includes a detailed classification of chains by type, material, and application, ranging from steel-based lifting and transport chains to lightweight, corrosion-resistant polymer types. Manufacturing methods and connection techniques are also discussed, underscoring the importance of proper assembly for mechanical reliability. Special attention is given to the role of materials, particularly the emergence of polymer composites reinforced with glass or carbon fibers, which offer promising alternatives to conventional metals. Although such composites exhibit advantageous properties--such as low weight, corrosion resistance, and energy efficiency--their application remains limited, insufficient load-bearing capacity, and the absence of standardized design guidelines. The review identifies critical knowledge gaps in the field, especially concerning shaping, dimensioning, and normative requirements for polymer-based load-bearing chains. It also highlights the lack of focused research on chain-specific geometries and the need for numerical simulations to optimize link design. The article concludes by emphasizing the importance of developing sustainable, durable, and standardized chain systems--particularly those utilizing recycled or novel materials--to meet both technical demands and environmental goals. This work supports future innovation in the design of advanced chain structures and provides a foundation for expanding the use of high-performance composites in civil and urban engineering applications.  
technical chain; shaping; geometry; composite; load-bearing capacity 2020
## 4 Discussion
### RQ1: Which Electrophysiological Signals May Index Emotional Processing?
#### 4.1.2 Open Access Databases  
The quality and profile of the data analyzed are important to achieve valid results and conclusions. All but one study collected their own data, which led to a wide diversity of induction methods, psychometric testing, electrophysiological measurements and analysis, experimental paradigms, pattern recognition techniques and applications. On the otherhand, one study decided to use the SEED and DEAP databases [26]. The use of previously recorded databases can limit the scope of the study, as the research goals should be adjusted to the experimental conditions and technical recording settings. However, their use reduces the efforts in collecting information, fosters the inter-agreement between scientists, and helps to fade cofounding factors due to methodological concerns.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 4 Results and Discussions
### Validation with Ground-Based TOCs  
The EMI DOAS TOCs were verified using reliable daily TOCs from several ground-based stations' measurements in the WOUDC and SAOZ datasets. In this study, the TOC results for the daily average EMI data using the ground pixels (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) of the corresponding ground-based stations were calculated. The relative differences between the EMI and ground-based TOC values are shown in Figure 7.  
Table 4 displays the average relative deviations observed between the EMI series and ground-based TOCs. Among them, EMI-DQ01 TOCs has the smallest deviation from the ground-based TOCs. The fluctuations between the EMI-GF5(02) and ground-based TOCs ranged from \(-\)5.41% to 4.12%, those between the EMI-DQ01 and ground-based TOC results ranged from \(-\)4.41% to 4.63%, and those between the EMI-GF5(01A) and  
Figure 7: Relative differences between the ground-based TOCs and those of the (**a**) EMI-GF5(02), (**b**) EMI-DQ01, and (**c**) EMI-GF5(01A) datasets.  
ground-based TOC values ranged from \(-5.27\%\) to \(2.57\%\). The deviations were relatively large in high-latitude regions. The 2023 daily average standard deviations between the EMI-GF5(01A), EMI-GF5(02), and EMI-DQ01 and ground-based TOCs from September to December were 4.08%, 4.12%, and 3.94%, respectively. The above TOC results indicated the consistency and accuracy of the EMI TOC retrieval, with the maximum deviation being 4%.  
Possible causes of biases could have been the different air masses probed by the space- and ground-based instruments. The a priori vertical profile information used in the retrieval algorithms may influence air masses. Information on the ten stations and the relative deviations are listed in Table 4, and the standard deviations are listed in Table 5. Figure 8 displays scatter plots comparing the time series of the EMI series and ground-based TOCs measured at several stations worldwide.