esa-sceva/satcom-chunk-collection huggingface dataset:
### Traditional Segmentation Methods  
Currently, the methods for LV segmentation can usually be divided into traditional segmentation methods and deep learning methods. However, the early traditional segmentation methods have some obvious drawbacks. Several of these algorithms can only obtain relatively accurate segmentation results when the pixel intensities of the LV and other tissues reach a high level of contrast. For example, the threshold-based segmentation method was used by [PERSON] et al. [27] in 1995 to extract the LV contour. It adaptively determines the threshold grayscale according to the global or local grayscale histogram of the image. Therefore, this method can only obtain better segmentation results on the condition that the grayscale of the region is significantly different from the background. Since its appearance, the K-Means clustering algorithm has been extensively applied in the fields of image analysis and data mining. However, because many regions in cardiac MR images are similar or even connected to the LV, the K-means clustering algorithm cannot achieve the expected results and needs further improvement. In 2006, [PERSON] et al. [12] employed the idea of split-merge to segment the LV, which could successfully extract the epicardium and endocardium of the LV under the condition of manually annotating the epicardium and endocardium of the first slice. This manual process obviously increases the clinical application complexity. On the whole, these traditional methods have the characteristic of relying on manual design, which contradicts the idea of automatic segmentation.
## 2 Materials and Methods
### Processing the Images
#### 2.3.2 Segmentation Post-Processing  
The images segmented with the range-constrained Otsu method (Figure 1c) were processed to:  
1. Calculate the area (_pixel\({}^{2}\)_) ) of the core (of the circular region) in each image \(i=1,2,\ldots,2100\), which was denoted \(A_{i}\). For this, binarization based on the active contour model (snakes) [34] segmentation method was used.
2. Identify and index the dark regions inside the core and label the pixels of each one with the index assigned to the region. The methods for detecting regions (blobs) in images identify regions that differ in brightness or color from the surrounding environment ([35]). A blob is a region where the brightness or color is approximately constant, such that all the points in a blob can be considered similar.
3. Calculate the white area \(b_{i},\ i=1,2,\ldots,2100\) (_pixel\({}^{2}\)_) to calculate the percentage of cement paste in each cross-section as described below.  
In parallel, after binarizing the images highlighting the gravel (Figure 1b) as described in the previous section, it was necessary to perform morphological opening and closing operations ([36]) to filter noise and connect the contiguous regions of the image. The result of these operations is shown in Figure 1c. Improved definition of the contours of the gravel regions and uniformity of the color inside those regions can be observed. The number of white pixels in each of the 2100 axial images of the binarized and corrected core enable estimation of the volumetric percentage of gravel. The white area in image \(i\) is denoted by \(B_{i},\ i=1,2,\ldots,2100\ (pixel^{2})\) ), and the volumetric percentage of gravel in the \(i\)-th section of the core can be estimated by the ratio \(B_{i}/A_{i}\). Thus, the volumetric percentage of gravel, \(G\), in the entire core can be approximated by the mean of the percentages of gravel in the 2100 cross-sections of the core, i.e.,  
\[G=\frac{100}{N}\sum_{i=1}^{N}\frac{B_{i}}{A_{i}}\ (\%), \tag{1}\]  
where \(N=2100\) in this case. Finally, in this post-processing phase, the percentage of cement paste in the core was calculated. The area of white pixels \(b_{i},\ i=1,2,\ldots,2100\) calculated in each cross-section in the image of Figure 2c represents the area of cement paste and gravel, since the area of black pixels corresponds to pores, EVA, and fiber. As we calculated the gravel area \(B_{i},\ i=1,2,\ldots,2100\) in the image of Figure 1c, the cement paste area in each cross-section can be calculated as \(c_{i}=b_{i}-B_{i}\), such that the cement paste percentage in the \(i\)-th section is given by \(r\ c_{i}/A_{i}\). The percentage of cement paste in the core, \(C\), can be estimated using the mean of the cement paste areas in the 2100 cross-sections, that is,  
\[C=\frac{100}{N}\sum_{i=1}^{N}\frac{c_{i}}{A_{i}}\ (\%). \tag{2}\]  
At this point, after estimating the percentages of gravel (\(G\)) and cement paste (\(C\)), it remained necessary to estimate the percentages of the light, coarse aggregates (EVA and fiber) and the pores. The pipeline executed thus far is illustrated in Figure 3.
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 4 Experimental Results and Analysis
### Analysis of ODIR
#### 4.3.1 ODIR: Analysis of Object Recognition  
For object recognition, we trained an RF classifier on the features extracted from the hand joint data to classify 16 objects. The numerical results obtained for object recognition using an RF classifier are shown in Table 3. The table presents the performance metrics obtained for each of the individual objects. It can be observed from these results that the recognition performances of \"Bottle\", \"Chair\", \"Kettle\", \"Laptop\", and \"Scissors\" were better compared with other objects. Each of these objects was recognized with an accuracy or F1-score of more than 80%. This depicts that it was easier to recognize these objects based on the hand joint information. More precisely, \"Chair\" was recognized with the highest accuracy of 91.2%. In contrast, objects such as \"Safe\", \"Toy Car\", \"Storage Furniture\", and \"Lamp\" achieved a recognition performance of less than 70% (in terms of accuracy and F1-score). Thus, this gave us a notion that these objects were difficult to classify based on the hand joint information.  
This was because the interactions related to these objects were quite similar to each other in terms of the hand motion patterns, which caused these objects to be confused during the classification stage. The confusion matrix shown in Figure 5 revealed strengths in distinguishing items such as \"Bottle\" and \"Mugs\" with high accuracy, as indicated by the high values on the diagonal. It also uncovered specific confusions, like mixing up \"Bowls\" with \"Buckets\", thus pointing to areas where the model misinterpreted similarities. The frequent mislabeling of \"Toy Car\" was a sign that this category needed particular attention for improvement. The overall average F1-score achieved for object recognition was 73%.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Object Category** & **Acc.** & **Pre.** & **Rec.** & **F1** \\ \hline Bottle & 0.88 & 0.86 & 0.88 & 0.87 \\ Bowl & 0.78 & 0.77 & 0.78 & 0.77 \\ Bucket & 0.71 & 0.67 & 0.71 & 0.69 \\ Chair & 0.91 & 0.88 & 0.91 & 0.89 \\ Kettle & 0.85 & 0.73 & 0.85 & 0.78 \\ Knife & 0.70 & 0.64 & 0.70 & 0.67 \\ Lamp & 0.61 & 0.77 & 0.61 & 0.68 \\ Laptop & 0.86 & 0.78 & 0.86 & 0.81 \\ Mug & 0.79 & 0.70 & 0.79 & 0.74 \\ Pliers & 0.66 & 0.65 & 0.66 & 0.65 \\ Safe & 0.56 & 0.62 & 0.56 & 0.59 \\ Scissors & 0.81 & 0.82 & 0.81 & 0.81 \\ Stapler & 0.67 & 0.72 & 0.67 & 0.70 \\ Furniture & 0.62 & 0.80 & 0.62 & 0.70 \\ Toy car & 0.57 & 0.65 & 0.57 & 0.60 \\ Trash can & 0.70 & 0.71 & 0.70 & 0.70 \\ \hline Average & 0.73 & 0.74 & 0.73 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ODIR numerical results for object classification.
# Treatment Methods for Antibiotic Mycelial Residues: A Review
## 3 Treatment Methods for AMR
### Physical Methods  
3.1.1. Landfill  
Within China's current environmental regulatory framework, AMR is explicitly designated as hazardous waste. Mandated by pertinent legislation, AMR landfill disposal is restricted to facilities specifically engineered for hazardous waste containment. The inherent characteristics of AMR--notably its elevated moisture content and rich organic composition--necessitate pretreatment prior to landfilling. This pretreatment aims to reduce moisture levels and stabilize organic constituents, ensuring adherence to landfill entry standards.  
The intricate landfill milieu, coupled with AMR's distinct chemical properties, hinders its complete post-disposal degradation. Consequently, fermentation-induced secondary pollution occurs, posing latent threats to the soil, groundwater, and broader ecosystems. This scenario also intensifies the technical demands and costs of subsequent leachate management, significantly inflating landfill operational expenditures and sustaining elevated long-term maintenance costs [61]. Moreover, AMR harbors substantial recoverable nutrients. Direct landfill disposal without resource recovery constitutes inefficient utilization  
Figure 1: AMR processing methods [25; 57; 62; 63; 64; 65; 66; 67].  
and squandering of valuable materials, fundamentally contradicting contemporary principles of circular economy and sustainable development. The significant land footprint required for landfills further compounds the issue, inflicting localized ecological harm and rendering large-scale AMR landfilling unsustainable and irrational over extended periods [3].  
Therefore, when evaluated against criteria of technical practicality, environmental impact, resource recovery efficiency, and economic feasibility, landfill technology has not achieved broad acceptance or widespread implementation for AMR management.
## 3 Experimental Results  
### Characterization of Fiber Bragg Grating  
The result of FBGs inscription on the MgO-NP fiber is shown in Figure 3, which displays the power backreflected at each section of the fiber for both polarizations. The lead-in fiber is a SMF, which has a scattering level around \(-\)91 dB and terminates at 4.58 m length (measured from the OBR lead-out connector). In the MgO-NP section, we observe a scattering gain, which is the increment of scattering with respect to the SMF, of 37.2 dB, similar to [17]. Due to the high scattering, the fiber has a high two-way loss estimated as 22.1 dB/m, i.e., cumulating both the forward and backward wave.  
Two FBGs have been inscribed at the lengths of 4.60 m and 4.63 m. The first FBG exhibits a signal increment of \(\sim\)10 dB over the scattering level and corresponds to a relatively weak FBG; the second FBG is stronger (28 dB over the scattering trace) and represents a strong FBG. We also observe that the polarization appears to fluctuate along the MgO-NP fiber, as previously observed in [22].  
We report in Figure 4 the reflection spectrum of the stronger of the two MgO-NP FBGs, i.e., the grating inscribed at the length of 4.63 m as the temperature increases; the results are similar to the FBG inscribed at 4.60 m. The reflection spectrum of the FBG appears as \(\sim\)28 dB over the noise floor, in compliance with Figure 3. As for a standard FBG, the spectrum appears to shift towards longer  
Figure 2: Setup of the FBG interrogation system. (**a**) Schematic diagram of the setup; (**b**) photograph.  
wavelengths as the temperature variation \(\Delta\)T increases from the reference value, maintaining the spectral shape. At the initial temperature (\(\Delta\)T = 0 \({}^{\circ}\)C, corresponding to the room temperature) the Bragg wavelength is 1552.2 nm, and rises to 1553.6 nm for \(\Delta\)T = 125.6 \({}^{\circ}\)C, at the maximum temperature.  
Figure 5 reports the Bragg wavelength of the FBG as a function of the temperature variation. As in [1; 5], the FBG shows a linear wavelength shift, with sensitivity equal to 11.45 pm/\({}^{\circ}\)C and reference wavelength of 1552.262 nm; the fit has coefficient of determination R\({}^{2}\) = 0.997, which shows a very accurate fit for over 125\({}^{\circ}\)C of temperature range.  
Figure 4: Reflection spectra of the MgO-NP FBG, for different values of temperature variation \(\Delta\)T; the reference temperature \(\Delta\)T = 0 \({}^{\circ}\)C corresponds to room temperature.  
Figure 3: Backreflected signal for from the MgO-NP fiber with 2 FBGs inscribed at 4.60 m and 4.63 m.
## 2 Methodology
### Prediction Model Structure Construction  
As explained in Section 1, to verify the effects of online sentiment data and the CNN-LSTM model on Taiwan's car sales prediction, three models, namely, the classical, sentimental and CNN-LSTM models, were created for forecasting car sales movement directions in Taiwan. The structures of these three prediction models will be described in this section.
# Intraoperative Imaging of Cortical Blood Flow by Camera-Based Photoplethysmography at Green Light
## 2 Methods and Clinical Cases
### Data Processing  
The recorded data were processed offline by using custom software implemented in the MATLAB@platform (Version, R2018b, The MathWorks, Inc., MA, USA, 2018). First, we compensated the heterogeneous motion of an open brain cortex. To this end, the whole image was divided on the segments of \(64\times 30\) pixels and, then, the motion of each segment was compensated independently. It was assumed that the motion-related component of the pixel-value variations is proportional to the image gradient and lateral offset. The latter was estimated in every segment by optical flow algorithm using the gradient method [25]. Thereafter, the motion-related component was reconstructed and subtracted from the original image. Second, we manually selected an area for evaluation of blood-pulsation parameters in one of the recorded images. PPG waveforms were calculated as frame-by-frame evolution of the average pixel value in every pixel within the selected area of the motion-compensated frames. The size of each pixel corresponds to 260 \(\upmu\)m at the cortex. To compensate uneven illumination of the cortex, the waveform was calculated as the ratio of the alternating component (AC) modulated at the heart rate and slowly varying (DC) component (AC/DC ratio). As it is conventionally accepted in papers devoted to photoplethysmography [26; 27], the sign of the obtained waveform was inverted so that its increase follows the increase of blood volume (and pressure) in vessels during a cardiac cycle. These waveform transformations are typically applied in photoplethysmography [14; 15]. Third, the noise and DC components were removed from the calculated waveforms by applying a band-pass (0.12-20 Hz) zero-phase digital filter.  
Typically, each oscillation of a PPG waveform follows the R-peak of the ECG signal [24]. These oscillations can be plotted together so that each R-peak is at the beginning of the time scale. Thereafter, we calculated in each small region of interest (ROI) the mean waveform by averaging the filtered one-cardiac-cycle oscillations during the recording time of 40 s. Since the minimum of the mean waveform corresponds to the beginning of the anacrotic wave with fast blood-pressure increase [28], the parameter PAT was calculated as the time delay between the R-peak and this minimum. Amplitude of the pulsatile component (APC) was estimated as the difference between the maximum and minimum values of the mean PPG waveform. In other words, APC is the amplitude of heart-related oscillations of light reemitted from the brain cortex.
## 3 Application of the Proposed Method
### Case Study Robotic Tasks  
Two robotic tasks are considered for the application of the presented method. A pick and place task and a path-following task.
# Studies on the Electrochemical Behavior of Sulfite on Incoloy 800 in a Neutral Environment
###### Abstract  
Avoiding atmospheric pollution with sulfur dioxide is generally achieved by its absorption from combustion gases in alkaline solutions and conversion to sulfites. Afterwards, sulfites can be transformed into neutral and environmentally safe chemicals by oxidation to sulfates. The oxidation of sulfites to sulfates can also be carried out in a cell in which the fuel will be sulfite ions. In this way, in addition to the beneficial effect of neutralizing large quantities of sulfite waste, electrical energy is also obtained. This is one of the reasons why study of the anodic oxidation of sulfite to sulfate on various electrode materials was necessary. Given the sensitivity of electrode materials in the presence of sulfur compounds, in our research we approached the study of sulfite oxidation on the Incoloy 800 anode in neutral solution (1 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\)). In this research, the results obtained in the study of the kinetic parameters of the anodic process as a function of the sulfite concentration (10\({}^{-1}\), 0.5, and 1 mol L\({}^{-1}\)), using linear voltammetry, are presented. The appreciable values of the exchange current density (3.4, 3.0, and 2.6 A m\({}^{-2}\)) show that Incoloy 800 has a significant catalytic effect in the anodic oxidation of sulfite. Chronoamperometric studies have shown that the anodic oxidation of sulfite is controlled by the mass transfer of sulfite ions from the bulk solution to the electrode surface. According to the chronocoulometric diagrams, it can be appreciated that, up to anodic potentials of +1.50 V, sulfite oxidation occurs on the electrode, while at more positive potentials, the oxygen evolution reaction is the main process. Electrochemical impedance data provide evidence of a chemical reaction coupled with electron transfer, which was modeled using a Gerischer impedance. At high sulfite concentrations, the charge transfer resistance (\(R_{ct}\)) decreases by a factor of 10, indicating that the sulfite oxidation reaction is fast at sufficiently positive potentials. On the other hand, the passivation tendency of stainless steels upon anodic polarization gives them a high corrosion resistance, so that Incoloy 800 can be a viable option as an anode material for sulfite/oxygen (air) fuel cells.  
stiffes anodic oxidation; anodic materials; stainless steel electrodes; chronoelectrochemical methods; electrochemical impedance spectroscopy +
Footnote †: journal: Applied Sciences
# CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
## 4 Experiment
### Result  
#### 4.4.1 In Domain  
In Figure 5, we present the results for six datasets, with CFP-AL demonstrating the best performance across all of them. For datasets like IMDB and SST-2, all methods show similarly good results. However, for tasks that require inferring relationships between two sentences (QNLI, QQP) or classifying into multiple classes (PUBMED, AGNews), there are clear differences in performance. Notably, among the baselines presented in Section 4.2, Entropy, CAL, and ALPS are uncertainty-based sampling methods. CFP-AL also exploits the uncertainty of model predictions. It showed the most stable and best performance by combining features. On the other hand, BERT-KM, like CFP-AL, is a method used when sampling embeddings and features. CFP-AL clearly showed superior performance in all Tasks compared with BERT-KM. This shows that a method that uses both features and predictions is superior to a method that simply relies on features alone, even if it just uses a simple method.  
Uncertainty-based sampling methods like Entropy, CAL, and ALPS rely heavily on the classifier. These methods are highly dependent on the classifier's performance in each iteration, and due to the cold-start approach that uses small initial data, they show significant variance. For example, it is abnormal for a model trained with \(11\%\) of the data in iteration 6 to perform worse than a model evaluated with \(9\%\) of the data in iteration 5. However, this issue was strong among uncertainty-based methods. In Table 11, we present the average and variance for all experiments, which also exhibit this issue. On the other hand, CFP-AL focused on features especially in the early iterations, showing the least  
Figure 4: Active Learning algorithm implementation details for SST-2, IMDB, AGNews, QNLI tasks. Red is data from labeled data pool, and blue is data from unlabeled data pool. Based on the data in the already sampled labeled data pool, the data in the unlabeled data pool are judged and sampled using each acquisition function.  
variance and no performance degradation at larger iterations. We analyze this issue in Section 5.
###### Abstract  
In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions. Simulation results demonstrate the superior performance of the proposed technique with high accuracy of the DNN in deciding offloading policy and partitioning of a task with minimum delay and energy consumption for UE. More than 70% accuracy of the trained DNN is achieved through a comprehensive training dataset. The simulation results also show the constant accuracy of the DNN when the UEs are moving which means the decision making of the offloading policy and partitioning are not affected by the mobility of UEs.  
mobile edge computing; computational offloading; deep learning: cost function; remote execution; energy efficiency