esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Legislation
### America  
#### 2.2.1 United States  
In the United States, state legislatures and the federal government are taking actions in order to protect the environment (water, soils) and public health [38].  
The US EPA, after the publication in 2016 of a nonenforceable health advisory on PFOA and PFOS, unveiled a formal PFAS Action Plan in 2019, then updated it in February 2020.  
In the plan, the following points are addressed: maximum contaminant level for states and local water utilities; listing PFOA and PFOS as hazardous substances under the CERLCA; and developing more accurate and innovative techniques to identify contaminants in drinking water, soil, and groundwater.  
In October 2021, EPA announced the Agency's PFAS Strategic Roadmap [39], which represents an important and meaningful step to safeguard communities from PFAS contamination, providing a more enduring and protective solution.  
In March 2023, the EPA moved forward with the development of a national drinking water regulation to establish maximum contaminant levels (MCLs) for 6 PFAS: PFOA, PFOS, PFNA, GenX compounds, PFHxS, and perfluorobutane sulfonic acid (PFBS).  
States will be forced to establish their requirements at least to those levels when the final regulations take effect.  
In parallel, state policymakers have targeted PFAS substances and have enacted laws restricting PFAS in firefighting foam; regulating the presence of PFAS in drinking water, food packaging and consumer products; and allocating funds for clean-up and remediation.  
From 2018 to 2021, lawmakers enacted more than 250 PFAS-related bills to regulate their use in several materials and setting maximum levels of contamination. More than 200 measures with PFAS-related terminology were discussed by legislators in 2022, and approximately 50 bills on PFAS in food packaging, firefighter personal protective equipment, and environmental remediation were passed in at least 18 states. The issue of PFAS in consumer items such as apparel, children's goods, cookware, furniture, cosmetics, and ski wax were also brought up by lawmakers in their legislation.  
After Maine became the first US state in 2021 to ban the sale of products containing intentionally added PFAS, several states took the lead to regulate PFAS.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.6 Unet3+  
In 2020, [PERSON] et al. proposed UNet 3+, an advanced version of the U-Net architecture designed for improved semantic segmentation in medical imaging. The architecture introduces full-scale skip connections, which effectively integrate low-level details with high-level semantic features across multiple scales, and employs deep supervision to enhance hierarchical representation learning. Additionally, a hybrid loss function and a classification-guided module are incorporated to improve boundary accuracy and minimize over-segmentation in non-organ regions. UNet 3+ achieves higher segmentation accuracy with fewer network parameters, offering computational efficiency. The model achieved Dice scores of 0.9675 and 0.9620 for liver and spleen segmentation, respectively [17].
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
###### Abstract  
With the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are widely used in numerous applications, from everyday items, such as smartphones, wireless vacuum cleaners, and wireless power tools, to transportation means, such as electric vehicles and bicycles. In this paper, the state of charge (SOC) of each cell of the lithium battery pack was estimated in real time using two types of neural networks: Multi-layer Neural Network (MNN) and Long Short-Term Memory (LSTM). To determine the difference in the SOC estimation performance under various conditions, the input values were compared using 2, 6, and 8 input values, and the difference according to the use of temperature variable data was compared, and finally, the MNN and LSTM. The differences were compared. Real-time SOC was estimated using the method with the lowest error rate.  
lithium battery Pack; State of Charge; Multi-Layer Neural Network; Long Short-Term Memory; real-time +
Footnote †: journal: Applied Sciences
# Numerical and Experimental Study of a Large-Scale Natural Circulation Helium Loop
###### Abstract  
This paper deals with the one-dimensional unsteady fluid flow model of a natural circulation loop. The governing equations are solved according to both the Euler and Lagrange approaches on two parallel computational grids. The linearization of equations and a semi-implicit discretization scheme are used to enhance the algorithm's effectiveness. The results of the simulations were verified by using experimental data obtained on an experimental rig that was a scale model of an emergency system for the removal of residual heat after reactor shutdown. The parameters compared were the helium temperature at two locations and the heater outlet pressure. The simulation results generally did not differ from the experimental data by more than 10%. The best agreement was obtained for scenarios in which the helium pressure was highest in combination with slow changes in the input parameters (less than 4%). Conversely, the results differed the most for the scenario with extremely fast device cooling (20%).  
gas-cooled fast reactor (GFR); natural circulation loop; emergency cooling; one-dimensional model +
Footnote †: journal: Physics Letters B  
1 Faculty of Mechanical Engineering, STU in Bratislava, Namstie Globody 17, 812 31 Bratislava, Slovakia 1  
a rectangular loop with a cooling capacity of 250 kW, and it works with helium with a maximum of temperature 520 \({}^{\circ}\)C and a pressure of 7 MPa. The loop was built as part of the efforts in Slovakia, Czech Republic, France, Hungary, and Poland to develop a European gas-cooled fast reactor (GFR) demonstrator (ALLEGRO). A similar facility is being built in the Czech Republic.  
The aim of the present paper is to present and verify our own numerical model of a system with natural circulation. The motivation for our efforts in this area was to develop a tool for rapid analysis of the behavior of a natural circulation system, which would reduce the number of costly experiments or allow us to investigate scenarios that cannot be implemented in a physical model for safety reasons. The model is one-dimensional, is transient, and uses a combination of fixed and floating computational grids. It is implemented in C++. The input parameters are defined as a combination of heating and cooling power. The model allows the simulation of the effects of leakages on the output power parameters. Verification of the results was performed by comparing the time histories of temperatures and pressures at selected points that coincided with the locations at which these parameters were measured in the experiment. The aim of the comparison was to determine the percentage agreement of the results in transient modes (heating, cooling) and in steady-state conditions.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 3 Results
### Dataset  
In this study, three healthy individuals participated, and the dataset was formed from the thirty-six characteristics of the EMG and FMG signals extracted from each activation in each of the files corresponding to each of the five gestures.  
In total, seventy data files suitable for the following stages of the study were recorded, distributed as follows: hand opening (14), hand closing (16), pinch (11), thumbs-up (16), and pointing (15). These files were selected after discarding others due to acquisition problems, such as excessive noise and incorrect positioning and/or improper fixation of the sensors.  
Figure 6a shows the dataset for each gesture, while Figure 6b shows how the total of thirty-six features extracted from each activation are distributed. In fact, the number of signal characteristics extracted from extensor and flexor muscles is equal.  
However, of these eighteen characteristics, only six are extracted from the FMG signal. Of these six characteristics, only two are different from those extracted from the EMG signal. In Figure 6c, all the characteristics are presented, showing whether they are common to both signals or from only one of the signals through the use of different colors.  
The collected data from the EMG and FMG signals of each muscle group, which consists of the relevant characteristics that were extracted, is used as input for the training of the Machine Learning models, through MATLAB's Classification Learner, in order to predict the execution of each gesture.
## 6 Issues and Future Directions
### Efficiency Improvement  
Low computing efficiency is a key factor which limits the wide application of spatiotemporal fusion methods. There are two reasons for the low computing efficiency of most spatiotemporal data fusion methods: pixel-wise calculation and moving window strategy. For weight function-based methods, they need to compute the weights from spatial, spectral, and temporal perspectives for each fine pixel. For the unmixing-based methods, they need to estimate the endmember values for each coarse pixel. In other words, pixel-wise calculation is used to model the local rules linking coarse and fine images. Meanwhile, many calculations are implemented within moving windows. Moving window is commonly used by STARFM-like methods to search for similar pixels. The demand of computing time of these methods increases dramatically with the size of moving window. Due to the above complexity, in terms of calculation, most existing spatiotemporal data fusion methods, applied to an image size of one Landsat scene, need several hours and sometimes more than one day to obtain the fused image when using a personal computer. This efficiency is acceptable for small study areas or when a limited number of images need to be fused. The efficiency must be largely improved for studies that cover large areas and long-length time series. There are two possible ways to improve the efficiency: (1) balancing the pixel-wise calculation and block-wise (or image-wise) calculation based on the homogeneity of the fused images; and (2) using more advanced programming strategies, such as parallel computing and GPU computing.
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 4 Discussion
#### Strengths and Limitations  
This study focuses on whether sEMG analysis could effectively assess deep squat performance. Even though such an approach has some limitations (homogeneity of the research group and lack of more detailed analysis of exclusions), our goal was to check how sEMG signals from different muscles together responded to incorrectly performed physical exercises, particularly the deep squat. The main parameter involved in the analysis was %MVC, unique and standardized for all study participants, making the results more reliable. Additional parameters, such as slope calculation of sEMG and muscle fatigue analysis, could be added to the analysis to verify how they influence the obtained results. The method based on sEMG could be applied in the case of limited access to physiotherapists and may also find application in predicting injuries, for example, in athletes, where an \"abnormal EMG\" may occur significantly earlier than changes in the typically assessed exercise performance parameters. The presented method can also support the objective assessment of rehabilitation progress. The results presented in this work concerned potentially healthy and young people; however, not all of them were qualified for the third group with the highest correctness of performing deep squat, which confirms that evaluating the correctness of exercise performance is essential to avoid bad habits and, in the long term, harmful consequences. Although the results are promising, the sample size of the study should be extended, mainly with regard to the third group.
## 3 Results and Discussion
### Structural Analysis from FTIR Spectroscopy  
FTIR was used for structural investigation, and the stacked spectra for the B0H group of materials are shown in Figure 4. The spectra transmittance vs. wavenumber demonstrates that the majority of the vibrations were caused by borate groups. The cage vibration of metal cations such as Bi\({}^{3+}\), Li\({}^{+}\)[26; 27] was characterized by three primary bands of 400-750 cm\({}^{-1}\), 750-1200 cm\({}^{-1}\) and 1200-1650 cm\({}^{-1}\) in the region with a weak band of less than 600 cm\({}^{-1}\). In the region of 650-750 cm\({}^{-1}\), the bending of several borate segments results in a peak. This band has a tiny inclination for moving to a higher wave number called the blue shift. A higher bond strength value reduces the length of bond [18; 19; 20; 21; 22; 23; 24]. In this way, Ho increased the structure groups' compactness. The higher concentration in the B0H package caused the rise in density to vary due to the difference in atomic weights of Ho and B. The result was a change in free volume. The outcome was that the band area between 750 and 1200 cm\({}^{-1}\) was connected with BO\({}_{4}\) deformation and non-bridging oxygen NBO movements. A symmetrical B-O stretching of the BO\({}_{3}\) group was assigned to the region from 1200 to 1650 cm\({}^{-1}\). In addition, the BO\({}_{4}\) field was employed to approximate the four-coordinated boron atoms for the bands and BO\({}_{3}\) units [28]. After integration in the B0H host, the production of NBOs was seen. The BO\({}_{3}\) units broke up into BO\({}_{4}\) units that create NBOs, also called dangerous bonds. Figure 5 shows the difference between the four-coordinated boron atoms and the B0H set.  
Figure 4: FTIR spectra for B0H set of samples.  
Figure 5: BO\({}_{4}\) fractions in B0H set of samples.
## 3 Kinematic Analysis
### Inverse Velocity Solving and Jacobian
#### 3.3.1 Velocity Analysis
Most importantly, Equation (22) involves only several easily accessible parameters, and the shield machine velocity can be easily determined using this equation. It will contribute significantly to the precise control of the speed of the shield machine during the tunneling process.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 2 Application Scenario and System Model
### Node Model and Energy Model  
Among the \(N\) nodes, there are \(N_{su}\) super nodes, \(N_{ad}\) advanced nodes, and \(N_{or}\) ordinary nodes. The advanced nodes have several times more energy than the ordinary nodes. The proportions of super nodes and advanced nodes are \(M_{s}\) and \(M_{a}\), respectively. The number of each type of node can be expressed by the following equation:  
\[N_{su}=N\times M_{s} \tag{1}\]  
\[N_{ad}=N\times M_{a} \tag{2}\]  
\[N_{or}=N\times(1-M_{s}-M_{a}) \tag{3}\]  
where \(N_{su}\), \(N_{ad}\), and \(N_{or}\) represent the number of super nodes, advanced nodes, and ordinary nodes, respectively.  
Assume that the energy of a super node is \(\beta_{su}\) times that of an ordinary node, and the energy of an advanced node is \(\beta_{ad}\) times that of an ordinary node. The energy consumption of an ordinary node includes energy expenditures for data acquisition, data processing, and data transmission. The total energy consumption of the \(i\)-th node can be expressed by the following equation:  
\[E^{i}_{total}=E^{i}_{sense}+E^{i}_{proc}+E^{i}_{trans} \tag{4}\]  
Figure 2: System model of HWSNs.  
where \(E^{i}_{sense}\) represents the energy consumption for data sensing, \(E^{i}_{proc}\) denotes the energy consumption for data processing and \(E^{i}_{trans}\) indicates the energy consumption for data transmission.  
The total initial energy for ordinary nodes can be expressed as  
\[E_{total\_or}=N_{or}\times E \tag{5}\]  
where \(E\) is the initial energy of a single ordinary node. The initial energy of super nodes is \(\beta_{su}\) times that of ordinary nodes. Consequently, the total initial energy of super nodes can be expressed as  
\[E_{total\_su}=N_{su}\times\beta_{su}\times E \tag{6}\]  
where \(\beta_{su}\times E\) is the initial energy of a single super node.  
The initial energy of advanced nodes is \(\beta_{ad}\) times that of ordinary nodes. Therefore, the total initial energy of advanced nodes can be represented as  
\[E_{total\_ad}=N_{ad}\times\beta_{ad}\times E \tag{7}\]  
where \(\beta_{su}\times E\) is the initial energy of a single advanced node.  
The total initial energy of the HWSNs can be calculated as  
\[E_{total}=E_{total\_or}+E_{total\_su}+E_{total\_ad} \tag{8}\]
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Encryption
#### 5.5.3 Steganography  
Steganography is very similar to encryption, although the goal of steganography is to hide data so that it is not clear any data are even hidden. [PERSON] and [PERSON] [243] proposed a technique using neural networks to determine the Regions Of Non-Interest (RONI) in an image that are optimal for implanting a watermark. In a similar proposal by [PERSON] et al. [221], the discrete cosine transform and ECC were used to encrypt the image first and then the well-known SegNet neural network to improve hiding.
## 3 Results
### Polyphenols  
Although polyphenols are water-soluble, most were found in the curd but very few in the whey (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Buffalo**} & \multicolumn{4}{c}{**Sheep**} \\ \cline{2-9}  & **BM\({}_{\mathbf{C}}\)** & **BM\({}_{\mathbf{K}}\)** & **SEM** & **S** & **SM\({}_{\mathbf{C}}\)** & **SM\({}_{\mathbf{K}}\)** & **SEM** & **S** \\ \hline Total solid (g/100 g) & 35.89 & 44.44 & 1.83 & \({}^{+++}\) & 43.68 & 50.69 & 0.46 & \({}^{+++}\) \\ Proteins (g/100 g) & 10.76 & 11.74 & 0.26 & \({}^{+}\) & 15.83 & 15.74 & 0.23 & ns \\ Lipids (g/100 g) & 20.60 & 27.40 & 1.24 & \({}^{++}\) & 21.72 & 27.55 & 0.36 & \({}^{+++}\) \\ Ashes (g/100 g) & 0.99 & 1.27 & 0.03 & \({}^{+++}\) & 1.33 & 1.69 & 0.05 & \({}^{+++}\) \\ Carbohydrates & 4.05 & 4.53 & 0.75 & ns & 5.16 & 5.52 & 0.47 & ns \\ Fe (\(\mu\)g/g) & 0.71 & 0.81 & 0.09 & ns & 1.57 & 1.93 & 0.09 & ns \\ Mg (\(\mu\)g/g) & 266.01 & 254.73 & 14.05 & ns & 268.27 & 271.63 & 4.58 & ns \\ K (\(\mu\)g/g) & 1721.61 & 2136.11 & 132.05 & ns & 1727.81 & 1696.26 & 56.39 & ns \\ Ca (\(\mu\)g/g) & 7985.18 & 7938.71 & 259.73 & ns & 8131.28 & 8689.25 & 285.27 & ns \\ Na (\(\mu\)g/g) & 522.83 & 346.34 & 42.47 & \({}^{+}\) & 1173.25 & 1004.39 & 23.37 & \({}^{+}\) \\ Colour & & & & & & & & \\ \hline L* & 93.73 & 92.15 & 0.37 & \({}^{+++}\) & 93.12 & 92.09 & 0.24 & \({}^{+}\) \\ a* & \(-\)1.75 & \(-\)1.83 & 0.04 & ns & \(-\)1.88 & \(-\)1.73 & 0.05 & ns \\ b* & 8.68 & 10.62 & 0.45 & \({}^{+}\) & 12.17 & 12.33 & 0.17 & ns \\ E* & 1.84 & 0.68 & & & 1.05 & & 0.36 \\ \hline \hline BMC, buffalo–calf rennet cheese; BMK, buffalo–kiwifruit cheese; SMC, sheep–calf rennet cheese; SMK, sheep–kiwifruit cheese; \(\Delta\)E between cheese from calf rennet—kiwifruit, \(\Delta\)E* = (\(\Delta\)L*)2 + (\(\Delta\)a*)2 + (\(\Delta\)b*)2)1/2. Values are expressed as the mean \(\pm\) standard deviation; SEM, standard error medium; S, significance. \({}^{+}\), 0.01\(<P<0.05\); \({}^{++}\), 0.01 \(<P<0.001\); \({}^{+++}\), \(P<0.001\); ns: not significant. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physico-chemical composition of the chees made with kiwi extract or calf rennet.  
Chees obtained with the kiwi extract showed a significantly higher content of polyphenols, both in buffalo (+466%) and sheep cheeses (+278%) (Table 3).  
The polyphenol content of SM-C was high, because the sheep milk used for our experiments was obtained from grazing animals. Our results are in line with [PERSON] et al. (2019) in cheese produced with milk from pasture fed goats.  
Cheeses obtained with the kiwifruit extract were higher both in flavonoids (quercetin, rutin and catechin) and in phenolic acids (caffeic acid, coumaric acid and cinnamic acid). They also contained coumaric and cinnamic acids, which were not present in the calf remnet cheese. The only polyphenol not affected by the cheesemaking was gallic acid.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.13 Random Forest  
Random forest (RF) is an ensemble-learning algorithm used in self-healing systems to classify and diagnose system faults or failures. It is a robust ML algorithm that combines multiple decision trees to achieve better accuracy and generalisation. RF is described by [39] as a set of decision trees that tend to produce accurate decisions based on growing multiple trees of different subsets of a dataset. RF is an ensemble classifier used for the regression analysis of intrusion detection in datasets [22; 24]. It functions by creating various decision trees in the training phase of the classification process, then produces an output of class labels with a majority vote. The decision trees are independent of each other, but they operate in an ensemble manner, as noted by [PERSON] [28]. Each decision tree produces an output and a class, and the majority class amongst them becomes the random forest. The RF classifier algorithm applies to self-healing functionality because it can detect the anomaly in the system's critical datasets and trigger its self-healing functions. RF relies on the need to remove trivial features because they do not affect the outcome of the results. The information gain of RF split on a dataset is calculated as:  
\[\mathrm{G(T,X)}=\mathrm{entropy(X)}-\mathrm{entropy(T,X)}\]  
The above equation is explained in [39] as a calculation where \"G\" is the gain, and the attribute with the highest gain value is chosen as the split/decision node. If the value of the entropy of the selected node is 0, then it becomes the leaf node. The algorithm runs recursively until no more splits are present on the nodes, whereby if the entropy value of the selected node is 0, it becomes the leaf node. RF could be used to predict the likelihood of a particular system component failing based on multiple features such as CPU usage, memory usage, network traffic, and disk I/O. It is a critical self-healing tool, and its ability to handle large datasets and high-dimensional feature space makes it useful for implementing complex self-healing systems.