esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### CNN-Based Learning Model for Classification of Penetration Condition  
A CNN model was applied to predict the penetration condition in the GMA root-pass weld of a V-groove. CNN, a deep learning model, is primarily used for processing image data. The CNN model extracts features from images and learns using the obtained feature maps. Feature maps are generated through operations utilizing kernels with weights, a process known as convolution. The layers where convolution takes place are called convolution layers. Additionally, pooling is employed to emphasize the data in the feature maps, and the layers where pooling occurs are referred to as pooling layers.  
The CNN model for predicting the penetration condition utilized a supervised learning classification model and was constructed using three convolution layers and two pooling layers. The rationale for choosing three convolution layers and two pooling layers was based on several considerations. First, the depth of the network was designed to be sufficient to capture complex patterns in the input data without overfitting. Three convolution layers allow the model to extract low-level, mid-level, and high-level features progressively. The pooling layers, placed after the first and second convolution layers, serve to reduce the spatial dimensions of the feature maps, thereby decreasing the number of parameters and computational load while retaining the most salient information. The convolutional layers and pooling layers were optimized using the Keras Tuner library. Supervised learning is as method where the model is trained using pre-labeled input variables and then makes predictions on new data. Classification models are used when the predicted output is a discrete value.  
The current and voltage data during welding were converted into dynamic resistance values to predict the penetration state during the root pass of the V-groove butt joint. The current and voltage during welding were converted into a dynamic resistance, which was then transformed into STFT-formatted image data and utilized as input data. The back-bead was measured using a laser vision sensor, categorized into classes based on height, and used as output data. A total of 2000 data points were randomly selected and divided into a 60% training set (1200 points), a 20% validation set (400 points), and a 20% test set (400 points). The selected input and output data were used to train a CNN model to predict and classify penetration conditions.
# Functional Magnetic Resonance Imaging in Research on Dog Cognition: A Systematic Review
## 4 Discussion
### Research Articles Considering Olfactory Stimuli  
Research on canine olfaction using fMRI has explored how dogs process and respond to various odors, highlighting their sophisticated olfactory capabilities. To establish the feasibility of fMRI for investigating the olfactory system in conscious dogs, both anesthetized and awake animals were used. The olfactory bulb and piriform lobes were activated in both groups. At the same time, the frontal cortex showed activation primarily in awake dogs, indicating that anesthesia suppresses pathways for the conscious processing of odor information [47]. Moreover, the response to odor intensity revealed differences in activation strength and spatial extent across several brain regions, including the olfactory bulbs, piriform lobes, cerebellum, and frontal cortex [49]. By presenting various stents, including familiar and unfamiliar human and dog odors, it was found that while all stents similarly activated the olfactory bulb, the caudate nucleus responded most strongly to familiar human odors, indicating a complex response to biological stents based on familiarity [50].  
Research on service dogs was focused on enhancing olfactory sensitivity, which is particularly important for working dogs, as they need to detect even subtle olfactory cues. The premise is that the signals the olfactory bulb receives and further transmits to the olfactory network are directly related to odorant molecules reacting with receptor neurons [8]. Zinc enhanced fMRI responses in the olfactory bulb and hippocampus, suggesting that these nanoparticles could amplify the olfactory response [8]. This issue was further explored, demonstrating that zinc strengthened connections from the olfactory bulb to a tight network of pathways between the frontal cortex, thalamus, caudate, amygdala, and hippocampus. This emphasizes the amygdala's role in processing emotionally significant olfactory stimuli [46].  
Another important study for working dogs focused on how dogs decode odor mixtures. This study's stimuli consisted of a target odor with or without a food reward, a distractor odor, and a mixture of the two. When predicting target odor identity, dogs showed significant brain activity in the caudate nucleus, amygdala, olfactory bulbs, perirhinal cortex, and posterior cingulate. The findings suggested that dogs perceive mixtures as distinct odors rather than as their components, which has implications for improving their performance in scent detection tasks. Furthermore, analysis of activated regions highlighted the importance of the amygdala for learned associations, indicating that these associations are maintained over time [48].
## 5 Conclusions and Future Work  
In this paper, a semi-active suspension control strategy based on the Proximal Policy Optimization (PPO) algorithm was proposed. Firstly, the road unevenness information was collected in real time by the sensor, and the three performance indicators of the vehicle were obtained as the state input for reinforcement learning by establishing a 2-DOF quarter suspension semi-system model, then the control strategy was continuously optimized according to the corresponding reward and punishment functions and the prints of the weight matrix under different road conditions until the convergence state was reached. The simulation experiments showed that the proposed control strategy could improve the vehicle performance better than the passive suspension and fuzzy control.  
However, the traditional reward function is not applicable in the face of complex and frequently changing roads, and the applicability of the control strategy involved to the full-vehicle suspension requires further exploration and research.  
Conceptualization, S.-Y.H.; methodology, S.-Y.H.; writing--original draft preparation, T.L.; writing--review and editing, S.-Y.H. and T.L.; supervision, S.-Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province for Key Project under Grant ZR2020 KF006, the National Natural Science Foundation of China under Grants 61903156 and 61873324, \"New 20 Rules for University\" Program of Jinan City under Grant No. 2021 GXRC077, the Natural Science Foundation of Shandong Province under Grant ZR2019 MF040, the University Innovation Team Project of Jinan under Grant 2019 GXRC015, the Higher Educational Science and Technology Program of Jinan City under Grant 2020 GXRC057, and the State Scholarship Fund of the China Scholarship Council.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Number of iterations.
# A Fast Ray-tracing Method for Locating Mining-induced Seismicity by Considering Underground Voids
## 2 Methodology
### Fermat Principle  
The Fermat principle [33] is one of the basic principles in the ray theory of seismic waves, which reflects the propagation law of seismic waves. The popular expression of the Fermat principle is that the travel time of the seismic wave along a ray is minimal compared to the travel time along other paths. As shown in Figure 1, in the uniform velocity model, the propagation path from point A to point B is a straight line (Figure 1a); in the layered velocity model, the propagation path from point A to point B is a polyline. Moreover, the inflection point is on the interface (Figure 1b). In the continuously changing velocity model, the propagation path from points A to B is a curve (Figure 1c). The propagation path of the wave varies with the velocity model. However, no matter how it changes, the propagation path always satisfies the Fermat principle. For underground mines, we only need to find the shortest path from the source to the receiver in the non-voids.
## 4 Methodology
### Evaluation Metrics  
To assess the quality of the predicted signal, we used two evaluation metrics. The first one is Mean Absolute Percentage Error (MAPE), which is widely used for time series forecasting [62]:  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|, \tag{6}\]  
where \(y_{i}\) is the true value and \(\hat{y}_{i}\) is the predicted value.  
The second one is a custom metric based on several fEPSP parameters that describe the functional properties of the neuron groups that directly generate the signal. We include the following signal parameters: rise time, decrease time, response time halfwidth, amplitude and slope, as shown in Figure 5. Namely, we define the evaluation metric as the combination of squares of slope, response time halfwidth, rise time and decay time with corresponding weights: 0.05, 0.4, 0.3, 0.05 and 0.2. Also, we normalized values of each part of metric to be in range from 0 to 1. The choice of the evaluation metric was specified by biological background of the problem.
## 3 Materials and Methods
### Followed Methodology
If the user delayed it, the application periodically showed a reminder aiming to gather all needed data. More information concerning these surveys is presented in Section 3.2. When the acquisition period was completed, we studied the collected data and generated a dataset for each user to implement a personalized machine learning (ML) model. Additionally, we decided to generate a dataset including all users' data and compare its results with the individual approach results. After organizing the data, we started exploring the creation of ML models to predict blood pressure values, testing multiple algorithms. Once we tested the suitability of these models, we extended the capabilities of SENIOR, including functionality to send personalized notifications to the users with predicted measures within hypertensive ranges. Remarkably, the predictive mechanisms implemented offer both real-time and future predictions. In terms of real-time calculations, the SENIOR framework determines the estimated blood pressure associated with the features gathered by the platform in that instant. Moreover, the framework offers blood pressure predictions within 8, 16, and 24 h temporal windows.
## 3 System Model and Problem Formulation
\right] \tag{9}\]  
SVD is applied to the matrix defined in Equation (9), as shown in Equation (10):  
\[M_{SVD}=SVD(M_{\text{C}})=U_{n\times p}\Sigma_{p\times p}V_{m\times p}^{*} \tag{10}\]  
\(U\) and \(V^{*}\) are orthonormal, which results in an optimal Euclidean distance between each point, that is fundamental to define the final constellation of SCMA. \(V_{SVD}^{*}\); which is a matrix of \(2\times 8\) similar to \(M_{\text{C}}\), with the difference that \(V_{SVD}^{*}\) is a set of orthonormal eigenvectors. \(X_{j}\) is a matrix of \(4\times 8\) that represents the six codebooks (\(J=6\)), where the rows represent the subcarriers and the columns the eight codewords; two rows are zero according to the scatter matrix \(V_{j}\). The works shown in [4; 5; 6; 7; 8; 9; 10; 11] encoded two user bits in SCMA codewords, and the distance between adjacent points in the mother constellation is fundamental for the detection and decoding of the codewords. In this work, unlike its predecessors, each SCMA codeword represents three bits, producing an IQ constellation with closest adjacent points; Figure 1 shows the spacing between points of the constellation using SVD and phase rotation methods. According to Figure 1a, the distance ratio between adjacent points of the SVD constellation is \(\frac{0.474}{0.158}=3\), while from Figure 1b it is observed that the distance ratio in the constellation by phase rotation is \(\frac{0.788}{0.427}=1.84\). When SVD is applied to the mother constellation, the resulting matrixes are orthogonal with orthonormal vectors, which produces constellations with improved distance between adjacent points. According to the FG showed in (2), the SC1 carries data of the Users 1, 3 and 5; each three-bit codeword could be represented by 64 possible combinations, since each codeword (three bits) of User 1 could be combined with the eight possible values of User 3 and the eight possible values of User 5, with the result that eight codewords for each user could be represented by up to 512 combinations. The IQ constellation is shown in Figure 2.
## 2 Background Theory
### DC Electrical Method  
As shown in Figure 1, the DC electrical method consists in injecting a current (\(I_{AB}\), Ampere) into the subsoil by using two current electrodes \(A\) and \(B\) and to measure the potential difference (\(\Delta V_{MN}\), Volt) through two other electrodes \(M\) and \(N\) on the ground surface. The resistance of the material (\(R\), \(\Omega\)) through which the current flows can be obtained by:  
\[\Delta V_{MN}=I_{AB}R. \tag{1}\]  
\(R\) depends on electrical resistivity distribution of the subsoil (\(\rho_{a}\), \(\Omega\)m) and the configuration of electrodes, according to:  
\[\rho_{a}=K_{g}R \tag{2}\]  
where \(K_{g}\) (m) is called geometric factor; it depends on electrodes geometry (array) and can be calculated by:  
\[K_{g}=2\pi\bigg{[}\bigg{(}\frac{1}{AM}-\frac{1}{BM}-\frac{1}{AN}+\frac{1}{BN} \bigg{)}\bigg{]}^{-1}. \tag{3}\]  
For inhomogeneous and anisotropic media, \(\rho_{a}\) is called apparent resistivity and is defined as the ratio between the measured value of the parameter in question and its theoretical value in a homogeneous and isotropic medium of unit resistivity [39]. Therefore, in real cases, inverse methods must be applied to turn from the apparent resistivity to the true spatial distribution of this parameter.  
Lateral and vertical resistivity variation of the subsoil can be determined by moving current (\(AB\)) and receiving dipoles (\(MN\)) on the soil surface. This is called electrical resistivity tomography (ERT) and the sequence of four electrodes \(A\), \(B\), \(M\) and \(N\) is defined as quadrupoles.  
In order to increase resolution at depth, [PERSON] [40] and [PERSON] [41] introduced the cross-hole electrical resistivity tomography (CH-ERT). In this case, plate or cylindrical electrodes are installed along two or more boreholes in contact with the host soils/rocks or with the formation fluid (Figure 1b). CH-ERT allows to identify lateral and vertical resistivity variations in the area between two or more boreholes; however, the ratio between boreholes depth and separation should be >1.5 otherwise sensitivity is reduced (Figure 1c).
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Senders' Side  
The operations of senders enable data compression, encryption, critical information concealment, and masking before data transmission.
## 4 The Role of Routing Protocols to Resilience in Self-Configuring WSNs
### Example of Self-Configuring Mechanisms in WSNs  
Figure 2 shows an example of the link configuration for a given WSN running the AODV, DSR and MPH protocols, this example consists of three phases: in the initial phase, the nodes are switching on; in the second phase, the network has been formed according to the underlying routing protocol; and in the third phase, we can see the resultant topology under a reactive jamming attack. In the second phase, each routing protocol establishes the links according to their rules. For AODV and DSR, links are established within the coverage radius of the nodes. Regarding MPH, parent-child links are set with a hierarchical topology with respect to a given coordinator node (node zero). The third stage presents how the topology for AODV, DSR and MPH is reconfigured after an attack caused by a jammer node  
Figure 1: Important features of the _ad hoc_ on-demand distance vector (AODV), dynamic source routing (DSR) and multi-parent hierarchical (MPH) protocols.  
represented by the shaded node. Let us assume that this node produces the reactive jamming (which is the worst case scenario for the WSN presented in this paper) and causes the links next to it to become unusable. The logical links are dropped due to the partial overlapping of the coverages of the jammer node with Nodes 2, 4 and 5 having in common the lost links. This situation is caused because the attacker node emits a packet when it listens to any packet transmission, causing collisions and corrupting the legitimate packet. The nodes under the MPH protocol update their neighbor tables as soon as they realize that there are no packets being received from some neighbors. In AODV and DSR, routing tables are also updated through error messages when the links are already dropped. The nodes in the WSN with AODV and DSR increase the average number of packet retransmissions and the number of MAC algorithm retries, because the jammer node constantly maintains the reactive attack. After the topology update, these links are lost, and error packets of these routes are received by the nodes. For MPH, nodes quickly realize the futility of these affected links due to persistence and reactivity mechanisms [6]. In addition, the periodicity of the neighbor tables' update produces these links that are discarded without sending extra packet notification. Finally, this reflects lower power consumption and better performance for a network under the MPH protocol.
# Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant
###### Abstract  
The chestnut tree (_Castanae sativa_, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic and antimicrobial properties. These metabolites can be mainly divided into monomeric polyphenols and tannins. In this piece of work, we investigated a sustainable protocol to isolate enriched fractions of the above-mentioned compounds from CW residues. Specifically, a sequential extraction protocol, using subcritical water, was used as a pre-fractionation step, recovering approximately 88% of tannins and 40% of monomeric polyphenols in the first and second steps, respectively. The optimized protocol was also tested at pre-industrial levels, treating up to 13.5 kg CW and 160 L of solution with encouraging results. Ultra- and nanofltrations were used to further enrich the recovered fractions, achieving more than 98% of the tannin content in the heavy fraction, whilst the removed permeate achieved up to 752.71 mg GAE/g\({}_{\text{ext}}\) after the concentration (75.3%). Samples were characterized by means of total phenolic content (TPC), antioxidant activity (DPPH- and ABTS-), and tannin composition (hydrolysable and condensed). In addition, LC-MS-DAD was used for semiqualitative purposes to detect rescalagin/castalagin and Vescalin/castalin, as well as gallic acid and ellagic acid. The developed valorization protocol allows the efficient fractionation and recovery of the major polyphenic components of CW with a sustainable approach that also evaluates pre-industrial scaling-up.  
subcritical water extraction; microwave-assisted extraction; semi-industrial scale; chestnut wood; tannins; polyphenols; membrane filtration; sequential extraction; fractionation; antioxidant activity +
Footnote †: journal: Applied sciences
## 4 Outlook  
In order to realize solar cells with high energy conversion efficiency, the charge carrier diffusion lengths should be larger than the penetration depth. However, several materials like microcrystalline silicon, amorphous silicon, organic and polymeric materials exhibit low charge carrier diffusion lengths [14; 15]. In order to increase the energy conversion efficiency of solar cells made of such materials, the electrical and optical properties of the solar cell have to be decoupled. Several approaches have been suggested to decouple the electrical and optical properties including radial nanowire or 3D solar cells [10; 11; 12; 13; 14; 15]. In the case of a radial solar cell, the solar cell acts like an optically thick and electrically thin solar cell. The solar cell can be fabricated by using a nanowire array as a template. The actual layers of the solar cell are subsequently deposited on such nanowire array [10; 11; 12; 14; 15]. However, such solar cell should be called 3D solar cell instead of nanowire solar cell. Such 3D textured solar cells can be described as surface textured solar cells using surface textures with nanowire/pillar heights larger than the thickness of the actual solar cell.  
In this manuscript, the optics of solar cells with silicon nanowires with diameters larger than 30 nm is studied. Decreasing the diameter of the nanowire down to 1-2 nm leads to the formation of quantum wires [2]. Quantum wells, quantum wires or quantum dots allow for the control of the band gap by tuning the size of the quantum structure [35; 36]. In the case of silicon, the bulk band gap of 1.14 eV can be increased to 1.7 eV or larger. Thus far, encouraging results have been demonstrated for silicon quantum dot solar cell [37]. Band gap engineering of the materials by just controlling the size of the quantum structure would allow for the realization of solar cells with increased energy conversion efficiency. All silicon tandem solar cells using a silicon quantum wire in combination with a conventional silicon bottom solar cell are proposed in the literature [38]. According to the detailed balance limit the energy conversion efficiency of tandem solar cells can exceed 40% [39]. However, further investigations are required to realize such solar cells with energy conversion efficiencies comparable to conventional multi-junction solar cells. Besides the generation of electrical energy at high energy conversion efficiencies the high open circuit voltage of multi-junction solar cells allows for the direct generation of hydrogen via water splitting [40; 41].
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Performance of the Proposed Method
#### 5.2.1 Image Color Restoration  
At this stage, the image that appears dim and hazy with a hue distorted from the original color is corrected by preserving the original color. In general, this stage is used for color restoration; the lighting component in the image is removed. Figure 21 shows the image after color restoration using the method in this paper. Figure 21 shows that our method can restore image color while preserving the original color. The iced transmission line is brighter and more precise than the original image.