esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Physiological Measurement Devices
#### 3.5.2 Non-Neural Physiological Measurements  
EOG requires two pairs of electrodes applied around the eyes to record both up-down-blink movements and left-right sideways eye movements. An advantage over an eye tracker is that they can give eye position indication even when the eye is nearly closed. However, the eye position from EOG is not as accurate as eye trackers when eyes are open and involve the sensation of electrodes attached to the skin and the wires leading away from the electrodes and thus are only suitable for laboratory setting.  
EMG measures muscle activity and can be placed over muscles relevant to the task (e.g., limbs for reaching, driving, etc.; facial muscles for attention and social engagement). [PERSON] [107] discusses benefits and caveats of the use of facial EMG for emotion/affect research. EMG on limbs can be used in the context of simulated driving [108] and driver distraction [109].  
GSR has a long history of use in detecting a person's arousal state, in particular related to stress or anxiety. In the context of detecting mind wandering and engagement, GSR is often used alongside other measures. GSR has been shown to be effective for recognizing a distracted state on its own while avoiding the privacy concerns that eye trackers or cameras suffer from [110].  
Heart rate, heart rate variability (HRV), and blood pressure can all be measured with a wristwatch-style monitor, also enabling this physiological measure of arousal for engagement purposes (e.g., CogWatch with low-cost research-grade outputs [111]). Specific examples of engagement using cardiac-derived measures include student engagement in different learning education environments [112], task demands interacting with ADHD diagnosis [113], interactions with time-on-task relevant for assessment of prolonged engagement [114], and links with cognition and neurological conditions [115].  
One notable study [116] used a combination of neural and non-neural measures to attempt to distinguish between different cognitive states, including workload-focus, distraction, sense of urgency, mind wandering, and cognitive interference. While results were modest, this paves the way for more precise dissociation of cognitive state beyond just on-task or off-task.  
However, physiological signals can be intrusive or obstructive in practical applications such as driving [2], although some applications may be possible, e.g., wearable GSR [110] and minimal size slim eye trackers.
## 2 Materials and Methods  
### Introduction  
Nowadays, several advanced simulation tools [4; 5; 6] are available to define areas in danger of landslides, foresee landslides' propagation paths, and obtain parameters to design structural and non-structural countermeasures in order to prevent the damage they cause. Here, we have selected GeoFlow-SPH and ravaflow, which were developed for the propagation modeling of rapid landslides, such as debris flows and rock avalanches, and have been widely validated through the back analysis of real events [11; 12; 13; 14]. GeoFlow-SPH and ravaflow both employ a two-phase model which uses an interacting solid and fluid mixture; it is similar to the model proposed by [PERSON] and [PERSON] (2005) [15], and based on the depth-integrated mathematical model of [PERSON] and [PERSON] (1984) [16].  
In propagation modeling, the mathematical formulations implemented in sets of code are usually simplified and reduced to two dimensions through depth integration approximations. Since many flow-like landslides can be long and wide compared to their depths, the governing equations can be integrated along the vertical axis. The resulting 2D depth-integrated model provides a good balance between accuracy and time consumption. GeoFlow-SPH and ravaflow are also based on integrated solutions of the balance equations of linear momentum and mass using the shallow water flow assumption.  
We start by explaining how the mentioned simulation programs can be represented. Since the equations are depth-integrated, the particles cannot be defined along the vertical axis. Thus, in depth-integrated models, the flowing mass is divided into a finite number of columns represented by particles. Therefore, two heights corresponding to the solid (\(h_{s}\)) and fluid (\(h_{w}\)) phases will appear in each model by considering the assumption that the fluidized soil is fully saturated, and the sum of these partial heights is the total height (\(h\)) of the mixture. Therefore, the phases are simulated in different layers and interact with each other by interaction force. In Figure 1, the evolution of both phases' heights with respect to the total height is depicted.  
Let \(\bar{n}_{w}=\bar{n}\) and \(\bar{n}_{s}=(1-\bar{n})\) be the fractions of volume \((V)\) occupied by the water and solid particles, respectively. The heights of the solid phase \((h_{s})\) and fluid phase \((h_{w})\) are given by:  
\[\begin{split}& h_{s}=\bar{n}_{s}h\\ & h_{w}=\bar{n}_{w}h\end{split} \tag{1}\]
## 3 Results
### Effect of Off-Normal Processing Parameters
#### 3.2.2 In-718  
First, process parameter sets D, E, and F were investigated using 60 mJ laser pulse energy generated Rayleigh waves received at 23 and 39 mm. The results are plotted in Figure 13. Aside from the amplitude for Set E being smaller than for Sets D and F, at 23 mm the waveforms are similar. However, at 39 mm the waveforms for both off-normal parameter sets E and F are substantially different from those for set D in that they have a sine wave appearance, while set D is primarily V-shaped. While no significant microstructural differences were observed in the optical micrographs, property-affecting microstructural features are often too small to resolve with optical microscopy. While we do not have mechanical test results to confirm that the off-normal process parameters have deleterious effects on the material, we feel that the different waveforms provide a useful first step.  
In the final experiment to be reported we assessed the possibility of receiving the waveforms at a single point and describing nonlinear waveform evolution by changing the amplitude of the source. In Figure 14 the process parameter sets D, G, and H, having different hatch spacings, were investigated using a low pulse energy (30 mJ) and high pulse energy (120 mJ) and receiving after a wave propagation distance of 28 mm. The waveforms have the characteristic V-shape indicative of linear behavior for the low pulse energy. However, for the high pulse energy the waveform is more like a sine wave followed by erratic secondary peaks. Unfortunately, we do not yet understand the cause of the secondary peaks or if there is a way to distinguish between the normal and off-normal process parameters.  
Figure 12: Frequency spectra for signals received at point 1 (Figure 11a) for process parameter sets A, B, C.  
Figure 13: IN-718 process parameter sets D, E, F where processing laser speed changes. Signals received at (**a**) 23 mm, (**b**) 39 mm.
# The Antiviral Potential of Probiotics--A Review on Scientific Outcomes
## 4 Clinical Studies on the Antiviral Potential of Probiotics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model System** & **Probiotic(s)** & **Duration of Probiotic Exposure** & **Findings** & **Ref.** \\ \hline \multirow{4}{*}{BALB/c mice} & \multirow{4}{*}{_Lactobacillus rhamnus_ GG} & \multirow{4}{*}{3 days (pre-exposure)} & \multicolumn{2}{c}{Activates the lung} \\  & & & natural killer & \\  & & GG & & \(\uparrow\) Cell-mediated & \\  & & & immune responses & \\ \hline \multirow{4}{*}{BALB/c mice} & _L. rhamnus_ GG and & \multirow{4}{*}{19 days (pre-exposure)} & \multirow{4}{*}{\(\downarrow\) Clinical symptom scores} \\  & _Lactobacillus gasseri_ & & & \(\downarrow\) Pulmonary virus titers \\  & & TMC0356 & & \\ \hline \multirow{4}{*}{BALB/c mice} & \multirow{4}{*}{_Lactobacillus plantarum_ DK119} & Oral administration for 10 days & \multirow{4}{*}{\(\downarrow\) lung virus loads} \\  & & (pre-exposure) and 14 days & & \\  & & (post exposure) & & \\ \hline \multirow{4}{*}{BALB/c mice} & _L. plantarum_ (Lp) & 14 days (pre-exposure) and & \multirow{4}{*}{\(\uparrow\) Cytokines IFN-\(\gamma\) and IL-12} \\  & heat-killed LP (nF1) & & 14 days (post exposure) & \\ \hline \multirow{4}{*}{BALB/c mice} & _L. plantarum_ (Lp) & 14 days (pre-exposure) and & \multirow{4}{*}{\(\downarrow\) viral replication in the lungs of mice} \\  & heat-killed LP (nF1) & & 14 days (post exposure) & \\ \hline \multirow{4}{*}{BALB/c mice} & _Lactobacillus delbrueckii_ & \multirow{4}{*}{21 days (pre-exposure)} & \(\uparrow\) Anti-influenza virus IgA} \\  & & ssp. bulgarcius & & and IgG1 \\  & & OLL1073R-1 & & \(\uparrow\) Natural killer (NK) \\  & & & cell activity & \\ \hline \multirow{4}{*}{BALB/c mice} & _Lactobacillus brevis_ & \multirow{4}{*}{14 days (pre-exposure)} & \(\uparrow\) Influenza specific IgA} \\  & KB290 (KB290) & & & levels\(\uparrow\) Serum \\ \hline \multirow{4}{*}{C57 BL/6N mice} & _Lactobacillus gasseri_ & \multirow{4}{*}{21 days (pre-exposure)} & \(\downarrow\) Lung virus loads \\  & SBT2055 (LG2055) & & \(\uparrow\) Expression of antiviral & \\  & & & genes Mx1 and Oas1a & \\ \hline \multirow{4}{*}{Gnotobiotic (Gn) pigs} & _Lactobacillus acidophilus_ & \multirow{4}{*}{exposure at 3, 5, 7, 9 and 11 days} & \(\uparrow\) Intestinal IgA and IgG \\  & NCFM\({}^{\text{TM}}\) & of age & \(\uparrow\) Serum IgA, IgM and \\ \hline \multirow{4}{*}{Gnotobiotic (Gn) pigs} & \multirow{4}{*}{\(\uparrow\): Increased; \(\downarrow\): Reduced.} \\  & & & \\ \end{tabular}
\end{table} 
Table 2: The antiviral potential of probiotics based on in vivo studies.
## 4 Equalization Technologies
### Equalization Based on Machine Learning  
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, which was coined in 1959 by [PERSON] [81]. Recently, machine learning algorithms have been utilized to process optical communications and achieve distinguished performance [82; 83; 84; 85; 86]. Some DSP techniques based on machine learning including SVM and NN are described in this section for PAM4 optical links.  
Figure 15: The structure of Volterra nonlinear equalizer (VNLE).
## 1 Introduction
From a variety of retrofitting schemes, this manuscript studies a particular retrofitting scheme that focuses in confining, if possible, the structural intervention only at the ground floor of a soft-story vulnerable building. This retrofit consists of constructing RC infills at the soft-story level together with RC jackets for the members of the surrounding RC frames. In this way, this intervention aims to counteract the described detrimental effect resulting from the increased stiffness of the upper stories due to the presence of the masonry infills. Such a retrofit can be selectively extended, if required, to the upper stories. In this framework, the objectives of the present study are:  
1. to briefly describe the influence of the presence of masonry infill within an RC frame, which leads to the formation of such a soft story, by presenting a summary of the effects of UMI-RC frame interaction. The importance of the peripheral mortar joint at the interface between the UMI and the surrounding RC frame is underlined, and the necessity of including this interface response mechanism in the vulnerability assessment is highlighted (Section 2);
2. to study the impact of the studied particular retrofit consisting of constructing an RC infill within the bay of an existing RC frame. This is done by an experimental study employing 1/3 scaled single-story one-bay RC frames. It is shown that such a retrofit, can be beneficial because it results in considerable increase of the stiffness, strength, and plastic energy consumption. The importance of the presence of properly designed steel ties connecting the RC infill with the surrounding frame is also shown (Section 3); and
3. to show the capabilities of a numerical methodology aimed at predicting the RC infill interaction effects, including the nonlinear response of the RC infill, the nonlinear response of the RC frame combined with the nonlinear response of the steel ties, and additional nonlinear response mechanisms at the interface. This numerical approximation, which is validated by utilizing the measured response, is shown to be capable of predicting reasonably well these important response mechanisms. It can therefore have the potential to be utilized for design purposes (Section 4).
## 4 Results
### Correlation Coefficient Comparison
Figure 9:3  
However, in analyzing the data from the three wells in Figure 21, we can observe that the Pearson correlation coefficient between water saturation and resistivity does not exhibit a clear linear relationship. This may be because the Pearson correlation coefficient can only uncover the linear correlation between variables and is insufficient to capture the nonlinear relationships between them. Therefore, employing additional correlation metrics, such as the Spearman correlation coefficient, the coefficient of determination, and the point-biserial correlation coefficient, is beneficial. These measures can elucidate the nonlinear associations between variables. They provide a broader insight into the links between reservoir attributes and production parameters, encompassing complex interactions. In this research, while model selection plays a significant role, data preprocessing and feature extraction are pivotal in enhancing the model performance. Effective data preprocessing helps cleanse and enhance the quality of the data, while precise feature extraction is essential for uncovering the potential of the data and refining valuable information. In Figure 5, we have elucidated the initial rationale for employing histograms and violin plots to determine the normalization technique. However, upon observing the uniform distribution of DEPTH in Figure 21, it became evident that StandardScaler may not be the optimal choice, as the mean and standard deviation of uniformly distributed data may not effectively capture their characteristics. In this scenario, employing max-min normalization or utilizing the original data directly may be more suitable. Furthermore, the data from Well 6 indicate that the permeability closely approximates a normal distribution, while the water saturation conforms to a Poisson distribution, and Well 7's gas production exhibits numerous outliers. These findings suggest the necessity of implementing distinct preprocessing methods for different well-logging data to ensure data quality post-processing and establish a robust foundation for subsequent feature analysis. In the process of feature extraction, principal component analysis (PCA) is dedicated to reducing the dimensionality of the dataset and decreasing the number of features, while the generation of polynomial features aims to enhance the model structure by introducing higher-order terms and interaction terms. The results in Figures 7 and 20 demonstrate that although the polynomial feature expands the applicability of the model and enhances adaptability, it may lead to overfitting issues. Simultaneously, while PCA sacrifices some information during data compression, it retains the majority of the variability, effectively simplifies the data structure, and reduces the processing costs. The choice of method should be based on the nature of the data and our requirements for model accuracy and explanatory power.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis  
The aim of this section is to demonstrate the low complexity and scalability of our algorithm. In order to do so, we developed an analytical mathematical model and used it to determine the mean number of messages generated by each location update. We also show that the locally generated updates in the location tables by each location update were only related to the mean number of generated messages. Thus, we only focus on the mean number of messages generated per location update. The section is organized as follows. First, the cost of SFLS in terms of messages sent is estimated and this shows the scalability of the solution. Secondly, SFLS is compared with an optimized flooding algorithm used in the OLSR protocol.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 2 UWA MIMO-OFDM System Model  
Consider a UWA MIMO-OFDM system with \(N_{t}\) transmitters and \(N_{r}\) receivers. The duration of the overall OFDM block is given by \(T_{bl}=T+T_{g}\), where \(T\) denotes the OFDM symbol duration and \(T_{g}\) denotes the guard interval. The subcarrier spacing can be given by \(\frac{1}{T}\) and for total number of \(K\) subcarriers, the bandwidth can be given by \(B=\frac{K}{T}\). For \((v,\mu)\) transmit-receive pair in a MIMO system, assume that the acoustic multipath channel consists of \(P_{v,\mu}\) discrete paths and the channel impulse response can be calculated as:  
\[h_{v,\mu}(\tau,t)=\sum_{p=1}^{P_{v,\mu}}A_{v,\mu,p}(t)\delta(\tau-(\tau_{v,\mu,p}-a_{v,\mu,p}t)), \tag{1}\]  
where \(A_{v,\mu,p}(t)\), \(\tau_{v,\mu,p}\) and \(a_{v,\mu,p}\) show the amplitude of the \(p\)th path of \((v,\mu)\) transmit-receive pair, delay at the start of the OFDM block and the Doppler factor corresponding to the \(p\)th path. The fast Fourier transform (FFT) output of the \(m\)th subcarrier of the \(v\)th receiver can be obtained as follows, where \(s_{\mu}[k]\) shows all the transmitted symbols:  
\[z_{v}[m]=\sum\limits_{\mu=1}^{Nt}\sum\limits_{k\in S_{A}}H_{v,\mu}[m,k]s_{\mu}[ k]+\eta_{v}[m], \tag{2}\]  
When the system does not have the influence of ICI, \(\mathbf{H}\) (the subscript is omitted here for convenience) is a diagonal matrix; whereas when ICI exists, \(\mathbf{H}\) is a regular matrix and can be written as:  
\[\mathbf{H}=\left[\begin{array}{ccc}H\Big{[}-\frac{K}{2},-\frac{K}{2}\Big{]}& \ldots&H\Big{[}-\frac{K}{2},\frac{K}{2}-1\Big{]}\\ \vdots&\ddots&\vdots\\ H\Big{[}\frac{K}{2}-1,-\frac{K}{2}\Big{]}&\ldots&H\Big{[}\frac{K}{2}-1,\frac {K}{2}-1\Big{]}\end{array}\right], \tag{3}\]  
where the element \(H[m,k]\) in the vector \(\mathbf{H}\) is the ICI factor, which represents the \(m\)th subcarrier interference with the \(k\)th subcarrier. Generally, \(\mathbf{H}\) is a large matrix and in most channel estimation algorithms, it needs to be inverted, which involves a higher amount of computation. Therefore, in order to decrease the complexity, we reduce \(\mathbf{H}\) to a strip matrix by assuming that each symbol is only interfered by the adjacent \(D\) symbols, i.e.,  
\[H[m,k]\approx 0,\forall|m-k|>D \tag{4}\]  
Figure 1 shows a visual illustration of \(\mathbf{H}_{D}\) with different values of \(D\).  
When \(D=0\), it indicates that the system has no influence of ICI, that is, all subcarriers of OFDM are orthogonal. Therefore, Equation (2) can be re-written as:  
\[z_{v}[m]=\sum\limits_{\mu=1}^{Nt}\sum\limits_{k=m-D}^{k=m+D}H_{v,\mu}[m,k]s_{\mu }[k]+\eta_{v}[m], \tag{5}\]  
Denote \(\sum\limits_{k=m-D}^{k=m+D}H_{v,\mu}[m,k]\) with \(\mathbf{H}_{v,\mu}^{D}\), Equation (5) can be rewritten in matrix form as:  
\[\mathbf{z}_{v}=\sum\limits_{\mu=1}^{Nt}H_{v,\mu}^{D}\mathbf{s}_{\mu}+\eta_{v} \tag{6}\]  
Figure 1: Channel model with different degrees of Inter-Carrier Interference (ICI).
## 2 Photovoltaic Modeling and Problem Formulation  
### Photovoltaic Modeling  
As can be seen from Figure 1, a real solar cell is modeled by the equivalent circuits comprising of a photocurrent source \(I_{ph}\), a lumped series resistance \(R_{s}\), a lumped shunt resistance \(R_{sh}\) and one or several parallel diodes with different ideality factors. In Figure 1a, the diode D of SDM incorporates a nonphysical ideality factor \(n\) to account for the combined effect of more than one type of conduction mechanism within the actual solar cell. In Figure 1b, the first diode D\({}_{1}\) represents the conduction phenomena in quasi-neutral regions, while the second diode D\({}_{2}\) corresponds to the recombination in the space-charge region of the junction. In Figure 1c, the first two diodes D\({}_{1}\) and D\({}_{2}\) of TDM carry the same physical meaning as they did in DDM, while D\({}_{3}\) is exclusively designed to take the recombination losses in defect regions and grain boundaries into account with the ideality factor \(n_{3}\). For a given irradiance and temperature, the \(I\)-\(V\) relationships in Figure 1a-c can be formulated by the SDM Equation (1), DDM Equation (2) and TDM Equation (3), respectively.  
\[I=I_{ph}-I_{0}\left[\exp(\frac{V+IR_{s}}{nV_{th}})-1\right]-\frac{V+IR_{s}}{R_{ sh}} \tag{1}\]  
\[I=I_{ph}-\sum_{i=1}^{2}I_{0i}\left[\exp(\frac{V+IR_{s}}{n_{i}V_{th}})-1\right]- \frac{V+IR_{s}}{R_{sh}} \tag{2}\]  
\[I=I_{ph}-\sum_{i=1}^{3}I_{0i}\left[\exp(\frac{V+IR_{s}}{n_{i}V_{th}})-1\right]- \frac{V+IR_{s}}{R_{sh}} \tag{3}\]  
where \(I\), \(V\), \(I_{ph}\), \(I_{0i}\), \(I_{0i}\), \(n\), \(n_{i}\), \(R_{s}\) and \(R_{sh}\) are the terminal current, terminal voltage, photocurrent, diode reverse saturation currents, diode ideality factors and series and shunt resistance, respectively. The thermal voltage \(V_{th}=kT/q\), where the Boltzmann constant \(k=1.3806503\times 10^{-23}\) J/K, electronic charge \(q=1.60217646\times 10^{-19}\) C and \(T\) is the absolute temperature in Kelvin.  
It is easy to see from Equations (1)-(3) that there are five parameters \(X=[n\), \(I_{ph}\), \(I_{0}\), \(R_{s}\), \(R_{sh}]\) in SDM, seven parameters \(X=[n_{1}\), \(n_{2}\), \(I_{ph}\), \(I_{01}\), \(I_{02}\), \(R_{s}\), \(R_{sh}]\) in DDM and nine parameters \(X=[n_{1}\), \(n_{2}\), \(n_{3}\), \(I_{ph}\), \(I_{01}\), \(I_{02}\), \(I_{03}\), \(R_{s}\), \(R_{sh}]\) in TDM. Since SDM is a good compromise between simplicity and accuracy [30], it has been widely used to simulate the electrical behavior of solar modules with \(I_{phm}=I_{ph}N_{p}\), \(I_{0m}=I_{0}N_{p}\), \(n_{m}=nN_{s}\), \(R_{sm}=R_{s}N_{s}/N_{p}\) and \(R_{slm}=R_{sh}N_{s}/N_{p}\), where \(N_{s}\) stands for the number of cells in series while \(N_{p}\) represents the strings of cells connected in parallel.
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 3 Experiment
### Testing Environment  
The experiments were conducted on a high-performance computing platform configured as follows: Server: Dell rack-mounted server equipped with an Intel Xeon Silver 4214R CPU, 128 GB RAM, and dual NVIDIA GeForce RTX 3090 GPUs (24 GB VRAM each) for parallel computation. 2. Storage: 1 TB hard drive to accommodate large-scale datasets and model checkpoints. Operating System: Linux Ubuntu 20.04 LTS for stable system-level operations. Computational Framework: PyTorch 1.9.0 with CUDA 11.1 integration to leverage GPU-accelerated training and inference. Python 3.8.11 as the primary programming language. OpenCV-Python 4.5.3.56 for image preprocessing and analysis. NumPy (\(\geq\)1.18.5), SciPy (\(\geq\)1.4.1), and Pandas (\(\geq\)1.1.4) for numerical and tabular data manipulation. Pillow (\(\geq\)7.1.2) and Matplotlib (\(\geq\)3.2.2) for image I/O and visualization. Strict version control for critical packages (e.g., torchvision = 0.8.1 to ensure compatibility with PyTorch 1.9.0, protobuf < 4.21.3 to prevent dependency conflicts). Auxiliary tools: TensorBoard (\(\geq\)2.4.1) for training metrics visualization, PyYAML (\(\geq\)5.3.1) for configuration parsing, and Seaborn (\(\geq\)0.11.0) for statistical graphics. TQDM (4.62.0) for progress tracking during iterative processes. Requests (\(\geq\)2.23.0) for HTTP communication in distributed workflows. This configuration ensures efficient utilization of GPU parallelism, reproducible deep learning workflows, and robust support for image processing tasks, aligning with the computational demands of residual-based models such as DnCNN.