esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 3 Finite Element Method
### Galerkin Approximation  
Equations (5) are defined on the spaces \(V\) and \(Q\), which are subspaces of \(H^{1}\) and \(L^{2}\) of infinite dimension. To develop a numerical scheme that is able to approximate the solution, we need to find an approximate problem defined on spaces of finite dimension. Consider, \(V_{h}\subset V\) and \(Q_{h}\subset Q\), both of finite dimension, then the problem becomes:  
\[\begin{cases}a(\mathbf{u}_{h},\mathbf{v}_{h})+c(\mathbf{u}_{h},\mathbf{u}_{h},\mathbf{v}_{h})+b(\mathbf{v}_{h},p_{h})=(\mathbf{f},\mathbf{v}_{h})&\forall \mathbf{v}_{h}\in V_{h}\\ b(\mathbf{u}_{h},q_{h})=0&\forall q_{h}\in Q_{h}\end{cases}. \tag{6}\]  
The problem with this formulation is that, following the same steps as before, the algebraic system that arises is no more linear, but, due to the trilinear form \(c(\cdot,\cdot,\cdot)\), it becomes nonlinear. This complicates enormously the method, since solving a nonlinear system of equations involves methods that are far more complicated and computationally costly (e.g., [PERSON] method). The simplest way to solve this problem is to use a fixed-point scheme (known also as Picard iteration) and try to linearize the nonlinear term \(c(\mathbf{u}_{h},\mathbf{u}_{h},\mathbf{v}_{h})\). This will require an iterative scheme: suppose to start from a given velocity field \(\mathbf{u}_{h}^{0}\); to find the next iterate \(\mathbf{u}_{h}^{1}\) one could solve Equation (6) where, instead of the nonlinear term, there is the linear term \(c(\mathbf{u}_{h}^{0},\mathbf{u}_{h}^{1},\mathbf{v}_{h})\). In this way, the trilinear form that was giving problems becomes simply a bilinear form and can be treated like the other ones. Accordingly, the method can be formalized, as follows  
\[\begin{cases}a(\mathbf{u}_{h}^{(k+1)},\mathbf{v}_{h})+c(\mathbf{u}_{h}^{(k)}, \mathbf{u}_{h}^{(k+1)},\mathbf{v}_{h})+b(\mathbf{v}_{h},p_{h}^{(k+1)})=( \mathbf{f},\mathbf{v}_{h})&\forall\mathbf{v}_{h}\in V_{h}\\ b(\mathbf{u}_{h}^{(k+1)},q_{h})=0&\forall q_{h}\in Q_{h}\end{cases} \tag{7}\]  
for \(k=0,1,\dots\) until convergence. The initial field \(\mathbf{u}_{h}^{(0)}\) must be taken divergence free, in order to be consistent with the problem. This can be achieved solving a Stokes problem at the beginning (which assures a divergence-free velocity field) and then using the solution as initial datum for the iteration. This is consistent with setting \(\mathbf{u}_{h}^{(-1)}=\mathbf{0}\). This formulation of the Navier-Stokes problem is known as Oseen problem and it is the method that will be used in this work. We refer e.g., to the book [16] for more details.
## 2 Results and Discussion
### Catalysts' Characterization
#### 2.1.2 Spectroscopy Studies (FTIR, UV-Vis, Raman, and \({}^{29}\)Si MAS-NMR)
The temperatures indicated refers to DTG maximums of observable peaks. Altogether, these observations indicate that cerium incorporation increases the surface polarity of the materials, somewhat expected since cerium is less electronegative than silicon. Also, the mass loss from silanol condensation was different for the three materials: 1.70%, 3.50%, and 2.67% for Si-MCM-41, Ce-MCM-Cl, and Ce-MCM-NO\({}_{3}\), respectively. Thus, it is noticeable that the isomorphic substitution leads to less thermally stable materials, highlighting the fact that Ce-MCM-Cl is less stable than Ce-MCM-NO\({}_{3}\), evidence of its higher degree of silicon substitution in the framework. This is expected once the insertion of heteroelements causes bond angle tension and loss of the crystalline mesophase as indicated by XRD data, which is in accordance with the disordering effect proposed for the crystallization of the materials and corroborated previously with other characterization techniques.
# Foxn1 Control of Skin Function
## 4 Foxn1 as a Skin Wound Healing Regulator
A study by [PERSON] et al., suggested that, similar to its role in the thymus, the Wnt pathway is involved in Foxn1 regulation during skin wound healing [69]. In particular, \(\beta\)-catenin-dependent Wnt10a and \(\beta\)-catenin-independent Wnt11 signaling seem to regulate Foxn1 in the process of reparative (scar-forming) skin wound healing. Similar patterns of Foxn1, Wnt10a, Wnt11, and \(\beta\)-catenin mRNA expression during excisional wound healing in mice together with co-localization of their protein products to the epidermis and epidermal leading tongue indicate a possible interaction, although the precise mechanistic explanation awaits exploration [69]. The observed differences between Foxn1\({}^{\
icefrac{{1}}{{+}}}\) and Foxn1\({}^{\
icefrac{{-}}{{-}}}\) mice in the process of skin wound healing suggest that Foxn1 acts beyond the epidermis, its tissue of origin. Indeed, a comparative analysis of dermal fibroblasts isolated from Foxn1\({}^{\
icefrac{{1}}{{+}}}\) and Foxn1\({}^{\
icefrac{{-}}{{-}}}\) mice showed vast differences between them [65; 71; 72]. Dermal fibroblasts from Foxn1\({}^{\
icefrac{{-}}{{-}}}\) mice showed features of neoteny. The population of Foxn1\({}^{\
icefrac{{-}}{{-}}}\) DFs contained a high percentage of cells expressing stem cell markers CD117 and Oct3/4, which are low (CD117) or absent (Oct3/4) in Foxn1\({}^{\
icefrac{{+}}{{+}}}\) DFs [71]. Foxn1\({}^{\
icefrac{{-}}{{-}}}\) DFs exhibited a higher cumulative number of population doublings (cumulative PD), increased adipogenic differentiation  
Figure 2: Foxn1 expression in injured skin of Foxn1:eGFP mice at post-wounded day 2 (**A**) and day 3 (**B**). wm—wound margin, arrows indicate leading epithelial tongue. capacity, higher expression of Tgf\(\beta\)3, and greater sensitivity to TGF\(\beta\)3 stimulation compared to Foxn1\({}^{+/+}\) DFs [72]. Moreover, dermal fibroblasts from Foxn1\({}^{-/-}\) mice produce higher levels of Mmp3, -9, and 13 and collagen III than Foxn1\({}^{+/+}\) DFs [72; 65]. All these features are common among sites prone to regenerative wound healing, namely, skin of mammalian fetuses and gingival tissues (scar-less healing of privileged tissues) [73; 74]. Since strong evidence exists that epidermal Foxn1 plays a critical role in differentiation, developmental processes, and maturation, it is conceivable that a lack of Foxn1 maintains keratinocytes and DFs in the immature (fetal) regenerative period of development.
## 2 Pump Geometry and Asymmetric Pitch
### Design of Asymmetric Pitch Control  
After the speed PI controller determines the current reference value, \(i_{s}^{ref}\), the additional instantaneous current reference value, \(i_{asymmetry}^{ref}\) for asymmetric pitch control is applied as shown in Figure 6.  
The magnitude of the current reference for the asymmetric pitch control is differentially applied to each tooth sequence, as defined in Equation (4) based on Equation (2), which is known as the asymmetric pitch function in previous studies and patents [5]. Through controlling the motor current, the motor torque is instantaneously controlled according to  
Figure 5: Control diagram of electric oil pumps.  
Figure 6: Proposed asymmetric pitch-simulated control.  
the number of pump teeth and tooth positions. Then, through instantaneously increasing or decreasing the motor torque according to the symmetric position of each tooth, the same effect as applying an asymmetric pitch can be expected.  
\[I_{\Delta\theta_{n}}=(-1)^{n}\times A_{m}\bigg{\{}sin\bigg{(}P_{1}\frac{2\pi}{N} n\bigg{)}\times cos\bigg{(}P_{2}\frac{2\pi}{N}n\bigg{)}\bigg{\}} \tag{4}\]  
The shape of the current and torque reference for the asymmetric pitch control should be determined by considering the instantaneous shape of hydraulic pulsation according to the pump design as described in Section 2.2.  
The instantaneous shape of an increase or decrease in the motor torque is presented as in Equation (5).  
\[y=e^{-B_{m}x^{2}} \tag{5}\]  
where \(-1\leq x\leq 1\), \(B_{m}\) represents factors for uneven pitch-simulated control.  
This is applied by adjusting the magnitude of \(B_{m}\), which is the asymmetric pitch control shape factor in Equation (5), according to the hydraulic pulsation shape of the pump, interpreted or measured as in Figure 4. Figure 7 shows the shape of asymmetric pitch control according to \(B_{m}\).  
Finally, the current reference for asymmetric pitch control for each tooth sequence is implemented as in Equation (6).  
\[i_{asymmetry}^{ref}=I_{\Delta\theta_{n}}\times e^{-B_{m}\left[mod(\theta, \frac{2\pi}{N})-1\right]^{2}} \tag{6}\]  
where mod(a,b) is the modulo operation returns of the remainder of the division of a/b.  
To maintain the pump's hydraulic performance, the average of the control reference should be zero, and its maximum magnitude of current needs to be experimentally determined without deteriorating the performance.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 3 Problem Formulation  
In this section, we give the formal definition of our general problem. Suppose that there is a base station \(B_{S}\) and a set of nodes of size \(s\)  
\[N=\{n_{1},n_{2},\ldots\,n_{s}\},\]  
where the nodes are independent, and each node \(n_{i}\) has a residual energy level \(e_{i}\). Without the loss of generality, assume all nodes have the same battery capacity \(E_{S}\), and the set \(N_{e}\) represents the set of residual energy levels of all nodes  
\[N_{e}=\{e_{1},e_{2},\ldots\,e_{s}\}.\]  
The set \(N_{d}\) represents the distance from node \(n_{i-1}\) to node \(n_{i}\), and each node is placed at distance \(d_{i}\) from the previous node, the first node is inserted at distance \(d_{1}\) from the base station \(B_{S}\), and so on  
\[N_{d}=\{d_{1},d_{2},\ldots\,d_{s}\}.\]  
A single MCR with total power \(E_{c}\) is utilized to charge a subset of the WRSN. We define the power needed to align the MCR charging coils to the center of the sensor node's receiving coils, referred to as concentrically localize the MCR and a node, \(\lambda\), the target power level of each node to be charged as \(l\), total localization and charge power as \(C\), total number of sensor nodes to charge as \(u\)  
\[C=u\times\lambda+\sum_{i=0}^{u}l-e_{i}.\]  
We define Y as the maximum feasible subset of nodes the MCR may charge to achieve the highest possible life extension, and \(\Gamma\) is the point enabling the MCR to reach the furthest node in Y  
\[u=|Y|,\]  
\[\text{Y}=\{n_{t1},...,n_{tu}\}\subset N,\]  
\[\Gamma=\sum_{i=1}^{tu}d_{i}.\]  
Charging Y leads to the maximum WRSN life extension \(\Psi\). We define \(M\) as the total moving power consumed by the MCR, and this includes the power needed to reach the furthest node to visit and return to \(B_{S}\). Assuming the unit moving cost is \(\Lambda_{m}\)  
\[M=\Lambda_{m}\times\Gamma,\]  
and the power constraint formula is such that  
\[E_{c}\geq C+M.\]  
Each sensor node discharges power at either rate \(r\) or zero, based on a discharge function \(g\) and a discharge probability \(p\), we use a fixed discharge probability for each run similar to [46]  
\[g(n)=\begin{cases}r,&\text{if node is activated,}\\ 0,&\text{otherwise.}\end{cases}\]  
Finally, we define the network life as the time until \(k\) nodes reach zero residual energy:  
\[k=\sum_{i=1}^{s}f(n_{i}),\]  
\[f(n_{i})=\begin{cases}1,&\text{if $e_{i}$ equals $0$,}\\ 0,&\text{otherwise.}\end{cases}\]  
The objective is to maximize the increase in network life. With no prior knowledge of \(N_{e}\), this objective is accomplished by identifying:  
* The best point to stop exploring and return to \(B_{S}\), \(\gamma\),
* The best possible set of nodes to charge, \(v\).  
Stopping at point \(\gamma\) leads to identifying \(v\). Charging \(v\) results in a network life increase \(\psi\). The problem is to bring \(\psi\) as close to \(\Psi\) using a least intrusive no knowledge charging algorithm.
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 3 Experimental Results
### Experimental Setup  
We implemented the proposed deep CNN solution for image multi-labeling in the Keras environment, which is a high level neural network application programming interface written in Python. We set the number of epochs to 100 and fixed the mini-batch size to 16. Additionally, we set the learning rate of the Adam optimization method to 0.0001. Regarding the exponential decay rates for the moment estimates and epsilon, we use the following default values: 0.9, 0.999, and 1e-8 respectively.  
The datasets contain images of size \(640\times 480\). Rescaling the images to the usual size of \(224\times 224\) is not necessary because the SqueezeNet model is able to accept images of different sizes directly. In fact, this is one the advantages of the SqueezeNet model. Based on the input size of \(640\times 480\), Model 3, presented in Figure 5c, now has a low number of weights between 756,559 to 858,191 only.  
The efficiency of the proposed framework is expressed in terms of the measures known as sensitivity (SEN), specificity (SPE), and their average (AVG) defined in [9; 40]. SEN expresses the probability of correct detection of the presence of an object, while SPE expresses the probability of correct detection of the absence of an object, respectively. They are computed as follows:  
\[\text{SEN}=\frac{\text{{True}\,\text{{Positives}}}}{\text{{True}\,\text{{ Positives}}}\;+\;\text{{False}\,\text{{Negatives}}}} \tag{6}\]  
\[\text{SPE}=\frac{\text{{True}\,\text{{Negatives}}}}{\text{{True}\,\text{{ Negatives}}}+\;\text{{False}\,\text{{Positives}}}} \tag{7}\]  
\[\text{AVG}=\frac{\text{{SEN}+\;\text{SPE}}}{2} \tag{8}\]  
All experiments are conducted on an HP-laptop with an Intel Core i7-7700 HQ CPU of 2.80 GHz, 16 GB of RAM, and the NVIDIA graphics card GeForce GTX 1050 Ti with a 4 GB dedicated memory.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Audio-Visual Assisting
#### 3.5.2 Visual Features Assisting Audio Recognition  
In 2019, [PERSON] et al. cultivated the complementary strengths of deep learning and acoustic modeling through filtering techniques to craft a deep learning regression model for speech enhancement. Specifically, they employed an enhanced visual Wiener filter (EVWF) to estimate the clean audio power spectrum by utilizing an inverse filter-bank transformation that facilitates speech recognition through visual features while effectively eliminating the need for voice activity detection (VAD) and noise estimation. This innovative approach mitigated the detrimental impact of noise on speech recognition, paving the way for clearer auditory understanding [123].  
Multi-modality information from visual and auditory cues demonstrated a remarkable improvement in recognition accuracy. These multi-modalities, grounded on the Transformer self-attention mechanism, employed Connectionist Temporal Classification (CTC) loss and the other utilizing sequence-to-sequence loss to explore how lip-reading enhances audio-based speech recognition. Their investigations into speech recognition under noisy conditions revealed that visual information significantly bolsters recognition performance, particularly when audio is compromised by noise [124].  
A Mel spectrogram, as a tool in the fields of speech and music research, represents the frequency spectrum of an audio signal on a Mel scale based on human auditory perception, and represents time with color intensity reflecting the energy levels at corresponding frequencies as visual representation. Inspired by Mel spectrograms, in 2022, a novel approach is designed by synthesizing sound from lip movements by encoding visual features into hidden states for identifying speakers by capturing appropriate lip movements in an unconstrained manner [82]. Subsequently, it employed an attention mechanism to autoregressively generate frames of the Mel spectrogram, effectively integrating the speaker's visual characteristics into the synthesized speech. This innovative framework underscores the potential of leveraging visual information to enhance speaker identification, paving the way for more robust multimodal recognition systems.  
These studies demonstrate that visual information can significantly aid in speech recognition, particularly in noisy environments. By leveraging visual cues, the effectiveness of speech recognition systems is greatly enhanced, which further provides a robust solution to the difficulties encountered in acoustic interference.
## 5 Conclusions  
In this study, an improved numerical method was proposed in order to calculate the shape of a floating bubble more precisely and robustly than the conventional method. The proposed method divides the cavity into three parts and accurately finds the location of the connection points at \(\varphi=\pi/4\), \(\pi\), \(3\pi/4\), and \(\pi\) by defining specific functions and solving them via the root-finding technique. The type of meniscus was used in order to simplify the conventional bisection method, and the improved method was validated by comparison with previously published numerical and experimental results. The bubble shapes and parameters were then obtained precisely for a more extensive range of Bond numbers (\(5\times 10^{-5}<Bo<5000\)), compared to the previously reported range of Bond numbers (\(0.003<Bo<241\)). The dimensionless maximum depth of submergence of the bubble below the undisturbed liquid surface was determined as \(H=0.7291015\) when \(Bo=4.755563\). The log-log plots of the parameters were divided into three distinct  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Limit** & \(a\) & \(b\) \\ \hline \multirow{2}{*}{\(X_{c}\)} & \(Bo\to 0\) & 0.8163 & 1 \\  & \(Bo\to\infty\) & 0.9063 & 0.4983 \\ \hline \multirow{2}{*}{\(Z_{c}\)} & \(Bo\to 0\) & 1.646 & 1.395 \\  & \(Bo\to\infty\) & 0.8945 & 0.01123 \\ \hline \multirow{2}{*}{\(\theta_{c}\)} & \(Bo\to 0\) & 33.09 & 0.5 \\  & \(Bo\to\infty\) & 81.56 & 0.009909 \\ \hline \multirow{2}{*}{\(R_{0}\)} & \(Bo\to 0\) & 1.414 & 0.5 \\  & \(Bo\to\infty\) & 0.9104 & 0.4978 \\ \hline \multirow{2}{*}{\(H\)} & \(Bo\to 0\) & 1.409 & 0.4997 \\  & \(Bo\to\infty\) & 2.197 & \(-0.4978\) \\ \hline \multirow{2}{*}{\(H_{0}\)} & \(Bo\to 0\) & 0.2357 & 1.5 \\  & \(Bo\to\infty\) & 0.7787 & 0.5136 \\ \hline \multirow{2}{*}{\(A_{cap}\)} & \(Bo\to 0\) & 2.094 & 2 \\  & \(Bo\to\infty\) & 4.454 & 1.011 \\ \hline \multirow{2}{*}{\(V_{cap}\)} & \(Bo\to 0\) & 0.2467 & 3.5 \\  & \(Bo\to\infty\) & 1.246 & 1.517 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Coefficients of the power law for asymptotic relations.  
regions of the Bond number, and each interval was fitted with a fifth-order polynomial function. Interestingly, it was possible to fit the parameter \(X_{c}\) with a single correlation for the entire range of Bond numbers. In addition, the asymptotic relations were presented with a simple power law as the Bond number tended to zero or infinity. Moreover, it was possible to approximate most of the bubble parameters to a power of \(Bo^{1/2}\). The correlations and asymptotic equations presented herein are expected to be helpful in scaling models of dynamic bubble-related phenomena such as aerosols and atomization when a bubble bursts.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20208901010010).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The author declares no conflict of interest.  
The authors declare no conflict of interest.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Slopes and Landslides Monitoring
#### 3.2.1 Distributed Strain Sensing for Slopes and Landslides Monitoring
Figure 18: The principle of an optical fibre inclinometer for displacement calculation (reproduced with permission from [214]).
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADM & Angular Distribution Model \\ AVHRR & Advanced Very High Resolution Radiometer \\ CPP & Cloud Physical Properties \\ Ch1, Ch2 & Channel 1, Channel 2 \\ CDR & Climate Data Record \\ CDS & Climate Data Store, from the Copernicus Climate Change Service (C3S) \\ CERES & Clouds and the Earth's Radiant Energy System (instrument and mission) \\ CM SAF & Climate Monitoring Satellite Application Facility \\ CLARA-A3 & CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data, 3 rd edition \\ Cloud\_cci & Cloud component in the ESA's Climate Change Initiative (CCI) programme \\ COT & Cloud Optical Thickness \\ CPP & Cloud Physical Properties \\ DLB & Daylight block \\ ECMWF & European Centre for Medium-Range Weather Forecasts \\ ERA5 & ECMWF Reanalysis 5 th Generation \\ ERB & Earth Radiation Budget \\ EUMETSAT & European Organisation for the Exploitation of Meteorological Satellites \\ CDR & Climate Data Record \\ FDR & Fundamental Data Record \\ FOV & Field of View \\ GAC & Global Area Coverage \\ GERB & Geostationary Earth Radiation Budget \\ GCOS & Global Observing System for Climate \\ GLCC & Global Land Cover Characterization \\ IGBP & International Geosphere-Biosphere Programme \\ ISCCP & International Satellite Cloud Climatology Project \\ LECT & Local Equator Crossing Time \\ MAB & (Global) Mean absolute bias calculated from daily RSF \\ MABH & (Global) Mean absolute bias calculated from hourly RSF \\ MB & (Global) Mean bias \\ MetOp-A & Meteorological Operational satellite, Satellite A (ESA) \\ Met & MetOp-A \\ NOAA-X & National Oceanic and Atmospheric Administration, Satellite X \\ N15, N16, \(\ldots\) & NOAA-15, NOAA-16, \(\ldots\) \\ NTB & Narrowband-to-Broadband \\ NWC SAF & Nowcasting Satellite Application Facility \\ OSI SAF & Ocean and Sea Ice Satellite Application Facility \\ PPS & Polar Platform System \\ RAA (\(\phi\)) & Relative Azimuth Angle \\ RMSB & Root Mean Square of Bias \\ RSF & Reflected Solar Flux \\ SR\({}_{\lambda}\) & Scaled Radiance (spectral) \\ SYN1 deg & Synoptic gridded 1\({}^{\circ}\) product (from CERES) \\ SZA (\(\theta_{0}\)) & Solar zenith angle \\ TOA & Top of Atmosphere \\ TSI & Total Solar Irradiance \\ TWL & Twilight \\ VZA (\(\theta\)) & Viewing zenith angle \\ \end{tabular}
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 3 Fundamentals of Hydrogels
### Hydrogel Composition  
The solid state of hydrogels takes the form of a three-dimensional network of crosslinked polymer chains. On a molecular scale, this arrangement is commonly termed the mesh, encompassing mesh size and polymer chains [24]. Hydrogels arise from the chemical or physical crosslinking of polymers. Chemical crosslinking can result from covalent bonds, hydrogen bonds, or ionic interactions [7]. Physical crosslinking may occur due to opposite charges. The product of crosslinking is a hydrogel mesh capable of retaining water. The elastic forces in the hydrogel mesh permit swelling and release of water to maintain the solidity of hydrogels. This characteristic is ascribed to hydrogel's fluid exchange and drug release capabilities. The mesh size plays a crucial role in determining the duration of drug release and is associated with the distance between two crosslinking points [5]. It has been reported that mesh size controls the diffusion and release of the drug. The mesh size depends on the polymer and crosslinker concentrations [5]. Mesh size controls steric interactions between the drugs and the polymer network. Therefore, the mechanism of drug release is dependent on mesh size. When the mesh size is expanded, drug release primarily occurs through diffusion, while with a smaller mesh size, drugs are released via free movement [5]. Table 2 outlines different hydrogel compositions along with their respective advantages and disadvantages.