esa-sceva/satcom-chunk-collection huggingface dataset:
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 3 Probiotic Encapsulation Techniques
### Drying Method
#### 3.4.3 Spray Chilling  
Spray chilling (cooling or congealing, which produces capsules with sizes of 20-200 \(\upmu\)m) is like spray-drying, but it injects cold air to atomize and solidify the particles instead of hot air. In the spray chilling process, the encapsulated agent is first dispersed in a molten lipid matrix before atomization in a chamber with cold air injection [62]. In this technique, lipids are utilized as the encapsulation material. During the passage through the gastrointestinal tract, when the temperature reaches the melting point of the carrier material (lipid), the lipases in the intestines digest the lipid wall materials and release the probiotics. Therefore, spray chilling was found to be very promising in the controlled release of probiotics. Spray chilling is reported to be the cheapest encapsulation technique as it provides a high process yield regardless of continuous or batch production. Spray chilling has also been recognized as more environmentally friendly as it requires only mild processing conditions, low operation energy, and time. Since spray chilling does not require heat, the viability of probiotics can be retained. Furthermore, this technique can be operated continuously with the elimination of hold times between manufacturing steps, making it suitable to be scaled up for industrial-scale production [43,48].  
_S. boulardi, L. acidophilus_, and _B. bifidum_ have been encapsulated using the spray chilling technique [63]. The results showed that the survivability of spray-chilled (_S. boulardi_, 97.89%; _L. acidophilus_, 83.57%; _B. bifidum_, 88.50%) and spray-dried (_S. boulardi_, 97.51%; _L. acidophilus_, 84.05%; _B. bifidum_, 90.10%) probiotics under simulated gastric conditions were similar. The spray chilling technique has also been used to encapsulate probiotics in probiotics-enriched cream-filled cakes [31] and savory cereal bars [34]. Spray chilling improved the survivability of _S. boulardi, L. acidophilus_, and _B. bifidum_ incorporated in cream-filled and marmalade-filled cake samples during refrigerated storage [31]. Similarly, the viabilities of spray-chilled _L. acidophilus_ and _B. animals_ subsp. _lactis_ were higher than freeze-dried and free probiotics in the savory cereal bars after being stored for 90 days at 4 \({}^{\circ}\)C [34]. However, low encapsulation capacities on the beads produced through this technique have been reported. A lower load (10-20%) was obtained when compared to spray-drying (5-50%) [64]. The beads produced through the spray chilling technique have low melting points (32-42 \({}^{\circ}\)C). Probiotics encapsulated using the spray chilling technique were also found to protrude from the beads during storage. Hence, proper handling and storage conditions are required to preserve spray-chilled probiotics [43,44].
## 2 Materials and Methods
### System Model
#### 2.1.1 UAV Movement Model  
In the MEC system, UAVs can move freely. In time slot t, the coordinates of the nth UAV are \(\omega_{n}(t)=[x_{n}(t),y_{n}(t),z_{n}(t)]^{T}\), where \(x_{n}(t),\ y_{n}(t)\) and \(z_{n}(t)\) represent the UAV's coordinates on the \(X\), \(Y\), and \(Z\) axes, respectively. Assuming that the flight distance of UAV n in time slot t is \(l_{n}(t)\) and the flight angle is \(\theta_{n}(t)\in[0,\ 2\pi)\), the position of the UAV in the next time slot using Equations (1) and (2) can be determined.  
\[x_{n}(t+1)=x_{n}(t)+l_{n}(t)cos(\theta_{n}(t)) \tag{1}\]  
\[y_{n}(t+1)=y_{n}(t)+l_{n}(t)sin(\theta_{n}(t)) \tag{2}\]  
The UAV's altitude can change within a certain range, constrained by Equation (3).  
\[Z_{min}\leq z_{n}(t)\leq Z_{max} \tag{3}\]  
where \(Z_{min}\) and \(Z_{>max}\) represent the minimum flight altitude and maximum flight altitude of the UAV, respectively.  
Since the horizontal flight speed and vertical flight speed of the UAV are limited, the flight distance within a time slot is also limited. The horizontal and vertical flight distances of the UAV are shown in Equations (4) and (5), respectively.  
\[l_{n}(t)=||v_{n}(t+1)-v_{n}(t)||\leq L_{max}^{h} \tag{4}\]  
\[\Delta z_{n}(t)=|z_{n}(t+1)-z_{n}(t)|\leq L_{max}^{v} \tag{5}\]  
where \(L_{max}^{h}\) represents the maximum horizontal flight distance of the UAV in a time slot, and \(L_{max}^{v}\) represents the maximum vertical flight distance of the UAV in a time slot.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 4 Proposed DNN-Based MIMO OFDM Models
### Model Type I  
With model type I, we are mostly still the receiver's DSPs. The DNN model is added after the signal detection phase to replace the demapping (demodulation) block. The goal is that the DNN model not only does the work of demapping block but can also improve the performance of the signal detection phase.  
Figure 2: General DNN structure.  
As shown in Figure 3, we explain everything in detail. In the channel estimation block, we use the LS algorithm to get the channel information and pass them to the signal detection block. At the signal detection block, as we have shown in the introduction, the ZF/MLD algorithm is used to recover the original signals. The DNN model has five layers, three of which are hidden layers. The numbers of neurons in each layer are 32, 256, 128, 64, and 16 for both 2 \(\times\) 2 and 4 \(\times\) 4 MIMO OFDM models, respectively. The input number corresponds to the number of real parts and imaginary parts of a part of the signal detector output and a part of the pilot values at the output of the FFT block. However, we do not need to feed all the signal detector output and the pilot values at the output of the FFT block into the DNN model. We only feed symbols corresponding to 16 bits in the output layer. Then, we need 8 QAM-4 symbols of the signal detector output and 8 QAM-4 symbols of the pilot values at the output of the FFT block. Every 16 bits of the transmitted data are grouped and predicted based on a single independently trained model, which is then concatenated for the final output. The ReLU function is used as the activation function in hidden layers except in the output layer, where the Sigmoid function is applied to map the output to the interval [0, 1].  
\[F_{ReLU}(x)=Max(0,x) \tag{11}\]  
\[F_{Sigmoid}(x)=\frac{1}{1+e^{-x}} \tag{12}\]  
Furthermore, the loss function of the DNN model is the mean square error (MSE ) shown below  
\[MSE=\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}(X_{i}-\hat{X} _{i})^{2} \tag{13}\]  
where \(\hat{X}_{i}\) is the predicted value, \(X_{i}\) is the actual value and \(n\) is the number of neural network labels.
# Drying Stress and Strain of Wood: A Review
## 3 Composition of Drying Strain
### Elastic Strain  
Elastic strain refers to the strain that wood is subjected to by an external force within the range of its elastic limit and recovers immediately from when the force disappears. In the early stage of drying, as the MC decreases, the wood surface first exhibits tensile deformation and then turns to compression deformation after reaching the limit [63].  
Surface checks occur as tensile deformation exceeds the limit value of the surface tensile elastic strain. The relationship between elastic stress \(\delta\) and strain \(\varepsilon_{E}\) conforms to [PERSON]'s law and can be expressed by the following equation:  
\[\varepsilon_{E}=\frac{\delta}{E(W,T)}, \tag{1}\]  
where \(E\) is the static flexural modulus in the transverse grain of wood, \(W\) is the moisture content and \(T\) is the temperature.  
The modulus of elasticity (MOE) of wood is related to wood MC and temperature. When the MC is above FSP, the MC has a weak effect on MOE; when the MC is below FSP, the MOE is affected by the combined effect of MC and temperature. At this time, the MOE decreases as MC increases, and the effect of temperature is more complicated.  
[PERSON] and [PERSON] pointed out that in the range of \(-40\) to \(80\)\({}^{\circ}\)C, the MOE of wood with MCs of 12 and 23% generally decreases with increasing temperature, but the MOE of wood with an MC of 23% had a significant step change at the freezing point [64]. [PERSON] et al. evaluated the MOE of wood with MCs of 4 and 12% in the temperature range of \(-26\) to \(66\)\({}^{\circ}\)C (in Figure 3). They concluded that the decrease in MOE has a linear relationship with the increase in temperature, which is similar to the conclusion of [PERSON] et al. [65]. [PERSON] further verified and explored the influence of temperature and MC on the MOE by using _Pinus koraiensis_ [PERSON]. et Zucc. [66]. It was concluded that the relationship between MOE and the temperature of wood was based on the MC. The moisture state of wood cells is the essence of the changes in the mechanical properties of wood. When the MC is lower than FSP, the MOE of small specimens is not significantly affected by the temperature below or above the freezing point. When the MC is higher than FSP, the MOE mainly responds to temperature. When the temperature drops below freezing point, the free water in wood cell vessels forms into ice crystals, resulting in MOE increasing rapidly; the decrease in MOE is relatively slow with temperatures above the freezing point.
## 6 Evaluation Measurements  
The obvious challenge when dealing with the binary classification of an imbalanced dataset is that the training model is biased toward the majority class, resulting in a high accuracy for the majority class but the model failing to predict instances from the minority classes. In this work, we used the following metrics: accuracy, which was calculated using the confusion matrix defined in Equation (10), where TP represents the number of true positives, TN represents the number of true negatives, FP represents the number of false positives, and FN represents the number of false negatives [47]; g-mean, which was the geometric mean of the sensitivity and specificity, as defined in Equation (13); F1 score, which was the harmonic mean of the precision and sensitivity, as defined in Equation (14), where \(\beta\) is the real positive factor, which was chosen such that the sensitivity was considered to be \(\beta\) times more important than the precision. In this work, we used \(\beta=1\), which allocated the same weighting to both the sensitivity and precision.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}} \tag{10}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{FP}+\text{TN}} \tag{12}\]  
\[\text{g-mean}=\sqrt{\text{Sensitivity}\times\text{Specificity}} \tag{13}\]  
\[f\text{1-score}=\frac{(1+\beta^{2}).\text{sensitivity}.\text{precision}}{ \text{sensitivity}+\beta.\text{precision}} \tag{14}\]  
where  
\[\beta\geq 0\]
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
## 3 Geoportal Implementation Framework
### Tools and Resources  
The portal was developed using an ensemble of cross-platform programming and scripting languages that can be used on various operating platforms such as Windows, Linux, and UNIX. The portal's front-end user interface was developed using ExtJS[c] version 6 and GeoExt[d] version 2. ExtJS is a cross-platform web application JavaScript framework and interactive application. GeoExt is also a JavaScript framework cross-platform application that adds Open Layers[e] and GIS functionalities into ExtJS. Agenda 2063 Geoportal uses PHP[f] (Hypertext Pre-processor) open-source, cross-platform language as a server-side language for database querying, deleting, updating, and adding new data. The portal also uses MapServer[g], an open-source, cross-platform application to publish the maps. MapServer uses PostGIS[h] and GeoExt to display the published interactive maps. Figure 2 below provides a concise depiction of the interactions between the front-end and the backend of the Geoportal.
## 2 Related Works
### Landscape Based Approaches  
Landscape based methods assume that physical properties of the environment, where cameras placement will take place, are known a priori. This family of methods aims to optimize the coverage area. The definition of coverage is variable from one domain to another [12]. A general definition of coverage is proposed by [13] as the quality of surveillance, which can be provided by a sensor network and how well an area of interest is monitored.  
One of the well-studied topics related to landscape based approaches is the Art Gallery Problem [14]. It intends to determine the number of observers (cameras for example) to monitor every point inside a polygon area (Art Gallery Room). In other words, this problem tries to find the optimal number and location of observers within the landscape in order to satisfy the desired coverage or to maximize the coverage ([8,15]).  
In the same vein, other researchers propose many discrete optimization methods for sensor placement. A genetic algorithm methodology to optimize sensor placement was proposed in [16]. An approach based on a parallel evolutionary optimization technique to choose and order candidate sites for sensors was developed in [17]. Another work presented in [18] aims to place sensors based on Simulated Annealing technique to find the best configuration of the network. All these three methods were applied on very simplest environments that do not reflect the complexity and the diversity of real landscapes.  
A Voronoi based algorithm was developed in [19] that integrates a Digital Surface Model of the area of interest. This Algorithm was inspired from the work of [20] which proposes three Voronoi strategies: (1) Vector-Based (VEC), (2) Voronoi-Based (VOR) and (3) Minimax. The authors in [19] propose first to generate a Voronoi Diagram from an initial position of sensors. Then, the sensor network is optimized by moving each sensor towards its farthest Voronoi vertex until reaching the highest elevation inside the Voronoi Cell. Likewise, a research work proposed by [21] combines a probabilistic sensing model with a Digital Surface Model to define the optimal placement of sensor network. This probabilistic sensing model consists of membership functions based on sensing range and sensing angle.  
It is important to mention that these latest two algorithms, which integrate the landscape reality through a Digital Surface Model, use the line of sight method in order to determine the percentage of coverage. When combined with a Digital Surface Model (or a Digital Terrain Model), the line of sight method is a very effective way for surveillance camera placement, because it allows introducing some important characteristics of cameras such as the 3D position of each camera, observation azimuth, field of view, the range of the camera, _etc._ (Please refer to Section 3 for more details).
## 4 Conclusions  
In this work, a detailed experimental analysis of CLD viscoelastic damping for comfort improvement in high-speed trains has been carried out. The methodology consisted of Experimental Modal Analyses of laboratory-scale aluminium beams taken from the floor of a lightweight rail vehicle to which different CLD configurations were added.  
Results have been extracted from the analysis of parameters such as the type of the constraining layer (uniform or honeycomb), thickness of the viscoelastic layer, location, covered area and continuity between patches. It should be emphasised that selecting honeycomb constraining layers yields damping ratios which are 52%, 70% and 107% higher (in modes 1, 2 and 3, respectively) than those with uniform constraining layers with similar added mass.  
Optimal configurations are highly dependent on the mass requirements of the design scenario. For local damping of existing lightweight structures, parameters that optimise the specific damping, such as partial coverage and thin layers of both viscoelastic and constraining layers, should be used. However, the main potential of CLD is achieved when  
Figure 11: FRF of the reference beam (A) and the configuration with the best absolute (C6) and specific (C1) damping coefficient.  
Figure 10: FRF of the reference beam (A) and configurations with honeycomb constraining layer (C).  
it is made part of the structural design contributing to its stiffness and strength. In this way, the modal damping could be up to 22 times that of the structures without CLD.  
Standard EN-12299 for comfort evaluation in railway applications states that the comfort-relevant frequency range for humans is 3-30 Hz, so our recommendations to increase the modal damping of the first two modes of vibration of the lightweight structure (which are within said range) can translate into significant improvements of the ride comfort index.  
Conceptualisation, M.M. and A.J.N.; methodology, M.M.; software, M.M. and E.P.; validation, M.M. and A.L.M.; formal analysis, M.M., A.J.N. and E.P.; investigation, M.M. and A.J.N.; resources, A.L.M. and C.R.; data curation, E.P.; writing--original draft preparation, M.M. and A.L.M.; writing--review and editing, M.M., A.J.N. and P.P.; visualisation, M.M. and C.R.; supervision, A.L.M.; project administration, J.M.C.; funding acquisition, A.J.N., A.L.M., J.M.C. and P.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grant TRA-2017-83376-R, funded by MCIN/AEI/10.13039/501100011033 and \"ERDF A way of making Europe\", and grant SBPLY/19/180501/000142, funded by JCCM-ERDF.  
The authors would like to show their gratitude to the railway vehicle manufacturer \"Patentes Talgo\" for providing specimens for tests.  
The authors declare no conflict of interest.
## 3 Turbo Equalization Concept
### The Basic Architecture of the Iterative Receiver Using the Turbo Principle
The extrinsic information \(L_{e}^{D}(\hat{s}^{\prime})\) provided by the decoder is computed as follows:  
\[L_{e}^{D}(\hat{s}^{\prime})=L^{D}(\hat{s}^{\prime})-L_{e}^{E}(\hat{s}^{\prime }), \tag{3}\]  
The vector \(L_{e}^{D}(\hat{s}^{\prime})\) is then interleaved to give a new sequence \(L_{e}^{D}(\hat{s})\), which is sent back to the MAP SISO equalizer, where it is used as a priori information for a new equalization step at the next iteration together with the received message yi [22]. As in the case of the equalizer, subtracting the a priori LLRs \(L_{e}^{E}(\hat{s}^{\prime})\) from the a posteriori LLRs \(L^{D}(\hat{s}^{\prime})\) is to avoid the equalizer obtaining information established on its own decisions, which were generated in the previous iteration in the turbo-equalization process. The iterative processing (equalization/decoding) continues until a pre-established end condition is met [36]. In the end, the output \(L(\hat{u}_{i})\) of the decoder delivers an estimate of the transmitted message based on decision rules:  
\[\hat{u}_{i}= \begin{cases}-1&\text{if}\\ +1&\text{otherwise}\end{cases} \quad L(\hat{u}_{i})<0. \tag{4}\]  
Methods for Achieving the Iterative Process of Equalization and Decoding Concerning the Proposed System
## 2 Materials and Methods
### Methods
#### 2.2.3 Fuzzy Analytic Hierarchy Process (AHP)  
[PERSON] (1983) ([PERSON], 1983) used the geometric mean method to calculate the fuzzy weights of each fuzzy matrix, a process which is often used in disaster zoning studies ([PERSON], 1983). The analytic hierarchy process AHP is a decision-making method that combines qualitative and quantitative methods and guarantees a unique solution for the reciprocal matrix. This study selected the weighted evaluation method of the geometric mean because it is simple and easy to use in a fuzzy environment. The comparison of the index weights with the positive and negative numbers of the evaluation factors is shown in ([PERSON], 1983):  
\[A=\left[\begin{array}{ccc}A_{11}&\cdots&A_{1n}\\ \vdots&\ddots&\vdots\\ A_{n1}&\cdots&A_{nm}\end{array}\right]\]  
The geometric mean of each row vector is calculated as follows:  
\[M_{i}=\prod_{j=1}^{n}A_{ij},i=1,2,\cdots,n\]  
Calculate the \(n\)-th root of \(M_{i}\)  
\[\overline{W_{i}}=\sqrt[n]{M_{i}},i=1,2,\cdots,n\]  
Normalize the vector \(\overline{W}\)  
\[W_{i}=\frac{\overline{W_{i}}}{\sum_{i=1}^{n}\overline{W_{i}}},i=1,2,\cdots,n\]  
\(W_{i}\) is the weighting factor of each variable.  
To evaluate the coefficient matrix, it is necessary to calculate the consistency index \(CI\) of the matrix. \(CI=\frac{\lambda_{max}-n}{n-1}\), where \(\lambda_{max}\) is the largest eigenvalue of \(A\). When \(CI=0\), the judgment matrix is completely consistent; the smaller \(CI\) is, the better the consistency of the judgment matrix. In the judgment matrix consistency test, the \(CI\) value is compared with the average random consistency index \(RI\) (Table 3), and \(CR\) is expressed as the random consistency ratio of the judgment matrix; that is, \(CR=CI/RI\). When \(CR<0.1\), the judgment matrix can be considered to have good consistency; when \(CR>0.1\), the judgment matrix needs to be adjusted until \(CR<0.1\).
# Theory of Electrical Breakdown in a Nanocomposite Capacitor
## 2 Methods
### Local Field in a Nanocomposite
For the purposes of the present derivation, one needs the limiting value of the tensor component \(f_{zz}(z,z^{\prime};k_{x},k_{y};\omega)\) when \(\omega\to 0\), which we denote as \(f_{zz}(z,z^{\prime};k_{x},k_{y};0)\). This quantity determines the \(z\)-component of the local field which dictates the potential barrier for injected electrons. In this limit, the reflected field is reduced to the field originating from the images of the NP-induced dipole in the two electrodes. Let us introduce the Fourier transform of the local field in the capacitor,  
\[E_{z}(\mathbf{r})=\frac{1}{(2\pi)^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{ \infty}e_{z}(z;k_{x},k_{y})e^{ik_{x}x}e^{ik_{y}y}dk_{x}dk_{y}, \tag{7}\]  
and substitute it into Equation (5). The obtained equation involves the quantity \(f_{zz}(z,z^{\prime};k_{x},k_{y};0)\) whose variation with \(z\) and \(z^{\prime}\) is determined by the factors \(\exp(\pm\kappa z)\) and \(\exp(\pm\kappa z^{\prime})\) with \(\kappa=(k_{x}^{2}+k_{y}^{2})^{1/2}\). Assuming the inequality \(\kappa d\ll 1\), which will be justified in what follows, and the model of a perfect conductor for the electrodes, one obtains (see Ref. [29] for the detail)  
\[f_{zz}(0,0;k_{x},k_{y};0)\approx\frac{4\pi}{\epsilon_{h}d}. \tag{8}\]  
We then obtain  
\[e_{z}(k_{x},k_{y})=\frac{e_{0}(k_{x},k_{y})}{1-(4\pi/\epsilon_{h})N\alpha}, \tag{9}\]  
where  
\[e_{0}(k_{x},k_{y}) =\int_{-L_{2}/2}^{L_{2}/2}\int_{-L_{1}/2}^{L_{1}/2}E_{0}e^{-ik_{x} x}e^{-ik_{y}y}dxdy\] \[=E_{0}L_{1}L_{2}\text{sinc}\bigg{(}\frac{k_{x}L_{1}}{2}\bigg{)} \text{sinc}\bigg{(}\frac{k_{y}L_{2}}{2}\bigg{)} \tag{10}\]  
is the Fourier transform of the applied electric field. Here, \(\text{sinc}\equiv\sin x/x\), \(L_{1}\) and \(L_{2}\) are the lateral dimensions of the electrodes along the \(x\) and \(y\) axes, respectively, and we have assumed that the field is zero outside the capacitor. Taking into account that the function \(\text{sinc}x\) is essentially nonzero within the range \(|x|\leq 3\), one concludes that the essential range of integration in Equation (7) is limited to the values \(|k_{x}|\leq 6/L_{1}\) and \(|k_{y}|\leq 6/L_{2}\). This means that the condition \(\kappa d\ll 1\), which we have used above, is justified provided \(d\ll L_{1},L_{2}\). Finally, the local field, Equation (7), takes the form  
\[E_{z}=\frac{E_{0}}{1-3f\beta}, \tag{11}\]  
with \(f=(4\pi/3)NR^{3}\) being the volume fraction of NPs and \(\beta=(e_{i}-e_{h})/(e_{i}+2\epsilon_{h})\). Let us note that the applicability of this approach which models NPs by point dipoles is limited to the range \(f\leq 0.2\)[16].