esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 2 Operation Principles  
### Time-Domain Equalization Implementation  
The previous section shows that digital signal processing (DSP) is playing a growing role in the CDE. In digital coherent optical receivers, the CD transmission function of an optical pulse in the channel given by  
\[G(z,w)=\exp\biggl{(}-\mathrm{i}\frac{D\lambda^{2}}{4\pi c}\omega^{2}z\biggr{)} \tag{1}\]Here, \(D\,,z\,,\lambda\,,c\) and \(\omega\) are dispersion coefficient, length of the fiber link, wavelength, light speed, and digital angular frequency. Therefore, we can derive an FD-FIR close to Equation (1) to compensation CD as  
\[H_{\text{Des}}\Big{(}e^{j\omega T}\Big{)}=\frac{1}{G(z,\omega)}=\exp\!\left(j \frac{D\lambda^{2}}{4\pi c}\omega^{2}z\right) \tag{2}\]  
By implementing IFFT of Equation (2), the continuous CDE pulse response function as  
\[g(z,t)=\sqrt{\frac{c}{jD\lambda^{2}z}}\exp(-j\varphi(t)),\quad\varphi(t)=\frac {\pi c}{D\lambda^{2}z}t^{2} \tag{3}\]  
Aliasing occurs when the digital angular frequency exceeds the Nyquist frequency determined by the sampling rate. To avoid aliasing, by direct sampling and truncation of Equation (3), we derived with a complex impulse response given by [17; 18]  
\[h(k)=\sqrt{\frac{\text{j}cT^{2}}{D\lambda^{2}z}}\exp\!\left(-\text{j}\frac{ \pi cT^{2}}{D\lambda^{2}z}k^{2}\right)-\left\lfloor\frac{N}{2}\right\rfloor \leqslant k\leqslant\left\lfloor\frac{N}{2}\right\rfloor \tag{4}\]  
Here, \(T\) is the sampling time. Where the length of the filter \(h(k)\) is the maximum number of filter taps so that aliasing does not occur, \(N\) is odd as  
\[N=2\times\left\lfloor\frac{|D|\lambda^{2}\text{z}}{2 cT^{2}}\right\rfloor+1 \tag{5}\]  
where \(\left\lfloor\,\right\rfloor\) is the rounding down operation and \(|D|\) indicates absolute value. So we can further obtain the FD response of the filter after truncation as \(H(k)\).  
One of the drawbacks of Equation (4) is that truncation is applied to Equation (7) to avoid the aliasing of frequency. Still, truncation in the TD introduces energy leakage in the FD, so the main lobe of the window spectrum can only be approximated as a rectangle. Equation (4) has another drawback that the complexity of TD-CDE and FD-CDE are \(N^{2}\) and \(N_{\text{FFT}}\log_{2}(N_{\text{FFT}})\), where \(N\) is the number of filter taps, and \(N_{\text{FFT}}\) is the FFT block size. Therefore, for larger accumulated CD, such as in long-distance fiber transmission, the complexity of FD-CDE is lower than that of TD-CDE [19].
# Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations
## 3 Materials: Experimental Setup
### Databases
#### 3.1.1 Speech Emotion Collection  
The process of collecting this type of audio typically involves engaging actors and actresses to read a text with specific emotions. Acted datasets offer certain advantages compared to real datasets. Firstly, researchers have complete control over the recording conditions, ensuring optimal audio quality. Additionally, it is easier to create label-balanced datasets as opposed to real datasets where the distribution of labels heavily relies on the recording conditions specific to each dataset. Furthermore, in acted datasets, the same sentences can be recorded with different emotions, allowing for greater flexibility in experimentation without relying solely on textual information.  
However, acted datasets also come with some disadvantages. Firstly, the expressed emotions may be less realistic compared to natural conversations where emotions tend to be more subtle and often appear in combination with one another. Moreover, the number of individuals recorded in acted datasets is typically limited, which can pose challenges in terms of generalizability to a broader population.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Database** & **Time (m)** & **Lang.** & **Spk** & **\#Neutral** & **\#Happy** & **\#Anger** & **\#Sad** & **Text** \\ \hline EmoDb & 16 & Ger. & 10 & 79 & 71 & 127 & 62 & Read \\ RAVDESS & 42 & Eng. & 24 & 96 & 192 & 192 & 192 & Read \\ CREMA-D & 203 & Eng. & 91 & 1087 & 1271 & 1271 & Read \\ IEMOCAP & 420 & Eng. & 10 & 1708 & 1636 & 1103 & 1084 & Improv. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about speech emotion databases included in the study.
# An Optimization Framework for Wind Farm Design in Complex Terrain
###### Abstract  
Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.  
wind farm; layout optimization; design; random search; complex terrain 2018  
1
## 2 Microchip Laser
### Discussion  
The laser emissions from the microchip laser cavities presented different efficiencies according to their configurations. It is observed that the additional thickness of the active medium in this case and/or the addition of the saturable absorber reduce the efficiency of the laser, as seen in Table 1. The resulting fixed optical cavity presented a better efficiency (16.7%) than the movable cavity with the same configuration (16.0%). This is likely due to the shorter cavity assembled on the substrate.
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### One-Dimensional Concrete Carbonation Model
#### 2.1.1 CO2 Diffusion Coefficient  
\(D_{CO_{2}}\) can be expressed as Equation (3) based on a previous study [20]. CEB-FIP 1990 proposes CO2 and O2 diffusion coefficients of concrete in the range of \(0.5\times 10^{-4}\)\(\sim\)\(6.0\times 10^{-4}\)[19; 21].  
\[D_{CO_{2}}(t)=D_{1}\cdot t^{-n_{d}} \tag{3}\]where \(D_{1}\) is the CO\({}_{2}\) diffusion coefficient after one year and \(n_{d}\) is the time coefficient reflecting reduction of the diffusion coefficient. The time coefficient (\(n_{d}\)) significantly depends on the _w/c_ ratio.  
Figure 1 shows that the higher the _w/c_ of concrete mix, the higher the CO\({}_{2}\) diffusion coefficient. Similar to existing study results, as carbonation is progressed, the diffusion coefficient gets decreased by the compaction of the concrete porous structure.  
As shown in Figure 2, this study used a CO\({}_{2}\) diffusion coefficient with an average of \(3.87\times 10^{-4}\) cm\({}^{2}\)/s and a standard deviation of \(3.79\times 10^{-4}\) cm\({}^{2}\)/s for a CO\({}_{2}\) diffusion coefficient based on previous studies [22].
## Materials and Methods
### Support Vector Machine Based on IFA  
#### 2.5.1 Support Vector Machines  
Support Vector Machine (SVM) is a supervised learning binary classification model [30]. For nonlinear classification problems, the data are transformed into a high-dimensional space through nonlinear changes, and the optimal separation hyperplane is calculated in the high-dimensional space, and the data are classified.  
The kernel function \(k\) is usually used to project the input data features into the high-dimensional feature space. The commonly used kernel function is radial basis function, as Equation (15). It has the ability to map samples to a higher dimensional space, can handle the non-linear relationship between different labels and features, and has high anti-interference ability. It requires fewer parameters to be set, which is simple and easy to use. Therefore, in this paper, the Gaussian radial basis kernel function is selected as the basis function of the support vector machine. The radial basis function needs to select the two main parameters of the penalty factor \(C\) and the radial basis kernel function parameter \(\gamma\), which has a greater impact on the training and test errors of the support vector machine. Therefore, IFA is used to optimize these two parameters.  
Gaussian radial basis kernel function:  
\[k\left(\overrightarrow{x_{i}},\overrightarrow{x_{j}}\right)=\exp\left(- \gamma\left\|\overrightarrow{x_{i}}-\overrightarrow{x_{j}}\right\|^{2}\right) \tag{18}\]  
\(\phi\left(\overrightarrow{x}\right)\) represents the feature vector in the high-dimensional space map after \(\overrightarrow{x}\) is transformed by the kernel function. In this feature vector, the form of the hyperplane is as follows:  
\[f(x)=\omega^{T}\phi\left(\overrightarrow{x}\right)+b \tag{19}\]  
The optimal constraint:  
\[\underset{\omega,b}{\text{min}}\frac{1}{2}\|\omega\|^{2}s.t.y_{i}(\omega \cdot\phi(x_{i})+b)\geq 1(i=1,2,\cdots,N) \tag{20}\]  
Use Lagrange operator to calculate, and finally get the optimal hyperplane formula:  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}y_{i}\phi\left(x_{j}\right)+b=\sum_{i=1}^{N} \alpha_{i}y_{i}k\left(\overrightarrow{x_{i}},\overrightarrow{x_{j}}\right)^ {T}\phi\left(x_{j}\right)+b \tag{21}\]
# Continuous-Wave and Mode-Locked Operation of an In-Band Pumped Tm,Ho,LuCaGdAlO4 Laser
## 2 Materials and Methods  
The tetragonal CALGO is an optically positive (\(n_{\rm e}>n_{\rm o}\)) uniaxial crystal. Thus, the spectroscopic properties of the dopant ions are described in terms of \(\pi\) (\(E//c\)) and \(\sigma\) (\(E\bot c\)) polarizations. The maximum absorption for the \({}^{3}\)H\({}_{6}\)\(\rightarrow\)\({}^{3}\)H\({}_{4}\) Tm\({}^{3+}\) transition in Tm,Ho,Lu:CALGO (Tm: 4.48%, Ho: 0.54%, Lu: 5.51%) from the ground level amounts to \(\sim\)5.1 cm\({}^{-1}\) close to 1700 nm for \(\pi\)-polarization and the corresponding bandwidth (FWHM) of this absorption band exceeds 100 nm [22]. The measured thermal equilibrium luminescence lifetime is 4.35 ms, for both ions emission near 2 \(\upmu\)m [22].  
As a pump source, we employed a home-made fiber Raman laser delivering 6 W of CW power at a central wavelength of 1678 nm, well matching the absorption peak of Tm\({}^{3+}\) in CALGO, with a spectral FWHM of 0.8 nm. The laser consists of an input fiber Bragg grating (highly reflective at 1678 nm and highly transmissive at 1560 nm) spliced to 390 m of OFS Raman fiber (non-polarization maintaining) with a flat fiber cleave (0\({}^{\circ}\) angle at fiber end) providing \(\sim\)4% feedback from the Fresnel reflection at the glass-air interface. The Raman cavity is pumped by a 12 W CW Er-fiber filtered amplified spontaneous emission source centered at 1560 nm, spliced directly to the Raman cavity. A high power fiber isolator (1550 nm) is used between the Er:fiber source output and the Raman cavity, to prevent back reflected 1550 nm radiation from the flat cleave at the output of the OFS Raman fiber damaging the Er-fiber pump source. The entire source is fiber integrated, and an\(f\) = 15.26 mm aspheric lens is used to collimate the fiber Raman laser at the flat cleave output. The source provides a stable and diffraction limited pump beam.  
Figure 1: Energy level schemes of Tm\({}^{3+}\) and Ho\({}^{3+}\) in the CaGdAlO\({}_{4}\) crystal: black and red arrows-pump and laser transitions, respectively, CR–cross-relaxation, ETU–energy-transfer upconversion, ET–non-radiative energy transfer, NR–multi-phonon non-radiative relaxation.  
A 6 mm long, \(a\)-cut and antireflection (AR)-coated Tm,Ho,Lu:CALGO crystal with the above composition was used at normal incidence. The lateral dimensions were 4 (\(a\)) \(\times\) 4 (\(c\)) mm\({}^{2}\). It was wrapped in indium foil and water cooled to 12 \({}^{\circ}\)C in a copper holder.  
The maximum available pump power from the unpolarized fiber source after the imaging lenses and the pump mirror M\({}_{1}\), see Figure 2, was 5.5 W. External polarizers had to be used to record the input-output characteristics at constant spatial quality with the pump laser kept at maximum power. The Tm,Ho,Lu:CALGO sample was mounted with its \(c\)-axis vertical and it was pumped in \(\pi\)-polarization for higher absorption. A polarizer was first used to impose linear polarization to the pump, followed by a combination of a half-wave plate and a second polarizer acting as an attenuator.
## 3 Solving Optimal Cutting Parameters Using Black Hole-Continuous Ant Colony Algorithm  
### Introduction to Continuous Ant Colony Algorithm  
The continuous ant colony algorithm (ACO\({}_{\text{R}}\)) is built upon the ant colony algorithm (ACO) and utilizes a Gaussian kernel function for sampling to generate new solutions. This extends the discrete search space to the continuous domain. The algorithm proceeds as follows:  
Step 1: Generate \(k\) ants randomly in the definition domain, calculate their function values \(F(x_{i})\) according to Equation (16), and rank them according to the results from smallest to largest.  
Step 2: Calculate the weight \(\omega_{i}\) of each solution based on the ranking number \(i\) with the following formula:  
\[\omega_{i}=\frac{1}{qk\sqrt{2\pi}}e^{-\frac{(i-1)^{2}}{2q^{2}k^{2}}} \tag{18}\]  
where \(i=1,2,\cdots,k\) denotes the \(i\)-th solution; \(q\) is the reinforcement factor, and the smaller its value, the more the algorithm tends to select the top-ranked solution.  
Step 3: Calculate the probability of each solution being selected (\(p_{i}\)).  
\[p_{i}=\frac{\omega_{i}}{\sum\limits_{i=0}^{k}\omega_{i}} \tag{19}\]Step 4: Select a guide solution \(x_{i}\) according to the probability of step 3, and sample the n dimensions of \(x_{i}\)\(m\) times using a weighted Gaussian kernel function \(G_{j}(x)\). The sampling process is expressed as follows:  
\[G_{j}(x)=\sum_{i=1}^{k}\omega_{i}g_{ij}(x)=\sum_{i=1}^{k}\omega_{i}\frac{1}{ \sigma_{ij}\sqrt{2\pi}}e^{-\frac{(x-\mu_{ij})^{2}}{2\sigma_{ij}}} \tag{20}\]  
where \(j=1,2,\cdots n\), the mean corresponds to \(\mu_{ij}=x_{ij}\), and \(\sigma_{ij}\) is the standard deviation, which is calculated as follows:  
\[\sigma\dot{ij}=\xi\sum_{\epsilon=1}^{k}\frac{|x_{\epsilon j}-x_{ij}|}{k-1} \tag{21}\]  
where \(\xi\) is the offset distance ratio, and its value is greater than 0. The larger the value of \(\xi\), the slower the algorithm converges.  
Step 5: After sampling to generate \(m\) new solutions, calculate their function values \(F(x_{i})\) again according to Equation (16) and merge them with the previous \(k\) ants; arrange them according to the function values from smallest to largest; and select the first \(k\) results from these \(k+m\) ants as better solutions, and the last \(m\) as inferior solutions, and discard them. Then, return to step 2 until the termination of the iteration.
## 1 Introduction  
Endodontics involves the prevention and treatment of pulpal and periradicular diseases [1, 2]. ProRoot(r) Mineral Trioxide Aggregate (MTA; Dentsply Maillefer, Ballaigues, Switzerland), a hydraulic silicate cement, has been perceived as the gold standard for vital pulp therapy, apexification, repair of root perforations and root-end fillings [3; 4]. Mineral trioxide aggregate is biocompatible and induces the formation of new mineralized tissue [5; 6]. However, disadvantages such as fluid consistency, make it difficult to handle and control, and long setting time challenges clinical handling. Biodentine(tm) (Septodont, Saint-Maur-des-Fosses, France) has been introduced as an alternative bioceramic material with reduced setting time and the ability to promote the formation of a dentinal bridge when used for direct pulp capping [7; 8]. Other bioceramic materials incorporate calcium phosphate into tricalcium silicate cements (CPCSs) for improving bioactivity and facilitating clinical handling [9; 10]. These bioceramic materials have been proposed for apexification, the repair of root defects and perforation [11]. They are also available in pre-mixed formulations and have been reported to be as biocompatible as MTA or Biodentine [12]. Endosequence BC root repair material purity (RRMPU; Braseler, Savannah, GA, USA) and Root Repair Material Paste (RRMPA; Braseler) are premixed bioceramics that have been reported to be biocompatible [13; 14] and promote fibroblast growth [15]. These pre-mixed bioceramics are available in \"regular\" and \"fast-set\" versions, with setting times ranging from 4 h to 20 min, respectively [10]. The fast-set formulation has similar mechanical properties as MTA and a higher final hardness value than other CPCS cement formulations [16]. Biocompatibility studies on novel bioceramics have reported the absence of cytotoxicity and genotoxicity; however, their ion release profiles, mineralization capability and interactions with bone marker are yet to be determined [17]. Although fast set bioceramic cements showed similar biocompatibility and better cell adhesion capacity compared with MTA [18], their osteogenic potential remains largely unknown. Human MG-63 osteoblastic-like cells are considered a reliable in vitro model for evaluating the osteogenic potential of biomaterials by examining the expression of cytokines, bone markers and osteoblast-derived proteins [19]. Accordingly, the objective of the present study was to compare the biocompatibility and osteogenic potential of fast-set bioceramic cements with MTA and other bioceramic materials using the MG-63 human cell line model. The null hypothesis tested was that there are no differences among the tricalcium silicate materials in terms of their biocompatibility and osteogenic potential.
## 3 Results
### Classification Performance Using the VGG-19 with Transfer Learning  
The VGG-19 is a deep and wide structure in which the number of computational parameters is well-optimized. In particular, the parameters were configured for training the network, including epochs (300), hidden layer active function (Tansig), output active function (Softmax), initial learning rate (0.00001), and batch size (60).  
In this research, the division of 16,010 tomato leaf images (100%) in the Plant Village dataset is performed as follows: 80% of the image set for training and validation, and 20% of the image set for testing. In addition, the images for training and validation are divided into the training (80%) and the validation (20%).  
Figure 9 demonstrates the confusion matrix of the model; where 1 is bacterial spot disease, 2 is early blight disease, 3 is late blight disease, 4 is leaf mold disease, 5 is Septoria leaf spot disease, 6 is two-spotted spider mites, 7 is target spot disease, 8 is yellow leaf curl virus disease, 9 is Mosaic virus disease, and 10 is the healthy leaf, respectively. We observed that the SEN value of most leaf diseases is high, at over 99%, except the SEN value of the mosaic virus type, at 97.4%. This result can be because the mosaic virus dataset is only 373 images, compared with the other datasets from 1000 to 3000 images. To improve, we can apply the up-sampling technique to increase the number of datasets or the balanced class weight method to enhance the accuracy of the mosaic type. Furthermore, the PPV value of most diseases is perfective at 100%. So, the model has an average accuracy of 99.71%. This value demonstrates that the proposed model is very effective for classifying the tomato leaf diseases.  
Figure 9: Classification evaluation results of nine leaf diseases and one healthy leaf: (**1**) Bacterial spot disease, (**2**) Early blight disease, (**3**) Late blight disease, (**4**) Leaf mold disease, (**5**) Septoria leaf spot disease, (**6**) Two-spotted spider mites, (**7**) Target spot disease, (**8**) Yellow leaf curl virus disease, (**9**) Mosaic virus disease, (**10**) Healthy leaf.
# Stability of MSW Landfill Slopes Reinforced with Geogrids
###### Abstract  
Slope stability is one of the main problems encountered in MSW (municipality solid waste) landfill designs. Slope stability calculations become difficult due to the heterogeneous structure of MSW landfills and leachate, and therefore, slope geometries are formed by choosing low slope angles for safe designs. This causes less waste to be stored on site. This study presents slope stability analyses of MSW landfills. Numerical analyses were performed using finite element and limit equilibrium methods. The stability behavior of landfill slopes was analyzed for both unreinforced and geogrid-reinforced conditions in order to investigate the effects of shear strength parameters, the unit weight of soil waste, and material model parameters. It has been seen that the stability of landfill slopes can be increased significantly using geogrid materials. When the optimum geogrid parameters obtained from the numerical analysis results are used, it has been observed that the safety factor of the slope can be increased by up to approximately two times. Slopes in landfills reinforced with geogrid reinforcements can be formed steeper, allowing more solid waste to be stored. Considering the high initial investment cost of MSW landfills, it has been concluded that storing more solid waste with the use of geogrids will provide significant economic gains. Based on the results, the optimum values of geogrid parameters were determined and suggested for maximum reinforcing effects in MSW landfill slopes.  
MSW landfill; slope stability; geogrid; finite element; limit equilibrium Article  
Stability of MSW Landfill Slopes Reinforced with Geogrids