esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Audio Equalization Filters by Deep Neural Networks
## 3 Proposed Method
### Multilayer Perceptron  
The MLP is a shallow network composed of several fully-connected layers: one input, one or more hidden layers, and an output layer. The input is constrained to the number of samples in the impulse responses, that are concatenated in a long vector. Considering \(\mathcal{S}\times\mathcal{M}\) impulse responses of  
Figure 1: Multi-point equalization problem: \(\mathcal{S}\) loudspeakers are displaced in an environment together with \(\mathcal{M}\) microhones. The equalizing filters \(g_{s}\) are designed to invert the environment impulse responses \(h_{m,s}\).  
length \(L\), the input will have length \(\mathcal{S}\times\mathcal{M}\times L\). The network produces a vector concatenating all the FIR coefficients, thus has size \(\mathcal{S}\times\mathcal{T}\), where \(\mathcal{T}\) is the number of taps for each filter. The architecture is shown in Figure 2.  
The loss function for the MLP is defined as the Euclidean distance between the given response at each iteration \(|\tilde{Y}_{m}(\omega)|\), computed according to (2), and the desired frequency response:  
\[J=\Bigg{(}\sum_{m=1}^{\mathcal{M}}\big{\|}|\tilde{Y}_{m}(\omega)|-|Y_{des}( \omega)|\big{\|}_{2}\Bigg{)}. \tag{3}\]
# Moving Mesh Strategies of Adaptive Methods for Solving Nonlinear Partial Differential Equations
## 2 The Improved Moving Mesh PDEs
### The Moving Mesh Equation Based on the Characteristic Line  
Taking the original PDE (1) as a Hyperbolic PDE locally, [PERSON] [30] let the mesh move along the characteristic line, for the original PDE (1), [PERSON] [30]'s moving mesh equation is:  
\[u_{t}=-L(u), \tag{15}\]  
which is the direction of character line of the original PDE (1).  
Discreting Equation (15) applying the explicit Euler discretization on time, one can get:  
\[x(t_{k+1})=x(t_{k})-\triangle t_{k}L(u(x(t_{k}),t_{k})),\]  
in order to interpret the moving mesh Equation (15) more precisely and do comparisons with other moving mesh methods, we divide the above into two steps:  
\[x^{(1)}(t_{k})=x(t_{k}), \tag{16a}\] \[x(t_{k+1})=x^{(1)}(t_{k})-\triangle t_{k}L(u(x(t_{k}),t_{k})), \tag{16b}\]where \(x^{(1)}(\xi,t_{k})\) is an intermediate term.  
As a velocity-based mesh movement method, [PERSON]'s moving mesh equation takes the moving of the knots to be leaves on the wave (PDE) and the mesh is gone with the wave (PDE). The method keeps the mesh's distribution on the solution according to time iteration. The benefit of the method is it considers the influence of the original PDE on the moving of the mesh. However, the method is not able to do the equidistribution on fixed time, if the initial mesh is not equidistributed, the mesh will always be unequidistributed until the end.
## 2 Results and Discussion  
Peach fruit produces a number of volatile organic compounds (VOCs) [20; 21]. The investigation of different peach varieties showed that C\({}_{6}\) compounds, alcohols, aldehydes, and lactones are the most powerful aroma-active compounds [13; 14]. Nectarines usually contain C\({}_{9}\) aldehydes, \(\gamma\)-decalactone, and terpen; peaches contain a majority of C\({}_{6-10}\) lactones; and flat peaches are dominant in benzaldehyde, \(\gamma\)-decalactone, and \(\delta\)-dodecalactone [22]. It is no coincidence that volatile compounds are of scientific interest. A quantitative comparison is not always feasible because of the variations in the extraction procedures and quantification procedures used. Different conclusions can be made based on the methods used for collection, the compound concentrations, and the nature of the volatiles produced by fruit. A recent study confirmed significant differences in the number of identified compounds and their quantities exist [23]. The typical taste of most fruits is not present during early formation, but develops after the ripening process [21]. During this period, metabolism changes to catabolism and volatile compounds are formed from the main plant constituents by various biochemical pathways [24]. The climacteric respiration of the fruit aids in the flavor formation during the post-climacteric maturation phase [25]. The richness of the aromatic compounds is of primary importance for consumer acceptance. Many researchers have confirmed that the same cultivars act differently when subjected to another environment. The varieties \"Morsiani 90\", \"Ufo- 4\", \"July Lady\", and \"Flat Queen\" are varieties that have been introduced to the Bulgarian geographical region, which means that the orchard management and the ecological factors can result in a different VOCs profile. \"Evmolpiya\", \"Laskava\", \"Filina\", and \"Gergana\", on the other hand, are local varieties, and their volatile profile is yet to be reported. \"Filina\" is a result of the breeding selection of \"Maycrest\" \(\times\) \"July Lady\". \"Gergana\" is created by combining Goldengrand and Aureliogrand varieties. \"Laskava\" is created by cross-species hybridization, with the participation of the species _Prunus persica_ L. [PERSON] and _Prunus ferganensis_ (Kost. and Rjab.) from the parent combination \"Hale\" \(\times\) (\"Elberta\" \(\times\) \"Fergana Yellow\"). \"Evmolpiya\" is a variety obtained by the interspecific hybridization with the participation of _P. persica_ var. nucipersica, _P. persca_ and _P. davidiana_, from the parent combination \"Fantasia\" \(\times\) (\"Halle\" \(\times\) _Prunus davidiana_). Gas Chromatography-Mass Spectrometry (GC-MS) Profiling of Volatile Compounds of Analyzed Peach Samples  
The volatile profiles of eight peach varieties (four local and four introduced) grown in Bulgaria were analyzed by GC-MS. Table 1 is a visual presentation of the results; sixty-five volatile compounds, belonging to seven chemical classes (aldehydes, ketones, alcohols, fatty acids, esters, hydrocarbons, and terpenes), were identified.
## 2 Materials and Methods
### Eddy Covariance and Meteorological Measurements  
Carbon dioxide fluxes were measured from June 2013 to December 2016, using the eddy covariance method. A 22-m-tall steel scaffold flux tower was established in the forest where the terrain was relatively flat with a highly homogenous land cover surface. The instruments included a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA) used to measure 3 coordinate speeds (\(u\), \(v\), \(w\)), and an open-path CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA) used to measure the water vapor density and carbon dioxide fluctuations, installed at 20 m above the ground. Air temperature (\(T_{\mathrm{a}}\), \({}^{\circ}\)C) and relative humidity (\(R\)H, %) were also measured at 20 m above the ground surface by air temperature/relative humidity sensors (HMP45C, Campbell Scientific, Logan, UT, USA). The turbulence fluxes and \(T_{\mathrm{a}}\) and \(RH\) data were recorded by a CR3000 data logger (Campbell) at a 10 Hz sampling frequency, and the 30 min averages were computed.  
In addition, the solar radiation components (i.e., upward and downward shortwave radiation and longwave radiation) were measured at 10 m above the ground by a 4-Component Net Radiation Sensor (CNR4, Kipp & Zonen, Delft, The Netherlands), and the net radiation (\(R_{\mathrm{n}}\), W m\({}^{-2}\)) was calculated as the difference of the net shortwave radiation and the net longwave radiation. One quantum sensor (LI-190 SA, Li-COR Inc.) was mounted at an approximately 6 m height above the ground to measure the photosynthetically active radiation (PAR, \(\mu\) mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)). Two soil heat flux plates were buried at approximately 5 cm below the soil surface to measure the soil heat flux (\(G\), W m\({}^{-2}\)). These data were recorded by a CR1000 data logger (Campbell).
###### Abstract  
Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial-temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP's trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.  
Keywords:load balancing; satellite network; Internet of Things; genetic algorithm; beam hopping +
Footnote †: journal:
## 3 Results  
### XRD and TEM for magnetite Nanoparticles  
X-ray diffractogram for a powder specimen of magnetic nanoparticles (MNPs) is shown in Figure 1. The indexed diffraction maxima correspond to Fe\({}_{3}\)O\({}_{4}\) (ICDD 01-072-8149 file), the only phase in magnetite samples: 1.3\({}^{\circ}\), 30.1\({}^{\circ}\), 35.5\({}^{\circ}\); 43.1\({}^{\circ}\), 53.4\({}^{\circ}\), 57.1\({}^{\circ}\), and 62.6\({}^{\circ}\), corresponding to interplanar distances of 4.85 A, 2.97 A, 2.53 A, 2,10 A, 1.72 A, 1.62 A, and 1.48 A and Miller indexes (111), (220), (311), (400), (422), (511), and (440). The as-prepared and indexed Fe\({}_{3}\)O\({}_{4}\) compound has a face-centered cubic crystalline lattice with a lattice parameter a = 8.394 A and a main diffraction maximum at Bragg angle 20 of 35.44\({}^{\circ}\). This corresponds to the Miller indices (311) with an interplanar distance of 2.53 A and an average crystallite size of 7.3 nm (Scherrer formula). Bright-field TEM images in Figure 1A show the morphological features of magnetite nanoparticles with round and almost spherical shapes and sizes between 5 and 12 nm (agglomerates). Figure 1C reveals nanoparticle sizes of 6-9 nm with a relatively narrow distribution. The energy-dispersive X-ray analysis (Figure 1D) shows iron and oxygen as major elements, a fact that confirms that the nanoparticles from the agglomerates are iron oxide.  
Figure 1: X-ray diffractogram and TEM characterization for magnetite nanoparticles: (**A,C**)—bright-field TEM images (BF-TEM); (**B**)—electron diffraction (SAED); (**D**)—Energy-dispersive X-ray analysis (EDAX).
## 3 SSDAE-DRN Network
### Improved Method of SSDAE
#### 3.2.3 Batch Normalization  
To extract features better, reduce the dependency between parameters, and alleviate the phenomenon of over-fitting, the nonlinear activation function is used after each layer of weighted summation in the feature extraction network to enhance the nonlinear expression. Meanwhile, in order to accelerate the convergence of the network, prevent gradient explosion, and improve the accuracy of the model, a batch normalization (BN) layer is added before the activation function so that the input will not always be in a negative interval. The calculation of the BN layer can be seen in formula (17).  
\[\left\{\begin{array}{c}\mu_{B}\leftarrow\frac{1}{m}\sum_{i=1}^{m}x_{i}\\ \sigma_{B}^{2}\leftarrow\frac{1}{m}\sum_{i=1}^{m}\left(x_{i}-\mu_{B}\right)^{2 }\\ \hat{x}_{i}\leftarrow\frac{x_{i}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\varepsilon}}\\ y_{i}\leftarrow\gamma\hat{x}_{i}+\beta\end{array}\right., \tag{17}\]  
where \(\sum_{i=1}^{m}x_{i}\) is the output of the dense layer, \(\mu_{B}\) and \(\sigma_{B}^{2}\) represent the mean and variance, respectively, \(y_{i}\) is the output of the BN, \(\varepsilon\) is the numerical stability constant, \(\gamma\) and \(\beta\) are the optimizable parameters of the BN layer to ensure that the normalization can optimize the distribution of activation values.
## 4 Test Results and Discussion
### Minor Axis Test  
Figure 16 presents the normalized bending moment-deflection curves for unreinforced, 200 mm and 300 mm reinforcement types where the flexural behaviour of the specimens under minor axis tests has been reported.  
The 200 mm reinforcement type captured the best flexural performance compared to the other two types. The normalized bending capacity for each specimen is also tabulated in Table 4.  
Figure 14: The normalized major axis test average curves.  
Figure 15: The ultimate normalized moment for major axis test with respect to the reinforcement type.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Specim** & **Normalized Ultimate Bending Moment (\(\frac{M}{q_{y}\mathsf{X}^{2}}\))** \\ \hline SMIM-Test1 & 0.193595703 \\ \hline SMIM-Test2 & 0.203694478 \\ \hline SMIM-Test3 & 0.195419898 \\ \hline Average & 0.197570026 \\ \hline Standard deviation & 0.005381785 \\ \hline
200 RMIM-Test1 & 0.232982073 \\ \hline
200 RMIM-Test2 & 0.239285101 \\ \hline
200 RMIM-Test3 & 0.242392113 \\ \hline Average & 0.238219762 \\ \hline Standard deviation & 0.004794624 \\ \hline
300 RMIM-Test1 & 0.211013767 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ultimate normalized bending capacity of specimens in the minor axis tests.  
Figure 16: Normalized bending moment–deflection about minor axis test curves: (**A**) SMIM; (**B**) 200 RMIM; (**C**) 300 RMIM.  
The reinforced specimens with 200 mm reinforcing space were subjected to the minor axis tests as shown in Figure 18. Based on the test observations, the mid-span deflections were much higher than the unreinforced specimens. The reinforced specimens exhibited no sudden torsion or twisting, but the bending capacity was enhanced significantly in the 200 mm reinforcing type.  
Figure 18: The observed failure mode for reinforced specimens with 200 mm reinforcing pitch during the minor axis test: (**A**) 200 RMIM-test1; (**B**) 200 RMIM-test2; (**C**) 200 RMIM-test3.  
The minor axis test was performed on 300 mm reinforced specimens. Observations proved that increasing the reinforcing space decreases the section flexural capacity compared to the 200 mm specimens as well as the ultimate deflection of the sections at the time of failure (Figure 19).  
The normalized mean bending moment versus deflection curves demonstrated in Figure 20 represent the effects of the reinforcements in the minor axis tests. According to Figure 20, non-reinforced  
Figure 19: The observed failure mode for reinforced specimens with 300 mm reinforcing pitch during the minor axis test: (**A**) 300 RMIM-test1; (**B**) 300 RMIM-test2; (**C**) 300 RMIM-test3.  
specimens had lower bending capacity. However, 200 and 300 mm reinforcement types presented improved behaviour, especially in the ultimate capacity and deflection.  
Figure 21 presents the influence of performed reinforcement types. The 200 mm reinforcement type resulted in 12% and 17% improvement in ultimate bending capacity compared to the 300 mm reinforcement and non-reinforced types, respectively. The 300 mm type showed a 5.5% improvement in ultimate bending capacity compared to the non-reinforced specimen.
## 3 Machine Learning (ML)
### Advanced ML Methods  
#### 3.1.1 Artificial Neural Network (ANN)  
The artificial neural network (ANN) model, drawing inspiration from the structure and information processing of biological brains, comprises nodes (i.e., artificial neurons) organized into layers (Figure 4a). Within each layer, a neuron performs a simple mathematical operation on its input signal and feed forwards the output to neurons in the subsequent layer [46]. Each neuron has a multiplication factor (also known as a weight) that is adjusted during the training process. These weights are adjusted through an optimization procedure to minimize the error of a loss function between the network's output and the desired output (e.g., labeled outputs in supervised learning or energy function in unsupervised learning).  
A key challenge in training an ANN lies in determining the optimal number of times the training data is passed through the model to adjust model parameters (i.e., the number of epochs) [46]. Overfitting occurs when an ANN captures unwanted data details (such as noise and experimental errors), leading to diminished performance. Conversely, stopping training prematurely can result in underfitting, where the ANN fails to comprehend the training dataset or generalize to new data. A recommended strategy involves selectingspecific training parameters (e.g., epoch number, batch size, and learning rate) as hyperparameters and fine-tuning them through a grid search with various values. This strategy, combined with k-fold cross-validation, provides a less biased and more realistic evaluation of training.  
It is essential to note that efficient training of an ANN requires a proper extracting of the most relevant features from the dataset [47]. The approach to feature selection differs between supervised and unsupervised problems. Performance of an ANN, particularly of regression models, degrades when the training dataset includes non-informative features.
###### Abstract  
Many diseases produce pathological changes in the fundus; analyzing the retinopathy of the fundus could help diagnose diseases in time. A fundus camera is a medical imaging device that specializes in taking fundus images to diagnose hypertension, coronary heart disease, diabetes, and others. The fundus optical imaging system is the core part of it. Nevertheless, the conventional fundus optical imaging system is large and not suitable for mobile examination and follow-up use. So, it has not been widely used in medical institutions. In this paper, a miniaturized fundus optical imaging system based on aspheric technology and non-coaxial illumination is proposed. The length of the imaging system is only 34.6 mm, the field of view is 50\({}^{\circ}\), and the MTF curve is greater than 0.2 at 100 lp/mm, which can resolve the structure of 5 um. The illumination system adopts a non-coaxial annular array illumination structure to avoid occlusion of the imaging system. Our study effectively tackles the pressing predicament of fundus optical system miniaturization. This innovative paradigm harbors the potential to revolutionize fundus image data acquisition, propelling the field of fundus diagnosis forward and efficiently catering to crucial applications, improving the versatility of fundus examination, and providing technical support for the intelligent diagnosis system.  
fundus imaging; non-coaxial illumination; retinopathy; intelligent diagnosis system Article  
A Miniaturized Large-Field  
Aspheric Imaging and Non-Coaxial Illumination
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 1 Introduction
With the growing popularity of a wake-up signal/wake-up radio for IoT applications, the main purposes of this paper are (1) to fast-track and summarize the recent 3 GPP activities on the NB-IoT WUS and the draft 5G LP-WUS/LP-SS (to be included in the 5G standard in 2025), which might be of interest to the cellular IoT community and (2) to discuss the considerations and challenges of cellular WUR design based on recent top IC publications. The remainder of this paper is organized as follows: Section 2 discusses the motivation to standardize WURs for cellular IoT and possible use cases. Section 3 provides an overview of WUR design metrics and requirements. Section 4 summarizes the recent 3 GPP activities on standardizing WUR, including the NB-IoT WUR standard introduced in Rel. 15 [13] and the ongoing 5G NR WUR work item for Rel. 19 [11]. Section 5 discusses common receiver architectures suitable for WUR. Section 6 presents the state-of-the-art NB-IoT WUR chips. Section 7 concludes the article.
## 5 Results and Discussion
### The Parallel Register  
The parallel register works in such a way that the \(i\)-th output remembers the state at the \(i\)-th input that was at the moment of the rising edge on clk. The first attempt to obtain a 4-bit parallel register was made using the table describing the operation of the system presented in Table 6.  
As a result of genetic programming, various systems were obtained that were inconsistent with expectations. For example, for the parameter \(number\_columnsCGP=8\) and the set of functions as in the previous examples (JK, AND, OR, NOT), the following genotype was obtained:  
([4]0 1 2), \"([5]1 2 2), \"([6]2 1 1), \"([7]2 3 3), ([8]3 1 1), ([9]1 6 5), ([10]0 6 8), ([11]1 9 10), 0 6 5 7 representing the system shown in Figure 11. As can be seen from the table used, it does not follow that the signals should be transferred from the inputs to the outputs only when clk appears. Only the table consisting of 4 sets of data, presented in Table 7, contains information that the state from a given input should appear in the appropriate output only after clk and the fact that the state at the output should not depend on inputs that do not correspond to it. Using this table and the \(number\_columnsCGP\) parameter and the set of functions left unchanged, the genotype was obtained, for example:  
*([4]3 3 1), \"([5]3 2 2), \"([6]3 0 4), \"([7]3 1 1), \"([8]0 0 6), \"([9]0 2 5), \"([10]0 3 4), \"([11]0 1 7), 8 11 9 10  
which corresponds to the system shown in Figure 12. All systems obtained using the presented table were in the form shown in Figure 12. As you can see, this is a classic parallel register system.  
\begin{table}
\begin{tabular}{c c c} \hline
**Clk** & **Inputs** & **Outputs** \\ \hline
1 & 1 0 0 0 & 1 0 0 \\ \hline \end{tabular}
\end{table}
Table 6: Description of the parallel register response.  
Figure 11: An example of a circuit obtained based on Table 6, which is intended for implementing a parallel register. As can be seen from the obtained result, the table does not indicate that the signals should be transferred from the inputs to the outputs only when clk appears.