esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and Neonatal jaundice  
A very interesting application of AI for the future is in jaundice diagnosis. A Saudi Arabian group explored the possibility of using deep learning and ML models for the diagnosis of neonatal jaundice using a dataset comprising images from a smartphone camera. The study population included late preterm and term newborns. In this study, the authors trained a neural network to recognize jaundice from the information obtained from the images of the skin and the eyes [81]. [PERSON] et al. studied the clinical data for a large population of newborns with the intention of building a risk stratification tool for neonatal jaundice using an ML approach and without using bilirubin measurements. Their model showed good accuracy in the risk stratification of neonatal jaundice [82].
## 2 Parameters Affecting HCP and ER Measurements
### Parameters Affecting the HCP  
HCP measurements can be impacted by various parameters which can result in incorrect interpretations of the data. One such factor is the moisture content, which was shown to reduce electrical resistivity and lead to lower (more negative) HCP values [1; 2; 3; 4; 5; 6; 7]. Another crucial parameter is the concrete cover thickness, which affects the distribution of the potential, causing the potential at the steel/concrete interface to be significantly different from the potential on the concrete surface as the concrete cover thickness increases [8]. However, the effect of the concrete cover is still not conclusive, with some studies suggesting it can impact the accuracy of HCP measurements [5; 9]. The anode/cathode ratio also affects the potential distribution due to corrosion, with a smaller ratio potentially leading to difficulty in detecting localized corrosion [8]. Furthermore, it was concluded that the relationship between HCP values and the size of the corroded area in the steel bar needs to be further investigated to improve HCP measurements [10].
# An Adaptive Procedure for the Global Minimization of a Class of Polynomial Functions
## 1 Introduction  
Global optimization over continuous spaces is an important field of the modern scientific research and involves interesting aspects in computer science. Given an objective function \(f\) modeling a system of points which depends on a set of parameters, the task is to determine the set of parameters which optimizes \(f\). For simplicity, in most cases the optimization problem is stated as a minimization problem.  
For high dimensional problems, numerical methods are suggested which construct converging sequences of points by employing local optimizers, for example search methods. In general, procedures of this type are stochastic and rely on intensive computation to explore the solution space, without too deep enquires concerning convergence proofs. Roughly speaking, there are two main approaches: a sampling approach and an escaping approach. Examples of the sampling approach are Pure Random Search algorithms [1], where an extensive sampling of independent points is considered, and Multistart algorithms [2; 3], which apply local optimizers to randomly generated starting points; then the best among the found local optima is considered the global optimum. Sampling algorithms have been shown [3] to converge in infinite time with probability 1, but in practice there is no guarantee that the best local solution found in a finite amount of computation is really the global optimum. In the escaping approach, various methods are devised for escaping from an already found local optimum, reaching other feasible points from which to restart a new local optimizer (see for example the Simulated Annealing [4]).  
In practice, most algorithms succeed for small and medium-sized problems, but the chance of finding the global optimum worsens considerably when the number of the dimensions increases, due to the necessary limitation of the sampling set size. In any case, the computation of a local solutioncan be very expensive and the convergence of differently started local procedures to the same local optimum is frequent and should be avoided.  
In this paper we propose an adaptive strategy for finding the global minimum of a polynomial function expressed through the Frobenius norm of matrices and tensors. The considered problem is outlined in Section 2. The Multistart algorithm, on which the adaptive strategy is based, is described in Section 3. It implements a particularly tailored local minimization procedure with an ad-hoc stopping condition. The basic step of the adaptive strategy is implemented through a priority queue and consists in splitting the runs starting from different initial points in segments of fixed length. The processing order of the various segments is interlaced, discarding those which appear less promising. The adaptive procedure is described in Section 4. Finally, in Section 5 the validity of the adaptive procedure is verified through a large experimentation on both nonnegatively constrained and unconstrained problems.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment
### Impacts on Nutrients Availability  
Biochar can incorporate many macronutrients and micronutrients into soil that can be used by plants ([PERSON] et al., 2018). The amount of incorporated carbon, which will be used by microbial communities or sequestered in soil, depends strongly on pyrolysis temperature and therefore varies from biochar to biochar. During pyrolysis and depending on carbonization temperature, some elements, such as C, N, H, O and S, are partly lost in the form of volatile substances, while other elements, including P and K and other metals, are more concentrated in the biochar than in the feedstock, which increases the fertility of soil ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). Many investigations reported that biochar-amended soil has a high ability to capture ammonium and nitrate ([PERSON] et al., 2018; [PERSON] et al., 2018), and to prevent nitrate leaching ([PERSON] et al., 2018; [PERSON] et al., 2018). [PERSON] et al. (2018) showed that biochar amendment significantly increased rice N-uptake and reduced leaching of N-forms from the soil.  
The effect of biochar on the concentration of N-forms in soils depends on many factors, including the pyrolysis temperature, the residence time of biochar in soil, the biochar application rate and the soil pH ([PERSON] et al., 2018). [PERSON] et al. (2018) showed that the concentrations of ammonium and nitrate are reduced following biochar amendment by about \(-11\%\) and \(-10\%\), respectively. The significance of reduction depends, according to the authors, on the residence time of biochar in the soil, and decreases as the biochar ages. Comparably, [PERSON] et al. (2018) reported that nitrate concentration decreased by 12% after biochar application. By contrast, [PERSON] et al. (2018) observed an averaged decrease in nitrate leaching by \(-13\%\) and N\({}_{2}\)O emissions by \(-38\%\) in the majority of biochars that were applied to soils. This reduction is, however, dependent on the soil type and the residence time of biochar in the soil. Similarly, [PERSON] et al. (2018) reported that biochars could prevent nitrate loss from agro-ecosystems and act as a slow-release fertilizer, which can reduce global N fertilizer demands. Following biochar addition, not only N but also P leaching is reduced. According to [PERSON] et al. (2018), the conversion of manure into biochar allows the formation of less soluble (Ca, Mg)\({}_{3}\)(PO\({}_{4}\))\({}_{2}\), which is preserved in the biochar produced. Similarly, [PERSON] and [PERSON] (2018) observed an enhancement of P availability in soil after biochar addition, which may help in reducing the demand for chemical fertilizers in biochar-amended soils.
## 2 Materials and Methods  
### Standard and Reagents  
Dichloromethane, 2-propanol, trifluoroacetic acid, acetonitrile (ACN) and methanol (CH\({}_{3}\)OH), all of analytical or chromatographic grade, were obtained from Merck (Darmstadt, Germany). Water was purified by a Milli-Q system (Millipore, Watford, Hertfordshire, UK). A standard mixture containing even n-alkanes with chains from 10 to 40 carbon atoms (C10-C40) was purchased from Sigma-Aldrich (Milan, Italy). Analytical standards of diphenyl sulfone (DPS), 2,4-ditter butylphenol (2,4-DTBP), ibuprofen (IBU), perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), gemfibrozil (GBZ), estrone (E1), triclosan (TCS), carbamazepine (CBZ), bisphenol-A (BPA), \(\beta\)-estradiol (E2), 17-\(\alpha\)-ethinylestradiol (EE2), benzophenone-3 (BP-3), ethylhexyl methoxycinnamate (EHMC), octocrylene (OC), octyl dimethyl p-aminobenzzoic acid (OD-PABA), naproxen (NAP), diclofenac (DIC), nefenamic acid (MEF) and caffeine (CAF) were obtained from Sigma-Aldrich (Milan, Italy). All standards were of high purity grade (>97%).
## 2 Numerical Analysis of the Erosion Model  
### Pipe Parameters and Mesh Segmentation  
In this paper, a typical pipe elbow is analysed as an example. The structural dimensions are shown in Figure 1, and the fracturing fluid parameters are shown in Table 1. The geometric model is a spatial anisotropic arrangement with a 90\({}^{\circ}\) angle. The upper right port is the inlet end, and the lower left port is the outlet end. The bend model consists of five segments: 1#, 3# and 5# are straight pipe segments, and 2# and 4# are 90\({}^{\circ}\) bend segments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters of Fracturing Fluid** & **Symbol** & **Numerical** & **Units** \\ \hline Inner diameter & D & 50 & mm \\ Bend diameter ratio & R/D & 2 & \\ Velocity & v & 20 & m/s \\ Particle diameter & d & 0.00045 & m \\ Density of sand & \(\rho\) & 2650 & kg/m\({}^{3}\) \\ Mass flow & mn & 7 & kg/s \\ Viscosity & \(\eta\) & 0.001 & Pa\(\cdot\)s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fracturing fluid parameters.  
The mesh is divided as shown in Figure 2, where high-quality hexahedral elements are used in all components.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 2 Network Based Systems
### Range Free  
Range free methods are based on the connectivity information of a wireless network, which can be used to estimate the position without computing any range measurement to an anchor node. There are mainly two kind of range free algorithms:  
* **Proximity methods:** these methods use the connectivity information to infer directly the position of the user based on the number of anchors in the neighbourhood.
* **Fingerprinting methods:** these methods are based on location dependent characteristics of the signals received from the wireless network. In a first step, a database of the characteristics and the real location where they were measured is collected. Then, in a second step, the position is estimated by selecting the position of the database sample that best matches the real data.
## 2 Mathematical Model
### Noise Equivalent Power  
The received signal is subject to amplifier and detector noises including background noise, shot noise and dark current noise, all of which should be considered in the ELPW simulation. The noise equivalent power (NEP) is a common metric that quantifies the sensitivity of a photodetector or the power generated by a noise source, which means the minimum optical power required to achieve a unity signal-to-noise ratio in a 1 Hz bandwidth. To take into account noise in the ELPW simulation, a Gaussian random variable with a zero mean and a standard deviation equal to NEP is generated and then added to the received power because the noises are independent and identically distributed Gaussian random variables [36]. The NEP resulting from the detector noise is given by  
\[NEP_{Detector}(t)=\frac{\sqrt{2 qB[l_{ds}+(l_{db}+l_{b}+l_{s})G^{2}F_{A}]}}{ \Re}, \tag{18}\]  
where \(q\) is the electronic charge, \(B\) is the electrical bandwidth, \(l_{ds}\) and \(l_{db}\) are the surface and bulk dark currents, respectively, \(l_{b}\) and \(l_{s}\) are the currents due to the background noise and received signal, respectively, \(G\) is the detector gain, \(F_{A}\) is the excess noise factor, \(\Re\) is the detector responsivity defined as \(\Re=q\eta_{Q}G/hv\) using the quantum efficiency of the detector \(\eta_{Q}\), \(h\) is [PERSON]'s constant and the laser beam frequency is \(v\). The excess noise factor is expressed as \(F_{A}=k_{eff}/G+\big{(}1-k_{eff}\big{)}(2-1/G)\) in terms of the detector gain and the ionization coefficient ratio \(k_{eff}\). Two currents owing to the received signal and background noise in Equation (18) can be obtained from [37]  
\[I_{s}(t)=\Re P_{r}(t), \tag{19}\]  
\[I_{b}=\Re P_{b}=I_{\lambda}\Omega_{FoV}\eta_{R}\eta_{F}A_{R}\Delta\lambda_{F}, \tag{20}\]where \(I_{\lambda}\) is the spectral radiance of the sky, \(\Omega_{FoV}\) is the receiver field of view in steradians, and \(\Delta\lambda_{F}\) is the bandwidth of the optical bandpass filter.  
The NEP corresponding to the amplifier noise is given by [35]  
\[NEP_{Amp}(t)=\sqrt{\frac{4 kTP_{N}}{R_{L}\Re^{2}}}, \tag{21}\]  
where \(\kappa\) is Boltzmann's constant, \(T\) is the temperature in Kelvin, \(F_{N}\) is the noise factor, and \(R_{L}\) is the load resistance. The NEPs for the detector and amplifier are combined into the total NEP which is interpreted as the standard deviation for the Gaussian distribution of the additive noises in the optical power domain:  
\[NEP_{Total}=\sqrt{NEP_{Detector}^{2}+NEP_{Amp}^{2}}. \tag{22}\]
## 3 Underwater Routing Protocols
### Dfr  
Directional flooding-based routing (DFR) considers the link quality to route packets from a source to a destination [25]. Every node knows its own position and the position of the forwarder and the sink (final destination at the surface of the water). Every node also knows the link quality of its one-hop neighbors. The protocol forwards packets using flooding in a restricted zone formed by the angle among the source, forwarder and sink. It also ensures that there is at least one forwarder in the flooding zone to avoid packet loss. Under the worst link conditions, the flooding zone can be extended to include more forwarders and then select the link with the best quality to forward packets. Redundant packet transmission is the major issue with this protocol, which unnecessarily consumes the energy of nodes.
## 2 Basic Concepts and Work Scope  
In the next subsections, we review the concepts necessary for understanding and putting our work into context, define the terminology, and explain the intended scope.
## 7 Results
### Temperature Measuring Performance  
The system was tested in real conditions for one month. The commodities were placed in a cold storage room and a land transport container. Three different setpoint temperatures, at 10, 5 and 2 \({}^{\circ}\)C, were selected for system performance purposes. Two slave nodes placed in different locations inside the container were used. Position 1 was placed just below the evaporator of the freezer, and position 2 was placed at the back of the container on the opening doors. A gateway node was used to communicate the data (see Figure 15).  
Figure 14: Trial 6: voltage evolution of the three nodes distributed in several places inside of the refrigerated truck container.  
Figure 13: Trial 5: voltage evolution of the three nodes distributed in several places inside of the refrigerated truck container.  
Charts in Figure 16 show the performance of the system. A temperature difference can be observed between the node positions. This difference is due to the different storage and transport conditions of the pallets containing the commodities.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 4 Proposed Method
### Configuration of the CUDA Kernel  
Our system primarily processes three-dimensional input and output arrays. To fully exploit the GPU's parallelism and enable efficient indexing, we adopted 2D threads combined with a 1D block structure aligned with the data structure. This approach effectively captures the inherent 2D spatial properties of the data, such as width and height, using 2D threads. The 1D block structure corresponds to the third dimension, allowing all elements in a 3D array to be indexed accurately and efficiently. Furthermore, maintaining independence between blocks maximizes computational parallelism.  
Another feature of the kernel design is the fixed parallel operations on the output array. By fixed parallelism, we refer to a structure where each GPU thread independently processes a specific element of the output array. This one-to-one correspondence between each thread and an output element minimizes data dependency between threads, ensuring deterministic results by guaranteeing that each thread processes exactly one output element. Additionally, this focus on the output array makes it easy to scale the number of threads up or down based on the size and demand of the output data, supporting dynamic computational requirements.  
An additional important aspect of the kernel design is the use of arrays for thread identifiers. This choice was made to efficiently handle CNN computations, where specific parts of the input are extracted with different weights. This structure increases the efficiency of parallel computations by allowing each thread to process multiple outputs simultaneously. Moreover, by utilizing the block index, we enabled computations to be performed only on specific blocks, improving efficiency by indexing multiple computed outputs at once. The  
Figure 2: The comparison of the routing inference architectures in the conventional and proposed methods. (**a**) In the conventional approach, all blocks of the inference are conducted only sequentially on a CPU. (**b**) In the proposed approach, several blocks of the inference are conducted in parallel on a GPU, which can reduce the overall computation time.  
specific configuration of the CUDA kernel is shown in Algorithm 1. Here, we also present additional methods proposed in this paper.  
```
1:Initialize thread indices and block index
2:Initialize sum array
3:Calculate output array
4:Initialize local input array
5:for\(i=0\) - input_channel
6:for\(j=0\) - input_width
7:for\(k=0\) - input_height
8: Fill local input
9: Copy shared memory
10:for\(i=0\) - output_array
11: Perform multiplication and add bias
12: ReLu
13: Write the output
``` ```  
**Algorithm 1**Configuration of the CUDA kernel
## 3 Results and Discussion  
### SIF Mean Analysis  
The magnitude and spatial pattern of the global monthly mean SIF averaged over 2007 to 2019 (Figure 1) show the highest SIF values in the tropical regions, intermediate values in the eastern United States, southeast Asia, and central Europe, and the lowest values in the barren regions (e.g., deserts, poles). This pattern is largely dependent on local climate conditions (air temperature, precipitation, and solar radiation) as well as vegetation types (e.g., [40; 41; 42; 43]). The 2007-2019 global mean SIF is 0.16 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\).
## 5 Conclusions  
A novel network model for providing IoT-based services with private sensor networks, using third party access infrastructure, has been studied. The model was analyzed as two games using game theory, population games, Logit discrete choice model, optimization and Nash equilibrium concepts.  
Firstly, a congestion model was proposed for the utility of the sensors and it was shown economically viable for the network operator to offer connectivity service, however, the system load never exceeded half of system the capacity.  
Secondly, a Logit discrete choice model was chosen to model users' decisions with two Internet of Things Service Providers competing for serving them maximizing their own profits. It has been shown that, in the equilibrium, both IoT-SPs obtain the same profits multiplied by the portion of the total sensors that each one has. We observed that the value of users' sensitivity to the data quality/price had to be \(1<\alpha<2\), in order to obtain a providers' pricing equilibrium other than (0,0). In addition, the number of potential clients had to be high enough to guarantee the feasibility for the IoT-Service Providers.  
Given that both stages have been shown feasible under specific conditions, we can conclude that the whole network model is conditionally feasible from an economic point of view.  
Future work will involve a sensor-data traffic differentiation model, with different delay requirements and different pricing schemes for each sensor-data traffic type, in order to improve the efficiency on the operator wireless network and create new business opportunities for the providers.  
This work was supported by the Spanish Ministry of Economy and Competitiveness through projects TIN2013-47272-C2-1-R and (co-supported by the European Social Fund) BES-2014-068998.  
[PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper and read and approved the final manuscript.  
The authors declare no conflict of interest.
# A Review of Deep Learning Based Speech Synthesis
## 2 An Overview of Speech Synthesis
### The History of Speech Synthesis  
With the development of digital signal processing technologies, the research goal of speech synthesis has been evolving from intelligibility and clarity to naturalness and expressiveness. Intelligibility describes the clarity of the synthesized speech, while naturalness refers to ease of listening and global stylistic consistency [16].  
In the development of speech synthesis technology, early attempts mainly used parametric synthesis methods. In 1971, the Hungarian scientist [PERSON] used a series of delicate bellows, springs, bagpipes and resonance boxes to create a machine that can synthesize simple words. However, the intelligibility of the synthesized speech is very poor. To address this problem, in 1980, Klatt's serial/parallel formant synthesizer [17] was introduced. The most representative one is the DECtalk text-to-speech system of the Digital Equipment Corporation (DEC) (Maynard, MA, USA). The system can be connected to a computer through a standard interface or separately connected to the telephone network to provide a variety of speech services that can be understood by users. However, since the extraction of the formant parameters is still a challenging problem, the quality of the synthesized speech makes it difficult to meet the practical demand. In 1990, the Pitch Synchronous OverLap Add (PSOLA) [18] algorithm greatly improved the quality and naturalness of the speech generated by the time-domain waveform concatenation synthesis methods. However, since PSOLA requires the pitch period or starting point to be annotated accurately, the error of the two factors will affect the quality of the synthesized speech greatly. Due to the inherent problem of this kind of method, the synthesized speech is still not as natural as human speech. To tackle the issue, people conducted in-depth research on speech synthesis technologies, and used SPSS models to improve the naturalness of the synthesized speech. Typical examples are HMM-based [19] and DL-based [20] synthesis methods. Extensive experimental results demonstrate that the synthesized speech of these models has been greatly improved in both speech quality and naturalness.