esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Constraint Satisfaction Problem  
A constraint satisfaction problem, as defined in [1], is a triple \(\mathcal{P}=\langle X,\,D,\,C\rangle\), where \(X=\langle x_{1},x_{2},\ldots,x_{n}\rangle\) represents an n-tuple of variables, \(D=\langle D_{1},D_{2},\ldots,D_{n}\rangle\) is a corresponding n-tuple of domains such that each variable \(x_{i}\in D_{i}\), and \(C=\langle c_{1},\,c_{2},\,\ldots,\,c_{t}\rangle\) is a t-tuple of constraints. Each constraint \(c_{j}\) is defined as a pair \(\langle R(S_{j}),\,S_{j}\rangle\), where \(S_{j}\) denotes the set of variables involved in the constraint (its scope) and \(R(S_{j})\) is the relation of these variables, specifying a subset of the Cartesian product of their domains.  
A solution [1] to the CSP \(\mathcal{P}\) is an n-tuple \(A=\langle a_{1},a_{2},\,\ldots,\,a_{n}\rangle\), where each \(a_{i}\in D_{i}\) and every constraint \(c_{j}\) is satisfied when \(A\) is projected onto \(S_{j}\). The problem may require finding all solutions (\(sol(\mathcal{P})\)), checking whether at least one solution exists, or determining that the CSP is unsatisfiable (i.e., \(sol(\mathcal{P})=\emptyset\)). The CSP framework, while simple, demonstrates broad applicability across various domains such as artificial intelligence, operations research, scheduling, supply chain management, graph algorithms, computer vision, and computational linguistics.  
Figure 1 shows an example of a constraint satisfaction problem and its solution. There are three variables, \(X_{1}\), \(X_{2}\), and \(X_{3}\), with domains \(D(X_{1})=D(X_{2})=D(X_{3})=\{1,\,2,\,3\}\) and constraints \(c_{12}\equiv(X_{1}<X_{2})\) and \(c_{23}\equiv(X_{2}=X_{3})\). On the left side is the problem definition, and on the right side is the solution to the CSP inspired by an example from the authors of [1].  
An instantiation \(I\) on a subset of variables \(Y=(x_{1},\ldots,x_{k})\subseteq X\) in a CSP \(P=(X,D,C)\) assigns values \((v_{1},\ldots,v_{k})\) to the variables, denoted as \(I=((x_{1},v_{1}),\ldots,(x_{k},v_{k}))\), where each pair \((x_{i},v_{j})\) indicates that \(x_{i}\) takes the value \(v_{i}\). The instantiation is valid if every assigned value \(v_{i}\) belongs to the domain of \(x_{i}\) (i.e., \(I[x_{i}]\in D(x_{i})\) for all \(x_{i}\in Y\)). It is locally consistent if it is both valid and satisfies every constraint \(c\in C\) whose variables \(X(c)\) are fully included in \(Y\). If \(I\) fails to meet these conditions, it is locally inconsistent. Figure 2 (left) provides examples of instantiations.
## 2 Geometric and Operational Parameters for the Numerical Runs  
The process for obtaining the correlations for the pressure loss coefficients per unit length, as will be explained later, employs the results from the 81 detailed numerical runs presented in [17]. Therefore, the geometric and operational parameters in the numerical runs for this work must be the same as those employed in that work. The parameters and their range are presented in Table 1. \"\(D\)\" is the size of the channel, which corresponds to the diameter of the shroud of the fan being analyzed (see Figure 2). \"\(D_{h}/D\)\" is the ratio between the hub of the fan and the shroud diameter (see Figure 2). \"\(S\)\" is the swirl number as defined by [PERSON] [30] and given in Equation (1). In this definition, \(L_{c}\) is a characteristic length assumed to be half of the simulated fan diameter in this work; \"\(r\)\" is the radial coordinate; and \"\(V_{a}\)\" and \"\(V_{t}\)\" are the local axial and tangential velocities, respectively. \"\(a\)\" is the distance between the outlet of the fan (in this work, the outlet of the fan is the domain inlet) and the inlet of the screen (see Figure 2). \"\(\varepsilon\)\" is the porosity of the screen, defined as the ratio between the open area and the total area of the cross section (\(D\times D\)). The next parameter, \"\(\xi\)\", is the ratio between the total lateral perimeter of the pores and the area of the cross-section. \"\(t\)\" is the thickness of the screen (see Figure 2), and \"\(V\)\" is the mean axial velocity in the channel. The 81 numerical cases are shown in Table 2; the \"-\", \"0\" and \"+\" level values used for each parameter are defined in Table 1.  
\[S=\frac{\iint_{A}\rho\cdot V_{a}\cdot V_{t}\cdot r\cdot dA}{I_{c}\cdot\iint_{A} \rho\cdot V_{a}^{2}\cdot dA} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \(D\) & \(D_{\text{B}}/D\) & \(S\) & \(a\) & \(\varepsilon\) & \(\xi\) & \(t\) & \(V\) \\  & **(mm)** & **(-)** & **(-)** & **(mm)** & **(\%)** & **(1/mm)** & **(mm)** & **(m/s)** \\ \hline Minimum value (-) & 40 & 0.25 & 0.4 & 5 & 30 & 0.25 & 0.5 & 1 \\ Middle value (0) & 70 & 0.4 & 0.7 & 15 & 52.5 & 0.31 & 1.5 & 1.75 \\ Maximum value (+) & 100 & 0.55 & 1 & 25 & 75 & 0.37 & 2.5 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operational and geometric parameters included in the study and their range.  
Figure 2: (**a**) Generic scheme of the domain and boundary conditions used for the simulations; (**b**) 3D representation of the flow domain.  
following [PERSON] et al. [17] by placing a circular wall whose diameter is \"\(D_{h}\)\" at the inlet cross-section. Consequently, the inlet velocity conditions are imposed on the area around it. In order to make it possible to compare the results of the detailed models from [PERSON] et al. [17] with the compact models from the present work, the channel length after the screen must be the same as that used in the first study. To that end, a value of 40 mm was fixed.
# Towards Explainable Augmented Intelligence (AI) for Crack Characterization
## 3 The AutoNDE Code for Semi-Automatic Crack Characterization
### Image Processing  
The image processing module is used to select those MTFM images, which lend themselves to easy interpretation. The basis for selection is _a priori_ knowledge that the crack to be characterized is large and plane. Therefore the crack image is expected to contain a straight segment, which is a specular reflection from the crack, or else two diffraction spots surrounding the crack tips. Sometimes only one crack tip can be picked up. AutoNDE differentiates the possible diffraction spots from the possible specular features by size, allowing for some overlap.  
The _ImageProcessing_ module of AutoNDE uses a variety of intensity thresholds. As mentioned above, the maximum intensity is 256. Thresholding is a standard tool in image processing, which is used to filter out noise. During the AutoNDE training, in most cases 125 has been found to produce the best results. However, some significant weak features could only be picked up at lower thresholds, while some noise could be filtered out only at thresholds that are higher. For this reason, AutoNDE normally utilizes three thresholds, 65, 125 and 185.  
AutoNDE analyse the resulting images using OpenCV (Open Source Computer Vision Library) functions [21]. Two submodules are involved, _FindSpecAndDiffFeatures_ and _BlobDetector_.  
1. _FindSpecAndDiffFeatures_ relies on the OpenCV _FindContour_ function to select two types of features, large (longer than 7 mm) and small (between 1 mm and 7 mm long).  
Figure 5: A schematic of MTFM.  
If one of the features is 7 mm or slightly smaller and there are several other small features smaller than 3 mm in extent the small features are neglected and the larger one is treated as a specular reflection.
2. The _BlobDetector_ relies on the OpenCV _DetectBlob_ function to filter blobs by size between 80 and 160 pixels. The _BlobDetector_ is particularly useful when dealing with surface-breaking cracks, because in these situations the probe often picks up only one crack tip. When only one blob is picked up the final crack characterization can be made only by a human inspector. In cases like this the AutoNDE flags the situation by putting the question mark after every defect characteristic and estimate of the report quality (the definition of quality is given below). All the feature and blob parameters mentioned above have been chosen by trial and error to maximize the number of true positives and minimize the number of false positives selected by the code.
# Approximate Networking for Universal Internet Access
## 2 Approximate Networking Technologies
### Approximate Networking Hardware

Table 1: Cont. Approximate computing can be done through circuit-level techniques such as _voltage over-scaling_ using which the voltage is deliberately reduced for improving power efficiency of circuits. Such a technique can be used in approximate networking to build hybrid memories with power efficient unreliable memory arrays that can drastically improve power efficiency through aggressive voltage over-scaling. Approximate networking can also use _functional approximation_ at various levels (particularly, at the architectural, circuit-level, and at the transistor level). This can include the use of neural networks for learning a simplified approximation of code [28]. We note here that approximate computing technology is not limited to general-purpose central processing unit (CPU) architecture only. Companies are already resorting to approximate computing to obtain energy and cost optimized application-specific integrated circuits (ASICs), e.g., Google has made a custom ASIC named the Tensor Processing Unit to run machine learning-based tasks at scale in their data centers, while requiring fewer transistor per operation. Similarly, IBM exploited the error resilience of Deep Neural Networks to loss of numerical precision for better area and power efficiency systems [12]. We believe it is only a matter of time before these ideas would find way to network ASICs and that more research is needed on how these technologies will interplay with the network-layer stack.
# Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine
## 3 The Proposed Adaptive Weight Online Sequential Extreme Learning Machine  
In lots of real online applications, with measurement noise and unexpected external disturbance, the sequential data chunks often have varying confidence coefficients. It can be easily found that OS-ELM cannot deal with the varying confidence coefficients very well. If a data chunk with a low confidence coefficient is employed to train the network in the normal way, the accuracy of the trained network is likely to be reduced. In this section, we propose the novel AWOS-ELM, where the confidence coefficient of each data chunk is assessed before being used to train the network, and the weight of each data chunk is obtained accordingly.
###### Abstract  
Free space optics (FSOs) is an emerging technology offering solutions for secure and high data rate transmission in dense urban areas, back haul link in telecommunication networks, and last mile access applications. It is important to investigate the performance of the FSO link as a result of aggregate attenuation caused by different weather conditions in a region. In the present work, empirical models have been derived in terms of visibility, considering fog, haze, and cloud conditions of diverse geographical regions of Delhi, Washington, London, and Cape Town. Mean square error (MSE) and goodness of fit (R squared) have been employed as measures for estimating model performance. The dual polarization-quadrature phase shift keying (DP-QPSK) modulation technique has been employed with hybrid mode and the wave division multiplexing (MDM-WDM) scheme for analyzing the performance of the FSO link with two Laguerre Gaussian modes (LG00 and LG 01) at 5 different wavelengths from 1550 nm to 1554 nm. The performance of the system has been analyzed in terms of received power and signal to noise ratio with respect to the transmission range of the link. Minimum received power and SNR values of \(-\)52 dBm and \(-\)33 dB have been obtained over the observed transmission range as a result of multiple impairments. Random forest (RF), k-nearest neighbors (KNN), multi-layer perceptron (MLP), gradient boosting (GB), and machine learning (ML) techniques have also been employed for estimating the SNR of the received signal. The maximum R squared (0.99) and minimum MSE (0.11), MAE (0.25), and RMSE (0.33) values have been reported in the case of the GB model, compared to other ML techniques, resulting in the best fit model.  
attuation model; clouds; DP-QPSK; free space optics (FSOs); fog; haze; LG modes; MDM-WDM; wireless communication 2025
# Setonix: Blockchain-Based Hierarchy Domain Name System for Web3
## 1 Introduction
### Basic Concepts and Services Relevant to This Paper
#### 1.1.3 Ethereum Name Service (ENS)  
As the name suggests, ENS is a DNS operating on the Ethereum platform that simplifies complex Ethereum addresses like 0x123... into a more readable format [21]. Unlike Handshake DNS, ENS is not designed to support a universal address system, so its scope is limited to the Ethereum network. Its actual implementation consists of three main smart contract components: registry, registrar, and resolver.  
The registry is the smart contract at the core of ENS resolution, as all ENS resolution requests begin with the registry. It maintains and manages the list of domains, records information such as the resolver, TTL, and owner for each domain, and allows domain owners to update these data upon request. Next, the registrar is divided into the BaseRegistrar and the ETHRegistrarController to reduce the attack surface. The BaseRegistrar is responsible for managing name ownership and transfers, while the Controller handles registration and renewal.  
Lastly, the resolver is a component that every name registered in ENS has, and it is responsible for retrieving the information linked to the name, such as actual addresses or avatars. Although users can implement a custom resolver themselves, they typically use a universal resolver known as the public resolver. For a standard forward resolution, a query is first sent to the ENS registry to find the resolver used by the ENS name, and the matching resolver's address is obtained. Once the matching resolver is obtained, another query is sent to the resolver to request the relevant data. For example, when requesting an Ethereum address, the addr() function is used. However, due to its complex structure, design, and multiple stages of resolution, it shows significantly lower performance compared to conventional DNS.
## 7 The Concept of the 'Effector Strain'  
Generally, the 'effector strain' is a microorganism with zero pathogenic potential that can persistently colonize infection-susceptible host tissues and prevent tissues conquest by pathogens. This mechanism has been described as'replacement therapy'. This method could be used for prevention, as much as for the cure of disease, and potentially could lead to 'herd protection' through effector strain's transmission from one individual to another [101]. In the case of dental caries, the use of certain _S. mutans_ strains as 'effector strains' has been proposed by [PERSON] [102]. Specific _S. mutans_ strains with low acidogenic potential due to a lactate dehydrogenase (LDH) deficiency and the ability to produce particular bacterios could be mobilized to serve as 'effector strains' [102; 103; 104]. LDH is an enzyme that plays a pivotal role in pyruvate to lactic acid conversion during the catabolism process of glucose by cariogenic bacteria [105]. On the other hand, bacterios possess antimicrobial properties against strains or species in close relativity with the producer one [106]. A series of _S. mutans_ strains, JH1000, JH1001, JH1005, JH1010 have been found to produce a specific bacteriocin ('mutacin 1140' or 'MU1140'), a lantibiotic, in particular, with close relativity to nisin's structure [104; 107], as the latter has been determined by [PERSON] [108]. Mutacin 1140 is highly bactericidal against a wide range of microorganisms, primarily Gram-positive (e.g., _Streptococcus sanguis_, _Streptococcus sanguis_, _Streptococcus sanguis_, _Streptococcus sanguis_, _Streptococcus sanguis_, _Lactobacillus salivarius_, _vacacillin_--and vancomycin-resistant _Staphylococcus aureus_ and _Actinomyces_ species), and Gram-negative bacteria as well [104; 109; 110]. This is attributed to its ability to powerfully connect with lipid II [111; 112], which is a crucial element in bacterial wall synthesis [113] and is targeted by a variety of antibiotics [114]. No adaptive resistance to MU1140 has been presented [109]. JH1000 and its close relatives JH1001, JH1005, and JH1010 were also tested for their ability to colonize rats' oral cavity [104]. JH1001 and JH1005 strains were significantly more competent to displace indigenous _S. mutans_ strains and, conversely, to a lesser extent, displaced by extrinsic bacteria (e.g., _S. mutans_ Ingbrittl) than JH1010 strains. The strong correlation between the bacteriocin production and the producer strain's ability to preemptively infect the rodents' oral cavity indicated that JH1001 and its successor, JH1005, could be used as human probiotic strains in the future. The finding reinforced this thesis that JH1001 strain was indeed superinfecting and could displace indigenous _S. mutans_ to a great extent in humans, but a minimal infection dose (MID) was not determined [115]. Two years later,
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 2 Data
### AOD Data  
Gaofen satellites, developed by China, are a series of high-resolution remote sensing satellites designed to provide high-quality data for a range of applications including environmental monitoring, resource management, urban planning, and disaster surveillance. In this study, Gaofen-1 AOD data were obtained from the China Remote Sensing Satellite Ground Station ([[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com))), while the multi-angle implementation of atmospheric correction (MAIAC) AOD product with a spatial resolution of 1 km \(\times\) 1 km was obtained from the MODIS Terra satellite ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))). Before use, raw AOD data underwent preprocessing steps, including reprojection, mosaicking, clipping, and rescaling. To improve retrieval accuracy, the dark pixel method [32] was applied to correct for atmospheric interference. Interpolation was performed to achieve a more continuous AOD dataset. Two AOD datasets, aligned to the same spatial resolution, were subsequently merged to generate a comprehensive daily AOD time series. Finally, meteorological data, latitude, longitude, and time information were integrated with the satellite data to form a complete dataset for subsequent analysis.
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 2 Mixed Signal Dereverberation
### Recorded Signal Model  
In the time domain, the signals recorded by the microphone can be modeled by the source signal convolved with the room impulse response (RIR). However, for speech signals, we often work within the TF domain rather than in the time domain [25]. Short-Time Fourier Transform (STFT) is utilized to obtain the time-frequency representation of the recorded signal. Here, \(s[k,n]\), \(x[k,n]\) and \(m[k,n]\) are the T-F domain coefficients of clean signals \(s(t)\), reverberant signals \(x(t)\), and the room impulse response (RIR) \(h(t)\). So, \(x[k,n]\) can be written as [21]:  
\[x[k,n]=\sum_{h=0}^{H^{\prime}}s[k,n-h]m[k,h] \tag{1}\]  
where \(H^{\prime}\) is the reverberation order which reflects the reverberation level. The expected value of the \(\left|x[k,n]\right|^{2}\) is denoted by \(X_{k,n}\in\mathbb{R}_{0,+}\), \(\mathbf{X}=\left(X_{k,n}\right)_{K\times T}\), which can be expressed as follows:  
\[X_{k,n}=\sum_{h=0}^{H}S_{k,n-h}M_{k,h} \tag{2}\]  
where \(S_{k,n}=\left|s[k,n]\right|^{2}\) and \(M_{k,h}=\left|m[k,n]\right|^{2}\) are the square of the magnitude of \(s[k,n]\) and \(m[k,n]\), respectively; \(H=\min\{H^{\prime}-1,n-1\}\); \(n=1,\ldots,T\) and \(k=1,\ldots,K\) are the time index and the frequency index, respectively.  
Subsequently, we aim to build a spectrogram model of the sound source signal using the NMF method to recover the sound source signal from the recorded signal. We assume that the non-negative spectrogram of sound source signal \(\mathbf{S}=\left(S_{k,n}\right)_{K\times T}S_{k,n}\in\mathbb{R}_{0,+}\) can be represented by two non-negative matrices \(\mathbf{W}=\left(W_{k,l}\right)_{K\times L}\), \(W_{k,h}\in\mathbb{R}_{0,+}\) and \(\mathbf{V}=\left(V_{l,n}\right)_{L\times T}\), \(V_{l,n}\in\mathbb{R}_{0,+}\), where \(L<min\{K,T\}\), \(min\{\cdot\}\) is the minimum value. Then, \(\mathbf{S}\) can be written as follows by an NMF model:  
\[\mathbf{S}\cong\mathbf{W}\mathbf{V} \tag{3}\]  
where \"\(\cong\)\" represents the approximate equation. \(\mathbf{W}\) and \(\mathbf{V}\) are the dictionary matrix and activation matrix, respectively. Next, we use \(\mathbf{W}\mathbf{V}\) to replace \(\mathbf{S}\) for convenience.  
Thereafter, Formula (2) can be rewritten as the following formula, which is the model of the recorded signal:  
\[X_{k,n}=\sum_{h=0}^{H}\sum_{l=1}^{L}W_{k,l}V_{l,n-h}M_{k,h} \tag{4}\]
###### Abstract  
The influence of tillage and nitrogen fertilization methods on soil quality attributes and crop agronomic characteristics has been studied broadly under different agroclimatic conditions. Nevertheless, the interactive effect of tillage and fertilization approaches on soil properties on different soil depths and yield is rarely addressed, particularly on the Loess Plateau belt, and requires more exploration. Thus, this research was conducted in order to evaluate the interactive impact of tillage and nitrogen fertilization methods on soil properties and wheat productivity. The treatments included conventional tillage (CT) and no-till (NT) with different fertilization approaches (no fertilization: CK, chemical nitrogen fertilizer: N, organic fertilizer: M, combined application of nitrogen fertilizer and organic fertilizer: NM) and were explored in a split plot arrangement under a randomized complete block design replicated thrice on soil properties (SWC, SOC, TN, TP, NO\({}_{3}\)\({}^{-}\)N, NH\({}_{4}\)\({}^{+}\)N, and stoichiometric ratio) and wheat yield. The results showed that sole no-tillage and NT in association with nitrogen fertilization (inorganic and organic) significantly increased the soil water content, SOC, TN, NH\({}_{4}\)\({}^{+}\)N, C/P, and N/P ratios and wheat productivity but did not significantly yield TP, whilst it reduced the NO\({}_{3}\)\({}^{-}\)N and C/N ratio compared with sole CT and CT together with nitrogen fertilization (organic and inorganic). Overall, NT in association with the joint application of inorganic and organic N fertilization are the best techniques to improve soil water status and nutrient status under the wheat mono-cropping system conditions and yield.  
climate smart agriculture; conservation tillage; fertilization methods; nutrients accumulation; semi-arid Loess Plateau; wheat yield +
Footnote †: journal: Applied sciences