esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
## 3 Improved CBBA
### Main Procedure of ICBBA
A consensus flag \(conf_{i}\) is also introduced; \(conf_{i}=1\) if the consensus is reached between \(sat_{i}\) and its neighbors currently, \(conf_{i}=0\) otherwise. \(h_{ij}\) is used as an indicator function that is \(1\) if \(DMG_{ij}>y_{ij}\) is true and \(0\) otherwise, which is expressed as: \[h_{ij}=\mathbb{I}\big{(}DMG_{ij}>y_{ij}\big{)}.\] (10)  
From the perspective of each decentralized node, each satellite starts to run the ICBBA algorithm after receiving the task set. The main procedure of ICBBA includes the following steps:(1) Bundle Construction: construct its own bundle and update planning information \(\mathbf{b}_{i}\), \(\mathbf{y}_{i}\), \(\mathbf{z}_{i}\); (2) Planning Information Sending: send its own planning information \(\mathbf{y}_{i}\), \(\mathbf{z}_{i}\), \(\mathbf{s}_{i}\) to neighbors; (3) Planning Information Receiving: receive the planning information \(\mathbf{y}_{i^{\prime}}\), \(\mathbf{z}_{i^{\prime}}\), \(\mathbf{s}_{i^{\prime}}\) from its neighbor \(sat_{i^{\prime}}\); (4) Consensus Check: update the time stamp \(\mathbf{s}_{i}\) and check whether the consensus has been reached, no operation if so; otherwise, change the corresponding part of \(\mathbf{b}_{i}\), \(\mathbf{y}_{i}\), \(\mathbf{z}_{i}\) and jump to Step (1). Step (1) is triggered by non-consensus. Step (2) is triggered by Step (1). Step (3) is triggered by Step (2) of neighbor \(sat_{i^{\prime}}\). Step (4) is triggered by Step (3). Because each satellite does not know whether the global consensus has been reached, the ICBBA on each satellite cannot be terminated spontaneously until the earliest task execution start time or the running time threshold set in advance is reached. After the algorithm is terminated, each satellite performs the observation according to the last updated decentralized planning scheme. Therefore, sufficient running time must be reserved to ensure that the algorithm has completed global convergence; otherwise, the consensus of the planning scheme cannot be guaranteed. The pseudo-code of ICBBA from the perspective of each decentralized node is described in Algorithm 1.
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 3 Application of 2D Nanofluids
### PV/T Systems
Table 8 presents some recent experimental and numerical works on the application of 2D nanofluids in PV/T systems in the field of technology. Further experimental works on the use of PV/T systems with phase change materials dispersed in water or other base fluids flowing in different piping arrangements inside the hybrid phase change materials are required. The combined usage of 2D nanofluids and phase change materials has proved that systems with this solution presented higher efficiencies than those operating with only phase change materials. Innovative configurations of PV/T systems should be developed and implemented, such as PV/T systems with loop pipes and 2D nanofluids. Moreover, geometrical and structural factors, such as the radius, thickness, and roughness of the involved channels and tubes should be optimized in the near future. In addition, there is a literature gap linked with the dynamic performance of PV/T systems using 2D nanofluids and its main results and influencing factors, including the reflective coating and TC of the base fluids. Furthermore, there is a lack of studies on two-dimensional nanomaterials dispersed in bio-friendly base fluids to work in PV/T systems. Finally, the viability of PV/T systems operating with 2D nanofluids should be further addressed considering technological, economic, and environmental features. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Authors/Year** & **Nanomaterial/Concentration** & **Base Fluid** & **Main Findings** & **Remarks** \\ \hline  & & & & Decreases of 23.9” C, 16.1” C, and 11.9” C in the FV temperature for nanofluid-based PYT/PCM system, water-based PYT/PCM system and PV/PCM system, respectively. & Increases of 23.9\%, 22.7\%, and 9.1\% in the electrical efficiency, respectively, in the hybrid PYT/PCM system in comparison to that of the water-based hybrid PYT/PCM system, and overall efficiency increase of 12\%. & Increased of 23.9\%, 2
# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## Theory and Methods  
### Bistatic Radar Equation  
The spaceborne GNSS-R remote sensing sea surface wind speed based on the onboard DDMI, which is capable of cross-correlation reflected signals with the local replica code in the receiver and mapping the scattering power over a range of time delay and Doppler frequency bins, is known as DDM. To generate the DDM, the coherent integration time commonly takes 1 ms during signal processing in the receiver to avoid the influence of strong speckle noise in short-time correlation, and 1 s non-coherent integration is performed to obtain higher SNR DDM. Both the current TDS-1 and CYGNSS projects belong to this traditional cGNSS-R. The bistatic radar equation (BRE) theoretically explains the physical meaning of DDM [3].  
\[\left\langle\left|Y_{s}\left(\hat{\tau},f\right)\right|^{2}\right\rangle=\frac{ P_{T}G_{T}\lambda^{2}T_{I}^{2}}{\left(4\pi\right)^{3}}\iint\limits_{A}\frac{G_{ R}\sigma^{0}}{R_{T}^{2}R_{R}^{2}}\Lambda^{2}(\hat{\tau}-\tau)sinc^{2}\left(f-f \right)dA \tag{1}\]  
where \(\mathrm{P}_{T}\) is the transmit power of GNSS satellite; \(\mathrm{G}_{T}\) is the transmit antenna gain; \(\mathrm{G}_{R}\) is the antenna gain of the receiver; \(\lambda\) is the wavelength of the signal carrier; \(\mathrm{T}_{I}\) is the relevant integration time; \(\mathrm{R}_{T}\), \(\mathrm{R}_{R}\) represent the distance from the transmitter to the sea surface and surface to the receiver, respectively; \(\Lambda(\hat{\tau}-\tau)\) is the GNSS signal spreading function in delay; and \(\hat{\tau}\) and \(\tau\) are the replica signal and incoming signal time delays, respectively. \(sinc^{2}\left(\hat{f}-f\right)\) is the frequency response of the GNSS signal; and \(\hat{f}\) and \(f\) are the replica signal and incoming signal frequencies, respectively. A is the effective scattering area of DDM and \(dA\) is a differential area within \(A\). \(\sigma^{0}\) is the normalized bistatic radar scattering cross-section, which can be expressed as:  
\[\sigma^{0}=\frac{\pi\left|\mathbf{\pi}\right|^{2}\overset{\rightarrow 4}{q}}{q_{z}^{4}}P\left(-\frac{\overset{ \rightarrow}{q}_{\perp}}{q_{z}}\right) \tag{2}\]  
where \(\mathbf{\Re}\) is the Fresnel reflection coefficient associated with the signal polarization characteristics; \(\overset{\rightarrow}{q}\) is the scattering unit vector; \(\overset{\rightarrow}{q}_{\perp}\) is the horizontal component of the scattering unit vector; \(q_{z}\) is the vertical component (surface normal direction); \(P\) is the probability density function of the rough sea surface slope. The scattering coefficient is related to geophysical parameters in the bistatic radar equation. The scattering intensity from the ocean surface is mainly affected by the sea surface roughness. For the wind speed retrieval, the sea surface roughness is affected due to the influence of local winds, and the change in sea surface roughness will be reflected in the variation of the scattering power.
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 1 Introduction
The results show that our CNN-LSTM model gives the best performance based on several evaluation measures. The main contributions of this paper are as follows:  
* Collection of an Arabic SMS dataset labeled as spam or not-spam, which can be helpful in performing studies on Arabic SMS spam. * Proposal of a model for detecting SMS spam in a mixed mobile environment that supports Arabic and English, based on a hybrid deep learning architecture combining the CNN and LSTM algorithms. * Comparison of the proposed model with other machine learning algorithms. * The proposed model outperforms traditional machine learning algorithms by achieving a remarkable accuracy of 98.37%. The remaining parts of the paper are organized, as follows: Section 2 reviews the related works. Section 3 describes the model design of our approach. Section 4 presents the experimental results. Finally, we conclude the paper in Section 5.
## 4 The Proposed Two-Step Access and Handover Strategy
### Importance-TOPSIS
#### 4.2.1 The Step of Original TOPSIS Scheme  
The steps for TOPSIS are as follows [22; 29].  
Step 1. Constructing decision matrix. The decision matrix is expressed as  
\[\mathbf{D}=\begin{array}{cccc}&C_{1}&C_{2}&\cdots&C_{m}\\ &Sat_{1}&\begin{bmatrix}d_{11}&d_{12}&\cdots&d_{1m}\\ d_{21}&d_{22}&\cdots&d_{2m}\\ \vdots&\vdots&\vdots&\vdots\\ d_{n1}&d_{n2}&\cdots&d_{nm}\end{bmatrix}\\ \end{array} \tag{12}\]  
where \(Sat_{1},Sat_{2},\cdots,Sat_{n}\) are the possible alternatives and \(C_{1},C_{2},\cdots,C_{m}\) are the criteria. Each element \(d_{ij}\) of the decision matrix \(\mathbf{D}\) is the value of the alternative \(Sat_{i}\) with respect to the criterion \(C_{j}\).  
Step 2. Obtain the normalized decision matrix. The normalized value of \(r_{ij}\) is computed as  
\[r_{ij}=d_{ij}/\left(\sum\limits_{i=1}^{n}d_{ij}^{2}\right)^{0.5};i=1,2,\cdots, n;j=1,2,\cdots,m \tag{13}\]  
Step 3. Obtain the weighted normalized decision matrix \(v_{ij}\). The weight vector satisfies the following formula:  
\[\omega=(\omega_{1},\omega_{2},\cdots,\omega_{m}),\sum\limits_{j=1}^{m}\omega_{ j}=1 \tag{14}\]After the weights are obtained, each column of the standardized decision matrix is multiplied by its associated weight.  
\[v_{ij}=\omega_{j}r_{ij};i=1,2,\cdots,n;j=1,2,\cdots,m \tag{15}\]  
Step 4. Determine positive and negative ideal solutions.  
\[Z^{+}=\big{(}v_{1}^{+},v_{2}^{+},\cdots,v_{m}^{+}\big{)},\text{ where }v_{j}^{+}=\left\{\begin{array}{l}\max\big{(}v_{ij}\mid i=1,2,\cdots,m\big{)},j \in I_{b}\\ \min\big{(}v_{ij}\mid i=1,2,\cdots,m\big{)},j\in I_{c}\end{array}\right. \tag{16}\]  
\[Z^{-}=\big{(}v_{1}^{-},v_{2}^{-},\cdots,v_{m}^{-}\big{)},\text{ where }v_{j}^{-}=\left\{\begin{array}{l}\min\big{(}v_{ij}\mid i=1,2,\cdots,m\big{)},j \in I_{b}\\ \max\big{(}v_{ij}\mid i=1,2,\cdots,m\big{)},j\in I_{c}\end{array}\right. \tag{17}\]  
where \(I_{b}\) denotes the set with the benefit criteria, and \(I_{c}\) denotes the set with the cost criteria.  
Step 5. Calculate the distance of each alternative from the positive (negative) ideal solution.  
The distance between the solution and the positive ideal solution is  
\[S_{i}^{+}=\sqrt{\sum_{j=1}^{m}\Big{(}v_{ij}-v_{j}^{+}\Big{)}^{2}};i=1,2, \cdots,n \tag{18}\]  
Similarly, the distance between the solution and the negative ideal solution is  
\[S_{i}^{-}=\sqrt{\sum_{j=1}^{m}\Big{(}v_{ij}-v_{j}^{-}\Big{)}^{2}};i=1,2,\cdots,n \tag{19}\]  
Step 6. Calculate the relative proximity of each alternative solution to the ideal solution.  
\[T_{i}=\frac{S_{i}^{-}}{S_{i}^{+}+S_{i}^{-}};i=1,2,\cdots,n \tag{20}\]  
the optimal satellite is the satellite with the highest \(T_{i}\).
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Technical Level in the CTE Model  
The Technical Level consists of three segments, which are as follows:  
* Coverage and accessibility to users (TL);
* Technological development (TL);
* IT Development (TL).  
According to the characteristics of these segments and what they provide in their outputs to assess potential of the telecom, these segments provide two areas.
## 2 Problem Description  
We assume that the measured projection \(y\in R_{+}^{I}\) in computed tomography is acquired according to the linear model  
\[y=Ax+\delta \tag{1}\]  
where \(A\in R_{+}^{I\times J}\), \(x\in R_{+}^{J}\), and \(\delta\in R^{I}\) are a projection operator, target image, and noise, respectively, with \(R_{+}\) denoting the set of nonnegative real numbers. The purpose of iterative reconstruction is to obtain a high-quality image similar to the target image by solving an initial value problem consisting of a difference equation or an equivalent iterative algorithm. Recently, a novel iterative image reconstruction algorithm [15], which is based on minimization of the EPDM as an objective function of the measured and forward projections, has been developed:  
\[z_{j}(k+1)=z_{j}(k)f_{j}(z(k);y,\lambda) \tag{2}\]  
with  
\[f_{j}(z;y,\lambda):=\frac{\sum\limits_{i=1}^{I}A_{ij}\bigg{(} \frac{y_{i}}{\big{(}A_{i}z\big{)}^{\alpha}}\bigg{)}^{\gamma}}{\sum\limits_{i=1 }^{I}A_{ij}\bigg{(}\frac{A_{i}z}{\big{(}A_{i}z\big{)}^{\alpha}}\bigg{)}^{\gamma}} \tag{3}\]  
for \(j=1,2,\ldots,J\) and \(k=0,1,2,\ldots,K-1\) with \(z(0)=x^{0}\in R_{+}^{I}\), where \(A_{i}\) and \(A_{ij}\) indicate the \(i\)th row vector and the element in the \(i\)th row and \(j\)th column of the matrix \(A\), respectively, and \(\gamma>0\) and \(\alpha\geq 0\) are parameters that are collectively referred to as \(\lambda:=(\gamma,\alpha)\). The PDEM algorithm described in Equation (2) can be rewritten as  
\[z(k+1)=z(k)\odot f(z(k);y,\lambda), \tag{4}\]  
with \(f:=(f_{1},f_{2},\ldots,f_{J})^{\top}\), where the symbol \(\odot\) denotes the Hadamard product and the superscript \(\top\) stands for the transpose.  
The purpose of this paper is to construct a system that estimates the most appropriate values of \(\lambda\) in the PDEM algorithm from the projection \(y\) by using a deep-learning approach.
## 2 Methodology
### Combination  
Multiple candidate regions can be obtained by the above methods in spatial and frequency domains. However, these candidate regions may intersect or overlap with each other. So confirmation is needed to get the final bounding boxes, i.e., \(B_{3}\). First, Intersection-over-Unions (IoUs) of the bounding boxes from spatial and frequency domains are calculated by Equation (10):  
\[IoU=\frac{Area(A\cap B)}{Area(A\cup B)}, \tag{10}\]  
where \(A\) and \(B\) are two bounding boxes from texture detection and saliency detection, respectively, and they constitute a pair of combinations. Note that in a certain video frame, there are \(M\times N\) similar combinations. \(M\) and \(N\) are the number of bounding boxes from texture detection and saliency detection, respectively. The final result from the candidates is obtained by an Intersection-over-Unions Based (IoU-based) strategy, as shown in Equation (11):  
\[C=\begin{cases}\text{A},&\tau_{1}<IoU\leq\tau_{2}\cap R_{1}>R_{2}\\ \text{B},&\tau_{1}<IoU\leq\tau_{2}\cap R_{1}\leq R_{2}\\ A\cap B,&IoU>\tau_{2}\\ \varnothing,&others\end{cases} \tag{11}\]  
where \(C\) is the result of combining texture detection and saliency detection; \(\tau_{1}\) and \(\tau_{2}\) are the thresholds; \(R_{1}\) and \(R_{2}\) are replacement rates. Experiments results show that the best thresholds are 0.2 and 0.8, respectively. The replacement rate is similar to the confidence of a bounding box, which is calculated through Equation 12:  
\[\left\{\begin{array}{l}R_{1}=\frac{Area(A,B)}{A}\\ R_{2}=\frac{Area(A,B)}{B}\end{array}\right. \tag{12}\]  
where \(R_{1}\) and \(R_{2}\) are the replacement rates of the results of texture detection and saliency detection, respectively. The combination strategy not only strictly judges the existence of floating objects, but also makes the final detection result close to the ground truth.  
Figure 3: Saliency detection in two different water scenes. They are **(a)** the turbulent surface with a low contrast object, **(b)** the detection result of **(a)**, **(c)** the clam surface with multiple floating objects of different sizes, **(d)** the detection result of **(c)**.
## 6 Conclusions  
It is virtually impossible to account for all of the potential situations in which we encounter casual or expected EMF exposure in our daily life. Moreover, although regulated by physical and mathematic laws, it is quite tricky to describe the variable interactions between RF and biological systems.  
One can envisage that EMF \"speaks\" to each organism and each cell with a different language. The answer to that call can potentially induce protein modification, ion exchanges and nucleic acid conformational changes that might cause positive, adaptive or destructive effects and the modulation of EMF can determine the benefit or the severity of the outcomes. Numerous works indicated that tuning the use of pulsating low and extremely high frequency (EMF) trains of stimulation might be used in cancer treatment [1] as well as in regenerative medicine [119, 120] where cells can be induced to differentiate with minimal manipulation and without pharmacological treatment or gene modification [121]. One of the most exciting findings is that using the ion cyclotron resonance of different elements, _i.e._, calcium (7 Hz 9.2 micro Tesla), it is possible to induce neuronal differentiation, reducing the carcinogenic phenotype [122]. We foresee the possibility that this tuning can be achieved also with RF in the range of 900 MHz, as some positive reposts on memory enhancement would suggest.  
It is therefore important that at least in research, the standardization of protocols, reproducibility of results and unbiased interpretation take place. We attempted an overview of the most recent advances in understanding the nature and the possible functional impact of the effects induced by RF exposure on living beings. We discussed the molecular path finding of the _in vitro_ investigation as well as the functional complexity of _in vivo_ experimental designs. The overwhelming amount of the sometimes contradictory incoming data about potential damage in humans has been briefly considered here as regulatory agency guidelines continuously monitor and regulate the issue.
# Towards Sentiment Analysis for Romanian Twitter Content
###### Abstract  
With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around \(15,000\) tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of \(\approx\)78% using standard machine learning and \(\approx\)81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data.  
Keywords:natural language processing; sentiment analysis; underrepresented language; machine learning; deep learning; Twitter +
Footnote †: journal: _Algorithms_
# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 2 Preliminary Remarks and Basic Systematisation
### Services of General Interest  
Services of general interest are often based on a broad understanding of provision, which includes the availability of immaterial (services) as well as physical goods (infrastructures) for the general public [32]. In addition, the concept of services of general interest is determined dynamically depending on context and time (cf. [33] (pp. 35-40); cf. [34] (p. 24), which makes a sharp definition difficult. The derivation of definitional aspects is often very complex and, hence, is not deepened here (deeper insights: [32; 34]). Generally, services of general interest are often described by examples: healthcare facilities (hospitals, medical practices, pharmacies), education (day-care centres, schools), and local amenities (food for daily needs) should be provided nationwide within reasonable distance at socially acceptable prices and minimum acceptable quality [32]. In this study, we restrict the analysis of services of general interest to the examples mentioned above. Such structural infrastructures have an impact on the supply situation of the population in a given area. We do not consider the more technical infrastructures such as the fresh water supply, wastewater disposal, waste disposal, or internet services.  
How the quality of supply can be defined across all the services and infrastructures is highly variable and therefore very difficult to capture. With the help of our integrated MC-SDSS, we provide a proposal for planners to keep the variability and still provide a powerful tool to measure the quality of supply.
# Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
## 1 Introduction
[15] optimized the VOF model and simulated the interface stability and interface waves during the impact jet of two liquid filaments. [PERSON] et al. [16] developed a Eulerian-Lagrangian computational fluid dynamics (CFD) model to describe two types of fluid atomization in a tapered fluidized bed coater using the air-blast/air-assisted atomizer model. [PERSON] [17] developed a number of extra length criteria for adaptive meshes and implemented the moving mesh interface tracking method to solve these multiple-length-scale problems with high fidelity. [PERSON] et al. [18] used a single fluid model to describe the two-phase flow motion and used the VOF method to capture the interface to simulate the growth of capillary instability and jet rupture. Regarding research on gas atomization, [PERSON] [19] developed a computational fluid dynamics (CFD) approach to examine complex fluids during atomization from different nozzle designs, using the volume of fluid (VOF) method and the Reynolds Stress Model (RSM). [PERSON] [20] modeled the GA process by combining the volume of fluid (VOF) model with a dynamic adaptive mesh and the discrete phase model (DPM) to simulate the formation of the powders and the evolution of defects. [PERSON], [PERSON] [21] studied the influence of the spray chamber flow on particle morphology. Regarding research on centrifugal atomization, [PERSON], [PERSON] et al. [22] used an incompressible smoothed particle hydrodynamics (SPH) method to numerically simulate the liquid flow scattering from rotary atomizers. [PERSON] et al. [23; 24] used the VOF model to study the influences of different casting speeds and casting heights on the liquid film's thickness and the flow velocity of molten slag in a rotating disk. Recently, with the continuous optimization of software functions, the VOF model and DPM model have been coupled for numerical simulation [25; 26]. However, research on the flow characteristics of the molten metal fluid film in the centrifugal atomization process by numerical simulation methods is still lacking. Therefore, in this work, the STAR-CCM+ 2022.1.0 software is used to simulate the atomization process of aluminum alloy powder via a rotating disk, the flow characteristics of the liquid film are studied intuitively and systematically, and the influence of the disk shape on the spreading and breakage of the liquid film into droplets by the rotating disk is obtained, which provides a theoretical basis for the development of efficient atomizers.