esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Collection, Experimental Setup and NN Model Selection
### Dataset
#### 3.1.2 Neural Network Comparative Analysis  
The proliferation of Deep Learning and the internet of things on low memory devices, increasing sensing and computing applications and capabilities promise to transform the performance of such devices on complex sensing tasks. The key impediment to the wider adoption and deployment of neural network-based sensing application is their high computation cost. Therefore, there is the need to have a more compact parameterization of the neural network models. To this end, we evaluate the performance of the MLP, IDNN, vanilla RNN (vRNN), GRU and LSTM on the roundabout scenario across different parameterization for model efficiency. From our study, we observe that the IDNN, vRNN and GRU achieves a max CRSE orientation rate of 0.34, followed by the LSTM recording a max \(CRSE\) of 0.35 rad/s, whilst the MLP provided the worst performance of them all with a max \(CRSE\) of 0.97 rad/s. In an almost similar fashion, the IDNN, vRNN, LSTM and GRU obtain a max CRSE displacement of 17.96 m, whilst the MLP obtains a max \(CRSE\) of 156.23 m. However, as the IDNN is characterised by a significantly lower number of parameters compared to the GRU, LSTM and vRNN whilst providing similar CRSE scores across all NN studied and weight connections explored, we adopt it for use in learning the sensor noise in the accelerometer and gyroscope in this study. Table 4 shows the number of parameters characterizing each NN across the various weights investigated; 8, 16, 32, 64, 96, 128, 192, 256 and 320. Furthermore, we observe that the number of weighted parameters has little influence on the performance of the displacement and orientation rate estimation model. However, we notice that the number of time steps in the recurrent NN models (recurrent in both layer architecture and input structure such as the IDNN) significantly influences the accuracy of the model's prediction in both the orientation and displacement estimation. The performance of the IDNN across several time steps ranging from 2-14 are presented in Tables 5 and 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**NN**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Time Steps** \\ \end{tabular} } & \multirow{2}{*}{**Motorway (m)**} & \multirow{2}{*}{**Roundabout (m)**} & \multicolumn{2}{c}{**Quick Changes in**} & \multirow{2}{*}{**Hard Brake (m)**} & \multicolumn{2}{c}{**Sharp**} \\  & & & & & **Acceleration (m)** & \\ \hline \multirow{2}{*}{IDNN} & 2 & 651.41 & 702.17 & 571.99 & 648.57 & 425.56 \\ \cline{2-6}  & 4 & 616.60 & 655.22 & 546.76 & 580.01 & 373.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Showing the performance evaluation based on the CRSE metric of the IDNN in each investigated scenario across several time steps on the displacement estimation.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 2 Background  
Research on algorithms for fusion systems using recognition sensors is being actively conducted in various fields. [PERSON] et al. [14] proposed a method for fusion of the raw data of a LiDAR sensor and a camera. In [15], a fusion system that works in real time is proposed for a single 3D LiDAR sensor and multiple 2D LiDAR sensors. In [16], an algorithm for collecting and recognizing data from recognition sensors is implemented using multiple LiDAR sensors. In [17], their article proposes a multiple instance multiresolution fusion (MIMRF) framework. The proposed MIMRF can fuse multiresolution and multimodal sensor outputs from multiple sensors while effectively learning from bag-level training labels. [PERSON] et al. [18] implemented an algorithm to detect pedestrians in the same way as [16]. In [19], they proposed a data fusion approach to integrate low-density LiDAR data and a single optical image for a precise estimation of the tree top heights at single tree level [20]. Refs. [16; 18] propose low-level fusion methods, as they directly merge raw point clouds. In [21], a detection algorithm using dual LiDAR is implemented for low-level centralized fusion [22]. In [23], a multiple feature-based superpixel-level decision fusion (MFSuDF) framework that takes full advantage of feature discriminability of different modules and superpixel structure is proposed for hyperspectral images (HSIs) and LiDAR sensor data classification. In [24], they propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. This research deals with applications using fusion sensors. Inorder to improve their performance, they need an efficient data fusion algorithm. The data fusion algorithm is the process that integrates the data of multiple sensors to produce more consistent, accurate, and useful information than that provided by the data of an individual sensor. Data fusion approaches can be distinguished according to the level at which data are fused: fusion of raw data, fusion of features extracted from the data, and fusion of decisions derived from the data [25]. Most of the research for data fusion is focused on the high level, that is, integration of separately and independently processed sensor signals [26]. We believe that the robustness gained through independent processing in a high-level fusion setup does not have to be sacrificed if the low-level fusion architecture and the modeling process are designed properly. Although some promising initial research into low-level fusion has already been performed, raw data fusion still remains a challenging task. We consider a low-level fusion architecture that deals with received raw data from each sensor and can optimize processing delay.
# Sample Reduction Strategies for Protein Secondary Structure Prediction
## 2 Related Studies  
In this section, we give a brief review of the literature that employs SVMs for secondary structure prediction and studies that propose methods for reducing the training time of the SVM through sample reduction. [PERSON] et al. proposed a multi-SVM ensemble to improve the performance of secondary structure prediction. Their method contains two layers: the first layer consists of an ensemble of five classifiers and the second layer is built by three SVMs. The multi-SVM ensemble employs bagging to resample the training dataset through bootstrap sampling and achieves improved performance on secondary structure prediction when a seven fold cross-validation is performed on the RS126 dataset [23]. [PERSON] et al. proposed a new method of protein secondary structure prediction which is based on the support vector machine (SVM). Their method achieves a three-state per-residue accuracy (Q3) of 73.5% by seven fold cross validation on the CB513 dataset [24]. Although there are many other publications that employ SVMs for protein secondary structure prediction, none of these include sample reduction for reducing the training time of the SVM. Therefore we continue with methods that improve the model training time of SVM in other problems. [PERSON] employed stratified sampling to select a subset of examples from training set [18]. In this work, the author selected 10% of the samples from each class, which reduces the size of the training set by 10-fold. Then an SVM classifier is trained using the reduced dataset. The method is applied to four datasets from UCI Machine Learning repository. Though the prediction accuracy of the models trained by 10% stratified sampling is maintained for the adult and iris datasets, it reduced considerably as compared to using all the samples for letter image recognition and protein location sites datasets. In another work, [PERSON] and [PERSON] reduced the number of features by F-score and stratified sampling for credit scoring problem and obtained similar accuracy as the other state-of-the-art methods while reducing the computational time significantly [25]. In addition to sampling strategies, there are also methods that employ clustering to reduce the sample size of the training sets. [PERSON] et al. [19] employed a hierarchical clustering approach to improve the training time of an SVM particularly for large datasets. They proposed three techniques named TCT-SVM, TCTD-SVM and OTC-SVM, which are shown to work efficiently for model training. Among those TCT-SVM performed better than the others in terms of accuracy but it had a higher model training time [19]. [PERSON] et al. have proposed a new method called CB-SVM (Clustering-Based SVM) that integrates a scalable clustering method for large datasets while generating high classification accuracy. The authors claim that the CB-SVM algorithm can reduce the total number of data points effectively for training an SVM [20].
## 2 Theoretical Framework  
The photons on their path undergo a scattering, reflection and absorption phenomena before they are transported from the sky to a building interior. The light scattering within the Earth's atmosphere and also the diffuse reflection from the clouds are by far the most important factors influencing the ground-reaching radiation; however, the absorption by aerosols can also play an important role especially in densely polluted (industrial) regions. The light guide can be fully characterized by its optical properties and boundary conditions, such as the light field entering the pipe at its upper interface. The latter can be predicted from the UniSky Simulator which is designed to model radiance patterns under arbitrary sky conditions [20].
# Code-Switching in Automatic Speech Recognition: The Issues and Future Directions
###### Abstract  
Code-switching (CS) in spoken language is where the speech has two or more languages within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research as ASR needs to recognise speech in bilingual and multilingual settings, where the accuracy of ASR systems declines with CS due to pronunciation variation. There are very few reviews carried out on CS, with none conducted on bilingual and multilingual CS ASR systems. This study investigates the importance of CS in bilingual and multilingual speech recognition systems. To meet the objective of this study, two research questions were formulated, which cover both the current issues and the direction of the research. Our review focuses on databases, acoustic and language modelling, and evaluation metrics. Using selected keywords, this research has identified 274 papers and selected 42 experimental papers for review, of which 24 (representing 57%) have discussed CS, while the rest look at multilingual ASR research. The selected papers cover many well-res  
language. The bilingual and multilingual speech recognition systems must identify each unique language uttered by users, where speakers may be uttering more than one language in a single speech.  
The notion of code-switching (CS) is common in bilingual and multilingual speech research, where the utterance includes two or more languages spoken within it [2; 3; 4; 5]. CS creates an issue in spoken-language technologies, such as the ASR systems that need to handle input from many types of languages [6].  
This study reviews the current works on CS in bilingual and multilingual ASR systems. The review focuses on the issues and solutions proposed in the selected papers in terms of the research focus, the databases, acoustic and language modelling, and evaluation metrics. The existing reviews on ASR cover mainly single language ASR systems, covering selected themes, including databases [7], feature extraction and classification [8; 9; 10] and performance [7; 11], among others. At the time of writing, no works have reviewed bilingual and multilingual ASR systems, particularly on CS. This review is essential in this domain because there is an increasing need for an ASR system to recognise several languages.  
The rest of the paper is organised as follows: Section 2 looks at the existing review of ASR systems and CS in bilingual and multilingual speech recognition systems. Section 3 looks at the methodology used in this research to review bilingual and multilingual speech recognition system development. The findings obtained for each research question are presented in Section 4. Section 5 discusses the significant findings. Section 6 provides the conclusion and Section 7 looks at future directions.
# Decision Optimization for Power Grid Operating Conditions with High- and Low-Voltage Parallel Loops
## 3 Generation of Parallel Loop Opening Schemes  
### Weighted Network and Weighted Modularity Indicator  
The original GN algorithm can only be applied in unweighted networks. In an unweighted network, the existence of an edge only means that the nodes on the two ends of the edge are connected. No more information is contained in the edge. However, most of actual networks, especially power grids, are weighted networks, where weights of network edges have practical significance. The edges in weighted networks can contain information about how the nodes on the two ends of the edge are connected or how closely they are connected. This information is represented by edge weights. If a weighted network is analyzed as an unweighted network, the information contained in weights will be lost, which will influence the reasonableness of analysis results. To solve this problem, [PERSON] found a way to convert the weighted network to multigraph, which makes GN algorithms applicable in weighted networks [PERSON] (2003).  
For a power grid network, better community separating results can be derived if the edge weights are considered in the separating process. Considering the length and physical parameters of transmission lines in power grid networks, the modulus value of branch admittance is defined as the edge weight. The weight of an edge in a complex network usually represents the connection tightness of the two nodes connected by this edge. Thus, the larger the admittance modulus value of a branch, the shorter the electrical distance between two substations connected by this branch.  
In a weighted complex network, the weighted edge betweenness can be defined as the ratio of the edge betweenness obtained from the corresponding unweighted network and the weight of this edge. Thus, a larger edge betweenness and a smaller weight value would jointly make a larger weighted edge betweenness of this edge, whose possibility of being removed from the network in community separating process would be larger.  
Based on the edge weight defined above, the weighted modularity indicator can be derived from (2) as:  
\[Q^{\prime}=\frac{1}{2s}\sum_{v=1}^{n}\sum_{w=1}^{n}\left(A_{vw}S_{vw}-\frac{s _{v}s_{w}}{2s}\right)\delta(c_{v},c_{w}), \tag{5}\]  
\[s=\frac{1}{2}\sum_{v=1}^{n}\sum_{w=1}^{n}A_{vw}S_{vw}, \tag{6}\]  
\[s_{v}=\sum_{w=1}^{n}A_{vw}S_{vw}, \tag{7}\]  
where \(S_{vw}\) is the weight of the edge connecting node \(v\) and \(w\).
## 2 Methods and Model  
### Methods  
#### 2.1.1 Monte Carlo Method (MCM)  
The MCM is a computational technique based on probability and statistics. It primarily uses random numbers to solve mathematical problems. The core principle involves obtaining a solution to a given problem by performing a large number of random samples.  
The MCM involves drawing simple subsamples \(x_{1},x_{2},\ldots,x_{N}\) from a population to conduct sampling experiments. The variables \(x_{1},x_{2},\ldots,x_{N}\) represent \(N\) independent random variables that share the same distribution, with the same expected value \(E(x_{i})\) and variance \(D(x_{i})\), where \(i=1,2,\ldots N\). According to the law of large numbers, for any \(\varepsilon>0\), the following holds:  
\[\lim_{N\to\infty}p\bigg{\{}\left|\sum_{i=1}^{N}x_{i}/N-E(x_{i})\right|> \varepsilon\bigg{\}}=0 \tag{1}\]  
According to Bernoulli's Law, if the probability of event \(A\) occurring is \(P(A)\) and in \(N\) independent and repeated trials, the event \(A\) occurs \(n\) times, then the frequency of event \(A\) is \(n/N\). Therefore, for any \(\varepsilon>0\), the following holds:  
\[\lim_{N\to\infty}p\Big{\{}\left|\ \frac{n}{N}-p(A)\right|<\varepsilon\Big{\}}=1 \tag{2}\]  
From the above analysis, it can be concluded that when \(N\) is sufficiently large, the sample mean converges to \(E(x_{i})\) in probability. Similarly, the frequency \(n/N\) approaches the probability \(P(A)\). This demonstrates the convergence property of the MCM, where the estimates derived from random sampling converge to the true values as the number of samples increases.
# GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III
## 3 Results Analysis  
### Measurements with NTSC's High-Gain Antenna  
The NTSC of the Chinese Academy of Sciences operates a GNSS space signal quality assessment system with a 40 m large-diameter antenna as the core. The antenna L-band gain is greater than 50 dBi, the beam width is about 0.3\({}^{\circ}\), and the tracking accuracy of the antenna is about 0.015\({}^{\circ}\)[16]. The signal received by the large-diameter antenna is transmitted to the computer room through the optical fiber [16]. The ground receiving station is also equipped with an accurate channel calibration subsystem to minimize the impact on signal quality. The parameters of the system can be found in [17]. The signal received by the large-diameter high-gain antenna is considered to be unaffected by multipath interference, and the software receiver can accurately estimate and eliminate the Doppler effects. The classical parallel frequency search method and bump-jump technique are used for code-phase and carrier frequency tracking [18]. Meanwhile, the following conditions are assumed to hold:  
* The in-phase and quadrature (I/Q) branch is completely separated, and the related outputs are irrelevant;
* The mixed effect of signal distortion and external noise will not lead to loss of lock, and the tracking jitter is negligible;
* In a short time, the influence of the spatial transport layer is ignored in a small area.  
Under the above assumptions, the actual received signal is considered to be very close to the navigation payload transmitted signal.
# Data Hiding Method for Color AMBTC Compressed Images Using Color Difference
## 4 Proposed Method
### DH Extraction Procedure  
We assumed that the marked image \(\mathcal{MI}\) is delivered to the receiving side. The recovering procedure hidden secret bits is performed by reversing order of the embedding procedure (Figure 3), i.e., read a block from \(\mathcal{MI}\) in order, and extract hidden bits from both of the quantization levels and a common bitmap. The detailed DH extraction procedure is described in stages.  
* The marked image, \(\mathcal{MI}\); block size, \(n\times n/(m\times m)\);.
* Extracted secret bits, \(\mathcal{S}^{\prime}\)
* Read a block from \(\mathcal{MI}\) and assign it to \(\mathcal{P}_{i}\), where \(i\in\{1\leq i\leq(n\times n)/(m\times m)\}\).
* To extract hidden bits in nine quantization levels, Equation (16) is executed. \[[S_{k}]=\sum_{j=1}^{9}\left\{\begin{array}{ll}\frac{\mathcal{Q}_{i}(j)}{2} \Big{|}\bmod 2;inc(k),\\ \frac{\mathcal{Q}_{1}\otimes\mathcal{Q}_{1}\otimes\mathcal{Q}_{1}}{2}\ \ X{\rm OR}\Big{(}\Big{|}\frac{\mathcal{Q}_{i}(j)}{2}\Big{|}\bmod 2,Q_{i}(j)\bmod 2\Big{)};inc(k).\end{array}\right.\] (16)
* If \(\mathcal{P}_{i}\) \((Q_{1}^{\rm G}-Q_{2}^{\rm G})\leq T\), the common bitmap in block \(\mathcal{P}_{i}\) is assigned to variable \(M\).
* Then, 16-bit data is extracted using Equation (17), where \(j\) is \(\{1\leq j\leq 32\}\), i.e., \(\mathcal{S}_{k}^{\prime}=\sum_{j=1}^{32}f(M_{j})\), where \(f\) is a function of the logic. \[[S_{k}]=\left\{\begin{array}{ll}0,&if(M_{j}:M_{j+1})=(00)|(10),\\ 1,&if(M_{j}:M_{j+1})=(01),\\ inc(j+2),inc(k).\end{array}\right.\] (17)  
* To extract hidden data from all blocks, the process of _Steps_ 1 to 3 is repeated.
## 1 Introduction
The main idea of our recent paper [29] was that the modeling of waves in annular flow had to consecutively deal with all the stages of the waves' evolution and be conducted in close relation to the experiments. The paper was focused on the investigation of the initial waves and liquid film at this stage of evolution. It was found that, after the film leaves the inlet slot, its thickness is comparable to that of the slot, until it abruptly falls to a much thinner value. The initial waves appear and grow on this \"thick\" film. They were found to be two-dimensional and surprisingly regular in the absence of external perturbations; both characteristics were lost when the film thickness dropped to the \"equilibrium\" value. The main parameters of the liquid film and initial waves were measured with the shadow method. The length of the initial area was found to increase with the liquid flow rate and decrease with gas velocity. Frequency, velocity and the increment of the amplitude growth of the initial waves were measured and compared to the properties of the waves of maximum growth predicted in frames of linear stability analysis. The velocity of the waves was predicted reasonably well. The predicted frequency of the waves corresponded well to the experiments for the moderate liquid flow rates, but for larger flow rates, the modeled values exceed the measured ones. The increment of amplitude growth is significantly underestimated by the model, so further development of the models is necessary. The results described above were obtained for downward air-water annular flow. As mentioned above, many parameters affect the liquid film and its wave structure. In the present paper, we aim at expanding the range of conditions within the same problem statement. We investigate the effect of flow orientation by comparing the initial waves in upwards and downwards flows and the effect of liquid viscosity by using water-glycerol solutions as working liquids.
# IoT-Based Implementation of Field Area Network Using Smart Grid Communication Infrastructure
## 2 Hierarchy-Based Smart Grid Communication Infrastructure for Smart Farming Applications
### Neighborhood Area Network (NAN)  
The neighborhood area network (NAN) communicates information collected by smart meters to the central controller. The NANs may contain a few hundred smart meters set up in HANs. Smart meters are connected with different gateways through NANs. The coverage area of NANs is around 1-10 square miles [31; 32; 33; 34; 35; 36; 37]. The requirement of data rates for NAN is around 10-1000 Kbps [38; 39; 40]. WLAN, cellular technologies, and PLC can be used for neighborhood area networks.