esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Feature Extraction  
Machine learning algorithms, needing numerical values as inputs, cannot directly run on text data. The process of converting text to numerical values is called feature extraction. There are numerous types of feature extraction methods. Some popular feature extraction methods for text are Bag of Words (BoW) and word embedding. We used the word embedding approach in our work.
# Approximate Networking for Universal Internet Access
## 1 Introduction
### What Is Approximate Networking?  
With approximate networking, we intend to seamlessly integrate the concepts from approximate computing along with traditional mechanisms for approximations in networking, in terms of approximations adopted by networking protocols and algorithms. The concept of _\"approximate networking\"_ is necessary since experience has shown us that universal commissioning of \"ideal networks\", which have extremely high capacity, bandwidth, and reliability, in addition to extremely low or negligible delays, errors, and congestion, is non-practical. It is important to emphasize that approximate networking is not a single standalone technique nor is it the first time that approximation has been proposed in networking. Indeed, a number of existing networking techniques already utilize approximation and are best effort. Our idea of \"approximate networking\" generalizes these classical ideas and importantly supplements these ideas with recent developments in the field of _cross-layered approximate computing_ (particularly at the hardware level) to facilitate the design of future energy-efficient and optimized network infrastructure as well as algorithms and protocols. We aim to enable end to end approximation principles/frameworks engaging hardware/software approximation as well as network layer approximations for systematic approximate networking. As this point in time, there has only been rudimentary work done in efficiently combining low-level, approximate computing modules to construct larger high-level modules and architectural components. Approximate computing has been deployed for a large number of applications including image processing, signal processing, machine learning, scientific computing, financial analysis, database search, and distributed computing, _however, its extension to the field of networking is practically non-existent at this point in time, with only some recent preliminary works as exceptions_[18; 19]. We anticipate that these hardware-focused approximate computing advances will percolate into the field of networking and in the future, there will be an increased interest in synergistic approximation management at different layers of the hardware and software stacks in networking.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Trusted Wireless Network
#### 8.2.1 Main Tank to Cluster Heads  
This is the backbone of the entire network as it will connect the CHs to the BS at the main tank. Starting with an omnidirectional antenna of 9 dBi gain, Figure 19 shows that only CH ww2 was able to achieve the margin due to its proximity, while the rest of the CHs fail to meet this threshold even at full transmission power. Therefore, the other CHs will require antennas with higher gains to improve their signal strength.  
Figure 5 shows that CH at T/1/364/0/96 isolated with no remote nodes to supervise would benefit from the directional RAD-ISM-2400-ANT-PAR-19-0 antenna, as it focuses the signal more effectively, enhancing connectivity. In contrast, other CHs managing data from remote nodes need the omnidirectional antenna with higher gain. Therefore, the Allendale Electronics omnidirectional antenna, offering a 15 dBi gain, will be used to improve connectivity.  
Figure 20 shows that the RSSI of the CHs has significantly improved with the use of higher gain antennas. CHs matched the performance of cluster head ww2, reaching the  
Figure 19: Transmit Power vs. RSSI plot for the main tank and the CHs.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Base Station, Remote Node, and Repeater**} \\ \hline Frequency & 2000–2400 MHz \\ \hline Antenna height & 5 m (vary) \\ \hline Wireless module & RAD-2400-IFS \\ \hline \multirow{3}{*}{Antennas} & Allendale Electronics’ Omnidirectional (15 dBi), \multirow{3}{*}{RAD-ISM-2400-ANT-OMNI-9 (9 dBi), \multirow{3}{*}{RAD-ISM-2400-ANT-PAR-19 (19 dBi)} \\  & RAD-ISM-2400-ANT-PAR-19 (19 dBi) \\ \hline Transmission power & 1–20 dBm (vary) \\ \hline Receiver sensitivity & \(-93\) dBm (250 Kbps) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Trusted Wireless network link parameters.  
desired margin at a transmitter power of 15 dBm, with CH T/1/364/0/96 achieving the highest RSSI due to the use of a directional antenna.  
However, CH T/1/235/0/96 displayed a significant drop in RSSI compared to the rest of the network, mirroring behavior observed in the Wi-Fi network. This drop is attributed to the antenna's low height, which resulted in higher path loss due to terrain interference. To address this issue, increasing the antenna height to 8.5 m improved the RSSI, as documented in Figure 21. The final link parameters are summarized in Table 10.  
Figure 21: Transmit Power vs. RSSI plot for the main tank and the CHs with adjustment to gain and height.  
Figure 20: Transmit Power vs. RSSI plot for the main tank and the CHs with improved gain.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 5 Simulation Results
### Bit Error Rate According to SNR  
Figures 9 and 10 show the bit error rate (BER) comparing the conventional hybrid beamforming system and the proposed hybrid beamforming system forming 16 beam patterns. As the results of the BER in Figures 9 and 10, the proposed hybrid beamforming system has a better error performance than the conventional hybrid beamforming system. The proposed hybrid beamforming system and the conventional hybrid beamforming system have a difference about 1.5 dB with the QPSK. Also, the proposed hybrid beamforming system and the conventional hybrid beamforming system have a difference about 1.7 dB with the 16-QAM. Because the proposed hybrid beamforming system forms the beam patterns around the path with the high power, it has a better error performance than the conventional hybrid beamforming system. Also, the azimuth and elevation angles for the multi-paths with a high signal have a great influence on the error performacne.  
Figure 8: The flow chart for the proposed hybrid beamforming scheme.
## 2 Related Work
* [PERSON] and [PERSON] [22] did a comparative analysis of the application, deployment, and routing techniques of underwater wireless sensor networks. Here, they emphasized the application of UWSNs and characterized them in terms of architecture, deployment level, communication type, and implementation. They also compared several deployment techniques in terms of deployment type, objective, application, and sensor type. They also focused on geographical-based routing protocols, in contrast to our paper, where all three classifications are explained and compared. They also compared two protocols per year from 2010 to 2020 but did not mention the category for the selection. * [PERSON], [PERSON], and [PERSON] [23] studied the protocols based on data forwarding. In their study, the authors focused on the numerical analysis of the protocols. In contrast to their study, we focus on the holistic operation of underwater network sensor protocols. All of these works, along with the summary of our work, are put together in Table 1. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref No.** & **Publisher** & **Year** & **Scope** & **Drawbacks** & **No of Protocols** \\ \hline
[16] & IEEE & 2021 & Survey on Recent Advancements in Energy-Efficient Routing Protocols for Underwater Wireless Sensor Networks. & Only covered the energy efficient routing protocols & 40 \\
[17] & IEEE & 2020 & State-of-the-art survey in all aspects of routing protocols. Categorized the survey in three main parts: energy-based, data-based, and geographical information-based. & Covers the protocols of from 2018 to 2020 & 45 \\
[18] & MDPI & 2016 & They further subdivided the protocols into five categories (two for non-cross-layer and three for cross-layer protocols. Focus on the application of UWSN and discussed the future of routing protocols and application. & Did not discuss route propagation & 67 \\
[19] & SAGE & 2015 & The authors used the same protocol as the one used in the previous section. & Did not focus on Protocols rather on the application of the sensors & Did not focus on Protocols rather on the application of the sensors & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of the existing surveys and our work.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 2 Related Work and Motivation
Although the dataset in the study was collected for different device poses as well, the authors did not study the effect of these on the localization. Hence, there is a need to address this research gap to determine the localization improvement attained when taking device pose into account, which is the novel contribution of this study (as contextualized within the broader literature in Table 1).
## 4 Experimental Results
### Analyze the Target, Uncertainty, and 50% Combination Pattern Relationships  
Increasing uncertainty ranks with decreasing target ranks, as we have seen with the **70 at** landslide trigger areas via the **70 atm7** process in Figures 6 and 7, are also found in the target patterns. We can visualize their suspected higher uncertainty (hinted by the curves in Figure 4b) for lower prediction ranks. This is carried out by generating plots similar to the one in Figure 6 but with 646,091 points corresponding to the pixels in the SA, using pairs of target and uncertainty patterns. Instead, the following simpler visualization procedure was followed.  
We have generated 200 equal-area ranks out of the prediction, target, and uncertainty ranks, each corresponding to 0.5% of the study area (\(\approx\)3230 pixels). Being concerned mostly with the highest ranks, e.g., the higher 80%, we have displayed the numbers of pixels in each combination pattern that corresponded with the 50% lower uncertainty ranks, leaving out those with higher uncertainty ranks. We observed, therefore, the decrease in pixel number for each rank due to the elimination of higher uncertainty ranking pixels and detected which ranks were consequently more uncertain. These were the intermediate ranks. Figure 10 compares this decrease in the 50% combination patterns from the five models with the respective target ranks, i.e., the straight lines. In all cases, the intermediate ranks show higher uncertainty, i.e., higher \"loss\" of target pixels in the combination ranks, between rank 160 th and rank 80 th. The curves in Figure 10 have strongly variable configurations with one or two concavities and different higher pixel numbers in the vicinity of the 200 th and the 40 th ranks. Figure 10 contrasts the curves from models FZ and LO. Note the absence of a drop in pixel number for the top ranks in Figure 10. It shows a sharper pick, as to be expected from the yellow prediction-rate curve from model BP in Figure 4a that appears to be less sensitive to the suspected outlier occurrences.  
We may wonder whether the curves indicate some form of database or modeling signature. The curves in Figure 10 show how the combination patterns reach high uncertainties at intermediate ranks. The relative uncertainties increase from initial low to reach one or two maximum values between rank 190 th and rank 60 th. Since this is observed for all the patterns from the five models, it appears as a database property. Observe the prediction-rate curves in Figure 4a and consider the curves in Figure 10 showing the number of target ranks corresponding to lower uncertainties close to the two extreme ranks displayed. Would we want to select the top 10% combination ranks, from 200 th to 180 th, as the acceptable part? Or alternatively, would we prefer the top 20% as more significant? The properties of uncertainty and combination patterns are still unknown and remain a research challenge.
# A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science
## 1 Introduction  
With more than ten years of prosperity and development, Twitter possesses 330 million monthly active users that send about 500 million tweets per day [1]. Previous reports [2, 3] indicated that Twitter was losing its users, but statistics show that the trend of active users in this social network platform is still relatively positive [4].  
Data from diverse social network platforms is being used by researchers to develop \"a better understanding of how people are using social media in specific circumstances\" [5]. Under the global tendency of using Twitter as a daily communication and information tool [6], scientific research about this social network platform has maintained a high growth rate year by year [7]. Twitter data, compared with other digital platforms (e.g., Facebook, Instagram, Snapchat, etc.), is more accessible and can contain valuable resources for academic research; besides, the wide range of data-retrieving method options makes Twitter one of the most studied objects in the social sciences [5, 8].  
Figuring out the focus of scholars when they study Twitter became a realistic problem in understating such a rapidly developing research field. There are some academic works focusing on this issue; for example, [PERSON], [PERSON] and [PERSON] [9] qualitatively reviewed the title and abstract of 1161 Twitter-related articles, they classified these remaining academic works across three dimensions: aspect, method and domain, they found that the majority of the publications relating to Twitter concentrates on messages sent and details of the users. [PERSON] and [PERSON] [10] applied a co-word analysis to a limited bibliographic data of the Korea Citation Index, revealing 53 different disciplines in Twitter scientific literatures. [PERSON] et al. [7] quantitatively ranked 4709 Twitter-related studies byvarious categories, including annual global publication, geographic distribution, subject distribution, top keywords, top productive institutions, top authors etc.  
Above-mentioned studies have successfully argued the current research environment about Twitter-related studies, but important limitations were also included: First, as the study of [PERSON] et al. revealed, the total number of academic output of Twitter study is growing rapidly; thus, their study may lose accuracy and representability in today's view. Second, none of the listed academic publications systematically analyzed the common characteristics of the Twitter scientific literatures, the current Twitter studies' community structure remains in blank. Third, fore-mentioned studies were mainly descriptive, no analytic insights were explicitly discussed or concluded regarding to how do the related study hotspots or domains were evolved across time.  
In this paper, we aim to update the current knowledge boundary in Twitter-related studies by amplifying the research sample, and provide a longitudinal analysis to discuss our proposed research gap.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials
### Deep Neural Network  
A deep convolutional neural network (DCNN) is a feedforward network whose artificial neurons can respond to surrounding units; they have been proved effective in multiple fields such as image classification [30; 31; 32], target detection [33; 34; 35], semantic segmentation [36; 37; 38], etc. VGG network, proposed by [PERSON] et al., achieved astonishing classification results [27]. ResNet was born in 2015 and has a top error rate of 3.57%. ResNet [30] proposes residual skip connections between layers, introduces forward information, alleviates gradient disappearance, and makes it possible to deepen neural networks. MobileNets [39] are lightweight DCNNs using depth-wise separable convolutions for mobile and embedded vision applications. The network properly balances latency and accuracy by introducing a width multiplier and a resolution multiplier. Motivated by these lines of work, we take in the capacities of deep learning to determine whether the SAR images are contaminated.  
The basic module of a CNN is composed of input and output layers and multiple hidden layers. The hidden layers consist of convolutional layers, pooling layers, activation layers, and loss layers.
###### Abstract  
This paper presents a characterization of the pore structure of tight-pore sandstones of the Achimov suite and examines the application of Lattice-Boltzmann method (LBM) simulations to estimate the permeabilities of rock formations with a single-scale porosity. Porosity is characterized by pore volume distribution, pore throat connectivity, and tortuosity, which are calculated from 3D computer tomography pore network maps. The tight sandstones are poorly permeable, with permeabilities from 0.7 to 13 mD. For comparison, sandstones and carbonates with higher porosity and permeability from the existing database are also considered. For the more permeable reference samples with wider pores (250 \(\upmu\)m), LBM simulations show good agreement with the experiments and somewhat outperform the selected state-of-the-art direct simulations from the literature. For samples with the tightest pores and lowest porosity, LBM simulations tend to somewhat overestimate the permeability in comparison with the direct simulation methods, whereas for samples of higher porosity, a slight underestimation is obtained. We explain the inconsistencies by an interplay between the compressibility effects neglected by our LBM simulations in wider pores and the friction at the pore-wall interface, which is underestimated due to the use of the bounce-back conditions. However,the general agreement with experimental and direct simulation methods is very reasonable and suitable for practical use,which means that LBM is fast, highly parallel, and computationally sound even in tight pores.  
tight sandstones; pore structure analysis; Lattice-Boltzmann method; permeability +
Footnote †: journal: Applied Sciences
## 2 Complete System Formation
### A Hybrid Proposed FLC-ANN Tuned FPA MPPT
where,  
\(\dot{D}\) = Crisp output  
\(\mu_{D}(D)\) = Membership function (Aggregated)  
\(D\) = Fuzzy output  
\(S\) = Subarea/Universe of discourse  
Also, neural-fuzzy network output (\(\overline{D}\)) expressed mathematically as:  
\[\overline{D}=\mu_{A}(E)\times\mu_{B}(dE)\times W_{Li} \tag{10}\]  
where,  
\(\mu_{A}(E)\) = Membership function of fuzzy set \(A\) in \(E\) universe of discourse  
\(\mu_{B}(dE)\) = Membership function of fuzzy set \(B\) in \(dE\) universe of discourse  
\(W_{Li}\) = Weight of consequent \(i\)th layer  
The ANFIS objective function is expressed mathematically as:  
\[RMSE=\left[\frac{1}{P}\sum_{i=1}^{P}(D-\overline{D})^{2}\right]^{1/2} \tag{11}\]  
where,  
\(P\) = Total sample  
\(D\) = Fuzzy output  
\(\overline{D}\) = Neural network output  
Rule I: Biotic pollination uses levy flight for transferring pollens and is called global pollination in which the \(i\)th pollen solution vector is expressed mathematically using Equation (12). The Levy flight factor is accountable for pollens transport which improves the methodology of pollination where scaling factor is responsible to limit step size. \[X_{i}^{T+1}=X_{i}^{T}+L_{f}\times\gamma_{scaling}\times\left(X_{i}^{T}-G_{best }\right) \tag{12}\]  
where,  
\(X_{i}^{T}\) = Vector representing solution  
\(T\) = No. of iteration  
\(L_{f}\) = Levy flight factor  
\(\gamma_{scaling}\) = Scaling factor  
\(G_{best}\) = Global best solution  
Rule II: Self-pollination is termed as local pollination and characterized mathematically as:  
\[X_{i}^{T+1}=X_{i}^{T}+P_{f}\times\left(X_{m}^{T}-X_{n}^{T}\right) \tag{13}\]  
\(X_{m}^{T}\) and \(X_{n}^{T}\) = two unlike pollen in the species  
\(P_{f}\) = Switched probability  
Rule III: The performance of the flower is assumed to be identical to the probability of reproduction which is equivalent to resemblance of two concerned flowers. Rule IV: Pollination is interchanged between global and local, which depends on switching probability which lies between 0 and 1. The proposed nature-inspired FPA algorithms are responsible for providing proper learning of the neural network to reduce root mean square error between outcomes of \(D\)(Fuzzy output) and \(\overline{D}\) (Neural network output). Pollen position (duty ratio) is updated using biotic/abiotic pollination for the next iteration. Under step variation in solar insolation, the corresponding variance in voltage/current threshold is expressed mathematically as [22]:  
\[\frac{dP_{PV}(n)}{dV_{PV}(n)}\geq 0.2 \tag{14}\]  
\[\frac{dI_{PV}(n)}{I_{PV}(n)}\geq 0.1 \tag{15}\]  
where,  
\(V_{PV}(n)=\)_nt_h iteration PV voltage  
\(I_{PV}(n)=\)_nt_h iteration PV current  
\(dV_{PV}(n)=\) change in PV voltage (_nt_h and (_n_ - 1)th iteration)
# Three-Dimensional Change of Lip after Two-Jaw Surgery in Facial Asymmetry Using Facial Scanner
## 2 Materials and Methods
### Superimposition Process
\begin{table}
\begin{tabular}{l l} \hline \hline
**Reference** & **Definition** \\ \hline Hard tissue landmarks & \\ Nasion (Na) & The most anterior point of the frontonasal suture in the midsagittal plane \\ Orbitale (Or) & The most inferior point of the lower margin of the bony orbit \\ Porion (Por) & The most superior point of the external auditory meatus \\ Upper canine (U3-D/ND) & Maximilary canine cusp tip on the deviated side (D)/non-deviated side (ND) \\ Upper 1 st molar (U6-D/ND) & Maximilary 1 st molar mesibuccal cusp tip on the deviated side (D)/non-deviated side (ND) \\ Mental foramen (Mf-D/ND) & The most inferior point on the lower edge of the mental foramen on the deviated side \\ Menton (Me) & The most inferior point in the middle of the mandibular chin in the frontal plane \\ Soft tissue landmarks & \\ Cheilion (Ch-D/ND) & The most lateral extent of the outline of the lip in the deviated side (D)/non-deviated side (ND) \\ Labrale superius (Ls) & The midpoint of the upper vermilin line \\ Labrale inferius (Li) & The midpoint of the lower vermilin line \\ Reference planes & \\ Horizontal plane & A plane constructed with both side orbitales and right porion (Frankfort horizontal (FH) plane) \\ Midsgittal plane (MSP) & A plane passing through the nasion and the basin as well as positioned perpendicular to the horizontal plane \\ Coronal plane & A plane positioned perpendicular to the horizontal and midsagittal planes as well as passing the nasion \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Landmarks and reference planes used in this study. Figure 4: Changes in the upper and lower lip planes. The upper lip plane was established by connecting both sides of the Ch and Ls. The lower lip planes were also established by connecting both sides of the Ch and Li. (**A–C**) The lip planes on the FS images, (**D**) The coronal view of the lip planes, (**E**) The sagittal view of the lip planes, (**F**) The axial view of the upper lip plane, (**G**) The axial view of the lower lip plane. Green arrows indicate significant changes in the landmarks. MSP, midsagittal plane; FH, Frankfort horizontal plane; Ls, labrale superius; Ch-D/ND, cheliion on deviated side/non-deviated side; Li, labrale inferius; and Ls, labrale superius. In addition, a correlation analysis was performed to ascertain the factors related to the vertical change in the corner of the mouth post-surgery. The correlation coefficients (p) between the vertical changes in both sides of the Ch and the changes in the hard tissue landmarks were evaluated in the transverse, anteroposterior, and vertical axes.