esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 3 Proposed Sentiment Analysis Method
### Methods Used for Text Analysis
#### 3.2.1 Long Short-Term Memory (LSTM)  
LSTM is a deep-learning algorithm based on recurrent neural network (RNN) architecture. An LSTM network can learn long-term dependencies between time steps of sequence data. Theoretically, the traditional RNNs can keep track of arbitrary long-term dependencies in the input sequences. However, in practice, RNNs are not capable of learning these dependencies. The RNNs work well in simple cases where the gap between the relevant information and the target location is small. Moreover, in classic RNNs, there is a problem of vanishing gradient in the back-propagation while training. The LSTM was able to learn long-term dependencies and partially resolve the issue of vanishing gradient.  
The LSTM is well suited to classify, process, and predict from sequences in data such as sequence in time series data, and textual data (naturally sequential) etc. Any text is a sequence of words (sequence of characters) which may have dependencies between them. The LSTM network is advised to be used for the classification of sequence data where long-term dependencies are used for accurate classification. The detailed architecture of LSTM and implementation method is given on Github ([[https://colah.github.io/posts/2015-08-Understanding-LSTMs/](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)]([https://colah.github.io/posts/2015-08-Understanding-LSTMs/](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)), accessed on 16 February 2021) and Mathworks ([[https://www.mathworks.com/help/textanalytics/ug/classify-text-data-using-deep-learning.html](https://www.mathworks.com/help/textanalytics/ug/classify-text-data-using-deep-learning.html)]([https://www.mathworks.com/help/textanalytics/ug/classify-text-data-using-deep-learning.html](https://www.mathworks.com/help/textanalytics/ug/classify-text-data-using-deep-learning.html)) accessed on 30 November 2020), respectively. The settings and options of our LSTM model is provided in Table 1.
## 4 Fire Retardation of Styrenic Polymers with Phosphorous-Containing Compounds
### Additive Modifications of Styrenic Polymers
#### 4.1.4 Transition Metal-Based Formulations (P\({}^{+5}\))  
The transition metal-based organophosphorus compounds are an emerging class of hybrid FRs suitable for the fire protection of styrene polymers. Recently, [PERSON] et al. (2018) applied organophosphorus hybrid phenylphosphinates (PPIs) of different transition metals to enhance the fire-retarding properties of PS [78]. The phenyl groups bonded to phosphorus atoms within the PPI were located above and below the plane containing the metal atoms. This arrangement resulted in the formation of a conjugation effect between the PS and PPI, which helped obtain better dispersion in the polymer matrix and enhance the thermal barrier action. Additionally, these formulations not only favoured the char formation but also suppressed the yields of smoke and toxic volatiles emitted during degradation of the polymers. For instance, the PPI containing transition metals such as cobalt (Co), nickel (Ni), iron (Fe), and lanthanum (La) were synthesised and added to the PS via the solution-blending method [78]. The metal PPI, which is well-dispersed in the polymer matrix, seemed to obstruct the transfer of degraded products to the gaseous phase, and the transition metal appeared to catalyse the conversion of attached fragments to the char. Moreover, the addition of these metal PPIs suppressed the heat release, and it also reduced the smoke release rate and the CO emissions [78]. In the similar way, layered metal phenylphosphonates (PP) have been synthesised through a hydrothermal method [79]. The prepared Co-PP, Ni-PP, copper (Cu)-PP, and Fe-PP were added to the PS via the solution-blending approach. The fire-retarding mechanism was found to be similar to the one occurring in metal-PPIs. It was explained by the fact that the layered PP acted as a barrier, which in turn decreased the thermal decomposition rate of the PS composites and limited the gas diffusion [79]. It is important to mention that the main drawback of high loadings common for the additive FRs was mitigated here: the transition metal organophosphorus compounds were used in the amounts ranging from 2 to 6 wt% [79]. Thus, the formulations based on the transition metal PP and/or PPI are the promising candidates for enhancing the fire retardance of PS.
## 2 Mathematical Setup
### Transmission and Reflection  
If the irradiated object is not a semi-infinite medium but a composition of at least two materials \(M\) and \(\tilde{M}\) (with thermal effusivities \(e\) and \(\tilde{e}\), respectively), the thermal wave travels through \(M\) first towards \(\tilde{M}\) and, if the planar thermal wave propagation direction is perpendicular to its interface, the thermal reflection and transmission coefficients are  
\[R=\frac{1-b}{1+b},\quad T=1+R=\frac{2}{1+b} \tag{7}\]  
with  
\[b=\frac{\tilde{e}}{e} \tag{8}\]  
being the thermal refraction index, which characterizes the thermal contrast between the two media. If there is no thermal contrast, i.e., if \(e=\tilde{e}\), it follows that \(R=0\). In this case, there would be no significant reflection from this interface and, therefore, no contribution to thermal wave interference effects influencing the surface temperature. Thus, for a determination of coating layer thicknesses it is crucial to guarantee that the materials are distinguishable, i.e., \(b\
eq 1\).  
In the following, whenever indices \(j=1,\ldots,n\) are added to the reflection or transmission coefficient, this is assigned to the interface between the materials \(M_{j}\) and \(M_{j+1}\). The direction of these coefficients has to be understood downwards, cf. Figure 1. For the upward direction, a prime is added to the notation. For example,  
\[R_{n} :=R_{M_{n}\to M_{n+1}},\] \[T_{1}^{\prime} :=T_{M_{2}\to M_{1}}.\]  
The only exception regarding the direction is for \(R_{0}\) and \(T_{0}\), where the top surface of first layer material \(M_{1}\) is exposed to air \(M_{0}\), i.e.,  
\[R_{0} :=R_{M_{1}\to M_{0}},\] \[T_{0} :=T_{M_{1}\to M_{0}}.\]  
The reader is referred to [4] for a more detailed derivation of the expressions above.
# EMF Assessment Utilizing Low-Cost Mobile Applications
## 2 Materials and Methods
### Measured Data Processing
#### 2.3.2 Correlation of Power Measured with the Two Types of Equipment  
In a previous study, a method for mapping the exposure from 4G LTE cellular networks using a mobile app was introduced [19]. This method was possible due to the correlation coefficient between the received power of the exposimeter and that of the app being above 0.5 for the entire dataset, indicating a strong linear relationship. Specifically, on days with low road traffic, the correlation coefficient exceeded 0.65, while on high-traffic days, it dropped below 0.5.  
Additionally, it was concluded that considering only the main and up to three neighbor cells out of the 17 available was sufficient for exposure mapping.  
By establishing this linear relationship, a cost-effective alternative for mapping electromagnetic radiation exposure using smartphones and an application was introduced. However, this estimation method had limitations due to variability in road traffic and communication load.  
Figure 4 displays the correlation coefficient results between the received power of the exposimeter and the app for several repetitions from the first route. The app's received power calculations varied based on the number of considered neighbor cells.  
From this figure, two key points can be confirmed: First, three neighbor cells were considered sufficient for the mapping, as the correlation coefficient did not significantly improve when all 17 neighbor cells were considered. Second, some route repetitions showed higher correlation coefficient results than others due to lower traffic and communication load. For instance, during low traffic conditions, such as Tuesday evening corresponding to Repetition 1, the correlation between the exposimeter and app data was higher compared to high traffic conditions, such as Friday noon corresponding to Repetition 6. This is because the exposimeter captures power from the entire 4G spectrum, while the app records only reference signals, i.e., RSRP. Specifically, during rush hours, the power emitted by BSs increased [20], affecting only the exposimeter data. Moreover, high street traffic led to multipath propagation in certain areas, affecting the exposimeter and mobile device differently, thereby disrupting the linear relationship. These effects reduced mapping accuracy on high-traffic days.  
Figure 4: Correlation Coefficient results between the exposimeter’s and the app’s received power.  
The goal of this study was to reduce these limitations by identifying areas with high variability in received power over route repetitions. This will allow us to predict where our estimations are reliable and where there is a high likelihood of misestimation, as it is observed that high variability can disrupt the linear relationship between the received power measured by the two types of equipment.
###### Abstract  
Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, capable of causing large-scale disruptions. DDoS operates by generating malicious traffic that mimics normal network activity, leading to service disruptions. It becomes imperative to deploy mechanisms capable of distinguishing between benign and malicious traffic, serving as the initial line of defense against DDoS challenges. In addressing this concern, we propose the utilization of traffic classification as a foundational strategy for combatting DDoS. By categorizing traffic into malicious and normal streams, we establish a crucial first step in the development of effective DDoS mitigation strategies. The deleterious effects of DDoS extend to the point of potentially overwhelming networked servers, resulting in service failures and SDN server downtimes. To investigate and address this issue, our research employs a dataset encompassing both benign and malicious traffic within the SDN environment. A set of 23 features is harnessed for classification purposes, forming the basis for a comprehensive analysis and the development of robust defense mechanisms against DDoS in SDN. Initially, we compare GenClass with three common classification methods, namely the Bayes, K-Nearest Neighbours (KNN), and Random Forest methods. The proposed solution improves the average class error, demonstrating 6.58% error as opposed to the Bayes method error of 32.59%, KNN error of 18.45%, and Random Forest error of 30.70%. Moreover, we utilize classification procedures based on three methods based on grammatical evolution, which are applied to the aforementioned data. In particular, in terms of average class error, GenClass exhibits 6.58%, while NNC and FC2 GEN exhibit average class errors of 12.51% and 15.86%, respectively.  
SDN; DDoS; genetic algorithm; grammatical evolution; packet classification +
Footnote †: journal:  
0000-0001-9399-4300][PERSON]  
1 Department of Informatics and Telecommunications, University of Ioannina, 47150 Arta, Greece; [EMAIL_ADDRESS] (E.D.S.); [EMAIL_ADDRESS] (C.S.)
## 2 Theory of the SPH Wind and Sand Flow Modeling Approach
### Basic Equations of SPH for Wind-Sand Flow
#### 2.2.3 Selection of the Kernel Function  
Pressure instability problems are triggered during the movement of wind-sand flow due to the complex energy exchange between the two types of particles [13]. To cope with this problem, the five times spline kernel function is chosen as the kernel function in this paper. The advantage of the quintic spline kernel function over other kernel functions is that its second-order continuous smooth derivative is inversely related to the interparticle distance. The smaller the distance, the larger the derivative; the more significant the distance, the smaller the derivative. This characteristic makes it highly applicable in solving pressure instability problems. The five times spline kernel function expression is given below.  
\[W(R,h)=\alpha_{d}\times\left\{\begin{matrix}(3-R)^{5}-6\big{(}2-R\big{)}^{5} +15\big{(}1-R\big{)}^{5},0\leq R<1\\ \big{(}3-R\big{)}^{5}-6\big{(}2-R\big{)}^{5}1\leq R<2\\ (3-R)^{5},2\leq R<3\\ 0,R>3\end{matrix}\right.\right) \tag{7}\]  
The values of \(\alpha_{d}\) in one, two and three dimensions are \(\frac{120}{h},\frac{7}{48\pi h^{2}}\) and \(\frac{3}{359\pi h^{3}}\).
## 5 Simulation Results and Analysis
While our simulation results demonstrate the effectiveness of the proposed ADMM-based resource allocation and interference coordination strategy, conducting real-world experiments remains a challenge due to significant infrastructure, regulatory, and cost barriers. Future work will focus on exploring collaborations with industry partners to facilitate field trials, which would provide valuable insights into the practical implementation of our method and its performance in operational satellite networks.
## 3 Results and Discussion
### Changes in pH, as Well as Lactic Acid, Reducing Sugar, and Uronic Acid Contents, During Pickling
#### 3.1.3 Reducing Sugar Content  
As shown in Figure 5, the amount of reducing sugar in all samples decreased along with fermentation time. The reducing sugar content decreased quickly in the first 7 days. After this interval, the decrease was slower and remained steady until the end of the fermentation period for all samples. According to our results, the reducing sugar content of pickled apples decreased from 15.9 \(\pm\) 0.45 g/100 g on the 1 st day to 6.52 \(\pm\) 0.67 g/100 g in the NaCl brine, to 4.7 \(\pm\) 0.39 g/100 g in the KCl brine, and to 5.03 \(\pm\) 0.46 g/100 g in the MgCl\({}_{2}\) brine on the 35 th day of fermentation. The polysaccharides and other macromolecules present in fruits and vegetables can be deteriorated by some microorganisms, mostly by _Lactobacillus_ species, which slowly increases the quantity of reducing sugar in pickles [37].  
Further fermentation and continuous reproduction of microorganisms means that reducing sugar is used rapidly by microorganisms or dissolved in the pickle brine through osmosis and soaking. Thus, the content of reducing sugar decreased quickly in the KCl- and MgCl\({}_{2}\)-brined apples compared to the NaCl-brined apples. This may be because soaking pickled apples in brine could stimulate the elimination of sugars from apple tissue cells and quickly convert the reducing sugar into organic acids, causing a drop in the pH values of the brine. Similar results were found in the case of other brine-pickled vegetables, like sauerkraut [38], pickled cucumber [39], and pickled Chinese radishes [40].
## 3 Experimental Results
### Quantitative Evaluation  
Quality evaluation is conducted from two aspects: subjective visual comparison and objective quantitative evaluation. Subjective visual comparison analyzes image clarity, artifacts, and depth of field, while objective quantitative evaluation selects commonly used image evaluation indicators for comparison.
## 3 Performance Analysis of iPyroxy Block
### S21 Transmission Coefficient With and Without Block  
The system's mutual coupling coefficient is quite good; it constantly measures well below \(-15\) dB across the operational range. The S21 transmission coefficient, measured  
Figure 6: (a) S11 VNA without iPyroxy block; (b) S11 Reflection coefficient without iPyroxy block.  
Figure 7: S11 Reflection coefficients with and without iPyroxy block.  
without a blocking device, indicates the system's intrinsic capacity to preserve low coupling among its constituents. Its low mutual coupling coefficient highlights the system's ability to convey signals efficiently without significant interference or energy transfer between coupled units (Figure 8). The S21 transmission coefficient performance, especially without a blocking device, demonstrates the system's intrinsic isolation and decoupling properties. This system is well-suited for use in wireless communication systems, phased-array antennas, and other scenarios where precise signal transmission and reception are crucial because of these attributes, which are critical in applications where signal integrity and isolation between different components or channels are paramount.
# SBNNR: Small-Size Bat-Optimized KNN Regression
## 4 Proposed Method
### Feature Selection  
The fundamental objective of feature selection is to identify and retain the most informative and relevant subset of features, a task of paramount importance in the context of small data.  
In essence, feature selection serves as a strategic filter, sifting through the multitude of available features to pinpoint those that contribute significantly to the model's predictive power. This selective process offers several advantages, including improved computational efficiency and a reduction in the risk of overfitting, which is particularly pertinent in scenarios where data are limited.  
Small datasets are particularly prone to overfitting, a scenario in which models excessively conform to the noise or peculiarities within the data. Feature selection aims to delicately curate the feature set for modeling, aiming to find an equilibrium between preserving crucial information and eliminating redundancy. This process enhances the model's ability to unveil significant patterns and ensures resilience in dealing with limited data.  
The process of feature selection involves exploring a vast search space of potential feature combinations, which can be a computationally challenging task, especially in high-dimensional datasets. To navigate this space effectively and identify the optimal subset of features, various techniques are employed. Evolutionary algorithms similar to those used in instance selection, such as the bat algorithm, offer a promising avenue for efficiently exploring the combinatorial possibilities and arriving at feature subsets that enhance model accuracy and generalization in the context of small data.  
For feature selection, we have a search problem in the space of \(2^{d}\), where \(d\) is the count of features in the dataset.
## 4 Result and Discussion
### Learning Convergence
The E2E performance of RTT during haptic communication with the external host are shown in Figures 12 and 13. Figure 12 illustrates the simulation results of the ITE in terms of RTT, whereas Figure 12d with 20 external host shows that the proposed system focus to endure the QoS/QoE of the tactile application as compared RTT with our system with 20 hosts in Figure 7d. The Flow Queue Controlling Queue Delay (FQ-CoDel) scheme without any intelligence was used as a baseline model to illustrate the effectiveness of the proposed ITE. The RTT simulation results of the FQ-CoDel with the random strategy to select haptic codecs are shown in Figure 13. Figure 14 also shows that the average throughput of the system fluctuates around 1.5 Mbps, which ensures the stability of the network and controls the congestion level while using the proposed ITE. Finally, a comparative analysis of the power function for ITE and FQ-CoDel-Random is depicted in Figure 15, which shows the cumulative power of the connection during haptic communication with the presence of all external hosts. The results indicates that the ITE outperforms the FQ-CoDel-Random model while providing QoS and QoS requirements of the emerging service of Tactile Internet. Since the proposed ITE simulation has one nested loop (Outer loop for iteration and inner loop for episodes), the time complexity of the ITE is \(n~{}\times~{}n=O(n^{2})\). Figure 12: Total E2E RTT of the system with ITE during haptic communication with different number of training epochs and external hosts; (**a**) External Host = 5, (**b**) External Host = 10, (**c**) External Host = 15, (**d**) External Host = 20. Figure 14: Total E2E throughput of the system during haptic communication with different number of training epochs and external hosts; (**a**) External Host = 5, (**b**) External Host = 10, (**c**) External Host = 15, (**d**) External Host = 20. Figure 13: Total E2E RTT of the PQ-CoDel-Random (Baseline) during haptic communication with different number of training epochs and external hosts; (**a**) External Host = 5, (**b**) External Host = 10, (**c**) External Host = 15, (**d**) External Host = 20.
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results
### Blass Matrix Results
Finally, for the third beam that aims towards \(-34^{\circ}\), which is affected the most from the spurious paths, compared to any other beam, the maximum amplitude deviation is \(\pm 0.35\) dB \(\pm 8.4\%\) (see Figure 19e), and the time delay differences between the output ports differ by \(\pm 2 ps\) or \(\pm 24\%\) at most. It is important to note that, even though the errors seem large at a first glance, they are not systematic and they exhibit a random-like behavior. As a consequence, both the directivity and the beam pointing angle of the resulting array factors are largely unaffected. The only degradation introduced by the spurious paths can be observed at the slightly increased SLL shown in Figure 20a-d as calculated directly from the S-parameters of our network. The maximum SLL of the three beams is 10 dB and occurs for the second beam's entire operational bandwidth. This is due to the spurious excitations, as predicted. The beam crossover level is at 4.5 dB at 33 GHz and goes up to 3 dB at 27 GHz. In summary, the operational bandwidth of the Blass design presented in this section is 27-33 GHz. Figure 19: _Cont._  
Figure 18: Example of array fed by the Blass matrix  
Figure 19: Proposed Blass matrix simulation results: (**a**,**c**,**e**) amplitudes \(|S_{ij}|\) for the \(i=1\)–4 outputs when the input ports \(j=1,2,3\) are excited; and (**b**,**d**,**f**) group delay for the \(i=1\)–4 outputs when input ports \(j=1,2,3\) are excited. Figure 20: Calculated array factor radiation pattern when the outputs of the proposed Blass matrix (Figure 8) is used to excite the phased array at: (**a**) 27 GHz; (**b**) 29 GHz; (**c**) 31 GHz; and (**d**) 33 GHz.
## 3 Design Approach and Methodology
### Application Design and Development
#### 3.2.2 User Interface (UI) and Experience Elements (UX)  
The functionality of the _Anat_Hub_ application user interface (UI) was created using Unity (cross-platform game engine). The application's touch interactions were kept to three variants: tap to interact with icons or models, double-tap to zoom in and out of models, and swiping up and down to scroll. The application's flow and structure allow the user to intuitively navigate through the application with simple and comprehensible guidance (Figure 2). The icons and design elements were created using graphics editors: Krita, Photopea and Adobe photoshop. Some 3-D models of the human upper and lower extremities were supplied to be incorporated for users to navigate and interact with. The software, Autodesk 3 ds max, was necessary to manipulate, rig, and animate the models with the modifier and motion tools. The sound incorporated into the application was used to enhance user experience and provide users with a feedback response for UI and 3D model interaction. Table 2 provides a detailed summary of various software and hardware required to achieve a functional immersive application.