esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structural Design
### Main Structural Design
#### 2.2.1 Design of the Separation Cage Structure
_{12\times 12} \tag{3}\]  
The coordinate transformation matrix is defined as follows:  
\[R_{ij}^{T}=\ \begin{bmatrix}r&0&0&0\\ 0&r&0&0\\ 0&0&r&0\\ 0&0&0&r\end{bmatrix} _{12\times 12} \tag{4}\]  
where the directional cosine submatrix \(r\) satisfies the following:  
\[r\ =\ \begin{bmatrix}\cos\theta_{x\overline{x}}&\cos\theta_{x\overline{y}}& \cos\theta_{x\overline{z}}\\ \cos\theta_{y\overline{x}}&\cos\theta_{y\overline{y}}&\cos\theta_{y\overline{z }}\\ \cos\theta_{z\overline{x}}&\cos\theta_{z\overline{y}}&\cos\theta_{z\overline{z }}\end{bmatrix}  \tag{5}\]  
Here, \(\theta_{x\overline{x}}\) represents the angle between the \(x\)-axes of global and local systems, and the rest goes the same way. Figure 4: Structure diagram of the fullerene. The expanded elemental stiffness matrices \(K_{ij}^{P}\)\({}_{360\times 360}\) are superimposed to construct the global stiffness matrix:  
\[K_{360\times 360}\ =\sum K_{ij}^{P} \tag{6}\]  
The fundamental equilibrium equation governing nodal displacements and forces is expressed as follows:  
\[K_{360\times 360}\Delta_{360\times 1}=P_{360\times 1} \tag{7}\]  
where \(\Delta_{360\times 1}\) denotes the nodal displacement vector and \(P_{360\times 1}\) the external force vector. Member internal forces are derived through the following:  
\[\overline{P}_{ij}=R_{ij}^{T}K_{ij}u_{ij}=\overline{k}_{ij}R_{ij}^{T}u_{ij} \tag{8}\]  
According to the Mises criterion, assuming that all member units in the simulated fullerene frame structure are ideal units, when the bending and torsion effects of the member units are ignored, the stress at the node is determined as follows:  
\[[\sigma_{i}]=\sqrt{\sigma^{2}\,+\,3\tau^{2}} \tag{9}\]  
where \(\sigma\) and \(\tau\) represent the axial and shear stresses, respectively. Multiplying Equation (9) by the cross-sectional area yields equivalent nodal forces:  
\[F_{i}\ =\sqrt{F_{x}^{2}\,+\,3(F_{y}^{2}\,+\,F_{z}^{2})} \tag{10}\]  
A MATLAB (2022b) implementation incorporates material parameters: \(E\) = 50 GPa, \(G\) = 19 GPa, \(\mu\) = 0.3, \(\rho\) = 1.5 g/cm\({}^{3}\), circumsphere radius \(R\) = 30 cm, and circular cross-section \(r\) = 1 mm, with centripetal loading. Simulation results demonstrate the structural performance as illustrated. As shown by Figure 5, the fullerene-inspired structure attains optimal load-bearing capacity at a truncation ratio of \(\lambda\) = 0.375, while showing low structural stiffness. The structure shows excellent load-bearing and stress-distribution characteristics. The tension form configuration ensures a uniform stress distribution and omnidirectional load-bearing capacity, resulting in a better structural integrity and resistance to stability. Figure 5: The influence of \(\lambda\) on the maximum equivalent force (F\({}_{\text{max}}\)) in the overall structural system.
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 4 Query Processing  
In our previous work [33], we provided a detailed classification of space-based and trajectory-based query types. We extend that work by adding a time-based query type. Moreover, we improve the continuous queries by including a flag to indicate trajectory-in or trajectory-out conditions.  
Our goal is to concentrate on space-based, time-based, and trajectory-based queries to reveal the performance level of the proposed approach in different scenarios. As a result, we focus on the following queries: Range Query, Interval Query, Continuous Range Query, Continuous Interval Query, Continuous Spatio-Temporal Query, Longest Trajectory, and Lookup Query.
###### Abstract  
Beetroot is a root vegetable with carotenoids, phenols, vitamins, minerals, and water-soluble betalain pigments such as betacyanins (red-violet color) and betaxanthins (yellow-orange color), which have many nutritional and health benefits. Its use in the food industry is mainly as a powdered natural dye. This study aims to investigate the effect of adding pea protein to beetroot juice as an encapsulating agent, and the spray-dried temperature on the physicochemical, structural, and functional properties of the powder. The spray drying was conducted at 125 and 150 \({}^{\circ}\)C with 3.5% and 7% pea protein used in the mixtures with the beetroot juice. The water content, bulk density, porosity, hygroscopicity, water solubility, water absorption index, color, and microstructure of the obtained powder were determined. In addition, betacyanin, total phenols, antioxidant capacity, and powder encapsulate efficiency were analyzed. Using pea protein in the spray drying of beetroot juice had shown high yields of spray drying and good characteristics of the powdered product. Beetroot powder with 7% of pea protein was more porous and luminous, and less hygroscopic than beetroot powder with 3.5% of pea protein. However, the use of 7% of pea protein increased the amount of water immobilized by the samples and reduced the soluble solids present in the product compared to beetroot powder with 3.5% of pea protein. The use of 7% of pea protein protected beetroot bioactive compound higher than the use of 3.5%. Higher spray-drying temperature (150 \({}^{\circ}\)C) significantly decreased phenols content and antioxidant capacity of the beetroot powders (\(p<0.05\)). Results showed using 7% pea protein mixed with beetroot juice and a 125 \({}^{\circ}\)C spray-drying temperature gave the most content of the studied bioactive compounds and antioxidant capacity. Moreover, the proposal gives more stable powders from a functionality viewpoint because it showed the higher encapsulate efficiency.  
beetroot; pea protein; spray drying; encapsulation; bioactive compounds; antioxidant capacity; physicochemical properties; powder +
Footnote †: journal: Applied Sciences
## 2 Results
### Pro10-1D Targets TLR4-Induced NF-\(\kappa\)B and MAPK Signaling in RAW264.7 Cells  
LPS of gram-negative infection is recognized by the TLR4 receptor and subsequently leads to the production of several downstream transcription factors, including NF-\(\kappa\)B, which contributes to pathogenesis of several inflammatory diseases [34]. To understand the effect of the newly designed peptides on TLR4 regulation, we analyzed the expressions of TLR4 and mitogen-activated protein kinase (MAPK) ( c-Jun N-terminal kinase (JNK) and Extracellular signal-regulated kinase (ERK)) and NK-\(\kappa\)B nuclear translocation in LPS-stimulated RAW264.7 cells via immunoblott assays (Figure 10). LPS treatment increased the expression of TLR4, followed by significant phosphorylation of MAPK and NF-\(\kappa\)B translocation in RAW264.7 cells. Treatment with 20 \(\upmu\)M Pro10-1 and Pro10-D effectively suppressed LPS-induced TLR4 expression as well as downregulated MAPK and NF-\(\kappa\)B proteins. A comparative analysis showed that Pro10-1D showed improved downregulatory effects compared to Pro10-1. Hence, we suggest that Pro10-D could influence the inflammatory cues by inhibiting TLR4-mediated cascades.  
Figure 8: Light microscopic images representing the antibiofilm effect of Pro10-1D against MDREC 1229: MDREC 1229 cells were allowed to adhere the polystyrene surface of the culture plate and were then exposed to Pro10-1D (0–16 \(\upmu\)M) for 16 h. Strains were cultured in Mueller–Hinton (MH) media, and biofilm formation was evaluated by crystal violet staining. Pro10-1D-exposed biofilms appeared thinner and scantier than untreated MDREC 1229; these effects were dose-dependent. Scale bar, 100 \(\upmu\)m.  
Figure 10: Pro10-1D is a target of toll-like receptor 4 (TLR4) and downregulates MAPK and NF-kB translocation in LPS-stimulated RAW264.7 cells. (**A**) Immunoblot analysis revealed the suppression of LPS-mediated TLR4, total/phospho-JNK, total/phospho-ERK, and cytosolic and nuclear translocation of NF-kB in RAW264.7 cells treated with peptides (20 uM) and LPS (50 ng/mL). \(\beta\)-actin was used as an internal control. (**B**) Graphical data represent the densitometry analysis of respective proteins, quantified by ImageJ. The values are expressed as the mean \(\pm\) SEM of three independent experiments and are statistically significant, based on _one-way ANOVA_ (_[PERSON]’s post hoc test_, \"\" \(p<0.01\), and \"\" \(p<0.001\); ns, not significant).  
Figure 9: Pro10-1D modulates lipopolysaccharide-inckharide (LPS)-induced inflammatory response in RAW264.7 cells. (**A**) Graphical data signify the dose-dependent inhibition of nitrite production and (**B,C**) expression of inflammatory cytokines (TNF-\(\alpha\) and IL-6) in RAW264.7 cells incubated with peptides (0–50 uM) and then challenged with LPS (20 ng/mL) for 16 h. Data are expressed as the mean \(\pm\) SEM of three independent experiments and are statistically significant, based on _one-way ANOVA_ (_[PERSON]’s post hoc test_, \"\" \(p<0.05\), \"\" \(p<0.01\), and \"\" \(p<0.001\); ns, not significant).
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Physical Layer
#### 3.1.1 Cell Acquisition and Synchronization
In [56], the authors presented the NB-IoT frequency diversity (FD) reception for NVSS as well as NSSS. In the reception mode, the NB-IoT UE alternatively receives the NVSS and NSSS in time domain radio frame by switching the received signals transmitted in different resource blocks in the frequency domain. Their simulation results show that using the proposed FD reception could improve the detection probability by 16% more than without applying the frequency diversity. Additionally, using FD with precoding vector switching (PVC) transmit diversity, achieves 90% of physical cell ID detection (PCID) probabilities at the average SNR of 0 dB with maximum carrier offset of 70 kHz. The method also achieves 97% of PCID detection probability without consideration of frequency carrier offset.
## 2 Experimental Section
### A Case Study for the Sensor Platform: A Vertical Garden System (VGS)
#### 2.1.1 Experimental Apparatus (Sensor Configuration)  
The wireless sensor nodes in the energy system collected physical information about the indoor environment, which is designed for human comfort. Three types of sensor network were used: dual-use temperature and humidity, illumination and wind speed sensors. The nodes were placed indoors and outdoors. The sensors, the signal processing, model, the measurement range and the power consumption of the wireless transmission modules and solar collector panels are shown in Table 2.  
The signal from the sensor was processed by a microprocessor (PIC16F526) and data was transferred by TI CC1101 (from TI Co. Ltd., Dallas, TX, USA), combined into a commercial wireless communication module (PIC16F526 + TI CC1101).  
The Cloud monitor platform used an Intelligent Energy Network-Application Service Provideri (IEN-ASP) system. iEN-ASP is a well-known network platform for energy saving control at Taiwan. This is provided by installing a computer monitoring device in the air-conditioning system, such as chiller, pre-cooling A/C box, air-conditioning unit, fan coil unit, water tower, pump and box-type A/C unit (package), window-type and split-type A/C, _etc._ Further, various environmental sensors will also be installed in the air-conditioning environment such as temperature sensor, humidity sensor, CO\({}_{2}\) sensor  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Item** & **Type** & **Measurement Range** & **Power Supply** \\ \hline Relative humidity and temperature sensor & SHT10 & 20\% \(\sim\) 80\% \(\pm\) 4.5\% & 550 \(\mu\)A at 3 V \\ \hline Photo sensor with signal processing circuits & Accuracy level was \(\pm\)2\% of the reading range, which yielded 1.5 \(\sim\) 4.5 \(\lx@math@degree\)C uncertainty. & 1 mA at 2.5 V \\ \hline MEMS flow sensor developed by the lab & 0.1 m/s \(\sim\) 0.45 m/s & 5 mA at 2.5 V \\ \hline Signal processor and RF transmission module & PIC16F526 & 0.5 \(\sim\) 10 mA at 3.5 V & Nominal power: 20 mW Standby power: 1.75 mW Transient power max: 35 mW \\ \hline Photovoltaic cell & SC 7035 & & \\ \hline \end{tabular}
\end{table}
Table 2: Wireless sensor node model and operating range.  
Figure 4: Case study: A building shielded by a vertical garden system (VGS) saves air-conditioning energy. (**a**) Structure of the VGS. (**b**) Climbing plants provide shade.  
and mobile sensor, _etc._, to collect the A/C equipment operation and environment status data. The environment was continuously monitored by all of the sensors, and information collected by the sensors was sent back to the server via the Internet. All of the necessary information from a specific place was provided by the iEN-ASP platform [17].
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 3 Methodology
### Tweets Preprocessing
#### 3.3.1 Textual Features Extraction
A variety of pre-trained BERT-based language models for Arabic are available, namely AraBERT [34], QARBi [35], ARBERT, and MARBERT [9]. This work focuses on ARBERT and MARBERT to extract contextualized embeddings. ARBERT is trained on Modern Standard Arabic (MSA) text extracted from six resources covering different Arabic countries. MARBERT is a large-scale model trained on 6 billion Arabic Tweets. Therefore, it has shown promising results for social media tasks. The following paragraph will explain the steps to extracting contextualized embeddings using BERT-based models. First, the input text is passed to the tokenizer to truncate it into tokens. The tokenizer uses the word-piece vocabulary size of around 100K. The word-piece tokenizer starts by searching for the whole word in the vocabulary, and if it does not exist, it iteratively splits this word into sub-words until the predefined words are reached. The advantage of this is that it forces the model to represent any word, and hence overcome the OOV issue [33]. After decomposing the text into tokens, it transforms the list of tokens into numerical IDs corresponding to vocabulary indices, indicated as input IDs. To make all input sequences have a fixed length, padding is performed, and to distinguish between tokens and padding elements in the input sequence, the attention mask was created. Input IDs and attention masks are fed into the model, giving an embedding vector of 768 for each token as the output. Two different approaches were considered here to use their embeddings: word-level embedding, which encodes each token as a 768-dimensional vector of the numerical values, and sentence-level embedding, which averages all the tokens' vectors to create a single 768-dimensional vector for the entire sentence.
## 3 Results and Interpretation
### Chemical Composition and Variations
#### 3.3.2 Chemical Variation  
The SEM observations showed that there were weathered surfaces at the edges of most glass beads. There was crystallization of what was assumed to be stabilizers and coloring materials. Therefore, the differences in composition were compared and analyzed by EDS for both weathered and non-weathered surfaces. In addition, the distributions of coloring materials and stabilizers were identified, and quantitative and qualitative analyses were carried out (Figure 5 and Table 3). The edges of the blue glass beads from tomb No. 8 were weathered, and the round materials, which were predicted to be coloring materials, were also weathered and shattered (Figure 5a). The bubbles were filled with crystalized materials, and the crystals were less than 100 \(\upmu\)m in size. The SEM-EDS analysis revealed that 4.94 wt.% Fe\({}_{2}\)O\({}_{3}\) was present in addition to the basic constituents. In addition, as there was a large concentration of Al\({}_{2}\)O\({}_{3}\) (19.42%), it was concluded that the coloring materials and stabilizers were combined (Table 3). There were no differences in the findings from the SEM-EDS analysis or the color composition for the brown glass beads from tomb No. 11 (Figure 5b). On the other hand, white impurities were identified on the surfaces of the green glass beads, and the SEM results showed the composition of the white impurities (Figure 5c). The white impurities were at least 20 \(\upmu\)m in size and had strange shapes, and there were round impurities of less than 2 \(\upmu\)m in size. The SEM-EDS analysis showed that the impurities had large concentrations of tin and lead (Figure 5c). The concentrations of SnO\({}_{2}\) and PbO in the large needle-shaped crystal impurities were 31.34 and 7.55 wt.%. The concentrations of SnO\({}_{2}\) and PbO from the small round impurities were 6.64 to 9.64 wt.%, and 30.07 to 37.50 wt.%, respectively (Table 3). Therefore, it is estimated that these compounds form PbSnO\({}_{3}\) making the glass opaque, instead of forming PbO and acting as stabilizers [7]. Figure 5: SEM-EDS analysis of microtextural constituents. **(a)** Analysis spots in five planes of a blue glass bead from tomb No. 8. **(b)** Four analysis points for a brown glass bead from tomb No. 11. **(c)** Analysis spots in one plane and at two points for a green glass bead from tomb No. 11. **(d)** Analysis spots in four planes for a blue glass bead from tomb No. 11. **(e)** Analysis spots in one plane and at one point for a brown glass bead from tomb No. 12. **(f)** Analysis spots in four points for a light-blue glass bead from tomb No. 18. **(g)** Analysis spots in one plane at two points for a brown glass bead from tomb No. 18. **(h)** Analysis spots in three planes and at one point for a brown glass bead from tomb No. 18.
# Heuristics for Spreading Alarm throughout a Network
## 4 Testing and Results  
For testing purposes, we have selected a wide range of networks, and both real as well as artificial examples. Their resulting burning numbers for all the tested algorithms are given in the Table 1. The first network is a path graph, similar to the one in Figure 2, only with 49 nodes instead of nine nodes. It was used for testing, as it is clear that its optimal burning number is seven (the maximum number of nodes in a path graph for a particular burning number can be calculated as \(\sum_{i=1}^{bg}(2i-1)\)). The second network, _squaredLedBurn7_, is an artificial example with known optimal burning number equal to seven, the network is shown in Figure 1. The _Reed98_, _polblogs_, _ba-1k-2k_, _mahindas_, and _netscience_ are networks from repository [29]. The 2D and 3D lattices and binary and ternary regular trees were standardly generated and are reproducible. The _Geometric random_ provides averaged results achieved for 10 randomly generated geometric networks, where 1000 random points on a unit square were connected by an undirected edge if they were closer to each other than a given radius 0.05. The _Erdos-Renyi_ provides averaged results achieved for 10 randomly generated networks, each with 1000 nodes, each couple of nodes connected with a probability 0.012. The _Barabasi-Albert_ provides averaged results for the networks produced by the Barabasi-Albert (BA) preferential attachment model, generated by starting with a triangle and adding each time a node together with three edges. The algorithms as well as the network generation used an igraph module in R [30], which was managed through the RStudio environment. Some of the required functions, like finding eccentricity, radius, eigenvalue centrality, number of components or the largest component, average path length, the vertices of the neighborhood of the given vertex within the given graph distance (EGO), or the weighted aggregated sum product assessment (WASPAS) method are directly available in standard R modules, mostly in the igraph module.  
The burning numbers obtained by Algorithms 1-3 were compared with the results by [PERSON] and Kamali algorithm [3]. The best-obtained results are emphasized by red font. The best-achieved values were obtained by Algorithm 3, which is however also the most complicated. Algorithm 2 was only slightly worse, and Algorithm 1 has the maximum deviations on artificial examples, while on real networks or their models its results were still close to optimum. Compared to the [PERSON] and [PERSON] algorithm [3], our best Algorithm 3 achieved on average a 37 percent improvement. However, this comparison is slightly unfair, the algorithm in [3] was used mainly to obtain an upper bound value for the burning number and it has the advantage of properly analyzed complexity. Although the complexity of Algorithm 1 is similar to [3], our Algorithm 3 has too many heuristically defined parameters to obtain its complexity easily. From a practical point, run times for the tested networks were comparable (within a minute on PC).
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 1 Introduction
To date, the only wind-induced attenuation model, caused by antenna vibration, relied on the experimental data in Japan [27], which has much different weather systems. Thus, the new data obtained in this paper present a significant contribution to assessing the applicability of combined rain and wind-induced attenuation models in new locations. During simultaneous wind and rain conditions in stormy weather, it was unclear whether the experimental data agree with either the current models of rain attenuation in the ITU-R P.530-18 recommendation [14] or the wind-induced attenuation in the APT-AWG-REP-81 model [6]. In addition, the wind effect as described in the APT report and the previous paper [27] used a wind speed sensor which showed statistical dependence between the wind speed and the received signal strength. However, several outliers were observed that were different from the expected values. This paper also shows that, by measuring the dynamic pole inclination angle using an improved setup to obtain accelerometer data, the effects of pole vibration on the link performance can be analyzed in more detail than that that was previously available. We collected data using the improved setup at Chiang Mai University, in Chiang Mai, Thailand during the rainy season of 2022. The data were analyzed against the aforementioned models. This paper is organized as follows. Section 2 presents the experimental setup with equipment specifications. Section 3 presents the link budget theory and calculation methods. Section 4 presents the results and analysis. Finally, Section 5 is the conclusions.
Therefore, the designed method not only provides strong privacy preservation but also maintains the data utility. In this paper, our contributions can be summarized as follows:  
We propose a method based on node differential privacy to preserve directed graphs in wireless mobile networks. Particularly, node differential privacy and edge modification are combined to generate a synthetic directed graph that provides strong privacy preservation for the original directed graph. We present four algorithms to maintain data utility in the proposed method. First of all, the Louvain algorithm is used to divide the original directed graph into several sub-graphs. Then, the node degree sequences in each sub-graph are generated by the GSEM (generating degree sequence based on exponent mechanism) algorithm and ADPRA (adding noise based on differential privacy with the ranking micro-aggregation) algorithm adds less noise on these node degree sequences. In the end, GGM (generating synthetic graphs based on graph modification) algorithm generates a synthetic directed sub-graph that maintains the properties of an original sub-graph. We demonstrate the performances of the proposed method on several different real data sets, and the experimental results show that the proposed method is effective in privacy preservation and data utility. The rest of this paper is organized as follows. Section 2 reviews the related methods to preserve the graph structure data. In Section 3, the preliminaries are introduced. Then, the proposed method is described in detail in Section 4. Section 5 demonstrates the performance of the proposed method in privacy preservation and data utility. In Section 6, the existing challenges and promising future directions are discussed.