esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Walker Coverage Analysis for Information Dissemination in Wireless Sensor Networks
## 4 Simulation Results
#### Geometric Networks on a Grid  
In order to test the proposed analysis in extreme conditions, simulations are also conducted on grid geometric networks. A grid geometric network is composed of nodes deployed on a 2D torus grid. In particular, each node is located on the intersection of the grid in a 2D-torus surface. All considered  
Figure 8: Simulation results for networks of various densities of links \(N=1000\), \(r_{c}=0.100\) (red), \(0.200\) (green), \(0.500\) (magenta), \(1.000\) (yellow), number of neighbors \(=35,158,834,997\). It depicts the coverage over the number of time steps. Full lines are for the analytic results and dashed for simulation results. Similarly to previous outcomes, the modeling is in accordance with the simulation. Moreover, it is clear that there is a difference only in the case of marginally-connected networks with the minimum value for radius \(r_{c}\).  
Figure 7: Simulation results for 100 sparse networks with number of nodes \(N=1000\), radius \(r_{c}=0.06\) and mean number of neighbors \(=12\). One can see that the simulation’s outcome is in accordance with the expected results from the analysis.  
networks are connected with \(10^{4}\) nodes each and varied transmission range. Simulation results are presented in Figures 9 and 10.  
As can be observed, even in these conditions, where the randomness of the network is repealed in the simulation, the results fully support the analytical model proposed in the main part of this work. Although this model was based on the random walker mechanism acting on randomly-generated graphs, the results of Figures 9 and 10 showed that it is able to describe a deterministic variant, as well.  
Figure 10: Simulation results for the grid geometric network, \(N=10^{4}\), \(r_{c}=0.033\), mean number of neighbors \(=34\). Similarly to the previous simulation results, there is a fit between the modeling and simulation.  
Figure 9: Simulation results for the grid geometric network (green dashed line), \(N=10^{4}\), \(r_{c}=0.011\), mean number of neighbors \(=4\). Coverage is depicted over the number of time steps. There is a strong correlation between the analytical part and the simulation one.
## 2 Material and Methods  
### CW System and Characteristics of the Support Bed  
The CW system was constructed at the Environmental Technology Laboratory of the Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza campus (coordinates: 3 deg44'47'', 38 deg32'08'', ), and featured a mixed filter bed comprised waste from the shellfish industry and from construction. The experiment was designed with one non-vegetated (Figure 1a) and one vegetated bed (Figure 1b), and both reactors were run in parallel under the same conditions to assess the impact of vegetation on greywater treatment efficiency. The system comprised two rectangular base reservoirs, each measuring 42 \(\times\) 29 \(\times\) 27 cm, with a total volume of 30 L. The filter media included ceramic bricks, construction debris, and bivalve mollusk shells from _Anomalocardia brasiliana_, sourced from shellfish harvesting in Acarau, Ceara. These materials were pre-washed with running water to eliminate coarse impurities. The materials were layered in the following sequence from the base: brick fragments (8.80 cm), seashells (8.80 cm), and medium sand (O 2.00 mm) (4.4 cm), creating a bed with a usable volume of 11.20 L and a porosity of 41%.  
Figure 1: Images of the non-vegetated (**a**) and vegetated (**b**) operated CW, and scheme of the CW system operated with the detail of _Echinodorus subalatus_, in the vegetated phase (**c**).  
The _Echinodorus subalatus_ seedlings were collected from a marsh area in the locality of Jacunda, municipality of Aquiraz, Ceara (\(3\lx@arcdegree 53\lx@arcdegree 57\lx@arcdegree\)\(\lx@arcdegree\)\(\lx@arcdegree\)\(\lx@arcdegree\)\(\lx@arcdegree\), \(38\lx@arcdegree 25\lx@arcdegree 29\lx@arcdegree\)\(\lx@arcdegree\) W) and transported in plastic bags to the laboratory, where they were washed with tap water. Dried and decomposed leaves were pruned, and the seedlings were placed in 1 L plastic beakers containing a nutrient solution composed of (g/L): H\({}_{3}\)BO\({}_{3}\) (0.3), MoO\({}_{3}\).H\({}_{2}\)O (0.13), CaCl\({}_{2}\) (0.13), MnCl\({}_{2}\) (0.35), KH\({}_{2}\)PO\({}_{4}\) (0.02), KCl (0.15), NaNO3 (0.07), (NH\({}_{4}\))\({}_{2}\)SO4 (0.07), CuSO\({}_{4}\).5H\({}_{2}\)O (0.35), ZnSO\({}_{4}\) (0.35), FeSO\({}_{4}\) (0.04), and MgSO\({}_{4}\) (0.04). The plants were maintained under artificial lighting using 15 W fluorescent lamps with a 12 h photoperiod at 24 \(\lx@arcdegree\)C for approximately 15 days before being planted in the wetland. _Echinodorus subalatus_ was planted with 20 cm spacing between individuals, resulting in a density of 18 plants/m\({}^{2}\). Figure 1c illustrates the layout of the wetland system, both with and without vegetation.
## 4 Analysis of the Existing Datasets
### ShanghaiTech Dataset  
The ShanghaiTech dataset [34] contains 330 training videos and 107 test videos. Thirteen scenes in the dataset feature complex lighting and camera angles. This dataset has 42,883 test frames and 274,515 training frames, each with a resolution of 480 \(\times\) 856 pixels. Examples of abnormal frames are shown in Figure 6. The ground truth is available at both the pixel and frame levels.
## 4 Conclusions  
The SCFs reinforced 2024 Al composites were fabricated by powder metallurgy. The effect of SCFs on the interfacial microstructure and mechanical properties of SCFs/2024 Al composites at different temperatures were investigated. The results obtained are summarized as follows:  
1. The dislocation accumulation is formed in the aluminum matrix due to the thermal expansion mismatch between carbon fiber and the aluminum matrix. With the testing temperature increasing, the size of interfacial product Al4C3 and precipitates Al2 Cu becomes larger, and the segregation of Al2 Cu is found coarsening around Al4C3.
2. From the result of nanoindentation, carbon fiber improves the hardness and modulus of the adjacent aluminum matrix. The indentation on the aluminum matrix nearest carbon fiber possesses the highest modulus (93.53 GPa) and hardness (2.51 GPa).
3. The addition of SCFs gives rise to the sharp decrease in the fracture strain of SCFs/2024 Al composites. The tensile strengths of the composites first increase and then decrease with the increasing volume fraction of SCFs. At room temperature, 423 and 523 K, the highest yield strengths are obtained by 4 vol. % SCFs/2024 Al, which are 19.6%, 45.6%, and 36.7% higher than those of 2024 Al, respectively.
4. The fracture surface of the SCFs/2024 Al composites at room temperature is mainly characterized by the sheared fracture of carbon fiber, due to the strong interfacial bonding between carbon fiber and the aluminum matrix caused by the residual stress generated at the interface during the cooling process. With the temperature increasing, the interfacial bonding is weakened because of the decreasing residual stress and the coarsening of interfacial product Al4C3 and Al2 Cu segregation. Therefore, the interface debonding and fiber pulled-out become predominant fracture morphologies for the fracture surface obtained at 523 K.  
[PERSON] and [PERSON] designed the experiment. [PERSON] carried out sample preparation and TEM analysis. [PERSON] and carried out the XRD and SEM analysis. [PERSON] carried out the mechanical properties' measurements, and [PERSON] wrote the paper. All of the authors discussed the data and commented on the paper.  
This work was financially supported by the Natural Science Foundation of China (No. 41872181 and 41502344).  
The authors declare no conflict of interest.
# Practical Model for Metamaterials in Wireless Power Transfer Systems
## 2 Practical Model for Low-Frequency Metamaterials
### Q-Based Design Theory in the MTM Slab  
MTMs used in WPT systems usually appear in the form of slabs composing of several MTM units arranged periodically, which can be regarded as effective media [39]. In order to obtain the relative permeability of the MTM slab, the common numerical model utilizing electromagnetic waves and the corresponding periodic boundary conditions is usually used as in [37]. In this numerical model, wave ports are used as the excitation, and perfect electronic conductor (PEC) and perfect magnetic conductor (PMC) are used as symmetric boundary conditions to obtain S-parameters. Then a retrieval algorithm is employed to calculate the relative permeability of the MTM slab. However, this technique is less rigorous when applied to systems on low MHz frequencies. The distance between the wave ports and the MTM unit is too large to be modeled in the low frequencies. Moreover, modeling the entire MTM slab at the unit cell level is also impractical due to the limitation of computation capability.  
Nevertheless, according to the \(Q\)-based design theory [38] and (1), the relative permeability of the entire slab can be easily and accurately derived from the numerical model shown in Figure 2. The \(Q\) factor of the effective medium response is essentially equal to the \(Q\) factor of an individual resonant particle. This implies that by measuring a single fabricated MTM unit, one can simply and accurately estimate the relative permeability or permittivity of an MTM slab without complicated simulation,  
Figure 1: The Swiss roll structure of the metamaterial (MTM) unit. Two reverse spirals on each side of Fr4 substrate.  
fabrication, or measurements. The lumped port is used as the excitation, and four PEC boundaries are used for surrounding outer boundaries. The solid model represents a two-dimensional (2D) extension of MTM units and is a proper way to describe the structure of an MTM slab. Unlike those researches inheriting high-frequency analysis methods, this is a single-port simulation without S-parameters or inversion calculations. After adding the lumped capacitor, the resonance frequency and quality factor of the MTM slab can be calculated by the admittance (Y-parameters) from the simulation results shown in Figure 3. The relative permeability of the MTM slab is then concluded by (1).  
It should be pointed out that after adding the PEC, in order to tune the MTM slab, the value of the series capacitance is different from the previous results of the MTM unit. Under the periodic boundary, the value of capacitance cannot be simply calculated by \(\omega=\sqrt{LC}\) because the influence of the surrounding MTM unit needs to be considered. The proposed simulation method can exactly reflect the difference due to the periodicity of MTM units, which makes the design and frequency modulation of MTMs more accurate and reliable.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 3 Locata Development History and Mathematical Models
### Locata Mathematical Model
#### 3.2.3 Locata Integrated with GNSS/INS  
During the development of Locata's prototype system, the main objective was to develop the proof-of-concept software and hardware for the LocataLite, the LocataNet, and the Locata Rover. In many applications, there may be less than four LocataLites in a LocataNet. Therefore, integrating GNSS and Locata measurements to provide a more accurate and reliable position and navigation solution in GNSS-deficient environments has been investigated for applications, such as open-cut mining, structural deformation monitoring, and aircraft landing [1; 17; 38; 39; 40; 41; 42; 43; 44].  
More recently, researchers have investigated \"triple integration\" of Locata, GNSS, and Inertial Navigation System (INS) measurements for PNT solutions. Locata is straightforward in integrating Locata with GNSS, or to extend the traditional GNSS/INS integration system to a Locata/GNSS/INS triple-integration system since it relies on GNSS-like range measurements to generate position and navigation solutions. For a Locata/GNSS/INS system, the INS error equations define the dynamic model [43], whereas the measurement model contains measurement information from the GNSS and Locata systems. Classifying according to measurement models used, there are loosely-coupled, cascaded-coupled, and tightly-coupled integration architectures [44]. Classifying by filter structures, the Centralised KF (CKF), Decentralised KF (DKF), Parallel KF (PKF), Federated KF (FKF), and Global Optimal Filtering (GOF), have been investigated for marine, aviation, and land vehicle navigation applications [45; 46; 47].
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 4 Global Regulatory Proteins Important for _F. tulareis_ Virulence
### AraC, a Novel Global Regulator  
The AraC protein (arabinose operon regulatory protein) belongs to the AraC/XylS family that is characterized by the presence of the HTH DNA-binding domain. This domain is conserved in all members of this family and is necessary for the protein's function as a transcription factor. The evidence of the HTH motif in _F. tulareis_ subsp. _holarctica_ LVS AraC was confirmed by sequence alignment. Subsequently, a mutant strain lacking the gene encoding AraC was characterized [51]. It was revealed that AraC is necessary for regulation of the oxidative stress response and that AraC is activated only during the oxidative stress conditions. It was also shown that AraC downregulates five FPI genes (pathogenicity determinant protein E (_pdpE_), _pdpC_, _iglI_, _iglI_, and _dotU_) during oxidative stress, but not during normal physiological conditions. Transcriptome analysis of the mutant strain also revealed the involvement of AraC in the regulation of _aroA_ and _rnhA_ (heat stress response and homeostasis) during oxidative stress conditions. AraC also plays a crucial role in the regulation of some key components of the TCA cycle during oxidative stress conditions [51]. The connection of the TCA cycle with _F. tulareis_ subsp. _holarctica_ LVS virulence has been described [114]. Pyruvate dehydrogenase and oxoglutarate dehydrogenase were downregulated in an _araC_ deletion mutant strain during oxidative stress conditions [51]. Moreover, AraC regulates the pantothenate synthesis that is a central component of coenzyme A, a molecule necessary for the TCA cycle. It was also observed to be involved in glucose metabolism [51]. Thus, AraC seems to be a global regulator with many functions. AraC could affect RNA polymerase subunits expression, because RpoB and RpoC (RNA polymerase subunit \(\beta\) and \(\beta\)') were downregulated in the _araC_ mutant strain after exposure to various stress conditions, indicating that AraC is a positive regulator of these two proteins. A recent study regarding the AraC describesits possible important role in the pathogenesis of _F. tulareis_ subsp. _holarctica_ LVS strain. However, there is no proof that there is an AraC-DNA interaction in _F. tulareis_ and this needs to be further explored [51].
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 5 Conclusions  
This study demonstrated the design and performance of an NFC potentiostat developed by Silicon Craft Technology PLC (Bangkok, Thailand). The NFC potentiostat is a flat, card-sized, lightweight device and is operated using an NFC smartphone, so it is portable and easy to use. The performance of the NFC potentiostat exhibits a good agreement with a commercial benchtop potentiostat for both cyclic voltammetry and chronoamperometry. The application of this device was demonstrated through its use in ascorbic acid measurement. This platform is suitable for various analytes, depending on the design and modification of the electrodes used as a sensor. Therefore, an NFC potentiostat can be an alternative device for electrochemical measurement in point-of-care testing and resource-limited settings in many fields, including healthcare, agriculture and the environment.  
The following are available online at [[https://www.mdpic.com/2076-341](https://www.mdpic.com/2076-341)]([https://www.mdpic.com/2076-341](https://www.mdpic.com/2076-341)) 7/11/392/s1, Video S1: Demonstration of NFC potentiostat operation. A 100 \(\mu\)L of 1.0 mg/mL ascorbic acid was drop-cast on screen-printed carbon electrode, followed by a cyclic voltammetry measurement by NFC potentiostat.  
Conceptualization, K.K., S.K. ([PERSON]) and R.P.; Formal analysis, K.K., and S.K. ([PERSON]); Funding acquisition, R.P.; Methodology, K.K., and S.K. ([PERSON]); Project administration, R.P.; Writing--original draft, K.K.; Writing--review and editing, S.K. ([PERSON]), S.K. ([PERSON]) and R.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Innovation Agency, Silicon Craft Technology PLC, the Center of Excellence on Medical Biotechnology [CD-61-005], Science and Technology Postgraduate Education and Research Development Office, the Office of Higher Education Commission of Thailand and the Science Achievement Scholarship of Thailand.  
Not applicable.  
Data Availability Statement: The data presented in this study are available within the article and its supplementary materials.  
The authors would like to thank Silicon Craft Technology PLC (www.sic.co.th) for developing the NFC potentiostat and providing the prototype to use in this study. We also thank Jyh-Myng Zen's Laboratory from the Department of Chemistry, Faculty of Science, Chung-Hsing University, Taiwan, for providing the electrochemical device (CHI 611E, Texas Instruments Inc., Austin, TX, USA)and laboratory facilities to conduct the experiments.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Comparative Analysis  
Here, the proposed system's performance is validated by analogizing it to related works, which are described in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Clustering Time (ms)** \\ \hline Proposed SKGC & 56,942 \\ \hline SC & 66,556 \\ \hline HC & 85,863 \\ \hline DBSCAN & 98,866 \\ \hline KMC & 115,059 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clustering time analysis.  
Figure 7: Performance analysis.  
Table 3 analyzes the proposed work's performance by comparing it to related models. The proposed GDP-LSTM utilizes the global decayed PAF to achieve efficient GT. Likewise, SDQL established SDER to identify the update or error of the testing results that enhance the GUI performance. Similarly, the existing models introduce techniques like You Only Look Once (YOLO), RL, and LSTM. The proposed model achieved PT, precision, accuracy, F-measure, and CC of 21,456 ms, 98.98%, 98.89%, 99.21%, and 92.45%, respectively. However, the existing works retained limited performance. Hence, it is proved that the proposed work has a higher prominence than traditional methods.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 3 Materials and Methods
### Data Collection and Preparation
#### 3.1.1 Data Labeling  
The accuracy of labeling affects the performance of the model in a way that a classifier may fall apart and there is no guarantee that the model can predict or classify correctly on low-quality labelled data. Labelling a large dataset is a critical building block and a key factor in supervised learning. Even for some machine learning task, this is the costly and time-consuming job. Due to this high cost of the labelling process, researchers often adopt semi-supervised approaches for many real-world applications, where a large amount of unlabelled data is given as input with the conjunction of a few labelled samples to perform a classification task.  
The two well-known approaches adopted by the research community for data labeling are manual annotation and automated annotations/data programming. In the automated-labeling approach, various dictionary resources and data programming tools are used in a way that they take dataset as input and generate a label for each sample in the dataset. The examples of such tools are _WordNet_, _SentiWordNet_ and _SentiStrength_ dictionaries that are easily available for data annotations. In [10; 14], an automated approach adopted in data labeling process for supervised classifiers. As we mentioned earlier that the limitation of automated labeling is that it replies only on a catalog of words and the class label is assigned based on the predefined polarity of words available in the dictionary for a given observed sample. Whereas in many applications, the context of the sentence is not only dependent on the word occurrence but even order of the words is also significant. In such a scenario, the context of  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Query-Terms**} \\ \hline \#KUNDUZ & \\ \#AIRSTRIKE & \\ \#TALIBAN & \\ \#TEFHEREK-E-TALIBAN & \\ \#AFGHANISTAN \#AFGHANI & \\ \#PEACEMARCH & \\ \#JIHADI & \\ \#MUJAHID & \\ \#TERRORIST, \#TERORISM \#TEROR & \\ \#VAR & \\ \#ATICK & \\ \#BLAST & \\ \#ARGHAN & \\ \#MADRASSA & \\ \#KILLING & \\ \#SHAHEED & \\ \#MUJAHID & \\ \#DESTROY & \\ \#USA & \\ \#NATO & \\ \#PAC & \\ \#HELMAND & \\ \#JIHAD, \#JHADI & \\ \#PASHTUN & \\ \#FAZLULLAH, \#HAIBATULLAH \#AKHUNZADA & \\ \#RUIN & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top trends used for data extractions.  
the overall sentence is beyond the word occurrence, automated labelling does not give accurate results. Therefore, researchers are trying their best to improve automated labelling mechanisms. Because of limitation of existing automated labelling to label content by ignoring sematic, we opted to choose manual labelling process and we labelled our dataset by carefully reading each tweet by not only considering occurrence of each word but also considering the context and semantics as well. Though hand labelling required more time and human resources, it is more accurate and reliable.
## 2 Algorithm Description  
### Traditional Single-Satellite Positioning Algorithm  
#### 2.1.1 Basic Principle of Positioning in a Static Environment  
We know that the GNSS positioning system requires at least four visible satellites to complete the positioning. However, the single-satellite positioning system cannot obtain multiple sets of pseudorange information at the same time since it has only one satellite. Therefore, this algorithm obtains the positioning solution by exchanging time redundancy for space redundancy. The principle of the single-satellite positioning system is shown in Figure 1.  
Figure 1: Schematic diagram of single-satellite positioning.  
As can be seen in Figure 1, the integral Doppler measurement value \(d_{12}=\rho_{2}-\rho_{1}\) is obtained at the terminal of baseline \(L_{12}\), and the terminal of baseline \(L_{13}\) obtains the measured value \(d_{13}=\rho_{3}-\rho_{1}\) plus the elevation value of the positioning target. Then, two positioning hyperboloids and a spherical surface are constructed. The coordinate value where the three curved surfaces intersect is the actual positioning result.