esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets
## 4 Experiments and Results
### Dataset  
In our experiments, we used the multi-label emotion classification dataset of SemEval-2018 Task1: Affect in Tweets [30]. It contains 10,983 samples divided into three splits: training set (6838 samples), validation set (886 samples), and testing set (3259 samples). For more details about the dataset, we refer the reader to [38]. We trained our system on the training set and used the validation set to fine-tune the parameters of the proposed system. We pre-processed each tweet in the dataset as follows:  
* Tokenization: We used an extensive list of regular expressions to recognize the following meta information included in tweets: Twitter markup, emoticons, emojis, dates, times, currencies, acronyms, hashtags, user mentions, URLs, and words with emphasis.
* As soon as the tokenization was done, we lowercased words and normalized the recognized tokens. For example, URLs were replaced by the token \"<URL>\", and user mentions were replaced by the token \"<USER>\". This step helped to reduce the size of the vocabulary without losing information.
# High-Efficiency Adsorption of SARS-CoV-2 Spike 1 Protein by Plasma-Modified Porous Polymers
## 1 Introduction
However, there are no oxygen-containing functional groups and microporous functional groups on the resin surface, which reduces the adsorption potential of the resin and hinders its practical application in virus removal [20]. Non-thermal plasma technology is a fast-processing and environmentally friendly modification method, which can greatly improve the adsorption performance of adsorbents [21; 22]. In the process of non-thermal plasma discharge, numerous reactive substances (radicals, electrons and ions) are generated, which can interact with the material surface to form new functional groups and to enrich pores [23; 24; 25; 26; 27; 28; 29]. Thus, plasma modification can be applied to the surface modification of adsorbents to improve their adsorption performance. Therefore, in this study, nanosecond pulsed discharge was used to modify macroporous polystyrene resin (MaPR) to enrich the number of pore sizes and surface functional groups and generate a modified porous polymer (MPP), in order to realize the effective adsorption of SARS-CoV-2 S1 protein. Western blotting, an enzyme-linked immunosorbent assay (ELISA), scanning electron microscopy (SEM) and Fourier-transform infrared spectrum (FTIR) were used to analyze the effects of plasma modification on the adsorption efficiency of SARS-CoV-2 S1 protein, morphological changes in the surface, the pore size and number, and surface functional groups of the porous polymers. Additionally, the interaction mechanism between plasma modification and the adsorbent was briefly analyzed. These results provide a research basis for the efficient removal of pathogenic microorganisms in indoor air and wastewater.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Backscatter Communication
#### 4.4.1 Sdr Pipeline
This could explain why the internal oscillator yields--despite its instability--a better result than the external one in some channels. Furthermore, we also evaluated if decoupling the harvester from the modulator circuit improves the signal quality (see Figure 15c). Surprisingly, we did not observe a significant difference between the cases where the harvester is connected and influences the tag's load impedance and when it is decoupled. Therefore, we conclude that the switch we used for decoupling, described in Section 3.3, is not required.
## 1 Introduction
With the popularity of UAVs and the emergence of high-resolution vehicle trajectory extraction technology in computer vision, videos of highway work zone areas can be recorded with UAVs, and vehicle characteristics can be analyzed based on the extracted trajectories. Many open-source, high-resolution vehicle trajectory datasets are extracted from UAV videos, including the SeuTraffic dataset ([[http://seutraffic.com/#/](http://seutraffic.com/#/)]([http://seutraffic.com/#/](http://seutraffic.com/#/)) (accessed on 1 May 2022)) and highD dataset ([[https://www.highd-dataset.com](https://www.highd-dataset.com)]([https://www.highd-dataset.com](https://www.highd-dataset.com)) (accessed on 1 May 2022)), and UCF-SST-CitySim dataset. To the best of the authors' knowledge, existing vehicle trajectory datasets rarely focus specifically on highway work zones, particularly in capturing both vehicle- and location-level factors that influence merging behavior. This study addresses this gap by introducing a high-resolution vehicle trajectory dataset, collected via UAV technology, which offers unprecedented detail and coverage of vehicle interactions in temporary highway work zones. This study uses the vehicle trajectory dataset to investigate vehicle merging characteristics in highway work zones with different traffic states and layouts. Specifically, we analyzed the merging behavior in a basic work zone scenario (the leftmost lane out of three lanes in one direction was closed) under light and heavy traffic conditions. Combined with the other two scenarios (one with solid white lines and the other with fewer lanes), all three types of work zone were performed to compare the merging characteristics in different work zone layouts. The main contributions of this study lie in:  
1. The construction of a high-resolution trajectory dataset in highway work zone areas, which provides critical insights into merging behavior across various traffic states and work zone layouts, filling the significant gap between available data and real-world highway conditions;
2. The analysis of vehicle merging behavior under different traffic states, which highlights how varying traffic volumes and densities influence merging decisions, offering a more comprehensive understanding of traffic dynamics in work zones;
3. The analysis of vehicle merging behavior under different work zone layouts, which reveals the impact of lane configurations and work zone designs on merging patterns, providing valuable information for optimizing traffic management and safety in work zones. The remainder of this study is organized as follows. Section 2 briefly reviews related work on vehicle merging behavior and models in work zones. A detailed introduction of the dataset and scenarios of this study is performed in Section 3, followed by empirical and statistical analysis and discussion of vehicle merging characteristics in Section 4. Finally, we conclude the findings and future research direction in Section 5.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 5 Conclusions  
This article has shown the relevance of an appropriate end-to-end coordination of resources to support practical IoT deployments. A design proposal for an IoT slice orchestrator has been presented. This orchestrator enables the cohabitation of multiple instances of an IoT solution for distinct customers, over a set of shared infrastructures. The proposed design leverages the capacity of orchestrating and managing the network slices of 5G networks [56], complementing it with the automated deployment of other specific IoT related functions, like an IoT gateway or an IoT analytics server. As a first step to prove the practical feasibility of our work, we have focused on the multi-tenancy aspects at the edge and cloud computing domains. In this respect, we have implemented a functional prototype of our design, supporting end-to-end orchestration procedures on a real environment, using a commercial cloud platform, i.e., Google Compute Engine, as well as an open-source microservice platform, i.e., Kubernetes, which serves to provide functions at the edge of the network.  
The prototype implementation has been used to validate the capacity of our design to support the deployment of the pieces composing an IoT slice (e.g., IoT gateway) at different network locations (at the edge and at the cloud), even on resource-constrained single board devices that can be available at the edge with very limited cost (for instance transported over SUAV platforms). In this respect, the paper evaluates the benefits of this approach in terms of latency using a specific experimental setup. As a result of this work, we have also made several contributions to the open-source community, which are summarized in Table 1 in Section 4.1.  
Our future work will cover the networking aspects that are relevant to end-to-end orchestration, particularly those related with the creation of network slices over the radio access, transport and core networks of telecommunication operators. In addition, we will address a detailed study of the options to support the decision logic regarding the placement of IoT functions and the selection of specific transport networks, aiming at optimizing specific parameters of the IoT service (e.g., reducing latency)  
Investigation, J.M.F.; Resources, J.M.F.; Software, J.M.F.; Supervision, I.V.; Validation, J.M.F., I.V. and F.V.; Writing - original draft, J.M.F.; Writing - review & editing, J.M.F., I.V. and F.V.  
The work of [PERSON] and [PERSON] was partially supported by the European H2020 5 GinFIRE project (grant agreement 732497), and by the 5 GCity project (TEC2016-76795-C6-3-R) funded by the Spanish Ministry of Economy and Competitiveness.  
Some of the small icons used in the figures are obtained from [[https://www.flaticon.com/](https://www.flaticon.com/)]([https://www.flaticon.com/](https://www.flaticon.com/)) under the license of CC 3.0 BY.  
Figure 11: Throughput measured at IoT server when gateway is deployed in GKE cluster.
# Structure and Principles of Operation of a Quaternion VLSI Multiplier
## 2 Short Background  
A quaternion \(q\in\mathcal{H}\) is a hypercomplex number which can be presented as [2]:  
\[q=q_{0}+iq_{1}+jq_{2}+kq_{3}, \tag{1}\]  
where \(\mathcal{H}\) is a four-dimensional vector space over the real numbers \(q_{0},q_{1},q_{2},\) and \(q_{3}\in\mathcal{R}\), and \(i,j\), and \(k\) are basic imaginary units satisfying the following multiplication rules  
\[jk=i,\ ki=j,\ ji=-k,\ kj=-i,\ ik=-j. \tag{2}\]  
Suppose we need to compute the product of two quaternions \(a\) and \(b\):  
\[c=ab, \tag{3}\]  
where  
\[a = a_{0}+a_{1}i+a_{2}j+a_{3}k,\] \[b = b_{0}+b_{1}i+b_{2}j+b_{3}k,\] \[c = c_{0}+c_{1}i+c_{2}j+c_{3}k. \tag{4}\] \[ab = (a_{0}+a_{1}i+a_{2}j+a_{3}k)(b_{0}+b_{1}i+b_{2}j+b_{3}k)\] \[= a_{0}b_{0}-a_{1}b_{1}-a_{2}b_{2}-a_{3}b_{3}\] \[+ (a_{0}b_{1}+a_{1}b_{0}+a_{2}b_{3}-a_{3}b_{2})i\] \[+ (a_{0}b_{2}+a_{2}b_{0}+a_{3}b_{1}-a_{1}b_{3})j\] \[+ (a_{0}b_{3}+a_{3}b_{0}+a_{1}b_{2}-a_{2}b_{1})k. \tag{5}\]  
It is well known that the quaternion multiplication can be represented as a matrix-vector product:  
\[\mathbf{y}_{4}=\mathbf{B}_{4}\mathbf{x}_{4}, \tag{6}\]  
where  
\[\mathbf{B}_{4} = \left[\begin{array}{cccc}b_{0}&-b_{1}&-b_{2}&-b_{3}\\ b_{1}&b_{0}&b_{3}&-b_{2}\\ b_{2}&-b_{3}&b_{0}&b_{1}\\ b_{3}&b_{2}&-b_{1}&b_{0}\end{array}\right],\] \[\mathbf{y}_{4} = \left[y_{0},y_{1},y_{2},y_{3}\right]^{T},\ \ \left\{y_{m}\right\}=\left\{c_{m}\right\},\ m=0,1,2,3,\] \[\mathbf{x}_{4} = \left[x_{0},x_{1},x_{2},x_{3}\right]^{T},\ \left\{x_{m}\right\}=\left\{a_{m}\right\},\ m=0,1,2,3.\]  
Calculating this matrix-vector product directly calls for 16 real multiplications and 12 real additions. If, when implementing matrix-vector multiplications in hardware, to achieve maximum performance, we use the paradigm of the maximum parallelization of calculations, then 16 multipliers and four four-input adders will be required. Our goal is to reduce the required number of multipliers while maintaining performance. Below we will show one way to do this. The proposed approach is based on the idea described in [40].
## 5 Simulation Results
### Velocity-Level Algorithm Results
#### 5.1.2 Disturbance Rejection Results  
Figure 10 shows the response of the system to an impulsive force on the end-effector after a linear horizontal trajectory. The impulse has a triangular shape. Its time duration is 0.2 s, and its maximum value is 12 N. The algorithm shows good robustness against these types of impulsive disturbances, with a positioning error that settles under 0.1 mm about 7 s after the impulse. The algorithm is also tested for lower peak values of the impulse obtaining lower errors and faster rejection dynamics. On the other hand, higher impact forces cause a large UAV horizontal translation, which in turn causes the arm to reach singular configurations and the algorithm to fail.  
Figure 10: System response in the case of an impulsive force acting on the manipulator end-effector at the end of a linear trajectory (\(t=T_{path}\)). The impulse is acting in the negative x direction. The system is controlled by a closed-loop velocity-level inverse kinematics algorithm (\(K_{P}=100\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|\mathbf{e}(t)\|\) (in blue), as a function of time. From the instant \(t=T_{path}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. The orange line indicates the impulsive force, \(F_{EE}\), on the end-effector. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAV CoG.  
Figure 9: Three-DOF system response in the case of wind disturbances and sensors noise. The system is controlled by a closed-loop velocity-level inverse kinematics algorithm (\(K_{P}=100\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. **(left)** Task-space error 2-norm, \(\|\mathbf{e}(t)\|\), as a function of time. From the instant \(t=T_{path}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAV CoG.
## 3 Simulation Analysis
### Optimization Parameter Analysis
#### 3.3.1 Influence of Orifice Diameter on Static Performance  
It can be seen from Figure 6a that the diameter of the orifice has little effect on the bearing capacity at low eccentricity. Under the same eccentricity, the increase in the diameter of the orifice leads to the deterioration of the throttling effect, which reduces the overall bearing capacity. As the gas consumption is proportional to the square of the orifice diameter, the increase in the orifice diameter will lead to a significant increase in gas consumption, consistent with the results in Figure 6b.
## 5 Results and Discussion
### Analysis of the Thermal Performance of the Storage Tank during Discharge with a c-h Ratio of 0.38
#### 5.1.2 Thermocline Thickness  
Due to the minimal differences in thermocline thickness among the three storage tanks at low flow rates, only the internal temperature contour distributions during discharge at flow rates of 0.2 m/s and 0.3 m/s are presented, as shown in Figures 9 and 10.  
Figure 8: Temperature distribution during discharge at different flow rates.  
At a flow rate of 0.2 m/s, Type b is less disturbed by water jet turbulence compared to Type a, with Type c being the least affected and exhibiting the most uniform thermocline distribution. As the flow rate increases, the mixing between water layers intensifies. At a flow rate of 0.3 m/s, the thermocline thickness in each storage tank significantly increases, with the thermoclines in Type b and Type a experiencing more severe turbulent disruption, while the thermocline in Type c remains uniformly distributed. At these two flow rates, the thermocline thickness in the tanks with porous obstacles is smaller. At a flow rate of 0.3 m/s, the thermocline thickness in Type a and Type b is 42% and 14.3% higher, respectively, than in Type c. Although the porous obstacles can still effectively mitigate the turbulent effects at the inlet even at higher flow rates, the increased flow rate still impacts stratification, with the thermocline thickness at 0.3 m/s being significantly greater than at 0.2 m/s.  
Both porous obstacles and orifice plate obstacles can mitigate water flow disturbances inside the storage tank, but the porous structure demonstrates superior performance in this regard. As observed from the temperature contour distributions in Figures 9 and 10, under the same discharge conditions, the internal thermocline in Type c storage tanks exhibits the highest uniformity, whereas. Type a storage tanks show the most uneven thermocline distribution, experiencing greater fluctuations. Although an increase in flow velocity results in a thicker thermocline, this phenomenon remains unchanged.  
Figure 10: Temperature contour map at a flow velocity of 0.3 m/s for c–h ratio 0.38: (**a**) conventional tank; (**b**) orifice plate obstacle tank; (**c**) porous obstacle tank.  
Figure 9: Temperature contour map at a flow velocity of 0.2 m/s for c–h ratio 0.38: (**a**) conventional tank; (**b**) orifice plate obstacle tank; (**c**) porous obstacle tank.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 3 Electromagnetic Irradiation
### Ultraviolet Light Irradiation  
[PERSON] et al. [109] treated MoS\({}_{2}\) crystals under ultraviolet-ozone exposures at room temperature. Oxygen-sulfur bonds were formed at the top sulfur layer of the MoS\({}_{2}\) surface without breaking sulfur-molybdenum bonds. [PERSON] et al. [136] investigated optical behaviors of azobenzene-functionalized MoS\({}_{2}\) mono-layers and exfoliated multi-layers on Au substrates. It was reported that UV light could tune doping and the Fermi level of the hybrid structures. [PERSON] et al. [137] irradiated liquid-exfoliated MoS\({}_{2}\) nanosheets in aqueous solutions and found that the MoS\({}_{2}\) layers were oxidatively etched because of photon-induced powerful OH\({}^{*}\) radicals.  
[PERSON] et al. [138] illustrated multilayer MoS\({}_{2}\) FETs under ultraviolet light in N\({}_{2}\) atmospheres. The multi-layers were micromechanically exfoliated from natural MoS\({}_{2}\) crystals and deposited on SiO\({}_{2}\) coated silicon. The wavelength of the UV light was 220 nm and the average intensity was 10 mW/cm\({}^{2}\). Nitrogen gas alone did not affect the electrical properties of MoS\({}_{2}\) nanosheets, but nitrogen gas in the presence of UV light remarkably affected the electrical properties of MoS\({}_{2}\) nanosheets. Charge-carrier mobility, carrier density, and drain current were enhanced after exposure to nitrogen gas under UV light irradiation because of a possible doping effect. Detailed investigations [139] showed that the charge-carrier density of single-layer, bi-layer, and few-layer MoS\({}_{2}\) nanosheets were reversibly tuned with nitrogen and oxygen gas in the presence of ultraviolet light. The device performance was adjusted by exposure to gases in the presence of UV light.  
[PERSON] et al. [110] fabricated single-layer and multilayer MoS\({}_{2}\) (mechanically exfoliated) on Si substrates into FETs. It was found that the electron mobility increased with UV exposure up to 3.4 \(\times 10^{10}\)-2.2 \(\times 10^{13}\) photos/cm\({}^{2}\) in a vacuum. Raman spectroscopy showed no significant crystalline radiation damage or oxidation degradation under UV exposure. Irradiation-hard MoS\({}_{2}\) FET devices are expected.
# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
###### Abstract  
One of the ultimate goals of studies on visualization literacy is to improve users' visualization literacy through education and training. Even though users' cognitive characteristics may significantly affect learning and responding processes in general, studies have addressed the relationships between users' cognitive characteristics and visualization literacy. As a first step toward discovering the relationships, we conducted an empirical study to investigate the correlation between cognitive characteristics and visualization literacy. Our first study focuses on testing the correlation between visualization and three cognitive characteristics: numeracy, need for cognition, and visualizer-verbalizer style. In this study, we measured 178 participants' visualization literacy and the level of the three cognitive characteristics using the Visualization Literacy Assessment Test (VLAT), the Decision Research Numeracy Test (DRNT), the Need for Cognition Scale (NCS), and the Verbalizer-Visualizer Questionnaire (VVQ) through a crowdsourcing experiment. Our test results confirmed that a correlation exists between visualization literacy and both numeracy and need for cognition. Based on our test results, we discuss the implications for education to enhance visualization literacy and future studies to investigate on related user characteristics.  
Keywords:visualization literacy; numeracy; need for cognition; visualizer-verbalizer +
Footnote †: journal: Applied Sciences
## Nomenclature  
Abbreviation  
ANN  
CNN  
Convolutional Neural Network  
DE  
Damage Evaluation  
DL  
Deep Learning  
IQR  
Inter Quantile Range  
FEM  
Finite Element Analysis  
GM  
Ground Motion  
Gated Recurrent Unit  
JMA  
Japan Meteorological Agency  
LRB  
Lead Rubber Bearing  
LSTM  
Long Short-Term Memory  
MDOF  
Multi Degrees Of Freedom  
ML  
Machine Learning Model  
MLP  
Multi-Layer Perceptron  
MSE  
Mean Square Error  
NTHA  
Nonlinear Time History Analysis  
NRB  
Natural Rubber Bearing  
OpS  
Operator Splitting  
RC  
Reinforced Concrete  
RMSE  
Road Mean Square Error  
RNN  
Recurrent Neural Network  
RPP  
Response Prediction Problem  
SI  
System Identification  
STERA_3D  
Structural Earthquake Response Analysis 3D
## 3 Mathematical Modeling
### Multi-Objective Model for FJSP-LS
#### 3.2.3. Makespan (\(Z_{1}\))  
The makespan is defined as the maximum completion time of a given schedule. Its minimization is a widely used objective function in scheduling research. The essence of minimizing the makespan is to finish production as soon as possible to expedite the delivery of products to customers and/or to quickly free up resources for the upcoming production and other tasks, such as development and maintenance. The first objective function (\(Z_{1}\)) of the proposed multi-objective model is makespan (\(c_{max}\)) as shown in Equation (21).  
\[Z_{1}=c_{max} \tag{21}\]
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Approximation Bases
#### 3.1.3 Model Updating Technique for Shear Modulus Extraction
If their values are considerably different, the new \(G_{0}\) estimate takes the place of the previous and Steps 3 to 5 are repeated. Conversely, if their values are within a tolerance defined by the user, a fixed point is found. The process stops and the fixed point is delivered to the user as the final shear modulus estimate. The instantaneous correlation corresponding to the fixed point is a confidence measure, reflecting the quality of the best correlation.