esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review and Hypotheses Development
### Physiological Features  
Measuring affective states using physiology is a predominant strategy employed within the field of UX. According to the circumplex model of affect, affective states emerge from two fundamental neurophysiological systems related to valence and arousal [39]. Two common physiological indices used to measure the valence and arousal dimensions defining affective state are facial micro expressions and EDA. Often captured via a webcam, facial micro expressions are generally quantified using some form of automated facial micro expression (AFE) analysis software and assessed through the lens emotional valence. Facial expression analysis remains one of the most reliable ways to physiologically measure emotional valence, as facial muscles' micromovements will involuntarily occur as a direct result of changes in affective state [10]. Indeed, in one study, it was found that data captured via facial micro-expressions were more effective in measuring instant emotions and pleasure of use in comparison to a user questionnaire [43].  
Emotional valence, characterized by negative emotions (e.g., fear, anger, sadness) and positive emotions (e.g., joy, surprise), on opposite sides of the spectrum, refers to the emotional response to a specific stimulus [44]. Simply put, it has been described as how users feel [45]. The dimension of valence can be studied alone or as a complementary construct to arousal, as described in the following paragraphs.  
As for EDA, it is a measurement of electrical resistance through the skin that captures changes of skin conductance response (SCR) from the nervous system functions [11; 46; 47]. Indeed, it relates to the sympathetic nervous system, an automatic response to different situations [48]. The easy to use and reliable physiological measure has been widely used in NeuroIS research [49; 50; 51; 52]. Often captured via electrodes on the palm of the hand, it is sensitive to the variations in skin pore dilation and sweat gland activation, which are in in turn sensitive to changes in emotional arousal [53; 54]. As suggested in the literature, it commonly infers levels of arousal through the measure of skin conductance [46].  
The arousal levels measured via EDA range from very calm to neutral to highly stimulated [55]. It has been suggested to be an ecologically valid portrait of the user's arousal, while being non-invasive and free of overt recoded behaviour [18]. In one study regarding child-robot interactions, the measured arousal via skin conductance was deemed as a valuable and reliable method in assessing social child-robot interactions [56].
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 4 Results and Discussion
### Misdetections  
Even though it provides excellent detection results on the weapon in various categories including pistols, revolvers, and rifles, the system had very few false positives that can be improved in the future. An example of a false positive is depicted in Figure 19, which shows a smartphone detected as a pistol with a very low confidence of 25% that can be overcome by model improvement, as well as by setting the threshold.
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
## 3 Optical Fiber Current Sensor Based on Magnetostirctive Composite Materials
### Sensor Structure
A special copper screw is placed at the upper right of the structure, and the beam of the T-shaped structure is pressurized by the screw, so as to achieve the purpose of pressurizing the T-shaped structure. Therefore, the sensitivity enhanced current sensor is composed of GMM, FBG, copper support structure, and screws made of copper. Experiments show that the sensitivity of the T-shaped structure is higher than that of the T-shaped structure. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Geometry** & **TD Mass** & **Linear Range** \\ \hline
[13] & Cylindrical & 40 & 250–700 A \\
[14] & Toroidal & 144 & 320–900 A \\
[15] & Cubic & 74 & / \\
[16] & Toroidal & 5.6 & 105–650 A \\
[17] & Toroidal & 2.0 & 50–400 A \\
[18] & Hyperbolic & 1.5 & 15–450 A \\
[19] & Cuboid & 0.42 & 100–800 A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison: FBG-based current sensorsfor high-voltage lines. Figure 12: Optical fiber current sensor based on 1.5 g hyperboloid magnetostrictive composite material. Figure 13: Optical fiber current sensor based on 0.42 g cuboid magnetostrictive composite material. sensor is significantly improved, and the cost of the proposed structure is low. As shown in Table 2, current sensors with different structures are summarized in this paper.
## 3 Research Design
### Research Method and Process  
#### 3.2.1 Research Method  
Two kinds of research methods are applied, as follows:  
* Systematic literature review (SLR). This is a specific type of literature review that is characterized as methodical, comprehensive, transparent, and replicable. It consists of raising research questions on a specific topic, searching and obtaining all relevant literature in a comprehensive way, and conducting a systematic assessment and integration of analysis to address the research question. This method contains five steps: defining research scope, developing selection criteria, planning the search, collecting and screening the literature, and presenting results [31];
* Bibliometric analysis. This is a quantitative method used to explore and describe previous studies, and is helpful for evaluating academic studies in a specific field [32; 33]. It offers a systematic, transparent, and reproducible review process, and thus enhances the reliability and quality of the review [34]. In this study, VOSviewer software has been used as a tool to perform the co-occurrence analysis, and then to realize the visualization of the intellectual structure;
* Classification method. This study uses the classification method to delineate the types of models and algorithms used in the literature for topic classification and prediction. The essence of classification is the grouping together of things or concepts that are similar in some way and, in doing so, comparing a given object with the object that the assessor believes best represents the category [35]. Here, we have used EXCEL to record the algorithms and models used in each article and the domain to which they belong, and to classify them accordingly.
## 3 Methodology and Data Sources
### Reconstruction of Hourly Transport Pathways and Wind-Field Grids  
The spatiotemporal distribution of hourly transport pathways and wind-field grids were reconstructed using TrajStat combined with ArcGIS software. First, air mass backward trajectories were calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT-4) model [38] loaded into TrajStat, which is a GIS-based software, and we converted the backward trajectory data to ESRI \"PolylineZ\" shape file format [39; 40]. The calculation is described in detail in Section 3.3.1. Second, the backward trajectory data in shape file format were added into ESRI ArcGIS software and we performed a series of processing steps, as elaborated in Section 3.3.2, to obtain the hourly transport pathways and wind-field grids during PEs.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 2 Materials and Methods
### Trajectory Generation  
The DAI-based controller requires a trajectory to track between the current state and the desired state. It is desirable that the trajectory be continuous with the system velocity and position, and needs to reach the desired position and velocity after some finite time interval, i.e., if the current position is \(x_{0}\), the final position is \(x_{f}\), and the desired time to complete the move is \(T\) then the trajectory \(q(t)\) must satisfy  
\[q(0) =x_{0},\ \dot{q}(0)=\dot{x}_{0} \tag{4}\] \[q(T) =x_{f},\ \dot{q}(T)=\dot{x}_{f} \tag{5}\]  
One way of satisfying these requirements is to choose a trajectory that is a cubic polynomial of the form  
\[q(t)=x_{0}+\Delta x(at^{3}+bt^{2}+ct+d) \tag{6}\]  
where \(\Delta x=x_{f}-x_{0}\). Solving for the coefficients gives  
\[a =(\dot{x}_{f}-\frac{2\Delta x}{T}+\dot{x}_{0})(\Delta xT^{2})^{-1} \tag{7}\] \[b =(\Delta x-\Delta xaT^{3}-\dot{x}_{0})(\Delta xT^{2})^{-1}\] (8) \[c =\frac{\dot{x}_{0}}{\Delta x}\] (9) \[d =0 \tag{10}\]  
If constant velocity is assumed for \(t<t_{0}\) and \(t>t_{0}+T\) then the trajectory becomes the piecewise defined functions  
\[q(t) =\begin{cases}\dot{x}_{0}(t-t_{0})+x_{0}&t<t_{0}\\ x_{0}+\Delta x(a(t-t_{0})^{3}+b(t-t_{0})^{2}+c(t-t_{0})+d)&t_{0}\leq t\leq t_{0 }+T\\ \dot{x}_{f}(t-t_{0}-T)+x_{f}&t>t_{0}+T\end{cases} \tag{11}\] \[\dot{q}(t) =\begin{cases}\dot{x}_{0}&t<t_{0}\\ \Delta x(3a(t-t_{0})^{2}+2b(t-t_{0})+c)&t_{0}\leq t\leq t_{0}+T\\ \dot{x}_{f}&t>t_{0}+T\end{cases}\] (12) \[\ddot{q}(t) =\begin{cases}0&t<t_{0}\\ \Delta x(6a(t-t_{0})+2b)&t_{0}\leq t\leq t_{0}+T\\ 0&t>t_{0}+T\end{cases} \tag{13}\]
## 4 Materials and Methods
### Real-Time Quantitative PCR Validation  
Eight DEGs associated with Okaramine B biosynthesis, regulators, ABC transporters, and tryptophan metabolism were selected to confirm the transcriptome data via RT-qPCR. \(\beta\)-actin (Gene ID 4.104) was used as the inner reference. Primers used in this study were designed using Primer3 web v.4.1.0 [71]. Total RNA from eight-day submerged and semi solid fermentation samples was extracted using the FastPure(r) Plant Total RNA Isolation Kit (Cat#RC401, Vazyne, Nanjing, China) and reverse transcribed to cDNA using the iScript(tm) gDNA Clear cDNA Synthesis Kit (Cat#172-5035, Bio-Rad, Hercules, CA, USA). The qRT-PCR analysis was performed on a C1000 Touch(tm) Thermal Cyler system (Bio-Rad, Hercules, CA, USA) using iTaq Universal SYBR(r) Green Supermix (Cat#1725122, Bio-Rad, Hercules, CA, USA). Three biological replicates were used in each experiment. The relative expression level of the genes was calculated using the \(2^{-\Delta\Delta\mathrm{CT}}\) method.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 3 Hierarchical Phoneme Classification
### Consonant Group Model Architectures  
As described in the previous section, we focus on making a better decision on consonants rather than on vowels. Consonants show various types of energy distributions in phoneme groups. Therefore, we implement appropriate models for individual consonant phoneme groups and verify their performance. MFCC_gws is a feature extraction method with group different window sizes, as well as autocorrelation features added to help distinguish voiced phonemes and unvoiced phonemes within the consonant group. By combining the two features [51], three phoneme group models are created.  
Figure 7 shows three different model configurations. Figure 6(a) simply concatenates MFCC_gws and the autocorrelation vector at the input unit and passes them to a BLSTM layer. A fully connected (FC) layer generates softmax outputs as many as the number phoneme to generate predictions for phoneme labels. Figure 6(b) has individual BLSTM layers for MFCC_gws and the autocorrelation vector, and concatenates the outputs to pass them through an FC layer. The advantage of output concatenation over input concatenation is that the mismatches of the different kinds of features, such as scale differences can be automatically adapted to each other. The concatenation at the BLSTM output layers is expected to be more reliable than Figure 6(a). Figure 6(c) adds another BLSTM layer to the concatenated output of Figure 6(b). These three configurations are used to construct the hierarchical phoneme classifier in Figure 4.  
Figure 6: Waveforms and autocorrelation functions of a voiced sound (phoneme [d]) and a unvoiced sound (phoneme [t]). Upper floats are waveforms and lower floats are computed autocorrelation functions.
## 3 Results and Discussion
### Evaluation of the Performance on Different Movements  
We further investigated the model performance for each FMS test individually. Figure 6a,b show the F1-micro-average and F1-macro-average for FMS movements on different methods. For all three models, we found that improved GMM-based model has better overall performance compared to the Naive-Bayes-based model and AdaBoost.M1-based model. Specifically, the improved GMM-based model performs better than the other methods across the four movements (m03, m05, m09, and m11); the performance of the three methods is essentially the same across the two movements (m07 and m14).
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods  
### Generation of Space-Time Images  
STIV is a non-contact measurement method that takes the velocimetry lines as the analysis area and estimates surface velocity by detecting the texture angle of the synthesized space-time images [18]. Firstly, a camera installed on the bank is used to collect an image sequence of \(m\) frames of the river surface in a certain time interval \(t\); then, a series of velocimetry lines are set along the direction of the actual movement of the water; these lines are only the width of a single pixel with a length of \(l\) pixels. Next, a space-time image (STI) with a size of \(l\ \times\ m\) pixels is synthesized in an \(x-t\) rectangular coordinate system constructed from the motion distance and time. Figure 1 shows the generation of ST images; the black and white texture trajectory is displayed in the space-time image, and the angle \(\theta\) between these texture trajectories and the vertical direction is defined as the main orientation of texture (MOT).  
Figure 1: Schematic diagram of space-time image synthesis.  
Suppose that in the physical coordinate system, the distance of the river surface flow feature moving along the velocimetry lines in time \(t\) is \(x\), corresponding to \(l\) pixels moving within \(m\) frames in the image coordinate system; then, the river surface velocity \(V\) on the velocimetry lines is shown in Equation (1).  
\[V=\frac{x}{t}=\frac{l\cdot k_{x}}{m\cdot k_{t}}=\tan\theta\cdot\frac{k_{x}}{k_{t }}=\tan\theta\cdot k_{x}\cdot fps \tag{1}\]  
where \(k_{x}\) (m/pixel) represents the actual distance of each pixel, \(k_{t}\) (s/pixel) represents the time interval between two frames, \(fps\) (pixel/s) represents the camera frame rate, and \(tan\theta\) represents the tangent of the texture angle.
## 3 Conventional Geolocation Techniques
### Angle of Arrival (AOA)  
AOA is the angle of the arriving signal at a sensor that was emitted by the target. This angle is utilized to determine the location of the emitter [38].  
Based on the direct measurement accuracy (\(\pm\theta_{s}\)), the location of the emitter will be determined about the Line of Bearing (LOB) path by \(2\theta\) angular spread. A simple triangulation in the AOA technique is used to determine the location of the emitter, as illustrated in Figure 3(a). Mathematically, the angle between the emitter and sensor (\(i\)) is  
\[\theta_{i}=tan^{-1}\Bigg{[}\frac{(y-y_{i})}{(x-x_{i})}\Bigg{]} \tag{1}\]  
The authors of [39] have proposed a closed-form solution for geolocation, employing an AOA technique in the presence of sensor position errors; the performance of the algorithm proposed was verified by CRLB and MSE; Figure 3(b) shows the asymptotic of the proposed algorithm performance to CRLB. The geolocation system can be regarded as that which employs a UAV applying on-board AOA in Line of Sight (LOS) conditions [40]. Employing more than two sensors will produce an increase in geolocation accuracy. When the primary scatter off the radio systems is positioned away from the sensors and located around the emitter, the AOA technique can present a reasonable location accuracy [41]. [PERSON] et al. proposed a weighted AOA-based localization approach; they computed the relationship between the Cumulative Distribution Functions (CDFs) and the localization errors of the three proposed methods with approximate factors, and they have presented a comparison of emitter localization performance based on AOA measurements implemented by two localization scenarios. The first scenario used two-antenna of sensors (L = 2) and the second used three-antenna of sensors (L = 3). From the results achieved, it is feasible that the Least Square Methods (LSM) with a relative factor can minimize the average error by approximately 20% compared to the basic LSM [41]. The AOA technique has some drawbacks, such as complex hardware; in addition, the accuracy and precision decrease when there are signal reflections from adjacent walls or something similar (multi-path), which produces an NLOS error. Therefore, the AOA technique is not suitable as in indoor or urban field geolocation systems [37].  
Figure 4: \(Cont\).
## 2 Materials and Methods
### DNA Extraction and MisSeq Illumina Sequencing  
Environmental DNA was extracted immediately after sampling to minimise significant DNA degradation. Prior to DNA extraction, water and sediment samples were aseptically homogenised by vortexing to prevent decantation or non-representative sampling. After homogenisation, water samples (W1 and W4) were filtered throughout nylon membranes with 0.45 um pore diameter (Merck KGaA, Darmstadt, Germany), at a rate of 1 nylon membrane per 500 mL, approximately. For the DNA extraction from sediments, 0.5 g of sediment sample were used, while for the water samples, nylon membranes obtained after filtering were used. Environmental DNA extraction was performed using the NucleSpin(r) Soil DNA extraction kit (Macherey-Nagel, Duren, Germany) according to the manufacturer's instructions with slight adaptation for water samples. Nylon membranes were sterilely cut into small fragments by a scalpel, immersed into lysis buffer and vortexed. The resultant supernatant was taken to further proceed to DNA extraction following manufacturer's instructions. Yield, quality and purity of extracted DNA was quantified by 1% agarose gel and spectrophotometer, using a multimode lector Cytation 5 with the specific software Gen5 (Biotek, VT, USA). DNA yield and quantitation were measured by absorbance at wavelengths 260, 280 and 320 nm. Purity ratio A260/280 was around 1.8 in water and sediment DNA. All extracted DNA samples were stored at \(-\)20\({}^{\circ}\)C until further processing.  
After DNA quality and quantity controls, amplicon sequencing (V3-V4 region) of 16S rRNA gene [20] was performed on DNA extracts on a MiSeq using v3 chemistry by Stabvida (Caparica, Portugal, Portugal). 16S amplicon PCR primers used in the library preparation was 5\({}^{\prime}\)-CCTACGGNGGCWGCAG-3\({}^{\prime}\) with the Illumina adapter 5\({}^{\prime}\)-TCGTCGGCAGGCTGTCAGATGTGTAAGAACAG-3\({}^{\prime}\) for forward sequencing and 5\({}^{\prime}\)-GACTACHVGGGTACTAATCC-3\({}^{\prime}\) with the Illumina adapter 5\({}^{\prime}\)-GTCTCGGCTCGGGAATGTGTAAGAGACAG-3\({}^{\prime}\) for reverse sequencing. Final taxonomic assignment was obtained after amplicon data processing and sequence alignment using QIIME 2\({}^{\text{TM}}\) v2018.6.0 [21] and the version 132 of SILVA database [22]. Generated sequences from epilimion, hypolimnon and sediment were deposited in the GenBank database under the Bio-Project PRJNA734791 and BioSample accessions number SAMN19539535-SAMN19539537.
## 6 Conclusions  
Combined with aPSO-DE hybrid algorithm, the convergence properties of angular light-scattering method (ALSM) and spectral extinction method (SEM) in retrieving ASDs are studied. Moreover, the sensitivity analysis of measurement signals to characteristic parameters in ASDs is also studied. Compared with the SEM, there are several advantages of using ALSM. The following conclusions can be drawn:  
1. To obtain more accurate results, the measurement wavelengths should be better selected from the short wavelength region for SEM (e.g., \(\lambda\in[0.2,\,1.4]\) m in this study), and the measurement angles should better be selected from the forward angle region for ALSM (e.g., \(\theta\in[0,\,30]\) deg in this study).
2. ThePSO-DE hybrid algorithm can converge much faster and obtain a lower fitness function value within a smaller number of generations than the SPSO algorithm in retrieving the ASDs.
3. The retrieval results by using ALSM show better convergence accuracy and robustness than those by using SEM, which attributes to the different distributions of objective function value for these methods. Moreover, considering only single spectral information of aerosol is required, the ALSM provides a more effective and reliable technique to obtain spherical ASDs. Future research will focus on the application of ALSM in studying spheroidal ASDs.  
**Author Contributions:** All authors have contributed equally to this work. [PERSON], [PERSON] and [PERSON] conceived the structure of paper; [PERSON] and [PERSON] wrote the simulation program; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Funding:** The supports of this work by National Natural Science Foundation of China (No: 51806103 and No: 51606095) and Jiangsu Provincial Natural Science Foundation (No: BK20170800, BK20160794) are gratefully acknowledged.  
**Acknowledgments:** A very special acknowledgement is made to the editors and referees who make important comments to improve this paper. Moreover, the authors are grateful to [PERSON] and [PERSON] for supplying the spectral optical constants of aerosols.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Distribution of retrieval results of monomodal ASDs for 30 calculations by using the SEM and ALSM: (**a**) and (**b**) for SEM; (**c**) and (**d**) for ALSM.
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
###### Abstract  
In this paper, we take into account a two-dimensional inverse scattering problem for localizing small electromagnetic anomalies when they are surrounded by small, randomly distributed electromagnetic scatterers. Generally, subspace migration is considered to be an improved version of Kirchhoff migration; however, for the problem considered here, simulation results have confirmed that Kirchhoff migration is better than subspace migration, though the reasons for this have not been investigated theoretically. In order to explain theoretical reason, we explored that the imaging function of Kirchhoff migration can be expressed by the size, permittivity, permeability of anomalies and random scatterers, and the Bessel function of the first kind of order zero and one. Considered approach is based on the fact that the far-field pattern can be represented using an asymptotic expansion formula in the presence of such anomalies and random scatterers. We also present results of numerical simulations to validate the discovered imaging function structures.  
Keywords:Kirchhoff migration; random scatterers; multi-static response matrix; Bessel function; numerical simulations +
Footnote †: journal: Applied Sciences