esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### 3D Microgestures' Design  
The designed 3D microgestures were guided by prior research studies and by currently used gestures [37; 41; 42; 43] (HoloLens, Microsoft, Albuquerque, NM, USA) (Magic Leap One, Magic Leap Inc., Florida, MIA, USA). To prevent computer interaction gestures from being mistaken for static non-computer gestures, only dynamic microgestures were considered. Two certified professional ergonomists with expertise in hand biomechanics and the design of tools and gestures were consulted to design microgestures that could optimize comfort and reduce the risk of hand fatigue and pain. For example, based on prior research that has identified sustained supination (palm up) of the forearm as a risk factor for pain and discomfort [34], microgestures that promoted pronated (palm down) to neutral (thumb-up) forearm postures were selected. Based on the design criteria described, a library of 33 microgestures was created and described pictorially (Figure 1) and literally (Table 2).  
\begin{table}
\begin{tabular}{l l c} \hline \hline  & **Task List** & **Score** \\ \hline
1A. Gesture on & 1B. Gesture off & 4.3 (0.7) \\
2A. Open menu bar & 2B. Close menu bar & 4.8 (0.4) \\
3A. Scroll left & 3B. Scroll right & 3.0 (1.2) \\
4A. Scroll up & 4B. Scroll down & 3.0 (1.2) \\
5A. Previous & 5B. Next, & 3.5 (1.2) \\
6A. Previous on a circle & 6B. Next, on a circle & 3.0 (1.6) \\
7A. Play & 7B. Pause & 4.5 (0.5) \\
8. Accept call & & 4.3 (0.3) \\
9. Decline call & & 4.3 (0.3) \\
10A. Mute & 10B. Undo mute & 4.7 (0.8) \\
11A. Volume up & 11B. Volume down & 4.7 (0.4) \\
12A. Enable marker & 12B. Disable marker & 3.3 (1.3) \\
13A. Confirm selection & 13B. Cancel selection & 5.0 (0.0) \\
14A. Shrink & 14B. Enlarge & 4.0 (1.2) \\
15A. Initialize & 15B. Restore & 3.3 (1.5) \\
16. Cursor & & 4.4 (0.6) \\
17. Translation & & 4.0 (1.2) \\
18. Rotation & & 4.3 (0.4) \\
19. Duplicate & & 3.0 (1.0) \\
20. Delete & & 3.0 (1.0) \\
21A. Add label & 21B. Remove label & 2.5 (0.5) \\
22. Back to center & & 2.8 (0.9) \\
23. Screen shot & & 2.8 (1.1) \\
24A. Fast forward & 24B. Fast backward & 2.5 (0.9) \\
25. Take picture & & 2.8 (1.1) \\
26. Take a video & & 2.5 (0.9) \\
27. Search & & 2.8 (1.3) \\
28A. Brightness up & 28B. Brightness down & 2.8 (1.3) \\
29. Battery save & & 2.0 (0.9) \\
30A. Enable keyboard & 30A. Disable keyboard & 2.8 (0.8) \\
31. Sleep mode & & 3.0 (0.9) \\
32. Shut down & & 2.7 (0.4) \\
33. Reset settings & & 2.8 (0.9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thirty-three commands with mean (\(SD\)) ratings by experts on their importance for using AR or VR systems (Likert Scale: 1 = least important, 5 = most important).  
Figure 1: The graphical description of 33 designed microgestures. Microgestures with N, P, or N–P indicated that the forearm in the neutral, pronated position, or rotating from neutral to pronated position while microgestures with * were identified as unfamiliar gestures. The instruction on how to perform each microgesture could be checked in Table 2, for example, microgesture \(a\) was performed from extended fingers to close fingertips or the reversed one.
## 4 Experiments and Discussion
### Feature Selection  
The selection of traffic flow features determines the prediction accuracy to a large extent. The specific traffic features are screened in Table 2, where the sequence features are the traffic flow of the prediction point, the spatial features are the traffic flow of other detection points in a certain area that influence the prediction point, and the external features are the influence of weather factors on the traffic flow; the periodic features mainly consider the periodicity of days and morning and evening peaks, and whether they are working days and holidays, with the label set to 1 for morning and evening peaks and 0 for the rest of the time. The working day label is set to 1, and the holiday label is set to 1. The time of the morning peak is selected as 8:00-10:00 and the evening peak period is 17:00-20:00.  
There are many factors affecting traffic flow, but when analyzing traffic flow in a specific environment, each factor has a different degree of influence on traffic flow, and feature selection is required to prevent the introduction of redundant features. Pearson correlation coefficients are used to filter features, and the correlation analysis is shown in Figure 5.  
The correlation between the prediction point G and the rest of the road observation points was obtained by analyzing the
###### Abstract
Consequently, the signal detection performance deteriorates. Thus, existing data-aided methods for signal detection will face the problem of performance degradation under low signal-to-interference ratio (SIR) and SNR circumstances. Meanwhile, such methods usually estimate CFO according to the characteristics of the phase shift between symbols in the training sequence. Similarly, their CFO estimation performance will also degrade as the signal phase is destructed by the in-band interferences. To tackle the problem of performance degradation of existing methods in the presence of in-band interferences, we propose an adaptive signal detection and CFO estimation method based on virtual array technology in this paper. The virtual array is not a real existence but an equivalent array constructed based on the array signal model that is derived. With three sub-training sequences whose symbols are different, we first construct virtual array snapshots from the first two sub-training sequences and use the root-MUSIC algorithm [16] to perform virtual spatial spectrum estimation to obtain frequency estimations of the strong signals, which include in-band interferences and the expected signal. Then, we identify the frequencies corresponding to the in-band interferences and the expected signal according to the relationship between the frequency of the modulated signal and the symbol of the training sequence. On this basis, we further perform beamforming on the virtual array snapshots constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, better signal detection and CFO estimation can be obtained by performing virtual spatial spectrum estimation on the beamformed signal. The feasibility and effectiveness of the proposed method are verified by theoretical analyses and simulation experiments, which show that the proposed method can detect signals with lower missed detection probability and false detection probability, as well as achieve higher CFO estimation accuracy compared to existing methods. The rest of this paper is organized as follows: Section 2 presents the signal model. Section 3 introduces the principle of the proposed method for the construction of a virtual array signal and beamforming. Section 4 presents the results of simulation experiments. The conclusion is drawn in Section 5.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 2 Proposed Method 1: Robust Models with Gabor Dictionary Analysis and Machine Learning
### Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) Model  
The audio cough signal data are split into \(a_{t}\) and \(b_{t}\) with \(t=1,2,\ldots,n\) and a specific lag \(l\). Between the two time series \(a_{t}\) and \(b_{t+l}\), the general formula for the covariance function is represented as follows:  
\[c_{ab}(l)=\frac{1}{n}{\sum_{t=1}^{n-l}}(a_{t}-\overline{\mu}_{a})(b_{t+l}- \overline{\mu}_{b}) \tag{7}\]  
where the means of \(a_{t}\) and \(b_{t}\) are expressed as follows:  
\[r_{ab}(l)=\left\{\begin{array}{l}\frac{c_{ab}(l)}{\sigma_{a}\sigma_{b}};l \geq 0\\ \frac{c_{ba}(-l)}{\sigma_{a}\sigma_{b}};l<0\end{array}\right. \tag{8}\]  
where the standard deviations of \(a_{t}\) and \(b_{t}\) are indicated by \(\sigma_{a}\) and \(\sigma_{b}\), respectively. \(a_{t}\) and \(b_{t+l}\) are correlated if \(l\) is a positive integer. \(a_{t}\) and \(b_{t-l}\) are correlated if \(l\) is a negative integer. When \(l=0\), then \(r_{ab}=r_{ab}(0)\), indicating the correlation coefficient. It is assumed that the cough audio signal \(a_{t}\) is correlated with the time-delayed signal \(b_{t+l}\) and the audio signal \(q_{t}\) is highly correlated with both \(a_{t}\) and \(b_{t+l}(t=1,2,\ldots,n)\). Based on CCF \(r_{ab}(l)\), it can be assessed with the help of partial CCF (PCCF) and represented as follows [32]:  
\[r_{ab|q}(l)=\frac{r_{ab}(l)-r_{aq}\cdot r_{qb}(l)}{\sqrt{\left(1-r_{aq}^{2} \right)}\sqrt{\left(1-r_{qb}^{2}(l)\right)}} \tag{9}\]  
where the CCF at lag \(l\) is represented by \(r_{ab}(l)\) and \(r_{qb}(l)\). A discrete PCCF which corresponds to lag \(l\) is represented by \(r_{ab|q}(l)\). The elimination of the linear influence \(q_{t}\) is conducted from \(r_{ab}(l)\). The PCCF manages to indicate the cross-correlation among the signal residuals.
## 2 System Characteristics
### The Theoretical Analysis of M-PPM Photon-Counting Communication System  
M-PPM photon-counting receiver has been shown to achieve high communication sensitivity [9; 37]. In the M-PPM symbol, a single pulse in one of the M transmission slots represents \(\log_{2}(\mathrm{M})\) bits of information. When the PPM system works, the laser can operate at both high peak power and low average power, with the number of signal photons received corresponding to the energy of the emitted single laser pulse energy [38]. For the photon-counting receiver, assuming the signal optical power incident on a single-photon detector is \(P_{r}\), the average photon arrival rate will be [39]:  
\[\lambda_{s}=\frac{\gamma P_{r}}{h\
u} \tag{10}\]  
where \(\gamma\) is the quantum efficiency of a single-photon detector, \(h\) is [PERSON]'s constant, and \(\
u\) is the frequency photon. Considering the influence of background optical power and the single-photon detector's dark count, the average noise photon rate is given as:  
\[\lambda_{n}=\frac{\gamma P_{b}}{h\
u}+\lambda_{d} \tag{11}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Water Types** & \(\lambda_{0}\) **[nm]** & \(a\) **[m\({}^{-1}\)]** & \(b\) **[m\({}^{-1}\)]** & \(c\) **[m\({}^{-1}\)]** & \(L_{\text{ext}}\) **[m]** & **Albedo \(\omega_{0}\)** \\ \hline Jerlov I & 450 & 0.018 & 0.0038 & 0.022 & 45.87 & 0.17 \\ Jerlov IA & 450 & 0.0221 & 0.00631 & 0.028 & 35.71 & 0.23 \\ Jerlov IB & 450 & 0.0235 & 0.068 & 0.092 & 10.87 & 0.74 \\ Jerlov IC & 450 & 0.105 & 0.514 & 0.619 & 1.62 & 0.83 \\ Jerlov II & 450 & 0.0241 & 0.504 & 0.528 & 1.89 & 0.95 \\ Jerlov III & 450 & 0.0388 & 2.38 & 2.419 & 0.41 & 0.98 \\ \hline \end{tabular} Parameters: \(\mathrm{I_{ext}}\): extinction length; \(\omega_{0}=b/c\).  
\end{table} 
Table 2: Optical characteristics parameters for the various water types [33]. where \(P_{b}\) is the background optical power, and \(\lambda_{d}\) is the dark count rate. Then, the average number of counted photons in a period of time \(t\) = \(T_{s}\) can be arrived at:  
\[\rho=\lambda T_{s} \tag{12}\]  
Since the single-photon detector is in a silent state during the dead time and cannot respond sequentially to arriving photons, the maximum number of photons detected by the single-photon detector in a period of t is \(\rho_{\max}\) = \(\left\lfloor T_{s}/\tau+1\right\rfloor\), with \(\left\lfloor x\right\rfloor\) denoting the largest integer that is smaller than \(x\)[40]. The photocount distribution of an active quenching single-photon detector with a dead time of \(\tau\) during the time interval of \(T_{s}\) is given by [41]:  
\[f\left(\rho,\lambda_{\rho}\right)=\left\{ \begin{array}{ll}\sum\limits_{i=0}^ {\rho}\psi\left(i,\lambda_{\rho+1}\right)-\sum\limits_{i=0}^{\rho-1}\psi\left( i,\lambda_{\rho}\right)&\rho<\rho_{\max}\\ 0&\rho\geq\rho_{\max}\end{array} \right.
# Context-Aware End-User Development Review
## 5 What Is behind the Tool? Implementation Approaches and Challenges  
### Rule-Based Programming  
Rule-based is the most common method to specify context-aware applications. The basic rule expression form is the \"IF condition Then action\" adopted by iCap [44], Context Cloud [60], GALLAG Strip [61], and VS-CaSP [64]. [PERSON] et al. [68] improve the implementation of Context Cloud, incorporating a rule engine to infer and iBeacons and algorithms to provide proximity functionalities. Situation API [50] enables decomposing context rules into references, parameters, and listeners. A reference abstracts input context (e.g., location, light level). The parameters are relevant parameters processed by references (e.g., bedroom light is on). The listeners receive notification of occurrences such as invoked actions or modified parameters (e.g., turn off the lights).  
Events complement rule-based expressions, such as T4 Tags 2.0 [70], which define trigger-condition-action (WHEN) rules. TARE [73] extends the rule expression to include IF-THEN, WHEN, AND, OR. Similarly, IVO [57] activity workflows model events. The architecture includes a rule-based system and an event-driven workflow engine. The rule system allows the definition of alternative actions (i.e., IF-THEN-ELSE), considering standard operations (e.g., greater than, equals, not, AND, OR). The workflow engine is implemented as a finite state machine (FSM) to execute workflows in mobile clients. A mobile client also includes the perceived environment (sensing) and process conditions (register/unregister context values in the workflow engine). The application definitions are expressed in IVOML--an XML-based language to develop mobile context-aware applications [88].  
SSB [62] allows for context registration and subscription of HNS (Home Network System) services. End-users can search and aggregate context, as well as verify, reuse, and refine existing services. The context registration allows defining conditions (logical expressions) for sensor services. Afterward, the context subscription allows binding registered contexts to appliance operations. In the end, end-users can create service rules for the HNS.
# Information-Aware Secure Routing in Wireless Sensor Networks
## 3 Model and Methods
### System Model  
We assume that a WSN consists of \(n\) nodes and adopts a mesh-network structure as its topology. For a WSN \(G=(V,E,W)\), \(V=N\cup\{sink\}\), \(N\) represents the set of sensor nodes, \(E\) represents the set of edges connecting the nodes, and \(W=\{w_{ij}|(v_{i},v_{j})\) is an edge of \(E\}\) and is a set of weights associated with edges. \(G\) has a unique source node (\(src\)) and sink node (\(sink\)). For any two nodes \(s\) and \(d\) of \(V\), a transmission path \(\langle m_{1},\cdots,m_{K+1}\rangle\) of length \(K\) is a \(K\)-hop path between them, where \(m_{1}=s\), \(m_{K+1}=d\), and \(\langle m_{i},m_{i+1}\rangle\) is an edge of \(E\), \(\forall i\ \in\{1,\cdots,K\}\). Consequently, a path from the source to the sink in \(G\) can be written as \(l_{s}=\langle src,\cdots,sink\rangle\). Along with a multi-hop path to the sink, data can be transmitted to the destination. However, in the presence of malicious nodes (i.e., some nodes of \(G\) are captured by attackers and act as malicious nodes), a \(K\)-hop path \(\langle m_{1},\cdots,m_{K+1}\rangle\) cannot guarantee successful data transmission between \(m_{1}\) and \(m_{K+1}\).
## 4 Results and Discussions
### Extended Solution  
The virtual SEA model can be used to extend the prediction of noise level up to 4 kHz using the ACTRAN extended solution [21; 22]. Figure 11 displays the noise level prediction  
Figure 10: Calculated pressure using VSEA vs. ATPA methods at (**a**) Target 1 and (**b**) Target 2.  
obtained up to 4000 Hz. For this simulation, the structure and cavity mode shapes are achieved for up to 1250 Hz with the FEM model. The extended solution predicts the higher frequency mode shapes and computes the noise pressure levels.  
Figure 11 depicts how this solution anticipates a noise trend consistent with the calculations derived from ATPA, particularly at high frequencies. Discrepancies between the model outcomes and ATPA results can potentially be attributed to the underlying assumptions of the SEA techniques. Furthermore, the SEA results are acquired via mode shapes sourced exclusively from the range of 900 Hz to 1250 Hz (via interpolation). For higher frequencies, an extrapolation technique is applied (referred to as the extended solution).
# Tunable and Switchable Erbium-Doped Fiber Laser Using a Multimode-Fiber Based Filter
## 2 Experimental Setup and Principle
Note that when tuning the paddles of the PC, the change of the fast axis orientation angle in the MMF may slightly affect the coupling coefficients of the dominant guiding modes, which could change the contrast of the bandpass filter. The model above could be extended to the general case with more than two modes by introducing an additional phase shift, \(\varphi_{ij}\), from the \(i\)th- and \(j\)th-order modes. Then Equation (1) could be modified to:  
\[I=\sum_{i=1}^{n}I_{i}+\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}2\sqrt{I_{i}I_{j}}\cos \biggl{(}\frac{2\pi(n_{i}-n_{j})L}{\lambda}+\varphi_{ij}\biggr{)}. \tag{6}\]  
When a few more modes are considered, the interferences between the relatively dominant higher-order modes and the fundamental mode could modulate the spectrum of the MMF-based filter. In contrast to the uniform periodic spectrum from a two-beam interference, the amplitude of the spectrum and the wavelength spacing of the MMF-based filter between adjacent peaks could be non-uniform. By adjusting the paddle of the PC, one could change the stress-induced additional phase shifts in the MMF, which enables the bandpass filter to be tunable; moreover, tuning the PC allows exciting different polarized mode components within the LP mode families, which helps the switchable function. Thus, by adjusting the PC, the wavelength spacing and the amplitude of the adjacent interference peaks could be adjustable, which could be used to dynamically reshape the spectrum of the MMF mode-beating filter. Therefore, the tuning mechanics of the MMF-embedded PC could be implemented in fiber lasers to realize tunable and switchable functions in single/multiwavelength operating modes.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 4 Conclusions  
TA/GO composites were successfully synthesized, and their adsorption behavior toward U(VI) ions was studied by bath sorption experiments. The resultant TA/GO composites had excellent adsorption capacity in concentrating or removing U(VI) ions from aqueous solutions at low pH. The adsorption of U(VI) ions on TA/GO was controlled by contact time, pH, ionic strength, and the solid-liquid ratio. In addition, the adsorption of U(VI) ions on TA/GO followed pseudo-second-order kinetics and the Langmuir model. Moreover, TA/GO composites have outstanding selectivity for uranium ions in aqueous solutions containing other ions (Cs\({}^{+}\), Sr2\({}^{+}\) Co2\({}^{+}\)) and good recyclability for uranium adsorption. Therefore, TA/GO has high selectivity, excellent adsorption, and good recyclability adsorbents is an ideal material to remove highly toxic U(VI) ions from wastewater.  
Conceptualization, B.Z., Z.G., and B.L.; methodology, B.Z. and Z.Z.; software, B.Z. and Z.Z.; validation, B.Z., F.S., and Z.Z.; formal analysis, B.Z.; investigation, B.Z.; resources, Z.G.; data curation, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing, B.Z.; visualization, B.Z.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number 20190431.  
We sincerely thank [PERSON] from College of Civil Engineering and Mechanics, Lanzhou University for the technical support during these studies.  
The authors declare no conflict of interest.
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 2 Materials and Methods
### Olfactory Stimulation  
According to previous studies, sandalwood essential oil and bergamot essential oil are commonly used in olfactory aromatherapy and are capable of eliciting significant emotional responses [16]. Specifically, sandalwood essential oil has been associated with emotional relaxation [17], while bergamot essential oil has been linked to mood enhancement [18]. These contrasting emotional effects are advantageous for developing machine learning models, as they provide distinct emotional states for classification and analysis. Therefore, in this study, undiluted sandalwood (_Santalum album_ L.) essential oil (SEO) and bergamot (_Cirrus medica_ L. var. _sarcodactylis_ Swingle) essential oil (BEO), sourced from Quintis Trading Co., Ltd. (Xiamen, China) and Zhejiang Golden Hand Biotechnology Co., Ltd. (Zhejiang, China), respectively, were employed to produce olfactory stimuli. For the convenience of the participants, unscented aroma diffuser woods were procured as carriers for the essential oils. The EEG data obtained during the inhalation of SEO was designated as 0 (negative sample), and the data corresponding to BEO inhalation was marked as 1 (positive sample).
# Evaluation of the Occurrence of Phthalates in Plastic Materials Used in Food Packaging
## 2 Materials and Methods
### HS-SPME Multivariate Optimization Process  
Several factors influence the SPME efficiency, the most important being the fiber coating, extraction time, and temperature. A multivariate optimization process using a mixed standard solution containing phthalates at 60 \(\upmu\)g/L was used to achieve the optimal extraction efficiency of HS-SPME. The study of the variables that were considered significant for the experiment was conducted using a 3\({}^{3}\) factorial design, namely, fiber coating (DVB/CAR/PDMS, PDMS, PDMS/DVB), extraction (15, 30, and 45 min), and extraction temperature (60, 70, and 80 \({}^{\circ}\)C). The optimal HS-SPME extraction conditions were used to analyze the samples. After sample preparation, an aliquot of 2 mL of sample and 0.2 g of NaCl was placed in an 8 mL vial. The vial was capped with a Teflon (PTFE) septum, and the fiber was introduced and exposed into the headspace for 30 min at 80 \({}^{\circ}\)C under constant agitation (400 rpm). Then, the fiber was removed from the vial and inserted into the GC injection port, where the extracted analytes were desorbed for 6 min at 250 \({}^{\circ}\)C. Each sample was analyzed in triplicate and blank chromatographic injections of the SPME device were performed before each set of analysis.