esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Light-To-Frequency Converter for Visible Light Communication Systems
###### Abstract  
PIN (positive intrinsic negative) photodiodes and analog-to-digital converters (ADC) are commonly used on visible light communication (VLC) receivers in order to retrieve the data on detected signals. In this paper, a visible light communication receiver based on a light to frequency converter (LTF) is proposed. We characterized the LTF and derived an equation for signal-to-noise ratio (SNR) estimation in terms of its input optical power, and the frequency of the output periodic signal. The experiments show that the periodic signal of the LTF converter has a maximum output frequency of 600 kHz at a distance of 6.2 cm. In this setup, measured SNR reached 18.75 dB, while the lowest obtained SNR with 1.1 m length was roughly \(-\)35.1 dB. The results obtained suggest that a bit rate of 150 kbps can be achieved with an on-off keying (OOK) modulation format. We analyzed the results and discuss the advantages and limitations of the LTF converter for optical wireless communication purposes.  
visible light communication; light to frequency converter; white-light LED; optical wireless communication +
Footnote †: journal: Accepted: 17 August 2018; Published: 28 August 2018
## 4 Defines and Clarifies the Problems--Why Is Biomechanical One Different from Other Biofeedback?
### Biomechanical Steps Required in Developing Wearables for Biomechanical Feedback  
As discussed before, a successful motor learning outcome can be supported by useful and timely biomechanical feedback to the athlete targeting performance defects. Systematic, objective, and reliable performance monitoring and evaluation, performed by means of quantitative analyses of biomechanical variables, can reinforce the biomechanical feedback training in sports practice [12; 37; 42]. Therefore, the ways of quantifying a motor skill with high spatial and temporal accuracy (i.e., the limbs coordination) would be the key for developing wearables for biomechanical feedback training [50].  
Currently, the most reliable biomechanical feedback method is three-dimensional (3D) motion capture, which identifies and tracks markers that are attached to a human subject's joints and body parts to obtain 3D skeleton information [51; 52; 53]. The spatiotemporal human representation based on 3D motion capture data is currently the most trustworthy approach in motor skill quantification, both in sports and arts performance [54; 55; 56; 57; 58; 59; 60]. This method, however, mainly supplies post-measurement feedback (i.e., not real-time) due to its drawbacks: multiple cameras placed in a room, long calibration and setup procedures, a time consuming course on data collection, processing, analysis, and interpretation, and the high cost of the equipment [61; 62; 63].  
For practitioners, real-time feedback is more useful. Yet, due to the drawbacks of 3D motion analysis technology and the diversity of human motor skills in sports and arts performance, research on biomechanical feedback has to undergo:  
1. selection of a specific motor skill,
2. 3D motion analysis of the skill,
3. verification of post-measurement feedback in practice, and
4. development of feedback device for monitoring the critical/vital parameter(s) (e.g., coordination among certain segments or joints) for the given motor skill.  
These steps are, at present, required for developing a reliable device that is capable of supplying real-time biomechanical feedback [27; 50].
# Angle-Resolved Fluorescence of a Dye Coupled to a Plasmonic Nanohole Array
## 2 Materials and Methods
### Dye Deposition  
ATTO/00 dye was selected to provide a distinct spectral overlap between the dye absorption and emission spectra, the plasmonic features, and the detection device in use [35; 41; 42]. The dye was deposited by drop-casting on the GNA and, in parallel, on a bare SiO\({}_{2}\) slide taken as a reference. To provide both uniformity and stability, alternating positively and negatively charged polyelectrolyte layers (PEL) were deposited on the targeted surface before the dye-deposition process.  
Operatively, poly(styrene) sulfonate at 2% and poly(diallyldimethylammonium) chloride at 2% solutions were alternatively deposited on the plasmonic metasurface, as shown in Figure 1. After each deposition step, the sample was washed with MilliQ water and dried under nitrogen flow. In this way, the PEL layers were stacked on the sample surface. The substrate charge was considered in the deposition procedure: positive for the PEF platform and negative for the SiO\({}_{2}\) one. Therefore, two layers on the PEF platform were deposited and three in the SiO\({}_{2}\) case. Consequently, the PEL coverage thickness was estimated to be 4 nm for the PEF platform and 6 nm for the SiO\({}_{2}\) sample.  
Finally, an 80 uM aqueous solution of ATTO/00 was deposited by drop-casting [43; 44]. In this way, dye molecules are supposed to form a uniform single layer over the sample surface. Panel (a) of Figure 2 reports the PEF platform stack layout.  
Figure 2: Panel (**a**): PEF platform stack layout and fluorescence measurement configurations. Panel (**b**): Scheme of the custom optical setup for either pumping (\(\alpha\)) or collection (\(\beta\)) of angle-resolved fluorescence measurements.
## 6 Comparative Analysis
### Comparison with IFRs, CIFRs and IVIFRs  
An IVCFR argues about both the degree of membership and non-membership, as do the structures of IFRs, CIFRs and IVIFRs. However, IFRs and IVIFRs involve only real numbers, which limits them to single variable problems. They cannot express the problems involving time (periodic) or with phase changes.  
The structure of CIFRs is based on complex numbers and, thus, consists of amplitude and phase terms. However, the advantage of an interval-valued structure of IVCIFRs over the crisp valued structure of CIFRs is that the interval represents a set, thus covering the uncertainties, including mistakes made by the person, experimental errors or computer approximations that lead to fuzziness. A detailed comparison between IVCIFRs and IVIFRs is given below.  
We look to solve the problem discussed in Section 5.3 by using IVIFRs. We present the following two IVIFSs \(\bar{\mathbb{F}}\) and \(\bar{\mathbb{F}}\) representing the set of securities and the set of sources of threats, respectively. We omit some of the securities and the sources in order to minimize the amount of calculation. The details of abbreviations used in the following sets are in Table 4.  
\[\bar{\mathbb{F}}=\left\{\begin{array}{c}\left(DD,[0.550,0.600],[0.300,0.400] \right),\left(PP,[0.475,0.525],[0.250,0.375]\right),\\ \left(STEM,[0.300,0.375],[0.275,0.325]\right)\end{array}\right\}\]  
\[\bar{\mathbb{F}}=\left\{\begin{array}{c}\left(USB,[0.375,0.450],[0.275,0.32 5]\right),\left(RA,\left[0.350,0.400\right],[0.325,0.350]\right),\\ \left(IC,[0.475,0.575],[0.250,0.350]\right),\left(OC,[0.500,0.550],[0.225,0.300 ]\right)\end{array}\right\}\]The IVIFR \(\bar{\mathbb{R}}\) between \(\bar{\mathbb{E}}\) and \(\bar{\mathbb{F}}\) is  
\[\bar{\mathbb{R}}=\left\{\begin{array}{l}\left((DD,USB),[0.375,0.450],[0.300,0.400]\right),\\ \left((DD,RA),[0.350,0.400],[0.325,0.400]\right),\\ \left((DD,IC),[0.475,0.575],[0.350,0.400]\right),\\ \left((DD,OC),[0.500,0.575],[0.300,0.400]\right),\\ \left((PP,USB),[0.375,0.450],[0.275,0.375]\right),\\ \left((PP,RA),[0.350,0.400],[0.325,0.375]\right),\\ \left((PP,IC),[0.475,0.525],[0.250,0.375]\right),\\ \left((SP,OC),[0.475,0.525],[0.250,0.375]\right),\\ \left((STEM,USB),[0.300,0.375],[0.275,0.325]\right),\\ \left((SIEM,RA),[0.300,0.375],[0.325,0.350]\right),\\ \left((SIEM,IC),[0.300,0.375],[0.275,0.350]\right),\\ \left((SIEM,OC),[0.300,0.375],[0.275,0.350]\right),\\ \left((SIEM,OC),[0.300,0.375],[0.275,0.325]\right)\end{array}\right.\right)\]  
From the above IVCFR \(\bar{\mathbb{R}}\), it is observed that it explains the effectiveness and ineffectiveness of securities against the sources of penetration. However, unlike IVCIFR, it does not involve the time frame. We are also interested in the time durations for which certain security can successfully handle the vulnerability. Therefore, the complex valued degrees must be involved to achieve the needed information.
## 1 Introduction
The rigid body method considers the shape and size of the object but neglects its deformation, making it a common method for building rockfall models [36]. Since the concentrated mass method treats falling rocks as an infinitely small point, it cannot reflect the impact of shape and size on rolling characteristics. In contrast, the rigid body method accurately represents the irregularity of the rock's shape [29]. Therefore, in theoretical analysis, this paper adopts the rigid body method, assuming the falling rock behaves as an ideal rigid body with no changes in shape and size during the rolling process. This allows for the investigation of how the shape of falling rocks influences their translation and rotation. To investigate the impact of shape on the movement characteristics of falling rocks prone to rockfall disasters, the selected rock shapes underwent model tests. Test blocks of varying shapes were released from slopes with different heights and inclinations. The rolling characteristics and changes in rolling speed of each test block were observed at different release heights, slope roughness levels, and slopes. The experimental results were then utilized to analyze the relationship between the shape of falling rocks and their rolling resistance. Proposed shape parameters were introduced into the kinematic formula, accounting for the influence of specimen shape, size, release height, slope, and slope roughness. This integration resulted in a formula for calculating the rolling speed and energy of rockfall. Subsequently, the accuracy and applicability of the formula were verified using data obtained from model experiments. By combining site characteristics with the shape of falling rocks, it becomes possible to infer the impact range of rolling falling rocks, providing valuable insights for the prevention and control of rockfall disasters.
## 5 Conclusions
Third, the co-registration errors between Landsat 8 and MODIS is still potential factor with side-effect on the fusion result, which are usually neglected. Previous researchers have done related research on the influence of co-registration errors on data fusion [39; 40]. Maybe, this is another way needing to be concerned to improve USTARFM performance. Last but not least, the applicability of the USTARFM method in inversing parameters, such as e.g. NDVI or biomass, need to be further tested. Although the work in this paper is based on Landsat and MODIS surface reflectance data are popular satellite sources, the USTARFM algorithm still has a prospect for other high resolution images (e.g., Chinese GF-1 satellite data with 16-m resolution, French SPOT-5/6 satellite data with 10/6 m resolution, _etc._) and medium resolution images (e.g., the NPOESS VIIRS data U.S. operational polar satellite constellation which is the new generation satellite after MODIS). This work are supported by the Natural Science Foundation of China (Grant No. 41301444), the \"Youth talent plan\" of Institutions of higher learning, Beijing, and the Major Project of High Resolution Earth Observation System, China. We thank USGS data center for providing free Landsat data and MODIS science team for providing free MODIS products. [PERSON] and [PERSON] designed research; [PERSON] performed research; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] analyzed data; [PERSON], [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest.
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence
#### 3.1.3 Bacterial Foraging Optimisation (BFO) Algorithm  
The BFO was proposed to mimic the E. coli bacteria's foraging behaviours in the intestine [40]. The algorithm became popular due to its high ability to escape from the local optimum, and when compared with other heuristic methods, it has faster convergence [41]. Some researchers are working to improve the BFO, which is used for area coverage as follows:  
[PERSON]. [41] proposed a bacterial chemotaxis optimisation (B.C.) algorithm. The B.C. is a decentralized algorithm used in swarm robots inspired by bacteria chemotaxis for target search and trapping within area coverage. This algorithm was adapted from the original bacterial foraging optimisation (BFO), and it considered each robot to be a bacterium and applied the mechanism of bacteria chemotaxis to solve problems of distributed controlling for swarm robots in area coverage. The B.C. set the target's position to a significant value, divided the area by the Voronoi method, computed the gradient for the robots' chemotaxis direction, and approached the target's position by bacteria swimming through the law in each Voronoi cell. Twenty-six robots can avoid six obstacles by moving robots in the direction to avoid these obstacles. The problem of detecting targets and surrounding them in a particular area was solved in this work. Simulation results have presented B.C. as having a good performance, robustness, and algorithm calculation complexity compared to several methods for swarm robots' distribution.  
Ref. [42] introduced a new proposal for a swarm robotic system called a bacterial chemotaxis-inspired coordination strategy (BCCS) for coverage and aggregation. Chaotic preprocessing is used to initialise the starting positions of the robots. Then, the area covered by the robot is computed as a fitness function value to compare with previous ones. Based on BCCS, every robot makes an action, running or rotating. The process continues until the maximum number of iterations is met or the number of covered cells satisfies the termination conditions. In addition, it has presented a random factor to guide robots to rendezvous at an undefined point to overcome aggregation. The simulation results showed the supremacy performance of the proposed strategy compared with other controllers in both success rate and iteration average number. BCCS has a high exploration ability for area coverage.
## 4 Experiment and Result Analysis
### Results and Comparison
#### 4.4.1 Statistical Test  
A non-parametric Friedman test was applied to verify the statistical significance of the differences between the performance results of the various machine learning classifiers. In this study, the prediction models were compared in terms of balanced accuracy score. The [PERSON] statistic test was calculated as 47.59 with a \(p\)-value of \(~{}1.18\cdot 10^{-7}\) and rejected the null hypothesis at a 99% significance level. According to the average ranks of the performances, SMOTE with GBT of 2.33 and SMOTE with RF of 3.33 models performed the top rank compared with other models. GBT, LR and DEEP MLP baseline models were determined low ranks by 20.41, 18.91 and 17.08, respectively.
# Multi-Layered Interactive Target Guidance with Visual Safety in Convex-Shaped Obstacle Environments
## 3 Safety Layer for Visual Safety
### Field of View Maintenance  
In this section, a controller for field of view maintenance with a control barrier function is presented.  
A candidate control barrier function for field of view maintenance, \(h_{fov}:\mathcal{D}_{fov}\to\mathbb{R}\), is introduced as  
\[h_{fov}(x_{pe})\coloneqq p_{co}^{T}T_{M}p_{co}-\Delta_{fov}. \tag{28}\]  
For this candidate control barrier function \(h_{fov}\), the following result holds.  
Theorem 1: _The function \(h_{fov}\) is a control barrier function for the pursuit-evasion system (5)-(7) in \(\mathcal{D}_{fov}\)._  
Proof: The time derivative of \(h_{fov}\) is  
\[h_{fov}(x_{pe},V_{wc}^{b}) =2p_{co}^{T}T_{M}\dot{p}_{co} \tag{29}\] \[=2p_{co}^{T}T_{M}(\dot{e}^{\dot{\theta}_{co}}v_{wo}^{b}-v_{wc}^{ b}-\dot{\omega}_{wc}^{b}p_{co}). \tag{30}\]  
Furthermore, \(-h_{fov}\leq 0\) holds on the set \(\mathcal{D}_{fov}\) from the definition of \(\mathcal{D}_{fov}\). Therefore, with any extended class \(\mathcal{K}\) function \(\alpha\), \(\dot{h}_{fov}\geq-\alpha(h_{fov})\) is satisfied with \(v_{wc}^{b}=-\|v_{aw}^{b}\|\frac{T_{M}p_{co}}{\|T_{M}p_{co}\|}\) and \(\omega_{wc}^{b}=0\). Consequently, (23) holds. In addition, \(h_{fov}\) is continuously differentiable on \(SE(3)^{2}\). Thus, \(h_{fov}\) is a control barrier function in \(\mathcal{D}_{fov}\).  
In the previous research [18], control barrier functions for maintaining field of view were defined for each feature point \(p_{ci}\). In contrast, this paper requires only a single control barrier function for the entire object.  
From the proof of Theorem 1, an upper bound can be given on the minimum value of the norm of the input required for field of view maintenance.  
**Corollary 1**.: _There exists an input that satisfies_  
\[\|v_{\text{nc}}^{b}\|\leq\frac{k_{e}}{\Delta_{fov}}+v_{max} \tag{31}\]  
_and achieves field of view maintenance._  
Proof.: Since \(\|p_{\alpha}\|\) is greater than \(\sqrt{\Delta_{fov}}\) on \(\mathcal{D}_{fov}\), and \(v_{\text{nc}}^{b}\) is assumed to be (6), the controller \(v_{\text{nc}}^{b}=-\|v_{\text{nc}}^{b}\|\frac{T_{M}p_{\alpha}}{\|T_{M}p_{ \alpha}\|}\) satisfies (31).  
We define the admissible control input set for field of view maintenance as  
\[K_{fov}(x_{pe}):=\Big{\{}V_{\text{nc}}^{b}\in\mathbb{R}^{6}\mid h_{fov}(x_{pe}, V_{\text{nc}}^{b})\geq-2h_{fov}(x_{pe})\Big{\}}. \tag{32}\]  
In the absence of obstacles, by applying a Lipschitz continuous controller to the pursuit-evasion system (5)-(7), which satisfies \(V_{\text{nc}}^{b}(x_{pe})\in K_{fov}(x_{pe})\) for all \(x_{pe}\) in \(\mathcal{D}_{fov}\), the set \(\mathcal{D}_{fov}\) is rendered forward invariant, as stated in Lemma 1.
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
## 5 Simulations
### Spectral Efficiency  
Figures 4 and 5 show the SE versus \(\log_{2}M\) for different OFDM-based generalized MIMO schemes when \(N=2\) and \(N=3\) are considered, respectively. When \(N=2\) and \(\log_{2}M=8\), the SE of the proposed TD-QGSM is, respectively, improved by 56.5%, 56.5%, and 99.8% compared to TD-GSM, FD-QGSM, and FD-GSM, while the SE of the proposed TD-QGSMP is, respectively, improved by 72.3% and 99.9% compared to TD-GSMP and FD-GSMP. The TD-QGSMP shows the highest SE among all the OFDM-based generalized  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Algorithm** & **Activated LED Number** & **Floating Point Operation Count** \\ \hline \multirow{4}{*}{ML-MRC} & 1 & 512 \\  & 2 & 12,288 \\  & 3 & 262,144 \\  & 4 & 1,310,720 \\ \hline \multirow{4}{*}{The proposed IVC-based OMP} & 1 & 64 \\  & 2 & 160 \\ \cline{1-1}  & 3 & 288 \\ \cline{1-1}  & 4 & 448 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complexity comparison.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of LEDs & 4 \\ Number of PDs & 4 \\ Room dimension & \(4\) m \(\times\) 4 m \(\times\) 3 m \\ Height of ceiling & 3 m \\ LED spacing & 2 m \\ Height of receiving plane & 0.85 m \\ PD spacing & 10 cm \\ Semi-angle at half power of LED & 60\({}^{\circ}\) \\ Responsivity of the PD & 0.53 A/W \\ Active area of PD & \(10^{-4}\) cm\({}^{2}\) \\ Refractive index & 1.5 \\ Half-angle FOV of optical lens & 60\({}^{\circ}\) \\ Gain of the optical filter & 0.9 \\ Number of activated LEDs & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
MIMO schemes. When \(N=3\) and \(\log_{2}M=8\), the improvement in SE of the proposed TD-QGSM is almost the same as when \(N=2\), while that of the TD-QGSMP is more significant, at least 81.6% higher than the other two multiplexing schemes. According to the analysis in Section 3.3, since the the proposed TD-QGSMP uses \(N\) LEDs to transmit different signals, resulting in an \(N-1\) fold increase in the SE of the constellation part, the SE's improvement will be more significant as the number of activated LEDs increases.
## 4 Experimental Study of the CAFB Using a Model Bridge
### Modal Test of Model Bridge  
To evaluate the performance of the CAFB-based IDAQ system, the modal test was performed by using the dynamic wired measuring system. The acceleration response in the vertical direction was obtained by selecting the position (at the 1/3 point of the main span) for both the wire and wireless acceleration response using an accelerometer (Dytran model 3134D). Figure 10 shows the dynamic wire and wireless measuring systems (IDAQ system) that were utilized; the dynamic wired measuring system used the iOtech (Model: 652U) from Measurement Computing Corporation (Norton, MA, USA) and ME'ScopeVES from Vibrant Technology (Centennial, CO, USA).  
Figures 11 and 12 show the raw data for the (a) time and the (b) frequency domains acquired by the dynamic wire and wireless measuring systems, respectively, and Table 3 shows the natural frequency analysis results for the raw data measured by both systems. Here, the natural frequencies for the three low-level bending modes were calculated, and the validity of the dynamic wireless sensing system configured in this article were evaluated by comparing the error ratio of the natural frequency based on the results obtained by the wired measuring system.  
\begin{table}
\begin{tabular}{c|c} \hline \hline
**Natural Frequency \& Mode Shape** \\ \hline
1 st bending mode & \\  & 2.877 Hz \\ \hline
2 nd bending mode & \\  & 4.194 Hz \\ \hline
3 rd bending mode & \\  & 6.242 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of FE analysis of the model bridge.  
Figure 10: Wired and wireless system for the modal test: (**a**) wired (iOtech); and (**b**) wireless (IDAQ system).  
The FE analysis results for the natural frequency displayed errors between approximately 1.4% and 5.2% compared to the wired measurement results and, thus, the conformity was excellent. It was observed that the detailed FE model configured in Section 4.1 successfully reproduced the dynamic characteristics of the model bridge. Additionally, as the wireless measurement results showed errors of between just 0.0036% and 2.471%, compared to the wire measurement results, it was determined that the IDAQ system configured in this article is valid.
# Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
## 2 Methods
### Local Geographically Weighted Flow Cross K-Function  
Although the global Geographically Weighted Flow Cross K-Function can measure the overall spatial associations between two types of spatial flows, it cannot capture the spatial variability of spatial associations [17; 33; 34]. To identify where a strong spatial association occurs, the local version of the Geographically Weighted Flow Cross K-Function was developed in this study. For the type A spatial flow \(f_{i}\), the local Geographically Weighted Flow Cross K-Function, which measures the spatial association between type A and type B at the reference spatial flow \(f_{i}\), is calculated as  
\[locGWFK_{A,B}^{i}=\lambda_{B}^{-1}\sum_{j}^{N_{B}}w_{ij} \tag{5}\]  
Similar to the global version, \(locGWFK_{A,B}^{i}\) uses the weighted number of spatial flows of type B near \(f_{i}\) as an indicator to show how the strength of the spatial association changes at different spatial locations (spatial flows), which means that the local version can measure the spatial association between two flow types at any reference spatial flow. Moreover, the spatial association detected by the local version can be different from that by the global version, e.g., the local Geographically Weighted Flow Cross K-Function can identify strong spatial associations at some reference spatial flows when the global spatial association is weak.