esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Discussion
### Noise Reduction Performance of the Simulation Signal  
Figure 3b shows that the signal was submerged by the noise compared with Figure 3a. It is difficult to extract the main components of the useful signal. Figure 4 shows the effect of noise suppression based on three methods. First, the initial SNR of the noisy signal is \(-11.21\) db; it can be seen in Figure 4a that the blue curve of the de-noised signal obviously deviates from the pure signal, and a waveform distortion has occurred in the whole time spectrum. This is because the effective order determined by MPODSOISE and MPOCSOISE is excessively small, which causes the loss of components of the original signal. By contrast, the coincidence of two curves in Figure 4b improves. Figure 4c shows that the de-noised signal curve fits the original signal better than the others; this is because the processing of de-noising based on MMBCSOISE takes the other major peaks, which contain significant information about useful signals, into account (not only maximum peak), completing the components of the pure signal. The components of the original signal were the best preserved based on MMBCSOISE. Next, this paper compared four methods from the view of the frequency domain.  
This paper conducts an FFT transform of the original and de-noised signals; Figure 5 reflects the corresponding frequency components. It is not difficult to extract the inherent frequencies of the original signal at 15 and 40 Hz in Figure 5a. Figure 5b is the frequency spectrum of the noisy signal, and the characteristic frequencies are submerged by the noise. Figure 5c-e are the de-noised frequency spectra, only the frequency component at 15 Hz appeared in Figure 5c and the signal component whose frequency is 40 Hz, are missing. Figure 5d recovers all of the frequencies; however, the amplitude of the 40 Hz frequency is smaller than the original spectrum. The frequency spectrum in Figure 5e is closest to the original signal. The characteristic frequencies of 15 and 40 Hz recovered although they introduced irrelevant ingredients, which also happened in Figure 5d, and it is for this reason that some residual noise is still preserved. What can be concluded from Figures 4 and 5 is that the method presented in this paper retains the most useful signal components when de-noising, and the distortion caused by reconstruction was minimal.  
Figure 4: Contrasting figure of de-noised effect about three different methods on the time-domain waveform with initial signal-to-noise ratio (SNR) is \(-11.21\) db. The time-domain waveform of the de-noised signal (blue curve) based on four methods should be compared with the pure signal (red curve). (**a**) De-noised signal based on MPODSOISE, MPOCSOISE; (**b**) De-noised signal based on MMBDOSISE; (**c**) De-noised signal based on MMBCSOISE.  
Figure 3: The time-domain waveform of the original signal and noisy signal. This paper uses white Gaussian noise to simulate the truthful noise of the vibration environment. (**a**) Pure signal; (**b**) Noisy signal.
## Results  
### UV-VIS Spectra Fingerprints Related to Botanical Origin  
The UV-Vis absorption spectra as a first indicator of oils' group fingerprint are presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Range (cm\({}^{-1}\))** & **Functional Group** & **Mode of Vibration** \\ \hline
3029–2989 & =C–H (trans and cis) & Stretching \\
2946–2782 & -C–H (CH\({}_{3}\), CH\({}_{2}\)) & Stretching (asym/sym) \\
1795–1677 & -C=O (ester, acid) & Stretching \\
1486–1425 & -C–H (CH\({}_{3}\), CH\({}_{2}\)) & Bending \\
1425–1409 & =C–H (cis) & Bending (rocking) \\
1396–1371 & -C–H (CH\({}_{3}\)) & Bending \\
1290–1211 & -C–O; CH\({}_{2}\)- & Stretching; Bending \\
1211–1147 & -C–O; CH\({}_{2}\)- & Stretching; Bending \\
1147–1006 & -C–O & Stretching \\
1006–885 & -HC=CH- (trans/cis) & Bending (out of plane) \\
885–802 & =CH\({}_{2}\) & Wagging \\
802–701 & -C–H; -HC=CH- (cis) & Bending (out of plane) \\ \hline \hline \end{tabular}
\end{table}
Table 2: FTIR wavenumber regions of vegetable oils, correlated with the mode of vibration for specific functional groups, considered as predictor variables for statistical data processing [6; 33; 42].  
The individual UV-Vis absorption spectra showed specific criteria of recognition based on the spectra shape and absorption maxima. The SFO and WO oils (group I and VI) had specific absorptions in the UV region (220-280, 325 nm) and 220-280 nm, respectively, with no relevant absorptions in the Vis region. The PO group II was recognized by intense peaks at 526, 568, 590 and 620 nm, indicating their carotenoid, acylated anthocyanin and chlorophyll content, respectively. The spectra of LSO and SO oils (group III and V) were similar, with maxima at 448 and 443 nm, respectively. Hempesed (HO, group IV) and olive oils (EVOO, group VIII) were characterized by high absorption intensities at 668 nm (indicator of chlorophylls). The sea buckthorn (SBO) oils group VII had a unique fingerprint with maxima at 447/470 nm (due to high carotenoid content) and a lower intensity at 668 nm (chlorophylls). These spectra and the peak intensities can be considered as preliminary, useful, informative criteria for the recognition of the botanical origin of these oil groups.
## 4 Numerical and Experimental Results
### Active Control of the Flow-Induced Resonance Noise
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Cavity** \\ **Depth/mm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Noise** \\ **Frequency/Hz** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Optimal** \\ **Stepsize** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Effective Reference** \\ **Frequency Range/Hz** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Optimal** \\ **Reference** \\ **Frequency/Hz** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Control Results/dB** \\ **Error** \\ \end{tabular} } \\ \cline{5-5}  & 340 & & & 19 & 20 & 18 \\ \cline{1-1}  & 680 & 0.011 & [328, 340] & 332 & 14 & 12 & 13 \\ \cline{1-1}  & 334 & & 22 & 13 & 11 \\ \cline{1-1}  & 668 & 0.008 & [326, 338] & 334 & 12 & 13 & 13 \\ \cline{1-1}  & 331 & & 331 & 343 & 337 & 11 & 12 & 20 \\ \cline{1-1}  & 662 & & & & 7 & 6 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental control performance of the flow-induced acoustic resonance noise. case with a 261 mm cavity, the optimum reference frequency 337 Hz becomes much higher than the resonance frequency 331 Hz. Figure 8: Experimental results with 236 mm cavity: the noise spectrum at the test (**a**) microphone I and (**b**) microphone II with a 328 Hz reference; the noise spectrum at the test (**c**) microphone I and (**d**) microphone II with a 332 Hz reference (best); the noise spectrum at the test (**e**) microphone I and (**f**) microphone II with a 340 Hz (resonance frequency) reference. Figure 9: Experimental results with 249 mm cavity: the noise spectrum at the test (**a**) microphone I and (**b**) microphone II with a 326 Hz reference; the noise spectrum at the test (**c**) microphone I and (**d**) microphone II with a 334 Hz (resonance frequency) reference (best); the noise spectrum at the test (**e**) microphone I and (**f**) microphone II with a 338 Hz reference. The offset of the optimal reference frequency and the control performance at the first harmonic indicate that the control mechanism should be nonlinear. By injecting tonal secondary noise near the resonance frequency, the narrowband ANC system changes the boundary condition of the cavity bottom at the controlling frequency and destroys the original nonlinear feedback excitation conditions. Therefore, significant reductions could be simultaneously obtained at both the original resonance frequency as well as its first harmonic. The higher tolerance for the reference FM could also result from the nonlinear mechanism of flow-induced cavity resonance. Finally, it can be seen from Figures 8-10 that resonance noise is suppressed without enhancements at other frequencies. This is because only tonal secondary noise is injected into the cavity, just as expected in Section 1.
# Subway Station Capacity Maintained by Optimizing a Maintenance Schedule of Key Equipment
## 2 Dynamical SSC and Problem Statement  
### Definition of Dynamical SSC  
For a typical subway station, the gathering and scattering process usually consists of three sub-procedures: arrival, departure and the alighting-boarding process, as shown in Figure 1. The arrival process begins when passengers buy tickets and ends when passengers arrive at the platform. In this process, passengers can buy tickets from the TVM (Ticket Vending Machine) or use e-tickets with their ID-Card or Smart phone. The passengers pass through the security check system and gates in the Hall, arrive at the platform by taking escalators or staircases, and board the trains. The departure process starts when passengers get off from the train, then take the escalators or staircases to the Hall and finally arrive at the leaving gates. The alighting--boarding process is a bridge connecting the arrival and departure processes.  
In the gathering and scattering process, the subway station is regarded as a service system according to the queuing theory [4], as shown in Figure 2. The input is the number of passengers entering the station system per unit time, the output is the number of passengers leaving the station system per unit time. The service time includes the arrival time, departure time, and waiting time on the platform. The service time reflects the efficiency of the subway station.  
The SSC is generally defined as the maximum number of passengers served by one station in a given period of time [9]. The SSC can be measured by the sum of inbound and alighting passengers or  
Figure 1: Passenger gathering and scattering process at a subway station.  
the sum of outbound and boarding passengers under certain level of service. It is a constant value which is related to the uncertain equipment state. In our case, the dynamical SSC (DSSC) needs to be considered. The influence of passenger inflow and the key equipment reliability on the SSC are considered. Therefore, in this paper, the DSSC is defined as the number of passengers served by one station in a given period of time, typically 1 h, with certain passenger inflow input, under specified operating conditions [9].
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
###### Abstract  
The generalized spectral subtraction algorithm (GBSS), which has extraordinary ability in background noise reduction, is historically one of the first approaches used for speech enhancement and dereverberation. However, the algorithm has not been applied to de-noise the room impulse response (RIR) to extend the reverberation decay range. The application of the GBSS algorithm in this study is stated as an optimization problem, that is, subtracting the noise level from the RIR while maintaining the signal quality. The optimization process conducted in the measurements of the RIRs with artificial noise and natural ambient noise aims to determine the optimal sets of factors to achieve the best noise reduction results regarding the largest dynamic range improvement. The optimal factors are set variables determined by the estimated SNRs of the RIRs filtered in the octave band. The acoustic parameters, the reverberation time (RT), and early decay time (EDT), and the dynamic range improvement of the energy decay curve were used as control measures and evaluation criteria to ensure the reliability of the algorithm. The de-noising results were compared with noise compensation methods. With the achieved optimal factors, the GBSS contributes to a significant effect in terms of dynamic range improvement and decreases the estimation errors in the RTs caused by noise levels.  
generalized spectral subtraction algorithm; noise reduction; decay range improvement; reverberation time
## 3 Results  
### Summary Statistics and Correlation between TCC (iCheck and Chroma Meter Measures) and DMC  
The summary statistics (Table 1) of the parameters showed that the value of TCC measured with iCheck ranged from 0.12 \(\upmu\)g/g to 10.18 \(\upmu\)g/g, and Chroma Meter ranged between 11.70 and 39.62. Similarly, the minimum and maximum values of DM were 7.56 and 42.70, respectively. The standard deviation values of TCC measured by iCheck and Chroma Meter were 2.47 and 6.78, respectively, whereas that of DMC was 7.08.  
There was a negative correlation between DMC and TCC measured by both iCheck and Chroma Meter. The correlation between DMC and iCheck TCC values was r = \(-\)0.37, and r = \(-\)0.33 between DMC and Chroma Meter TCC values. There was a high correlation (r = 0.72) between iCheck TCC values with Chroma Meter TCC values (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trait** & & **TCC** & **DMC (\%)** \\ \hline
**Methods** & **iCheck (\(\upmu\)g/g)** & **Chroma Meter (b*Values)** & **Oven-Drying** \\ \hline
**Min.** & 0.12 & 11.70 & 7.56 \\ \hline
**Max.** & 10.18 & 39.62 & 42.70 \\ \hline
**Mean** & 3.78 & 26.62 & 29.49 \\ \hline
**SD** & 2.47 & 6.78 & 7.08 \\ \hline Min. = minimum value, Max. = maximum values, SD = standard deviation, iCheck = iCheck CAROTENE. \\ \hline \end{tabular}
\end{table}
Table 1: Summary statistics of traits.  
Figure 1: Trait association between total carotenoid content (TCC) (measured with iCheck (TCC_iCheck) and Chroma Meter (TCC_Chroma Meter)) and root dry matter content (DMC).
# Recent Advances in Positive-Instance Driven Graph Searching +
## 3 A Gentle Introduction to Positive-Instance Driven Graph Searching
### Execution Modes  
It is worth noting that positive-instance driven algorithms only solve the problem for a fixed \(k\). Hence, to obtain an _optimal_ treedepth decomposition (or any other decomposition), one has to run the algorithm for various values of \(k\). The order in which these values are tested is called the _execution mode_ of the algorithm. Positive-instance driven algorithms differ from other strategies with respect to values that are \"easy\". For instance, branch-and-bound algorithms usually solve the problem quickly if \(k\) overestimates the optimal value. However, overestimating \(k\) means _more_ positive subproblems, making these instances hard for positive-instance driven algorithms. On the other hand, underestimating the optimal value yields instances that are usually hard but that are solved quickly by positive-instance driven algorithms as there are only few positive instances. Positive-instance driven graph searching is, thus, especially suited to compute lower bounds [63]. Therefore, the typical execution mode is to start with \(k=1\) and to increase this lower bound until either an optimal solution was found or a heuristically obtained upper bound is met [48; 49].  
Figure 4: The _glue operation_: We have already guessed the vertex \(v\) and currently consider the set \(X=\setset{(C_{1},\rho_{1}),(C_{2},\rho_{2}),(C_{3},\rho_{3})}\). Note that all blocks are adjacent to \(v\) and are pairwise non-intersecting. The combined neighborhood is highlighted. This area is not allowed to be larger than \(k-\max\{\rho_{1},\rho_{2},\rho_{3}\}\), as the searchers must clean it before they can proceed the search on one of the blocks. From this situation, we generate the block \((C_{1}\cup C_{2}\cup C_{3}\cup\{v\},1+\max\{\rho_{1},\rho_{2},\rho_{3}\})\).
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) has a high correlation with Gross Primary Production (GPP). However, studies focusing on the impact of drought on the SIF-GPP relationship have had mixed results at various scales, and the mechanisms controlling the dynamics between photosynthesis and fluorescence emission under water stress are not well understood. We developed a leaf-scale measurement system to perform concurrent measurements of active and passive fluorescence, and gas-exchange rates for winter wheat experiencing a one-month progressive drought. Our results confirmed that: (1) shifts in light energy allocation towards decreasing photochemistry (the quantum yields of photochemical quenching in PSII decreased from 0.42 to 0.21 under intermediate light conditions) and increasing fluorescence emissions (the quantum yields of fluorescence increased to 0.062 from 0.024) as drought progressed enhance the degree of nonlinearity of the SIF-GPP relationship, and (2) SIF alone has a limited capacity to track changes in the photosynthetic status of plants under drought conditions. However, by incorporating the water stress factor into a SIF-based mechanistic photosynthesis model, we show that drought-induced variations in a variety of key photosynthetic parameters, including stomatal conductance and photosynthetic CO2 assimilation, can be accurately estimated using measurements of SIF, photosynthetically active radiation, air temperature, and soil moisture as inputs. Our findings provide the experimental and theoretical foundations necessary for employing SIF mechanistically to estimate plant photosynthetic activity during periods of drought stress.  
photosynthesis model; photosynthetic CO2 assimilation; pulse-amplitude modulation (PAM); remote sensing; solar-induced chlorophyll fluorescence (SIF); water stress +
Footnote †: journal: plants
## 4 Experimental Results
### Practical Text Files  
The algorithms were also compared using the following three practical text files downloaded from the Large Canterbury Corpus:  
1. _E. coli_: the sequence of the _Escherichia coli_ genome consisting of 4,638,690 base pairs with \(|\Sigma|=4\);
2. The Bible: The [PERSON] version of the Bible consisting of 4,047,392 characters with \(|\Sigma|=63\);
3. World192: A CIA World Fact Book consisting of 2,473,400 characters with \(|\Sigma|=94\).  
The experiments were carried out the same way as in the case of randomized text files. The same 19 varying pattern lengths are used, namely, m = 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 80  
For _E. coli_, FQS is much better than the other algorithms. The QS and HOR are in the second group rank (Figures 10(a) and 10(a)). For the Bible, the four algorithms have a similar performance. FQS is slightly better than the others (Figures 10(b) and 10(b)). For World192, QS, FQS and HOR have a similar performance, with FQS showing a slightly better performance (Figures 10(c) and 10(c)).  
For these practical files, FQS is the overall best algorithm among the four. Each of the three practical files has a symbol alphabet with size \(|\sigma|\leq 128\). This suggests that FQS might be the algorithm of choice for practical use, especially for searching genomic databases with typically smaller alphabets.
# Painting Path Planning for a Painting Robot with a RealSense Depth Sensor
## 1 Introduction  
This paper presents a specific robotic application based on the processing of depth images captured using a low-cost Intel RealSense D435 stereo camera [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. This work is part of an industrial-research project aiming to build a robot that would automatically--based on data obtained from the depth camera--be able paint a building's facades or interior walls with wall paint. The goal was to develop a robust and simple computer vision algorithm to detect and extract windows and coarse obstacles on walls using depth images recorded with a stereo camera, which is also able to notify the control system [15; 16; 17; 18; 19; 20; 21; 22]. The acquired depth images and point clouds of the environment serve as resources for the robot to determine which surfaces to paint.  
This paper introduces a procedure for window and obstacle detection and for extracting these objects from walls using captured depth images.  
This project is a part of the KFI_16-1-2017-00485 project, which involves the development of a robot for painting and thermal insulation of facades of monument buildings. The client's instruction and the main goal of the project is to use already proven and reliable algorithms to ensure smooth and reliable operation of a painting robot. Almost all wall painting robots on the market are not automated, meaning the operator fully controls the painting arm and decides what should be painted. In this project, the client requires automation using a computer vision system. This approach is new in in the design of commercial wall painting robots. The basic hardware in the vision system is the Intel RealSense depth camera.  
The wall extraction algorithm is based on real-time, appearance-based mapping (RTAB-Map) [23; 24; 25; 26; 27] and a random sample consensus model (RANSAC) [28; 29]. Theinput point cloud is formed via an RTAB-Map algorithm using zig-zag robot movements along with a built-in RealSense depth camera by merging recorded point clouds during movement [30; 31; 32]. Further, before extracting the RANSAC algorithm, the initial step is to apply a statistical filter [30] to remove most of the outliers from the captured point clouds. Finally, the RANSAC algorithm combined with the clustering procedure is performed for the wall extraction process. RANSAC is a powerful iterative method used to assess the parameters of a mathematical model from a set of data containing outliers.
## 4 Simulation Experiments: Security Analysis and Comparison
### Comparative Analysis of Cryptographic Attacks  
We conducted a security analysis of our proposed text encryption scheme and compared its performance with existing encryption schemes [14; 38; 39; 40; 44]. The results are presented in Table 7, which outlines the evaluation of both the proposed scheme and the existing schemes in terms of key spacing, key sensitivity, the ciphertext-only attack, and the known-plaintext attack. The results from Table 7 indicate that the proposed scheme offers superior security against cryptographic attacks when compared with the text encryption schemes [14; 38; 39; 40; 44].
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Vertical Structure of Rainfall
#### 5.1.2 Precipitation Height and the Rainfall Rate  
Most studies yielded that there was a high positive correlation between the precipitation height and the rainfall rate [106; 116; 117], but this might not demonstrate the necessary and sufficient condition for each other. [PERSON] et al. [118] found that an extreme amount of rainfall was characterized by less intense convection with strong radar echoes not extending to very high altitudes (around 8-9 km) compared with extreme convective cases with altitudes more than 10 km even 16-17 km. They indicated the importance of warm rain processes in producing high rainfall rates. [PERSON] et al. [113] found that a high rainfall rate was associated with a deep rain system but a deep rain system was not always associated with a high rainfall rate. These results imply that the rainfall rate may not be simply related to the vertical extent of precipitation. Specific conditions of dynamic, thermodynamic and water vapor need to be taken into detailed consideration. For example, the rainfall of large vertical extention might need moderate water vapor because abundant water vapor would be condensed easily in low altitudes.