esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Growth Conditions and Photodynamic Inactivation  
_P. aeruginosa_ strain DSM 50071 (DSMZ, Braunschweig, Germany) was used as a Type strain. The bacteria were routinely grown in Brain Heart Infusion (BHI; Oxoid, Wesel, Germany) at 37 \({}^{\circ}\)C. For each experiment, the bacteria were plated out from the 30% glycerol frozen stocks onto BHI agar plates and incubated at 37 \({}^{\circ}\)C for 24 h. A single colony was picked from the agar plate, inoculated in BHI broth and incubated at 37 \({}^{\circ}\)C for 16 to 17 h with agitation at 125 rpm for preparation of overnight culture.  
In order to evaluate the bactericidal and the biofilm disruption of CHL and PEI alone and in combination, antimicrobial susceptibility testing was performed using broth microdilution checkerboard assay [56]. As described in Figure 12, the peripheral wells were filled with sterile distilled water to reduce dehydration throughout the incubation time. Negative control without the bacteria, checking the non-contamination, and positive  
Figure 11: Emission spectrum of applied red LED-based light source.  
Figure 10: Chemical structure of chlorophylllin sodium salt **(a)** and polyethylenimine **(b)**.  
growth control without the antimicrobial compounds were added, followed by twofold dilutions of each antimicrobial in the inner 60 wells. To test the susceptibility of the planktonic cells to the tested antimicrobials, a diluted overnight culture adjusted to an optical density at 600 nm (OD\({}_{600}\)) of 0.1 corresponding to 4 \(\pm\) 1 \(\times\) 10\({}^{7}\) colony-forming units (CFU) mL\({}^{-1}\) was used to inoculate the microtiter plate (100 \(\upmu\)L per well), then covered with transparent seal (Greiner, Frickenhausen, Germany) to avoid cross-contamination, followed by exposure to red light. In order to determine the minimum bactericidal concentration (MBC), aliquots (3 \(\upmu\)L) from each well were subcultured on BHI agar. A dark control was evaluated in parallel.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 4 Speech Enhancement Experiment
### Objective Evaluation  
The objective measures were used to evaluate the quality and intelligibility of the enhanced speech with respect to the corresponding clean speech. The following objective evaluation metrics have been used in this paper:  
* Perceptual Evaluation of Speech Quality (PESQ) for objective quality evaluation [28]. The PESQ score ranged between \(-0.5\) and \(4.5\). A higher PESQ score indicates better speech quality;
* Signal to distortion ratio (SDR) for objective quality evaluation [29]. The SDR score ranged between \(-\infty\) and \(+\infty\). A higher SDR score indicates better speech quality;
* Short-time objective intelligibility (STOI) measure for objective intelligibility evaluation [30]. It ranged between 0 and 1 (or 0 and 100%). A higher STOI score indicates better speech intelligibility.  
Figure 7: Comparing \(K_{0}(n)\) obtained using KF-pracle, proposed, and AKF-RMBT [22] methods from the utterance sp05 corrupted with 5 dB: (**a**) non-stationary (_bubble_) and (**b**) colored (_f16_) noises.
## 3 System Model and Problem Formulation
### Computing Model  
In each time block \(t\), the computation task for each user \(i\in K\) is partitioned into two parts. Assume that the total computation task is \(Z_{i}(t)\geq 0\), local and offloading computing are represented by \(l_{i}(t)\geq 0\) and \(q_{i}(t)\geq 0\) (bit).
# Individual Differences in Signal Perception for Takeover Request in Autonomous Driving
## 2 Related Works
### Warning Signal Design in Autonomous Vehicle
However, it is essential to keep in mind that humans have a limited capacity to divide their attention between two sensory channels. When two signals are simultaneously presented to distinct sensory channels, one may be ignored [15]. While this is acceptable if all cues presented at the same time have the same meaning, people are more likely to remember only one if they convey distinct information. The ability to divide attention across multimodal displays declines with age [16]. Accordingly, extensive research has been conducted on the advantages and disadvantages of multimodality, based on the degree of autonomy and the presence of dual tasks. Numerous guidelines for in-vehicle visual, auditory, and tactile stimuli have been devised based on existing signal guidelines from human factors research [17; 18; 19]. Researchers have performed quantitative analyses to investigate the effects of signals on the human perceptual system, providing both general guidelines (e.g., salience, discrimination) and specific values for their contexts [17; 20]. Guidelines suggest that for visual display, the text should be at least 0.25 inches high [17]. Visual guidelines emphasize providing vital information in the driver's central field of vision, utilizing colors that correspond to the severity of a hazard, and modulating luminance to match driving conditions [1; 19]. Regarding auditory signals, various reports provide slightly divergent recommendations. Auditory tones should be approximately 15 to 30 dB above the masked threshold but should not exceed 115 to 120 dB in absolute amplitude [17; 18]. The frequency range for sound should be between 100 and 4000 Hz, audible between 50 and 90 dB but not uncomfortable [17; 18; 19]. [PERSON] et al. suggests signal durations between 100 and 500 ms [18]. A minimum of 80 dB in the 1-5 kHz frequency range has been suggested for audible warning signals [21]. In addition, it has been suggested that the duration of the warning should be shorter than the average response time expected. Another study found that a warning tone with a pulse rate greater than 6 Hz is perceived as more urgent than one with a pulse rate below 6 Hz [22]. Depending on the body part, tactile detection thresholds vary, with guidelines recommending that amplitudes be set at 15-20 dB above the detection threshold [23; 24]. Amplitudes greater than 0.6-0.8 mm must be avoided, as they may induce a painful sensation. The frequency of the tactile signal should be between 150 and 300 Hz, and the burst duration should be between 50 and 200 ms, as prolonged vibrations can be irritating [24]. These guidelines serve as the foundation for vehicle-driver interaction research and as suggestions for in-vehicle information systems.
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
## 5 Conclusions  
This paper firstly applies the elastic parabolic equation to study the acoustic propagation characteristics and influencing factors of low-frequency signals in a polar shallow water environment. Combining the elastic parabolic equation method and boundary conditions, a numerical model of a polar acoustic field is established. The ice layer and sea bottom of the model is regarded as an elastic medium, and its effect is considered. The validity and accuracy of the elastic parabolic equation method are verified by the elastic normal mode method. The results show that the method can be well applied to the study  
Figure 6: Contour plots (**a–d**) of propagation loss at different underwater depths.  
of low-frequency signals in polar shallow water. At the same time, the influence factors of low-frequency sound propagation in a polar environment are analyzed, which reveals the influence of several key parameters on the propagation characteristics of sound pressure in water. With the decrease of distance between the sound source and ice layer, the value of the propagation loss is larger. A thicker ice layer and smaller compressional wave velocity of the seafloor also cause larger propagation loss. In these cases, it is not conducive to the long-distance transmission of acoustic signals. This research has important value in the field of underwater acoustic scientific research and engineering applications. At the same time, understanding the Arctic environmental sound field can be helpful to improve the environmental adaptability of ship sonar equipment and meet the research needs of detection, communication, and navigation technology. The polar sound field environment simulated in this paper is parallel to the seabed. More complex marine environments such as tilt are not discussed, and the characteristics of sound vector parameters need to be further studied.  
Conceptualization, S.L. ([PERSON]) and S.Y.; methodology, S.L. ([PERSON]) and S.Y.; software, S.Y.; validation, S.Y. and S.L. ([PERSON]); formal analysis, S.Y. and J.W.; investigation, S.L. ([PERSON]) and S.Y.; resources, S.L. ([PERSON]); data curation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.L. ([PERSON]); visualization, S.Y. and J.W.; supervision, Q.H., S.L. ([PERSON]) and Z.Z.; project administration, J.W.; funding acquisition, S.L. ([PERSON]) and Z.Z. 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 under Grant 51575201 and the Natural Science Foundation of Hubei Province under Grant 2020 CFB510.  
Not applicable.  
This work was supported by the National Natural Science Foundation of China under Grant 51575201 and the Natural Science Foundation of Hubei Province under Grant 2020 CFB510. The useful contribution and discussions from project partners are also acknowledged.  
The authors declare no conflict of interest.
## 6 Discussion  
On the basis of the simulation and experiment analysis in Sections 3 and 4, the proposed formation tracking method was demonstrated to be a feasible and effective for multiple small spherical robots. According to Tables 3 and 4, the root square errors of the trajectory tracking and formation constraints almost converged to zero in the numerical simulation. However, the physical simulation and experiment result was a little poor. One reason for this was that there was an interaction between the water environment and spherical robots in the physical simulation and experiment. The second reason was that the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Robot Number** & \(\Delta x\)**(m)** & \(\Delta y\)**(m)** \\ \hline
1 & 0.07 & 0.11 \\
2 & 0.09 & 0.09 \\
1–2 & 0.07 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Root mean square error of the trajectory tracking and formation constraints in the experiment.  
Figure 31: Position errors of the two robots. (**a**) Position error of Robot 1. (**b**) Position error of Robot 2.  
Figure 32: Formation error in the experiment.  
location of the spherical robot was inaccurate. The position of the robot was obtained based on a global vision location system, which was achieved by using the kernel correlation filter (KCF) algorithm. The last reason was that the four thrusters of the spherical robots were of the same type, but they also had minor differences. The differences indicated that the different thrusts were outputted under the same PWM signal, which led to the real control input being different with that which was proven by the controller algorithm.  
Most of the current multi-underwater robot formation studies have focused on pure studies that were verified only by numerical simulations [4; 8; 9; 10; 26]. Although the effects based on these methods are preferred, it is hard to apply these methods to the robot platform. Some studies have carried out experiments and have showed satisfactory results. [PERSON] addressed the distributed formation reconfiguration control problem for a fleet of nonlinear AUVs in three-dimensional (3-D) ocean environments and verified the effectiveness with experiments [27]. However, the results of this experiment showed that the robots can achieve formation reconfiguration and do not show the concrete formation error. Reference [28] applied the ADRC to the formation of multiple self-made AUVs. The results of the numerical simulations and experiments showed that the AUVs were able to form the desired shape and achieve formation switching. However, the results lacked concrete error analysis. Compared with references [27; 28], this paper focuses on solving the problem of formation and formation trajectory tracking, as well as on proving a method that is able to easily deployed to small underwater robots, which possess many limitations such as computation ability, perception ability, and actuator maximums. In addition, all of the results, including the numerical simulations, physical simulations, and experiments, were analyzed qualitatively and quantitatively, which can provide reference for further studies.
## 3 Data Analysis  
### Variability of Seismic Noise Parameters' Spatiotemporal Distribution  
Main features of the noise parameters' spatiotemporal distribution for the considered timeframe, 2011-2021, are shown on maps in Figure 4. On these maps, the areas of danger of strong earthquakes are distinguished by the minimum \(\alpha^{*}\), \(\Delta\alpha\), and \(\beta\) values and maximum \(En\) values in accordance with the conceptual model used.  
During the first seven years in 2011-2018, the danger area was located in the northern part of Kamchatka Peninsula, at the junction of the Kurile-Kamchatka and Aleetian island arcs (Figure 4, maps on the left). All four earthquakes in 2013-2018 with \(M_{\mathrm{up}}=7.2\)-8.3 occurred in the latitude range 54-58\({}^{\circ}\) N (Table 3), identified in previous authors' publications (35,36) as \"dangerous\" for the emergence of strong earthquakes with \(M_{\mathrm{up}}\geq 7.5\). In this case, the magnitudes of two events out of four, the Sea of Okhotski (No. 1 in Table 3) and Near Islands Aleetian (No. 3 in Table 3), corresponded to the magnitude range of expected events. The Sea of Okhotski deep-focus earthquake on 24 May 2013, with \(M_{\mathrm{up}}=8.3\) was the strongest seismic event in the region of the Kamchatka Peninsula during detailed seismological observations since 1961 (20). Its seismic moment exceeded by 7-48 times the seismic moments of all other considered earthquakes (Table 3).  
In 2017-2018, seismicity intensified in the indicated danger area over a section about 750 km long in the zone of contact of the Pacific oceanic plate with the Beringia and Okhotsk small continental plates and the Commander block. The epicenters and focal areas of the two strongest earthquakes with magnitudes 7.7 and 7.3 (No. 3, 4 in Table 3) are shown in Figure 1. The Near Islands Aleetian earthquake (No. 3 in Table 3), with a rupture length of 500 km (23), occurred on July 17, 2017, also in the \"dangerous\" range of latitudes 54-58\({}^{\circ}\) N.  
Over the next three years, 2019-2021, significant changes took place in the spatial distribution of seismic noise parameters (Figure 4, maps on the right). The danger area moved to southern part of the region in the latitude range of 50-54\({}^{\circ}\) N (39). On 25 March 2020, the North Kuril earthquake with \(M_{\mathrm{up}}=7.5\) (No. 5 in Table 3) occurred to the south of the indicated area.  
According to the maps of the spatial distribution of noise parameters at the end of 2021, the position of dangerous areas remained in the southern part of the region, which may indicate the possibility of strong earthquakes here.  
Thus, a certain correspondence between the distinguished areas of strong earthquake danger and the occurred seismic events gives reason to believe that the processes of preparation for strong earthquakes are reflected in the regular behavior of seismic noise parameters.
## 2 Materials and Methods  
This study was conducted on 484 regions measured in 58 patients who visited the department of orthodontics, Korea University Guro Hospital between 2014 and 2019, and obtained diagnostic records including CBCT images. Inclusion criteria were as follows.  
1. Patients without defects in the trabecular bone density measurement area.
2. Patients with no symptoms of temporomandibular disorder (TMD).
3. Patients without a history of bone diseases such as osteoporosis.
4. CBCT images having a quality suitable for research.  
Table 1 showed the number of samples, mean age, and standard deviation. This study was approved by the Institutional Review Board (IRB) of Korea University Guro Hospital (no. 2019 GR0189).  
The CBCT equipment used in this study was PaX-Reey3D (Vatech Korea, Seoul, Korea), the tube voltage was 80 kV, the tube current was 3.3 mA, and the voxel size was 250 \(\upmu\)m. Before the trabecular bone density measurement, the head position was reoriented with the Frankfort Horizontal plane (right and left orbitale, right portion).  
The trabecular bone density was measured using the Hounsfield unit and trabecular microstructure parameters in the anterior basal bone beside the incisive foramen of the maxilla, the mandibular body; medial side of mental foramen, the mandibular condyle; center of medial and lateral pole; and the lateral mass of first cervical vertebra, at the right and left region, respectively (Figure 1).  
The Invivo5 (Anatomage, San Jose, CA, USA) software was used for the Hounsfield unit measurement, and the CTan (Bruker micro-CT, Kontich, Belgium) software was used for the trabecular microstructure analysis (Figure 2).  
Each region was evaluated in three-dimensions (3D) with a size of \(4\times 4\times 4\) mm\({}^{3}\) cube. The trabecular microstructure parameters used in this study were described in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Male** & **Female** & **Total** \\ \hline Number of regions measured & 272 & 192 & 464 \\ Number of patients & 34 & 24 & 58 \\ Mean age (SD) & 23.8 (6.5) & 19.5 (7.0) & 22.0 (7.0) \\ \hline \hline SD: Standard deviation. & & & \\ \end{tabular}
\end{table}
Table 1: Characteristics of the samples.  
The Wilcoxon signed-rank test was performed to determine whether there was a difference in the measured values of left and right of each region and a difference in the measured trabecular bone density for each region. The Mann-Whitney U test was used to identify differences in the measured trabecular bone density between genders. Spearman correlation was used to check the correlation coefficient between two measurement methods of the measured trabecular bone density. The SPSS software (ver 25.0; IBM Corp., Armonk, NY, USA) was used for all the analysis.  
Figure 1: Regions of trabecular bone density measurement (yellow square points): (**a**) Anterior basal bone beside the incisive foramen in maxilla (Mx); (**b**) body of mandible, medial side of mental foramen (Mn); (**c**) condyles of mandible, center of medial, and lateral pole (Co); (**d**) lateral mass of the first cervical vertebra (Cv).
###### Abstract  
Tropical forests are acknowledged for providing important ecosystem services and are renowned as \"the lungs of the planet Earth\" due to their role in the exchange of gasses--particularly inhaling CO\({}_{2}\) and breathing out O\({}_{2}\)--within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450-650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for 'forest' classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.  
above-ground biomass; remote sensing; tropical forest; pan-tropics; carbon stock; biomass