esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges to Developing a Remote Sensing Based EHA System
### Uncertainties in Ecosystem Health Indicators
#### 4.4.2 Active Remote Sensing  
Radar data have been widely used for structure and moisture content related ecosystem health indicators. However, the accuracy of estimation is dependent on many factors, such as characteristics of the instrument including frequency or wavelength, polarization, incident angle, look direction, and spatial resolution [159], and the properties of the land surface including surface roughness, and moisture content [160]. A discussion on those factors was provided in [59].  
LiDAR data have also been used for retrieving ecosystem health indicators. The application of LiDAR data is basically based on the structure information LiDAR data can detect. The way LiDAR data are received (discrete return and full waveform LiDAR) and the footprint [161] may cause uncertainties in estimating health indicators. Full waveform LiDAR can provide more structure details than discrete LiDAR data. Generally, small footprint LiDAR has an advantage over detailed local mapping, and large footprint LiDAR is more suitable for investigating more interactions with multiple vertical structures and taking more complete ground sampling [161].
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 4 Numerical Simulation
### Only Some of the Satellites Are Scheduled to Serve for a Single Target (SSST)  
In order to not affect the normal work of navigation constellations, only some of the satellites can be scheduled to serve other spacecraft. Assuming that the normal work can be maintained by the half of the satellites, three scenes consisting of 1/2, 1/4, 1/8 satellites were discussed: the satellites numbered 1, 3, 5, 7 of each orbit; the satellites numbered 1, 5 of each orbit; and the first satellite of each orbit, respectively.  
The probability that an arbitrary grid point is available is the ratio of the visible time and the simulation time. Figures 5-7 illustrate the tendency that part of the constellation is the same as the whole constellation. When there are 12 available satellites, all locations below 40,000 km and no less than 97.6% (global average) of the locations within 72,000 km can view at least one satellite; 100% of the locations lower than 34,000 km and no less than 96.2% (global average) of the locations within 36,000 km may view at least four satellites. When there are six available satellites, the number of locations that can view at least one satellite is 100% within 33,000 km and 95.1% (global average) within 70,000 km; the locations (global average) that can view at least four satellites are constrained to be at an altitude between 6000 km and 16,000 km, and no locations can reach four-fold coverage above 34,000 km. When the number of usable satellites is 3, then 95% of the locations between 4000 km and 18,000 km can get one-fold coverage. Thus, the availability of ISLs decreases as the number of scheduled satellite resources decreases.  
Figure 5: Availability as a function of altitude (1/2): (**a**) number and (**b**) probability.  
Figure 6: Availability as a function of altitude (1/4): (**a**) number and (**b**) probability.  
Based on these results, the single-fold SSV can be extended to 70,000 km and the four-fold SSV can be expanded to 16,000 km when there are not many satellites scheduled (1/4).
###### Abstract  
Boron-doped diamond (BDD) electrodes have been advocated as a potential treatment alternative to the established methods for root canal disinfection. As healing of periapical tissue is important in this context, the associated risk of host tissue damage was to be evaluated. Following in vitro cell culture experiments (HeLa cells), root canal treatment was performed in an animal trial comparing BDD electrode application and the currently used rinsing media with respect to cell viability and host tissue damage. Statistical analyses comparing the size of radiolucency were based on [PERSON]'s All Pairs tests (\(\alpha\) = 0.05). The direct application of BDD electrodes had a time-dependent effect on cell viability comparable to H\({}_{2}\)O\({}_{2}\), NaOCl, and CHX application. In contrast to the chemical treatment, the effect of BDD electrodes was transient. Conventionally treated teeth and teeth additionally treated with BDD electrodes did not significantly differ from each other with respect to the size of the periapical radiolucency as observed radiographically (vertical \(p\) = 0.998 and horizontal \(p\) = 0.878) and histologically (vertical \(p\) = 0.940 and horizontal \(p\) = 0.862). While showing greater disinfection efficiency, the application of BDD electrodes for the electrochemical disinfection of root canals does not have a greater risk of host tissue damage compared to the conventional treatment.  
chlorhexidine; CHX; endodontics; hydrogen peroxide; root canal treatment; sodium hypochloride; dog model +
Footnote †: journal: Applied Sciences  
an electric current for the simple electrolysis of water using already installed metallic dental implants as electrodes has been introduced [9; 10; 11; 12; 13]. A newer study on the clinical application of this technology reported that the re-osseeintegration of implants following electrochemical treatment and augmentation was possible [14], which may indicate that electrochemical treatment per se does not harm host tissue.  
A more specific approach of using electrochemical disinfection applies to boron-doped diamond (BDD) electrodes and has been shown to effectively inactivate bacterial biofilm on dental implant surfaces and in root canals [15]. The working principle of BDD electrodes is the formation of OH radicals and its subsequent reaction products acting as potent, transient, but also unspecific disinfecting agents [16]. The application of these electrodes in the future may follow conventional instrumentation of root canals. Prior to clinical application, the potential risk of host tissue damage by applying electrochemical disinfection must be estimated. It was the goal of this combination of in vitro cell culture experiments and animal trials (reported according to the PRIASE 2021 checklist and flowchart [17]; see Supplementary Materials) to compare BDD electrode application and the currently applied rinsing media with respect to cell viability and host tissue damage.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Exploratory Spatial Data Analysis
Some traditional methods such as Moran's I analysis, hotspot analysis, and grouping analysis are used frequently in spatial clustering analysis. We used hotspot analysis, supported by the Getis-Ord \(G^{\star}\) statistic, to identify statistically significant clusters of high values (hot spots) and low values (cold spots) in this study. \(G^{\star}\) is positive for a 'hot spot' and negative for a 'cold spot'. The Getis-Ord \(G^{\star}\) statistic with n observations \((x_{1},\ x_{2},\ldots,\ x_{n})\) can be calculated using the following formula:  
\[G^{\star}=\frac{\sum_{j=1}^{n}w_{ij}x_{j}-\overline{x}\sum_{j=1}^{n}w_{ij}}{ \sqrt{\frac{\sum_{j=1}^{n}x_{j}^{2}}{n}-\frac{1}{x^{2}}}\sqrt{\frac{\left[n \sum_{j=1}^{n}w_{ij}^{2}-\left(\sum_{j=1}^{n}w_{ij}\right)^{2}\right]}{n-1}}} \tag{4}\]  
where \(\overline{x}\) is the mean of the observations \((x_{1},\ x_{2},\ldots,\ x_{n})\), \(w_{ij}\) is the spatial weight between \(x_{i}\) and \(x_{j},\ i,\ j=1,\ 2,\ \ldots,\ n\). The z-scores and \(p\)-values derived from the hotspot analysis are measures of statistical significance indicating whether or not to reject the null hypothesis that the values analyzed have a random spatial distribution. In effect, they indicate whether the observed spatial clustering of high or low values is more pronounced than one would expect in a random distribution of the same values. The critical \(p\)-values and z-scores are shown in Table 4.  
We analyzed P-scores, Q-scores, and D-scores using exploratory spatial data analysis methods to describe the spatial distribution of ecosystem services output, efficiency, and development trend, respectively.
# Practical Model for Metamaterials in Wireless Power Transfer Systems
## 4 Discussion  
The approximations and uncertainties of the proposed model are clarified here. Studies on magneto-inductive (MI) waves have pointed out that the existence of MI waves may add considerably to the losses of the MTMs [41]. Therefore, in MTM-enhanced WPT systems, the losses will cause a drop in system transmission efficiency. This is also the main reason for the disparity between experimental results and theoretical results. However, incorporating MI theory into the MTM-enhanced WPT system would incur complicated expressions and complex results. According to [41], the expression of two-dimensional (or even three-dimensional) MI waves involves various factors under different conditions. In order to solve this problem, we simplified the MI theory and kept the \"practical model\" concise and the error was controlled under a permissible level, which makes it much more convenient to be applied in practice.  
As the analytical method in high-frequency applications usually utilized the \(S\) parameter to represent the system efficiency, the efficiency obtained using lumped parameters in this paper cannot be compared directly with previous results. The higher efficiency shown in Figure 13 is owing to different reference standards of efficiency.  
As for the future development of the researches on MTM-enhanced WPT systems, the proposed model can help researchers to clarify the resonance frequency of MTMs and to estimate the transmission efficiency of the system. In addition, as the lumped parameters are adopted in the model, the efficiency of the system can be obtained simply using circuit parameters (voltage and current), without resorting to the expensive device, which works in a demanding environment. The proposal of the model will bring great convenience to the subsequent development of the MTMs-enhanced WPT systems.  
Figure 13: Comparison of different efficiency results after adding MTM slab in a WPT system.
# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 4 Algorithm and System Experiment
### Performance Evaluation
#### 4.2.3 Comparison of Performance before and after Human Tracking  
In order to test the effect on performance before and after adding underwater human tracking, we used part of the underwater dataset as the test set. Taking MobileNetV3 (small) as the test model, the recognition results of adding tracking and not adding were tested. Some gesture recognition results are shown in the Figure 11. Subfigures (a.1), (b.1), and (c.1) in Figure 11 show the results of using only gesture recognition. Subfigures (a.2), (b.2), and (c.2) show the results of adding tracking before gesture recognition. From the comparison of (a.1) and (a.2), it can be seen that the detection score of gesture recognition has been greatly improved from 0.76 to 0.99. In addition, there was a large number of samples that cannot be detected by gesture recognition alone in the test set, as shown in (c.1). After adding underwater human tracking, better performance was obtained, as shown in (c.2).  
We used the trained MobileNetV3-small model to test the underwater gesture recognition accuracy before and after tracking. Table 5 shows the mean average precision (mAP) index of gesture recognition before and after the addition of tracking in detail. It can be seen from the table that the mAP index improved greatly after the addition of underwater human tracking. With the increase of the score threshold (i.e., removing network detection results below the threshold), the improvement rate increased from 40% to 105%.  
Additionally, from Figure 12 it can be seen that with an increase of the set score threshold, after adding underwater human tracking, the performance of the gesture recognition was more stable, and it continued to maintain a high recognition accuracy. The reason is that the tracking removes a large number of interfering pixels and makes the gesture recognition focus on finding preset gestures from the person body image rather than the whole image. To sum up, adding the step of person tracking before underwater gesture  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backbone** & **mAP** & **Speed** & **Model Size** \\ \hline VGG-16 & 0.9998 & 10.8 fps & 94.1 MB \\ V2 & 0.9990 & 21.3 fps & 12.7 MB \\ V3-Small & 0.9994 & **23.5** fps & **5.71** MB \\ V3-Large & **0.9998** & 21.2 fps & 14.4 MB \\ \hline \hline \end{tabular} Bold indicates the best result.  
\end{table}
Table 4: Performance of different backbones on gesture recognition dataset.  
Figure 10: Comparison of test results between air dataset and diver dataset. (**a**) The results of the model trained by the air dataset. (**b**) The results of the model trained by the Diver dataset.
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 5 Simulation Setup and Results
### Simulation Setup  
For our experiments, we assume a fixed number of static nodes that are randomly distributed in a large two-dimensional area and a high processing power \(BS\) located randomly within the nodes' geographic area. The nodes are generated through a uniform random distribution where all values of the interval have equal probability. We assume that all nodes have the same amount of energy, at the beginning of the protocol. The benefit of the protocol is tested through a series of simulations with _MATLAB_ by varying the number of rounds and the number of misbehaving nodes (\(MN\)). For comparison purposes, \(SSP\) is compared with the \(SNCR\) protocol; the same confidential routing protocol but without incorporating the security scheme against \(DoS\) and pollution attacks. The benefit of the protocol is also tested by varying the size of the network, i.e., the number of total sensor nodes in the network, to verify its scalability.  
The clustering phase is operated with a \(K\)-means algorithm for simplicity and efficiency. Both protocols use direct transmission from \(CH\)s to \(BS\) as a communication pattern. The simulations are performed into two phases. The first allows routing of packets from member nodes to \(CH\) nodes while the second phase processes the required computations within the \(BS\) and verifies which packets have been successfully recovered.  
Data availability against selfish nodes is evaluated in terms of the parameters of the Packet Delivery Ratio (\(PDR\)) and correct messages ratio, whereas data integrity against pollution attackers is evaluated in terms of the number of correct messages received. Table 1 shows the input parameters for the experiments. We provide an example of the \(BS\) coordinates used in this simulation and illustrate it later, in the network presentation figures.
## 3 Experiments and Results  
Two sets of experiments were conducted to verify the correctness of the algorithm put forward in this work. In the first set of experiments, 150 fingerprint images were selected from a database and blurred to three different levels (3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7 kernels) using Gaussian smoothing (Figure 5). Then, 150 sets of images (450 images) were imported into the proposed Power Spectrum + Prewitt filter algorithm, the conventional Power Spectrum algorithm and the Structural Similarity Index (SSIM) to determine whether the evaluation values of the blurred images were negatively correlated with the degree of blurring. SSIM is a classic full reference metric, which measures the degradation of image quality due to data compression or transmission losses. In the second set of experiments, 150 good and faulty fingerprint images (Figure 6) were selected from a database to test whether the proposed and conventional algorithms could correctly classify the two groups. Finally, a comparison of the experimental results was performed between the proposed algorithm, the Power Spectrum algorithm, and SSIM. All the algorithms were implemented using LabVIEW.
## 5 Experiment Validation  
### Data Acquisition and Pre-Processing  
To monitor for abnormal conditions in gearboxes, vibration measurement sensors were attached to the drive side (DS) and non-drive side (NDS), as shown in Figure 2. The sensor specifications are as follows: Sensitivity (\(\pm\)10%) 100 mV/g, Setting Time <2.5 s, Spectral Noise at 10 Hz 14 \(\mu\)g/Hz; Spectral Noise at 100 Hz 2.3 \(\mu\)g/Hz Data for Sampling Speed was collected at 10\({}^{-4}\) s, but it was converted to 10\({}^{-3}\) s before pre-processing. We extracted one image for every 500 ea of pre-processed vibration data (window size = 500). To monitor equipment abnormalities, the gear teeth were arbitrarily damaged, and the operation was started, ultimately reaching a catastrophic failure in which the gear teeth were destroyed. Data were collected from both the DS and the NDS through acceleration and vibration sensors. The experimental device consisted of a 37 kW six-pole motor and four gears (a reduction ratio of 1/26.5 and torque of up to 297 N-m). The collected continuous data included vibration data with time series characteristics in units of gravitational acceleration: g (m/s\({}^{2}\)). Data were collected with synchronized time stamps and stored in a database. Source data were collected every 10\({}^{-4}\) s and stored every 10\({}^{-3}\) s because of computation power. We aggregated 500 pieces of data into one bundle and defined the window size (w) for analysis. Some original vibration data from the gearbox have been captured. Some of the original vibration data of the gearbox are shown in Figure 3. The original dataset, 46 ea files, is divided into 10 equal parts, resulting in 10 steps. A step divides the data into 10 sections, and each section contains 27,600 images in Figure 4. A gearbox was damaged by missing gear teeth, and alarm and warning signals were obtained through the proposed method in advance.  
Figure 3: DS and NDS gearbox vibration signal examples; step = 1, 2.
## 3 Methodology
### Tests Description
First, we measured how much current is drained from the battery when the robot is moving (\(\approx\)100 mA). All of our robots move at constant speed, both in real tests and under simulation. Then, we measured how long it takes for a robot to move 1 m ahead (\(\approx\)20 s). This provides the motor battery discharge per distance unit (\(\approx\)0.56 mA\(\cdot\)h/m). Finally, in our simulation, this current drain is calculated for each robot depending on its traveled distance. It needs to be noted that this model is over-simplistic, and consumption would probably change in real conditions, especially if the terrain were uneven, robots could accelerate and/or they carried a load. Nevertheless, in all of our tests, we found that power consumption due to motor operation is neglectable with respect to power consumption due to communications, because: (i) the covered area is not too large, so robots do not travel too far even in the worst case scenario (robots in boundary area in environments with obstacles); and (ii) after deployment, robots do not move too often.
## 1 Introduction
The proposed scheme is divided into two broad categories, i.e., (1) IoV-based congestion detection (IoV-based CD) used for real-time congestion detection, (2) IoV-based congestion avoidance (IoV-based CA) for proactive rerouting to avoid congestion. The roadside unit (RSU) on every junction computes the capacity \(C\) of the entrance segment. The capacity is defined as the maximum number of vehicles in a given road's segment without producing traffic jam [37]. If the number of vehicles exceeds a specific threshold, then congestion will be detected, and rerouting using EG-Dijkstra [35; 36] will be started to resolve the congestion. The contributions of this paper are given below:  
* We proposed IoV-based CDA for a particular road intersection highly vulnerable to massive traffic jams using two prominent parameters, i.e., the occupancy rate and occupancy time. We assessed our proposed scheme through simulation and noted that it reduced path cost and travel time as compared to the no-rerouting scheme (i.e., MCDP) significantly. * Secondly, our proposed IoV-based CDA employed rerouting to assess the traffic load of any alternate route before selection. Thus, it prevents traffic jams migration from one place to another. Lastly, we measured the robustness of our proposed IoV-based CDA by investigating under different levels of congestion (i.e., low, medium, high, and massive) scenarios. The rest of the paper is structured as follows. Section 2 describes the IoV-based CDA scheme system model. Section 3 presents the proposed IoV-based CDA algorithm. Section 4 discusses the experimental results, and Section 5 concludes the paper and outlines future works.
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 5 Conclusions  
Based on one year of observations performed at the FINO3 research platform in the North Sea, it was found that the wind speed distribution is shifted towards higher values at all height levels  
Figure 11: Left panel shows the mean wind speed as a function of data availability for evenly spaced data in time for CNR threshold values of -22 dB and -35 dB, and for 126 m and 626 m height levels. The right panel shows the standard deviation of the observations for the same combinations of observation levels and CNR threshold values.  
(126 m to 626 m) when the Carrier-to Noise-Ratio threshold value is changed from ~35 dB to ~22 dB. As a consequence, the wind power density is shifted even more (32%) applying the CNR threshold for the whole profile (126 m to 626 m) and somewhat less (18%) when imposing the CNR threshold at 126 m only. Furthermore, the wind rose is shifted towards westerly and south-westerly directions for increasing CNR threshold values.  
Consequently, the Carrier-to Noise-Ratio threshold filtering should be done cautiously when creating a wind climatology. Here it is suggested that the lower limit for the Carrier-to-Noise-Ratio of the observations should be -29 dB or less, to obtain a threshold independent estimate of the mean wind speed and wind rose. Alternatively, all valid observations should be used for the analysis. Although this study is specific for the synoptic conditions in the North Sea, we suggest that for a representative estimation of the wind resource with wind lidars, the effect of the CNR threshold filtering on the wind distribution should be studied when the lidar recovery rate is less than 100%.  
The preparation of the manuscript is common work. S.E.G prepared the figures.  
This study was supported by the Danish Council for Strategic Research, project number 2104-08-0025 named \"Tall Wind\" and the work is related to activities of SEG within the TOPROF COST Action ESI303.  
The meteorological measurements from the mast at the FINNO research platform were kindly made available by the BMU (Bundesministerium fur Umwelt, German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety) and the PTJ (Projekttrager Julich, project executing organization). We thank the Test and Measurements section of DTU Wind Energy for operation and maintenance of the FINNO3 wind lidar database. [PERSON] kindly read through the manuscript.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.