esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
## 2 Formation Algorithm
### Multi-Ring Tree Formation Algorithm
#### 2.2.1 Min-Path Algorithm
When \(N=34\) and the constraint \(|P-R|=4\), \(N\) can be divided into (\(R=1\), \(P=34\)) and (\(R=2\), \(P=17\)); the resulting \(R\) and \(P\) sets do not meet the constraint. Next, \(N\) decreases by 1 into 33 with (\(R=1\), \(P=33\)) and (\(R=3\), \(P=11\)) sets, which do not satisfy the constraint. Here, \(N\) decreases by 1 again until the constraint is satisfied by two computed sets: (\(R=4\), \(P=8\)) and (\(P=8\), \(R=4\)). As a result, the optimal configuration can be determined by comparing the average hop length of (\(R=4\), \(P=8\)) and (\(P=8\), \(R=4\)); the resulting configuration is (\(R=4\), \(P=8\)).
# A Staring Tracking Measurement Method of Resident Space Objects Based on the Star Tacker
###### Abstract  
Measuring resident space objects (RSOs) by star trackers has become a research hotspot in space situational awareness. However, the arc length measured by star trackers is too short to complete the high-precision orbit determination of the RSO. In this paper, a staring-tracking measurement (STM) method is proposed to obtain a sufficiently long arc. Firstly, the three-axis attitude calculation model of the satellite is established for tracking RSOs during the staring process. Secondly, an observation method of the RSO location is proposed based on the principle of the angular distance invariance and the imaging mapping function of the star tracker. Finally, the numerical simulations and the ground experiment were conducted to verify the proposed algorithm. Simulation results show that the acquisition time of the measurable arcs is longer than 6 min, and the accuracy of the declination and the ascension can meet the standard for RSO orbit determination. The physical experiment shows that the acquisition time is consistent with that in the simulation. Our work provides a new idea for the realization of high-precision space-based optical measurement.  
resident space objectives (RSOs); optical measurement; target tracking; star tracker +
Footnote †: journal: _Photonics_  
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Footnote 0: Copyright** © 2023 by the authors. Liconsse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)]([https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0))).
# Coastal Wind Measurements Using a Single Scanning LiDAR
## 3 Results  
### Wind Condition during the Experimental Campaign  
Before presenting the VVP-processed results, we briefly describe the wind characteristics of the measurement site. Figure 4 shows the time series of wind speeds and wind directions at a height of 107 m ASL obtained from V1 during the current measurement period (22 January to 23 September 2019). The data availability for V1 during this period was 91%. Figure 5 presents the distribution of these wind speeds and wind directions in the form of occurrence frequencies and a wind rose. Figure 5a also presents the Weibull probability density function (PDF) obtained by fitting to the following Weibull function:  
\[f(U)=\frac{k}{A}\bigg{(}\frac{U}{A}\bigg{)}^{k-1}\exp\bigg{[}-\bigg{(}\frac{U} {A}\bigg{)}^{k}\bigg{]}, \tag{8}\]  
where \(U\) indicates the horizontal wind speed, k is the shape parameter and A is the scale parameter. For the current site, k = 2.09 and A = 7.9. Furthermore, a mean wind speed of 7.0 m/s was observed. A north-easterly wind was dominant during the experimental period, though a south-westerly wind was also significant. A typhoon approached HORS on 8 to 9 September 2019, when strong winds with more than a 35 m/s 10-minute mean were observed.  
As described in [PERSON] et al. (2018) [7], at the HORS site, winds coming from 335\({}^{\circ}\) to 145\({}^{\circ}\) were categorized as sea winds (from sea to land sectors), while those from 155\({}^{\circ}\) to 325\({}^{\circ}\) were categorized as land winds (from land to sea sectors). Notably, land winds coming from between 175\({}^{\circ}\) and 230\({}^{\circ}\) were influenced by the neighboring wind turbines situated on the coastline. As a homogeneous wind field was assumed in the VVP method, the winds blowing from the wake sector may reduce the measurement accuracies, relative to the other sectors. Consequently, observations with a wind direction that was directly disturbed by the wake of the wind turbines were excluded from the validation.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Ranging Module  
The ranging algorithm inputs the circuit module's voltage signal \(v_{c}[i]\). It then calculates each scan point's target range \(R\) and signal intensity \(I\). The range \(R\) is given in meters while the intensity \(I\) is mapped linearly to an arbitrary integer scale from 0 to 4096 as used in the Cube 1 products. The algorithm is applied to several threshold levels to distinguish between internal reflection, noise, and target peaks. The target range is determined based on the relative position of the target peaks to the internal reflection, while the signal intensity is calculated from the peak voltage levels [4].
## 3 Results
### Total Organic Carbon (TOC) and Total Nitrogen (TN) in Leachates  
Figure 3a,b report the TOC of the leachates collected after the irrigations.  
Figure 3: _Cont._  
As reported by [27], BCdryQ has a higher C content than BCwetQ. By considering the leachates, the TOC from BCdryQ and BCwetQ-treated columns (see Figure 3a) were not statistically different, except for the leachates collected on the 26 th day after sowing. By looking at the mean values, it appeared that the TOC mean value from the WET Q treatment was always higher than the DRY Q treatment. Table 2 reports the \(p\)-values of the pairwise comparison for the factors DAS and treatment for the variable TOC results from the two-way ANOVA.  
The two treated substrate did not differ, but they were significantly different from the CTRL treatment. In particular, the biochar application resulted in a lowering of the leachable organic C, regardless of the type of quenching.  
As for the irrigations, the TOC in the leachates was such that the first two leachates (occurring at 5 and 12 DAS) were significantly higher than the last two leachates (occurring at 33 and 40 DAS), while the TOC mean values for the leachates 19 and 26 DAS were not statistically different from the first and second irrigations (5 and 12 DAS) and from the second irrigation (12 DAS), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **(a)** & & & \\ \hline
**DAS** & **5** & **12** & **19** & **26** & **33** & **40** \\ \hline
5 & ns & ns & ns & 0.028 & 0.001 & 0.004 \\
12 & & ns & ns & ns & 0.003 & 0.011 \\
19 & & & ns & ns & ns & ns \\
26 & & & & ns & ns & ns \\
33 & & & & & ns & ns \\
40 & & & & & & ns \\ \hline \multicolumn{8}{c}{**(b)**} \\ \hline
**Substrate** & **CRTL** & **DRY** & **WET** \\ \hline CTRL & ns & & 0.000 & 0.000 \\ DRY & & & ns & ns \\ WET & & & & ns & ns \\ \hline ns = not significant (\(p\)-value \textgreater{} 0.05\). & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(p\)-values from the ANOVA performed on the variable TOC, considering (a) the factor leachates that is indicated by the days after sowing (DAS) and (b) the factor substrates.  
Figure 3: Total organic carbon (TOC) (**a**) and total nitrogen (TN) (**b**) in ppm (mg L\({}^{-1}\)) of the leachates collected after the 6 irrigations of the 12 columns (n = 4).  
With regards to the TN, Figure 3b shows a clear difference between the TN in the CRTL leachates that was separate from the leachates collected from the treated substrates (WET and DRY), being much higher when the biochar was not applied to the soil.
## 1 Introduction
Although the ultimate goal is to maximize the performance metrics, given the very central role of the user, such performance is very dependent on not only how the user is able to execute the task, but how the system influences the perception of that task and if that may influence its execution positively. Simplification, effort reduction, and intuitiveness are some keywords that positively influence the user and therefore his/her performance. To this end, some guidelines on which aspects are important to evaluate in a teleoperation system are also presented. Structure of the article: Section 2 provides insights about skills and performance in human activity. Section 3 describes the human role in teleoperation, namely the control and feedback flows with the human in the loop operating a remote device. Section 4 analyzes the technological constraints and human perceptual factors with relevance for immersive teleoperation design, providing a set of recommendations for the construction of these systems. Section 5 presents an evaluation methodology for immersive interfaces in teleoperation. As an example, an immersive interface is proposed, and the influence of perception on task execution is analyzed. Section 6 presents the quantitative and qualitative results and a discussion of the proposed immersive interface. Section 7 presents the findings and conclusions.
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 4 Results
### Results in the Quaternion Processing
Generally, the results that produce the highest levels of detection are the frames included within the first 5 results of the processed sequence, increasing the noise level and losing detection capacity for frames with further positions. Results with lower noise levels lose the ability to detect most subtle defects. The results that allow the detection of a greater number of defects, located deeper, also contain a certain level of noise, so that in general the optimal detections do not occur in the first result. As with QFT processing, QPCA results generate similar color tones for defects of the same type and located at the same depth (Figure 9a-c), while defects located at different depths generate different color tones (Figure 9d-f). Finally, quaternionic filtering by spatial convolution (QFILT) has been applied to the results obtained with the application of QFT and QPCA. The objective of this processing is to enhance the edges of the defects to improve the visualization and facilitate their location. For its application, the pair of conjugate filters defined in the Quaternion Spatial Convolution section together with a scale factor \(S=1/6\) has been used to average the sum of the 6 pixel values defined by the pair of filters. Some results obtained in this analysis are shown in Figure 10, where the upper row corresponds to the starting results obtained with QFT and QPCA, and the lower row corresponds to the results obtained after the quaternionic filtering. It is observed that the edges of the defects appear colored with different shades depending on the original color of the defect, while the internal areas appear with colors tending to shades of gray. Additionally, the edges of the defects are not shown with a single color but are represented with two different tones. This effect occurs as a result of the path followed in the convolution process. In this study, the path followed in the convolution went from top to bottom and from left to right. The change of the path produces different tones at the edges. Figure 8: Results obtained with the application of QFT processing to the sequence colorized by Gaussian KPCA: (**a**) \(\sigma=0.01\), direction 15 and frame 1, (**b**) \(\sigma=0.05\), direction 63 and frame 7, (**c**) \(\sigma=0.5\), direction 54 and frame 17, (**d**) \(\sigma=5\), direction 26 and frame 3, (**e**) \(\sigma=5\), direction 66 and frame 1, (**f**) \(\sigma=1\), direction 67 and frame 12.
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 1 Introduction
### Motivations and Objectives  
In this paper, we aim to develop a multidisciplinary version of the Mermaid float, making it possible to combine different monitoring applications during the same campaign. Although we focus on UPA monitoring, the float is not limited to acoustic and can integrate other sensors.  
The main motivation of our work is to enable scientists to write signal processing applications for the instrument. Indeed the sensors, and more especially the acoustic, generates high volume of data (e.g., a five minutes recording at 78.1 kHz produces 70 MB of data, and 7 TB for one year). These data can be stored by the floats, but, due to cost effectiveness, the floats are usually not recovered from the oceans, as it is the case with most Argo floats. The satellite communication system has a very limited bandwidth and it is not capable of transmitting such an amount of data. Moreover, many applications such as monitoring of earthquake or volcanic activity, require data transmission in (quasi) real time. An algorithm that is generic enough to handle different signal processing applications from different domains does not exist. Even machine learning algorithms have different architectures, depending on the application. Thus, the Mermaid cannot be configured with just a few parameters, it must be programmed with fully fledged applications.  
However, developing signal processing applications to be embedded in an instrument such as Mermaid is challenging for the following four reasons:Embedded software programming requires specific technical skills, and it can be off-putting for less technically skilled scientists who will have to learn C language programming and know specialized technical details regarding the instrument, such as the operation of the real time operating system, micro-controller, sensors, etc.
2. The embedded applications must comply with the limited resources of the instrument. Otherwise they may not behave as expected, induce elevated costs of data transmission, or considerably reduce the instrument life time.
3. The embedded applications must be reliable, without software bugs, and efficient, with a minimum impact on the instrument resources. Any miss-conceived code may compromise the instrument that is not directly accessible when deployed in the oceans. Less technically skilled developers are more prone to writing miss-conceived code.
4. The embedded applications developed independently and installed on the same instrument must not interfere with each other. Whether the applications are installed alone or with other applications their behavior must not change.  
To overcome these challenges, we have developed a programming language dedicated to the Mermaid. This language is designed in order to meet the needs of signal processing experts and it does not require embedded software programming skills. The language is called HeLa, for Mermaid Language, and it is presented in the next section.
## 5 Temperature Effects  
The CSEM method was able to detect the subtle resistivity changes in the TU Delft campus reservoir with the preferential acquisition setup, as shown in Figure 3. Thus, the CSEM monitoring of the TU Delft campus reservoir allows for tracking the growth of the resistivity change over decades. Hence, we checked whether the time-lapse electric field responses acquired with the surface-to-borehole setup were sensitive to an increased  
Figure 3: Time-lapse electric field response and difference at different source offsets (1, 2, or 3 km from (**top**) to (**bottom**)). The (**left column**) shows the electric field response of the base model, the (**middle column**) shows that of the monitoring model, and the (**right column**) provides their relative difference.  
resistivity change radius. With an expected fluctuation in temperature of the injected water depending on heat demand, the quantity of the resistivity change will fluctuate as well; see Equation (1). Thus, we considered the monitoring of temperature effects under different volumes.  
Figure 4 shows the difference in monitoring fields determined at a zero offset for different disk radii and magnitudes of the resistivity change. The source had a frequency of 1 Hz and was located with a 1 km offset. At a certain return temperature, the difference in time-lapse fields increases when increasing the radius of the resistivity change; meanwhile, for a certain radius, the difference increases with temperature. Figure 4 indicates that the time-lapse electric fields acquired with the surface-to-borehole configuration are sensitive to the growth and magnitude of the resistivity change. Considering these results, we next tested the robustness of CSEM monitoring in the presence of undesired effects.
###### Abstract  
Herein, we report the melt blending of amorphous poly(lactic acid) (PLA) with poly(styrene-_co_-methyl methacrylate) (poly(S-_co_-MMA)). The PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blends were made using amorphous PLA compositions from 50, 75, and 90 wt:%, namely PLA\({}_{50}\)/poly(S-_co_-MMA)\({}_{50}\), PLA\({}_{75}\)/poly(S-_co_-MMA)\({}_{25}\), and PLA\({}_{90}\)/poly(S-_co_-MMA)\({}_{10}\), respectively. The PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blend pellets were extruded into filaments through a prototype extruder at 195 \({}^{\circ}\)C. The 3D printing was done via fused deposition modeling (FDM) at the same temperature and a 40 mm/s feed rate. Furthermore, thermogravimetric curves of the PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blends showed slight thermal decomposition with less than 0.2% mass loss during filament extrusion and 3D printing. However, the thermal decomposition of the blends is lower when compared to amorphous PLA and poly(S-_co_-MMA). On the contrary, the PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blend has a higher Young's modulus (\(E\)) than amorphous PLA, and is closer to poly(S-_co_-MMA), in particular, PLA\({}_{90}\)/poly(S-_co_-MMA)\({}_{10}\). The PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{y}\) blends proved improved properties concerning amorphous PLA through mechanical and rheological characterization.  
Keywords:amorphous poly(lactide acid); poly(styrene-_co_-methyl methacrylate); polymer blends; filament extrusion; 3D printing +
Footnote †: journal:
## 5 Results and Discussion  
### Electrode Design  
Based on the results obtained in Figure 2, optimization was performed to maximize the performance metric of the MZI (Detailed in Appendix A), and values of wg and wc were found to be 8.25 \(\upmu\)m and 5.75 \(\upmu\)m. Using the optimized dimensions for electrodes neff(and \(\Delta\)n ) and \(Z_{0}\) (\(Z_{\text{in}}\) and R) versus RF frequency are plotted in Figure 5a,b, respectively. Here, \(Z_{\text{in}}\) is the input line impedance calculated using the Equation in [12]. It is evident that n\({}_{\text{eff}}\) and \(Z_{\text{o}}\) decrease sharply in the sub-GHz region and remain very close to n\({}_{\text{op}}\) and 50 \(\Omega\), respectively, in the sub-THz region, providing high velocity and impedance matching. \(\Delta\)n, \(Z_{\text{in}}\) and R further verify the results plotted in figures.  
Apart from velocity and impedance matching, the MZI bandwidth also depends on the RF response of the electrode lines. As the length (L) increases, the RF losses increase, which hinders the performance of the MZI. The result in Figure 3b suggests that \(V_{\pi}L\) for the proposed design is 1.74 V.cm. Using this result, the RF response of the designed electrodes for L = 2.9 mm is plotted against the RF frequency in Figure 5c. The figure shows that \(5_{21}\) remains moderately above \(-\)10 dB at 200 GHz and \(S_{11}\) remains around \(-\)30 dB for the complete RF frequency range.  
The electro-optic and RF responses are plotted together in Figure 6a. The figure also displays the electro-optic and RF bandwidths of the proposed device. The 3 dB electro-optic bandwidth for the proposed device is 136 GHz. Modulation efficiency and phase error (refer to Appendix A Equations (A5)) are plotted in Figure 6b, and it remains at \(-\)1.24 dB at the maximum bandwidth, while phase error remains below 0.08 rad (4.6\({}^{\circ}\)). Due to deterioration of electro-optic transmittance with frequency, \(V_{\pi}L\) also changes with frequency. Variation of \(V_{\pi}L\) against the RF frequency is plotted in Figure 6c. The figure shows that \(V_{\pi}L\) increases with the RF frequency. Since L is fixed, it means that the \(V_{\pi}\) of the device changes with the frequency. The performance of various CPW-based TFLN modulators has been listed in Table 2.  
Figure 5: (**a**) n\({}_{\text{eff}}\) (and \(\Delta\)n), (**b**) \(Z_{\text{o}}\) (\(Z_{\text{in}}\) and R) and (**c**) RF response of the CPW for the design parameters versus RF frequency.  
Figure 6: (**a**) RF (S\({}_{21}\)) and electro-optic response, (**b**) modulation efficiency and phase error and (**c**) \(V_{\pi}L\) versus RF frequency.
## 4 Conclusions  
This paper proposes a deep learning approach based on AE data. The objective is to characterize and classify different stages of deformation (elastic and plastic) and different configurations of the specimen (Txy, Tz, T45) on AlSi10 Mg samples obtained during the SLM process. The process started with tensile tests. The AE signals were extracted. A time-frequency analysis was performed, using CWT to extract the spectrograms. The time-frequency content allowed the AE signals to be distinguished from the stages of damage (elastic and plastic) and from the different configurations of the specimen. Subsequently, these spectrograms were used to train three different CNNs, including AlexNet, SqueezeNet, and a new model. A comparison was made among these three CNN models, considering the accuracy and development time of the different CNN models. In addition, a series of training modes were performed to determine the amount of data required to train a network to achieve 100% accuracy. Changing the parameters from Max-Pooling to Average-Pooling does not affect the results. Using the sigmoid activation function results in 50% accuracy for the damage stage classification and 33.3% accuracy for the classification based on the specimen configuration for all the CNN models used. Using the ReLu activation function, 100% accuracy is achieved for each CNN model. The CNN achieves the same accuracy but with a different learning time. The new, simplified CNN model classified AE signals in less time compared to AlexNet and squeezeNet, which have a high number of parameters. The AE signals, which are from an individual specimen, were classified with  
Figure 19: Classification based on the signals generated from individual specimens using TM 2: (**a**) AlexNet, (**b**) SqueezeNet, and (**c**) the Proposed model CNN.  
the optimal CNNs by changing the input data. The results show, first, that increasing the input data affects the accuracy of CNN classification. Second, the simplified CNN model can achieve the same accuracy as AlexNet and squeezeNet but in a short time.  
Conceptualization, C.B., G.P., V.P.K. and D.K.M.; methodology, C.B., G.P., V.P.K. and D.K.M.; software, D.K.M.; validation, C.B., G.P., V.P.K. and D.K.M.; formal analysis, C.B., G.P., V.P.K. and D.K.M.; investigation, C.B., G.P., V.P.K. and D.K.M.; resources, C.B. and G.P.; data curation, C.B., V.P.K. and D.K.M.; writing--original draft preparation, D.K.M.; writing--review and editing, C.B., G.P., V.P.K. and D.K.M.; visualization, D.K.M.; supervision, G.P. and C.B.; project administration, G.P. and C.B.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.