esa-sceva/satcom-chunk-collection huggingface dataset:
# Additive Manufacturing of Bone Scaffolds Using PolyJet and Stereolithography Techniques
## 3 Results
### Numerically Calculated Permeability  
The numerically calculated permeability values showed that the cubic scaffold structures had higher permeability, ranging between 0.3 and \(2.4\times 10^{-10}\) m\({}^{2}\), as compared to the hexagonal closed packed scaffold structures, which ranged between 0.13 and 2.0 \(\times\) 10\({}^{-10}\) m\({}^{2}\). The Polylet 3D-printed scaffold structures and \(\upmu\)SLA 3D-printed scaffold structures had the same calculated permeability because the Kozeny-Carman equation contains only structural properties. The specific surface area and porosity values are very critical factors, which varied with each scaffold  
Figure 4: Standard samples drawn in \(x\)-, \(y\)-, and \(z\)-directions and printed in the \(x\)-direction, while lines on the sample shows the build layer.  
structure design and were obtained from CAD models. The CAD models were similar for both 3 DP techniques used in this study. Figure 5 showed that the permeability was increased with the increases in pore size and porosity in both cubic and hexagonal closed packed scaffold structures.  
Figure 5: Numerically calculated permeability values: (**a**) Scaffold structures with cubic pore shape; (**b**) Scaffold structures with hexagonal pore shape.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Liquids**} & \multicolumn{2}{c}{**Contact Angle**\({}^{\circ}\)} & \multicolumn{2}{c}{**Surface Energy mN/m**} \\ \cline{2-5}  & **VeroClear** & **PlasWhite** & **VeroClear** & **PlasWhite** \\ \hline Tap Water & 67.0 & 69.1 & 41.7 & 38.5 \\
15\% Glycerol–Water & 70.0 & 72.6 & 40.0 & 37.9 \\
20\% Glycerol–Water & 72.9 & 73.7 & 39.8 & 37.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contact angle and surface energy values for Polylet (VeroClear) and \(\upmu\)SAL (PlasWhite) 3D-printed scaffold structures.  
Equation (3) showed that specific surface area was an important parameter that affected the permeability and changed with every scaffold structure; therefore, it was also important to observe the effects of the pore size and porosity of scaffold structures on the specific surface area, which ultimately affects the permeability. The surface area decreased with increases in pore size [45]. Figure 6 also shows the same results, i.e., the specific surface area decreases with increases in pore size and porosity. The lower pore sizes plotted in this figure were 0.34 mm and 0.6 mm and then 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm; therefore, this drastic change in the surface area may also have been due to the difference in steps between 0.6 mm and 1.5 mm. Moreover, Equation (3) also represents the inverse relation between the porosity and surface area; hence, the increase in pore size decreased the specific surface area and resulted in increased permeability.  
Figure 6: Specific surface area with respect to the pore size and porosity: (**a**) cubic structures; (**b**) hexagonal closed packed structures.
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 4 Growth of GaN Nanorods Using MSE
### Self-Assembled GaN Nanorods Grown Using MSE
[16] have grown self-assembled nanorods on multiple functional and cost effective templates and substrates, including Si, SiC, TiN/Si, ZrB\({}_{2}\)/Si, ZrB\({}_{2}\)/SiC, Mo, and Ti. The nanorods show high purity and quality, as evidenced by the strong band-edge emission from room-temperature cathodoluminescence spectroscopy. The work demonstrates the capability of MSE-grown self-assembled nanorods as a simple and affordable fabrication option, making it attractive for future device fabrications.
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 3 Materials and Methods  
This paper presents remote sensing as an Internet service, using a UAS for a simple tree counting case. We use the images captured during a flight over a field of olive trees in Andalucia, Spain, with the objective of counting the number of olive trees in it. We show how to process the images one by one, as they are captured by the camera. A RaspberryPi [17] was used to demonstrate that the information relevant to the end-user can be obtained during the flight. The requirements are that the counting will be computed and delivered in real time, while flying, and that the final value has an accuracy similar to that obtained by the post-processing of the mosaic.
# An Evaluation Framework and Algorithms for Train Rescheduling
## 7 Description of the Experiment
### Train Delays  
According to the average values in Table 16, ALG2 outperforms ALG1 in obtaining solutions with smaller train delays. The statistical significance of the difference in the performance of the two algorithms could be confirmed only for TFD\({}_{3}\) (\(p=0.002\)). For TAD\({}_{3}\), statistical significance could not be confirmed from the obtained results (\(p=0.29\)). This means that the difference in the values of TAD\({}_{3}\) for the two algorithms is more likely to have occurred by chance.  
For several of the scenarios 1-10, the TFD\({}_{3}\) in ALG1's solutions is often rather far from the optimal value (see Table 12). In contrast, ALG2 always obtains a solution with optimal TFD\({}_{3}\), even if it means causing a large delay to a single train. For example, in the solution obtained by ALG2 for scenario 6, although the solution's TFD\({}_{3}\) is minimized (54.9 min), a train experiences a delay \(\geq\) 30 min at its final station (see Table 10). ALG1's solution for scenario 6 has a significantly larger TFD\({}_{3}\) (78.1 min). However, in that solution, no train experiences a final delay \(\geq\) 30 min. This is a good example of the trade-off between reducing individual train delays and reducing total train delays.  
For a majority of the scenarios 11-30, both the algorithms found either ideal solutions or solutions that are very close to ideal (see Table 12). This is an interesting result, as the ideal point is generally expected to be unattainable [33]. A trade-off was expected between minimizing final and accumulated delays; we did not expect to obtain a solution with optimal TFD\({}_{3}\) as well as optimal TAD\({}_{3}\). It is surprising that such a trade-off between TFD\({}_{3}\)-TAD\({}_{3}\) does not occur more frequently in Table 12.  
An interesting observation can be made from the results obtained for scenarios 3, 6, 14, and 18. For these four scenarios, ALG1 produces solutions that have a smaller TAD\({}_{3}\), compared to ALG2 (see Table 12). Note that ALG1 does not try to minimize TAD\({}_{3}\). However, in the obtained solutions, the delays accumulated by trains at commercial stops are close to optimal. On the other hand, ALG2 has minimizing TAD\({}_{3}\) as its second objective. A reason for this anomaly is that ALG1, while minimizing the passenger delays, indirectly reduces the delays experienced by trains at commercial stops. On the other hand, once ALG2 optimizes the final delays (i.e., the value of TFD\({}_{3}\)) and uses it as a bound, it cannot reduce the accumulated delays beyond a certain point.
## 3 Role of Lipids for Membrane Function and Intracellular Transport
### Effect on Endocytosis and/or Retrograde Transport due to Manipulations of the Lipidome
#### 3.7.3 Effect of Cell Density  
Several studies have shown that changes of the cell density in culture could modulate the effect of plant and bacterial toxins, as less toxicity was observed at increased cell density [30; 188]. To look more closely into this effect, HEp-2 cells were grown to three different cell densities, from 20-30% density on Day 1 to 80-90% density on Day 3 [33]. As expected, a decrease in Shiga toxin induced cell toxicity was observed with increasing cell density. Increasing cell density also resulted in an increase in Gb3 (no change in the relative amount of Gb3 species) and cholesterol and a decrease in ether lipids, as discussed in Section 3.7.2. Remarkably, the cells with a small increase in Gb3 levels showed some decrease in binding/uptake of Shiga toxin, whereas a stronger reduction of the toxicity was found to be due to a reduced transport from endosomes to Golgi or further to the ER (Table 1). A detailed quantitative lipidomic study showed some changes in a few PA and DAG species which might be related to the decreased Shiga toxicity [33], but further studies are needed to understand why Shiga toxin is much less toxic to cells at a higher cell density.  
The group of [PERSON] later reported an extensive study on cell crowding effects of mouse embryonic fibroblasts either expressing or lacking focal adhesion kinase (FAK) [34]. They reported that more than 1000 genes adapted their transcript abundance to cellular crowding, of which 80% required the presence of FAK to adapt. Notably, most changes reported in the lipidome of the fibroblasts as a function of increased cell density [34] were different from those reported in the study with HEp-2 cells [33]. Thus, increasing the cell density of various cell lines has been reported to result in different changes of the lipidome, and more studies are needed to understand how and why such changes occur.
## 3 Results and Discussion
### Vibration  
The highest average RMS acceleration of 0.17 ms\({}^{-2}\) was achieved by train C. In five of the six measurement points (2, 3, 5, 8, and 10), this train achieved the highest values of this measure (Figure 11). In most of these points, train A, with congruent technical specifications to train C, recorded similar parameters, thus taking second place among the highest average RMS acceleration values obtained by the train. The next pair was formed by the latest metro cars, trains D and E, which achieved comparable values at all measurement points, which were the lowest in each case. Train E achieved the lowest average RMS acceleration value of 0.06 ms\({}^{-2}\), and train D had the second smallest--0.07 ms\({}^{-2}\).  
The weighted RMS values of accelerations in the one-third-octave bands (Figure 4) did not exceed the permissible values in any of the cases. The highest values were achieved for the vertical direction in the frequency range of 10-60 Hz for the constructions with coil springs in second-stage suspensions. In contrast, the designs with pneumatic springs have lower acceleration levels. The traction rod between train D's bogie and car body had no adverse effect on the acceleration level relative to other vehicles. In contrast, the other vehicles without traction rods had higher acceleration levels in the horizontal plane direction. In the vertical direction, the train equipped with a traction rod had the lowest acceleration levels measured at the car's body ends, compared to other vehicles. The levels at the car body center were higher in the 8-25 Hz frequency range but still comparable to B and E trains and less than A and C trains. The absence of a traction rod requires using a traction-torsion device at the bogie pivot, which can generate additional stiffness parallel to the second-stage springing at a level higher than the basic stiffnesses (pneumatic springs only) for the transverse and vertical guides. This may impact passenger comfort, especially with poorer track quality.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Arm–Hand, RMS** & **Whole Body, RMS** & **Whole Body, VDV** \\ \hline Vibration threshold value & 2.5 m/s\({}^{2}\) & 0.5 m/s\({}^{2}\) & 9.1 m/s\({}^{1.75}\) \\ Maximum dose value & 5 m/s\({}^{2}\) & 1.15 m/s\({}^{2}\) & 21 m/s\({}^{1.75}\) \\ \hline \end{tabular}
\end{table}
Table 2: Worker vibration exposure limits for an eight-hour reference period [33].  
and amounts to 19.5. In contrast to the \"x\" direction, train D had the second-lowest average crest factor score of 13.7, and train B had the highest crest factor value of 21.6.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 4 Merging Method
### Formulation
#### 4.1.1 Creation of Fake Tuples  
A published table with generalization has less data utility than the published table with fake tuples. The presence of fake tuples does not have any effect on the utility of the published dataset, but will increase the probability of false matches during a composition attack on the published table [25]. In our anonymization method, we create \(n\) (\(n\)=1,2,3,...) fake tuples with the same \(QI\) values as in the original table and assigned sensitive values to them based on the sensitive value distribution in the initial dataset.
## 7 Conclusions  
This publication describes the use of an interferometric sensor based on the Mach-Zehnder interferometer as an alternative to current standard seismic stations. The presented sensor operates with single-mode telecommunications optical fibers according to standard G.652.D., with a wave length of 1550 nm and a radiation source with a power output in the order of milliwatts. The paper compared original results based on an evaluation of long-term time records. A total of 769 passes was recorded for various types of tram over five months of practical measurements under various climatic conditions. The fiber-optic sensor detected all vehicles at a 100% rate, and the recorded results were compared to the results from a standard seismic station. Amplitude and especially frequency significantly agreed in the results from both items of equipment. Amplitude showed that all tram vehicle axles were detected in individual time records. Frequency showed an agreement in the bandwidth, generally corresponding to rail transport of individual types of tram. The results published in this paper show a potential use for this type of sensor in monitoring selected rail transport parameters as an alternative to existing conventional sensors. The primary reason for reducing the use of conventional sensors is the electromagnetic interferences generated by the new traction drives in rail vehicles and signal contamination caused by the return traction current in rails.  
[PERSON] proposed the sensor system idea, designed the measuring sensory systems and edited the manuscript. [PERSON], [PERSON] developed, tested and validated the LabView application and MATLAB scripts. [PERSON], [PERSON], [PERSON], M.P., and [PERSON] performed the experiments and wrote the manuscript. [PERSON] and [PERSON] critically evaluated the quality of the research data and experimental methods used to generate/acquire them as well as the soundness/validity of the scientific and engineering techniques, wrote the manuscript, and performed its final edits.  
This article was supported by the Ministry of Education of the Czech Republic (Project Nos. SP2018/170, SP2018/184, and SP2018/55). This research was partially supported by the Ministry of Education, Youth and Sports of the Czech Republic through Grant Project No. CZ.1.07/2.3.00/20.0217 within the framework of the Operation Programme Education for Competitiveness financed by the European Structural Funds and from the state budget of the Czech Republic. This article was also supported by the Ministry of the Interior of the Czech Republic within Project Nos. VI20152020008 and VI2 VS/444, as well as the Ministry of Industry and Trade of the Czech Republic within Project Nos. FV10396 and FV20581. It was also supported by the Grand Agency of the Czech Republic (Project #15-21547S) and the European Regional Development Fund in the Research Centre of Advanced Mechatronics Systems project, Project Number CZ.02.1.01/0.0/0.0/16_019/0000867, within the Operational Programme Research, Development and Education.  
The authors declare no conflict of interest.
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 2 Methodology
### Experimental Technical Scheme  
This study used the GNSS data preprocessing software TEQC (2019.5) to process the received satellite observation and navigation files to extract signal-to-noise ratio, elevation, and azimuth data. Specifically, daily SNR data for each satellite were segmented into individual ascending or descending arcs based on changes in elevation angle. Following the steps outlined in Section 2.1, data were preprocessed to obtain phase characteristic parameters for different arcs of each satellite. Ultimately, a soil moisture retrieval model based on Random Forest was established and validated against Support Vector Machine, RBF neural network, and Convolutional Neural Network models through comparative analysis. The specific experimental workflow is depicted in Figure 3.  
Figure 3: Flow of Random Forest-based multi-satellite data fusion GNSS-IR soil moisture retrieval approach.
# Shape Modelling and Volume Optimisation of Salt Caverns for Energy Storage
## 1 Introduction
They found that a D/H ratio of 0.33 showed the best performance in terms of maximum displacement and the safety factor, but a D/H ratio of 0.82 was best in terms of effective volume. [PERSON] et al. [3] proposed an optimal H/D ratio of 7/3 for ellipsoidal caverns. New complex designs of cavern shapes were proposed by [PERSON] et al. [29; 33], [PERSON] and [PERSON] [30], [PERSON] et al. [34] and [PERSON] et al. [5]. In these models, the cavern was divided into two parts: the lower structure and upper structure. The shape and dimensions of the lower and upper structures were designed, and stability analyses were performed for each part. The shape of the caverns considered in these studies was ellipsoidal, with slightly enlarged upper or lower parts. It should be underlined that most shapes considered in the aforementioned papers [25; 26; 29; 32; 33] were characterised by sharp edges. Sharp edges of underground workings--e.g., the corners and sharp angles between walls--are prone to stress concentration [35; 36; 37]. This sharp edge effect is also reflected in numerical simulations. Consequently, the stability performance of these caverns is worse than caverns with shapes based on an ellipsoid or a hemisphere. The real shape of a salt cavern is usually more complex [31] than the geometric figures evaluated in numerical simulations. Thus, the determination of a sharp and smoothed edge effect on cavern stability is important. The motivation of this paper is to evaluate the stability of typical cavern shapes (cylindrical, enlarged top and enlarged bottom) in three variants that differ by diameter in the geological and mining conditions of the bedded salt deposit. The influence of sharp and smoothed edges on the cavern's stability performance is studied. This evaluation is also aimed at finding the optimal cavern shape and volume in the implemented geological conditions to maximise storage capacity. The analysis employs the stratigraphy and material properties of the Mechelinki salt deposit (Northern Poland).
## 3 Technology  
### Advantages of Single-Photon Avalanche Diodes (SPADs) for TG Measurements  
The efficient fast time-gating of single-photon detectors is not straightforward and represented a challenge in the field of single-photon counting for diffuse optical measurements. Photocathode-based detectors [62], the historical choice for TD-DO, have been gated in two different ways: (i) by applying fast voltage transitions to the electron beam amplification stage (i.e., dynodes in photomultiplier tubes or micro-channels in microchannel plates or in intensified charge-coupled devices) to selectively enable and suppress the multiplication [28; 63; 64; 65; 34]; (ii) by seeping photoelectrons generated by undesired photo-detections out of the phosphor screen in streak cameras [36]. However, these techniques offer limited performance for TD-DO at small/null \(\rho\) as the photocathode is not shielded by the burst of early-photons, thus increasing its dark count rate due to extreme light exposition, also risking a persistent damage. For this reason, single-photon avalanche diodes (SPADs) [37] were considered. SPADs are solid-state detectors exploiting high electric field in a reversely biased p-n junction (above its breakdown voltage) to multiply photo-generated electron-hole pairs through impact ionization, thus resulting in macroscopic avalanche current pulses (milliampere range) in response to single-photons [37]. As compared to typical photocathode-based detectors, SPADs have much smaller active areas (with typical diameters \(\leq\) 200 \(\upmu\)m), but can be time-gated by swiftly changing their bias voltage above or below breakdown, turning them respectively ON and OFF [31]. In this way, a burst of early photons can just generate electron-hole pairs in the device, without persistent alteration of the SPAD performance.
## 2 Materials and Methods
### Data Generation  
The data used to develop the proposed method represent the harmonic response of the system over the frequency range over which the structure will be tested, typically 5 Hz to 100 Hz. In this study, in order to train and evaluate the networks to compare the different ANNs, data had to be generated for the three different scenarios. The training, testing, and validation data were generated by conducting a finite element harmonic analysis to compute the accelerations and stresses or forces at given nodes and elements, respectively, over a determined frequency range (5-200 Hz, step of 2 Hz).  
This data set was complemented by another set of data that was generated by conducting a finite element harmonic analysis over the same frequency range, with the same structure but different material properties. The Young's modulus of the JWST's optical bench was decreased by 5% in order to shift the natural frequencies of the structure, and to account for material property uncertainty. The remaining material properties were left unchanged. These artificial data helped the trained models to generalize and make better predictions when the material of the test structure was not identical to the material data considered in the finite element analysis.  
The data were then divided into a training set, a testing set, and a validation set to enable the assessment of the training progress and process. In order to improve training, the data at natural frequencies of the structure were included in the training data set, while the remaining frequencies were  
Figure 3: Architecture of the considered ANN: (**a**) frequency-dependent ANN and (**b**) nonlinear autoregressive exogenous (NARX) model.  
randomly distributed between the training and the validation data set. Thus, it was ensured that the model learned the connections at the natural frequencies that were the most critical, since the structure experiences the stresses with highest amplitudes. In general, a small random number of frequencies can also be used as a test set to evaluate the model's accuracy. However, in this investigation, the models were assessed on independently generated test data with a changed [PERSON]'s modulus. In this way, uncertainties, as experienced in reality, were taken into account.
# Candidate Waveforms for ARoF in Beyond 5G
## 5 Comparison of Candidate Waveforms
Focusing on the main requirement of URLLC, pure single-carrier waveforms are the best solution to reach extremely low latency because of their low block processing delays. On the other hand, the reliability can be achieved by adapting the modulation order according to the channel conditions. We can reduce the modulation order because the bit rate is not critical in this scenario. SC amplitude and phase waveforms allow more flexibility to change the modulation order compared to SC amplitude only waveforms. Therefore, SC amplitude and phase waveforms are good candidate waveforms in URLLC both for downlink and uplink. An alternative solution could consist in adaptive waveform formats according to the type of service. Namely, to select the waveform that best adapts to the requirements of a particular service. This can be achieved through intelligent software-defined radio (SDR) [49] and software-defined networking (SDN) [56].