esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Within the present paper the authors propose the use of a kinetic energy storage system as a power source for an unmanned aerial vehicle launcher. The work contains dynamics equation of the model, results of performed simulations and even a CAD model of a kinetic launcher prototype.  
In general, main advantages of the kinetic launcher for UAVs are:  
* Simple design that allows to actively control the acceleration and speed of the launched objects;
* Much higher energy density than in the magnetic launchers (the stored energy that can be used is proportional to the square of the rotational speed of the flywheel and can be adjusted for different class of lauded objects);
* A much more compact solution than pneumatic launchers to provide similar performance (no large tank, no compressed air preparation device, no complex valves system);
* Easy scalability for different classes of launched objects.  
Simulation results clearly revealed that the proposed approach may provide an alternative for currently used pneumatic and rubber launchers. One important advantage of the described system is the possibilities it creates for motion control. When compared to magnetic launchers, the proposed solution is characterized by a much higher energy density allowing it to be used for bigger UAVs. Only a simple binary algorithm for launch control is presented in the article. However, the use of active electromagnetic systems as actuators for torque transmission makes it possible to implement any other control rule. One idea for future research is the investigation of a control system for quasi-continued stabilization of UAV acceleration during the launch using a PID controller. The work provided many conclusions connected with the practical realization of the project. First of all, it is necessary to do trials to verify whether a transmission system based on synchronous toothed belts can carry loads which may appear during the launch. It may also be necessary to supplement it with belt tension subsystems. Secondly, the linear motion of the launched object should be supported through a subsystem of linear tracks. This can improve the stability of the UAV during the launch and make it possible to use the developed kinetic launcher for airplanes of higher mass. Additionally, the CAD project assumed the utilization of an electromagnetic actuator for braking. However, other solutions mentioned within the paper, such as mechanical bumpers or rubber catch ropes may make it possible to decrease the total length of the launcher. Although the results presented in the paper are very promising, within this  
Figure 9: Results of the 3 rd simulation for flywheel speed \(n_{S0}=2000\) rpm, \(x_{h}=2\) m and non-zero damping and friction parameters.  
last paragraph it must be stated that the future of all further investigations are connected with the construction of a model in the laboratory allowing the verification of all assumptions and simulation results. The authors recognize this task as quite challenging and treat it as the next stage of any future work on the kinetic launcher for UAVs.
## 3 Results and Discussion
### Thermal Analysis and Flame Retardancy of EVA and its Composites
Figure 7: Cone calorimetry results of EVA and its composites with 20 wt. % LDHs. (**a**) Heat release rate (HRR) curves; (**b**) total heat release (THR) curves; (**c**) smoke production rate (SPR) curves and (**d**) production rate of CO (COP) curves. It is well known that most injuries and deaths result from the smoke and toxic gases in the case of fire. The peak smoke production rate (pk-SPR) for EVA, S-NiMgAl-Mn/EVA, S-NiMgAl-Co/EVA, S-NiMgAl-Cu/EVA and S-NiMgAl-Zn/EVA are 0.146, 0.100, 0.107, 0.0997 and 0.102 m\({}^{2}\)/s, respectively. Furthermore, peak production rate of CO (pk-COP) are 0.0147, 0.0044, 0.0049, 0.0045 and 0.0044 g/s for EVA, S-NiMgAl-Mn/EVA, S-NiMgAl-Co/EVA, S-NiMgAl-Cu/EVA and S-NiMgAl-Zn/EVA, respectively. The pk-SPR and pk-COP for all the composites decrease significantly compared to EVA as listed in Table 1. In addition, MCOY is the mean release yield of CO of burning materials. The MCOY values of EVA, S-NiMgAl-Mn/EVA, S-NiMgAl-Co/EVA, S-NiMgAl-Cu/EVA and S-NiMgAl-Zn/EVA are 0.0362, 0.0277, 0.0281, 0.0268 and 0.0271 kg/kg, respectively. Compared to the EVA, MCOY values for composites show an evident decrease. In particular, S-NiMgAl-Cu/EVA has the smallest MCOY value among all the composites. The above concealorimeter data provide evidence that the composites show important improvement of both the flame retardancy and the smoke/toxic gas suppression, especially for S-NiMgAl-Mn/EVA.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 1 Introduction
[PERSON] et al. used ResNet50 and DenseNet121 models for TSOM measurement [19]. They achieve a larger size range of nanoscale measurement compared to CHO's, but its accuracy is still limited as a classification method. The common deficiency of the above three kinds of TSOM measurement methods is that only a single TSOM image is used to retrieve the measurement parameters. That means only a sectional view of the entire TSOM cube is used, which leads to the low image information utilization and further leads to the limited accuracy of these TSOM measurement methods. [PERSON] et al. studied the relationship between the TSOM height and TSOM volume with the defects of nanodevices, indicating that there is a regression relationship between the entire TSOM cube and parameters of the measuring target [25]. This conclusion indicates that parameters of the measuring target can be retrieved by inputting more images of the TSOM cube to improve the accuracy rather than inputting only a sectional view. Since only the defocus distance between different images that constitutes the TSOM cube changes, and the focused image has the highest definition and contains more information relatively among the TSOM cube, a single focused image is the most suitable to be added as the supplementary input of TSOM image. On this basis, a two-input deep-learning TSOM method based on CNN is proposed in this paper. The two-input CNN uses the focused image and the TSOM image as two inputs (instead of using only a single TSOM image as previous TSOM methods) and uses the measuring parameters of the target as the output to make full use of more effective information of the TSOM cube. Additionally, the regression model is used to solve the problem that the measurement accuracy is limited by the classification categories. The structure of the rest of this paper is as follows: the second part introduces the materials and methods, including the process, the experimental devices, the acquisition of the dataset, the structure of the proposed two-input CNN and how to train it. The third part introduces our experiment, including model-evaluation indices, the measurement results, analysis, and the uncertainties of the model. The fourth part introduces our discussion, mainly concerning the effect of focusing position error and the influence of the shift of the measuring target. The fifth part introduces our conclusion.
# Concept of a 3D-Printed Voronoi Egg-Shaped Habitat for Permanent Lunar Outpost
## 3 Materials and Methods
### Possible Construction Method of a Lunar Habitat  
The construction process described below is a conceptual consideration, but just like the Artemis mission, the use of robotic technology is also expected here. In this regard, it is possible to consider preparing the base, or a large part of it, even before the landing of the human crew. All the stages described below are also depicted in Figure 7.  
Phase A--Finding a suitable place for the future habitat and transporting construction equipment and materials. At this stage, it is necessary to evaluate the actual conditions and possible complications of the given place. Subsequently, preparations for the base at the bottom of the crater and the surrounding area, for easier transportation, were conducted. At the same time preparations are being made for the production of printable geopolymer mixture from regolith.  
Phase B--Laying and anchoring of the basic building block. It is a critical part of the building, which is followed by 3D printing of the external and internal supporting part of the structure. At this moment the printable mixture is already prepared, together with a robotic arm and other supporting equipment.  
Phase C--Layer after layer of the future habitat is now being continuously 3D-printed. At the same time, regolith is constantly being sprinkled to the same level at which the printing is currently taking place.  
Phase D--Once the structure reaches the ground floor level and the access area (with a retention wall) is 3D-printed, the connection module with the pressure chamber and space suit chamber will be installed. Regolith will continue to be piled up around it.  
Figure 6: Scheme of an egg curve with marked parameters.  
Phase E--The entire retention wall protecting the entrance and habitat structure is printed. Once the connecting ring of the habitat on top is in place, work will begin on the internal structure. Firstly, the membrane is inflated (only with a small pressure), which completely adheres to the walls, to which it is anchored at points. The internal structure is assembled from collapsible beams and columns, and a frame is prepared for the installation of the modular floor. At the same time, the light tube is assembled and ready to be used. All equipment is installed, and the habitat can be fully pressurized.  
Phase F--The connecting ring and the mirror are installed. The regolith embankment is complete. The lunar base is fully operational.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 4 Security Analysis  
In this section, the security analysis of the CP-ABE-based multi-authorization revocable access control method will be introduced, including data confidentiality, forward security and post-security, and collusion attacks by multiple consumers.
###### Abstract  
Satellite remote sensing is an important method for forest phenological studies at continental or global scales. Sentinel-1 (S1), a polar orbit satellite with a spatial resolution of 10 m, provides an opportunity to observe high-resolution forest phenology. The sensitivities of S1 C-band backscatter measurements to vegetation phenology, such as crops, meadows, and mixed forests, have been discussed, whereas their performance for different forest types has not yet been quantitatively assessed. It is necessary to evaluate accuracy before adapting S1 datasets in forest phenological studies. This study discusses the seasonal variations in S1 backscatter measurements and assesses the accuracy of S1-based forest phenological metrics in two types of typical forests: deciduous and coniferous. S1 C-band SAR dual-polarization backscatter measurements for the period 2017-2019 were used to extract forest phenology metrics using the Fourier transform (FT) and double logistic (DL) functions. Phenological metrics from the ground-based PhenoCam dataset were used for evaluation. The S1 backscatter VV-VH signal peaks for deciduous and coniferous forests occur in the winter and summer, respectively. The S1 backscatter could reasonably characterize the start of season (SOS) of deciduous forests, with R\({}^{2}\) values up to 0.8, whereas the R\({}^{2}\) values for coniferous forest SOS were less than 0.30. Moreover, the retrieved end of season (EOS) was less accurate than the SOS. The differences in accuracy of S1 backscatter phenological metrics between deciduous and coniferous forests can be explained by the differences in seasonal changes in their corresponding canopy structures. To conclude, S1 C-band backscatter has a reasonable performance when monitoring the SOS of deciduous broadleaf forests (R\({}^{2}\) = 0.8) and relatively poor performance when extracting EOS of deciduous broadleaf forests (R\({}^{2}\) = 0.25) or phenology of evergreen needleleaf forests (R\({}^{2}\) = 0.2).  
sentinel-1; forest phenology; time-series; PhenoCam; NDVI +
Footnote †: journal: remote sensing  
0000-0002-4640-3330][PERSON]  
0000-0002-1467-1886][PERSON]  
0000-0002-1467-1886][PERSON]  
0000-0002-1467-1886][PERSON]  
3  
[PERSON]  
3,4
# Synthesis of Mesoporous MWCNT/HKUST-1 Composite for Wastewater Treatment
## 2 Experimental Procedures
### Functionalization of MWCNTs and Characterization  
The as-received MWCNTs were functionalized by treatment with acid. Due to the diameter of the as-received MWCNTs, it was decided that a mild acid treatment should be used in order to not damage the carbon nanotubes. Approximately 2 g of MWCNTs were treated with 130 mL of 55% HNO\({}_{3}\) in a round-bottom flask of 500 mL. The solution was oxidized under reflux at 80 \({}^{\circ}\)C for 6 h. The solution was washed severally with deionized water and then with ethanol to prevent the agglomeration of carbon nanotubes during drying. The water from the functionalized MWCNTs had a pH of approximately 7. The slurry of the functionalized MWCNTs was dried at 80 \({}^{\circ}\)C for 24 h. The morphology of the as-received MWCNTs and functionalized MWCNTs was examined through high-resolution scanning  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **S\({}_{\text{BET}}\) (m\({}^{2}\)g\({}^{-1}\))** & **V\({}_{\text{P}}\) (cm\({}^{3}\)g\({}^{-1}\))** & **Ref.** \\ \hline HKUST-1 & 1009.00 & 0.064 & [PERSON] et al. [35] \\ HKUST-1 & 841.00 & 0.433 & [PERSON] et al. [9] \\ Cu–BTC MOF & 856.00 & 0.031 & [PERSON] et al. [14] \\ HKUST-1 & 1345.90 & N/A & [PERSON] et al. [36] \\ MOF-199 & 1370.00 & 0.57 & [PERSON] and [PERSON] [13] \\ CNT/Cu–BTC MOF & 123.25 & 0.081 & [PERSON] et al. [14] \\ CNT/MOF-199 & 1280 & 0.49 & [PERSON] and [PERSON] [13] \\ HKUST-1 & 1615 & 0.69 & [PERSON] et al. [34] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural properties of Cu–BTC metal–organic framework (MOF) (Hong Kong University of Science and Technology (HKUST-1)) synthesized using the hydrothermal method.  
electron microscopy (HRSEM Joel 7800F) equipped with EDS analysis. Powder X-ray diffraction (RXD) was used to identify the phases present. The presence of functional groups on the carbon nanotubes was examined through Fourier-transform infrared spectroscopy (FTIR). This technique helped in accessing the carboxylic functional group on the treated MWCNT surfaces. Since carbon nanotubes exhibit strong absorbance due to their black characteristics, a very small number of CNTs were blended with KBr powder to form a pellet that was used for FTIR analysis.
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 3 BPNN Model for Noise Limits Prediction during Vehicle Acceleration
### Performance Analysis on Machine Learning Models  
There are several commonly used machine learning models in engineering practice, and thus, it is important to perform a comparative analysis to choose an appropriate one, which is specifically suitable to learn noise limits in ECE R51 regulations [27]. These machine learning models, including the support vector machines (SVM) [28; 29], the gaussian process regression (GPR) [30; 31], the ensembles of trees (ET) [32; 33], and the regression trees (RT) [34; 35], are compared and discussed.  
The comparative analysis for these aforementioned machine learning models aimed to investigate their performance in terms of accuracy of the predicted responses they made, more precisely, by a five-fold cross-validation procedure. All the four mentioned-above models were employed to learn noise limits described in Table 1, and each one was checked with the classic five-fold cross-validation procedure. The yielded MSE values of the predicted responses are shown in Table 3. The performance evaluation in terms of accuracy of predicted response was based on the MSE values between true and predicted values.  
According to Table 3, the SVM, GPR and BPNN models have much better performances than the ET and RT models. On the one hand, the MSE values from ET models using Boosted Trees and Bagged Trees algorithm are 23.6 and 14.8, respectively, while the MSE value of Regression Trees are 4.76, 14.4 and 14.4, respectively. The two models-the ET models and RT model were found not suitable for the noise limit prediction. On the other hand, the BPNN and GPR models have high accuracy on the noise limit prediction, and better than the SVM models. As a result, the BPNN model was chosen because it has a small MSE value and quite good performances in training data set, compared to the GPR model.
# Modeling Trust in IoT Systems for Drinking-Water Management
## 3 Methods and Proposed Approach
### Proposed Approach
#### 3.2.1 Trust Relationship Modeling  
Modeling trust relationships in a water-treatment plant like Sherbrooke's requires a mathematical approach to calculating trust levels. This approach takes into account the dynamic nature of trust, reflecting changes over time. In addition, it recognizes the non-bi-directional aspect, where trust can vary depending on the direction of the interaction, and the non-transitive aspect, acknowledging that trust is not necessarily transitive between all parties involved. This mathematical modeling thus offers a realistic and flexible representation of trust relationships in a drinking-water management environment.  
Let us define some notations to formalize our model:  
* Let \(N\) be the set of critical elements in the water-treatment station, including sensors, PLCs, operators, etc.
* Let \(C_{ij}\) be the trust variable between critical element \(i\) and critical element \(j\), where \(C_{ij}\) represents the trust level of \(i\) in \(j\). To calculate trust levels, we have employed an approach based on dynamic weights. The weights are used to model factors influencing trust.
* Each weight \(W_{ij}\) reflects the influence of a specific factor on the trust relationship between \(i\) and \(j\).
# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 4 Experimental Results and Analysis
### Evaluation Indicators and Data Set Preparation  
The following metrics were utilized in this study: Accuracy (P); Recall (R); Average Precision (AP); Mean Average Precision (mAP), and frames per second (FPS). These metrics were employed to evaluate the model from different perspectives. The calculation expressions of P, R, AP, and mAP are presented in Table 1.  
The original data used in this experiment is sourced from a power grid company in Yunnan, which provides a small-target data set of transmission lines. This data set contains various small targets of transmission lines, such as insulators and bolts. To augment the data, random horizontal flips, rotation transformations, brightness adjustments, and other data expansion techniques were applied to the original set of 1076 images, resulting in a new data set containing 3200 images. The new data set was annotated using image labeling software labeling and split into the training, validation, and test sets with an 8:1:1 ratio. For training, 300 epochs were used with an initial learning rate of 0.01 and a weight decay coefficient of 0.0005. The same data set, namely the small-target data set of transmission lines, was utilized for both training and testing in this experiment.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pollutant** & **Example Product** & **Measurement Method** & **Resolution** & **Accuracy** & **Range** & **Price (USD)** \\ \hline \multirow{4}{*}{Carbon Monoxide (CO)} & Teledyne Model T300U & IR Absorption & \multirow{2}{*}{0.1 ppb} & \multirow{2}{*}{\(\pm\)0.5\% of reading} & 0-100 ppb & About $30,000 \\  & Gas Filter Correlation & with Gas Filter & & or 0-100 ppm & \\  & Carbon Monoxide Analyzer [62] & Correlation Wheel & & & & \\ \cline{2-6}  & Aerogonal Series 500 & Electrochemical & & & & \\ \cline{2-6}  & with CO Sensor Head [63] & Sensor & & & & \\ \cline{2-6}  & Alphasense & Electrochemical & & & & \\ \cline{2-6}  & B4 Series CO Sensor [64] & Sensor & & & & \\ \cline{2-6}  & Humanized & Solid-State & & & & \\ \cline{2-6}  & MQ-7 CO Sensor [65] & Sensor & & & & \\ \hline \multirow{4}{*}{Nitrogen Dioxide (NO\({}_{2}\))} & Teledyne Model T500U & Cavity Attenuated & \multirow{2}{*}{0.1 ppb} & \multirow{2}{*}{\(\pm\)0.5\% of reading} & 0-100 ppb & About $30,000 \\  & Nitrogen Dioxide Analyzer [66] & Phase Shift & & & & \\ \cline{2-6}  & Spectroscroy & & & & & \\ \cline{2-6}  & Aerogonal Series 500 & Electrochemical & & & & \\ \cline{2-6}  & with NO\({}_{3}\) Sensor Head [67] & Sensor & & & & \\ \cline{2-6}  & Alphasense & Electrochemical & & & & \\ \cline{2-6}  & B4 Series NO\({}_{3}\) Sensor [68] & Sensor & & & & \\ \cline{2-6}  & SCASensorTech & Solid-State & & & & \\ \cline{2-6}  & MICS-2714 NO\({}_{2}\) Sensor [69] & Sensor & & & & \\ \hline \multirow{4}{*}{Ozone (O\({}_{3}\))} & Teledyne Model T500U & Chemiluminescence & \multirow{2}{*}{0.1 ppb} & \multirow{2}{*}{\(\pm\)0.5\% of reading} & 0-100 ppb & About $525,000 \\  & Chemiluminescence & & & & & \\ \cline{2-6}  & Ozone Analyzer [70] & Detection & & & & \\ \cline{2-6}  & Aerogonal Series 500 & Solid-State & & & & \\ \cline{2-6}  & with O\({}_{3}\) Sensor Head [71] & Sensor & & & & \\ \cline{2-6}  & Alphasense & Electrochemical & & & & \\ \cline{2-6}  & B4 Series O\({}_{3}\) Sensor [72] & Sensor & & & & \\ \cline{2-6}  & Hanwei & Solid-State & & & & \\ \cline{2-6}  & MQ-130 S Sensor [73] & Sensor & & & & \\ \hline \multirow{4}{*}{Sulfur Dioxide (SO\({}_{2}\))} & Teledyne Model & \multirow{2}{*}{0.1 ppb} & \multirow{2}{*}{\(\pm\)0.5\% of reading} & 0-50 ppb or & About $30,000 \\  & & & & & & \\ \cline{2-6}  & 6400T/6400E & UV Fluorescence & 0.1 ppb & & & \\ \cline{2-6}  & Sulfur Dioxide Analyzer [74] & & & & & \\ \cline{2-6}  & Aerogonal Series 500 & Electrochemical & & & & \\ \cline{2-6}  & with SO\({}_{2}\) Sensor Head [75] & Sensor & & & & \\ \cline{2-6}  & Alphasense & Electrochemical & & & & \\ \cline{2-6}  & B4 Series SO\({}_{2}\) Sensor [76] & Sensor & & & & \\ \cline{2-6}  & Hanwei & Solid-State & & & & \\ \cline{2-6}  & MQ-136 SO\({}_{2}\) Sensor [77] & Sensor & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Instruments used in air quality monitoring systems (Part B).