esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Detection System of Safety Hazards in Industry 5.0
## 3 Related Work and Background
### Object Detection Algorithms
Several studies focus on the exploitation of Machine Learning or Deep Learning methods to detect more than one object of PPE. The authors in [43] present three DL models based on YOLO architecture to identify if the workers are wearing hard hats and vests based on the Pictor-v3 dataset, a self-obtained and annotated image dataset that is created by crowd-sourcing and web-mining techniques. One of the main limitations of vision-based detection methods is that they are prone to occlusion, poor illumination, and image blurriness. The work in [44] introduces a system designed to inform supervisors when a worker is not wearing a helmet or vest. Another relevant paper in this field is demonstrated in [45], in which the authors develop a detector that detects in real time if people wear face masks, face shields, and hand gloves. The detector is trained on a combination of both captured and collected images from Google. In both [44,45] papers, YOLO v4 is used as an architecture model with satisfactory results. The work described in [46] presents a system that enhances workers' safety by employing a camera that detects the utilization of PPE using the YOLO v4 algorithm. To achieve this, the researchers collect data from public resources and manually annotate it. The created dataset consists of five classes, namely hardhats, safety vests, safety gloves, safety glasses, and hearing protection. The system provides good detection accuracy; however, it shows poor performance in uncontrolled environments and in the detection of small and occluded objects. One relevant paper in that field is [47], which proposes a system for real-time detection of PPE, namely helmets, vests, and gloves, using deep neural networks on video streams. The authors of this work also enhance public datasets with manually annotated ones while they deploy the application on a low-cost embedded system located near the camera and directly connected to it, adopting the edge computing paradigm.
## 3 Algorithm and Strategies of Data Processing
### Multipath Analysis  
GNSS phase multipath is a main error source in absolute and relative determination of antenna phase center variations due to the fact that there hardly exists an environment unaffected by multipath effects [8]. In order to derive precise PCV-independent stations in field procedures, multipath must be separated carefully from PCVs in the data processing [5], but it is impossible to exploit a perfect mathematical model to precisely describe multipath which is suitable for any general station situation. However, MP can be considered as time-variant error source since it fluctuates with time in the local station environment. Instead, PCVs can be treated as static parameters which are only dependent on elevation and azimuth (_i.e._, antenna type dependent). Therefore, the MP and PCV are essentially independent from each other and can be separated with different measurements and strategies. In this paper, three measures were taken to mitigate multipath as much as possible: (1) choosing a less multipath-disturbing field. The calibration baseline is situated on top of the roof of a teaching building, with no significant sky obstructions and without nearby electromagnetic interference. The two solid concretes were intentionally built at 1.6 m high up the ground to effectively reduce multipath sources from ground reflections; (2) Double observables differencing with two stations. There is no doubt that two stations at both ends of a short baseline are always under similar multipath conditions. The station distance in our calibration procedure is about 3 m. Double differencing (DD) with these two stations will differentially eliminate most parts of any multipath effect; (3) Time-wise triple difference. The remaining multipath of DD, which will still disturb antenna phase pattern modeling (see Figure 3, demonstration only), cannot be neglected. One can triple difference the DD observables of two successive mean sidereal days since multipath periodically appears after a mean sidereal day [4]. Although the so-called sidereal filter approach is able to further reduce multipath, it takes several continuous days to accomplish an antenna calibration. In addition, partially differential multipaths may not work for some signals from day to day due to the flaky weather, e.g., 2:00 GPST to 4:00 GPST in the Figure 3. In our calibration approach, similar to [8,19], we adopt the epoch-difference approach which is more efficient due to the fact that a change of the multipath behavior in a very short time is extremely similar. The epoch-differences on the basis of DD observations results demonstrate that the multipath has further reduced (_cf._ Figure 3).
# Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
## 4 Results and Discussion  
A modern mobile wireless network is comprised of a combination of different site types and different access technologies that work together as a heterogeneous network. The RF planning teams in the MNOs select the appropriate site type according to many inputs, such as the target coverage range, number of users, forecasted traffic growth, total cost, and nature of the area (rural, urban, suburban, dense, indoor buildings, etc.). This study looked into multi-operator, multi-technology CDs for two types of sharing sites, macro and indoor IBS sites, which are currently the most commonly used.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 6 Conclusions  
This research proves the viability and feasibility of developing an adaptable system for small satellites using commercial off-the-shelf components. The software design of the system is verified. In this research, we created an open-source tool that is compatible with the Raspberry Pi devices already used in small satellites. Next, the technology must be demonstrated by testing the hardware implementation and creating an experimental subsystem to be operated from space. This will be discussed in detail in the following paper. Adaptive systems are indeed the future of satellite communication, especially for low Earth orbits.  
Conceptualization, Y.M.O.A. and K.A.; data curation, Y.M.O.A.; formal analysis, Y.M.O.A.; funding acquisition, K.A.; investigation, Y.M.O.A.; methodology, Y.M.O.A.; project administration, K.A.; resources, Y.M.O.A. and K.A.; software, Y.M.O.A.; supervision, K.A.;validation, Y.M.O.A.; visualization, Y.M.O.A.; writing--original draft, Y.M.O.A.; writing--review and editing, Y.M.O.A. and K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Materials and Methods  
### Sample Preparation and Initial Properties of As-Cast and As-Compressed ZM6  
#### 2.1.1 Casting Process, Chemical Composition, and Hot Compression  
The ZM6 alloy was prepared using pure magnesium (Mg \(\geq\) 99.9 wt%), the Mg-25.0 wt% Nd master alloy, the Mg-33.3 wt% Zr master alloy, and pure zinc (Zn \(\geq\) 99.9 wt%). The addition of aluminum, iron, silicon, manganese, and magnesium oxide should be avoided because these elements and impurities hinder the grain refinement in zirconium. During the smelting process, the alloy was melted in a resistance furnace and was protected by CO\({}_{2}\) and SF\({}_{6}\) gas in order to ensure that the alloy liquid did not become oxidized, thereby reducing the loss of alloy elements by burning and improving the casting quality. The test alloy was added in the form of a mixed rare earth metal and was dried at 200 \({}^{\circ}\)C before casting, and it was then melted in a steam crucible furnace. The crucible and the other tools were cleaned and preheated with flux before use, and they were then cast in a metal mold. The ingot was preheated in a preheated crucible at 200 \({}^{\circ}\)C and kept at 720 \({}^{\circ}\)C for 25-30 min. After the alloy was completely melted, the pure Zn and Mg-Nd master alloys were added at 750 \({}^{\circ}\)C and were held there for5 min; then, the Mg-Zr master alloys were added at 780 \({}^{\circ}\)C and were kept there 5 min, and then the 1% refining agent was added and stirred. Finally, the master alloys were kept at 780 \({}^{\circ}\)C for 20 min, and the metal mould (160 mm \(\times\) 130 mm \(\times\) 20 mm) was poured at 720 \({}^{\circ}\)C.  
The chemical composition of the as-cast ZM6 (Mg\({}_{2.6}\)Nd\({}_{0.4}\)Zn\({}_{0.4}\)Zr) alloy was within the standard content region as follows: 0.2-0.7% Zn, 2.0-3.0% Nd, 0.4-1.0% Zr, Si: 0.01%, Cu: 0.03%, Ni: 0.01%, Fe: 0.01%, others: 0.05% (all in mass fraction). The as-cast bar stock was then compressed at 400-450 \({}^{\circ}\)C and at a compression degree of 8%. The tensile strength and the elongation of the as-cast and as-compressed ZM6 heat-resistant magnesium alloys were tested by the vendor of the Xi'an Brake Branch of Avic Aircraft Co., Ltd. (Xi'an, China).
## 1 Introduction  
Due to global warming and energy crisis, efforts have been taken to obtain highly efficient construction materials. Aerated concretes are increasingly being adopted, especially in non-loadbearing walls, in order to condition the inside environment while using the least possible amount of energy.  
Aerated concretes are mainly produced from Portland cements and raised by mixing with metal powders, like aluminium, and then reacting with acidic ashes, which create hydrogen gas. Unfortunately, such aerated concrete technology involves the use of Portland cement and autoclaves to create expansive reactions and consolidate the bricks. Both the raw materials and the technology are not environmentally friendly [1]. Researchers are therefore trying to obtain sustainable construction materials from waste materials like bottom ash without the need of expensive autoclaves.  
With growing industrial energy demands, most countries are now fulfilling their energy requirements from coal-burning power plants [2]. The coal used in the power plants for combustion produces bottom ash (BA) which is used for landfills. Twenty percentage of the total coal ash produced is the BA. The particle size of BA is larger than that of fly ash and nearly equals the size of sand particles [3; 4]. The coal ash fuses, agglomerates, and settles down to the bottom of the furnace, therefore it is called bottom ash [5].  
In order to get aerated concretes from coal ashes, a mixture of different approaches have been undertaken.H\({}_{2}\)O\({}_{2}\) and organic precursors have been combined and reacted [6]. CaCO\({}_{3}\), which releases CO\({}_{2}\)while heat treating at higher temperatures (800-850 \({}^{\circ}\)C), creates foams [7,8]. [PERSON] et al. have used sodium silicate as a foaming agent to produce fly ash-based foam by thermal treatment using sodium borate as a fluxing agent [9]. In our previous work, we developed a geopolymer foam by microwave process [10]. Sodium silicate (SS) is a commonly used industrial material which swells with the application of heat; it was also utilized in our present research as a foaming agent [11].  
In this research work, we investigated a thermal foaming process at a lower temperature compared to other works, without using sodium borate or any other fluxing agents. Different compositions were tested, and all samples were characterized for their physical, microstructural, and mechanical properties.
## 1 Introduction
Experimental results show that the proposed ultrasonic range measurement method provides stable TOF estimation under the tolerable deviation and improves the performance of target localization and tracking in the application of human finger tracking. The proposed system and algorithms can be applied to applications such as handwriting systems on screens or smartphones and hand gesture recognition systems. One application for the proposed system is the large-scale non-touch screen. Especially in recent days, avoiding physical contact to prevent virus infection is an important issue. This system can be built in the business district for an interactive application with the people without physical contact. It is also a safe solution for healthcare professionals to perform contactless panel control in the operating room. The rest of the paper is organized as follows. Section 2 gives the mathematical formulation of the proposed algorithms, including the VAD, demodulation for envelope extraction, the NRLM with the GN model and the DE model for TOF estimation, and the localization method of the EKF with outlier rejection. Section 3 presents the theoretical simulation for the performance comparison of the NRLM with the GN and DE models. The experimental description and result analysis of the system are described in Section 4. Finally, the conclusion is given in Section 5.
## 1 Introduction
Thus, statistical models are often needed to describe maritime traffic characteristics, such as traffic volume, heading, and speed distribution. Based on this analysis, we have designed a supervised learning method using machine learning for multi-feature, multi-output learning of vessel behavior within fairways based on AIS data. First, we acquire AIS trajectory data within channel boundaries and preprocess the data. Then, we organize training data based on the preprocessed AIS trajectories. Finally, we apply three methods to learn vessel behavior within fairways. The resulting models can accurately predict vessel latitude, longitude, heading, and speed at specific future times based on berth positions and vessel dimensions. These models support traffic volume prediction, risk assessment, traffic management, and vessel scheduling within fairways. The remainder of this paper is organized as follows: Section 2 reviews existing research on vessel behavior learning and trajectory prediction, discussing their advantages, disadvantages, and application scenarios. Section 3 defines the multi-attribute vessel behavior learning method within fairways based on AIS data. Section 4 presents experiments and analyzes the results using AIS data from the fairways of Tianjin Port, China. Section 5 concludes the paper and provides an outlook for future research.
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Architecture of In-Line Measuring Station  
An in-Line measurement system was designed based on the specifications described in the previous section. A non-contact measurement device laser probe was proposed from the start to reduce the risk of collision between the machine and part. To compensate for the fact that the laser probe can make only one measurement at a time, a high speed positioning system was needed. A cartesian positioner, Gantry type, actuated by linear motors was selected because of the high speeds that can be achieved. The total work volume of the position system is 600 \(\times\) 600 \(\times\) 270 mm.  
Figure 6 shows the structure of the full measurement system. There are two different sections in the system: The loading area and the measuring area. The measuring station is enclosed for the purpose of protecting the laser sensor as well as the linear motors from the environment. Parts are loaded (and retrieved) in the loading station, and then transported into the measuring area by a pneumatic positioner. This configuration requires a workpiece fixturing system (Figure 6c), which uses a novel locating system for accurate positioning of the casting in the measuring area [47].  
Figure 5: Current gaging inspection system: (**a**) View from operator’s perspective showing linear variable displacement transducers (LVDTs) and reference pins, and (**b**) view from the opposite side showing a workpiece mounted and ready for measurement operation.  
The measuring process starts when the operator mounts the part on the transportation plate. Two button boxes are used to start the measurement cycle and require both hands to be occupied when an action is started by the operator. The part is fixed by pneumatic clamps and then transported into the working area, where the measurement process is conducted. If the part is within specifications, the system automatically unclamps the part, which allows the operator to remove it. If the part is not within specs, the operator has to push buttons to release the part, which then goes through a reprocessing step. The measurement cycle is repeated until the part is accepted or rejected if it could not be fixed. Results of a measurement are displayed on a computer screen and the data are sent to the Cloud.  
The control and processing system coordinate all the functions of the equipment and manage the data from the process. Figure 6 shows the elements of this system: A Galil Controller (GC), a driver for the laser, an XDK Bosch multisensory, an accelerometer, and a minicomputer that coordinates the interaction of all and provides Connectivity.  
The manner in which the hardware design meets the characteristics of flexibility, accuracy, robustness, and speed that are needed for in-line measurement are now discussed.
## 4 Results
### Spatiotemporal Patterns in Global GPP  
A global GPP product was produced based on an improved LUE model that simultaneously considered multiple environmental factors and the canopy N concentrations index. The global mean annual GPP, from 2001 to 2018, was \(129.7\pm 3.02\) PgC. The spatial pattern of global annual GPP was shown in Figure 7. The GPP was high in tropical regions where sufficient water and suitable temperature can satisfy the photosynthesis of vegetation.  
Figure 5: Relationship between Cl\({}_{\text{green}}\) and canopy N concentrations (%). The grey area indicates the 95% confidence interval.  
Figure 6: 10-fold cross validation of the model driven by ERA5 meteorological data for different vegetation types, using (**a**) the coefficient of determination (R\({}^{2}\)), (**b**) the root-mean-square error (RMSE) (gC/m\({}^{2}\)/d), (**c**) the mean absolute error (MAE) (gC/m\({}^{2}\)/d), and (**d**) the index of agreement (IOA) as the accuracy evaluation indices. The vegetation types include cropland (CRO), closed shrubland (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grassland (GRA), mixed forest (MF), open shrubland (OSH), savanna (SAV), permanent wetland (WET), and woody savanna (WSAV).  
While the GPP gradually decreased with increasing latitude, cold or arid environmental conditions limited the growth of vegetation.  
Figure 8 showed the interannual variation trend of global GPP estimated by the NI-LUE model from 2001 to 2018. In the past 18 years, GPP showed an increased trend in about 60.1% of terrestrial ecosystems. In addition, the GPP in Central Africa, Central South America, Western Europe, Eastern China, and western India showed a rapidly increasing trend. However, there was a significant decreasing trend in eastern South America and tropical Southeast Asia. In high latitudes, GPP varied less and showed an increasing trend.  
Additionally, we used 10 sets of parameters obtained by a 10-fold cross-validation to simulate the global GPP separately to determine the uncertainty of the model. Globally, the mean uncertainty of the annual GPP simulation was 15.86 \(\mathrm{gC/m^{2}/yr}\). The spatial pattern of the uncertainty in the global GPP simulation (Figure 9) showed that GPP uncertainty was low at middle and high latitudes, while GPP uncertainty was high in eastern South America and central Africa.  
Figure 8: Spatial pattern of interannual variation trend of global GPP estimated by the NI-LUE model from 2001 to 2018.  
Figure 7: Spatial pattern of global GPP estimated by the NI-LUE model during 2001–2018.
# Electrical Property Measurement on Antistatic Coating During Aging Process
## 3 Results and Discussion  
### Charging Current Measurement  
Looking at Table 1, where the charging currents are listed, we can say that the least conductive paints on a glass sheet were those containing CaTiO\({}_{3}\).  
The following values for particular coating types are conductive currents with measured resistance and calculated surface resistivity (according to ANSI/ESD STM11.11-2006 Surface Resistance Measurement of Static Dissipative Planar Materials standard) [48] (Table 1).  
We can also say that perovskite CaTiO\({}_{3}\) with a combination of conductive polymers behaves as an absorber. A decrease in \(I_{G}\) value can be observed; thus, clean perovskite has a higher value than the one with a conductive addition. The paints on glass containing TiO\({}_{2}\) without additions of calcium oxide have approximately the same value of conductivity currents as well as the commercially available paint BISIL (Table 2).  
The exception is a PANI polymer. This sample did show conduction capability, as the \(I_{G}\) current value was a 10 times higher value than the current of non-conductive paint: PANI: \(I_{G}\) = 350 \(\mu\)A, where the charging current on conductive paint PANI before the aging process on a glass sheet is presented.  
For paints on steel pads, we measured higher value currents in general due to the pad material. There is also a considerably higher effect of conductive polymers (Table 3).  
We can also observe an opposite effect of CaTiO\({}_{3}\) in combination with metallic material compared to the expectations. The constant current flowing through the paints on a glass  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coating Material** & \(I_{G}\) **(\(\mu\)A)** & \(R\) **(M\(\Omega\))** & \(\rho_{S}\) **(M\(\Omega\))** \\ \hline CaTiO\({}_{3}\)/PPY & 35 & 0.286 & 44.874 \\ CaTiO\({}_{3}\)/PANI & 80 & 0.125 & 19.632 \\ CaTiO\({}_{3}\) & 210 & 0.048 & 7.479 \\ \hline \end{tabular}
\end{table}
Table 1: Measured and calculated data on the Keithley 6517B electrometer.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coating Material** & \(I_{G}\) **(\(\mu\)A)** & \(R\) **(M\(\Omega\))** & \(\rho_{S}\) **(M\(\Omega\))** \\ \hline TiO\({}_{2}\)/PANI & 100 & 0.1 & 15.706 \\ TiO\({}_{2}\)/PPY & 110 & 0.091 & 14.278 \\ BISIL & 110 & 0.091 & 14.278 \\ \hline \end{tabular}
\end{table}
Table 2: Measured and calculated data on the Keithley 6517B electrometer.  
sheet increased by 90 \(\mu\)A. The most conductive paints were those that contained PANI polymer, this time in combination with TiO\({}_{2}\). In this case, the conductive current was 1000 times higher in comparison with the clean PANI polymer case.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload
### Performance Analysis  
Nominal values of the parameters and associated uncertainties used for the Monte Carlo simulations are listed in Table 10. The atmospheric mean density uncertainty (\(\pm 3\sigma\)  
Figure 19: Effect of density variation on Venus aerocapture corridor. The black dash-dot line indicates the selected EFPA, and the black dotted lines indicate the assumed \(\pm 3\sigma\) error.  
Figure 20: Evolution of altitude rate and altitude during Venus aerocapture. The red circle indicates the time at which drag skirt jettison is commanded.  
and high-frequency perturbations are used from Venus-GRAM [45]. All 1000 entry trajectories propagated achieved successful orbit insertion. Figure 21 shows the distribution of the achieved apoapsis and periapsis altitude at atmospheric exit. Figure 22 shows the distribution of the peak g-load, peak stagnation-point heat rate, and the total heat load.  
statistics from the Monte Carlo simulations. The 95 percentile peak deceleration and peak heat rate are 8.8\(g\) and 168 W/cm\({}^{2}\). The 95 percentile periapsis increase in \(\Delta V\) is 27 m/s.
## 2 Study Area and Materials
### Datasets and Preprocessing
#### 2.2.2 DEM Data  
The digital elevation model (DEM) data with a spatial resolution of 90 m were downloaded from the Shuttle Radar Topography Mission (SRTM) database ([[http://strm.csi.cgiar.org/](http://strm.csi.cgiar.org/)]([http://strm.csi.cgiar.org/](http://strm.csi.cgiar.org/)), accessed on 7 November 2022, CGIAR-CSI, Montpellier, France). The SRTM DEM is the most popular and highest-quality terrain data, consistently covering a large proportion of the global surface [64; 65].