esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Detecting Abnormal Activity in a Group of Mobile Robots
## 2 Related Work
That is, other types of attacks were not detected. To properly track an intruder, the system should have special monitor nodes, which track the network behavior, and their quantity must be at least six. This fact brought extra limitations to the network. Also, the authors did not analyze the effects of variability in the number of intruders and the top quantity of deflected intruders. A system for detecting anomalous behavior of the internet of robot network was discussed in Reference [25]. This system was that it had two subsystems. The first system was a group of robots that collected data using a sensor system and transmitted it to a central server, which was connected to an external network. The second one was a mobile network, where the following modules were available: data acquisition module, anomaly classification module, and order module. The disadvantage of this system was that it was completely centralized; the robots did not communicate with each other and acted only through an intermediary. Anomaly detection was performed using a classifier that was trained using a pre-formed training sample. Thus, the following conclusions can be made:  
* The topics of robotized systems security are the focus of research worldwide. Authors offered various approaches to robot security. * There are no complex approaches to security of mobile robot networks on the network and physical layer, as well as on the control level [26]. The works discussed above were based on the need to compare the behavior of the robot with a reference one or the presence of an external observer, or to consider detection systems based on rules or signatures. In this case, the parameters analyzed to identify anomalies were very specific and may not be suitable for all systems. Our contributions to the state of the art are summarized as follows:  
* Analysis of various architectural solutions, protocols, and standards for building a communication infrastructure for a group of robots. * Analysis of the main approaches to the creation of group control systems for robots, and developing a group control algorithm for the experimental stand. * Design and implementation of an experimental stand simulating the work of a group of robots. * A methodology for in-depth traffic analysis based on mathematical statistics for a group of robots. * We prove the effectiveness of using metrics, based on which the further development of an anomaly detection system in a robotic system is planned. As a result, the method allows determining the presence of an attack rather quickly and with a minimum loss of resources. This method takes into account the features of robotic systems and will be effective precisely for such systems. * A software tool that implements the method of deep traffic analysis is developed and tested at the experimental stand. * We conduct an experimental study proving the effectiveness of detecting anomalous behavior of a node based on a deep analysis of traffic, and we also confirm the low cost of resources for implementing calculations by network nodes.
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 2 Inorganic Nanomaterials in Cultural Heritage Conservation
### Application of Inorganic Nanomaterials
#### 2.2.2 Development of Protective Barriers Against Environmental Factors  
Nanocomposite coatings incorporating silver or zinc oxide nanoparticles exhibit antimicrobial properties that protect surfaces from biofilm formation. This is particularly crucial for outdoor monuments exposed to biological growth [26; 27].  
Nanoparticles can also provide UV protection for sensitive materials like textiles and paintings. By incorporating UV-blocking nanoparticles into varnishes or coatings, conservators can mitigate light-induced damage [28; 29; 30].
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 5 Conclusions  
This paper presents some insights about the impact of the DEM used for applying terrain correction on SAR data, more specifically on COSMO SkyMed SAR images. To evaluate the results, an application case was selected for windthrow detection, and five different DEMs were evaluated at different pixel spacings, 2 m and 30 m, derived from different sources. The results showed that there are significant discrepancies in windthrow detection across the five corrections. Among the five different DTMs, the local DTM at VHR (2 m pixel spacing) was the one that provided terrain-corrected images most suitable for windthrow detection, while the VHR DSM at 0.5 m pixel spacing had the worst performance. Concerning the global DEM, images corrected with ALOS DEM seem to be the ones most suitable for windthrow detection, while the ones corrected with the COP DEM are the least.  
For future works, it would be interesting to not only evaluate different DEMs but also to compare them with other SAR data at lower spatial resolutions, for instance, data from Sentinel-1.  
Conceptualisation, M.D.; methodology, M.D., D.M. and Y.T.S.-C.; software, M.D.; validation, M.D. and D.M.; formal analysis, M.D., D.M. and Y.T.S.-C.; investigation, M.D., D.M. and Y.T.S.-C.; data curation, M.D.; writing--original draft preparation, M.D. and Y.T.S.-C.; writing--review and editing, M.D., Y.T.S.-C. and D.M.; supervision, M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is not available due to the provider source.  
**Acknowledgments:** This work was carried out using CSK(r) Products, C ASI (Italian Space Agency), delivered under an ASI license to use. The authors are thankful to ASI for their help with distributing the data for the different experiments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Material Preparation  
To prepare the SA-G bioink, low-viscosity sodium alginate (154725, Lot Number: SR01864) and gelatin (bovine skin type B, G9391, Lot Number: SLCF9893) powders were purchased from MP Biomedicals (Santa Ana, CA, USA) and Sigma-Aldrich (St. Louis, MO, USA), respectively.  
The hydrogel precursor solution was prepared as follows. First, the powders were separately sterilized in 99.99% ethanol (Sigma-Aldrich, St. Louis, MO, USA) for 15 min. The sterilization process was repeated three times. After the complete evaporation of ethanol, 0.4 g of sodium alginate powders were dissolved in 10 mL of sterile phosphate-buffered saline (PBS, 1X, \(w/o\) Calcium, \(w/o\) Magnesium, pH = 7.4, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) overnight under vigorous magnetic stirring. Similarly, 1 g of gelatin was dissolved in 10 mL of sterile PBS at 37 \({}^{\circ}\)C until total solubilization. The two resulting solutions were mixed together in a volume ratio of 1:1. Hence, the final alginate and gelatin concentrations in the precursor were 2% and 5% \(wt./v\). These concentrations were selected after an optimization process based on preliminary extrudability and printability tests. The precursor solution was stored at 4 \({}^{\circ}\)C and used within one week.  
The crosslinking solution was prepared by dissolving 2% \(wt./v\) of calcium chloride (CaCl\({}_{2}\), Sigma-Aldrich, St. Louis, MO, USA) into sterile distilled water and stored at 4 \({}^{\circ}\)C until use. The CaCl\({}_{2}\) concentration and the crosslinking duration were optimized based on previous studies [24]. The use of CaCl\({}_{2}\) is widely reported in the literature to crosslink sodium alginate [25]. The crosslinking mechanism is based on ionic bonds between calcium ions and carboxylate ions present in the alginate. Briefly, sodium alginate is a linear polysaccharide containing carboxylate ions, each bonded with a sodium ion. When alginate is immersed in a CaCl\({}_{2}\) solution, the dissociated calcium ions replace the sodium ions in the alginate, ionically bonding with the carboxylate ions. Since each calcium ion can bond with two carboxylate ions, the process results in the crosslinking of the polymer chains and, thus, in the formation of a hydrogel [25].
# Environmental Footprint Assessment of a Cleanup at Hypothetical Contaminated Site
## 1 Introduction
A reduction in the emissions of CO2 by 571,000 pounds per year, sulfur dioxide (SO2) by 1600 pounds per year, and nitrogen oxide (NOx) by 1100 pounds per year is expected from this hybrid remedial system design [17]. The EPA's Spreadsheet for Environmental Footprint Assessment (SEFA) is among the key tools available for such investigations. However, very limited published literature is available about using SEFA for environmental impact assessment of remediation of contaminated sites. [PERSON] et al. [18] study the environmental impact of the remediation of an aquifer below an industrial site in the Bologna area. Three proposed systems were investigated for environmental impact using two environmental footprint analysis tools, i.e., SiteWiseTM and SEFA. The three solutions studied and compared for environmental footprint are (i) groundwater extraction system, treatment and reinjection, (ii) reductive bioremediation, and (iii) in situ chemical oxidation (ISCO). Based on the results obtained from both tools, bioremediation is found to be the appropriate remediation with minimum GHG and lower levels of environmental impacts. In addition, the higher environmental impacts caused by ISCO is due to frequent multiple injection events with Potassium Permanganate, in contrast with the general single injection performed with bioremediation. In this paper, the environmental footprint of a cleanup at a hypothetical contaminated site is studied by using EPA's Spreadsheet for Environmental Footprint Assessment (SEFA). The underlying idea is to develop a methodology for analyzing the environmental impact of a cleanup of any contaminated site using this easily accessible tool. Therefore, a hypothetical contaminated site is selected from a metropolitan city in Pakistan and the environmental footprint of the cleanup is analyzed under three different scenarios: cleanup without any renewable energy sources at all, cleanup with a small share of renewable energy sources, and cleanup with a large share of renewable energy sources.
## 2 Computational Modeling
### Mesh Generation  
Computational meshes are generated in the universal mesh generator within the ANSYS Workbench. Due to the complex geometry of the individual elements, an unstructured mesh with tetrahedral elements is used for Impeller 1, Impeller 2, Radial diffuser 1 and Radial diffuser 2. A structured mesh of hexahedral elements is generated for the Annular seal and Outlet. To perform mesh independence analysis, four meshes with different maximum sizes (largeness) of the tetrahedral elements are generated: Mesh1.4, with a maximum element size (MES) of 1.4 mm; Mesh1.3, with a MES of 1.3 mm; Mesh1.2, with a MES of 1.2 mm and Mesh1.0, with a MES of 1.0 mm. Fifteen layers of prismatic elements are generated on the walls of all fluid-flowed parts. The total thickness of the layers is 1.2 mm, with a growth rate of 1.1. Information about Numbers of Elements (NE) and Numbers of Nodes (NN) is provided in Table 2. Numbers of Elements represent the total number of volume elements used in designing the computational grid. Numbers of Nodes indicate the number of points used to connect the volume elements to each other. The volume elements can be of different types, such as tetrahedra, wedges, pyramids, hexahedral and polyhedral.
# _remote sensing_
## 2 Materials and Methods
### Retrieval of Biophysical and Agoronomical Variables
#### 2.1.3 SAFYsw Model Calibration and Data Assimilation
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Meaning** & **Mean** & **Range 1** & **Source** \\ \hline \multicolumn{4}{c}{Crop growth parameters} \\ \(\epsilon\)C & Climatic efficiency & 0.48 & & [13; 17] \\ \(\epsilon\)I & Light interception coefficient & 0.5 & & [13; 17] \\ SLA & Specific leaf area (m\({}^{2}\):g\({}^{-1}\)) & 0.22 & & [13; 17] \\ Topt & Optimum temperature for development (C) & 20 & & [13; 17] \\ Tmin & Minimum temperature for development (C) & 0 & & [13; 17] \\ Tmax & Maximum temperature for development (C) & 37 & & [13; 17] \\ DAM\({}_{0}\) & Initial dry aboveground biomass (g\({}^{-}\)m\({}^{-2}\)) & 4.55 & LAI/SAH with LAI = 0.1 [13; 17] \\ D\({}_{0}\) & Emergence date (day of year) & 354 & \multicolumn{2}{c}{Varies on visual inspection of LAI curve} \\ ELUE & Effective light use efficiency (g\(\cdot\)M)\({}^{-1}\) & 3.45 & 2.9–4.0 & Calibrated \\ Py & Partition to grain (grain filling rate) & 0.0049 & 0.0028–0.007 & Calibrated \\  & & Phenology parameters & & \\ P\({}_{\rm LA}\) & Parameter A of partition to leaf & 0.121 & 0.067–0.175 & Calibrated \\ P\({}_{\rm LD}\) & Parameter B of partition to leaf & 0.0025 & 0.002–0.003 & Calibrated \\ Str & Growing degree days (GDD) to senescence & 1225 & 850–1600 & Calibrated \\ R\({}_{\rm S}\) & Rate of senescence (GDD-day\({}^{-1}\)) & 20,500 & 8000–33,000 & Calibrated \\  & Soil parameters & & & \\ RooftRatio & Root weight to length ratio (cm\({}^{-}\)g\({}^{-1}\)) & 0.98 & & [41] \\ RGrRate & Root growth rate (cm\({}^{-}\)GDD\({}^{-1}\)) & 0.22 & & [41] \\ MxRDP & Maximum root depth (cm) & 120 & & [41] \\ Ruff & Runoff factor (ratio to total rainfall) & 0.15 & & [41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of SAFYsw parameters and values used in the simulations.
## 6 Conclusions  
In this paper, we investigated the scaling laws of scheduling gain for uplink massive MIMO systems with a linear receiver. From the analyses and simulation results, we verified that the scheduling gain for the MF receiver increases as the number of antennas increases, regardless of the user selection algorithm and CSI availability for user scheduling. Furthermore, we verified that the scheduling gain for a ZF receiver can be increased only when optimal user selection is employed, under the assumption of a large channel estimation error for data demodulation and near-ideal CSI for user selection. However, optimal user selection is not practically feasible because of the enormous computational complexity of the exhaustive search. Therefore, we can conclude that random user selection is sufficient for the ZF receiver and user scheduling is still beneficial for the MF receiver in uplink massive MIMO systems. This study can be extended to more generalized massive MIMO systems with multi-antenna users and multi-cell environments under pilot contamination, as well as to state-of-the-art massive MIMO systems, such as the mmWave scenario with hybrid beamforming and large intelligent surfaces.  
Conceptualization, T.K. and S.P.; methodology, T.K.; software, T.K.; validation, T.K. and S.P.; formal analysis, T.K.; investigation, T.K. and S.P.; visualization, T.K.; writing-original draft preparation, T.K.; writing-review and editing, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (No. NRF-2019R1C1C1003202).  
The authors declare no conflict of interest.
## 1 Introduction
From an experimental point of view, several optical diagnostic techniques have been developed for monitoring the dust particle size, such as Mie ellipsometry [49; 50; 51; 52; 53; 54], light extinction spectroscopy (LES) [55; 56], time-resolved laser-induced incandescence (TIRE-LII) [57; 58; 59; 60; 61], and laser-induced particle explosive evaporation (LIPEE) [62; 63]. In this work, we introduce a new method for in situ particle sizing, based on time-synchronized measurements of the free electron density in the plasma and laser light extinction by the dust cloud confined in it. Using these two quantities allows resolving both the dust particle size and the dust density. The experimental implementation of this method is relatively straightforward and, more importantly, the measurements are non-intrusive. This work can be seen as a follow-up after our previous work [36]. The basis of the experimental setup and the primary data set is the same, but the focus is on the development of the fitting method. The newly developed method proves to expand the understanding of the measurements, which supports the choice of using the same data set for a direct comparison between the results. The structure of this work is as follows: In Section 2 the experimental setup for the measurement of the free electron density and the laser light extinction is explained in more detail. Section 3 continues by explaining how these types of measurements can be used to disambiguate the dust particle size and the dust density. Section 4 shows the typical results of these measurements and Section 5 summarizes the most important findings of this work.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 3 Results
### Water Surface Time-Series  
Climate change has had a huge impact on water resources, and may increase the number and the intensity of flood events. Essential Climate Variables (ECV) are defined by the Global Climate Observing System to characterize and monitor the Earth's climate [24]. Lake Water Extent (LWE) is one of the ECVs that was monitored through EEO. Indeed, lakes are sentinels of climate change [25].  
Lake Fitri, located in the semi-arid zone of Chad within the Sahelian band, is a flat-bottomed lake with high intra- and inter-annual variability, related to the fluctuations of the West African monsoon, which are expressed in Lake Fitri by fluctuations of great amplitude in the surface area of open water and in the depth of the lake, on all time scales (from one season to several millennia). In recent decades, since the lows of the 1970s-1990s droughs, open water and swamp areas have doubled and stabilized since the early 2000s [26; 27].  
The EEO pipeline downloaded 299 Sentinel-2 images acquired from 2017 to 2021 covering Lake Fitri. The lake surface was calculated on each exploitable date (i.e., no clouds over the lake) from all lake masks. Then, the results fed the LWE curve illustrated in Figure 8. The variations of the lake's surface were impressive, with a minimum surface of 194 km\({}^{2}\) and a maximum surface of 1249 km\({}^{2}\) over the observed period. Moreover, the dynamics of the water surfaces were similar for the years 2017, 2018, and 2019. In 2020, the huge rise in the LWE was significant, as the 2020 maximum extent corresponded to double the maximum LWE observed during the three previous years.  
Occurrence products were also derived from the lake masks, including annual occurrence (illustrated in Figure 9 for 2020) and overall occurrence over the 4-year observation period. These maps were very useful in helping to understand the lake's inter- and intra-annual dynamics, with the results offering very rich information for the study and management of this lake. A regular monitoring of Lake Fitri's LWE variation, in addition to rainfall, would provide excellent indicators of climate trends in this poorly studied region, as well as stresses on the resources in the context of population growth and stability/security. This was a success for EEO in terms of automatically deriving high quality water and cloud masks over a time series (from Sentinel-2 and Sentinel-1), computing areas to build a LWE monitoring curve and, compiling occurrence products. Details and maps are available through a dedicated ArcGIS Story Map [28].  
Figure 8: Lake Fitri LWE curve derived from Sentinel-2 data acquired between 2017 and 2021.
# DSCope: Development of Automatic Program for Detecting Fractures and Measuring Dip Angles
## 2 Development of an Automated Program (DSCope)  
The Detecting Structures (of Cores) Code Over Practical Eyes (DSCope) program automatically detects fractures in the photographs of drilling cores and determines the dip angles of the detected fractures through linear function fitting. It was developed using Python; therefore, it can be used regardless of the operating system (e.g., Windows or Linux). Because the version of the package differs from the Python version, the results obtained can vary even if the same photograph and settings are given as the input. To prevent this, DSCope was developed using Python version 2.7. The update for Python 2 is no longer supported after Python version 2.7 released in January 2020. Therefore, the same result could be obtained using Python 2.7 regardless of the computer environment. Moreover, the standard library can be installed easily using Anaconda, and the DSCope code was written with the packages of the standard library that support convenient installation on Anaconda.  
DSCope progresses in three steps. For DSCope to work, the setting file is created first as shown in Figure 1. A list of drilling core photographs is generated based on the input values of the setting file. Next, the number of cores is input into the list of drilling core photographs. Each core photograph is obtained by equally splitting the drilling photographs by the number of cores. During this process, each drilling core photograph is converted into grayscale. They are then rotated 90\({}^{\circ}\) clockwise to sort the direction and determine the depth from top to bottom. A list of each drilling core photograph is simultaneously created and used in the subsequent step. Finally, the fractures are identified, and the dip angles are measured by receiving the setting file created in the first step and the list of each drilling core photograph created in the previous step.  
Figure 1: Flow chart of the DSCope program.  
In DSCope, multiple drilling cores are taken in a single box to capture multiple photographs of the drilling core boxes and analyze them. It assumes that the drilling core box gets deeper from the top to bottom and from left to right. Based on this assumption, each core photograph is generated and numbered sequentially, starting with the top core. These photographs are then arranged in terms of the depth direction after being rotated clockwise. Under the assumption that they are color photographs, the RGB channel is combined into the average value to transform them into grayscale (see Figure 2).
## 1 Introduction
In [26; 27], a nonlinear disturbance observer was designed to estimate external disturbances. In addition to these observers mentioned above, the most famous one is extended state observer (ESO) [28], but almost none of them have been tested in real flight conditions. In this work, a Backstepping Active Disturbance Rejection Controller (Backstepping -ADRC) hybrid is proposed to achieve attitude tracking for an autonomous helicopter under the influence of environmental disturbances and nonlinear aerodynamic forces. The forces from external disturbances and internal nonlinear model are treated together as total disturbances. An ESO and a backstepping controller are designed to estimate and compensate for the total disturbances without reliance on a precise system model to eliminate their effects simultaneously and achieve precise attitude tracking. As disturbance and nonlinearity rejection are separated from attitude tracking control, the performance and robustness could be guaranteed independently without compromise. The utility of the proposed controller was validated through simulations and flight tests and compared with a PID controller. The rest of the article is organized as follows. Section 2 presents the related work of this paper. The mathematical model of helicopter and wind gust are presented in Section 3. In Section 4, firstly, the ESO is designed to estimate total disturbances mainly including flapping dynamics and wind gust in this paper, secondly, the procedure of backstepping controller design is presented, at last, considering parameter uncertainties. Then, simulation results and flight tests are shown in Sections 5 and 6, respectively. The conclusions of the paper are described in Section 7.