esa-sceva/satcom-chunk-collection huggingface dataset:
# Lead Tolerance and Enrichment Characteristics of Three Hydroponically Grown Ormamental Plants
## 3 Results and Discussion  
### Effects of Pb Stress on the Biomass of Ornamental Plants  
The fresh weight and dry weight of the shoots and roots of three ornamental plants decreased to varying degrees with the increasing Pb concentration (Figure 1), and all reached a significant level of difference (\(p\) < 0.05). Compared with CK, the fresh weights of the shoots of _T. patula_, _S. nigrum_, and _M. jalapa_ decreased by 21.3% to 63.7% (2.52 to 7.51 g), 31.1% to 77.8% (2.08 to 5.21 g), and 17.1% to 61.6% (2.49 to 8.99 g), with mean values of 40.4%, 54.0%, and 43.3%, respectively. The fresh weights of these roots decreased by 19.0% to 69.8% (0.29 to 1.06 g), 21.9% to 66.7% (0.15 to 0.47 g), and 18.5% to 50.8% (0.72 to 1.97 g), with mean values of 49.6%, 49.0%, and 35.8%, respectively. The dry weight of the shoots decreased by 25.2% to 69.3% (0.41 to 1.13 g), 26.7% to 68.9% (0.22 to 0.58 g), and 19.9% to 62.4% (0.42 to 1.33 g), with mean values of 49.8%, 50.1%, and 42.5%, respectively. The root dry weight decreased by 30.5% to 75.9% (0.18 to 0.44 g), 25.3% to 78.9% (0.08 to 0.25 g), and 27.7% to 61.9% (0.19 to 0.42 g), with mean values of 57.6%, 57.1%, and 48.1%, respectively. Growth inhibition and reduced biomass production are general responses of higher plants to heavy metal toxicity [23]. Similar results were obtained in our experiments. Different concentrations of Pb stress significantly inhibited the growth of the shoot and root parts of the tested ornamental plants. Compared with the control, the shoot and root biomasses of the tested ornamental plants were reduced by 35.8% to 57.6%, which is consistent with the results of [PERSON] et al. (2013) in three ornamental plants (_Tagetes patula_, _Dahlia pinnata_, and _Ipomoea quamoclit_) [24]. The possible reason for this is that the metabolic processes of plants, including photosynthesis, are altered under heavy metal stress [25]. These phenomena are some of the physiological responses exhibited by plants to Pb treatment, as the presence of Pb in cells, even in small amounts, may have a wide range of adverse effects on physiological processes [26]. In this study, the aboveground biomass of the test ornamental plants, _T. patula_ and _S. nigrum_, decreased similarly compared to the control treatment, while _M. jalapa_ decreased less than _T. patula_ and _S. nigrum_, indicating that _M. jalapa_ is more tolerant to Pb than _T. patula_ and _S. nigrum_.
## 2 Related Terms and Works
### Research Using the Mirai Source Code  
While the public availability of the Mirai source code helps cybercriminals to create new variants of Mirai, it also helps to ensure that ethical research is actively conducted using the Mirai source code.  
Based on the analysis of the Mirai source code, [PERSON] et al. created rules for Snort, one of the intrusion detection systems, to detect communications between bots, a C&C server, and a DNS server [11]. The effectiveness of the created rules was then checked using the Mirai worm created using the actual Mirai source code. They focused only on the bot alone and not on its behavior as a botnet. The proposed Snort rules are useful in understanding the presence of Mirai-infected devices on a network, but they do not take into account disinfecting the Mirai botnet.  
[PERSON] et al. used the Mirai source code to capture packets by running each of the elements that make up the Mirai botnet on real hardware. The captured packets were then used to create an LSTM model to detect DDoS attacks and bot communications from the botnet, which showed a discrimination performance of over 90% [15]. Unlike [16], which focuses on the behavior of a botnet, it is intended for detection and does not target botnet disinfection.  
[PERSON] et al. proposed a digital forensics technique that uses the Mirai source code to actually run the bot and collect data such as RAM and network traffic from the servers that make up the botnet to obtain evidence about the targets of DDoS attacks that were performed, etc. [17]. In the study, the authors not only ran the Mirai botnet on real hardware, but also analyzed various aspects of Mirai, including the source code and packet capture, with the goal of obtaining information about the DDoS attacks performed by the attackers and the bot-enabled devices; they did not target botnet disinfection.  
[PERSON] et al. analyzed the Mirai source code, including not only the Mirai bots but also the elements that make up Mirai, and pointed out the existence of SQL injection vulnerabilities in the C&C server [18]. It is the first research to reveal vulnerabilities in the components of the Mirai botnet and proposed a new way to fight back against the Mirai botnet, but the research is limited to an analysis of the source code and does not reveal how a SQL injection can actually attack the Mirai botnet or how it can be affected. The research is limited to an analysis of the source code.  
Although many studies have been performed using the Mirai source code and methods have been proposed to combat the Mirai botnet, none have proposed specific methods to disinfect Mirai bots.
## 2 Materials and Methods
### LIDAR Data Set  
The first data set was captured over downtown Toronto (Canada). Optech's ALTM-ORIONM was used to acquire the ALS data at a flying height of 650 m in six strips with a point density of about 6 points/m\({}^{2}\). The area contains a mixture of low- and high-storey (58) buildings, showing various degrees of complexity in rooftop structures. The reference for building detection and for 3D building reconstruction was generated by stereo plotting. The accuracy of well-defined points is 20 cm in planimetry and 15 cm in height. For more details, refer to [26] and the web site of the test [27]. The scene also contains trees and other urban objects.  
The second data set was captured over a complex building in downtown Matera (Italy). The LIDAR survey in the historical center of Matera (Italy) (Figure 1a,b) was carried out by GEOCART S.p.A. using a full-waveform scanner [28], RIEGL LMS-Q560 on board a helicopter to obtain a higher spatial resolution. The flight was operated with a share of around 400 m, a speed of 25.7 m/s, and an opening angle at 60\({}^{\circ}\). The scanner acquired data in the direction SN-EW, with a divergence of the radius 0.5 mrad, and a pulse repetition rate at 180,000 Hz. The average point density value of the dataset was about 30 points/m\({}^{2}\). The accuracy was 25 cm in x, y and 10 cm in z (altitude). The raw data of a small tile extracted by the survey have been orthorectified and radiometrically corrected in order to provide a ready-to-use point cloud to realize as output a group of watertight mesh models that could be used for various applications, such as energy demand estimation, classification of building types, visibility analysis, 3D cadastre, visualization for navigation, urban planning, emergency response, or flooding [4]. The LIDAR data is provided as a group of unorganized discrete points in which each individual point has x, y and z value, plus the intensity value that represents the reflective proprieties of surface encountering (Figure 1b).
###### Abstract  
Transmissive metasurface-based dual-wide-band dual circular polarized operation is needed to facilitate volume and size reduction along with polarization diversity for future THz wireless communication. In this paper, a novel dual-wide-band THz linear polarization to circular polarization (LP-to-CP) converter is proposed using transmissive metasurfaces. It converts incident X polarized waves into transmitted left-hand circular polarized (LHCP) and right-hand circular polarized (RHCP) waves at two frequency bands. The structure consists of bi-layered metasurfaces having an outer conductor square ring and three inner conductor squares diagonally intersecting each other. The proposed converter works equally well with incident Y polarizations. Operational bandwidths for the dual-band LP-to-CP are 1.16 THz to 1.634 THz (34% fractional bandwidth) and 3.935 THz to 5.29 THz (29% fractional bandwidth). The electromagnetic simulation was carried out in two industry-standard software packages, High Frequency Structure Simulator (HFSS) and Computer Simulation Technology (CST), using frequency and time domain solvers respectively. Close agreement between results depicts the validity and reliability of the proposed design. The idea is supported by equivalent circuits and physical mechanisms involved in the dual-wide-band dual polarization operation. The impact of different geometrical parameters of the unit cell on the performance of LP-to-CP operation is also investigated.  
metasurfaces; linear to circular polarization converter; dual-band polarization converters; transmission-based polarization conversion +
Footnote †: journal: Electronics Letters  
1
## 3 Applications of Infrared Spectroscopy in BSFL as Feed or Food
### Assessing Components with Specific Functional Properties--Chitin  
The characterization and determination of chitin in BSFL is one of the fields in which infrared and Raman spectroscopy have been extensively evaluated [58; 59; 60; 61; 62; 63]. The different published studies showed that changes in intensity at specific wave numbers can be used to identify and characterize chitin in BSFL samples from different treatments and processes [58; 59; 60; 61; 62; 63]. For example, the use of IR spectroscopy has been reported as a tool to optimize the processing steps of chitin extraction in BSFL where steps such as demineralization, deproteinization, and bleaching as well as methods to optimize the extraction of high-purity chitin from BSFL samples were evaluated [63].  
A comparative study of chitin and chitosan and their broad-spectrum antimicrobial activities were evaluated using FTIR spectroscopy [62; 63]. The FTIR spectra was interpreted where chitin showed characteristic chitin peaks at 1650 and 1550 cm\({}^{-1}\) wave numbers, corresponding to amide I stretch and amide II bending, respectively [62; 63]. Changes in these wave numbers were associated with the inhibition of the growth of some of the pathogenic bacteria evaluated [63]. The FTIR spectra of extracted chitin was also characterized, showing the specific functional groups at wave numbers associated with O-H stretch, C=O stretch, N-H bend, CH\({}_{2}\) bending, CH\({}_{3}\) deformation, C-N stretch, and C-O-C stretch [64].
# A Computational Study on the Aeroacoustics of a Multi-Rotor Unmanned Aerial System
## 3 Results and Discussion
### Sound Pressure Level
#### 3.1.4 Noise on the Wings and Fuselage  
In this section, the noise level on the wings and the fuselage is evaluated, using the microphones shown in Figure 2b. The propeller phases \(\gamma\) are shown in Figure 11 to be linked to the discussion. The acoustic pressure fluctuation over one propeller rotation and SPL for the probes on the wings are shown in Figure 12.  
Figure 10: Directivity of SPL (**top**) and OASPL (**bottom**) around CRC-3: (**a**) varying velocity; (**b**) varying RPM.  
The acoustic pressure fluctuation is obviously dependent on the propeller phase, as the unsteadiness imposed on the wing's load is due to the propeller's rotation. \(t=0.03\) s corresponds to the propeller phase angle of \(\gamma=45^{\circ}\).  
Figure 12: Acoustic pressure fluctuation and sound pressure level on the wings close to: (**a**) wing tip; (**b**) fuselage.  
It can be seen that, for all conditions, at \(\gamma=90^{\circ}\) (\(t=0.0325\) s), the level of acoustic pressure fluctuation tends to become equal for the three pitch angles and the difference becomes maximum at \(\gamma=45^{\circ}\), \(135^{\circ}\). Both microphones on wing-2 show very similar pressure fluctuations, since they are positioned at the location of propeller tip-vortex interaction with the wing. On wing-1, however, the pressure fluctuation is different for the two points, due to interference with the fuselage. In addition, wing-1 experiences a slightly smaller level of pressure fluctuation compared with wing-2. Therefore, the SPL spectra shows that wing-1 benefits acoustically over wing-2, while it also benefits aerodynamically by experiencing a larger lift for most of the conditions [49]. This is different from the trend observed by [PERSON] et al. [37], probably due to the different configuration in that study. While low and mid \(\theta\) represent relatively similar trends and levels of fluctuation, high \(\theta\) shows a significantly different pattern, with larger fluctuations, leading to a higher level of SPL. This confirms the conclusion made before based on Figure 7.  
The pressure fluctuation and SPL on the microphones on the fuselage are shown in Figure 13. In contrast to [PERSON] [33] conclusion on smaller contribution to noise by the fuselage compared to the wing, seen here is a larger level of acoustic pressure fluctuations on the fuselage and consequently sound pressure level compared to the wings. This is firstly due to the fact that the fuselage directly interacts with four propellers and, secondly, due to its larger surface compared to the wing.
While SM acquisitions are not as commonly available as IW or EW products, the use of SM products for geometric calibration naturally also improves the expected accuracy of the wide swath products, acquired using the same beams. For the validation of the S1A SM product geolocation accuracy, it is important to understand and exclude the largest known sources of error. Unavoidable technical challenges for such systems are orbital stability and positional accuracy; both set limits on the achievable localization accuracy of imaged ground targets. In addition to orbit- and sensor-specific technical challenges, achieving the highest-possible accuracy from SAR image products requires correcting for at least two perturbing factors [6, 7, 8]:  
* The effect of the troposphere and ionosphere on the signal travel time, called atmospheric path delay (PD), can cause errors typically on the order of several meters if neglected. The tropospheric component is the largest, caused by hydrostatic (air pressure), wet (water vapor), and liquid (water droplet) variations along the signal travel path. * The solid Earth tide (SET), a periodic (mainly vertical) oscillation of the Earth's crust on the order of 1-2 decimeters, caused primarily by the moon and sun. When using surveyed reference targets on the ground, an additional correction needs to be made. In this study, a set of four and two corner reflectors (CRs), deployed consecutively at two test sites, were surveyed using Differential GPS (DGPS). Because of the drift between local and global geodetic reference frames resulting from plate tectonics, the coordinates provided by the GPS alone are usually shifted relative to the International Terrestrial Reference Frame (ITRF) employed to describe the spacecraft positions. This effect can be corrected by transforming the GPS position into the global frame through the use of a plate tectonic drift model. We refer to perturbations caused by SET and plate tectonics together as \"solid-Earth perturbations\", since they affect the position of targets situated on solid ground. Even smaller effects also exist: e.g., Earth deformation by ocean tide loading. As the oceans are periodically redistributed by the relative positions of the Sun-Moon-Earth system, the changing weight distribution of the water mass on the ocean bed causes the Earth to deform. These vertical fluctuations usually do not exceed several centimeters for inland sites [9] and are neglected in this study.
## 4 Assessment of the Developed Approach
### Datasets and Thresholds Used for the Assessment
#### 4.1.1 Datasets for the Approach Assessment  
During our project, a call for point clouds has been launched. Consecutively to it, four datasets were provided by French Land Surveyors Offices and a French company. It deals with indoor point clouds issued from laser scanning acquisitions. Among these datasets, two datasets were more specifically chosen for the assessment of the approach. They present the advantage of being relatively complete compared to the two others and they concern buildings with different functions and surfaces. Figure 11 presents the two selected datasets. The first dataset deals with an individual house composed of a garden level and a first floor for a total surface of about 200 m\({}^{2}\). Indoor point clouds of this house were acquired with a FARO Focus3D S120 laser scanner. The second dataset concerns an office building composed of two blocks and four floors. Indoor point cloud of the biggest block of about 1000 m\({}^{2}\) was considered. The laser scanner used for the acquisition was a Leica ScanStation C10. For both datasets, deliverables created manually from point clouds were also provided by the professionals.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 2 Background
### Problem Definition  
Let \(C\subset\mathbb{R}^{d}\) be the configuration space of the path planning task, and \(d\) be the dimension of the configuration space. \(C_{obs}\subset C\) represents the impassable obstacle area. \(C_{free}=C\backslash C_{obs}\) represents the free area that can be traversed. \(x_{init}\in C_{free}\) is the initial state, and \(x_{goal}\in C_{free}\) is the target state. If there is a collision-free path between the two nodes \(x_{1}\in C_{free}\) and \(x_{2}\in C_{free}\), let a continuous function \(\eta:[0,1]\to C_{free},\eta(0)=x_{1},\eta(1)=x_{2}\), \(\eta(k)\in C_{free},\forall k\in[0,1]\) represent this path. Let \(f(\eta)\) be the Euclidean distance cost function of the path \(\eta\), such that:  
\[f(\eta)=\sum_{i=1}^{n-1}\lVert x_{i}-x_{i+1}\rVert_{2},\eta=\left\{x_{init} \ldots x_{k}\ldots x_{goal}\right\} \tag{1}\]  
Two important questions are defined as follows:  
Problem 1 (Feasible Path Planning): the feasible planning task involves finding a collision-free path, \(\eta:[0,1]\to C_{free}\), connecting \(x_{init}\) and \(x_{goal}\), such that \(\eta(0)=x_{init}\) and \(\eta(1)=x_{goal}\). If the path is not found within the given time, an error is reported.  
Problem 2 (Optimal Path Planning): let \(\sum\) be the set of feasible paths. The optimal planning task is to find a path \(\eta^{*}\) with the lowest distance cost, connecting \(x_{init}\) and \(x_{goal}\), within the given time, such that \(f(\eta*)=min\{f(\eta),\eta\in\sum\}\).
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 1 Introduction
Finally, our experimental simulation results verify the effectiveness of the proposed baseline optimization method.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 4 Experimental Results and Analyses
### Experiments with Simulation Data
Moreover, the correctness of the navigation parameters and effectiveness of the anti-jamming by the proposed algorithm were also proven.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 4 Results  
### Determining of the Harvest Dates by the Optical Data  
The calculations were carried out in terms of the following parameters: \(\Delta NDVI=0.08\), \(NDVI_{prev}=0.3\), \(NDVI_{hav}=0.4\), \(\Delta T=40\) days, and \(\mu=0.9\). Window width of the modified median filter is \(k=3\).  
The results of quality assessment for the harvest dates determining are: True Match = 45, False Match = 39, False Not-match = 32, True match rate = 0.58, Match predictive value=0.53.  
Figure 9 represents the general distribution of harvest dates determined from the optical data.  
Dynamics of the average fraction of the cloud-covered image area (Figure 10a) is determined from Sentinel-2 images metadata (cloudy pixel percentage). According to the graph, the most favourable observation conditions were during the period of March-August 2018 when the cloud fraction was not more than 45% of the image area. It is no wonder that the overwhelming majority of the harvest dates coincidences fall on the period from March to August 2018 (Figure 10b).  
Figure 9: Harvest dates: (1) identified by visual interpretation; (2) determined by the algorithm from the optical data; (3) coinciding harvest dates determined by the two mentioned methods.  
Omissions of the harvest dates have two clear maximums. The June maximum falls on the low-cloudiness period. According to Figure 9, there are comparatively minor errors of the harvest dates determining during that period; they are rarely more than 10 days. The October minimum falls on the end of the year--a period of dense clouds.  
There is a similar situation for false responses (Figure 10b). The June maximum corresponds to the comparatively minor errors in the harvest dates determining since both false responses and omissions fall on June. Such errors will not affect the evaluation of the sugarcane area harvested within that period. Apparently, the September maximum of false responses is related to the October omissions of harvest dates (Figure 10b): first, there is a false response of the algorithm due to the cloudiness; it is followed by the omission of the real harvest date. The November maximum of false responses is also connected with cloudiness since the cloud-cover peak falls on November 2018.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method
### Least Mean Square Parameter Estimation Algorithm Based on Virtual Frequency Characteristics
Initialization: make \(k=1\), set the initial value \(\hat{n}_{0}(\omega_{0})\) to any real number, \(\hat{\alpha}_{1}(\omega_{0})\), \(\hat{\alpha}_{2}(\omega_{0})\), \(\hat{\beta}_{1}(\omega_{0})\) and \(\hat{\beta}_{2}(\omega_{0})\) to any real vector, and set the parameter estimation precision \(\varepsilon\). 2. A sinusoidal excitation signal is applied to collect the observed data \(N(\omega_{k})\) of virtual frequency characteristics. 3. Consider \(k\) as odd or even and calculate the information vectors \(\varphi(\omega_{k})\) and \(\psi(\omega_{k})\) with Equations (15) and (16) or (21) and (22). 4. Calculate the innovation using Equation (37). 5. The gradient vector \(g_{k}\) is calculated and formed by Equation (39), and the optimal step size \(\mu(\omega_{k})\) (approximate step size can be taken) is calculated by Equation (38). 6. Use Equation (32) to calculate parameter estimation \(\hat{n}_{0}(\omega_{k})\), Equation (33) to calculate parameter estimation \(\hat{\alpha}_{1}(\omega_{k})\), Equation (34) to calculate parameter estimation \(\hat{\beta}_{2}(\omega_{k})\), Equation (35) to calculate parameter estimation \(\hat{\beta}_{1}(\omega_{k})\), and Equation (36) to calculate parameter estimation \(\hat{\beta}_{2}(\omega_{k})\). 7. The parameter vector \(\hat{\vartheta}(\omega_{k})\) is formed by Equation (40). 8. If \(\|\hat{g}(\omega_{k})-\hat{g}(\omega_{k-1})\|>\varepsilon\), \(k\) increases by 1, and go to step 2; otherwise, output parameter estimation \(\hat{n}_{0}(\omega_{k})\), \(\hat{\alpha}_{1}(\omega_{k})\), \(\hat{\alpha}_{2}(\omega_{k})\), \(\hat{\beta}_{1}(\omega_{k})\), \(\hat{\beta}_{2}(\omega_{k})\) and terminate the cycle calculation process.