esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Environment Fusion of Visual System for Automatic Pear Picking
###### Abstract  
A visual system is a key tool for automatic fruit harvesting. It faces the challenges of varied occlusion and illumination in the field, resulting in difficulties in fruit recognition and picking. At present, there are many studies that lack a comprehensive analysis of the impact of the environment on harvesting. This study proposes an object-environment fusion visual system. It comprises modules for object perception, environment perception, and picking pose estimation. The object perception module aims to identify and locate pears. The environment perception module is deployed to analyze the three-dimensional (3D) information of objects and obstacles. Finally, the picking pose estimation module fuses the information of the objects and environment to calculate the collision-free picking position and orientation. Additionally, specific implementations are employed for each module. It compares three networks to identify pears for object perception. Voxel-based representation is used to simplify point clouds for environmental perception. A sampler and evaluator are applied for picking pose estimation. The S1 and S2 datasets were acquired in a laboratory pear tree model and the orchard of Zhejiang Academy of Agricultural Sciences, respectively, to evaluate the proposed method. In the S2 dataset, the success rate for picking pose estimation reached 87.11% within the distance range of 30\(\sim\)50 cm. All of the results demonstrate that the proposed method can be applied to visual perception for automatic pear harvesting.  
visual system for automatic pear picking; object perception; environment perception; picking pose estimation; fruit recognition +
Footnote †: journal: Applied Sciences  
1
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 2 Related Background  
### The Communication System Model of UAVs with VLC  
UAVs with VLC technology are used in a wide range of applications, such as indoor positioning and rescue scenarios [14]. To achieve positioning or distance measurement, UAVs exploit flashing LEDs to send useful information, such as their own coordinates, to the receiver, which can be any physical device with a photo diode, such as another UAV. After receiving the information, the receiver will perform further inference work [15]. Such a communication system can be abstracted as shown in Figure 1[13, 16, 17]. Transmitted binary data \(\mathbf{u}\) are encoded by the ECC encoder and RLL encoder to generate the encoded frames \(\mathbf{x}\) and \(\mathbf{v}\), respectively. Later, the encoded frames are modulated by an OOK modulator into the electrical signal \(\mathbf{r}\), which is employed to drive the LEDs for lighting and transmission. After that, the modulated sequences are transmitted over an additive white Gaussian noise (AWGN) channel; hence, the received signal \(\mathbf{y}\) is denoted by  
\[\mathbf{y}=\mathbf{r}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{n}\) follows \(\mathcal{N}\big{(}0,\sigma^{2}\big{)}\). The receiver consists of a detector, an RLL decoder, and an ECC decoder. The detector outputs the hard estimates by photo diodes (PDs), which are decoded by the RLL decoder and ECC decoder successively to obtain the transmitted data estimates.
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 2 Background and Related Work
### Ensemble Machine Learning-Based Attack Detection  
The authors of [29] demonstrate how ensemble machine learning, neural networks, and kernel methods can be used to detect abnormal behavior in an IoT intrusion detection system. In this study, ensemble methods outperform kernel and neural networks in terms of accuracy and error detection rates.  
To detect webshell-based attacks, ensemble machine learning is used in [30]. In webshell attacks, a malicious script installed on a web server for remote administration executes malicious code written in popular web programming languages. Ensemble techniques, including random forest and extremely randomized trees, are applied in this work, and voting is used in order to improve their performance. The study concluded that random forests and extremely randomized trees are best for IoT scenarios involving moderate resources (CPU, memory, etc). Nevertheless, voting is proved to be most effective in scenarios requiring heavy resources. In [31], cyberattacks are detected using ensemble methods for IoT-based smart cities. Ensemble methods were found to be more accurate than other machine learning algorithms, including linear regression, support vector machines, decision trees, and random forests.  
Further, anomalies are detected using ensemble methods applied to software-defined networking (SDN) in IoT at [32]. In SDN, IoT networks could be controlled from a central server called a controller [33; 34]. Further, in [35], DDoS attacks are detected by using an ensemble method that uses traffic flow metrics to classify attacks. The applied approach yields fewer false alarms and a high degree of accuracy. Moreover, cyberattacks are detected by enabling cloud-fog architecture on the Internet of Medical Things (IoMT) using ensemble machine learning, in [36]. In this work, decision trees, naive Bayes, and random forest machine learning techniques are used as a base classifier, and XGBoost is used at the next level. This method achieved a high detection rate of 99.98% on the NSL-KDD dataset.  
The detection of anomalies in the smart home is carried out by ensemble machine learning rather than binary classification in [37]. Ensemble machine learning was able to detect anomalies in categorical datasets with minimal false positives. In [38], adaptive learning is used to boost the intelligence of ensemble machine learning for the Internet of Industrial Networks. This approach proved effective under ROC curve calculations.
## 5 Numerical Simulation Activities
### Impacts with GEO Objects  
The second set of simulated collisions involved GEO telecommunication satellites with sizes comparable to the Intelsat spacecraft family [53]. The main goals of this work were to assess the transition from non-hypervelocity to hypervelocity regime, as low-velocity scenarios are of primary importance for GEO collisions, and to evaluate the influence of structural properties and impact point (when appendages interact first) to possibly dissipate impact energy and dampen the event. In the simulation campaign, both impactor and target had the same design, apart from a geometric scale factor equal to 1:0.535 (target and impactor mass were 3280 kg and 500 kg, respectively) and contained detailed elements, such as internal components, instrumentation, and antennas. The two impact conditions can be seen in Figure 14 and consist of direct impacts on the central bodies or involving the spacecraft appendages first (solar panels). For each configuration, three impact velocities were simulated, 0.1, 1, and 10 km/s, which led to an EMR of 0.66, 66, and 6600 kJ/kg, respectively.  
Among the main results collected by this work and available in [53], it was found that the large satellite is never completely fragmented, despite the increase in the number of fragments with the impact velocity. In addition, for the 10 km/s scenarios, the impact configurations have a marginal effect on the total number of generated fragments and the number of fragments larger than 10 cm is higher in the collisions involving the appendages.
## 5 Case Study: CV Joint Replacement in Korean Automotive Market
### Sensitivity Analysis on Preference Change  
The environmental and economic impacts were evaluated in the previous section, respectively. As some parts are reused in the remanufacturing process, reductions in energy consumption, raw material usage, and CO2 emissions can be achieved. As a result, the environmental impact of remanufactured CV joints can be reduced if the proportion of remanufactured CV joints in replacements increases. However, profit could fall to almost 40% when a CV joint is replaced with a remanufactured one due to the relatively low price of remanufactured CV joints.  
The proportion of remanufactured CV joints in replacement scenarios affects both environmental and economic impacts. Thus, a sensitivity analysis was conducted to evaluate the changes in environmental and economic impacts by increasing the proportion of remanufactured parts. In the baseline study, it was assumed that the preference for CV joint replacement when the vehicle age exceeds seven years is fixed at 10% (new CV joint) and 90% (remanufactured CV joint). The proportion of remanufactured CV joints (\(\beta_{1}\)) was considered as a variable and was increased from 0% to 100%.  
The sensitivity analysis results, according to the change in the proportion of remanufactured CV joints, are shown in Figure 6. It can be seen from the figure that the total environmental impact (new and remanufactured together) gradually decreases as the proportion of remanufactured CV joints increases. Additionally, the environmental impact attributed solely to remanufactured CV joints increases as anticipated. These results indicate that replacing damaged CV joints with remanufactured CV joints can minimize environmental impact.  
The total profit, representing economic impact, also decreases as the proportion of remanufactured CV joints increases. The results show that while the market size of the remanufacturing industry is expanded, the total market profit is simultaneously reduced. Thus, increasing the proportion of remanufactured CV joints offers advantages in terms of environmental impact but disadvantages in terms of industry profit.  
Figure 5: The price, cost, and profit of new and remanufactured CV joints.
The application of PGRs stimulated the development of the root system, improved aboveground biomass, and increased the content of photosynthetic pigments in the leaves. Our previous study, in which Regoplant and Stimpo were tested during one vegetation year of _M._\(\times\)_giganteus_ in soil slightly contaminated with TEs, indicated the effect of PGR utilization on morphological indicators and biomass parameters when the crop was produced in soil of moderate quality [36]. The influence was greatest when PGRs were combined to treat rhizomes before planting and used in plantation spraying during vegetation [36]. However, the research illustrated that the application of PGRs were not effective when the crop was cultivated in pure nutrients and organic matter soil. _Miscanthus_ spp. is a perennial grass, and the first two production cycles are the most important for establishing a good stable feedstock [37]. In this regard, PGR influence must be monitored in at least two growing processes. In the previous study [36], it was concluded that the development of plants in pure soil was not sensitive to the application of PGRs; however, the conclusion was based on simple graphical analysis, while the influence of multi-factor interactions, i.e., soil-PGRs, soil-years of cultivation, PGRs-years of cultivation was not statistically evaluated. To overcome this gap, the current research was designed to examine the influence of PGRs on _M._\(\times\)_giganteus_ cultivation during two vegetation seasons with a statistical evaluation of the primary process factors: soil, time, and PGR type. Consequently, the goals of the current study were as follows:  
* To test the impact of three commonly used PGRs applied in the _Miscanthus_ spp. production scheme, i.e., Regoplant, Stimpo, and Charkor, on dry biomass value during two vegetation seasons;
* To analyze the impact and value of three main factors involved in the production cycle: soil, PGRs, and year of cultivation on plant development.
# New Perspectives in the Development of the Artificial Sport Trainer
## 3 Illustrative Example
### Integration with the AST-Control Monitor  
[PERSON] et al. (2018) proposed a low cost embedded device for monitoring cyclist training sessions called the AST-control monitor. The device is totally flexible and open, as it supports connecting different sensors and is small enough to be embedded on the bicycle. Further, it supports the Internet connection, which allows the integration with the AST running on the host server computer as well as connections with other web services (like weather services for forecasting weather conditions). A simple Graphical User Interface (GUI) is developed on top, for interaction and feedback between the cyclist during individual training sessions.  
The AST-control monitor is based on the Raspberry Pi computer, which has almost two advantages necessary for our study: (1) It can be mounted on the cycle, and (2) It supports a wide range of external sensors with which an environment can be sensed. The monitor is capable of carrying out the following functions:  
* loading training plans from the AST+,
* monitoring the realization of the cycling training sessions,
* uploading the finished training activities to the AST+ for a detailed analysis.  
The original AST-control monitor operates on the principle of feedback systems ([PERSON] et al., 2019), and minimizes the difference between the prescribed and measured HR. On the other hand, the system is adapted for monitoring the interval cycling sessions. In line with this, three modifications need to be performed in order to satisfy our demands:  
* a power meter must be connected to the Raspberry Pi via a USB ANT+ stick,
* the feedback system must be replaced with the proposed DT for cycling,
* the Graphical User Interface (GUI) must be adapted for monitoring the regular cycling training sessions instead of interval ones.  
However, the purpose of the study was to find a cognitive predictive model of the athlete that could be run within the DT. Indeed, this DT could monitor the current results of the cyclist during the realization of a training session, and advise he/she what to do in specific situations in order to achieve the goal set by the AST+. Therefore, the testing of the AST-control monitor stays an unsolved task for the future. In the remainder of the paper, the advantages and weaknesses of the proposed solution are discussed in detail.
###### Abstract  
Wildfire damage severity census is a crucial activity for estimating monetary losses and for planning a prompt restoration of the affected areas. It consists in assigning, after a wildfire, a numerical damage/severity level, between 0 and 4, to each sub-area of the hit area. While burned area identification has been automatized by means of machine learning algorithms, the wildfire damage severity census operation is usually still performed manually and requires a significant effort of domain experts through the analysis of imagery and, sometimes, on-site missions. In this paper, we propose a novel supervised learning approach for the automatic estimation of the damage/severity level of the hit areas after the wildfire extinction. Specifically, the proposed approach, leveraging on the combination of a classification algorithm and a regression one, predicts the damage/severity level of the sub-areas of the area under analysis by processing a single post-fire satellite acquisition. Our approach has been validated in five different European countries and on 21 wildfires. It has proved to be robust for the application in several geographical contexts presenting similar geological aspects.  
Keywords:cepernicus; sentinel; satellite; wildfire; severity; U-Net; convolutional neural network; artificial intelligence +
Footnote †: journal: Applied Sciences
## 4 Results
### Results of the Analyses of a Single Factor's Effects
#### 4.1.3 Analysis of the Camera Rotation  
The next to be analyzed was the camera rotation. The authors wanted to see whether the rotation of the virtual scene influenced the results of the users. 106 tests were performed with a rotation of \(-45^{\circ}\), 294 with \(-30^{\circ}\), 294 with \(-15^{\circ}\), 1251 with no rotation, 312 with \(15^{\circ}\), 313 with \(30^{\circ}\) and 139 with \(45^{\circ}\). The numerical values of the average rates are presented in Table 10. The results of the logistic regression analysis are summarized in Table 4.  
The basic level was \(-15^{\circ}\). According to the \(p\)-values, the \(-45^{\circ}\), \(0^{\circ}\), \(45^{\circ}\) rotations presented significant increases in the probability of correct answers to that of \(-15^{\circ}\). The latter is not significant at the 1% level, but it is close to being significant. To further validate the results, the authors grouped the results into two groups. The first group named \"IMP_R\" contained those rotations that positively affect the probabilities. These are the rotations of \(-45^{\circ}\), \(0\), \(45^{\circ}\). The other group, named \"NO_R\", contained the rotations that did not have a significant positive effect.  
1496 tests fell into the IMP_R group and 1213 tests fell into NO_R. The numerical results are presented in Table 11. When analyzing the results of these two groups through logistic regression analysis, the results were as presented in Table 5.  
The reference point was NO_R. Table 5 indicates that the two groups previously defined in this subsubsection had significantly different probabilities. These results prove that the groups can be distinguished from each other, and the authors will use these groups later in the case of variable camera rotation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Rotation** & **Estimate** & **Standard Error** & \(z\) **Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.620184 & 0.33839 & 18.327 & \textless{}2\times 10^{-16}\) \\ \(-45^{\circ}\) & 0.176147 & 0.065570 & 2.686 & 0.00722 \\ \(-30^{\circ}\) & \(-0.009832\) & 0.047978 & \(-0.205\) & 0.83763 \\ \(0^{\circ}\) & 0.147985 & 0.037690 & 3.926 & 8.62 \(\times 10^{-5}\) \\ \(15^{\circ}\) & 0.015393 & 0.047061 & 0.327 & 0.74360 \\ \(30^{\circ}\) & 0.012121 & 0.046995 & 0.258 & 0.79646 \\ \(45^{\circ}\) & 0.145807 & 0.059162 & 2.465 & 0.01372 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Logistic regression analysis results of the camera rotation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field of View** & **Estimate** & **Standard Error** & \(z\) **Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.768746 & 0.018226 & 42.179 & \textless{}2\times 10^{-16}\) \\ \(60^{\circ}\) & 0.005655 & 0.056640 & 0.100 & 0.9205 \\ \(75^{\circ}\) & 0.012530 & 0.054223 & 0.231 & 0.8172 \\ \(90^{\circ}\) & 0.134485 & 0.058957 & 2.281 & 0.0225 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Logistic regression analysis results of the camera field of view without the orthographic field of view.
# Aggregate and Particle Size Distribution of the Soil Sediment
## 2 Materials and Methods
### Evaluation of Soil Samples--Particle Size Distribution
#### 2.3.2 Laser Diffractometer--Mastersizer 3000  
The Mastersizer 3000 uses optical model Mie theory, which is recommended by ISO 13320 for particles smaller than 50 \(\upmu\)m. Mie theory is based on [PERSON]'s electromagnetic field equations and provides solution for the calculation of particle size distribution from light scattering data. To obtain accurate results, which the Malvern software calculates based on the data from the laser diffractometer, information about the analyzed material and dispersion medium (refractive and absorption indices which characterize the passage of light through) must be entered before the measurement starts [49].  
Silica (SiO2), which has the most similar properties to soil, was selected as a referential material from the software database. Based on the selected material, the software assigned values for the refractive index, 1.457 [22], and the absorption index, 0.010. Water was selected as a dispersion medium with a refractive index value of 1.33. Adjusted values were in agreement with the user manual [49].  
The air dried soil sediment was and thoroughly mixed before the measurement to ensure its representativeness. For 40 s, the background data of distilled water was measured. To a beaker of 550 mL distilled water, the amount of the soil sample (approximately one gram) was added in order to achieve the concentration required by the software (obscuration 10-30%).  
The analysis of each sample was set up to run 25 records for 20 s each. Laser beam scattering information was recorded during these measurements. During the first five records, the soil sample with aggregates was analyzed. Then, the ultrasound was manually started. [PERSON] et al. [20] deduced that 4 min of ultrasound application is enough to destroy all soil aggregates, and that there is no upper limit whose exceeding could cause breaking of the particles themselves. Accordingly, the time of active ultrasound was set to 320 s. By this time soil aggregates had been destroyed, and the five records after ultrasound were carried out on the soil sample, already without aggregates.  
[PERSON] et al. [51] found that only one reading per sample is sufficient. Therefore, for further comparison of the particle size analysis, two curves from analysis of each soil sample were used--the average of the first five (soil with aggregates) and the average of the last five records (soil with already destroyed aggregates).
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation
### Classification of Channel Estimation Schemes
#### 3.1.2 Overview of Semi-Blind Channel Estimation  
In semi-blind CE, signal's statistical properties as well as training sequences are incorporated to estimate the CIR of the channel. This means that semi-blind methods are hybridisation of the pilot based techniques and blind methods. In [96], semi-blind iterative SAGE maximisation CE algorithm is proposed for massive MIMO systems that makes use of data symbols for CE. In this system, the base station is assumed to have knowledge of its own cell's large scale fading coefficients without being aware of interfering cells. This novel CE algorithm is designed to update the training-based MMSE CE iteratively using SAGE algorithm where the initial estimate is improved by employing pilot symbols and soft information of the transmitted data. This proposed algorithm demonstrates superior performance in comparison with other semi-blind and blind CE schemes reported in the literature. In [98], a low complexity pre-coding and semi-blind CE scheme is proposed for a two-way multi-relay networks under frequency selective fading channels. The proposed method makes use of a rotation based matrix for pre-coding, and the composite CIR of each source relay-destination link is estimated using second-order statistics of the received signals. A small number of pilot symbols is adopted to eliminate the ambiguity in the proposed CE which is caused by the channel information of the direct link. The results obtained in [98] shows that the MSE of the channel estimate varies inversely to the number of training blocks and the SNR as the number of data blocks approaches infinity. It is worth mentioning that a quasi-Newton method for semi-blind CE named BFGS estimation scheme is proposed in [99] for uplink C-RANs. The CE scheme utilises unknown data symbols and known training sequence to estimate the channel distortion. Furthermore, [PERSON] [99] adopted the maximum likelihood (ML) principle to improve the performance of the CE. Results show that their algorithm decreases the overhead of bandwidth without sacrificing the MSE performance. Besides, the semi-blind algorithm demonstrates high data throughput and spectral efficiency for C-RAN as compared to the pilot aided CE scheme investigated in [99].