esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The development of robotic applications for agricultural environments has several problems which are not present in the robotic systems used for indoor environments. Some of these problems can be solved with an efficient navigation system. In this paper, a new system is introduced to improve the navigation tasks for those robots which operate in agricultural environments. Concretely, the paper focuses on the problem related to the autonomous mapping of agricultural parcels (i.e., an orange grove). The map created by the system will be used to help the robots navigate into the parcel to perform maintenance tasks such as weed removal, harvest, or pest inspection. The proposed system connects to a satellite positioning service to obtain the real coordinates where the robotic system is placed. With these coordinates, the parcel information is downloaded from an online map service in order to autonomously obtain a map of the parcel in a readable format for the robot. Finally, path planning is performed by means of Fast Marching techniques using the robot or a team of two robots. This paper introduces the proof-of-concept and describes all the necessary steps and algorithms to obtain the path planning just from the initial coordinates of the robot.  
orange groves; autonomous mapping; satellite images; outdoor navigation +
Footnote †: journal:  
doi: 10.3390/a12090193
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 3 Methods
### Monitoring of Non-Agriculturalization
#### 3.2.2 Change Vector Analysis  
CVA performs difference calculations on images from the T1 and T2 periods, computing the change in each band on a pixel-by-pixel basis. The changes in each band are then summed to produce a difference image. In this paper, optical imagery and SAR data are used for CVA, with six bands in total: three commonly used optical bands (B2, B3, and B4), as well as the VH, VV, and VH/VV ratio bands from SAR data. The calculation formula is shown in Equation (1). A larger calculated change value indicates a greater intensity of change in the pixel, suggesting a higher likelihood of a change in the type of ground object.  
\[\Delta\text{V}=\sqrt{\sum_{i=1}^{n}(X_{i}(T1)-X_{i}(T2))^{2}} \tag{1}\]  
Here, \(\Delta\text{V}\) represents the change magnitude at each pixel, while n denotes the number of image bands. \(X_{i}(T1)\) and \(X_{i}(T2)\) represent the i-th band of the image at times T1 and T2, respectively.  
After performing change vector analysis to obtain the difference image, the image can be binary classified using a threshold to distinguish between unchanged and changed regions. In this study, [PERSON]'s method is used to determine the optimal threshold, which adaptively selects the threshold for image segmentation. For a detailed explanation of the OTSU method, please refer to Appendix A.
## 3 Results and Discussion
### Development of Hybrid Mixes
#### 3.3.1 Rheological Properties  
Figure 15 shows the average diameter for the J-Ring flow and slump flow for the hybrid mixes. As shown in the figure, all hybrid mixes had the minimum diameter to be initially considered SCC. Figure 16 shows the numerical difference between the J-Ring flow and slump flow for each trial. All hybrid mixes met both requirements of average diameter and numerical difference as stated in ASTM C162.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Replacement Material \({}^{*}\)** & **Hybrid mix 1 (H01)** & **Hybrid mix 2 (H02)** & **Hybrid mix 3 (H03)** \\ \hline CWCA (replacing 4.75 mm CA) & 0 & 10 & 0 \\ CWFA (replacing sand) & 10 & 0 & 0 \\ CWP (replacing cement) & 0 & 0 & 5 \\ EAFS (replacing 9.5 mm CA) & 5 & 10 & 5 \\ \hline \hline \end{tabular}  
* Values cited are the percentage replacement of conventional concrete materials shown in parentheses.  
\end{table}
Table 4: Testing matrix for the hybrid mixes used in the study.  
Figure 14: Variation of compressive strength for samples containing various dosages of EAFS compared to the benchmark SCC mix.  
The hybrid mixes had no effect on the flow-ability of the SCC. All samples of SCC had ideal flow-ability as their H2/H1 ratio was 1.0. This, along with the J-Ring test results, show that the hybridization of the benchmark mix in all hybrid mixes was done correctly, as the benchmark SCC's fresh properties were not compromised.  
Figure 17 shows the time it takes for the hybrid SCC samples to pass through a V-Funnel. It is clear that all hybrid mixes reduced the viscosity of the SCC, which is shown in the significant drop in time required to clear the V-Funnel. H02 had the best results (6 s) whereas H01 and H03 had similar results (8 s). All hybrid samples showed desirable viscosity.  
Figure 16: Numerical difference of the J-Ring flow and slump flow for hybrid SCC samples.  
Figure 17: The time taken for hybrid SCC samples to pass through a V-Funnel.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 3 IoT Service Definition
### Communication  
In addition to simple synchronous or asynchronous communication, distributed IoT applications often involve different types of group communications, with both multiple recipients as well as multiple senders. For example, consider a number of sensors autonomously sending their sensing feeds to a server to be used in aggregate form as the basis for a service. What is required to support this is high-level communication primitives, which do not mix functional concerns of services with complex communication concerns.
###### Abstract  
The feasibility of early disease detection in potato seeds storage monitoring of volatile organic compounds (VOCs) and plant physiological markers was evaluated using 10 fungal and bacterial pathogens of potato in laboratory-scale experiments. Data analysis of HS-SPME-GC-MS revealed 130 compounds released from infected potatoes, including sesquiterpenes, dimethyl disulfide, 1,2,4-trimethylbenzene, 2,6,11-trimethyldodecane, benzothiazole, 3-octanol, and 2-butanol, which may have been associated with the activity of _Fusarium smubucinium_, _Alternaria tenuissima_ and _Pectobacterium carotovorum_. In turn, acetic acid was detected in all infected samples. The criteria of selection for volatiles for possible use as incipient disease indicators were discussed in terms of potato physiology. The established physiological markers proved to demonstrate a negative effect of phytopathogens infecting seed potatoes not only on the kinetics of stem and root growth and the development of the entire root system, but also on gas exchange, chlorophyll content in leaves, and yield. The negative effect of phytopathogens on plant growth was dependent on the time of planting after infection. The research also showed different usefulness of VOCs and physiological markers as the indicators of the toxic effect of inoculated phytopathogens at different stages of plant development and their individual organs.  
seed potatoes; phytopathogens; phytopathogen volatiles; potato physiology; markers; soft rot; early blight; dry rot +
Article  
Volatile Organic Compounds and Physiological Parameters as Markers of Potato (_Solanum tuberosum_ L.) Infection with Phytopathogens
## 2 System Model
### 5G Receiver Measurement Model  
Considering the scene of 3D positioning, assuming that the number of line-of-sight 5G base stations is \(n\), the signal-measurement model of the 5G positioning receiver is as follows:  
\[y^{n}_{\text{5G}}[t_{k}]=[\tau[t_{k}],\varphi[t_{k}],\theta[t_{k}]]=\text{h}^{n }_{\text{5G}}(\text{ s}[t_{k}])+\omega^{n}_{\text{5G}}[t_{k}] \tag{3}\]  
where y represents the collection of measurements; n denotes the number of 5G base stations; \(t_{k}\) indicates the measurement time of the receiver; and \(\tau,\varphi\), and \(\theta\) represent the measurements of the Time Of Arrival (TOA), azimuth Direction Of Arrival (DOA), and elevation DOA. \(s[t_{k}]\) is the state of the 5G receiver at the timestamp \(t_{k},\omega[t_{k}]\) denotes the measurement uncertainty of the receiver, and the nonlinear measurement function \(\text{h}^{n}_{\text{5G}}(\text{ s}[t_{k}])\) can be expressed as follows:  
\[\text{h}^{n}_{\text{5 GUE}}(s[t_{k}])=\left[\begin{array}{c}\frac{d^{n}[t_{k }]+\delta[t_{k}]}{c}\\ \arctan\Bigl{(}\frac{\Delta y^{n}[t_{k  
to estimate the clock offset of the device. It is shown in [29] that the clock offset of two consecutive adjacent times can be expressed as  
\[\rho[t_{k}]=\rho[t_{k-1}]+\alpha[t_{k}]\Delta t \tag{5}\]  
where \(\Delta t\) represents the time interval between two consecutive measurement times \(t_{k-1}\) and \(t_{k}\), and\(\alpha[t_{k}]\) denotes the clock skew, which is also a time-variable quantity  
\[\alpha[t_{k}]=\omega\alpha[t_{k-1}]+\eta[t_{k}] \tag{6}\]  
where \(\eta[t_{k}]\sim\mathcal{N}\Big{(}0,\sigma_{\eta}^{2}\Big{)}\) is additive white Gaussian noise sequence. \(\omega\) is a coefficient describing the correlation between \(\alpha[t_{k}]\) and \(\alpha[t_{k-1}]\). In this paper, \(\omega\) is set to 1 based on empirical values.
## 3 The GAGAN Framework: Evolutionary Optimization in GAN Architectures
### Genetic Operators in GAGAN
#### 3.2.4 Evaluation and Fitness Function  
In the GAGAN framework, once selection, crossover, and mutation have been applied, an individual in the population undergoes an evaluation based on its effectiveness in distinguishing real images from generated ones. This evaluation is essential, guiding the genetic algorithm's optimization by providing a measure of performance for an individual.  
The fitness function in GAGAN combines key performance metrics to evaluate the quality of each discriminator configuration. A primary component for this assessment is the discriminator loss, which indicates the model's proficiency in distinguishing real from generated images. Additionally, the fitness function incorporates the generator's loss, enabling the algorithm to gauge how effectively the generator deceives the discriminator. By balancing these elements, the fitness function prioritizes configurations that improve the discriminator's capacity to identify subtle differences between real and synthetic images, ultimately promoting higher-quality image generation.  
After calculating the fitness scores, the GA identifies the highest-performing individuals, selecting them to proceed to the next generation. This continuous cycle of evaluation and selection sharpens the population, allowing GAGAN to explore new configurations and optimize the discriminator's performance over time, ultimately improving the GAN's overall quality and stability.
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 2 Antenna Design and Simulation  
### Simulation Environment  
As shown in Figure 1, the proposed antenna is simulated in a three-layer tissue numerical model with the dimensions of 50 mm \(\times\) 50 mm \(\times\) 58 mm that imitates the real human environment. The human tissue is composed of skin, fat, and muscle. The electrical properties of the tissues vary with frequency. Table 1 lists the values of dielectric properties for skin, fat, and muscle in the simulated model at 2.45 GHz. To be closer to external devices and reduce the path loss in the tissue, the proposed antenna is implanted in a depth of 2 mm from the top of skin. The simulated tool employs ANSYS High Frequency Structure Simulator (HFSS) software (v.13, Ansys Inc., Canonsburg, PA, USA) for modeling, optimizing, and analyzing.  
Figure 1: Simulation environment of the proposed antenna.
# A Unified Nonlinear Elastic Model for Rock Material
## 4 The Characteristics of the New Model  
### Sensitivity Analysis of the Parameters  
The new unified nonlinear elastic model has two parameters: \(E_{ni}\) and \(m\). The influence of the \(E_{ni}\) values was studied by fixing the \(m\) value to 1.0. The stress-strain curves of the new model when \(E_{ni}\) is equal to 1 MPa, 5 MPa, 10 MPa, 15 MPa and 20 MPa are listed in Figure 4. All the curves rise linearly when the strain is less than 0.4, and nonlinearity gradually appears when the strain is greater than 0.4. The higher the \(E_{ni}\) values are, the higher the position of the curves, and the distance between adjacent curves is equal when the strain is the same. The slope of the curve for the unified nonlinear elastic model in its initial stage is determined by the \(E_{ni}\) value.  
The influence of the \(m\) value was studied by fixing the \(E_{ni}\) value to 10. The stress-strain curves of the new model when the \(m\) value is equal to 0, 0.5, 1.0, 1.5 and 2.0 are listed in Figure 5. All the curves are coincident when the strain is less than 0.4, and differences appear quickly when the strain is greater than 0.4. The curvature of the curves for the unified nonlinear elastic model in its middle-late stages is determined by the \(m\) value.  
Compared with Figures 4 and 5, the mechanisms of influence for the two parameters of the unified nonlinear elastic model on the stress-strain curves are different since they represent different physical properties of the nonlinear elastic deformation of the rock. The initial stage of the curve for the unified nonlinear elastic model is determined by the \(E_{ni}\) value, and its middle-late stages are determined by the \(m\) value. The unified nonlinear elastic model uses the above two parameters to jointly adjust and control in order to achieve accurate descriptions of the nonlinear elasticity of rock.  
In fact, the nonlinear deformation of the rock or joint is not obvious when the deformation is small. During the closure of the rock pores and fractures or the joint, the anti-deformation ability increases as the rock matrix gradually contacts, and then the nonlinear elastic characteristic gradually becomes obvious. The new unified nonlinear elastic model can accurately describe these properties.
## 1 Introduction
Another method proposed to measure the variation of the total magnetic field flux through the yoke rather than measuring the magnetic field leakage is MMF (Main Magnetic Flux). Since the MMF method can quantify the section loss of the tendon, it can be utilized in combination with the MFL method [19]. Although the MMF method basically estimates the relative sectional loss, a method to figure out the absolute sectional loss by measuring the maximum and minimum of the magnetic flux change after delivering a strong bi-polar magnetic field to the tendon was proposed [20]. Another study proposed to quantify the sectional loss by comparing the slope of the B-H loop with the reference slope [21]. However, there was a difficulty in distinguishing the slope change resulting from the sectional loss and the tensile stress in the tendon. The IMF method that is conceptually similar to the MMF method has shown a capability of detecting the damage location but there remains some doubt about the quantification of the damage [22]. In addition, another method called spontaneous MFL was suggested to estimate the sectional loss by measuring the magnetic flux delivered by the Earth's magnetic field instead of an artificial magnetic field to the tendon [23; 24]. However, the leaked magnetic flux was so small that an extremely sensitive magnetic sensor had to be used to measure it and the method also required separating the measured signal from the noise and the external magnetic field source. For the external tendon of the PSC structure, a heavy magnetizing system using a yoke for NDE is not suitable practically. It is also difficult to move the magnetizing system along the length of the tendon with a constant speed. Accordingly, this research adopts a simple solenoid to generate the magnetic field instead of the heavy yoke. In this framework, when the DC (direct current) is delivered to the solenoid, the induced voltage of the secondary coil related to the sectional change can be influenced by the moving speed of the device. Therefore, the AC (alternating current) is delivered to the primary coil to reduce the effect of the moving speed of the device and generate larger induced voltage in the secondary coil so that the flux leakage can be measured easily. The proposed method is expected to identify the sectional damage of the external tendon effectively by increasing the sensitivity of the secondary coil. This paper proposes a novel damage-detecting method for an external tendon of PSC structures using a solenoid-shaped device. The principle of detecting sectional loss using a magnetic field is explained in Section 2, and the experimental demonstration of the method is presented in Section 3. The discussion of the experimental results and the conclusion of the research are drawn in Sections 4 and 5.
# Study on the Influence of Rock Shape on Rolling Distance
## 5 Experimental Results and Theoretical Formula Calculation Comparison
### Comparison Analysis for Composite Slope Characteristics  
To confirm the suitability of the formula for calculating composite ground materials, we assessed the rolling range of test blocks. This assessment considered their shape characteristics, deviation, and the velocity at which they left the slope. The test results of rolling the test block on the rolling surface over 20 trials at an initial velocity of 2.2 m/s are directly plotted on the composite ground. The points in the figure correspond to the final stop position of the test block as shown in Figures 32-34.  
In Figure 32, test block A's rolling range consistently fell within the calculated parameters, with rolling distances slightly shorter than the calculated values. Notably, 60% of the test blocks did not transition between two types of ground materials, and 30% of these came to an immediate halt after crossing the boundary. This can be attributed to the elevated rolling resistance of the test blocks. As they neared the \"step\", their rolling speed had considerably decreased. The minor ground irregularities could easily impede their progress, leading to a subset of test blocks remaining at the boundary.  
Figure 31: Comparison between experimental and calculated values for test block F.  
Figure 32: Rolling behavior of test block A on composite ground.  
Test block B, characterized by reduced rolling resistance, retained a more significant amount of rolling kinetic energy as it approached the \"step\". In this scenario, 65% of the test blocks successfully traversed the boundary between two ground materials. For the subset that did not cross, their average deviation was 2.1 times greater than those that did, as indicated in Figure 33. The introduction of the \"step\" led to deviations in test block B's path after transitioning to new material, resulting in relatively substantial deviations. Nevertheless, the rolling range after deviation remained consistent with the calculated parameters.  
Test block F, which encountered three types of ground materials, experienced the least rolling resistance due to its shape and the higher circularity of its two-dimensional cross-sectional shape. This enhanced its ability to navigate different materials. Remarkably, all test blocks successfully surpassed the minimum rolling distance, as illustrated in Figure 34. In comparison to test blocks A and B, their lateral deviation was notably smaller, averaging a mere 23%. This reduction in deviation minimized the energy dissipation during rolling, enabling the test blocks to swiftly traverse the boundaries between various ground materials. Consequently, the impact of the ground material on the test blocks during rolling was relatively minor, resulting in a reduced margin of error between calculated and experimental values.
## 4 Assessment of the Developed Approach
### Datasets and Thresholds Used for the Assessment
#### 4.1.3 Processing of Datasets  
The developed approach was applied to the two presented datasets with the default threshold values proposed Table 1. The automatic 3D reconstruction of walls and slabs required a processing time of 8 min for the individual house and 19 min for the office building. Figure 13 shows for each dataset the repartition of processing time regarding the main steps of the approach namely the loading of point clouds, the segmentation into sub-spaces, the segmentations into planes and the point classification and finally the description of walls and slabs into obj format. One can observe that the repartition is quite similar for the two datasets. The most time consuming step is the segmentations into planes and the point classification which represents almost 50% of the processing time. The percentage of time required for the segmentation into sub-spaces is higher for the office building since the considered surface is five times higher than the surface of the individual house.  
\begin{table}
\begin{tabular}{c c} \hline Process & Threshold value \\ \hline \multicolumn{2}{c}{**Spatial resampling and/or space dimensions**} \\ \hline
3D region growing in point clouds & voxel size = multiple of resampling \\ \hline Filter for 2D regions removal in binary image & \(s_{unit}=1\) square metre (\(\sim\)1100 pixels) \\ \hline Filter for 3D regions removal & \(h_{min}=1.8\) m (French “Carrez” law) \\ \cline{2-3}  & \(v_{min}=\)1.8 m\({}^{3}\) \\  & (92,000 points for a spatial resampling of 1 cm) \\ \hline Minimal number of points for a plane & \(nb_{min}=1800\) for a spatial resampling of 1 cm \\  & (10 cm \(\times\) 1.80 m) \\ \hline \multirow{4}{*}{Conditions to perform an additional plane segmentation (_while_ loop)} & number of remaining points: \\  & \(>\)5\% (\%pts_{remaining}\) of initial point cloud \\ \cline{1-1}  & OR \(>400\) (\(nbpts_{remaining}\)) for a spatial resampling of 1 cm \\ \cline{1-1}  & (20 cm \(\times\) 20 cm) \\ \cline{1-1}  & the last segmentation must have extracted \\ \cline{1-1}  & at least one plane of 1800 points minimum \\ \cline{1-1}  & (\(nbpextract_{segm-1}\geq 1\)) \\ \hline \multicolumn{2}{c}{**Constraints and quality criteria**} \\ \hline Maximum distance between inliers and plane & \(t=2\) cm for grounds and ceilings; \\  & \(t=5\) cm for walls \\ \hline RMSE of planes & \(\sigma_{p}<2\) cm \\ \hline Horizontal and vertical constraints & maximal angular value of 1 degree \\  & (\(\alpha_{hz}\) et \(\alpha_{vert}\)) \\ \hline \multirow{4}{*}{Criteria for the identification and the reconstruction of walls} & maximal distance between planes \\  & between 5 cm (\(th_{min}\)) and 50 cm (\(th_{max}\)) \\ \cline{1-1} \cline{2-3}  & parallelism: maximal angular value of \\ \cline{1-1}  & \(\alpha_{\beta}=5\) degrees \\ \hline \end{tabular}
\end{table}
Table 1: Main thresholds involved in the developed approach and default values.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 7 Open Challenges and Future Directions
### Users Devices Renderged by Drones  
In our approach, we have considered that the users' devices can only deplete their energy and that the drone only transfer data to them. However, some new approaches suggest that drones can also transfer energy to end devices [33]. The ground multi-tier scheme should dynamically adapt to this characteristic with no modification. However, this could impact the drone behavior since based on the energy it sends to the nodes, it would need to adapt its trajectory.