esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System
## 4 Exact Solution and Validation  
This section presents the implementation of the algorithms designed by the authors of this paper for operations D and E on the ellipsoid by means of highly accurate fast-convergent iterative algorithms which were initially presented in [18]. As explained later, however, the iterative algorithm for operation E has been improved for the purpose of a better and even faster convergence to the exact solution.  
We made two different implementations of the algorithm: in Java, creating a library (API) that allows the user to integrate it with their own applications, and in PL/SQL (SQL stored procedures) with a PostGIS spatial DBMS. With PostGIS, a GIS user can take advantage of all the spatial functionality and run the geodesic intersections with its own cartography easily.  
Both implementations can be found in a public repository [25], files src/GeodesicSpatial Op.java and src/GeodesicSpatialOp.sql.  
For the implementation of the algorithm, we need to call some functions to get the inverse and direct solutions of geodesics on the ellipsoid. Any algorithm that gives a high accuracy (100 nm or more) can be used. We chose [PERSON]'s implementation [13], due to its accuracy, robustness and a wide range of supporting programming languages. The numeric precision for floating types is 64 bits, which is supported by PostgreSQL or Java.  
Scheme 4 shows the inputs and outputs of the functions InverseGP and DirectGP that solve the inverse and direct geodesic problem. All azimuths and coordinates are in radians. Both functions are working with WGS84.  
[aAB, aBA, sAB] = InverseGP (IonA, latB, IonA, latB)  
Input: Longitude and latitude of the points A and B that define the geodesic line  
Output:  
aAB = Azimuth AB  
aBA = Azimuth BA  
sAB = Distance on the geodesic between points A and B  
[IonB, latB] = DirectGP (IonA, latA, aAB, sAB)  
Input: Longitude and latitude of the point A, from which the new point B will be calculated, using the Azimuth AB (aAB) and the distance sAB.  
Output: Longitude and latitude of the calculated point B  
**Scheme 4.** Function signatures of direct and inverse geodesic problems.  
Scheme 5 shows the pseudocode of the Geodesic Intersection algorithm which returns the longitude and latitude of the intersection point X, and the number of iterations needed. Comments are denoted by #.  
FUNCTION GeodesicIntersection  
( IonA, lata, IonB, latB, IonC, latC, IonD, latD )  
RETURNS [ IonX, latX, nIterations ]  
R = 6378137 # WGS84 (a)  
threshold = 0.0001 # 0.1 mm  
nIterations = 0  
DO  
nIterations = nIterations + 1
# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 2 Problem Description  
### Scenario Description  
The problem of multi-UAV cooperative inspections and task assignment can be described as follows: For the inspection task composed of multiple transmission towers in multiple transmission lines in three-dimensional space, multi-UAVs can maximize the cooperative operation efficiency of UAVs through reasonable task segmentation, balanced distribution, and path planning. In the execution of the task, the UAV needs to take off from the designated charging station (hive) and return to the hive after the completion of the task. There are a certain number of redundant hives for the UAV to charge. Because the transmission towers in different lines belong to different entrusting parties, there are various types of inspection tasks in the scenario. The inspection task defines a number of checkpoints and auxiliary points for each tower. The UAV needs to hover at the checkpoint to take photos and complete operations such as turning at the auxiliary point. Figure 1 shows a scenario example with three transmission lines, two power change hives, and a total of 51 towers to be inspected.  
Figure 1: Top view of the transmission line scene.
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 4 Proposed Work
### Deploy Nodes and Define the Source and Destination Node  
Initially, N number of nodes are deployed within a defined length and width of (1000 \(\times\) 1000). Each node is labeled by (N\({}_{1}\), N\({}_{2}\),..., N\({}_{\text{n}}\)), including the source (N\({}_{16}\)) and the destination node (N\({}_{36}\)) where n is the number of deployed nodes.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 3 Proposed Approach
### IoT Network
#### 3.2.1 Edge  
The number of edge nodes to use on each farm mainly depends on the size of the farm. With more nodes, it is possible to know more quickly and reliably how the number of pests is evolving. However, as the pests are attracted by the traps inside each structure, just one node is enough to see the daily evolution of the pests.  
For each farm, we used one ESP32 microcontroller with a camera attached to obtain the trap images. This is a low-cost, low-power microcontroller suitable for when there is no external power source. It has only 520 kilobytes (KB) of memory, with 320 KB of DRAM (for storage) and 192 KB of IRAM (for instruction execution); the rest is RTC memory (which persists when the device is on standby) [32].  
We attached the OV2640 camera to the ESP32 to create the ESP32-CAM. This camera allows the obtaining of images of different qualities (jpeg quality) and resolutions (320 \(\times\) 240, 352 \(\times\) 288, 640 \(\times\) 480, 800 \(\times\) 600, 1024 \(\times\) 768, 1280 \(\times\) 1024 and 1600 \(\times\) 1200).Finally, we programmed the ESP32-CAM to send daily images (1280 \(\times\) 1024) to the cloud through the HTTP method [33].  
To test if the ESP-Cam had the capacity to detect pests, we counted the WF in the testing dataset (we counted 1371 WF) and then resized those pictures to different dimensions and sizes that are allowed by the Esp32-Cam. Next, we used the YOLOv5 (XLarge) model to make detections on those pictures, as shown in Figure 7.  
The previous table shows that it is possible to use the Esp32-Cam with 1280 \(\times\) 1024 images for pest detection without losing significant performance in YOLOv5 (XLarge) (between 10.3% and 16.3% less detections).  
To power the ESP32-CAM, we used two 18650 batteries and also solar panels to extend the battery life (see Figure 8). The daily power consumption of each edge node is 24 mA and the used batteries have 3350 mAh of power capacity, providing more than four months of duration for each battery. The solar panels with 150 mA and an average of 5 h of direct  
Figure 7: YOLOv5 (XLarge) performance (number of whitefly detections) for different image dimensions and sizes.  
sun exposure during the year (in Coimbra, where these edge nodes are placed) allow the edge nodes to run without the need to change batteries.  
In crops, the edge is subject to adverse conditions that can affect the durability of the devices and the quality of the images. With that in mind, we designed a structure (see Figure 9 in this paper and Figure A1 in the Appendix A) to protect the ESP32-CAM from atmospheric conditions and to better control the image quality (such as brightness). At the base of this structure, we placed the yellow sticky trap, and the ESP32-CAM at the top.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
###### Abstract  
This comprehensive review explores the ecological significance of the Argane stands (_Argania spinosa_) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts.  
Argania spinosa; remote sensing; satellite imagery; forest monitoring; drone; vegetation health; drought stress +
Footnote †: journal: _Geographies_  
0024
## 4 Discussion
### Interannual and Seasonal Variation of R\({}_{\rm S}\) along Succession in TDF  
In seasonally dry ecosystems such as TDF, carbon, water and energy fluxes are tightly linked to the intra-annual and interannual seasonal variations in precipitation and moisture availability [83] and successional changes in the forest affecting R\({}_{\rm S}\)[56, 84, 85].  
In the TDF of northwestern Mexico, R\({}_{\rm S}\) followed a typical seasonal pattern for seasonally dry ecosystems where after a period of water stress and dormancy, a large CO\({}_{2}\) pulse is stimulated by the first precipitation events. Soil respiration then reaches a maximum rate coinciding with the highest overall biological activity of the TDF, and finally a drastic decrease as the soil dries towards the end of the growing season [16, 56, 86]. We hypothesized that R\({}_{\rm S}\) would have significant variations across forest succession sites at annual and seasonal scales. The mean annual R\({}_{\rm S}\) rate in the old-growth (OG) was higher than at the mid-secondary (MS) and early-secondary (ES) forests, but contrary to our expectations, during the study period, the mean annual R\({}_{\rm S}\) rates did not reveal significant differences between the sites. However, at interannual scales, R\({}_{\rm S}\) mean rates differed significantly between seasons and according to forest succession (Table 3). These temporal R\({}_{\rm S}\) variations across sites suggest that these seasonal differences could be related to site-specific direct and indirect interactions between biotic and abiotic factors that include environmental conditions and vegetation attributes (i.e., root density, above- and belowground biomass) coupled with physical and chemical soil properties, substrate availability and microbial activity along the forest succession [87, 88, 89].  
Noteworthy, we found that R\({}_{\rm S}\) responded to precipitation inputs differently according to the state of forest succession, where the sites on early- and mid-secondary successional stages depict a faster increasing response in this flux than an old-growth forest. However, the ES and MS sites resulted in a much more ephemeral flux with a rapidly decreasing trend at the end of the wet season, while the OG site showed sustained high R\({}_{\rm S}\) rates for a longer time during the summer-fall transition (Figure 1c). A likely explanation for the sustained R\({}_{\rm S}\) rates in the OG site may be related to the amounts of soil organic matter (5.5 \(\pm\) 0.2%) and litterfall production (4.32 Mg ha\({}^{-1}\) year\({}^{-1}\)) from previous seasons in this site. Furthermore, accumulated organic matter still available for decomposition from tree mortality that occurred in 2011 due to an extreme freezing event in the region may play an additional role [61, 90].
###### Abstract  
With the continuous expansion of low-Earth-orbit (LEO) satellite networks, the services within these networks have exhibited diverse and differentiated demand characteristics. Due to the limited onboard resources, efficient network resource allocation is required to ensure high-quality network performance. However, the dynamic topology and differentiated resource requirements for diversified services pose great challenges when existing resource awareness or prediction methods are applied to satellite networks, resulting in poor awareness latency and the inaccurate prediction of resource status. To solve these problems, a network resource allocation method based on awareness-prediction joint compensation is proposed. The method utilizes the node awareness latency as a prediction step and employs a long short-term memory model for resource status prediction. A dynamic compensation model is also proposed to compensate for the prediction results, which is achieved by adjusting compensation weights according to the awareness latencies and prediction accuracies. Furthermore, an efficient, accelerated alternating-direction method of multipliers (ADMM) resource allocation algorithm is proposed with the aim of maximizing the satisfaction of service resources requirements. The simulation results indicate that the relative error between the compensation data and onboard resource status does not exceed 5%, and the resource allocation method can improve the service resource coverage by 15.8%, thus improving the evaluation and allocation capabilities of network resources.  
l ow-Earth-orbit (LEO) satellite network; network resource allocation; in-band telemetry (INT); long short-term memory (LSTM); joint compensation
# Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
## 4 Control Design
### Robust Stability Analysis of a Closed Loop Control System  
In this section, we demonstrate the robust stability of the closed loop control system. The R2R system has model uncertainty because the inertia of the web in roll form changes during the process. To reflect this model variation, the actual plant is expressed as follows:  
\[P(s)=P_{n}(s)[1+\Delta(s)] \tag{26}\]  
where \(\Delta\) is the multiplicative model uncertainty.  
In Equation (8), the actual plant can be represented by the following parameters as follows:  
\[P(s)=\frac{2\omega s\theta R(s)^{-1}}{1+\tau_{n}s}=\frac{1}{As+B} \tag{27}\]  
Actually, these parameters have confidence intervals, not single values (i.e., confidence interval A = (0.005, 0.0015), B = (0.15, 0.25)) Moreover, the multiplicative model uncertainty \(\Delta(s)\) is calculated as follows:  
\[\Delta(s)=\frac{P(s)\,-\,P_{n}(s)}{P_{n}(s)} \tag{28}\]The small-gain theorem was applied to demonstrate the robust stability of the closed loop control system. As shown in Figure 6b, the following conditions are given by the small-gain theorem:  
\[\left|\Delta(j\omega)\times T\left(j\omega\right)\right|<1 \tag{29}\]  
where \(T(j\omega)\) is the complementary sensitivity function and is expressed as follows:  
\[T\left(j\omega\right)=\frac{P_{n}(j\omega)C_{Fb}(j\omega)+Q(j\omega)}{1+P_{n}( j\omega)C_{Fb}(j\omega)} \tag{30}\]  
In the bode plot of Figure 7, the magnitude of the complementary sensitivity function \(T(s)\) is below the magnitude of inverse multiplicative uncertainty. This means it is possible to implement good tracking performance over the whole range of frequencies.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Key Protocols
#### 4.1.3 Fragmentation  
The other service provided by the 6 LoWPAN adaptation layer is fragmentation and reassembly. Fragmentation is only required when the entire IPv6 packet cannot fit in a single IEEE 802.15.4 frame, _i.e._, when it is larger than the available space for the MPDU payload (typically 106 bytes, see Section 3.2). According to RFC 4944, fragmentation breaks a single IPv6 packet into smaller pieces and a fragmentation header is included in every fragment. The document further specifies using two different types of fragmentation headers. The fragment header of the first fragment contains only the datagram size (11 bits) and datagram-tag (16 bits) fields, while subsequent fragments of the same IPv6 packet also include the datagram-offset (8 bits) field. Datagram size is the length of the entire unfragmented IPv6 packet, datagram-tag identifies to which datagram (_i.e._, packet) a particular fragment belongs. Therefore, these values have to remain the same for all fragments of a single IPv6 packet. The datagram-offset indicates the offset of the fragment from the first fragment. Figure 7 shows the 6 LoWPAN fragmentation header.
## 1 Introduction
However, the measurements of these parameters were complicated, and the separated results obtained were difficult to apply in deformation calculation. To simplify the calculation, an easier method is to directly use the flexural rigidity (\(EI\)), the product of the modulus of elasticity and moment of inertia. [PERSON] et al. (1998) calculated the flexural rigidity of a rice stalk through deflection deformation experiments and substituted it into a large deflection formula to fit the deformed curve [13]. Furthermore, [PERSON] et al. proposed a bending model of rice stalks based on formula deduction and empirical data [14]. [PERSON] et al. (2019) deduced a numerical calculation method for deflection deformation of rice stalk [15]. However, the above methods can only be used for rice stalks calculation. Therefore, assumptions that the rice plant is bent elastically and the panicle is  
Figure 1: Diagram of a double-cutterbar structure. (1) Lower cutterbar, (2) upper cutterbar, (3) auger, (4) header frame, (5) reel. moved translationally during harvesting were made in this paper to calculate the deformation of the whole rice crop. The shearing level angle is an important factor affecting cutterbar working efficiency. Since the heights of both cutterbars are subject to the harvesting conditions and requirements, it is necessary to investigate the influence of shearing level angle on shearing properties within whole range of rice stalk. So far, plenty of studies have reported various shearing properties of rice crops. [PERSON] and [PERSON] (2016) found that the cutting energy of rice stems in the second internode decreased by an average of 32.5% compared to third internode [16]; [PERSON] and [PERSON] (2016) reported that rice cultivar and blade velocity had significant effects (\(p<0.01\)) on the specific cutting energy, and blade cutting and level angles were not solely influential on the specific cutting energy but they interacted with rice cultivar and impacted it [17]; [PERSON] and [PERSON] (2006) found that the static and dynamic shearing strength was different among varieties, and blade level angle and blade type had no significant effect on the shearing strength [18]. However, these studies only focus on limited areas of rice stalk. To provide comprehensive information for multi-cutterbar level angle configuration, a series of rice stalk shearing tests using three shearing level angles (\(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\)) were conducted within whole range of rice stalk (15 cutting positions) in this study. The primary goal of this study was to develop a double-cutterbar structure designed based on rice plant deformation and shearing properties for a wide header to solve the problem of main crop rice harvesting. The detailed objectives are to: (1) develop and verify configuration methods for upper and lower cutterbars; (2) study the influence of cutterbar level angle on shearing properties within whole range of rice stalk; and (3) fabricate and test the double-cutterbar prototype to validate its functionality and practicability.
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 4 System Architecture
### Operator Interaction  
The system requires minimal input from the operator, only the limits of the inspection region are required, along with starting the UAV within an obstacle-free zone. A starting zone of at least \(8\times 8\times 3\) m provides a safe operational baseline to execute a small predefined flight plan, which is the starting manoeuvre. At its start location, with the initial manoeuvre, the UAV creates a bubble of known-safe space that will be incrementally expanded through exploration. Since the UAV only navigates within known free space, the starting manoeuvre boot-starts the generation of the map to enable the first autonomous manoeuvres. Consequently, the initial obstacle-free zone is a requirement to operate with the proposed approach. The mission ends either when this bubble completely encompasses the limits defined by the operator or when there are no more avenues to expand the known space (due to obstacles).  
With the exploration finished, the resulting output can be used in three broad ways. (1) Immediately, an operator can see a rough reconstruction of the environment. (2) The flight plan generated by the system can be logged and reused in following inspections to gather further measurements, either with the same platform or with another of similar configuration. (3) Finally, all the point clouds generated by the sensor during the flight, cross-referenced with location and time tags, can be processed with off-the-shelf 3D reconstruction software to generate a highly detailed 3D reconstruction of the structure. This last step feature is beyond the scope of this paper.
# Multi-Objective Optimization Study on Production of AlSi10 Mg Alloy by Laser Powder Bed Fusion
## 4 Conclusions  
In this study, in the production of the AlSi10 Mg alloy by the Laser Powder Bed Fusion method, the effects of the laser power, hatch distance, and scan speed parameters on surface roughness and relative density at different levels were analyzed using a full factorial experimental design and the TOPSIS method. The results obtained are presented below. As a result of the analyses carried out with the full factorial experimental design, it was found that the surface roughness decreases with increasing laser power (W) and hatch distance (mm) but increases with increasing scan speed (mm/s). In addition, it was observed that the relative density increases with increasing laser power (W) but decreases as the hatch distance (mm) and scan speed (mm/s) increase. As a result of TOPSIS analysis, it was determined that the most suitable experimental conditions were obtained with a combination of 275 W laser power, 0.14 mm hatch distance, and 800 mm/s scan speed. These parameters determined the surface roughness. According to these parameters, the surface roughness was 4.421667 \(\upmu\)m, and the relative density was 0.972.  
Future research could focus on various areas to deepen the understanding of AlSi10 Mg alloy production through the LPBF method and enhance efficiency in applications. This includes investigating different alloys, exploring the effects of preheating, and systematically evaluating the impact of powder particle size on surface roughness and density. Additionally, analyzing the long-term mechanical properties of parts produced by LPBF, utilizing digital twin technology for optimizing production processes, and conducting comparative analyses of different processing combinations and parameters are also significant considerations. Such studies will provide valuable insights that can significantly contribute to improving the effectiveness of applications.  
I.B.T.: Writing--original draft, Writing--review and editing, Resources, Formal analysis, Data curation, Project administration, Software, and Investigation. N.D.: Writing--review and editing, Methodology, Investigation, Conceptualization, and Validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to express our sincere thanks to the employees of Gazi University Additive Manufacturing Technologies Application and Research Center (EKTAM) for their contribution to the production processes of this study. We would also like to thank the employees of Gazi University Faculty of Engineering Powder Metallurgy Laboratory for their assistance in experiments.  
The authors declare no conflicts of interest.