esa-sceva/satcom-chunk-collection huggingface dataset:
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 5 Commercial Case Studies
### H3 Dynamics  
H3 dynamics is developing the future of business drone services through the combination of machine learning, remote tele-robot, and off-grid power to ensure the deployment and management of condition control, surveillance, and security solutions from anywhere. H3 dynamics provide huge volumes of smart, professional-validated inspection reports into complex images, videos, and other sensor data, a cloud-based service for owners, airlines, contractors, and financial companies [62].  
H3 dynamics have developed a station named \"DBX\" that can easily link all its functions with third-party networks, including particular security and communications networks, using an open application program interface (API) architecture. The DBX can respond in any mobile or fixed sensor to geo-located signals, such as an intelligent camera in a CCTV network, mobile phone, fire sensor or radar coordinate after an earthquake or movement of some sort. To facilitate the fight against COVID-19, Rooftop DBX drone facilities are being re-used as rooftop drone distribution and delivery networks for test kits, blood samples, and provision of supplies. H3 dynamics provide services to many sectors such as real estate, mining, and oil and gas [62].
## 3 CGR-NER Architecture
### Representation Layer
#### 3.1.1 Contextual Representation  
In previous studies, Word2 Vec [35] was often used to generate a contextual representation. However, the contextual representation generated by Word2 Vec was context-independent and could not effectively represent polysemy words. To solve this problem, CGR-NER uses BERT to dynamically generate a contextual representation. BERT is a pre-trained language representation model, which is stacked by multiple layers of Transformer encoders. It no longer uses the traditional one-way language model or shallow concatenation of two one-way language models for training, but uses the masked language model to train the two-way Transformer encoder, and finally generates a deep two-way language representation that can fuse contextual semantic information. The process of generating the contextual representation sequence \(E_{c}=\{e_{1},e_{2},\ldots,e_{n}\}\) corresponding to sentence \(S\) can be denoted as  
\[E_{c}=BERT(S) \tag{1}\]  
where \(e_{i}\) is the contextual representation corresponding to the \(i\)th character of sentence \(S\), and \(n\) is the sentence length. \(E_{c}\in\mathbb{R}^{n\times d_{c}}\), \(d_{c}\) is the dimension of the contextual representation.
## 5 Numerical Investigation  
### Finite Element Modeling  
The deck plate bolted with truss girder has been numerically modeled based on the test results to further examine the structural responses not identified from the tests. A finite element code, LS-DYNA [14; 15], was employed for these simulations. The deck plate was modeled using shell elements with the Belytschko-Tsay element formulation with two through thickness integration points. The truss girder consisting of upper rebar, lower rebar, and lattice steel wire was modeled using the Hughes-Liu element formulation with the cross-section integration with the \(2\times 2\) Gauss quadrature rule. The spacers, which enable connection of deck plates and truss girders by bolts, were modeled using one-point quadrature eight-node solid elements, with which the Flanagan-Belytschko hourglass control with exact volume integration for solid elements and the hourglass coefficient of 0.1 recommended by [PERSON] [21] was defined using CONTROL_HOURGLASS to prevent the zero energy deformation modes.  
A full-scale model for each specimen has been built, as shown in Figure 18a for 135-ED1-3.2. All steel components were connected by merging nodes assuming perfect bond. The models were simply supported at 50 mm away from the ends, similar to the given experimental conditions. An element size of approximately 8 mm was used consistently for beam, shell, and solid elements and this size has been considered to be sufficient to result in mesh-converged solutions. Figure 18b shows the modeling details including the information for element size in the vicinity of the support. The global and local responses of the numerical models are evaluated later in this paper by comparison to the test results.
## 3 Quantum Modelling of FMO Quantum Excitation
### Main Dipole Interactions among BChls  
Changes in the absorption spectrum of chromophores are usually different for excitonic coupled chromophores compared to the isolated ones [21]. Both in the ground and excited state, the molecule has a permanent dipole momentum, \(\mu_{g}\) and \(\mu_{e}\), respectively, which can differ significantly for asymmetric pigments like bacteriochlorophylls [21]. In the presence of a linear electric field \(F_{E}\), the energy levels \(E_{g}\) and \(E_{e}\) for the ground and excited states change (together with the difference \(\Delta E\) between them). Those changes are:  
\[E_{g,e} = E_{g,e}^{0}-F_{E}\cdot\mu_{g,e}\quad\rightarrow\quad\Delta E= \Delta E^{0}-F_{E}\Delta\mu\cos\theta \tag{1}\]  
Figure 6: Diagram of the excitation energy transfer pathways in the FMO complex depicting the energies of each site and considering the participation of the additional eighth bacteriochlorophyll (blue).  
where \(\Delta E=E_{e}-E_{g},\Delta E^{0}=E_{e}^{0}-E_{g}^{0},\Delta\mu=\mu_{e}-\mu_{g}\), and \(\theta\) is the angle between \(F_{E}\) and \(\Delta\mu\). The superscript 0 refers to the case \(F_{E}=0\). We avoid the report of the spatial form of fields and forces \(F_{E}\) for the dipole-dipole interactions because it is common in the electromagnetism literature [40]. The main aspect is highlight that the knowledge about the FMO structure is essential to understand the energy transfer dynamics and their performance between chromophores. Nevertheless, the protein itself may undergo structural and electrostatic changes depending on its environment conditions. Then, to understand the EET dynamics within the molecule, one must first to understand the coherent distribution of excitation among the different BChl sites.  
This is usually achieved taking two assumptions into consideration: (1) BChl sites are modeled as two-level systems, and (2) only one site can be excited at a time for EET to occur, making negligible the probabilities of bi-excitonic and other higher states compared to the single excitation [41]. Those assumptions can be attributed to the dipole blockade, an effect in which excitation of one site shifts other sites out of resonance due to the interaction energy being added to or subtracted from, the excitation energy for attractive and repulsive interactions, respectively [41; 42]. The strongest forces are exerted by the electric dipole interaction, arising from the closely spaced arrangement of the BChl sites [41; 42]. Since only exciton temporal dynamics have been experimentally observed through spectroscopy techniques, site excitation dynamics still must be indirectly modeled [43; 44; 45; 35] in order to get an entire description of the Hamiltonian.
## 3 Data Collection and Preprocessing
### Description of Data Sources
#### 3.1.1 Module-Level Measurements  
At the meteorological station of the PV power plant used for this study, the following parameters can be measured: total irradiance, ambient temperature of the power plant, surface temperature (module temperature) of the PV panels, and wind speed. These data were taken directly from the power plant's meteorological station. The active power obtained from the PV output can be calculated from the measured current and voltage values and recorded directly with a wattmeter. It is well known that temperature negatively impacts PV module performance; this effect can be roughly expressed as a linear relationship. An increase in ambient temperature and module temperature will cause a decrease in open-circuit voltage and a slight increase in short-circuit current, thereby reducing the PV module's efficiency and power output [81]. When the total irradiance (W/m\({}^{2}\)) increases at  
Figure 1: The satellite image of the PV power plant in Canakkale, Türkiye (reference: Google Earth).  
a constant temperature, the output current of the PV module will increase, which will lead to an increase in output power [82].  
Wind speed is a positive factor in PV panel efficiency and power output, ensuring PV modules work well in energy conversion thanks to their strong cooling effects [83]. The effect of temperature change on the current-voltage (I-V) curve of the PV module and its efficiency is shown in Figure 2. The effect of radiation on the I-V curve of the PV module can be seen in Figure 3.
# Propagation Characteristics of a Twisted Cosine-Gaussian Correlated Radially Polarized Beam
## 2 Theory
Within the framework of paraxial approximation, the elements of the CSD matrix of a twisted CGC radially polarized beam through an ABCD optical system can be written as [2]:  
\[\begin{array}{ll}W_{\alpha\beta}\left(\mathbf{r}_{1}^{{}^{\prime}},\mathbf{r }_{2}^{{}^{\prime}}\right)&=\frac{k^{2}}{4\pi^{2}g^{2}}J_{-\infty}^{\infty} \int_  
On the basis of the CSD matrix of an electromagnetic beam, there are three important fundamental statistical characteristics that can be defined as:  
the spectral density  
\[S\left(\mathbf{r}^{{}^{\prime}}\right)=W_{xx}\left(\mathbf{r}^{{}^{\prime}}, \mathbf{r}^{{}^{\prime}}\right)+W_{yy}\left(\mathbf{r}^{{}^{\prime}},\mathbf{r }^{{}^{\prime}}\right), \tag{9}\]  
the spectral degree of coherence (DOC)  
\[\eta\left(\mathbf{r}^{{}^{\prime}}_{1},\mathbf{r}^{{}^{\prime}}_{2}\right)= \frac{\mathrm{Tr}\left[\overline{\mathbf{W}}\left(\mathbf{r}^{{}^{\prime}}_{1}, \mathbf{r}^{{}^{\prime}}_{2}\right)\right]}{\left(\mathrm{Tr}\left[\overline{ \mathbf{W}}\left(\mathbf{r}^{{}^{\prime}}_{1},\mathbf{r}^{{}^{\prime}}_{1} \right)\right]\mathrm{Tr}\left[\overline{\mathbf{W}}\left(\mathbf{r}^{{}^{ \prime}}_{2},\mathbf{r}^{{}^{\prime}}_{2}\right)\right]\right)^{1/2}}, \tag{10}\]  
and the degree of polarization (DOP)  
\[P\left(\mathbf{r}^{\prime}\right)=\left(1-\frac{4 Det\left[\overline{\mathbf{W }}\left(\mathbf{r}^{{}^{\prime}},\mathbf{r}^{{}^{\prime}}\right)\right]}{ \left(\mathrm{Tr}\left[\overline{\mathbf{W}}\left(\mathbf{r}^{{}^{\prime}}, \mathbf{r}^{{}^{\prime}}\right)\right]\right)^{2}}\right)^{1/2}. \tag{11}\]  
In Equations (9)-(11), Tr represents the trace and Det denotes the determinant. It should be noted that there is another definition of the spectral DOC for electromagnetic beams introduced by [PERSON] et al., which is also widely used [9].
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 4 Distributed Coordinated Resource Scheduling Algorithm Design Based on ADMM  
This section presents a distributed iterative method based on the Alternating Direction Method of Multipliers (ADMM). Firstly, we tackle the consistency constraints of the problem P4 by constructing an augmented Lagrangian formulation, enabling the complete decomposition of the problem into subproblems that can be independently solved by each satellite system. Subsequently, we develop a distributed coordinated resource scheduling algorithm based on the ADMM which effectively solves the problem in parallel.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 2 Study Area and Data Sets  
### Study Area  
The AP is a northward extension (approximately 1300 km) of Antarctica toward the southern tip of South America (Figure 1). The AP is ice-covered and mountainous, with long melt seasons ([PERSON], 1998). Strong rising trends in the melting conditions were found in the records of the meteorological stations in the AP, where warming was more rapid than in the rest of Antarctica ([PERSON], 1998; [PERSON], 1998). Recent studies found that the loss of ice shelf mass in west AP is accelerating ([PERSON], 1998). Meltwater percolated into the bottom of the ice will weaken and even disintegrate the ice shelves. Recently, some ice shelves in the AP experienced dramatic break-ups, including the catastrophic collapse of Larsen B in 2002 when the surface snowmelt was three times greater than the average ([PERSON], 1998; [PERSON], 1998).  
Figure 1: Map of the Antarctic Peninsula (AP). (**a**) Location of the AP on Antarctica; (**b**) Digital elevation model (DEM) of the AP obtained from the National Snow and Ice Data Center (NSIDC, [[http://nsidc.org/](http://nsidc.org/)]([http://nsidc.org/](http://nsidc.org/))). The black line is the boundary of the study area, and the colored rectangles are the frames of Sentinel-1 images used in this study. The base map was obtained from Earthstar Geographics ([[http://www.terracolor.net/](http://www.terracolor.net/)]([http://www.terracolor.net/](http://www.terracolor.net/))).
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 2 Materials and Methods  
### Definitions  
As discussed in the previous section, the detection ability of our network is significantly affected by the presence of existing TRs within the sensing range of our SRs. The field of view of each SR is influenced by the number of TRs surrounding it, leading to obstruction and a decrease in the overall coverage performance of the WSN.  
To provide a clearer understanding of our coverage methodology, below is an outline of the setup:  
* We define an AoI, denoted as \(A\), of a predetermined size located on a designated map.
* Within area \(A\), a set of \(n\) SRs are strategically positioned at fixed locations.
* Each SR, labelled as \"\(SR_{i}\)\" where \(i=1,\ldots,n\), is positioned at coordinates \((x_{i},y_{i})\) within \(A\) and has a predetermined sensing range denoted as \"\(R_{i}\)\".
* The field of view of each \(SR_{i}\) is determined by a starting and ending angle.
* A position in area \(A\) is considered to be covered by \(SR_{i}\) if it falls within the \(SR_{i}\)'s sensing range.
* Within area \(A\), there are a total of \(M\) known TRs with their positions already identified.
* Due to the presence of TRs, each \(SR_{i}\)'s field of view is obstructed within certain angular intervals ranging from \(\delta_{before}\) to \(\delta_{after}\) degrees from the actual direction of each known TR (SR blindness).
* Each \(SR_{i}\) can thus detect any new TRs within its remaining sensing range.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Definition** \\ \hline WSN & Wireless sensor networks \\ SR & Sensor \\ TR & Transmitter \\ AoI & Area of interest \\ OOP & Object-oriented programming \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of acronyms.  
* A sub-area of area \(A\) can be covered by the sensing range of \(k\) SRs. In our specific scenario, a minimum of three SRs is required for triangulation (\(k\geq 3\), see also [4; 7]), defining \"triangulation\" as per this study's definition.
## 2 Background
### Monitoring Technologies  
The Global Navigation Satellite System (GNSS) is a technology that utilizes satellite signals to determine the precise position of a receiving device on Earth. This device comprises an antenna and a system to decode the satellite signals to calculate its location. GNSSs are beneficial for monitoring large areas or regions with limited visibility, such as landslide-prone areas [14]. This monitoring process involves the placement of measurement points (rovers) within the landslide area, while control points (references) are established in stable adjacent zones. These reference points are crucial for compensating and enhancing the accuracy of the data collected by the rovers.  
This method is relevant for the study of landslide risk because it provides precise data on the position and movement of landslides, which is essential for the creation of accurate predictive models. The interferometric synthetic aperture radar (InSAR) technique is used to assess the activity status of known landslides and identify connections between weather conditions and instability [15]. It enables the delineation of unstable slope areas and the measurement of displacement. It employs radar electromagnetic waves to measure phase changes between two images taken at different times by detecting ground displacements. These images are captured as the transmitting and receiving antennas move along a predefined trajectory.  
This technology is crucial for an integrated approach, as it allows for the monitoring of ground deformations in real time, improving the ability to predict landslides. Radar Doppler systems utilize the \"Doppler\" technique to monitor real-time landslide movements [16]. They emit an electromagnetic signal in the microwave domain using a frequency-modulated continuous wave radar sensor. The signal is collected by several receiving antennas, which check for frequency deviations indicating the presence of moving bodies. This system enables the measurement of landslide speed and the geolocation of its trajectory. This system is very useful as it provides essential data for the analysis of landslide dynamics in the predictive model.  
In addition to modern technologies, traditional methods also play a significant role.
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
###### Abstract  
Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model (\(R^{2}=0.96\)) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards.  
orchard canopy; leaf area density; plant protection; fitted leaves +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], X. Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology. _Appl. Sci._**2025**, _15_, 7411. [[https://doi.org/10.3390/app15137411](https://doi.org/10.3390/app15137411)]([https://doi.org/10.3390/app15137411](https://doi.org/10.3390/app15137411))
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 3 Problem Statement for Scheduling Request
As the active time increases by the random-access procedure, both the battery life and cell capacity can decrease due to failure on contention resolution. Besides the handshake triggered by application packets, the energy consumption problem arises by the transport protocol stack used in the IoT network, such as constrained application protocol (CoAP), datagram transport layer security (DTLS), and multicast domain name system (mDNS). The DTLS protocol, which is intensively investigated for the IoT [16], needs additional handshakes for security-context creation and resuming [17]. Given the memory cost for the server to maintain several security contexts from IoT devices, the refresh operation at the server is inevitable. In this case, an inappropriate timeout value can cause more scheduling request procedures and increase energy consumption. Figure 10 illustrates the initial 6-way handshake to create security context in the CoAP/DTLS protocol. The uplink DTLS and RLC ACK packets need a scheduling request procedure per transmission. Figure 8: Scheduling request procedure with random access.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Path Planning
#### 3.2.3 \(A^{*}\) Search  
The \(A^{*}\) algorithm was employed for path planning along the points of interest. The proposed viewshed-based method was compared with the classical method based on raster data. The results are shown in Figure 10, where the planned path passes through the optimal sequence of points of interest. The classic map is constrained by the terrain and takes the shortest path between the points of interest without considering the blockage region. On the other hand, the proposed viewshed approach finds a path that stays in the visibility region, despite taking a longer route.  
The average number of successful links per meter of flight was used as a metric to assess the efficiency of each method. Table 2 summarizes the comparison results, and shows that the blockage map path obtains 40.12% more links when compared to the classic path. This means that, on average, the blockage map links with 1.7 more points of interest per meter of flight, despite increasing the map path length by approximately 22.45%. Figure 11 further shows the cumulative links obtained in each case.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Node A** & **Node B** & **Distance (m)** & **Node A** & **Node B** & **Distance (m)** & **Node A** & **Node B** & **Distance (m)** \\ \hline
1 & 4 & 209 & 21 & 22 & 215 & 47 & 48 & 149 \\ \hline
1 & 34 & 2645 & 21 & 30 & 152 & 47 & 71 & 3828 \\ \hline
2 & 3 & 517 & 22 & 33