esa-sceva/satcom-chunk-collection huggingface dataset:
# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
###### Abstract  
The simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to be limited when modeling a large number of antenna elements combined with a complex and realistic representation of propagation conditions. In this paper, we propose an efficient implementation of a 3 rd Generation Partnership Project (3 GPP) three-dimensional (3D) channel model, specifically designed for graphics processing unit (GPU) platforms, with the goal of minimizing the computational time required for channel simulation. The channel model is highly parameterized to encompass a wide range of configurations required for real-world optimized 5G NR network deployments. We use several compute unified device architecture (CUDA)-based optimization techniques to exploit the parallelism and memory hierarchy of the GPU. Experimental data show that the developed system achieves an overall speedup of about 240x compared to the original C++ model executed on an Intel processor. Compared to a design previously accelerated on a datacenter-class field programmable gate array (FPGA), the GPU design has a 33.3% higher single-precision performance but a 7.5% higher power consumption. The proposed GPU accelerator can provide fast and accurate channel simulations for 5G NR network planning and optimization.  
3 GPP 3D channel model; massive-MIMO; 5G; 5G NR; hardware acceleration; GPU; CUDA optimization techniques; network planning simulation +
Footnote †: journal: Electronics Electronics  
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Footnote 0: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 5 Multi-Operator Game for SF Assignment  
We first propose a game for SF selection. This game is played among the different network operators under the assumption of previously selected channels per operator. Each network aims to find the SF distribution \(p_{s}^{i}\), which maximizes its own normalized traffic throughput. Nonetheless, networks can not do this independently. Otherwise, within the same geographical zone i.e., devices would have the same radio conditions, all of the devices would default to similar SF selections. This would increase the risk of collisions. The situation makes non-cooperative game theory ideal for SF selection in a multi-operator LoRaWAN deployment, where rational players (the operators) are competing for rather contradicting objectives.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 4 Simulations Environment and Performance Evaluation  
ABORt performance is evaluated by means of simulation using a Cooja emulator [21] with the parameters shown in Table 1. We used an area of 100 \(\times\) 100 m\({}^{2}\) where sensor nodes are randomly scattered, but we ensure that the network is connected (no isolated nodes). In Cooja, each node is emulated with the same sensor nodes capacities as sky motes. This ensures realistic parameters and behavior of nodes. Indeed, the simulation code can be used for experiments on physical sensor nodes.  
In order to show that multichannel routing helps to improve the network throughput, we compared ABORt with two single channel protocols. The routing protocol for low power and lossy networks (RPL) with the ETX objective function and a Collaborative Load Balancing Algorithm to avoid queue overflow in WSNs (CoLBA) [22]. In RPL, we fixed the minimal interval for sending out Destination oriented directed acyclic graph Information Object (DIO) messages to 500 milliseconds (\(I_{min}=500\) ms) and the number of time \(I_{min}\) is continuously doubled is 16 (\(I_{doubling}=16\)). CoLBA is a queueing delay-based dynamic routing protocol that avoids queue overflow. The routing metric is exchanged using specific control frames referred to as beacons. To evaluate ABORt efficiency compared to other multichannel routing protocols, we compared it to M-HopCount and M-HopCount-Sync. M-HopCount uses a routing metric that selects the shortest path in terms of number of hops that we implemented using the same ACK-based metric exchange method of ABORt. M-HopCount-Sync also uses a routing metric based on shortest path in terms of number of hops, but node activity is organized in cycles. Each cycle is divided into two phases: a synchronization phase and a data transmission phase. During the synchronization phase, all nodes in the network tune their radio interface to the same channel to exchange beacon frames. The data transmission phase is dedicated to exchanging data packets and nodes have to switch from their reception channel to the reception channel of their neighbors. All of these protocols start with the same network set-up phase during which neighbor discovery and channel selection are done.  
Note that the presented results are extracted once our proposed routing scheme has completely taken over (except for the overhead results). The duration of the transient phase from hop count routing to ABORt varies from 8 s to 1 min depending on the network size and the network depth.
# Verification of a Probabilistic Model and Optimization in Long-Range Networks
## 4 Discussion
Integrating these advanced features into the proposed model would provide a more comprehensive framework for improving LoRa-based networks, bridging the gap between simple LoRa configurations and the advanced capabilities of LoRaWAN. In summary, this work makes significant strides in advancing the understanding of interference modeling and optimization in LoRa networks. By combining theoretical models with practical validation, it provides actionable insights for network designers and highlights pathways for future research to further enhance the scalability and reliability of LoRa and other LPWANs.
# Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization
## 2 Background and Related Work
### The Algorithm Selection Framework  
Rice [20] proposed an algorithm selection framework based on measurements of the characteristics of the problem. Successful implementations of this framework can be found in graph coloring, program induction, satisfiability, among other problem domains [28; 29; 30; 31]. Our interpretation of the framework results in the selector illustrated in Figure 1, whose components are:  
* **Problem set** (\(\mathcal{F}\)) contains all the relevant BBO problems. It is hard to formally define and has infinite cardinality.
* **Algorithm set** (\(\mathcal{A}\)) contains all search algorithms. This set potentially has infinite cardinality.
* **Performance evaluation** is a procedure to evaluate the solution quality or speed of a search algorithm \(\alpha\) in a function \(f\), using a performance measure, \(\rho\).
* **Feature extraction** is a procedure where an _input sample_, **X**, is evaluated in the problem instance, resulting in an _output sample_, **Y**. These two samples are then used to calculate the ELA features, **c**, as illustrated in Figure 2.
* **Algorithm selection** is a procedure to find the best algorithm \(\alpha_{b}\) using **c**. Its main component is the _learning model_[28], as illustrated in Figure 2. The learning model maps the characteristics to the performance of a subset of complementary or well-established algorithms, \(\textbf{A}\subset\mathcal{A}\), also known as algorithm portfolio. A method known as pre-selector constructs **A**. The learning model is trained using experimental data contained in the knowledge base [32]. To predict the best performing algorithm for a new problem, it is necessary to estimate **c** and evaluate the model.  
Figure 1: Diagram of the algorithm selector based on [PERSON]’s framework [20]. The selector has three main components: Performance evaluation, Feature extraction, and Algorithm selection.
## 3 Methodology
### Light Use Efficiency Model
#### 3.2.3 The Calculation of FAPAR  
Some earlier research on LUE models employed the fraction of PAR absorbed by the vegetation canopy (\(\mathrm{FAPAR}_{\mathrm{canopy}}\)) to estimate APAR, using a linear relationship between \(\mathrm{FAPAR}_{\mathrm{canopy}}\) and VIs as well as LAI as \(\mathrm{FAPAR}_{\mathrm{canopy}}\) proxies [5; 8; 56]. Subsequently, as communities discovered that the fraction of PAR absorbed by chlorophyll (\(\mathrm{FAPAR}_{\mathrm{chl}}\)) or photosynthetically active vegetation (PAV, \(\mathrm{FAPAR}_{\mathrm{pav}}\)) contributes more to vegetation photosynthesis, \(\mathrm{FPAR}_{\mathrm{chl}}\) or \(\mathrm{FAPAR}_{\mathrm{pav}}\) were found to be appropriate for estimating GPP. In this study, we selected four common relationships between \(\mathrm{FAPAR}_{\mathrm{chl}}\)/\(\mathrm{FAPAR}_{\mathrm{canopy}}\) and satellite-based vegetation indicators as follows:  
\[\mathrm{FAPAR}_{\mathrm{chl1}}=\mathrm{a}\times\mathrm{EVI} \tag{14}\]  
\[\mathrm{FAPAR}_{\mathrm{chl2}}=(\mathrm{EVI}-\mathrm{b})\times\mathrm{c} \tag{15}\]  
\[\mathrm{FAPAR}_{\mathrm{chl3}}=\mathrm{min}(\mathrm{max}(\mathrm{d}\times \mathrm{NDVI}+\mathrm{e},0),1) \tag{16}\]  
\[\mathrm{FAPAR}_{\mathrm{canopy}}=1-\mathrm{e}^{\mathrm{k}\times\mathrm{ LAI}} \tag{17}\]  
where Equations (14) and (15) are linear functions between EVI and \(\mathrm{FAPAR}_{\mathrm{chl}}\) where a is set to 1 [53], and \(\mathrm{b}\) and \(\mathrm{c}\) are, respectively, 0.1 and 1.25 [10]. Equation (16) removes the influence of bare soil on NDVI, where \(\mathrm{d}\) and \(\mathrm{e}\) are set to 1.24 and \(-\)0.168 [57]. \(\mathrm{k}\) is the extinction coefficient set to 0.5 in Equation (17) [58].
# In-Depth Aroma and Sensory Profiling of Unfamiliar Table-Grape Cultivars
## Results
### Odor-Active Compounds of Unfamiliar Table Grapes  
To identify the true aroma contributors, we determined the odor activity values (OAVs). Thirty-one volatiles (22 in pulp and 27 in skin) existed as aroma-active compounds (OAVs > 1; Tables S3 and S4). Among these, C\({}_{6}\) compounds, terpen, esters, and C\({}_{13}\)-norisoprenoids were the dominant contributors, including hexenal and (\(E\))-2-hexenal, ethyl hexanoate, d-limonene, rose oxide I (trans), linalool, cedrol, geranic acid, \(\beta\)-damascenone, and \(\beta\)-ionone. For instance, hexenal and (\(E\))-2-hexenal were the most active C\({}_{6}\) compounds contributing to flavors in all samples, except for the pulp of H and R cultivars. Furthermore, the pulp of J, K, and L cultivars showed high values for ethyl butyrate and ethyl 2-methylbutanoate. The OAVs of these compounds were greater than 100. Conversely, ketones and acids were low in content, and hence, showed no odor (<1).
# _remote sensing_
## 1 Introduction
Many global MSS models developed by Technical University of Denmark (DTU), Collecte Localisation Satellites of Centre National d'Etudes Spatiales (CNES_CLS), Shandong University of Science and Technology (SUST2020), and Wuhan University (WHU) were constructed using the altimeter data, tide gauge data, and other auxiliary data [13; 14]. For tidal corrections, the more precise global or regional tide models were used in the Cal/Val of altimeters such as FES2014, NAO99b, GOT, etc. [15]. The WSCS has been chosen to be the first specified site for Cal/Val of the satellite altimeters in China since 2013 by the National Satellite Ocean Application Service (NSOAS) [16]. They plan to accomplish Cal/Val for HY-2 series satellites. Its facilities are placed on four islands that are adjacent to the footprint of HY-2A/B/C. Fortunately, the Sentinel-3A (S3A) and Jason-3 altimeters also fly over this site. This makes it possible to calibrate such altimeters. In this research, we aim to assess the accuracy of the above altimeter missions by comparing the SSH with in situ measurements of WSCS. In contrast to previous work in the region [15], our study is more comprehensive and particular. We have introduced the equipment and data processing strategies of WSCS in detail since August 2019. The ocean models such as the MSS and tide were also evaluated and used in this research. Moreover, our research can provide a unified datum for multiple altimeters, and ensure the consistency and continuity of the measurements by long-term series altimeters. A detailed introduction of the facilities for altimeter calibration in WSCS and the altimeter calibration methods were provided in Section 2. In Section 3, the datum of altimeter calibration has been defined using the PGSs and DBG. Moreover, the related data and correction models for Cal/Val of altimeters such as the MSS and tidal differences of WSCS were measured and evaluated. The calibration results of HY-2B, HY-2C, Jason-3, and S3A altimeters were shown in Section 4. Sections 5 and 6 were the discussions and conclusion, respectively.
## 5 Enhancing QoS and QoE in ICN
### Transmission and Flow Control
Simulation results from ndnSIM demonstrated higher throughput, lower latency, and minimal packet loss. Table 5 lists various ICN frameworks that enhance QoS and QoE metrics by optimizing transmission, flow, and congestion control. Comparative Analysis of Transmission and Flow Control-Based Solutions  
To compare the strategies listed in Table 5, we can divide them into the following subgroups: (i) transmission fairness, (ii) hop-by-hop flow control, and (iii) client-driven adaptations. The fairness-focused flow control strategies primarily aim to improve the end-user experience by ensuring equitable resource sharing and preventing highly popular content from monopolizing bandwidth. A potential drawback is that strict fairness might some times lead to suboptimal overall network utilization if resources are not allocated based purely on demand. Hop-by-hop flow control techniques involve intermediate routers along the forwarding path making local decisions to manage network traffic and congestion. The localized control and class-based interest shaping allow better management of Differentiated Services and improved QoS and QoE support. The hop-by-hop control approaches add state management overhead on routers; plus, they require implementing new scheduler components. In client-driven adaptation methods, consumers actively participate in optimizing their content retrieval. Clients fetch content fragments from multiple sources over different paths and dynamically adjust retransmission timeouts and window size. The negotiation-based gaming approach enables clients to negotiate with content caches based on price and desired QoS levels. Note that these methods rely on accurate client-side measurements (as shown in Figure 5).
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
###### Abstract  
This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth's surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.  
Sentinel-2; drought; soil moisture; evapotranspiration; vegetation response; surface water and wetland analysis; land use and land cover analysis 2021  
Reviewing the Potential of Sentinel-2  
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M.; [PERSON]; [PERSON], [PERSON].  
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2021
While ML applications for estimating \(G_{\rm s}\) have emerged, most studies have been limited to specific plant species or confined to certain geographic or climatic regions [35; 36; 37; 38]. [PERSON] et al. leveraged meteorological and drone-recorded data to forecast \(G_{\rm s}\) using multiple ML models [35]. Their findings highlighted challenges in predicting stomatal conductance from remotely sensed data, indicating a need for further exploration. [PERSON] et al. assessed the performance of a Jarvis-type model against ML models for predicting \(G_{\rm s}\) in wheat [37]. They found that although ML models demonstrated high predictive accuracy with extensive datasets, their capacity to generalize to new data regions was limited. [PERSON] et al. developed ML models using environmental predictors for 36 tree species across five forest biomes and six continents, advocating for future studies to test the effectiveness of ML in predicting \(G_{\rm s}\) across diverse climatic conditions and vegetation types [38]. [PERSON] and [PERSON] explored a variety of ML models for \(G_{\rm s}\) across multiple plant functional types (PFTs) to determine the extent to which nonparametric models could accurately simulate \(G_{\rm s}\) both within and across PFTs [39]. However, their approach was constrained as only factors relevant to the empirical models were considered, limiting the comprehensive assessment of environmental impacts. This study aims to elucidate the influence of biophysical controls on ecosystem-scale vegetation \(G_{\rm s}\), enhance our comprehension of how \(G_{\rm s}\) is affected by the environment, gain insights into plants' ecological adaptation and growth mechanisms to climate change, provide a deeper understanding of the physiological processes involved in vegetation-atmosphere water and carbon exchange, and help to develop water utilization strategies for vegetation by investigating the role of hydrothermal meteorological conditions in \(G_{\rm s}\). Hence, this study addresses three scientific questions: (1) How does the contribution of environmental factors differ in different vegetation types of \(G_{\rm s}\)? (2) Are environmental factors' effects on \(G_{\rm s}\) consistent under a similar hydroclimatic regime? (3) What are the effects of ET delineation methods on \(G_{\rm s}\) under different scientific assumptions?
## 3 Proposed Approach
### Data and Pre-Processing
#### 3.1.4 Standardization (Z-Score Scaling)  
Each feature \(x_{i}\) is standardized as follows:  
\[x_{i}^{\text{std}}=\frac{x_{i}-\mu_{x}}{\sigma_{x}}\]  
where \(\mu_{x}\) is the mean and \(\sigma_{x}\) is the standard deviation of feature \(x\).
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 3 Mathematical Models
### Forces Acting on Particles by Seauwater  
In the horizontal direction, the particle is mainly affected by Magnus lift force, \(\mathbf{F}_{M}\), Saffman lift force, \(\mathbf{F}_{S}\), and turbulence force, \(\mathbf{F}_{t}\). Magnus lift force and Saffman lift force, pointing to the center of the pipe, are caused by the velocity difference between two sides of the particle. Turbulent force originates from the fluctuation of the liquid flow parameters. In the vertical direction, the particle suffers from gravitational force, \(\mathbf{F}_{S}\), pressure gradient force, \(\mathbf{F}_{p}\), and drag force, \(\mathbf{F}_{D}\). The gravitational force is opposite to the flow direction while the directions of pressure gradient force and drag force are consistent with the flow direction [23; 24; 25; 26].  
When a particle rotates in the flow field, a force perpendicular to the relative velocity of the particle will be produced, which is termed Magnus lift force.  
\[\mathbf{F}_{M}=\frac{1}{8}\pi d_{s}^{3}\rho_{l}\mathbf{\omega}_{i}\times(\mathbf{u}_{l}-\mathbf{ u}_{s}) \tag{21}\]  
where \(\mathbf{u}_{s}\) is the velocity of particle (m/s) and \(d_{s}\) is the diameter of particle \(i\) (m).  
When a particle moves in the flow field with high velocity gradients, the particle undergoes the force from high pressure to low pressure, which is termed Saffman lift force.  
\[\mathbf{F}_{S}=1.61d_{s}^{2}(\mathbf{u}_{l}-\mathbf{u}_{s})\sqrt{\rho_{l}\mathbf{u}_{l}}\bigg{|} \frac{d\mathbf{u}_{l}}{dy}\bigg{|}^{1/2} \tag{22}\]  
where \(\mu_{l}\) is the dynamic viscosity of the liquid (Pa-s).  
Gravity is the force produced by the earth and is given by:  
\[\mathbf{F}_{g}=\frac{1}{6}\pi d_{s}^{3}\rho_{s} \tag{23}\]  
When a particle moves in the flow field with pressure gradients, it will be subjected to the pressure gradient force:  
\[\mathbf{F}_{p}=-\frac{1}{6}\pi d_{s}^{3}\
abla\mathbf{P} \tag{24}\]  
Due to the velocity gap between particles and liquid, the particles suffer from the drag force affected by the particle Reynolds number \(Re_{p}\), the turbulent parameters, and the shape and concentration of particles, and so on.  
\[\mathbf{F}_{D}=C_{D}\frac{\rho_{l}|\mathbf{u}_{l}-\mathbf{u}_{s}|(\mathbf{u}_{l}-\mathbf{u}_{s})}{ 2}\frac{\pi d_{s}^{2}}{4} \tag{25}\]  
where \(C_{D}\) is the drag coefficient, which is set to 0.44 when \(Re_{p}>1000\).
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## 3 Proposed SDN-Based Identifier-Locator Separation Architecture for IoT Networks  
In this section, we introduce the architecture and the namespaces that are used in this paper. Each procedure for registration, packet forwarding, and route optimization are described with some examples. From now on, we use the term MN to indicate mobile nodes, sensor nodes, and endpoint nodes. In the proposed scheme, it is assumed that an MN can send and receive data using an IPv6 over low power wireless personal area network (6 LoWPAN) protocol [21; 22].
## 3 Improved Q-Learning Algorithm for Better Management of Channel Utilization in UWCNs  
### State Space (\(S_{s}\))  
A variety of variables that affect energy efficiency and transmission quality can be combined to define the state space of UWCNs, as follows:  
* Battery level (\(B_{l}\)): How much power the sensor node has left.
* Interference level (\(I_{l}\)): The degree of interference in communication within the proximity of the node.
* Data packet queue length (\(Q\)): The quantity of data packets that are waiting for transmission.
* Recent collision history (\(C\)): A quantitative or binary metric showing the frequency of recent collisions. As a result, a vector S = (\(B_{l}\), \(I_{l}\), \(Q\), \(C\)) may be used to describe a state \(s\in A\).