esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Earth observation that provides timely and spatially explicit information about land surface processes, such as vegetation dynamics, represents an advanced digital agriculture tool to derive important cropland use information over large areas, supporting decision making and sustainable agricultural land management. Biophysical parameters that have been largely used to monitor vegetation status, compared with the vegetation spectral indices calculated from optical multispectral satellite sensors, allow to quantitatively measure vegetation properties. The Leaf Area Index (LAI), a biophysical index defined as one half the total green (i.e., photosynthetically active) leaf area per unit horizontal ground surface area, can be estimated from the surface reflectance data acquired by satellite sensors. Vegetation biophysical characteristics, such as the canopy structure and photosynthetic capacity, are well described by LAI, which is largely used in agricultural studies [1].  
LAI information is widely adopted for crop monitoring and for numerical modeling applications [2], to derive phenological metrics [3], and to identify and map the main crop types [4]. It has also been demonstrated to be effective to monitor vegetation dynamics [5] and to map terrestrial natural habitats [6].  
A number of techniques, evolved in the past to derive the biophysical variables of vegetation using remote sensing data, can be grouped into four categories [7]: (i) parametric regression methods; (ii) non-parametric regression methods; (iii) physically based methods, using Radiative Transfer Models (RTMs); and (iv) hybrid retrieval methods, combining non-parametric and physically based approaches.  
During the last decades, satellite optical multispectral sensors sensed the Earth's surface either at medium spatial resolution and high revisit frequency or at high spatial resolution and low revisit frequency. Recently, the Copernicus Sentinel-2 satellite constellation, equipped with an MSI sensor, allows to sense the Earth's surface at high spatial, spectral, and temporal resolutions, expanding the capacity for vegetation status and plant phenology monitoring from the time series of biophysical parameters.  
The aim of this study is to compare the LAI estimates from the Sentinel-2 MSI and Landsat-8 OLI satellite data over croplands and other vegetation cover types in a selected test area, using the different biophysical processors available in the SNAP software, that belong to physically based estimation methods. Simple linear regression analysis is used to determine the linear relationship between different LAI estimates, coefficients, and error metrics.
## 4 Experimental Stations
### 2D X-ray Detectors
#### 4.3.6 First Characterization Results  
The first full size JUNGFRAU modules have been extensively characterized with continuous X-ray sources and electrical or optical stimulation. The following performance has been measured:  
* Noise in HG0 of 52 e.n.c. This allows single photon detection at energies <2 keV
* Noise in G0 lower than 70 e.n.c.
* Noise in G1 and G2 of 0.5 and 4 12 keV photons rms, well below Poisson statistical fluctuations
* Saturation level higher than 10,500 12 keV photons
* Linearity better than 1% rms.
* Radiation hard up to 10 MGy  
A noise distribution for all the pixels of one module, measured using copper X-ray fluorescence radiation, is presented in Figure 21. A 1 M JUNGFRAU system has been tested at the LCLS X-ray Correlation Spectroscopy (XCS) instrument. Data was collected using Cu fluorescence, providing uniform illumination of the sensor, and with powder and single crystal diffraction samples. The data from LCLS confirmed the previous characterization, in terms of noise, dynamic range and spatial resolution. As an example, Figure 22 shows the measured number of photons hitting the sensor, averaged over a number of LCLS pulses, for a silver behenate powder sample and 9.3 keV illumination. The pixels in the inner rings recorded intensities covering most of the medium gain range (G1). The width and double structure of the rings is due to the geometry of the capillary.  
Figure 21: Distribution of the rms noise (in electrons) for all the pixels of a sample module. An average value of 50.5 e.n.c. is measured.
# A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
## 5 Discussion
Because it is difficult to verify the accuracy of each way object one by one, random sampling analysis is used in our experiments. To reduce the bias in the sampling process, each reputation interval is sampled to verify the accuracy of our experimental results. This study considers that mainly the similarity between different versions of the same object is calculated. The area overlap rate can quantify the topological relationship between versions, to a certain extent. Therefore, this study considers only the area overlap rate as a metric of topological similarity for the time being. This method cannot distinguish between the topological relationships of disjoint and meet, and how to quantify these two cases can be considered in the next step. The results of the sampling experiment show that users with higher reputation tend to provide higher quality data, as shown in Table 3. The overall high quality of data in the region verified the conclusion that the reputation of OSM contributors in the region is generally high. Contributor's evaluation depends mainly on factors such as semantic similarity, geometric similarity, and topological similarity, as shown in Figure 8c. When the way object matches the actual object well in terms of geometric and topological relationship, but there is a semantic deviation, it results in the contributor's reputation being low. The model proposed in this paper considers only the semantic changes of feature class tag and linguistic change of other tags of OSM objects rather than the semantic changes of tags of non-feature class, which need to be further improved.
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 3 Related Work
Queries are decomposed into work units that are assigned to different threads. The authors use the number of query terms and the sum of lengths of the respective posting lists for performing the classification required to set the number of threads to be assigned to each co-occurring query. The work in [31] proposed a prediction algorithm for multi-stage retrieval systems where an initial document candidate generation stage is followed by one or more re-rankers. The work in [32] proposed an analytical performance modeling framework for user queries. The overhead introduced by the proposed analytical model varies between 5% and 6% depending of the particular test. More recently, the work in [33] proposed a reinforcement learning based approach for search engines. During query evaluation, the query is classified using pre-defined categories, and consequently a match plan is selected. The authors proposed a method to predict which match plan to employ for each incoming query. The Discrete Fourier Transform (DFT) technique has been previously used in contexts such as patterns recognition in data mining [34; 35], and to predict the popularity of videos by analyzing videos view count traces in the frequency domain [36]. In the Web search engine application domain, it has been used for determining (i) document relevance [37], (ii) document semantic representation [38] and (iii) document classification [39]. In this paper, we show that the DFT technique can also be useful for predicting the running time of query processing algorithms.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### Deep Neural Network
#### 8.3.6 LSTM Model Architecture of Experiment 2  
The architecture of LSTM model for Experiment 2 can be observed in Figure 13. This model consists of the following layers:  
1. Sequence input layer, where the input dimension is equal to 256, since the number of subcarriers for Experiment 2 is 256.
2. LSTM layer with 100 hidden units.
3. A dropout layer with 40% probability of dropping out the input elements.
4. Six fully connected layers.
5. Softmax layer.
6. A classification layer for the activity classifications.  
Figure 11: LSTM network architecture.  
As an optimizer, the ADAM optimizer with default parameter values was used. A max epoch size of 60 was set, since a higher max epoch size was not giving better results; however, the learning time was increasing. As a mini-batch size, the value of 32 was chosen.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 3 Simulation Environment and Settings
### The INET Framework  
The INET framework is an open-source model library for the OMNeT++ simulation environment. It helps to provide network models, various protocols and agents for researchers and students to configure and develop communication networks. The framework is useful for the design and validation of network protocols, as well as the exploration of new and rare networking scenarios. The simulator contains models for the Internet protocol stack (TCP, UDP, IPv4, IPv6, and OSPF), as well as link layer protocols (e.g., Ethernet). There is also support for mobility and MANET routing protocols and other protocols and components. Other simulation frameworks have extended the INET framework further whilst using it as a base to extend in specific directions, such as peer-to-peer networks and LTE (4G).  
INET builds on the concept that network modules communicate through message passing (routing). Networks can be built using devices such as hosts (nodes), routers and switches and a network operating system. New networking devices can be developed, but the existing components help users to understand and make it easier to modify certain areas of components. It benefits from the infrastructure that the OMNeT++ simulator provides, and models can be developed, tested, and evaluated from the comfort of the simulation IDE. The command line can also provide the same functionalities as the simulation IDE.  
INETMANET has been based on the INET framework, and to this day, it is continuously being developed, which is like the INET framework, except that INETMANET includes additional protocols and components that become useful when developing, testing, and evaluating wireless communication scenarios. OMNeT++ and the INET framework help to provide all the necessary components and models needed for simulating networks and protocols in general. And because of the architecture that OMNeT++ provides, it ismore than suitable for modeling, simulating and implementing sophisticated, complex network protocols.
## 3 Results and Discussion
### Emission Characteristics
#### 3.5.2 HC Emissions  
Figure 8 shows the overall emissions of hydrocarbons (HC) as a function of the engine load.  
In terms of unburned hydrocarbons, pure gasoline features the highest emissions levels with nearly 0.042 g \(\bullet\) kWh\({}^{-1}\). Dual-fuel operation facilitates HC minimization since ABE reduces emissions levels while hydroxy doping upgrades this share. Noticeably, HC emissions are significantly lower than CO levels but still represents an undesired pollutant that affects human health and air quality. The overall trend of HC emissions can be explained based on the same fundamentals of CO emissions. It is important to note that  
Figure 8: HC emissions for fuels tested.  
alcohols additives (ABE) feature improved oxygenated characteristics that further improve combustion efficiency and promote a homogeneous air/fuel mixture, which reduces HC formation. These concluding remarks are consistent with that of [PERSON] et al. [10], which unravels the effect of engine speed on ABE blend overall performance. The enhanced laminar flame speed of ABE and HHO is another contributor supporting HC minimization.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 5 Performances
### Analysis
#### 5.2.1 Complexity of the Computations  
We leave to users the choice of the cryptographic primitives, as we believe that they are interchangeable in our protocol. For our experiments, we have arbitrarily chosen to use \(SHA256\) as the hash function, and \(RSA2048\) as the Signature algorithm. Those choices are voluntarily poor performance-wise. However, they allow us to give an upper complexity bound.  
For each session, \(\mathcal{R}\) (\(\mathcal{S}\), respectively) performs Hash&Sign (Hash&Verify, respectively) over the packets \(\{p_{i}\}_{0\leq i<n}\) and the random bits \(\{r_{i}\}_{0\leq i<n}\) and \(\{s_{i}\}_{0\leq i<n}\). This is \(n\cdot(8p+2)\) bits of data to be hashed. \(SHA256\) is based on the Merkel-Damgard construction, this means that the message to hash is separated into blocks of identical size which are processed by a compression function. Hence, its complexity is linear in the number of blocks involved. \(SHA256\) uses 512-bits blocks, so for each session, the Hash complexity will be \(\mathcal{O}(np)\) in the number of compression function, given by the following computation: \(\frac{n(8p+2)}{512}=\frac{n(4p+1)}{256}\).  
Table 1 shows the number of applied compression functions and the corresponding hashing time depending on \(n\) with a fixed value of \(p=512\).  
Regarding the signature and the verification, the input value is always a 256 bits string, and so the time taken for this operation remains constant for both operations.  
Finally, \(\mathcal{S}\) runs the decision function on the sample. This process is linear in the size \(n\) of the sample as it goes through the table of RTTs and increments a counter every time the treated value is higher than the chosen threshold. Note that \(n\) should not be too large because the verification is performed for each \(n\) packet sent. Hence, a high \(n\) leaves a wider amount of data to be relayed before the detection. We believe \(n=256\) or \(n=512\) to be the most suitable choices. These values being very small, we can consider the decision complexity to be negligible.  
Note that, the slower the overall verification process is, the later a suspicious sample will be detected. However, as the authentication and verification are performed concurrently with the other processes, those times do not impact the throughput performances.
# Rod-Airfoil Interaction Noise Reduction Using Gradient Distributed Porous Leading Edges
###### Abstract  
Rod-airfoil interaction noise is a major concern in several practical industrial and aeronautical applications. In this study, we constructed bio-inspired gradient distributed porous leading edges to reduce rod-airfoil interaction noise. Noise radiations by NACA 0012 airfoils with nonporous aluminum and porous leading edges were experimentally compared in an an anechoic wind tunnel by changing the streamwise gap between the upstream rod and the downstream airfoil, as well as the angle of attack of the airfoil. The results of detailed acoustic tests showed that the proposed gradient distributed porous leading edges can significantly reduce noise radiation around and above the peak frequency of the baseline rod-airfoil interaction. Parametric studies on the piecewise porous characteristics showed that rod-airfoil interaction noise reduction is sensitive to the coverage percentage, position, and arrangement order of the porous materials. Porous leading edges with lower pores per inch, larger coverage, and gradually sparse distributed pores better reduced noise. Moreover, the position of the porous material affected the frequency band of noise reduction, and the noise reduction performance was better when it was located in the downstream strips of the porous leading edge.  
rod-airfoil interaction noise; leading edge; noise reduction; porous material; shedding vortex addressref=aff  
url
# RF Energy Harvesting Wireless Communications:
## 2 Urban and Semi-Urban RF Environment
### Weather Dependent Attenuation  
In the outdoor environment, the weather can considerably impact the propagation attenuation of EM waves. As shown in Figure 7, the attenuation of EM waves in sunny winter is as low as 0.1 dB/km. If the weather is fog, rain, or the air is humid, the attenuation increases to 4 dB/km, 40 times higher than that on a sunny day. For an energy harvesting communication system, the variation of propagation attenuation not only impacts the communication range but also affects the energy harvesting rate. Therefore, to improve the reliability of an RF-EHWC system in different weather conditions, a system designer needs to reserve sufficient margin for the power consumption and receiving SNR when making a communication budget.  
Understanding the temporal and spatial features of ambient RF power density is essential to the efficient hardware design, power management, as well as routing and security design. The energy harvester is designed to work in a specific narrowband signal. As discussed in Section 3, the power conversion efficiency of the energy harvesting circuit severely depends on the strength of the RF signal. Therefore, the design of energy harvester needs to know both the central frequency and the range of signal strength for the highest power conversion efficiency. In Section 4, we introduce that efficient power management methods need to know the amount of energy that can be harvested in the near future, which relies on the temporal feature of the RF signal strength. In Sections 5 and 6, we also discuss how the low power density of ambient RF signal challenges the routing and security design in the RF-EHWC networks.  
Figure 6: RF power density in an out-door environment with respect to time and frequency [42].
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 3 Materials and Methods
### Data Analysis for Improving the Quality of Aerial Sensor Observations
#### 3.2.3 Aerial Data Validation and Calibration Process  
The HSP aerial data validation and calibration process consists of several steps and involves data observations from both aerial and ground-based sensors, i.e., humidity and temperature, infrared, and spectrometer data. Each farm under observation by the HSP is divided into multiple farm regions that include a single ground node and are used to grow the same crop. More elaborate farm regions can be also supported to take into account soil quality, irrigation, fertilisation, etc., but specialising regions further is outside the scope of this paper, as such extensions can be easily supported by modest extensions to the core contributions of this paper. The aerial data collection is performed in three cycles a day. Cycles are defined as the period where the UAV is used to collect aerial data. The HSP validation and calibration process pseudo-code is presented in Algorithm 1.  
The proposed algorithm is continuously applied on each aerial data collection cycle to determine the most optimum line slope and intercept values to be used for aerial data calibration. As more data are collected using the HSP, the size of the available data sets to be analysed using the data analytic software increases and therefore the most optimum line slope and intercept values can be determined.
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 5 Conclusions  
This study aimed to develop and evaluate a complete system to check compliance with the mandatory buffer zones around buildings and roads, as determined by Portuguese law 10/2018 for rural areas. The system has been fully developed and is based on off-the-shelf drones equipped with RBG cameras, an AI model to detect the contour of buildings, and CHAMELON bundles to detect trees and vegetation within buffer zones.  
The AI model that was trained in this study is effective at detecting the contour of buildings from aerial images, with a precision of 0.871, a recall of 0.835 and a mAP50 of 0.888, and it was able to process the aerial images provided by Mapbox. The system is able to automatically delineate the buffer zones around buildings and roads, in accordance with Portuguese legislation. The WebGIS that has been developed is able to display the use case orthophoto on a map, show the boundaries of the buffer zones, and highlight trees and vegetation within buffer zones that are not in compliance. The results from the two use cases show that this system is effective in detecting non-compliant trees and vegetation within buffer zones.  
All in all, the main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to identify and cut down the excess vegetation and trees in buffer zones, thus helping to reduce the risk of wildfire propagation in wildland-urban interfaces. The work carried out in this study has paved the way for the launch of a commercial service for estimating wildfire propagation risk in wildland-urban interfaces.  
Conceptualization, O.S. and N.S.; methodology, O.S. and N.S.; software, O.S.; validation, O.S. and N.S.; formal analysis, O.S.; investigation, O.S.; writing--original draft preparation, O.S. and N.S.; writing--review and editing, O.S. and N.S.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has indirectly received funding from the European Union's Horizon Europe research and innovation action programme, via the CHAMELON Open Call #2 issued and executed by Astron Systems under the CHAMELON project (Grant Agreement no. 101060529).  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] both were owners of the company Astron Systems Company. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 2 Background and Rationale  
### Proba-V Mission  
The Proba-V mission [13; 14], launched by the European Space Agency (ESA) in May 2013, was designed to ensure the continuity of the SPOT-VEGFATION (SPOT-VGT) 15-year archive [16] of global daily observations over land and coastal zones. The Vegetation sensor on board Proba-V is a pushbroom multi-spectral radiometer acquiring measurements in four spectral bands: Blue, Red, Near-Infrared (NIR) and Short-Wave Infrared (SWIR) over an across-track swath of approximately 2250 km. This wide swath is obtained thanks to three overlapping cameras with an optical field of view of 34\({}^{\circ}\) each (see Figure 1). The Ground Sampling Distance (GSD) across-track ranges from 100 m at nadir to 350 m at the edges of the swath for the visible and NIR focal plane, while in the SWIR, GSD ranges from 200 m to 700 m [13]. Proba-V platform lacks an on-board calibration device; therefore, the sensor radiometric calibration was entirely based on vicarious approaches [17].  
Proba-V products are released to the users as segments per camera (L2A) Top-Of-Atmosphere (TOA) product, as well as temporal composites S1 (daily) TOA and S1/S10 (daily and 10 days) atmospherically corrected Top-Of-Canopy (TOC), i.e., surface reflectance products [18]. The data are projected onto the Plate Carree grid, and provided at 1 km, 333 m and 100 m spatial resolution. The cloud detection is based on a dynamic threshold method [18]. The cloud shadow pixels are flagged using a hybrid radiometric and geometric approach, while the snow/ice detection is based on a decision tree algorithm [18]. The atmospheric correction is performed using the Simplified Method for Atmospheric Correction (SMAC) approach [19]. The aerosol optical depth (AOD) at 550 nm is estimated using an optimization algorithm applied to the Blue channel over vegetated surfaces [20]; over desert sites and bare soils, a latitude-dependent AOD climatology is used instead.