esa-sceva/satcom-chunk-collection huggingface dataset:
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 2 Materials and Methods
### Methodology
#### 2.3.2 Pixel-Based and Object-Oriented Fusion Methods
The SNIC algorithm is a mature and efficient image segmentation algorithm on the GEE platform, which is available as 'ee.Algorithms.Image.Segmentation.SNIC' [16; 59]. The advantage of this algorithm is that the image segmentation results can be adjusted by changing the input segmentation parameters. The main parameters are size, seedsize, compactness, connectivity and neighborhoodsize [60]. Among them, \"size\" refers to the pixel-based spacing of superpixel seed positions, that is, segmentation size. \"Seedsize\" is the superpixel seed position spacing. \"Compactness\" defines the shape of the clusters, meaning that the larger its value, the closer the segmentation result is to a square. \"Connectivity\" takes continuity into account based on the merging of neighboring clusters. And the \"neighborhoodsize\" is to avoid tile boundary artifacts. Based on the actual situation of cropland in Shandong and combined with several parameter adjustment experiments, the best parameters' combination was finally obtained as \"size\" = 10, \"seedsize\" = 15, \"compactness\" = 0, \"connectivity\" = 8 and \"neighborhoodsize\" = 256. Finally, the pixel-based classification and the image objects obtained from SNIC segmentation were integrated based on the following rules. The main strategy of these rules is to improve the classification accuracy by redefining the pixel values within each object. These rules have been repeatedly experimented and verified to be reasonable, and the details are as follows:  
If >80% of the pixels in an object have the same attribute value, then it is final for the whole object;  
If <20% of the pixels in an object have an attribute value, then the value is removed from the object and assigned to the most abundant one;  
If none of the above conditions are satisfied, the pixel-based classification results will be retained, which may result in the existence of winter wheat and non-winter wheat pixels in an object. This fusion strategy retains the advantages of the pixel-based classification method while fully considering the characteristics of object-oriented methods. As a result, it eliminates pixel-based classification noise while maintaining a spatial correlation between pixels, which greatly improves the accuracy of classification.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 2 Background  
This section concentrates on cryptographic key management for DTNs by examining its unique characteristics, and the associated challenges. To familiarise the reader with this topic, the section starts with a brief introduction containing the basic methods for key management and the corresponding terminology.
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 3 Proposed Algorithm
### SNR Estimation and Distance Estimation  
According to [23], the signal to noise ratio (SNR) can be defined as the ratio of the signal energy at the carrier frequency to the noise energy at this frequency, which can be expressed as  
\[\text{SNR}=20\log_{10}\left(\frac{P_{r}}{Q_{r}}\right) \tag{17}\]  
where the signal power is  
\[P_{r}=\sum_{k=(f_{start}-2.4\times 10^{9})/f_{w}}^{(f_{end}-2.4\times 10^{9})/f_{w} }\dot{\textbf{y}}_{(i/t_{a})k} \tag{18}\]  
and the noise power is  
\[Q_{r}=\sum_{k=1}^{(f_{start}-2.4\times 10^{9})/f_{w}-1}\dot{\textbf{y}}_{(i/t_{a })k}+\sum_{k=(f_{end}-2.4\times 10^{9})/f_{w}+1}^{(2.5-2.4)\times 10^{9}/f_{w}} \dot{\textbf{y}}_{(i/t_{a})k} \tag{19}\]  
The RSSI is used to estimate the distance between the receive antenna and signal source, as the signal strength decreases with distance. The fading characteristics of the channel have a log-normal distribution, so the path loss can be expressed as  
\[PL(d)=PL(d_{0})+10\times n\times\log_{10}\left(\frac{d}{d_{0}}\right)+X_{\sigma} \tag{20}\]  
where \(d\) is the distance between the source and receiver, \(n\) is the path loss index, \(X_{\sigma}\) is a Gaussian distributed random variable with zero mean and standard deviation \(\sigma\), and \(d_{0}\) is the reference distance. The received signal power is then  
\[P_{r}=P_{t}-PL(d) \tag{21}\]  
Figure 5: (**a**) The improved cross-correlation using accumulation with the UAV present, (**b**) the improved cross-correlation using accumulation with no UAV present, (**c**) frequency estimation with a UAV present, and (**d**) direction of arrival (DOA) estimation at frequency \(f_{start}\).  
where \(P_{t}\) is the transmit power and  
\[P_{0}=P_{t}-PL(d_{0}) \tag{22}\]  
so that  
\[PL(d_{0})=P_{t}-P_{0} \tag{23}\]  
Using (20) and (23), the path loss at a distance \(d\) after averaging multiple measurements is  
\[PL(d)=P_{t}-P_{0}+10\times n\times\log_{10}\left(\frac{d}{d_{0}}\right) \tag{24}\]  
where \(X_{\sigma}\) is neglected because the mean of \(X_{\sigma}\) is 0. Then, from (21) and (24), the received signal strength at a distance \(d\) is  
\[P_{r}=P_{0}-10\times n\times\log_{10}\left(\frac{d}{d_{0}}\right) \tag{25}\]  
so the distance between the receiving antenna and signal source can be estimated as  
\[d=d_{0}\times 10^{\frac{P_{0}-P_{r}}{10m}} \tag{26}\]
## 2 Methods
### Improved Path Point Sampling Strategy
#### 2.2.1 Initial Sampling  
The original FMT* algorithm utilizes pseudo-random sampling to sample a certain number of path points in the search space. Sobol sampling, on the other hand, is a deterministic low-discrepancy sampling method. Compared to random sampling, Sobol sampling can better fill the search space, reducing overlapping and redundant samples, as shown in Figure 3. Therefore, Sobol sampling finds wide applications in numerical integration, signal processing, computer graphics, and other fields.  
The Sobol sequence can be represented as the product of the Van der Corput sequence and the Sobol-generating matrix \(C\). For a Sobol sequence \(X_{n}\) with \(n\) numbers, it can be expressed as follows:  
\[X_{n}=[(\varphi_{2},C(0)),(\varphi_{2},C(1))\ldots,(\varphi_{2},C(n))] \tag{2}\]  
where  
\[\varphi_{b},C(i)=(b^{-1}\ldots,b^{-M})[C(a_{0}(i)\ldots,a_{M-1}(i))^{T}] \tag{3}\]  
In the Equation (2), \(\varphi_{b}\) represents the Van der Corput sequence in base b. The variable i represents the i-th number in the Van der Corput sequence (\(i\) = 0, 1, 2\(\ldots n\)). In Equation (3), \(a_{M}(i)\) represents the value of the \(M\)-th digit of the \(i\)-th number in the Van der Corputsequence in base b. The Sobol-generating matrix \(C\) is calculated based on the generating polynomial and the direction numbers. For Sobol sampling in different dimensions, the generating matrix \(C\) is different. For example, a 2-dimensional Sobol sampling requires a \(2\times k\) generating matrix, where k represents the number of direction numbers. The calculation of the generating matrix is complex and will not be further discussed here. [PERSON] et al. have found the highest-dimensional Sobol-generating matrix of dimension 21,201, which can be referenced for interested readers [24; 25]. In practical applications, pre-calculated generating matrices are used, eliminating the need for complex calculations and resulting in higher sampling efficiency.  
Using Sobol sampling in the search space for initial sampling, followed by setting a larger connection radius \(r_{n}\), the original FMT* algorithm is used to generate the initial feasible path. Sobol sampling ensures a more uniform distribution of sampling points, while using a larger connection radius allows for faster expansion of the feasible paths. As a result, collision-free paths from the start to the goal can be quickly generated.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 3 Method
### Flood Inundation Modeling  
In this study, the FLORA-2D hydraulic model [45] was used to simulate flood dynamics and the extent of two Bradano River flood scenarios referred, respectively, to November 2010 and December 2013, described in Section 2. The two-dimensional hydrodynamic model, already proposed in [45], is based on the numerical solution of a simplified subset of an equation derived from Navier-Stokes shallow water equation formulation. It was developed with the aim of simulating flood propagation in flat areas, taking into consideration the dynamic spatio-temporal effect of vegetation. Indeed, the topography and land-use roughness are the most critical factors in 2D hydraulic modeling [46], as they influence estimation of flood extension and its flow characteristics, like water depth that can be considered the main factor of flood losses according to [9]. The numerical solution was developed for the specific needs of providing a good compromise between simulation time and data accuracy for a correct representation of the process [37], namely to obtain high computational efficiency through the use of a simplified set of initial and boundary conditions.  
The initial flood stage condition was a null riverbed water level. Moreover, the upstream boundary condition was constructed by considering the effective water depth measurements along the river, available as readings from a gauge station in the analysis area, called S.S. 106 (see Figure 1). Conversely, in [45; 47] the authors used the output of the hydrological model as a boundary condition simulating FLORA-2D for the 1 March 2013 Bradano flood event, (not analyzed in this study), because the information that was recorded by the S.S.106 hydrometric station was not available due to malfunctionscaused by the impact of the flood. The above-mentioned hydrometric levels were assigned as upstream boundary conditions, while a constant water level equal to 0.5 m a.s.l. was considered a downstream boundary condition (sea level was assumed to be slightly above standard conditions). Finally, the spatial distribution of flow resistance was derived from LiDAR data, used to generate maps of vegetation heights [48]. In this application, the computational domain was defined by a square grid with a resolution of 10 m, while the time step was set to 2 s to ensure model stability.
## 2 Methods, Study Area and Data Analysis
### Study Site and Ground Data Collection  
The research was conducted at Risley Moss Nature Reserve, situated in the valley of the River Mersey near Birchwood, Warrington (latitude 53.420 N, longitude 2.506 W; Ordnance Survey Grid reference: SJ674922) in north-west England. The topography is flat with an elevation of around 25 m above sea level. The elevated flood plain is characterized by acidic soil, and there is an adjacent area of peat bog. Risley Moss has local and international nature reserve designations; the vegetation is mainly deciduous woodland, covering approximately 80 hectares, growing in mixed stands of different ages and heights, with silver birch (_Betula pendula_) the dominant tree species. The stem density is approximately 900 trees per hectare, and tree heights range between 10 m to 20 m. The study site selected comprised a homogeneous plot of 60 by 60 m, dominated by silver birch, located away from tracks and paths. The plot size was designed to allow for the 32-m spatial resolution of the DMC. LAI measurements were made at ground level by sampling along the diagonals of the 60 by 60 m plot. Measurements were made at intervals of approximately 2 m using a Delta-T SunScan LAI probe, selected for this study, because it allows for rapid, non-destructive estimation of LAI, even under different sky conditions [54]. Measurements were made every two to three weeks from March, 2005-February, 2006, with approximately 100 measurements made along the two orthogonal transects at each visit, and the average plant area index (PAI, the total plant area per unit ground area) was determined on each date. Minimum leaf-off PAI was measured in January, 2006, and was subtracted from the other measured PAI values to remove the woody component and to provide an estimate of true LAI.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
###### Abstract
As research and development forge ahead, integrating security from the inception of device design, rather than as an afterthought, will be crucial in defining the future of a secure and efficient IoT landscape [13; 14; 15]. The author in [16; 17] introduces an enhanced aggregate segmentation mask RCNN model (AS Mask RCNN) for grading mixed aggregates. The study conducted three different experiments and found that the AS Mask RCNN achieved an impressive accuracy of over 89.13% across all experimental scenarios. Compared to the faster RCNN  
Figure 1: The top IoT Applications. and mask R-CNN models, it demonstrated an accuracy improvement of 8.85%. It reduced the processing time for single image segmentation by 1.29 s, making it suitable for near real-time field detection requirements. The paper also presents a self-developed noncontact testing platform for aggregate grading that can be applied in complex environments. This platform facilitates digital, automated, and intelligent noncontact grading of mixed aggregates, ultimately enhancing the accuracy of aggregate grading testing and supporting the high-quality development of reservoir dam construction in China. The author's work focuses on the significant role of the human microbiome in predicting certain diseases. They acknowledge the challenges posed by limited samples and high-dimensional features in microbiome data for machine learning methods. The author introduces a novel ensemble deep-learning disease prediction method to address this. The approach combines unsupervised and supervised learning techniques. It starts with unsupervised deep learning to discover potential sample representations. Then, these representations are used to develop a disease-scoring strategy, creating informative features for ensemble analysis. A score selection mechanism is implemented to ensure optimal ensemble performance, and performance-enhancing features are incorporated with the original data [18]. Certainly, summarizing the main contributions into three points:  
* we introduce a flexible and highly efficient approach that utilizes ensemble-based deep learning models to swiftly and accurately detect intrusions in IoT environments while mitigating false positives and negatives;
* we evaluate and characterize the performance of deep learning algorithms;
* we provide a systematic and comparative experimental analysis.
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 2 Related Work
### [PERSON] work on dynamic resource allocation of SDN controllers has its origins in the context of Wide Area Networks (WAN) [36]; such a reference presents the Dynamic Controller Provisioning Problem, an optimization strategy to minimize the cost of gathering statistics data from switches, configuration flows, synchronization, and large scale assignment of switch to controller. The authors formulated the optimal controller provisioning problem as an Integer Linear Program (ILP) and it was modeled as a Single Source Unsplittable Flow Problem. Since it is a NP-hard problem, two heuristics were proposed to solve it a greedy approach based on the knapsack problem and a simulated annealing based meta-heuristic approach.  
In [38] it is proposed a solution to the assignment of master controller to switches in a distributed SDN controller environment. While switches are connected to multiple SDN controllers for resiliency, only a master controller can install flow rules into switches. The master assignment strategy determines the master SDN controller for each switch. Thus, a master controller assignment problem is formulated in order to minimize the average setup flow latency taking into account the number of assigned master controllers along a path and the flow arrival rate among switches. A heuristic named low-complexity controller master assignment (LCMA) is presented to solve the problem.
## 2 Literature Review
### Thermal Management and Energy Efficiency  
Effective thermal management is essential for both hydrogen combustion and fuel cell systems in aviation. This ensures operational stability, system longevity, and energy efficiency [38]. Hydrogen must be stored at cryogenic temperatures, and without proper insulation and boil-off mitigation, significant energy losses can occur. Therefore, aircraft systems increasingly incorporate multi-layer insulation materials, vacuum-jacketed lines, and actively controlled cryogenic tanks to minimize thermal losses and vaporization during storage and transit [23]. [PERSON] et al. (2024) conducted a thermodynamic analysis of hydrogen-fueled turbojet engines and proposed a thermal management framework that leverages both heat and cold exergy for energy recovery, utilizing advanced heat exchangers and flow control systems to maximize efficiency [38]. Similarly, [PERSON] et al. (2024) emphasized the structural role of insulation in cryogenic hydrogen tanks, including material durability under temperature cycling and its contribution to overall system safety [4].  
Advanced energy recovery systems, such as thermoelectric generators and organic Rankine cycles, have also been proposed to recapture waste heat and enhance onboard energy conversion [38]. [PERSON] et al. (2024) reviewed progress in hydrogen transport systems and highlighted the importance of reducing boil-off through enhanced insulation and active cooling techniques [5]. [PERSON] et al. (2024) discussed integrated cryogenic management strategies for liquid hydrogen fuel systems in aircraft, including vaporization control, pressure regulation, and heat integration with turbine engines [26]. These innovations are crucial for achieving stable operation across varying flight conditions and optimizing the energy density of hydrogen systems. Together, these developments demonstrate a strong focus on integrated thermal design as a key enabler of hydrogen-powered aviation.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 1 Introduction
Several basis functions were explored in the field, and some of the popular ones include the Slater basis function showing cusps at the nucleus and exponential decay [7], the primitive Gaussian functions suggested by [PERSON] in the 1950s [8], and the Gaussian functions with contraction coefficients and exponents, also known as Contracted Gaussian Functions (CGFs) [9]. When the Hartree-Fock machinery uses Gaussian basis functions, the molecular orbital functions \(\{\psi_{i}\}\) are expressed as a linear expansion of Gaussian-type orbitals (GTOs). A GTO is a mathematical function used to approximate atomic orbitals in quantum chemistry calculations. It takes the form of a Gaussian function, which allows for efficient computation of integrals due to its simple analytical properties. Unlike Slater-type orbitals (STOs), which more accurately describe the exponential decay of atomic orbitals, GTOs facilitate faster integral evaluations. As a result, GTOs have been widely adopted in recent studies on quantum chemistry. As such, wave functions become computable from linear combinations of Gaussian-type orbitals (GTOs), and the computation of Basis-ERIs is reduced to the problem of computing GTO-ERIs.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications
### Marine Navigation  
Sydney Harbour by Locata Corporation, UNSW Sydney, and several New South Wales government agencies conducted a field trial in October 2012 to demonstrate Locata's positioning capability in harbour areas. Experiment analyses that were focused on Locata carrier phase processing and Locata/INS loosely-coupled integration systems [33], on GPS/Locata precise point positing [23], and on different data fusion algorithms applied to triple-integrated GPS/Locata/INS systems [45]. However, the publications that focused on evaluating the positioning performance at the central harbour areas where the Locata signals were relatively stable and there was high availability. Section 5.2 presents a detailed analysis of the Locata signal spatial characteristics and the positioning performance diversity within the whole test area.
## 3 Results
### Comparison with Existing Literature
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Modelo** & **nMBE [\%]** & **nRMSE [\%]** & **R\({}^{2}\)** \\ \hline _Locally evaluated models_ & & & \\ kNN CIESOL & 6.27 & 27.65 & 0.78 \\ LightGBM CIESOL & **1.65** & **22.30** & **0.86** \\ CatBoost CIESOL & 1.89 & **24.23** & 0.83 \\ ANN CIESOL & 2.07 & 31.91 & 0.71 \\ XGBoost ( Cyprus) & \(-5.86\) & 64.42 & 0.37 \\ LightGBM ( Cyprus) & \(-9.24\) & 68.08 & 0.39 \\ CatBoost ( Cyprus) & \(-6.68\) & 59.81 & 0.47 \\ _Extrapolated models_ & & & \\ kNN CIEMAT & 19.56 & 76.32 & \(-0.12\) \\ LightGBM CIEMAT & 2.27 & 69.18 & 0.08 \\ CatBoost CIEMAT & 8.71 & 70.87 & 0.03 \\ ANN CIEMAT & 4.01 & 66.12 & 0.16 \\ Kimber ( Cyprus) & \(-0.85\) & 61.01 & **0.56** \\ You ( Cyprus) & 50.01 & 73.95 & 0.43 \\ Coello ( Cyprus) & \(-5.19\) & **55.57** & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of normalised metrics and \(R^{2}\) for machine learning models.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 6 Conclusions  
In this paper, we presented our investigation of state-of-the-art models for transcribing printed mathematical formulas from images into spoken English text. This task holds particular relevance for the digital processing and comprehensibility of mathematical content. Our focus encompassed two approaches: (1) The integration of MER models and NLP models to convert formula images initially into LaTex code and then into text, and (2) the direct conversion of formula images into text using VL models. Since no suitable dataset could be identified that contained both printed mathematical formulas and the corresponding transcriptions, we created the new dataset _Formula2 Text_ to fine-tune and evaluate our systems. The best performance was achieved using the combination of the MER model _LaTeX-OCR_ and the NLP model _BART-Base_. This combination achieved a TER of 36.14%. The best VL model _TrOCR_ obtained a TER of 42.09%, resulting in a relative difference of 16.46% compared with the best model combination. Our work shows that decent results can be achieved even with a comparatively small dataset. It also demonstrates that VL models, which are primarily used for common image captioning tasks, have great potential for transcribing mathematical formulas in images--particularly since _TrOCR_ was able to outperform seven combinations of MER and NLP models, which represent 50% of the tested systems.