esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries and Problem Statement
### Network Model and Problem Definition
\right.\]  
The mathematical formulation of the moving object coverage problem is presented as follows:  
\[\min\sum_{k=1}^{m}\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{k}, \tag{1}\]  
\[\min\sum_{u,v\in M,\ u\
eq\left|\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{u}- \sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{v}\right| \tag{2}\]  
Subject to:  
\[\sum_{k=1}^{m}y_{i}^{k}=1\ \text{for every spot $i$ }\in\ V\backslash\{0\}, \tag{3}\]  
\[y_{0}^{k}=1\ \text{for every sensor $k$ }\in\ M, \tag{4}\]  
where each spot, except the initial base station, must be monitored by exactly one mobile sensor (3) and all mobile sensors must return to the base station (4). \[\sum_{j=1}^{n}x_{0j}^{k}=1\ \ \text{for every sensor $k$ }\in\ M, \tag{5}\]  
\[\sum_{i=1}^{n}x_{i0}^{k}=1\ \ \text{for every sensor $k$ }\in\ M, \tag{6}\]  
where all mobile sensors must start from the base station (5) and stop at the base station (6). \[\sum_{j=0}^{n}\sum_{k=1}^{m}x_{ji}^{k}=1\ \ \text{for every spot $i$ }\in\ V\backslash\{0\}, \tag{7}\]  
\[\sum_{j=0}^{n}\sum_{k=1}^{m}x_{ij}^{k}=1\ \ \text{for every spot $i$ }\in\ V\backslash\{0\}, \tag{8}\]  
\[\sum_{i=0}^{n}x_{iz}^{k}-\sum_{j=0}^{n}x_{zj}^{k}=0\ \ \text{for every spot $z$ }\in\ V\ \text{and for every sensor $k$ }\in\ M, \tag{9}\]  
where each spot must have exactly one incoming and outgoing route [(7)-(9)]. \[\sum_{z=1}^{h}x_{ij}^{k}=0\ \text{for every spot $i$, }j\in\ V_{z}\ \text{and for every sensor $k$ }\in\ M, \tag{10}\]  
\[\min\sum_{u,v\in M,\ u\
eq\left|\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{u}- \sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{v}\right| \tag{2}\]0  
where each mobile sensor cannot travel to the spot in the same subset of its current position (10) and it must travel to one spot in each subset of spots exactly once (11). \[\min\sum_{u,v\in M,\ u\
eq\left|\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{u}- \sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{v}\right| \tag{2}\]1  
where each mobile sensor cannot return to a previously monitored spot. \[\min\sum_{u,v\in M,\ u\
eq\left|\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{u}- \sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{v}\right| \tag{2}\]2  
\[\min\sum_{u,v\in M,\ u\
eq\left|\sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{u}- \sum_{i=0}^{n}\sum_{j=0}^{n}d_{ij}x_{ij}^{v}\right| \tag{2}\]3  
where spot \(j\) must be monitored by the mobile sensor that followed the path from spot \(i\) to \(j\) [(13) and (14)]. The two objective functions [(1) and (2)] represent the total traveled distance to be minimized and the total difference in the distance traveled of all mobile sensors to be minimized, respectively.
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Specific Attenuation Model for Rain, Fog, Snow, and Gases
#### 2.3.3 Snow  
The specific attenuation caused by snow is derived from the model presented in [15], as shown in Equation (8). Snow can be classified into dry and wet snow based on its specific liquid content [36]. This paper only considers the model for dry snow, with a default ambient temperature of 0 degrees Celsius.  
\[\gamma_{\rm snow}=0.00349\frac{R_{\rm snow}^{1.6}}{\lambda_{cm}^{4}}+0.00224 \frac{R_{\rm snow}}{\lambda_{cm}}\quad({\rm dB/km}), \tag{8}\]  
where \(R_{\rm snow}\) represents the snowfall rate in mm/h, typically ranging within 0-10 mm/h, and \(\lambda_{cm}\) denotes the wavelength in centimeters.
## 2 The Model
### Source Terms  
#### 2.3.1. Plume Entrainment  
Following CFAST [5], [PERSON]'s correlation [13] is used to evaluate the mass entrained by the fire/plume from the lower layer to the upper layer at a height z above the base of the fire. The equations of [PERSON]'s plume are as follows:  
* Above the mean flame height \(z_{L}\): \[\dot{m}^{ent}(z)=C_{1}Q_{\mathrm{C}}^{1/3}(z-z_{0})^{5/3}\Big{[}1+C_{2}Q_{ \mathrm{C}}^{2/3}(z-z_{0})^{-5/3}\Big{]}\] (11)
* At and below the flame height, mass flow rates in fire plumes have been found to increase linearly with height [14], from zero at the fire base to the flame-tip value, leading to: \[\dot{m}^{ent}(z)=\dot{m}^{ent}(z_{L})z/z_{L}\] (12) where \(Q_{c}\) is in kW and \(c_{p}\) in kJ/kg/K, \(z_{L}=D\big{(}-1.02+3.7Q^{*0.4}\big{)}\), \(z_{0}=D\big{(}-1.02+1.4Q^{*0.4}\big{)}\), and \(Q^{*}=Q/\big{(}c_{p}\rho_{\infty}T_{\infty}\sqrt{gDD^{2}}\big{)}\). \(D\) is the diameter of the fire source (or effective diameter for noncircular fire sources such that \(\pi D^{2}/4\) is the area of the base of the fire). The constants \(C_{1}\) and \(C_{2}\) are given by: \(C_{1}=0.196\big{(}g\rho_{\infty}^{2}/c_{p}T_{\infty}\big{)}^{1/3}\) and \(C_{2}=2.9/\big{(}\sqrt{g}c_{p}\rho_{\infty}T_{\infty}\big{)}^{2/3}\). The subscript \(\infty\) refers to ambient conditions. For most fire calculations, it is accurate enough to neglect the effect of the change of the molecular weight from that of air, so that density is determined primarily by its temperature: \(p_{\infty}T_{\infty}=353\) kg\(\cdot\)K/m\({}^{3}\)[15]. For weak plumes, a limit on the mass entrainment is introduced [5]: \(\dot{m}^{ent}(z)<Q_{\mathrm{C}}/\big{[}c_{p}(T_{u}-T_{l})\big{]}\).  
In [13], [PERSON] assumed that the plume velocity profile at a given height z, \(u^{p}(r,z)\) can be represented as a Gaussian function of the plume radius r. Therefore, the mean plume velocity \(\overline{u}^{p}(z_{i})\) at the interface height that appears in the momentum equation can be obtained by integrating the velocity Gaussian function between \(r=0\) and the plume radius where the gas velocity has declined to half the value at the centerline. This gives: \(\overline{u}^{p}(z_{i})=0.5\pi u_{0}^{p}(z_{i})\sigma_{u}^{2}(z_{i})\), where \(\sigma_{u}(z_{i})\) is a measure of the plume width at \(z=z_{i}\), as defined in [5].
## 2 Evolution of NMR Methodologies
### Multidimensional and Advanced NMR Techniques  
Multidimensional and advanced NMR techniques have significantly expanded the scope of polysaccharide research by overcoming the limitations of one-dimensional methods and enhancing spectral resolution. These methods are very important for studying complex molecular structures. They can also link structural modifications with functional results. Moreover, they can show the dynamic behavior in heterogeneous systems. For example, 2D and 3D experiments like COSY, HSQC, TOCSY, and NOESY, candistinguish overlapping signals. In addition, advanced methods such as ordered diffusion spectroscopy (DOSY) are available for characterizing molecular interactions and diffusion dynamics [6; 47].  
Beyond structural elucidation, multidimensional NMR is also useful for studying how molecules in polysaccharides interact. For example, it can be used to study the gel, hydration, or stable interactions in food matrices. Techniques like DNP-enhanced NMR have also greatly improved the sensitivity. This improvement allows us to study the low-abundance components in complex systems [5; 8]. These advances have made multidimensional magnetic resonance a key part of polysaccharide research. It combines research at the molecular level with practical uses in food, biomedical, and environmental sciences.
## 4 Material and Methods  
We did a review of papers published in specialized Journals from 2015 to 2020, checking algorithms, classes, samples, overall accuracies (OA), and others. Some classical references of concepts, and the reference of VIs were incorporated. Our information sources were the following databases: AGORA (FAO); Current Contents-Physical Chemical & Earth Sciences (Clarivate Analytics); DBLP Computer Science Bibliography (Universitat Trier); DOAJ--Directory of Open Access Journals; Ei Compendex/Engineering Village (Elsevier); Genomics JournalSeek; HINARI (WHO); Inspec (IET); Journal Citation Reports/Science Edition (Clarivate Analytics); Science Citation Index Expanded--Web of Science (Clarivate Analytics); Scopus (Elsevier), and Web of Science (Clarivate Analytics).  
In order to perform the protocol and registration to integrate the papers, linking sources, eligibility criteria, selection, and data collection, we adopted the \"Preferred Reporting Items for Systematic reviews and Meta-analyses\" (PRISMA) [53] and the \"Protocol, Search, Appraisal, Synthesis, Analysis, Research Protocol, and Reporting results\" (PSALASAR) [54] methods. The search strategy involved the determination of hierarchical topics to guide the selection of papers. We organized them in the Mendeley platform ([[https://www.mendeley.com/](https://www.mendeley.com/)]([https://www.mendeley.com/](https://www.mendeley.com/))) considering the following topics: \"Land use and land cover mapping\", \"Sentinel-2 and Landsat 8 time series\", \"Pixel- and Object-based image analysis with Sentinel-2 and Landsat 8\", \"Landsat 8 and Sentinel-2 integration\", \"Red-edge and SWIR-associated vegetation indices\", \"Random Forest (RF)\", and \"Support Vector Machine (SVM)\". We designed a schematic process based on [PERSON] et al. [26] to filter, select, and organize the papers (Table 2).  
We selected the papers according to their scope and objectives, and characteristics such as the geographic area mapped, sampling strategy, number of samples and classes, classification method, and accuracy. We especially selected papers of the agricultural or natural vegetation domain because of their dynamics and heterogeneity, favoring innovative and reproducible methods that permit the reasoning about landscape dynamics, considering the capability to distinguish different classes. We previously selected 989 papers, and we included 193 of them in this systematic review (Figure 1).  
Based on [PERSON] et al. [55], a text mining analysis through term network extraction using the VOSviewer software was performed on 193 recent papers to analyze the evolution of the research fields in the evaluated period. Including classical references, we cited 221 papers and 2 web sites.
## 2 Materials and Methods
### Multi-Layer Perceptron (MLP) Architecture  
The MLP architecture consisted of seven fully connected/dense layers, which alternated with dropout layers (i.e., dropout was applied before every weight layer in the MLP), as depicted in Figure 3. Dropout was used to regularise the network and prevent the latter overfitting to the training data [34]. The hidden units in all layers of the network (except the final layer) employed Rectified Linear Units (ReLU) as non-linear activation functions. Weights in all dense layers were initialised using the He normal method (kernel_initialiser = \"he_normal' in Keras). Bias terms were initialised to small constant values, typically zero, which is standard practice for ReLU-based networks.  
The final layer of the MLP comprised a single output unit with a sigmoid activation function, producing a single scalar value for the growth rate of each AAA. The regression-MLP was trained using the standard back-propagation algorithm, by minimising the mean-squared-error loss function (evaluated between the ground truth and predicted AAA growth rates) using the root-mean-squared gradient propagation (RMSprop) optimiser (with an initial learning rate = 0.01) [35]. This optimiser was chosen empirically as it provided the best performance throughout cross-validation. The same network architecture was employed for all experiments.  
Figure 2: A flow diagram illustrating the machine learning (ML) framework: AAA and aortic arch blood pool regions are segmented for texture analysis, with radiomic features extracted from both. A heatmap (green, yellow and red grid) visualises texture features, such as Grey-Level Co-occurrence Matrix (GLCM) metrics. Dimensionality reduction (top right), as described in Section 2.5, uses Recursive Feature Elimination (RFE) based on Random Forest regression to select 29 features from an initial set of 93 radiomic features (details provided in Table 2). Both full and reduced feature sets are then input into a neural network-based ML model (bottom right) to predict AAA growth rate. The final outputs predict aneurysm growth rate.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Impact of Training Dataset Size on Accuracy
#### 4.6.2 Accuracy in Function of the Train/Test Split Ratio  
This second analysis focuses on assessing the impact of the train/test split ratio on model accuracy using datasets from different EDFAs sourced from multiple vendors. The goal is to evaluate how the size of the measurement dataset influences the model's performance. Rather than using datasets from separate devices, the models are built by merging the six available measurement datasets and varying the proportion of data allocated to training and testing. The results, shown as the cumulative distribution of absolute error, are presented in Figure 20. The results in the figure refer to the Cisco L-band EDFA device, which is used as a reference for this analysis. Figure 20a and, in detail, Figure 20b, demonstrate that model accuracy remains stable across different train/test split ratios. The curves largely overlap, indicating minimal performance variation, except for the 1% test size case (in blue), which shows a slight improvement in the zoomed-in region. Figure 20c, which illustrates the 90%- and 99%-thresholds as a function of the test size, further supports the observation that the absolute error remains consistent across different splits.  
Based on these findings, along with the results from the previous section, we conclude that the most effective strategy for achieving good model performance while minimizing the time required for EDFA power density spectrum measurements is to use data from multiple devices, even if the datasets are relatively small. For instance, increasing the step  
Figure 19: Cumulative distribution of the absolute error for the L-band EDFA device, as a function of the number of training datasets. **(a)** Overview of absolute errors; **(b)** Zoomed-in view of the \(y\)-axis region between 80% and 100%; **(c)** 90% and 99% error thresholds versus the number of datasets.  
Figure 20: Cumulative distribution of the absolute error for the L-band EDFA device, as a function of the train/test split ratio. **(a)** Overview of absolute errors; **(b)** Zoomed-in view of the \(y\)-axis region between 80% and 100%; **(c)** 90% and 99% error thresholds versus test size.  
size of total input power, gain, and tilt can help reduce the number of required power spectrum measurements. However, reducing the number of devices tested would not be advisable, as data diversity across different devices contributes significantly to model accuracy and generalization.
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 4 Discussion
This results in a significant number of additional over-detections for \(I_{VV}\) and \(I_{VH}\) that are not detected by \(C_{VV-VH}^{un-norm}\), the latter presenting a reduced number of false alarms, as discussed in Section 3.2, where the visual analysis shows that the coherence is more immune to false alarms than CFAR. Figure 8: Different Sentinel-I SAR vessel signatures represented in the \(C_{VV-VH}^{un-norm}\), \(I_{VV}\) and \(I_{VH}\) images.
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
## 5 HEAD Metamodel: Development Approach to Access Control in Dynamic and Heterogeneous Environments
### Metamodel Development
#### 5.1.2 HEAD Metamodel: Meta-Components and Relationships  
The unified AC components of the previous step have been used to develop the needed metamodel. The meta-components \(E_{x}\), \(I_{m}\) (AU and PU), and \(S_{t}\) and the relationships between them form the HEAD metamodel. As shown in Figure 17[3]:  
* The relationship between \(E_{x}\) and AU is to assign, for example, zero or many (0..*) subjects to roles, groups, categories, or any other authorization unit.
* The relationship between AU and PU, and PU and \(E_{x}\), is to represent which AUs are able to perform zero or many PU (e.g., actions, permissions, etc.) and access some, for example, objects or services.
* \(E_{x}\) and \(I_{m}\) components might have zero or many \(S_{t}\) (e.g., contextual and/or non-contextual constraints) before accessing/performing tasks on some other \(E_{x}\) components.
* The self-association edge exists on each of the classes to allow formulating AC models and hybrid models for different policies by allowing AUs to be associated with other AUs, PUs to be associated with other PUs, and so on.
* The hierarchical relationships are depicted by aggregation association for each of the entities, \(E_{x}\), AU, PU, and \(S_{t}\), to create a hierarchy of classes.  
The importance of the HEAD metamodel is that it even allows defining any new component/attribute for non-existing model(s) to formulate new model(s), besides its ability to derive the common models.  
Figure 16: An example of hybrid policy.  
Figure 17: HEAD metamodel [3].
# Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants
## 2 Materials and Methods  
### Materials  
To apply the IR inspection strategy posed in this study, two different thermographic cameras will be used that are suitable for each of the two stages. Stage one of the inspection with aerial IR thermography employs a Workswell WIRIS 2 nd-gen 336 camera installed on a drone [27] as shown in Figure 1. This camera with an uncooled microbolometer detector has a spectral range of 7.5-13.5 um, a resolution of 336 \(\times\) 256 pixels and weighs less than 400 g. This inspection entails an initial sweep to detect relevant incidents quickly. The drone used was an ATYGES FV8 octocopter. Flight plans were programmed and executed at heights of 15 m, flight speeds from 1 to 2 m/s and a thermographic recording interval of 2, 3 and 5 s. To calculate the maximum inspection height, the thermal imaging camera was equipped with a 45\({}^{\circ}\) FOV lens, with a 9 mm focal length and an IFOV of 1.889 mrad. The maximum spatial resolution is 3 cm per pixel, as required in specification IEC-62446-3 [12]. Therefore, IFOV \(\times\) h (m) = pixel-side size (mm), resulting in a maximum length of 15 metres from the PV modules.  
Stage two of the inspection is done by ground-level IR thermography, but only for the most relevant faults. This second sweep was done with a ThermoCam S60 [28]. This camera, equipped with an uncooled microbolometer detector, has a spectral range of 7.5-13 um, a resolution of 320 \(\times\) 240 pixels and a weight of 2 kg. The thermographic camera is positioned facing the PV module at an angle that prevents the influence of solar reflection during the test.
## 2 Materials and Methods
### Vulnerability Assessment  
The vulnerability assessment methodology used was based on the identification of the components of earthen buildings and their susceptibility. The latter is defined as the potential damage that each component may suffer as a result of a flood, considering its main physical characteristics. Thus, flood vulnerability can be defined as the degree to which an asset is susceptible and unable to cope with the adverse effects of flooding. The estimation of the susceptibility was the result of a study of the existing literature on the characterization of the earthen architecture and the effects of the action of water on its materials [1, 22, 37]. The construction characteristics of the asset and the relationship with its environment was considered, as well as the state of conservation, which represents an influencing factor for potential damages. Each susceptibility factor was assigned a value of 1 to 5, depending on its response to the action of floodwater, where the value of 1 corresponds to a low susceptibility and 5 to a high susceptibility. The global resistance of a wall highly depends on its construction technique. The more a wall is monolithic and homogeneous, the more it will have resources of resistance against the action of horizontal hydrostatic pressure that generates overloads, proportionally, to the height of the water. Thus, a minimum susceptibility value (1) was assigned to rammed earth walls, whereas adobe and half-timber walls were assigned medium (3) and high (5) values. In addition, the different variants of each construction technique were assigned values between 1 and 5, according to the previous consideration. The metric characteristics of the building (footprint and number of floors) represent the size of the building and its responsiveness to water drag. The position of the building related to the ground level, as well as its building type, have a great influence on the water drag and absorption. The function of the plinth at the base of the walls is also considered relevant. This construction element guarantees the separation of the walls from the ground, allowing to reduce the action of rising damp. However, the insulation provided by the plinth changes depending on its material. Ashlars plinths have a high cohesion and thin joints, thus their susceptibility to water is low, while brick and masonry plinths have a greater absorption rate which results in higher susceptibility values. The presence of an additional protection and of rendering can improve the resistance of the structure, according to the hygrometric characteristics of the material. The state of conservation of the buildings plays a crucial role. The more a material is deteriorated the more susceptible to the impact of water. Erosion processes and cracks facilitate the penetration of water and humidity, increasing the susceptibility. Susceptibility values were corrected by weighting factors, obtained by consulting the opinion of 43 experts and applying the Delphi method [32, 33]. The experts were selected from various fields (including architecture, engineering, restoration, geology, heritage,
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 2 Related Works  
Path planning algorithms can be categorized into two, namely _offline_[7; 8; 12; 13; 14; 15; 16; 17; 18] and _online_ schemes [19; 20; 21; 22; 23; 24; 25; 26; 27]. The offline path planning schemes are based on conditional restrictions to determine the globally optimal path because a MAN visits the path and the location of the broadcast has been pre-planned. Thus, the computational capability of a MAN is low, and the number of broadcast times of a MAN can be optimized, thereby facilitating the reduction of energy consumption of the MAN and unknown nodes. The online path planning schemes are based on observable environments or sensor deployment situations. A MAN dynamically adjusts the feasible path to satisfy the system requirements, although online path planning has considerable flexibility. A MAN must have high computing capability because it must make decisions (e.g., move path trajectory or broadcast timing) during movements. In addition, the online path planning method can only provide the optimum solution locally, thereby failing to guarantee that all the unknown nodes can receive three location information of the MAN to estimate their locations [2]. Table 1 summarizes the comparison between the offline and online path planning algorithms for MANAL.