esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 2 Materials and Methods
### Analysis Workflow
The training dataset was employed to train a Random Forest (RF) algorithm, a robust ensemble-based supervised machine learning classifier known for its robustness against overfitting [43]. The selection of RF was based on its capacity to avert overtraining [44] and its demonstrated efficacy in coastal habitat mapping across diverse scales [45,46]. By leveraging multiple decision trees, RF effectively captures nonlinear relationships between classes, making it particularly effective for distinguishing _Posidonia oceanica_ from sedimentary and carbonate substrates [47]. We used 100 trees and allowed the maximum tree depth to effectively capture the complex relations of the seascape. In addition, we applied regression-based spectral unmixing [48, 49, 50, 51, 52], using the EnMAP toolbox, leveraging the RF model to estimate fractional cover for the _Cystoseira_ s.l. class, which constitutes a novel application for marine habitat mapping.
## 3 Results and Discussion
### Correlation Analysis  
As the \(I_{\text{C=O}}\) exhibited to have been the most sensitive to oxygen, the relationship of \(I_{\text{C=O}}\) to the adhesion of COBA-aggregate was analyzed before and after thermal oxygen aging, as shown in Figure 9.  
Figure 8: Functional group index of COBA.  
As illustrated in Figure 9, there is a high linear relationship between \(I_{\text{C=O}}\) of COBA and adhesion work between COBA and aggregates, and R\({}^{2}\) is mostly around 0.9. This indicates that the change of COBA oxygen-sensitive functional groups before and after thermal oxidation is well correlated with the adhesion property of COBA-aggregate. \(I_{\text{C=O}}\) can be used as the evaluation index of asphalt-aggregate adhesion under thermal oxidation. After short-term aging, the R\({}^{2}\) of \(I_{\text{C=O}}\) and adhesion work is low. This is likely because after short-term thermal oxidation, \(I_{\text{C=O}}\) increased with bio-oil content, and C=O with a higher concentration is easily converted to form alcohols and carboxylic acids, which makes the asphalt brittle. At the same time, C=O is prone to pyrolysis reaction at high temperatures, which makes the COBA molecular chain break, thus leading to asphalt aging and peeling. Notably, the increase in adhesion work between the COBA and aggregates is abrupt at 10% of bio-oil content, and the two follow an inconsistent trend change, so the correlation is low.
## 3 Discrete Event Simulation Model
### Buffer and Transportation Modeling  
System buffers have a certain capacity. The model accepts that, if a buffer is filled with products, it will not accept more products until the pallets are unloaded (the transportation events will not be carried out). In other words, if a machine does not have any place to drop the products, it will stop production.  
Each of the injection machines delivers to a buffer of two pallets, each containing 100 products, then a buffer of ten pallets feeds the painting station.  
Regarding transportation modeling, semi-elaborated product movements were modeled exclusively. During the modeling process, movements between the following were considered: (i) a machine and a buffer location; (ii) two machines; (iii) a buffer location and a machine; and (iv) two buffer locations. To mention transportation types, for Movements (i)-(iii), products are transported by the piece, while, for Movement (iv), products are moved using pallets. During the modeling process of these movements, a delay is introduced in the system. Therefore, at time \(t\), the element is at the initial point, whereas, at time \(t+delay\), it is at the destination point. To simplify the whole process, Movements (i)-(iii) are not modeled (delay = 0). Empty pallets are inserted into the system (and removed out of it) to transport products automatically and immediately. The lift truck transport is modeled with a delay having a uniform distribution range between 14.4 and 28.8 s.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
###### Abstract  
Acoustic microimaging (AMI), a technology for high-resolution imaging of materials using a scanning acoustic microscope, has been widely used for non-destructive testing and evaluation of electronic packages. Recently, the internal features and defects of electronic packages have reached the resolution limits of conventional time domain or frequency domain AMI methods with the miniaturization of electronic packages. Various time-frequency domain AMI methods have been developed to achieve super-resolution. In this paper, the sparse representation of AMI signals is studied, and a constraint dictionary-based sparse representation (CD-SR) method is proposed. First, the time-frequency parameters of the atom dictionary are constrained according to the AMI signal to constitute a constraint dictionary. Then, the AMI signal is sparsely decomposed using the matching pursuit algorithm, and echoes selection and echoes reconstruction are performed. The performance of CD-SR was quantitatively evaluated by simulated and experimental ultrasonic A-scan signals. The results demonstrated that CD-SR has superior longitudinal resolution and robustness.  
non-destructive testing and evaluation; acoustic microimaging; sparse representation; super-resolution Article
# Secure Localization in the Presence of Colluders in WSNs
## 6 Experimental Results
### Comparative Evaluation on Cost  
Figure 8 plots the average number of neighbours per location-unknown node, plus the 95% confidence intervals in each scenario. As expected, the number is always below 30 active neighbours In the uniform deployment simulations, the number of active neighbours per location-unknown nodes is always 12 neighbours This is because, in the uniform case, location-unknown nodes are always covered by four anchors and eight regular (non-anchor) location-known nodes. In the random case, there is no predetermined number of location-known nodes covering a location-unknown node. We recall that the topologies in the random deployment case consist of the random placement of exactly 80 non-colluders, 10 location-unknown sensors and an incremental number of additional location-known sensors acting as colluders. For random deployment, the curve in Figure 8 was computed by averaging the exact number of neighbours per location-unknown node in each of the 100 runs per simulation scenario (i.e., ten scenarios, from 10% to 90% colluders).  
As a function of the number of active neighbours per location-unknown node, we compare the cost of executing algorithms SCC and CC. Figure 9 shows the average cost per algorithm, plus the 95% confidence intervals. The cost is determined by the amount of exchanges that a location-unknown node needs to handle. The description of Algorithms 1 and 2 shows that such cost mainly depends on the number of active neighbours; that is, the cost is bounded by the number of processed replies (cf. Lines 2 and 8 of Algorithm 1 for SCC; and Lines 2 and 7 of Algorithm 2 for CC). We can see that the number of processed replies in the SCC algorithm is always slightly higher than the equivalent  
Figure 7: Random deployment.  
number of processed replies in the CC algorithm. This leads to the conclusion that the cost of SCC is always higher than the cost of CC. The experimental results depicted in Figure 9 show that the increase is generally reasonable.
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
###### Abstract  
The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.  
apple leaf disease; disease identification network; Xception; channel attention; lightweight network +
Footnote †: journal: Medical Image Processing  
**2021**, _11_, 4614. [[https://doi.org/10.3390/app11104614](https://doi.org/10.3390/app11104614)]([https://doi.org/10.3390/app11104614](https://doi.org/10.3390/app11104614))
# Cold and Thermal Neutron Single Grating Dark-Field Imaging Extended to an Inverse Pattern Regime
## 1 Introduction
The probed autocorrelation length \(\xi\) is defined as \(\xi=\mathrm{L_{s}}\lambda/p\)[7] and increases linearly with the wavelength \(\lambda\), the distance \(\mathrm{L_{s}}\) between the sample and the detector, and the reciprocal of the period \(p\) of the grating. In a quantitative dark-field imaging experiment, a set of measurements at multiple autocorrelation lengths is acquired to characterize microstructural features of the sample. The visibility \(V\) can be described as the normalized amplitude of the measured modulation produced by the gratings. For the SG approach, the visibility, for a sufficiently absorbing grating, is not influenced by the wavelength but only by the capability of the setup to spatially resolve the absorption pattern. The ratio between the visibilities \(V\) and \(V_{0}\) measured with and without the sample, respectively, delivers the dark-field contrast \(V/V_{0}\) of the sample. Hence, the precision and accuracy of the dark-field contrast relies strongly on achieving a high setup visibility \(V_{0}\). For a single absorption grating setup, this implies a trade-off between beam flux, controlled by the pinhole size used for the source collimation, and accessible autocorrelation length range. The latter is effectively limited by the distance between the absorption grating and the imaging detector, since the wavelength and the grating period are often fixed. To achieve suitable measurement conditions, the single grating needs to be placed relatively close to the detector, limiting the autocorrelation length range that can be probed through dark-field imaging. When the distance between the grating and the imaging detector increases, the blurring effect of the beam divergence degrades the visibility of the measured grating pattern. However, in this work, we show that after observing a visibility loss, the visibility increases again at sufficiently large separation distances and an _inverse pattern_ of the projected grating is observed. We demonstrate that this pattern allows for extracting attenuation and dark-field images that are consistent with the results from the non-inverse pattern. Exploiting the inverse pattern can hence significantly extend the accessible range of autocorrelation lengths for dark-field imaging.
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 4 Computing \(F\) in Metric Space Based on Geometric Distance
### 3D Metric Distance Criterion of \(F\)  
As mentioned above, almost all the computation of \(F\) uses the distance of feature pixel to epipolar line as criterion to discard outliers of features matching. However, our algorithm uses the 3D metric distance criterion and is detailed as followsCompared with the traditional methods of \(F\), the 3D metric distance criterion is more robust and has a similar performance on precision. It is the average distance between the projection of \(X\) on two image plains, \(P_{E}X\) and \(P_{E}^{t}X\), and feature pixels \([u,\ u^{\prime}]\) in Equation (11), denoted by \(\epsilon_{t}^{metric}\). If \(\epsilon_{t}^{metric}\) of one point \(X_{i}\) is less than threshold presented, it is an inlier. Then Equation (11) is designed to compute the average distance \(\epsilon^{metric}\), our 3D metric distance criterion of \(F\), with all \(N\) matching inliers.  
\[\epsilon^{metric}=\frac{1}{N}\sum_{i=1}^{N}\|u_{i}-P_{E}X_{i}\|+\|u_{i}^{ \prime}-P_{E}^{t}X_{i}\| \tag{11}\]  
This 3D metric distance criterion can be applied into any RANSAC framework of \(F\) computation to judge whether one feature matching is an outlier or not. This criterion is more robust and has better performance especially for the images satisfied the assumption that the optical center lies at the center of image, \([w/2,h/2]\). The validation of this criterion by experiment is discussed in Section 5.
## 2 Related Work  
One of the simplest ON algorithms is _Epidemic Routing_[9], a stateless and flooding-based epidemic protocol wherein messages behave as contagious diseases, infecting new devices when they are met. This method achieves the highest possible delivery rate and the lowest latency possible at the cost of a massive load on the network, as every forwarding opportunity is taken, and every node ends up with a copy of every packet.  
Various algorithms improve upon Epidemic Routing to reduce the network load. For example, _BSW_[10] is a version of _Spray and Wait_[11] which proposes the following method for controlling the propagation: first, messages have attached a \"number of copies\" attribute, a configuration parameter set at transmission time, which controls the total number of copies of the message in the network. Then, when an \"infected\" node meets a non-exposed one, half of the copies are handed over and half kept by the emitter. Once a copy count reaches 1, no more forwardings are performed until the message's final destination is encountered.  
Unlike _BSW_, which does not collect information on the network, _PRoPHET_[12] attempts to learn the best forwarders for different destinations. _PRoPHET_ is based on the exchange of distance tables between nodes in a process conceptually similar to _Distance Vector_ protocols. The main difference is that the distance is represented by a \"predictability\" or delivery probability. These predictability vectors are exchanged between nodes upon meeting. The predictability of a node as a target is reinforced when meeting it and is subject to exponential decay as time passes. Furthermore, it can be transitively reinforced: when node \(A\) meets node \(B\), then \(A\)'s predictability values for nodes in \(B\)'s table are reinforced. This transitive reinforcement is affected by the \(B\)'s own predictability. The basic idea is that nodes encountered more frequently have higher predictability, as are nodes reachable through high-predictability nodes.  
This process of learning better opportunistic routes can be attacked with other methods (for an overview of current algorithms, see [7]). We consider that machine learning techniques are a promising alternative for the following reasons:  
* ML is very efficient at detecting and capitalizing on patterns, which are the basis of efficient ON routing.
* ML effectiveness frequently depends on the availability of training data, which an ON can provide, as every packet can be considered an instance of the routing problem.
* ONs are complex systems with different components -such as mobility, data generation, and wireless interference- that are difficult to model and characterize. One of the ML strengths is that it can be applied without a deep understanding of the system's dynamics.  
In this work, we will discuss the application of reinforcement learning to ON routing.
## 2 Theoretical Calculation of Additional Internal Forces in Composite Foundation Piles and Soil  
Based on the results of three-dimensional finite element numerical calculations of composite foundations, this paper calculated the additional axial force on the cross-sectionof the target pile and the additional vertical force on the target soil caused by the top load. Specifically, the additional axial force of the pile was equal to the axial force of the pile when the top load was applied minus the axial force of the pile generated by the initial ground stress. The additional vertical force of the soil was equal to the vertical force of the soil when the top load was applied minus the vertical force of the soil generated by the initial ground stress.  
The main analysis steps were as follows:  
1. Three-dimensional modeling of composite foundations: A three-dimensional finite element model of composite foundations was established by finely constructing three-dimensional numerical models of concrete piles, inter-pile soil, cushion layers, and lower rock-soil structures.
2. Calculation of initial ground stress field: The initial ground stress field of composite foundations was calculated through the finite element method by simulating the consolidation deformation of ground soil due to the self-weight stress before construction. At this time, concrete piles were assigned the material of the corresponding soil layer.
3. Loading condition calculation: Concrete material parameters were assigned to the concrete piles, and the vertical loads generated by the upper buildings on the surface of the composite foundations were applied to simulate the actual loading conditions of the composite foundations.
4. Calculation of additional internal forces of piles and soil: Based on the results of the three-dimensional finite element numerical calculations, the additional axial force on the cross-section of the target pile and the additional vertical force on the target soil were calculated. Then, the proportion of the additional axial force of the pile was calculated.
# A Methodological Approach to Securing Cyber-Physical Systems for Critical Infrastructures
## 3 Open (Sub-)Challenges
### Monitoring Challenges  
**Ontology-based Monitoring.** As mentioned in Section 2, current research focuses on behavioral or ontology-based monitoring. However, despite the growing interest, some practical challenges still need to be addressed to make the monitor an effective tool for predicting and addressing behavioral patterns and properties. These issues can be summarized as follows:  
* _CH1: White-box and black-box assessment_: Distinguishing between white-box and black-box assessment processes is paramount. The objective is to gather internal execution data (white-box) from critical infrastructure components without delving into their source code structure (black-box). This methodology promotes transparency and facilitates the evaluation of both functional and non-functional properties, all while maintaining alignment with the principles of loose coupling and implementation neutrality.
* _CH2: Data collection vs. data management_: It is crucial to differentiate monitoring data collection features from those necessary for knowledge management and validation. This distinction is vital for smart collection classification and prediction, assisting in development and assessment activities. By clearly delineating these aspects, the monitor can enhance its ability to categorize and predict data effectively, contributing to a more innovative and informed approach to development and assessment tasks.
* _CH3: Managing countermeasures_: The knowledge management process enables the customization of manual or automatic countermeasures tailored to risk analysis results. This flexibility empowers the mitigation of vulnerability detection risks during monitoring activities. By incorporating this capability, the system gains the advantage of adapting countermeasures in response to the identified risks, enhancing overall security measures.  
**Advanced Secure Monitor.** A significant obstacle to virtual machine introspection is that it requires highly privileged access to the host system. Current hypervisors see the virtual machine as a black-box. As such, the information which the hypervisor can access is quite limited (this problem is known as a semantic gap). Monitoring through introspection a large application or a full VM software stack can prove overkill for performance, and its usage is very limited. [22] In fact, introspecting containers and Unikernels can also be hard, due to the compressed nature of their memory footprint. Nevertheless, the limited amount of occupied memory can also provide some benefits as it limits monitoring scope [54]. Further investigating these approaches is vital to allow for more protected and secure computations leveraging actual CPU capabilities.
## 3 Materials and Methods
### Remote Sensing Indices and Analytical Tools for Shoreline Monitoring
#### 3.2.1 Spectral Indices for Shoreline Delineation  
The Normalized Difference Water Index (NDWI) enhances the presence of water features in remotely sensed imagery. It is calculated using the formula:  
\[\text{NDWI}=(\text{G}-\text{NIR})/(\text{G}+\text{NIR}), \tag{1}\]  
where G is the green spectral band reflectance and NIR is the near-infrared spectral band reflectance. NDWI values close to +1 represent water bodies, whereas values close to 0 or negative are indicative of non-water surfaces. The NDWI is useful for distinguishing between water bodies and land masses, as well as for shoreline change detection. For example, the NDWI has been used to identify land masses versus water bodies in shoreline detection applications [33].  
The NDWI (primarily) and NDVI (indirectly) are indices broadly accepted as global standards for measuring vegetation and water body behaviors in coastal areas. However, in comparison with research based on high-resolution commercial satellites like WorldView, there is valuable insight that can be gained. WorldView satellites' ability to capture fine-grained coastal details, such as narrow coastlines, patches of vegetation, and finely grained anthropogenic structures, as seen in [PERSON] et al., 2022 [21], is unparalleled. Such detail at this level significantly increases effectiveness in detection accuracy and shoreline change analysis, particularly in urban or sediment-rich coastal areas where NDVI and NDWI values derived via lower-resolution satellites such as Sentinel and Landsat are at a disadvantage [5; 40]. While NDVI and NDWI are useful in large-scale analysis, effectiveness is lost when high spatial accuracy is required for analysis, where sub-meter resolutions are optimal for detecting subtle changes in vegetation or waterbody boundaries. Integrating high-resolution comparisons emphasizes how commercial satellite systems may complement or outshine open-access systems, particularly coastal monitoring applications [53].  
Other spectral indices, apart from the NDWI, have been proposed to enhance water feature recognition in different environments. The Modified Normalized Difference Water Index (MNDWI) developed by [PERSON] (2006) [75] substitutes the use of the NIR band in the NDWI with the Short-Wave Infrared (SWIR) band, allowing for better discrimination of water features, particularly in urban environments, to counteract the interference of built-up features in the water extraction process. Such is the case with the Automated Water Extraction Index (AWEI) by [PERSON] et al. (2014) [76], wherein several spectral bands are fused in order to better discriminate water from non-water features, such as shadows and dark targets, thus leading to better water extraction accuracy in complicated environments. The integration of these indices with NDWI can improve shoreline extraction accuracy in a wide range of coastal environments.