esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based System for Efficient Detection of Cotton Pest
## 1 Introduction
### Related Work
#### 1.1.1 Pest Monitoring Applications  
Many agricultural pest detection applications using WSN have been proposed [3; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18], some of the most effective applications are discussed here:  
Red Palm Weevil Detector is a complete system to monitor and detect Red Palm Weevil (RPW) caterpillar [7]. It uses an acoustic sensor device to detect RPW based on the noise-generating habit of these caterpillars. Another team of researchers suggested a self-sufficient protection system [8] for palms from their RPW larvae. Some reviews [3; 5; 6; 9] proposed acoustic devices as detection technology while the other research studies [13; 16] are based on optical devices. Researchers in [16] designed a sensor and proposed a sensing mechanism with a detection algorithm to monitor Xylophagous insects in wood, especially termites. Authors in [14] proposed a photonic fence (PF) system, in which they suggested insect monitoring and control by implementing advanced mid-range insect tracking and identification technologies. Recently, some optoelectronics sensors were used to monitor underground soil micro-arthropods [16], but nowadays many types of sensors and many solutions have been proposed [17] that are based on a combination of these technologies. An IoT-based cotton whitefly prediction system was developed and proposed in [18] that worked on Radial Basis Function Network (RBFN) algorithms and used a deep learning technique to predict the existence of the cotton whiteflies timely.
## 2 Proposed Simulation Framework
### Module 1: High-Efficiency Modeling of Temporary Hospitals
#### Input of model information  
This function allows users to input model information such as the parameters of the mean wind speed profile in the UBL, the vertex coordinates of the geometric model, the coordinate and ventilation volume of each air outlet, the concentration of harmful air in the exhaust air from each air outlet, and the locations of monitoring points and slices.  
The ventilation volume, \(V_{R}\), of each air outlet can be easily calculated based on the design drawings, and the concentration of harmful air in the exhaust air from each air outlet can be obtained through the following method. Generally, two patients share a ward in a COVID-19 temporary hospital. The average rate of breathing air from a seated person is reported to be between 0.27 and 0.33 m\({}^{3}\)/h, with significant variation due to physiological activity conditions [1]. In this study, the reference breathing rate, \(V_{P}\), of a COVID-19 patient is assumed to be 0.30 m\({}^{3}\)/h. This value was also adopted by [PERSON] et al. [1] for the simulation of indoor SARS transmission. Thus, the total amount of harmful air exhausted by patients in \(N\) wards sharing one air outlet is \(2V_{P}\)-\(N\). Considering the positive effect of the filtration devices installed at the air outlets, the mass ratio of harmful air in the exhaust air from one air outlet can be calculated through Equation (1):  
\[R=2\beta NV_{p}/V_{R}, \tag{1}\]  
where \(\beta\) is a coefficient reflecting the filtering capacity of the filtration device for harmful air.  
Mesh generation  
This feature allows users to design specific grid schemes, including a single-resolution grid and a multi-resolution grid.  
Reading, writing, and visualization  
The \"reading\" function enables users to read the pre-constructed building geometry model or the completed FDS model in the program, as well as other information required for the CFD simulation (i.e., coordinates and ventilation volumes of air outlets, and locations of monitoring points and slices) in pre-defined formats. The \"writing\" function allows users to generate the FDS model file based on the information entered, and output simulation results at monitoring points. The \"visualization\" feature enables users to observe the constructed objects (i.e., building shapes, as well as locations of air outlets and monitoring points) in a specific window on the right side of the GUI. Additionally, users can freely delete incorrect objects through this function.  
Based on the FDSGenerator program, the corresponding high-efficiency modeling methods for different design phases are proposed and will be introduced in detail in the following sections.
## 3 Calculation of Mechanical Variables Using a Strong Form-Based Meshfree Method
### Particle Difference Method (PDM) Approximation
Assuming that the number of nodes included in the influence domain of the reference point \(\mathbf{y}\) is N, and using the residual functional of the moving least squares method, the relationship between the approximation of Equation (7) and the nodal solution \(u_{il}\ =\ u_{i}(\mathbf{x}_{l})\) can be given as follows:  
\[J\ =\ \sum_{I\ =\ 1}^{N}\omega_{I}(\mathbf{y})\Big{\{}\mathbf{p}_{m}^{T}( \mathbf{x}_{l},\mathbf{y})\ \mathbf{c}_{i}(\mathbf{y})-u_{il}\Big{\}}^{2}, \tag{8}\]  
where \(\omega_{I}(\mathbf{y})\ =\ \omega\Big{(}\frac{|\mathbf{x}_{l}-\mathbf{y}|}{r} \Big{)}\) denotes the weight function, and \(r\) denotes the radius of the influence domain that determines the number of nodes included in the approximation construction. Then, \(\mathbf{c}_{i}(\mathbf{x})\) can be found by minimizing Equation (8) as follows:  
\[\mathbf{c}_{i}(\mathbf{x})\ =\ \left(\sum_{I\ =\ 1}^{N}\omega_{I}(\mathbf{x}) \ \mathbf{p}_{m}(\mathbf{x}_{l},\mathbf{x})\ \mathbf{p}_{m}^{T}(\mathbf{x}_{l},\mathbf{x})\right)^{-1}\cdot(\omega_{1}( \mathbf{x})\ \mathbf{p}_{m}(\mathbf{x}_{1},\mathbf{x}),\ \cdots\,\omega_{N}(\mathbf{x})\ \mathbf{p}_{m}( \mathbf{x}_{N},\mathbf{x}))\cdot\mathbf{u}_{i}^{h}, \tag{9}\]  
where \(\mathbf{y}\) of \(\mathbf{c}_{i}(\mathbf{y})\) in Equation (8) is replaced with \(\mathbf{x}\), and \(\mathbf{u}_{i}^{h}\ =\ (u_{i1},\cdots,\ u_{iN})^{T}\) is the vector of the nodal solution included in the influence domain. According to the definition of the Taylor polynomial, \(\mathbf{c}_{i}(\mathbf{x})\) contains all the derivative approximations from the zero-th to the \(m\)-th order, and is written in the matrix form as follows:  
\[\mathbf{c}_{i}(\mathbf{x})\ =\ \left( \begin{array}{c}D_{\mathbf{x}}^{\alpha_{1}}u (\mathbf{x})\\ \vdots\\ D_{\mathbf{x}}^{\alpha_{K}}u(\mathbf{x})\end{array} \right)\simeq\left( \begin{array}{ccc}\phi_{1}^{[\alpha_{1}]}(\mathbf{x})&\cdots&\phi_{N}^{[ \alpha_{1}]}(\mathbf{x})\\ \vdots&\ddots&\vdots\\ \phi_{1}^{[\alpha_{K}]}(\mathbf{x})&\cdots&\phi_{N}^{[\alpha_{K}]}(\mathbf{x })\end{array} \right)\cdot\left( \begin{array}{c}u_{i1}\\ \vdots\\ u_{iN}\end{array} \right) \tag{10}\]  
where \(D_{\mathbf{x}}^{\alpha}\ =\ \partial_{x_{1}}^{\alpha_{1}}\cdots\partial_{x_{n}}^{ \alpha_{n}}\) is the differential operator expressed in a multi-index, and \(\phi_{I}^{[(0,\ldots,0)]}(\mathbf{x})\) and \(\phi_{I}^{[\alpha_{K}]}(\mathbf{x})\) represent the zero-th order shape function and the \(\alpha_{K}\)-order shape function, respectively, for node \(I\) at point \(\mathbf{x}\). Note that the actual differential calculation is not required, and the derivative approximation of all orders can be calculated at once in the process of calculating the unknown vector \(\mathbf{c}_{i}(\mathbf{x})\) for obtaining the discrete version of the Taylor series. In this study, the new PDM method proposed by [PERSON] et al. [33] was applied, and approximations only up to the first differential were used. [PERSON] et al. [33] can be referred to for a more detailed derivation process.
###### Abstract  
In this article, we present a link budget analysis for Global Navigation Satellite System (GNSS) signals scattered off the sea surface in arbitrary acquisition geometries. The aim of our study is to investigate the reliability of the Geometrical Optics (GO) scattering model, which accurately describes sea surface scattering at and near the specular reflection direction, in properly modeling the sea surface return in far-from-specular acquisition geometries, which are of interest for maritime surveillance purposes and where GO is expected to fail. To this end, we adopted the recent Bistatic Anisotropic Polarimetric Two-Scale Model (BA-PTSM), which revealed good agreement with advanced scattering models, such as the second-order Small Slope Approximation (SSA2), regardless of the acquisition geometry, with the advantage of a reduced computational complexity. Numerical results have been derived for both circular polarization channels and for both spaceborne and airborne GNSS-Reflectometry (GNSS-R). It has been shown that, as long as conventional GNSS-R processing is assumed, GO can be safely adopted for simulation and analysis of spaceborne GNSS-R data regardless of the acquisition geometry and sea state, whereas more accurate scattering models, e.g., BA-PTSM, should be used for airborne receivers in far-from-specular configurations.  
GNSS-Reflectometry; sea surface scattering; bistatic radar; two-scale model; link budget analysis [table]capposition=top
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Case Studies of Cyber-Physical Attacks
#### 3.1.1 The Zotob Worm  
In August 2005, 13 Daimler Chrysler car manufacturing plants (among over 170 other major corporations) were attacked by a worm called Zotob. The worm caused a temporary (5 to 50 min) shutdown of the entire production line in each of these plants, affecting more than 50,000 production personnel, and resulting in financial damage in the order of thousands of US dollars, apart from the financial damage caused by the loss of the man-hours of more than 50,000 personnel [24].  
The Zotob worm came into effect shortly after Microsoft announced a security vulnerability in the Windows 2000 operating system. Within a matter of days from the announcement of the vulnerability, the code for the Zotob worm had already been developed and distributed within the computer networks of several institutions before IT security personnel could apply the required security patch. In particular, the Zotob worm exploited the Microsoft Windows Plug and Play Buffer Overrun Vulnerability on TCP port 445, which opened a back door to the operating system (OS) that enabled the hacker to perform malicious actions with full user rights and privileges [25; 26]. The vulnerability also made it possible for the hackers to install malicious software on the operating system. From there on, the worm cold replicate and progress further into the computer network, and thus affect any systems connected to the manufacturing network. This is exactly what happened in the attack on the Daimler Chrysler manufacturing plants.  
The Zotob worm successfully illustrated how fast the black hat hacking community can respond to the announcement of a security vulnerability before any preventative action can be taken by cyber-security personnel in a particular industry.
# Pneumatically Actuated Microfluidic Platform for Reconstituting 3D Vascular Tissue Compression
###### Abstract  
In vivo, blood vessels constitutively experience mechanical stresses exerted by adjacent tissues and other structural elements. Vascular collapse, a structural failure of vascular tissues, may stem from any number of possible compressive forces ranging from injury to tumor growth and can promote inflammation. In particular, endothelial cells are continuously exposed to varying mechanical stimuli, internally and externally, resulting in blood vessel deformation and injury. This study proposed a method to model biomechanical-stimuli-induced blood vessel compression in vitro within a polydimethylsiloxane (PDMS) microfluidic 3D microvascular tissue culture platform with an integrated pneumatically actuated compression mechanism. 3D microvascular tissues were cultured within the device. Histological reactions to compressive forces were quantified and shown to be the following: live/dead assays indicated the presence of a microvascular dead zone within high-stress regions and reactive oxygen species (ROS) quantification exhibited a stress-dependent increase. Fluorescein isothiocyanate (FITC)-dextran flow assays showed that compressed vessels developed structural failures and increased leakiness; finite element analysis (FEA) corroborated the experimental data, indicating that the suggested model of vascular tissue deformation and stress distribution was conceptually sound. As such, this study provides a powerful and accessible in vitro method of modeling microphysiological reactions of microvascular tissues to compressive stress, paving the way for further studies into vascular failure as a result of external stress.  
blood vessel compression; biomechanical stress; microfluidic chip; pneumatically actuated valve; perfusable blood vessel +
Footnote †: journal: Applied Sciences
## 4 Results
### Gravel Treated with Ash and Lime
#### 4.2.3 Variation in Unconfined Compressive Strength (UCS)  
The unconfined compressive strength of gravels treated with different materials is a decisive factor in the dimensions of the different base and sub-grade layers.  
The results in Table 7 show the special behavior of this ash and lime mix. For values of added ash of 15% and a 28-day curing period, simply by adding 3% lime, the maximum strength can be obtained. Moreover, it can be seen that simply adding lime does not increase the strengths, that is, the ash needs a specific proportion of lime, with respect to its own added value, to enable activation. Given that an increment of between 2 and 3% of added lime does not lead to a great variation in the compression strengths, and bearing in mind that for values of 4% of added lime the strengths start to decrease, the optimum value of added lime for a mix with 15% of added ash would be between 2 and 3% of lime. Even so,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Maximum Density (KN/m3)**} & \multicolumn{2}{c}{**Optimum Wet (\%)**} \\ \hline  & \multicolumn{2}{c}{**Lime Content**} & \multicolumn{2}{c}{**Lime Content**} \\ \hline Gravel & 0\% & 2\% & 3\% & 0\% & 2\% & 3\% \\ G-00\% & 22.20 & 22.05 & 21.93 & 6.00\% & 6.90\% & 7.50\% \\ G-10\% & 21.03 & 21.32 & 20.71 & 7.65\% & 7.80\% & 8.15\% \\ G-15\% & 20.80 & 20.50 & 20.49 & 8.60\% & 9.20\% & 8.75\% \\ G-20\% & 20.04 & 19.99 & 19.98 & 9.72\% & 9.10\% & 9.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum densities and optimum wet. Gravel Treated with Ash and lime.  
Figure 6: Particle size analysis for gravels.  
a slightly better compression value is found with mix G-152 than with G-153 for a curing period of 90 days. Moreover, the optimum value of added lime does not depend only on the bottom ash added, but also on the curing time. In fact, for curing periods of 90 days, the optimum is about 20% of added bottom ash and 3% of added lime. As we add more bottom ash, it is necessary to add more lime and the curing time must be increased to obtain the optimum value. Therefore, the optimum ratio of added bottom ash to lime is 6.5.  
Analyzing the results, as well as the simple compression strength, the modulus of the material can be obtained, demonstrating the evolution of the modulus with the curing time of the mix (Table 8). The values of the modulus increase significantly after 28 days, obtaining an increase between 28 and 90 days of 300%, where the activation of the ash with the lime over an increasing curing time can be seen.  
Comparing these results with the values obtained in two different studies for the case of fly ash, [21; 22] similar values can be seen with respect to simple compression strength.
## 2 Related Work
### Handcrafted vs. CNN-Based for Image Classification  
With their deep learning architecture, convolutional neural networks (CNNs) learn from the data directly and deliver highly accurate recognition results. CNNs can extract, and process features internally to perform tasks such as image classification, object detection, and recognition.  
Comparisons of classification results using these methods (handcrafted and CNN-based) have been studied. In [18], the handcrafted methods LBP and HOG are compared with the deep features for the classification of histopathology images, with the LBP method giving the best results. On the other hand, in [19], the classification accuracy obtained using neural networks was 22% higher than that obtained using various manual methods for ear recognition. In [20], the fusion of features derived from both methods seems to perform better than each case separately for identifying the adequacy of contrast-enhanced magnetic resonance liver images. In [21], eighteen datasets containing images from various categories, ranging from medical and subcellular to butterfly species, materials, flora, smoke images, paintings, etc., are classified using both deep learning-based and handcrafted features. The former includes principal component analysis network (PCAN) and the compact binary descriptor (CBD), as well as transfer learning methods. The latter includes the use of the methods local binary pattern (LBP) and eight variants of it, local ternary pattern (LTP), and local phase quantization (LPQ). Dimensionality reduction in the features extracted from the CNNs was also carried out with the discrete cosine transform (DCT) and principal component analysis (PCA) methods. The comparison between the handcrafted and non-handcrafted features and their combination showed that the two feature extraction systems provide different information, and therefore the fusion of handcrafted features with the CNN-based outperforms the standard approaches. In our case study, the fusion of the features of the two methods resulted in the classification accuracy taking an intermediate value between the results of the two methods separately. Classification accuracy results of the bag-of-visual-words (BoVW) model, CNN-based features, and transfer learning on AlexNet are compared in [22], with the last one to outperform. The classification error rate on fingerprint images for fingerprint liveness tasks with handcrafted and deep features has been studied in [23]. The handcrafted features outperformed under the within-dataset category, while on cross-sensor evaluation, deep features obtained higher accuracy but handcrafted lower misclassification rate.
# Example usage calculate_urban_resilience('input_file_name')
### Monitoring and Reviewing Risks  
The process of monitoring and reviewing risks in the context of enhancing urban resilience, particularly regarding earthquake hazards in Portugal, is very important. This involves the continuous assessment of implemented measures and their effectiveness. To ensure that urban areas remain resilient against seismic activities, it is important to regularly revisit and update the risk management strategies based on new data, technological advancements, and changing environmental conditions. Regular monitoring can be achieved through various methods, including: (i) data analysis; (ii) technology utilization; (iii) stakeholder feedback; and (iv) performance metrics  
Data analysis includes utilizing updated earthquake and demographic data to reassess risk levels in different municipalities. Changes in population density, infrastructure developments, or recorded seismic activities can significantly alter the risk landscape. Technology utilization includes employing advanced technologies such as seismic monitoring systems and GISs to track seismic activities and their impact on urban areas. Stakeholder feedback involves gathering input from local authorities, emergency services, and residentsto understand the practical effectiveness of current resilience measures. Finally, performance metrics allow for the establishment of clear performance indicators to measure the effectiveness of the implemented resilience strategies. These metrics should align with the initial goals and objectives of the urban resilience plan.
# Streaming-Based Anomaly Detection in ITS Messages
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AUC-ROC & Area under the curve of the receiver operating characteristic \\ AUCPR & Area under the curve of precision-recall \\ CAM & Cooperative awareness message \\ C-ITS & Cooperative intelligent transport systems \\ ELSCP & Enhanced locally selective combination in parallel outlier ensembles \\ ETSI TS & European Telecommunications Standards Institute Technical Specifications \\ FDM & Filter-discovery-match \\ FP & False positive \\ FPR & False positive rate \\ FN & False negative \\ GPS & Global positioning system \\ HBOS & Histogram-based outlier score \\ IForest & Isolation forest \\ ITS & Intelligent transportation systems \\ IoT & Internet of Things \\ KD tree & K-dimensional tree \\ kNN & k-nearest neighbours detector \\ LOF & Local outlier factor \\ LoTAD & Long-term traffic anomaly detection \\ LSCP & Locally selective combination in parallel outlier ensembles \\ MAD & Median absolute deviation \\ MCD & Minimum covariance determinant \\ MTTD & Mean time-to-detect \\ OMNET & Objective modular network testbed \\ P & Precision \\ PKI & Public key infrastructure \\ PyOD & Python outlier detection \\ PySAD & Python streaming anomaly detection \\ R & Recall \\ ROC & Receiver operating characteristic \\ RRCF & Robust random cut forest \\ RSU & Roadside unit \\ STORM & Stream outlier miner \\ SUMO & Simulation of urban mobility \\ TP & True positive \\ TPR & True positive rate \\ TN & True negative \\ VANET & Vehicular ad hoc network \\ V2I & Vehicle-to-infrastructure \\ V2V & Vehicle-to-vehicle \\ V2X & Vehicle-to-everything \\ \end{tabular}
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 4 PALANTIR General Architecture for Threat Remediation
### Remediation Awareness  
Security operators and end-users who are members of a client organisation need to be informed of the security-related events occurring in the network. As such, the PALANTIR Portal has real-time monitoring as a core functionality, and the ability to check past threats.  
All the authenticated users who operate as members of client or PALANTIR provider organisations receive live notifications from the platform as they occur. The alerts and notifications are pop-ups at the top of the screen, regardless of the view they are currently in. The alerts and notifications originate from the PALANTIR platform as a whole, as well as the SCs their organisation has subscribed to. These pop-up notifications are colour-coded based on severity, with low-severity or simple notifications being green, medium-severity alerts being yellow, and critical or operation failure notifications being red. Unknown severity incidents or generic notifications just have a grey background. If the notification pertains to an incident, it is kept in an incidents page, and the pop-up contains a link to it in that view/page. In general, any notification that pertains to an element that is viewable somewhere on the dashboard in more detail, or is managed or controlled through a view system, then the notification contains appropriate links. Mitigation and remediation actions appear as notifications, stating briefly the kind of action that was performed on the network, along with the threat in response to which this action has been undertaken. The link to inspect the remediation action leads to the details of the threat and the corresponding remediation.  
The dashboard also provides a consolidated view of past threats, including the applied remediation for each. Each remediated threat is indicated in the list of past threats. Viewing the details of such an element will show the details of both the detected threat and the remediation steps taken.  
Information that is intentionally absent from the UI is remediation action prompts. Indeed, the PALANTIR platform strives to minimise the required human interaction, while ensuring network and systems integrity. This is conducted in order to ensure a faster response than the one CIRTs or CSIRTs comprised of humans would provide. As such, focus is given to (a) optimising the incoming threat and remediation action notifications and (b) providing a compact but detailed view of past threats and applied remediation actions.