esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 4 Design and Implementation
### Discussion  
We present, in this subsection, some main points for discussing our solution in regards to the method used for ensuring the security aspects of availability and integrity, and confidentiality.  
The main algorithm, the \(NC\) technique that creates a \((t,p)\) data redundancy, is effective with respect to size; that is to say that rebuilding \(t\) measures only needs \(t\) parts whose total size is identical. It is, however, possible to use more parts \((p>t)\) to help protect the network against \(Do5\) attacks or pollution attacks. In other words, an attacker needs to compromise at least \(p-t\) separate paths, i.e., in the worst case, \((p-t)\)\(CH\) nodes. If the attacker does not know the topology set up to route data in the network, it has no choice other than to randomly compromise the nodes. As a result, it will likely need to capture more than \((p-t)\)\(CH\) nodes.  
This strong availability and integrity is obtained at the cost of extra data parts transmission for each source node, i.e., transmitting \(p\) parts instead of \(t\) parts where \(p>t\). However, our approach does not require heavy operations: dispersal and reconstruction are computationally efficient [33] and the systems of equations have to be solved only by the \(BS\).  
It is not possible for a few compromised nodes to reconstruct the measures. In other words, there is no technique for rebuilding, even partially, the measurements from \(t-1\) parts. This is the major advantage of dispersing the information in the network.  
The solution, based on \(NC\), provides complete confidentiality, so that no information can be obtained by intercepting coded symbols or random values of \(p_{i}\). In fact, a wiretapper cannot recover the original data packets of a source node \(N_{i}\), whether it is an outsider node or a captured \(CH\) inside the network. The main reason is that the corresponding matrix \(A_{i}\) belonging to a source node \(N_{i}\) is secret and not communicated to the destination when routing data packets. Moreover, the chosen values of \(p_{i}\) for each node \(N_{i}\) are changing and chosen randomly each round, so no other node but the \(BS\) is able to reconstruct the original data. A detailed demonstration is available in our previous paper [15].  
Apart from security requirements, our solution minimizes the overheads compared to conventional network coding systems. First, instead of transferring all the coded vectors, i.e., the coefficients \(a_{k,q}\), where \(k,q\in\{[1,t],[1,p]\}\), along with the coded \(p\) symbols, a source node \(N_{i}\) adds only a single digit \(p_{i}\) to the coded vector to be sent. Second, the solution performs encoding only at the level of source nodes; there is no re-coding at the level of intermediate nodes, i.e., the \(CH\) nodes. This decreases the energy consumption and increases the network lifespan.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 2 Related Work
### Other Link Failure Detecting Algorithms  
Besides \(\phi\) FD, there are other algorithms aiming at detecting link failures. Analogous to \(\phi\) FD, [PERSON] et al. [9] and [PERSON] et al. [11] assume that the interval of heartbeat responses follows exponential distribution or Weibull distribution and calculate the probability of time interval between the current time and the time of last heartbeat response. If the probability is lower than a threshold, the monitor suspects the monitored node. [PERSON] et al. (2010) uses two windows with different sizes to collect the intervals of heartbeat messages. By comparing the current time and a predicted next receipt time calculated based on these two windows, the monitor decides whether the remote node is reachable or not. [PERSON] et al. (2012) proposes an IFDS framework which can handle multiple concurrent applications with different QoS requirements, whose purpose is different from us.
## 3 Proposed Method
### Overall Procedure  
The overall flow of the proposed identification approach based on the IPC is shown in Figure 2. First, a search query is used to find a patent set from the patent databases. Second, structured data of the patents, such as the IPC symbols and application dates, are gathered for the determination of the value of technological novelty (VON) of each technology manifested in the patent set. In this step, bifurcation and the hierarchical features of the IPC system are used to find difference of the IPC symbols, which determines the novelty of technical features among patents after attaching time attributes. Third, R&D themes and their values of difficulty (VOD) are decided, by analyzing the unstructured data of individuals in the patent set. In this step, features of a complex network composed of IPC symbols are used as the value of difficulty. Then, an IPC-based coordinate system is built based on VON and VOD, to identify technological opportunities for radical inventions. The details of these steps are as follows:  
Figure 1: Two-dimensional coordinate system for classifying different technological opportunities.
## 6 Theoretical and Practical Steps for the SLR Study  
This section presents and explains the steps involved to conduct the SLR study in detail, including the tools, software, and techniques used. The process is replicable in other SLR studies in any field of academic research. The following are the practical steps undertaken for each database.  
* **Google Scholar**: First, we inserted the second group of keywords (G.2) into the search box; then, we selected the range of publication years, resulting in the required documents displayed and available to us. However, we had to exclude several duplicate documents found in other databases when using the first group of keywords throughout the search process. Then, we imported the residue documents into the My Library  
Figure 3: Taxonomy of existing research literature in RPL.  
feature in Google Scholar before exporting them to Mendeley Software using the Mendeley Web Importer Tool's extension [43].
* **Scopus Database:** The first group of keywords (G.1) is inserted into the search box. Then, the search results are refined using year and document type filters. Afterward, we exported the selected studies using the Research Information System (RIS) before importing them into Mendeley software for further analysis.
* **Springer Link:** We began by entering the first group of keywords (G.1) into the search engine, then refined the returned results based on document type and year of publication. Then, we downloaded the links to the selected articles as separate CSV-formatted files. Next, we downloaded the articles from the available links and imported them into Mendeley software using Mendeley's Web Importer Tool extension [43].
* **IEEE Xplore(r) digital library:** We began by inserting the research keywords (G.1) into the search box, then applied the filter and selection criteria. Then, we export the selected documents using (Jib) format, which is then imported into Mendeley Software.
* **Science Direct:** The search process in Science Direct is similar to that for the Scopus database. We selected the displayed documents according to the pre-defined criteria. Then, we exported the documents in RIS format, which can be imported into Mendeley Software later. Figure 4 shows the whole process of conducting the SLR stages.
# Application of Machine Learning to Assess the Quality of Food Products--Case Study: Coffee Bean
## 3 Results and Discussion  
### Color Analysis  
In Tables 1 and 2, the results of the L*a*b* and RGB color space models for each class of coffee are summarized. As a result of analysis by the Tukey test, the existence of subclasses for coffee beans was confirmed. Indeed, the test classes differ from each other [34]. In Table 1 on L*a*b* color, it was observed that statistically, the greatest difference in color was shown by the burnt coffee class against the other classes. Analyzing the L*, a* and b* components of the L*a*b* model [47, 48], the under-roasted and standard classes showed a statistically significant similarity between them. In fact, these classes have similar values in the color attributes, which translates into difficulty in distinguishing them from each other. In the case of the over-roasted coffee class, even organoleptic differences in brightness (L*), red-green (a*) and yellow-blue (b*) can be effectively discerned [34].  
increasing consumer awareness, it is also required that a food product contain information as described for authenticity in the production and the distribution of products [21; 51]. By the same means, the RGB color space model, by distinguishing more precisely between different classes of coffee beans than the L*a*b* model, can be of greater benefit in assessing the quality of these products.
# ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things
## 2 IoFT within the Education Domain
### IoFT in the Classroom  
In December 2015, the Australian Education Ministers endorsed a 10-year (2016-2026) National STEM strategy focusing on delivering foundational skills in Science, Technology, Engineering and Mathematics. This plan promotes creative and critical thinking for K-12 students undertaking project-based learning [15]. This growth of Project-Based STEM projects has led to the increase in the use of UAVs within the education sector [14]. The UAV's that stand out within the education K-12 domain (Adapted from \"7 Best Drones For Education To Build, Learn To Code and Configure\" By [PERSON], 2020 ([[https://www.dronezon.com/learn-about-drones-quadcopters/best-educational-drones-kits-to-build-and-code-uavs/](https://www.dronezon.com/learn-about-drones-quadcopters/best-educational-drones-kits-to-build-and-code-uavs/)]([https://www.dronezon.com/learn-about-drones-quadcopters/best-educational-drones-kits-to-build-and-code-uavs/](https://www.dronezon.com/learn-about-drones-quadcopters/best-educational-drones-kits-to-build-and-code-uavs/)) (accessed on 18 January 2022)). Copyright 2020 by Drone-Zon.com (accessed on 18 January 2022) due to their price and features discussed below:  
* Robolink CoDrone Lite: 8 min of flight time, Programable in Snap, Python and Blockly.
* Sky Viper e1700 Stunt: Two flight modes, Controller can be adjusted to match the best sensitivity, Auto Launch and Land.
* Ryze Tello EDU: Programable in Python, Swift and Scratch, 13 min of flight time, HD video streaming, Auto Launch and landing.
* Parrot Mambo Fly: Programmable in Blockly, Tynker, Python and JavaScript, 60 fps camera and Fly range 20 m with smart phone or 100 m with remote controller.  
Of particular significance of UAVs referenced was the Ryze Tello. The Tello was DJI's first UAV directly targeted to the education sector and allows for students to learn to control the UAV using programming languages such as python or scratch. Although the Tello is not directly produced by DJI, it does utilise DJI's high-quality flight technology that is utilised in their high-end drones [16]. The Ryze Tello is currently heavily used in schools in Japan [17]. However, the use of the DJI Tello does pose cyber risks to the learning institute, staff and students that utilise this and similar IoFT devices.
## 4 Case Verification and Result Analysis
### Result Analysis  
Combined with the operation results of the above example, it can be concluded that the selective maintenance planning of the fleet under the age reduction model in this paper has the following characteristics:  
1. Fleet mission satisfied capability has a high correlation with the cost of maintenance tasks invested, and the benefit of unit maintenance cost invested decreases with the increase in fleet mission satisfied capability. The mission satisfied capability of the equipment cluster is determined by the health status of each equipment unit. The higher mission satisfied capability has higher requirements on the health status of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Option 1** & **Option 2** & **Option 3** \\ \hline Maintenance cost & 9.28 & 14.38 & 22.63 \\ Mission capability & 0.72 & 0.83 & 0.91 \\ Maintenance hour & 40.27 & 42.21 & 42.69 \\ Maintenance schedule & 3-4-1-2 & 1-2-3-4 & 4-1-2-3 \\ Cost-effectiveness ratio & 0.0776 & 0.0577 & 0.0402 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Options selected after evaluation of maintenance hours.  
Figure 7: The Gantt chart of maintenance schedule.  
each equipment; so, it needs to invest a lot in maintenance costs. In the case of a high requirement of mission satisfied capability and sufficient budget, more cost should be invested in exchange for higher mission success rate. In the case of low mission satisfied capability requirements and tight budgets, maintenance strategies with slightly lower mission satisfied capability can be selected to reduce costs.
2. There is little difference in maintenance hours corresponding to different maintenance strategies. Since a reasonable maintenance schedule is established to maximize the use of maintenance time, when the fixed maintenance hours are longer than the hours associated with the maintenance level, the corresponding maintenance hours of different maintenance strategies are not different; so, the maintenance hours can be considered as a supernumerary optimization objective.  
In summary, the two-stage selective maintenance planning algorithm for the fleet proposed in this chapter can quickly solve the nondominated solution set that is evenly distributed in the target space, evaluate the solution set and give the maintenance schedule with the shortest maintenance time so as to provide the mission and health condition driven decision scheme and its theoretical basis for the selective maintenance task of a fleet.
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 2 Related Work
### Feature Extraction-Based Method  
Deep learning feature extraction architectures have been widely used in many computer vision tasks, such as image super-resolution [31], image detection [32], and live searching [33]. For VIPR, the common feature extraction method is to modify general deep learning feature extraction architectures into two-stream networks [1; 2; 14; 34; 35] whose shallow layers are independent to learn features from different modalities, and whose deep layers are shared to align cross-modal features. For example, [PERSON] et al. [2] first introduced the two-stream network for deep cross-modal shareable feature learning, which contains a visible image stream and infrared image stream. Still, there are some methods [1; 34] that introduced an attention mechanism to the two-stream network, which learned intra-modal discrepancy and inter-modal correlation to achieve better feature alignment. Furthermore, [PERSON] et al. [18] used an attention module to aggregate part features to global features to enhance discrimination ability of feature learning. Such a part-level feature learning method is also applied in [14; 16; 17]. Nevertheless, these methods improve feature learning architecture via extra modules (e.g., attention [1; 18; 34; 36] and transformer [35]) that usually consume additional computations.
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 2 Method
### Convolutional Neural Network  
A shallow convolutional neural network (CNN) similar to the generic network shown in Figure 6 is adopted for all cases in this paper. Figure 6 shows the working of a CNN through convolution and pooling operations performed on a \(32\times 32\times 1\) image. The stride values for the convolution and pooling layers are 1 and 2, respectively. Once the image is fed to the CNN, the convolution layer applies a series of filters to help the network capture the various characteristics of the image. The image size thus changes after the convolution operation. For an image of dimensions \(I\times I\), filter width \(F\times F\), zero-padding \(P\), and stride \(S\), the output dimensions of the image are \(\left[\frac{(I-F+2P)}{5}+1\right]\times\left[\frac{(I-F+2P)}{S}+1\right]\)[32]. This is followed by a pooling layer that downsamples the image, thereby reducing its size while preserving the dominant features. The dimensions of an \(I\times I\) image after a \(F\times F\) max-pooling filter operation are \(\left[\frac{(I-F)}{5}+1\right]\times\left[\frac{(I-F)}{5}+1\right]\). The resulting image is flattened to a vector and fed to a fully connected layer as part of a regular feed-forward network. The loss and activation functions in the final layer are chosen depending on the nature of the case, i.e., whether it is a regression problem or a classification problem. The CNN compares its prediction with the ground truth, calculates the loss/error, and adjusts its learnable parameters to minimize the loss.  
Figure 6: An example showing a convolutional neural network and the impact of convolution and pooling layers on the image size and information transfer.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
###### Abstract  
Helminthosporium leaf blotch (HLB) is a serious disease of wheat causing yield reduction globally. Usually, HLB disease is controlled by uniform chemical spraying, which is adopted by most farmers. However, increased use of chemical controls have caused agronomic and environmental problems. To solve these problems, an accurate spraying system must be applied. In this case, the disease detection over the whole field can provide decision support information for the spraying machines. The objective of this paper is to evaluate the potential of unmanned aerial vehicle (UAV) remote sensing for HLB detection. In this work, the UAV imagery acquisition and ground investigation were conducted in Central China on April 22 th, 2017. Four disease categories (normal, light, medium, and heavy) were established based on different severity degrees. A convolutional neural network (_CNN_) was proposed for HLB disease classification. The experiments on data preprocessing, classification, and hyper-parameters tuning were conducted. The overall accuracy and standard error of the _CNN_ method was 91.43% and 0.83%, which outperformed other methods in terms of accuracy and stabilization. Especially for the detection of the diseased samples, the _CNN_ method significantly outperformed others. Experimental results showed that the HLB infected areas and healthy areas can be precisely discriminated based on UAV remote sensing data, indicating that UAV remote sensing can be proposed as an efficient tool for HLB disease detection.  
U  
A 2019 9 558 (); 31 December 2018 (); 4 February 2019 (); 8 February 2019 (); 31 December 2018 (); 4 February 2019 (); 588; doi:10.3390/appp9030558
## 5 Biofilm and Heterogeneity of _A. baumannii_ Populations
### Persisters and Heterogeneity of A. baumannii Populations
Dispersed cells accumulate a wide diversity of mutations that enhance biofilm formation and antibiotic resistance. For example, the efflux transport system AdeABC was upregulated in the presence of both ciprofloxacin and tetracycline, whereas the expression of RecA and UmuD, which are involved in DNA repair and mutagenesis, was increased during ciprofloxacin treatment. Phenotypic alteration between opaque (VIR-O) and translucent (AV-T) colonies is another example of _A. baumannii_ population heterogeneity. It was shown that both phenotypes exhibited significant differences in cell morphology, biofilm formation, surface motility, antibiotic resistance, and virulence [173]. VIR-O cells were covered with a thicker coating of the extracellular capsule, and they were more resistant to disinfectants, ROS, antibiotics, lysozyme, and the cathelin-related antimicrobial peptide [53]. This highly virulent subpopulation dominated in the bloodstream of human patients. The AV-T cells produced more dense biofilms and a larger quantity of OMVs in comparison with the VIR-O variant [174]. The analysis of VIR-O and AV-T transcriptomes suggested that the AV-T subpopulation is better adapted for natural environments outside the host than VIR-O cells. The phenotype switching between VIR-O and AV-T subpopulations depends on several factors, including a TetR-type transcriptional regulator ABUW_1645, the ArpAB efflux system, the EnvZ/OmpR two-component system [174], and ppGpp [127]. Antibiotic heterosexual is another common phenotype that may contribute to the heterogeneity of bacterial populations. We describe this phenomenon in the next section, discussing the broader problem of multidrug resistance _A. baumannii_.
# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 2 Materials and Methods
### CMR Imaging  
Cardiac magnetic resonance imaging was performed using a 1.5 T GE Signa HDxt scanner (General Electric, Milwaukee, WI, USA) as part of the routine standard clinical protocol [19; 20; 21; 22]. Left ventricular (LV) volumes and ejection fraction were assessed with cine-CMR using a steady-state free-precession technique (FIESTA) with the following imaging parameters: 20 phases per slice location, FOV 32 \(\times\) 32 cm, TR 1.6 ms, TE 2.8 ms, FA 20-30\({}^{\circ}\), matrix 256 \(\times\) 160, slice thickness 8 mm. Delayed enhancement images were acquired 15-20 min after a bolus of gadolinium using an inversion recovery gradient-echo sequence with the following imaging parameters: FOV 42 \(\times\) 42 cm, TR 8 ms, TE 3.8 ms, FA 40-50\({}^{\circ}\), slice thickness 8 mm. The parameters of the LV were analyzed with commercially available software QMass(r) MR analysis software version 7.6 (Medis Medical Imaging Systems bw, Leiden, The Netherlands). The cut-off values of LV mass (LVM) and LV end-diastolic volume (LVEDV) were obtained from a study performed by [PERSON] et al. [23]. An absolute LVEDV \(>\) 232 mL was considered as abnormal in males or \(>\)175 mL as abnormal in females (16), while an indexed LVEDV \(>\) 117 mL/m\({}^{2}\) was considered as abnormal for men or \(>\)101 mL/m\({}^{2}\) as abnormal for women. Left ventricular hypertrophy (LVH) was defined as LVM \(>\) 148 g in men or \(>\)96 g in women, or an LVM/body surface area (BSA) \(>\) 72 g/m\({}^{2}\) in men or \(>\)55 g/m\({}^{2}\) in women [23].