esa-sceva/satcom-chunk-collection huggingface dataset:
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 8 Geospatial Analysis  
During the experiments the location of all vehicles was tracked so the density of attacking nodes could be visualised (Figure 5). This shows that although distributed attacks provided the opportunity for wider spread disruption, the less structured approach meant that attacking nodes could cluster together, rendering them less effective. Testing them in a dynamic environment did, however, provide the opportunity for the densities of nodes to change providing a necessary overall view of a complex strategy of both running and attacking autonomous vehicles.  
The visualisations can show the entire experiment (animated), a single position in time or a summary of the entire monitoring time. An effective way to ensure the attacks were covering the entirety of the city was to show where the attacking cars had got to within the given time frame (Figure 6). The coverage shows that the attacking nodes reached most of the areas and so the disruption caused should have been relatively high. As anticipated, in the more outlying areas the density of vehicles was less than in the city centre, however this would be true for regular and attacking vehicles so the proportions should not differ greatly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Varied (Yes/No)** & **Value(s)** \\ \hline Time (seconds) & Yes & 10,800; 18,900; 27,000; 35,100; \\  & & 43,200 \\ \hline Transmission & No & Bluetooth \\ \hline Number of Ordinary Cars & Yes & 50, 45, 40, 35, 30, 25, 20, 15, 10, 5, 0 \\ \hline Number of Public Transport  Vehicles & No & 6 \\ \hline Number of Attacking Cars & Yes & 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 \\ \hline Protocol & Yes & Binary Spray and Wait, PRoPHET, Maxprop, Epidemic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table to show the experiment parameters.
## 4 CNN-GRU Neural Network Prediction-Assisted Positioning Method
### CNN-GRU-Based GNSS Outage Assisted Prediction  
In GNSS/INS integrated systems, there are already many neural network-assisted models. The common approach is to establish a relationship between INS outputs (angular velocity, specific force, velocity, position, etc.) and certain GNSS information. When GNSS signals are available, these data are used to train the neural network. If GNSS signals are interrupted, the trained neural network is capable of generating pseudo GNSS data. Before beginning to construct the neural network model, it is necessary to establish a system model and select appropriate parameters as inputs and outputs, which helps to enhance the efficiency of model training and the accuracy of predictions. A commonly used model is model \(O_{INS}-\delta_{GNSS,INS}\)[35], because \(\delta_{GNSS,INS}\) represents the difference between INS and GNSS positions, and the constructed model is always affected by errors from both GNSS and INS. To avoid this problem, model \(O_{INS}-\Delta P_{GNSS}\) is proposed, where the model output (\(\Delta P_{GNSS}\)) is only the GNSS position increment. The ideal scenario for neural networks is when training data and prediction data are in the same motion state. However, in practical applications, the motion state of vehicles always changes due to complex road environments. A single neural network may not effectively train with these mixed data. Therefore, CNN is used to extract the motion state of the vehicle. CNN has excellent feature extraction capabilities and can accurately capture changes in motion states from raw data in complex and dynamic motion states. Additionally, GRU is introduced to help the model better understand long-term dependencies in time series data. These mechanisms enable the model to more effectively filter and utilize historical information when processing navigation data with temporal dynamics, enhancing navigation and positioning performance in complex environments.  
The specific structure of the CNN-GRU neural network-assisted model is shown in Figure 6. Here, \(\omega,f\) represents the angular velocity and specific force output by the IMU, \(v_{INS},p_{INS}\) represents the velocity and position output by the inertial navigation system, \(p_{G}\) is the GNSS output position, and \(\delta p\) is the position error. The assisted prediction model includes a training phase and a prediction phase. In the training phase, the CNN-GRU model parameters are determined based on the acceleration, specific force, and velocity of the INS at the previous and current times, as well as the GNSS position increment \(\Delta P_{G}\). In the prediction phase, the acceleration, specific force, and velocity of the INS at the current and next times are input to the CNN-GRU model to predict the GNSS position increment. Subsequently, the predicted position increment \(\Delta P_{G}\) is added to the initial position information \(P_{G0}\) to generate the pseudo GNSS position \(P_{G}^{psr}\), thereby suppressing the divergence of the inertial navigation system errors.
## 1 Introduction
### Our Contribution  
This work presents a novel approach to implementing an integrated, efficient, and reliable oil pipeline monitoring system. It covers the approach of enhancing the accuracy of system functionality, in addition to utilizing machine learning techniques over wireless sensor networks to enhance network performance. The contributions of this work are explained as follows:  
* To the best of our knowledge, this work is the first of its kind to introduce an integrated and efficient solution in the field of oil pipeline monitoring.
* The accuracy and efficiency of a real-time transient model (RTTM)-based LoRa WAN oil pipeline leak detection system is maintained and increased in terms of continued monitoring and a quick detection response time by applying machine learning techniques that enhance node power consumption and extend the node battery lifetime.
* The number of hardware resources utilized to implement the wireless network of the proposed system is optimized due to the selection of long-range (LoRa) technology, which offers several benefits over other wireless communication technologies, especially in terms of environmental monitoring systems that deploy the Internet of things (IoT) and low-power, wide-area network (LP-WAN) applications.
* The whole of the LoRaWAN network's performance, especially in the context of LoRa node power consumption, was significantly improved by implementing different approaches of machine learning techniques using the LoRa network. The hybrid deep extreme learning machine model provided impressive results in improving network performance, e.g., power consumption, packet loss, and packet delay, compared with that of other models.
## 3 Materials and Methods
### Neural Network Backbone  
In this work, we adopt the deep residual network (ResNet-18) [25] as our network backbone. As shown in Figure 2, ResNet begins with convolving and pooling the input spectrogram to extract low-level features. This is followed by four basic blocks consisting of multiple convolutional layers, each with the skip connection that allows the input to bypass the block and flow directly to the output.  
The basic block in ResNet-18 is called a \"residual block\", which is designed to solve the problem of vanishing gradients in deep neural networks. The residual block takes inputs and passes them through a series of convolutional layers, batch normalization, and activation functions--ReLU (we omit the ReLU and batch normalization in Figure 2 for brevity). The outputs from this series of layers is then added to the inputs, creating a \"shortcut connection\". The purpose of the shortcut connection is to preserve the gradient information from the inputs, which can be lost as it passes through the convolutional layers. By adding the inputs to the outputs, the residual block allows the network to learn residual functions, which are easier to optimize than the original functions. The convolutional  
Figure 2: The framework of network backbone—ResNet18. “Conv” represents the convolutional layer (yellow box) and “FC” represents the fully connected layer. For brevity, we omit the ReLU and Batchnorm layers in the figure.  
layers in the basic blocks have a filter size of 3 \(\times\) 3 and a stride of 1, with padding used to keep the spatial dimensions of the inputs and outputs the same. The batch normalization layer normalizes the outputs from the convolutional layers, helping to reduce the effects of internal covariate shift and improve the stability and speed of the training process. The activation function used in the residual block is a rectified linear unit (ReLU), which helps to introduce non-linearity into the model and increase its capacity to learn complex representations.  
ResNet also includes several pooling layers that downsample the feature maps to reduce the dimensionality of the input. The last adaptive pooling layer will pool the feature map into 512 \(\times\) 1 \(\times\) 1, and the representation vector can be obtained after the flatten operation. The classic ResNet is followed by a fully connected layer to output the classification result, while our LGNet uses this representation for subsequent locally gated activation and assignment. We refer to this ResNet-based network architecture that transforms the input spectrograms into one-dimensional representations as \"Front-end network backbone\".
## 3 PPK2 Enzymology
### Class I PPK2 Enzymology  
The following sections will summarize the biochemical characteristics of representative members of the class I, class II, and class III PPK2 families. The Kornberg lab conducted the first biochemical characterization of a class I PPK2 after having purified the enzyme to homogeneity from _P. aeruginosa_ lysate. This enzyme (later called PPK2A/PA0141 to disambiguate from the other two _P. aeruginosa_ PPK2s [39]) has a 75-fold preference for nucleoside diphosphate phosphorylation relative to polyP synthesis. Among nucleoside diphosphate acceptors, GDP is favored over ADP, as demonstrated by a roughly 2.5-fold lower Michaelis constant (Km). PPK2A shows no detectable AMP-phosphorylulating activity; hence, it is a class I PPK2. Interestingly, Mg2+ is favored over Mn2+ for nucleoside diphosphate phosphorylation [34], but the inverse is true for polyP synthesis--Mn2+ elevates the PPK2A polyP synthesis rate to a level comparable to that of PPK1 [35]. A similar phenomenon was observed for the class I _Corynebacterium glutamicum_ PPK2, which shows optimal activity with Mn2+, but remarkably exhibits a strong preference for polyP synthesis from ATP/GTP regardless of the cation provided [49]. A second class I _P. aeruginosa_ enzyme PA2428 (dubbed PPK2B [39]) can readily phosphorylate ADP and GDP [36; 65], and recent work from our lab has shown that this enzyme also exhibits exceptionally strong in vitro polyP synthesis activity from ATP, but only in the presence of an ATP regeneration system [57]. Other characterized class I enzymes include those of _S. melidiot_, Agrobacteriumium tumefaciens, Rhodoposeudomonas palustris [36], _Mycobacterium tuberculosis_[54], and _F. tulareis_[52]. These enzymes fit the more traditional mold for class I PPK2s, demonstrating Mg2+-dependent ADP or GDP phosphorylation by polyP. An unusual member of the class I family is a PPK2 from _Silicibacter_ (_Ruegeria_) _pomeroy_--dubbed PPK3 by [PERSON] and [PERSON]--that preferentially uses pyrimidine substrates [50; 59]. Specifically, PPK3 catalyzed the polyP-dependent phosphorylation of CDP and UDP to CTP and UTP with roughly twofold higher activity compared to ADP/ATP and GDP/GTP [59]. Recently, two more class I enzymes were discovered to have the broadest nucleotide specificity to date. Both PPK2C from _Ralstonia etuppha_[66] and PPK2 AT from _Agrobacteriumum tumefaciens_ were shown to phosphorylate ADP, GDP, CDP, dTDP, and UDP, in addition to performing the reverse reaction (polyP synthesis) from all five NTTs. This marks the first time PPK2s have been shown to use all the natural nucleotides--including thymidine--as substrates, highlighting the potential of these so-called universal PPK2s as tools for biotechnological NTP regeneration [66]. Specifically, universal PPK2s could be a boon for the regeneration of UTP and CTP required for the enzymatic synthesis of complex glycans [59; 67; 68], in addition to more traditional ATP regeneration systems (reviewed in [63]).
## Materials and Methods
### Eddy Covariance and Satellite Observations  
Eddy covariance data from these sites has been used to explore the patterns and trends in carbon dioxide and evapotranspiration in California and the Southwestern US [33; 35]. The ten sites share the same instrumentation, data processing, and maintenance [36]. The eddy covariance systems consist of a closed-path infrared gas analyzer (LI-7000, LiCor Biosciences) and a 3D sonic anemometer (CSAT-3, Campbell Scientific). Data processing includes corrections for sensor lag, mean wind rotation, and energy budget closure. The net CO\({}_{2}\) fluxes were partitioned into respiration and gross uptake (half-hour GPP) by extrapolating light-response curves to darkness. Our analysis focused on the gross uptake during sunny, midday (11:00 to 13:00 local time) periods and we removed observations during cloudy periods or with high vapor pressure deficits (>2 KPa). We focused on midday observations to avoid the confounding effects of seasonal changes in sunlight duration, and the effects of meteorological constraints on photosynthesis, such as low light or high evaporative demand.  
We used surface reflectance data for the sites from the MODerate resolution Imaging Spectrometer (MODIS) MCD43A4.006 product (downloaded from AppEEARS; [[https://lpdaacsvc.cr.usgs.gov/](https://lpdaacsvc.cr.usgs.gov/)]([https://lpdaacsvc.cr.usgs.gov/](https://lpdaacsvc.cr.usgs.gov/))). This product provides daily coverage of surface reflectance (atmosp spherically corrected) at 500 m spatial resolution adjusted to a nadir view angle and local noon. For most sites, we extracted the time series of the pixel containing the tower location and VIs were estimated from bands 1 (Red), 2 (NIR), and 3 (Blue) according to Equations (1)-(3). Some sites in Southern California have relatively small areas of homogeneous vegetation around the towers relative to the MODIS pixel, and for these sites we used observations for nearby larger patches of well-matched vegetation [33] (Supplementary Material 1). We selected the nearby pixels as the closest ones showing the most similar VIs values to the area around the towers using Landsat imagery, and the similarity in vegetation type and composition at the pixel area was confirmed by visual inspection. MODIS data were filtered for clouds, high aerosols,and conditions leading to poor corrections of sensor view and sun angles according to the quality bands in the MCD43A4.006 product, and for patchy snow based on the Normalized Difference Snow Index (NDSI).  
The time spans of eddy covariance and satellite observations used are shown on Figure 1, and ranges from 5 to 10 years of observations for each site, including all seasons of the year and all stages of canopy development.
## 2 Antenna Configuration and Design Strategy
### Systematic Design Process  
The proposed frequency-agile antenna is extracted from the conventional CPW printed antenna by following two major steps. Step 1 includes the designing of a compact antenna to operate at multiple frequency bands, depending upon the presence of the stub along with the main radiator. Step 2 focuses on achieving frequency reconfiguration without affecting the overall size of the antenna along with the multi-mode operation.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 4 Dropped Object Frequency and Consequence Analyses
The corresponding weight was derived from the suction drum bundle in Drop Zone PD03:  
* Impact Energy: 51 kJ,
* Shape: Cylinder type,
* Material Type: AH36 steel plate,
* Size: 1.6 m \(\times\) 6.8 m,
* Weight: 1.3 MT (metric tons),
* Height: \(<\)5 m.  
The six parameters affecting the process deck of the fractionation module are drop weight, position, impact energy, impact angle, impacted body type, and stiffener position. Sensitivity was analyzed according to the drop weight and impact frequency. As illustrated in Figure 9, the sensitivities to the drop weights were confirmed to be 0.5, 0.7, 1.0, 1.5, and 2.0 \(\times\) W (W = 1.3 metric tons). We checked each exceedance curve based on the proposed acceptance criteria \(1\times 10^{-4}\), and the impact energies were 25.5, 35.7, 51.0, 76.5, and 102.0 kJ, respectively. Figure 7: Drop zone and drop point definition. The sensitivity analysis of the impact frequency was conducted at 0.5, 0.7, 1.0, 1.5, and 2.0 \(\times\) F (F = Impact Frequency), as shown in Figure 10. Impact energies of 35.3, 36.9, 51.1, 56.9, and 60.8 kJ were obtained for the acceptance criterion of 1 \(\times\) 10\({}^{-4}\) based on each exceedance curve. The minimum value was 1.72 times less sensitive than the maximum value for drop frequency and 4.0 times for the drop weight. Figures 9 and 10 show that drop weight affects the impact energy more than impact frequency. The results drive the focus towards the relationship between impact energy and impact orientation in Section 5. Figure 8: Impact energy exceedance curve. Figure 9: Impact energy exceedance curve by drop weight.
_Applied_  
_sciences_  
Breast Cancer Mass Detection in DCE-MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach  
[PERSON] 1,2,6, [PERSON] 1,2,7,8, [PERSON] 3, [PERSON] 4, [PERSON] 5 and [PERSON] 1,2,6  
1 Laboratory of Biomedical Physics and Environment, Department of Mathematics and Physics \"[PERSON]\", University of Salento, 73100 Lecce, Italy; [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (G.D.N.)  
2 Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), University of Salento and ASL (Local Health Authority), 73100 Lecce, Italy  
3 Operative Unit of Radiotherapy, ASL (Local Health Authority), Brindisi, and 'Di Summa-Perrino' Hospital, 72100 Brindisi, Italy; [EMAIL_ADDRESS]  
4 Operative Unit of Radidiagnostics, ASL (Local Health Authority), Brindisi, and 'Di Summa-Perrino' Hospital, 72100 Brindisi, Italy; [EMAIL_ADDRESS]  
5 Operative Unit of Medical Physics, ASL (Local Health Authority), Brindisi, and 'Di Summa-Perrino' Hospital, 72100 Brindisi, Italy; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]  
Received: 6 August 2020; Accepted: 29 August 2020; Published: 3 September 2020  
Featured Application: Automatic CAD system in detecting tumors as a valuable support to radiologists for detection and characterization of breast cancers in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images.  
**Abstract:** Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups--in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels--(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE-MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.  
**Keywords:** breast cancer; Radiomics; machine learning; deep learning; segmentation; in situ breast cancer; infiltrative breast cancer
## 3 Results and Discussion  
### Topic Visualization Analysis  
To create a profile of the most important issues arising in the field and to perform a visualization of main topics, we carried out a text-mining analysis using the VOSviewer suggested procedure to text-mine the keywords of the papers.  
VOSviewer applies natural language calculation methods to analyze the keywords. We followed indications and considerations from [56].  
With this procedure, we generated what is called a \"concurrence network\" (Figure 3).  
Figure 3: Keywords topic visualization analysis—concurrence network.  
Figure 2: Number of documents in list “A” for each year.  
In this network, the most relevant terms were highlighted, and a cluster analysis was performed identifying four clusters that theoretically gather themes that share some higher connection. Once the procedure was completed, we gave a title to each cluster in agreement with what seems to be the leading topic. We applied the VOSviewer option of eight for the minimum number of occurrences of a term. From a total of 1524 keyword terms, 20 met the threshold. The selected terms were grouped in four clusters, as shown in Figure 3 and Table 1. It must be noted that the procedure we applied to extract the terms and to create the clusters is completely automatic, the clusters are created according to an algorithm that puts together terms that are not necessarily logically connected. We manually gave a label to each cluster, and some of the terms, thus, might not be connected with the specific topic label we have chosen.