esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Application  
### Representation and Analysis of Process-to-Process Input-Output Exchange  
Table 1 (Section 2.3) illustrates two examples of SAMs that map various system architecture aspects to each other. Both SAMs include a mapping of processes to each other. Table 1(a) includes explicit mapping of process to process via operands. The operands serve as indirect process mediators: the output of one process is the input of another. However, there is no explicit mapping in the model in the form <P,P>. Therefore, this mapping must be composed. This composition is defined in the following manner, using SQL queries to retrieve and cross the data from the raw set of RSTUV tuples:  
1. Find all the processes, i.e., targets in tuples with S = 'OPM.Process' and R = 'Classification'.
2. Find all outputs, i.e., targets in tuples where R = 'Result', and retrieve the source process item.
3. Find all tuples in which the above outputs are sources in a Consumption relation (i.e., inputs), and retrieve the target process items.
4. Cross the process-output set with the input-process set such that output = input.
5. Cross process set (1) with output-generating process set (2). Keep all processes including those that are generating no output.
6. Cross process set (1) with input-receiving process set (3). Keep all processes including those that are receiving no input.
7. Layout a matrix with output-generating processes as rows, input-receiving processes as columns, and identity of matching output-input (_output_
# Multi-Objective Evolutionary Neural Architecture Search with Weight-Sharing Supernet
## 2 Related Works
### Evolutionary-Computation-Based Neural Architecture Search
#### 2.3.3 Population Evaluation Strategy
In this work, we combine the advantages of evolutionary-computation-based and gradient-based NAS. Specifically, a population strategy in EC is used to optimize and search for diverse candidate network architectures. For the training and evaluation processes, we adopt the idea of one-shot models to optimize the weights of networks. All the possible candidate network architectures are represented by a supernet. In the training stage, a candidate network architecture refers to a subnet masked from the supernet. The weight parameters involved in the masked subnets are continuously updated using stochastic gradient descent (SGD). In the evaluation stage, all the candidate network architectures share the trained weight parameters of the supernet, and there is no need to re-train the networks from scratch. Thus, the candidate networks can be directly evaluated on the validation data, and their performance will further guide the optimization of the evolutionary algorithm. In this framework, the optimization of architecture parameters and weight parameters alternates. In each update period, the weights of the supernet will inherit the weights from the previous period. Next, we will further elaborate on the proposed framework from the aspects of search space, supernet configuration, and algorithm flow.
# A Review on Solar Energy Utilization and Projects: Development in and around the UAE
## 7 International Solar Projects: Sponsored by the UAE
### Bagnouna Solar Plant, Jordan  
As illustrated in Figure 19f, this project began as a Power Purchase Agreement (PPA) between Masdar and Jordan's state energy supplier, the National Electric Power Company. This USD 240 million projects will create 563.3 GWh of electricity per year, which is equal to 4% of Jordan's annual energy consumption. Construction is set to commence in the first quarter of 2018, with Masdar serving as the project's primary stakeholder. Jordan will be able to meet its objective of producing 10% of its domestic electricity needs from renewable sources thanks to the 200 MW solar power plant and Masdar's 117 MW Tafila windfarm. Jordan aims to build 1.8 gigatonnes of renewable energy by 2020, and the two projects will account for roughly 18% of that total.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
In summary, the main contributions of this paper are as follows:  
(1) This paper combines depth map estimation with Mask-RCNN instance segmentation to propose a vision-based absolute distance estimation method for ADAS. For depth map estimation, we constructed a semi-supervised deep learning network that uses sparse depth labels and left and right views in the offline training process to compute right and left disparity maps, thus improving the performance of offline depth estimation. In the process of online depth map estimation, this deep learning network can convert an RGB (Red, Green, Blue) image into an RGB-D (depth) image, providing depth information corresponding to each pixel of the input image;  
(2) To measure the distance between the subject vehicle and the target pedestrian, we propose a depth histogram-based method that calculates the average depth values of all pixels whose depth values are in the peak range of the depth histogram of the pedestrian;  
(3) To measure the distance between the subject vehicle and the target vehicle, we propose a method that first fits a 3-D plane based on the locations of target points in the camera body coordinateusing RANSAC, then projects all the pixels of this target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. Although the proposed system needs to use stereo image pairs and sparse depth information to train a semi-supervised network, it is considered to be a monocular vision-based approach because it only needs to input a single image when used online in V-DAS. The experimental results on the public dataset KITTI and in real road scenarios illustrate that the proposed system can use a single vehicle forward-looking image to obtain its corresponding pixel-level depth information and accurately predict the distances to different targets to meet the needs of ADAS. The remainder of this paper is organized as follows: in Section 2, the related works are briefly discussed. Section 3 presents the details of the proposed depth map estimation method. Section 4 presents the two distance measurement methods based on a forward-looking image and the corresponding depth map. Section 5 gives the flow chart of the proposed distance measurement system, which combines a pretrained semi-supervised network for depth map estimation and the Mask R-CNN network for instance segmentation. Section 6 describes and analyzes the experimental results. The conclusions and future work are presented and discussed in the final section.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 1 Introduction
### Research Methodology  
The IoT is also known as the internet of objects capable of connecting actuators, things, sensors, and many other smart technologies; thus, enabling communication between devices. It is typically connected to cloud services. The IoT system can be a heterogeneous environment that allows the different substantial devices to interact with both users and other devices, be they hardware or services offered. IoT conveys meaning to different people. Therefore, we use a research methodology that evaluates the overall study about malware on the smart home and answers three main questions:  
Figure 1: Smart home architecture.  
* Can a smart home be hacked? Every device connected to any other network without exceptions can be compromised. For this question, the answer is yes. The smart home system is not totally safe. There are several examples of smart devices affected by malware attacks.
* How can smart home cyber-security be compromised by an attacker? In a smart home, every device or anything with the smallest piece of firmware and networking capability can be compromised. We could be forgiven for having secured strong built-in security and for presuming that any successful attack is only possible with highly determined cybercriminals. It is not just about one hacker trying to target your camera to watch you making lunch. This is far from the truth, however. Vulnerabilities and exploits are much more common than we can imagine.
* What is the impact of different attacks against smart home occupants? As we know, the typical smart home system is configured for energy efficiency, convenience, and security. Therefore, we should keep in mind that cyber-security attacks cause serious disruption, leading to adverse experiences in users' daily lives, ranging from inconvenience, to loss of time, and intense frustration due to goal blockage.  
In this research paper, we discuss how to handle a device that may be infected, as well as techniques on how to defend against malware. Table 1 presents the contribution of this survey in related to existing surveys. The rest of this paper is organized as follows: Section 2 describes the taxonomy of malware in the smart home network; Section 3 discusses VPNFilter malware in the smart home network; Section 4 describes the impact on the smart home network as well as physical, cyber-security, and daily life impacts; Section 5 discusses the open issues, challenges, and proposed solutions; Section 6 presents the conclusions. We expect our discussion to help readers gain an overall understanding of the studies related to these issues.
## 3 Methods
### Improvement to the YOLOv5
#### 3.2.2 Improvement on Loss Function  
In this research, we used the varifocal loss function to replace the focal loss function, which was used in the original YOLOv5 to obtain better model training results. Focal loss is dedicated to distinguish two category samples, one is difficult to classify (described as 'difficult samples' below) and another is easy to classify (described as 'easy samples' below). Focal loss is used in the learning stage to focus on difficult samples by reducing the weight of easy samples. The formula is expressed as:  
\[FL(p,y)=\begin{cases}-\alpha(1-p)^{\gamma}\log(p)&\text{if }y=1\\ -(1-\alpha)p^{\gamma}\log(1-p)&\text{otherwise}\end{cases} \tag{2}\]  
where \((1-p)^{\gamma}\) represents the loss contribution of easily separable samples and \(\gamma\) represents the hyperparameter, respectively [34]. The difference between the difficult samples and the easy samples is directly proportional to the value of \(\gamma\). When \(\gamma\) is equal to 0, the focal loss function will degenerate into a cross-entropy loss.  
The varifocal loss function is expressed as:  
\[VFL(p,q)=\begin{cases}-q(q\log(p)+(1-q)\log(1-p))&q>0\\ -\alpha p^{\gamma}\log(1-p)&q=0\end{cases}, \tag{3}\]  
where \(p\) represents the predicted IoU-aware classification score (IACS) [35]; \(q\) represents the IoU score [35] between its basic facts (actual category label and actual accurate position) and the generated bounding box. For positive samples in training, \(q\) was set to the IoU score between the generated bounding box and ground truth box; while for negative samples, the training target q of all categories was 0. The training focused on these high-quality samples in the candidate detection samples with higher IACS. \(\alpha\) and \(\gamma\) are hyperparameters. \(\alpha\) is a scale parameter belonging to 0 to 1, used to adjust the loss between positive and negative samples. In this way, training can focus less on the negative samples. By applying this loss function, the model can make judgments and trade-offs between difficult samples and easy samples, which can further increase the loss contribution of difficult samples.
## 4 Discussion
### Result Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision(\%)** & **Recall(\%)** & **F-measure** \\ \hline HDD & 86.17 & 88.38 & 0.8726 \\ HDC & 82.94 & 83.56 & 0.8325 \\ HDB & 77.33 & 81.44 & 0.7933 \\ HDM & 91.77 & 91.94 & 0.9185 \\ Our method & 94.79 & 95.44 & 0.9511 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision, Recall and F-measure of our method and four competing methods. Figure 17 shows the receiver operating characteristic (ROC) curve and the Precision-recall curves of our method and four other methods. These two curves are important indicators for evaluating the performance of the harbor target extraction method. We can evaluate the performance of the methods by comparing the area under the curve of these methods: The larger the area under the curve, the better the performance of the method. According to Figure 17a,b, our method performs better than four other methods.
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters
### PowerCast  
Powercast was founded in 2003. Its aim was to develop a wireless charging solution. Such a solution was supposed to provide power to devices without using batteries. The very first RF harvesting solution was presented in 2007 during CES (Consumer Electronics Show). Further work ended with the release of commercial products on the market in 2017. Powercast allows energy harvesting from a signal of power of \(-17\) dBm and a frequency range from 10 MHz to 6 GHz. The company says that their modules have an efficiency of up to 75% [10]. A Powercast transmitter can be used to charge devices at a distance of up to 24 m [11].  
Among all other solutions, the Powercast product is the most commercialised one. The company provides developer sets and states that they have already sold about 15 million modules [12]. However, this product does not harvest ambient RF energy but uses a dedicated generator to provide enough power to the harvester [13].
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 3 Results
### Measurement of the ARC
#### 3.3.1 Gain Stability  
Based on the gain calibration method mentioned in the previous section, the gain stability is very good. We measured the gain of the multi-band ARC for about two weeks from 28 November to 14 December 2018. During these two weeks, we measured the gain several times every day using a vector network analyzer. Every measurement was made after the gain had been calibrated using our gain calibration circuit. Figure 11 shows the gain during the two weeks. It can be seen from Figure 11 that the gain stability is less than \(\pm\)0.2 dB for the L-band, \(\pm\)0.22 dB for the C-band, \(\pm\)0.21 dB for the X-band, and about \(\pm\)0.25 dB for the Ka-band. The gain is very stable over a long time, thus guaranteeing the RCS stability of the ARC.  
Figure 11: Cont.  
Figure 10: Outer view of the RF box: (**a**) one side; (**b**) the other side.
# Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II
## 2 Path Optimization Problem Description
### Traveling Salesman Problem  
The toolpath optimization problem can be expressed using the TSP framework, where each toolpath is considered a city coordinate that requires a visit. In describing the toolpath, the decision variable \(k\) is employed, where \(k_{ij}=1\) indicates that the salesman (cutting tool) moves from city (machining) \(i\) to city \(j\) to the ending toolpath. Conversely, \(k_{ij}=0\) signifies that the cutting tool does not move from city \(i\) to city \(j\) within the total toolpath. The TSP problem, which is a minimization problem, can be formulated as follows [48]:  
\[\min\sum_{i=1}^{n}\sum_{j=1}^{n}c_{ij}k_{ij} \tag{1}\]  
where \(c_{ij}\) represents the Euclidean distance from city \(i\) to \(j\). To guarantee that each city \(j\) is visited a single time during the tour presented in (2), Equation (3) guarantees that the cutting tool departs from each individual city only once.  
\[\sum_{i=1}^{n}k_{ij}=1;\ \forall_{j} \tag{2}\]  
\[\sum_{j=1}^{n}k_{ij}=1;\ \forall_{i} \tag{3}\]  
Finding the most efficient solution for TSP problems is not feasible, and heuristic algorithms are employed to obtain solutions that closely approximate the optimal solution within a relatively short running time.
## 4 Implementation and Results
### Study I  
We simulate the multimedia traffic in two different scenarios. First we measure the delay, PSNR and throughput in scenario where there is no mechanism exists for detection of malicious data and malicious node as shown in Figures 2-4.  
\begin{table}
\begin{tabular}{|l|c|} \hline
**Parameters** & **Values** \\ \hline Channel & Wireless \\ Vehicles & 3 \\ MAC protocol & 802.11 \\ Radio Propagation Model & Two-Ray Ground \\ Time & 50 s \\ Data type & multimedia \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Settings.  
Figure 2: PNSR.  
In this study we have three Vehicles (V1, V2 and V3) that are moving at very high speed.V2 and V3 want to share multimedia traffic with V1 and V2 is a malicious node that sends malicious data to V1 and affects the performance of network. V1 has no framework to determine the validity of data and it considers both V2 and V3 as fair nodes. The delay in this case is higher and throughput is lower because of the effect of malicious data.
# InP-Based Foundry PICs for Optical Interconnects
## 2 InP-Based Generic-Foundry Photonic Integrated Circuits
### InP-Based Integration Technology for Generic-Foundry Service
The diluted waveguide has a much larger mode size (mode size = \(10\times 7\) um\({}^{2}\)) than that of the other waveguides, and it allows for an efficient and alignment-tolerant in/out-coupling to a single-mode fiber (SMF; less than 2 dB coupling loss). After completion of the epitaxial growth steps, the individual Tx BBs are mesa-structured using lithography, dry-, and wet etching processes. The semi-insulating doping eliminates optical waveguide loss by free carrier absorption and favors achieving a high electrical bandwidth. Since the substrate is semi-insulating, the n- and the p- metal contacts are both on the top surface of the PIC, and this facilitates on-wafer electrical testing without introducing any electrical crosstalk between the different devices. Additionally, our integration platform also offers polarization converters and splitters. In order to achieve polarization rotation, a so-called asymmetric waveguide is needed, and Figure 2b shows one of the two possible asymmetric waveguides that we can realize in our platform (the asymmetry is introduced wet-chemically, which depends on the crystallographic direction of the waveguide). The working principle of the polarization rotator is described in detail in a previous study [17]. Figure 2: Schematic of the spot-size converter seen in a bird ’s view perspective with a plot of a simulation of the light propagation on the side (**a**), and a scanning-electron-microscope (SEM) photograph of the asymmetric waveguide used in the polarization rotator (**b**).
## 3 Results and Discussion
### SnowSense Service for the Island of Newfoundland
#### 3.2.2 Exploits River  
The analyzed run-off results for the down-stream gauges \"Below Noel Pauls Brook\" and \"Charlie Edwards Point\" at the Exploits River show a very notable agreement regarding timing and volume with the measured values in the winter season from December 2017-May 2018 (Figures 9 and 10). Due to the strong influence of the controlled water release at Millertown Dam, and the not daily adapted model parameters during the demo, the results are a little below the ones for Humber River. The coefficient of determination (\(\mathbf{R}^{2}\)) reaches 0.80 at Noel Paul Brook. For the downstream section of the Exploits River, after the conjunction with Badger River, the occurrence of river ice impeded the full evaluation of the results. Within the period of mid-December to late April, the measurement point was affected by ice jams, which resulted in an increased water level and therefore a false determination of the run-off. Due to the thawing in April, the flood peak from the snow melt could be well compared (Figure 10).
## 2 Islanding Detection Methods
### Hybrid Methods
#### 2.3.4 Hybrid Sandia Frequency Shift and Q-F Method  
Because the optimal gain kf, relies on the quality factor of the load, the Sandia Frequency approach is modified in this method by adding a Q-f droop curve to reduce NDZ. This gain value may be excessive when the quality factor exceeds five, which leads to erroneous detection and system instability. Reactive power is managed by the main grid after it is connected. While DGs operate at unity power factor and produce no reactive power during islanding operation, a frequency gap between the actual and rated system frequency is formed. This technique, with a detection period of 1.4 s, tracks this frequency variation for islanding detection [31].
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
###### Abstract  
The present work numerically investigates the flow behavior of Newtonian fluid between rotating disk systems. Rotating disk flow is an embranchment of fundamental flow research, which forms the theoretical basis for the flow media in many disk-type hydraulic machinery, and has been widely applied in energy, aerospace, vehicles, medical, and chemical areas. The Reynolds equation model of the rotating disk flow is established based on a series of assumptions, and considers the characteristics of hexahedral surface textures on the friction disk, and the control equation is dimensionless. The velocity, pressure, and pressure coefficient in oil film are solved by finite difference algorithm, and the shear cavitation incipient position is predicted. Graphical visualization and numerical data about cavitation characteristics are also presented. Furthermore, the cavitation process is researched in CFX and the experiment, eventually verifying the correctness of the numerical analysis. The pressure perturbation caused by texture boundaries and the low pressure at the vortex center induce the initial generation of cavitation, and cavitation effect can obviously enhance lubricating performance. The cavitation region extends from micro-dimples towards non-textured region when texture rate increases. This study can guide design of surface structures such as mechanical seals, wet clutches, and disk turbines.  
hydraulic machinery; cavitation prediction; hydrodynamic lubrication; surface texture Article
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Evaluation  
In order to compare the performance of underlaying schemes following evaluation metrics are considered.  
* Stability period: Time from the start of network operation till the death of first nodes is termed as stability period, whereas time from the death of first node till the death of last node is termed as instability period.
* Halflife: Halflife of network is defined as the time till the death of half nodes.
* Lifetime: Lifetime of network is the time till the death of all nodes.
* Average end to end delay: Average end to end delay is mean of accumulated sum of propagation and transmission delays of all nodes.
* Throughput: Throughput is considered in terms of following two aspects: 1. Packet sent to and received at sink: Number of packets sent to sink are dependent on type of application. In event driven or threshold sensitive applications some selected nodes are packet generator nodes, while in other type all the nodes have equal probability to generate the packet after some regular interval and sent to sink. Number of packets successively received at sink is less than the number of packets sent to sink. 2. PDR--Packet Delivery Ratio: PDR is the ratio of number of packets received at sink to number of packets sent to sink. In ideal case where no packet is dropped PDR is equal 1, otherwise its value is less than 1 and greater than 0.
* Transmission loss: Transmission loss is the average decrease in sound energy/intensity level as it propagates through water.  
Each metric is explained in details in the following subsections.