esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Expressway PTV Detection Algorithm for Over-the-Horizon
### PTV Quantitative Evaluation Algorithm
#### 4.5.1 Definition of PTV  
The preprocessed ETC data can show information about the vehicles travelling in expressways. Therefore, in this work, the PTV is extracted from ETC data. In this paper, four typical metrics are defined based on ETC data.  
**Definition 1**.: _(Over Speed (OS)) OS is one of the key factors leading to traffic accidents, indicating that if the current vehicle speed exceeds the speed of over-the-horizon vehicles, that is, if the speed is greater than \(V\) (for example, \(V=100\) km/h), it is considered as OS._  
**Definition 2**.: _(Vehicle Type (VT)) VT represents the type of vehicle in transit. Different types of vehicles driving at the same speed on the expressway have different levels of danger. Therefore, it is necessary to determine the type of vehicle and conduct dynamic hazard ratings based on different vehicle types._  
**Definition 3**.: _(Longtime Driving (LD)) LD represents the cumulative driving time of a vehicle on an expressway section. If the cumulative driving time of a vehicle is too long, it will lead to sleep-deprived driving of the driver, which will lead to traffic accidents. If the cumulative driving time exceeds a certain threshold, it is considered as LD. According to Chinese traffic regulations, this threshold is usually set to 4 h._  
**Definition 4**.: _(Traffic Flow (TF)) TF represents the number of vehicles passing through the same road section (in the same direction) within a certain period of time. When the traffic flow in a section exceeds a certain threshold, it is considered more dangerous, such as TF > 1500._  
Based on the above four indicators, if a vehicle has a large traffic volume, is of a type that is prone to accidents, has traveled for a long time on that road section, and is fast, then the vehicle may be considered a PTV. Of course, this also requires a comprehensive judgment based on other actual situations and data and cannot rely solely on a single indicator for evaluation. Although more indicators may improve the accuracy of detection, this will increase the difficulty of data collection and the cost of deploying and maintaining new equipment. For example, if a truck or specialized work vehicle is driving in a fast lane or frequently changing lanes, it will be detected as a PTV, but it is difficult to extract vehicle lane data from ETC data.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 6 Evaluation
### Experimental Settings and Methodologies
First, the awake EH-nodes will keep active for 20 s, generating and forwarding packets, and then sleep for a period randomly chosen from 20 s to 30 s. Second, the transmit power of all nodes increases up to \(-\)10 dBm, in order to avoid the occurrence of too many segments in the network. In scenario AllEH, each awake node generates packets at the rate of 0.25 Hz. Unlike EH1 and EH2, the debugging logs of each AllEH node were stored in the on-board ROM (read-only memory) which actually is a kind of serial flash chip. Since the Telosb ROM involved in our testbed is only 1024 KB in size, the AllEH experiment only ran 10 min for each protocol, aimed at avoiding the ROM overflow caused by a great amount of runtime log data. After each experiment was done, we transfered the in-ROM logs of each node, through USB wire, to the PC for further analysis. During the initialization of AllEH experiments that had no USB connections with the PC, we let the sink (node 1) broadcast a set of messages with the maximum transmit power (0 dBm), which were used to (1) synchronize the global clock of each node and (2) boot each node with a random delay ranging from 30 s to 60 s such that not all the nodes would enter their first sleeping state at the same time. The time synchronization done only once in the network initialization is sufficient because the local clock skew at each node is reasonably negligible in the experiment that lasted for only 10 min [44; 45]. Followed are five metrics used to evaluate the performance of both La-CTP and CTP. * _Network throughput_: the ratio between the number of packets received by the sink and the number of packets generated during experiment. * _Number of parent updates_: the total number of parent updates that all the nodes perform during experiment. This metric can reflect the dynamics of the network topology. * _Number of detected loops_: the total number of loops that occurred during experiment. This metric is used to weigh the performance that the protocol handles the loops. * _Average time of loop removement_: if parent node \(u\) determines a loop-detection event just upon receiving a packet from child \(v\) at time \(t_{\mathrm{s}}\) and in the following time \(t_{e}\), \(u\) receives another packet from \(v\), yet without reporting loop detection, then this loop through \(u\) and \(v\) is reckoned to have been removed and the removement time of this loop is calculated by \((t_{e}-t_{\mathrm{s}})\). We use the average time of loop removements in the routing performance evaluation. * _Numbers of beacons_: the number of beacons sent by nodes and the number of beacons received by nodes in the routing establishment and maintenance. We use this metric to approximately measure the energy overhead needed by the routing protocol, because the exact energy consumption cannot be measured at the nodes powered by USB lines.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 3 Smart Public Lighting: Infrastructure Architecture and Main Tasks
### Electric Parameters Monitoring  
LCs can be differentiated also according to the quantity and quality of information they can acquire about the electrical network. For instance, beyond active and reactive power consumption, further parameters can be of interest: voltage, current, power factor (cos phi), average consumption (hourly, daily, weekly, monthly, yearly).  
The real-time acquisition of such values turns LCs into a distributed analyzer of the lighting infrastructure. Collected information, although not directly concerned with the energy saving objective, are useful to optimize the maintenance of the lighting plant. For instance, hardware failures and faults can be immediately detected and even, in some cases, anticipated. The real-time monitoring allows therefore predictive maintenance, which reduces expenditures avoiding costly emergency repairs, which sometimes also requires modifications to vehicular circulation. Moreover, such real-time distributed analyzer provides insightful data on the operating condition of the city power grid to which it is connected, which are valuable information for the grid operator.  
Therefore, LCs can be classified according to their capability of providing data with a high time resolution, rather than simply providing aggregated (e.g., averaged) data on a daily basis. In terms of technical requirements, a real-time monitoring of a multitude of parameters claims for higher technical performance, but allows a more effective infrastructure management.
# Embedded Deep Learning for Ship Detection and Recognition
## 2 Related Work
### Embedded Object Recognition  
Embedded image processing has been attracting a lot of efforts. In [15], [PERSON] et al. designed a full-featured license plate detection and recognition method using DSP. The processing core is a single Texas Instruments fixed point DSP with 1 MB RAM. Additionally, a slower SDRAM memory block of 16 MB exists. It can achieve real-time performance. In addition, [PERSON] and [PERSON] [16] implemented a license plate detection system on a DSP using the Hough transform. [PERSON] et al. [17] implemented a vehicle tracking and license plate recognition system on a PDA. An FPGA was used by [PERSON] et al. [18] to speed up parts of their license plate recognition system.  
There was research that ran the Fast R-CNN on Jetson TK1 platform [19]. Although additional modifications on the Fast R-CNN were made to fit TK1, the detection speed was very low (1.85 frames per second-fps). The work in [20] ran a seven-layer CNN on TDA3x SoC for object classification, and the overall system performance was 15 fps. Therefore, a powerful software/hardware platform is needed to support efficient embedded deep learning based real-time video processing.
## 3 Results and Discussion
### Optimization of Sample Preparation
#### 3.2.2 Re-Dissolution of the Samples  
Re-dissolution is a basic operation in the pretreatment of samples because its efficiency directly affects the recovery. To optimize the re-dissolution effects of the analytes, the efficiency of solutions with different percentages were compared, including: (1) an acetonitrile-water mixture containing 5 mmol/L ammonium acetate (10/90, _v/v_), (2) an acetonitrile-water mixture containing 5 mmol/L ammonium acetate (20/80, _v/v_), (3) an acetonitrile-water mixture containing 5 mmol/L ammonium acetate (30/70, _v/v_), (4) an acetonitrile-water mixture containing 5 mmol/L ammonium acetate (40/60, _v/v_), and (5) an acetonitrile-water mixture containing 5 mmol/L ammonium acetate (50/50, _v/v_). As shown in Table S1, with an increase in the proportion of acetonitrile, the recovery of ZEN increased sharply and then decreased slowly, while ZEN-14G always showed a steady trend. Satisfactory recovery rates were obtained for the two analytes when the acetonitrile-water mixture containing 5 mmol/L ammonium acetate in water (30/70, _v/v_) was chosen. This might be related to the fact that both analytes, which had different properties, had relatively good solubility in this re-dissolving solution.
## 2 Materials and Methods
### Dataset  
Developing an automated glaucoma detection system requires a lot of data to be trained. In this study, we used the ORIGA-light public dataset provided [25], which contained 650 images and was equipped with OD and OC ground truth, classification ground truth, CDR value, and diameter of OD. The ORIGA-light dataset provided 168 glaucomatous images and 482 healthy images. Table 1 illustrates the summary of the dataset used in our study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Description** \\ \hline Case of dataset & Glaucoma \\ \hline Type of data & RGB image \\ \hline Dimension of data & 3072 by 2048 pixel \\ \hline Availability & Online access in [19] \\ \hline Number of classes & 2 classes (normal and glaucomatous) \\ \hline Ground truth information & CDR value, segmentation ground truth of OD and OC, classification ground truth (normal and glaucomatous classes) \\ \hline Number of data in each class & 168 glaucomatous images and 482 healthy images \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic of ORIGA-light dataset.  
Figure 2: Flowchart of our proposed method.  
The ORIGA-light dataset has been studied intensively for several years and has produced various successes. As summarized in Table 2, several researchers used the ORIGA-light dataset in three ways, i.e., (1) analyzing the presence of glaucoma using CDR value; (2) segmenting the OD and OC; (3) recognizing the glaucoma damage using the characteristics of OD and OC. However, most of the studies only focus on image segmentation parts rather than glaucoma detection and classification. Therefore, in our study, we proposed an alternative method to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL).
## 3 Results and Analysis
### Simulation Experiment and Result Analysis  
The algorithms' comparison and analysis are arranged as follows: (1) first, compare AFS-MMSBAS algorithm with two basic algorithms and the hybrid algorithm of the basic algorithm based on different dimensions; (2) further analyze the stability and convergence speed of the AFS-MMSBAS algorithm in high dimensions; and (3) compare the performance of the AFS-MMSBAS algorithm and other related algorithms in high dimensions.
## 3 Residual Dense Convolutional Graph-U-Net-Based GAN for Target Speech Enhancement
### Generative Adversarial Network: Basic Concepts  
GAN was proposed by [PERSON] et al. in 2014 [35]. Its ability to generate realistic images and generalize well to pixel-wise, complex distributions has led to its widespread use in numerous applications in image restoration, computer vision, speech processing,wireless communications, etc. In speech enhancement, GAN architecture is capable of providing fast enhancement performance due to the absence of any recursive operation like RNN. Moreover, GAN architecture is able to learn from different speakers and noise types, which renders it capable of providing good target speech enhancement performance under noisy conditions. A GAN includes two models, the generator (\(G\)) and the discriminator (\(D\)). The generator (\(G\))'s main task is to learn an effective mapping, which can imitate the real data distribution to generate novel samples related to those of the training set. This is achieved by G when it solely maps the data distribution characteristics to the manifold defined in the prior distribution, without memorizing input-output pairs. The GAN operates through an adversarial process which can be formulated as a mini-max game between \(G\) and \(D\). The objective function of the mini-max game can be expressed as follows:  
\[min_{G}max_{D}V(D,G)=E_{x\sim p_{data}}[\log(D(x))]+E_{z\sim p_{z}(z)}[\log(1-D (G(z)))] \tag{16}\]  
Several GAN architectures have been applied to target speech enhancement [21; 22; 23; 24; 25], where most of these are based on the conditional GAN (c-GAN) approach [24]. While traditional GAN can generate realistic samples, there is no control on the data being generated in such as an unconditional generative architecture. For speech enhancement tasks, it is necessary to control the generated data samples based on the noisy input samples (for e.g., received multi-channel speech mixture). This issue is addressed by the c-GAN framework, where the neural architecture is conditioned to control the data generation process based on the input data. c-GANs have demonstrated promising performance in noise suppression. The c-GAN architecture is trained to generate the spectrogram of clean speech given the noisy speech spectrogram; however, it ignores the phase mismatch problem.
## 3 Numerical Examples
### Effect of Diameter-to-Thickness Ratio on the Longitudinal Modes of Thermoelastic Guided Waves  
In this section, the effect of different diameter-to-thickness ratios on the dispersion relation of thermoelastic guided waves is investigated. That is, the phase velocity dispersion curves of four longitudinal thermoelastic guided wave longitudinal modes at the same wall thickness and different diameter-thickness ratios are calculated. The thickness is taken as 1 mm, and the outer diameter is taken as 5 mm, 10 mm, 15 mm, and 20 mm, and their diameter-to-thickness ratios are 5, 10, 15, and 20.  
The calculation is carried out with the up-to-term M = 10 and the circumferential order n = 0. The results are shown in Figure 6 below, where the diameter to thickness ratio is denoted by \(\eta\).  
Figure 6: Axial thermoelastic waveguide dispersion curves for silicon nitride pipes with different diameter-to-thickness ratios for the same wall thickness: (**a**) \(\eta\) = 5; (**b**) \(\eta\) = 10; (**c**) \(\eta\) = 15; (**d**) \(\eta\) = 20.  
In order to facilitate the analysis of the change rule of phase velocity under the above four diameter-to-thickness ratios, the first three modes in the above four diagrams are combined into the following Figure 7 for analysis. By analyzing the local enlargement of the three regions in Figure 7a-c, it can be seen that the effect of the diameter-thickness ratio on the dispersion of the longitudinal modes is as follows: In the dispersion curves of the first two modes, with the reduction in the diameter-thickness ratio, the cutoff frequency is gradually increasing. That is, in the figure, it is expressed as the curve shifted to the right. And, for the thermal modes, the cutoff frequency with the reduction in the diameter-thickness ratio is gradually reduced. The two modes show opposite trends.  
Figure 7: Dispersion curves of pipes with different diameter-to-thickness ratios: (**a**) dispersion curves for the first three modes; (**b**) localized enlarged view of region a; (**c**) localized zoom in region b; (**d**) localized zoom in region c.
# Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
## 2 State of the Art: Renewable Energy Zones (REZs)  
In order for the grid to be capable of supporting the planned development scenarios [4], it is necessary to create and optimize so-called \"Renewable Energy Zones\" in the area. A Renewable Energy Zone is a geographical area connected to the local electricity grid that allows for the profitable and cost-effective production of renewable energy [8].  
The United States Agency for International Development (USAID) proposes a comprehensive methodology for developing a REZ in six operational phases: process design and vision statement, renewable energy resource assessment, candidate zones selection, transmission options development, final transmission plan design, transmission system upgrade [8].  
One of the first countries to make explicit use of the REZ approach is Australia. Australia has some of the world's most abundant renewable energy resources, but the current grid capacity to connect these high-potential areas is limited. As highlighted in the Australian Energy Market Operator's (AEMO) Integrated System Plan, the establishment of Renewable Energy Zones (REZs) will be essential to harness these robust resources and enable new generation and storage capacity in the National Electricity Market (NEM) at the lowest cost to consumers [9].  
To address this, the Australian Renewable Energy Agency (ARENA) commissioned Baringa Partners, in collaboration with DIgSILENT Pacific, to undertake a study of the technical, commercial, and regulatory challenges of developing REZs in the NEM. The study also aimed to identify practical commercial and regulatory solutions to support the efficient and sustainable implementation of REZs [9]. DIgSILENT is an independent consulting company providing highly specialized services in the field of electrical power systems. DigSILENT's software called \"PowerFactory (version 3.0)\" is used for the analysis and simulation of electrical systems. It allows you to model transmission and distribution networks and perform power flow calculations, fault analysis, dynamic simulations, and harmonic studies.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 4 Detection Algorithm and Its Applicability
### Implementing Algorithm and Its Procedures
#### 4.1.1 SSD Model  
We used an input image of size \(300\times 300\) pixel for implementing the SDD model. Then, we used a VGG16 net as its backbone network without its fully connected (FC) layers. Six auxiliary convolutional layers were added with distinct kernel sizes that aid in detecting our target objects at multiple scales. However, the convolutional layers decreased the feature map sizes, while increasing the depth progressively (see Figure 9).  
Figure 8: SG2-ADA network architecture. (**a**) SG2 generator (**b**) SG2 discriminator Hence, SG2 performs data augmentation after the input vector for both (**a**,**b**).  
Our feature maps are of sizes \(38\times 38\), \(19\times 19\), \(10\times 10\), \(5\times 5\), \(3\times 3\), and \(1\times 138\times 38\), \(19\times 19\), \(10\times 10\), \(5\times 5\), \(3\times 3\), and \(1\times 1\) while computing both the location and the class scores using a \(3\times 3\) convolution filter. For the loss function, regression (localization) loss, and classification (confidence scores) loss (see Equations (10), (11) and (12), respectively).  
\[L(x,c,l,g)=\frac{1}{N}\left(L_{conf}(x,c)+\alpha\ L_{loc}(x,l,g)\right) \tag{10}\]  
\[L_{loc}(x,l,g)=\sum_{i\in Pos}^{N}\sum_{m\in\{cx,cy,w,h\}}x_{ij}^{k}smooth_{L1} \left(l_{i}^{m}-g_{j}^{m}\right) \tag{11}\]  
\[L_{conf}(x,c)=-\sum_{i\in Pos}^{N}x_{ij}^{p}log\left(x_{i}^{p}\right)-\sum_{i \in Neg}\log\left(x_{i}^{p}\right) \tag{12}\]  
where  
\(\hat{c}_{i}^{p}=\frac{exp(\hat{c}_{i}^{p})}{\sum_{p}exp(\hat{c}_{i}^{p})}\)  
\(N=number\ of\ posit\ matches\)  
\(\propto=weight\ for\ the\ localization\ loss\)  
\(l=predicted\ bounding\ box\)  
\(g=ground\ truth\ bounding\ box\)  
\(\hat{c}_{i}^{p}=softmax\ activated\ class\ score\ for\ default\ box\ i\ and\ category\ p\)  
\(x_{ij}^{p}=matching\ indicator\ between\ default\ box\ i\ and\ groundtruth\ box\ j\)  
\(\hat{c}_{ij}^{k}=matching\ indicator\ between\ default\ box\ i\)  
and\(ground\ truth\ box\ j\ of\ category\ k\)
## 2 Materials and Methods  
### Experimental Set-Up  
X-ray beams from a static X-ray tube (Quantum Medical Imaging, New York, NY, USA) were perpendicularly incident on 12 V DC SMD 5050 cold white LED strips. These cold white LED strips constitute a gallium-nitride (GaN) semiconductor that was coated with a phosphor. Each strip had 12 chips, and the sensitive area for each chip was approximately 12.6 mm\({}^{2}\). During the LED strip fabrication, these chips were connected in parallel by the manufacturer.  
The X-ray tube was operated in a non-automatic exposure control (AEC) mode, and the field size was collimated to 26 \(\times\) 26 cm\({}^{2}\) for all of the exposures in this study. The tube voltage was manually adjusted in steps of 10 from 70 to 140 kVp, while maintaining a constant tube current-time product and source-to-detector distance (SDD) of 80 mAs and 60 cm, respectively. However, the tube current-time product setting had pre-set values of 20.2, 25.6, 32, 40, 51.2, 64, 80, 102.4, 128, 160, 201.6, and 256 mAs and the tube voltage and SDD were kept constant at 120 kVp and 80 cm, respectively. While fixing the tube voltage and current-time product at 120 kVp and 256 mAs respectively, the SDD was varied from 60 to 90 cm--in 5 cm steps. A 30 cm \(\times\) 30 cm \(\times\) 8 cm solid water phantom slab absorbed the treatment couch sourced backscattered radiation, as observed in Figure 1.  
Ambient light signals were mitigated by executing the irradiations in a light-tight surrounding and then masking the LED chip sensitive areas with black vinyl tape. Despite these mitigations, an ambient signal (0-0.2 mV)--perhaps due to ambient temperature/heat, was observed before irradiations; whilst directly injecting the signal into the multimeter. The ambient signal was deducted from the total detected signal to obtain the net radiation-induced signal.  
A single-channel dosimeter probe (A-CF1E1003, Iba Dosimax plus: Schwarzenbruck, Germany) was simultaneously irradiated with the LED chips. The dosimeter's measurable parameter ranges were absorbed dose; 200 nGy-9999 mGy, dose rate; 80 nGy/s-70 mGy/s for 50-150 kV; and time (1 ms-19,999 s).  
An electric cable wire for LED strip lighting (Red and Black Tinned Copper 22 AWG wire, Lustreon) was deployed for inter-strip connections. The cable also transmitted the raw photovoltaic signal (radiation-induced current) to a digital multimeter/oscilloscope (Model JDS2012A, Jinhan Company: Hefei, Anhui, China). The signal reading was displayed in the form of voltage--mV.