esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Spatial Variability Assessment  
The estimation accuracy of the evaluated precipitation products over seven regions of Malaysia that differed in their precipitation regime and topography, with information on the spatial distribution of R\({}^{2}\) for daily and monthly precipitation, is presented in Figures 4 and 5, respectively. At the daily scale, 3B42V7 and APHRODITE were in best agreement with the rain gauges, with the highest R\({}^{2}\) recorded of 0.57 (station 48616) and 0.82 (station 48618), respectively. At the monthly scale, good performance of R\({}^{2}\) was found for 3B42V7 and APHRODITE, which varied from 0.3 to 0.94 and 0.3 to 0.91, respectively. Interestingly, the highest R\({}^{2}\) values for all products were generally found in the eastern and southern PM and the northern EM, all characterized by high rainfall amounts because of the NEM. Similar  
Figure 4: Coefficient of determination (R\({}^{2}\)) of daily precipitation between rain gauges and (**a**) 3B42 RT, (**b**) 3B42V7, (**c**) GPCP-1 DD, (**d**) PERSIANN-CDR, (**e**) CMORPH and (**f**) APHRODITE over Malaysia.  
findings, _i.e._, better SPP performance in wetter regions than dry ones, were previously reported by [PERSON] _et al._[45].  
The poorest daily and monthly performance was observed in the western PM at low terrain. This finding contradicted previous studies [13, 61], showing that SPPs are generally more reliable in smooth terrain compared to mountainous terrain. A possible reason for the trend observed in the present study is that the heavy rain brought in by the Southwest and Northeast monsoon is dramatically reduced by the Sumatra mainland and Titiwangsa Range before reaching the western PM. This indicated that SPPs have difficulty in estimating the convective precipitation that occurred during the inter-monsoon periods. Surprisingly, for two rain gauges (station 48631 and 48632) located in high mountainous regions, all precipitation products showed a moderate R\({}^{2}\) value (0.48-0.83) for a monthly timescale. The fact that the SPPs could not achieve very good performances over mountainous regions might be due to the strong scattering of the microwave signal by mountains [35] and miss the discrimination of warm clouds by IR sensors that commonly appear on the top of mountainous regions [11].
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
## 2 Related Work
### Unsupervised Deep Learning Methods  
In contrast to supervised learning, unsupervised deep learning methods enhance low-light images without paired image datasets. Some approaches treat image enhancement as an image-specific mapping estimate and will employ deep learning to calculate the best mapping. Zero-DCE [24] was the first to propose a low-light enhancement network that does not need paired training data. It designed a per-pixel high-order curve, demonstrating the potential of training image enhancement networks through a no-reference loss function in the absence of reference images. Subsequently, an improved version, Zero-DCE++ [25], was proposed, which leverages the advantages of a mini-network with only 10K parameters, resulting in significantly improved computation speed. Additionally, [PERSON]. [26] developed a novel self-calibrating illumination (SCI) learning framework and established a cascade illumination learning process with weight sharing to handle this task. GANS [20] is also used for low-light image enhancement [18; 19; 27; 28]. These methods employ generative adversarial networks to encourage the distribution of the generated images to approach the distribution of the target images without pairing supervision. The CycleGAN model [19; 29] introduced in this paper presents a method for learning to translate images from the source domain X to the target domain Y without paired examples, providing inspiration for generating \"paired\" images. There are, indeed, several image enhancement methods based on improved CycleGAN [30]. [PERSON] et al. [31] proposed an improved CycleGAN model that incorporates perceptual loss into the generator's objective function. This enhancement aims to retain more image details and structural information in the low-light image enhancement process.
### Literature Review
#### 1.2.2 Method of Mooring Area Detection  
Some valuable work was carried out on MASS mooring area detection. The essence of mooring area detection is to detect a circular anchor area, which can be obtained by the method of Euclidean space distance, image identification [38; 39] and so on. In [40], the adaptive genetic algorithm was applied to optimize the automatic detection results of mooring area based on the gray system and an artificial neural network, and the final detection results of mooring area were output while the artificial intelligence system completed the regional detection of the mooring area. However, the anchorage circle radius and safety distance model were inaccurate. In [35], the disc packing algorithm was applied to optimize anchorage space utilization and specify the anchor position. In [41], the calculation model of the anchorage space capacity of standard ships was established based on the Monte-Carlo algorithm. In [26], the Multi-Objective Anchorage Planner (MOAP) algorithm was studied based on safety and efficiency, and verified with historical data and the Monte-Carlo algorithm. The MOAP algorithm could provide safer anchorage planning while maintaining the same optimization level. The Monte-Carlo algorithm can provide a fast simulation method. In [42], the reliability calculation of anchorage capacity and the redundancy optimization model was proposed, which would effectively solve the problem of optimizing anchorage space utilization under the influence of anchorage capacity uncertainty.  
In conclusion, the current research of anchorage detection mainly focuses on the length of the anchoring chain, anchorage radius, safety spacing of anchoring ships, anchorage planning, utilization rate of anchorage space and so on. The factors mentioned above could play a certain supporting role in the detection of mooring area. Only a few of studies have applied the Adaptive Genetic Algorithm [20] to optimize the automatic detection results of the mooring area in the gray system and artificial neural network system and the artificial intelligence system to detect the mooring area, and to output the final detection results of mooring area. However, the anchorage radius and anchor spacing model used in this method might be inaccurate.
# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 4 Conclusions  
In this study, we constructed a 2D CNN Taguchi parametric optimization method for recognizing lung cancer from CT images. We successfully applied the Taguchi method to determine optimal 2D CNN architecture to increase the classification accuracy of lung cancer recognition, which was verified by running an eight-factor mixed-level experimental design. Additionally, our results identified the significant factors affecting the extraction yield. In this study, the LIDC-IDRI and SPIE-AAPM datasets were used to verify the proposed method. In the LIDC-IDRI dataset experimental results, the deep features + kNN + mRMR method for lung nodule type classification in a CAD system, the accuracy was 99.51%. Although this is better than the 98.83% result for the proposed method, the sensitivity, specificity, and FPR for the proposed method are superior to their results. In the SPIE-AAPM dataset experimental results, the best accuracy of the BPNN-VIW method in classifying lung CT was 97.2%. The proposed method had an accuracy of 99.97%, so it is better than the previously proposed CAD system. The experimental findings showed the proposed method provides increased classification accuracy, the essential model design with parametric optimization is necessary.  
The classification accuracy of lung cancer using the original 2D CNN was 91.97% on the LIDC-IDRI dataset; the lung cancer classification accuracies for the three experiments with the proposed method were 98.04%, 98.91%, and 99.53%. Hence, the average accuracy rate of three observations of the proposed method was 98.83%. In addition, the lung cancer classification accuracy of the original 2D CNN was 94.68% on the SPIE-AAPM dataset; the accuracies were 100%, 99.90%, and 100% for the proposed method. Hence, the average accuracy rate of three observations of the proposed method was 99.97%. The accuracies of the proposed method were 6.86% and 5.29% higher than the original 2D CNN on the LIDC-IDRI and SPIE-AAPM datasets, respectively. The findings show that the proposed 2D CNN with Taguchi parametric optimization is more accurate for this task.  
The limitation of this study is the depth of layers. However, the 2D CNN with Taguchi parametric optimization shows potential feature learning and is robust to variable sizes of training and testing datasets. Future studies should focus on the optimal size of the input patch for the deep learning algorithms development of new architectures to improve the computational efficiency and diagnosis of human diseases.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 108-2221-E-167 -026.  
The authors declare no conflict of interest.
## 4 Implementation and Discussion of Results
### Selecting the ARIMA Parameters  
The parameters used to construct the ARIMA model, along with their values, are: ARIMA (1,1,1) is a model with one auto-regressive (AR) term, one first-order difference, and one moving average (MA) term applied to the z variable, which indicates the linear trend in the data.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Method Based on Deep Learning  
Beyond 2014, deep learning methods have developed rapidly with special effects and excellent applications. In general, deep learning models mainly use the learnability of the network to extract features from multi-focus images and separate focused and defocused regions to generate full-focus fusion images. At present, convolutional neural networks (CNNs) are one of the popular models in this field. In addition, pulse-coupled neural networks (PCNN) and generative adversarial networks (GAN) also have many applications.
## 3 Discrete Event Simulation Model
### Equipment Modeling
#### 3.1.2 Costs Related to Reduced Speed Due to Aging  
The equipment model assumes that the production rate falls proportionally in relation with the age of a component following a factor \(\tau\):  
\[V_{m}(w)=V_{0}-\tau^{2}\cdot w_{m}(t,\varepsilon). \tag{10}\]  
In the same way as CM, an averaged production speed for the lifetime of a component, \(V^{*}\), is calculated:  
\[V^{*}=\frac{\sum_{m=1}^{e}V_{m}^{*}\cdot M_{m}}{M_{m}}. \tag{11}\]  
Adopting this value of the speed production average \(V^{*}\), the \"production time lost\" related to a reduced speed (\(t_{sl}\)) can be calculated. If only a fraction of the production system is considered, the production time lost is available as follows:  
\[t_{sl}(\mathbf{x})=L\cdot\left(1-A_{s}(\mathbf{x})\cdot\frac{V^{*}}{V_{0}} \right), \tag{12}\]  
where \(A_{s}(\mathbf{x})\) is the availability system resulting from the combination of the unavailabilities of the components. To finish with the different type of costs, the ones related to the production speed loss of the equipment (\(C_{sl}\)) in the period \(L\) can be evaluated proportionally to the production time lost:  
\[C_{sl}(\mathbf{x})=C_{lsl}\cdot t_{sl}(\mathbf{x}), \tag{13}\]where \(C_{lsls}\) is the average hourly cost due to non-produced items. By substituting Equation (12) into Equation (13), we have:  
\[C_{sl}(\mathbf{x})=C_{lsls}\cdot L\cdot\left(1-A_{s}(\mathbf{x})\cdot\frac{V^{* }}{V_{0}}\right) \tag{14}\]
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 1 Introduction
In comparison with the existing literature, the main contributions of the paper are mainly twofold. Firstly, the concept of quasi-cyclic switching signals (QCSS) is introduced to describe a more practical scenario, compared with the multi-agent systems with connected switching topologies [28; 29; 30; 31; 32; 33] or strict cyclic switching topologies including unconnected cases [35; 36; 37]. Secondly, the stability of the multi-agent tracking system with both stable subsystems and unstable subsystems is guaranteed as well as the conservativeness of switched system conditions is reduced by finding a set of switching signals with admissible quasi-cyclic signals. The remainder of the paper is organized as follows. Some preliminaries and the model formulation are presented in Section 2. The switched system with quasi-cyclic switching topologies (QCST) are introduced and the main theoretical results are established and proved in Section 3, followed by the simulation results with illustration of theoretical analysis in Section 4. Finally, the conclusions are drawn in Section 5.
# River Water Quality Monitoring Using LoRa-Based IoT
## 4 System Design  
The developed prototype is divided into two isolated subsystems, IoT Node and Gateway, as shown in the block diagram in Figure 10.  
Figure 8: Packet duration and spreading factor relationship (CR = 1, BW = 125 kHz).  
Figure 9: Packet duration and bandwidth relationship (CR = 1, SF = 7).  
In the IoT Node, which is installed next to the river under study, the water quality parameters (temperature, pH, conductivity and turbidity) are measured using independent and specific sensors. The data acquired are processed on a microcontroller board and then sent using LoRa modulation with a radio module (LoRa SX1276 transceiver (Semtech Corporation, Camarillo, CA, USA)).  
The microcontroller board, used in IoT Node, is the Arduino Mega 2560 Rev3 [21], which is based on the Atmega2560. This is an eight-bit microcontroller at 16 MHz, with 256 kB Flash program memory, 8 kB Static Random Access Memory (SRAM), and 4 kB Electrically Erasable Programmable Read-Only Memory (EEPROM). This board offers several interfaces: four Universal Asynchronous Receiver/Transmitter (UART), I2C, Serial Peripheral Interface (SPI), 54 digital inputs/outputs and 16 analog inputs.  
On these analog inputs, it is possible to measure signals that are multiplexed and converted to digital, using the microcontroller's ADC. The ADC has ten-bit resolution and a sampling rate of 15,000 samples per second and accepts input values of up to five volts, the same value as the supply voltage.  
In the Gateway, these data are received from the LoRa network with an equal transceiver, processed, stored and displayed in a dashboard developed in Node-RED [22].  
In this subsystem, the single-board computer Raspberry Pi 3 is used [23]. It is equipped with a 64-bit quad-core 1.4 GHz processor and 1 GB of Synchronous Dynamic Random Access Memory (SDRAM) memory. It has WIFI in 2.4 GHz and 5 GHz bands, Bluetooth 4.2, Gigabit Ethernet, four USB 2.0, HDMI ports and is fed to five volts. It has a Micro SD card slot where the operating system is installed. It also has a 40-pin connector where multiple pins of General-Purpose Input/Output (GPIO), a UART serial port, an I2C interface, and an SPI interface are made available.
## 4 Adaptive OFDM-IM Method
### Fuzzy Logic Optimization
#### 4.4.4 Defuzzification  
The fuzzification of output variables is a process that generates numerical results based on the output membership functions and the corresponding degree of point qualification. The definition of the output membership function is shown in Figure 13. Setting the output membership function as a Gaussian curve, compared to linear and trapezoidal curves, helps to smooth the relationship between the input and output variables to a certain extent, thereby reducing the system's sensitivity.  
To obtain an accurate output value, it is necessary to perform precise calculation on the fuzzy output obtained from fuzzy inference. This is achieved through defuzzification using the center of gravity method. The input value is passed through the corresponding membership function to obtain the corresponding output value. The defuzzified value is taken as the x-coordinate of the center of gravity of the shaded area formed by the output variable's membership function. More specifically, the output membership function can be sliced horizontally according to the corresponding degree, with the top portion removed. Then, the centroid of this shape is calculated, and the x-coordinate of the centroid becomes the defuzzified value. If \(Y(x)\) represents the resulting function and \(x\) denotes the horizontal axis, the center of gravity is calculated as follows:  
\[\text{COG}=\frac{\int Y(x)xdx}{\int Y(x)dx}. \tag{27}\]  
COG represents the suitability of the index parameter in the energy efficiency evaluation system, with higher values indicating better suitability and alignment with the fuzzy logic criteria. In the optimization of OFDM-IM systems based on fuzzy logic, by defining the fuzzy membership functions and rules for the given scenario, the system's performance for different index parameters can be evaluated. A larger fuzzy logic value indicates that the parameter better meets the energy efficiency maximization requirements of the scenario. By comparing the fuzzy output results for different parameters, the optimal index parameter for the OFDM-IM system in vehicular network scenarios can be determined.  
Figure 13: Membership function of fuzzy system output.
###### Abstract  
The precision and consistency of the microdroplet jetting procedure are crucial for the casting sand mold's performance during binder injection. The generation and jetting of microdroplets in piezoelectric printheads were examined in this study in relation to changes in specific jetting process parameters. Using finite element analysis and a simplified physical model of a microdroplet jetting device, an electromechanically coupled model of a microdroplet jetting device was created in order to study the characteristics of microdroplet jetting. A volume-of-fluid model was also created in order to study the microdroplet jetting process and perform repeatability tests. The effects of altering nozzle radius, actuation pulse width, intake velocity, and fluid viscosity on microdroplet jetting properties were then investigated using the models. We were able to control the development of satellite droplets thanks to the knowledge we gained about how each process parameter affected droplet status. This study demonstrates how the radius of the nozzle and the pulse width of the piezoelectric actuation signal have a significant impact on the jetting properties of piezoelectric printheads and the production of microdroplets. The quantitative correlations between process factors and jetting characteristics can be used to optimize microdroplet production and reduce droplet size. Finally, this study will help create control systems for microdroplet jetting operations and enhance the precision of 3D printed casting sand molds.  
3D printing; casting sand mold; piezoelectric printheads; microdroplet injection; forming accuracy
###### Abstract  
This paper presents a new shape (s-shape monopole) of a super wideband antenna using stepped meander lines, a quarter waveguide transformer feeding line, and a defected ground structure (DGS). The antenna will be used for multiple wireless communication applications like WIMAX/WLAN/ISM/UWB, and also for several wireless communication applications. The total dimensions of the proposed antenna are 35 mm \(\times\) 35 mm \(\times\) 1.57 mm or 0.36 \(\lambda\)o \(\times\) 0.36 \(\lambda\)o \(\times\) 0.016 \(\lambda\)o, which are the corresponding electrical dimensions with free-space wavelength (\(\lambda\)o) at the lower operating frequency. The antenna is designed and simulated into two steps: the first (Antenna 1) covers a bandwidth of 18.2 GHz, while the second (Antenna 2, using DGS) covers a super wide bandwidth of 37.82 GHz (3.08-40.9 GHz). The measured fractional bandwidth and bandwidth ratio of the antenna are 174.68% and 13.009:1, respectively, which is operating from 3.09-40.2 GHz. The maximum calculated gain and efficiency are 5.9 dBi and 92.7%, respectively. The time-domain performance is good due to the calculation of the system fidelity factor, group delay, and its linear and constant phase variation.  
bandwidth ratio; DGS; stepped meander line; super wide band; time-domain analysis 2020201735; doi:10.3390/s20061735
# Application of Mammalian Nudix Enzymes to Capped RNA Analysis
## 6 Other RNA Decapping Enzymes  
Decapping of (m)RNA cap structures was shown in vitro for NUDIX enzymes in other domains of life--in plants [138], bacteria, or viruses [139]. Among four tested enzymes of the model plant _Arabidopsis thaliana_, the AtNUDT27 appeared as effective enzymes with a broad activity towards various cap structures: unmethylated, monomethylated m7G-caps, and non-canonical caps [138]. RppH bacterial NUDIX hydrolase was initially identified as activity that hydrolyzes 5\({}^{\prime}\)-triphosphate from RNA leaving 5\({}^{\prime}\)-monophosphate RNA [140], and subsequently it was demonstrated to cleave m7 GpppG- and GpppG-capped RNA [116] and NAD-capped transcripts [133]. deNADB activity was also attributed to bacterial NudC enzyme [141].  
Apart from the NUDIX-type decapapping enzymes, at least three other classes of enzymes are involved in processing cap structures at the 5\({}^{\prime}\)-RNA end: DXO family proteins, HIT (histidine triad) proteins, and ApaH-like phosphatase found in trypanosomes [139]. In the case of DXO, it removes cap structure through cleavage of the entire dinucleotide from the RNA body (e.g., GpppG, m7 GppG, m3\({}^{\text{2,2,7}}\)GppG), with a preference towards unmethylated structure, which was connected to functions in the mechanism of 5\({}^{\prime}\)cap quality control [142]. 2\({}^{\prime}\)O-methylation of the first and the second transcribed nucleotide found in the Cap 1 and Cap 2 structures of mRNA, respectively, caused resistance to DXO decapping [101,143]. Apart from decapping activity, DXO can also remove non-canonical caps (NAD, FAD, and dpCoA) and has activity toward uncapped triphosphorylated RNA [144].
## 5 Results
### Security Analysis  
In the proposed system, it is assumed that some vehicles and RSUs may be malicious or attacked. Therefore, the system should be able to tolerate these abnormalities. In accordance with the work in [33], malicious vehicles may send fake messages to others, which can lead to disturbance. In the proposed system, trust is calculated based on the Bayesian inference on the reputation of the vehicles and RSUs. Therefore, sending fake messages cannot disturb the system. Another scenario is that vehicles send fake message reliability to RSU, but due to Bayesian inference in RSU, this case cannot cause disturbance.  
Trust to RSUs is the most important issue in this kind of VANET. All agents (i.e., RSUs and vehicles) in the network are selfish. This means that they are intending to maximize their own payoffs. Ignoring this concept can result in failing mechanisms. For example, a recent study by [PERSON] et al. [33] ignored the RSU's selfishness. They have assumed each RSU mines a block to add to the Blockchain, with a network's difficulty determined by itself. The network's difficulty is calculated based on the received messages. However, due to the rationality (or hacking), a RSU can set a low network's difficulty in order to faster mining and getting more payoffs. In our proposed system, committees use a public random string epochRandomness to make certain that the members randomly (not precomputed) compose the committee.