esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data  
### Study Area  
The study area is located in Zhuzhou city, which is a primary grain production base for the Hunan Province, China. Historically, Zhuzhou is one of eight key national industrial cities. Therefore, with the development of industry, Zhuzhou has become an area with serious heavy metal pollution. The Xiangjiang River, which is the main irrigative river through the region, continually transports industrial pollutants to farmland [34; 35; 36]. Paddy rice is the main grain crop in Zhuzhou. Boyou9083 is the primary type of rice in this area.  
Three subareas were selected (A, B, C) area A (\(113^{\circ}12^{\prime}30.38^{\prime\prime}\) E, \(27^{\circ}47^{\prime}31.01^{\prime\prime}\) N) is located northeast of the Zhuzhou municipal districts and is far away from the Xiangjiang River; area B (\(113^{\circ}10^{\prime}18.65^{\prime\prime}\) E, \(27^{\circ}40^{\prime}28.35^{\prime\prime}\) N) is adjacent to the Lujiang River, which is a tributary of the Xiangjiang River; area C (\(113^{\circ}2^{\prime}38.43^{\prime\prime}\) E, \(27^{\circ}50^{\prime}22.72^{\prime\prime}\) N) is a rice paddy near the Xiangjiang River (Figure 1). The physicochemical characteristics of the soil were investigated for the three areas, and it was concluded that the heavy metal concentration of area A was lower than that of its background level, whereas the concentration was greater than the background levels in areas B and C (Table 1). According to the Environmental Quality Standard for Soils in China (GB15618-1995, EQSSC) and the pollution index, the Cd concentration in the Zhuzhou clearly exceeded these standards. With respect to local soil background values, these areas were classified as being at a \"Safe\" level, \"Level I\" or \"Level II\". As environmental indicators, the mean annual temperature of the region is approximately 16-18 \({}^{\circ}\)C,and all of the areas belong to a subtropical monsoon climate zone with sufficient sunlight to grow paddy rice. The main soil type is orthicacrisol, which has a sufficient amount of organic matter (2-3%). The pH of the soil in the study area is between 5.0-8.5 [37]. To avoid other uncertain factors that might lead to growth stress, it was ensured that the paddy rice grew under adequate water with an irrigation channel system and timely fertilization.
## 1 Introduction  
Age-related macular degeneration (AMD) is the third leading cause of severe irreversible vision loss worldwide, and it represents the major cause of central blindness in developed countries, especially among people older than 60 years [1; 2; 3]. Prevalence data suggest that about 200 million of people are nowadays affected by AMD, and this value is expected to increase to nearly 300 million by 2040 [4].  
The advancement of imaging technology, in particular the use of optical coherence tomography (OCT) and OCT angiography (OCTA), has improved scientific knowledge on AMD, making mandatory a multimodal imaging approach for retinal and choroidal conditions. OCTA allows a clear and detailed visualization of retinal and choroidal microvasculature, and it is useful either for reaching the diagnosis or for guiding treatment choice and monitoring AMD patients [5; 6].  
In 2013, [PERSON] proposed a five-stage AMD clinical classification based on the risk of progression [7]. According to this classification, the presence of only small drusen \(\leq\)63 \(\upmu\)m without pigmentary abnormalities is considered normal aging change; early AMD is characterized of medium drusen \(>\)63 \(\upmu\)m and \(\leq\)125 \(\upmu\)m, while intermediate AMD (iAMD) is defined with the presence of large drusen \(>\)125 \(\upmu\)m and/or pigmentary abnormalities. Late AMD develops when macular neovascularization (MNV) or geographic atrophy (GA) occur [7].  
MNVs are a growth of abnormal vessel and associated tissues into the outer retina, subretinal space, or subretinal pigmentary epithelium (RPE) space. They are classified according to the anatomic location determined by OCT imaging into three types: type 1 is a growth of vessels from choriocapillaris that proliferates into and within the sub-RPE space; type 2 originates from the choroid, and it passes through the Bruch's membrane and the RPE monolayer proliferating in the subretinal space; type 3 develops from the retinal circulation, usually in the deep capillary plexus, and it grows toward the outer retina [8; 9].  
MNV are usually characterized by intraretinal or subretinal fluid within the macula, but recent studies demonstrated that type 1 MNV can present without exudation on OCT, but it can be well visualized by means of fluorescein angiography (FA), indocyanine green angiography (ICGA), and OCTA [10; 11]. The Consensus on Neovascular AMD Nomenclature (CONAN) group stated that this form of MNV could be identified more commonly with the improvement of imaging technology, but there is still not a consensus on which term to use [9]. In this article, we will use the term nonexudative MNV, even if there is a substantial difference between the term \"treatment-naive quiescent neovascularization\" used by [PERSON] and colleagues [10] and \"asymptomatic, nonexudative or subclinical MNV\" used by other authors [12; 13; 14; 15]. Treatment-naive quiescent neovascularization are MNVs without sign of activity for at least 6 months from baseline, while all the other terms refer to new diagnosis MNVs without exudation [10; 12; 13; 14; 15].
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 4 Results  
### Cross-Validation between MERSI-II, and MYD11 and MYD21 MODIS LST Products  
The validation of LST based on the measured data is a difficult task. [PERSON] et al. [42] put his extensive efforts toward correctly verifying the MODIS LST products with the measured data. His findings reveal that the MODIS MYD11A1 LST product in the experimental area are accurate to within 1 \({}^{\circ}\)C. Hence, for relative error analysis, we used the MYD11A1 and MYD21A1 LST products, and the MERSI-II-estimated LST data. The LST of two different raster datasets (MYD11A1/MYD21A1 and MERSI-II) were extracted by using the shapefiles of different sub-regions over the study area; the results of differences in LST between the MERSI-II, and the MODIS MYD11A1 and MYD21A1 products are shown in Figures 4 and 5.  
It was observed that the RMSD between MERSI-II, MYD11A1 and MYD21A1 ranged from 1.71 to 5.31 \({}^{\circ}\)C throughout the year, over the sub-regions of Pakistan. The highest RMSDs were recorded in region IV in both the LST products, and they were the lowest in region V, which was an average of annual record. Regarding bias, region II showed the highest negative trend when compared to MYD11A1, and the highest positive trend of bias were observed in region IV in a comparison of both products. The highest error in RMSE was possibly due to various reasons, such as heterogeneous surfaces producing larger errors due to uneven surface features. The LST retrieved from remote sensing data was the instantaneous LST at the moment of sensor imaging. The imaging time of the original images used for the retrieval of MERSI-II LST and MYD11 AI and MYD21A1 LST differed, and the sensor calibration accuracy also differed. Thus, the LST results of the same area slightly differed due to retrieval using different remote sensing data sources.  
Figure 4: Cross-validation errors between LSTs of MERSI-II, and MODIS (**a**) MYD11A1 and (**b**) MYD21A1 LST products over five sub-regions of Pakistan.  
The LST distributions in all datasets were almost same under clear sky conditions (as shown in Figure 5), with only minor deviations being observed in MERSI-II, possibly the stripe noise problem of the sensor. According to the latitudinal profile, it can observed that the LST of MERSI-II was close the MYD11A1 LST product as compared to the MYD21A1, and the overall RMSD errors were also smaller when compared with the MYD11A1 product. However, the image acquisition times of both sensors were similar to some extent, but not identical, and the MODIS LST product itself contained some minor errors when compared to the ground data. In this regard, when we compared MERSI-II's retrieved LST with the MODIS LSTs products, the results are merely a spatial analysis of the LSTs, and relative errors are not perfectly precise.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### Dynamic Assembly Pose Tracking
#### 3.2.2 Depth Modality  
Combined with the RGBD data of the depth camera, we can add depth modality to our method to improve the stability of assembly part tracking in practical applications. Our method calculates the pose transformation vector between two frames by extracting the correspondence point \(P\) from the depth data so that it matches the 3D surface points \(X\) sampled from the sparse viewpoint model closest to the pose result of the previous frame.  
The correspondence point \(P\) is a 3D point, and the depth modality first needs to search for the correspondence point. Firstly, the 3D surface point \(X\) sampled from the sparse viewpoint model closest to the pose in the previous frame is projected into the depth image. Based on the user-defined radius and stride, 3D points in the vicinity of \(X\) are then  
Figure 2: Visualization results of correspondence lines in different iterations: (**a**) first iteration; (**b**) second iteration; (**c**) third iteration; (**d**) fourth iteration.  
reconstructed. Finally, we select the nearest reconstructed point to the 3D surface point \(X\) as the correspondence point \(P\). And the correspondence point \(P\) whose distance from the 3D surface point \(X\) is greater than the threshold is deleted.  
As shown in Figure 3, the yellow points are the correspondence points in the depth image, the blue points are the 3D surface points sampled from the CAD model, and the red line segments represent the corresponding associations of the correspondence points.  
For the posterior probability model of depth modality, in this paper, we use point-to-plane error measurement to establish the normal distribution. Therefore, the distance between the 3D surface point \(X\) and the correspondence point \(P\) is projected to the normal vector \(N\) direction of the corresponding 3D surface point for calculation. Given the correspondence point \(P\), we can calculate the probability of the pose transformation vector \(\theta\).  
\[p(\theta\mid P)\propto exp\left(-\frac{1}{2d_{Z}^{2}\sigma_{d}^{2}}\Big{(} \ MN^{\top}(\ MX-\ MP(\theta))\Big{)}^{2}\right) \tag{4}\]  
where \(\sigma_{d}\) is the user-defined standard deviation in the depth modality, \(d_{Z}\) is the depth of the correspondence point, and \(\ MX\) is 3D points in the model coordinate system.  
In practical use, \(\sigma_{d}\) is given by the user, considering that the quantity and quality of the data measured by the depth camera will decrease as the distance between the tracked object and the depth camera increases, so \(\sigma_{d}\) should increase as \(d_{Z}\) increases. At the same time, \(\sigma_{d}\) also ensures compatibility with the uncertainty of the region modality, which also increases with the camera distance.
# Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine
## 2 Modeling of the Turboshaft Engine Performance  
### The Configuration and Operating Principle of the Turboshaft Engine  
The turboshaft engine in this paper is a widely used thermodynamic system to provide power to helicopters. It uses air as working medium to continuously generate power by converting thermal energy into mechanical energy [1; 2]. The configuration of the engine is presented in Figure 1. There are five main components in the engine, i.e., inlet, compressor, combustor, turbine, and nozzle. The compressor is a combination of a three-stage axial compressor and a single-stage centrifugal compressor. The turbine is divided into two parts, including a high-pressure turbine (HPT) and a low-pressure turbine (LPT). These two parts play different roles in the engine. The HPT is used to drive the combined compressor, whereas the LPT is used to provide useful power. In general, the compressor, the HPT and a connecting shaft constitute the high-pressure rotor (HPR). The LPT is connected to the helicopter rotor through machine driven system.  
The engine runs in a continuous working cycle. A working cycle of the engine incorporates three main processes, i.e., compression, combustion, and expansion. The path of air through the engine is indicated by the blue arrows shown in Figure 1. When the engine is running, the air is firstly drawn into the engine from the inlet. Then, the air is compressed in the compressor. During the compression process, the pressure and temperature of the air increase due to the work done by the rotating compressor. Meanwhile, there is a corresponding decrease in volume. Next, the high-pressure air is mixed with the fuel in the combustor and the fuel is burnt to rise the temperature of gas (i.e., mixture resulting from combustion). The process can considerably increase the volume while remaining the pressure almost constant. After that, a large quantity of thermal energy is taken from the expanding gas stream by the turbine and then turned into mechanical power. A proportion of power is used to drive the compressor while the residual power is the available power to drive the rotor. During the expansion process, there is a significant decrease in temperature and pressure with a corresponding increase in volume. Finally, the remainder of the thermal energy is discharged to the atmosphere with the gas.
# Development of a High-Precision Touch-Trigger Probe Using a Single Sensor +
###### Abstract  
To measure various components with nano-scale precision, a new high-precision touch-trigger probe using a single low-cost sensor for a micro-coordinate measuring machine (CMM) is presented in this paper. The sensor is composed of a laser diode, a plane mirror, a focusing lens, and a quadrant photo detector (QPD). The laser beam from the laser diode with an incident angle is reflected by the plane mirror and then projected onto the quadrant photo detector (QPD) via the focusing lens. The plane mirror is adhered to the upper surface of the floating plate supported by an elastic mechanism, which can transfer the displacement of the stylus's ball tip in 3D to the plane mirror's vertical and tilt movement. Both motions of the plane mirror can be detected by respective QPDs. The probe mechanism was analyzed, and its structural parameters that conform to the principle of uniform sensitivity and uniform stiffness were obtained. The simulation result showed that the stiffness was equal in 3D and less than \(1\,\mathrm{mN/\SIUnitSymbolMicro m}\). Some experiments were performed to investigate the probe's characteristics. It was found that the probe could detect the trigger point with uniform sensitivity, a resolution of less than \(5\,\mathrm{nm}\), and a repeatability of less than \(4\,\mathrm{nm}\). It can be used as a touch-trigger probe on a micro/nano-CMM.  
Keywords:touch-trigger probe; stiffness; quadrant photo detector; coordinate measuring machine +
Footnote †: Academic Editor: [PERSON]
## 4 Verification and Simulation  
### Simulation Scenario and Software  
In order to verify the applicability of the generated driving model, two simulations have been carried out. Firstly, a simulation with a fuzzy control model representing human drivers has been performed and compared with real driving data. Here, the behavior of the subject in the study is imitated--the desired speed/target speed is given to the driver model in the same order as in the experiment, and the pedal position output by the driver model is recorded and compared with real test data. Next, a simulation of the Level 0, Level 2 and Level 5 assistance systems was accomplished. In this simulation, the microscopic traffic simulation system SUMO was chosen for providing the simulation scenario. The TraCI (Traffic Control Interface) interface was used to connect the driver model in MATLAB with the vehicle model in SUMO. Vehicles with different degrees of automation were implemented on a simple intersection in SUMO and the vehicle counters were located at the end of each road.
## 2 Materials and Methods
### ElGamal System  
AICLDH has a symmetric cryptosystem using the key share of the ElGamal system and the Diffie-Hellman protocol [27] that works with prime numbers. This study suggests building a prime \(p^{*}\) in the following way: \(p^{*}=2k\times p_{1}p_{2}+1\). Where \(p_{1}\) and \(p_{2}\) are numbers of high primality with a bit size of \(2^{512}\), and \(k=1,2,\cdots\). Finally, the proposed \(\alpha\) has to satisfy Equation (18) [28] for all the prime factors of \(p^{*}-1\) and \(0<\alpha<p^{*}-1\).  
\[\alpha^{p^{*}-1/q}\
eq 1\mod p^{*} \tag{18}\]  
where \(q\) are the prime factors of \((p^{*}-1)\) different from \(1\). It is pointed out that the high primality of \(p_{1},p_{2}\) is verified using the Miller-Rabin algorithm [29].  
On another topic, both the sender \(A\), and the receiver \(B\), share the following value: \(\beta=(\beta_{a_{B}})^{a_{A}}\) mod. \(p^{*}=(\beta_{a_{A}})^{a_{B}}\) mod. \(p^{*}\). Considering that \(\beta_{a_{B}}=\alpha^{a_{B}}\) mod \(p^{*}\) and \(\beta_{a_{A}}=\alpha^{a_{A}}\) mod \(p^{*}\). The sender receives the value \(\beta_{a_{B}}\) from the receiver, and the receiver obtains the integer \(\beta_{a_{A}}\) from the sender. For increasing security, the integers \(a_{A}\) and \(a_{B}\) can change in each communication between the sender and the receiver. Although, they could also remain fixed for a while. Furthermore, both integers satisfy the following condition: \(0<a_{A},a_{B}<p^{*}-1\).  
In this work, we calculate the values \(\beta_{i}=\alpha^{i}\times\beta\) mod \(p^{*}\) to send a pair of constants using the Hash SHA-512 algorithm. In addition, the authors consider it convenient to present an example with particular values. If we assume that \(p_{1}=101\), \(p_{2}=103\), it is not difficult to verify that the prime we are looking for is \(p^{*}=2k\times p_{1}p_{2}+1=20,\)807, when \(k=1\).  
In this order of ideas, if we propose \(\alpha=17,\)742, it can be verified that \(\alpha^{p^{*}-1/q}\
eq 1\mod p^{*}\) when the primes \(q\) are equal to: \(2\), \(101\), and \(103\). On the other hand, if we assume that sender \(A\) and receiver \(B\) have the private keys \(a_{A}=600\) and \(a_{B}=1500\), it is easy to check that the public keys of the sender and receiver are: \(b_{A}=11,\)147 and \(b_{B}=\)16,143. Therefore, \(\beta=11,\)425 can be computed by the sender and the receiver. To finish this example, \(\beta_{1}=(17,\)742\()(11,\)425\mod 20,\)807 \(\equiv 556\).
## 4 Result  
### Reliability of Approximate Calculations  
In the FPGA implementation, numerous approximate calculations based on CORDIC and lookup table (LUT) multipliers were employed to reduce computational latency, save computation time, and lower hardware costs. To decrease the computational load of post-FFT data processing, the FFT results were resampled to lower the precision for calculations. To minimize the resource consumption of the LUTs, the lookup tables provided only eight decimal places of precision. Additionally, due to the input interface width limitations of DSP slices, some data truncation was performed during energy accumulation and square root calculations. Furthermore, all operations were executed in fixed-point arithmetic due to FPGA constraints. These factors could potentially impact the validity of the final results. Therefore, the methods used in this process were simulated to ensure that the detection accuracy achieved on the FPGA aligns with the experimental results implemented on the ESP32. As shown in Figure 8, the simulation results indicate that even with a significant number of low-precision approximate calculations, a clear distinction between fault modes and normal modes can still be observed.  
Figure 7: Module reuse design in IMFs extraction stage.  
We attempted to improve the method using sampling localization for failed detections but were unable to succeed. As shown in Figure 8, the significance of fault features exhibits a certain periodicity. Combined with the need for sufficient sample lengths in EMD to extract required features, the dead time may be relatively long, leading to a failure in timely fault detection. Efforts are currently underway to replace relative Shannon entropy with SVM for outputting results.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 5 Conclusions  
This study presented a method for identifying wheat yellow rust using optimized GhostNetV2, which can accurately and efficiently diagnose the severity of wheat yellow rust. Firstly, the Ghost module was improved, and the information transfer between feature maps was improved through the channel transformation operation so as to promote the feature learning ability of the model. Then, the Fused-MBConv module was used instead of the shallow network to shorten the training time of the model and increase the training efficiency. Finally, the ECA was used to further enhance the model's attention to the disease and reduce the parameters of the model. We obtained accuracy rates of 98.22%, 98.82%, and 98.36% with the improved method on the Plant-Village, LWDCD2020, and Rice Leaf Diseases Dataset datasets, respectively, illustrating the generalization ability of the model. By improving the GhostNetV2 model, we achieved a 2.24% improvement in accuracy, a 37.49% reduction in training time, and a 1.16 M reduction in parameters. It is an effective technical means that can run quickly on mobile terminals. In the actual detection of disease severity, mobile devices can be used to detect diseases, and pesticides can be sprayed according to the guidance to reduce pollution and increase wheat yield.  
However, the improved method also has shortcomings. The calculation load of the improved model increased by 0.48 G and had a higher calculation load. Future tasks will focus on how to further reduce the calculation load. Ultimately, we simply implemented severity detection for a single disease, but crop diseases are complex and diverse. Therefore, future work will further collect disease data to achieve severity identification of multiple diseases.  
Conceptualization, Z.L., X.F., T.Z. and Y.Z.; methodology, X.F.; software, Y.Z.; validation, Z.L. and Y.Z.; formal analysis, X.F. and Y.Z.; investigation, Z.L.; resources, X.F.; data curation, T.Z.; writing--original draft preparation, X.F.; writing--review and editing, Z.L.; visualization, Z.L. and T.Z.; supervision, Y.Z.; project administration, T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset is available at [[https://www.kaggle.com/datasets/tolgahayit/yellowrust19-yellow-rust-disease-in-wheat](https://www.kaggle.com/datasets/tolgahayit/yellowrust19-yellow-rust-disease-in-wheat)]([https://www.kaggle.com/datasets/tolgahayit/yellowrust19-yellow-rust-disease-in-wheat](https://www.kaggle.com/datasets/tolgahayit/yellowrust19-yellow-rust-disease-in-wheat)), accessed on 29 December 2022.  
This work was supported by the National Key Research and Development Program of China: Research and Development of Quality Information Management and Control Technology for Efficient Connection of Grain Multimodal Transport (project no.: 2022 YFD210020).  
The authors declare no conflict of interest.