esa-sceva/satcom-chunk-collection huggingface dataset:
# Oxygenated Diesel Fuels and Their Effect on PM Emissions
## 4 Tests Results and Discussion
Butanol has the advantage that it can be produced from biomass in a fermentation process [73; 74; 75; 76] as a renewable fuel (biobutanol). Fermentation occurs under anaerobic conditions by bacteria of the genus Clostridium. Hence, it is currently enjoying a lot of interest, also as a fuel for spark ignition engines. In the literature, there are studies conducted with high contents of butanol, which indicate that under such conditions a significant reduction in PM emissions occurs compared to the fueling with conventional diesel fuel. In an extensive review [77] on the use of butanol as a fuel component for diesel engines, it was found that most of the tests with butanol described in the literature were performed under stationary conditions, and only a few in dynamic driving cycles. In most cases, however, significant reductions in PM emissions are recorded. In [78], the authors showed that blends containing 50% diesel fuel and 50% butanol effectively reduce soot emissions from a modern diesel engine. For low load (BMEP = 0.4 MPa) the soot emission reduction was 50%. At high load (BMEP = 1.2 MPa) the reduction was even greater, reaching 80%. In addition, it has been shown that all butanol isomers had a similar effectiveness in reducing soot emissions at low load. At high load, there were already clear differences between the individual isomers. The most effective was n-butanol (soot reduction by 80%). The least effective was sec-butanol, which reduced soot emissions by 50%. The authors of [79] showed that with lower butanol additions (20%), regardless of the type of isomer, a similar reduction in soot emissions was obtained (by about 35%). Only when the content of butanol was higher, the differences in the effectiveness of the various isomers appeared. Iso-butanol was the most effective (soot reduction by maximum of 85%), and the least effective was tert-butanol (soot reduction by maximum of 68%). The authors of [80] showed that the concentration of PM in exhaust gas decreases linearly with the increase in butanol content in diesel fuel in the tested range of 10% to 30% \(v/v\). [PERSON] [81] showed a similar dependence in the scope of 5% to 20% butanol content and smoke opacity. In [PERSON] et al. [82; 83], the beneficial effect of butanol addition to diesel fuel on PM emissions from a Euro 6 passenger car was confirmed. The effect of butanol on PM emissions was also investigated for heavy-duty engines. The tests described in [84] showed that when using a mixture containing 40% n-butanol, smoke emissions close to zero were achieved, even using high levels of exhaust gas recirculation (EGR) to reduce NOx emissions. [PERSON] et al. [85] tested the influence of n-butanol/diesel blends on performance and emissions of a heavy-duty diesel engine over the World Harmonized Steady-State Cycle (WHSC). They observed significant reductions in PM emissions for fuel blends that contained 10% and 20% of butanol. At 10% butanol concentration, PM emissions decreased by 22-88%, while at 20%, they decreased by 15-87%, as compared to neat diesel fuel.
## 4 Results  
### Ground Displacement Monitoring Result  
After processing the data as described above, the average velocity of ground displacement of the methods (PS-InSAR, SBAS-InSAR, and the proposed method) in the study area were obtained and the results are shown in Figure 4. The average velocity of ground displacement from May 2017 to December 2018 was \(-\)186 mm/year-41 mm/year, \(-\)206 mm/year-31 mm/year, and \(-\)230 mm/year-28 mm/year, determined by the PS-InSAR method, the SBAS-InSAR method, and the proposed method, respectively. All four methods could acquire positions of ground displacement in the study area effectively and there were four subsidence areas (shown as red wireframes in Figure 4) of A, B, C, and D in the study area. The ground displacement monitoring results in the four areas are shown in Figure 5. The A subsidence zone mainly covers the southern side of the Li Lou coal mine and the northern area of the Guo Tun coal mine, the two coal mines are connected to form a subsidence area, which mainly includes Che Lou village, Wang Lou village and Guo Tun town. As the Guotun mine continued to be mined, the extent of ground displacement gradually presented a south-westerly trend. The B subsidence zone is located in the Zhao Lou coal mine, and the area covers Tangmiao town. During the study period, the coal mines in the area were at the beginning of their mining, showing a certain distribution of displacement and the velocity of displacement was relatively small. The C subsidence zone is located in Pengzhuang coal mine, within the villages of Chenhekou and Zhangguantum. There are two working faces where mining started in early 2018; as coal mining continues, the ground displacement is gradually increasing; the area has a large ground displacement velocity. The D subsidence zone is the Liangbaosi Mine, located in the town of Liangbaosi, which presents a small area of ground displacement. As shown in Figures 4 and 5, the results of the ground displacement monitoring are becoming more and more evident as the density of CPs increases by integrating DS InSAR techniques and conventional time series InSAR techniques. The results show that different methods will obtain different densities of CPs, which will have an impact on the ground displacement monitoring results, where the higher density the better ability to identify the ground displacement monitoring results.  
Figure 4: Average velocity of ground displacement in the study area obtained by different methods: (**a**) PS-InSAR; (**b**) SBAS-InSAR; and (**c**) the proposed method.
## 5 Experimental Results and Evaluation
### Performance Evaluation
#### 5.2.3 Computational Cost by Preserving Meta-Data Privacy  
In our test scenario, the average size of the meta-data file was about 2 KB. Figure 8 shows the processing time of accessing data from (to) the DDS and executing encryption over a meta-data file on the client side. The delays incurred by uploading a meta-data file to the Swarm network and then downloading it from a service site are almost the same on the desktops and Rpis. However, the RPis took longer to encrypt and decrypt the data than desktops did due to the limited computational and memory resources. Compared to the Swarm operations, performing encryption algorithms on meta-data brings extra overheads in the data verification process on both platforms. As a trade-off, using encrypted meta-data to ensure privacy preservation is inevitable at the cost of a longer latency in the service process.
## 1 Introduction  
Underwater wireless communication has found considerable attention during last couple of decades because of its applications in the field of military, commercial, and monitoring applications. Among the different modes of communication technologies tried for underwater, such as acoustics [1, 2], magneto-inductive [3], radio frequency, and optics [4], acoustics are applied for applications that require long range communication despite the severe physical layer challenges. Serious research efforts have been carried out to improve LWAC technology [5, 6, 7, 8, 9] to deal with these challenges, however, achieving high bandwidth and data rate still remain bottlenecks in LWAC advancement. Learning and borrowing from terrestrial networks, to maximize the bandwidth efficiency, increase data rate and throughput researchers have started to explore full duplex (FD) implementation. Users can send and receive data at the same time using FD links in the complex underwater environment, and they can listen to their neighbors' transmitted data using FD links [10]. SinceFD links consider the projected traffic load, they increase network bandwidth efficiency by reducing the effect of under-utilized channels on system throughput and traffic latency [11]. Due to major physical changes in the ocean, the FD-UWAC channel faces challenges such as synchronization and delays, self-loop and SI, LTI, noise, attenuation, multipath effects, and Doppler spread, limited channel power and bandwidth, low bandwidth utilization, collisions, and hidden and exposed terminal problems [12; 13]. Furthermore, limited bandwidth and large propagation delay problems in the FD channel cause frequency fading, which affects control timings [14]. As a result, this paper reviews various FD physical layered UWAC systems and MAC layered protocols for underwater communication in great detail. The advantages and difficulties that the above systems and protocols have encountered are also highlighted. Different metrics, such as spectral efficiency, BER, transmission range, and data rate, are used to compare the performance of various physical layered FD systems. Similarly, a comparison of various FD MAC protocols is conducted using various metrics such as throughput, delay efficiency, energy efficiency, transmission rate, and data rate. In terms of the physical and MAC layers, Figure 1 depicts how the aforementioned systems and protocols are classified.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 1 Introduction
Studies conducted both domestically and internationally have demonstrated that point cloud-based plant height measuring is more accurate and efficient than conventional techniques [21]. For instance, researchers have obtained impressive results in measuring the plant height of various crops by reconstructing point cloud data using LIDAR or multi-view geometry. However, the extensive use of LIDAR equipment is restricted by its expensive cost [22]. On the other hand, the multi-view geometry approach achieves low-cost, high-quality point cloud data capture, which is particularly appropriate for the field crop population study situation [23]. However, because of plant shade and close spacing, field crop populations have more difficulty extracting plant height and segmenting point cloud data [24]. Consequently, research on point cloud-based techniques for measuring plant height in field populations may yield fresh concepts for digital agriculture. In this study, we gather field image sequences of maize populations and create point cloud data by combining UAV remote sensing with multi-view geometric 3D reconstruction technology [25; 26]. Based on this, a plant height estimate model is developed, a new technique for measuring plant height in field populations is suggested, and the pre-processing and segmentation algorithms of the point cloud data are examined [27]. This study not only addresses the limitations of conventional plant height measurement techniques but also contributes to advancing UAV-based phenotyping methods by proposing a novel column space approximate segmentation algorithm [28]. This algorithm is designed to overcome the challenges associated with dense canopy structures and overlapping plants, improving the accuracy of plant height estimation at the field scale. In addition to promoting the use of point cloud technology in agriculture and offering technical assistance for the informatization and unmanned measurement of crop phenotypes, this study intends to close the gap in the field of plant height measurement of point cloud data of crop populations at the field scale. It will also be extremely important for the collection of maize growth parameters and variable management.
## 2 Actuator Structure and Fabrication
### Consideration of Actuator Design Parameters
#### 2.2.2 Modular Thumb Actuator  
The actuator module was 23 mm long, which is long enough to fit into any of the thumb joints (Figure 1b). The fiber was wrapped in 12 loops of two-way hitching [28]. Two modules were connected head-to-tail, one for each of the IP joints and MCP joints, for impelling joint flexion. The bottom surface of the flexion module was made of stiffer silicon than the rest to limit the extension of the bottom surface, hence facilitating the module to flex.  
The actuator unit for the CMC joint was designed based on the concept of utilizing pre-existing flexion modules to realize multi-DoF support without a noticeable sacrifice of ROM of abduction. According to the previous studies [16; 18; 23] that used a modular actuator for thumb abduction assistance, the modular unit cannot be simply placed transversely between the metacarpal bones of the thumb and the index finger because its initial length would diminish the thumb abduction RoM. Therefore, we designed a CMC unit attaching the bottom surfaces of two modules via an inextensible nylon thread (1.2 mm diameter) that ran longitudinally between them. The left module is responsible for CMC abduction, and the simultaneous air inflation of both modules induces CMC flexion. It is noteworthy that the two bottom surfaces cannot be completely attached because the two-module unit would have to overcome the elasticity of a shore 20A silicone (the stiffness of the silicone used in this study) block with a width of 4 mm (two times the chamber thickness) and a thickness of 15 mm (the width of the module), causing the unit to barely flex during simultaneous inflation of the two modules. Moreover, the CMC unit was wrapped with 12 loops of the single loop instead of two-way hitching because fibers in two-way hitching would restrict the elongation of the actuator surface that makes contact with the thumb, resulting in unwanted flexion during the air inflation of the left module (abduction). Accordingly, the optimization of the position of the inter-module thread would be important for effective multi-DoF support. We made two versions of the CMC actuator unit: one with the connective thread positioned at the center axis of the bottom surfaces (version 1), and the other with the thread located at a deeper position nearer to the thumb that equals the 3/4 of the module's width (version 2).
## 3 Determining Material Parameters in the da/dN-\(\Delta\)G-R Equation by FCP Tests
### Analysis of Test Results  
The _a-N_ curves under different stress ratios measured in the tests are shown in Figure 5a, indicating that the specimen crack propagates faster as the stress ratio increases. For example, at \(N\) = 6 \(\times\) 10\({}^{4}\), the crack lengths are about 12 mm, 13 mm, 16 mm and 26 mm when \(R\) equals to 0.1, 0.3, 0.5 and 0.7, respectively. In order to obtain the FCP rate _da/dN-AK_ curve, a seven-point incremental polynomial method recommended by GB/T6398-2017 is used to process the data [32]. That is, for each data point (\(a_{i}\), \(N_{i}\)), the previous three points and next three points are taken, and these seven data points are used to fit a quadratic polynomial. The slope of the fitted curve at this data point is regarded as the FCP rate _da/dN_. \(\Delta K\) under different crack lengths can be calculated using Equation (8). The FCP rate curves in Figure 5b show that the FCP rate increases as the stress ratio increases. Since the slopes and intercepts of these fitted curves vary with the stress ratio, this means that the Paris equation including only two material parameters and without considering the influence of the stress ratio is inaccurate. Equation (8) is shown below:  
\[\Delta K=\frac{\Delta P}{B\sqrt{W}}\cdot\frac{2+\alpha}{\left(1-\alpha\right) ^{3/2}}\left(0.886+4.64\alpha-13.32\alpha^{2}+14.72\alpha^{3}-5.6\alpha^{4}\right) \tag{8}\]  
where \(\Delta P\) is the load amplitude, \(B\) is the specimen thickness, \(W\) is the horizontal distance from the loading point to the edge of the specimen (as shown in Figure 2), _a_' is the calculated crack length which is the horizontal distance from the loading point to the crack tip, and \(\alpha=a^{\prime}/W\).  
Figure 4: Fatigue crack propagation process. (**a**) Crack propagation initial stage; (**b**) Crack propagation close to fracture.  
Figure 5: Fatigue crack growth curve under different stress ratios. (**a**) _a–N_ curve; (**b**) FCP rate curve.  
Equations (1) and (2) are expressed in logarithmic form, as shown in Equations (9) and (10), respectively. The material parameters \(C_{0}\), \(m_{0}\) in the Paris Equation (9) and \(C\), \(\gamma\), \(m\) in the Walker Equation (10) of steel Q345 qD can be obtained by experimental data fitting, as shown in Figure 6. The material parameters in the da/dN-\(\Delta\)G-R equation are the same as those in the Walker Equation (10) and are obtained according to the derivation process described in the last section. The material parameters of steel Q345 are shown in Table 3.  
\[\text{Paris}:\ \log(da/dN)=\log C_{0}+m_{0}\log(\Delta K) \tag{9}\]  
\[\text{Walker}:\ \log(da/dN)=\log C+m\log(\Delta K)+m(\gamma-1)\log(1-R) \tag{10}\]
## 6 System Optical Transmittance Analysis  
The optical energy loss of the optical system is determined by factors such as the number of reflective surfaces in the optical path, the reflectivity of the reflective surface, the number of lenses, the transmittance of the lenses, and the size of the central opening of the reflective surface. The camera's visible light and long-wave infrared share the primary and secondary mirrors. The working spectrum ranges from 450 nm to 10,500 nm, and the spectral range is large. It is difficult to achieve high optical efficiency of the mirror in the full spectrum through conventional coating methods.  
Under normal circumstances, the wide-spectrum reflective coating of a large-aperture mirror is a metal-protected or metal-enhanced film structure. Commonly used metal materials for reflective films include aluminum, silver, and gold. Among them, gold has high reflectivity only in the wavelength band greater than 550 nm, which cannot meet the requirements of the camera's operating spectrum. Aluminum and silver have their own advantages and disadvantages. Aluminum has a high reflectivity in the ultraviolet band, but there is a low reflectivity near 800 nm, which has a greater impact on the reflectivity in the visible and near-infrared region, and its average reflectivity in each spectrum is low. Silver has good consistency and high reflectivity in a wide range of wavelengths greater than 350 nm [18].  
According to the existing optical film system theory and coating process technical conditions, the film system design of the primary and secondary mirrors in the camera in this paper is carried out by using silver as the main film layer. The average reflectivity of the reflective film on the surface of the primary and secondary mirrors in the visible spectrum is about 0.97, and the average reflectivity in the long-wave infrared spectrum is about 0.985. The reflectivity of other mirrors in the optical system is 0.97. The central opening radius of the primary mirror is 75 mm, and the radius of the primary mirror is 250 mm, so the actual light transmission rate of the main mirror is 91%. After calculation, the transmittance of the visible light band system is 0.806, and that of the long-wave infrared band system is 0.806.  
Figure 9: MTF value changes with ambient temperature (**a**)Visible light band (**b**) Long-wave infrared band.
## 2 Material and Methods
### CHAID Model  
CHAID was first developed by [53]. CHAID algorithms, as well as CART models, are used for classification, and the outcomes are usually classification trees, which are nonparametric methods. In other words, these methods are called decision tree methods, while this tree offers specific rules along with their inputs and outputs. Each input variable in these methods is divided into subgroups. Despite black-box methods, the internal procedures of these tree models are visible for the user, and they have been called white-box methods [54]. The main difference between decision trees and regression models is in their relations between their variables, in which there are simple linear combinations for regression methods with classification and categorization based on output variables in decision methods [55].  
CHAID algorithm uses Pearson's Chi-square when a target variable is categorical and uses the likelihood ratio Chi-square statistic as a separation reference when a target variable is continuous. The function of likelihood ratio [PERSON]'s Chi-square statistic is calculated as [53]  
\[x^{2}\ =\ 2\sum_{i,j}f_{i,j}\times log_{e}(\frac{f_{ij}}{e_{ij}}) \tag{5}\]  
Overall, the steps of CHAID model application are as follows:  
(1) The best division for each input variable is found.  
(2) The best input variable was selected.  
(3) The whole data were divided into subgroups.  
(4) Each of these subgroups is divided into new subdivisions.  
More detailed of this method can be found in [55].
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 3 CAV Trajectory Prediction
### LSTM Network  
The LSTM is a specialized type of recurrent neural network (RNN) primarily designed for handling and predicting time-series data as well as dependencies within sequences. Traditional RNNs often encounter issues such as vanishing or exploding gradients when processing long sequences, making it difficult to learn long-term dependencies. The LSTM addresses this challenge through a series of unique structural designs, effectively overcoming these limitations.  
The LSTM can effectively retain important long-term information while discarding unnecessary data, allowing it to process sequences of varying lengths more flexibly.  
The Flatten layer is used to convert multi-dimensional input into a one-dimensional array. It transforms the data into a one-dimensional vector so that it can be processed by subsequent fully connected layers.  
The fully connected layer is located at the final stage, receiving the features output by the previous CNN and LSTM layers. Each neuron in the fully connected layer is connected to all neurons from the previous layers, integrating various features to perform classification tasks. The ReLU activation function is also used in the fully connected layer to help the model capture complex patterns.