esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 5 Conclusions  
In conclusion, the addition of the FOP will greatly reduce the CLA. We simulated and analyzed the different CLA losses caused by different FOP schemes. By selecting appropriate FOP schemes, it is feasible to appropriately reduce the loss of CLA of the monocentric imager with FOPRIT schemes. By analyzing the periodic in-plane displacement of the FOP relative to the image sensor, the lowest CLA that the monocentric imager can withstand can be obtained. The main conclusions are as follows: (1) The CLA of the monocentric imager without the FOPRIT scheme is 0.002 pixels by defocusing the imaging spot. (2) By analyzing the influences of FOP parameters, including fill factor and fiber diameter, on CLA, we have obtained two suitable FOP schemes: scheme one: \(d_{f}\) = 4 \(\upmu\)m, \(K=0.7\) and \(CLA\) = 0.031 pixel; scheme two: \(d_{f}\) = 6 \(\upmu\)m, \(K=0.7\) and \(CLA\) = 0.037 pixel. (3) By analyzing the influences of FOP in-plane displacement, including rotation and translation on CLA, the lowest CLA of the monocentric imager with FOP scheme one has been reduced from 0.031 pixel to 0.038 pixels, and the lowest CLA of the monocentric imager with FOP scheme two has been reduced from 0.037 pixels to 0.045 pixels. Compared with FOP scheme two, the loss of the lowest CLA that the monocentric imager can withstand caused by the addition of the FOP is reduced by 20% in FOP scheme one.  
For space point target imaging, given clear FOP parameters, the lowest CLA that the monocentric imager can withstand can be given by the method in this paper. We provided some constructive guidance on the selection of the FOP, which can appropriately reduce the loss of the CLA caused by the addition of FOPs and provide some feasibility for the wide application of monocentric imagers in the field of the space-based space target surveillance. FOPs are being processed and prepared. We also aim to report a comprehensive experimental verification of our simulation and analysis in our future work.  
Conceptualization, Y.H. and C.W.; methodology, Y.H.; software, Y.H. and D.X.; validation, C.Y. and C.W.; formal analysis, Y.H.; investigation, Y.H. and D.X.; resources, C.W.; writing--original draft preparation, Y.H.; writing--review and editing, Y.H.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 61805235.  
The authors declare no conflict of interest.
## 3 4-SSB System Design
### The Concept of Application of 4-SSB into OFDM M-QAM 4-SSB Uncoded System Using the Shadow Equalizer  
In this section, we apply the multiple feedback success interference cancellation with shadow area constraints (MF-SIC-SAC). This algorithm is utilized to address the error propagation in compensating the ISI and make feedback decision using the SIC technique to test the SNR symbol as feedback. We then propose the new scheme of M-QAM 4-SSB OFDM with MF-SIC-SAC which declines the first symbol estimation using the third aforementioned equalizer type by decreasing the number of feedback symbols. The correct constellation is still allocated in the remaining feedback symbol [13].  
The selective algorithm is then optimized from the set of candidate's symbol feedbacks. This optimization algorithm is performed by selecting only one branch in the lattice tree. Consequently, this approach realizes a smart interference canceller, which is different from the hard decision of SIC or sphere decoder by searching in the optimized branch of the lattice tree to prevent from growing complexity. The shadow area constraint is also introduced to decide whether the symbol estimation is reliable or not. The feedback output is a reliable symbol whereas the non-reliable symbol will be replaced by the concentrated symbol producing by SAC.
# The Current Development Status of Agricultural Machinery
## 4. Chassis Leveling Systems of Agricultural Machinery  
In agricultural production environments, farmland terrain often presents complex and variable topographies. Agricultural machinery, including combine harvesters, sprayers, and seeders, frequently experiences tilt during field operations due to uneven ground surfaces, slope gradients, and varying degrees of soil compaction. This tilting not only degrades operational performance--evidenced by uneven seeding, incomplete spray coverage, and increased harvesting losses--but also accelerates mechanical wear, ultimately shortening equipment service life. As a result, extensive research has been conducted globally on active leveling chassis systems, yielding significant advancements in the areas of leveling mechanism design, suspension system optimization, control strategy development, and scenario adaptability [117, 118, 119, 120, 121]. A standard active leveling chassis system typically consists of three key components: sensor networks, hydraulic actuators, and an intelligent control module. The system continuously monitors vehicle posture using inclination sensors and dynamically adjusts hydraulic cylinder displacements through an intelligent control unit. This unit integrates a chassis dynamics model that accurately captures the interactions between the machinery, terrain, and load under multi-degree-of-freedom motion, thereby enabling precise regulation of both lateral and longitudinal balance. Currently, such systems are predominantly applied in harvesting machinery, crop protection equipment, and orchard-specific vehicles, showing strong performance in challenging environments such as hilly and mountainous regions and orchard settings.
# Anonymous Access System with Limited Number of Uses in a Trustless Environment
## 3 Background
\begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Meaning** \\ \hline \(\mathcal{AP}\) & Attribute Provider \\ \(\mathcal{SP}\) & Service Provider \\ \(id_{\mathcal{U}}\) & User identifier \\ \(id_{\mathcal{U}}^{5}\) & User identifier for scope \(S_{i}\) \\ \(sk_{\mathcal{U}}\) & User secret key \\ \(sk_{\mathcal{U}}\) & Fake User secret key \\ \(sk_{\mathcal{AP}}\) & Attribute Provider secret key \\ \(sk_{\mathcal{SP}}\) & Service Provider secret key \\ \(\delta_{\mathcal{AP}}\) & Signature of Attribute Provider \\ \(P\) & Generator of the cyclic group \(\mathbb{G}\) \\ \(sk_{\mathcal{U}}^{\prime}\) & Blinded User secret key \\ \(\delta_{\mathcal{AP}}^{\prime}\) & Blinded signature \\ \(n\) & Maximum number of authentications \\ \(e(_{\mathcal{U}})\) & Pairing function \\ \(\mathcal{U}\) & User \\ \(\bar{A}\) & Adversary \\ \(S\) & Scope (arbitrary string) \\ \(i\bar{d}_{\mathcal{U}}\) & Fake User identifier \\ \(pk_{\mathcal{U}}\) & User public key \\ \(pk_{\mathcal{U}}\) & Fake User public key \\ \(pk_{\mathcal{AP}}\) & Attribute Provider public key \\ \(pk_{\mathcal{SP}}\) & Service Provider public key \\ \(H(S)\) & Hash of scope \\ \(b\) & Random blind factor \\ \(pk_{\mathcal{AP}}^{\prime}\) & Blinded User public key \\ \((R^{\prime},t_{i})\) & Modified NI-Schnorr ZKP \\ \(i\) & Auth counter in \([0,\cdots,n]\) \\ \hline \end{tabular}
\end{table} 
Table 1: Notation guide. 5. \(\mathcal{SP}\) also verifies the correction of the credentials: \[e(C^{\prime}+pk^{\prime}_{\mathcal{AP}},\delta^{\prime}_{\mathcal{AP}})\stackrel{{?}}{{=}}e(P^{\prime},pk^{\prime}_{\mathcal{U}})\] (5) 6. \(\mathcal{SP}\) continues to verify the correction of \(id_{\mathcal{U}}\) by computing: \[e(C^{\prime}+pk^{\prime}_{\mathcal{U}},H(S)\cdot id_{\mathcal{U}})\stackrel{{?}}{{=}}e(P,pk^{\prime}_{\mathcal{U}})\] (6) 7. Finally, \(\mathcal{SP}\) verifies that \(\mathcal{U}\) possesses the private key that checks the correctness of the NI-Schnorr ZKP as follows: \[t^{\prime}\cdot P\stackrel{{?}}{{=}}R^{\prime}+pk^{\prime}_{ \mathcal{U}}\cdot h^{\prime}\] (7)
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 3 Experimental Studies and Modeling  
### Materials and Methods  
Portland cement (type I) and blast furnace slag were used to prepare the paste specimen. Table 1 shows the chemical composition of cement and slag. The SiO\({}_{2}\) content in slag is much higher than that in cement, which favors the production of secondary calcium silicate hydrate and contributes to the strength development. Table 2 shows the physical properties of cement and slag. Table 3 shows the specimens and test stages. The water/binder ratio of the specimens was 0.5. The slag replacement percentages were 0%, 30%, and 50%.  
The hydration heat and the compressive strength of the paste specimen were measured. The curing temperature of the specimens was 20 \({}^{\circ}\)C. The hydration heat was measured  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **SO\({}_{3}\)** & **Na\({}_{2}\)O** & **TiO\({}_{2}\)** & **Loss on Ignition** \\ \hline Cement & 64.7 & 21.1 & 5.77 & 3.14 & 0.89 & 1.61 & 0.25 & 0.22 & 2.32 \\ \hline Slag & 34.63 & 35.85 & 9.79 & 0.46 & 13.84 & 3.45 & 0.35 & 0.62 & 1.01 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cement and slag.  
Figure 1: The simulation framework of cement–slag concrete.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Siag Replacement Percentages**} & **Water/Binder Ratio** & **Curing Temperature** & **Isothermal Calorimetry Tests** & **Strength Tests** \\ \hline \(0\) & & & & & \\ \(30\%\) & 0.5 & 20 \({}^{\circ}\)C & From mixing time to 7 days & 3,7, 14, 28, and 90 days \\ \(50\%\) & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Specimens and tests.  
using isothermal calorimetry from the time of mixing to 7 days. The strengths were measured from the early stages (three days) to the late stage of 90 days. Cubic specimens (\(50*50*50\) mm\({}^{3}\)) were used for strength testing. Sealed curing was used for the specimens in the compressive strength test.
## 3 Control System Design  
The overall control framework consists of three main components: the curved road driver model, the AEB control system, and the LKA control system. The curved road driver model simulates how a human driver typically manipulates the longitudinal and lateral movements of the vehicle when driving on a curved road. It provides a basis for understanding and replicating human driving behavior in the control system. When the vehicle encounters potential collision risks on the curved road, the AEB system intervenes proactively by applying emergency braking measures, to prevent rear-end collisions with the vehicle in front. The LKA system, on the other hand, is designed to curb the lateral deviation that might occur during emergency braking on curved roads, preventing the vehicle from veering out of its original lane.  
Once the AEB system is activated, the LKA system can be triggered under two different conditions. Figure 2a illustrates the first control system framework, where both subsystems operate independently. Longitudinally, the AEB system actively applies braking pressure based on its designated strategy. Laterally, the LKA system determines if there is a potential lane deviation by analyzing factors such as the vehicle's position within the lane and the direction and magnitude of deviation. If a deviation tendency is detected, the LKA system intervenes by adjusting the steering wheel angle to prevent the vehicle from veering out of the lane. If no deviation tendency is identified, the LKA system remains inactive. In contrast, the second control system framework, depicted in Figure 2b, differs in terms of the triggering mechanism for the LKA system. In this case, the LKA system is triggered  
Figure 1: Lateral offset scenario during emergency braking on a curved road.  
when the AEB system engages emergency braking. Table 1 summarizes the functions of each control system and the corresponding triggering conditions for the subsystems.  
Figure 2: Control system framework.
## 3 Results
### Statistical Analysis of the Monitoring Data
#### 3.3.2 Box-and-Whisker Plots  
The box-and-whisker plots displayed in Figures 15-17 show the minimum, median and maximum tendency, and the extreme results of monitoring data from 1998 to 2016 collected four times a year, usually in March, June, September and November.  
The results show that the minimum BOD levels are in the range of 0.5 mg O\({}_{2}\)/L for all piezometers but the maximum concentrations are observed in piezometers P-9, P-7A, P-11A and P-2A (800, 650, 600 and 260 mg O\({}_{2}\)/L, respectively). However, these values were obtained from the period before the closure of the vertical barrier.  
A similar situation was observed in the case of COD concentrations in groundwater. During the 1998-2016 monitoring period, the maximum COD levels were observed in piezometers P-7A (1758 mg O\({}_{2}\)/L), P-9 (1157 mg O\({}_{2}\)/L), P-2A (705 mg O\({}_{2}\)/L) and P-11A (377 mg O\({}_{2}\)/L).  
In the case of TOC concentrations in groundwater, the minimum levels were observed in piezometers P-10A, P-12, and P-15 (1 mg C/L), and the maximum levels in piezometers P-9, P-2A, and P-7A (437, 359 and 129 mg C/L, respectively). This confirms that the BOD, COD, and TOC concentrations in groundwater are determined by the location of these piezometers.  
Figure 16: Box-and-whisker plots of the COD level in particular piezometers.  
Figure 15: Box-and-whisker plots of the BOD level in particular piezometers.
## 4 Conclusions  
This paper addresses the issue of missing data in structural health monitoring (SHM) and proposes a dropout mechanism for data-driven approaches that can handle missing data. The results show that instead of imputing missing data, we can treat it as dropped input units and develop a robust model for damage identification and other tasks.  
The paper proposes an unsupervised learning method based on an LSTM-structured AE model, which serves as a baseline agent model for the correlation between a group of cables. The reconstruction error of the model can be used as a damage indicator. The baseline model can be trained with the monitoring data in the early period of the structure's operation and establishes a many-to-many spatiotemporal mapping model of cable tension for cables in the group. This improves the efficiency of SHM data processing and health diagnosis.  
The unsupervised learning representation of the LSTM-structured AE model and the dropout mechanism used in this paper can handle missing data effectively, allowing for accurate and robust baseline agent models to be developed. This model enables the unified handling of missing data imputation and damage identification simultaneously and reduces the requirements for data quality.
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### Design of the Lower-Lever MOEA/D Algorithm
#### 4.3.2 Generation of Weight Vector  
In the MOEA/D algorithm, weight vectors are used to decompose a multi-objective optimization problem into multiple single-objective subproblems [34]. After decomposition, each subproblem corresponds to a specific weight vector. These weight vectors guide the search direction of the algorithm, and well-designed weight vectors ensure that the solutions are evenly distributed along the Pareto front. Since the lower-level optimization model involves only two objectives, an equal-spaced weight generation method can be used. This method first determines the number of intervals and then generates weight vectors uniformly using the reciprocal of the interval count as the step size [35]. The mathematical expression is given in Equation (45).  
\[\omega=(\frac{i}{H},1-\frac{i}{H}),i\in[0,H+1] \tag{45}\]
# Crude Glycerol as an Innovative Corrosion Inhibitor
## 1 Introduction
Spent organic and inorganic inhibitors are released into the environment as aqueous wastes which causes harmful effects to living organisms. In offshore operations, corrosion inhibitors are used to form a barrier layer between the oil and the water phase. The water-inhibitor system is eventually released into the sea, which has toxic effects on marine life [26]. The increasing emphasis on renewable energy is due to the rapid increase in demand for clean transportation fuels, environmental concerns, and shrinking oil reserves. Biodiesel is a biodegradable and renewable fuel that has recently been viewed with increasing interest. Although global biodiesel production was once expected to reach higher levels, the high production cost and the production of high volumes of crude glycerol, a by-product of the biodiesel making, have slowed the rate of biodiesel production [27]. For every 100 kg of biodiesel produced, approximately 10 kg of crude glycerol is generated [28; 29]. Crude glycerol is typically transformed to pure glycerol using energy-intensive and expensive processes for applications in the food, pharmaceutical, and cosmetics industries. Various methods for conversion and utilization of crude glycerol have been proposed such as combustion, composting, anaerobic digestion, animal feeds, and thermochemical/biological conversions to value-added products. The chemical composition of crude glycerol depends on the composition of the feedstock, the type of catalyst used in the biodiesel production process, the trans-esterification efficiency, the recovery efficiency of biodiesel, and whether the methanol and catalysts were recovered [30]. Disposal of crude glycerol, which contains salt, methanol, free fatty acids, residual catalyst, etc., represents an economic and environmental challenge. The high cost of purification and market saturation behooves biodiesel producers to seek cheaper and alternate ways to utilize crude glycerol [31]. The presence of impurities has a negative effect on the fermentation process by inhibiting product formation. Hence, microbial fermentation is an alternative and competitive approach for biodiesel producers. Canada's annual biodiesel production in 2018 is estimated at 750 million liters [32]. Hence, large quantities of crude glycerol (~75 million liters) will need to be either purified for use in secondary industries or use in nonconventional applications. In this study, non-purified crude glycerol from biodiesel production was evaluated as a potential green inhibitor for steel specimens in a hydrochloric acid medium.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Method Based on Machine Learning
#### 4.4.2 Convolutional Neural Network  
Convolutional neural networks (CNNs) belong to a branch of ANN. As a network structure with fewer layers, ANNs have limited representation ability for complex functions, and generalization ability for complex classification problems is restricted to some extent. However, CNNs can realize complex function approximation by learning a deep nonlinear network structure. The deep neural network (DNN) has more layers (8-152 layers) than ANN, and needs more training data (4000-10,000 images).  
An end-to-end detection network model was outlined in [PERSON] [40]. Since the feature detection layer of CNNs is learned by training data, explicit feature extraction is avoided when using CNNs, while implicit learning is carried out from the training data. Moreover, since the weights of neurons on the same feature mapping surface are the same, the network can learn in parallel. It is worth mentioning that the detection accuracy is 99.29%. The CNN's structure for surface defect recognition model is presented in Table 5. In a recent study, [PERSON] et al. [82] employed CNNs to detect several types of defects on textured and non-textured surfaces, which was difficult to achieve by traditional machine learning methods. The recognition rate was 98%, and the time consumed for single image recognition was 0.01135 s. [PERSON] et al. [21] proposed a max-pooling convolutional neural network method for the classification of steel defects. Compared with the commonly used support vector machine (SVM) classifier for feature descriptor training, this method can not only obtain better detection effects, it can also be directly used to detect the original image and segmentation defects, avoiding further time consumption and difficulty in optimizing adaptive preprocessing. However, changes in image size in a particular classification task have not yet been addressed by standard CNNs;  
Figure 7: Artificial neural networks (ANN) model.  
nevertheless [PERSON] et al. [41] put forward the multi-scale pyramidal pooling network, which had three characteristics: (1) a pyramidal pooling layer that made the net independent of input image size; (2) multi-scale feature extraction; and, (3) an encoding layer emulating standard dictionary-based encoding strategies. Hence, the problem of image scale in traditional CNNs was solved.
# A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions
###### Abstract
The proposed estimator had a more accurate estimation of vehicle longitudinal speed during a cornering manoeuvre. [PERSON] et al. proposed a wheel-slip control approach that ensures ESC functions normally for decentralized drive EVs [18]. This approach used the wheel rotation and chassis acceleration to estimate the maximum transmissible torque. [PERSON] et al. presented a continuous slip control algorithm designed for the case in which the vehicle velocity is precisely known. The control strategy is based on both wheel slip and wheel acceleration regulation and ensures global asymptotic stability in a closed loop [19]. [PERSON] et al. presented the analysis and design of a novel traction control system (TCS) based on sliding-mode control (SMC) and the maximum transmissible torque estimation (MTTE) technique and applied it to four-wheel independent drive electric vehicles (EVs) without chassis velocity or acceleration sensors [20]. However, when all four wheels are slipping, the precision level that is available with only acceleration and wheel speed sensors is limited. This paper aims to develop a novel longitudinal speed estimator for four-wheel drive vehicles when more than two wheels have high slip rates on snowy roads. First, a vehicle speed estimator is created to estimate vehicle speed from wheel speed by fuzzy control logic. This vehicle speed estimator based on acceleration estimates vehicle speed by integrating the acceleration variable. During four-wheel slip (especially large slip), a single fuzzy controller cannot obtain accurate vehicle speed from the wheel speed. An additional drawback is the inaccuracy of the initial vehicle speed condition from the vehicle speed estimator based on wheel speed. Consequently, it is necessary to obtain the initial vehicle speed condition. Due to the importance of judging wheel slip in the second fuzzy controller, we need relatively accurate estimation of vehicle speed. Therefore, the Local Vehicle Speed Estimator has been developed to combine two vehicle speed estimation methods, wheel speed encoders and an acceleration sensor, to obtain a more accurate estimation of vehicle speed. A weighting coefficient has been introduced to obtain a relatively accurate speed estimation by balancing the contribution between the estimated vehicle speed from the wheel encoders and acceleration sensor. The second fuzzy controller can obtain accurate vehicle speed estimation using relative slip ratio inputs. Finally, its accuracy is verified by driving experiments on split roads covered by snow. The result shows that the speed estimated by our estimator is similar to the vehicle speed obtained from a production car. The focus of this paper is longitudinal speed estimates of four-wheeled slip and in conditions of snowy road. The purpose is to obtain accurate speed estimates under snowy road and four wheels' slip condition.