esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
## 1 Introduction  
A VC is a torque-transmission device that uses the shearing force of high-viscosity fluid. VCs continue to be an important part of modern automobile drive systems and provide a cost-effective and reliable traction-control system. Several million units are built into vehicles each year [1]. VCs can improve the torque-distribution characteristic of the conventional differential to increase the dynamic performance and trafficability of the vehicle [2]. A limited-slip differential (LSD) equipped with a VC will improve the traction capability of vehicle on a wet road surface, and will also increase the degree of understeering while the vehicle is being driven, especially on the split-road, and the steering characteristics will be improved significantly [3]. However, the driving torque will change the distribution between the front and rear axes and affect the vehicle's steering performance [4, 5]. The effect on automobile performance caused by a VLSD depends on its own working characteristics. The application of VLSDs in vehicles requires an accurate calculation method for the delivering torque characteristics, that is, the accurate calculation of the working characteristics of the VC [6].  
The two working modes of the VC are oil-film-shearing operation and hump operation [7], both of which are closely related to temperature. When a VC is operating during the oil-film-shearing operation mode, the heat generated by shearing friction will bring about an increase in the internal pressure and temperature, as well as a decline in viscosity and shear torque. Reference [7] showed the calculation method of shear torque in a certaintemperature; however, the temperature was unable to be predicted. If a VC operates at a large speed difference for a long period of time, the generated heat will continue to increase the internal pressure and temperature, and eventually form a quasi-rigid connection of the driving and driven plates, the delivering torque will increase sharply, and the VC moves into hump operation mode [1]. The internal heat of the VC is transferred from the inside to the outside of shell through all parts of the VC; only by accurately calculating the heat transfer of each part can the real-time working characteristics of the coupling be obtained. In reference [8], according to the gradient of velocity and torque, the temperature change was calculated using the finite element method inside the VC, but it was too complex for real-time calculation of the working characteristics. In references [7; 9; 10], the flow phenomenon of silicon oil and effect of main factors on the hump phenomenon were analyzed while a VC was in operation. There is no calculating method for real-time temperature so far.  
Therefore, only by performing an accurate analysis of the absorbed heat and the temperature of the silicone oil, the real-time characteristics of the VC can be predicted accurately. This is one of the most important aspects of VCs, and the problem to be resolved in this paper.
## 2 Related Work
### IEEE 802.11 ac (Wi-Fi 5)  
This standard has been a key factor in the adoption of high-speed wireless networks, providing the foundation for performance-demanding industrial applications such as the remote telecontrol of machinery. Wi-Fi 5 operates exclusively in the 5 GHz band, eliminating the congestion commonly experienced in the 2.4 GHz band and taking advantage of channel capacities of up to 160 MHz, four times wider than its predecessor.  
The introduction of 256-QAM modulation improved spectral efficiency by encoding more bits per symbol, and the use of new coding schemes enhanced the error resistance and reliability of the wireless network. Additionally, the incorporation of Multiple-User-Multiple-Input-Multiple-Output (MU-MIMO) technology allows multiple devices to communicate with an access point simultaneously rather than taking turns, improving network capacity and reducing latency in high-density device environments. Another technology incorporated is the beamforming technique, which directs Wi-Fi signals towards specific devices instead of scattering them in all directions, resulting in more robust and stable connections.
## 2 Materials and Methods  
### Study Area  
As shown in Figure 1a, the GKM region (50\({}^{\circ}\)10\({}^{\prime}\)-53\({}^{\circ}\)33\({}^{\prime}\)N, 121\({}^{\circ}\)12\({}^{\prime}\)-127\({}^{\circ}\)0\({}^{\prime}\)E) is located in the northernmost area of China. This study area is adjacent to the Lesser Khingan Mountains in the east, near the vast Hulunbeir Prairie in the west, bordering the fertile Songnen plain in the south, and separated from Siberia in Russia by the Heilongjiang River. Lying in the east slope of the Greater Khingan Mountains ridges (GKMR), the study area has an obvious decrease in elevation from southwest to northeast (Figure 1b). The GKM belongs to the rigid temperature monsoon climate zone, which has an annual average temperature of \(-\)2.6 \({}^{\circ}\)C. The annual average precipitation is 400-600 mm, and the majority of the precipitation is concentrated in July to September, providing a relatively humid environment for vegetation growing. The soils are mainly dark brown coniferous forest soils. The GKM is an important state-owned natural forest region that serves as a biological protective screen for northeastern and northern China. Also, it includes a first-grade National Nature Ecological Reserve with abundant forest resources whose area covers about 80% of this region (Figure 1c), including the largest and best preserved primeval cold-temperature bright coniferous forest in China (Figure 1a). However, its forest structure is relatively simple due to the cold weather. The dominant species are _Larix gmelinii_ and Mongolian pine [45].  
Figure 1: (a) Distribution of vegetation type and location of the Greater Khingan Mountains (GKM) in China, based on the Vegetation Regionalization Map of China (1:6,000,000) [47]; (b) DEM(Digital Elevation Model) and the meteorological station of the GKM; and, (c) Land cover of the GKM. The land cover data is from the MODIS dataset of 2001.
# Quadrotor Stabilization and Tracking Using Nonlinear Surface Sliding Mode Control and Observer
## 1 Introduction
To derive the appropriate control input \(u(t)\) by SMC, we use the augmented quadrotor dynamics and the angles estimated by SMO. In SMC, the desired angle \((\phi_{d},\theta_{d})\) is derived from the target position \((x_{d},y_{d},z_{d})\) and the control input \(u(t)\). With the desired angle \((\phi_{d},\theta_{d})\), we operate the quadrotor system to reduce the error between the desired and measured angles. Moreover, to solve the time delay problem in the target tracking time due to the deceleration curve in SMC, we set up the nonlinear sliding surface to reduce the reaching time of a sliding mode controller. The design of a nonlinear sliding surface incorporates the deceleration curve of SMC into the sliding surface so that the delay of reaching time can be effectively reduced. As a result, the quadrotor follows the target position \((x_{d},y_{d},z_{d})\) by the SMO and control system. This paper is structured as follows. In Section 2, we obtain the quadrotor dynamic model. In Section 3, the SMO estimates the unmeasured angle and augmented dynamics of the quadrotor. In Section 4, a nonlinear sliding surface is designed to reduce the influence of the deceleration curve. In Section 5, the desired angle \((\phi_{d},\theta_{d})\) and control input \(u(t)\) are obtained by SMC with the estimated angle and augmented dynamics by SMO. In Section 6, we prove the proposed method by simulation. Finally, in Section 7, we present our conclusion.
# Cellulose as a Delivery System of Raspberry Juice Volatiles and Their Stability
## 1 Introduction
Also, since volatiles are generally larger than water molecules they may not readily diffuse or are trapped within the carbohydrate matrix during drying [24, 23]. Process variables during freeze-drying also play an important role in the retention of volatiles on carbohydrate matrices. During freeze-drying the formation of microregions occurs and they become impermeable to organic compounds when their water content decreases below a critical level [24]. Several studies have investigated the effect of some types of carbohydrates like dextrin, starches, \(\lambda\)-carrageenan, \(\beta\)-cyclodextrintrins, xanthan, guar gum and pectins on the stability and retention of volatile compounds [25, 26, 27, 28, 29]. Cellulose is the polysaccharide composed of glucose molecules, and it is insoluble in water and common organic solvents. It is the main component of the plant cell walls, and widely distributed and abundant in nature. The structure and properties of cellulose make it a suitable material for the encapsulation of different active ingredients in order to formulate stable and efficient delivery systems [30, 31, 32, 33, 34, 35, 36], thus we choose it as a delivery system for raspberry volatiles. In addition, cellulose is one of the main dietary fibers. Generally, during the last several decades, dietary fibers have been the focus of many studies on their potential health benefits. Many epidemiological and clinical studies have demonstrated that the consumption of dietary fibers has positive effects on obesity, type 2 diabetes, cancer and cardiovascular disease [37]. The potential use of cellulose as a delivery system for flavor compounds was investigated in this study. In these complexes, the benefits of one plant compound would be combined with the benefits of other plant compounds producing an additive, which could then be used for development and/or improvement of novel, innovative foods. For that purpose, freeze-dried complexes of cellulose and raspberry volatiles were prepared. Complexation of cellulose and raspberry volatiles was conducted for 15 and 60 min in order to evaluate if prolonged complexation would result in a better adsorption of volatiles onto the cellulose. In addition, the amount of cellulose used was varied (2.5, 5, 7.5 and 10%). Adsorption of raspberry volatiles onto cellulose was investigated after the preparation of complexes, but additionally, the stability of the complexes during 12 months of storage was also investigated.
## 3 Technologies for Treating Landfill Leachate
### Biological Treatment Processes
#### 3.3.3 Constructed Wetlands  
On-site treatment using constructed wetlands (CWs) is one of the low cost methods of landfill leachate treatment which has been widely practiced in several countries for many years ([PERSON] et al., 2017). A wetland system comprises a permeable substrata, such as gravel, which is typically planted with emergent wetland plants, such as _Schoenoplectus_, _Typha_, _Phragmites_, and _Cyperus_([PERSON] et al., 2017). [PERSON] et al. (2017) employed three discontinuous batch shallow CW systems with reed, willow, and plant-free configurations featuring recirculating subsurface horizontal flow to evaluate nitrogen removal from landfill leachate in relation to HRT and climatic conditions. This study revealed vegetation significantly influenced both conditions and processes within the shallow CW systems. [PERSON] et al. (2014) expressed that more than 50% of nitrogen can be eliminated by microbial activities, such as the nitrification/denitrification process, while around 25% of nitrogen may be absorbed by plant roots. [PERSON] et al. (2014) achieved good removal efficiencies of phenol (88-100%), bisphenol A (9-99%), and 4-tert-butylphenol (18-100%) from synthetic landfill leachate using a vertical flow constructed wetland system.  
Biological treatment demonstrates distinct advantages in landfill leachate management, particularly through efficient organic matter mineralization and cost-effective nitrogen elimination under optimized conditions ([PERSON] et al., 2017). However, its application encounters three principal limitations: (1) prolonged treatment cycles due to refractory organic fractions and microbial inhibition from complex contaminants ([PERSON] et al., 2017); (2) inadequate removal of non-biodegradable organics ([PERSON] et al., 2017); (3) little effect on mature leachate ([PERSON] et al., 2017). Table 5 lists the efficiency of biological treatment for landfill leachate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **pH** & **Parameter** & **Concentration** & **Removal** & **Ref.** \\  & & & **(mg/L)** & **Rate (\%)** & **Ref.** \\ \hline ASP & 7.85 & \begin{tabular}{c} COD\({}_{\text{i}}\) \\ COD\({}_{\text{f}}\) \\ \end{tabular} & 950.0 & 69.9 & [138] \\ SBR & 8.4 & \
\begin{tabular}{c} COD\({}_{\text{i}}\) \\ COD\({}_{\text{f}}\) \\ \end{tabular} & 4975.0 & 50.0 & [145] \\ GSBR & 7.5 &
\begin{tabular}{c} TAN\({}_{\text{i}}\) \\ TAN\({}_{\text{f}}\) \\ \end{tabular} & 498.0 & 99.0 & [139] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Efficiency of biological treatment at laboratory scale.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods
### AdaBoost Algorithm  
The AdaBoosting algorithm [28] is an important ensemble learning technique, which can enhance a weak learner whose prediction accuracy is only slightly higher than random guessing into a strong learner with high prediction accuracy, which provides a new idea for solving the difficulty of directly constructing a strong learner. AdaBoost is one of the most successful algorithms.  
A brief description of the AdaBoost algorithm is as follows:  
* First initialize the weight distribution D1 of the training data. When there are \(N\) training sample data, each training sample is given the same weight at the beginning: \(w1=\frac{1}{N}\);
* Then, train the weak classifier H. In the specific training process, if a certain training sample point is accurately classified by the weak classifier H, then its corresponding weight should be reduced when constructing the next training set. Conversely, if a training sample point is misclassified, its weight should be increased. The sample set whose weights have been updated is used to train the next classifier, and the entire training process will continue to iterate.
* Finally, combine the weak classifiers obtained from each training into a strong classifier. After the training process of each weak classifier is over, the weight of the weak classifier with a small classification error rate is increased so that it plays a greater decisive role in the final classification function. The weight of the weak classifier with a large classification error rate is reduced so that it plays a less decisive role in the final classification function.
## 3 Numerical Validations
### Continuous Beam Example
Generally, all the cases have been identified. No ill-conditioned problem is found even without regularization. The following is the discussion on different influencing factors. The results of both types of forces illustrate that the recognition via D1 presents better accuracy than that via D2. Observing the relative position of D1 and D2, it can be found that D1 is on the same span with force, while D2 is on the other span. The error is caused by the restraining effect of the mid-span support to small forces. Due to the TLSF response matrix, \(\mathbf{\Lambda}\) is constituted of the responses excited by the small TLSFs, the inhibition of mid-span support is amplified. This results in a distortion in \(\mathbf{\Lambda}\), and hence leads to a large error in determination during the inversion of Equation (17). Figure 4: Fixed force identification results. (**a**) Harmonic force identification results; (**b**) Square wave force identification results. The type of the excitation force  
Comparing Figure 4a,b, it is found that both the harmonic and square wave forces can be determined, and the accuracy of harmonic force is better. Taking the results through D1 as an example, the curve of the identified harmonic force closely follows the true force, while the curve of identified square wave force has visible fluctuations around the true force, especially at the corner when the force rapidly changes. This is because the large difference and discontinuity of the square wave force misled the method to determine the force as an 'impulse force' and hence produce a mutation at the corner. The cases containing noises have shown that measurement noises only affect the accuracy of the peak recognition in a small amplitude, and the impact on the overall curvature is negligible. The robustness of the proposed method to noise is further proved.
# Using Artificial Intelligence for Space Challenges: A Survey
## 3 Challenges in Space Applications
### Earth Observation  
Satellites generate thousands of images every minute of the day, collecting terabytes of data. There are several applications of Earth Observation (EO), such as agriculture, forestry, water and ocean resources, urban planning and rural development, mineral prospecting, and environment and disaster management [44]. Without a data analysis tool, humans were previously in charge of understanding these data and gathering relevant information for the required application. This effort resulted in a long time to select, filter, refine, and analyze a vast number of images. With the advancement of AI, there have been significant signs of progress in reviewing and analyzing millions of images produced by satellites. Furthermore, these algorithms can detect any issues with the images by speeding up the image-gathering process.  
Satellites are often used to respond to natural disasters from space. For example, these images can help ground operators to detect the course of the disaster or determine victims and damages to buildings and the environment [45]. Furthermore, geospatial information of the area affected by the event is rapidly provided to the first responders thanks to EO satellites. Indeed, satellite data can provide information to reduce the disaster risk assessment and plan the post-disaster response [46]. Studies about the population's exposure to air pollution and demographic data have been carried out to highlight the highest exposure risks [47]. Therefore, EO can analyze several of Earth's physical, chemical, and biological parameters to understand the Earth's conditions better and propose action plans to improve life on the planet.
## 2 Materials and Methods
### Statistical Analysis  
The Kolmogorov-Smirnov test was employed to determine whether parametric tests were appropriate to be used. In the case of the statistical analysis of electrophysiological data at the distractor array onset, the mean amplitudes of lateralized early P\({}_{\rm D}\) and N2 pc were submitted to mixed ANOVAs, which had a between-subject factor relative to the state depression group (low and high) and a within-subject factor relative to VWM capacity loads (low 1 and high 4). To evaluate the presence of a significant magnitude of early P\({}_{\rm D}\) and N2 pc components for each of the two state depression groups at different levels of VWM capacity loads, one-sample t-tests with a test value of 0 were also conducted.  
In terms of behavioral data, after removing omissions and response times less than 200 ms and above 2100 ms, the mean accuracy (%) and mean correct response times (RTs; ms) in performing the task were analyzed. The mean accuracy (%) and mean correct RTs (ms) were subjected to a 4-way mixed-design ANOVA with a between-subject factor relative to the state depression group (low and high) and three within-subject factors with respect to the VWM capacity load (low 1 and high 4); the distractor-to-memory-probe with horizontal and spatial compatibility (compatible and incompatible); and the distractor-to-memory probe with vertical and spatial compatibility (compatible and incompatible). In all repeated-measure ANOVAs for lateralized ERPs and behavioral data, Greenhouse-Geisser epsilon adjustments for non-sphericity were applied where appropriate. Post hoc pairwise comparisons of the means were conducted using the Bonferroni method unless differently specified. For all statistical tests, \(p<0.05\) (2-tailed) was considered significant.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 3 Unific-Measurement-Based Beam Switching and Handover Scheme  
In this section, we propose a unific-measurement-based beam switching and handover scheme that improve the mobility performance in mmWave massive MIMO networks. First, we define a new measurement event that leads to a trigger for both beam switching and handover. Next, we introduce the entire procedure of beam switching and handover that can be operated based on 3 GPP specifications.
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 2 Document Image Degradation Problems
### Poor Lighting Conditions  
In the process of document digitization, in the past, scanners were commonly used digital instruments, and scanning resulted in high-quality documents. However, with the development of science and technology, the types of digital image acquisition equipment are increasing, such as digital cameras, mobile phones, cameras, and so on. More people now use their cell phone cameras to get digital copies of their files. However, this approach may encounter problems, such as uneven lighting, excessive shadows, or too dark. In addition, for some old documents or antique literature, there may be wormholes, translucency, or unclear handwriting. This interference with the readability of the acquisition of documents has a harmful impact. The unevenness approach focuses on evaluating shadows on the document image and trying to remove them, making the document clean again and readable. Figure 2d shows an example of shadow removal under uneven lighting.