esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Deformation Analysis Method of Excavation Based on Random Field Theory  
#### 3.3.1 Method and Steps of Random Field Simulation  
In this study, taking the log-normal random field of the parameter \(i\) as an example, a brief description of the method and steps of the midpoint method based on the Cholesky decomposition and Latin hypercube sample (LHS) [61] is presented as follows:  
1. LHS was adopted to generate a random sample matrix \(\xi\) with independent standard normal distribution.
2. For the standard normal equivalent cross-correlation matrix \(R_{0}=(\rho_{0,i_{2}})_{mm}\), the Cholesky decomposition \(L_{1}L_{1}{}^{T}=R_{0}\) was performed to obtain the lower triangular matrix \(L_{1}\). Multiply the transposed matrix \(L_{1}{}^{T}\) with the sample matrix \(\xi\) so as to obtain the associated standard normal distribution sample matrix \(\chi^{D}=\xi L_{1}{}^{T}\).
3. With regard to the autocorrelation coefficient matrix \(\sum_{\chi,\chi^{D}}^{G}\), the Cholesky decomposition \(L_{2}L_{2}{}^{T}=\sum_{\chi,\chi\chi}^{G}\) was performed to obtain the lower triangular matrix \(L2\), thereby obtaining the correlated standard Gaussian random field by the following equation: \[H_{i}^{D}(x,y)=L_{2}\cdot\chi^{D}=L_{2}\cdot\xi\cdot L_{1}^{T}\] (9)
4. The correlated standard Gaussian random field \(H_{i}^{D}(x,y)\) was converted to the correlated non-Gaussian random field by an equal probability conversion method as the following equation: \[H_{i}(x,y)=G_{i}^{-1}\Big{\{}\varphi\Big{[}H_{i}^{D}(x,y)\Big{]}\Big{\}}\] (10) where \(G_{i}^{-1}(\cdot)\) is the inverse function of the marginal cumulative distribution of the non-Gaussian distribution and \(\varphi(\cdot)\) is the cumulative distribution function of the standard normal distribution.
5. Taking the exponential correlated standard Gaussian random field, the correlated log-normal random field of the parameter was obtained employing the equation: \[H_{i}(x,y)=\exp\Big{(}\mu_{\ln i}+\sigma_{\ln i}\cdot H_{i}^{D}(x,y)\Big{)}\] (11) where \(\mu_{\ln i}\) and \(\sigma_{\ln i}\) denote the mean and standard deviation of normally distributed variable \(\ln i\).  
A couple of points should be stated here: (1) the purpose of LHS is to improve the representativeness of the random sample; (2) the above random parameters require a standard normal distribution, otherwise it is required to be transformed in advance.
## 3 Results and Discussion
### Comparison to Numerical Particle Model--Wheat Straw and Salix
Since the gas phase reaction kinetics were not modelled, this indicates that diffusion is the dominant process, and that the hot plume was increasingly diluted by the surrounding air at higher HAP. The good agreement is also evident from the shape of the K(g) time series during devolatilization. For Salix, a concentration peak is observed in the beginning, after which the level is fairly constant. However, for wheat straw, the concentration is first low and then rises steadily until a maximum is reached at the end of devolatilization. This is most probably due to the high availability of quickly volatilising chlorine in wheat straw (Table 1), leading to the release or formation of KCl instead of K(g). An important input parameter that can affect the outcome of the model is the fraction of organic K in the ash. According to reaction R3, the release of atomic K is directly proportional to the available organic K, thus, by increasing the organic content in the model, higher values of K(g) can be expected. In future studies, the amounts of organic and inorganic K should be determined through the ash analysis to directly improve the predictability of the model. However, one should note that the ratio between organic and inorganic K in the biomass ash cannot be chosen arbitrarily. There are some limitations related to the amount of Cl in the ash: since chlorine is primarily bonded to potassium, the inorganic K content is at least equal to the stoichiometric Cl content. The percentage of organic/inorganic sulphur, on the other hand, mainly affects the release of SO2 (not measured in this work) and has negligible effect on the final K release. Figure 4: Comparison between measured and simulated H2O (**a**,**d**), temperature (**b**,**e**) and K(g) (**c**,**f**) at HAP 4 mm for wheat straw (**a**–**c**) and Salix (**d**–**f**). Solid lines represent results obtained by the numerical particle model, whereas markers display TDLAS data.
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
## 2 Architectures of VANETs  
In VANET, vehicles communicate through wireless links that are mounted on each vehicular node. Each node within VANET acts as both the participant and router of the network, as the nodes communicate through other intermediate nodes that lie within their own transmission range. There is no fixed architecture of VANETs due to their self-organizing nature. The architecture of VANETs can be classified into three types: (a) Pure cellular wireless local area network; (b) Pure ad-hoc networks; (c) Hybrid networks [16; 17; 18].  
Figure 1 shows the cellular wireless local area network. The vehicular nodes access the internet through cellular gateways and wireless local area network access points. It assists vehicular nodes by giving information about traffic congestion and traffic control. It also provides infotainment services such as downloading data, latest news, parking information and advertising. The deployment of such types of architecture is very hard due to the high cost of cellular towers, wireless access points and geographic limitations [16; 17; 18].  
Pure ad hoc network architecture is shown in Figure 2. It provides communication between vehicles and among nearby vehicles. It is also called an inter-vehicles ad-hoc network. This type of architecture collects and disseminates road related information without considering any fixed infrastructure. The vehicles are free to move on the road, high mobility of the vehicles induces quick changes in the network topology. The rapid topology changes create fragments in the network. In the pure ad-hoc mode, the frequent partitions of the network due to the high mobility of vehicular nodes make routing the data more challenging. The advantage of the pure ad-hoc mode is that it overcomes the deployment cost of base stations [16; 17; 18].  
Figure 1: Pure cellular wireless local area network.  
The hybrid architecture, which is shown in Figure 3, is composed of ad-hoc domain and infrastructure domain. It provides communication between vehicles to vehicles (V2V) as well as a vehicle to infrastructure (V2I). This type of architecture is helpful in providing richer contents and improved flexibility in contents sharing [16; 17; 18].
## 4 Results and Discussion  
### Quality of the Synthetic Datasets  
Using the synthetic forest stand data, the individual tree detection method achieved an average commission error (number of unassigned detections per number of detections) of 11% and an average omission error (number of missed trees per number of reference trees) of 72%. The planar RMSE of the tree positions was about 0.75 \(\pm\) 0.39 m, and the RMSE of the heights was about 1.21 \(\pm\) 0.55 m. These accuracy descriptors are consistent with most of the methods benchmarked in [3,11], which indicates an appropriate modeling of the synthetic point clouds. The increased omission rate might be caused by the simplistic detection method and the bias to dominant trees.  
Table 3 summarizes the results of the statistical evaluation of the synthetic training data compared to the original data (overall and on plot level).  
The test results for the amount of trees and the amount of detections per reference tree indicate realistic stand densities. The increased average NNDs of the synthetic datasets indicate an overestimation of the tree distances. This effect might be caused by the fact that the original stands are more clustered (e.g., because of aisles or leaning trees), while the simulated trees are distributed more uniformly (with consideration of tree competition). This effect also leads to an incomplete coverage of the value range for this attribute.  
As lower NNDs reduce the separability of points, the synthetic inventory plots might be easier to co-register than the original ones. Nevertheless, since the variance within the synthetic datasets is greater than within the original datasets and the value ranges of the other attributes are completely covered, the simulated datasets can be assumed to be appropriate. Thus, by training the algorithm with the synthetic datasets, it should be applicable to the original datasets without losing accuracy.
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
###### Abstract  
This paper proposes a novel controller, fast fractional-order terminal sliding mode control (FFOTSMC), for a seven-degree-of-freedom (7-DOF) robot manipulator with tracking control. The new controller applies the fractional-order derivative on both the sliding surface design and the sliding control/reaching law. Compared to previous research, which only applies the fractional-order derivative on the sliding surface design, the proposed controller has a faster convergence for reaching the sliding surface and maintaining stay on it because of the new fractional-order control law, which helps the tracking accuracy. To implement the controller on the robot with less chattering, a sliding perturbation observer (SPO) is used to estimate the disturbance and uncertainties. Stability analysis is analyzed using Lyapunov functions for fractional-order systems. The controller performance is evaluated by a simulation of a single-input and single-output (SISO) system in MATLAB Simulink and experiments on the robot manipulator.  
robot control; sliding mode control; robust control; fractional calculus +
Footnote †: journal: Applied Sciences
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 2 Related Works
### Lighting Control  
The architecture of a lighting system control can be centralized or distributed [34]. In a centralized control system, sensor data are transmitted to a central controller, where the dimming level for the corresponding luminaire is determined by a control law and transmitted to the luminaire [22]. In contrast, in a distributed control system, each sensing module has a controller and communicates with neighbors to calculate the dimming levels [17; 19; 40]. In wireless networks, compared with the centralized-control networks, a distributed control system requires larger bandwidth and has a higher cost due to larger amounts of processing and storage in a sensing module [41]. However, a centralized control system needs more time to process data in its central node, thus making the central node more complex and vulnerable and increasing the system communication traffic and delays. Considering the robustness and the latency, a distributed control system architecture is used in this study.  
In order to determine dimming levels of luminaires, two different control methods are utilized: open-loop method and close-loop method. The open-loop method measures the incoming daylight and signals a controller to proportionately dim the luminaire based on the daylight contribution [42]. The closed-loop method adjusts the luminaire dimming level to ensure that the combined contribution from daylight and artificial light maintains the desired illumination level. Specifically, proportional integral (PI) and proportional integral differential (PID) are two commonly used closed-loop methods [34]. Compared with the closed-loop method, the performance of the open-loop method is affected by the absence of feedback. However, the open-loop method is more economical due to considering the limited processing capability of microcontroller in a sensing module and also, more robust to errors in the placement of light sensors. In addition, optimization algorithms and machine learning are applied to control methods to minimize total illuminance [10; 14; 34] and make the control system smart [42], respectively. Although the optimization algorithms can achieve high energy savings, they consume more computational resources and much time and seldom consider the movement of people.
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 6 Algorithm Analyses
### Key Space Analysis  
For a desirable encryption algorithm, its key space should be large enough to resist the brute-force attack. (1) For [PERSON]'s algorithm, the AES key is also the key of [PERSON]'s algorithm. This algorithm security main depends on the used key length, i.e., 128, 192, or 256 bits. Therefore, the size of key space is also \(2^{128}\approx 3.4\times 10^{38}\), \(2^{192}\approx 6.3\times 10^{57}\), or \(2^{256}\approx 1.16\times 10^{77}\); (2) For [PERSON]'s algorithm, authors adopt AES-256 during the process of image encryption. Therefore, the key space is \(2^{256}\approx 1.16\times 10^{77}\); (3) For [PERSON]'s algorithm, authors adopt AES-128 during the process of image encryption. Therefore, the key space is \(2^{128}\approx 3.4\times 10^{38}\); (4) for the proposed algorithm, the keys are the AES key, the variables \(x_{0}\) and \(q\) of PWLCM. In our experiment, we used AES-256 to encrypt the plain image. If 16 digits exists after the decimal point for the key, then the key space is \(10^{32}\times 2^{256}\approx 1.16\times 10^{109}\). Therefore, the proposed algorithm has the largest key space to withstand the brute-force attack.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 2 Materials and Methods
### Introduction of ZUPT Aiding INS
#### 2.3.1 Zero-Velocity Detection  
The walking process of pedestrians can be modeled as a repeating sequence of heel strike, stance, push off, and swing. The velocity of our foot should be zero during the stance phase when the foot is bearing the weight of the whole body. The zero-velocity can be utilized as a \"virtual measurement\" to correct the IMU's attitude, velocity, and position if we can detect the occurrence of the zero-velocity. While there are many approaches to help detect zero-velocity, we chose the generalized likelihood ratio test (GLRT) to derive the detector [48]. The IMU is considered stationary at epoch \(k\) if Equation (40) is satisfied.  
\[\frac{1}{N}\sum_{i\in\mathcal{W}_{k}}(\frac{1}{\sigma_{f}}-\left| \left|\boldsymbol{f}_{i}-\boldsymbol{g}\frac{\overline{f_{k}}}{\left| \overline{\boldsymbol{f}_{i}}\right|}\right|\right|+\frac{1}{\sigma_{\omega}} ||\boldsymbol{\omega}_{i}||^{2})<\boldsymbol{\gamma} \tag{40}\]  
In Equation (40), \(\overline{\boldsymbol{f}_{k}}\) is the mean of accelerometer measurements over the time window \
# Dento-Skeletal Class III Treatment with Mixed Anchored Palatal Expander: A Systematic Review
## 1 Introduction  
The treatment of skeletal Class III malocclusion is sometimes a challenge in orthodonducities. The prevalence of this type of malocclusion presents high variability among and within populations ranging from 0% to 26%: the populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, Middle Eastern nations had a mean prevalence rate of 10.2%, European countries had a lower prevalence rate of 4.9% and the Indian population showed the lowest one of 1.2% [1]. The etiology of Class III malocclusion is generally genetic, as has been demonstrated in several studies. [1; 2; 3]. A wide range of environmental factors have been suggested as contributing to the onset of Class III malocclusion (enlarged tonsil, difficulty nasal breathing, habit of protruding the mandible) [2; 3; 4; 5; 6]. Crani features may be attributed to the incongruity of position and the size of the craniofacial structures at the skull base, maxilla and/or mandible [7; 8; 9; 10; 11; 12; 13; 14; 15]. Several tooth-borne anchorage treatments have been proposed to treat Class III dentoskeletal disharmony, including intraoral and extraoral appliances, such as the rapid maxillary expansion [16; 17; 18; 19] along with the facial mask (RME/FM) and two occlusal acrylic splints associated with Class III elastics and chin-cup (SEC III) [20; 21].  
Some adverse effects were reported with the use of conventional dental anchorage as RME/FM such as upper incisors proclamation and extrusion and mesial tipping of the upper molars, gingival recessions, [3, 22, 23, 24, 25, 26, 27, 28] fenestrations of the buccal cortex and root resorption of the posterior teeth [29, 30, 31, 32, 33, 34]. The SEC III appliances also present some limits, such as uncomfortable dimensions of splints and the impossibility of having expansion in the upper arch. Therefore, a modified SEC III protocol including a maxillary bonded expander has been used with the remaining limit of the mesializing effect of the upper arch [20]. To overcome tooth-borne anchorage treatment limitations, the use of bone anchorage has recently been proposed [10, 35, 36, 37, 38]. The use of micro-implants allows for the achievement of skeletal anchorage without the need for surgical procedures such as mini-plate placement and removal [39, 40, 41, 42, 43, 44, 45, 46]. The goal of this systematic review of the literature is to determine the efficacy of using a mixed anchored palatal expander to treat Class III malocclusions, as well as to see if using a bone-anchorage device induces more maxillary advancement with fewer dental side effects.
## 2 Overview of the Human Gut Microbiota
### Composition and Functional Organisation of the Human Gut Microbiota
The most frequently detected genera include _Streptococcus_, _Granulicatella_, _Actinomyces_, _Solobacterium_, _Rothia_, _Gemella_, and members of the candidate phylum TM7 (G-1). Dominant species comprise streptococci from the _mitis_ and _sanguinis_ groups, _Streptococcus salivarius_, _Rothia mucilaginous_, and _Actinomyces_ spp., particularly from the _A. meyeri/ odontolytica_ group. In contrast, strict anaerobes and _Pseudomonadota_ are present in low abundance. This composition reflects a microbial continuity between the oral cavity and distal small intestine, which markedly differs from the colonic microbiota [48; 49]. The large intestine is distinguished by a substantial bacterial density, reaching up to 10\({}^{12}\) CFU/mL, with a preponderance of the _Bacilota_ and _Bacteroidota_ phyla [39]. The most abundant bacterial families include _Bacteroidaceae_, _Prevotellaceae_, and _Rikenellaceae_ from _Bacteroidota_, as well as _Lachospiraceae_ and _Ruminococcaceae_ from _Bacilota_. These families play key roles in the fermentation of complex polysaccharides and the production of SCFAs, which are essential for colonic health and host metabolism [7; 50]. Understanding this close relationship and microbial transition from the oral cavity to the gut is essential, as disturbances in the oral microbiota may influence gastrointestinal health and systemic diseases through their impact on the gut microbiome [4]. Despite its complexity and inter-individual variability, the gut microbiota exhibits well-conserved functional capacities, referred to as core functions, which are consistently present in the host. These include metabolic processes, the modulation of the immune system, and protection against pathogens [51; 52].
# Integrated Module Antenna for Automotive UWB Application
## 1 Introduction
Moreover, a simulation with a model of a real vehicle is conducted to verify the feasibility of the proposed antenna. Based on this work, the proposed antenna can be utilized as a universal device that simultaneously performs UWB localization outside the vehicle and rear passenger detection sensor inside the vehicle as a single module. This paper is organized in the following order. First, we discuss considerations for antenna design, such as antenna design space with substrate, antenna covering case, and the actual mounting position on the vehicle. Second, the prototype antenna is designed with monopole and patch array. In addition, the modified ground stub and parasitic radiators are applied to compensate for the degradation of the antenna performance due to shared ground in the limited module size. Third, the optimized antenna is fabricated and measured. The antenna simulation with an actual vehicle model is conducted. Then, we verify the performance of the proposed antenna by comparing it with other findings in the literature.
## 4 Results
#### 4.3.1 Rectifier Systems and Their RF Energy Harvesting Efficiency
Such rectifiers employ advanced materials and sophisticated design techniques to attain optimal performance at microwave frequencies, rendering them indispensable in applications like wireless power transfer and RF energy harvesting. Their inherent capacity for efficient operation in the GHz range underscores their significance in contemporary energy harvesting technologies, as detailed in Table 22.
## 4 Experiment
### Discussion of Limitations and Future Analysis  
It is necessary to analyze the limitations of the proposed method, which can provide ideas for future work. There are two limitations worthy of further discussion. The first point is that the performance of our detection method is affected by the contextual sensitivity of the background. This is because the inter-class variance between the foreground and background affects the effective segmentation of [PERSON]'s method. Inaccurate shadow detection results lead to subsequent compensation methods being unable to handle missed and false detection pixels. Therefore, it is necessary to improve the adaptability of shadow detection methods to background information. The second point is that when processing hyperspectral data with a large amount of information, it is necessary to improve computational efficiency to promote practical applications. The exposure fusion stage in the compensation method processes the entire image, but in reality, only the shadowed areas need to be processed. Excess data calculations reduce efficiency and require further improvement. To explore and overcome the limitations of the proposed method, we conduct experimental analysis and improvement with regard to the two points mentioned above. By cropping non-shadowed areas in the original image, we investigate the impact of contextual sensitivity of the background on shadow detection performance. Figure 10 shows the false color images and shadow detection images under different backgrounds based on the Airport and Houston datasets, indicating that the shadow detection results are sensitive to changes in the background. To improve the adaptability of the shadow detection method, we explore Formula (1) in Section 3.2. This formula constructs the shadow feature map, which is the key to detecting shadows. It is improved as follows  
\[F=\frac{H+\alpha}{I+\beta}\cdot\delta \tag{20}\]  
where \(\alpha\), \(\beta\) and \(\delta\) are fine-tuning parameters. Figure 11 compares the effect of fine-tuning parameters on the shadow detection result. We use the same fine-tuning parameters for images in the same dataset, and the detection performance is significantly improved compared to that achieved without fine-tuning parameters. This indicates that using fine-tuning parameters can improve the shadow feature map, thereby reducing the limitations of background contextual sensitivity on the shadow detection method. However, improving adaptability can lead to a decrease in some detection results. Balancing the adaptability and detection accuracy of the detection method will be a key research direction in the future.