esa-sceva/satcom-chunk-collection huggingface dataset:
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Ultrasonic Pretreatment Effects  
#### 3.3.1 Comparison Analysis  
This study examined the leaching effect of lithium from the roasting products at different R\({}_{\text{fs}}\) and roasting durations. A comparison was conducted to assess the impact of ultrasonic treatment, both with and without its applications, prior to the roasting process. The results are shown in Figure 6a.  
As can be seen from the figure, without ultrasonic pre-treatment for the roasting process the leaching rate reached its maximum at an R\({}_{\text{fs}}\) of 1:2, but in the presence of ultrasonic treatment, the maximum as value almost reached at an R\({}_{\text{fs}}\) of 1:1, and the leaching rate was also slightly higher than the former, in the range of 87.39% to 90.74%. The ultrasonic treatment resulted in more uniform mixing of the fly ash and sodium carbonate. As a result, the leaching rate increased, which required less additive. The leaching rate at different roasting times was analyzed. A possible impact of ultrasonic pre-treatment in reduction of energy utilization of the roasting process was observed. The results are shown in Figure 6b. It shows that while at a roasting time 1.5 h, the leaching rate was higher than that with the ultrasonic pre-treatment, ultrasonic pre-treatment resulted in a higher leaching rate in the roasting process for a reaction of 1.5 h and the maximum rate was achieved at 89.04% for roasting reaction of 2 h. At a roasting time over 2.5 h, the leaching rates for the both methods were similar, which suggests that ultrasonic treatment has potential in saving energy. Roasting time for 2 h was recommended for a better leaching in the presence of ultrasonic pre-treatment.
## 4 Experimental Setup
### Evaluation Metrics  
Before applying the attacks to the 3D models, different experiments were conducted to evaluate the effectiveness of the proposed method in terms of imperceptibility and robustness. The distortion introduced by the watermark insertion is compared objectively and visually. The robustness of the proposed scheme is evaluated using the normalized correlation (NC).
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 3 Classification of Schemes for Congestion Control and Load Balancing in RPL
of one-hop \\ neighbors \\ deadline constraints \\ \\ duty cycle \\ \\ \(\surd  
**Utilizing path diversity. ** RPL provides a framework where a node can exploit path diversity by maintaining multiple parents. An obvious way to fully exploit path diversity for congestion mitigation is to forward packets via multiple parents on a per-packet basis. Section 4.2 presents many schemes adopting this approach.
# Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer
## 1 Introduction
Examples of the single-objective algorithms include the Particle Swarm Optimization (PSO) [13], Genetic Algorithm (GA) [14], Ant Colony Optimization (ACO) [15], Grey Wolf Optimizer algorithms (GWO) [16; 17], which have the extended multi-objective optimization variations, namely MOPSO [18], MOGA [19], m-ACO [20], and MOGWO [21], respectively. In our previous study [8], we used the coordinated multi-robot exploration [22] and GWO algorithms together as a hybrid. In this study, our interest is to solve the multi-robot exploration problem as the MOOP using Multi-Objective Grey Wolf Optimizer (MOGWO). Using the MOGWO exploration, we defined two objectives for optimization, namely the maximization of the search for new area and the minimization of the inaccuracy of the explored map. It can be said that the search process is divided into two stages (Figure 1). They switch during the simulation run depending on the value of the GWO parameter. If the value is greater than one, it searches occluded space. If the value is less than one, it increases the map accuracy by repeated visits in the explored space. It needs to be emphasized that the occupancy grid map with probabilistic values is used in this study [23]. The MOGWO exploration employs static waypoints in the simulation, which promotes the efficient exploration of an indoor simulated environment. It can be noted that the waypoints belong to the programmed logic of the algorithm and are not supposed to be used in the real environment [24]. The waypoints are grey wolf agents with some costs of probability values. In each iteration, the robots search alpha, beta, and gamma waypoints and save them in an archive wherein avoiding the selection the same non-dominated waypoints for several robots. After the selection, the robots can compute the next position, which is the closest to the average position of alpha, beta, and gamma waypoints, from among the frontier cells [25]. This paper is organized as follows: in Section 2, we briefly recall different algorithms of multi-robot exploration and evolutionary optimization techniques used in related works. In Section 3, the theory of GWO and MOGWO is presented. Sections 4 and 5 are dedicated to the proposed MOGWO exploration and its performance. Section 6 concludes the present study.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 3 Results
### Screening of Cyanobacterial Strains for Toxin, Biomass and Polysaccharide Production
#### 3.1.4 Viscosity of the Cell-Free Medium  
The higher viscosity of the cell-free cultivation medium was observed in three out of five strains when cultivated in the medium BG11 N\(-\) (Figure 2). In contrast, higher viscosity of the cell-free BG11 N+ medium of the _Chroococcidiopsis_ sp. L1 strain was observed (12.01 mPa-s). This value also represents the highest viscosity measured in this study. The viscosity of _Tylyopthrix_ sp. L4 cultures was similar in both cultivation media.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**Med.**} & **Total Polysaccharide** & **RPS** & **RPS Share** & **Total Polysaccharide Yield** & **RPS Yield** \\  & & **(mg/L)** & **(mg/L)** & **(\%)** & **(mg/g Biomass)** & **(mg/g Biomass)** \\ \hline \multirow{2}{*}{L1} & N+ & 3.1 \(\pm\) 0.3 & 1.4 \(\pm\) 0.0 & 45.6 & 48.4 & 22.1 \\  & N\(-\) & 1.6 \(\pm\) 0.1 & 0.6 \(\pm\) 0.0 & 39.3 & 196.6 & 77.4 \\ \hline \multirow{2}{*}{L2} & N+ & 5.4 \(\pm\) 0.3 & 1.9 \(\pm\) 0.1 & 35.4 & 38.7 & 13.7 \\  & N\(-\) & 3.3 \(\pm\) 0.2 & 0.7 \(\pm\) 0.0 & 20.9 & 41.4 & 8.7 \\ \hline \multirow{2}{*}{L4} & N+ & 7.1 \(\pm\) 0.6 & 2.7 \(\pm\) 0.1 & 37.9 & 54.5 & 20.7 \\  & N\(-\) & 1.6 \(\pm\) 0.2 & 0.5 \(\pm\) 0.0 & 29.3 & 18.2 & 5.3 \\ \hline \multirow{2}{*}{L5} & N+ & 2.3 \(\pm\) 0.2 & 1 \(\pm\) 0.1 & 42.6 & 4.7 & 2.0 \\  & N\(-\) & 2.9 \(\pm\) 0.3 & 1.1 \(\pm\) 0.0 & 37.6 & 37.1 & 13.9 \\ \hline \multirow{2}{*}{L6} & N+ & 4.0 \(\pm\) 0.3 & 1.7 \(\pm\) 0.1 & 41.9 & 145.6 & 61.0 \\  & N\(-\) & 3.7 \(\pm\) 0.2 & 1.6 \(\pm\) 0.1 & 44.4 & 75.9 & 33.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Production of exopolysaccharide (RPSs) and total polysaccharides by isolated cyanobacterial strains. The polysaccharide content of strains in the 7 th week of cultivation in BG11 N+ and BG11 N\(-\) media is presented.  
Figure 2: The viscosity of cell-free media of isolated strains in the 7 th week of cultivation. The error bars indicate standard deviation.
## 1 Introduction
The simplest correction methods are presented by the Kalman filtering [33] and LASSO regression [34] approaches, as well as the measurements variation limits [35] approach. According to manufacturers, the lifetime of low-cost electrochemical sensors is set at 1 to 2 years, and during its operation the change in sensor response is referred to as aging [36; 37]. Specifically, [37] reports on the identification of aging factors in low-cost electrochemical sensors for measurement corrections. Other research works present the application of low-cost sensors for air quality monitoring [38; 39; 40], and [41] presents the artificial intelligence method of a memory-based precise calibration of a cost-efficient NO\({}_{2}\) sensor, and data reliability and fault diagnostics for an air quality monitoring station based on low-cost sensors and active redundancy, studied in [42]. As a statistical model, nonlinear regression is the application of regression analysis, where observational data are captured by a function that includes a nonlinear combination of the model parameters and depends on one or more independent variables. The data are tabulated and adjusted accordingly using a method of successive approximations. In general, linear regression shows the correlation between two variables using a straight line, while nonlinear regression shows the correlation between variables using a curve. The authors of [43] show the results of nonlinear regression on low-cost microparticle data from sensors. By applying probabilistic gradient boosted decision trees (GBDTs), direct field calibration of raw PM2.5 sensor data is achieved. The use of probabilistic GBDTs improves point accuracies and distribution compared to the linear model. In [44] the application of calibration models using nonlinear machine learning boosting algorithms, namely Stochastic Gradient Boosting Regressor, yielded better results than those obtained using artificial neural networks and linear regression approaches. In this research work, the degree of correction in low-cost sensor measurements is investigated by applying nonlinear regression to data obtained by low-cost electrochemical pollutant gas sensor measurement, aiming to identify a polynomial function that serves as a correction factor in low-cost sensor measurements, with the final result being the optimization of these measurements. In addition, the methodologies of time scaling and seasonality scale are investigated to evaluate the results using the nonlinear regression approach. The choice of a polynomial function as a correction tool finds immediate application for integration in IoT devices, as it does not require computational resources, unlike other techniques, such as artificial neural networks, etc. The article is structured as follows: Section 2 presents the Materials and Methods, Section 3 discusses the Results, Section 4 covers the Discussion and Section 5 covers the Conclusions.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Aerospace
#### 4.1.3 Selected Wireless Communication Protocols Used in Aviation
The main areas of use of the ARINC 653 specification are integrating different systems into a single environment (CPU environment), integrating different OS systems, standardization of safety-critical control systems and time and space partitioned systems [38]. * The IEEE 1451 standard describes the common functions, communication protocols, as well as transducer electronic data sheet formats. The purpose of the standard was to standardize plug and play technology, making it easy to connect a transmitter from any manufacturer to any control and measurement network so as to make the transmitter independent of the protocol used in a given network. This standard's key element is to define the TEDS (Transducer electronic data sheet) format that stores relay identification, correction data, calibration, as well as manufacturer information [39]. * The WAIC system (Wireless Avionics Intra-Communication) is also based on the IEEE 802.15.4 standard. Studies indicate that the best band for WAIC is 4.2-4.4 GHz, as it shares a similar band with a radio altimeter. Moreover, too strong electromagnetic waves from wireless devices inside an aircraft are exposed to high attenuation. As they penetrate various metal and composite objects, it is essential to choose the right frequency not to interfere with other components. WAIC is designed to integrate multiple devices in a single aircraft fully. The system itself is adapted to low range (<100 m) and low power consumption (10 mW for small transmitters and 50 mW for large transmitters). It should be emphasized that the standard does not provide a connection to the Internet. It only serves as an intermediary for, among others, data transmission methods, i.e., satellite network [31]. * 7 ECMA-368 (High Rate Ultra Wideband PHY and M.A.C. Standard) is a standard for high-data-rate networks within the airplane. There are solutions for optimal node placement, quality of service mechanisms, and loss-free mobility support. Ultra-wideband technology provides better indoor performance compared to traditional narrowband systems. The wide bandwidth allows resistance to channel effect in dense environments and allows very accurate satisfactory time-space resolutions for very precise positioning inside UWB nodes. The standard sets a low spectral density below ambient noise for a low probability of signal detection and consequently increases communication safety. The standard provides high periscosity data transmission over short distances (up to 100 Mbps at <10 m) [40].
## 2 Fault Diagnosis Algorithm Selection
### Introduction of Probabilistic Neural Network  
Probabilistic neural networks are based on the Radical Basis Function (RBF) neural network, and they are developed by combining Bayesian decision and density function estimation theory. Unlike the RBF neural network, probabilistic neural networks are specifically applied to classification problems [8; 16]. Since all neural networks are progressive theories which ask the number of samples tends to infinity, theoretically, only the number of training samples is large enough to achieve optimal classification performance.  
Bayes Rule was proposed by the British mathematician named with [PERSON] in 1763 [17]. Bayesian decision theory is the theoretical basis of probabilistic neural networks. The mathematical expression of the Bayesian formula is in Equation (1).  
\[P(A_{i}|B)=\frac{P(A_{i})P(B|A_{i})}{\sum_{j=1}^{n}p(A_{i})P(B|A_{i})},i=1,2, \cdots,n \tag{1}\]  
In Equation (1), \(A_{1}\) to \(A_{n}\) form a complete event group, and with each \(A_{i}\) there exits \(P(A_{i})>0\). Every member of this group is the different cause of event \(B\).  
Probabilistic neural networks are mainly composed of input layer, hidden layer, summation layer, and decision layer [18]. The input vector \(x\) reaches the hidden layer through the input layer. In this paper, the Gaussian function is used to define the relationship between the input and output of the \(j\)-th neuron in the \(i\)-th class of the hidden layer. The specific formula is in Equation (2).  
\[\Phi_{ij}(x)=\frac{1}{(2\pi)^{\frac{1}{2}}\sigma^{d}}e\frac{\big{(}x-x_{ij} \big{)}\big{(}x-x_{ij}\big{)}^{t}}{\sigma^{2}} \tag{2}\]  
where the value range \(i\) is [1,\(N\)]. \(N\) represents the total number of classes in the training sample, and \(d\) is the dimension of the sample space, \(x_{ij}\) represents the \(j\)-th neuron center of the sample.  
Summation layer weighted average the input from the same layer of implicit layers in the corresponding sample. The specific form is shown with Equation (3), where \(V_{i}\) represents the category output of the \(i\)-th class, and \(n\) represents the number of neurons of the \(i\)-th class.  
\[V_{i}=\frac{\sum_{j=1}^{n}\phi_{ij}}{n} \tag{3}\]  
Set \(\omega\) as the weighting coefficient, and the input vector \(x\) must be multiplied by the weighting coefficient \(\omega\). Then put it into the hidden layer, if \(x\) and \(\omega\) have been normalized to unit length, and then use radial basis operation to obtain the Equation (4).  
\[f(x,\omega_{i})=\exp[-\frac{(\omega_{i}-x)^{T}(\omega_{i}-x)}{2\sigma^{2}}] \tag{4}\]  
where \(\sigma\) represents the smoothing factor of the probabilistic neural network, which has a great influence on the final classification result of the network.
## 3 Treatment of Curved Boundaries
### Geometry Representation  
The integrals that appear in Equation (18) are intended to be calculated in reference elements [30] using the Gaussian quadrature rules. The physical coordinate \(x=(x,y,z)\) associated with the reference coordinate \(\mathbf{\xi}=(\xi,\eta,\zeta)\) can be expressed as  
\[\mathbf{x}=\sum_{i=1}^{n_{d}}\psi_{i}(\mathbf{\xi})\mathbf{x}_{i}\quad\forall\mathbf{\xi}\in\kappa, \tag{21}\]  
where \(n_{d}\) is the total number of the control points \(\mathbf{x}_{i}\), and \(\psi_{i}(\mathbf{\xi})\) are the corresponding shape functions that are fixed in the reference element \(\kappa\).  
As illustrated in Figure 1, with the mapping relationship (21), the linear reference element can be used for representing simple straight-edged elements exactly, while quadratic reference elements can be applied for approximating curve-edged elements only. For enhancing the accuracy of high-order approximation, special treatment, so-called high-order re-mesh, is further required [9] and addressed in the following.
# Three-Dimensional Geological Modeling Method Based on Potential Vector Fields
## 3 Results
### Influence of Parameters
#### 3.2.2 Influence of Data Point Spacing  
The parameter \(d_{\mathrm{max}}\) controls the maximum spacing between data points. Figure 13 shows the local geological interfaces when \(n_{\mathrm{min}}=5\) and \(c=10\), with \(d_{\mathrm{max}}\) set to \(10\,\mathrm{m}\), \(15\,\mathrm{m}\), \(20\,\mathrm{m}\), and \(25\,\mathrm{m}\), respectively. In contrast to the geological interface shown in Figure 13a, the interfaces in Figure 13b-d exhibit noticeable undulating distortion at higher slopes. This is because a larger data point spacing leads to sparse data points in the \(z\)-axis direction, resulting in an unreasonable potential vector field. Based on experimental comparisons, it is recommended that \(d_{\mathrm{max}}\leq 1.0c\) to achieve higher-quality geological interfaces, with the shape parameter \(c\) selected according to references [33; 34; 35].
## 2 Baselines in the Literature
### Baseline Methodologies
#### 2.2.5 Calculated  
The calculated baseline methodology introduces a baseline based on a mathematical description rather than measurements and data. This mathematical description may still use data, but aims to eliminate the necessity of accurate and reliable measurements at the point of connection of flexibility assets. The calculated methodology is discussed by [16] and implemented by [12].  
A subcategory of the calculated baseline methodology is the regression-based methodology. Here, a regression model is used to calculate the baseline. An example is the spline fixed effect change point model, proposed in [26]. The regression-based baselining methodology is broadly referred to and applied in the literature, and can be found in, e.g., [11; 13; 14; 15; 16].  
In general, the calculated baselines perform worse than the previously discussed methodologies in terms of simplicity and transparency. It is less clear how the baseline is determined, which depends on mathematical algorithms rather than measurements. The upside of the use of algorithms can however be found in the proneness to manipulation. As models determine the outcome of the baseline, aggregators do not have an opportunity for gaming and the user dilemma is eliminated. The accuracy of calculated baselines largely depends on the accuracy of the models of the flexibility sources considered.
# Cable Force Health Monitoring of Tongwamen Bridge Based on Fiber Bragg Grating
## 4 Conclusions  
According to characteristics of Tongwamen bridge suspension cables, a cable force health monitoring scheme was proposed based on the theory of string vibration and FBG sensing. FBG sensors with high sensitivity and good repeatability suitable for detection of suspension cable frequency were developed. A total of 16 sensors were installed on 16 suspension cables. Vibration frequencies of the cables were measured and cable forces were calculated. From the monitoring results, two cables with abnormal tension forces were found using the proposed method. This monitoring method has strong practical applications in structural health monitoring of significant engineering and equipment, owing to its broad application prospects.  
This research was partially supported by the Natural Science Foundation of China under Grant (No. 51605348, 51174153, 51374164), and the Natural Science Foundation of Hubei province under Grants No. 2016 CFB116. [PERSON], [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON], [PERSON] wrote the paper; [PERSON] modified and improved the paper.  
The authors declare no conflicts of interest.
# A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring
###### Abstract  
The monitoring and forecasting of particulate matter (e.g., PM\({}_{2.5}\)) and gaseous pollutants (e.g., NO, NO\({}_{2}\), and SO\({}_{2}\)) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data noises, environmental and other factors. This paper proposes a general solution to analyse how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty, with a comparative case study of atmospheric pollutant concentrations prediction in Sheffield, UK, and Peshawar, Pakistan. The Neumann series is exploited to approximate the matrix inverse involved in the Gaussian process approach. This enables us to derive a theoretical relationship between any independent variable (e.g., measurement noise level, hyperparameters of Gaussian process methods), and the uncertainty and accuracy prediction. In addition, it helps us to discover insights on how these independent variables affect the algorithm evidence lower bound. The theoretical results are verified by applying a Gaussian processes approach and its sparse variants to air quality data forecasting.  
Gaussian process; uncertainty quantification; air quality forecasting; low-cost sensors; sustainable development 2021
## 4 Experimental Results  
### Scenes and Platforms Introduction  
To validate the performance of the proposed localization method, this paper conducted experiments in two typical underground parking scenes, as shown in Figure 8. Figure 8a,b depicts an industrial park's underground parking scene with a total experimental path length of approximately 146 m, an area of approximately 1145 m\({}^{2}\), and a total of 24 berths. Figure 8c,d represents a mall's underground parking scene with a total experimental path length of around 362 m, an area of approximately 6669 m\({}^{2}\), and a total of 141 berths.  
Figure 8: Experimental scenes. (**a**) Depicts the experimental path in the industrial park’s underground parking scene; (**b**) shows the distribution of the berths in the industrial park’s underground parking scene; (**c**) illustrates the experimental path in the mall’s underground parking scene; (**d**) displays the distribution of the berths in the mall’s underground parking scene.  
The test vehicle used in the experiments was the BYD e5 electric car, as shown in Figure 9a. Figure 9b illustrates the sensor installation positions. The vehicle was equipped with four fisheye cameras, installed respectively on the front and rear bumpers, as well as the left and right mirrors of the vehicle, to form an AVM. The data acquisition frequency of the AVM was 10 Hz, and the captured images had a pixel size of 416 \(\times\) 416, with a physical scale factor of 0.02 m per pixel. The pixel coordinates at the rear axle center of the vehicle were \(\mathbf{U}_{\text{r}}=\) (207, 285). Additionally, a high-performance inertial navigation system (INS) was installed at the rear axle center of the vehicle. The INS can still collect high-precision pose information even in enclosed indoor environments, such as underground parking scenes, serving as the ground truth for the localization system. The data acquisition frequency of the INS was 50 Hz. Time synchronization between the AVM and the INS was achieved through clock synchronization and interpolation. The computing platform used for the experiments was a laptop computer equipped with a 2.8 GHz Intel i7-7700 HQ CPU and 4 GB RAM.