esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Seaweeds from South West England as a Biorefinery Feedstock
## 1 Introduction
A biorefinery-based approach is likely the most effective way to achieve this and has the added benefit of potentially being able to accept both natural harvest and cultivated seaweed biomasses and, crucially, has the potential to utilise biomasses of diverse origin (i.e., multiple species). Whilst offshore cultivation provides plenty of scope for controlled, regulated and monitored activity around particular seaweed crops, increasing natural harvest levels of mixed communities by existing commercial entities may create an ecological imbalance and will need to be managed carefully. Yet, the harvesting of natural biomass to \"sanitise\" beaches to promote tourism is already an established and socially acceptable activity in the South West. For example, between 1 April 2018 and 28 June 2018, 734.8 tonnes of seaweed were collected (and sent for composting) from 18 beaches by Torbay council, giving an average monthly collection of approximately 248 tonnes during the peak of the tourist season (Personal Communication, Torbay council). Storm-generated beach material in the winter months also represents a significant biomass which could be used as feedstock for the generation of refined non-food-based products. Assuming biomass generation and availability is not a limitation in the future, the question remains as to what exactly is/are the best input biomass(es) for a marine seaweed biorefinery in the South West. It is important to recognise that whilst seaweeds have a variety of different attributes, most of these are specific to individual species and so assumptions cannot be made about the potential of \"seaweeds\" as a crop without being tempered by the reality of what any given species can produce. There are a broad range of different seaweeds with different properties growing around the UK coast [9]and these different species have a range of components that can make them valuable for the production of nutraceuticals, fuels, fertilisers and fine chemicals. Here, we explored some of the components that can make individual seaweeds (commonly found in the South West) valuable as feedstocks in a biorefinery-based process, such as lipids and high-value omega oils, pigments with antioxidant and antibacterial properties, plant hormones and macronutrients, minerals, total carbohydrate and proteins. We avoided established food additives derived from seaweed sugars, such as agar and carrageenan, as they have been investigated previously and already offer established markets [1; 10]. In addition, we also assessed heavy metal levels which could potentially hinder commercialisation opportunities.
## 3 Results and Discussion
### Effect of BTH Treatment on the Activities of Key Enzymes of LOX Pathway
#### 3.4.3 C6 Esters  
A total of two C6 esters, specifically bound and free hexyl acetate and Z-3-hexenyl acetate, were detected at low levels during grape ripening. Free Z-3-hexenyl acetate accumulated mainly in the early stages of the ripening process. The continuous decrease in free Z-3-hexenyl acetate during grape ripening observed in our study may be attributed to the progressive conversion of its free to bound form, as bound Z-3-hexenyl acetateincreased during grape ripening. Z-3-Hexenyl acetate can be used for the identification of grapes at the fruit-set and pre-veraison stages [35]. In comparison, the evolutionary pattern of hexyl acetate is less stable than that of Z-3-hexenyl acetate. The hexyl acetate concentration generally showed a decreasing trend and then an increasing trend during grape ripening. BTH treatment significantly enhanced the free hexyl acetate concentration in veraison and ripe grapes, especially in ripe grapes, with a significant increase of 192.71%. Similarly, the free Z-3-hexenyl acetate concentration was significantly increased by 214.32% in ripe BTH-treated grapes compared with the CK (Table S3). This could be explained by the decrease in the concentration of bound Z-3-hexenol, the enhancement of AAT activity, and the increase in the conversion of Z-3-hexenol and hexanol to their corresponding esters. In this study, the high concentration of free hexyl acetate corresponds to the high activity of AAT during grape ripening, which is similar to the results of the study described by [PERSON] et al. [33] using BTH.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 4 Proposed Framework for Climate Forecast Error Reduction
### Climate Series Clustering
Consequently, for a given \(K\), it minimizes \(\text{intra}(K)\) while maximizing \(\text{inter}(K)\), resulting in minimizing \(\text{Validity}(K)\). As a result, the smaller the value of Equation (5), the better the clustering. This way, one learns the appropriate number of filters to be designed for the considered geographical region and time frame. To define the clusters, one uses the means of the climate forecast variables in each cell during the forecast period. This approach attempts to group cells presenting similar climates over time and not similar forecast errors. The latter could lead to grouping cells presenting very similar error behavior but from different climates. This could hamper the moving average model in Equation (2) and Figure 3 to follow the time series behavior over time. For the clustering process, we define the vector \(\mathbf{z}_{i}=[\overline{\mathbf{x}_{1,i}(n)},\overline{\mathbf{x}_{2,i}(n)},\overline{\mathbf{x}_{3,i}(n)}]\), where the first subscript is used to indicate the forecast variable, the second indicates the cell, and \(i\) refers to ordered pair \((lat_{i},lon_{i})\). More precisely, for the first subscript, 1 denotes the meridional wind (m/s), 2 denotes the zonal wind (m/s), and 3 denotes the geopotential height; the overline indicates the average over time \(n\), i.e., \(\overline{\mathbf{x}_{1,i}(n)}=L^{-1}\sum_{n=1}^{L}\mathbf{x}_{1,i}(n)\). Once we learn the number of filters to be employed, the filters that the time series within each cluster for forecast error reduction need to be learned. However, in this case, one can use supervised learning from the forecast error in previous time series. This is accomplished using adaptive filtering. The time series within each cluster are randomly concatenated in time, and the resulting series is used to adapt the correspondent filter using the Recursive Least Squares algorithm.
## 3 Results and Discussion
### Electronic Properties  
To find origins of band structures, the total density of states and partial density of states are calculated, as shown in Figure 7. From Figure 7a,c, the main bonding peak between \(-\)2.5 eV and \(-\)0.5 eV are dominated by Co-d and Zr-p, Zr-d for Co\({}_{2}\)Zr (see Figure 7a), and between \(-\)3.5 eV and \(-\)0.5 eV are contributed by Co-d and Ti-p, Ti-d for Co\({}_{2}\)Ti (see Figure 7d) at 0 GPa. It is illustrated that pressure has little effect on the density of states of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti in Figure 7b,c,e,f. However, the energy dispersion spectrum crosses the Fermi level (E\({}_{\rm f}\) = 0 eV), indicating Co\({}_{2}\)Zr and Co\({}_{2}\)Ti compounds are both electrical conductors. In addition, there are two spikes on both sides of the Fermi level where the value of DOS (density of states) is not zero, and the width between two spikes represents the pseudo gap. The wider the pseudo gap is, the stronger covalence is. Therefore, Co\({}_{2}\)Zr has greater covalence than Co\({}_{2}\)Ti.  
Electron density difference can be used to study transfer and accumulation of electrons when atoms form compounds. Positive values indicate an increase in electron density while negative ones show a loss of electron density. To see the electron transfer and bonding property of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti, we calculated the electron density difference in the (110) plane of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti, as shown in Figure 8 at 0, 10 and 30 GPa. It is seen that the atoms at the four vertices of the (110) plane of two crystals form metal ions with losing electrons while electrons accumulate at the center of the plane to form electron clouds. There are no electrons around the rest of the atoms, and they exist in crystalline atomic form. Therefore, there are both metal ions and metal atoms in the two crystals, indicating that both Co\({}_{2}\)Zr and Co\({}_{2}\)Ti are metal crystals. With the increase in pressure, the degree of electron accumulation deepens. Comparing the electron density difference of two substances at the same pressure, the gain and loss of electron capacity of Co\({}_{2}\)Ti is stronger than that of Co\({}_{2}\)Zr, which proves that the covalent property of Co\({}_{2}\)Zr is stronger than that of Co\({}_{2}\)Ti.  
Figure 6: Thermal expansion \(\alpha\) as a function of (**a**) temperature at 10, 30, and 90 GPa; and (**b**) pressure at 500, 1000, and 1500 K for Co\({}_{2}\)Zr and Co\({}_{2}\)Ti.  
Figure 7: The total density of states and partial density of states of Co\({}_{2}\)Zr at (**a**) 0, (**b**) 10, and (**c**) 30 GPa; and that of Co\({}_{2}\)Ti at(**d**), (**e**) 10, and (**f**) 30 GPa.
## Theory  
### Continuous Wavelet Transform  
The continuous wavelet transform defined by [PERSON] and [PERSON] is as follows [18]:  
\[W_{f}^{\Psi}(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}x(t)\Psi\left(\frac {t-b}{a}\right)dt \tag{1}\]  
where \(x(t)\) denotes the signal, \(a\) is a scale parameter, \(b\) is a shift parameter and \(W_{f}^{\Psi}(a,b)\) is the wavelet coefficient. All the parameters and functions are defined in the domain of real numbers.  
The outstanding characteristic of the CWT, which can be known from this equation, is to expand or reduce the predefined wavelet \(\Psi(t)\) using the scale factor \(a\) and shift the wavelet using the shift factor \(b\) which indicates a location in the time domain.  
As for the CWT, the wavelet is gradually dilated with increasing scale parameter \(a\) and reduced as the scale parameter gets smaller. Therefore, the scale parameter is small at high frequencies and large at low frequencies. These properties of the CWT make it possible to plot signal characteristics on a plane of two axes, time and frequency. Signals can become easy to interpret when their wavelet coefficients are plotted on the time-frequency plane, and the time-frequency interpretation is useful for the interpretation of abnormal signals.
## 3 Construction of the Control Strategy
### Fuzzy-BPNN-PID Controller Design
#### 3.2.2 Design of BP Neural Network PID Controller  
A BP neural network is a multi-layer, feed-forward neural network, which contains three layers of a network structure, namely, the input layer, hidden layer and output layer. The number of nodes in the input and output layers is determined by the dimensionality of the input vector and output vector, respectively, and the hidden layer plays an important role in the function and structure of the network [28].  
The neural network controller in this paper used a three-layer BP neural network, the inputs to which are fuzzy-processed system state variables. The network contains three input layer nodes, nine implicit layer nodes and three output layer nodes. The structure of the fuzzy preprocessed neural network is shown in Figure 8. The role of each layer of the neural network and the input-output relationship between the layers are as follows.  
The first layer is the input layer, where the input is the system state variable after fuzzy processing; the second layer is the hidden layer, and the input is:  
\[\left\{\begin{aligned} net_{i}^{(2)}(k)&=\sum\limits_{j= 1}^{3}\omega_{ij}^{(2)}O_{j}^{(1)}(k)\\ O_{j}^{(2)}(k)&=f\Big{[}net_{i}^{(2)}(k)\Big{]}\\ (i=1,2\cdot\cdot\cdot\cdot\cdot 9)\end{aligned}\right. \tag{3}\]  
where \(\omega_{ij}^{(2)}\) is the implied layer weighting factor. \(f\left[\cdot\right]\) is the hidden layer activation function, selected as \(f(x)=\tan\hslash(x)\).  
Figure 8: Fuzzy-BPNN-PID structure diagram.  
The final layer is the output layer, with the following inputs and outputs:  
\[\left\{\begin{aligned} & net^{(3)}_{li}(k)=\sum\limits_{i=1}^{9} \omega^{(3)}_{li}O^{(2)}_{i}(k)\\ & O^{(3)}_{l}(k)=g\big{[}net^{(3)}_{l}(k)\big{]}\\ &(l=1,2,3)\\ & O^{(3)}_{1}(k)=k_{p}\\ & O^{(3)}_{2}(k)=k_{i}\\ & O^{(3)}_{3}(k)=k_{d}\end{aligned}\right. \tag{4}\]  
where \(\omega^{(3)}_{li}\) is the output layer weighting factor. \(f\left[\cdot\right]\) is the output layer activation function, selected as \(g(x)=e^{x}/(e^{x}+e^{-x})\).  
After determining the neural network outputs \(k_{p}\), \(k_{i}\) and \(k_{d}\), they are brought into the incremental PID formula for calculation. The incremental PID control is able to remember and maintain the state of the system at the previous moment, thus making the impact of possible errors minimal; therefore, incremental PID is used to obtain the optimal control signal, and the incremental PID formula is:  
\[\left\{\begin{aligned} &\Delta u(k)=K_{P}[e(k)-e(k-1)]+K_{I}e(k)\\ &+K_{D}[e(k)-2e(k-1)+e(k-2)]\\ & u(k)=u(k-1)+\Delta u\end{aligned}\right. \tag{5}\]  
where \(\Delta u(k)\) is the control signal increment and \(k\) is time; \(k_{P}\) is the proportionality factor; \(k_{i}\) is the integration factor; and \(k_{d}\) is the differentiation factor.
## 3 Background
### Trust Management in IoV
#### 3.1.3 Attributes of Trust  
Various trust attributes have to be considered when computing the components of trust mentioned earlier.  
* Similarity: This refers to the degree to which two vehicles are similar in terms of content and services. Euclidean distance is often used to describe the similarity of messages or vehicles in the literature. The direction of travel of the two nodes, which is usually the location based on trajectory similarity, is known as Euclidean distance [29].
* Timeliness: The attribute of timeliness relates to how recently the two vehicles have interacted with each other. It is usually determined by adding the current time to the time when the interaction happened. Maintaining the timeliness of data and the trust score contributes to higher levels of trust; however, old data reveal an outmoded trust value, which can lead to negative consequences [30].
* Duration of Interaction: This refers to the length of interaction among the two nodes. Longer interactions allow the entity to learn more about the other entity's conduct and capabilities, and as a result, long interactions have been seen to lead to better interactions among entities, which leads to higher trust levels [31].
* Familiarity: This attribute exhibits the level of acquaintance the two vehicles have with one another. This feature was derived from social networks, and it is worth noting that increased familiarity leads to increased trust in interpersonal relationships. Higher familiarity with the trustee is frequently a reflection of the evaluator's past understanding and knowledge of the trustee [32].
* Packet Delivery Ratio: This can be described as the degree of connection between the trustor and the trustee. The only criterion required to calculate direct trust toward a trustee is the packet delivery ratio. In the literature, this is typically referred to as the packet transmission rate between nodes. Furthermore, it is a main goal in the development of trust models and a key criterion for detecting harmful activity [33].
* Frequency of Interactions: On a regular basis, the trustor and trustee communicate with one another., and this is measured by the frequency of their interactions. When two nodes communicate, they can learn each other's communication and behavioral patterns to improve trust computations [31].
## 2 Method for Clock and Data-Acquisition Synchronization
### Clock Synchronization  
The slave clock is synchronized with the master clock by PTP. A PTP daemon (PTPd) for Linux OS [24] is performed as a background process and is independent of the data-acquisition and processing software. From the statistical data generated by the PTPd, the clock synchronization error is considered to be a few microseconds theoretically. This is an acceptable error for the 500 Hz or higher sampling rate experimental system. The accuracy of the clock synchronization is evaluated in Section 4.1.
## 4 System modeling
### Partial least square regression (PLSR)
#### 4.1.6 Back-propagation neural network (BPNN)  
From this subsection on, the discussion of supervised learning methods will focus on artificial neural network (ANN) models. Some modifications of linear learning methods such as stepwise functions and polynomial regression can be applied for non-linear systems, but they do not work for highly nonlinear modeling problems. Presenting a good mimic of human brain cognition process, ANN has been extensively used for machine learning tasks. A variety of ANN models have been developed for sensor design, but this paper will focus on three most widely used ANN models, since many of others can be considered derivatives of these versions.  
BPNN is one of the most widely used neural network models, with extensive applications in function approximation (classification is also a type of function approximation problem). Typically BPNN is composed of three layers: input, hidden, and output. A sigmoid function is a standard nonlinear activation function for each hidden and output neuron. Given the error between the stipulated and predicted results for the \(k\)th sample, \(E_{\text{k}}\), the learning process (i.e., the adjusting process for connecting weights) based on the gradient descent algorithm can be represented as:  
\[\Delta\mathbf{v}=-\alpha\bigg{[}\frac{\partial E_{k}}{\partial\mathbf{v}} \bigg{]}\;\text{ and }\;\Delta\mathbf{w}=-\beta\bigg{[}\frac{\partial E_{k}}{\mathbf{w}}\bigg{]} \tag{8}\]  
where, \(\alpha\) and \(\beta\) are the learning rates, \(\mathbf{v}\) and \(\mathbf{w}\) are the connecting weight vectors between hidden and input layers and between output and hidden layers, respectively. This learning process is repeated for all the training samples, which is called one iteration. This iteration process continues until a convergence criterion is reached.  
BPNN has been used for developing various E-nose systems including bacterial infected illness diagnosis and warfare agent stimulants classification [62-65]. A three-layer BPNN with sufficient hidden neurons has proven able to approximate any classification boundaries [66], but a possible over-fitting problem will lead to a failure in the prediction of new samples. A constructive approach for building BPNN could be either starting with a small structure followed by a gradual increase of hidden neurons or using pruning strategies to remove extra hidden neurons from a big initial model structure.  
Possible overlap learning time and local optima are two major problems for BPNN. Adaptive learning strategies can help BPNN reach a convergence and jump out of local optima, but designing such a self-adjusting process is empirical and difficult.
## 5 Mathematical Modelling for Revealing Vibration Signal Properties  
An impeller consists of a cover component, a disk component and several blades. The finite element model of an impeller is depicted in Figure 24.  
Figure 23: Photo of C fan after repair welding.  
Figure 22: Photo of the crack on B fan.  
Figure 24: Finite element model of an impeller and a sector model.  
According to many experienced engineers, cracks initiate mostly at the weld toe on the cover sides of the blade, as shown in Figure 24. In this paper, the effects of a crack located at such a position on the vibration response of the impeller are of interest, and the cracks at other position can be modeled without much modification. For details of the mathematical modelling and mathematical formulation readers may refer to [34; 37].  
After component synthesis, the equation of motion of a complete impeller is represented by:  
\[\mathbf{M}\ddot{\mathbf{p}}+\mathbf{C}\dot{\mathbf{p}}+\mathbf{K}\mathbf{p}= \mathbf{b}+\mathbf{f}_{nl}(\mathbf{p}) \tag{7}\]  
where \(\mathbf{M}\), \(\mathbf{C}\) and \(\mathbf{K}\) are the mass, damping and stiffness matrices of the ROM; \(\mathbf{p}\) is the vector of the DOFs retained; \(\mathbf{b}\) denotes the external excitation acting on the impeller; and \(\mathbf{f}_{nl}(\mathbf{p})\) is the nonlinear forces caused by intermittent contact of the crack surfaces. More details of this modelling process can be found in [34].  
One of the most important issues for crack detection and identification is to search for sensitive indicators. An effective indicator should possess several features, such as robustness, monotonicity and industrial testability. The resonant frequencies discussed in the previous sections are potential indicators for the quantitative detection of crack faults. Two other kinds of frequency based indicators are studied in [34].  
In sum, the frequency-based indicators for crack identification of centrifugal impellers were studied and discussed. However, an effective and reliable tool with a sensitive indicator for crack identification of impellers in operation still faces a lot of challenges at present.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 3 Design of Integrity Monitoring System with Multi-Source Observation Links
### Monitoring Processing
Similarly, residuals of observation data in the clock can be gained using Formula (8) minus Formula (9). \[\begin{array}{l}\left|\overrightarrow{r}_{A}(T_{0})-\overrightarrow{r}_{B} (T_{0})\right|=\frac{\rho_{BA}(T_{0})+\rho_{AB}(T_{0})}{2}+\frac{\Delta\delta _{BA}+\Delta\delta_{AB}}{2}\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+c\cdot\frac{\tau^{\mathit{ Recv}}+\tau^{\mathit{Send}}+\tau^{\mathit{Recv}}+\tau^{\mathit{Send}}_{A}}{ 2}\\ \\ \mathit{clk}_{A}(T_{0})-clk_{B}(T_{0})=\frac{\rho_{BA}(T_{0})-\rho_{AB}(T_{0})}{ 2}+\frac{\Delta\delta_{AB}-\Delta\delta_{BA}}{2^{\mathit{\mathit{\mathit{\mathit{ \mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{ \mathit{\mathitwhere \(URE_{\text{Orb},t_{i}}\) represents the three-dimensional error of orbit determination at time \(t_{i}\) and \(\Delta R\), \(\Delta T\), and \(\Delta N\) represent the orbit error in radial, tangential, and normal directions, respectively. Clock errors are the differences between clock parameters of autonomous messages and the main control station to evaluate whether the satellite's clock is abnormal.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 2 Influencing Factors of City Gas Daily Load  
### Air Temperature  
Residents' living habits will be directly affected by the change of temperature, thus affecting the actual gas load. A small change in temperature of 1 \({}^{\circ}\)C will lead to a 5-6% change in daily gas load [40]. After sorting out the annual air temperature data and gas data of a city, the yearly changes in daily gas load and average temperature of a city are shown in Figure 1. It can be seen that the daily gas load was negatively correlated with temperature, and the gas load decreased with the increase of temperature.