esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
EOs are liquid plant isolates containing complex mixtures of volatile and highly aromatic compounds. Traditionally, they are obtained by distillation from plant material by exposing the plants to steam generated in situ or ex situ. In this process, EO compounds are released from the plant material by vaporization, the steam volatilizes the oil, and the vapour mixture of EO and water condenses. The recovered oil is separated from the water due to the difference in density [17]. As the isolated oil remains in contact with water for some time, various polar hydrophilic volatile compounds that can form hydrogen bonds with water are distributed in the water phase, which is then called hydrolate. The content of volatile aromatic compounds from EOs in hydrolate is usually less than 1 g/L, and they mainly contain oxygenated compounds due to their relatively higher solubility in water compared to the solubility of hydrocarbons. Therefore, hydrolates are also characterized by specific flavours and organoleptic properties [8; 11; 18]. Another by-product is the non-distilled aqueous phase or wastewater. This fraction is also valuable as it contains large amounts of valuable secondary plant compounds, mainly water-soluble polar phenolics [13; 14].  
According to [PERSON] et al. (2018), there are two main MHD mechanisms involved in EO isolation. The first mechanism is based on the selective heating of water in the sample matrix, where microwaves interact with free water molecules distributed in vascular systems and plant glands, which then expand dramatically. This leads to rupture of the tissue and extraction of the oil components into the water phase. The second mechanism is related to the dipolar moments of the EO organic compounds that are able to absorb microwave energy. The compounds with a high dipolar moment are easier to extract because they interact more strongly with microwaves than those with a low dipolar moment.
## 5 Research Methods  
### Sentiment Analysis Method  
A text sentiment analysis, also known as opinion mining, propensity analysis, etc., is a modality represented by textual information, and its relevant sentiment information is extracted via computer technology [14]. At present, the research on text sentiment analysis methods is mainly based on two components: text sentiment analyses based on sentiment dictionaries and machine learning algorithms [15]. A sentiment analysis of text based on sentiment dictionaries is generally carried out by querying the sentiment values of sentiment words in sentiment dictionaries to obtain the sentimental tendency expressed by the text. In sentiment analyses, sentiment dictionaries take into account not only the text content but also the emoticons and the text expressions [16]. Machine-learning-based sentiment analysis methods first use manual methods to screen features from a large corpus, then represent the whole text with the selected features and, finally, use machine learning algorithms to classify the text, while deep learning, as a special kind of machine learning, does not require manual feature extraction, and it can automatically extract features, combine them and perform automatic sentiment classification [17].  
Since the vocabulary of microblog text is time-sensitive and the update speed of the sentiment dictionary cannot meet the current needs of online terms, this paper uses a machine learning algorithm to analyze the expressions of people's sentiments. The Tencent Cloud natural language processing (NLP), based on massive data and deep learning algorithms, can more accurately describe people's sentiments in different scenarios, meeting the research needs of this paper. Therefore, this paper uses the Tencent Cloud natural language processing platform with API sentiment analysis tools, based on a large-scale Internet corpus and deep neural network models, such as LSTM and BERT, for training, to identify and analyze people's post-disaster sentiments.
## 2 Literature Review
### Liveness Detection as Sensor-Level Attack Mitigation in Fingerprint-Based Systems  
Liveness Detection (LD) technologies have been proposed to combat the use of fake or decapitated fingerprints to ensure that the presented fingerprint is alive and authentic. The authors in [11] used digitized statistical image features with sampling from Gaussian distribution to detect fake fingerprints as a method for liveness detection. They concluded that the points from the center of the fingerprint present more information to help detect a fake fingerprint than the pixels from the edge. As a result, they were able to improve the traditional digitized statistical image feature from 85% to 91% accuracy.  
The authors in [12] used joint time frequency analysis to detect a fake fingerprint. They tested a profiling approach that reached 90% accuracy, a wavelet-based approach using a Daubechies wave that reached 81% accuracy, and a cascaded system reaching 100% accuracy in differentiating between a live fingerprint and a silicon one.  
Other researchers used convolutional neural networks (CNN) to detect fake fingerprints. In [13], the authors used CNN to evaluate the LivDet 2019 database, which is a liveness detection competition that includes 2000 live and fake fingerprints, reaching 95% accuracy. The authors in [14] also used CNN with an enhancement, reaching 98% in terms of accuracy in differentiating between fake and real fingerprints. Further, the authors in [15] used Slim-ResCNN applied to the LivDet 2017 database and reached 95.25%.  
An evaluation of liveness detection-based software was carried out by [16] using CNN and local binary patterns, reaching 95.2% accuracy. Moreover, a density-connected CNN was used and optimized using a genetic algorithm to achieve a liveness detection rate reaching a 98.22% accuracy [17]. The authors in [18] also used CNN on the LivDet 2015 database and reached 95.5% accuracy based on 50,000 fake and real fingerprints. Finally, the authors in [19] used a template-probe CNN on the LivDet 2015 database, reaching 97.24% accuracy in differentiating between real and fake fingerprints.  
Various other approaches have been proposed to detect liveness in a fingerprint, including using a score-level fusion [20], reaching 96.88% accuracy, using automatic template updating using the fusion of ECG and Fingerprint [21], reaching 97.4% accuracy, usinga two-layer parallel SVM network based on aggregated local descriptors [22], reaching 95.32% accuracy, and using SVM [23], reaching 100% accuracy.  
Table 1 summarizes the methods described in the literature and the accuracy reached in differentiating between fake and real fingerprints.  
The literature documents a high accuracy in differentiating between fake and real fingerprints using various approaches. However, none of the previous approaches examined the analysis of the micro-behavior during fingerprint placement to detect if a fingerprint is fake or real.
# Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis
###### Abstract  
Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.  
deep belief network; fault diagnosis; hierarchical structure; feature learning; bearing
## 2 RESs Uncertainty
### PV Modeling  
#### 2.2.1 Accurate PV Modeling  
There several models that are presented in the literature to model the PV cell and modules as [30; 31; 32; 33; 34]. Among these models, the double diode model of the PV cell in Figure 1 presents an accepted accurate compared with single diode model in [35; 36]. PV output power depends on solar irradiance (G) and ambient temperature (T).  
The relation between the cell voltage and current can be obtained from the following [37]:  
\[I=I_{ph}-I_{sh}-I_{d1}-I_{d2} \tag{2}\]  
where,  
\[I_{ph}=\frac{G}{G_{STC}}\times(I_{ph_{STC}}+\alpha_{I_{c}}\Delta T) \tag{3}\]  
\[I_{sh}=\frac{V+R_{S}I}{R_{P}} \tag{4}\]  
\[I_{d1}=I_{S1}(e^{\frac{(q_{1}V+R_{S}I)}{q_{1}\kappa T}}-1) \tag{5}\]  
\[I_{d2}=I_{S2}(e^{\frac{(q_{1}V+R_{S}I)}{q_{2}\kappa T}}-1) \tag{6}\]  
where, \(I_{ph}\) is the photo-generated current, \(I_{S1}\) and \(I_{S2}\) are the reverse saturation current for the first and second diodes, respectively. \(R_{P}\) and \(R_{S}\) are the cell shunt and series resistance. \(q\) and \(K\) are referring to the electron charge and the Boltzmann Constant respectively. \(a\) is the diode ideality factor. \(T\) refers to ambient temperature. \(STC\) refers to the standard conditions (\(G_{STC}\) = 1 kW/m\({}^{2}\), \(T_{STC}\) = 25 \({}^{\circ}\)C).  
For a certain climate conditions, PV module output power can be calculated by forming the (I-V) characteristic curve and obtaining the point of maximum power from the curve. Each point in the curve is calculated by solving Equation (2) at a certain voltage. The maximum power point is chosen as the operating point.
# Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum
###### Abstract  
This study proposes the design of an active stabilizing system (ASAS) for a single-track vehicle. Using the gyroscopic effects of two flywheels, this system can generate control torque to stabilize the vehicle in cases where there is centrifugal force of turning. To control the flywheel gimbals to generate stabilizing torque, a model predictive controller (MPC) is applied to control the system. For the controller design and performance evaluations, a model of a gyroscopic inverted pendulum is developed. Control strategies are proposed to stabilize the vehicle in the cases of straight running, circular motion, and path following. The results of the proposed stratgies when controlling the gyroscopic inverted pendulum showed good performance even with physical limitations of the control torques. In order to evaluate the real-time performance and the feasibility of the MPC, a real-time simulator is employed, which includes two embedded STM32F407 boards. The dynamic system and the control algorithms are respectively embedded into two STM32F407 boards for real-time simulation. Implementations of the MPC in this study demonstrate that the proposed controllers are feasible for real-time applications.  
model predictive control; real-time implementation; gyroscopic effect; bicycle; inverted pendulum +
Footnote †: journal: Applied Sciences
## 1 Introduction
A wavefield modeling based on 2D and 3D array is constructed in [32], the output of an array can be written as the product of a wavefield independent sampling matrix and an array independent coefficient vector. However, the truncation errors exists because of the infinite dimensional of Hilbert space. Also, the error of array interpolation exists. The mutual coupling effect of a spherical antenna arrays is elaborated as the effect of current density [33]. Due to mutual coupling, currents are induced in all other dipole elements, which in turn influence also the current distribution in the active element. Based on the wavefield modeling, some computationally efficient algorithms are proposed in [34]. The Root-MUSIC is extended to 3D array and wavefield. However, both truncation errors and array interpolation error exist, which would affect the accuracy of DOA estimation. The accuracy of array interpolation is affected by the scope of interpolation sector. A Root-MUSIC algorithm based on sparse uniform circular arrays is proposed in [35]. However, the truncation errors exist because of the phase modes of order. The algorithm can only deal with 2D array and 1D-DOA estimation. In this paper, an arbitrary baseline \"2D and 3D\" direction finding algorithm based on conformal array antenna is proposed. The algorithm provides unambiguous direction finding using the rotation comparison method. The virtual baseline combined with matrix inversion is used to achieve ultra-wideband direction finding. Averaging the DOAs obtained by different sub-arrays are to ensure the stability of the proposed algorithm. Comparing with MUSIC-based algorithms, the computational complexity of the proposed algorithm is much lower. In addition, the elements with separation can be much larger than half of the wavelength. Comparing with other interferometer algorithms, the antennas can be arranged anywhere theoretically. The proposed algorithm only need some phase differences operation for DOA estimation. The implementation of the proposed algorithm is very simple. Comparing with the algorithm proposed in [34,35], the transformation errors do not exist. Thus the transformation errors such as the error of the phase modes of order and the error of array interpolation could not affect the estimation result of the proposed algorithm. The computation complexity of the proposed algorithm is much lower than that of the algorithms proposed in [34,35], which is suitable for real-time application. The remaining of the paper is organized as follows. Section 2 introduces the preprocessing of conformal antenna. Section 3 describes the model of incident signal and 2D arbitrary baseline algorithm in details. Section 4 and 5 contain the core contributions of this paper, which is the 3D arbitrary baseline method for conformal array. Section 6 presents the simulation result. Section 7 provides some final conclusions.
# A Data Processing Framework for Polar Performance Diagrams
## 3 The hrosailing Framework
### Computing Costs of Travel  
We include the following approach in the hrosailing framework, which includes cost functions used in the literature, but is much more versatile and considers weather conditions along the way in contrast to only considering the weather conditions at the  
Figure 9: A polar plot of the combined convex hull (dashed lines) of polar diagram slices of multiple sails (solid lines). Dashed lines corresponding to a single sail (same color) indicate manoeuvres without sail changes. Grey dashed lines indicate manoeuvres with sail changes.  
starting point. Note that this approach is more general than the typically used algorithms. We support two kinds of costs:  
1. absolute costs \(\text{cost}_{abs}\), depending on the conditions at the start point, the conditions of the end point, the total travel time and the total travel duration;
2. relative costs \(\text{cost}_{rel}\), which is a density function depending on position and time.  
The absolute cost can be used to model costs which do not depend on the weather conditions of each point, while relative costs can be used to model costs that do. Altogether, we then need to calculate the integral  
\[\text{cost}(A,B)=\text{cost}_{abs}(A,B,t_{total})+\int_{0}^{|B-A|}\text{cost}_ {rel}(s,t(s))ds.\]  
Here, \(t_{total}\) is the total travel time, \(s\) is the absolute distance travelled on the segment from \(A\) to \(B\) (assuming a flat map after a suitable mercator projection), and \(t(s)\) is the time travelled in dependency of \(s\). Note that \(t\) is the solution of the ordinary differential equation  
\[\frac{dt}{ds}=\frac{1}{v(s,t)},\ t(0)=0 \tag{1}\]  
where \(v\) is the velocity depending on \(s\) and \(t\) (obtained from the used WeatherModel and polar diagram). Such equations tend to be hard to solve analytically but can be solved numerically using standard tools (see [26]). Then, numerical integration methods can be applied to the solution for \(t(s)\) to calculate the costs.  
For clarification, consider the following example. You aim to find a fast route, but you want to avoid bad weather. For simplification, we assume we have a reasonable way to distinguish good from bad weather. You could choose the cost functions \(\text{cost}_{abs}(A,B,t_{total})=t_{total}\), \(\text{cost}_{rel}(s,t(s))\) is 1 if the weather at the position \(A+s_{*}(B-A)\) and at the time \(t(s)\) (computed by the WeatherModel) is bad. Otherwise, we set \(\text{cost}_{rel}(s,t(s))\) to zero. Thus, the total cost of the travel from \(A\) to \(B\) is basically the total travel time plus the distance we had to travel in bad weather.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 4 Future Trends  
The evolution of underwater wireless communication systems has been marked by significant progress and ongoing challenges. As we look to the future, the development of these systems will center on three key directions: technological innovation and system integration, intelligent and sustainable development, and the exploration of emerging application domains. These advancements are geared toward overcoming existing limitations and promoting all-around progress in marine exploration and resource utilization. Technological innovation will focus on developing new materials and devices to improve communication performance, while system integration will aim to combine different communication technologies to achieve more efficient and reliable underwater communication. Intelligent development will involve the application of artificial intelligence and ML to optimize communication protocols and enhance system adaptability. Sustainable development will emphasize energy efficiency and environmental friendliness. The exploration of emerging application domains will drive the expansion of underwater wireless communication into new fields such as smart oceans, underwater IoT, and deep sea resource development.
## 4 Analysis
### Bias and Covariance  
The performance of the proposed solution is ultimately determined by the GN iteration. Therefore, attention will be directed towards the solution provided in Section 3.1. The bias and covariance of a column vector estimation derived from minimizing a cost function are summarized in [53]. This approach will be followed to evaluate the performance of the proposed MLE.  
The estimate of \(\mathbf{\beta}\) implies  
\[\mathbf{0}=\left.\frac{\partial J(\mathbf{\beta})}{\partial\mathbf{\beta}}\right|_{\mathbf{ \beta}=\mathbf{\beta}}, \tag{41}\]  
where \(\mathbf{\beta}\) is the estimated value. Taking the Taylor-series expansion of the right side around the true value \(\mathbf{\beta}^{o}\) yields  
\[\mathbf{0}\approx\left.\frac{\partial J(\mathbf{\beta})}{\partial\mathbf{\beta}}\right|_{ \mathbf{\beta}=\mathbf{\beta}^{o}}+\left.\frac{\partial^{2}J(\mathbf{\beta})}{\partial \mathbf{\beta}\partial\mathbf{\beta}^{oT}}\right|_{\mathbf{\beta}=\mathbf{\beta}^{o}}(\mathbf{ \beta}-\mathbf{\beta}^{o}). \tag{42}\]  
The gradient vector and Hessian matrix evaluated at the true value are  
\[\bigtriangledown\mathcal{J}(\mathbf{\beta})= \left.\frac{\partial J(\mathbf{\beta})}{\partial\mathbf{\beta}}\right|_{ \mathbf{\beta}=\mathbf{\beta}^{o}}=2\bigg{(}\frac{\partial\mathbf{\mathsf{a}}^{o}}{ \partial\mathbf{\beta}^{oT}}\bigg{)}^{T}\mathbf{\mathsf{Q}}_{\alpha}^{-1}(\mathbf{\alpha}- \mathbf{\mathsf{a}}^{o}), \tag{43}\] \[\mathcal{H}(\mathbf{\beta})= \left.\frac{\partial^{2}J(\mathbf{\beta})}{\partial\mathbf{\beta}\partial \mathbf{\beta}^{oT}}\right|_{\mathbf{\beta}=\mathbf{\beta}^{o}}=2\bigg{(}\frac{\partial^ {2}\mathbf{\mathsf{a}}^{o}}{\partial\mathbf{\beta}^{o}\partial\mathbf{\beta}^{oT}}\bigg{)} ^{T}\mathbf{\mathsf{Q}}_{\alpha}^{-1}(\mathbf{\alpha}-\mathbf{\mathsf{a}}^{o})-2\bigg{(} \frac{\partial\mathbf{\mathsf{a}}^{o}}{\partial\mathbf{\beta}^{oT}}\bigg{)}^{T}\mathbf{ \mathsf{Q}}_{\alpha}^{-1}\frac{\partial\mathbf{\mathsf{a}}^{o}}{\partial\mathbf{\beta} ^{oT}}. \tag{44}\]Using (43) and (44) to (42) yields  
\[\hat{\beta}-\beta^{o}\approx-\bigtriangledown\mathcal{J}(\beta)\mathbf{\mathcal{H}}( \beta)^{-1} \tag{45}\]  
Taking the expectation on both sides, we arrive at the bias  
\[\mathrm{E}[\hat{\beta}-\mathbf{\beta}^{o}]\approx-\mathrm{E}[\bigtriangledown \mathcal{J}(\beta)]\{\mathrm{E}[\mathbf{\mathcal{H}}(\beta)]\}^{-1}=\mathbf{0}. \tag{46}\]  
And the covariance matrix is  
\[\mathrm{E}[(\hat{\beta}-\beta^{o})(\hat{\beta}-\beta^{o})^{T}]\] \[\approx\{\mathrm{E}[\mathbf{\mathcal{H}}(\beta)]\}^{-1}\,\mathrm{E}[ \bigtriangledown\mathcal{J}(\beta)\bigtriangledown\mathcal{J}(\beta)^{T}]\{ \mathrm{E}[\mathbf{\mathcal{H}}(\beta)]\}^{-1}. \tag{47}\]  
Putting (43) and (44) to the right side of (47) verifies that  
\[\mathrm{E}[(\hat{\beta}-\beta^{o})(\hat{\beta}-\beta^{o})^{T}]\approx\mathbf{ CRLB}(\mathbf{\beta}^{o}) \tag{48}\]  
holds when the small noise conditions  
\[n_{i,k}/r_{i,k}\approx 0,\quad e_{i,k}/r_{i,k}\approx 0, \tag{49}\] \[\text{or }n_{i,k}\approx 0,\quad e_{i,k}\approx 0, \tag{50}\]  
are satisfied.
## 4 Simulation Results and Analysis
The bandwidth of the SU with a strong detection capability is wider, while the bandwidth of the SU with a weak detection capability is narrower (represented by the green curve). It can be seen from the above analysis that the EDSAA based on the Cournot model is positively correlated to the detection capability and the demand factor given by the SUs. This algorithm results in the spectrum distribution being more consistent with the user's actual situation and reduces the spectrum of waste, which significantly improves the system spectrum utilization. Finally, we propose the EDSAA based on the Cournot model. When the detection capabilities of SUs are different, the convergence results are also different. This is because the EDSAA can dynamically adjust their price function according to the detection capability of the system feedback. As shown in Figure 6, the detection capability of the \(SU_{1}\) is constant (\(w_{1}=1.0\)), which results in the detection capability (\(w_{2}\)) and the evaluation criteria (\(\alpha\)) influencing the \(SU_{1}\) and \(SU_{2}\) price function in the spectrum allocation process. (Similar results are shown when the \(SU_{2}\) detection capability (\(w_{2}\)) is constant and the simulation results of the SUs price are similar, which is not provided in this present paper.) Figure 5: Iteration curve of the dynamic game of secondary user (SU). It can be seen from Figure 6 that the adjustment trend of the proposed method is negatively correlated to the detection capability of SUs. Essentially, SUs with a higher detection capability has a smaller price function. At the same time, the EDSAA introduces the system evaluation criteria (a) to adjust the adjustment rate of the user price function, which is used to balance the sensitivity and filtering ability of the EDSAA.