esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Tracking Accuracy Analysis in Different Conditions
#### 3.3.4 Driving Mode  
The rear wheel speed is used in this paper as an input in the differential drive model for the calculation of real-time vehicle localization, so it is applicable for both forward driving and backward driving modes. To verify the effect the two different driving modes have on path tracking accuracy, this experiment also performed backward autonomous  
Figure 16: Tracking error with different start angles.  
Figure 15: Different start position angles. (**a**) The initial position without angle deviation; (**b**) The initial position with angle deviation.  
driving tests, and three sets of comparison experiments were conducted in forward and backward modes, the results of which can be seen in Figure 18. The experimental curves and the average error show the average error in backward mode to be slightly larger than in forward driving, but the difference between them is not considered significant.
# Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition
## 1 Introduction
This fact has motivated some authors to use EEG channels only [21] to classify sleep stages. The training process analysed in the present paper results in the evaluation of the coefficients of a mathematical model for multiclass pattern recognition [34; 35; 36; 37]. The classification accuracy and cross-validation error were then used for comparing the results. The resulting mathematical model can be further updated using expanded databases of individuals and visual sleep scoring as well as the experience of a neurologist in the associated interactive process. The proposed mathematical model forms a contribution to further automatic systems of sleep stages classification and data analysis for the evaluation of proportion of Wake and REM segments associated with the age of an individual. These information can provide an additional diagnostic tool in the clinical environment. The present paper is organized as follows. Section 2 presents PSG data acquisition, pattern matrix construction and the methodology of machine learning for pattern recognition of sleep stages. Section 3 describes results of the age analysis of sleep stages and their classification using both multimodal and EEG features of individuals with different diagnosis. The final Section 4 is dedicated to the discussion and conclusion.
## 4 Review of Microgrid Control and Design
### Primary Control Level
#### 4.3.2 Grid Forming Control  
When MGs operate in autonomous mode, the renewable energy sources (RESs) and storage systems must be responsible for the stability of voltage and frequency, which therefore requires optimal control of active and reactive powers to guarantee that the devices share power in an appropriate manner. [PERSON] et al. [88] have already handled this kind of control in their work for islanded MGs. Depending on the needs, a portion or all of the RES inverters will operate in parallel to ensure microgrid voltage control in grid-forming mode and in grid-following mode for the remaining ones [89]. The following are the two primary configurations in which this control approach has been used:  
- Single grid forming unit:  
In this scenario, one sustainable energy source is interfaced to a grid-forming power converter to generate the voltage and frequency references, while all other units operate in grid-following mode and are controlled by the current to extract the maximum possible energy from the RESs [90; 91].  
- Multiple grids forming units:  
This approach involves the use of several renewable energy generation units operating in grid-forming mode. Consequently, synchronization is required to ensure the voltage  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Primary** & **Secondary** & **Tertiary** \\ \hline Objective & \begin{tabular}{c} Maintain voltage and frequency \\ stability at the DER level \\ \end{tabular} & \begin{tabular}{c} Restore voltage and frequency \\ deviations caused by primary \\ control actions \\ \end{tabular} & \begin{tabular}{c} Handle supervisory tasks, such as \\ economic optimization, energy \\ management, and demand \\ response programs \\ \end{tabular} \\ \hline Responsiveness & \begin{tabular}{c} Fastest response time \\ (milliseconds) \\ \end{tabular} & \begin{tabular}{c} Slower response time (seconds \\ to minutes) \\ \end{tabular} & \begin{tabular}{c} Slowest response time \\ (minutes to hours) \\ \end{tabular} \\ \hline Mechanisms & Droop control, inverter control & \begin{tabular}{c} Communication-based control \\ and adaptive control \\ \end{tabular} & \begin{tabular}{c} Optimization algorithms, \\ decision-making tools \\ \end{tabular} \\ \hline Role & \begin{tabular}{c} Ensures fast power sharing \\ among DERs to respond to \\ sudden changes in generation \\ or load \\ \end{tabular} & \
\begin{tabular}{c} Coordinates power dispatch \\ among DERs to optimize energy \\ consumption and minimize \\ operational costs. \\ \end{tabular} &
\begin{tabular}{c} Interacts with the main grid for energy \\ exchange and backup power, ensuring \\ long-term economic and operational \\ efficiency \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Functionalities of hierarchical control levels.  
and frequency stability of the microgrid while meeting power demands. Depending on whether or not a communication network links the interface converters, the techniques in this field can be classified into several categories [92; 93; 94].
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 2 XPCS Concepts
### X-ray Speckle Visibility Spectroscopy  
Many current XPCS experiments are limited by the detector, i.e., the fastest accessible time scale is defined by the lowest detector repetition rate. Nowadays, two-dimensional megapixel detectors used for XPCS experiments reach several kHz up to MHz, allowing to measure sub-ms correlations, see Section 3.2 for details. X-ray speckle visibility spectroscopy (XSVS) is one way to circumvent the repetition rate and reach faster time scales [49; 50; 51; 52].  
In an XSVS experiment, single speckle patterns are measured with different exposure times. Different from XPCS, all patterns are treated individually. If the sample shows any dynamics during the exposure, the speckle patterns will smear out and loose contrast. An examples is shown in Figure 5a. Here speckle patterns from diffusing disks are calculated for four different exposure times of 1, 10, 30 and 100 arb.u., respectively. Especially with increasing \(q\) the reduction of speckle contrast becomes visible. The contrast obtained close to the minimum intensity drops from \(\beta_{1}=1\) for the fastest exposure to \(\beta_{100}=0.48\) for a 100 times longer exposure. This is even more pronounced at larger \(q\).  
The contrast in XSVS experiments can be calculated from the exposure time \(t_{\text{exp}}\) and the intermediate scattering function via [49; 51]  
\[\beta(q,t_{\text{exp}})=\frac{2\beta_{0}}{t_{\text{exp}}}\int_{0}^{t_{\text{ exp}}}(1-t/t_{\text{exp}})|g_{1}(q,t)|^{2}\text{d}t, \tag{9}\]  
with the coherence factor, i.e., the speckle contrast for \(t_{\text{exp}}\to 0\), \(\beta_{0}\). For an exponential \(g_{1}(q,t)=\exp(-\Gamma(q)t)\) as in the case of diffusion, Equation (1) can be solved analytically to  
\[\beta(q,t_{\text{exp}})=\beta_{0}\frac{\exp(-2t_{\text{exp}}\Gamma(q))-1+2t_{ \text{exp}}\Gamma(q)}{4t_{\text{exp}}^{2}\Gamma^{2}(q)}. \tag{10}\]  
An example comparing directly \(g_{2}\) from XPCS with the result in Equation (10) is shown in Figure 5b.  
As XSVS does only depend on the exposure time, it can be used for faster processes than conventional XPCS if the exposure time is controlled by, e.g., fast shutter systems or electronic detector triggers. It allows in particular the reduction of the radiation dose because the sample has to be exposed only up to \(t_{\text{exp}}\), and may allow more flexible control of intensity. In particular for fast processes in the fs range probed at FEL sources this is the only way to obtain dynamics via speckle correlations.  
Figure 4: (**a**) Two-time correlation matrix \(C(t_{1},t_{2})\) modelled for an aging system that continuously slows down during the measurement. (**b**) Cuts through \(C\) at given \(t_{2}=t^{*}\) as indicated in (**a**). The lines are exponential fits up to \(\Delta t=t_{1}-t^{*}=20\), afterwards the modelling fails because of the aging. (**c**) Variance \(\chi\) from (**a**). The maximum \(\chi_{\text{max}}\) is found at about \(\Delta t=61\) and marked by the dashed line.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 2 Materials and Methods
### Watermark Detection Procedures  
Since watermarks may be embedded into digital models, G-code programs, and physical parts, different decoding methods are derived to handle these contents accordingly. Their flowcharts are illustrated in Figure 5. The procedures for verifying G-code programs and digital models are similar. They rely on virtual manufacturing, volume rendering, and similarity evaluation to achieve their goals. On the other hand, the authentication procedure for physical parts invokes physical manufacturing, image capturing, and visual comparison. These three watermark detection algorithms are described in this subsection.  
The method dedicated to authenticating a G-code program is shown in part (a) of Figure 5. At first, the G-code program is emulated by using a simulator to generate a voxel model. The design, implementation, and functionalities of this simulator are presented in [27]. Then, the model is volume-rendered to search for a trace of watermark. If a watermark appears in the resultant images, we extract the watermark by using the recorded view angle and ROI. At the following step, the similarity between this watermark and the recorded one is computed by using the dHash method of [28]. If the resultant dHash value is higher than a predefined threshold, we assume that this G-code is genuine.  
To examine a digital model, we volume-render this model to confirm the existence of a watermark. Then, this watermark is retrieved by using the recorded ROI and view parameter. At the following step, the extracted watermark is compared with the recorded one by using the dHash method. If the similarity index is higher than the predefined threshold, this digital model passes the authentication test.  
The verification procedure for a physical part in depicted in part (b) of Figure 5. At first, we retrieve the standard model, fabricated in the encoding process. At the second step, we illuminate the physical part and the standard model by using a bright light source to uncover their watermarks. Then, the revealed watermarks are visually compared. If an image capture device is available, we can generate watermark images from both objects and make comparisons by using the dHash method.  
Figure 5: Watermark verification methods for (a) digital model models and G-codes, (b) physical parts.
## 4 Results
### Early Warning System
#### 4.3.1 Software Database  
The software is programmed on a computer that controls the operation of iMETOS stations and processes information from images, reports and transmits if a forest fire occurs. The software database includes: (1) the detailed information about the potential fire risks including: the coordinates, lot number, plot, sub-zone, name of forest owner; (2) the photographs of the current fire taken from the monitoring station; (3) the information about the fire transferred to the website for the administrators to confirm the information and transmit the information to manage the fire. A model of the overall system is shown in Figure 10.
# Preconception Health Attitudes and Behaviours of Women: A Qualitative Investigation
## 3 Results
### Partner and Community Influences and Support  
Most women kept their pregnancy intent private between their partner and themselves and rarely sought support from family and friends during the preconception period.  
\"Um I guess we have not really talked a lot about it to family and friends, we have been a bit vague, we have not been telling people that we are going to start trying, so just in case it takes a while and those kind of things.\"--(P2, preconception, 33 years old, no children).  
However, some noted learning from the experiences of friends and family members who had previously given birth.  
\"A lot of my friends do have kids and things like that. So, I guess you hear out of that default what they might have done and things like that, or more so I would probably pick up from that, um times if they had had bad experiences. Like pain and problems during pregnancy and things like that,and, and I would be like right, I do not want to have that, I am going to go into it nice and fit and strong and things like that yeah.\"--(P3, pregnant, 30 years old, no children).  
Whilst women described their partners as being supportive during the preconception period, majority recalled that their partners made little to no behavioural or lifestyle changes themselves in preparation for pregnancy. This was mainly attributed to the partner's perception that they already had a relatively healthy lifestyle prior to preconception.  
\"He is um, so supportive... I do not think he would have done anything--like he would have changed anything for himself. I think he would have just gone along with whatever I said really. [laughs] I do not think he would have driven any changes... he probably just thought, she has got it under control... he is very healthy\"--(P11, pregnant, 37 years old, one child).  
However, a few women felt that their partners were simply unaware of what changes needed to be made but would be inclined to take action if necessary.  
\"If the advice was that he ought to do it then, yes, I imagine that he would. Um so it is one of those things that he is perfectly capable and prepared to um receive advice. But he may not--I doubt he is aware of anything and I doubt that unless I actually sort of pushed him to go to the doctor, um, that he would find out for himself.\"--(P6, preconception, 32 years old, no children).  
Of the few partners who adopted changes, these included reducing alcohol consumption, improving diet, undertaking a preconception health check-up, engaging in physical activity, quitting smoking and taking men's multivitamins.
# Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
## 1 Introduction  
According to the problems of data sparsity and cold start in the recommendation system, relevant information is introduced to complete missing information in the traditional method, which mainly includes user attribute, item attribute, user social network, context and so on [1; 2; 3; 4]. User-based recommendation algorithm and collaborative filtering algorithm build user's interest model according to user's behavior information. Although the above algorithms have low dependence on expert knowledge, the number of users and items in the commercial recommendation system is very large, in addition users have limited access to items, which leads to sparse user behavior information on items and reduce the performance of the algorithm. Knowledge Graph is a structured semantic knowledge base, which contains abundant entity information and the relationship among entities. On the one hand, the semantic information of Knowledge Graph provides auxiliary information to describe the relevant information of users and tourist attractions, to enhance the density of data and improve the accuracy of the recommendation algorithm. On the other hand, the semantic association of Knowledge Graph provides different relation connection, and it uses knowledge reasoning to mine user's deep interests according to context information, as well as spready mine the different semantic relations of the project, to improve the diversity of recommended results [5]. To sum up, this paper proposes a new algorithm of personalized tourism recommendation based on Knowledge Graph.  
The main contributions of this paper are be summarized as follows:  
1. In response to the lack of the public Chinese tourism Knowledge Graph, this paper introduces a Knowledge Graph into the tourism field, analyzes knowledge characteristics of tourist attractions, realizes the division of the entities and the relationshipamong tourist attractions, and constructs a complete Chinese tourism Knowledge Graph.
2. Aiming at the problem of a high error rate in constructing negative triples, this paper introduces Bernoulli sampling strategy and proposes a new knowledge representation model of B-TransD (Bernoulli-TransD) to reduce the probability of false negative triples.
3. Because the traditional recommendation algorithm only relies on the user's historical behavior, but does not consider the attribute information of users or tourist attractions, this paper proposes a new method of user interest based on the attribute of the tourist attraction, and excavates the user preferences for tourist attraction, building the user interest model to improve the performance of the recommendation system.  
The rest of the paper is organized as follows: a related literature survey is summarized in Section 2, the details of the proposed framework and Knowledge Graph are introduced in Section 3, experimental results are presented in Section 4, and the study is concluded in Section 5.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Boundary Segmentation
#### 2.2.1 Block-Based Methods  
The earliest block-based segmentation scheme is to divide the source image into several fixed-size blocks, obtain the fused block by using the threshold based adaptive fusion rule, and finally use the consistency test method to achieve the fused image. Due to the fixed block size, the boundary of the multi-focus fusion image is prone to the fuzzy phenomenon.  
[PERSON] et al. [19] proposed a multi-focus image fusion method based on adaptive region segmentation, which decomposed pre-registered source images into approximate coefficients and detail coefficients using the Laplace pyramid transform. In order to avoid the defect of fixed block size, an adaptive differential evolution algorithm is designed to calculate the optimal block size. Figure 7 shows a fusion framework for this approach. This method can effectively reduce the noise with high computational efficiency. [PERSON] et al. [20] adopted the quadtree structure realized adaptive segmentation to fuse multi-focus images. The varied block sizes were determined by each specific content, which effectively solved the problem of the block effect.  
It can be seen from the above that the size of the block has a crucial influence on the final fusion effect, and it is prone to the phenomenon of fuzzy boundary. In addition, the compatibility between adjacent blocks needs to be considered.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
###### Abstract
Ultra-massive MIMO considers using more than 128 antenna elements at both the transmitter and receiver [11]. The Saleh-Valenzuela (SV) channel model has been selected by many researchers and is applicable in various communication formats, including hybrid beamforming, hybrid precoding, and spatial multiplexing [12]. Machine learning architecture, particularly ELMx-based systems, that includes ELM is widely used in communication for channel estimation [13], including articles of interest regarding the application of AI in traffic prediction [14]. The main contributions of this work are summarized as follows:  
* We propose the CNN-SD, which integrates three machine learning algorithms of ELM, RELM, and ORELM for signal detection. The hidden layer bias and input weight in CNN-SD are randomly generated from distributions [15, 16, 17]. * We foresee greater complexity with the larger number of antennas. The application of deep learning to signal detection contributes to improved performance and reduced complexity, rather than using more complex channel estimation methods. * We developed a modeling framework for detailed learning and simultaneous regression, incorporating real and imaginary components of complex matrices into the input layers of artificial neural networks to minimize potential errors. This approach allows us to reduce the system's overall complexity and enhance its efficiency. The simulation results demonstrate that, in terms of mean square error (MSE), bit error rate (BER), channel capacity, outage probability, and computational time, CNN-SD performed better for signal detection. The remainder of this paper is summarized as follows: Section 2 details the materials and methods. The proposed CNN-SD algorithm is specifically described in Section 2. Section 3 gives simulation figures to illustrate the signal detection performance of the proposed algorithm. Section 4 is the conclusions.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 2 Mathematical Model of the Components of the Proposed System
### Heat Subsystem
#### 2.2.3 Heat Storage Model  
The HS operation is expressed by the following formula [34]:  
\[{W_{HS}}^{t}=({W_{HS}}^{t-1})\cdot(1-\rho_{HS})-\Delta t\cdot\left(\frac{{H_{HS- dis}}^{t}}{\eta_{dis}}\right)+\Delta t\cdot{H_{HS-ch}}^{t}\cdot\eta_{ch} \tag{17}\]  
where \({W_{HS}}^{t}\) and \({W_{HS}}^{t-1}\) are heat levels stored in the HS at \(t\) and \(t-1\). \({H_{HS-dis}}^{t}\) and \({H_{HS-ch}}^{t}\) heat are power delivered of or absorbed from the HS (kW),\(\rho_{HS}\) is the heat loss rate.
###### Abstract  
_Pallenis spinosa_ (L) Cass. is a widespread plant in the Mediterranean region. Traditionally, it is used as a medicinal species to treat several ailments, from inflammation to skin injuries. Although the phytochemical content of this plant has already been investigated, there is currently limited data on Algerian _P. spinosa_. In this work, we focused on volatile compounds and non-volatile secondary metabolites extracted using HS-SPME and methanol from the aerial parts of _P. spinosa_ collected from Northeast Algeria. Volatile constituents were analyzed by GC-MS, while non-volatile compounds were analyzed by NMR and HPLC-MS(tm). In total, 48 volatile compounds were identified, including sesquiterpene hydrocarbons (65.8%), monoterpene hydrocarbons (16.9%), and oxygenated monoterpene and sesquiterpenes (8.3% and 6.5%, respectively). \(\beta\)-Chamigrene (16.2%), \(\alpha\)-selinene (12.8%), \(\beta\)-pinene (10.6%), and \(\beta\)-caryophyllene (9.2%) were assessed as the main constituents. Concerning non-volatile metabolites, 23 polyphenols were identified (7.26 mg/g DW), and phenolic acids were predominant (5.83 mg/g DW). Tricaffeoylhexatic acid (1.76 mg/g DW), tetradeofyleylehexatic acid (1.41 mg/g DW), 3.54-diefeoylquin acid (1.04 mg/g DW), cathegoral diboside (0.35 mg/g DW), and chlorogenic acid (0.29 mg/g DW) were the most abundant ones. Several known flavonoids, such as tricin and palutetian glycosides, kaempfer, and apigenin, were also identified, and myricetin hexoside was detected in _P. spinosa_ for the first time. Overall, our work is the first to report an exhaustive characterization of volatile and non-volatile secondary metabolites from Algerian _P. spinosa_. The results represent a step forward in revealing the chemistry of this widespread plant species. Furthermore, they may contribute to rationalizing its traditional medicinal applications and preserve the biodiversity of Algerian flora.  
Pallenis spinosa; secondary metabolites; terpen; polyphenols; headspace analysis; mass spectrometry +
Footnote †: journal: Applic. Sci.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms  
In this section, we propose a decomposition strategy to solve the MTASU problem using the Lyapunov optimization method. Then, we devise an online algorithm, named DTEM, to obtain optimum solutions without prior statistics knowledge about the stochastic variables (i.e., ISL contact state, data acquisition rate, transmission rate, and energy harvesting rate on user satellites) for any time slot.