esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The microencapsulated phase change materials in the PCL matrix were successfully manufactured by an electrospray process, using two types of solvents, ethyl acetate and chloroform, and three loading contents (\(30\%\), \(50\%\), and \(70\%\) by weight). The process not only produces particles with a mono-dispersed particle size distribution, an average micrometric diameter, a non-porous morphology, but also a high encapsulation efficiency. The microparticles have a latent heat correlated to the theoretical load and good thermal stability. The break up process in the early stages of the electroplating method mainly determines the particle size of the mPCM. Thus, depending on the choice of \(n\)-alkane, \(n\)-hexadecane, or \(n\)-eicosane, two phenomena occur. On the one hand, in the case of \(n\)-hexadecane, which is in a liquid state at room temperature, the break-up of the charged droplets takes place throughout the flight process, from the nozzle to the collector, which allows the solvent to evaporate entirely and thus obtain a low mean diameter. On the other hand, the droplets loaded with \(n\)-eicosane break-up only in the earliest stages of the flight process, as the evaporation of the solvent leads to the \(n\)-alkane crystallization, which stops the process, resulting in higher mean diameters and broad size distribution. The surface morphology of the electro-sprayed mPCM is either smooth, and dense or rough and wrinkled when \(n\)-hexadecane, and \(n\)-eicosane are used, respectively. Besides, \(n\)-eicosane tends to be on the surface of particles as its loading content increases. Also, the choice of solvent affects the particle size and morphology of the particles, which may be related to changes in the physico-chemical properties of solutions, such as surface tension, vapor pressure, and viscosity. The loading content depends  
Figure 7: SEM micrographs of \(n\)-hexadecane/PCL microparticles after thermal treatment.  
mainly on the diffusion of _n_-alkane chains during the process, which is also influenced by solvent evaporation or phase separation, the interactions between PCL macromolecular chains, and _n_-alkane chains, and the evolution of the physical state of PCM. Thus, the encapsulation efficiency and charge content of an _n_-alkane liquid at room temperature are respectively higher and lower than those of a solid _n_-alkane in ethyl acetate, but in chloroform, it is the opposite.  
Conceptualization, S.Z. and F.S.; methodology, S.Z., C.C. and F.S.; validation, C.C. and F.S.; formal analysis, S.Z. and F.S.; writing--original draft preparation, S.Z.; writing--review and editing, F.S.; supervision, C.C. and F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are very grateful to Sichuan University (Chengdu, China) for microscopy analyses (SEM) and the technical and financial support from GEMTEX laboratory (Lille, France). The author [PERSON] is also thankful for the financial support for a PhD from the China Scholarship Council (CSC).  
The authors declare no conflict of interest.
# End-Users' Augmented Reality Utilization for Architectural Design Review
## 2 Literature Review
### Application of Augmented Reality in the Construction Industry
Only a few prior studies have explained how AR utilization affects a design review task, especially from the view of end-user experience. Overall, to find an appropriate method for involving end-users in the design review process, it is essential to understand an AR system's strengths and weaknesses in terms of visualization, acceptability, and user experience.
## 3 Proposed Method
### Cross-Level Information Interaction  
While extracting detailed features of the liver and tumor, shallow a priori knowledge like organ boundaries and texture is often neglected. To address this concern, we introduced a cross-level information interaction mechanism based on bidirectional residual connections. This mechanism enhanced the network's capability to learn and represent features by modeling both the encoder and decoder. As depicted by the blue arrows in Figure 6, the cross-level information interaction mechanism comprised shallow forward residuals and deep reverse residuals, detailed below.  
Suppose \(x_{i}\in R^{H\times W\times C}\) denotes the shallow forward residual input originating from layer i of the encoder, and \(f_{i-1}\in R^{\frac{H}{2}\times\frac{W}{2}\times\frac{C}{2}}\) represents the deep reverse residual input received from layer \(i-1\) of the decoder.  
First, an upsampling operation is conducted on \(f_{i-1}\) to bring its resolution in line with that of \(x_{i}\) for subsequent operations. This upsampling is achieved through bilinear interpolation.  
\[f_{i-1}{}^{\prime}=upsample(f_{i-1}) \tag{7}\]  
Then, \(x_{i}\) and \(f_{i-1}{}^{\prime}\) are summed element-wise and directed into the \(DLE\) unit for feature extraction. The features obtained are combined with \(x_{i}\) and subsequently reduced in dimensionality using linear mapping to produce the ultimate output \(y_{p}\).  
\[y_{b}=\big{(}Concatenate\big{(}DLE\big{(}x_{i}+f_{i-1}{}^{\prime}\big{)},x_{ i}\big{)}\big{)}G \tag{8}\]  
Figure 6: The structure of the cross-level information interaction mechanism (CII).  
where \(DLE\) denotes the double-flow linear pooling enhancement unit, and \(G\) denotes the linear mapping function, which is implemented by \(1\times 1\) convolution.  
The cross-level information interaction mechanism, founded on bidirectional residuals, achieves the fusion of contextual information across various layers. It effectively addresses the problem of forgetting a priori knowledge during training and expedites feature fusion within the network, serving as an automatic learning mechanism.
## 2 Methodology
### Linearization of Vehicle Dynamics Model  
The lateral motion control of autonomous vehicle is to control the front wheel rotation angle for lateral movement, and then realize the path tracking purpose. Therefore, this paper selects path tracking as the ultimate goal of autonomous vehicle lateral control and the tracking accuracy as the main indicator to measure the performance of the control system. Model predictive control can be divided into linear time-varying model predictive control (LMPC) and non-linear model predictive control (NMPC) [22; 23]. The linear time-varying model predictive control algorithm uses the linear time-varying model as the predictive model. Compared with the nonlinear model predictive control, the real-time performance of the linear time-varying model predictive control is much better. For the motion control of autonomous vehicles, the real-time performance of the control algorithm is very important; therefore, we use LMPC for lateral motion control of autonomous vehicles in our work.  
Firstly, the non-linear dynamic vehicle model needs to be linearized. The state quantity and control quantity of the system satisfy the following relationship:  
\[\dot{\xi}_{dyn}=f\Big{(}\xi_{dyn},u_{dyn}\Big{)} \tag{4}\]  
Performing the Taylor expansion at any point \((\xi_{r},u_{r})\), retaining the first-order term and ignoring the higher-order terms, we can get:  
\[\dot{\xi}=f(\xi_{r},u_{r})+\left.\frac{\partial f}{\partial f\xi}\right|_{ \begin{array}{l}\xi=\xi_{r}\\ u=u_{r}\end{array}}\begin{array}{l}(\xi-\xi_{r})+\left.\frac{\partial f}{ \partial u}\right|_  
After introducing the incremental model, the state space equation is written as:  
\[\left\{\begin{array}{l}\Delta\xi(k+1)=A(k)\Delta\xi(k)+B(k)\Delta u(k)\\ \zeta(k)=C\Delta\xi(k)\end{array}\right. \tag{8}\]
# A Text-Generated Method to Joint Extraction of Entities and Relations
## 2 Related Work  
Entity and relation extraction methods can be divided into pipeline and joint learning.  
Pipeline method regards entity recognition and relation extraction as two separate tasks, and extracts relations based on entity recognition. Some pipeline methods based on RNN and CNN models have been proposed. Ref. [17] first used RNN for relation extraction. Ref. [4] first introduced CNN to this task. Refs. [2; 18; 19; 20], improved on the previous work and enhanced the effect of relation extraction. The pipeline method has the disadvantages of error propagation, ignoring the relevance between these two subtasks and generating redundant information, while joint learning method proposed in recent years which uses a single model to extract entities and relations simultaneously and can use the close interaction information between entities and relations.  
The joint learning method is further divided into feature-based structured systems [21] and neural network models. Ref. [7] first used neural network methods with the dependency tree to jointly extract entities and relations. Ref. [22] proposed a hybrid neural network which has two channels after the encoding layer, one links to the NER module, the other feeds into the relation extraction module. Ref. [5] first introduced the attention mechanism in combination with bidirectional LSTMs for joint extraction of entities and relations. Ref. [8] proposed an entity-relation extraction method based on a novel tagging scheme. This method completely transforms the joint learning model into sequence labeling problem, it can extract multiple relations, but cannot deal with entity overlap because the model can only assign a label to each word. Ref. [11] based on [PERSON]'s work [8], further transformed the joint task into a graph problem and proposed a transition-based method, can model underlying dependencies between relations and identify overlapped relational triples.  
Ref. [9] first proposed a solution for overlapping relation extraction, and divided the sentences into three classes according to the degree of entity overlap: Normal, Entity Pair Overlap, and Single Entity Overlap. They proposed an end-to-end model based on sequence-to-sequence learning with copy mechanism, copying entity pairs from the original input, and classifying the relation types in the predefined relational table. Unlike [9], our model uses a unified way to generate token at any time in the decoding process, instead of judging whether to copy entities or predict relations at different steps. By calculating a generate probability distribution, the model can automatically learn whether entities or relations should be generated at each moment. At the same time, because [9] presupposes that a relational triple is generated every three steps, they can only recognize the last word of a multi-word entity. While our model does not limit the number of words contained in each relational triple, it can recognize multi-word entities and can copy entity words from the original text.
## 1 Introduction
It is worth noting that modeling the interface and connection joints between substructures significantly influences the dynamic response of the assembled system. The accuracy of these models depends on the chosen connection models, which range from rigid joints to multi-joint damper/spring models [6; 15; 16; 17]. [PERSON] and [PERSON] [18] reviewed the advancements in substructure synthesis methods using frequency response functions and introduced the general joint description method (GJDM). In addition to the foundational work in dynamic system modeling, recent advances have focused on predicting dynamic behaviors in complex mechanical systems, particularly in machining operations. [PERSON] et al. [19] developed a method for predicting frequency response by modeling the stiffness and damping characteristics of shrink-fit tool holder connections, offering a significant improvement in milling accuracy and vibration control. [PERSON] and [PERSON] [20] applied substructure coupling techniques to predict spindle dynamics in milling, highlighting the effectiveness of dynamic substructuring for improving spindle stability. The generalized receptance coupling method has been used in the domain of the dynamic response prediction of tool tips due to its simplicity, its low computational cost, and the possibility of developing analytical formulations to determine the system receptance matrix and response in terms of substructures' FRFs [21; 22]. Recent developments by [PERSON] and [PERSON] [23] provide a fundamental review based on various substructuring schemes using RCFBS, highlighting the effectiveness of these techniques for predicting road noise and vibrations in vehicle dynamics. The main aim of the work presented here is to demonstrate the effectiveness of RCFBS in predicting dynamic responses for systems with both continuous and lumped-mass properties involving translational and rotational degrees of freedom (DoFs). By applying RCFBS to two case studies--one with distributed parameters and the other with lumped parameters--we validate the method against the numerical FEA, the modal method, and direct solutions. The results show that, while both RCFBS and the modal method are based on linear theory, RCFBS provides superior reliability for systems with distributed parameters in the high frequency range by avoiding mode truncation limitations. This paper contributes to the growing body of research on modular modeling approaches by showing that RCFBS is particularly well-suited for applications in which higher-frequency dynamics, substructures with distributed parameters, and the coupling of translational and rotational DoFs are critical. The method's superior performance in predicting system behavior across various frequency ranges highlights its potential for applications in fields such as automotive engineering, robotics, and vibration analysis.
# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
## 6 Discussion
### Comparison with Matrix Banding  
In this section, we describe another option for our proposed method, matrix banding. Matrix banding is a simple method to make arbitrary matrix a band matrix [69]. The band matrix is a sparse matrix all non-zero elements of which are in at most some consecutive and diagonally bordered bands including main diagonal. In other words, all out-of-band elements of the band matrix are zero. Therefore, the matrix banding is the method that can make arbitrary matrix sparse with less computational burden than the GLASSO. We compare the performances of the proposed method (GLASSO-PLDA) with those of the matrix banding-based PLDA (denoted as banding-PLDA), in which the within-class precision matrix is regularized with the banding rather than the GLASSO. There is no reason for using the GLASSO-PLDA if the banding-PLDA generally shows better performances than the GLASSO-PLDA.  
Tables 3 and 4 summarize the EERs of the proposed method and banding-PLDA for each embedding on RSR 2015 part 1 and 2, respectively. For the both proposed method and banding-PLDA, all deep speaker embeddings were orthogonalized using the PCA. Therefore, the EERs of the proposed method in Tables 3 and 4 are the same as those in Tables 1 and 2, respectively. For the banding-PLDA, we constrained the bands to symmetric because within-class precision matrix must be symmetric. \(k\) means the bandwidth for one side (left or right). The total bandwidth is \(2k+1\) because the bands are constrained to be symmetric. \(k=0\) means the diagonal matrix. The GLASSO-PLDA generally exhibited better performances than the banding-PLDA. In TD-SV, therefore, the GLASSO, which is based on the \(L_{1}\)-penalized Gaussian log-likelihood, is more effective than the banding.  
Figure 11: The EERs (%) of the PLDA (dashed red line) and GLASSO-PLDA (solid blue line) according to \(\rho\) on the (**a**) validation and (**b**) evaluation trials of the VoxCeleb dataset.
## 5 Performance and Simulations of Instantaneous Frequency Rate Tracking Approach
### Selection of Processing Parameters
To evaluate the influence of the second-order Doppler rate, numerical computations are made for the coherence loss function based on Equation (55). The complex sampling frequency is 200 kHz,and the data rate is 4 kHz after down-sampling. The total coherent gain is plotted versus different coherent integration times, which is shown in Figure 15.  
As is seen in Figure 15, the coherent gain gets to the maximum value of about 30.6 dB with 0.35 s of integration when the second-order Doppler rate is 200 Hz/s\({}^{2}\), and the coherent gain is about 30 dB when the integration time decreases to 0.25 s. When the second-order Doppler rate is 500 Hz/s\({}^{2}\), the maximum of coherent gain is attained also with 0.25 s of integration. Thus, the coherent integration time is selected to be 0.25 s.  
It should be noticed that the above analysis gives the theoretical foundation about the selection of integration time. If the time value exceeds the recommended value, the coherent gain will decrease. Besides, the dynamic response of the algorithm is also influenced by the coherent integration time, which still needs more investigation. In our processing approach, we use numerical simulations to evaluate the dynamic response of the algorithm given a certain data rate and coherent integration time.
# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 1 Introduction
### Problem Statement  
The inefficient management of network resources and load balancing accelerates quality-of-service degradation and energy losses. Due to increased traffic overhead on fog gateways, delays have also increased, which cannot be tolerated in real-time applications. High energy usage and load balancing are major areas of research in the fog-cloud environment. To conquer this issue, the IoT-fog-cloud model is introduced in this article by efficiently dispensing the workload among different devices at different levels. Nevertheless, only a restricted amount of information is forwarded to the cloud for further computation, which leads to enhanced bandwidth and reduced latency in the network.  
This article highlights the challenges in emergent IoT, the colossal quantity of data produced through sensors, and the trouble in reporting these challenges with available networking and computing models. Thus, this research article presents the need for a new model and architecture called fog computing. Fog computing aids in filling the technological gaps of cloud and IoT models betweenness, viz., quality of service, time latency, and location awareness. The main objective of this article is to proffer a balancing load mechanism in a fog computing environment for a uniform distribution of incoming traffic among available cloud servers or fog devices, considering underloading or overloading  
Figure 1: Integration of IoT– fog–cloud model.  
of nodes. This improves performance, energy consumption, throughput, and resource utilization while reducing the response time. Without dependence on the cloud, IoT devices send data to a fog server for processing. Thereby, it can efficiently save cloud storage and network bandwidth for important processes and data [6; 7; 8]. A few current challenges of load balancing are efficiency, fault tolerance improvement, overhead, and the consequences of implementation.  
The primary objectives are as follows:  
1. Investigate the existing literature on the IoT-fog-cloud architecture and models considering energy factors.
2. The three significant issues covered in this article are load balancing, energy consumption, and computation delay or latency.
3. An energy-efficient and load-balanced mechanism for fog environments, considering available CPU capacity.
4. Performance evaluation of the proposed mechanism with existing techniques.  
The organization of the rest of the article is as follows: Section 2 inspects the prior studies and analyses their weaknesses and strengths. The methodology of the proposed scheme is illustrated in Section 3. The experimental setup and simulation of this work are described in Section 4. The results and discussion are presented in Section 5. The conclusion of the proposed work is presented in Section 6.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 2 Preliminaries  
### Syndrome Decoding Problem (SDP)  
Code-based cryptographic algorithms derive their security from the difficulty of the Syndrome Decoding Problem (SDP), which is known to be NP-hard.  
In SDP, a secret vector \(e\in\mathbb{F}_{2}^{k}\) is challenging to recover from the syndrome (ciphertext) \(c=He\), even when the parity-check matrix \(H\in\mathbb{F}_{2}^{n\times k}\) (which is public) is known. It is essential to recognize that the vector \(e\) has a specific low Hamming-weight \(t\) in SDP.
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 2 Materials and Methods
### Data Processing and Analysis  
Chemometric analysis of the NIR and MIR spectra was conducted using the Unscrambler X software (Camo Analytics; Oslo, Norway). Figures were drawn in the Unscrambler X and in R Studio running R 4.0.2 [27].  
For the development of the predictive chemometric models, the data set was split into a calibration set and a validation set, comprising 80% and 20% of the sample set, respectively. Two regression methods were trialed--partial least squares regression (PLS-R) and support vector regression (SVR). PLS-R is a linear quantitative modeling algorithm that has been extensively used in spectral analysis and particularly in the infrared wavelength range (NIR and MIR). It is always used to avoid multiple collinearities by reducing a large number of independent variables into a new set of unrelated variables called latent variables (LVs). In our case, the X matrix containing the MIR and NIR spectra was used to build the PLS2 models. The response matrix Y contains four variables to be predicted, namely DM, TPC, FRAP, and CUPRAC. The number of latent variables chosen in this study is based on cross-validation using the leave-one-out cross-validation procedure [28]. Support vector analysis (SVR) is oriented from SVM (support vector machine). This method is very interesting because it can solve practical problems such as a small sample, nonlinearity, and high dimension. In this study, the SVM method was applied using linear kernel algorithms applied using nu-SVM.  
Before calculating regression models, the NIR and MIR spectra were subjected to different methods of pre-processing, including standard normal variate (SNV), multiplicate scatter correction (MSC), and the 1 st and 2 nd derivatives using a Savitzky-Golay algorithm (10 pts/side). Both SNV and MSC are generally applied to spectral data to decrease the influence of light scattering and the baseline shift. The purpose of the derivatives was to reduce the influence of random noise, highlight the subtle frequency band shape, maintain relative frequency band intensity information, and improve the resolution of overlapping frequency bands ([PERSON] et al. [29]). The Savitzky-Golay derivative uses a smoothing procedure that is useful to decrease random noise and increase the signal-to-noise ratio.  
The accuracy of the models was evaluated by root mean square error of prediction (RMSEP), cross-validation (RMSECV), calibration (RMSEC), determination coefficients calibration (R\({}^{2}\)cal), cross-validation (R\({}^{2}\)CV) and prediction (R\({}^{2}\)pred) and the ratio of prediction to deviation (RPD). RPD was calculated by dividing the standard deviation of the sample set by the RMSEP. [PERSON] et al. [30] considered that an RPD of 3 indicated an excellent model; between 2.5-3 indicated a good model; between 2-2.5 indicated that coarse quantitative predictions are possible; while an RPD of 1.5-2 suggested that the model could discriminate between low and high values of the analyte.