esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive Infrared Evaluation of Thermo-Physical Parameters in Bamboo Specimens
## 1 Introduction
In wood inspection, active IT has important applications such as the estimation of wood density [13], the imaging of moisture content distribution [14; 15; 16] and the detection of adhesion defects in laminated wood composites [17]. In bamboo inspection, active IT studied the glue interface between bamboo laminate, since it affects bamboo flooring quality. The researchers used active thermography using frequency modulated wave imaging (FMTW) and discovered glue deficiencies of gaps between laminates at different frequencies [18]. In spite of these applications of active IT in wood and bamboo inspection, to the extent of our knowledge, there are no previous works in inversion algorithms for the estimation of thermal parameters in bamboo cylindrical specimens. Nonetheless, active IT and inversion algorithms have provided good results in scenarios such as defect parameter estimation in composites [19], determination of material properties [20], and heat flux estimation [21]. In this work, we conduct the non-destructive estimation of Guadua a.k. thermal parameter \(k\) and \(\rho c_{p}\) based on the inversion of infrared thermography data. This inversion method inspects the specimen in its cylindrical shape based on the discretization of a 1D direct heat transfer model in cylindrical coordinates. In spite of the importance of chemical preservation in Guadua a.k. to decrease its vulnerability to insect attacks, few studies have analyzed the effects of preservation on the thermal properties. In this paper, we compare the estimated values of \(k\) and \(\rho c_{p}\) obtained from preserved and non-preserved specimens to observe how preservation impacts these parameters. The main contributions of this work are: (i) the estimation of thermal parameters \(k\) and \(\rho c_{p}\) in Guadua a.k. through nonlinear least squares and infrared thermography; and (ii) the sensitivity analysis of the direct model with respect to thermal parameters \(k\), \(\rho c_{p}\), \(h\), and \(r\). The remainder of this article is organized as follows: Sections 2 and 3 describe the theoretical thermal model and inverse problem formulation. Sections 4 and 5 introduce the estimation of thermal parameters in the presence of additive Gaussian noise and the experimental set-up. Finally, Sections 6 and 7 estimate the thermal parameters in bamboo specimens and conclude the paper.
# Age and Source of the Jinjingzui Skarn-Type Gold Deposit in the Southeastern Hubei Province, China
## 2 Regional Geology and Ore Deposit Geology
### Local and Mine Geology  
The Jinjingzui deposit is situated near Dave City, Hubei province. This ore cluster is severely covered with the Quaternary strata, comprising 80% of the material present. Drilling conducted by the Dave group exposed strata comprising thin/thick bedded marble, dolomite, and dolomitic marble. The intrusive rocks in the Jinjingzui deposit are typically diorite and diorite porphyry, which are mostly distributed at the edge of the pluton and have a phase change relationship with diorite. The main structural directions in the ore district are NE and NW. The diorite and gold ore bodies are located in structural convergence positions, controlled by the NNE reverse anticline and NW fault (Figure 3a). There are approximately eight discrete ore bodies in the Jinjingzui district. Among these, the No. 1 ore body between exploration lines 9-14 (elevation \(-\)374-7 m) has relatively large resources and is currently mined, having known Au reserves of nearly 8 tons. The No. 8 ore body is found in the contact zone situated at the apex of the intrusion, while the remaining ore bodies are positioned close to the interior of the smaller intrusive rocks, forming regular and irregular ring shapes, or appearing within the intrusions as veins. (Figure 3b).  
cluding native and small quantities of kustelite. The morphology of the microscopic gold includes sphere-, plate-, foliaceous, dendritic, angular, polyangular and irregular. Based on the relationship between gold and other minerals, the occurrence characteristics of microscopic gold can be categorized into two types: intergranular-crack gold and encapsulated gold. The former includes intergranular gold found in the spaces between mineral grains and crack gold present within the internal fractures of minerals. These gold particles are relatively coarse and can be readily liberated. Encapsulated gold is characterized by a small particle size and its difficulty in dissociating from pyrite, marcasite, altaite or other minerals. Wall-rock alteration related to Au mineralization includes skarnization and silicification. Based on field observations of cross-cutting relationships between veins, the occurrence of mineral assemblages and the textural/growth relationships of adjacent minerals and the crystallization sequence of ore-forming and skarn minerals can be divided into the following three stages: (i) skarn stage; (ii) sulfide-carbonate stage (as the temperature decreases, a significant quantity of sulfides commences to precipitate); and (iii) a supergene oxidation stage. The paragenetic sequence of minerals from these stages is shown in Figure 5.  
Figure 4: Photograph of hand samples (**a**,**b**) and microphotograph (**c**,**d**) reflection light from Jinjingzui. Au deposit, Middle-Lower Yangtze River Valley metallogenic belt. Q—quartz; Py—pyrite; Cyy—chalopyrite; Gl—Galena; Mal—Malachite; Cc—calcite; Lm—limonite.
# A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL
## 4 Case Study
 \tag{10}\]  
In the third case, the avionics system sends an application request through RPC to make the SOA system execute intelligent target analysis application. At this time, the image information from the radar triggers the agent to transition to the path planning state, and Equation (11) shows the calculation process of system delay time \(T_{3}\) in this case. \[ \begin{split} T_{3}=T_{radar1\_signal}+& T_{FPGA1}+T_{data\_switch1}+T_{Signal\_ processing\_FPGA}+T_{SOA\_System}+T_{Signal\_ reception\_service}\\ +& T_{path\_planning}+T_{signal\_transmission\_ service}=5+10+4+15+5+5+5+35\\ =& 84\text{ ms},\end{split}  \tag{11}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Service Name** & **Value (ms)** \\ \hline SOA\_System & 5 \\ Signal\_reception\_service & 5 \\ Signal\_transmission\_service & 5 \\ Communication\_management\_service & 5 \\ Signal\_transmission\_service & 2 \\ Signal\_reception\_service & 5 \\ Equipment\_signal\_acquisition\_service & 3 \\ Equipment\_status\_analysis\_service & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The delay time of services in SOA_System. \begin{table}
\begin{tabular}{c c} \hline \hline
**Working State** & **Value (ms)** \\ \hline Object\_identification & 20 \\ Obstacle warning & 27 \\ Path\_planning & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The delay time of target analysis service. Through the comparison of the system delay time in Table 4, the same task path in the system has different results, and the SOA executes different applications and the external input triggers different agent working states. It can be observed that the modeling method proposed in this paper can more accurately describe the working state of the agent, the SOA working process and the system attributes in the execution of different applications, and in practice, can improve the accuracy and effectiveness of the analysis.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods  
Data applications in many diverse fields now primarily use _artificial neural networks_ (ANNs) as their main model for processing. ANNs are capable of adapting to wide ranges of data [41]. Their nonlinear processing allows them to learn highly complex patterns, but they are very difficult to interpret and generally require models with huge numbers of parameters and calculation, which often exceed the capacity of low-resource devices (e.g., cell phones). ANNs transform data or recognize patterns, based on automatic training from examples. An ANN maps an input sequence (e.g., speech waveform samples) nonlinearly to an output sequence (e.g., a speaker-labelled set of time stamps).
## 3 Empirical Orthogonal Function  
The sound underwater propagation path and attenuation are closely related to the change of underwater sound speed. The change of the sound speed profile (SSP) is one of the important factors affecting the sound propagation in the ocean [15]. The sound speed profile is a structural distribution of the path and velocity variations of an acoustic wave propagating in the seawater. Due to the marine environment misalignment caused by the environmental factors such as the non-uniformity of seawater medium, and acoustic propagation channel interference [16], climate change [17; 18; 19], and other various reasons such as failure of the equipment for sound speed measuring, the sound speed profile measured in real time has diversity and errors. The empirical orthogonal function is a very effective method for inversion of the sound velocity profile. The EOF can obtain accurate enough sound speed profiles with the history sound speed data. The research of [PERSON] et al. [20] showed that  
Figure 1: Ultra-short baseline positioning principle.  
The above \(N\) feature values \(\lambda_{1},\lambda_{2},\ldots,\lambda_{N}\) are arranged in ascending order. The sound velocity profile at any point in the measurement region can be represented by the first \(K\)-order EOF approximation as  
\[c(z)=\overline{c}+\sum_{i=1}^{K}\alpha_{i}f_{i}(z) \tag{11}\]  
In Equation (11), \(\alpha_{i}\) is the EOF coefficient of the sound speed profile. The determination method of \(\alpha_{i}\) is  
\[\alpha_{i}=[\alpha_{1}\ \alpha_{1}\ \cdots\alpha_{i}(\text{K})]^{\text{T}} \tag{12}\]  
where \([\alpha_{1}\ \alpha_{2}\ \cdots\alpha_{i}\ \cdots\ \alpha_{M}]_{\text{K}\times M}=A_{ \text{K}\times M}\), \(A_{\text{K}\times M}=\frac{\Delta C}{F_{N\times K}}\).  
Equation (11) can be used to represent the values of each sound velocity profile retrieved in the SSP. Normally, the sound velocity profile can be well inverted by selecting 3 to 6 orders of the EOF [23].
# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
## 3 The Affinity of SERS-Active Substrates for Biochemical Molecules  
Currently, there is a wide range of solid and dispersed substrates used as SERS enhancement media ranging from roughened metals to thin metal films to colloidal NPs. Colloids based on coinage metals--Ag and Au--are predominantly used in bioanalytical science partly due to ease of preparation and modification, low-cost, high stability and large EF [50]. Also, Ag and Au nanomaterials exhibit naturally high affinity for molecules that possess highly electronegative or charged atoms (e.g., oxygen, nitrogen, sulfur, etc.). Interestingly, numerous biomarkers such as metabolites, nucleic acids and proteins (the reactants, intermediates and end products of genetically encoded processes), contain one or more electronegative atoms and polarisable delocalised \(\pi\)-conjugated system and so they have strong SERS activity [51; 52]. Moreover, since molecular symmetry properties change when molecules are bonded to the NP surface, centrosymmetric biomolecules are detectable by SERS. Thus, SERS has unlocked new prospects to apply unique diagnostic information obtained from symmetrical biomarkers, which would otherwise not be amenable by Raman or Fourier-transform infrared (FT-IR) spectroscopies, according to the mutual exclusion principle [52].  
In general, there are two approaches to accomplish SERS measurements namely, label-free and label-based techniques as shown in Figure 1. Label-free or intrinsic SERS measures direct interactions between analytes and NPs [53]. The resultant spectral bands provide detailed intrinsic structural information and dynamics in biomolecules directly attached to NPs. By contrast, label-based or extrinsic SERS combines optical activity of plasmonic materials (Ag, Au, Cu, etc.) functionalised with SERS-active messenger molecules (so-called Raman'reporters'), which are resonant with a wide range of available excitation lasers [30]. The recognition element, e.g., antibody, enzyme, aptamer, etc., attached to NP surface binds to epitope(s) of specific target analytes (e.g., a metabolite, nucleic acid or bacterium) and its plasmonically enhanced characteristic SERS signal is measured indirectly through the Raman reporter. When several biocompatible recognition elements are employed, extrinsic SERS offers quantitative multiplexed analysis of biomarkers in complex fluid matrices.
## 3 Distributed Estimation of Laplacian Spectrum
### Distributed Estimation of Non-Zero Laplacian Eigenvalues
#### 3.1.1 Implementation of Decoupled Nonlinear Problems  
Firstly, in order to solve the decoupled NLP (7) for \(t=1,\ldots,h\), the Augmented Lagrange method is applied here. Hence, we introduce the Augmented Lagrange function with the Lagrange multiplier \(\mathbf{\beta}\) and penalty parameter \(\mathbf{c}\) as below:  
\[\mathbf{H}_{1,t}=\frac{1}{2}\mathbf{a}_{t}^{T}\mathbf{L}\mathbf{\alpha}_{t}+\mathbf{ \lambda}^{T}(\mathbf{\alpha}_{t}-\mathbf{y}_{t})+\frac{\rho}{2}\|(\mathbf{\alpha}_{t}- \mathbf{y}_{t})\|_{\mathbf{\Sigma}_{t}}^{2}+\beta^{T}(\mathbf{x}(h)-\bar{ \mathbf{x}})+\frac{c}{2}\|\mathbf{x}(h)-\bar{\mathbf{x}}\|_{2}^{2} \tag{14}\]  
The solution of this problem can be obtained by applying a gradient descent method iteratively:  
\[\mathbf{\alpha}_{t}[k+1]=\mathbf{\alpha}_{t}[k]-b\frac{\partial\mathbf{H}_{1,t}}{ \partial\mathbf{\alpha}_{t}} \tag{15}\]  
\[\mathbf{\beta}[k+1]=\mathbf{\beta}[k]+c(\mathbf{x}(h)-\bar{\mathbf{x}}) \tag{16}\]  
where \(b\) stands for stepsizes of the gradient descent method, which can be chosen by fix constants or be determined by deploying a line-search algorithms such as Wolfe Condition, Back-stacking, or Armijo ones in [28], while \(c\) dedicates for the penalty parameter of the Augmented Lagrange method.  
**Lemma 2**.: _The derivatives of this Lagrange function (14) is obtained as follows:_  
\[\frac{\partial\mathbf{H}_{1,t}}{\partial\mathbf{\alpha}_{t}}=\mathbf{L}\mathbf{ \alpha}_{t}+\mathbf{\lambda}+\rho\mathbf{\Sigma}_{t}(\mathbf{\alpha}_{t}-\mathbf{y}_{t})- diag^{-1}(\mathbf{\alpha}_{t})diag(\mathbf{x}_{t-1}-\mathbf{x}_{t})\mathbf{\delta}_{t}- diag^{-1}(\mathbf{\alpha}_{t})diag(\mathbf{x}_{t-1}-\mathbf{x}_{t})\mathbf{e}_{t} \tag{17}\]  
_where \(\mathbf{\delta}_{h}=\beta\), and \(\mathbf{\delta}_{t}=\mathbf{W}_{t+1}\mathbf{\delta}_{t+1}\), while \(\mathbf{e}_{h}=\mathbf{x}(h)-\bar{\mathbf{x}}\) and \(\mathbf{e}_{t}=\mathbf{W}_{t+1}\mathbf{e}_{t+1}\)._  
The proof is showed in the Appendix A.
# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 2 Materials and Methods
### Development of a Neural Network  
For all three tasks (three datasets), sequential-type models were implemented in the Python programming language using the keras library, as well as the sklearn library. Programmatically, the model was created by declaring the Sequential() model and sequentially adding layers through model.add using the Dense module from keras.layers. All three neural network models are aimed at solving multiple input factor regression problems with one or more dependent variables.  
The task of the designed neural network for the dataset \"Traffic\" is predicting the values of the dependent variable characterizing the travel time in a loaded network from independent variables characterizing the influence of 32 values of traffic factors.  
The neural network has 1 input layer with 32 neurons equal to the number of independent variables, 1 hidden layer with 40 neurons in it, and 1 output layer with 1 neuron equal to the number of independent variables. The model training parameters were chosen experimentally and are equal to the following values: loss ='mean_squared_error', optimizer = 'adam', batch_size = 20, epochs = 50. The hidden layer activation function is reluThe task of the designed neural network for the dataset \"Mobility\" is forecasting the values of dependent variables characterizing the structure of the mobility of the population of the area, in the amount of 4 items from 50 values of independent variables. The neural network has 1 input layer with 50 neurons equal to the number of independent variables, 1 hidden layer with 60 neurons in it, and 1 output layer with 4 neurons equal to the number of independent variables. The model training parameters were chosen experimentally and are equal to the following values: loss ='mean_squared_error', optimizer = 'adam', batch_size = 20, epochs = 50. The hidden layer activation function is relu.  
The task of the designed neural network for the dataset \"Areas\" is forecasting the values of dependent variables characterizing the share of trips of the population of the area by various modes of transport from the area and back, in the amount of 8 items from 24 values of independent variables. The neural network has 1 input layer with 24 neurons equal to the number of independent variables, 2 hidden layers with 40 neurons each and 1 output layer with 8 neurons equal to the number of independent variables. The training parameters of the model were chosen experimentally and are equal to the following values: loss ='mean_squared_error', optimizer = 'adam', batch_size = 20, epochs = 50. The activation function for the 1 st hidden layer is tanh, and that for the 2 nd hidden layer is relu.  
The architectures of neural networks developed for the conditions of this study are shown in Figure 2.
## 3 Results and Discussion
### Vapour Pressures and Vaporisation/Sublimation Enthalpies of Alkylbenzenes

Table 2: Compilation of the enthalpies of vaporisation, \(\Delta_{1}^{\mathbf{S}}H_{\mathbf{m}}^{\mathbf{O}}\) for the tert-butyl-benzene derivatives determined in this work and from the data available in the literature. temperatures available from the literature [30]; \(S_{x}\) = derived from correlation of vaporisation enthalpies with the solvent-accessible surface (see text); b Vapour pressures available in the literature were treated using Equations (3)-(5) with the help of heat capacity differences from Table S1 to evaluate the enthalpy of vaporisation at 298.15 K. Uncertainties in the vaporisation enthalpies \(U(\Delta_{\text{cr}}^{\text{S}}H_{\text{m}}^{\text{o}})\) are the expanded uncertainty (0.95 level of confidence). They include uncertainties from the fitting equation and uncertainties from temperature adjustment to \(T\) = 298.15 K. Uncertainties in the temperature adjustment of vaporisation enthalpies to the reference temperature \(T\) = 298.15 K are estimated to account for 20% to the total adjustment; c Weighted mean value (uncertainities were taken as the weighting factor). Values in bold are recommended for further thermochemical calculations. The tables for both iso-propyl-benzenes and tert-butyl-benzenes can create the first impression that these compounds have been sufficiently studied. However, this is not correct as most of the entries come from our validation of structure property relationships described in the next section. In fact, in both series, only di-alkyl-substituted benzenes have been extensively studied, and the tri- and poly-alkyl-substituted benzenes have received significantly less attention. Three main methods have been used for vapour pressure measurements of alkylbenzenes: transpiration, static and ebuliometry. These methods are applied for a specific temperature range: transpiration is generally used below 383 K, the static method can cover a wider temperature range from room temperature up to 450 K (using multiple gauges) and ebuliometry usually works up to boiling temperature. The vaporisation/sublimation enthalpies were not always of interest in the original work and even when these values were derived using the Clausius-Clapeyron equation they were not comparable as they referred to different average temperatures. It should be noted that a systematic development of suitable methods for temperature adjustment of vaporisation enthalpies to the reference temperature \(T\) = 298.15 K began in the 1990s [40; 41], but even today it is not completely sufficient for proper calculations (see further information in ESI). Since the vaporisation/sublimation enthalpies at the reference temperature are the focus of this study, we carefully analysed the \(p\)-\(T\) data available in the literature to derive the \(\Delta_{\text{cr}1}^{\text{S}}C_{p,m}^{\text{o}}\)-values specific to the alkylbenzene series (see Table S2). Our complementary
## 3 Data Analysis Method  
### Basic Procedure  
Based on the dataset obtained from the unsteady CFD numerations, a program for data analysis, mining, and visualization was developed using Matlab R2022a software. A flowchart of the program is illustrated in Figure 7.  
As shown in Figure 7, the initialization of the data analysis environment is carried out first. The numerical analysis results under various flow conditions are read in the form of data tables to construct a raw dataset, with the dataset generation explicitly incorporating CFD model and test validation components (see Sections 2.2-2.5) as foundational inputs. The data analysis is conducted within the Exploratory Data Analysis (EDA) framework [36]. That is, functions such as indexing and screening are first used to extract specific data fields, and the basic characteristics of the data are observed through simple visual plotting. Subsequently, data aggregation is performed, where new fields are added and specific fields are exported according to the research requirements, further enriching the data structure. Finally, data analysis and mining are carried out, including applying Fast Fourier Transform (FFT) analysis to explore the frequency characteristics of relevant parameters and using trigonometric function fitting to uncover the potential variation patterns of certain variables.  
Figure 7: Flowchart of the data analysis and mining program.  
When the data analysis is completed, the data are visualized for better understanding, with a new module composition law and application enlightenment appended to the workflow to highlight the derivation of physical insights and their specific engineering implications.  
It should be noted that the source code in the Supplementary Materials of this paper covers the entire process of data analysis, including data visualization, to facilitate its invocation and analysis by researchers and engineering designers. While the source program provides data visualization plots, it also separately exports the original data involved in the plotting, thus enabling the use of third-party tools for independent plotting or other analytical processing of the data.