esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Grey Relational Analysis  
Grey relational analysis was used to study the internal relationship between the properties of the load-caused cracking resistance and temperature shrinkage cracking of the SBS/CRM-modified asphalt mixtures under low temperature. As one of the Grey system analysis methods, the Grey relational analysis method measures the correlation degree between factors according to the similarity or dissimilarity of the development situation among factors (Grey relational degree) [20].  
As the constrained test piece temperature strength test is one of the best evaluation methods for evaluating the low temperature performance of SMA in the engineering field [21], the freezing fracture temperature and strength of the SMA were taken as reference series in this study. The bending beam test indexes for evaluating vehicle load cracking were taken as comparison series. Grey relational degree analysis was carried out on the bending fracture strain (X1-X3) and stiffness (X4-X6) of SMA at different test temperatures. The results are shown in Table 11 and Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence** & **Properties** & **Freezing Fracture Temperature** & **Freezing Fracture Strength** \\ \hline X1 & Bending tensile strain at \(-10\)\({}^{\circ}\)C & 0.804 & 0.679 \\ X2 & Bending tensile strain at \(-5\)\({}^{\circ}\)C & 0.807 & 0.688 \\ X3 & Bending tensile strain at \(0\)\({}^{\circ}\)C & 0.776 & 0.676 \\ X4 & Bending stiffness modulus at \(-10\)\({}^{\circ}\)C & 0.775 & 0.662 \\ X5 & Bending stiffness modulus at \(-5\)\({}^{\circ}\)C & 0.716 & 0.638 \\ X6 & Bending stiffness modulus at \(0\)\({}^{\circ}\)C & 0.700 & 0.634 \\ \hline \hline \end{tabular}
\end{table}
Table 11: SBS/CRM-modified SMA performance index at low temperature.  
Figure 6: Bending fracture stiffness affected by NMS.  
It can be observed from Figure 7 that the correlation degree between freezing fracture temperature and low temperature bending beam test in the temperature-strength test of constrained specimens is the best. Both are above 0.7, which was greater than the correlation coefficient of freezing fracture strength, indicating that freezing fracture temperature can better characterize the crack resistance of SMA under a low temperature load. Secondly, the correlation degree between bending fracture strain and freezing fracture temperature at three different temperatures was about 0.8. The correlation degree of the stiffness at each temperature was lower than that of the bending fracture strain, indicating that bending fracture strain was more suitable than stiffness to characterize the low temperature performance of the SMA with hybrid modification. In addition, the variation trend of the correlation degree between freezing fracture strength and low temperature bending beam test was basically similar to that of the freezing fracture temperature. The correlation degree between freezing fracture strength and bending stiffness at 0 \({}^{\circ}\)C is poor, only 0.634. It is consistent with the analysis results of stiffness above.
## 3 Proposed Methodology
### Data Pre-Processing
#### 3.2.2 Segmentation  
Segmentation is a crucial process that involves dividing a signal into smaller sections or windows. In our research, we utilize segmentation for two primary purposes. The first challenge we encounter is the variability in the captured CSI signals, which can differ in length and belong to different subjects. This variability hinders the identification procedure. The second challenge relates to the temporal aspect of processing an extensive volume of CSI data, which requires significant time and computational resources.  
To address these challenges effectively, our study adopts a predetermined window size. This window size allows us to partition the denoised CSI signal into multiple smaller signals. By doing so, we can treat each small signal as an independent instance during the training phase of the CNN-GRU-AttNet model. This approach not only enhances efficiency but also improves the accuracy of our results.  
Figure 2: Some CSI signal representations from CSI-HAR dataset after pre-processing: (**a**) before denoising; (**b**) after denoising.
## 2 Analytical Properties of 5-Point-Window \(L_{1}\) Splines  
The splines that we will consider in this paper are calculated locally as described in this paragraph. For the interpolation problem under consideration in the present paper, the function values are given. In the 5-point window with middle point \(x_{i}\), \(2\leq i\leq I-2\), the derivative at \(x_{i}\) is calculated by minimizing  
\[\int_{x_{i-2}}^{x_{i+2}}\left|\dfrac{\text{d}^{2}z}{\text{d}x^{2}}\right|\, \text{d}x \tag{3}\]  
over the finite-dimensional spline space of \(C^{1}\) piecewise cubic polynomials \(z\) that interpolate the data. The free parameters in the minimization of functional (3) are the derivatives \(b_{i}\) of the spline at the five nodes. The derivative at node \(x_{i}\) that occurs at the minimum of functional (3) is denoted by \(b_{i}^{*}\). Whenever the minimum of functional (3) is nonunique, we choose \(b_{i}^{*}\) to be the scalar in the optimal set (the interval \([b_{i}^{l},\,b_{i}^{u}]\)) closest to the slope \(\delta_{i}\) of the chord between the neighboring points, that is, median\(\{b_{i}^{u},b_{i}^{l},\delta_{i}\}\). Previously, nonuniqueness was resolved by \"regularization\" of the spline functional, specifically, by adding to the spline functional (3) a sum consisting of the absolute values of various expressions involving the derivatives at the nodes times a sufficiently small number \(\varepsilon\) (_cf._[5, 9, 16]). The method for resolving nonuniqueness that we use in the present paper differs from the regularization approach used in previous \(L_{1}\) spline work but leads to both simpler analysis and simpler computational procedures. The derivatives at the points \(x_{0}\) and \(x_{1}\) are determined by \(b_{2}^{*}\) which is calculated by minimizing (3) for \(i=2\). Analogously, the derivatives at the points \(x_{I-1}\) and \(x_{I}\) are determined by \(b_{I-2}^{*}\) which is calculated by minimizing (3) for \(i=I-2\). After obtaining all of the \(b_{i}^{*}\), a \(C^{1}\) piecewise cubic interpolant \(z\) is set up by Hermite interpolation  
\[z(x)=z_{i}+b_{i}^{*}(x-x_{i})-\frac{1}{h_{i}}(2b_{i}^{*}+b_{i+1}^{*}-3\triangle z _{i})(x-x_{i})^{2}+\frac{1}{h_{i}^{2}}(b_{i}^{*}+b_{i+1}^{*}-2\triangle z_{i} )(x-x_{i})^{3} \tag{4}\]  
for \(x\in(x_{i},x_{i+1})\), \(i=0,\ldots,I-1\) (_cf._[9]). The \(C^{1}\) piecewise cubic interpolant calculated in this manner is the \(L_{1}\) spline (locally calculated cubic \(L_{1}\) spline). In the remainder of this section, we investigate the relation between the geometry of the 5 points in each window and the derivative at the middle point of the window.
## 2 Problem Definition
### Intersatellite Links for SPDOP  
ISLs are considered crucial for enhancing the accuracy of orbit determination and satellite clock estimation [30]. This is improved using a combination of GS observations and ISLs, which eventually enhance the precision of navigation without supplementary GS network augmentation [31; 32; 33]. In this study, ISLs are used to improve SPDOP, which is a criterion of the precision of orbit determination [18; 29]. The specific observation model, when only the satellite position parameters are considered, is the geometric range between satellite \(i\) and satellite \(j\), which can be expressed as  
\[\rho^{ij}=\sqrt{\left(X^{i}-X^{j}\right)^{2}+\left(Y^{i}-Y^{j}\right)^{2}+\left( Z^{i}-Z^{j}\right)^{2}}, \tag{12}\]  
where \((X^{i},Y^{i},Z^{i})\) and \((X^{j},Y^{j},Z^{j})\) are the three-dimensional coordinates of satellite \(i\) and satellite \(j\), respectively.  
Using Equations (7) and (12), the coefficient matrix \(\mathbf{A}_{ISL}\) of the observation equation for satellite \(i\) with GSs observations and ISLs can be expressed as [29]  
\[\mathbf{A}_{ISL}=\begin{bmatrix}cx_{1}^{i}&cy_{1}^{i}&cz_{1}^{i}\\ cx_{2}^{i}&cy_{2}^{i}&cz_{3}^{i}\\ \vdots&\vdots&\vdots\\ cx_{m}^{i}&cy_{m}^{i}&cz_{m}^{i}\\ c\tilde{x}_{1}^{i}&c\tilde{y}_{1}^{i}&cz_{1}^{i}\\ \vdots&\vdots&\vdots\\ c\tilde{x}_{n}^{i}&c\tilde{y}_{n}^{i}&cz_{n}^{i}\\ \end{bmatrix}, \tag{13}\]  
where \(cx_{k}^{i}\), \(cy_{k}^{i}\), \(cz_{k}^{i}\) is the direction cosine from the satellite \(i\) to the \(k^{th}\) ground station (\(k=[1,\ldots,m]\)), \(m\) is the total number of GSs that are visible to satellite \(i\) at a certain epoch, \(c\tilde{x}_{l}^{i}\), \(c\tilde{y}_{l}^{i}\), \(c\tilde{z}_{l}^{i}\) is the direction cosine from satellite \(i\) to satellite \(l\) (\(l=[1,\ldots,n]\)), and n is the total number of satellites that are visible along with the \(i^{th}\) satellite at a certain epoch. Therefore, the cofactor matrix \(\mathbf{Q}_{ISL}\) of the observation matrix \(\mathbf{A}_{ISL}\) can be expressed as  
\[\mathbf{Q}_{ISL}=\left(\mathbf{A}_{ISL}^{T}\mathbf{A}_{ISL}\right)^{-1}= \begin{bmatrix}\tilde{q}_{11}&\tilde{q}_{12}&\tilde{q}_{13}\\ \tilde{q}_{21}&\tilde{q}_{22}&\tilde{q}_{23}\\ \tilde{q}_{31}&\tilde{q}_{32}&\tilde{q}_{33}\\ \end{bmatrix}. \tag{14}\]  
Therefore, SPDOP with GS and ISL observations can be expressed as [18; 29]:  
\[SPDOP_{ISL}=\sqrt{\tilde{q}_{11}+\tilde{q}_{22}+\tilde{q}_{33}}. \tag{15}\]
###### Abstract  
The extent of the feathers covering a bird's body and the condition of the plumage largely determine the bird's well-being and aesthetic condition. This investigation aimed to compare changes in the plumage status of laying hens in dissimilar housing systems in the egg-laying period. The experiment was carried out with 1460 non-beak-trimmed laying hens, provided by Babolna TETRA Ltd., representing a part of the Rhode-type elite lines (Rhode Island Red (RIR) and Rhode Island White (RIW)) used for breeding brown layer hybrids. Hens were kept in the closed experimental laying house of MATE University (Kaposvar Campus), where the RIR and RIW hens were placed into three types of housing systems: EU standard furnished cage (EU) (7560 cm\({}^{2}\); 10 hens/cage; 756 cm\({}^{2}\)/hen); Alternative pen (A) (5.52 m\({}^{2}\), floor and litter combination, 53 hens/pen; 1040 cm\({}^{2}\)/hen); and Conventional cage (C) (3780 cm\({}^{2}\); 6 hens/cage; 630 cm\({}^{2}\)/hen). The feathering status of the hens was examined at five body parts: neck, breast, wings, back and tail at 33, 45 and 61 weeks of age, which was evaluated on a four-grade scale by the same experienced score, and the cumulated plumage point (5-20) was calculated for each hen. The RIW genotype had a better plumage condition than RIR at each of the examined times (at the 33 rd week: 19.0, 18.3; at the 45 th week: 18.4, 17.4; at the 61 st week: 17.2, 15.5). The status of the plumage of the RIR hens had deteriorated to a greater extent. As age progressed, the plumage condition of both genotypes significantly deteriorated in all the three housing technologies. The relatively high level of deterioration can be partly explained by the fact that the animals were non-beak-trimmed. At all test times, the total feather score of the birds in the alternative cage housing was significantly better (at the 33 rd week: EU: 18.4, A: 19.1, C: 18.5; at the 45 th week: EU: 17.6, A: 18.6, C: 17.4; at the 61 st week: EU: 15.9, A: 16.9; C: 16.1). The condition of the neck plumage was better in group RIW than in RIR, and the best in the alternative housing at each test time. At the end, the high decomposition results were striking, since at the beginning there were values close to the maximum (3.98; 3.99). It can be concluded that the reason for the dominantly favorable aivars results is that the hens in this type of housing have to come into contact with fewer mechanical factors, their companions do not jump on their backs when changing places, they do not have to stick their heads out of the grid to collect feed, etc. In addition to all this, they also had a littered scratching area and a nest lined with artificial grass, where they could scratch and take a dust bath.  
hen; beak trimming; plumage; egg production; mortality; housing system +
Footnote †: journal: Applied Sciences
## 3 Results
### Assessment of Physicochemical Properties and Morphology of the Printing Substrate
#### 3.1.3 Thickness and Moisture Content of the Printed Substrate  
The thickness and moisture content of the gum film are crucial properties of printing substrates. An adequate printing substrate should be thick enough to resist a bending force during the printing process. However, too thick of a substrate might retard film disintegration. Furthermore, film moisture acts as a plasticizer to improve film flexibility. Nevertheless, too high of a moisture content might affect film stability and ink deposition efficiency [6]. As presented in Table 2, the thickness of the prepared printing substrate was between 106.56 um and 151.33 um. The substrate thickness significantly rose when the gum concentration increased. The thickest gum substrate was that of 5% gum and the gum:sorbitol ratio of 97:3. On the other hand, an increment of sorbitol in the formulation did not alter any film thickness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gum Concentration (\% _w/w_)** & **Sorbitol Percent from Gum** & **Moisture Content (\%)** & **Thickness (\(\mu\)m)** & **Disintegration Time (\(\mu\)m)** \\ \hline
3.5 & 0 & 9.30 \(\pm\) 1.04 & 109.11 \(\pm\) 23.24 & 19.31 \(\pm\) 0.88 \\
3.5 & 1 & 9.29 \(\pm\) 0.25 & 106.56 \(\pm\) 30.63 & 11.16 \(\pm\) 1.76 \\
3.5 & 3 & 8.31 \(\pm\) 0.36 & 106.89 \(\pm\) 10.47 & 8.14 \(\pm\) 0.98 \\
3.5 & 5 & 7.49 \(\pm\) 0.62 & 109.11 \(\pm\) 18.74 & 9.33 \(\pm\) 0.13 \\
4 & 3 & 9.40 \(\pm\) 1.12 & 117.22 \(\pm\) 21.28 & 17.57 \(\pm\) 3.24 \\
4.5 & 3 & 9.71 \(\pm\) 1.57 & 131.33 \(\pm\) 30.71 & \textgreater{}30 \\
5 & 3 & 9.38 \(\pm\) 0.32 & 151.33 \(\pm\) 18.21 & \textgreater{}30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Moisture content, film thickness, and disintegration time of different printing substrate formulations.  
Figure 3: Scanning electron microscope images of printing substrates prepared from different concentrations of sorbitol and gum, and also comparison of (**a**) non-printed, (**b**) printed substrate surfaces and (**c**) a cross-sectional view of the printed substrate at 3.5% gum and 3% sorbitol.  
Film moisture content ranges from 7.49% to 9.40% as shown in Table 2. An increase in the gum concentration did not affect the moisture content; however, high sorbitol portions lowered the film's moisture content. This result is consistent with a previous study by [PERSON] et al. where they explained that the resemblance of the high molecular structure of glucose units with that of sorbitol caused stronger molecular interactions between the sorbitol and the intermolecular polymer chains. Therefore, the chances of sorbitol interacting with water molecules decreased [30].
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 3 Double Panel Air Cavity, Sandwich and Composite Structures
### Sandwich Composite Structures  
The use of sandwich composite panels with anisotropic properties has been adduced to give higher STL than those with isotropic ones [66]. Sandwich composite structures are particularly suited in the aerospace industry owing to their ability to protect materials and equipment in the aircraft from external noise that could cause their damage. Apart from the good noise reduction behavior of these structures, they also exhibit higher stiffness, higher strength and lower density than their metallic counterpart. The shape of the composite panels investigated by researchers are typically made flat, cylindrical or curved as previously mentioned. However, no sufficient literature has been found to compare the effect of the different geometrical shapes on the STL of sandwich composite structures. For sandwich structures made with metallic skins, [PERSON] et al. [67] proposed a methodology for solving the  
Figure 4: Illustration of a unit cell sandwich core support structure for curved and flat plates [48] (reproduced with permission from [PERSON] et al. Polymer Testing; published by Elsevier, 2018).  
STL of periodic flat, cylindrical and curved DPS under aerodynamic and acoustic excitation. Moreover, poroelastic, viscoelastic and aeroelastic materials have been used in the core to further improve their vibro-acoustic properties.
## 4 Example
\begin{array}{c}\overline{\theta}(\xi,\tau)=\frac{\pi}{L^{\tau}}\bigg{\{} \sum\limits_{n=1}^{\infty}2e^{-(h+\frac{\omega_{n}^{2}\cdot 2}{L^{2}})t^{\tau}\tau}\cos(n\pi\xi)J(n,\tau)+e^{-ht^{ \tau}\tau}J(0,\tau)\bigg{\}},\\ J(n,\tau)=\int\limits_{0}^{\tau}e^{(h+\frac{\omega_{n}^{2}\cdot 2}{L^{2}})t^{ \tau}\eta}\Big{(}-\frac{Q}{P_{A}}\rho_{1}(\eta)+(-1)^{n}\frac{Q}{P_{A}}\rho_{2 }(\eta)\Big{)}d\eta.\end{array}  \tag{38}\]  
The accuracy of the solution of Equation (36) with \(n_{1}=n_{2}=7\) and 9 terms of Equation (38) taken into account: \(\Delta\approx 0.001\). The simulation results in MATLAB for \(n_{1}=n_{2}=7\) and constant heat transfer coefficient are shown in Figure 2.  
Let us consider the case when \(\alpha(t)\) is time variable:  
\[\alpha(t)=5.82+8.73\cdot\left\{ \begin{array}{cc}2t,&t\in(0,\,0.5t^{*}]\;, \\ 2(1-t),&t\in(0.5t^{*},\,0.75t^{*}]\;,\\ 0.5,&t\in(0.75t^{*},\,t^{*})\;.\end{array} \right. \tag{39}\]In this case the operating matrix \(P_{k}^{\tau}\) is defined as:  
\[P_{k}^{\tau}=\left[ \begin{array}{cccccc}8.12&0.58&-0.72&-0.40&0.25&\cdots\\ 0.58&7.99&-0.53&-0.87&-0.14&\cdots\\ -0.72&-0.53&7.83&-0.27&-0.95&\cdots\\ -0.40&-0.87&-0.27&7.75&-0.37&\cdots\\ 0.25&-0.14&-0.95&-0.37&7.75&\cdots\\ \vdots&\vdots&\vdots&\vdots&\vdots&\ddots\end{array} \right]. \tag{40}\]  
The simulation results for \(n_{1}=n_{2}=7\) and variable heat transfer coefficient are shown in Figure 3.  
This method is applicable for the heat conduction in case of two or three spatial variables, with mixed type boundary conditions and a time-dependent heat transfer coefficient. Figure 3: Temperature distribution graph is case of time-variant \(\alpha\). Figure 2: Temperature distribution graph is case of constant \(\alpha\).
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Data Integration and Interoperability
#### 5.1.2 Existing Solutions  
Currently, several methods are available to achieve data integration and interoperability. For example, the Artificial Intelligence Modern Data Platform (AIMDP) integrates the core features of the modern data platform with data science capabilities to handle various data types. In practice, this platform can manage both structured data (e.g., electronic health records) and unstructured data (e.g., medical images). For instance, in a large healthcare institution, AIMDP can integrate data from different departments, such as laboratory results, patient monitoring data, and clinical notes. By utilizing its experimentation and knowledge extraction modules, the platform helps clinicians extract valuable insights from integrated data, thereby optimizing patient treatment plans [94]. The second method involves transforming available data into data with similar properties and structure. This can be achieved by developing a data harmonization pipeline that adheres to the common FHIR data standard. The process includes querying data from the hospital database, performing FHIR mapping, conducting syntactic validation, transferring harmonized data into a patient-model database, and exporting data in an AI-friendly format. For example, in diabetes management, a hospital can consolidate patient blood glucose data, weight, and dietary records from various sources into a unified FHIR standard. This ensures that the data can be consistently used across different medical applications, enhancing the personalization and accuracy of treatment [95]. The third method uses Health Level 7 FHIR as a health data content modeling and exchange standard. This involves extracting health data from various sources, converting them into a standardized FHIR format, and ensuring data consistency and interoperability. For example, in a cross-regional healthcare network, hospitals can share patient medical records using the FHIR standard, facilitating seamless information exchange. A specific instance is in cancer treatment, where genetic information, treatment history, and current clinical data can be integrated through FHIR standards, allowing specialists across different hospitals to access comprehensive patient information on a unified platform and devise the best treatment plans [96].
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
### Related Works
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Authors** & **Year** & **Objective** & **Technologies** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline [PERSON] [9] & 2008 & RFID tag sticks to cigarettes and taxes & RFID & N & Y & N & N & Y & N \\ \hline [PERSON] et al. [11] & 2011 & RFID tag integration into a cigarette pack & RFID & N & Y & N & N & Y & N \\ \hline [PERSON] et al. [8] & 2012 & Deploy RFID to fashion supply chains & RFID & N & N & Y & Y & Y & N \\ \hline [PERSON] et al. [12] & 2012 & Track and trace system for supply chains & RFID & N & N & Y & Y & Y & Y \\ \hline [PERSON] et al. [13] & 2012 & Logistics vehicle load balancing and tracking & RFID, GPS, & & & & & & & \\  & & & mechanism & GSM & & N & Y & Y & Y & N \\ \hline [PERSON] et al. [14] & 2016 & Tobacco logistics retroactive system & RFID, database & N & Y & Y & Y & Y & N \\ \hline [PERSON] et al. [15] & 2018 & Financial service platform for tobacco supply chain & Blockchain & Y & Y & Y & N & N & N \\ \hline [PERSON] et al. [16] & 2020 & Smart logistics and transportation using IoT and & Blockchain & Blockchain, IoT & Y & N & Y & Y & N & N \\ \hline \hline \end{tabular}  
* Notes: 1: Focus on a blockchain, 2: Focus on tobacco products, 3: Proposing an architecture or framework, 4: Focus on logistics, 5. RFID/GPS-enabled, 6: Security analysis, Y: Yes, N: No.  
\end{table} 
Table 1: Survey of related works. this research, we applied blockchain, RFID, and GPS technology to achieve the complete tobacco products' logistics system. The remaining sections of this paper are organized as follows. Section 2 briefly introduces the technologies that are used in our proposed scheme. Section 3 proposes our scheme. Section 4 analyzes the security issues. The computation cost, communication performance, and comparison discussion are given in Section 5. Finally, Section 6 concludes this paper.