esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Material Models  
Three material models were employed for the three separate material components simulated in this work, namely the TNT explosive, the surrounding air, and the Lexan structural barrier. Only the TNT and air appear in the free-field simulations, and only the air and Lexan appear in the blast mitigation simulations. For the explosive TNT, a simple Jones-Wilkens-Lee (JWL) equation of state [28] is used:  
\[P(v,e)=A\bigg{(}1-\frac{\omega}{R_{1}v}\bigg{)}\exp(-R_{1}v)+B\bigg{(}1-\frac {\omega}{R_{2}v}\bigg{)}\exp(-R_{2}v)+\frac{\omega}{v}e \tag{2}\]  
Here, \(P\) is the pressure, \(v=V/V_{0}=\rho_{0}/\rho\) the relative volume, and \(e\) is the material energy per reference volume. \(V,\rho\) are the volume and density, respectively, while \(V_{0},\rho_{0}\) are the initial (reference) values of these properties. The parameter \(\omega\) is the Gruneisen coefficient; \(A,B,R_{1}\), and \(R_{2}\) are free parameters. \(\omega,R_{1}\) and \(R_{2}\) are dimensionless, while \(A\) and \(B\) have units of pressure. The parameter values used in simulations for Equation (2) are given in Table 1.  
The equation of state of air is given by a simple Gamma-law:  
\[P(\rho,e)=(\gamma-1)\frac{\rho}{\rho_{0}}e \tag{3}\]  
The only free parameter \(\gamma\) is dimensionless and typically has a value of 1.4 for air. The initial (atmospheric) pressure \(P_{0}\) is obtained through Equation (3) by prescribing and initial energy per unit volume:  
\[e_{0}=\frac{P_{0}}{\gamma-1}\]  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline _A (kPa)_ & _B (kPa)_ & _R_\({}_{1}\) & _R_\({}_{2}\) & _\(\rho_{0}\)_(g/cm\({}^{3}\))_ & _\(\omega\)_ \\ \hline
3.712 \(\times 10^{8}\) & 3.231 \(\times 10^{6}\) & 4.150 & 0.950 & 1.630 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: JWL parameters for TNT.  
The parameter values used in simulations for Equation (3) are given in Table 2.  
The Lexan mitigation barrier is modeled using a power law constitutive model:  
\[\sigma =k(\epsilon_{0}+\epsilon)^{y_{c}}, \tag{4}\] \[\epsilon_{0} =\left(\frac{E}{k}\right)^{\frac{1}{y_{c}-1}} \tag{5}\]  
Here, \(\sigma\) is the current yield stress and \(\epsilon\) an equivalent plastic strain. \(\epsilon_{0}\) is an initial yield strain determined by parameters \(k,E\) and \(y_{c}\). \(E\) is a standard Young's modulus with dimensions of pressure, \(k\) the yield stress coefficient with dimensions of pressure, and \(y_{c}\) is a dimensionless strain-hardening coefficient. An additional equation of state relates pressure \(P\) to the bulk modulus \(K\) and the relative volume \(v\):  
\[P=K\mu \tag{6}\]  
Here, \(\mu=(1/v)-1\), and the bulk modulus is derived from the Young's modulus and Poisson ratio \(\
u\):  
\[K=\frac{E}{3(1-2\
u)}\]  
The values used for Lexan in the present work are given in Table 3. Given the nature of the low pressure incident waves studied in this paper, only small (elastic) deformations of the barrier are expected. Therefore the values used for the barrier material are not expected to have significant effect on the analysis results.
## 3 Results
### Determination of Pseudo-Activation Energy  
The pseudo-activation energies of decomposition (EAs) for the investigated samples were determined using the Kissinger and Ozawa methods (Table 2), using ignition/explosion temperature measurements (Figures A2-A13). Data analysis indicates that the addition of NC-KNO\({}_{3}\) to the respective materials significantly increases their the  
Figure 4: SEM analysis of AN for MNC fibres (**left**) and AN for MNC-KNO\({}_{3}\) fibers (**right**).  
Figure 3: SEM analysis of AN for NC fibres (**left**) and AN for NC-KNO\({}_{3}\) fibres (**right**).  
mal resistance. The most pronounced changes were observed for the NC sample, whose activation energy was 122 kJ/mol (Kissinger) and 123 kJ/mol (Ozawa), while for the KNO\({}_{3}\) sample, these values increased to 269 kJ/mol and 263 kJ/mol, respectively. This indicates a considerable enhancement of the thermal stability of the system in the presence of the oxidising agent.  
A similar trend, although less pronounced, was observed for the MNC and ANNC samples, where the EA values also increased, further confirming the stabilising effect of the additive. For example, the MNC sample showed an activation energy of 110 kJ/mol  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **E\({}_{\text{A}}\)([PERSON]) [kJ/mol]** & **E\({}_{\text{A}}\)([PERSON]) [kJ/mol]** \\ \hline NC & 123 & 123 \\
**NC-KNO\({}_{3}\)** & 269 & 263 \\ MNC & 110 & 131 \\ MNC-KNO\({}_{3}\) & 125 & 126 \\ ANNC & 88 & 92 \\ ANNC-KNO\({}_{3}\) & 111 & 114 \\ ANMNNC & 77 & 82 \\ ANMNNC-KNO\({}_{3}\) & 88 & 92 \\ ANNC-D & 97 & 101 \\ ANNC-KNO\({}_{3}\)-D & 133 & 135 \\ ANMNC-D & 94 & 98 \\ ANMNNC-KNO\({}_{3}\)-D & 129 & 131 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the pseudo-activation energies of decomposition for the investigated samples. [PERSON] and blue row colours respectively denote the use of NC and MNC; lighter colours are used for cellulose nitrates produced using the HNO\({}_{3}\)-based esterifying mixture, darker ones are for the KNO\({}_{3}\)-based mixture.  
Figure 5: SEM-EDS analysis of MNC-KNO\({}_{3}\). The labels give first, the identified element, and second, the line used for identification of this element, as described above.  
([PERSON]) and 131 kJ/mol ([PERSON]), while for MNC-KNO\({}_{3}\), these values increased to 125 kJ/mol and 126 kJ/mol, respectively. For the ANNC sample, the EA values rose from 88 kJ/mol and 92 kJ/mol to 111 kJ/mol and 114 kJ/mol for ANNC-KNO\({}_{3}\), respectively.  
However, samples containing ANMNC exhibit noticeably lower activation energy values, suggesting that the modifications applied reduce the thermal stability of the material and facilitate its decomposition. For ANMNC, the values were 77 kJ/mol ([PERSON]) and 82 kJ/mol ([PERSON]), while for ANMNC-KNO\({}_{3}\), a moderate increase was recorded, reaching 88 kJ/mol and 92 kJ/mol.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 5 Proposed Scheme EIA
### Pseudoidentity Update  
Regularly changing the pseudonyms of vehicles can effectively prevent vehicles from being tracked and personal information leaked. Pseudonymous updates protect the privacy of the vehicles while allowing the vehicles to continue participating in necessary communications and data exchanges without revealing their true identities.  
Step 1: The validity period of each pseudonym is \(\triangle t\). When \(|T-T_{b}|\geq\triangle t\), a pseudonym update is required, where \(T\) represents the current time, \(\triangle t\) is the period of the system parameter, and \(T_{b}\) is approximately equal to the certification end time. Then, the OBU at the vehicle sends \(\{\sigma,ID_{i}A,R_{i}\}\) as the pseudonym update request to the AMF.  
Step 2: Upon receipt of the request, the AMF verifies the legitimacy of the identity by calculating \(\sigma B=h_{1}(ID_{i}||A||T)P+Y_{i}\). After the validation, the AMF embarks on the calculation of \(RID_{i}=ID_{i}\bigoplus h_{1}(s||R_{i})\) and simultaneously selects a new random variable, referred to as \(r^{\prime}_{i}\in Z_{q}^{*}\), by which the AMF proceeds to compute and generate a new pseudonym, identified as \(ID^{\prime}_{i}=RID_{i}\bigoplus h_{1}(s||R^{\prime}_{i})\). In the subsequent steps, the AMF performs the computation of \(Q=h_{1}(y_{i})\bigoplus ID^{\prime}_{i}\) culminating in the dispatch of \(\{Q,T_{c}\}\) to the vehicle. \(T_{c}\) represents the current timestamp. By hashing the ID with the \(y_{i}\), it avoids the transmission of the ID in plaintext and the illegal user obtaining the ID.  
Step 3: After receiving the message \(\{Q,T_{c}\}\), if \(T_{c}\) is not fresh, \(OBU_{i}\) adopts this session. Otherwise, it then computes to generate the new pseudonym \(ID^{\prime}_{i}=Q\bigoplus h_{1}(y_{i})\).
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 4 Experiments  
### Dataset  
We used an open accessed dataset, which comprises nine heart sound databases that were collected by various organizations, provided by PHYSIONET [42]. Due to a large number of tested subjects and long recording time, we choose the database that was collected by the Dalian University of Technology heart sounds database (DLUTHSDB) from these nine sound databases as our experimental dataset. In this dataset, subjects included 174 healthy volunteers and 335 patients with coronary artery disease. PHYSIONET resamples the original waveform at 2 kHz. Figure 2 illustrates three recorded heart sound in the DLUTHSDB database.
# Behavior of Circular Hollow Steel-Reinforced Concrete Columns under Axial Compression
## 5 Parametric Analysis
### Spacing of Stirrup  
Figure 14h plots the load-deflection curves of the circular HSRC columns at various stirrup spacings. Reducing the spacing of the stirrup from 150 mm to 100 mm and 50 mm increases the axial force from 1113.3 kN to 1163.8 kN and 1233.1 kN, respectively, i.e., 4.5% and 10.8%. Changing the spacing of the stirrup only has a particular impact on restraining the longitudinal rebar. However, considering the wrapping restraint provided by the external concrete, it cannot significantly impact the bearing capacity and overall stiffness of the columns under axial compression. Thus, the spacing of the stirrups has no remarkable effect on the bearing capacity and stiffness of the circular HSRC columns.  
It can be concluded that the strength of the concrete, slenderness ratio, diameter of the steel tube, yield strength of the longitudinal rebar, and yield strength of the steel tube dramatically influence the bearing capacity of the circular HSRC columns under axial compression. Additionally, the stiffness of circular HSRC columns is notably affected by the slenderness ratio and the strength of the concrete. The impact of various parameters on the axial compression performance of circular HSRC columns is primarily attributed to the buckling failure of composite columns at different heights under axial compression, as well as the alterations in the strength and cross-sectional area of the constituent materials. The primary factors influencing the initial stiffness of circular HSRC columns under axial compression include the flexural stiffness of concrete, longitudinal rebar, and steel tube, as well as the variation in flexural stiffness resulting from instability and damage caused by changes in the slenderness ratio.
## 1 Introduction
Therefore, EWT is unable to correctly determine the number of separation modes, and thus cannot effectively separate the intrinsic modes from the measured bearing fault signal. To solve these problems, an improved EWT strategy is proposed in this paper. First, a novel envelope demodulation-based EWT (DEWT) is proposed. The signal is decomposed by DEWT, each component is treated as a virtual observation signal, and all components constitute a new multi-channel virtual observation signal. The multi-channel virtual observation signal is then input into singular value decomposition (SVD). The mode number is determined by a method of adjacent singular value difference, according to which the number of DEWT components is selected, and DEWT is performed once more. Finally, the bearing health status is determined via spectrum analysis. The main contributions of this paper are as follows. 1. An envelope demodulation-based strategy for EWT boundary detection is developed. The high-frequency modulation problem of the bearing vibration signal is solved, and the improved EWT can be better used in the field of rolling bearing signal processing. 2. A source number estimation method based on the DEWT and SVD is proposed to determine the number of decomposed modes, which solves the problem of compound bearing fault diagnosis with single-channel acoustic signals. The remainder of this paper is organized as follows. The basic theory of the proposed method is briefly introduced in Section 2. Section 3 describes the proposed method. Experimental validation and method comparison are presented in Section 4. Finally, the conclusions are given in Section 5.
## 2 Methodology and Experiments
### Experiments  
The surface morphology of the fabricated SERS substrate was investigated by using a scanning electron microscope (SEM, JSM-7800F, JEOL Ltd., Tokyo, Japan) and an atomic force microscope (AFM, Bruker Dimension Icon, Bruker Corporation, Billerica, USA). The SERS performances of fabricated substrates were tested with R6G and malachite green solutions. Five microliters of different concentrations of R6G aqueous solution and malachite green solution were separately dropped onto the prepared SERS substrates and allowed to dry at room temperature for Raman spectroscopy measurements. SERS spectra were measured by a microscope Raman spectrometer (Nanofinder 30, Tokyo Instruments, Tokyo, Japan) under excitation of a 632.8 nm HeNe laser, wherein the effective power of the laser source was set to \(0.1\) mW. The diameter of the focused laser spot on the surface of  
Figure 1: The fabrication process of the described micro/nano hierarchical structure. (**a**) The silicon wafer was wet etched in a mixed solution of NaOH, water, and isopropyl alcohol. (**b**) Depositing a gold thin film on the micro-pyramid array using a sputtering system. (**c**) Attach the chip to a slide before electrochemical treatment. (**d**) The sample was treated with electrochemical oxidation and reduction cycles to create nanostructures on the surface of the gold thin film.  
the sample was approximately 2 \(\upmu\)m, and the integration time per measurement was 10 s. The SERS substrate performance was then characterized by calculating the SERS enhancement factor.
## 4 Spatiotemporal Evolution Characteristics
### Change in Spatial Core Distribution Structure  
The Kernel Density Estimation (KDE) analysis depicted in Figure 6 offers an in-depth examination of the spatial organization of Paomo restaurants within Xi'an's central urban area over the study period, revealing a prominent \"center-periphery\" spatial structure. This analysis highlighted significant high-density zones and areas of aggregation primarily centered around the Bell Tower, encapsulated within a high-value cluster surrounding the Xidajie Huifang. However, the secondary cores showed considerable dynamism.  
In 2012, the spatial distribution exhibited an \"east-west + scattered\" pattern with notable sub-core distributions extending along the East-West Avenue, reaching into areas such as the High-tech Zone, Weiqu, Sanqiao, Zhangba, and Textile City. This pattern suggested a strong linkage to the local residential areas, indicating that the spatial placement of Paomo restaurants was closely aligned with the living quarters of local inhabitants.  
Figure 6: \(Cont\).  
By 2023, the spatial dynamics within Xi'an's central urban area displayed an increased trend towards multipolarization. While the primary core around the Bell Tower remained intact, it appeared slightly reduced in scale. Concurrently, there was a significant shift in the sub-core configurations: the core along the East Avenue extension line not only strengthened but also expanded towards Nan Daije, reflecting a geographic widening of culinary hotspots. Conversely, the cores along West Avenue and in Textile City saw a notable decline. Noteworthy is the emergence of new sub-centers at strategic locations such as Longshouyuan-Daming Palace, Gaoxin Road-TV Tower, and Chanba Peach Blossom Dam, alongside the persistence of Weiqu, Sanqiao, and Zhangba sub-centers, albeit with trends indicating dispersion and contraction.  
These transformations are likely influenced by the evolution of the city's north-south axis as the predominant spatial conduit for population flow, with significant construction developments around areas like the TV Tower, Chanba Ecological District, and Daming Palace Site Park catalyzing the intensification and clustering of POI facilities into defined \"cores\". These changes have substantially impacted the spatial distribution patterns of Paomo restaurants, reshaping the urban culinary landscape.  
Interestingly, despite its status as a major tourist draw, the Qujiang Dayan Pagoda area did not evolve into a third-level or higher agglomeration. This observation raises pertinent questions regarding the effectiveness of planning guidance and the dynamics of market competition within this locale, suggesting a potential misalignment between tourist attractions and local commercial development. This scenario underscores the necessity for further focused research and a nuanced understanding of the planning strategies and market forces that influence such crucial urban development outcomes.
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
## 2 Theory  
This section presents a theory of the proposed binaural room-acoustics auralization method using a massively parallel wave-based room-acoustics solver. First, the relevant theory of the massively parallel wave-based room-acoustics solver is given briefly: dispersion-optimized explicit TD-FEM with DDM-based parallel computing implementation. Spatial discretization is consistently performed with eight-node linear hexahedral FEs in the study. Then, the theory of a hybrid binaural room-acoustics auralization method based on Ambisonics with HRTF is given. A novel approach is also formulated for accessing expansion coefficients of spherical harmonics using spatial differentiation of the finite element shape function.
## 2 Results and Discussion
### Repressed Exopolysaccharides (EPS) Biosynthesis  
Many strains in sphingomonads can produce EPS [12; 13]. The transcriptome data showed the gene encoding a putative glucan biosynthesis protein (WP_052117974) and two genes involved in capsule biosynthesis and export (WP_052118064, WP_007015819) were up-regulated 3.03, 2.07 and 3.01-fold respectively in strain \(\Delta\)_rsh_. Glucans are major constituents of capsular materials [54]. Thus, we analyzed \(\Delta\)_rsh_ for Congo red binding. Congo red binds certain polysaccharides as well as polymers that display amyloid-like properties [12]. On Congo red plates, the colony biofilm of \(\Delta\)_rsh_ was light red, while the wild-type strain appeared slightly orange. The complemented strain showed a phenotype similar to that of the wild-type strain (Figure 6a). Furthermore, the culture of \(\Delta\)_rsh_ bound more Congo red than the wild type, leaving less Congo red in the cell-free supernatant. The Congo red bounding of \(\Delta\)_rsh_ (pCM62-rsh) showed no significant difference with that of strain US6-1 (Figure 6b). The results suggested that Rsh\({}_{\text{US6-1}}\) affected EPS biosynthesis as a repressor, consistent with the changes of the expression levels of the above genes. This phenomenon was also observed in _Sinorhizobium meliliot_[55]. When the local environment becomes unfavorable (e.g., nutrient or oxygen deficiency), the motile behavior is important for bacteria to move towards a better environment to survive. In several strains of sphingomonads, the presence of EPS can result in a phenotypic shift from planktonic cells to non-motile cells [13]. Therefore, the repression of Rsh\({}_{\text{US6-1}}\) on the EPS biosynthesis might be a survival strategy of strain US6-1.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 7 End-To-End Delay Analysis
### Model Validation  
Some aspects of the Markovian model have been validated in previous work. In particular, in [36] the FSA as RAP was validated for a wide range of traffic load by extensive computer simulations, showing excellent accuracy. With regard to the TDMA model, the validation has been conducted to the particular case of very low data traffic. For that purpose we have derived a parallel model, rather simpler than the previous one, that allows the calculation of the E2E delay of an isolated data packet generated at an arbitrary sensor located at a given CH. Then, the result has been compared with the previous general model with very low traffic load. The results of this verification procedure offer a perfect fit. Due to the lack of space, we have omitted the details of that particular queuing model. Here we only outline the main aspects in the following way.  
Let us consider a data packet generated at a sensor in the coverage area of CH \(\langle 4,3\rangle_{h}\), see Figure 5. Then, since our tagged data packet is alone, it will gain the access with no collisions. Then, \(\overline{AC}_{\langle 4,3\rangle_{h}}=N_{msC}\) mini-slots (the first term of Equation (24)) since our tagged packet is transferred to its CH at time slot \(C\#=1\) of the CONT sub-frame. Next we account for the \(CT_{\langle 4,3\rangle_{h}}\) term (the second term of Equation (24)) and it is given by \(CT_{\langle 4,3\rangle_{h}}=(N_{intra}-C\#)\cdot N_{mmC}+(T\#+1)\cdot N_{mmT}= \left(3-1\right)\times N_{mmC}+(3+1)\cdot N_{mmT}\) minislots. Next we take into account the third term of Equation (24), \(\overline{RW}_{\langle 4,3\rangle_{h}}\), whose expression is given in Equation (26). Since our tagged data packet travels alone the WSN, according to Equation (22) and to Equation (23), \(r_{\langle 4,3\rangle_{h}}=\overline{W}_{\langle 4,3\rangle_{h}}^{\prime}=0\). Finally, with regard to the term \(TT_{\langle 4,3\rangle_{h}\langle 3,2\rangle_{h}}\) in Equation (26) we have to account for the TDMA subframe of CH \(\langle 4,3\rangle_{h}\) as \((N_{inter}-T\#-1)\cdot N_{mmT}=3\cdot N_{mmT}\) minislots, next for the whole duration of a CONT sub-frame\(N_{intra}\cdot N_{mmC}=3\cdot N_{mmC}=\text{minislots}\) and for the TDMA sub-frame of CH \(\langle 3,2\rangle_{h}\) as \((T\#+1)\cdot N_{mmT}=1\cdot N_{mmT}\) minislots; in total \(TT_{(4,3)_{h}\langle 3,2\rangle_{h}}=3\cdot N_{mmT}+3\cdot N_{mmC}+N_{mmT}\) minislots. As another illustrative example, it is quite straightforward to see that \(TT_{(42)_{h}\langle 3,1\rangle_{h}}=4\cdot N_{mmT}\).