esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To acquire Johnson-Cook (J-C) constitutive parameters that accurately depict the mechanical behavior of powder liner under conditions of high pressure, elevated temperature, and large deformation, as well as Holmquist-Johnson-Cook (HJC) constitutive parameters that precisely describe the dynamic damage of hard rock and make them suitable for numerical simulations for hard rock perforation, the present study introduces a constitutive parameter inversion method based on finite element simulation. Firstly, based on the experiments of perforating steel targets and underground perforating hard rock targets, a dynamic simulation of the perforating process of a shaped charge perforating target was carried out using ANSYS/LS-DYNA, and the influence law of each constitutive parameter on perforating depth and perforating aperture was systematically analyzed. Subsequently, the key parameters of the J-C constitutive model for powder liner and the HJC constitutive model for hard rock were optimized and determined using a response surface method, multi-genetic algorithm, and experimental data. A numerical simulation of the perforating process was finally conducted using the retrieved constitutive parameters of powder liner and hard rock, which were then compared with the experimental results. The results demonstrated that the discrepancy between the experimental and simulated data was within 5%, indicating that the constitutive parameters obtained through this inversion method could more reliably reflect the mechanical behavior of the powder mold and hard rock used in this study during perforation.  
Keywords:perforation experiment; perforation numerical simulation; J-C constitutive model; HJC constitutive model; response surface method; multi-genetic algorithm Article
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## Appendix A  
Proof.: Firstly, note that, since the pair \((S,C)\) is assumed to be observable, or at least detectable, there exists a feedback matrix gain \(K\), such that the proper values of \([S-KC]\) are located in the closed left-half of the complex plane.  
Then, consider the quadratic form \(H(e)=\frac{1}{2}e^{T}Me\), which is positive for any \(e\
eq 0\), given that \(M\) is a positive definite symmetric matrix. Besides, note that \(H(e)\) is continuous at the origin and that \(H(0)=0\). Therefore, \(H(e)\) qualifies as a candidate Lyapunov function.  
Calculating the derivative of \(H(e)\) along the (11)-(12) leads to  
\[\dot{H}(e) = e^{T}M\dot{e}\] \[= e^{T}MJ(y)\frac{\partial H(e)}{\partial e}+e^{T}M[S-KC]\frac{ \partial H(e)}{\partial e}+e^{T}M\bar{F}(x,\hat{x})\]  
Now, using \(\frac{\partial H(e)}{\partial e}=Me\), one has that  
\[\dot{H}(e)=e^{T}MJ(y)Me+e^{T}M[S-KC]Me+e^{T}M\bar{F}(x,\hat{x})\]Thus, since \(J\) is a skew-symmetric matrix, one has that \(e^{T}MJ(y)Me=(Me)^{T}J(y)(Me)=0\). Then,  
\[\dot{H}(e)=e^{T}M[S-KC]Me+e^{T}M\tilde{F}(x,\hat{x})\]  
Besides, considering that any scalar is trivially a \(1\times 1\)-symmetric matrix, \(e^{T}M[S-KC]Me=e^{T}M[S^{T}-C^{T}K^{T}]Me\), or equivalently \(e^{T}M[S-KC]Me=e^{T}M\bigg{[}S-\frac{1}{2}(KC+C^{T}K^{T})\bigg{]}Me\) due to \(S\) is symmetric, producing  
\[\dot{H}(e)=e^{T}M\bigg{[}S-\frac{1}{2}(KC+C^{T}K^{T})\bigg{]}Me+e^{T}M\tilde{F }(x,\hat{x})\]  
with matrix \(\bigg{[}S-\frac{1}{2}(KC+C^{T}K^{T})\bigg{]}\) being at least negative semi-definite.  
Finally, expanding Taylor series of \(F(x)\) around of \(\hat{x}\), one has that  
\[F(x)=F(\hat{x})+\frac{\partial F(\hat{x})}{\partial x}e+\theta(e),\]  
for some \(\vartheta(e)\), with \(||\vartheta(e)||\leq k_{\
u}||e||^{2}\), for constant \(k_{\
u}\) and small enough \(e\); or equivalently  
\[\tilde{F}(x,\hat{x})=\frac{\partial F(\hat{x})}{\partial x}e+\theta(e).\]  
Moreover, using  
\[e^{T}M\frac{\partial F(\hat{x})}{\partial x}e =\frac{1}{2}e^{T}\bigg{[}M\frac{\partial F(\hat{x})}{\partial x} +\frac{\partial F(\hat{x})}{\partial x}M\bigg{]}e^{T}\] \[=e^{T}\Pi e^{T}\]  
one has that  
\[\dot{H}(e) =e^{T}M\bigg{[}S-\frac{1}{2}(KC+C^{T}K^{T})\bigg{]}Me+e^{T}\Pi e^ {T}+e^{T}M\vartheta(e)\] \[=e^{T}\Xi e+e^{T}M\vartheta(e),\]  
where the maximum eigenvalue of \(\Xi\) fulfills \(\sigma_{M}(\Xi)<0\). Then, for a sufficiently small \(e\), one has that \(\dot{H}(e)\leq-\gamma H(e)\), for some \(\gamma>0\), and consequently \(e\to 0\) exponentially.
## 3 Results and Discussion
### Four-Ball Rolling Contact Fatigue Test  
The lubricity performance of oil samples was investigated based on their tribological behavior in terms of wear and friction. Damages due to rolling contact fatigue are predominant in mechanical applications. A good lubricant should protect the mating surfaces from sliding wear and protect them from damage occurring due to contact fatigue. The fourth ball was fitted to the ball chuck--Figure 3b, assembled to the tribometer spindle--Figure 3c, rotated at 500 rpm at room temperature of about 25 \({}^{\circ}\)C. The load was applied gradually from 0 to 7262 N. The examples of reports generated by the tribometer are presented in Figure 7: (a) for fresh oil and for comparison (b) for oil with 32 working hours.  
In these reports, changes in several parameters can be observed: friction torque, friction coefficient, and load on the measuring node (four balls). Figure 7 shows examples of the variability trends of selected tests for oil samples (for spec. oil operating time) generated automatically by tribometer application.  
However, reading the detailed values from the graph generated automatically by the tribometer software proves to be difficult due to its low accuracy. Therefore, the measurement data were exported to an Excel file where they were analyzed and a more accurate graph was prepared and presented in Figure 8. Since the entire range of data recorded by the tribometer is not needed to analyze the parameters considered important for the tested oil, the range was narrowed to the values from 4.5 to 7.5 s of the test, as shown by the green rectangle in Figure 7b. Thus, Figure 8, based on exported data, shows examples of the friction torque between balls immersed in the tested oil (for different oil samples). The graph also presents the trend line of the load for each presented sample (black line) and points for which LSL was estimated (red points). Additionally, the following points were highlighted: LSL for fresh oil as a reference (1) and LSI for fresh oil (2).  
There were no changes in measured parameters such as the friction torque, load seizure limit (LSL), load seizure index (LSI), load seizure pressure (LSP), etc., in relation to the changes in the amount of fuel in the oil, change in viscosity, or density. Observed changes are rather accidental. For example, the load seizure limit (\(F_{\text{co}}\)) for clean oil did not reach the highest values (what was expected) compared to the other samples--oil samples after 2 and 8 h of operation reached LSL later and oil samples after 2, 24 and 32 h of operation reached higher values of friction torque. The straight line that represents the load trend shows a proportional and constant increase for different samples.  
Figure 7: Examples of reports generated by tribometer: (**a**) for fresh oil sample; (**b**) for oil sample with the longest operating time (32 h).
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 1 Introduction
exhibit comparable performance in weak multipath communication channels. Numerous studies have explored the integration of channel estimation [22; 23; 24; 25] and coding techniques [26; 27] to enhance system performance. [PERSON] et al. [22; 23] introduced a novel block structure designed to ensure the phase continuity of a single symbol block, replacing the traditional training sequence for channel estimation with a cyclic prefix (CP). This approach reduces transmission information redundancy while maintaining channel estimation performance. [PERSON] et al. [26] developed a receiver based on turbo equalization combined with various channel coding methods, though they continued to employ the Viterbi algorithm for symbol detection in CPM signals. While existing FDE algorithms for CPM signals demonstrate effectiveness in frequency-selective fading channels, their application to symbol detection becomes increasingly complex when using the Viterbi algorithm. Furthermore, simple phase detection techniques fail to account for the inherent memory of CPM signals, which leads to a degradation in performance. This study addresses the issues of high complexity in the Viterbi algorithm and performance loss due to simple phase detection in CPM signal reception by proposing a posterior probability-based symbol detection algorithm based on the CPM symbol detection concept from [16]. The posterior probability-based symbol detection combined with FDE (PS-FDE)can significantly enhance the reception performance of CPM signals while incurring only a marginal increase in computational complexity. This symbol detection algorithm relies solely on the inherent coding gain of CPM signals and does not depend on additional channel coding. After passing through FDE, the CPM signal obtains the initial likelihood values for each symbol via simple phase detection. Then, based on the characteristics of CPM signals, the contained information is separated, delayed, and merged to obtain the symbol log-likelihood value after the information merging. The estimated log-likelihood value can be used as the initial likelihood value to adjust the weight of information merging. PS-FDE detection, after 2-3 rounds of the information merging, can effectively enhance the reception performance of CPM signals. The remainder of this paper is organized as follows: Section 2 introduces the system model of CPM UWA communication based on Laurent decomposition that includes the frame structure and preprocessing before PS-FDE; Section 3 describes the proposed algorithm and analyzes the computational complexity; Section 4 presents the numerical simulations used to evaluate the proposed algorithm performance; Section 5 discusses the sea trial performed to verify the proposed algorithms; and Section 6 concludes the paper.
## 3 Results
### Patient Characteristics  
A total of 1184 patients were included in the study, with more females (68.5%) than males (31.5%). White patients accounted for the largest proportion, 84.9%. Most patients were aged between 50 and 80 years. They were married, lived in large cities, and had a  
Figure 2: Deep learning process diagram. higher income. The most frequent histological subtype was DLBCL (56.8%), followed by marginal zone or MALT lymphoma (21.3%). Further, 36.9% of patients refused chemotherapy and 63.1% accepted chemotherapy; 44.4% of patients did not undergo surgery and 55.5% underwent surgery; and 56.8% of patients refused radiotherapy and 43.2% received radiotherapy. The clinical characteristics of the patients are shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Training Set** & **Validation Set** & _p-Value_ \\ \hline Sex (\%) & & & 0.703 \\ Female & 574 (68.99) & 238 (67.61) & \\ Male & 258 (31.01) & 114 (32.39) & \\ Race (\%) & & & 0.967 \\ American Indian & 1 (0.12) & 3 (0.85) & \\ Asian & 69 (8.29) & 39 (11.08) & \\ Black & 24 (2.88) & 7 (1.99) & \\ White & 738 (88.70) & 303 (86.08) & \\ Histologic subtypes (\%) & & & \textless{}0.001 \\ NOS & 89 (10.70) & 27 (7.67) & \\ Composite-site Hodgkin’s lymphoma & 3 (0.36) & 0 & \\ Small lymphocyte B cell & 6 (0.72) & 4 (1.14) & \\ Mantle cell lymphoma & 3 (0.36) & 3 (0.85) & \\ Mixed diffuse B cell & 2 (0.24) & 1 (0.28) & \\ DLBCL & 455 (54.69) & 190 (53.98) & \\ Burkitt’s lymphoma & 24 (2.88) & 13 (3.69) & \\ Follicular lymphoma & 68 (8.17) & 36 (10.23) & \\ Marginal zone lymphoma & 177 (21.27) & 76 (21.59) & \\ T-cell lymphoma & 5 (0.60) & 2 (0.57) & \\ Pathological grade (\%) & & & 0.107 \\ B-cell & 823 (99.52) & 348 (98.86) & \\ Grade I & 3 (0.36) & 3 (0.85) & \\ Grade III & 1 (0.12) & 1 (0.28) & \\ T-cell & 5 (0.60) & 3 (0.85) & \\ Distant metastasis surgery (\%) & & & 0.832 \\ Other regional sites & 828 (98.92) & 348 (98.86) & \\ Distant lymph node & 1 (0.12) & 2 (0.57) & \\ Distant site & 3 (0.36) & 2 (0.57) & \\ Radiation (\%) & & & 0.146 \\ Beam radiation & 342 (41.11) & 144 (40.91) & \\ Method or source not specified & 16 (1.92) & 2 (0.57) & \\ Recommended & 5 (0.60) & 2 (0.57) & \\ Refused & 469 (56.37) & 204 (57.95) & \\ Order of surgery and radiotherapy (\%) & & & 0.183 \\ No radiation & 643 (77.28) & 260 (73.86) & \\ Radiation prior to surgery & 188 (22.60) & 92 (26.14) & \\ Radiation after surgery & 1 (0.12) & 0 & \\ Chemotherapy record (\%) & & & 0.526 \\ Yes & 525 (63.10) & 221 (62.78) & \\ No & 307 (36.90) & 131 (37.22) & \\ Systemic “B” symptoms at diagnosis (\%) & & & 0.386 \\ Yes & 44 (5.29) & 13 (3.69) & \\ No & 726 (87.26) & 314 (89.20) & \\ Not documented & 62 (7.45) & 25 (7.10) & \\ Marital status (\%) & & & 0.272 \\ Single & 100 (12.02) & 42 (11.93) & \\ Married & 657 (78.97) & 276 (78.41) & \\ Divorced & 75 (9.01) & 34 (9.66) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Patient clinical characteristics.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 2 Problem Analysis
### Vulnerabilities of the SSI Network Platform  
The security risks vary across different IoT networks and devices, as depicted in Figure 1. These risks include latency in fast wireless networks such as 5G, DoS (Denial of Service (DoS) attacks targeting cloud application servers, increased power usage in low-power and limited-mobility devices, data interception in home IoT setups, and replay attacks across IoT systems. While traditional endpoint, network, and application systems share these security concerns, the unique constraints of IoT environments necessitate novel solutions.  
Figure 1: Security threats in IoT services: security threats manifest differently based on the characteristics of the IoT service.  
The SSI was proposed to implement MACsec for Layer 2 encryption communication, providing Connectivity Association Keys (CAKs) to authenticated IoT devices for symmetric key generation [9]. The CAK, used as the initial key to create the SAK for session encryption, usually remains the same for a long time; thus, it is crucial to protect it from exposure [20]. In SSI, IoT devices receive configuration information in plaintext after initial authentication for the L2 TP session setup. This vulnerability may invite hackers to intercept, a risk not explicitly addressed by SSI. Moreover, vulnerabilities exist in the exchange of secret keys for MACsec encryption. The MACsec CAKs, distributed over the public internet using the Extensible Authentication Protocol Transport Layer Security (EAP-TLS) protocol, aim to safeguard against CAK exposure. However, the advent of quantum computing undermines the security assurances of current EAP-TLS implementations employing RSA, DH, and ECC cipher suites becasue PKI systems like RSA, DH, and ECC risk becoming obsolete with the advancement of quantum computing capabilities.
## 7 Optimization of the Space Mission Supply Chain
Minimize  
\[\lambda_{1}\sum_{i\in I}\sum_{j\in I}a_{ij}x_{ij}-\lambda_{2}\sum_{i\in I}\sum_{j \in I}b_{ij}x_{ij}-\lambda_{3}\sum_{i\in I}\sum_{l\in L}c_{il}y_{i} \tag{1}\]  
subject to  
\[\sum_{i\in I}x_{ij}=z_{j},\ \forall j\in J \tag{2}\]  
\[\sum_{j\in J}x_{ij}=y_{i},\ \forall i\in I \tag{3}\]  
\[\sum_{i\in I}y_{i}=Y \tag{4}\]  
\[\sum_{j\in J}z_{j}=Z \tag{5}\]  
\[x_{ij}\ \in\{0,1\},\forall i\in I,\ \forall j\in J \tag{6}\]  
\[y_{i}\in\{0,1\},\forall i\in I \tag{7}\]  
\[z_{j}\in\{0,1\},\forall j\in J \tag{8}\]  
The multi-objective model (SMORS) is expressed in Equation (1), compounded by the criteria described in Table 3, making Equation (2) to Equation (8) define the constraints:  
1. Equation (1) describes multi-criteria (triple-objective) function, where efficiency of component/tasks assignment, risks factors of space mission are minimized while, sustainability factors of space mission are maximized, and supply chain efficiency (fulfillment of resources required for space mission tasks) are maximized. 2. Equations (2) and (3) describe the condition of the assignment considered for space mission required components and tasks. 3. Equation (4) describes the fulfillment of required number of space mission tasks. 4. Equation (5) describes the fulfillment of required number of space mission components. 5. Equations (6)-(8) describe the variable ranges. Computational experiments with the use of only experimental data were performed using the AMPL programming language and the Gurobi 9.0.2 solver on a MacBookAir laptop with Dual-Core Intel Core i7 processor running at 1.7 GHz and with 8 GB RAM. The size of the integer (binary) program for the example problem was relatively small. The set of non-dominated optimal solutions was obtained within seconds. Due to the lack of access to real data for computations, the above mathematical programming multi-objective optimization model (SMORS) was verified with the use of experimental data. The size of the experimental data set was the following: it takes into account 10,000 sustainability and risk factors with 10,000 space mission components to 10,000 space mission tasks and 1000 suppliers in the space mission supply chain. The author names this model conceptual. For this reason of experimental data use only, even the proposed set of mathematical equations gives promising solutions. This part of the research is going to be continued in the future as the next stage of this project. Based on the best knowledge of the author and after a careful check of published research papers [96], this proposed multi-objective optimization model SMORS is one of only a few proposed multi-objective optimization models [97] in the area of space mission risk, sustainability and supply chain.
## 1 Introduction  
The exterior claddings of building facades are gradually degraded from the moment of construction due to various environmental damages. This process can be significantly accelerated by harsh service circumstances, such as a marine environment and exposure to polluted air, which are well recognized to have a detrimental impact on the durability of building components [1]. The salinity of marine aerosols makes this environment particularly hostile since the salt particles can enter porous construction materials and crystallize there [2]. Because of the enormous crystallization pressure that may ruin even the most resilient materials, this process can induce volume changes inside building materials and result in cracking and spalling [2; 3; 4].  
Due to their rich aesthetic quality and exceptional durability, stone claddings provide a desired exterior finish [5]. Nevertheless, even if [PERSON] claims that the method of fixing plays an essential role in their durability [5], an investigation of the long-term toughness of \"dry fixed\" stone claddings has not yet been conducted, despite the rising interest in research into new methodologies to analyze the service life of various types of exterior cladding (Double-Skin Facades). A relatively new building technique could be dry-fixing in natural stone claddings, where the old practice of attached stone cladding (wet-fixed) is replaced mainly with dry-fixed stone cladding [6; 7]. This construction approach is commonplace in current building practice [8]. According to Israeli Standards [9; 10], Figure 1 shows a typical wet- and dry-mounting technique system. According to [PERSON] [6], dry-fixed stone cladding includes a cavity space between the facade layers, making it more attractive than standard skin facades because of the resulting acoustic and energy efficiency and its adaptability for tall structures. The main drawback is the substantial increase in building costs over single facades.  
The objectives of the current study were set using the method created by [PERSON] et al. [11; 12] and the findings of a prior investigation of wet-fixed stone cladding subjected to typical service conditions [13]. In order to assess the potential impact of a marine environment on both stone cladding techniques, it is essential to (1) characterize the deterioration patterns of exterior dry- and wet-fixed stone claddings exposed to either standard or marine environment service conditions and (2) measure the life expectancy limiting coefficient (modifying factors).  
When selecting between the two alternative anchoring systems for exterior stone claddings, designers, construction managers, and facility managers will consider the study's findings very helpful. They serve as an example of the significance of assessing environmental factors, whether standard or marine, when making design and construction decisions. They also serve as a critical component for maintenance managers when formulating an efficient preventive maintenance strategy.
# Study on Impact-Echo Response of Concrete Column near the Edge
## 5 Conclusions  
According to the research, when the impact-echo method is used to detect concrete columns, the analytically obtained predominant frequency is affected by the edge and the frequency value is reduced. In this paper, to determine the influence range of the edge, the method of field test combined with numerical simulation was used to analyze the frequency under relevant working conditions. The vibration mode of the square cross-section was simulated using the plane strain finite element model, and the natural frequency of each mode was calculated to further explain the influence of the boundary on the predominant frequency. Through the above research, the main conclusions are as follows:  
1. When the impact point is located in the edge region's influence range, the predominant frequency's value decreases. When the impact point is farther away from the edge, the predominant frequency returns to the normal level and the predominant frequency is related to the width of the concrete column.
2. When the impact-echo method is used in the actual detection, when the detection area is located at 0.3 times the width of the measured object, in addition to considering the influence of the internal defects of concrete and the dominant frequency of material degradation, it is also necessary to consider the influence of the component boundary on the detection results.
3. Through the plane strain finite element simulation, the dominant frequency at the edge is 0.9 times the thickness frequency. The dominant frequency is the natural frequency corresponding to the specific mode under the cross-section. When the distance between the acquisition point and the edge of the concrete column becomes larger, the influence of the edge is also reduced and the dominant frequency is restored to the thickness frequency corresponding to Mode 2.  
Conceptualization, writing--review and editing, Y.L.; writing--original draft preparation, methodology and software, H.X.; formal analysis and validation, X.M.; project administration, D.W.; supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Anhui Provincial Universities Natural Science Research Project, grant number KJ2020 ZD43; Anhui Housing Urban-Rural Development Scientific and Technological projects (2022-RK057); Research Fund Project of Anhui Jianzhu University (2019 QDZ51); and Anhui Provincial Universities Natural Science Research Project (KJ2021A0607).  
Not applicable.  
The datasets included in and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors are grateful to the Prefabricated Building Research Institute of Anhui Province for funding and providing research facilities for this study.  
The authors declare no conflict of interest.