esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Derivation of Boundary Conditions of 4D C/C Composite
### The Temperature Boundary Conditions of UC1  
The temperature boundary conditions of the composites can be derived by the displacement boundary conditions proposed by [PERSON] et al. (2018), combining the homogenization theory and [PERSON]'s law of thermal conduction. As for calculating the effective thermal conductivity in x direction, \(\lambda_{xx}\), the heat flux \(q_{x}\) and temperature gradient \(\
abla T_{\rm x}\) of the two sides on the unit cell need to be acquired first. The \(\
abla T_{\rm x}\) is a specified value that is prescribed by the equation \(\
abla T_{x}=\Delta T/l\) in this work. The results of heat flux are acquired in the post-processing. In this work, \(\Delta T=300k\) is prescribed when predicting the thermal conductivities of UC1 and UC2. For calculating \(\lambda_{yy}\) and \(\lambda_{zz}\), \(\
abla T_{y}=\Delta T/w\) and \(\
abla T_{z}=\Delta T/h\) can be used, respectively. After determining the temperature gradient, the temperature boundary conditions should be used for the UC1. The temperature boundary conditions in the x direction can be described as ([PERSON] and [PERSON], 2016):  
\[T_{(l,y_{1},z_{1})}-T_{(0,0,0)}=\
abla T_{x},\ T_{(x_{1},w,z_{1})}-T_{(0,0,0) }=0,\ T_{(x_{1},y_{1},h)}-T_{(0,0,0)}=0, \tag{3}\]  
where \(x_{1},y_{1}\), \(z_{1}\) are arbitrary values in \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively. The heat flux and temperature of the face, edge and node are expressed by a subscript of its coordinates in the following discussion.  
Similarly, the temperature boundary conditions in the \(y\) direction and \(z\) direction can be described as Equations (4) and (5).  
\[T_{(l,y_{1},z_{1})}-T_{(0,0,0)}=0,\ T_{(x_{1},w,z_{1})}-T_{(0,0,0)}=\
abla T_{y },\ T_{(x_{1},y_{1},h)}-T_{(0,0,0)}=0, \tag{4}\]  
\[T_{(l,y_{1},z_{1})}-T_{(0,0,0)}=0,\ T_{(x_{1},w,z_{1})}-T_{(0,0,0)}=0,\ T_{(x_{1},y_{1},h)}-T_{(0,0,0)}= \
abla T_{z}. \tag{5}\]  
Equations (3)-(5) are the temperature boundary conditions of the UC1. UC2 is constructed from UC1 by rotational transformations, so the boundary conditions of UC2 need to be derived on the basis of UC1. The derivations of the temperature boundary conditions of UC2 are discussed in detail in the following section. It should be noted that deriving the temperature boundary conditions of UC2 also need to consider three conditions: (1) the temperature gradient \(\
abla T_{x}\) when calculating the \(\lambda_{xx}\), (2) the temperature gradient \(\
abla T_{y}\) when calculating the \(\lambda_{yy}\), (3) the temperature gradient \(\
abla T_{z}\) when calculating the \(\lambda_{zz}\).
## 2 IL-1 Family Cytokines, Receptors and Co-Receptors
### IL-1 Subfamily  
IL-1\(\alpha\) and IL-\(\beta\) are both pro-inflammatory cytokines with some distinctive characteristics. IL-1\(\alpha\) is constitutively expressed in hematopoietic immune cells and other cell types such as intestinal epithelial cells and cutaneous keratinocytes (KCs) [13]. Although non-processed full-length IL-1\(\alpha\) has some functional activity, cleavage by calpain and extracellular proteases such as neutrophil elastase, granzyme B, and mast cell chymase enhances its pro-inflammatory activity [14; 15]. Its major role is played locally, since IL-1\(\alpha\) is mostly found bound to membranes. It is also expressed intracellularly in the cytosol--acting as an alarm upon release from necrotic cell death--and in the nucleus--activating transcription of pro-inflammatory genes or tissue homeostasis and repair genes [16]. In addition, IL-1\(\alpha\) expression can be induced by proinflammatory stimuli, leading to IL-1R1 binding and pro-inflammatory gene expression targeting type 1 or type 17 immune responses. In turn, this produces recruitment and activation of T cells, dendritic cells (DCs), neutrophils, and monocytes/macrophages that will release further pro-inflammatory cytokines and chemokines, leading to an autoinflammatory amplification loop [17]. On the contrary, IL-1\(\beta\) is the primary circulating form, and its expression is inducible only in monocytes, macrophages, and DCs [17]. Full-length IL-1\(\beta\) precursor protein (pro-IL-1\(\beta\)) is stored in the cytoplasm and is cleaved by caspase-1 to its active form in response to activation of pattern recognition receptors (PPR) by pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) in an inflammasome-dependent process [18]. In addition, pro-IL-1\(\beta\) can also be activated in the extracellular space by neutrophils and mast cells-derived proteases or by microbial proteases [19]. The antagonist IL-1 Ra exerts its anti-inflammatory properties by binding to the IL-1R1 receptor and competing with IL-1\(\alpha\) and IL-\(\beta\)[20].
## 2 Bi-Objective Model for the Design and Management of RMSs
### Optimization Model Formulation  
The technical objective function (1) minimizes the time needed for RMTs reconfiguration, i.e., first and second terms, respectively, for the assembly and disassembly of the auxiliary modules, and for the part and auxiliary module travel time, i.e., respectively, the third and the fourth terms. The analytic formulation is as follows. \[ \begin{array}{l}\lambda=\sum\limits_{t=1}^{T}\sum\limits_{r=1}^{R}\sum \limits_{n=1}^{N}MAR_{nrt}\cdot\left(\sum\limits_{m=1}^{M}at_{mr}\cdot B_{mm}\right) \\ \qquad+\sum\limits_{t=1}^{T}\sum\limits_{r=1}^{R}\sum\limits_{n=1}^{N}MDR_{nrt} \cdot\left(\sum\limits_{m=1}^{M}dt_{mr}\cdot B_{mm}\right)+\sum\limits_{p=1}^ {P}\sum\limits_{c=1}^{C}\sum\limits_{c_{1}=1}^{C}\sum\limits_{t=1}^{T-1}PBF_{pcc_ {1}t}\\ \qquad\cdot tp_{pcc_{1}}+\sum\limits_{n=1}^{N}\sum\limits_{c_{1}=c}^{C}\sum \limits_{c_{1}=1}^{T-1}AMF_{nc_{1}t}\cdot\left(\sum\limits_{m=1}^{M}ttam_{mcc_{1 }}\cdot B_{mm}\right)\end{array}  \tag{1}\]The ergonomic objective function (2) minimizes the overall repetitive movements performed by the human operators for the auxiliary module assembly/disassembly on/from the RMTs, as follows. \[\mu=\sum\limits_{t=1}^{T}\sum\limits_{r=1}^{R}\sum\limits_{m=1}^{M}\sum\limits_{ n=1}^{N}B_{nm}\cdot\left[\left(\frac{NTA_{mr}}{\xi_{mr}^{MA}\cdot at_{nr}}\cdot MAR_{nrt} \right)+\left(\frac{NTD_{mr}}{\xi_{mr}^{MD}\cdot dt_{nr}}\cdot MDR_{nrt}\right)\right] \tag{2}\]  
Moreover, the model is subject to the following feasibility constraints, adapted from [PERSON] et al.
## 3 Net Impact and Capturing Risks
### Net Impact Contact Forces and Moments  
The resulting force and moment loads from the net contact dynamics can be found in Figures 6 and 7. The time series for contact forces and moments are artificially extended with artificial dissipation in order to determine the longer-term effects of the net contact dynamics on the Envisat satellite.  
The highest forces and moments are 12 kNand 160 kNm, respectively, occurring at the impact moment with the ASAR antenna (near \(t\approx 0\) s). As anticipated, the greatest force is exerted in the \(y\) direction (along-track), resulting in a maximum contact moment occurring in the \(Z\) direction (yaw).  
Finally, these values undergo a substantial decrease, fluctuating around \(-1.5\) and \(1.5\) kN for forces and \(-15\) to \(15\) kNm for moments. This decline is primarily attributed to the main net wrapping process, taking approximately 6 s, after which the contact dynamics gradually stabilise as the net successfully envelops the target. Notably, a significant occurrence occurs at approximately \(t\approx 4.7\) s (refer to Figure 7), involving the contact between one of the bullets and the Envisat ASAR antenna. This collision, of an impact force magnitude of 4.5 kN, has the potential for fracture-induced damage. However, this study primarily focuses on examining the impact of these loads on the overall system dynamics and their associated risks of secondary space debris generation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Input** & **Value** \\ \hline \hline Mock-up Envisat reduction factor & 0.75 \\ Thread radius & 1 mm \\ Bullet radius & 1 cm [15] \\ Static friction coefficient & 0.19 [10] \\ Dynamic friction coefficient & 0.152 [10] \\ Material constant & 1.5 [10,15] \\ Contact stiffness & \(1.622\times 10^{8}\) N/m\({}^{1.5}\) \\ Hunt-Crosley damping constant & 0.05 [10] \\ Maximum penetration & \(1.58\times 10^{-4}\) m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation inputs related to contact detection and dynamics.  
Figure 6: Contact force time series in the \(\mathcal{L}\)-frame.
applied sciences Article  
Prediction of Phase Equilibrium Conditions and Thermodynamic Stability of CO\({}_{2}\)-CH\({}_{4}\) Gas Hydrate [PERSON], [PERSON], [PERSON], [PERSON] *, [PERSON] + and [PERSON] *  
School of Engineering, China University of Petroleum-Beijing at Karamay, Karamay 834000, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (N.L.)  
Footnote †: journal: Applied Sciences  
**Abstract:** With the large-scale promotion and application of CO\({}_{2}\) flooding, more and more engineering problems have emerged. Due to the high CO\({}_{2}\) mole fraction, the associated gas of CO\({}_{2}\) flooding very easily forms solid hydrates, compared to conventional natural gas. This has resulted in production decline or shutdown. Understanding the phase equilibrium conditions for hydrate formation in production fluids is crucial for hydrate prevention and control. In this study, accurate predictions of CO\({}_{2}\)-CH\({}_{4}\) mixed gas hydrate formation conditions were performed using theoretical models. The temperature and pressure ranges for hydrate formation were calculated for different CO\({}_{2}\) mole fraction, ranging from \(-\)11.5 \({}^{\circ}\)C to 20.85 \({}^{\circ}\)C and from 0.81 MPa to \(-\)28.1 MPa, respectively. Based on the calculated phase equilibrium data, a multi-parameter empirical model was developed using polynomial fitting. The calculation errors for the multi-parameter empirical model were 3.09%. The multi-parameter empirical model established in this study can avoid complex thermodynamic equilibrium calculations and has the advantages of simplicity, high accuracy, and wide coverage of downhole conditions. Based on the calculated phase equilibrium data, the dissociation enthalpy of CO\({}_{2}\)-CH\({}_{4}\) hydrate below and above the freezing point of water was calculated. The results showed that an increase in CO\({}_{2}\) mole fraction led to an increase in hydrate dissociation enthalpy and enhanced thermodynamic stability, making hydrate prevention more challenging. Our work can contribute to the optimization of CO\({}_{2}\) production fluid treatment processes and the development of hydrate prevention and control technologies.  
gas hydrates; CO\({}_{2}\)-CH\({}_{4}\) mixed gas; formation conditions; prediction
## 3 Materials and Methods
### Model Formulation
#### 3.2.1 Nomenclature  
Sets  
* \(s\in S\): set of assembly stations.
* \(p\in P\): set of products.
* \(h\in H\): set of quality inspection methodologies.
* \(k\in K\): set of quality characteristics.
* \(t\in T\): set of time steps.  
Parameters  
* \(D_{p,t}\): demand for product \(p\) in period \(t\).
* \(A_{s,p,k}\): 1, if characteristic \(k\) of product \(p\) can be controlled at station \(s\). 0, if otherwise.
* \(q^{*}_{s,p,k}\): time required for quality control of product \(p\) for characteristic \(k\) using inspection methodology \(h\) at station \(s\).
* \(q^{\text{max}}_{t}\): maximum time available for quality control in period \(t\).
* \(rep^{\text{time}}_{s,p,k}\): time required to repair characteristic \(k\) of product \(p\) at station \(s\).  
Figure 1: Process flow through the assembly system with integrated inspection and rework activities.  
\(det_{s,p,h,k}^{prvb}\): probability of detecting a defect in characteristic \(k\) of product \(p\) using inspection methodology \(h\) at station \(s\).
* \(det_{p,k}^{prvb}\): probability of a defect occurring for characteristic \(k\) of product \(p\).
* \(C_{h}^{inv}\): annualized investment cost for quality inspection methodology \(h\).
* \(C_{h}^{var}\): variable cost per inspected unit using quality inspection methodology \(h\).
* \(C_{p,k}^{pen}\): penalty cost for non-compliance in characteristic \(k\) of product \(p\).
* \(C_{s,p,k}^{pen}\): cost to repair characteristic \(k\) of product \(p\) at station \(s\).  
Decision variables  
* \(c^{inv}\): total cost for investments in quality inspection methodologies.
* \(c^{var}\): total variable cost for inspection.
* \(c^{pen}\): total penalty cost for non-compliance products.
* \(c^{rep}\): total rework cost.
* \(Y_{s,h}\): 1, if inspection methodology \(h\) is adopted at assembly station \(s\). 0, if otherwise.
* \(X_{s,p,h,k}\): 1, if inspection methodology \(h\) is adopted at assembly station \(s\) for controlling characteristic \(k\) of product \(p\) in period \(t\). 0, if otherwise.
* \(N_{s,p,h,k}^{contr}\): number of inspections performed at assembly station \(s\) for characteristic \(k\) of product \(p\) using inspection methodology \(h\) at period \(t\).
* \(NC_{p,k,t}^{prvd}\): number of non-compliant units of product \(p\) with defect in characteristic \(k\) at period \(t\).
# Model Test Study on a Shield Tunnel Adjacent to Pile in the Sub-Clay of a Coastal Area
## 2 Design of Model Test System Crossing Existing Piles by Shielding
### Design of Test Materials  
#### 2.2.1 Equivalent Materials  
The actual soil is a typical silty clay layer in the coastal area, and the model material is selected according to the classification of surrounding rock for a railway tunnel of grade V and VI. The geometric similarity constant \(C_{\text{I}}\) = 100, the bulk density similar constant \(C_{\text{Y}}\) = 1, according to the similarity of the two mechanical behaviors and phenomena, the following similarity law indicators can be obtained:  
\[C_{\varepsilon}=C_{\mu}=C_{\varphi}=1 \tag{1}\]  
\[C_{\sigma}=C_{E}=C_{c}=100 \tag{2}\]  
The results show that the clean sand, 200 mesh parite powder and Vaseline were the appropriate main simulation materials. After repeated tri-axial and shear tests, a mass ratio of 8:3:1 with a similar relationship was determined. The stress-strain relationship at different confining pressures is presented in Figure 5. The deformation modulus \(E\) = 13.1 MPa and the relationship between shear stress-strain is shown in Figure 6. The relationship between shear strength and vertical pressure is shown in Figure 7. The cohesive force \(C\) = 0.375 kPa, the internal friction angle \(\varphi\) = 31.35\({}^{\circ}\); the physical-mechanical parameters of actual and model for surrounding rock are shown in Table 1. The deformation modulus, cohesion and internal friction angle presented the similar relationship to the railway tunnel surrounding rock of V, VI.  
Figure 5: The stress–strain relation at different confining pressure.  
Figure 6: Shear stress–strain relations.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 3 Communication
### Underwater Acoustic Communication  
Currently, the only truly viable option for long range underwater communication is using acoustic signals. Other types of signals used in terrestrial networking rely on electromagnetic waves of high frequency which attenuate rapidly in water [3]. While greatly increasing the range of communication, the drawbacks of using acoustic communication are low data throughput, large delays in packet delivery, and high probability of transfer failure. These issues are due to the relatively low bandwidth of the signal, slow propagation of sound through water, and a high chance of signal multipath and attenuation.  
Successful acoustic communication within a swarm of robots is much more complex than merely supplying them with acoustic modems. Since water is the transmission medium through which all signals propagate during acoustic transmission, collisions can occur during data transfer attempts if two or more agents try to communicate at the same time. To prevent signal collision issues and allow successful communication between all agents sharing the same transmission medium, a communication protocol was developed and implemented.  
Multiple access methods are generally divided into the following categories [17]: frequency division multiple access (FDMA), time division multiple access (TDMA), and code division multiple access (CDMA). Due to the limited capabilities of the acoustic modem used in the swarm, implementing a TDMA-based communication protocol is the only viable option. TDMA allows multiple agents to share the same frequency channel by dividing the signal into different time slots. Each agent can send data to other agents or request data from them within its assigned time slot.  
Figure 11: Swarm network topology. Green lines represent mesh network traffic routed over the 5 GHz interface, while red lines represent Access Point—Client traffic over the 2.4 GHz interface.  
Several different modes of communication were implemented in order to provide the best possible utilisation of the available communication system. The communication protocol was modelled as a finite-state machine (FSM), where each state represents one mode of acoustic communication, shown in Figure 12.
# A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
## 4 Conclusions  
This study proposes a physics-guided neural network for predicting the fatigue life of materials. Taking the loading environment parameters as the output, the parameters of the S-N curve corresponding to the loading environment are predicted instead of directly predicting the fatigue life, which greatly simplifies the prediction process of fatigue life. The predicted fatigue performance parameters can help further research on fatigue phenomena. The proposed method can obtain more accurate fatigue life prediction results based on smaller training samples, and has a certain data extrapolation ability. The main conclusions are as follows:  
* According to the characteristics of the power-law relationship between fatigue life and load, predicting the S-N curve instead of directly predicting the fatigue life can simplify the fatigue life prediction process. This study can provide a reference for the prediction of fatigue life by machine learning methods in the future.
* Predicting the S-N curve under a specific loading environment means that the model is not completely a black box for the prediction process of fatigue life under corresponding conditions, which is helpful for the community to further study the fatigue phenomenon.
* The prediction accuracy of machine learning methods is more dependent on the selection of training samples. The prediction results of the models obtained by selecting different training samples are different.  
Machine learning methods have shown great potential in the field of fatigue life prediction. In order to obtain more reliable prediction results, on the one hand, more complex models can be studied, and on the other hand, it is necessary to simplify the research problems. In addition, the influence of the randomness of training samples on the prediction accuracy of the model should also be paid attention to. Only in the ideal case are the training samples sufficient and in many cases, only a limited number of training samples are available. The ideal model should be able to reduce the randomness caused by small batches of samples, which can make the prediction results more reliable.  
Conceptualization: D.Y. and A.J. Validation: A.J. Data curation: D.Y. Funding acquisition: A.J. Programming: D.Y. and Y.L. Writing--original draft: D.Y. Writing--review and editing: D.Y., A.J. and Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 5 Simulation and Results Analysis
It can be seen from the results that the proposed approach has a better speed of convergence and can be used in the cloud computing platform with a huge number of slave nodes. The random algorithm has a large fluctuation and more execution time because it is an unpredictable algorithm. LBVS is better than a random algorithm but it also fluctuates, like the random algorithm. Therefore, the proposed approach has good efficiency for load balancing in cloud computing, which satisfies the needs of dynamic task allocation in the cloud platform. The proposed approach can make the cloud platform evolve over time and speed up the convergence time. Compared to a random algorithm and LBVS algorithm, it has better performance and is less time consuming.
# New Magnetstrictive Transducer Designs for Emerging Application Areas of NDE
## 5 Reversed Wiedemann Effect Sensors  
MsTs were developed as an alternative to direct Wiedemann effect sensor options. The physics of operation are unchanged. However, the magnetic and structural anisotropy of the ferromagnetic material should be considered, because it will influence the transducer performance. Figure 3 shows two different reversed Wiedemann effect sensor designs: (a) a MsT utilizing a ferromagnetic strip that is used for testing pipes, and (b) an EMAT MsT that is used for testing tubes.  
Figure 3: Sensors utilizing a reversed Wiedemann effect: (**a**) a magnetostrictive transducer (MsT) for the guided wave testing of pipes; (**b**) an EMAT MsT for the testing of heat exchanger tubes.  
Figure 2: Sensors utilizing a direct Wiedemann Effect: (**a**) electromagnetic acoustic transducer (EMAT) heat exchanger probe for the guided wave testing of carbon steel heat exchanger tubing, (**b**) magnetostrictive sensor (MsS) used for the guided wave testing of pipes.  
MsTs for piping, as illustrated in Figure 3a, have several notable engineering advantages over MsSs. Since guided wave sensors are typically much longer in the particle motion direction than transverse to the particle motion direction, the reversed Wiedemann effect style means that the magnetic biasing length is relatively short. Rare earth magnets are more readily available in this size to induce stronger and more consistent bias fields. Also, efficient solenoidal coil designs can be used to reduce impedance, which allows the use of simpler electronics, reducing power requirements or increasing signal strength.  
The EMAT MsT for heat exchanger tube inspection that is shown in Figure 3b has been found to be advantageous for the guided wave testing of magnetically soft ferromagnetic alloys such as SeaCure and Duplex. This is because the RF coil does not create any time-varying magnetic field in the axial direction of the tube (only a circumferential magnetic field is generated). The results of successful field trials on 5/8 inch OD SeaCure tubing using the fundamental T(0.1) mode have been previously discussed [25; 26].  
Another interesting version of an omnidirectional MPT probe was proposed by [PERSON], [PERSON] and [PERSON] [27]. We believe that this probe falls into a category of reversed Wiedemann effect transducers, because its permanent magnetic bias direction is parallel to the shear horizontal wave propagation direction.