esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 4 Data Collection Scheme Based on Opportunistic Node Connections
### Energy Efficient Probe Message Forwarding Mechanism
// suppose that \(v_{m}\) has forwarded the probe message to \(v_{m}\), but then receives this message again from it
12. Explore the neighbors that have not forwarded this probe message;
13. Delete the IDs of \(v_{m}\), \(v_{n}\) from the \"Forwarders ID\" field;
14. Node \(v_{m}\) adds its ID in the \"Forwarders ID\" field;
15. Calculate NF based on the \"Forwarders ID\" field;
16. Calculate (EOH-NF);
17. _if_ (the explored neighbors exist)
18. Select the neighbor whose EOH is closest to (EOH-NF) as an optimal forwarder;
19. Forward the probe message to it;
20. end if
21. _end if_
22. _if_ (only the neighbor \(v_{p}\) of \(v_{m}\) is in working status)
23. Forward the probe message to \(v_{p}\);
24. else //\(v_{m}\) is in sleeping status
25. Stop forwarding this probe message;
26. end if
```  
**Algorithm 1**Energy efficient probe message forwarding mechanism  
Considering the asynchronous working-sleeping cycle strategy for nodes, when \(v_{m}\) receives this probe message from \(v_{p}\), if only the neighbor \(v_{p}\) is in working status, as shown in Figure 3c, it forwards the message to \(v_{p}\). However, if \(v_{p}\) is also in sleeping status, as shown in Figure 3d, \(v_{m}\) stops forwarding it. Algorithm 1 gives the detailed energy efficient probe message forwarding mechanism. Figure 3: The illustration of four cases when \(v_{m}\) receives the probe message, (**a**) \(v_{m}\) receives the probe message for the first time; (**b**) \(v_{m}\) has forwarded this message; (**c**) only the neighbor \(v_{p}\) of \(v_{m}\) is in working status; (**d**) \(v_{m}\) has no neighbor.
## 3 SDN, NFV, and SFC: An Overview
### Service Function Chaining  
The vNFs implemented in NFV constitute the NF forwarding graph, which consists of network functions connected via logical network links to achieve the goal of packet processing by the vNFs. SFC is made up of NFs connected in a chain (based on service requirements and specifications) to deliver end-to-end services to end users [54; 57]. A typical service chain consists of NFs such as a NAT function, a firewall, and a traffic load balancer. As briefly introduced in Section 2.2, in order for an SFC deployment to be complete, components such as the service classifier, SFF, Service Function Path, SFC proxy, and service function need to be in place.  
The IETF SFC architecture presented by [PERSON] et al. [37] shows that the SFCs are either bidirectional or unidirectional, where packet processing is performed through an ordered list of service functions in a unidirectional scenario [37]. A bidirectional SFC scenario requires packet processing elements (SFs) to be placed in both directions of the service chain. SFCs are deployed as network service graphs with SFs placed carefully  
Figure 6: ETSI NFV architectural framework.  
Figure 7: Service function chaining scenario.  
at different parts of the service chain. The ability to add and remove SFs dynamically along the service path is essential for the design of any SFC framework. NFV and SDN are integrated to achieve instantiation, management, and orchestration of service chains [58].  
Intelligent service orchestration is important when handling various service functions, and this can be achieved when the NFV is properly integrated with SDN [59]. Figure 7 depicts a typical SFC scenario with service requests generated by users based on the application requirements. A classification of the user traffic is carried out by the flow classifier, which helps in deciding what network function(s) need to be traversed by the traffic before reaching its destination. In a scenario where the service requests traverse more than one NF, a service orchestrator is used to create a chain of NFs that forms the final processing pipeline toward the destination (requested service).
## 6 Utilizing MRSO for Solving Engineering Design Issues
### Overview of Constrained Engineering Design Problems
#### 6.1.7 Tire Design Problem  
The Tire Design problem focuses on minimizing tire deflection, which is critical to improving vehicle performance, safety, and longevity. Tire deflection occurs when a tire deforms under load, impacting factors such as handling, fuel efficiency, and wear. The optimization involves key parameters, including the aspect ratio, section width, load capacity, pressure, and rim diameter, which influence the tire's stiffness and ability to maintain its shape under varying loads and road conditions. By adjusting these parameters, engineers aim to strike a balance between minimizing deflection and maintaining comfort, traction, and durability [47; 48]. This problem, as illustrated in Figure 9, is common in vehicle dynamics optimization and is often addressed using techniques such as constrained nonlinear optimization.  
Figure 9: Tire Design problem schematic diagram.  
The design variables are as follows:  
Consider the following:  
\(\overset{\rightarrow}{P}=[S_{1},\ S_{2},S_{3},S_{4},S_{5}]=[\textit{Aspect Ratio, Width Section, load Capacity, Pressure, Rim Diameter}\ ]\)  
The objective is to minimize the following:  
\[f\left(\overset{\rightarrow}{P}\right)=F_{z}\times\frac{W_{1}+W_{2}+W_{3}}{K_{1}}\]  
subject to the following:  
\[R=\frac{S_{5}\times 25.4}{2}\]  
\[H=\frac{S_{2}\times S_{1}}{100}\]  
\[T=\frac{\left(\frac{H}{2}\right)\times\left(1-\left(\frac{S_{1}}{100}\right) \right)}{1000}\]  
\[I=\left(\frac{H^{3}}{12}\right)\times T\times\text{1,000,000}\]  
\[F_{z}=\frac{S_{3}}{4}\]  
\[P=S_{4}\times 1000\]  
\[A=\sqrt{(R^{2}-(\frac{H}{2})^{2})}\]  
\[B=\sqrt{(R^{2}-(T+\frac{H}{2})^{2})}\]  
\[W_{1}=K_{1}\times\left(\frac{F_{z}}{P}\right)^{0.9}\]  
\[W_{1}=K_{2}\times\left(\frac{F_{z}}{P}\right)^{0.8}\times\frac{A^{1.1}}{B}\]  
\[W_{1}=K_{3}\times\left(\frac{F_{z}}{P}\right)^{0.7}\times\frac{A^{1.3}}{B} \times\frac{I}{H^{3}}^{0.7}\]  
where \(K_{1}=5000,\ K_{2}=10,000,\ \text{and}\ K_{3}=20,000\).
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band ON and K/Ka-Band OFF
#### 7.2.3 Reflection Coefficients  
Figure 23 shows the input and output reflection coefficients for the active X-band arm. Both reflection coefficients are better than \(-5\) dB across 8-12 GHz, which is acceptable for many practical applications.
# Overexpression of _PtDXS_ Enhances Stress Resistance in Poplars
###### Abstract  
1-Deoxy-D-xylulose-5-phosphate synthase (DXS) is the rate-limiting enzyme in the plastial methylerythnitol phosphate pathway (MEP). In this study, _PtDXS_ (XM_024607716.1) was isolated from _Populus trichocarpa_. A bioinformatics analysis revealed that _PtDXS_ had high homology with the _DXS_s of other plant species. _PtDXS_ expression differed among plant tissues and was highest in young leaves and lowest in roots. The recombinant protein was produced in _Escherichia coli_ BL21 (DE3), purified, and its activity evaluated. The purified protein was capable of catalyzing the formation of 1-deoxy-D-xylulose-5-phosphate (DXP) from glyceraldehyde-3-phosphate and pyruvate. A functional color assay in _E. coli_ harboring pAC-BETA indicated that _PtDXS_ encodes a functional protein involved in the biosynthesis of isoprenoid precursors. The treatment of _P. trichocarpa_ seedlings with 200 uM abscisic acid (ABA), 200 mM NaCl, 10% polyethylene glycol6000, and 2 mM H2O2 resulted in increased expression of _PtDXS_. The ABA and gibberellic acid contents of the transgenic lines (Poplar Nanlin 895) were higher than wild types, suggesting that DXS is important in terpenoid biosynthesis. Overexpression of _PtDXS_ enhanced resistance to _S. populiperda_ infection. Furthermore, the transgenic lines showed decreased feeding by _Micromelalopha trogloduta_, supporting the notion that PtDXS is a key enzyme in terpenoid biosynthesis.  
_Populus trichocarpa_; DXS; elicitor treatments; ABA; GA
## 5 Experimental Results
### Synthetic Data in Native Network Form
#### 5.2.5. BTER Benchmark  
A BTER network [68] of 1899 nodes was generated using the FEASTPACK Matlab distribution [69] (version 1.2, Sandia National Laboratories, Albuquerque, NM, USA). In this network model, Erdos-Renyi [70] communities are created with sizes following a scale-free distribution and then connected with edges chosen according to the [PERSON] and [PERSON] model [71]. This graph is different from the other tested models because, in addition to having a scale-free degree distribution, the community sizes follow a scale-free distribution. It has 198 clusters. Of these, 47 clusters contain only three nodes, 31 clusters contain four nodes, and the largest cluster contains 101 nodes. The graph is not connected. A visualization of this network is shown in Figure 6. VAT, integrity and tenacity performed very well on this network with a minimum of 84% accuracy, and all but 5 out of 198 communities were successfully detected. VAT clustered this network at 86% accuracy, and all but 3 of 198 communities were successfully detected.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 6 Conclusions  
In conclusion, our E-GTN framework has proven to be a robust solution to the challenges posed by unmanned excavator operations. By integrating advanced multisensor fusion, we have significantly enhanced the geometric representation of the terrain, which is foundational for high-fidelity environmental reconstruction. Our GridNet has successfully processed grid data to extract salient terrain features, demonstrating its critical role in the reinforcement learning tasks for autonomous excavation. The decision-making module, informed by these extracted features, has facilitated efficient and intelligent operation, showcasing the potential of our framework to revolutionize the construction machinery industry. The successful application of our E-GTN framework underscores its value as a comprehensive platform for scholars and practitioners in related fields, paving the way for future advancements in autonomous excavation technologies.  
Expanding on our work, future iterations of the E-GTN framework could employ adaptive algorithms for better performance across varied soils. Real-time learning capabilities may enable self-optimizing excavators. Additionally, deploying this technology in extreme settings, like underwater or underground, could redefine automated digging. These forward-looking applications not only build on the current strengths of the E-GTN framework but also chart a course for its evolution, solidifying its role as a transformative force in the industry.  
Conceptualization: Q.Z.; methodology: Q.Z., D.W. and X.M.; formal analysis and investigation: Q.Z. and D.W.; writing--original draft preparation: Q.Z.; writing--review and editing: J.Q. and J.H.; funding acquisition: L.G. and J.H.; resources: L.G.; supervision: J.H. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (52035007, U23B20102), Ministry of Education \"Human Factors and Ergonomics\" University Industry Collaborative Education Project (No. 202209 LH16) and the Xie Youbai Design Scientific Research Foundation (XYB-DS-202401).  
Not applicable.  
Data Availability Statement: Data are available on request to the authors.  
Figure 8: Comparison of feature map activations.  
**Acknowledgments:** We would like to extend our sincere thanks to Sany Company for their support and contribution to this research.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Sany Heavy Machinery Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Three-Dimensional-Bioprinted Liver Chips and Challenges
## 2 Material
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**3D Bioprinting Method** & **Materials and Bioink** & **Application** & **Reference** \\ \hline \multirow{2}{*}{Inkjet printing} & FN-G film: housing & & \\  & Hep2 Gs and HUVECs: cellular parts & Drug screening & [PERSON] et al. [76] \\ \hline \multirow{2}{*}{Inkjet printing (organovo)} & PDMS and PMMA: housing & & \\  & HepG2/G3A: cellular parts & Drug screening & [PERSON] et al. [75] \\ \hline \multirow{2}{*}{Digital light processing printing} & PECDA: housing & & \\  & hMSCs and HUVECs: cellular parts & (therapeutic transplantation) & [PERSON] et al. [77] \\ \hline \multirow{2}{*}{Extrusion-based printing} & PCL: housing & & \\  & Hep2 Gs and HUVECs cellular parts & & \\ \hline \multirow{3}{*}{Extrusion-based printing} & PMMA and poly(ethylene/vinyl acetate): housing & & \\  & HepaRG-layer dECM biink and HUVECs: cellular parts & & \\ \hline \multirow{6}{*}{In the table, FN: fibronectin, G: gelatin, Hep2 Gs: human hepatocellular carcinoma cells, HUVECs: human umbilical vein endothelial cells, PEGDA: poly(ethylene glycol) diacrylate polymer, hMSCs: primary human mesenchymal stem cells, PCL: poly(\(\epsilon\)-caprolactone) polymer, PDMS: polydimethylsiloxane, PMMA: poly(methyl methacrylate).} & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three-dimensional-bioprinted liver-on-chip platforms. material for biomolecules besides being biocompatible and water-soluble. Poly(ethylene glycol) diacrylate (PEGDA), a conjugated PEG, can be used as a cell-encapsulating ink to improve stiffness, cell adhesion, cellular interactions, bioactivity, cell viability, and proliferation [81, 82]. For 3D-bioprinted porous scaffolds, PEGDA is a good alternative to other biocompatible and non-immunogenic materials (e.g., PDMS) owing to its photopolymerizability and resistivity to protein adsorption. Photopolymerizable acrylate groups provide the unique ability to perform photolithographical patterning of biochemical and biomechanical architectures by allowing the matrix to be covalently cross-linked by UV light after printing [83, 84]. Confocal microscopy images show that structures bioprinted with PEGDA form homogeneous and high-density cellular encapsulation throughout the structure [82]. PCL is a promising biocompatible platform with less protein absorption than PDMS, increasing the sensitivity of the metabolism measurements and drug screening [78]. Additionally, a PCL-based platform was developed for mimicking liver functions during drug administration, showing higher cell viability compared to other biocompatible platforms since it has a relatively low melting point (less thermal stress for cells in the printing process). However, this platform faced challenges in imaging due to its low optical transparency (compared to PDMS) [78].
# Design of Encoding Algorithm for Underwater Wireless Optical Communication Based on Spinal Code
## 5 Simulation Verification
Compared to Spinal codes mapped to 43 bits through RNG, while the overall communication performance is similar, the Superposition UEP-Spinal Codes enhance the communication performance of head codes by sacrificing part of the tail code performance. This makes them highly suitable for use in underwater wireless optical communication, where channel conditions change randomly, as they improve the reliability of transmitting critical data.
# Control Synthesis as Machine Learning Control by Symbolic Regression Methods
## 5 Computational Experiment  
Now let us consider the solution of the control synthesis problem as MLC with the considered symbolic regression methods. Consider the problem of stabilization system synthesis for a mobile robot Khepera II [27]. The mathematical model of the control object is  
\[ \begin{array}{rcl}\dot{x}_{1}&=&0.5(u_{1}+u_{2})\cos(x_{3}),\\ \dot{x}_{2}&=&0.5(u_{1}+u_{2})\sin(x_{3}),\\ \dot{x}_{3}&=&0.5(u_{1}-u_{2}),\end{array}  \tag{40}\]  
Figure 3: The BCGP graph of the function (30). where \(\mathbf{x}=[x_{1}\ x_{2}\ x_{3}]^{T}\) is a vector of state, \(\mathbf{u}=[u_{1}\ u_{2}]^{T}\) is a control vector. The control has restrictions  
\[-10\leq u_{i}\leq 10,\ i=1,2. \tag{41}\]  
The given set of initial conditions included 26 elements:  
\[\tilde{\chi}_{0}=\{[-2.5\ \ -2.5\ \ -5\pi/12]^{T},[-2.5\ \ -2.5\ \ 0]^{T},\]  
\[[-2.5\ \ -2.5\ \ 5\pi/12]^{T},[-2.5\ \ 0\ -5\pi/12]^{T},\]  
\[[-2.5\ \ 0\ \ 0]^{T},[-2.5\ \ 0\ \ 5\pi/12]^{T},[-2.5\ \ 5\pi/12]^{T},\]  
\[[-2.5\ \ 2.5\ \ 0]^{T},[-2.5\ \ 2.5\ \ 5\pi/12]^{T},[0\ \ -2.5\ \ -5\pi/12]^{T},\]  
\[[0\ \ -2.5\ \ 0]^{T},[0\ \ -2.5\ \ 5\pi/12]^{T},[0\ \ 0\ -5\pi/12]^{T}, \tag{42}\]  
\[[0\ \ 0\ 5\pi/12]^{T},[0\ \ 2.5\ \ -5\pi/12]^{T},[0\ \ 2.5\ \ 0]^{T},\]  
\[[0\ \ 2.5\ \ 5\pi/12]^{T},[2.5\ \ -2.5\ \ -5\pi/12]^{T},\]  
\[[2.5\ \ -2.5\ \ 0]^{T},[2.5\ \ -2.5\ \ 5\pi/12]^{T},\]  
\[-10\leq u_{i}\leq 10,\ i=1,2. \tag{41}\]0  
\[-10\leq u_{i}\leq 10,\ i=1,2. \tag{41}\]1  
\[-10\leq u_{i}\leq 10,\ i=1,2. \tag{41}\]2  
The terminal conditions were set as one point  
\[-10\leq u_{i}\leq 10,\ i=1,2. \tag{41}\]3  
It is necessary to find a control function in the form  
\[-10\leq u_{i}\leq 10,\ i=1,2. \tag{41}\]4  
where \(r_{1},r_{2},r_{3}\) are constant parameters, \(i=1,2\), such that a robot from all 26 initial conditions (42) got to terminal condition (43) with minimal total time and high accuracy. For solution of this problem, the network operator method, the Cartesian genetic programming, and the complete binary genetic programming were used with the principle of small variation of the basic solution. Proportional controllers for each variable were used as a basic solution in all algorithms. The operations of addition and multiplication were used as binary operations, and a set of 28 smooth elementary functions was used as unary operations. The description of these functions can be found in the supplementary material to [28]. The network operator found the following control law  
\[u_{i}=\left\{ \begin{array}{l}10,\text{if}\ \tilde{u}_{i}>10\\ -10,\text{if}\ \tilde{u}_{i}<-10\\ \tilde{u}_{i},\text{otherwise}\end{array}
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 2 Overview and Related Works
### Related Works
Authors in [29] proposed a scheduling approach for M2M devices. This mechanism uses the current and past information about resource allocations, channel quality and QoS requirements to control the impact of M2M and H2H communication and to ensure fairness in resource allocation. [PERSON] et al. [29] propose extending the nine QoS classes defined in [31] which are adapted only to H2H applications, by adding new classes for M2M communication. Their propositional scheduler consists of two phases. In the first phase, M2M devices which will be scheduled are selected according to their delay tolerance. In the second phase, available resources are divided equally between selected M2M devices. The propositional mechanism in [29] allows one to satisfy the QoS requirements and ensure fairness in resource allocations, but it does not reduce the consumed energy for M2M devices. In this paper, we propose two mechanisms of resource allocation which take into consideration the coexistence of M2M and H2H communications, ensure reduction of energy consumption for M2M devices and maximise the satisfaction of QoS requirements.
## 2 Test Design
### Design of the Model Segmental Lining  
#### 2.2.1 Geometric Dimension of the Model Lining Ring  
The engineering background of the test is the shield tunnel project of the Beijing East Sixth Ring Road reconstruction project, whose external diameter, lining thickness, and average width are 15.5 m, 0.65 m, and 2 m, respectively, as shown in Figure 2.  
The tests aim to investigate the mechanical characteristics of the mainline tunnel when lateral openings of different types, i.e., small circle, large circle, and rectangle, were made to construct cross passages. In general, the larger the dimension of the model tunnel, the easier to machine the detailed parts, such as joints of the model lining. Due to the limitation of the soil box and the requirements of the tunnel boundary conditions, considering the symmetry of the investigating issues, only one mainline tunnel was set in the model box. The primary geometric similarity ratio of the centrifugal tests is 1:50, namely that the external diameter (D) and the ring width of the model lining are 310 mm and 40 mm, respectively. The general  
Figure 1: TK-C500 centrifuge.  
Figure 2: Segment layout and dimensions.  
similarity laws of the centrifuge model tests can be deduced according to the similarity theory, as shown in Table 1.  
The lining thickness was determined considering the similarity of the bending stiffness (E.I.). In the tests, Mg-Al alloy (AZ31B) is employed as the material of the model lining, with the advantages of high strength and good machinability. In addition, the elastic modulus of the selected material is 44.8 GPa, close to that of the prototype lining material, C60 concrete with an elastic modulus of 36.5 GPa, making the thickness similarity ratio close to the primary geometric similarity ratio.  
According to the similarity principle of the model test, the thickness of the model lining can be calculated by  
\[t_{m}=\frac{1}{n}\times\left(\frac{E_{p}}{E_{m}}\right)^{\frac{1}{3}}\times t_ {p} \tag{1}\]  
where \(t_{m}(t_{p})\) is the model (prototype) lining thickness; \(E_{m}(E_{p})\) is the elastic modulus of model (prototype) material; \(n\) is the primary geometric similarity ratio. The lining thickness in the tests is 12.5 mm.
## 2 Microseismic Monitoring Method under Tunnel Construction  
### Principle of Microseismic System  
Under the action of stress redistribution due to excavation disturbance, the microfractures in the surrounding rock release energy to the surroundings in the form of elastic waves. The microseismic monitoring system collects the elastic waveforms released by the fracture of the rock mass in the monitoring range through the sensor array arranged behind the working face of the tunnel or near the key monitoring area. The signal type is identified on the basis of the waveform characteristics, and the interference signal is eliminated. In the process of rock fracture, both primary waves (P-wave, which is a transverse wave) and secondary waves (S-wave, which is longitudinal wave) are generated at the source. The P- and S-waves naturally separate as they travel at different speeds. The P- and S-wave arrival times for different channels are picked, and the source location is realized by the time difference and other information. Finally, the source parameters such as energy and magnitude are solved. Figure 1 shows the flow of the microseismic signal acquisition and data processing system.  
Figure 1: Microseismic signal acquisition and data processing.