esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Vibration and Post-Buckling Behaviors of Metal and FGM Pipes Transporting Heavy Crude Oil
## 6 Numerical Results
### Engineering Implications and Model Extensions  
The current model provides valuable insights into the structural response of FGM pipelines under thermal and mechanical loads, specifically, when transporting high-viscosity fluids like heavy crude oil. These findings are especially relevant in applications such as oil sands pipelines, geothermal energy transport, and thermally enhanced recovery processes, where elevated temperatures and pressure-induced instabilities are prevalent.  
Importantly, the ability to adjust the vibration characteristics and critical flow velocity by tuning the volume fraction index (\(N\)) and material configuration enables engineers to design pipelines that are more robust against resonant excitation and buckling. For instance, selecting a ceramic-rich outer surface may be advantageous in high-temperature, erosion-prone environments, while a metal-rich inner core improves ductility and strength under flow pressure.  
Moreover, the current analytical framework is versatile and can be extended to include the following:  
* Geometric imperfections and initial curvature effects;
* Complex foundation models such as nonlinear Winkler or Pasternak foundations;
* Turbulent flow or pulsatile excitation effects;
* Time-varying temperature profiles or transient thermal shocks;
* Multi-mode Galerkin approximations for higher-order dynamic responses.  
These extensions would further enhance the model's applicability in more realistic field conditions and are proposed as future directions.
## 2 Definitions  
Let us consider a \(N\)-DOF (degree of freedom) linear, time-invariant, vibrating system. Its mass, damping, and stiffness matrices are respectively denoted by \(\mathbf{M},\mathbf{C},\mathbf{K}\in\mathbb{R}^{N\times N}\) and its equations of motion are therefore  
\[\mathbf{M}\mathbf{\ddot{q}}(t)+\mathbf{C}\mathbf{\dot{q}}(t)+\mathbf{K} \mathbf{q}(t)=\mathbf{B}u(t), \tag{1}\]  
where \(\mathbf{q}\) is the generalized displacement vector and \(\mathbf{\dot{q}}\), \(\mathbf{\ddot{q}}\in\mathbb{R}^{N}\) its derivatives with respect to the time \(t\). \(\mathbf{B}\in\mathbb{R}^{N\times N_{B}}\) is the force influence matrix and \(u\in\mathbb{R}\) is the independent external control force.  
The rank-one control law for a regulation problem in the case of state feedback control, and by assuming that delay affects the measurements, is defined as follows:  
\[u(t)=\mathbf{f}^{T}\mathbf{\dot{x}}(t-\tau_{f})+\mathbf{g}^{T}\mathbf{x}(t- \tau_{g}), \tag{2}\]  
where \(\mathbf{f},\mathbf{g}\in\mathbb{R}^{N}\) are the velocity and displacement gain vectors and \(\tau_{f}\) and \(\tau_{g}\) the respective time delays. State references are omitted in Equation (2) since do not affect the eigenstructure; their inclusion is, however, trivial.  
The closed-loop controlled system in the Laplace domain denoted by \(s\) is inferred from Equation (1), leading to  
\[\Big{(}s^{2}\mathbf{M}+s\Big{(}\mathbf{C}-\mathbf{B}\mathbf{f}^{T}e^{-s\tau_{f}} \Big{)}+\Big{(}\mathbf{K}-\mathbf{B}\mathbf{g}^{T}e^{-s\tau_{g}}\Big{)}\Big{)} \mathbf{q}(s)=\mathbf{0}, \tag{3}\]  
The left-hand side of Equation (3) is the transcendental characteristic equation of the closed-loop system, \(P_{c}(s)\), whose \(i\)-th solution \(p_{i}\) is the \(i\)-th closed-loop pole of the system. If \(\tau_{f}=\tau_{g}=0\) then \(P_{c}(s)\) is a polynomial and therefore the system features \(2N\) eigenpairs that completely describe the system dynamics. Conversely, as studied in this paper, if the time delays are not null, the characteristic equation has an infinite number of roots: \(2N\) roots are the \"primary roots\", while an infinite number of \"secondary roots\" (often denoted as the \"latent roots\") arise [21; 24].
## 8 Limitations and Further Directions of Research  
The scope of this research does not advance object perception and path planning algorithms, predictive simulation and image processing tools, and mobile sensors and actuators in digital twin-driven manufacturing systems. Big data analytical and acoustic environment recognition tools, computer vision and swarm intelligence algorithms, and artificial neural networks configure multi-agent robotic systems in digital twin environments. Condition monitoring data, plant equipment diagnosis, and digital twin capabilities are pivotal in autonomous production and virtual manufacturing systems. Additional research should address how Internet of Things-enabled control systems, data mapping and processing tools, and prognostic and diagnostic algorithms articulate manufacturing task management in smart networked environments. Sensory data acquisition and deep reinforcement learning tools, autonomous robotic and digital twin technologies, and computer vision and predictive maintenance algorithms shape machining process performance. Virtual twin data, smart interconnected and cognitive robotic devices, and machine learning and context awareness algorithms are instrumental in Internet of Things-enabled automation and process manufacturing systems.  
Future research should investigate how sensing and computing technologies, autonomous visual object detection tools, and mobile robotic systems enable reconfigurable manufacturing processes. Neural network and deep learning algorithms, digital twin modeling and predictive maintenance tools, and environment mapping and data stream clustering algorithms assist virtual manufacturing technologies and cyber-physical production systems. Smart interconnected devices, swarm robotic and machine learning algorithms, and process simulation and scheduling tools articulate smart manufacturing systems and digital twin-based virtual factories. Attention should be directed to how digital twin and cloud computing technologies, navigation management tools, and convolutional neural networks further autonomous swarm robots in smart manufacturing environments. Behavior pattern clustering and virtual simulation tools, data mining and intelligent process planning algorithms, and predictive and prescriptive analytics optimize planning and scheduling data in cyber-physical production and swarm robotic systems on digital twin shop floor. Digital twin simulations, robotic operating systems and agent behaviors, and environment perception sensors configure Internet of Things-based cloud manufacturing.
## 3 Feature Extraction with GraphSAGE
### Learning Parameters  
The parameter matrix is the core of GraphSAGE when the aggregation of node feature vectors is calculated, which contains the mapping relationship of information aggregation between the node feature vectors and neighboring features. So, this subsection explains the parameter learning process of the GNN-DRL algorithm. In the pre-training phase, the network parameters of the DQN model are fixed while the weight parameters of the GNN model are adjustable. Since the goal of model training is to maximize the cumulative reward \(R\) of the routing task, a gradient descent algorithm is used to calculate the gradient of the entire sample in order to learn the optimal weight parameter \(W\) of the GraphSAGE model, as shown in Equation (8):  
\[W^{t}=W^{t-1}-\gamma\
abla_{W}R(W) \tag{8}\]  
where \(\gamma\in(0,1)\) is the learning rate, \(W^{t}\) denotes the weight parameter based on GraphSAGE model at step \(t\) and \(R(W)\) is the cumulative reward of the parameter.  
The implementation of the GraphSAGE-based feature extraction algorithm is shown in Algorithm 2.  
```
Input: Undirected graph \(G\), Node feature vectors \(\{x_{i},\forall v_{i}\in V\}\), sample size \(q\), network depth \(L\), weighting matrix \(W\) Output: hidden feature vector of the node \(\{h_{i},\forall v_{i}\in V\}\)
```  
**Algorithm 2** GraphSAGE-based feature extraction algorithm
## 3 Results
### Technological Implications  
Silicon (Si) and sulfur (S) can significantly impact coal utilization, primarily through their effects on combustion and ash behavior. Si in coals, as a major component of coal mineral matter, contributes to ash formation and slagging issues. S in coals, often present as pyrite, can react with silica during combustion to form clinkers, potentially inhibiting coal combustion [2; 55].  
While silicon (Si) in the form of quartz (hardness 7) causes significant abrasion to mining and grinding equipment, it also occurs in softer minerals such as clays and other silicates, which contribute less to abrasion [2]. In the studied samples, silicon content ranges from 0.9% to 11.9% (average 3.9%) in Jurassic coal, and from 0.1% to 20.2% (average 11.3%) in Carboniferous coal. The high silicon values of all samples may suggest a technological impact, but the effects may be mitigated by the presence of kaolinite, in addition to quartz, as observed through SEM analysis.  
In the coal ash samples from the five coal mines, the average sulfur concentration is 0.9% in the Jurassic samples and 0.2% in the Carboniferous samples. According to [56], coal with sulfur levels ranging from 1.42% to 5.3% (average 3.36%) by weight is considered high-sulfur and low-quality. This indicates that the sulfur content in these samples is relatively low, which positively affects their technological suitability.  
Research on high-ash coal combustion highlights complex interactions between coal properties and combustion efficiency. A higher volatile matter content reduces unburned carbon, facilitating easier ignition and more complete combustion. However, it can also increase emissions of elements like Hg, Pb, Zn, and Cd, which behave differently depending on furnace temperature [57; 58]. Additionally, high-ash fusion temperatures and the presence of refractory minerals such as alumino-silicates, iron oxides, and titanium oxides impact combustion efficiency, as well as the potential for slagging and fouling [59]. Optimal combustion typically occurs with 25 \(\pm\) 1.75% moderate volatile matter and 20-35% moderate ash content [60] is ideal for combustion because it provides efficient burning with fewer problems like emissions and slagging. The coal samples from the five mines show varying ash yields, with lower values in Jurassic samples and higher values in Carboniferous samples, along with higher volatile matter (Table 5). These differences may result in deviations from the optimal combustion range (20-35% ash yield) and the ideal volatile matter content (around 25%). This suggests that different combustion technologies may be needed for the coal from the five mines to handle these variations effectively.
## 2 Materials and Methods
### Device Fabrication  
Device fabrication consists of several steps: spin coating, photolithography, surface treatment, electron beam evaporation deposition, lift-off and annealing treatment. In the spin-coating process, the WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction was coated with ultraviolet photoresist (LOR, Microchem company, Austin, TX, USA) at 5000/min rotation for 1 min and baked at 180 \({}^{\circ}\)C for 10 min. Then, the WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction was coated with ultraviolet photoresist (RZJ-304-25, Ruihong Electronic Chemicals Co., Ltd., Milpitas, CA, USA) at 5000/min rotation for 1 min and baked at 115 \({}^{\circ}\)C for 1 min. In the photolithography process, source and drain electrodes are patterned via lithography (Microw writer ML3 laser direct-writing system) on MoS\({}_{2}\) at a laser dose of 160 mJ-cm\({}^{-2}\). Then, the WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction was soaked in developer solution for 30 s, followed by deionized water soaking and blow-dry process. In the surface treatment process, the WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction was treated in Ar-plasma with at 4 mTorr for 1 min by using a plasma cleaner (Harrick PDC-002, NY, USA) to remove the remained photoresist. In the electron beam evaporation deposition process, 5 nm Ni and 50 nm Au were deposited by the electron beam evaporator (GX1350 from Tianxingda Vacuum Coating Equipment Co., Ltd, Shenzhen City, China). In the lift-off process, the WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction was soaked in N-Methylpyrrolidone (NMP) at 40 \({}^{\circ}\)C for 30 min, followed by isopropanol soaking and blow-dry process. In the annealing treatment process, the WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction was annealed at 300 \({}^{\circ}\)C in an Ar atmosphere for 2 h by using a tube annealing furnace (OTF-1200X-S from HF-keping Equipment Co., Ltd., Hefei City, China) to realize better contact. The device fabrication process is shown in Figure S3 in the Supporting Information.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 5 Enabling Technologies to Support Smart Mobility
### Role of Enabling Technologies in Smart Mobility Services and Applications
#### 5.2.2 Role in Traffic Flow Optimization and Optimization of Logistics  
Traffic optimization is one of the primary needs in terms of mobility. In [131], the authors propose a blockchain-based approach focused on air traffic optimization, where blockchain technology is used to ensure a secure, transparent and decentralized platform.  
The transport sector's contribution to CO\({}_{2}\) emissions is 23%, with traffic congestion accounting for \(\frac{3}{4}\) of this value [132], hence the optimization of the traffic flow (private and also from the point of view of logistics) is essential if CO\({}_{2}\) emissions are to be reduced.  
Real-time analysis of traffic information is essential to efficiently manage urban traffic. In [133], the authors propose the monitoring and analysis of traffic through unmanned aerial vehicles (UAV) for real-time video collection, and artificial intelligence techniques for the processing and identification of moving objects. In [134], the authors propose a model for collecting and analyzing traffic patterns, using cameras at intersections in order to help reduce traffic congestion. In order to reduce network congestion, the authors propose processing at the edge, without sending the Big Data set to the cloud. Processing is performed using deep learning techniques and algorithms. Is it important to highlight that these types of approaches can also help to solve problems related to transportation more related to the logistics sector.  
The use of sensors and an IoT network for traffic monitoring and optimization is the result of numerous contributions in the literature [135]. In [136], a solution is proposed to optimize the traffic flow at intersections based on ultrasonic sensors integrated with a Raspberry Pi that operate on the road lines, taking into account the density of the traffic. The authors stress the importance of using IoT to drive for smarter safety and safer devices on the road.  
The performance of the various stakeholders of the supply chains, including transport logistics, needs transparency so that it can be a connected, intelligent and efficient system. In [137], the authors propose a new approach to the use of blockchain to ensure the integrity of the performance monitoring of the logistics sector. On the other hand, in [138], and given the lack of a traceability mechanism in the logistical transaction, as well as the integrity and security of the information, the authors propose a traceability algorithm in logistical transactions based on blockchain.
# Transforming Group Codes in Mealy Finite State Machines with Composite State Codes
###### Abstract  
A new state assignment method focusing on Mealy finite state machines (FSMs) is proposed. The proposed codes are an alternative to composite state codes (CSCs). CSCs are represented as concatenations of group codes and partial state codes. Both group and partial state codes are maximum binary codes. We propose encoding groups using one-hot codes. The main goal of this method is improving the value of the FSM cycle time without a significant degradation of the spatial characteristics. The method can be applied if FSM circuits are implemented using the look-up table (LUT) elements of field-programmable gate arrays (FPGAs). The resulting FSM circuit includes three logic blocks. The first block generates partial input memory functions and FSM outputs depending on maximum binary state codes and one-hot group codes. The partial codes are assigned in a way minimizing the number of arguments in the partial functions. This allows for the generation of most partial functions by single-LUT circuits. The second block generates the final values of the input memory functions and FSM outputs. This block does not require group codes to generate functions, as in CSC-based FSMs. The third block transforms maximum binary group codes into their one-hot equivalents. The proposed approach allows for a reduction in the number of series-connected LUTs in comparison with CSC-based FSMs. Due to this reduction, the temporal characteristics of an FSM circuit are improved. This paper includes an example of FSM synthesis applying the proposed method. The experiments were conducted using standard benchmark FSMs. The results of the experiments show that the proposed method allowed for an improvement in the cycle time of an average of 8.81%. Moreover, in relation to CSC-based FSMs, the LUT counts decreased by an average of 4.00%.  
Mealy FSM; FPGA; LUT; synthesis; composite state assignment; one-hot group codes +
Footnote †: journal: Applied sciences
# Trusted Academic Transcripts on the Blockchain: A Systematic Literature Review
## 4 Results
### Publication Year and Geographic Distribution  
Figure 2 shows the distribution of articles ordered by year of publication. It can be seen that although no restriction was added, the timeframe is only five years (2016-2020). While one article was published in 2016, the number increases in 2017 with a further boost in 2018, which quintuple the retreived amount.  
Figure 1: Literature Review Strategy.  
Figure 2: Publications distribution per years.  
A peak is seen to be reached in 2019 with 20 articles published, while 2020 shows a slight decrease with ten items. Since the data gathering took place around October 2020 (articles were retrieved from the Scopus, WOS, and EBSCO host databases on the 30 th of September, 8 th of October, and 10 th of October, respectively), this literature review lacks publications that had not yet been indexed and those from the last two months of 2020. Identifying whether the fewer publications imply a decrease of interest in the subject is troublesome. It must also be noted that since most of the papers are of an empirical nature and tend to be published in conferences, the COVID-19 pandemic may have interfered with tests and delayed publication processes.  
Regarding geographical distribution, Figure 3 shows that publications hailed from Asia (35% n = 17), Europe (33% n = 16), North America (6% n = 3), and South America (2% n = 1). Concerning specific countries, the majority of contributions came from China (4) followed by India, Pakistan, Portugal, and the UK (3). Finally, the USA, Spain, and Portugal produced two while the others produced just one.
## 2 Background and Proposed Method
### Proposed Method Overview
#### 2.4.1 Network Architecture  
The proposed model architecture comprises two components. The first component, as illustrated in Figure 3, integrates a CNN encoder with Transformer encoders, incorporating a projection matrix. This projection matrix is utilized exclusively during the pre-training phase. The Transformer encoders consist of 12 layers, each performing self-attention operations. Each layer has an output channel dimension of 768, with 12 attention heads, and a projection dimension of 256 for the loss function. The second part shown in Figure 4 depicts details of the improved ECAPA-TDNN. Among them, Res2 net-U, SE-attentive mean, and multi-heads attentive statistics pooling are shown in Figure 4b, Figure 4c and Figure 4d, respectively. The PNR-TDNN and ECAPA-TDNN share similarities in their capability to process speech segments of varying lengths. Signals with a duration exceeding 0.5 s are used for training and testing. When the signal length exceeds 3 s, it is truncated to 3 s.  
Figure 3: The pipeline of PNR. The PNR processes contaminated speech, which is encoded sequentially through CNN and HuBERT encoders. The representation is then transformed into output by a projection layer.
## 3 Results and Discussions
### Compressive Properties
#### 3.3.3 Compressive Test Results for Rotation Structures  
In the rotation lattice structures, the uniform and RG specimens outperformed the other specimens in terms of both first compressive strength and elastic modulus (Figure 11). This indicates that these two configurations allow a more effective load distribution on the lattice.  
Figure 11: Stress–strain curves for rotation specimens.  
Figure 10: Stress–strain curves for re-entrant specimens.  
According to the compression test results, the highest compressive stress and elastic modulus values are obtained in the rotation structure (Figure 12a,d). Maximum Young's modulus values were obtained in the uniform and radial configurations of the rotation structure. This is due to the fact that these two configurations reach high compressive stress values at very low strain values.  
The greater strength of the rotation structure compared to the other structures resulted in high compressive stress, while the brittle material properties resulted in a low strain value. FGLSs were seen to have the highest mechanical strength values among all of the lattices. Therefore, FGLSs gave the best results for the elastic modulus. The lowest mechanical strength and strain values were observed in the VG specimens (Figure 12c).  
The results show that the gradient structure provides a more balanced load distribution. As seen Figure 12, although the mechanical strength and elastic modulus results obtained with the chiral structure were worse than the rotation structure, the ductile material property was reflected in the strain values. This was also reflected in the energy absorption capacity of the chiral structure.