esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Examples
### Series System  
Consider a series structure system comprising two failure modes, with the corresponding function denoted as follows.  
\[y_{1}=2x_{1}+2x_{2}-4.5x_{4}-100 \tag{11}\]  
\[y_{2}=x_{1}+x_{2}+2x_{3}-4.5x_{4}+380 \tag{12}\]  
where \(x_{1}\), \(x_{2}\), \(x_{3}\), and \(x_{4}\) are normal random variables, specifically denoted as \(x_{1}\sim N(200,30^{2})\), \(x_{2}\sim N(200,30^{2})\), \(x_{3}\sim N(200,30^{2})\), and \(x_{4}\sim N(80,15^{2})\), respectively. The failure conditions of the system are defined by \(y_{1}<0\) or \(y_{2}<0\).  
The adaptive AdaBoost surrogate model is employed to assess the failure probability of the series system. The variation in error concerning the number of iterations throughout the training process (resubstitution loss) is illustrated in Figure 3.  
As depicted in Figure 4, it is evident that with an increasing number of iterations, the error of the adaptive AdaBoost surrogate model gradually decreases and converges to zero, indicating its efficacy in failure discrimination for the series system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Sampling Frequency** & **Failure Probability** & **Relative Error** \\ \hline Monte Carlo method & \(10^{6}\) & \(4\times 10^{-5}\) & / \\ Adaptive AdaBoost & 500 & \(3.7\times 10^{-5}\) & 7.5\% \\ method & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated failure probability of the parallel system.  
Figure 3: Variation in the parallel system error with the number of iterations during training.  
As illustrated in Figure 5, it is evident that with an increasing number of iterations, the error of the adaptive AdaBoost surrogate model gradually decreases and converges to zero; this suggests that the surrogate model possesses a robust fitting capability.  
The trained model is employed to ascertain the failure probability, which is subsequently compared with that determined using the conventional Monte Carlo method. The results are presented in Table 4, in which the relative error between the results obtained from the adaptive AdaBoost surrogate model and those obtained from the conventional Monte Carlo method is 1.59%, satisfying accuracy requirements and demonstrating the effectiveness of the adaptive AdaBoost surrogate model. In comparison to the traditional Monte Carlo method, which necessitated millions of samples, only 70 samples are required by the adaptive AdaBoost surrogate model, indicating considerably improved sampling efficiency and a reduction in calculational time.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 5 Experimental Results
### Broker-Subscriber Delay  
In the third experiment, we measured the time lapse extending from the moment the broker sends the PUBLISH packet to the point at which it receives the PUBREC packet from the subscriber. This temporal duration was referred to as the broker-subscriber delay. During this investigation, both MQTT and E-MQTT transmitted two packets (PUBLISH and PUBREC) between the broker and the subscriber. However, it is important to note that the broker of E-MQTT incurs an additional computational overhead, attributed to its verification process involving the designated subscriber fields within the PUBLISH packet before sending it to the intended subscribers.  
As the number of subscribers increased, we measured the broker-subscriber delay and calculated the average value according to the number of subscribers. As demonstrated in Figure 16, the delay duration experienced by both MQTT and E-MQTT exhibited a comparable pattern. The average broker-subscriber delay values of MQTT, asynchronous E-MQTT, and synchronous E-MQTT were 1.72, 1.48, and 0.85, respectively. Given that both E-MQTT and MQTT transmitted an equal number of packets, discernible dissimilarities in the delay period were not observed. In essence, the message transmission rates of MQTT and E-MQTT were not significantly different.
## 3 System and Methodology
### Object Tracking  
In the tracking phase, the UAV moves to the target according to the detection results. To maintain real-time detection while tracking, with full consideration of the correlation in sequence frames, we propose an innovative strategy in which the current detection region is centered on the detected area of the previous frame and expands it twice. Finally, the size of the cropped image was adjusted to 300 \(\times\) 300 pixels as the input of the detector to output the current real-time detection results of each frame.
# Weather Files for the Calibration of Building Energy Models
## 3 Results and Discussion
### Simulation and Calibration Results: On-Site vs. Third-Party Weather Data  
Table 2 shows the results of both the base model and the model obtained after the calibration process of the two case studies: N2 and O2 houses. The weather files used were developed using the on-site and third-party weather data during the training and checking periods. The left side of the table shows the housing (N2 or O5); the model (base or calibrated); the type of weather file used, and the simulation period. The right side shows the adjustment results obtained by the thermal zone, following the same scheme proposed in Annex 58. Finally, the adjustment is presented; we performed an area-weighted average of the thermal zones involved in the process (global index). The results (MAE and \(\rho\)) are subdivided by columns highlighted in red showing the ones that exceed the limits proposed by Annex 58. The first result to highlight in Table 2 is that all the models (base and calibrated) that used the on-site weather file were within the acceptable limits proposed by Annex 58. The calibrated models have a better fitting. When looking at the results produced by the third-party weather file, the base models did not comply with the acceptable limits in either house. Their global indices were far from these limits, both in the training period and in the checking period (marked in red), except for one parameter in the O5 house during the training period, whose \(\rho\) index reached 95.0%. This result reinforces the need to use calibrated models for complex energy-saving strategies, such as P2H solutions, MPC applications, etc. On the other hand, when calibrated models from both houses were used, all the weather files (third-party and on-site) were within the proposed limits. Therefore, the calibration process not only makes better use of the indoor data but also overcomes errors in the environmental sensors. This way, a third-party weather file could be considered an option for improving models with an acceptable error index. The fact that the same results were achieved in two different models and that both were checked in an independent period shows that they are robust and avoid the risk of overfitting in the solutions.
## 3 DMPs-DPF Approach for Obstacle Avoidance  
### Overall Framework of DMPs-DPF Approach  
Although the DMPs can generalize the learned skills to new environments, it is still a tricky problem when some obstacles exist in the re-planned motion path. Here, we only consider the obstacles existed in the motion path are stationary. Considering the excellent performance of dynamical potential field (DPF) in obstacle avoidance, it is a good try to combine the DPF with the DMPs model so as to extend its functionality. Essentially speaking, the DMPs model and DPF model are all force field models guided by attractors, which can both be described by a set of differential equations. Therefore, it is reasonable and feasible to insert the DPF force field model after reasonable abstraction and correction into the DMPs model.  
When inserted the DPF term into the transformation system as a coupling term, the transformation system of DMPs can be described as  
\[\tau\dot{v}=K(x_{g}-x)-Dv-K(x_{g}-x_{0})s+Kf(s)+p(o,x,v), \tag{10}\]  
Figure 1: Learning and generalization process of dynamic movement primitives (DMPs).  
Figure 2: Multiple degrees of freedom schematic in DMPs generalization.  
where, \(o\) is the position of the obstacle, \(x\) is the current position of the system, \(\
u\) is the relative velocity of the obstacle and system, \(p(o,x,\
u)\) is the coupling term. The modified DMPs can be called \"DMPs-DPF\" for short.  
The block diagram of DMPs-DPF approach is shown in Figure 3. It mainly contains two parts (the DMPs model and the DPF coupling term) with three functions (learning, obstacle avoidance, and generalization). The DPF coupling term is used to avoid obstacles existed in its motion paths. This term is inserted into the DMPs model through the transformation system shown in Equation (10). Just with this modification, the robot can easily follow the similar steps shown in Figure 1 to learn and generalize related movements, even avoid obstacles may be encountered.  
Compared with the DMPs approach, the main difference is the structure of the transformation system in the DMPs-DPF approach. Just with this modification, the robot can increase the function of avoiding obstacles while maintaining the original learning and generalization functions.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 2 Piezoelectric Materials
### Perovskite
#### 2.2.2 Lead-Free Perovskite Materials
It is the most promising candidate for enhancing or modifying the ferroelectric properties of BTO and SrTiO\({}_{3}\) solid-solution systems [45]. (III) A\({}^{\text{I}}\)B\({}^{\text{V}}\)O\({}_{3}\)  
The key advantages of the typical A\({}^{\text{I}}\)B\({}^{\text{V}}\)O\({}_{3}\) class are its Curie temperature (T\({}_{\text{C}}\) = 420 \({}^{\circ}\)C) [46], good ferroelectric properties, with remnant polarization of Pr = 3.3 \(\mu\)c/cm\({}^{2}\)[47], as well as its excellent electrochemical coupling factors. Owing to these properties, A\({}^{\text{I}}\)B\({}^{\text{V}}\)O\({}_{3}\) materials are widely used as lead-free piezoelectric materials; furthermore, these materials are eco-friendly and biocompatible [48]. Potassium sodium niobate (KNN) (K\({}_{\text{x}}\)Na\({}_{\text{(1-x)}}\)NbO\({}_{3}\)) is commonly used for PENG applications. The phase transition of pristine KNN occurs from cubic (paraelectric) to tetragonal to orthorhombic, and finally to rhombohedral (ferroelectric); the KNN orthorhombic phase is typically observed at room temperature [49]. Various KNN morphologies have been reported in the literature, including nanoparticles [50], nanotubes [51], nanofibers [52], and nonpellets, which have been obtained using different physical and chemical synthesis methods.
# Nickel-Based Structured Catalysts for Indirect Internal Reforming of Methane
## 1 Introduction
Concomitant side reactions take place with DRM, such as reverse water gas shift reaction (RWGS) Equation (2) that lowers the H\({}_{2}\)/CO ratio, methane decomposition Equation (3), and Boudouard reaction Equation (4), both deactivating the catalyst due to the carbon deposits:  
\[\text{CO}_{2}+\text{ H}_{2}\rightleftarrows H_{2}\text{O}+\text{ CO} \Delta\text{H}^{0}=41\text{ kJmol}^{-1} \tag{2}\] \[\text{CH}_{4}\rightleftarrows C+2\text{H}_{2} \Delta\text{H}^{0}=75\text{ kJmol}^{-1}\] (3) \[\text{2 CO}\rightleftarrows C+\text{CO}_{2} \Delta\text{H}^{0}=-172\text{ kJmol}^{-1} \tag{4}\]  
Recently, some authors of the present paper reported that nickel supported on samarium doped ceria (SmDC) is a highly performing catalyst showing remarkable carbon resistance. The oxygen vacancies introduced by Sm enhanced the CO production, leading to a high O uptake by the supports, favoring the coke oxidation [28; 29]. The catalytically active Ni particles are involved in the chemisorption of CH\({}_{4}\) and CO\({}_{2}\), producing the intermediates, CHx and O*, that subsequently form CO and H\({}_{2}\). However, the progressive dehydrogenation of CHx species may form carbon residues, which can cause the deactivation of the catalyst. The Ni species interact strongly with CeO\({}_{2}\), and this interaction is favored in CeReOx solid solutions by the oxygen vacancy formed by the aliovalent dopants (i.e., La, Sm, and Pr). These oxygen vacancies allow the activation of CO\({}_{2}\), producing CO and O* species which can be transferred quickly near the Ni sites favoring the oxidation of the carbon residues, limiting the deactivation of the catalyst. Thus, the deposited carbon amount during the DRM was related to the nature and the amount of oxygen vacancies [1; 30; 31; 32; 33]. In this work, Ni-Ce\({}_{0.85}\)Sm\({}_{0.15}\)O\({}_{2-\delta}\) was used as catalyst powder and deposited on NiCrAl foam by wash-coating to fabricate structured catalysts for DRM. Powder, support, and final structured catalyst were investigated by using different techniques such as: X-ray diffraction (XRD), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), focused ion beam (FIB), temperature programmed reduction (H\({}_{2}\)-TPR), and Raman spectroscopy. Catalytic tests for DRM were performed to evaluate activity, selectivity, and stability at SOFC operating conditions.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 2 Related Work
also focused on network coverage and connectivity issues, in Reference [25], they proposed a sleep-scheduling scheme that ensured a coverage degree requirement based on the dangerous levels of toxic gas leakage area, while maintaining global network connectivity with minimal awake nodes. All these schemes can reduce the nodes' energy consumption, but the working-sleeping cycle strategy for sensor nodes will cause opportunistic node connections, which can further result in the link instability. Therefore, modelling this kind of WSNs is a challenging task, and thus random graph theory was introduced in WSNs. In Reference [26], the authors indicated that WSNs could be represented as a graph with a set of vertices consisting of the sensor nodes of the network and a set of edges consisting of the wirelesslinks between the nodes, and they showed that it was appropriate to use a random geometric graph for modeling WSNs. The random geometric graph evolved from the random graph where edges only exist between adjacent nodes. In Reference [27], the authors further considered heterogeneous sensor nodes that may have different transmission ranges and designed a new routing metric, and then they proposed a novel random geometric graph model based on this metric for heterogeneous WSNs. In Reference [28], the authors focused on the weaknesses of modeling WSNs topology by the means of random graph theory, proposed a new weighted topology model for WSNs based on random geometric theory, in which the strength of the transmitted signal would decrease rapidly along the increasing of distance between different nodes was taken into consideration. In Reference [29], the authors proposed a model of WSNs on a two-dimensional plane using site percolation model, a kind of random graph in which edges are formed only between neighboring nodes, and based on which, they investigated WSNs connectivity and energy consumption at percolation threshold when a so-called phase transition phenomena happens. However, in some scenarios, it is not necessary to study the connectivity of the whole network. For example, in Reference [30], authors studied the problem of optimally determining source-destination connectivity of random networks, and by viewing the network as a random graph, they determined an optimal policy for establishing whether the designated source and destination were connected. In this work, for any node who needs to send its source information that included in the probe message to the mobile sink, an energy efficient probe message forwarding mechanism is proposed. On receiving these probe messages and by analyzing the included source information, the mobile sink can determine the neighbor relationships of these nodes and calculate the link connectivity between any two adjacent nodes, and according to which, the mobile sink can construct an OCRG.
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 2 System Model
### Data Detection  
#### 2.2.1 For Perfect CSI  
For a receiver with perfect CSI, the maximum likelihood (ML) decision rule for the kth bit and the optimum threshold are defined, respectively, as in [13]:  
Figure 1: Synoptic diagram of the proposed blind detection free-space optical (FSO) system.  
\[\left(r[k]-\frac{h}{2}\right)>0;\;\hat{s}[k]=1 \tag{4}\] \[\left(r[k]-\frac{h}{2}\right)<0;\;\hat{s}[k]=0 \tag{5}\]  
where \(\hat{s}[k]\) is the detected bit.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Methods and Approaches for Scheduling in TSN
#### 4.1.1 Schedulability and Dynamism
For example, a GRASP metaheuristic is developed in [45, 46] to enhance the chances of AVB traffic while serving the HTS traffic. Alternatively, study [22] discussed the impact of temporal distribution on schedulability by altering the inter-frame time (i.e., sparsity of traffic) in three different setups. In contrast to [22, 51, 58], they argued that minimizing the window count or traffic sparsity on a link provides more opportunities for other traffic types and yields better scheduling results.
# Development and Testing of a Two-UAV Communication Relay System
## 3 Hardware Platform  
In this section, the hardware construction of the miniature two-UAV system is introduced. A miniature UAV relay system with the ability to relay radio communications was built. The hardware mainly consisted of three communication nodes: a GCS; a UAV that relays radio communication, which was named \"Mom\"; and a second UAV named \"Son\", whose communication with the GCS was relayed through the UAV \"Mom\". The schematic of the major hardware platform for the \"Mom and Son\" relay system is illustrated in Figure 2 and the detailed communication subsystem will be introduced in Section 3.3 later.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 3 ISLs Design  
Due to the rapid change in relative positions between LEO satellites, the ISLs between satellites in different orbital planes are of short duration, which tends to cause frequent link switching. This not only poses a major technical challenge to the recapture, targeting, and tracking of satellites, but also hinders the smooth operation of inter-satellite communication networks. In this case, this paper establishes ISLs based on the principle of full period visibility of the satellites and minimization of the maximum inter-satellite distance. This approach assures the permanence of the link between the satellites and avoids link reconstruction during data transmission.  
In this section, based on the design of the mega-constellation in this paper, we propose a solution for establishing stable ISLs between the satellites to ensure that any two satellites can communicate efficiently
# Reactive Molecular Dynamics Simulations of Polystyrene Pyrolysis  
[PERSON]  
1 College of Sciences, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 College of Sciences, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 College of Sciences, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (C.N.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 College of Sciences, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 College of Sciences, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
2 Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, China; 2 [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] 233444533345 material [19]. In attempts to prepare activated carbon by pyrolysis of PS, it has been found that the heating rate and the target temperature are critical elements in determining the intermediate and final products [13; 20; 21; 1; 22]. Since microstructures of carbon materials play a vital role in the properties and characterization, the controlled pyrolysis of polymers has been studied by [PERSON] et al. [14]. There are only a few reports on preparing carbonaceous materials using PS as a pyrolysis precursor [1; 13]. Experimentally, although PS thermal decomposition yields relatively simple products compared to other commodity polymers, such as PE [23] and PP [24; 25], it is still unsatisfactory in controlling pyrolysis product type, morphology, and pore size [11].  
Working towards the controllable preparation of PS pyrolysis, this work adopts the reactive molecular dynamics (ReaxFF-MD) method to study the effects of heating rate and target temperature on the pyrolysis mechanism. The evolution of the microstructure and the generation of pyrolysis products is investigated in detail to provide a clear cognition of the pyrolysis mechanism from the atomic level. It is found that the heating rate would mainly affect the diversity of pyrolysis intermediates, while the target temperature would affect the tissue morphology of the final equilibrium products. Carbon ring analysis bridges theoretical simulations and experimental observations, making ReaxFF-MD simulations practically relevant.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 4 PHANToM Omni Device Model (MD & SD)  
Based on the scheme presented in Figure 1, this section presents details associated with the MD and SD used as reference for the hardware system proposed in this research. The MD and SD are characterized as a three degree of freedom robotic manipulator, 3-DoF, called the PHANToM Omni [37] (Figure 2). The PHANToM Omni has been widely used in literature as presented in [38; 39]. In this work two of this devices are going to be used: one as an MD and the other as a SD.  
As can be seen from Figure 3, the PHANToM Omni physical structure is formed by a base, an arm with two segments \(L_{1}\) and \(L_{2}\) which are interconnected by three rotary joints \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{3}\) and a tool. The variables presented in Figure 3 are represented by: \(L_{1}\) = 0.135 mm, \(L_{2}\) = \(L_{1}\), \(L_{3}\) = 0.025 mm and \(L_{4}\) = \(L_{1}+A\) where \(A\) = 0.035 mm as described in [40]. These detailed features of the device are essential for performing the kinematics and dynamic calculations.