esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Adaptive PCNN Combined with GWO and BEMD  
### Introduction of Intrinsic Mode Functions  
Unlike the traditional integral transform techniques, EMD is a multi-scale partial wave analysis method. However, BEMD offers higher frequency resolution and more accurate timing of nonlinear and nonstationary signal events. After a sifting process is applied to the input signal, the intrinsic mode functions (IMFs) can be obtained. The IMF components can be defined using two conditions: (1) the number of extrema and the number of zero-crossings should either be equal or differ only by no more than one; (2) at each point, the mean of the defined envelope through the local maxima and the envelope that is defined by the local minima must be zero. Then, the decomposed result of the IMFs denoted by \(m_{i}\) and the residue _r_are as shown:  
\[s=\sum_{i=1}^{n}m_{i}+r \tag{1}\]  
where \(s\) is defined as the input signal, \(n\) demonstrates the number of the IMFs.  
The IMF components represent each of the frequencies of the local data and thus correspond to both high-frequency and low-frequency data. The residue function component represents the tendency of the original image. The exact results of the IMFs constitute the BEMD [28].
## 2 Materials and Methods
### Equipments  
Scaffolds were printed using a fused deposition modelling process (FDM) on a commercial 3D-printer (CR-10 Max, Shenzhen Creality 3D Technology Co. Ltd., Shenzhen, China). Electrospinning was done in an apparatus consisting of a 5 mL luer-lock glass syringe (Hamilton Gastight, Franklin, MA, USA) connected to a PTFE tubing with a PEEK luer-lock connector with a flat-end 22G metallic needle, a syringe pump (NewEra Pump Systems Inc, Farmingdale, NY, USA) to control the feeding rates, a plastic plate covered with a rectangular metallic mesh, a high voltage DC power supply (Genvolt High Voltage Ind. Ltd., Bridnorth, United Kingdom) connected to the metal needle and to the ground metallic mesh. SEM images of fibrous materials were obtained in CrossBeam Scanning Electron Microscope (FIB-SEM Auriga Compact, Zeiss, Aalen, Germany). SEM-EDS images were obtained in a variable pressure SEM (PV-SEM EVO MC10, Zeiss, Aalen, Germany) possessing an X-ray microanalysis detector (Oxford Instruments, Abingdon, United King). Wettability of fibrous materials was measured by the sessile drop method in a contact angle meter (HO-IAD-CAM-01, Holmarc Opto-mechanotronics PVT. LTD., Kochi Kerala, India). Thermal and mechanical properties of 3D-printed scaffolds and composites were measured using a dynamic mechanical analyzer (DMA 8000, Perkin Elmer, Waltham, MA, USA). Tensile tests were carried in the fibrous materials by stress-strain tests using a texture analyzer (CT3-1000, Brookfield, Middleboro, MA, USA). Colorimetric MTT assays were performed using a UV-vis spectrophotometer (Bio-MateTM 3S, Thermo Fisher Scientific Inc, Waltham, MA, USA). Simulated body fluid assay was done in a thermostated orbital shaker (WiseCube WIS-20, Witeg Laborthencik GmbH, Wertheim, Germany).
## 2 Materials and Methods Background
### Basic BEM Design with IoT Integration  
The Internet of Things (IoT) is at the heart of smart urban area (city) implementation. In other words, IoT represents the intelligent technical backbone of smart urban areas (cities), as shown in Figure 9. Smart \"urban areas\" (cities) must have three key characteristics:  
* Intelligence,
* Network connection,
* Instrumentation, that IoT can provide. It can be said that by using IoT, smart areas or cities will become feasible. The use of smartphones, smart meters, smart sensors, and radio frequency identification (RFID) essentially forms the framework of IoT in smart cities (see Figure 10) [38].  
In addition to the key characteristics of a smart urban area, or more precisely a smart city, the smart energy system (SES) is a compatible and integrating element. \"A smart energy system consists of the smart integration of decentralized sources of sustainable energy, efficient distribution, and optimized energy consumption. Therefore, smart energy consists of three independent building blocks that must be interconnected and communicate with each other effectively to form a unified smart energy system\" [39]. The fulfilment of  
Figure 10: Optimization of energy consumption, IoT and building management system.  
Figure 9: Smart Cities and IoT.  
the SES is achieved by proposing a new approach to the solution thereof, which is shown structurally (model) in Figures 4 and 5. The solution for energy consumption optimization is documented in Figure 10. Subsequently, the SES is verified in our experiment, which will be described in the following section.  
The sensor (see Figure 10) can be considered the lowest layer of the IoT architecture that allows collecting data from the environment and sending it to SCADA for further processing. The sensors can be integrated into various electrical appliances to create an IoT-integrated device. These \"smart\" appliances can be remotely controlled and managed by a PLC (Programmable Logic Controller) or FOXTROT/KNX/ABB-free@home@, and they can read information. They can be connected via structured wiring or wireless communication via a cloud. Communication can be carried out directly from the device to the cloud (device-to-cloud), or the device communicates with a gateway connected to the Internet (device-to-gateway). Typically, the gateway includes software facilitating the data transfer to the cloud application.
# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
### Paper Organization  
Table 4 summarizes the organization of this paper. Section 1 presents the introduction of FANETs, the 5G access network, and the structure of vertical clustering in 5G-based FANET. Furthermore, this section contains the distinguishing aspects of our research and contributions. Section 2 presents the core elements of the 5G access network (i.e., network planes and controllers) and their functions, and the hybrid framework and its advantages. This section also explains the categories of data based on their lifetime, and the significance of fresh data. Section 3 presents the traditional clustering scheme, DQN-based vertical routing, cluster maintenance, the three main components of DQN, the DQN algorithm, and the reinforcement learning algorithm. Section 4 presents research implementation, baseline approaches, ranges of important parameters, energy models, the selection of various performance measures, the analysis of RL and DQN approaches based on learning rate, convergence, simulation results, and complexity analysis. Section 5 presents the significant research outcomes. Section 6 presents future research directions.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{227.6 pt}} \hline \hline
**Section** & **Detail** \\ \hline Introduction & Section 1 presents the introduction of FANETs, 5G access network, and the structure of vertical clustering in 5G-based FANET. Furthermore, this section contains the distinguishing aspects of our research, contributions, and organizational structure of paper. \\ \hline Network Architecture & Section 2 presents the discussion about core elements of 5G access network (i.e., network planes and controllers). It also presents the discussion on the hybrid framework, functions of controllers, and advantages. It defines the categories of data based on their lifetime, and the significance of fresh data. \\ \hline System Model and Functions & Section 3 presents the traditional clustering approach, routing mechanism, and cluster maintenance. It presents a detailed discussion of vertical routing based on a use case scenario as shown in Figure 1. Furthermore, it presents DQN-based vertical routing, the three main components of DQN, and the DQN algorithm as shown in Algorithm 1. It also presents the discussion and algorithm of reinforcement learning as shown in Algorithm 2. \\ \hline Performance Evaluation, Results and Discussion & Section 4 presents a detailed discussion of the implementation of research, baseline approaches, ranges of important parameters, energy models, the selection of various performance measures, the analysis of RL and DQN approaches based on learning rate, the convergence of proposed schemes, and a comprehensive discussion of simulation results. Furthermore, it presents a complexity analysis including its parameters. \\ \hline Conclusion and Future Work & Section 5 presents the significant research outcomes and the future research direction. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Organization of this paper.
## 3 Results and Discussion
### Effect of Tread Knobs
#### 3.2.4 Static Lateral Stiffness  
To understand the contribution of tire tread knobs to total (treaded) tire lateral stiffness, knob shear stiffness, and bald carcass lateral stiffness were measured separately and then combined, in the manner of springs in series, as in Equation (7), to compare against the measured lateral stiffness of the knobby tire.  
\[\frac{1}{K_{total\text{ }tire}}=\frac{1}{K_{bald\text{ }arcass}}+\frac{1}{K_{tread}}, \tag{7}\]  
As illustrated in Figure 13, the shear stiffness of the tread knobs was measured by isolating an integral number of knobs (six shown) of an unmounted tire between matching aluminum plates. Vertical load was applied directly from the bare rim to the top aluminum plate. Then, the rim was pulled laterally while two cap screw heads prevented it from sliding relative to the upper plate. The applied lateral force and resulting lateral deflection of the rim were recorded simultaneously. The measured tread lateral stiffness was then divided by the number of knobs present between the plates to yield an approximate individual knob stiffness.  
Figure 12: Force and moment fitted curves for \(29\times 2.3\)” knobby, \(29\times 2.3\)” bald, \(29\times 2.5\)” file-tread, and \(29\times 2.5\)” bald tires at 25 psi (1.7 bar) on 25 mm inner width rim with (**a**) normalized lateral force vs. slip angle, (**b**) normalized lateral force vs. camber angle, (**c**) normalized self-aligning moment vs. slip angle and (**d**) normalized twisting torque vs. camber angle.  
Finally, per Equation (8), the tread shear stiffness was calculated as springs in parallel by multiplying the individual knob stiffness by the number of knobs evident in the tire's footprint.  
\[K_{trad}=n_{knobs}*k\;n_{knob}, \tag{8}\]  
These respective stiffness values and the comparison of measured versus calculated total tire stiffness are shown for the \(29\times 2.3\"\) knobby tire in Figure 14.  
As can be seen, the approximate total tire stiffness matched well (within 8%) with the measured lateral stiffness of the inflated knobby tire. Possible sources of variation included difficulty in determining exactly how many knobs, or fractions thereof, were included in the contact patch. This approach also assumed equal stiffness contribution from various knob typologies and ignored possible variation in load distribution across the patch. A similar investigation was not repeated for the  
Figure 14: Bar chart of knob, tread, bald carcass, measured, and calculated total tire stiffness for \(29\times 2.3\"\) knobby tire comparing relative stiffness magnitudes at 25 psi (1.7 bar).  
Figure 13: Schematic illustrating non-destructive measurement of lateral shear stiffness of tire tread knobs via loads applied to unmounted tire squeezed between appropriately sized aluminum plates.  
file-tread pattern because of the impracticality of counting and measuring the stiffness of an integral number of the file-tread knobs.
# Electrochemically Activated Screen-Printed Carbon Electrode for Determination of Ibuprofen
## 3 Results and Discussion  
### Electrochemical Behaviour of IBP  
In the first stage of experimental work, the electrochemical behaviour of IBP was examined at the SPCE in the 0.25 M acetate buffer solution of pH 4.5 \(\pm\) 0.1 containing 0.1 mM IBP. The cyclic voltammograms (CVs) were recorded for the different scan rates from 5 to 500 mV s\({}^{-1}\), and two time of stirring solution (5 and 60 s without applying potential to the working electrode) before IBP determination. CVs indicated the presence of one peak in the anodic run (around 0.9 V). In the backward scan of the CVs no IBP signal was visible (Figure 1A). The stirring time extending contributed to an increase in the intensity of the IBP peak current (6.7 vs. 8.5 uA). This is connected with facilitated diffusion of IBP molecules in the supporting electrolyte to the electrode surface during stirring [16]. It was confirmed on the basis of the dependence between the IBP peak current (Ip) and the square root of the scan rate (v\({}^{1/2}\)). As can be seen in Figure 1B, the linear correlations (r = 0.9974 for 5 s and r = 0.9981 for 60 s) suggest that the process is diffusion-controlled. Moreover, the effect of solution stirring time under open circuit (0-120 s) on the analytical signal of 20.0 uM IBP was evaluated (Figure 1C). It was found that the peak current of IBP is lower without stirring solution before the voltammetric signal recording. The IBP signal reached maximal and stable value in the time of 45 s, so for further studies the stirring solution time of 45 s was chosen.
## 2 New EMI Shielding Materials: Biochars, Scaffolds, Rare Earth, and Ferrite-Based Materials
### Iron Oxide-Based Materials as EMI Shielding Material
A significant effect on the EMI shielding effectiveness of the composites was assigned to the morphology of the metallic nanoparticles and the layered structure of the C component of the composites. They achieved an EMI SE of \(-\)52.14 dB. Record values of EMI shielding effectiveness were achieved for MXene by incorporating magnetic nanoparticles as intercalators between layers [133] and 3D N-doped GO with silver nanowires (\(-\)79.99 dB) at a thickness of 2.660 mm [134].
## 4 Experimental Testing
### Training Objectives  
In their iterative releases, the \"BigScience\" workshop team [15] introduced multiple versions of 'BLOOM' [15] that was implemented using a range of parameters along with clearly specified hyperparameters and configurations for each version, as seen in Table 1.  
Given the project's initial aim of developing a chatbot model requiring limited hardware resources, the 'BLOOM' model with a 7 billion parameter set (BLOOM-7B1) has been identified as the most suitable model. Related studies have also identified the BLOOM-7B1 model as their backbone including LLaMA-7B for Alpaca [48] and BLOOM-7B1 for the Phoenix model. All experiments conducted in this study use the BLOOM-7B1 model as the backbone. Recall that an autoregressive language model defines a conditional distribution, where the probability of the \(i\)th word--\(x_{i}\)--depends on the contextual meaning or the words \(x_{1:i-1}\) as shown in Equation (6):  
\[p(x_{i}\mid x_{1:i-1}) \tag{6}\]  
Equation (6) uses the following steps:  
* **State 1** Map \((x_{1:i-1})\) to contextual embeddings \(\phi(x_{1:i-1})\);
* **Step 2** Apply an embedding matrix \(E\in\mathbb{R}^{V\times d}\) to obtain scores for each token \(E\phi(x_{1:i-1})_{i-1}\);
* **Step 3** Exponentiate and normalize it to produce the distribution over \(x_{i}\).  
Steps 1-3 are succinctly shown as Equation (7)  
\[p(x_{i+1}\mid x_{1:i})\ =\ \text{softmax}(E\phi(x_{1:i})_{i}) \tag{7}\]  
**Maximum likelihood:** Let \((\theta)\) be all of the parameters of large language models. Let \((\mathcal{D})\) be the training data consisting of a set of sequences. Following the maximum likelihood in the principle function, we define the following negative log-likelihood objective function as a loss function \(\mathcal{L}(\theta)\) given in Equation (8):  
\[\mathcal{L}(\theta)\ =\ \sum_{x_{1:L}\in\mathcal{D}}-\log p_{\theta}(x_{1:L})\ =\ \sum_{x_{1:L}\in\mathcal{D}}\sum_{i=1}^{L}-\log p_{\theta}(x_{i}\mid x_{1:i-1}) \tag{8}\]
## 4 Results of Monitoring Methods
### Conveyor Force Measurements  
Monitoring of the lateral mistracking of the belt based on the compressive force measured at the cylinders can only be performed when hydraulic tensioning devices are used. The standard design of the hydraulic tensioning device is based on two actuators powered by a hydraulic engine, which move the structure of the tensioning carriage and cause the initial tensioning force of the belt.The measured forces on the hydraulic cylinders are shown in Figure 18. In the case of lateral mistracking, the force measured on one of the cylinders is greater. In the discussed case, the difference is approx. 2.5 kN. Assuming the correct geometry of the joint, the force on the two actuators should be approximately similar, which will allow for symmetrical belt tracking. The system shown in the Figure 18 approaches equilibrium, which results in the belt moving towards the actuator on which the lower value of the compressive force is measured. Mistracking occurs until about 120 s, when the forces equalize. However, due to the shifting of the belt along the length of the pulley, there is already friction of the belt edges and the conveyor structure.  
Figure 17: Temperature change on the edge of the belt.  
Figure 18: Compressive forces on the piston rods of hydraulic cylinders of the belt tensioning mechanism in the case of lateral belt mistracking.  
The difference of the two signals coming from the actuators of the tensioning device can be used for monitoring the operating status and early detection of mistracking. The Figure 19 shows the difference of the forces measured on the actuators in the case of normal operation and for the mistracking state. For normal operation, the signal from strain gauges is stable in time with a standard deviation of 44 N. The standard deviation of the signal at the moment of mistracking is greater as 695 N. In the diagnostic signal, which is the absolute value of the difference between the measured forces or pressures on two actuators, the rising edge appears first. This is the result of a sudden difference in the forces acting on the actuator piston and indicates a tendency to mistracking. Then a trailing edge appears, suggesting that the belt is running laterally.
# A Feasibility Study of a Novel Piezo MEMS Tweezer for Soft Materials Characterization
## 3 Modeling Piezoelectric Actions on the Microbeams  
Concerning the piezoelectric actions, many studies [63; 80; 81; 82] have confirmed that, under certain assumptions (piezoelectric plates are perfectly bonded to the structure, the ratio between their thickness and the thickness of the beam is very low and their inertia and mass negligible with respect to those of the beam) the stresses applied to the beam can be considered as they were concentrated at the piezoelectric plate ends. Moreover, if the electric field to the upper plate is opposite in sign to the one applied to the lower plate, such action is equivalent to a flexural moment (see Figure 3) applied to the end of the beam of magnitude equal to:  
\[M_{a}(t)=\frac{\psi}{6+\psi}E_{a}cT_{a}T_{b}\Lambda(t) \tag{1}\]  
with  
\[\left\{\begin{array}{l}\Lambda(t)=\frac{d_{31}}{T_{a}}V(t)\\ \\ \psi=\frac{E_{a}T_{b}}{E_{a}T_{a}}\end{array}\right. \tag{2}\]  
By activating the piezoelectric plates pairs on the two jaws-beams of the tweezer in opposite manner the two moments \(M_{a}\) will have opposite sign and the beams will bend in opposite direction allowing the gripper to grasp the soft tissue, as depicted in Figure 4.  
Figure 3: Piezoelectric action (PIN force model).  
Figure 2: An alternative layout for the microgripper: outer layers with a same voltage \(V_{0}\) (**a**); structural material for the beams (**b**); internal layers with voltage \(V_{i}\) (**c**) and structural layer for the handle (**d**).
# A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching
## 4 The Proposed HDQN Approach
### Hierarchical Deep Q-Network Approach  
In the process of a microgrid optimization decision, the agent can meet the actual demand of the load through a series of battery-charging and -discharging power decisions and the power distribution of each line. However, the charging and discharging decision of the battery is a discrete variable decision, and the power distribution of each line is a continuous variable decision. Therefore, when considering the line loss of the low-voltage distribution power grid, the energy dispatching problem of the microgrid is a mixed-decision problem with complex constraints, including discrete action-continuous variables. To solve these problems, an optimization approach based on DQN is proposed in this paper. The neural network is used to estimate the action values, so as to realize the decision of continuous variables. At the same time, the discrete action decision is realizedby combining with the DQN network, and, finally, the energy optimization dispatching of the microgrid is realized.  
The action space of comprehensive optimization dispatching of micrometers includes two types of actions. One is the charging/discharging of energy storage equipment, which is a discrete action. The other is the specific transmission power of each line, which is a continuous energy distribution. The energy storage equipment at the time of the charging and discharging decisions will affect the future energy status of the microgrid system and has long-term regulatory significance. Therefore, this paper designs a double-layer HDQN framework in which the upper-layer agent is used for the charging and discharging decisions of energy storage devices, and the lower-layer agent receives the decision-making actions of the upper layer. Based on the upper-layer decision, an Actor-Critic network is used to obtain the optimal action that minimizes the system operation cost. All actions of the upper-layer agent and the lower-layer agent constitute the entire action space at that moment, acting together on the environment to obtain the next state.  
The HDQN approach framework is shown in the Figure 2, including environment, experience pool, rewards and different neural networks. Agents at both the upper and lower layers can sense the current state of the environment. The evaluation network of the upper-layer agent can solve the long-term battery charging and discharging decisions, and the action and evaluation network of the lower-layer agent can solve the optimal line power distribution and output of each controllable unit at the current moment.
## 2 Experimental System and Working-Conditions  
### Experimental System and Method  
The schematic diagram and physical objects of the experimental system are shown in Figure 1. Argon, ammonia, oxygen, methane, carbon monoxide, and other gases used in the experiment were stored in high-pressure gas cylinders, and the pressure-reducing valve was used to control the opening of the gas cylinder. Air flow was controlled by the flow meter and D08-3E flow indicator with an accuracy of \(\pm\)0.5%.  
Figure 1: High-temperature gas combustion system. (**a**) Schematic diagram and; (**b**) Physical image objects.  
The gas was mixed and then supplied to a corundum tube for reaction. The electric heating system could control the furnace temperature through a temperature control program. The inner diameter of the corundum tube was 30 mm and the length of constant-temperature zone was 290 mm.  
The amount of NO produced by combustion was determined by the testo 335 gas analyzer with a range of 0-2000 \(\mu\)L/L and an accuracy of \(\pm\)10% for 0-99 \(\mu\)L/L and \(\pm\)5% for 100-1999 \(\mu\)L/L. Each case was repeated three times and the root-mean-square error was used to represent the test error. One end of the gas analyzer was connected to the exhaust gas and the other end was connected to a computer, and the exhaust gas composition data was monitored by testo easyEmission software.