esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Input Impedance Matching of All Modes  
It is known that the input impedance matching of all modes at the desired frequency 35 GHz is essential to the resonant antenna. In this article, the antenna is fed by a 50 \(\Omega\) microstrip line, and the input impedance is transformed by a microstrip impedance transformer. The simulated return loss  
Figure 6: Radiation patterns of reconfigurable antenna. (**a**) Theoretical evaluation of patterns; (**b**) Simulation of patterns.  
Figure 7: Photograph of the proposed antenna. (**a**) Antenna with testing holder; (**b**) Pure antenna fabricated on high resistivity silicon.  
of all modes at the desired frequency are all greater than 20 dB, as shown in Figure 8a. As shown in Figure 8b, the measured return losses of all modes are greater than 10 dB. The discrepancies between the measured results and the simulated ones are mainly caused by conductor loss and the manufacture process, such as the lithographic resolution, the residual polyimide, the inhomogeneity of thickness, and the asymmetry between the left RF MEMS switch and the right RF MEMS switch. However, the shapes of the curves in Figure 8a,b are similar, and the measured return loss reached 10 dB at desired frequency. Thus, the manufactured antenna has acceptable return loss curves. The proposed antenna is in impedance matching and resonant states at all pattern reconfigurable modes.
## 1 Introduction  
In the 1930s, [PERSON] [1], proposed a reaction-diffusion model to describe the interactive dynamic of genes. In parallel, [PERSON], [PERSON] and [PERSON] [2] proposed the same equation in combustion theory. In both cases, the models considered a Gaussian order two-diffusion model with a non-linear reaction of the form \(f(u)=u(1-u)\). The authors introduced the concept of travelling wave (TW) solutions to describe the propagation features of each involved species. Afterwards, the Fisher-KPP model was subjected to remarkable mathematical research to explore further applications (see [3, 4, 5]). More recently, some analyses [6] have shown new patterns of formation in chemistry and biology (compared to those existing in the current literature, see the remarkable references [7, 8]) for travelling waves solutions and making use of numerical algorithms.  
The aim of this paper was to define a model and obtain results to predict the behaviour of interacting gases in a global medium with forced convection (advection) and diffusion. The gas interacting process, aimed to model, consists of the removal of oxygen from a fuel tank to prevent any hydrocarbon combustion in the case a generated spark or hot spot occurs [9]. One of the technical solutions raised for this purpose is known as an inerting system, which shall be understood as a fire preventive [10]. As a short description, the inerting system consists of a filter or membrane that separates the nitrogen and oxygen in the air. The nitrogen is introduced into the fuel tank while the oxygen is expelled outwards. The inerted atmosphere prevents the fire propagation as there is not sufficient oxygen concentration to produce and sustain a combustion process.  
One possibility to model the nitrogen-oxygen interaction is to consider the nitrogen replacement by oxygen in the fuel tank air space. In [11], the authors developed an algebraic model based on a mass balance to determine the oxygen and nitrogen concentrations in fuel tank bays. Additionally, in [12], the algebraic model was compared with a transport differential equation based on a mass balance in a differential tank element. In both cases, the modelling exercise does not introduce diffusion into the interacting species,although the forced convection is introduced by the transport (in the sense of particle movement) phenomenon modelled.  
The diffusion in the fuel tank was introduced by [PERSON] et al. [13] for simulating the interactive process of fuel vapours in a fuel tank. Such diffusion was proposed to be governed by the classical parabolic homogeneous equation:  
\[\frac{\partial C_{f}}{\partial t}=D\frac{\partial^{2}C_{f}}{\partial z^{2}}, \tag{1}\]  
where \(C_{f}\) is the fuel concentration in the air, and \(z\) is the vertical coordinate. This problem considers only the diffusion, disregarding the natural convection terms and reaction or absorption phenomenon. The intention of this paper was to consider the forced convection together with a proposed interaction between the substances in the fuel tank.
## 2 Simulation of Bowden Cable-Fabric Force Transfer and Analysis of Lower Limb Motion
### Angle and Torque Fitting of Knee Joint
By using this method, the system can better fit the torque curve of the knee extension required by the human body in the walking process through the combination of two sine curves. \[F_{d}(t)=\left\{ \begin{array}{ll}F_{d}{}^{p}sin\left(\frac{\pi\left(t-T_{d} ^{0}\right)}{2\left(T_{d}^{p}-T_{d}^{0}\right)}\right)&T_{d}{}^{0}\leq t<T_{d} {}^{p}\\ F_{d}{}^{p}sin\left(\frac{\pi\left(T_{d}^{p}-t\right)}{2\left(T_{d}^{p}-T_{d} ^{p}\right)}\right)&T_{d}{}^{p}\leq t<T_{d}{}^{e}\\ 0&otherwise\end{array} \right. \tag{2}\]  
where \(F_{d}\) and \(F_{d}{}^{p}\) are the magnitude of expected force and peak force, respectively, \(t\) is the current gait cycle, and \(T_{d}{}^{0}\), \(T_{d}{}^{p}\), and \(T_{d}{}^{e}\) are the start time, peak time, and end time of the force curve. According to Figure 4b, the actual peak torque of the knee joint is 40 Nm. According to the parameters of the lower leg model, the expected peak was set to 40 N, 50 N, and 60 N when the exoskeleton auxiliary efficiencies were 40%, 50%, and 60%. By substituting the peak value of the expected force into Formula (2), the expected force curve of the knee joint under three different peak values was obtained and is shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Amplitude** & **Value** & **Period** & **Value** & **Phase** & **Value** \\ \hline \(a_{1}\) & 48.16 & \(b_{1}\) & 0.01951 & \(c_{1}\) & \(-3.112\) \\ \(a_{2}\) & 525.2 & \(b_{2}\) & 0.1239 & \(c_{2}\) & \(-0.02836\) \\ \(a_{3}\) & 540.2 & \(b_{3}\) & 0.1232 & \(c_{3}\) & \(-3.159\) \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of the Sinusoidal fitting curve. Figure 5: Fitting curve of knee joint motion angle.
## 3 Methods
### Framework for Spatiotemporal Feature Extraction Based on Dual-Stream Networks
#### 3.1.1 STA-Block
The latter's task is to map these advanced features back to the original input dimensions to fuse them with the outputs of other layers. Although the goals of these two convolutional layers differ, they operate in the same space, namely the dimensions of the original input. Thus, they can perform dot product calculations in the same space, effectively merging the outputs of the two convolutional layers. This design  
Figure 2: Multi-Scale Transpose Attention (MSTA) module. allows the network to operate in different feature spaces and merge these feature spaces when necessary. In this work, we made two fundamental modifications to the Feedforward Network (FN) to enhance representation learning: (1) introducing a gating mechanism and (2) adopting depthwise convolutions. The Multi-Scale Feedforward Network (MSFN) structure we designed is shown in Figure 3.  
The gating mechanism helps regulate the flow of information in the network hierarchy, enabling each layer to focus on finer image attributes. This mechanism is achieved through the element-wise product of the two parallel paths of the linear transformation layer, with one path passing through the GELU non-linearity [32] activation. Similar to Multi-Scale Transpose Attention (MSTA), we also introduced depth-wise convolutions in Multi-Scale Feedforward Network (MSFN) to encode information from spatially adjacent positions, allowing the model to capture channel-specific information better. This helps improve the model's ability to distinguish between different features, enabling it to learn more discriminative feature representations more effectively. Given that the input tensor \(\mathbf{Y}\in\mathbb{R}^{\mathrm{H}\times\mathrm{W}\times\mathrm{C}\times \mathrm{T}}\), the representation of MSFN is formulated as:  
\[ \begin{split}\mathbf{Z}=\mathbf{W}_{p}^{0}\mathrm{Gating}( \mathbf{Y})+\mathbf{Y},\\ \mathrm{Gating}(\mathbf{Y})=\Phi(\mathbf{W}_{d}^{1}\mathbf{W}_{p} ^{1}(\mathrm{LN}(\mathbf{Y})))\odot\mathbf{W}_{d}^{2}\mathbf{W}_{p}^{2}( \mathrm{LN}(\mathbf{Y})),\end{split}  \tag{2}\]  
where \(\odot\) represents element-wise multiplication, \(\Phi\) represents the GELU non-linearity, and LN is Layer Normalization [29]. Overall, MSFN controls the flow of information at each hierarchical level in our pipeline, allowing each level to focus on complementary fine details with other levels. In other words, compared to MSTA, MSFN provides different roles, focused on enriching features with contextual information). In summary, MSFN can further blend token context at each spatiotemporal position to improve classification accuracy.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 5 Results and Discussion  
In this section, we examine the effectiveness of the proposed CNN-based method for shape sensing in continuum robots using a soft e-textile sensor. We start by assessing the ability of the e-textile sensor array to dynamically detect the robot's shape alterations via a 4 \(\times\) 4 grid of sensing points, as illustrated in Figure 7. The electrical resistance at each sensing point varies inversely with the pressure exerted by the robot's deformation, providing a detailed insight into its structural modifications.  
After evaluating the sensor, we focus on the performance of the CNN model. By carefully tracking the training and validation losses, we pinpoint key areas for improvement, enhancing the model's robustness and effectiveness. The assessment further involves testing the model on a separate dataset to confirm its reliability and accuracy in predicting the robot's curvature and the angle of the curvature plane.  
Additionally, to determine the most effective model architecture, we utilized a 5-fold cross-validation technique across five different models. This thorough analysis provided a comprehensive understanding of each model's strengths and weaknesses, helping us identify the most appropriate architecture for our application.  
Finally, we assessed the performance of the feedback shape control for the continuum robot, which integrates Jacobian-based control with the CNN shape sensing model and the e-textile soft sensor.
## 1 Introduction
Results from a study on Ti-6 Al-4V alloys highlight the decrease in cutting force with increasing workpiece preheating temperature during hot machining, albeit with only a marginal improvement in cutting tool life when the preheating temperature is raised to 250 \({}^{\circ}\)C [7]. Conversely, pushing the workpiece preheating temperature to 350 \({}^{\circ}\)C results in a reduction in cutting tool life compared to conventional turning. Given the extensive use of AISI630 hardened stainless steel in industries like the military, power plants, and petrochemical sectors, researchers have dedicated attention to studying tool wear and machinability for this particular steel [8]. For instance, [PERSON] et al. explored the effects of various turning parameters on tool wear and surface roughness in AISI630 steel with a hardness rating of 35 HRC, offering recommendations for cutting speed ranges for carbide cutting tools [9]. [PERSON] and [PERSON] delved into the study of cutting tool flank wear and machined surface roughness in AISI630 steel during conventional turning [10]. They also employed cryogenic cooling techniques using liquid nitrogen and minimal quantity lubrication to mitigate tool wear. [PERSON] et al. focused on assessing the impact of preheating temperatures on cutting tool wear during hot turning of AISI630 steel with a hardness rating of 43 HRC, incorporating plasma and simultaneous cooling of the cutting tool [11]. Their results indicated that a combination of hot turning and cryogenic cooling, known as hybrid machining, could effectively reduce cutting tool wear and surface roughness. Lastly, laser hot milling experiments were conducted on AISI630 stainless steel specimens in a study. The findings demonstrated that preheating the workpiece to 300 \({}^{\circ}\)C in hot milling resulted in a 33% reduction in cutting forces and a consequent 50% reduction in tool wear when compared to conventional milling operations. In the present investigation, both conventional dry turning and hot turning techniques were employed to machine AISI630 hardened stainless steel specimens, focusing on exploring the mechanisms behind cutting tool flank wear and crater wear [12; 13; 14; 15; 16; 17; 18]. Workpiece surface morphology and roughness have also been studied under different conditions and at different workpiece preheating temperatures. Tool tip temperature has been estimated and calculated by applying a validated finite element model to analyze and interpret the reasons for cutting tool wear in preheating machining contrasted to traditional machining operation.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### System Setup, Simulation Parameters, and Assumptions
#### 6.1.2 TowerCo  
In the RANaaS scenario depicted in Figure 8, the TowerCo is responsible for the resource allocation and cost distribution. As a host of multiple operators, we assumed the TowerCo could support the simultaneous maximum resource utilization by all the MNOs meaning a maximum bandwidth utilization and the maximum number of users served simultaneously on all three bands by each operator. This gave us a cumulative TowerCo's bandwidth of 30/60/300 MHz and TowerCo's spatial multiplexing factor of 3/3/24 on the three bands 700/2100/3500 MHz, respectively. Furthermore, it should be able to serve all the users during a given time slot. To do so, we assumed that the TowerCo had enough BB-processing capacity to meet these requirements.
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 4 Behavior-Based Sharding Hashgraph Framework
### Security Problem  
However, the dynamic shards caused by nodes dynamically joining or exiting incur some security problems.  
* Sybil attack. A malicious node intends to create multiple identities to participate in shards, or an identity is used in multiple shards. Intuitively, only when the identity is validated by multiple shards is the node allowed to join a shard. Nevertheless, under unstable communications among shards, high transmission delay makes the node spend a long time waiting for identity authentication.
* Targeted attack. Malicious nodes intend to control a specified shard by compromising honest nodes or joining the shard. When the number of malicious nodes exceeds the fault tolerance of the shard, it is corrupted. Intuitively, the reconfiguration of shards in a small epoch can alleviate the targeted attack. Nevertheless, it results in the frequent interruption of services during shard reconfiguration, reducing the performance of the system.
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 3 Methods and Experimental Procedures
### Experimental Procedures
#### 3.2.4 Automatic Exhibition Guide System  
In the painting exhibition area, before visitors enter the exhibition area, they first register an account, and the system takes a photo of the visitor to store their facial features. At the same time, the guide app is installed and linked to the visitor's mobile device. When the visitor stays in front of a painting for more than 3 s, the hidden camera in the frame captures and recognizes the visitor's identity. The system then automatically sends the audio-visual guide file of the painting to the corresponding mobile device registered by the visitor and triggers the guide app to automatically play the guide introduction of the painting. When the visitor leaves the painting for more than 3 s, the system stops playing the guide introduction of the painting.  
This system integrates facial image recognition technology developed in previous projects as well as real-time detection of the handheld electronic device's position coordinates and orientation. Based on the facial image recognition results, the system can determine the identity, position coordinates, and orientation of the handheld electronic device user, thereby automatically selecting and playing the matching exhibit explanation content. Handheld electronic devices mainly refer to modern mobile phones or tablets, which include display screens, storage devices, global positioning systems (GPS), electronic compasses, multiple processors, and playback modules. These components are used to retrieve and play the matching exhibit explanation content from the device's storage. Additionally, the device needs to be equipped with a data acquisition module to receive the user's selected exhibition category and an adjacent exhibit determination module. The adjacent exhibit determination module detects the device's position coordinates through GPS and calculates the distance between each exhibit and the device based on the position coordinates of each exhibit in the selected exhibition category, thereby obtaining exhibits with a distance less than or equal to the preset value as the adjacent exhibits of the device.
## 4 Structure of the First Diagram for Robot Control, the Iterative Learning Method
### Algorithm Content According to the First Control Diagram  
It is obvious that the Euler-Lagrange model (1) of robot manipulators is equivalent to  
\[\ddot{\vec{q}}=-A_{1}q-A_{2}\dot{q}+\underline{u}_{kh}+\underline{\mu} \tag{14}\]  
Figure 6: Results of the output tracking following 60 repetitions of the first joint.  
Figure 7: Results of the output tracking following 60 repetitions of the second joint.  
where \(A_{1},A_{2}\) are two arbitrarily chosen matrices and  
\[\underline{\underline{\mu}}=\underline{\tau}_{d}+\Big{[}I_{n}-H(\underline{q}, \underline{\underline{\theta}})\Big{]}\underline{\dot{q}}-\Big{[}C(\underline{q},\underline{\underline{\dot{q}}},\underline{\underline{\theta}})-A_{2}\Big{]} \underline{\dot{q}}-\Big{[}g(\underline{q},\underline{\underline{\theta}})-A_{ 1}\underline{q}\Big{]}. \tag{15}\]  
From (15), we see that the route cost calculation of model (1) is contained in this vector \(\underline{\underline{\eta}}\). Based on the equivalent model (14), the proposed intelligent controller for the robot is as follows:  
First, the estimation errors of the new unknown function vector \(\underline{\underline{\eta}}\) will be adjusted, as shown below.  
\[\underline{\underline{u}}_{kk}=\underline{\underline{\theta}}-\underline{ \underline{\mu}} \tag{16}\]  
With the estimation of bias \(\underline{\underline{\delta}}\) as (17) system (14) will become linear:  
\[\underline{\underline{X}}=A\underline{\underline{X}}+B[\underline{\underline{ \theta}}+\underline{\underline{\delta}}],\underline{\underline{\chi}}= \underline{q}=(I_{n},0_{n})\underline{\underline{X}} \tag{17}\]  
with  
\[\underline{\underline{\chi}}=\begin{pmatrix}\underline{q}\\ \underline{\underline{q}}\end{pmatrix},A=\begin{pmatrix}0_{n}&I_{n}\\ -A_{1}&-A_{2}\end{pmatrix},B=\begin{pmatrix}0_{n}\\ I_{n}\end{pmatrix},\underline{\underline{\delta}}=\underline{\underline{\mu}}- \underline{\underline{\mu}} \tag{18}\]  
and \(0_{n}\), \(I_{n}\) are \(nxn\) square matrices with zero.  
Secondly, we control the compensation system in Equation (17) so that its output \(\underline{\underline{\chi}}(t)\) will converge to a desired reference value \(\underline{\underline{R}}(t)\).
## 2 Materials and Methods
### Fabrication of DSSCs Using the AZ Films  
The aerosol-deposited ZnO films on FTO glass substrates were used as working electrodes (WEs) in the fabrication of DSSCs. Firstly, the WEs were immersed in 0.3 mM of N719 dye/ethanol solution for 24 h in dark condition. Next, the WEs were rinsed with distilled water for three times and dried at room temperature. As for the counter electrode (CE), platinum (Pt) coated FTO glass substrates were used. A sandwich-type configuration was used for the fabrication of DSSCs as shown in Figure 2. The two electrodes (WE and CE) were sealed with the thermal adhesive (50 \(\upmu\)m thick) and heated at about 100 \({}^{\circ}\)C on the hot plate to perfectly assemble the two electrodes together. Iodide/triiodide (I/I\({}_{3}\)) was used as an electrolyte prepared by mixing 0.05 M of iodide (WAKO, Osaka, Japan) into 50 mL acetonitrile. After that, 0.1 M of lithium iodide (WAKO, Osaka, Japan), 0.6 M of 1,2-di-mehtyl-3-proyimidazolium iodide (WAKO, Osaka, Japan) and 0.5 M of 4-tert-butylpyridine (WAKO, Osaka, Japan) were add and stirred until homogeneous solution were obtained at room temperature. The electrolyte was injected into the sealed electrodes through the small opening holes at the CE. Finally, the AZ DSSC was sealed using polytetrafluorethylene sealant to prevent leakage of the electrolyte solution. The current density-voltage (J-V) curves were measured using an ADCMT 6244 DC Voltage/Current Source/Monitor and an HAL-320 W solar simulator (Asahi spectra) with a 300 W Xenon Lamp and an air-mass 1.5 global filter was used for light irradiation. The incident light intensity and the active area were kept at 100 mW/cm\({}^{2}\) and 1 (1 \(\times\) 1) cm\({}^{2}\), respectively.  
Figure 2: Photographs showing the (**a**) Pt counter electrode (CE), (**b**) N719-sensitized working electrode (WE) and (**c**) fabricated sandwich-type ZnO film based DSSCs.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
## 1 Introduction
They achieve an efficient load balancing under predefined reliability constraint. The key contributions of this paper are summarized as follows: First, we presented a mathematical analysis for the selection of relay nodes based on the remaining energy and reliability. Second, we proposed MMREP to select the path that maximizes the remaining energy of its nodes subject to the reliability constraint. Third, we introduced the entropy-like function to measure the uniformity of residual energy distribution. Fourth, we proposed OREBP, which selects the path based on the entropy of residual energy of the path. The remainder of the paper is organized as follows:  
* In Section 2, we provide an overview of related work in the literature. * In Section 3, we introduce the framework in which the different communication protocols will be discussed. * In Section 4, we describe MMREP to select the path which keeps the residual energy as maximum as possible. * In Section 5, we describe OREBP to select the optimal path that maintains uniform residual energy distribution. * In Section 6, we give the numerical results of a detailed performance analysis of the algorithms where their performances are compared with other protocols. * In Section 7, some conclusions are drawn, and remarks on future research directions are given.