esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
## 3 Simulation Results
### Effect of Modulation Scheme  
Figure 3 shows the BER of the proposed system, compared to the OFDM, STC OFDM and OTDM using QPSK as a modulation technique with a number of subcarriers N = 16. It is clear that the BER of the proposed system outperforms the other techniques. The difference in SNR between the proposed system and OTDM technique equals 6 dB at BER = \(10^{-3}\). If we compare the STC OTDM with STC OFDM, we find that at BER = \(10^{-3}\), the difference in SNR between them approximately equals 0.7 dB and the SNR between the proposed system and the OFDM technique equals 16.5 dB. Furthermore, the eavesdropper cannot decode the data correctly because the eavesdropper and the transmitter are not in the same channel or both have different basis functions as clearly shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Channel** & **Rayleigh Fading Channel** \\ \hline
**Number of channel taps, L** & 6, 12 and 15 \\
**Type of modulation** & QPSK, 64-QAM and 256-QAM \\
**Number of subcarrier, N** & 16, 64 and 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3: Bit error rate (BER) of the proposed technique (STC OTDM) in comparison with other multicarrier techniques at N = 16 and L = 9 using QPSK.  
In Figure 4, 64-QAM is used with the same value of N. The effect of increasing the M-ary appears as the BER increases with the modulation order. This is because the constellation becomes closer to each other [16]. This appears at BER = \(10^{-3}\), where the difference in SNR between the proposed system and OTDM system is approximately equal to 5 dB, where the SNR of the proposed system is 22.5 dB and 10 dB at 64-QAM and QPSK, respectively, at BER = \(10^{-3}\). Thus, in 256-QAM, the performance of the proposed system degrades to reach 28 dB at the same BER as shown in Figure 5.
# Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks
###### Abstract  
According to the data from Alzheimer's Disease International (ADI) in 2018, it is estimated that 10 million new dementia patients will be added worldwide, and the global dementia population is estimated to be 50 million. Due to a decline in the birth rate and the development and great progress of medical technology, the proportion of elderly people has risen annually in Taiwan. In fact, Taiwan has become one of the fastest-growing aged countries in the world. Consequently, problems related to aging societies will emerge. Dementia is one of most prevailing aging-related diseases, with a great influence on daily life and a great economic burden. Dementia is not a single disease, but a combination of symptoms. There is currently no medicine that can cure dementia. Finding preventive measures for dementia has become a public concern. Older people should actively increase brain-protective factors and reduce risk factors in their lives to reduce the risk of dementia and even prevent the occurrence of dementia. Studies have shown that engaging in mental or creative activities that stimulate brain function has a relative risk reduction of nearly 50%. Elderly people should develop the habit of life-long learning to strengthen effective neural bonds between brain cells and preserve brain cognitive functions. Playing chess is one of the suggested activities. This paper aimed to develop a Chinese robotic chess system for the elderly. It mainly uses a camera to capture the contour of the Chinese chessman, recognizes the character and location of the chessman, and then transmits this information to the robotic arm, which will grab and place the chessman in the appropriate position on the chessboard. The camera image is transmitted to MATLAB for image recognition. The character of the chessman is recognized by convolutional neural networks (CNNs). Forward and inverse kinematics are used to manipulate the robotic arm. Even if the chessmen are arbitrarily placed, the experiment showed that their coordinates can be found through the camera as long as they are located within the working scope of the camera and the robotic arm. For black chessmen, no matter how many degrees they are rotated, they can be recognized correctly, while the red ones can be recognized 100% of the time within 90\({}^{\circ}\) of rotation and 98.7% with more than a 90\({}^{\circ}\) rotation.  
Chinese robotic chess system; convolutional neural network; dementia; forward and inverse kinematics +
Footnote †: journal: _Journal of the Royal Society: Series A_
## 5 Mechanism
### Interfacial Tension Reduction  
Capillary force restricts oil recovery in a reservoir system [94]. The value of capillary force is determined by the IFT between the reservoir fluids and rock wettability [169]. By reducing the IFT and altering the rock wettability, the capillary pressure will decrease, leading to improved oil recovery [170]. IFT is usually measured with the pendant drop method [38] and the spinning drop method [4].  
Decreasing the IFT by using a surfactant is an important mechanism for mobilizing residual oil [94]. Nanoparticles are proposed to assist in decreasing IFT during EOR processes with or without a surfactant. The presence of NPs in a surfactant mixture can improve the rheology of the solution and enhance the surfactant's effect on IFT reduction [171]. Adsorption of NPs onto the surface of the fluid will effectively reduce the IFT between those fluids [38].  
The ability of silica NPs to reduce IFT has been demonstrated by several studies. [PERSON] et al. [172] reported that NWP and HLP silica NPs were able to reduce the oil-water IFT from 26.3 mN/m to 2.55 mN/m and 1.75 mN/m, respectively. [PERSON] et al. [152] added that the magnitude of IFT reduction is directly related to the concentration of NPs, with a higher concentration favorable for lower IFT. In addition, alumina NPs dispersed in brine were able to reduce brine-oil IFT from 19.2 mN/m to 12.8 mN/m, while silica-based NPs were only able to reduce it to 15.7 mN/m [2]. CNTs have also been proven to significantly reduce interfacial tension, demonstrating their potential for use as an EOR agent [147].  
The presence of NPs in a surfactant mixture can improve the rheology of the solution and enhance the surfactant's effect on IFT reduction [171]. Adsorption of NPs onto the surface of the fluid will effectively reduce the IFT between those fluids [38]. Zirconium oxides mixed in a surfactant solution have been shown to significantly reduce the IFT of heptane-fluid and air-fluid [138]. [PERSON] [173] found that zinc oxides could improve the efficiency of a sodium dodecyl sulfate (SDS) surfactant in reducing IFT. The pH of the fluid had a significant effect on IFT, with an acidic and neutral pH favored.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 6 Conclusions  
This paper comprehensively reviews the evolution and optimization strategies of the PBFT consensus algorithm in coalition chains, and provides an in-depth analysis of the core challenges such as communication complexity, master node selection mechanism, and incentive structure. To address these issues, the researchers propose a series of improvements, including hierarchical group consensus, reputation-based node selection, multi-master node architecture, and a hybrid consensus model incorporating economic incentives. Among these strategies, hierarchical consensus mechanisms and high-quality node selection are the most commonly adopted approaches, as they effectively reduce the number of participating nodes and optimize message transmission paths, thereby lowering communication complexity. Additionally, the multi-leader consensus model alleviates the computational burden on a single master node, enhancing system throughput, while the introduction of incentive mechanisms improves node honesty and strengthens system resilience against malicious attacks. These optimizations provide a more efficient and secure consensus framework for consortium blockchain applications, though they also introduce challenges such as increased centralization tendencies and the complexity of incentive models. Future research could further explore the application of machine learning in dynamic node selection, enhance cross-chain interoperability, and optimize the communication overhead to facilitate the widespread application of the PBFT consensus algorithm in large-scale blockchain systems.  
This paper was supported by Innovative Research Group of Chongqing Municipal Education Commission (CXQT19026), and Cooperative Project between Chinese Academy of Sciences and University in Chongqing (HZ2021011). Moreover, this work was supported by the Research Startup Fund of Chongqing University of Technology (0119240197).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Experimental Results and Analysis
### Effect of Electric Field Type on the Breakdown Voltage of Transformer Oil  
In this experiment, a pair of spherical cap electrodes was used to simulate a slightly non-uniform electric field, while a sphere-to-plate electrode was used to simulate an uneven electric field. Breakdown tests were conducted on the two types of oil samples with low water contents.  
The experimental results of nanofluids at low water contents are shown in Figure 10. From the figure, it can be observed that there was a clear difference in the test results between the two electrodes, and the breakdown voltage under the slightly non-uniform electric field was higher than that under the uneven electric field. This is because the fault breakdown in transformer oil is more likely to occur in situations with a less uniform electric field.  
Figure 10: Results of the breakdown experiment of nanofluids under different electric fields.  
Figure 11 shows the breakdown test results of pure oil under two different electrodes. In the breakdown test of pure oil, the difference between the two electrodes was relatively small in terms of the absolute value of the breakdown voltage. Within the experimental temperature range, the breakdown voltage under the slightly non-uniform electric field was slightly higher than that under the uneven electric field. At a temperature of \(-20\)\({}^{\circ}\)C, the breakdown voltage under both electrodes was equal, deviating from the expected results. This indicates that the breakdown voltage of pure oil in an uneven electric field exhibits instability. In comparison to Figure 10, it can be inferred that the nanoparticle material enhanced the stability of the breakdown voltage.  
Comparing the experimental results of the two oil samples, it can be inferred that the electric field type had an influence on the breakdown voltage of pure oil, but the overall difference was not significant. In the breakdown tests of the two oil samples, the absolute value of the breakdown voltage under the slightly non-uniform electric field was greater than that under the uneven electric field. Comparing the two, it can be observed that transformer oil with the addition of nanoparticle materials had an advantage in a uniform electric field. Additionally, the addition of TiO\({}_{2}\) nanoparticle materials played a stabilizing role in the breakdown voltage of transformer oil, especially under low-temperature conditions.
# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
## 3 Proposed IoT-Enabled Healthcare Architecture
### Manager
Based upon the above discussion, the number of messages increases such that it may represent an overload for the healthcare system from a communication perspective. Two strategies are executed to solve this problem. The first is that each group of messages is treated as one message with many flags. For example, the first category (i.e., from UM to HMs) has three message types: guidance, stop, and inform. Hence, one message comprises three flags in its header: a 'G' flag for guidance messages, an 'S' flag for stop messages, and an 'T' flag for inform messages. When the UM needs to send a special message type, its flag should be transformed into one, and the other flags stay at zero values. If two flags are settled into one value, two message types are sent in one message, and the data part in the message is divided into two main parts: one for the data of the first message and the other for the data of the second message. The first strategy can be executed on each message category; thus, the result is only four main control message types. Figure 3 contains a simple structure of one type of control message. The second strategy to decrease the control message overload is message prioritization (see Section 3.3). The functions of HM are summarized as follows:  
* Manage its cluster(s). * Monitor the activities of each thing in its cluster(s). * Update its cluster by deletion/addition of old/new thing(s). * Predict the future events in its cluster(s). * Control the message exchange process in its cluster(s). * Gather information about the things in its cluster periodically. * Send summarized report about its cluster to UM. * Communicate with neighbor HMs. * Select a computing center to process a specific data inside its cluster region. The functions of UM are summarized as follows:  
* Manage the entire domain of the proposed IoT-based healthcare environment. * Select additional HMs for a specific cluster (If required). * Achieve the load balancing issue between clusters. * Achieve the fault tolerance issue inside clusters. * Achieve the communication between clusters' HMs. * Evaluate the clusters in the healthcare environment periodically. * Exchange between the messages' transmission strategies. * Select a computing center to process a specific data outside a cluster region.
## 2 Models  
### Dynamic Equations  
For simplicity, it is considered that Earth is a homogeneous sphere with a radius of 6378 km. The Coriolis inertial force is ignored, and the gravitational acceleration is regarded as a constant at 9.8 m/s\({}^{2}\). The force analysis of aircraft is shown in Figure 1, where \(T\) is the thrust vector generated by the aircraft engine, \(R\) is the aerodynamic force vector that contains lift and drag, and \(G\) is the gravity vector.  
The dynamic equation of the aircraft mass center in a form of vectors is as follows:  
\[m\frac{d^{2}\overset{\rightarrow}{r}}{dt^{2}}=\overset{\rightarrow}{T}+ \overset{\rightarrow}{R}+\overset{\rightarrow}{G} \tag{1}\]  
In the velocity coordinate system, Equation (1) can be decomposed into  
\[\left\{\begin{array}{l}m\frac{d\
u}{dt}=T\cos\alpha-D-G\sin\gamma\\ m(v\cdot\frac{d\gamma}{dt}-\frac{v^{2}\cos\gamma}{r})=T\sin\alpha+L-G\cos\gamma \end{array}\right. \tag{2}\]  
where \(v\) denotes the flight velocity, \(\gamma\) is the elevation angle of trajectory, \(\alpha\) is the angle of attack, and \(m\) is the mass of the aircraft. Due to  
\[\left\{\begin{array}{l}h=r-R_{e}\\ v=Ma\cdot c\end{array}\right. \tag{3}\]  
Figure 1: Force analysis diagram of aircraft.  
where \(h\) denotes the flying height (km) and \(Ma\) is Mach number, then the dynamic equation of the cruise phase is [6]:  
\[\left\{\begin{array}{l}\frac{dh}{dt}=Ma\cdot c\cdot\sin\gamma\\ \frac{MAa}{dt}=\frac{T\cos a-D-mg\sin\gamma}{m<c}\\ \frac{dT}{dt}=\frac{T\sin a+L}{mMa+c}+\cos\gamma(\frac{Ma<}{R_{e}+h}-\frac{g} {Ma<c})\end{array}\right. \tag{4}\]  
Based on the dynamic equation, the trajectory can be solved by numerical method. Before that, the magnitude of lift, drag, and thrust need to be determined.
## 3 Evaluation
### Experiments  
Several experiments were conducted on the prototype manipulator to compare simulations with actual bending shapes. Figure 9 shows the experimental setup to obtain the stiffness values of the flat springs. Four motors (Maxon, Swiss) and linear actuators were arranged radially in a drive system. A load cell was connected to the cable (SUS 304, Carl Stahl Sava Industries, Riverdale, NJ, USA) driving the manipulator, enabling the tension applied to the cable to be measured. First, we checked if the bending curvature of the manipulator changed when configurations of flat springs with different thickness, arranged at the joint, were changed. To intuitively analyze the movement of the manipulator, it was observed on a plane surface. Figure 10a shows the result of the configuration of T1 and Figure 10b shows the configuration of T2 of the manipulator. The operating cable of the manipulator of the two configurations (T1, T2) was pulled by 0.5, 1.0, 1.5, and 2.0 (mm). It was confirmed that the proximal part in the case of T1, and the distal part in the case of T2, had a small radius of curvature about 4.27 mm when it maximally bent. Additionally, the distal part in the case of T1 and the proximal part in the case of T2 had a small radius of curvature about 14.67 mm. The joint configuration of T1 had a larger workspace than T2. Next, the similarity between the simulations and the experiments was confirmed. To check for the similarities, the simulation results were superimposed on the manipulator of the actual experiments in Figure 11. The experimental values and the position errors against the simulation are shown in Table 2. In both combinations, the position error of the endpoint increases as the cable displacement increases.  
Figure 9: Experimental settings for manipulator’s bending motion. The driving cable is moved using a linear actuator with a fixed load cell. After connecting the manipulator, as shown in the lower right picture, the movement is observed from the top vertically using a telecentric lens and a camera.  
Figure 11: Graphs comparing experimental results and simulations. The two combinations (T1, T2) were compared, respectively, and the solid line represents the experimental data, and the dotted line represents the simulation data.  
Figure 10: Results of manipulator bending experiment of two combinations; (**a**) Experimental result of T1 (red line) combination manipulator, moving the cable up to 2 mm at 0.5 mm intervals; (**b**) Experimental result of T2 (green line) combination manipulator performed under the same conditions.
## 2 Materials and Methods
### Material Properties of AlSi10 Mg  
The phases of the AlSi10 Mg powder and the solid phases were considered separately in the model presented in this paper, which used temperature\(-\)dependent thermophysical properties including the thermal conductivity, density, specific heat capacity, and enthalpy. The temperature\(-\)dependent physical properties of the solid phase were obtained using JMatPro software. The density of the AlSi10 Mg changes with the temperature and can be expressed as [34]  
\[\rho_{p}=\left\{\begin{array}{ll}(1-\varphi)\rho_{s}&T<T_{m}\\ \rho_{s}&T>T_{m}\end{array}\right. \tag{11}\]  
where \(\rho_{p}\) is the powder bed density, \(\rho_{s}\) is the solid phase density, and \(\varphi\) is the porosity, which was set as 0.4 in this paper [35].  
The thermal conductivity \(k\) is more significantly affected by the material phase. In the model, it was assumed that the thermal conductivity of the AlSi10 Mg powder is the same as 0.01 of the solid phase. The relationship between conductivity and temperature is as follows [36]:  
\[k=\left\{\begin{matrix}k_{p},T<T_{m}\\ k_{s},T\geq T_{m}\end{matrix}=\left\{\begin{matrix}0.01\times k_{s},T<T_{m}\\ k_{s},T\geq T_{m}\end{matrix}\right.\right. \tag{12}\]  
where \(k_{p}\) is the equivalent thermal conductivity of the powder bed, \(k_{s}\) is the density of the solid phase material, and \(T_{m}\) is the melting temperature of the powder.  
Table 3 shows the thermophysical properties of the AlSi10 Mg material used in this model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature (\(\mathrm{\SIUnitSymbolCelsius}\))** & **20** & **120** & **320** & **520** & **820** & **1120** \\ \hline Density (kg/m\({}^{3}\)) & 2660 & 2643 & 2606 & 2568 & 2374 & 2266 \\ Thermal conductivity & 150 & 144 & 130 & 120 & 107 & 63 \\ (W/(m-K)) & & & & & & \\ Specific heat capacity & 700 & 748 & 848 & 1066 & 922 & 925 \\ Density (powder) & 1596 & 1586 & 1563 & 1541 & – & – \\ (kg/m\({}^{3}\)) & & & & & & \\ Thermal conductivity & & & & & & \\ (powder) & 1.5 & 1.4 & 1.3 & 1.2 & – & – \\ (W/(m-K)) & & & & & & & \\ Specific heat capacity & 420 & 449 & 509 & 1066 & – & – \\ (J/(kg-K)) & & & & & & & \\ Coefficient of thermal & & & & & & \\ expansion (\(10^{-6}/\mathrm{K}\)) & 21.7 & 21.8 & 31.4 & 20.6 & – & – \\ Young’s modulus (GPa) & 76 & 72 & 64 & 52 & 0 & 0 \\ Poisson’s ratio & 0.32 & 0.33 & 0.34 & 0.36 & 0.5 & 0.5 \\ Yield strength (MPa) & 250 & 150 & 105 & 70 & – & – \\ Latent heat (J/kg) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature\(-\)independent physical properties of AlSi10 Mg alloy [37; 38; 39; 40].  
Figure 2: Finite element model: (**a**) model geometric information and (**b**) local amplification.
## 4 Case Study
### Virtual Sensor Fault-Detection Performance Metrics
#### 4.3.2 Precision  
Precision indicates the proportion of all the faulty data that are identified by the virtual sensor as a fault. The precision has a value ranging from 0 to 1. The proportion of FP decreased as the precision increased. The precision is calculated using the following expression:  
\[\text{Precision}=\frac{TP}{FP+TP} \tag{11}\]
## 1 Introduction
In practice, users can select a portion of the KMS for particular applications. Although the properties of messages sent through communication channels can be determined by the function needed, communications are also not fixed. To address this issue of managing keys for multiple smart devices in AMI systems, this research proposes three different key management methods for broadcast, unicast, and multicast communications, which are designed based on functional requirements and message types. Each of these methods includes specific procedures for key regeneration and refreshing, considering factors such as the computation and storage limitations of SMs, the time requirements of functions, and other relevant factors. The remaining sections of this paper are arranged as follows: Section 2 examines the framework and also the message flow for an AMI system. KMS design difficulties with the AMI system are introduced in Section 3. Section 4 represents the KMS design difficulties with an AMI system. The key management and key refreshing policy for unicast, broadcast and multicast communication is discussed in Sections 5-7, respectively. Section 8 introduces the security examination. In Section 9, the performance analysis in terms of the cost of storage, the time cost for computation, and the time cost for distributions are discussed. In Section 10, a conclusion is introduced.