esa-sceva/satcom-chunk-collection huggingface dataset:
# A Knowledge Graph Embedding Model Based on Cyclic Consistency--Cyclic CKGE
## 2 Related Work
### Method Based on Convolutional Neural Network  
While translation models use simple operations and limited parameters to learn embeddings, they produce lower-quality representations. Recent research has shown that models based on convolutional neural networks (CNNs) [33] can generate richer, more expressive feature embeddings and therefore also perform well in relation prediction. [PERSON] et al. proposed ConvE [34], which was the first model to apply CNN to KG tasks. It is characterized by structuring head entities and relationships into \"pictures\" and using convolutional networks to calculate the features of the pictures. The tail entity is then viewed as a \"label\" for classification. This constitutes a data set for supervised learning. The whole process also did not use the translation model. It uses stacked 2D convolution to reshape entity and relational representations, thereby improving their expressiveness while maintaining parametric efficiency. ConvKB [35] is another convolution-based approach that applies width convolution to stacked entity, relationship and object embeddings.  
### Method Based on Convolutional Neural Network  
A graph neural network (GNN) [36] is a method of structuring data into a directed graph, and the knowledge graph itself is a directed graph. Therefore, this network has an inherent advantage for knowledge graph embedding research. There are two popular variants of GNN, namely, the Graph Convolutional Network and the Graph Attention Network (GAT) [37]. Existing methods learn KG embeddings either by focusing only on entity features, or by considering entity features and relationships in a disjoint manner.
It is well established that the segmental mobility of a polymer matrix is frozen in the crystallinity phase, and the lithium ion transportation occurs only in the amorphous region of the polymer hosts [9; 10]. Although the excellent chain flexibility of PEO causes lithium salts to be easily dissolved and promotes the smooth movement of dissociated ions, PEO-based electrolytes still exhibit relatively low ionic conductivity (10\({}^{-7}\)-10\({}^{-5}\) S cm\({}^{-1}\)) due to the high degree of crystallinity present in PEO at room temperature [11]. Consequently, finding an ideal SPE with good ion transport properties is still a challenging task. Several research groups have improved the performance of the polymer electrolytes by reducing the crystallinity of the polymer, increasing the concentration of ions, and increasing the proportion of the amorphous regions [12]. Some of the notable strategies include decreasing glass transition temperature (T\({}_{\text{g}}\)) of the polymer electrolyte system and improving the magnitude of lithium ion dissociation [13] through the methods of grafting, crosslinking, mixing with ionic liquids, and blending various polymers materials [14; 15; 16; 17]. Recent studies involving polymer nanoparticles containing composite polymer electrolytes (CPEs) have received great attention because of their superior electrolytic properties such as high amorphous content, low Tg, and high thermal and mechanical properties [18; 19]. In light of this development, in this research study, we designed and synthesized three types of crosslinked polystyrene nanoparticles containing covalently functionalized lithium salt moieties (PSLSs) for the purpose of destroying the crystallinity of PEO-based electrolytes, leading to higher ionic conductivity. In order to achieve superior SPE properties (viz., compatibility, electrolytic and mechanical properties), we blended these polymer-bound lithium salts with a traditional polymer electrolyte system made of high molecular weight PEO and LiTFSI [15]. Polymer composites containing nanoparticles reinforce the polymer matrix, leading to enhancement of the mechanical properties. The high surface area of the nanoparticles is responsible for more physical interaction with the polymer matrix. We also included a low molecular weight polyethylene glycol dimethyl ether (PEGDM) plasticizer in the SPE formulations. The addition of a plasticizer has been extensively used to improve the ionic conductivity of PEO-based SPEs [16].
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 1 Introduction
### Control System Applications
The authors in [31] have proposed a controller design utilising a feedforward PIDD2 controller for the valve-controlled hydraulic motor system, which shows better performance in position tracking capability when simulated and compared to PID, PIDD, and differential PID controllers. It is clear from the results that the PIDD2 controller design is a promising option for controlling various systems, providing better performance than traditional PID controllers. Plant processes can be modelled as differential equations of varying orders, with the behaviour and order of the system being influenced by its degree of freedom and direction of motion. For instance, the transfer function of a DC motor is a second-order system, with the input voltage and shaft angle or position serving as input variables, including the armature's inductance, resulting in a third-order transfer function. To optimise predictive algorithms, the authors in [32] proposed a universal searchless method for parametric optimisation, using a PIDD2 controller to control a third-order model with time delay. The control parameters are tuned using the universal searchless method, with simulation showing an improvement in the transient response of the set-point signal compared to the predictive PID controller. Similarly, in [33], researchers proposed a PIDD2 controller, tuned through a maximal stability degree method with iteration, for a second-order model with inertia and time delay. The proposed method outperforms the PID controller regarding control time and voltage overshoot, exhibiting superior performance. The authors in [34] presented a PIDD2 controller tuned with the identification algorithm REDIC to attain adaptive self-tuning of controller parameters based on the analog model of a control plant. The proposed method significantly outperforms traditional PID controllers in terms of transient responses. Finally, in [35], researchers proposed a PIDD2 controller for an IPDT plant model, with control parameters tuned using the quintuple real dominant pole tuning method resulting in improved performance in terms of IAE, with the proposed design surpassing PI, PIDD2 controllers. Diabetes mellitus is a medical condition linked to the improper control of blood glucose levels and is caused by inadequate insulin production or ineffective use of insulin in the body. Type 1 diabetes is characterised by a complete lack of insulin production, requiring a syringe or implanted micro-pump for insulin administration. Automated closed-loop devices are being explored to improve the accuracy and reliability of insulin regulation. In a recent study by the authors in [36], the FPIDD2 controller with a two-delay differential model is proposed to simulate this scenario. The study demonstrated that this approach improved MAPE, RMSE, and insulin usage compared to other controllers, such as PD, PI, parallel PID, and single-rule-based FPID. The particle swarm optimisation algorithm enhanced performance with linearly decreasing weight, ensuring optimal results.
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 4 EEDCF Algorithm
### Cluster Build Phase  
The final elected CHs broadcast message CH_Message containing node ID within the communication radius \"R\". The member nodes can estimate the distance to the CH node by received signal strength indication. A member node may receive more than one CH_Message from different CH nodes. If that happens, the node will choose the CH node with the shortest distance as the appropriate CH node and send Node_JOIN message to this CH node. Once receiving Node_JOIN message, the CH will return CH_ACCEPT message to the corresponding node to confirm its join request. Moreover, in the worst case, when there is no suitable CH within its communication radius \"R\" for a member node to join, it will elect itself as the CH node. Thus, we can finish the cluster formation process.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 2 Preliminaries  
### Hyperelliptic Curve  
A hyperelliptic curve is a generalized form of an elliptic curve with a genus of \(\geq\)1. In cryptography, this sort of curve is considered a viable approach because its vital length and parameter size is significantly smaller than for an elliptic curve.  
Let \(Hec\) be the hyperelliptic curve over the finite field \(f\); the equation of hyperelliptic curve '\(Hec\)' over the field '\(f\)' having genus '\(g\)' is shown in the equation below:  
\[Hec\,:u^{2}+h(v)u=f(v)\]  
In the equation above, \(h(v)\) is a polynomial of degree \(\leq g\), while \(f(v)\) is a polynomial of degree \(2g+1\). The above equation represents a nonsingular curve, and it can verify the given curve equation and two partial derivatives \(2u+h(v)=0,\ h^{\prime}(v)u=0\).
# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
## 3 Results
### Estimation of Winter Wheat Carbon Flux at Different Growth Stages
To further assess the model's performance across the four growth stages, additional evaluation metrics were computed, as presented in Table 2. The adjusted R\({}^{2}\) values for the model's predictions across all growth stages in 2023 ranged from 0.79 to 0.94, with an average of 0.88, indicating strong overall predictive performance. Notably, the R\({}^{2}\) values for the first three growth stages exceeded 0.89. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values provide insight into the model's prediction errors during different growth stages. The MAE values ranged from 2.15 to 5.33 gC\(\cdot\)m\({}^{-2}\cdot\)8d\({}^{-1}\), with an average of 4.07 gC\(\cdot\)m\({}^{-2}\cdot\)8d\({}^{-1}\), while the corresponding RMSE values ranged from 2.87 to 6.9 gC\(\cdot\)m\({}^{-2}\cdot\)8d\({}^{-1}\), with an average of 5.31 gC\(\cdot\)m\({}^{-2}\cdot\)8d\({}^{-1}\), reflecting the squared errors between predicted and actual values. The Normalized Root Mean Square Error (nRMSE), which expresses the error as a proportion of the actual values, offers a useful measure of the RMSE's relative size. For all growth stages, the nRMSE values were approximately 0.05, indicating consistent predictive performance throughout 2023, with values close to zero suggesting minimal RMSE errors and robust accuracy. Specifically, the jointing stage exhibited the highest adjusted R\({}^{2}\) value and the lowest nRMSE value, signifying the best fit and smallest prediction errors. Based on the overall performance across these metrics, it can be concluded that the model demonstrates strong predictive capability, with high interpretability of the results and minimal errors. It shows a solid ability to accurately predict the carbon flux of winter wheat in the Guanzhong Plain.
###### Abstract  
A novel adjustable three-dimensional plasmonic Archimedes spiral lens (3D PASL) has been investigated and analyzed in detail by numerical simulations. The 3D PASL consists of a spiraling Archimedes helix slot that is engraved on the inner wall of a funnel-shaped gold film on a silicon dioxide substrate. When the incident light from the bottom of substrate is composed of left-hand circularly polarized (LCP) waves, the transmitted light field will converge completely to a focused point that floats in the hollow funnel. This light field will change into an optical vortex when the incident light is changed to right-hand circularly polarized (RCP) waves. The performance of our 3D PASL is discussed for particle trapping or rotation applications. In addition, the position of the optical focus or vortex can be adjusted by varying the height of the structure. Our 3D PASL is highly flexible for practical optical manipulation applications and overcomes the problem where the previous two-dimensional PASL could only manipulate particles on the surface.  
plasmonic; archimedes spiral; optical manipulation +
Footnote †: journal: Applied Physics Letters
# The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator
## 4 Discussion
### The Application in Fast Process Imaging  
Benefitting from the periodical working mode of the e-LINAC, this system can be used for the investigation of fast repetitive processes. When the e-LINAC works at the typical repetition frequency of 100 Hz, the engine mentioned in [18] can be inspected with a running speed as high as 6000 rpm. The e-LINAC will be triggered by the synchronization signal provided by the engine, with an adjustable time delay, to produce X-rays and neutrons. The adjustable time delay permits the X-ray pulses to inspect the working engine for the user-specified phases. The 10-minute-flight-time for thermal neutrons ranges from 1.32 ms (300 meV neutron) to 7.23 ms (10 meV neutron); thus, the \({}^{n}\)MCP detector with high temporal resolution readout should be applied to successively register the image slices of the working engine. As the sub-microsecond temporal resolution can be easily realized with the readouts of cross delay line [19], cross strip [20], or Medipix/Timepix [21], successively acquiring the neutrons' images of the working engine would not be difficult. However, it must be pointed out that, as the e-LINAC works at the pulsed mode, the fluxes of both X-rays and neutrons are not temporally uniform. In each period of the e-LINAC, the flux of X-rays (4 \(\times\) 10\({}^{5}\)/cm\({}^{2}\) incident photons within the 5 \(\upmu\)s X-ray emission duration) is far larger than that of thermal neutrons (10/cm\({}^{2}\) incident thermal neutrons within about the \(\sim\)10 ms duration) at the position of the detector; therefore, the throughput of the detector  
Figure 13: The measured spectra of neutrons penetrating different materials with the time-of-flight method. The corresponding reference spectra are also shown for the comparison.  
system should be large enough, and perhaps Timepix is the only proper choice for the readout. Although the neutron yield of the e-LINAC driven neutron source in this stage is quite low, the repetitive processes permit a long acquisition time for the detection system to achieve the good enough counting statistics.  
In the case of non-repetitive processes [22], the detection system has no opportunity to investigate the same phase of the inspected object for the second time, the poor counting statistics then prohibit the application of bi-modal imaging with this system in practice. Apparently, increasing the neutron yield is the only way to fulfill the requirements for the inspection of non-repetitive processes. However, increasing the neutron yield of the e-LINAC photoneutron source as high as that of the spallation neutron source, for example, 10\({}^{17}\) n/s, would be a formidable challenge, as the heat power deposited on the target by the bombarding electrons will be as high as decades of megawatt.
## 1 Introduction
Grey box modeling has been deeply studied and widely used in the modeling of practical engineering systems because it combines the advantages of first principle modeling and system identification modeling [8]. In order to track the desired voltage trajectory of the dead-end cascade-type polymer electrolyte membrane (PEM) fuel cell stack, [PERSON] [9] obtained the mathematical model of proton exchange membrane fuel cells by using a nonlinear grey box modeling method and linearized the established model. [PERSON] [10] put forward a grey box modeling method based on ordinary differential equation (ODE) parameter identification to solve the problem that the hydraulic servo system is difficult to model. In order to understand the engine performance of homogenous charge compression ignition (HCCI), [PERSON] [11] used the grey box model to predict the major variables of HCCI. [PERSON] [12] obtained the accurate mathematical model of a domestic electric boiler by using the gray box modeling method. [PERSON] [13] proposed a grey box modeling method by combining the LR model and the first principle model and established an accurate mathematical model of pipe temperature. Because the chemical reaction process of aviation kerosene combustion is very complex and the combustion mechanism is not fully understood, it is difficult to obtain a more accurate mathematical model by using first principle modeling and identification modeling. Therefore, a grey box modeling method was proposed to obtain an accurate lumped parameter model of gas temperature for the HHHTS. Firstly, a preliminary lumped parameter model of gas temperature was established through first principle modeling, and some prior knowledge was obtained. Then, uncertain parameters in the gas temperature model were identified by using the auxiliary variable recursive least squares method and correlation analysis method, and the precise lumped parameter model of gas temperature was obtained.
# Combined Channel Estimation with Interference Suppression in CPSS
## 3 Channel Estimation with Interference Suppression
### Interference Suppression in CIR
The simplest way to suppress interference on a subcarrier is zero force, which means to set the samples on the interfered subcarriers in CIR to zero. However, the zero members will also participate in data demodulation and output error soft bits, which will have the adverse effect to channel decoding and degrade the overall system performance. On the other hand, the channel estimation will be deteriorated by setting the samples on the interfered subcarriers to zero in CIR. The CIR in interfered subcarriers can not reflect the fading characteristics of the subcarriers. It is assumed that the channel is stationary for a continuous spectrum transmission. Thereby, the difference between CIR in the interfered subcarriers and the adjacent subcarriers will be small. Hence, we propose to suppress interference by linear interpolation of the CIR of frequency domain. The interpolation algorithm is presented as Equation (10):  
\[H(k_{I},I)=(1-\frac{b}{a+b})\hat{H}^{(1)}_{LS}(k_{I}-a,l)+\frac{b}{a+b}\hat{H }^{(1)}_{LS}(k_{I}+b,l), \tag{10}\]  
where \(a\) is the normal carrier at the left of the interfered subcarriers, and \(b\) is the normal carrier at the right of the interfered subcarriers. Both \(a\), \(b\) will be at the left/right of the interfered subcarriers, which are at the far right/far left. Through linear interpolation on the interfered subcarriers, \(J(k_{I},I)\) in Equation (4) will be cancelled, and it will be rewritten as Equation (11):  
\[\hat{H}^{(2)}_{LS}(k,I)=H^{\prime}(k,l)+\frac{W(k,l)}{X(k,l)}. \tag{11}\]  
It must be noted that linear interpolation on the interfered subcarriers is to improve the accuracy of channel estimation, which can not restore original data on interfered subcarriers. The \(K\)-means algorithm can achieve the local optimization in unsupervised learning [25]. On the premise of NBI, the \(K\)-means algorithm will be an effective scheme for interference detection. However, even if the signal component is misjudged as interference, the line interpolation over adjacent subcarriers will not have a serious negative impact on CIR.
# Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality
## 2 Design of Agricultural Product Quality and Safety Evaluation System  
### Screening of Evaluation Indicators  
In terms of heavy metal enrichment, microorganisms in the soil cannot fully decompose heavy metal pollutants, resulting in the accumulation of a large number of heavy metal pollutants due to the failure to decompose. After the absorption and enrichment of heavy metals by crops, various biochemical processes are disordered, which not only affects the growth of crops and the safety of agricultural products but also causes various diseases after the accumulation of heavy metals in the human body. Although the quality and safety of agricultural products involve a wide range, the problem of heavy metal pollution is particularly prominent in the quality and safety of agricultural products. There are both natural and social indicators, both qualitative and quantitative indicators in the analysis of heavy metal pollution sources. When constructing the index system, the design of each index should reflect a certain level.  
In general, for the risk of exceeding the standard of heavy metals, by analyzing the content level and spatial distribution characteristics of eight kinds of heavy metal in agricultural products, the factor analysis method was used to analyze the monitoring-related indicators, including total content of heavy metals in soil, soil pH value, soil organic matter content, soil redox potential, planted crop varieties, nutritional status, cultivation measures, heavy metal content in pesticides, heavy metal content in fertilizers, heavy metal content in atmosphere, and heavy metal content in irrigation water. Three principal components were extracted from the total and determined as being first-class indicators: the available content of heavy metals in soil, the absorption coefficient of heavy metals in crops, and other sources of heavy metals. After a literature review and data analysis, the second-class indicators under each first-class indicator were further defined. The selection of specific agricultural product quality and safety risk assessment indicators is shown in Table 1.
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
## 3 Dual-Band Reflectarray System Design
### Results  
The reflectarray antenna is required to steer the beam towards \(\theta_{o}=20^{\circ}\) at both 20 GHz and 30 GHz frequencies. The feed antenna is located to \((91\) mm, \(0\) mm, \(250\) mm) of the coordinate system. Firstly, each of the reflectarrays are simulated separately. The performance levels of the single layer reflectarrays are also analyzed analytically using array theory. Figure 7 shows the simulated and calculated normalized radiation pattern of the reflectarrays in \(\phi=0^{\circ}\) plane. It should be noted that, in this figure, a 30 GHz reflectarray is simulated with a whole copper ground plane (without FSS and 20 GHz reflectarray). Similarly, a 20 GHz reflectarray is simulated without FSS and 30 GHz reflectarray layers. The main beam is oriented towards \(\theta_{o}=20^{\circ}\) with both reflectarrays. The directivities of the single layer reflectarrays are obtained as 22.5 dB and 24.8 dB at 20 and 30 GHz, respectively.  
Figure 6: Phase distribution and corresponding reflectarray surface at **(a)** 20 GHz and **(b)** 30 GHz.  
Figure 7: Normalized radiation patterns of the reflectarrays in \(\phi=0^{\rm o}\) plane: (a) 20 GHz reflectarray; (b) 30 GHz reflectarray.  
The multilayer reflectarray system consisting of the 30 GHz reflectarray at the top, 20 GHz reflectarray at the bottom, and FSS in between is given in Figure 8. As exhibited in this figure, the FSS structure is sandwiched between the two reflective surfaces. Simulated three-dimensional directivity patterns of the reflectarray at 20 and 30 GHz are given in Figure 9. The maximum directivity of the reflectarray antenna system is obtained as 20.9 dB at 20 GHz, 21.8 dB at 20.9 GHz, and 24.9 dB at 30 GHz. The figure clearly shows that the beam is oriented to \(\theta_{o}=20^{\circ}\) at both target frequencies. Normalized radiation patterns of the dual-band FSS-backed reflectarray system are given in Figure 10 in \(\phi=0^{\circ}\) plane. Higher sidelobes are observed at 20 GHz. However, they are still below the \(-10\) dB level.  
Figure 8: Dual-band FSS-backed reflectarray system.  
Figure 9: Directivity patterns of the FSS-backed reflectarray system at (a) 20 GHz and (b) 30 GHz.