esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Photon counting has been proven to possess excellent signal detection capabilities at low power levels and has extensive potential applications in sixth-generation (6G) communications. However, the inherent dependency between the signal and noise complicates system analysis, and optimizing achievable rates in photon-counting visible light communication (VLC) systems remains unresolved. This paper introduces a new method aimed at minimizing multi-user interference (MUI) through a zero-forcing (ZF) scheme and maximizing the weighted sum rate of the proposed downlink multi-user photon-counting multiple-input multiple-output (MU-PhC-MIMO) VLC system by solving an optimization problem. The key point lies in our utilization of the ZF approach to derive a reasonable asymptotic approximation expression for the weighted sum rate. Subsequently, we use variable substitution and methods like successive convex approximation (SCA) to iteratively convexify the non-convex optimization problem and maximize the weighted sum rate under the ZF form. Compared to other algorithms, this approach can save 2.5 dB of transmission power to achieve the same system-weighted sum rate and significantly outperforms the repetition coding scheme at sufficient transmission power.  
visible light communication; multi-user; photon counting; Poisson shot noise; precoding +
Footnote †: journal: Applied sciences
# Estimating Thermal Material Properties Using Solar Loading Lock-in Thermography
## 4 Results and Discussion  
### Thermal Simulations  
Multiple transient thermal simulations were carried out to validate the theoretical formulas. The aluminum reference plates were simulated by a mass point with a thermal mass of \(m_{th}=\rho Vc_{p}\) each, subjected to a periodic heat input, as well as to convective and radiative cooling.  
Convective cooling is assumed to be linear in respect to temperature difference and uses a free convection rate of \(5\,\mathrm{W}/\left(\mathrm{m^{2}K}\right)\) for all specimen. Radiative cooling was simulated using a constant ambient temperature of \(21\,\mathrm{\SIUnitSymbolCelsius}\) and emissivity of \(\epsilon=1.00\) for all specimen, to simplify radiation calculations.  
The simulation results, shown in Figure 1, correlate well with the analytical results according to Equations (1) and (2). For greater heat input amplitudes, the simulation temperature amplitude is lower than expected, as the theoretical formulas do not take the cooling effects into account. Overall, a good correlation is shown for common solar irradiance values (<1200 W/m\({}^{2}\)). Moreover, non-linear effects are negligible.
## 3 Background and Related Works  
### Authentication Goals in IoT  
Authentication in IoT is similar to that in the current Internet but, particularly, it concerns the following:  
* Devices with low memory and computational capabilities;
* New applications and services are provided between users and devices in IoT;
* Solid nodes and secure communication for users;
* IoT devices can be integrated into all aspects of existing domains;
* Security of communications between nodes in WSN is critical because it is difficult or even impossible for human intervention after the deployment of sensors;
* Devices are battery-powered in general, and embedded applications must have low computational costs to save energy;
* Real-time applications in IoT require a high level of security with lightness in execution;
* IoT domains become very wide and distance between the sensor node and/or servers must not have a consequence on the communication overhead.  
In the following section, we focus on detailing the existing HIP-based solution that is the subject of this paper, then we provide an overview of several works of proposed authentication solutions in IoT.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 2 Computational Model
### Numerical RTM Model
#### 2.2.1 Solve for Pressure  
Equation (2) can be written in a weak form using the Galerkin finite element method, for a given mesh. The derivation of this is given in Appendix A. This gives the stiffness equation to solve:  
\[\mathbf{E}(\mathbf{K})\mathbf{p}=\mathbf{f}, \tag{8}\]  
where \(\mathbf{E}(\mathbf{K})\) and \(\mathbf{f}\) are the finite element stiffness matrix and the forcing vector, respectively, as defined in Appendix A. All nodes with respective CVs in \(U(t)\) are fixed at \(p=0\), as discussed in Section 2.1. Applying boundary conditions based on these time-varying sets alters \(\mathbf{E}(\mathbf{K})\) to \(\mathbf{A}(\mathbf{K},t)\), which is defined as follows, using MATLAB notation:  
\[A(i,:)=\begin{cases}\mathbf{E}(i,:)&CV_{i}\in\mathcal{F},\\ \mathbf{e}_{i}^{T}&CV_{i}\in U(t)\text{ or }n_{i}\cup\delta D_{I}\cup\delta D_{O} ),\end{cases} \tag{9}\]  
where \(CV_{i}\) represents the \(i\)th CV, \(n_{i}\) represents the \(i\)th node, and \(\mathbf{e}_{i}^{T}\) is the standard unit basis vector. The vector \(\mathbf{f}\) does not change as \(\mathcal{U}\) and \(\mathcal{F}\) change and is defined as follows:  
\[\mathbf{f}(i)=\begin{cases}p_{\text{in}}&n_{i}\in\delta D_{I},\\ 0&\text{otherwise}.\end{cases} \tag{10}\]  
Figure 1: Control volumes (blue) offset from the triangular finite elements (black) for a basic mesh (a more complex mesh is used in the numerical simulations).  
Equation (8) is never actually solved, but, instead, the following is solved every timestep:  
\[\mathbf{A}(\mathbf{K},t)\mathbf{p}(t)=\mathbf{f}. \tag{11}\]  
It is worth noting that \(\mathbf{A}(\mathbf{K},t)\) is generally very sparse.
## 4 Results and Discussion
### Influential Distances of the Boundary Effect  
In order to know the range of influence of the boundary effect, the data of pipeline boundary distances of 0, 2, 4, 6, 8, and 10 m were deleted for inverse computation, and the model changed at the different distances observed; the results are shown in Figure 18. The resistivity profile charts within 6 m from the boundary medium had significantly abnormal electrical resistivity regions, and the boundary effect was apparent; when the distance to the boundary medium was 8 m, the boundary effect had little influence.
## 4 Results
### Pore Throat Size Distribution from MICP Measurements  
The MICP results show that the capillary pressure curves of the sandstone samples are different in morphology (Figure 5A), suggesting complex pore structures and microscopic connectivity. The longer the flat segment of the capillary curves, the better the sorting and microscopic connectivity. The capillary pressure curves of samples Cn371-1 and F153-1 exhibit a longer flat segment during the mercury intrusion (Figure 5A), indicating a high proportion of large and connected pore throats and good microscopic connectivity. As shown in Figure 5B, the pore throat radius of samples Cn371-1 and F153-1 are concentrated between 0.5 and 5.0 \(\upmu\)m. In contrast, the flat segments of sample F119-1 and Sample G351-1 are short (Figure 5A), suggesting poor sorting and microscopic connectivity of pore throats.  
Figure 4: Microscopic characteristics of pore systems in the E\({}_{\text{s4}}\) sandstone reservoirs. (**A,B**) Intergranular pores and dissolution pores; (**C,D**) Micro-fractures; (**E,F**) Intercrystalline pores; (**G**) Dissolution pores; (**H,I**) Intergranular pores and dissolution pores; (**J**) Residual intergranular pores and dissolution pores; (**K,L**) Extensive carbonate cementation.  
Highly variable pore throat radii are quite apparent from the MICP data with the average pore throat radius being mostly less than 2.0 \(\upmu\)m with a non-uniform distribution (Figure 6B). Most of the mercury withdrawal efficiency is in the range of 25-45% (Figure 6A). The maximum mercury saturation has a concentrated distribution from 65% to 95%, with an average value of 89.69% (Figure 6C). Other parameters that can reflect pore structure characteristics, such as porosity (\(\varphi\)), permeability (\(\ell\)), and displacement pressure (Pd) also have a wide range of distribution (Figures 3 and 6D). Overall, the pore structures in the E\({}_{\text{s4}}\) sandstone reservoirs are characterized by strong reservoir heterogeneities, highly variable pore throat sizes, and poor microscopic connectivity.
## 4 End to End Security Analysis on the Proposed IIoT Security Architecture
### Data Processing Layer  
Data processing layer is responsible for transforming network data flows into valuable information (e.g., data pre-processing and curation), subsequently storing in a data warehouse and using high-level data processing such as aggregating, analyzing and interpreting data to serve client applications. In most cases, IIoT data comes with the Big Data characteristic when there is a large scale of \"things\" generate and exchange data continuously, such as in smart cities, aviation industry, logistic and shipment industry. IIoT technologies offer automated mechanisms such as cloud, edge and fog computing, machine-to-machine (M2M) communication technologies and underlying architecture to capture, collect and transmit data into the warehouse. On the other hand, big data technologies provide a data processing platform to support real-time analytics, such as Hadoop in a data warehouse to curate, process and analyze these machine data and turn it into valuable information. Because of the technological challenges of implementing in-house big data processing, these data are frequently stored and processed by third-party service providers such as Hortonworks, AmazonEMR, Cloudera, IBM, Zettaset, HDInsight, etc. As a result, the issues of data are exacerbated [108]. This section discusses end-to-end data protection for the data processing layer. * Data-in-Transit Data-in-transit refers to data being transmitted from the network layer to the data processing layer and application layer, either forwardly or backwardly. Most of big data technologies relies on Kerberos authentication scheme, public key infrastructure (PKI) and network encryption algorithm such as Hadoop remote procedure call (RPC), secure socket layer (SSL), HDFS data transfer protocol, simple authentication and security layer (SASL) mechanism, to ensure data confidentiality and authentication. However, these security mechanisms provide limited access control and authorization capabilities. A more sophisticated access control scheme such as role-based, identity-based and attribute-based can be plug-in with additional security packages such as Cloudera Sentry, DataGuise, IBM Infosphere Optima Data Masking, Datastax Enterprise, Zettaset Secure and others [108,109]. While data-in-transit protection in IIoT is mainly constructed based on the conventional security mechanism such as PKI, it is recommended to employ the bottom-up approach for heterogeneous and distributed networks infrastructure. The more challenging issue here is the key management and distribution problem, includes distribute key across distributed network and communication technologies, maintaining a large scale of the certificate, key revocation, recovery, and updating process. The key management proposal should be able to solve the scalability and interoperability issues to solve these problems.
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 1 Introduction
Furthermore, in this paper one of our goals is based on a theorem of \"no free lunch\" which states that there is no metaheuristic algorithm best suited for solving all optimization problems [28]. It means that one algorithm might obtain excellent results on a given set of challenges but underperform on an alternative set of issues. Another goal in this paper is to consider the strength of different algorithms based on the types and properties of the problem. In this paper, we extend our previous work of the compact bat algorithm (cBA) [2] for the unequal clustering of wireless sensor networks (uWSN) problems. By extending our work we aim to deal with the issues of saving variable memory and selecting parameters according to the unequal clustering problem of WSNs. The reasons for extending our work include an added need to control the probability of sampling for perturbation vector, a set of tests for benchmark functions, a solution to the unequal clustering problem, and an extensive comparison with other compact algorithms in the literature. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for solving the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems.
## 1 Introduction  
Terahertz (THz) wave refers to electromagnetic radiation with a frequency of 0.1-10 \(\times\) 10\({}^{12}\) Hz (corresponding to wavelength of 3 mm-30 \(\upmu\)m). Because of its unique characteristics such as superior spatial resolution, perspective, and spectroscopic fingerprints, terahertz wave has been widely used in security or industrial inspection [1, 2], material composition identification [3, 4], and biosensing [5]. Since commercially available high-power terahertz sources are expensive, it is crucial to enhance the localized field intensity, thus enabling strong light-matter interactions in diverse exciting applications such as detection [6], sensing [7], absorption [8], spectral filtering [9], and emission [10], and significantly improving the performance of the corresponding devices. For example, by confining most electric energy to a small region filled with microfluid, the sensitivity of a terahertz sensor can be greatly improved [11].  
By efficiently coupling and confining the free-space terahertz radiations to a small (usually subwavelength) region, a terahertz antenna can greatly enhance the localized near fields. Therefore, terahertz antennas have been widely used in a diverse range of applications including biosensing [11, 12, 13, 14, 15, 16] and detection [17, 18]. An important figure of merit for these terahertz antennas is the near-field enhancement, which is defined as the ratio of the near-field electric field intensity and the electric field intensity of the incidence [19]. In particular, for a dipolar type antenna, of which the strongly enhanced fields are mainly confined to the gap region of the dipole unit, the field enhancement usually refers to the enhancement of the near field in the gap. Since the field enhancement strongly depends on the antenna geometry, various antenna structures have been designed, including dipole antennas [20; 21; 22], log-periodic antennas [23; 24] and spiral antennas [17; 25]. To achieve large near-field enhancement, major efforts have been put on careful design and optimization of terahertz antennas.  
In this work, we propose a novel approach to further improve the near-field enhancement of the terahertz dipole antenna. We show that by adding a hyperbolic metamaterial with hole arrays beneath the antenna, the near-field electric field intensity within the gap of the antenna can be further enhanced by more than three times. We will also analyze the underlying physics for this improvement and discuss the effects of the gap width and of the hyperbolic dispersion relationship of the metamaterials. We expect this proposed approach can be extended to other types of terahertz antennas.
## 6 Implications  
### Theoretical Significance  
This study advances the understanding of public attitudes and dominant sentiments regarding the application of AIGC in education. By carefully selecting a substantial number of text-based comments from well-known social media platforms as data samples, we directly captured diverse societal perceptions and insights into AIGC' s role in education. Leveraging Python tools, we employed a combination of data mining techniques, sentiment analysis methods, and public opinion topic analysis strategies to conduct a comprehensive and in-depth examination of the collected data. The findings not only reveal the complex emotional attitudes of the public toward AIGC in education but also pinpoint key issues and trending topics that draw public attention.  
Integrating the TOE framework, this study further uncovers the impacts of AIGC in the education sector and forecasts its developmental trajectory from three dimensions: technological, organizational, and environmental. For instance, the introduction of AIGC depends not only on technological advancement but also on whether the organizational structures of educational institutions can adapt to these new technologies and whether external social, cultural, and legal policies support its application. This multi-faceted analysis offers a holistic understanding of how AIGC can function effectively in educational settings and overcome potential barriers and challenges. Policy-makers, technology developers, and educators can use this framework to gain deeper insights into the technological, organizational, and environmental challenges associated with implementing AIGC, enabling them to design more precise strategies for advancing educational technologies and facilitating the intelligent transformation of education.  
Moreover, by exploring the multi-dimensional impacts of AIGC in education, this study not only sheds light on the current application landscape but also provides strategic guidance for future educational technology innovations. The TOE framework allows us to predict future technological trends and potential challenges from the perspectives of technology, organization, and environment, offering strategic references for the sustainable development of educational technologies. The study also emphasizes the importance of fostering harmonious interactions between the public and generative AI, laying a theoretical foundation for promoting the deep integration and sustainable development of AIGC in education.
## 4 Steam Electrolysis and Thermo(-Electro-)Chemical Cycles
### High-Temperature Electrolysis  
In high-temperature electrolysis (HTE), water is heated by an external heat source, entering the electrolytic cell in the form of steam, to promote the decomposition of the water molecule (see Figure 3).  
One possible heat source is CSP: the high radiative fluxes allow for operations at desired temperatures (600-800 \({}^{\circ}\)C). Figure 4 shows a schematic representation of a possible P&ID of the process.  
Considering hydrogen production efficiency as a performance indicator, defined as the ratio between the higher heating value of hydrogen and the sum of the gross electrical energy plus the thermal energy required to produce the hydrogen, it is possible to achieve values of approximately 52.6% at 0.4 MPa and 800 \({}^{\circ}\)C [87]. More recently, some companies with \"Sunfire\" reported conversion values of 84% in practical tests (PCI H\({}_{2}\) to AC) [88].  
HTE requires both thermal and electrical energy to decompose steam into hydrogen and oxygen. In the electrolysis reaction, the electrical energy input is equivalent to the Gibbs free energy change (\(\Delta\)G), while the thermal energy input corresponds to T\(\Delta\)S (where T is the reaction temperature, and \(\Delta\)S is the entropy change). As the reaction temperature rises, the required electrical energy \(\Delta\)G decreases, while the necessary thermal energy TAS increases (Figure 5). Consequently, high-temperature electrolysis demands less electrical energy input compared to electrolysis conducted at lower temperatures. In comparison with conventional electrolysis, HTE can present the following benefits: (1) It requires lower working voltages (<1 V vs. standard 1.23 V) due to the energy contribution of the thermal component (\(\Delta\)H = \(\Delta\)G + TAS) (Figure 5). (2) It allows for the combination with feedstock  
Figure 4: Schematic P&ID of a CSP-HTE process, adapted from Ref. [87].  
Figure 3: Schematic representation of HTE process, adapted from Ref. [87].  
(e.g., biomass) at high temperatures to react with oxygen ions. In practice, part of this energy from this feedstock is used to \"assist\" the electrolysis process and thus reduce hydrogen production costs [89].
## 2 Problem Formulation
### Navigation Task Description  
The task operation environment within a mobile robot system is a 2D plane with random generated obstacles as shown in Figure 1, for example. In this figure, the red dot and green dot are denoted as the start position \((x_{0},\,y_{0})\) and the destination position \((x^{d},\,y^{d})\) in the 2D plane, respectively. This task requires the mobile robot to start from the red dot and then reach the green dot without colliding with any obstacles. To perform this task, an omnidirectional mobile robot with a state vector  
\[s(t)=[x(t)\;y(t)\;\theta(t)]^{\top} \tag{1}\]  
at each sample decision-making time index \(t\) is considered in this paper, where \(x(t)\) and \(y(t)\) are the horizontal and vertical coordinates of the mobile robot, and \(\theta(t)\) is the heading direction of the mobile robot. The motion of this vehicle is conducted by a finite number of motion types \(\mathcal{M}_{i}\), \(i=1,\ldots,n\). Here, each motion type should be linked to an implementable action of the mobile robot, e.g., go ahead, turn left or turn right, and associate with a particular state change function  
\[s(t+1)=\mathcal{F}_{i}(s(t)),\;i=1,\ldots,n. \tag{2}\]Since the motion function is simplified to motion types, it enables this task to be carried out by sequentially making appropriate decisions of motion types at each sample time index \(t\). To help the achievement of the latter design, two assumptions are made as below.  
The motion types of this mobile robot guarantee it to reach any feasible position within the given 2D plane.  
The motion types must differ significantly from each other, i.e., for a given state \(s\), there exists  
\[\left\|\mathcal{F}_{i}(s)-\mathcal{F}_{j}(s)\right\|\geqslant\sigma,\text{ for any }i,\ j, \tag{3}\]  
where \(\sigma\) is a nonnegative scalar.  
Assumption 1 makes sense in practice, since it guarantees the mobile robot task achievement, as there is no need to design any autonomous mobile robot navigation approach for an impossible task. Assumption 2 ensures all motion types will be different, which increases the success rate of classifying the appropriate motion type using a neural network in Section 3.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 5 Analysis of Fatigue Life  
The random vibration load applied to the structure lasted for only a short time and the structure remains unaffected by fatigue when designing space optical remote sensors. However, the designing scheme becomes unacceptable when only traditional mechanical analysis is adopted. The stress and the temperature of mirror components will increase with vibration, while the fatigue limit of the flexible structure will decrease and cause fatigue failure.