esa-sceva/satcom-chunk-collection huggingface dataset:
# Service for Deploying Digital Twins of QKD Networks
## 3 Functional Overview  
One of the main properties we pursue in the development of the QKD network digital twin service is that the architecture of the digital twin environment follows current standards. Specifically, we focus on those that present a general definition of QKD node components and define an API between applications and the QKD module of a node [27; 28]. In addition, we require the ability to distribute the network nodes among different machines, as discussed in the Section 2.4, and we also look for the convenience of automatic deployment of the desired QKD network, allowing the use of an existing NFV orchestration tool.  
In our service, for each QKD node, the user may indicate specific configuration parameters, including the ETSI APIs and the QKD protocol that must be supported. With this, our solution provides a management and orchestration service, handling the complete lifecycle (creation, configuration, and termination) of a digital twin of the QKD network. The digital twin implements each QKD node in the software, enabling its execution in a virtual machine or a virtualization container. We use an emulator to support quantum links between QKD nodes and qubit operations, such as creating and exchanging Bell pairs. The management and orchestration service handles the deployment of the digital twin of the QKD network, as a composition of virtualized QKD nodes. Moreover, QKD nodes may be placed at different locations at the discretion of the user. To this purpose, our solution can leverage ETSI NFV-compliant cloud and edge infrastructures, (e.g., based on OpenStack), if available. Alternatively, it may set up the digital twin of the QKD network using physical or virtual machines, or virtualization containers pre-provisioned by the user. After the deployment process, user applications can access the different QKD nodes to retrieve cryptographic key material, using their respective ETSI standard interfaces.  
As can be deduced from the above discussion, in the developed QKD network digital twins service, there are three important roles: the digital twin orchestrator, the QKD network nodes, and the QKD network clients.
## 2 FE Model and Validation
### Modeling Method  
To improve simulation efficiency, a two-dimensional FE model was built to determine the mechanism of double-column self-centering pier with shear links, using the general-purpose FE program MSC_Marc (MSC Software Corporation, CA, USA, 2007) [40]. A sketch illustrating the main model components is shown in Figure 2. Four-node quadrilateral plane stress elements are employed to simulate the columns, bent cap, and footing; 2-node truss elements with initial stress are employed to simulate the unbonded prestress steel strands. According to previous relevant investigations [8; 9; 10; 11; 12; 13], during the rocking process of the self-centering pier, the footing, bent cap, and prestress steel strands remain elastic, with limited damage in the contact area at both ends of the column. Therefore, the aforementioned parts are set as elastic. In addition, the shear links are modeled by elasto-plastic springs as presented in Section 2.2.2. Furthermore, the contact between the columns and the bent cap/footing is simulated by the contact function provided by MSC_Marc [40]. The elements in the potential contacting parts of the columns and bent cap/footing are defined as two separated sets of contact bodies. During the iteration process, each node in the contact bodies is repeatedly checked. The program calculates the contact tolerance according to the size of elements. If a node is within the contact tolerance, it will be considered to be in contact with other elements. In a tangential direction of the contact interface, an approximation of the Coulomb friction model (i.e., arctangent model) is adopted to represent the relationship between the normal stress and tangential stress at the contact interface.
## 1 Introduction
The PH path is parameterized by polynomials as a function of the path length and provides closed-form polynomials for path length and curvature. In addition, the position and direction at the initial and final locations are directly considered as boundary conditions, trading off the path length and its curvature to meet the maximum-curvature constraint. In order to take full advantage of the PH path, the PH curve is adopted from other literature as the parameterized path of the UAV in this paper. In this paper, the focus is on generating a group of cooperative paths for UAVs arriving at the rendezvous point simultaneously and forming a desired formation configuration. A distributed cooperative particle swarm optimization (DCPSO) algorithm with a cooperation mechanism is proposed, which also serves as the contributions of the paper. The PH paths of all UAVs are optimized cooperatively by using the DCPSO algorithm. Using a distributed cooperation mechanism, the particles in each sub-swarm are modified so that the kinematic constraints of UAVs, the collision avoidance, and the simultaneous arrival of the UAV formation are achieved. Meanwhile, the optimal or sub-optimal paths are obtained. The rest of this study is organized as follows. The multi-UAV formation rendezvous problem formulation is presented in Section 2. Section 3 describes the distributed cooperative particle swarm optimization algorithm for multiple UAVs. Simulation results are shown in Section 4. The summary and conclusions are given in Section 5.
## 1 Introduction
CSRR-based slot structures are etched onto the defected ground plane with the aims of bandwidth enhancement and mutual-coupling reduction. The MIMO antenna was designed using multilayer solver analysis in commercial Computer Simulation Technology (CST) software. Detailed MIMO-antenna and single-element topologies are presented and analyzed. The measurement results, performed on the fabricated prototype of the UWB-MIMO antenna, indicate that the proposed antenna has a compact size (\(30\times 20\times 1.6\) mm\({}^{3}\)), an ultra-wide impedance bandwidth spreading from \(3.01\) GHz to \(12.34\) GHz, less than \(-22\) dB mutual coupling, and a good diversity performance. Additionally, the metamaterial structure is integrated into the antenna design to provide artificial electromagnetic properties that further enhance the isolation and reduce the antenna size. The rest of this paper is organized as follows. Section 2 describes the proposed antenna design and configuration. In Section 3, we explain the design process of the compact single-element monopole antenna. Section 4 describes the design of the proposed UWB-MIMO monopole antenna. In Section 5, we investigate the MIMO-antenna diversity performance using the diversity gain, among other parameters. Finally, the paper concludes in Section 6.
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 2 Related Work  
Prior to the emergence of AI-based grading, programming assignments were typically evaluated using manual or semi-automated methods, including the use of test cases, trace analysis, rubrics, and unit testing frameworks such as JUnit or PyTest to verify functional correctness [11, 12, 13, 14, 15]. Although these methods have proven effective, they are time-intensive and demand substantial manual effort from instructors. The following summary outlines the main characteristics of four commonly employed assessment methods.  
Various strategies have been employed for code evaluation in educational settings, each with distinct advantages, limitations, and appropriate use cases [16]. One of the most prominent approaches is the use of test cases, valued for being objective, automatable, and capable of rapidly assessing the functional behavior of code [17]. Although well-designed test cases can infer the existence of logical errors, they often cannot directly evaluate the quality of internal reasoning or code style. Consequently, it is best suited for assignments where functional correctness is the primary focus. The feedback generated by this method is typically binary (pass/fail), although it can be enhanced through advanced tools and customized error messages designed to guide students [18].  
This technique is especially valuable for assessing a student's understanding and for identifying flaws in control-flow logic. It offers detailed feedback on logical decision-making, flow-related errors, and the student's grasp of program structure [16]. Trace analysis is particularly effective for small-scale exercises or exam settings involving pseudocode or basic programs. However, it is inherently subjective and time-consuming, which limits its scalability in large groups.  
Rubrics offer a flexible and detailed assessment framework that also promotes good programming practices. This approach is well suited for larger projects or final evaluations, as it enables a qualitative analysis of multiple dimensions of the code. Nevertheless, its implementation can be time-consuming and is sensitive to variations in evaluator consistency [17]. The feedback generated through rubrics typically highlights strengths and weaknesses, along with specific suggestions linked to each assessment criterion.  
Finally, unit testing is distinguished by its precision and reusability, and it encourages the adoption of test-driven development practices. While it requires initial setup and ongoing maintenance and entails a learning curve, it is particularly appropriate for advanced courses or contexts focused on professional software testing methodologies. The feedback it provides is technical in nature, reporting function-specific errors and indicating the success or failure of each individual test.
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 4 A Cross-Modal Hash Retrieval Method with Fused Triples
### Hash Learning
#### 4.3.4 Quantifying Losses  
In the method of this paper, the features of the two modalities obtained in the feature extraction part are represented as vector codes. In the hash learning part, to achieve cross-modal hash retrieval, the vector encoding needs to be converted to binary hash codes to reduce storage space and achieve fast retrieval between modalities.  
To ensure the vector encoding is as consistent as possible with the binary hash code, quantization errors need to be kept to a minimum. The hash codes \(G\) and \(F\) are continuous actual values generated by the image and text networks. They must transform into binary hash codes \(\mathbf{B}^{(x)}\) and \(\mathbf{B}^{(y)}\) in the computation process. At the same time, \(\mathbf{B}^{(x)}\) and \(\mathbf{B}^{(y)}\) should maintain cross-modal similarity in \(S\). Supervised information and hash code learning to integrate into a unified learning framework, effectively preserving the similarity information in the original data. Therefore, the quantization error loss of the method can be taken as:  
\[L_{\text{S}}=\eta_{1}\bigg{(}\big{\|}\mathbf{B}^{(x)}-F\big{\|}_{F}^{2}+\big{\|}\bm {B}^{(y)}-G\big{\|}_{F}^{2}\bigg{)} \tag{7}\]
## 1 Introduction
The regional nodes act as value added providers by offering tiered levels of assistance to users including developing tools and tutorials, providing training and hands-on assistance, locating or creating products in response to users' needs and working directly with users on projects. The West Coast Node (WCN, [[https://coastwatch.pfeg.noaa.gov](https://coastwatch.pfeg.noaa.gov)]([https://coastwatch.pfeg.noaa.gov](https://coastwatch.pfeg.noaa.gov))) is housed at NOAA's Southwest Fisheries Science Center (SWFSC) and caters primarily to the needs of NMFS scientists. The WCN works closely with the data group at the SWFSC Environmental Research Division (ERD), which hosts the WCN/ERDDAP server. The WCN developed the NOAA Ocean Satellite Course [9] to increase the awareness and utilization of satellite data by fisheries scientists and ocean resource managers. The course has been offered and refined annually for over a decade. In this paper, we describe the course content and structure, present results of post-course surveys designed to measure course effectiveness, and review \begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Since** \\ \hline Sea ice & 1978 \\ Sea Surface Temperature & 1981 \\ Sea Surface Height & 1992 \\ Ocean Color (Chlorophyll) & 1997 \\ Precipitation & 1997 \\ Surface Vector Winds & 1999 \\ Surface Salinity & 2011 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of oceanographic satellite measurements and the start of their continuous time series of data. the course impacts on operational oceanography. We also describe efforts to extend the reach of the course, both nationally and internationally, and discuss lessons learned.
## 3 Numerical Simulation
### Model Introduction  
The numerical model dimensions refer to an actual orthotropic steel box girder bridge, whose cross-section is shown in Figure 4. Considering the significant local force characteristic, we built a local finite element model with two lanes with a width of 7.5 m in the transverse direction and three-span diaphragms with a length of 9.6 m in the longitudinal direction; the diaphragm was cut out with 0.8 m down from the deck in the vertical direction. The deck, U-rib, and diaphragm thicknesses were 14 mm, 12 mm, and 8 mm, respectively. The general commercial FEM software ANSYS was adapted to simulate the orthotropic steel box girder model. The whole model was simulated using shell elements; the material elastic modulus was E = 2.1 \(\times\) 10\({}^{5}\) MPa and the Poisson ratio was 0.3. Without a loss of generality, the load identification of the vehicle driving in the middle lane was mainly considered, so the mesh size of the middle lane was refined to 2 cm and the mesh size of other parts was set as 4 cm. The bottom of the diaphragms at the outermost end was simple and support-constrained, and the middle diaphragms were constrained in the vertical and transverse directions. The whole finite element model is shown in Figure 5.  
Figure 4: Half of the cross-section of an orthotropic deck steel box girder.
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 2 Materials and Methods  
### Samples  
The test articles used in this study are four kinds of surface-modified and unmodified nano-silicas, namely, sample A (SA), modified sample A (mSA), sample B (SB), and modified sample B (mSB). The silica nanoparticles were obtained by hydrolysis of tetraethoxysilane (TEOS) in an ethanol medium. The nanomaterials were obtained from the Industrial Technology Research Institute (ITRI, Zhudong, Taiwan). R.O.C. ITRI also provided us with the physical and chemical characteristic information of those materials. The structure of test particles was examined using transmission electronic microscopy (TEM). After diluting in purified water, samples were placed over a copper grid coated with carbon film then stained with 2% phosphothurgistic acid. The samples were air-dried prior to placement in the TEM instrument for analysis. The shapes and sizes of nano-silicas were determined using a TEM, which showed that particles diameters in four different preparations were in the nanosized range (1-100 nm). The average particle sizes of SA, mSA, SB, mSB are 55.16, 82.29, 22, and 86.64 nm, and the solid content are 41.13, 3.77, 35.8, 3.77%, respectively (Figure 1; Table 1). The nanomaterials were prepared in their powder form, which was stable and kept away from light at all times. However, to reduce instability that may occur during our experiments, we only weighed out enough powder to make the solution for one experiment and used them immediately.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Principal Component** & **Solid Content (\%)** & **Diameter (nm)** & **pH** \\ \hline Sample A & Colloidal SiO\({}_{2}\) & 41.13 & 55.16 & 7–9 \\ Modified & Sample A & & & \\ sample A & TEOS a & 3.77 & 82.29 & 2–4 \\  & H\({}_{3}\)Psi & & & \\ Sample B & Colloidal SiO\({}_{2}\) & 35.8 & 22 & 7–9 \\ Modified & Sample B & & & \\ sample B & TEOS a & 3.77 & 86.64 & 2–4 \\  & H\({}_{3}\)Psi & & & \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) TOES: tetraethoxysilane.  
\end{table}
Table 1: The chemical properties of unmodified and modified nano-silica.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 5 Future Roadmap
Apart from these approaches, domain-specific real-time SR as well as adaptive and dynamic approaches for SR, are emerging trends in real-time SR. Domain-specific real-time SR focuses on applying SR methods to specific applications, such as medical imaging, satellite photography, or video streaming, where unique limits and requirements necessitate customized models. These tailored models can improve accuracy and efficiency by taking use of the target domain's unique properties. Meanwhile, adaptive and dynamic methods to SR entail creating models that can modify their complexity and processing capacity in real time based on available computing resources or task-specific requirements. These strategies are intended to improve performance in a variety of contexts, ranging from high-performance servers to low-power constrain devices, while maintaining constant quality and speed. These changes, taken together, imply a move toward more adaptable, efficient, and application-specific solutions in real-time SR.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.1 Energy-Efficient Clustering Algorithm (EECA)  
In [47], [PERSON] et al. proposed an algorithm that reduced the energy loss of each round of clustering, and had fewer transmission times and better data hierarchy [48]. The algorithm is divided into five parts, as shown in Figure 9. In this protocol, cluster heads for each round are selected from all multi-point relays in the network which is energy-based of all the multi-point relay (MPR) nodes and the distance between them and the base station. Considering load balancing at each round, different cluster heads are selected for transmission of packets to the base station. The dissipated energy of each multi-point relay is calculated based on the number of transmissions and the distance between the sender and the receiver node, each MPR's residual energy is calculated by  
\[E(K,D,T)=SUMMATION(T)\{K*E_{elec}+K*E_{amp}*D^{2}\}. \tag{6}\]  
where \(K\) is the number of the bits of packets, \(E_{elec}\) is the energy dissipated in electronic circuits, \(E_{ampl}\) is the energy dissipated for transmission in power amplifier, and \(D\) is the transmission distance.  
The cluster heads are selected by considering the remaining energy levels of the MPR and the distance between them and the base station.  
**Advantages:** (1) Suitable for dense networks and assuming that communication occurs more frequently between a large number of nodes, it is much better than the traditional protocols with respect to end-to-end latency, transmission times and energy consumption. (2) In this protocol, most of the aggregated data resides in the sub-cluster head of the cluster where the persistent data is stored, so this protocol extends the life cycle of the system and optimizes the transmission rate of packets.  
**Disadvantages:** All the nodes which wants to transmit the data send the data to its corresponding sub-cluster heads and then aggregate the data, without considering limited buffer space of nodes.
## 2 Overview of the Power Ramp-Rate Control Methods
### Second Order Low-Pass Filter (SOLPF) Method  
The SOLPF is similar to the FOLPF. However, the attenuation factor for the SOLPF is \(-40\) db/DB. This implies that the SOLPF is more selective below the cutoff frequency, compared to the FOLPF. This implies that high-frequency fluctuations in the PV power can be more attenuated, which helps to reduce PRR violations. The discrete transfer function for the SOLPF is given by [28]:  
\[\mathrm{G}_{\mathrm{solpf}}(\mathrm{z})=\frac{\mathrm{T}_{\mathrm{s}}^{2} \omega_{\mathrm{n}}^{2}\mathrm{z}^{-2}}{1+(2\zeta\omega_{\mathrm{n}}\mathrm{T }_{\mathrm{s}}-2)\mathrm{z}^{-1}+\left(1-2\zeta\omega_{\mathrm{n}}\mathrm{T} _{\mathrm{s}}+\mathrm{T}_{\mathrm{s}}^{2}\omega_{\mathrm{n}}^{2}\right)\mathrm{ z}^{-2}} \tag{8}\]  
where \(\omega_{\mathrm{n}}\) is the natural frequency and \(\zeta\) is the damping ratio. As the eigenvalue location of \(\mathrm{G}_{\mathrm{solpf}}(\mathrm{z})\) is not as explicit as \(\mathrm{G}_{\mathrm{folpf}}(\mathrm{z})\), it is important to analyze them before implementation.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.4.1 Cost Savings  
When the cost savings are calculated for different growth scenarios (6%, 10%, 20%, 30%, and 40%) vs. different number of factories (2, 10, 100, and 500), deployment scenarios A (all in the central cloud) and B (UP in the factory and CP in the central cloud) show significantly lower costs as compared to scenario C (all in factory). The lower costs are attributed to the scale benefits of centralization vs. the full distribution of the major cost components. Furthermore, the scale benefits and relative cost benefits are studied by varying the CAGR for different scenarios of device growth and for the different number of factories, as shown in Figure 11.  
With a single factory as the slice customer in the market, scenario A and C cost the same and both are lower than scenario B; however, in all other cases, when there is more than one factory, scenario C is the most expensive and scenario A is the least expensive. Figure 10 shows that a percentage of the cost savings relative to scenario C continues to increase with an increase in the number of factory slice customers, indicating that the higher the number of slice customers, the higher the cost savings through scenario A and B. However, for a given number of factories, lower cost savings are possible as the CAGR increases. This is due to the fact that a large factory will have less to gain in terms of cost savings in comparison to a small factory, again highlighting scale benefits. Furthermore, it should be noted that the absolute costs within each scenario are expected to grow with a higher number of factories; the percentage of cost savings relative to scenario C have a weaker trend.  
Additionally, scale benefits can be achieved through volume discounts for larger volume purchases. However, some strategic uncertainties exist such as the complexity of human costs and automation levels, which can vary based on the stakeholders involved in the ecosystem. A factory might have more expertise in managing their industrial processes rather than managing the networking infrastructure, while slice providers and network operators are expected to be better suited for network management tasks. However, network slice management for the request and configuration of the slices can be customer-managed, slice provider-managed, or network operator-managed, depending on the business models. The sensitivity analysis above shows that the model is applicable to different market scenarios with different device growth, customer size, and number of customers.