esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
Symbols  
\(M^{\kappa}\)  
mass accumulation of component \(\kappa\), (kg/m\({}^{3}\))  
\(F^{\kappa}\)  
mass flux of component \(\kappa\), kg/(m\({}^{2}\cdot\)s)  
\(q^{\kappa}\)  
sink/source of component \(\kappa\), kg/(m\({}^{3}\cdot\)s)  
\(M^{\theta}\)  
energy accumulation (J/m\({}^{3}\))  
\(F^{\theta}\)  
energy flux, J/(m\({}^{2}\cdot\)s)  
\(q^{\theta}\)  
sink/source of heat, J/(m\({}^{3}\cdot\)s)  
\(V\)  
volume (m\({}^{3}\))  
\(\Gamma\)  
surface area (m\({}^{2}\))  
time (s)  
composite thermal conductivity  
\(k_{dry}\)  
dry thermal conductivity  
\(k_{ave}\)  
wet thermal conductivity  
\(\varphi\)  
porosity  
\(\lambda_{I}\)  
average thermal conductivity of ice  
\(P_{cap}\) cap  
capillary pressure (Pa)  
\(P_{\theta}\)  
initial capillary pressure (Pa)  
saturation for capillary pressure  
saturation of phase \(\beta\)  
maximum reference aqueous saturation of capillary  
\(S_{nrA}\)  
irreducible saturation of aqueous phase  
\(k_{fp}\)  
relative permeability of phase \(\beta\)  
\(S_{nG}\)  
irreducible saturation of gas phase  
\(nA\)  
permeability reduction index for aqueous phase  
\(nG\)  
permeability reduction index for gas phase  
\(\lambda\)  
porosity distribution index  
\(F_{\theta\xi}\)  
coefficient of permeability reduction  
effective permeability of the sediment
## 4 Experimental Results
### Propeller Measurement Results
#### 4.2.1 Precision Analysis in Continuous Measurement Mode  
The one-way ANOVA homogeneity test revealed that, under the continuous measurement mode, the values measured for the first, second, and third blades were nonsignificantly different to the second decimal place. Thus, the developed system consistently achieves precision up to the second decimal place for all three blades when operated in the continuous measurement mode (Table 4).
## 4 Comparative Analysis of AI Language Models Based on Scholarly Literature
### Real-World Applications of AI Chatbots
#### 4.8.1 Education and Learning  
AI chatbots have shown remarkable potential in transforming education and learning across various disciplines and levels. ChatGPT, in particular, has been at the forefront of this educational revolution, with numerous case studies highlighting its impact on student engagement, learning outcomes, and research efficiency.  
One of the most promising areas for AI chatbot applications is in the field of education. ChatGPT has shown strong potential as an educational tool, especially for novice learners. Its ability to generate multiple choice questions and evaluate educational materials, such as those related to sleep apnea [26], underscores its utility in creating and curating learning content. Moreover, ChatGPT 3.5's proficiency in programming across languages like Java, Python, and C++, as well as its adaptability to cater to both non-programmers and experienced coders [33], further highlights its potential to revolutionise coding education and practice.  
In the domain of higher education English teaching, ChatGPT has demonstrated its ability to enhance language learning by providing instant feedback, generating diverse conversational scenarios, and facilitating language practice [34]. This interactive and personalised approach has led to improved student language skills and increased engagement in the learning process.  
Beyond language learning, ChatGPT has also proven valuable in streamlining the academic research processes. The use of ChatGPT for thematic analysis showcases its potential to assist researchers in coding qualitative data, identifying themes, and drafting thematic reports, although it is not without challenges [35]. This human-AI collaboration significantly reduces the time and effort required for analysis, thus allowing researchers to focus on higher-level tasks.  
The impact of ChatGPT on higher education extends further to provide assistance in tutoring, content creation, and administrative support [36]. Many related studies reported increase student satisfaction, learning efficiency, and an improvement to the overall educational experience when ChatGPT was integrated into the learning environment.  
In experiential learning environments, GenAI tools like ChatGPT were also used to facilitate authentic assessment by providing realistic scenarios and personalised feed back [37]. This approach enhances students' critical thinking and problem-solving skills, thus preparing them for real-world challenges.  
As AI chatbots continue to evolve and integrate into educational systems, their impact on learning and teaching practices is set to grow exponentially. As researchers, we have the responsibility to create methods that allow for the full exploration of the AI potentials, which could revolutionise traditional learning and teaching. This mainly includes the provisions of the necessary ethical and responsible use guidelines and practices.
## 2 Related Work
### Bi-Lstm
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{7}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{8}\]  
As in the Figure 6, the last step is to determine what to export to the output gate layer. First, we take input data on the sigmoid layer and decide which part of the cell state to export as output. Then, burn the cell state into the tanh layer, receive the value between \(-1\) and \(1\), and multiply it by the output of the sigmoid gate that was calculated just now. Through this calculation, we can export only the part we want to send as output. Output \(O_{t}\) is expressed by multiplying \(x_{t}\) and \(h_{t-1}\) by each weight and multiplying the sigmoid function by the sum of the value calculated and bias. Hidden state \(h_{t}\) is represented by multiplying output \(O_{t}\) by \(\tanh(C_{t})\). Figure 4: LSTM input gate layer. Figure 3: LSTM forget gate layer. Although LSTM solves the gradient vanishing/exploding problem well, future information is not considered in the output. Therefore, As in the Figure 7, Bi-LSTM has emerged, which considers both directions of the hidden state to analyze more patterns and perform sophisticated learning by considering information in both directions. The model of Bi-LSTM is expressed as a formula, as follows:  
\[y_{t}=W_{hy}h_{t}+b_{y} \tag{2}\]0  
\(h_{t}^{f}\) and \(h_{t}^{b}\) refer to a forward layer and a backward layer, respectively. \(W\) represents the weight connecting input \(x\) to hidden layer \(h\). \[y_{t}=W_{hy}h_{t}+b_{y} \tag{2}\]1  
\[y_{t}=W_{hy}h_{t}+b_{y} \tag{2}\]2 LSTM cell state update. \[y_{t}=W_{hy}h_{t}+b_{y} \tag{2}\]3 LSTM output gate layer. Hidden layer \(h_{t}\) is expressed by multiplying the input value \(x_{t}\) and the previous hidden layer \(h_{t-1}\) by each weight and substituting the value obtained by adding the bias \(b\) vector into the \(tanh\) function. The backup hidden layer uses a value of \(h_{t+1}\) instead of \(h_{t-1}\). \[y_{t}=W_{hy}h_{t}+b_{y} \tag{2}\]4  
Output layer \(y_{t}\) is calculated by multiplying the weight W by the hidden layer in both directions and adding bias \(b_{y}\). By considering both hidden state values in both directions, the output value is output as a learned value not only for forward but also for backpropagation patterns. The process of calculating \(\mathcal{Y}_{t}\) by adding the hidden states in both directions through the above equation is as follows. The input values in each step are input to the layers in both directions. The layers in both directions generate an \(h_{t}\) value through the LSTM information processing process, and the two \(h_{t}\) values are connected to the output \(y_{t}\) of each step. Bi-LSTM minimizes the loss to output, and end-to-end learning is performed to simultaneously learn all parameters in both directions. Thus, more sophisticated models can be built than traditional LSTMs. In addition, the similarity between each input and existing patterns can be embedded in the input vector \(i_{t}\) to improve the performance of the model, and the long data length does not degrade using the underlying performance and attention mechanism of LSTM.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 3 Proposed Continuous Sinusoidal Model
### Analysis Phase: Feature Extraction
The phases are obtained recursively in a minimum phase response between harmonics in adjacent frames:  
\[\varphi_{k}^{i}=Im\Big{\{}C_{k}^{i}\Big{\}}+k\gamma^{i} \tag{10}\]  
\[\gamma^{i}=\gamma^{i-1}+\frac{T}{2}\Big{(}w_{0}^{i}+w_{0}^{i-1}\Big{)} \tag{11}\]  
where \(k\gamma^{i}\) is a linear-in-frequency term which can be attributed to the underlying excitation, and \(T\) is the frame shift measured in samples (typically it corresponds to a \(5 ms\) interval). The synthetic noise part \(n(t)\), with this work building on our previous study [32], is firstly filtered by a high-pass filter \(f_{h}(t)\) with a cutoff frequency equal to the local MVF, and then modulated by its Hilbert envelope \(e(t)\):  
\[s_{n}^{i}(t)=e^{i}(t)\;\Big{[}f_{h}^{i}(t)*n^{i}(t)\Big{]} \tag{12}\]  
In unvoiced frames, on the other hand, the harmonic part is zero (MVF = 0) and the synthetic frame is formed only by noise. Hence, we can reconstruct the speech signal exactly by summing up the harmonic and noise components.
## 3 Results and Discussion
### Integration of Smart Microgrids with Virtual Power Plants (VPPs)  
Virtual Power Plants (VPPs) are emerging as a complementary and scalable paradigm for managing distributed energy resources. Although VPPs differ from microgrids in structure and scope, they share several operational functions, such as distributed energy aggregation, peak shaving, demand response, and ancillary services. Recent studies suggest that microgrids can act as functional subsystems within VPP frameworks, enabling coordinated scheduling and market participation. In this context, microgrids provide local autonomy and resilience, while VPPs offer centralized control and market interfacing. This convergence is particularly relevant for the deployment of advanced control strategies and intelligent energy management systems, enabling dynamic interaction with grid operators and virtual aggregators. The incorporation of VPP-related functionality into microgrid design further reinforces their strategic role in the future of decentralized and transactive energy systems [89; 90].
## 8 Conclusions  
Wireless mesh networking is a promising technology that has tremendous capabilities to support multimedia and QoS-demanding applications and services. To enable multimedia and QoS-demanding applications and services over WMNs, QoS provisioning routing is required, which is an emerging field. Available QoS provisioning routing protocols uses greedy parameters to find and select end-to-end QoS-provisioned paths. Furthermore, the deployment of WMNs is random. Thus, QoS provisioning routing and random topology deployment causes severe wastage of physical layers resources, ultimately increasing the deployment and operating costs.  
In this paper, we have proposed a novel ILP-based optimization solution and a polynomial time heuristic algorithm to reconstruct an optimized mesh backbone topology with a minimum number of wireless communication links and backbone WMRs. Our simulation study shows that more than 20% of physical layer resources are being wasted by QoS provisioning routing and random topology deployment. Our proposed heuristic LNR-RCTD algorithm achieved near-optimal results and the time complexity of LNR-RCTD is polynomial. Our reconstructed optimized backbone mesh topology also  
Figure 6: (**a**) Identification of extra mesh nodes; (**b**) Identification of extra wireless links.  
maintains a specific level of redundancy, which is the core design objective of WMNs. In this paper, we only considered single-path routing between source and destination pairs. This work can be further extended to multipath routing between source and destination pairs.  
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1002) supervised by the NIPA (National IT Industry Promotion Agency).  
All authors have made significantly contributions to the paper. [PERSON] designed and implemented both ILP-based solution and LNR-RCTD Algorithm, conducted the experiments, parsed the results, and prepared the manuscript. [PERSON] provided realistic QoS requirements of real-time and multimedia applications which will occupy most resources of future mobile networks. He also checked the proposed methods satisfy those requirements and provided the funding for publication. [PERSON] and [PERSON] contributed to the paper organization and technical writing of early versions of the manuscript. They also contributed in several rounds of critical revisions. All authors have contributed to the interpretation and discussion of the results and have read and approved the final version of the manuscript.  
The authors declare no conflict of interest.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 4 Implementation and Evaluation Setup
### Hardware Board  
Since no current UWB hardware boards support sub-GHZ technologies, an existing sub-GHz IoT platform was used: the Zolertia Re-Mote [31]. The Zolertia Re-Mote supports two low-power IoT radios (CC1200 and CC2538), but only the CC1200 sub-GHz radio was used in this paper. For this IoT platform, a custom UWB add-on board using the Decawave DW1000 [32] UWB Transceiver was designed. To improve the performance, the UWB hardware board supports an SMA connector, allowing to utilize external antennas instead of chip antennas.
## 3 Beamforming Techniques
### Robust Beamforming with ODL Technique  
In this optimization technique, the loading level mostly depends on the noise power, signal power, and norm of steering vector with and without a look direction error. Besides, the LCCAA-based ODL technique is an automatic system that relies on steering vector distortion bound. The array correlation matrix can be written using the ODL technique as Equation (3) [7].  
\[R_{new}=R+(\beta\times I) \tag{3}\]  
here \(\beta\), refers to  
\[\beta=\frac{\epsilon\left(\sigma_{n}^{2}+P_{s}\,\left\|S_{0}\right\|^{2} \right)}{\left\|S_{ac}\right\|-\varepsilon} \tag{4}\]  
\(P_{s}\) denotes the signal power, \({\sigma_{n}}^{2}\) is the symbolized noise power, \(\left\|S_{0}\right\|\) indicates the norm of the steering vector without the look direction disparity, and \(\left\|S_{ac}\right\|\) refers to the steering vector with the look direction disparity. The steering vector distortion bound \(\varepsilon\) is represented in Equation (7).
# Advanced First-Principle Modeling of Relativistic Ruddlesden--Popper Strontium Iridates
## 4 Results and Discussions  
### Crystal Structures  
The RP series of iridates Sr\({}_{n+1}\)Ir\({}_{n}\)O\({}_{3n+1}\) is a family of materials, with \(n\) being the number of SrIrO\({}_{3}\) perovskite layers sandwiched between SrO layers [143]. Figure 1 displays the crystal structures of the three iridates. Sr\({}_{2}\)IrO\({}_{4}\) is a single-layered quasi-two-dimensional (2D) compound for which the symmetry is lowered from \(14/mmm\) to \(14_{1}/acd\) due to the rotation of IrO\({}_{6}\) octahedra along the \(c\) axis by about \(11.5^{\circ}\)[144]. Besides rotation, the IrO\({}_{6}\) octahedra is elongated along the \(c\) axis, giving rise to a bond length ratio \(c/a\approx 1.04\)[144]. Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\) exhibits a similar crystal structure to Sr\({}_{2}\)IrO\({}_{4}\), but it consists of stacking bilayers of corner-shared IrO\({}_{6}\) octahedra along the \(c\) axis [145]. The octahedral rotations for the neighboring IrO\({}_{6}\) octahedra in Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\) are opposite not only within each layer but also between the two layers (see Figure 1b). The perovskite-like SrIrO\({}_{3}\) is a high-pressure phase and has an orthorhombic three-dimensional (3D) structure [146; 147].  
Figure 1: Crystal structure of the Ruddlesden–Popper (RP) series of perovskite-like iridates Sr\({}_{n+1}\)Ir\({}_{n}\)O\({}_{3n+1}\) (\(n=1\), \(2\), and \(\infty\)). Sr, Ir, and O atoms are shown in green, blue, and red, respectively. Reproduced with permission from Reference [100]. Copyright 2018 by the American Physical Society.  
In our calculations, we adopted the experimental lattice parameters and fully relaxed internal atomic positions using the LSDA+\(U\)+SOC method until the forces were smaller than 0.01 eV/A. Experimentally measured magnetic orderings (\(n=1\): in-plane canted AFM [15; 17]); \(n=2\): \(c\)-collinear AFM [30; 31; 32; 33], and \(n=\infty\): nonmagnetic [147]) were adopted. The detailed optimized structural and magnetic data are given in Reference [100].
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 4 Numerical Simulation
### Validation of Inter-Satellite Links Pointing and Tracking  
Based on the virtual target satellite in Section 3.2, the test method for inter-satellite link pointing and tracking is validated. The half angle of the beam is set as 0.3\({}^{\circ}\). The ground stations receive the inter-satellite link signals from the local satellite. The orbit of the local satellite and the ground projection of the beam pointing are shown in Figures 6 and 7, respectively. The time durations of the inter-satellite links pointing to the ground stations are shown in Figure 8 and Table 2.  
angle and rotation angle are about 5.2\({}^{\circ}\) and 5.0\({}^{\circ}\), respectively. The rates of change in these angles are acceptable in practical missions.
# Switchable Filter with Four Operating Modes Implemented on a 50-Ohm Microstrip Line
## 4 Switchable Filter Design with Bais Circuit
(**a**) dual-band filter (\(D_{1}\): off, \(D_{2}\): on, \(D_{3}\): off) (**b**) dual-band filter (\(D_{1}\): off, \(D_{2}\): off, \(D_{3}\): off) (**c**) wideband filter (\(D_{1}\): on, \(D_{2}\): off, \(D_{3}\): off) (**d**) all pass filter (\(D_{1}\): don’t care, \(D_{2}\): don’t care, \(D_{3}\): on). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Function** & **Operating Frequency Range [GHz]** & **BW [\%]** & **IL [dB]** & **Num. of Diodes** & **Structure** & **Circuit Size [\(\lambda_{\mathbf{g}}\times\lambda_{\mathbf{g}}\)]** \\ \hline
[6] & BSF/ASF/ & 1.5–2.5/-/-1.83-2/ & 50.2/-/-9.7/22.1 \& & 1.99 & 4 & Parallel filter branches & 0.21 \(\times\) 0.22 \\  & BPF/DBSF & 1.44–1.78 \& 2.12–2.6 & 20 & & & & \\ \hline
[7] & BPF/BSF & 1.41–2.43/ & & & & & \\  & DBSF & 1.63–2.21/1.55-1.61 \& & 53.1/30.2/3.8 \& 2.7 & 0.9/2/2.2 & 6 & & \\  & DBSF & 2.2–2.26 & & & & & & \\ \hline
[8] & WBPF / BPF / & 1.51–4.38/ & 95.7/25.3/106 & 0.94/0.8 & 6 & & & \\  & BSF & 2.55–3.29/1.41–4.59 & & 95.7/25.3/106 & 0.94/0.8 & 6 & & & \\ \hline
[9] & BPF/DBPF/BSF & 1.19–2.93/1.25–1.87 \& & 84.5/39.7 \& & 0.9/1.13 \& 6 & & \\  & BPF/DBPF/BSF & 2.25–2.89/ & 24.9/29.6 & 1.5/2 & 2 & & & \\  & & 1.73–2.33 & 2.53 & 2.6 \& 20.4/30 \& 4.6/5.6 & 7 & & \\ \hline
[10] & DBPF/ & 1.7–2.2 \& 2.2–2.7/1.7–2.3 & 25.6 \& 20.4/30 \& 4.6/5.6 & 7 & & \\  & DBSF & \& 2.3–2.9 & 23.1 & & & & \\ \hline
[11] & BPF/BSF & 1.47–2.13/1.67–1.9 & 36.75/13 & 1.73/0.8 & 7 & & & \\ \hline
[13] & BBPF/ & 1.23–1.78/1.61–1.99 \& 36.7/20.7 \& 16.1 / & & & \\  & DBPF/ & 2.76–3.24/0.36–0.44 \& 36.7/20.7 \& 16.1 / & & & \\  & TBPF & 1.29–1.71 \& 2.41–2.89 & 20 \& 27.4 \& 18.1 & & \\ \hline
[14] & BPF/DBPF & 0.8–1/0.65–0.94 \& & & & & \\  & 0.87–1.13 & 36.07/26.42 \& 22.9–4.9 & 13 & & & \\ \hline This work & DBPF/ & & & & & & \\  & DBPF/ & -6–/-8–/2.05–4.41 & 23.5 \& 14.42/23.33 \& 1.33 \& 1.3/0.88 & & 50 \(\Omega\) line \\  & WBPF/APF & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with previous switchable filters.
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 3 Simulation Results
Increasing the number of transmitters has little effect on the fairness index of both CP and RP methods, while increasing the number of receivers reduces the fairness index of both methods. The reason can be found in the broader range of users' data rates. Furthermore, the fairness index of CP and RP methods reduces slightly with the increase of the D2D search radius. Figure 2: The stability as a function of (**a**) the number of potential D2D transmitters, (**b**) the number of co-channel D2D receivers, and (**c**) the D2D search radius. Finally, in Figure 4, the sum rate as a function of the number of potential D2D transmitters around a receiver, the number of co-channel D2D receivers, and the D2D search radius are compared. In Figure 4a, the sum rates of all four methods are compared when the number of transmitters is increased. The MSP method has the highest data rate. Nevertheless, the FP method approaches the MSP method by increasing the number of potential transmitters. The other two methods (RP and CP) do not follow the MSP method in this case. In Figure 4b, similar to Figure 4a, increasing the number of D2D receivers, the FP method sum rate follows the MSP method and also the sum rate of the RP method increases slowly, but after increasing the number of receivers (from 3 to 4), due to the effect of interference on the rate, the sum rate of the CP method tends to a constant value. In Figure 4c, the sum rate is plotted as a function of the D2D search radius for the four methods mentioned. As expected, due to the increased interference, the sum rate decreases in all four methods as the search radius increases. However, the FP method is still the closest method to the MSP method.