esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
## 3 Image Motion Measurement System and Experimental Environment
### Measurement of Background Noise in the Experiment Environment  
The image motion measurement experiment based on an inertial reference laser depends on a precise measurement system. To reduce measurement error, the stability of the parallel light pope and image target system should be ensured. Therefore, this system was placed on a vibration isolation platform to isolate the micro vibrations from the ground. Before the experiment, the angular displacement noise around the \(Y\)- and \(Z\)-axes direction of the working face of the vibration isolation platform was measured using a high-precision angular displacement sensor (the definition of the coordinate system is shown in Figure 4). The measurement conditions were the same as those during the experiment, that is, the integrating sphere fan was kept on, the rest of the surrounding vibration experiment was stopped, the ventilation devices in the experimental area were turned off, and the shading curtains were closed.  
Figure 5 shows the micro vibration power spectral density curves measured in two directions under the aforementioned conditions, and Table 1 shows the corresponding RMS noise. The background noise of the vibration isolation platform within the 1-200 Hz range did not exceed 0.037\({}^{\prime\prime}\) and was less than 1/20 pixels, while in the 3-200 Hz frequency range, the background noise did not exceed 0.014\({}^{\prime\prime}\) and was less than 1/50 pixels, thereby meeting the accuracy requirements of the experiment.
# Source Microphone Identification Using Swin Transformer
## 4 Experiments and Discussion
### Investigating the Preprocessing of Inputs to Improve System Accuracy
#### 4.4.2 Input Projection Kernel Shape Effect on Accuracy  
As was previously mentioned in the methodology, the input sound is converted into a spectrogram and then divided into 4 \(\times\) 4 batches, and as it is known that the spectrogram differs from the images as there is a temporal relationship in the second axis, and accordingly, experiments were conducted to study the effect of the different shape. The batch is based on the accuracy of the classification. Experiments were conducted using different forms of batches, as shown in Figure 4. The results of experiments that were conducted on all microphones are shown in Figure 5. We note that the best results are at projection 4 \(\times\) 4, and the results are uneven whenever the batch shape is changed from the shape on which the system was built, where the worst results are 1 \(\times\) 16 in addition to 8 \(\times\) 2. This implies that using frequency resolutions and more bands gives a better performance than time resolution and intervals.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Mic Model** & **\# Mic** & \multicolumn{6}{c}{**Frequency Band**} \\ \cline{4-8}  & & & **(0–4) k** & **(4–8) k** & **(8–12) k** & **(12–16) k** & **All Bands (0–16) k** \\ \hline M1 & AKG0451 & 2 & **0.7208** & 0.646 & 0.558 & 0.626 & 0.6752 \\ M2 & COL4038 & 2 & **0.992** & 0.697 & 0.637 & 0.603 & 0.976 \\ M3 & ELE0020 & 2 & **0.8308** & 0.823 & 0.599 & 0.722 & 0.7876 \\ M4 & SEN0421 & 3 & 0.8 & **0.859** & 0.747 & 0.711 & 0.8122 \\ M5 & SHU0058 & 3 & **0.7444** & 0.606 & 0.593 & 0.584 & 0.6874 \\ M6 & TBO0088 & 6 & 0.6002 & **0.864** & 0.457 & 0.453 & 0.8518 \\ M7 & AKG3000 & 2 & 0.5608 & **0.783** & 0.52 & 0.514 & 0.8388 \\ M8 & NEU0184 & 2 & 0.6242 & **0.676** & 0.346 & 0.304 & 0.7984 \\ \hline \end{tabular}
\end{table}
Table 2: Impact of the frequency band on the classification accuracy with respect to the microphone model.  
Figure 4: The shapes of the projection kernel for the input spectrogram are presented in five different shapes, denoted as (1 \(\times\) 16), (2 \(\times\) 8), (4 \(\times\) 4), (8 \(\times\) 2), and (16 \(\times\) 1).
## 3 Case Study
### Performance Comparison Analysis of Different Solar Irradiance Forecasting Models
#### 3.5.2 Comparison Analysis under Cloudy Day  
Based on the dataset of Elizabeth City State University and Desert Rock Station, the performance comparisons among different cloudy day's forecasting models are presented in Tables 10 and 11, respectively. As previously discussed in Table 5, the DWT-CNN-LSTM model of cloudy days has the highest forecasting precision at WD level 2 when using the dataset of Elizabeth City State University. Therefore, as shown in Table 10, the proposed DWT-CNN-LSTM model with WD level 2 is selected to make comparisons with the other kinds of forecasting models.  
First of all, it should be noted that all the error index values of DWT-CNN-LSTM (WD level 2) model is better than that of single CNN-LSTM. This result indicates that the DWT based solar irradiance sequence decomposition has the capability to further improve the forecasting performance of combined CNN-LSTM models. As discussed in Section 3.5, the obvious performance improvement can be attributed to the fact that the solar irradiance curve of cloudy days presents high volatility, variability and randomness. Therefore, the cloudy day's solar irradiance sequence includes nonlinear and dynamic components in the form of spikes and fluctuations. The existence of these components will undoubtedly deteriorate the precision of the solar irradiance forecasting models. Additionally, the application of WD could well mitigate the above problems.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Forecasting Models** & **MAE** & **RMSE** & **R** \\ \hline DWT-CNN-LSTM (WD level 2) & 81.466 & 118.645 & 0.928 \\ CNN-LSTM & 86.754 & 121.922 & 0.925 \\ \hline CNN & 87.043 & 122.042 & 0.923 \\ LSTM & 87.997 & 122.479 & 0.921 \\ Manually extracted features-ANN & 90.310 & 125.871 & 0.905 \\ ANN & 89.743 & 123.532 & 0.917 \\ Persistence forecasting & 95.370 & 168.443 & 0.849 \\ ARIMA & 110.334 & 207.694 & 0.772 \\ \hline \end{tabular}
\end{table}
Table 10: The performance comparison of different cloudy days’ forecasting models using the dataset of Elizabeth City State University.  
Figure 7: Actual and forecasted solar irradiance on sunny day pattern using dataset of Elizabeth City State University.  
When compared to the manually extracted features-ANN, as well as the traditional forecasting models (i.e., ANN, persistence forecasting and ARIMA), the comparison results verify our proposed model's advantages in the following two respects. One is the ability to automatically extract representative and significant information from the raw input data, and the other is the ability to capture the long dependencies among the time series input data. In addition, the performance improvement of CNN-LSTM over CNN and LSTM also reveals the benefits of the combination of them. A similar discussion can also be made according to Table 11. Figure 8 shows the actual and forecasted solar irradiance curve on cloudy day pattern using dataset of Elizabeth City State University.
# Progress in Topological Mechanics
## 3 Mechanical Higher-Order Topological Insulators
### C\({}_{n}\)-Symmetric HOTIs
(**a**) Composite unit cells with \(\Delta\gamma=0.164\), \(\Delta m=0.5\) (top) and \(\Delta\gamma=-0.2\), \(\Delta m=-0.5\) (bottom) [109]. (**b**) The square elastic phononic crystal sample. The black circles signal the locations of defects [109]. (**c**) The corner mode at 1792 Hz [109]. (**d**) A composite unit cell consists of stainless-steel prisms(blue) and an aluminum plate(red) [110]. (**e**) The squared metamaterial formed by the two phononic crystals \(\theta=-25^{\circ}\) and \(\theta=50^{\circ}\), respectively. (**f**) The corner state at 34.01 MHz [110]. (**g**) The photo of the fabricated sample chip [111]. (**h**) The unit cell of the square lattice photonic crystal [111]. (**i**) One corner mode in the experiment system [111]. rotation angle \(\theta\) of the pillars could open the crystal bandgap and induce band inversion at the Brillouin zone corners. Specifically, the topological nature can be characterized by the topological index which can be calculated from the symmetry eigenvalues at high-symmetry momenta of the bands below the bandgap and corner charges. The topological index is \(\chi=(-1,-1,0)\) and the corner charge is \(Q_{c}=1/2\) when \(-90^{\circ}<\theta<0^{\circ}\), while the topological index is \(\chi=(-1,-1,-2)\) and the corner charge is \(Q_{c}=0\) when  
edge transitions that could be realized with tunable phase modulation speeds, which has potential applications for the controlled transmission of information across the waveguide. Figure 8: Temporal pump in electromechanical waveguides [135]. **(a)** The electromechanical elastic beam  
Figure 7: Spatially modulated elastic lattices. **(a)** 1D lattices model with modulated spring stiffness \(k_{n}=k_{0}[1+a\cos(2\pi np/q+\phi)]\)[128]. **(b)** 2D surface \(S(x,\phi)=\cos(2\pi\tau x+\phi)\). The black lines denote stiffness variation with \(\phi\) at a lattice node, and the blue lines denote cross-sections at \(\phi_{\
u}=(2\pi\tau/3)(r\in[0,3])\)[128]. **(c)** The dispersion surfaces with Chern numbers labeled on the gaps [128]. **(d)** The energy spectrum as a function of \(\phi\). The red lines signals the edge modes [128]. **(e)** The transition of the edge wave across the system from left to right as the variation of \(\phi\) in he second gap. [128]. **(f)** The array of continuous beams: the thick black lines signal the notional deformed beams, the thick red lines signal the undeformed beams, and the thin black horizontal lines correspond to the distributed springs with coupling constant \(\gamma_{n}\)[128]. **(g)** The square wave modulated continuous elastic plate employed in experiments [130]. with modulated bending stiffness \(D_{n}(\phi(t))\). (**b**) The schematic of the transient pump. (**c**) The dispersion diagram in quasistatic conditions (top) or in the time domain (bottom). (**d**) The influence of different phase modulation durations (top, 2 ms; middle, 1.5 ms; bottom, 1 ms) on edge-to-edge transitions.
## 2 New EMI Shielding Materials: Biochars, Scaffolds, Rare Earth, and Ferrite-Based Materials
### Rare-Earth-Based Materials as EMI Shielding Material
#### Synthesis, Morphology, and EMI Shielding Efficiency of RE-Based Materials
A schematic representation of the impact of hazardous EM waves radiated from various electronic sources on livelihood and a demonstration of the most possible EMI shielding mechanism to prevent it are presented in Figure 5e [111]. Figure 4: Morphology and particle size analysis by FESEM images at different resolutions of **(a,b)** Gd- and **(c,d)** Er-doped \(\alpha\)-MnO\({}_{2}\) samples. The figure is adapted from reference [111]. Copyright 2023, Elsevier B.V.  
Figure 3: Schematic representation of in situ chemical oxidative polymerization technique. The figure is adapted from reference [110]. Copyright 2025, Elsevier B.V.  
Rare earth elements exhibit high magneto-dielectric effects, and their dielectric properties can be tuned by applying an electric or magnetic field. This property makes them attractive for use in electromagnetic shielding applications where the magnetic field can be used to control the dielectric constant of the material. For example, the doping of MnO\({}_{2}\) with Er and Gd can enhance its dielectric properties, which make it a promising material for high-performance electronic devices. The incorporation of Er ions into the MnO\({}_{2}\) matrix can potentially modify the material's electrical conductivity, magnetic properties, and structural stability, thereby influencing its effectiveness in attenuating electromagnetic waves [111].
# Competition with the Industrial IoT: A Service-Dominant Logic Approach
## 1 Introduction
### Addressing the IIoT under an S-D Logic Perspective  
Despite some recently developed IIoT-based systems representing methods in service knowledge [6; 7; 8], no solution oriented toward creating value through cooperation, namely, with manufacturing SMEs, was found.  
Further bolstering the IoT and embedded systems era [32], the S-D Logic literature links the IoT and IIoT to ecosystems [15]. For S-D Logic, in ecosystems, resources are connected through any form of a sensor [23], enabling resources to be located, identified, and even operated upon [33] to promote more interactions among actors.  
The S-D Logic focuses on value co-creation involving actors through operant and operand technologies [15], while the IIoT combines the global reach of the Internet with industrial capabilities to control, coordinate, and manage the physical world of goods, machines, factories, and infrastructure [21].  
Aligned with this S-D Logic perspective on operand and operant resources [18], some authors in ICTs and service management refer to intelligent artefacts or systems able to execute specific functions [34] and reacting toward user interaction according to property sets of objects. However, for other authors, although this feasibility occurs successfully in some simple cases, in complex processes, the in-box thinking smart objects lead to creativity needing to be improved [35].  
Viewing the IIoT through the S-D Logic perspective presents a strategic approach to leveraging technology for value co-creation. IIoT technologies facilitate the sharing of information and resources, enhancing the visibility and responsiveness of firms within networks [18]. By enabling more effective resource integration and service exchange, the IIoT empowers firms to engage in S-D Logic's core principle of creating value with and for their customers and partners. This integration highlights the role of technology not just as a tool or enabler but as a participatory resource in the service ecosystem, actively shaping opportunities for value creation [36].  
From this perspective, IIoT technologies catalyze cooperation and competition among SMEs within coopetition networks. By providing a platform for data exchange and collaborative innovation, the IIoT enables firms to share insights and resources while maintaining competitive advantages [37; 38]. This dual functionality supports the development of coopetition networks where SMEs can mitigate weaknesses like scale and explore new market opportunities without diluting their unique value propositions. The dynamic interplay facilitated by IIoT technologies underscores the complex nature of coopetition networks, where shared value creation and competitive differentiation coexist and reinforce each other.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 1 Introduction
Finally, moderate linearity performance is shown with a third-order intercept point of \(-\)3.28 dBm and a 1 dB compression point of \(-\)13 dBm, respectively [10]. This study presents a novel SiGe HBT-based differential double-balanced down-conversion mixer. The proposed mixer is specifically designed to address key challenges studied in [4; 5; 6; 7; 8; 9; 10; 11] in terms of conversion gain, low noise figure, excellent linearity, wide dynamic range, and port-to-port isolation. Compared to existing designs, this mixer achieves impressive performance metrics, such as a single-sideband (SSB) noise figure of approximately 7 dB \(\pm\) 0.4 dB, a conversion gain of 12 dB \(\pm\) 1 dB, and an RF-to-IF isolation exceeding 35 dB, and LO-to-RF and LO-to-IF isolation are better than 50 dB in the frequency band of 0.5 GHz-1.8 GHz. Furthermore, it demonstrates excellent linearity, with a third-order intercept point (IP3) of +4.7 dBm and a 1 dB compression point of \(-\)8 dBm. The design combines several state-of-the-art features, including a single-ended-to-differential signal conversion by eliminating the need for a transformer at the RF stage,and a dual feedback linearization technique to improve nonlinear transfer function to achieve better impedance matching while minimizing signal distortion, resulting in a highly efficient and more linear mixer architecture. To avoid cross-talk interference of LO and RF signal, symmetrical active device layout and multilayer board technology are used, which significantly reduced LO to RF feedthrough, resulting in improved port-to-port isolation. The present paper is structured in the following way. Section 1 discusses general topics related to mixers and introduces a double-balanced mixer operating principle in Section 2. Section 3 focuses on the design and optimization of a fully monolithic double-balanced down-conversion Gilbert-cell mixer. Section 4 presents simulation results (linearity, noise, and gain calculations) and looks at the experimental validation of the proposed mixer. Concluding remarks are given in the final part of the paper.
## 3 Comparative Analysis on Accuracies/Techniques  
This section is divided into a concise description of the experiments, the interpretation of the experimental outcomes, and the conclusions. In the following, Table 1 present the authors, year, methodologies, accuracy, sensitivity, and specificity where the information is available.  
The review of soft computing in the identification, classification, and detection of heart disease in Table 1 showed that Fuzzy Logic and Convolutional Neural Networks achieved the highest accuracy of 99.30%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Authors** & **Year** & **Methodology** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline
01 & [PERSON] et al. & 2017 & PNN, GRNN & 98\% & NA & NA \\ \hline
02 & [PERSON] et al. & 2015 & HybridGA-Fuzzy,ANN & 86\% & 0.80 & 0.90 \\ \hline
03 & [PERSON] et al. & 2014 & FuzzyK-NN & 91\% & NA & NA \\ \hline
04 & [PERSON] et al. & 2016 & GFDSS & 80\% & 0.60 & 0.95 \\ \hline
05 & [PERSON] et al. & 2017 & Fuzzy Logic, feedforward neural network & 83\% & 0.84 & 0.89 \\ \hline
06 & [PERSON] et al. & 2017 & Gain-Ratio algorithm, SVM-REF & 84.16\% & 0.85 & 0.82 \\ \hline
07 & [PERSON] et al. & 2017 & HCWV & 82.54\% & NA & NA \\ \hline
08 & [PERSON] et al. & 2017 & XGBoost and logistic regressor & 85.86\% & 0.86 & 0.69 \\ \hline
09 & [PERSON] et al. & 2017 & Fuzzy Logic, GA & 88.11\% & 0.92 & 0.27 \\ \hline
10 & [PERSON] et al. & 2018 & Hybrid intelligent system-MLR & 88\% & 0.75 & 0.96 \\ \hline
11 & [PERSON] et al. & 2018 & KNN, SVM, Naive Bayes, LR, hybrid Naïve Bayes-LR, Vote & 87.41\% & 0.79 & NA \\ \hline \end{tabular}
\end{table}
Table 1: Methodology and accuracy of soft computing techniques for prediction of heart disease.
## 2 Related Works
### Overview of Existing Mitigation Approaches  
Several ADI mitigation strategies have been proposed and evaluated across different wireless communication technologies, including TD-LTE and 5G networks. Table 3 provides a comparative overview of representative methodologies, highlighting the diversity in detection mechanisms, performance metrics, and network contexts.  
[PERSON] et al. [10] introduced a Remote Interference Management Reference Signal (RIM-RS) design tailored for 5G NR systems. Their method demonstrated a high detection probability of 0.900 and a remarkably low false alarm probability of 0.002, indicating its reliability and precision in identifying ADI in real-time operational environments.  
In another notable study, [PERSON] et al. [17] presented a hybrid mitigation model combining digital signal processing (DSP), Long Short-Term Memory (LSTM) networks, and convolutional neural networks (CNNs). Applied to QAM-OFDM-based communication systems, their approach achieved a significant improvement in system performance, reducing the symbol error rate from 0.37618 to 0.0003. This result illustrates the efficacy of integrating deep learning with classical signal processing in enhancing ADI mitigation.  
[PERSON] et al. [3] proposed an interference mitigation mechanism for TD-LTE networks by adjusting the special subframe configuration, specifically the guard period. While detailed performance metrics were not provided in their study, this approach is recognized for its practical implementation potential within existing LTE infrastructure without requiring significant architectural changes.  
Similarly, [PERSON] et al. [14] adopted a guard period adjustment strategy in TD-LTE systems to mitigate ADI. Though this study did not specify quantitative results, it emphasizes system-level configuration tuning as an effective and low-complexity method for interference control.  
As summarized in Table 3, these diverse approaches reflect the multidisciplinary nature of ADI mitigation, encompassing signal design, machine learning, hybrid architectures, and protocol-level adjustments. However, variations in evaluation metrics and incomplete performance reporting in some studies underscore the need for standardized benchmarking frameworks to facilitate cross-comparative assessments and advance the field toward more unified, adaptive mitigation solutions.
## 3 Preliminaries
### Linguistic Term Set and Hesitant Fuzzy Linguistic Term Set  
**Definition 1**.: _Let \(S\) be a linguistic term set where \(S=\{S_{t}\,|\,t=-\varsigma,\ldots,-1,0,1,\ldots,\varsigma\}\), with odd cardinality. The midterm is symbolic of indifference, and the other linguistic terms are placed symmetrically centered around the midterm._  
During the decision-making process, it is often difficult for decision-makers to express their opinions using crisp values. Therefore, the concept of Linguistic Term Set (LTS) is useful to provide decision-makers the opportunity to better express themselves. An LTS is a set of linguistic terms that a decision-maker may pick from to evaluate a particular criterion. One issue that arises is that decision-makers may feel hesitant to give their opinions on a criterion. The Hesitant Fuzzy Linguistic Term Set (HFLTS) [38] was proposed as a solution to this problem, whereby a decision-maker is able to select more than one linguistic value from the LTS to evaluate a given criterion.  
**Definition 2**.: _Let \(S=\{S_{t}\,|\,t=-\varsigma,\ldots,-1,0,1,\ldots,\varsigma\}\) be an LTS. An HFLTS is defined as an ordered finite subset of \(S\)[38]._  
**Example 1**.: _Let \(S=\{s_{-2}=\text{very low},s_{-1}=\text{low},s_{0}=\text{medium},s_{1}=\text{ high},s_{2}=\text{very high}\}\) be an LTS with five terms. An HFLTS is \(h_{s}(x_{1})=\{s_{1},s_{2}\}\), which signifies high and very high. Another HFLTS signifying 'at least medium' is \(h_{s}(x_{2})=\{s_{0},s_{1},s_{2}\}\), which is a subset of linguistic terms from \(S\) comprising'medium', 'high', and'very high'._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \(p\) & probability value & \(L(p)\) & probabilistic linguistic term set \\ \(\#L(p)\) & number of terms in \(L(p)\) & \(\hat{Z}^{\mathbf{\pi}}\) & z probabilistic linguistic term \\ \(\hat{Z}\) & z probabilistic linguistic value & \(\phi\) & number of terms \\ \(S\) & linguistic term set & \(S^{\prime}\) & linguistic term set \\ \(\varsigma\) & max term in \(S\) & \(\zeta\) & max term in \(S^{\prime}\) \\ \(S(\cdot)\) & score & \(D(\cdot)\) & deviation degree \\ \(d(\cdot)\) & distance & \(\Lambda\) & quantitative matrix \\ \(\Gamma\) & decision-makers evaluation matrix & \(m\) & number of alternatives \\ \(n\) & number of criteria & \(t\) & number of quantitative criteria \\ \(b\) & decision-maker & \(E\) & decision matrix \\ \(\omega\) & criterion weight & \(\alpha\) & concordance threshold \\ \(\beta\) & discordance threshold & \(S^{F}\) & strong outranking \\ \(S^{f}\) & weak outranking & \(\psi(\cdot)\) & assigned rank \\ \(N^{F}\) & non-dominant strong alternatives & \(N^{f}\) & non-dominant weak alternatives \\ \(G^{F}\) & strong outranking graph & \(G^{f}\) & weak outranking graph \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of mathematical symbols and notations.