esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Super-Resolution with Regional Focus for Recurrent Network
## 3 Proposed Model
### Residual Calculation and Regional Focus in the BM Module
The regional focus block not only retains the details learned currently, but also performs regional focus on some features. The calculation process in Figure 5 can be expressed using Formula (3) as follows:  
\[F_{m}=Conv_{1\times 1}\{Concat\{F_{n},F_{n}\cdot Mask\}\} \tag{3}\]  
where \(F_{n}\) represents the feature of the input regional focus block (RF-Block). _Mask_ represents the focus weight calculated by the pooling, up-sampling, and sigmoid functions. \(Concat\{\cdot\}\) represents the splicing operation, \(Conv_{1\times 1}\{\cdot\}\) represents fusing the features with \(1\times 1\) convolution kernels, and \(F_{m}\) represents the output features.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 4 Overall Flow and Discussion of the Experiment
### Overall Flow of the Experiment
#### 4.2.1 Experiment 1: Causal Effect Estimation  
External factors other than temperature were considered binary variables in the present study, following the analysis in Section 3.3. The specific setting ranges for the binary variables are shown in Table 2. The causal effect of temperature was calculated by treating temperature as a continuous variable with 1 \({}^{\circ}\)C intervals. Depending on the temperature in the region where the power plant was located, the causal effect of the temperature from minus 1 \({}^{\circ}\)C to 39 \({}^{\circ}\)C on the load was analyzed.  
After data preprocessing, the causal effects of various external variables on load were calculated using the DML and PSM algorithms. The specific calculation results are presented in Figures 7 and 8, respectively. An observation can be made that the causal effect on load increased rapidly when the temperature increased above 30 \({}^{\circ}\)C. When the temperature increased above 36 \({}^{\circ}\)C, the plant was close to the upper limit of generation capacity, resulting in a decrease in causal effect. Other binary variables in addition to temperature had a significant impact on causal effect. The power plant in the present study had two generating units, and the units were shut down for servicing by rotating them during periods of low power consumption to extend their service life. The plant's single-day generation load was reduced by an average of 987 million kwh when a unit was under servicing. In addition, there was a significant causal effect by workdays and GDP on electricity generation. The causal effect of rainfall was the weakest in relative terms.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 2 Problem Formulation
### Spherical Harmonic Decomposition of Sound Field in Frequency-Space Domain  
An incident sound field at any arbitrary point \(\mathbf{x}=(r,\theta,\phi)\) inside a source free 3D spherical region \(\Omega\), where \(r\) refers to the distance between the point \(\mathbf{x}\) and the origin, \(\theta\) and \(\phi\) denote elevation and azimuth angles, respectively [40], can be expressed in the frequency domain as [1; 41]  
\[S(\mathbf{x},k)=\sum_{n=0}^{N}\sum_{m=-n}^{n}\alpha_{nm}(k)j_{n}(kr)Y_{nm}(\theta, \phi), \tag{1}\]  
where order \(n\) (\(n\geq 0\)) and mode \(m\) are integers, \(N=\lceil kR\rceil\)[1], \(k=2\pi f/c\) is the wave number, \(f\) is frequency, \(c\) is the speed of sound, \(R\) is the radius of \(\Omega\), \(\alpha_{nm}(k)\) is a set of spherical harmonic coefficients representing the sound field inside \(\Omega\), \(j_{n}(kr)\) is the \(n\)th order spherical Bessel function of the first kind, \(Y_{nm}(\theta,\phi)\) are the spherical harmonic functions. For convenience, we use real spherical harmonics in this paper, given by [42]  
\[\begin{split} Y_{nm}(\theta,\phi)=&(-1)^{|m|}\sqrt {\frac{2n+1}{4\pi}\frac{(n-|m|)!}{(n+|m|)!}}\\ &\times\left\{\begin{array}{ll}P_{nm}(\cos\theta)\cos(m\phi)&m \geq 0\\ P_{nm}(\cos\theta)\sin(m\phi)&m<0\end{array}\right.,\end{split} \tag{2}\]where \(P_{nm}(\cdot)\) is the associated Legendre function. Real spherical harmonics have the orthogonality property of  
\[\int_{0}^{2\pi}\int_{0}^{\pi}Y_{nm}(\theta,\phi)Y_{n^{\prime}m^{\prime}}(\theta, \phi)\sin\theta d\theta d\phi=\delta_{nm^{\prime}}\delta_{mm^{\prime}}. \tag{3}\]  
If the spherical harmonic coefficients \(\alpha_{nm}(k)\) are available for a sound field, then these coefficients can fully describe the sound field over the continuous spatial region of interest. Traditionally, when spatial harmonic processing is used to record a spatial sound field \(S(\mathbf{x},k)\), it is recorded over a spherical surface of radius \(R_{Q}\) (\(R_{Q}\geq r\)). The corresponding \(\alpha_{nm}(k)\) are extracted by integrating (1) over the spherical surface while exploiting the orthogonality property of \(Y_{nm}(\cdot)\) in (3), which gives [2]  
\[\alpha_{nm}(k)=\frac{1}{j_{n}(kr)}\int_{0}^{2\pi}\int_{0}^{\pi}S(r,\theta,\phi,k)Y_{nm}(\theta,\phi)\sin\theta d\theta d\phi. \tag{4}\]  
In practice, this integration is realized using an equivalent discrete summation of spatial samples over the sphere.
## 3 Results
### Parametric Analysis
(2) External operating parameters  
To demonstrate the energy saving potential of the coupled system, we further calculated the effect of climate conditions, mainly referring to the temperature and humidity ratio of the feed air, on the system performances. During the calculation, we maximized the \(ESR\) by optimizing the approach temperature of SAHEX, while keeping the suction temperatures of compressor II and III as close as possible to the minimum suction temperatures. Figure 10a shows how \(ESR_{max}\) varies with the feed air temperature and humidity ratio. \(ESR_{max}\) increases with the decrease in air temperature and humidity ratio, whereas the growth tendency of \(ESR\) with the decrease in air humidity is more notable than that with air temperature. This is because the feed air temperature only has a direct influence on the energy consumption of compressor I, while feed air humidity ratio also indirectly influences the energy consumption of compressor II and III, due to its impact on the minimum suction temperature for each compressor. Maximum \(ESR\) reaches 8.045% when air temperature is 5 \({}^{\circ}\)C and the humidity ratio is 0.002 kg/kg. When air temperature and the humidity ratio increase above 30 \({}^{\circ}\)C and 0.01 kg/kg, the energy saving potential becomes insignificant. To achieve the maximum \(ESR\) for each temperature and humidity condition, \(AT_{sahex}\) has to be optimized, as shown in Figure 10c. Under the maximum \(ESR\) case, the corresponding \(COP\) varies from 0.667 to 0.803. As can be observed from Figure 10b, there are turning points distributed on the iso-humid line of \(d\) = 0.0047 kg/kg. The turning points occur because the evaporation temperature of OAR is set constantly as 12 \({}^{\circ}\)C when \(d\) > 0.0047 kg/kg to satisfy the temperature demand for the molecular sieve, even though the minimum suction temperature of the compressorsis higher than 12 \({}^{\circ}\)C. Moreover, \(T_{euv}\) of OAR has to be adjusted according to the minimum suction temperature of compressor II, which is lower than 12 \({}^{\circ}\)C when \(d\) < 0.0047 kg/kg, in order to satisfy the cooling demand of CWAHEX2. Therefore, _COP_ decreases with the decrease in air humidity ratio below the turning point due to the decrease in \(T_{euv}\). _COP_ increases with the decrease in air humidity ratio above the turning point mainly because the required air for regeneration can be decreased as the air humidity ratio decreases (the air condition for regenerating is the same as the feed air), which further reduces the air heating loss and enhances _COP_. Though decreasing air heating loss is also applied for working conditions of \(d\) > 0.0047 kg/kg, clearly, decreasing evaporation temperature plays a more critical role.
# Seismic Design and Ductility Evaluation of Thin-Walled Stiffened Steel Square Box Columns
## 3 Finite Element Analysis
### FE Meshing  
In thin-walled, stiffened steel square box columns, excessive local deformation can inhibit stress redistribution. Test results indicate that local buckling typically occurs near the column base over a region approximately equal to the cross-sectional size (\(d\)) [3; 44; 46].  
To address this, a two-node beam element (B31) is used for the upper segment of the column, with a height of \(h-3d\), while reduced integration four-node shell elements (S4R), which effectively capture localized deformation, are applied to the lower segment, with a height of \(3d\), as shown in Figure 2b. The interface between the shell (S4R) and beam (B31) elements is modeled using a multi-point constraint (MPC). The elements used are available in the Abaqus/Standard library [52].  
For computational efficiency and accuracy, the bottom and middle portions of the lower segment of the column (equal to the side width, \(d\)) are, respectively, divided into 30 and 20 S4R elements, while the remaining height (\(d\)) is divided into 15 elements, with an additional 20 elements used in the width direction for the lower segment, see Figure 2b. The upper segment of the column, with a height of \(h-3d\), is divided into beam elements (B31), each sized at 90 mm. Finally, each longitudinal stiffener and subpanel between the stiffeners, respectively, have 3 and 6 columns of S4R. These mesh sizes were optimized through trial and error to ensure efficient and accurate results. A default of five integration points through the thickness of the S4R element is used. The analysis employs a displacement convergence criterion with a tolerance of \(10^{-5}\). Initial geometric imperfections and residual stresses are not considered, as these were not quantified in the tested columns [3,43]. Additionally, these factors are believed to have a negligible influence on the overall cyclic behavior after the first half-cycle [6,31]. Further details on elastoplastic large displacement analyses are provided in [6].
## 3 Methodology
### Ablation Experiments
#### 4.2.3 Network Architectures  
To comprehensively evaluate the influence of different network architectures on the enhancement effect, we conducted additional ablation studies focusing on the layer number within the UNet architecture employed in our methodology. The results, presented in Table 3, showcase performance metrics and corresponding scores for networks with one, two, three, four, and five layers. And, our experimental findings reveal intriguing insights. Firstly, networks with one, two, or three layers may lack sufficient nonlinear mapping capability to capture complex features, which can pose challenges when generalizing to unseen data. Secondly, increasing network depth does not exhibit a linear correlation with improved performance. A five-layer network introduces issues such as gradient instability and network degradation, despite partial mitigation attempts. Paradoxically, adding depth may even lead to performance decline. Moreover, deeper networks can suffer from saturation effects due to increased depth, which adversely impacts their enhancement capabilities. Additionally, deepening the model may diminish learning capabilities in shallower layers, thereby restricting the overall learning potential of the deeper network [46; 47; 48; 49; 50]. Considering these observations, we selected a 4-layer UNet architecture as it consistently delivers the best overall performance. The crucial role of structures like skip connections in facilitating effective learning within deeper networks underscores their significance in LLIE tasks.
## 3 Secret Key Rate in Inter-Satellite Links  
### Channel Model  
In recent years, THz communication is considered to be one of the key technologies to prop up the growing demand of high-speed wireless communication networks. When THz waves propagate through a free space channel, it can be impaired by turbulence, scattering, and absorption, etc. As we mentioned in the introduction, these atmosphere effects limit the transmission performance of THz waves. Nevertheless, in inter-satellite links, the beamdrift effect can be neglected and the absorption of atmospheric water molecules is nearly insignificant, which can allow us to approximatively present a diffraction-only channel model as a immovable attenuation. The loss caused by the diffraction effect is only derived from the size of the diffracted beam at the receiving aperture. Thus, the transmittance \(T\) can be expressed as [18]  
\[T=1-\exp[-2(r_{a}/l)^{2}], \tag{1}\]  
where, \(r_{a}\) stands for the receiving aperture radius, and \(l\) stands for the beam radius of THz waves at transmission distance \(d\). Taking advantage of the Gaussian approximation, \(l\) can be expressed as  
\[l=r_{b}\sqrt{1+(\lambda d/\pi r_{b}^{2})^{2}}, \tag{2}\]  
where \(r_{b}\) is the beam-waist radius, and \(\lambda\) is the wavelength of THz waves.  
In this channel model, we assume that both the receiving aperture radius \(r_{a}\) and the beam-waist radius \(r_{b}\) to 0.6 m [13] and the environment temperature is 30 K [37].
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Compared Methods  
To assess the effectiveness of our method, we compare FedCDAE-MN with several existing approaches, including the baseline few-shot learning methods (i.e., Finetune, FeatureKNN1, and FeatureKNN2) and related state-of-art methods (i.e., DS-MN and FS-MN). Here is a specific introduction to these methods:  
* Finetune: The basic transfer learning method involves pre-training the model using source domain data and then fixing the feature extraction layer. Subsequently, the model is fine-tuned using target domain data.
* FeatureKNN1: After pre-training with source domain data, FeatureKNN1 calculates similarity weights based on the feature distance between query samples and support samples. The weighted labels of all support samples are then utilized as the classification results.
* FeatureKNN2: An extension of FeatureKNN1, FeatureKNN2 replaces the feature extractor with a convolutional denoising auto-encoder, building upon the initial approach.
* DS-MN [30]: A few-shot learning method based on metric learning. It differs from other approaches as it is directly trained in the data space rather than the feature space.
* FS-MN [29]: This method, tested only in a single source domain, focuses on metric learning in the feature space.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Relationship between Source**} & \multicolumn{2}{c}{**Relationship between**} & \multirow{2}{*}{**Name**} \\  & & **Domain and Target Domain** & & **Source Domains** & \\ \cline{2-2} \cline{4-6}  & **FT** & **WC** & **FT** & **WC** & \\ \hline Single Source & \(\times\) & ✓ & & / & SCDF \\ \hline Multi-Source & ✓ & \(\times\) & ✓ & \(\times\) & DCSF \\  & \(\times\) & \(\times\) & ✓ & \(\times\) & DCDF \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments Settings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multicolumn{2}{c}{**CPU**} & \multirow{2}{*}{**GPU**} & \multirow{2}{*}{**Mem**} & \multirow{2}{*}{**Storage**} \\ \cline{2-2} \cline{4-6}  & **Model** & **Frequency** & & & \\ \hline Device & IntelR XeonR Gold 6130 & 2.1 & 40 & GeForce RTX 2080 & 528 G & 2150 G \\ \hline \hline \end{tabular}
\end{table}
Table 3: Device Configurations.  
* **FedCAE-MN**: Our proposed method, FedCAE-MN, is based on FS-MN. It modifies the feature extractor, employing a convolutional auto-encoder instead.
* **FedCDAE-MN**: Our proposed method, FedCDAE-MN, is also based on FS-MN. It similarly replaces the feature extractor, but this time using a convolutional denoising auto-encoder.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 2 Passive Optical Network Standardization Overview
### The Road to 100G PON  
In the transition from 50G PON to 100G PON, both coherent and IM/DD solutions have been explored to meet the increasing demands for higher data rates. Building on the foundation of 50G PON, our approach follows the parameters and modifications proposed by [31] to leverage the cost efficiency and simplicity of IM/DD systems for 100G PON [32].  
The development of 100G PON involves critical modifications to components such as transmitters and receivers, which need to operate at higher bandwidths to support the increased data rates. This adjustment is necessary to maintain signal integrity over the optical distribution network (ODN). Improved digital signal processing (DSP) is crucial for managing the increased impact of fiber dispersion and other signal distortions at higher data rates. Stronger DSP equalization and enhanced Forward Error Correction (FEC) mechanisms are required to support higher error rates, ensuring reliable data transmission under challenging conditions. Maintaining a power budget similar to 50G PON is essential for 100G PON feasibility. The launch power for 100G transmitters remains within the same range as 50G PON, allowing the existing infrastructure to be used without significant modifications.  
By addressing these technical challenges, 100G PON aims to provide high-performance, cost-effective solutions for next-generation PONs. The proposed parameters and changes ensure that 100G PON can meet the increasing demands for higher data rates while maintaining compatibility with existing network infrastructures.
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 4 The Proposed Risk-Sensitive Intelligent Control Method
### The Structure of the Control Method
#### 4.1.3 Temperature Coefficient and Its Loss Function  
The temperature coefficient \(\alpha\) balances the ratio between real-time rewards and policy entropy at each time step, and therefore, can affect the randomness of the optimal strategy. \(\alpha\) is a learnable parameter. At the beginning, an initial value is given, and then, the value of \(\alpha\) can be dynamically adjusted via training of the algorithm. The objective function for optimizing the temperature coefficient \(\alpha\) is shown in Equation (13).  
\[J(\alpha)=E_{(s_{t},g_{t})\sim\mathcal{D},a_{t}\sim\mathcal{T}}[-\alpha\log \pi(a_{t}|h_{t},s_{t},g_{t})-\alpha\mathcal{H}_{0}] \tag{13}\]  
where \(\mathcal{H}_{0}\) represents the target value of policy entropy. In this paper, \(\mathcal{H}_{0}\) is set to the negative of the duty cycle's dimension.  
Similar to the value network or the actor network, the temperature coefficient is updated by iteration as well. When the entropy of the current strategy is lower than \(\mathcal{H}_{0}\), the value of \(\alpha\) is increased. Otherwise, it is decreased. By adjusting the importance of policy entropy in the objective function of the strategy network, the direction of policy improvement can be controlled.  
The pseudocode of the proposed risk-sensitive intelligent control method is summarized in Algorithm 1.  
```
0: Parameters of value network \(\theta_{1}\) and \(\theta_{2}\), parameter of policy network \(\phi\), and experience pool \(\mathcal{D}\).
1: for each iteration do
2: for each environment step do
3: obtain the duty cycle \(a_{t}\) according to the state \(s_{t}\), the current target \(g_{t}\) and the sampling strategy \(\pi_{\phi}\);
4: execute duty cycle \(a_{t}\), and then, obtain the next state \(s_{t+1}\), the next target \(g_{t+1}\) and the risk evaluation \(l_{t}\);
5: store sample \((s_{t},a_{t},l_{t},s_{t+1},g_{t})\) into experience pool \(\mathcal{D}\);
6: for each gradient step do
7: evenly extract a batch size sample from \(\mathcal{D}\) for training;
8: update value network parameters \(\theta_{1}\) and \(\theta_{2}\);
9: update policy network parameter \(\phi\);
10: update temperature coefficient \(\alpha\);
11: end for
12: end for
13: end for
```  
**Algorithm 1** The proposed Risk-Sensitive Intelligent Control Method.
# Facile Preparation of Graphene Oxide-MIL-101(Fe) Composite for the Efficient Capture of Uranium
## 2 Materials and Methods
### Characterization  
The dimension and morphology of all the samples were obtained from scanning electron microscopy (SEM) (Hitachi S4800, Tokyo, Japan). X-ray diffraction (XRD) D/max-TTRIII (Rigaku, Tokyo, Japan) was used to provide the crystalline information of the samples. Attenuated total internal reflection Fourier (ATR-FTIR) spectra were obtained on a Bruker TENSOR-27 (Bruker Optics, Bremen, Germany). The surface composition and binding state information were achieved from X-ray photoelectron spectroscopy (XPS) (Thermo Escalab 250 Xi, Waltham, MA, USA). Pore information and specific surface area were analyzed by a Micromerities ASAP 2010 (Norcross, GA, USA). \(\zeta\) potential data were collected from a Zetasizer Nano ZS (Malvern Instrument, Worcestershire, UK). For measuring the zeta potential, the pH values of the achieved samples (\(\epsilon_{\rm NaCl}\) = 10 mM) were adjusted to values between 1 and 11 by adding HCl or NaOH, respectively.
## 5 Evaluation and Discussion  
### Decentralized Data Mining  
In the following, we present the experimental setting (Section 5.1.1) and simulation results (Section 5.1.2) of the application of the distributed data mining (DDM) method, presented in Section 4.2, on the air-quality data collected at the \"Mrakova Farm\" in Bled, Slovenia.  
The aim of the simulation is to show a decision tree learned in an incremental way (Hoeffding tree) in a distributed sensor environment does not significantly differ from a decision tree learned in batches from all available data from all of the sensors. Moreover, we want to show that the sequence, in which the nodes are visited in the WSN does not significantly affect the final decision tree.