esa-sceva/satcom-chunk-collection huggingface dataset:
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 3 Inverse Halftoning Technology for Scanned Halftone Images  
The inverse halftoning method for scanned halftone images refers to converting the scanned halftone image into a continuous-tone image. This method is used to preserve valuable works of art in electronic form, such as ancient books, calligraphy, and paintings. Due to some obvious quality reduction in the scanned halftone image, such as dot deformation, ink smear, and paper changes with time, the restoring of the scanned halftone image is more complex compared to the digital halftone image. In addition, there is no paired real data for scanned halftone images. Thus, the means of restoring a high quality continuous-tone image from a scanned halftone image is a challenging process.  
Fundamentally, inverse halftoning is an image restoration problem. Thus, some general purpose DCNNs for image restoration can also be used for inverse halftoning, such as super-resolution networks, style transfer networks, and Generative Adversarial Networks (GANs). In this section, according to whether the network is specially designed for inverse scanned halftone images, the inverse halftoning method for scanned halftone images is divided into two types: the method based on general purpose DCNNs and the method for specially designed DCNNs.
## 3 Results and Discussion  
### Overview on PM\({}_{2.5}\) Composition in the Investigated Area  
A total of 100 PM\({}_{2.5}\) daily samples were collected to determine the concentrations of ten inorganic species, three carbonaceous components and sixteen metals. Results show that, on average, OC and EC accounted for 15.4% and 3.4% of PM\({}_{2.5}\) mass, respectively, while in terms of organic matter (OM) (that is obtained multiplying OC average concentration for \(f_{\text{OM/OC}}=1.95\)) we obtained the 30.3% of PM\({}_{2.5}\) mass. Moreover OC, WSOC, SO\({}_{4}\)\({}^{2}\), and NH\({}_{4}\)\({}^{+}\) concentrations were generally larger than 1 \(\mu\)g m\({}^{-3}\); this result was explained by considering the expected contribution from anthropogenic sources for PM\({}_{2.5}\). Marine aerosol and suspended dust, that are sources mainly present in the PM coarse fraction, affected in minor amount the fine fraction. The predominant anthropogenic species (OC, EC, WSOC, SO\({}_{4}\)\({}^{2-}\), NO\({}_{3}\)\({}^{-}\), and NH\({}_{4}\)\({}^{+}\)) showed concentrations in the same range of values observed in other European urban areas. Mineral elements, such as Fe, Al, Ti, Ca, reached the highest concentrations associated to South wind directions. This can be explained in terms of long-range transported soil emissions, mainly from the Saharan desert, but also from the local soil resuspension. Considering the chemical composition of the samples only 6 days were characterized by Saharan dust episodes during the sampling campaign. Removing them from the dataset, a PM\({}_{2.5}\) mean variation of only 0.2 \(\mu\)g m\({}^{-3}\) can be evicted, and these samples where then excluded to further analysis. The marine contribution to PM, described by sea salt components (mainly Cl\({}^{-}\), Na\({}^{+}\) and Mg\({}^{2+}\)), sometimes showed high values during Saharan dust outbreaks. The nss-K\({}^{+}\) is correlated to OC (r = 0.82), suggesting its biomass burning origin, and two PM\({}_{2.5}\) samples, which showed particularly high levels of both OC and nss-K\({}^{+}\), were excluded by the dataset. EC were consistent with mixed sources for carbonaceous particles (fossil fuel, heavy fuel oil, and biomass burning). As mentioned in Section 2.1, given that the site was downwind to different main sources (port, urban, and industrial areas), the dataset was split in two subsets considering the prevalent winds (the samples of days in which the wind direction was variable were excluded from the analysis). Multivariate analysis methods (PCA, LDA) were applied to the dataset to evaluate if significant differences among the PM\({}_{2.5}\) samples divided by the two prevailing wind directions can be observed. In Table 1, a descriptive statistic of variables obtained on samples divided in Urban-Industrial and Port prevalent wind directions is shown.
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 2 Background and Related Work
### Distributed Load Control  
Operated at different levels of the grid, load control can alleviate voltage regulation problems (primary level) or matching desired consumption profiles (secondary/tertiary level). Direct Load Control (DLC), for example, is a specific mechanism of demand side management that allows electric utilities to turn specific users' appliances off during peak demand periods and critical events. Most of the current DLC programs work on thermostatic loads [3, 4], such as air conditioners and heating systems because they allow a fine-tuning regulation of power demand.  
Multiple load typologies, including interruptible or deferrable loads, have been considered for responding to different frequency components of the regulation signal. For example, in [5], it has been shown that the pool pump control can work at a time scale between the few minutes characterizing the typical control interval of thermostatic loads, and the few hours characterizing the deferral of electric vehicles charging. Internet connections available in most households may enable more flexible programs managed by energy suppliers or third parties [6]. Other solutions are considering the use of enhanced smart plugs which consider user habits and consumption profiles to optimize controllable resources [7].  
For a given demand, control signals or control decisions can be programmed by using analytical tools, such as queuing models [8] that have been widely adopted for teletraffic engineering [9]. Indeed, loads on the electrical grid are multiplexed at different aggregation levels (distribution transformers, primary station, price zone), similarly to traffic from data sources multiplexed at a router of a different hierarchical level. In [9], teletraffic analogy has been exploited for sizing the power distribution network as a function of the statistical characterization of the loads, while, in [8], the queuing models are used to study an optimal scheduling of energy suppliers working on renewable sources, without knowing the statistics of the energy production and demand processes. Queuing theory is also used in [10] and [11] for sizing the population of customers subscribing a DLC program under a given maximum tolerable delay for activating the controlled appliances.
# Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture
## 5 Conclusions  
In this paper, we present a groundbreaking end-to-end microscope image fusion model, built on the Swin Transformer backbone, known as the U-Swin fusion model. Leveraging the innate capabilities of transformers to capture long-range dependency features, our innovative transformer-based models consistently produce fusion images that either match or surpass the state-of-the-art (SOTA) image fusion algorithms. Additionally, they exhibit remarkable generalization ability for fusing multi-focus microscope images. Notably, the pure transformer-based U-Swin fusion model, incorporating channel mix fusion rules, attains superior performance in numerous evaluation metrics compared to the majority of existing end-to-end fusion models. This work lays a pioneering foundation for applying transformer-based networks within the realm of microscope image fusion. Despite the extensive experimental results that validate the advantages of our proposed models, there remain several avenues for improvement in pursuit of even more robust image fusion models. For instance, exploring complex transformer backbones and fusion rules holds promise for further enhancing the model's performance in the future.  
Conceptualization, H.G. and H.S.; Methodology, H.H.X.; Formal analysis, H.H.X. and K.G.; Investigation, W.L.; Data curation, H.H.X.; Writing--original draft, H.H.X.; Writing--review & editing, H.G.; Funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key R&D Program of Zhejiang Province (2021C01016) and National Natural Science Foundation of China (61827805). And the APC was funded by Jiaxing Key Laboratory of Visual Big Data and Artificial Intelligence, Zhejiang Province, China.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.  
Figure 6: The comparison example of the U-Swin fusion model with channel mix fusion rule for various color spaces.
# HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
## 4 Experiments and Results
This is in line with our expectation, as we limited the backpropagation starting from the measure loss to the range of the measure network; the prediction performance of the likelihood network was therefore not affected by the measure network. In the second experiment, we trained the HPS-Net models with different latent levels and different numbers of radiologists to evaluate the performance of HSP-Net on predicting different measurement values. Table 2 shows the results of the second experiment, from which we could observe the following:  
* Compared with HPS-Net (L = 1) and HPS-Net (L = 3), HPS-Net (L = 5) had the lowest mean squared error (MSE) and the lowest standard deviation (std.) in most cases. * The predictions on TNR were much more accurate than the predictions on TPR and precision, where the MSE\(\pm\)std. on TPR was lower than the MSE \(\pm\)std. on precision. * The MSE \(\pm\)std. on TNR was as low as 0.0001 \(\pm\) 0.0062 (all annotation) and 0.0010 \(\pm\) 0.0219 (single annotation), which is close to the requirements of practical application. However, the MSE \(\pm\)std. on TPR and precision was still as high as 0.0938 \(\pm\) 0.1080 and 0.1160 \(\pm\) 0.1449 with single annotation and L = 5, which is far from practical application and still needs to be improved. To further verify the performance of HPS-Net on image segmentation and measure prediction, an additional experiment was conducted on the ISIC2018 dataset [12], where we trained the HPS-Net models with different latent levels and compared it with three benchmark methods, namely, det. U-Net, prob. U-Net, and PHiSeg. From Table 3, we can observe that HPS-Net had the highest Dice score, which means it had the best segmentation performance. Similarly, the MSE \(\pm\)std. of TNR was the lowest among all three measure predictions, while the MSE \(\pm\)std. of precision was still the highest. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **\# Radiologists** & \(\mathbf{D_{GED}^{2}}\) & \(\mathbf{S_{NCC}}\) & **Dice** \\ \hline Prob. U-Net & All & 0.2393 & 0.7749 & - \\ PHiSeg (L = 1) & All & 0.2934 & 0.7944 & - \\ PHiSeg (L = 5) & All & 0.2248 & 0.8453 & - \\ HPS-Net (L = 1) & All & 0.2410 & 0.8025 & - \\ HPS-Net (L = 5) & All & **0.2218** & **0.8414** & - \\ \hline Det. U-Net & 1 & - & - & 0.5297 \\ Prob. U-Net & 1 & 0.4452 & 0.5999 & 0.5238 \\ PHiSeg (L = 1) & 1 & 0.4695 & 0.6013 & 0.5275 \\ PHiSeg (L = 5) & 1 & 0.3225 & 0.7337 & 0.5408 \\ HPS-Net (L = 1) & 1 & 0.2997 & 0.7822 & 0.5475 \\ HPS-Net (L = 5) & 1 & **0.2513** & **0.8003** & **0.5521** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segmentation performance of different methods on LIDC-IDRI dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **\# Radiologists** & **MSE \(\pm\) Std. of TPR** & **MSE \(\pm\) Std. of TNR** & **MSE \(\pm\) Std. of Precision** \\ \hline HPS-Net (L = 1) & All & 0.2025 \(\pm\) 0.2329 & 0.1441 \(\pm\) 0.1450 & 0.3671 \(\pm\) 0.3736 \\ HPS-Net (L = 3) & All & 0.153
## 2 State-of-the-Art
Consequently, the field distribution in resonator DR3 undergoes orientation reversal, causing the cross-coupling mechanism to alternate between electric and magnetic coupling in the equivalent circuit representation (corresponding to capacitive and inductive coupling, respectively). In contrast, microstrip filters exhibit significantly lower quality factors (typically in the range of tens to hundreds) [21], rendering them unsuitable for demanding applications. Furthermore, when implementing designs requiring high attenuation through transmission zeros, the resulting responses often display asymmetry. This asymmetric behavior stems from several factors as follows: parasitic effects (as microstrip resonators operate in quasi-TEM mode rather than pure TEM mode, leading to increased dispersion at higher frequencies) [25], manufacturing misalignments, and geometric variations between resonators. These asymmetries particularly affect transmission zeros, which may demonstrate unequal attenuation levels and slight frequency shifts. Conventional microstrip cross  
Figure 4: Primary modes in a cylindrical DR.  
Figure 5: Third-order dielectric resonator-based cavity filter (DRCF). coupling implementations typically use stubs [26]--asymmetric distributed elements with carefully designed lengths and positions relative to the center frequency. Common microstrip filter implementations include various coupled-line structures, such as Hairpin, parallel-coupled line, and directly coupled filters [27]. Key observations highlight the following three fundamental trade-offs: First, DRCFs provide superior frequency stability through high-Q ceramic resonators with low insertion loss. Second, microstrip designs benefit from simpler planar workflows but suffer from substrate tolerances that need correction, either by multiplying circuit variants through tolerance sweeps or implementing electronic tunability. Third, DRCF manufacturing requires precision machining versus automated PCB production for microstrip alternatives. The DRCF assembly process requires significant characterization time, and in production, this reduces yield in terms of electrical performance consistency between final circuits. While high-Q resonators demonstrate better temperature stability, they come with larger size (volume) and higher design complexity, requiring more time to properly match experimental results with simulation predictions. These factors collectively justify the need for an intermediate technology to bridge the existing gaps between DRCFs and microstrip filters for space applications, as summarized in Table 1.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
###### Abstract  
Communication in deep-sea shadow zones remains a significant challenge due to high propagation losses, complex multipath effects, long transmission delays, and strong environmental influences. In recent years, orthogonal chirp division multiplexing (OCDM) has demonstrated promising performance in underwater acoustic communication due to its robustness against multipath interference. However, its high peak-to-average power ratio (PAPR) limits its reliability and efficiency in deep-sea shadow-zone environments. This study applies a recently proposed generalized orthogonal chirp division multiplexing (GOCDM) modulation scheme to deep-sea shadow-zone communication. GOCDM follows the same principles as orthogonal signal division multiplexing (OSDM) while offering the advantage of a reduced PAPR. By segmenting the data signal into multiple vector blocks, GOCDM enables flexible resource allocation, optimizing the PAPR without compromising performance. Theoretical analysis and practical simulations confirm that GOCDM preserves the full frequency diversity benefits of traditional OCDM, while mitigating PARR-related limitations. Additionally, deep-sea experiments were carried out to evaluate the practical performance of GOCDM in shadow-zone environments. The experimental results demonstrate that GOCDM achieves superior performance under low signal-to-noise ratio (SNR) conditions, where the system attains a 0 bit error rate (BER) at 4.2 dB and 6.8 dB, making it a promising solution for enhancing underwater acoustic communication in challenging deep-sea environments.  
underwater acoustic communication; multicarrier modulation; deep-sea communication; orthogonal chirp division multiplexing
## 4 Results and Discussion
### Design of Channel Group Allocation  
Figure 13 shows a hypothetical design procedure for channel allocation sets to cellular base stations, where there are 12 channels in a cell cluster (i.e., N = 12).  
Considering a circular section of the cluster segment from Figure 14, the reuse distance can be calculated for the grid cells. The center spread between two co-channel cells is known as the reuse distance. The cluster size in the grid as shown in Figure 14 is equal to 12.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Consecutive Colors** & **Manhattan Distance** & _i_**Parity** & _j_**Parity** \\ \hline (0,1) & \(\geq\)3 & even, odd & even, even \\ (1,2) & \(\geq\)3 & odd, even & even, even \\ (2,3) & \(\geq\)3 & even, even & even, odd \\ (3,4) & \(\geq\)3 & even, odd & odd, odd \\ (4,5) & \(\geq\)3 & odd, even & odd, odd \\ (5,6) & \(\geq\)3 & even, even & odd, even \\ (6,7) & \(\geq\)3 & even, odd & even, even \\ (7,8) & \(\geq\)3 & odd, even & even, even \\ (8,9) & \(\geq\)3 & even, odd & even, even \\ (9,10) & \(\geq\)3 & odd, odd & even, odd \\ (10,11) & \(\geq\)3 & odd, even & odd, odd \\ \hline \end{tabular}
\end{table}
Table 2: Relationships between successive vertices for the L(3,1) coloring algorithm.  
Figure 13: Hypothetical design of a cellular grid labeled with the L(3,1) coloring algorithm.  
Co-channel reuse means that within a specific area of coverage there are many cells utilizing a similar group of channels [37]. Such cells are called co-channel cells. Co-channel cells also exhibit interference signals, which are referred to as co-channel interference. The only way to eliminate co-channel interference is by ensuring that the cells are separated by a sufficient distance [38]. In [39], the authors showed that in situations where cell sizes and base station power transmission are equal, the ratio of co-channel interference is often not a function of the power transmitted, but rather, that it depends on the cell radius (R) and the center spread of the nearest co-channel cells (D).
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 2 Background and System Model  
### Notation  
Scalar random variables are denoted by uppercase letters \(X\). Vectors and matrices are denoted by lowercase boldface letters, \(\mathbf{x}\), and uppercase boldface letters, \(\mathbf{X}\), respectively. The determinant and trace of a matrix \(\mathbf{A}\) will be denoted as \(\det(\mathbf{A})\) and \(\operatorname{tr}(\mathbf{A})\). \(\mathbf{I}\) is the identity matrix. Probability density functions are denoted by \(p_{Y}(y)\). The superscript \((\cdot)^{*}\) denotes complex conjugate, and \((\cdot)^{T}\) denotes transpose. \(E[\cdot]\) denotes mathematical expectation, and \(h(Y)\) denotes the differential entropy of a continuous random variable. \(\mathbf{x}\sim\mathcal{CN}(\mathbf{0},\mathbf{R})\) indicates that \(\mathbf{x}\) is a complex circular Gaussian random vector of zero mean and covariance \(\mathbf{R}\), while \(\mathbf{x}\sim\mathcal{CN}(\mathbf{0},\mathbf{R},\bar{\mathbf{R}})\) indicates that \(\mathbf{x}\) is a complex non-circular Gaussian random vector of zero mean, covariance \(\mathbf{R}\) and complementary covariance \(\bar{\mathbf{R}}\).
### Main Contributions of the Paper  
Considering the evolution of the deep learning-based image segmentation and its anticipated future development, this article is organized as follows:An overview of the research advancements in deep learning-based image segmentation with focus on panoptic segmentation (PS) is presented.
* The article draws attention to several interesting works towards PS that include Panoptic Feature Pyramid Network, Attention-guided network for PS, Seamless Scene Segmentation, Panoptic Deep lab, Unified panoptic segmentation network, and Efficient panoptic segmentation. A top-down approach to PS is discussed and suggested improvements in predicted outputs are highlighted.
* Research efforts by leading companies supporting the bigger picture of developing computer vision models for PS are presented.
* Performance metrics of both scene recognition and panoptic segmentation models are discussed. Several comparisons have been performed to measure the performance using different datasets under different metrics and highlight the potential benefits and challenges.  
From this point on, the paper is organized as follows: after Section 1 (the current section), Section 2 discusses the literature review. Section 3 presents the concept of PS along with the metrics and discusses some interesting models used to date. It also presents a top-down view of PS and discusses certain challenges and further improvements. Section 4 presents the research efforts to date by companies such as Apple, Facebook, and Tesla in developing computer vision models using PS. For example, the use of on-device PS to enhance the camera vision that use transformers, the subject lifting network architectures by Apple, and Detectron2 by Facebook AI research, and self-driving PS support vehicles by Tesla are presented. Section 5 introduces the publicly available datasets and benchmarks to both scene recognition and PS models. Finally, Section 6 concludes the paper with future directions.
## 3 Results
### Optimization Results and Analysis
#### 3.1.2 The Impact of Orbital Inclination on LEO Satellites  
Using a satellite at an orbital altitude of 1100 km and an inclination of 55\({}^{\circ}\) as a case study, this analysis examines the distribution characteristics of average coverage for a single satellite with inclinations ranging from 40\({}^{\circ}\)\(\sim\)70\({}^{\circ}\) along different latitudes. The average number of visible satellites is illustrated in Figure 6.  
As depicted in Figure 6, the number of visible satellites in mid-latitudes decreases with increasing inclination, while it increases in polar regions. Given China's geographical position between the north latitude 10\({}^{\circ}\)-55\({}^{\circ}\), LEO tilt constellations primarily serve the mid-latitude area. The selection of satellite inclination must also consider the carrying capacity of ground rockets; greater orbital inclinations will result in higher energy consumption by  
Figure 5: Analysis of altitude parameters for orbital constellation: (**a**) Distribution of ionospheric, Van Allen radiation belts, and the satellite orbital altitudes; (**b**) the relationship between satellite orbital altitude and inclination and the period of regression period.  
launch vehicles. Therefore, the satellite inclination for LEO tilt constellations should be chosen between around \(45^{\circ}\) and \(55^{\circ}\).