esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Reputation Based on Functionality  
Table 6 shows the reputation of the same applications in terms of functional aspects. This is realized based on Appbot from which an evaluation of functionality on Android applications is provided depending on the point of view of ratings and review polarity.  
It reveals for example that \"Security Master\" has a very good reputation (86%) in terms of what it has been designed for. Users feel very comfortable while using this application. Unlike, Express Union which has a bad reputation (15%) meaning that users are not satisfied when using this application to perform banking transactions.
# Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs
## 3 Results
### Additional Metrics  
In this section, we analyze the results obtained from the additional evaluation metrics: inception score (IS), Frechet inception distance (FID), and kernel inception distance (KID). These metrics provide a quantitative assessment of the quality and diversity of the generated images, offering insights beyond the qualitative probability metrics D(G(z1)) and D(G(z2)).  
The inception score measures both the quality and diversity of generated images, with higher scores indicating better performance. In our experiments, the IS varied significantly. Experiment 1 scored 1.373 \(\pm\) 0.015, Experiment 2 scored 2.391 \(\pm\) 0.152, and Experiment 3 scored 1.673 \(\pm\) 0.049. The highest score in Experiment 2 suggests that the images generated were more diverse and of higher quality compared to the other experiments. This improvement is likely due to the adjustment in batch size, which provided a more stable training process.  
Frechet inception distance measures the distance between feature distributions of real and generated images, with lower scores indicating better alignment with real image distributions. Experiment 1 had an FID score of 1124.113, Experiment 2 scored 456.309, and Experiment 3 scored 686.987. These scores indicate that Experiment 2 produced images closest in distribution to the real images, followed by Experiment 3, while Experiment 1 had the highest divergence. This finding aligns with the inception score results, reinforcing that the changes in training parameters for Experiment 2 positively impacted generative quality.  
The Kernel inception distance also measures similarity between real and generated image distributions, with lower scores indicating better performance. The KID scores were 3.350 for Experiment 1, 2.153 for Experiment 2, and 3.012 for Experiment 3. Similar to the FID results, the KID indicates that Experiment 2 had the best performance, suggesting more effective learning and generation of images closely resembling the real ones.  
The additional metrics provide a comprehensive evaluation of the GAN's performance. Experiment 2 consistently showed the best results across all metrics, indicating that the adjustments made to the training parameters were effective for the training dataset.
## 2 Datasets and Methods  
### Study Area  
As shown in Figure 1, the YRD is located in the northeast of Shandong province, China, and is bounded by the Bohai Sea to the north and Laizhou Bay to the east [12; 39]. It is a fan-shaped area formed by sedimentation of the Yellow River as it flows into the Bohai Sea, where newly formed wetlands are increasing at a rate of 30 km\({}^{2}\) per year. The terrain of the region is flat, with diverse types and complex geomorphic forms and an average slope of 2.14% [39; 46; 10; 36]. Tidal creeks are widely distributed in the coastal areas on the north and south sides of the YRD. This region is in the temperate monsoon climate zone, with an average annual temperature of 14.7 \({}^{\circ}\)C and average precipitation of approximately 526 mm/year during January 2019 and December 2020 [7].  
The YRD wetland is a typical estuarine wetland ecosystem of salt marsh, which belongs to the coastal wetland classified by the Ramsar Wetland Convention [47; 48]. The total area of wetland in the YRD is approximately 700 km\({}^{2}\), of which the area of natural wetland accounts for approximately 89%, mainly including shallow sea, tidal flat, river, and vegetation, and the area of human-made wetland accounts for approximately 11%, mainly including aquaculture ponds, salt pans, and farmland. Four dominant salt marsh plant species, namely, _Phragmites australis, Tamarix chinensis_, _Spartina alterniflora_, and _Sueda salsa_, occupy the broad tidal flats of the YRD [20; 37]. _Sueda salsa_ and _Tamarix chinensis_ are the only halophytic plants in heavy saline soil and tidal flats. When the soil is desalted, the vegetation type changes to _Phragmites australis_.
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 2 Related Work  
### Multi-Scale Feature Extraction  
As CNNs typically utilize small kernels of sizes such as \(1\times 1\) and \(3\times 3\) for feature extraction, CNNs can only extract local features within a small range of perceptions and cannot extract multi-scale features. Multi-scale features represent samples of the signal at different granularities, and different features can be observed at different granularities to perform different tasks. Smaller or denser sampling can reveal more details, whereas larger or sparser sampling can reveal overall trends. Multi-scale features contain overall global information and local detailed information that is useful for image restoration tasks [21].  
Multi-scale feature extraction can be achieved through two main approaches. The first method involves employing parallel Conv with different kernel sizes, followed by merging the features obtained from each parallel branch across channels. This structure, illustrated in Figure 1a as the separation-transformation-fusion structure of Inception [22], utilizes \(1\times 1\) Conv, \(3\times 3\) Conv, \(5\times 5\) Conv, and \(3\times 3\) maximum pooling operations to obtain features at different scales. The second method utilizes sampling to acquire feature maps at different scales. In [23], the authors proposed a feature pyramid network (FPN) that samples features from various layers at different scales for prediction, as depicted in Figure 1b. This approach effectively handles scale variations while maintaining a balance between expressive power, speed, and resource consumption. Unlike traditional image pyramid methods, the FPN approach eliminates the need to generate images at different scales before feature extraction. In the widely used U-Net method [24], pooling operations are employed to increase feature map channels during downsampling, and feature maps of different scales are fused during upsampling. While this technique reduces parameter counts and enhances network inference speed, it also leads to a loss of feature information, which can hinder image denoising tasks. In contrast, the MWCNTs approach [25] replaces pooling operations with wavelet transforms to reduce the feature map size. Since wavelet transforms are reversible, this network can extract multi-scale features while preserving as many image texture details as possible.  
Figure 1: Illustration of multi-scale feature extraction. (**a**) Inception [22]; (**b**) FPN [23].
## 2 Materials and Methods
### Acidogenic Fermentation  
The cheese whey (CW) acidogenic fermentation was carried out in an anaerobic membrane bioreactor (AnMBR) comprised of a 10 L glass bioreactor (BioStat(r), B-Plus Sartorius), coupled to a hollow-fibre ultra-filtration module (5 \(\times\) 10\({}^{5}\) MW cut-off, UFP-500-E-4X2 MA, GE Healthcare) and operated under continuous flow. The fCW was obtained as a permeate and biomass was recirculated back to the bioreactor. The AnMBR feeding was obtained by diluting sweet whey powder (supplied by a Portuguese cheese factory--Bel Portugal, Fabrica da Ribeira Grande). It consisted of the following composition (wt.%): lactose 79.3; proteins 9.1; fats 0.7; ashes <8.5, with tap water to achieve a final concentration of 15 g-sugar\(\cdot\)L\({}^{-1}\). No nutrient (nitrogen and phosphorus) were supplemented, since previous experiments had shown that the CW contains protein and other components which are used as a nutrients source for the anaerobic culture metabolism [15]. The anaerobic bioreactor was operated under constant conditions of mixing, 300 rpm, temperature, 30 \({}^{\circ}\)C, and pH, 6 by dosing 2 M NaOH. The organic-loading rate (OLR), hydraulic (HRT), and sludge retention times (SRT) were kept constant (Table 1).  
Since the focus of this study was on the enrichment of a PHA-accumulating culture stage, it was necessary to ensure that the fCW fed to the selection reactor was constant throughout the entire operation. Hence, 20 L of fCW were collected under sterile conditions, during ca. 3 days of AnMBR stable operation, fully characterised (Table 1), and preserved at \(-\)20 \({}^{\circ}\)C, in individual 0.5 L packages, prior to being used as feedstock for the following experiments.  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{3}{l}{**Operational Parameters**} \\ \hline
**OLR** & \\ (Cmol\(\cdot\)L\({}^{-1}\).d\({}^{-1}\)) & 0.53 \\ (gCOD\(\cdot\)L\({}^{-1}\).d\({}^{-1}\)) & 20 \\ \hline
**Feed proteins** & \\ (gProt\(\cdot\)L\({}^{-1}\)) & 1.7 \\ \hline
**Feed Total-N concentration** & \\ (Nmmol\(\cdot\)L\({}^{-1}\)) & 26.4 \\ \hline
**HRT** & \\ (d) & 1 \\ \hline
**SRT** & \\ (d) & 3 \\ \hline
**Stable operation time** & \\ (d) & 34 \\ \hline
**Performance Parameters** & \\ \hline
**YFFS** & \\ (Cmol-FP\(\cdot\)Cmol-S\({}^{-1}\)) & 0.64 (0.05) \\ (gCOD-FP\(\cdot\)gCOD-S\({}^{-1}\)) & 0.72 (0.06) \\ \hline
**Protein removal** & \\ (gProt\(\%\)) & 30 (7) \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the operational conditions tested and the performance of the AnMBR, and fermented cheese whey characterisation.
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.1 Updating Training Matrix on Contact  
The analysis for the used dataset involves simulating data analysis with a similar methodology to the one that is used to train Machine Learning models. This simulation includes dividing the data into a training subset and a test subset. As mentioned earlier, we allocate 75% of the simulation data to train a set of adjacency matrices. In these matrices, we simply increment the counter for each connection between pairs of nodes (device, another host). Algorithm 1 generates adjacency matrices that not only indicate connected nodes but also capture the connectivity capacity of nodes, making them likely to be chosen as message transporters to other nodes.  
It is important to note that matrices are calculated with a specific frequency to ensure that different adjacency matrices are collected, reflecting the evolving connectivity of the nodes.  
```
Input:\(TM\) (Training Matrix), \(N1\) and \(N2\) (new contact between two nodes) Output:\(TM\) (a new version of Training Matrix)
1 begin
2 if\(TM\)[\(N1\), \(N2\)] == nullthen
3\(TM\)[\(N1\), \(N2\)] = 0;
4 if\(TM\)[\(N2\), \(N1\)] == nullthen
5\(TM\)[\(N2\), \(N1\)] = 0; \(TM\)[\(N1\), \(N2\)] \(\leftarrow\)1;
6\(TM\)[\(N1\), \(N2\)] \(\leftarrow\)1;
7\(TM\)[\(N1\), \(N2\)] \(\leftarrow\)1;
8 return\(TM\)
```  
**Algorithm 1** Updating training matrix on contact  
Computational complexity analysis allows us to evaluate the efficiency of an algorithm in terms of resource usage, such as time and memory [37]. By understanding the complexity, we can estimate how the algorithm will respond to varying input sizes. In the case of Algorithm 1, the complexity is simply O(1) because when two nodes meet each other, the corresponding cells of the adjacency matrix (TM[\(N1\), \(N2\)] and TM[\(N2\), \(N1\)]) are updated,indicating how many times those nodes have been found throughout the considered period. The spatial complexity, on the other hand, is determined by the need to store contact information in an N \(\times\) N matrix, where N is the number of nodes in the topology. However, since we also store M matrices to reflect the evolution of the adjacency matrix between nodes, the spatial complexity turns out to be O(M \(\times\) N2).  
For each matrix, we generate an intermediate ranking of nodes with good connectivity. These rankings are then combined to obtain a final ranking, as described in Section 3, Influence Metrics, depending on the metric used. The rankings assist the routing algorithm in determining whether to forward a new copy of the package to the identified node.
## 4 Trends and Challenges
### Low-Earth Orbit  
The independently developed AIRSAT-08 marks a major breakthrough for China in the field of low-earth-orbit (LEO) SAR. AIRSAT-08 is the country's first low-inclination ultra-low-orbit SAR remote sensing satellite, as shown in Figure 29. It was successfully launched in December 2024, marking a major leap from experimental verification to practical deployment in this technical domain. The lower orbital altitude significantly enhances radar echo strength and signal-to-noise ratio, enabling higher imaging resolution and stronger target identification capabilities under the same system parameters. In addition, the shorter revisit cycle and denser ground tracks of the LEO allow for the high-frequency coverage of observation areas, making it well suited for time-sensitive tasks. AIRSAT-08 is equipped with an X-band multi-polarization radar system and adopts a miniaturized design, with the onboard SAR payload weighing less than 80 kg. It supports multiple imaging modes including stripmap and spotlight, and achieves a maximum resolution of 0.1 m. On-orbit tests have demonstrated high-quality imaging and multi-polarization data acquisition, successfully validating the potential of low-orbit small SAR systems in high-resolution imaging, rapid revisit, and multi-dimensional observation [121].  
Figure 28: LT-4(01) Satellite scanning [image credit: CAST].  
Figure 27: LT-4(01) Satellite work diagram [image credit: CAST].  
In addition, the successful launch of the HJ-2-06 satellite further enhanced China's deployment of low-Earth-orbit SAR systems. As a key component of the HJ-2 series, this satellite operates in LEO, together with two optical satellites, forming a \"2 + 2\" observation constellation. Its low-orbit configuration enables high revisit frequency and all-weather imaging, playing a vital role in disaster monitoring and emergency response. The improved temporal resolution and high-precision observation capability provided by LEO significantly strengthened data support for rapid response to emergencies in China [122].
# Understanding the Seismic Resilience of Metallic Cylindrical Tanks Through Parametric Analysis
## 4 Discussions on FE Results and Parametric Sensibility
### Tank H/R = 0.5  
To study the effect of both rigid and flexible wall tanks, the trend of pressures exerted by the fluid on the tank walls has been thoroughly investigated. The seismic response, expressed in terms of total wall pressure, of a tank with an \(H/R\) ratio of 0.5, obtained from numerical analyses, is compared with the design predictions of the AWWA [40], EC8-4 [34], and NZSEE [33] standards. These standards use the input motion response spectrum from Figure 5 and apply the combination rule for impulsive and convective pressure components specified in each respective code. Figure 6a shows the pressure profiles normalized by the tank height (\(z/H\)) for five different configurations of the \(R/t\) ratio, while Figure 6b compares the predictive expressions for impulsive and convective pressure distributions with the results of the FE simulations. As observed in Figure 6a, the pressure profiles exhibit a similar trend for all five different \(R/t\) ratios investigated. Pressure peaks occurred at the top of the wall due to the convective waves both for rigid and  
Figure 5: (**a**) Input motion considered for all case studies analysis; and (**b**) response spectral acceleration of input motion for 5% damping and 0.5% damping.  
flexible wall tanks. These peaks were generally similar in magnitude, except for the rigid tank (\(R/t=600\)), where the peak was smaller, occurring at approximately 0.9 normalized height (\(z/H\)). In Figure 6b, a comparison between the numerical results and the standard codes is presented. Generally, the results obtained from the FE analyses align well with the analytical predictions in the lower part of the tank wall. As expected from the preliminary analyses performed to calibrate the FE model, the impact of convective waves on the tank shell caused a pressure peak at the top of the wall, which was not captured by the design approaches proposed by the three regulations considered in this study. Under the assumption of flexible circular tanks, EC8-4 predicted the highest values for the pressure, as it recommended directly summing the impulsive and convective effects. On the other hand, when assuming rigid circular tanks, EC8-4 underestimated the pressure profiles compared with the FE simulations. The NZSEE standard provided more or less the same pressure values along the tank wall for both rigid and flexible tanks. However, the AWWA code tended to underestimate the pressure profiles compared to the FE results.  
These observations highlight the limitations of current design codes in accurately predicting the pressure distribution for tanks subjected to seismic loading, especially at the top of the wall where convective waves play a significant role.
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 2 The Reliability Algorithm
In this Section, a new reliability algorithm is proposed, with a main contribution lying in the fault isolation time analysis and the failure mode and effect analysis. A simple radial feeder is taken as an example to illustrate the specific implementation of Steps 3 and 4 in the proposed algorithm, as shown in Figure 2. The remaining Steps can be referred to the literature [14]. Let \(t_{1}\), \(t_{2}\), and \(t_{3}\) denote the time for remotely switching in the master station, the time for fault isolation and the time for fault repair, respectively. \(t_{2}\) consists of the travel time to the faulty feeder (defined as \(t_{21}\)), the time for locating the fault (defined as \(t_{22}\)), and the time for operating the switches manually to isolate the fault to the shortest feeder segment (defined as \(t_{23}\)). The calculation of \(t_{2}\) can be expressed as:  
\[t_{2}=t_{21}+t_{22}+t_{23} \tag{1}\]  
Figure 1: The procedure of the reliability algorithm for the distribution network with three kinds of feeder terminal units (FTUs).
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 1 Introduction
Existing literature on density evolution analysis of LDPC codes are all BP algorithm or derivative algorithms of the BP algorithm, such as the Normalized Min Sum (NMS) algorithm and the Offset Min Sum (OMS) algorithm in References [18; 24; 25]. Nevertheless, in this paper we present density evolution and Gaussian approximation of the TDMP algorithm based on the BP algorithm, and analyze the decoding convergence of the TDMP algorithm from the perspective of density evolution and Gaussian approximation. Thus, we can provide a more theoretical basis for proving the superiority of the TDMP algorithm. Meanwhile, we simulated six code rates of LDPC codes under the IEEE 802.16 standard and obtained performance curves of the BP algorithm and the TDMP algorithm based on density evolution and Gaussian approximation, respectively. Thereby, we can verify that the convergence speed of the TDMP algorithm was faster. Under the degree distribution of 1/2 code rate, the two algorithms were further compared under the 802.16 and Digital Television Terrestrial Multimedia (DTMB) standards. Then, based on the DE theory, the calculation of the normalized factor is given according to the PDF of the check node information of TDMP and simplified TDMP algorithm, and simulation results are explained.
# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 1 Introduction
This paper examines the potential of optical fibres for the monitoring of railway infrastructure conditions that have already been installed alongside railway tracks. A sensing technique named distributed fibre optic sensing (DFOS) is used for the continuous and permanent monitoring of railway tracks from the cable duct whereby two distributed acoustic sensing (DAS) interrogator units were applied to detect all events taking place on or in the proximity of the monitored route. In order to not affect the day to day railway operations, no modifications regarding the fibre layout were made. The systems should prove its ability under realistic boundary conditions to detect potential track failures and isolated track defects. In contrast to new measuring systems, which are developed and installed especially for their respective requirements, in this case, already installed optical fibres are used for monitoring. Such optical fibres were originally installed for signalling and telecommunications and are therefore not actually intended for measurement purposes. The aim of this paper is to investigate whether the condition of the railway infrastructure can be monitored without additional trackside measuring equipment and consequently the number of sensors in the track does not increase. Thus, the focus is clearly on the addition of information about the track that can be generated with existing fibre optic infrastructure and not on the development of the measurement system.