esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning
#### 4.6.1 Smog Profiling over Zywie  
The complex scenario focused on smog monitoring via a zigzag-shaped flight over Zywie city in Poland. Its severe air quality issues, mountainous landscape, and proximity to an airport present a unique and challenging environment, making it an ideal candidate for testing the APP in planning a low-emission smog profiling mission. The map in Figure 5 illustrates the mission plan. The satellite imagery used is sourced from Google Earth. The mission begins and concludes at the nearby Zar Airport (ICAO: EPZR). Situated on the slopes of Mount Zar in the Little Beskids of southern Poland, the airport lies just to the north of the city. A no-fly zone (NFZ) is designated above the city to protect urban infrastructure. The UAV's task is to navigate to the region with the highest anticipated pollution levels and then perform a zigzag-shaped flight within a designated measurement area. During the return flight, an unexpected error occurs, resulting in the loss of communication with the Ground Control Station (GCS) and the current route. Consequently, the UAV must locally calculate an alternative route back to the airport. A comprehensive overview of this mission scenario is provided in Table 7. \begin{table}
\begin{tabular}{|l|l|c|c c c c|c c c c|} \cline{3-10} \multicolumn{1}{c|}{} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Weight**} & \multicolumn{4}{c|}{**Grade**} & \multicolumn{4}{c|}{**Weight \(\times\) Grade**} \\ \cline{3-10} \multicolumn{1}{c|}{} & & & **I-GWO** & **ACO\({}_{\mathbb{R}}\)** & **PSO** & **GA** & **I-GWO** & **ACO\({}_{\mathbb{R}}\)** & **PSO** & **GA** \\ \hline \multirow{6}{*}{**Coverage**} & CT & 7 & 0 & 3 & 2 & 1 & 0 & 21 & 14 & 7 \\  & CFEs & 6 & 3 & 1 & 2 & 0 & 18 & 6 & 12 & 0 \\  & COST & 10 & 0 & 3 & 2 & 1 & 0 & 30 & 20 & 10 \\  & SMOO & 8 & 1 & 3 & 2 & 0 & 8 & 24 & 16 & 0 \\  & EEE & 9 & 1 & 3 & 2 & 0 & 9 & 27 & 18 & 0 \\ \hline \multirow{6}{*}{**Coverage**} & CT & 2 & 0 & 1 & 2 & 3 & 0 & 2 & 4 & 6 \\  & CFEs & 1 & 1 & 0 & 2 & 3 & 1 & 0 & 2 & 3 \\  & COST & 5 & 2 & 0 & 1 & 3 & 10 & 0 & 5 & 15 \\  & SMOO & 3 & 2 & 0 & 1 & 3 & 6 & 0 & 3 & 9 \\  & EEE & 4 & 0 & 1 & 3 & 2 & 0 & 4 & 12 & 8 \\ \hline \multicolumn{10}{c|}{**Weighted sum**} & 52 & 114 & 106 & 58 \\ \cline{2-10}  & \multicolumn{4}{c|}{**Maximum points**} & 165 & 165 & 165 & 165 \\ \cline{2-10}  & \multicolumn{4}{c|}{**Solution fitness**} & 31.52\% & 69.09\% & 64.24\% & 35.15\% \\ \end{tabular}
\end{table} 
Table 6: Multi-criteria point-based evaluation of GPP algorithms.
## 1 Introduction
However, the use of these supports poses problems of its own, such as the rapid passivation of the surface, decrease in the mineralization reaction rate, and inactivation of the surface. This work aims to study the feasibility of the use of a commonly encountered austenitic stainless steel (X5 CrNi18-10, AISI 304) as a zero-valent iron source in the photo-Fenton degradation of methylene blue and methylorange dyes in the presence of peroxide and persulfate ions. Austenitic stainless steel has several advantages compared to pure iron, such as a good corrosion resistance, allowing for a low amount of Fe\({}^{2+}\) ions being generated (which reduces sludge formation); constant photodegradation rate; and better overall control of the process. Using an iron-rich support, such as stainless steel, could mitigate the issue of ZVI surface passivation, but the use of stainless steel in photo-Fenton processes is not as widely researched compared to the use of ZVI particles [22; 23]. Using persulfate ions to degrade oxidant sulfate radicals for the degradation of the two model dyes in the presence of stainless steel as ZVI at pH values close to 7 has been identified as a gap in the literature [24; 25]. pH values close to neutral while generating a lower amount of iron ions in the presence of oxidant species could ensure the reuse and prolonged maintenance of the mechanical properties of the substrate. Under these conditions, the photo-Fenton catalyst can be structural steel from the lining or manufacture of a wastewater storage or treatment tank, without adding supplementary particulate or mesh catalysts. To possibly contribute to future applications of the photo-Fenton process, a comparison between the degradation efficiency and kinetics of the two model dyes was performed for the two source oxidant species, hydrogen peroxide and potassium persulfate. The persulfate-initiated photo-Fenton process has been addressed less often in the reference literature. Additionally, the photo-Fenton process under nearly neutral pH conditions is a less well-addressed issue associated with solid iron-based catalysts. The novelty of this study, as previously stated, is not related to using custom-made catalysts or \"unconventional\" oxidant sources, but to documenting the feasibility of the use of stainless steel as a photo-Fenton catalyst for various oxidant concentrations. This type of catalyst has been insufficiently well-studied in the reference literature with photo-Fenton processes. Although stainless steel could be considered rather mundane, its wide use, known behaviour in different environments, and the fact that it is a structural material could constitute an advantage over custom-made ZVI sources for upscaling wastewater treatment installations. This upscaling is not possible without knowing the material's behaviour (performance, compositional stability) under different reaction conditions, and it is the aim of this study to contribute to this.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 3 Results
### NPP Estimates across Different Scales
Positive differences indicate that MODIS NPP overestimates NFI NPP and vice versa. regions and most countries. Only for Poland and Germany \(\Delta\)NPP\({}_{\text{GLOB}}\) is smaller than \(\Delta\)NPP\({}_{\text{EURO-}}\)\(\Delta\)NPP\({}_{\text{GLOB}}\) is positive for most countries (negative in only 2 countries), while the discrepancy of MODIS EURO is more randomly distributed in Europe and the 4 regions (\(\Delta\)NPP\({}_{\text{EURO}}\) positive in 5 countries and negative for 7 countries). In addition, Table 2 shows that Rel. \(\Delta\)NPP\({}_{\text{EURO}}\) is smaller than 10% for all countries except five (France, Germany, Czech Republic, Poland and Spain). Figure 4: Comparison of MODIS EURO using European climate data and NFI NPP (MODIS GLOB vs. NFI NPP in the subplot in the bottom-right corner), we present median by country, solid line is 1:1 line, dashed line represents the linear trend of the 12 countries, Coefficient of determination R\({}^{2}\), Residual standard error (RSE) and the trend function are given. Figure 3: Comparison of MODIS GLOB and MODIS EURO with NFI NPP: The box represent the Median and the 25 th and 75 th percentile, the diamond give the arithmetic mean, the whiskers extend to 1.5 of the interquartile range, values outside this range are indicated by circles, on the bottom the number of values represented by the boxplots are given. The number of observations is different since climate data is missing for certain pixels to compute MODIS NPP. To enhance the interpretability of the image, NFI NPP results larger 2100 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) (445 observations) are not shown, but are included in the boxplot. This suggests that the discrepancy between MODIS EURO and NFI NPP is smaller than for MODIS GLOB and NFI NPP and we wanted to confirm this along the NPP gradient by showing the country medians in Figure 4.  
Figure 4 provides the results by country of the NPP estimates resulting from the NFI data versus MODIS EURO with an R\({}^{2}\) 0.68, a residual standard error (RSE) of 52.0 gC\(\cdot\) m\({}^{-2}\). year\({}^{-1}\) or 9.7% of median of the NFI NPP. Aside from Germany and Poland MODIS EURO and NFI NPP are similar across the NPP gradient for the analyzed countries. The results for MODIS GLOB in the right corner exhibit consistent overestimation of NFI NPP, smaller agreement (R\({}^{2}\) = 0.59) and larger error (RSE 80.6 gC\(\cdot\) m\({}^{-2}\). year\({}^{-1}\) equal 15.0% of median NFI NPP). We used in Figure 4 the aggregated NPP of all inventory plots of one country, since the spatial coverage and thus the error structure of the two NPP sources are very different (one MODIS pixel covering 1 km\({}^{2}\) or 100 ha and the size of an NFI plot ranging from approx. 0.01 to 0.2 ha; Table S1). A direct plot-to-pixel comparison is provided in Figure S1 in the Supplementary Material.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 3 Experimental Verification  
### Experimental Area and Visualization Platform  
In this paper, experimental validation was conducted in Lianyungang City, Jiangsu Province. A fishery farming area measuring 100 m in length and 100 m in width was selected for testing unmanned boat-related functions. The usual environmental conditions at the test site are shown in Table 2.  
Data is transmitted using the MQTT protocol [38; 39], with the EMQX [40] server employed to ensure high concurrent connections and large throughput, guaranteeing the timeliness and low latency of the data. To better visualize and manage the data, we developed a visualization platform based on the Vue framework and the Leaflet framework. This platform can display the real-time operational status of the UBP and supports user functions, such as feeding point configuration. Figure 10 illustrates the data integration display interface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Measured Value** & **Measurement Method** \\ \hline Average wind speed & 1–3 m/s & Handheld anemometer 1 \\ Ambient temperature & 18–25 \({}^{\circ}\)C & Digital thermometer 1 \\ Wave height & 0.1–0.3 m & Wave buoy monitoring 2 \\ Current velocity & 0.2–0.5 m/s & Doppler current meter 2 \\ \hline \hline \end{tabular} \({}^{\dagger}\)Manufactured by DFRobet, Shanghai, China; Purchased via Taobao; 2 Manufactured by LinkOcean, Qingdao, China; Purchased via Taobao.  
\end{table}
Table 2: Parameters of environment.  
Figure 10: Visualization interface.  
Figure 9: YOLOv5 network architecture.  
On the hardware side, data is transmitted to the EMQX server via the internet, where it is processed and displayed by the software side. Location data is sent in the JSON format, including a timestamp and latitude-longitude coordinates. Water quality data, which includes water temperature, pH value, and TDS value, is also sent in the JSON format, containing a timestamp and the water quality data values. The results of the AI edge detection are transmitted in the JSON format as well, including a timestamp, species, confidence level, and detection box coordinates. On the software side, by parsing the JSON data and utilizing the timestamps for matching, different types of heterogeneous data are integrated and displayed.
# _Pseudomonas aeruginosa_ Response to Acidic Stress and Imipenem Resistance
###### Abstract  
The present study aimed to unveil the phenotypic heterogeneity and heterosexual of _P. aeruginosa_ to acidic stress and imipenem. Furthermore, the growth, morphology, and potential for biofilm formation of the subject isolates at different pHs were assessed. Isolates of _P. aeruginosa_ were recovered from juice samples and confirmed by molecular analysis. Antibiotics sensitivity was evaluated using the Kirby-Bauer-disk diffusion method, and the MIC for imipenem was determined, followed by a biofilm formation assay and population analysis. Scanning electron microscopy (SEM) was used to visualize biofilm formation. The subject isolates persisted in an acidic environment and adopted a biofilm lifestyle. The population analysis assay indicated the presence of two distinct phenotypes, i.e., a normal colony phenotype (NCP) and slow growing colony phenotype (SGCP). NCP showed visible colonies after 48 h, while SGCP colonies appeared after 72 h of incubation. Both displayed heterosexual to imipenem and susceptibility to other antibiotics. Biofilm formation at acidic pH was observed in both phenotypes. Interestingly, the recovery of SGCP was increased in an acidic environment. Biofilm consortia were highly resistant to imipenem. The present study indicated that _P. aeruginosa_ persisted for a long time in an acidic environment, through phenotypic alteration. The subject isolates adopted a biofilm lifestyle and reduced metabolism, to neutralize the effects of acidic pH and imipenem toxicity. Interestingly, the biofilm consortia harbored metabolically active (NCP), as well as inactive populations, of (SGCP), to maintain an active growth and persistency. SGCP retained the potential to revert to NCP upon subsequent sub-culturing in plentiful nutrients and optimum conditions.  
heterogeneity; pH stress; biofilm formation; imipenem resistance
## 1 Introduction
### Inspiration  
In this paper, our proposed new idea in defending against adversarial attacks is inspired by analyzing communication war in the real world, as described below. Suppose a war scenario or simulation in which the Blue team uses satellite communication to operate its military. If the other side, the Red team, is somehow capable of modifying the Blue team's satellite communication without being detected, then the Blue team is misled and could lose the war eventually. In defending against such an attack in disruption of its communication, the Blue team could add a secondary radiotelegraphy system to complement its main satellite communication because radiotelegraphy, relying on a completely different mechanism, cannot be disrupted by the Red team's satellite attack methodology. Although radiotelegraphy using Morse code has very limited bandwidth, it can transmit summary data that matches the complete data transmitted via satellite communication. In this system, if the receiver finds out that the information between the radiotelegraphy and the satellite communication does not match, it can tell that a Red team satellite-based attack is ongoing and will therefore not be fooled by the misinformation.  
Our proposed defense system against adversarial attacks uses the same philosophy as the war scenario described above. The deep learning image classification system is an analogy to satellite communication and can be compromised by various adversarial attacks. However, we propose the use of a traditional ML algorithm, such as RF, in analogy to radiotelegraphy, as the secondary verification system. Although it is less accurate than a normal deep learning image classification system, it is immune to most known adversarial attacks because it does not rely on a neural network structure. In this way, we can detect adversarial attacks easily when there is a mismatch between the outputs of the primary deep learning module and the secondary RF module.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
###### Abstract  
Image restoration aims to recover high-quality, clear images from those that have suffered visibility loss due to various types of degradation. Numerous deep learning-based approaches for image restoration have shown substantial improvements. However, there are two notable limitations: (a) Despite substantial spectral mismatches in the frequency domain between clean and degraded images, only a few approaches leverage information from the frequency domain. (b) Variants of attention mechanisms have been proposed for high-resolution images in low-level vision tasks, but these methods still require inherently high computational costs. To address these issues, we propose a Frequency-Aware Network (FreANet) for image restoration, which consists of two simple yet effective modules. We utilize a multi-branch/domain module that integrates latent features from the frequency and spatial domains using the discrete Fourier transform (DFT) and complex convolutional neural networks. Furthermore, we introduce a multi-scale pooling attention mechanism that employs average pooling along the row and column axes. We conducted extensive experiments on image restoration tasks, including defocus deblurring, motion deblurring, dehazing, and low-light enhancement. The proposed FreANet demonstrates remarkable results compared to previous approaches to these tasks.  
deep learning; frequency information modeling; image restoration; multi-domain feature extraction; multi-scale attention mechanism Article
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Data Augmentation by Using DC-GAN  
Figure 16 shows some examples of constellation data generated by the DC-GAN based on the actual constellation data obtained in our experiment in comparison with the original data. The constellation heat map cloned by the DC-GAN closely resembles the real data. The final value of the discrimination signal is 0.922/1.000, indicating that the data generated by the DC-GAN is sufficiently similar to the actual data.
## 3 Results
### Dynamically Induced Porewater Pressure Characteristics
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Quantity Description** & **Symbol** & **Scaling Factors (Prototype/Model)** \\ \hline Length & \(l\) & \(\lambda\) \\ Saturated soil mass density & \(\rho_{sat}\) & \(1\) \\ Shear strain & \(\gamma\) & \(\lambda^{0.5}\) \\ Shaking time & \(t\) & \(\lambda^{0.75}\) \\ Total stress & \(\sigma\) & \(\lambda\) \\ Effective stress & \(\sigma^{\prime}\) & \(\lambda\) \\ The bulk modulus of soil solid particles & \(K_{\mathrm{s}}\) & \(\lambda^{0.5}\) \\ Pore water pressure & \(u\) & \(\lambda\) \\ Soil permeability & \(k\) & \(\lambda^{0.75}\) \\ Soil velocity & \(\dot{u}\) & \(\lambda^{0.75}\) \\ Soil acceleration & \(\ddot{u}\) & \(1\) \\ The relative displacement of pore water & \(w\) & \(\lambda^{1.5}\) \\ to soil skeleton & \(\ddot{w}\) & \(\lambda^{0.75}\) \\ Rate of flow of pore water & \(n\) & \(1\) \\ Soil porosity & \(K_{\mathrm{u}}\) & \(\lambda^{0.5}\) \\ Hydraulic gradient & \(i\) & \(1\) \\ Frequency & \(f\) & \(1\) \\ Shear modulus & \(G_{max}\) & \(\lambda^{0.5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summarized applicable similitude laws for shaking table as per [18]. Figure 10: Excess PWP during the third shaking cycle. for interpreting the physical model results. Most importantly, the in-situ soil confining stresses are far greater than those produced in physical models. As found in [18], some soil parameters are significant parameters to scale with the similitude laws, including the ground thickness, soil density, small strain shear modulus, large-strain damping ratio, shaking circular frequency, acceleration, cyclic displacements, shear strain, and reference strain. In summary, the time scale in the model is reduced by the square root of the prototype time and the application of reduced soil densities (loose soils) in models for reasonable comparisons with prototype densities. Figures 8-10 show that the dynamically generated excess PWP reduces as the clay contents within the fabrics of ECS increase, irrespective of the shaking intensities.
## 3 Results and Discussion
### Elemental Analysis
However, the general trend of the elemental concentrations measured by PXRF and ICP-MS is running parallel, especially when speaking on the slags' major elements. Bulk and surface chemical compositions equally suggest that at least bloom refining and/or the refined iron processing took place on the sites investigated. The slag samples seem to be most of all byproducts of the bloom refining process carried out in pit-furnaces. Concluding about questions related to the provenance of raw materials (including the raw bloom supposed to be refined) would be very difficult as the quantity of the slag pieces collected was individually far below the recommended minimal amount of 200-300 g recommended for reliable bulk analysis of such highly heterogeneous materials [18; 19; 20]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Element**} & \multicolumn{8}{c}{**Iron Slag Samples**} \\ \cline{2-11}  & **4137** & **4251** & **4264** & **4009** & **4169** & **4222** & **9487** & **9445** & **9454** & **Vat** \\ \cline{2-11}  & **C1/2034** & **C1/2035** & **C1/2038** & **C2/2039** & **C2/2039** & **C1/2038** & **A/1** & **A/3** & **A/3** & **V** \\ \hline \multirow{6}{*}{Pb} & LOD & 25 & 28 & 21 & 15 & 39 & 26 & 22 & 19 & 26 & 53 \\  & Mean & 442 & 316 & & 104 & 281 & 96 & 921 & 406 & 157 & 527 \\  & Min & 359 & 122 & & 56 & 160 & 67 & 75 & 214 & 105 & 485 \\  & Max & 534 & 673 & \textless{}LOD & 164 & 393 & 119 & 2107 & 510 & 208 & 610 \\  & SD & 88 & 309 & & 55 & 117 & 26 & 1058 & 167 & 52 & 72 \\  & CV & 0.199 & 0.978 & & 0.529 & 0.416 & 0.271 & 1.149 & 0.411 & 0.331 & 0.137 \\ \hline \multirow{6}{*}{Bi} & LOD & 10 & 11 & 12 & 19 & 15 & 10 & 9 & 8 & 10 & 22 \\  & Mean & 119 & 33 & & 40 & 49 & & & 120 & 38 & 176 \\  & Min & 52 & 29 & & 34 & 36 & & & 75 & 27 & 163 \\  & Max & 155 & 42 & \textless{}LOD & 46 & 61 & \textless{}LOD & \textless{}LOD & 145 & 52 & 199 \\  & SD & 58 & 12 & & 8 & 18 & & 39 & 13 & 20 \\  & CV & 0.487 & 0.364 & & 0.200 & 0.367 & & & 0.325 & 0.342 & 0.114 \\ \hline \hline \end{tabular}  
* LOD: limit of detection; Mean: mean value of the measurements in three different surface points; SD: standard deviation; CV: coefficient of variation.  
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Iron Sug Samples**} \\ \cline{2-10}
**Element** & **4137** & **4251** & **4264** & **4009** & **4169** & **4222** & **9487** & **9445** & **9454** & **Vat** \\ \cline{2-10}  & **CI/2034** & **CI/2035** & **CI/2038** & **CI/2039** & **CI/2039** & **CI/2038** & **CI/2038** & **A/I** & **A/I** & **A/I** & **A/I** \\ \hline \multicolumn{10}{c}{_Major elements_} \\ \hline K & 1408.08 & 913.50 & 1982.75 & 984.88 & 209.88 & 627.52 & 720.45 & 264.74 & 585.83 & 296.72 \\ \hline Ca & \multicolumn{6}{c}{