esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The coupled process of electrocoagulation with ozone was successfully tested in the treatment of wastewater from a tannery from the riparian zone. Parameters such as applied voltage potential, feed flow, initial turbidity concentration, pH, and ozone flux were studied on the percentage of turbidity reduction and energy consumption in the electrocoagulation cell. It was found that parameters have the greatest influence on turbidity reduction and the effects separately of each process such as ozone, electrocoagulation and ozone-assisted electrocoagulation on turbidity.  
The result showed that the factor that has the greatest influence on reducing turbidity is voltage. The present study showed that the coupled electrocoagulation and ozone system reduced more turbidity than the processes alone. The optimal conditions for the removal of turbidity, Chemical Oxygen Demand (COD) and Biochemical Oxygen Demand (BOD) were obtained at 10 volts, 7.5 pH, 360 L/h of wastewater recirculation flow, 2400 mg/h of ozone flow, and 1130 NTU of initial turbidity of the sample in 60 min of treatment. Finally, under these conditions, a removal of 99.75% of turbidity, 33.2% of COD, and 39.36% of BOD was achieved. Likewise, the degree of biodegradability of the organic load obtained increased from 0.467 to 0.553.  
Conceptualization, P.A.-M.; Data curation, A.B.C.-S.; Investigation, J.A.R.-H.; Project administration, A.B.C.-S.; Software, J.T.M.-C.; Validation, P.A.-M.; Writing--original draft, E.J.F.-G.; Writing--review & editing, G.E.R.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 7: Effect of process parameters on specific energy consumption.
## 3 Experimental Methods  
### Laser Phase Microscopy (LPM)  
To confirm the presence of emulsion droplets and obtain information about their parameters, we carried out LPM experiments with the help of a modulation interference microscope MIM-320 (manufactured by Amphora Labs, Moscow, Russia). The main advantage of such a device is that it combines a conventional far-field reflective microscope with a laser interferometer using a semiconductor laser operating at a wavelength of 405 nm, and thus allows not only determining the size of microscopic objects (droplets) but also estimating their refractive index. Using the image of the sample (on a reflective substrate) in white light, it is possible to select a fragment of interest--a drop of emulsion, which can then be examined in the mode of coherent phase microscopy. The laser channel scheme includes a Mach-Zehnder interferometer with the use of micro-objectives and a phase modulator in the reference arm of the interferometer. The LPM is based on measuring the two-dimensional spatial distribution of the phase shift between the reference beam and the beam passed through the object under study (and then reflected from the substrate)--the object wave. The phase shift is described by the expression:  
\[\delta=4\pi(n-n_{0})r/\lambda, \tag{1}\]  
where \(n_{0}\) is the refractive index of the medium, \(\lambda\) is the laser wavelength (\(\lambda=405\) nm), \(r\) is the radius of the particle (emulsion drop). If \(n>n_{0}\), then the shape of the dependence of the phase shift on the transverse coordinates will be convex or, otherwise, concave. In a phase microscope, the phase shift is actually measured in units of the optical path difference (OPD) between the object and reference beams along a chosen plane (droplet cut) in the liquid volume. The optical path difference \(\Delta h\), according to [24], for a spherical particle is related to its size (diameter \(d\)) and refractive index (\(n\)) by the expression:  
\[\Delta h=\delta\cdot\lambda/2\pi\alpha=d\Delta n/\alpha, \tag{2}\]  
where \(\Delta n=n-n_{0}\) and \(\alpha\) is the instrumental coefficient of the device, which depends on the particle size. For example, it can be calibrated experimentally using latex particles [25].  
The piezo-scanned reference beam of the interferometer is mixed with the object beam and received by a CMOS matrix. The dynamic interference pattern arising on the matrix is processed by a personal computer. The distribution of the phase difference in the plane transverse to the laser beam makes it possible to simultaneously determine the size and refractive index of the particle.
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 1 Introduction
### Analysis of Known Results  
Modern air quality monitoring systems embody a seamless integration of cutting-edge technologies and diverse methodologies. These systems combine in situ measurements, ex situ analyses, and remote observations from satellite platforms to provide comprehensive insights into air quality. Recent advancements in low-cost, Internet of Things (IoT)-based sensor networks have enabled dense, real-time monitoring with high spatial resolution, significantly enhancing traditional monitoring stations. This is particularly valuable in urban areas where pollutant levels vary widely. When combined with satellite data, these networks offer unprecedented detail on pollutants like particulate matter, ozone, and nitrogen dioxide (NO\({}_{2}\)). Studies reveal that integrating satellite data, ground-based measurements, and traffic information identifies correlations between NO\({}_{2}\) levels and traffic patterns, deepening our understanding of urban pollution sources [3].  
Advanced data processing techniques, including physical models and artificial intelligence, analyze large datasets, uncover patterns, and provide accurate forecasts. Decision support systems (DSSs) [4; 5] leverage these tools to address air quality challenges on both local and global scales, promoting sustainable environmental management. Notable national initiatives, such as the US EPA's AirNow [6] and the EU's Copernicus Atmosphere Monitoring Service (CAMS) [7], integrate ground-based and satellite data to provide real-time information. However, these systems often lack the granularity to capture hyper-local pollution dynamics, particularly in smaller communities.  
While localized efforts in cities like Houston [8], Beijing [9], Rotterdam [10], and Sao Paulo [11] address specific industrial and environmental challenges, they often focus on localized concerns and miss broader interconnected pollution trends.  
Moreover, air quality monitoring systems face challenges related to spatial coverage, pollutant scope, temporal resolution, and the interaction effects between pollutants [12; 13; 14]. These limitations hinder their ability to deliver comprehensive evaluations.
## Appendix A  
**Theorem A1**. : _For the problem of SRM for both DL and UL, it can be transformed into a WMMSE problem by introducing auxiliary variables \(a_{\mathrm{D},k}\) and \(a_{\mathrm{U},j}\), i.e., weighting coefficients, which can be reformulated as follows:_  
\[\arg\max_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F}}R_{\mathrm{D},k} =\arg\min_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F}}\big{(}a_{\mathrm{ D},k}\epsilon_{\mathrm{D},k}-\log_{2}\big{|}a_{\mathrm{D},k}\big{|}\big{)}, \quad\forall k,\] (A1) \[\arg\max_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F}}R_{\mathrm{U},j} =\arg\min_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F}}\big{(}a_{\mathrm{ U},j}\epsilon_{\mathrm{U},j}-\log_{2}\big{|}a_{\mathrm{U},j}\big{|}\big{)}, \quad\forall l.\] (A2)  
Figure 5: Residual SI power of BIOS-assisted FD systems with different \(M_{y}\). Proof of Theorem A1. : In the DL scenario, considering the independence between UEs, we take the \(k\)-th DL UE as an example to prove the equivalence between SRM and WMMSE. The proof is to demonstrate that maximizing the rate of the \(k\)-th DL UE is equivalent to solving the following optimization problem:  
\[\min_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F}}\quad g_{\mathrm{D},k}=a_{\mathrm{ D},k}\epsilon_{\mathrm{D},k}-\log_{2}\bigl{|}a_{\mathrm{D},k}\bigr{|}. \tag{10}\]  
It can be observed that when variables \(\mathbf{\Phi}_{1},\mathbf{\Phi}_{2}\), and \(\mathbf{F}\) are fixed, the optimization problem (10) becomes convex with respect to \(\mathbf{A}_{\mathrm{D},k}\), \(u_{\mathrm{D},k}\).
## 6 Evaluation
### Accuracy Evaluation of Energy Model
#### 6.3.1 Variation of Input Data Size  
This experiment evaluates how the costs of each allocation policy change according to the data size for tasks from Application 1, shown in Figure 5. We built a simulation with 500 tasks, 100 IoT devices, two MEC servers, and energy cost coefficient configured to 4/5. The \(x\)-axis shows the input sizes. Figure 4(a) shows 3.6 MB, 36 MB and 362 MB, and Figure 4(b) shows 3.6 GB. The \(y\)-axis indicates the cost of policies.  
As shown in Figure 5, when the data entry size increases, MEC and CC policies have cost increments. The IoT execution cost in devices remains the same, as no data transmissions are carried out. When data entry size scales, allocation policies that require data transmissions become costly, and allocation on the IoT device itself turns increasingly advantageous. This increase in cost for MEC and CC policies is quite evident in Figure 4(b), for inputs of 3.6 GB, even the cost scale had to be adjusted to represent the values better. Therefore, it is crucial to design applications so that data transfers over the network are not too large per task, avoiding high data transmission costs. An approach to do this is to create more tasks with lower data size.  
We also designed two other cases, one with 5000 tasks and 362 MB per task and another with 500 tasks, Figure 4(a), and 3.6 GB per task, with roughly 1.8 TB in total each. The system energy consumption and the total elapsed time for the 500 tasks case were 59,160.92 Joules (J) and 14,515.21 s. For the experiment with 5000 tasks, the costs were 45,011.49 J and  
Figure 5: Cost policies for input data size variation in Application 1. (**a**) Cases A, B and C. (**b**) Case D.  
10,305.94 s, that is, a decrease of 23.92% and 29%, respectively. Therefore, tasks should preferably not be super data-intensive, if dependent on MEC or CC, as data transmissions add additional energy and time expenses.
## 3 Related Work and Literature Review
### Robustness Against Geometric and Compression Attacks  
A primary challenge in watermarking involves ensuring the survival of embedded watermarks during manipulations, such as cropping, scaling, and rotation. This consideration is of particular importance in forensic applications, where evidence may undergo transformation during analysis or sharing. Robustness against these distortions is a key requirement for forensic watermarking techniques.  
[PERSON] et al. [23] address this issue using machine learning models to predict scaling parameters and extract watermarks from heavily cropped images. Their approach applies Discrete Fourier Transform (DFT) to distribute watermarks across images to enable extraction despite arbitrary scaling and cropping.  
Although the machine learning approach shows promise to improve watermark robustness [47], its forensic application raises concerns about the trade-off between complexity and practicality. In forensic investigations, simplicity and transparency are often preferred to maintain the workflow and avoid interference with evidence analysis. Moreover, while machine learning models can improve robustness, they can also introduce black-box elements that may raise concerns about the reproducibility and transparency of the watermarking process [48]. A balance must be struck between improving robustness and maintaining transparency in forensic watermarking.  
Although robustness protects the watermark from malicious manipulation, imperceptibility is equally important to preserve the integrity and utility of the original content.
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
###### Abstract  
In order to suppress the cavitation of an airfoil under random operating conditions, a deformable covering was constructed in the cavitation prone area of the NACA0012 airfoil. By sensing the pressure difference between the inner and outer sides of the airfoil, the covering of the airfoil can be changed adaptively to meet the requirement of suppressing random cavitation of the airfoil. The simulation results show that the cavitation influence range of the airfoil with a shape memory alloy covering can be reduced by more than 70%, and the cavitation is well reduced and suppressed. Moreover, the backflow near the wall of the airfoil was reduced under random working conditions. When the maximum bulge deformation of the covering was between 3-6 mm, the airfoil produced a cavitation range only on the covering surface of the airfoil, and there was no cavitation erosion on other parts. This method with locally variable airfoil to suppress cavitation provides a good reference value for other hydraulic machinery to suppress cavitation.  
NACA hydrofoil; cavitation suppression; random operating condition +
Footnote †: journal: Applied sciences
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 5 Experiments and Discussion
Figure 7: Magnitude and depth estimation error using SimpleRNN, GRU, and LSTM. of experiment, we used the normalized models of the SimpleRNN, GRU, and LSTM to compare their performance. After analyzing the effects of the normalization techniques on the RNN models, we ensembled the SimpleRNN, GRU, and LSTM models in different settings to make a powerful and efficient stacked model. The output of the first model was given as input to the second model, and so on. The results of the stacked models and individual models are shown in Figure 9. We conclude a significant decrease in the RMSE values of the stacked models compared to the individual models. Although the performance of stacking two models is better, stacking three models obtains the lowest RMSE values. From the results, we can easily conclude that ensemble methods are suitable for the task of magnitude and depth detection by overcoming the weaknesses of the base models. Figure 8: Magnitude and depth estimation error using SimpleRNN+BN, SimpleRNN+LN, GRU+BN, GRU+LN, LSTM+BN, and LSTM+LN. Figure 9: Magnitude and depth estimation error using SimpleRNN, GRU, LSTM, SimpleRNN+GRU, SimpleRNN+LSTM, GRU+LSTM, and SimpleRNN+GRU+LSTM. The stacked RNN achieves the lowest RMSE values, 3.27 and 3.65, for magnitude and depth detection. Again, the GRU with either LSTM or the SimpleRNN does not show a high performance. In Figures 8 and 9, we see that the stacked-based ensemble model significantly outperforms the individual models, and the models with the RNN models with normalization layers outperform the models with batch normalization layers. Finally, our proposed model, the stacked normalized RNN (SNRNN), is an ensemble model of the SimpleRNN, GRU, and LSTM models where each model has a normalization layer. Normalization is applied to the model input in the stack. The normalization layer normalizes each input in the batch independently across all the features. Batch normalization depends on the batch size, while normalization is independent of the batch size. The RMSE values show that the proposed model SNRNN (on top) outperforms all the models, where normalization helps to achieve 3.16 and 3.24 RMSE values for magnitude and depth detection. Our proposed SNRNN model takes advantage of both the ensemble learning and normalization techniques. All the results are summarized in Table 4.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 1 Introduction  
CubeSats have evolved significantly over the last 10 years. While they were once used primarily for educational purposes, they are now capable of performing more complex tasks. The goal of the research at the Institute of Space Systems of Technical University of Braunschweig (IRAS) is to further enhance the performance of small satellites. In order to tackle even more complex tasks, such as supporting active debris removal (ADR) or scientific missions, new technologies are implemented. The chair of Information Technology for Aerospace at the University of Wuerzburg is mainly researching reliable distributed and autonomous computing systems, focusing on space and aeronautic projects. Both universities parthered and are jointly developing a 3U-CubeSat for innovative technology advancement and practical, sustainable education of students. The aim of the project is the technology demonstration of two innovative technologies, which have been previously developed and funded by German Aerospace Center (DLR) InnoSpace Masters program: SKITH (SKIPTheHarness) and Wall#E. InnoCube concludes its three year development phase with the launch into orbit in 2023, aiming for a mission of at least one year. The planned CubeSat will be developed by students and research staff, going through all phases of a space project life cycle. The following article describes the mission framework witha focus on the design criteria and operational concept. Therefore, Section 2 covers the mission, payloads and boundary conditions, Section 3 gives an overview of the satellite bus including the software, while Section 4 gives a summary and outlook.
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 4 Conclusions  
Deep learning has been widely applied in the field of agriculture. Particularly in the cultivation of citrus, its impact is notable for tasks such as detecting crop diseases, insect pests, and the level of fruit maturity. This study involves adjustments and optimizations to the ShuffleNet network architecture to more effectively identify and classify insect pests during the citrus-growing process. The dataset was also expanded to enhance the model's ability to recognize insect pests under different environmental conditions. Several key improvements were made to the original ShuffleNet network, including changing the activation functions, incorporating an enhanced attention mechanism module, and adjusting the network structure. These improvements significantly increased the accuracy of the model and achieved a more lightweight design. Moreover, by changing the training strategy through transfer learning, not only was the training time reduced but the costs were also lowered. Experiments with comparison tests, ablation studies, and heatmap validations have proven that these improvements indeed enhance the performance of the network. Ultimately, the improved ShuffleNet network model in this paper has a better overall performance with a recognition accuracy of 94.48% and a model size of 3.84 MB, which can be used as a reference for citrus pest recognition and classification techniques.  
While prioritizing lightweight computations, the SCHNet pest identification model achieved a high identification rate. The subsequent phase will focus on deploying the model within a WeChat mini-program or mobile app.  
Conceptualization, Y.-N.Y. and C.-L.X.; methodology, C.-L.X. and J.-C.Y.; software, J.-C.Y. and C.-L.X.; validation, Y.-B.M.; formal analysis, S.-Q.D.; investigation, Z.-H.W.; resources, R.-F.Y.; data curation, Y.-B.M.; writing--original draft preparation, C.-L.X.; writing--review and editing, C.-L.X. and J.-C.Y.; visualization, C.-L.X. and J.-C.Y.; supervision, Y.-N.Y.; project administration, C.-L.X.; funding acquisition, Y.-N.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 42061059), Guilin Technology Application and Promotion Plan (grant number 20210226-2), and China-ASEAN Huawei Artificial Intelligence Innovation Center 2022 subsidy project.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Strengths and Limitations  
Strengths of the present review are its exhaustive search of the available literature, reproducibility, systematic assessment of evidence and grading of recommendations. However, there are important limitations. Neonatology is rapidly developing and changing. E.g., nutritional recommendations changed several times within the last 20 years. Using historical controls rather than contemporaneous controls carries a high risk of bias and grossly limits the level of evidence. Improvements in nutritional status may rather be a consequence of increased nutritional awareness than a consequence of new developed PN approaches. The observed effects may have been noticed by chance and adequately powered, randomized controlled trials based on up-to-date parenteral nutrition recommendations are urgently required to evaluate the real clinical benefits of SPN bags. Given current parenteral nutrition recommendations a considerable proportion of infants of the reported trials are undernourished. In addition, the composition of SPN and IPN nutritional regimens and consequently the observed nutritional status, varied across and within the different studies, limiting the validity of the results of the meta-analyses. In the clinical experience of the authors, one SPN does not fit all preterm infants. The smaller the infants are, the more often adjustments are required, commercial SPN bags are more expensive than hospital pharmacy produced SPN bags, and finally commercial SPN bags still require pharmacy-based adjustments (e.g., supplementation of vitamins and trace elements).
## 4 Fuel Cell Designs
### Cell Design
However, high thermal expansion of NiO as compared to zirconia and large change in volume due to reduction of the anode material during cell operation makes it prone to electrolyte cracking in period of fabrication and cell operation. The problem of  
Figure 4: Different types of fuel cell design: (**a**) electrolyte; (**b**) anode; (**c**) cathode and (**d**) metal or ceramic supported [8]. electrolyte cracking in order to maintain performance of cell can be sorted out by controlling the thickness of electrolyte, microstructure support and carefully selecting the content of NiO in composite of NiO-YSZ [68; 102; 103; 109]. The supported cell designs are further segregated into two sub categories namely; ceramic and metal supported designs shown in Figure 5. From these two sub categories, ceramic support materials are usually preferred for the ceramic based fuel cell due to their inertness and low thermal expansion coefficient. The cathode degradation becomes limited due to reduced use of high chrome in high temperature alloys [87]. The cell designs based on ceramic support materials are generally considered not to be suitable for the system that undergoes fast and continuous thermal cycles due to their high stiffness and lower toughness [8; 102; 103]. Cell designs based on metal support are somehow difficult to manufacture since metal cannot be heated to the high temperature which is normally needed for processing of ceramic components. Also, high temperature metal alloys are chemically unstable in the cathode's oxidizing environment, which results in the reduction of cell's lifetime. Beside above shortcoming of metal support design, it is still a very attractive design due to its very high electrical conductivity, high strength and fine resistance to thermal shock which helps its usage in automotive and residential micro-CHP sectors [68; 102; 103].