esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Experimental Data  
In this work, the experimental data from Prognostics Center of Excellence at National Aeronautics and Space Administration (NASA) Ames is employed to train and validate the proposed approaches [34]. This approach leads the comparison of the proposed techniques with that of recently published papers using the same dataset to be easier. The dataset consists of four batteries, aged through three different operational profiles conducting alternately in the dataset, namely impedance, charge, and discharge profiles. The impedance measurement process was performed by employing the electrochemical impedance spectroscopy (EIS) technique. Moreover, in the regular charge and discharge cycle, the batteries were charged and discharged at CC of 1.5 A and 2 A, respectively. In the charge step, 1.5 A is imposed to the batteries to reach the maximum voltage of 4.2 V, followed by the CV process, until the current decreased from 1.5 A to 20 mA. Nevertheless, in the discharge profile, the CC discharge step was conducted by reaching the voltage of 2.7 V, 2.5 V, 2.2 V, and 2.5 V for batteries, No. 05, 06, 07, and 18, respectively. As a consequence of reoccurring the above procedure, the capacity of the batteries reached 70% of the nominal capacity.
## 3 Results and Analysis
### Characteristics of Stress Distribution around a Single Roadway
#### 3.2.3 Characteristics of Stress Distribution around a Single Roadway  
Figure 7 illustrates the distribution curve of hoop stress at the cross-section boundary of the roadway, derived from the calculation model depicted in Figure 3, in scenarios where no adjacent roadway exists. The stress curves within the roadway cross-section represent the tensile stress, while those outside represent the compressive stress. In this case, given the closeness in magnitude between the vertical and horizontal stresses, no tensile stress is observed around the roadway. The straight-wall corners of the roadway are identified as areas of stress concentration, with the peak stress reaching 72.0 MPa, whereas the stress values in other regions do not exceed 35 MPa. The monitoring points on both sides of the roadway indicate a hoop stress of 14.5 MPa, and the hoop stress at the middle monitoring points of the top and bottom plates is measured at 17.0 MPa and 5.8 MPa, respectively. Stress concentration areas are observed at the two straight-wall corners at the bottom of the roadway and at the two arch shoulder areas. Due to the axisymmetrical nature of the roadway shape, the stress distribution also exhibits symmetric characteristics. An important point to note is that, as per the stress function derived, the position of the maximum hoop stress is located at the straight-wall corners at the bottom of the roadway, registering at 72.0 MPa. Although these data effectively highlight the degree of stress concentration in this region, they may lead to the underestimation in the real elastic solution. This underestimation primarily stems from the slight rounding at the turning angle of the opening in the mapped shape, despite the mapped shape being very close to the ideal original shape, resulting in a slight deviation from that of the original shape. However, in practical engineering scenarios, even if straight-wall corners exist at the opening boundary, they may become rounded due to excessive stress; hence, the stress solution at this part retains its engineering reference value.
## 3 Results and Discussions
### Spatially Resolved Delayed Luminescence of Individual Grains of The Yeast  
An example of the spatially resolved delayed photon emission of the yeast grains in response to photo-irradiation is presented in Figure 9. Grains of the yeast were placedin 16 small cells \(1/8^{\prime\prime}\) in diameter that were carved onto a sample platform. Figure 9A shows the layout of the cells, and Figure 9B shows a photograph of the samples placed in the receded cells. The sample platform was held in the custom light-tight chamber for more than 2 h before the illumination. The sample was imaged with the timing sequences shown in Figure 4, up to a total integration time of 1000 s. Figure 9C gave a raw image after the time of integration, wherein the brighter region spreading between the 10 o'clock and the 1 o'clock positions was the residual luminescence of the tip of the fiber after the irradiation to the sample was switched off. That position/extent of the residual luminance of the fiber corresponds to the position of the fiber being reflected by the reflectance standard, as was marked in Figure 5 by the white circle. A baseline image of the integrated background luminescence of the dark chamber was acquired at the same setting, except that no yeast samples were placed in the receded cells. Simple subtraction of the baseline image of the time-integrated acquisition of the empty chamber post-irradiation from the time-integrated acquisition of the yeast-surfaced chamber post-irradiation, as shown in Figure 9C, resulted in Figure 9D, which revealed the delayed luminescence of the individual grains of the yeast. However, the grains of the yeast placed in the cells at the lower \(1/3\) segment of the FOV were not as clear as those at the middle segment. That was due to the nonuniform irradiation of the light delivered by the fiber to the sample surface that was placed approximately \(1^{\prime\prime}\) away from the fiber tip. Additionally, the azimuthal symmetry of the light projected from the fiber was distorted due to the fiber being side-attached to the wall of the sample chamber, which caused wall reflection of the fiber-delivered irradiation. The overall effect was a nonuniform irradiation of or dose to the sample surface, which would cause nonuniform delayed luminescence from the sample of the same photic activity when assessed over the same duration of time after the photic stimulation. Spatially resolved accurate measurement of the true activities of delayed luminescence would require compensating the dose dependency imposed by the nonuniformity of the irradiation.
## 3 Results and Discussion
### Desorption Studies
Oxyanions are heavily de-bonded from the iron oxide surface due to the creation of chemically bonded complexes either on the external or internal surface, i.e., outer- and inner-surface complexes [21; 34; 35], which can also be stated for the hybrid EGS@APTES-GT adsorbent. On the other hand, weak electrostatic bonding within the organic structure Iprodione could be easily be desorbed by either a strong base (NaOH) or ionic species (NaCl), a more effective process using a stronger base/nucleophile can be achieved, and the decision can depend on the desorption effectiveness, techno-economical parameters, and environmental impact. Figure 7: Adsorption-desorption regeneration cycles for the repeated adsorption of As(V) and Iprodione onto EGS@APTES-GT.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 2 Semantic Segmentation Models for Multi-Damage Detection
### Efficient Symmetric Network (ESNet)  
An efficient symmetric network (ESNet) is another lightweight state-of-the-art model that was proposed by [PERSON] et al. (2016). ESNet consists of only 1.6 million parameters and is considered appropriate for embedding into ECDs. The ESNet was trained on the Cityscapes dataset, and its performance was compared with other lightweight models. ESNet achieved 70.7% mIoU on the Cityscapes testing dataset and achieved better performance, in terms of accuracy and inference speed, compared with other models in the study. To reduce the computational cost, ESNet employed two types of residual modules, known as factorized convolutional units (FCUs) and parallel FCUs (PFCUs), in their encoder and decoder parts. These are non-bottleneck modules, in which the standard convolution is decomposed into two 1D convolutional layers.  
Figure 3(a) shows the entire FCU block, which consists of three decomposed 1D convolutional layers with addition, and each convolutional layer is followed by a rectified linear unit (ReLU) activation function with BN. K in Figure 3(a) represents the kernels size, which is not fixed to vary the receptive fields, depending on the feature map.  
Figure 3: Overall architecture of CGNet.  
Figure 2: Structure of CGblock used in the CGNet.  
Figure 4b shows the PFCU block consists of decomposed 1D convolutional layers and adopts a transform-split-transform-merge strategy. The PFCU comprises two decomposed 1D convolutional layers that are then connected to three parallel dilated 1D convolutions. The dilation rates in the corresponding convolutional layers are 2, 3 and 5, and the outputs from these three dilated convolutions are added.  
Figure 5 displays the entire architecture of ESNet, which is symmetric with the same number of layers in the encoder and decoder. The encoder part of the ESNet contains three blocks. Block 1 consists of three FCU modules, whose filter size (K) is 3, to downsample the input image to half. Block 2 consists of two FCU modules, with K of 5, to capture a wide-scaled context, and the downsampling ratio in this block is 1/4. Three PFCU blocks are used in block 3 to further downsample the input to 1/8 of its original resolution. For upsampling, the decoder comprises two blocks. Blocks 4 and 5 contain two FCU modules with filter sizes of 5 and 3, respectively. In blocks 4 and 5, the upsampling layer is employed in front of each FCU unit to resize the output to its original resolution map.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 3 Data
### Artificial Neural Networks  
Artificial neural networks represent a learning algorithm inspired by the neural networks of biology. With the recent advancement of deep learning, the use of artificial neural networks has yielded excellent performance in classification and can be used for regression when a mean-square error (MSE) is the loss function. Below are the neural network methods we used in this study.  
Multi-layer perceptron (MLP) is a basic neural network structure that adds hidden layers into the perception structure. We built a hierarchical model with four inputs, two outputs, and one hidden layer.  
Radial basis function network (RBFN) is a type of neural network that represents the proximity to the correct answer using Gaussian probability and Euclidian distance [34]. One hidden layer based on Gaussian probability distribution is used, and the training process is extremely fast.  
Deep neural network (DNN) increases training parameters by adding hidden layers in MLP. Figure 2 shows the settings of the input and output values in the basic DNN structure. It is often essential to use a rectified linear unit (ReLU) [35] and Dropout [36] to prevent the vanishing gradient problem [37].  
Recurrent neural network (RNN): the connection between units is characterized by a circular structure [38] and can be used when continuous data is given. RNNs can be used to predict trajectories since the movement of a drifter is sequential data. The input data for MLP or DNN has a fixed size of 4, as depicted in Figure 2. However, RNNs should incorporate all the drifter moves in sequence; thus, the data length can be different. The maximum length that the model can receive as an input sequence is set, and the remaining data space is padded with zero. For example, if the maximum length is set to 200 and given data set has 120 h of movement information, the remaining data is filled with 80 zeros. Figure 3 shows the input and output data structure of the RNN model.  
Long short-term memory (LSTM): the RNN model creates the next unit based on product operation and suffer from the vanishing gradient problem when dealing with long data sequences. LSTM [39], which has the function of forgetting past information and of remembering current information by adding a cell-state to the hidden state of RNN, has emerged as one of the most widely used RNN methods and can effectively solve not only the vanishing gradient problem but the long-term dependency. This model is expected to remember the movement of a drifter according to the specific short sequence of wind and flow.
## 9 Samples of the Results
### Accelerated Learning in Robotic Navigation with Assisted DDPG  
[PERSON] et al. [27] introduced the Assisted Deep Deterministic Policy Gradient (AsDDPG) framework, which incorporates an external controller, such as a PID controller, to enhance the training efficiency of Deep Reinforcement Learning (DRL) for robotic navigation. The framework initially uses the external controller to guide policy exploration, enabling the agent to learn basic behaviors like moving in a straight line. Over time, the actor network surpasses the controller's performance and operates independently, while the controller is gradually discarded. The experimental setup involved training in simulation environments (Stage and Gazebo) with three levels of complexity: empty, simple, and complex obstacle layouts. Performance was evaluated using metrics like collision rates, goal-reaching frequency, and learning curves under varying network configurations and reward functions.  
Figure 18 shows the smoothed learning curves of AsDDPG and standard DDPG under different network hyperparameters, demonstrating that AsDDPG not only accelerates training but is also more robust across hyperparameter variations. AsDDPG achieves higher average performance compared to DDPG, even under challenging conditions. This result underscores the effectiveness of leveraging an external controller to stabilize and speed up learning in DRL-based navigation tasks.
# Photocatalytic Activity of Monosized AuZnO Composite Nanoparticles
## 1 Introduction  
Emissions of large amounts of industrial organic wastewater and domestic sewage have resulted in serious surface and groundwater pollution. Water pollution not only poses a great threat to the environment and people's health, but also aggravates the shortage of water resources [1]. In order to deal with the organic pollutants in the water and obtain more water resources, many physical and chemical methods have been tried, such as coagulation/flocculation, chemical oxidation, electrocoagulation, coagulation/carbon adsorption process, etc. [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Compared with these methods, photocatalytic degradation of organic pollutants is considered to be the most promising technology.  
The use of heterogeneous catalyst (semiconductor materials) photocatalytic technology aroused widespread concern, due to the effective and rapid elimination of organic compounds and the relatively low cost [12]. Nowadays, many kinds of semiconductor materials have been studied as photocatalysts, including TiO, ZnO, WO\({}_{3}\), CdS, and so on [13; 14; 15; 16; 17]. Among the semiconductor materials, ZnO has theadvantages of wide band gap energy (3.37 eV), high sensitivity, high thermal stability, easy preparation, and low cost, and has been widely used in various fields, such as sensors, electronics, solar cells, and optoelectronics [18; 19; 20; 21; 22]. However, its applications are somewhat restricted [23]. Research shows that doping ZnO with the appropriate amount of precious metal can effectively improve its photocatalytic activity [24; 25; 26; 23].  
We previously reported the one-pot polyol synthesis of poly(ethylene glycol)-block-poly(propylene glycol)-blockpoly(ethylene glycol) (PEO-PPO-PEO)-coated AuZnO composite nanoparticles, using PEO-PPO-PEO molecules as the surfactant [27]. The AuZnO composite nanoparticles have two absorption bands, one centered at 350-360 nm from the band edge of ZnO, and the other at a maximum of 520-550 nm from the surface plasmon resonance (SPR) of the nanostructured Au. The wide optical absorbance of AuZnO composite nanoparticles is promising for photocatalysis, photodegradation, biosensing, and optoelectronic devices. In this work, the AuZnO composite nanoparticles with different compositions were prepared. The characterization shows that the nanoparticles are monosized and have high crystallinity, manifesting excellent dispersibility and optical performance in both organic and aqueous media. The photocatalytic activity of AuZnO composite nanoparticles with different composition were investigated by the photodegradation of RhB under UV and visible light irradiation, and the reusability of AuZnO catalyst is also studied. The results reveal that the AuZnO composite nanoparticles with proper Au loading can improve photocatalytic activity and stability, and are expected to be applied in the field of photocatalysis.
# Adversarial Training Methods for Deep Learning: A Systematic Review
## 4 Findings
### Adversarial Training Extension
#### 4.9.1 Method-Based Ensemble Adversarial Training  
The method-based ensemble adversarial training (MBEAT) utilizes varieties of adversarial attacks in the training process [31]. This method further increases the model's generalization across the data that are attacked by different attack methods.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 8 Conclusions  
This work dealt with channel feed-forward software defined equalization in a QAM VLC system. The whole platform based on LabVIEW and National Instruments hardware was introduced. The first part presented the results from simulation software, which roughly estimated the expected results. QR-RLS was preliminarily identified as the best algorithm for deployment; however, this argument had to be backed by certain data.  
The second part described the whole measuring phase, which backed up the earlier statement. QR-RLS managed to improve BER values by up to one whole order. It also improved EVM up to 10%. LMS and NLMS improved the measured values as well, but the impact was lower. NLMS managed to improve EVM up to 7% in the best-case scenario, whereas LMS reached only up to 5%.  
Certain weak spots were identified and discussed. Further research was outlined with the focus on new parts, which will significantly influence the performance of future platforms.  
Conceptualization, R.M. and L.D.; Methodology, R.M.; Software, L.D. and R.M.; Validation, R.M., L.D. and R.J.; Formal Analysis, R.M. and L.D.; Investigation, R.M. and R.J.; Resources, R.M.; Data Curation, L.D.; Writing--Original Draft Preparation, L.D.; Writing--Review & Editing, R.M. and R.J.; Visualization, R.J.; Supervision, R.M.; Project Administration, R.M.; Funding Acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education of the Czech Republic, Project No. SP2020/156 and by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, Project Number CZ.02.1.01/0.0/0.0/16_019/0000867.  
This article was supported by the Ministry of Education of the Czech Republic, Project No. SP2020/156. This work was supported by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, Project Number CZ.02.1.01/0.0/0.0/16_019/0000867, within the Operational Programme Research, Development and Education.  
The authors declare no conflict of interest.
## 3 Results  
The Levene test showed significant results (\(p<0.05\)) and values of \(F\) near to 1 in all comparisons performed, so all data were considered homogeneous and were subjected to the analysis of variance (ANOVA). This analysis showed \(p<0.001\) for all comparisons, so data were subjected to the LSD test, for intergroup analysis.  
The colony forming units for mL were expressed in terms of log\({}_{10}\) (Figure 2). UE samples were characterized by a count of 7.001 \(\pm\) 0.311 (log\({}_{10}\)CFU/mL). All groups treated with CMFs showed a fungal reduction in respect to controls, but the P1 program alone (6.981 \(\pm\) 0.001) was not effective on providing a statistically significant difference. The other protocols showed a significant reduction of _C. albicans_, compared to controls and P1; P1-P5, 1 antibacterial, and 2 antibacterial were characterized by 6.833 (\(\pm\)0.048), 6.814 (\(\pm\)0.001), and 6.902 (\(\pm\)0.002) log\({}_{10}\)(CFU/mL), respectively.  
The SEM observations of polyacrylic resin discs (Figure 3) showed a significant reduction of _C. albicans_ adhesion on both test discs, without differences between 1 and 2 sessions of the antibacterial program.  
Figure 2: Comparison of the viable planktonic _Candida albicans_ Log\({}_{10}\) CFU/mL (error bars show \(\pm\) standard deviation) after treatment with CMFs Antibacterial protocol, with different times of exposure.  
The quantification of CFUs, after 24 h of fungal cultivation on the resin discs, showed a significant reduction of the viable cells on test groups (Figure 4): 1 antibacterial, 6.119 (\(\pm\)0.240); 2 antibacterial, 6.421 (\(\pm\)0.347); with respect to UE, 7.0725 (\(\pm\)0.294) log10(CFUs/mL). No significant differences were found between 1 and 2 antibacterial programs.  
Figure 3: _Candidia albicans_ adherence on polyacrylic resin discs. (**A**) Yeasts exposed to a double antibacterial treatment. (**B**) Yeasts exposed to a single antibacterial program. (**C**) Unexposed yeasts. (**D**) Average cell count on SEM scans of area of 135 \(\upmu\)m\({}^{2}\). A1: one complete session of the antibacterial protocol with CMFs. A2: two complete sessions of the antibacterial protocol with CMFs. * _p_-value < 0.05. Red arrows show the _Candidia_ cells.
# Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
## 3 Results
### Stress Events Have a Greater Impact on Vegetation Health Compared to Other Factors  
The predicted trends for stress events are predominantly negative, accounting for 85.64% of the cases. Excluding the Yunnan-Guizhou Plateau, South China, and parts of Heilongjiang, other regions show a negative trend with the maximum trend being \(-0.027\cdot\mathrm{a}^{-1}\) (Figure 10a,b). In contrast, other events exhibit an opposite trend, with the maximum trend reaching \(-0.03\cdot\mathrm{a}^{-1}\), representing 27.16% of the total. The contribution of stress events to vegetation health significantly exceeds that of other events, with areas where stress events contribute more than 50% accounting for 69.41% (Figure 10c,d). The contribution rates of stress events are the lowest in South China and the southern arid and semi-arid regions of northern China. Overall, except for R5, stress events contribute more than 50% to vegetation health across the country with R7 showing the highest contribution at 65.4%. Conversely, R5 exhibits the highest contribution from other events at 66.9%. The contribution rates of stress events across regions R1 to R9 are ranked as follows: R7 > R1 > R6 > R4 > R3 > R2 > R9 > R8 > R5. In R1, R6, and R8, stress events contribute up to 100% (Figure 10e).  
Figure 10: Contribution of stress and other events to vegetation health. (**a,b**) Slopes of stress and other events, (**c,d**) contribution rates, and (**e**) contribution rates of stress and other events across nine subregions.