esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis for the LLB Method
### Analysis Results for the LLB Method  
Figure 7 shows the numerical analysis results of applying the LLB method in three dimensions. The images show the rock (grey), large-diameter uncharged hole (grey), explosive (blue), and stemming (yellow). These results show the blasting process over time; the elements deleted by detonation of the explosives are shown in deep black.  
The explosive materials detonate after 0 s, and rocks around the explosives are fragmented due to explosive energy. At the same time, a compressive stress induced by the detonations propagates through the rock. When the propagated compressive stress reaches the large-diameter uncharged hole (free surface), a tensile stress wave is generated due to the reflection of the compressive stress (Hopkinson effect). After 0.5 ms, rocks near uncharged holes begin to fragment due to their vulnerability to tensile stress. Thereafter, the extent of failure near the large-diameter uncharged hole increases. In addition, tensile failure was observed at the tunnel face due to the tensile stress wave generated by the reflection of the compressive stress wave at 2.0 ms.  
Theoretically, the LLB method is more advantageous for long hole blasting due to the large-diameter uncharged hole formed at a greater depth than normal blasting holes. However, this analysis was focused on rock fragmentation in the area charged with explosives and is therefore limited to simulating flying rocks by gas pressure. Although a large-diameter uncharged hole is situated at the center of the explosives, large-scale rocks may not be sufficiently accommodated. In other words, the blast-induced high-pressure gaseous product may fail to sufficiently push away the remaining rocks.
## 5 Application Results and Analysis
### Measured Signal  
The aforementioned PD online measurement system is installed in a hydro-plant. In order to further examine the denoising performance of the proposed scheme, it is applied to the measured PD signal from the on-line measurement system performed on hydro-generators in the hydro-plant, and the denoising results are compared with those of EBWS, SNRBWS and NewEBWS by visual inspection.  
(1) Case 1  
The first case is the PD signal measured on a hydro-generator with rated parameters of 20 kV, 50 Hz and 460 MW, as shown in Figure 13a. The whole noised signal is denoised by EBWS, SNRBWS, NewEBWS and the proposed method, the denoising results are shown in Figure 13b-e, respectively. As can be seen, only the proposed method extracts nearly all pulses, which is very meaningful for the accurate phase resolved partial discharge (PRPD) patterns and PD pattern recognition, while the other three methods all produce different degrees of pulses omission. Besides, the proposed method causes the minimum amplitude errors by comparing the amplitude of the pulses in original measured signal and denoised signals, while the SNRBWS gets the worst denoising results, specifically in the most missed pulses and the maximum pulse amplitude errors.  
Figure 12: The times percentages of each method obtaining the best result for \(s_{3}\).  
Figure 13: Denoising results of measured PD signal immersed in noise (**a**) original measured signal (**b**) EBWS (**c**) SNRBWS (**d**) NewEBWS (**e**) proposed method.  
Case 2  
The second case is from another hydro-generator in the same plant with the same parameter.as shown in Figure 14a. The results comparison is shown in Figure 14b-e. From the results it can be found that the EBWS, SNRBWS and NewEBWS produce more pulses loss than the proposed method. Besides, the SNRBWS causes larger amplitude error especially for the pulses with small amplitude, for the SNRBWS is mainly focused on the pulse with maximum amplitude at each scale. In general, the proposed method outperforms other three methods in the denoising of the measured PD signal.
## 3 Experimental Results and Discussion
### HMI Dashboard
An example of the historical trend display is illustrated in Figure 12, which shows the historical trends in the air temperature, leaf, and background temperatures of Plant-1 and Plant-2. In the figure, the range of the displayed trend is three hours, where the SCADA program can adjust the range. The trend display provides valuable information to the user/operator about the trends in the monitored parameters. The historical data saves a group of data into HMI internal memory. The historical data group can be displayed in the table format in the HMI. It consists of four groups:  
1. Historical data group of soil parameters of Plant-1. It displays the historical data of soil pH, moisture, temperature, EC, and NPK of Plant-1. 2. Historical data group of soil parameters of Plant-2. It displays the historical data of soil pH, moisture, temperature, EC, and NPK of Plant-2. 3. Historical data group of air parameters and leaf temperature of Plant-1. It displays the historical data of air temperature and humidity, an average temperature of the thermal image, threshold, leaf, and background temperatures of Plant-1. 4. Historical data group of leaf temperature of Plant-2. It displays the historical data of an average temperature of the thermal image, threshold, leaf, and background temperatures of Plant-2. Figure 11: Thermal camera data display. An example of the historical data display is illustrated in Figure 13, which shows the historical data of soil pH, moisture, temperature, EC, and NPK of Plant-1. The figure shows that the historical data are saved at every one-minute interval. The SCADA program can adjust this interval. Furthermore, the table can be saved into a CSV file for offline analysis. Theses historical data represent an essential feature in the sensor data analysis, as described in the next section.
###### Abstract  
Infrared (IR) and visible (VIS) image fusion enhances vision tasks by combining complementary data. However, most existing methods assume normal lighting conditions and thus perform poorly in low-light environments, where VIS images often lose critical texture details. To address this limitation, we propose VCAFusion, a novel approach for robust infrared and visible image fusion in low-light scenarios. Our framework incorporates an adaptive brightness adjustment model based on light reflection theory to mitigate illumination-induced degradation in nocturnal images. Additionally, we design an adaptive enhancement function inspired by human visual perception to recover weak texture details. To further improve fusion quality, we develop an edge-preserving multi-scale decomposition model and a saliency-preserving strategy, ensuring seamless integration of perceptual features. By effectively balancing low-light enhancement and fusion, our framework preserves both the intensity distribution and the fine texture details of salient objects. Extensive experiments on public datasets demonstrate that VCAFusion achieves superior fusion quality, closely aligning with human visual perception and outperforming state-of-the-art methods in both qualitative and quantitative evaluations.  
Keywords:image fusion; adaptive low-light adjustment; local detail; saliency-preserving; multi-scale decomposition +
Footnote †: journal: applied sciences
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 5 Discussion  
In this study, irrigated areas were mapped over a study site in Catalonia, Spain using Sentinel-1 (S1) SAR time series and optical NDVI time series by applying two classification approaches: Random forest (RF) and a convolutional neural network (CNN). The potential of each data type was investigated, and the combined use of both data types was also presented for both classification approaches.
## 5 Measurements and Experimental Results
### Experimental Results of Deep Learning  
As shown in Table 1, the accuracy increases as the learning rate decreases. The three learning rates set in this research are \(10^{-4}\), \(10^{-5}\) and \(10^{-6}\).  
The accuracy assesses the number of correctly predicted images from the deep-learning model with respect to the total number of images. The validation (Val) shown in Figure 21 represents the validation error based on the validation data set. The validation set has been used to estimate the predictive error of the selected model. Similarly, training in Figure 21 represents the training errors created from the training data sets. When the correct label of a testing image is not within the five highest possible labels that the model predicts for this testing image, the top 5 err will be high. Similarly, the top 1 err indicates that the correct label of a testing image is not the label that the model predicts for this testing image. A smaller top 1 err and top 5 err indicates better results. As shown in Table 1, the accuracy increases when the learning rate decreases from \(10^{-6}\) to \(10^{-4}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning Rate** & **Spending on Training** & **Spending on Testing** & **Accuracy** \\  & **Time (s)** & **Time (s)** & **Time (s)** \\ \hline \(1\times 10^{-4}\) & 52,798.313 & 19.36 & 89.77\% \\ \(1\times 10^{-5}\) & 56,821.985 & 20.826 & 88.06\% \\ \(1\times 10^{-6}\) & 55,571.778 & 19.451 & 78.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time for deep learning of training and testing data (seconds).  
Figure 20: (**a**) Bacteria type 1 image; (**b**) bacteria type 2 image; (**c**) allergy image; (**d**) dandruff image.
## Materials and Methods
### Protein Expression and Purification  
LB broth was inoculated with _E. coli_ BL21(DE3) containing the recombinant vectors and incubated O/N with an agitation of 180 rpm. O/N cultures were diluted 1:10 in fresh LB broth, incubated until the OD\({}_{600}\) reached 0.5-0.8 and induced with 1 mM Isopropyl \(\beta\)-D-1-thiogalactopyranoside (IPTG)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strains/Plasmids** & **Species/Purpose** & **Reference** \\ \hline
**Strains** & & & \\
10-beta & _E. coli_/cloning strain & NEB \\ BL21(DE3) & _E. coli_/expression strain & Novagen \\
**Plasmids** & & & \\ pET30a & Expression plasmid & Novagen \\ pUC18::_gfp_ & GFP amplification & Clontech/Takara \\ pET30:_gp221_ & Test PlyB221 lytic activity & This study \\ pET30:_gpf_(linker)::_gp221_cbd_ & Test PlyB221 CBD binding activity & This study \\ pET30:_gp32_ & Test PlyP32 lytic activity & This study \\ pET30:_gpf_(linker)::_gp32_cdb_ & Test PlyP32 CBD binding activity & This study \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strains and plasmids used for cloning and expression experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Target** & **Primer Name** & **Sequence (\(\mathbf{5^{\prime}}\rightarrow\mathbf{3^{\prime}}\))** \\ \hline _gfp_ & GFP\_EcoRL\_F & TTCCGAATTCAAAGGAGAAGAACTTTTCACTGGAG \\  & GFP\_EagL\_linker\_R & TTCGGCCGTCCACTACTGCATCCATACCTTGTAGAGCTCCTACCACC \\ _gp221_ & gp221\_Ncol\_F & TATCCATGGTGCAATGTCTTAGATACTTTAATCA \\  & gp221\_Ncol\_R & TATTTCCGAGCTCTCTCCAAAAGGTTGATGATG \\ _gp221\_cbd_ & gp221\_CBD\_Eag\_F & **ACGGCGCGCGCGCGCGCGCGCAGAGACACT \\  & gp221\_Ncol\_R & TATTTCTCGGACTTCCCATCAGAAGTGTATG \\ _gp32_ & gp32\_Ncol\_F & TATCCATGGAAAAAGATTTTAGATATTTCACACC \\  & gp32\_Ncol\_R & ATTACCTGAGTTAAAGATTATTACGTGTACCCA \\ _gp32\_cbd_ & gp32\_Ncol\_R & **AAACG(Sigma). _E. coli_ cultures were incubated during 6 h at 28 \({}^{\circ}\)C and 180 rpm to allow protein expression. The cells were then centrifuged (4000\(\times\)_g_, 4 \({}^{\circ}\)C for 15 min) and the pellet stored at -20 \({}^{\circ}\)C. The pellet was thawed on ice for 30 min and resuspended in lysis buffer (50 mM NaH\({}_{2}\)PO\({}_{4}\), 300 mM NaCl, 10 mM imidazole pH 8) supplemented with a protease inhibitor cocktail (1x, SIGMAFAT\({}^{\text{TM}}\) Protease Inhibitor Cocktail Tablet, EDTA-Free, Sigma) and 1 mg/mL lysozyme (Sigma). After incubation for 30 min at 10 \({}^{\circ}\)C to lyse the cells, three units/mL of Benzonase Nuclease (Sigma) were added to reduce viscosity (15 min incubation). The lysate was centrifuged (30 min, 4 \({}^{\circ}\)C at 10,000\(\times\)_g_) and the supernatant, containing the soluble proteins, was filtered on 0.45 \(\upmu\)m (VWR, Oud-Heverlee, Belgium). The recombinant proteins were then purified on Ni-NTA column (Qiagen, Hilden, Germany) according to the manufacturer's recommendations. The purity was assessed by SDS-PAGE and the recombinant proteins were dialyzed O/N with agitation against PBS supplemented with 300 mM NaCl. The protein concentration was determined by the Bradford assay [30].
## 1 Introduction
Any shift in the ratio of the amplitudes of the first and second harmonics leads to fluctuations in the higher statistical wave moments, wave symmetry and the symmetry of orbital near-bottom velocities, which directly affects the processes of cross-shore sediment transport [23]. As shown in [24], periodical fluctuations in the biphase are the main reason for the impossibility of the unambiguous parameterization of changes in wave symmetry in the coastal zone. The hypothesis about spatially periodic energy exchange between the first and second non-linear harmonics being one of the mechanisms of formation of underwater sand bars was already stated in [25]. This was later confirmed by comparing real and modeled (monochromatic waves with the parameters of a typical storm were used) underwater bottom profiles for the Georgian Bay of Lake Ontario. Similar results were obtained for two selected underwater profiles for the Lake Huron coast and on the Atlantic coast of Canada [26]. However, this hypothesis has not been carefully tested with field data, which include simultaneous measurements of wave parameters and the evolution of the underwater profile. It also remains unclear what happens with the coast profile if it already has underwater bars. For example, will the spatial periodic energy exchange between harmonics contributing to the formation of new morphological forms, will it support the existing ones, or will it be a force that moves the existing bars? On the other hand, it is considered as a well-known fact, confirmed in many laboratory experiments, that \"strong\" storms erode the shores and underwater profile, while \"small\" or \"weak\" storms, on the contrary, restore them (for example, [27]). However, the exact mechanism of the underwater profile restoration in relation to the changes in wave regimes and scenarios of wave transformation is unknown. The main goals of this work are to investigate: (1) how the change in the amplitudes of harmonics and the phase shift between them affects the wave transport of sediment, (2) whether there are characteristic features, and (3) what happens when the scenario of nonlinear wave transformation changes. Despite the success of numerical modeling in engineering practice, semi-empirical formulas are still used and, as was noted in [5], the verification of the applicability of various engineering formulations to describe the physical processes remains very important. Therefore, at the same time, we will verify one of the main widely used Bailard's formulations [8] for the ability to adequately reproduce the effect of a non-linear wave transformation scenario on cross-shore sedimentation processes.
## 4 Case Study
### Performance Evaluation Using Fuzzy MCDM Model in the Construction Project  
The proposed fuzzy TOPSIS model of fall protection system decision-making is employed to select the optimal fall protection plan. The involved experts shown in Table 4 reviewed the performance of all alternatives and assessed each criterion for each hazardous working area. The linguistic variables were obtained from the respondents, such as \"very low\", \"fair\", \"medium high\" and \"very high\". In order to transform the linguistic variables into quantitative expressions, the transformation rules for criteria performance need to be set. Therefore, the fuzziness of human thoughts is incorporated into the decision-making model through linguistic evaluations, which is illustrated in Table 6. Thus, the linguistic rating judgments on performance of criteria of each alternative are performed by the respondents. The results of initial fuzzy rating are averaged to generate the accurate performance values of all criteria in each alternative. The fuzzy evaluation data for Alternative 1 are illustrated in Table 7.  
As given in Table 8, based on the normalized weights and the fuzzy matrix obtained previously, the fuzzy-weighted normalized matrix is calculated using Equation (12). Then, the fuzzy ideal positive solutions (\(A_{j}^{+}\)) and fuzzy ideal negative solutions (\(A_{j}^{-}\)) for the sub-criteria for each fall protection plan alternative can be determined through Equations (13) and (14), as illustrated in Table 9. \begin{table}
\begin{tabular}{c c} \hline
**Linguistic Terms** & **Corresponding Triangular Fuzzy Number** \\ \hline Very low (VL) & (0,0,1) \\ Low (L) & (0,1,3) \\ Medium low (ML) & (1,3,5) \\ Fair (F) & (3,5,7) \\ Medium high (MH) & (5,7,9) \\ High (H) & (7,9,10) \\ Very high (VH) & (9,10,10) \\ \hline \end{tabular}
\end{table} 
Table 6: Transformation rules of linguistic variables of decision makers to fuzzy numbers for criteria performance of planning alternatives.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 2: What Are the Features of Blockchain That Could Benefit Education?
#### 4.2.2 Transaction Verification  
In blockchain, all proposed transactions are verified according to agreed rules by the active participants of the network before being included in a block. As a consequence, the warehoused data can be considered trustful.  
In the educational field, organizations issue, manage and revoke information that should be trustful, tamper-proof stored, and verifiable to avoid forgery; thus, this blockchain feature is a key component to achieving this. As a consequence, all proposals reviewed take into account this characteristic at some level.  
For example, [PERSON] et al. [31] develop a single platform to record all of the academic achievements of students and make them trustfully available to several educational entities.
## Appendix A Supplementary Information  
Our study showed that red light in autotrophic condition proved to be an affordable and effective option to increase _P. tricornutum_ biomass and lipid accumulation. Thus, in the second series of experiments, we further explored the effect of red light on _P. tricornutum_ biomass and lipid accumulation in autotrophic conditions. Several studies had reported that light color and intensity bring changes in the photon flux density, which have a strong impact on the growth and metabolic pathways [31; 56]. Light shift from one spectrum to another will change the light ambience and culture environment, which can trigger different photoreceptors to activate signaling pathways allowing a better conversion of light energy. However, the exact growth phase on which to exert a light shift is unknown. Thus, we investigated the effect of red light shift on _P. tricornutum_ growth during different growth phases under batch cultivation for 10 days (Appendix A--Figure A1). Specifically, the effect on growth was studied using optical density for six conditions. The impact of red light shift to a full spectrum during different growth phases i.e., lag, exponential, and stationary are labeled as RT1, RT2, RT3; RS (Table 2), along with continuous red light (R), was investigated. RT2 and RT3 were found to be less effective at promoting growth, whereas RS was significantly better to promote _P. tricornutum_ growth (Appendix A--Figure A1). The red light shift treatment during the specific growth phases--lag, exponential, and stationary phase (RT1, RT2, and RT3)--was not an efficient method to increase growth (Figure A1). Thus, these treatments are not interesting for our investigation, since we aimed to enhance the overall biomass and total lipid count. Similarly as observed in Figure 2, 2 lag is observed between day 4 and day 7 (exponential to stationary) where it is not clear whether cell growth continued before slowing down (Figure A1). However, RT1, RT2, and RT3 could be interesting parameters to consider for studying _bonafide_ metabolic pathways in diatoms once there is more information on the characterization of these photoreceptors such as phytochromes. Cell morphology is sometimes indicative of changes in response to stress. Three morphotypes in _P. tricornutum_ culture have been reported: oval, fusiform and triradiate [48]. Microscopic analyses (Figure 4c,d) revealed a high occurrence of fusiform cells, a limited number of triradiate-shaped cells, and no appearance of the oval form in all of our growth conditions. However, our study showed the presence of a limited number of triradiate cells (Appendix A--Figure A2). Figure A1: Analysis of _P. tricornutum_ growth determined using optical density at 680 nm in different light conditions: red light during the exponential phase and then shifting in the stationary phase (RS), red light exposure during the lag phase (RT1), red light during the exponential phase (RT2), and red light during the stationary phase (RT3). Figure A2: Light microscope images of _Phaodactylum tricornutum_ in different light conditions (**A**) in white light and (**B**) in red light.
## 7 Methodology and Experimentation
### Reinforcement Learning Agent Results  
After the training process described in Section 7.3, we compared the job completion rate with other algorithms using the same simulation environment. The results, as shown in Figure 12, demonstrate that a trained model can quickly adapt to environmental variations and surpass the performance of human-designed job completion heuristics. Notably, around iteration 50, the model in the environment mentioned in Section 7.3 outperforms the best human-designed heuristic regarding job completion, and it continues to improve in this metric in subsequent iterations. One of the challenges in training a reinforcement learning agent for this use case is that the model needs to learn from multiple scenarios to generalize effectively, which is both time-consuming and resource-intensive. However, our objective in this paper is to provide evidence that, despite being trained in different environments, the model can quickly adapt to new ones and excel in the metrics mentioned.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline _Job Set_ & **Random** & **BPA** & **eSEAS** & **RelBPA** & **ReleSEAS** \\ \hline _1500_ & 73.68 & 109.30 & 111.60 & 109.65 & 112.23 \\ _36,000 (full set)_ & 860.52 & 1588.68 & 1604.73 & 1868.67 & 1934.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average performance in GIPS across all simulations using random waypoint.  
Figure 11: Average performance in GIPS for each simulation using random waypoint to generate connection events and the full set of jobs.  
Figure 10: Average performance in GIPS for each simulation using random waypoint to generate connection events and the 1500 jobs set.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline _Job Set_ & **Random** & **BPA** & **eSEAS** & **RelBPA** & **ReleSEAS** \\ \hline _1500_ & 64.36 & 67.05 & 67.18 & 82.03 & 86.25 \\ _36,000 (full set)_ & 231.69 & 303.27 & 305.30 & 935.17 & 1029.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average performance in GIPS across all simulations using random walk.  
Using the obtained model, we trained new and specific environments using the weights obtained in the first training phase, transferring the learned weights to the new environment. The results show that this new RL agent converges on the job completion rate for each new simulation. Figure 13 details how the agent converges to a solution in each training iteration. The new environments, as said in Section 7.3, were new random waypoint environments, with 1500 JOBS and 25 devices.  
Figure 12: RL convergence for job completion for each simulation using random walk compared to other algorithms in the 1500 jobs set.  
Figure 13: RL Convergence for job completion for each simulation using transfer learning in a random waypoint environment compared to other algorithms in the 1500 jobs set.