esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Submerged Cavitating Jet Experiment
### Erosion Experiment  
In order to verify the practical performance of each nozzle, an erosion experiment was designed, and pure aluminum blocks were used as the objects for erosion, as shown in Figure 4. The nozzle was connected to the high-pressure hose, and the high-pressure water impacted the aluminum block after cavitation through the nozzle. The nozzle was fixed on the height adjustment clamp through which the height of the nozzle could be adjusted to achieve different standoff distances. The aluminum block was fixed by a clamp to avoid moving. Both the aluminum block and the nozzle were submerged in the water tank during the experiment. The output pressure of the pump was constant at 30 MPa during the experiment; that is, the water flow into the water tank was constant. By adjusting the tank outlet valve, the water outflow could be adjusted to be the same as the water inflow, therefore all nozzles were always at the same water depth, removing the influence of water pressure difference caused by different water depths on the actual effect of cavitating jet. The optimal standoff distance of the nozzle is between 30 mm and 50 mm [49; 50]. In order to compare the performance under different standoff distances, two groups of experiments were designed. For the first group, the aluminum blocks were eroded by the seven nozzles listed in Table 1 for ten minutes at a standoff distance of 30 mm, and three nozzles with better erosion effect were selected. The selected three nozzles form the second group, and the experiment was conducted at a standoff distance of 50 mm. The aluminum block was weighed before and after the experiment and the masses were recorded.
## 8 Phytoremediation  
Phytoremediation relies on the use of plants to mitigate the toxicity of pollutants. The extensive application of phytoremediation can be generally classified into seven mechanisms; phytoextraction, phytovoltailisation, phytodegradation, rhizdegradation, rhiziltration, phytostabilisation and hydraulic control based on the parts of plants involved and the effects on contaminants [67]. Remediation of organic pollutants are predominantly by degradation, containment and volatilisation [68]. The resource-rich root exudate of plants stimulates active microbial activity that in turns enhances the degradation of contaminants as previously described in microbe-algae consortia systems. In fact, the ability of higher plants with extensive fibrous root system to accumulate petroleum hydrocarbon independently has been well studied in tropical regions (i.e., legumes and grasses). These plants  
Figure 3: Proposed pathways for microbial catabolism of PAH (adapted [65]).  
were highlighted to have the capacity to absorb, immobilise and transform the organic and inorganic toxicants within the rhizosphere. Studies on non-leguminous and lower plants such as algae and moss are relatively limited; however, a few reports have validated algae as a potential degrader.  
[PERSON] et al. [69] suggested that eukaryotic algae are capable of biotransforming aromatic compounds sequentially into hydroxylated intermediates, leading to overall degradation of the pollutants. PAH biodegradation was tested on the microalgae _S. captricornutum_ showing 78% fluoranthene removal upon 7 days of treatment [70]. Besides the ability to uptake hydrocarbons in plant metabolism, Antarctic moss and algae demonstrated a high tolerance towards SAB fuel, which contrasts strongly with the high sensitivities reported for indigenous microbial communities and invertebrates [30]. The heterotrophic mode of algae to metabolise a diverse range of hydrocarbon has been suggested to be associated with the habitat where the Antarctic algae receive minimal irradiance below the ice sheet.  
Although biochemical mechanisms employed by these algae are not elucidated well, the first report of a purely algal biotransformation of diaryl ethers by [PERSON], [PERSON] and [PERSON] [71] proposed a possible mechanism involving mono- and di-oxygenase pathways in prokaryotic and eukaryotic microalgae. The hypothesis was supported by the formation of monohydroylated derivatives suggesting the use of cytochrome P-450 monooxygenase in the obligate presence of oxygen. Meanwhile, the dioxygenase pathway was implicated through the route via _cis_-dihydrodiols that resembled the fungus.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis
### Algorithm Verification and Analysis  
In the Earth's aerocapture scenario, we first conduct simulations regardless of parameter dispersions. The nominal results are shown in Figure 4, from which we can see that the spacecraft's velocity decreases from 11,000 m/s to about 7800 m/s through aerocapture, and the bank angle basically follows a saturation function profile. In addition, there is a bank reversal in about 200 s. The required total \(\Delta V\) is 64.67 m/s for this case, and the target apoapsis altitude and orbit inclination are well met. Based on the nominal results, we can further verify the method's performance by Monte Carlo simulation.
## 2 Literature Review
They employed two strategies for the local search--two-random swap and two-opt testing of the combined method with the benchmark instances--and demonstrated that such a hybrid method performed better than other existing DE algorithms. [PERSON] and [PERSON] [23] modified DE further by using greedy subtour crossover in the mutation operation and modified ordered crossover in the crossover operation. In addition, local search with only the two-opt strategy was used in the decoding process. In addition to DE, PSO was also used frequently in the VRP field. [PERSON] et al. [24] developed an advanced PSO to solve a VRP variant where the demands of customers were uncertain and demand distribution was undetermined, a special form of capacitated VRP (CVRP). They devised a new decoding method for the PSO and were able to obtain superior solutions compared with those from well-known studies using the same test instances. Another variant of the capacitated VRP, called the capacitated location-routing problem (CLRP), was also studied by [PERSON] et al. [25], in which PSO was combined with clustering and local search techniques to solve the problem. [PERSON] et al. [26] proposed the use of multi-adaptive PSO (MAPSO) with three different strategies to solve the VRPTW, where each strategy had a certain role in achieving optimal solutions faster, yielding promising results compared with those of other PSO variants. [PERSON] and [PERSON] [27] used hybrid PSO (HPSO) combined with simulated annealing (SA) to determine solutions for the multi-compart VRP with a time window (MCVRPTW), where each vehicle possessed multiple compartments, each one containing goods with the same characteristics. They tested the algorithm with benchmark instances, and the results showed that HPSO had better performance than normal PSO (PSO without SA). [PERSON] et al. [28] performed two types of PSO, namely discrete PSO (DPSO) and neural-like DPSO (NDPSO), on the periodic vehicle routing problem (PVRP). The PVRP deals with the tasks of finding an ideal customer service mode and determining optimal vehicle routing in accordance with the found mode. [PERSON] and [PERSON] [29] combined PSO and the artificial bee colony (ABC) algorithm to solve the VRP, which converged faster than individual algorithms (PSO and ABC) and also yielded better solutions. Lastly, [PERSON] et al. [30] employed PSO with self-adaptive inertia weight to solve the open VRP with time-dependent travel time (OVRPTD), a special VRP variant where vehicles do not have to return to the depot after finishing a delivery job and travel time is time-dependent, producing better solutions compared to those of PSO and the nearest-neighboring search (NNS) algorithm.
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 5 Discussion  
The \(p\)-value of the ADF test is one indicator of the stationarity of a time series. In the case of industrial equipment, some variables, such as vibration, would be stationary in ideal conditions. However, what normally happens is that vibration increases with increased workload or between maintenance interventions. The present research shows that analysis of the \(p\)-value may be a promising method to detect trends, or even outliers, in the vibration signals of three industrial pumps. The ADF test is conducted using a rolling window, thus avoiding direct repetition of the test on the same data points, reducing the likelihood of false positive inflation. Balancing false positives and false negatives is always challenging and the cost analysis of further domain-specific errors must be conducted before this approach is used in the industry.  
To the best of the authors' knowledge, this method has never been tried before in wood chip pumps. Nonetheless, the analysis of the \(p\)-value could have several advantages compared to other methods of detecting and predicting possible faults:  
* One advantage is that it is known that a \(p\)-value above 0.05 indicates the non-stationarity of the series. This threshold is easier to establish than fixing limits for each variable. So this could be a kind of universal approach to detect the non-stationarity of a variable in a given time window.  
Figure 11: \(p\)-value and VCP3 mean variation (15-day rolling average).  
Another advantage is that the ADF test is able to work with noisy data, simplifying the analysis process. The presence of noise in the data has some impact on the results but does not cause a catastrophic failure of the method.  
On the other hand, the method also has some drawbacks, as demonstrated in the results of the experimental work. One important drawback is that it is necessary to determine the best time windows, both for calculating the \(p\)-value and for noise filters, if they are applied, because different window sizes will produce different results and different numbers of possible alarms. Also, even though the \(p\)-value method may be universal for different types of equipment, the noise and quality of the data still require careful analysis and specific cleaning or treatment methods. An analysis of possible cointegration issues may also be necessary for ruling out the influence of external factors [27]. Table 8 shows the advantages and disadvantages of the proposed method, making its interpretation more straightforward.
## 2 Numeric Modeling of NPP Equipment
### Modeling with Tuned Mass Damper (TMD)  
#### 2.4.1 Mathematical Modeling of the TMD System  
A TMD is a substructure that serves as a vibration absorber for the main structure [50; 51; 52; 53; 54]. A TMD device consisting of a spring (with stiffness \(k\)), a mass (\(m\)) and a damper (coefficient \(c\)) is attached to a structure having stiffness (\(K\)), a mass (\(M\)) and a damping (\(C\)) to mitigate undesirable vibrations. In other words, TMD helps to reduce the response of the structure under the effects of seismic loading condition by increasing considerably modal damping in some first important modes. The simple explanation of the TMD device is described in Figure 6.  
In this study, in order to validate the effectiveness of the TMD device, the seismic response of the electric cabinet with and without TMD subjected to different groups of ground motion are analyzed and compared to each other.  
Figure 5: First three mode shapes of the cabinet: (**a**) Mode 1 (\(f=14.43 Hz,\text{ MPMR}=1.09\%\)); (**b**) Mode 2 (\(f=14.56 Hz,\text{ MPMR}=17.63\%\)); (**c**) Mode 3 (\(f=15.13 Hz,\text{ MPMR}=68.00\%\)).  
Figure 6: Schematic diagram of a tuned mass damper (TMD) attached to the cabinet.  
The specifications of a TMD device include its tuning, mass and damping ratio. The ratio between the fundamental frequency of the TMD \(\omega_{t}\) and the main structure \(\omega_{o}\) is defined as the tuning ratio \(f\), which expressed in the Equation (2).  
\[f=\omega_{t}/\omega_{o} \tag{2}\]  
Moreover, the mass ratio is defined by Equation (3)  
\[\mu=m/M \tag{3}\]  
where \(M\) is the generalized mass for a certain mode of vibration of the structure. The damping ratio of the TMD device is defined by Equation (4).  
\[\xi=c/2m\omega_{t} \tag{4}\]  
To reduce the response of the main structure under earthquake loading conditions, many studies were conducted to assess the effects of parameters \(f\) and \(\xi\) for a given \(\mu\). According to [PERSON] et al. [55], the optimum mitigation in the response of the main structure can be achieved when \(f\) is close to unity.
## 2 Materials and Methods
### EO Image Processing
#### 2.8.3 Affected Area Detection  
The sub-module of affected area detection aims to detect the affected areas using the images retrieved from the sentinel-2 parser as input. Using two different methods, this sub-module can detect both the areas affected by water, through the water change detector and the burned areas, through the burned area detector. The water change detector processes images from the sentinel-2 and outputs processed images and numerical data relating to flood disasters. Similarly, the burned area detector also processes images from the sentinel-2 sub-module and extracts processed images and numerical data relating to fire disasters.  
Both modules provide the processed images as output, depicting the affected areas with the use of additional layers over the initial image and numerical data including the percentage of the affected area. The images and the numerical data resulting from this analysis are used for the composition of the final article. The images can be included in the final article, while the numerical data will be used within the context of the generated text, aiming to provide additional information to the user, which in other cases might not be feasible to be retrieved.
## 4 Experimental Results of Active Localization in a Reverberant Environment
### Localization Performance in a Reverberant Environment  
Figure 14 depicts the energy maps obtained from the experimental results. Case 1 shows the test results when the PVC pipe was considered as a silent intruder, and Case 2 shows the results when a person is the silent intruder. Each image shows the intruder position using relative power values (dB). The square marker is the actual position and the cross marker is the estimated position. To analyze the experimental results in Figure 14, we compared the estimated position results with those in Table 1, which lists the simulation results of the reverberation environment. In Table 1, when examining the results of \(\alpha\) greater than 0.7, which is the range in which the active localization method operates, no error of angle was observed and the error of distance was up to 19% (distance error 0.38 m). The experimental results of the reverberation environment in Figure 14 indicate that the angle had no error, and the error for distance was within 6.5% (distance error 0.13 m). Therefore, the proposed active localization method can be implemented if the operating conditions of Equations (22) and (23) are satisfied, as discussed in Section 3.2. However, the position detection results of Case 2 shown in Figure 14 indicate an increased error compared with the results of the PVC pipe. To analyze this, the results in both an anechoic chamber and a classroom are summarized quantitatively in Table 4 as the error between the actual and estimated values of each experimental configuration. These position errors represent angle and distance errors. The results of the localization performances are compared in terms of the type of silent intruder (a PVC pipe or a person). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Position** & **EDT (ms)** & **RT20 (ms)** \\ \hline  & 1 & 8.1 & 24.0 \\  & 2 & 2.7 & 20.0 \\ In a microphone & 3 & 2.0 & 11.6 \\ array & 4 & 2.0 & 5 \\  & 5 & 2.1 & 15.5 \\  & 6 & 2.6 & 20.1 \\  & 7 & 9.0 & 23.5 \\ \hline  & 8 (A) & 10.5 & 22.7 \\ In control space & 9 (B) & 22.8 & 23.5 \\  & 10 (C) & 13.2 & 23.6 \\  & 11 (D) & 12.2 & 25.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of room acoustic parameters measured in the control space as in Figure 13 at positions shown in Figure 10a. Figure 14: Energy maps of Case 1 and Case 2 to verify the localization performance in a classroom: The Case 1 of a PVC pipe in (**a**) position A; (**b**) position B; (**c**) position C; (**d**) position D; Case 2 of a person in (**e**) position A; (**f**) position B; (**g**) position C; (**h**) position D. The square marker is the actual position and the cross marker is the estimated position.
## 6 Computational Results  
From the above problems, we select two, TSP and PFSSP, that are NP-hard in the decision versions [69; 70].  
To give an idea of the cardinality of the search space of these permutation problems, we present a few factorials as follows:  
\(10!=3628800\approx 3.6\times 10^{6}\)  
\(20!=2432902008176640000\approx 2.4\times 10^{18}\)  
\(30!=26525285981219105863630848000000\approx 2.6\times 10^{32}\)  
\(40!=81591528324789773434561126959611589427200000000\approx 8.1\times 10^{47}\)  
\(50!\approx 3\times 10^{64}\)...  
\(1001\approx 9.3\times 10^{157}\)  
The traditional approaches to such problems are based on computations using heuristic methods [71; 72] for large instances such as genetic algorithms, simulated annealing, tabu search, differential evolution [73], firefly algorithm, particle swarm optimization, and ant colony optimization. Then, statistical tests are applied to examine at a certain significance level (e.g., \(\alpha=0.05\)), to what extent the mean value of the results obtained by different methods and different settings of their parameters at a larger number of runs is the same or different (and, therefore, one of the methods gives better results). For the \(t\)-test, we assume that the sets of values have a normal distribution. However, this assumption may be false, and then one of the non-parametric tests, such as the Wilcoxon test, must be used.  
Since, given the validity of the No Free Lunch Theorem [74; 75], one should not expect a general conclusion that any of the heuristics for each problem instance gives better results than other heuristics.  
In this paper, we do not explore heuristics using instead a mixed integer programming model with software tools built as solvers in the GAMS environment [76; 77] to find an exact solution by deterministic computation.  
Statistical evaluations are, therefore, meaningless here. What can be said, however, is that the power of this software today is considerably greater than it was 20 years ago, when, in our experience, for a problem with a complexity of \(\mathcal{O}(20!)\), the system ended up with a runtime error and the message \"insufficient space to update U-factor...\". The performance of GAMS has been steadily increasing over the years, although the source code of the solvers is not freely available, from [78] it can at least be seen that it includes, among others, CPLEX, GUROBI, Lindo, and the results of the work of academic departments of Princeton University, Stanford University, and Zuse Institute Berlin. Today, GAMS calculates the exact solution for PFSSP with 20 jobs on a laptop with Intel(R) Core(TM) i5-10210U CPU @ 1.60 GHz 2.11 GHz processor, 8 GB operational memory and 64-bit operating system in less than 3 min, as shown in the following subsection.  
Of course, with a computer of better technical parameters for the same time limit we get results for larger instances of the problem, but it seems to be better to use a heuristic beyond this boundary, e.g., our GA 'war elimination' modification [79].  
Since PFSSP has a more complex model than TSP, we start with it and include its complete GAMS code.
## 3 Results and Discussion
### Effect of Thermal Annealing on Hysteresis
With increasing annealing time, the slope in the Bode plot increased, meaning that the electrode was becoming more capacitive (hence, the kinetic hysteresis of the 4 h device will be the strongest). However, the change from a Faradaic to non-Faradaic response explains the different strength of the kinetic hysteresis at high scan rates; still, it does not explain the origin of the non-kinetic hysteresis at low scan rates. The pronounced non-kinetic hysteresis (\(\psi\approx 3\) V) only occurred in the device with a capacitive character, while the non-annealed device was primarily resistive and only showed very low non-kinetic hysteresis (\(\psi=0.51\) V), the origin of which still  
Figure 3: (**a–c**) Scan rate-dependent transfer curve measurements for OECTs where the PEDOT:Tos film is annealed for 0, 1, and 4 h, respectively. (**d**) Hysteresis strength \(\phi\) plotted against the scan rates. The kinetic hysteresis strengths (at higher scan rates) increase with increasing annealing time. Non-kinetic hysteresis appears fastest in the 1 h-annealed film. Further, the 1 and 4 h-annealed devices show similar non-kinetic hysteresis (\(\phi\approx 3\) V), whereas the non-annealed device shows rather small non-kinetic hysteresis (\(\phi=0.51\) V). Here, \(\mathrm{W,L,g=150\,\mu m},150\,\mu\mathrm{m},50\,\mu\mathrm{m}\); \(\mathrm{V_{D}=-0.2\,V}\); the film thicknesses for 0, 1, 4 h annealing are \(103,119,141\,\mathrm{nm}\), respectively. remains elusive. The CPE in the impedance spectra tells us that the annealed PEDOT:Tos devices possessed a distribution of states at the interface between the electrolyte and the OMIEC, which is possibly connected to the appearance of the non-kinetic hysteresis. This transition in the impedance response of the PEDOT:Tos film is connected to the annealing-induced polymerization. We speculated that this polymerization process might trigger a demixing of the two components (PEDOT and Tos), similar to PEDOT:PSS [27], where, e.g., PSS-rich domains might act as deep trap states (as described by the CPE). However, to analyze this in more detail, further studies on the micro- and nano-scale formation of such films are needed.
# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 3 Experiments and Results
### Experiments
#### 3.1.2 Data Preparation  
The selected images were randomly assigned to training (80%) and validation (20%) sets. To mitigate the limited data volume and enhance model robustness, we applied preprocessing and augmentation techniques, notably rotating the images between \(-45^{\circ}\) and \(45^{\circ}\) at \(5^{\circ}\) intervals. This process expanded our dataset to 3800 images, enhancing the diversity and generalizability of our training set.