esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Predictive Model to Detect Water Quality and Pollution
###### Abstract  
The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation. Typical water quality assessment requires continuous monitoring of water and sediments at remote locations with labour-intensive laboratory tests to determine the degree of pollution. We propose an automated water quality assessment framework where we formalise a predictive model using machine learning to infer the water quality and level of pollution using collected water and sediments samples. Firstly, due to the sparsity of sample collection locations, the amount of sediment samples of water is limited, and the dataset is incomplete. Therefore, after an extensive investigation on various data imputation methods' performance in water and sediment datasets with different missing data, we chose the best imputation method to process the missing data. Afterwards, the water sediment sample will be tagged as one of four levels of pollution based on some guidelines and then the machine learning model will use a specific technique named classification to find the relationship between the data and the final result. After that, the result of prediction can be compared to the real result so that it can be checked whether the model is good and whether the prediction is accurate. Finally, the research gave improvement advice based on the result obtained from the model building part. Empirically, we show that our best model archives an accuracy of 75% after accounting for 57% of missing data. Experimentally, we show that our model would assist in automatically assessing water quality screening based on possibly incomplete real-world data.  
a 2022143244320221432  
on water and sediment quality. This research is an extension of [3] which evaluated the water and sediment dataset collected in Australian ports located in six different areas. That research tested the water sediment samples and indicated the main pollutants. Several pollution indexes were calculated and pollution indexes helped to identify the water sediment quality and degree of pollution. While facing a large number of possible pollutants, it is almost impossible to monitor all metals and make regulations [4]. Those indicate that distinguishing the main pollutants will assist in pollution regulation.  
In this paper, we tried to find the method to make an improvement for the previous model. Using an Artificial Intelligence learning-based approach helps to identify the essential pollutant in a data-driven approach, which helps to assist the pollution regulation. We tested the water and sediment samples and identified the primary pollutants. Several pollution indexes were used to determine the water and sediment quality and degree of pollution. We empirically combined different water and sediment sources to learn the correlation between various sediment content levels and their contribution to water pollution. Experimentally, we show that our model can achieve the state-of-the-art predictive capability to identify highly polluted ports based purely on collected data.
## 7 Conclusions and Further Work
### Underwater Demonstration  
The demonstration tank consists of a sample 2.5 m long, Schedule 40, 6-inch diameter carbon steel pipe which is placed through a tank enclosure as shown in Figure 22. The tank is supported by a steel frame and the pipe is supported by two pipe supports. The section of the pipe which is encapsulated by the tank is sealed using O rings and vacuum lubrication. The pipe ends are closed using flanges where one end includes a valve to allow water to be filled within the pipe. The tank consists of a tap at the bottom panel to allow the water to be drained out.  
With the completion of the tank assembly, the HPUT collar is placed underwater as shown in Figure 23. Due to the length of the pipe, a single UGW collar is placed at a pipe end outside of the tank for pulse-echo configuration. The power amplifier hardware is used to drive the marinized HPUTs, and the Teletest unit [23] is used to pressurize the UGW collar and monitor the pipe.  
Figure 22: Assembly of the integrated system demonstration tank.  
The development of a testing tank can be utilized for further characterization and optimization of the ultrasonic cleaning technique for underwater applications. Examples of improvement include the modification of the cleaning transducers by material selection, addition of a sonotrode, and improvement of the ultrasonic cleaning system hardware to drive the transducer excitation.  
[PERSON] is the lead author and main contributor to the paper. She conducted the experimental investigation and has written the original draft of the manuscript and carried out the literature review and formal analysis of the data collected. [PERSON] has contributed to the conceptualization to carry out this investigation as well as supervision of the research, project management and coordination of the research activity, visualization of the manuscript and, furthermore, contributed toward reviewing and editing the original manuscript. [PERSON] has contributed plenty as the academic supervisor of the PhD research, also contributing toward funding acquisition and further reviewing and editing of the original paper. [PERSON]-[PERSON] has contributed by supervision of the PhD research, reviewing of the original paper, and also acquiring funding to carry out the current investigation.  
This research was funded by Innovate UK, grant number 102491.  
The authors would like to give their appreciation to [PERSON] and [PERSON] of Brunel University London for their contributions toward this work.  
The authors declare no conflict of interest.
## 2 Numerical Model Description
### Four Variable Valve Train Configurations  
Four different variants, as shown in Table 3, stood out as candidates for simulation testing: (1) \"Base\" case, for comparing with other cases; (2) CIC V1 for a recompression strategy that reduced valve lifting and duration; (3) CIC V2 for recompression that reduced valve lifting and duration about the exhaust valve; and (4) CIC V3 for added post fuel injection based on CIC V2, as shown in Table 3. With the exhaust recompression valve strategy, the cylinder charge temperature is controlled by trapping the hot exhaust gas from the previous engine cycle. This is done by closing the exhaust valve early during the exhaust stroke while opening the intake valve late in relation to the exhaust valve closing timing. The crank-angle period when both intake and exhaust valves are closed around the engine top dead center (TDC) is defined as the negative valve overlap (NVO).  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Unit** & **Specification** \\ \hline Bore x Stroke & mm & 88 \(\times\) 90 \\ Displacement & cc & 499 \\ Valve per cylinder & - & 4 (2 intake and 2 exhaust) \\ Compression ration & - & 16 \\ \hline \multirow{2}{*}{Intake valve} & \multirow{2}{*}{CAD} & IVO & bTDC 10 \\  & & IVC & aBDC 28 \\ \hline \multirow{2}{*}{Exhaust valve} & \multirow{2}{*}{CAD} & EVO & bBDC 54 \\  & & EVC & aTDC 4 \\ \hline \end{tabular}
\end{table}
Table 2: Specification of R-engine used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Valve Profile** & **Description** \\ \hline
**Base** & **Valve Profile** & **All valve lift 8 mm** \\
**CIC V1** & **Crank Angle (deg)** & **Recommend** \\
**CIC V2** & **Crank Angle (deg)** & **+ All valve lifting 6 mm** \\
**CIC V3** & **Crank Angle (deg)** & **+ Negative valve overlap** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four variable valve train configurations.  
Figure 2 shows the schematic diagram of this analysis process. In order to study the effects of internal EGR by variable exhaust valve actuation, a methodology was first developed to investigate recompression internal EGR, wherein a variable exhaust valve timing mechanism is used to transfer exhaust gas from one cycle to the next, just as in the valve strategies referred to as \"Base\" case, CIC V1, and CIC V2. The additional step in the process begins with CIC V2 as normal and then adds three different timings of post fuel injection.
## 4 Results and Discussion
### Swelling Ratio  
All of the PEC films showed a fast swelling rate (Figure 4) that can be attributed to their large capacity (macro pores in the film), which let the films absorb large amount of water [57]. TM-PEC %0.4 film had a final lower swelling ratio in comparison with that of TM-PEC %1. The reason may be  
Figure 3: FTIR results.  
because TM-PEC %1 has less crosslinked functional groups, so there is less resistance against expansion and penetration of water molecules into its structure. While TM-PEC %0.4 has more functional groups leading to a more confined structure and less swelling ratio. After 5 h, the swelling ratio of both PEC films reached a relatively constant maximum value. Both structures had a gradual weight loss and deswelling. Due to the incomplete reaction, there are some unreacted alginate and chitosan molecules in the solution, which are solved from the membrane into water and can be responsible for some weight loss of the chitosan-alginate PEC membranes after swelling.
## 5 Simulation and Discussion
### Intraday MPC Rolling Optimization Simulation Analysis
#### 5.3.3 MPC Structure Evaluation  
Taking Case B, the most complex scenario in this study, as an example, the solution speed is shown in Table 9. From the listed data, it can be observed that the intraday solution time for the MPC structure reaches the order of seconds, while the day-ahead solution time is at the minute level. After decoupling the timescales, the MPC rolling optimization strategy is faster than the overall DRO optimization, enabling real-time optimization and thereby improving scheduling efficiency, as well as providing better engineering application results.  
This high computational efficiency suggests that the global optimum is readily found, which in turn ensures precise system tracking. This tracking performance is illustrated in Figures 9 and 10, which, respectively, show the MPC rolling optimization results for the upper-layer and lower-layer models. It can be observed that the trend of changes in the upper-layer coupling variables is consistent, reflecting the constraint effect of the coupling restrictions. It is essential to note that the hydrogen blending ratio, \(r\), which forms the core of the adjustment mechanism, is not tracked like the outputs of the devices but rather optimized as a variable. From the results, it is evident that the trend of \(r\) over time is similar to that of the day-ahead forecast, tending toward the boundary values of 0 and 0.2, with a more pronounced step characteristic. This indicates that to mitigate uncertainty error, \(r\) will further enhance its characteristics during different periods. When \(r\) is low, it will  
\begin{table}
\begin{tabular}{c c c c} \hline  & **DRO** & **MPC Upper-Layer** & **MPC Lower-Layer** \\ \hline Average solving time & 60.819 s & 0.366 s & 0.384 s \\ Time scale & 24 h & 1 h & 5 min \\ \hline \end{tabular}
\end{table}
Table 9: Multi-timescale methods: speed comparison.  
decrease further, and when high, it will increase further, reflecting the reasonableness of the THB strategy.  
Additionally, the lower-layer model shows greater tracking deviation compared to the upper-layer model. This is partly because the error from the upper layer propagates to the lower layer, causing cumulative deviations. Furthermore, the lower-layer model has a shorter timescale, and the disturbances designed for it are more frequent.  
Overall, the MPC structure effectively integrates system states with day-ahead optimization results during intraday rolling optimization, maintaining the system's stability and optimization effectiveness. Through this approach, MPC not only responds quickly to real-time data but also ensures that the long-term goals of the system are consistent with the day-ahead plan, improving the overall scheduling performance.  
Figure 10: Lower-layer MPC rolling result.  
Figure 9: Upper-layer MPC rolling result.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.6 Input Sentence Length Analysis  
To assess the sensitivity of various models to the length of input sentences, we deliberately partitioned the training set into multiple sub-training sets with varying lengths. The detailed experimental results are depicted in Figure 11a.  
Figure 10: \(\alpha\) impact on model performance.  
Figure 9: Model parameter analysis. (**a**) The accuracy of the model varies with the number of iterations; (**b**) \(\lambda\) Impact on model performance.  
The results presented in Figure (a)a clearly demonstrate that the proposed Bi-Dual Inference model outperforms other baseline models in terms of overall performance. Notably, the performance of the Bi-Dual Inference model is optimal when the sentence length is 20. On the other hand, the LSTM model also exhibits a noticeable trend. As the length of the training sentence increases, the model tends to forget more information, resulting in diminished performance.
# Mapping Recent Shoreline Changes Spanning the Lateral Collapse of Anak Krakatau Volcano, Indonesia
## 5 Conclusions  
This paper discusses the application of an optimal remote method for rapidly and automatically mapping shorelines using only sentinel-2 multispectral imagery. The results demonstrate that the combined image pre-processing, thresholding and edge detection algorithm shows good potential for providing an efficient solution to obtain shoreline data, especially if multi-decennial time spans are considered. Here, the method has only been tested for a small coastal area that showed considerable shoreline changes over a short time interval, so further work is necessary to evaluate the applicability of the method presented here for different types of coasts (e.g., rocky and delta) where a supervised image classification may be necessary. In addition, further research is required to determine whether it is possible to further enhance the shoreline mapping results by incorporating other datasets, like tidal data and topographic profiles which can account for the seasonal variability of the coast. The shorelines changes document a remarkable pattern of regrowth initiated by the lateral collapse of  
Figure 7: Correlation between the area of the Anak Krakatau Island obtained from Google Earth Engine procedure and manual interpretation of the Sentinel-2 imagery for four different dates. The linear regression fit is represented by a black solid line.
## 2 Passive Wake-Up Radio
### Passive Wake-Up Radio Hardware Architecture
#### 2.2.4 DC to DC Converter  
To transform the direct current (DC) to a different voltage level, a DC/DC converter is connected at the output of the voltage multiplier. The DC voltage source can be either increased or decreased by the converter depending on the voltage level and converter type. In the energy harvesting circuit shown in Figure 2, the DC/DC converter block is a boost DC converter (also known as step-up chopper). This converter steps up the output voltage delivered to the capacitor. The value of the inductor (L) depends on the output voltage and current requirements.
# Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems
## 3 Results
### Integration of the Rossler System by DTM  
In some studies on the DTM, one can find comparisons of results for different orders of the DTM or comparisons between the DTM and other integration schemes. Usually, these are results in the form of time series or phase diagrams that are compared. These comparisons are similar to those made in this study (Figures 1, 3 and 4) in order to determine the optimal parameters of the DTM for solving the Rossler system. The question remains, however, how the DTM solution of the Rossler system will be affected by changing the coefficients of the system (6). To assess this effect for the fractional system, a comparison is made between bifurcation diagrams that were determined using two integration methods: (a) a method based on the Grunwald-Letnikov definition of fractional derivative with the use of RK4 method (G-L RK4) and (b) the DTM. In the G-L RK4 method, the integration step was set to \(h=0.001\) and 17,500 history points of the system (its previous states) were taken into account. For the DTM case, \(h=0.01\) (i.e., the optimal \(h\) value detected in the preliminary tests) and orders of 8 and 14 were tested. The coefficient \(a\) was used as a bifurcation parameter. The system fractionality was induced by the fractional order of only the first equation (\(\alpha_{1}\
eq 1\)). The results obtained for \(\alpha_{1}=0.98\) are presented in Figure 5 and for for \(\alpha_{1}=0.96\) in Figure 6.  
Figure 5: Bifurcation diagrams obtained for the Rössler system using two methods of integration: the RK4 scheme based on the Grünwald–Letnikov definition of fractional derivative and DTM for \(\alpha_{1}=0.98\).  
The results shown in Figure 5 for the system with a relatively low fractionality are similar. One can only observe a slight shift to the right of the DTM result in relation to the G-L result, towards the positive values of the bifurcation parameter \(a\). As the fractionality of the system increases (\(\alpha=0.96\)) (Figure 6), the result obtained by the G-L method changes qualitatively, while the overall characteristic of the DTM result remain unchanged. The shift of both results to the right increases. Based on these results and the results of previous tests, it can be concluded that the DTM results are more resistant to the change in the dynamics of a chaotic system. In the G-L RK4 method, the accumulation of errors in the iterative procedure quickly leads to qualitative changes in the obtained solutions. These changes depend on the dynamic characteristics of the system, i.e., on the value of the bifurcation parameter. As a consequence, when the fractionality of the system is changed, the bifurcation diagrams obtained by the G-L RK4 method are considerably modified while the DTM results are changed to an insignificant extent. Obviously, the DTM solution is not perfect; however, in the analyzed range of parameters, it is more resistant to iteration errors, which makes the obtained results more stable and thus resistant to changes in the system dynamics.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Skill Degradation  
Using autonomous systems over a long period of time may cause the operators' skill to degrade. A study of UAV operations highlighted this, stating that complacency and overconfidence are the main problems that experienced operators can have [33]. Operators who execute the same job for a long time think that they can perform the procedures and checklist operations by heart, which can lead them to miss a significant step in emergency situations and can blind them to hazards [16]. This situation refers to skill degradation in an automated environment over a long period of time, similar to automated driving and automated mining domains.  
Remote ship operators are at similar risk of skill degradation. During remote and autonomous operations, vessels are under constant human control and monitoring. Hence, a human is in the loop in all modes of unmanned and autonomous operations and may take full direct remote control over the ship even at the highest level of autonomy. This implies that humans must always be prepared to take over operations. However, high reliance on autonomy for a long period of time degrades operators' skills.
# Research on SWIM Services Dynamic Migration Method +
## 5 Experimental Results and Analysis  
SWIM is a large-scale network system and used in civil aviation systems. It utilizes existing civil aviation equipment resources, as a heterogeneous data resource integration system, unifies services data formats, and as a virtual information pool to provide services to civil aviation organizations. In order to verify the performance of the SWIM service dynamic migration method, a SWIM simulation test platform was built. The topology is shown in Figure 8.  
The SC acquires the SWIM services as a SWIM services consumer, SP\({}_{1}\), SP\({}_{2}\) and SP\({}_{3}\) composes an aeronautical information services migration cluster to provide aeronautical information service for SWIM system, SP\({}_{4}\) and SP\({}_{5}\) provides a flight information service. In simulation test, SC acquires a flight information services via SWIM access point. Among them, the flight information services data and aeronautical information services data were respectively provided by an airline and an air traffic control technology center in China, the SWIM flight information exchange model (FIXM) [17] and the SWIM aeronautical information exchange model (AIXM) [18] is a standard that provides flight information services and aeronautical information services to SCs. The experimental PC hardware configuration is shown in Table 2.  
Figure 8: SWIM simulation test platform.  
The simulation experiment mainly analyzed the feasibility and effectiveness of the SWIM service dynamic migration method from three parts. The first part was based on the SWIM network topology structure constructed in Figure 8 to simulate the SWIM services interaction process and analyze the feasibility of the SWIM service dynamic migration method according to the statistical data of the SWIM. The second part is a mathematical model for establishing the SWIM key services dynamic migration method, it was compared with the SASS and TOKEN models to verify the validity of the SSM model. The third part was to evaluate the method from the two parts of throughput and service average delay.
## 2 Terminology  
In this paper, classical terms employed by the clinicians to describe ultrasound findings in lung ultrasonography (A-Lines, B-Lines, and Interstitial Syndrome) will not be used [23]. According to recent observations addressing the origin of the ultrasound lung artifacts [24; 25], we will use terms consistent with the principles that govern the ultrasound propagation through aerated and variously porous tissues. When necessary, the usual lung ultrasound lexicon will be recalled and addressed to the involved physical phenomena.  
A-Lines is a generic term indicating horizontal artifacts related to a normal lung surface. In this review, we consider these artifacts as the summation of the reverberation effects, due to the pleural-line and myofascal acoustic interfaces of the chest wall, and the mirror effects (variable in its expression in relation to the thickness of the chest wall) reproducing beyond the pleural line, in a specular way, the myofascal planes of the chest wall (Figure 1).  
B-Lines are defined as discrete laser-like vertical hyperechoic artifacts that arise with a narrow base from the pleural line, extending to the bottom of the screen without fading, and moving synchronously with lung sliding [1; 6]. As B-lines show great heterogeneity and they look different at different levels [7], we will simply call the B-Lines \"vertical artifacts\" (Figure 2).  
From a clinical point of view, they indicate a denser sub-pleural lung, but not yet consolidated, caused by: (1) interstitial pathology enlarging the interstitial tissue but sparing residual peripheral air spaces; (2) pathologic deflations of a normal healthy lung or pathologic subversion of peripheral air spaces; and (3) mixed situations [23; 24]. The term \"Interstitial Syndrome\" can thus be transformed in \"Hyperdense pre-consolidated sub-pleural lung\". In cognitive terms, the vertical artifacts in lung sonography represent acoustic information relative to a physical state (porosity or full/empty ratio) of a relatively thin and superficial layer of the lung.