esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The density-based method can group clusters in any shape, so many researchers favor it. According to the type of input sample, the method can be divided into clustering using points and clustering using trajectories [19]. The attributes of points are used for clustering, such as the location, course or speed. [PERSON], et al. [20] used time and date, position, SOG, and COG details as variables, and DBSCAN was used to identify the stationary points and turning points. [PERSON], et al. [21] used the ship position, direction in and direction out as sample attributes, and DBSCAN was used to generate turning nodes. In addition, methods based on statistics [22], HDBSCAN [23] and OPTICS [24] have been proposed. Clustering using points conforms to the distance measure requirements of the basic theory of the clustering algorithm, and can be easily used in existing clustering algorithms. However, this method is easily influenced by local attribute changes; for example, collision avoidance behavior of ships may lead to a different result. So, clustering using trajectories was proposed. Trajectory similarity measure is used to compute the distance between trajectories for clustering. Trajectory similarity measure design is a special problem for this method. Dynamic time warping [25], Edit Distances [8], Hausdorff distance [26], Frechet distance [27] and longest common sub-sequence (LCSS) [28] have been used as the trajectory similarity measure. [PERSON] and [PERSON] [29] proposed a structural similarity measure considering speed, course, and position, which was used as the distance measure of DBSCAN to cluster, and inferred the optimal route using the Ant Colony Algorithm. In the study of a ship's behavior, maritime traffic pattern recognition is a fundamental problem that needs to be solved. The above studies have proposed their own solutions, but they did not explicitly consider the impact of environmental change on the existing patterns or prove that the patterns were still feasible after environmental changes, such as sea building, shipwck, and waterway construction, among others. If a pattern fails to adjust to environmental changes, it will lose its practical application and cannot achieve fully automatic MSA. Consequently, a maritime traffic pattern dynamic recognition method is proposed. The method preprocesses the trajectory online, including trajectory cleaning, simplification, and partition. An improved LCSS distance is invoked as the similarity measure of the incremental DBSCAN to recognize the maritime traffic pattern. The remainder of this paper is organized as follows: Section 2 introduces the method, Section 3 the effectiveness of the method, Section 4 discuss the results. The study's conclusions are outlined in Section 5.
### Current Challenges in Complex Maritime Systems  
Complex maritime systems face a multitude of challenges that underscore the urgency for advanced, data-driven solutions. Below, we enumerate some of the most significant challenges currently affecting these systems:  
1. Increasing Vessel Traffic: The continuous growth of global trade has led to an increase in vessel traffic, resulting in congestion, especially in crucial maritime zones such as the Strait of Malacca, the English Channel, and near major port areas [21]. Managing this rising traffic effectively to avoid collisions, groundings, and delays is a critical challenge.
2. Environmental Impact: Maritime activities have a substantial impact on the marine environment. Vessel operations contribute to air pollution and greenhouse gas emissions, and accidental spills can have disastrous consequences for marine ecosystems [22; 23]. Mitigating these impacts requires improved management and predictive capabilities.
3. Security Threats: The maritime industry faces threats such as piracy, smuggling, and illegal fishing [24; 25; 26]. Addressing these threats and ensuring the security of vessels and their cargo is a considerable challenge.
4. Regulatory Compliance: International, regional, and local regulations govern maritime operations. Ensuring compliance with these regulations, particularly concerning environmental standards, safety, and security, requires sophisticated management systems [27].
5. Data Management: The maritime industry generates a vast amount of data from different sources, including AIS, radar, weather monitoring systems, and vessel logs. Managing, integrating, and making sense of this big data is a significant challenge, especially in real-time scenarios [28].
6. Human Error: Many maritime accidents are attributable to human error. Reducing the reliance on human judgment in navigation and decision-making processes could help mitigate this risk [29].
7. Adaptability to Changing Conditions: The maritime environment is highly dynamic, with changing weather conditions, sea currents, and geopolitical circumstances. Maritime systems must be capable of quickly adapting to these changes [30].  
Machine Learning, with its ability to analyze and learn from large datasets, make predictions, and automate decision-making processes, holds significant potential to address these challenges. The next sections of this review will explore the role of Machine Learning in tackling these issues and enhancing the management and predictive capabilities within complex maritime systems [16].
## 3 Results and Discussion
### Thermodynamics of Adsorption  
To understand the effect of temperature on the adsorption capacity of the CDM-HCP towards anilines, the results are discussed for the different temperatures. The changes in thermodynamic parameters, including enthalpy change (\(\Delta H\)), entropy change (\(\Delta S\)), and Gibbs free energy change (\(\Delta G\)), were calculated by \(K_{\mathrm{L}}\) at the different temperatures and were fitted based on the isothermal equation and the van \({}^{\prime}\)t Hoff equation. The results are given in the Supplementary Materials. The thermochemical data for the retention of anilines onto the CDM-HCP are listed in Table S2 (Supplementary Materials).  
As shown in Table S2 (Supplementary Materials), \(\Delta H\) values were negative, demonstrating that the adsorption process was of an exothermic nature. Moreover, the negative \(\Delta G\) also implied that the adsorption was a spontaneous process. The increase of \(\Delta G\) with temperature increasing indicated that the adsorption of anilines was more beneficial at lower temperatures. Furthermore, the negative \(\Delta S\) showed that the adsorption was an entropy reduction process, which might occur because a large number of anilines were adsorbed onto the adsorbent.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 1 Introduction
### Solving the Power Flow Problem for Hybrid AC/DC Grids with MT-HVDC Systems
#### 1.2.1 The Unified Method  
The unified method solves the PF problem for the entire hybrid AC/DC grids using a modified Jacobian matrix [12, 13, 14], where all the AC and DC variables, such as the impact of DC links in the Jacobian matrix, can be calculated in each iteration process. Many techniques are proposed and developed to improve the efficiency of the unified method, but the main drawback of those methods is neglecting the impact of droop parameters' settings on the AC/DC PF [12, 13, 14]. Another issue of the unified method to solve the PF problem is that it needs an alternation of the extension of an existing AC PF.
## 6 Simulated Results  
After the calibration process, the simulations of two specific scenarios are conducted, and described as follows:  
* Presence of an audience with 50% occupancy;
* Presence of an audience with 100% occupancy.  
The absorption coefficients assumed for the audience are summarized in Table 6, related to 117 m\({}^{2}\) for 50% occupancy and 243 m\({}^{2}\) for 100% occupancy, respectively [25]. The occupancy for the 50% scenario was uniformly distributed across the audience area, in both stalls and balconies--specifically, every other seat. The main acoustic parameters are reported in the octave bands between 125 Hz and 4 kHz, to be considered as the averaged values of all the receivers' positions. Figure 11 shows the graphs of the simulated results compared with the calibrated values representing the condition without any audience.  
The graphs in Figure 11 show that with a medium-high upholstery of the seats, the presence of an audience does not result in any appreciable difference [26], meaning that the acoustics of the Ponchielli theater do not change and are independent of the degree of occupancy [27]. This outcome is positive, especially for actors and musicians who do not have to change their effort between rehearsal and a live show in the presence of the public [28]. The negligible change between 50% and 100% occupancy is due to the high absorbing coefficients of the seats that are very similar to those of a human body. In addition to the standard deviation error bars added to graphs (c) and (d) of Figure 11, a spatial distribution at 1 kHz of the parameters C\({}_{80}\) and D\({}_{50}\) is shown in Figure 12, where it is possible to see that the values are higher for positions close to the stage and 2-3 points less in the middle of the hall. The acoustic maps in Figure 12 show the values at both 50%  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{6}{c}{**Absorption Coefficients—Octave Frequency Bands (Hz)**} \\ \cline{2-7}  & **125** & **250** & **500** & **1k** & **2k** & **4k** \\ \hline Audience & 0.65 & 0.66 & 0.80 & 0.88 & 0.83 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Absorption coefficients of the audience considered in the model of Ponchielli theater of Cremona.  
Figure 11: Simulated results of the acoustic parameters: EDT (**a**), T\({}_{20}\) (**b**) C\({}_{80}\) (**c**) and D\({}_{50}\) (**d**).  
and 100% occupancy, shown above and below the main axis of the plan, respectively. The spatial distribution of C\({}_{80}\) and D\({}_{50}\) has been added since they are acoustic parameters that vary with the position inside the space, hence the average values are only indicative for all the octaves.
## 4 Experimental Study
### Comparison against Existing Methods
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **AUC\_ROC** & **ACC** & **SPEC** & **F1 SCORE** & **PREC** & **SENS** \\ \hline [PERSON] et al. [30] & 98.75 & **97.13** & 97.98 & 81.91 & **86.64** \\ \hline [PERSON] et al. [37] & **99.14** & 97.12 & 98.62 & **84.75** & 82.29 \\ \hline [PERSON] et al. [38] & 98.58 & 96.85 & 97.61 & & 83.91 \\ \hline [PERSON] et al. [39] & 98.12 & 96.48 & 97.68 & & 82.75 \\ \hline [PERSON] et al. [40] & 94.92 & 97.71 & & 70.19 \\ \hline [PERSON] et al. [41] & 95.16 & 97.31 & & 67.13 \\ \hline [PERSON] et al. [42] & 95.73 & 98.63 & & 70.23 \\ \hline [PERSON] et al. [43] & 86.50 & 88.06 & & 83.42 \\ \hline [PERSON] et al. [44] & 96.01 & 98.02 & & 68.46 \\ \hline
**OUR MODEL** & 97.57 & 96.92 & **98.70** & 79.00 & 82.00 & 75.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the introduced convolutional autoencoder on the STARE dataset. Figure 8: Comparison against existing method. (**a**) An attempt of automatic segmentation by the Octave Convolution Neural Network [45]. (**b**) The corresponding automated vessel segmentation of our proposed convolutional autoencoder. The experimental results reveal pretty impressive findings. First, the results highlight the quite good performance of our model. Our autoencoder achieved quite good accuracy. Our model's quite good performance is the architecture we designed and the balanced number of layers it consists of. In addition, the results also point out that our model reports the best performance among the models in the literature in terms of specificity. Indeed, in this regard and to the best of our knowledge, the best performance is achieved by our model on both the STARE and DRIVE datasets. Finally, our model achieves a quite good F1 score while the sensitivity is on a good level compared to the related works in the literature. Last but not least, it is worth indicating that we designed, formulated and evaluated our model in a machine with the characteristics in Table 3.
## 6 Conclusions  
In order to simplify meridional configuration, three impellers are designed with innovative shape. Based on the Computational Fluid Dynamics (CFD) method, detail analysis of steady and unsteady flow field has been carried out with single-passage-model and full-passage-model respectively. Furthermore, the experiment results are used to evaluate the accuracy of CFD results. The conclusions can be drawn as following:  
Figure 19: Dimensionless static pressure coefficient distribution at different time steps. DTS: dual-time step.  
* The processing time and manufacturing cost are reduced because the hub is described by the straight line for the optimized impeller, LL impeller. Moreover, the hollow geometry in the disk may also be beneficial to the rotor dynamic design and bearing design.
* Within the operating range, the prototype impeller has stable performance. Nevertheless, its operating range cannot fully match the industrial requirement. Meanwhile, the simplified impeller, LL impeller, is improved with excellent stall margin as well as its efficiency and pressure ratio are decreased little.
* The calculation result of the full passage model is matched with experiments very well. So, in order to obtain comprehensive performance of a centrifugal compressor, full passage model with volute is necessary to be considered.
* In this case, unsteady calculation is conducted due to \(St\) 0.12, but the unsteady characteristics of the steam compressor is not obvious. Meanwhile, the amplitude of pressure oscillation around volute tongue is only about 1.5%. Thus, for this case, the steady numerical simulation result is accurate enough to investigate asymmetric circumferential flow pattern.  
Conceptualization, B.Y.; Investigation, H.X.; Software, X.L. and M.S.; Resources, C.G.; Writing-Review & Editing, H.X.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Methodology
### OD Segmentation Using DeepLabv3+ Semantic Segmentation  
In this stage, the OD was segmented using DeepLabv3+ [29] and a bounding box was set, see Figure 3. DeepLabv3+ uses two phases: an encoder and a decoder. The encoder uses four parallel atrous convolutions at different rates to extract features. The extracted features were then concatenated, using average pooling, on the last feature map. To reduce the number of channels and obtain the best encoder features, \(1\times 1\) convolution was used. The decoder recovered the object boundaries encoded from CNNs model. The encoded features were first bilinearly upsampled by a factor of four and then concatenated with the corresponding low-level features. Since the corresponding low-level features usually contain a large number of channels, a \(1\times 1\) convolution filtered the important encoder features. Afterwards, a \(3\times 3\) convolution was used to refine the features followed by another bilinear upsampling by a factor of four. Five deep CNNs--ResNet18, ResNet50, XceptionNet, MobileNet and InceptionResNet--were used in the encoder phase and their segmentations were compared to find the best one for the encoder of DeepLabv3+. This generated the semantic mask for the OD region. OD extraction is illustrated in Figure 4. First, our segmentation formed a mask by labeling every pixel as OD or non-OD. The mask was laid over the original retinal image to extract the OD region. Finally, a bounding box cropped the OD region, which was input to the classification stage.
###### Abstract  
In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R\({}^{2}\) of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy.  
Sentinel-2; soil texture; multiple regression model; water efficiency; irrigation
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
###### Abstract  
During its commissioning phase, the Copernicus Sentinel-3B satellite has been placed in a tandem formation with Sentinel-3A for a period of 6 months. This configuration allowed a direct comparison of measurements obtained by the two satellites. The purpose of this paper was to present the range of analyses that can be performed from this dataset, highlighting methodology aspects and the main outcomes for each instrument. We examined, in turn, the benefit of the tandem in understanding instrument operational modes differences, in assessing inter-satellite differences, and in validating measurement uncertainties. The results highlighted the very good consistency of the Sentinel-3A and B instruments, ensuring the complete inter-operability of the constellation. Tandem comparisons also pave the way for further improvements through harmonization of the sensors (OLCI), correction of internal stray-light sources (SLSTR), or high-frequency processing of SRAL SARM data. This paper provided a comprehensive overview of the main results obtained, as well as insights into some of the results. Finally, we drew the main lessons learned from the Sentinel-3 tandem phase and provided recommendations for future missions.  
calibration; Copernicus Sentinel-3; OLCI; SRAL; MWR; SLSTR; tandem formation 2020
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.2 Constraints in Edge Computing and the Application of Reinforcement Learning  
In Table 9, MOBA-CV-SARSA shows a 0.9% improvement in the hypervolume indicator compared to NSGA-RL-CV. In contrast, its improvement over QLPSO-CV reaches 15.07%. In 30 comparative experiments, MOBA-CV-SARSA exhibits a slightly higher standard deviation than NSGA-RL-CV. Although MOBA-CV-SARSA's worst performance yields a hypervolume value of 10,771.17, somewhat lower than NSGA-RL-CV's average, it still outperforms QLPSO-CV. In Table 10, MOBA-CV-SARSA's diversity indicator surpasses NSGA-RL-CV by 4.72% and QLPSO-CV by 0.1%. In Table 11, both MOBA-CV-SARSA and NSGA-RL-CV never have zero retained non-dominated solutions in 30 comparisons. QLPSO-CV has this issue seven times, indicating SARSA's superiority over Q-learning. Under the \"most retained non-dominated solutions\" scenario, MOBA-CV-SARSA consistently preserves the most solutions, thus achieving the best overall performance. Table 12 illustrates the average computation time for each method. Notably, MOBA-CV-SARSA computes 64.338 s faster than NSGA-RL-CV and 200.524 s faster than QLPSO-CV.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
###### Abstract  
To address the challenges of a long measurement period, high testing cost, and environmental pollution of traditional milk composition detection methods, a portable detection instrument was developed by combining multi-spectral sensors, machine learning algorithms, and an embedded system to rapidly detect the main components of milk. A broadband near-infrared (NIR) LED constant-current driver circuit and multi-spectral sensor module were designed to obtain six NIR features of milk samples. Based on a comparison of several machine learning algorithms, the XG-Boost model was selected for training, and the trained model was ported to a Raspberry Pi unit for sample detection. The validation results showed that the coefficients of determination (\(R^{2}\)) for the investigated protein and fat models were 0.9816 and 0.9978, respectively, and the corresponding mean absolute errors (_MAE_) were 0.0086 and 0.0079. Accurate measurement of protein and fat contents of milk can be facilitated in a short time interval by using the proposed low-cost portable instrument.  
m +
Footnote †: journal: _sciences_
## 4 Results and Discussion
### Pros and Cons of Using Machine Learning
#### 4.9.2 Limitations of Machine Learning  
* Risk of False Positives: One of the significant challenges with ML is the risk of generating false positives--incorrectly predicting that a student may drop out. This can lead to unnecessary interventions, potentially wasting resources and adversely affecting the student involved.
* Data Privacy and Security: The need for substantial data to train ML models raises concerns about privacy and data security, especially when sensitive student information is involved. Ensuring the integrity and security of this data is paramount but can be resource-intensive.
* Complexity and Resource Requirements: ML models, particularly those like random forest or neural networks, are complex and require significant computational resources and expertise to develop, maintain, and interpret. This may pose barriers for institutions without sufficient technical staff or infrastructure.
# An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem
## 3 Improved Adaptive Genetic Algorithm
### Heuristic Placement Algorithm  
When using the fitness function to evaluate individuals in the population, it is necessary to arrange the rectangles into the sheet according to a certain heuristic algorithm for  
Figure 3: Initial population with \(PS\) individuals (\(n=8\)).  
Figure 2: Individual chromosome two-stage encoding (\(n=8\)).  
the packing sequence, rotation state, the width and height of rectangles, and the width and height of the sheet to calculate the sheet filling rate and figure out the fitness.  
[PERSON] proposed the IBL algorithm [15] to solve the problem that the left side of the BL algorithm [12] is too high and the unfilled cavity area can not be effectively used. Although it can overcome the disadvantage of the BL algorithm, which is prone to the waste area of the sheet, it will lead to the problem of a high right side. In contrast, the lowest horizontal line algorithm proposed in this paper can effectively solve the shortcomings of the BL and IBL algorithms and improve the filling rate [33]. Therefore, the placement algorithm in this paper adopts the heuristic lowest horizontal line algorithm of Ref. [23], and the procedure is as follows in Algorithm 1.  
```
HeuristicPacking(\(R\), sheet):
1 Initialize the lowest level line \(l\) is 0
2 while\(R\) is not empty or \(l\) is beyond the sheet \(H\):
3 Get filling area on \(l\) and hollow area under \(l\), the area size store 2-tuple set (\(AW\), \(AH\))
4 Priority to sorting hollow area in increasing order of \(AW\) for (\(AW\), \(AH\))
5 Prepare to place \(r_{i}\) from \(R\) in order and rotation state
6 if\(r_{i}\) need to rotate:
7 Exchange the width \(w_{i}\) and height \(h_{i}\) of \(r_{i}\)
8 if\(AW\geq w_{i}\) and \(AH\geq h_{i}\):
9 Place \(r_{i}\), update area size (\(AW\), \(AH\))
10 Record \(r_{i}\) the left-bottom corner position coordinates \((x_{i},y_{i})\), placed state, size, rotation state, etc.
11 Remove \(r_{i}\) from \(R\)
12 else:
13 If exist the next area:
14 Move the next area to continue placing
15 else:
16 Sort these placed \(r_{i}\) in increasing order of \(y_{i}\)
17 Update \(l\) and the area size (\(AL\), \(AH\))
18 return the placed information for \(R\)
```  
**Algorithm 1** Heuristic packing procedure