esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Existing infrared small target detection methods often fail due to limited exploitation of spatiotemporal information, leading to missed detections and false alarms. To address these limitations, we propose a novel framework called Spatial-Temporal Fusion Detection (STFD), which synergistically integrates three original components: gradient-enhanced spatial contrast, adaptive temporal intensity accumulation, and temporal regional contrast. In the temporal domain, we introduce Temporal Regional Contrast (TRC), the first method to quantify target-background dissimilarity through adaptive region-based temporal profiling, overcoming the rigidity of conventional motion-based detection. Concurrently, Regional Intensity Accumulation (RIA) uniquely accumulates weak target signatures across frames while suppressing transient noise, addressing the critical gap in detecting sub-SNR-threshold targets that existing temporal filters fail to resolve. For spatial analysis, we propose the Gradient-Enhanced Local Contrast Measure (GELCM), which innovatively incorporates gradient direction and magnitude coherence into contrast computation, significantly reducing edge-induced false alarms compared with traditional local contrast methods. The proposed TRC, RIA, and GELCM modules complement each other, enabling robust detection through their synergistic interactions. Specifically, our method achieves significant improvements in key metrics: SCRG increases by up to 36.59, BSF improves by up to 9.48, and AUC rises by up to 0.027, reaching over 0.99, compared with the best existing methods, indicating a substantial enhancement in detection effectiveness.  
infrared small target detection; local contrast measure; spatial-temporal fusion detection (STFD); temporal profile +
Footnote †: journal: Applied sciences
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids  
A new energy structure called a microgrid combines energy storage systems, renewable and other energy resources, loads, and the power grid. Microgrids must have efficient energy management in place to guarantee maximum energy efficiency. However, integrating renewable energy resources has made it more difficult because of the stochastic nature of these resources. Thus, ensuring economical and flexible operation with a range of resources and arranging unpredictable operating conditions of distributed generation are among the challenges in energy management and microgrid optimization. An energy management system is essential for making the best use of these distributed energy resources in a microgrid in a way that is coordinated, safe, smart, and dependable. A microgrid's energy management system can monitor, analyze, and forecast power generation from distributed generation systems, load consumption, energy market prices, and meteorological factors, among other things. Energy management systems can optimize the microgrid with the help of these features [18].  
In a microgrid, energy management systems are control software that allocates power output among distributed generation units and finds the most cost-effective way to feed the load. This is done by taking into account safety, reliability, and power quality. In general, a microgrid energy management system needs to coordinate various distributed generation sources, each with its constraints, to provide energy in a sustainable, reliable, environmentally friendly, and cost-effective manner. Energy management systems receive multiple inputs and then act on the available information to achieve the goals set by the microgrid owner. Figure 2 provides an illustrative overview of a microgrid energy management system.  
Energy management is facilitated using energy storage systems in microgrids. Energy management enables the realization of scenarios such as storing excess power in energy storage devices if the power demanded by the system is less than the power produced by renewable energy resources, and meeting the required power from energy storage devices if the power demanded exceeds the power produced by renewable energy resources. Between the production, consumption, and storage system and the control of battery charging and discharge, an effective energy management system is needed. Controllers can thus collaborate with the load's demand to achieve appropriate energy management. Differentapproaches applied to energy management systems in microgrids are shown in Figure 3. Classical method-based energy management systems, energy management systems based on meta-heuristic approaches, energy management systems based on artificial intelligence methods, energy management systems based on stochastic (variable) and powerful programming approaches, energy management systems based on model predictive control, and multi-agent energy management systems are used in energy management system applications in microgrids [16].
## 4 PSA in a DOF: Dual Pump Configuration  
In the preceding section, we focused on a single pump PSA system with a DOF. However, a dual pump PSA system is often preferred to a single pump system, owing to its ability to provide a larger gain [46], or for the amplification of polarization multiplexed optical signals [47; 48]. Since here our focus will be on achieving a larger gain for the PSA system, we will restrict our discussion to the fundamental gain band (\(q=0\)) of the system, that typically can reach gains much larger than the high-order sidebands [49].  
In this section we aim to optimize the dispersion modulation parameters of the DOF, such that a high PSA gain can be attained. To this aim, we first introduce an analytical model for a dual-pump PSA considering dispersion oscillation. The dual pump system configuration is illustrated in Figure 8.
# Ship Network Traffic Engineering Based on Reinforcement Learning
## 2 Materials and Methods  
### Ship Network Structure  
Ship networks carry a variety of data types crucial for vessel operations, safety, and communication. Understanding the characteristics and requirements of these data types is essential for effective network management and optimization. Control and command data are used for remote control of vessel systems, such as propulsion, navigation, and safety systems. Control and command data often require low-latency transmission to ensure timely response and control of vessel systems. While individual control commands may be small, frequent transmission and low-latency requirements necessitate moderate to high bandwidth allocation.  
Ship network structure is a critical infrastructure designed to meet internal communication, data transmission, and management needs. Its design includes physical, access, and core layers. Each layer plays an important role in its design and helps provide reliable communications, data transfer, and ship management.  
The physical layer forms the basis of the entire network structure and handles hardware devices and physical connections. In ship networks, the physical layer includes various transmission media, such as cables, optical cables, and satellite communications. This layer ensures that the physical connections between various parts of the ship are stable and reliable to support efficient data transfer. The access layer acts as a bridge connecting the physical layer and the core layer. It is responsible for data exchange, forwarding, and processing. Through the access layer, various ship equipment and sensors can connect to the network and perform data exchange and communication, including network switches, routers, and wireless access points connected to various devices. The core layer is the backbone of the entire network. It manages and schedules data traffic to ensure efficient data transmission from source to destination. In a ship network, the core layer may include high-performance switches and routers to ensure fast, reliable data transmission between different devices. This layer usually involves strategic design to ensure the network's high availability and fault tolerance. The core layer also provides connections to external networks, allowing the ship to communicate and exchange data with the outside world.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 4 Results
### Qualitative Results  
We also conducted qualitative evaluation of the proposed method and other SOTA methods on the UIEB test dataset. Some sample images and their corresponding enhanced results are shown in Figure 3.  
It can be seen that the proposed method produces visually pleasing images from these samples. In case of greenish and bluish underwater images, the proposed method can remove these unpleasant color deviations. Such capability is important since greenish or bluish color deviations are common for underwater images taken especially in open water due to the fact that the red light attenuates first, followed by the green light and then the blue light. In case of underwater images with low contrast, the proposed method can substantially remove the backscatter light, leading to clear results.  
Compared with other underwater image enhancement methods, the proposed method produces results with higher visibility and less artifacts, proving the effectiveness of Retinex transmission map estimation and adaptive color correction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR \(\uparrow\)** & **SSIM [27]\(\uparrow\)** & **UCIQE [28]\(\uparrow\)** & **UIQM [29]\(\uparrow\)** \\ \hline Fusion [7] & 19.16 & 0.73 & **0.63** & 2.73 \\ \hline RGHS [22] & 18.86 & 0.76 & **0.63** & 2.62 \\ \hline UDCP [6] & 12.88 & 0.56 & 0.58 & 2.26 \\ \hline GDCP [16] & 13.42 & 0.75 & 0.57 & 2.76 \\ \hline ULAP [19] & 14.37 & 0.69 & **0.63** & 2.44 \\ \hline [PERSON] et al. [20] & 15.66 & 0.66 & 0.55 & 2.14 \\ \hline The proposed method & **19.31** & **0.79** & **0.63** & **2.83** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative full-reference evaluation and non-reference evaluation of the proposed method and six other SOTA underwater image enhancement methods. The best results in terms of each metric are shown in bold.
## 5 Airfoil Design Problem
### Evaluation Methods
#### 5.2.3 Airfoil Representation  
In this study, the class-shape function transformation (CST) parameterization method [36] was used for airfoil shape parameterization. The benefits of CST are that it could can generate several kinds of aerodynamic shapes and that it has a high degree of freedom due to the changeable number of numbers of the controlled parameter to generate the airfoil shape; whereas the other type of airfoil representation, such as NACA's airfoil representation, parametric section (PARSEC) and non-uniform rational B-spline (NURBS), have the fixed controlled parameters. The product of a class function, \(C(x/c)\), and a shape function, \(S(x/c)\), can be represented geometrically by adding a term that characterizes the trailing edge thickness:  
\[\frac{y}{c}=C\Big{(}\frac{x}{c}\Big{)}S\Big{(}\frac{x}{c}\Big{)}+\frac{x}{c} \frac{\Delta z_{te}}{c}, \tag{24}\]  
where \(\Delta z_{te}\) is the trailing edge thickness, and \(C(x/c)\) is given in generic form by:  
\[C\Big{(}\frac{x}{c}\Big{)}\equiv\Big{(}\frac{x}{c}\Big{)}^{N_{1}}\Big{[}1- \frac{x}{c}\Big{]}^{N_{2}}\text{ for }0\leq\frac{x}{c}\leq 1. \tag{25}\]  
The shape function, \(S(x/c)\), is defined on the basis of the Bernstein binomials [37], by the introduction of weight factor \(b_{i}\) as follows:  
\[S\Big{(}\frac{x}{c}\Big{)}=\sum_{i=0}^{n}\Big{[}b_{i}\cdot K_{i,p}\cdot\Big{(} \frac{x}{c}\Big{)}^{i}\cdot\Big{(}1-\frac{x}{c}\Big{)}^{p-i}\Big{]}, \tag{26}\]  
where \(p\) is the degree of the Bernstein binomials. In this study, \(N_{1}\) was set to 0.5 and \(N_{2}\) was set to 1.0. Further, third-degree Bernstein polynomials were used to generate the airfoil shape for the lower side \(b_{1}-b_{3}\) and the upper side \(b_{4}-b_{6}\). The ranges of the design parameters are defined in Table 1.
## 6 Discussion and Limitations  
Our analysis reveals valuable insights into player performance through the predictions of actual goals per game (\(aGg\)) and expected goals per game (\(xGg\)). The ridge regression model proved effective in predicting these metrics, showcasing its capability in managing our dataset's characteristics and avoiding overfitting. The results indicate that Player ID 227 stands out with the highest \(aGg\), reflecting exceptional goal-scoring efficiency. On the other hand, Player ID 2099, while having the lowest \(aGg\) among the top players, shows remarkable efficiency in relation to their predicted \(xGg\). The alignment between predicted \(xGg\) and actual \(aGg\) for top players highlights the model's accuracy in identifying consistently high performers. The classification of players into efficient scorers and consistent performers provides actionable insights for teams and coaches. Efficient scorers, who significantly exceed their predicted \(xGg\), can be valuable targets for enhancing finishing skills and refining game strategies. Conversely, consistent performers offer reliability and stability, contributing to overall team consistency. By focusing on these insights, teams can better  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Player ID** & \(aGg\) & \(xGg\) & \((aGg-xGg)\) \\ \hline
227 & 1.41 & 1.09 & 0.32 \\
2371 & 1.37 & 1.13 & 0.24 \\
2097 & 1.13 & 0.99 & 0.14 \\
2099 & 1.10 & 0.76 & 0.34 \\
2098 & 1.14 & 1.09 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Top players with maximum predicted actual goals per game (\(aGg\)) and predicted expected goals per game (\(xGg\)), sorted by difference (\(aGg-xGg\)).  
strategize player recruitment, training, and in-game tactics to optimize performance and achieve better results.  
Even though our analysis provides valuable insights into player performance and predictive accuracy, several limitations should be acknowledged. Firstly, the dataset used is limited in scope and may not fully represent the diversity of leagues and player conditions. Factors such as injuries, changes in team dynamics, and variations in playing conditions are not accounted for, which could impact the accuracy of predictions. Another significant limitation is that the model's performance could be improved with a more extensive dataset, particularly if it includes historical performance data from individual teams. If a specific soccer team were to collect and utilize detailed historical data on its players, it could enhance the model's accuracy and predictive power, providing more tailored insights for that team. Lastly, the focus on expected goals per game (\(xGg\)) does not encompass other critical aspects of player performance, such as defensive contributions and teamwork. A more comprehensive model incorporating these elements could provide a fuller picture of player effectiveness. Addressing these limitations in future research could enhance the robustness and applicability of predictive models in soccer analytics.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results  
In this section, we present the results achieved in the selected study areas by applying the abovementioned methodologies. In the following figures, we use the polygon of definitive flooded areas as a benchmark.
## 5 Influence of SDIEC on Optimization Results
### Comparison of Blade Loading and Circulation Distribution for the First and Second Cases  
The optimized impellers F1 and S2 mentioned in Section 4.3 were selected as preferred impellers for the first and second cases, respectively. The blade loading distribution and SDIEC of impeller F1 and S2 are shown in Figure 16. In impeller F1, the SDIEC is uniform and the distributions of blade loading are aft-loaded and fore-loaded on the shroud and hub, respectively. However, in impeller S2, the SDIEC is a second-order parabolic and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Impeller** & \(RV_{h}\) & \(RV_{s}\) & \(DRVT_{h}\) & \(ND_{h}\) & \(K_{h}\) & \(DRVT_{s}\) & \(ND_{s}\) & \(K_{s}\) & \(\beta\) \\ \hline S1 & 0.3005 & 0.3365 & 0.249 & 0.513 & \(-1.729\) & \(-0.144\) & 0.706 & 1.999 & \(-16.269\) \\ S2(Preferred) & 0.3318 & 0.3363 & 0.199 & 0.893 & \(-1.464\) & \(-0.088\) & 0.503 & \(-1.439\) & \(-13.804\) \\ S3 & 0.3129 & 0.3365 & 0.199 & 0.500 & \(-1.50\) & \(-0.200\) & 0.900 & \(-1.499\) & \(-19.864\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Design parameters for the second case.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & \multicolumn{2}{c}{**RSM**} & \multicolumn{2}{c}{**CFD**} \\ \cline{2-6}
**Impeller** & \(\mathbf{\eta_{0.8}\) **(\%)**} & \(\mathbf{\eta_{1.2}\) **(\%)**} & \(\mathbf{\eta_{0.8}\) **(\%)**} & \(\mathbf{\eta_{1.2}\) **(\%)**} & \(\mathbf{H_{1.0}\) **(m)**} & \(\mathbf{\eta_{1.0}\) **(\%)**} \\ \hline S1 & 80.68 & 83.10 & 80.87 & 82.54 & 12.52 & 88.4 \\ S2 (Preferred) & 81.23 & 80.75 & 81.75 & 81.08 & 12.18 & 88.87 \\ S3 & 82.00 & 76.45 & 81.80 & 77.84 & 11.97 & 88.64 \\ Baseline model & & & 80.48 & 73.84 & 12.10 & 85.21 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison for the second case.  
Figure 15: Comparison of Pareto front for the first and second cases.  
the blade loading at shroud and hub are both fore-loaded. Therefore, when SDIEC is considered in the optimization process, the blade loading and circulation distribution on the entire blade is greatly changed.
# A Symmetric Matrix-Aided MIMO to Improve Reliability for Maritime Visible Light Communications
## 3 Symmetric Matrix-Aided MIMO-VLC System
### Receiver Structure  
The receiver structure and the decoding process of the received signal are shown in Figure 3. At the \(j\)th user's receiver, the received signals \(\mathbf{y}_{j}\)[15] is  
\[\mathbf{y}_{j}=\mathbf{H}_{j}\mathbf{s}+\mathbf{n}_{j}, \tag{17}\]  
Figure 2: The symmetric matrix-aided modulator.  
where \(\mathbf{n}_{j}\) represents the additive white Gaussian noise (AWGN) vector [26].  
From Figure 3, we can see that the received signals can be attained from the equation \(\mathbf{r}_{j}=\mathbf{U}_{j}^{T}\mathbf{y}_{j}\). Due to the orthogonal properties, \(\mathbf{H}_{j}\) and \(\widetilde{\mathbf{V}}_{i}^{(0)}\) satisfy the following condition \(\mathbf{H}_{j}\cdot\widetilde{\mathbf{V}}_{i}^{(0)}=\mathbf{0}\), \(i\
eq j\). When the DC bias is removed [26], we can obtain  
\[\overline{\mathbf{r}}_{j}=\mathbf{\Lambda}_{j}\mathbf{x}_{j}+\mathbf{U}_{j}^{ T}\mathbf{n}_{j}=\mathbf{\Lambda}_{j}\mathbf{S}_{j}\mathbf{u}_{j}+\mathbf{U}_{j}^{ T}\mathbf{n}_{j}. \tag{18}\]  
Then we use the maximum likelihood (ML) detection method to recover the signal \(\mathbf{x}_{j}\) of the user \(j\)[26] as  
\[\widehat{\mathbf{x}}_{j}=\arg\max_{\mathbf{x}_{j}}p(\overline{\mathbf{r}}_{j} |\mathbf{x}_{j},\mathbf{\Lambda}_{j})=\arg\min_{\mathbf{x}_{j}}\|\overline{ \mathbf{r}}_{j}-\mathbf{\Lambda}_{j}\mathbf{x}_{j}\|_{F}^{2}, \tag{19}\]  
where \(\widehat{\mathbf{x}}_{j}\) that will be transmitted to the user \(j\) is the estimates of \(\mathbf{x}_{j}\).
## 2 Materials and Methods
### Extracting Significant Travel Time Patterns and Computing the Statistics of These Patterns
#### 2.2.1 Extracting Significant Travel Time Patterns  
The significant travel time patterns in this paper are defined as maximal repeats [8] that appear at least twice in gantry timestamp sequences and can not always be subsequence of the other patterns in these sequences. As shown in Figure 6, significant travel time patterns were extracted from gantry timestamp sequences downloaded from the \"TDCS\"; meanwhile, class frequency distribution of these patterns were computed. Note that the types of classes can be the combination of timestamp and vehicle types and are determined by observers or analyzers on purpose.  
To have a significant travel time pattern extracted for illustration, Figure 7 (resp. Figure 8) gives one significant travel time pattern whose \"Travel Time\" is 34 min (resp. 39 min) extracted from the gantry timestamp sequences in the southern (resp. the northern) direction of Freeway No. 5. In Table 4, for example, some of the class frequency distributions of the significant travel time pattern in Figure 7 (resp. Figure 8) is in the 1 st (resp. 4 th) row. The format of class frequency distribution of one pattern in this study is defined as (\"Date\" \"Weekday\" \"VehicleType\"#\"TF\"), where the \"Date\" and \"Weekday\" present the date and the weekday that pattern happened, respectively, the \"VehicleType\" gives the type of vehicle, and the \"TF\" means the frequency of vehicles matching that case of class frequency distribution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Taxel Time Pattern** & **Gantry Sequence** & **TimeStamp (h/min)** & **Taxel Time (h/min)** & **Frequency** & **Class Frequency Distribution (Date Weekay Vehicle TypeFT)** \\ \hline \multirow{4}{*}{**1**} & 05F0000S & 0.09 & & & (2017-01-05 Thu 32 ft) \\  & 05F005S & 0.13 & & & (2017-01-30 Mon 318 ft) \\  & 05F0287S & 0.30 & & & (2017-01-31 Fri 3162) \\  & 05F0309S & 0.31 & & & \\  & 05F049S & 0.39 & & & \\  & 05F049S & 0.43 & & & \\ \hline \multirow{4}{*}{**2**} & 05F0000S & 5.55 & & & (2017-01-28 Sat 3144) \\  & 05F005S & 6.02 & & & (2017-01-28 Sat 3263) \\  & 05F0287S & 6.26 & & & \\  & 05F0309S & 6.28 & & & \\  & 05F049S & 6.36 & & & \\  & 05F049S & 6.39 & & & \\ \hline \multirow{4}{*}{**3**} & 05F0528N & 0.11 & & & (2016-12-19 Mon 318 ft) \\  & 05F0493N & 0.17 & & & (2017-01-03 Me 31 ft) \\  & 05F0309N & 0.25 & & & (2017-01-06 Fri 328 ft) \\  & 05F052N & 0.27 & & & (2017-01-24 Tue 318 ft) \\  & 05F050S & 0.42 & & & (2017-03-13 Mon 32 ft) \\  & 05F001N & 0.46 & & & \\ \hline \multirow{4}{*}{**4**} & 05F0528N & 13.03 & & & (2016-12-68 Thu 418 ft) & (2017-01-22 Sun 318 ft) \\  & 05F0493N & 13.09 & & & (2016-12-15 Thu 318 ft) & (2017-02-22 Wed 418 ft) \\  & 05F0309N & 13.17 & & & (2016-12-25 Fri 418 ft) & (2017-03-10 Fri 418 ft) \\  & 05F0528N & 13.37 & & & (2017-01-19 Tu 42 ft) & \\  & 05F0010N & 13.42 & & & (2017-01-20 Fri 418 ft) & \\ \hline \end{tabular}
\end{table}
Table 4: Examples of significant travel time patterns extracted from the Freeway No. 5 gantry timestamp sequences.
## 2 Materials and Methods
### Similarity Criterion and Control Scale
Sediment suspension similarity condition: \(\lambda_{\rm w}~{}=~{}\lambda_{\rm V}\Big{(}\frac{\lambda_{\rm H}}{\lambda_{ \rm L}}\Big{)}^{\rm m}\)  
Similarity conditions for sediment incipient motion: \(\lambda_{\rm v}~{}=~{}\lambda_{\rm v_{c}}\)  
Similarity conditions for bed deformation caused by a suspended load: \(\lambda_{\rm t_{2}}~{}=~{}\frac{\lambda_{\rm v_{0}}\lambda_{\rm L}}{\lambda_{ \rm s}\lambda_{\rm V}}\)  
In the above equations, \(\lambda_{\rm L}\) is the horizontal scale ratio, \(\lambda_{\rm H}\) represents the vertical scale ratio, \(\lambda_{\rm J}\) denotes the slope scale ratio, \(\lambda_{\rm V}\) indicates the flow velocity scale ratio; \(\lambda_{\rm v_{C}}\) is the sediment incipient velocity scale ratio, \(\lambda_{\rm w}\) represents the sediment settling velocity scale ratio, \(\lambda_{\rm n}\) denotes the roughness scale ratio, \(\lambda_{\rm s}\) indicates the sediment concentration scale ratio, \(\lambda_{\rm s}\), represents the sediment carrying capacity scale ratio, \(\lambda_{\rm t_{2}}\) denotes the bed deformation time scale ratio, and \(\lambda_{\rm v_{0}}\) indicates the unit weight scale ratio. According to [PERSON]'s formula for the sediment settling scale and [PERSON] settling formula [24], the grain size scale for suspended sediment was determined as 1.24. The model layout is shown in Figure 4. The specific model test scale is shown in Table 2 below. Figure 4: Model production process and layout rendering. Figure 3: Generalization of incoming water and sand from the model test. (**a**) Discharge generalization chart and (**b**) sediment concentration generalization chart.