esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Comparative Experiments
#### 4.1.2 Comparative Experiments on ISIC2017
Red contours represent ground truth boundaries, while green contours indicate predicted segmentation boundaries. Figure 13: Comparison results on the ISIC2017 dataset. (**a**) Segmentation result from U-Net; (**b**) segmentation result from LCAUnet; (**c**) segmentation result from EGE-Unet; (**d**) segmentation result from MA-DenseUNet. each model in a more comprehensive and accurate manner, a more thorough analysis of the outputs from existing advanced models has been conducted. The specific segmentation visualization results are shown in Figure 13. For skin lesion images characterized by large background areas and relatively small lesion regions, U-Net and other mainstream models are generally capable of accurately segmenting the principal lesion regions; however, significant discrepancies in boundary delineation remain when compared with the ground truth (as shown in Figure 14a,b). When the lesion occupies a larger proportion of the image, the performance differences among the models become more pronounced. As exemplified in Figure 14a, U-Net tends to exhibit under-segmentation, failing to fully capture the extent of the lesion area. Although LCAUnet and SCSONet demonstrate improvements over U-Net in segmenting the primary regions, LCAUnet produces excessively smoothed boundaries, leading to a loss of fine-grained details, while SCSONet struggles to accurately preserve irregular boundary structures when compared to the proposed MA-DenseUNet. Overall, these visual results substantiate that the proposed MA-DenseUNet can more effectively capture intricate lesion features and suppress noise-induced artifacts, thereby generating segmentation maps that more closely approximate the ground truth annotations. Figure 15 illustrates the variation in the accuracy and Dice coefficients during the validation process as a function of the number of training epochs. As the training progresses, the model gradually converges, exhibiting enhanced stability. Specifically, the fluctuations in the accuracy and Dice coefficients decrease over time, indicating that the model is continuously optimized during the training process, leading to stabilized performance with improved accuracy and robustness. Furthermore, the model's accuracy and Dice coefficients are significantly higher than those of other comparative models, demonstrating its superiority in the image segmentation task. This suggests that, as training advances, the model not only achieves more accurate segmentation, but also exhibits enhanced adaptability and generalization capabilities.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## 2 Methodology
### Energy Analysis Method  
In order to analyze the developed numerical model for the PV/T system, a performance evaluation is required. Therefore, the solar irradiance as an energy source was supposed uniform and perpendicular on the PV/T surface. Absorber solar irradiance by the PV cells layer was given as function of the glass layer transmittance \(\tau_{g}\), the cell absorptivity \(\alpha_{PV}\), and the incident solar irradiance \(E_{sun}\) by [33]:  
\[E^{\prime}_{sun}=\tau_{g}\alpha_{PV}\;\;E_{sun} \tag{24}\]  
The electrical production from the PV cells is determined by Equation (24):  
\[E_{e}=A_{pv}\;\eta_{e}E^{\prime}_{sun} \tag{25}\]  
For evaluation of the electrical performance, the following expression was used [30]:  
\[\eta_{e}=\eta_{ref}[1-\beta(T_{PV}-T_{STc})] \tag{26}\]  
where \(\eta_{ref}\) is the reference efficiency; \(\beta\) is the temperature coefficient; \(T_{PV}\) is the PV cells temperature; and \(T_{STc}\) is PV cells temperature at STC conditions equal to 25 \({}^{\circ}\)C. According to the AE320 HM-60 PV commercial unit with characteristic reported in Table 1, the \(\eta_{ref}\) is about 19.30% and \(\beta\) is equal to 0.0045 C\({}^{-1}\)[34].  
The thermal production of PV/T system is a function of fluid mass flow rate \(in\), fluid specific heat capacity \(C_{f}\), and the temperature difference of the fluid at the inlet and outlet of the PV/T system [35]:  
\[E_{th}=inC_{f}\;(T_{out}-T_{int}) \tag{27}\]  
Figure 2: Flow chart of the PV/T numerical modeling.  
For assessment of thermal efficiency, the fraction of the thermal production on the absorbed solar irradiance was conducted according to Equation (28):  
\[\eta_{th}=\frac{E_{th}}{E_{sun}^{2}} \tag{28}\]  
Combining both electrical and thermal efficiencies, the PV/T global performance was obtained by:  
\[\eta_{g}=\eta_{e}+\eta_{th} \tag{29}\]
## 2 Methodology
### Sampling Sites
(1) 3 plots in Fontainebleau forest: a forest plot of oaks and hornbeams sampled in winter 2010, a wild broadleaf forest plot sampled in summer 2012, and a plot of the same location sampled in winter 2013, which represent temperate deciduous trees in two typical seasons; (2) 1 plot of white oaks in OHP Region, representing temperate deciduous trees in spring; (3) 2 plots in Landes forest in September 2008, for different mature (\(\sim\)50 and \(\sim\)10 years, respectively) maritime pines, representing temperate conifer trees, which do not change much with the seasons; (4) 2 plots of tropical forests on Reunion Island in May 2014, one for tropical lowland rainforest, and the other for tropical montane cloud forests; (5) 1 plot of plantation of poplars in OHP Region in June 2012. The _FOT_ and _TTH_ of every lidar profiles in each plot were calculated through the inverse model; their mean and standard deviation values are shown in Figure 3. The temperate deciduous forests have a small _FOT_ in winter but a big _FOT_ in summer, because of tree crowns with dense leaf amounts. The _FOTs_ of temperate conifer forests of different ages (different _TTHs_) are similar. The tropical forests are dense and have a big _FOT_ as expected. However, the results show that the wild temperate broadleaf forests in summer can be denser than the studied tropical forests. These distributions of FOTs and _TTHs_ will be considered in our simulator. In the previous plots, several representative profiles were selected as the input of the EES for the numerical simulations. The vertical profiles of the extinction coefficient of sampling profiles were calculated through the inverse model; six of them are shown in Figure 4 as examples. The extinction coefficient not only significantly varies from one site to another, but also varies within each sampled site. The selected sample profiles are located at the center of the distribution in terms of both \(\alpha\) and _FOT_ in the sampled site. Figure 3: Mean (marker) and standard deviations (line segments) of lidar-derived forest optical thickness (_FOT_) and tree top height (_TTH_) for 9 forest plots. Trop. : tropical, Temp. : temperate, Deci. : Deciduous, Conif. : conifer. The tropical montane forest represents an approximate average of the measured biomes (Figure 3). A detailed distribution of the _FOT_ is given in Figure 5 for this case, which will be considered as the reference in the following. Figure 4: Examples of extinction coefficient profiles of 6 selected lidar profiles: (**a**) tropical montane cloud and rain forest; (**b**) temperate deciduous forest in both summer and winter, and (**c**) temperate confer forest with young (10 years old) and old trees (50 years old). Figure 5: Blue bars: Forest optical thickness (_FOT_) distribution in tropical montane cloud forest (Table 4). Black curve: cumulative probability density functions (CPDF)/Cumulative distribution function **F** of _FOT_.
## 4 Eliminating the Low Weight Non-Structured Codewords
### Rules for Designing Practical LDPC-CCs  
Through the above analysis, to obtain good distance spectrum when designing practical LDPC-CCs based on a given weight matrix, we propose two design criteria to follow:  
* ensuring that each super code does not contain any codewords with weight smaller than the minimum weight of the structured codewords calculated using Lemma 1;
* eliminating the low weight non-structured codewords of the super codes as many as possible.  
In order to eliminate the non-structured codewords in the super codes, a two-step calculation is performed as follows:  
**Step 1**: Compute the enumerator for the structured codewords \(E_{\mathcal{C}}\) that the super code contains. In this paper, the structured codewords are calculated based on the formation of the unavoidable cycles [21]. For example, the weight matrix of the super code formed by the second and the third columns of Equation (12) is an all-one matrix of size \(5\times 2\). According to the result in [21], this weight matrix contains \(\binom{5}{3}\) = 10 unavoidable cycles of length 12. If an all-one weight matrix contains only two columns or two rows, the structure of an unavoidable cycle of length \(n\) in the weight matrix forms a structured codeword of weight \(n/2\). Hence, we have \(E_{\mathcal{C}}=10\) for the super code formed by arbitrary two columns of Equation (12).  
**Step 2**: For each entry in the polynomial syndrome former matrix of the super code, we randomly choose a monomial with power smaller than the syndrome former memory \(m_{s}\) and calculate the number of the codewords that the associated super code has. If it is larger than \(E_{\mathcal{C}}\), this monomial is replaced by another one until a valid one is found. If all of the monomials with power smaller than \(m_{s}\) have been tested, we simply increase the value of \(m_{s}\) and repeat the process in Step 2.  
Figure 1: Decoding performance of the codes defined in Equations (12) and (8).
# Deep Learning of GNSS Acquisition
## 6 Conclusions and Future Work  
Deep learning is a powerful data-driven tool which is increasingly being used in multiple fields and applications. This work proposes to use deep learning as a substitute to standard GNSS signal acquisition processing, a well-understood block present in all GNSS receivers. The proposed approach leverages a DNN classifier to output posterior class probabilities when the input is a region of the CAF for a specific satellite. The splitting of the CAF enables the flexible use of the method on CAFs of different dimensions (depending on the delay/Doppler bin sizes), as well as allowing for the parallelization of the process through multiple smaller DNN models. It is shown that the deep learning method can outperform standard approaches, even exceeding their fundamental limits in moderate-to-high signal-to-noise ratios. This result can be explained by the fact that standard methods are based on the bin maximization of the CAF, whereas the proposed data-driven method exploits the correlation across neighboring bins. Additionally, an optimal fusion rule is provided in order to extend the methodology to noncoherent integration schemes, which is also seen to improve the overall classification performance. The use of deep learning for advanced GNSS receiver design is in its infancy, from which many research directions can be foreseen. In the context of the framework proposed in this paper, future work includes the study of the proposed deep learning methodology in the presence of other sources of  
Figure 9: Test statistic histograms under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) hypotheses for a 1 ms coherent integration time and \(K=6\) noncoherent integrations for a range of relevant \(C/N_{0}\) values. In this case, the two histograms are clearly separated, which supports the performance results in Figure 6.  
errors, such as receiver clock instabilities, higher receiver dynamics and the presence of jamming interferences or spoofing signals, as well as testing on real datasets.  
P.B.-D.: conceptualization, software, methodology, and writing--original draft; H.L.: methodology, discussions, and review; P.W.: methodology, discussions, and review. P.C.: conceptualization, funding acquisition, methodology, supervision, validation, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the NSF under Award ECCS-1845833.  
The authors declare no conflict of interest.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 3 Experimental Analysis
### Comparion and Analysis of the Proposed Model with Other Models
#### 3.4.2 Comparison of the Proposed Model with Traditional Machine Learning Algorithms  
In machine learning-based time series forecasting algorithms, SVR can handle the nonlinear issues in the data, BP effectively trains deep networks using the backpropagation algorithm, and RF enhances prediction robustness by aggregating multiple decision trees. Therefore, these three methods are commonly used for wind power forecasting [32; 33; 34]. The model proposed in this paper is a time series forecasting model based on deep learning, aiming to suppress noise and enhance the model's ability to capture nonlinear relationships through EWMA and KAN. Compared to traditional algorithms, this model can automatically learn deep features of the data, making it superior when dealing with high-dimensional and nonlinear characteristics while also exhibiting greater generalization capability. To verify the superiority of the proposed model over traditional machine learning algorithms, it is compared with traditional machine learning algorithms such as SVR, BP, and RF. The comparison of model prediction curves is shown in Figure 16, and the test set evaluation metrics are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE/MW** & **RMSE/MW** & **R\({}^{2}\)/\%** \\ \hline SVR & 4.55 & 17.52 & 92.88 \\ BP & 7.00 & 12.86 & 96.16 \\ RF & 4.55 & 8.47 & 98.33 \\ EWMA-KAN-Transformer & 4.38 & 7.37 & 98.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of test set evaluation metrics between the EWMA-KAN-Transformer model and traditional machine learning algorithms.  
Figure 16: Comparison of prediction curves between the EWMA-KAN-Transformer model and traditional machine learning algorithms.  
From Figure 16, it is evident that the model proposed in this paper has the best prediction accuracy compared to traditional machine learning algorithms. In Table 6, the proposed EWMA-KAN-Transformer model shows improvements compared to the SVR model; MAE and RMSE are reduced by 0.17 MW and 10.15 MW, respectively, while R\({}^{2}\) increases by 5.85%. Compared to the BP model, MAE and RMSE are reduced by 2.62 MW and 5.49 MW, respectively, with an increase of 2.57% in R\({}^{2}\). Compared to the RF model, MAE and RMSE are reduced by 0.17 MW and 1.10 MW, respectively, and R\({}^{2}\) increases by 0.40%. Thus, the proposed model demonstrates the highest prediction accuracy compared to the four traditional machine learning algorithms.
# Minimizing Travel Time and Latency in Multi-Capacity Ride-Sharing Problems
### Motivation  
Many ride-sharing companies (see [1]) provide a service (carpooling, ride-sharing, etc.) where customers submit their requests and then wait for the company to assign them a car. Consider a large number of requests in a working day morning, each consisting of a pick-up and a drop-off location. The company has a number of available cars whose locations and capacity \(c\) are known. The value of capacity \(c\) can be seen as the capacity of each car over time, i.e., the number of requests a car can accommodate in a relevant period of time. This value may well differ from the instantaneous capacity of a car (say the number of seats), as a pair of requests served by the same car may not be served simultaneously as one request is dropped off before the other request is picked up. In order to achieve a balanced allocation of requests to cars, each car receives no more than \(c\) requests. The task then is to assign the maximum number requests to available cars with respect to the capacity constraint.  
It is a fact, however, that in many practical situations, \"each request is allowed to occupy at most two seats in a car\" (see Uber [1]). A regular vehicle has 4-8 seats; thus, only a limited number of requests can be combined in a single vehicle; this can be modeled by taking \(c\leq 4\).  
Consider the application of our problem in the area of collective transport. For instance, the company TransVision [2] provides transport service for specific groups of people (patients, commuters, etc.), and they organize collective transport by collecting requests in a particular region of The Netherlands in advance, combine these requests, and assign them to some regular transport companies. To access their service, customers must make their request the evening before the day of the actual transport; the number of requests for a day often exceeds 5000. In this application, each server (car, bus, etc.) may pick up more than four requests during its working period; hence, a value of \(c>4\) can be appropriate.  
We can capture the above scenarios by the following problem: There is a set of customers who have specified their pick-up locations and drop-off locations to the vehicle provider, and the provider has a set of cars (also with drivers) that have a specified location and capacity \(c\). The task, in this paper, is to assign customers to vehicles without exceeding the vehicles' capacities and plan a service route for each of the vehicles based on optimization criteria, either minimizing the total travel time in \(\text{CS}_{sum}\) or minimizing the total latency in \(\text{CS}_{lat}\).  
The problems \(\text{CS}_{sum,s=t}\) and \(\text{CS}_{lat,s=t}\) are natural special cases of \(\text{CS}_{sum}\) and \(\text{CS}_{lat}\), respectively, and can be used to model situations where parcels have to be delivered to clients (whose location is known and fixed). For instance, one can imagine a retailer sending out trucks to satisfy clients' demands where each truck is used to satisfy multiple clients.
# YOLOv7 Optimization Model Based on Attention Mechanism Applied in Dense Scenes
## 1 Introduction  
Since the development of computer vision, object detection in dense small targets has been a very challenging topic. In dense scenes, many targets need to be detected, the background is complex, and the occlusion problem is common, which greatly affects the accuracy. However, due to the practical application requirements, such as mask recognition detection in high traffic scenarios, crop fruit detection, etc., the human detection workload is large, the recognition accuracy is unstable, and the execution efficiency is low. So, the high-accuracy object detection recognition method for dense scenes is of great research significance.  
Nowadays, object detection has been widely used in the field of deep learning, which can be divided into two main types, respectively, one-stage-based regression and two-stage-based RPN (Region Proposal Network) [1]. The one-stage algorithm starts from the original YOLO (You Only Look Once) [2] and gradually develops SSD (Single Shot MultiBox Detector) [3], YOLOv2 [4], RetinaNet [5], YOLOv3 [6], etc., and it directly uses the whole image as the network input and obtains the position of the target-enclosing frame and the class of the target by only one forward propagation. The one-stage algorithm is fast in detection but suffers from low accuracy and poor detection of small objects. While two-stage starts from the original R-CNN [7], and gradually develops SPPNet (Spatial Pyramid Pooling) [8], Fast-RCNN [9], and Faster-RCNN [10]. The two-stage algorithms need to generate region proposals by heuristic methods (selective search [11]) or CNN networks (RPN) and then classify region proposals. Although its accuracy will be higher than that ofthe one-stage algorithm, the feature repetition calculation is too large and the training speed is too slow. And, after the one-stage algorithm is continuously optimized, the drawbacks are compensated in YOLOv4 [12], YOLOv5 [13], YOLOv6 [14], and YOLOv7 [15], which are continually developed in the YOLO series.  
Most of the previous studies have used older model versions, and suitable application scenarios for the latest YOLOv7 have not yet been found. So, this paper improves the latest YOLOv7 deep learning model and proposes the YOLOv7B-CBAM network model to deal with small target detection in dense scenes. The main innovations and contributions of this paper are as follows:  
1. An improved YOLOv7B-CBAM model based on the attention mechanism is proposed to enhance the performance of the YOLOv7 model using the CBAM attention mechanism to achieve high-precision, real-time detection in complex and dense scenes.
2. Comparing the results of three improved YOLOv7 models based on different attention mechanisms on the VOC dataset and proposing the YOLOv7B-CBAM model with the highest accuracy, which demonstrates the superiority of the proposed model in accuracy.
3. Realizing real-time, high-accuracy detection on the two different datasets demonstrates the generalization and applicability of the proposed model in different complex scenes.
## 4 Experiments
### Results  
#### 4.5.1 Luminance Enhancement Curve Effectiveness Experiment  
Figure 7 shows an example of the curve parameter plot \(A_{n}\) for the three RGB channels, demonstrating the validity of the luminance enhancement curve (Equation (11)). For visualization, we averaged the curve parameter plots over eight iterations and normalized the values to the range [0, 1]. The average best-fit curve parameter plots for the R, G, and B channels were denoted by the letters \(A_{n}^{R}\), \(A_{n}^{G}\), and \(A_{n}^{B}\), respectively. Heat maps were used to visualize the mappings, as shown in Figure 7 images (b), (c), and (d). There were correlations and differences between the three channels of the low-light image, as seen by the best-fit parameter maps for the various channels, which had similar tuning trends but with distinct values. It can be seen that for any of the RGB channels, the enhancement values were smaller in the bright regions and larger in the dark regions.
# A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
## 3 Multi-Input Machine Learning Modelling
### Autoencoder Model  
To evaluate the performance of iterative models, a comparison with the state-of-the-art autoencoder (AU) algorithm is carried out. The AU has been regarded as an anomaly detector algorithm [17; 25; 26], which can be applied in applications where it is necessary to establish whether a system is moving away from the nominal condition. This algorithm (represented in Figure 5) is a particular type of neural network that performs two main operations:  
* Encoder function: it applies different transformations to the input data (**X**) by means of successive layers, which generates a compressed representation of the input (**H**) in a new feature space usually called _latent space_. The mathematical expression is: \(\mathbf{H}=f(\mathbf{X})\).
* Decoder function: starting from the latent space, it applies different transformations to determine a reconstruction (**R**) of the input. The mathematical expression is: \(\mathbf{R}=g(\mathbf{H})\).  
Figure 5: Autoencoder structure.  
To evaluate the performance in reconstructing the input, the residual between the input and output is evaluated:  
\[\mathbf{E}_{\text{AU}}=\mathbf{R}-\mathbf{X} \tag{5}\]  
where \(\mathbf{R}\) and \(\mathbf{X}\) are vectors with dimensions \(N\times 1\) (number of measurements). So, \(e_{\text{AU}}\) is a vector with the same dimension, whose elements represent the reconstruction error for all measurements. If the AU is trained with nominal data, it learns to minimise the reconstruction error when nominal data are processed and to raise it when anomalous data are encountered.