esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
Thus, the objective was to determine the group of bands where the models exhibit the best performance using the 10-fold cross-validation method (K-Fold), which is a technique that, by partitioning the dataset into folds, allows for the assessment of the model's robustness and generalization capabilities across different subsets of data. Finally, in the deployment phase, the model that achieved the best cross-validation performance with the optimal number of bands was identified to obtain the confusion matrix along with its associated metrics. Additionally, considering that decision tree-based models yielded the best results, the rules or criteria used to determine the condition of an AC roof were identified. Figure 2: (**a**) Synthetic data included in the dataset; (**b**) zoom of the synthetic spectra signature. The selection of the five machine learning models--decision trees, K-Nearest Neighbors (KNN), logistic regression, support vector machine (SVM), and random forest--was made based on their distinct characteristics and effectiveness in classification tasks. Decision trees were chosen for their interpretability and ability to handle complex decision boundaries, which aids in understanding the conditions of asbestos--cement roofs. K-Nearest Neighbors (KNN) provides a straightforward approach to classification based on similarity, which is useful for comparing spectral signatures. Logistic regression offers a baseline method for binary classification problems, against which more complex models can be compared. Support vector machine (SVM) is well-suited for high-dimensional spaces and complex patterns, while random forest combines multiple decision trees to enhance accuracy and robustness, particularly in noisy datasets. This diverse selection of models ensures a comprehensive evaluation and robust results, addressing different aspects of the classification challenge.
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 2 Materials and Methods
### The Improvement of Detection Method with Multi-Object Tracking
The Mahalanobis distance measure is ignored owing to the large jitter of motion onboard camera in this paper. Finally, cascade matching and IOU (Intersection over Union) association are employed to relieve trajectory changes or loss caused by occlusion.
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 2 Ground Tests and on-Orbit Validation  
This research was designed in two parts: the ground test and on-orbit validation. The ground test was used to collect data to train the model and to verify it by using the on-orbit data. The ground testing site and on-orbit situation were described in detail as follows.
## 3 Results and Discussion
### Experimental Setup  
The experimentation was carried out using a Dell Alien machine, Intel (R) Core (TM) i7--7700 HQ CPU @ 2.80 GHz, having 32 GB RAM. The operating system used was Microsoft Windows with open source software and library programs. Numerous parameters for computing EMAPs have been found, concerning mean value 2.5-10%, standard deviation value 2.5%, and threshold range is (200-500). The experiment is performed by running independent Monte Carlo iterations 10 times and has empirically found the optimum number of samples to generate outstanding results. Before and after this sampling threshold, accuracy suffered degradation. After classification, the MAP estimation was passed to the segmentation algorithm, where the MLL algorithm was used for segregating the classes. Here, different accuracy measures were tabulated for each class corresponding to AL methods, namely RS, ME, MI, MB, and MBT. The classification results in terms of overall for all these AL techniques, in the case of the Indian Pines dataset, varied from 98.13 to 99.93%. The Indian Pines ground truth is pictorially shown in Figure 2(a), while the classification and segmentation pictogram are shown in Figure 2(b), respectively. These results may be compared subjectively based on the perceptual information within the original images shown in Figure 2(a). The quantitative performance of classification and segmentation is illustrated in Tables 1 and 2 for Indian Pines. The results were outstanding for classification for the Indian Pines dataset as shown in Table 1, having OA, AA, and kappa statistics as 99.93, 99.86, and 99.84%, respectively. Whereas for segmentation, each of the performance measures (OA, AA, and kappa) have been found as 99.98%, as shown in Table 2. For classification, most of the past methods were based on the exploitation of spectral information [50]. Researchers having similar thoughts relied on the fact that single spectral information is enough for better classification [14; 51; 52]. These extracted spectral features, representing independent characteristics, were fed to the classifier without using the spatial information in contiguous pixel dependency. [PERSON] et al. [53] addressed the solution of this issue, that exploitation of the spectral information only is not enough for classification. Spatial information should incorporate with spectral information for better results [54; 55; 56; 57; 15; 30; 54]. [PERSON] et al. [28] have also shown the importance of spatial information with spectral information. [PERSON] et al. [58] have elaborated hyperspectral image classification with a combination of SVM using self-organizing maps (SOMs). This technique is divided into two phases; the first one is based on the training of the SVM using SOM, while the second is based on the calculation of probabilities by finding the interior and exterior pixels, so the best label should be decided based on an optimal threshold. The comparison of existing techniques using the Indian Pines datasets is shown in Table 3 (Section 3.2).
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Synthetic Data
#### 3.1.1 Evaluating the Performance of Marker Detection  
The performance of the marker detection algorithm was evaluated by comparing the ground truth and detected feature center points for each of the 20 images of the main synthetic dataset, as shown in Figure 9. The difference in image coordinates between the detected and ground truth points is shown in Figure 10 in the \(x\) and \(y\) dimensions. This representation shows that error in feature detection is well in the sub-pixel range for the studied range of positions. For comparison, results of the gold-standard algorithm for checkerboard detection with sub-pixel refinement of [52] are included.  
Figure 8: Example synthetic images of the checkerboard (**a**) and the bead array (**b**) targets, at the same relative position.  
Figure 9: Cropped section of a synthetic image of the bead array target. The center of the marker detected from the image is shown in red, while the ground truth value is shown in blue.
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
## 1 Introduction
Therefore, it is necessary to predict the tank pressure due to the vaporization of liquid hydrogen in order to design the structure of cryogenic liquid hydrogen tanks. The maximum storage pressure of the tank is one of the important considerations in the structural design of a hydrogen fuel tank. The design of gaseous hydrogen tanks is relatively simple because the design pressure is determined at the beginning of the design. In contrast, the design of a cryogenic liquid hydrogen tank is complicated by additional factors. These design considerations include the tank shape, the insulation performance, the amount of liquid hydrogen filling, and the fuel consumption rate. These considerations affect the maximum pressure and pressure behavior of the liquid hydrogen tank. The structures and the insulation area are changed according to the shape of tank. The insulation performance of the tank affects the amount of vaporized liquid hydrogen and the pressure inside the tank. In addition, the maximum pressure of the tank varies according to the amount of liquid hydrogen stored in the tank [24], and the fuel consumption rate affects the pressure behavior [25; 26]. Therefore, in designing a liquid hydrogen fuel tank, a technique for predicting the maximum internal pressure that considers the heat inflow, liquid hydrogen filling amount, and fuel consumption rate is required. This study aims to develop a thermodynamic model that can predict the maximum internal pressure and pressure behavior of cryogenic liquid hydrogen tanks. The heat leakage of the tank by heat transfer is applied by dividing it into a liquid area and a gaseous area. The thermodynamic model considers the phase change from liquid hydrogen to gaseous hydrogen [27; 28], and estimates the temperature and pressure in the gas region. The analysis is performed as a transient analysis. The tank pressure behavior depends on fuel discharge and is investigated by selecting a commercial fuel cell to calculate the fuel consumption rate. The analysis of the cryogenic liquid hydrogen tank is carried out for three cases: without fuel consumption (storage tank), with fuel consumption immediately after liquid hydrogen filling, and with time delay before fuel consumption after fuel charging. Finally, the prediction accuracy and applicability of the proposed thermodynamic model are confirmed.
## 2 Materials and Methods
### Determination of the Effectiveness of the EOs Against the Tested Bacteria  
The minimum inhibitory concentration (MIC) of EOs isolated from seeds and cones, along with positive controls, such as thymol (Sigma-Aldrich, Darmstadt, Germany) and gentamicin sulfate (Krka, Novo Mesto, Slovenia), was established against the above bacterial strains according to the serial dilution method in a Mueller-Hinton broth (MHB, Sigma-Aldrich, Darmstadt, Germany), according to the Clinical and Laboratory Standards Institute recommendations [11]. A stock solution of the tested essential oils was prepared with the incorporation of Tween 80 (1.0%, \(v/v\)) (Sigma-Aldrich, Darmstadt, Germany), resulting in a range of concentrations from 250 to 0.03 \(\mu\)L/mL. Subsequently, 50 \(\mu\)L of each EO concentration was dispensed into a 96-well microplate, followed by the addition of 50 \(\mu\)L of a bacterial suspension with a concentration of 106 CFU/mL. After an incubation period of 18 h at 37 \({}^{\circ}\)C, the MIC was determined by adding 20 \(\mu\)L of 0.02% a resazurin solution (Sigma-Aldrich, Darmstadt, Germany) to the wells. The presence of bacteria was indicated by a color change from navy blue to pink after incubation with resazurin at 37 \({}^{\circ}\)C for an additional 3 h. The MIC value was regarded as the first well where the navy blue color persisted. Additionally, a positive control was conducted utilizing gentamicin (Krka, Slovenia) at a concentration of 40 mg/mL, and the test was replicated for each substance in triplicate. The minimum bactericidal concentration (MBC), defined as the lowest oil concentration that results in the death of 99.9% of the bacterial population, was identified by transferring 20 \(\mu\)L of bacterial cultures from wells with concentrations exceeding the MIC into fresh MHB-filled wells. This incubation also occurred for 18 h at 37 \({}^{\circ}\)C, and the MBC was established as the concentration at which no bacterial growth was detectable in the respective well [12]. To check the efficacy of the substances, the ratio of MBC to MIC was calculated, where a ratio of MBC/MIC \(\leq\) 4 indicated a bactericidal effect, and a ratio of MBC/MIC > 4 denoted a bacteriostatic effect.
## 1 Introduction  
Breast cancer is the most frequent cancer among females, amounting to 24% of all cancer occurrences in 2018 [1], accounting for 684,996 deaths in 2020 worldwide [2]. The World Health Organization indicates screening programs aimed at early detection as one of the key factors in reducing mortality [3]. Magnetic Resonance Imaging (MRI) is an increasingly popular procedure for the screening of high-risk groups [4] and evaluating the response to neo-adjuvant chemotherapy [5]. MRI has several benefits over X-ray mammography; it does not utilize ionizing radiation, generates high-resolution images and contains dynamic information. Moreover, recent developments in MRI image processing [6] indicate that MRI is gaining popularity even with its major drawbacks (time-consuming, stressful and costly) and is actively being targeted by the research community.  
Dynamic Contrast Enhanced MRI (DCE-MRI) outputs four-dimensional data (three spatial dimensions + one temporal dimension), consisting of images acquired before and after the intravenous injection of a contrast agent (CA). The change in tissue appearance in response to the CA is tissue-specific and, therefore, indicative of the presence of malignant breast lesions [7]. These changes in tissue appearance are extremely similar to the ones of the internal organs such as the heart, making automatic lesion detection in breast DCE-MRI sequences challenging. Manual delineation of the chest wall is an extremely time-consuming activity. Therefore, the automatic removal of the internal organs from the images by segmenting the chest cavity is instrumental to the development of an automatic lesion detection methodology. Figure 1 shows the results of an automatic lesion detection algorithm for a properly segmented image and a poorly segmented image of a breast DCE-MRI sequence. The algorithm is based on the statistical properties of the whole sequence and indicates areas with a high likelihood of containing a lesion, with red indicating the most likely candidate area. By not excluding the chest cavity, the system evaluates the heart as an area of high likelihood. As the chest cavity is segmented, the actual lesion is correctly highlighted.  
The current state of the art addresses the challenge by training a three-dimensional cluster of 2D UNets [8]. We argue that implementing more recent deep learning (DL) techniques can lead to better results and generalization performance.  
In this study, a novel, DL-based methodology for the segmentation of the chest cavity from breast DCE-MRI scans is proposed. The solution aims to address the main challenges in chest cavity segmentation, especially sternum detection, by using a data-based approach. To showcase the potential of the solution, the target area to segment is selected to be the upper half of the chest cavity.  
The paper is organized as follows. In Section 2, a selection of previous studies with relative contributions is presented; in Section 3, the proposal is presented in detail; in Section 4, the results of the experiments are presented and discussed; finally, in Section 6, conclusions and suggestions for further work are presented.
## 1 Introduction
[15] proposed a Laplacian regularized LRR (LapLRR) to enhance low-rank subspace clustering by utilizing manifold property of data. [PERSON] at el. [16] improved the low-rank representation via sparse manifold adaption for semi-supervised learning. [PERSON] et al. [17] proposed a Non-negative Sparse Laplacian regularized LRR model (NSLLRR) to improve the performance of LRR. Recently, [PERSON] et al. [18] proposed a LRR model on Grassmann manifold with Laplacian graph constraint. [PERSON] et al. [19] proposed an adaptive way to construct the affinity matrix for eliminating the interference of noise in data. Except for the above Laplacian graph constraints, other constraints like the block-diagonal form of the affinity matrix [20; 21; 22] and the covariance of the data [23] are also exploited to improve subspace clustering performance. In spite of obtaining improvement by utilizing the non-linear property hiding in the high dimensional data, most of the current methods justly represent this non-linear property by the Laplacian graph constructed directly from the similarity of the raw data under Euclidean distance, which is hard to fulfill modeling the intrinsic manifold structure. Additionally, these methods are easily biased by the noise and outliers existing in the raw data. Facing these problems, we think that the manifold learning methods, such as Local Linear Embedding (LLE) [24], Isomap [25] and Laplacian Eigenmaps (LE) [26], etc. give a possible solution, as they adopt a data-driven manner to learn and represent the non-linear structure of data and show robustness to noise or outliers. So we try to integrate manifold learning methods and the subspace clustering methods, and construct a subspace clustering method for high dimensional data based on manifold learning. Here, we adopt the classic LLE manifold learning method to learn the non-linear structure in the data and embed it into SSC and LRR subspace clustering procedure, thus we propose two new subspace clustering methods, namely LLE-SSC and LLE-LRR. The main contributions of this paper are listed as follows:* A new framework of clustering is constructed by integrating the manifold learning methods with subspace clustering methods. * Instead of building the Laplacian graph from the raw data, the manifold learning method, LLE, is adopted to reveal the non-linear property of the data for clustering, and this strategy is considered superior in dealing with noise and outliers. * To solve the complicated optimization problem involved in the proposed LLE-SSC and LLE-LRR models, an efficient algorithm is determined as the solution. The rest of the paper is organized as follows. In Section 2, we summarize the SSC and LRR subspace clustering methods and review the related works of our model. In Section 3, our proposed models, LLE-SSC and LLE-LRR, are described in detail and their solutions are given. In Section 4, the performance of the proposed method is evaluated on several public datasets. Finally, conclusions and future work are provided in Section 5.
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 4 Simulation Results
### Scenario 2: GPEBO Performance Test  
The performance of GPEBO is tested in this scenario. \(k_{p}=0.015,k_{i}=0.15\), \(\mu=1\times 10^{-6}\). By selecting a different observer gain \(\gamma\), the convergence result of GPEBO is revealed, as shown in Figure 6. One can observe that the larger \(\gamma\) results in a quicker convergence rate. Interestingly, the initial amplitude of the estimated current does not increase with the rise in the gain. The reason is that the control gain is fixed during the GPEBO performance test. In this case, the real curve of the current is confirmed. Hence, the larger observer gain only allows the estimate of the current to converge to the real value more quickly. Instead, the smaller \(\gamma\) may result in this convergence process becoming slower, which may provide a bigger initial amplitude of the estimated current. This fact is clearly shown in the figure.  
Figure 4: Boost converter transient under the proposed sensorless controller. (**a**) Output voltage \(v\), (**b**) estimated current \(\hat{i}\), (**c**) duty ratio \(d\).  
Figure 5: Response curves of boost converter under the proposed sensorless controller with different references. (**a**) Output voltage \(v\), (**b**) estimated current \(\hat{i}\), (**c**) duty ratio \(d\).
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 5 Conclusions  
This paper researches an active STAR-RIS-assisted SWIPT system with a focus on minimizing transmission power from AP while satisfying communication rate and minimum energy collection requirements. The optimization strategy involves a joint tuning of the transmission precoding matrix, the reflection and transmission coefficient matrix, and the amplification matrix within the active STAR-RIS. The process begins by fixing the passive beamforming variables and applying SDR algorithm to determine the optimal active beamforming variables. Following this, the derived active beamforming variables are held constant, and a penalty-based algorithm is used to fine-tune the passive beamforming variables. This AO approach continues iteratively until the system reaches convergence. Compared to passive RIS, active RIS, and passive STAR-RIS configurations, the active STAR-RIS-assisted system provides substantial energy savings while maintaining consistent variables such as the quantity of transmitting antennas, the quantity of intelligent reflector units, the minimum power accepted by ERs, and the SINR at IRs. However, this paper only investigates the minimum transmission power of AP under perfect channel states and linear energy receiver models. In the future, we will study the problem of minimizing AP transmission power in more complex situations, such as in imperfect channel states and nonlinear energy receiver models.  
Conceptualization, C.G. and S.L.; methodology, C.G.; software, C.G.; validation, C.G., S.L. and M.W.; formal analysis, S.D.; investigation, Y.W.; resources, B.Y.; data curation, C.G.; writing--original draft preparation, C.G.; writing--review and editing, S.L.; visualization, S.L.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Chunhui plan international cooperation project of China Education Ministry under Grant 202201406 and Grant HZKY20220171, in part by the key research and development plan of Xuzhou under Grant KC23134 and Grant KC2083, and in part by 2023 Jiangsu Graduate Practice Innovation plan under Grant SJCX23_1378.  
Please contact the corresponding author for the data in this paper because the data in the research involve intellectual property.  
The authors declare no conflicts of interest.