esa-sceva/satcom-chunk-collection huggingface dataset:
# CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements
## 2 Methodology
### Wind Turbine Model  
The twenty-five 2 MW wind turbines in the wind farm are designed and manufactured in China by China Shipbuilding Industry Corporation (CSIC) Haizhuang (HZ) Windpower Equipment Co., Limited (Chongqing, China). Table 1 contains the basic information about the wind turbine. A computer-aided design (CAD) model of the blade was provided by the blade manufacturer (confidential), which was used to extract six airfoil profiles needed for the AD and AL models. The airfoil profiles were used as input to the viscous-inviscid airfoil flow solver, \(Q^{3}UIC\)[15], to generate the polar data containing the lift and drag coefficients as a function of the angle of attack.  
The power and thrust as a function of wind speed were provided by the manufacturer, but not the rotor RPM (revolutions per minute) and pitch schedule. Therefore, the AD and AL models were developed iteratively to match as closely as possible to the manufacturer's data as well as the wind turbine measurement data for the RPM and pitch. Figure 1a,b depict the electrical power and rotor thrust as a function of the wind speed from the manufacturer's data and the developed model used in the AD and AL techniques. Figure 1b shows that the developed wind turbine model over-predicts the rotor thrust between 3 and 10 m/s compared to the manufacturer's data. However, an estimate of rotor thrust based on strain-page measurements showed deviations from the manufacturer's data as well [16], and is in close agreement to the wind turbine model. Nevertheless, rotor thrust discrepancies will influence the wake deficit comparisons shown in Section 3.2.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 2 Related Work  
### Traditional Methods  
Routing problems have garnered significant attention in recent decades due to their broad applicability across diverse domains. Traditional solution strategies fall into two primary categories: exact and heuristic algorithms. Exact algorithms, such as Concorde [14], branch-and-cut [15], and branch-cut-and-price [16], leverage pruning and partitioning techniques to systematically reduce the search space and identify theoretically optimal solutions. Notable advancements include the work of [PERSON] et al. [17], who introduce novel valid inequalities for routing problems and develop cut-pool and separation procedures to enhance solution quality. Similarly, [PERSON] et al. [18] propose a price-cut-and-enumerate approach that establishes lower bounds for tour optimization, accelerating the overall solv  
Figure 1: Overall framework of the proposed DRL-DM approach. Our DRL-DM is built upon an encoder–decoder architecture designed to facilitate efficient routing decisions. The encoder is responsible for transforming raw input features into high-dimensional node embeddings, enabling effective extraction and representation of structural patterns within the routing instance. It comprises \(L\) stacked layers, each consisting of an MHA sublayer followed by an FFN sublayer, which jointly models complex inter-node dependencies. The decoder operates in an autoregressive manner to construct routing solutions sequentially. At each decision step, it employs an MHA sublayer and a SHA sublayer to compute a context-aware probability distribution over the candidate nodes. This distribution guides the selection of the next node to visit, progressively generating a feasible route until completion.  
ing process. While exact algorithms guarantee optimal solutions for small-scale instances, their computational complexity escalates exponentially with problem size, limiting their practical applicability to larger-scale routing scenarios.  
By contrast, heuristic algorithms, while lacking guarantees of optimality, offer a pragmatic balance between solution quality and computational efficiency, making them well-suited for large-scale routing problems. [PERSON] et al. [19] introduce a genetic algorithm with an extended sorting scheme to approximate Pareto-optimal solutions for TSPs. [PERSON] et al. [20] develop a simulated annealing approach integrated with variable neighborhood search to optimize routing for heterogeneous fleets, providing valuable decision-support tools for government agencies and logistics enterprises. [PERSON] et al. [21] address the challenges of electric vehicle routing by incorporating charging constraints into a hierarchical ant colony optimization framework. Despite their effectiveness, heuristic algorithms require extensive trial-and-error tuning and rely heavily on domain-specific expertise, limiting their adaptability across diverse problem settings.
## 2 Applied Theory
### Design of Stability Augmentation Systems  
The longitudinal stability augmentation system (SAS) can improve the characteristics of the oscillation frequency and damping of the short-period modal by feeding back the angle of attack and pitch rate to the elevator, thereby improving the flight quality of the aircraft over a wider range [38]. Figure 1 is the schematic diagram of the longitudinal stability augmentation system, in which the change in the angle of attack is felt by the sensor. After amplifying the signal by the system, the rudder control is deflected to reduce the change in the angle of attack through the steering gear and booster. The pitch rate sensor is usually a mechanical gyro device that measures the angular rate around the pitch axis.  
Figure 1: Longitudinal stability augmentation system. The characteristics of the oscillation frequency and damping of the short period modal are modified by feeding back the angle of attack and pitch rate to the elevator.  
Figure 2 shows the lateral/directional stability augmentation system adopted in this study, in which the body-axis roll rate is fed back to the ailerons and sweep angle to modify the roll subsidence modal, and the yaw rate is fed back to the rudder on the V-tail to modify the Dutch roll mode. Generally, the lateral (roll) motion is coupled with yaw and sideslip (directional) motions. Therefore, the augmentation systems will be analyzed with the aid of multivariable state equations (three inputs, ailerons, rudder, and asymmetric sweep angle, and two or more outputs). The purpose of the stability augmentation yaw rate feedback is to use the rudder to generate a yaw moment that opposes any yaw rate that builds up from the Dutch roll mode. The resulting feedback configuration is a type 0 yaw rate command system with zero command input. This raises the following difficulty: in a coordinated steady-state turn, the yaw rate has a constant non-zero value. It may be thought that the pilot could easily coordinate the turn by applying the correct yaw rate command through the rudder pedals. A simple solution to the problem of implementing the yaw damper is to use a washout circuit on the output of the yaw rate sensor [38]. The high-pass filtering action of the washout circuit removes the steady-state component of the yaw rate during turns. The output of the washout approximates the differentiated yaw rate. In Figure 2, \(G_{w}\) is the washout circuit, \(G_{a}\) represents the transfer function of the control surface at the trailing edge of the outer wing part, \(G_{\Lambda}\) represents the transfer function of the asymmetric sweep angle change in the outer wing part, and \(G_{r}\) is the rudder actuator. The transfer functions \(G_{F}\) represent noise filtering and any effective lag at the output of the roll rate and yaw rate gyros.
## 4 An Illustrative Design Example
### Effectiveness of the CICBISs

Table 4: The cost index \(J_{\text{F}}\) of CASE A, CASE B and CASE C.  
Figure 10: The displacement frequency response functions (FRFs) of CASE A, CASE B, and CASE C: (**a**) the fourth layer inter-layer drift; (**b**) the top layer displacement. Hence, the seismic control effectiveness is investigated herein with a time history analysis. An artificial accelerogram and three recorded earthquake accelerograms, of which the peak accelerations are scaled to be 0.1 g, are employed to conduct the time history analysis. The artificial accelerogram is the BCJ-L2, provided by the Building Center of Japan. The recorded earthquake accelerograms extracted from the Pacific Earthquake Engineering Research Center database ([[http://peer.berkeley.edu/nga](http://peer.berkeley.edu/nga)]([http://peer.berkeley.edu/nga](http://peer.berkeley.edu/nga))) are the Kobe accelerogram (1995), the Chi-chi accelerogram (1999), and the El Centro accelerogram (1940). Figure 12 shows the time history displacement responses of the fourth layer, where the black dash line is the uncontrolled structure response. The drift reduction ratio \(J_{\text{p}}\) of each seismic accelerogram is calculated based on the time history responses and is listed in Table 5. All drift reduction ratios are lower than the target drift reduction ratio \(J_{\text{target}}\)= 0.5. CASE A, CASE B, and CASE C show almost the same control effects. Figure 13 shows the deformation of the bottom layer CBIS and its damping element in CASE A, CASE B, and CASE C, with the BCJ-L2 artificial accelerogram as the excitation. It shows that the design method proposed in Section 3.2 ensures the damping enhancement effect of the inerter system, which means the deformation of the damping element in the inerter system is enlarged compared with traditional damper. As is mentioned in [19], the damping deformation enhancement factor, defined as the ratio of RMS deformation of the damping element to that of the inerter system, represents the degree of the damping enhancement effect. The enhancement factors of the CICBIS-A11, CICBIS-1, and IICBIS-1 showed in Figure 13 are 1.7764, 1.506, and 1.399, respe  
Figure 11: The root mean square (RMS) inter-story drift angle and the RMS control forces: (a) RMS inter-layer drift angles; (b) RMS control forces \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title 1** & **BCJ-L2 Artificial** & **Kobe** & **Chi-chi** & **El Centro** \\  & **Accelerogram** & **Accelerogram** & **Accelerogram** & **Accelerogram** \\ \hline CASE A & 0.4714 & 0.4081 & 0.4906 & 0.4416 \\ CASE B & 0.4690 & 0.4182 & 0.4953 & 0.4469 \\ CASE C & 0.4600 & 0.4111 & 0.4874 & 0.4393 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Normalized maximum controlled structural drift ratios. enhancement effect partly explains the smaller damping requirements of the CICBISs compared to the IICBISs, as is shown in Table 2.  
Figure 12: Displacement responses of the fourth layer: (**a**) BCJ-L2 artificial accelerogram; (**b**) Kobe accelerogram; (**c**) Chi-chi accelerogram; (**d**) El Centro accelerogram.
## 3 Results and Discussion
### Effect of Bending Stiffness (EJ) of Stay Cable  
The stay cables for long-span bridges have quite a big diameter. Especially, they are always covered by mortar or epoxy. As a result, the bending stiffness of the cable increases significantly. Therefore, cable bending stiffness should be considered during the analysis of cable vibration. Generally, the damping ratio of the stay cable depends on not only the spring factor, loss factor, installation position but also its bending stiffness, cable tension, and cable length.  
The non-dimensional parameter \(\Delta=\pi^{2}\frac{EI}{5.L^{2}}\) will be used for investigating the effect of bending stiffness. Parameter \(\Delta\) reflects the cable stiffness and its sectional properties. The variation of the 1 st modal damping ratio with non-dimensional parameter \(\Delta\) can be seen in Figure 10. In this Figure 10, the value of \(\Delta\) changes from 0 to 0.15 and the installation positions of HDR dampers varies from 0.02 to 0.05. Obviously, an increase in the damping ratio is directly proportional to \(\Delta\). The highest damping ratio is around 3.13% when the bending stiffness is taken into account in the current model.  
Figure 8: Relationship of \(\xi_{1}\) and \(l_{1}/L\) with different \(K_{1}\).  
Figure 9: Relationship of \(\xi_{1}\) and \(l_{2}/L\) with different \(K_{2}\).
## 2 Background and Related Technologies
### LPWANs  
Low-Power, Wide-Area Network (LPWAN) technology supports long-range communication with very low power consumption, making it ideal for IoT and M2M applications. LPWANs have shown significant promise in various fields, such as security, agriculture, smart cities, and smart homes, thanks to their energy efficiency and low maintenance costs [12].  
Traditional short-range wireless technologies (e.g., Zigbee and Bluetooth) offer low power consumption but suffer from a limited range, making them less suitable for large-scale deployments. In contrast, cellular technologies (3G, 4G, and 5G) provide long-range communication and high data rates, yet they typically demand higher power, which is a drawback for battery-powered IoT devices. Therefore, in IoT-NTN environments, where low-power operation is crucial, LPWANs strike an optimal balance between energy efficiency and extended coverage.  
While LPWANs achieve energy-efficient operation, they generally support lower data rates compared to conventional satellite communications. This limitation can be mitigated by integrating LPWANs with AIoT--where edge computing techniques process data locally--thereby compensating for the lower data throughput and creating a synergistic effect.
## 5 Limitations and Future Outlook  
In this study, several limitations should be acknowledged. Firstly, in order to facilitate the development of coaching, we focused solely on the performance of field testing and did not conduct extensive physiological assessments. To gain a deeper understanding of the characteristic of adolescent throwing athletes, future research will need to necessitate more comprehensive testing and analysis. Secondly, we only studied adolescent throwers aged 14 to 18 and the chronological age of athletes. The lack of maturity analysis [51] for athletes limits the depth of this study. We are uncertain about the impact of early and late maturation on adolescent throwers. Furthermore, high-performance during adolescence may not necessarily mean a higher performance at later ages [16; 17]. Many factors have an impact on the performance of athletes, like the relative age effect [57], biological age, hormones [58] and so on. Further research is also needed to delve into the optimal development for adolescents that contributes to their performance in adulthood.  
Finally, it is possible that, due to the limited number of participants, correlations were observed only between specific spin hammer throw and age as well as the 2000 m run. It is also possible that the included variables were insufficient to fully capture the characteristics of hammer throw athletes, and further in-depth analysis will be required in the future. It has to be stressed that the regression model was solely employed to assess specific throwing performance for throwers aged between 14-18. Its applicability needs further validation.  
In the future, it is essential to take into account factors such as maturity status and variability in physiological profiles in order to better comprehend, identify, and nurture the physical attributes of adolescent athletes. More extensive research is required to further investigate the physical and physiological attributes of track-and-field throwers. Specifically, future researches should consider standardized testing batteries and explore the links between physical fitness performance and throwing performance, expanding current knowledge for talent identification and informing tailored conditioning programs for adolescents within the sport. It is possible to build upon this study by enriching the indicators and content of the tests, and developing more effective talent identification schemes and evaluation models.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 5 Experimental Study
### ML-Based Diagnosis
Table 3 reveals that the most accurate classifier, RF, takes approximately 3660 milliseconds (3.66 s), whereas the second most accurate took much less than 1 s for the same process, yet returned a reliable test accuracy. However, its efficiency is limited to the underlying assumption that the input data distribution is Gaussian--which is not often the case for real-life applications. On the high side, GBC revealed itself as a greedy algorithm, as shown by its high computational cost of 31,210 milliseconds (31.2 s); yet it ranks third in the test comparative assessment. This is followed by GPC (whose cost is 5030 milliseconds), which is costlier than RF but still the second least accurate on the test data. Based on the comparison, a choice of classifier can be made according to the metric being assessed. Typically, computational speed is highly considered in most real-world scenarios, but not at the expense of predictive efficiency. Considering such circumstances, NBC may be preferred, since it offers both low computational cost and significantly high test accuracy. However, RF is an appropriate choice when there is abundant computing power available or when accuracy is critical. Digging deeper into the algorithms, we assessed the class-specific predictive performance of the classifiers using the traditional confusion matrix, which reveals the probability of correct predictions of a classifier for each of the classes of interest, i.e., operating conditions. Figure 8 illustrates the confusion matrix resulting from ten-fold cross-validation of the algorithms based on the test data. Close examination of Figure 8 reveals that GPC and Gaussian SVM returned the highest false positives (FP) per class (except NOM-4), as shown in Figure 8e,k. However, class-specific efficacy in prediction for RF (see Figure 8f) is observed in its high true positive values (across the diagonal). Unfortunately, the false-alarm rate for ISC-3 is observed in the 72.6% false negative to BRB-2. This implies that the model mostly recognizes data  
Figure 8: Confusion matrix on test data: (**a**) LR, (**b**) ABC, (**c**) NBC, (**d**) kNN, (**e**) GPC, (**f**) RF, (**g**) GBC, (**h**) DT, (**i**) MLP, (**j**) Linear SVM, (**k**) Gaussian SVM, and (**l**) QDA. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Classifier** & **LR** & **ABC** & **NBC** & **kNN** & **GPC** & **RF** & **GBC** & **DT** & **MLP** & **SVM-** & **SVM-** & **RBF** & **QDA** \\ \hline Cost (mSecs) & 60 & 810 & 3 & 1 & 5030 & 3660 & 31210 & 72 & 0.71 & 51 & 370 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational costs of training and testing process. belonging to ISC-3 as belonging to BRB-2, and this reveals itself as a major limitation of our study.
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 1 Introduction
To verify the proposed method, multiple independent experiments were conducted on the publicly available Xiongan hyperspectral dataset. Unlike the smaller datasets commonly used in the hyperspectral image literature, abundant natural unlabeled samples are available in the Xiongan dataset. The Xiongan dataset is a state-of-the-art high-resolution hyperspectral dataset covering a large area. The corresponding labels for the images were accurate to the species levels. The three contributions of this study are summarized as follows. * We propose a novel preprocessing solution called NearPseudo, to utilize the natural features of unlabeled data to improve the classification of imbalanced data; NearPseudo generates pseudo-labels for unlabeled samples and creates a feedback mechanism based on a consistency check to increase its reliability. * Compared to other common processing methods in different imbalanced environments, NearPseudo performs better in hyperspectral image classification, especially in the case of an extremely imbalanced dataset. Simultaneously, NearPseudo can effectively improve the accuracy of most minority classes. * We report the simplicity and universality of NearPseudo. After balancing by NearPseudo, the classification accuracy of most common classifiers, including RF, classification and regression tree (CART), LR, and \(k\)-nearest neighbors (\(k\)NN), was improved. The remainder of this paper is organized as follows. In Section 2, NearPseudo and its pseudocode are described in detail. Section 3 describes the materials and experimental setup. Section 4 presents the experimental results for the Xiongan dataset. Finaly, Section 5 concludes the paper.
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 2 Related Works
### Liver Vessels Segmentation  
In order to perform blood vessel segmentation, as well as for liver segmentation, common methods exist such as region growing, thresholding, edge detection, pixel-based clustering, variational algorithms, or morphological approaches such as in [12; 28; 29; 30].  
Furthermore, methods for vessel segmentation have been developed with DL networks based on different imaging techniques (e.g., see the reviews [2; 7]). [PERSON] et al. [31] introduced a reduced 3D U-Net for hepatic vessel reconstruction from US data, and obtained accurate vessel segmentation in true 3D and stacked 2D US images. [PERSON] et al. [32] proposed a method for training liver vessel segmentation on noisy labels from contrast CT imaging. The authors of [33] developed a TopNet architecture, which is a multi-task DL architecture for vessel tree reconstruction. The network architecture simultaneously solves the task of detecting voxels on vascular centerlines (i.e., nodes) and estimates connectivity between centervoxels (edges) in the tree structure to be reconstructed. [PERSON] et al. [34] proposed an automatic vessel segmentation approach that uses a multipathway DL network. The proposed framework learns a network for binary classification based on extracted training patches in three planes (sagittal, coronal, and transverse planes) of MRI images. [PERSON] et al. [35] used U-Net with variant Dice loss functions for robust liver vessel extraction. In [36], the authors introduced an improved fuzzy connectedness method for automatic 3D liver vessel segmentation in CT scans. [PERSON] et al. [37] proposed a pipeline network comprising a convolutional neural network (CNN) followed by unsupervised clustering to perform vessel segmentation in hepatic US images. The authors of [38] segmented vessels from abdominal MRI images using an adaptive and reconstructive approach. [PERSON] et al. [39] proposed combining DL with anatomical analysis for automatic segmentation of the liver portal vein. They applied CNN to learn the consistent appearance patterns of the portal veins using a training set with reference annotations. Finally, [PERSON] et al. [40] proposed a solution for liver vessel segmentation based on an extreme learning machine tested on CT images.