esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital Twin Representation
### Navigation Graph  
In extension of the structure of the physical environment, the digital twin representation specifies a navigation graph that mobile agents can use for efficient motion planning. When the assigned goal position for an agent's task is not in direct line of sight to the agent, i.e., when an obstacle needs to be circumnavigated, a graph search algorithm will be used (here: Dijkstra) to find the shortest path along the navigation graph to the goal position. It will then use the nodes along this path as waypoints for the navigation towards the goal position.  
The navigation graph can be designed and provided manually, or created using a probabilistic roadmap algorithm [46] or other approaches for automatic generation of navigation graphs [47].
## 4 Experiment
### Limitations  
A major technical limitation of our method is that the detection of openings are highly dependent on the geometric quality of point clouds, which for indoor scenes with high amount of noise, could be very problematic. Also, the curved walls are represented with many small polygons, indicating that irregular structures could not be expressed as meshes. Then, the output results in this study are surface models; however, BIM standard models are often represented as volumetric building entities with walls, floors, ceilings, and topological information, thus, the surface models will lead to limit the expression of model thickness in practice. Lastly, in 5G signal simulation, the type of wall materials, which could create varying degrees of signal loss, was ignored for simplification, which results in some errors with actual situation.
## 4 Experimental Results and Discussions
### Forecasting Performance of MLS-SVR  
To train the MLS-SVR model, we use the six-order features of pseudo-Zernike moments as the input parameters [38; 39], and the scaling, rotation, and translation parameters of the image subjected to geometric attack as output parameters. This experiment includes 114 training and 30 test samples. The training prediction errors of scaling, rotation, and translation are 0.0069, 0.0052, and 0.0066, respectively. Table 6 shows pseudo-Zernike moments of five random images from training samples. The forecasting results of MLS-SVR are shown in Table 7.  
The experimental results show that the prediction accuracy of the MLS-SVR network remains relatively high. The corrected watermark image can improve the accuracy of watermark extraction. When the watermarked image is subjected to rotation, translation, and scaling attacks with correction, the watermark extraction bit error rate is shown in  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Images** & **(0,0)** & **(2,2)** & **(4,4)** & **(8,9)** & **(9,9)** & **(11,11)** \\ \hline  & 118.470609 & 10.63005807 & 6.520395 & 10.98115 & 9.578474 & 7.771317 \\  & 108.780323 & 3.0475966 & 5.046498 & 19.59498 & 10.36659 & 6.043941 \\  & 108.975487 & 4.159313509 & 5.991258 & 19.21224 & 11.81801 & 5.619767 \\  & 112.28917 & 4.159313509 & 5.991258 & 11.92792 & 11.70199 & 5.490806 \\  & 83.8989961 & 23.28029947 & 8.259456 & 7.213874 & 8.284153 & 1.5930389 \\ \hline \end{tabular}
\end{table}
Table 6: The low-order pseudo-Zernike moments among different images.  
\begin{table}
\begin{tabular}{c c c} \hline
**Channel Combination** & NC & **Extraction Watemark** \\ \hline  & 0.9531 & \\  & 0.9687 & \\  & 0.9692 & \\  & 0.9499 & \\  & 0.9427 & \\ \end{tabular}
\end{table}
Table 5: \(Cont.\)Table 8. It can be seen from Table 8 that \(BER\) is very small, which indicates the watermark can be be almost completely extracted after correction.
## 2 Data and Methodology
### Interaction between Wind Direction and Building Orientation
#### 2.2.2 Selection of Facade for Installation  
After reassuring that this kind of turbine can work with a wide range of wind directions, we can now examine the wind resource on the building's roof. With this information, the best facade or facades can be selected, and, after that, the hypothetical wind energy output of a installation on that or those facades can be estimated.  
For this purpose and for simplicity's sake, we will suppose that the \(C_{p,max}\) does not increase nor decrease from \(0^{\circ}\) to \(60^{\circ}\) of skew angle, as well as that it drops to zero with higher values. This is consistent with the tendency found at the wind tunnel experiment explained in the previous Section 2.2.1, as well as with Reference [25]. This means that, in our analysis, a turbine mounted between the roof and a facade absorbs the wind coming from a range of \(120^{\circ}\) in front of the facade.  
After studying the wind rose of the wind measurements taken on the building, it is very clear that the valley in which the town of Eibar is located concentrates the wind, since the predominant wind directions are the ones parallel to the valley's direction. This means that the building primarily receives wind from the NE and specially the SW, as seen in Figure 6. Even if various facades could be selected to augment the overall produced energy, selecting only the best facade is the best option for optimizing the capacity factor and thus making the installation more economically viable. Apart from that, wide facades are desirable over narrow ones because they concentrate the wind better and because they give more room for the installation. For all these reasons, the south facade was selected. This facade directly faces the wind coming from \(197^{\circ}\), so the absorption range covers any wind coming from \(137^{\circ}\) to \(257^{\circ}\). Figure 7 shows that the \(120^{\circ}\) range is wide enough to absorb the strongest winds.  
Figure 5: Boxplots of rotational speed of the small-scale longitudinal turbine at different wind speeds and skew angles.
# Directly Measuring the Pulse Front Distortion of High-Peak-Power Femtosecond Lasers
###### Abstract  
Pulse front distortion occurring in lenses can broaden the temporal profile of the pulse at focus and therefore decrease the focused intensity, especially for large-aperture femtosecond lasers. A previously proposed self-reference cross correlator was improved to directly measure the pulse front distortion of high-peak-power femtosecond lasers. The measured results of a 200 TW/27 fs laser are in good accordance with the calculated value. Moreover, the temporal intensity distribution of the pulse in the focal region was also investigated, in order to better guide and further promote the strong laser-matter interaction investigations. According to the measured PFD, the effective pulse duration at far field of this 200 TW laser was theoretically simulated to be \(\approx\)49 fs, which is almost two times the generally regarded 27 fs. As a result, the actually available focused intensity of this laser is only 55% of the case without pulse front distortion.  
femtosecond laser; pulse front distortion; ultrafast measurements; pulse duration +
Footnote †: journal: Applied Physics Letters
## 2 Theoretical Background
### Analysis of the System of Two Sub-Beams  
The equilibrium equations of the system of the two sub-beams (applicable to all joint models) can be derived considering the free-body diagram illustrated in Figure 3a:  
\[\frac{d\mathcal{N}^{(1)}}{dx} = B\tau,\ \ \frac{d\mathcal{N}^{(2)}}{dx}=-B\tau \tag{6}\] \[\frac{d\mathcal{Q}^{(1)}}{dx} = B\sigma,\ \ \frac{d\mathcal{Q}^{(2)}}{dx}=-B\sigma\] (7) \[\frac{d\mathcal{M}^{(1)}}{dx} = \mathcal{Q}^{(1)}-B\frac{h^{(1)}}{2}\tau,\ \ \frac{d\mathcal{M}^{(2)}}{dx}= \mathcal{Q}^{(2)}-B\frac{h^{(2)}}{2}\tau \tag{8}\]  
where \(\sigma\) denotes the normal and \(\tau\) the shear stress located on the interface between the two sub-beams. Both \(\sigma\) and \(\tau\) are functions of \(x\).
# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 2 Related Studies
### Generative Adversarial Networks in Medical Imagiting
[36] introduced a novel automation pipeline utilizing generative adversarial networks (GANs) for synthetic data augmentation, aiming to determine an optimal multiple for improving deep learning-based diagnostic performance with limited datasets. The study demonstrates superior diagnostic performance compared to conventional data augmentation using Waters' view radiographs of patients with chronic sinusitis. However, limitations include a relatively small pool of subjects, the arbitrary choice of the auxiliary classifier GAN (ACGAN), and the omission of some conventional data augmentation methods. Evaluating synthetic medical pictures is difficult because of the complexity and subjectivity of medical imaging. The FID score, which calculates the statistical similarity of actual and manufactured images using features extracted from a pre-trained neural network, is a quantitative measure of image quality [11]. The SSIM measures structural similarities between images, whereas perceptual similarity considers human perception [37]. These metrics provide a comprehensive evaluation technique that takes into account statistical, structural, and perceptual elements of picture quality. The training procedure for GANs is an important factor impacting the quality of generated images. A GAN consists of a competitively trained generator and discriminator [38]. During training, the generator learns to create realistic images, while the discriminator develops the ability to differentiate the difference between genuine and artificially produced images. Finding a balance between these two networks is critical for producing high-quality, realistic medical pictures. Notably, the choice of a certain GAN architecture influences the training process as well as the quality of the produced pictures. For example, the employment of a Wasserstein GAN (WGAN) or auxiliary classifier GAN (ACGAN) requires special training techniques [39]. The WGAN tackles mode collapse and instability difficulties by including the Wasserstein distance, resulting in more stable training [40]. The ACGAN, on the other hand, uses auxiliary classifiers to direct the generator towards specified classes, hence improving image synthesis for specific diseases [12]. One of the main contributions of our study is the utilization of generative adversarial networks (GANs) for synthesizing medical images, particularly for sinus-related pathologies. Also, it is clear from the above discussion that evaluating the quality of synthesized images is a challenging but critical aspect of the GAN-based image generation process. To deal with this challenge, we employed several metrics, including the Frechet Inception Distance (FID) score, structural similarity index (SSIM), and perceptual similarity, to comprehensively assess the synthesized images.
## 3 Experiments  
To validate the performance of the Beidou-Binocular Cooperative Navigation Model (BBCN), experiments were conducted to evaluate its global positioning accuracy, dynamic obstacle avoidance capability, and path planning efficiency in complex environments. The experiments utilized a robotic platform to test its performance in a substation's complex environment. The experimental scenarios included fixed obstacles, dynamic obstacles, and electromagnetic interference areas. The evaluation focused on the BBCN model's own performance while also comparing it with other models to highlight its advantages in multi-sensor fusion and adaptability to complex environments.
# Automated Exploratory Clustering to Democratize Clustering Analysis
## 2 Related Work
### Automated Clustering  
In the field of clustering, a few approaches for AutoML have been proposed. In [43], neural networks are trained to cluster the objects of a dataset based on examples of objects belonging to the same or different clusters provided by the practitioner. While this can find solutions streamlined for the user, it requires additional input, which might not be easy to generate. Furthermore, this method will most likely only find the clusterings a user is looking for, meaning fewer novel insights might be found and existing biases might be reinforced. AutoClust [17] uses meta-learning, where a classifier has been trained on a number of meta-datasets with a known ground truth to predict the best clustering algorithm. This is used as a start to an iterative hyperparameter tuning procedure, a regressor on multiple Clustering Validation Indices (CVIs), to examine the best clustering. However, this approach will only find clusterings that behave like the ground truth in the meta-learning datasets, where novel clusterings, which share little to no features with the datasets (or the labels) in the ground truth, cannot be found.  
Two similar approaches are cSmartML [44] and cSmartML-Glassbox [45], using meta-features to find promising algorithms and hyperparameter optimization using genetical algorithms. TPE-Autoclust [46] is a similar approach, focusing on the need for complete clustering pipelines. The search for these pipelines is conducted using genetic algorithms and is warmstarted by meta-learning. Another method is PoAC [47], which uses a surrogate model for meta-learning. kClusterHub [48] is a method limited to partitioning-based algorithms, using the elbow method instead of CVIs to find the correct number of clusters.  
All these approaches and a few of the following share the fact that they only retrieve a single \"best\" clustering. Due to the ambiguous nature of the clustering problem, this cannot be the fitting solution for every situation.
## 4 Discussion
### Uncertainty Analysis and Future Improvements  
The spatiotemporal characteristics of the WUE in IM in the last 18 years as well as the corresponding influencing factors were analyzed in this study. We found that LAI played a dominant role in the WUE variation. However, there are still some limitations and uncertainties. Firstly, the WUE was calculated according to GPP/ET; the accuracy of GPP and ET data can lead to uncertainties in estimation of WUE. Although the availability of the data was verified through flux stations, there are still some uncertainties in data verification due to limitation in station number and distribution. Secondly, the annual variables at stations were interpolated. The spatial distribution of stations in the study area and the interpolation method may influence the results to some extent. Finally, other factors, such as human activities, nitrogen sedimentation, and soil water changes may also influence the WUE of vegetation. These factors could be considered holistically in future studies.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication  
[PERSON]  
1 Department of Computer Science, Ariel Cyber Innovation Center and Astrophysics, Geophysics & Space Science Research Center, Ariel University, Ariel 4070000, Israel; [EMAIL_ADDRESS] (R.M.); [EMAIL_ADDRESS] (B.B.-M.)  
[PERSON]  
1 Department of Computer Science, Ariel Cyber Innovation Center and Astrophysics, Geophysics & Space Science Research Center, Ariel University, Ariel 4070000, Israel; [EMAIL_ADDRESS] (R.M.); [EMAIL_ADDRESS] (B.B.-M.)  
[PERSON]  
2 Ariel Cyber Innovation Center, Department of Industrial Engineering & Management, Ariel University, Ariel 4070000, Israel  
may be applicable for several store-and-forward use-cases, and specifically in cases in which there is a need for high bandwidth with no real-time restrictions. Figure 1 illustrates the main two use-cases of FSO satellites from both the satellite's and the ground station's point of view.  
This paper offers a comprehensive view of the two sides of satellite-terrestrial FSO communication. The first side is of the ground station. In this context, we describe the components that enable the required functionality of the ground station. We then explain the steps to initiate and maintain an FSO communication link with a satellite. Additionally, there are situations in which the LOS temporarily disappears, e.g., due to local weather conditions that obscure the satellite; we explain how tracking can be maintained in such situations via position prediction of the obscured satellite. We also discuss a scheduling problem that arises when the ground station needs to communicate with multiple satellites.  
The second side of the communication is of the satellite. As in the case of the ground station, we also describe the satellite's components that enable its required functionality. We then explain the FSO communication steps from the satellite's viewpoint. Additionally, we provide a link-budget formulation, as well as performance evaluation. Table 1 demonstrates the expected (average) performance of various communication technologies for nano-satellites (RF vs. laser).
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 2 A Lung Ultrasound Dataset for COVID-19 Detection
### Data Collection  
#### 2.2.1 Northumbria Data  
Patient data was made available by the Northumbria Healthcare NHS Foundation Trust. The Trust serves a population of approximately 600,000 over a large geographical area in the North East of the United Kingdom. During the COVID-19 pandemic, inpatient respiratory services were centralised onto the acute care centre. Thoracic ultrasound was conducted with a convex probe Venue(tm) ultrasound machine by GE Healthcare (2-5 MHz). Patients were scanned according to the established BLUE protocol [62] which has high diagnostic sensitivity, specificity and accuracy for pleural effusions, alveolar consolidation and interstitial syndromes [63]. RT-PCR was done to confirm/reject COVID-19 diagnosis and standard care (including thoracic X-Ray and CT) was done to identify bacterial pneumonia. Local Caldicott approval was sought and granted. Patient were consented appropriately [64; 65]. Recordings with appropriate pathology were stored, anonymized and electronically shared. A total of 70 videos and images from 44 male and 26 female patients were provided.
## 3 Methodology and Model
### Three-Dimensional PSA-DV-Hop Algorithm Based on Optimal Subset of Nodes
#### 3.3.2 The Subset of Optimal Nodes  
Random sample consensus (RANSAC) is a parameter estimation algorithm that uses randomly selected initial hypotheses and test sets. However, this approach can result in slow convergence. The PROSAC algorithm improves upon this by ranking all points in the sample set according to rank, assigning higher ranks to the inner points that are more likely to estimate the correct model. By selecting points with higher ranks, PROSAC increases the probability of obtaining the correct model [23]. Incorporating the correlation between beacon nodes into the PROSAC algorithm helps address noise and anomalies in the beacon nodes, thereby improving the robustness of the average distance calculation. The specific implementation steps in the second phase of the DV-Hop algorithm are as follows:  
1. Beacon node sorting: All beacon nodes are sorted based on their relevance, which is determined by their correlation to the target node.
2. Progressive sampling: Sampling begins from the highest-ranked nodes based on the sorting results, and the sampling set is gradually expanded as the number of iterations increases.
3. Model evaluation and selection: The Euclidean distance between the selected beacon nodes is calculated and divided by the hop count to obtain the average distance per hop. The quality of the selected beacon node combination is then evaluated by assessing the stability and consistency of the computed average distance per hop.
4. Termination condition: The current optimal combination of beacon nodes is selected if it meets the predetermined accuracy requirement.  
By utilizing the PROSAC algorithm-based correlation node subset strategy, highly correlated beacon nodes are selected for inclusion in the average hop distance calculation, thereby reducing the influence of nodes with significant errors.
## 1 Introduction
In [15], the speed and the current loop are controlled via SMC and DPCC, respectively, with a high-order sliding mode observer estimating and compensating for the disturbance of both, which improves robustness and current-tracking accuracy. Furthermore, an integral can be introduced for error compensation. In [16], the current error integral is introduced, and the parameter of the flux linkage is dynamically adjusted to cancel the error. In [17], a new method that introduces the current error into the control signal through an integral sliding mode term for error compensation is proposed. This method enhances robustness and guarantees the control capability. However, methods that introduce the integral term inevitably cause integral saturation when the error is large, and this affects the system control. In this article, a weakened integral sliding mode compensation method is proposed for the first time. The method is intended to address the issue of steady-state errors generated by parameter mismatches in conventional DPCC and integral saturation generated by adding an integral term with a large error. This method introduces the concept of a boundary layer, inside of which is the normal integral, and outside of which the integral is weakened to achieve an anti-windup effect. Secondly, weight factors are introduced to reduce the effect of current fluctuation issues resulting from excessive inductance mismatch. Finally, the one-step delay compensation is used to solve the time delay problem in the practical control system. Our simulation and experimental results indicate that the proposed method eliminates the error effectively when the motor parameters mismatch, solves the integral saturation problem, and strengthens the robustness of the system. This article is structured as follows. Section 2 describes the principles of DPCC with one-step delay compensation and error analysis in case of parameter mismatch. Section 3 shows how to compensate for the error using a weakened integral sliding mode, proves its stability, and introduces the method of adding weight factors to enhance robustness. In Sections 4 and 5, the effectiveness of the proposed algorithm is verified by testing the current loop and comparing conventional DPCC with the optimized method through simulation and experiment. Finally, Section 6 summarizes this article.