esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
Objectives: High cystatin C(CysC) levels are associated with impaired cardiovascular outcome. Whether CysC levels are independently related to the atherosclerosis burden is still controversial. Methods: We enrolled 31 non-ST-segment elevation acute coronary syndrome patients undergoing percutaneous coronary intervention. Patients were divided into 2 groups on the basis of median value of serum CysC. Using the high CysC group as a dependent variable, univariable and multivariable analyses were used to evaluate the association between CysC and three different features of atherosclerosis: 1) coronary plaque vulnerability as assessed by optical coherence tomography (OCT), 2) coronary artery calcium (CAC) by means of computed tomography scan, and 3) aortic wall metabolic activity, as assessed using \({}^{18}\)F-Fluorodeoxyglucose-positron emission tomography (\({}^{18}\)F-FDG-PET). Results: After univariable and multivariable analyses, \({}^{18}\)F-FDG uptake in the descending aorta (DA) was independently associated with a low level of CysC [(Odds Ratio = 0.02; 95%CI 0.0004-0.89; p = 0.044; \({}^{18}\)F-FDG uptake measured as averaged maximum target to blood ratio); (Odds Ratio = 0.89; 95%CI 0.82-0.98, p = 0.025; \({}^{18}\)F-FDG uptake measured as number of active slices)]. No trend was found for the association between CysC and characteristics of OCT-assessed coronary plaque vulnerability or CAC score. Conclusions: In patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS), \({}^{18}\)F-FDG uptake in the DA was associated with a low level of serum CysC. There was no relation between CysC levels and OCT-assessed coronary plaque vulnerability or CAC score. These findings suggest that high levels of CysC may not be considered as independent markers of atherosclerosis.  
cystatin C; \({}^{18}\)F-fluorodeoxyglucose-positron emission tomography; frequency domain-optical coherence tomography; coronary artery calcium score; non-ST-segment elevation acute coronary syndrome
# Practical Real-Time Phase Drift Compensation Scheme for Quantum Communication Systems
## 3 Experiment and Results
Both of these situations can cause an instantaneous increase in the QBER. Figure 4: Obtained QBER (**a**) and driving voltage of the phase modulator (**b**) with a feedback compensation frequency of 1 s per iteration. To further validate the long-term stability of the system, we conduct an extended stability test lasting nearly 16 h with a feedback compensation frequency of 0.1 s per iteration. Figure 6 presents the QBER, its statistical distribution, and the variations in feedback driving voltage. The obtained QBER achieves an average value of 1.60% and a standard deviation of 0.0583%. Figure 5: Obtained QBER (**a**) and driving voltage of the phase modulator (**b**) with a feedback compensation frequency of 0.1 s per iteration.
## 3 Experimental Results and Analysis
### Bearing Capacity and Deformation Analysis  
The test results for each specimen characteristic point are shown in Table 5. The yield load and the yield displacement were determined with the energy equivalent method. (1) After a comparison of the test results of W-1 and W-5, it was observed that the bearing capacity of each stage decreased. The ultimate bearing capacity decreased the most obviously, and the ultimate bearing capacity of W-1 decreased by 7.1%. (2) After a comparison of the test results of W-1 and W-4, it was observed that the cracking load of W-1 increased by 22.6%, and the yield load increased by 1%, but the ultimate load decreased by 4.5%. It could be seen that the selection of ceramics concrete could improve the premature cracking of the wall slab, but this would compromise the load-bearing capacity of the wall slab. (3) The cracking, yielding, and ultimate loads of specimen W-2 were compared with those of W-1. The loads of W-2 were increased by 22.2%, 7.8%, and 3.2%, and the corresponding displacements were increased by 42.1%, 16.9%, and 18.3%, respectively. This result suggested that the addition of fibers was more effective in retarding cracking, and the plastic deformation capacity of the wall panel was improved after the yielding of the specimens. In contrast, the cracking, yielding, and ultimate load were reduced by 29%, 31.3%, and 15.7%, respectively, and the corresponding displacements were increased by 26.9%, 7%, and 1.3%, respectively, for W-1 compared with W-3. This indicated that the deformation capacity of the wall panel was stronger. However, the stiffness is smaller when the fiber admixture was 0.4%, which led to a substantial decrease in the load-carrying capacity of the wall panel.  
Figure 9: Skeleton curves of specimens.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Cracking** \\ **Load** \\ **No.** \\ \end{tabular} & \begin{tabular}{c} **Cracking** \\ **Displacement** \\ **\(P_{g}\)/NN** \\ \end{tabular} & \begin{tabular}{c} **Yield Load** \\ **Displacement** \\ **\(P_{g}\)/NN** \\ \end{tabular} & \begin{tabular}{c} **Yield Load** \\ **Displacement** \\ **\(P_{g}\)/NN** \\ \end{tabular} & \begin{tabular}{c} **Ultimate** \\ **Load** \\ **\(\Delta\)/mm** \\ \end{tabular} & \begin{tabular}{c} **Doctility** \\ **Factor** \\ **\(\Delta\)/mm** \\ \end{tabular} & \begin{tabular}{c} **Increment** \\ **\(P_{g}\)/NN** \\ \end{tabular} & \
\begin{tabular}{c} **Energy** \\ **Disipation** \\ **\(\Delta\)/mm** \\ \end{tabular} &
\begin{tabular}{c} **Increment** \\ **\(P_{g}\)/NN** \\ \end{tabular} \\ \hline W-1 & 92.3 & 1.9 & 197.5 & 7.1 & 315.2 & 23.5 & 3.31 & 0 & 0.51 & 0 \\ W-2 & 112.8 & 2.7 & 212.8 & 8.3 & 325.3 & 27.8 & 3.35 & 1.2 & 0.54 & 5.8 \\ W-3 & 65.4 & 2.6 & 135.7 & 7.6 & 265.7 & 25.3 & 3.33 & 0.6 & 0.49 & \(-3.9\) \\ W-4 & 75.3 & 1.7 & 195.6 & 8.6 & 330.2 & 23.8 & 2.77 & \(-16.3\) & 0.50 & \(-2.0\) \\ W-5 & 103.4 & 2.2 & 230.8 & 8.9 & 339.3 & 21.3 & 2.39 & \(-27.8\) & 0.49 & \(-3.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test result.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 2 Related Works  
### Point Cloud Semantic Segmentation  
As a core task of 3D visual understanding, point cloud semantic segmentation aims to endow each spatial point in the unstructured point cloud with an accurate semantic label. Many works design network architectures for point clouds, such as PointNet [26], PointNet++ [27], KPConv [28], and RandLA-Net [29], and utilize a fully supervised learning paradigm that depends on large-scale labeled datasets for training. However, the fine-grained annotation of point clouds requires enormous human resources, which significantly limits the application and advancement of these methods in practical scenarios.
## 3 Algorithm Flow  
### Polarization Image and Spectral Decoupling and Spectral Reconstruction  
The decoupling process of the polarization atlas is shown in Figure 4. According to the grayscale difference between image units of different interference order, the polarization interference image array is segmented by using the method of convolution kernel traversing the image and Canny edge detection. The interference order is determined by the phase space modulation mode of the system interference core device. The segmented image units are matched according to the optical path difference. The image units after the optical path difference matching are image-registered by SUSAN Corner Detection combined with the SIFT Feature Matching Algorithm, and the polarization interference intensity sequence of any point in the target scene is obtained. The polarization interference intensity of each polarization channel is transformed by Fourier transform to obtain the spectral information and polarization information of the target scene, and then the distributions of Stokes vectors of each polarization channel in the wavenumber domain, \(B_{S_{0}}(v)\), \(B_{S_{1}}(v)\), and \(B_{S_{2}}(v)\), are obtained.
###### Abstract  
Among all the existing possibilities within the renewable energies field, wind energy stands out due to the significant expansion of offshore turbines installed in coastal and deep-sea areas. Although the latter represent considerable energy generation potential due to their larger size and location in areas of strong winds, they are exposed to harsh environmental disturbances, particularly waves, causing these structures to experience vibrations, increasing in this way fatigue, reducing efficiency, and leading to higher maintenance and operational costs. In this work, vibration reduction is achieved using two structural control systems for a 5 MW barge-type floating offshore wind turbine (FOWT), tuned via a metaheuristic method, with genetic algorithms (GAs). Firstly, the standard deviation of the Top Tower Displacement (TTD) is used as a cost function in the GA to optimize a passive Tuned Mass Damper (TMD), resulting in a vibration suppression rate of 34.9% compared to a reference standard TMD. Additionally, two semi-active structural control systems based on a gain scheduling approach are proposed. In one of the approaches, the TMD parameters are optimized based on the amplitude of oscillations, achieving a suppression rate of 45.4%. In the second approach, the TMD parameters are optimized in real time for the identified wave frequencies, demonstrating superior performance for medium-high frequencies compared to the other TMDs.  
floating offshore wind turbine; semi-active structural control; tuned mass damper (TMD); meta-heuristic optimization; genetic algorithms (GA) +
Footnote †: journal: Applied sciences
## 3 Methodology
### Data Preprocessing
#### 3.2.1 Data Cleaning  
This section covers the critical preprocessing tasks of dropping duplicates, dropping columns with one unique value, filling NaN values, and removing leading space characters in the CICIDS2017 dataset. Dropping duplicates is an essential step for maintaining the integrity of the dataset. A total of 24 duplicate records were identified and removed for binary classification, and 12 duplicate records were removed for multi-class classification to ensure that each entry was unique, avoiding bias and overfitting, to enhance the reliability and performance of the machine learning models. Dropping columns with one unique value helped to reduce the dimensionality of the dataset by removing columns that provided no meaningful variance or discriminative power. Columns such as Bwd PSH Flags, Bwd URG Flags, Fwd Avg Bytes/Bulk, Fwd Avg Packets/Bulk, Fwd Avg Bulk Rate, Bwd Avg Bytes/Bulk, Bwd Avg Packets/Bulk, and Bwd Avg Bulk Rate, all of which contained only a single unique value, were removed. This step ensured that the dataset included only features that contributed to the learning process, improving model efficiency and predictive accuracy. Filling the NaN values was another critical preprocessing step. A total of 1358 missing values were imputed with the column's mean value of 1358. This imputation ensured that the dataset was complete and improved the model's reliability by addressing gaps in the data. Lastly, removing leading space characters from feature names was necessary to ensure consistency in the dataset. A total of 59 feature names contained leading spaces for binary classification, and 46 feature names contained leading spaces for multi-class classification, including columns such as Destination Port, Flow Duration, and Total Fwd Packets. Removing these spaces improved the dataset's structure, making it easier to handle programmatically and reducing the likelihood of errors. These data cleaning steps collectively ensured that the CICIDS2017 dataset was robust, consistent, and ready for machine learning tasks.
## 1 Introduction  
The shear modulus \(G\) and the damping ratio \(D\) are the key soil parameters in geotechnical issues in which the soil is subjected to dynamic loads, for example, machines foundation [1], city tram and subway traffic, driving of sheet pile or precast pile, or mechanical soil compaction at the bottom of the excavation. These two mentioned parameters are also used in the soil-structure interaction when a large area of soil is employed to interaction with construction [2], e.g., in diaphragm walls' construction, tunnel housing, or subway stations. Each one of the abovementioned geotechnical problems should be solved based on geotechnical parameters obtained in small [3] and medium strain range, in which soil behaves like an elastic and elastic-plastic medium. Reliable evaluation of the stress-strain response of the soil, leading to a correct prediction of ground and structure deformations under static and dynamic loading and is directly dependent on the accurate assessment of the dynamic properties [4].  
However, when energy dissipation is small, undamped analysis can be employed. Nevertheless, every abovementioned geotechnical problem is connected with significant damping, so its effect should be included in the dynamical analysis [5]. Damping is used to describe the ability of subsoil or structures to dissipate energy during dynamic loading [6]. However, the damping process relates to several possible processes, not to a unique process, so it is challenging to model it properly. The value of damping depends on many factors, like vibration amplitude, material properties, structural configurations, fundamental periods of vibration, mode shape, etc. [7]. In engineering practices, several types of damping are used, i.e., hysteretic damping [8], viscous damping, damping caused by wave scattering, radiation damping, numerical damping, and damping as an alternative [9]. From the geotechnical point of view, the most important are hysteretic and viscous damping. There are two types of viscous damping: external damping and internal damping. External damping is caused by the interaction between the considered body and outside of the system. Internal damping is caused by the difference of the internal velocity between moving particles [9].  
This paper focuses on the internal viscous damping ratio (\(D\)) of Quaternary cohesive soils from Warsaw and factors determining this parameter in studied material. It presents a literature review of the most important factors affecting the damping ratio. Moreover, the authors discuss the influence of identified factors on their results. For the damping ratio of cohesive soil in small and medium shear strain range obtained from resonant column tests, the authors created the new empirical model. A comparison with other models from the literature is also included in the paper. Conclusions from conducted research are drawn at the end of this work.
## 2 Materials and Methods
### Reference Signals Generation Process  
Both, the accuracy and the repeatability studies of the proposed method, were conducted on the dataset generated by the J-R model.  
First, 100 signals were generated with random parameter values, from ranges listed in Table 2. Then, to somehow simulate real measurement, additional white Gaussian noise was added to each signal. Signal to noise ratio was equal to 0 dB for every signal, which means that the power of added noise was equal to the power of the original signal. Next, for each noised signal, the parameters were obtained 10 times using the proposed method.  
This process is graphically presented in Figure 4. For clarity, only one signal generation was visualized.  
Each reference signal was generated with 1 kHz sampling frequency using a mid-point numerical method of solving differential equations and lasted for 20 s.
## 3 Experiments and Results
### Results and Analysis of Datasets  
In this section, we juxtapose the performance of the MixerNet-SAGA model with several cutting-edge deep learning models in the realm of remote sensing image-based road extraction. Below is a brief overview of the models under scrutiny:  
U-Net: An iconic fully convolutional network crafted specifically for medical image segmentation. Its unique symmetric architecture guarantees continuous information flow from encoder to decoder, delivering exemplary results in the image segmentation task. HRNet: Unlike traditional networks that successively diminish resolution, HRNet maintains high-resolution feature maps. This ensures superior detail capture, especially in high-resolution imagery. ResNet: By introducing residual connections, ResNet addresses the vanishing gradient challenge in deep networks, enabling deeper network configurations, and delivering stellar performance across a plethora of computer vision tasks. ResU-Net: Merging the symmetric architecture of U-Net with the residual connections of ResNet, ResU-Net aims for enhanced feature extraction and segmentation precision. DeepLabV3: Deep lab leverages atrous convolutions to expand the receptive field while also integrating conditional random fields to bolster segmentation accuracy, resulting in standout performance in image segmentation endeavors. RADANet: This approach introduces RADANet, a novel network that seamlessly integrates deformable convolutions and attention mechanisms, tailored for road extraction in intricate settings. Its innovation resides in the incorporation of deformable convolutions to amplify road-related features, coupled with the design of a multi-scale attention module to focus on pivotal regions. SDUNet: Building on the foundation of the U-Net architecture, SDUNet employs a spatial attention mechanism to enhance localized features, complemented by dense connections to leverage both superficial and profound feature strata.  
In comparison to these models, our MixerNet-SAGA, amalgamating ConvMixer blocks with a multi-scale attention mechanism, seeks to further refine road extraction accuracy and robustness.
# AMANDA: A Middleware for Automatic Migration between Different Database Paradigms
## 4 Evaluation  
In this section, we evaluate AMANDA efficiency by observing both feasibility and query response aspects, with respects to the four parameters below:  
* **Migration Correctness**: Queries on each entity (e.g., Suppliers, Products) were conducted on Dgraph in order to ensure that all table rows have been successfully migrated into the target database;
* **Query Execution**: Six queries were performed in Postgres, MySQL, and Dgraph in order to ensure that the same queries in the source databases can be executed in the target one. In this context, Postgres and MySQL are both source databases, whereas Dgraph is the target database.
* **Query Performance**: Time execution assessment of six queries in Postgres, MySQL, and Dgraph. This comparison intends to verify whether the migration between databases is worth being executed. Each query (Q1-Q6) ran 100 times for each database (Postgres, MySQL, and Dgraph), and we calculated the average execution. The queries are presented later in this section.
* **Migration Speed**: The time required to produce a graph database starting from a Relational Database Management System (RDBMS). We executed and registered the execution time of the migration script in two scenarios: MySQL \(\Rightarrow\) Dgraph and Postgres \(\Rightarrow\) Dgraph.  
Figure 3: UML Diagram representing the migration between Postgres and Dgraph.  
Based on previous works described in Section 2 i.e., [36; 37; 42], we evaluated our solution with two different datasets: (a) Northwind [44] with 778 kilobytes presented in Section 4.1 and (b) the IMDB [45] dataset with 5.38 gigabyte, shown in Section 4.2. The Northwind database consists of data representing a small-business system with the following columns: suppliers, products, categories, employees, orders, customers, shippers, territories, and regions. When it comes to the IMDB dataset, it encompasses comment reviews for movies and TV shows with information about individual media titles, film crew, TV episodes, and user ratings. The AMANDA experiment's performance was conducted with both a small and a big dataset, both presenting strong relationships between entities, making the datasets suitable for comparing RDBMS and graph databases.
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 2 System Framework  
The causes of unstable tactile rendering must be managed to enable real tactile interaction between humans and VE. In practice, high stiffness and negative damping of virtual models are the dominant factors causing instability. The passivity theorem has been used in the design of haptic interfaces for VE. High stiffness or negative damping can cause energy to overaccumulate; therefore, a compensatory mechanism is required for passive systems. A passive-based approach to visual coupling design was proposed [21], in which the net power flow is observed and adjusted to ensure that the tactile system is energetically passive over time. The advantage is that it immediately dissipates only the required amount of energy, which is generated if it is negative at any time and if it might contribute to instability later. However, the stabilizing time is relatively long when the initial stored energy is high.  
By using the passivity theorem, we could determine the stability region of the VE parameters; the use of an additional IPC was considered to improve the stability of tactile interactions. Without loss of generality, the manipulation of a haptic interface is based on a one-degree-of-freedom (DOF) setup. To assess the effect of haptic simulation on stiffness, a previous study described a passivity stability analysis for a sampled-data system [18]; in such an analysis, sufficiently high values of \(K\) and \(B\) should be selected to ensure device transparency. If the values of \(K\) and \(B\) are sufficiently low, the haptic sensation of the visual objects will be soft. However, considerably high stiffness may cause the haptic feedback system to become unstable. Accordingly, the present study designed the IPC for haptic system stabilization; in this controller, the stable region of \(K\) and \(B\) can be determined, and the required controller parameter can be obtained.  
The framework of the IPC system is presented in Figure 1, where \(G(s)\) is the haptic device model, \(\frac{1-e^{-T_{h}}}{s}\) is the zero-order holder (ZOH), and \(C(z)\) and \(E(z)\) are the IPC and VE models, respectively. In Figure 1, \(x(t)\) represents the displacement of the stylus, \(f_{h}\) represents the force of the human operator, and \(f_{e}\) and \(f_{c}\) represent the feedback forces from the VE and controller, respectively. Because the VE and controller were implemented with a personal computer, the associated discrete-time representations were used.