esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To evaluate the proposed methodology, we perform a set of experiments with both synthetic and real-world datasets to, respectively, validate our approach and showcase the methodology against real decision process outcomes. This work extends our previous work [10], which only provides a high-level overview of the approach along with a proof-of-concept on a synthetic dataset. In particular, we refined the approach and extended the experimental evaluation to real-world datasets. The main contributions of this work can be summarized as follows:  
* We identify general desiderata for a bias detection technique targeted at addressing sub-groups analysis;
* We propose a data-agnostic, evidence-based approach to identify well-grounded hypotheses of bias in any decisional process which aids policy makers identify potentially biased and systematic decisions affecting a group or sub-group(s) of entities of interest (e.g., sensitive subjects);
* We perform a set of experiments on synthetic data and showcase the application of our method in the domain of discrimination detection by employing two real-world datasets used in previous research on discrimination detection;
* We show that the descriptive statistics (mean, 95% confidence intervals) of the effects of interest returned by our approach can be used to further guide the policy maker in pertaining follow-up regulatory actions. This paper is organized as follows. The next section provides background on association rule mining and regression analysis. Section 3 introduces our methodology and Section 4 presents its experimental evaluation. Section 5 provides a discussion of our method and results. Finally, Section 6 discusses related work and Section 7 provides conclusive remarks.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
## 2 Modelling
### Primary Structure with Two Half TMDs  
The TMDI topology with the serial arrangement produces an additional degree of freedom, i.e., the primary structure with this TMDI behaves as a three-degrees-of-freedom system. Therefore, the appropriate benchmark is the primary structure with two different TMDs, resulting in a three-degrees-of-freedom system as well. The mass ratio of each TMD is selected to be \(0.5\%\), whereby the mass ratio of both TMDs together is equal to \(1\%\). The equations of motion of the primary structure with two different TMDs but with the same mass are as follows (Figure 1e):  
\[\left[\begin{array}{cccc}m_{1}&0&0\\ 0&m_{2}/2&0\\ 0&0&m_{2}/2\end{array}\right]\left[\begin{array}{c}\ddot{x}_{1}\\ \ddot{x}_{2}\\ \ddot{x}_{3}\end{array}\right]+\left[\begin{array}{cccc}c_{1}+c_{2a}+c_{2b} &-c_{2a}&-c_{2b}\\ -c_{2a}&c_{2a}&0\\ -c_{2b}&0&c_{2b}\end{array}\right]\left[\begin{array}{c}\dot{x}_{1}\\ \dot{x}_{2}\\ -k_{2b}&0\\ 0\end{array}\right] \tag{15}\]  
The parameters \(k_{2a}\), \(c_{2a}\), \(k_{2b}\) and \(c_{2b}\) are numerically optimized for minimum primary structure displacement (Table 2). Notice that the numerically optimized stiffness and viscous damping coefficients are not equal because one half of the TMD is tuned to mitigate the left peak of the primary structure with classical TMD and the other half of the TMD is tuned to the right peak.
## 4 Experimental Results and Discussions
### Dataset Generalizability and Robustness  
#### 4.1.1 Testing with Noisy Samples  
In everyday life, texts often contain various sources of noise such as spelling mistakes, repeated characters, slang, emojis, and abbreviations. In particular, users on social media, messaging applications, or review platforms tend to write quickly and informally rather than using a formal style. Therefore, the model must be prepared to handle the diverse forms it may encounter in real-world contexts. As shown in Figure 6, the model effectively handled noisy elements across different examples.  
In the noisy positive example in Figure 6, \"Bn bugn is  
manunda uygulanan yenilikci immutorapi protokolu, tumor boyutunu beklenenden huzh kucultltu doktorlar bile bu iylesmeye hayran kald!\", which includes specialized terms such as \"onkoloiji\" and \"immunoterapi\", the model classified the statement as positive with a confidence score of 69.97%. In the domain-specific negative example, \"Rekonstruksiyon ameliyat sonrasi beklenmeyern komplikasyonlar gelisince, hastanu durumu kottulesti\", the model recognized the negative emphasis in a medical context with a confidence score of 76.82%. Lastly, in the domain-specific neutral example, \"Istinaf mahkemesi, dosyadaki eksik belgeler nedenijle davanu veniden degerlendirilmesine karar verdi.\", the model identified the legal term \"istinaf\" and classified the sentence as neutral with a confidence score of 76.48%.  
These results suggest that the model performs reasonably well in both noisy environments and domain-specific terminology. In future work, the aim is to improve the model in these respects by increasing data diversity and enhancing its resilience against various linguistic errors.
## 3 Results and Discussion
### Discussion of the Practical Advantages of Annealing for Uncooled Monopole Antennas  
Most clinically available microwave ablation applicators utilise active liquid or gas cooling via a jacket surrounding the antenna [4; 5; 6; 7; 8; 9; 10]. While cooling facilitates higher energy delivery with minimised coaxial cable diameters and reduces reverse heating effects, it  
Figure 8: Measured return losses at 2.45 GHz, acquired from the power meter during ablation of porcine liver at 50 W.  
Figure 9: A comparison of ablation lesion ellipticity fitted to a normal distribution between annealed and non-annealed antennas.  
also introduces a significant limitation for dual-mode applications [2, 42]. Specifically, the presence of an interface medium between the antenna and the tissue can interfere with sensing capabilities.  
In contrast, this study focuses on an uncooled monopole antenna, which must withstand all generated heat during treatment. Unlike cooled designs, the uncooled antenna eliminates intermediate media, simplifying direct tissue interaction. However, this approach introduces potential risks, including PTFE dielectric expansion, as discussed in Section 3.1, and subsequent performance degradation due to elevated heating, as discussed in Sections 3.2 and 3.3. To address these challenges, this study has thoroughly investigated the feasibility of an uncooled monopole antenna and has concentrated on its structural stability when subjected to ablative temperatures. It was shown that annealing of the coaxial cable potentially suppresses PTFE expansion.  
While previous studies have examined alternative antenna topologies like dipoles and slotted-type antennas for dual-mode applications [19, 20], this research uses a monopole design, which was used in a preliminary dual-mode ablation investigation described in [21] and will be utilised in future studies. The monopole's replicable and simple design facilitated better isolation of the PTFE expansion effects compared to more complex designs not used in this study. Furthermore, by quantifying changes in the antenna structure, this investigation enables future numerical modelling analysis and dual-mode experiments to distinguish more effectively between scattering parameter variations caused by permittivity changes in surrounding tissue and those arising from antenna structural changes.  
Alternative designs utilising ceramic or zirconium tips have been proposed, providing improved thermal insulation and stability due to the intrinsic material properties [43, 44]. However, these solutions significantly increase the cost and complexity of the antenna. This study demonstrates that controlled annealing of standard RG405 coaxial cables can potentially be an alternative solution.
## 2 Materials and Methods
### Methods for FCO\({}_{2}\) Prediction
#### 2.3.2 Evaluation Approaches  
To evaluate the performance and accuracy of the RF model, we used the 10-fold cross-validation method for independent validation. The data was equally split into ten folds. Nine folds were used for training and the remaining one set was used for model validation. Meanwhile, three statistical metrics, the root mean square error (RMSE), mean absolute prediction error (MAE), and coefficient of determination (R\({}^{2}\)) were employed. As we expected, the smaller of RMSE and MAE, the higher accuracy of the RF model. On the contrary, the larger R\({}^{2}\), the higher accuracy of the RF model. The three statistical metrics were calculated as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(\text{FCO}_{2}^{meta}(i)-\text{ FCO}_{2}^{pre}(i)\right)^{2}}{n}} \tag{6}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|\text{FCO}_{2}^{meta}(i)-\text{FCO} _{2}^{pre}(i)\right| \tag{7}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(\text{FCO}_{2}^{meta}(i)-\text{FCO} _{2}^{pre}(i)\right)^{2}}{\sum_{i=1}^{n}\left(\text{FCO}_{2}^{meta}(i)- \overline{\text{FCO}_{2}^{meta}}\right)^{2}} \tag{8}\]  
where the \(\text{FCO}_{2}^{pre}\) and \(\text{FCO}_{2}^{meta}\) respectively represent the ith predicted and measured values of \(\text{FCO}_{2}\); \(\overline{\text{FCO}_{2}^{meta}}\) is the mean measured values; \(n\) is the total number of samples.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 2 Modeling
### Blade Element Momentum Theory
#### 2.2.3 Validation of the BEMT and CFD  
We selected a 3-blade NACA 5868-9 propeller (\(R=1.5024\) m and \(\theta=25^{\circ}\) at 0.75 R) as the test model. [PERSON] et al. [31] gave the parameter distribution (Figure 7); its geometry was remodeled and is presented in Figure 8.  
An experiment on the test propeller model was conducted in a NACA 6 m propeller wind tunnel, which had an open throat and was capable of producing air speeds up to 50 m/s. The dynamic pressure, thrust, torque and RPM were measured with an accuracy from 1 to 2% [32]. The thrust and the torque forces were measured on recording balances situated in the balance house on the test chamber floor, which is described in [31] in detail. In the wind tunnel experiment, the revolutionary speed of the propeller was held at 1000 r/min and the flow velocity was increased by steps up to 50.14 m/s. The revolutionary speed of the propeller was then decreased by steps whilst the flow velocity was held at 50.14 m/s to reach higher advance ratio conditions. A 3D CFD method was also used to validate the optimization result and will be discussed in Section 4.2.4. Before that, the CFD  
Figure 5: The grid of airfoil for 2D CFD simulation.  
Figure 6: The regression of the trained BP-ANN surrogate model for 2D airfoil aerodynamics. (**a**) \(C_{d}\) regression; (**b**) \(C_{L}\) regression.  
method was validated by a comparison with the wind tunnel test data. There were, in total, 7.84 million structural grids for the 3-blade NACA 5868-9 propeller (Figure 9). The frame motion method was adopted to simulate the revolution of the propeller and k-omega shear stress transportation (SST) turbulence model was used to solve the boundary layer flow. The flow velocity and the revolutionary speed of the propeller were consistent with the wind tunnel test settings.  
The thrust coefficients of the propeller and its efficiency obtained by the BEMT, CFD and wind tunnel test are shown in Figure 10. It was concluded that both the BEMT and 3D CFD method were able to calculate the thrust and efficiency effectively with the experiments. The result of the CFD method based on the k-omega SST model was consistent with that of the wind tunnel test in a wide range of advance ratios. The BEMT had lower accuracy than the CFD method. Nevertheless, the BEMT was accurate enough and appropriate for the  
Figure 8: Geometry of 3-blade NACA 5868-9 propeller. (**a**) Top view; (**b**) axonometric view.  
Figure 7: The distribution of chord-length, twist angle and relative thickness of a 3-blade NACA 5868-9 propeller.  
Figure 9: Structural grid of the 3-blade NACA 5868-9 propeller for 3D CFD simulation.  
rapid optimization of the VTOL propeller blade shape, especially when the computational cost of the CFD method was considered.
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 3 Uncertainty Management  
Automation naturally generates uncertainty, which is related to the accuracy of the mechanism. Such uncertainty becomes critical for the reliability of applications.  
Focusing specifically on the process, uncertainty is mainly associated with the ontology matching phase and is commonly caused by two independent factors: the domain semantics and the strategy underlying the matcher.  
Concerning the semantics, even manual approaches may lead to errors due to potential ambiguities. Indeed, people may not sufficiently understand the domain vocabulary and, therefore, may provide imprecise and incorrect correspondences. Additionally, certain domains can be intrinsically critical and naturally error-prone, as the intended correspondences maybe be unclear.  
For example, concepts can have ambiguous semantics when they are closely related but not completely synonymous [33; 34; 35]. In this case, matching systems become uncertain [36]. Additional issues are related to the \"Open World Assumption\", where concepts are supposed to overlap and share a certain amount of common information (even if it is not yet specified) [36], as well as to different representations within the same domain or closely related domains [37].  
As previously mentioned, significant uncertainty is introduced by matchers, regardless of their strategy (e.g., structural-, semantic-, or instance-based [38]).  
Uncertainty management plays a crucial role in real-world applications, and it has been widely recognized as one of the key issues in data integration [39]. The actual challenge to manage uncertainty in such a context is to be able to maximize the reliability of the generated correspondences [40]. Techniques to reduce uncertainty (and, consequently, increase the quality of the output) may be manual or may assume a certain degree of automation.  
One example from the latter class of solutions is alignment trimming [41], which aims to trim the alignment with a given contextual threshold in order to ensure that only the best correspondences are maintained in the alignment. Alternatively, different disambiguation techniques may be applied.
## 4 Evaluation
### Thresholding Singular Values for Calculating the Pseudo-Inverse Matrix  
To extract the proposed feature instantaneously, the Moore-Penrose pseudo-inverse of the frequency basis matrix needs to be calculated in advance using Equation (17). As follows from Equations (19) and (20), the pseudo-inverse matrix can be calculated via singular value decomposition with thresholding of the small singular values. The threshold, which is one of the design parameters, is related to stability and sparsity of the pseudo-inverse matrix. Thus, it may affect the performance of the extracted features. Therefore, we evaluated the effect of the threshold on the classification performance.  
Table 4 presents the classification performances with various threshold values. As follows from Equation (27), \(\delta\) denotes the ratio of the threshold value to the maximum singular value. Among the test values, \(\gamma=0.01\) exhibits the best performance, while \(\gamma=0.005\) and \(\gamma=0.05\) exhibit comparable performances. However, \(\gamma=0.001\) and \(\gamma=0.1\) result in significantly degraded performances. Therefore, \(\gamma\) values between 0.005 and 0.05 are good choices for our system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**NMF**} & \multicolumn{2}{c}{**NMF**} \\  & \multicolumn{2}{c}{**(Iterative)**} & \multicolumn{2}{c}{**(Non-Iterative)**} \\ \cline{2-5}  & **F1-Score [\%]** & **F1-Score [\%]** & **F1-Score [\%]** & **F1-Score [\%]** \\  & **(Micro)** & **(Macro)** & **(Micro)** & **(Macro)** \\ \hline STR & 40.12 & 39.23 & 40.02 & 38.45 \\ WEAK(U) & 40.02 & 38.89 & 38.98 & 37.65 \\ WEAK & 41.53 & 38.28 & 39.01 & 37.76 \\ STR + WEAK(U) & 38.91 & 37.50 & 38.39 & 38.79 \\ STR + WEAK & 38.51 & 37.39 & 39.97 & 38.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results with different frequency basis matrices from various parts of the database.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Azure Resource Provision
#### 4.2.6 Private Endpoints and DNS Zones  
Private Endpoints are critical for enforcing network security. The Bicep deployment provisions private endpoints for the following elements:* The PostgreSQL Flexible Server;
* Azure AI Search;
* The Azure Container Registry (optional, if blocking public access).  
Private DNS Zones are also created or linked to resolve these private endpoints automatically within the VNet. Bicep modules ensure the following:  
* Creation and association of DNS Zones with the Vnet;
* Configuration of necessary A records for Private Endpoint IP addresses;
* All Private Endpoints are part of the relevant resource module.  
To enforce strict network isolation and eliminate public access to PaaS services, the infrastructure leverages Azure Private DNS Zones linked to the virtual network. This enables automatic name resolution of private endpoints for services like PostgreSQL, Azure AI Search, and Azure Content Safety. The Bicep code for provisioning and linking these Private DNS Zones is included in Appendix F.  
This design guarantees that all inter-service communication occurs privately and securely.
###### Abstract
An architecture called DL-Net [21] was designed to determine the type of grasp used in hand prostheses. Industries have gradually been incorporating them due to the precision they provide. In [22], they develop a lightweight CNN called WearNet for surface scratch detection. The authors aim to use the built network in embedded systems; for this, they design a small and fast network, achieving an accuracy of 94.16%. In [23], researchers employed machine learning and UNet++ deep learning to investigate porosity and permeability features in CT images. As a result, they found that the random forest technique was more effective at identifying weakly connected fractures. Mining is one of the areas that would greatly benefit from introducing the learning techniques described earlier. Currently, several mining tasks are performed manually. This increases operational costs and decreases production owing to operators' lack of experience or visual contamination, such as suspended dust, snow, and fog. Most research in this area uses haptic teleoperation [24] and does not consider the presence of visual contamination. A prototype of an autonomous robotic system for rock hammers was designed in [25], which uses a 3D perception system. In this case, they used a stereo camera array and employed the YOLO version 3 algorithm for rock detection. A system is proposed in [26] to learn to break rocks with a rock hammer using Deep Double Deep-Q Networks (DDDQNs). In [27], they analyze different clustering algorithms to recognize minerals in rocks. In this study, they explore various color spaces and conclude that these are useful, especially the HSV space, for segmenting rocks using the k-means algorithm. In [28], they compare ResNet 1 and ResNet 2 for Mineral Grain Segmentation and Recognition. As a result, they achieve a validation accuracy of 90.5 % using ResNet 2. Due to all of the above, the following article has the following contributions:  
1. Various classic and deep learning methods for rock detection in mining operations are analyzed. Classic and advanced learning algorithms were evaluated in terms of parameter variations using recall, precision, AP\({}_{50}\), F1-score, and average detection time metrics. 3. Various visual contamination situations, such as rain, fog, and high brightness, were considered. 4. One of the analyzed algorithms was proposed for rock detection in mining environments with visual contamination. The rest of the article is organized as follows. Section 2 analyzes and describes the described algorithms. Section 3 describes the configurations used and discusses the results. Section 4 concludes the study and outlines future work.
## 4 Experimental Analysis
### Results and Discussion
#### 4.4.3 Evaluation of Real-World Objects  
**RQ3:**: _How well does the proposed method operate real-world objects with the Pix3D dataset?_  
To evaluate the proposed WGA-SWIN method's performance in real-world image processing, we conduct experiments on the Pix3D dataset, which is a difficult benchmark for single-view 3D reconstruction. The dataset contains real-world images with complicated backgrounds, different lighting conditions, and occlusions. Following the setting in [7; 9], the chair category from ShapeNetChairRFC is used to generate the training set, which uses 60 synthesized images generated for each object with randomly selected backgrounds from the Sun database [35; 36].  
For this evaluation, we compared our WGA-SWIN framework with available state-of-the-art methods, including UMIFormer [13] and LRGT [11], as shown in Table 3. Although Pix3D provides only a single-view input for each object, which slightly limits the performance of methods, like WGA-SWIN, designed for multi-view inputs, our framework still achieves the best performance in both the IoU and F- Score@1% metrics. However, WGA-SWIN outperforms all other approaches, with an IoU of 0.321 and an F-Score@1% of 0.147, compared to the closest competitor, UMIFormer, which scores 0.317 IoU and 0.142 F-Score@1%. This demonstrates how reliable our framework is in reconstructing real-world objects.  
Furthermore, the excellent performance can be attributed to the architectural innovations of WGA-SWIN, especially the progressive hierarchical decoder, which combines attention processes with convolutional layers. This design enables the model to capture complex structural details, preserve fine-grained features, and produce accurate reconstructions, even in single-view settings. As shown in Figure 5, WGA-SWIN excels in reconstructing fine details, such as the legs and backrest of a chair, which are often overlooked by competing methods. These results demonstrate that the proposed WGA-SWIN framework is not only effective in multi-view situations but also highly adaptable and competitive in real-world single-view reconstruction tasks. This adaptability and generalizability make it a strong candidate for 3D reconstruction in various application scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**UMIFormer [13]** & **LRGT [11]** & **WGA-SWIN** \\ \hline
0.317/0.142 & 0.302/0.129 & **0.321/0.147** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation on Pix3D using IoU/F-Score@1%. The best result is highlighted in bold.