esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Discussion of Major Quantitative Findings and Qualitative Feedback from the Users
#### 4.1.3 Perception of Internal Control  
In terms of POIC, this study finds that the joystick control is superior to hand gesture recognition because it provides a perception of standard operation to its users. Users only need to know where the buttons are to operate the system (see Table 8). This verifies the fact that the joystick controller is not difficult for users to learn, as suggested by [PERSON] et al. [47]. For the use of hand gesture recognition, this study observed that some users, whose hand gestures were standard, completed the tasks efficiently (in 15 min), and these users believed that the hand gesture recognition was not challenging for them to learn. These users gave relatively higher evaluations for their user experiences. However, we also observed that many hand gestural users used the interface after a short time (after 15 min); their hand gestures became non-standard due to users' habit (or fatigue) and unfamiliarity \begin{table}
\begin{tabular}{l} \hline \hline
**Qualitative question:** \\
**How did you feel about the operation of hand gesture recognition/joystick control (e.g., easy to control, easy to operate, you can efficiently complete the tasks, etc.)?** \\ \hline \hline Feedback for hand gesture recognition: \\ - & The operation made me tired. \\ - & No good, difficult to operate, sore hands after using it for a long time. \\ - & Tired, easy for wrong identifications or detections. Sometimes it identified another \\ - & gestural command. \\ - & It was ok. Inaccurate recognition. \\ - & Not bad, but there was a problem aiming the button. \\ \hline \hline Feedback for joystick control: \\ - & It was convenient to control. \\ - & The joystick controller simply needed me to click the button, and I did not worry about the \\ - & erroneous detection of hand gestures. \\ - & Easy to use. \\ - & Relatively standard. \\ - & Normal detection, convenient operation. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Users’ Qualitative Feedback on PU and PEOU for Hand Gesture Recognition and Joystick Control. with the gestural detection of the helmet. Therefore, they provided low ratings for their experience. In this respect, hand gesture recognition poses limitations and issues to its users' proficiency in using it, which affects POIC and the efficiency with which tasks are completed, as suggested by [PERSON] et al. (2017).
## 6 Conclusions and Future Work
### Limitations and Future Directions  
Despite its promising performance, the proposed framework has several limitations. First, the evaluation was limited to the xAPI dataset, which may restrict the generalizability of the results to other learning environments or academic domains. Second, although AutoML simplifies the modeling pipeline, the resulting DL ensembles are computationally demanding, which may hinder real-time deployment in resource-constrained settings. Third, while our approach prioritizes predictive performance, it still lacks transparency in decision-making, a critical factor in educational applications.  
Therefore, future research should focus on several directions. First, we aim to extend the proposed framework to other publicly available EDM datasets and domains to assess its broad applicability. Second, we plan to integrate explainable AI (XAI) techniques into the pipeline to improve transparency and support educational decision-making. Third, we explore more efficient modeling strategies to improve interpretability and reduce training overhead.
## 5 Results and Discussion
### Models Performance  
By integrating single-well facies diagrams and analyzing a subset of representative samples, we conducted analytical validation of the final CNN-2D model's outcomes.  
Figure 14 illustrates the sedimentary sequence in the depth interval of 1170-1218 m for Well W2-18-10, characterized by a transition from the lowstand system tract to the transgressive system tract, with sedimentary bodies evolving from channel deposits to thin sandstones. Within the depth range of 1190-1194 m, the measured depositional facies is sheet sand, but the model misclassifies it as a natural levee. Additionally, at depths of 1214-1216 m, the actual depositional facies is a main channel, yet the model erroneously as  
Figure 12: Learning curve diagram.  
Figure 13: t-SNE feature space visualization diagram.  
signs it to a distributary channel. These misclassifications primarily stem from discrepancies between resistivity response characteristics and microfacies attributes, potentially caused by GR curve noise interference due to wellbore enlargement or insufficient model sensitivity to the difference between deep and shallow resistivity measurements (LLD/LLS).  
Figure 15 illustrates the stratigraphic interval from 1200 to 1250 m of Well W2-21-19, which represents a depositional sequence from the lowstand system tract. The overall sedimentary body is dominated by thick channel deposits. Notably, the basal sheet sand is predicted with high accuracy, while the intermediate strata show prediction bias where distributary channels were misidentified as main channels, and sheet sands were erroneously interpreted as natural levees. The upper channel recognition remains relatively accurate. The sample misclassifications primarily stem from SP curve distortions caused by multiple interfering factors, compounded by wellbore enlargement-induced noise that disrupts curve imaging. Overall, the interpretation demonstrates satisfactory reliability despite localized inaccuracies.  
Figure 14: Model verification for microfacies recognition of well W2-18-10 in Weixing oilfield.
## 4 Parameter Setting and Analysis
### Angle Threshold  
Selecting an appropriate angle threshold is a crucial step in the region growing segmentation of highway road surface point clouds. In this study, the angle threshold was determined based on two adjacent supervoxels, denoted as \(S_{i}\) and \(S_{j}\), respectively. First, the normal vectors \(N_{i}\) and \(N_{j}\) between supervoxels \(S_{i}\) and \(S_{j}\) were computed using Principal Component Analysis (PCA). Then, the angle \((\arccos(N_{i}\cdot N_{j})\times\frac{180}{\pi})\) between these two normal vectors was calculated to check whether it satisfied the angle threshold requirement. If the angle was less than or equal to the angle threshold, the two supervoxels were merged into the same region. Otherwise, if the angle was greater than the angle threshold, this indicates that they did not belong to the same region, and the calculation continued with other neighboring supervoxels.  
There are two main cases of adjacent supervoxels on highway road surfaces. In one case, both supervoxels are on the same side of the road, resulting in a small angle or even an angle close to zero between their normal vectors. The other case occurs when the two supervoxels are on opposite sides of the road center. In this study, we conducted multiple experiments and adjustments to the angle threshold to select an appropriate value that met the segmentation requirements.  
From Figure 12a, it can be observed that when the angle threshold varies from \(2^{\circ}\) to \(5^{\circ}\), there is a slight variation in the accuracy for Site 1. This is because, with a smaller angle threshold, the segmentation precision improves in terms of details, but it may also lead to over-segmentation and noise generation. Similar results were obtained at Site 2 (Figure 12b).  
Figure 11: Accuracy analysis of four different slope threshold values: (**a**) Site 1; (**b**) Site 2.
## 4 Experimental Results
### Experimental Criteria and Performance Measures
#### 4.2.1 Environment and Libraries  
The experiments were carried out on the Google Cloud Platform (GCP) and Google Colaboratory on the GCP server (us-west1-b region) with a computed engine virtual machine and additional machine learning and deep learning libraries. To speed up the complex computing jobs, the authors used 16 virtual central processing units (vCPUs), with 104 GB random-access memory (RAM) and a single NVIDIA graphics processing unit (GPU) Tesla K80. The experiments were implemented using the Python 3 programming language and the libraries below.  
Environment setup:  
1. Python version (Python 3.6.3), installed using PyPI;
2. Virtual environment from Anaconda;
3. TensorFlow (1.13), Theano, and Keras (as backend) for complicated deep learning classification.  
Figure 8: The number of samples was 4522, 12,875, 3323, 867, 2624, 239, 253, and 628 in classes 0, 1, 2, 3, 4, 5, 6, and 7, respectively.  
Figure 9: Representation of augmented image samples after applying image augmentation techniques; (**a**) represents the augmented image sample for melanoma skin disease, (**b**) represents the augmented image sample for melanocytic nevus skin disease.  
Library setup:  
1. TensorFlow federated;
2. Federated core API;
3. Scikit-learn library to perform basic machine learning tasks;
4. OpenCV to perform image processing tasks;
5. NumPy and Pandas for data manipulation and processing;
6. Seaborn and Matplotlib for visualization of the results.
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 5 Conclusions  
This paper presents an implementation of the Internet of Energy into 5G infrastructure. We discussed that the requirements of the IoE may be fulfilled by the 5G network. We afforded the challenge of obtaining a communication network that may implement specific solutions for the IoE and concluded that Open RAN implementation is an acceptable approach that reduces costs (by simplifying some parts of the network) and increases the potential applications (by increasing the flexibility of the deployment in the network). Open RAN approach also has its limitations, and some limitations need to be overcome in order to be used by IoE applications. One such limitation is the possibility of deploying the beamforming mechanism based on Multiple-In Multiple-Out antennas.  
We introduced zero-forcing in Open RAN that may decrease the traffic in the fronthaul. The results showed that zero-forcing and MMSE reduce interferences and may be a solution for deploying beamforming in Open RAN implementations.  
Conceptualization, J.M.B. and K.W.; methodology, J.M.B.; validation, M.M.; formal analysis, C.X.M.; investigation, K.K.-P. and G.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Centre for Priority Research Area Artificial Intelligence and Robotics of Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) programme (Contract No. 1820/29/Z01/POB2/2021). The research of [PERSON] has been undertaken under the \"Context-Aware Adaptation Framework for eMBB services in 5G networks\" project supported by the National Science Centre, Poland, under the grant Agreement No. 2018/30/E/ST7/00413. Contribution of [PERSON] was funded by the Poznan University of Technology project No. 0312/SBAD/8160.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Comparison among ZF-BF and MMSE-BF: Achievable rate varying SNR in the range \([-10;30]\) dB with 64 Tx-antennas and 8 mobile devices covered by the antennas.
# Using Machine Learning for Quantum Annealing Accuracy Prediction
## 2 Methods
### Classification
To weigh solvable MC instances by D-Wave more heavily than unsolvable ones, the option _class_weight=\"balanced\"_ was employed. The option of balanced class weightings was chosen as only about \(p\approx 0.11-0.13\) of the test problems are solvable. Therefore, a model that classifies all instances as unsolvable will still be 87-89% accurate. By setting _class_weight=\"balanced\"_, we assign a weight to the solvable problems that is inversely proportional to how often they appear in the dataset, thus penalizing incorrectly classified solvable problems by a factor of about \(1/p\). During training, we noticed that the decision tree machine learning model appears to have trouble with problems that return a clique size of zero. This was due to a suboptimal QUBO formulation we used for MC, and the problem disappeared when using the formulation of [1; 4]. However, similar issues might occur with other problems, highlighting that the present approach might be most suitable for problem classes that can be solved sufficiently well on D-Wave. Additionally, it is challenging to tune the decision tree in such a way as to obtain good performance and interpretable results simultaneously. Applying the decision tree classifier using default parameters usually results in very large trees having many redundant branches, which are poorly interpretable. However, this issue can be alleviated by increasing the Gini impurity (parameter _min_impurity_decrease_) while simultaneously decreasing the maximal depth of the tree (parameter _max_depth_).
## 4 Discussion
Future work should implement a structured validation framework involving (1) testing on multi-institutional datasets with varied acquisition protocols; (2) evaluation across different patient demographics and clinical presentations; and (3) comparison of segmentation performance across various contrast enhancement phases. Such comprehensive external validation would provide stronger evidence of generalizability and clinical utility across diverse real-world settings. We have initiated collaborations with multiple institutions to facilitate this validation process in subsequent studies. The clinical translation of automated adrenal segmentation tools requires careful consideration of integration pathways. [PERSON] et al. [46] outlined critical factors for successful clinical AI deployment, including seamless workflow integration, interpretability, and alignment with radiological practice patterns. Our time performance metrics provide initial insights into potential deployment considerations, but further usability studies and clinical validation would be necessary before implementation. Effective clinical integration requires consideration of both technical and human factors. Beyond segmentation accuracy, successful deployment depends on seamless workflow integration, minimal disruption to established clinical processes, and appropriate presentation of AI-derived information to clinicians [46; 47]. User acceptance studies specifically focusing on radiologists' interaction with automated adrenal segmentation tools would provide valuable insights for optimizing deployment strategies. Future research directions include expanding the dataset with more diverse cases, incorporating advanced architectural elements such as attention mechanisms [43] or transformer-based designs [41], and conducting rigorous external validation studies to better assess generalizability. Implementation of sophisticated postprocessing techniques such as conditional random fields, as demonstrated by [PERSON] and [PERSON] [48], or ensemble learning approaches could potentially enhance segmentation performance. Multi-task learning frameworks that simultaneously segment adrenal glands and classify potential pathologies might provide synergistic benefits for clinical applications, an approach that has shown promise in liver lesion analysis, as reported by [PERSON] et al. [49]. Such integrated approaches align with emerging \"detect-then-segment\" paradigms that leverage the relationship between detection and segmentation tasks to improve overall performance [50]. Additionally, self-supervised pre-training on large unlabeled datasets could potentially address the data scarcity challenge in adrenal imaging [51]. In conclusion, this study confirms the feasibility of adrenal gland segmentation using deep learning methods while highlighting the persistent challenges associated with small organ segmentation. Our findings establish a methodological foundation for further optimization and eventual clinical integration of AI-assisted adrenal imaging tools, potentially contributing to improved efficiency and accuracy in radiological workflows.
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
## 2 Discussion of Orbital Parameters, Sampling Process, and SpOCK
### Overview of Observation Sampling Process and Instrument Considerations  
#### 2.2.1 GNSS-R Surface Reflection Global Distribution  
The global distribution of GNSS-R surface observations is the result of an asynchronous process between the orbits of the transmitting GNSS satellites (GPS, Galileo, SBAS, etc.) in Medium Earth Orbit (MEO, -20,200 km altitude) and occasionally Geostationary orbit (-35,786 km altitude) and a constellation of Low Earth Orbit (less than 2000 km altitude) receivers below the transmitters. The GNSS-R observation point is the Earth surface location between the transmitter and receiver that results in a forward specular reflection in the direction of the receiver. An Illustration of three example GNSS-R reflection geometries is shown in Figure 1, with the transmitters above and outside the figure and a single LEO receiver capturing the parallel surface reflections. The specular reflection location point between transmitter and receiver at a single epoch can be calculated using an iterative process as described in [3]. The resulting total of GNSS-R surface reflection points is therefore driven by the pseudo-random alignment of the multiple GNSS satellites and the multiple LEO receivers that result in fortuitous surface reflection locations in the receiver antenna footprint of the GNSS-R instruments. The number of GNSS transmitters can well exceed 100 if multiple constellations are utilized (GPS, Galileo, Beidou, GLONASS) for example, while the optimal number of receivers can range into several dozen in varied orbits resulting in a dense web of overlapping surface observations. Given the large numbers of transmitters and receivers a simulation is used to generate these large data sets of surface observation points over time (nominally one day). The achieved GNSS-R observation coverage was then analyzed with respect to optimizing the spatial and temporal coverage metrics described below.
# Multiblock Analysis Applied to TD-NMR of Butters and Related Products
## 2 Material and Methods
### Data Processing
#### Figures of Merit  
Two data subsets were prepared, one to calibrate the model (with 37 samples) and another one to externally validate the previous calibration model (with 13 samples). The 50 samples were sorted in ascending order of \(F_{w}\). One in four samples were placed into the test set and the rest into the calibration set, providing a similar distribution of \(Fw\) in both sets. Since all the samples were measured in triplicate, the calibration set contained \(N_{c}=37\times 3=111\) signals and the test set \(N_{t}=13\times 3=39\) signals.  
PLS, CovSel, SO-PLS and SO-CovSel were applied on the two blocks \(\mathbf{X_{L}}\) and \(\mathbf{X_{S}}\) to predict \(\mathbf{y}\), with \(K\), \(K_{1}\) and \(K_{2}\) varying from 0 to 10. A one-sample-out cross-validation provided an estimate \(\hat{\mathbf{y}}\) of the \(N_{c}\) calibration samples for each \(K\) or pair (\(K_{1}\), \(K_{2}\)). The RMSECV cross-validation error was calculated using the following formula:  
\[RMSECV(K\ )=\sqrt{\frac{\sum_{i}(y_{i}-\hat{y}_{i}(K\ ))^{2}}{N_{c}}}\text{ for PLS and CovSel} \tag{7}\]  
\[RMSECV(K_{1},K_{2})=\sqrt{\frac{\sum_{i}(y_{i}-\hat{y}_{i}(K_{1},K_{2}))^{2}} {N_{c}}}\text{ for SO-PLS and SO-CovSel} \tag{8}\]  
The model was calculated on the whole calibration set and then reapplied to this set, which provided an estimate \(\hat{\mathbf{y}}\) of the \(N_{c}\) values of \(F_{w}\) for each \(K\) or pair (\(K_{1}\), \(K_{2}\)). The RMSEC calibration error was calculated according to the following formula:  
\[RMSEC(K)=\sqrt{\frac{\sum_{i}(y_{i}-\hat{y}_{i}(K\ ))^{2}}{N_{c}-K\ -1}}\text{ for PLS and CovSel} \tag{9}\]  
\[RMSEC(K_{1},K_{2})=\sqrt{\frac{\sum_{i}(y_{i}-\hat{y}_{i}(K_{1},K_{2}))^{2}} {N_{c}-K_{1}-K_{2}-1}}\text{ for SO-PLS and SO-CovSel} \tag{10}\]  
Examination of the evolution of RMSEC and RMSECV values as a function of \(K\) or (\(K_{1}\), \(K_{2}\)) made it possible to choose the optimal numbers of latent variables \(K_{opt}\) or (\(K_{1 opt}\), \(K_{2 opt}\)). A model was then calibrated with these values on the whole calibration set and was applied to the test set, providing an estimate \(\hat{y}\) of the \(N_{t}\) fat values. The following figures of merit were calculated:  
\[\text{RMSEP}=\sqrt{\frac{\sum_{i}\bigl{(}y_{i}-\hat{y}_{i}\bigr{)}^{2}}{N_{t }}}\ \ Bias=\frac{\sum_{i}\bigl{(}y_{i}-\hat{y}_{i}\bigr{)}}{N_{t}}\ \text{SEP}=\sqrt{\frac{\sum_{i}\bigl{(}y_{i}-\hat{y}_{i}-\mathit{ Bias}\bigr{)}^{2}}{N_{t}}} \tag{11}\]  
All the data processing was carried out similarly on the signals acquired at 6 \({}^{\circ}\)C and at 15 \({}^{\circ}\)C. All the data processing was made using Matlab (The Mathworks Inc., Natick, MA, USA). Matlab code of SO-PLS and SO-Covsel can be freely downloaded from: [[https://www.chem.uniroma1.it/romechemometrics/research/algorithms/](https://www.chem.uniroma1.it/romechemometrics/research/algorithms/)]([https://www.chem.uniroma1.it/romechemometrics/research/algorithms/](https://www.chem.uniroma1.it/romechemometrics/research/algorithms/)).
## 1 Introduction
The electromagnetic problem is described in Section 2, while in Section 3 the adopted Mitzner GBCs are discussed and two auxiliary sub-problems are introduced, each of which leads to DIEs for which the unknown is an equivalent spectral electric or magnetic current density. The procedure for the solution of these two sets of DIEs is outlined in Section 4, where all the quantities of interest are calculated as well. In Section 5, we propose a new boundary condition for thick conductive disks with no magnetic properties. This allows for formulating the problem in terms of only one set of DIEs, with consequent savings in terms of computation time compared with the original full formulation. In addition, we propose a series expansion (which in many practical case reduces to a single term) to efficiently evaluate the involved improper integrals. It is worth pointing out that the linear algebraic system resulting from the discretization of the involved DIEs defines a Fredholm problem, and as such has guaranteed convergence. Numerical results are presented in Section 6, showing the dependence of the magnetic shielding effectiveness on different geometrical parameters; these point out possible critical situations in particular configurations. Our conclusions are drawn in Section 7. Finally, we want to clearly point out the contribution of the paper. The present manuscript addresses thick circular plates, and its novel contribution is twofold: (i) for general magneto-conductive plates, a set of established DIEs having both electric and magnetic currents as unknowns is solved rigorously, and (ii) for nonmagnetic conductive plates, an original alternative DIE formulation having only electric currents as unknowns is introduced. This allows an original and efficient tool to be derived, allowing for the evaluation of, e.g., the shielding effectiveness of material disks against a near-field source such as a circular current loop coaxial with the disk.