esa-sceva/satcom-chunk-collection huggingface dataset:
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 1 Introduction
Also, we investigated two Transformer-based models: BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa-large (Pre-trained BERT model for Romanian). * Performance evaluation: Comparing models using metrics like accuracy, precision, recall, and F1 score. * Results analysis: Identifying strengths, limitations, and future improvements for each model. While our research employs established Transformer architectures, the novelty lies in their specialized application and optimization for Romanian fake news detection, making several significant contributions. Our approach introduces specific adaptations for Romanian language processing, including careful preservation of diacritics and language-specific tokenization patterns. The models demonstrate superior performance through optimized configurations, with BERT achieving 96.5% accuracy and RoBERTa reaching 94.4% accuracy on the combined test set, significantly outperforming previous approaches for Romanian fake news detection. The findings from this research have been implemented in a practical web application (ai-fake-news-ro.vercel.app) that allows users to verify Romanian news content in real time. The platform utilizes the best-performing BERT model, trained on our enhanced dataset, making the research directly applicable and accessible to the public. The study contributes a new enhanced dataset combining FakeRom with over 1000 newly collected articles through systematic web scraping of the Veridica platform. This datasetexpansion provides more current and diverse examples of Romanian fake news, enabling better model training and evaluation. The research findings have been transformed into a functional tool through the web platform, which provides real-time fake news detection for Romanian content, showcases detailed research findings and methodology, demonstrates the practical application of Transformer models in a real-world context, and makes advanced AI technology accessible to Romanian users. These contributions advance the field by establishing new benchmarks for Romanian fake news detection and providing empirical evidence for effective model configuration in low-resource-language scenarios, while also bridging the gap between academic research and practical applications. This paper is organized as follows: Section 2 presents the state of the art in fake news detection, Section 3 includes the used methods, including Used Technologies, Dataset Collection, Dataset Preprocessing, Machine Learning and Transformer-Based Models, Performance Evaluation, Model Implementation and Training, and the application of the models to detect fake news using the best-performing AI model that resulted from our research. Section 4 presents the obtained results and Section 5 highlights the main key findings from the results. Finally, Section 6 includes the conclusions for this research.
# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
###### Abstract  
Truscutaneous injection laryngoplasty (TIL) is a commonly used method to treat vocal fold paresis, where the affected vocal folds are augmented through injection. Determining the injection site and route is a major step during the preprocedural planning of TIL. In this communication, we propose and investigate an automatic method for needle route computation in preoperative neck CT. Recently, deep reinforcement learning (RL) agents showed noteworthy results for localizing the vocal folds. In this work, we focus on finding the optimal needle trajectory from the neck skin to the vocal folds localized by such RL agents. Identifying critical structures and constraints in the medical routine, we propose a minimal cost-based search to find the optimal path. Furthermore, we evaluate the proposed method with neck CT volumes from 136 patients, where it is shown that our computed needle paths have high accuracy.  
injection laryngoplasty; neck CT; deep reinforcement learning; needle trajectory; needle route; vocal fold augmentation; preoperative planning 2023
###### Abstract  
In this paper, a new receive antenna subset (RAS) selection scheme is proposed for precoding-aided spatial modulation (PSM). First, a lattice reduction (LR)-based precoder is employed instead of a conventional zero-forcing (ZF) precoder. It is analytically shown that a full diversity gain can be achieved by the LR-based ZF precoder without RAS selection. Then, an optimal LR-based RAS selection criterion is derived for the over-determined LR-based PSM systems, and a suboptimal selection algorithm is additionally presented. It is also shown that optimal and suboptimal RAS selection algorithms based on LR improve the BER performance of the LR-based PSM system. Further, the overall diversity order of the over-determined LR-based PSM systems with optimal LR-based RAS selection is analyzed. Finally, diversity analysis and simulation results show that the LR-ZF-based PSM system with optimal LR-based RAS selection outperforms the conventional ZF-based PSM system with conventional optimal RAS selection.  
antenna selection; multiple-input multiple-output (MIMO); precoding; zero-forcing (ZF); spatial modulation; lattice reduction +
Footnote †: journal: _Sensors_  
In this paper, the concept of LR operation is first applied to the precoding scheme of the PSM system, and a new optimal RAS selection criterion based on the LR is presented. It is shown that the LR-assisted optimal RAS of selecting \(N_{T}\) antennas among \(N_{R}\) receive antennas in the LR-aided ZF-based PSM system with \(N_{T}\) transmit antennas and \(N_{R}\) receive antennas achieves a diversity order of \(N_{T}(N_{R}-N_{T}+1)\). Meanwhile, it has been shown in [18] that the conventional optimal RAS of selecting \(N_{T}\) antennas among \(N_{R}\) receive antennas in the ZF-based PSM system with \(N_{T}\) transmit antennas and \(N_{R}\) receive antennas can obtain the diversity order of \((N_{R}-N_{T}+1)\). It is shown that the LR-aided ZF precoding can offer a better BER performance in comparison with the conventional PSM with RAS selection.  
The remainder of this paper is organized as follows. In Section 2, an RAS selection scheme of the conventional PSM system is briefly presented. In Section 3, the PSM system with LR-based ZF precoding is introduced. In addition, the optimal LR-RAS selection and complexity-reduced LR-RAS selection algorithms are derived with their computational complexity analysis. Simulation results are presented in Section 4, where the diversity order analysis is also included. Finally, some conclusions are drawn in Section 5.  
_Notations:_ We use lower-case and upper-case boldface letters for vectors and matrices, respectively. Superscripts \({}^{*}\), \({}^{T}\), and \({}^{H}\) denote the complex conjugate, transposition, and Hermitian transposition, respectively. The notations \(\textit{Tr}(\cdot)\) and \((\cdot)^{-1}\) denote the trace and the inverse of a matrix, respectively. \(E[\cdot]\), \(|\cdot|\), and \(\left\lVert\cdot\right\rVert_{F}\) represent the expectation, the absolute value, and the Frobenius norm, respectively. \(\textbf{X}(k,\cdot)\) indicates the \(k\)-th row vector of a matrix **X**.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method  
The identification model is established and analyzed in this section. In order to solve the channel mismatch problem of the TIADC system more effectively and improve the universality of the data acquisition system in various application scenarios, with the analysis of the TIADC system's structure, this paper designs an identification model to describe the frequency characteristics of the TIADC system based on system identification theory. This process will be elaborated on in Section 2.1. The frequency characteristic parameters of the TIADC system are analyzed in Section 2.2. The recursive parameters are estimated by the method described in Section 2.3, and the identification model is established using the frequency characteristic observation data of the TIADC system. Among them, the larger the input signal frequency range used by the observation frequency characteristics, the more accurate the estimation of the recursive parameters and the wider the scope of application of the system. Finally, in Section 2.4, through the frequency domain correction method, a digital compensation filter is established. In Section 2.5, we summarize the correction methods and expound on the methods to correct the whole TIADC system error. The working mechanism of the TIADC error correction system based on parameter estimation of the identification model is shown in Figure 2. The signal conditioning module in the front-end of analog signal input contains an analog filter, which can reduce the noise of the signal output from the sensor, reduce the order of the identification model, reduce the complexity of digital signal processing and improve the real-time performance of the system.
###### Abstract  
The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes.  
Keywords:tightly coupled navigation; measurement noise covariance estimation; adaptive Kalman filter (AKF); unscented Kalman filter (UKF); satellite selection +
Footnote †: journal: Sensors
## 5 Discussion  
### Performance Analysis of Relgraph  
From the benchmark results presented in Table 1 and the drug repurposing experiment outcomes in Table 3, it is evident that the Relgraph outperforms the baseline model in terms of performance across various KGC tasks. This underscores the advantage of Relgraph in leveraging the relation graph for extracting information exchange between predicates.  
Notably, the Relgraph exhibits the most significant performance improvement compared to baseline methods on the FB15K-237 dataset, as evident from Table 1. Improvements achieved by Relgraph-RotatE and Relgraph-TransE over suboptimal baseline methods amount to approximately 56.5% and 52.9%, respectively.  
On the WN18 RR dataset, the Relgraph exhibits a relatively smaller improvement over the baseline method. Among them, the Relgraph-RotatE approach exhibits the most notable improvement, reaching approximately 1.9%, followed by the Relgraph-TransE approach with approximately 0.4%.  
Relgraph exhibits a significant performance improvement over the baseline method on the UMLS dataset. When compared to the suboptimal baseline, Relgraph-RotatE and Relgraph-TransE achieve improvements of 22.0% and 21.6%, respectively.  
The Relgraph model exhibits an advantage compared to the baseline in its ability to identify new indications for existing drugs, achieving a performance improvement of 4.4% for Relgraph-RotatE relative to RotatE and 1.3% for Relgraph-TransE relative to TransE. This suggests that in large-scale knowledge bases, Relgraph remains effective.  
Upon comparing different experimental tasks, it is observed that the Relgraph model exhibits its greatest advantage on FB15K-237, which has the highest number of predicates, and relatively least advantage on WN18 RR with the lowest number of predicates. This underscores the unique advantage of the relation graph in handling complex KGs with numerous predicates.
###### Abstract  
Traditional fundus image-based diabetic retinopathy (DR) grading depends on the examiner's experience, requiring manual annotations on the fundus image and also being time-consuming. Wireless sensor networks (WSNs) combined with artificial intelligence (AI) technology can provide automatic decision-making for DR grading application. However, the diagnostic accuracy of the AI model is one of challenges that limited the effectiveness of the WSNs-aided DR grading application. Regarding this issue, we propose a WSN architecture and a parallel deep learning framework (HybridLG) for actualizing automatic DR grading and achieving a fundus image-based deep learning model with superior classification performance, respectively. In particular, the framework constructs a convolutional neural network (CNN) backbone and a Transformer backbone in a parallel manner. A novel lightweight deep learning model named MobileViT-Plus is proposed to implement the Transformer backbone of the HybridLG, and a model training strategy inspired by an ensemble learning strategy is designed to improve the model generalization ability. Experimental results demonstrate the state-of-the-art performance of the proposed HybridLG framework, obtaining excellent performance in grading diabetic retinopathy with strong generalization performance. Our work is significant for guiding the studies of WSNs-aided DR grading and providing evidence for supporting the efficacy of the AI technology in DR grading applications.  
wireless sensor networks; diabetic retinopathy; deep learning; HybridLG framework; MobileViT-Plus +
Footnote †: journal: Applied Sciences  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. A Wireless Sensor System for Diabetic Retinopathy Grading Using MobileViT-Plus and ResNet-Based Hybrid Deep Learning Framework
# SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
## 3 Methodology  
The method proposed in this paper is primarily divided into two main tasks: object segmentation and the subsequent rotation and adjustment of the images. Figure 1 summarizes the proposed method.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Strengths** & **Limitations** \\ \hline \multirow{2}{*}{Traditional Camera Calibration} & Accurate estimation of & \\  & intrinsic/extrinsic parameters & \\  & using precise patterns & \\ \hline \multirow{2}{*}{SIFT/SURF} & Robust to rotation and scale & High computational cost \\  & changes & (especially SIFT), \\  & & not suitable for real time \\ \hline \multirow{2}{*}{Lightweight feature extractors (e.g., ORB)} & Low computational cost, suitable & Lower accuracy and precision \\  & for real-time applications & compared to SIFT/SURF \\ \hline \multirow{2}{*}{Deep-learning-based keypoint detection} & High performance under various & Requires large-scale training \\  & conditions & datasets \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing algorithms for correspondence point detection.  
Figure 1: Overview of the proposed method.
## 4 Results and Discussion
### Nanofluid Characterization
#### 4.2.4 The Specific Heat Capacity of the Nanofluids  
Figure 9a depicts the measured specific heat capacity of the NFs as a function of temperature and NP volume concentration. From this figure, it is evident that \(C_{pNF}\) decreases as the volume fraction of the NPs in the NFs increases. The obtained results at 35 \({}^{\circ}\)C indicate that the decrements in the specific heat of the NFs at 0.2, 0.5, 1.0, and 1.5 \(v/v\)% are 0.7%, 2.4%, 3.1%, and 3.2%, respectively, compared to the base fluid. Figure 9a also indicates that the specific heat capacity of the NFs used in this study moderately increases with an increase in temperature. This experimental behavior was also observed by [PERSON] and [PERSON] [50] in other metal oxide NFs.  
Figure 9b shows a comparison between the measured \(C_{pNF}\) data at 35 \({}^{\circ}\)C and that computed by the [PERSON] and [PERSON] [51] model, which is defined as:  
\[C_{pNF}=\frac{\phi\qty(\rho C_{p})_{NP}+(1-\phi)\qty(\rho C_{p})_{BF}}{\rho_{BF}} \tag{11}\]  
where \(\phi\) is the volume fraction of the NPs, \(\rho_{NP}\) is the density of the NPs, \(C_{pNP}\) is the specific heat capacity of the NPs, \(\rho_{BF}\) is the density of the base fluid, and \(C_{pBF}\) is the specific heat capacity of the base fluid.  
Figure 9b indicates that the theoretical model given by Equation (11) overestimates the specific heat capacity of the prepared TiO\({}_{2}\)-W NFs, which is similar to that reported by [PERSON] and [PERSON] [52] and [PERSON] et al. [53].  
Figure 8: (**a**) Experimental density of TiO\({}_{2}\)–W NFs as a function of temperature and NP volume concentration. (**b**) Comparison of the measured density at 35 \({}^{\circ}\)C and that predicted by the mixture rule principle [48].
## 3 Results and Discussion
### Joining of CFRP to Aluminum Using a Diode Laser  
For the laser joining process, a PC sheet with a thickness of 150 \(\upmu\)m was used as a filler material for joining. The sample size was 5 mm \(\times\) 20 mm. Before joining procedures, the effect of pretreatment laser power on the tensile strength of CFRP/aluminum joints was investigated, as shown in Figure 9. When the CFRP was pretreated with a 300 mW and 500 mW femtosecond laser, the tensile strength of the obtained joints remained stable at about 12 MPa. As the laser power increased to 800 mW, the tensile strength of the joints decreased to around 9 MPa. Two pieces of PC sheet can improve the tensile strength of joints between CFRP and aluminum. In the investigation of joining with different laser powers, the surface of the CFRPs were treated with a 300 mW femtosecond laser. A 50 W to 150 W diode laser was applied on the CFRP/aluminum laser joining; while the laser power was above 150 W, the temperature of the joints was higher than the decomposition temperature of the PC sheet and epoxy matrix of CFRP, resulting in a joint with no shear strength.  
Figure 10 shows the tensile strength of joints under different joining laser powers. With the increase of laser power, the tensile strength of the joints increased gradually, and with two pieces of PC sheets, the CFRP/aluminum joints reached a higher tensile strength. The highest tensile strength of 20 MPa was obtained using the laser power of 150 W and two pieces of PC sheets were used as a filler material. Figure 11 presents the fracture surfaces of both sides of the 20 MPa CFRP/aluminum joint. Only a small amount of PC remained in the fracture surface. The fracture happened in the carbon fibers, and a layer of carbon fiber adhered to the surface of the aluminum, which demonstrated that the real tensile strength of the joint was higher than 20 MPa, and higher than the strength of carbon fibers.  
Figure 8: SEM image of fracture surface (**a**) CFRP side; (**b**) aluminum side; (**c**) magnified red area in (**b**).  
Figure 9: Effect of the pretreatment laser power on tensile strength.
## 5 Conclusions  
In this paper, a novel framework for HI extraction, called MSCAE, is proposed. It can overcome the disadvantages of traditional methods that require manual extraction of time-frequency domain indicators as features and the need to set failure thresholds by experience in RUL prediction. It relies solely on the raw sensor vibration signal to extract HI and does not require additional determination of the failure threshold. MSCAE can use convolutional kernels of different sizes to effectively exploit the global and local information of vibration signals, enhancing the HI extraction capability. A quadratic function-based label is first constructed for the original vibration data, after which the model is trained using the training data, and the internal parameters are optimized using a compound loss function. Then, HI is extracted using the test-bearing data to verify the validity of MSCAE. Finally, the RUL prediction is performed using LSTM. The HI extraction capability of MSCAE is verified to be superior to that of CAE models using a single scale with the PHM2012 dataset. Furthermore, it is compared with two state-of-the-art HI construction methods, CRNN and CNN, to judge the prediction performance of RUL using five evaluation metrics. The comparison results confirm the superiority of the proposed MSCAE-extracted HI for RUL prediction. In this paper, HI extraction and RUL prediction for rolling bearings achieved excellent results; however, the generalization capability for mechanical components such as gears and engines needs further validation. The future direction is to apply MSCAE to HI extraction and RUL prediction of other mechanical components.  
Data curation, Q.Z.; Methodology, Z.Y.; Project administration, S.S., T.N. and Y.Z.; Resources, Q.Z.; Supervision, S.S., T.N. and Y.Z.; Validation, S.S.; Writing--original draft, Z.Y.; Writing--review & editing, Z.Y. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shaanxi Provincial Natural Science Foundation Project: 2022 JQ-344 and Fourteenth Five-Year Plan Advance Research Project: 50902060401.  
Not applicable.  
The authors declare no conflict of interest.
# Investigation of Microservice-Based Workflow Management Solutions for Industrial Automation
## 3 Challenges and Requirements Set by the Industry for the Workflow Lifecycle
### Requirements for Workflow Execution
* **Discovery**: Microservice-based architectures and the constantly changing availability of services at the edge cause the discovery of available services at run-time to be an essential challenge for workflow orchestrators. The discovery of the available services and computing resources at the edge must go beyond predefined contracts and addresses. The probability of trying to contact a device that is no longer available is much higher at the edge, which causes device/service registration to be a pivotal aspect for assuring workflow execution efficiency [55]. **Challenge 7. Security and privacy**: IoT and edge networks are vulnerable to new types of attacks related to data flows. Attacks might come from inside or outside the company control domain and affect the management of workflows. The security requirements at the edge are numerous: control of data flows, authentication at different levels, authorization and access management for microservices, etc. Managing the security and privacy-related risks is a complex task for CPSs, although standard compliance guidelines are available for all industrial stakeholders to follow [63]. Mechanisms to correctly identify challenges related to WFM and their impact are necessary to support companies with the selection of the best WFM tools and languages to represent their manufacturing orders or recipes.