esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental  
Commercial metal granules of Hf (99.95% purity, Alfa Aesar) and Ta (99.95% purity, Alfa Aesar) were used as raw materials for the preparation of Hf-Ta alloys with different compositions. The atomic fraction of Ta in the Hf-Ta alloy was varied in a specific range in which it is known to form the Hf\({}_{6}\)Ta\({}_{2}\)O\({}_{17}\) superstructure [12]: Hf-xTa (x = 20.2, 27.2, 35.2 in at. %; hereafter, the compositions are denoted in at. % unless otherwise stated). In order of increasing Ta content, the three kinds of samples are denoted hereafter as HT-1, HT-2, and HT-3, as summarized in Table 1. The proportioned granules were alloyed by arc melting into ingots. In order to prevent chemical homogeneity, each ingot was flipped over several times (more than five times) during the arc melting. Ingots obtained from the arc melting were sliced into several mm sizes. The surface was polished using diamond suspensions, finishing at 1 micron.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Ta \% in Hf-Ta Alloy**} & \multirow{2}{*}{**Ref. [12] (at. \%)**} \\ \cline{2-3}  & **(wt. \%)** & & \\ \hline HT-1 & 20.2 & 19.8 & 19.7 * \\ HT-2 & 27.8 & 27.2 & 26.7 * \\ HT-3 & 36.3 & 35.2 & 34.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of the Hf-Ta alloy samples.  
For the oxidation, the Hf-Ta alloy ingots were heat treated at 1500 \({}^{\circ}\)C for 10 min in laboratory air, resulting in the Hf\({}_{6}\)Ta\({}_{2}\)O\({}_{17}\) superstructures according to a previous study [12]. The crystal structure of the Hf-xTa alloy samples was examined using X-ray diffraction (XRD, Rigaku Mini-Flex, Rigaku, Japan) before and after the oxidation treatment. The diffraction patterns of the samples were identified using MDI Jade 6 (Materials Data, Inc. Liverpool, CA). The microstructures of the samples were observed by scanning electron microscopy (SEM, Philips XL-30, Eindhoven, The Netherlands) and energy-dispersed X-ray spectroscopy equipped with SEM. For the purpose of this study, the Hf-Ta alloys with the Hf\({}_{6}\)Ta\({}_{2}\)O\({}_{17}\) superstructures were examined in terms of whether they endured the sintering conditions in the head-end process for pyroprocessing. Therefore, the oxidized alloy samples were heat treated under the same conditions for 1 h at 700 \({}^{\circ}\)C and then for 24 h at 1450 \({}^{\circ}\)C in 4% H\({}_{2}\)-Ar atmosphere. After repeated heat treatment, the residual samples were analyzed by XRD, SEM, and EDS.
# Hybrid-Recursive Feature Elimination for Efficient Feature Selection
## 3 Results
### Experimental Results  
Using the feature importance ranking list from the three previous RFE methods and two proposed RFE methods, four classifiers were tested to find the best feature set that produced the highest classification accuracy. The results of the basic experiment are summarized in Table 3. Each cell expresses the highest classification accuracy, and the number of selected features is shown in parentheses next to the accuracy value. For example, 0.904 (36) from SVM-RFE and KNN indicates that the SVM classifier produced the highest accuracy of 0.904 when it used the first 36 features from the SVM-RFE feature ranking list. The data in bold in the table represent the top values for a specific classifier. If two or more methods produced equal top accuracy, the method that used the least number of selected features was chosen.  
From Table 3, it is evident that the top values were mainly produced by the two proposed methods. This means that the feature ranking lists generated by the proposed methods were better than those generated using the previous methods. The frequency of top accuracies for the compared RFE methods are summarized in Table 4. The frequencies of Hybrid-RFE with the simple sum function and weighted sum were 10 and 11, respectively (total of 21). The frequencies of SVM-RFE, RF-RFE, and GBM-RFE were 2, 4, and 4, respectively. In our experiment, Hybrid-RFE with the weighted sum function showed the best performance, whereas SVM-RFE showed the worst performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{5}{c}{**RFE Methods**} \\ \cline{3-8}  & & **on** & **Entire Feature** & **SVM-RFE** & **RF-RFE** & **GBM-RFE** & **Hybrid-RFE** & **Hybrid-RFE** \\  & & **Set** & & & & & **(SS) 1** & **(WS) 2** \\ \hline satellite & KNN & 0.904 & 0.904 (36) & 0.904 (36) & 0.904 (36) & 0.907 (31) & 0.904 (31) \\  & SVM & 0.898 & **0.  
If two RFE methods A and B produce the same classification accuracies and the numbers of selected features are 15 and 10, respectively, method B is more efficient than method A. The ideal feature selection method produces a small feature subset size and high classification accuracy. Therefore, we compared the average size of the set of selected features from the five RFE methods. The results are summarized in Table 5. Only the GDS datasets were used in the comparison because the others have a small number of features in the original datasets. The proposed Hybrid-RFE methods were more efficient than previous RFE methods. In particular, Hybrid-RFE with a weighted sum function produced a remarkably small feature subset.
## 3 Potential Security Implications of IoT in 6G Environment
### 6G Usage Scenario-Related Vulnerabilities
#### 3.2.5 Artificial Intelligence and Communication  
The integration of Artificial Intelligence (AI) with IoT in 6G gives rise to several distinct security challenges. AI-driven IoT applications, such as autonomous vehicles, medical devices, and distributed computing, rely on vast amounts of data and real-time processing, which can expose IoT devices to new vulnerabilities. IoT devices involved in distributed AI processes, such as training models or conducting inferences, are at risk of data breaches or manipulation [5]. For example, attackers may exploit weaknesses in AI models through techniques like data poisoning or adversarial attacks, leading to erroneous decision-making by the devices [45]. Additionally, the offloading of complex computational tasks across diverse networks and devices can expose sensitive data to interception or tampering during transmission, especially in scenarios requiring low latency and high reliability [47]. Privacy breaches are another significant concern, as the massive data exchange required for AI-driven applications can expose personal or sensitive information to unauthorized access [5]. The integration of AI with quantum communication and edge computing could further complicate the security landscape [2].
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 5 Experimentation
### Experiment Parameter Configuration  
To validate the effectiveness of the BERT_hybrid model on the DuReader2 dataset, BERT (base) and RoBERTa-wwm-ext were used as the encoding layers. The model's parameters were adjusted to achieve the best experimental results. During the experiment, iterative parameter tuning was conducted based on the code parameters provided by the Joint Laboratory of Harbin Institute of Technology and iFLYTEK Co., Ltd. (Hefei, China). Various combinations of hyperparameters were manually adjusted and repeatedly tested, such as different learning rates (0.00007, 0.00005, 0.00003), maximum sequence lengths (400, 450, 500), number of epochs (1, 2, 3, 4), and hidden_size (199, 398, 768). Furthermore, the parameters of the pretrained RoBERTa-wwm-ext model were set to have adjustable gradients, necessitating fine-tuning based on the training data. The optimal parameter combination was selected based on the experimental results, as outlined in Table 2.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Reference Value** \\ \hline seq\_length & 512 \\ Learning-rate & 0.00005 \\ batch\_size & 8 \\ Optimization & Adam \\ hidden\_size & 768 \\ Num\_hidden\_heads & 12 \\ warmup\_proportion & 0.1 \\ epochs & 4 \\ Hidden\_Activation & Gelu \\ Directionality & Bidi \\ \hline \hline \end{tabular}
\end{table}
Table 2: Super parameter configuration.  
To assess the effectiveness of the hybrid attention mechanism model, the following comparative experiments were conducted. The experimental models from the BERT series, including Baseline_Bert, Baseline_MacBert, Baseline_RoBERTa_wwm, and others, were compared. The final scores were evaluated using two metrics: ROUGE-L and BLEU-4. The experimental data for the development set and test set are presented in Tables 3 and 4, respectively, where the bold entries represent the experimental indices of the models used in this study.  
As can be seen from Tables 3 and 4, traditional reading comprehension models such as match-LSTM and BiDAF yielded unsatisfactory results. In contrast, pretrained models exhibited a high degree of fitting on both the development and test sets. In particular, the proposed hybrid attention mechanism model yielded a remarkable ROUGE-L score of 61.1 and a BLEU-4 score of 60.9 on the development set. Similarly, on the test set, the model exhibited a commendable performance with a ROUGE-L score of 61.1 and a BLEU-4 score of 60.9. A significant enhancement compared to traditional models and BERT-based models was observed. Based on the experimental results, it can be deduced that the proposed hybrid attention model represents a substantial improvement in model prediction accuracy. Moreover, the model exhibits superior data fitting capabilities, thereby greatly enhancing the prediction accuracy in Chinese reading comprehension tasks.
# From Discourse Relations to Network Edges: A Network Theory Approach to Discourse Analysis
## 2 Discourse Relations and Discourse Units  
There are numerous formal theories of discourse representation in the literature, and corpora have been annotated for discourse structure based on the principles of these theories (Segmented Discourse Representation Theory (SDRT; [21; 23], Rhetorical Structure Theory (RST; [33]), the Linguistic Discourse Model (LDM; [34]), the Discourse Graphbank model ([35]) and Discourse Lexicalized Tree Adjoining Grammar (DLTAG; [36])). Briefly, Segmented Discourse Representation Theory (SDRT) is a dynamic semantic theory of discourse interpretation that focuses on the interplay between discourse interpretation and discourse coherence. It is an extension of Discourse Representation Theory (DRT), which is a framework for exploring meaning under a formal semantics approach. In SDRT, a text is segmented into constituents related to each other by means of rhetorical relations, resulting in a structure known as a segmented discourse representation structure (SDRS).  
Among the above approaches, SDRT offers a well-principled model of discourse inference and interpretation and has also been adopted as the annotation model for the annotation of the STAC corpus as well as the C58 corpus. The availability of both corpora serves the process of stressing the differences and similarities between the three different types of recorded discourse data: single-author written texts, nonsituated and situated dialogues. Additionally, SDRSs, the discourse representations that SDRT adopts and that is analyzed in the rest of the paper, can be mapped to non-tree-like graphs, contrary to the above-mentioned discourse theories.  
Although SDRT has been chosen as the basis of the current work, all discourse theories are more or less aligned as to how to proceed in annotating linguistic utterances. The basic premise is that discourse structure is hierarchically organized around discourse units, the _elementary discourse units_ or EDUs in SDRT parlance. However, SDRT includes also _Complex Discourse Units_ or CDUs to cope with semantic groupings of EDUs in the discourse and, recently, refs. [28, 29, 37] extended SDRT's classic definition of a discourse graph with a third type of node, the _elementary event units_ or EEUs, so that nonlinguistic events are considered as nodes during the construction of the discourse graph. Here is the definition of a discourse graph by [28]:  
**Definition 1**.: _A discourse graph_ G _is a tuple (_V_, E1, E2, \(\ell\)_, Last), where V is a set of nodes (_EDUs_,_ EEUs_, and_ CDUs_);_ E1, a set of edges representing discourse relations;_ E2, a set of edges relating each Complex Discourse Unit (_CDU_) to its sub-units; \(\ell\), a labelling function from elements of_ E1 _to discourse relation types; and Last, a label for the last unit in_ V _relative to textual order._
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### Asset Management
#### 4.1.2 Predictive Maintenance and Forecasting  
Predictive maintenance and forecasting are critical in minimizing downtime and operational costs. Digital Twins play a crucial role here by predicting when maintenance should be performed based on the current condition of the assets rather than on a predetermined schedule. This method significantly reduces unnecessary maintenance, extends the assets' life, and ensures their optimal performance [92].
# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
###### Abstract  
Advancements in science, technology, and computer engineering have significantly influenced biometric identification systems, particularly facial recognition. However, these systems are increasingly vulnerable to sophisticated forgery techniques. This study presents a novel deep learning framework optimized for texture analysis to detect facial forgeries effectively. The proposed method leverages high-frequency texture features, such as roughness, color variation, and randomness, which are more challenging to replicate than specific facial features. The network employs a shallow architecture with wide feature maps to enhance efficiency and precision. Furthermore, a binary classification approach combined with supervised contrastive learning addresses data imbalance and strengthens generalization capabilities. Experimental results, conducted on three benchmark datasets (CASIA-FASD, CelebA-Spoof, and NIA-ILD), demonstrate the model's robustness, achieving an Average Classification Error Rate (ACER) of approximately 0.06, significantly outperforming existing methods. This approach ensures practical applicability for real-time biometric systems, providing a reliable and efficient solution for forgery detection.  
facial forgery detection; biometrics; human identification; image forensics; data imbalance; texture analysis Article
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 4 Applied OMA Methods
### Poly-Reference Least-Squares Complex Method (PolyMAX)  
PolyMAX is a parametric frequency domain model of operational modal analysis [1; 7; 12; 13]. It is a method based on least-squares complex-frequency (LSCF) -type estimators [1]. Its main advantage is the possibility of obtaining very clear stabilization diagrams [1; 12]. As a parametric method, it has a high complexity and high computational effort. It is based on the right fraction of the FRF (frequency response function) matrix.  
\[[H(\omega)]=[B_{R}(\omega)][A_{R}(\omega)]^{-1} \tag{5}\]  
where \([H(\omega)]\) is the FRF matrix; \([B_{R}(\omega)]\) is the numerator polynomial matrix and \([A_{R}(\omega)]^{-1}\) is the denominator polynomial matrices.  
The FRF matrix relates the inputs exciting the system to the system's response. In the case of operational modal analysis, the system inputs are unknown, so the analogy between PSD matrix and FRF matrix is used, assuming that the input is wide-band noise. The common-denominator model (also known as the scalar matrix-fraction model) of the FRF represents a special case of the right fraction of a matrix, where the numerator is a polynomial matrix and the denominator is a polynomial characterized by scalar coefficients.  
\[[H(\omega)]=\frac{[B(\omega)]}{[A(\omega)]}=\frac{\sum_{j=0}^{n}[\beta_{j}( \omega)]\Omega^{j}(\omega)}{\sum_{j=0}^{n}\alpha_{j}\Omega^{j}(\omega)} \tag{6}\]  
where \(\Omega^{j}(\omega)\) is the polynomial basis function, which is used to describe a frequency-domain model that is derived from a discrete-time model; \(n\) is the model order; \(\beta_{j}(\omega)\) are the numerator matrix polynomial coefficients; and \(\alpha_{j}\) are denominator matrix polynomial coefficients.  
The least squares complex frequency (LSCF) method is based on the common denominator model shown in (6). The PolyMAX method is an extension of the LSCF motivated by some limitations arising from the application of the common denominator model in the LSCF method. The most important limit of LSCF is the difficulty in identifying very close modes [1].  
By deriving the modal parameters for different model orders, the stabilization diagram can be constructed. As in the Cov-SSI algorithm, the pole alignment indicates the modal parameters of the system under examination.  
For the complete and exhaustive mathematical treatment of the method, reference can be found in [1; 13].
## 2 Materials and Methods  
### Materials  
Commercial AnodiscTM wafers (13 mm wafer diameter and \(\sim\)230 nm pore diameters, Whatman) were used for the synthesis of most nanorods in this study. For the synthesis of thinner nanorods, an AAO membrane was manually fabricated by anodizing Al foil (99.999%, from Sigma-Aldrich, Seoul, Korea) in 0.3 M oxalic acid solution at 47 DC V of applied voltage. The electro plating solutions, Orotemp 24 RTU for Au and Techni Silver 1025, were purchased from Technic Inc. (Seoul, Korea) Other chemicals, pyrrole, CdSe quantum dot nanoparticles, tetrahydrofuran, and tetrabutylammonium tetrafluoroborate were purchased from Sigma-Aldrich. Deionized (DI) water was purified to the resistivity of 18.2 M\(\Omega\) through a Millipore water purification system (USA) and used for all the aqueous solutions and necessary steps.
## 4 Conclusions  
This paper presents a computer application that determines the optimal tilt and orientation angles of flat surfaces to maximize solar insolation capture based on existing mathematical models. The most populated cities of Ecuador were selected for this study, since there are significant changes in weather conditions across cities due to geographic conditions, and it is not representative for the entire country to estimate a single average optimum tilt angle. The data was obtained through meteorological stations. The city of Cuenca was selected as a reference, and its data measured during three years from the local meteorological stations, comparing them with the TMY files of the same. Significant variations were observed in the various selected cities in all the variables studied. It should be noted that the typical year files are a database without updating and that their content will significantly affect the simulations and calculations carried out. Note that the most common and studied climate scenario currently predicts an increase between 1% and 3.49% in annual mean global temperatures.  
The error in TMY files (which ranges between 22.2% in inclination and 19.11% in insolation values) must be taken into account while modeling. It is observed that the cities of the Sierra region--Quito, Cuenca, Loja, and Ambato--have a higher insolation index than the cities of the Costa region: Guayaquil, Santo Domingo, Machala, and Portoviejo. In this study, the inclination and orientation values together with those of maximum irradiance analyzed with data measured from meteorological stations have primarily coincided with other studies carried out mainly in the city of Cuenca, where the optimal inclination of a flat surface for maximizing insolation pickup is between 9\({}^{\circ}\) and 16\({}^{\circ}\), and orientation between 20\({}^{\circ}\) and 60\({}^{\circ}\).  
In a future work, it could be helpful to analyze the effects of orientation and irradiance on DC power.  
Conceptualization, X.S.-G.; Data curation, E.C., J.-J.F.; Formal analysis, E.C., J.-J.F.; Investigation, E.C., J.-J.F.; Methodology, X.S.-G., E.C., J.-J.F.; Supervision, X.S.-G.; Validation, X.S.-G., A.B.-E., J.-M.C.; Writing--original draft, E.C., J.-J.F., J.-M.C.; Writing--review & editing, X.S.-G., A.B.-E., J.-M.C. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Universidad de las Americas-Ecuador.  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TMY & Typical Meteorological Years \\ PV & Photovoltaic \\ RE & Renewable Energy \\ \end{tabular}
## 3 Numerical Model
### Flexural Behavior  
The flexural behavior has been modelled with a distributed plasticity approach using a nonlinear force-based beam-column element, with a fiber discretization of the section.  
The peculiarity of a finite element with a force formulation is that it employs force shape functions (derived by equilibrium considerations) to obtain the internal forces (at section level) once the external forces acting at the nodes of the element are known.  
The vantage of that approach is that, provided no loads are applied along the element length, just one element can be sufficient to capture the bending behavior of the whole cantilevered pier, despite the fact that the formation of plastic hinges at the fixed end of the element will produce a concentration of non-linear curvatures.  
The overall response of the element is obtained through the integration of the non-linear responses obtained over the different sections of the element. In practice, the integral is substituted by a weighted summation adopting some numerical integration scheme over a certain number of monitored sections. In our case, a Gauss-Lobatto 5 node scheme was adopted.  
In order to analyze the non-linear response of the element cross-sections, those have been discretized in fibers, as depicted in Figure 3a. Since a reinforced concrete element is essentially composed by two different materials, namely casted in place concrete and steel reinforcing bar, two different kinds of constitutive relationship are used to describe the mechanical behavior of those materials, and assigned to relevant fibers within the element sections.  
The longitudinal steel reinforcement has been modelled by the Menegotto and Pinto [19] constitutive law (Steel02 uniaxial material in OpenSEES). With reference to Figure 3b f\({}_{y}\) and \(\varepsilon_{y}\) are, respectively, the yield strength and strain of the reinforcement whilst E\({}_{0}\) is the elastic modulus and b\({}_{o}\) an adimensional parameter accounting for post-yield stiffening.  
The concrete has been modelled in OpenSEES using the Concrete04 uniaxial material which is based on the Popovics law [20]. The concrete on the section cover has been considered unconfined, whilst the concrete in the section core has been considered as confined, using the [PERSON] et al. model [21]. With reference to Figure 3c, f\({}_{co}\) and f\({}_{cc}\) are, respectively, the strength of unconfined and confined concrete, whilst \(\varepsilon_{co}\) and \(\varepsilon_{cc}\) are the corresponding strains.  
Figure 2: Numerical finite element model adopted in this study (x is the element local axis).