esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
###### Abstract  
The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modeling, short- and long-term drift detection, interpretability, and a drift-aware federated paradigm. By leveraging self-supervised or unsupervised learning, M2D2 constructs a resilient baseline of nominal behavior across numeric, textual, and categorical features, thereby facilitating the early detection of both rapid spikes and slow-onset deviations. An interpretability layer--using attention visualization or SHAP/LIME--delineates which sensors, logs, or batch identifiers precipitate each drift alert, accelerating root-cause analysis. An active learning loop dynamically refines threshold settings and model parameters in response to real-time feedback, reducing false positives while adapting to evolving production conditions. Crucially, M2D2's drift-aware federated learning mechanism reweights local updates based on each site's drift severity, preserving global model integrity at scale. The key scientific breakthrough of this work lies in combining advanced multi-modal processing, short- and long-term anomaly detection, transparent model explainability, and an adaptive federated infrastructure--all within a single, coherent framework. Evaluations of real WBG fabrication data confirm that M2D2 substantially improves drift detection accuracy, broadens anomaly coverage, and offers a transparent, scalable solution for next-generation semiconductor manufacturing.  
active learning; anomaly detection; attention-based interpretability; autoencoder; Bayesian threshold; contrastive learning; drift-aware federated learning (FL); graph neural network (GNN); multi-modal data; parameter drift; self-supervised learning; semiconductor manufacturing; transformer
# Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
## 3 Materials and Methods
### Barriers to Maintenance 4.0
#### 3.1.3 Data-Related and Cybersecurity Main Criteria  
* Issues in data quality and quantity: Maintenance applications will also be successful if accurate, complete, and reliable data is provided. With a successful maintenance application, production continuity and the efficient operation of the machines are ensured [53].
* Inability to perform real-time monitoring: Real-time monitoring is an important criterion for predictive maintenance 4.0 applications. Predictive analytical maintenance models are developed by analyzing the data coming from IoT sensors in real-time. In this way, maintenance costs are also minimized [2].
* Data security and privacy concerns: Data security and privacy are key factors for the continuity and success of Maintenance 4.0 implementations. Therefore, cybersecurity is an integral part of maintenance strategies [53].
* Complexity in big data management and analytics: To implement Maintenance 4.0 policies flawlessly, a large and diverse amount of data needs to be collected and analyzed. For this, a problem-free storage and access service must be provided. In addition, data analysis tools must be compatible. Inaccurate and unreliable data analysis leads to incorrect maintenance estimates [54].
* Lack of digital literacy among staff: Increasing the digital literacy of personnel within the business is important for the success of Maintenance 4.0 applications. Good digitalliteracy is required for the use of new technologies and the analysis and interpretation of the data obtained [55].
* Risks in information security and data sharing (internal and external): This is a criterion that is often overlooked, but which is very important when it comes to sharing data with both internal and external stakeholders. The fact that the system is so digital and that all devices are connected and communicate with each other underlines the need for intensive security measures [1].
## 2 Inverse Analysis Containing to LFWD Test  
### Overview of LFWD Test and Its Inverse Analysis  
The section DK65 + 629 on the Baotou-Shenmu HAL railway is selected to carry out the LFWD field test (Figure 1) [25; 26]. As shown in Figure 2a, the LFWD used in this study is developed and manufactured by the Rincent BTP company. The operation mode of the device is simple. As the sliding drop weight is released, it strikes the cylindrical shock absorbers so that an impulse load of 40 ms duration with a maximum force ranging up to 35 kN is transferred through the loading plate into the ground [27]. The force is measured using a load cell on the center of the plate, and geophones are used to measure the surface accelerations at various distances from the footplate [14]. The vertical displacements (\(d_{n}\)) can be then obtained by integrating the accelerations. On this basis, the surface deflection at different points away from the load application point can be then determined.  
The geophone distances were selected to target the displacement responses that are a result of, and representative of, the differing layers of material that comprise a railway track bed (see Figure 2a). They were based on previous works that empirically relate the depth of materials affected by the load to the displacement responses measured at differing distances from the applied load [14; 18; 28]. The geophones at 0.2, 0.3, and 0.6 m (\(M_{1}\)-\(M_{3}\)) provide a combined measure of the whole of the support structure; and the 1.2, 1.5, and 1.8 m (\(M_{4}\)-\(M_{6}\)) geophones provide a measure of the combined stiffness of the embankment and subgrade. The depth of materials varies over the length of the railway structures and, as a result, the geophone placements are rarely optimal; however, experience has shown these placements to be practical [14; 18; 28].  
Subsequently, the deflection data resulting from an LFWD test can be used in combination with a model of the railway track structure to determine the values of the resilient modulus necessary to characterize the material properties of the track substructure [14; 29]. To this end, in the initial stage, the estimated values of the modulus are adopted for each layer of the substructure in the FE modeling (Figure 2b), and the comparison between the resulting deflections from the model and the field is then conducted.  
In the end, the influence of the resilient modulus and Poisson's ratio of each layer on the magnitude of the deflections can be determined by performing a trial and error process or a sensitivity analysis (Figure 2c).  
Figure 1: LFWD test plan: (**a**) location of the Baoshen heavy axle load (HAL) railway; (**b**) test site; (**c**) LFWD test; (**d**) LFWD device; (**e**) vibration pickup (891-II); and (**f**) data acquisition system (DH5922D).  
Figure 2: Inverse problem (**a**) input: LFWD field test; (**b**) process: finite element modeling; and (**c**) output: comparison of measured and computed deflections.
# Easing DApp Interaction for Non-Blockchain Users from a Conceptual Modelling Approach
## 2 Related Work  
Despite being quite a recent field of research, we can find several works related to the development of DApp's. For instance, the authors of [13] presented a DApp to share everyday objects. By using this DApp, which was deployed on the Ethereum blockchain, users can register and rent objects by scanning QR codes. Besides, a different scenario was considered in [14], whose authors presented a DApp for real-time ride-sharing services. By means of this DApp, they propose a blockchain version of the Uber platform, consisting in \"decentralized, community-owned-and-managed transportation network without unsatisfactory centralized decision-making or risks\". In a different vein, a DApp for the healthcare domain is presented in [15]. In this work, they presented FHIRChain, a DApp to store and share clinical data, thus benefiting from blockchain's security. Therefore, they use digital health identities (blockchain addresses) to authenticate patients in this DApp, thus maintaining data ownership and privacy.  
As far as conceptual modeling and software engineering for blockchain is concerned, we can find only a handful of works. Indeed, [PERSON] and [PERSON] started the discussion regarding the need for applying software engineering to the development of smart contracts in [16]. They propose using UML's class diagrams to model the structure of smart contracts since they are class-based artifacts. In this sense, the authors of [17] applied several UML diagrams to model an Ethereum-based DApp for managing workers contracts. In this work, they applied the blockchain oriented software engineering approach [18], which in turn, acknowledges the need for applying software engineering to blockchain-based applications. Despite of the fact of not being directly related to UML, in [19] it is proposed a fine-grained approach to identify which are the elements of an application architecture that could be implemented by using blockchain. They illustrate such approach by means of a case study based on a system for the coordination and payment of craftsmen constructing buildings.  
As far as we know, there is no work aimed at easing the interaction of non-blockchain expert users with a DApp. Therefore, this constitutes the main motivation for this work: to conceptually model and analyze the DApp structure, with the aim of creating a middleware. It would help users to interact with DApps without the need of being used to blockchain concepts which could be cumbersome and overwhelming for non-expert users.
## 2 AUV Design and Construction
### System Configuration and Dynamics  
The constructed AUV is equipped with four thrusters, \(T_{s1}\), \(T_{s2}\), \(T_{r1}\), and \(T_{r2}\), where \(T_{r1}\) and \(T_{r2}\) are for surge and yaw thrusting, and \(T_{s1}\) and \(T_{s2}\) are for heating and pitching, as shown in Figure 3. \(T_{s1}\) and \(T_{s2}\) are allocated at the side of the AUV by \(l_{r}\) along the Y-axis and by \(l_{p}\) along the X-axis. \(T_{r1}\) and \(T_{r2}\) are allocated at the side of the AUV by \(l_{y}\) along the Y-axis.  
In this paper, to control the heave, pitch, roll, and yaw motion of the AUV, the vector and moment expression of the dynamics of the AUV are expressed as follows [19]:  
\[M\dot{v}\ +C(v)v+G(\eta)=\tau \tag{1}\]  
where  
\[\begin{bmatrix}F_{z}\\ K\\ M\\ N\end{bmatrix}=\begin{bmatrix}0&0&1&1\\ 0&0&l_{r1}&l_{r2}\\ 0&0&l_{p1}&l_{p2}\\ l_{y1}&l_{y2}&0&0\end{bmatrix}\begin{bmatrix}T_{s1}\\ T_{s2}\\ T_{r1}\\ T_{r2}\end{bmatrix}\]  
In Equation (1), \(M\) is the inertia matrix, \(C(v)\) represents the Coriolis and centripetal force matrix, \(G(\eta)\) is the restoring force and moment vector due to gravity and buoyancy, and \(\tau\) is the vector of external forces and moments. Also, \(F_{z}\) represents the vertical force, and \(K\), \(M\), and \(N\) correspond to the roll, pitch, and yaw moments, respectively.  
Figure 3: Thruster allocation of AUV.  
Figure 2: System of AUV.  
Based on the allocation of the thrusters, the related configuration matrix between the input torque, \(\tau\), and thrusting forces is expressed as  
\[\tau=Bu \tag{2}\]  
where  
\[B=\begin{bmatrix}0&0&1&1\\ 0&0&l_{r1}&l_{r2}\\ 0&0&l_{p1}&l_{p2}\\ y_{1}&l_{y2}&0&0\end{bmatrix},\ \tau=\begin{bmatrix}F_{z}&K&M&N\end{bmatrix}^{T},\ u= \begin{bmatrix}T_{s1}&T_{s2}&T_{r1}&T_{r2}\end{bmatrix}^{T}\]  
The AUV is experimented with in the calm water tank, and it is slow in heave motion and roll, pitch, and yaw rotations, such that the added mass and moment of inertia can be neglected. Also, the AUV is designed to be symmetrical with respect to the xy and yz planes, it has no sway direction thrusters, and its center of mass is centered on the AUV axis. Hence, the dynamics of the AUV in the hovering state are expressed with the same expression as [19]:  
\[\begin{array}{c}\text{m}\big{[}\dot{w}-uq\big{]}=\sum F_{z}\\ I_{xx}\dot{p}+\big{(}I_{zz}-I_{yy}\big{)}\text{qr}=\sum K\\ I_{yy}\dot{q}+\big{(}I_{xx}-I_{zz}\big{)}\text{rp}=\sum M\\ I_{zz}\dot{r}+\big{(}I_{yy}-I_{xx}\big{)}\text{pq}=\sum N\end{array} \tag{3}\]
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 2 Method
### Monte Carlo Simulation  
To analyze the spatial dispersion characteristics of laser transmission, this study used Monte Carlo photon tracking technology to obtain the underwater light field distribution [38]. This method enables the tracking of photons from the light source to the receiver plane. The fundamental concept involves sampling variables with known probability distributions, which include the transmission distance of the photons, the interaction type between the photon and the medium, and the scattering direction. By calculating the trajectories of the transmitted photons that reach the receiving plane, it is possible to further analyze the relevant information related to the light field.  
Sampling from the cumulative distribution function \(\Psi(x)\) is based on the following basic rule:  
\[\zeta=\int_{x_{min}}^{x_{max}}\ p(x)\mathrm{d}x=\Psi(x) \tag{2}\]  
where \(\zeta\) is a uniform random variable distributed between 0 and 1, \(x\) is the sampling parameter, \(x_{\mathrm{min}}\) and \(x_{\mathrm{max}}\) are the minimum and the maximum values of \(x\), \(p(x)\) is the probability density distribution function, \(\int_{-\infty}^{+\infty}p(\mathrm{x})\mathrm{d}x=1\).
## 2 Related Work
[16] proposed a novel framework based on Convolutional Neural Networks with various hyperparameters optimizations that achieved perfect accuracy and F1-scores when applied to CAN bus data. Conversely, traditional machine learning models remain competitive in certain scenarios due to their lower computational requirements and ease of interpretability. For example, [PERSON] et al. [22] developed HDL-IDS, a hybrid IDS architecture that combines convolutional and recurrent layers to capture both spatial and temporal features of CAN traffic. By analyzing time-series data from CAN messages, their system achieved an F1-score of over 99.9%. [PERSON] et al. [3] used a Deep DTL-based IDS, and compared the results with TML and DL models. Although the highest F1-score achieved was in the proposed DTL model, with a value of 97.83%, the results of the TML models are higher than the DL models, specifically with Random Forest, Decision Trees, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Also, [PERSON] et al. proposed a lightweight IDS using MobileNetV2 [23] and MobileNetV3 [12], which achieved perfect accuracy and F1-scores but limited adaptability to novel attacks. [PERSON] et al. [24] introduced IoV-BERT-IDS, which applies Large Language Models (LLMs) such as BERT to extract contextual semantics from CAN bus traffic. This innovative approach improved detection rates for complex attack scenarios, resulting in a 99.97% accuracy and 99.85% F1-score. The focus on CAN-specific intrusion detection underscores the critical need for methods that prioritize optimization and effectively address the challenges posed by imbalanced datasets. Real-world CAN environments are inherently imbalanced, with benign traffic significantly outnumbering malicious instances, making it essential to evaluate models in realistic scenarios. The literature review highlights the consistent high performance of TML even under these challenging conditions. Their ability to maintain high detection rates and F1-scores across multiple types of attacks without relying on computationally intensive methods demonstrates the practicality and efficiency of TML models in resource-constrained vehicular systems. While advanced techniques such as DL and DTL models provide precision, optimization-driven approaches using TML models continue to be highly effective in ensuring scalable and reliable CAN intrusion detection in real-world applications, highlighting the importance of both methods. In contrast to previous studies, our research focuses on evaluating machine learning models on the CICIoV2024 dataset [8] without applying artificial data balancing techniques, ensuring a more realistic evaluation of model performance under imbalanced conditions. In addition, we employ advanced hyperparameter optimization using Optuna [18] to provide fine-tuned models that balance detection accuracy across all traffic classes, including minority attacks. This comprehensive approach addresses the gaps in existing research and delivers practical and effective solutions tailored to real-world IoV environments.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 5 Numerical Simulation of Linear MIMO FSE Receiver for MDL-Impaired Optical Channel
### Channel Model
The two orthogonal matrices after QR factorization of two independent realizations of the random matrix are used as \(\underline{U}^{(k)}\) and \(\underline{V}^{(k)}\), respectively. We consider a total number of \(K_{amp}=100\) spans, each \(\ell_{span}=50\) km long. For the fiber parameters, we used the multi-core fiber data reported in [41], considering the number of modes \(D=6\) and the central wavelength \(\lambda_{c}=1469\) nm. The selection of this multi-core fiber allow us to compare the results of this work with those presented in [28], and to obtain the fiber parameters needed for the numerical simulations from [41]. We take 2% as the underestimation dispersion factor that is applied to the dispersion coefficient \(D_{CD}\) to obtain the residual CD experienced by the receiver. For the gain STD \(\sigma_{g}\), we considered several values in the range of the systems referenced in [27]. For the numerical simulations, we compute a total of \(N_{ch}=10{,}000\) realizations of the channel frequency response \(\underline{\underline{H}}_{tot}(\omega)\) defined in (5).
## 6 Result and Discuss
### Data Prediction Performance Analysis
#### 6.2.3 Forecast Model Analysis  
Exponential smoothing (ES) is a classical time series prediction method [55]. The prediction generated by the exponential smoothing algorithm is the weighted average of the past observations. As the number of observations grows, the weighted values decay exponentially. This is suitable for short- and medium-term forecasting and has good accuracy. The prediction accuracy of the ES model depends on the smoothing factor  
Figure 10: Prediction performance analysis of different parameters selected using ARMA in different periods. Based on the temperature data of (0, 960), (960, 1920), (1920, 2880), (2880, 3840) periods, ARMA models with different (p, d, q) parameters are used to predict the temperature data of the next 24 h period; calculate the MAE, MSE and RMSE formed by the predicted values and the original monitoring values; and draw the analysis curves (**a–d**).  
\(\alpha\), which is set between 0.1 and 0.9. The smoothing factor \(\alpha\) can be acquired using the trial-and-error method. In this experiment, the value of \(\alpha\) is 0.22.  
GRU is a variant of the RNN network and a type of long-term memory network that can effectively capture the nonlinear relationship between sequence data [56; 57]. GRU adds a gating unit to the standard RNN model. The gating unit can control the flow status of information in different time steps in the network. GRU is a simplified version of the LSTM network, but the number of parameters used is still huge. The GRU structure used in the experiment adopts the structure of two layers of GRU and one layer of full connection, having 74,661 parameters.  
The ES, ARMA, GRU, and EP-ARMA (0.1) models were used to predict the underwater temperature data based on three different training and prediction periods. The RMSE representing the performance of the four methods is shown in Figure 11.  
Figure 11 shows that ES, ARMA, GRU, and EP-ARMA have similar prediction effects when forecasting with '168-4' and '960-24'. ARMA, GRU and EP-ARMA have better prediction effects when forecasting with '1920-48'. Compared with GRU, ARMA and EP-ARMA have fewer parameters and smaller computational complexity, which is very important for resource-constrained underwater communication nodes. As the EP-ARMA model corrects the data, it gains a large return in accuracy; therefore, its RMSE is the lowest under the '168-4', '960-24', and '1920-48' combinations. When selecting the prediction model, we should comprehensively consider the prediction performance and computing performance. Among the above four models, the EP-ARMA model is the best.  
Figure 11: Comparison of RMSE of prediction performance of ES, ARMA, GRU, and EP-ARMA algorithms. ’168-4’ means that the training data cover 168 periods, and the prediction data cover 4 periods. ’960-24’ means that the training data cover 960 periods and the prediction data cover 24 periods. ’1920-48’ means that the training data cover 1920 periods and the prediction data cover 48 periods.
# Gaussian Process Modeling of Specular Multipath Components
## 3 Gaussian Process Regression
### Learning  
The GP prediction in Section 3.2 assumes the hyper-parameter vector \(\mathbf{\theta}_{k}^{\text{abs}}\) given in (14) (or \(\mathbf{\theta}_{k}^{\text{ph}}\) given in (15)) is known. If the hyper-parameter vector \(\mathbf{\theta}_{k}^{\text{abs}}\) is not known, it needs to be estimated from the training database \(\mathcal{D}_{k}=\{\mathbf{\phi}_{k},y_{k}\}\) for each SMC. The joint probability density function of the observed measurements \(\mathbf{y}_{k}\) conditioned on the training angles \(\mathbf{\phi}_{k}\) is given by a Gaussian distribution with mean and covariance both dependent on the hyper-parameter vector \(\mathbf{\theta}_{k}^{\text{abs}}\) (see for (12) and (13)), which we make explicit by writing \(\mathbf{\mu}_{\mathbf{\theta}_{k}^{\text{abs}}}\) and \(\mathbf{K}_{\mathbf{\theta}_{k}^{\text{abs}}}\), respectively. The parameter vector \(\mathbf{\theta}_{k}^{\text{abs}}\) (or \(\mathbf{\theta}_{k}^{\text{ph}}\)) is estimated by maximizing the log-likelihood function, that is,  
\[\begin{array}{rl}\hat{\mathbf{\theta}}_{k}^{\text{abs}}&=\arg\max_{\mathbf{\theta}_{ k}^{\text{abs}}}\log p(y_{k}|\mathbf{\phi}_{k},\mathbf{\theta}_{k}^{\text{abs}})\\ &=\arg\max_{\mathbf{\theta}_{k}^{\text{abs}}}-\frac{1}{2}\log|\mathbf{K}_{\mathbf{\theta}_ {k}^{\text{abs}}}(\mathbf{\phi}_{k})|-\frac{1}{2}(y_{k}-\mathbf{\mu}_{\mathbf{\theta}_{k}^{ \text{abs}}}(\mathbf{\phi}_{k}))^{\text{T}}\mathbf{K}_{\mathbf{\theta}_{k}^{\text{abs}}}( \mathbf{\phi}_{k})^{-1}(y_{k}-\mu_{\mathbf{\theta}_{k}^{\text{abs}}}(\mathbf{\phi}_{k})) -\frac{N}{2}\log 2\pi\,.\end{array} \tag{20}\]  
In general, this maximum likelihood estimation cannot be done in closed-form. Therefore, numerical approximation needs to be used. Since the function may be highly non-convex, \(\hat{\mathbf{\theta}}_{k}^{\text{abs}}\) (or \(\hat{\mathbf{\theta}}_{k}^{\text{ph}}\)) has to be found by a global search over the domains of \(a_{k}^{\text{abs}}\), \(\sigma_{k}^{\text{abs}}\), and \(\sigma_{v,k}^{\text{abs}}\) (or \(a_{k}^{\text{ph}}\), \(\sigma_{k}^{\text{ph}}\), and \(\sigma_{v,k}^{\text{ph}}\)).