esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Precipitation Data
#### 2.1.4 IMERG Precipitation Data  
The GPM IMERG product represents a new generation of global satellite precipitation products released in recent years [33]. Being equipped with an advanced dual-frequency precipitation radar and using improved algorithms, it has a higher accuracy, wider coverage range, and higher spatiotemporal resolution than its predecessors. Rainfall and snowfall data on a global scale are provided every 30 min based on microwaves within 3 h and the IMERG algorithm, and they can be used to promote research and applications in disciplines such as hydrology, meteorology, agriculture, disaster management [19; 52; 53; 54].  
The IMERG product has a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and a temporal resolution of 0.5 h. To meet the needs of different users, three IMERG product types have been developed, i.e., near real-time \"Early-run\" (IMERG_E), \"Late-run\" (IMERG_L), and post real-time \"Final-run\" (IMERG_F), releasing data with delays of 4 h, 14 h, and 3 months, respectively [33]. To reduce product bias, climate-related adjustments are made in IMERG_E and IMERG_L, while adjustments related to monthly precipitation observations are made in IMERG_F. Various versions of these IMERG products have been developed and the technology has been continuously improved [55]. In this study, data obtained from IMERG-F V6 covering the period from January 2001 to December 2020 and the region between 60\({}^{\circ}\)N and 60\({}^{\circ}\)S were selected for analysis.
# Imitating Human Go Players via Vision Transformer
## 1 Introduction
In this study, we trained a ViT model on a dataset of game records from human experts. Our model achieved a top-one accuracy of 51.49% and a top-five accuracy of 83.12% using a simple feature set, surpassing a CNN-based model's top-one accuracy of 50.45%. In addition to conventional top-k accuracy metrics, we adopted extended top-k accuracy to account for equivalent moves--logically identical moves with different coordinates--addressing a unique evaluation challenge in Go. This study makes several key contributions to the field of AI-driven Go training and gameplay:  
1. **Application of Vision Transformers (ViTs) in Go**: We introduce a ViT-based model that captures long-range dependencies in Go and predicts future moves, providing deeper insights into game progression and improving gameplay understanding. 2. **Bridging AI and Human Intuition**: Our model emulates human decision-making, providing a more intuitive training tool for Go players to engage with the AI as if playing against a professional. 3. **Improved Top-one Accuracy Compared to Existing Studies**: By utilizing the ViT model, we achieved higher top-one accuracy compared to previous studies, demonstrating the superior capability of ViT in Go move prediction. The remainder of this article is structured as follows: Section 2 reviews related work on applying deep learning models to Go, with a focus on CNNs, and introduces the background of ViT. Section 3 describes the dataset and the features used as model inputs. Section 4 presents the experimental setup, comparing the performance of ViT with that of two CNN-based models from prior studies on imitating human moves. Section 5 concludes the study and outlines potential directions for future work.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 2 Short-Circuit Fault Level Estimation for Active Distribution Networks  
Taking a radial distribution network with DGs as an example, the network and the configuration of the circuit breaker (CB) are shown in Figure 1. Without losing generality, the IBDGs are represented by the photovoltaic generators, while the rotating DGs are represented by the DFIGs. The short-circuit fault level to be estimated was chosen as the current through CB8 if a fault occurs at the point downstream of CB8. The fault point is marked by the red symbol in the figure. The magnitude of the short-circuit current is not constant, as it changes with the network reconfiguration corresponding to the switching operation.  
The equivalent circuit at the breaker CB8 is illustrated in Figure 2, where \(\dot{E}\) and \(Z_{eq}\), respectively, the voltage source and the impedance derived from Thevenin's equivalence of both the upstream grid and the relevant rotating generators (including the conventional synchronous generators and the rotating DGs); \(\dot{I}_{DG}\) is the equivalent current source modeling the upstream IBDGs, and its value is determined by the internal controls of IBDGs [5]; \(Z_{L}\) is the equivalent impedance of the lines connected to the feeder; \(\dot{E}_{L}\) and \(\dot{I}_{DGL}\) are the equivalent voltage source and current source modeling the rotating DGs and IBDGs connected to the feeder, respectively; and \(Z_{eqL}\) represents the equivalent network impedance between the downstream voltage-source-type DG and the point of interconnection.
## 5 Experiments and Discussion
### Comparative Evaluation and Analysis of Performance
#### 5.3.1 Recommended Performance Comparison  
In theis experiment EIAFM was contrasted against other cutting-edge recommendation models, with the difference in their model performance represented in Figure 3:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Values** \\ \hline Iteration count for the model & 100 \\ \hline Training batch size per iteration & 128 \\ \hline Size of the test dataset & 20\% \\ \hline Hidden units in a two-layer configuration & (64,64) \\ \hline Factor of attention & 32 \\ \hline Units in a two-layer CIN configuration & (4,4) \\ \hline Strength of L2 regularization & 0.001 \\ \hline Rate of dropout & 0.5 \\ \hline Value of seed & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Default hyperparameter settings across all models.  
Figure 3: Contrasting the EIAFM model with FM-based models in terms of service recommendation performance.  
From Figure 3, it can be seen that EIAFM performs the best in terms of the log-loss and AUC metrics; xDeepFM performs the next best, followed by DeepFM, while basicFM performs the worst. AFM shows improved performance, indicating that measuring the varying importance of the second-order crossover is effective, and xDeepFM compares much better with the other models, which suggests that it is important to take into account both explicit and implicit higher-order feature interactions. The AUC values and log-loss of EIAFM show a significant improvement in model performance, and EIAFM is more effective than any other advanced FM-based recommendation model. Hence, mastering both explicit and implicit interactions of higher order features while quantifying the varying significance of second-order interactions can lead to a substantial enhancement of the model's recommendation capabilities.
## 5 Simulated Jamming Results Analysis  
The performance of the GNSS receiver is assessed based on its ability to provide a reliable positioning solution and track GNSS satellites under both low and high jamming conditions. The reference solution was logged using the Orolia GSG-8 GNSS simulator. The jamming signal was inserted from minute 14 to minute 16 on the logged data for both the low and high jamming conditions. The timing interval of the jamming signal insertion was chosen to be away from high dynamics up to a certain extent to make sure that the effect on the receiver performance mainly results from the impact of the jamming signal.
# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
###### Abstract  
This paper investigates the integration of intrusion detection systems (IDSs) within Digital Twin (DT) architectures to enhance cybersecurity in industrial environments. Using the CICIDS2017, CIC Modbus, and 4 SICS 2015 datasets, we evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) in detecting network intrusions. Results indicate that RF achieves an accuracy of 99.9% for CICIDS2017, with high precision, recall, and low false positives. In contrast, SVM exhibits an accuracy of 94.2% for the same dataset, struggling with high rates of false positives and moderate recall. Similarly, for 4 SICS 2015, RF demonstrates an accuracy of 93%, being balanced and reliable for industrial applications, while SVM shows only 88% accuracy, with a low precision of 65% and a high false alarm rate. For the CIC Modbus dataset, RF displays an accuracy of 95% in validation and 93% in testing, highlighting strong detection in ICS networks. However, SVM maintains an accuracy of 88%, with weak separation between benign and malicious traffic, and a higher misclassification rate. Our findings highlight the importance of DT-IDS integration in real-time threat detection and system resilience, paving the way for future research in deep learning-based IDS solutions.  
digital twins; intrusion detection systems; machine learning; CICIDS2017 dataset; CIC Modbus; cybersecurity; support vector machine; random forest Article
## 4 Direct Position Determination Method
\begin{array}{l}\mathbf{A}(\mathbf{p})=\left[\mathbf{A}_{1}^{\rm H}(\mathbf{p})\ \ \mathbf{A}_{2}^{\rm H}(\mathbf{p})\ \ \cdots\ \ \mathbf{A}_{N}^{\rm H}(\mathbf{p})\right]^{\rm H},\\ \overline{\mathbf{X}}={\rm blkdiag}[\overline{\mathbf{x}}_{1}\ \ \overline{\mathbf{x}}_{2}\ \cdots\ \ \overline{\mathbf{x}}_{N}]\,.\end{array} \right. \tag{12}\]  
It is important to stress that the second equality in (10) holds owing to the fact that given any matrix \(\mathbf{Z}\), the non-zero eigenvalues of \(\mathbf{Z}^{\rm H}\mathbf{Z}\) and \(\mathbf{Z}\mathbf{Z}^{\rm H}\) are identical [63]. Moreover, note that the dimensions of matrices \(\mathbf{B}(\mathbf{p})\mathbf{B}^{\rm H}(\mathbf{p})\) and \(\mathbf{B}^{\rm H}(\mathbf{p})\mathbf{B}(\mathbf{p})\) are respectively \(K\times K\) and \(N\times N\). In practice, \(K\) is typically much greater than \(N\) and it is therefore more computationally efficient to perform the eigendecomposition on \(\mathbf{B}^{\rm H}(\mathbf{p})\mathbf{B}(\mathbf{p})\) instead of \(\mathbf{B}(\mathbf{p})\mathbf{B}^{\rm H}(\mathbf{p})\). Because the cost function in (10) is not a closed-form expression for \(\mathbf{p}\), the most straightforward method of solving (10) is to perform a grid search, as recommended in [29]. Note that when the location is estimated in multipath environments, the localization accuracy may obviously improve if the information contained in the non-line-of-sight signal components is exploited with the aid of appropriate channel modeling [35; 36]. As a consequence, the signal model in (1) and (3) and the estimation criterion in (5) must be further adjusted to give a desired solution for the multipath model. Indeed, our performance analysis method also applies to the case of multipath propagation, but we only consider the single-path signal model in this paper owing to limited space.
## 5 Validation
### Receiver Operating Characteristics (ROC)  
The receiver operating characteristics (ROC) curve is used to validate the resulting LSMs from the geons model. The ROC method enables an evaluation of the true positive rate (TPR) and the false positive rate (FPR) in the resulting landslide susceptibility map [64]. The ROC curve was calculated for the geons model. The TPs are the pixels that were correctly classified as landslides, and the FPs are the pixels that were incorrectly classified as landslides [65]. The ROC curve is derived by plotting the TPR against the FPR. The AUC is the degree that stipulates the accuracy of the resulting landslide susceptibility map. The resulting AUC values indicate the probability of the pixels being labelled correctly. A greater AUC value indicates a higher accuracy of the model, whereas a lower AUC value indicates a lower accuracy of the model. The AUC values obtained for three SPs are shown in Figure 12. Figure 12 shows the ROC curve for the geons model using three different scale parameters (1300, 1500, and 1700). The AUC is very similar for all the three scale parameters used in the geons model.  
The DST can be used to optimize the overall accuracy of the resulting LSMs. Figure 13 shows the optimized results derived through DST for three scale parameters, along with the DST AUC curve.  
Figure 12: The optimized area under the curve (AUC) values using the geons model for three different scale parameters.
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 2 Beamforming and Deconvolution
### Deconvolved Beamforming  
#### 2.2.1 The Equivalent Process of CBF  
We rewrite Equation (1) as follows:  
\[B(\sin\theta)=\int_{-1}^{1}B_{p}(\sin\theta-\sin\alpha)S_{p}(\sin\alpha)d\sin\alpha \tag{3}\]  
where  
\[B_{p}(\sin\theta)=\bigg{|}\frac{\sin c(\pi N(d/\lambda)\sin\theta)}{\sin c(\pi (d/\lambda)\sin\theta)}\bigg{|}^{2} \tag{4}\]  
This is called the beam pattern, which relates to the array element number, the array element spacing, and the signal wavelength. That is, this function is known to be a certain horizontal linear array.  
\[S_{p}(\sin\theta)=[\sum_{i=1}^{M}\Big{\langle}|S_{i}|^{2}\Big{\rangle}\delta( \sin\theta-\sin\theta_{i})] \tag{5}\]  
Equation (5) is the source power distribution function, which consists of several impulse functions that denote targets from different directions.  
Therefore, CBF can be regarded as a process where the impulse function passes a linear system and the output is the direction spectrum. Figure 2 shows the process. Its physical explanation is that the real target becomes blurred when it is observed because of the finite length of the array, and the blurring process is the convolution of the real azimuth spectrum of the target and the beam pattern of the array. The ambiguous results we observed (i.e., those obtained by conventional beamforming) are the convolution of the true azimuth spectrum of the target and the array beam pattern. Following this idea, we can deconvolute the ambiguous results with the azimuth spectrum of the array, and then we can get the real azimuth spectrum of the target.  
The deconvolution can be done with the direction spectrum \(B(\sin\theta)\) (the output of CBF) and the beam pattern \(B_{p}(\sin\theta)\), and then the impulse function of the signal is obtained. The impulse function is only valued at the variable value 0 and valued zero at any other positions so that the very narrow main lobe can be obtained and there is no side lobe. Thus, high-resolution beamforming is achieved.
## 3 End-to-End Speech Synthesis
### Objective Evaluation of Phone-Level Prosody Prediction  
In the standard FastSpeech2 system, prosody modeling is not explicitly considered, which makes it hard to objectively evaluate the prosody prediction performance of the TTS systems without a subjective listening test. Accordingly, in this work, we introduce a phone-level prosody prediction module [13] to our models, which autoregressively predicts the distribution of prosody embeddings for each phoneme. It can not only lead to improved naturalness compared to the standard FastSpeech2 system, but also allow the use of prosody embeddings log-likelihood to easily evaluate phone-level prosody prediction performance in an objective way. The structure is illustrated in Figure 1.  
Specifically, in the training stage, the prosody embeddings  
\[\mathbf{e}=[\mathbf{e}_{1},\mathbf{e}_{2},\ldots,\mathbf{e}_{K}] \tag{3}\]  
are extracted for all the \(K\) phonemes by a prosody extractor from the corresponding mel-spectrogram segment. It is then projected and added to the corresponding hidden state sequence \(\mathbf{h}\) in order to better reconstruct the mel-spectrogram. We use \(\mathbf{e}_{k}\) to represent the prosody embeddings for the \(k\)-th phoneme. In this work, the distribution of \(\mathbf{e}_{k}\) is assumed to be a GMM whose parameters are predicted by the prosody predictor. In the inference stage, we sample the \(\mathbf{\hat{e}}_{k}\) from the predicted prosody distribution for each phoneme.  
The training criterion for the prosody prediction is the negative log-likelihood of the prosody embeddings \(\mathbf{e}\), so we obtain the loss function for training the prosody predictor  
\[\mathcal{L}_{\text{PP}}=\sum_{k=1}^{K}-\log p(\mathbf{e}_{k};\mathbf{e}_{<k}, \mathbf{h}) \tag{4}\]  
Consequently, the overall architecture is optimized with the loss function  
\[\mathcal{L}=\beta\cdot\mathcal{L}_{\text{PP}}+\mathcal{L}_{\text{FastSpeech2}} \tag{5}\]  
where \(\mathcal{L}_{\text{FastSpeech2}}\) is the loss function of FastSpeech2, which is the sum of variance prediction loss and mel-spectrogram reconstruction loss as described in [5], and \(\beta\) is the relative weight between the two terms.
## 3 Results and Discussion
### Estimated Maize Yield Change Analysis from 1984 to 2018 in Harare  
Using cropland class area and the three estimated maize yield per hectare scenarios in Harare, the estimated change in maize from 1984 to 2018 is shown in Table 7. Table 7 reveals that cropland area for maize, and hence maize produced in Harare, decreased by approximately four times from 1984 to 2018.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Season** & **Yield (T/ha)** \\ \hline
2005–06 & 4.97 \\
2006–07 & 4.23 \\
2007–08 & 4.81 \\ \hline Mean & 4.67 \\ Standard Deviation & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average maize yields in Harare for the June 2005, July 2006, and August 2007 seasons.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Coverage (Ha) and in Brackets Is Percentage (\%) of Total Area** \\ \hline  & **1984** & **2018** \\ \hline CBD Industrial & 2359 (2.6) & 4672 (5.2) \\ Cropland & 11,120 (12.4) & 2631 (2.9) \\ High density residential & 27,650 (30.7) & 49,741 (55.2) \\ Low-medium density residential & 23,733 (26.4) & 24,246 (26.9) \\ Vegetation & 24,470 (27.2) & 8230 (9.1) \\ Water & 698 (0.8) & 510 (0.6) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Changes in proportional coverage of LULC categories between 1984 and 2018.
# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 2 Literature Background  
Both CPS and DT are aimed at achieving the cyber-physical integration needed for smart manufacturing, but each approach emphasises something different. While CPS focuses on sensors and actuators, DT focuses on data and models [8]. The relationship between CPS and DT can be described in that CPS uses DT, and existing DT is a prerequisite for CPS [9]. Cyber-physical systems produce a large number of data; thus, big data analytics is used for them, e.g., for predictability, planning, and decision making [10]. The relationship between CPS and DT can be seen in Figure 1.  
Big data analytics is a core component of data-based smart manufacturing and Industry 4.0 initiatives [12]. The term big data originated in the early 1990s and refers to data that are large in volume, variously heterogeneous (mainly in terms of structure), and with uncertain credibility due to their possible inconsistency or, for example, incompleteness, and with fast processing often required. Thus, the basic characteristics of big data are the three Vs--Volume', 'Veracity', and 'Variety' [13]. Over time, the three Vs have been extended to five Vs by the addition of 'Value,' the value of the data (often relative to a point in time), and 'Velocity,' the rapid increase in the volume of the data [14]. Big data processing often encounters the limitations of traditional databases, software technologies, and established processing methodologies [15].  
Generally, there are two main functional components--system infrastructures and data analytics--used to address the issue of big data in CPS in Industry 4.0. System infrastructure provides real-time communication between devices and cyber devices, while  
Figure 1: Relationship between CPS, DT, and IoT [11].  
data analytics focuses on improving product personalisation and efficiency of resource utilisation [16].  
As the processing of large volumes of data exceeds the capabilities of commonly used computers, supercomputers or clusters are used. Since supercomputers are very expensive, distributed computing systems (clusters) are more available, more cost effective, and widely used in industry [17].  
Big data mostly generates CPS, ERP systems, etc. within the Industry 4.0 concept. These big data are analysed, cleansed, and mined, and the obtained data are then used as input for simulations, based on the results of which the parameters and properties of the CPS are then adjusted, or the simulations are used as a semantic validator, as shown in Figure 2.  
The approach presented in this paper is inspired by this standard approach. The difference, however, is that the simulation models generate large volumes of data that are reduced using a hashing algorithm. Network architecture is used to communicate over a shared database to reduce unnecessary computations on the simulation model if a given variant has already been realised by a simulation optimiser. Moreover, it is possible to set up different scenarios with data storage in the memory of each simulation optimiser.
# Algorithms for Drug Sensitivity Prediction
## 3 Feature Selection
Some commonly-used wrapper feature selection approaches in drug sensitivity prediction include: (i) Sequential Floating Forward Search (SFFS) [32; 38], where at each forward iteration, the feature from the remaining features that maximizes the reward or minimizes the cost is selected, and the floating part provides the option to remove an already selected feature if it improves the objective function; (ii) recursive feature elimination [39], which initially involves the ranking of features based on a model fit to all features and recursively eliminating the lowest ranked ones. * Embedded feature selection: As compared to wrapper feature selection approaches that are evaluated based on objective functions without incorporating the specific structure of the model, embedded feature selection approaches consider the specific structure of the model to select the relevant features, and thus, the feature selection and learning parts cannot be separated. A commonly-used embedded approach is regularization that penalizes the norms of feature weights, such as ridge regression [40; 41] penalizing the \(L-2\) norm, LASSO [42; 43] penalizing the \(L-1\) norm and elastic net regularization [23; 44] penalizing a mix of \(L-1\) and \(L-2\) norms. Figure 3: Average trajectory of generalization and training error with the increase in the number of features with a fixed number of training samples. * Feature extraction: Dimensionality reduction can be approached based on feature extraction from the input data where input data vectors are mapped to new coordinates using different functions. One common example of feature extraction approach is Principal Component Analysis (PCA) [45; 46], which maps the input data to a coordinate system that is orthogonal to each other and with the property that the variance along each component projection is maximized. * Biological knowledge-based feature selection: This approach is based on using prior biological knowledge to reduce the size of the relevant feature set. For instance, if the drugs considered are kinase inhibitors, a potential approach to feature selection might involve modeling from a set of around 500 available kinases [47; 48]. Other approaches can include incorporating pathway knowledge in data-driven feature selection, such as pathway-based elastic net regularization [49], biological pathway-based feature selection integrating signalizing and regulatory pathways with gene expression data to select significant features that are minimally redundant [50] or the use of the activation status of signaling pathways as features [51].