esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Settings
#### 4.1.3 Performance Evaluation  
To achieve comprehensive assessments, we exploit the pixel-wise output of these algorithms and make comparisons with baseline algorithms using four metrics: image-level AUROC (area under the receiver operating characteristic), image-level F1-score, pixel-level AUROC, and pixel-level F1-score. All the performance metrics are evaluated based on the confusion matrix, which consists of four classes: true positive (TP), false positive (FP), false negative (FN), and true positive (TP). Note that the positive samples in the defect inspection task refer to images with defect regions or pixels in a defect area.  
The AUROC is calculated as the area under the ROC curve. A ROC curve shows the trade-off between the true positive rate and false positive rate across different decision thresholds. For the following unsupervised binary image-wise or pixel-wise classification tasks, AUROC is more informative in telling the discrimination ability of the model considering the potential data imbalance, especially from the pixel level.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Backbone** & **Pretrained** & **Decoder** & **Flow Steps** & **Learning Rate** & **Batch** & **Image Size** \\ \hline Fastflow & Resnet18 & True & - & 8 & 0.001 & 32 & 256 \\ \hline Cflow & Wide\_resnet50\_2 & True & freia-cflow & - & 0.0001 & 32 & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental settings of the baseline OOD models.  
Moreover, F1-score leverages precision and recall into a single measure that captures both properties. The calculation process of the F1-score is obtained through Equations (4)-(6).  
\[precision=\frac{TP}{TP+FP}\times 100\% \tag{4}\]  
\[recall=\frac{TP}{TP+FN}\times 100\% \tag{5}\]  
\[F1-score=2\times\frac{precision\times recall}{precision+recall}\times 100\% \tag{6}\]
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 1 Introduction
Various concept drift detection methods have been proposed by scholars, including the drift detection method (DDM) [11], the Adaptive Sliding Window (ADWIN) [12], concept drift detection methods based on online performance tests (CDPTs) [13], a K-Means clustering- and SVM-based hybrid concept drift detection technique for network anomaly detection [14], and a semi-supervised concept drift detection method by combining sample output space and feature space with its application [10], among others. Most of these methods are based on traditional machine learning techniques, such as Support Vector Machines (SVM), Principal Component Analysis (PCA), Naive Bayes, and Hoeffding Trees. However, traditional machine learning methods rely on feature extraction processes to handle data, which inherently limits their representational capacity and restricts them to shallow learning. In contrast, deep learning employs multi-layer neural networks to autonomously learn the deep features of data, enabling it to handle more complex datasets and nonlinear problems, thus offering superior learning capabilities. While a limited number of studies have explored deep network models, the application of deep learning to concept drift detection is still in its infancy and requires further systematic investigation. Based on this, this paper proposes a concept drift detection method, termed DNN+AE-DD, which integrates a deep neural network model with an autoencoder. In this approach, a deep neural network serves as the base classifier for model training. A structurally identical neural network is employed for processing streaming data, with certain network weights frozen to preserve learned representations. Finally, the method leveragesautoencoders and the 3\(\sigma\) principle to detect concept drift by analyzing the outputs of the model's hidden layers.The contributions of this paper are as follows:  
* Propose a new drift detection framework. * Apply autoencoders combined with the 3\(\sigma\) principle for concept drift detection. * Validate the ideas presented in this paper using both synthetic and real-world data. The results demonstrate that the model proposed in this paper performs excellently based on the evaluation metrics and datasets used. The structure of this paper is organized as follows: Section 2 offers a concise review of drift detection methods developed by various researchers in recent years to address the challenge of concept drift. Section 3 provides a comprehensive description of the proposed method. Section 4 presents and analyzes the experimental results. Finally, Section 5 concludes the paper and outlines potential directions for future research.
## Appendix B Position Optimization  
The positions of subarrays are optimized based on an iterative convex optimization. Let us consider the expanded radiation field of the array in \((\vec{u},\vec{v})\)-space as:  
\[f_{\zeta}(\vec{u},\vec{v})=\frac{1}{N}\sum_{n=1}^{N}e^{ik\zeta(\vec{u}x_{n}+ \vec{v}y_{n})}, \tag{2}\]  
As it is discussed in Ref. [44], when the positions of antennas are optimization variables, the problem is nonlinear and non-convex, however it can be solved as a convex problem iteratively by first-order Taylor expansion, as in:  
\[\begin{split}& f_{\zeta}^{i}(\vec{u},\vec{v})=\frac{1}{N}\sum_{n=1} ^{N}e^{ik\zeta\vec{u}x_{n}^{i-1}}(1+jk\zeta\vec{u}e_{n}^{i})e^{jk\zeta\vec{v} y_{n}^{i-1}}(1+jk\zeta\vec{v}\beta_{n}^{i})\\ &\approx\frac{1}{N}\sum_{n=1}^{N}e^{ik\zeta\vec{u}x_{n}^{i-1}}e ^{jk\zeta\vec{v}y_{n}^{i-1}}(1+jk\zeta\vec{u}e_{n}^{i}+jk\zeta\vec{v}\beta_{n} ^{i}),\\ &\text{where }|\beta_{n}^{i}|\ll 1,|\epsilon_{n}^{i}|\ll 1\end{split} \tag{3}\]where \(i\) denotes the iteration index, and \(e_{n}^{i}\) and \(\beta^{i}\) are the movement in \(x\) and \(y\) axes at \(i\)th iteration, respectively. \(x_{n}^{i-1}\) and \(y^{i-1}\) are the position in \(x\) and \(y\) axes at \((i-1)\)th iteration, respectively. (A3) is a linear convex function that can be solved by Matlab CVX [45]. To enforce only subarray movements, \(\{e_{n}^{i},\beta_{n}^{i}\}\)s are equal for all antennas in a subarray. Finally, the optimization can be written as:  
\[\begin{split}\min_{E^{i},B^{i}}&\gamma^{i}\\ \text{s.t.}&\max(|f_{\{\mathbf{\tilde{v}}\}}^{i}(\{\mathbf{\tilde{u}},\mathbf{ \tilde{v}}\}_{\mathbf{SL}})|)\leq\gamma^{i},\\ &\text{real}(f_{\{\mathbf{\tilde{v}}\}}^{i}(0,0))=1,\\ &|e_{n}^{i}|\leq\mu,|\beta_{n}^{i}|\leq\mu,\qquad\forall n\in\{1,2,...,N\} \end{split}\] (A4)  
where \(E^{i}=\{e_{1}^{i},e_{2}^{i},...,e_{N}^{i}\}\) and \(B^{i}=\{\beta_{1}^{i},\beta_{2}^{i},...,\beta_{N}^{i}\}\). \(\{\mathbf{\tilde{u}},\mathbf{\tilde{v}}\}_{\mathbf{SL}}\) is the set of all side lobe angles \((\tilde{u},\tilde{v})_{\text{SL}}\) and \(\mu\) is a predetermined number. As the elements are domino tiles, there should be a distance of at least \(\lambda\) between the phase center of each domino at one subarray with the phase centers of other dominoes at adjacent subarrays for fabrication possibility. This could also be added as a constraint to (A4), as discussed in Ref. [44].
## 3 Energy and Economic Analysis Model
### Simulation Model for Energy and Economic Analysis  
In this study, DOE reference buildings of large office and primary school, a simulation model for passive elements analysis, were modified, as shown in Table 2, so that the S/V ratio can have different characteristics within the same volume [6,16].  
The midrise apartment building has the form shown in Figure 3, in which 88.24 m\({}^{2}\) of households are distributed on 4 floors with a total of 32 households. The window area ratio of the exterior wall is about 15%, and the model presented by the DOE was used without modification (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Building Type** & **Total Floor Area** & **Number of Floors** \\ \hline Large Office & 46,320 & 12 \\ \hline Medium Office & 4982 & 3 \\ \hline Small Office & 511 & 1 \\ \hline Warehouse & 4835 & 1 \\ \hline Stand-alone Retail & 2319 & 1 \\ \hline Strip Mall & 2090 & 1 \\ \hline Primary School & 6871 & 1 \\ \hline Secondary School & 19,592 & 2 \\ \hline Supermarket & 4181 & 1 \\ \hline Quick Service Restaurant & 232 & 1 \\ \hline Full Service Restaurant & 511 & 1 \\ \hline Hospital & 22,422 & 5 \\ \hline Outpatient Health Care & 3804 & 3 \\ \hline Small Hotel & 4013 & 4 \\ \hline Large Hotel & 11,345 & 6 \\ \hline Midrise Apartment & 3135 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DOE Reference Buildings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & \multicolumn{2}{c}{**Large Office**} & **Primary School** \\ \hline \multirow{2}{*}{Floor area (m\({}^{2}\))} & Original & 46,320 & 6871 \\ \cline{2-4}  & Modified & 3656 & 7312 \\ \hline \multirow{2}{*}{Floors (Story height = 4 m)} & Original & 12 & 1 \\ \cline{2-4}  & Modified & 4 & 2 \\ \hline Total floor area (m\({}^{2}\)) & 14,624 (3656 \(\times\) 4) & 14,624 (7312 \(\times\) 2) \\ \hline \multirow{2}{*}{Volume (m\({}^{3}\))} & 58,496 & 58,496 \\  & (3656 \(\times\) 4 \(\times\) 4) & (7312 \(\times\) 4 \(\times\) 2) \\ \hline \multirow{2}{*}{Surface area (m\({}^{2}\))} & Wall & 6073 & 10,667 \\ \cline{2-4}  & Window & 1523 & 1677 \\ \cline{2-4}  & Roof & 3656 & 7312 \\ \cline{2-4}  & Floor & 3656 & 7312 \\ \hline S/V ratio & & 0.25 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modified properties of DOE Reference Buildings for simulation.
## 2 Machine-Learning Algorithm and Factor Analysis  
### Overview of Gradient-Boosting Decision Tree  
The gradient-boosting decision-tree algorithm [30] combines weak learners sequentially to build a single predictive model. During the training process, weak learners are connected to minimize the overall loss of the model. This is achieved by repeatedly adjusting the weights at each stage to reduce the error between the predicted and actual values, thereby explicitly fitting the relationship between the explanatory and target variables. Through this process, the model performs feature extraction of explanatory variables and learns data-trend patterns, enabling it to make predictions and provide explanations when applied to new datasets. Notably, it actively generates interaction features from the product of explanatory variables, allowing for consideration of causality with multiple variables. This enables effective feature extraction, because the combination of multiple features enables the representation of interactions in higher dimensions.  
Gradient-boosting decision-tree algorithms are often used to solve regression problems due to their dual advantages of low computational cost during training and high prediction accuracy. These make them extremely effective when handling large amounts of data, and thus suitable for building robust regression-prediction models.
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
## 6 Conclusions  
The design of a mm-wave antenna for wideband wireless communications on-chip, WiNoC, is presented in this work. WiNoC is an emerging trend in on-chip communications to solve power consumption and latency issues of conventional metal-wires in the case of links above 1 cm in size (e.g., distant tiles in a multi-processor system-on-chip). The aim is to interconnect multiple macrocells of homogenous or heterogeneous MPSoCs. The proposed bow-tie antenna design has been characterized in the range from 50 GHz to 100 GHz in terms of area, efficiency, and beamwidth. With respect to the state-of-the-art, the antenna stands out for its better trade-off in terms of size, gain, HPBW and bandwidth. With respect to the zig-zag antenna, typically used as a reference for WiNoC design in literature, the proposed design stands out for its much higher gain, about 19 dB higher and almost doubled beamwidth. The higher gain is useful to connect tiles at opposite sides of large MPSoC designs (in [31], the zig-zag antenna is limited to 2 cm) or--giving the same distance and path loss--to reduce the transmitted power, thus saving power.
# Effect of Audio-Visual Factors in the Evaluation of Crowd Noise
## 2 Materials and Methods
### Questionnaires  
Questionnaires were distributed in the laboratory to investigate participants' evaluations of the recorded landscapes. The questionnaire consisted of three main parts, which corresponded to our three research questions. Each part had its own evaluation indicators, as shown in Figure 3. Answers were given on a five-point Likert scale, from very dislike to very like.  
A total of 33 people participated in this study (54.5% females). To reduce intra-group differences, we chose students from our university as the participants. Participants were an average age of 26 (SD = 4.0; Min = 21; Max = 37), and all had normal hearing. Each participant was randomized to assess audio-only, visual-only, and audio-visual environments.
## 4 Simulation Results and Discussion
### TMR Effect in BPMR Channel  
In this section, we study the effects of TMR. In practice, TMR results in performance degradation; however, it is often difficult to estimate how often it occurs. However, we are able to estimate the ITI with the TMR effect when using the three-way GPR target. In the model, when the TMR is \(+\Delta_{off}\), the upper interference \(h_{-1,0}\) is larger than the lower interference \(h_{1,0}\) (the estimation of GPR results \(\alpha_{1}\geq\alpha_{2}\)). Then, the coefficient of the feedback line is \(\alpha_{1}\) to reduce ITI. Meanwhile, \(\alpha_{2}\) is the estimation of the lower interference \(h_{1,0}\). Therefore, \(\varepsilon\) is \(\alpha_{2}\) (\(\varepsilon=\alpha_{2}\)). When the TMR is \(-\Delta_{off}\), the upper ITI \(h_{-1,0}\) is smaller than the lower one \(h_{1,0}\) (the estimation of GPR results \(\alpha_{1}<\alpha_{2}\)). Then, the coefficient of the feedback line is \(\alpha_{2}\) to reduce ITI. Meanwhile, \(\alpha_{1}\) is the estimation of the lower ITI \(h_{-1,0}\). Therefore, \(\varepsilon\) is \(\alpha_{1}\) (\(\varepsilon=\alpha_{1}\)). In general, the feedback coefficient is equal to max \((\alpha_{1},\alpha_{2})\) and \(\varepsilon=\min(\alpha_{1},\alpha_{2})\). In our study, we simulated the channel conditions for both 10% and 15% TMR [12].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SNR** &  
Figure 14: BER performance for our proposed algorithm.  
Figure 13: Bit error rate (BER) versus \(\varepsilon_{VA}\) for modified VA.  
The results in Figure 15 show that our algorithm was minimally impacted by the variation in TMR as the TMR is reflected on the ITI information and our GPR target estimation is based on the ITI.  
In Figure 16, we varied the TMR value from 10 to 30% at SNR = 15 dB. This confirms that our algorithm is not significantly affected by TMR. TMR occurs when the read-head deviates from the main track. This changes the ITI on the receiving signal. The change in ITI is not quantifiable with the conventional scheme. However, with a three-way GPR target, the model is able to estimate the ITI information using \(\alpha_{1}\) or \(\alpha_{2}\) when TMR occurs. This allows the algorithm to offset the amount of ITI during detection. The proposed scheme is quite resistant to the TMR effect.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
###### Abstract  
Solar activity has been subject to increasingly more research in the last decades. Its influence on life on Earth is now better understood. Solar winds impact the earth's magnetic field and atmosphere. They can disrupt satellite communication and navigation tools and even electrical power grids and several other infrastructure crucial for our technology-based society. Coronal mass ejections (CMEs), solar energetic particles, and flares are the main causes of problems that affect the systems mentioned. It is possible to predict some of those by monitoring the sun and analyzing the images obtained in different spectra, thus identifying solar phenomena related to its activity, such as filaments, pores, and sunspots. Several studies have already been carried out on the subject of automation of the mentioned analysis, most of which use neural networks and other machine learning approaches. In this work, we develop a method for sunspot detection based on the YOLOv5 network, applying it to a dataset of images from the Geophysical and Astronomical Observatory of the University of Coimbra (OGAUC), which has one of the oldest and more complete datasets of sun images in the world. Our method reaches mAP@.5 over 90% with YOLOv5s, which is higher than other methods previously applied for the same dataset. This shows that CNN models can be used in spectroheliographs for detecting and tracking sunspots.  
sunspot detection; convolutional neural network; YOLO; spectroheliograph; OGAUC 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
Some of the routine activities in everyday life, like running due to being late for important events, or undesirable hindrances or disruptions in common tasks can also trigger APS, which may be easily masked by the concurrent PA [13; 17; 18]. Appropriate signal processing and feature extraction can improve the accuracy of discriminating between physical activity and psychological stress. The necessity of feature extraction in ML is well established [31]. The development of signal processing techniques to handle the noise and artifacts in the measured physiological biosignals from wearable devices is an active research area [19; 22; 23; 24; 25; 26]. Motivated by the above considerations, in this work, we studied the effects of data filtering and adaptive noise cancellation techniques on the accuracy of detecting and discriminating PA and APS, and quantifying the PA intensity using a variety of ML algorithms and physiological measurements collected from a wristband. We demonstrate that effective signal processing and feature extraction are important to ensure high accuracy for ML algorithms (naive Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine) to discriminate among different types of individual or concurrent incidences of PA and APS and quantify the intensity of the PA through estimates of energy expenditure (EE). The PA and APS classification provides users with important information on the individual or concurrent simultaneous occurrences of physiological and psychological stressors that they are experiencing, and EE provides an assessment of the physical exertion. The results show that the proposed signal processing techniques increase the detection and discrimination accuracy for PA and APS and decrease the EE estimation error.
## 2 Backgrounds
### Dimensionality Reduction Methods  
A reduction method is used to reduce the \(p\)-dimensional input space into a lower \(k\)-dimensional feature space (\(k<p\)).  
* Principal component analysis (PCA) [41; 42; 43]--PCA extracts \(k\) principal components by using a linear transformation of the singular value decomposition (SVD) to maintain most of the variability in input data.
* Latent semantic analysis (LSA) [44]--Contrary to the PCA, LSA performs the linear dimensionality reduction by means of the truncated SVD.
* Feature agglomeration (FAG) [45]--FAG uses the Ward hierarchical clustering, which groups features that look very similar to each other. Specifically, it recursively merges a pair of features ina way to increase the total within-cluster variance as less as possible. The recursion stops when the remaining number of features is reduced to \(k\).
* Gaussian random projection (GRP) [41]--GRP projects the high-dimensional input space onto a lower dimensional subspace using a random matrix whose components are drawn from the normal distribution \(N\big{(}0,\frac{1}{k}\big{)}\).
* Sparse random projection (SRP) [46]--SRP reduces the dimensionality by projecting the original input space using a sparsely populated random matrix introduced in [47]. The sparse random matrix is an alternative to a dense Gaussian random projection matrix to guarantee a similar embedding quality while saving computational cost.
## 5 Simulations and Discussions
### Comparison with Existing Approaches  
The proposed algorithm is compared with the existing approaches in [11] (marked as 'Para'), [6] (noted as 'Tri'), and [19] (marked as 'FIR'), and the VFD parameters included in the simulation procedure are consistent with those of scenario 2. Here, the parameters of 'FIR' are set to \(M=10\) and \(\tau=0.1\)[19]. By comparing it with Figure 6, it can be seen that in terms of estimation, the 'FIR' algorithm outperforms the other three algorithms when the \(SNR\leq 10\) dB; however, the MSE of the proposed algorithm is closest to CRLB when the \(SNR>10\) dB. The parabolic algorithm has a larger bias due to the increasing time-delay difference because of the misfit phenomenon, as well as being insufficient for broadband multichannel acquisition systems. Furthermore, Figure 6 obviously shows that the 'Tri' is more suitable for spread-spectrum communications [6] than the scenario described in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Arguments** \\ \hline Normalized bandwidth \(BT\) & \(1/2,1/3,1/4,1/6\) \\ Delay difference \(\tau_{0}\) & \(0.01,0.1,0.3,0.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of simulation parameters.  
As far as the computational complexity is concerned, as presented in Table 2, the 'FIR' method has the heaviest computational load, meaning that it is not appropriate for highly dynamic situations. In contrast, the algorithm proposed in this paper only uses \(P+1\) more multipliers than the parabolic algorithm to achieve a performance close to that of CRLB. Therefore, it is more suitable for application in wideband large-scale multichannel systems for real-time time-delay estimation.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 3 Results and Discussion
### Precision of Original TRMM Products
#### 3.1.1 Grids with the Gauge Station
On the contrary, the TRMM precipitation is lower in the high gauged precipitation area (HGPA), including the stations of WL, DLH, DL, CHWS, XRH, XRD, KE, SGB and CHH. As a result, the average MAP of TRMM is approximately equal to that of the gauged precipitation, but the underestimated HGPA and overestimated LGPA will lead to systematic bias. The systematic error is considered the result of planarization of the original TRMM when calibration was done with too limited gauged data. The average RMSD of the original 3B42 RT and 3B43 in the Qaidam Basin is 72.25 mm and 64.24 mm, respectively. Considering with the bias together, the minimum RMSD (23.17 mm of 3B43 RT and 18.42 mm of 3B43) relates to the overestimation (bias of 106.79%, 82.07% and value of 21.26 mm, 16.31 mm) of 3B42 RT and 3B43 in the LGPA (LH station), while the maximum RMSD (189.76 mm of 3B43 RT and 187.08 mm of 3B43) relates to the underestimation (bias of -40.49%, -40.20% and value of -175.04 mm, -174.09 mm) in the HGPA (CHH station). It is obvious that the bias of underestimation is smaller than that of overestimation, but the absolute amount of underestimation is far greater than that of overestimation, as shown in Figure 3. Therfore, it is supposed that the average TRMM precipitation in the Qaidam Basin would be an underestimation overall.