esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis and Verification  
### Preparation of the Experimental Data  
#### 4.1.1 Introduction of the Dataset  
This paper uses the motor rolling bearing dataset from Case Western Reserve University [29] (CWRU) to validate the proposed method. This dataset includes three types of faults: an outer ring fault, rolling element fault, and inner ring fault, as well as the normal types. Each type contains vibration signals of bearings with different damage sizes, speeds, and load faults. The CWRU bearing testing equipment consists of a 2 HP torque motors, sensors, power testers, etc. The drive end bearing model is SKF6205, and the fan end bearing model is SKF6203 (SKF, Yokohama, Japan).  
The CWRU bearing test collected data from various working conditions. There are four different dimensions of damage size: 0.356 mm, 0.533 mm, and 0.714 mm. There are four types of loads: 0 HP, 1 HP, 2 HP, and 3 HP. There are four types of rotational speeds: 1797 r/min, 1772 r/min, 1750 r/min, and 1730 r/min. The health status includes the normal state, inner ring fault, rolling element fault, and outer ring fault. Specific information on the bearing dataset is shown in Table 1.
# Texture-Based Analysis of \({}^{\bf 18}\)F-Labeled Amyloid PET Brain Images
## 2 Materials and Methods
### Image Analysis  
All images are preprocessed using Statistical Parametric Mapping 12 (SPM12) (Wellcome Centre for Human Neuroimaging, University College London, London, UK) [25]. Preprocessing includes a manual orientation of the PET/CT images, coregistration between PET and CT images, spatial normalization to the Montreal Neurological Institute (MNI) space based on the unified segmentation algorithm of SPM12 using the CT image as an anatomical reference [26], and segmentation of cortical brain regions. Spatially normalized PET images have a matrix size of 91 \(\times\) 109 \(\times\) 91 and voxel size of 2 \(\times\) 2 \(\times\) 2 mm\({}^{3}\).  
The segmentation of cortical brain regions for the textural analysis is based on the Automatic Anatomical Labelling (AAL2) atlas [27; 28]. This way, 6 volumes of interest (VOI) are defined for each brain hemisphere due to potential asymmetry of the amyloid burden [29; 30], corresponding to the frontal, occipital, parietal and temporal lobes, the combined posterior cingulate/precuneus (PCC) and the anterior cingulate. These 6 VOIs are selected based on the specifications of the visual interpretation [8; 9; 10]. Additionally, a global VOI is defined as the sum of the 12 VOIs. Spatially normalized images are segmented by these VOIs as the goal is to study the difference between grey matter and white matter image intensities, except in the case of SUV calculations where a previous grey matter segmentation is performed. The AAL2 segmentation provides regions with higher cortical thickness and is, therefore, best-suited for the proposed analysis. For each region, the 3-dimensional (3D) GLCM is constructed. The process is based on the Radiomics toolbox ([[https://github.com/mvallieres/radiomics](https://github.com/mvallieres/radiomics)]([https://github.com/mvallieres/radiomics](https://github.com/mvallieres/radiomics)), accessed on 23 February 2021) by [PERSON] for MATLAB (The MathWorks Inc., Natick, MA, USA) [31; 32; 33]. Before GLCM computation, image intensities are quantized into 128 bins using equal probability quantization. A total of 6 HFs are calculated, namely energy, contrast, entropy, homogeneity, correlation, and dissimilarity [24; 33], which have previously been used while analyzing amyloid PET images and have shown good performance [22; 23]. On the other hand, the SUVRs are obtained by normalizing the average SUV of each region to the average SUV of the grey matter part of the cerebellum [8; 14; 34].
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 3 System Model
### Migration Cost Model  
During the container migration process, the container migration cost mainly consists of two parts: the network delay caused by the transmission of intermediate and final result data between any edge nodes and the migration time of the container itself [8], that is, the downtime. The downtime is mainly affected by the container memory size [28]; therefore, this study uses the following equation to calculate the migration cost incurred when container _Ci_ migrates from edge node \(N_{i}\) to \(N_{j}\).  
\[Mig^{i}_{\textit{Scos }t}=\frac{CD_{i}+CM_{i}}{Band_{i,j}} \tag{1}\]where \(CD_{i}\) represents the amount of data transmitted by container \(C_{i}\); \(CM_{i}\) represents the memory resources allocated to container \(C_{i}\); and \(Band_{ij}\) represents the available network bandwidth between devices \(i\) and \(j\). \(Mig_{cos~{}t}^{i}\) represents the cost of migrating the deployed container to another healthy device. Therefore, for all the faulty devices in the edge network, the total migration cost is given by the following:  
\[Mig_{cos~{}t}^{total}=\sum_{c_{i}\in C}y_{ij}Mig_{cos~{}t}^{i} \tag{2}\]  
Here, \(C\) represents all containers on the faulty device that need to be migrated, and \(y_{i,j}\) is a binary variable, which represents whether or not there exists migration from faulty device \(i\) to healthy device \(j\) based on Equation (1).
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 2 Related Work  
Concept drift can be roughly classified into four types based on different drift characteristics [15]: sudden drift, gradual drift, incremental drift, and reoccurring drift, as illustrated in Figure 1 (The blue line represents the distribution of the old concept to which the data belongs, the red line indicates the distribution of the new concept, and the black line depicts the transition from the old concept distribution to the new concept distribution). Relevant studies typically focus on detecting some or all of these drift types. For instance, the Active Drift Detection based on Meta learning (Meta-ADD) [16] detects sudden, gradual, and incremental drifts. The LSTM Drift Detector (LSTMDD) [17] is designed to detect sudden and gradual drifts. The Weakly Supervised Conceptual Drift Detection method based on the Online Deep Neural Network (WSCDD) [18] is capable of detecting all four types of concept drift.  
Currently, several methods for detecting concept drift have been proposed. This paper categorizes these existing detection methods into two groups based on the models employed: concept drift detection methods that rely on traditional machine learning models and those that utilize deep learning models.  
Figure 1: Different types of concept drift.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 3 Research Methodology  
This section introduces our reliable SGP, which incorporates an optimized MQTT broker. The proposed protocol SGP enables the transmission of structured messages with various topics, presenting flexible and extensible capabilities for connecting to a cloud platform through pub-sub systems. We also highlight the importance of reducing complexity and cost, minimizing processing time, and decreasing the number of transferring messages for both the MQTT broker framework and the primary server.
## 8 Results
### Secrecy Capacity Results
In a more realistic setup, the main link can typically be expected to experience a higher average SNR than the eavesdropper link, in particular with modern communication systems where the use of multiple antenna technologies and beamforming techniques are used to direct the transmitted signal towards the desired recipient. This should lead to a much lower SNR at any potential eavesdroppers (unless they are perfectly aligned in the same direction as the transmitter and the legitimate receiver, which is rather unlikely in practical scenarios). When the legitimate receiver in the main link experiences a higher average SNR than the eavesdropper link, then the use of CA can effectively result in an improvement in the secrecy capacity, as illustrated in Figure 10, where the eavesdropper link remains at an average SNR of 10 dB and the main link experiences a higher average SNR of 20 dB. By only increasing the average SNR in the main link by 10 dB with respect to the eavesdropper link, the secrecy performance of CA can be substantially improved (if the number of CCs is correctly configured). This is true not only when assuming the ideal model of effective SNR (Figure 10) but also when the more conservative and realistic average model of effective SNR is considered (Figure 11). From the discussion above, it can be concluded that the secrecy capacity can potentially be improved with the use of CA as a diversity technique when compared to the single carrier scenario; however, it may also be degraded depending on the average SNR of the eavesdropper links, which in practical scenarios may or may not be known. In a worst-case scenario, the use of CA would lead to a degraded secrecy capacity compared to the equivalent single carrier scenario; however, this does not mean that confidential communication may not be achievable. A degraded secrecy capacity means that the level of confidentiality is reduced at the physical layer, from an information-theoretic point of view. However, confidentiality can still be guaranteed by taking appropriate measures at  
Figure 9: Secrecy capacity as a function of the number of CCs for various values of the bandwidth overhead parameter \(\alpha\) (ideal effective SNR model, 10 dB SNR in both main and eavesdropper links). higher layers of the protocol stack, which usually involves the use of encryption techniques. The results presented and discussed in this work suggest that CA can be used as a diversity technique to increase the user data rates and system ergodic capacity; however, when doing so, special attention should be paid to higher-layer techniques for communication confidentiality since the secrecy capacity may in some cases be degraded when using CA as a diversity technique. With an adequate consideration of both ergodic and secrecy capacities, a robust system design for CA as a diversity technique can be accomplished. In this context, the analytical results presented in this work constitute a useful tool to achieve this end.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 5 Results: Algorithm Verification Using On-Orbit Data  
This section presents an initial validation of the algorithm using a 60 s capture of raw sampled GPS L1 data from the NASA CYGNSS satellite. Two parallel reflection tracks, one which traverses from ocean to land are processed independently and compared to the algorithm predictions. The raw sampled data (or high resolution data, logged at 16.0362 \(\times\) 10\({}^{6}\) samples per second) from CYGNSS does not undergo any digital signal processing by the CYGNSS GNSS-R instrument. This data is captured by the CYGNSS zenith and nadir instrument antennas with a common clock, amplified and down converted as described in [28] and then digitally sampled into a large memory buffer for downlink. This permits a comparison of the actual delay and Doppler characteristics of the real reflected signal and the predicted delay and Doppler from the algorithm presented here.  
Section 6 presents a larger statistical validation of the algorithm performance as a function of geographic location and performs an analysis of the algorithm's convergence speed.
###### Abstract  
As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows indicating the flow direction are detected. In the third step of post-processing, using the results of line sign detection, continuous lines that require changing of the line type are determined, and the line types are adjusted accordingly. Then, the recognized lines are merged with flow arrows. For verification of the proposed method, a prototype system was used to conduct an experiment of line recognition. For the nine test P&IDs, the average precision and recall were 96.14% and 89.59%, respectively, showing high recognition performance.  
deep learning; image processing; line object; object recognition; piping and instrumentation diagram +
Footnote †: journal: Applied Sciences
###### Abstract  
The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of the highest priority. Security research in this area has two key issues: the lack of datasets for training artificial intelligence (AI)-based intrusion detection models and the fact that most existing datasets concentrate only on one type of network traffic. Thus, this study introduces Dragon_Pi, an intrusion detection dataset designed for IoT devices based on side-channel power consumption data. Dragon_Pi comprises a collection of normal and under-attack power consumption traces from separate testbeds featuring a DragonBoard 410c and a Raspberry Pi. Dragon_Slice is trained on this dataset; it is an unsupervised convolutional autoencoder (CAE) trained exclusively on held-out normal slices from Dragon_Pi for anomaly detection. The Dragon_Slice network has two iterations in this study. The original achieves 0.78 AUC without post-processing and 0.876 AUC with post-processing. A second iteration of Dragon_Slice, utilising dropout to further impede the CAE's ability to reconstruct anomalies, outperforms the original network with a raw AUC of 0.764 and a post-processed AUC of 0.89.  
IoT time series power dataset; IoT hardware security; IoT intrusion detection; convolutional autoencoder; unsupervised learning; cyber attacks on IoT; explainable AI +
Footnote †: journal: Future Internet
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 2 System Description and Virtual Flux Measurement  
Figure 1 illustrates the schematic of a two-level Voltage Source Converter (VSC) connected to the grid through a \(LC\) filter, where \(L_{f}\) and \(R_{f}\) represent the inductance and resistance of the coil, respectively. The converter current \(i\) is measured at this point, while the capacitance \(C_{f}\) is where the output voltage \(v\) is measured. The parameter \(e\) represents the internally generated voltage of the converter.  
On the \(DC\) side of the inverter, both the \(DC\) current (\(I_{dc}\)) and \(DC\) voltage (\(V_{dc}\)) are measured. Using these measurements, along with the reference values for active power (\(P^{*}\)) and reactive power (\(Q^{*}\)), the four control schemes analyzed in this article can be implemented. These schemes generate the corresponding switching pattern defined by the six switching signals (\(S_{1...6}\)).  
On the other hand, from the schematic shown in Figure 1, the following electrical equation can be obtained, if \(R_{f}\) is neglected  
\[e=L_{f}\frac{di}{dt}+v \tag{1}\]  
In practical applications, the inductors used in power converter filters are designed with a high quality factor, with a typical \(X/R\) ratio of around 10 or higher. Therefore, the impedance of the inductor is dominated by the inductive term, making the resistive component negligible in comparison.  
As described in [57], the grid voltage \(v\) can be expressed as  
\[v=\frac{d\psi}{dt} \tag{2}\]  
Figure 1: Generic diagram of a GFM.  
where \(\psi\) represents the grid flux. Substituting (2) into (1) yields  
\[e=\frac{d}{dt}(L_{f}i+\psi) \tag{3}\]  
The derivative term in (3) represents the variable known as virtual flux, which can be expressed as  
\[\psi_{v}=L_{f}i+\psi \tag{4}\]  
Once the converter is connected to the grid, the grid voltage and the converter output voltage become equal (\(v\)). Therefore, isolating the grid flux in (2) and substituting it into (4), the virtual flux of the converter can be expressed as  
\[\psi_{v}=L_{f}i+\int v\,dt \tag{5}\]  
As discussed in [57], the expressions for active power (\(P\)) and reactive power (\(Q\)) are given by  
\[P=\frac{3}{2}\Bigg{(}\frac{v}{j\omega L_{f}}\Bigg{)}e\sin\delta \tag{6}\]  
\[Q=\frac{3}{2}\Bigg{(}\frac{v}{j\omega L_{f}}\Bigg{)}(e\cos\delta-v) \tag{7}\]  
where \(j\omega L_{f}\) represents the reactance of the system based on the grid frequency (\(\omega\)), and \(\delta\) is the phase angle between the converter's internal voltage \(\vec{e}\) and its terminal voltage \(\vec{v}\). Since the internal voltage \(\vec{e}\) is directly related to the virtual flux \(\vec{\psi}_{v}\), its adjustment enables the regulation of both active and reactive power in the converter.
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 7 Conclusions  
In this paper, we have reviewed the main modal properties and design strategies of photonic crystal (PhC) cavities and cavities for extreme dielectric confinement (EDC).  
In Section 2, we have provided an overview on the main properties of PhC cavities. In Section 3, we have discussed why PhC lasers are promising light sources for chip-scale optical interconnects. In Section 4, we have derived in detail the resonance condition and electric field distribution of line-defect cavities. These properties naturally follow from the spatial and frequency dependence of the Bloch modes of the line-defect waveguide on which the cavity is based. In Section 5, we have reviewed the tuning characteristics of PhC Fano lasers, with one mirror based on Fano interference. In this study, optical confinement factor and group index have been directly extracted from Bloch modes and dispersion of the line-defect waveguide. This partially accounts for slow-light and provides a more quantitative basis as compared to previous investigations. Finally, in Section 6 we have covered EDC cavities, which embed bow-tie structures to greatly enhance the electric energy density around a given point. As a result, EDC cavities squeeze the diffraction-limited mode volume of conventional PhC cavities and strengthen the light-matter interaction.  
PhC and EDC cavities open up a wide range of intriguing opportunities, such as energy-efficient semiconductor lasers, nonlinear photonics and quantum electrodynamics. This paper provided an introduction to this realm of photonic cavities and their modal properties. There are many aspects left for future research, such as the limits to the light-matter interaction in these cavities and the designs of structures for particular applications.  
**Funding:** This work was supported by the Danish National Research Foundation through NanoPhotot--Center for Nanophotonics (grant number DNRF147); the European Research Council (ERC) under the European Union Horizon 2020 Research and Innovation Programme (grant number 834410 Fano); and Villum Fonden through the NATEC Center (grant number 8692).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** [PERSON] gratefully acknowledges fruitful discussions with [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 16: (**a**) Slot and (**b**) antislot effect to enhance the electric energy density. (**c**) Interlocking of slots and antislots leading to a bow-tie with either disconnected or connected tip.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 2 Cyber Physical Wind Energy System  
The CPWES is considered to be a large and complex system, as it comprises multiple heterogeneous domains that interact with each other. Figure 1 shows the proposed CPWES, which consists of four layers: a WF power system layer, data acquisition and monitoring layer, communication network layer, and application layer.
## 5 Experiments
### Analysis of Instruction Vectors  
In this subsection, we show the relevance of instructions from different ISAs, AMD64 and AArch64 using GeneDiff. First, we use the training dataset to train the representation model; the dimension of the instruction vector \(d\) is 100, and the number of iterations (_iteration_) is 50. Then, we extract the numeric vectors of the combinations of VEX instructions from the trained model and establish a map \(M\): assembly instructions \(\rightarrow\) combinations of VEX instructions \(\rightarrow\) vectors. After obtaining the numeric vectors for each assembly instruction via the map \(M\), we utilize T-SNE [31] for visualization. T-SNE can map high-dimensional vector spaces into two dimensions via nearest neighbour approximation, where a smaller geometric distance indicates a higher lexical semantic similarity. Figure 7 shows the relationship between assembly instruction vectors mapped onto a two-dimensional space. From Zoom 1 in Figure 7, the numeric vectors of the _mov_ family instructions from different ISAs are mapped together in two-dimensional space. In zoom 2, the numeric vectors of the _add_ family instructions from different ISAs are also grouped together in two-dimensional space.  
To better illustrate the similarity in the semantic vectors of instructions of the same family from different ISAs, we obtain the cosine similarity of the instructions in Zoom 2 from Figure 7. Table 3 shows the results of calculating the cosine similarity of some _add_ family instructions. The cosine similarity is approximately 1, which indicates that the two instructions are highly similar. It can be found that two instructions processed in the same bit registers have higher similarity; ADDS X0, X21, #1 and ADD EAX,1 have lower similarity because they process registers of different bits, and the ADDS instruction updates the condition flag.
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 3 Experiment and Analyses
### Experimental Results and Discussions
#### 3.4.3 Evaluation on Three Multi-Focus Images  
Our method was also suitable for more than two multi-focus images. The three source images for \"Toy\" (\(512\times 512\)) are shown in Figure 14a-c, and close-up views are shown at the bottom for better observation. Figure 14d,e show that the fused images obtained by the DCHWT and SOMP methods showed serious blurring effects at the \"ball\" in the right corner. The GF fusion method produced jagged edges around the \"puppet\", as shown in Figure 14f. The IM fusion method exhibited slight blurry artifacts in the upper-right corner of the \"ball\", as shown in Figure 14g. Compared with other methods, the CNN and DMSR performed well. As shown in Figure 14h,i, all focused areas from the source images were merged into the fusion image with imperceptible artifacts. The values of \(Q_{\text{MI}}\), \(Q^{AB/F}\), and _VIFF_ for various fusion methods are presented in Table 2, with the best results indicated in bold.  
Figure 14: The three multi-focus image and fusion results obtained by different fusion methods.