esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
### Concept of Digital Twins  
A digital twin is defined as a dynamic digital representation of a physical entity (which could be an object, a system, a process or a workflow) that allows for a two-way automatic data exchange, ensuring that any changes in the physical entity are mirrored in the virtual replica model and vice versa [8]. This integration facilitates real-time updates and interactions, enhancing the accuracy and utility of the digital model in analyzing and predicting the physical counterpart's behavior. Digital twins originated from NASA in the 1960s Apollo program to create a virtual twin of the spacecraft [8], and it was later expanded for various applications in manufacturing processes to create virtual replicas of factories [15]. A digital twin is different from the conventional concepts of digital model and digital shadow. The former involves a mathematical or a theoretical model to study the behavior and/or performance of an actual physical system (such as simulation, 3D models and Computer-Aided Designs), whereas the latter represents an evolving digital representation of an object, where a change in the physical object's state is reflected in the digital model but not vice-versa (such as emulators, anomaly detection and visualizations). Superseding these capabilities, a digital twin represents a \"personalized\" virtual model of the physical object instead of a generic model that is dynamically updated with real-time data to make informed decisions to fully realize the physical object's value. These personalized models of a digital twin are built using behavioral and contextual (features) data to track the performance variable of the system in real time. The autonomous exchange of data between the physical object and digital twin requires continuous learning to keep the twin in sync with the model under dynamic environments. Figure 3 illustrates digital model, digital shadow and digital twin capabilities and the flow of information between physical and digital counterparts.  
Figure 2: Challenges posed by concept drift on data-driven digital twin anomaly classification.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Methods Deployed Across Studies  
This section includes a summary of the methods employed by authors across the studies reviewed. As illustrated in this section, there is a wide variety of methods employed throughout the literature with little standardization or consistency. This could be due to the diversity of end-uses analyzed and/or the relative novelty of this field of study.  
Of the studies reviewed, authors either quantified flexibility potential for individual buildings, aggregated buildings, or aggregated loads. Methods frequently employed by the authors  
included clustering  
optimization [6; 7; 8; 9; 11; 15; 17; 23; 28; 35; 44; 47; 55; 59; 64], regression [16; 19; 24; 25; 27; 46; 52; 53; 58], and simulation [7; 11; 15; 17; 22; 24; 28; 31; 33; 34; 37; 38; 40; 50; 51; 54; 58; 59; 62; 65; 66], with many researchers employing multiple techniques and methods in one study. Clustering methods generally tend to group buildings into subsets that have similar characteristics in terms of electricity consumption, load shape, or demand flexibility. Regression models often try to estimate the amount of flexibility based on various building or user characteristics. These can then be employed as models in system-wide analyses or might be used for prediction purposes. Optimization-based approaches, on the other hand, typically incorporate user and/or load characteristics into a building-level or system-level optimization model, to reduce demand, reduce costs, or meet a certain operational target. However, many authors developed novel models to illustrate the flexibility of loads and building types.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### SVM-Based Feature Classification  
Feature sets were created for seizure and non-seizure classes, ensuring balanced data representation to mitigate data imbalance. These sets were utilized to train a binary SVM classifier with a linear kernel and penalty parameters (\(C\)). The linear kernel was chosen for its computational simplicity, as it only requires the dot product, unlike more complex kernels such as RBF or Polynomial. Additionally, the dataset exhibited a quasi-separable distribution between the two classes, with the sets being reasonably well-defined on both sides of the hyperplane, despite some overlap in the middle. The parameter \(C\) values were determined through cross-validation training on one-fifth of the total dataset, considering a loss threshold between 1% and 5% of the total classified features during validation. Higher \(C\) values reduced error rates but also led to SVM models prone to overfitting, resulting in diminished generalization capability.  
Models were trained separately for the alpha and beta bands, significantly reducing the amount of computations and processing time to assess the performance of each one. Specifically, the processing time for the alpha band was shorter than the beta band because the alpha band was constructed with 16 samples, whereas the beta band was constructed with 32 samples.  
Model performance was evaluated on a separate test dataset. The class of each test feature vector was determined by applying the SVM's decision function, classifying a negative output as non-seizure and a positive output as seizure. Three SVM models were generated: the first for the dataset from CHB-MIT, the second for the dataset from Helsinki hospital without pre-filtering, and the third for the pre-filtered dataset from Helsinki hospital. The aim of models 2 and 3 was to compare the performance of the model trained on EEG signals without pre-filtering and filtered signals using the Savitzky-Golay (frame length 35, order 22). This was intended to evaluate how effectively this setup can enhance model performance. The parameters were chosen through extensive trial and error due  
Figure 6: Wavelet decomposition based on the Mallat algorithm.  
to the risk of information loss with improper Savitzky-Golay filter settings, potentially resulting in poorer performance compared to models trained on unfiltered signals.
## 2 Human Activities and HAR Sensors  
### Human Activities  
This section provides a structured representation of different human activities, enabling better analysis of these activities. A Unified Modelling Language (UML) diagram is used to illustrate the relationship and attributes of these activities. Figure 1 shows the UML diagram of the different types of activities and their attributes. Human activities can be simple or complex. Complex human activities (CHA) are made up of multiple simultaneous or overlapping actions like cooking or cleaning, while simple human activities (SHA) are single repeated actions such as walking or sitting [11]. Complexity is used as an attribute in the UML diagram to show whether an activity is complex or simple. These activities can also be carried out alone or with other people. This is important to consider when discussing social activities and is represented by the attribute no_of_persons, which is an integer showing the number of people involved in that particular activity. Other attributes that human activities have include location, duration, start, and end time.  
There are five basic types of human activities: physical activities, intellectual activities, social activities, recreational activities [12], and daily activities. Intellectual activities include activities like reading, writing, working at a desk, and most other activities around school or office work. Physical activities include exercises and other activities that require a lot of energy expenditure [13]. Social activities are activities that require communicating and interacting with others [14]. Recreational activities are activities undertaken for pleasure, leisure, or relaxation. Daily activities are activities carried out regularly that do not fit into the other four types such as cooking, cleaning, and eating.  
Each type of activity has attributes that are relevant to them based on the task of HAR. With physical activities, health vitals such as heart rate and pulse rate are relevant. Specific activities like swimming have attributes such as speed, stroke rate [15], and body temperature. Activities like jogging, running, biking and swimming all have the attribute speed. Some activities can be classified under more than one type, like walking, which is a physical activity but can also be classified as a daily activity. For the human activity UML model, activities are classified based on the type they best fit into.  
Figure 1: UML diagram of human activities.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 5 Design and Development Process
### Calculation of Scaling Factor
#### 5.1.1 Detail View  
This view was composed of 3D scenery of the airport. Therefore, it was required to map the air traffic on runways, taxiways etc. Hence, for calculating the scaling factor for this view, the actual distance/length of one of the runways of the airport was considered. This distance could also have been calculated using the GPS coordinates of an airport runway's starting and ending points. The scaling factor was derived for this view by substituting that distance at D\({}_{2}\) in Equation (1) against one unit of the mixed reality world represented as D\({}_{1}\).
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 2 Materials and Methods
### Key Technology of Faster-RCNN
#### 2.1.3 Loss Function  
The loss function of Faster-RCNN mainly consists of two parts, including the loss function of RPN and the loss function of RCNN, and each loss function includes the classification loss and the regression loss. The classification loss \(L_{cls}(*)\) was calculated using cross-entropy, and the regression loss \(L_{reg}(*)\) was calculated using smooth-L1. RPN loss function and RCNN loss function are shown in Equations (1) and (2).  
\[L_{RPN}(\{p_{i}\},\{t_{i}\})=\frac{1}{N_{cls}}\sum_{i}L_{cls}(p_{i},p_{i}^{*} )+\lambda\frac{1}{N_{reg}}\sum_{i}p_{i}^{*}L_{reg}(t_{i},t_{i}^{*}) \tag{1}\]  
where \(N_{cls}\) represents the number of images in each minibatch, and \(N_{reg}\) represents the number of anchor boxes in each minibatch, with a difference for nearly 10 times. Therefore, in order to balance the classification loss and regression loss, a coefficient \(\lambda=10\) is added in the regression loss function section. \(p_{i}\) represents the category score vector of the proposal box, \(p_{i}^{*}\) represents the label of the proposal box, \(t_{i}\) represents the position parameter of the proposal box, \(t_{i}^{*}\) represents the position parameter of the proposal box, and \(i\) represents each annotation box.  
\[L_{RCNN}(p,u,t^{\mu},v)=L_{cls}(p,u)+L_{reg}(t^{\mu},v) \tag{2}\]  
where \(p\) represents the score of the category predicted by the network, \(u\) represents the score of the ground truth, \(t^{\mu}\) represents the coordinate of the ground truth, and \(v\) represents the coordinate of the predicted box.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### OFDM Based UWAC Systems  
The most promising physical layer for underwater acoustic network (UWAN) communication is orthogonal frequency division multiplexing (OFDM). There has been a lot of research done in this field. However, the use of OFDM for FD communication is still being researched. The OFDM technique divides the available frequency band into orthogonal subcarriers. Symbols are emitted concurrently by these mutually orthogonal subcarriers with overlapping frequency bands [53, 54].  
The OFDM system has been proven an efficient modulation technique for the terrestrial communication system [55]. However, if the signal has to pass through a multipath channel, such as the UWAC channel, orthogonality suffers from ICI. To remove ICI, a guard interval was added after each individual channel in the OFDM system, as seen in the diagram, as shown in Figure 2.  
Furthermore, selective Automatic Repeat reQuest (ARQ) is needed to improve the reliability of the FD-OFDM channel in difficult channel conditions [55]. Other parameters for the FD-OFDM channel are necessary for effective operation in the physical and data link layers, as shown below.  
* The OFDM signal's entire bandwidth should cover all the data transmission frequencies, which is dependent on the bandwidth of the mounted transducers [55].
* The inverse fast Fourier transform (IFFT) algorithm is used to make the sub-carriers orthogonal for successful transmission using the UW communication transceiver [16].  
Figure 2: Frequency domain of OFDM system.  
* The Doppler distribution \(f_{m}\) of the OFDM channel must be smaller [16, 20]. The coherence band-width is denoted by \(\Delta f_{c}\), and the rate of signaling I is denoted by \(W\): \[\Delta f_{c}>W>>f_{m}\] (1)
* As shown below, the channel coherence time is denoted by denoted by \(\Delta t_{c}\). \(T_{S}\) is the upper limit of the OFDM symbol length, \(\tau_{rms}\) is the rms access delay and \(\tau_{max}\) is the maximum access delay: \[\Delta t_{c}<<T_{s}<\tau_{rms}\] (2)
* A cyclic prefix is applied to the OFDM symbol to reduce the SNR in the OFDM channel. The symbol length is five to six times longer than the cyclic prefix (CP) period, resulting in a throughput loss of less than one decibel.
## 4 Discussion
#### Limitations and Implications for Further Research  
This pilot study has several limitations: Due to COVID-19 pandemic restrictions, we were not able to complete data collection, hence the small sample (n = 12) and the lack of statistical power. As such, this study can be considered a pilot study for integrating a systematic assessment of mechanisms underpinning feedback effects. It is possible that inter-individual differences in anthropometrics, muscle tension, or spinal mobility may have impacted our results. Therefore, even though interpretations must be taken with caution, we paved the way for an interesting research avenue. Importantly, we only included a short-term retention test, and long-term retention tests are necessary to better investigate learning effects. Future research should focus on increasing the sample, targeting workers or subjects with back pain, who can directly benefit from such an intervention. Long-term retention tests should also be included to evaluate learning. Further, the repositioning task should be further developed, i.e., displacements from a lumbar angle target could be assessed instead of the torso angle target that was used in this study. Importantly, any future investigation should further emphasize the mechanisms that underlie the effects of tapes with different elastic properties and differentiate between performance and learning effects, as the reductions in the lumbar range of motion noted in this work may have been generated due to a variety of interacting and intersecting phenomena (supplementary sensory feedback, mechanical limits imposed by stiffer tape, changes in the stretching of the muscles imposed by tape applications, etc.)
## 1 Introduction
It is important for a grid-tied inverter, even when the grid is subject to harmonics, to maintain the harmonic contents of inverter output currents below the specified limit. Therefore, some research is focused on the inverter PLL performance to analyze the PV PCC harmonic generation mechanism in a micro-grid [32; 33]. The studies above reveal a fact that the analysis of harmonic generation mechanisms are the key issues of harmonic mitigation for the power systems containing distributed generations, such as photovoltaics and wind generations. All the methodologies above of micro-grid harmonic research include theoretical analysis, modeling, simulation and experimental research. The study results obtained by these methodologies are not completely consistent with the actual results in field, such as PV PCC harmonics in micro-grid. Therefore, it is necessary to study the power quality problems in PV plants based on effective field testing and data measurement. In this paper, a field test and analysis method of a PV plant harmonics is presented, and a new generation mechanism of PV PCC harmonics is proposed.
###### Abstract  
A Mach-Zehnder modulator (MZM) is necessary for implementing a decoy-state protocol in a practical quantum key distribution (QKD) system. However, an MZM bias control method optimized for QKD systems has been missing to date. In this study, we propose an MZM bias control method using \(N\) (\(\geq\)2) diagnostic pulses. The proposed method can be efficiently applied to a QKD system without any additional hardware such as light sources or detectors. Furthermore, it does not reduce the key rate significantly because it uses time slots allocated to existing decoy pulses. We conducted an experimental demonstration of the proposed method in a field-deployed \(1\times 3\) QKD network and a laboratory test. It is shown that our method can maintain the MZM extinction ratio stably over 20 dB (bit error rate \(\leq\)1%), even in an actual network environment for a significant period. Consequently, we achieved successful QKD performances.  
quantum key distribution; Mach-Zehnder modulator; bias control; decoy-state protocol +
Footnote †: journal: Electronics
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 5 Simulated Analysis
### Output Voltage Stability Comparison of Variable Loads  
In the actual wireless energy transmission system, the load is not constant and sometimes changes, which requires real-time adjustment of the closed-loop system structure to keep the output voltage and output current stable within a specific load variation range.  
According to Figure 10, the output voltage suddenly changes when the system is disturbed by the load for 0.001 s. Due to the closed-loop regulation in the system, the output voltage can still be maintained at 110 V. The red line represents the result of the system output voltage under traditional sliding mode control, and it can be seen that the overshoot of the output voltage is 30 V. The blue line shows the result of the output voltage under the improved sliding mode control, where the load is subjected to the same disturbance at 0.01 s; it can be seen that the output voltage overshoots, and compared with the conventional sliding mode control, the amount is more minor; the overshoot is 25 V, the time to stabilize is also shorter, and the adjustment time is 0.0002 s. Compared with the traditional sliding mode control, the adjustment time is five times faster, so the improved sliding mode control algorithm is more suitable for complicated situations.
# Quantum 3D FFT in Tomography
## 3 Theory of Tomography
### Reconstruction Simulation  
In the experiments we conducted, the 3D data cube (fandom), shown in Figure 6, was employed. The random contains a large sphere with the same intensity (no. 2), a truncated cone with increasing intensity downwards (no. 1), an inclined cylinder with increasing intensity upwards (no. 3), a white parallelepiped inside the sphere (no. 4), and four spheres with various intensities inside the cone (nos. 5-8).  
The 2D projections of the 3D random were obtained every 0.7 degrees around it, covering 180 degrees so that a total of 256 projections are obtained. Projections that could be obtained from the rest aspects from the supplementary angles are not needed since they provide the same information as the initial 256 projections. Three of the projections are given in Figure 7. Specifically, the projections 1 st, 110 th, and 180 th are presented, which correspond to rotation angles 0.7, 77, and 126 degrees.  
For each one of the 256 projections, the corresponding 2D spectrum is evaluated by means of the 2D QFT. In Figure 8, the amplitude of the 2D Fourier Transform for the 110 th projection is given, i.e., the projection with a 77-degree angle. All of these 256 projections are combined in a radial arrangement, as shown in Figure 5 (left Figure, top view) to give a cube of spectra of the random in Figure 6. In actuality, this cube has the spectral content of the random in Figure 6 but with greater density in the center. Before inversely transforming by means of the inverse 3D QFT to the original random, the cube with the spectral content must be multiplied with the quantity  
\[\sqrt{1+\omega_{x}^{2}+\omega_{y}^{2}\,+\omega_{z}^{2}} \tag{19}\]  
Figure 5: Image reconstruction using the Fourier intersection theorem.  
The whole procedure includes the following steps:  
1. Projections acquisition as those shown in Figure 7.
2. Evaluation, using the 2D QFT, of the 2D spectra of the above projections.  
Figure 8: The amplitude of the 2D QFT for the 110 th projection, i.e., projection angle 77 degrees.  
Figure 6: A 3D random (data cube) containing a large sphere with the same intensity (no. 2), a truncated cone with increasing intensity downwards (no. 1), an inclined cylinder with increasing intensity upwards (no. 3), a white parallelepiped inside the sphere (no. 4), and four spheres with various intensities inside the cone (nos. 5–8).  
Figure 7: Three of the projections of the 3D data cube of Figure 6. The projections 1 st, 110 th, and 180 th corresponding to rotation angle 0.7, 77, and 126 degrees.  
3. Synthesis of the 3D spectrum of the type of Figure 8, in a radial manner, as shown in the left of Figure 5.
4. The derived 3D spectrum is filtered (multiplied) by a 3D filter of the type \(\sqrt{1+\omega_{x}^{2}+\omega_{y}^{2}+\omega_{z}^{2}}\).
5. On the new 3D spectrum, the inverse 3D QFT is applied in order to derive the 3D random.  
In the next section, the Quantum 2D and 3D versions of the Fourier Transforms are applied in order to implement the above five-step procedure.
## 4 Proposed Scheme (Multi-BSM Approach)
### Attacker's Model
#### 4.1.1 Active or Passive Attack  
The mode of attack in which an attacker is directly involved in the participation of tempering of the information and/or degrading the network is known as the active attack, while the passive attacker only eavesdrops on the information that is being conveyed in the network which can be used for personal or malicious purposes.
## 3 Materials and Methods
### Traffic Scenario Map Construction
#### 3.1.1 Object Tracking  
To fully understand the dynamic scene, three main feature extraction pipelines are used as inputs to recognize different objects and determine the spatial relationships of vehicles in the video. The key components for dynamic feature extraction include instance segmentation, semantic segmentation, pixel-by-pixel optical flow, and depth data in video frames. First, Mask R-CNN [36] is used, for instance, as segmentation to detect and track moving objects over time. Second, Detectron2 [37] is employed to achieve a semantic understanding of the scene, particularly for static objects such as traffic signs and lane markings, which serve as important landmarks. Finally, RAFT (Recurrent All-Pairs Field Transforms) [38] is used to compute the optical flow between consecutive video frames, enabling the tracking of each object in the scene by combining optical flow information with instance features and semantic segmentation.