esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Unsupervised Methods  
Unsupervised methods are the most extensively used methods since they do not need labeled training data. Modeling the training dataset is carried out using an unsupervised learning approach.  
The underlying premise used by the strategies in this category is that regular cases in the test data are significantly more common than abnormalities. If this presumption is incorrect, the effectiveness of these methods will drop dramatically.  
By employing a portion of the unlabeled dataset as training data, many semi-supervised approaches may be modified to function in an unsupervised manner. This kind of adaptation is predicated on the test data having relatively few abnormalities and the model to be trained being resilient to those anomalies.  
A detailed description of these methods can be found in [22]. Isolation Forest is a representative for this type of method [33].  
Some researchers have applied unsupervised anomaly detection for health testing [34] and time-series anomaly detection [35]. A novel method based on mutual information and reduced spectral clustering was developed in [36].  
The advantage of this type of method is that it can perform anomaly detection without label data, which means it is more suitable in most situations; its main disadvantages are that the interpretability is relatively poor, and the decision process is less direct and more obscure than supervised anomaly detection, especially when using artificial intelligence technologies such as neural networks and deep learning.
## 3 Proposed PUF Design  
Our proposed PUF platform, as shown in Figure 1, consists of a microcontroller, the control logic (also referred to as Evaluation Board), and the PUF core.  
The platform enables direct access to the analog PUF in a full discrete board-level design, especially designed for the used PUF core fabricated with \(M=8\) inverters. Furthermore, the proposed PUF allows nonlinear bit-width scaling by increasing the number of PUF core inverters, comparable to related PUF architectures as, e.g., RO-PUFs [5].  
Each PUF core inverter is realized with a commercially available NMOS-transistor \(T\) and a resistive load \(R_{L}\) with \(1\,\mathrm{\char 37}\) variation. The sequential addressing process selects two inverters in parallel. Due to process variations, the transistor drain currents \(I_{D}\) differ slightly [29]. In combination with the impact of resistive loads, this results in a variation of the inverter's output voltage \(V_{out}\). The PUF core inverters are set up such that \(V_{out}\geq V_{ov}\) with \(V_{ov}=V_{in}-V_{th}>0\,\mathrm{V}\), ensuring that the transistors operate in the active region for nominal devices.  
For signal routing, a constant input biasing voltage \(V_{in}\) is fed to the system. \(V_{in}\) is routed to the two addressed inverter input terminals via two bi-directional multiplexers (MUXs), used as demultiplexers (DEMUXs), as shown in the middle part of Figure 1. Each PUF core inverter output voltage \(V_{out}\) is buffered by a voltage-follower configured operational amplifier (op-amp) to separate the intrinsic variation source from the readout circuitry. Each buffered output voltage is routed to its corresponding MUX input channel in both MUXs. Therefore, the same bi-directional multiplexers asfor demultiplexing are deployed. The MUX1 output signal is routed to the positive comparator input terminal and ADC channel 1, respectively. The output of MUX2 is routed to the negative comparator input terminal and ADC channel 2, as shown in Figure 1. Each readout path utilizes a first-order RC low-pass filter around a cut-off frequency of \(f_{c}\approx 10\,\mathrm{kHz}\), which limits the signal-bandwidth of the readout path.
## 2 Electromagnetic Model
To investigate the resonance properties of CEBs we have used a cooled generator based on high-temperature Josephson junctions as it was done in [21; 22; 23]. Long Josephson junction samples based on YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\delta}\) film (YBCO) on the surface of 24\({}^{\circ}\) [001]-tilt Zr\({}_{1-x}\)Y\({}_{x}\)O\({}_{2}\) bicrystal substrates were deposited by magnetron sputtering using preliminary topology mask method [24; 25]. The preliminary CeO\({}_{2}\) mask was applied to the substrate, and with further deposition of the superconducting film, an insulator grew in the modified regions and a superconducting film in the unmodified regions. Thus, a pattern of a planar structure of a superconducting generator on a substrate was formed. At the same time, the superconducting film did not grow on the bicrystal boundary resulting in the formation of a superconductor-weaklink-superconductor structure, which is a Josephson junction. Figure 3 (top left) shows a fragment of a chip that consists of a long Josephson junction with a dipole antenna. When a direct current is passed through such a structure, an alternating voltage (electromagnetic field) arises, and the frequency of this generation is strictly determined by the average voltage in the junction and is in the range of 50-800 GHz [23]. Thus, Josephson junctions are effective generators for studying the amplitude-frequency characteristics of bolometric and detector systems. Figure 3 shows a schematic view of the experiment. The generator chip consisted of a Josephson junction integrated with dipole antennas was mounted on a holder with a Si hyperhemisphere lens (4 mm diameter) located on a 4 K cryostat plate. The control magnetic field, perpendicular to the Josephson junction boundary, was created by current flowing through a coil of a copper wire. A bolometric system with the lens on the backside of the silicon substrate was placed on a 300 mK plate. The copper shield, covered with a black body, was used to avoid signal reflections from the shield back to the generator. The distance between the detector and the source was 3 cm. Varying the current through the Josephson junction, we measured the bolometric response (voltage difference dV\({}_{\text{CEB}}\) at a fixed bias current through the CEB) and the voltage on the YBCO generator (the Josephson radiation frequency f = 2 eV\({}_{\text{YBCO}}\)/\(\hbar\)). The generator was calibrated independently by a broadband CEB receiver, studied in [23], and compared with the response of the backward wave oscillator (BWO). In addition, the oscillator antenna was investigated by measuring the Shapiro steps [25] using the same BWO. Substituting the Josephson relation between the frequency and the voltage on the generator, we obtained the amplitude-frequency characteristic of the receiving system. Figure 2: SEM image of fabricated CEB integrated into the double-folded slot antenna. Short red arrows show the electric field distribution in the slot and in the coplanar waveguide (CPW).
# Deep Learning Methods in Image Matting: A Survey
## 1 Introduction
### Classification of Image Matting Methods  
Over the years of research and development, researchers have designed a series of effective algorithms for various application scenarios of image matting; these algorithms can be categorized into three groups: sampling-based, propagation-based, and learning-based methods. Sampling-based algorithms predict the opacity of the unknown region by collecting a series of pixels or pixel blocks from the known regions of the trimap. Propagation-based algorithms typically establish connections between adjacent pixels and then use an optimization strategy to propagate the opacity information of the known regions to the unknown regions in order to predict the opacity of each pixel in the unknown region. Learning-based algorithms learn the features of the image by using a considerable amount of data and use these features to predict the opacity. As deep learning algorithms have already been applied to various visual tasks and have completely surpassed traditional learning-based algorithms, they are gradually being introduced into image matting.
## 4 Discussion  
The fault interpretation is vital in understanding the subsurface structural framework [41]. A multistep hybrid workflow was devised to avoid falling into the classic pitfall of cumbersome computation. The objective was to make the fault interpretation efficient and automatic. [PERSON]'s innovative fault likelihood coupled with conventional attributes were used to detect and auto-extract the faults. The method can be regarded as 3D semblance as it measures semblance at a variety of dips and azimuth. This novel approach has been successfully applied on a faulted reservoir under a gas chimney in offshore, Malaysia. It is comprised of four main steps: (1) seismic data conditioning using dip steered median filter (DSMF); (2) fault detection and enhancement by generating fault likelihood and combining it with conventional attributes; (3) automated fault extraction; and (4) fault data analysis. The application of the steering cube as well as the structural oriented filtering significantly enhanced and sharpened the discontinuity features. The effective integration of discontinuity attributes enabled the automatic fault extraction, which helped to build a structural framework for mapping fault relationships with juxtaposed strata. This paper also discussed several tools and techniques available for fault data analysis that can quickly filter, sort, organize, and merge the fault surfaces. These include the iteration process to clean up and refine the resulting volume by eliminating any unwanted discontinuities. Another approach is to use statistical graphs to filter the faults based on dip, azimuth, surface area, and confidence of the faults to be tracked. By applying this automatic workflow, more than 500 fault surfaces were generated in a few hours. The fault surfaces were filtered and chosen for further analysis based on several criteria using the fault analysis tools. Major surfaces were merged to make fault planes and were then converted into the fault interpretation. Subsequently, the fault interpretation was sent to the fault model to form a fault framework, as shown in Figure 13. This framework can now be incorporated into static modeling to understand the subsurface structural framework. To conclude, this study provided an advanced workflow that identified and auto extracted the faults with unprecedented clarity. It is fast and completed in a fraction of time compared to what would have been required for traditional manual picking. As future work, the methodology can be tested to explore fracture corridors and stratigraphic features to gauge its adequacy and effectiveness. It is customizable and can use other attributes and algorithms to further improve it.  
Figure 12: (**a**) 3D view of filtered major fault surfaces using fault data analysis tools. Inset (**b**) shows all major and minor discontinuities in the planner view.
# Equivalent Analysis of Thermo-Dynamic Blow-Off Impulse under X-ray Irradiation
## 1 Introduction  
Since the 1950s and 1960s, the study on multiple effects of X-ray has aroused extensive attention, especially on X-ray thermodynamic effects as a basic mean of spacecraft damage [1, 2, 3] has aroused extensive attention. In terms of the mentioned effects, a combination of laboratory and numerical simulations is required [4, 5, 6]. In the past half century, people have extensively developed a variety of simulation experimental equipment, such as low-energy relativistic electron beam accelerator [7, 8], etc., as well as a series of related software in terms of calculation [9, 10, 11]. These studies play a very important role in the development and application of anti-X-ray reinforcement technology [12, 13].  
The intense pulsed X-ray can exert a robust damaging effect [14]. When a certain intensity of pulsed X-ray irradiates the medium surface, X-ray energy will suddenly deposit on the surface of this material with a certain thickness, and the material temperature will rise and expand rapidly to excite robust stress wave, which is termed as thermal shock wave [15]. In the meantime, when the temperature or internal energy reaches a certain value, the material will melt or vaporize [16], which will have effects on the dielectric material at a certain expansion speed and spray outward, with the material or structures exhibiting the form of recoil, which is termed as blow-off impulse. The impulse can induce structural responses (e.g., vibration [17], deformation [18] and buckling of structural shells [19]). Thermal shock wave, blow-off impulse and structural response refer to three basic forms of damaging effect of intense pulse X-ray, and these studies on such forms are of high practical significance [20, 21].  
[PERSON] [22] studied the energy deposition profiles of three mono-energetic electron beams of aluminium at different angles of incidence and found that low-energy electron beams at a large angle could simulate the radiating effect of intense pulse X-ray. [PERSON] [23] reported that electron beam had an angle of incidence distribution in the spot range of the anode target in the study on thermal-mechanical effect of multi-spectral electron beams, and that the distribution of the angle of incidence significantly impacted the energy deposition profile of the multi-spectral electron beam in the target material.  
In this study, the X-ray blow-off impulse of two mono-energetic and blackbody spectra on aluminium alloy materials was calculated with self-developed numerical software. The mono-energetic and blackbody X-rays in the laboratory were discussed as the equivalent method, and point source was found feasible in simulating parallel light radiation source based on the mentioned results.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods
### Mathematical Model
#### 2.1.2 Mapping Relation  
In the general imaging model, the mapping relation between the target in a world coordinate system and a pixel in the image coordinate system can be described as a straight incoming line, as shown in Figure 3. Plucker coordinates can describe the incoming light with unique homogeneous coordinates in a five-dimensional projective space [14], as shown in Equation (1).  
The coordinates of two points are \((x_{1},x_{2},x_{3})\) and \((y_{1},y_{2},y_{3})\) in a three-dimensional space. The line through the two points can be expressed by Plucker Coordinate as \(l_{ij}=x_{i}y_{j}-x_{j}y_{i}\), \(x_{0}=y_{0}=0\) Rewritten in vector form as  
\[\overset{\rightarrow}{l}=(l_{01},\,l_{02},\,l_{03},\,l_{23},\,l_{31},\,l_{12}) \tag{1}\]  
Assuming the mapping relation is continuously changing, we can express the mapping relation by a vector valued function \(f(x)\), which can be solved by radial basis function interpolation as shown in Figure 3.  
Figure 1: Structure of the orthogonally splitting imaging pose sensor.  
Figure 2: 2d image coordinate of the target point \(l\left(u,v\right)\) can be restored by two 1d images.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 4 Verification of the Proposed Method
### Results of Algorithm Optimization  
Combined with the WNN, the WDO algorithm optimizes parameters \(E_{1}\), \(E_{2}\), \(E_{3}\), and \(\rho\). The four updating parameters correspond to the four dimensions of air particles, and 100 air particles are used for optimization calculation. The velocity limit is \([-1,\,1]\), the maximum number of iterations is 100, and the coefficients \(a\), \(g\), \(RT\), and \(c\) in the velocity update equation are 0.85, 0.65, 0.5, and 2, respectively. The iterative curve of the objective function is shown in Figure 7.  
It can be seen from the figure that the objective function converges at 58 iterations, and the objective function value is as small as \(2.3\times 10^{-3}\), which shows that the optimization effect of the algorithm is obvious, and it is suitable for multi-parameter optimization. The corrected values of physical parameters are shown in Table 4.  
Figure 6: The training process of WNN.  
Figure 7: The iterative curve of WDO.  
The differences in updated and actual values are shown in Table 5. Based on the parameters, the WNN and the updated finite element model were used to calculate the structural response, respectively, to obtain the network value and the updated value of the structural response and then compared with actual responses. The comparison is shown in Table 6. Among them, the difference I is: (network value \(-\) actual value)/actual value; difference II is: (updated value \(-\) actual value)/actual value.  
It can be seen from Table 5 that after updating parameters \(E_{1}\), \(E_{2}\), \(E_{3}\), and \(\rho\), the differences become smaller, and the maximum difference is reduced from 25.00% to 3.92%. It can be seen from Table 6 that the values of difference I and difference II are both small, and the network value, updated value, and actual value of the static and dynamic responses are relatively close; thus, the model updating is proven to be effective. Therefore, the WNN can approximately reflect the nonlinear relationship between structural response and parameters and has good simulation performance; using the WNN as a surrogate model can effectively update the finite element model and improve work efficiency.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### BA and BS Estimates
#### 4.1.1 Spectral Indices and Mapping Techniques
All of the studies reported the expected temperature increase in the immediate post-fire environment. For example, NBR and three indices in which the NBR is enhanced with LST revealed the best performance in assessing burn severity in chaparral shrublands [210]. [PERSON] et al. [211], based on the time series of daily Aqua-MODIS LST data (2003-2017) and the harmonic analysis of time series (HANTS) algorithm, demonstrated that the LST anomaly is a covariate of both burned area and fire duration. The comparison of the above publications in relation to the burn severity may aid in better understanding of forest burn patterns at different spatio-temporal scales, as well as providing reliable information for forest managers' systematic monitoring of wildfires and restoration efforts.
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Classical Algorithms
#### 3.2.5 Deployment Entropy with Potential Fields Strategy  
Deployment entropy was presented to cover persistent areas, using many sensing swarm robots, which depend on partitioning the area into many regions. Deployment means the uniformity of agents per region across all regions when covering an area. The study showed that a good spread of agents and growing sensor coverage resulted when compared with previous results, which did not use potential fields with deployment entropy. Fifty-agent deployment was used for the simulation. Two redeployments are global, which happens in partition regions, and local, which occurs in subregions. As a result, the robots cluster together more in corners at the end. The attractive and repulsive fields are applied receptively between robots, leading to the greater spread of robots to achieve area coverage. Deployment entropy is suitable because of its scalability and potential for robot system implementation that uses a decentralised control type. The simulation results show that the potential field approach is more effective than the non-potential field approach in generating a uniform group of distributed robots [64].
###### Abstract  
A CDMA RoFSO link with receivers' spatial diversity is studied. Turbulence-induced fading, modeled by the _M_(alaga) distribution, is considered that hamper the FSO link performance along with the nonzero boresight pointing errors effect. Novel, analytical closed-form expressions are extracted for the estimation of the average bit-error-rate and the outage probability of the CDMA RoFSO system for both directions of the forward and the reverse link. The numerical results show clearly the performance improvement of using spatial diversity, even in the most adverse atmospheric conditions with strong and saturated atmospheric turbulence with enhanced misalignment. Also, the effects of nonlinear distortion, multiple access interference and clipping noise aggravate the performance of the link, where cases with large number of users are taken into account.  
free-space optical communications; spatial diversity; RoFSO; CDMA; malaga distribution; nonzero boresight pointing errors +
Footnote †: journal:  
_Acticle_
## Wavelet-Based Watermarking and Compression for ECG Signals with Verification Evaluation  
**[PERSON] 1,** [PERSON] 1,** [PERSON] 2,** [PERSON] 3,** [PERSON] 4 and [PERSON] 5  
1 Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China; E-Mails: [EMAIL_ADDRESS] (K.-K.T.); [EMAIL_ADDRESS] (X.H.)  
2 Department of Exercise and Health Promotion, College of Education, Chinese Culture University (CCU) and Department of Neurosurgery, Lo-Hsu Foundation, Lotung Poh-Ai Hospital, Luodong, Yilan 265, Taiwan; E-Mail: [EMAIL_ADDRESS]  
3 Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan  
4 Department of Software Engineering, Xiamen University, Xiamen 361005, China; E-Mail: [EMAIL_ADDRESS]  
5 Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 14 December 2013; in revised form: 6 February 2014 / Accepted: 18 February 2014 / Published: 21 February 2014_  
**Abstract:** In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user's data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.  
**Keywords:** integrating; watermarking; ECG; compression
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 3 Proposed Security Solution for Spatial Files
### Strong Encryption for Spatial Files
Selecting a spatial file security solution based on AES presents a safe and wise decision as it provides the best encryption security, regulatory coverage, and position for future development.