esa-sceva/satcom-chunk-collection huggingface dataset:
# Wheel-Based MDM-PON System Incorporating OCDMA for Secure Network Resiliency
###### Abstract  
Wheel-based network resilience passive optical network (PON) based on mode division multiplexing (MDM) can be integrated with optical code division multiple access (OCDMA) schemes efficiently for the fixed and backhaul traffic under normal and break/failure fiber operating conditions. In this work, a bidirectional \(10/2.5\) Gbit/s hybrid MDM-OCDMA-PON system using multi-weight zero cross-correlation (MWZCC) code is proposed. Donut modes \(0\) and \(1\) are incorporated by the MDM technique in the proposed system. The benefit of this work is to offer an inexpensive, high-bandwidth and advanced long-haul network with satisfactory resource utilization ability for fiber links with protection against faults and to improve the reliability along with survivability of the network. The simulation results show the successful realization of the multimode fiber (MMF) link at \(1.6\) km in the uplink and \(1.2\) km in the downlink directions under an acceptable bit error rate (BER). The minimum accepted received power of \(-31\) dBm in uplink and \(-27\) dBm in downlink over \(1\) km link at \(10/2.5\) Gbit/s rate is obtained. Moreover, the minimum received power of \(-20\) dBm in uplink and \(-30\) dBm downlink is achieved by using MWZCC code compared to other codes handling \(58\) simultaneous end users. Further, the influence of fiber impairments and connected devices on the proposed approach is numerically evaluated. Moreover, it is shown that the wheel based proposed approach performs well than other topologies for the bidirectional network resilience transmission.  
mode division multiplexing (MDM); donut modes; optical code division multiple access (OCDMA); multimode fiber (MMF) +
Footnote †: journal: _Photonics_  
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Footnote 0: Copyright** © 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/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 1 Introduction
[20] conducted in-depth research of the innovative composite-anchor pile that integrates an internal composite anchoring system comprising three key components: steel strands, high-strength steel pipes, and grout. By utilizing a combination of on-site tests and numerical simulations, they examined the load-carrying mechanism and bearing performance of these newly developed uplift piles. However, a notable limitation exists as there is a lack of a well-defined design method for determining the bearing capacity of these composite-anchor piles, which poses a hindrance to their practical implementation in engineering projects [20]. To address this gap, this study aims to evaluate the influence of crucial parameters, such as the pile diameter, the number of composite-anchor cables, and the steel strand diameter, on the load-bearing capacity of uplift piles. This assessment will be based on a three-dimensional finite element refined model that has been validated against on-site test results. Moreover, this paper proposes a straightforward and pragmatic calculation method grounded in current standards, ultimately providing theoretical support for the practical application of composite-anchor uplift piles in engineering projects [21; 22]. The composite-anchor cable structure incorporated within the pile enables the adaptability of the composite-anchor pile to diverse working conditions, rendering it a promising solution to revolutionize the current utilization of uplift piles. This innovation, for instance, mitigates steel reinforcement wastage and improves the longevity of uplift piles in corrosive environments.
# Means and Issues for Adjusting Principal Component Analysis Results
## 1 Introduction
Since experimental data inherently contain noise, it is expected that noise-sensitive PCA will exhibit such errors. In such situations, there may be a temptation to adjust PCA results or train PCA to correct rotations. Accordingly, PCA now offers various options for data manipulation. One approach, for instance, involves reducing the dataset to be subjected to SVD by selecting it in a specific way [10]. Another involves altering the direction of rotation derived from SVD [11, 12]. Both orthogonal and oblique rotations are available, each offering several methods. Naturally, these variations result in different solutions, thereby compromising the objectivity of PCA. This subjectivity arises because the outcomes depend on the analyst's choices, which poses a significant challenge when analyzing scientific data. Oblique rotations, in particular, introduce a high degree of freedom, potentially leading to arbitrary results. This study introduces a straightforward method to rotate data in relation to the two principal components while maintaining their orthogonality. The impact of such rotations on the original PCA results is evaluated in terms of the contributions of the principal components. The findings reveal that mild rotations do not significantly alter the results.
## 4 Experiments and Analysis of Results
### Ablation Experiments  
To verify the effectiveness of each module in the MDFT-GAN model and its contribution to overall performance, a series of ablation experiments were conducted, as summarized in Table 12.  
Figures 24 and 25 visually illustrate the results of the comparison. The comparison includes three model variants: ACGAN, ACGAN with Conv-RES, and MDFT-GAN. The visual quality and quantitative classification performance of the generated images highlights the contribution of each module to the overall improvement.  
Obviously, the MDFT-GAN with Conv-RES and Transformer is highly consistent with the ground truth in terms of both local details and global distribution, thus greatly improving the quality of the generated samples. The classification accuracy results further validate the above conclusions: among 50 samples, the classification accuracy of ACGAN is 83.60, which is improved to 89.60 after the introduction of the Conv-RES module, which indicates that Conv-RES can effectively enhance the local feature extraction, and the classification accuracy reaches 99.41 after the Transformer is introduced.
## 4 Application
### Multi-Parameter Selective Sensing
This can be achieved by averaging the total duration of multiple heartbeats or by performing a Fast Fourier Transform (FFT). According to  
Figure 11: Selective multi-parameter sensing diagram. Figure 12a, the magnetic field signal is obtained through differential measurements between adjacent SAWS units with different material compositions. The midpoint between the two SAWS units involved in the differential measurement is chosen as the recording point for the signal. After converting the differential signal to a magnetic signal, a two-dimensional MCG is obtained. Generally, measuring different physical signals requires distinct measurement materials, modules, and sensing logic. The MP-SAWS can selectively output different physical quantities without changing the measurement devices. This design inherently offers high integration. Moreover, due to its small size, the device has the potential for further integration into medical devices.
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
## 3 Fabrication and Measurement of Non-Foster Circuit  
Having validated the NFC performance using simulations, the optimized NFC was fabricated as shown in Figure 7. It was implemented on an FR-4 substrate with a footprint of 14.4 mm \(\times\) 20 mm, which is smaller than the previous report [6]. It was miniaturized by reducing the length of microstrip lines connecting the transistors and RLC components. This also reduced the overall losses consumed by the NFC, resulting in the use of lower R values to satisfy the stability conditions.
## An Adaptive Dynamic Multi-Template Correlation Filter for Robust Object Tracking  
**[PERSON] \({}^{*}\) and [PERSON]**  
Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, No. 1001,  
Daxue Rd. East Dist., Hsinchu City 300093, Taiwan  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract:** In the field of computer vision and robotics, scholars use object tracking technology to track objects of interest in various video streams and extend practical applications, such as unmanned vehicles, self-driving cars, robotics, drones, and security surveillance. Object tracking is a mature technology in the field of computer vision and robotics; however, there is still no one object tracking algorithm that can comprehensively and simultaneously solve the four problems encountered by tracking objects, namely deformation, illumination variation, motion blur, and occlusion. We propose an algorithm called an adaptive dynamic multi-template correlation filter (ADMTCF) which can simultaneously solve the above four difficulties encountered in tracking moving objects. The ADMTCF encodes local binary pattern (LBP) features in the HSV color space, so the encoded features can resist the pollution of the tracking image caused by illumination variation. The ADMTCF has four templates that can be adaptively and dynamically resized to maintain tracking accuracy to combat tracking problems such as deformation, motion blur, and occlusion. In this paper, we experimented with our ADMTCF algorithm and various state-of-the-art tracking algorithms in scenarios such as deformation, illumination variation, motion blur, and occlusion. Experimental results show that our proposed ADMTCF exhibits excellent performance, stability, and robustness in various scenarios.  
**Keywords:** adaptive-adjustment; local binary patterns; multi-template
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 5 Security Aspects
Organizations must carefully assess their security priorities, regulatory requirements, and DevSecOps strategies to determine which platform aligns best with their software security objectives. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Subcategory** & **Azure DevOps** & **GitHub** & **Remarks** \\ \hline Authentication and Access Control & Multi-Factor Authentication (MFA) & Supported & Supported & Both platforms enforce MFA for enhanced security. \\ \hline \multirow{4}{*}{Single Sign-On (SSO)} & \multirow{2}{*}{Integrated with Azure AD} & SAML-based SSO (Enterprise Cloud/Server) & Azure DevOps has deeper integration with Azure AD for enterprise nodes \\ \hline \multirow{4}{*}{Repository Security} & Role-Based Access Control (RBAC) & Advanced RBAC with hierarchical permissions & Basic RBAC with repository-level permissions & Azure DevOps offers more granular access control. \\ \hline \multirow{4}{*}{Commit Signing} & Fine-Crained Access Tokens (PATs) & Supported (limited scope and duration) & Supported (limited scope and duration) & Both platforms reduce token leakage risks. \\ \hline \multirow{4}{*}{Commit Signing} & Default Repository Visibility & Private by default & Public by default (unless explicitly set to private) & Azure DevOps enforces stricter access controls from the outset. \\ \cline{2-5}  & \multirow{2}{*}{Branch Protection Security} & Prevents unauthorized direct commits, gated check-ins, and branch locking & Protected branches, required reviewers, draft pull requests & Azure DevOps provides more enterprise-grade branch controls. \\ \hline \hline \multirow{4}{*}{Commit Signing} & \multirow{2}{*}{Supported} & GPG/SSH commit signature verification & GitHub enforces signed commits for better authenticity. \\ \cline{2-5}  & & Required reviewers, automated security checks in pull requests & Both platforms enforce code reviews, but GitHub integrates automation better. \\ \hline \multirow{4}{*}{Repository Access Controls} & IP whitelisting, geographical restrictions, device compliance checks & Self-hosted private repositories, SAML-based access controls & Azure DevOps offers more comprehensive access controls. \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Security aspects are divided by category.
## 2 The Mathematical Modeling of OPF Objective Function
### Objective Function
#### 2.3.1 Fuel Cost (FC)  
The cost of fuel required by generators in thermal power plants is regarded as the major issue in electricity generation. OPF challenges focus on the entire fuel cost of the system, with generator cost characteristics stated as a quadratic function of the output power.  
The OPF solution targets reducing the total generator costs (USD/h), the cost objective function is presented below in Equation (21):  
\[F_{1}(x,u)=\sum\limits_{i=1}^{NG}\left(a_{i}+b_{i}P_{G_{i}}+c_{i}P_{G_{i}}^{2}\right) \tag{21}\]where \(NG\) is the number of generators, \(a_{i}\), \(b_{i}\), and \(c_{i}\) are the fuel cost coefficients of the ith generator unit, and \(P_{G_{i}}\) is the real power generation of the ith generator.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Demodulation Method Comparison  
Three demodulation techniques were evaluated: integration, PLL, and quasi-FM-PWM conversion. Each method successfully recovered the original information signals from both conventional and proposed PPM formats:  
* Integration Method: Demonstrated effective signal recovery for both sinusoidal and triangular waveforms, with the integrator circuit providing consistent demodulation performance across different signal types.
* PLL Method: Operating with a 12 kHz carrier frequency and 9.6-14.1 kHz lock range, the PLL successfully tracked frequency variations and recovered the modulating signals with acceptable fidelity.
* Quasi-FM-PWM Conversion: The monostable multivibrator approach with RC low-pass filtering showed robust performance, particularly suitable for applications requiring implementation simplicity.
## 3 Results
### Water Proton Spin Relaxivity Values  
The r\({}_{1}\) and r\({}_{2}\) relaxivities of the FA-PAA-coated MnO nanoparticles, i.e., the slopes of the 1/T\({}_{1}\) and 1/T\({}_{2}\) plots versus the Mn-concentration, respectively, were estimated to investigate their potential as an MRI contrast agent. As shown in Figure 8a and Table 1, the estimated r\({}_{1}\) and r\({}_{2}\) values were 9.3 \(\pm\) 0.1 and 20.4 \(\pm\) 0.4 mM\({}^{-1}\)s\({}^{-1}\) (r\({}_{2}\)/r\({}_{1}\) = 2.2), respectively. In addition, dose-dependent contrast enhancements were clearly observed in the longitudinal (R\({}_{1}\)) and transverse (R\({}_{2}\)) map images (Figure 8b), evidencing that the FA-PAA-coated MnO nanoparticles can act as both T\({}_{1}\) and T\({}_{2}\) MRI contrast agents. However, the r\({}_{1}\) value was high and the r\({}_{2}\)/r\({}_{1}\) ratio was somewhat close to one, corresponding to highly suitable conditions for successful application as a T\({}_{1}\) MRI contrast agent [7, 8]. Thus, the FA-PAA-coated MnO nanoparticles were applied as a T\({}_{1}\) MRI contrast agent in this study.  
Figure 6: Mass-corrected magnetization versus the applied field (i.e., M-H) curves (\(-2.0\) T \(\leq\) H \(\leq\) 2.0 T) at T—100 and 300 K. The M values are the net M values of the MnO nanoparticles without FA-PAA (see text for details). The M-H curve at T = 100 K was slightly noisy owing to the slight temperature instability of the instrument.  
Figure 7: Cell viabilities of NCTC1469 and DU145 cells for the FA-PAA-coated MnO nanoparticles, exhibiting very low toxicities of up to 200 \(\upmu\)M Mn.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 1 Introduction  
Augmented Reality (AR) applications allow a user to interact with digital entities on top of a real environment [1], often linking real objects to digital information. This linking process requires recognizing the semantics of an environment, a task which proves to be difficult. One solution to this problem is localization in digital twins. If the exact location of the device in the environment is known, then a digital twin can provide semantics to the environment. Determining this location is called localization or pose estimation. While systems such as GPS are able to provide a georeferenced location in outdoor environments, they are prone to errors in indoor environments, making them unusable as localization for many applications. Thus, indoor localization can be seen as a separate problem from outdoor localization. In Section 2, we will discuss different existing approaches to global localization and their advantages and disadvantages.  
Especially the Architecture, Engineering, and Construction (AEC) industry could profit from using augmented reality in areas yet to be digitalized. One such area is maintenance, where augmented reality can already be used for contextual data visualization and interactive collaboration [2]. Worker efficiency could be increased by allowing direct access of an object's digital twin through augmented reality. Data could be visualized directly on top of the real world, and changes or notes could be sentback to a central service immediately. Direct data availability could also improve the construction process itself. Comparing the as-is state to the as-should-be state of a construction site would be easier and faster, which in turn would uncover mistakes earlier. Applications in these areas would have to rely on localization to attribute data to its real world counterpart.  
Global indoor localization requires digital information about the building a device is operated in, such as a digital twin. Digital building information is accumulated through a concept called Building Information Modeling (BIM) [3]. BIM encapsulates geometry, structure, and semantics of a building over the whole building lifecycle. Over the years, BIM has seen a rise in popularity in the construction industries, and thus, the availability and sophistication of digital twins has improved. A more widespread use of digital building models would allow for more applications to procure detailed data directly from available sources, allowing for a direct connection between buildings and users. Therefore, BIM can benefit from a reliable localization as much as localization can be aided by using BIM data.  
In this paper, we introduce a novel approach to localization in indoor spaces through the help of BIM. We aim for a computational, efficient 6 degrees of freedom (6-DOF) pose estimation algorithm that can accurately determine the position of an AR device in a known building. To achieve this, we use point clouds, BIM, and template matching.
## 3 Mathematical Model of the System
### Power Electronics Systems Controlling the Flow of Power
The following indicators are used further in this paper:  
* total annual time of power generation with a value lower than P\({}_{3\text{min}}\), covering only the periods lasting up to T\({}_{\text{max}}\) (according to the system operation algorithm, the generated power deficit is replenished by the energy from the storage) at the storage capacity A\({}_{\text{FESS}}\): \[T_{sumTmax}(A_{FESS})=\sum_{i=1}^{M}T_{Tmax(i)}\] (8) where: \(T_{Tmax(i)}\) is the duration of the \(i\)th interval which is shorter than or equal to \(T_{max}\) in which the power supplied to the grid \(P_{3}\) is lower than the assumed minimum power \(P_{3 min}\), and M is the number of intervals \(T_{Tmax}\) in which power deficit occurs,
* percentage coefficient of elimination of periods when the generated power value is lower than P\({}_{3\text{min}}\), considering only the periods which last up to T\({}_{\text{max}}\): \[\Delta T_{sumTmax\%}=\frac{T_{sumTmax}(0)-T_{sumTmax}(A_{FESS})}{T_{sumTmax}( 0)}\times 100\%\] (9) where: \(T_{sumTmax}(0)\) is time \(T_{sumTmax}\) identified for a system without storage, \(T_{sumTmax}(A_{FESSn})\) is the time \(T_{sumTmax}\) identified for a system with energy storage with a rated capacity \(A_{FESS}\) (the diagram explaining the difference between \(T_{max}\), \(T_{sumTmax}\) and \(T_{sumTmax}(0)\) was presented in Figure 5),
* power deficit accounting only for periods lasting up to T\({}_{\text{max}}\): \[\Delta A=\sum_{i=1}^{M}T_{Tmax(i)}(P_{3 min}-P_{3\text{arg}(i)})\] (10) where: \(P_{3\text{arg}(0)}\) is the mean power supplied to the grid from the wind farm--kinetic energy storage system in the \(i\)th interval shorter than or equal to \(T_{max}\), in which the power released to the grid \(P_{3}\) is lower than the assumed minimum power \(P_{3 min}\). Equation (8) identifies the total time composed of periods no longer than \(T_{max}\), in which the deficit of power generated by the system (\(P_{3}<P_{3 min}\)) was not completely covered by the energy in the storage. Efforts are made to minimise the indicator, while its value close to zero means complete elimination of power outages in the assumed maximum duration \(T_{max}\). High values of Equation (9) mean elimination of the majority of outages with duration up to \(T_{max}\). Efforts are then made to keep the value at 100%. Equation (10) characterises the system operation on the power side and helps to identify the power which was not supplied to the power grid in periods lasting up to \(T_{max}\). Efforts are made to keep the value of Equation (10) as low as possible, and ideally at 0.
# Optimization of Optical Trapping and Laser Interferometry in Biological Cells
## 3 Materials and Methods  
### Experimental Setup  
Details for the OT and LI setup incorporating the feedback tracking of a probe particle is given elsewhere [15]. Briefly, the experimental setup is shown schematically in Figure 2. The optical trapping of particles (melamine: microParticles GmbH, Berlin, Germany, silica and latex: Polysciences, Warrington, PA, USA) was made with a 1064 nm laser (Nd: YVO4, Compass, Coherent, Santa Clara, CA, USA). The diameter of particles used for all experiments in this study was \(2a=1\)\(\upmu\)m. Laser light is tightly focused on to a probe particle by a high NA objective lens (NA = 1.3, Neofluar, oil immersion, Zeiss, Oberkochen, Germany). The laser that passed through the sample was collimated with a condenser lens (NA = 1.0, water immersion, Nikon, Tokyo, Japan), and the back-focal plane of the condenser lens was then projected on the quadrant photodiode (QPD). With this arrangement, the particle position was detected with the back-focal-plane laser interferometry [6]. The origin of the coordinate (position of the laser focus) in the \(z\) direction was determined as follows. First, the focus of the microscope image and the focus of the laser were overlapped by observing the bottom surface of the sample and the laser reflection from it. After confirming that probe particles in purely viscous reference materials were trapped at the focus of the microscope under this condition, the microscope image of the probe particle in the measurement sample was then used to estimate the displacement \(z\). OT and LI were performed in cells while the piezo stage on which the sample was placed was controlled via feedback. For horizontal directions (\(x\), \(y\)), the QPD signal was read by the proportional-integral-derivative controller (PID controller) (SIM960; 100 kHz, Stanford Research Systems, Inc., Sunnyvale, CA, USA). The controller generated the signal which was sent to the piezo-mechanical stage so as to align the QPD signal with the target value (setpoint) (Figure 2). The QPD signal and the displacement signal of the piezo stage were recorded simultaneously, and the net displacement of the probe was then obtained by summing each signal after calibrating. For three-dimentional feedback control, the probe position in \(z\) direction was obtained by analyzing the microscope image of the particle. The analysis was made with a custom-made LabView program and the voltage signal to control the piezo stage was made with PCI-1408 analog I/O board (National Instruments, Austin, TX, USA). The detailed procedure of the analysis is given in Section 4.2.