esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation of Magnetically Driven Nickel Phosphide Nanowires and Their Electrochemical Properties
## 2 Experimental Procedure
### Evaluation of Electrochemical Properties of Nickel Phosphide Nanowire Electrodes  
The fabricated nickel phosphide nanowires were tested as electrodes for redox systems. Nickel foam (Invisible Inc., Gyeonggi-do, South Korea, 99.5%) was used as a substrate, and ultrasonic cleaning was performed in ethanol for 1 h to remove surface impurities. An electrode was prepared by applying a pressure of 1 MPa after placing the nickel nanowires on the nickel foam substrate. Here, the additives (e.g., binder or conductive material) were not used to confirm the unique characteristics of the synthesized materials.  
A three-electrode beaker cell was constructed to evaluate the electrochemical properties of the electrode and understand the intrinsic properties of the nanowires. The fabricated electrode was used as a working electrode. Platinum foil and a Hg/HgO electrode were selected as a counter and a reference electrode, respectively. A 2 M KOH aqueous solution was used as an electrolyte. To understand the basic characteristics of the electrode and estimate the amount of reaction charge, cyclic voltammetry was performed at a scan rate of 5 mV/s in the potential range of 0.2-0.6 V vs. Hg/HgO, and the rate characteristics of the reaction were estimated under various current densities ranging from 0.5 to 4 A/g. A constant cathodic/anodic current density of 3 A/g was applied 500 times to evaluate the degree of deterioration during the repetitive redox cycles.  
The electrochemical active surface area (ECSA) was determined through the \"capacitance method\" [23, 24]. For this purpose, cyclic voltammetry was performed on the prepared electrodes in a 0.1 M KOH electrolyte. At this time, the potential range corresponded to that of the open circuit potential (OCP) \(\pm\) 50 mV, and the scan rates were set to 5-400 mV/s. The ECSA per unit mass was determined according to the following equations:  
\[I_{\text{c}}=I_{\text{anodic (OCP-50 mV)}}-I_{\text{cathodic (OCP+50 mV)}} \tag{1}\]  
\[\text{ECSA}=\frac{C_{dl}}{c\cdot m}=\frac{1}{c\cdot m}\cdot\frac{\Delta I_{ \text{c}}}{\Delta v} \tag{2}\]  
where \(I_{\text{c}}\) is the charging current at a specific scan rate obtained through cyclic voltammetry; \(I_{\text{anodic(OCP-50 mV)}}\) and \(I_{\text{cathodic(OCP+50 mV)}}\) denote the current values at OCP \(-\) 50 mV and OCP + 50 mV, respectively; \(C_{dl}\) is the double-layer capacitance, determined through the slope (\(\Delta I_{c}/\Delta v\)) of the charging current vs. the scan rate plot; \(c\) denotes the specific charge density (40 uF/cm\({}^{2}\)[23, 24]); and \(m\) denotes the mass of nickel phosphide. All electrochemical experiments were performed using a Solartron 1470E potentiostat (AMETEK Scientific Instruments, West Sussex, UK).
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Data Analysis  
The data analysis section of this research utilizes diverse datasets to examine crime trends across Maryland, focusing on incidents and their clearance rates across multiple jurisdictions and time periods. By employing advanced machine learning techniques, such as random forests and clustering methods, alongside time series regression models, the study analyzes the demographic and geographic patterns of crime victimization using data from census and law enforcement sources. Clustering techniques identify patterns in victimization across different demographic factors, such as age, gender, race, and geographical distribution, offering critical insights into the dynamics of crime. The findings from this section illuminate the complex factors contributing to crime trends and their differential impact across various population segments, offering guidance for targeted interventions and informed policy decisions. However, potential biases, such as changes in crime reporting practices during this period, should be considered when interpreting the results. The approach and findings have the potential to inform similar crime analysis efforts in regions with diverse urban and rural contexts.
# Silicon Waveguide Optical Isolator with Directly Bonded Magneto-Optical Garnet
## 2 Device Structure and Operation Principle  
Figure 1 shows schematic of an MZI isolator with Si waveguides and Ce:YIG upper cladding layer. It has nonreciprocal phase shifters in the two arms and a phase bias in one of the arms. Since NPS caused a direction-dependent propagation constant, phase differences were nonreciprocal when external magnetic fields in anti-parallel directions were applied transversely to the light propagation along \(L_{\text{NPS}}\). In addition, the two arms were slightly asymmetric by the length \(L_{\text{RPS}}\), which provided reciprocal phase bias. Nonreciprocal and reciprocal phase differences are designed with the waveguide lengths as  
\[\theta_{\text{NPS}}=(\beta_{+}-\beta_{-})L_{\text{NPS}}=\mp\frac{\pi}{2}, \tag{1}\]  
\[\theta_{\text{RPS}}=\beta L_{\text{RPS}}=\frac{\pi}{2}+2m\pi, \tag{2}\]  
where \(\beta_{+}\) and \(\beta_{\pm}\) are slightly different propagation constants of two arms due to the NPS, \(\beta\) is the propagation constant at the phase bias, \(m\) is an integer determined by \(L_{\text{RPS}}\). The sign of \(\theta_{\text{NPS}}\) is minus and plus for forward and backward directions, respectively. In the forward direction, light launched to the input port was transmitted to the output port by a constructive interference, because the total phase difference between the two arms was in-phase. In the backward direction, light coming back to the output port was radiated to the side ports of the input side by a destructive interference, because the total phase difference was out-of-phase. In this way, an optical isolation was achieved. Forward and backward transmittance between the input and output ports is given by  
\[T_{\text{for,back}}=\cos^{2}\left(\frac{\theta_{\text{RPS}}\mp\theta_{\text{ NPS}}}{2}\right), \tag{3}\]  
where 3-dB couplers are assumed to work ideally.  
Si waveguides were fabricated on a silicon-on-insulator (SOI) wafer by an electron-beam lithography and reactive ion etching. In order to ensure sufficient area of top Si layer for bonding, a 2.5-\(\mu\)m-wide Si layer beside the waveguide was removed as a side cladding region. The single-crystalline Ce:YIG grown on substituted-GGG wafer was cut into a 1.5-mm square die and bonded on the Si waveguideby surface-activated bonding. The detailed process was described in [19]. Figure 2 shows a microscopic image of a fabricated MZI isolator. Though the whole MZI pattern was covered by the Ce:YIG die, an effective device footprint was about 500-\(\upmu\)m-wide square.
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 3 Analysis of the Control Strategy
### Performance Comparison of the PR and QPR Controller  
In the current control of the grid-connected inverter in a \(\alpha\)-\(\beta\) coordinate system, the PR controller can obtain higher gain at the fundamental wave of the power grid or a specific frequency and can eliminate the steady-state error of the grid-connected current or inhibit the influence of a specific subharmonic on the grid-connected current. The QPR controller has good robustness and adaptability to power grid frequency fluctuation. The following is a comparative analysis of these two controllers.  
The transfer function of the PR controller is:  
\[G_{PR}(\mathrm{s})=K_{1}+\frac{K_{2}\mathrm{s}}{s^{2}+\omega_{0} ^{2}} \tag{10}\]  
The transfer function of the QPR controller is:  
\[G_{QPR}(\mathrm{s})=K_{3}+\frac{K_{4}\omega_{i}\mathrm{s}}{s^{2} +2\omega_{i}\mathrm{s}+\omega_{0}^{2}} \tag{11}\]  
\(K_{1}\) and \(K_{2}\) in Equations (10) and (11) are the respective proportional coefficients and resonant coefficients of the PR controller; \(K_{3}\) and \(K_{4}\) are the respective proportional coefficient and quasi-resonance coefficient of the QPR controller; \(\omega_{0}=2\pi f_{0}\) is the fundamental angular frequency; \(\omega_{i}\) is the bandwidth of the resonance term considering the \(-3\) dB requirement, i.e., the gain of the resonance term is \(0.707K_{4}\) at \(\omega_{0}\pm\omega_{i}\).  
Figure 5 is the Bode diagrams of \(G_{\mathrm{PR}}(\mathrm{s})\) and \(G_{\mathrm{QPR}}(\mathrm{s})\). For the LCL three-phase grid-connected inverter, the gain of the PR controller can obtain infinite gain at fundamental frequency, thus realizing non-static error control of the fundamental component of the grid-connected current. However, due to load changes and various interferences, the actual power grid frequency will fluctuate. When the grid frequency deviates from the  
Figure 4: Bode diagram of \(T\)(s) with capacitor current proportional feedback.  
fundamental frequency \(f_{0}\) set by the PR controller, the gain of the PR controller will decrease rapidly, which will lead to the rapid increase in the steady-state error of the grid-connected current. The QPR controller adds parameter \(\omega_{1}\) to the PR controller, so that it can obtain higher gain than the PR controller in a wider frequency band and can adapt to the power grid frequency fluctuation in a certain range at the same time. However, the gain at the fundamental frequency \(f_{0}\) is much smaller than the gain of the PR controller, which indicates that the accuracy of the QPR controller is lower than that of the PR controller and will produce steady-state errors. Increasing the quasi-resonance coefficient \(K_{4}\) can improve the gain at the fundamental frequency of the QPR controller, but due to the limitation of system stability, the value of \(K_{4}\) cannot be too large.
# Development of a CALPHAD Thermodynamic Database for Pu-U-Fe-Ga Alloys
## 3 Literature Review
### The Plutonium-Uranium-Iron Phase Diagram  
The Pu-U-Fe system was experimentally investigated by [2; 3; 73; 74; 75; 76]. The first two papers consider a metallic fuel, either U-Pu-Zr [73] or U-Pu [74] with iron as a diffusion couple at 650 \({}^{\circ}\)C and 670 \({}^{\circ}\)C respectively. The experimental techniques include electropoke microanalysis, EDX and SEM. It was found that the higher Pu containing fuels lead to increased liquid formation, since the liquid phase field extends into the (Pu,U)\({}_{6}\)Fe domain from the low-melting Pu\({}_{6}\)Fe compound. The solubility limits were found to be 16 at. % Pu at 650 \({}^{\circ}\)C and estimated (based on models) to be 12 at. % Pu at 670 \({}^{\circ}\)C in the U\({}_{6}\)Fe compound. [PERSON] [75] and [PERSON] [2] prepared ternary Pu-U-Fe samples of varying composition by arc-melting, with [75] using DTA to elucidate ternary phase transitions from the prepared samples and [2] annealing the samples at 650 \({}^{\circ}\)C and characterizing them using XRD, SEM-EDS and chemical analysis. They were therefore able to establish a thermodynamic database derived from the information of these studies. This was later amended by [PERSON] et al. [3] to incorporate minor actinides within a CALPHAD database.
## 3 Results
### Generalization Test  
To statistically evaluate the differences in generalization performance for each axial level before and after GAN-based DA, we built one model (non-augmented) that was trained using only the training set and another model (augmented) that was trained using both the training and generated  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Label** & **Metric** & **TL(4 th)** & **FL(10 th)** & **PP1(16 th)** & **PP2(22 nd)** & **PL1(28 th)** & **PL2(34 th)** & **Avg(SD)** \\ \hline A\(\beta\) (\(-\)) & MMD & 0.0990 & 0.0983 & 0.0822 & 0.0911 & 0.0896 & **0.0813** & 0.0902(0.00) \\ t/o 1 & FID & 5.6293 & **5.4589** & 5.6456 & 5.7796 & 6.2149 & 6.4051 & 5.8556(0.37) \\  & 1-NN accuracy & 0.4565 & 0.4928 & **0.4130** & 0.4855 & 0.5507 & 0.4928 & 0.4819(0.05) \\  & MMD & 0.1239 & 0.1120 & 0.1104 & 0.1130 & **0.1036** & 0.1144 & 0.1129(0.01) \\ A\(\beta\) (\(+\)) & FID & 6.3801 & **6.2410** & 6.4176 & 6.6572 & 6.8022 & 7.5409 & 6.6732(0.47) \\  & 1-NN accuracy & **0.4203** & 0.4493 & 0.4928 & 0.4855 & 0.5435 & 0.4420 & 0.4722(0.04) \\  & MMD & 0.3779 & 0.3245 & 0.2849 & 0.2317 & **0.2284** & 0.3054 & 0.2921(0.06) \\  & FID & 9.4479 & 7.9763 & 7.9686 & **6.8253** & 7.2652 & 8.2666 & 7.9583(0.90) \\  & 1-NN accuracy & 0.9625 & 0.9125 & **0.8562** & 0.8687 & 0.8625 & 0.9375 & 0.9000(0.04) \\ A\(\beta\) (\(+\)) & MMD & 0.2860 & 0.2482 & 0.2123 & **0.1865** & 0.3289 & 0.2645 & 0.2544(0.05) \\  & FID & 7.4191 & 6.8910 & 6.7300 & **5.8919** & 7.6111 & 7.197 & 6.9566(0.61) \\  & 1-NN accuracy & 0.8261 & 0.7391 & 0.6522 & **0.6233** & 0.7536 & 0.8551 & 0.8418(0.07) \\ \hline \hline \end{tabular}  
* 1/o: Similarity between training and test set. 2 \({}^{\circ}\)/g: Similarity between test set and generated set. TL: Temporal lobe. FL: Frontal lobe. PP: Posterior cingulate/Precuneus. PL: Parietal Lobe. **Bold** represents the axial level with the highest similarity.  
\end{table}
Table 2: Comparison of synthetic similarity using quantitative measurements of amyloid negative and positive images.  
Figure 7: Quantitative measurements to estimate synthetic similarity between real and generated images with respect to each axial level of pre-processed FBB imaging.  
sets. The model was evaluated independently for each axial level class using the same test set. The Mann-Whitney U test was used because the given distribution was not found to exhibit normality.  
Figure 8 shows a comparison of the generalization performance of the target model based on ML with and without the data generated in the training set. Regardless of the augmentation, the classification performance of the target model tended to decrease at both ends. In both models, SVM and NN, GAN-based DA was performed independently at each axial level, resulting in a statistically significant improvement in generalization performance. Thus, DA was confirmed to work with stronger evidence in the NN-based model rather than the SVM-based model (median-SVM: 0.943 to 0.956, \(p<0.0454\); median-NN: 0.946 to 0.963, \(p<0.0047\)).
## 2 Comparative Evaluation of UCS and TCP Estimation Methods
### Overview of the Compared Methods  
The following methods were included in the comparative evaluation:  
Method A--robot's internal three-point UCS and internal TCP measurements. This baseline method utilizes the robot controller's native functionality for UCS definition via the so-called three-point method, in which the user manually defines the UCS using three spatially distinct points on a calibration fixture. The robot's internal TCP measurement routine is used to localize the tool frame. This method reflects standard industrial practice and serves as a reference for real-world usage accuracy [13].  
Method B--absolute orientation via [PERSON]'s closed-form solution. In this approach, the UCS is estimated using [PERSON]'s absolute orientation algorithm, which provides a closed-form least-squares solution for rigid transformation between two sets of corresponding 3D points. In this method, a set of measured points in the robot's base coordinate system is matched to the same points represented in the UCS. The robot's internal TCP measurement is used directly without modification. This provides a mathematically optimal UCS transformation under the assumption of noise-free point correspondences [14].  
Method C--UCS Estimation via geometrical measurement of the robot's world frame. This method involves manually measuring the geometric location and orientation of the robot's World Coordinate System (WCS) using physical reference of the robot's construction. The orientation and position of the WCS are then used to construct the UCS using a transformation matrix. The TCP remains as defined by the robot's internal measurement. This method is representative of a hybrid manual-geometric approach often used in industrial calibration scenarios. Geometrical estimation methods are also under constant development with multisensory studies [15] and terrestrial scanner systems [16].  
Method D--joint UCS and TCP estimation via the proposed algorithm. The novel method under evaluation estimates both the UCS and TCP parameters simultaneously. The method minimizes residual errors between observed robot poses and reconstructed transformations using an iterative estimation approach, inspired by the absolute orientation problem; however, it is extended to include both TCP offset and UCS transformation. This method is particularly advantageous in scenarios where neither the TCP nor the UCS is known a priori.
## 5 Formal Modeling and Analysis of SAPS
### User Un-Traceability  
In our scheme, each session-based communication contains parameters \(\{X_{A},\ P_{A},\ T_{1}\}\) based on \(X_{A}\) and \(P_{A}\) that are dynamic for each authentication/verification session of all participants and are different for each transmission. Moreover, a timestamp is appended to every session. The value of \((X_{A},\ P_{A})\), \(Z_{TS}\) and \(X_{B},P_{B}\), as shown in the proposed protocols to fulfill the requirements for common session key establishment, are executed in a distributed manner. Consequently, the adversary is unable to figure out that two procedures have the same users involved. It ensures that our proposal accomplishes user un-traceability. According to the pattern of the projected protocol, the participant generates a new random number \(r\in Z^{*}_{q}\) to compute \(X_{A}\), \(Z_{TS}\) and \(X_{B}\) in each session. Due to the randomness of the secret parameters, the adversary cannot find and link among messages sent by the end parties or the trusted server, and therefore is unable to follow their actions. Therefore, the proposed protocol is able to provide un-traceability during authentication, session key management, and the following data transmission procedures.
## 5 Fuels from Isoprenoid and Fatty Acid Pathways
### Bacterial Biofuels from Fatty Acids  
Engineering bacteria to produce FFA has received attention lately due to its minimal metabolic technology and process economy. A few common approaches have been applied to enhance the fatty acid production in _E. coli_. For instance, malonyl-CoA and acetyl-CoA levels (precursors of fatty acids) in _E. coli_ have been raised, and even the ACC enzyme is overexpressed to produce more FAs. Along with it, even the \(\beta\)-oxidation pathway was blocked by knocking out genes responsible for fadE and fadD enzymes. [PERSON] et al.  
Figure 4: Graphical representation of the fatty acid biosynthesis pathway for the production of biofuel and the \(\beta\)-oxidation pathway in _S. cerevisiae_. The native pathway is presented with black arrows and the heterologous pathway with blue arrows (adapted from [65]).  
Figure 5: An altered \(\beta\)-oxidation pathway in _E. coli_ for the production of methyl ketones (adapted from [65]).  
produced good amounts (1.2 g/L) of FFA in _E. coli_ by knocking out fadE and fadD genes, along with increased expression of thiolase ('tesA) [86]. In one study, depending on OptForce predictions, the fadD gene was knocked out and 'tesA along with fabZ were overexpressed in _E. coli_, which resulted in increased (1.7 g/L) FFA production [84]. For eliminating the metabolic pathway issues, a fed-batch process was designed with an engineered _E. coli_ strain. The strain overexpressed various genes associated with the fatty acid pathway (fabG, fabA, fabI, fabH, and ACC) and plant thioesterase enzymes (CnFatB2, BnFatA, and EgTE) along with the knockout of the fadD gene [87] (Table 1). While _E. coli_ is one of the most ideal hosts for producing FA-related products, enhancing their production beyond a certain limit in _E. coli_ is challenging due to the tough feedback inhibition, highly regulated transcription, and the post-transcriptional process [84]. Bacteria normally give out fatty acyl-ACP as an end product, and it has to be further hydrolys to give FFA [86]. To overcome these drawbacks, a eukaryotic system has to be employed, and yeasts would be an interesting choice.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 5 Validation and Discussion
### Fatigue Test
Therefore, the noticeable increase in rise-time (Figure 18a) at around the red dashed line could be from the plastic zone. It can be observed Figure 18b that the duration of the events did not show any trend. The duration of events during the initial yielding and final fracture was expected to be higher (since these events had a higher peak amplitude). However, no distinct increase in duration could be observed during these stages. The fixed window length settings (820 us) used for this test masked the performance of duration. Research on fatigue damages using AE are mostly conducted by analysing the cumulative trend of count and energy [46; 48; 50; 51]. Therefore, in order to further evaluate the performance of AE entropy, its cumulative trend was compared with the traditional features in Figure 19.  
It can be observed in Figure 19 that in the beginning of the test, there was a small increase, and at the end, there was a significant increase in the cumulative energy. The increase in the cumulative energy in the beginning is attributed to the yielding as a result of the onset of loading, whereas the significant increase in the cumulative energy at the end is due to fracture of the specimen. The plastic zone identified by DIC for this test was at 22,911 seconds. However, there was no significant change observed in the cumulative energy at that time. In fact, in the period between the yielding and fracture of the specimen, there was no noticeable increase in the cumulative energy. Highly energetic AE events towards the end may have masked the cumulative trend in energy between yielding and fracture. It is clear from Figure 19 that the cumulative entropy exhibits the same trend as the cumulative count for the entire duration of the test. At around 23,000 s, a significant jump could be noticed in both the cumulative features. This time corresponds to the plastic zone formation in Figure 17. Hence, it can be concluded that the noticeable jump in the cumulative AE entropy and count is an indication of the formation of the plastic zone. It is also evident from Figure 19 that there was a noticeable increase in the cumulative AE entropy and count after 20,000 s. However, DIC images in Figure 17 only shows a noticeable change in the strain map from 21,556 s onwards. Therefore, the noticeable increase in the cumulative AE entropy and count, from 20,000 s, could be a result of the growth and coalescence of micro-cracks, which is not reflected in DIC results.