esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 3 Stimuli Dependent Techniques
### Physical Stimuli-Based Self-Assembly of Nanomaterials
#### 3.2.3 Electric Field-Induced Self-Assembly
Most of the traditional optoelectronic tweezers utilize a uniform electric field and light to create a gradient force on photoconductive surfaces for the SA of NPs. These gradient forces on photoconductive surfaces allow for the positioning, trapping, and transport of particles for SA structures [113]. However, in a classical example, [PERSON] and co-workers demonstrated the SA of NPs through optoelectronic tweezers on a non-uniform electric field for effective trapping and SA nanostructures. A non-uniform electric field provides spatial variation in the electric field to enable the fine-tuning of particle movement [114]. This integrated optoelectronic system shows the capability to arrange NPs into linear chains and 2D lattice structures depending on the light pattern and configuration of the non-uniform electric field. On the other hand, combinatorial systems are widely used for the SA of complex structures. In a classical example, photovoltaic optoelectronic tweezers were used for the patterning of nanostructure ranges from clusters to grid-like structures. The patterning of NPs in photovoltaic optoelectronic tweezers was controlled by altering electric field parameters and illuminated light [115]. Electrophoresis is a technique where charged particles move towards and deposit on the oppositely charged electrode when the DC current is applied. The mobility and deposition of particles are mostly influenced by the size, molecular weight, and density of the particles [116]. Both of these techniques are mainly used in assembling biomolecules such as DNA, RNA, protein molecules, and cellulose. However, in electrostatic interactions, NPs that repel each other at the liquid-liquid interface due to their functionalized charged surfaces can assemble at two liquid interfaces under an applied potential. [PERSON] and co-workers demonstrated this process experimentally by assembling the gold NPs at the water-1,2-dichloro interface by applying potential; 1,2-mercaptoethanoldodecanoic acid stabilized gold NPs were dispersed in immiscible solvents. When a negative potential was applied (\(-\)0.2 V), the particle movement was noticed, and those gold NPs were self-assembled at the liquid-liquid interface. However, the reversibility of the self-assembled gold NPs was obtained by applying a positive potential of (+0.2 V) [117]. Thus, the electric field-induced SA of NPs can be controlled precisely, and it is reversible in nature.
## 5 Results and Discussion
### Validation of the Methodology on a 136-Bus Microgrid
BESS C exhibits intermediate behavior with regular full recharge cycles but avoids extreme depletion, except for the period between hours 57 and 69. On the other hand, for the objective function focused on minimizing emissions, Figure 22 illustrates different SoC dynamics. BESS A displays a highly fluctuating SoC pattern, with frequent charging and discharging cycles, indicating a reactive strategy to counter emission peaks. BESS B maintains prolonged discharge intervals, especially between hours 0-16 and 52-117, likely aimed at supporting the system during high-demand or high-emission periods. In contrast, BESS C shows smoother transitions with a significant charging period between hours 57 and 72, possibly aligning with lower-emission windows. Figure 21: SoC profile as the solution achieved by the PMVO for a representative week of operation for a 136-node system in the grid-dependent mode. Figure 22: SoC profile as the solution achieved by the PMVO for a representative week of operation for a 136-node system in the self-powered mode. This behavior suggests that BESS C pursues a more balanced trade-off between operational stability and emission reduction. Notably, the weekly operation shows consistent alignment with renewable generation patterns, strategic depth-of-discharge allocation based on unit function, and adaptive responses to changing grid conditions while maintaining safe operating limits. This configuration demonstrates an effective multi-objective optimization strategy that simultaneously addresses grid stability, renewable integration, and battery longevity requirements. These results further support the scalability of the proposed technique, demonstrating its applicability to larger networks without compromising performance. Moreover, the method has shown strong adaptability to scenarios with high variability in both renewable generation and demand, making it a robust tool for the optimal operation of microgrids under dynamic and realistic conditions. This operational flexibility is particularly valuable in modern systems, where uncertainty and variability play a central role in decision-making.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 3 Proposed Approach
### Data and Variables  
For DEA to obtain the PSEI of each industry, we set the inputs and outputs. In general, safety performance is the result of the investment in safety measures and productivity is the total result. Thus, as inputs, we use the injury rate (IR), frequency rate (FR), and severity rate (SR), and as outputs, the labor productivity (LP), production capacity (PC), operation ratio (OR) are applied to satisfy the condition of definition. From the KOSIS and Korea Occupational Safety and Health Agency (KOSHA), three productivity indexes and safety performance indexes, respectively, of 15 manufacturing industries are gathered from 2015 to 2018 as summarized in Table 3. The mean value of each index, from 2015 to 2018, is shown in Table 3. Because the recent data are biased due to COVID-19, we use stable index data on the basis of 2015 for addressing a consistent national economy.  
Figure 2: Research gap and purpose.  
It is difficult to decide whether an industry shows good productivity with good safety performance because the rank of multiple variables is inconsistent. Some industries have low level of safety performance but indicate high level of productivity. To analyze these differences in performance among the multiple variables, the DEA can extract the integrated measures, considering three safety performance indexes and productivity indexes.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 4 Simulations
### Case 1  
This case considered the stationary disturbance. The step size of the FXLMS was 0.000005. The frequency of the disturbance signal was set to 120 Hz, and the phase error was in turn set to \(\phi=0,\ \pi/4,\ \pi/2,\ \pi\). The MSE of the pseudo-error under different phase errors is presented in Figure 3. It can be seen that the convergence time constant increased when the phase error changed from zero to \(\pi\). However, the MSE performances of the pseudo-error at the steady state were nearly identical to each other. In addition, the estimation results when the command and disturbance signals were out of phase are shown simultaneously. It can be seen that the ratio of Equation (46) provided an excellent estimation of the performance when the command signal was out of phase with the disturbance signal, both at the convergence stage and the steady state.  
As shown in Figure 4, at the convergence stage, the difference \(\Delta\xi(n,\phi)\) under different phase errors decreased when the phase error increased from zero to \(\pi\), which implied that the phase error increased the convergence rate, as given in Equation (43). However, as shown in Figures 3 and 4, the MSE difference under different phase errors \([\xi(n,\phi)-\xi(n,0)]\) was large, and this increase in the convergence rate was insufficient to counteract the considerable disparity, due to which the phase error led to \(\mathbf{w}^{o}(n,\phi)\) being far from \(\mathbf{w}(0,\phi)\). The adaptation of \(\left\|\mathbf{w}^{o}(n,\phi)\right\|_{2}\) under different phase errors \([\mathbf{w}(0,\phi)=0]\) is presented in Figure 5, where the norm \(\left\|\mathbf{w}^{o}(n,\phi)\right\|_{2}\) increased when the phase error changed from zero to \(\pi\). The convergence time constant result of \(\mathbf{w}(n,\phi)\), which represented a change from \(\mathbf{w}(0,\phi)\) to \(\mathbf{w}^{o}(n,\phi)\), was in accordance with the performance presented by the MSE of the pseudo-error.  
Figure 3: The MSE result of the pseudo-error for different iteration numbers under different phase errors.
## 4 Light Attenuation
### Shading/Blocking  
Shading occurs when radiation coming from the sun that should illuminate the heliostat mirror is obstructed before by some other heliostat. Blocking occurs when radiation reflected by the heliostat mirror is obstructed by some other heliostat before reaching the target (see Figure 5). These effects (see for example [7; 8]) may occur when the heliostats are too close or when the target is too low. The sun being at low altitude (in winter and at dawn and dusk) contributes significantly to these effects. Clearly, they should not play a major role in well-designed heliostat fields collecting thermal energy.  
Fortunately, shading and blocking can be easily simulated in an elegant, robust way in ray-tracing software. For each generated reflection point in the heliostat surface, there is an input wave vector coming from the sun and an output wave vector pointing to the target. If the line defined by the input wave vector intercepts any other heliostat, a shading event occurs, and this ray is lost. If the line defined by the output wave vector intercepts any other heliostat, a blocking event occurs, and this ray is lost. To find out whether a ray intercepts some other heliostat:  
1. Use the four vertices defining the rectangle polygon of each neighbor heliostat to calculate its center and normal vector (see Figure 6). The neighbor heliostats can be in the rest position, sun-orientated, or in any other arbitrary orientation defined by the rotating angles \(\alpha\) and \(\beta\), with varying results.
2. Determine the ray interception with the neighbor heliostat plane (see [19; 20] for details).  
Check whether the interception point is inside or outside the neighbor heliostat polygon (see Figure 7). When the point is inside, the cross product of successive vectors from the polygon center to the polygon vertices always points to the same polygon side, so the dot product of these cross vectors is always positive. When the point is outside, some of the cross products will produce vectors in opposite directions, so at least one of the dot products of these cross vectors will be negative (our group has used this technique extensively in the past to study the behavior of homogenizers in high-flux solar furnaces [21]).  
Figure 5: Radiation accumulated at the target of a 3 \(\times\) 2 heliostat field (simulated with 10\({}^{8}\) rays), showing heliostat blocking: the three back-row heliostats (pointing above) are partly blocked by the three front-row heliostats (pointing below). In this example, the target is 6 m higher than the 2 m \(\times\) 2 m heliostats, which are 2 m apart.  
Figure 6: Adding the cross products between successive edge vectors is an effective way to calculate the normal vector of a convex, flat polygon.
# Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence
###### Abstract  
The purpose of anomaly detection is to identify special data points or patterns that significantly deviate from the expected or typical behavior of the majority of the data, and it has a wide range of applications across various domains. Most existing statistical and machine learning-based anomaly detection algorithms face challenges when applied to high-dimensional data. For instance, the unconstrained least-squares importance fitting (uLSIF) method, a state-of-the-art anomaly detection approach, encounters the unboundedness problem under certain conditions. In this study, we propose a scaled Bregman divergence-based anomaly detection algorithm using both least absolute deviation and least-squares loss for parameter learning. This new algorithm effectively addresses the unboundedness problem, making it particularly suitable for high-dimensional data. The proposed technique was evaluated on both synthetic and real-world high-dimensional time series datasets, demonstrating its effectiveness in detecting anomalies. Its performance was also compared to other density ratio estimation-based anomaly detection methods.  
a _Article_
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 2 Silicon Photonics for High-Speed Data Communications
### Silicon Photonics
Samsung has also demonstrated the feasibility of a multi-drop bus topology based on silicon photonics. Figure 12 illustrates an optical link with the hybrid integration of the EIC and the PIC, which shall be developed into a fully monolithic EPIC in the future. The implemented optical link consists of two sets of TXs and RXs processing 8 DQ signals, and simply emulating a read or a write operation of the controller-memory communication. The link successfully demonstrates an error-free operation at the data rate of up to 2.5 Gb/s per fiber.
## 4 Discussion  
A comparison between the proposed bandpass FSS filter and other S-C band solutions found in the literature is shown in Table 3.  
The comparisons confirmed a better performance of the designed FSS in terms of thickness and angular stability with respect to the state-of-the-art, with competitive advantages concerning the fractional bandwidth (FBW) and the overall low profile. It is worth underlining that the proposed structure had the lowest thickness. When the herein-discussed FSS filter was compared with the ones in [29; 30] that exhibited similar profiles, a significant improvement in the FBW could be observed.  
Figure 6: (**a**) Fabricated prototype of the bandpass FSS filter. (**b**) Photo of the measurement setup in the anechoic chamber.  
Although the FSS in [31] showed a broader bandwidth, this was achieved by considering a hybrid 2.5D-3D unit cell, which significantly added complexity to the implementation and fabrication cost of the FSS. Only the FSSs proposed in [28, 29, 32] offered superior angular stability up to 60\({}^{\circ}\), but with significantly reduced bandwidth.  
As can be further deduced from Table 3, the other proposed works exhibited a smaller unit cell size. However, they employed multiple layers or multidimensional FSSs, thus enlarging the overall thickness of the unit cell.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 4 Experiment Results
### Recognition Accuracy of Two Classifiers
#### 4.1.2 Recognition Accuracy under Unconventional Feature Spaces  
The recognition rates of the two classifiers under different unconventional feature spaces are listed in Table 3. Down-sample\({}^{1}\), Down-sample\({}^{2}\) and Down-sample\({}^{3}\) stand for reducing the dimensions of the cropped image by down-sample to sizes of 100, 400 and 1600, respectively. PCA\({}^{1}\), PCA\({}^{2}\) and PCA\({}^{4}\) represent that the dimensions of the cropped image are reduced by PCA to sizes of 25, 100 and 400. Additionally, PCA\({}^{3}\) refers to reducing the dimensions to the extent where the accumulative contribution rate approximately equals 80%, because the empirical value guarantees the reduced  
Figure 4: The recognition accuracy of the two classifiers under different feature spaces; (**a**) and (**b**) refer to the recognition accuracy achieved by SRC and SVM, respectively. DM\({}^{1}\), DM\({}^{2}\) and DM\({}^{3}\) stand for the dimensions of feature spaces adopted here that have sizes of 16, 36 and 81, respectively.  
dimensions will cover all the primary information. In this experiment, PCA\({}^{3}\) stands for reducing the dimensions to a size of 125. From Table 3, SRC achieves the highest recognition rates in both feature spaces. The recognition rates of SRC are 3.03%, 4.18% and 4.46% better than SVM in Down-sample\({}^{2}\), PCA\({}^{3}\) and PCA\({}^{2}\), respectively. Figure 5 is plotted here to study the tendency of the recognition accuracy.  
Figure 5 shows the recognition rates under different unconventional feature spaces. Figure 5a,b are two forms of the recognition rates. Figure 5a is a column diagram, and Figure 5b is a line chart. The two feature spaces are divided in Figure 5b. Meanwhile, different colors are adopted here to represent different classifiers. Obviously, SRC is indeed superior to SVM in Down-sample\({}^{2}\), PCA\({}^{3}\) and PCA\({}^{2}\). However, with increments of decreases of the dimensions, the performances of both classifiers tend to descend. The reason is that speckle noise will be added into the data with increasing dimensions [10]. Simultaneously, when the dimensions of the data decrease, the data is too small to cover all the primary information. The performance of SRC descends a little faster than SVM. Additionally, the performances of both classifiers in the down-sampling spaces are better than that in the spaces of PCA. That is to say, down-sample contains more effective information than PCA in this experiment.
###### Abstract  
Comparing electroanalysis and chromatography, this study highlights that electroanalysis, specifically using a glassy carbon sensor (GCS), is the most appropriate choice for quantifying recalcitrant organic compounds. Octoberle (OC), an organic compound commonly found in sunscreens, is of particular concern in swimming pool water monitoring, as its presence above legal limits poses health risks. OC quantification was performed using both high performance liquid chromatography (HPLC) and electroanalysis in sunscreen formulations and water matrices. The limits of detection (LODs) and quantification (LOQ) for OC were approximately 0.11 \(\pm\) 0.01 mg L\({}^{-1}\) and 0.86 \(\pm\) 0.04 mg L\({}^{-1}\) by electroanalysis, and 0.35 \(\pm\) 0.02 mg L\({}^{-1}\) and 2.86 \(\pm\) 0.12 mg L\({}^{-1}\) by HPLC. Electroanalysis successfully quantified OC in real sunscreen samples, and the results were comparable to those obtained by HPLC. The matrices tested--swimming pool water and distilled water (containing 0.002 M Cl\({}^{-}\)) contaminated with 0.4 \(\pm\) 0.2 g L\({}^{-1}\) of sunscreen (based on a maximum concentration in sunscreen and cosmetic formulations of 10%)--showed OC concentrations below 10% in the formulation, with no significant differences observed between the two techniques. GCS was further utilized to monitor OC degradation via anodic oxidation at current densities of 5 and 10 mA cm\({}^{-2}\), using a boron-doped diamond (BDD) anode. The combined approach demonstrated high efficacy in both detecting and eliminating OC from various water matrices, making it a reliable and efficient alternative for environmental and water quality monitoring.  
octorylene; electroanalysis; glassy carbon; boron-doped diamond; sunscreens; HPLC
## 2 Methodology
### Mechanical Property and Thermophysical Property Evolution  
The deformation and failure process of the element obeys the constitutive relationship of elastic damage mechanics. When the stress state of the element meets the specific failure criterion, the element is damaged. We introduce a damage variable \(D\) to describe the cumulative damage expression of the elastic modulus:  
\[E=(1-D)E_{0} \tag{8}\]  
where \(E\) is the elastic modulus value after damage, \(E_{0}\) is the initial value of elastic modulus, and \(D\) is the damage variable.  
We adopt the Mohr-Coulomb strength theory with the tensile criterion as the criterion of elementary failure. The characteristics of rocks in tension are non-linear and additionally, rocks also experience plastic deformations in tension [40]. However, the non-linear problem in macro-scale can be changed into linear problem in meso-scale, by considering the heterogeneity of rock in meso-scale [33]. When the maximum tensile principal strain of the element reaches its given strain threshold, it begins to undergo tensile damage (Figure 4). The damage variable \(D\) is transformed into:  
\[D=\left\{\begin{array}{ccc}0&&\varepsilon<\varepsilon_{t_{0}}\\ 1-\frac{\sigma_{tr}}{\varepsilon E_{0}}&&\varepsilon_{t_{0}}\leq\varepsilon \leq\varepsilon_{t_{u}}\\ 1&&\varepsilon>\varepsilon_{t_{u}}\end{array}\right. \tag{9}\]  
where \(\sigma_{tr}\) is the residual strength after the tensile damage, \(\varepsilon_{t_{0}}\) is the tensile strain corresponding to the ultimate elastic state of the element, and \(\varepsilon_{t_{u}}\) is the ultimate tensile strain of the element. \(D=0\), \(0<D<1\), and \(D=1\) corresponds to the non-damaged, incomplete damaged, and completely damaged, respectively.  
Figure 3: Weibull distribution with different \(m\) index.  
Meanwhile, the thermal conductivity and heat capacity of the element varies with the temperature and damage variable \(D\) when the element exceeds the damage threshold, according to the following equation [41]:  
\[\lambda_{T}=\left\{\begin{array}{cc}1+\frac{\lambda_{0}}{0.99+T(a-b/\lambda_{0} )}&(0<D<1)\\ \xi_{1}\lambda_{0}&(D=1)\end{array}\right. \tag{10}\]  
\[c_{T}=\left\{\begin{array}{cc}c_{0}(1+\varpi T)&(0<D<1)\\ \xi_{2}c_{0},&(D=1)\end{array}\right. \tag{11}\]  
where \(\lambda_{T}\) is the thermal conductivity, \(c_{T}\) is the heat capacity at \(T\), \(\lambda_{0}\) and \(c_{0}\) are the thermal conductivity and heat capacity at \(0\)\({}^{\circ}\)C, \(a\) and \(b\) are empirical parameters [41], and \(\varpi\) is the empirical coefficient that is \(3\times 10^{-3}\)\({}^{\circ}\)C\({}^{-1}\) in general. \(\xi_{1}\) and \(\xi_{2}\) are the damage effect coefficient of thermal conductivity and heat capacity, respectively, equal to the ratio of the thermal conductivity and heat capacity of the element to the air.