esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Underwater Image Processing Based on Polarization Imaging and Scattering
The asymmetry of the returned beam was observed by using different propagation depths, transverse coherence lengths, and propagation angles of the beam. This asymmetric model was converted into a surface inclination angle and different surface gradients were obtained by collecting two-beam scans to reconstruct the basic surface contours of the target. The experimental results showed that the asymmetric method is capable of target detection and contour analysis using the differential parts of the backscattered signal when the backscattered light interference is strong. This method is also compatible with continuous-wave and pulsed lasers, which can provide a low-cost platform using a continuous wave laser or can be combined with the time-of-flight concept, using a pulsed laser to enhance the effect. Compared with the traditional visual limit of high-scattering underwater environments, this method can extend the detection range of these features by three to five times under certain turbidity conditions.
## 3 Results
### Cs+ Adsorption Efficiency and Magnetized Zeolite Recovery Efficiency
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multirow{2}{*}{**Size**} & **Saturated** & **Residual** & & \\  & & **Magnetization** & **Magnetization** & **Rectangle Ratio** & **Coercive Force** \\  & & **M\({}_{\text{s}}\) (A\(\cdot\)m\({}^{2}\)/kg)** & **M\({}_{\text{r}}\) (A\(\cdot\)m\({}^{2}\)/kg)** & & **(H\({}_{\text{c}}\)/G)** \\ \hline Magnetized zeolite A & 1–3 mm & 5.8257 & 0.40636 & 0.07 & 72.115 \\ Magnetized zeolite B & 60–80 mesh & 10.722 & 0.68184 & 0.064 & 63.611 \\ Magnetized zeolite C & 120 mesh & 12.055 & 0.82313 & 0.068 & 71.612 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: VSM result of the magnetized zeolites. The performance of the magnetized zeolite studied herein was further compared to the previous literature research [36; 37; 38; 39; 40; 41; 42; 43; 44], as shown in Figure 11c and Table S3. Compared to the literature research, the Cs removal efficiency is significantly higher (99.92%) for the study herein, while the electrokinetic method can achieve a Cs removal rate of 97.6%, which ranks second in table. Although the electrokinetic washing method can reach as high Cs removal efficiency as the magnetized zeolite, the application of electrokinetic washing might pose other environmental concerns, which limits its application. Although the treatment conditions might vary depending on different scenarios, which makes it relatively difficult to make an accurate comparison, it is suggested that the magnetized zeolite studied herein demonstrates certain advantages for Cs-contaminated soil treatment. Overall, the magnetized zeolite studied herein enjoys advantages, including high efficiency, easy recovery, and low cost, which is of great significance for emergency radioactive soil remediation. However, the application of current adsorption and desorption methods based on magnetized zeolite is still limited by its Cs adsorption capacity, especially in an acid/base or high-salinity environment, which is closer to the real industrial scenario.
## 3 Problem Statement and Schedule-Based Network
### Schedule-Based Network Representation
#### 3.2.5 Arrive Arc  
\(a\left(va_{s}^{l},ve_{s}\right)\) denotes the arrival arc from arrival node \(va_{s}^{l}\) to virtual destination node \(ve_{s}\). The virtual temporal node \(ve_{s}\) represents the destination of the travel path. The virtual arrival arcs are only used to ensure the integrity of the travel path, and there is no time cost when calculating the travel path cost. In other words, the travel time of the arrival arc is 0.  
In the schedule-based network, paths of spatiotemporal O-D pairs are a sequence of arcs. For example, one path of spatiotemporal O-D pair \(\left(vo_{s}^{2},ve_{7}\right)\) can be expressed as \(\left(vo_{3}^{2},vd_{3}^{2}\right)\rightarrow\left(vd_{3}^{2},va_{5}^{2} \right)\rightarrow\left(va_{5}^{2},vd_{5}^{2}\right)\rightarrow\left(vd_{5}^{2 },va_{6}^{2}\right)\rightarrow\left(va_{6}^{2},vd_{6}^{1}\right)\rightarrow \left(vd_{6}^{1},va_{1}^{1}\right)\rightarrow\left(va_{7}^{1},ve_{7}\right)\) based on the schedule-based network shown in Figure 3. It corresponds to the trip that passengers take train No. 2 from station No. 3, then transfer to train No. 1 at station No. 6 and take train No. 1 arrive the terminal station No. 7.
## 2 Theory and Method
### Time Series Forecasting Model Based on ARIMA-LSTM
#### 2.2.2 LSTM Model  
ARIMA(p,d,q) model can well deal with the linear part of the chemical composition content in the time series, but it has certain limitations because the obtained residual series results have nonlinear characteristics, and the process of the content of some chemical components changing with the degree of weathering is a nonlinear process. This requires a deep learning model to solve the nonlinear trend of chemical composition changes [39]. The LSTM (Long Short-Term Memory) model is a deep learning model that is very good at solving nonlinear data. Its nonlinear gate unit can adjust the information flowing into and out of memory tuples at each time point so as to better fit the trend of nonlinear data changing over time.  
LSTM is a special type of recurrent neural network (RNN) that performs very well with long sequences of data, mainly solving gradient disappearance, gradient explosion, and overfitting problems when training long sequences [40; 41; 42; 43]. RNN is an artificial neural network that operates on time-series data and can use back-propagation algorithms to learn and adapt to the relationship between inputs and outputs. In contrast to standard RNN, LSTM has an input gate, a forgetting gate, and an output gate that control the way information flows through the network. These gates of the LSTM allow it to store past information and update the current state appropriately, thus providing a significant advantage when dealing with long sequences of data. The basic structure is shown in Figure 1[44; 45].  
The basic unit of the LSTM network contains an oblivion gate, an input gate, and an output gate. The oblivion gate determines the oblivion part of the state storage unit by combining the input \(x_{i}\) with the state storage unit \(C_{i-1}\) and the intermediate output \(h_{i-1}\), while the input gate transforms \(x_{i}\) by means of the \(\Sigma\) and tanh functions. The associated intermediate output \(h_{i}\) is determined by the updated \(C_{i}\) and the output \(B_{i}\)[32]. The calculation formulas are shown in (22) to (27):  
\[f_{i}=\sigma\Big{(}W_{f}\cdot[h_{i-1},x_{i}]+b_{f}\Big{)} \tag{22}\]  
\[e_{i}=\sigma(W_{e}\cdot[h_{i-1},x_{i}]+b_{e}) \tag{23}\]  
\[\widetilde{C}_{i}=\tan\mathrm{h}(W_{e}\cdot[h_{i-1},x_{i}]+b_{c}) \tag{24}\]  
\[C_{i}=f_{i}*C_{i-1}+e_{i}*C_{i} \tag{25}\]  
\[B_{i}=\sigma(W_{B}\cdot[h_{i-1},x_{i}]+b_{B}) \tag{26}\]  
\[h_{i}=B_{i}*\tan\mathrm{h}(C_{i}) \tag{27}\]  
\(f_{i}\), \(e_{i}\), \(\widetilde{C}_{i}\), \(C_{i}\), and \(B\) are the forgetting gate, input gate, new candidate vector, updated cell state, and output gate, respectively, \(W_{f}\) and \(b_{f}\) are the corresponding weight coefficient matrix and bias term, \(tanh\), and \(\sigma\) represent the hyperbolic tangent activation function and S-shaped activation function [45]:  
\[\tanh\mathrm{h}(x)=\frac{1-\exp(-2x)}{1+\exp(-2x)} \tag{28}\]  
\[\sigma(x)=\frac{1}{1+\exp(-x)} \tag{29}\]
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 2 Categorization of Bridge Boost Converter Topologies
### Conventional Interleaved BBC  
An interleaved BBC converter has several boost converters connected between the rectifier bridge and DC link capacitor for providing current levels, which improves power quality; consequently, ripple current in input is reduced. In addition, the levels make it possible to reduce the size of inductors, diodes, and circuit switches since thermal stress is reduced. An interleaved BBC converter is recommended for regulating the DC voltage in medium voltage applications since it provides voltage levels in the DC link bus; however, it is not recommended for high voltage applications since power losses and EMI issues are high due to the rectifier bridge. In medium voltage, a robust filter is necessary (EMI filter with several stages) between the AC power supply and the converter; in addition, the use of resonant circuits is recommended for each level for providing soft switching which reduce losses [32; 76; 77; 78]. Figure 3 corresponds to the interleaved BBC converter that is composed of two stages which is commonly implemented in industrial applications [10; 28; 79; 80]. In [28; 81; 82], and it presented a methodology for the design of inductors, capacitors, diodes, and power switches. The authors also included the design of the EMI filter. Simulation and experimental results presented an efficiency of around 97% and a power factor up to 0.995.  
Figure 3: Interleaved PFC topology.  
Figure 2: Conventional PFC topology with pseudo-continuous conduction.  
The conventional Interleaved BBC with two states can be modified as it was presented in [7]. The authors proposed to move switches and inductors before the rectifier bridge. Simulation results presented \(THD_{i}\) into a normative range with classical PI control for applications in low voltage and power. In addition, operation in DCM and CCM was considered, obtaining an efficiency greater than 96%.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Precision
#### 3.1.5 Summary  
In summary, the particular wavelength of measurement really made no difference in repeatability for the ASD-based systems. In general ASD-based systems can measure brighter targets with a precision of 0.01-0.02 and darker targets at a precision of 0.001-0.002. UAS-based systems using the Headwall sensors perform very comparable to ASD-based systems for bright targets. However, for dark targets, UAS precision was closer 0.005 reflectance units. Large, pristine panels, such as the Labsphere Permaflect targets, exhibited better precision than the other small targets.  
Due to the stable atmosphere during the morning and noon collection times on both days, little difference could be seen between the EROS single and double ASD approach.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 4 Discussion
### Future Work  
Work of this paper focus on uniform metadata storage models design, spatial-temporal retrieval performance optimization and GDAL compatible I/O interfaces development. However, some parts of RSIMS still needs to be improved further. The process of different remote sensing image metadata integration is performed manually based on personal knowledge and experience, which can't ensure that the metadata with different structures can be correctly imported into RSIMeta. To solve the problem, we would conduct more research on the metadata formats and standards of different remote sensing images, and try to make the integration process more automatic. Considering that read operations of remote sensing applications are more frequent than write operations, so we perform more optimizations on data access than data write. In subsequent work, we would optimize write and update operations for better performance. Besides that, I/O interfaces would be extended further to provide better support for remote sensing applications, such as reading data by geospatial regions, exporting raster files to images, etc. Combining with popular distributed computing systems (e.g., Spark, Hadoop) to achieve better process and analysis efficiency is another important research direction.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation
### Evaluation Matrix  
This evaluation aims to demonstrate the OOD (opportunistic and on-demand) and OODC (opportunistic and on-demand network coding) capabilities for increasing the LPWAN coverage and reliability while preserving the network capacity. We compare our work with FP (fixed-path) forwarding that represent the majority of relay forwarding protocol used by other related work. For each simulation, we increase the number of end-devices to evaluate scalability performance. We used PDR to evaluate performance with regard to extending LoRaWAN coverage and scalability. The PDR defines the ratio of successfully received packets in the network server to the transmitted packets from end-devices over a period. Conversely, the number of duplicated packets in the network server shows unnecessary forwarding from the mechanism. By Evaluating the effect of unnecessary forwarding to reliability performance, we can show the trade-off between network reliability and efficiency.  
For these purposes, we present two experiments with different network topologies. First, we focus on the performance of OOD, OODC, and FP in increasing LoRaWAN coverage via network topology 1 in Figure 6a. In this topology, there are two relays close to the border that can communicate with each other. The end-devices are placed outside gateway coverage according to the \(P_{xx}\) estimation. Conversely, the second experiment is used to show the benefit of the relay to make efficient use of network resources via ADR mechanism. Thus, it can increase network scalability. The network topology 2 in Figure 6b consists of four relays and several end-devices inside the gateway coverage. The coverage is based on SDR settings to make the devices capable of choosing between SF 7 and SF 12 in ADR scenario. While the gateway coverage is approximately 8900 m, the relay distance to the gateway is approximately 4000 m.
# Green Analytical Chemistry--Recent Innovations
## 5 Application of Green Materials
### Green Chromatographic Columns  
Traditional chromatographic columns rely on silica-based stationary phases, which often require environmentally hazardous modification processes. Recent advancements have led to the development of hybrid organic-inorganic phases, bio-based silica alternatives, and polymer-based stationary phases, which reduce energy-intensive synthesis steps while maintaining high separation efficiency. Additionally, water-compatible stationary phases have facilitated the transition toward aqueous-based mobile phases, further supporting green chromatography principles.  
[PERSON] et al. [88] demonstrated the potential of limonene, a bio-based pore expander, as a sustainable alternative to fossil-based trimethylbenzene (TMB) for micellar templated mesoporous silica synthesis. Their study found that limonene effectively expands silica pores while reducing environmental impact, as confirmed by a comparative life cycle assessment (LCA). This innovation aligns with green chromatography principles, offering a renewable, waste-derived alternative for eco-friendly column material production. By adopting bio-based silica synthesis approaches, chromatographic methods can move towards more sustainable practices, reducing reliance on fossil-based reagents while ensuring optimal separation performance.  
Polymeric stationary phases have emerged as sustainable alternatives in green chromatography due to their high thermal stability and solvent-free compatibility. [PERSON] and [PERSON] [89] reviewed polystyrene divinylbenzene (PS-DVB) and PRP-1 crosslinked polymer phases, commonly used in superheated water chromatography (SHWC), demonstrating their ability to withstand prolonged operation at temperatures above 200 \({}^{\circ}\)C. Additionally, novel amino acid-modified polymeric phases exhibit stability in heated water conditions,extending column lifespan and reducing solvent dependency. While polymeric phases offer enhanced durability, they may exhibit lower efficiency compared to silica-based phases, requiring optimization for specific applications. Their chemical robustness and compatibility with water-based mobile phases, however, make them an essential advancement in green chromatography, promoting reduced solvent consumption and improved sustainability.
## 2 Materials and Methods
### Multivariate Polynomial Regression  
A polynomial regression can be used to fit a nonlinear relationship between independent variables and a dependent variable. The equation for a \(k\)th-order polynomial regression (with \(k\) greater than 1) is represented by the coefficients \(\omega_{1}\), \(\omega_{2}\),... \(\omega_{k}\), the intercept \(\omega_{0}\), the input variable \(x\), and the outcome variable \(\hat{y}\)[36; 37].  
\[\hat{y}=\omega_{0}+\omega_{1}x+\omega_{2}x^{2}+\omega_{3}x^{3}+\cdots+\omega_{ k}x^{k} \tag{1}\]  
When multiple variables are used in a polynomial regression, it is referred to as Multivariate Polynomial Regression (MPR). For a system with lease, please find attached my revision of the article, which includes proposals to improve the text, some suggestions,  
Figure 1: Research methodology.  
and also some comments for you to analyze input variables, the \(k\)th-order multivariate polynomial regression can be written as [37,38]:  
\[\begin{array}{c}\hat{y}=\omega_{0}+\sum\limits_{l_{1}=1}^{m}\omega_{l_{1}}x_{l _{1}}+\sum\limits_{l_{1}=1}^{m}\sum\limits_{l_{2}=l_{1}}^{m}\omega_{l_{1}l_{2}}x _{l_{1}}x_{l_{2}}+\cdots+\\ \sum\limits_{l_{1}=1}^{m}\sum\limits_{l_{2}=l_{1}}^{m}\cdots\sum\limits_{l_{ 2}=l_{1}}^{m}\omega_{l_{1}l_{2}\cdots l_{2}}x_{l_{1}}x_{l_{2}}\cdots x_{l_{k}} \end{array} \tag{2}\]  
The input variables in the polynomial regression are represented by \(x_{l_{1}}\), \(x_{l_{2}}\),..., \(x_{l_{k}}\) and the coefficients by \(\omega_{l_{1}}\), \(\omega_{l_{1}l_{2}}\),..., \(\omega_{l_{1}l_{2}\cdots l_{k}}\). The MPR uses a nonlinear model to fit the data, but it is linear in terms of its coefficients. Therefore, the solution of the MPR problem is similar to that of the Multiple Linear Regression (MLR) problem, which uses the least-squares method. The least-squares technique finds the optimal polynomial coefficients by reducing the residual sum of squares, which can be mathematically represented as the sum of squared errors between the predicted outcome and the actual outcome.  
\[\text{min Loss }(y,\overrightarrow{y})_{\text{MLR}}=\sum\limits_{i=1}^{N} \left(y_{i}-\omega_{0}-\sum\limits_{j=1}^{p}x_{ij}\omega_{j}\right)^{2} \tag{3}\]  
In the multivariate regression model, \(x_{ij}\) represents the \(i\)th value of the \(j\)th input feature, \(\omega_{j}\) is the coefficient for the \(j\)th input feature, and \(p\) is the total number of input features used in the model.
## 2 Rotational Flow Control  
### Pressures  
A (contiguous) fluid segment of volume \(U_{0}\) and density \(\varrho\) possessing a radial extension \(\Delta r=r-r_{0}\) and mean radial position \(\overline{r}=0.5\cdot(r+r_{0})\) between its inner and outer menisci \(r\) and \(r_{0}\), respectively, which resides in a valve of (static) geometry \(\Gamma\), experiences a centrifugal pressure head as follows:  
\[p_{\omega}=\varrho\cdot\overline{r}\Delta r\cdot\omega^{2} \tag{1}\]  
when spinning at an angular frequency \(\omega=2\pi\cdot v\). Given typical experimental values, e.g., \(\varrho=10^{3}\) kg m\({}^{-3}\), \(\overline{r}=3\) cm, \(\Delta r=1\) cm, lower elevated spin rates \(\
u=\omega/2\pi=10\) Hz and 50 Hz, Equation (1) yields \(p_{\omega}\approx 12\) hPa and 300 hPa, respectively. So even the larger value of \(p_{\omega}\) (13) only induces roughly one third of the standard atmospheric pressure \(p_{\rm std}=1013.25\) hPa.  
Additional pressures \(p_{\rightarrow}\) and \(p_{\leftarrow}\) may act on the fluid, which are directed parallel or against the main z-axis of a channel, respectively. The pneumatic is as follows:  
\[p_{V}=p_{0}\frac{V_{0}}{V} \tag{2}\]  
which results from the compression of an original gas volume \(V_{0}\) initially held at ambient pressure \(p_{0}\) to \(V\) (law of Boyle-Mariotte). Theoretically, \(p_{V}\) (2) can be made arbitrarily large through sufficiently shrinking the final volume \(V\) (for a static \(V_{0}\) defined by \(\Gamma\)). Note that, in addition to the compressibility, a volume-pressure relationship akin to (2) can also arise in fluidic systems containing flexible, solid elements, such as elastic lids or membranes [29]. The base pressure \(p_{0}\) may also be modified by heating or cooling [60] according to \(V\propto T\) ([PERSON]'s law).  
A capillary pressure  
\[p_{\Theta}=\frac{4\sigma}{D}\cdot\cos\Theta \tag{3}\]  
is exerted on a liquid of surface tension \(\sigma\) with a contact angle \(\Theta\) to a (cylindrical) channel of diameter \(D\). For water with \(\sigma\approx 72.8\times 10^{-3}\) N m\({}^{-1}\), and a hydrophobic contact angle \(\Theta\approx 120^{\circ}\) (approximately corresponding to a Teflon coating in contact with aqueous) in a conduit with \(D=100\) um, the capillary counterpressure amounts to \(|p_{\Theta}|\approx 15\) hPa \(\ll p_{\rm std}\), only, which is matched by rather low spin rates in the region \(\
u=\omega/2\pi\approx 10\) Hz. In refined models, the pressures resulting from the viscosity- and geometry-dependent hydrodynamic resistance (law of Hagen-Poiseuille), the deceleration of flow or inertial agitation of liquid samples and reagents driven by \(r\cdot d\omega/dt\) ought to be factored in.  
In hydrostatic equilibrium,  
\[p_{\omega}+p_{\rightarrow}=p_{\leftarrow} \tag{4}\]  
a liquid distribution \(\Lambda=\Lambda(\omega)\) stabilizes within a given geometry \(\Gamma\) in response to \(p_{\omega}\) (1), \(p_{\rightarrow}\) and \(p_{\leftarrow}\), e.g., \(p_{V}\) (2) or \(p_{\Theta}\) (3). The outer meniscus \(r=r(\omega)\) is a function of \(\omega\), and \(r_{0}\) can then be directly determined for a given \(\Gamma\) and \(U_{0}\).
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 2 Habitat Integration
### Research  
In this demo, we aimed to reduce the cognitive load the user received at once by displaying singular procedure steps in sequence. The demo also looked to test technology-related features such sensor detection and AR visual output. The prototype was internally validated by a naive user with no prior knowledge or experience with the prototype by running through an ISS-like procedure utilizing the SHARP tool. The user was asked to execute a procedure aiming to remove and replace a failed Fan Power Supply Unit. SHARP presented the task, grouped into three sections: actions required before the procedure, actions required during the procedure, and actions required after the procedure was completed. In the demo, the user pointed the iPad's camera at the locker prototype, which allowed the iOS application to locate the orientation of the object and display a border around the detected volume in AR. Once the object was captured, the interface provided the user with the first step of the procedure. Sensors were placed in key locations within the physical mock-up where the user's action would be detected. Once the sensor detected the expected motion for that specific procedure step shown, the interface displayed the next step for the user to perform. By representing sensor action within the digital interface, the SHARP tool demonstrated how to provide users with real-time feedback on the progress of the procedure.