esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Configurations of Mueller Polarimeter for Gaussian-Poisson Mixed Noise
## 2 Materials and Methods
### Effect of Linear Retarders on Polarization States
#### 2.2.1 Constant Fast Axis Angle and Variable Retardance  
LCVR is typical of variable retarders. These retarders usually require a controlled cable connection, so they cannot change the fast axis angle during the measurement. Only when the measurement system is building, the variable retarder can change its angle. But the retardance of a variable retarder can be changed by the control cable. As shown in Figure 2, we selected three typical types of polarized light as incident light (linear polarization, circular polarization, and elliptical polarization), then calculated the polarization state by the Mueller matrix of retarders, and plotted the change of polarization state on the Poincare sphere.  
To show the general regulation of how variable retarders change the polarization states, we should have randomly selected their fast-axis angles and retardance, but to show them more clearly and to facilitate the reader's understanding, we selected three special variable retarders with fixed fast-axis angles and retardance variation ranges: (a) the fast axis angle is 0\({}^{\circ}\) and the retardance changes between 0\({}^{\circ}\) to 270\({}^{\circ}\); (b) the fast axis angle is 22.5\({}^{\circ}\) and the retardance changes between 0\({}^{\circ}\) to 180\({}^{\circ}\); (c) the fast axis angle is 125\({}^{\circ}\) and the retardance changes between 0\({}^{\circ}\) to 320\({}^{\circ}\).  
From the top view of the Poincare sphere, the trajectory of the polarization state changed on the Poincare sphere will form a straight line in the projection of the \(S_{1}S_{2}\) plane. In addition, the angle of this projected straight line with the \(S_{2}\) direction is 2\(\theta\). Note that the angle of the purple line in Figure 2a appears to be reversed from B and C. This is because the polarization state of the incident light in Figure 2a is a linear polarization, which makes the trajectory cross the hemisphere and form a reversal. Similarly, when the \(S_{3}\) of the incident light Stokes vector is smaller than 0, it also makes the direction of trajectory lookreversed. Therefore, when the fast axis angle is constant, the trajectory direction is also determined. Then, with the increase in the retardance, the trajectory will form an arc, and the radian of the arc is equal to retardance.  
In this way, when the polarization state of incident light is known, we can easily generate the arbitrary polarization state we need through the variable retarder by first determining the directional relationship between the projection of the target polarization state and the current polarization state in the \(S_{1}S_{2}\) plane, thus determining the fast axis angle of the retarder. However, as mentioned before, the fast axis angle of the variable retarder cannot be changed easily, so we need two variable retarders to have orthogonal fast axis angles to each other (45\({}^{\circ}\) difference) to achieve the modulation of an arbitrary polarization state.
## 3 Results and Discussion
### Comparative LCA of Hydrogen Production: Biogas Reforming vs. Agricultural Residue
#### 3.2.5. Fossil and Mineral Resource Scarcity  
Fossil resource scarcity (FRS) is a critical concern in hydrogen production sustainability. This study shows that gasification requires significantly fewer fossil resources (3.203 kg oil eq) compared to biogas reforming (10.416 kg oil eq). Similarly, mineral resource scarcity (MRS) is notably higher in biogas reforming (0.1168 kg Cu eq) than in gasification (0.0106 kg Cu eq), demonstrating that gasification is a more resource-efficient process.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 2 Materials and Methods
### Recurrent Neural Network (RNN) Design  
LSTM and GRU are both types of RNNs designed to handle sequential data. An RNN is a type of neural network designed to work with sequential data where the order of the data matters. It is a neural network with a form of memory, allowing it to capture temporal dependencies in the data. RNNs can receive inputs at each time step and use their internal state (hidden layer) to process the current input and remember previous inputs [34]. They are explained using Figure 5.  
\[tf(\lambda)=\sigma(W_{hh}*tf(\lambda-1)+W_{hx}*x(\lambda)+b_{h}) \tag{8}\]  
\[of(\lambda)=\sigma\Big{(}W_{yh}*tf(\lambda)+b_{y}\Big{)} \tag{9}\]  
The key feature of an RNN is that the activation at each time step depends on the previous activations. This allows the RNN to capture temporal dependencies in sequential data. In particular, the previous activation \(tf(\lambda-1)\) serves as a form of memory passed from one time step to the next. Equation (1) computes the activation \(tf(\lambda)\), which is a function of the previous activation \(tf(\lambda-1)\), the current input \(x(\lambda)\), and biases \(b_{h}\). The equation uses the \(\sigma\) activation function to squash the weighted sum of the previous activation and current input. Equation (2) computes the output of \((\lambda)\), a function of the current activation and biases \(b_{y}\). The equation uses the \(\sigma\) activation function to squash the weighted sum of the current activation. The key feature of an RNN is that the activation at each time step depends on the previous activations, allowing the RNN to capture temporal dependencies in sequential data. In particular, the previous activation \(tf(\lambda-1)\) serves as a form of memory that is passed from one time step to the next.
## 4 Mathematical Calculation of PSD
### Unipolar PSD  
The unipolar method expresses the modulation in one signal with a width \(\mathbf{T_{b}}\). Equation (2) shows the PSD of the unipolar method in simplified form. The high polarity duration for each bit is \(\mathbf{T_{b}}\) with amplitude \"A\". The low polarity or absence of a pulse \"0\" demonstrates the \(\mathbf{T_{b}}\). The methods of both unipolar and polar NRZ are calculated as per Equation (2) with signal plus and a DC term. Therefore, the PSD of both encoding methods is similar without the delta function in DC. \"A\" is used for \(\mathbf{\sqrt{2}}\), which can normalize the unity of the signal for PSD [24].  
\[P(f)=\frac{A^{2}T_{b}}{4}(\frac{sin\pi fT_{b}}{\pi fT_{b}})^{2}\left[1+\frac{ 1}{T_{b}}\delta(f)\right] \tag{2}\]
## 8 Conclusions and Future Scope  
The radio spectrum, which is used for wireless communication, is a limited resource. This study explores different OSA methods to enhance the QoS for both PUs and SUs. This study introduces innovative priority queueing models that incorporate PU delay and finite buffer size. These models have demonstrated a substantial enhancement in the QoS for both PU and SU. As a result of utilizing solely the model described in reference [4], the SU encounters no challenges in receiving prompt service without excessive waiting time. Nevertheless, the processing unit experiences a small increase in its waiting time. Therefore, the authors of this work conducted more research by incorporating priority models with the PU delay. Previous research has shown that implementing the non-preemptive priority  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Related Works** & **Buffer Size** & **Delay** & \begin{tabular}{c} **Network** \\ **Congestion** \\ \end{tabular} & **Throughput** \\ \hline \hline \begin{tabular}{c} [PERSON] et al. [27] \\ \end{tabular} & ✗ & ✓ & ✓ & ✗ \\ \hline \hline \begin{tabular}{c} [PERSON] et al. [28] \\ \end{tabular} & ✓ & ✗ & ✓ & ✗ \\ \hline \begin{tabular}{c} [PERSON] et al. [29] \\ \end{tabular} & ✓ & ✗ & ✓ & ✓ \\ \hline \begin{tabular}{c} [PERSON] et al. [30] \\ \end{tabular} & ✗ & ✓ & ✗ & ✓ \\ \hline \
\begin{tabular}{c} [PERSON] et al. [31] \\ \end{tabular} & ✗ & ✓ & ✓ & ✓ \\ \hline
\begin{tabular}{c} Proposed Framework \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of state-of-the-art literature with the proposed framework.  
with PU delay leads to a further decrease in the average overall waiting time for the SU. Similarly, the pre-emptive priority with the PU delay model significantly enhanced the performance of the average total PU waiting time. The combination of non-preemptive priority and PU delay undeniably yields a favorable effect on the system compared to the PU delay model.  
Conceptualization, J.R.B., A.L.I. and S.S.S.; methodology, J.R.B.; software, S.S.T. and S.B.; validation, J.R.B., A.L.I, S.S.S., S.S.T. and S.B.; formal analysis, J.R.B.; investigation, J.R.B., A.L.I, S.S.S., S.T. and S.B.; resources, J.R.B., A.L.I, S.S.S., S.S.T. and S.B.; data curation, J.R.B.; writing--original draft preparation, J.R.B.; writing--review and editing, A.L.I, S.S.S., S.S.T. and S.B.; visualization, J.R.B., A.L.I, S.S.S., S.S.T. and S.B.; supervision, A.L.I. and S.S.S.; project administration, A.L.I., S.S.S., S.S.T. and S.B.; funding acquisition, A.L.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data will be made available on request from the authors.  
The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Animals and Treatment  
Male BALB/c (5-week-old) mice were procured from Koatech Lab Animal Inc. (Seoul, Korea). The Institutional Animal Care and Use Committee of Dongguk University approved all animal experiments (Approval no. IACUC-2019-7). All mice were fed with the commercial pellet diet (5L79, PMI Nutrition, St Louis, MO, USA) and water ad libitum. The animal room was maintained at 22 \(\pm\) 2 \({}^{\circ}\)C, humidity 65 \(\pm\) 2%, and 12 h light/dark cycle (light on/off, 8:00 a.m./8:00 p.m.). The mice were assigned into five groups (\(n\) = 6 per group): NC group (normal control group), DEX group (dexamethasone (DEX) treated group), DEX+puerarin 0.1% group, DEX+puerarin 1% group, and DEX+_Centella asiatica_ group. DEX (D4902) was purchased from Sigma-Aldrich (St. Louis, MO, USA). Puerarin (CFN99169, purity 98%) was obtained from Chemfaces (Hubei, China). The powdered _Centella asiatica_ extract (#1297770) was obtained from United States pharmacopeia (Rockville, MD, USA). _Centella asiatica_ extract containing asiaticoside as a main component was known to promote wound healing [17; 18; 19; 20]. Therefore, _Centella asiatica_ extract was used as a positive control drug to assess test validity and compare with the wound healing efficacy of Puerarin. After shaving the backs of the mice, excision wounds were produced using 5 mm biopsy punches (Kai Industries Co., Ltd., Saki, Japan), as described previously [21]. The wounds were treated daily with DEX (0.1% in acetone) for 14 days. For therapy, Puerarin (0.1%, 1%) and _Centella asiatica_ extract (1%) in 50 \(\upmu\)g Vaseline were applied topically to the wounds, daily for 14 days. The wound sizes were measured on 0, 7, and 14 days after wounding. The mice were sacrificed on day 14 using isoflurane (Piramal Critical Care Inc., Bethlehem, PA, USA), and the wounds were collected using 6 mm biopsy punches for further analysis.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 2 Origin, Characteristics, and Properties of Heavy Metals and Metalloids
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Chemical and Physical Properties** & **Application** & **World Production (ton\(\cdot\)y\({}^{-1}\))** \\ \hline \multirow{3}{*}{Chromium (Cr)} & Density: 7190 kg\(\cdot\)m\({}^{-3}\) & Industrial application, alloys, & 15,000,000 \\  & Atomic mass: 51.99 & \multirow{3}{*}{tanning agents, paint pigments, catalysts, photography.} & \multirow{3}{*}{(year: 2017)} \\  & Heat of fusion: 21.00 kJ\(\cdot\)mol\({}^{-1}\) & & \\ \hline \multirow{3}{*}{Copper (Cu)} & Density: 8960 kg\(\cdot\)m\({}^{-3}\) & Electrical and electronics, & \\  & Atomic mass: 63.55 & \multirow{3}{*}{transport equipment, construction, industrial machinery, pesticides.} & \multirow{3}{*}{(year: 2017)} \\  & Heat of fusion: 13.26 kJ\(\cdot\)mol\({}^{-1}\) & & \\ \hline \multirow{3}{*}{Zinc (Zn)} & Density: 7140 kg\(\cdot\)m\({}^{-3}\) & Paints, rubber, cosmetics, & \\  & Atomic mass: 65.38 & \multirow{3}{*}{soaps, batteries, textiles, and electrical equipment} & \multirow{3}{*}{(year: 2019)} \\  & Heat of fusion: 7.32 kJ\(\cdot\)mol\({}^{-1}\) & & \\ \hline \multirow{3}{*}{Cadmium (Cd)} & Density: 8650 kg\(\cdot\)m\({}^{-3}\) & \multirow{3}{*}{\begin{tabular}{c} Electroplating, paint pigments, plastics, silver-cadmium batteries, coating operations, machinery \\ and baking enames, photography, television phosphors. \\ \end{tabular} } & \multirow{3}{*}{24,670 (year: 2019)} \\  & Heat of fusion: & 6.21 kJ\(\cdot\)mol\({}^{-1}\) & \\ \hline \multirow{3}{*}{Lead (Pb)} & Density: 11,340 kg\(\cdot\)m\({}^{-3}\) & Electrical accumulators and & 11,600,000 \\  & Atomic mass: 207.2 & batteries, building construction, & (year: 2018) \\ \cline{1-1}  & Heat of fusion: 4.77 kJ\(\cdot\)mol\({}^{-1}\) & & \\ \hline \multirow{3}{*}{Mercury (Hg)} & Density: 13,530 kg\(\cdot\)m\({}^{-3}\) & \multirow{3}{*}{\
\begin{tabular}{c} Dental preparations, \\ thermometers, fluorescent and \\ ultraviolet lamps, \\ pharmaceuticals, fungicides, \\ industrial process waters, seed \\ dressings. \\ \end{tabular} } & \multirow{3}{*}{4000} \\  & Atomic mass: 200.59 & \multirow{3}{*}{
\begin{tabular}{c} Pharmaceuticals, fungicides, \\ industrial process waters, seed \\ dressings. \\ \end{tabular} } & \multirow{3}{*}{(year: 2019)} \\  & Heat of fusion: 24.44 kJ\(\cdot\)mol\({}^{-1}\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Heavy metals and metalloids characteristics. Figure 1: Anthropogenic sources of polluting metals and metalloids. It is evident that these elements are used in all fields and that annual production around the world is very high, especially chromium, copper, zinc, and lead. The major producing countries are China, Peru, Australia, the United States of America, Russia, and Mexico.
## 1 Introduction
[16] conducted a study on fly ash (FA) and ground granulated blast furnace slag (GGBFS)-based self-compacting geopolymer concrete (SCGC), and assessed its efficacy in marine applications. The findings indicated that the mixture can be effectively used in marine environments, with a design strength of 40 MPa attained under harsh marine conditions. [PERSON] et al. [17] developed a new type of synchronous grouting material for tunnelgrouting and determined the optimal mix ratio of geopolymer grouting material through a series of performance tests and Response Surface Methodology (RSM). This material was subsequently applied to metro projects. In summary, geopolymer grouting materials have demonstrated excellent performance and application potential. However, existing research on grouting materials does not fully meet the reinforcement needs for silty mudstone slopes. The strength disparity of existing geopolymer- and cement-based grouting materials and silty mudstone is too significant. This renders the process of grouting repair susceptible to secondary cracking, which has the potential to compromise the efficacy of the repair and the long-term stability of the slope [18]. As a result, there is a research gap in the development and application of geopolymer grouting materials for silty mudstone slope reinforcement. To address this gap, this study develops a new environmentally friendly grouting material using GGBFS and zeolite powder as the base materials, CL as the retarder, and NaOH solution as the alkali activator. The tests focused on the setting time, flowability, bleeding rate, correction rate, and compressive strength and used scanning electron microscopy (SEM) and X-ray diffraction (XRD). The one-factor standard deviation and Grey Relational Analysis (GRA) were used to quantitatively analyze the influence of different material components and performance indicators on the mix ratio, and determine the optimal mix ratio of alkali-activated GGBFS-zeolite powder grouting materials. These findings could provide new materials for the grouting reinforcement of fractured rocky slopes in the southern region of China.
## 4 Experiments
### Quantitative Analysis  
We choose 25 images (512 \(\times\) 512) from the Granada dataset for our test experiment. The fidelity of each watermarked image is evaluated from PSNR and SSIM criteria. Figure 13 shows the PSNR of watermarked images generated by all trained networks with fixed strength factors (\(\alpha=1\)). The average of PSNR is 36.094852.  
The robustness of a watermarking algorithm can be determined using two approaches. One approach is to enhance the watermark embedding strength. High-intensity embedding increases the robustness but reduces the PSNR. We balance the robustness and imperceptibility of the trained network by adjusting the watermark strength factor (\(\alpha=1\)). The bit error rate of the watermark is calculated under a variety of attacks with fixed strength factors to prove the robustness of the proposed framework. The other approach is to increase the diversity of image attack types to enhance the robustness of the framework, as shown in Table 4. Our framework can effectively embed the watermark and avoid  
Figure 12: Simulating image distortions for screen-shooting.  
Figure 13: Test images (25) PSNR of SSDeN.  
various image distortions. Figure 14 illustrates the original and watermarked images as well as the difference image between the original image and the watermarked images with an enhancement of 50\(\times\).
# Three-Dimensional Division of Visible Light Communication Irradiation Area
## 4 VLC Experiment Setup and Discussion
### Results Discussion  
This paper describes our investigation of the effect of lampshade height on VLC. The light irradiation area was divided into three different signal receiving areas according to the BER distribution. We experimentally obtained the volume ratio of the different receiving areas in the entire illumination area, and modeled the optimal area for VLC mathematically accordingly. Following the results of this study, the effect of light reflection from the ground on BER needs to be considered in subsequent experiments. Meanwhile, the gradual weakening of the light in the edge part of the light illumination caused the BER to change to 100% in a very short distance. How to improve the detection ability of weaker light signals needs to be studied in future work. For visible light communication, three parts are generally studied: the transmitter, the communication channel, and the receiver. Since the focus of this paper is on the communication channel aspect, a more efficient modulation and demodulation method is used at the transmitter side: the use of LED devices with higher luminous flux, and the enhancement of the sensing performance of the photoelectric sensor for weak light at the receiving end.All of these strategies can improve the communication distance of the channel and reduce the BER of the communication to a certain extent.
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 1 Introduction  
Recently, there has been growing interest in the use of inexpensive off-the-shelf smartphones in various innovative applications beyond regular voice calls. A modern smartphone is equipped with multiple sensors, including a microphone, which is perhaps the most ubiquitous and universally available sensor to third-party application developers. Several emerging mobile applications in such fields as healthcare and environmental monitoring require mobile devices that continuously perform microphone sensing and real-time digital signal processing in background mode. Examples of such applications include:  
* Mobile applications that measure a person's exposure to background acoustic noises to assess the risk of noise-induced hearing loss (also known as \"noise dosimeters\") [1; 2];
* Applications for crowd-sourced noise pollution monitoring and urban noise mapping [3; 4; 5];
* Sleep quality monitoring [6], estimation of respiratory rates [7], and obstructive sleep apnea monitoring applications [8];
* Smartphone-based TV and radio audience research applications [9; 10];
* In-door location and proximity detection based on near-ultrasound acoustic beacons [11; 12].  
One of the major challenges in developing practical applications that require background audio monitoring is the power consumption of the microphone sensor and the associated digital signal processing. The Android operating system (OS), which is one of the most widely used OSs in the smartphone market, allows audio recording in background mode. However, the commercial feasibilityof the abovementioned applications strongly depends on the impact of background audio sensing on a smartphone's battery life [13].  
A number of previous studies have been devoted to the analysis of smartphone power consumption in different use scenarios [14; 15; 16; 17; 18]. However, most of these works do not explicitly analyze the background audio recording mode. For example, in [14] the authors analyze the power consumption of Wi-Fi use, the graphical processing unit, and video recording of four mobile devices (Samsung Galaxy line). In [18], the authors report the results of power consumption measurements for typical multimedia applications that extensively use a smartphone display. Finally, in [19], the authors report the power consumption of the microphone recording mode, yet they only provide the results for the Samsung Galaxy S6 smartphone.  
In this paper, we provide a systematic analysis of smartphone power consumption in background audio recording scenarios. We report comprehensive measurement results for several Android smartphones with diverse hardware and software specifications. We also report the power consumption results for typical real-time digital signal processing algorithms that are frequently used simultaneously with background audio recording.