esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Methodology
### Materials and Methods
Region II includes five areas: Ar, Al, Ha, Al, and Najfan. Region III includes two areas, namely Al Hudud ash Shamaliyah and Ash Shardiyah. This partitioning method also divides Saudi Arabia into three regions: eastern (Region III), central (Region II), and western (Region I), which is convenient for studying the regional characteristics of aerosols over Saudi Arabia. In this study, we used the dataset on cloud-free conditions (30%) for day and night, and only the highest quality (uncertainty \(\leq\) 3) was considered, null values and the values less than or equal to zero were eliminated, while the remaining data were resampled to a \(1^{\circ}\times 1^{\circ}\) grid, and the vector mask file of the study area was used to crop out the data of the study area. The dataset includes the Saudi regions I, II, and III under cloud-free daytime and nighttime conditions. To study seasonal changes, the data were divided into four seasons (spring: March, April, and May (MAM); summer: June, July, and August (JJA); autumn: September, October, and November (SON), and winter: December, January, and February (DJF)). A 12-month estimate was utilized to study the annual changes. From 2007 to 2019, several parameters of Saudi Arabia's aerosol layer during the daytime and nighttime were calculated based on the annual and seasonal averages. The interannual and seasonal variation characteristics of the physical and optical properties of the aerosol layer in the three regions of Saudi Arabia were compared and statistically analyzed. At the same time, the correlation between aerosol parameters is analyzed, to better understand the temporal and spatial distribution characteristics of aerosols in Saudi Arabia.
## 2 Materials and Methods
### Methodology
#### 2.3.5 Accuracy Assessment  
The confusion matrix (CM) technique, a commonly used methodology based on comparing classification outputs with ground truth data, was used to assess the accuracy of the various approaches [70; 71]. The matrix was used to produce specific accuracy measures: Overall Accuracy (OA, Equation (3)), Producer's Accuracy (PA, Equation (4); also known as \"recall\"), and User's Accuracy (UA, Equation (5); also known as \"precision\"), according to the following formulas:  
\[\text{OA}=\frac{\text{Number of correct predictions}}{\text{Total number of predictions}} \tag{3}\]  
\[\text{PA}=\frac{\text{Number correctly identified in a given map class}}{\text{Number actually in that reference class}} \tag{4}\]  
\[\text{UA}=\frac{\text{Number correctly identified in a given map class}}{\text{Number claimed to be in that map class}} \tag{5}\]  
The OA is commonly expressed as a percentage, with 100% accuracy indicating that all reference sites were correctly classified [72]. At the class level, PA is the ratio of true positives to the total number of true positives and false negatives, whereas UA is the ratio  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CODE** & **Textural Index Description** \\ \hline ASM & Angular Second Moment: measures the number of repeated pairs in the image \\ CONTR & Contrast: measures the local contrast of an image \\ CORR & Correlation: measures the correlation between pairs of pixels in the image \\ VAR & Variance: measures how spread out the distribution of gray-levels \\  & is in the image \\ IDM & Inverse Difference Moment: measures the homogeneity of the image \\ \hline \hline \end{tabular}
\end{table}
Table 4: Textural indices calculated using the Gray-Level Co-occurrence Matrix (GLCM).  
of true positives to the total number of true positives and false negatives. The UA is the omission error's counterpart, whereas the PA is the commission error's complement. The F-score is a composite measure with better sensitivity and specificity that assesses algorithm reliability [73]. It is calculated as a harmonic mean of PA and UA (Equation (6)), and it seeks to depict the balance of PA and UA [74,75] (Equation (6)). In the LULC classification, a high UA indicates a low representation of commission errors, whereas a high PA indicates a low proportion of omission errors [76].  
\[\text{F-score}=2*\frac{\text{PA}*\text{UA}}{\text{PA}+\text{UA}} \tag{6}\]  
The statistical significance of OA differences was assessed according to previous similar studies [52,77], using the McNemar test without continuity correction (with \(p<0.05\) as the significance threshold), which is appropriate for situations involving non-independent test datasets [78]. The kappa coefficient of agreement [79] was not calculated because the test datasets used to assess the various classification approaches were not independent [80]. Moreover, the use of kappa in assessing land cover classification accuracy has been strongly discouraged [81,82].
## 3 Computation of Inertial Manifolds for DDEs
### The \"Fast RNA\" Case
The model without a delay (i.e., \(m=0\)) does not have an Andronov-Hopf bifurcation, but the \(m=1\) ODE model does, as seen in \[\beta\varepsilon_{1}\dot{c} =s(e-c)-(\gamma+\beta\varepsilon_{1})c,\]0 The limit cycle in the \(m=1\) model is a little larger than that in the DDE model, and is a little distorted. Nevertheless, there is clearly a wide range of parameters near the Andronov-Hopf bifurcation where the two models have similar behavior. In past work, it was found that an \(m=1\) model accurately reproduced the Andronov-Hopf bifurcation curve of a related DDE model [39]. Although such a calculation has not been done for this model in order to focus on other issues, it seems clear that this will be true for this model as well. \[\beta\varepsilon_{1}\dot{c} =s(e-c)-(\gamma+\beta\varepsilon_{1})c,\]1 (a) Approximate inertial manifolds for the three-variable model obtained at the same level of iteration (\(k=3\)) for different orders of expansion of the delayed term. Parameters: \(\varepsilon_{1}=\beta=\alpha=0.1\), \(\gamma=1.5\), \(\kappa=0.2\), \(n=4\) and \(\delta=0.4\). On the scale of this figure, regardless of how we rotate the graph, the three surfaces appear very similar to the eye. (b) Difference between \(\mathcal{C}_{3}^{(3)}\) and \(\mathcal{C}_{3}^{(1)}\). \[\beta\varepsilon_{1}\dot{c} =s(e-c)-(\gamma+\beta\varepsilon_{1})c,\]2 Trajectories of (**a**) the three-variable model and of (**b**) the \(m=1\), (**c**) \(m=2\) and (**d**) \(m=3\) reduced models for the same parameters as \[\beta\varepsilon_{1}\dot{c} =s(e-c)-(\gamma+\beta\varepsilon_{1})c,\]3 All trajectories were started from initial conditions near the origin, \(s(0)=0.02\), \(c(0)=0\). For the three-variable model, \(e(0)=0\) and the initial function was \(s(\theta)=0\) for \(\theta<0\) (i.e. there is a small discontinuity in the initial function at \(\theta=0\)). \[\beta\varepsilon_{1}\dot{c} =s(e-c)-(\gamma+\beta\varepsilon_{1})c,\]4 Trajectories of (**a**) the three-variable DDE model and of (**b**) the \(m=0\) and \(m=1\) reduced ODE models. The \(m=1\) model has an Andronov–Hopf bifurcation and can generate limit-cycle oscillations, like the three-variable model, whereas the \(m=0\) model never undergoes an Andronov–Hopf bifurcation. All parameters are as in Figure 4, except \(\delta=0.7\).
## 4 Discussion
At sufficiently small concentrations, the SERS spectrum is largely created by a very small number of analyte molecules which happen to land in the hotspots of the active area; thus, the diffusion of the analyte molecules due to thermal effects, however small they may be, can make the spectrum behave in a highly unstable manner, changing in time and not allowing a trustworthy collection of data points. Consequently, a very large amount of measurements (as was the case for the surface maps presented in this paper) is required, in order to at least adequately gain confidence in the data describing the performance of the substrate, the concentration of the analyte, or any other relevant metric. This investigation also highlights the issues associated with the definitions of the enhancement factor. As we have shown in the results, the intensity of enhanced Raman lines does not follow the analyte concentration in a linear manner, likely due to the non-linear nature of the adsorption of analyte molecules near the nanostructured active surface. Additionally, due to the fact that the analytical enhancement factor and theoretical enhancement factor are defined in conceptually different ways, a one-to-one comparison between the experiment and theoretical models is not possible using just this one metric. Moreover, the theoretically calculated enhancement factor does not take into account the contribution of some chemical enhancement, or resonance due to the fluorescence of Rhodamine B. The discrepancy between the experimental and theoretical enhancements could also be attributed to the fact that analyte molecules may adsorb better on the surface of substrate C compared to substrate B--something that the FEM model inherently cannot take into consideration. Despite this, the enhancement factor obtained theoretically does hint to the reality of the substrate performance. These models also show the possibility of modelling not just idealised nanostructures and particle configurations but also more complex morphologies reflecting those of real substrates. Therefore, such modelling can help with getting an idea of the potential real-life enhancement strength of different nanostructured configurations.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 2 Related Works on ITS Applications
### Adaptive Traffic Signal Control (ATSC)  
[PERSON] et al. [14] proposed an adaptive traffic signal control with the Oldest Arrives First (OAF) strategy by using a vehicular network (802.11p). The OAF algorithm controls the traffic with two phases where Phase I platoons the vehicles using the real-time vehicle positions, it first maximizes the size of the platoon, and then minimizes the size difference of platoons. In Phase II, OAF schedules each platoon to pass the intersection by the oldest arrives first principle. The simulation results show that the OAF algorithm has the best performance compared with traditional traffic signal scheduling algorithms, including the pre-timed, vehicle actuated logic, and Webster methods [14]. [PERSON] et al. presents a novel de\(-\)centralized flexible phasing scheme, where the traffic signal controller uses a Nash bargaining game\(-\)theoretic framework [15]. Although the proposed algorithm is innovative, the lack of some traffic protection measures, such as maximum green time and minimum green time, may lead to unsafe cases for pedestrians.  
Some well-known ATSC models are commonly adopted in urban intersections, as illustrated in Table 1, including: SCOOT [16], SCAT [17], RHODES [18], COMDYCS III [19], LHOVRA [20], and OPAC [21]. The common issue is that the spatiotemporal coverage of traffic information is not enough for optimal control, and the costs for construction and maintenance of the loop detector are high.
# Determination of Cellulose Degree of Polymerization in Historical Papers with High Lignin Content
## 2 Materials and Methods
### pH Measurements  
The pH of the water extracts was measured according to the standard of [27], modified to a smaller sample amount [28]: 7 mL of deionized water was added to 100 mg of paper sample. The pH was determined in the water extract after 1 h using a flat membrane electrode (Metrohm 6.0256.100, Metrohm AG, Herisau, Switterland) connected to a Mettler Toledo MP 220 (Mettler Toledo, Columbus, OH, USA) pH meter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Sample No.**} & \multirow{2}{*}{**Year of Publication**} & \multirow{2}{*}{**pH Value**} & **Groundwood Fiber** & **Bleached Chemical** & \multirow{2}{*}{**Cotton Fibers (\%)**} \\  & & & **Content (\%)** & **Pulp (\%)** & \\ \hline
1 & 1983 & 5.1 & 85 & 15 & \\
2 & 1960 & 6.4 & 66 & 34 & \\
3 & 1958 & 6.2 & 65 & 35 & \\
4 & 1903 & 4.8 & 64 & 36 & \\
5 & 1982 & 5.3 & 84 & 14 & 2 \\
6 & 1987 & 5.6 & 45 & 26.6 & 28.5 \\
7 & 1953 & 6.9 & 58 & 42 & \\
8 & 1965 & 5.8 & 64 & 36 & \\
9 & 1889 & 4.9 & 81 & 19 & \\
10 & 1899 & 5.1 & 61 & 39 & \\
11 & 1898 & 5.3 & 58 & 42 & \\
12 & 1896 & 5.3 & 68 & 32 & \\
13 & 1909 & 5.5 & 35 & 65 & \\
14 & 1911 & 4.9 & 66 & 34 & \\
15 & 1887 & 4.6 & 57 & 20.5 & 22.5 \\ \hline \end{tabular}
\end{table}
Table 1: List of real paper samples, publication years, pH values, and fiber furnish analysis. The pH values are calculated from two measurements with an average standard deviation of 0.1 pH units.
## 1 Introduction
used three machine learning models (XGBoost, SVM, MARS) to estimate the amount of daily diffuse solar radiation (Rd) based on local inputs, stating that three machine learning models can estimate daily Rd based on local inputs [13]; [PERSON] et al. investigated the potential of four machine learning (ML) models, namely, extreme Learning Machine (ELM), Genetic Programming (GP), and RNN [14]. [PERSON] et al. presented a support vector machine (SVM) approach for predicting solar radiation. They stated that the SVM method would be a promising alternative to standard methods for evaluating solar radiation at any location [15]. This demonstrates how machine learning methods can simulate and forecast temperature data in the research domain. In the North China Plain, there are essentially no studies on the use of machine learning algorithms for extreme temperature prediction. This study uses statistical downscaling methods to downscale the data of seven GCMs in CMIP6 in time and space. It uses Support Vector Regression (SVM) [16] to regress and analyze the simulated data of multiple climate models to obtain a relatively reasonable prediction model. In this paper, nine commonly used extreme temperature indices were selected to firstly simulate and evaluate the extreme temperature indices in the historical period of the North China Plain and then predict the spatial and temporal variability of the extreme temperature indices in the future climate scenarios of the North China Plain, to provide a scientific basis for addressing the disaster risks associated with extreme temperature events. Therefore, predicting future extreme temperature trends in the North China Plain can aid in understanding the extreme climate risks that global warming may bring to agricultural production, allowing agricultural production levels to be stabilized and regional food security to be ensured [7; 17].
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 3 Problem Description
In LEO satellites, where real-time processing is critical, if the computation time of a deep learning model conflicts with real-time requirements, other essential tasks on the satellite could be delayed, which would negatively impact the entire network system [23]. To address these challenges, it is necessary to simplify the computation. This simplification can be achieved through hardware acceleration. In particular, the parallel processing capabilities of GPUs, which are highly effective at accelerating AI-based algorithms, can offload intensive computations from onboard computers, enabling faster routing decisions. Figure 1 provides a visual representation of how an agent performs routing using an embedded board with constrained onboard resources in an environment modeled by MDPs. First, the dynamic satellite environment is represented as a lattice map, with each cell corresponding to a state. Each cell represents either a communicable zone or an obstacle in the satellite routing problem. We applied the MDP model to simulate the dynamic environment. In this setup, the Dueling DQN routing algorithm is employed. While it is an effective routing method, it is computationally intensive, and the satellite's onboard computer struggles to handle the workload effectively. As a result, developing methods that consider the limited resources of onboard computers in the routing environment of low-orbit satellites remains a significant challenge. Traditional onboard systems on most satellites are currently limited in their ability to handle large-scale computations. Therefore, methodologies that reduce the computational burden through parallel processing, utilizing hardware like GPUs, are essential.
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs), built from many battery-operated sensor nodes are distributed in the environment for monitoring and data acquisition. Subsequent to the deployment of sensor nodes, the most challenging and daunting task is to enhance the energy resources for the lifetime performance of the entire WSN. In this study, we have attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimizes energy consumption and prolongs the overall network lifetime of WSNs. Initially, a wireless portable charging device (WPCD) is assumed which periodically travels on our proposed routing path among the nodes of the WSN to decrease their charge cycle time and recharge them with the help of wireless power transfer (WPT). Further, a scheduling scheme is proposed which creates clusters of WSNs. These clusters elect a cluster head among them based on the residual energy, buffer size, and distance of the head from each node of the cluster. The cluster head performs all data routing duties for all its member nodes to conserve the energy supposed to be consumed by member nodes. Furthermore, we compare our technique with the available literature by simulation, and the results showed a significant increase in the vacation time of the nodes of WSNs.  
wireless sensor networks (WSNs); wireless portable charging device (WPCD); energy consumption; vacation time; wireless power transfer; grid clustering routing protocol; cluster head Article
# Space Weather Effects on Satellites
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCD & Charge-coupled device \\ CIR & Co-rotating interaction region \\ CME & Coronal mass ejection \\ EM & Electromagnetic \\ EUV & Extreme ultraviolet \\ GCR & Galactic cosmic rays \\ GEO & Geostationary Earth orbit \\ GIC & Geomagnetically induced current \\ GLE & Ground-level enhancement \\ GS & Geomagnetic storm \\ ICME & Interplanetary CME \\ IMF & Interplanetary magnetic field \\ IP & Interplanetary \\ LEO & Low Earth orbit \\ MEO & Medium Earth orbit \\ SEE & Solar energetic electron \\ SEP & Solar energetic proton \\ SF & Solar flare \\ SIR & Stream interaction region \\ SSC & Sudden storm commencement \\ SW & Space weather \\ SXR & Soft X-ray \\ UV & Ultraviolet \\ VLEO & Very low Earth orbit \\ \end{tabular}
## 4 Intrinsic Visible Fluorescence Emitted by Individual Amino Acids and Their Variants
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Excitation, Emission Wavelength/Range**} & \multirow{2}{*}{**Methodology Used**} & \multirow{2}{*}{**Physical State**} & \multirow{2}{*}{**Ref.**} \\  & & **(\(\lambda\)exc, \(\lambda\)em)** & & & \\ \cline{2-5}  & **UV–Blue** & **Green** & **Experiment** & & \\ \hline \hline \multirow{9}{*}{H} & & 405 nm, 488 nm, & & \\  & & and & & \\  & 360 nm, 424/445 nm, & 420 nm, 500 nm, & Fluorescence & \\  & and & and & spectroscopy and & \\  & 380 nm, 450 nm, & 440 nm, 515 nm, & confocal & Microplates & [120] \\  & & and & microscopy & \\  & 400 nm, 452–500 nm & 460 nm, 550 nm, & & \\  & & 480 nm, 560 nm & & \\ \hline \hline Q & 360 nm, 430 nm & & Fluorescence & Solid state & [122] \\  & & & spectroscopy & \\ \hline W & 350 nm, 400/450 nm & & Emission-excitation & \\  & & matrices (EEMs) & Solution & [40] \\ \hline Y & 350 nm, 400/440 nm & & Emission-excitation & \\  & & matrices (EEMs) & Solution & [40] \\ \hline \hline pyro-Q & 340 nm, 420 nm & & Fluorescence & \\  & & spectroscopy & Solid state & [122] \\ \hline Amino acid crystals & 405 nm, N.R. ** & & Confocal & \\  & & spectroscopy & Crystals & [121] \\ \hline Ornitin & 405 nm, N.R. ** & & Confocal & \\  & & spectroscopy & Crystals & [121] \\ \hline
2.4-diaminobutyric acid & 405 nm, N.R. ** & & Confocal & \\  & & & spectroscopy & [121] \\ \hline D-2,3-diaminopropionic acid & 405 nm, N.R. ** & & Confocal & \\  & & spectroscopy & Crystals & [121] \\ \hline \multirow{9}{*}{F} & & 370 nm, 452 nm, & 400 nm, 458 nm, & \\  & & and & & Fluorescence & Fibrillar \\  & 380 nm, 454 nm, & 410 nm, 500 nm, & spectroscopy & solution \\  & & and & & \\  & 390 nm, 456 nm & 420 nm, 500–540 nm & & \\ \cline{1-1} \cline{2-5}  & & 350 nm, 400/430 nm & & Emission-excitation & Solution & [40] \\ \hline \hline K & 340 nm, 433 nm & & Fluorescence & Concentrated & \\  & 365 nm, and 440/488 nm & 460 nm, 511 nm & Fluorescence & \\  & & spectroscopy & solution & \\ \hline K & 300–365 nm, & 365 nm, 524 nm & Fluorescence & Recrystallized & \\  & 385–420 nm & & spectroscopy & solids & \\ \hline S & 320–440 nm, & 320 nm, 513 nm, & Fluorescence & Recrystallized & \\  & 385–484 nm & 440 nm, 484/598 nm & Fluorescence & \\  & & 440 nm, 484/598 nm & & Spectroscopy & solids \\ \hline S & 312 nm, 427 nm & 480 nm, 529 nm & Fluorescence & Concentrated & \\  & & & spectroscopy & solution \\ \hline I & 320–440 nm, & 320 nm, 507 nm, & Fluorescence & Recrystallized & \\  & 384–484 nm & 440 nm, 484/598 nm & Fluorescence & \\ \hline N-acetyl-A & 260–400 nm, 440 nm & & Fluorescence & Solution & [109] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Individual amino acids emitting visible fluorescence. Amino acids are reported using one-letter code.
## 2 Background
### Vehicle Routing Problem
Robustness means maintaining feasibility as well as maintaining the best possible performance under uncertainty constraints. The goal of robust optimization is to make full use of the search space to find the best optimal solution while minimizing the effect of uncertainty. [PERSON] et al. [40] proposed a novel, robust, two-stage planning model for charging station placement that is determined by fuzzy inference considering distance, road traffic, and grid stability. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm was proposed to obtain the Pareto front, and fuzzy decision-making was used to compare the Pareto optimal solutions. [PERSON] [41] et al. proposed a novel strategy to solve the VRPTW with uncertain demands using adaptive credibility thresholds, which try to find good solutions with relaxed capacity constraints. For this purpose, a new split decodification algorithm that combines a genetic algorithm with local search strategies was designed. [PERSON] [42] et al. proposed a robust routing optimization model for multivehicle electric delivery that considers random changes in demand and transportation time at different customer points to minimize the distribution cost and carbon emissions; additionally, an improved genetic algorithm was proposed to solve this problem. [PERSON] [43] et al. designed a new form of disturbance on travel time that is determined by the maximum disturbance degree and constructed a robust multiobjective VRPTW, which adopts two conflicting objectives to capture the uncertainty characteristics. Moreover, a robust multiobjective particle swarm optimization approach that adopts a coding method involving priority assignment and decoding based on a greedy strategy is proposed. A new metric, Rob, was designed to measure the robustness of the solutions. To take full advantage of the problem characteristics, the algorithm adopts problem-based and route-based local searches. In the current literature, robustness methods rarely take into account the JIT objective of the VRP, and there are fewer studies on the combination of robust VRPs with MOPs. In this paper, the solutions obtained by the algorithm are subjected to an uncertainty test. Robust solutions that take into account diversity are obtained based on the distribution of the solutions in the solution space.