esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecularly-Imprinted SERS: A Potential Method for Bioanalysis
## 3 Surface-Enhanced Raman Spectroscopy  
Raman spectroscopy was invented in 1930. However, its use is restricted due to its low sensitivity and weak Raman signal intensity. Along with the invention of the LASER and [PERSON]'s study, which demonstrated an increase in signal related to the silver electrode's surface roughness induced by adsorption of tiny molecules, it developed a signal-enhancing phenomenon known as surface-enhanced Raman scattering. Because SERS has a high sensitivity (up to \(10^{4}\)), it offers great promise for application in studying and detecting single molecules [25; 117].  
Gold (Au), silver (Ag), and copper (Cu) are usually utilized as SERS substrates. They are stable, give strong SERS signal enhancement, and are inexpensive. Because the size of metal particles influences its efficacy as a substrate and SERS signal enhancer, those metals are often utilized in the form of nanoparticles. The distance between metal nanoparticles and the analyte (also known as _hotspots_) improves the SERS signal. Salt can also trigger nanoparticle agglomeration, increasing hotspots and resulting in stronger SERS signals [25; 118; 120; 121; 122].  
SERS signal enhancement may be explained by two mechanisms: electromagnetic enhancement (EM) and chemical enhancement (CE). SERS offers various benefits in analysis, including high detection sensitivity, rapid signal creation if the target molecule is adsorbed on the surface of the SERS-active material, and fingerprint features [121] that can validate the chemical structure based on energy levels. SERS has applications in agricultural chemicals [26; 117], adulteration [117], biological toxins found in agricultural goods [117], clinical diagnostics [118], veterinary drugs, food contaminants [119], environmental pollutants [120; 121], and biology [122; 123].  
In the presence of a complex matrix, the SERS signal might be disturbed. Interfering substances can create false-negative signal readings or fail to identify the investigated component in complex matrix. On the other hand, interfering chemicals can result in erroneous positive signal readings. As a result, SERS was combined with other approaches to limit the influence of matrix interfering chemicals. Furthermore, MIP may be utilized as a material in the adsorption process and to eliminate interfering chemicals from the matrix (capturing the SERS substrate to get closer to the target molecule) [27; 123; 124].
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 2 Principle of the LC-DM AO System  
### System Composition  
Figure 1 presents the block diagram of the LC-DM cascade AO system. This system is mainly composed of an LC wavefront corrector, a DM, a tip-tilt mirror (TTM), a Hartmann WFS, a wavefront processing computer, and an imaging camera. The density of the LC is 256 \(\times\) 256 pixels, with a pixel size of 24 \(\upmu\)m, and the aperture of the LC is 6.14 mm. The number of actuators of the DM is 145, the pitch of the DM is 2.5 mm, and its aperture is 30 mm. The aperture of the TTM is 25 mm and the number of subapertures of the WFS is 20 \(\times\) 20, and the full aperture is 5.8 mm; these devices are placed in the conjugate position by lens pairs in the optical path for high spatial frequency small amplitude aberration correction, low spatial frequency large aberration correction, tilt aberration correction, and wavefront measurements, respectively. The wavefront processing computer is used for wavefront reconstruction, wavefront decoupling, and the calculation of the drive signal of each corrector. The wavefront obtained using the WFS is decomposed and decoupled and sent to the controller of each corrector. The controller is used to calculate the driving signal and to send this signal to each corrector. The imaging camera is employed to image the corrected light beam. In the design, the compactness and complexity of the system are comprehensively considered, and the device is selected by ensuring energy utilization, resolution, the field of view, and the zoom beam, and is completed with the smallest volume and fewest devices.
### Characterization
#### 2.3.3 De-Icing Test  
A de-icing test was carried out for the specimen that showed the best results in terms of the average temperature during the self-heating test. First, a 2.5 mm thick ice layer is generated in a freezer with deionized water. Then, the de-icing test was carried out by applying 1000 V with the Keithley 2410 equipment to the specimen, placed in vertical position to promote water evacuation during the test. Here, the minimum temperature was recorded using the FLIR E50 thermal camera to evaluate the de-icing time.
# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 3 Methodology
### Scale Optimization for Vehicle Target Detection Scenario
#### 3.2.1 Multi-Scale Training  
In the target detection problem, the size of the input image has a significant influence on the precision of the model. Although a bigger input image may indicate better precision performance of the model, it also means a higher computation cost and slower inference speed. In practical applications, it generally requires achieving a balance between the model precision and inference speed, so the size of input images should not be too big.  
After the image is input into the basic network, it will generally generate a feature map ten times smaller than the input image, which makes it difficult for the network to capture the small objects in a small feature map. Therefore, by imputing bigger and more images to train the model, the model's robustness against object size can be improved to a certain degree. As a result, the model can learn more common features of the target rather than specific details of target, and the model's generalization ability can be improved. During the inference stage, small input images are still used for inference, which does not incur any loss in inference speed. However, more optimal model performance can be obtained through multi-scale training, so when the same size of input image is used during inference, the model precision obtained through multi-scale training is higher.  
This paper has defined the scale rule as shown in Formula (5) during training, in which n is a constant number. For each batch, the image of a scale will be randomly selected from the scale rule and inputted into the neural network for training and learning.  
\[\text{S}(\text{n})=320+32\text{n}\ 0\leq\text{n}\leq 9 \tag{5}\]
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Driving Behavior Characteristics Analysis Results
#### 3.1.2 Acceleration Characteristics  
Acceleration features are linked to both the behavioral adaptability of a driver's speed change and their driving style. The acceleration statistical indicators of different sections are shown in Table 4. We looked into both longitudinal (speed changes) and lateral (turning) accelerations from the simulation data, which encompassed 22,620 measurements of acceleration.  
\[J=\sqrt{\frac{1}{T}\int_{0}^{T}((\frac{da_{x}}{dt})^{2}+(\frac{da_{y}}{dt})^{ 2})dt} \tag{4}\]  
In the equation, \(J\) represents the rate of acceleration change (m/s\({}^{3}\)), where \(a_{x}\) and \(a_{y}\) denote the longitudinal and lateral accelerations (m/s\({}^{2}\)), respectively, and \(T\) stands for the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index (m/s\({}^{2}\))** & **Straight-Line Section** & **Curved Section** \\ \hline Mean longitudinal acceleration & 0.35 & 1.12 \\ Longitudinal acceleration standard & 0.42 & 0.86 \\ deviation & 0.28 & 1.50 \\ Mean lateral acceleration standard & 0.37 & 1.15 \\ deviation & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical indicators of acceleration in different sections.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index (km/h)** & **Straight-Line Section** & **Curved Section** \\ \hline Mean & 63.5 & 48.2 \\ Standard deviation & 8.6 & 12.4 \\
85\% quantile value & 72.1 & 60.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Speed statistics of different sections.  
time taken to pass through a sharp bend (s). A larger rate of acceleration change indicates more frequent and abrupt driver control.  
\[DR=3.27-1.08\ln R+0.82J \tag{5}\]  
where DR represents the level of driving risk (1-5, with higher values indicating greater risk), R stands for the radius of the curve (m), and J denotes the rate of acceleration change (m/s\({}^{3}\)). Experimental verification has shown that this model can be used for the quantitative assessment of driving risk on sharp turns.
# Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications
## 5 Conclusions  
In this paper, a sequential DPD architecture was proposed and experimentally validated. Compared to conventional predistortion systems, the proposed DPD reduces the complexity of the feedback path hardware by using reduced sampling rates for the acquisition of the PA output signal. Moreover, the computational complexity of the proposed predistorter is reduced by using a coarse delay resolution for the identification of the memory polynomial DPD. The two-step sequential characterization and predistortion function synthesis does not add any hardware overhead, reduces the sampling rate in the feedback path, eliminates the need for signal de-embedding required for the identification of the MP DPD sub-function, and alleviates the computational complexity of the delay estimation and alignment. Experimental results show that the proposed DPD can successfully linearize a PA driven by a 20 MHz test signal while operating the feedback path at 25 MSa/s, and using a coarse delay alignment resolution. Future work can investigate the suitability of the proposed architecture for other two-box digital predistortion functions, and the possibility of extending the complexity reduction to the transmit path.  
Conceptualization, O.H. and S.B.; methodology, M.A. and R.A.; validation, M.A.; formal analysis, M.A. and R.A.; writing--original draft preparation, M.A. and R.A.; writing--review and editing, O.H. and S.B.; supervision, O.H.; project administration, O.H.; funding acquisition, O.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Office at the American University of Sharjah under Grant FRG20-M-E85. The work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah.  
The data used for this research work are not publicly available.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 A Review on Machine Learning and Neural Network Algorithms
### Long Short-Term Memory (LSTM)  
LSTM is considered as an artificial recurrent neural network (RNN) architecture. It has been proposed to solve the vanishing and exploding gradient problems of conventional RNNs. LSTMs originally include special units called memory blocks in the recurrent hidden layer. Each memory block has a memory cell with self-connections storing the temporal state of the network and special multiplicative units (i.e., gates). An input gate is used to control the flow of input activations into the memory cell and an output gate to control the output flow of cell activations into the rest of the network [26].
## 4 Discussion
To balance the normal (0) and attack (1) records, we have to scale our confusion matrices so that real Recall and F1 Score values can be obtained. Figures 9-15 and Table 5 show the comparison of results of all the performance metrics that have been calculated only for the optimal feature set. From these statistics, we see that, once again, RF gives the highest value of Recall, which is 0.993, and of F1 Score, which is 0.997. The second-best performance is exhibited by the Naive Bayes, giving the Recall and F1 Score values as 0.989 and 0.994, respectively. Hence, from the obtained results and performance values, we can conclude that Random Forest and the Naive Bayes algorithms of machine learning have a better classification performance when used in the scenario of real-time IoT networks. Because it is the first time a realistic IoT dataset has been generated and released publicly, further research on similar real-time IoT datasets must be done to confirm our claim in the future. Machine-learning algorithms behave differently on the same type of data, and each machine-learning model works better on certain kinds of datasets than others. This is why  
Figure 9: A comparison of achieved accuracies of all algorithms. we have found which one works better with IoT data. We must now ask the question of how we compare different machine-learning algorithms. This depends on different aspects like forecast accuracy, sample complexity, bias-variance trade-offs, assumptions, and objectives, as well as the time and space complexity of the models. The predictive ability and performance of machine-learning models hence vary, and different performance parameters that rely on this fact also output different values. It is worth discussing why the performances of each classifier are different from each other while using the same data and feature sets. This is because machine-learning algorithms are data-oriented, flexible, and adaptive. The output and different performance depend on many factors like the problem under consideration, the type and size of data, the number of features, the kind of output we want, speed and linearity, and, most importantly, their interpretability [26].
# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 1 Introduction
### Novelty of This Study  
This study applied the previously described calibration method (hereafter referred to as \"classic\"), incorporating infiltration values estimated through in situ tests to leverage the impact of these data on energy model performance. Dynamic infiltration values were calculated using both the decay equation and the E\({}^{+}\) infiltration models with regressed coefficients, procedures that were previously explained. A key research objective was to compare and validate the novel calibration method against the classic and baseline models to determine if it offers enhanced predictive accuracy. To achieve this, three core objectives (O) were established, each aligning with a specific research question (RQ) to guide the study:  
1. O1: Validate the performance of the novel calibration approach against the classic method, focusing on the accuracy and robustness of model parameters. * RQ1: Does the novel calibration approach provide better performance and more accurate, robust estimates of model parameters compared to the classic method?
2. O2: Evaluate whether energy models that incorporate regression-adjusted EnergyPlus equation coefficients can reliably predict thermal performance, leveraging the validated indoor air quality model. * RQ2: Do energy models that use regression-adjusted EnergyPlus equation coefficients accurately predict thermal building performance, given the already verified indoor air quality model?
3. O3: Assess whether the infiltration model calibrated using the classic method complies with the ASTM D5157 standards to ensure physical relevance in representing air leakage.  
Figure 1: Model calibration process flowchart.  
RQ3: Does the infiltration model calibrated using the classic method comply with ASTM D5157 standard requirements?  
To accomplish these objectives, a triple validation was conducted to assess model performance using three variables: two thermal indicators and one related to indoor air quality. The agreement between simulated and measured dry-bulb temperature and CO\({}_{2}\) curves, along with total energy consumption during the free oscillation period, were analyzed.
## 3 TMD Design Method
### TMD with RIs (TMD1)  
In the first proposed solution, RIs are employed to provide the TMD with both stiffness and damping. The RI damping ratio, \(\xi_{is}\), usually ranges between 10% and 15%. The use of lead-rubber isolators (LRIs) can be considered when a higher damping coefficient is required, up to 35%.  
To implement RIs in the FEM of the building, the non-linear link element called 'Rubber Isolator' is used. This element has a bilinear force-displacement (F-d) relationship, like the one shown in Figure 10. The first branch of the diagram, of slope \(k_{1}\), describes the isolator behavior up to the yielding point of coordinates \((d_{1},F_{1})\), while the second branch, with slope \(k_{2}\), describes the post-yielding behavior up to the maximum force \(F_{2}\) and the maximum displacement \(d_{2}\). \(k_{eq}\) is the secant stiffness corresponding to the displacement that the RI undergoes.  
The isolators' design consists of choosing the number of devices and of determining their stiffness \(k_{eq}\) and equivalent damping factor \(\xi_{is}\) through the design method described above. In particular, \(k_{eq}\) is obtained by dividing the value of \(k\) obtained from Equation (7) by the number of isolators, and \(\xi_{is}\) coincides with the value of \(\xi\) calculated with Equation (8). Then, the values of \(k_{1}\), \(F_{1}\) and \(k_{2}\) are computed with the formulas provided by FEMA [45].
# Validation of Omega Subunit of RNA Polymerase as a Functional Entity
## 2 The Transcription Machinery of the Cells  
Expression of the genetic information stored in DNA is central to the sustenance of life. Gene expression consists of two major steps: transcription and translation. Transcription is the first step of gene expression, which starts with the synthesis of RNA from DNA by the RNA polymerase (RNAP) enzyme. Transcription is a tightly controlled process, necessitated by the essentiality of precise temporal and spatial gene expression. This exquisitely specific enzyme is multi-subunit machinery. It exists with varying degrees of complexity in all cells; however, it is structurally and functionally similar in all distant life forms. Bacterial RNAP can be considered as the most well-studied member of this family of polymerases. It consists of five main subunits, i.e., two \(\alpha\) (RpoA) subunits and one subunit of \(\beta\) (RpoB), and \(\beta^{\prime}\) (RpoC), the smallest subunit \(\omega\) (_RpoZ_) forming the enzyme, \(\alpha 2\beta\beta^{\prime}\omega\)[2]. Together they facilitate the elongation step of transcription, but they require \(\sigma\) factor (RpoD) to initiate transcription reaction from a specific promoter sequence. A single RNA polymerase primarily mediates the transcription of a bacterial genome. The purified _Escherichia coli_ RNA polymerase holoenzyme has a subunit structure of \(\alpha 2\beta\beta^{\prime}\omega\)\(\sigma\). Holoenzymes can be reversibly dissociated to yield core enzyme (\(\alpha 2\beta\beta^{\prime}\omega\)) and \(\sigma\) subunit. Like holoenzymes, the core enzyme is catalytically active, but transcription proceeds non-specifically without promoter recognition ability [3].
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 1 Introduction  
The continuing integration of unmanned aerial system (UAS) operations into the National Airspace System (NAS) requires new or updated regulations, policies, and technologies to maintain safety and enable efficient use of the airspace. One enabling technology to help address several UAS airspace integration gaps are airspace encounter models, which have been fundamental to quantifying airborne collision risk for manned and unmanned operation [1; 2; 3; 4; 5]. These models represent how aircraft behavior and their relative geometries evolve during close encounters. They have supported the development of surveillance and communication requirements [6; 7].
## 8 Technical Challenges and Potential Solutions
### Design Standards Issues  
Building standards are the key components that describe the architecture, structure, durability, facilities, reliability, sustainability, and safety requirements for designing traditional and PFISB structures. The current design standards for PFISB structures are based on the conventional limit state design. Therefore, the uniqueness of the design should be considered. Without proper technical specifications, designers and contractors spend a considerable amount of time designing and inspecting, influencing the project's quality and safety. Meanwhile, developing standards for PFISB technologies necessitates plenty of testing, particularly in the areas of an explosion, collision, fatigue, earthquake, winds, collapsing, and fire resistance.
## 5 Results
### Mediating Effects Analysis
#### 5.2.2 Empirical Test of the Mediating Effect of Corporate Sustainability Performance  
The mediating mechanism by which GPP impacts urban carbon emissions intensity through corporate sustainability performance is illustrated in Table 4. In Column (1), GPP exhibits a significant negative effect, which indicates that a 1% increase in GPP expenditures leads to a 1.548% reduction in urban carbon emissions intensity, thereby confirming the efficacy of the policy in reducing such intensity. Column (2) reveals that GPP significantly enhances corporate sustainability performance, which satisfies the first condition for mediation effects. This suggests that such procurement motivates companies to improve supply chain management and environmental practices, while simultaneously  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **(1) Urban Carbon** & **(2) Urban Green** & **(3) Urban Carbon** \\  & **Emissions Intensity** & **Innovation** & **Emissions Intensity** \\ \hline Green public procurement & \(-1.548\)*** & \(12.454\)*** & \(-1.400\)*** \\  & (0.402) & (4.518) & (0.399) \\ Urban green innovation & & & \(-0.012\)*** \\  & & & (0.002) \\ Control Variables & Yes & Yes & Yes \\  & \(-10.212\)*** & \(78.877\)*** & \(-9.270\)*** \\ Constant & (2.494) & (28.017) & (2.476) \\ Individual Fixed Effects & Yes & Yes & Yes \\ Temporal Fixed Effects & Yes & Yes & Yes \\ N & 2565 & 2565 & 2565 \\ R-squared & 0.633 & 0.025 & 0.640 \\ \hline \hline \end{tabular} Notes: Standard errors in parentheses. All variables mean-centered. ***\(p<0.01\).  
\end{table}
Table 3: Mediating effect of urban green innovation.  
improving corporate reputation through dual drivers of regulatory pressure and market incentives. In Column (3), corporate sustainability performance negatively affects urban carbon emissions intensity. Specifically, each unit improvement in corporate sustainability performance results in a decrease of 0.171 units in urban carbon emissions intensity, thus meeting the second condition for mediation effects.  
Overall, the carbon emissions intensity reduction effect of GPP in urban areas can be quantitatively assessed through corporate sustainability performance, thereby yielding a result of 1.206 \(\times\) (\(-\)0.171) = \(-\)0.206, which accounts for 13.32% of the total effect (\(-\)1.548). After controlling for corporate sustainability performance, the direct effect of GPP decreases to \(-\)1.115 (\(p\) < 0.01). Both the direct and indirect effects remain negative with no evidence of compensation, thereby supporting a partial mediation effect and thus validating H3.