esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Methodology  
### Research Methodology  
Intelligent Transportation Systems aim to provide a high level of safety and efficiency for drivers, and monitoring highway traffic in real-time is one of its highest priority applications. However, highways extend for hundreds of miles and vehicles move very fast making monitoring and predicting traffic flow patterns in real-time by utilizing a centralized system very challenging [52; 53]. This research proposes a scalable distributed machine learning-based scheme for Internet of Vehicles to predict highway traffic average speed and detect abnormal traffic flow patterns. The experimental environment included the use of Python (3.10) for data preprocessing, Google Cloud Platform (GCP) for deployment, and Apache Spark for data processing and real-time analytics using Kappa Architecture. The proposed model was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-Squared (R\({}^{2}\)), and Adjusted R-Squared (AR\({}^{2}\)) metrics. We assume that the I-95 highway is divided into Complex Segments (CSs) where each CS is located between two exits as represented in Figure 3. Each CS consists of 4 segments and each segment has a Road Side Unit (RSU). In each segment (cluster), a leader vehicle (cluster head) will collect traffic information from the vehicles in that segment using Vehicle to Vehicle (V2V) communication. This information will be sent to the RSU, where machine learning models are implemented and ready to provide prediction results in real-time. This method allows us to break down a complex problem into small sub-problems where different machine learning algorithms can collaborate and provide the traffic prediction results in parallel. Since the geographical location of each cluster provides different traffic flow patterns, different learning algorithms trained on different hyper-parameter's values are utilized to build a distributed machine learning model. These hyperparameter's values are determined based on the different traffic flow patterns. So, each segment has specific hyper-parameter's values based on its historical traffic flow data. The block diagram in Figure 4 provides an overview of the DMLRF model's structure for predicting the average speed of highway traffic. More details on the proposed model is available in Section 6.  
Figure 3: A highway distributed machine learning architecture.
###### Abstract  
Two novel esterase genes, _est2L_ and _est4L_, were identified from a previously constructed metagenomic library derived from an oil-polluted mud flat sample. The encoded Est2L and Est4L were composed of 839 and 267 amino acids, respectively, without signal peptides. Est2L was a unique fusion type of protein composed of two domains: a domain of the CzcO superfamily, associated with a cationic diffusion promoter with CzcD, and a domain of the acetylesterase superfamily, belonging to family IV with conserved motifs, such as HGG, GXSAG, and GXPP. Est2L was the first fused esterase with a CzcO domain. Est4L belonged to family V with GXS, GXSMGG, and PTL motifs. Native Est2L and Est4L were found to be in dimeric and tetraneric forms, respectively. Est2L and Est4L showed the highest activities at 60 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, respectively, and at a pH of 10.0. Est2L preferred short length substrates, especially _p_-nitrophenyl (pNP)-acetate, with moderate butyrylcholinesterase activity, whereas Est4L showed the highest activity with pNP-decanoate and had broad specificity. Significant effects were not observed in Est2L from Co\({}^{2+}\) and Zn\({}^{2+}\), although Est2L contains the domain CzcD. Est2L and Est4L showed high stabilities in 30% methanol and 1% Triton X-100. These enzymes could be used for a variety of applications, such as detergent and mining processing under alkaline conditions.  
metagenomic library; CzcO-containing family IV esterase; family V esterase; substrate specificity 22 June 2021
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating the Water Environment Impact on Energy Production
#### 3.1.3 Loss of Efficiency of PV Modules Over Time  
The combination of high temperatures and high relative humidity has been shown to significantly impact the performance loss rate (PLR) of photovoltaic (PV) modules. In environments with relative humidity exceeding 70%, the risk of moisture penetration increases, leading to greater corrosion of metal joints, necrosis of the polymer, and yellowing of its color [42].  
However, initial experimental evidence from the largest testbed of FPV systems currently in operation, located in the Tengeh Basin (Singapore), a tropical climate location, indicates that over a three-year observation period, the PLRs of floating systems are comparable to those of a rooftop reference system installed near the basin [43].
## 3 Results and Discussion  
### Impact of the ICXT on the System Performance  
The ICXT varies randomly over time. The time variations can be of the order of a few minutes or even hours [20]. Therefore, the concept of time fraction and short-term average inter-core crosstalk (TAXT) has been introduced to assess the performance of MCF-based systems [20]. A time fraction is a small-time duration much shorter than the ICXT decorrelation time, where the ICXT is considered constant [18]. STAXT is the average power of the ICXT measured during a time period much shorter than the ICXT decorrelation time. If the time interval between time fractions is much larger than the ICXT decorrelation time, then the ICXT varies from time fraction to time fraction. This random variation of the ICXT can cause high performance changes over time.  
Figure 7 shows the impact of the ICXT on the system. In Figure 7a, the STAXT is presented as a function of the time fractions. The results show the high variation of the ICXT power at the output of the interfered core, such as reported in [5; 18; 20; 22]. In Figure 7a,b, the ICXT level is \(-15\) dB. Figure 7b shows the calculation of the BER along 500 different time fractions. The results show that the ICXT can cause high BER variations along time which can remain for several minutes or even hours, leading to system outage.
## 4 Discussion
Despite these challenges, the ability of LSTMs to process sequences and their robustness to \"input shifts\" (changes in the input data distribution over time) make them invaluable for dynamic systems analysis. The results contribute to the ongoing debate regarding the selection of appropriate machine learning techniques for predictive maintenance in manufacturing. While SVM and boosting algorithms are well-established in the literature, their comparative analysis with deep learning techniques like LSTM in the specific context of milling operations offers new insights. This study highlights the need for further investigation into hybrid models that combine the memory capability of LSTM with the robust classification features of SVM or decision trees, potentially leading to models that better capture the temporal and spatial characteristics of tool wear. Furthermore, our findings underscore the importance of developing domain-specific pre-trained models for time-series analysis in industrial applications. This could significantly reduce the data requirements and computational costs associated with training models from scratch. This study is not without limitations. The relatively small dataset and the specific focus on milling operations limit the generalizability of the findings. Future research could explore the application of these models across different types of machining processes and with more diverse datasets to validate and extend our results. Additionally, integrating sensor fusion techniques to combine multiple types of data, such as vibration, acoustic, and force measurements, may enhance the accuracy and robustness of the predictive models. Finally, exploring feature engineering and selection methods tailored to sequential data can further optimize LSTM performance.
## 4 Results and Discussion  
### \"Perfect\" Signal Due to Validated Simulated Fiber Break  
The experimental signals and the simulated signals are presented in order to show the validation of the modeling approach before expanding this numerical investigation to different media and different damage mechanisms. Figure 5a,b shows the direct comparison between experimental signals and simulated signals resulting from fiber break, in time and frequency domains. In this case, sensor C was located 12 mm from the fiber break. Most of the energy of the wave was concentrated below 400 kHz. We could observe a good agreement between the experimental and simulated signals. Figure 5c shows the evolution of two descriptors (amplitude and roll-off frequency) versus the distance of propagation for the experimental data and the simulated data. We also observed a good agreement.  
Accordingly, we could use this simulation to extrapolate the signal recorded by a perfect virtual point-contact sensor located at the same position (Figure 6a,b). Contrary to the case with the AE sensor, most of the wave energy was concentrated up to 400 kHz, and the peak frequency was around 500 kHz. Hence, on the surface of the specimen, without the sensor effect, the frequency content was higher. This material surface data, without the sensor effect, could be used to optimize the choice of sensor. The simulation also provided waveforms computed along a straight line on the surface (Figure 2a) in order to further analyze the characteristics of the wave propagation in the medium. Figure 7 shows the results for the 2D-FFT obtained with a virtual perfect point-like contact sensor and with the sensor nano 30 for the simulated data. Even if the identification of the excited modes was not straightforward due to the high number of eigenmodes able to be excited over the studied frequency range, we could observe the F11 bending mode, which could be compared to the well-known A\({}_{0}\) antisymmetric mode for an infinite plate. For a specimen of such thickness, in the frequency band of 1 MHz or for a frequency-thickness product up to 2.8 MHz.mm, the source excited a significant number of modes, the predominant ones being bending modes. The 2D-FFT of the signals convoluted by the response of a nano 30 sensor are shown in Figure 7b. When this sensor was used, frequencies above 400 kHz were eliminated. These results clearly show the loss of information related to the choice of sensor. Some of the analyses in the following parts were performed for the perfect virtual point-contact sensor in order to avoid the sensor effect for the parametric studies.
## 3 Detailed Design of Viscous Plane Damper
### Theory of Viscous Plane Dampers Using Orifices  
The proposed viscous plane damper is designed to create a pressure differential across a piston, using a small orifice to force fluid through, which generates a damping force. Energy dissipation occurs due to the resistance encountered by the fluid as it moves through these orifices, which leads to a pressure drop and converts mechanical energy into heat. Therefore, in the developed viscous plane damper, the fundamental mechanism involves the movement of fluid (oil or another viscous liquid) through small orifices as the piston moves within a sealed cylinder. The damping force arises from the pressure differential generated by the fluid being forced through these orifices. The Key Theoretical Concepts are demonstrated as follows:  
Figure 4: Elevation section of the viscous plane damper.  
Figure 5: Detailed drawing of parts of viscous plane damper.  
Fluid Dynamics and Pressure Drop: The primary theory governing viscous plane damper is based on fluid mechanics, particularly the principles of Bernoulli's equation and orifice flow. As the piston inside the damper moves, it pushes fluid through small orifices, causing a pressure drop between the two sides of the piston  
The relationship between the pressure drop (\(\Delta\)P) and the fluid flow rate (Q) through an orifice is nonlinear, typically governed by the orifice equation:  
\[\Delta\mathrm{P}\;=\;\mathrm{K}\times\frac{\mathrm{Q}^{2}}{\mathrm{A}^{2}}\]  
where  
\(\Delta\mathrm{P}\) is the pressure differential across the orifice;  
\(\mathrm{K}\) is a constant depending on fluid properties and orifice characteristics;  
\(\mathrm{Q}\) is the volumetric flow rate of the fluid; and  
\(\mathrm{A}\) is the cross-sectional area of the orifice.
## 2 Materials and Methods  
Data  
Meteorological and social statistical data were used to conduct drought risk assessments (Table 1). Based on precipitation, temperature, and drought-affected areas as indicators in the drought hazard assessment, total agricultural output value, water resources, and forest area were used as vulnerability assessment factors, while permanent population, cultivated land area, and the gross output value of agriculture, forestry and fisheries were used as exposure assessment factors. Many semiarid and arid-prone areas have significant competitive advantages for irrigated agriculture because they are rich in resources, agricultural activities in the area are inexpensive, and there are few land use alternatives [18]. Therefore, the use of water resources and agroforestry to assess drought risks is considered reasonable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Index Name** & **Historical Period** & **Source** \\ \hline \multirow{2}{*}{Meteorological data} & Temperature, & \multirow{2}{*}{1988–2017} & National Meteorological \\  & precipitation, etc. & & Information Center \\ \hline \multirow{2}{*}{Social statistics} & \multirow{2}{*}{Population, GDP, etc.} & \multirow{2}{*}{1988–2017} & China Statistical \\  & & & Yearbook \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sources of drought risk assessment data.  
Wide-ranging uncertainties about future climate change coercion and responses are expected by the 21 st century, so future scenarios need to be utilized to explore the potential consequences of distinct response options [19]. When applied to climate change research, scenarios help to assess the uncertainty of human contributions, the responses of Earth to anthropogenic activities, and the impacts of different mitigation and adaptation methods (actions that promote responses to new climatic conditions). Specifically, we conducted drought risk estimates from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) [20], land use and population GDP data (see Table 2 for a complete list of all model expansions).  
Consistent with many other studies, we focused on lower emission RCP (Representative Concentration Pathway) 4.5 scenario and high emission RCP 8.5 scenario [26]. Temperature, precipitation and evaporation were used to estimate hazards; primary land, second land, urban and secondary land transition to crop were used to estimate vulnerability; and population, GDP, crop, and pasture were used to estimate exposure. Studies have shown that changes in land use caused by human activities can affect global climate change by changing the carbon content of the atmosphere [22; 27]. For example, vegetation degradation can cause increased drought and ecosystem degradation [28]. Therefore, it is reasonable to consider land use in drought risk estimation.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Machine Learning Algorithm
#### 4.2.1 Neural Network  
The neural network (NN) algorithm is based on the information obtained by each UAV sensor, and it quickly obtains the actions that the UAV should take. The neural network has a nonlinear complex network structure composed of a large number of nonlinear unit connections. By simulating the control and feedback functions of the human brain function, a nonlinear mapping system is formed to obtain the mapping relationship between the state space and the action space; then, it completes the UAV formation trajectory planning through its own powerful learning ability and rapid planning ability. Figure 22 is a neural network model.  
[PERSON] and [PERSON] [120] designed a fast trajectory planning method using an improved neural network algorithm. This method combined a dynamic adjustable step size with a neural network and added adaptive learning factors for trajectory planning, which solved the problem that the trajectory of a traditional neural network algorithm may not be optimal in the presence of threats.  
[PERSON] et al. [121] proposed a method to cover the trajectory planning problem of UAVs driven by artificial intelligence, which combined a distributed artificial neural network (ANN) and A* algorithm to solve the problems of inadequate grid resolution and low trajectory efficiency of traditional methods.
###### Abstract  
Isomers of hexachlorocyclohexanes (HCHs) and metabolites of dichlororibhenyltrichloroethanes (DDTs) are still frequently detected worldwide in considerable amounts, even decades after their prohibition. Carbonaceous materials (CMs) have been shown to significantly reduce risks of propagation to humans by binding the hydrophobic organochlorine pesticides (OCPs) present in aquatic sediments. In the present study, black carbons extracted from natural sediments, and artificially produced black carbons, including black carbons by burning rice straw at 450 and 850 \({}^{\circ}\)C, and a commercial activated carbon were compared to investigate the factors affecting the sorption of \(\gamma\)-HCH (lindane) and \(p\),\(p^{\prime}\)-dichlororibhenyldichloroethylene (\(p\),\(p^{\prime}\)-DDE) on CMs. The results indicated that when the proportion of CMs to total organic carbon (\(f_{\text{CM}}/f_{\text{OC}}\)) was greater than 0.35, CMs played a leading role in the sorption of lindane and \(p\),\(p^{\prime}\)-\(DDE\) by the sediments. The sorption contribution rate of CMs could reach up to 64.7%. When the ratio of \(f_{\text{CM}}/f_{\text{OC}}\) was less than 0.10, CMs played a minor role in the sorption. In addition, the nonlinearity of the sorption isotherms was strengthened with the increasing the proportion of CMs to total organic carbon. Our findings show that \(f_{\text{CM}}/f_{\text{OC}}\) value is a principal parameter for assessing the sorption capacity of sediments added by CMs for OCPs.  
**Keywords:** organochlorine pesticide; hydrophobic organic compound; persistent organic pollutant; black carbon; activated carbon; rice straw
# Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants
## 1 Introduction
This method effectively reflected a number of characteristics including the diversity among different individuals, the randomness of behavior, the environmental relevance, and the diversity among different individuals. [PERSON] et al. [PERSON] et al. (2020) established a quantitative stochastic model based on user actions to predict the air-conditioning usage status. The mathematical descriptions affecting the use of air-conditioning were regarded as a series of conditional probabilities, which had a high accuracy rate. [PERSON] et al. [PERSON] et al. (2019) proposed a new method for describing personal behavior and built an energy using behavior model and algorithm process based on the \"subject and event mechanism\". This model focused on the overall process of people in buildings from mobile behavior to energy use behavior. Influenced by the surrounding physical environment, social environment, and personal subjective factors, such as emotional changes, habit adjustments, etc., the behaviors of office occupants have a large degree of uncertainty. The traditional point prediction models cannot describe them well, while probabilistic models can provide more information about the uncertainty. This paper proposes an MCMC based method for predicting office occupants' energy use behavior. The on-off state data of electrical equipment and indoor environmental parameters are collected through the indoor electrical Internet of Things system (IEIoTS). The Metropolis-Hastings MCMC algorithm is used for random sampling of the parameter values of the fitting function, and the sampling theorem is used for judgment, so as to obtain the optimal energy use action model, energy use working hours model, and air-conditioning energy use behavior model of the office occupants. Compared with other prediction approaches, this method can predict the user behavior more quickly, because the accept-reject step in Metropolis-Hastings MCMC algorithms can make the probability distribution model converge more efficiently to the optimal parameters. Understanding users' energy use behaviors can provide decision-making reference for intelligent control of building equipment. For example, air-conditioners and water dispensers in the office can be turned on automatically during electricity price valley periods and maintained in the specified state in advance before working, which can effectively reduce the electricity bill while providing users with a comfortable working environment. The rest of the paper is organized as follows: Section 2 introduces the properties of the Markov chain and the principle of the Metropolis-Hastings (MH) MCMC algorithm. Section 3 presents the procedure of the proposed method to predict energy use behaviors of office occupants. The comparative experiments are carried out and the results are discussed in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Case Study and Simulation
### Bandwidth of the Control Loop  
The accuracy of the PLL depends on the bandwidth. Analysis of the behavior of bandwidth in Figure 12 shows that clock stability improves with the decreasing bandwidth. However, PLL bandwidth cannot be set arbitrarily small because the dynamic stress tolerance may be exceeded. Hence the performance of the clock must be taken into account when designing the bandwidth of PLL.  
Figure 11: Comparison of the Allan deviations of a free run slave clock and a controlled clock for different residual errors. Data from the 1 st hour to the 24 th h are truncated to evaluate the synchronization performance. The standard deviations of the stable loops and the free run clock are 0.004 nanosecond, 012 nanosecond, 0.24 nanosecond, 0.47 nanosecond and 10 nanosecond, respectively.