esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 2 Materials and Methods  
In this study, we developed an innovative route optimization system that integrates multiple technologies to select the optimal path from a set of alternatives based on distance, average travel time adjusted for congestion, road safety concerning potholes, and road sensitivity incidents. The foundation of our system leverages TomTom's routing API [67] to gather comprehensive route data, including distance, estimated travel time, and traffic conditions. To enhance real-time road safety assessment, we employed an ESP32 microcontroller equipped with an MPU-6050 accelerometer and gyroscope sensor, alongside a GPS module, to detect and log potholes by identifying sudden variations in vehicle movement.  
To further enhance the system's responsiveness to traffic incidents, such as accidents or violent events, we integrated an ESP32-CAM module. This module continuously captures images of the traffic environment, which are then processed using Azure's Cognitive Services. Specifically, Azure's Content Safety feature was employed to automatically identify and classify incidents involving violence or accidents. The dataset used to test the efficacy of Azure's Content Safety consists of 300 images gathered from the internet that cannot be made available to the public through a reference due to the impossibility of obtaining the copyright from all its authors and the lack of a database with these type of images that can be cited. Upon detection, these incidents are reported in real time, ensuring that users receive up-to-date information on potential hazards along their routes.  
The software development was conducted using Microsoft Visual Studio with C#, providing an environment for building the application's backend and user interface. Visual Studio facilitated seamless integration with Structured Query Language (SQL) Server, which was used to store and manage data collected from the ESP32 devices. SQL Server ensured the handling of large datasets, including real-time pothole reports and traffic incidents. The Arduino IDE programmed the ESP32 microcontroller, enabling precise control over sensor data acquisition and real-time GPS tracking. This setup allowed the system to capture accurate location data and detect potholes effectively.  
The integration of these components results in a comprehensive route optimization tool that considers traditional metrics like distance and travel time and dynamically responds to real-time road conditions and safety concerns. By combining SQL Server for data management, Visual Studio with C# for application development, Arduino IDE for micro-controller programming, and Azure's AI capabilities for incident detection, our system provides a solution for optimal route selection.
## 2 Materials and Methods
### Determination of Genes Involved in Flavour Formation in Grape Berries  
The total RNA was extracted by using the PEXBIO Plant Fruit Kit (Beijing APEXBIO Biotechnology, Beijing, China) according to the manufacturer's instructions. The purity and integrity of RNA were analyzed by Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). cDNA was synthesized by using 1 \(\mu\)L of total RNA as a template using Takara PrimeScript(tm) RT reagent Kit with gDNA Eraser (Takara Biomedical Technology, Beijing, China). Reverse transcription dilution can be used directly for quantitative PCR. Actin (Accession No. EC969944) was selected as a reference to calculate expression profiles, primers sequences of _VvEcar_, _VvCCD1_, _VvE_ and _VvLIS_ used for quantitative real-time PCR (RT-qPCR) were designed according to Genebank ([[http://www.idtdna.com/primerquest/Home/Index](http://www.idtdna.com/primerquest/Home/Index)]([http://www.idtdna.com/primerquest/Home/Index](http://www.idtdna.com/primerquest/Home/Index)) accessed on 1 May 2022) and listed in Table S3.  
RT-qPCR was performed on a qTOWER 2.0 PCR detection system (Analytik Jena AG, Jena, Germany). The reaction mixture (25 \(\mu\)L) contained a1 \(\mu\)L cDNA template, 0.5 \(\mu\)L-specific forward, and reverse primers to each gene, 12.5 \(\mu\)L 2 \(\times\) UltraSYBR Mixture (CWBIO), and 10.5 \(\mu\)LddH2O. Thermal cycling conditions were as follows: 95 \({}^{\circ}\)C for 10 min followed by 95 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 1 min under 40 cycles. Fluorescence was collected in the second step, and the ddH2O was used to replace the cDNA as the control. Each sample was run in three replicates. Gene transcripts were quantified upon normalization to Actin gene by comparing the threshold cycle (Ct) of each target gene with the geometric mean of Actin and EF Ct. The relative quantification per each gene was calculated by the 2\({}^{-\Delta\text{Act}}\) method [20].
## 5 Results and Discussions  
We focused on using the Genetic Algorithm and the Support Vector. The dataset is unbalanced and consists mainly of living patients (85%); only predicting all live patients would result in a high accuracy score of 85%. As a result, accuracy would not be an acceptable score method for the model, and average precision score was used, which summarizes the precision-recall curve. There are also alternatives for balancing the classes, such as down-sampling or modifying class weights. Modification of the class weights was concentrated on because down-sampling would result in a tiny dataset to deal with, while up-sampling may have further confused the data.  
The death prediction rate and accuracy level increased with the class weight parameter at the expense of live-patient prediction. The average accuracy score also served as a decent summary of the precision-recall curve in this scenario. Figure 2 illustrates the confusion matrix or classification report that may be used to examine death predictions to understand how effective the model is for present purposes.  
To balance the death-to-life ratio, which was 15 to 85, the class weight argument was set to 'balanced.' The ramifications of changing this argument's ratio may be observed in Figure 3.  
Although higher class weights on deaths enhanced correct death forecasts, false death predictions increased with the drop in incorrect live predictions. In Figure 4, the impact of class weights can be seen. It is worth noting that the score dropped considerably at the 5.67 value, which is the equalizing point of the ratio of 15 to 85.  
Figure 2: Confusion matrix for the GA/SVM approach.  
As shown in Table 2, Random Forest performed relatively better than FPSO-SVM + SMOTE, PSO-SVM + SMOTE, SVM + SMOTE, and KNN + SMOTE. It achieved 0.83 accuracy, which was better than all of the other classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** \\ \hline RANDOM FOREST [1] & 0.83 & 0.91 \\ FPSO-SVM + SMOTE [22] & 0.6890 & 0.6612 \\ PSO-SVM + SMOTE [22] & 0.6435 & 0.5089 \\ SVM + SMOTE [23] & 0.6291 & 0 \\ KNN + SMOTE [24] & 0.6630 & 0.6545 \\ GA-SVM+ SMOTE & 0.85 & 0.91 \\ Proposed method & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 2: Comparison of existing approaches with respect to Accuracy and F1 score.  
Figure 4: Summary of the accuracy score curve with balanced weights.  
Figure 3: Summary of the precision–recall curve.  
As shown in Table 3, FPSO-SVM + SMOTE performed relatively better than PSO-SVM + SMOTE, SVM + SMOTE, and KNN + SMOTE. It achieved 0.6942 and 0.6813 for the G-mean and AUC, respectively, which were better results than those of all of the other classifiers. The proposed method method has been performed better as compared to FPSO-SVM + SMOTE and the other state-of-the-art algorithms.  
In Table 4, the proposed method GA-SVM + SMOTE outperformed PSO + SMOTE, SVM + SMOTE, and KNN + SMOTE. Its Accuracy and AUC results, which were 0.85 and 0.69, were better than those of the other classifiers. Table 5 shows that the proposed method performed better than PSO + SMOTE, SVM + SMOTE, and KNN + SMOTE.
## 2 Materials and Methods
### The Formulation of QASK, QFSK, and QPSK  
Multilevel transmission may be achieved by varying the amplitude, frequency, or phase of the carrier between more than two distinct values. The throughput of data transmission may be increased by having numerous carriers, as opposed to only one. Depending on the kind of modulation utilized, the carrier for the M-piece symbol may  
Figure 1: Examples of _ASK_, _FSK_, and _PSK_ modulation for a string bit of 10110100.  
have variable amplitude (MASK), frequency (MFSK), or phase values [37]. The binary information signal's bits are often separated into groups, each of which receives a distinct carrier. The bit group's amount of information is sent by this carrier. Multiple-level modulations often use the modulations QASK, OFSK, or OPSK. These modulations, which send out two bits at once, quadruple the transmission speed. The data transmission rate is significantly boosted with the addition of 8, 16, and 32 carriers. Moreover, as the number of carriers increases, the complexity of the demodulator circuits must also increase. Figure 2 illustrates the frequency _ASK_, _FSK_, and _PSK_ modulation with AWGN in 5 dB SNR.  
Figure 3 illustrates binary data ranging from 0 to 255 for quadrat-type modulations, while Figure 4 illustrates corrupted signals with SNR = 5 dB noise.
## 4 Case Study  
### Test System and Parameter Settings  
To validate the proposed model, a local grid in northeast China is used as a test system. The empirical data from this validation case exhibit a relatively low renewable energy penetration rate. However, MSTS demonstrates superior economic performance in scenarios with high renewable penetration. The power sources connected to the main grid comprise four CSs and five IPPs that are directly integrated into the grid without flexibility retrofitting. Each CS consists of two coal-fired generating units, one PV station, and one wind power plant. The specific composition of the case study power sources is detailed in Table 1. The scheduling time step (\(\Delta T\)) is set to 30 min, with a total rolling scheduling horizon (\(T_{\text{s}}\)) of 24 h, resulting in a total of \(N_{\text{Ts}}\) = 48 scheduling periods. Under this short-term scheduling framework, the short-term expected values of renewable energy generation are updated and corrected in real time at each time period, significantly reducing the impact of uncertainty in wind and PV power forecasting within the CS. The OPF voltage operational range is constrained within 0.94 p.u.-1.06 p.u. The DPR parameters are derived from [22]. Table 2 contains other parameters for the CS.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 3 SPinDP: Stream Purifier in Distributed Platform
### Purification Library  
The second component, Purification Library (Purification Library is available at [[https://github.com/dke-knu/i2](https://github.com/dke-knu/i2) am/tree/master/i2 am-core]([https://github.com/dke-knu/i2](https://github.com/dke-knu/i2) am/tree/master/i2 am-core) (accessed on 1 December 2023)), is composed of sampling and filtering algorithms specialized for data streams. To design Purification Library, we first analyzed existing algorithms that are applicable to data stream environments, and then developed new sampling and filtering algorithms specifically for  
Figure 5: Class structure of RDMA-Storm.  
data streams. In this section, we will provide a detailed description of Purification Library, which is built by constructing the enhanced algorithms.  
The Purification Library consists of a set of Apache Storm Topologies. Each Topology is composed of sampling and filtering algorithms optimized for data streams, and each algorithm can be executed individually or combined with other algorithms using a unified interface of the user authoring tool. SPinDP provides eight sampling algorithms, i.e., hash [14], systematic [15], reservoir [16], priority [17], KSample [19], UC KSample [26], binary Bernoulli sampling [20], and distributed binary Bernoulli sampling [27], and five filtering algorithms, including query filtering [21], Bloom filtering [22], Kalman filtering [23], and noise recommendation Kalman filtering [24]. Each algorithm is developed through the Topology-based design of Spouts and Bolts, which is optimized for stream environments and adopted to operate on Apache Storm and Kafka.  
In particular, UC KSample [26], distributed binary Bernoulli sampling [27], and noise recommendation Kalman filtering [24] are newly developed algorithms aimed at improving purification effectiveness and algorithm usability. Firstly, UC KSample aims to improve the reliability and accuracy of the sampling results. Secondly, distributed binary Bernoulli sampling aims to improve processing efficiency to support large-scale data streams. Finally, noise recommendation Kalman filtering aims to improve algorithm usability, making filtering accessible to non-experts and improving its utility. In SPinDP, these algorithms are implemented in the Apache Storm environment to provide a more advanced stream purification technology.
of energy utilization and uniformity, this paper changed the traditional light quantum model to an irradiance model, established a particle swarm algorithm suitable for this optical model, added a comprehensive evaluation of uniformity and energy utilization to the algorithm function, and finally used the algorithm to conduct experiments and simulations to provide a way for subsequent scholars to conduct plant lighting design to improve energy efficiency.  
Lasers have characteristics of narrow line width and concentrated energy. [PERSON] et al. used a single wavelength laser for plant lighting, which proved that lasers can be used for plant lighting such as traditional light sources [21; 22]. Plants rely on plant pigments to capture light energy [23]. The absorption spectrum of each plant pigment is specific. Combining the characteristics of plant pigments to capture light and the characteristics of laser light emission, we can use a variety of wavelength lasers to combine the same plant light source with the plant absorption spectrum. This improves plants' use of light energy as well as increases plant photosynthesis and yield. However, due to the limited current semiconductor technology, the production of full-band lasers is not possible, and the photoelectric conversion efficiency of high-power LEDs is low. Some scholars have proposed the use of lasers and LEDs to approximate plants with light, i.e., low-power LEDs provide the broad spectrum required by plants. The laser accurately fills up light at the absorption peak of the plant, as shown in Figure 2, so that the use of laser and low-power LED hybrid lighting has become a new generation of plant factory lighting technology research hotspot. Due to the narrow laser beam angle and the luminous intensity following the Gaussian distribution, new requirements have been put forward for the uniformity design of plant lighting. In order to solve the above problems, this paper developed hybrid laser and LED models suitable for plant lighting. Further, we established a particle swarm algorithm that comprehensively considers energy utilization and uniformity and uses this algorithm for experiments and simulations.  
Figure 1: Plant factory. (**a**) shows the plant factory laboratory; (**b**) is the model of the plant factory laboratory.
## 4 Experimental Results
### Visual Tracking Result
The results of AMF-MSPF are convincing as compared to the TSHT and PFKBOT methods as shown in Figure 5. Contrasting TSHT and PFKBOT, the proposed AMF-MSPF framework selects a new set of \(N_{\text{r}}\) features that are used to initialize MS procedure for every particle. These features are selected by the ranking module that is triggered whenever the re-sampling step occurs due to the degeneration problem. In contrast to other methods, our method is more accurate because it re-initializes the object based on local features. For a quantitative analysis and highlights of some of the prominent characteristics of our method Tables 3 and 4 are given in this regard.
## 2 Literature Review
[37] established the least squares SVM (LS-SVM) prediction model optimized by Aquila Optimizer (AO), which surmounted the problem of prediction error generated by the subjective key parameters of the LS-SVM model, and brought about an average increase of 7.25% in the predictive accuracy of the machine tool motorized spindle thermal errors. For the purpose of optimizing the thresholds and weights of the BP neural network, the PSO algorithm with variable inertia factor was adopted by [PERSON] et al. [38] to establish the thermal error model for the machine tool in 2014. As a result, the improved BP neural network model was capable of overcoming the restrictions of conventional BP neural network model, which gave rise to a higher predictive accuracy of 93.15%. Considering that BP neural network could not adjust separate arguments according to data updates in thermal error modeling, a revised BP neural network was proposed on the basis of global adjustment tactics by [PERSON] et al. [39] in 2020. By minimizing the square error of whole data, the revised BP neural network model induced a 50% increase in predictive accuracy. Furthermore, in the study by [PERSON] et al. [40] in 2011, the RBF neural network was adopted to make the established thermal error model more accurate. However, large amount of time was spent in determining the quantity of neurons in the hidden layer. For promoting the predictive precision of the RBF neural network model, [PERSON] et al. [41] put forward an improved RBF neural network model for the thermal error prediction in 2019, in which the PSO method was adopted to optimize the key arguments of the RBF neural network. Moreover, other improvements to the neural network have been made, such as deep learning convolutional neural network (CNN). Deep learning architectures are multilayer stacks of simple modules that allow computational models to learn data expression with multilevel abstraction [42]. Deep learning CNN has generated many breakthroughs in image processing [43; 44], finance [45; 46] and data mining [47; 48]. Based on deep learning CNN, [PERSON] et al. [49] proposed the axial and radial thermal error models for machine tool spindle, coupled with thermal images and thermocouple data to reflect the temperature field of the spindle. The results indicated that the predictive precision of the thermal error model may reach 90-93%.
# Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach
## 1 Introduction and State of the Art
### Characteristic Diagram-Based Correction  
Characteristic diagrams represent a set of input variables continuously mapped onto defined output variables. Between support points, data can be generated by multilinear interpolation, polynomials, B-splines, or other functions, depending on the type of the diagram [24]. Characteristic diagrams are largely independent of the structure they describe, making this approach flexible and locally applicable at the component level. In the state of the art, characteristics are currently mostly determined from experimental results. Alternatively, characteristic diagrams can also be obtained very efficiently by simulation based on finite element (FE) models using temperature and displacement fields [25]. These models are characterized by a very good representation of the temperature fields [26]. Because the physical machine tool is not required to generate the characteristic map, it is possible to create a wide variety of load case configurations by means of calculations based on the FE model that has been created and calibrated once. This modularization reduces the computational effort and enables the assignment of locally relevant input variables to each component input variable [27]. Current research focuses on the development of efficient grid structures of high-dimensional characteristics and FEM with multigrid solvers [28; 29; 30]. FEM-based sensitivity analysis is used to determine the optimal positions for temperature sensors as the basis of simulation-based characteristics, and smoothed grid regression is used in research work as an extended calculation methodology for characteristic diagrams [31]. It has been shown that a 40-80% reduction in the mean thermally induced error can be achieved through the application of simulation-based characteristics [32; 33; 34] and even more, when the characteristics can be updated with information regarding the current load case [35; 36; 37].
## 6 Testing and Measuring Performance: Analysis of PSO, GWO, and Hybrid PSO-GWO Methods
### Results
#### 6.4.5 Throughput Improvement  
The bar graph below shows the increase in total capacity (that is, the number of vehicles passing through the intersection) of each road.  
The index of performance improvement is 0.2090 for PSO, 0.6563 for GWO, and 0.89912 for hybrid PSO-GWO. The hybrid approach has far more momentum, demonstrating its ability to increase traffic flow. Figure 7 shows the improvement in performance achieved by the three methods.  
Figure 6: Average reduction in waiting time for the three proposed methods.