esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 1 Introduction  
With the rapid development of the economy and the continuous increase in the total length of highways and the number of vehicles, the demand for highways is rising. However, the rapid growth of vehicle numbers does not match the speed of infrastructure development, leading to increasingly prominent traffic congestion issues [1]. Promoting a balance between the supply and demand of highways can no longer rely solely on increasing supply [2]. Instead, it should focus on optimizing the efficiency of existing resources from the perspective of traffic management and control.  
Toll stations are an essential component of highways, and as intermittent flow facilities, they are essentially bottlenecks in traffic flow [3,4]. If the toll station's capacity is insufficient, vehicles queuing and congesting at the toll station will limit the overall traffic efficiency of the highway. Congestion on highways and toll booths will have a huge cost [3,5,6]. Therefore, improving the toll station's capacity is one of the key measures to ensure the efficient operation of highways. Recognizing the severity of this issue, an increasing number of countries, such as China [7,8], the United States [9], India [10], Japan [11], and Serbia [12,13], are devoting greater attention to addressing congestion and queuing problems at highway toll stations.  
Currently, the toll collection methods on highways are diversifying, including Electronic Toll Collection (ETC for short) and Manual Toll Collection (MTC for short) [14,15]. However, if the distribution of ETC lanes and MTC lanes is not properly balanced, it may result in queuing congestion at MTC lanes, while ETC lanes remain idle for extended periods. In addition to the diversification of toll methods, the categories of highway users are also becoming more varied [16]. As shown in Figure 1, based on different tolling methods, users can be roughly classified into three categories. First, Connected Autonomous Vehicles (CAVs) can only use ETC lanes for automatic toll payment throughout the journey. Second, manual toll-collection human-driven vehicles (MTC-HVs) must use MTC lanes for payment. Finally, electronic toll collection human-driven vehicles (ETC-HVs) can flexibly choose between ETC or MTC lanes. This diversification of user types reflects the trend in transportation technology development and also places higher demands on the management of toll systems. It requires further optimization of lane allocation and management strategies to accommodate the needs of different types of vehicles. Moreover, vehicle composition and traffic demands vary across different countries and regions at different times, and this dynamic demand requires toll systems to have sufficient flexibility to adapt to traffic fluctuations [12,17].  
Therefore, the rational distribution of ETC and MTC lanes at toll stations is an effective way to improve highway traffic efficiency [18].
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 2 Related Work
This paper directly addresses the new continuous space wireless communication tower placement problem. Given a two-dimensional area, a list of users distributed in the area, and the number of wireless communication towers to be deployed in this area, the new continuous space wireless communication tower placement problem is to find a placement of the wireless communication towers in the area such that the total number of users that can be covered by the wireless communication network is maximal. Figure 1: A scenario where a discrete algorithm cannot find an optimal solution. In this figure, users are marked with ‘\(\circ\)’, A, B, C, and D are grid points where a wireless communication tower can be placed, the solid line circle represents the coverage of the wireless communication tower placed at marked ‘\(\times\)’, and broken line circles are the coverage of the wireless communication tower placed at the grid points A, B, C and D.  
In this paper, we propose a hybrid simulated SA approach to the new continuous space wireless communication tower placement problem. To our knowledge, no other researcher has applied SA to any continuous wireless communication tower placement problem. In this paper, we model the continuous wireless communication tower placement problem as a continuous function optimization problem and solve it using a hybrid SA algorithm that capitalizes on the powerful exploration capability of SA and the efficient and effective exploitation capability of a local search procedure.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 3 Experimental Setup and Linearization Results
### Experimental Setup  
The proposed ET PA linearization approach was evaluated using a remote Matlab-controlled digital linearization test bench, as shown in Figure 8, interfacing arbitrary waveform generation (AWG M8190A from Keysight) and signal acquisition (DSO RTP084 from Rohde and Schwarz) instruments. The system-on-chip ET PA board was developed by HiSilicon for mobile applications. The envelope signal is generated by the AWG and is pre-amplified by the differential amplifier, then sent to the ETM input with a differential connection. The following tests results were obtained by operating the ET PA with a 5G NR signal of 60 MHz bandwidth and 6.8 dB of PAPR at band 7 (i.e., 2.55 GHz). The shaping function to dynamically supply the ET PA is the slew-rate reduction (SR) envelope described in Section 2.1.
## 3 Research Results
### Questionnaire Survey Results
#### 3.2.3 Adequacy and Reliability of the Collected Data  
The collected data listed in Table 7 should be verified to be adequate and reliable before they can be used for further analysis of the effectiveness of the proposed system. For this purpose, the SPSS software package was used to compute the Kaiser-Meyer-Olkin  
(KMO) measure values and the significance values of [PERSON]'s sphericity test, as well as the Cronbach's alpha coefficient value of the collected data. The results are shown in Tables 8 and 9. From Table 8, it can be seen that the value of the KMO measure is 0.851, larger than the threshold value of 0.50, and that the significance value of [PERSON]'s sphericity test is 0.000 (nearly zero), smaller than the threshold value of 0.05 [42]. Furthermore, the computed [PERSON]'s alpha coefficient value was as high as 0.909, as seen in Table 9, which is larger than the threshold value of 0.70 [43]. These facts indicate that the data set is both adequate and reliable for further analysis, as described in the subsequent sections.
## 5 Case Studies
### Impact of Noise Included in the Measured Signal  
When the signal of (13) includes Gaussian white noise, the signal-to-noise ratio is 10 dB. Figure 7 shows the detection performances of STFT, CWT, FSST, and SSWT. It is observed that SSWT leads to the most accurate harmonic detection compared to the other TFA methods.  
Figure 6: Performances of detection for multiple frequency components signal of (13): (**a**) frequency-modulated signal, and results of (**b**) STFT, (**c**) CWT, (**d**) FSST, (**e**) SSWT.
# Preparation and Characterization of Polymer-Modified Sulphoaluminate-Cement-Based Materials
## 3 Results and Discussion
### Characterization  
#### 3.3.1 Fourier Transform Infrared Spectroscopy (FTIR) Analysis  
The Fourier transform infrared spectrometer was utilized to compare and analyze the sulphoaluminate cement specimen (a) and polymer-modified sulphoaluminate cement specimen (b). The experimental results are presented in Figure 3.  
The characteristic peak at 3467.3 cm\({}^{-1}\) observed in the sulphoaluminate cement specimen can be attributed to a strong stretching vibration of O-H bonds in cement hydration product ettringle. Additionally, a minor peak at 1618.05 cm\({}^{-1}\) corresponds to its bending vibration of O-H bonds. In the polymer-modified sulphoaluminate cement specimen, a new band at 3634.75 cm\({}^{-1}\) appeared, indicating stretching and shrinking vibrations of N-H bonds in polymers. Furthermore, a relatively intense absorption peak at 3431.19 cm\({}^{-1}\) was observed due to stretching vibrations of O-H bonds in cement hydration product ettringle. The polymer-modified sulphoaluminate cement specimen exhibited a significant C=O stretching vibration peak for carboxylate ions at 1671.81 cm\({}^{-1}\) and a C-O stretching vibration at 1431.73 cm\({}^{-1}\), suggesting that monomers undergo polymerization reactions leading to polymer formation. Compared with the sulphoaluminate cement specimen's peak width, there was decreased intensity for the [SO4\({}^{2-1}\)] (a hydration product of ettringle) stretching vibration phase observed at 1115.7 cm\({}^{-1}\). Moreover, the N-H vibrational peak shifted towards lower wavenumbers (1022.98 cm\({}^{-1}\)), providing evidence of new chemical bond formation between cement hydration products and polymers. Peaks near 900 cm\({}^{-1}\) and 500 cm\({}^{-1}\) could be ascribed to bending and stretching vibrations of Si-O and Al-O bonds, respectively.  
Based on the comprehensive analysis above, it can be seen that the monomer acrylamide was in situ-polymerized in the cement-based material to form a polymer. At the same time, a new network structure was formed between the polymers and cement hydration products. This network structure improved the compatibility and stability of cement-based materials.  
\begin{table}
\begin{tabular}{c c c} \hline
**Optimal Combination** & **Flexural Strength** & **Compressive Strength** \\ \hline A1B4C5 & 4.97 & 28.68 \\ A3B4C5 & 9.55 & 36.03 \\ A5B4C5 & 5.76 & 30.44 \\ \hline \end{tabular}
\end{table}
Table 6: Optimal combination comparison.  
Figure 3: Infrared spectrogram of the sulphoaluminate cement specimen (**a**) and polymer\(-\)modified sulphoaluminate cement specimen (**b**).
###### Abstract
Moreover, an SNG metamaterial with dimensions of 5 \(\times\) 5 \(\times\) 1 mm\({}^{3}\) which comprising a concentric ring, along with a cross line, was designed on an FR-4 substrate [25]. It exhibited dual resonance peaks at 13.9 GHz and 27.5 GHz and was used to enhance the performance of a microstrip transmission line. A metamaterial was designed on an elliptical graphene nanodisk with a periodic pattern on a thin SiO\({}_{2}\) dielectric layer, as reported in [26]. Recently, two ceramic dielectrics were synthesized using MGa\({}_{2}\)O\({}_{4}\) (M = Ca, Sr) and LiF for to enhance the gain and performance of antennae [27; 28]. An MM was designed using critical coupling at the gaps of two SRRs for total broadband transmission electromagnetic (EM) waves in a C-band application [29]. A cadmium sulfide (CdS) nanocrystalline coating with conducting polyaniline was designed to synthesized polyaniline-coated CdS nanocomposites characterized by UV-vis absorption [30]. In 2022, [PERSON] et al. designed a nanocomposite using a potentiostatic method, which offered excellent electrocatalytic activity for nitrite oxidation [31]. In his research article, we present a new metamaterial that is an aggregation of three modified octagonal rings, along with a split-ring resonator. This power tiller wheel-shaped MM is compact in size, with an EMR of 11.61. In numerical simulation, it exhibits quadband resonance peaks at 2.35, 7.72, 9.23 and 10.68 GHz, covering the S, C and X bands. Moreover, it also exhibits negative permittivity (\(\epsilon\)) and a negative refractive index (\(n\)), with simultaneous near-zero permeability (\(\mu\)). Such characteristics can be applied to comprehend various electronic components with different features and utilities. The main aim of this simple but first-hand design is to target versatile uses in wireless communication. The simulated results are verified by validation processes, confirming the reliability, consistency and efficiency of the proposed metamaterial. The ADS simulated result using a circuit model and Ansys HFSS 3D high-frequency software (high-frequency structure simulator) results show excellence harmony with the CST results.
## 2 Data and Methods
### Study Area and Data
#### 2.1.2 Earthquake Event Data  
The United States Geological Survey (USGS) provides real-time updated global earthquake catalog data, encompassing detailed information on seismic events such as occurrence time, location, magnitude, depth, and more. This dataset serves as a valuable resource from which the features for sample analysis can be directly extracted.
# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 4 Experimental Results and Analysis
### Original Model  
The experimental data were divided into 70% for training and 30% for testing. Considering the convenience and resource requirements of the computational environment, the experiment was conducted using the following hardware configuration: CPU: 12 th Gen Intel(R) Core(TM) i5-12500H, GPU: NVIDIA GeForce RTX 3050 Laptop. The Python version used was 3.9.18, and the ResNet18 model was built based on this version. The deep learning frameworks and related Python libraries involved include PyTorch version 1.12.0 + cu116, Torchvision version 0.13.0 + cu116, NumPy version 1.26.4, Pandas version 2.2.1, and other components. After the data underwent the aforementioned segmented GASP method for two-dimensional transformation, they were input into the ResNet18 model for training and  
Figure 5: Example of a mixed gamma spectrum at a distance of 5 cm: (**a**) mixed gamma spectrum of \({}^{60}\)Co and \({}^{137}\)Cs; (**b**) mixed gamma spectrum of \({}^{238}\)U and \({}^{232}\)Th.  
testing. The model's performance was evaluated and analyzed using precision, recall, F1 score, and loss metrics.  
Through experimental testing, it was found that the learning rate (LR) did not converge when it exceeded 0.05. Therefore, the initial learning rate was set to 0.05. The Adam optimizer was used during training, with a dynamic decay strategy that reduces the learning rate to 30% of its previous value every 10 epochs to improve training efficiency. The loss function used was CrossEntropyLoss. To prevent overfitting, Dropout was applied, randomly dropping neurons to force the model to generalize better, with a dropout rate set to 0.5.  
After 100 epochs, the results are shown in Figure 6. The model converged around the 55 th epoch, achieving a precision of 99.9%, recall of 99.6%, F1 score of 99.7%, and a loss value of 0.0023 on the test set. These metrics reflect the model's efficient classification ability in the nuclide recognition task, demonstrating the powerful potential of deep learning techniques in complex feature extraction and pattern recognition. The overall recognition accuracy of the model reached 99.8%, validating the fact that the deep learning-based nuclide recognition model, combining residual networks and GASF technology, can achieve efficient and precise nuclide identification. The model's efficiency and reliability meet the standard recognition requirements.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 3 Supercritical Water Gasification (SCWG)
### Biomass SCWG Process  
Gasification with supercritical water, as well as other types of gasification, has been studied extensively for biomass [98]. These studies allowed laying the foundations for the understanding of the SCWG of plastic material.  
Supercritical water gasification of biomass is typically performed at temperatures between 600 and 650 \({}^{\circ}\)C and at a pressure of about 30 MPa. Above 600 \({}^{\circ}\)C, water acts as a strong oxidant. Carbon atoms are oxidized and CO\({}_{2}\) is preferably formed. Moreover, the use of water results in a high hydrogen yield, in fact hydrogen atoms not only from biomass but also from water are released to form hydrogen [99]. We can write an overall chemical reaction that describes the biomass SCWG process [96]:  
\[\mathrm{CH_{x}O_{y}+(2-y)H_{2}O\to CO_{2}+(2-y+x/2)\ H_{2}} \tag{1}\]  
where x and y are the elemental molar ratios of H/C and O/C of the biomass, respectively. The product of the reaction is the synthesis gas, whose quality depends on x and y.  
During the process, however, three other competitive reactions can occur:  
1. Steam reformation \[\mathrm{CH_{x}O_{y}+(1-y)H_{2}O\to CO+(1-y+x/2)\ H_{2}}\] (2)
2. Methanation for CO \[\mathrm{CO+3H_{2}\leftrightarrow CH_{4}+H_{2}O}\] (3)
3. Methanation for CO\({}_{2}\) \[\mathrm{CO_{2}+3H_{2}\leftrightarrow CH_{4}+H_{2}O}\] (4)
4. Water-gas shit reaction \[\mathrm{CO+H_{2}O\leftrightarrow CO_{2}+H_{2}}\] (5)  
Figure 3: Schematic diagram phase of water.  
As described by [PERSON] et al. (2020), the effects of pressure on the SCWG process are still not very clear. With an increase in pressure, the methanation reactions are shifted to the right (towards a smaller number of molecules) with an increase in the formation of methane at the expense of CO, CO\({}_{2}\), and H\({}_{2}\). Moreover, the increase in pressure causes a change in the density of the water. In particular, ionic reactions are favored at high pressure due to the ion stabilization effect given by the high density water. On the contrary, the radical reactions are disadvantaged.  
A typical SCWG plant is described in detail by [PERSON] et al. (2016). Below, we summarize the main features of the plant:  
1. Water and feedstock must be pressurized to the supercritical pressure required for the process. The biomass is then ground and mixed with water using an emulsifying agent, in order to obtain a pumpable liquid.
2. The resulting slurry is pumped into the reactor. The pressurized water is preheated by passing it through a heat exchanger, which exploits the heat of the product leaving the reactor. The pressurized water also passes through an externally heated preheater before entering the reactor.
3. The syngas obtained in the process is cooled and passed through a gas-liquid separator. The gas mixture passes through further purification equipment such as the scrubber or pressure-swing adsorption unit.
# Quadratic Model-Based Dynamically Updated PID Control of CSTR System with Varying Parameters +
## 2 Methods  
### The PID Controller  
In industry, more than ten particular forms of PID realizations can be found [4; 21]. In this paper, the parallel PID realization is used. The mathematical definition of parallel PID realization is given with Equation (1). As it can be observed, the control value is produced in one step and it depends on the current error as well as on previous control errors. The control error in the current iteration \(e^{(i)}\) is defined as the difference between the current values of the set-point (SP) and the process value (PV), \(e^{(i)}=SP^{(i)}-PV^{(i)}\). The PID tuning parameters are \(K_{p}\), \(K_{i}\) and \(K_{d}\), each corresponding to the P, I and D terms respectively. Finally, \(u_{0}\) is the bias in the control signal and \(T_{i}\) is the simulation time step:  
\[u_{PID}^{(i)}=u_{0}+K_{p}e^{(i)}\ +K_{i}\sum_{m=1}^{i}e^{(m)}T_{i}+K_{d}\frac{e^{( i)}-e^{(i-1)}}{T_{i}}. \tag{1}\]  
In Figure 1, the feedback control loop consisting of a PID controller and a control object (CO) is given.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 4 Methodology
### Theoretical Analysis
#### 4.1.3 Slant Range Analysis  
In the context of the above methodology, the visibility of spacecraft as per slant range is possibly denoted from 0 to 90\({}^{\circ}\). However, in practice it is fairly difficult to communicate with spacecraft due to urban obstacles at the lowest elevation angle [25]. The variation of the range of spacecraft with a different angle is shown in Figure 5 using the values from Table 3.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 3 Results and Discussion  
OpenDSS was used with MATLAB, interfaced through the COM server to implement CVR by changing the taps of four tap-changing transformers for a well-known and widely used test system, IEEE 30-Bus system [52]; its single-line modified diagram is shown in Figure 5. The test system has a 100 MVA apparent power base and three voltage bases of 132, 33 and 11 kV. The implemented algorithms are solely concerned with the 33-kV region, which has the most loads in the test system. Details on the test system concerning its loads, lines generators and other parameters can be found in [52]. A voltage-sensitive load model was used to represent the power consumed by a load as a  
Figure 4: Combined CVR and DLC algorithm flowchart.  
function of voltage, and hence each load in the IEEE 30-Bus system is represented with the appropriate ZIP coefficients model. The detailed development for ZIP model is described and documented in [53]. The IEEE 30-Bus system was also used to implement a DLC algorithm that shuts off certain loads if a certain criterion is met. In addition, the optimal DG placement algorithm was implemented to locate the optimal location for PV unit.  
The verification of the proposed algorithms was tested on the basis of different scenarios. Different cases were established by the commutation between the existence of DG, CVR and DLC, as follows:  
* Case I: Base case (without DG, CVR and DLC)
* Case II: with DG, without CVR and DLC
* Case III: with CVR, without DG and DLC
* Case IV: with DLC, without DG and CVR
* Case V: with DG and CVR, without DLC
* Case VI: with DG and DLC, without CVR
* Case VII: with CVR and DLC, without DG
* Case VIII: with DG, CVR and DLC.  
Figure 5: IEEE 30-Bus modified single line diagram.
## 2 Results and Discussion
### Volatile Compounds Analysis
#### 2.2.1. Esters  
Ester is one of the most important volatile species in wine aroma. It could be processed through the chemical interactions between alcohols and acids or released by yeast strains during alcohol fermentation [19; 20; 23]. The esters could mainly be categorized as acetate esters, ethyl esters, and other esters, which could contribute to wine fruity aroma [20; 23]. A total of 20 esters were found in the experiment wine samples (Table 2) and among them, the control wine showed the highest level in both the quantities and contents of volatile compounds. MF treatment showed the most significant influence in ester compounds, however, 9 of them were not detected and the total contents reduced by 59.87%, followed by BT fining with a 47.74% decrease, while SP fining wine showed only a 16.97% decrease in total ester content and without the absence in quantities. This is in agreement with what [PERSON] et al. found. Bentonite treatment resulted in significant removal of ethyl esters and fatty acids [24]. Ethyl hexanoate, ethyl octanoate, ethyl decanoate and ethyl dodecanoate were the dominant ethyl esters present in wine in appreciable concentrations, while ethyl acetate and isoamyl acetate were the most abundant acetate esters. Most of the esters followed the same decrease tendency in treated wines. MF wine showed the lowest concentration, whereas the contents of ethyl butyrate, diethyl succinate and diethyl malate were lower in BT fining wine.