esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 1 Introduction
The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry, so that re-training of the network is not needed for different towing vehicles. The results show that the mass estimation error is less than 10%. By analyzing the algorithms mentioned above, two issues can be found relating to a lack of detailed research. Firstly, driving safety, tracking ability, driving comfort, and energy consumption should be optimized during ACC, and how to complete online computation efficiently should be solved. Secondly, the bus mass will change due to the passengers getting on and off at stations. Besides the vehicle mass, the road slope is another important parameter that can affect the ACC algorithm performance. In order to solve these two issues, a deep fusion method with MPC and EKF is designed in this paper. The contributions and novelties of this paper are as follows: (1) a novel multi-objective optimization algorithm based on nonlinear MPC is proposed; (2) the road slope and vehicle mass estimation make the proposed control strategy more applicable to actual situation; (3) some search skills are used in preceding horizon optimization to reduce the computing burden. This paper is mainly organized as follows. In Section 2, the dynamic model of the pure electric city bus is built. Section 3 mainly formulates the problems and introduces the optimizing indexes of ACC. The MPC multi-objective optimizing algorithm and vehicle mass and road slope estimated by EKF are designed in this section. Simulations and HIL test are carried out and results are analyzed in Section 4. Finally, conclusions of this paper are obtained in Section 5.
## 4 Data Processing  
Data processing consists of three main procedures: preprocessing, ship's attitude and magnetization correction, and post-processing. These procedures are detailed in the flow chart shown in Figure 4.
## 2 Fundamental Principles
### Mathematical Formulation
Signal Analysis for Singular Reflecting Objects  
When taking measurements of opaque, singular reflecting objects such as metallic surfaces, as is often the case in manufacturing scenarios \((N=1)\); this allows for a reduction of Equation (7) to the form shown in Equation (9). This removes the interference noise, which occurs between reflectors at different OPDs for \(N>1\):  
\[I_{D}(k)=\frac{\rho}{4}S\left(k\right)[R_{R}+R_{S1}]+\frac{\rho}{2}S\left(k \right)\left[\sqrt{R_{R}R_{S1}}(cos\left[2 kn\left(z_{R}-z_{S1}\right)\right]) \right]. \tag{9}\]  
Depth information can be obtained by performing an inverse Fourier transform on Equation (9) to form Equation (10):  
\[i_{D}(z)=\frac{\rho}{8}\gamma\left(z\right)[R_{R}+R_{S1}]+\frac{\rho}{4}\gamma \left(z\right)*\left[\sqrt{R_{R}R_{S1}}(\delta\left[(z\pm 2n(z_{R}-z_{S1})) \right])\right]. \tag{10}\]
## 3 Results
### Influence of Temperature and ER on Gas Composition  
Figure 3 shows the effect of temperature on gas composition for the different ER levels analyzed. Of the three components that provide energy to the gas, and which were possible to quantify, the most contributing is CO, followed by H\({}_{2}\) and CH\({}_{4}\). The percentage of CO in the gas was in the range of 6.9-20.2 mol %, while H\({}_{2}\) presented a proportion of 2.3-8.6 mol %. For its part, CH\({}_{4}\) showed gas concentration variations between 1.7 mol % and 5.8 mol %. Regarding CO concentrations, higher temperatures generally increased the CO concentration due to the enhanced breakdown of olive cake and increased reactions involving CO formation due to the Boudouard reaction, which is favored at higher temperatures. On the other hand, the data do not reveal a significant relationship between changes in temperature and the concentrations of H\({}_{2}\) or CH\({}_{4}\) in the gas product. At an ER of 0.1, the concentrations of H\({}_{2}\) and CH\({}_{4}\) were relatively higher compared to ERs of 0.3 and 0.5 and exhibited less variation with changes in temperature. At an ER of 0.3, the concentrations of hydrogen (H\({}_{2}\)) and methane (CH\({}_{4}\)) demonstrated less consistent behavior across different temperatures, with distinct peaks observed. At an ER of 0.5, the H\({}_{2}\) and CH\({}_{4}\) concentrations were relatively lower than at ERs of 0.1 and 0.3 and showed less variation with temperature changes. The results are consistent with previous works [22; 23]. The increase in CO concentration with the rise in temperature can be explained by the increase in the rate of both water gas and Boudouard reactions [24]. Regarding the slight change in CH\({}_{4}\) concentrations, it has been reported that this is mainly due to the simultaneous formation and consumption of CH\({}_{4}\) in exothermic reactions at low and high temperatures, respectively [25].  
In relation to the molar percentage of N\({}_{2}\), this was in the range 45.1-61.7 mol %, increasing as the ER used was raised, but it was practically constant with the temperature. A deep analysis suggests that both the H\({}_{2}\) and CH\({}_{4}\) percentages decrease as the ER increases. This is because increasing the ER introduces more air in the gasifier, improves the oxidation reaction rate more than reforming and cracking reactions [26], and eventually enhances the formation of more CO\({}_{2}\) and H\({}_{2}\)O. However, the percentage of CO increased as the ER increased from 0.1 to 0.3, and then it decreased when the ER was increased to 0.5. Then, lower ER values resulted in higher CH\({}_{4}\) and H\({}_{2}\) concentrations, but CO concentrations depended on the range of ER analyzed. This different effect on CO concentrations was also observed by [PERSON] et al. [27] in their experimental study of the gasification of waste tires with air in a bubbling fluidized bed gasifier. The results shown are also like those obtainedby [PERSON] [28], [PERSON] et al. [29], and [PERSON] et al. [19], in which a high ER turned into low yields of CH\({}_{4}\), CO, and H\({}_{2}\) in gaseous products.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 5 Future Potential of SSIM in Medical Image Analyses and Conclusions  
This review offers comprehensive insight related to state-of-the-art SSIM approaches for medical imaging, as well as the potential medical image examination research methods that can be improved using SSIM. It is likely that, in the future, SSIM may be used not only for IQM but also for segmentation, clustering, classification, image fusion, and object detection in regard to image processing in medical imaging, and as a feature extractor and loss function relative to the deep learning method [133; 134; 135]. Furthermore, SSIM can be extended to CAD for assisting physicians and clinicians in their predictions and for providing conclusions when diagnosing a specific disease based on the work of radiologists. Here, SSIM can act as a second opinion to improve the confidence of radiologists. In addition, using an objective measure, SSIM can be efficient in terms of cost and time consumption. However, we must give more attention to the drawbacks of SSIM. As reviewed, the original SSIM showed some shortcomings when evaluating badly blurred images, scaling, rotation, and spatial translation images. Nevertheless, these issues can be overcome by implementing an improved SSIM, such as MS-SSIM, three- and four-component weighted SSIMs, GSSIM, CW-SSIM, ISSIM-S, and other types of SSIM. SSIM has been identified as easy to use and improve because, based on image processing knowledge, it can be upgraded based on the goal of a particular study. Therefore, future studies should address the SSIM implementation by focusing on the improved SSIM for obtaining the optimal SSIM model when assigning it as an IQM or other image processing approach in medical imaging.  
Conceptualization, V.M. and S.-w.C.; investigation, V.M. and S.-w.C.; writing--original draft preparation, V.M. and S.-w.C.; writing--review and editing, V.M., M.K. and S.-w.C.; supervision, V.M. and S.-w.C.; project administration, S.-w.C.; funding acquisition, M.K. and S.-w.C. All authors have read and agreed to the published version of the manuscript. This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [NRF-2019R1F1A1062397, NRF-2020R11A3074098] and the Brain Korea 21 FOUR Project (Dept. of IT Convergence Engineering and Dept. of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology). Not applicable. The authors declare no conflict 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.
## 5 Numerical Analysis of the Supporting Performance of the Spatial Reticulated Shell
### Numerical Results and Analysis  
The cloud diagrams of the supporting arches are shown in Figure 20. The numerical results show that the maximum stress at the arch foot of the SRS arch is 187.7 MPa, which does not exceed the yield strength of the steel tube. To further improve the bending capacity of the arch in the joint area, the back of the end-plate can be strengthened by adding reinforcement. In addition, the maximum stress of the main reinforcement at the arch foot of the grid arch is 240.7 MPa, which does not reach the yield strength of 400 MPa.  
Figure 19: Constitutive relation curves of the concrete. (**a**) The tensile stress–strain curve and (**b**) the compressive stress–strain curve.  
According to Figure 21, the plastic failure zone of the concrete structure is mainly concentrated in the arch foot and vault, where the compressive stress is relatively focused and the plastic strain value is larger. The arch vault and arch bottom are tensile cracking areas, the plastic value and distribution range of the arch shoulder are small, and no obvious damage has occurred. When supported by the SRS arch, the plastic damage area of the concrete can reach 45%, while the grid arch is more than 60%. Due to the high bearing capacity of the SRS arch, it can provide high support resistance when the concrete is damaged and reduce the plastic zone.  
Beyond that, the numerical results also show that the vault settlement is 18.0 mm and the peripheral convergence is 11.2 mm in the case of the SRS arch support, and the vault settlement is 24.6 mm and the peripheral convergence is 14.3 mm in the case of grid arch support. The comparative analysis indicates that the SRS arch has a positive impact on the control of the surrounding rock deformation and the improvement of structure stress and has higher supporting stiffness and bearing capacity, which can meet the safety and quality requirements of tunnel engineering.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 4 Results and Discussions
### Transient EMI in LTE-A  
A double-sided exponential jammer with adjustable gain and varying centre frequencies was tested. The impact was substantial at a transmit gain of 18 dB, with KPIs being summarized in Table 4 below. The findings from the experiment demonstrate that LTE-A link performance is highly dependent on the interferer frequency, with 2.2 GHz exhibiting the most severe impact, as reflected by the highest EVM peak (1985.256%), EVM RMS (85.549%), and BER (0.32059). This is likely due to increased spectral overlap with the desired signal, leading to significant degradation. In contrast, at 2.1955 GHz, the EVM peak (829.12%) and BER (0.24165) are lower, indicating better resilience to interference. The observed frequency offset (2132.578 Hz at 2.2 GHz vs. 1680.86 Hz at 2.1955 GHz) further supports the conclusion that certain frequencies are more vulnerable to transient EMI.  
The experimental results indicate a significant degradation in the SINR, which decreased from 15.1226 dB (no interference) to \(-0.2355\) dB. This drop represents the maximum performance degradation observed in the LTE communication link, despite employing a high transmit gain intended to mitigate such losses.  
Figure 5 illustrates a peak centred at 2.2045 GHz, resulting from the injection of transient EMI. This EMI not only contributes to the SINR reduction but also leads to substantial distortion in the signal constellation. As shown in Figure 6, the constellation exhibits the effect of noise that can vary between symbols. This noise around the ideal symbol configuration suggests that the received symbols are displaced from their intended positions, reflecting the detrimental effects of frequency distortion on the integrity of the signal and the overall performance of the LTE communication system. This can be verified by comparing Figure 7, which a perfectly received 64 QAM constellation, with Figure 6, showing distortion. Additionally, the received constellation appears to be a single noise cloud around the origin if the magnitude of the interference increases to a level where the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interferer** & **EVM Peak** & **EVM RMS** & **BER** & **Frequency Offset (Hz)** & **SINR (dB)** \\
**Frequency** & **(\%)** & **(\%)** & **BER** & **Offset (Hz)** & **SINR (dB)** \\ \hline
2.1955 GHz & 829.12 & 79.212 & 0.24165 & 1680.86 & 1.89 \\
2.2 GHz & 1985.256 & 85.549 & 0.32059 & 2132.578 & 4.5046 \\
2.2045 GHz & 1387.908 & 83.565 & 0.26159 & 2082.8 & \(-0.2355\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: KPI measurements of the LTE-A link at different RF frequencies in the presence of transient interference. The gain of the interfering USRP is set to 18 dB.
## 4 Overlapped Discretization Concepts
### Numerical Stability  
The discretization of the domain and its multi-zones have to be managed in a numerically efficient manner to avoid numerical instabilities and excessive iteration. For the simulation of engineering systems with internal zones (i.e., fatigue prone zone), the high resolution zonal grid interfacing technique can be employed and zonal discretization with a dynamic grid-to-grid interface (GGI) can be implemented. The dynamic mapping of zones into the main domain is performed by considering a void zone (VZ) concept. The new concept of a VZ creates a virtual zone within the domain, which replaces the actual zone to exchange the variables at zone boundaries. Different methodologies are used for the mapping scheme; namely, direct mapping to the zone boundaries or indirect mapping through overlapping of the zone grid with VZ mesh. Figure 5 shows some partial and full overlapping grids for simple 2D numerical domains [22].  
The multi-zone simulations are developed within this framework, where physical phenomena at different scale/resolutions (e.g., micro-cracks and fatigue) can also be handled using continuous and discrete high-resolution zones. For the discrete approach, the physical phenomena at high resolution are simulated at discrete points (gauss integration points in FE, among others) using lattice/structured grid and the results correspond to the higher scale grid. While for the continuous technique, full or partial overlapping zones can be employed in several layers to bridge the scale/resolution within the numerical domains. In Figure 5b, simple overlapping 2D meshes are outlined, where the concept of VZ is initially used to define the high-resolution area and its boundaries. The introduction of a second overlapping high-resolution zone is carried out using the balance material/energy initiation scheme, where the distribution of material and energy through boundaries is carried out [22].  
Figure 4: Schematic architecture of finite zone computational system. AI, artificial intelligence; ML, machine learning.
## 3 Results and Discussion
The adjusted model yields the result shown in Table 3 and Figure 11. The results indicate that computation time takes 50 min longer for an 80/20 and 90/10 split, with the resulting accuracy reaching over 0.7 than the previous test. The most significant increase in accuracy is for 150 epochs with a 90/10 split of testing and validation data, which shows an  
Further testing with 140, 145, 155, and 160 epochs to investigate the optimum combination of accuracy with shorter time yields the results shown in Figure 12. Since the testing accuracy is greater with 150 epochs than 100 epochs, we assume that accuracy will improve within 150 \(\pm\) 10 epochs. Testing with a 70/30 split confirms that as epochs increased from 140 to 160, accuracy gradually improved by 13.6% from 0.567 to 0.703, while computing time increased by 12% from 240 min to 269 min. A similar trend is also observed during testing using an 80/20 data split with an accuracy increase by 13.5% from 0.577 to 0.712 but with a much longer computing time from 243 min to 304 min, representing an increase of 25.1%. The more significant increase is due to the additional time needed to perform more training for the 80/20 than the 70/30. As for the testing with a 90/10 data split, the peak accuracy performance is achieved at 150 epochs with 0.719. Testing with 140, 145, 155, and 160 epochs gives lower results. In Table 4, the three-dimensional CNN without any combinations with other methods results in higher accuracy than what was achieved by [PERSON] et al. at 0.685 [36]. It is comparable to [PERSON] et al. [11] on open trees flood accuracy at a range of 0.55 to 0.70, which is similar in conditions to flooded areas in Jakarta. Figure 12 shows the flood map of the proposed model compared to the SAR image, as shown in Figure 2, where the model could detect most of the dark areas of the flood while leaving out the similarly dark Jakarta Bay. Compared to the sub-district-level flood map publicly released by the government [2], it is also shown that the flood has occurred in the reported sub-districts. There are discrepancies between the detected and reported areas since the report classifies floods as a whole sub-district coverage.
## 3 Research Methodology  
This study aims to facilitate the design and development stages of applications to be developed with MSA and to serve as a guide. For this purpose, it is intended to identify the features in the MSA and to classify the technologies according to these features. In this way, the decision-making process will be accelerated, and it will be determined which factors the technology choice depends on. In order to achieve this goal, we have determined the following research questions:  
RQ1--What are the current key MSA approaches in the state of the art?  
RQ2--What are the key features of these MSA approaches in RQ1?  
RQ3--What are the current implementation approaches for the MSA features in RQ2?  
RQ4--What are the common and different features of the selected MSA vendor's approaches?  
We developed and applied a research methodology shown in Figure 2 to reliably analyze all of the published work involved in this study. This protocol starts by performing domain analysis for MSAs and components; then, a characterization framework is developed according to this domain analysis. Domain analysis is the systematic process for analyzing and modeling the corresponding domain knowledge necessary for the engineering process. Domain analysis includes two key sub-steps of domain scoping and domain modeling. In the domain scoping process, the scope of the investigated domain is defined. In the domain modeling step, the domain knowledge is modeled for further reuse [18]. In this article, we use feature diagrams, which is one of the approaches for domain modeling [19]. Feature diagrams represent the common and variant features of a domain or system.  
This process is followed iteratively because, in the meantime, the missing points in the characterization framework can be completed by returning to domain analysis again. Then, to validate our characterization framework, the studies that suggest technology and patterns from both the key providers and MSA area are handled separately and the related technologies are structured according to the characterization framework developed. While selecting and evaluating related MSA technologies and key vendors' infrastructure, the characterization framework can be updated again by going back to the domain analysis phase. Finally, we will eventually present a general evaluation of the work done.  
Figure 1: Reference model for MSA. Source: adapted from [17].
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 4 Significance Analysis of Mental Workload and Feature Based on SVM Classifier
### Feature Extraction
#### 4.2.2 Feature Extraction Based on Power Spectral Density and Energy  
To minimize the time effects, EEG data of the middle 5 min is selected. Segmentation processing, fast Fourier transformation, power spectrum estimation, and energy calculation are performed in sequence. When segmentation is performed, the length of each segment is 1 s. Based on the idea of the averaging period method, half of the adjacent segments data is overlapped making the EEG characteristic curve smoother. Then, the Fourier transformation is performed on each segment to obtain \(F(n)\) (\(n\) = 1, 2,..., 1000), and the corresponding frequency and amplitude.
## 2 Materials and Methods
### Analytical Methods  
TP, COD, TN, and N-NH\({}_{4}\) in the wastewater were measured using a DR 5000 spectrophotometer with an HT 200 s mineralizer (Hach-Lange GmbH, Dusseldorf, Germany). TOC content was determined by means of a TOC-L analyzer (Shimadzu, Kyoto, Japan). VS, MS, and TS were determined by weighing using the dry-matter/ignition method (PN-EN 15935:2022-01). TC, TOC, and TN in the digestate were measured with a Flash 2000 analyzer (Thermo Scientific, Waltham, MA, USA). TP was determined colorimetrically at 390 nm (DR 2800 spectrophotometer, Hach-Lange GmbH, Dusseldorf, Germany) after prior mineralization. Total protein was determined by multiplying the TN value by 6.25. Reducing sugars  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{\begin{tabular}{c} **Target OLR** \\ **(gCOD/dm\({}^{3}\).d)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **COD** \\ **in the** \\ **H-LPW** \\ **(gO2/dm\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Volume of** \\ **H-LPW** \\ **(dm\({}^{3}\)/day)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Reactor** \\ **Volume** \\ **(dm\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **HRT** \\ **(day)** \\ \end{tabular} } \\ \cline{5-6}  & & & & \(\approx\)0.26 & & \(\approx\)15.4 \\ \cline{5-6}  & & & & & \(\approx\)7.7 \\ \cline{5-6}  & & & & & \(\approx\)5.1 \\ \cline{5-6}  & & & & & \(\approx\)3.8 \\ \cline{5-6}  & & & & & \(\approx\)1.30 & \(\approx\)3.1 \\ \cline{5-6}  & & & & & \(\approx\)1.56 & \(\approx\)2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: OLR and HRT across experimental variants.  
were detected colorimetrically with an anthrone reagent at 600 nm (DR 2800, Hach-Lange GmbH, Dusseldorf, Germany). Lipids were determined using the Soxhlet method (Buchi AG, Flawil, Switzerland). The pH was measured using a pH meter (HQ11D, Hach-Lange GmbH, Dusseldorf, Germany).  
The instantaneous and total biogas amounts were measured with a mass flow meter (Aalborg Instruments Inc., Orangeburg, NY, USA). The composition of biogas produced at each section of reactor was measured every 24 h using a gas-tight syringe (20 cm\({}^{3}\) injection volume) and a gas chromatograph (GC, 7890A Agilent) equipped with a thermal conductivity detector (TCD). The GC was fitted with a Porapak Q column (80/100), two molecular sieve columns (60/80 mesh), and two Hayesep Q columns (80/100 mesh), operating at 70 \({}^{\circ}\)C. The temperature at the injection and detector ports was 150 and 250 \({}^{\circ}\)C, respectively. Argon and helium were used as the carrier gases at a flow rate of 15 cm\({}^{3}\)/min. The levels of methane (CH\({}_{4}\)) and carbon dioxide (CO\({}_{2}\)) were also measured.