esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Average Energy Consumption  
The performance comparison for the proposed RALAR, KALAR, and LAR protocol in terms of average energy consumption has been presented in Figure 8. The results show that the energy consumption for the proposed RALAR algorithm is much lower than the KALAR and LAR protocol. The proposed RALAR algorithm achieved an average energy consumption value equal to \(2.724\times 10^{4}\) joule, whereas, the KALAR and LAR protocol achieved an average energy consumption value equal to \(3.052\times 10^{4}\) and \(3.050\times 10^{4}\) joule, respectively. The results prove that the proposed RALAR algorithm performed better because it is limited to the rectangular zone along with the utilization of the GA  
Figure 6: Average E2E Delay vs. Time.  
Figure 7: Routing Overhead vs. Time.  
optimization to estimate the exact timeout. This helps to prolong the lifetime of the network by using the RALAR protocol, which is not the case for the LAR and KALAR protocols. Moreover, it is also noticed that the LAR and KALAR results are very similar; however, the LAR delivers slightly lower results as compared to the KALAR approach.
## Appendix B Short-Circuit Formulation Notation  
Appendix B documents the notation required to define the fundamentals of the mathematical formulations. The notation discussed here is used to model short-circuit analysis.  
**Sets:**  
\(\mathcal{F}\)--set of faults, indexed by \(f\)  
\(\mathcal{G}_{pqi}\)--set of grid-following inverters, indexed by \(g\)  
\(\mathcal{G}_{vi}\)--set of grid-forming inverters, indexed by \(g\)  
**Parameters:**  
\(i_{f}\)--the bus of fault \(f\)  
\(\mathbf{G}_{f}\)--admittance matrix of fault \(f\)  
\(\mathbf{\alpha}\)--complex parameter used for calculating sequence components  
\(\mathbf{\hat{r}}_{g}\)--virtual impedance real part upper limit, indexed by \(g\)  
\(\mathbf{\hat{x}}_{g}\)--virtual impedance imaginary part upper limit, indexed by \(g\)  
\(M_{g}\)--scaling for voltage upper and lower limit, indexed by \(g\)  
\(V_{gf}^{\Phi ref}\)--reference voltage of inverter, indexed by \(g\)  
**Variables:**  
\(I_{gf}^{\Phi}=\left(\ell_{gf}^{\Phi}+\mathbf{j}\mathbf{m}_{gf}^{\phi},\ \phi\in\Phi\right)\in\mathbb{C}^{3}\)--vector of complex fault current generation at \(g\in\mathcal{G}\) for fault \(f\)  
\(I_{sif}^{\Phi}=\left(\ell_{if}^{\Phi}+\mathbf{j}\mathbf{m}_{i,f^{\prime}}^{\phi}, \ \phi\in\Phi\right)\in\mathbb{C}^{3}\)--vector of complex fault current at \(i_{f}\in\mathcal{N}\) for fault \(f\)  
\(I_{if}^{\Phi}=\left(\ell_{if}^{\
## 4 Results and Discussions
### Data Collection and Analysis  
#### 4.1.1 Precomputing Clusters  
Before the actual validation runs, two training sets were generated by running 1000 shoppers with unique lists containing 15-21 items; the first set used a pool of 60 out of 555 items and the second set used all 555 items. These were used to precompute clusters for the DProSA setups during the validation runs.
###### Abstract  
The study aimed to evaluate for the first time the degree of contamination of soil and crops with major and trace elements (Cd, Co, Cr, Cu, Ni, Pb, Zn, F, Na, Mg, Si, P, Cl, Fe, Al) in agricultural lands situated in the Lower Danube Basin, Galati and Braila counties (SE Romania), impacted by the steel industry. Soil samples, as well as leaves and seeds of wheat, corn, and sunflower, were collected from two depths in 11 different sites. Along with elemental and mineralogical analyses, performed by HR-CS AAS, PIGE, SEM-EDX, and ATR-FTIR, the soil pH, texture, organic matter, electric conductivity, and CaCO\({}_{3}\) content were investigated. The results showed that the levels of Cr (83.27-383.10 mg kg\({}^{-1}\)), Cu (17.11-68.15 mg kg\({}^{-1}\)), Ni (30.16-55.66 mg kg\({}^{-1}\)), and F (319-544 mg kg\({}^{-1}\)) in soil exceeded the Romanian regulations for sensitive use of the land. Igeo, EF, PI, and PERI pollution indices indicate that the soil is moderate to highly contaminated with Cr, Ni, and Cu, while the CSI and mERMQ indices suggested a relatively low risk for metal contamination. The elemental concentrations in plant tissues and bioaccumulation factors (BFs) provide valuable insights into the soil-plant relationship, health risks, and the selectivity of plant compartments for different elements. Thus, the results revealed that the wheat plants tended to exclude the bioaccumulation of particular elements in their tissues, while exhibiting a different bioaccumulation pattern for Zn and Cu. In the case of corn, most BFs were below one, indicating a limited phytoaccumulation capacity. However, exceptions were observed for Cd, Zn, and Cu with the sunflower BFs indicating higher bioconcentration of these elements in leaves and seeds compared to other elements. Chromium (Cr) contributes to non-carcinogenic dermal contact and ingestion hazards, children being more susceptible to the adverse effects of this contaminant.  
soil; wheat; sunflower; corn; trace elements; pollution; heavy metals; bioaccumulation factor; Lower Danube Basin; health risk +
Footnote †: journal: Applied Sciences  
Article  
Major and Trace Element Accumulation in Soils and Crops  
(Wheat, Corn, Sunflower) around Steel Industry in the Lower Danube Basin and Associated Ecological and Health Risks
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 3 System Model and Performance Metrics  
The resource allocation problem in the downlink single-cell NOMA system is the primary concern to be investigated. The CSI is considered to be known to the BS. To ensure that the receiver complexity is reduced, the SIC technique is applied for the multiplexed users. The HNG algorithm, which is an optimal assigning tool for resource allocation problems, is first used to pair two users in a subchannel, followed by equal power allocation consideration for assigning each subchannel. Thereafter, the optimal power allocation technique is applied to achieve the optimal power allocation for each of the paired users in the given subchannel. In this section, description of the utilized system model, step-by-step mathematical formulation and definitions of the performance metrics are discussed.
# On the Measurement of Particle Contact Curvature and Young's Modulus Using X-ray \(\mu\)CT
## 4 Experimental Protocol  
A single zeolite particle with diameter 2.27 mm was imaged by X-ray \(\upmu\)CT, which was carried out in X-ray \(\upmu\)CT laboratory in the School of Geoscience, University of Edinburgh. The reason for choosing a zeolite particle is because of its scientific merit and widespread industrial application [24; 30; 31; 32]. The particles mentioned in this work are intended to be granulated zeolite particles instead of primary particles. The mean diameter for zeolite granules, d\({}_{50}\) is varied from 1.0 to 3.5 mm in the literature and their mechanical response subject to compressive and impact loading can be found elsewhere [21; 22; 24]. The projection data were collected by incrementing a small rotation angle around the rotation axis during X-ray scanning. The collected data were digitally processed in an image processing software Avizo Fire [33]. In this way the imaged particle could be visualized in 3D by data reconstruction and the digital information of the reconstructed particle could be captured and processed. The global and local curvature estimation at the contact point was conducted with two different fitting methods. The effect of the fitting scope is investigated and the detailed procedure for image processing is described as follows.
## 2 Theoretical Background and Hypotheses Development
### Effort Expectancy (EE)  
This construct has shown in previous studies to have a significant impact on the intention to use a certain technology [36; 47; 48]. In particular, effort expectancy is defined as \"the degree of ease associated with the use of the system\" [43]. In the context of the research, effort expectancy was related to the ease of use of the IOT by teachers. They were proposed as hypotheses:  
**Hypothesis 4 (H4):**: _Effort expectancy has a significant effect on behavioral intention to use IoT._  
**Hypothesis 5 (H5):**: _Gender is a factor that has a significant effect on effort expectancy of IoT._  
**Hypothesis 6 (H6):**: _Age is a factor that has a significant effect on effort expectancy of IoT._
# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
## 2 Related Works
### Domain Adaptation and Generalization  
Domain adaptation aims to train a model on a labeled source domain that performs well on an unlabeled target domain [19; 20; 21], while the goal of domain generalization [8; 22; 23] is to enable the trained model to generalize unseen domains appropriately. Domain generalization is more robust than domain adaptation because multiple source domains containing the same visual classes are available for model training.  
The aforementioned works only focus on the source and target domains. However, many studies have proven that the intermediate domains between source and target domains are useful for addressing the domain adaptation problems. [PERSON] et al. [7] characterized samples from each domain as one covariance matrix and interpolated some intermediate points between source and target domains to bridge them. [PERSON] et al. [24] proposed a method for continuously indexed domain adaptation by combining traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution. [PERSON] et al. [8] designed a model that can generate a continuous sequence of intermediate domains flowing from one domain to the other for semantic segmentation.  
Unlike the studies mentioned above, we generated intermediate domains based on GAN and conducted the UDA baseline on the source domain and intermediate domains. Then, the model fine-tuned on intermediate domains was transferred to the target domain.
## 4 Results and Discussion
### Energy Considerations for Industrial Application
#### 4.7.1 Energy Demand for Bone Ash Calcination  
In this study, bone ash was produced by firing sun-dried animal bones at 800 \({}^{\circ}\)C for 90 min. While laboratory furnaces were used in this case, transferring this process to industrial production involves significant energy input. Although exact consumption data for bone calcination is not available in the literature, firing at 800 \({}^{\circ}\)C for 90 min typically requires prolonged operation of gas- or electric-powered kilns. Therefore, this step should be considered a major contributor to the embodied energy of the AB-modified binders. Further research is necessary to quantify this stage through industrial measurements or life cycle assessment (LCA).
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
###### Abstract  
This paper proposes a novel and efficient low-complexity chromatic dispersion equalizer (CDE) based on finite impulse response (FIR) filter architecture for polarization-multiplexed coherent optical communication systems. The FIR filter coefficients are optimized by weights to reduce the energy leakage caused by the truncation effect, and then quantization is used uniformly to reduce the number of real number additions and real number multiplications by utilizing the diversity of the quantized coefficients. Using Optisystem 15 to build a coherent optical communication system for simulation and experimental demonstration, the results show that after the filter coefficients are optimized by weights. Compared with the time-domain chromatic dispersion equalizer (TD-CDE), the proposed design has a lower bit error rate (BER) and better equalization effect. When the transmission distance is 4000 km and the system quantization stages M = 16, the multiplication operation and addition operations reduce computing resources by 99% and 43%, and the BER only increases by 5%. Compared with frequency-domain chromatic dispersion equalizer (FD-CDE), widely used in long-distance communication, the multiplication operation reduces computing resources by 30%. The proposed method provides a new idea for high-performance CDE in long-distance coherent optical communication systems.  
coherent optical communication; chromatic dispersion equalization; weight optimization; uniform quantization +
Footnote †: journal: _photonics_
# Water Quality Evaluation and Prediction Based on a Combined Model
## 5 Conclusions  
Water quality evaluation and prediction are not two completely independent procedures. On the contrary, they form a mutually dependent system and complement each other. Accordingly, in this study, we established a water quality evaluation-prediction system, established a water quality evaluation model using a T-S fuzzy neural network and constructed research samples by interpolating water quality index data evenly distributed on the basis of each index grading standard stipulated in the _Environmental Quality Standards for Surface Water_. A stratified sampling method is used to construct training samples. The trained T-S fuzzy neural network was applied to the water quality evaluation of the Lanzhou Xincheng Bridge section in the Yellow River Basin and the Longdong section in the Sichuan Panzhihua section in the Yangtze River Basin, with total positive water quality grade evaluation rates of 90.38% and 88.46%, respectively, indicating the positive water quality evaluation effect and generalizability of the model.  
Figure 23: Water quality evaluation of Longdong section in 2016.  
Figure 22: Water quality evaluation of Lanzhou Xincheng Bridge section in 2016.  
For the prediction of water quality, in this paper, we proposed a new combined model, ARIMA-WNN, which establishes the combined prediction model for each water quality index of the two basins and compares the prediction results with the combined model. The results show that compared with the single model, the combined model (ARIMA-WNN) has a higher prediction accuracy, and the prediction ability can be improved by up to 68.06%. Compared with commonly used water quality prediction models (BP neural network and LSSVM), we found that the MAPE, MAE and RMSE of the combined model are significantly lower, which demonstrates the excellent water quality prediction ability of the combined model.  
Determining reasonable weight coefficients for each single model in the combined model is the basis for obtaining accurate prediction results, and in this study, we used the bat algorithm to achieve this process. Swarm intelligence optimization algorithms have developed rapidly in recent years, and a variety of new methods have emerged in succession [41]. Determining the optimal weight is a subject that can be studied in depth, and additional methods should be proposed and tested in subsequent work [42].  
Conceptualization, [PERSON]; Methodology, [PERSON]; Writing--original draft, [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON], F.Z., J.C. and J.J.; Writing--review & editing, S.C.; Supervision, [PERSON] and Z.W. All authors have read and agreed to the published version of the manuscript.  
The research was supported by National Natural Science Foundation of China Under Grant No.41271038.  
Not applicable.  
The data presented in the present study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 2 Similarity Theory of the Scale Model
### Kinematic Similarity  
The velocity and acceleration at any point in the model are proportional, by constant scale factors, to those at the corresponding point in the prototype. The time similarity ratio \(\lambda_{t}\), the velocity similarity ratio \(\lambda_{u}\), and the acceleration similarity ratio \(\lambda_{a}\) can be expressed as:  
\[\lambda_{t}=\frac{t_{m}}{t_{p}} \tag{4}\]  
\[\lambda_{u}=\frac{u_{m}}{u_{p}}=\frac{\lambda_{l}}{\lambda_{t}} \tag{5}\]  
\[\lambda_{a}=\frac{a_{m}}{a_{p}}=\frac{\lambda_{l}}{\lambda_{t}^{2}} \tag{6}\]
## 5 Conclusions  
In this paper, a new GKL method is proposed for suspension control. Based on the traditional LQR control and genetic algorithm, the machine learning idea is introduced to obtain more objective weighting coefficients for different driver types.  
The following conclusion can be drawn:  
1. The proposed GKL algorithm may offer good weight coefficients for the suspension control system's optimization, and these weights are independent of expert experiences;
2. It could be proved that the construct method of weighting space is reasonable. The generated random weights are important to build the machine learning dataset. The dataset could provide 60 thousand sample points for suspension control engineers.  
Figure 14: Comparison curves of time domain response for two suspensions (passive suspension and comfort mode of active suspension: (**a**) BA; (**b**) SWS; (**c**) DTD.  
Automotive developers can directly select an appropriate weight matrix according to this dataset, which can greatly improve design efficiency by reducing the optimization process of repeated trial and error;
* Through K-means clustering, the active suspension is classified into safety mode, comprehensive mode, and comfort mode, which can adapt to three driving styles: aggressive, normal, and soft. This method offers the LQR control system with two extra solutions, except for the unique value optimized by GA. In future research, we can also use similar ideas combing the support vector machine or deep convolution network with the dataset for parameter optimization;
* The GKL control could improve the suspension performance. The adhesion between the tires and ground increase by 11% in safety mode, and the body acceleration refines by 40% in comfort mode. Moreover, the three performance indexes show good stability for the road input and driving velocity;
* The LQR controller designed based on machine learning ideas has more intuitive design results and richer performance modes. It can meet the different requirements of different drivers for suspension performance or recommend appropriate weighting coefficients according to the different purposes of the vehicle.  
Conceptualization, [PERSON] and J.L. ([PERSON]); methodology, K.W.; software, K.W.; formal analysis, K.W.; data curation, [PERSON] and J.L. ([PERSON]); writing--original draft preparation, K.W.; writing--review and editing, [PERSON] and J.L. ([PERSON]); supervision, [PERSON]; project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]) and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51575288) and the Natural Science Foundation of Shandong Province (ZR2019 MEE072).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
## 2 Proposed Self-Test Platform  
### Conventional SAR ADC Architecture  
The basic block of the traditional SAR ADC includes a track-and-hold circuit (T/H), an N-bit DAC capacitor array, a comparator circuit, and a SAR controller (Figure 1). The SAR ADC generally uses a binary-weighted method to implement the circuit [9]. According to the results for the switching capacitor array, the digital output signal can be obtained as Logical High (\"1\") or Logical Low (\"0\"). In addition, some biomedical applications require an adaptive resolution using a multi-switch operated in 8 bit or 12 bit mode [9]. However, the non-linearity of the multi-switch in the T/H circuit will limit the SAR ADC performance. The proposed calibration method also has the capability of eliminating the non-linearity and enhancing the performance of the multi-mode SAR ADC. Figure 2 depicts the multi-mode SAR ADC structure. In this, switch S2 is applied to control the two operation modes, namely the 8 bit and 12 bit modes. The bootstrapped switch is also adopted to provide a small on-resistance with a constant value and better linearity.
## 1 Introduction
The aim of the present study was to assess the deformation behavior of three different aligner systems--CA(r) Pro+ Clear Aligner (Scheu Dental, Iserlohn, Germany), Taglus Premium (Taglus Company, Mumbai, India), and Spark Trugen (Ormco Corp., Orange, CA, USA)--through a compression method by the digital image correlation (DIC) technique. The null hypothesis states that no differences in deformation behavior are recorded between the three aligner systems.
## 2 Materials and Methods
### Model Applicability  
The potential of the nUBEM model is demonstrated according to the following points:  
* Its ability to cover the current lack of data;
* Its capacity for mapping zones with better and worse energy performance at the building level;
* The potential to calculate progress indicators.  
Several of the progress indicators required by the 2024 EPBD recast for the MS [2] were obtained using the nUBEM model. The selected indicators are those considered to best define the characteristics of the residential building stock and its energy performance:  
* Number of buildings and total floor area per energy performance class;
* Number of buildings, number of dwellings and total floor area per the 43% worst-performing buildings (including a definition);
* Annual operational greenhouse gas emissions per building type.  
In relation to the applicability of the model, several issues must be defined:  
Spain is divided into different climate zones, which are defined in Spanish regulations [48] using letters from A to E for winter climates and the letter alpha for the Canary Islands, and numbers from 1 to 4 for summer climates, both ranging from mild to harsh. This results in a total of 15 climate zones from the combination of values for winter and summer.  
The energy classes in Spain are divided into NRPEC energy classes and GHG emissions energy classes, with labels ranging from A to G, and the threshold values are organized according to [52]. This document sets threshold values for each energy class depending on three factors: the climate zone, the type of building (single-family house and multi-family house), and whether the building is in a peninsular or extrapeninsular climate.  
Figure 3 shows, as an example, the threshold value ranges to determine the energy class of multi-family buildings in peninsular climates according to their climate zone. The figure illustrates how the threshold values for the classes vary significantly by climate zone, as the heating and cooling requirements vary greatly. The model automatically assigns the corresponding class to each building according to the regulation. The results of the model validation are presented in Section 3.3.