esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transport Model
### Transport Model for a Graphene Nanoribbon  
Along a graphene nanoribbon, in the operating conditions assumed here, the \(\pi\)-electrons mainly move along the longitudinal axis, _i.e._, the transverse components may be neglected. Therefore, the transport model is derived from that exposed in Section 3.1, assuming a 1D case: the electrons are quantum confined laterally and, thus, occupy quantized energy levels, whereas along the longitudinal axis, the wave-vector, \(k\), is assumed to be almost continuous.  
If T is the translational length of the unit cell (see Figure 3), the first Brillouin zone is given by the interval (\(-\pi/T\),\(\pi/T\)) and the longitudinal conductivity in the spatial and wavenumber domain obtained from Equation (13) is given by the sum of the contribution of the \(N\) conduction sub-bands in the circumference \(C_{F}\):  
\[\hat{\sigma}_{xx}(\beta,\omega)=\sum\limits_{\mu=1}^{N}\hat{\sigma}_{\mu}( \beta,\omega), \tag{14}\]  
where:  
\[\hat{\sigma}_{\mu}(\beta,\omega)=i\frac{4e^{2}}{\pi W}\int\limits_{0}^{-\pi/T} \frac{v_{\mu}^{(+)^{2}}(k)}{1-v_{\mu}^{(+)^{2}}(k)\frac{\beta^{2}}{\left( \omega-i\,\
u_{\mu}\right)^{2}}}\frac{dF}{dE_{\mu}^{(+)}}dk. \tag{15}\]  
Using the above results, the generalized [PERSON]'s law Equation (9) may be expressed for a GNR as follows:  
\[\Big{[}1-\psi(\omega)\beta^{2}\Big{]}\hat{J}_{z}(\beta,\omega)=\frac{\sigma_{ 0}}{1+i\omega/\
u}\hat{E}_{x}(\beta,\omega) \tag{16}\]  
where we have introduced the following functions:  
\[\psi(\omega)=\frac{\alpha(\omega)v_{F}^{2}}{\
u^{2}\left(1+i\omega/\
u\right) ^{2}},\quad\sigma_{0}=\frac{2v_{F}M}{\
u R_{0}\pi W} \tag{17}\]  
\(R_{0}=12.9\) k\(\Omega\) being the quantum resistance, and:  
\[M=\frac{2\hbar}{v_{F}}\sum\limits_{\mu=1}^{N}\int\limits_{0}^{\pi/I}v_{\mu}^{( +)^{2}}(k)\Bigg{(}-\frac{dF}{dE_{\mu}^{(+)}}\Bigg{)}dk, \tag{18}\]  
\[\alpha(\omega)=\frac{2}{M}\frac{\hbar}{v_{F}^{3}}\sum\limits_{\mu=0}^{n-1} \int\limits_{0}^{\pi/I}v_{\mu}^{(+)^{4}}(k)\Bigg{(}-\frac{dF}{dE_{\mu}^{(+)}} \Bigg{)}dk \tag{19}\]The quantity \(M\) in Equation (19) is the equivalent number of conducting channels, a measure of the number of sub-bands that effectively contribute to the electric conduction, _i.e._, those that cross or are closer to the Fermi level. A detailed discussion on the behavior of such a number \(M\) for CNTs and GNRs may be found in [18; 19; 20], where it is clearly shown how such a quantity strongly depends on the chirality, size and temperature of such carbon nanostructures.
# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 1 Introduction  
### Residential Real-Estate Domain:Motivation and Challenges  
The real-estate sector plays a vital role in the economy and includes various domains such as residential, commercial, industrial, and retail. It relates to a broad spectrum of activities centered on the purchase, sale, rental, lease, and management of properties [1]. Its dynamics and developments have a pertinent impact on people, companies, and state authorities around the globe [2].  
The residential real-estate sector is clearly an integral part of the overall property market, especially the purchase and sale or leasing of residential property (single-family homes, townhomes, and condominiums) [2]. This segment has a huge impact for the overall economy [3]. Residential properties represent about 65% of the entire real-estate market in the USA. Home sales in the United States hit an all-time high of nearly seven million in 2021, after consistently increasing from a low level that year. The average price per square foot of floor space in new single-family housing fell after the great financial crisis and then stagnated for several years. Since 2012, the price has continuously increased, reaching approximately USD 144 per square foot in 2021. In the same year, the average sales price of a new home was over USD 454,000, and, in 2022, house prices increased further ([[https://www.statista.com/statistics/682549/average-price-per-square-foot-in-new-single-family-house%20s-usa/](https://www.statista.com/statistics/682549/average-price-per-square-foot-in-new-single-family-house%20s-usa/)]([https://www.statista.com/statistics/682549/average-price-per-square-foot-in-new-single-family-house%20s-usa/](https://www.statista.com/statistics/682549/average-price-per-square-foot-in-new-single-family-house%20s-usa/)) accessed on 28 November 2024). The above indicates that understanding its current state, trends, and prospects is essential for both industry participants and investors.
## 2 Materials and Methods
### Groundwater Storage--Water Table Fluctuation Method  
In order to assess changes in groundwater storage, dS/dt(gw), we used a Water-Table Fluctuation (WTF) method which estimates dS/dt(gw) from changes in the water level of wells in unconfined aquifers. This method has been employed in a number of other analyses, including recent studies [28] and [29]. For each hydrological unit, we selected 10 wells from the USGS Climate Response Network (CRN) which contains wells certified as structurally sound, minimally affected by pumping, and representative of one specific hydrological unit [30] ([[http://groundwaterwatch.usgs.gov/net/ogwnetwork.asp?ncd=crn](http://groundwaterwatch.usgs.gov/net/ogwnetwork.asp?ncd=crn)]([http://groundwaterwatch.usgs.gov/net/ogwnetwork.asp?ncd=crn](http://groundwaterwatch.usgs.gov/net/ogwnetwork.asp?ncd=crn))). Wells were sampled to best represent the spatial extent of the hydrological unit and the longest period of record. We note that the small number of wells may not offer an ideal observational representation of water table fluctuations on the hydrological unit scale, but a very limited number of CRN wells contain a length of record long enough to warrant use in our long-term analysis. For each well, we determined a specific yield; a proportion that translates change in the water level height of a well into an equivalent amount of precipitation. The proportion is heavily dependent on the aquifer's geologic make-up and represents the fraction of the total aquifer volume that does not consist of geologic material and can be occupied by water. We determined the predominant geologic make-up of each aquifer and assigned an appropriate specific yield based on previously published average values unique to each geologic feature [31; 32; 33]. Estimates of dS/dt(gw) were obtained by multiplying the annual change in water level height for each well by the well's specific yield, and then averaging per hydrological unit over a common period of 56 years extending from 1955 to 2011. The years before this timeframe were not used, as the amount of data available was deemed insufficient.  
The runoff, precipitation, and groundwater-derived dS/dt(gw) time series were calculated in terms of water years (October 1 to September 30). To reduce the effect of inter-annual variability, a 3-year moving average was applied to both the runoff and precipitation time series of each hydrological unit to isolate low frequency variability and make all trend calculations more representative of the long-term changes we wish to analyze.
# Sentiment Analysis of Online New Energy Vehicle Reviews
## 3 The Proposed Model EDC
### Loss Function  
In this study, a sentiment analysis is performed on the dataset of comments about new energy vehicles. It is observed that there are variations in the presence of sentiment-related words within the comment data. Moreover, the categories that represent positive and negative sentiment tendencies are not equally distributed across the dataset. This imbalance in class distribution can lead the model to prioritize easily classifiable samples during training, potentially neglecting the more challenging ones.  
To address this issue, a Focal Loss function [42] is introduced to account for the varying difficulty levels of sample classification within the dataset. The standard categorical cross-entropy loss formula, as depicted in Equation (14), is refined and adapted to better handle the imbalanced nature of the dataset and to put more emphasis on difficult-to-classify samples.  
\[CE(p,y)=\left\{\begin{array}{l}-\log p,y=1\\ -\log(1-p),y\
eq 1\end{array}\right. \tag{14}\]  
The equation above represents the standard cross-entropy loss function, where CE denotes the cross-entropy, p represents the predicted probability, and y represents the label value. However, when dealing with imbalanced datasets, where one class has significantly more samples than the other, the standard cross-entropy loss function may be ineffective.  
To address the issue of data imbalance in the cross-entropy loss function, a modified formula is proposed, as shown in Equations (15) and (16):  
\[p_{t}=\left\{\begin{array}{c}-p,y=1\\ -(1-p),y\
eq 1\end{array}\right. \tag{15}\]  
\[Focal\ Loss=(1-p_{t})^{\gamma}\log(p_{t}) \tag{16}\]  
The experimental results presented in this study provide evidence that setting the adjustable parameter gamma (\(\gamma\)) to 2 yields the optimal performance for the Focal Loss function. By assigning greater weights to misclassified samples and lower weights to easily classified samples, this value effectively enables the model to concentrate on samples with ambiguous sentiment expressions and sentiment-related keywords within the automotive review dataset.  
To accomplish this objective, the Focal Loss function is employed for the multi-class sentiment analysis. Equation (17) outlines the modified loss function, which is specifically tailored to meet the requirements of this task. By implementing this enhanced loss function, the model can effectively focus on samples featuring ambiguous sentiment expressions and sentiment-related keywords, thereby aiding in capturing subtle emotional nuances within the textual data.  
\[FL=-\sum_{i=0}^{n}{(1-p_{p})^{\gamma}p_{t}\times\log(p_{p})} \tag{17}\]  
Here, _pp_ represents the predicted probability for class \(p\), _p_\(t\) represents the corresponding label value, and gamma (\(\gamma\)) is the adjustable parameter that controls the degree of emphasis on difficult examples.
## 3 Results
### Sizing and Scaling of the Utilization Plant
#### 3.4.3 Equipment Sizing  
For equipment sizing, in addition to considering the mass flows that make up the scaled process, references and technical data sheets of real equipment were consulted on international websites. From these references, the equipment with the highest capacities or at least the ones closest to the required capacities were selected. As a result, Table 5 was obtained, which includes details such as the operation or unit process to be developed, the equipment where it takes place, the capacities required according to scaling, the capacities offered by the equipment, and the number of required equipment units.  
In Table 6, the determination of size and quantity of units required for each process is elucidated. For instance, in defining the quality and quantity of CSTR reactors, considerations encompassed the magnitude of inflow to the unit process and the hydraulic retention time specified in the experimental phase for the combination C6. This facilitated the determination of necessary useful volume, and subsequent exploration of market references yielded selections based on optimal performance. Notably, reactor volume served as the primary selection criterion. Similarly, for the centrifugal pumps, outcomes were informed by the maximum nominal flow rate of the chosen reference. This line of reasoning extends to the determination of the number of crushers and mixing tanks. Nonetheless, while results are grounded in market-available equipment, consideration of alternatives, such as bespoke designs, warrants attention. Such an approach could potentially streamline unit numbers but may also necessitate specialized logistics, potentially impacting spare-part availability and equipment maintenance practices.  
Additionally, it is pertinent to mention that for the flash separator, the plate tower, and the heat exchanger, references were found where the construction of these equipment items will depend on the flows and volumetric flows provided by the client. In other words, these industrial equipment items are built according to the mass streams with which they are intended to operate.  
Regarding the hydrogen combustion cell, a phosphoric acid fuel cell (PAFC) model was selected due to the characteristics defined by the National Hydrogen Center (2018). These characteristics include the ability to accept impure hydrogen streams as a power source, such as the stream delivered by the flash separator, and a maximum generation capacity equivalent to 11,000 kWh, resulting in a total of 19 units.
## 2 Experiment and Method  
### Experiment Setup  
In order to investigate the new method proposed in this study, laboratory experiments were conducted. As we all know, due to the buoyancy, gas tends to flow upward underground as shown in Figure 1b, so a vertical one-dimensional cylindrical freezing model was adopted. The freezing model is made of a stainless-steel pipe with an inner diameter of 80 mm, an outer diameter of 89 mm, and a length of 1 m. A sufficient vertical dimension could ensure sufficient heat exchange and be conducive to the development of a larger frozen range. As shown in Figure 2, the bottom of the model acts as the inlet of cold gas with a surge chamber to homogenize cold gas flow, and the top acts as the outlet; two thermocouple temperature sensors (T1 and T6) were set accordingly. The freezing model was equipped with thermocouple temperature sensors (T2-T5) every 20 cm between two ends along the vertical direction (z coordinate), the thermocouple temperature sensors used ranged from \(-50\) to \(50\,^{\circ}\)C, with a calibration accuracy of 0.5%; meanwhile, another thermocouple temperature sensor (T7) was set beside the nitrogen cylinder, outdoors, to monitor the ambient temperature in real time. The pressure sensors with an accuracy of 2.5% were arranged at the inlet and outlet to monitor pressure difference. Therefore, seven thermocouple temperature sensors and two pressure sensors were organized in total. A glass rotameter (LZB-10 WB) was used to measure the gas flow rate with an accuracy of 2.5%, which was installed at the entrance of the experimental model. The gas flow rate can also be adjusted through the glass rotameter [24; 25]. The outer side of the stainless-steel pipe was wrapped with a heating cable; the initial temperature of the freezing model could be effectively controlled jointly by the heating cable and cold gas. The freezing model and injection pipes were tightly wrapped with insulating cotton; the thermal conductivity of the insulating cotton was 0.032 W/(m\(\cdot\)K) so as to minimize the heat exchange between the freezing model and the external environment during the experiments. A KNOWELL paperless recorder was used for data acquisition in the experiments, and it can be accurate to two decimal places. Since a group of experiments took a long time, the temperature and pressure data fluctuated a little during the experiment; we set the data acquisition interval of temperature and pressure at 15 s.
## 3 Method and Materials
### Construction of Digital Monitoring System for Asphalt Concrete Pavement Construction  
#### 3.3.1 Overall System Design  
The construction of the digital monitoring system for the whole process of asphalt concrete pavement construction adopts the \"1+5\" mode. Specifically, it is composed of a data management system and an asphalt material production monitoring system; an asphalt mixture mixing monitoring system; an asphalt mixture transportation monitoring system; an asphalt mixture paving monitoring system; and an asphalt mixture rolling monitoring system; a total of 4 subsystems. The system architecture is shown in Figure 2.  
(1) Data acquisition layer. This includes monitoring and extraction of key parameters during asphalt mixture production, mixing, transportation, paving, and rolling through IoT, sensors, and the BeiDou navigation system. Compared with the GPS, the BeiDou system can realize two-way communication and has significant advantages [34].  
(2) Data transmission layer. The whole lifecycle process monitoring system of asphalt concrete pavement construction adopts a 5G wireless communication network for data transmission. As a new generation of communication technology, the 5G network has the characteristics of low latency, wide connection, and high speed [35]. Compared with the 4G network, the transmission rate of the 5G network is increased by 10-100 times, and the delay can reach the millisecond level, which can meet the transmission efficiency of massive data during the construction process. The 4G network faces a major problem in the current construction stage. Mobile broadband and real-time indicators cannot meet the needs of application scenarios such as high-definition video surveillance and remote control [36; 37]. The comparison between 5G and 4G is shown in the following Table 3[38].  
(3) Application layer. The application of the monitoring system includes basic modules and subscribe modules. Guided by the actual needs of asphalt concrete pavement construction, it provides construction parties and partners with application functions and technical services for project management and construction monitoring. Users can enter through the
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 3 Results and Discussion  
### Path-Tracking Simulation Test  
In order to verify the path-tracking control algorithm, the simulation model was established by MATLAB/Simulink, as shown in Figure 14. The model mainly consists of vehicle kinematics model, adaptive pure tracking model, vehicle tracking trajectory generation model and so on.  
In the simulation, with set the vehicle speed \(v_{0}=0.45\) m/s and the fixed forward-looking distance 2 m, the initial position of the controlled object is (0 m, 0.1 m), the tracking distance is 30 m. Set two kinds of simulation tracking trajectories. The linear tracking path is shown in Equation (13), and the curve tracking path is shown in Equation (14). The path-tracking simulation results are shown in Figure 15.  
\[y_{1}=0.5x \tag{13}\]  
\[y_{2}=x\sin 0.2x \tag{14}\]  
It can be seen from Figure 15c that there was a large tracking error in the linear path-tracking in the initial distance. We found that this was because the model sets the initial deviation. After the experiment was adjusted by the controller, when the vehicle was running to about 5.4 m, the pure tracking controller with fixed front view distance reached the basic steady state, and the steady-state average tracking error was 0.089 m. When the vehicle was running to 2.5 m, the adaptive pure tracking controller can achieve steady state, and the steady-state average tracking error was 0.054 m. Therefore, the adaptive pure tracking controller designed in this paper has faster response speed and higher tracking accuracy. It can be seen from Figure 15d that the adaptive pure tracking controller designed in the section with large curvature has better stability than the pure tracking controller with fixed forward-looking distance.  
Figure 14: Path-tracking simulation block diagram.
## 4 Conclusions  
This paper analyzes the single-event transient (SET) sensitive nodes in a low-dropout regulator for an LC voltage-controlled oscillator. A symmetrical multi-path-splitting (SMPS) hardening structure for SET mitigation in LDO is proposed. Based on commercial 28 nm CMOS technology, the Spice simulation results indicate that SMPS reduces 80.8% of SET-induced LDO output fluctuation. This structure has no additional power consumption and does not effect the LDO power-supply rejection ratio.  
Conceptualization, X.C. and Q.G.; methodology, X.C. and Q.G.; validation, X.C.; formal analysis, X.C.; investigation, X.C.; writing, X.C.; visualization and supervision, Y.G.; funding acquisition, Y.G. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61772540, 62104257; National University of Defense Technology Pre-research foundation, grant number ZK21-34; National Defense Science and Technology Key Laboratory fund, grant number WDZC20215250110.  
Not applicable.  
The data presented in this article can be obtained from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l} ASET & analog single-event transient \\ CP & charge pump \\ DC & direct current \\ DIV & frequency divider \\ EA & error amplifier \\ LDO & low-dropout regulator \\ LPF & low-pass filter \\ PLL & phase-locked loop \\ PSRR & power-supply rejection ratio \\ RH & radiation-hardened \\ RHBD & radiation-hardened-by-design \\ RHBP & radiation-hardened-by-process \\ VCO & voltage-controlled oscillator \\ SEE & single-event effect \\ SET & single-event transient \\ SEU & single-event upset \\ SMPS & symmetrical multi-path splitting \\ TMD & three-mode redundancy \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref. No.** & **Ref. [27]** & **Ref. [23]** & **Ref. [31]** & **This Work** \\ \hline Method & time-skewed space-split & built-in filter & bulk tied to source & symmetrical multi-path splitting (SMPS) \\ Technology & 180 nm & 180 nm & 40 nm & 28 nm \\ ASET mitigation 1 & 63\% & 64.6\% & 75.8\% & 80.8\% \\ Hardening level & circuit & circuit & transistor & circuit \\ \hline \end{tabular}  
* Compare in analog single-event transient (ASET) peak voltage.  
\end{table}
Table 2: Comparison with other state-of-the-art analog single-event transient (ASET) mitigation methods.
## 4 Experimental Verification and Results Analysis
### Experimental Results and Analysis
From Tables 2 and 3 and Figure 13, it can be observed that the average measurement error and the maximum measurement error of the system at a measurement distance of 8 m have almost all increased compared to those at 4 m. This is because, in the system described in this article, the distance of measurement only affects the measurement accuracy of the vision system and has almost no impact on the measurement accuracy of the IMU. As the  
Figure 13: The comparative analysis between the results of IMU integrated solution and data fusion solution. measurement distance increases, the number of pixels occupied by the feature points on the target in the camera image will decrease, leading to a relative increase in the positioning error of the feature points and a decline in the imaging quality of the camera. Moreover, with the increase in the measurement distance, the shape and size of the feature points in the images captured by the camera will also change, resulting in a decrease in the matching accuracy of the feature points, which in turn further affects the measurement accuracy of the camera. Therefore, the measurement accuracy of the system decreases with the increase in the measurement distance. By analyzing the comparison chart, it is evident that as the rotation angle increases, the cumulative error of the IMU-integrated solution shows a gradually increasing trend. In contrast, the attitude angles obtained from the data fusion solution do not exhibit significant cumulative error even at larger rotation angles. This phenomenon indicates that the employed data fusion algorithm effectively suppresses the cumulative error of the IMU. Therefore, the data fusion algorithm holds significant value in the field of high-precision attitude estimation, significantly enhancing the accuracy and reliability of IMU solutions. The results of this study provide empirical evidence for the effectiveness of the IMU data fusion algorithm, further validating its superior performance in complex dynamic environments.