esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 5 Conclusions  
In this paper, first, the superpixel segmentation algorithm based on information measures with color histogram driving was introduced. The spectral bands were selected based on entropy and CMF. In these selected bands, the redundant information was removed while the significant information was retained for further superpixel segmentation. Then, the mutual information was used to select the final three groups of bands, i.e., \(3n\) bands, to composite false color images. The smaller value of the mutual information indicated the less redundancy between the spectral bands and the greater amount of contained information. In our study, the segmentation results were obtained using the hill climbing optimization algorithm. In addition, parameter selection experiments and comparative experiments were designed and implemented in this study. Under the optimal parameter, the proposed IM-CHD was compared with other similar existing methods. The experimental results demonstrated the effectiveness and superiority of the proposed IM-CHD method. Therefore, the proposed method achieved the purpose of efficient preprocessing of hyperspectral images.  
Previously, we proved the validity of obtaining the main spectra of HSIs through information measure. In the future, we will study the application of superpixel segmentation based on information measurement in the classification of hyperspectral images. That is, the IM-CHD method proposed in this paper is used to preprocess HSIs and classify the processed HSIs. The classification results are compared with the previously proposed HSIs classification method, and it is discussed whether IM-CHD method can improve the classification accuracy of HSIs.  
Conceptualization, L.L. and S.Z.; methodology, L.L.; software, S.Z.; validation, L.L. and S.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, L.L.; project administration, L.L.; funding acquisition, L.L. 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 No. 61671170 and National Key R & D Plan, grant number No. 2017 YFB1302701. The authors declare no conflict of interest.
## 4 Modelling and Simulation
### Geometry Mesh and Materials  
The ANSA pre-processor is used for the development of the geometry mesh and volume elements are used for higher results accuracy (Figure 5). The mesh of the swingarm has 97,259 volume elements, from which 87,475 are Tetras and 9784 are Pyramids elements. The total mesh of all the components included in the swingarm assembly has 116,818 volume elements (Figure 6).  
Figure 5: Mesh of the initial swingarm design.  
The modelling of material properties used for the preliminary simulations can be found in Table 4, where the required properties used by the pre-processor are: (a) Elastic modulus, (b) Poisson ratio, (c) Shear modulus, (d) Density and (e) Yield strength. The materials initially considered are: (a) aluminum 7075-T6 for the swingarm and the suspension link, (b) stainless steel AISI-304 for the swingarm axle and the bearings, (c) steel AISI-130 for the suspension, all modelled as isotropic materials. Suspension has been simplified to a steel round beam (shown with a line in Figure 6) in order to reduce mesh elements number and is assumed to be a rigid component.
## 4 Analysis of Examples  
### Data Acquisition  
In this paper, a section of the second ring road in Chengdu is selected as the target path. It is 10.5 km long, with a speed limit of 80 km/h and four lanes in both directions, which is one of the main roads in Chengdu. The geographical location of the path is shown in Figure 7.  
The authors collected the track records of taxis in Chengdu from 3 August 2014 to 16 August 2014. After data cleaning, abnormal records and duplicate records were removed, and the data during the period from 06:00:00 to 23:59:59 were retained as the floating car data set under normal conditions. VISSIM software was used for simulation to obtain the post-earthquake floating car data set by combining the traffic flow information of this section under normal conditions. Hence, the travel time simulation value of the post-earthquake floating car trajectory was obtained, as shown in Tables 2-4. To test the impact of earthquake damage on the reliability of path travel time, this paper assumes that the earthquake damage can be divided into three types: basically intact, medium damage, and destroy, the earthquake damage index is 0.1, 0.5, and 0.9, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle ID**} & **Start Time of Floating** & **End Time of Floating** & **The Trajectory Observation** & **Average Earthquake** \\  & **Car Track** & **Car Track** & **Travel Time is T\({}_{i}\)/Second** & **Damage Index _ind_** \\ \hline
2801 & 17 August 2014 07:41 & 17 August 2014 8:55 & 4429 & 0.1 \\
2802 & 17 August 2014 08:04 & 17 August 2014 8:41 & 2210 & 0.1 \\
2803 & 17 August 2014 08:00 & 17 August 2014 9:00 & 3601 & 0.1 \\
2804 & 17 August 2014 07:43 & 17 August 2014 8:39 & 3412 & 0.1 \\
2805 & 17 August 2014 08:51 & 17 August 2014 9:29 & 2289 & 0.1 \\
2806 & 17 August 2014 07:36 & 17 August 2014 8:53 & 4630 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The simulated FCD set (_ind_ = 0.1).  
Figure 7: Selected route (clockwise direction).  
As can be seen from Figures 9 and 10, the scaling factors are mostly concentrated around 1. This is because the nodes of the road network are fixed from beginning to end, and the fluctuation caused by the changing coverage path is relatively small.
## 4 Core Topology Optimisation
Additionally, the wavy configuration of the cork core induces a pattern of plastic strain distribution that further contributes to the increase in the energy absorption. The energy absorbed by the cork core increases by more than 20%, as indicated in Table 8.  
Figure 14 shows the evolution of the kinetic and internal energies for the individual components of the sandwich plate (skins and core), for both reference and optimum case. It can be clearly seen the different behaviour between both plates. The maximum kinetic energy reached by all components in the optimum case is clearly higher than the reference case. As it was previously said for Figure 11, this effect is especially observed for the front skin produced, because the sinusoidal shape delays the contact between the core and the rear skin. This change in the kinematic induces the higher internal energy absorbed by each individual component and, therefore, the sandwich itself for the optimum configuration compared with the reference. Figure 12: Energy absorption in optimised case, as function of the geometrical (**a**) amplitude and (**b**) frequency. \begin{table}
\begin{tabular}{l l r r r} \hline \hline
**Case** & \multicolumn{2}{c}{**Reference**} & **A4-f2** & **Difference [\%]** \\ \hline \multirow{4}{*}{Absorbed energy} & Total & 40.64 & 56.61 & 15.97 & 39.3\% \\  & Cork & 32.61 & 39.56 & 6.95 & 21.3\% \\  & Front skin & 2.63 & 6.00 & 3.37 & 128.1\% \\  & Rear skin & 5.39 & 11.03 & 5.64 & 104.6\% \\ \hline \hline Cork compression & Maximum & 0.187 & 0.242 & 0.055 & 29.4\% \\ \hline \hline \multirow{2}{*}{Maximum displacement} & Front skin & 9.8 mm & 20.5 mm & 10.7 mm & 109.2\% \\  & Rear skin & 8.3 mm & 12.1 mm & 3.8 mm & 31.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Energy absorption in the reference and optimised case.
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 3 The Solution Algorithm for the Hybrid Sensor Deployment
### The Solution Procedure of Step I  
The main idea of this section is to find a feasible deployment plan by solving two sub-models, where sub-model I is used to determine variable \(x_{ij}^{k}\) and sub-model II is adopted to achieve variables \(y_{iu}\) and \(z_{u}\). Since the purpose of solving sub-model I is to give a multi-sensor deployment plan as a reference, only constraints related to sensors are involved (Equations (2)\(\sim\)(5)). In addition, the budget constraint for sensors should be considered. Thus, the formulation of sub-model I is stated as follows.  
\[\begin{split}& min\sum_{i,j\in\mathbf{N}\cup\{0,n+1\},k\in \mathcal{S}}\pi_{ij}^{k}x_{ij}^{k}\\ & s.t.\quad\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
\[\begin{split}&\min\sum_{i,u\in N}\phi_{iu}y_{iu}\\ s.t.&\sum_{u\in N}\varpi z_{u}\leq W-\omega_{1}\\ &\sum_{i\in N}y_{iu}f_{i}\leq z_{u}F,\forall u\in N\end{split} \tag{14}\]  
_Equations_ (8) \(\sim\) (10)  
where \(\phi_{iu}\) is the distance between cells \(i\) and \(u\).  
The above model is a linear expression that can be easily solved by commercial solvers. In this way, the number and location of sensors and servers can be determined. This solution method intends to relax the hard constraint (Equation (7)), while the interaction between servers \"location and sensors\" site selection is not reflected. This part only imposes the influence of variable \(x_{ij}^{k}\) on variables \(y_{iu}\) and \(z_{u}\). There is a lack of discussion on the reverse relationship. So it is necessary to investigate how to determine the variable \(x_{ij}^{k}\) by presetting the value of \(z_{u}\). Step II of the proposed solution algorithm is used to complete this task, which will be explained in the following part.
## 6 Navigation and Interface
### Dynamic Trajectories  
The opposite concept is represented by dynamic trajectories shown in Figure 10. In this case, the user only defines some individual stops within the environment. After that, Phollower performs the route calculation within the virtual map between the stations using the A star algorithm [PERSON] (1996). This type of trajectory determination is suitable for environments where it is not strictly defined  
Figure 9: An example of a predefined trajectory operation.  
where the robot can or cannot move. As dynamic trajectories are executed cyclically, it is also suitable in environments where many moving obstacles occur randomly. Hospitals and shopping malls are potential environments with demanding requirements for which dynamic trajectories are particularly suitable. For both operation modes, the execution of the chosen path is a task for the position controller. Phollower uses the well-known pure pursuit algorithm iterated in every cycle, which means continual updating of the actual vehicle position and a new look-ahead point determination. Detailed information about the implementation of the pure-pursuit algorithm can be found in [1].
# Study About the Performance of Ascon in Arduino Devices
## 4 Security Analysis
### Side-Channel Attacks Against Arduino Devices  
In this subsection, we present the main studies related to side-channel attacks that have been published against Arduino devices. It is noteworthy that most of them only implement the AES cryptosystem.  
In [33], the authors implemented correlation power analysis (CPA) attacks using ATmega (ATmega 328 P - PU, Arduino Uno) cryptographic module for configuration and the oscilloscope to obtain the experimental result, and the MATLAB program (R2021b) for the verification process and design technology to analyze countermeasures.  
Power analysis attacks against different Arduino boards were presented in [34], but only Speck and AES systems were implemented. Authors concluded that software countermeasures such as random instruction injection and randomly shuffling S-boxes are good enough for their simplicity and cost.  
In [35], the authors presented two contributions. First, they show how a complete side-channel test bench can be built with affordable and mainstream components. For this purpose, [PERSON] and Durvaux have taken advantage of classic use cases: an AES implementation running on an 8-bit ATmega328P embedded on an Arduino Uno and later on a 32-bit ATSAM3X8E (ARM Cortex-M3) embedded on an Arduino DUE.  
[PERSON] et al. studied CPA attacks in [36] and they found that light-weight cipher SPECK may not resist side-channel attacks because it requires too few resources for a successful attack. Furthermore, the authors investigated the minimum number of plaintexts needed for attacking it. As a result, it was found that 50 plaintexts are sufficient for a successful attack.  
In [37], authors conducted the side-channel attack for the XOR in the final round of the SIMON cryptosystem. As a result, they could attack SIMON with little computational resources and time, which means that this cryptosystem can be weak against side-channel attacks. Furthermore, the authors checked the relationship between the Hamming weight of the round key and the ease of attacking. The authors confirmed that there was no relationship between them.  
In [38], [PERSON] et al. presented power analysis traces and practical segmentations of power consumption measurements charts for Russian encryption standard Kuznyechik cipher [39] on original and modified Arduino nano boards.  
Finally, in [40], the authors evaluated the effectiveness of the countermeasure based on voltage modulation against CPA and DPA side-channel attacks. The variations in voltage introduce variations in the device current, making it more difficult for the attacker to find the secret key. They implemented the countermeasure on the Arduino platform and tested it for several different voltage sine frequencies. The results shown that the proposed countermeasure is extremely effective against cryptographic attacks.
## 6 Conclusions  
We explored the effects of the airborne LiDAR scanning angle, flying altitude, and pulse density on forest foliage profile retrieval for the first time, to our knowledge. The results indicated that the scanning angle is an important influence factor in foliage profile retrieval, and the optimal scanning angle for foliage profile estimations is 20\({}^{\circ}\). The optimal scanning angle was independent of the flying altitude and pulse density. In addition, scanning angle combinations of 15\({}^{\circ}\) and 25\({}^{\circ}\) and 15\({}^{\circ}\) and 30\({}^{\circ}\) can improve the foliage profile estimation accuracy. A more accurate method that combines multiple scanning angles for foliage profile estimations should be studied in future research. The effects of flying altitude and pulse density on foliage profile retrieval were small with no obvious regularity, so they could all be ignored.  
In general, the forest foliage profile estimation was significantly affected by the airborne LiDAR scanning angle, and the effects of flying altitude and pulse density on the foliage profile estimation were small and could be ignored. These sensitivity analyses were very important for selecting optimal instrument operational parameters to acquire more accurate foliage profiles and minimize data acquisition costs. Overall, our results provide reliable and useful information for the forest and ecological researchers using LiDAR data.  
**Acknowledgments:** This work was supported by the National Key R&D Program of China (No. 2017 YFA0603002) and National Natural Science Foundation of China (Nos. 41371350, 41671434, and 41431179). We received funds for covering the costs to publish in open access.  
**Author Contributions:** [PERSON] designed the study, collected and analyzed the data, and wrote the manuscript. [PERSON] designed the study, and wrote the manuscript. [PERSON] searched the literature, analyzed the data, and revised the manuscript. [PERSON] analyzed the data and revised the manuscript. [PERSON] collected the data and revised the manuscript. The manuscript was improved by the contributions of all of the co-authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 The Selection Parameters of the MPPT Algorithms
### Tracking Speed  
Tracking speed is the rate at which the MPPT algorithm adjusts to changes in response to changing environmental conditions such as irradiance and temperature to maintain optimal power output. Faster tracking minimizes the loss of power during transient changes. In PV systems, environmental factors like shading or temperature fluctuations can cause rapid changes in the amount of power generated by the PV modules. The MPPT algorithm must be able to quickly adjust the operating point (voltage and current) to match the new optimal power point.
## 1 Introduction
Additionally, WLAN standards support MIMO technology, which allows users to use different spatial streams to improve throughputs. WLAN also supports multiple bands, so it is necessary to design antennas [23; 24; 25] with compact, weak coupling and high radiation efficiency performance to achieve high signal efficiency and high linearity transfer. Moreover, WLAN uses orthogonal frequency division modulation (OFDM) technology to support higher throughputs, which lead to a high peak-to-average ratio (PAPR). Therefore, PAs need to have high efficiency both at peak output power and at large back-off output power in order to save power. To improve efficiency at the power back-off level, various technologies have been developed in recent years. Supply envelope tracking is an effective method [26; 27; 28], but in the CMOS process, the power supply voltage is relatively low, which makes it difficult to apply to CMOS PAs; the Doherty PA is another popular way to improve back-off power efficiency [29; 30; 31; 32], but there are also difficulties regarding auxiliary PA matching and layout routing. Recently, some new technologies have been used to further improve PA efficiency at deep PBO levels. Multi-ways Doherty PAs can achieve multiple efficiency peaks at PBOs with a relatively larger size [33; 34; 35; 36]; references [37; 38] propose a class-G Doherty PA, which achieves efficiency peaks at 6- and 12-dB PBOs, with additional hardware overhead. In this article, we propose a fully integrated linear CMOS PA operating from a 3.3-V supply for 2.4 G WLAN applications. It not only achieves high linearity using gm linearization, PMOS compensation, and large signal MGTR techniques [39], but also has a high efficiency at high back-off power with the proposed method. This article is organized as follows: In Chapter 2, the efficiency-enhanced method is discussed. The detailed circuit implementation is described in Chapter 3. Chapter 4 presents the measurement results, Chapter 5 discusses the future research direction of CMOS PAs for WLAN, and Chapter 6 concludes this article.
## 5 Case Study and Simulation Results  
### Simulation Parameters  
The simulation model of the energy router-based HEMS is built on the MATLAB/Simulink platform. The DC bus voltage, AC bus phase-phase voltage and AC frequency are set to 750 V, 380 V and 50 Hz, respectively.  
Photovoltaic (PV) cells are connected to DC bus through a boost circuit converter, which utilizes maximum power point tracking (MPPT) to make full use of the PV power. Incremental conductance algorithm is used for MPPT. The maximum output power of PV cells is 5 kW when the solar irradiance is 1000 W/m\({}^{2}\) and the temperature is 25 \({}^{\circ}\)C.  
It is assumed that the battery unit is used to undertake a load current of 80% at most and its continuous energy supply time \(t\) is 3 h. When the load demand is 5 kW, the rated load current \(I_{\text{load}}\) can be calculated according to the following equation,  
\[I_{\text{load}}=\frac{P_{\text{load}}}{\sqrt{3}U_{\text{rated}}} \tag{17}\]  
where \(U_{\text{rated}}\) is the AC bus voltage. Then battery capacity \(C_{\text{bat}}\) is calculated by the following equation,  
\[\gamma\times I_{\text{load}}\times t=C_{\text{bat}}\times k \tag{18}\]  
where \(k\) = 0.4 is the average charging or discharging current coefficient, the value of \(\gamma\) is 50%. The calculated battery capacity is 22.8 Ah. Finally, the battery capacity is chosen to be 30 Ah, and its rated voltage is 50 V.  
It is assumed that the supercapacitor can supply loads with rated power of 5 kW for 15 s when power of PV output is zero, and its safe voltage range is from 245 V to 375 V. Then the supercapacitor capacity \(C_{\text{sc}}\) can be calculated by the following equation,  
\[C_{\text{sc}}=\frac{2E_{\text{sc}}}{u_{\text{scmax}}^{2}-u_{\text{scmin}}^{2}} \tag{19}\]  
where \(E_{\text{sc}}\) = 75 kJ, \(u_{\text{scmax}}\) = 375 V and \(u_{\text{scmin}}\) = 245 V, respectively. The supercapacitor capacity is chosen to be 2F.  
Other main parameters are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
###### Abstract  
Urban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance by leveraging the variability of the signal-to-noise ratio (SNR), without requiring auxiliary sensors. Analysis of 24 h observational datasets collected across diverse environments, including open-sky (OS), city streets (CS), and urban canyons (UC), demonstrates that multipath-affected non-line-of-sight (NLOS) signals exhibit significantly greater SNR variability than direct line-of-sight (LOS) signals. The HK model classifies received signals based on the standard deviation of their SNR and assigns corresponding weights during position estimation. Comparative performance evaluation indicates that relative to existing weighting models, the HK model improves 3D positioning accuracy by up to 22.4 m in urban canyon scenarios, reducing horizontal RMSE from 13.0 m to 4.7 m and vertical RMSE from 19.5 m to 6.9 m. In city street environments, horizontal RMSE is reduced from 11.6 m to 3.8 m. Furthermore, a time-sequential analysis at the TAEE site confirms consistent improvements in vertical positioning accuracy across all 24-hourly datasets, and in terms of horizontal accuracy, in 22 out of 24 cases. These results demonstrate that the HK model substantially surpasses conventional SNR- or elevation-based weighting techniques, particularly under severe multipath conditions frequently encountered in dense urban settings.  
GPS; SNR; multipath; weight model; urban canyons +
Footnote †: journal: Sensors
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 2 Materials and Methods  
### Study Area and Datasets  
Bayannur city is located in the west of Inner Mongolia, China, with a total length of 378 km, a width of 238 km, and an area of 64,000 km\({}^{2}\), with a temperate continental climate [11]. The location is shown in Figure 1.  
The sample air pollutant data were obtained from the weather report website ([[http://www.tianqihoubao.com](http://www.tianqihoubao.com)]([http://www.tianqihoubao.com](http://www.tianqihoubao.com)) (accessed on 9 January 2023)), and the meteorological data for the same period were obtained from the Inner Mongolia Meteorological Bureau, containing a total of 1771 sets of daily average monitoring values from 1 January 2015 to 13 January 2020. Since the percentage of missing values in the wind direction factor is more than 50% of the total sample size, this factor is not included in the dataset. There are 12 factors in each sample dataset, including date, air pressure (AP), air temperature (AT), wind speed (WS), sunshine hours (SH), relative humidity (RH), daily precipitation (DP), PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), CO, and O\({}_{3}\). AQL is the predicted grouping variable mapped from the value of the AQI. Some of the sample data are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **AP** & **AT** & **WS** & **SH** & **RH** & **DP** \\  & **(0.1 hPa)** & **(0.1 \({}^{\circ}\)C)** & **(0.1 m/s)** & **(0.1 h)** & **(1\%)** & **(0.1 mm)** \\ \hline
1 January 2015 & 9109 & \(-\)96 & 14 & 81 & 39 & 0 \\
2 January 2015 & 9060 & \(-\)70 & 7 & 78 & 47 & 0 \\
3 January 2015 & 8985 & \(-\)42 & 8 & 83 & 38 & 0 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meteorological factors sample.