esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### MODIS Data  
In order to test and validate GPP models at the flux sites, MODIS products (collection 5, including eight-day surface reflectance MOD09A1, eight-day MOD17, and eight-day land surface temperature (LST) product) in the period of 2003-2005 were downloaded [17]. MOD17 values were calculated using the following algorithm:  
\[\mathrm{GPP}=\varepsilon_{\mathrm{max}}\times\mathrm{f}(\mathrm{T}_{\mathrm{ min}})\times\mathrm{f}(\mathrm{VPD})\times\mathrm{FPAR}\times\mathrm{SW}_{\mathrm{rad}} \times 0.45 \tag{1}\]  
where \(\varepsilon_{\mathrm{max}}\) is the maximum LUE obtained from LOOKUP table on the basis of vegetation type [17]. The attenuation scalars \(\mathrm{f}(\mathrm{T}_{\mathrm{min}})\) and \(\mathrm{f}(\mathrm{VPD})\) are simple linear regression functions of daily minimum temperature (\(\mathrm{T}_{\mathrm{min}}\)) and vapor pressure deficit (VPD), and values of \(\mathrm{T}_{\mathrm{min}}\), VPD and \(\mathrm{SW}_{\mathrm{rad}}\) were obtained from the Date Assimilation Office (DAO) dataset [6,17]. MOD09A1 (500 m spatial resolution) was calculated from MODIS level 1B, which provides seven spectral bands: red (620-670 nm), NIR\({}_{1}\) (841-876 nm), blue (459-479 nm), green (545-565 nm), NIR\({}_{2}\) (1230-1250 nm), SWIR\({}_{1}\) (1628-1652 nm), and SWIR\({}_{2}\) (2105-2155 nm) [18]. Based on MOD09A1, we computed the Land Surface Water Index (LSWI) [19] and Enhanced Vegetation Index (EVI) [20], respectively.  
\[\mathrm{LSWI}=\frac{\mathrm{NIR}_{1}-\mathrm{SWIR}_{1}}{\mathrm{NIR}_{1}+ \mathrm{SWIR}_{1}} \tag{2}\]  
\[\mathrm{EVI}=\frac{\mathrm{NIR}_{1}-\mathrm{red}}{\mathrm{NIR}_{1}+6\times \mathrm{red}-7.5\times\mathrm{blue}+1} \tag{3}\]  
The S-G filter was used to smooth the noise of remote sensing data induced by cloudy and atmospheric contaminations [21]. Based on latitude and longitude information of eight flux sites, MOD17, LST, EVI, and LSWI at eight-day composites were finally extracted from 3 \(\times\) 3 km (3 \(\times\) 3  
Figure 1: Spatial distributions of eight flux sites in this study. EBF: evergreen broadleaf forests; ENF: evergreen needleleaf forests; MF: mixed forests.  
pixels for 1 km spatial resolution data and 6 \(\times\) 6 pixels for 500 m spatial resolution data) around the center of flux towers [11,22,23].
## 3 Experimental Results
### Radionetry of an IR Target  
Images of the targets at temperatures from 20 to 100 \({}^{\circ}\)C at an interval of 20 \({}^{\circ}\)C were collected, and the integration time of the infrared imaging system was set to 2000 and 3000 \(\upmu\)s. The gray image of the target at a distance of 830 m is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(t\)/\(\upmu\)s & **2000** & **3000** & **3500** \\ \hline G/DN & 3421 & 5073 & 5896 \\ \(\tau\) & 0.7924 & 0.8002 & 0.8005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of transmittance at different integration times.  
Figure 4: Errors of high-speed calibration considering the integration time.  
Figure 6a,b shows the radiometric results at 2000 and 3000 \(\upmu\)s in the outfield, respectively. The blue lines represent the radiation inversion result of the atmospheric transmittance correction MODTRAN. The black lines represent the radiation inversion by [PERSON]'s formula with the correction of the spectrum. The red lines represent the radiation inversion by the method proposed in this paper. The \(x\)-axis is the corresponding temperature of target from 50 to 120 \({}^{\circ}\)C at intervals of 10 \({}^{\circ}\)C and the leftmost \(y\)-axis is the radiance of the target.  
Figure 5: The site of IR radiometry.  
Figure 6: The precison result of radiometry. (**a**) The comparative result between conventional and the proposed method at 2000 \(\upmu\)s; (**b**) The comparative result between conventional and the proposed method at 3000 \(\upmu\)s.  
As shown in Figure 6 and Table 2, the results calculated by MODTRAN4.0 at 2000 \(\upmu\)s show that the maximum error was 13.81% and the average error was 7.00%; on the whole, the system error of the root mean square (RMS) was 7.63%. The results using the proposed method demonstrate that the maximum error was 4.78% and the average error was 1.92%, the RMS of the system error is 2.67% at 2000 \(\upmu\)s.  
Similarly, at 3000\(\upmu\)s, the results by software show that the maximum error was 10.86%, the average error was 6.94%, and the RMS of the system error was 7.41%. In contrast, the results by the constant reference show that the maximum error was 4.89% and the average error was 1.15%; and the RMS of the system error was only 2.12%.  
Both integration time experiments reveal that the method based on constant references is more accurate for atmospheric inversion than the method based by software. The error tends to be moderate in the range of 50\({}^{\circ}\) to 120\({}^{\circ}\) at any random integration time.
## 6 Conclusions  
In this study, we aimed to control the transmission rates of data packets in CSMA/CA wireless networks more accurately using MAB algorithms. We proposed the use of the logarithmic values of the transmission rates of MCS levels instead their nominal values in the computation of their expected throughput estimations. To evaluate the effectiveness of the proposed method, we introduced two MAB algorithms which adopt the logarithmic rates and a sliding window over time. The proposed MAB algorithms were also designed to support frame aggregation schemes available in wireless network standards for high throughput such as IEEE 802.11 ax.  
The experimental results using the event-driven network simulator, ns-3, demonstrated that the proposed MAB algorithms outperformed their counterparts that do not adopt the logarithmic rates in both the stationary and non-stationary scenarios. Additionally, the proposed MAB algorithms achieved better performance than their counterparts when frame aggregation was enabled. Furthermore, the proposed MAB algorithms achieved comparable performance to non-MAB-based transmission rate control algorithms such as Minstrel in rapidly changing wireless channel environments.  
We expect that the use of the logarithmic rates of the transmission rates can be adopted to improve the performance of MAB algorithms that involve other types of wireless communications such as cellular networks. However, by comparing the experimental results of the proposed algorithms with those of the Ideal algorithm in the IEEE 802.11 ax network, we can see that there is room for improvement. As a future work, we consider the adoption or development of techniques to reduce the size of the search space to improve the performance particularly when the search space is large. In addition, we would like to investigate whether the adoption of sophisticated change detection techniques could result in better performance than using the sliding window.  
**Funding:** This work was supported by 2021 Hongik University Research Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Materials  
The main part of the low-cost device is the Insta360 X3 spherical camera (Arashi Vision Inc., Shenzhen, China). This camera is equipped with a \(1/2^{\prime\prime}\) sensor to capture 360\({}^{\circ}\) videos at resolutions up to 5.7K/30 fps. The camera includes two fisheye lenses with a focal length of 6.7 mm and an aperture of F1.9. The camera also includes an inertial system that allows it to stabilize the orientation of the images. All data collected are stored in an INSV format.  
Another important part of the equipment is the low-cost GNSS. The GNSS consists of a ZED-F9P receiver and an Ardusimple ANT2B antenna [24] (ArduSimple, Lleida, Spain). The GNSS Controller application, developed by students at CTU Prague, is used to record RTK positions and log RAW data. The accuracy of RTK \(\sigma\) measurements is 1 cm + 1 ppm, as specified by the receiver manufacturer.  
To integrate these components into a single device, a custom case was designed to securely hold both devices. This assembled device can then be mounted on a pole. The important thing is not to obscure the GNSS antenna with devices that could interfere with the reception of the GNSS signal. The complete low-cost device is shown in Figure 1.  
A Trimble R2 GNSS device (Trimble Inc., Westminster, CO, USA) was used to create a reference absolute point field based on GCPs. In the Lipova street case study, several checkpoints were surveyed using a Trimble S8 total station. The reference point clouds were created using a Faro Orbis mobile scanning system, where the manufacturer specifies  
Figure 1: Assembled low-cost mapping device.  
a one-sigma 5 mm accuracy, and a Mavic 3E drone (SZ DJI Technology Co., Ltd., Shenzhen, China) with an RTK module.
## 3 Results and Discussion
### Quantification of Individual and Total Sugars by Regression Analysis
PLSR loading vectors reveal which bands are responsible for explaining the highest variation in the model and assist in understanding which functional groups account for the correlation in the regression model. The marker bands associated with the PLSR loading vectors for the first latent variable (factor) for the sucrose, glucose, fructose, and total sugar content in ground breakfast cereals, shown in Figure 4, indicated that the bands at 2205, 2091, and 1940 nm explained most of the variance in their corresponding PLSR models. The NIR bands located near 2205 nm correspond to C-H combination bands that are common to the various sugars [44]. As mentioned previously, bands centered at 2091 nm is associated with O-H combination stretching and H-O-H deformation of polysaccharides [45; 46; 47], and the band at 1940 nm corresponds to O-H bending second overtone [20; 47]. To evaluate the FT-NIR sensor's performance for intact breakfast cereals, spectra were collected only from the samples purchased from the grocery stores (\(n=20\)). Prediction models were generated using PLSR analysis, and similar or slightly lower performance was obtained in comparison with the ground samples (Table 2). Usually, the breakfast cereals are manufactured by coating an unsweetened base with a sweetener solution [48]. Because the sugar is located mostly on the surface of the cereal samples, the prediction performance of models was not adversely affected by the light scattering within the unground samples. The calibration models generated for the individual sugars and total sugar content in ground and intact breakfast cereals were externally validated using an independent sample set that has not been used before (20% of the total ground and intact samples). The performance statistics obtained from the external validation set were similar to the calibration model performance statistics in terms of  
Figure 3: Partial least square regression (PLSR) plots for sucrose (**a**,**c**) and total sugar (**b**,**d**) content in ground and intact breakfast cereal samples. The gray diamonds represent the calibration set samples; the black diamonds represent the external validation set samples. correlation coefficients and error (Table 2), which confirmed the robustness and predictability of the models. Figure 3a-d demonstrate the external validation set samples' distribution within the range of calibration set samples. The calibration and external validation models for individual and total sugar content using the handheld FT-NIR sensor showed similar or superior performance in R and SEP or SECV to previously reported studies using laboratory instruments (Table 3).
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 1 Introduction
### Classification of Valve Actuation Systems
#### 1.3.2 Camless Valve Actuation Systems  
Without the constraint from the cam mechanism, a camless system is capable of adjusting valve timing, duration, and lift independently to achieve more desired target levels that can be varied cycle-by-cycle. It also provides independent control of engine valves for each cylinder. For example, it is able to provide asymmetric opening for two intake valves for one cylinder, resulting in improved charge air and fuel mixing. It can also perform cylinder deactivation under low load conditions. It, thus, offers more control with greater benefits than conventional cam-based valve system. Camless systems include the following:  
* Opposed solenoid electro-magnetic (or electromechanical) camless VVA (EMVVA) [29, 30, 31, 32, 33, 34];
* Electro-hydraulic camless VVA (EHVVA) [35, 36, 37];
* Electro-pneumatic camless VVA (EPVVA) [38, 39];
* Rotary motor EMVVA, also called intelligent valve actuation (IVA) system by Camcon [33, 34].  
Camless systems are a key technical enabler for other advanced engine technologies, such as air hybrid vehicles [40], HCCI [41], and high-efficiency diesel engines [42].  
In the subsequent sections, various valve actuation systems are grouped and reviewed based on the valve actuation control parameters and structures.
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## 2 Materials and Methods
### Optimization Process  
The optimization of the antenna structure to achieve the desired goal (broadening the antenna bandwidth to include 5G NR) uses novel metaheuristic algorithms, known for their efficiency in solving a wide range of well-defined and parametrized problems, as highlighted in the existing literature. To make the algorithms applicable to the structure, the authors first parameterized the antenna (at least the degrees of freedom that would be used for the optimization), and then a fully functional API between the electromagnetic simulation software CST 2023 and the Matlab programming environment was designed by the authors to automatically implement the changes in the structural parameters and obtain the behavioral responses (i.e., \(S_{11}\)).  
These changes are defined by the BeSD algorithm [3], during the iteration process, in which each agent corresponds to a possible solution (antenna design with algorithm-defined parameters) and is evaluated by its user-defined cost function, which is calculated based on the \(S_{11}\) graph. The design with the lowest cost function (minimization problem) is deemed optimal, and the algorithm ultimately converges at it, successfully achieving the posed goal. Our objective is to develop an antenna that includes a wide operating bandwidth. Therefore, the objective function is defined as  
\[\begin{split} F(\tilde{x})&=S_{11}^{f_{c}}+\eta \times max(0,\gamma(\tilde{x})-U_{dB})\\ \gamma(\tilde{x})&=max\Big{\{}S_{11}^{f_{h}}(\tilde {x}):k=1,..K\Big{\}}\end{split} \tag{1}\]  
where \(\tilde{x}\) is the vector of the geometric parameters, \(\eta\) denotes a very large number, \(U_{dB}\) is the limit in dB, \(K\) is the number of frequency points taken, and \(f_{c}=3.55\) GHz the center frequency of the n78 5G NR band. We set \(U_{dB}=-10\) dB while the frequency points are taken from 1.4 to 3.9 GHz. The optimization process was carried out using the CST Matlab API in conjunction with the BeSD algorithm code.
## 5 Results and Discussion
### Experimental Validation of Quantitative Reliability Calculations  
The mission reliability of a ship electronic information system is measured by the mission success rate. The evaluation range of the mission success rate depends on the specific characteristics and requirements of the mission. Generally speaking, the higher the mission success rate, the better, but in practice, the success rate of the mission is also affected by a variety of factors, such as the complexity of the mission, the execution environment, and the time of mission execution. For the ship electronic information system in this study, it is required that the success rate of sea-to-air, sea-to-underwater, and sea-to-land missions is greater than 0.8.
## 2 Usual Rotor Faults in Conventional Machines
### Faults Shared by Conventional Machines--Unbalance, Misalignment, Eccentricity  
Among the common faults for all topologies, eccentricity and misalignment are the focus of diagnosis research [12]. The eccentricity between rotor and stator is related to the uneven airgap along one mechanical rotation. Eccentricity possesses a different nature depending on the position of the minimum airgap. The eccentricity is static when the minimum airgap position is always fixed along the airgap periphery, as depicted in Figure 2b. This is normally caused by inherent mounting or manufacturing imperfections. On the other hand, dynamic eccentricity takes place when the minimum airgap position depends on the instantaneous rotor position (i.e., dynamically changes with rotating frequency), as illustrated in Figure 2c. Some frequent causes for dynamic eccentricity are rotor ovality and bearing misalignment.  
Mixed eccentricity combines both static and dynamic cases and represents the most common practical scenario. The eccentricity condition is inherent to all machines and, thus, low eccentricity levels are normally accepted up to 10% of the airgap thickness [38].  
In turn, misalignment refers to the positional deviation between the rotational axes of two coupled shafts. This misalignment can induce rotor-stator relative displacement, analogous to the effects observed in eccentricity. The misalignment is defined in two planes and in two modes. The parallel misalignment occurs when the rotational axes of two coupled shafts are parallel but not collinear, as shown in Figure 3b. Angular misalignment takes place when the rotational axes of two coupled shafts intersect but are not parallel, as illustrated in Figure 3c. This means that the shafts are angled relative to each other, resulting in a situation where the shafts are closer together at one end and further apart at the other. The combination of parallel and angular misalignment can also occur in real cases, further increasing the additional forces on rotors, bearings, couplings, and seals.  
The acceptable levels of shaft misalignment depend on the nominal rotating speed and the utilized mechanical coupling, as well as on the type of load [39]. Different misalignment modes are related to eccentricity indicators since they induced a relative rotor-stator displacement [40].  
The unbalance fault derives from the mass uneven distribution in rotating components such as the coupling or the driven load. It results in the generation of unbalanced centrifugal forces and moments, which can potentially damage various machine components. It is closely associated to the dynamic eccentricity case since it induces a rotating relative displacement between stator and rotor [41].  
Figure 2: Different eccentricity types in electric machines: (**a**) faultless machine; (**b**) machine with static eccentricity; (**c**) machine with dynamic eccentricity.  
In summary, from an electromagnetic perspective, common faults related to inherent eccentricity, misalignment, and unbalance faults can be converted to the analysis of static, dynamic, and mixed eccentricity.
## 4 Results and Discussion
### Comparison of Overall Accuracy of Model Predictions and Variance in Prediction Error
The last column of Table 3 presents the results obtained for average RMSE of predictions of all the models in repeated five-fold cross validation tests. As can be seen from Table 3, the trend in RMSE values is consistent with the trends observed in MAPE and MAE, and it leads to the same conclusion: among the six nonlinear ML models, the ANN model turns out to be the most accurate, the second most accurate model is the RFR, and it is followed by the ABR, GBR, and KNN models.
# Rock Indentation Behavior: Effects of Penetration Rates and Indenter Types
## 2 Methodology
### Physical Properties of Rock Specimens  
Three types of rock, consisting of artificial rock, limestone, and granite, were manufactured into cylindrical rock specimens (size of \(\phi\)50 \(\times\) 100 mm) for indentation test performance by indenters. These materials were selected to represent a range of rock types with varying mechanical properties, which allow for a comprehensive analysis of rock indentation behavior. Concrete, an artificial rock, was chosen due to its reproducibility and controlled properties, making it a suitable candidate for consistent experimental testing. Limestone was selected for its relatively soft and brittle nature. Meanwhile, granite, a harder and more durable rock, was included to represent materials with higher hardness. In this study, both limestone and granite rock specimens were drilled from intact rock with no obvious cracks or damage zones. Therefore, rock specimens were initially assumed to be homogeneous with no obvious cracks. Moreover, according to the X-ray diffraction tests, the limestone specimen exhibits a high content of calcite and dolomite, resulting in a lower overall hardness. In contrast, the granite specimen contains significant amounts of quartz and feldspar, contributing to a higher overall hardness, while artificial rock is a mixture of water, concrete, and sand in the ratio of 1:4.8:0.8. Artificial rocks were then standardized for 14 days at a room temperature of 5-10 \({}^{\circ}\)C. These rock specimens were then subjected to serial rock mechanics tests to obtain their compressive and tensile strengths for rock brittleness calculation. The results are shown in Table 1. Accordingly, the compressive and tensile strengths of the specimens are distributed across a certain gradient.
## 6 Results and Discussion  
A simulation environment is created using MATLAB-2015A where DT, LEACH and LEACH-PRO are implemented to evaluate and compare their performance under different scenarios. The simulation parameters used are similar to those used in many state of the art works in this topic. The main performance evaluation metrics used are NDR (Nodes Death Rate), SRE (System Remaining Energy) and Packets to BS. NDR is calculated by measuring the First Node Death rate (FND), Half of Nodes Death rate (HND) and 90% of nodes death rate. Similarly, SRE is also calculated by measuring the remaining energy in percentage as 10% SRE, 50% SRE and 90% SRE to analyze the overall performance of the protocols. Packets to BS refers to the number of packets sent towards the base station. A summary of the simulation setting is presented in Table 4.  
Besides the above metrics, packet delivery ratio and end-to-end (E2E) delay measurements are very important metrics as well since they will uncover any potential negative effect of the proposed LEACH-PRO scheme. Indeed, a decrease in packet delivery ratio and/or a high end-to-end delay will make LEACH-PRO unsuitable for certain application scenarios where low delay and high reliability are required. To investigate these two metrics thoroughly we will measure them in the experiments to be conducted in the monitoring environment setup at QUEST, in Nawabshah and the results will be reported in another experimental paper.