esa-sceva/satcom-chunk-collection huggingface dataset:
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model  
### UAV Model  
In this paper, we assume a fixed-wing UAV which is generally efficient in wide-area reconnaissance. We use the simple UAV model used in [12], as shown in Table 1. To fulfill realistic motion for the turn of fixed-wing UAV, we set the rotation radius to 500 m. We set the communication range among UAVs to 8000 m. We do not take account of packet loss in communicating with each UAV. The UAV is furnished with a camera of which the scanning range is 2000 m \(\times\) 1000 m. The UAV speed is fixed at 150 km/h and its altitude is fixed at 3500 m. Since UAVs fly at the same altitude, we denote the coordinate of a UAV \(u_{i}\) as \((x_{i},y_{i})\) for the simplicity.
## Materials and Methods
### Measurement of Albedo
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Specifications**} & \multicolumn{2}{c}{**Pyranometer Type**} \\ \cline{2-3}  & **CMP6 Downward** & **CMP11 Upward** \\ \hline Field of view & 180\({}^{\circ}\) & 180\({}^{\circ}\) \\ Response time & 18 s & 5 s \\ Sensitivity & \(9.05\times 10^{-6}\) V W\({}^{-1}\) m\({}^{2}\) & \(4.96\times 10^{-6}\) V W\({}^{-1}\) m\({}^{2}\) \\ Temperature dependence of & \(<\)4\% & \(<\)1\% \\ sensitivity & 285–2800 nm & 285–2800 nm \\ Operating temperature & \(-\)40 to + 80\({}^{\circ}\)C & \(-\)40 to + 80\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pyranometer sensor type, properties, and specifications. Figure 3: Setup of the pyranometers for measuring albedo at International Rice Research Institute (IRRI) in Los Banös, Republic of the Philippines (14°9’55′′, N, 121°15′37′′ E). the upright pyranometer, \(\gamma_{diff}\) (-) is the diffuse fraction, and the view factors F between the sensor and the six surfaces are: (a) unshaded paddy sample \(F_{(sensor\text{-}a)}\), (b) unshaded circular shield \(F_{(sensor\text{-}b)}\), (c) pyranometer shadow \(F_{(sensor\text{-}c)}\), (d) support rod shadow \(F_{(sensor\text{-}d)}\), (e) crescent shade of the circular shield \(F_{(sensor\text{-}e)}\), (f) and shaded part of the circular shield \(F_{(sensor\text{-}f)}\), these surfaces are shown in Figure 4.  
The clearness index \(k_{t}\) was used for the calculation of \(\gamma_{diff}\), according to [PERSON] et al. [57] and is valid for latitudes between 8.5\({}^{\circ}\) and 28\({}^{\circ}\) N.  
\[\gamma_{diff}=\qquad\left\{ \begin{array}{cc}0.98\cdot k_{t}&k_{t}<0.2\\ 1.024+0.47\cdot k_{t}-3.62\cdot k_{t}^{2}+2\cdot k_{t}^{3}&0.2<k_{t}<0.77\\ 0.16&k_{t}>0.77\end{array} \right. \tag{14}\]  
where \(k_{t}\) is the ratio of total solar radiation \(G_{up}\) and extraterrestrial radiation \(G_{ext}\). \(G_{ext}\) is calculated as:  
\[G_{ext}=1353\cdot\left[1+0.033\cdot cos\left(\frac{360\cdot N}{365}\right) \right]\cdot\cos\theta \tag{15}\]  
where \(N\) is the Julian day of the year and \(\theta\) is the solar zenith angle, which is represented in (16):  
\[\theta=\arccos(\sin\phi\cdot\sin\delta+\cos\delta\cdot\cos\phi\cdot\cos\omega) \tag{16}\]  
where \(\delta\) is the declination angle, \(\phi\) is the latitude, and \(\omega\) is the hour angle. For modeling the daytime-dependent course of albedo, an approach of [PERSON] et al. [58] was chosen:  
\[\alpha=a+b\cdot exp^{c\cdot\theta} \tag{17}\]  
where \(a\), \(b\), and \(c\) denote statistically estimated coefficients. Finally, the albedo was integrated as a variable into the model for the sun drying of paddy rice.
# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 3 Methodology
### Commodity Classification with Logo Data  
The linkage between logo recognition and the commodity classification is provided by a commodity database we built. We utilized the North American Industry Classification System (NAICS), a standard that classifies business establishments with the aim of collecting and analyzing business-related statistical data. It is a comprehensive and well-structured system that classifies economic activities hierarchically into levels of groups, such as sectors, subsectors, and industry groups.  
We built our commodity database by searching the NAICS code for each logo class and stored its corresponding commodity description. Samples of NAICS code and commodity description correspondences are shown in Table 1. With the database, the results from our logo detection and recognition pipeline are linked to their commodity description. This process completes our commodity classification solution. To the best of our knowledge, the proposed pipeline is the first attempt in this direction.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 2 Methodology
### Data Processing: THD and Harmonic Contributions  
For each harmonic component, querying the database of Elspec data returns the harmonic voltage as a fraction of the fundamental voltage component. We use this value (i) directly, (ii) to calculate THD, and (iii) to calculate the contribution of the harmonic to THD. This is done as follows.  
For the \(i\)-th phase, the voltage in the \(j\)-th harmonic is \(v_{i,j}=c_{i,j}v_{i,0}\), where \(c_{i,0}\) is the value returned by the device. \(v_{i,0}\) is the value of the fundamental voltage of the \(i\)-th phase. The THD for the \(i\)-th phase is:  
\[\mathrm{THD}_{i}=\frac{\sqrt{\sum_{j=1}^{j,\mathrm{max}}v_{i,j}^{2}}}{\sqrt{v_ {1,0}^{2}}}=\sqrt{\sum_{j=1}^{j,\mathrm{max}}c_{i,j}^{2}}, \tag{1}\]  
and the contribution of the \(j\)-th harmonic to the overall THD is \(c_{i,j}^{2}/\mathrm{THD}_{i}^{2}\). Depending on the site and temporal coverage, data is available for either 8 or 96 harmonics (\(j,\mathrm{max}=\{8,96\}\) as well as phase-to-ground (\(i=\{1,2,3\}\)) or phase-to-phase voltages (\(i=\{12,23,31\}\)).
## 5 Quantitative Evaluation of the Influence of Some Typical Parameters on the Balise Uplink Signal
The results in Figure 21 show that these four curves had the same value of main lobe peak power. However, the side lobe peak power in the active balise mode was the largest. The side lobe peak power in the passive balise mode decreased with the decrease of the 27 MHz tele-powering current intensity. In Figure 17, the influence of installation mode on the uplink received signal is discussed in the system composed of active balise and OAU. Compared with Figure 17, Figure 22 replaces the active balise with the passive balise, and other parameters are the same as those in Figure 16:0 By comparing Figures 17 and 22, it can be seen that the difference between passive and active balises in the received signal power caused by the installation mode is not obvious, and the curve envelope in the main lobe zone is very similar. However, under the passive balise condition, the signal power of the side lobe zone was significantly reduced, which was consistent with the analysis in Figure 16:1
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 4 Results
### Simulation Results  
First, consider the temperature at the photovoltaic panels shown in Figure 9, which illustrates a scenario where no heater action is taken, that is, no temperature control. Sides 1 and 4 of the satellite become hotter because the attitude favors these surfaces towards the Sun. The decrease in the temperature in the middle of the figure results from the orbit's period under the Earth's shadow.  
Figure 10 shows the power input (\(P_{in}\)) of the satellite concerning the variables battery voltage (\(V_{B}\)), average temperature of solar panels (\(T_{PV}\)) for each instant, and average solar irradiation (\(Q^{\prime\prime}_{que}\)). All the parameters were normalized and valid for the portion of the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Mode:**\(L\) & **SoC [\%]** & \(P_{L}\) **[W]** \\ \hline
5 & 95–100 & 2.75 \\
4 & 90–95 & 2.25 \\
3 & 85–90 & 1.75 \\
2 & 80–85 & 1.25 \\
1 & \textless{}80 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Operational mode of the satellite.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \(\theta_{i}\) & \(\vec{u}_{i}\) & \(\theta_{j}\) & \(\vec{u}_{j}\) \\ \hline
90\({}^{\circ}\) & \([0,1,0]\) & Equation (12) & \([0,0,1]\) \\ \hline \end{tabular}
\end{table}
Table 4: Input for the orbit model.  
orbit eclipse, and this behavior was captured for \(T_{set}=273\) K. The integration of thermal-electrical parameters in this figure shows a temperature increase that should decrease the solar panels' efficiency. However, this loss in efficiency is compensated by the increase in battery voltage due to the energy input, resulting in a closer behavior for the available irradiance and the power generation when the \(V_{B}\) is higher.
## 2 Data Acquisition and Preprocessing
### Data Preprocessing
#### 2.2.2 Manual Ground Truth  
We used Adobe Photoshop CS 6.0 to generate a segmentation mask for each plot; these masks were then used to manually annotate the boundary of each leaf in the image. To reduce any errors in the final masks, each image was treated three times by different workers to produce three unique masks, and the most accurate mask for each image was manually selected based on the overlay of the mask and the original image.
## 5 Conclusions and Future Work
Questions remain about potential other reasons that would explain why urban areas such as Los Angeles or Seoul only show small OSM communities and not similar success as in Europe. Possibly differences in Internet access, culture, mentality, personal interests or acquaintance to the project due to language barriers could play a role. Others would argue that countries with freely available datasets, e.g., provided by the government such as the TIGER/Line datasets in the US, are slowing down data contribution efforts in OSM. Other influential indicators could most likely only be determined by conducting an extensive survey. The assessment of the quality of the data collected by external OSM members in comparison to local members was not part of this study. However, it was clearly shown that large data contributions have been made in selected areas by members that maybe never collected data locally in person and lack the \"local expertise\" [42] that are making VGI projects unique. Based on these findings, investigations planned for the future will reveal some answers to questions such as: Do external or remote members provide a better, equal or worse data quality when contributing to the project? A similar approach to the one chosen during the analysis of Wikipedia and \"The Roles of Local and Global Contribution Inequality\" [43] could provide some meaningful insights. Geometric differences such as inconsistencies in positional accuracy will most likely be limited due to the high resolution images that the mappers can utilize when tracing data for OSM, as long as they are not outdated. However, a metadata analysis including street names, street types or turn restrictions could introduce some of the caveats of remote data contributions in OSM. The authors would like to thank [PERSON] for his valuable comments towards the improvement of this paper.
## 2 Concept of Operations
### Local Rate Regulators for Communications and Clock Data Services
#### 2.4.2 Integrated Communication and Clock Data Regulators  
As illustrated in Figure 4, the next contribution of this work emphasizes the systematic disclosure of procedures for composition, feedback design, and optimal control of linear discrete-time regulators aimed at enhancing wireless communication rates and clock data transport services. In the previous section, it has been noted that the dynamic CN0 allocation by the local autonomous agent \(i\) is treated as a distributed controlled system, specifically the subsystem \(\mathcal{S}_{i}\) for \(i=1,\ldots,N\). This is described by Equations (21) and (22), where \(x_{i}(l)\in\mathbb{R}^{6}\) represents the state vector and \(u_{i}(l)\in\mathbb{R}^{2}\) denotes the control input. The constant matrices  
It is important to note that the locally controlled systems \(\mathcal{S}_{i}\), where \(i=0,1,\ldots,N\), are decoupled. The local optimal control \(u_{i}^{*}(l)\), which minimizes \(J_{i}\) as described in (24), is now determined by the dynamic constraints (21) and (22)  
\[u_{i}^{*}(l)=-(R_{i}+B_{i}^{T}K_{i}^{*}B_{i})^{-1}B_{i}K_{i}^{*}A_{i}x_{i}(l)\,, \tag{27}\]  
where \(K_{i}^{*}\geq 0\) is a unique stabilizing solution for the algebraic Riccati equation; i.e.,  
\[K_{i}^{*}=(A_{i}^{T}K_{i}^{*}A_{i}+C_{i})-A_{i}^{T}K_{i}^{*}B_{i}(R_{i}+B_{i}^{ T}K_{i}^{*}B_{i})^{-1}B_{i}^{T}K_{i}^{*}A_{i} \tag{28}\]  
with the associated optimal cost governed by (24)  
\[J_{i}^{*}(l_{0},x_{i}(l_{0}))=x_{i}^{T}(l_{0})K_{i}^{*}x_{i}(l_{0})\,. \tag{29}\]
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 2 System Schematic  
Figure 1 shows a schematic diagram of the proposed single-stage transformerless buck-boost inverter system, where the dc-link voltage \(V_{dc}\) is maintained as a constantby a dc power supply. Through operating the semiconductor switches \(S_{1}\)-\(S_{4}\), the inductor \(L_{f}\) can be charged from both directions, and then it will be discharged to supply the filter capacitor \(C_{f}\) and the load \(R_{o}\) so long as switch \(S_{5}\) or \(S_{6}\) are turned-on. In Figure 1, \(i_{L}\), \(i_{R}\), and \(v_{c}\) denote the inductor current, load current, and capacitor voltage, respectively.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 2 Background  
### Hyperspectral Imaging and Hyperspectral Data  
Hyperspectral images from imaging spectrometers or hyperspectral cameras consist of \(L\times S\) spatial pixels with \(B\) bands each. Hence, a hyperspectral image is also referred to as a hyperspectral data cube with dimensions \(L\times S\times B\). A hyperspectral data cube is usually captured by spatial or spatial-spectral scanning and usually contains many dozens and up to hundreds of bands, with bandwidths less than 15 nm. In a push-broom design like HYPSO-1, spatial scanning can be done via the satellite's motion, or by rotation of either the camera platform or an optical component.  
There are three common kinds of data formats, also called sample ordering, that describe data streaming order from the sensor or data cube arrangement in memory: Band Interleaved by Pixel (BIP), Band Interleaved by Line (BIL) and Band Sequential (BSQ). The data serialization of a hyperspectral data cube can be understood as the mapping from the three-dimensional coordinates of the cube to a unique one-dimensional index. It is important to be aware of this format when implementing hyperspectral data processing algorithms and especially on Field Programmable Array Gate (FPGA), which accesses the raw data in memory directly. In the case of Hyperscout and its snapshot spectral camera, images are captured in BIL sample order and after georeferencing and radiometric calibration, the cubes are converted to BSQ, before gridding [22]. In a transmission grating hyperspectral camera, depending on sensor orientation with respect to slit, the raw data are streamed usually either in BIP or BIL format, with raw HYPSO-1 data using the BIP format.
## 4 Discussion
### Scientometric Analysis Insight: Role of Countries, Authors, Institutions, and Journals
Furthermore, there is a substantial lack of knowledge because of the limited global access to research outputs from native language journals other than English. Hence, in order to boost research output on a significant geographic level, it is essential to strengthen collaborations between researchers from both developed and developing nations. Encouraging researchers from underrepresented regions to participate more in article review and editorial processes may foster innovative research in this area. Moreover, providing additional capacity-building workshops for researchers and practitioners from underrepresented countries and increased funding from international agencies could be key to achieving optimal results.
## Appendix A
### GAM Model Performance by Lag Time  
The comparison of the lag-based performance of the GAM models ordered by their performance in 1-month lag is provided in Figure 11. A summary of the descriptive statistics of the models based on lag time is presented in Table 11.  
The models build using 1-month lag variables are shown to perform better than the 2-month and 3-month lags except in 8 out of the 34 cases when 2-month lag time models outperform the 1-month lag models. Even in these 8 cases, the performance of the 2-month lags are still below \(R^{2}\) of 0.5.  
From Table A1, a summary of performance of all the GAM models shows that 1-month prediction has the best performance as compared to the 2-3 months prediction ahead. Despite posting the highest range, 1-month predictions still post a mean performance of \(R^{2}\) = 0.62 as compared to 0.44 and 0.25 for 2-month and 3-month lag times, respectively.
## 2 Materials and Methods
### Methods  
#### 2.4.1 Theoretical Runoff Calculation Method  
Different land use types used different methods to calculate theoretical runoff. The runoff of paddy fields (\(R_{\text{pf}}\), in mm) was calculated by the infiltration curve method. The runoff of dry fields (\(R_{\text{dr}}\), in mm) was calculated by the saturation-excess runoff formula [32] using the 1-layer evaporation model. The runoff of wood meadows (\(R_{\text{wm}}\), in mm) was regarded as 40% of dry field runoff. The runoff of construction land (\(R_{\text{cl}}\), in mm) was calculated by the runoff coefficient. Combining the area and runoff results of different land use types in different years, the yearly runoff for 1-day and 3-day rainfall at 3-, 5-, 10-, and 20-year return period was calculated according to the area weighting method. Therefore, theoretical runoff was calculated as follows [41]:  
\[\begin{split} R_{\text{pf}}=P-\text{h}-(E+D)\text{t}\\ R_{\text{df}}=P-(WM-W_{0})-E_{0}\frac{W_{0}}{WM}\\ R_{wm}=40\%R_{df}\\ R_{\text{cl}}=\alpha P\\ R=f_{pf}R_{pf}+f_{df}R_{df}+f_{wm}R_{wm}+f_{cl}R_{cl}\end{split} \tag{1}\]  
where \(R\) is theoretical runoff (mm); \(f_{pf}\), \(f_{df}\), \(f_{wm}\), and \(f_{cl}\) are the area proportion of paddy fields, dry lands, wood meadows, and construction land, respectively (%); \(P\) is rainfall (mm); h is the depth of paddy submergence tolerance (mm); \(E\) is the evaporation rate of paddy fields (mm/d); \(D\) is the infiltration rate of the surface water (mm/d); \(W_{0}\) is the average initial water storage (mm); WM is the average maximum water storage (mm); \(E_{0}\) is water surface evaporation (mm); and \(\alpha\) is the runoff coefficient. According to the relevant references [16; 41; 42; 43], h = 30, \(E\) = 4.29, \(D\) = 3.59, \(W_{0}\) = 40, \(WM\) = 80, \(E_{0}\) = 3.1, and \(\alpha\) = 0.3.
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 4 Design Consideration of SM Capacitors
### Transient SM Voltage Constraint  
For industrial MMC projects, a series of protection strategies are embedded in the control system to avoid switching devices (e.g., IGBTs) getting over-stressed. Once the operating SM voltage reaches a preset threshold \(V_{th}\), the SM will be bypassed by the control blocks [34]. If a large amount of SMs get over-charged at the same time, a system-level shutdown may be triggered. Thus, the transient SM voltage \(V_{tmax}\) must be constrained under the threshold \(V_{th}\), which can be written as \(V_{tmax}<V_{th}\).  
By substituting Equations (27) and (28) into the equation, it can be obtained as  
\[C_{sm\_ij}\geq\frac{\max[\int{(i_{cj\_tran}+\frac{1}{2}i_{j\_tran})n_{uj\_tran} }]}{V_{th}-\max[\frac{v_{uj}(t_{0})}{N}]} \tag{36}\]  
\[C_{sm\_ij}\geq\frac{\max[\int{(i_{cj\_tran}-\frac{1}{2}i_{j\_tran})n_{lj\_tran} }]}{V_{th}-\max[\frac{v_{lj}(t_{0})}{N}]} \tag{37}\]  
It should be noted that the maximum transient SM voltage for upper and lower arms can be different during grid faults. The selected SM capacitance should fulfill the Equations (49) and (50) in the meantime.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 2 Materials and Methods
A high-reflectance, white Spectralon plate was used to calibrate the sensor. Five readings were taken from each leaf sample; the data were later processed using the ViewSpec Pro software and analysed with an electronic spreadsheet. After eliminating any non-representative measurements, the mean radiance was calculated. The values for spectral radiance were converted into bidirectional reflectance factors as per Equation (1):  
\[\text{FRB}_{\lambda}=\frac{\text{La}_{\lambda}}{\text{L}r_{\lambda}} \tag{1}\]  
where:  
\(\text{FRB}_{\lambda}=\text{bidirectional reflectance factor (dimensionless)}\)  
\(\text{La}_{\lambda}=\text{spectral radiance of the target (W cm${}^{-2}$ sr${}^{-1}$ \mu m${}^{-1}$)}\)  
\(\text{L}r_{\lambda}=\text{spectral radiance of the reference plate (W cm${}^{-2}$ sr${}^{-1}$ \mu m${}^{-1}$)}\). correlation analysis was carried out between the spectral data and the nutrient content obtained from the leaf analysis. The bands with the highest correlations were then selected. A 2D-correlation graph was generated to select the significant bands to be used in the band ratio, employing a statistical tool for testing collinearity [42] available from the online platform of the Brazilian National Institute for Space Research (INPE). Two-dimensional correlation is a statistical technique that allows the correlation between a dependent variable and various independent variables to be quantified. According to [PERSON] [42], this technique is commonly applied when estimating biogeochemical parameters by means of spectral data, where the use of correlation graphs is an important tool in band selection, reinforced by using hyperspectral sensors. This technique was used by [PERSON] [42] to select the best Normalised Difference Indices in order to estimate soil texture. When carrying out the partial least squares regression, the spectral characteristics from 350 to 399 nm were removed due to the high level of noise. The PLSR was carried out using the Unscrambler(r) software, to which values for the reflectance factor of each wavelength (X) and the variable response values (Y) referring to the nutrient content of _Vigna unquiculata_ (L) Walp was added. Of the total number of data employed, \(2/3\) were used to calibrate the models, and \(1/3\) for validation [43]. To evaluate the performance of the models, the coefficient of determination (R2), root mean square error (RMSE) and the residual prediction deviation (RPD) were used. Equation (2) shows the expression for the RMSE, while Equation (3) refers to the RPD. \[\text{RMSE}=\sqrt{\frac{\Sigma\left(\text{Y}_{\text{i}}-\text{Y}\right)^{2}}{ \text{n}}} \tag{2}\]  
where:  
Y\({}_{\text{i}}\) = measured values  
Y = predicted values  
n = sample size  
Figure 1:0