esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 3 Methodology
### Collection and Preparation of the Dataset  
The process of preparing and consolidating the data used in the research was developed in three steps: collection, pre-processing and distribution. First, images were collected from different datasets for coffee leaf rust detection. Specifically, we collected images from: RoCoLe [39], Bracol [40], Digipathos [10], D&P [41], and Licole [21]. The purpose was to have images that vary in background (e.g., natural background or white background), rust severity (see Figure 3), and image brightness.  
As a result, Table 2 summarizes the number of images used from each dataset for the three classes: healthy, rust and other diseases: leaf miner, phoma, cercospora, red spider mite, bacterial blight, blister spots and sooty molds.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Healthy** & **Rust** & **Other** \\ \hline Bracol [40] & 272 & 272 & 751 \\ \hline RoCoLe [39] & 789 & 602 & 167 \\ \hline D\&P [41] & 0 & 285 & 257 \\ \hline Digipathos [10] & 4 & 49 & 139 \\ \hline Licole [21] & 621 & 515 & 614 \\ \hline
**Total** & **1686** & **1723** & **1928** \\ \hline \end{tabular}
\end{table}
Table 2: Number of images used for coffee leaf rust classification in the present study from state-of-the-art datasets: H (Healthy), R (Rust), and Other.  
Figure 3: Different levels of coffee rust severity. The higher the level, the more the coffee leaf is affected by coffee rust. Level 1 implies that between 1% to 5% of the leaf has rust, level 2 means that between 6% to 20% of the leaf is affected, level 3 has an affectation between 21% to 50% of the leaf, while level 4 implies that more than 50% of the leaf has rust.  
Figure 2: Proposed methodology for quantitative evaluation of coffee rust detection models using transfer learning.  
Second, several data preparation tasks were applied with the objective of having a clean dataset. For example, distractors in the images that could bias the classification process were removed. More specifically, the tasks focused on:  
* Apply crop to have a single leaf per image.
* Remove background that does not belong to the white background or natural environment of the leaf.
* Discard images that contain rust along with other diseases on the same leaf.
* Enlarge the image to fit the coffee leaf.  
Finally, the new dataset was divided into training, validation, and testing according to the distribution shown in Table 3.
## 3 Methodology
### Functional Principal Component Analysis (fPCA)  
The fPCA technique is an approach used to reduce the dimensionality of large datasets while helping to enhance interpretability, yet loses minimum information from the data. Herein, we briefly introduce the main idea of fPCA with an example of voltage measurement data \(v_{ij}\). The fPCA can be thought of as an extended version of the traditional multivariate principal component analysis (PCA) with infinite-dimensional vectors, i.e., the function. To apply the fPCA, the data has to be given in the form of a smooth function. As the voltage data are discrete measurements over time, we first attempt to represent \(v_{ij}\) as a function \(v_{i}(t)\) using a B-spline basis expansion with an appropriate number of knots.  
The fPCA aims to find weight functions \(\phi(t)\) that mostly explain the function-to-function variabilities. The first functional principal component \(\phi_{1}(t)\) is chosen to maximize the mean square \(\sum_{i=1}^{N}\xi_{i1}^{2}/N\) where:  
\[\xi_{i1}=\int\phi_{1}(t)v_{i}(t)dt \tag{1}\]  
with the constraint of the unit squared norm \(\int\phi_{1}(t)^{2}dt=\|\phi_{1}\|^{2}=1\). The second and subsequent fPCs can also be found by solving the same optimization problem with the additional orthogonality constraints of \(\int\phi_{k}(t)\phi_{m}(t)dt=0,\;\;k<m\). Let us define the  
Figure 1: Graphical representation of workflow.  
covariance function \(G(s,\,t)=\sum_{i=1}^{N}v_{i}(s)v_{i}(t)/(N-1)\); then, it can be shown that the above optimization problem is reduced to the following eigen-equation:  
\[\int G(s,t)\phi(t)dt=\lambda\phi(s) \tag{2}\]  
where \(\phi(\cdot)\) is an eigenfunction and \(\lambda\) is an eigenvalue. This continuous functional eigen-analysis problem can be solved by an approximately equivalent matrix eigen-analysis task. For more details, see chapter 8.4 of [PERSON] and Silverman [15]. It can also be shown that each curve of \(v_{i}(t),\,\,i=1,\,\,\dots,\,\,N\) is approximated by the expansion in terms of a small number of orthonormal basis functions \(\phi\)'s by the following form:  
\[v_{i}(t)\cong\mu(t)+\sum_{k=1}^{K}\xi_{ik}\phi_{k}(t) \tag{3}\]  
where \(\mu(t)=\sum_{i=1}^{N}v_{i}(t)/N\) is the mean function, \(\xi_{ik}=\int\phi_{k}(t)v_{i}(t)dt\) is the \(k\)-th fPC score, and \(\phi_{k}(\cdot)\) is the \(k\)-th eigenfunction. The same procedure can be conducted for the current and temperature curves. In our research, we extracted five fPC scores from each curve of voltage, current, and temperature. These 15 fPC scores for each cycle are used as the input features of the battery capacity prediction model.
## 2 Data and Method
### Method  
#### 2.3.1 Mapping Fire Damage  
In the present study, the difference in vegetation activity obtained from the NDVI between the pre- and post-fire periods was used to estimate fire damage. Fire damage represents the reflectance changes between the pre-fire vegetation and post-fire burn scar that can be interpreted as the extent of degradation of the pre-fire vegetation community. Therefore, fire damage (D) can be measured as a difference between pre- and post-fire NDVI values, as given by  
\[\mathrm{D}=\mathit{NDVI}_{\mathit{pre-fire}}-\mathit{NDVI}_{\mathit{0}} \tag{1}\]  
where \(\mathit{NDVI}_{\mathit{pre-fire}}\) is the average NDVI value in the pre-fire period, _i.e._, from 1984 to 1986, while \(\mathit{NDVI}_{\mathit{0}}\) is the NDVI value in the fire year, _i.e._, 1987. In this paper, we prefer fire damage to burn severity because the latter definition is often associated with numerous factors that include the effects on soil composition, the amount of organic material consumed by the fire, the effects on vegetation, e.g., amount of char on shrubs, scorch height and crown scorch, tree mortality or the presence of colonizers [22].
# Photocatalytic Activity of Monosized AuZnO Composite Nanoparticles
## 2 Experimental
### Synthesis of PEO-PPO-PEO-Coated AuZnO Composite Nanoparticles  
As illustrated in Scheme 1, the PEO-PPO-PEO-coated AuZnO composite nanoparticles were synthesized according to the methods previously reported in the literature [27]. The synthesis was carried out in a 100 mL three-necked flask-for instance, the AuZnO sample coded as S1 was obtained by mixing Au(OOCCH\({}_{3}\))\({}_{3}\) 0.0112 g (0.03 mmol) and Zn(acac)\({}_{2}\) 0.3875 g (1.47 mmol) in 10 mL octyl ether with 1,2-hexadecanediol 0.4851 g, and PEO-PPO-PEO 0.7878 g under vigorous stirring. The reaction mixture was first heated to 125 \({}^{\circ}\)C in 2 h and held at 125 \({}^{\circ}\)C for 1 h. The temperature was then rapidly warmed to 280 \({}^{\circ}\)C in 15 min and held at 280 \({}^{\circ}\)C for 1 h. After cooling to room temperature, the purple black product was obtained by centrifugation. The product was washed several times with ethanol/hexane at a volume ratio of 2:1, and dried to obtain a sample of about 88 mg. The other two AuZnO samples were prepared analogously, except for the various amounts of precursor, reductant, and surfactant. The AuZnO sample coded as S2 had the precursor, reductant, and surfactant amounts of Au(OOCCH\({}_{3}\))\({}_{3}\) 0.0281 g (0.075 mmol), Zn(acac)\({}_{2}\) 0.3756 g (1.425 mmol), 1,2-hexadecanediol 0.4851 g, and PEO-PPO-PEO 0.7878 g, and about 93 mg of sample was finally obtained, while about 102 mg of sample S3 was obtained from the mixture of Au(OOCCH\({}_{3}\))\({}_{3}\) 0.0561 g (0.15 mmol), Zn(acac)\({}_{2}\) 0.3559 g (1.35 mmol), 1,2-hexadecanediol 0.4851 g, and PEO-PPO-PEO 0.7878 g. For a comparative investigation, more composite nanoparticles were similarly synthesized alone from Au(OOCCH\({}_{3}\))\({}_{3}\) (coded as Au) and Zn(acac)\({}_{2}\) (coded as ZnO) separately. The nominal composition was Au\({}_{0.03}\)(ZnO)\({}_{1.47}\) for S1, Au\({}_{0.075}\)(ZnO)\({}_{1.425}\) for S2, and Au\({}_{0.15}\)(ZnO)\({}_{1.35}\) for S3, respectively. For comparison, ZnO nanoparticles were prepared similarly, using only zinc acetylacetonate as the precursor (Figure S3, shown in supplementary materials).
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.1 Automatic Gain Control  
Software receiver processing starts from reading the IF data from the disk. The MVDR algorithm works efficiently when the data received from different antenna gains/RF chain has similar distribution. Hence, IF data from each antenna is amplified by a gain term which is managed by an automatic gain control function. The objective of the gain control is to equalize the different noise  
\begin{table}
\begin{tabular}{l l l} \hline ICP initialization and & Perform the initialization and difference & every 1 ms \\ calculation of the & calculation of ICP for determining the steering & \\ differential ICP & vectors. & \\ \hline Adaptive MVDR & Implement the adaptive MVDR beamforming & every 1 ms \\ beamforming & including covariance matrix calculation and & \\ -Covariance calculation & weight updating. & \\ -Weight updating & Hand-coded assembly for covariance & \\  & calculation. & \\ \hline Weight and sum & Perform the signal combination as Equation & every 1 ms \\  & (1). & \\  & Hand-coded assembly. & \\ \hline Bias calculation for & Perform the calculation of cabling/RF chain & One time after first \\ receiver performance & bias. & positioning \\ validation & & \\ \hline Calculation of angle- & Perform the calculation of angle-frequency & every 5 s \\ frequency response for & response. & \\ performance validation & & \\ \hline System Program & Schedule threads to achieve real-time & every 1 ms \\  & capability. & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: Flowchart of the software from reading IF data to validating receiver performance.  
power of all elements of antenna array caused by different RF chains/antenna gains. The gain is updated iteratively by finding a maximum value across one ms data as follows:  
\[MAX=\underset{i=1}{\max}\left|s_{i}\right| \tag{10}\] \[g=\left(2^{N_{s}}-1\right)/MAX\] \[G[k+1]=G[k]+\beta(g-G[k])\]  
where \(s_{i}\) is \(i^{\text{th}}\) digitized sample of the IF signal, \(N_{s}\) is the number of samples in one ms, \(N_{b}\) is the number of bits for representing data, and \(\beta\) is updating step.
## 2 Ranging Principle and Analyses of the Questions  
The dual-one-way measurement of an ISL consists of two satellites alternately performing a pseudo-code phase ranging process and the mutual transfer of the measurement results. It contains two one-way pseudo-range measurements.  
As shown in Figure 1,  
\[T_{B}=\Delta t+t_{A}+\tau(t_{1},t_{2})+r_{B}=t_{2}-t_{0}, \tag{1}\]  
where \(T_{B}\) is the measurement pseudo-range, \(\Delta t\) is the system clock difference between satellite A and satellite B, \(t_{A}\) is the transmitting circuit delay of satellite A, \(r_{B}\) is the receiving circuit delay of satellite B, \(\tau(t_{1},t_{2})\) is the spatial delay, \(t_{1}\) is the transmitting point time of satellite A, and \(t_{0}\) and \(t_{2}\) are the reception point time and the measurement point time of satellite B, respectively. Next, we describe two questions in the development of the ISL payload engineering prototype.
# Design Procedures for Sustainable Structural Concretes Using Wastes and Industrial By-Products
## 3 Results
### Physical Properties
#### 3.3.3 Permeability to Air and Water  
For air and water absorption, two different coefficients were obtained. In case of air permeability, the modified D'Arcy's law used for gases was applied according to the following equation:  
\[K_{G}=\frac{2v\cdot\eta\cdot L\cdot P_{2}}{A\left(P_{1}^{2}-P_{2}^{2}\right)} \tag{8}\]  
where \(K_{G}\) is the coefficient of gas permeability, \(v\) is the gas flow, \(\eta\) is the dynamic viscosity of the gas (considered as 18.2 \(\times\) 10\({}^{-6}\) Ns/m\({}^{2}\)), \(L\) is the thickness of the concrete cross-section, \(A\), is the concrete cross-section traveled by the gas, \(P_{1}\) is the absolute inlet gas pressure (in the current case 3 bar) and \(P_{2}\) is the absolute outlet gas pressure (atmospheric pressure, 1 bar). In order to measure the gas flow, \(v\), five constant flow readings were taken by measuring the travel time of a soap bubble for a known distance of 10 cm (\(T_{10}\)). For this purpose, a pipette with an inner diameter of 2.5 mm was used. For the determined water permeability coefficient, \(k_{W}\), the _Valenta_ equation was employed (Equation (9)):  
\[k_{w}=\frac{W_{vc}\times{d_{p}}^{2}}{2\times t\times h} \tag{9}\]  
where \(W_{vc}\) is the porosity of the specimen (determined above), \(d_{p}\) is the water penetration depth, \(t\) is the time during which the pressure was applied, i.e., the time taken to reach a penetration depth \(x_{d}\) (adopted as 3 h), and \(h\) is the equivalent pressure to the water head (adopted 3 bar = 30.621 mH\({}_{2}\)O). In order to measure the depth of water penetration, \(d_{p}\), eight readings were taken on each of the two broken faces of the specimen obtained after splitting tensile test. To improve the readings, a color indicator--phenolphthalein--was used, in combination with the water, which evidences the boundary of water penetration. The conversion factor for the coefficient, \(k_{W}\), when using phenolphthalein solution, from m/s to m\({}^{2}\), is: \(K_{W}\) (m\({}^{2}\)) = 1.3 \(\times\) 10\({}^{-7}\)\(k_{W}\) (m/s). Tables 16 and 17 show the results of permeability to air and water obtained for EAFSBD1 mixture, including the gas and water coefficients (\(K_{G}\) and \(K_{W}\)), the average specimen length (\(L_{m}\)), cross-section area (\(A_{m}\)), travel time of a soap bubble for a distance of 10 cm (\(T_{10m}\)) and depth of water penetration (\(d_{pn}\)). The coefficients of variation obtained for the parameters analyzed were somewhat high, which is normal due to the heterogeneity of the porous structure. Figure 8 shows a representative EAFSBD1 specimen where water penetration was measured after performing the water permeability test in LPC.  
Figure 7: (**a**) Set-up for water absorption test by capillarity; (**b**) representative specimens after a certain time of exposure to water, during the absorption test by capillarity.
## 4 Results and Discussions
### Time-series Normalized Difference Vegetation Index (NDVI)  
As shown in Figure 4, the study area was in an agricultural region. Majority of the landscape was covered by cropland (~66.3%), followed by grassland (~15.8%), shrubland (~9%), and deciduous forest (~1.6%). The four types of vegetation covered about 92.6% of study area. The rest was waterbody, buildup area, and others. As part of effort to check the quality of the reconstructed time-series Sentinel-2 images, we calculated the NDVI time-series of the four vegetation types of the study period as shown in Figure 10 from sample data of the clear-sky Sentinel-2 images, MODIS, synthetic Sentinel-2 images, and reconstructed Sentinel-2 images. The clear-sky Sentinel-2 and MODIS images were used to generate synthetic Sentinel-2 images when cloud-free images were not available. The sample data used for calculation of NDVI were randomly selected from these images and the only requirement was that all its neighbor pixels of 25 by 25 (the size of a MODIS pixel) had the same land cover type. The requirement for the deciduous sample was relaxed to 15 by 15 pixels due to a small portion of deciduous forest land cover in the study area.  
As shown in Figure 10, although different land cover has different NDVI shapes, the NDVI patterns from the four images were similar for all the same land cover types. The NDVI time-series from MODIS and the synthetic Sentinel-2 images looked similar with exception of the deciduous forest. The NDVI time-series from the synthetic Sentinel-2 images were generally larger than that from the MODIS data though the general patterns of the two were the same. This was because the sample MODIS pixel for the NDVI calculation was not a homogeneous deciduous forest land cover. In general, the NDVI time-series from the synthetic Sentinel-2, reconstructed and clear sky images were very similar. This confirmed that first the synthetic images were well predicted and the second reconstructed cloud-free Sentinel-2 image time-series was well produced.  
Figure 9: Cont.  
Figure 10: The Normalized Difference Vegetation Index (NDVI) time-series of vegetation: upper left: Cropland; upper right: Deciduous forest; lower left: Grassland; and lower right: Shrubland.  
Figure 9: Comparison of the reconstructed images before and after the normalization process of the dates of 16 May, 15 June, 30 July, 21 August, and 3–5 October 2018 (from top to bottom).
## 2 Design, Modeling, and Control
### Proposed Models
#### 2.4.1 Motor Model  
As in the past [22], the generic model for a DC motor is used, since the motor-gearbox system is equivalent. Equation (1), rearranged to obtain Equation (2), are the main equations used to model the motor. In both equations, \(u\) expresses the voltage (V) applied to the motor, \(R\) is the motor resistance (\(\Omega\)), \(i\) stands for the current flow (A), \(k\) for the motor constant (N\(\cdot\)m/\(\sqrt{\text{W}}\)), and \(\omega\) the angular speed (rad/s). The static friction torque \(T_{q}\) (N\(\cdot\)m) of the motor is obtained through the multiplication \(k\cdot i\). As already mentioned in [21], it is noteworthy that this motor model assumes that the static torque friction \(T_{q}=T_{m}\) while \(T_{m}<T_{q\_max}\).  
\[u =R\cdot i+k\omega \tag{1}\] \[i =\frac{u-k\omega}{R}. \tag{2}\]  
The performed hardware tests to estimate the mentioned parameters were detailed in [22]. Figures 13, 14, and 15 present the collected data, which allow the estimation of the motor model parameters. The performed tests were carried out with the planetary reduction gear coupled to the motor shaft. This way, the gear effects are incorporated in the motor model, instead of the joint model.  
Figure 13: Motor Angular Speed versus Voltage.  
The obtained estimated parameters for the motor are presented in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Parameters** \\ \hline R & 2.466 \(\Omega\) \\ k & 6.9632 N\(\cdot\)m/\(\sqrt{\text{W}}\) \\ T\({}_{q}\) & 1.1633 N\(\cdot\)m \\ B\({}_{v}\) & 0.67 N\(\cdot\)m\(\cdot\)s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated motor parameters.  
Figure 15: \(k\times\) Current versus Motor Angular Speed.
## 3 Results and Discussion
From the results of Figures 3-5, it is seen that the \(f_{AT}\) shows two regions, the strong microwave \(\Omega_{MW}\) linear region and the weak field nonlinear region. At the linear region, \(f_{AT}\approx\Omega_{MW}\), which can be used to measure precisely the microwave electric field with uncertainty less than \(1\%\), see Figure 5a,b, one orders of magnitude better than the conventional microwave field measurements. At the nonlinear region, \(f_{AT}\) strongly depends on the microwave-free EIT linewidth, relying on the probe and coupling laser Rabi frequency. From Figures 3 and 5c-e, the smaller the coupling \(\Omega_{c}\) is, the narrower the microwave-free EIT linewidth is and the smaller the \(f_{AT}\) can be separated, see Figure 5c,d. However, when the coupling \(\Omega_{c}\) is further decreased, the EIT-AT spectrum shows good two-peak profile but with low signal-noise ratio, which also leads to the large measurement uncertainty in Figure 5c. For the electric field measurement using EIT-AT, the narrow EIT linewidth is expected to obtain a large \(f_{AT}\) linear region and precise microwave field measurements. From Figure 3, we note that this nonlinear behavior also leads to a critical value for \(\Omega_{cri}\) before AT splitting occurs. The critical value corresponds to the minimum splitting that can be detected and is given by the locations where the curves cross the \(x\)-axis, \(\Omega_{cri}\), see the short-dashed arrow in Figure 3. For \(\Omega_{MW}\lesssim\Omega_{cri}\), the microwave electric field cannot be measured with this EIT-AT method. The \(\Omega_{cri}\) is relative to the EIT linewidth, \(\gamma_{EIT}\). The narrower the \(\gamma_{EIT}\) is, the smaller \(\Omega_{cri}\) is. The narrow linewidth EIT spectrum can be obtained with a weak coupling(probe) laser beam (small Rabi frequency). However, this strategy to reduce EIT linewidth has a limitation that is limited by the Doppler broadening. For reducing the EIT linewidth further, it need to lower the temperature of the atomic vapor and using the laser trapped atoms. In other hand, using weak probe(coupling) laser Rabi frequency will increase the background noise and reducing the sensitivity, see Figure 5c. Figure 4: (color online) Measurements of \(f_{AT}\) as a function of probe Rabi frequency \(\Omega_{p}\) (**a**) and coupling Rabi frequency \(\Omega_{c}\) (**b**) for fixed microwave electric field \(\Omega_{MW}/2\pi=7.61\) MHz and \(9.48\) MHz, corresponding to electric field \(E=0.46\) V/m and \(0.57\) V/m, respectively. The error bars show the standard error of four times measurements.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 3 Experiments and Results
### Study Datasets
#### 3.1.1 S3 DIS Dataset  
The S3 DIS dataset is an important resource for indoor scene understanding and 3D modeling studies. The dataset consists of detailed 3D point clouds data collected from  
Figure 9: An example of extracted data structure in JSON format.  
six large single-floor indoor areas within three different buildings, covering a total area of over 6000 m\({}^{2}\). These buildings cover a variety of functional spaces such as offices, conference rooms, hallways, and lobbies. The dataset also provides annotated semantic information on elements such as floors, ceilings, walls, beams, columns, windows, doors, and furniture. These richly annotated data are well suited for the development and evaluation of algorithms for automatically extracting indoor navigation networks from 3D semantic point clouds of large-scale indoor scenes. In this study, we chose Area_1, Area_4, and Area_6 in the S3 DIS dataset for our experiments, as shown in Figure 10.  
It should be noted that the S3 DIS dataset does not include elevator lobbies in its space category; after data checking, we did not find where the elevator lobby was located either. Additionally, the dataset does not separately classify stairwells, instead grouping them together with hallways under the hallway category. To ensure the consistency of space classification, we added a separate category for stairwells. According to the data preprocessing strategies in Section 2.1, we preprocessed the S3 DIS dataset, which mainly includes the re-segmentation of some hallways and the supplementation of virtual doors.