esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Single Shooting Pattern
#### 4.1.3 Random Forest Feature Importance
the result indicators in a non-linear manner rather than a simple linear relationship, or that it may contribute to the prediction performance through interactions with other variables (e.g., overlap). This emphasizes that complex relationships between variables that are difficult to capture with linear models alone may exist. In conclusion, Random Forest variable importance analysis partially confirms the results of multiple regression analysis (e.g., the importance of the overlap variable in the Surface pattern) while providing a clearer understanding of the relative importance of variables from a nonlinear model perspective. Additionally, it offers specific insights into how the influence of each shooting variable varies depending on the shooting pattern and the target outcome metric. For example, when applying the Circular pattern, if improving geometric accuracy or completeness of reconstruction is the top priority, controlling the shooting radius is essential, but if maximizing the model's detail (number of points) or processing efficiency management is important, it is necessary to focus more on optimizing the camera tilt angle.
## 3 Results
### Circadian Stimulus Autonomy (CSA)
#### 3.3.1 Influence of Window-to-Wall Ratio (WWR, 15% to 45%)  
The \(CSA2\) values are higher with larger window dimensions, demonstrating the significant impact of WWR on circadian stimulus performance. For a WWR of 35% or greater and centered configurations, \(CSA2\) values near the facade approach 100%, decreasing linearly to 90% in Seville and 82% in Barcelona. In configurations with smaller window sizes, particularly 15% WWR, \(CSA2\) values are significantly lower; for north orientations, the \(CSA2\) value is often null, and for south orientations, it barely reaches 15%.  
\(CSA1\) values are generally lower in all scenarios with a 15% WWR, regardless of location or window position (centered or upper). Specifically, for dark coatings and a north orientation at a 15% WWR, \(CSA1\) values are null. Conversely, with increased WWR (up to 45%), \(CSA1\) values for bright coatings and a south orientation range from 62% (Seville, centered, 15%) to 95% (Barcelona, upper, 45%).
## 2 Results and Discussion
### Effects of Rootstocks on Vine Growth
\begin{table}
\begin{tabular}{c c c c c} \hline
**Source of** & **Stock/Scion** & **Estimated Yield** & **Pruning Weight** & **Crop Load** \\
**Variation** & **Diameter** & **(kg/Vine)** & **(kg/Vine)** & **(Yield/Pruning Weight)** \\ \hline
**Rootstock (R)** & & & & \\ CS & 1.00 a 1 ** & 4.27 & 1.26 & 4.26 \\ CS/101-14 & 0.78 bc & 3.38 & 1.41 & 3.02 \\ CS/110R & 0.75 bcd & 3.52 & 1.38 & 3.08 \\ CS/5A & 0.77 bcd & 4.10 & 1.40 & 4.15 \\ CS/5 BB & 0.75 bcd & 3.28 & 1.36 & 2.79 \\ CS/Ganzim 1 & 0.69 cd & 3.47 & 1.40 & 3.11 \\ CS/Harmony & 0.83 b & 3.08 & 1.29 & 3.30 \\ CS/Riparia Gloire & 0.67 d & 3.79 & 1.29 & 3.91 \\ CS/SO4 & 0.71 cd & 3.92 & 1.48 & 3.21 \\
**Vintage (V)** & & & & \\
2015 & 0.77 & 2.26 b & 1.79 b & 1.30 b \\
2016 & 0.77 & 5.03 a & 0.93 a & 5.55 a \\
**Significance**2  & ns 3  & ns & ns & ns \\ \hline \end{tabular}
\end{table} 
Table 1: Effects of rootstocks on vine growth parameters in two seasons (2015–2016). was significantly higher in 2016 than in 2015 (Table 1). [PERSON] et al. [11] also reported that most of the variations in the yield to pruning weight ratio were attributed to the seasonal effects followed by rootstock effects. In this study, the average vine yield to pruning weight ratio in 2015 was far blow 4, indicating that vines were excessively vigorous due to the young vine age. Despite no significant differences being observed among grafted and own-rooted vines, own-rooted vines with the highest yield and the lowest pruning weight produced the highest crop load in comparison to grafted vines (Table 1). In addition, the vine yield was negatively correlated with the pruning weight (r = \(-\)2.45, \(p\)\(<\) 0.001, \(n\) = 54) in this study, which was also reported in the previous study [15]. Variations in crop load among grafted and own-rooted vines were probably due to the rootstock effects on biomass partitioning between roots and shoots, and then between the fruit and the rest of vine [24]. In addition, water and/or nutrient uptake capability may differ among grafted and own-rooted vines due to different root system architectures, and vine vigor and crop yield may then be modified by rootstocks [19].
## 3 Results and Discussion
### Taguchi Methods Results
The optimum results were 100 mg/1 catalyst load, pH 2, 7.63 W/m\({}^{2}\) irradiation intensity, and 15 ppm Cr concentration. Studies on visible light irradiation are only found for composite materials with TiO\({}_{2}\) on pollutants not commonly used. For instance, CoFe\({}_{2}\)O\({}_{4}\)-TiO\({}_{2}\) (p-n catalyst) was used for optimizing the Malachite Green degradation process, using a Taguchi experiment of four factors and five levels. The tested parameters were the catalyst load, the irradiation time, the pH, and the stirring speed, and the optimum results were 0.1 g/1 catalyst load, 10 min irradiation time, pH 7, and 300 rpm stirring speed [61]. Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/clinoptilolite catalyst was tested on optimizing Acid Black 172 dye, on a 4-factor and 3-level Taguchi experiment. The pH, the catalyst load, the dye concentration, and the irradiation time were investigated, resulting in the optimum conditions of pH 2, 1 g/1 load, 60 min irradiation time, and 50 mg/1 dye concentration [62]. Our research contributes to the photocatalytic process investigation because the choice of pollutant is one factor that has not been tested until now. The same applies to the distance from the irradiation source, as only the irradiation intensity was previously tested. The irradiation time and the catalyst load parameters are of great significance in a photocatalytic process, so they were also included in the Taguchi experiment of N-TiO\({}_{2}\). The findings of this study are close to the findings of the abovementioned literature [59; 60; 61; 62]. The pH was not studied, as it has been widely studied before, and was fixed at 6, which is adjacent to the result from most of the relevant publications. In this study, the chosen set of variables does not correspond to any of the experiments performed. Therefore, the degradation of the pollutant when following the proposed photocatalytic protocol is presented in Figure 13.  
It is evident that after 150 min of visible light irradiation, Rhodamine B degrades up to 60%. Therefore, the photocatalytic activity of the N-doped TiO\({}_{2}\) catalyst is profound and the protocol is suitable for testing various photocatalysts. The photocatalytic kinetics of the experiment shows satisfactory adaptation to pseudo-first-order kinetics, with k = 0.00637 and R\({}^{2}\) = 0.98285.
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
## 2 Advanced Sliding Mode Control Framework for UAV Security
### Adaptive and Fault-Tolerant Mechanisms
#### 2.3.6 Adaptive Sliding Mode Control  
To further enhance fault tolerance, the control gains \(K_{\phi}(t)\), \(K_{\theta}(t)\), and \(K_{\psi}(t)\) are adapted based on the sliding surface magnitude and detected faults:  
\[\begin{split} K_{\phi}(t)=K_{\phi_{0}}+\gamma_{\phi}\|S_{\phi}(x) \|+\alpha_{\phi}\Delta K_{\phi},\\ K_{\theta}(t)=K_{\phi_{0}}+\gamma_{\phi}\|S_{\theta}(x)\|+\alpha_{ \phi}\Delta K_{\theta},\\ K_{\psi}(t)=K_{\psi_{0}}+\gamma_{\psi}\|S_{\psi}(x)\|+\alpha_{ \psi}\Delta K_{\psi},\end{split} \tag{21}\]  
where \(K_{\phi_{0}},K_{\phi_{0}},K_{\psi_{0}}\) are the baseline gains, \(\gamma_{\phi},\gamma_{\theta},\gamma_{\psi}\) are the adaptation rates, and \(\alpha_{\phi},\alpha_{\theta},\alpha_{\psi}\) are fault-dependent gain adjustment factors.
## 3 PCC\({}^{2}\)LS for the Minimum Connected Dominating Set Problem
### The Local Search Procedure in PCC\({}^{2}\)Ls
#### 3.2.3 Vertex Weighting Strategy  
During the local search phase, optimizing the current solution involves continually flipping vertices both within and outside the solution. Hence, selecting which vertices to flip becomes paramount. We have observed that relying solely on the _Dscore_ value for vertex selection easily traps the algorithm in local optima. To address this, we propose a vertex weighting strategy wherein the weight value assumes a pivotal role in guiding vertex selection. If a vertex remains undominated for an extended period, its weight value increases, influencing the overall vertex score and thereby prompting its flipping. Remarkably, the strategy boasts low complexity and straightforward implementation. Each vertex's weight value, denoted as _Wscore_, undergoes updates based on predefined rules.  
**Weight_Rule1**: In the initialization phase of the algorithm, the _Wscore_ value for each vertex is initialized to 1;  
**Weight_Rule 2**: In the local search phase, at the end of each iteration of the loop, the _Wscore_ value for vertices that are not dominated is incremented by 1.  
Based on the vertex weighting mechanism and the _Dscore_ value, we introduce a novel scoring function termed _Cscore_, which amalgamates the individual _Dscore_ value per vertex with the _Wscore_ value. While the _Dscore_ value facilitates dominance over more vertices, the _Wscore_ value enables the algorithm to escape locally optimal solutions and explore alternative vertices. The formula for computing the _Cscore_ value is presented in Equation (3).  
\[\begin{split}\textit{Cscore}(v)=&\left\{\begin{array}{ ll}\sum_{u\in M1}\textit{Wscore}(u)+\textit{Dscore}(v),\text{if}\ v\
otin D\\ -\sum_{u\in M2}\textit{Wscore}(u)+\textit{Dscore}(v),\text{if}\ v\in D\end{array}\right. \end{split} \tag{3}\]  
where \(M1\) represents the subset of vertices within \(N[v]\) that are undominated, while \(M2\) signifies the subset of vertices in \(N[v]\) that are solely dominated by vertex \(v\). The vertex's _Cscore_ value incorporates both its _Dscore_ and _Wscore_ values.
# Trypsin Inhibitor from Soybean Whey Wastewater: Isolation, Purification and Stability
## 3 Results and Discussion
### Stability Analysis of STI
#### 3.4.2 The Effect of pH on the STI Activity  
As shown in Figure 7a, no changes were evident in the residual inhibitory trypsin activity between pH 3.0 and pH 12.0. Therefore, the STI displayed excellent stability in a wide pH range. [PERSON] reported that the trypsin inhibitor obtained from mung bean seeds maintained high inhibitory activity between pH 3.0 and pH 10.0 [36]. Moreover, [PERSON] revealed that the trypsin inhibitor obtained from pigeon- and cowpeas also exhibited high inhibitory activity between pH 4.0 and pH 10.0 [37] The trypsin inhibitor purified from Cassia _leiandra_ seeds also displayed high inhibitory activity between pH 2.0 and pH 10.0 [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **\(\alpha\)-Helix** & **\(\beta\)-Sheet** & **\(\beta\)-Turn** & **Random Coil** \\ \hline NO heating & 0.00 \(\pm\) 0.00 & 39.23 \(\pm\) 0.58 & 15.30 \(\pm\) 0.50 & 45.30 \(\pm\) 0.98 \\
40 & 0.20 \(\pm\) 0.16 & 39.27 \(\pm\) 1.25 & 15.53 \(\pm\) 0.12 & 44.97 \(\pm\) 1.48 \\
50 & 0.27 \(\pm\) 0.09 & 39.33 \(\pm\) 1.32 & 15.03 \(\pm\) 0.52 & 45.30 \(\pm\)1.87 \\
60 & 0.00 \(\pm\) 0.00 & 40.17 \(\pm\) 1.27 & 14.20 \(\pm\) 1.44 & 45.63 \(\pm\) 1.37 \\
70 & 0.33 \(\pm\) 0.34 & 40.87 \(\pm\) 1.45 & 15.10 \(\pm\) 0.78 & 43.70 \(\pm\) 1.93 \\
80 & 0.07 \(\pm\) 0.09 & 41.33 \(\pm\) 1.52 & 15.30 \(\pm\) 0.50 & 43.33 \(\pm\) 1.92 \\
90 & 0.43 \(\pm\) 0.61 & 41.07 \(\pm\) 0.41 & 15.20 \(\pm\) 0.22 & 43.33 \(\pm\) 1.08 \\
100 & 0.13 \(\pm\) 0.19 & 41.73 \(\pm\) 0.52 & 15.17 \(\pm\) 0.12 & 42.97 \(\pm\) 0.52 \\ \hline \hline \end{tabular} Results are presented as mean values \(\pm\) standard deviation (\(n\) = 3). The data in each column are not significantly different (\(p\) > 0.05).  
\end{table}
Table 1: The secondary structures of STI.  
Figure 6: The effect of temperature on the activity of STI (**a**). The circular dichroism spectra of STI (**b**). Different letters indicate significant differences (\(p\) < 0.05) among the different conditions. Residual trypsin inhibitory activity is expressed as the percentage of trypsin inhibitory activity after the treatment accounting for trypsin inhibitory activity of the untreated STI (25 \({}^{\circ}\)C).  
A CD analysis was performed of the STI treated at different pH levels. The results were shown in Figure 7b and Table 2. Strong negative peaks were evident at 190-220 nm, indicating that the STI mainly contained \(\beta\)-fold structures regardless of the pH conditions. In addition, no \(\alpha\)-helical structures were present in the STI secondary structure in different pH conditions. The \(\beta\)-sheet content decreased, and the random curl content increased at higher pH levels, indicating gradual destabilization of the STI structure as the pH increased. In acidic conditions, the STI was more stable than in alkaline conditions, which could be due to the de-ionization of hydrogen bonds in the \(\beta\)-fold structure, resulting in structural de-folding [15].
## 2 Literature Review
### Healthcare and Edge Computing Technology  
The accuracy, speed, and continuity of patient monitoring systems are critical in healthcare. Cloud dependency poses risks, leading to the adoption of edge computing. Challenges in cloud implementation may render some applications impractical. Edge computing alternatives include mobile edge computing (MEC), fog computing, and cloudlets. Fog computing, with its features like internode cooperation and short delays, proves suitable for healthcare systems [18]. There are some challenges for IoT-based cloud implementation, so it may be impossible to implement some applications inside the cloud layer. Some of the challenges are summarized in Figure 2. Challenges such as security issues may have destructive social effects as well as interruption issues, delays in sending and receiving data, and the need for high bandwidth, making the cloud an unsuitable tool for healthcare applications.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 4 Performance Metrics
### Algorithmic Complexity  
This section studies the algorithmic complexity in terms of the number of multiplications and iterations. The complexity of SQUID is thoroughly discussed in [38] and Table 3 shows the order estimates take from [38] (Table I). Note the large number of iterations.  
The complexity of MSM depends on the choice of optimization algorithm and [42] considers a simplex algorithm. Unfortunately, the simplex algor  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Multiplications per Iteration** & **Iterations** & **Pre-Processing Multiplications** \\ \hline QLP-ZF & \(\mathcal{O}(TK^{3}+TK^{2}N)\) & \(1\) & - \\ \hline SQUID & \(\mathcal{O}(8 KNT+8 NT\log T)\) & 20–300 & \(2T\cdot(\frac{5}{3}K^{3}+3K^{2}N+(6N-\frac{2}{3})K)\) \\ \hline MSM & \(\mathcal{O}(4 KNT^{2}+4 KT+2 NT)\) & \(\approx\)8400 & \(4 KNT\) \\ \hline MAGIQ \& QCM & \(\mathcal{O}(KNTL+KNL|\mathcal{X}|)\) & 4–6 & \(KNT+4 NT\log T\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithmic complexity.  
number of iterations to converge because this number is proportional to the number of variables and linear inequalities that grow with the system size \((N,K,T)\). An interior point algorithm converges more quickly but has a much higher complexity per iteration.  
For MAGIQ and QCM, Equation (8) shows that updating \(x[.]\) requires updating \(L\) of the \(T\) terms that each require a norm calculation. The resulting terms \(\|\mathbf{u}[t]\|^{2}\) do not affect the maximization; terms such as \(\|\mathbf{\alpha}\mathbf{H}\mathbf{x}\|_{2}^{2}\) can be pre-computed and stored with a complexity of \(NKL|\mathcal{X}|\), and then reused as they do not change during the iterations. On the other hand, products of the form \(\mathbf{\alpha}\mathbf{u}^{\mathrm{H}}\mathbf{H}\mathbf{x}\) must be computed for each of the \(L\) terms for each antenna update and at each time instance, resulting in a complexity of \(\mathcal{O}(NKLT)\). The initialization requires \(KNT\) multiplications and one must transform the solutions to the time domain. We neglect the cost of updating \(\alpha\) because the terms needed to compute it are available as a byproduct of the iterative process over the time instances.
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 4 Design of Trajectory Tracking Control Strategy Considering Load Transfer
### Lateral Load Transfer IMPC Control Strategy
#### 4.2.2 Design of Objective Function  
The objective function should be able to ensure that the AGV tracks the desired trajectory quickly and smoothly while considering the load transfer. Therefore, the following objective function is established with the objective of minimizing the deviation of system state variables, lateral load transfer, and control increment:  
\[J=(Y-Y_{r}+F)^{T}Q_{w}(Y-Y_{r}+F)+\Delta U^{T}R_{w}\Delta U+\rho e^{2} \tag{34}\]  
where \(Q_{w}\) and \(R_{w}\) are weight matrices, \(Q_{w}=\begin{bmatrix}q_{w}&0&\cdots&0\\ 0&q_{w}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&0&q_{w}\end{bmatrix}_{N_{p}\times N_{p}},\;R_{w}=\begin{bmatrix}r_{w}&0& \cdots&0\\ 0&r_{w}&\cdots&0\\ 0&0&20&0&0\\ 0&0&0&0.3&0\\ 0&0&0&0&10\end{bmatrix}\), \(r_{w}=\begin{bmatrix}10&0&0\\ 0&10&0\\ 0&0&10\end{bmatrix}\), \(Y_{r}=\begin{bmatrix}0&0&0\\ 0&0&10&0\\ 0&0&0&10\end{bmatrix}\), \(Y_{r}=\begin{bmatrix}0&0&0\\ 0&0&0&10\end{bmatrix}\), \(Y_{r}=\begin{bmatrix}0&0&0\\ 0&0&10\\ 0&0&10\end{bmatrix}\), \(Y_{r}=\begin{bmatrix}0&0&0\\ 0&0&0\\ 0&0&0&10\end{bmatrix}\), \(Y_{r}=\begin{bmatrix}0&0&0\\ 0&0&0\\ 0&0&0&0\end{bmatrix}^{T}\), \(f_{g}(k)=\begin{bmatrix}0_{N_{k}\times 1}\\ \delta_{fr}(k)-\delta_{rr}(k)\end{bmatrix}\).  
The first term in (34) reflects the ability of the system to track the reference trajectory and the optimization of the lateral acceleration. The second term reflects the stability of the change in the input control variables of the system. The third term is a relaxation factor term introduced to ensure that each optimization goal has a feasible solution, where \(\rho\) is the weight of the relaxation factor, and \(\varepsilon\) is the relaxation factor.  
Substituting (33) into (34) can obtain  
\[J=E^{T}Q_{w}E+\Delta U^{T}(\Theta^{T}Q_{w}\Theta+R_{w})\Delta U+2E^{T}Q_{w} \Theta\Delta U+\rho\varepsilon^{2} \tag{35}\]  
where \(E=\Psi\zeta(k)+F_{\beta}\), \(E^{T}Q_{w}E\) is a constant, which can be discarded in the solution.  
Further processing of (35) to convert it into a QP problem:  
\[\underset{\Delta U}{\min}J=\frac{1}{2}\Delta V^{T}H\Delta V+G^{T}\Delta V \tag{36}\]  
where \(\Delta V=\begin{bmatrix}\Delta U&\varepsilon\end{bmatrix}\), \(H=\begin{bmatrix}\Theta^{T}Q_{w}\Theta^{T}+R_{w}&0\\ 0&\rho\end{bmatrix}\), \(G^{T}=\begin{bmatrix}E^{T}Q_{w}\Theta&0\end{bmatrix}\).
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 4 OFDM in PLC Transmission
### Filtered-OFDM  
As in conventional OFDM, inputs to an F-OFDM system are signals of the form  
\[s_{k}(t)=\sum_{n}s_{k}[n]\delta(t-nT), \tag{6}\]  
where \(s_{k}[n]\) are the subcarrier data symbols, \(k\) is the subcarrier index and \(T\) is the symbol duration. In general, the difference between OFDM and FBMC lies in the choice of the impulse responses of the shaping filters \(p_{T}(t)\) and \(p_{R}(t)\) used at the transmitter and the receiver, respectively. In OFDM, \(p_{T}(t)\) is a rectangular pulse of duration equal to \(T\), whereas \(p_{R}(t)\) is also a rectangular pulse but of smaller duration \(T_{FTT}<T\), where \(T_{FTT}=1/B\) and \(B\) is the subcarrier frequency spacing. In FBMC systems designed for maximum spectral efficiency, the duration of both shaping pulses is chosen equal to \(T_{FTT}\). Moreover, the durations of \(p_{T}(t)\) and \(p_{R}(t)\) are longer than the symbol duration \(T\) (usually an integer multiple of \(T\)), causing the overlapping of successive data symbols [16].  
Under distortionless transmission, a simple choice for \(p_{T}(t)\) and \(p_{R}(t)\) would be a pair of rectangular pulses both of duration \(\tau\). When channel distortion exists, the transmitted symbols undergo a transient state before reaching the steady state. To alleviate the transient phenomenon, the duration of \(p_{T}(t)\) is extended in time by a time period greater than the duration of the channel impulse response by adding a cyclic prefix (CP) to every OFDM symbol. At the receiver, \(p_{R}(t)\) is aligned in time with the transmitted symbol after the latter has reached its steady state.  
In F-OFDM, the rectangular shaping pulse is replaced by a pulse (window function) exhibiting a smoother transition at the edges. A \(p_{T}(t)\) option often used in practical F-OFDM transmission is the raised cosine pulse  
\[p_{T}(t)=\Pi\left(\frac{t-T/2}{T}\right)\bigotimes h(t), \tag{7}\]  
where \(\bigotimes\) denotes convolution, and  
\[\Pi\left(\frac{t}{T}\right)=\begin{cases}1,&|t|\leq T/2\\ 0,&\text{otherwise}\end{cases} \tag{8a}\] \[h(t)=\frac{\pi}{2T_{0}}\sin\left(\frac{\pi t}{T_{0}}\right)\Pi\left(\frac{t-T _{0}/2}{T_{0}}\right), \tag{8b}\]  
and \(T_{0}\) is the overlapping period of adjacent F-OFDM symbols, referred to as the roll-off period.
## 3 Test Experiment
### Classification Results of Public Hyperspectral Databases  
To test the usefulness of the method, three public hyperspectral databases were chosen for study. In all of the experiments, the deep belief network and a self-organizing network were applied. The color difference between the classification map and ground truth image is present, since the self-organized network is an unsupervised clustering method. The following are the experimental results.  
As we can see from Figure 8, the scene was acquired by the ROSIS sensor during a flight over Pavia in northern Italy. The original data is composed of 115 spectral bands, ranging from 0.43 to 0.86 \(\upmu\)m, each with a bandwidth of 4 nm. Noisy bands were previously discarded, leaving 102 channels in the image cube. The image of Pavia Center is a 1096 \(\times\) 715 pixel image. Both of the image ground truths differentiate 9 classes each. For this data set, a total of 148,125 pixels were available for the study. As can be seen in Figure 8b, on the right is the ground truth, while in Figure 8a, on the left are the experimental results based on DBN and SOM. The edge of every class can be recognized easily. The algorithm has basically clustered related pixels into individual classes.  
Figure 8: Pavia Image. (**a**) Classification map based on Deep Belief Networks (DBN) and Self-Organizing feature Map (SOM). (**b**) Reference data.  
Figure 7: Classification accuracy varying with the labeled sample.  
The scene shown in Figure 9 was also captured by the ROSIS sensor during a flight over Pavia in northern Italy. Noisy bands were also discarded, and the final number of spectral bands was 102 for the image of Pavia Center University. The image of Pavia Center University is comprised of 610 \(\times\) 340 pixels. There were nine categories that needed to be classified. Figure 9a on the left is the result of this analysis, while Figure 9b on the right is the ground truth. Using DBN and SOM worked well visually on this hyperspectral image.  
The scene in Figure 10 was collected by the 224-band AVIRIS sensor of the Salinas Valley, California and is characterized by a high spatial resolution (3.7-m pixels). The area covered is composed of 512 \(\times\) 217 pixels. The Salinas ground truth contains 16 classes. We generated the ground truth image on the right and classified the hyperspectral image into 16 classes using the DBN and SOM. The result is displayed on the left in Figure 10a.  
Figure 10: Sanilas Image. (**a**) Classification map based on DBN and SOM. (**b**) Reference data.  
Figure 9: Pavia University. (**a**) Classification map based on DBN and SOM; (**b**) Reference data.