esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 1 Introduction
This is shown in Figure 1 (Source for Figure 1a: Vegetation analysis: using vegetation indices in ENVI [16]). The details of the measures used for the band selection, mathematical equations, range of the measures, and BS procedure are discussed in the Section 2. The contributions of the paper are shown below:  
* There are different successful methods in the literature for HSI classification. However, not all methods are suitable for all the available application areas to perform classification. In the present paper, we designed a band selection model for crop classification based on the physical and biological properties of plants. * A new framework for informative band selection is proposed by partitioning the original hyperspectral cube based on the reflectance nature in the visible, near-infrared, and short wave infrared regions of the electromagnetic spectrum. This further uses measures such as entropy, NDVI, and NDWI, respectively, for band quantization. * A two-dimensional convolution network for hyperspectral image classification is designed and implemented for the accurate classification of agriculture crops with the selected bands. Detailed analysis of the results in crop classification is showcased. The following is a breakdown of the paper's structure: materials and methods are presented in Section 2, which consists of a technical description of the proposed method. Dataset description is described in Section 3, followed by Section 4, which includes HSI classification, experimental results, and analysis. Finally, the conclusions are presented in Section 5.
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 3 Model Development
### Lazy Locally Weighted Learning (LLWL)  
Similar to the K-star technique (i.e., an instance-based classifier), locally-weighted learning (LWL) [44] is one of the common types of lazy learning-based solutions. Lazy learning approaches provide valuable training algorithms and representations for learning about complex phenomena during autonomous adaptive control of complex systems. Commonly, there are disadvantages in employing such methods. Lazy learners create a considerable delay during the network simulation. More explanations about this model are provided by [PERSON], et al. [44].  
The key options we have in LLWL include number of decimal places (numDecimalPlaces), batch size (batchSize), KNN (following the k-nearest neighbors algorithm), nearest neighbor search algorithm (nearestNeighborSearchAlgorithm), and weighting Kernel (weightingKernel). More explanations are provided below for each of the above influential parameters.  
* numDecimalPlaces--The number of decimal places. This number will be implemented for the output of numbers in the model.
* batchSize--The chosen number of cases to process if batch estimation is being completed. A normal value of the batch size is 100. In this example we also consider it to be constant as it did not have significant impact on the outputs.
* KNN--The number of neighbors that are employed to set the width of the weighting function (noting that KNN \(\leq\) 0 means all neighbors are considered).
* nearestNeighborSearchAlgorithm--The potential nearest neighbor search algorithm to be applied (the default algorithm that was also selected in our study was LinearNN).
* weightingKernel--The number that determines the weighting function. (0 = Linear; 1 = Epnechnikov; 2 = Tricube; 3 = Inverse; 4 = Gaussian; and 5 = Constant. (default 0 = Linear)).  
A good example of k-nearest neighbors algorithm is shown in Figure 6. The test sample (red dot) should be classified either as blue squares or as green triangles. If k = 3 (i.e., depicted in solid line circle) it is depicted by the green triangles as there are two triangles (reversed in shape) and only one rectangle through the inner (i.e., continuous line) circle. If k = 5 (dashed line circle) it is assigned to the blue rectangles (three blue rectangles vs. two green triangles inside the outer circle). Variation of the correlation coefficient (R\({}^{2}\)) versus number of used KNN neighbors is shown in Figure 7. It can be seen that changing the KNN could significantly enhance the correlation coefficient. For the cases of KNN = -1, KNN = 2, KNN = 4, KNN = 6, KNN = 8, and KNN =10, the training correlation coefficients were 0.9025, 0.9579, 0.9861, 0.9916, 0.9937, and 0.9943, respectively. In the case of our study we proposed the KNN = -1 as it considers all neighbors.  
Figure 6: Example of k-nearest neighbors (KNN) regression/classification.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 2 PPP Mathematical Model and Satellite Selection Algorithms  
### Dual-Frequency Multi-GNSS PPP  
As another alternative to traditional PPP which uses the ionosphere-free combination model, undifferenced and uncombined PPP has attracted a lot of attention in the GNSS field [25; 26; 27]. This solution is more flexible in processing multi-frequency GNSS observations, avoids noise amplification caused by traditional linear combination, and can extract ionospheric delays [28; 29; 30]. In the dual-frequency undifferenced and uncombined PPP model, the receiver uncalibrated code delay (UCD) absorbed by both receiver clock offset and Line-of-Sight (LOS) ionospheric delay parameters should be considered. Additionally, the GPS and BDS are both based on the code division multiple access while GLONASS is based on frequency division multiple access. Hence, the inter-frequency bias should be considered. The linearized equations of pseudorange and carrier phase observations are written as follows [25]:  
\[\left[ \begin{array}{c}p_{r,1}^{1,T}\\ l_{r,1}^{1,T}\\ \vdots\\ p_{r,2}^{m,T}\\ l_{r,2}^{m,T}\\ l_{r,2}^{m,T}\end{array} \right]=\left[ \begin{array}{cccccc}-\mathbf{u}_{r}&\mathbf{1 }&\mathbf{M}_{\mathrm{W}}&\mathbf{K}&\mathbf{R}_{1}&\mathbf{R}_{2}\end{array} \right]\left[ \begin{array}{c}\mathbf{x}\\ \vec{d}_{r}^{T}\\ \vec{L}_{r,1}^{T}\\ \vec{L}_{r,1}^{T}\\ \vec{N}_{r,1}^{T}\end{array} \right]+\left[ \begin{array}{c}\epsilon_{r,1}^{T} \\ \vec{\epsilon}_{r,1}^{T}\\ \vec{\kappa}_{r,1}^{T}\end{array} \right]\mathbf{Q}_{L}, \tag{1}\]  
\[\left\{ \begin{array}{l}\vec{d}_{r}^{T}=d_{r}+d_{r,1}^{T}\vec{\mathbf{r}_{12}}\\ \vec{L}_{r,1}^{T}=\vec{l}_{r,1}^{S,T}+\vec{l}_{12}^{T}\cdot(\mathrm{DCB}_{r,P _{1}P_{2}}^{T}-\mathrm{DCB}_{P_{1}P_{2}}^{S,T})\\ \vec{N}_{r,1}^{S,T}=\vec{\lambda}_{1}^{T}\cdot(N_{r,1}^{S,T}+b_{r,1}^{S,T}-b_{1 }^{S,T})+d_{1r,1}^{S,T}-d_{r,1}^{S,T}+\vec{l}_{12}^{T}\cdot(\mathrm{DCB}_{r,P _{1}P_{2}}^{T}-\mathrm{DCB}_{P_{1}P_{2}}^{S,T})\\ \vec{N}_{r,2}^{S,T}=\vec{\lambda}_{2}^{T}\cdot(N_{r,2}^{S,T}+b_{r,2}^{S,T}-b_{2 }^{S,T})+d_{1r,1}^{S,T}-d_{r,1}^{S,T}+\vec{\lambda}_{2}^{T}\cdot\vec{\beta}_{12 }^{T}\cdot(\mathrm{DCB}_{r,P_{1}P_{2}}^{T}-\mathrm{DCB}_{P_{1}P_{2}}^{S,T}) \\ \end{array} \right., \tag{2}\]  
\[\left\{ \begin{array}{l}\alpha_{mm}^{T}=\frac{(\vec{\sigma}_{m}^{T})^{2}}{( \vec{\sigma}_{m}^{T})^{2}-(\vec{\sigma}_{m}^{T})^{2}},\\ \mathrm{DCB}_{P_{m}P_{m}}^{S,T}=\vec{d}_{m}^{T}-\vec{d}_{n}^{S,T}\end{array}  \right. \begin{array}{l}\vec{\beta}_{mm}^{T}=-\frac{(\vec{\sigma}_{m}^{T})^{2}}{( \vec{\sigma}_{m}^{T})^{2}-(\vec{\sigma}_{m}^{T})^{2}}\\ \mathrm{DCB}_{P_{m}P_{m}}^{S,T}=\vec{d}_{m}^{T}-\vec{d}_{n}^{S,T}\end{array}  \right. \begin{array}{l}\mathrm{DCB}_{P_{m}P_{m}}^{S,T}=\vec{d}_{r,m}^{T}-d_{r,n }^{S,T}\\ \vec{d}_{r,1}^{T}=\vec{\alpha}_{mm}^{T}-\vec{d}_{n}^{S,T}+\vec{\beta}_{mm}^{T} \cdot\vec{d}_{n}^{S,T}\end{array}
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 2 Related Work in Anomaly Detection for IoT Networks
### Selected Methods for IoT Anomaly Detection: A Review
By traversing the tree from root to leaf, a classification is made. For anomaly detection, the leaves represent the classes 'normal' or 'anomalous'. However, these methods have limitations in terms of overfitting and modeling complex dependencies [16; 19; 20]. * Fuzzy Logic: This is a multi-valued logic system that permits each logical value to be any real number between 0 and 1. Unlike classical logic's binary truth values, fuzzy logic allows for degrees of truth. Its capacity to handle imprecise information makes it particularly advantageous in contexts characterized by noisy and uncertain data [16; 18]. * Recurrent Neural Networks (RNNs): These are ideal for analyzing time-series data, which are typical of IoT technology. With their ability to remember past information, RNNs can detect patterns, trends, and anomalies in time-series data [17; 19; 20]. * Long Short-Term Memory (LSTM): These methods constitute a special type of RNN that is particularly effective at modeling long-term relationships in data. Thanks to a gating mechanism, LSTMs can remember relevant information for longer periods of time, which is useful for complex patterns of anomalies [17; 19; 20]. * Convolutional Neural Networks (CNN): These are able to extract salient features from the data, such as shapes or textures, allowing the detection of anomalies based on local patterns [17; 19; 20]. * Autoencoder: This is a special type of neural network whose task is to learn the most salient features of the data. It works on the principle of compressing and decompressing information: it first encodes the input data into a more compact representation and then tries to reconstruct the original data based on this representation. The entire learning process takes place without the need to feed the network with additional information about the data, making it an unsupervised learning method [23; 24]. The Autoencoder is a tool that allows the automatic detection of anomalies in data without the need to first define what exactly an anomaly is. The Autoencoder's ability to learn complex data structures enables it to detect even subtle deviations from the typical pattern. In summary, choosing the right ML method for anomaly detection in IoT networks depends on a number of factors, such as data characteristics, accuracy requirements, and problem complexity. Each of the methods discussed has its own unique advantages and limitations [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32], which means that the choice of the optimal solution requires a case-by-case approach [16].
# Stress Analysis on the Ankle Joint during Incline and Decline Standing
## 4 Discussion
Instead of that, bones are robust and strong, able to sustain compressive loads [25]. Although cartilage can resist compression, it is also capable of bearing some tensile loads [26] but to a lesser extent than ligaments. Due to these differences in material properties, ligaments tend to experience higher stress under tensile loading compared to bones and cartilage. Other than that, specific anatomical functions might explain the various von Mises stress distributions found at each component of the ankle. Ligaments primarily function to stabilise joints by reducing excessive movement and providing structural support [27]. During slope standing, ligaments may uphold a significant portion of the load to maintain joint stability. Bones, on the other hand, support the body's weight and provide a framework for muscle attachment [28]. cartilage acts as a cushion between bones and helps distribute forces within joints [29]. However, it generally experiences lower stresses compared to ligaments and bones. There are some limitations that are associated with this study. First, the cartilage and ligament of the ankle joint were assumed as linear elastic. Although these soft tissues can be considered as viscoelastic materials, the present analysis was performed under linear elastic region. The loads applied are relatively low compared to the material yield limit of all ankle parts. There are even studies that show that linear elastic model can stimulate bulk behaviour for cartilage accurately [30; 31]. Despite limitations of the present model, particularly on ligament, the results showed that the stresses obtained on bones are not much different compared to other studies [11; 15]. Hence, this limitation does not give a significant effect on the present findings. Second, the cartilage and ligaments were constructed to ensure the connectivity on foot structure that mainly consisted of bone. Other soft tissues such as skin and plantar fascia were omitted. Future research considering all soft tissues in the foot may need to be conducted to observe the effect of parts' interaction on the ankle biomechanics. Moreover, simulation of the ankle joint as a flexure hinge in the field of compliant mechanism is a quite interesting topic to be explored in future, particularly for prosthetic design.
# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 3 Method
### Scene Analysis Module
#### 3.2.1 Density Estimation  
Figure 3a shows a tracking frame and its corresponding responses after cross-correlation. The tracking frames were obtained from the OTB dataset [11]. The backbone was obtained from SiamRPN++ [15]. Figure 3b shows the responses in three dimensions. We can see that with the improvement in training [17] and the [15] network, the responses distribute densely within the potential tracking targets. Therefore, we take the responses as a distribution and first sample it using the accept-reject algorithm. Specifically, given the responding distribution \(d(x)\), we select a known probability distribution \(q(x)\) and a sufficiently large  
Figure 3: (**a**) Score map visualized by a heat map. (**b**) Score map in three dimensions. (**c**) Density estimation outcome of GMM. The GMM components are visualized using ellipses in different colors. (**d**) Instance segmentation. The GMM components are further segmented in instance-aware clusters, where each cluster corresponds to a potential object.  
constant \(m\), such that \(\forall x\), we have \(mq(x)\geq d(x)\). Then, we repeatably sample from a uniform distribution \(U(0,1)\). If the \(i\)th sample satisfies  
\[u_{i}\leq\frac{d(x_{i})}{mq(x_{i})}, \tag{1}\]  
we accept \(x_{i}\) as a sample, or reject it otherwise. Figure 3c shows the sampled points from the score map.  
After sampling from the score map, we adopt the Gaussian mixture model (GMM) to estimate the probability density. The GMM is a parametric probability density function represented as the sum of Gaussian densities. The representation of GMM is  
\[p(\mathbf{x}|\mathbf{\Theta})=\sum_{c=1}^{C}\rho_{c}\mathcal{N}(\mathbf{x}|\mathbf{\mu}_{c}, \Sigma_{c}), \tag{2}\]  
where \(\mathcal{N}(\mathbf{x}|\mathbf{\mu},\Sigma)\) is the multivariate Gaussian densities, whose parameter \(\mathbf{\mu}\in\mathbb{R}^{2},\Sigma\in\mathbb{R}^{2\times 2}\) are the mean vector and the covariance matrix. The scalar \(\rho_{c}\) is the weight of the Gaussian component.  
Since there is no closed-form solution for the GMM, the expectation-maximization (EM) algorithm [27] is commonly used to find a solution by iteratively maximizing data likelihood until the average data log-likelihood converges to a threshold. Figure 3c depicts the fitting outcome of the example image. The components of the GMM are visualized as different-colored ellipses.
## Instrument Description
### Offline Signal Processor
#### 2.6.1 Beamformer
The method relies on the properties of the GNSS signals, and the idea is to apply the clean-replica approach at each antenna element and to compute the phases that align in the complex plane all obtained waveforms to the reference. This is done for each array individually. Therefore, for each antenna array, the process consists of:  
1. Selection of a visible PRN (Pseudo-Random Noise) transmitter to point towards. 2. Obtain the Doppler frequency of the selected PRN by computing the cross-correlation using the FFT of the reference antenna signal and the clean-replica code modulated with a carrier tone over a given frequency range. The Doppler frequency of the signal corresponds to the one that maximizes the amplitude of the correlation function. 3. FFT-based cross-correlation of the data from each antenna element with the clean-replica code modulated with the Doppler obtained to generate a complex waveform for each case. 4. Computation of the beamformer phases as the differences between the phases of the complex waveform's peaks for each antenna element against the same term at the reference element. Notice that this method also compensates for the remaining instrumental offsets of the system. On the other hand, this technique is computationally costly, and it is affected by the signal's noise and other factors such as multipath. Selected Approach  
After taking into account the trade-offs of each method, the solution adopted combines both. During the first stage, the GNSS-tracking method is employed to retrieve the beamformer phases during certain tracks with a proper visibility and high SNR. Then, by assuming that the instrumental phases are not affected by the geometry (they shall be the same for different PRNs), we can obtain them as the average difference between the beamformer phases provided by the theoretical method and those phases obtained with the GNSS-tracking method. Once the instrumental offsets have been estimated, the theoretical method, which is computationally faster and not affected by multipath and noise, is employed to obtain the corrected beamformer phases to properly combine the signal contributions of the different channels in the antenna array. The beamformer phases are updated every second for both up- and down-looking antenna arrays.
## 6 Case Study Analysis
### Comparison of Grid Abnormal Evolution Under Coupled Power Flow and Weather vs. OPA Model
From a large number of simulation results, the 10 most frequently occurring fault chains are selected and presented in Table 4.  
Based on the fault propagation paths shown in Table 4, when large-scale power grids face extreme weather disasters, the cascading fault evolution process exhibits the following characteristics: initial faults are concentrated in key structural branches such as 167, 169, 159, and 164, and then spread rapidly to secondary key branches like 104, 110, 30, and 106 through power flow redistribution. Notably, in the later stages of fault evolution, multi-region splitting occurs, leading to localized power supply-demand imbalances. The research results show that the risk assessment method considering the dynamic spatiotemporal evolution of the power grid can effectively handle the complex topology of large power grids. This method demonstrates strong applicability in large grid risk assessment under extreme weather conditions. \begin{table}
\begin{tabular}{c c c} \hline
**Number** & **Fault Chain Path** & \(\mathbf{R_{E}}\) \\ \hline
1 & 14, 8, 15, 4, 5, 3–6, 2, 1 & 3.6079 \\
2 & 14, 8, 15, 3–4–5–6, 1 & 0.5634 \\
3 & 15, 14, 6, 8, 5, 4, 2 & 2.3900 \\
4 & 8, 14, 15, 6, 3, 2–5, 4 & 1.4646 \\
5 & 14, 15, 3–6, 4, 2–5–8 & 3.6842 \\
6 & 14, 15, 6, 8, 5, 4, 1–2–3 & 2.1570 \\
7 & 15, 14, 8, 6, 3–4, 5, 1, 2 & 1.9806 \\
8 & 14, 15, 4–8, 6, 3, 5, 1, 2 & 1.8060 \\
9 & 14, 15, 4–6–8, 3, 5, 2 & 5.1136 \\
10 & 14, 8, 15, 6, 3, 4, 2, 5 & 3.3041 \\ \hline \end{tabular}
\end{table} 
Table 3: Risk assessment results of the IEEE 14 system using the OPA model.
## 2 Materials and Methods
### Observation of Differentiation Ratio over Time  
Images of SH-SY5Y cells cultured for a total of 22 days after induction of differentiation were observed every second day by phase-contrast microscopy using a 10\(\times\) objective lens. Each image was manually cropped to 200 \(\times\) 200 pixels with a single SH-SY5Y cell at the center. The images were then input to the trained model and classified as 'differentiated' or 'undifferentiated'. The differentiation ratio, which we defined as the ratio of the number of images classified as 'differentiated' to total number of images classified as 'differentiated' and 'undifferentiated', was then calculated for each day the images were recorded. To confirm that there was gradual differentiation once differentiation was induced, we obtained the feature map output from the fully connected layer of our trained model using all of the phase-contrast photomicrographs (i.e., test data + time-series data). The number of dimensions of the obtained feature map were reduced to two by t-SNE. The distribution of the obtained feature map was visualized in the two-dimensional space as a scatter plot.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Layer** & **Description** \\ \hline Convolution Layer 1 & Filter size = 4 \(\times\) 4, Number of filters = 155, Stride size = 1 \\ Max Pooling Layer 1 & Filter size = 3 \(\times\) 3, Stride size = 3 \\ Convolution Layer 2 & Filter size = 9 \(\times\) 9, Number of filters = 130, Stride size = 1 \\ Max Pooling Layer 2 & Filter size = 5 \(\times\) 5, Stride size = 5 \\ Convolution Layer 3 & Filter size = 3 \(\times\) 3, Number of filters = 152, Stride size = 1 \\ Max Pooling Layer 3 & Filter size = 3 \(\times\) 3, Stride size = 3 \\ Fully Connected Layer 1 & Number of units = 2863 \\ Fully Connected Layer 2 & Number of units = 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of our convolutional neural network model, as optimized by using SigOpt [28]
## 6 Simulation Analyses
### The Relationship Between Orbit Determination Accuracy and Measurement Accuracy
#### 6.4.2 The Effect of Range Measurement Accuracy on Orbit Determination Accuracy  
When the angle measurement accuracy is 0.01\({}^{\circ}\), the range measurement accuracy is set to 5 m, 30 m, and 50 m. The relationship between the thrust parameter pre-identification accuracy and the time span is shown in the Figure 24.  
Figure 23: Relationship between average relative error and time interval in acceleration identification at different angle measurement accuracies.  
As the ranging accuracy decreases, the average relative error of thrust parameter pre-identification also decreases. When the ranging accuracy is 5 m, a 5% solution error can be achieved within 2 h; when the ranging accuracy is 30 m, a 5% solution error can be achieved within 6 h; when the ranging accuracy is 50 m, a 5% solution error can be achieved within 8 h.  
Both angular measurement and range measurement accuracy affect the accuracy of thrust parameter pre-identification, and the degree of impact is basically the same. When angular measurement or range measurement accuracy decreases by a factor of 10, the average relative error of thrust parameter pre-identification increases by about 8 times.