esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methodology
### Extraction of Water Surface Areas from MODIS Data  
To understand the ability to identify the details of the water, the classification accuracy of water extracted from the MODIS image was evaluated based on the ground-truth water extracted from OLI. The MODIS image was acquired on 27 September 2016. The green band (B4 of MODIS, center wavelength 555 nm) and the shortwave infrared band (B6 of MODIS, center wavelength 1640 nm) were used to compute the MNDWI according to Equation (1). The red band (B1 of MODIS, center wavelength 645 nm) and the near-infrared band (B2 of MODIS, center wavelength 860 nm) were used to compute the NDVI according to Equation (6). If the MNDWI value of a pixel is more than 0 and the NDVI value of a pixel is less than 0.15, it is classified as water. Thus, the water image form MODIS was obtained. Then, the water image form MODIS was resized to 30 m using the nearest-neighbor method when building the accuracy assessment confusion matrix.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 5 Discussion
### About the Loss Function  
YOLOX uses the BCE function as the classification and confidence loss function, and the IoU loss as the positioning loss function. Some studies [39; 41] have shown that using a focal loss-type of loss function is better than using the BCE function because focal loss can alleviate the imbalance in the number of positive and negative samples. Based on these theoretical foundations, we replaced the BCE function in YOLOX to the latest focal loss function, Varifocal Loss, and obtained the accuracy improvement (as shown in Table 4).  
We have also studied the replacement of the positioning loss function. In addition to the IoU function, the positioning loss functions include GIoU [55], DIoU [56], CIoU [56], and SIoU [57]. They all add other punishment items on the basis of IoU to obtain a better convergence effect. We tried to replace the IoU function in YOLOX into these functions, but the improvement effect of the detection accuracy was not obvious, and the reasoning time was increased. So, we did not use these more advanced loss functions in our final model.
## 2 Deformation Allowance of Loess Tunnel and Statistical Monitoring Samples
### Surrounding Rock Deformation
At the beginning, as the deformation of the surrounding rock increases, the required support force gradually decreases. In an ideal scenario, the support force could be reduced to a minimal level or even become negligible. However, at this critical stage, represented  
Figure 1: The support principle based on the GRC and SRC [24; 25; 26]. by point D in Figure 1, the surrounding rock may experience significant structural failure, including disintegration or complete collapse, which indicates a potential loss of stability. At this stage, the pressure exerted by the surrounding rock on the support is no longer the pressure from deformation, but rather the pressure resulting from the collapse of the surrounding rock, often referred to as the loosening pressure, which is primarily due to the weight of the disintegrated rock. As a result, anchor shotcrete support becomes unsuitable, and the only viable option is to employ the traditional construction method of cast-in-place concrete lining. On the other hand, the optimal location for support should be positioned to the left of point D, close to point C, as shown in the figure. At this location, the surrounding rock is capable of undergoing significant deformation, which helps to redistribute a larger portion of the rock mass pressure. Meanwhile, the support itself will experience minimal deformation pressure, ensuring more efficient load distribution and stability. This configuration ensures the stability of the surrounding rock. During construction, once the displacement and deformation of the first support stabilize, the second support is introduced when it reaches point C on the diagram. As construction progresses, both the surrounding rock and the support undergo creep deformation, at which point the support and deformation pressure will stabilize at PE. This process ensures that the support force maintains a sufficient safety margin, providing enhanced stability and reliability. Given a specific amount of displacement in the tunnel surrounding rock and lining, as represented by the FG section in Figure 1, two treatment options can be considered. The first option involves allowing continued deformation of the surrounding rock, which is denoted as \(\Delta\mathsf{u}\) in Figure 1. However, the first approach is clearly impractical due to the significant encroachment of the lining on the building boundary. As an alternative, the second option involves increasing the support force, denoted as \(\Delta P\) in the figure, which can help address the displacement issues without compromising the structural integrity of the building. Firstly, this approach requires further excavation of the tunnel while allowing the deformation to continue, effectively redistributing the load. Secondly, it necessitates an enhancement in the support strength of the tunnel to accommodate these changes. Therefore, the rational design of deformation allowances is a critical factor in ensuring the stability and safety of tunnel construction.
## 2 The Proposed Model
### Hermite Interpolation  
After obtaining the three basic points, how to build a continuous smooth M-N curve based on the above three points became the focus of this study. A quadratic parabola can meet the fitting requirements based on only three-points. However, the peak point of the M-N curve constructed by a quadratic parabola often deviates from the balanced failure point. To address this problem, the Hermite interpolation is used in this work. There are only two interpolated points required to establish the Hermite function, and four boundary conditions, including the function values and the first order derivative at the two nodes, must be satisfied. As the continuity and differentiability of the original function are presented, the Hermite function is also named as the shape-preserving function. The value and derivative of the Hermite function at any interpolation knot equals the real value and derivative of the original function, which could make the interpolation function much more easily converged to the prescribed target [37].  
In the general case, \(a=x_{0}<x_{1}<x_{2}<\cdots x_{n}=b\) is a partition of the interval [a, b], for the given points \((x_{i},y_{i},d_{i})\), where \(i=0,1,2,\cdots,n,y_{i}\) and \(d_{i}\) are the function value and the first derivative value at the point \(x_{i}\), assuming \(h_{i}=x_{i+1}-x_{i}\), \(t=\frac{x-x_{i}}{h_{i}}\); the cubic Hermite interpolation spline curves on the subinterval \([x_{i},x_{i+1}]\) can be expressed as  
\[H_{i}(x)=\alpha_{i}(t)y_{i}+\alpha_{i+1}(t)y_{i+1}+\beta_{i}(t)h_{i}d_{i}+ \beta_{i+1}(t)h_{i}d_{i+1}, \tag{35}\]  
where \(\alpha_{i}(t)\), \(\alpha_{i+1}(t)\), \(\beta_{i}(t)\), \(\beta_{i+1}(t)\) are the basis functions, which can be expressed as  
\[\left\{\begin{array}{l}\alpha_{i}(t)=1-3t^{2}+2t^{3}\\ \alpha_{i+1}(t)=3t^{2}-2t^{3}\\ \beta_{i}(t)=t-2t^{2}+t^{3}\\ \beta_{i+1}(t)=-t^{2}+t^{3}\end{array}\right., \tag{36}\]  
According to the definition and Equation (36), the basis functions should satisfy the following conditions:  
\(\alpha_{i}(0)=1\), \(\alpha_{i+1}(0)=0\), \(\beta_{i}(0)=0\), \(\beta_{i+1}(0)=0\), \(\alpha_{i}(1)=0\), \(\alpha_{i+1}(1)=1\), \(\beta_{i}(1)=0\), \(\beta_{i+1}(1)=0\), \(\alpha_{i}^{\prime}(0)=0\), \(\alpha_{i+1}^{\prime}(0)=0\), \(\beta_{i}^{\prime}(0)=1\), \(\beta_{i+1}^{\prime}(0)=0\), \(\alpha_{i}^{\prime}(1)=0\), \(\alpha_{i+1}^{\prime}(1)=0\), \(\beta_{i}^{\prime}(1)=0\), \(\beta_{i+1}^{\prime}(1)=1\), and \(\alpha_{i}(t)+\alpha_{i+1}(t)=1\), \(\beta_{i}(t)=-\beta_{i+1}(1-t)\).  
Standard cubic Hermite interpolation spline curves defined by Equation (36) satisfy \(H_{i}(x_{i})=y_{i}\), \(H_{i}(x_{i+1})=y_{i+1}\), \(H_{i}t(x_{i})=d_{i}\), and \(H_{i}t(x_{i+1})=d_{i+1}\). In addition, the second derivative of the polynomial used in the PCHIP function is not continuous. This lack of continuity in the second derivative ensures that the original shape of the peak is preserved with only a minimal degree of curvature existing between data points, resulting in an interpolated peak that retains the original maxima and minima of the sampled peak [38].
## 4 Proof Examples (two MIHP on GHT(\(m\), \(n\), \(n\)/2) )
### m _Even_, n \(\geq\) 4; y1 = y2, _Even_; (x2 - x1) _Odd_  
The two basic Lt. and Rt. Hamiltonian path patterns in a GHT(2, 4, 2) network shown below can have no collision except for end nodes. Assume \(n\) = 4 first to show the scalability performance of mutually independent Hamiltonian paths (MIHP) existed in GHT (\(m\), 4, 2), m even, for end nodes with a same, even \(y\) coordinate. As below, the underline shows the location of L-embeddings and R-embeddings, and the italic underline is for the R-embedding. Coordinates (0, 0) and (1, 3) represent the lowest-left and highest-right positions of GHT(2, 4, 2). 03 is the coordinate (0, 2) and 04 is the coordinate (1, 2) in Figure 3(a).  
03,01,02,08,06,05,07,04 Lt.  
03,08,06,05,07,01,02,04 Rt.  
For both Lt. and Rt. patterns, 01.02 and 06.05 have exactly same nodes and nodes' sequence; hence they can be deemed as a special node. 08, 06.05 and 07 can be collisionfree, because basic patterns' leading difference is 2 but the adding node differences between 01,02 and 03,08 are multiples of 4. Rt. 03.08 and Lt. 07.04 have enough separation to be collision free in accommodating their adding nodes, which have consistent sequence in both patterns.  
Figure 3: Mutually independent Hamiltonian paths (MIHP) performance exist in GHT(2,4,2) (**a**) without vertical extension (X-ext.); (**b**) with vertical extension (X-ext.).  
We then considered scalable vertical extension, as in Figure 3(b), and showed the vertex transitiveness. By the half vertical span nature of the third coordinate of GHT(\(m\), \(n\), \(n\)/2), scalable vertical extensions can be configured through the mechanism of continual symmetrical vertical extensions, X-shaped extensions, which do not affect the original nodes' connections. We can find that the vertical extension can have the rhythm, incorporating with the horizontal embeddings, and the rhythm of both patterns will not collide, despite extension increases. It is noted that after the first extension, the compared nodes can get more separation spaces. Besides, the adaptation is applied in R-embeddings, and whether collisions exist can be easily checked.
# A Deep Reinforcement Learning Approach for Active SLAM
## Appendix B On the Complexity  
Consider a neural network with \(n\) the number of inputs, \(m\) the number of outputs, \(H\) the number of hidden layers, \(m_{i}\) the intermediate outputs of the \(i\)-th hidden layer, and \(h_{i}\) the number of neurons in the \(i\)-th hidden layer. The computations required to propagate the information trough the network (feed-forward) during testing are one matrix multiplication and one activation function per layer. Thus, the number of multiplications performed will be,  
\[n\times m_{1}+m_{1}\times m_{2}+\cdots+m_{H-1}\times m_{H}+m_{H}\times m\] (A18) \[=n\times m_{1}+m_{H}\times m+\sum_{i=2}^{H-1}m_{i}\times m_{i-1}\] (A19)  
which allows to express the complexity as:  
\[\Theta\left(n\times m_{1}+m_{H}\times m+\sum_{i=2}^{H-1}m_{i}\times m_{i-1}\right)\] (A20)  
where \(\Theta(\cdot)\) is a strict bound, following the Bachmann-Landau or asymptoticwhere \(\Theta(\cdot)\) is a strict bound, following the Bachmann-Landau or asymptotic notation.  
Consider now the number of hidden units per hidden layer to be equal for all hidden layers, and denoted as \(\hat{m}\), and the number of hidden layers to be \(H=2\). Then, Equation (A20) can be rewritten as:  
\[\Theta\left((n+m+\hat{m})\times\hat{m}\right)\] (A21)  
On the other hand, the complexity of applying the activation function can be expressed, under the previous assumptions, as \(\mathcal{O}\left(\hat{m}\right)\), where \(\mathcal{O}(\cdot)\) is the upper bound. Therefore, the global complexity of the problem will be \(\mathcal{O}\left((1+n+m+\hat{m})\times\hat{m}\right)\), or, as a function of only the number of inputs and outputs, \(\Theta\left(n+m\right)\). The number of outputs is equal to the dimension of the action set,\(\text{dim}(A)\ \equiv\ |A|\ =\ m\), and the number of inputs directly affects the observation set, \(O\). For example, in the studied case where a laser scan is used, the dimension of the observation set would be,  
\[\text{dim}(O)\equiv|O|=\left((\max_{\text{range}}-\min_{\text{range}})\times 1 0^{2}\right)^{n}=k^{n} \tag{10}\]  
Thus, the time complexity of solving the problem will be linear in the action set and logarithmic in the observation set according to \(\Theta\left(|A|+\log(|O|)\right)\).  
Traditional resolution of the problem may result in different complexities depending on the method selected. For example, value iteration for a MDP is known to be \(\mathcal{O}\left(|A|\times|S|^{2}\right)\) complex, where \(|S|\) is the dimension of the state set; and for a POMDP, \(\mathcal{O}\left(c^{|A|}\times c^{|O|}\right)\) with \(c>1\), i.e., exponentially complex in the action and observation sets. Note that the previous complexity corresponds to the most demanding stage, being lower order terms ignored, such as the search over the action set, or the computation of the utility function that would be, for example, for \(D\)-opt, \(\Omega(|S|^{2.373})\), where \(\Omega(\cdot)\) is a lower bound.
# Classification and Fast Few-Shot Learning of Steel Surface Defects with Randomized Network
## 6 Testing Few-Shot Learning with EffNet+RC
Figure 6: The accuracy of the different classification models for all classes (base and new). Fit EffNet: fine-tuned EfficientNet-B7; EffNet+RC: fixed EfficientNet-B7 backbone plus randomized classifier; EffNet+FiC: fixed EfficientNet-B7 backbone plus fine-tuned classifier; Fit EffNet Unbal: fine-tuned EfficientNet-B7 with unbalanced dataset. When talking about few-shot incremental learning, in some scenarios, only few samples from the new and base classes are available for re-training. In our tests, except for **Ft EffNet Unbal**, the same number of shots were used from all classes for re-training. One would first expect that **Ft EffNet** could have the highest accuracy, since the whole network is re-trained, but this is not the case. While it can quickly adapt to the new classes, it gives the worst performance for the old ones (see corresponding curves in Figures 7 and 8). This is similar to catastrophic forgetting, and the drop from almost 100% to 87.5-93% is significant and the worst among all. Contrarily, if we involve more samples from the base classes to the re-training (and thus generate an unbalanced training dataset), the performance on old classes remains almost 100% but gives quite bad results for one to three shots of the new classes. It is illustrated by the green curve (**Ft EffNet Unbal**) in Figure 8.  
Now, compare the proposed **EffNet+RC** with **EffNet+FiC**. These two networks are similar, except for the fact that the latter does not have a randomized fully connected layer,  
Figure 8: The accuracy of the different classification models for new classes. Ft EffNet: fine-tuned EfficienNet-B7; EffNet+RC: fixed EfficientNet-B7 backbone plus randomized classifier; EffNet+FiC: fixed EfficientNet-B7 backbone plus fine-tuned classifier; Ft EffNet Unbal: fine-tuned EfficientNet-B7 with unbalanced dataset. Figure 7: The accuracy of the different classification models for base classes. Ft EffNet: fine-tuned EfficienNet-B7; EffNet+RC: fixed EfficientNet-B7 backbone plus randomized classifier; EffNet+FFC: fixed EfficientNet-B7 backbone plus fine-tuned classifier; Ft EffNet Unbal: fine-tuned EfficientNet-B7 with unbalanced dataset. but all of its weights are trained by backpropagation (see Figure 5 for illustration). As can be read out from Table 5 and Figure 6, **EffNet+RC** outperformed not only **EffNet+FIC (100)** and **EffNet+FIC (200)** but all other models at \(K=1,3,10\). At a higher number of shots, it still remained in the mid-range. Our proposed randomized model behaved quite well for the old classes; meanwhile, it could nicely follow the best curve of the fine-tuned EfficientNet-B7 (**Ft EffNet**) for the new ones.
## 4 Results and Discussion
### Limitations  
The results obtained for the simulated data show that our proposal is, unlike the other tested approaches, able to detect statistically significant, directed phase-amplitude interactions in data with high levels of noise. However, it tends to be less frequency-specific than the CFD and the conventional approach for the estimation of directed phase-amplitude interactions through TE. Therefore, our method is well-suited to the analysis of noisy data, but care should be taken with results under more ideal conditions. The proposed approach  
Figure 8: Topoplots of the average nodal (channel) relevance for each tested frequency band pair. The arrows represent the most relevant connections. For visualization purposes, only the connections with the highest average relevance values are depicted (1% of all connections). The rows indicate the frequency band driving the phase-amplitude interactions, while the columns correspond to the frequency band of the driven oscillations (e.g., the topoplot in the top right corner shows the nodal relevance and relevant connections obtained when \(\text{TE}^{\theta\omega}_{\text{ex}}\) was computed, assuming directed interactions from the oscillations phases in the \(\theta\) band to the amplitude envelope of oscillations in the \(\beta_{l}\) band).  
also relies on the kernel-based, phase TE-estimation method introduced in [22]; therefore, it suffers from the same limitations, especially regarding the selection of the many parameters involved in TE computation. Here, we employed relatively simple parameter selection strategies, as described in Section 3.3, but it is possible that more elaborate strategies [52] may lead to better results. Additionally, the TE estimator used assumes stationary or weakly non-stationary data, and cannot distinguish direct interactions from those originating from unobserved common causes [16]. Finally, our proposal is strictly limited to the detection of directed phase-amplitude interactions. It is unable to capture any of the other types of cross-frequency interactions that arise in oscillatory neural activities (phase-phase coupling, phase-frequency coupling, etc.) [3]. it was also not designed to be full information-theoretic analysis that simultaneously accounts for the multiple rhythms that can interact at both the sender and receiver side of coupled dynamical systems [1].
## 4 Result and Discussion
### Summary of Results  
The model developed with the proper handling of the imbalanced dataset can achieve an overall high F1-score on all 18 activity classes for both the full-body and upper-body dataset. The model results for all the experiments are presented in Table 5. Table 5 shows a comparison of the developed model with other works. There are several deep neural network models that were used to train the model for human activity recognition. InnoHAR used a multi-level neural network structure model based on the combination of Inception Neural Network and GRU. The experiment was conducted on the Intel Atom E3826 processor platform but the imbalanced class distribution still remains unsolved, as reported in [34].  
InnoHAR has a slightly better performance in the HAR classification, as shown by its F-measure of 0.94 compared to our works with an F-measure of 0.91. A ConvLSTM network has the advantage of having lower parameters and a low memory footprint from hidden representations, which is more suitable for wearable activity recognition during the deployment stage. Although the developed model is slightly lower in F1-score (3%) as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gamma (Hyperparameter)** & **Model Accuracy** & **F1-Score** \\ \hline
0.5 & 0.87 & 0.89 \\
1.0 & 0.90 & 0.90 \\
1.5 & 0.85 & 0.89 \\
2.0 & 0.83 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of model performance using multiclass focal loss.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiments** & **Model Accuracy** & **F1-Score** \\ \hline InnoHAR [34] & - & 0.94 \\ b-LSTM-S [39] & 0.92 & - \\ CNN [28] & 0.88 & - \\ CNN [27] & 0.85 & - \\ ConvLSTM [33] & - & 0.91 \\ ConvLSTM (Full body sensors) & 0.85 & 0.83 \\ \(\bullet\) & removal of NULL class and cross-entropy function & 0.80 & 0.80 \\ \(\bullet\) & resampling method and cross-entropy function & 0.89 & 0.88 \\ ConvLSTM (Upper body IMU sensors) & 0.86 & 0.84 \\ \(\bullet\) & removal of NULL class and cross-entropy function & 0.85 & 0.85 \\ \(\bullet\) & multiclass focal loss function & 0.90 & 0.90 \\ \(\bullet\) & resampling and cross-entropy function & 0.91 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of results in this paper compared to previous approaches.  
compared to InnoHAR [34], it remains at the same level of performance with state-of-the-art technologies. More importantly, a home-based rehabilitation assessment system can be developed with this model and a minimal number of sensors.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 2 Materials and Methods
### Problem Statement  
The ongoing generation of data by IoT devices offers immense potential to optimize urban services and decision-making while at the same time exposing smart cities to vulnerabilities. One of the most pressing problems is the presence of anomalies in this data.  
Anomalies cover a wide spectrum of irregularities in the context of smart cities. These irregularities can manifest as sudden spikes in energy consumption, deviations in transportation patterns, unexpected environmental changes, or even malicious cyberattacks on critical city infrastructure. The implications of these anomalies are profound, from the loss of operational efficiency to the potential danger to public safety and urban resilience.  
Unaddressed anomalies can have far-reaching consequences. For example, anomalies in power consumption data may indicate equipment malfunctions or even unauthorized access to the electrical grid. Failure to detect and respond to these anomalies promptly can lead to service interruptions, lost revenue, and, in some cases, catastrophic losses. Similarly, anomalies in transportation data can indicate traffic congestion, accidents, or irregular travel patterns. Without effective anomaly detection mechanisms, these problems can lead to increased travel times, environmental pollution, and a decrease in urban dwellers' overall quality of life.  
The emergence of smart cities as centers of innovation and progress requires a proactive approach to address these challenges. Traditional urban management methods are not prepared to handle the complexity and scale of urban data generated by IoT devices. Manual monitoring and intervention are no longer sufficient, given smart cities' enormous amount and velocity of data. The ability to detect, analyze, and respond to anomalies in real-time is paramount to smart cities' continued success and sustainability.