esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials And Methods
### Classification Model  
Once the set of statistical features was extracted from different frequency sub-bands of the Haar wavelet transform calculated on each original image and its relative GLCMs, as described above, a classification model was trained to discriminate microcalcification clusters into benign and malignant on a subset of significant features. We used a well known machine learning method, like random forest (RF) [28].  
Moreover, in order to evaluate the minimum number of significant features needed to solve the classification problem, we developed an embedded feature selection [29; 30]. Embedded methods allow optimization between the interaction of the selected features and the machine learning algorithm used, because the selection criterion is grafted into it. In particular, the RF classifier processes an analysis of feature importance with respect to its expected result; therefore, these methods are essentially fulfilling the goal, i.e., optimizing the classifier performance. At each step of cross-validation, a feature ranking was calculated with respect to the predictive power assessed with the embedded method on the training set. Then, a binary classification model was trained by selecting iteratively an increasing number of features sorted by their discriminating power. In particular, the tree based strategy used by RF naturally ranks by how well they improve the purity of the node: nodes with the greatest decrease in impurity are at the start of the trees, while nodes with the least decrease in impurity occur at the end of trees measured by Gini's diversity index. Thus, the RF algorithm allows estimating predictor importance values by permuting out-of-bag observations among the trees. In this work, we adopted a standard configuration for RF with 100 trees and 20 features (as described in [28]) randomly selected at each split. More complicated architectures did not give any significant classification improvement.  
The performance of the prediction model was evaluated on 100 ten-fold cross-validation rounds in terms of Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve and also accuracy, sensitivity, and specificity calculated, identifying the optimal threshold by means of [PERSON]'s index on ROC curves [31]. Performing 100 rounds of cross-validation allowed having an estimate of the error on the average value of the performance.  
All the analysis algorithms used in this work (images' pre-processing, feature extraction and, learning models) were implemented in MATLAB2017a (MathWorks, Inc., Natick, MA, USA) software.
## Materials and Methods
### Integration of ESA Land Use Data  
Land use data from the European Space Agency (ESA) were imported via the Earth Engine API and reclassified to delineate areas designated for agricultural production. This reclassification was crucial for assessing land use dynamics within the study area and its implications for agricultural productivity and ecosystem services.
## 3 Tunnel Size and Support Types  
The tunnel shapes and sizes used for the construction time analyses are presented in Figure 3, which are the standard tunnel cross-sections and tunnel support designs established by the Korean Ministry of Land, Infrastructure and Transport (MOLIT) in 2021. The design is for a tunnel with two lanes and consists of a total of five sizes and tunnel support types according to RMR. The tunnel widths and heights are between 10.5 and 10.7 m, and between 7.0 and 7.4 m, respectively, as shown in Table 1, which is summarised from MOLIT [35]. The allowed overbreak and drill length decreases from 19 cm to 12 cm, and from 3.8 m to 1.3 m, respectively, with lower RMR (higher tunnel support types).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Tunnel** \\ **Support Type** \\ \end{tabular} & **RMR** & \begin{tabular}{c} **Bottom** \\ **Width (m)** \\ \end{tabular} & \begin{tabular}{c} **Height** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Contour** \\ **Length (m)** \\ \end{tabular} & \begin{tabular}{c} **Overbreak (m)** \\ **Arch** \\ \end{tabular} & \begin{tabular}{c} **Cross-Sectional** \\ **Area (m\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Rock Volume** \\ **/Round (m\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Drill/Round** \\ **Length (m)** \\ \end{tabular} \\ \hline Type 1 & 100–80 & 10.484 & 7.009 & 22.14 & 0.19 & 0.18 & 76.805 & 268.8 & 3.8/3.5 \\ \hline Type 2 & 80–60 & 10.484 & 7.399 & 22.14 & 0.19 & 0.18 & 76.805 & 268.8 & 3.8/3.5 \\ \hline Type 3 & 60–40 & 10.554 & 7.399 & 22.26 & 0.19 & 0.18 & 78.457 & 156.9 & 2.2/2.0 \\ \hline Type 4 & 40–20 & 10.648 & 7.279 & 22.16 & 0.12 & 0.12 & 79.397 & 119.1 & 1.6/1.5 \\ \hline Type 5 & 20–0 & 10.740 & 7.279 & 22.33 & 0.12 & 0.12 & 81.533 & 97.8 & 1.3/1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tunnel specifications by support type.  
Figure 2: Effects of rock mass quality variation in the NTNU model (Modified from [29]).  
The kinds and number of tunnel supports increased from Tunnel Support Type 1 through 5, as presented in Table 2, summarised from [35]. In Tunnel Support Type 1, 5 cm of shotcrete and 3 m of rock bolts were randomly installed. For Type 1 through Type 3, only shotcrete and rock bolts were installed. For Tunnel Support Type 4, steel ribs or lattice girders were also installed with a 1.5 m interval, in addition to the shotcrete and rock bolts. For Type 5, forepoling was added to the tunnel supports used in Type 4. In Korea, a concrete lining of at least 30 cm thickness is installed for the whole length of a typical tunnel after the breakthrough of the tunnel or far behind the face.  
Figure 3: Analysed tunnel size and support types (Modified from [35]).  
The rock mass quality in the updated NTNT D&B model is expressed by a Q-value; however, the tunnel sizes and support types vary with RMR. Hence, the RMR values in Table 2 are converted into Q-values by the relationship suggested by [PERSON] [36].
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### System Model
#### 3.1.4 Trustworthy Nodes Forwarding for Routing  
If the selected preferred parent node is not detected as an attacker node, then it is forwarded as a trustworthy parent node for routing decisions in the network. Since the child node can send packets only to the selected parent node, and the parent nodes in SMTrust are verified to be trustworthy using the trust metrics computation and trust threshold for the parent selection procedure. Hence, only the legitimate nodes will take part in the network.
## 4 Improved Routing Protocol
### Route Discovery
#### 4.2.3 Data Forwarding  
After each node builds the routing discovery neighbor table of the forward/backward list based on their positions, the source node first sets a route option to transmit a data packet. Occupying one-bit length in the protection information field, the route option is the index of route mode. The index makes nodes on the route aware that protection is the priority and blocks the sensor collection. After setting the source route option for the data packet, the source gets the next hop to the destination node by looking up its neighbor table of the forward/backward list. The strategy of choosing the next node from the forward/backward list is given as follows: if data packets are transmitted forward, the next node should be chosen from forward list. The furthest node is chosen to decrease the number of hops based on the distance parameter in Table 1 if its LQI satisfies the required minimum link quality. If there is no node that satisfies the required minimum link quality in the list, the node with the best link quality is chosen to be the next node.
## 4 Experimental Results and Discussion
### Research on the Influence of Undersampling Neighbor Count  
This section analyzes the performance of different algorithms at various undersampling neighbor counts to evaluate the impact of undersampling neighbor counts on model performance. The experiments were conducted using the Cora, Citeseer, and PubMed datasets with well-defined constraints. The imbalance ratio was set to 0.5, the loss weight was set to 0.1, and the undersampling neighbor count K varied as [3; 4; 5]. Each experiment was repeated three times, and the results were averaged.  
From the experimental results in Figure 2, the following observations can be made:  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset and Metric**} & \multirow{2}{*}{**Cora**} & \multicolumn{3}{c}{**Citeseer**} & \multicolumn{3}{c}{**PubMed**} \\  & & **F1-** & **AUC-** & **F1-** & **AUC-** & **F1-** & **AUC-** & **F1-** & **Times** \\  & **ROC** & **Macro** & **ROC** & **Macro** & **ROC** & **Macro** & **ROC** & **Macro** \\ \hline GraphSnote & 0.8942 & 0.5960 & 532.7449 s & 0.7193 & 0.3987 & 830.3261 s & 0.8457 & 0.6881 & 14.938.8945 s \\ GraphSnote + ENN undersampling & 0.9144 & **0.6657** & 574.32 s & 0.7407 & 0.4399 & 705.73 s & 0.8729 & 0.7209 & 14763.00 s \\ GraphSnote + Attention mechanism & 0.8991 & 0.6465 & **220.66 s** & 0.7525 & 0.41 & 379.96 s & 0.8647 & 0.7066 & **8834.59 s** \\ ESA-GCN & **0.8997** & 0.6392 & 223.30 s & **0.7614** & **0.4475** & **378.08 s** & **0.8746** & **0.7229** & 8973.52 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the ablation experiment.  
In the Cora dataset, the AUC-ROC metric is optimal when K = 4, while the F1-Macro metric is optimal when K = 5. This suggests that increasing the undersampling neighbor count improves model performance on this dataset.  
In the Citeseer dataset, K = 3 yields the best performance, while both AUC-ROC and F1-Macro metrics are worse than the original model when K = 5. This indicates that a smaller undersampling neighbor count is more favorable for model performance on this dataset.  
In the PubMed dataset, K = 4 yields the optimal AUC-ROC and F1-Macro metrics, but the F1-Macro metric is worse than the original model when K = 5. This suggests that an undersampling neighbor count of 4 is more advantageous for model performance on this dataset.  
In general, choosing a too small value for K may result in selecting inappropriate neighbors, thus impacting model performance. On the other hand, choosing a too large value for K would increase the number of nearest neighbors for each sample, leading to an inadequate number of retained minority class samples and compromising undersampling effectiveness while reducing model generalization capacity. Therefore, selecting an appropriate K value in the ENN algorithm requires careful consideration based on specific datasets and application requirements.
## 3 Materials and Methods  
### Materials and General Methods  
The starting materials and reagents purchased from commercial suppliers were generally used without purification unless otherwise mentioned. _E. coli_ BL21 (DE3) cells were purchased from Novagen (San Diego, CA, USA). The Ni SepharoseTM 6 Fast Flow was obtained from GE Healthcare (Uppsala, Sweden). All the other chemicals were obtained from Sigma-Aldrich (Milano, Italy). Reverse-phase high-performance liquid chromatography (RP-HPLC) analyses of P13#1 and CG-P13#1 were performed on a JASCO LC-NET II/ADC equipped with a JASCO Model PU-2089 Plus Pump and a JASCO MD-2010 Plus UV-vis multiple-wavelength detector set at 220 nm. The column used was a C18 reversed-phase analytical column (Waters, Bondapak, 10 \(\upmu\)m, 125 \(\AA\), 3.9 mm \(\times\) 300 mm), which was operated with linear gradients of acetonitrile (ACN) containing 0.1% trifluoroacetic acid (TFA) in H\({}_{2}\)O (0.1% TFA) over 30 min at a flow rate of 1.0 mL/min for the analytical runs. The high-resolution mass spectra (HRMS) were recorded on a Bruker Solarix XR Fourier-transform ion cyclotron resonance mass spectrometer (FTICR-MS) equipped with a 7T magnet, using matrix-assisted laser desorption/ionization (MALDI). Yields refer to chromatographically pure materials.
## 5 Conclusions  
In this paper, we propose a fuzzy learning-based expression recognition method for human emotion prediction. We construct a fuzzy label distribution system using fuzzy sets and present two fuzzy label distribution prediction methods based on fuzzy rough sets. To compare our algorithm with the label distribution approach, we conduct experiments on the fuzzy label distribution task and the label distribution task, respectively. The experimental results show that the accuracy of the proposed method is superior to the other methods on both tasks. The other methods cannot directly deal with the human emotion fuzzy label distribution task; therefore, the proposed method is a more general label distribution prediction method that is more suitable for deployment on AI IoT.  
This work only proposes the framework of fuzzy learning for label fuzzy distribution, and there are still some drawbacks: firstly, the accuracy of the proposed method, although better than the label distribution learning, still has some room for improvement; secondly, the efficiency of the proposed method and how to quickly output the fuzzy prediction of multiple composite labels; finally, the scalability of the proposed method, and how to extend the proposed method to multi-modality data.  
Therefore, in future work, we will try to improve the following aspects: firstly, the fuzzy rough set can be replaced by a more advanced fuzzy learning method such as intuitionistic fuzzy rough set and neutrosophic rough sets, thus further improving the accuracy of the method; secondly, the parallel computing framework of the proposed method is increased to improve the efficiency of the framework; finally, we study how to extend the fuzzy relationship calculation method between samples so that it can be applied to multi-modality data.  
Figure 7: Example of the video emotion recognition.  
**Author Contributions:** Conceptualization, L.Z.; Methodology, L.Z.; Software, L.Z.; Resources, Y.X., T.Y. and L.L.; Writing--original draft, L.Z.; Writing--review & editing, H.Z. and M.M.; Funding acquisition, H.Z., Y.W. and L.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Key Research and Development Projects of Zhejiang Province (under Grant 2022C01200), Natural Science Foundation of Hebei Province (under Grant F2020202028), and Natural Science Foundation of China (under Grants 62232005).  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture
### Layer 5: Output Layer  
The last layer in the proposed architecture is the output layer which is a fully connected softmax layer. Here, the output of the LSTM model is the input of this layer. The outcome is the final detection made by the SNRNN model about the magnitude and depth of the earthquake event.
## 5 Evaluation and Discussion
In this way, time and resources are saved and so is the consumption of electrical energy, which is important today for several reasons, including reducing resource consumption, protecting the environment, reducing greenhouse gas emissions, and ensuring long-term sustainability and stability in the energy sector.
## 2 Background
### WaveCluster  
#### 2.2.1 WaveCluster Algorithm  
WaveCluster [6] is a grid and density-based clustering technique unique for performing clustering directly in wavelet space. This confers a number of advantages associated with wavelet transformations, including native multiresolution and multilevel analysis, linear runtime, and localization in space and time, rather than solely in frequency. The idea behind WaveCluster is as follows:  
* Quantize data to a grid, using the cell counts in place of the original data.
* Apply a wavelet transformation using a hat-shaped wavelet (such as the 2.2 or 4.2 biorthogonal wavelets), retaining the approximation coefficients. These wavelets have the effect of emphasizing dense regions and suppressing sparse ones.
* Threshold cells in the transformed space. Cells with values above a user-specified density threshold are \"significant\", while other cells are dropped.
* Apply a connected component algorithm to the significant cells to discover and label clusters.
* Map the cells back to the data using a lookup table built during quantization.  
This approach is illustrated in Figure 2:WaveCluster is notable for its robustness to noise, linear runtime, ability to discover complex shapes, cluster significance thresholding, and native multiresolution analysis. Unlike k-means, the number of clusters need not be specified. However, WaveCluster has several additional parameters: grid cell size, significance threshold, wavelet name, and wavelet level. An additional drawback of WaveCluster is its tendency to underestimate border regions (which makes it useful for outlier detection). As outlined in [7], we adopt a variant of WaveCluster replacing grid cell counts with a weighted sum of intensity values to extend to grayscale segmentation.
# Staircase Detection, Characterization and Approach Pipeline for Search and Rescue Robots
## 2 Materials and Methods
### Description of the Cameras Used  
In this work, we tested the proposed pipeline with two different low cost RGD-D cameras, the Intel(r) RealSense(tm) D435i and the OpenCV AI Kit with Depth (OAK-D) from Luxonis Holding Corporation. The RealSense(tm) D435i contains a stereo camera system, an RGB camera and an Intel(tm) RealSense D4 Vision Processor Unit (VPU). The RGB camera can obtain video with a resolution up to 1920 \(\times\) 1080 and at 30 Frames per Second (FPS). On the other hand, the stereo system uses an infrared pattern projector and the VPU to acquire depth images with a resolution up to 1280 \(\times\) 720 and up to 90 FPS. In comparison, the OAK-D camera uses a Myriad X VPU, which contains two Neural Compute Engines (NCEs) which are capable of executing 4 Tera Operations per Second (TOPs). By being able to perform neuronal inference on the camera. This camera is also capable of obtaining RGB images at a resolution of 4K and 30 FPS and depth images at a resolution of 1280 \(\times\) 720 and 30 FPS.  
The proposed pipeline was designed to increase autonomy in rescue robots robots, and for this reason, communication to the teleoperator computer should only be for monitoring purposes. The RealSense(tm) D435i pipeline requires additional hardware to perform the detections, and the OAK-D does not. For this reason, the OAK-D camera is the best option for robots are limited not only in computing resources, but also in space inside their chassis and energy consumption (for example for prebuilt rescue robots). Unfortunately, one of the disadvantages of this camera are the low disparity levels it produces. Due to this, the clustering Algorithm 1 was implemented and it allowed for better comparisons between the two cameras used.  
To implement the pipeline using the D435i camera, a teleoperator computer with an Nvidia(r) RTX 2070 with Max-Q Design was used. This allowed a detection of over 100 FPS, but increased the implementation cost of the pipeline and it required a continuous communication to the teleoperator computer. In comparison, the OAK-D camera can be obtained for about the same price as a D435i camera and it includes NCEs that allows neural inference on the camera. This benefit outweighs the drawbacks of using this camera (for example the low disparity levels).
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 5 Case Studies
### Case Study for Lateral Stability
#### 5.2.3 Leveled Configuration  
The leveled configuration can also be adjusted, as shown in Figure 2(c). The aim of this section is to compare the lateral stability of the initial state and leveled configuration. Specifically, the robot is at its lowest ground clearance in its initial state. Then, it adjusts its roll angle by expending the linear actuator on the downhill side.  
The relationship between the lateral stability criteria (\(\beta_{l}\)) and the slope angle is shown in Figure 12. Generally, the leveled configuration is more stable than the initial state. When the slope angle is \(5^{\circ}\), \(\beta_{l}\) increases from \(39.78^{\circ}\)to \(40.22^{\circ}\)(\(1.11\%\)); when the slope angle is \(10^{\circ}\), \(\beta_{l}\) increases from \(34.78^{\circ}\) to \(36.32^{\circ}\)(\(4.43\%\)); when the slope angle is \(12^{\circ}\), \(\beta_{l}\) increases from \(32.78^{\circ}\) to \(34.91^{\circ}\)(\(6.5\%\)).  
Figure 11: Lateral stability with expending the downhill side actuator: (**a**) Adjustable angle; (**b**) Roll angle; (**c**) Lateral stability criteria.
# Efficient Algorithms for the Maximum Sum Problems
## 4 Improved Sequential Algorithm  
We can observe that Algorithm 5 not only takes \(O(n^{3})\) time, but also requires \(O(n^{3})\) memory. The reason for this memory requirement is that the algorithm stores maximum \(W\) and maximum \(N\) for all possible anchor columns.  
We can improve the memory requirement of Algorithm 5 significantly to \(O(n^{2})\) with a simple modification. Firstly, we observe that there is no reason to keep all maximum \(W\) and maximum \(N\) for all \(O(n^{3})\) possible anchor columns. Instead, we can iterate over the possible anchor column sizes and compute the \(O(n^{2})\) maximum \(W\) and \(N\) for the given anchor column size in each iteration, thereby computing the maximum \(WN\) for the given anchor column size in each iteration. Thus, in each iteration, we only need \(O(n^{2})\) memory, and there is no need to store maximum \(W\) and \(N\) values from previous iterations. Note that \(W\) and \(N\) on shorter columns are available for the \(t\)-th iteration.  
Algorithm 7 is the pseudocode for achieving the \(O(n^{2})\) memory bound. The pseudocode has been simplified, since much of the details have already been provided in Section 3.  
```
1:for\(t\gets 1\) to \(n\)do
2: Compute maximum \(W\) for all anchor columns of size \(t\)
3: Compute maximum \(N\) for all anchor columns of size \(t\)
4: Combine \(W\) and \(N\) for all anchor columns of size \(t\)
5: Store the current maximum \(WN\)
6:endfor
```  
**Algorithm 7** Sequential algorithm for \(MCS\).  
We note that the reduction in the memory bound is very significant in practical terms. The difference between \(O(n^{2})\) memory and \(O(n^{3})\) memory, if we take image processing as an example, is the difference between being able to process a mega-pixel image entirely in memory and having to resort to paging the results in an incomparably slow execution time.