esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Handcrafted Representation-Based Approach
### Other Approaches
#### 2.3.1 Local Binary Pattern (LBP)-Based Approaches  
Local binary patterns (LBP) [99] is a type of visual descriptor for texture classification. Since its inception, several modified versions of this descriptor such as [100, 101, 102] have been proposed for different classification-related tasks in computer vision. A human action recognition method was proposed in [103] based on LBP combined with appearance invariance and patch matching method. This method was tested on different public datasets and proved to be efficient for action recognition. Another method for activity recognition was proposed using LBP-TOP descriptor [104]. In this method, the action volume was partitioned into sub-volumes and feature histogram was generated by concatenating the histograms of sub-volumes. Using this representation, they encoded the motion at three different levels: pixel-level (single bin in the histogram), region-level (sub-volume histogram), and global-level (concatenation of sub-volume-histograms). The LBP-based methods have also been employed for multi-view human action recognition. In [105], a multi-view human action recognition method was proposed based on contour-based pose features and uniform rotation-invariant LBP followed by SVM for classification. Recently, another motion descriptor named Motion Binary Pattern (MBP) was introduced for multi-view action recognition [106]. This descriptor is combination of Volume Local Binary Pattern (VLBP) and optical flow. This method was evaluated on multi-view INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and achieved 80.55% recognition results.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## Materials and Methods
### Dual Process: Adsorption and Photocatalytic Reactions  
The acid green 1 (4 \(\times\) 10\({}^{-4}\) M) solutions containing 0.1 g of well-dispersed particles of the prepared material were mixed and ultra-sonicated for few minutes. Then, a small amount of the mixture was separated after its keeping in the dark for 1-2 h to determine equilibrium state of adsorption process. After that, the mixture was irradiated in sunlight. The irradiation area was 10 cm\({}^{2}\). Photocatalytic decomposition of the AG1 solutions was characterized using UV-vis spectrophotometer. The main characteristic peak of AG1 occurs at 714 nm, while, the other wavelengths (320, 283, and 232 nm) were due to the internal benzene ring of the structure of AG1 as shown in Scheme 1. Therefore, the extent of decomposition was determined by calculating the integrated area of the peak at 714 nm.  
The procedure of photocatalytic degradation of the pollutant was monitored by separating a certain quantity of the solution at regular intervals and measuring the absorbance by UV-vis spectrophotometer. The degradation and the decolorization of the solutions were followed by determining the concentration of the remaining dye in the solutions through measuring its absorbance. All photocatalytic degradation experiments were performed under irradiation of sunlight between 9:30 A.M. and 11:30 A.M. during winter season (November) in Saudi Arabia. The intensity of sunlight was 25 W cm\({}^{-2}\).
## 4 Parametric Analysis
### Effect of Steel Channel Model  
On the basis of the numerical simulation of the above typical cross-sections, and under the premise of keeping other parameters of the model unchanged, only the model of the steel channel is changed. The equivalent spring stiffness in different cases is shown in Table 5.  
Figure 16: The relationship curve between the opening of circumferential joints and the number of rings under different quantity.  
Figure 17: The relationship curve between the quantity of steel channels and the opening of circumferential joints for different rings.  
As shown in Figure 18, the effect of steel channel models and quantity on longitudinal displacement is similar. Due to the change of the section of the steel channel of No. 14 and above, there is a sudden change in the maximum longitudinal displacement at No. 14. Changing the model of the steel channel can not only reduce the maximum longitudinal displacement, but also save the tunnel construction space. A reasonable matching of the model and the quantity of steel channels during reinforcement can help to achieve a better reinforcement effect.  
As shown in Figures 19 and 20, the change of the steel channel model and the increase in the quantity of steel channels have roughly the same reinforcement effect on the opening of each ring circumferential joint, and the relationship curves are also very similar. The openings of the first four rings are the largest. When the model of steel channels is No. 14, the maximum opening is 5.97 mm. If the model of steel channel is less than No. 14, the opening of circumferential joint will exceed the critical value of 6 mm, which does not meet the specification requirements.  
Through the above analysis, the increase in the model and quantity of steel channels is equivalent to increasing the total cross-sectional area of the reinforced steel channels, so the effect of the two reinforcement methods is almost the same. Considering the safety and practicality of the project, when 24 steel channels are used to reinforce the 20-ring segment, it is more appropriate to make sure that the model of the steel channel is no less than No. 14.  
Figure 18: Maximum longitudinal displacement under different models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Model of Steel Channel** & \
\begin{tabular}{c} **The Total Area** \\ **(m2)** \\ \end{tabular} &
\begin{tabular}{c} **Equivalent Spring Stiffness** \\ **(N/m)** \\ \end{tabular} \\ \hline
10 & \(3.06\times 10^{-2}\) & \(1.61\times 10^{8}\) \\
12 & \(3.69\times 10^{-2}\) & \(1.94\times 10^{8}\) \\
14 & \(5.11\times 10^{-2}\) & \(2.69\times 10^{8}\) \\
16 & \(6.04\times 10^{-2}\) & \(3.17\times 10^{8}\) \\
18 & \(7.03\times 10^{-2}\) & \(3.69\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Equivalent spring stiffness of steel channels under different models.
# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Synthesis of the Selected Works
#### 3.8.11 Neurosurgery  
Neurosurgery is one of the surgical specialties with the highest operation risks [129]. [PERSON] et al. developed NeuroTouch, a virtual simulator for cranial microsurgery training [104]. It is currently known as CAE NeuroVR. The neurosurgical simulator was developed using a physics-based engine, a 3D graphics engine system for rendering, a pair of Geomagic Touch Hamitic devices, procedure controls, and two screens. NeuroTouch was designed to enable residents to practice their skills in tumor-debulking and tumor caueterization tasks. This system computes tissue deformation and topology changes according to tissue rupture, cut, or removal. Assessment of technical skills involved in craniotomy procedures is calculated using operation time, errors, instrument forces, and the amount of virtual tissue removal.  
[PERSON] et al. created an augmented reality neurosurgical training simulator with haptic feedback [105]. The simulation provides holographic guidance for pre-operative training using Microsoft HoloLens during brain tumor resection. The organs were reconstructed using MRI and a Voxelwise Residual Network, _VoxResNet_[130]. The simulator also provides the features of cutting, deformation, and bleeding. The Microsoft HoloLens provides an overlay with information about the hidden target organs using a benchtop 3D-printed skull model. The authors used two Geomagic touch devices, and the simulation was implemented in Unity and the UNET module. Finally, [PERSON] et al. performed a user study with ten participants using and a perception questionnaire to validate the system. Results from this study state that the simulation was realistic, it provided an immersive training environment, and the tool interaction was accurate.  
Another work in the area of neurology is conducted by [PERSON] et al. [106]. They designed a neurosurgical training application for repairing cerebral aneurysms in the Sylvian fissure region. The authors used the SOFA library, NVIDIA Flex, and Open Haptics Toolkit to develop their simulator. One Geomagic Touch haptic device is used to provide haptic feedback during the procedure. The anatomy models were constructed using coronary angio CT scans. In their study, users are asked to perform aneurysm clipping in the carotid region. The simulator was validated by an expert neurosurgeon, who stated that the realism of deformation and visualization were correct; however, additional validation tests are needed to demonstrate that the simulator helps students or residents to improve their surgical skills.
# Crude Glycerol as an Innovative Corrosion Inhibitor
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM)  
Scanning electron microscopy (SEM) images for the surfaces of fresh and corroded steel specimens were collected using a JSM5600 (JEOL, Tokyo, Japan). This study was performed to determine the surface morphology of the steel specimen. For each sample, images were collected at 2000\(\times\) magnification. SEM images of a fresh steel specimen, a steel specimen after corrosion using a 0.5 M HCl medium, and a specimen after corrosion inhibition with different concentrations of crude glycerol were taken to investigate the metal surface changes after three days of residence time in hydrochloric acid solutions with different concentrations of crude glycerol. Figure 8 shows the SEM images of the different tested scenarios. As can be seen from the SEM images, the steel surface is severely damaged and altered due to an aggressive attack of the corroding medium. Figure 8c shows that the corrosion marks on the surface of the metal decrease in the presence of crude glycerol. This suggests that the crude glycerol has the ability to adsorb on the surface of the steel specimen exhibiting greater protection towards steel corrosion. The protective crude glycerol layer becomes thicker as the concentration of inhibitor is increased. The parallel lines on the carbon steel surface can be attributed to polishing scratches.
## 3 AI Techniques and Their Applications
### Machine Learning Approaches
#### 3.1.2 Limitations and Case Studies  
Machine learning approaches, while generally lightweight and interpretable, often face challenges in generalizing across diverse or complex scenarios. Their effectiveness tends to diminish when confronted with noisy data, cluttered environments, or soil types that differ from those seen during training. Despite these limitations, ML methods continue to hold practical value in specific GPR applications, particularly in contexts where computational efficiency and model transparency are essential. For example, they are well-suited for utility detection in structured environments where subsurface conditions are relatively consistent and predictable. In transportation infrastructure projects, such as highways and railways, ML models are frequently employed for identifying layer boundaries, aiding engineers in assessing subsurface stratigraphy with minimal manual intervention. Additionally, ML techniques are often used as a preliminary step in anomaly detection, effectively serving as an initial screening tool that flags regions of interest for further analysis. These flagged regions can then be passed on to more complex deep learning models for refined interpretation, enabling a tiered analysis pipeline that balances speed, interpretability, and accuracy. By incorporating ML methods at the early stages of the GPR data processing workflow, practitioners can efficiently reduce data volume and focus computational resources on the most relevant segments. Furthermore, the use of ML in this hierarchical framework provides an added layer of robustness by combining fast decision-making with deeper, more nuanced analysis downstream. As such, ML approaches, while limited in adaptability, remain a valuable component in GPR interpretation pipelines when deployed strategically within their strengths [38; 39]. The traditional ML algorithms used with GPR are summarized in Table 3.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 1 Introduction
Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Surveillance Technology** & **Localization/Tracking Method** & **Detection Range** & **Challenges** \\ \hline Radar & Doppler-based tracking Delay-based localization & 10,000 m & Low radar cross section Low speed and altitude \\ \hline Acoustic & TDOA\(\backslash\)AOA-based localization & 0–300 m & High ambient noise \\ \hline Vision & Motion-based tracking & 100–1000 m & Confuse with birds Indistinguishable small objects \\ \hline RF & RSS\(\backslash\)AOA-based localization & 5000 m & Ambient RF noise Multipath Non-line of sight \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of UAV surveillance technologies. application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security. The rest of the paper is organized as follows. Section 2 gives the comprehensive review of UAV detection and identification methods. Section 3 describes ways of UAV localization and tracking. Anti-UAV system is introduced in Section 4. Finally, the conclusion is presented in Section 5.
## 2 Method
### Drift Elimination
#### 2.2.2 Positional Encoding  
Unlike traditional RNNs, the Transformer's self-attention mechanism does not process information on the order of elements in the input sequence. This indicates that, without processing the input data, the model fails to distinguish the positional relationships of the elements in the sequence, and cannot capture the dependencies between signals. The purpose of positional encoding is to add an encoding related to its position in the sequence to the representation of each input element at the input stage of the model so that the model can understand the sequential and temporal relationships in the sequence by using this information. There are generally two types of position encoding: sinusoidal functions and learnable position embeddings. The sinusoidal version, using sine and cosine functions at different frequencies, allows the model to handle sequences longer than those seen during training [30]. In contrast, learnable positional encoding involves adding a trainable weight matrix of the same dimension to the input data, optimizing these positional encoding parameters throughout model training. This method enables the model to learn the positional relationships of elements within the sequence [34]. Since both perform equally well, this paper chooses the sine-cosine function version of the position embedding. It can be expressed as  
\[PE_{(pos,2i)}=sin\left(\frac{pos}{10,000^{\frac{2i}{2 mold}}}\right) \tag{1}\]  
\[PE_{(pos,2i+1)}=cos\left(\frac{pos}{10,000^{\frac{2i}{2 mold}}}\right) \tag{2}\]  
Figure 2: The Transformer-based model architecture.  
where _pos_ represents the position of the element in the sequence, \(i\) represents the dimension in the embedding vector, and _dmodel_ represents the dimension of the vector. In this way, each element of the positional encoding is assigned a corresponding sine wave, which enables the Transformer model to understand the relative positional relationships among elements in the sequence [35].
# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 2 Materials and Methods
### Machine Learning
#### 2.2.2 Artificial Neural Networks  
Artificial neural networks (ANNs) are a machine learning technique that imitates the learning method of the human brain and performs functions such as learning, remembering, and generating new data from the data obtained using generalization methods. ANNs are synthetic systems that mimic biological neural networks. As in the human biological structure, the aim is to train machines to learn through artificial neural networks and to make decisions with what they have learned. Artificial neural networks can be trained and adapted to be self-organized, self-learning, and self-evaluating to model the learning structure of the human brain [27].  
ANNs are mathematical models consisting of many neurons connected to each other by weights. The network consists of input, intermediate (hidden), and output layers. Each input is multiplied by a connection weight. After the neurons weigh the input information, they sum it linearly and the threshold converts this information into output information by processing it in a linear or non-linear function. Other neurons connected to the cell receive this output as input information [28].  
ANNs can also be classified structurally. ANNs are categorized into two main groups, feed-forward networks and feedback networks, in terms of the structure of the connections between neurons, depending on the direction of information flow. In feed-forward networks, the flow of information from input to output layer through the intermediate layer proceeds in only one direction. They consist of an input, hidden, and output layer. Information flow on the network proceeds from the input layer to the output layer. In other words, neurons are fed one after the other. In feedback artificial neural networks, the information flows from the output of any neuron to its input. In this type of network structure, feedback connections are in question [29].
## 7 Module III: Temporal Privacy
### Energy Controlled Anti Temporal Scheme (ECAT)
#### 7.2.3 Energy Conservation by Forwarding Messages to Energy-Rich SNs  
When a node senses data or receives data that needs to be forwarded to the BS, it has to select the next one-hop node from the neighborhood set \(\text{N}_{i}\). During the setup phase, each \(\text{SN}_{i}\) has information about the hop-distance for each of the neighbors stored in its table Ti. Typically, there are three sets: one set where the hop-distance is less than its own (uplink set), a set where the hop-distance equals to itself (equal-link set) and a set where the hop-distance is larger than itself (downlink set). Choosing a node randomly or by round robin from the uplink set will be ideal in terms of delays since it will give the shortest path to the BS. However, that will cause the nodes in this set to consume more energy compared to the other two sets of the neighbors. After each transmission, the SN consumes some energy. The residual energy for SNi will be calculated as below:  
\[\Delta_{residual}=\frac{\Delta_{current}}{\Delta_{initial}} \tag{46}\]  
Each node will calculate its residual energy and share it with its one-hop neighbors. When the node sends fake messages, it will send its residual energy with it. The neighbor SNj will store the value in its Tj for each of its neighboring nodes. This way, any sensor node will have some information about the residual energy level for its immediate neighbors. Figure 16 exhibits the mechanism for selecting the forward node.  
Figure 16: How to choose the forwarding node according to the energy levels of the neighbors. The sensor calculates the average energy levels for all the neighbors. Then it will select a neighbor, which has energy level higher than the calculated average energy, from uplink nodes if it is available. If not, then from equal-link nodes and then from downlink nodes.
# GNSS-Based Monitoring Methods for Mining Headframes
## Abbreviations  
The following abbreviations are used in this manuscript:  
GNSS - Global Navigation Satellite System  
BDS - BeiDou Navigation Satellite System  
GPS - Global Positioning System  
RTK - Real-Time Kinematic  
SPSS - Statistical Package for the Social Sciences  
STFK - Spatiotemporal Kalman Filter  
GESD - Generalized Extreme Studentized Deviate  
BIMRWP - Beidou + Typical Geological Hazard Intelligent Monitoring and Rapid Warning Cloud Platform  
PCC - Pearson Correlation Coefficient  
PPP - Precise Point Positioning  
LEO - Low Earth Orbit
# Nano-Polymers as Cas9 Inhibitors
## 3 Results
### Effects of Polymer Length on Cas9 Inhibition  
By screening five different LLL-conjugated nano-polymers, we identified three of them as Cas9 inhibitors with varying inhibitory efficiencies with a ranking order of PMLA-LLL \(>\) PGA200-LLL \(>\) PLD200-LLL. We further asked whether the length of the polymers has any impact on the Cas9 inhibitory efficiency. To answer this question, we first chose a short PGA20 conjugated with LLL (PGA20-LLL) and determined its Cas9 inhibition efficiency with various concentrations. The DNA cleavage results showed that even with a PGA20-LLL/Cas9 molar ratio of 50:1, PGA20-LLL was still unable to completely inhibit Cas9 function with traces of digested DNA (Figure 4C, left). In comparison, PGA200-LLL only required a molar ratio (polymer/Cas9) of 10:1 to reach complete Cas9 inhibition (Figure 4B, left). Lastly, we compared the PGA-LLL polymers with three different backbone lengths (PGA20, 50, and 100) in their ability to inhibit Cas9 function. All three polymers were incubated with Cas9 at a polymer/Cas9 molar ratio of 20:1, and we found that only the longest PGA100-LLL exhibited a complete inhibitory effect, while both PGA20-LLL and PGA50-LLL showed slight traces of digested DNA in the gel (Figure 4B, right), indicating that longer forms of polymers have a stronger Cas9 inhibition effect.