esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Data and Evaluation Metrics
#### 4.1.2 Evaluation Metrics  
The following are the primary quantitative evaluation metrics employed in this study to assess the improvement of the algorithm:  
(1) Reprojection Error: R\({}_{e}\)  
Reprojection Error refers to the geometric deviation between the projected coordinates of a three-dimensional reconstructed point cloud and the observed coordinates of feature points in the actual image plane when back-projected onto a two-dimensional image. The unit of measurement is pixels, and it is mathematically represented as follows:  
\[R_{e}=\frac{1}{N}\sum_{i=1}^{N}\left\|P_{j}X_{i}-x_{ij}\right\|^{2} \tag{11}\]  
In this context, \(P_{j}\) represents the projection matrix of the \(j\) perspective camera, \(X_{i}\) denotes the coordinate value of the \(i\) three-dimensional point in the model, and x\({}_{ij}\) indicates the two-dimensional observation coordinate value of the \(i\) three-dimensional point in the \(j\) perspective image.  
(2) Reconstructing the quantity of point clouds: \(N\)  
The density of reconstructed point clouds positively correlates with feature matching density, demonstrating both the algorithm's capability in tracking features under low-texture/repetitive-structure scenarios and the completeness of reconstructed regions. This study employs point cloud quantity N as a quantitative evaluation metric.  
(3) Aligning the view edges: \(N_{C}\)  
The edge count in matching views reflects the feature-matching frequency required by incremental SfM. Redundant matches not only consume computational resources but also degrade reconstruction accuracy, particularly in cross-temporal similar structures. This study quantifies algorithmic improvements through edge count metrics, with comparative analyses in section \(N_{C}\), demonstrating performance enhancements.  
(4) Feature matching-accuracy of matches  
Matching accuracy serves as a key metric for evaluating feature matching algorithm precision, defined as the ratio of correct matches to total matches. Higher values indicate enhanced algorithmic precision. The calculation formula is:  
\[Accuracy=\frac{N_{(correct\ matches)}}{N_{(matches)}}\times 100\% \tag{12}\]  
In this context, \(N(matches)\) denotes the total number of matched point pairs obtained through the matching algorithm, which includes both accurate feature correspondences and erroneous matching results. \(N\big{(}correct\ matches\big{)}\) represents the number of correct matched point pairs within the results. The accuracy of the matches is one of the most critical metrics for evaluating algorithm performance, as it is directly linked to the reliability of the algorithm in practical applications.  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Number of Images** & **Resolution** \\ \hline Statue1 & 30 & 4096 \(\times\) 3072 \\ Statue2 & 40 & 4032 \(\times\) 2268 \\ Archi1 & 60 & 4032 \(\times\) 2268 \\ Archi2 & 100 & 4032 \(\times\) 2268 \\ \hline \end{tabular}
\end{table}
Table 2: Detailed information on self-collected data.
## 2 Overall Design of the Integrated System  
### System Architecture  
According to the landform, a coal mine can be divided into lineways, mining areas and mined-out areas. The lineways can be further divided into main tunnels and branch tunnels [3]. In the main tunnel and most of the branch tunnels and mining areas, the landform is relatively wide, where the CMS based on an optical fiber backbone network is deployed to monitor the environment. However, the landform in some of the branch tunnels, mining areas, and all of the mined-out areas, is narrow and irregular. It is very inconvenient or even impossible to deploy CMS in these areas. In this case, WSNs are used for environment monitoring because of their flexibility [4; 5]. That means the WSN is mainly deployed where is difficult to deploy the CMS. The deployment of the WSN depends on the requirements of comprehensively monitoring the coal mine, which has characteristics of regional dispersion and small relevance with individual WSN. Based on these considerations, an integrated environment monitoring system for underground coal mines is constructed, which can comprehensively monitor the coal mine environment. Figure 1 shows the architecture of the integrated environment monitoring system. As shown in Figure 1, the WSN is mainly deployed in branch tunnels, and communicates with the Ground Monitoring and Dispatching Center (GMDC) via the optical fiber backbone network.  
Figure 1: Architecture of the integrated environment monitoring system.  
In this architecture, the WSN is composed of three types of nodes: sink nodes, routing nodes and sensor nodes. The sink node establishes a WSN based on the ZigBee protocol [6]. The routing node and the sensor node connect to a sink node to join a WSN. Environmental information about the underground coal mine are collected by the sensor nodes, and are sent to the GMDC through the sink nodes. According to the landform of the coal mine and the environmental monitoring requirements, different WSNs may consist of different kinds of sensors to collect different environment information. The environmental information that the CMS and the WSNs collect are transmitted to the GMDC via the optical fiber backbone network. After receiving the information, the GMDC can know the environmental conditions of the underground coal mine in real time, such as the level of gas and CO, and can rapidly respond to urgent cases. In this case, the monitoring of the environment of the underground coal mine and early warning of dangers could be achieved effectively. In addition, the connection between the GMDC and Internet make it possible for the remote manager to monitor and manage the overall safety conditions of local coal mines.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 5 Results
### Xilinx FFT Model  
The challenge in hardware implementation is the significant time needed to obtain the final sampled output signal after the removal of interference. Simulation testing depends on the collected input data from the GNSS receiver. To overcome this challenge, the Xilinx FFT model was developed to be part of the testing and validation. This model was developed according to [43]. The Xilinx FFT model contains the FFT MEX function, which provides the MATLAB software interface to the FFT Xilinx model. This function produces identical results to the Xilinx FFT IP core. This model is useful as it has a lot of variable parameters to be changed as required. Three of these important parameters are the formatting type, scaling option, and input width. By testing this model with different precision for fixed-point formatting, the Xilinx FFT model results state that increased precision provides a better performance signal.  
Significantly, there are matching differences in the precision between the fixed-point 8-bit Xilinx FFT model points and FFT MATLAB-based points. This is because the FFT MATLAB-based point uses the 64 double-precision float-point while the 8-bit precision fixed-point is used for the Xilinx FFT model. The percentage error of the fixed point 8-bit precision Xilinx FFT model and the FFT MATLAB-based point is 9.5622%. The points of the fixed-point 16-bit precision Xilinx FFT model and FFT MATLAB-based point match better  
Figure 19: Averaged C/No degradation for one jammer (freqj = 6.5055 MHz), five jammers (freqj = 6.5055 to 7.5055 MHz), and the sweeping bandwidth jammer (freqj = 6.5055 MHz) with the applied filter.  
Figure 18: Averaged C/No degradation for one jammer (freqj = 6.5055 MHz), five jammers (freqj = 6.5055 to 7.5055 MHz) and sweeping bandwidth jammer (freqj = 5.5 to 7.5 MHz) without the applied filter.  
than the 8-bit precision and the percentage error is 2.3677%. The points of the fixed-point 22-bit precision Xilinx FFT model and FFT MATLAB-based point are almost identically matched and the percentage error is 0.0074%. The points of the float-point with the single precision of the Xilinx FFT model and FFT MATLAB-based point are the closet matched results due to the similarity of the data format, which is the float-point, and the percentage error is 0.0013%.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Optimization of Silicon Nitride Thickness  
To further optimize the sensor's performance, the thickness of the silicon nitride (Si\({}_{3}\)N\({}_{4}\)) layer was varied from 5 to 20 nm, and its effects on SPR metrics were analyzed. Figure 4 and Table S5 display the results obtained.  
Figure 4a shows the SPR reflectance curves as a function of the angle of incidence, with S\({}_{3}\)N\({}_{4\_}\) base as the reference configuration in water with 5 nm thickness. As the Si\({}_{3}\)N\({}_{4}\) thickness increases, the SPR dip shifts toward higher angles and broadens. This shift and broadening indicate enhanced interaction with the evanescent field, which can increase sensitivity. However, excessive thickness (such as 20 nm) leads to a pronounced broadening, which may negatively affect the sensor's precision and lead to high attenuation.  
Figure 4b and Table S5 present the attenuation percentages for each Si\({}_{3}\)N\({}_{4}\) thickness. A noticeable increase in attenuation is observed as the thickness increases, especially at 20 nm, where attenuation reaches 90.64%. This high attenuation indicates substantial energy loss, which can reduce sensor effectiveness dramatically. Conversely, at 15 nm, attenuation remains relatively low (8.30%), offering a reasonable balance between signal strength and energy retention. This manageable attenuation value, at 15 nm, suggests an optimal balance, enabling effective SPR response without significant signal loss.  
Figure 4c and Table S5 display the FWHM values for each thickness. The FWHM increases with the Si\({}_{3}\)N\({}_{4}\) thickness, reaching a maximum of 14.16\({}^{\circ}\) at 20 nm, indicating a broad and less distinct SPR dip. For practical biosensing, a sharp resonance (lower FWHM) is preferable, as it correlates with higher signal-to-noise ratios. At 15 nm, the FWHM is 4.99\({}^{\circ}\), which is broader than lower thicknesses but still within an acceptable range (<5\({}^{\circ}\)) for effective sensing.  
Figure 4d and Table S5 illustrate the sensitivity enhancement percentage for each Si\({}_{3}\)N\({}_{4}\) thickness, while Figure S2 shows a linear trend in sensitivity as Si\({}_{3}\)N\({}_{4}\) thickness increases. Sensitivity enhancement improves with greater thickness, peaking at 19.82% for 20 nm. However, this increase in sensitivity comes at the cost of significant attenuation and an excessively broad FWHM at 20 nm, making it less practical. At 15 nm, sensitivity enhancement reaches 15.09%, providing a substantial improvement over lower thicknesses while avoiding excessive attenuation and broad FWHM seen at 20 nm.  
Based on these metrics, a Si\({}_{3}\)N\({}_{4}\) thickness of 15 nm is selected as the optimal configuration. This thickness provides a balanced performance with manageable attenuation (8.30%), an FWHM of 4.99\({}^{\circ}\) (ensuring adequate resonance sharpness), and a high sensitivity enhancement of 15.09%. The choice of 15 nm achieves a practical compromise between maximizing sensitivity and maintaining a sharp, low-loss SPR response.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Data Preparation
#### 4.1.3 Dimensionality Reduction  
The preprocessed data, labeled as \"prDATA\" and comprising seventy-six features, undergoes dimensionality reduction through the basic autoencoder (bAE) as depicted in Figure 5. As a feature extraction technique, bAE systematically reduces dimensionality while considering all features' influence on the outcome. This process involves three internal submodules: the encoder, decoder, and loss function. Together, they contribute to extracting essential features, resulting in a condensed feature representation.  
The encoder transforms \"prDATA\" into a lower-dimensional \"zDATA\" using weights W and biases b with an activation function f. Among activation functions, the rectified linear unit (ReLU) is represented as max(0, prDATA), while the sigmoid activation function is defined as in Equation (2):  
\[Sigmoid=\frac{1}{(1+\epsilon(-prDATA))} \tag{2}\]  
ReLU and sigmoid introduce non-linearity into the model's output. The decoder aims to reconstruct the original input data \"rcDATA\" from \"zDATA\" using weights W and biases b with another activation function \"g\". The loss function, typically the mean squared error (MSE), quantifies the disparity between \"prDATA\" and \"rcDATA\". The reconstruction formula is defined in Equation (3) as follows:  
\[rcDATA=g\big{(}\big{(}W^{\prime}*zDATA\big{)}+b^{\prime}\big{)} \tag{3}\]  
After dimensionality reduction, the features, now in \"zDATA\", are ready for further processing. These reduced-dimensional data points, derived from the latent space, are crucial for subsequent analytical and modeling tasks.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 5 Environmental Application of Biochar for Wastewater Treatment
### As a Catalyst during Heterogenous Oxidation  
Biochar-based catalysts have been widely used in different systems for the degradation of biorecalcitrant compounds and the remediation of contaminants in wastewater, including catalytic ozonation processes, Fenton like reactions, and photocatalytic systems. [PERSON] et al. [194] tested the catalytic potential of a biochar prepared from pistachio hull biomass and presented a macroporous structure and hydroxyl and phenolic surface functional groups for the ozonation of a water recalcitrant contaminant (reactive red 198 dye). Higher catalytic efficiency was exhibited by the prepared biochar at a pH of 10.0, with a catalytic potential of about 58% and a dye solution mineralization of 71% after a reaction time of 1 h. The excellent ability of carbon materials to activate various oxidants, such as oxygen and hydrogen peroxide, and form reactive oxygen species for the degradation of refractory organic contaminants has been widely reported in the literature [195; 196; 197]. [PERSON] et al. [198] demonstrated that biochars originated from pine needles, wheat, and maize straw can induce OH generation in the presence of oxygen. Authors reported that free radicals in biochar transfer electrons to oxygen to form superoxide radical anion and hydrogen peroxide, which reacts further with free radicals to produce 'OH, OH generated from biochar suspensions was found very efficient to degrade organic contaminants. [PERSON] et al. [199] found that the hydrochar prepared by HTC enhanced Alachlor degradation in the Fe(III)/hydrogen peroxide Fenton-Like reaction by promoting Fe(III)/Fe(II) cycling via electron transfer from biochar to Fe(III). In another study performed by [PERSON] et al. [200], hydrochars were prepared by HTC of two-phase olive mill wastes impregnated with ferric chloride and used as catalysts for the degradation of methylene blue. The results showed that the hydrochar prepared at 250 \({}^{\circ}\)C HTC temperature during 4 h and with the ratio of ferric chloride to biomass of 1.5 has been effectively used as a catalyst in heterogeneous Fenton-Like oxidation, allowing for a degradation efficiency of methylene blue of about 91%.
## 3 The Proposed Routing Mechanism
### CCS-DTN Routing  
After clustering, every node will belong to a cluster. As shown in Algorithm 2, it is assumed that if node \(i\) needs to send messages to node \(j\), there could be only two cases, which are that the two nodes are in the same cluster and that the two nodes belong to different clusters. And when messages are transmitted over clusters, the network coding technique will be used.  
```
INPUT: messages, any node \(i\) OUTPUT: messages are forwarded to the destinations  Upon reception of message \(m\)do  Upon \(i\) meets \(j\)do if\(i\) is the destination node \(d\) of \(m\)then  messageDelivered(\(m\)); if\(i\) is the source node \(s\) of \(m\)then if\(C_{i}==C_{d}\)then  cacheIntraClusterMessage(\(m\)); else  cacheInterClusterMessage(\(m\));  checkForCoding(); if\(i\) is a relay node then if\(m\) is an intra-cluster message then  cacheIntraClusterMessage(\(m\)); else if\(C_{i}==C_{d}\)then  cacheIntraClusterMessage(\(m\)); else  cacheInterClusterMessage(\(m\)); else  cacheInterClusterMessage(\(m\)); else  cacheInterClusterMessage(\(m\)); end if
```  
**Algorithm 2**Routing Phase of the Proposed CCS-DTN
# Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
## 2 Materials and Methods
### Model Selection and Recommendation  
When comprehensively considering the precision, recall, F1 score, and accuracy, three types of models (SVM, RT, and ET) had better performance among the 10 machine learning algorithm models. Their average precision, recall, F1 score, and accuracy were above 0.8, except for the recall of grade-IV surrounding rock. In particular, the recall of the grade-V surrounding rock, which has a great influence on the safety of the tunnel, was above 0.95.  
Thus, these machine learning models based on SVM, RT, and ET have some degree of safety and high practical value.
# Multi-Classifier Pipeline for Olive Groves Detection
## 2 Materials and Methods
### Satellite Imagery Classification  
Table 2 describes the different pipelines that are used and compared in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Phenological Steps** & & **Feile Name** & **Acquisition Date** & **Cloud Cover** \\ \hline Dormancy period after harvesting & \(\text{S2B\_MSIL2A\_20190125T103319\_N0211\_R108\_T32 TLP\_20190125T134253}\) & 25 January 2019 & 6.74\% \\ \hline Flowering period & \(\text{S2A\_MSIL2A\_20190430T103031\_N0211\_R108\_T32 TLP\_20190430T140106}\) & 30 April 2019 & 5.86\% \\ \hline Early-stage of fruit growth & \(\text{S2B\_MSIL2A\_20190724T103029\_N0213\_R108\_T32 TLP\_20190724T135539}\) & 24 July 2019 & 3.67\% \\ \hline Late-stage of fruit growth & \(\text{S2B\_MSIL2A\_20190912T103019\_N0213\_R108\_T32 TLP\_20190912T141218}\) & 12 September 2019 & 0.18\% \\ \hline Harvest season & \(\text{S2A\_MSIL2A\_20191106T103231\_N0213\_R108\_T32 TLP\_20191106T112019}\) & 6 November 2019 & 11.87\% \\ \hline \end{tabular}
\end{table}
Table 1: Earth Observation data selection and their corresponding phenological stage.  
\begin{table}
\begin{tabular}{c c c} \hline
**Pipeline** & **Classifier(s)** & **Description** \\ \hline Baseline pipeline & Random Forest & The pipeline that used one classifier \\ \hline Multi-classifier pipeline 1 & Random Forest + Multi-layer & Pipeline using a multi-classifier system random forest and \\  & Perceptron & a binary classification approach in its second classifier \\ \hline Multi-classifier pipeline 2 & Random Forest + One-class Support & Pipeline using a multi-classifier system, random forest \\  & Vector Machine & and a unary classification approach in its second classifier \\ \hline \end{tabular}
\end{table}
Table 2: The list of pipelines used in this study.
## 4 Experimental results
### Summary  
The main parameter of the principal component aggregation, which determines the tradeoff between the compression accuracy and the communication costs, is the number of principal components to extract. As illustrated in Fig. 10 or 14, the network load incurred by the computation of the principal components quickly increases with their number. The PCAg scheme therefore proves useful only if a few components are required. In such a case, we emphasize that the gains can be dramatic, as was reported in Fig. 10 or 14.  
The amount of information retained by a set of principal components depends on the correlation existing between sensor measurements. We illustrated the method for a compression task using a real-world temperature dataset where measurements had an degree of correlation that can be assumed to be representative of a typical sensor network scenario. An interesting result to recall is that for \(5\) principal components, \(90\%\) of the variance could be retained. This was shown to return a nice approximation of the original signals (Fig. 8), and to reduce the highest network load from about \(100\) packets per epoch to about \(40\) packets per epoch (Fig. 10).  
Regarding the scalability, an interesting property of the PCAg is that the network load is better distributed in the network. This property first prevents congestion issues observed at the root node using the default scheme. Second, as the radio communication is a primary source of energy depletion for a wireless node, it better distributes the lifetime of the nodes in the network. The PCAg therefore provides an interesting framework for dealing with data in multi-hop networks.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 2 Related Works
As a result, it achieves enhanced detection performance and significantly lowers false alarm rates compared to conventional supervised classifiers. The study highlights concern regarding the algorithm's applicability across various IoT environments and underscores the necessity for additional testing to ensure its scalability and robustness. The authors propose investigating advanced semi-supervised learning methods and improving the system's ability to adapt to diverse IoT environments. Furthermore, future research should consider integrating alternative machine learning algorithms and enhancing the system's scalability. In the work [35], [PERSON] et al. present a framework named Cyber Threat Dictionary (CTD), which maps attacks to defense mechanisms, aligning the MITRE ATT&CK matrix with the NIST framework. The CTD comprises a search engine offering attack details and solutions, along with a suggestion component for providing appropriate countermeasures. The current studies offer promising approaches to IoT intrusion detection; however, they also demonstrate significant shortcomings. [PERSON] et al. [26] achieve high accuracy using CNN and LSTM models but encounter extended training durations attributed to large datasets. [PERSON] et al. [27] illustrate vulnerabilities in feature caching stability, affecting consistency when there are changes in dataset content. [PERSON] et al. [28] demonstrate significant accuracy using random forest, yet their approach is constrained in its adaptability to various IoT environments. [PERSON] et al. [29] exhibit superior performance through their ensemble framework, yet they face difficulties in handling the complexity and computational resources necessary for effectively implementing ensemble methods. [PERSON] et al. [30] face difficulties in achieving generalized self-supervised learning across new attack types. [PERSON] et al. [31] achieve high accuracy but still experience significant false alarms in dynamic cyber-attack scenarios. [PERSON] et al. [32] encounter challenges in achieving consistent accuracy in detecting OMM and zero-day attacks. Considering earlier investigations, crafting an efficient intrusion detection and prevention system (IDPS) requires tackling feature engineering for high-dimensional data and choosing potent detectors. In this study, we present a pioneering method employing autoencoders (AEs) and three distinct anomaly detectors, enhancing IDPS efficacy in identifying and mitigating potential security threats to network integrity.