esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Understanding Non-Fungible Tokens
### Storage of NFTs  
Given that digital collectibles by definition represent digital and physical assets, it is deemed necessary to examine where these assets that NFTs represent are stored. In the case of physical assets, the tangible artefact is usually shipped to the NFT collector, an indicative example of which is the NFT trading cards [68]. In the case of digital assets, however, storage is more complex. As a digital asset could be lost forever rather easily (e.g., being mistakenly erased due to human error, or by accident due to hardware damage), so systems have emerged for the persistent storage of assets represented by NFTs. In contrast to the actual tokens, which are stored on blockchains (e.g., on the Ethereum blockchain), the assets they represent (i.e., the digital files) are in most cases stored off-chain, as it would be prohibitively expensive otherwise. Indicatively, in July 2020, when gas fees were also significantly lower than in 2021, the fees to store (i.e., register) a 1 MegaByte image on the Ethereum blockchain cost more than 13,000 USD (i.e., 13.82 USD per KiloByte) [69]. To link the two, i.e., the blockchain-based tokens with the digital assets that they represent, which are stored in external storage systems, NFT platforms utilise different ways of associating the token with the asset; in the simplest of forms, one such reference can be a web link accompanying the token in its metadata.  
There are mainly two types of storage systems for digital assets represented by NFTs, i.e., distributed storage infrastructures and centralised storage solutions. The most-widely used storage system at present, IPFS, is distributed; IPFS is where the assets of leading marketplaces of crypto collectibles such as Rarible.com and Foundation.app are stored by default [70; 71]. On the other hand, an indicative example of a centralised storage solution is the Origin NFT Launchpad (originprotocol.com) platform. When an NFT is created and sold through the Origin NFT Launchpad, although the token transaction takes place on the Ethereum blockchain as normal (as if this NFT was created and sold through the aforementioned marketplaces), the asset represented by this NFT is stored on the company's own centralised servers instead (information provided by Origin Protocol directly, as of June 2021).
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Microstructure Properties of Mortar
#### 3.4.3 Thermogravimetric Analysis  
Thermogravimetric analysis (TGA) aids in quantification of hydration reactions, as various compositions of cement hydrates can decompose at different temperature ranges during the heating process. This enables the calculation of chemically bound water at a specific age, based on the mass loss of different compounds in pastes, providing an indication of the degree of hydration [39; 40].  
Figure 15 shows the TGA results obtained from the investigations carried out on mortar samples with 0%, 10%, and 30% of MGWP after being cured for 7 and 28 days. The TGA results presented in Figure 15 indicate that the mass loss of mortar samples containing MGWP is greater than that of the control mix for both 7 and 28 days of curing, with a more pronounced difference observed at a later curing age. Specifically, the difference in mass loss between the control mortar and mortar containing 10% MGWP is 2% at a curing age of 7 days, whereas it is 6% at a curing age of 28 days. The observed mass loss could be attributed to the release of chemically-bound water compounds and the decomposition of calcium carbonate present in MGWP. Although mortar containing 0% MGWP experiences significantly less mass loss compared to mortar with 10% MGWP, its compressive strength is lower. The high mass loss observed in the 10% MGWP mortar can be attributed to the release of chemically-bound water compounds within the MGWP. However, despite this mass loss, the mortar containing 10% MGWP still exhibits higher compressive strength than the mortar without MGWP when both are exposed to high temperatures. This phenomenon can be explained by the pozzolanic reactions occurring between the 10% MGWP and calcium hydroxide, as described in Section 3.4.1. These reactions result in the formation of additional C-S-H gel, leading to a more tightly packed microstructure, which ultimately contributes to the higher compressive strength, even in the presence of potential mass loss.  
Figure 15: _Cont._
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 1 Introduction
Building upon the collision point matrix template, the SMPM is first introduced to achieve local sparsity of the template, thereby reducing the data scale required for the data-driven collision localization method;
2. Comparative experiments are conducted by constructing SMPMs of various forms and degrees of sparsity, exploring the optimal way to build SMPMs effectively while maintaining high collision localization performance with a reduced data scale;
3. A data-driven collision localization method combining a convolutional neural network (CNN), an echo state network (ESN), and a support vector machine (SVM) is proposed to enable the SMPM to achieve optimal performance in collision localization.
# Framework for the Ensemble of Feature Selection Methods
## 3 Results
### Implementation of the Conceptual Framework
#### 3.3.3 Subsets of Relevant Features  
To facilitate the analysis of the results in the evaluation, the features contained in each dataset were named _Fi_, and the target column was called class.  
Figure 4 shows the subset of features selected by each algorithm on the Sonar dataset. In this test, the features F9, F10, and F12 were considered relevant by at least two selection algorithms. All algorithms selected F11, F36, and F45, and the other features were selected by only one algorithm. Thus, if the selection threshold defined by the user is 0, the set of features selected would be: {F9, F10, F11, F12, F21, F35, F36, F45, F46, F49}.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sonar** & **SPECTF** & **WDBC** \\ \hline SelectKBest & 73.91\% & 63.44\% & 89.47\% \\ RFE & 65.21\% & 68.81\% & 87.71\% \\ Feature Importance & 78.26\% & 72.58\% & 89.47\% \\ F-EFS & 73.91\% & 74.73\% & 92.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results using decision trees.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Features Selected** & **Features Selected** & **\% of Elimination of** & **\% of Elimination** \\  & **by F-EFS** & **in [39]** & **Features by F-EFS** & **of Features by [39]** \\ \hline Sonar & 60 & 10 & 24 & 83.30\% & 40\% \\ SPECTF & 44 & 10 & 19 & 56.70\% & 43.20\% \\ WDBC & 30 & 10 & 10 & 66.70\% & 66.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the EFS method constructed and the results reported in [39].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sonar** & **SPECTF** & **WDBC** \\ \hline SelectKBest & 84.05\% & 53.22\% & 90.35\% \\ RFE & 85.50\% & 53.22\% & 93.65\% \\ Feature Importance & 84.05\% & 59.67\% & 92.10\% \\ F-EFS & 86.95\% & 60.75\% & 93.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results using Logistics Regression.  
Figure 5 shows the subset of features selected by each algorithm on the SPECTF dataset. The Venn diagram indicates that more than one algorithm chose the features F25, F26, and F40. In comparison, the other features were only considered relevant by only one algorithm. Thus, if we assume that the selection threshold defined by the user is 0, the set of features selected would be [F4, F25, F26, F28, F30, F36, F40, F42, F43, F44].  
Figure 6 shows the subsets of features selected by each algorithm for the dataset WDBC. In this result, the features F23 and F24 were chosen by the algorithms Select K Best and Feature Importance, feature F21 by the RFE, and the Feature Importance algorithms, while the rest of the features were selected by only one algorithm. To get all features selected by all FS algorithms, assuming that the threshold defined by the user is 0, the set of features selected would be [F3, F4, F7, F8, F14, F21, F23, F24, F27, F28].  
Figure 4: Sonar.  
Figure 5: SPECTF.
## 4 Discussion
### Challenges
#### 4.1.1 Performance  
As an attacker is a legitimate user of the system, this notion poses the difficulty of drawing a clear line between what is legitimate and what is malicious [112]. Most of the existing approaches used for insider threat detection apply the anomaly detection approach, which is supervised or unsupervised methods that classify small deviations from normal activity patterns in anomaly detection as an abnormality and, thus, classify this abnormality as malicious. However, most of these abnormalities are nonmalicious activities. These methods tend to raise unnecessary false alarms in handling such cases [36]. Thus, traditional approaches suffer from the well-known issue of false positives due to this notion, which makes such approaches difficult to apply in enterprise environments [37]. In other words, reducing false positive and negative alarms for insider threat detections without affecting the detection accuracy remains a major challenge.
# QiOi: Performance Isolation for Hyperledger Fabric
## 4 Design and Implementation
### QiOi Architecture  
QiOi is designed as follows: (1) monitor the performance of Fabric to detect performance interference, (2) dynamically control the _CPU share_ of orderers and peers to minimize scheduling delay. The architecture of QiOi consists of performance monitor and QiOi controller as in Figure 5. QiOi is implemented as a user-level daemon on the host Linux with Hyperledger Fabric-based blockchain network. It adjusts _CPU share_ of the Fabric components while monitoring Fabric performance.  
We assume that the isolation threshold is given via Fabric service manager in Figure 5 because determining the isolation threshold is an orthogonal problem to the performance interference. The isolation threshold is a baseline for detecting performance interference and indicates the maximum throughput of Fabric, which varies depending on Fabric network configuration. Then, the Fabric service manager delivers the isolation threshold to the QiOi controller. The performance monitor periodically collects the actual throughput of Fabric and delivers it to the QiOi controller. The QiOi controller detects performance interference by comparing the isolation threshold with the monitored throughput. Then, QiOi adjusts _CPU share_ of orderers and peers using cgroups. A detailed explanation of the architecture and implementation is as follows:  
* **Performance monitor** periodically collects the Fabric throughput by analyzing Fabric network information in the container log of the peers. The container log is stored in the file [_container id_]-_json.log_ under the directory _/var/lib/docker/ containers/[container id_]. The Fabric network information contains transaction proposal, chaincode verification,  
Figure 5: QiOi architecture.  
and block commit with the timestamp. The performance monitor parses the Fabric network information every second and calculates throughput using the block commit information. Note that the monitoring period in QiOi is also adjustable.  
Throughput is the rate at which transactions are committed by the Fabric network in a time period. This rate is expressed as transactions per second (i.e., tps) [12]. During a period of time from \(t_{i}\) to \(t_{j}\), throughput is calculated as in Equation (3). \(N(t)\) is the total number of committed transactions at \(t\).  
\[throughput=\frac{the\ number\ of\ committed\ transactions}{time\ period}=\frac{N(t_{j})-N(t_{i})}{t_{j}-t_{i}} \tag{3}\]  
* **QiOi controller** adjusts _CPU share_ of orderers and peers when performance interference occurs. The QiOi controller regards a situation when the actual throughput is less than the isolation threshold received from the Fabric service manager as performance interference. Then the QiOi controller adjusts the _CPU share_ of orderers and peers with the algorithm explained in Section 4.3. The adjusted _CPU share_ is applied to CFS.
# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
## 2 Important Concepts and Background  
To assess the relevance of a feature within a given space, we employ the Pearson correlation coefficient (\(\rho\)). This coefficient, ranging between \(-1\) and \(1\), quantifies the linear relationship between two variables. For variables \(A\) and \(B\), each having \(N\) scalar observations, \(\rho(A,B)\) is calculated using the formula in Equation (1), where \(\mu\) and \(\sigma\) represent the mean and standard deviation of each variable [16].  
\[\rho(A,B)=\frac{1}{N-1}\sum_{i=1}^{N}\left(\frac{A_{i}-\mu_{A}}{\sigma_{A}} \right)\left(\frac{B_{i}-\mu_{B}}{\sigma_{B}}\right) \tag{1}\]  
In simpler terms, when \(\rho\) approaches \(1\), it signifies a positive linear relationship because both variables increase together. Conversely, when \(\rho\) approaches \(-1\), it indicates a negative or inverse correlation: when one variable increases, the other decreases. A \(\rho\) close to zero suggests no clear relationship between the two variables.  
Another important concept to bear in mind about class separability is the silhouette coefficient (s). This is a metric to calculate the goodness of a clustering technique and its value ranges from \(-1\) to \(1\). For a point \(i\), let \(a_{i}\) represent the average intra-cluster distance (the distance between points within a cluster) and \(b_{i}\) the average inter-cluster distance (the distance between all clusters) [17].  
According to Equation (2), we can say that the clusters are spaced well apart from one another when s is close to \(1\). If the coefficient is around \(0\), the clusters are indifferent because the distance between cluster centers is not significant. If \(s\) is close to \(-1\), this ultimately means that the clusters are incorrectly assigned. In short, as the second stage of the proposed method is applied, we expect to notice an increase in the \(s\) value.  
\[s_{i}=\frac{b_{i}-a_{i}}{\max(a_{i},b_{i})} \tag{2}\]Classification is required in many real-world problems. Although many classification methods have been proposed to date, such as neural networks and decision trees, there are still some limitations associated with these methods. Some lack generalization ability and are sensitive to an imbalanced number of instances in each class. Also, even when some classification methods outperform others in training sets, they may perform worse when applied to new datasets (test sets), an issue known as overfitting.  
While not a guarantee of better results, a preprocessed dataset with strongly related features and well-defined cluster centers increases the odds of further applications manipulating these data. Taking into account all the points addressed so far, Section 3 dives deeper into the core of the proposed algorithm, detailing each of the two dimensionality reduction stages.
## 3 Design of Proposed Structure
### Placement of Cylindrical Units Based on Radial Distance  
As described previously in Section 3.1, the respective cylindrical rods are placed for defined circular rings. In total, there are seven different rounds which are indicated by seven different colors as Round 1, Round 2, Round 4, Round 5, Round 6 and Round 7, where the cylindrical rods have the permittivity values varying as of 1.99, 1.95, 1.86, 1.72, 1.54, 1.32 and 1.05, respectively. This extends from the center towards the edge where the diameter of cylindrical rods is greater within the center region where as its radius gets decreased toward the edge. For seven different rounds of Round 1, Round 2, Round 4, Round 5, Round 6 and Round 7 the respective cylindrical rods radii are 3.5 mm, 3.5 mm, 2.5 mm, 2.5 mm, 2 mm and 1.5 mm. Similarly, the number of cylindrical rods in Round 1, Round 2, Round 4, Round 5, Round 6 and Round 7 are respectively 1, 4, 16, 24, 32, 51 and 175. The cylindrical rods have a uniform height of 11 mm where the base is 1.5 mm in thickness. The diameter of the base is 100 mm, which is the same as of the two parallel plate's diameters. The regions of the respective circular rings depend on the Luneburg lens principle. The number of cylindrical rods is adjusted in the analytical form so that adjacent cylinders does not overlap to each other. The cylinder height is kept constant to maintain a fixed gap between two parallel plates, which determines the thickness of the considered plates that operate in TE10 mode. Figure 4 shows the respective cylindrical rods arrangement with a feed of the WR-75 waveguide along with its direction of rotation. The rotation of WR-75 is in the counter clockwise direction for 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\) angles.  
Figure 3: Flat concentric circular seven different rings.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 4 Simulation Results and Analysis
### Simulation Results
#### 4.2.3 Confusion Matrix: Proposed Method vs. Comparative Methods  
The confusion matrix is an important index used to measure the performance of classification, which can intuitively show the classification of each sample. Figure 10 shows the confusion matrix results of the four methods. It can be seen that the proposed method has the best classification performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Contents** \\ \hline Modulation type & BPSK, QPSK, 8 PSK, 16 QAM, 64 QAM \\ Data format (I/Q) & 2 \(\times\) 128 \\ Standard deviation of the sampling rate offset & 0.01 Hz \\ Maximum sample rate offset & 50 Hz \\ Carrier frequency offset standard deviation & 0.01 Hz \\ Maximum carrier frequency offset & 500 Hz \\ No. of sine waves in frequency selective fading & 8 \\ Sampling rate & 200 kHz \\ Noise & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset parameters.  
Figure 9: The classification accuracy of the proposed AMC method and comparative AMC methods.
## 3 Influence Factors of \(R_{s}\)
### Influence of Sampling Device on \(R_{s}\)  
#### 3.3.1 Membrane Properties  
The membranes used in passive sampler play a key role in hindering mass transfer of analytes and preventing direct contact of analytes with the adsorbent material during deployment. [PERSON] et al. [102] compared two different passive samplers for the uptake of steroid and the results showed that the chemcatcher-type sampler without diffusion-limiting PES membrane had a greater \(R_{s}\) value than the POCIS. However, considering the uncertainties (such as DOM, suspended particles,  
Figure 4: Summary of the \(R_{s}\) (L/cm\({}^{2}\)/d) of POCIS determined in previous studies within the range of molecular mass (the \(R_{s}\) values were normalized to the surface area of the passive sampler and data source was referenced from 23 previous studies).  
and biofouling) caused by long-term deployment in the water environment, the POCIS with membrane could be better for monitoring trace contaminants. As a channel connecting the aqueous phase with sorbent, the exposed surface area of membrane is a crucial effect factor on the number of compounds accumulated. [PERSON] et al. [103] found a significant positive relationship between exposure area and the \(R_{s}\) of the POCIS.  
The properties of a membrane are crucial in mass transfer of the target compounds. It has been indicated that some chemicals have significant sorption onto the membrane, which results in a slow response for analyte concentrations in fluctuating aqueous phase and exhibits substantial lag times [29; 59; 104; 105]. [PERSON] and [PERSON] [106] characterized the sorption and permeation properties of membranes PES and PTFE by experimental and modelling methods. The results showed that the PES possessed strong sorption for some compounds, owing to its molecular structure with phenyl and sulfonate groups, whereas the sorption of those compounds on the PTFE was generally weak and their mass transfer was controlled by the WBL and the diffusion in membrane pore. Several sorption mechanisms, for instance, \(\pi\)-\(\pi\) cloud, electrostatic, van der Waals' force (VDW), and hydrogen bonding, controlled the behavior of these compounds on both the membrane and internal surface of sorbent [107; 108].
## 2 EO CEP Shifter
### Experiments  
This section presents previously published results demonstrating the validity of our model and giving an example of the performances obtained with the EO shifter. Equation 3 was checked in [17] for LiNbO\({}_{3}\) using a femtosecond 800 nm Ti:S laser source and an f-2f interferometer. The results show a good agreement between theory and experiments. Another approach using spectral interferometry with a broadband laser source [18] was applied to measure CEP shifts and confirmed again, with a better accuracy, the above theory. Stabilization of the CEP (slow loop) of a Ti:S femtosecond laser source was finally demonstrated in [19] with very promising results. The CEP-stable 20W range kHz laser and the EO device for CEP control arrangement is given in Figure 2. In this case, a LiNbO\({}_{3}\) crystal longitudinal EO shifter was used.  
Shot to shot and 10 ms averaged measurement of stabilized CEP drift over 10 min of amplified pulses at 3 W output are compared in Figure 3, with and without slow feedback control.  
Figure 2: Carrier-envelope-phase (CEP)-stable 20 W range kHz laser and LiNbO\({}_{3}\) electro-optic (EO) CEP shifter.
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
## 4 Results
### Groundwater Flow Characteristics
The deviations between the type of curve and measured temperature values, especially in the section of \(0.2<z_{D}<0.4\) in Figure 9 can be explained by the deviations of the centering of the loggers [57] or change of fracture direction or interconnection with other feature sets [27]. The deviation can also be noticed on the EC profile in Figure 6.  
In BH-3, an upward flow with a dip angle of \(-90^{\circ}\) occurred at 138-381 m along a fault of 400-403 m in the basic dyke with a flow velocity of \(-5.27\times 10^{-6}\) m/s, corresponding to the EC anomaly at 381-400 m.  
In BH-4, an upward flow with a dip angle of approximately \(65^{\circ}\) occurred 567-600 m along a fault of 501-502 m in the sandstone/mudstone with a flow velocity of \(-8.07\times 10^{-5}\) m/s, corresponding to the EC anomaly at 585-598 m. Alternatively, downward flows with dip angles of \(30^{\circ}\)-\(90^{\circ}\) occurred in three sections: 303-350 m along the fault of 281.9-282 m in the crystal tuff with a flow velocity of \(2.17\times 10^{-4}\) m/s, corresponding to the EC anomaly at 301 m; 394-480 m along the fault of 402-402.1 m in the crystal tuff with a flow velocity of \(7.96\times 10^{-5}\) m/s, corresponding to the EC anomaly at 394-397 m; and 873-967 m along the fracture zone of 1000-1127 m in the sandstone/mudstone with a flow velocity of \(4.28\times 10^{-5}\) m/s, corresponding to the EC anomaly at 850 m. Figure 9 shows the upward flow in the zone of 567-600 m, with \(\beta=-3.39\) (RSS = 0.010). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rock Types**} & \multicolumn{3}{c}{**Thermal Diffusivity (mm\({}^{2}\)/s)**} & \multicolumn{3}{c}{**Thermal Conductivity (W/mK)**} \\ \cline{2-7}  & [53; 54] & [55] & **Measured Data** & [53; 54] & [55] & **Measured Data** \\ \hline Dacite & - & 1.06 & - & - & 2.73 & - \\ Sandstone/mudstone & 0.80–0.96 & 0.80–1.51 & 1.22 & 2.04–2.47 & 2.04–3.86 & 3.01 \\ Basic dyke & - & - & 0.74 & - & - & 2.86 \\ Rhyolite & 1.40 & 1.20 & 1.51 & 3.59 & 3.08 & 3.87 \\ Tuff & 0.80 & 1.17 & - & 2.05 & 3.00 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of reference and measured values of thermal diffusivity and thermal conductivity for different rock types.