esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Platform
### Reliability Verification Results  
The reliability of the wheel sinkage detection method was verified by conducting qualitative and quantitative analyses of the perspective transformation and Hough circle detection results. To analyze the result of Hough circle detection, a circular sticker with spokes that corresponds to the wheel surface's dimensions is employed, as depicted in the lower-left corner of Figure 6. The precise pixel coordinates of the wheel center should be determined by analyzing the wheel surface stickers and spokes and then compared  
Figure 7: Results of eight different wheel sinkage depth tests: (**a**) Origin image; (**b**) WCP results; (**c**) WTB results; (**d**) detection results.  
with the results obtained from the Hough circle detection, which are shown in Figure 7b. Table 3 displays the results, and Figure 8 illustrates the pixel error, actual error, and error as a percentage of the radius for each test. According to the results, the pixel coordinate error of the Hough circle detection result is found to be below 10 pixels, with the actual error falling within 2 mm. In comparison to an actual wheel with a diameter of 160 mm, the error as a percentage of radius does not exceed 2%. The average pixel error for the eight experiments was 3.62 pt, with an actual error of 0.77 mm and a percentage error of 0.96% relative to the wheel radius. The method exhibits a high degree of accuracy and reliability.
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 3 Methodology  
### Assumptions  
As the excavation process is complex, the superposition method with multiple factors is widely used for the calculation of ground deformation [61; 70]. In this study, ground movements caused by bulkhead additive thrust, friction on jacking machine, grouting pressure, and ground loss were calculated separately and subsequently combined. To simplify the theoretical analysis, the following assumptions will be used: (1) the object is a three-dimensional homogenous isotropic elasticity with the jacking machine and segments; (2) the ground movement is caused by the construction of pipe-jacking rather than by change in the volume of the soil; (3) the ground loss is uniform along the tunnel axis and the profile is considered to be oval-shaped; (4) the grouting pressure is uniform along radial direction and tunnel axis; (5) the effect of grouting on ground loss was ignored. Based on these assumptions, Figure 3 shows the schematic of pipe-jacking and the mechanical condition of surrounding soils. \(R_{8}\) is the external radius of segment.
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
## 4 Algorithms for Waveform Optimization
### Closed-Form Solution of Transceiver Waveform  
For the closed-form solution, a candidate algorithm is the so-called Match Filter Bound given in [40], a well-known optimization algorithm. Herein, we optimize the transmit filters and the receive filter is matched with the transmitted waveform. The upper bound of the SINR for fixed transmit filters is obtained by \(\mathbf{h}=\
u\mathbf{R}_{v}^{-1}\mathbf{T}\mathbf{f}\), where \(\
u\) is a scalar constraint for the power. Consequently, the optimization problem is expressed as  
\[\begin{split}\underset{\mathbf{f}}{\max}&\mathbf{f}^ {\dagger}\mathbf{T}^{\dagger}\mathbf{R}_{v}^{-1}\mathbf{T}\mathbf{f}\\ \text{subj. to}&\|\mathbf{f}\|^{2}\leq 1.\end{split} \tag{22}\]  
This is a notable Rayleigh quotient given in [40]. The optimization solution of vector \(\mathbf{f}\) is reported in [18] and it is the principal component of \(\mathbf{T}^{\dagger}\mathbf{R}_{v}^{-1}\mathbf{T}\) and the maximization of the objective function which is achieved by the dominant eigenvalue of \(\mathbf{T}^{\dagger}\mathbf{R}_{v}^{-1}\mathbf{T}\), denoted as \(\lambda_{1}(\mathbf{T}^{\dagger}\mathbf{R}_{v}^{-1}\mathbf{T})\).
## 3 Boundary Conditions
### Definitions  
The following concepts are used in this research:  
Reflect boundary condition: This type of boundary usually used as particle boundary condition at wall, symmetry, and axis boundaries [33]. It should be stated that the particle rebounds off of the boundary in regard with a variation in its momentum as specified by the coefficient of restitution. Coefficient of restitution is defined by  
\[\text{Coefficient of restitution}=\frac{V_{2,n}}{V_{1,n}} \tag{24}\]  
where \(V_{1,n}\) and \(V_{2,n}\) are the particle velocities before and after particle-wall collision. This boundary has a restitution coefficient of 1.  
Trap boundary condition: This type of boundary usually used as particle boundary condition at wall boundaries [33]. The trajectory calculations are terminated and the destiny of the particle is saved as trapped. This boundary has a restitution coefficient of 0.  
Escape boundary condition: This type of boundary usually used as a particle boundary condition at all flow boundaries containing pressure and velocity inlets, pressure outlets, etc. [33]. The particle is considered as having \"escaped\" when it collisions the boundary in question. Trajectory calculations terminate for this type of boundary.  
Deposition: The deposition is defined as the ratio of the number of deposited (trapped) particles on all involved surfaces containing obstacle and duct surfaces to the number of particles injected to the duct at specific time.  
Concentration (Solid volume fraction): The void fraction of each cell in discrete particle model can be determined by  
\[\varepsilon=1-\frac{\sum V_{i}}{\Delta V} \tag{25}\]where \(V_{i}\) is the volume of \(i\)th particle in the cell. Moreover, the summation is taken over all the particles in the cell volume \(\Delta V=\Delta x\Delta yd_{p}\). This means that the two-dimensional domain is regarded as a pseudo three-dimensional one with a thickness of one particle diameter \(d_{p}\)[34].  
Parcel: In DPM, it is not possible to track all physical particles. Instead, representative particles or parcels should be tracked. A particle in each parcel is representative of the entire particles on that parcel and motion and heat exchange equations are solved only for this particle and extended to others. After solving the equations for particle, a Gaussian distribution function is used to make the connection between the particle and parcel parameters with  
\[\theta_{parcel}=\sum N_{particle}G_{w}\theta_{particle} \tag{26}\]  
\[G_{w}=\left(\frac{a}{\pi}\right)^{\frac{3}{2}}\exp\left(-a\frac{\left|x_{prcel }-x_{particle}\right|^{2}}{\Delta x^{2}}\right) \tag{27}\]  
where \(\theta\) is particle or parcel variable. \(G_{w}\) and \(N\) denote the Gaussian function and number of particles.
## 3 Data Processing Methods
### GB-SAR Deformation Detection with SAR Images
#### 3.4.3 Observation Geometry Analysis  
When the corner reflector is displaced, the slant range between it and the CBS alters, resulting in a corresponding change in the phase of the echo signal received from the corner reflector. The magnitude of this phase change is contingent upon the projection of the displacement along the LOS of the CBS. The geometric cross-section diagram illustrating the CBS observation is presented in Figure 8, depicting the displacement direction of the corner reflector as it moves horizontally along the ground towards the CBS.  
Figure 7: The influence of temperature, atmospheric pressure, and relative humidity on (**a**) atmospheric phase and (**b**) deformation monitoring.  
0 illustrates the actual observation geometry of the outdoor experiment, revealing that the displacement direction of the corner reflector (horizontal) is not aligned with the LOS of the CBS (with an incidence angle greater than horizontal). In fact, the deformation values detected by the CBS represent the projection of the corner reflector's displacement onto the LOS (referred to as the actual LOS deformation value). Therefore, to facilitate comparison with the detected LOS deformation values, the actual LOS deformation values (\(d_{actLOS}\)) were calculated based on the corner reflector's displacement (\(d_{actHOR}\)) and the incidence angle (\(\theta_{in}\)); the relationship between them is as follows:  
\[\cos\theta_{in}=\frac{d_{actLOS}}{d_{actHOR}} \tag{19}\]
## 4 Light Attenuation
### Mechanical Collisions  
The software also tests for mechanical collisions between the heliostat mirrors or with the ground. For each mirror vertex with \(x\),\(y\),\(z\) coordinates, the \(z\) coordinate is compared with the soil \(z\) coordinate for that \(x\),\(y\) location. Each heliostat mirror can be represented by a sphere, with a radius going from the center to a vertex. If this sphere intercepts the sphere of a neighbor mirror, a collision occurs. Unlike shading and blocking, which need to be tested only after each heliostat alignment, mechanical collisions must be tested throughout the mechanical rotation process (fortunately, this is a quick test).
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 3 Reversible Colour Transforms (RCTs)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transform** & **Forward RCT** & **Reverse RCT** & **Reference** \\ \hline RDgDb & \(\left[\begin{array}{c}R\\ D_{g}\\ D_{b}\end{array}\right]=\left[\begin{array}{ccc}1&0&0\\ 1&-1&0\\ 0&1&-1\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]\) & \(\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1&0&0\\ 1&-1&0\\ 1&-1&-1\end{array}\right]\left[\begin{array}{c}R\\ D_{g}\\ D_{b}\end{array}\right]\) & [PERSON] et al. [45] \\ \hline LDgEb & \(\left[\begin{array}{c}L\\ D_{g}\\ E_{b}\end{array}\right]=\left[\begin{array}{ccc}1/2&1/2&0\\ 1&-1&0\\ 0&1&-1\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]\) & \(\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1&1/2&0\\ 1&-1/2&0\\ 1&-1/2&-1\end{array}\right]\left[\begin{array}{c}L\\ D_{g}\\ D_{b}\end{array}\right]\) & [PERSON] et al. [45] \\ \hline YA2 UA2 VA2 & \(\left[\begin{array}{c}Y_{A2}\\ U_{A2}\\ V_{A2}\end{array}\right]=\left[\begin{array}{ccc}0&1&0\\ 0&-1&1\\ 1&-1&0\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]\) & [PERSON] et al. [45] \\ \hline YA6 UA6 VA6 & \(\left[\begin{array}{c}Y_{A6}\\ U_{A6}\\ V_{A6}\end{array}\right]=\left[\begin{array}{ccc}1&0&0\\ -1&0&1\\ -1&1&0\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]\) & [PERSON] et al. [45] \\ \hline YA7 UA7 VA7 & \(\left[\begin{array}{c}Y_{A7}\\ U_{A7}\\ V_{A7}\end{array}\right]=\left[\begin{array}{ccc}0&0&1\\ 0&1&-1\\ 1&0&-1\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]\) & [PERSON] et al. [45] \\ \hline mYA2 UA2 VA2 & Modular Arithmetic Variant of YA2 UA2 VA2 & \\ \hline mRDgDb & \(R=R\) & \(R=R\) & \\ mDg = \((R-G)smod2^{N}\) & \(G=(R-mDg)smod2^{N}\) & [PERSON] et al. [45] \\ \hline mLDgEb & \(mDg=(R-G)smod2^{N}\) & \(R=(mL+[mDg/2])mod2^{N}\) & \\ mL = \((R-[mDg/2])mod2^{N}\) & \(G=(R-mDg)mod2^{N}\) & [PERSON] et al. [45] \\ \hline mLDgDb & Modular Arithmetic Variant of LDgDb & \\ \hline RCT & \(\begin{array}{c}Y=\left[\frac{R+2G+B}{4}\right]\\ U=R-G\\ V=B-G\end{array}\) & \(G=Y-\left[\frac{U+V}{4}\right]\) & \\ \hline mRCT & Modular Arithmetic Variant of RCT & [30] \\ \hline YCoCg-R & \(\left[\begin{array}{c}Y\\ C_{g}\\ C_{O}\end{array}\right]=\left[\begin{array}{ccc}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1&-1&1\\ 1&1&0\\ 1&-1&-1\end{array}\right]\left[\begin{array}{c}Y\\ C_{g}\\ C_{O}\end{array}\right]\) & JPEG XR Standard \\ \(\left[\begin{array}{ccc}1/4&1/2&1/4\\ -1/4&1/2-1/4\\ 1/2&0&-1/2\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of RCTs and their forward and reverse equations.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 7 Overcoming the Leakage via the Adaptive Quantum Thermal Bath (adQTB)
### Application to Hydrogen-Bonded Systems: Liquid Water  
In a very recent study, liquid water was simulated using adQTB [18] in its adaptive random force version (i.e., adapting \(\gamma_{r}(\omega)\) while keeping the friction Markovian with a constant coefficient \(\gamma\)). Different observables such as average energies and radial distributions were compared to PIMD simulations and shown to closely follow this reference technique (see Figure 12). Constant-pressure simulations were also performed to evaluate the density and the vaporization enthalpy as a function of temperature, in good agreement with the path integral calculations, at a computational cost comparable with that of classical simulations.  
Water is a particularly challenging problem for the (ad)QTB method. Indeed, it was shown in the literature that in hydrogen-bonded systems, two opposite NQEs are in competition. On the one side, the stretching zero-point motion tends to strengthen hydrogen bonding, while on the other, the bending zero-point energy weakens it [82,83]. In liquid water, these two effects almost cancel each other, so that overall NQEs remain limited. Capturing this subtle balance is therefore essential to reproduce the properties of water accurately. In the standard QTB method, the molecular structure of water is strongly washed out by the ZPEL, as illustrated in Figure 12, but the adaptive procedure restores the correct distribution of energy, including zero-point contributions, and hence recovers radial distribution functions very close to the PIMD ones.  
Figure 13: Panel (**a**) Adapted \(\gamma_{r}(\omega)\) for the Ne\({}_{13}\) clusters of [17] at 4 K (blue) and 18.3 K (orange). Panel (**b**) Radial pair distribution function \(g(r)\) for the QTB (dotted red) and adQTB (dash-dotted blue) compared to classical (dashed grey) and PIMD (solid black) at 4 K (results converged using 32 beads). The friction coefficient was set to \(\gamma=2\) ps\({}^{-1}\) in all simulations.  
Interestingly, this study also confirms the potential interest of the adQTB for the inclusion of NQEs in the estimation of vibrational spectra: the infrared absorption spectrum of water was computed and compared to thermostated ring-polymer molecular dynamics (TRPMD), one of the most common approximations derived from the path-integral formalism for the calculation of time-dependent properties. The adQTB provides comparable accuracy to TRPMD, and it even captures better the high-frequency part of the infrared spectrum, where overtone and combination bands appear, whose amplitude the TRPMD method is unable to reproduce [16; 84; 85].
###### Abstract
It divides the interval into intervals to reflect the input and output sequences. While the length of input and output sequences is long, the interval that must be divided grows, the borders between intervals blur, it is difficult to distinguish when mapping, and mistakes are common. It is, therefore, necessary to propose a distribution matcher with better performance even when the output symbol length is short. A multiset-partition distribution matcher (MPDM) has been proposed to reduce the needed output lengths and can be considered as layered CCDM operations [20; 21]. MPDM also adopts the idea of pairwise optimization; it needs to build a Huffman tree structure to index different complementary pairs and uses serial processing for the input data. The coding process is complex and error-prone. Many distributed matcher techniques with a parallel structure have been proposed to address the extremely serial coding approach of CCDM, such as bit-level distribution matcher [22; 23], parallel-amplitude distribution matcher [24], and Hierarchical distribution matcher [25]. In addition, widely used enumerative sphere shaping (ESS)[26] and shell mapping (SM) are notable sphere shaping (SpSh) methods, which are different from CCDM. The SpSh considers amplitude sequences in a sphere. ESS orders these sequences lexicographically, while SM orders them based on their energy. Compared with ESS and SM, CCDM has similar latency and is significantly superior in terms of storage complexity [27]. In this work, we propose a parallel distribution matcher based on CCDM to improve the performance of CCDM at short block lengths. Contrary to the constant composition of the CCDM, this DM can output variable composition by using pairwise optimization and parallel structure. The proposed structure has a lower rate loss than CCDM for short output lengths (n less than 100), and the output block lengths can be lowered by up to 30% with the same rate loss. When the value of generalized mutual information is the same as it is when using CCDM, the OSNR tolerance of the PS-64 QAM signal using the proposedapproach can be enhanced by 0.12 dB and the block length can be reduced by 40%. At the same time, the transmission distance in the standard single-mode fiber link becomes longer.
## 2 The Ubiquitous Sensor Network Platform
### Field Tests  
Built on NGN infrastructures, the USN-Platform offers scalable message routing mechanisms across several administrative domains, to support smart device communications in similar fashion to how it is done for the millions of user terminals any Telco Platform is designed to deal with. The use of NGNs supporting context-aware applications in connected car scenarios has already shown improved performance in routing protocols compared to traditional vehicular communications over Vehicular Ad-hoc Networks (VANETs) [24]. According to [PERSON] _et al._, the use of NGNs to provide backup for data communication, as well as efficient mechanisms for the dissemination of relevant information, can provide a 20% increase in packet delivery ratio compared to VANET protocols, under situations of constrained mobility (e.g., streets without enough traffic density for uninterrupted V2V communication).  
We have already evaluated our Telco USN-Platform (without the semantic extension, which will be discussed in the next section) in several field trials for smart metering, smart places, and environmental monitoring [25]. Particularly noteworthy is its current deployment under the SmartSantander (www.smartsantander.eu/) EU Project: an IoT testbed facility that is being developed in a Smart City environment aiming to integrate up to 20,000 IoT devices by the end of 2013. So far it has integrated around 2500 sensors of different types providing mainly environmental (temperature, relative humidity, CO, noise level, _etc._) and parking information (sensing whether each particular parking space is free or taken). The USN-Platform implementation used in the development of services for SmartSantander is based on a scalable distributed architecture currently composed of a reduced set of specialized servers, and is suitable to provide horizontal support for multi-service deployments. This set-up is capable of managing information from more than 20,000 sensors providing observations every minute.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 6 Discussion
### Salminen Dataset  
Figure 12 and Table 9 provide a comparative analysis of performance metrics for the Salminen dataset. It compares FakeDetectionGCN to traditional models such as the crowd-sourced model, NBSVM, fakeRoBERTa, reported by [35] and the best-performing ML classifier.  
Figure 11: Bar Graph of model performance on OTT dataset (80%) [2].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **F1 Score** \\ \hline CNN-LSTM [25] & 95.5 & 89.5 & 87.0 \\ MLP with BERT & 85.93 & 85.38 & 86.04 \\ MOB-DeBERTa [28] & 87 & 87 & 87 \\ FakeDetectionGCN & 88.12 & 90.5 & 87.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative analysis on the OTT dataset (80%).  
The crowd-sourced model shows significantly lower performance. It has an accuracy of 55.36%, a precision of 77.78%, and an F1 score of 64.24%. These figures establish a basic benchmark for model performance on this dataset. In contrast, the NBSVM and fakeRoBERTa models exhibit exceptionally high performance. The NBSVM achieves an F1 score of 95%. fakeRoBERTa scores 97%.  
FakeDetectionGCN demonstrates robust performance. It has an accuracy of 92.3%, a precision of 93%, and an F1 score of 91.5%. The AUC of 97% indicates superior capability in distinguishing between classes. This reflects the model's effectiveness in handling diverse and complex scenarios. In particular, the performance of FakeDetectionGCN is notably superior to that of the OpenAI model. The OpenAI model reported lower scores in all metrics, including an accuracy of 83% and an F1 score of 82%.  
[PERSON] et al. [35] also tested their model's performance on the OTT dataset to evaluate its effectiveness in detecting human-written fake reviews. Figure 13 compares the AUC scores reported by [35] against FakeDetectionGCN.  
It can be seen that the fakeRoBERTa and NBSVM models had near-perfect accuracy on the Salminen dataset, but their performance dropped drastically on the OTT dataset. This shows that their models are only effective in detecting computer-generated fake reviews and fail to detect human-written fake reviews accurately. In contrast, FakeDetectionGCN shows consistent and robust performance across all variations of the two datasets. The recent MBO-DeBERTa [28] shows much better results for the Salminen dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline Crowd [35] & 55.36 & 77.78 & 54.71 & 64.24 & 54.66 \\ NBSVM [35] & 95.82 & 95 & 95 & 95 & 95.76 \\ OpenAI [35] & 83 & 83 & 82 & 82 & 83.33 \\ fakeRoBERTa [35] & 96.64 & 97 & 97 & 97 & 96.62 \\ MLP with GloVe & 89.3 & 89.2 & 89.4 & 89.34 & 95.2 \\ MOB-DeBERTa [28] & 98 & 98 & 99 & 98 & – \\ FakeDetectionGCN & 92.33 & 93.33 & 91.52 & 92.4 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis on the Salminen dataset (100%).  
Figure 12: Bar graph of model performance on Salminen dataset (100%) [35].
# Therapeutic Applications of Halloysite
## 2 Nanoclay Types
### Bentonite  
Bentonite is an aluminum and phyllosilicate clay formed by weathering volcanic ash by water. Montmorillonite is a significant component of bentonite in addition to feldspar and quartz [11]. Being hydrophilic makes bentonite a very absorbent clay. Exfoliation of sodium and potassium salts from bentonite might result in plates 1 nm in thickness [11]. Bentonite being absorptive would make a suitable wound dressing additive. As a nanoclay in combination with scaffold materials, Bentonite has been studied for hemostatic effect in wound healing [20; 21; 22; 23]. Bentonite or any other nanoclay material is a cost-efficient alternative to biological hemostatic agents such as fibrin, as biological hemostatic agents cost more in production and purification [24; 25]. Since Bentonite has montmorillonite as its primary component, it has also been explored to manufacture scaffold materials used for skeletal tissue engineering applications. It would improve the mechanical properties of the soft scaffolding materials such as hydrogels [25; 26; 27]. Montmorillonite layers and sheets in bentonite make it a suitable nanoclay for drug delivery [28; 29]. Its use in sustained and targeted drug release for targeted chemotherapy has also been investigated [30; 31; 32].