esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 4 Analysis and Results
### Analysis 5: Impacts of a Dual-Frequency Solution
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Case 1, Baseline 1** & **East [m]** & **North [m]** & **Up [m]** \\ \hline \(GPS\), \(L1\) & 0.1540 & 0.1366 & 1.0834 \\ \(GPS\), \(L1\) + \(L2\) & 0.1540 & 0.1359 & 1.0823 \\ \(GPS\) + Gal, \(L1\) & 0.1412 & 0.0687 & 0.6971 \\ \(GPS\) + Gal, \(L1\) + \(L2\) & 0.1302 & 0.0436 & 0.6404 \\ \(GPS\) + GLO, \(L1\) & 0.0040 & 0.0148 & 0.0149 \\ \(GPS\) + GLO, \(L1\) + \(L2\) & 0.0045 & 0.0145 & 0.0158 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Standard deviation from known position in the east, north, and up direction for PPK solutions using different constellations and frequencies during the jamming incident in Case 1, baseline 1. Looking at Figure 16, though, it is clear that the difference between the single- and dual-frequency solutions is mostly noticeable following the start of the HIP. As with jamming Case 1, it is clear that using two frequencies mostly provides two benefits for PPK/RTK positioning:  
1. In periods with heavy jamming, and only a few available satellites, a dual-frequency solution performs better than a single-frequency solution. 2. Following a heavy jamming incident, a dual-frequency solution quickly recovers, while the single-frequency solution seems to take a while in reacquiring a solid position fix. Figure 15: Comparison of positioning performance of single- vs. dual-frequency GPS + Galileo solutions for Case 1, baseline 1. Top: 3D positioning error. Bottom: horizontal positioning—the true position of the rover station is (0,0). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case2, Baseline 2** & **East [m]** & **North [m]** & **Up [m]** \\ \hline _GPS + Gal, L1_ & 0.0338 & 0.0158 & 0.1107 \\ _GPS + Gal, L1_ + L2_ & 0.0208 & 0.0122 & 0.0722 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Standard deviation from known position in the east, north, and up direction for PPK solutions using GPS + Galileo and one or two frequencies in Case 2, baseline 2. Summary: It makes sense to use two (or more) frequencies in RTK positioning solutions during jamming incidents. However, with the processing software used for these analyses, benefits are few and limited to some specific circumstances.
## 5 Experimental Validation
### Experiments in the Real Environment  
#### 5.2.1 Experimental Platform  
A landing line test platform was constructed to verify the effectiveness of the landing line method in the real environment, and consisted of three main components: a test stand,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **X (m)** & **Y (m)** & **Z (m)** \\ \hline RMSE & 0.0426 & 0.0345 & 0.0781 \\ Error at landing & \(1.4\times 10^{-4}\) & \(-2.0\times 10^{-3}\) & \(5.0\times 10^{-3}\) \\ Maximum error & 0.47 & 0.06 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Velocity errors between the planned and actual trajectories.  
Figure 14: Velocity variation and error of the FPLIR during trajectory tracking of landing.  
an FPLIR, and a ground control station. The landing test stand comprised a power line, a slope adjustment device, and a protection device. One end of the power line was fixed onto the frame, and the other end was fixed onto the slope adjuster device, which was used to simulate the slopes of overhead transmission lines. The protective device was there to prevent accidental injuries caused by failure of the inspection robot during testing. The overall size of the landing line test platform was \(6.4\times 5.8\times 3.8\) m (length, width and height), as shown in Figure 15a, where the ends of the power line were fixed at a height of 2.6 m on the experimental stand, and the lowest point was 2.5 m above the ground. To perform the landing line task, the FPLIR was equipped with visual sensors and an onboard computer to carry out the autonomous landing method. Moreover, the monitor at the ground control station showed the status information of the FPLIR and the landing line process, as shown in Figure 15b,c. The FPLIR weighed 38 kg (including all sensors), and the rotational inertia was \(\mathrm{I_{xx}}=5.6\) kg\(\cdot\)m\({}^{2}\), \(\mathrm{I_{yy}}=8.5\) kg\(\cdot\)m\({}^{2}\) and \(\mathrm{I_{zz}}=5.6\) kg\(\cdot\)m\({}^{2}\). The binocular resolution of the camera was set at 1280 \(\times\) 720 pixels, and VINS-Fusion was utilized as the VIO for global localization. All software modules were run in real time on the ROS platform.
## 4 Discussion
After identifying the most suitable reconstructions, further investigation was conducted on these two reconstructions and two additional ones with the same parameters but incorporating full MC collimator modeling. Figures 8 and 9 demonstrate that the inclusion of full MC collimator modeling enhances the values of both \(Q_{\mathrm{H}}\) and ARC. A significant challenge in \({}^{166}\)Ho SPECT/CT image acquisition arises from the limitations of conventional scatter correction techniques, which are optimized for discrete energy spectra. This issue is particularly notable due to the continuous Bremsstrahlung spectrum of \({}^{166}\)Ho. The use of Hybrid Recon(tm), which incorporates scatter modeling within the detector and collimator, represents a valuable advancement in improving the quantification of \({}^{166}\)Ho images, as verified in this study. However, despite the improved results achieved with full MC collimator modeling, a significant limitation is the extended computation time (processor used: 12 th Gen Intel(r) Core(tm) i7-12700K; no GPU acceleration supported by the MC algorithm), which is often impractical in clinical settings. For instance, reconstructing 5 iterations with 15 subsets required approximately 2 h, while reconstructing 10 iterations with 8 subsets took nearly 5 h when full MC collimator modeling was applied. Therefore, using 5 iterations with 15 subsets or 10 iterations with 8 subsets without incorporating full MC collimator modeling remains a highly suitable and practical choice for patient data. Finally, as observed in Figure 9A, Equation (7) provides a satisfactory fit for the data from both methods. However, it generally aligns better with the second method, which is expected since Equation (7), as proposed by [17], was specifically designed for determining ARC values using the second method. Additionally, Figure 9A highlights a clear limitation of using only spheres with diameters up to 31 mm, as the recovery curve fails to reach a plateau. This limitation should be considered in future studies as larger-diameter spheres become available. In summary, multiple methods for determining ARC values are available. The appropriate approach to performing PVE correction on small volumes depends on their specific usage context and the type of SPECT/CT reconstruction applied. Another significant insight from the ARC values analysis was the substantial potential underestimation of activity in small volumes caused by PVE. This highlights the critical need to account for and rectify PVE in local absorbed dose calculations.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### Random Forest Regression for Pilot Overhead Reduction  
In this section, we evaluate the performance of random forest regression SVDHB. The training model utilized features including the number of transmitter and receiver antennas, data streams, pilot bits, data symbols, and channel capacity.  
As depicted in Figure 11, the random forest regression SVDHB system achieves pilot overhead reduction comparable to the linear regression.  
Figure 10: Linear Regression SE vs. SNR.  
Figure 9: Linear Regression BER vs. SNR.  
Random forest regression outperforms k-clustering and linear regression in terms of BER performance. Figure 12 illustrates this improvement, with a significant 16% reduction in BER at \(-10\) dB SNR compared to previous methods. The system's robustness is further evident in its ability to maintain lower BER levels across a wide SNR range. These results highlight the potential of random forest regression for enhancing the reliability of wireless communication systems. It is also noted that the system accuracy reaches 99%, which is a major improvement over linear regression.  
Figure 13 demonstrates that the proposed system, enhanced with random forest regression, maintains comparable spectral efficiency to the linear regression approach.  
Figure 11: Random Forest Regression Pilot Overhead.  
Figure 12: Random Forest Regression BER vs. SNR.
# A Split Ring Resonator-Based Metamaterial for Microwave Impedance Matching with Biological Tissue
###### Abstract  
A metamaterial lens based on a split ring resonator (SRR) array has been designed and optimized to improve the focusing and the penetration depth in human biological tissue of a microwave beam irradiated by a substrate integrated waveguide (SIW) cavity backed patch antenna. The impedance matching of the antenna loaded with human tissue is strongly improved. The simulations have been performed by using CST Microwave Studio(r). A prototype of the device has been fabricated with the printed board circuits (PCB) process and has been characterized using a Network Analyzer and an antenna measurement system in anechoic chamber. A novel microwave applicator for hyperthermia therapy of skin cancer could be developed. The performances of the proposed applicator have been evaluated in terms of measured \(S_{11}\) scattering parameter modulus and simulated power loss density. The obtained results indicate that an SRR-based metamaterial is a promising solution for external microwave applicators to employ in dermatology.  
split ring resonator (SRR); metamaterials; substrate integrated waveguide (SIW); planar antennas; microwave devices +
Footnote †: journal: Applied Physics Letters
## 5 Autonomous Navigation Technology
### Path Planning  
Path planning technology is important for autonomous navigation of underground intelligent LHD [3]. When the scraper finishes shoveling the ores, there must be a planned collision-free path from the starting point to the destination, through which the distance is short, the transportation is efficient and energy is saved. According to the degree of access to environmental information, they can be divided into global path planning methods and local path planning methods [31], as shown in Figure 9 in detail.  
\begin{table}
\begin{tabular}{c c c} \hline
**Positioning Method** & **Advantage** & **Disadvantage** \\ \hline Dead Reckoning & It can achieve high accuracy with low cost in the short term & Errors will accumulate over a long period of time \\ Inertial Navigation & It is unaffected by external factors and shows good concealment & Errors will accumulate over time and the equipment are expensive \\ UWB Positioning & It is insensitive to channel fading, with simple system and high positioning accuracy & Multiple base stations are required, which is costly \\ Visual Positioning & It shows high positioning accuracy & Roadway dust, light intensity and other environmental factors affect the positioning \\ Information fusion positioning & It is extensively applied, with high positioning accuracy & The cost and calculating complexity increase \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of positioning technologies.  
Figure 9: Path planning methods for underground intelligent LHD [12; 85; 86].
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Advantage** & **Disadvantage** \\ \hline Correlation-based feature extraction & Simple and easy to implement, provides insight into pairwise relationships & Assumes linearity of relationships and may not capture higher-order relationships \\ \hline Statistical feature extraction & Can capture various statistical properties of the data, easy to compute & Assumes that the data follows a specific statistical distribution, may not capture nonlinear relationships \\ \hline Fourier Transform-based feature extraction & Captures periodic patterns in the data, efficient to compute & Assumes that the data is stationary and has a fixed frequency, may not capture non-periodic patterns \\ \hline Wavelet Transform-based feature extraction & Captures non-stationary patterns in the data, efficient to compute & The choice of wavelet function and level of decomposition can be subjective and impact results \\ \hline Neural Network-based feature extraction & The choice of wavelet function and level of decomposition can be subjective and impact results & Requires significant computational resources, sensitive to the choice of hyperparameters and architecture \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method with other existing feature extraction methods. **Funding:** This publication has emanated from research conducted with the financial support of the Science Foundation Ireland Research Professorship grant to [PERSON] [Grant number 15/RP/2765]. For Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. [PERSON] acknowledges financial support from the University of Birmingham Dynamic Investment Fund. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Raw data supporting the conclusions of this manuscript is made available by the corresponding author at [[https://github.com/italha-d/Stress-Predict-Dataset](https://github.com/italha-d/Stress-Predict-Dataset)]([https://github.com/italha-d/Stress-Predict-Dataset](https://github.com/italha-d/Stress-Predict-Dataset)) (accessed on 25 October 2022). **Conflicts of Interest:** All the authors declare that they have no conflict of interest in relation to this work.
# The BCL Method for DSSC: Basis and Applications +
## 5 The \(\mathbf{I_{T}}\) Parameter: A Measure of the Electronic Density on the Anchor Group  
In the previous chapter, two mechanisms for electronic transfer where proposed. In the first mechanism, the electronic injection was proposed to occur from a hot excited state in which the absorbed energy is directed towards the ligands, and where no vibrational deactivation is observed. In the second mechanism, after vibrational deactivation, the thermally balanced excited state is reached and, from there, by means of the anchor group, electronic injection towards the conduction band occurs. In this mechanism, the anchor group plays a very important role and, therefore, is central to optimize and understand its characteristics as an electronic transfer agent. Its properties of absorption on the surface of titanium dioxide are also important. From an experimental point of view, different types of chemical groups are known that can act as anchors, such as phosphonates and carboxylates, the latter being the most used for its stability.  
Given the importance of the anchor group, the objective of this chapter is to analyze the performance of different types of functional groups (OH, COOH, COOEt and CN) when acting as anchors in ruthenium complexes. The study of the optical and electrochemical properties of these complexes will allow us to determine which is the most likely to act as a sensitizer for solar cells [8].  
The complexes selected for this study obey the general formula [Ru(dmbpy)\({}_{2}\)R\({}_{2}\)ppl]\({}^{+2}\), where dmbpy is 4,4-dimetyl-2,2-bipyridine and R\({}_{2}\)ppl is disubstituted pyrazino[2,3,f][1,10] phenantroline (R = CN, COOH, COOEt and OH). The general structure of these complexes is shown in Figure 13.  
Figure 13: General structure of [Ru(dmbpy)\({}_{2}\)R\({}_{2}\)ppl]\({}^{2+}\) complexes.
###### Abstract  
Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector.  
Keywords:food authenticity; paprika powder; spectral angle mapper; Raman imaging +
Footnote †: journal: Applied Sciences
## 6 Results and Discussion
### Discussion  
The results show differences in prediction using different models, especially in MAPE prediction errors (see Table 4) and in the scatterplots of real fare values versus predicted values (see Figure 6).  
To better understand and explore the reasons for 'highly mispredicted' fares, cluster analysis was applied, using only the MAPE prediction errors from the three models as variables (see Section 6.2). The results of this analysis show that the prediction of ride-hailing fares become more difficult in the following cases: (i) the supply value of the fare variable is very high; (ii) there is higher rain precipitation; (iii) there is a taxi strike; (iv) during peak hours; and (v) during weekends and holidays. All the previous cases appear to be related to unexpected demand rises that produce an imbalance between supply and demand. The analysis thus helps identify in which circumstances there are imbalances between supply and demand.  
As mentioned before, machine learning models were applied considering the complete dataset, thus working with fixed and dynamic prices, not only dynamic prices--as noted by [PERSON] et al. (2021) [5]. It is also noteworthy that these authors explored ride-hailing fares using an econometric model (adopting the Generalized Linear Model--GLM) but  
Figure 8: Percentage of frequency of observations per hour and cluster.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Percentage of Observations**} & \multicolumn{3}{c}{**\#1 (Low Error)**} & \multicolumn{3}{c}{**\#2 (Medium Error)**} & \multicolumn{3}{c}{**\#3 (High Error)**} \\ \cline{2-10}  & **LR** & **DT** & **RF** & **LR** & **DT** & **RF** & **LR** & **DT** & **RF** \\ \hline Over-prediction & 75\% & 78\% & 71\% & 44\% & 53\% & 52\% & 67\% & 74\% & 74\% \\ Under-prediction & 25\% & 22\% & 29\% & 56\% & 47\% & 48\% & 33\% & 26\% & 26\% \\ Total & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Over-prediction and under-prediction per cluster and model.  
decided to reduce the number of combinations to just 40 OD pairs (representing 42% of the entire sample of this research) by looking only at fare data with notable variations over time (i.e., dynamic prices). All Uber minimum fare data (fixed price equivalent to EUR 3.50) was excluded because the econometric model adopted did not work well with fixed prices. Although the sample is smaller than that of this research, these authors found similar prediction difficulties in the econometric model (such as the occurrence of taxi strikes and peak hours).  
This research shows that machine learning methods can also handle the analysis of more data (which skewed the results of econometric models). The Random Forest model is the machine learning model with the best performance metrics (see Table 4) and is also one of the models that most contribute to the group with small MAPE prediction errors (see Table 5). The models also show more over-prediction than under-prediction in all clusters (see Table 8).
## 2 Problem Statement and Preliminaries
### Frequent Itemsets and Association Rules  
In this paper, we will use an established data mining technique, called association rule mining, that describes the frequent co-occurrence of sets of items in a large amount of collected data. Association rules have been initially exploited to identify correlations among items in the market basket data analysis context. However, they have been used also in several other contexts such as network traffic data analysis, functional diagnosis, and medical data analysis. The input data, in the association rule mining context, is a dataset \(\mathcal{TR}\) composed of a set of transactions \(tr\) of arbitrary length, where each transaction \(tr\in\mathcal{TR}\) is a set of items. Given an arbitrary dataset \(\mathcal{TR}\) as input, the association rule mining problem consists of mining the set of rules of the form \(X\to Y\) that are frequent in \(\mathcal{TR}\), i.e., it consists of mining the most frequent correlations among items in \(\mathcal{TR}\) and the \"direction\" of the extracted implications. More formally, an association rule \(r\) is represented as an implication in the form \(X\to Y\), where both \(X\) and \(Y\) are arbitrary sets of items, called antecedent and consequent of the rule, respectively. \(X\) and \(Y\) are disjoint sets, i.e., \(X\cap Y=\emptyset\).  
The quality of an association rule is usually measured by means of support and confidence. The support measure corresponds to the frequency of the set \(X\cup Y\) in \(\mathcal{TR}\) (i.e., the percentage of transactions in \(\mathcal{TR}\) that contain both \(X\) and \(Y\)) while the confidence measure corresponds to the estimation of the conditional probability of finding \(Y\) in \(\mathcal{TR}\), having found \(X\), and is given by \(\frac{sup(X\cup Y)}{sup(X)}\) (i.e., the confidence of \(X\to Y\) is the percentage of transactions containing both \(X\) and \(Y\) among those transactions containing \(X\)). Only the association rules with a support higher than a minimal support threshold \(min\_sup\) are mined in order to extract only statistically significant rules.  
In our context, each pair \((station,critical\ status)\) is an item that represents a specific critical status of a station. Hence, in our application, each transaction \(tr\in\mathcal{TR}\) is a set of pairs \((station,critical\ status)\) and represents the set of stations that are in a critical status in a specific timestamp \(t\). \(\mathcal{TR}\) contains one transaction for each timestamp of the time period under analysis. The rules mined by our approach represent frequent co-occurrences among stations that are simultaneously critical.