esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Computer-Aided Medical Image Processing
## 2 The Formation of Medical Images
### Defects in Medical Imaging  
#### 2.2.1 Defects of MRI  
In Section 2.1, for MRI imaging technology, we introduce the advantages. However, MRI scanners rely on complex superconducting electromagnets and excellent electronic equipment for imaging, which leads to high cost, a large area of site occupancy, and demanding imaging site conditions. Therefore, people have begun to promote the development of low-cost MRI technology with ultralow field (ULF) intensity for large-scale medical image detection [53; 54], which leads the MRI images' disadvantage in that most clinical images are lost to computers [55]. Figures 1-3 show the differences between T1-weighted images, T2-weighted images, and FLAIR-weighted images based on a high magnetic field, standard MRI images, and an ultralow field MRI, respectively. It can be seen from the comparison that these ultralow field MRI images have low brightness, poor contrast, no obvious details, and considerable noise, which is not convenient for computer and doctor detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tumor Site** & **Dataset Series Name** & **Dataset Composition** \\ \hline \multirow{4}{*}{Liver} & LiTS challenge 2017 & 22,500 images for training, 2550 images for validation, and 16,125 test images [38; 39] \\  & SLiver07 & 30 CT scans, including 20 training and 10 testing [40] \\  & 3 DIRCADb & 20 CT scans with 15 liver tumors [39; 41] \\ \hline \multirow{4}{*}{Lung} & NSCLC & 50 patients with 128-slice PET-CT [42] \\  & LUNA & 888 CT images of 1084 tumors, with the help of experts, excluding tumors \\  & STSs & smaller than 3 mm [43] \\  & Lung cancer patient dataset & CT and MRI from 51 patients [42; 44] \\  & COVID-19 dataset & 48 training cases and 36 test cases [45] \\  & LIDC-IDRI & 2500 high-quality images of COVID-19 [46] \\  & TCGA & 1018 patients’ CT data and their marker information, [47] \\  & NIH Deeplesion dataset & 51 patients’ CT data [48; 49] \\  & NIH Deeplesion dataset & over 32,000 annotated lesions identified on CT images, representing \\  & & 4400 unique patients [50] \\  & NIST & More than 75,000 CT images and more than 1200 pathological images from \\  & NIST lung cancer patients [51] \\  & SPIE-AAPM & 70 patients who underwent CT and received diagnoses [52] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: Article context: shows the logic of our life review, including medical image data, methods of medical image enhancement, multimodal image fusion technology, medical image feature extraction, and medical image segmentation. The line between the tumor image and CT or MRI represents the type of dataset suitable for this tumor site.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 1 Introduction
[33] presented the design of a \(H_{\infty}\) sliding mode and an unknown input observer for Takagi-Sugeno systems to deal with the problem of inexact measurements of the premise variables. In Reference [34], a data-driven system based on PCA was designed to detect and quantify fluid leaks in an experimental pipeline. Although these fault diagnosis approaches are able to detect certain industrial system faults, it remains a challenge to design a fault diagnosis model that meets practical production requirements [35; 36; 37]. In addition, how to accurately detect faults with a low complexity is still a challenge.
# The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review
## Results
### User Studies  
From the total number of analyzed papers, only 17 (42.5%) present user studies for testing the AR systems. Table 4 presents an overview of these studies in terms of number of participants, environment, type of investigations and statistical analysis: 5 of them fall into the first category and 12 into the second category.  
As a way to validate their research results, most of the authors have preferred to use subjective evaluations through questionnaires (nine studies--52.94%) or through interviews (four studies--23.53%), while the others chose other means.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Type of Investigation** & **Statistical Analysis** & **No. of Participants** & **Age Range** & **Gender Ratio** \\ \hline
[72] & interview & bi- and multivariate & 11 & 20–51 & 11:00 \\
[73] & interview & bi- and multivariate & 23, 52 & 20-53, 30–53 & 23:00 \\
[77] & questionnaire & – & 10 & N/A & N/A \\
[78] & SUS1 questionnaire & – & 21, 22 & 17–18 & N/A \\
[66] & questionnaire & ANOVA & 24 & 18-54 & 20:04 \\
[47] & NASA-TLX & ANOVA & 22 & mem. 20.3 & 13:09 \\
[58] & log files & – & 5 & N/A & N/A \\
[62] & stereogram test & – & 24 & 22–53 & 19:05 \\
[54] & interviews, NASA-TLX & ANOVA & 24,33 & 19-41, 65–91 & N/A \\
[48] & interview & ANOVA & 16 & mean: 42 & N/A \\
[49] & questionnaire & ANOVA & 24 & 18–40 & 17:07 \\
[63] & video analysis & – & 14 & 15–53 & 11:03 \\
[36] & questionnaire & ANOVA & 30 & 22–52 & 23:07 \\
[64] & SPSS software & – & 12 & 23–53 & N/A \\
[52] & AttrakDiff questionnaire & ANOVA & 88 & 20–54 & 70:18:00 \\
[53,84] & AttrakDiff questionnaire & ANOVA & 80 & 22–55 & 70:10:000 \\
[73] & TAM questionnaire & ANOVA & 26, 18 & 19–35, 19–41 & 15:11, 12:6 \\ \hline \end{tabular} \({}^{1}\)System Usability Scale.  
\end{table}
Table 4: List of user studies.  
Figure 16: Percentage of AR tracking methods (**a**) and distribution of papers according to tracking method per year (**b**).
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 2 The System Model  
In the system model, a ship sailing near the shore is connected to an onshore base station through a relay ship, with both ships traveling at different speeds (\(v\)) along their respective routes, thereby forming a dynamic network environment. At two separate time instants \(t_{1}\) and \(t_{2}\), the positions of the ships and their communication connections change. This change mirrors the instability caused by the movement of ships in maritime communication networks. It is assumed that the decode-and-forward (DF) protocol is employed during this process. The link connection probability from the nearshore ship to the base station is calculated using the channel state information and marine environment parameters collected over the past \(N_{i}\) time slots. A brief illustration of the system model is shown in Figure 1.
## 4 Initial Analyses and Findings
### The Factor Analysis
#### 4.3.2 Factor Rotation  
Factor rotation is a mathematical procedure that rotates the solution for a better interpretation of the results. The rotation does not change the factor extraction or the total variance explained. However, it is a geometrical interpretation feature that allows the observation of the effect of variables within a factor. This interpretation also changes the coefficients of variables and the distribution of variance; thus, factor scores are also affected. There are two types of rotations: orthogonal and oblique. Here, the main difference is that orthogonal rotation produces uncorrelated factors and oblique rotation vice versa [59]. The analyses indicated that the soil properties are correlated, and they all represent the plasticity characteristics of the soil regardless of the positive or negative effect. Therefore, altering one property will alter the overall plasticity. Consequently, the two extracted factors only include the variables to explain the plasticity-based characteristics due to confirmatory approach of the chosen factor analysis. Accordingly, the factors should be also correlated with each other. Based on these reasons, oblique rotation was chosen for the analysis. The rotated solution of the factors also clarified that the sole effect of calcium carbonate should be assigned as another factor (Appendix A). Also, if the soil properties are plotted based on their rotated loadings within the factor, distinguishable groups can be observed (Figure 4). These groups also indicate the validity of extracting two factors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Main Influencers** & **Contribution (\%)** & **Remarks** \\ \hline F\({}_{1}\) & N\({}_{1}\), N\({}_{2}\), N\({}_{3}\), N\({}_{4}\) & 69.61 & Positive effect on plasticity \\ F\({}_{2}\) & N\({}_{5}\) & 14.53 & Negative effect on plasticity \\ \hline \hline \end{tabular}
\end{table}
Table 4: The variables in this analysis and their corresponding contributions and remarks.  
Figure 4: Grouping of the extracted factors in rotated solution.
## 2 Results and Discussion
### Monomer Concentrations in Particles  
St and DVB concentrations in the particles along the course of the polymerizations were calculated as follows: from the recipe, phase-organic dosing rate and conversion data at a given time, the amounts of toluene, St and DVB in the latex were known. The analysis by GC gave the St/DVB weight ratio in the particles, which with the amounts mentioned above, allowed to know the composition of the organic phase in the latex. Then, the assumption that the composition of the mixture of toluene, St and DVB swelling particles and micelles was the same was made. The partitioning of this mixture between particles and micelles was estimated from the polymer/surfactant weight ratio, since this datum, along that of the total surfactant in the recipe, allowed to know the surfactant available to form micelles and from here the number of micelles and its content of organic phase. In this calculation an aggregation number of 60 [26] and a molecular surface coverage for SDS micelles of 0.5 nm\({}^{2}\)/molecule [25] were taken. With the partitioning results, the particle composition was known and St and DVB concentrations were calculated under the assumption of additive volumes of the components and discarding the volume of the cross-linked polymer from the total volume available for carrying out the propagation reactions. Tables 1 and 2 summarize the results achieved and Figure 9 shows the variation of total monomers concentration in the particles during the polymerizations. The data in Tables 1 and 2 show that St concentrations in the particles are consistently higher for the polymerization at both dosing rates. Consequently, the total monomer concentration (Figure 9) is higher for the polymerization at the lower dosing rate practically along all the reaction. This behavior is somewhat expected due to the higher \(x_{i}\) values obtained at the polymerization carried out at the higher dosing rate. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{t_{r}}\)} & \multicolumn{4}{c}{**0.05 g/min**} & \multicolumn{4}{c}{**0.15 g/min**} \\ \cline{2-13}  & \multicolumn{2}{c}{**St (mol/L)**} & \multicolumn{2}{c}{**DVB (mol/L)**} & \multicolumn{2}{c}{\(\boldsymbol{t_{r}}\)} & \multicolumn{2}{c}{**St (mol/L)**} & \multicolumn{2}{c}{**DVB (mol/L)**} \\ \cline{2-13}  & \multicolumn{2}{c}{**M**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**M**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**MR**} \\ \hline
0.23 & 4.12 & NA & 0.52 & NA & 0.14 & 2.32 & 1.56 & 1.40 & 1.08 \\
0.41 & 3.18 & NA & 0.17 & NA & 0.36 & 1.28 & 2.14 & 2.49 & 1.71 \\
0.58 & 2.41 & NA & 0.23 & NA & 0.53 & 2.32 & NA & 0.17 & NA \\
0.84 & 2.30 & NA & 0.26 & NA & 0.76 & NA & NA & NA & NA & NA \\
1.00 & 2.25 & 2.39 & 0.19 & 0.21 & 1.00 & 0.89 & 1.19 & 0.47 & 0.71 \\
1.18 & 1.74 & 1.74 & 0.12 & 0.15 & 1.51 & 0.09 & 0.12 & 0.08 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monomer concentration in particles along polymerizations as function of organic phase dosing rate.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Emerging Trends and Future Directions
#### 2.4.1 Energy Harvesting  
New materials and technologies: recently, energy-harvesting technologies, such as flexible solar cells, thermal energy-harvesting materials, and vibration-based technologies have emerged [34]. Tiny energy sources in the environment, such as differences in light and temperature, are harnessed by flexible nanogenerators and thermal energy converters to generate electricity. Meanwhile, device vibration or motion is utilized by vibration-based energy-harvesting technologies to generate electricity for wireless sensors and wearable devices.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 5 Conclusions  
With the intention of addressing the problem which the target tracking algorithm fails to track due to fast movement and background interference, this paper proposes a feature pyramid optical flow estimation Siamese network target tracking algorithm. The algorithm jointly trains optical flow and tracking tasks under the framework of the Siamese network. On the basis of the pyramid correlation mapping, the optical flow network is utilized to assess the movement information of the target in two contiguous frames, thereby improving the accuracy of feature representation. At the same time, SiamFP utilizes spatial attention and channel attention to effectively suppress background noise and better extract target features, effectively improving the success rate of the tracking algorithm. This paper conducts comparative tests with various algorithms on the OTB50, OTB2015, and VOT2016 datasets, and conducts qualitative analysis of the video sequences. The experimental results show that the SiamFP algorithm based on SiamFC reduces the occupation of computing resources, improves the computing speed, and improves the tracking effect. The designed SiamFP algorithm has good tracking effect in various challenging scenarios and shows a good balance between accuracy and real-time performance.  
Conceptualization, W.Z.; methodology, W.Z.; software, W.Z.; validation, W.Z., M.D. and C.C.; formal analysis, W.Z.; investigation, W.Z; resources, M.D.; data curation, C.C., writing--original draft preparation, W.Z.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, D.Z.; project administration, D.Z.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Key R&D Program of China (2018 YFE***********02), and the Major Public Welfare Project of Henan Province (201300311200).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracker** & **EAO** & **Accuracy** & **Robustness** & **FPS** \\ \hline SiamFP & 0.335 & 0.537 & 0.318 & 50 \\ CCOT & 0.331 & 0.529 & 0.238 & 0.3 \\ TCNN & 0.332 & 0.532 & 0.268 & 1.5 \\ SSAT & 0.321 & 0.534 & 0.496 & \textless{}25 \\ MLDF & 0.301 & 0.489 & 0.574 & \textless{}25 \\ Staple & 0.294 & 0.527 & 0.688 & 80 \\ DDC & 0.287 & 0.458 & 0.378 & 5 \\ EBT & 0.277 & 0.531 & 0.752 & \textgreater{}25 \\ SRBT & 0.258 & 0.528 & 0.900 & \textless{}25 \\ STAPLEp & 0.236 & 0.526 & 0.786 & 86 \\ DTN & 0.151 & 0.461 & 0.773 & \textgreater{}25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tracking Results on VOT Dataset.  
Figure 7: EAO Results on VOT2016 Dataset.
## 4 Conclusions  
In summary, we have developed a two-channel, time-wavelength interleaved PADC system with a sampling rate of 10.4 GSa/s, and demonstrated a concise method to measure and correct channel sampling timing walk-off of a PADC in signal recovery. Two detection RF signals of \(f_{1}\) = 100 MHz and \(f_{2}\) = 200 MHz were selected for measuring the channel sampling timing misalignment by finding the MRMSEs. By shifting the data from channel 2 forward by 12 positions, we obtained MRMSE values of 44.4174 and 28.9738 for the two detection signals, respectively. Additionally, we employed the PLM and normalization processing to mitigate the impact of signal peak jitter and power inconsistency between the channels. The ENOB of the directly synthesized signal from two channels of 100 MHz data was only \(-\)0.1051 bits, while it improved up to 4.0136 bits after correcting the channel delay misalignment. Furthermore, the ENOB reached 4.5668 bits when the PLM and normalization were added. Through the joint correction of the PLM, normalization, and shift synthesis, the ENOB of the 200 MHz signal increased from the \(-\)3.1627 bits to 4.3831 bits. Finally, the established two-channel PADC system was applied to detect the 21.1 GHz signal. The ENOB had increased from the initial \(-\)0.9181 to 4.1913 bits using our correction method. The signals collected from the two channels, as well as the corresponding direct synthesis signals and shift synthesis signals, were analyzed and discussed in both the time and frequency domains. The experimental results have demonstrated that our proposed method can effectively recover the test signal, being immune to channel delay misalignment.  
**Author Contributions:** Conceptualization, J.Q. and X.L.; methodology, J.Q., M.F. and D.S.; software, W.Y., D.S. and T.X.; validation, J.Q., X.C. and H.Z. ([PERSON]); formal analysis, H.Z. ([PERSON]), X.W. and B.S.; investigation, J.Q. and M.F.; resources, X.C. and H.Z. ([PERSON]); data curation, J.Q., X.C. and H.Z. ([PERSON]); writing--original draft preparation, J.Q.; writing--review and editing, X.L., W.W. and M.F.; visualization, D.S. and M.F.; project administration, X.L.; funding acquisition, X.L., W.W. and B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Magnetic Confinement Fusion Program of China (2018 YFE0310400); National Key Research and Development Program of China (2020 YFA0713504); Research Program of NUDT (ZK22-17); National Natural Science Foundation of China (62005317, 11704411, 119/5022, and 12275001); and the Natural Science Foundation of Hunan Province (2021J40695).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Methods and Data
### Pseudo-Measurement Generation
#### 2.2.1 Scenario Generation  
For testing the effects of different levels of measurement uncertainty, five pseudo-measurement scenarios are devised, representing increasing levels of precision. The method of consumption pattern generation is identical for residential and controlled consumers. These are detailed below, using the following notations:  
* \(l=1,\ldots,L\) are indices of the loads of the dataset (\(L=344\) for residential, and \(L=69\) for controlled datasets);
* \(t=1,\ldots,T\) are indices of time periods (\(T=34,944\) for the whole year);
* \(P_{l}(t)\) is the consumption value of load \(l\) at time period \(t\) in the original MSDS dataset;
* \(p(t)\) is the SLP value for time period \(t\) scaled for unit annual consumption;
* \(P_{\text{sc}}^{l}(t)\) is the consumption value of load \(l\) at time period \(t\) according to scenario sc.  
**Method 1.** All loads consume the same amount of energy in each time step. The constant consumption value is the overall average of the entire dataset.  
\[P_{1}^{l}(t)=P_{\text{avg}}\triangleq\frac{1}{LT}\sum_{l=1}^{L}\sum_{l=1}^{T}P _{l}(t) \tag{1}\]  
**Method 1m.** SLP-based consumption, where each load consumes the same amount as the other loads at a given time step.  
\[P_{1\text{m}}^{l}(t)=P_{\text{avg}}\cdot p(t) \tag{2}\]  
**Method 2.** All loads consume constant energy. This constant value is unique for each load and is the average of their annual consumption.  
\[P_{2}^{l}(t)=P_{\text{avg}}^{l}\triangleq\frac{1}{T}\sum_{l=1}^{T}P_{l}(t) \tag{3}\]  
**Method 2m.** SLP-based consumption. The mean consumption differs for each load and is based on the aggregated annual consumption of the given dataset.  
\[P_{\text{2m}}^{l}(t)=P_{\text{avg}}^{l}.p(t) \tag{4}\]  
**Method 2 mm.** Load consumptions directly correspond to the smart meter time series data from the MSDS.  
\[P_{\text{2 mm}}^{l}(t)=P_{l}(t) \tag{5}\]  
These scenarios help to distinguish between varying levels of precision in the input data, with the first scenario (1) being the least accurate, and the last scenario (2 mm) being the most accurate compared to the validating dataset consisting of smart metered data. Of these scenarios, (1), (2) and (2m) are the ones used in DSO practice. Scenario (1m) would be theoretically applicable; however, due to its low accuracy in terms of estimation result, it is not covered in detail in this paper. Scenario (2 mm) will be applicable in future research once smart consumption meters are deployed in these pilot environments.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 5 Results and Discussion
### Power Line Channel Performance  
This section discusses the BER performance of the power line channel employed in the current Hanadu kits. Two scenarios are considered: (i) the BER of different modulation schemes; and (ii) the effects of impulsive noise on BER.  
Figure 12 compares BER performance for different modulation schemes with only coloured background noise. From the communications requirements outlined in [37], the HEMS application requires 99.0%-99.99% reliability, which translates to a BER of \(10^{-3}\) (median). This is the typical BER needed to guarantee the QoS of HEMS application traffic. Generally, differential Phase Shift Keying (PSK) schemes allow non-coherent detection. Hence, phase-tracking circuits are not required at the receivers, which consequently reduces the design and implementation complexities, as well as the cost. The current Hanadu is based on DBPSK, and as seen in Figure 12, with coloured background noise  
Figure 11: Effect of impulsive noise on one-way delay.  
alone, the current Hanadu requires an SNR of 7.8150 dB to achieve a BER of \(10^{-3}\). It can also be seen that, to guarantee 99.99% reliability, SNRs of 10.4164 dB, 12.8197 dB and 18.5068 dB are required by Quadrature PSK (QPSK), Differential QPSK (DQPSK) and Differential 8-PSK (D8 PSK) respectively. Therefore, compared with QPSK-based systems, such as HomePlug Green PHY, this result shows that a 2.6014 dB power saving can be achieved by employing DBPSK. Although DQPSK and D8 PSK can offer higher rates, employing them will potentially undermine the energy efficiency of the system. Since HEMS applications typically require a low data rate, which has been satisfied by DBPSK, such a trade-off is not necessary. The effects of impulsive noise on BER are illustrated for various impulsive noise probabilities in Figure 13.  
Figure 12: System Bit Error Rate (BER) performance as a function of Signal to Noise Ratio (SNR).  
Figure 13: BER performance with various impulsive noise probabilities.  
Figure 13 shows the BER when the coloured background and impulsive noise are present, where \(p\) represents the probability of the appearance of impulsive noise. The figure illustrates BER performance with impulsive noise probabilities of 0.001, 0.01, 0.02 and 0.05. These values signify that impulsive noise occurs 0.1%, 1%, 2% and 5% of the time. As seen in the figure, to achieve the same BER of \(10^{-3}\) with \(p=0.001\), the SNR requirement increased to 8.1266 dB for the current Hanadu, meaning that an additional 0.3116 dB of transmit power are needed in the current kits to combat the effect of impulsive noise. Another inference from Figure 13 is that for higher probabilities of impulsive noise, higher power levels are required at the transmitter. For example, with a probability of 0.05, an SNR of 10.9381 dB is required to achieve a BER of \(10^{-3}\), translating to an additional 2.8115 dB, compared with the probability of 0.001.