esa-sceva/satcom-chunk-collection huggingface dataset:
# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### X-ray  
The fourth discussion on medical images is focused on X-ray images. Unlike MRI, CT, and US images, SSIM implementation for medical X-ray images was introduced in 2012 for the first time to the best of our knowledge. As is typical, SSIM was constructed to measure a designated algorithm to estimate the similarity score between the ground-truth image and the denoised images. In 2012, SSIM, PSNR, SNR, and MSE were appointed to evaluate an improved algorithm [120]. Similarly, in 2016, a method to eliminate Gaussian noise was established using an edge preserved wavelet packet transformation [121], and the denoising of a contrast-enhanced X-ray image was accomplished [122]. In addition, SSIM was allocated together with the MSE, RMSE, SNR, PSNR, recall, accuracy, precision, and error rate as an objective IQA. Subsequently, in 2017, a security watermark algorithm was developed to secure the diagnostic information of a patient, as well as the ownership and authentication [123] in X-ray and MR images. Again, SSIM has a role as an IQM along with wavelet domain SNR (WSNR) and PSNR. The feasibility of SSIM was not only shown for a numerical analysis but also in regard to deep learning, such as a CNN. The improved algorithm with deep learning may be measured using SSIM related to compression and noise reduction [124; 125; 126; 127]. Moreover, comparisons of filter techniques used to reduce the noise and image reconstruction, as well as apply image compression, were conducted using X-ray images [128; 129; 44; 130].  
To conclude this subsection, Table 4 exemplifies the purposes of using SSIM for X-ray medical images. As in Table 4, we describe the whole article using X-ray images conveying the SSIM metric as an IQM.
## 4 Frequency Analysis and Vibration Evaluation
### The Evaluation of Vibration  
The vibration in the area of the diversion pipeline has been analyzed. However, there is currently no globally unified standard for evaluating ambient vibration and noise. The research on vibration and noise in this area primarily relies on urban area noise and vibration standards. The ambient vibration control standard for the diversion pipelines of PSPSs is mainly based on GB1007-88 [21]. Table 9 presents the standard values of Z-direction vibrations in various urban areas.  
According to the regional characteristics of PSPSs, the vibration standards for residential, cultural, and educational areas are applied in these zones, while partial areas adopt the vibration standards for mixed areas (a \"mixed area\" refers to a general commercial and residential area or an industrial, commercial, light-traffic, and mixed residential area). The vibration acceleration level is defined as 20 times the logarithm base 10 of the acceleration-base acceleration ratio, expressed as _VAL_. The specific formula is as follows:  
\[VAL=20 lg\frac{a}{a_{0}}(\mathrm{dB}) \tag{4}\]  
where '\(a\)' is the effective value of vibration acceleration and \(a_{0}=10^{-6}\ \mathrm{m/s^{2}}\) is the base acceleration.  
According to the vertical vibration standards for various urban areas, the daytime vibrations in the diversion pipeline area should not exceed 70 dB (0.316 cm/s\({}^{2}\)), while the nighttime vibration should not exceed 67 dB (0.224 cm/s\({}^{2}\)). The vibrations in specific  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Application Scope** & **Daytime (dB)** & **Nighttime (dB)** \\ \hline Special residential area & 65 & 65 \\ Residential, cultural, and educational areas & 70 & 67 \\ Mixed and central business areas & 75 & 72 \\ Industrial concentration area & 75 & 72 \\ Both sides of the traffic trunk road & 75 & 72 \\ Both sides of the main railway line & 80 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Vertical vibration standards in different urban areas.  
regions should not exceed 75 dB (0.562 cm/s\({}^{2}\)) during the daytime and 72 dB (0.398 cm/s\({}^{2}\)) at nighttime.  
Combining these data with the test data, the vibration of the lower flat tunnel was prone to exceeding the standard, especially if the number of units running together exceeded three. For this reason, the initial phase of the construction of the lower flat tunnels was situated within mountainous regions to reduce the influence on the ground. The vibration acceleration of the other measuring points generally fell within an acceptable range.
## 3 Numerical Examples
### Validation of the Proposed MRRM  
Consider the bi-coupled periodic beam with Binary-I unit cell. The first several phase-constant spectra below 2.0 kHz obtained by proposed MRRM based on the Timoshenko beam theory (TBT) are shown in the right half-part of Figure 3. Also provided are the counterpart results by TMM obtained by ourselves and by DQM in Ref. [55]. The comparison of the boundary frequencies of the first four band gaps obtained by the aforementioned three methods are also listed in Figure 3. In order to also validate the proposed MRRM based on the EBT and for the sake of illustrating the effects of rotary inertia and shear deformation on the phase constant spectra and boundary frequencies of band gaps, the calculations are also conducted based on the EBT. In the left half part of Figure 3, the results by the MRRM, TMM and DQM are depicted corresponding to the right half part.  
In both cases of TBT and EBT, the good agreement between the results by the MRRM, TMM and DQM validates our formulation. Moreover, the good agreement between the first spectrum by TBT and EBT indicates both EBT and TBT work well in the relatively low frequency range. But the discrepancy between the second upwards spectra and the boundary frequencies of band gaps based on TBT and EBT becomes more and more distinct with the increasing of spectra order, which indicates that the rotary inertia and shear deformation have more and more obvious influences on the phase constant spectra and boundary frequencies of band gaps with the increasing frequency. EBT, which neglects the effects of rotary inertia and shear deformation, ceases to be effective in a somewhat high-frequency range above the second pass-bands.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit Cell Type** & \multicolumn{2}{c}{**Configuration of the Unit Cell**} \\ \hline Binary-I & 1.0 m Concrete beam + 1.0 m Steel beam \\ Binary-II & 1.0 m Aluminum beam + 1.0 m Epoxy beam \\ Ternary-I & 0.5 m Aluminum beam + 1.0 m Concrete beam + 0.5 m Steel beam \\ Ternary-II & 0.5 m Aluminum beam + 1.0 m Epoxy beam + 0.5 m Steel beam \\ Quaternary & 0.5 m Aluminum beam + 0.5 m Concrete beam + 0.5 m Steel beam + 0.5 m Epoxy beam \\ \hline \hline \end{tabular}
\end{table}
Table 1: The configuration of five kinds of unit cell in our exemplified bi-coupled periodic beams.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \begin{tabular}{c} **Young’s Modulus** \\ \(E\) **(GPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Shear Modulus** \\ \(G\) **(GPa)** \\ \end{tabular} &
\begin{tabular}{c} **Mass Density** \\ _\(\rho\) **(kg\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} \\ \hline Concrete & 25 & 9.40 & 2300 \\ Steel & 210 & 78.95 & 7850 \\ Aluminum & 77.56 & 28.87 & 2730 \\ Epoxy & 4.35 & 1.59 & 1180 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The material constants of the four materials involved.
###### Abstract  
Significant racial/ethnic disparities in healthcare and diabetes technology use have been observed in Type 2 diabetes mellitus (T2 DM), which are associated with nonengagement in diabetes self-management and out-of-range glycemia. This study aimed to assess whether there were differences in the blood glucose levels achieved by several racial/ethnic groups using the same digital tool. Study objectives were to determine whether engagement with the digital tool and blood glucose levels differ among ethnic groups, and to determine whether any differences in the in-target-glycemia are related to engagement levels. The retrospective real-world analysis followed a group of 1000 people with Type 2 diabetes who used the DarioTM digital therapeutic platform over 12 months. Participants included in the study had a blood glucose average \(>\) 180 mg/dL (hyperglycemia, high-risk) in their first month. The differences between/within the groups' average blood glucose level (Avg.bg) and glycemic variability were evaluated. Furthermore, three general linear models were constructed to predict the Avg.bg by the number of blood glucose measurements (Bgm) in Model 1 (with the moderator White persons (WP)/people from racial and ethnic minority groups (REM)) and by the frequency of measurements by months (F.m) within REM and WP in Model 2 and Model 3, respectively. The Avg.bg was significantly reduced in each group over a year with no differences between REM/WP users. Blood glucose measurements in Model 1 and frequency of measurements by months in Model 2 and Model 3 predicted the Avg.bg (\(\beta_{1}=-0.20\), \(p=0.045\); \(\beta_{2}=-4.38\), \(p=0.009\); \(\beta_{3}=-3.77\), \(p<0.001\), respectively). Findings indicate a positive association between digital engagement and glycemia, with no differences between REM and WP participants.  
digital therapeutics; digital engagement; blood glucose; diabetes; disparities Article
## 9 Estimation of Mechanical Parameters Based on RQD Value
### Estimation of Deformation Modulus Using RQD Value
#### 9.1.2 Power-Law Formula  
[PERSON] and [PERSON] [48] established the empirical relationship between RQD and the ratio of deformation moduli of the rock mass (E\({}_{\rm m}\)) and the intact rock (E\({}_{\rm r}\)) using the large number of published data (the rocks for the data included mudstone, siltstone, sandstone, shale, dolerite, granite, limestone, greywacke, gneiss, and granite gneiss).  
They discovered the following relationships by analyzing a significant number of measurements (see Figure 21):  
* Lower bound: \[\rm E_{m}/E_{\rm r}=0.2\times 10^{0.0186 RQD-1.91}\] (34)
* Upper bound: \[\rm E_{m}/E_{\rm r}=1.8\times 10^{0.0186 RQD-1.91}\] (35)
* Mean: \[\rm E_{m}/E_{\rm r}=10^{0.0186 RQD-1.91}\] (36)  
The E\({}_{\rm m}\)/E\({}_{\rm r}\) values can differ by up to five times at 100% RQD, as the figure below illustrates. Among other things, the varying borehole lengths [23] can help to explain this.
## 4 Numerical Results
### Experimental Setup  
All experiments were conducted on a system featuring dual Intel Xeon Gold 5118 CPUs operating at 2.30 GHz (2.29 GHz per processor) with 64 GB of RAM via MATLAB R2021b. The system was equipped with four NVIDIA GeForce RTX 2080 Ti GPUs, each possessing 11 GB of memory. In this paper, the experimental performance is illustrated mainly through Receiver Operating Characteristic (ROC) curves and tables. For the evaluation metrics, FAR denotes the false alarm rate and TPR denotes the true positive rate, i.e., the likelihood of correct detection of a result. Some experimental parameters could be set reasonably in advance; for example, the parameter H of the non-overlapping segment was set to 256.
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
## 5 System Testing
### System Testing  
After enabling the phase correction module and the corresponding PID algorithm, the phase delay of the high-frequency reference signal and the ambient temperature over time are illustrated in Figure 11. The figure indicates that after approximately 8 h of continuous testing, with a temperature fluctuation of 1 \({}^{\circ}\)C, the average phase delay is 15.782 fs, and the root mean square (R.M.S.) value of the phase delay is 19.925 fs.  
The experimental results demonstrated a significant impact of the phase correction module on the stability of the high-frequency signal phase. Before the activation of the phase correction module, the signal phase exhibited considerable fluctuations and instability. However, once activated, the fluctuations in the signal phase markedly decreased, resulting in significantly improved stability. As shown in Table 1, the average phase offset of the signal decreased from 1374.104 fs to 15.782 fs, the RMS value reduced from 1537.92 fs to 19.925 fs, and the maximum phase offset dropped from 2484.75 fs to 74.85 fs. This indicates a substantial reduction in the average phase offset and a notable enhancement in signal stability.
## 5 Discussion
### Ablation Study
#### 5.3.5 Performance of Individual Embedding Types  
To further understand each embedding type's contribution, we evaluated the model using only one embedding type at a time:  
Character Embedding Only: Precision was 0.65, recall was 0.78, the F1 score was 0.7732, and accuracy was 81%. While character embeddings alone provide substantial recall, they lack the semantic depth needed for high precision.  
Word Embedding Only: Precision improved to 0.7811, recall to 0.95, F1 score to 0.84, and accuracy to 81%. Word embeddings alone enable the model to capture semantic information, achieving a more balanced precision and recall compared to character embeddings alone.
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 3 Theoretical Framework of the IoT OS Platform  
Recognizing the development needs of the IIoT and addressing the key problems in the practical implementation of IIoT technologies, we propose a theoretical framework for the IoT OS platform. This framework aims to guide the architectural design and system construction of next-generation IIoT solutions scientifically.  
The IoT OS platform, serving as the software infrastructure for IIoT systems, manages various entities downwards and provides services support to IIoT applications upwards. Based on the operating system concept, it extends its computing resource management capabilities outward, moving from traditional operating systems' management of local computing resources to include any entity that can participate in the construction of IoT applications across multiple dimensions, such as sensor resources, network resources, and software resources. Its core philosophy can be divided into five aspects: connectivity, real-time, intelligence, openness, and security, thereby supporting various applications at the upper layer of the IIoT.
###### Abstract  
As an important target for deep to ultra-deep carbonate oil and gas exploration, Fractured-Vuggy dolomite reservoirs have strong heterogeneity. Accurate characterization of reservoir facies is crucial for their exploration and exploitation. Three methods, including the unsupervised intelligent clustering method of improved Fuzzy C-means clustering Algorithm Based on Density Sensitive Distance and Fuzzy Partrition (FCM-DSDFFP), the fusion method of Principal Components Analysis (PCA) dimensionality reduction and noise reduction, and the principle of clustering feature analysis are applied to identify reservoir facies based on logging data. Based on the PCA method, the logging response characteristics of the reservoir facies are excavated, and the fusion characterization data of dimensionality reduction and noise reduction are extracted. The FCM-DSDFP unsupervised intelligent clustering method, a model that approximates the subsurface conditions is established, and the reliability of the model is tested according to the elbow rule and silhouette coefficient. Combining drilling core observation, Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy type, Pore Type I, Pore Type II, Tight Type I, and Tight Type II are divided in the Dengying Formation 4 th Member. Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy Type I, Pore-Vuggy Type II, Pore Type II, and Tight Type are divided in the Dengying Formation 2 nd Member, respectively. Two methods were applied to verify the reservoir facies identification results based on intelligent algorithms. The first method is to compare the identification results with the reservoir facies types identified by core observations (Well PT103 and PS13). The second method is to verify the recognition results of intelligent algorithms by utilizing the relationship between reservoir facies types and bitumen. The test results show that the accuracy of the reservoir level identification is higher than 0.9, and the applicability is better than the commonly used algorithms such as FCM and K-means.  
Ediacaran Dengying Formation; fractured-vuggy dolomite; logging identification; reservoir facies; intelligent algorithm +
Footnote †: journal: Applied sciences  
Article  
Logging Identification Method for Reservoir Facies in Fractured-Vuggy Dolomite Reservoirs Based on AI: A Case Study of Ediacaran Dengying Formation
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
###### Abstract
Furthermore, anchor-based schemes may follow a rendezvous (RV) approach, in which a central entity keeps track of the location of the mobile producer in the network, or a proxy-based approach, where all communications with a mobile producer must pass through the proxy. Anchorless schemes, on the other hand, may apply either a routing-based approach, which includes updating of the FIBs along the path from the consumer to the producer, or a data-plane based approach, where the path leading to the producer's new location is re-established using the data plane. Although many producer MM schemes following these four approaches are proposed in the literature, their suitability for handling producer mobility for latency sensitive traffic has yet to be determined. We are motivated to find the best producer mobility management approach to efficiently accommodate the ever-increasing latency-sensitive traffic of media streaming, videoconferencing, and live video and audio broadcasting. This class of latency-sensitive applications requires the most stringent performance requirements, namely negligible data packet loss and data delivery delay. The main objective of this article is to specify the best-suited producer mobility management approach to transparently and continuously transport latency-sensitive traffic in NDN. The main contributions of this paper can be summarized as follows: follows:  
* Classify the producer MM schemes proposed for NDN into four general approaches;
* Select, according to the defined criteria, a representative producer MM scheme from each approach;* Implement the selected schemes in the ndnSIM simulator and then conduct a comparative analysis to ascertain their effectiveness and suitability for transporting real-time media streaming traffic under the same network topology and mobility settings;
* Show that the data plane is the most suitable producer MM approach that delivers the best seamless streaming of real-time multimedia content. The organization of this paper is as follows. First, we review the literature in Section 2. Next, we detail the operation of three promising MM schemes, each representative of a different producer MM approach, in Section 3. In Section 4, the simulation set-up, network topology and parameters, and performance metrics are detailed. Following that, we present and discuss the achieved results in Section 5. Lastly, we conclude our with findings and recommendations in Section 6.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AI artificial intelligence  
ANFIS Adaptive Neuro-Fuzzy Inference System  
AQ air quality  
AQFS Air Quality Fuzzy System  
ARIMA Autoregressive Integrated Moving Average  
CO carbon monoxide  
EET Equivalent-Effective Temperature  
GMRAE geometric mean of the relative absolute error  
IoT Internet of Things  
LOM largest of maximum  
MAPE mean absolute percentage error  
MdAPE median absolute percentage error  
MdRAE median relative absolute error  
MF membership functions  
\(\upmu\)g/m\({}^{3}\) micrograms per cubic meter  
MOM middle of maximum  
MSE mean square error  
MPPT maximum power point tracking  
NLP natural language processing  
NO\({}_{2}\) nitrogen dioxide  
NO nitrogen monoxide  
O\({}_{3}\) ozone  
PM\({}_{2.5}\) particulate matter sized 2.5 microns  
ppb parts per billion  
ppm parts per million  
RAE relative absolute error  
RMSE root mean square error  
SO\({}_{2}\) sulfur dioxide  
SOM smallest of maximum  
SPEM2.0 Software & Systems Process Engineering Metamodel  
UoD universe of discourse  
USEPA United States Environmental Protection Agency
## 2 Related Work
### Time Series Decomposition  
As a standard method for time series analysis, time series decomposition [22] decomposes time series into several different levels of representation, each of which can represent a predictable potential category and has mainly been used to explore historical changes over time. For prediction tasks, decomposition was usually used as a preprocessing step for historical sequences before predicting future sequences [23; 24; 25], such as trend-seasonal decomposition in Prophet [26], basis expansion in N-BEATS [27], and matrix decomposition in DeepGLO [28]. However, such a reprocessing operation was limited by the simple decomposition of historical sequences and ignored the hierarchical interaction between underlying patterns of long-term future sequences. COST [29] utilized contrastive representation learning that was aware of seasonality and trends. LaST [13] decomposed seasonal-trend representations in latent space based on variational inference, and supervised and separated representations from the perspective of self and input reconstruction to achieve optimal performance. However, traditional methods used the moving average operation to extract trend features, which resulted in weak robustness to noise. In this paper, we explored the idea of decomposition from a new perspective. Specifically, we mapped the time series to the frequency domain and then separated the high-frequency part as the seasonal component and the low-frequency part as the trend component through frequency domain masking. At the same time, for the high-frequency part, we filtered the Top-K amplitudes corresponding to the frequency to complete denoising, making it more robust.
## 5 Simulation Results and Discussion
### Test Systems and Results
#### 5.1.5 Test System 5  
This case considers a complex system with 160 testing units having multiple options and a valve-point effect. The total load demand is 43,200 MW. The input parameters for 160 generators are generated by multiplying those for the 10-unit system up to reflect a 160-unit system. The data system for 10 generators was taken from [14]. Transmission loss is ignored in this case. The best generating cost obtained by the proposed RLGWO is shown in Table 9. The convergence characteristic of 160 generators with GWO and RLGWO methods for fitness value is presented in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Generation Costs**} & \multirow{2}{*}{**Time (s)**} & \multirow{2}{*}{**No. of Hits of the**} \\ \cline{2-2} \cline{4-6}  & **Max** & & & **Min** & **Average** \\ \hline RLGWO & 1,559,389.896 & 1,559,389.595 & 1,559,389.746 & 44.2 & 49 \\ OIWO & 1,559,405.88 & 1,559,405.45 & 1,559,405.61 & 46.8 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between different methods taken after 50 trials (140-unit system with loss).  
Figure 11: Results obtained using different methods for the 140-unit system (50 trials): (**a**) boxplot of the final generation cost; (**b**) time and number of hits of the minimum solution.  
Figure 10: Comparative convergence characteristic of original GWO and proposed RLGWO algorithm for the 140-unit system.