esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 2 Geometrically Nonlinear Analysis of Trusses
### Tangent Stiffness Matrix for Members  
The condition describing the forces at the end of members and end displacements is represented in Equation (4)  
\[\{\Delta P\}=[T]\{\Delta u\} \tag{4}\]  
in which, \(\{\Delta P\}\) is the load at each increment \([T]\) is related to the member tangent stiffness given in Equation (5) and \(\{\Delta u\}\) is the increments of displacement [22], as illustrated in Figure 1.  
\[[T]=\left(\frac{AE}{L}\right)\{B\}{B\}^{T}+Q[g] \tag{5}\]  
in which, \(A\) is the cross-sectional area of the element; \(E\) represents the elastic modulus, the \(Q\) is the axial force in the truss member, \(L\) is the length of the member, \(\{B\}\) is transformation vector which summarizes the transformation from the global coordinate systems with local coordinates, and the geometric matrix [g] is given by Equation (6) [22].  
\[[g]=\frac{1}{L^{\prime}}\left[\begin{array}{ccccc}-(m^{2}+n^{2})&lm&ln&(m^{2}+ n^{2})&-lm&-ln\\ lm&-(l^{2}+n^{2})&mn&-lm&(l^{2}+n^{2})&-mn\\ ln&mn&-(l^{2}+m^{2})&-ln&-mn&(l^{2}+m^{2})\\ (m^{2}+n^{2})&-lm&-ln&-(m^{2}+n^{2})&lm&ln\\ -lm&(l^{2}+n^{2})&-mn&lm&-(l^{2}+n^{2})&mn\\ -ln&-mn&(l^{2}+m^{2})&ln&mn&-(l^{2}+m^{2})\end{array}\right] \tag{6}\]  
where \(l\), \(m\), and \(n\) show the direction cosines of the member in the deformed shape and \(L^{\prime}\) describes the after-deformation length.
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 3 Experimental Results
### Detection of the Uneveness of the Ballast  
After determining the position of the sleepers, the algorithm in Algorithm 1 detects areas of ballast irregularity whose levels exceed the assumed threshold = 35 mm relative to the sleeper level. Because the assessment of unevenness of the ballast boils down to an instance segmentation process, we checked the quality of the proposed algorithm using AP, IoU and Pixel Accuracy metrics for images from the _test_data_ set. These three metrics were obtained for the areas where the height is out of the range of +/ \(-\) 35 mm. Table 3 shows these metrics.  
Figure 10 shows the results of the proposed algorithm for the automatic detection of ballast irregularities. Figure 10a,c,e,g show the range images (depth map) of the track section  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Average Precision AP** & **IoU** & **Pixel Accuracy** \\ \hline
0.91 & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection and segmentation metrics for ballast irregularity detection algorithm.  
and Figure 10b,d,f,h show the image with the detected sleepers and area of the ballast with the height out of range +/ \(-\) threshold overlaid on it (the area marked in light green).  
Figure 10: Example results of the proposed algorithm for automatic detection of ballast irregularities, (**a**,**c**,**e**,**g**) range image (depth map) of track section, (**b**,**d**,**f**,**h**) image with the detected sleepers and area of the ballast (marked in light green) with the height out of range +/\(-\) 35 mm overlaid on it.
## 2 Materials and Methods
### Analysis  
The criteria for the inclusion of the individual studies in the meta-analysis were the same as for the systematic review, with the addition of the following three criteria: the MT or SE intervention was compared with a control or another intervention; the same outcome measures analyzed were found in at least three studies; and detailed statistical data, comparative data on exposure factors, treatment response data, and therapeutic intervention data were included in the results. When studies included more than two groups, only the group with MT or SE, and the control group were included in the meta-analysis. A random effects model was used. Means and standard deviations were compared between groups before and after the intervention by calculating the mean of the difference between the two times, and estimating the standard deviation of this difference. These data were extracted from the published results. For articles that did not report these data, the authors were contacted. If no response was received, medians and interquartile, ranges, or confidence intervals were used. [PERSON]'s Q test and the inconsistency index (I2) were used to analyze the heterogeneity among the studies. The mean effect size was calculated using Hedges' g. For each study, g was calculated with its corresponding confidence interval and \(p\)-value to determine whether each study had a significant effect size. The overall g is also reported with a 95% confidence interval (95% CI) and its \(p\)-value to determine whether there is an overall significant result. The assessment of g was interpreted according to a 4-point scale: (1) <0.20, negligible effect; (2) 0.20 to 0.49, small effect; (3) 0.50 to 0.79, moderate effect; and (4) \(\geq\)0.80, large effect [29]. The Egger test and Funnel plot were used for publication bias. The meta-analysis was performed using SPSS v.29.
# Surface Motion for P-Wave Scattering by an Arbitrary-Shaped Canyon in Saturated Half-Space
## 1 Introduction
Based on [PERSON] theory [24; 25; 26] and the big-arc method [4; 5], [PERSON] et al. [27] proposed an approximate analytical solution of SV wave scattering by a cylindrical canyon in a saturated elastic half-space. [PERSON] et al. [28] obtained an analytical solution to the scattering problem of SV waves by a shallow circular-arc canyon in a saturated elastic half-space. [PERSON] et al. [29] applied [PERSON]'s theory to simulate the earthquake response of a saturated soil site and develop hazard maps against seismic hazards for Vijayawada city, India. [PERSON] et al. [30] applied the Hankel integral transform method to solve the analytical solution of the P-wave scattering problem in a semicircular canyon in a complex plane. Some researchers have begun to pay attention to seismic wave propagation in partially saturated soil; [PERSON] et al. [31] investigated inhomogeneous plane harmonic wave propagation in dissipative partially saturated soils. Then, [PERSON] et al. [32] presented the procedure for the exact evaluation of the horizontal and vertical motion at the surface of partially saturated soils induced by faster waves. With the development of ocean engineering, some scholars began to pay attention to the scattering of seismic waves by canyons in the sea. [PERSON] et al. [33] showed numerical results of seismic amplification on the seabed where there is the presence of deposits formed by soft soils. [PERSON] et al. [34] studied the effects of underwater canyons on plane seismic waves (SV wave) propagation. It is worth pointing out that the analytical solution is limited in that it can only solve some terrain with regular geometric shapes, such as circular arc-shaped canyons [27; 28; 30]. At present, there is little research on the analytical solution of seismic wave scattering by complex terrain in saturated half-space. In this paper, based on Biot fluid-saturated porous media theory [24; 25; 26], the wave function expansion method [6], and the momentsmethod [20], a semianalytical solution is obtained for the P-wave scattering problem of an arbitrary-shaped canyon in saturated half-space. Then, the effects of canyon shape, the porosity of the soil, and the incident angle and frequency of the incident wave on the site seismic response are analyzed, which can provide a theoretical basis for seismic design of engineering construction of canyon sites in saturated half-space.
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 5 Conclusions  
In conclusion, the impact of irreversible compression on DL models is complex and not immediately apparent. JPEG and JPEG2000 compression algorithms eliminate high-frequency image information, which may contain details critical for training DL models, even when such distortions are imperceptible to the human eye. However, the high-frequency information discarded during irreversible compression often encompasses noise, which can inadvertently aid models in identifying key image features.  
Notably, model performance typically remains largely unaffected by compression until it reaches significantly high levels. In certain instances, the literature indicates that DL models may even perform better when trained with irreversibly compressed images. Conversely, there is a risk that models may learn compression artifacts, which could improve classification accuracy on the training data while complicating generalization to new datasets.  
A summary of the advantages and disadvantages of using irreversibly compressed images in DL for medical image analysis is provided as follows:  
Advantages:  
* May significantly reduce dataset size, leading to shorter model training times.
* Reduces storage costs.
* Allows the utilization of older irreversibly compressed data.  
Disadvantages:
* Models may learn compression artifacts during training, reducing their ability to generalize to new images.
* Insufficient published research on the topic.  
Overall, the current body of research establishes a strong foundation for addressing this complex issue. Continued investigation is essential to expand our understanding and support the advancement of reliable AI-driven medical technologies.  
General Further Research Suggestions on Using Irreversible Compression for Training DL Models in Medical Image Analysis  
Based on the literature review presented, the following list outlines suggested directions for researchers to further explore the use of irreversibly compressed images for training and testing DL models and to assess their accuracy and performance in medical image analysis:  
* Train and test models on mixed sets (uncompressed and compressed images) based on the compression ratios recommended for radiology.
* Modify models to handle irreversibly compressed images, such as using DCT frequency coefficients as input to DL models.
* Establish compression thresholds based on model prediction accuracy for more general datasets based on modality and anatomy.
* Create a virtual image dataset featuring basic shapes with subtle variations in shades of gray to represent the range of pixel intensities found in typical medical images for specific modalities and anatomical regions. Use this dataset to evaluate how different compression thresholds impact the accuracy of DNN predictions. This approach of using shapes and grayscale variations to replicate the structures and edges commonly found in medical images has been proposed in the literature as a method for assessing the quality of scaled medical images [73].
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 3 Problem Formulation and Existing Challenges
### Inherent Challenges on the Use of MIMO in 5G IoT Communication System  
Currently, a range of wireless communication technologies such as ZigBee, Bluetooth, Wi-Fi, LTE, Bluetooth, LoRa, etc. are contending to be used in IoT applications [1]. Each communication technology has its own features, including data transfer range, data rate, depletion of energy, and operating frequency. A few communication technologies are likely to deplete less energy than others (which usually corresponds to a lower data rate). Recent studies have shown that by using MIMO in the transmitter and the receiver, numerous data streams which are independent can be multiplexed at certain frequency-time window [28]. Accordingly, the multiplexing of various signals over separate antennas increases the probability of data rate of a link. This is achieved through the propagation of many highly correlated signals over independent fading channels. Yet, data transmission is still a challenging factor in IoT systems. When routing, the nodes' data should consider the following factors.  
1. The distance among nodes and service requirements on the network.
2. The transmission restriction given for the radio access technology.
# Evaluation of Impact Energy Absorption by Natural Fiber Composites in Motorcycle Helmets
###### Abstract  
In response to the growing concerns regarding motorcyclists' safety and advancements in the motorcycle industry, this study investigated the potential of natural fibers as a sustainable approach for enhancing helmet protection, thus replacing the traditional use of expanded polystyrene. Utilizing statistical tools such as the Shapiro-Wilk test, Chauvenet's criterion, and the interquartile range, we compared the impact energy absorption of composites reinforced with natural fibers, including sugarcane bagasse, coconut, and sisal, added to expanded polyurethane prototypes. The results, evaluated through confidence intervals, indicated that composites reinforced with 5% sugarcane bagasse, 5% and 10% coconut, and 10% and 15% sisal exhibited significantly superior impact absorption performance compared to pure expanded polyurethane. Composites with agarose sisalana fibers exhibited low variability and reliable performance, with the 10% concentration showing the best results. Sugarcane bagasse fiber demonstrated strong stability, especially at a 5% concentration. Coconut fiber performed well at both 5% and 10% concentrations but showed the greatest variability among the fibers tested. These findings underscore the potential of natural fibers in enhancing helmet safety and suggest promising directions for future research into the ideal composite materials for motorcycle helmets, an inquiry that is currently underway.  
s +
Footnote †: journal: Applied sciences  
_Article_
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 1 Introduction
Despite the flexibility of simulation tools for this purpose, most of the quantum simulators are not focused on facilitating the estimation of rate and fidelity while simultaneously giving precise control of the variables of the main network elements: sources, channels, and quantum protocols. In this paper, we present the design principles, the operating model, and the implementation of a set of experiments in NetSquid to simulate advanced routing experiments on entanglement-based quantum networks. We also provide a comparison of two network configurations that are difficult to study analytically. More specifically, our contributions are the following: First, we design and implement an experimental setting (based on NetSquid [13]) that simplifies the preparation and running of numerical experiments on quantum networks based on entangled resources. Different to other simulation approaches in the literature that rely on the abstraction of quantum circuits or ignore the real behavior of the devices, our focus is put on the quantum processes, devices, and entities that generate, manipulate, or consume entanglement as a fundamental resource. Secondly, our setting adopts a similar protocol to the one described in [21]--the E2E fidelity-aware routing--and fidelity on a path is estimated in advance to the creation of the entanglement. Finally, we conduct two detailed experiments to assess the performance of two configurations: a multi-hop network of repeaters, and a two-hop network wherein QRs have a finite but arbitrary number of qubits. The remainder of this paper is organized as follows. Related works are briefly reviewed in Section 2. Section 3 introduces the quantum concepts and mechanisms that are needed for subsequently understanding the system model presented in Section 4. We describe the details of our implementation in Section 5, present the network architectures in Section 6, and discuss the numerical results obtained with the tool in Section 7. The concluding remarks of our work are given in Section 8.
## 4 Experimental Validation
### Case 2  
#### 4.2.1 Jiangnan University Dataset Description  
In this paper, the Jiangnan University (JNU) dataset is selected as Case 2 for evaluating and validating the MCR-KAResNet-TLDAF method. The test rig setup is shown in Figure 11, and the bearings used in the experiments are the N205 and NU205 models. Acceleration sensors were used to collect vibration signals from the bearings at different positions on the motor. The vibration signal of the bearings was selected for this study. The details of the bearings are shown in Table 10.  
There are four health states for the bearings: healthy, outer ring failure, roller failure, and inner ring failure. The first three health states are from the N205 bearing, while the fourth is from the NU205 bearing [30]. The four health states in the JNU dataset are detailed in Table 11. In addition, the timing waveforms of different health states of the bearings are demonstrated in Figure 12.  
The vibration signals in the JNU dataset are collected using a vibration acceleration device (PCB MA352A60) at a sampling frequency of 50 kHz. The vibration accelerometers have a bandwidth of 5 Hz to 60 kHz, with an output of 10 mV/g. The dataset is divided into three different operating conditions based on three different speeds, as shown in Table 12. The sampling points for the JNU dataset are calculated using Equation (11), with the final selected sampling point set to 5000. During the experiments, the training and test sets are divided in the same way as the CWRU dataset. The number of iterations for the JNU dataset is set to 15  
**First group:** When using MTF and RP as the highlighted modules, the fault diagnosis performance was significantly lower than with CWT images. This may be because CWT better captures the time-frequency characteristics of the signal, providing a greater advantage in handling bearing signals.  
**Second group:** Placing CWT in the green channel resulted in the highest accuracy. This could be because the green channel makes the fault features more prominent.  
**Third group:** Using 0.1 MTF-CWT-0.1 RP as the input led to a decrease in the average fault diagnosis accuracy. This might be due to the parameters for the MTF and RP channels being set too low, which weakened the image features excessively. When using MTF-CWT-RP as the input, the average fault diagnosis rate decreased by 2.499%, significantly reducing fault diagnosis accuracy. This could be because the lack of a primary focus among the three channels led to important features being overshadowed. When changing the order of RP and MTF to use 0.2 RP-CWT-0.2 MTF as the model input, the average accuracy also decreased compared to the method proposed in this paper.  
These three sets of experiments gradually verified the feasibility of setting the parameters to 0.2 MTF-CWT-0.2 RP.
# A Survey of Deep Learning for Electronic Health Records
###### Abstract  
Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research.  
electronic health records (EHR); machine learning (ML); deep learning; de-identification; privacy preservation; deep EHR; natural language processing (NLP) +
Footnote †: journal: Medical Data Mining  
The purpose of this survey is to provide technical references for readers and researchers to advance the research and development of deep learning on EHR. The evolution of EHR is discussed first, followed by a summary of the most recognized EHR datasets and deep learning models in EHR. Following that, it goes through current achievements and algorithm innovation in this area. Finally, this survey will discuss the existing limitations and provide some suggestions for the development of deep learning applications based on EHR.
## 3 Research Results--Analysis and Discussion
### Uniaxial Compressive Strength (UCS)
#### 3.1.1 Effects of Conventional Stabilisers on UCS
However, the trend of constant increase in UCS value with increasing cement content for all the considered time intervals imposes the necessity for further research on the effects of using a higher percentage of cement in order to precisely determine the optimal stabiliser content in the clay mixture. By comparing these two types of conventional stabilisers, it was observed that for all test time intervals after the chemical treatment of clayey soil, better results are achieved with the addition of 3% lime than with the addition of 5% cement binder. By comparing the results obtained for the case of addition of 5% lime and for the case of addition of 10% cement binder, it can be observed that almost matching UCS values were achieved. The addition of 15% cement contributed to slightly higher UCS values compared to the addition of 7% lime. Finally, it could be concluded that when stabilising clayey soil, from the aspect of this soil property, the use of lime is more rational than cement.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
###### Abstract  
State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy.  
SOC; second-order RC model; model parameter optimization; AUKF +
Footnote †: journal: Applied Sciences