esa-sceva/satcom-chunk-collection huggingface dataset:
# Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
## 3 Results
### Between-Condition Fingerprinting
#### 3.5.2 Between-Condition Fingerprinting with Resting-State Matched-to-Task Scanning-Length  
In order to account for scanning length effects, the between-condition matching rate results were obtained when resting-state FCs are estimated by matching the time-series length to each fMRI task. Results are shown in Figure 7. For this analysis, we fixed the brain parcellation rank to 414, which was the optimal choice in terms of fingerprinting, as shown in Figure 5. For each participant rank across all between-condition settings, we computed matching rates with 100 different samplings of time points for the resting-state time series. In Figure 7A, we display the results of randomly sampling an initial time point and choosing the remaining time points to be consecutive to it. In Figure 7B, we display the results of randomly sampling all time points. When comparing both sampling strategies, randomly sampling all time points is a more robust and effective approach in terms of fingerprinting, as measured by the standard deviation and mean of each box plot.  
Figure 6: Between-condition comparison between highest matching rate obtained with the ST-HOSVD based fingerprinting framework and original FC matrices for parcellation granularity of 414. Despite being the shortest task and the one with worse within-condition fingerprints, Emotion displays one of the highest between-condition fingerprints. Contrarily, Working Memory possesses one of the highest fingerprints in the within-condition setting, but one of the lowest in the between-condition setting.
## 3 Results
### Results of Cyclic Tests
The mean values for the three shape descriptors continued to increase (when compared to Table 2), suggesting that the particles became even more spherical, rounder and more regular after 120 cycles. The damping ratio also continued to fall, implying an even lesser stiffness (more flexibility) for the samples with additional cycling (from 30 to 120 cycles). This section investigates the effects of loading cycles on the three particle shape descriptors and the damping ratio. The experimental results are presented in Figures 14 and 15, where the circle diameters represent the magnitude of the damping ratio. It was observed that an increase in the number of loading cycles led to an increase in the shape descriptors \(S\), \(R\) and \(\rho\), indicating that the particles became more spherical, rounder and more regular. Moreover, an increase in the number of cycles (i.e., an increase in \(S\), \(R\) and \(\rho\)) resulted in a decrease in the circle diameters and subsequently the damping ratio. These findings, which demonstrate the relationships between the number of loading cycles, particle shape and damping ratio, highlight the importance of considering these factors in the design and optimization of granular materials for various geoengineering applications. Figure 15a,b illustrate the variations in vertical stress and the CSR against the number of applied loading cycles, respectively; in these figures, the size of the circle diameters represents the magnitude of the damping ratio. The findings indicate that the damping ratio increases with increasing vertical stress levels, as evidenced by the increases observed in the circle diameters. These results provide insight into the influence of loading stresses on the damping behavior of sand, which has significant implications for the design and construction of various geotechnical structures. Figure 14: Effects of number of loading cycles, along with (a) \(R\), (b) \(S\) and (c) \(\rho\), on the damping ratio.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 3 Proposed Methodology  
### Hierarchical Sparse Parameter Strategy  
The loss function is very important in the training of the neural network. Choosing an appropriate neural network loss function can make the neural network model parameters converge to a value that can better describe the characteristics of the trained model [35]. The loss function is mainly divided into regression loss function and classification loss function, and different loss functions have different convergence characteristics. Therefore, the appropriate neural network loss function should be selected according to the purpose  
Figure 2: The structure diagram of SSAE model.  
and nature of the neural network. Since the purpose of this research is to achieve classification, combined with the unsupervised nature of the autoencoder, the mean square error is selected as the basic loss function of the autoencoder. The traditional SSAE connects multiple identical SAE in series and uses the output of the previous SAE as the output of the next SAE. The loss function \(E_{SSAE}\) of traditional SSAE is:  
\[E_{SSAE}=\frac{1}{m}{\sum\limits_{i=1}^{m}}{\left\|{x(i)-\hat{x}(i)}\right\|^{2 }}+\beta{\sum\limits_{k=1}^{N}{\sum\limits_{j=1}^{n_{k}}{KL(\rho\parallel \hat{\rho}_{j})}}} \tag{7}\]  
In the formula, \(N\) is the number of stacked layers of SSAE and \(n_{k}\) represents the number of neurons in the \(k\)-th layer.  
It can be deduced from the formula that the traditional SSAE sparsity strategy is to give each layer of neurons the same expected activation \(\rho\) and the same sparsity parameter weight \(\beta\), as shown in Figure 3a. Although it is impossible to reach the activation state shown in the figure during the training process, the figure shows the desired state of the network. This same sparse expectation may limit the learning ability of the deep sparse network to a certain extent.  
However, considering that the number of neurons in each layer of a sparse network is usually not the same, the same sparse activation and sparse weight are not the optimal parameter selection. By aiming at this problem, a hierarchical sparsity strategy is proposed to randomize the sparsity activation and sparsity weights to expand the sparsity of the sparse network, as shown in Figure 3b. The loss function \(E_{HS}\) of the improved SSAE using the hierarchical sparse strategy is:  
\[E_{HS}=\frac{1}{m}{\sum\limits_{i=1}^{m}}{\left\|{x(i)-\hat{x}(i)}\right\|^{2 }}+\sum\limits_{k=1}^{N}{\sum\limits_{j=1}^{n_{k}}{\beta_{k}KL(\rho_{k} \parallel\hat{\rho}_{j})}} \tag{8}\]  
where \(\beta_{k}\) is the sparse weight of the \(k\)-th layer of neurons and \(\rho_{k}\) is the expected activation degree of the \(k\)-th layer.  
Since the hierarchical sparse strategy is equivalent to increasing the randomness between hyperparameters, it is difficult to achieve the best results in manual tuning. Therefore, combined with the particle swarm optimization algorithm, the network sparsity is adaptively optimized to obtain reliable, optimal sparsity parameters.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 3 System Composition
### Image Acquisition Module  
A band-pass filter film [30] matching with the laser frequency is coated on the exterior surface of the filter imaging screen [31]; this allows the laser beam to go through the screen. A thin layer of diffuse reflection coating is present on the interior surface of the filter imaging screen [32]. On this layer, the laser beam forms a laser spot with Lambertian reflection. The HD industrial camera constantly takes photos of the laser spot, extracting the position information of the laser spot in the image coordinate system with machine vision. The band-pass filter film and darkroom [33] helps to reduce the stray light interference.
###### Abstract  
The laser tracker has characteristics of high measurement accuracy and wide measurement range. Laser tracker technology, as an effectively large-scale measuring approach, plays a critical role in dynamic measurement. Currently, the static performance of laser trackers has been well studied. However, the dynamic characteristics of the laser tracker remain unclear in terms of evaluating its dynamic performance. The circular trajectory generator measurement system can quantify the dynamic performance of the laser tracker. We developed a standard circular trajectory generator using a stable servo system and then conducted an in-depth study on the dynamic performance of the laser tracker through statistical analysis. Numerous experiments have shown that if the laser tracker is set at equal spacing, the dynamic indication error is smallest when the measurement distance is 3 m, indicating that the fitted diameter at a distance of 3 m is closest to the diameter of the circular trajectory generator. If the laser tracker is set with equal sampling frequency, the dynamic indication error is smallest when the measurement distance is 5 m. When the circular trajectory generator is at low speed, the measurement spacing of sampling points of the laser tracker is fixed proportional to the number of measurement points, while at low or high speed the sampling frequency of the laser tracker is fixed proportionally to the number of measurement points. These conclusions will facilitate the application of the laser tracker in dynamic measurement.  
laser tracker; circular trajectory generator; dynamic measurement; dynamic limit velocity; dynamic indication error; dynamic indication variable; equal spacing measurement; equal frequency measurement +
Footnote †: journal:
# Study of Root Canal Length Estimations by 3D Spatial Reproduction with Stereoscopic Vision
## 1 Introduction
Electronic apex locators were developed in 1958 and have undergone improvements in accuracy and miniaturization [23; 24; 25]. However, electronic apex locators cannot be used in patients with cardiac pacemakers, and try-in radiographic imaging cannot provide accurate measurements if the anatomical apical foramen is not aligned with the apex [26]. CT/CBCT data are output as DICOM (Digital Imaging and Communications in Medicine) data, which are the international standard for medical data communication. The 3D models of DICOM data are typically displayed on 2D devices. Performing root canal length measurements using applications on 2D devices requires the user to manually set the maximum axial length and cross-section of the tooth, which can be a cumbersome process. In this study, we performed region segmentation using 3D medical image segmentation software on the DICOM data of dental models, which were obtained by conventional clinical dental techniques. The segmented models were then converted into 3D computer graphics (3 DCG) and displayed on a spatial reproduction display to determine the accuracy of stereoscopic content perception without wearing a head-mounted display (HMD). We used an application called \"SR View for Endo\", which enables three-dimensional visualization and measurement of root canal length by understanding the three-dimensional morphology of the root canal. SR View for Endo was newly developed for this study. In this study, we analyzed the accuracy and usefulness of root canal length measurements by using a naked-eye stereoscopic display of 3 DCG root canal models.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 5 Experiment  
In order to verify the positioning accuracy of the BICI-IMM and BICI fusion positioning algorithms, we designed a multi-sensor fusion positioning equipment and carried out the fusion positioning algorithms experiments in the open and semi-open areas. As shown in Figure 10, the multi-sensor fusion positioning equipment includes two GPS, IMU, co-processor and embedded CPU. The two GPS receivers adopt ZED-F9P and MAX-7Q of u-blox company. The IMU sensor is the ADIS16470. The co-processor adopts the STM32F407 with uC/OS-III for data acquisition and time synchronization between sensors. The collected sensors data are sent to the embedded processor through rosserial. The embedded CPU is Allwinner H6 and carries a Linux operating system with a robot operating system. Allwinner H6 is the sensors' data-processing unit for data recording and fusion processing. During the experiment, we used the high-precision real-time kinematic (RTK) receiver of CHCNV E91 as the reference positioning and the continuously operating reference stations (CORS) adopted the FindCM service of Qianxun.
## 4 Materials and Methods
### Western Blot Analysis  
For the Western blot assay, hBMSCs were cultured on 6-well plate (at a density of \(1\times 10^{5}\) cells/well), with either blank or DFO-loaded NPs dissolved in the medium. After culturing for 3 days, cells were collected and lysed to extract total cellular proteins. Next, the protein concentrations were quantified by a BCA protein assay kit (Beyotime, Nantong, China). Then, each protein sample weighing 40 \(\mu\)g was separated by sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes. After blocked with bovine serum albumin (BSA) for 1 h, the membranes were incubated with primary antibody of anti-HIF-1\(\alpha\) (1:500; Abcam, City, UK), and detected using a horseradish peroxidase (HPR)-conjugated secondary antibody (1:5000; Sigma, Santa Clara, CA, USA). \(\beta\)-actin antibody (1:2000) was used for normalization of protein loading. The HIF-1\(\alpha\) protein was visualized using the Enhanced Chemiluminescence System (Cell Signaling Technology Inc., Boston, MA, USA).
# Statistical Analysis and Modeling for Optical Networks
## 2 Literature Review on Statistical Analysis and Modeling for Networks  
Statistical analysis and modeling of networks is essential to understand their characteristics and nature. In recent times, it has emerged as a critical field for understanding complex systems across diverse domains, employing various methodologies to decipher underlying structures and dynamics. Researchers have focused on characterizing network topologies through statistical properties like degree distributions, clustering coefficients, and path lengths, revealing insights into different characteristics, such as small-world and scale-free phenomena [5]. In order to explain the formation of observed network structures,statistical models such as Exponential Random Graph Models and latent space models are utilized, inferring social processes and spatial influences on network evolution [6]. Community detection, crucial for functional understanding, employs statistical approaches like stochastic block models to identify densely interconnected node groups [7]. Furthermore, dynamic network analysis addresses the temporal evolution of real-world networks, developing models to capture growth, adaptation, and response to external stimuli [8]. Foundational texts and comprehensive surveys provide a robust framework for understanding these methodologies, covering topics from basic statistical network measures to advanced modeling techniques for both static and dynamic networks [9; 10; 11; 12; 13; 14]. These advancements in statistical network analysis are vital for extracting meaningful information from the increasingly complex interconnected systems that define our contemporary world.
###### Abstract  
This study aimed to compare the percentage of biocecaramic sealer penetration and maximum penetration sealer depending on the fluorescent staining method when evaluated with confocal laser scanning microscopy (CLSM). In total, 42 single-rooted premolars were instrumented. Two groups of 20 teeth each were filled using the single cone technique. TotalFill BC Sealer(r) mixed with Rhodamine B was used for the first group and TotalFill BC Sealer(r) mixed with Fluo-3 for the second group. Two teeth made up the control group. All teeth were sectioned at 3 and 6 mm from the apex and were analyzed with CLSM. The canal perimeter where the tubular penetration happened and the maximum penetration of each sample were calculated. There were no significant differences between the two groups in either the apical or medial sections. In terms of maximum penetration, Rhodamine B had greater tubular penetration in the apical sections, and it had similar results to Fluo-3 in the medial sections. TotalFill BC Sealer(r) has a higher tubular penetration in the medial slices than in the apical slices regardless of the fluorescent stain used. Rhodamine B has higher results than Fluo-3 in terms of the percentage of sealer penetration and maximum penetration.  
bioceramic sealer; confocal laser scanning microscopy; Fluo-3; Rhodamine B; root canal filling +
Footnote †: journal: Applied Sciences  
Article  
A Comparison of Two Fluorescent Stains for the Evaluation of Bioceramic Sealer Penetration into the Tubular Dentine Using Confocal Microscopy
## 5 Conclusions  
In this paper, a method of generating boundary-scan MTV by combining the genetic algorithm and improved AO* model is proposed, which effectively solves the problem of optimizing the \(P_{\text{MTV}}\) of MTV under the optimal compactness index. We use walk-1, PSO, and our method to generate MTVs for the same module and calculate the \(P_{\text{MTV}}\) of each MTV. At the same time, we also used these MTVs for the actual boundary scan test. Based on the analysis and experiments, we can draw the following conclusions:  
* Compared with the MTVs generated by Walk-1 and PSO, the MTVs generated by our method have lower \(P_{\text{MTV}}\), and MTVs generated by our method have lower apriorism probability of misjudgment and confusion in the boundary scan test.
* Through the experiment of fault injection based on probability of shorted circuit, it is shown that the MTVs generated by our method have lower fault incidence than those generated by walk-1 and PSO. The MTVs generated by our method have lower failure rate from a posteriori point of view.  
Our research can be used to generate MTV in the boundary scan test and to optimize the \(P_{\text{MTV}}\) of existing MTVs. It has certain practical value. In the future, we will do more research on the generation of larger MTVs and adaptive optimization with failure rate and compactness.  
Conceptualization, Y.S. and X.G.; methodology, X.G. and H.L.; software, X.G. and J.W.; writing--original draft preparation, Y.S. and X.G.; writing--review and editing, H.L. and Z.L.; visualization, H.L. and X.G.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Project of Sichuan Youth Science and Technology Innovation Team, China (Grant No. 2020)IDTD0008).  
Not applicable.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Number of Tests** & **Walk-1** & **PSO** & **Our Method** \\ \hline
50 & 48.3\% & 34.1\% & 27.4\% \\
100 & 44.1\% & 33.2\% & 26.5\% \\
150 & 47.6\% & 34.3\% & 26.6\% \\
200 & 45.7\% & 34.1\% & 26.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The rates of misjudgment or confusion in the test using MTVs generated by each algorithm.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Fiber Monitoring System Applied to Railway Bridge Structures in a Near-Fault Region
## 2 Measurement Theory  
### Brillouin Frequency Shift in Single-Mode Optical Fiber  
A portion of the light is backscattered when it is beamed into optical fiber. The collision between the photons of the incident light and acoustic phonons causes this phenomenon. The frequency of the incident light shifts into the stoke and anti-stoke zones. This shift in frequency is proportional to the speed of acoustic phonons, which is the acoustic wave velocity. The temperature and strain acting on the sensing fiber will affect the density and tension of the glass, thus affecting the speed of the phonons \(V_{A}\)[16]. This Brillouin frequency shift (BFS) in relation to the acoustic wave velocity can be expressed as Equation (1):  
\[v_{B}=2\text{n}V_{A}/\lambda, \tag{1}\]  
\(n\) is the reflective index of the fiber sensor, \(V_{A}\) is the acoustic wave velocity, and \(\lambda\) is the wavelength of the incident light [17].  
BFS can further be expressed in relation to the external factors acting on the fiber. [PERSON] et al., 1989, and [PERSON] et al., 1990 [18], analyzed how temperature and external strain would affect the speed of the phonons \(V_{A}\) traveling in the fiber, thus causing BFS, and could be expressed in terms of linear parameters (2):  
\[v_{B}=v_{B0}+C_{\epsilon}(\epsilon-\epsilon_{0})+C_{T}(T-T_{0})\Delta v_{B}=C_{ \epsilon}\Delta\epsilon+C_{T}\Delta T \tag{2}\]  
where \(v_{B}\) is the measured Brillouin frequency shift; \(v_{B0}\) is the initial Brillouin frequency shift; \(\epsilon\) is the strain and T is temperature; and \(C_{\epsilon}\) and \(C_{T}\) are the coefficients of strain and temperature. The coefficients are related to the material properties of the fiber, which vary with different fiber sensors from different suppliers. This equation can be further arranged into Equation (3):  
\[\Delta\epsilon=(\Delta v_{B}-C_{T}\Delta T)/C_{\epsilon} \tag{3}\]
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Unmanned Aerial Vehicles and BackCom  
UAVs have revolutionized many areas of life. BackCom can be used with UAVs to extend the lifetime of communication. UAVs can be considered in relaying utilizing D2D communications in BackCom. Figure 42 shows the connectivity of BackCom tags using UAVs. The \"RFLY\" drone was proposed to relay information from tag to reader [180]. RFLY extends the communication range of the tag using UAV. Moreover, RFLY can read data of various tags at a time while flying in the air. Similarly, [PERSON] et al. highlighted opportunities and challenges for BackCom-enabled UAVs [194]. UAVs in rural areas are utilized to supply ambient power to passive sensors for efficient communication [195].
# Multilayer Backbones for Internet of Battlefield Things +
## 1 Introduction  
The progress in IoT inevitably impacted upon the modern battlefield, which is populated by thousands of \"things\", such as humans, sensors, vehicles, unmanned aerial vehicles (UAVs), aircrafts, etc. carrying out various tasks including environmental sensing, communicating, acting in isolation and/or in cooperation [1,2]. Therefore, the Internet of Battlefield Things [1] or the Internet of Military Things (IoMT) was born, which interconnects \"devices\" aiming to meet multiple and diverse missions, to operate in a (semi)autonomic mode, and to execute battlefield operations supporting end-to-end control and command; its ultimate goal is to carry out a commander's intent in a safe, responsive and resilient manner. Thus, IoBT presents at the same time all of the following significant and very challenging characteristics [3] which distinguish it from traditional IoT:  
* _Diversity in tasks and aims_. There will be many networks operating simultaneously to achieve a particular goal, e.g., tracking, surveillance, attack.
* _Operation in dynamically changing and resource starving environments_. Some \"devices\" of the IoBT network might be energy-starving (sensors, drones), others might not have energy issues (armored vehicles), others might be obliged to travel in non-chartered territories (e.g., planes).
* _Extreme device heterogeneity_. IoBT networks are expected to include from tiny little sensors to some as big as armored fighting vehicles.
* _High variance in network's density and size._ An IoBT network might be comprised e.g., by the cluttered network of a swarm of drones, or by the \"union\" of the ad hoc network of a battalion's soldiers and the ad hoc network of a tank platoon.  
The so-called _assured synthesis_ is one of the major challenges identified for IoBT, and in particular the _recruitment_ and _network composition_[3] tasks. The former task is about the discovery of cyberphysical assets, the human assets and their particularities, and also about the resilience to adversaries. The latter task is about the issue of dictating the nodes that must be considered in order for the requirements and constraints of the planned mission to be satisfied.