esa-sceva/satcom-chunk-collection huggingface dataset:
# Competing Failure Modeling for Systems under Classified Random Shocks and Degradation
## 2 System Description and Notation  
### System Description  
Generally speaking, competing failure modes for the system consists of soft failure (degradation risk mode) and hard failure (shock risk mode). As shown in Figure 1, there are two failure processes for a system, the soft failure and the hard failure. Soft failure occurs when the total degradation exceeds its threshold. Hard failure occurs when a random shock magnitude is greater than the threshold value. These two processes compete with each other, which means that whichever failure occurs first, the system will be disrupted.  
For hard failure, once an external shock exceeds the failure threshold \(D_{0}\), hard failure occurs. The appearance of external shocks follows a Poisson process with rate \(\lambda\). In addition, the sizes of shocks are random variables \(W_{i}\) with specific distribution, so the probability of hard failure has great relation to the \(W_{i}\).  
For soft failure, total degradation is composed of pure degradation due to regular wastage (such as wear, aging, and fatigue) and instantaneous increases caused by random shocks. The magnitudes of increases are related to the shock sizes. Three kinds of instantaneous increases for degradation are taken into consideration in this paper, as shown in Figure 1. If the size of one shock is less than a given degradation increase threshold \(D_{2}\), the increase would be type \(A_{i}\), or more specifically, no increase. If the size of one shock is between the given thresholds \(D_{1}\) and \(D_{2}\), the increase would be type \(B_{j}\), which is a random variable. Similarly, the increases would be type \(C_{k}\) if the size of one shock is greater than the given condition \(D_{1}\).  
The system is under competing risk modes and will stay valid if neither hard failure nor soft failure occurs. No matter which risk mode (hard failure or soft failure) occurs first, the system fails, and then the other risk mode will not occur anymore.
## 2 Materials and Methods
### Study Protocol  
The prospective study included 10 patients: 5 with intra-articular and extra-articular disorders (myofasical pain and intra-capsular incoordination) and 5 with only extra-articular disorders (myofasical pain). Enrolment was based on the diagnosis of TMD based on a standardised clinical valuation that fulfils the Research Diagnostic Criteria (RDC TMDs) [13].  
These patients were affected by headaches and orofacial pain from myofasical pain syndrome, caused by unconscious clenching [9], and were treated with gnathological therapy: passive aligners and biofeedback exercise.  
Operators identified 'parafunction' in the awake brain state using direct questions and visual observation of patients' behaviour [14]. During visual observation, we could identify patients with parafunctional habits by diagnosing damaged tooth structures, fractured teeth and restorations, craze lines, or loss of teeth. Other intraoral clinical signs are tongue indentations; bony exostoses or tori; periodontal changes, including widening of the periodontal ligament, tooth mobility, and recessions. If clinicians note this kind of damage, they should discuss the damage with the patients and review their medical history to determine the cause [15].  
All patients underwent the following:  
* Quantitative pain assessment (VAS and palpation test) during every follow-up.
* Measurement of masseter thickness before and after the treatment.
* Some studies evaluated the actual variation in the activity of some masticatory muscles in patients suffering from TMD. These studies revealed a correlation between TMD pain and an increased tone of masticatory muscles (greater tenderness scores, higher sEMG activity), in particular temporalis and masseter muscles. These studies proved a significant correlation between headaches and mastication muscle tone changes [9; 11; 12; 13; 14; 15; 16].
* fMRI and TMJ MRI before and after the gnathological treatment.  
This study evaluated the functional connectivity (fcMRI) of the pain network (PN) and the default mode network (DMN). Some research showed a stable variation in pain and cerebral behavioural networks in therapeutic procedures through the functional magnetic resonance of the brain [17; 18].  
The PN is the cortical network of the physiology of pain. In contrast, the DMN manages unconscious processes involving perceived pain.  
In the first stage of the study, the subjects underwent CBCT, clinical examination, palpation test, and VAS to diagnose unconscious teeth clenching [19] and myofasical pain TMD. Then, they underwent a TMJ MRI to assess TMJ condition and an fMRI of the brain.  
In the study's second phase, all the patients were treated with passive aligners and biofeedback exercises. Patients underwent VAS and palpation tests once a month.  
After 12 months, all patients underwent, once again, TMJ MRI, fMRI of the brain, and CBCT.  
The same operator followed each patient from the beginning until the end of the study.
# Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality
## 4 Results and Discussion
### Variation Laws of Overall Compaction Quality of Subgrade
#### 4.1.3 Variability of \(E_{vib}\)  
The variation law of variability in \(E_{vib}\) on the subgrade top surface with roller passes under different filling height and the underlying layer's strength is analyzed by above results of field tests.  
According to the recommendation of Technical Conditions for Continuous Compaction Control System of Highway Subgrade Filling Project [13], the uniformity of compaction  
Figure 6: Influence law of strength of underlying layer on subgrade compaction quality: (**a**) final compaction value \(E_{vibj}\); (**b**) mean value \(E_{vib}\) with number of roller passes; (**c**) increment of mean value \(E_{vib}\) with number of roller passes; (**d**) growth rate of \(E_{vib}\) with number of roller passes.  
can be controlled by the variability in the test data. The formula of the control conditions is as follows.  
\[ICMV_{i}\geq\eta\overline{ICMV} \tag{7}\]  
The coefficient \(\eta\) is 0.8; that is, if the continuous compaction control index value of each point is greater than 80% of the detection target value, it is considered that the current roller uniformity meets the requirements. Figure 7 shows that the influence of different working conditions on the compaction variability is obvious. At the beginning of rolling, the compaction variability of each working condition is higher and ranges from 32.5% to 42.5%. It was noted that the degree of irregularity in the condition of 40 cm thickness and 93 MPa strength decreases rapidly with roller passes, and the value becomes zero at five passes that means that the variability meets the requirement. Then, the condition of 40 cm thickness and 80 MPa strength has the same variation law, but it meets the variability requirement at six passes. Another three conditions of the data analysis are similar to the above two condition results. It was also found that with seven passes for the condition of 40 cm thickness and 60 MPa strength, 50 cm thickness and 60 MPa strength, and eight passes for 60 cm thickness and 60 MPa strength that the compaction variability reaches the standard. In view of compaction variability control, it was proven that increasing the strength of the underlying layer and reducing the thickness of the filling layer are beneficial to achieving a higher quality of the compaction state.
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 3 Metal-Organic Frameworks
### Synthetic Procedures  
Different procedures have been proposed to synthesize MOFs, having a great impact on their structure and physical properties, namely morphology, lattice dimension, porosity, and surface area [31; 38]. The main strategies used to produce MOF-based coatings rely on hydro/solvothermal methodology, microwave-assisted synthesis, sonochemistry, mechanochemical, and electrochemistry, as depicted in Figure 3.  
Both the hydrothermal and the solvothermal approaches are based on the dissolution of metal salts and organic ligands in either water (hydrothermal) or an organic (solvothermal) medium, followed by reaction at high temperatures in pressurized reactors. The obtained material is then cooled, washed, and vacuum-dried to remove the solvent and sub-products from the pores of the framework. Although these approaches are simple, long reaction times and the use of high volumes of organic solvents or harsh conditionsare usually required. Hydro/solvothermal synthesis has been frequently used for the in situ growth of MOFs [17; 29; 30; 31; 38]. To fasten the nucleation process, microwave-assisted synthesis has been proposed, obtaining MOF particles with a homogeneous size distribution in a very short time. Similarly, the ultrasound-assisted method has been proposed as a valid alternative to the previously described approach, being able to synthesize MOFs in shorter times, under milder conditions, and with high reaction yields. In fact, the acoustic cavitation provides a high local temperature and pressure, followed by high cooling rates, which facilitate both the reaction and the nucleation processes [38]. Microwave- and ultrasound-assisted synthesis are usually not compatible with in situ deposition; therefore, they are mostly proposed using the two-stage processes [31; 38]. Recently, mechanochemical synthesis (MC), based on the use of a mechanical force to break the intramolecular bonds and promote chemical transformation, has emerged as a valuable green approach [39]. Being solvent-free, improvements in terms of reaction time, energy conversion efficiency, and reduction in organic waste, can be achieved. Finally, the electrochemical approach can be used to synthesize the MOFs directly onto the SPME fiber: the metal ions are obtained by anodic dissolution of the metal salts and react with the dissolved organic linkers.
# Current Context and Research Trends in Linear DC-DC Converters
## 2 Switch Mode Power Supplies (SMPS)
### Issues with SMPS
#### 2.1.1.  
Inductors and Transformers  
Inductors play a major role in switch-mode power supplies in storing energy. Hysteresis and the eddy current losses are inherent losses in electromagnetic devices. Both the hysteresis loss (\(P_{h}\)) and the eddy current loss (\(P\)) are proportional to the operating frequency (\(f\)), as shown in Equations (1) and (2) [3], respectively.  
\[P_{h}\propto fB_{max}^{2} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Linear Regulators and LDOs** & **SMPS** & **Charge Pumps** \\ \hline Design flexibility & Step down & Step up/Step down/Inversion & Step up/Step down/Inversion \\ \hline Circuit Diagram & & & \\ LDO & Buck Converter & Switch capacitor voltage doubler \\ \hline Efficiency (typical) & Linear regulators: 50–60\% & & Higher than 95\% & 70–80\% \\  & LDOs up to 98\% & & \\ \hline Complexity & Low & High & Low \\ \hline Size & small & Smaller than LDO & Smallest \\ \hline Total Cost & Low & High & Low \\ \hline Electromagnetic & Low & High & Medium \\ interference (EMI) & Low & High & Medium \\ \hline Noise & Low & High & Medium \\ \hline Input voltage & Narrow & Wide & Narrow \\ \hline Thermal & Moderate (mainly the series & Complex (multiple devices to deal & Moderate (switches) \\ management & pass device) & with where the heat is dissipated) & \\ \hline Output current & Medium & High & Low \\ capability & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance of SMPS, charge pumps, and linear regulators.  
\[P_{e}\propto fB_{max}^{2} \tag{2}\]  
where \(B_{max}\) is the maximum flux density of the core.
## 4 Statistical Analysis of Surrounding Rock Pressure
### Vertical Pressure in Deep Tunnel  
The cover depth had a significant influence on the surrounding rock pressure in deep tunnels. The function relationship between the cover depth and the surrounding rock pressure was studied. Single-factor linear regression was used to study the variation of surrounding rock pressure at 44 m and 95 m depth. The regression equation is \(y=\mathrm{ax}-\mathrm{b}\), such that the regression equation between pressure and cover depth was as follows:  
\[y=3.655x-109.54 \tag{24}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Position** & **Cover Depth Levels** & **F** & **F-Crit** & _p-Value_ & **Testing** \\ \hline A & 44; 62; 95 & 20.195 & 5.143 & 0.002 & Sign. \\ B & 44; 62; 95 & 6.252 & 3.592 & 0.009 & Sign. \\ D & 44; 62; 95 & 18.716 & 3.592 & \textless{}0.001 & Sign. \\ E & 44; 62; 95 &  
The chart of Equation (24) is shown in Figure 7. Figure 7 shows that r-square was 0.95, which means that linear fitting was effective.  
The upper limit of 95% estimation for parameter a was 4.051 and for parameter B was 6.808. The regression equation of cover depth and pressure was as follows:  
\[y=4.051x+6.808 \tag{25}\]  
Equation (25) was suitable for calculating the vertical pressure of tunnels with a depth of 44 to 90 m.  
Generally, the dimensional weight of loess is 16.7 kN/m3. Figure 7 shows that the surrounding rock pressure in Equation (25) was equal to 0.24 times the gravitational stress.
###### Abstract  
Microimplant-assisted rapid palatal expansion (MARPE) is a technique widely used to treat transverse discrepancies in adult patients. The present study aims to examine how age, sex, maxillary width, and suture bone density might influence MARPE efficacy. It also analyzes bone density variations across the midpalatal suture regions. Materials and Methods: This retrospective study included 30 adult patients who underwent MARPE treatment. Pre- and post-treatment CT scans were analyzed to quantify the maxillary width and bone density measured in Hounsfield units (HUs) in the anterior, middle, and posterior nasal spine regions. Statistical analyses were carried out and included descriptive statistics, t-tests, and effect size calculations. Results: Younger patients (age \(22.13\pm 4.58\)) had significantly higher success rates compared to older patients (aged \(25.66\pm 4.67\)). No significant differences were found regarding sex or the initial maxillary width. Lower bone density in the middle and posterior nasal spine regions was correlated with higher success rates. Data showed that the anterior nasal spine exhibited higher bone density, but this finding did not affect treatment outcomes significantly. Conclusions: Age seems to be a crucial factor in MARPE success, with younger patients showing better outcomes. Regarding bone density, results showed that its values in the middle and posterior nasal spine regions could be a determinant of treatment success. On the contrary, sex and the initial maxillary width did not appear to affect outcomes. These findings emphasize the importance of preoperative assessments and the consideration of individual anatomical variations for optimal MARPE treatment planning.  
borate density; midpalatal suture; microimplant-assisted rapid palatal expansion (MARPE); Hounsfield units (HUs)
## 4 The Verification of CGH
### Interference Type CGHs  
While previous simulations focused on Fourier transform holography, computational holography extends beyond this approach. We conducted experiments using Fresnel holography with a He-Ne laser (\(\lambda\) = 632.8 nm). The flowchart included computation of the diffracted object field, generation of continuous CGHs via object-reference beam interference, binarization of interference patterns to produce binary CGHs, and reconstruction of binary CGHs under illumination.  
The continuous CGH (Figure 27,e) exhibits intact interference fringes and high reconstruction fidelity, preserving both amplitude and phase information of the original wave-field. In contrast, the binarized CGH (Figure 27,f) discretizes fringe transmittance into 0/1 levels, introducing quantization artifacts (edge blurring and texture distortion) that significantly degrade reconstruction quality.  
Fundamentally, continuous CGH encodes amplitude through fringe contrast (brightness modulation), while binarization enforces amplitude quantization via thresholding, resulting in dynamic range compression and high-frequency detail loss.  
Figure 26: PSNR and SSIM values of Kinoform reconstructed images under varying Gaussian noise coefficients (_ef_1 = 0.0–1.0).
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Spectroscopic Techniques
#### 4.1.1 Near-Infrared (NIR) Spectroscopy
A diverse and representative sample set that covers a wide range of texture attributes is essential for developing robust models that can accurately predict the texture traits in different types of meat. Overall, the studies that do not reach high performance values may be due to a combination of these factors influencing the accuracy and reliability on NIR spectroscopy for meat sensory trait analysis.
## 3 Results
### Changes in GPP of Different Land Cover Types  
To better understand the importance of urban forests to the regional GPP, the Sentinel-2 and MODIS GPP changes in different land cover types were shown in Figure 7. According to the result of Sentinel-2, the average monthly GPP of vegetation in the built area (e.g., urban forest) and non-built area (e.g., crop, tree, grassland) was 0.35 Tg C and 0.43 Tg C (0.32 for crops, 0.08 for trees, and 0.03 Tg C for grassland), respectively. Additionally, for MODIS, GPP of the built area and non-built area was 0.28 Tg C and 0.42 Tg C (0.18, 0.01, and 0.23 Tg C for crop, tree, and grassland, respectively). The MODIS GPP of grassland was 0.20 Tg C higher than Sentinel-2 GPP, and the MODIS GPP of cropland was 0.14 Tg C lower than Sentinel-2 GPP. This may be caused by the use of different land cover datasets in statistics. MODIS land cover products might be misplaced between farmland and grassland due to the lower spatial resolution. In fact, as an industrialized city, Suzhou does not have a lot of grasslands.  
The GPP of each land cover type varied throughout the year (Figure 7a,c). Similar to the change in total GPP, each land cover type had two peaks in spring and summer. Additionally, they also showed decrements in GPP in May. These decrements in GPP for crops and urban forests were obviously about 32.33% and 23.57%, respectively, according to Sentinel-2 GPP.  
Sentinel-2 results indicated that crops and urban forests accounted for a majority of the total GPP in Suzhou in 2021, with an average of about 38.15% and 44.43%, respectively. Trees and grasses in non-built area made relatively small contributions to total GPP, accounting for approximately 11.87% and 4.17%, respectively. According to MODIS GPP, urban forests still accounted for the majority (39.66%) of total GPP, followed by grassland and crops at 33.66% and 25.60%, respectively.  
Figure 6: The relationship between the estimated GPP and climate factors. (**a**) and (**b**) are the correlations between temperature and monthly GPP for Sentinel2 and MODIS, respectively. (**c**) and (**d**) are the correlations between precipitation and monthly GPP for Sentinel2 and MODIS, respectively.  
The contribution of different land cover types also varied throughout the year (Figure 7b,d). Taking the most contributed urban forests as an example, their Sentinel-2 GPP exceeded 50% of the total GPP for five months of the year, with the highest percentage at 55.39% in June. Additionally, the contribution made by crops was also large, even more than the contribution of urban forests in growing peak seasons, such as April and August.
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 4 Analysis and Experiments of Visual-Inertial Odometry Optimization
### Analysis of VINS Odometry Optimization
After adjustment, the framework still supports multiple sensor choices, such as stereo cameras, a monocular camera with a VIMU, and stereo cameras with a VIMU. Each sensor is treated as a general factor. Factors which share common state variables are summed together to build the optimization problem. The fusion of multi-sensor information factors is shown in Figure 7.  
Figure 7: Flowchart for Multi-Sensor Information Fusion. The maximum a posteriori estimation result is obtained by minimizing the prior sum of all measurement residuals, and the Mahalanobis norm through visual-inertial bundle adjustment is as follows:  
\[\min_{X}\left\{\sum_{(l,j)\in C}\rho\bigg{(}\left\|r_{C}\Big{(}\hat{z}_{l}^{C_{j }},X\Big{)}\right\|_{P_{l}^{c_{j}}}^{2}\bigg{)}+\sum_{k\in B}\left\|r_{B}(\hat{z }_{h_{k+1}}^{b_{k}},X)\right\|_{P_{h+1}^{b_{k}}}^{2}+\sum_{k\in VB}\left\|r_{VB }(\hat{z}_{b_{k+1}}^{b_{k}},X)\right\|_{P_{h+1}^{b_{k}}}^{2}+error_{Z\text{UPT }}\right\} \tag{7}\]  
where \(r_{C}\Big{(}\hat{z}_{l}^{C_{j}},X\Big{)}\) represents the residual of the visual measurement, \(r_{VB}(\hat{z}_{b_{k+1}}^{b_{k}},X)\) represents the residual of the VIMU measurement, and \(error_{Z\text{UPT}}\) represents the zero-velocity update constraint. Specifically, the residual of the visual measurement and the residual of the IMU, VIMU (with ZUPT), and zero-velocity constraint are all included in the cost function. Although the cost function slightly differs after adding the ZUPT for the VIMU, the dimension of the VIMU residual remains the same as that of the IMU residual, and the dimension of the state to be solved remains unchanged. By adding the zero-velocity constraint, the positioning accuracy and smoothness of the visual-inertial odometer are improved in complex environments.
## 3 Set Up Buildings' Settlement Prediction Model
### Set Up LSTM Model  
#### 3.2.1 LSTM Model  
In order to solve the problem of long-term dependency, the Long Short-Term Memory Neural Network was proposed [44]. LSTM is a special network in RNN with a mechanism that can regulate the flow of information, which consists of three kinds of gating, namely input gate, forgetting gate, and output gate, as well as memory cells. The network uses time-series data, the input layer in the network is a known dataset, the hidden layer uses the three gating controls to trade off the data, and the predicted values are obtained from the output layer.  
The LSTM model is shown in Figure 4, where \(X_{t}\) and \(Y_{t}\) are the inputs and outputs corresponding to moment \(t\), respectively, \(\sigma\) is the sigmoid activation function, \(tanh\) is the hyperbolic tangent function, and \(c_{t}\) and \(h_{t}\) are the unit state vectors obtained under the control of the forgetting gate and the final output values obtained under the control of the output gate, respectively. The network flowchart of the building settlement prediction model based on LSTM is shown in Figure 4a. Figure 4b includes the Oblivion gate calculation system, the input gate and the current candidate cell state, the current cell state, the output gate and the final output. The model takes geological parameters, spatial parameters, and building settlement monitoring values as inputs and building settlement predictions as outputs.  
In this paper, the settlement prediction model of neighboring buildings for deep foundation pit construction is set up with multiple hidden layers, and the LSTM layer is connected with the dense layer sequentially; the initial learning rate, the number of hidden units, the maximum number of back generation rounds, and the dropout are taken to be 0.007, 100, 80, and 0.2, respectively; and the loss transfer adopts the Adam algorithm.  
Figure 3: Initial dataset.