esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Three-Wavelength Radiation Temperature Measurement Based on Reflection Correction
We obtain a relationship between the error function \(e_{1}(T)\) and the temperature \(T\) at the measured point \(P_{0}\) of the target blade, which is represented by the green curve in Figure 10. The function \(e_{1}(T)\) has two zero points: one at the real temperature \(T_{0}\) = 1150 K, and the other at \(T_{1}\) = 930.6 K. Both temperatures are within the working range of the turbine blade and are easily confused with its actual surface temperature. Then, three wavelengths, 1, 2, and 3, are selected. According to the error function \(e_{1}(T)\), we can obtain two more error functions \(e_{2}(T)\), and \(e_{3}(T)\):  
\[e_{2}(T)=\frac{S_{3}-S_{e3}-S_{D3}}{S_{2}-S_{e2}-S_{D2}}-\frac{S_{B3}(T)-S_{e3} }{S_{B2}(T)-S_{e2}} \tag{28}\]  
\[S-S_{D}-S_{e}=\epsilon_{b}(S_{B}(T)-S_{e}) \tag{21}\]0  
We define the error function of three-wavelength radiation thermometry algorithm:  
\[S-S_{D}-S_{e}=\epsilon_{b}(S_{B}(T)-S_{e}) \tag{21}\]1  
We choose the center wavelengths of the three narrow bands beyond the strong absorption band of the engine gas as 1500 nm, 1600 nm, and 1700 nm and further analyze the relationship between the error functions \(e_{1}(T)\) (wavelengths 1600 nm and 1500 nm), \(e_{2}(T)\) (wavelengths 1700 nm and 1600 nm), and \(e_{3}(T)\) (wavelengths 1500 nm and 1700 nm) and the temperature \(T\) at the measured point \(P_{0}\) of the target blade at a specific temperature. The green curve in Figure 10 shows the relationship between \(e_{1}(T)\) and temperature \(T\); the red curve shows the relationship between \(e_{2}(T)\) and temperature \(T\); and the blue curve shows the relationship between \(e_{3}(T)\) and temperature \(T\). The three curves have the same zero point at \(T\) = 1150 K, and the other zero points appear at \(T_{1}\) = 930.6 K, \(T_{2}\) = 920.03 K, and \(T_{3}\) = 925.55 K. The positions of these three zero points are not the same. Therefore, when Equation (30) is used as the three-wavelength error function, only the minimum function value 0 is obtained when \(T\) = 950 K, and the function values at other positions are all greater than 0, which ensures the uniqueness of the solution. The temperature of the target surface can be reversed by the three-wavelength radiation temperature measurement algorithm, which only requires the radiation signal (\(S_{1}\), \(S_{2}\), and \(S_{3}\)) of the target surface and the ambient radiation signals (\(S_{e1}\), \(S_{e2}\), and \(S_{e3}\) are obtained from the simulation results of the reflection model combined with the measurement).
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 5 Conclusions  
In order to meet the challenges of variable working conditions and noise pollution and further improve the generalization and anti-noise performance of the algorithm, a novel capsule network model based on wide convolution and multi-scale convolution (WMSCCN) for bearing fault diagnosis is proposed. The proposed WMSCCN algorithm takes a one-dimensional vibration signal as input and no additional manual processing is required. In addition, the AdaBN algorithm further improves the robustness of the model, and in particular obtains a higher fault recognition rate under noisy conditions. In generalization experiments under different loads, the proposed WMSCCN and WMSCCN-AdaBN algorithms achieve average accuracy rates of 96.44% and 97.44%, respectively, which is superior to other advanced algorithms. In the noise resistance experiment, WMSCCN-AdaBN could maintain a 92.3% diagnostic accuracy in a strong noise environment with an SNR = \(-\)4 dB, showing a very strong anti-noise ability. When the SNR exceeds 4 dB, the accuracy reaches 100%, indicating that the proposed algorithm has a very good accuracy at low noise levels. Two experiments have effectively verified the effectiveness and generalizability of the proposed WMSCCN algorithm.  
Data collection, data analysis, python programming, manuscript writing, Y.W.; manuscript review, comparative experiment design, funding acquisition, D.N. and S.F. This version of the manuscript has been read, agreed on, and substantially contributed to by all the authors. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the green manufacturing system integration project of the Ministry of Industry and Information Technology of the People's Republic of China--GE Green Supply Chain Innovation Project.  
The authors would like to thank the Shanghai Economic and Information Technology Commission Industrial Internet Innovation Development Special Fund Project-the security and operation and maintenance service platform for industrial key basic information facilities and the Shanghai Industrial Field (Steel Manufacturing) Big Data Joint Innovation Laboratory for their support.  
The authors declare no conflict of interest.
## 3 Results and Analysis
### The Fragility Curves of Community Components  
The fragility curves of community components play a critical role in quantifying flood resilience. For this analysis, a simplified hypothetical community is considered, encompassing four key sectors: residential (R.S.), business (B.S.), educational (E.S.), and public service (P.S.). Each sector comprises multiple building archetypes, each designed with distinct service objectives and structural characteristics. For instance, the residential sector includes one-story single-family homes, one-story multi-family homes, two-story single-family homes, and two-story multi-family homes. The fragility curves for these archetypes, as derived from [6], are shown in Figure 5a.  
Similarly, the fragility curves for business facilities (Figure 5b), educational buildings (Figure 5c), and public service structures (Figure 5d) illustrate the probability of damage states at varying flood depths. These curves depict the exceedance probability, which quantifies the likelihood of reaching or exceeding specific damage states given a certain  
Figure 4: Zoning map of the virtual community.  
Figure 5: Fragility curves in four sectors: (**a**) R.S.; (**b**) B.S.; (**c**) E.S.; and (**d**) P.S.  
-flood intensity. Such probabilities are critical for understanding how individual components within each sector contribute to sector-wide functionality loss during flood events.  
Table 3 provides the lognormal parameters--logarithmic medians (\(\lambda\)) and standard deviations (\(\xi\))--for the developed fragility curves of each building archetype. For example, one-story single-family residential buildings (R1) exhibit a \(\lambda\) of 1.173 and a \(\xi\), of 0.278, indicating their relative vulnerability compared to other archetypes. These parameters enable a probabilistic understanding of structural performance under varying flood intensities. It can be observed that these fragility curves underscore the heterogeneity in resilience across building types. They reveal that while some archetypes are more robust against flood hazards, others face significant vulnerabilities, requiring targeted mitigation strategies. By integrating these fragility curves with interaction matrices, this study provides a comprehensive framework for evaluating the resilience of individual sectors and the community as a whole.
## 4 Discussion
While conducting the experimental tests, we observed dependencies of PPG, skin temperature, and UV measurements regarding the wearing location, tight or loose mounting, and relative angle of sensors. Furthermore, external material must not cover the device. Hence, the user must make sure that the physiological layer is appropriately located and tight on the wrist. The prototype is required to be outside-oriented, as the used microphone is omni-directional. Also, more comprehensive evaluations and field test are required before the device can be confirmed in clinical trials. In future, if a user is willing to extend his/her health monitoring conditions and already has an integrated diagnostic system in the private spaces, our prototype can be readily integrated to smart-homes [75] and smart-cars [76] for data correlation, expanding the application usage, and improving the reliability of collected data. Assuming stable communication between device and smart environments, the off-line time can be extended significantly when the device is worn in such environments. Furthermore, medical application is not limited to COPD. The acute respiratory distress syndrome (ARDS), for instance, also needs monitoring of environmental, behavioral, and physiological parameters [77], as ARDS patients are encountering an often severe shortness of breath, have a cough, and may have fever [78]. Furthermore, ARDS patients have difficulties when inhaling and exhaling, and thus a chest-worn device is infeasible [79]. Most people with ARDS stay in bed, many in critical states [80]. Therefore, embedding our device in smart environments will further broadband the monitoring and link vital signs to non-medical data (e.g., environmental and behavioral) for status assessment and early detection of abnormalities. In future studies we plan to shift the concentration from testing the functionality to improve the accuracy of measured data. Thus we will compare the data measured with the developed prototype with those measured with gold standard instruments to enhance the performance in terms of accuracy and uncertainty. For the next version of the prototype, we plan to implement a notification system to alert abnormalities detected from domain-fused sensor data. Currently, we have reserved the required circuit on Layer 5. Furthermore, we aim at driving research towards self-powered devices and consider a hybrid power-harvesting scenario. With a more comprehensive evaluation, we also plan to use the prototype for COPD patients in the clinical setting.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### 2D Plane Localization Accuracy  
We first validate the performance of ANTSpin in a 2D plane. The scenario is deployed, as shown in Figure 15a. We move the tags to be located and then collected about 150 test cases. Figure 16 plots the CDF of localization error in 2D. The mean error of relative incident angle is 0.0674, 80% of the errors are less than 0.1 with minimal error of 0.0006 and maximal error of 0.231. The mean error of distance calculation is 4.37 cm and 90% of the errors are less than 10 cm with minimal error of 0.12 cm and maximal error of 20.4 cm. Errors over 15 cm are individual phenomena. The mean distance error of ANTSpin under the 2D plane scenario is 5.11 cm in the X-axis, 6.82 cm in the Y-axis, and 9.3 cm in combined dimension; 80% of the errors are less than 15 cm with minimal error of 1.16 cm and maximal error of 26.3 cm. We tested some existing schemes in a similar experimental environment, and the experimental results show that ANTSpin performed better than Landmarc (33 cm), Vire (19 cm), SVR-Landmarc (23 cm), and BKNN (15 cm) [9; 10; 12; 35].
# Current Uncertainties and Future Challenges Regarding NAD+ Boosting Strategies
## 3 NAD+ Precursors and Boosters  
NAD+ boosters include NAD+ precursors, pharmacological inhibitors of enzymes consuming NAD+, and modulators of enzymes of the NAD+ biosynthesis pathways [68]. As NAD+ is mainly produced by the NAD+ salvage pathway from niacinamide/nicotinamide, nicotinamide riboside (NR), and nicotinamide mononucleotide (NMN) [4; 7; 53; 54; 55; 56], these seem to be the most promising NAD+ boosters, since more steps are required for nicotinic acid (NA) and tryptophan to form NAD+ [69]. NAD+ availability can be elevated by PARPs pharmacological inhibitors (BGB-290, olaparib, rucaparib, veliparib, CEP-9722, E7016, talazolapir, iniparib, niraparib (MK-4827), PJ34, DPQ, 3-aminobenzamide) [70; 71] and CD38 inhibitors [72; 73; 74], such as flavonoids including epigenin, luteolin, quercetin, kuromanin, luteolinidin, or thiazolquin(az)olinones such as the compound \"78c\" [75; 76; 77; 72]. There are also chemical inhibitors of the NAD+ biosynthesis enzymes, such as FK866, an NAMPT inhibitor, gallbladder, an inhibitor of NMNAT and phthal acid (PA), an inhibitor of QPRT [78; 67] hydroxynicotitic acid, an inhibitor of NAPRT [79]. However, little is known regarding their pharmacology and safety for human use to increase NAD+. The use of PARP inhibitors might have detrimental effects as PARPs are involved in fundamental cellular process and PARP inhibition induces genomic instability [80]. The mentioned inhibitors, including CD38 homologue CD157, are currently investigated as drugs for cancer treatment [24], and the inhibition of CD38 and PARPs in non-oncogenic conditions is poorly explored so far. Activation of NAD+-generating enzymes by AMPK and NAMPT activators is another approach for boosting NAD+ levels [81; 82]. Such activators are 5-aminoimidazole-4-carboxamide epigallocatechin gallate, leucine, metformin, resveratrol, ribonucleudelotides, P7C3, small molecule SBI-797812, and proanthocyanidins [81; 83; 84; 85; 86; 87; 88; 89; 90; 91]. The most important NAD+-generating enzyme is NAMPT, which converts NAM to NMN [81]. NAMPT biosynthesis can be modulated by the small synthetic molecule activators P73C and SBI-797812 [81], or notoginsense leaf triterpenes and a natural peptide IRW (Ile-Arg-Trp), natural compounds that activate Nampt gene expression and/or increase intracellular NAMPT protein abundance [92; 93; 94]. Further studies are needed to evaluate the health risks and benefits of continuous use of NAMPT activators and PARP and CD38 inhibitors in humans for medical purposes to increase NAD+ bioavailability.  
Figure 1: Major NAD+ biosynthesis, consumption, and redox-cycling routes.
# Experimental Research on In Situ Uniaxial Tensile Response of Silica-Based PLC Optical Splitters
## 3 Results and Discussion
### Observed Failure Mode
Consequently, more advanced techniques, such as a digital image correlation (DIC) technique, are required for further study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Insertion Loss Change (\(\Delta\Pi\)L)** & **Classification of Damage Degree** \\ \hline \((-5\) dB; \(0\) dB] & The bonding layer is slightly deformed with dispersed micro-defects and micro-cracks, that is, light damage. \\ \hline \((-15\) dB; \(-5\) dB] & As the deformation of the bonding layer increases, the micro-defects and micro-cracks of the bonding layer further extend and the propagation of some micro-cracks may cause local debonding of the interface, that is, moderate damage. \\ \hline \((-\infty\), \(-15\) dB] & Crack unstable propagation, adhesively-bonded joints lose load-bearing capacity, and the bonding interface is almost or completely separated, that is, severe damage. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The corresponding relationship between \(\Delta\Pi\)L at \(1.55\)\(\upmu\)m wavelength and the damage degree of PLC optical splitters.
## 2 Device and Methodology
To mitigate frequency shifts as a result of temperature variation, the reduction in the back cavity size should be considered to enhance the device's temperature stability. The effect of varying the thickness of the top silicon layer on the temperature stability of the device is shown in Figure 5. As the thickness of the silicon layer increases, the temperature stability changes significantly. When the thickness of silicon above is 2 \(\upmu\)m, the frequency change rate due to temperature reaches 77.2%, while it is only 1.9% at 5 \(\upmu\)m. The results indicate that, at higher temperatures, the frequency shift exhibits a nearly linear trend, and as the device stiffness decreases (i.e., with decreasing silicon thickness), this linear slope increases. Future experiments will further validate this observed trend. Figure 4: Frequency variation with temperature for devices with different back cavity sizes (FEM). Figure 5: Frequency variation with temperature for devices with different Si thicknesses (FEM).
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 4 An Efficient Conflict Detection Algorithm for Non-Prefix Fields  
For 5D conflict detection, we notice that when Condition 1 defined in Section 3 is satisfied, in the five fields, up to ten (\(C_{2}^{5}\)) types of two-field (\(i\), \(j\)) combinations exist to satisfy Condition 2 defined in Section 3. To determine the two fields to satisfy Condition 2, an extremely complex execution process is required. Moreover, since the fields of filters are stored in different data formats, it is difficult to design an effective data structure and algorithm to detect all conflicts. Therefore, if the existing algorithms are extended to detect 5D conflicts, the performance of most of them will be seriously affected. In order to simplify conflict detection, the key idea of our study is to divide the conflict detection into the detection of prefix fields and detection of non-prefix fields. We firstly analyze the comparison result combinations of filters \(F\) and \(G\) in prefix fields, then match them with the combinations of non-prefix fields of \(F\) and \(G\) to report conflict with our pre-computed information, which can reduce the number of comparisons of non-prefix fields and further speed up the execution of 5D conflict detection.  
The remainder of this section is organized as follows: Section 4.1 introduces our combined detection method, which can reduce the complexity of 5D conflict detection by analyzing all combinations for 5D filter conflicts. Section 4.2 explores the characterization and classification of non-prefix fields and defines the results of comparing two filters in a single non-prefix field. Section 4.3 explores the results of comparing all non-prefix fields and describes how to use our pre-computed information to reduce conflict detection for non-prefix fields. Section 4.4 describes how to construct a lookup table based on our pre-computed information and illustrates how to extend the existing 2D algorithm to 5D conflict detection with our proposed algorithm. The database in Table 2 is used as a supplementary explanation.
# Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions
## 1 Introduction
A control protocol based on distributed sampling data was proposed to achieve system consensus, but the delay condition was not taken into account in the algorithm. Reference [15] studied the consensus of second-order multi-robot systems under non-uniform and multi-time delays using the frequency domain analysis method. Compared with the Lyapunov method, it has lower conservativeness, and the results were extended to higher-order multi-robot systems. However, it did not take noise into consideration, which is unavoidable in practical environments. Reference [16] studied the consensus of second-order multi-robot systems under uniform time delay and noise environments, and designed different control protocols for different types of noise, thus achieving the consensus of the system. These algorithms provide some basic solutions to the second-order system consensus problem, but the problems encountered by multi-robots in practical applications are far more varied than these. On the basis of these algorithms, this paper performs a more in-depth analysis, especially considering the consensus of the second-order system in which there are many different time delays and multiplicative noises in the system, laying the foundations for a formation control algorithm for second-order multi-robot systems that can be truly implemented in real robot systems. In summary, this paper analyzes the consensus problem of second-order multi-robot systems under various delay and noise conditions. The system character equations are transformed into quadratic polynomials of pure imaginary eigenvalues based on frequency domain analysis, and then solved. Finally, its critical steady state is obtained and verified using Matlab numerical simulation. Compared with existing algorithms, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Since the omnidirectional mobile robot is a fully driven robot, and the horizontal and vertical directions can be separately controlled, it can be constructed as two one-dimensional second-order multi-robot systems. Therefore, experiments were carried out on a multi-omnidirectional mobile robot platform built in the laboratory using the proposed algorithm [17; 18], which verifies the effectiveness of the proposed algorithm.
# Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints
## 5 Conclusions  
1. In this paper, it is proposed that cement mortar can fill the the bottom of the well in advance to improve the stability of the wellbore before the wall is suspended and sunk to the bottom. According to the contact characteristics of the bottom of the wall after optimization, the wall cylinder is regarded as an equal-section compression rod hinged at the upper end and solidly supported at the lower end, and based on the mechanism of cusp catastrophe characteristics, the critical depth of instability of an equal-section drilling wall in a non-full-water state is deduced from catastrophe theory.
2. Compared with the traditional method, the critical depth of the wellbore structure after the optimization method can be increased by 41.39 \(\pm\) 5%. Through numerical simulation using a sub-well as an engineering example, the eigenvalue of the traditional method is 0.9978, which is smaller than 1, indicating that the wall is unstable, while the eigenvalue of the optimization method is 2.7471, which is larger than 1, indicating that the wall is stable, which is in line with the theoretical calculation. Both the theoretical and numerical analysis reveal that the optimization method is reliable and applicable.
3. The critical depth of the wellbore structure by using optimization measures is influenced by multiple factors. It increases with the increase in the elastic modulus and the thickness of the wellbore, decreases with the increase in the self-weight of the wellbore, and shows a trend of first decreasing and then increasing with the increase in the height of counterweight water.
4. The critical depth of the wellbore structure by using optimization measures is influenced by multiple factors to varying degrees. The degree to which they are affected in descending order is elastic modulus, counterweight water height, self-weight of the wellbore, and thickness of the wellbore.
5. In practical engineering, it is of great practical significance to use high-strength steel with a higher modulus of elasticity or increase the height of appropriate counterweight water and reduce the thickness of internal and external steel plates to improve the stability of the wellbore.  
Conceptualization, R.P.; methodology. J.L. and H.C.; software, J.L.; validation, R.P.; formal analysis, R.P.; investigation, H.F.; resources, R.P.; data curation, H.F.; writing--original draft preparation, R.P.; writing--review and editing. J.L.; visualization, J.L.; supervision, J.L. and H.C.; project administration, J.L. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Anhui Province Key Laboratory of Building Structure and Under-ground Engineering (Hefei), Anhui Jianzhu University (KLBSUE-2022-01).  
Not applicable.  
The data presented in this study are available on request from the corresponding author (privacy).  
The authors declare no conflicts of interest.