esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 2 Application of MFP Algorithms to Damage Identification  
In this section, we provide the fundamentals of the application of conventional beamformers, also called matched field processing (MFP) algorithms, to damage identification problems in beam structures. Since these algorithms are widely used for source localization in acoustics, it will be useful to present a one-to-one correspondence between the notions and the quantities used in the field of source localization and those of damage identification. The source is defined by its coordinates only, which are the arguments of a suitable function (i.e., Dirac delta function), and the measured quantity is the time history of the response of the field/continuum. The identification is carried out by means of matching the simulated data to the measurements, which match perfectly in an ideal case and when the source is at the exact point where one \"looks\". In this sense, the \"look direction\" is often used to describe the correct parameters of the source, that is, its position. In the case of damage identification, we replace the response of the system with the modal curvatures of the beam. The reason for this particular selection is discussed in Section 3. The \"source\" in our case is not the force acting on the beam but the damage itself; therefore, the parameters of the source function are the damage parameters, which are its location along the beam axis, and its severity. Therefore, the so-called \"look direction\" in our problem corresponds to damage location and severity.  
A means of forward solution provides the replica vector \(\mathbf{\chi}_{i}(\mathbf{x})\in\mathbb{R}^{m}\), which is a vector whose components are the mass-normalized modal curvatures of the \(i\)-th mode at \(m\) sensor locations for given damage parameters listed in the vector \(\mathbf{x}\). Beamformers operate comparing the replica vector to the data vector \(\mathbf{d}_{i}\in\mathbb{R}^{m}\), which is a vector that collects the \(i\)-th mass-normalized experimental modal curvature, obtained through a modal analysis of the responses recorded by \(m\) sensors. The comparison can be extended to as many modes as we can rely on. Sweeping the damage parameters over chosen intervals provides the set of replica vectors. An estimate of the damage parameters is then obtained by applying suitable means of correlation between the set of replica vectors and the data vector.  
The original contribution of this paper consists of the proof that both the damage location and intensity can be identified based on the use of modal strains experimentally measured with strain gauges as related to modal curvatures. Moreover, it provides an extension to the vibration data of algorithms that are popular in acoustics.
## 1 Introduction
Colorimetric analysis has been applied to evaluate the staining susceptibility of clear aligners and thermoformed retainers after exposure to various staining agents. For example, studies have employed colorimetric and spectrophotometric measurements to assess the discoloration levels in aligner materials subjected to substances like coffee, red wine, and tea, demonstrating their utility in understanding both material behavior and external influences over time [21]. These applications highlight the relevance of colorimetry as a diagnostic tool not only for aesthetic evaluation, but also for monitoring the aging and functional performance of orthodontic appliances in the oral environment [22]. In this context, the aim of this study was to evaluate patient compliance with NOXI 3D-printed night-time aligners (Sweden&Martina, Due Carrare, Padova, Italy) through an innovative method based on material discoloration. A well-documented effect of thermo oxidative degradation in aliphatic polyamides (PAs) is progressive yellowing, attributed to the formation of chromophoric structures--specifically, \(\alpha\)-ketoamide groups--resulting from the oxidation of methylene units adjacent to the amide carbonyl group [23; 24; 25]. This phenomenon was exploited in the present study as an indirect yet objective indicator of intraoral wear time. Accordingly, this study was designed to test the null hypothesis that different durations of intraoral wear (7 h vs. 12 h per day) would not result in a statistically significant difference in the degree of color change in polyamide aligners, as measured by RGB analysis. This hypothesis was tested over a standardized 14-day period using quantitative colorimetric measurements. Rejection of the null hypothesis would support the potential of RGB-based measurements as a reliable, non-invasive indicator for monitoring patient compliance.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 2 Theoretical Analysis and Methods
### Methods
#### 2.2.1 Averaged Amplitude Ratio of Low Energy Zone and High Energy Zone  
The first observable named EMR is the averaged correlation amplitudes ratio of the low energy zone and high energy zone. And the EMR is shown as  
\[EMR=\frac{\overline{Y}_{r}^{l}}{\overline{Y}_{r}^{l}} \tag{5}\]  
where \(\overline{Y}_{r}^{l}\) and \(\overline{Y}_{r}^{l}\) represent the average amplitudes of the low energy zone and high energy zone  
\[\left\{\begin{array}{l}\overline{Y}_{r}^{l}=\frac{1}{\tau_{2}-\tau_{1}} \int_{\tau_{1}}^{\tau_{2}}Y_{r}(\tau)d\tau\\ \overline{Y}_{r}^{l}=\frac{1}{\tau_{1}-\tau_{0}}\int_{\tau_{0}}^{\tau_{1}}Y_{ r}(\tau)d\tau\end{array}\right. \tag{6}\]  
where \(\tau_{1}\) and \(\tau_{2}\) are the delay values when the correlation amplitudes equal _Thershold2_ and _Thershold1_, respectively. \(\tau_{0}\) is the delay value when the amplitude is the maximum.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Radio Optimization
#### 2.1.3 Transmission Power Control  
Transmission Power Control (TPC) has been proposed in the literature to improve energy efficiency by adjusting the transmission power of the radio [23]. However, the TPC mechanism has an effect not only on energy but also on delays, interference and connectivity. In fact, when the transmission power is reduced, the risk of interference also diminishes. Conversely, the delay increases, because more hops will be needed to forward a packet. TPC has also been an active research area in recent years, essentially in the scope of Wireless Body Area Networks (WBANs) with medical applications, as shown in these two recent surveys [24; 25]. Important work in this area is due to [PERSON] et al. [26; 27; 28].  
The aim of TPC mechanisms in WBANs, which can be combined with other energy-saving mechanisms [29; 30; 31], is to reduce the energy consumption and external interferences by dynamically adjusting the transmission power output, with the minimum effect on other performance aspects, such as reliability and latency. Other recent works in this field are adaptive algorithms based on variations in body conditions [32] or on human motion [33].  
More recently, [PERSON] et al. [34] investigated TPC in 802.15.4 + RPL WSNs and proposed a mechanism where each node dynamically adjusts its transmission power based on channel conditions before sending every data and ACK packet. Their results confirm that this power control method improves network lifetime. A TPC mechanism is also proposed for Underwater Wireless Sensor Networks (UWSNs) in [35], where source nodes adjust transmission power according to the location of the destination node.
## 2 Results and Discussion
### Optimization of Screening Conditions  
In order to ensure the efficiency of screening and to give sufficient reaction time, the screening time from 0 min to 180 min of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles with extract was investigated. When Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles were incubated with extract for a certain time, the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles were retained by a magnet and washed with buffer solution. Finally, the nanoparticles were eluted by a methanol solution and the absorbance of the elution was detected by Ultraviolet-visible spectroscopy (UV-VIS) Spectrophotometer at 254 nm. Because the elution contained screened compounds and those compounds showed absorbance at 254 nm, higher absorbance of the  
Figure 4: The magnetization curves of (a) Fe\({}_{3}\)O\({}_{4}\) nanoparticles, (b) Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles and (c) Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles.  
Figure 3: The Fourier transform infrared spectroscopy (FT–IR) spectrum of (a) Fe\({}_{3}\)O\({}_{4}\) nanoparticles, (b) Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles and (c) amino Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles.  
elution at 254 nm meant that a higher concentration of compounds were screened out and, thus that the screening was closer to complete. Therefore, the screening capacity was expressed as the absorbance at 254 nm in a relative form (%) with the maximal value as 100%. As shown in Figure 5a, after 90 min screening, the screening capacity reached maximum. Hence, 90 min was enough for the screening of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles with extract.  
Reuse of magnetic nanoparticles could apparently reduce experimental cost and improve efficiency. The reusability of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles was investigated and shown in Figure 5b. After one round of screening, the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles were washed with buffer solution and then transferred into another extract solution to start a new round of screening. The elution of each round was monitored by UV-VIS Spectrophotometer at 254 nm and the screening capacity was expressed as the absorbance at 254 nm in a relative form (%) with the maximal value as 100%. As a result, the screening capacity of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles after five rounds screening retained 93% of its initial capacity. As a support for enzyme, Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles could effectively protect the enzyme activity and increase the practicality of this method.
## 5 Numerical Simulations
### Simulation Results and Analysis
#### 5.2.2 Case 2  
In the second case, the position and the attitude of UAV base are not controlled, similar to the free-floating space robot [13] in outer space environment. The kinematic equation of the AEROM system is shown in Equation (10). The pose disturbance acting on UAV base is considered. The cost function in (24) includes the UAV disturbance (21) and the angular variations of joints (22), shown as \(\Gamma=\Gamma_{1}+\Gamma_{1}\). The weight matrix \(\mathbf{M_{1}}\) is assigned as \(\mathbf{M_{1}}=\begin{bmatrix}\mathbf{E_{3}},\ \mathbf{O_{3}};\ \mathbf{O_{3}},\ \frac{1}{\sin \left(\frac{\pi}{80}\right)}\mathbf{E_{3}}\end{bmatrix}\). The weight matrix \(\mathbf{M_{2}}\) is assigned as \(\mathbf{M_{2}}=\frac{1}{\sin\left(\frac{\pi}{80}\right)}\mathbf{E_{6}}\). \(\mathbf{E_{3}}\) and \(\mathbf{E_{6}}\) are respectively 3-order and 6-order unit matrices. The AEROM system is commanded to move from initial state \(\mathbf{q}^{0}=[5,-10,10,20,-10,-20](\deg)\) to desired state \(\mathbf{q}^{f}=[-10,-20,0,0,0,-40](\deg)\). Figure 9 shows the variations of the cost function. The proposed HPSO algorithm stops when \(k_{\rm eq}=101>100=k_{\rm max}\), where \(N=278\) and the final value is 1.0789. Figure 10 shows the time histories of joint angles, meeting aforementioned conditions. Figure 11 shows the variations of joint velocities with the final value of \(\left(5.0\times 10^{-15},\ 5.0\times 10^{-15},\ -1.0\times 10^{-17},\ 1.0\times 10^{-19}, \ -7.5\times 10^{-15},\ -1.0\times 10^{-19}\right)(\deg)\). Figure 12 shows the attitude and the position variations of UAV base. The actually planned UAV attitude and UAV position are respectively \(\left(-5.3\times 10^{-3},\ 5.5\times 10^{-3},\ -6.4\times 10^{-3}\right)(\deg)\) and \(\left(4.1\times 10^{-4},\ 3.3\times 10^{-3},\ 2.7\times 10^{-4}\right)(\rm m)\). It means the disturbance order magnitude acting on UAV base is \(-3\). Therefore, the planned error is within the predefined threshold range, where \(\left\|\left[-5.3\times 10^{-3},\ 5.5\times 10^{-3},\ -6.4\times 10^{-3} \right]\right\|=9.9649\times 10^{-3}<\varepsilon(1)=0.1\) with the unit of degree and \(\left\|\left[4.1\times 10^{-4},\ 3.3\times 10^{-3},\ 2.7\times 10^{-4} \right]\right\|=3.3363\times 10^{-3}<\varepsilon(2)=0.1\) with the unit of meter. The error vector \(\delta\mathbf{q}\) in Equation (18) is \(\delta\mathbf{q}=[0.01,0.11,0.51,2.09,-3.73,4.1]\times 10^{-7}(\deg)\). According to the simulation results, the joints move smoothly and the robotic manipulator successfully arrives at the desired state. Furthermore, the objective of minimizing UAV pose disturbance is realized.  
Figure 8: Velocity variation curves of \(i\)th joint, where (**a**) \(i=1,\ 2,\ 3\), and (**b**) \(i=4,\ 5,\ 6\).  
Figure 7: Angular variation curves of \(i\)th joint, where (**a**) \(i=1,\ 2,\ 3\), and (**b**) \(i=4,\ 5,\ 6\).  
Figure 11: Velocity variation curves of \(i\)th joint, where **(a)**\(i=1\), 2, 3, and **(b)**\(i=4\), 5, 6.  
Figure 12: Pose variation of UAV base, where **(a)** UAV attitude, and **(b)** UAV position.  
Figure 9: The best values of cost function in case 2.
# Ensemble-Based Correction for Anomalous Diffusion Exponent Estimation in Single-Particle Tracking
## 1 Introduction
Unlike the first AnDi Challenge, which involved noisy trajectories generated by five different underlying diffusion models, the second challenge focused exclusively on noise-free trajectories simulated using fractional Brownian motion (fbm). In this more controlled yet demanding setting, we observed that classical statistical methods such as the time-averaged mean squared displacement [22] and Hurst exponent estimators such as the Whittle method [23] performed in a manner comparable to more complex machine learning approaches. Furthermore, classical methods offer a practical advantage since they can be applied directly without the need for training. The objective of this study is to develop ensemble-based correction methods for improving anomalous diffusion exponent estimation from short single-particle trajectories. Rather than developing new estimation algorithms like [17; 20; 24], our approach leverages ensemble statistics to correct individual trajectory estimates, following the paradigm of ensemble-based reliability enhancement, similar to [25]. By using collective information across multiple particles, we compensate for the noise and bias inherent in single-trajectory analysis, extending the reliability of existing methods to challenging short-trajectory regimes. Our contributions are as follows:  
1. Variance-Based Ensemble Estimation: For any \(\alpha\) estimation method, we propose empirically characterizing the method-specific noise component \(\sigma^{2}\) (\(\alpha\), trajectory length) to enable shrinkage correction, optimally balancing individual trajectory information with ensemble statistics to improve estimation accuracy and robustness. 2. Bias Correction in Short Trajectories: In normal and superdiffusive regimes, finite-length effects can introduce systematic biases into exponent estimates, especially when trajectories are extremely short. To address this, we propose using the time-ensemble averaged mean squared displacement (TEA-MSD) as a more reliable and length-invariant method for characterizing diffusion behavior in fractional Brownian motion. This approach enables accurate correction of the ensemble mean. 3. All code and experimental results supporting the findings of this study are publicly available at: github.com/SophiaLavr/Correction-AnDi-Exponent (accessed on 17 July 2025).
## 4 Test Results and Analysis
### Characteristic Points of the Skeleton Curve  
Based on the \"Regulations for Seismic Test Methods for Buildings\" (JGJ 101-2015), the values of vertical and horizontal coordinates corresponding to the peak points of the \(P\)-\(\Delta\) curve have been defined as ultimate load \(P_{m.t}\) and ultimate displacement \(\Delta_{m.t}\), respectively. The vertical and horizontal coordinates corresponding to 0.85 times of \(P_{m.t}\) are defined as the damage load \(P_{ft}\) and damage displacement \(\Delta_{ft}\), respectively. Meanwhile, \(P_{y,t}\) is the yielding load value and \(\Delta_{y,t}\) is the yielding displacement. The characteristic values of specimens SF-1 and SF-2 are shown in Table 4.  
Figure 16: Skeleton curves of SF-1 and SF-2.  
Table 4 summarizes the three special points on the skeleton curve, which indicate the yielding load and the yielding displacement, the ultimate load and the ultimate displacement, and the damage load and the damage displacement of SF-1 and SF-2 at PD and ND, respectively. In the PD direction, the yielding load of SF-2 is increased by 8.38% compared to SF-1. The yielding displacement of SF-2 is increased by 2.53 mm compared to SF-1. In the ND direction, the yielding load of SF-2 is 11.65% higher than that of SF-1, and the yielding displacement of SF-2 is 0.23 mm greater than that of SF-1. In the ultimate case, the bearing capacity of specimen SF-2 in the PD direction increased by 14.18 kN and the displacement increased by 14.34 mm compared with that of SF-1. In the ND direction, the ultimate bearing capacity of SF-2 increased by 16.53 kN and the displacement increased by 0.33 mm compared with SF-1. The damage load capacity of specimen SF-2 in the PD direction increased by 12.06 kN and the displacement increased by 24.72 mm compared with SF-1. In the ND direction, the damage load capacity of SF-2 increased by 14.04 kN and the damage displacement increased by 24.87 mm compared with SF-1.  
The ultimate bearing capacity and yield bearing capacity of specimen SF-2 are greater than those of specimen SF-1 because, under the same conditions, NEDCs can reduce the impact that frame deformation has on the AAC panels and alleviate the damage caused to the frame and panel. Therefore, the ultimate horizontal load capacity of the composite frame with NEDCs is higher than that of the composite frame using rigid connection nodes (hook bolts) under low-cyclic loading [28].
## 2 Materials and Methods
### Phenolic Compounds Extraction  
Briefly, 1 g of lyophilized extract from each cultivar was firstly stirred with 70% ethanol at 300 rpm for 2 h, protected from light at room temperature, and then centrifuged at 4000 rpm for 15 min. Next, the supernatant was evaporated, dissolved in deionized water, and placed into a C18 solid-phase extraction column (70 mL/10,000 mg; Macherey-Nagel, Duren, Germany) previously conditioned with ethyl acetate, ethanol, and 0.01 mol/L HCl [6]. Then, the sample was passed through the column and the non-coloured fraction was eluted with ethyl acetate and the coloured fraction with ethanol containing 0.1% HCl. This procedure was repeated and the total extract (rich in both phenolic subclasses, i.e., in anthocyanins and non-coloured phenolic compounds) was eluted with ethanol containing 0.1% HCl. Finally, each fraction was evaporated to dryness and frozen at \(-\)20 \({}^{\circ}\)C until analysis. The extraction yields of non-coloured fraction, coloured fraction, and total extract were 7.3 \(\pm\) 0.02%, 9.1 \(\pm\) 0.005%, and 16.76 \(\pm\) 0.021% for the Legacy blueberry cultivar, respectively, and 6.59 \(\pm\) 0.0070%, 10.9 \(\pm\) 0.04%, and 17.9 \(\pm\) 0.02% for the Duke blueberry cultivar, respectively. Regarding the Sweetheart cherry cultivar, the extraction yields were 6.0 \(\pm\) 0.004% for the non-coloured fraction, 2.9 \(\pm\) 0.02% for the coloured fraction, and 8.4 \(\pm\) 0.02% for the total extract, respectively. The extraction yield concerning the Saco sweet cherry cultivar was already reported in a previous work conducted by our research team and the extraction yields were 8.5 \(\pm\) 0.02%, 3.6 \(\pm\) 0.005%, and 13.0 \(\pm\) 0.01% for the non-coloured, coloured, and total extract, respectively [5].  
Figure 1: Image of the samples studied in this work (image courtesy of the authors).
# Provision of Energy- and Wavelength-Efficient Traffic
## 4 Simulation and Performance Evaluation
### Analysis of the Number of Wavelengths and Hops  
The following performance evaluations are based on the DSCNs with a 22-node topology. Figure 5 shows the comparison of the average number of wavelengths per node between the DLG, TPTG, DLG_GA, and TPTG_MA. As we can observe, the wavelength consumption per node of the TPTG_MA is fewer than that of the other three algorithms. When the traffic intensity is 300 Erl, the TPTG_MA consumes an average of 33.3%, 55.5%, and 66.7% few wavelengths per node than the DLG_GA, TPTG, and DLG, respectively. This is because the TPTG_MA grooms the traffic requests into the wavelengths in a finer way. In Section 2, we formulate the traffic grooming as a two-phase ILP problem, i.e., the first phase for TAASA, and the second phase for SG. In this way, most wavelengths are fully exploited, hence decreasing the number of wavelengths.  
Figure 4: Comparisons of the ECS between the DLG, TPTG, DLG_GA, and TPTG_MA under (**a**) 6-node topology, (**b**) 12-node topology, and (**c**) 22-node topology.  
Figure 6 presents the properties of the average number of hops per-flow between the proposed TPTG_MA and the other three algorithms. It can be observed that with the traffic intensity increasing, the average number of hops per-flow is improved. The average number of hops per-flow for the proposed TPTG_MA is slightly more than that of the other three algorithms. The reason is that the proposed TPTG_MA improves the utilization of wavelength capacity at the expense of elongating the hops of a few traffic requests. Although parts of traffic requests suffer the detour, the overall enhancement of network capacity utilization is more than the limited expense.  
Figure 5: Comparison of the number of wavelengths per node between the DLG, TPTG, DLG_GA, and TPTG_MA.  
Figure 6: Comparison of the number of hops per-flow between the DLG, TPTG, DLG_GA, and TPTG_MA.