esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### CFRP\({}_{\text{PROD}}\) Aircraft Panel  
Finally, the CFRP\({}_{\text{PROD}}\) aircraft panel with horizontal backside stiffeners and a complex cluster of production defects was discussed (see Figure 1c). The results of the post-processing techniques are presented in Figure 9. DTT's maxigramT (Figure 9a) provides no noticeable defect detectability. Only the backside stiffeners can be extracted, while the remainder of the component is governed by noise. In the timegram (Figure 9b), additional features can be observed in between both stiffeners (at the indicated location of the defect cluster in Figure 1c). However, a comparison with the damage map obtained through broadband vibrational NDT (see Figure 1c) shows that not all features of the defect cluster were detected in DTT's timegram. The features that were detected in DTT's timegram (around 5 s) indicate that the first 10 s was a good integration range for TSA. This was confirmed in the output of TSA (see Figure 9c), where many more of the defect features were retrieved. This indicates that the defect features that provide only a limited thermal contrast were not always properly detected by DTT. The integrated TSA technique, on the other hand, successfully highlights the presence of multiple defects. However, compared to the vibrational map (see Figure 1c), the defect indications in TSA were blurry (due to lateral heat diffusion) and there was more background noise. The small defect below the bottom stiffener (indicated with the red arrow in Figure 9c) was not detected by TSA.  
Figure 9: Results of processing techniques for the CFRP\({}_{\text{PROD}}\) aircraft panel: (**a**,**b**) DTT maxigramT \(\Delta T_{max}\) and timegram \(t_{\Delta T,max}\); (**c**) TSA (0–10 s); (**d**,**e**) FDT maxigramT \(\Delta\varphi_{max}\) and frequencygram \(f_{\Delta\varphi,max}\); and (**f**) ASBI damage index map.  
FDT's maxigramF (Figure 9d) revealed the locations of many of the small defect features, but their detectability was strongly hindered by the non-uniformity, which was introduced by the flash excitation (see Figure 3g). The frequencygram is presented in Figure 9e, and only highlighted the backside stiffeners and part of the complex defect cluster. ASBI (Figure 9f) shows a good agreement with the broadband vibrational map (Figure 1c). Both backside stiffeners were highlighted by an increased damage index, and the small defects in the complex defect cluster were detected more clearly than in the TSA output. Additionally, the very small isolated defect below the bottom stiffener was well represented in the ASBI damage map (see arrow in Figure 9f). However, note that there was still a remnant of the non-uniform heating profile.
## 4 Results and Discussion
### Diaphragm Movement and Stress Characteristics of the DSDC
#### 4.2.2 Diaphrag Movement Under Different Experimental Conditions  
Figure 16 shows the radial deformation behavior of diaphragms under varying experimental conditions across different crank angles. As depicted in Figure 16a,b, the center deflection exhibited similar trends under different operating conditions, while minor differences emerged at the BDC position, attributed to the coupled effects of volumetric efficiency variations and hydraulic oil compressibility. Specifically, case #1 operating condition achieved maximum center deflection at the BDC position, with the first-stage diaphragm reaching 4.14 mm and the second-stage diaphragm reaching 2.53 mm. Figure 16c,d shows the radial diaphragm stress variations under the specific case #3 condition across different crank angles. The results indicated that during piston movement  
Figure 14: Variations in oil and gas pressures with the crank angle under different experimental conditions. (**a**) Case #1. (**b**) Case #2. (**c**) Case #3. (**d**) Case #4. (**e**) Case #5.  
from the TDC position to the BDC position, the diaphragm center region (\(\rho_{r}=0\)) detached from the gas-side cavity surface earlier than the edge regions (\(\rho_{r}=0.8\)). Moreover, both first-stage and second-stage diaphragms reached zero center deflection at approximately 105\({}^{\circ}\) crank angle, while the second-stage diaphragm exhibited a steeper deflection gradient near the zero-deflection point.
## 2 Related Work
### Shadow Detection  
Property-based methods do not require any prior knowledge and directly extract features that can recognize shadows from image data. [PERSON] et al. (2019) utilized a region-growing process in specific color space and assessed pixel saturation, intensity, and edge gradients to detect shadows. [PERSON] et al. (2020) developed a shadow detection method using the Tricolor Attenuation Model (TAM) for single outdoor images. The method employs [PERSON]'s blackbody irradiance theory to estimate the spectral power distributions of daylight and skylight, allowing shadows to be detected without prior knowledge. [PERSON] et al. (2021) introduced a model that identifies shadows based on their high hue values compared to those of non-shadowed areas. This method utilizes a thresholding strategy within the hue, saturation, and intensity (HSI) color space to accurately differentiate shadowed regions from their surroundings. Similarly, [PERSON] et al. (2020) and [PERSON] et al. (2019) provided a simple and effective approach for extracting shadow features by utilizing shadow attributes in invariant color spaces.  
Model-based methods depend on prior knowledge, such as atmospheric lighting conditions and radiation, to simulate the physical interactions between light and the Earth's surface and atmosphere. [PERSON] et al. (2020) used a four-step shadow detection process, which included using the Digital Surface Model (DSM) for initial shadow estimation, training a supervised classifier to identify shadow regions, using the support vector machine (SVM) for shadow detection, and refining the classification results through post-processing. [PERSON] et al. (2021) introduced a shadow detection approach that merges photogrammetry with image analysis, using shadow simulation via DSM and sun position, alongside ray tracing and histogram-based segmentation to accurately identify shadows.  
Machine learning includes unsupervised learning to discover data patterns and supervised learning using labeled samples for prediction. [PERSON] et al. (2020) developed a dynamic shadow detection method using Gaussian Mixture Models (GMM) for identification. This method utilizes color space conversion for shadow differentiation and minimizes segmentation errors and false positives. [PERSON] et al. (2021) used a Bayesian framework to extract shadows from a single image. By using [PERSON]'s equation and Bayesian optimization, this method accurately distinguishes between shadowed and non-shadowed regions.  
In summary, the features extracted by property-based methods are insufficient to characterize shadows. Model-based methods require assumptions about the interaction between light and matter, combined with prior knowledge ([PERSON] et al., 2020) to identify shadows, which does not apply to complex scenes ([PERSON] et al., 2020; [PERSON] et al., 2021). Machine learning-based methods require a large amount of data, while hyperspectral data collection is difficult, and labeled data are limited.
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 3 Control Algorithms
### Nacelle Feedback Control  
The nacelle feedback control algorithm was designed to reduce the platform pitch motion of the target FOWT in regions where the wind speed is higher than the rated wind speed. To increase the damping ratio of the platform motion, the nacelle angular acceleration \(\ddot{\psi}_{nacelle}\) is converted to the nacelle angular velocity \(\dot{\psi}_{nacelle}\) through the integrator with the filter required for signal processing, and the feedback control is conducted [13; 18]. Figure 10 shows the block diagram of the nacelle feedback control algorithm. In the nacelle feedback control, the nacelle feedback control gain \(k_{nf}\) is adjusted as the control parameter.
###### Abstract  
Vegetables are usually thermally processed before consumption to improve their flavor and safety. In this work, the effect of boiling (BO), blanching (BL), steaming (ST), air-frying (AF), and pan-frying (PF)on the nutritional value and bioactivity of broccoli (_Brassica oleracea_ var. _italica_) heads was investigated, including a comparative analysis of the tissue and the cooking water remaining after the treatments. Using spectrophotometric methods, AF broccoli was found to have the highest levels (\(p\leq 0.05\)) of hydroxycinnamic acids (1.58 \(\pm\) 0.71 mg CAE/g few), total glucosinolates (3.76 \(\pm\) 2.09 mg SinE/g few), carotenoids (6.73 \(\pm\) 2.89 mg/kg few), and lycopene (0.91 \(\pm\) 0.19 mg/kg few). Steamed and AF broccoli had the highest total phenolics (0.72 \(\pm\) 0.12 mg GAE/g fw and 0.65 \(\pm\) 0.15 mg GAE/g fw, respectively; \(p\leq 0.05\)). ST broccoli also had the highest levels of soluble sugars (11.04 \(\pm\) 2.45 mg SucE/g fw) and total tannins (0.46 \(\pm\) 0.19 mg GAE/g fw). The water remaining after cooking broccoli (BOW) had the highest total flavonoids (2.72 \(\pm\) 0.59 mg QE/g fw) and antioxidant capacity (ABTS and FRAP, 57.57 \(\pm\) 18.22% and 79.34 \(\pm\) 3.28%, respectively; \(p\leq 0.05\)). The DPPH assay showed that AF (36.12 \(\pm\) 15.71%) and ST (35.48 \(\pm\) 2.28%) had the strongest antioxidant potential. DNA nicking assay showed that BOW and BLW were the most effective in preserving plasmid DNA supercoiled form (99.51% and 94.81%, respectively; \(p\leq 0.05\)). These results demonstrate that thermal processing significantly affects the phytochemical composition and functional properties of broccoli, with steaming and air-frying generally preserving the highest nutritional quality. Additionally, cooking water, often discarded, retains high levels of bioactive compounds and exhibits strong antioxidant and DNA-protective effects. To the best of our knowledge, this is the first study to investigate how different thermal processing techniques of vegetables influence their ability to protect plasmid DNA structure. Furthermore, this is the first study to compare the DNA-protective effects of broccoli tissue extracts and the water remaining after cooking broccoli.  
air-frying; _Brassicaceae_; DNA nicking; glucosinolates; heat treatment; nutritional quality; oxidative stress; polyphenols; soluble sugars
## 1 Introduction
Sensor nodes have many failure modes [7]. Each failure degrades the network performance. This study proposes a novel mechanism involving a hybrid cluster-based routing protocol for sensor computing that selects the most reliable routing path. The proposed mechanism can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. The rest of this paper is organized as follows. Section 2 provides the background knowledge of wireless sensor computing related work on reliability and cluster-based routing in sensor networking. Section 3 presents the proposed mechanism and algorithm. Section 4 then describes the simulation and implementation, and analysis of the results. Conclusions are finally drawn in Section 5. Figure 2: Cluster-based architecture.
## 5 Discussion and Future Research Directions
### Signal Processing and Data Preparation
#### 5.2.4 Challenges and Future Directions in Signal Processing  
Although wavelets and FFT-based methods have been well-studied in the literature, real-world deployment in industrial environments faces practical hurdles. Non-stationary speed profiles, time-varying loads, and multi-fault interactions create complex signal patterns that may require adaptive transforms or online parameter tuning. Additionally, model interpretability remains a challenge; while wavelet coefficients or frequency spectra can be visually inspected by domain experts, fully automated pipelines might obscure the relationship between raw signals and final diagnoses. Future research could explore lightweight wavelet implementations, real-time approximate transforms, or even learned wavelet dictionaries that adapt to the specific signal characteristics of each machine. Furthermore, collaborative platforms (e.g., federated or cloud-edge computing) can balance the computational overhead of advanced transformations with the real-time constraints of condition monitoring on plant floors.  
A recurring research challenge in this domain is striking a balance between transformation complexity and real-time applicability. Methods based on wavelets or EMD capture non-stationary signals effectively but often require high computational power, potentially exceeding the limits of edge devices. Future work might explore lightweight versions, such as approximated wavelets or adaptive sampling, to achieve near-online signal processing without compromising core diagnostics. Another issue is sensor synchronization and alignment of signals from multiple channels or components. Though cross-correlation and advanced synchronization approaches exist, their application in large-scale industrial settings remains limited, calling for more systematic validation and best-practice guidelines.
## 5 Findings and Discussion
An artificial fish swarm algorithm was proposed to solve the VRP by considering constraints such as dispatching time, type of vehicle, energy consumption, and vehicle capacity [37]. Effective vehicle routing results in the minimization of cost and emissions. * Unit capacity: Unit capacity falls in level I of the TISM hierarchy, and according to MICMAC analysis, it is in the first quadrant and is considered an autonomous type. Transportation and inventory holding unit capacities must be considered during PFSC modeling [34; 47]. * Fuel consumption: TISM hierarchy level II accommodates this enabler. According to MICMAC analysis, it is an autonomous type because it is in Quadrant I. The main objective of vehicle routing is to optimize the total logistic cost, which includes the fuel consumption cost. This objective can be achieved using a multi-objective evolutionary algorithm [28]. Being temperature-dependent, perishable products require extra fuel to maintain their quality. Hence, identifying the path that uses less fuel for refrigeration and traction should also be considered an objective [23; 35]. * Cost-benefit analysis: This is classified as a linkage enabler because if its high dependency and driving value, and it lies in quadrant III of the MICMAC analysis and the second level of TISM hierarchy. During the inbound and outbound logistics of cross-docking, it is needed to minimize the operational and transportation cost for cost-benefit analysis [48]. All the enablers affect cost directly or indirectly. * Freshness-keeping: Freshness-keeping lies in level four of the TISM hierarchy, which falls under quadrant I and is considered an autonomous type. Its significance in perishable products is due to shelf life deterioration during transportation. The freshness of the product must be maintained throughout the SC, which directly relates to the selling price of the product and the related wastage. [24; 27]. * Global warming: Global warming is seen in level I of the TISM hierarchy. This enabler is one of the most dependent enablers and falls under the fourth quadrant due to its high dependency and low driving values. Perishable products require refrigeration in SC during transportation to reduce product waste. Greenhouse gas emissions occur throughout, due to refrigeration gas leakage and high energy consumption. Thus, a mixed-integer problem has been developed to reduce global warming and total costs [38]. * Carbon emission: Lying in level III of the TISM hierarchy, carbon emissions are one of the most dependent enablers, falling in the fourth quadrant. Apart from the total cost,optimizing the carbon emissions in SC is challenging. Such a problem was solved using a multi-objective evolutionary algorithm [28]. * Energy utilization: Energy utilization is in level I of the TISM hierarchy and falls under quadrant III. The enabler is considered a linkage enabler according to MICMAC analysis. Refrigeration in transportation causes high energy utilization in PFSC, resulting in high carbon emissions [33].
## 2 Network Model and Operation
### Time Schedule
#### 2.2.3 Unequally Spaced Nodes  
Let us consider now the general case of unequally spaced nodes. To make sure that there will be no interference, in this scenario, the transmission time, which is given by the packet size, cannot be equal to the time slot. To avoid interference, two constraints must be met. First, the time slot must be set so that the longest propagation time is considered; this way, all transmissions will reach their destinations before the end of the second time slot after their start. Second, the packet size must be adjusted to the shortest propagation time; this way, no transmission will reach its destination node before the end of any other possible interference in that node.  
These ideas are depicted in Figure 7, where the worst case of the shortest and longest links being adjacent is shown. We denote \(\mathbf{t}_{\min}\) to be the propagation time of the shortest link, and \(\mathbf{t}_{\max}\) the propagation time of the longest link. The link distances are, respectively, \(\mathbf{d}_{\min}\) and \(\mathbf{d}_{\max}\). In this general case, scheduling is obtained using the proposed method. The throughput will decrease by a factor of \(\mathbf{\tau}=\mathbf{t}_{\min}/\mathbf{t}_{\max}=\mathbf{d}_{\min}/\mathbf{d}_{\max}\) compared to the results for the equidistant network.  
Figure 6: Example search of the optimal schedule for a string network with six nodes. In the upper-left corner, there is the legend. To its right, the three patterns considered (1, 2, and optimal) with a size of \(3\times 3\) (time slots \(\times\) nodes). At the bottom, test results for pattern 1 are shown: alignment (**left**) and overlap (**right**). Both tests indicate collisions in the 2D space-time lattice, so pattern 1 is not a valid pattern.  
Figure 7: To illustrate the general case of unequally spaced nodes in the event of adjacent shortest and longest links.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 2 Data and Methods
### Data Analysis Methods  
#### 2.3.1 Theil-Sen Median Trend Analysis and the Mann-Kendall (M-K) Test  
Theil-Sen median trend analysis and the M-K test were performed to investigate the variation rates and significance of the GPP, ET, and ecosystem WUE at a pixel scale. Theil-Sen median trend analysis can eliminate the effects of anomalous data on the trend to some extent and is more suitable to assess the variation rate of variables in short-term series. The M-K test does not require the assumption of variance normality and has a more solid statistical theoretical basis for testing of significance [21], rendering the results more scientifically credible, and is widely used in ecology-related studies [22; 23; 24].  
\[Sen=Median\left(\frac{x_{j}-x_{i}}{j-i}\right),\;2003\leq i<j\leq 2017 \tag{1}\]  
where Sen is the slope and \(x_{j}\) and \(x_{i}\) are the data values at time \(j\) and \(i\), respectively.  
The \(Z\) statistic for the M-K test was:  
\[Z=\left\{\begin{array}{l}\frac{s-1}{\sigma_{s}},s>0\\ 0,\;\;\;s=0\\ \frac{s+1}{\sigma_{s}},s<0\end{array}\right. \tag{2}\]  
where  
\[\sigma_{s}=\sqrt{\frac{n(n+1)(2n+5)}{18}} \tag{3}\]  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}\left(x_{i}-x_{j}\right) \tag{4}\]  
\[\text{sgn}\left(x_{i}-x_{j}\right)=\left\{\begin{array}{l}1,\;if\left(x_{i }-x_{j}\right)>0\\ 0,if\left(x_{i}-x_{j}\right)=0\\ -1,if\left(x_{i}-x_{j}\right)<0\end{array}\right. \tag{5}\]  
The \(Z\) statistic is the standard normal distribution, where \(\mid Z\mid>1.96\) and \(2.58\) indicate significance at the 95% and 99% confidence levels, respectively.
## 5 Two-Stage Iterative Algorithm Design
### Stage 1: Optimizing Task Offloading Decision
#### 5.1.1 DQN Agent  
We employ the DQN agent to solve the task offloading problem, which can be formulated as a Markov Decision Process (MDP) problem as follows.  
* State: The system state is defined as \(st=\{un,\beta_{un}\}\), where \(un\) represents the current vehicle, and \(\beta_{un}\) denotes the bandwidth ratio allocated to vehicle \(un\) by the associated RSU \(n\).
* Action: The action is denoted as \(a=\{0,1,2,\dots,N\}\), representing the task offloading decision. Given the current system state, \(a=0\) denotes that vehicle \(un\) computes the tasks locally. \(a=n\) indicates that the tasks of vehicle \(un\) are offloaded to the associated RSU \(n\) for processing, while \(a=v(v\
eq n)\) means that the tasks are offloaded to RSU \(v\) through RSU \(n\) for processing.
* Reward: To achieve the optimal task offloading decision, it is crucial to design a reward function related to the objective function. Considering the limited computational resources of the vehicle, we prioritize offloading tasks to RSU with greater computational resources to minimize task processing delay. Accordingly, the reward function is defined as \[r=\begin{cases}\dot{u}^{vch}_{un}-t_{un},&\text{s.t.}\ C1,C4,C5,\\ -\mu,&\text{otherwise},\end{cases}\] (9)where \(\mu\) represents the penalty term, which is applied when constraints \(C1\), \(C4\), and \(C5\) are not satisfied.
## 1 Introduction
Results of this study identified a unique panel of VOCs that discriminated between healthy and tumor-bearing mice with 94% accuracy and differentiated the end points (cancer week 1 and cancer week 3) with 97% accuracy. In the same study, we also reported a principal component regression model that predicts the number of days after tumor injection with R\({}^{2}\) equal to 0.71, indicating the capability of VOCs to noninvasively monitor breast cancer progression in real time as early as one week after induced tumor injection [23]. Another study based on the same cohort of mice investigated the use of pitavastatin as a therapeutic agent. Statins inhibit 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA), the enzyme leading to the biosynthesis of cholesterol [24]. Therefore, these drugs also inhibit the production of mevalonate and are used clinically to lower cholesterol [25]. Blocking the production of mevalonate and cholesterol has been previously demonstrated to induce antitumor properties [26; 27; 28]; therefore they were previously used to treat induced mammary tumorsin mice. This study showed that pitavastatin administered at high doses (8 mg/kg body weight) resulted in significant antitumor effects. This was also reflected in the VOC profile analyzed three weeks after tumor injection and pitavastatin treatment [29]. However, urine samples were not analyzed in mice administered pitavastatin at low doses, and the previous studies did not profile VOCs over the course of time. Herein, we extend these previous studies using the same cohorts of mice [23; 29] to analyze the ability of urinary VOCs to determine the efficacy of pitavastatin (pita) at high and low doses over the course of three weeks using two approaches (approach 1 and 2).