esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory of Signal Noise Reduction Method
### EMD Noise Reduction Method by Dominant Eigenvalues  
#### 2.3.1 Noise Reduction Principle by EMD  
The EMD is to decompose the signal into the sum of a series of IMFs and one residual term. With the increase of decomposition order the frequency of IMF will decrease, so the signal filtering can be realized.  
Assuming the input signal is \(x(t)\), the EMD by Equation (6) can be carried out to obtain all IMF components including \(s_{i}(t)\) and \(r_{n}(t)\). The signal after noise reduction can be obtained by the reconstruction of different IMF components.  
\[x(t)=\sum_{i=1}^{n}s_{i}(t)+r_{n}(t) \tag{6}\]  
The EMD flow is shown in Figure 4. The main processing steps include the determination of local maximum values and minimum values, cubic spline fitting of signal, mean value calculation of envelope and extraction of IMF component.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 2 Design, Composer, and Experimental Setup
### Theoretical and Analytical Design
#### 2.3.2 Anisotropic Friction Analysis  
Directional locomotion in the crawler is enabled by anisotropic friction forces generated by the interaction of kirigami pop-ups with the substrate. As the actuated kirigami structures buckle upward, they form inclined flaps that generate differential friction in the  
Figure 3: Quantitative and visual analysis of the pop-up height of triangular and trapezoidal kirigami structures. (**a**) Plot showing the measured pop-up heights (\(h_{p}\)) at six locations (labeled 1–6) across each kirigami pattern for both triangular and trapezoidal cuts with cut lengths of 10 mm and 7 mm. (**b**) Front and side views of the kirigami structures during actuation. Yellow lines represent pop-up heights extracted using ImageJ image processing and correspond to the data shown in (**a**).  
forward and backward directions. This behavior was analytically modeled by expressing the effective forward and backward friction coefficients such as:  
\[\mu_{forward}=\mu_{0}+k_{f}h\rho,\quad\mu_{backward}=\mu_{0}-k_{f}h\rho \tag{3}\]  
where \(\mu_{0}\) is the intrinsic base friction coefficient of the PET material against the substrate; \(k_{f}\) is a friction amplification constant that encapsulates both the inclination angle and surface engagement; \(h\) is the pop-up height as described earlier; and \(\rho\) is the number of kirigami units of density. Using experimental results for triangular patterns at \(l=7\,\)mm, configuration--where \(h=5.61\,\)mm, \(\rho=0.0112\) for \(4\times 20\) kirigami units over \(51\,\)mm \(\times 140\) mm, \(N=0.58\,\)N, \(\mu_{\text{forward}}=2.241\) and \(\mu_{\text{backward}}=0.776\). Based on the experimental result, the base coefficient and enhancement factors were computed as follows:  
\[\mu_{0}=1.5085,\quad k_{f}\approx 11.66\,\text{mm}^{-1} \tag{4}\]  
The corresponding net propulsive force can be calculated as follows:  
\[F_{\text{prop}}=N(\mu_{\text{forward}}-\mu_{\text{backward}})\approx 0.850\, \text{N} \tag{5}\]  
This propulsive force aligns closely with experimental locomotion performance and clearly demonstrates the functional contribution of directional friction enabled by different kirigami structures (Section 3). Overall, this integrated theoretical framework provides a valuable predictive tool for rational design and iterative optimization of kirigami-based soft robotic systems.
## 4 Results and Discussion
RHCP radiates in the -Z direction (clockwise) and LHCP in the +Z direction (counterclockwise), resulting in bi-directional radiation. The simulation and measured results were in good agreement. Table 4 summarizes the antenna parameters, RLBW, and ARBW of previous studies and this current research. Given the target L-band frequency (1-2 GHz), the ARBW of the proposed antenna scheme (77.87%) was significantly wider than that of [13,15] (19.86% and 36.87%, respectively). In comparison with [16,22,23,24], for C- and S-band applications (30.8-72.9%), the proposed antenna scheme could achieve a wider ARBW (77.87%). Figure 19: The simulated and measured axial ratio of the proposed antenna. Figure 18: The simulated and measured reflection coefficient of the proposed antenna. Figure 21: The simulated and measured radiation patterns: (**a**) LHCP, (**b**) right-hand circular polarization (RHCP). Figure 20: The simulated and measured gain of the proposed antenna. Essentially, the proposed inverted L-shaped patch antenna with a corner-truncated partial ground plane diagonally adjoined with a square branch for L-band spectrum could achieve an ARBW of 77.87% (1.09-2.48 GHz), rendering the technology suitable for satellite communication and navigation applications.
## 3 SDMob Architecture  
In this section, the structure of the SDMob architecture is addressed in more detail. First, we review the basic design of SDMob, which was presented in the previous work [12]. Next, we describe the mechanisms introduced in the enhanced SDMob to address the challenges that arise in networks with multiple mobile nodes, high density, and more realistic mobility patterns. For simplicity, we are using SDMob for the enhanced version throughout this paper. Finally, we analyze the tracking algorithm employed in the paper (particle filter).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Mobility Solutions** & **Infrastructure** & **Metric** & **Predictive Mechanism** \\ \hline mRPL [13] & fixed and mobile & ETX and RSSI & average RSSI/SNR \\ mRPL+ [20] & fixed and mobile & ETX and RSSI & average RSSI/SNR (overhearing) \\ ARMOR [26] & all can be mobile & Time To Reside & Relative velocity \\ RMA-RP [27] & fixed and mobile & Time To Stay & 2 consecutive RSSI values \\ D-trickle [23] & all can be mobile & ETX,ELT,RSSI,distance & - \\ Kalman-RPL [7] & fixed and mobile & predicted ETX & Kalman Filter \\ EKF-RPL [9] & fixed and mobile & position of MN & Extended Kalman Filter \\ EKF-LOADng [8] & fixed and mobile & position of MN & Extended Kalman Filter \\ DAO projection [33] & No mobility & priority for projected routes & - \\ Coral SDN [35] & fixed and mobile & OF and trickle set by controller & - \\ SD-MIOT [36] & fixed and mobile & link quality & proactive route installation \\ SDN-UAISE [41] & Mobile sink & not RPL-based & Decision tree \\ MobiFog [42] & fixed and mobile & ETX and RSSI & Average RSSI \\ MMF-SDN [40] & fixed and mobile & not RPL-based & - \\ FTS-SDN [39] & fixed and mobile & not RPL-based & - \\ BRPL [18] & all can be mobile & backlog drift plus ETX & Lyapunov Optimization \\ GTM-RPL [25] & fixed and mobile & ETX & Nash Equilibrium \\ RL-Probe [24] & all can be mobile & ETX (same as RPL) & epsilon-greedy learning \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobility management extensions for RPL.
## 4 Discussion
### Methodological Trends and Technological Shifts
in [92] have demonstrated the superiorperformance of data-driven algorithms in detecting inverter failures, switching anomalies, and component degradation in high-frequency converter environments. These trends underscore the expanding applicability of ML-based methods beyond traditional thermal and mechanical fault contexts. For example, [PERSON] et al. [41] used LSTM models for early fault detection in heating substations, highlighting real-time adaptability. [PERSON] et al. [42] proposed a hybrid DL-thermodynamic model, achieving better fault localization in HVAC systems. These cases demonstrate how algorithm selection influences diagnostic coverage and practical viability.
## 3 Uncertainty Analysis Model
### Approximation Model  
In the process of the uncertainty propagation analysis, a calculation complexity problem exists for the numerical simulation procedure of the temperature and thermal stress analyses. Therefore, the complex temperature and thermal stress models should be surrogated by the approximation model, which develops the relationship between the uncertain input parameters and the response with equations. There are various approximation models, such as the response surface model, the Kriging model, and the neural network model. In this paper, the response surface model [35] was chosen because of its low computational cost and great applicability. Then, the relationship between the uncertain input parameters and the response is fitted with a polynomial equation and expressed as follows:  
\[\widetilde{y}=\sum\limits_{i=1}^{M}\beta_{i}\varphi_{i}(p_{1},p_{2},\cdots,p_ {k}) \tag{7}\]  
where \(\widetilde{y}\) is the approximate model output; \(\beta_{i}\) is the regression coefficient, which can be obtained by the least square method; \(\varphi_{i}(p_{1},p_{2},\cdots,p_{k})\) is the basic function of \(p_{1},p_{2},\cdots,p_{k}\); and \(M\) is the number of terms of \(\varphi_{i}(p_{1},p_{2},\cdots,p_{k})\).  
To measure the reliability of the approximation function for the receiver tube model under multi-source uncertainties, the coefficient \(R^{2}\) is used to conduct an error analysis and calculated as follows:  
\[R^{2}=1-\frac{\sum\limits_{j=1}^{n}{(y_{j}-\widetilde{y}_{j})^{2}}}{\sum \limits_{j=1}^{n}{(y_{j}-\overline{y})^{2}}} \tag{8}\]  
where \(y_{j}\) and \(\widetilde{y}_{j}\) are, respectively, the actual and approximate model outputs; \(\overline{y}\) is the average value of the actual model output; \(n\) is the sample size for building the response surface model.  
Due to the advantages of high efficiency, the optimal Latin hypercube (OLH) sampling method [36] was adopted to gain the sampling points of the uncertain input parameters for the response surface model construction. In total, 146 sampling points were chosen to construct an approximation model of the receiver tube model under multi-source uncertainties, and 73 sampling points were chosen to perform the error analysis. The error analysis results of the model outputs of the maximum temperature and thermal stress inside the tube wall are, respectively, shown in Figures 4 and 5. The results demonstrated that the approximation values were consistent with the simulation values for model outputs of the  
Figure 3: Conceptual representation of uncertainty propagation.  
maximum temperature and thermal stress inside the tube wall, with \(R^{2}\) values of 1 and 0.9883, respectively.
## 5 Experiment Results and Discussion
Furthermore, the previous research on RFF identification have not focused on the mobile multipath factor in the V2X environment and lack experiments in the case of a high-speed moving state. Thus, the research done in this paper takes special account of the relative movement scene, in which the communication modules are placed in the motion of random path for data collection, and high mobility simulation tests are carried out to make the experimental results more persuasive. The identification accuracy rates of the eight modules in the LOS and NLOS moving state experiments and high-speed simulation tests all exceed 99%, indicating that the method proposed in this paper has the ability to extract the inherent physical characteristics of V2X devices based on DSRC technologies and realize identity recognition.
## 1 Introduction
Once the level set equation, as well as the energy parameters, is well-defined, edges of components of the medical images can be efficiently determined. Meanwhile, the users have to initially define the foreground and background to train the classifiers by themselves. The level set function typically requires re-initialization to avoid irregularities during its evolution [48]. Thus, the naive active contour models have inevitable inconvenience on the issues of determination of the energy parameters, the intrinsic algorithmic properties, and the intervention of marking the foreground and background of the region of interest. To circumvent these deficiencies, state-of-the-art machine learning methods in probabilistic perspectives have brought several useful techniques [5; 9; 10; 32; 48; 50; 51; 52; 53]. By combining the graph theory with topological priors [53], morphologies of three-dimensional biological images could be delineated and reconstructed visually. These machine learning-based algorithms rely on some prerequisites to fulfill the biological structure recognition, such as the seeding voxels [52], the regular curves, the shape of axons, the designated sizes [5], and so forth. Among these investigations, the method of spectral matting [53] provides a sequential searching to extract biological components by optimizing a Laplacian-type cost function under a well-defined window size of the cost function. Bayesian Sequential Partitioning algorithm [52] is also an applicable method for reconstruction of three-dimensional biological configuration. However, the time complexity caused by the detection of voxel growth is a challenging issue. The CNN-based algorithms also have the issue of high computational complexity [49; 50; 51]. Therefore, with user-supervised interventions and the computational complexity would probably become inevitable under the frameworks on the mentioned methods. Therefore, to achieve the purpose of medical image segmentation with appropriate computational complexity and to simultaneously resolve the issues of image segmentation and image compression, the techniques of data density functionals based on the density functional theory [32] is utilized in the article. Under the proposed theoretical framework, all information of employed medical imageries is globally mapped into a specific energy space to estimate the relevant energy functionals. Since the medical images are analyzed globally under the proposed method, global or partial image compression is allowed. Similar components also can be identified and localized by their connectivity and significance. Thus, distortion might be avoided. Two types of medical imageries, MRI and cell culturing, were utilized to validate the feasibility of the proposed method. The proposed method reveals its potential on the trade-off between accuracy and computational complexity in medical image segmentation and its relevant biomedical applications.
## 4 Discussion
### Sensory Analysis  
Evaluating the juices' overall taste, appearance, aroma, and texture is essential. In the case of apple-peach and apple-chokeberry juices, sensory analysis plays a crucial role in determining consumer acceptance. The juices' color, clarity, and visual appeal are essential to consumer perception. Additionally, the aroma and taste profile are key attributes that influence the overall sensory experience. Consumers highly value the appearance and taste of juices, paying particular attention to the external characteristics of food products [91; 92; 93; 94]. However, according to the literature, when the total color difference (\(\Delta\)E) between a treated and untreated sample is less than 1.5, it can be judged virtually imperceptible to the human eye [95].  
The sensory analysis revealed a well-balanced combination of apple and peach flavors and a pleasing aroma of apple-peach juice. The natural sweetness from the fruits and the absence of any off-flavors contributed to a positive sensory perception. The overall appearance of the juice, including its color and clarity, was also found to be appealing, which is essential for consumer preference. Similarly, in the case of apple-chokeberry juice, the sensory evaluation highlighted the distinctive deep red color and rich aroma, contributing to a visually and aromatically appealing product. The taste profile exhibited a harmonious blend of the apple's mild sweetness and the unique tartness of chokeberries, which was well-received during sensory analysis. The highest differences were observed in the color evaluation during the overall assessment of the juices. Changes in color in the juices may result from the activity of tissue enzymes (PPO and POD) and non-enzymatic Maillard browning reactions [96]. Additionally, research conducted by [PERSON] et al., 2011 showed that the products of enzymatic oxidation of phenolic compounds can oxidize anthocyanins, leading to the precipitation of polymeric compounds that sedimentation of the juices [97].  
Overall, the sensory analysis indicated that of apple-peach and apple-chokeberry juices were well-received in appearance, aroma, and taste, suggesting that these juices have the potential for positive consumer acceptance. This positive sensory evaluation is crucial for the success of these products in the market and underscores the importance of sensory analysis in product development and quality assessment.
## 3 GNC System with LOS-Based Model
### Modular System for the Path-Following Algorithm  
Figure 9 describes the modular GNC architecture with the required ROS topics. The coverage path module creates the waypoints for the path-following algorithm based on a predefined coverage planar area. Then, the guidance and control module receives the position and orientation of the AUV from the COLA2 navigation module [31]. The COLA2 navigation module includes the complete underwater localization of the Girona500 AUV. The LOS-based path-following algorithm is the only performance considered for analysis in the AUV implementation. The guidance and control module checks the AUV position to choose the next waypoint in the created trajectory and the necessary course angle for a continuous path operation. Furthermore, the guidance and control module incorporates the calculation of the required thruster setpoints based on the motion control system. The AUV thruster drivers receive these thruster setpoints to reach the defined waypoint from the coverage path.
# The Additive Manufacturing of Glass: A Critical Review
## 5 Summary and Future Needs  
### Summary  
The review herein has surveyed a body of work, most of which consists of relatively recent studies, which indicate that the 3D printing of glass continues to progress significantly--and appears to be here to stay. The processes explored reveal associated relevance for demonstrating considerable potential in various industries, such as architecture, art, optics, and healthcare. The additive manufacturing of glass has, to date, employed approaches and methods which are relevant across a very broad range of length scales (from microns to many tens of centimetres).  
Innovations in this field are broad and are associated with unique methodologies which are being employed for the AM of glass. For example, the EAM process with semi-solid glass processing (both at high and low viscosity) has transitioned from research settings to commercial uptake. Binder-based methods, which involve glass feedstock held in a green object that is later fused, has also seen broad uptake. Uniquely, even laser-based methods are being explored, with a varying degree of success as they remain in the R&D phase. Researchers are also exploring hybrid techniques combining 3D printing with traditional glassmaking processes to enhance the properties and aesthetic qualities of the final products. Of the works reviewed herein, the variety of glasses that have been utilised in glass AM to date has been confined to a very small subset of glass chemistries (of the thousands of available glasses explored). However, recycled soda-lime glass has been one of the most widely explored materials--which augurs well from a sustainability perspective. Despite some of the challenges expounded in this review (and future work/needs elaborated below), the potential for customised, complex structures makes glass AM a critical future technology. Geometry control and the emergence of metamaterials through generative design will allow the preparation of glass structures by AM for a potentially wide variety of new applications for glass.
## 3 Network Models and Problem Description
### Theoretical and Complexity Analysis  
This section provides theoretical guarantees for the existence and properties of minimum cross-degree backup paths, along with an analysis of the problem's computational complexity.  
**Theorem 1** (Existence of a Feasible Solution).: _For any connected undirected graph \(G=(V,E,W)\) and any source-destination pair \((s,d)\), there exists a backup path \(sp^{\prime}(s,d)\) that minimizes the cross-degree \(K(sp(s,d),sp^{\prime}(s,d))\)._  
Proof.: Let \(sp(s,d)\) be the optimal path computed via [PERSON]'s algorithm. The set of all possible \(s\to d\) paths, \(\mathcal{P}\), is finite and nonempty (since \(G\) is connected according to Definition 3). The cross-degree \(K\) for any path \(p\in\mathcal{P}\) is bounded by \(0\leq|K(p)|\leq|sp(s,d)|\). Thus, the set of cross-degree values \(\{|K(p)|:p\in\mathcal{P}\}\) is a finite set of non-negative integers. By the well-ordering principle, this set has a minimum value \(k_{\min}\). Any path \(p^{*}\in\mathcal{P}\) achieving \(|K(p^{*})|=k_{\min}\) is a valid backup path with minimum cross-degree.  
**Theorem 2** (Optimality Condition).: _A backup path \(sp^{\prime}(s,d)\) attains minimum cross-degree \(k_{\min}\) if and only if it shares no edges with \(sp(s,d)\) that could be avoided while maintaining connectivity._Proof.: Suppose \(sp^{\prime}(s,d)\) achieves \(k_{\min}\) but shares an avoidable edge \(e\in sp(s,d)\). Removing \(e\) from the graph and recomputing the backup path would yield a new path \(sp^{\prime\prime}(s,d)\) with \(|K(sp(s,d),sp^{\prime\prime}(s,d))|<k_{\min}\), contradicting minimality.  
Let \(sp^{\prime}(s,d)\) share no avoidable edges. If a path \(p^{\prime}\) exists with strictly smaller cross-degree, \(sp^{\prime}(s,d)\) must include at least one edge that could have been excluded without disconnecting \(s\) and \(d\), violating the \"no avoidable edges\" condition. Thus, \(sp^{\prime}(s,d)\) minimizes cross-degree.  
**Theorem 3** (Computational Complexity).: _Finding a minimum cross-degree backup path for a given \(sp(s,d)\) is **NP-hard**._  
Proof.: We reduce the **Minimum Shared Edges (MSE)** problem, known to be NP-hard [31], to our problem. The MSE problem is given as follows: given a graph \(G=(V,E)\), nodes \(s,d\), a path \(P\), and integer \(k\), does there exist an \(s\to d\) path sharing \(\leq k\) edges with \(P\)?  
**Reduction:** For any MSE instance, assign unit weights \(W\equiv 1\). Compute \(sp(s,d)\) using Dijkstra. The MSE instance is equivalent to decide whether a backup path \(sp^{\prime}(s,d)\) exists with \(|K(sp(s,d),sp^{\prime}(s,d))|\leq k\). A polynomial-time algorithm for our problem would solve MSE in polynomial time, contradicting its NP-hardness. Thus, our problem is NP-hard.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 4 Construction and Operation of SOUNET
### Phase 2: Network Maintenance  
In this phase, during the data transfer between the sensor nodes to the sink, updates are performed on the communication links according to the topological changes such as node additions and losses.