esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper extends the ordinary IBS method and presents a method for coupling multi-domain substructures, including IBS, FEM and modal substructures, with non-matching interfaces. The method has engineering application value. The following conclusions are drawn:  
1. A method for assembling multi-domain substructures with non-matching interfaces is proposed in this paper. It is an extension of the available IBS techniques and makes the IBS method more practical for coupling multi-domain substructures. Like the original IBS, this method can deal with complex dynamics efficiently.
2. The basic rule of generating the interpolation functions is discussed, and by achieving the normalization condition, force equilibrium and energy conservation can be conveniently satisfied. Compared with the approach of constructing transition elements, it is unnecessary to implement a complicated element construction process; only the distribution of support nodes and the coefficients are needed, and a different model can be used for interpolation functions when it satisfies the normalization condition.
3. The advantageous performance of this method in dynamic prediction is validated by two numerical examples: one is the dynamic simulation of the landing process of a lunar lander. With high accuracy, the time-consuming simulation of the landing process is drastically reduced by the employment of the proposed method.
4. Not all mesh grid points on interfaces are needed, and the support nodes can be selected properly considering the features of the interfaces and the engineering accuracy requirements. A more rigorous study about the support node selection considering their precise influence will be further studied as an alternative reduction of the interfaces.  
Methodology, Z.C. and L.L.; Investigation, S.C. All authors have read and agreed to the published version of the manuscript.  
This research and APC was funded by [the National Natural Science Foundation of China] grant number [52171290].  
Not applicable.  
The authors declare no conflict of interest.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 1 Introduction
As remote sensing science and technology have progressed and the utilization of remote sensing data has become more prevalent, the acquisition of thematic information on surface parameters from multi-source and multi-temporal remote sensing data has become feasible [46]. The indices calculated via multiple remote sensing sources enable the monitoring of drought events from different perspectives and accomplish the completion of multi-source datasets [47]. The FY-4 series satellite, which is the latest generation of geostationary meteorological satellites in China, enables unprecedented three-dimensional atmospheric monitoring in the geostationary orbit. Minimal research is dedicated to harnessing the potential of FY-4 for drought monitoring purposes [48]. We introduced a novel methodology for computing the daily TVDI using hourly FY-4A satellite data. The daily drought monitoring capabilities of FY-4A TVDI were validated using the Soil RelativeHumidity Index (SRHI) and Meteorological Drought Composite Index (MCI). To illustrate the need for geostationary meteorological satellite-based drought monitoring, we compare the FY-4A TVDI with the TVDI by earth observation satellite (Landsat-8 and MODIS). The diversity of satellites enables data acquisition from different spatial-temporal perspectives, facilitating a more comprehensive characterization of drought monitoring. In conclusion, the method suggested in this study improves the frequency and timeliness of drought monitoring in addition to introducing innovative data sources for multi-source data. It enables early detection and warning of drought for government and agricultural sectors during rapid drought processes. It contributes to safeguarding agricultural production, maintaining water resource supply, and reducing the economic and societal losses caused by drought. Additionally, it provides critical data, information, and scientific support, making significant contributions to manage drought, make decisions, and mitigate disaster, all of which promote sustainable development and social well-being.
## 2 Theory of the Non-Detection Zone
### The Equivalent Model of Low-Frequency Ultrasonic Guided Wave in a Square Steel Bar
#### 2.1.1 The Mode Selection of Ultrasonic Guided Wave  
Based on previous research findings, the reasons for the mode selection of ultrasonic guided wave in this study are as follows:  
Firstly, the use of low-frequency ultrasound in long-distance ultrasonic guided wave inspection is preferred because the attenuation of the high-frequency ultrasound transmission over long distances is too fast, which limits the measuring distance [4]. High-frequency ultrasound waves tend to be concentrated on the surface or in localized areas of the propagation medium [1]; therefore, the meaning of detecting the ultrasonic guided wave as the body wave in the entire medium is lost. Because of the multi-mode caused by the high-frequency ultrasonic excitation, the identification of detection becomes difficult. In short, the low-frequency section and less-mode ultrasound are appropriate for the detection requirements of long-distance ultrasonic guided wave inspection. For example, a similar low-frequency ultrasonic guided wave equivalent simulation was applied to rails (a rod-like structure) by [PERSON] et al. [7].  
Secondly, the simulated effect of an ultrasonic equivalent model is better in the low-frequency range than in the high-frequency range. For the long wavelength in the low-frequency ultrasonic range, the equivalent model for a rod-like structure is similar to the model of a plate-like structure for  
Figure 1: The equivalent model in low-frequency (**a**) The equivalent model of low-frequency ultrasonic guided wave in a square steel bar; (**b**) Low-frequency phase velocities of \(S0\) and \(A0\) modes provided by the beam, plate and elastic layer models.  
the Lamb wave mode in a uniform medium. However, the mode distribution generated by the high-frequency section in a rod-like structure is not similar to the Lamb wave mode, thus there is no equivalent effect [3].  
Finally, the equivalent model of low-frequency ultrasonic guided waves takes full advantage of the proven Lamb theory and can be used to theoretically control the modes of the excited pulse wave and the mode number [16; 17]. The relationship between the \(S0/A0\) impulsive waves and the flaw in related research can be used to analyze the mode transformation under the finite boundary conditions used in this study. Therefore, we use the ideal assumptions described in Section 2.
## 4 Deep Learning-Based MEC System Energy Saving Framework
### Task Arrival Rate Prediction with STGCN
STGCN is trained by the loss \(L(\hat{w}_{x}(t);\psi)=\sum_{t\in T}||\hat{w}(t)-w(t)||^{2}\). The MECS workload predictor takes \(\hat{w}(t)\) and outputs \(\hat{\lambda}(t)=\{\hat{\lambda}_{i}(t):i\in\mathcal{M}\}\), where  
\[\hat{\lambda}_{i}(t)=\sum_{x\in B_{i}}\hat{w}_{x}(t). \tag{11}\]  
Our proposed task arrival rate prediction approach is systematically outlined in Algorithm 1. ```
1:Input:\(A_{|B|\times|B|}=[a_{x,y}],\forall x,y\in B\), \(O_{|B|\times h}(t)=[w_{x}(t-i)],x\in B,i\in\{1,\ldots,h-1\}\)
2:Output: predicted \(\hat{\lambda}(t)=\{\hat{\lambda}_{i}(t):i\in\mathcal{M}\}\) (i.e., \(\hat{\lambda}(t)=\{\hat{\lambda}_{i}(t):i\in\mathcal{M}\}\))
3:At the end of a time slot \(t\):
4: Get \(\hat{w}(t)=\{\hat{w}_{x}(t):x\in B\}\) by using STGCN with \(A_{|B|\times|B|}\) and \(O_{|B|\times h}(t)\)
5:while\(i\in\mathcal{M}\)
6: Calculate \(\hat{\lambda}_{i}(t)=\sum_{x\in B_{i}}\hat{w}_{x}(t)\)
7: Return \(\hat{\lambda}(t)=\{\hat{\lambda}_{i}(t):i\in\mathcal{M}\}\)
```  
**Algorithm 1** Task Arrival Rate Prediction Algorithm
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Comparison Model Extreme Learning Machine (ELM)  
ELM is a new type of feedforward neural network proposed by [PERSON] et al. in 2006, which consists of an input layer, a hidden layer and an output layer. In this article, the neurons in the input layer correspond to the \(n\) input variables \((x_{1},x_{2},x_{3}\ldots\ldots\ldots x_{n})\) in the model, and the neurons in the output layer correspond to the horizontal displacement of the concrete dam crest. The specific principles of ELM are as follows:  
Given a Q group sample \((x_{n},t_{m})\), where \(x_{n}=[x_{n1}.x_{n2},\ldots,x_{nQ}]^{T}\),and \(t_{m}=[t_{m1},t_{m2},\ldots,t_{mQ}]^{T}\). Assuming that the hidden layer activation function is \(g(x)\), the output vector is:  
\[T=\big{[}t_{1},t_{2},\ldots,t_{Q}\big{]}_{m,Q} \tag{14}\]\[t_{j}=\left[t_{1j^{\prime}},t_{2j^{\prime}},\dots,t_{mj}\right]^{T}=\sum_{l=1}^{l} \beta_{im}g(\omega_{i}x_{i}+b_{i})(j=1,2,\dots,Q) \tag{15}\]  
Among them, \(b_{i}\) represents the threshold of the i-th neural node in the hidden layer; \(\omega_{\rm i}=\left[\omega_{\rm i1},\omega_{\rm 2i},\dots,\omega_{\rm mi} \right]^{\rm T}\) is the weight from the input layer of the i-th neural node to the hidden layer; \(\beta_{\rm i}=\left[\beta_{\rm 1i},\beta,\dots,\beta_{\rm mi}\right]^{\rm T}\) represents the weight from the hidden layer to the i-th neural node of the output layer Formula (15) can be expressed as:  
\[H\beta=T^{\prime} \tag{16}\]  
According to the ELM theorem, when the number of training set samples is large, the number of hidden neurons K is usually less than Q. At this time, the training error of ELM can be infinitely approximated to any real number \(\varepsilon\) greater than 0.  
\[\sum_{j=1}^{Q}\bigl{|}\bigl{|}t_{j}-y_{j}\bigr{|}\bigr{|}<\varepsilon \tag{17}\]  
The connection weight \(\beta\) between the hidden layer and the output layer can be obtained by finding the least squares solution of \(\min_{\beta}\bigl{|}\bigl{|}\mathrm{H}\beta-\ \mathrm{T}^{\prime}\bigr{|}\bigr{|}\), then \(\beta=\mathrm{H}^{+}\mathrm{T}^{\prime}\). Where \(\mathrm{H}^{+}\) is the generalized inverse Moore-Penrose matrix of matrix H.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Adhesins as a Target of c-di-GMP  
In addition to pili, non-fimbrial adhesins contribute to biofilm formation. The non-fimbrial adhesive protein LapA from _Pseudomonas putida_ and _P. fluorescens_ participates in bacteria binding to the surface and cell-cell interaction, stabilizing the biofilm. The type I secretion system transports the LapA protein to the outside of the cell; subsequently, LapA is proteolytically processed at its N-terminus by the periplasmic protease LapG. The proteolytic activity of LapG is regulated by the transmembrane protein LapD. Low levels of c-di-GMP regulate LapD activity since LapD has a degenerate EAL domain [18].
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Mobile Devices  
The participants were asked to use their own telephones linked to the OppNet terminals in mesh throughout the experiment, both for mesh communications, as well as for image capture and editing. The goTenna app is compatible with both Android and iOS terminals, and 7 terminals used here had an Android operating system (OS) and 7 had Apple's iOS system. Although the variation in the terminals did not affect the test with goTenna communicators, it produced differences in editing and in the quality of the images captured during the experiment, depending on the terminal's camera. As explained, a workshop was previously held to verify that all terminals had the app installed and correctly configured, without using their phone numbers but rather a generated identity (GID, a randomly generated 14-digit number starting with a 9, e.g., 9502 0359 4079 49), anonymously generated by the goTenna device by linking it via Bluetooth to the corresponding mobile terminal.
# New Trends in 4D Printing: A Critical Review
## 4 Shape Memory Materials
### External Transition Stimuli for 4 DP
#### 4.2.2 Liquid/Moisture  
In liquid-responsive materials, the transformation is designed to induce different swelling in distinct compartments, which is dependent upon spatial and temporal factors.  
The behavior of polymeric materials is influenced by moisture. Hydrophilic polymers contain functional groups that form secondary bonds with water molecules, leading to absorption and swelling of the polymer and an increased volume. Functional groups such as carboxyl, hydroxyl, and amines facilitate water absorption, while methyl groups increase hydrophobicity, preventing water absorption. Moisture acts as a trigger in 4 DP, particularly in hydrophilic polymers. The combination of active single layers, which respond to moisture, and flexible passive layers allows for volume variations and shape changes. The active layer reacts to moisture, while the passive layer adapts to the shape changes induced by the active layer. However, restoring the original dimensions after cycles of water sorption and desorption may not be guaranteed. Acrylamide-based hydrogels and PEG derivatives are examples of polymeric materials that exhibit shape-changing responses to moisture stimuli [83; 84; 85].  
Liquid-responsive materials supply different functionalities, such as cell encapsulation, controlled drug delivery, and reversible actuation for smart valves. However, it is important to consider some factors when the suitability for specific applications is evaluated, such as the response time, the mechanical properties after expansion, and the possible degradation or hydrolysis after various swelling and de-swelling cycles. It is important to address these challenges to ensure the long-term performance and durability of liquid-responsive materials [86].
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 2 Results
### Simulation Geometry  
The simulation geometry is shown in Figure 2, where the airborne radar is at an altitude \(H\) above the surface traveling with velocity \(\mathbf{v}=[v_{X},v_{Y},v_{Z}]^{T}\). We will assume a coordinate system fixed to the phase center of the radar antenna, such that the antenna is at the origin, the \(X\)-axis is pointed parallel to the surface and in the plane of symmetry of the aircraft, the \(Y\)-axis is pointed out the right side of the aircraft and parallel to the surface, and the \(Z\)-axis is pointing down. In this work, we will assume that \(\mathbf{v}\) always lies in the \(X\)-\(Z\) plane (i.e., no crabbing). Because we are seeking to model the scattering characteristics as a function of \(t\) and \(\tau\), we will need to express all spatial quantities in terms of these temporal variables.  
Figure 1: Wide-sense stationary uncorrelated scattering (WSSUS) system function relationships. Knowledge of any one of these functions grants complete knowledge of the second-order statistics of the system.  
We will model the radar signal using an approach similar to Barrick [50; 51], i.e., as a spherical wave emanating from the source located at the origin as is shown in Figure 3. Each small segment of the wave reflects off of successive isorange rings on the sea surface; the total return is therefore the superposition of the returns from each isorange ring. The incremental power gain as a function of delay can be obtained using the radar range equation:  
\[\frac{dP_{R}}{P_{T}}=\frac{G^{2}(\phi(\tau,\rho),\theta(\tau))\lambda^{2}\sigma ^{0}(\alpha(\tau))dA(\tau)}{(4\pi)^{3}\left(\frac{1}{2}c\tau\right)^{4}} \tag{9}\]  
where:  
* \(P_{T}=\) transmit power
* \(dP_{R}=\) incremental received power from isorange ring at delay \(\tau\)
* \(\phi,\theta=\) azimuth and depression angles relative to platform
* \(\alpha=\) grazing angle, which equals \(\theta\) in a flat earth model \(\implies\alpha=\theta=\sin^{-1}(H/(\frac{1}{2}c\tau))\)
* \(G(\phi,\theta)=\) one-way antenna power gain
* \(\lambda=\) carrier wavelength
* \(\sigma^{0}=\sigma^{0}(\alpha,\lambda,\text{sea state},\ldots)=\) surface normalized radar cross section (NRCS)
* \(dA=\frac{\pi}{2}c^{2}d\tau=\) incremental area of isorange ring at delay \(\tau\).
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 4 _Ulva_ Compounds of Interest  
### Ulvan  
Ulvans are a distinct polydisperse sulfated heteropolysaccharide made up of variable quantities of uronic acids, including glucuronic acid (2.6-52.0%) and iduronic acid (0.6-15.3%mol), interspersed with sugar moieties. Neutral examples are rhamn (5.0-92.2%mol), xylose (0.0-38.0%mol), and glucose, connected by \(\alpha\) and \(\beta\)-1 \(\rightarrow\) 4 bonds [118], representing according to the literature between 8 and 29% of the dry weight of biomass [119].  
Ulvans are the principal polysaccharide compound of the cell wall of _Ulva_ sp. [120], and they present gelling properties [120]; water solubility [119]; and antiviral, antioxidant, anticoagulant, antihyperpipidemic, and anticancer activity, adding to immunostimulant effects (for more information read [PERSON] 2013 [121], [PERSON] and [PERSON] 2007 [122], [PERSON] et al., 2013 [123]) and the ability to accumulate heavy metals (for more information read [PERSON] and [PERSON] 1996 [124], [PERSON] et al., 2009 [125], [PERSON] and [PERSON] 2010 [126], [PERSON] et al. [118]).  
The literature has demonstrated the significance of grammose owing to its impact on the biosynthetic pathways of the dermis and plant immunity. Uronic acids, including glucuronic and iduronic acids, along with their sulfate esters, are vital components of mammalian glycosaminoglycans (GAGs) such as heparin, heparan sulfate, and dermatan sulfate [127].  
Cellulose is a linear polysaccharide composed of repeating glucose units linked together by \(\beta\)-1,4 glycosidic bonds; it is the most abundant polysaccharide found in nature and serves as a structural component in the cell walls of plants and algae, providing strength and rigidity, thus contributing to their overall structural integrity or mechanical support.  
The molecules are organized into microfibrils, long fibers, and insoluble forms, which form a network [128] that is also known for its high resistance to enzymatic degradation [127].  
The composition and structure of polysaccharides in _Ulva_ sp. can be influenced by several factors, including environmental conditions, growth stage, and extraction methods [128]. As techniques used to analyze the composition and structural characteristics of these polysaccharides, we have Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) spectroscopy [128].
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Fingerprinting
For cost reduction, volunteers and self-guided devices are commonly used for the signal's features collection and radio map creation. Although it is mostly used for indoors, in [61], fingerprinting was used outdoors for comparing different wireless technologies, such as SigFox and LoRaWAN. Analytically, three SigFox datasets were collected in rural and urban areas, as well as a LoRaWAN dataset in an urban area. All datasets contained the GPS coordinates, the RSS, and other additional measurements for each particular location. Each fingerprint distance was estimated by using the Euclidean distance, and the centroid of the \(k\) nearest fingerprints was used as the location estimation. It was concluded that the fingerprinting localization technique provided a high level of accuracy and reliability for both tested wireless technologies.
## 4 Experimental Settings
### Algorithms
#### 4.1.2 Re-ID and Target Tracking
This demonstrated that the threshold can be appropriately adjusted, depending on the deployment environment. Additionally, when an object was first detected, the Re-ID procedure was performed, to determine whether the detected object was the actual target. In this process, the features of the detected object, obtained by cropping the relevant image, were extracted using the Re-ID model. These feature vectors were then sent to the server, allowing all the edge devices in the network to share consistent target information. This ensured that the tracking process remained unified and accurate across the distributed devices. If the target object  
Figure 3: Diagram of (**a**) target-tracking process and (**b**) re-identification (Re-ID) process for our experiments. was known beforehand, its feature vectors could be precomputed and distributed to edge devices through the server, bypassing the need for on-the-fly feature extraction during the first detection. To maintain clarity and avoid overcomplicating the visualization, the details of this specific case have been omitted from Figure 3. This approach ensured that the framework remained flexible, accommodating both scenarios where the target was predefined and where it needed to be identified dynamically.
## 4 Experimental Results
### Results of TIR Ship Detection  
#### 4.2.1 Effects of Parameters  
According to the above Section 2.4.1 part analysis, the STAEM values of ship targets are almost all much larger than those of non-ship targets. Accordingly, in the experiment, we randomly selected 500 ship target blocks and 500 non-ship target blocks and calculated their STAEM values one by one. The calculated STAEM values of the selected blocks are shown in Figure 13. The blue point line denotes the STAEM values of ship targets, and the red point line denotes the STAEM values of non-ship targets. As can be seen from Figure 13, because the STAEM values of ship target blocks and non-ship target blocks have a distinctive distinguishability, the OTSU method [46] is utilized to find the optimal threshold:  
\[Thr^{\star}=OTSU(\textbf{STAEM}), \tag{27}\]  
where \(\textbf{STAEM}=[STAEM_{1},STAEM_{2},\dots,STAEM_{j},\dots,STAEM_{1000}]\) is the group of STAEM values of selected blocks, and \(j\) denotes the index of the selected block. Then, the computed distinctive threshold \(Thr^{\star}\)= 0.2094 can be used to clearly discriminate ship targets and non-ship targets, as shown by the purple line in Figure 13. Therefore, the distinctive threshold is empirically set \(Thr^{\star}\)= 0.2094 in our experiments to reliably separate candidate ship targets and non-ship targets.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 3 Architecture for a Cement Factory Based on Blockchain Technology
### Physical Layer  
The proposed architecture applies numerous sensors and actuators and a microcomputer in the setting of a cement plant. We use five types of sensor to record the kiln temperature, the kiln rotation speed, the moisture of the mixture, the inlet-outlet pressure, and the weight. These sensors are interfaced with ultra-low-power STM32 Microcontroller, which pre-processes the collected data before transmitting them to the blockchain service layer. Several motors, actuators (variable frequency drive (VFD) gear-motors), and solenoid valves are used in this architecture.