esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Modern mechanical structures, especially intelligent flexible mechanical structures, are densely distributed with sensors, processors, and actuators [1]. Some transducers may apply inertial actions to the flexible structure, although they are also parts of the whole structure. In this study, structures with distributed gyroscopes will be studied, which has been verified as applicable in the control of soft structures such as space manipulation arms [2; 3; 4].  
The gyroelastic continua have been proposed by [PERSON] and [PERSON] to describe the mathematical modeling of structures with continuously distributed gyroscopes [5; 6]. The dynamics of flexible structures with distributed appendages can be investigated by modal discretization techniques such as the Galerkin method by introducing a set of trial mode functions, which are usually the modal functions of the corresponding structure without appendages [7; 8; 9]. Modal discretization techniques have shown powerful applications to structures with regular shapes (explicit modal functions) [10; 11; 12; 13; 14]. However, modal discretization becomes unpractical when treating structures with irregular or complicated contours. Without analytical modal functions, modal discretization loses the configuration base. Although the base modal shapes can be obtained by the finite element method and transferred to the modal discretization procedure, the manipulations are apparently cumbersome.  
Spatial discretization techniques such as the finite element method could tackle the dynamics of structures with arbitrary shapes. However, the available commercial finite element software provides no general modules to treat flexible structures with distributed gyroscopes. The distributed gyroscopesintroduce a new dynamic effect to the structures and the most important contribution is the gyroscopic coupling effect, which is usually neglected in low angular momentum examples. With increasing angular momentum, the gyroscopic coupling becomes dominating and varies the frequency and modal motion drastically [15; 16; 17; 18; 19; 20]. Gyroscopic coupling can be employed as a mechanism of sensor to detect rotating angles, which has been discussed in the literature [21; 22; 23].  
Although gyroscopic continua such as axially moving materials [24] and rotating components [25] have been studied widely, structures with discrete rotors have received less attention. In this study, we propose a spatial discretization technique designed to tackle flexible structures with distributed gyroscopes. The eigenfrequencies are studied and discussed. Both modal discretization and spatial discretization will be studied and compared by an example of gyroscope-distributed beam. The current study may expose the gyroscopic structures to more general numerical techniques.
## 5 Potential Use Cases
### Smart City Video Surveillance over 5G
Similarly, if an emergency event was a false alarm, or if it is no longer active video broadcast from edge to core is to be stopped thus releasing network bandwidth. In this context, the proposed orchestration service can handle autonomously this behaviour optimizing network resource usage.
# Variable Selection Using Adaptive Band Clustering and Physarum Network
## 4 Results and Discussion
### Corn Data Set
This may suggest that by using AP before PN, AP-PN-GA-PLS can further eliminate the redundant information in variables. Different from PN, AP optimized both the number of the sub-spectral bands and the number of the wavelengths  
Figure 4: Corn data (predicting protein content) wavelength selection result by using AP-PN-GA-PLS in the first round: (**a**) spectral responses vs. wavelengths, the 184 wavelengths selected by AP-PN are marked with circles; (**b**) times of selection (by GA-PLS) vs. wavelength identifier (1–184), the wavelengths above the lower line were selected; (**c**) spectral response vs. wavelengths, the 16 wavelengths selected by AP-PN-GA-PLS are marked with circles; (**d**) scatter plot of predicted values vs. the measured values. PLS = partial least square; GA = genetic algorithm. within each sub-spectral band, therefore, AP-PN-GA-PLS can achieve similar prediction performance with less wavelength. It is also shown in Table 4 that AP-GA-PLS can achieve RMSEP of 0.0643% with 39 wavelengths. This wavelength selection performance is comparable to that of PN-GA-PLS but is better than GA-PLS in terms of achieving similar RMSEP with less wavelength. This result suggests that the exemplars selected by AP can be input into GA-PLS directly, which can improve the wavelength selection performance. However, AP-GA-PLS's wavelength selection performance (39 wavelengths) is worse than that of AP-PN-GA-PLS (25 wavelengths). Every exemplar selected by the AP is a representative of each sub-spectral band, but it only represents the local information of the corresponding sub-spectral band. These exemplars do not consider the global information of the whole spectrum. They do not guarantee that they have the least correlation in a whole. This limitation of AP can be overcome by using PN afterwards. The PN selects one wavelength from each sub-spectral band to ensure that all the wavelengths in a whole have the least correlation. By combining the advantages of both AP and PN, the AP-PN-GA-PLS can thus achieve the best performance. AP-PN-GA-PLS achieved very good prediction performance (R = 0.9970, RMSEP = 0.0397%) with the least number of wavelengths among PLS, GA-PLS, PN-GA-PLS, AP-GA-PLS, and AP-PN-GA-PLS, whose number of selected wavelengths were 700, 67, 35, 39, and 25, respectively. There were 25 wavelengths selected by AP-PN-GA-PLS. The selected wavelengths 1778 nm and 1780 nm are similar to 1778 nm, which is the absorption wavelength of wheat gluten [31]. The selected wavelengths 2154 nm, 2156 nm, 2176 nm, and 2178 nm are within the range of 2100 nm-2200 nm, which are the absorption wavelengths of wheat gluten [31]. The selected wavelength 2220 nm is similar to 2230 nm, which is the local minimum absorption wavelength of wheat gluten [31].
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 2 Background and System Model
### A Motivating Example
Obviously, if we maximize the mutual information, the SU will cause more harm to the PU, but still, this harm is upper bounded by the harm caused by a Gaussian signal with the same \(\kappa_{s}\), so the PU will sustain its rate.
## 3 Results and Discussion
### Spatial Cumulative Effects Between Two Open-Pit Mines  
Due to the abnormal fluctuations of TVDI-CFS in the \"SW\" direction, further analysis of the cumulative impacts between two adjacent mining areas reveals interesting findings. In the rectangular sub-samples between the two open-pit mines (Figure 9), the Xiwan open-pit mine, which began mining in 2016, caused a significant increase in SM in the surrounding 420 m, altering the hydraulic gradient and leading to greater fluctuations in groundwater levels. By 2019, further mining at Xiwan expanded the disturbance range from 420 m to 480 m, while the commencement of production at open-pit mine 2 in the same year created a noticeable impact on TVDI-CFS with a disturbance range of 270 m. The area between the two mines showed a more pronounced increase in SM, and by 2021, the disturbance ranges of Xiwan and open-pit mine 2 had further increased by 30 m and 180 m, respectively. The TVDI-CFS distribution between the two mines shows a gradual deepening of color, with values decreasing, indicating that as mining operations expand, the long-term impact on the surrounding SM intensifies, with an increasing disturbance range and cumulative effects. This aligns with studies in regions like the Appalachian area of the United States and the Fuxin pen-pit mine in China, which have shown similar alterations to SM dynamics due to mining activities [57; 58].  
Figure 8: Disturbance distance graphs of the mining area’s impact on the TVDI in different directions.  
Figure 7: Fitting curve graphs of the mining area’s impact on the TVDI in different directions. (Green is the disturbed ares, pink is the undisturbed ares).  
However, the time frame of this study (2000-2021) and its focus on a single mining area limit the generalizability of the findings. Extending the temporal scope to include longer pre-mining and post-mining phases would provide a more comprehensive understanding of the long-term impacts of mining on SM. Additionally, conducting comparative studies in different ecological and climatic regions could help assess whether similar patterns of SM disturbance are observed globally, thus aiding in the development of broader environmental impact models for open-pit mining.
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## 5 Discussion  
Despite satisfactory quantitative results in the case study, there is still room for improvement in the model efficiency. The performance of the K-means clustering algorithm relies on various factors: the suitable number of clusters \(k\), an outlier sensitivity problem, and a selection of initial centroids. If there are many clusters, only a few data points are situated in each cluster, leading to erratic variograms. On the other hand, for a small number of \(k\), spurious correlation can affect the model prediction. Identification of the minimum number of cluster elements is additionally required to capture spatial variability. Furthermore, outliers can cause a misspecification of cluster centroids, causing less reliable results. In this work, data preprocessing was carried out to eliminate outliers. Additionally, the Calinski-Harabasz index was also employed to ascertain the optimal number of clusters. However, the suitability of this approach may be contingent upon the specific characteristics of the data and the research objectives. Alternative methods, such as the gap statistics, the silhouette index, and the density canopy algorithm [44], could offer valuable insights and potentially yield different results. Last, but most importantly, the time complexity of OK is typically \(O(n^{3})\)[45; 46; 47] where \(n\) is the number of data points, whereas that of our algorithm depends on two processes: the ordinary kriging and the K-means clustering system. The computational complexity of the K-means algorithm is \(O(nkt)\)[48; 49; 50] where \(t\) is the number of iterations. To mitigate such issues, some effective algorithms that can find appropriate initial centroids can simultaneously reduce the number of \(k\) and \(t\), resulting in a reduction in computational cost. The proposed window selection algorithm is not limited to the OK model. However, it could be applied to broader ranges of kriging models that incorporate auxiliary variables influencing the target variable via the trend function, such as regression kriging (RK) and kriging with external drift (KED) [51; 52]. These techniques specifically operate on a subset of data confined within the selected window for the estimation.  
Figure 5: The annual humidity kriging estimate maps using the different spatial interpolation methods: (**a**) OK, (**b**) MWK, (**c**) MWKK with \(k=5\), and (**d**) AMWKK with \(k=5\).
## 4 Numerical Modelling and Analysis
### Establishment of Numerical Analysis Model  
In this study, ABAQUS is employed as the analysis platform for the numerical modeling and analysis of Ultra-High-Performance Concrete (UHPC) wet joints precast steel-reinforced concrete variable cross-section bridge decks. The established finite element model is depicted in Figure 12.  
Figure 11: Bridge deck slab load—crack widths.  
Figure 10: Damage to bridge deck slabs at various locations. (**a**) Damage on the slab side. (**b**) Damage at variable section locations.  
The simulation model employs eight-node hexahedral reduced integration solid elements to simulate the variable cross-section bridge decks, wet joints, and shear studs; while 3D two-node truss elements are used to model the reinforcement within the structure. Steel reinforcement is embedded within concrete instances to simulate the steel-concrete contact relationship. Interaction between two sections is realized by defining tangential friction coefficients. The shear studs are welded onto the steel beam, and the finite element model adopts the TIE formulation to simulate their welding relationship. A cohesive force model is utilized to simulate the wet joint interfaces of the bridge deck. The finite element model in this section employs a quadratic nominal stress criterion to assess the damage extent at the wet joint interfaces, primarily induced by the combined effect of normal and tangential stresses leading to concrete failure.  
\[\left[\frac{s_{n}}{s_{n}^{0}}\right]^{2}+\left[\frac{s_{s}}{s_{s}^{0}}\right] ^{2}+\left[\frac{s_{t}}{s_{t}^{0}}\right]^{2}=1 \tag{1}\]  
where: \(s_{n}\), \(s_{s}\), \(s_{t}\)--are the normal stress, the first tangential stress, and the second tangential stress on the old and new interfaces, respectively; \(s_{n}^{0}\), \(s_{s}^{0}\)\(s_{t}^{0}\) are the critical stresses corresponding to the stresses in each direction, respectively; and the specific parameters selected for the finite element model are shown in Table 1:  
Considering that the model's steel beams do not undergo rigid body displacements, boundary conditions are established by restraining the translational motion of the lower flange of the steel beams to simulate real structural conditions. During meshing, the wet joints and the bridge decks on both sides are finely divided into meshes, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Causality** & **Smoothness** & **Roughness** & **Rougher** \\ \hline \(K_{\text{nn}}\) (N/mm\({}^{3}\)) & 1358 & 1358 & 1358 \\ \hline \(K_{\text{ss}}\), \(K_{\text{tt}}\) (N/mm\({}^{3}\)) & 20,358 & 20,358 & 20,358 \\ \hline \(s_{n}^{0}\), \(s_{s}^{0}\), \(s_{t}^{0}\) & 3.02 & 5.01 & 5.63 \\ \hline Totle, plastic displacement (mm) & 0.018 & 0.117 & 0.241 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the cohesion model [23].  
Figure 12: Finite Element Modelling of UHPC Wet Joint Flexural Test.  
Figure 13: Full-size bridge deck UHPC wet joint flexural test model.
## 4 Discussions and Comparative Analysis
### Complexity  
The selection of an appropriate MPPT, keeping in mind its design complexity for a specific PV system, is regarded as one of the critical factors. The degree of accuracy with which the algorithm seeks for the real GMPP in the presence of several LMPPs determines the complexity of the MPPT approach. Otherwise, the PV system will not be able to capture the maximum solar energy. Moreover, the configuration and implementation of the MPPT are dependent on the user's knowledge of the device, with one person being skilled in dealing with analog circuits, while the other prefers digital circuits. However, intelligent MPPT algorithms are implemented in digital form, which necessitates computer programming and software experts.
## 2 Materials and Methods
### High-Speed PIV  
Figure 2a also illustrates the laser setup for the application of high speed-PIV (HS-PIV) diagnostic in the optical engine. An Nd:YLF laser (Litron Lasers Ltd., LDY304) beam of 527 nm wavelength was converted to a 500 \(\upmu\)m thin sheet through a series of optics and then introduced into the engine combustion chamber from the bottom by reflecting off the 45\({}^{\circ}\) mirror and then passing through the piston-top quartz window. The pulse width of the laser shot is 150 ns at 1 kHz repetition rate. An area of 37 mm \(\times\) 54 mm in the middle of the side-view of the pent-roof, which includes the spark plug and its surrounding region was targeted as the measurement area for this study. Figure 2b displays the schematics of this measurement area (right) as well as the bottom-view of the fire deck to illustrate the relative position of the laser. The PIV images were captured using a high-speed camera (Vision Research Inc., Phantom v1611) at 1280 \(\times\) 800 resolution and the laser was operated at 7.2 kHz (20 kHz maximum) for a temporal resolution of 1\({}^{\circ}\) CA in the current engine operating condition. The time interval for two images was set to 40 \(\upmu\)s. A 527 nm bandpass filter with band width of 20 nm FWHM was utilized to isolate PIV signal from other interferences. The laser and camera operations were synchronized to engine rotation by using a pulse generator. The pulse generator was triggered by the engine rotating signal and the pulse generator generated the trigger signals for laser and camera. PIV data was captured for full crank angles of 19 consecutive fired cycles due to the high-speed camera memory limitation.  
In this study, HS-PIV diagnostics was applied under engine-firing conditions which in turns introduces special challenges and difficulties. These will be described and discussed in the later sections. For optimum signal intensity with high traceability for kHz order, the material of the seeds  
Figure 2: (**a**) Optical engine and Particle Image Velocimetry (PIV) set up, (**b**) PIV measurement area and position of the laser sheet with respect to the cylinder.  
need to be resilient to the harsh conditions of high pressure, high turbulence and high temperature inside a cylinder of a combustion engine while being inert and not affect the chemistry of combustion. As the practicability under the combustion condition was proven by previous LDV experiment at our laboratory [4], we selected a SiO\({}_{2}\) particle with 4 \(\upmu\)m diameter as our tracer particles. The process on tuning for the best seeding amount will also be discussed in later sections. The particles in the tank were carried by the compressed air and seeded into the intake air flow upstream of the fuel injector of the optical engine to ensure the homogeneous mixing and trace to the intake air motion. Table 2 summaries the PIV setup used.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 2 Experimental
### In Vitro Release Tests
#### 2.4.3 Biocompatibility Testing of Hydrogels  
The preparation of CAA-CS polysaccharide hydrogel followed the previously described method. The hydrogel was cut into discs with a diameter of approximately 2 cm, soaked in ethanol for 48 h, and subsequently washed with PBS (pH = 7.34). The gel discs were then incubated in complete culture medium for 48 h. The resulting gel extracts were mixed with complete medium at concentrations of 25%, 50%, 75%, and 100% extract. L929 fibroblasts were seeded in 24-well plates at a density of 2 \(\times\) 10\({}^{4}\) cells per well and cultured in these solutions. The complete medium consisted of DMEM supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, and 89% DMEM. The cultures were maintained in an incubator at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\), with media changes performed daily. Cell viability was assessed using the CCK-8 assay. Specifically, 500 \(\mu\)L of medium containing 50 \(\mu\)L of CCK-8 solution was added to each well and incubated at 37 \({}^{\circ}\)C for 4 h to allowformazan formation. The optical density (OD) of the solution was measured at 450 nm using a microplate reader, and cell viability was calculated accordingly. Each sample was tested in duplicate.
# Aerial Mapping of Coseismic Surface Rupture of 2021 M\({}_{\rm w}\) 7.3 Maduo Earthquake, China
## 2 Tectonic Setting  
The Tibetan Plateau, which was created by the collision between the Eurasian plate and the Indian plate, can be divided into smaller tectonic blocks that are bound by several major strike-slip faults. The Maduo earthquake occurred in the Bayan Har Block (Figure 1), which is the most active block in Tibet. In this century, some major earthquakes have happened on the faults surrounding the Bayan Har Block [4; 5; 6; 7; 8; 9; 10]. Historical seismic records show that strong (magnitude M \(\geq\) 6.0) earthquakes have mostly occurred at the edge of the block and that very few large earthquakes have occurred inside the block since 1900 (Figure 1).  
Figure 1: Active faults map of Tibet. Large fault zones (thick black lines) divide Tibet into several blocks. The red line represents the surface rupture, and the white star is the earthquake epicenter of  
the 2021 Maduo earthquake. The red circles represent the locations of historical (from 1900 to the present) earthquakes in the Bayan Har block with magnitudes greater than or equal to M 6.0. Larger circles correspond to higher earthquake magnitudes. The black arrows show the relative movement of blocks.  
The Bayan Har block is surrounded by the Xianshuihe fault in the south, the Kunlun fault in the north, NE-striking faults to the west, and SE-striking faults to the east. Animaqing Mountain is located on the eastern part of the Kunlun fault; the elongated geometry and anomalously high topography of the mountain are the result of tectonic compression and uplift along the restraining bends of the Kunlun fault. The primary seismogenic fault of the Maduo earthquake, which extends out from the western segment of the Kunlun fault and is located on the western side of Animaqing Mountain, has a strike of about 105\({}^{\circ}\)[11,12]. This fault is oriented roughly parallel to the strike of the central segment of the Kunlun fault and obliquely intersects the faults surrounding Animaqing Mountain (Figure 1).