esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The State-Dependent Constitutive Model for Saturated Sand
### State-Dependent Dilatancy  
The state-dependent dilatancy function proposed by [PERSON] and [PERSON] [19] is employed in this study:  
\[D=\frac{d\varepsilon_{v}^{p}}{d\varepsilon_{d}^{p}}=\frac{d_{0}}{M}\big{(}Me^{ m\psi}-\eta\big{)} \tag{4}\]  
where \(d\varepsilon_{v}^{p}\) is the plastic volumetric strain increment while \(d\varepsilon_{d}^{p}\) is the plastic deviatoric strain increment. \(M\) is the special value of the \(\eta\) at the critical state. \(d_{0}\) and \(m\) are two material constants related to dilatancy. \(\psi\) is the state parameter and it is defined by [PERSON] [20] as follows  
\[\psi=e-e_{c}=e-\left[e_{\Gamma}-\lambda_{c}\big{(}p^{\prime}/p_{a}\big{)}^{ \zeta}\right] \tag{5}\]  
where \(e_{c}\) is the critical void ratio on the critical state line in the \(e-p^{\prime}\) space corresponding to the current \(p^{\prime}\), improved by a power relationship \(e-\left(\frac{p^{\prime}}{p_{a}}\right)^{\zeta}\) and prevented by [PERSON] and [PERSON] [21], and \(p_{a}\) is the normal atmospheric pressure. \(e_{\Gamma}\), \(\lambda_{c}\) and \(\zeta\) are the material constants of the critical state. \(D>0\) and \(D<0\) mean contractive and dilative behavior. This formulation includes the following key features of dilatancy of sand subjected to shear [15]:  
1. At a loose state (\(\psi>0\)), the sand exhibits a contractive behavior (\(D>0\)), as \(\eta\) is always lower than \(Me^{m\psi}\) when \(\psi>0\)[22];
2. At a dense state (\(\psi<0\)), the sand displays either zero dilatancies when \(\eta=Me^{m\psi}\) (upon phase transformation), or a dilative behavior (\(D<0\)) if otherwise [22];
3. At the critical state, the dilatancy vanishes (\(D=0\)) being irrespective of the initial state by \(\eta=M\) and \(\psi=0\);
4. The equation can be recovered to the dilatancy function of the original Cam clay model (i.e., \(D=M-\eta\)), by setting the two material constants as \(d_{0}=M\) and \(m=0\).
# The Emerging Role of Cyclodextrin Metal-Organic Frameworks in Ostheotherapetics
## 3 Cyclodextrin-Based MOFs
### \(\gamma\)-CD-MOFs Loaded with Anti-Inflammatory Agents for the Management of Osteoarthritis
While _c.a._ 73% of pure diclofenac sodium had dissolved after the first 30 min in simulated gastric fluid, slowly reaching 100% dissolution over 6.5 hours and as the pH increases, the amount of the drug dissolved from the \(\gamma\)-CD-MOFs was 10-17% after the first 30 min, increasing to 35-40% after 4 h immersion at pH 7.4; for the Na\({}^{+}\) and K\({}^{+}\)\(\gamma\)-CD-MOFs, no further release was observed, with a total of 40% diclofenac sodium dissolved after 24 h (the total observation time of the assay), while the Fe-\(\gamma\)-CD-MOF kept releasing diclofenac sodium in a gradual way over the entire length of the assay, that is, 18 h of immersion at pH 6.8, reaching a dissolution value of _c.a._ 60% (Figure 3). _In vivo_ studies using the carrageenan-induced paw edema model in mice further confirmed that the anti-inflammatory activity of sodium diclofenac when carried by the Fe-\(\gamma\)-CD-MOF (88.7% effectiveness) was quite superior to that of the pure drug (30.4% effectiveness) and to those obtained with the other two \(\gamma\)-CD-MOF carriers (17.3% and 26.8%) [38]. Ibuprofen, one of the most widely used NSAIDs, was loaded into the \(\gamma\)-CD-MOF with K\({}^{+}\) and the product was evaluated regarding oral bioavailability in mice [39]. The most significant alteration in comparison with the profile of the drug was an increase in the plasma half-life, from 1.07 \(\pm\) 0.29 h (plain ibuprofen potassium salt) to 2.35 \(\pm\) 0.68 for the \(\gamma\)-CD-MOF loaded with ibuprofen. The \(\gamma\)-CD-MOF was thus an adequate delivery vehicle for extending the duration of the analgesic and anti-inflammatory effects of ibuprofen. \(\gamma\)-CD-MOFs with K\({}^{+}\) metal centres were further demonstrated to suitably load ketoprofen and fenbufen, [24] but release profile and pharmacokinetics were not evaluated. An overview of the reported \(\gamma\)-CD-MOF delivery systems with arthritis-relevant NSAIDs is presented in Table 1; drug structures are in Figure 4.  
Figure 3: Release rates of diclofenac sodium, either in the free form (blue triangles) or when carried by K-\(\gamma\)-CD-MOF (pink squares), Na-\(\gamma\)-CD-MOF (green dots) and Fe-\(\gamma\)-CD-MOF (red triangles) in different simulated biological fluids: HCl 0.2 M (pH 1.2) to simulate the gastric fluid, phosphate buffer solution (PBS) at pH 7.4 to simulate the intestinal fluid and PBS at pH 6.8 to simulate the colonic fluid. Reproduced with permission from [PERSON] _et al._[38]. \(\gamma\)-CD-MOFs Stabilised into Cross-Linked Materials and Their Application in Periosteum Bone
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
###### Abstract  
Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm--A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches.  
VANETs; genetic algorithm; MAC/NET; QoS; energy consumption +
Footnote †: journal: Article  
0022231387
# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
## 3 Proposed Method
### ResNet+ConvLSTM  
So far, we have discussed ResNet and ConvLSTM, where each works well in the tasks they are designed for. In this section, we propose our method by adding the ConvLSTM model to the ResNet structure, in that the output of ResNet goes through to the ConvLSTM network.  
The following describes the whole idea and procedure of the proposed ResNet50 ConvLSTM model shown in Figure 1.  
Figure 3: ConvLSTM structure [63].  
* At first, each input video is divided into \(n\) frames. Then, the difference between each frame and the next frame goes to preprocessing phase as an initial part of the ResNet structure, including data augmentation, de-noising, and normalizing.
* Next, the difference of each two frames goes to our ResNet50 structure. It passes through 34-layers of Batch-Normalization, Convolutional, pooling and fully-connected layers to provide a suitable feature extraction of a vector size of 1000.
* Subsequently, the output for each image will be the input for the related ConvLSTM. Each ConvLSTM layer will be provided with two inputs: one from the previous ConvLSTM (256 feature maps) and the second from the data processed by ResNet50. As a result, this structure is significantly helpful for spatial-sequential data, particularly videos. The ConvLSTM that we used in our model consists of 256 filters with the size of 3 \(\times\) 3 and stride 1.
* Finally, the output of the last ConvLSTM layer, which includes all information from previous stages with size \((n,4,4,128)\), goes to a \((2\times 2)\) MaxPooling layer followed by some fully connected layers to provide the desired classification.
## 3 Results
### Raw Materials Characterization
#### 3.1.2 Physical Characterization  
The physical characterization of biochar (Table 3) shows that it was characterized by a high percentage, around 40%, of grains with diameter less than 100 \(\upmu\)m. In accordance with the granulometric analysis, the biochar had a large specific surface area (210.50 \(\pm\) 5.94 m\({}^{2}\) g\({}^{-1}\)). The total porosity was estimated using Equation (1) from the apparent and absolute densities. According to [PERSON] et al. [22], the bulk density was very low (0.16 g/cm\({}^{3}\)) compared to other values in literature. Instead, the absolute density (2.23 g/cm\({}^{3}\)) had a by higher value, because the biochar was derived by the gasification process at a high temperature. Increasing the processing temperature led to an increase in graphitization degree that produced an absolute density similar to that of solid graphite (2.25 g/cm\({}^{3}\)) [22]. The total porosity obtained by the BET analysis was 92.8%, which is a high value and is similar to that found in other studies [50; 51].  
The teabag method provided realistic information about the amount of water that biochar absorbed and the available water that could be released from it at a later time [45]. The absorption occured in the first half an hour, while for longer times the weight remained almost constant, showing a saturation phenomenon; the same behaviour was observed in the cement pastes of [PERSON]'s research [45], where all the materials reached their equilibrium absorption during the measurement time. As showed in the [PERSON] study, the high absorption of material could be attributed to its molecular structure, morphology and large surface area. Biochar was characterized by high capacity to absorb water, as showed by Figure 5, indeed it absorbed a quantity of water about six times its weight.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Unity** & **Biochar** \\ \hline BET Surface Area & m\({}^{2}\) g\({}^{-1}\) & 210.50 \(\pm\) 5.94 \\ \hline Apparent Density & g cm\({}^{-3}\) & 0.16 \\ \hline Absolute density & g cm\({}^{-3}\) & 2.23 \(\pm\) 0.0004 \\ \hline Total Porosity & \% & 92.8 \\ \hline \end{tabular}
\end{table}
Table 3: BET, bulk density and total porosity results on biochar.  
Figure 5: Water absorption trend of biochar.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 4 Conclusions  
In this study, we took advantage of the wind profile Lidar's high spatial and temporal resolutions, high measurement accuracy, long-distance detection, and strong anti-interference characteristics to develop a method to measure the three-dimensional atmospheric wind speed field. In addition, by dividing the range gate to calculate the wind speed at different altitudes, the noise data and mirror data were limited to the first three range gates. Based on this, we proposed a method to calculate the CNR of the wind profile coherent wind measurement Lidar system. The experimental results show that the wind profiles obtained using the different techniques are quite consistent and the  
Figure 11: The relationship between the CNR and the detection range.  
Figure 10: Variation in the absolute deviation with height between the different beam measurements.  
standard error is less than 0.42 m/s compared with the three-beam and five-beam wind measurements. In summary, based on an in-depth analysis of the power spectrum of the echo signal, the proposed method has good accuracy and provides robust atmospheric wind measurements.  
Conceptualization, Y.G. and R.S.; investigation, Y.Z. ([PERSON]), J.D. and K.W.; methodology, X.Z.; resources, Y.Z. ([PERSON]); supervision, J.F.; writing--original draft, X.Z.; writing--review and editing, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province, grant number ZR2020 MA082 and Key Technology Research and Development Program of Shandong, grant number 2016 GGX101016.  
The authors declare no conflict of interest.
## 2 Numerical Modelling
### Stress Intensity Factor (SIF)  
The SIFs used were those obtained by [PERSON] and [PERSON] (2007) by a three-dimensional (3D) finite element analysis and the nodal-force method. These authors adjusted their results to an equation that calculates the SIF \(K\):  
\[K=\sigma\sqrt{\frac{\pi a}{Q}}F \tag{1}\]  
where the function \(Q\) is the shape factor for an ellipse (given by the square of the complete elliptic integral of the second kind) and depends on the aspect ratio \(a/b\) as follows:  
For \(\frac{a}{b}\leq 1\),  
\[Q=1+1.464\Big{(}\frac{a}{b}\Big{)}^{1.65} \tag{2}\]  
For \(\frac{a}{b}>1\),  
\[Q=1+1.464\Big{(}\frac{b}{a}\Big{)}^{1.65} \tag{3}\]  
\(F\) is a function of the parameters: \(a/b\) (crack aspect ratio, see Figure 1), \(a/t\) (relative crack depth, see Figure 1), and \(\phi\) the angle representing the specific position at the crack front (see Figure 2). The function \(F\) is different depending on if the crack is embedded, surface, or corner-type and is calculated by means of the expression,  
\[F=\bigg{[}M_{1}+M_{2}\Big{(}\frac{a}{t}\Big{)}^{2}+M_{3}\Big{(}\frac{a}{t} \Big{)}^{4}\bigg{]}gf_{\phi} \tag{4}\]  
where the parameters \(M_{1}\), \(M_{2}\), \(M_{3}\), \(g\), and \(f_{\phi}\) are obtained through the equations in Appendix A.  
In Table 1 the ranges are given of the variables \(a/b\), \(a/t\), and \(\phi\) for which the Equations in Appendix A are valid for the calculation of the SIF in an embedded, surface, or corner crack placed in a plate subjected to tensile cyclic loading.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Configuration**} \\ \cline{2-4}  & **Embedded Crack** & **Surface Crack** & **Corner Crack** \\ \hline \(a/b\) & 0 to \(\infty\) & 0 to 2 & 0.2 to 2 \\ \(a/t\) & \(<\)1.25 (\(a/b+0.6\)) if \(0\leq a/b\leq 0.2\) & \(<\)1 \\  & \(<\)1 if \(a/b>0.2\) & & \\ \(\phi\) & \(-\pi\) to \(\pi\) & 0 to \(\pi\) & 0 to \(\pi/2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Applicability ranges for the equations by [PERSON] and [PERSON] (2007).  
Figure 2: Angle \(\phi\) defining a specific point \(p\) over the crack front: (**a**) \(a/b\leq 1\); (**b**) \(a/b>1\).
## 3 Comparisons of Maintenance Effect  
### Stress Intensity Factors at the Crack Tip  
The stress intensity factor \(K\) is a significant parameter for characterizing the stress field at the crack tip of an elastic object under an external force [19]. With the increase in \(K\), the crack is more likely to propagate. According to linear elastic fracture mechanics, the stress intensity factors can be divided into three types: \(K_{\text{I}}\) (under in-plane tension), \(K_{\text{II}}\) (under in-plane shear), and \(K_{\text{III}}\) (under out-of-plane shear) [20]. The values of the stress intensity factors were extracted using ABAQUS software [21]. Comparing the stress intensity factors at the crack tip before and after the reinforcement is an important method for evaluating the maintenance effect. Then, based on vector accumulative theory [22], the effect stress intensity factor of \(K_{\text{eq}}\) can be obtained via Equation (1).  
\[K_{\text{eq}}=\sqrt{K_{\text{I}}^{2}+K_{\text{II}}^{2}+\frac{K_{\text{III}}^{2} }{1-v}} \tag{1}\]  
where \(K_{\text{eq}}\) is the effect stress intensity factor, and \(v\) is Poisson's ratio, which was 0.3 in this case.  
The stress intensity factors obtained via the finite element method are shown in Figure 4. It was seen that the stress concentration at the crack tip was obvious before reinforcement. After the steel plate reinforcement, the stress concentration was reduced, and the stress intensity factors fell by more than 93%. \(K_{\text{eq}}\) before the reinforcement reached 813.68 MPa\(\cdot\)mm\({}^{1/2}\), which decreased to 31.75 and 37.70 MPa\(\cdot\)mm\({}^{1/2}\) respectively after being reinforced with polygonal plate and rectangular plate. The values of the stress intensity factors after polygonal plate reinforcement were slightly higher than those after rectangular plate reinforcement. The results indicated that, both polygonal plate reinforcement and rectangular plate reinforcement had a significant effect on hindering crack propagation. In addition, before reinforcement, the crack that grew obliquely from the diaphragm-to-rib weld was shown as a mixed mode crack.  
Figure 4: Comparison of stress intensity factors before and after steel plate reinforcement.
## 4 Detection and Mitigation Mechanism
### Dynamic Model
According to the dynamic equation and the manipulator dynamic parameters given in Table 1, the manipulator dynamic model is established in Adams 2013, as shown in Figure 5.  
In order to test the correctness and accuracy of the manipulator dynamic model, the dynamic simulation experiment was performed by defining the rotation drive and torque of the seven joints. The simulation adopted the basic mode and the simulation type selected default. Moreover, the simulation time was 16 s. In this simulation, we added the same drive function to joint 5 and joint 6. The force of each joint in heavy-duty manipulator could be obtained through the Adams post-processing module. As shown in Figure 6a, the forces of the three joints at the end of the manipulator is given. And the force curves of joint 5 and joint 6 are similar, which is consistent with our drive equations, demonstrating the correctness of the dynamic model. In addition, Figure 6b shows the follow-up of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Joint**} & **Mass** & **Centroid Position** & **Terminal Position** & **Density** & **Elastic Modulus** \\ \cline{2-6}  & **m\({}_{i}\)** & \(c_{i}\)/m** & \(c_{i}\)/m & **kg/m\({}^{3}\)** & **kN-mm\({}^{2}\)** \\ \hline
1 & 745 & [0 0 0.21] & [0 0 0.4] & 7800 & 200–235 \\
2 & 910 & [0.48 0.4] & [1.13 0 0.4] & 7850 & 200–235 \\
3 & 882 & [1.26 0.0 4] & [1.54 0.4] & 7650 & 200–235 \\
4 & 64 & [1.7 0 0.25] & [1.7 0 0.12] & 7820 & 200–235 \\
5 & 301 & [1.83 0 0.12] & [1.92 0.12] & 7810 & 200–235 \\
6 & 279 & [2.01 0 0.1] & [2.08 0 0.1] & 7800 & 200–235 \\
7 & 198 & [2.27 0.1] & [2.5 0 0.1] & 7800 & 200–235 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of heavy-duty manipulator. Figure 5: (a) Heavy-duty manipulator; (b) Adams dynamic model. dynamic model's trajectory of the first three joints and the actual joint trajectory for the same control input based on a heavy-duty robot, where the blue line represents the manipulator and the red line represents the dynamic model. It can be seen from the figure that the established model and the actual trajectory are mostly consistent, ignoring the leading or lagging in some places, which proves the accuracy of the established model.
## 6 Open Challenges, Future Prospects and Research Directions
### Adaptive Modulation  
Traditional modulation techniques, while foundational, are being complemented and surpassed by more advanced methods. Among these, adaptive modulation stands out. Consequently, adaptive modulation techniques will be essential in future FSO systems. These techniques adjust the modulation parameters in real-time based on the current channel conditions. For instance, during periods of favorable atmospheric conditions, higher-order modulation schemes such as 64-QAM can be used to enhance data rates. Conversely, during periods of high turbulence or fog, the system can switch to more robust modulation schemes like BPSK to maintain link reliability. The dynamic nature of adaptive modulation ensures that FSO systems can optimize their performance under a diverse array of environmental conditions [93].  
Advanced Modulation Schemes in Conjunction with Sophisticated Error Correction  
Advanced error correction techniques are also playing a crucial role. By correcting errors that occur during data transmission, these methods enhance the reliability and efficiency of the systems, even in the presence of atmospheric disturbances like fog, rain, and turbulence. Thus, future modulation schemes will also incorporate advanced error correction methods to alleviate the impact of channel impairments. Techniques such as LDPC codes and Turbo codes will be integrated into the modulation process to detect and correct errors in the transmitted data. These error correction methods will enhance the reliability of FSO communications, ensuring that data are accurately received even amid interference and noise [93].  
Figure 16: Schematic of the tutorial organization with related content. AMS-SEC: Advanced Modulation Schemes in conjunction with Sophisticated Error Correction, OptSM-AI/ML: Optimized Spatial Modulation with AI and  
ML, ML-Aided-ORA: ML-Aided Optimization and Resource Allocation, AMC: Adaptive modulation and coding, AAA: Advanced Adaptive Algorithms, ECC-MS: Error Correction Codes and Modulated Signal, EPRT: Enhanced Performance in Real-Time, PAS: Probabilistic Amplitude Shaping, xFT: Extensive Field Trials, WS-DM: Wavefront Sensors and Deformable Mirrors, NI: Noise and Interference, NG: Next-Generation, mMTC: Massive Machine-Type Communications, ADT: Adaptive Detection Thresholds, AMBS: Adaptive Modulation and Beam Steering, CCTRX: Compact and Cost-Effective Transceivers.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 4 Discussion
### Method Transferability and Improvement  
Our approach provided a large degree of improvement in solving the problem of abandoned cropland in hilly areas. On the one hand, we obtained good available images at a monthly scale through cloudy interference removal, interpolation, and SG filtering. On the other hand, we improved efficiency by obtaining reliable samples through multisource data filtering and developed a visualization tool for GEE. The annual land-cover results were obtained by pre-classification and secondary classification, and then the definition was used to generate the final spatial distribution of cropland abandonment. However, additional adjustment may be needed according to local phenological characteristics, as also applied in other regions. For example, in this study, the abandoned cropland refers to areas not cultivated for two consecutive years, while the FAO defines it as cropland that has not been cultivated for more than 5 years. In addition, the timeseries window of the SG filter can be modified according to the degree of the impact image. There is still some roomfor improvement in our method. When a single optical remote sensing image is disturbed by clouds for a long period, the fill-and-fit (FF) approach may not be sufficient for surface phenology monitoring. It is necessary to use other images for filling in and to ensure a sufficient number of observations to effectively extract surface coverage changes. With the successful launch of Landsat-9, remote sensing images of each scene for 8 days can be jointly acquired with Landsat-8. Combining the timeseries of Landsat and Sentinel will increase the availability of remote sensing imagery in hilly areas. Furthermore, with the further development of the GEE platform, more reliable algorithms and data with higher precision will be available to improve the accuracy of cropland abandonment. In the future, free and publicly accessible images with a higher spatial resolution can reduce the influence of mixed pixels in hilly areas, as well as ameliorate the reliability of classification results.