esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Physical Property Characteristics  
The maximum porosity of turbidite tight reservoirs is 12.2% and the minimum value is 4.29%, with an average value of 9.34%, which is mainly distributed in the range of 4-12.2%, and the porosity of 47.6% of the samples is 10-12%. In addition, the permeability is distributed in the range of 0.01-100 mD, with a maximum value of 10.09 mD, a minimum value of 0.09 mD, and an average value of 1.64 mD. The 0.1-1 mD and 1-10 mD samples accounted for 71.4% and 19.0% of the total samples, respectively, but the permeability of 4.8% of the samples was greater than 10 mD (Figure 4a,b). For the tight reservoirs of the delta front subfacies, the porosity is distributed in all intervals within the range of 12%, with a maximum value of 11.67%, a minimum value of 0.68%, and an average value of 5.82%, but the main distribution range of porosity is 4-8%. The permeability range is 0.01-10 mD, the maximum value is 2.46 mD, and the minimum value is 0.03 mD, with an average of 0.37 mD. Among them, 70.2% of the samples have permeabilities of 0.1-1 mD, with 21.3% of the samples having permeabilities of only 0.01-0.1 mD (Figure 4a,b). The physical properties of turbidite tight reservoirs are significantly better than those of delta front tight reservoirs.  
According to the porosity and permeability intersection diagram, the turbidite sandstones clearly have a relatively strong positive correlation between porosity and permeabil  
Figure 3: Petrological characteristic of reservoirs: (**a**) Triangular plot of sandstone classification; (**b**) Box plot of diffraction analysis results of whole rock minerals.  
ity. However, there is no significant correlation between the porosity and permeability of delta front sandstone reservoirs (Figure 4c). Moreover, some sandstones from different sedimentary reservoirs have similar porosities, but obvious differences in permeability. Therefore, the pore structure of reservoirs under the control of different sedimentary systems in the study area needs to be studied, and the corresponding differences in the pore structure should be clarified. This study elucidates the effects of pore structure differences on permeability, and the influence of the pore structure on oil-bearing properties is discussed.
## 4 Discussion
The literature suggests that UTC provides numerous benefits to urban communities and that GIS, remote sensing, and spatial analyses are valuable and essential tools for communities to understand the distribution of UTC and the impacts that it has on at-risk populations. Spatial analyses, such as those performed in this study, can be used to inform policy and used in the decision-making process for the management and improvement of UTC for all urban communities. As cities grow over time, the effects of climate change increase as the natural land cover is converted; thus, it is vital to understand the associations between UTC and its benefits to the urban environment. Decision-makers may be more acutely aware of the detriments to any populations lacking UTC benefits and adjust their policies accordingly. Therefore, these types of spatial analyses should be performed routinely to understand gaps in tree cover and where these gaps need to be filled in order to maintain sustainable and build more equitable communities.
# Numerical Simulation of Subdam Settlement in Ash Disposal Based on CGSW Optimization
## 2 Geology and Distribution of CGSW
### Distribution Characteristics  
Accurate distribution data of CGSW are the basis of subsidence prediction. We conducted an intensive geological engineering investigation to ascertain the CGSW's boundary and thickness. The shape of the buried CGSW was constrained by 68 boreholes. Drilling revealed that the buried CGSW covers an area of 20,000.00 m\({}^{2}\) with total volume of 125,780 m\({}^{3}\) in an elevation range from 1631.67 m to 1643.78 m. The CGSW appears as two layers, both approximately parallel to the surface, as shown in Figures 2 and 3. The upper layer covers an area of 20,000 m\({}^{2}\), with a volume of 68,400 m\({}^{3}\) and an average thickness of 3.42 m. The lower layer, by contrast, covers an area of 9500 m\({}^{2}\), with a volume of 57,380 m\({}^{3}\) and an average thickness of 6.04 m.
## 3 Results and Discussion
### Phase Separation or Equilibrium Time  
To ensure that phase equilibrium was achieved within the period (\(\geq\)1 month) of incubation, a set of mixtures of lactic acid, water, and AE with the same composition (within the two-phase region) was prepared and used to determine the system equilibrium time at 308.15 K. The time to reach equilibrium is critical to the establishment of the phase diagram, for the obvious reason that the system must be at equilibrium during phase composition measurements. Operationally, if the solvent is to be used for liquid-liquid extraction, the equilibrium time determines the settling time or retention time needed for the separation process.  
The results (Figure 2) indicated that the lactic acid + water + AE system attained equilibrium after 168 h (7 days) of incubation at 308.15 K. This suggests that 1 month of incubation was more than enough for the establishment of the phase diagram. However, 7 days is not a very ideal extraction retention time, especially if the process is to be conducted in batch or semi-batch mode. Nevertheless, the results also indicated that the system was within 80% of equilibrium after 3.5 h of incubation. This is a more reasonable operational retention time for lactic acid extraction that could be applied for an actual separation process.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 3 Results and Discussion  
### Synthesis of Block Copolymers  
The triblock copolymers PLLA-PEI-PLLA, PLLA-PEG-PLLA, PDLA-PEG-PDLA and P(DL)LA-PEG-P(DL)LA were prepared as previously reported [31]. Although the controllable ring-opening polymerization (ROP) of cyclic lactide monomers readily produced well defined triblock copolymers, only synthesis of PLLA-PEI-PLLA required a multi-step route to yield the comparative quality of copolymer. In general, the synthesis of PEI/PLA block copolymers has considerable difficulties such as incompatible solubility of PEI with that of lactide or PLA, aggressive nucleophilic nature of PEI amines, and low reactivity of PLA/PEI macromolecular coupling reactions. By utilizing the amine-protection chemistry, we were able to develop a facile and well-controllable synthetic procedure to yield well-defined block copolymers (Scheme 1). The PEI-N-Boc intermediate is soluble in a range of organic solvents and the potential initiation sights of the secondary amines are protected leaving only the terminal hydroxyl groups for ROP to occur. The final triblock copolymer of PLA-PEI-PLA was obtained by the mild deprotection of Boc groups using 50 wt % TFA/CHCl3 solution for 5 min. The deprotection step leads to protonation of the PEI block rendering a positive charge which is necessary for the complexation step with negatively charged DNA.
## 2 HGV Tracking with Modified Aerodynamic Model
### HGV Aerodynamic Model
Furthermore, in HGV trajectory design, the nominal attack angle is set based on engineering experience and the bank angle is used as the main control variable within the constraints of air heating, loading and dynamic pressure [20; 21; 22; 23]. There are three main reasons for using above control approach [24]:  
First, the attack angle determines the flight distance. Furthermore, once the flight mission is determined, the attack angle is determined correspondingly. Second, the attack angle has a direct effect on aerodynamic force. The adjustments to attack angle frequently make aerodynamic force complicated, resulting in aggravating the burden of control system. Third, the proper thermodynamic environment is provided by using big angle for HGV flight. It is harmful to the thermal protection system when adjusting attack angle frequently. However, in some papers [17; 25] the attack angle is not a constant and the two control variables can change at the same time to a large degree. However, we will study HGV tracking using the typical trajectory with the control variables changing at the same time in this paper, which will be more convincing to validate the tracking algorithm. Through analysis of Equations (3)-(7), we can know that the bank angle and attack angle have an important effect on flight distance. Furthermore, for an appropriate flight distance the variation ranges are usually set as follows [8]: attack angle, \(6^{\circ}\) to \(12^{\circ}\); bank angle, \(-20^{\circ}\) to \(20^{\circ}\). Taking the variation ranges of attack angle and bank angle into consideration, the control variables do not change at a big degree. From the aspect of kinematic models to study the trajectory HGV maneuvers strongly, but from the aspect of aerodynamic model to study the trajectory HGV does not maneuver that strongly in the long flight distance.
## 3 Governing Equations and Numerical Scheme  
### Governing Equations  
The depth-integrated continuity and momentum equations of tidal flow are as follows:  
\[\frac{\partial\zeta}{\partial t}+\frac{\partial Hu}{\partial x}+\frac{\partial Hv }{\partial y}=0 \tag{12}\]  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{\partial u }{\partial y}-fv+g\frac{\partial\zeta}{\partial x}=\frac{\partial}{\partial x }\bigg{(}\varepsilon_{x}\frac{\partial u}{\partial x}\bigg{)}+\frac{ \partial}{\partial y}\bigg{(}\varepsilon_{y}\frac{\partial u}{\partial y} \bigg{)}+\frac{1}{\rho H}\bigg{(}\tau_{x}^{s}-\tau_{x}^{b}\bigg{)} \tag{13}\]  
\[\frac{\partial\sigma}{\partial t}+u\frac{\partial\sigma}{\partial x}+v\frac{ \partial\sigma}{\partial y}+fu+g\frac{\partial\zeta}{\partial y}=\frac{ \partial}{\partial x}\bigg{(}\varepsilon_{x}\frac{\partial v}{\partial x} \bigg{)}+\frac{\partial}{\partial y}\bigg{(}\varepsilon_{y}\frac{\partial v} {\partial y}\bigg{)}+\frac{1}{\rho H}\bigg{(}\tau_{y}^{s}-\tau_{y}^{b}\bigg{)} \tag{14}\]  
where \(t\) is the time; \(x\) and \(y\) are horizontal Cartesian coordinates; \(\zeta\) is the water level from the mean sea level; \(h\) is the water depth from the seabed to the mean sea level; \(H=\zeta+h\) is the total water depth; \(u\) and \(v\) are depth-averaged flow velocities in the \(x\) and \(y\) directions, respectively; \(\varepsilon_{x}\) and \(\varepsilon_{y}\) are the horizontal eddy viscosity of turbulent flow, respectively; \(f\) is the parameter; \(g\) is the gravitational acceleration; \(\tau_{x}^{s}\) and \(\tau_{y}^{s}\) are surface wind shear stresses; \(\tau_{x}^{b}\) and \(\tau_{y}^{b}\) are bottom shear stresses, and they can be expressed as \(\tau_{x}^{b}=gn^{2}u\sqrt{u^{2}+v^{2}}/H^{1/3}\), and \(\tau_{y}^{b}=gn^{2}v\sqrt{u^{2}+v^{2}}/H^{1/3}\); and \(n\) is Manning's roughness coefficient.  
The transport of non-uniform sediment in water is governed by the following differential equation:  
\[\frac{\partial S_{k}}{\partial t}+\frac{\partial uS_{k}}{\partial x}+\frac{ \partial vS_{k}}{\partial y}=\frac{\partial}{\partial x}\bigg{(}\varepsilon_ {s,x}\frac{\partial S_{k}}{\partial x}\bigg{)}+\frac{\partial}{\partial y} \bigg{(}\varepsilon_{s,y}\frac{\partial S_{k}}{\partial y}\bigg{)}+\frac{F_{ sk}}{H} \tag{15}\]  
in which \(\varepsilon_{s,x}\) and \(\varepsilon_{s,y}\) are horizontal diffusion coefficients for sediment, and \(F_{sk}\) is the source/sink term or the bed erosion/deposition function, expressed as:  
\[F_{sk}=E(k)-D(k) \tag{16}\]  
The erosion rate \(E\)(\(k\)) and the deposition rate \(D\)(\(k\)) are calculated by Formulas (29) and (31), respectively.  
The bed deformation equation can be written as:  
\[\rho_{s}^{\prime}\frac{\partial z_{b}}{\partial t}+\sum_{k=1}^{N}F_{sk}=0 \tag{17}\]  
where \(z_{b}\) is the bed elevation. The dry density of sediment \(\rho_{s}^{\prime}\) can be obtained from experimental data or calculated by formulas, such as [PERSON] and [PERSON]'s formula [38].
## 6 Results and Discussion
### Convolutional Neural Network-Based Cooperative Spectrum Sensing
Figure 7: Probabilities of detection and false alarm according to average SNRs for different sensing schemes. ED: energy detection. that were trained with \(K\in\{5,10,20\}\), while keeping the number of sensing samples unchanged at \(N_{i}=300\). For each value of \(K\), the performance metrics were calculated again for comparison purposes. As can be seen from the figures, the increases in the number of SUs that cooperate in spectrum sensing can significantly improve the performance of the CBCSS. This is caused by the increase in spatial diversity when using more SUs, which can help the CNN to extract more information from the sensing data. Moreover, in Figure 10, there is almost no sensing error at \(SNR=-10\) dB with \(K=20\) sensing nodes. Finally, we measured the performance of the CBCSS by varying the number of sensing samples, \(N_{i}\), as shown in Figures 11 and 12, for \(K=10\) secondary users. The training process is the same as with the changing \(K\), but now the number of sensing samples is varied instead of \(K\): \(N_{i}\in\{200,300,400\}\). We assert that the effectiveness of the new cooperative spectrum sensing system can be improved by increasing the number of sensing samples that are collected by the SUs for individual spectrum sensing using the energy detection method. Again, the larger value of \(\gamma_{i}\) provides better detection accuracy as well as a lower sensing error. Figure 10: Sensing error with the proposed CBCSS according to average SNRs when the number of SUs, \(K\), changes. Figure 9: Probabilities of detection and false alarm with the proposed CBCSS according to average SNRs when the number of SUs, \(K\), changes. Since in the paper we focus on developing a new CNN-based cooperative spectrum sensing technique, for the sake of simplicity, we use a simple energy detection method for local spectrum sensing. However, the sensing efficiency can be further enhanced by improving the local spectrum sensing. That is, if the local sensing outcomes provide more accurate sensing data, the CNN can learn the features of the data with higher accuracy, which will produce more precise classification results. From the simulation results, we can observe that larger values of the channel SNR can ensure the better local sensing results, which leads to better overall sensing performance of the system.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 2 Related Work on Point Cloud Attribute Compression
### Attribute Inter-Cluster Prediction and Intra-Cluster Analysis
The total texture complexity variation \(\Delta E\) for all \(m\) points in the constructed graph is defined as [35]:  
\[\Delta E=\sum_{i=1}^{m}\sqrt{\left(\frac{\Delta L_{i}^{\prime}}{k_{L}S_{L}} \right)^{2}+\left(\frac{\Delta C_{i}^{\prime}}{k_{C}S_{C}}\right)^{2}+\left( \frac{\Delta H_{i}^{\prime}}{k_{H}S_{H}}\right)^{2}+R_{T}\frac{\Delta C_{i}^{ \prime}}{k_{C}S_{C}}\frac{\Delta H_{i}^{\prime}}{k_{H}S_{H}}} \tag{5}\]  
where \(\Delta L_{i}^{\prime}\), \(\Delta C_{i}^{\prime}\), and \(\Delta H_{i}^{\prime}\) are attribute differences between the graph node \(i\) and the average attribute of all graph nodes in lightness, chroma, and hue, respectively. \(k_{L}\), \(k_{C}\), and \(k_{H}\) are adjustment factors. \(S_{L}\), \(S_{C}\), and \(S_{H}\) are weighting functions. \(R_{T}\) is a rotation function to address hue rotation issues. Among all constructed graphs with varying sparsity levels, the graph exhibiting the lowest variation in texture complexity is chosen as the initial graph for the subsequent graph optimization process. The corresponding sparsity value of the selected graph is considered the desired graph sparsity \(k_{d}\). We define the selection process of the initial desired graph \(\mathcal{G}_{d}=(\mathcal{V}_{d},\mathcal{E}_{d},\mathbf{W}_{d})\) as:  
\[\mathcal{G}_{d}(\mathcal{V}_{d},\mathcal{E}_{d},\mathbf{W}_{d})=arg\min_{i}( \Delta E_{1},\cdots,\Delta E_{i},\cdots,\Delta E_{k_{0}}), \tag{6}\]  
where \(\Delta E_{i}\) represents the texture complexity variation for the \(i\)th constructed graph. \(\mathcal{G}_{d}\) denotes the selected initial desired graph. \(\mathcal{V}_{d}\), \(\mathcal{E}_{d}\), and \(\mathbf{W}_{d}\) represent the vertex set, edge set, and weighted adjacency matrix of the selected graph, respectively. The connectivity between \(n\) graph nodes in the selected graph \(\mathcal{G}_{d}\) is encapsulated by the adjacency matrix \(\mathbf{W}_{d}\), which is transformed into a graph connectivity mask matrix \(\mathbf{A}\in\mathbb{R}^{n\times n}\). We define the graph connectivity mask matrix \(\mathbf{A}\) as:  
\[\mathbf{A}_{ij}= \begin{cases}1&\text{if }(i,j)\in\mathcal{E}_{d},\\ 0&\text{otherwise}.\end{cases}  \tag{7}\]  
It is notable that the derived graph sparsity parameter, denoted as \(k_{d}\), for each cluster of the point cloud must be encoded into the bitstream. This necessity arises from the utilization of texture information during intra-cluster analysis to determine \(k_{d}\). Consequently, during decoding, the process begins with the reorganization of the point cloud and uniform clustering based on the reconstructed geometry. Subsequently, the decoder retrieves and employs the decoded value of \(k_{d}\) to construct k-NN graphs within each cluster, ensuring consistency between the encoder and decoder regarding the desired graph sparsity \(k_{d}\) and the resulting graph connectivity mask matrix \(\mathbf{A}\) for every cluster of the point cloud.
## 5 Performance Evaluation
### Comparison of EMS, IPS and SOS Schemes for Selfish Nodes of 20% to 80%  
In this section, the performance of the proposed system is compared with the existing scheme, namely IPS and SOS, for different percentages of selfish nodes. In the first case, 20% of selfish nodes are taken. In this case, the packet delivery probability of the proposed scheme EMS, IPS, and SOS are 52%, 33%, and 29%, respectively, 31% and 37% higher than IPS and SOS, as seen in the Figure 6a. In another case, 80% of selfish nodes are taken. Here, the packet delivery probability of EMS, IPS, and SOS is 24%, 19%, and 15%, which is still 9% and 15% better than IPS and SOS.  
As shown in Figure 6b-d, the packet delivery delay, energy consumption, and the number of packages dropped in the Proposed EMS scheme is lower. For 20% selfish nodes, the packet delivery delay of EMS, IPS, and SOS is 120, 127, and 190 min, which is 2% and 23% lower than IPS and SOS. While taking 80% selfish nodes, the packet delivery delay of the EMS, IPS, and SOS is 160, 215, and 225 min, respectively, which is 18% and 22% lower than IPS and SOS. In Figure 6c, the energy consumption of the EMS, IPS, and SOS schemes is 35, 55, and 65 joules, respectively, which is 23% and 37% lower than IPS and SOS when 20% of nodes are behaving selfishly in the network. Similarly, for 80% selfish nodes, the energy consumption of the EMS, IPS, and SOS schemes is 45, 65, and 75 joules, respectively, which is 25% and 37% lower than IPS and SOS. In addition, for 20% of nodes behaving selfishly in the network, the number of dropped packages for the EMS, IPS, and SOS schemes is 4500, 6200, and 7800, respectively, as shown in Figure 6d, which is 17% and 33% lower than IPS and SOS. Similarly, for 80% selfish nodes, the number of dropped packages of the EMS, IPS, and SOS schemes is 6500, 6700, and 8100, respectively, which is 2% and 16% lower than IPS and SOS. The main reason for this is that the EMS approach enables selfish nodes to partake in packet forwarding, hence preventing selfish behavior. The other two strategies did not go into detail about how selfishness affects the network. Furthermore, the nodes show cooperativeness in the network due to the fear of punishment. Furthermore, the findings demonstrate that the EMS scheme can accurately handle a huge variety of selfish nodes by permitting them to collaborate in a network to improve network performance.  
Figure 6: Comparison of EMS, IPS, and SOS schemes for Selfish Nodes of 20% to 80%. **(a)** Packet Delivery Probability; **(b)** Packet Delivery Delay; **(c)** Energy Consumption; **(d)** Number of Dropped Packages.
## 4 Conclusions  
The CML environmental monitoring technology was introduced and has developed into academic research. By negotiating with local cellular providers, multidisciplinary research groups all over the world have received access to CML measurements in their countries, mostly for no cost, and are studying different aspects of this technology. Most of these groups are now collaborating and sharing experience, tools and knowledge in different ways, so a new scientific community has been built. While the achievements of this community are impressive, the road ahead is challenging.
## 4 Discussion and Conclusions
Finally, geometric analysis of the structural elements confirmed that the support system can be optimized by using shorter nails, reducing overall costs. Sensitivity analysis revealed that the GSI and parameters significantly impacted horizontal displacement and safety factor calculations. Consequently, the geomechanical properties defining the GSI were reassessed, and new values were determined. Further geometric parametric analyses were conducted using the HB constitutive model. The HB model, as proposed by [PERSON] et al. [28], exhibits non-linear behavior under varying stress conditions. Analyses using the HB model demonstrated that accurately identifying in situ weathered rock parameters can lead to cost-effective and safe designs. In this study, we evaluated the field performance of a soil-nailed slope design by comparing predicted horizontal displacements (u\({}_{\text{x}}\)) with field measurements. The discrepancies between the design predictions and field data prompted a reassessment of geotechnical parameters, particularly the GSI and (m\({}_{\text{i}}\)) values. Sensitivity analyses conducted using the Hoek-Brown model identified the GSI as a key factor influencing horizontal displacement and safety factor values. The revised RMR/GSI values improved prediction accuracy, aligning displacement estimates more closely with field observations. Geometric analysis highlighted that soil nail length is the most critical factor affecting stability. Shorter nails (4.00 m) provided sufficient safety, even for steeper slopes, offering a more economical solution. These findings highlight the importance of incorporating field data and sensitivity analyses in order to optimize slope design and reduce implementation costs. Conceptualization, M.E.S. and O.U. ; analysis, A.Y. ; writing--review and editing, A.Y., M.E.S. and O.U. This manuscript is based on [PERSON]'s Master's thesis (unpublished, 2023) entitled \"Investigation of Soil Nailed Wall in Weathered Rock\", conducted under the supervision of M.E.S. and O.U. at the Department of Civil Engineering, Graduate School of Science and Engineering, Yilduz Technical University. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. **Conflicts of Interest:** The authors declare there are no conflicts of interest.