esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IoT Service Orchestration via NDN
### Distributed Mechanisms
Therefore, for the latter ones, to speed up the provisioning at the expenses of a larger overhead, FoX is enhanced with the Find-and-Execute (FaX) strategy. In FaX, the computation is started immediately in the first available edge node and, in parallel, the interest is forwarded towards the data source to find a cached result. If the search is successful, the computation is stopped and the result is forwarded to the consumer. Although extremely promising in terms of targeted objectives and envisioned workflows, the mentioned orchestration strategies in NFN have been only theoretically discussed so far and a comprehensive evaluation of their performance in a realistic network topology is still work in progress. A free software implementation of NFN, with Berkeley-style licence, is under development at the University of Basel and it is available at www.named-function.net. The work in Reference [34] shows the opportunities of enabling edge computing over NDN. It assumes that fog nodes advertise the services they support and their resource utilization (e.g., CPU, GPU, memory, storage, and energy). Such information can be shared periodically in a proactive way by using an NDN routing protocol like Named-Data Link State Routing Protocol (NLSR) [47] or in a reactive way at the reception of the request. The node with the lowest resource utilization can be selected as executor. Such an approach targets well the fair distribution of computation efforts among nodes in the edge domain. However, it accounts neither for the closeness to the raw data to be processed nor for the time needed to collect them. In Reference [7], a more sophisticated IoT service orchestration strategy called IoT Named Computation Networking (IoT-NCN) is proposed. There, service execution is assigned to the edge nodes in order (i) to limit the raw IoT data traffic crossing the network and (ii) to not exceed their available processing resources. A weighted function is defined to compute the cost of service execution at the edge nodes on the path towards the data source, which well balances the aforementioned objectives. Two costs are included in the function: the closeness cost, expressing the distance of the potential executor to the data source, and the processing cost, expressing the processing capability of the potential executor with respect to the computing load required by the service. There, the candidate service executors are limited to the on-path nodes, and the one offering the lowest cost is selected. The possibility to resort to the cloud is not investigated.
# Spatiotemporal Analysis of Urban Expansion in Beijing, China
## 4 Discussion
Beijing's economic advantages can attract floating populations and accelerate urban expansion [25]. The distribution of construction land in Beijing is closely related to the distribution of transportation arteries [26], especially the distribution of subway lines, which greatly affects the direction and speed of urban expansion. The impact of the relocation of the municipal government and the construction of Daxing International Airport on Beijing's urban expansion is smaller than that of the subway line. The integrated development of Beijing-Tianjin-Hebei also has an important impact on the urban expansion of Beijing. The phenomenon of rapid development in Beijing is consistent with the view of Professor [PERSON] that the faster a city develops, the more inclined it is to expand towards the outskirts of the city. Beijing has a large increase in new population and housing demand, but land supply is tightening and new buildings are on the low side, resulting in an increasing supply-demand gap and leading to a contradiction between population and urban development. With the continuous development of urban expansion, the boundaries of Beijing have become increasingly complex, tortuous, and unstable. Overall, the center of gravity of Beijing is moving toward the northeast, and the elasticity coefficient of urban expansion is 1.67 times that of a reasonable coefficient. The Beijing Urban Master Plan (2016-2035) proposes to \"curb the development of urban sprawl\" and proposes a spatial layout orientation of \"increasing residential land and housing supply in areas outside the central urban area\" [27]. In the future, the spatial layout of Beijing's construction land will shift outward, and the expansion focus will continue to strengthen the trend of spatial expansion in areas outside the central city [28]. To prevent the excessive dispersion of spatial patterns during outward transfer, optimize the spatial structure of urban land use, and curb the trend of land expansion due to \"spreading big cakes\", it is necessary to strengthen the guidance and control of residential land supply locations and strengthen the rational planning of public routes. Therefore, the spatial dimension plays a crucial role in urban expansion, as it not only affects the physical form of the city, but also influences its economic, social, and environmental development. Therefore, urban planning and management need to comprehensively consider these factors to achieve the sustainable development of the city.
# Litholytic Activities of Natural Bioactive Compounds and Their Mechanism Insights
## 4 Litholytic Activity of Phenolic Acids
### In Vivo Investigations
#### 4.2.3 Ferulic Acid  
The anti-nephrolithiasis effect of ferulic acid (Figure 4) was evaluated using ethylenedylcol-induced kidney stones in a rat model. The oral administration of ferulic acid at 40 and 80 mg/kg by [PERSON] et al. [34] showed significant regulation of biochemical parameters in serum and kidneys in a dose-dependent manner. At 80 mg/kg, aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma glutamyl transferase (GGT), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH) in the kidneys were markedly mitigated by ferulic acid to near normal (5.12 \(\pm\) 0.38 U/mg protein, 31.21 \(\pm\) 1.13 U/mg protein, 28.36 \(\pm\) 0.98 U/mg protein, 24.96 \(\pm\) 0.89 U/mg protein, and 9.36 \(\pm\) 0.28 U/mg protein, respectively) [34]. Simultaneously, this bioactive molecule significantly decreased renal markers (calcium, oxalate, uric acid, magnesium, urea, creatinine, and total protein) in urine and plasma compared to the EG-treated group. Hyperovaluria reduced markedly the antioxidant enzymes (GSH, SOD, GPx, and CAT levels) due to a collapse of the antioxidant defense system, which leads to the formation of kidney stones. Ferulic acid at 80 mg/kg has the ability to increase the levels of these antioxidant enzymes compared to the EG group (1.20 vs. 1.02 U/mg protein, 1.41 vs. 0.94 U/mg protein, 0.72 vs. 0.42 U/mg protein, and 1.20 vs. 0.96 U/mg protein, respectively), while decreasing LPO (0.73 vs. 0.94 U/mg protein), thereby regulating the ROS [34]. In addition, histopathological analysis revealed that ferulic acid treatment significantly protected EG-induced tissue damage in a dose-dependent manner. However, further studies elucidating the mechanism involved in anti-lithiasis by ferulic acid should be conducted.
## 4 Algorithms Used
### XGBoost  
Gradient Boosting is a Machine Learning technique developed to solve classification and regression problems. It creates prediction models from an ensemble of smaller prediction models, typically represented by decision trees. Model building is done stepwise, in the same way as other boosting algorithms, but the advantage of this technique lies in the fact that it can generalize by allowing the optimization of an arbitrary differentiable loss function. This approach differs from others in that it is suitable for a wide range of tasks and has excellent portability as it supports a cross-platform between different programming languages and different operating systems.  
XGBoost [26] improves the Gradient Boosting Machines (GBM) framework on which it is based through optimizations in the system and improvements in the algorithms. XGBoost takes advantage of cache-aware algorithms by allocating internal buffers in each of the threads to store computed statistics. XGBoost uses parallelization, as the process of tree construction is done in a parallel way, due to the exchange of nested loops used in tree construction. The max_depth parameter allows for adjusting the division of each tree, instead of using the basic stop criterion. The features that enable algorithmic improvements are:  
* Allows for avoiding the phenomenon of overfitting in the training phase, ensuring regularization;* Capability of adaptation in the presence of features with missing values, since it automatically selects a value to replace the missing ones, based on the training data;
* Cross-validation at each iteration.  
XGBoost models currently represent an excellent solution in classification and regression tasks, both for their performance in terms of the results obtained and computation time compared to other algorithms.
## 1 Introduction
However, since it is based on the LW theory, its computational efficiency remains low. To address this limitation, the application of transition elements can be considered. In a previous study, the author [24] proposed a transition element for plates incorporating \(p\)-refinement, where ESL elements were assigned to the global region, while LW elements were applied to the local region where the patch was introduced. This approach improved computational efficiency compared to using only LW elements. However, when applying transition elements, displacement discontinuities may occur between ESL and LW elements, leading to unstable convergence. To mitigate this issue, the mesh configuration must be adjusted during modeling, and in some cases, additional modifications, such as significantly increasing the order of shape functions within the transition elements, may be necessary. Consequently, this approach introduces additional complexity in the modeling process. To overcome these limitations, this study proposes a finite element approach that integrates the advantages of ESL and LW theories into a single element type, effectively reducing the degrees of freedom while maintaining accuracy. Additionally, in cylindrical shells under internal pressure, axial cracks are structurally more common than circumferential cracks. Therefore, axial cracks should be a primary consideration when evaluating the structural integrity of cylindrical shells under internal pressure [28]. The proposed model is validated using benchmark problems involving orthotropic laminated shell elements and is applied to axial cracks repaired with composite patches. Fracture analysis is performed using the virtual crack closure technique (VCCT) based on linear elastic fracture mechanics to determine the stress intensity factor (SIF). Finally,parametric studies assess how patch material, size, thickness, and adhesive properties influence the performance of composite-repaired pipes.
## 4 Discussion
### Adaptability to Dynamic Shading Conditions  
Dynamic shading conditions are frequently encountered in real-world applications, caused by rapid changes such as moving clouds or temporary obstructions. Adapting to these conditions is a critical requirement for ensuring the system's energy efficiency and stability.  
The performance of the proposed method under dynamic shading conditions is ensured by the real-time operation of the control mechanism, which constantly monitors the PV array's power levels. In cases such as moving clouds or temporary obstacles, inevitable changes in energy production levels occur. When these changes are detected, the proposed method automatically determines the optimal connection configuration to achieve maximum energy efficiency under the new shading conditions. The results presented in Figure 8 and Table 7 demonstrate the effectiveness and stability of the method in various shading scenarios. For example, the system's ability to continuously achieve low CVI values under dynamic shading conditions shows that it can operate with high stability while minimizing energy production losses. Furthermore, the convergence times shown in Table 8 validate that the method can respond quickly to changing conditions.  
Consequently, the proposed method can operate steadily in real-world scenarios, such as dynamic shading conditions, and optimize energy efficiency through fast reconfiguration processes.
## 5 Discussion
The increase in recovery efficiency at a lower concentration (as shown in Figure 6) is a surprising result and it could be due to a varied number of factors. The ability of the artificial sputum sample to dissolve into the eluent fluid could be influenced by the particle loading. High particle loading could make it more difficult to dissolve samples that are embedded in the deeper layers of the swab and therefore produce lower elution efficiency. Alternatively, the process of reabsorption of the eluent fluid after local impact into the swab and the particle loading of the surface layers due to the reabsorption can be influenced by the concentration of the particles in solution. In low concentration samples, the number of particles in the eluent solution is comparatively low. The reabsorption could preferentially absorb some of the fluid post elution and therefore retain more of the particles in theeluent fluid as compared to the high concentration case. Finally, after the elution process is complete, some of the fluid is adsorbed on the outer surface of the swab while it is being taken out, which is also directly proportional to the concentration and the number of available adsorption sites. _E.coli_ was chosen as a model as it is one of the common infections in many clinical situations. It is also similar in size to many other infectious pathogens. Additionally, it is easy to grow in laboratory conditions, and therefore was considered as a suitable model. The results with _E.coli_ showed a similar trend as the polystyrene beads indicating that the characterization performed with the beads would be useful in determining the optimal conditions for bacterial samples as well. Similar to the polystyrene beads, the bacterial cells also undergo higher rates of recovery efficiency at higher speeds, as shown in Figure 7. The recovery efficiency was found to increase with concentration of the bacteria in the control case (no agitation). This is associated with the dissolution of the surface layer of artificial sputum dissolving into the eluent solution. In all of the other cases, the impingement of the magnetic particles on the swab creates impact forces that physically squeeze the fluid from the fibers and push the sample out, which results in higher elution efficiency.
## 2 Damping Force Characteristics of MREA under Impact Load  
### Design and Manufacture an MREA  
A linear stroke MREA with a two-stage electromagnetic coil is designed and manufactured as shown in Figure 1. The schematic of the MREA is shown in Figure 1a, and the photo of the MREA is shown in Figure 1b. The MREA consists of two piston rods (same diameter) (1 and 7), a piston (8), a hydraulic cylinder (5), and MR fluid (6) filling the reservoir of the MREA. The piston contains a piston core (2), two coils (3), and a piston cylinder (4). There are two winding grooves on the piston core, where the coil can be wound. There is an annular gap between the piston core and piston cylinder, through which the MR fluid flows and is magnetized. The MR fluid flows through this annular gap as a result of piston motion when shock or vibratory loads are applied to either piston rod. Two electromagnetic coils are inserted inside the annular gap. When a current passes through the coils, magnetic flux is generated radially across the annular gap. The magnetic particles form chains in the direction of the magnetic field (i.e., radially) and are perpendicular to the direction of flow. Thus, the chains in the fluid must be sheared before fluid can flow in the gap. Upon the initiation of fluid flow, a pressure drop develops between the two chambers separated by the piston. Therefore, as the pressure drop on the piston surface increases, the damping force of the MREA also increases. The design parameters are shown in Table 1.  
Figure 1: The double-ended MR damper configuration with two electromagnetic coils. 1—Upper piston rod, 2—piston core, 3—electromagnetic coil, 4—piston cylinder, 5—hydraulic cylinder, 6—MR fluid, 7—underneath piston rod, 8—piston, 9—seal ring, and 10—hydraulic cylinder seal cap.
## 2 Dataset
### An Example of Ionospheric Irregularities
The equivalent signal-to-noise ratio (S/N) and S4c index can be derived from the carrier-to-noise ratio (C/N) data released by common geodetic GNSS receivers [26], which provide a new method for the calculation of the amplitude scintillation index. A value of 0.015 is used as the threshold of S4c to indicate the strong amplitude scintillation. To verify the feasibility of this method on a satellite-based receiver, Figure 1e,f displays the detrended S/N and S4c derived from C/N recorded by Swarm A. Similar trends are shown in the detrended SNR and S4c index from GPS satellites from 19:35 to 19:39 UT. However, the significant fluctuations around 19:44 UT were not reflected in the amplitude scintillations.
## 3 Results
### Frequencies of the Daily Differences between the Four Selected Methods of SD
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \multicolumn{13}{c}{**Larnaca 2009–2012, dSD Gl–CS**} \\ \hline
**Class** & **Hours/Month** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **All** \\ \hline \(-\)C4 & \(<\)\(-1.5\) & 2.5 & 8.0 & 4.8 & 5.8 & 2.2 & 1.1 & 0.0 & 0.0 & 0.0 & 3.2 & 6.7 & 2.2 & 3.2 \\ \(-\)C3 & [\(-1.5\) to \(-0.5\)) & 22.1 & 29.5 & 20.2 & 20.8 & 15.1 & 6.7 & 0.0 & 3.2 & 4.4 & 20.4 & 17.8 & 17.2 & 15.5 \\ \(-\)C2 & [\(-0.5\) to \(-0.1\)) & 27.9 & 16.1 & 11.3 & 21.7 & 17.2 & 12.2 & 4.3 & 22.6 & 14.4 & 12.9 & 28.9 & 34.4 & 18.7 \\ C1 & [\(-0.1\) to 0.1] & 20.5 & 13.4 & 10.5 & 8.3 & 21.5 & 13.3 & 4.3 & 9.7 & 15.6 & 23.7 & 18.9 & 17.2 & 14.6 \\ C2 & (0.1 to 0.5\) & 18.9 & 17.0 & 26.6 & 25.8 & 19.4 & 31.1 & 45.2 & 39.8 & 26.7 & 22.6 & 18.9 & 18.3 & 25.6 \\ C3 & (0.5 to 1.5] & 6.6 & 12.5 & 25.0 & 16.7 & 22.6 & 31.1 & 45.2 & 24.7 & 38.9 & 16.1 & 8.9 & 9.7 & 20.9 \\ C4 & \(>\)1.5 & 1.6 & 3.6 & 1.6 & 0.8 & 2.2 & 4.4 & 1.1 & 0.0 & 0.0 & 1.1 & 0.0 & 1.1 & 1.5 \\ \hline \multicolumn{13}{c}{**Larnaca 2009–2012, dSD CSD3_Pent–CS**} \\ \hline
**Class** & **Hours/Month** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **All** \\ \hline \(-\)C4 & \(<\)\(-1.5\) & 11.3 & 1.8 & 3.2 & 3.3 & 0.0 & 3.3 & 6.5 & 6.5 & 0.0 & 3.2 & 13.3 & 16.1 & 5.6 \\ \(-\)C3 & [\(-1.5\) to \(-0.5\)) & 27.4 & 8.9 & 8.1 & 5.0 & 12.9 & 33.3 & 22.6 & 29.0 & 0.0 & 6.5 & 16.7 & 41.9 & 16.5 \\ \(-\)C2 & [\(-0.5\) to \(-0.1\)) & 12.9 & 14.3 & 6.5 & 16.7 & 29.0 & 26.7 & 38.7 & 41.9 & 6.7 & 6.5 & 20.0 & 16.1 & 17.9 \\ C1 & [\(-0.1\) to 0.1] & 16.1 & 16.1 & 3.2 & 6.7 & 0.0 & 10.0 & 6.5 & 3.2 & 6.7 & 9.7 & 20.0 & 3.2 & 8.9 \\ C2 & (0.1 to 0.5] & 6.5 & 21.4 & 12.9 & 20.0 & 12.9 & 10.0 & 9.7 & 3.2 & 16.7 & 22.6 & 3.3 & 6.5 & 12.8 \\ C3 & (0.5 to 1.5] & 17.7 & 19.6 & 41.9 & 25.0 & 25.8 & 10.0 & 16.1 & 12.9 & 53.3 & 32.3 & 16.7 & 9.7 & 24.1 \\ C4 & \(>\)1.5 & 8.1 & 17.9 & 24.2 & 23.3 & 19.4 & 6.7 & 0.0 & 3.2 & 16.7 & 19.4 & 10.0 & 6.5 & 14.2 \\ \hline \multicolumn{13}{c}{**Larnaca 2009–2012, dSD CSD3_Men–CS**} \\ \hline
**Class** & **Hours/Month** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **All** \\ \hline \(-\)C4 & \(<\)\(-1.5\) & 16.3 & 18.7 & 12.9 & 12.5 & 16.1 & 6.7 & 4.6 & 1.1 & 1.1 & 10.8 & 10.0 & 7.5 & 10.3 \\ \(-\)C3 & [\(-1.5\) to \(-0.5\)) & 24.4 & 22.4 & 23.4 & 16.7 & 16.1 & 23.3 & 10
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.5 Region-Scalable Fitting and Optimized Laplacian of Gaussian Energy (Rsfolge)  
The RSFOLGE applies a LoG energy term optimized by an energy functional. Then, it integrates the optimized LoG energy term with the region-scalable fitting energy term. The advantage of this is that it makes use of of local region information to drive the curve towards the boundaries. The advantage of the RSFOLGE model is that it achieves accurate image segmentation and it is insensitive to the positions of initial contour [11]. The energy functional used to optimize the LoG of the image is as follows:  
\[E^{LQG}(L)= \iint_{\Omega}g([\
abla I])\times(L-0)^{2}+(1-g(|\
abla I|)) \tag{12}\] \[\times(L-\beta\times\Delta(G_{\sigma}*I))^{2}dxdy \tag{13}\]  
where the value of optimized LoG of the image is represented with \(L\), and \(g(\
abla I)=e^{-a|\
abla G_{\sigma}*I},a,\beta\) are positive constants. A edge indicator is defined by \(g(\
abla I)\). The values of \(g(\
abla I)\) are small and approximately equal to 0 at the locations near the object boundaries. \(\Delta(G_{\sigma}*I)\) is:  
\[\Delta(G_{\sigma}*I)=\left[\frac{\partial^{2}G_{\sigma}(x,y)}{\partial x^{2}}+ \frac{\partial^{2}G_{\sigma}(x,y)}{\partial y^{2}}\right]*I(x,y) \tag{14}\]  
where \(G_{\sigma}(x,y)\) is a Gaussian kernel function with standard deviation \(\sigma\).  
The advantages of this method are that it develops the robustness of initialization and it has accurate segmentation results compared with the original region scalable fitting(RSF) method. Moreover, the optimization process is applied to the edge stopping function which used in edge-based ACMs.  
The disadvantage of this method is that it fails when objects inside the image have significantly different intensity values which can be tackled by using multi-phase segmentation.  
A simple and universal method of improving the robustness of initial contour for these local fitting-based models is presented [12]. The core idea of proposed method is exchanging the fitting values on the two sides of contour, so that the fitting values inside the contour are always larger (or smaller) than the values outside the contour in the process of curve evolution. In this way, the whole curve will evolve along the inner (or outer) boundaries of object and is less likely to be stuck in the object or background.