esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Experimental HVSR Results  
After the 76 HVSRs were calculated, each fundamental frequency, f\({}_{o}\), was selected along with corresponding amplitude A\({}_{o}\). The following four categories are proposed in terms of fundamental frequency and shown in Figure 12 (left):  
Figure 11: The multi-layered half space model considered by [64]. Point sources are located at the \(Z\) axis, and receivers at (r, \(\theta\), z), with the displacement components of the Green’s functions U\({}_{r}\), U\({}_{\theta}\), U\({}_{z}\) (modified from [64]).  
Category 1: 0.20 Hz \(<\) f\({}_{\rm o}\) \(<\) 0.60 Hz  
Category 2: 0.61 Hz \(<\) f\({}_{\rm o}\) \(<\) 0.98 Hz  
Category 3: 0.99 Hz \(<\) f\({}_{\rm o}\) \(<\) 1.70 Hz  
Category 4: 1.89 Hz \(<\) f\({}_{\rm o}\) \(<\) 20 Hz  
Correspondingly, four categories are proposed in terms of amplitude and shown in Figure 12 (right):  
Category 1: A\({}_{\rm o}\) \(<\) 2.0  
Category 2: 2.01 \(<\) A\({}_{\rm o}\) \(<\) 2.99  
Category 3: 3.00 \(<\) A\({}_{\rm o}\) \(<\) 3.99  
Category 4: 4.00 \(<\) A\({}_{\rm o}\) \(<\) 5.60  
In Figure 12, the bedrock depth iso-contours are also given. For Category 1 (0.20 Hz \(<\) f\({}_{\rm o}\) \(<\) 0.60 Hz), the fundamental frequency, f\({}_{\rm o}\), dominates the central part of the basin with the deepest recent deposits. Category 2 (0.99 Hz \(<\) f\({}_{\rm o}\) \(<\) 1.70 Hz) appears at the western part of the basin, while at the eastern part there is a broader transition from Category 2 to Category 3 (0.99 Hz \(<\) f\({}_{\rm o}\) \(<\) 1.70 Hz), ending with Category 4 (1.89 Hz \(<\) f\({}_{\rm o}\) \(<\) 20 Hz).  
The amplitude, A\({}_{\rm o}\), reaches high values mainly in the western part of the basin with a maximum of \(\sim\)6, as well as in the central part, with 3.0 \(<\) A\({}_{\rm o}\) \(<\) 4.0. A broad zone with lower amplitudes, 2.0 \(<\) A\({}_{\rm o}\) \(<\) 3.0, is evident in the eastern part of the basin.  
In Figure 13 the dominant frequency, f\({}_{\rm d}\), of the examined sites--that is, the frequency where the largest amplitude of the HVSR appears--is presented. Similarly, four categories as in the case of f\({}_{\rm o}\) can be distinguished. The amplitude of the dominant frequency, A\({}_{\rm d}\), also gives a similar pattern to that of the fundamental frequency, A\({}_{\rm o}\).  
Figure 12: Distribution of the fundamental frequency, fo, in the city of Durres (**left**); Distribution of the corresponding to the fundamental frequency, A\({}_{\rm o}\), amplitude (**right**). Bedrock depth iso-contours by [30].  
Figure 13: Distribution of the dominant frequency, f\({}_{\rm d}\), in the city of Durres (**left**); distribution of the corresponding to the dominant frequency, A\({}_{\rm d}\) amplitude (**right**). Bedrock depth iso-contours by [30].
## 6 Discussion  
From the comparison of the 24-node case and the real-region case, it can be seen that the system planning results and frequency response results differ significantly across the different schemes.  
Scheme 1 only needs to meet the future load growth demand and the requirements for power transmission, resulting in the lowest total planning cost. In the model with economic optimization as the objective function, since coal-fired power units have the highest fuel costs, the model will minimize the added installed capacity and online capacity of coal-fired power units, as well as reduce their output, thus achieving the goal of reducing coal-fired power unit operation costs. However, this will result in an insufficient level of system inertia.  
Compared to Scheme 1, Schemes 2 and 3 ensure that the system inertia meets the requirements by adding coal-fired power units that can provide stable inertia. However, due to the addition of coal-fired power units with higher operational costs, the total planning cost increases accordingly.  
Building upon Scheme 2, Scheme 3 not only considers system inertia constraints but also adds node inertia constraints to avoid significant inertia distribution differences. To adjust the inertia distribution, Scheme 3 requires more coal-fired power units, thereby increasing the total cost.  
With the increasing proportion of new energy capacity, the impact of new energy uncertainty on new power systems will grow. Stochastic programming methods have poor robustness, traditional robust methods cannot meet economic requirements, and though distributionally robust methods can balance economy and robustness, they struggle to handle nonlinear security constraints. Therefore, effectively addressing the issues of uncertainty and security in the planning of new power systems will be a key focus of future research.
## 5 Simulation Results
### Performance Evaluation
More interestingly, the increased frequency selectivity gives rise to the performance degradation for all IFO estimators, when compared to the case of the flat fading channel. This phenomenon is more pronounced in the case of the blind estimation schemes (6) and (9). Due to its high vulnerability to frequency selectivity and residual FFO, there are some limitations to applying the conventional schemes (6) and (9) to the ISDB-T system. In Figure 5, the probability of erroneous estimation of the IFO estimation methods for the ISDB-T 4k mode is illustrated as a function of \(\varepsilon_{f}\) when \(\text{SNR}=6\) dB is fixed. Here, we adopted the TU and HT channels. From the presented results, the conventional detection schemes (6) and (9) fail to properly estimate the IFO in the presence of the FFO. On the other hand, it can be found that the proposed IFO detection scheme is not significantly affected by the amount of the FFO, whereas the detection accuracy of the conventional estimation method (10) severely deteriorates at \(\varepsilon_{f}=0.07\) or higher. This observation indicates that the presence of residual FFO is not a crucial issue for the proposed IFO detection. Therefore, the proposed detection strategy can alleviate the need for sophisticated FFO estimation techniques. Figure 4: Performance of the conventional and proposed IFO estimation schemes for the TU channel: (a) \(\varepsilon_{f}=0\), (b) \(\varepsilon_{f}=0.08\). Figure 5: Performance of the conventional and proposed IFO estimation schemes versus \(\varepsilon_{f}\) when \(M=5\): (a) TU channel, (b) HT channel.
###### Abstract  
Virtual power plant (VPP) is an effective technology form to aggregate the distributed energy resources (DERs), which include distributed generation (DG), energy storage (ES) and demand response (DR). The establishment of a unified and coordinated control of VPP is an important means to achieve the interconnection of energy internet. Therefore, this paper focuses on the research of VPP construction model. Firstly, a preliminary introduction on all kinds of the DERs is carried out. According to the relevant guidelines, the decision area of the VPP is carefully divided, and the decision variables representing the various resources in the area are determined. Then, in order to get a VPP with low daily average cost, good load characteristics, high degree of DG consumption and high degree of resource aggregation, a multi-objective VPP construction model based on decision area division is established, and various constraints including geographic information are considered. The improved bat algorithm based on priority selection is used to solve this model. Finally, the correctness and effectiveness of the model are verified by an example.  
virtual power plant; decision area; improved bat algorithm; multi-objective optimization 2018 8 1484 doi:10.3390/app8091484
## 3 Simulation Methods  
### Computational Models  
#### 3.1.1 Asphalt Model  
Asphalt is a residual petroleum mixture composed of hydrocarbons, sulfur and nitrogen compounds, and their non-metallic derivatives. These molecules have significant differences in polarity and molecular weight, making it difficult to represent their composition with a single molecule. In order to understand the molecular structure of asphalt, [PERSON] et al. constructed an average molecular structure based on the nuclear magnetic resonance (NMR) spectra of eight asphalt samples from the Strategic Highway Research Program (SHRP) in the United States. However, these structures cannot reflect the interactions between asphalt components. Subsequently, the asphalt system was divided into four components: saturates, aromatics, resins, and asphaltenes (SARA) using the standard test method (ASTM D4124-09 [49]). Through continuous improvement, [PERSON] and [PERSON] constructed three 12-component asphalt models (AAA-1, AAK-1, and AAM-1) based on the SARA components. Therefore, in this study, the AAA-1 asphalt 12-component model was selected as a representative of the matrix asphalt for research. Figure 3 shows the molecular structure of the 12-component asphalt model. Figure 4 shows the model parameters of the 12-component asphalt model.  
Figure 3: Asphalt 12-component model.  
In this study, the density and glass transition temperature (Tg) of the asphalt model were computed to verify the accuracy of the model. In this case, the density of asphalt was calculated using the isobaric-isothermal ensemble (NPT) at 1 atm pressure. As shown in Figure 5a, when the simulation time is greater than 30 ps, the density curve of the asphalt model tends to stabilize, so we calculated the average density of the asphalt model from 50 to 100 ps. From the analysis of the asphalt density curve, it can be concluded that the density of asphalt after equilibrium at 298 K is 1.02 g/cm\({}^{3}\). The experimental density value of SHRP AAA-1 asphalt is 1.01-1.04 g/cm\({}^{3}\), which is consistent with the density of the asphalt model.  
After obtaining the asphalt density at different temperatures, the curve of asphalt specific volume (reciprocal of density) versus temperature was plotted, and linear regression analysis was performed. As shown in Figure 5b, the Tg of the asphalt is the intersection point of the two fitted lines. The Tg of the asphalt is lower than 273.15 K (0 \({}^{\circ}\)C), being at  
Figure 4: Asphalt 12-component model parameters: (**a**) the number of molecules; (**b**) the mass fraction of asphalt component.  
Figure 5: Physical properties of asphalt model: (**a**) density of asphalt; (**b**) glass transition temperature of asphalt.  
269.61 K, which is consistent with previous research results [50; 51; 52]. Therefore, it can be considered that the asphalt model is reasonable.
## 2 Electric Propulsion Shaft System Model and Static Characteristic Analysis
### Static Analysis of Propulsion Shafting System
#### 2.3.2 Modal Analysis  
To comprehensively analyze the vibration characteristics of the propulsion shafting system, a modal analysis was conducted. Considering the significant impact of bearing stiffness on the natural frequencies of the shafting system, the role of bearing stiffness was meticulously taken into account during the modal analysis. The finite element model used for static analysis was adopted and further defined with boundary conditions. Within the modal analysis module of the finite element software, the propulsion shafting system's modal analysis can be simulated similarly to statics using two perpendicularly constrained spring elements to represent the action of the bearings. The vertical and lateral stiffness of the spring elements are set according to the type of bearing and the given conditions. The thrust stiffness of the thrust bearing is set as axial stiffness, without considering the coupling effect of bearing stiffness. Corresponding constraints and boundary conditions are also applied to the propulsion shafting system; alternatively, the bearing elements in the module can be used to achieve the effect of the bearings, which are relatively simpler to set up than spring elements. A single-bearing element can simultaneously impart both lateral and vertical stiffness, replicating the function of two perpendicular spring elements. The propeller's mass is treated as a concentrated mass point, and for static treatment, the propeller's rotational inertia is not set. Depending on the actual analysis requirements, a reasonable number of modal analysis orders are set, and the minimum and maximum analysis frequencies can also be set based on the analysis frequency. In the actual calculations of this paper, 150 modal frequencies and modes were set. By selecting the overall deformation in the post-processing module, the modal deformation shapes for each order can be obtained, as shown in Figures 6-10, which are the main modal shapes of the propulsion shafting system.  
Figure 5: Deflection curve of the propulsion shaft system.  
Figure 6: Modal shape and natural frequency (6.327 Hz).
## 3 Modeling the Relationship between Field AGB and Satellite Data
### Machine Learning Methods  
The machine learning methods adopted in this study were modeled in WEKA software (version 3.8, The University of Waikato, Hamilton, New Zealand). The models defined the best parameters with the highest correlation coefficient (\(r\)) and the lowest root mean square error (RMSE) for the prediction of AGB, and then AGB mapping was implemented in ArcGIS.
# An Effective Hybrid Routing Algorithm in WSN:
## 4 ACOHCM Routing Algorithm
### Probabilistic Selection for the Best Next Hop Node  
The probabilistic node state transition formula in the ant colony optimization algorithm mainly consists of two key components: pheromone intensity on alternative paths and the heuristic factor. ACO utilizes those two parameters: search experiences represented by pheromone intensity, and domain knowledge in form of heuristic factor information to accelerate the search process. Pheromone intensity values are the results of the long-term collective learning of good paths from ants' actions, while the heuristic values reflect the node's local situation. The heuristic factor changes in the different fields of application. For the wireless sensor network routing optimization, we comprehensively take into consideration the residual energy of the alternative next hop nodes and the distance between node \(i\) and \(j\), and using the weighting factors \(\xi_{1}\) and \(\xi_{2}\) (\(\xi_{1}+\xi_{2}=1\) ) to indicate their role importance respectively, therefore the heuristic factor \(\eta_{ij}\) in ACOHCM algorithm is defined as Formula (5).  
\[\eta_{ij}=\frac{E_{j}}{C}\xi_{1}+\frac{d_{sum}}{d_{ij}}\xi_{2} \tag{5}\]  
From (5) it is clear to see that \(\eta_{ij}\) is intended to favor the selection of a node with higher level of residual energy and with shorter communication distance between the sender and receiver so as to save the data transmission energy consumption and balance the energy usage. Where \(C\) is the initial energy value of the sensor nodes; \(E_{j}\) is the remaining energy value of the node \(j\); \(d_{ij}\) is the distance between node \(i\) and node \(j\); \(d_{sum}\) is the sum of the distances between all alternative next hop nodes and the source node. Thus, the probabilistic node state transition formula indicating the probability of the ant \(k\) transferring from node \(i\) to node \(j\) at time \(t\) is shown as Formula (6).  
\[P_{ij}^{k}(t)=\begin{cases}\frac{\tau_{ij}^{k}(\frac{E_{j}}{\xi_{1}+\frac{d_{ max}}{d_{ij}}\xi_{2})^{\beta}}}{\sum\limits_{p\in allon\times k}\tau_{ip}^{k}( \frac{E_{p}}{\xi_{1}+\frac{d_{max}}{d_{ij}}\xi_{2})^{\beta}}}&j\in allowed_{k} \\ 0&\text{otherwise}\end{cases} \tag{6}\]  
where \(\tau_{ij}\) is the pheromone value on the path \(L_{ij}\); \(\alpha\) and \(\beta\) indicate the relative influence of cumulative pheromone on the path and the local heuristic factor respectively on path selection for an ant; \(allowed_{k}\) is the set of next hop nodes allowed to be selected for ant \(k\). According to Formulas (5) and (6), the more residual energy the alternative node \(j\) has, and the shorter the distance from the node \(j\) to the node \(i\) is, the larger the value of the heuristic factor will be, and the bigger the probability of the node \(j\) will be selected.
## 4 Applications of AI in Modern Communication Networks
### Traffic Prediction and Load Balancing
#### 4.5.3 Load Balancing  
AI-based load-balancing algorithms dynamically distribute network traffic across available servers or paths to prevent overload on any single node. This improves the efficiency of the network, ensuring high availability and low latency [123]. Traditional load balancing methods, on the other hand, are often static, relying on fixed rules and thresholds that do not adapt to changing network conditions [122].  
To better illustrate the impact of AI on load-balancing performance, Table 7 compares the efficiency of AI-based load-balancing methods with traditional static load-balancing techniques. As shown in the table, AI-based load balancing methods achieve up to 90% efficiency, outperforming the traditional approach which achieves only 75% efficiency. This improvement highlights the adaptability and scalability of AI in handling dynamic traffic patterns, leading to more efficient use of network resources and better overall performance.  
Table 7 shows that AI-based methods significantly outperform traditional static load balancing, both in terms of efficiency and adaptability to network conditions.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Probability-Based Resilience Approach
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resilience Approach** & **Metric** & **Model** & **Threat Type** \\ \hline Indicator-based approach & ANL [36] & Level-based & All-hazards \\  & LIOH [38] & Level-based & All-hazards \\  & REWI [38] & Level-based & All-hazards \\  & SmartResilience [39] & Level-based & All-hazards \\  & DHS-ISIT [40] & Level-based & All-hazards \\  & AHA [41] & Level-based & All-hazards \\ \hline Quantitative (holistic) & MOP-GR [42] & Time-based & All-hazards \\  & FLEP [43] & Time-based & All-hazards \\  & QRMP [44] & Indicator-based & All-hazards \\  & RCM [46] & Time-based & All-hazards \\  & MDRM [47] & Time-based & All-hazards \\ \hline Dynamic system approach & \(C_{r}\)[48] & \begin{tabular}{c} Functional- \\ dependent \\ \end{tabular} & Cyber \\ \hline Empirical approach & Event metrics [49] & Data-based & \
\begin{tabular}{c} Cascading \\ All-hazards \\ System failures \\ \end{tabular} \\ \hline Economic approach & Operability [53] & Graph-based & All-hazards \\  & SER [54] & Static-based & All-hazards \\ \hline Network-based approach & R(T) [31] & Time-based & Hurricane \\ \hline Vulnerability analysis & \multirow{2}{*}{ieffect [57]} & Network-based & Cyber \\ approach & & & \\ \hline Simulation-based approach & \(R_{T}\)[58] & Time-based &
\begin{tabular}{c} Terror-attack \\ \end{tabular} \\ \hline Seismic resilience approach & RL [37] & Time-based & earthquakes \\ \hline Probability-based approach & Prob\({}_{R}\)[61] & Probability-based & earthquakes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Resilience Measures for Smart Critical Infrastructure in the Literature. Figure 17: Measuring probabilistic resilience [61]. The approaches are categorized based on the method used to quantify resilience and the type of data used, such as historical data, news data, and data based on previous events. While all methods can be used, some methods are more preferred than others depending on the situation, such as the availability of accurate and reliable data. For example, if the available information is based on news and reports, such as the number of available power lines during a disruption event, then empirical models are preferred. On the other hand, if the interdependent infrastructures can be modeled as a graph, and accurate data are available, then the network-based approach is more preferred in such a situation.
# Balance of Antioxidants vs. Oxidants in Perinatal Asphyxia
## 5 Free Radical Biomarkers in NE
### Peroxidation-like Compounds  
Prostaglandin-like compounds, including isoprostanes, neuroprostanes, and neurotransmitters, are formed from the free radical-induced breakdown of arachidonic acid and docosahexaenoic acid (DHA), which are types of polyunsaturated fatty acids. F2-isoprostanes appear to be the most reliable indicators of the extent of oxidative stress following hypoxia-reperfusion events [89], and they can be measured accurately using ultra-high-performance liquid chromatography-mass spectrometry techniques, even in newborns. Elevated levels of 8-iso-15(R)-FGF2\(\alpha\) and total isoprostanes were found in infants with acidosis and depression compared to those without these conditions; 8-iso-15(R)-FGF2\(\alpha\) levels were correlated with the severity of asphyxia [90]. However, a study that longitudinally measured serum F2-isoprostanes within the first 5 days after birth in neonates who experienced severe, moderate, or mild asphyxia found no differences between the groups and no correlation with brain damage detected through neuroimaging [91]. This limited and conflicting evidence suggests that more research is needed to determine if prostaglandin-like compounds can serve as biomarkers following perinatal asphyxia.
# Tumor-Induced Osteomalacia as a Result of a Phosphaturic Mesenchymal Tumor: Detecting Rare Origin
## 1 Introduction  
PMT is rare tumor with roughly 450 cases reported, with the first case reported by [PERSON] in 1947 [1]. Most PMTs have been discovered in middle-aged adults with no gender predilection [2]. They are mostly found at appendicular sites, while only rare cases have been reported in the oral and maxillofacial region [3], including the paranasal sinuses, mandible, intracranial localization, maxilla, and oral cavity, in descending order of prevalence [4].  
Clinically, patients with PMT present with systemic symptoms related to osteomalacia. TIO, also known as oncogenic osteomalacia, is a rare parameoplastic syndrome that results in abnormal phosphate and vitamin D metabolism. It is mainly characterized by hypophosphatemia, hyperphosphaturia, reduced or inappropriately normal 1,25-dihydroxyvitamin D, elevated serum alkaline phosphatase, elevated or inappropriately normal plasma fibroblast growth factor 23 (FGF23), normal serum calcium, and normal parathyroid hormone concentrations [5]. These biochemical alterations eventually cause osteomalacia, of which clinical symptoms include diffuse body pain, muscle weakness, gait disturbance, fragility of bone, and multiple bone fractures due to defective mineralization of the osteoid [2].  
Complete surgical resection with an adequately wide margin is the most appropriate treatment [6]. Postoperative normalization of laboratory values is typically observed following successful resection of the tumor. As the origin of TIO, the intraoral region is very rare. Thus, we report a case in which a tumor of oral origin affected a patient systemically.