esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation  
### Coupling Model Validation  
In order to ensure the accuracy of the simulation of the coupled model by the numerical tools used in this paper, SIMA was used to simulate the response of the coupled model for 3 h. The obtained results were compared with the 1:50 model test results of OC4-DeepCWind released by NREL and the FAST simulation results [27]. First verify the free decay simulation of the floating wind turbine. Figure 2 shows that the simulated vertical oscillation response of SIMA under the action of irregular waves alone is basically the same as the experimental and Fast results, and the longitudinal oscillation response is basically the same as the Fast results but slightly smaller than the experimental results. Secondly, in order to further judge the accuracy of the coupled model simulation results, and experiments using the same turbulent wind and irregular wave joint action for verification. Figure 3 shows that the mean values of longitudinal oscillation, longitudinal rocking and tension of SIMA's simulated floating wind turbine under combined wind and wave conditions are basically the same as the test results, and the maximum value of tension is slightly smaller than the test value. This result should be due to the different amplitudes produced by the different random seeds generated by turbulent winds and irregular waves during the simulation. Overall the results are generally consistent, indicating that SIMA is able to accurately predict the kinematic response of the floating wind turbine in the presence of turbulent winds and irregular waves, demonstrating the accuracy of the numerical tool used in this paper for coupled model simulations.  
\begin{table}
\begin{tabular}{l l l} \hline
**Load Cases** & & **Value** \\ \hline JONSWAP & \(H_{s}\) & 6.42 m \\  & \(T_{p}\) & 11.58 s \\  & \(U_{ref}\) & 21 m/s \\  & \(U_{c}\) & 1.85 m/s \\ \hline \end{tabular}
\end{table}
Table 4: Environmental parameters of floating wind turbine.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Type of mooring system & catenary & Type of mooring system & tension \\ Number of mooring lines & 3 & Number of mooring lines & 3 \\ Mooring line length/m & 320 & Mooring line length/m & 313 \\ Breaking strength/KN & 13,583 & Breaking strength/KN & 12,790 \\ Depth to anchors/m & 40 & Depth to anchors/m & 40 \\ Fairlead depth/m & 14 & Fairlead depth/m & 14 \\ Dry weight/kg/m & 299.5 & Dry weight/kg/m & 357.7 \\ Anchor distance from platform center/m & 352.87 & Anchor distance from platform center/m & 352.87 \\ Mooring line diameter/m & 120 & Mooring line diameter/m & 175 \\ Axial stiffness/MN & 1123.5 & Axial stiffness/MN & 318 \\ \hline \end{tabular}
\end{table}
Table 3: Mooring system parameters.
## 3 Integrated Disaster Resource Scheduling Modeling
### User Satisfaction-Oriented Dynamic Scheduling Model
#### 3.2.3 User Satisfaction Maximization Objective Function  
Under extreme conditions, the user satisfaction model is primarily determined by the timeliness of electricity and freshwater supplies as well as the extent to which supply meets demand. To analyze the impact of response time on user satisfaction, this study proposes an upper-level comprehensive satisfaction model based on response time, formulated as follows:  
\[S_{i}=\begin{cases}1-\alpha\cdot\frac{T_{s}^{\text{response}}}{T_{\text{max}}},& \text{if }T_{s}^{\text{response}}\leq T_{\text{max}}\\ 0,&\text{if }T_{s}^{\text{response}}>T_{\text{max}}\end{cases} \tag{22}\]  
\[T_{s}^{\text{response}}=T_{\text{travel},\text{min}}+T_{s}^{\text{setup}} \tag{23}\]  
where \(T_{s}^{\text{response}}\) represents the response time of community \(s\), which refers to the duration from the moment a community experiences a water and power outage to when it receives resources, while \(T_{\text{max}}\) denotes the maximum allowable response time of the system; if this time is exceeded, user satisfaction drops to zero. In this study, \(T_{\text{max}}\) is a preset value mainly governed by the expected economic loss of prolonged power and water outages and the  
Figure 5: Path selection and optimization in resource scheduling.  
daily electricity and freshwater demand of the affected population; \(T_{s,c}^{\text{travel}}\) represents the time required for vehicle \(c\) to travel from its current location to community \(s\), while \(T_{s}^{\text{setup}}\) refers to the loading and unloading time required for resupply operations in community \(s\). In addition, \(S_{i}\) represents the user satisfaction level of community \(i\), indicating the degree of satisfaction of community residents with the timeliness of service in disaster emergency response, while \(\alpha\) is the adjustment coefficient.  
To optimize overall community user satisfaction, this study defines a maximization function for comprehensive user satisfaction \(S_{\text{total}}\), achieved by minimizing the response time for each community, as follows:  
\[\max S_{\text{total}}=\frac{1}{n}\sum_{i=1}^{n}w_{i}\cdot S_{i}, \tag{24}\]  
where \(n\) is the number of communities and \(w_{i}\) denotes the weight of community \(i\).
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 4 Qualitative Analysis
### Operation and Maintenance Phase
#### 4.4.2 Sustainable Operation  
Building sustainability management is the application of sustainability principles to minimize environmental impacts, while providing a comfortable, healthy, and sustainable built environment. In addressing carbon emissions, it is imperative to verify the efficacy of carbon reduction strategies through rigorous monitoring and data acquisition. For instance, [PERSON] et al. [97] developed an acquisition system based on Supervisory Control and Data Acquisition (SCADA), integrated with a single-wire bus network to monitor the energy efficiency of building envelopes. This system underwent validation to analyze the thermal behavior of concrete slabs reinforced with phase-change material, offering insights to enhance energy efficiency.  
At the level of specific carbon reduction methods, [PERSON] et al. [98] advocated retrofit interventions employing cross-laminated timber panels, coupled with the sensor-based monitoring of building performance to achieve material carbon reduction. Addressing building material reuse, [PERSON] et al. [99] proposed a numerical guide addressing the scarcity of data concerning reused building components. In the realm of noise reduction, [PERSON] et al. [100] utilized digital sound level meters to identify noise sources in precast buildings surpassing prescribed limits. Moreover, [PERSON] [101] established digital replicas of industrial buildings by integrating BIM data with sensors and historical facility data. Nonetheless, prioritizing the owner's occupancy experience remains an ongoing challenge in building operations and maintenance.  
The current state of research on the operation and maintenance phase of industrialized construction can be summarized as follows. First, predictive maintenance leverages data collection and monitoring through sensors and BIM to anticipate equipment failure or performance degradation. Digital twins can further enhance predictive accuracy and inform maintenance strategies. Second, sustainable operation aims to minimize environmental impacts through strategies such as monitoring energy efficiency retrofits, implementing carbon reduction methods, and promoting material reuse solutions. The research has integrated digital replicas to support performance monitoring and validate carbon reduction tactics. However, challenges remain in comprehensively addressing owner experience priorities alongside sustainability goals in operations.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.4.3 Resource Overbooking  
The case factory requires high availability for all services; thus, the costs were calculated using the worst-case scenario in which all services are active all the time. However, when services are not used continuously, some VM consolidation can be applied by overbooking the resources. Overbooking implies that less resources are needed compared to the worst-case scenario, while still maintaining the Service Level Agreement (SLA) for every service, as the services are not active all the time [37, 38]. For example, if the server supports eight VMs, but the requested number of VMs are not active all the time, then more than eight VMs can be supported per server. Thus, the number of servers and the supporting infrastructure to be dimensioned can be fewer than in a worst-case scenario. However, the statistical analysis of the required number of VMs is complicated and depends on service availability requirements and the statistical properties of service demand. If the services are needed only occasionally, a high overbooking can be applied. On the other hand, if services are active more often, then low overbooking can be applied. For the sensitivity analysis, the original input scenario from chapter four is utilized, and the TCO with different average overbooking ratios for each deployment scenario is calculated, as shown in Figure 12. Here, an overbooking ratio of one means no overbooking is applied, while a smaller value indicates a higher overbooking ratio. Significant cost savings can be achieved in each of the deployment scenarios with resource overbooking. A higher overbooking is suitable and may provide significant cost savings if less services are active at the same time. The risk of overbooking is that if the actual service demand is underestimated, the quality of service (for example, waiting times and available throughputs) would deteriorate, resulting in SLA violation. Thus, overbooking is a reasonable approach when there is sufficient information about service demand based on measurements made in a real environment, making sure that the SLAs are not violated.  
\begin{table}
\begin{tabular}{c c c c} \hline
**TCO and Cost of Isolation** & **Scenario A (All in** & **Scenario B (UP in Factory** & **Scenario C (All in Factory)** \\ **(M Euros)** & **Central Cloud)** & **and CP in Central Cloud)** & **Scenario C (All in Factory)** \\ \hline TCO with full isolation & 0.75 & 4.73 & 7.57 \\ TCO with no isolation & 0.36 & 1.98 & 2.79 \\ Cost of isolation & 0.39 & 2.75 & 4.78 \\ \hline \end{tabular}
\end{table}
Table 5: Cost of isolation.
# Numerical Analysis for Ground Subsidence Caused by Extraction Holes of Removed Piles
## 2 Analysis and Target Ground  
### Analysis Method  
In order to clarify the strength of the filler to be injected into the hole as the pile is pulled out, a total stress analysis [18; 19; 20] is performed on the extraction holes one, two, and three of removed piles as they are formed in the raw ground. The compressive stress acting on the holes is compared. As the fillers are seen to exhibit strength over time and to have different physical properties, it is deemed important to consider the ground displacement and ground stress that change over a period of time.  
The analysis method used here is a three-dimensional elasto-plastic consolidation analysis [18; 19; 20; 21] using MIDAS GTS NX [22; 23; 24] that can take into account the ground displacement and ground stress over time. MIDAS GTS NX is a FEM-based modeling software. It includes CAD-based 2D and 3D commands for modeling. MIDAS GTS NX analyses foundation stability subjected to lateral pressure and differential settlements.
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 2 Materials and Methods
### Data Analysis and Model Building  
#### 2.3.1 Definition of Fruit Ripeness  
Growth degree days can reflect the comprehensive impact of climate conditions on crop growth and analyze the heat conditions for crops. They can be used to determine the growth stages and corresponding physiological characteristics of crops under certain climate conditions, thus guiding the timely harvesting of crops [26; 27]. This study selects growth degree days (\(GDD\)) during the maturation process of passion fruit as the fundamental reference for fruit ripeness. The formula for calculating \(GDD\) for the fruit is as follows [28]:  
\[GDD=N*(T-T_{L}) \tag{4}\]  
where GDD are growth degree days, \({}^{\circ}\)C-d; \(N\) is the development period in days; \(T\) is the daily average temperature in \({}^{\circ}\)C; \(T_{L}\) is the lower limit temperature in \({}^{\circ}\)C, set at 10 \({}^{\circ}\)C.  
Observations and statistics in orchards reveal that fruit with \(GDD\) less than 700 \({}^{\circ}\)C-d, due to their insufficient growth period, are still in the fruit expansion period and unlikely to be mistakenly harvested. Fruit are considered fully mature when they naturally fall off the vine after reaching a maximum of 1400 \({}^{\circ}\)C-d in \(GDD\). Thus, this paper primarily investigates fruit with \(GDD\) in the range of 700-1400 \({}^{\circ}\)C-d. The \(GDD\) data are sourced from meteorological statistics in Zhangzhou, Fujian, where the fruit are grown. The calculation of \(GDD\) for each passion fruit starts from the fruit setting date.
# Study on the Mechanism of the Variable-Speed Rotor Affecting Rotor Aerodynamic Performance
###### Abstract  
The variable-speed rotor has proven to be a promising means to improve helicopter performance. Previous investigations on the aeromechanics of the variable-speed rotor are scarce. This work studies the mechanism of the variable-speed rotor affecting the rotor aerodynamic performance by means of dimensionless parameter analysis and reveals the various effect mechanisms under the various flight speeds and take-off weights. The current work shows that, during hover, the variable-speed rotor improves the rotor aerodynamic performance by reducing the blade dynamic pressure, while the non-uniform thrust distribution is attributed to the reduced rotor speed suppresses the performance improvement. In slow forward flight, the blade dynamic pressure reduction improves the rotor aerodynamic performance. In cruise and fast forward flight, both the blade dynamic pressure and advancing blade tip compression loss reductions improve the rotor aerodynamic performance. The study also shows that in forward flight, the rotor loading is smaller, and the effect of reducing the advancing blade tip compression loss through the variable-speed rotor is greater.  
Keywords:variable-speed rotor; rotor performance improvement; nonlinear aerodynamics; dimensionless parameter analysis +
Footnote †: journal: Applied Sciences
## 3 Results  
### Identification of VOCs in Calamondin Flowers at Different Developmental Stages  
Subsubsection  
The total ion chromatograms reveal significant differences in the floral scents at the four developmental stages (Figure 1). A total of 41, 49, 49, and 47 VOCs were detected in the bud (BS), half-open (BS), full bloom (FS), and senescence (SS) stages, respectively, with the highest number of VOCs observed at full bloom and the lowest at the bud stage (Figure 2A). In total, 67 VOCs were identified, including forty-eight terpenoids, six esters, five aromatics, four aldehydes, one olefin, one alcohol, and two others (Figure 2B; Table S1). Terpenoids accounted for 71.6% of the total VOCs, showing an initial increase followed by a decrease as the flowers developed. Esters made up 9% of the total VOCs, with two esters detected in the BS, HS, and FS stages, increasing to five in the SS stage. The number of aromatics decreased gradually with flower development, while aldehydes showed an initial increase followed by a decrease (Table S2).  
The Venn diagram of VOCs across the four stages showed 33 common compounds (Figure 2C). Among the three-stage intersections, HS, FS, and SS shared the most compounds (three), while the BS, HS, FS, and BS, HS, SS intersections had no unique VOCs. In the two-stage intersections, HS and FS shared the most compounds (five), while BS and HS, and FS and SS shared three and two unique VOCs, respectively. SS had the highest number of unique VOCs (nine), followed by FS (four), with BS and HS having the least (three) each.  
Figure 1: Total Ion Chromatograms (TIC) of calamondin’s floral scent at four different developmental stages detected by HS-SPME-GC-MS. The highlighted numbers indicate the main identified peak areas. 1: \(\beta\)-Pinene; 2: Limonene; 3: \(\beta\)-Ocimee; 4: Linalool; 5: Methyl anthranilate; 6: \(\alpha\)-Farnesene. IS: Internal standard, ethyl caproate.
# An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
## 2 Materials and Methods
### Item Similarity
#### 2.2.1 Textual Representation  
In many scenarios, it is possible to obtain a textual representation for the items to be considered by an RS. Such representations can range from short descriptions to complete item representations in the case of textual documents (e.g., books, articles, contracts). The most common approach to build a textual similarity function is to use the vector space model approach [34], which consists of building a word-item matrix from the collection of texts to be compared. The entries of the matrix \(\mathbf{M}=m_{w,i}\) are weights associated with the occurrence of the word \(w\) in the text associated with the item \(i\). The weights \(m_{w,i}\) are commonly determined using the well-known term frequency-inverse document frequency _tf-idf_ weighting schema  
\[m_{w,i}=f_{i,w}\log\frac{N}{n_{w}}, \tag{3}\]  
where the term frequency (TF) \(f_{i,w}\) is the number of times \(w\) occurs in the textual representation of \(i\), \(N\) is the total number of items in the collection, and \(n_{w}>0\) is the number of items in which \(w\) occurredin their textual representations. The inverse document frequency (IDF) \(\frac{N}{n_{w}}\) is the proportion of items in the collection containing the term \(w\). Finally, the item similarity score is computed according to the cosine similarity:  
\[s_{i,j}^{\text{TFDF}}=\frac{\sum_{w\in W}m_{w,j}m_{w,j}}{\sqrt{\sum_{w\in W}m_{ w,i}^{2}}\sqrt{\sum_{w\in W}m_{w,j}^{2}}}. \tag{4}\]  
Here, \(W\) is the vocabulary of words used in the textual collection. A CBF recommender, which we refer to in our experiments as TFIDF, is produced when the similarity function \(s_{i,j}\) in Equation (4) is used in Equation (1).  
Another common approach to compare texts is latent semantic indexing (LSI) [35], which addresses the sparsity of \(\mathbf{M}\) due to the usual large size of the vocabulary \(W\). This is achieved by factorizing \(\mathbf{M}\) using SVD: \(\mathbf{M}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\text{T}}\). The resulting matrices \(\mathbf{U}\) and \(\mathbf{V}\) are orthogonal and \(\mathbf{\Sigma}\), which is diagonal, contains the singular values of the decomposition. By selecting the \(p\)-largest singular values and replacing the remaining ones by zeros in \(\mathbf{\Sigma}\), it is possible to obtain a representation of dimension \(p\) for each text that can be used to build a similarity function by using again Equation (4). These \(p\)-dimensions are known as latent factors. Usually, \(p\) is a free parameter to be determined for each data collection and application. In our experiments, we refer to the CBF recommender obtained using this method as LSI-\(m\).
## 5 Conclusions  
In this paper, an FPAR inversion model (the DnD model), which distinguishes direct and diffuse radiation, was proposed based on the energy budget balance theory. Because of the different transmission processes of direct and diffuse skylight in the vegetation canopy, the DnD model has different solutions for these two cases. Instantaneous FPAR at any given time can be calculated through different solar zenith angles and diffuse PAR proportion inputs. On the assumption that LAI and surface albedo do not change over short periods, FPAR on a cloudy day can also be obtained from the DnD model. From a comparative analysis of the DnD model with models that do not distinguish direct and diffuse radiation, the diffuse PAR proportion was found to be larger in the FPAR calculated by the DnD model and closer to the SAIL model simulation results than those from the other kind of model. This shows that the DnD model has good performance under cloudy sky.  
Therefore, this method can improve the temporal resolution of FPAR products. To validate the accuracy of the DnD model, direct, diffuse and total FPAR for 2012 in the Heihe River Basin were inversed. Instantaneous FPAR was validated by measured FPAR and measured APAR. The RMSE  
Figure 11: Map of the temporal AC for Heihe River Basin.  
between inversed and measured FPAR is 0.03; \(R^{2}\) is 0.85. In this paper, diurnal changes in instantaneous inversion FPAR were also compared with measured diurnal change data. It could be concluded that the characteristics of diurnal change in FPAR can be expressed by the DnD model. Daily average FPAR was validated by the MODIS FPAR product. The inversion results and the MODIS FPAR product were found to be highly correlated, but the MODIS FPAR product slightly overestimated FPAR in forest areas. The inversed FPAR was closer to the true value. Through producing the regional FPAR products in a year and validation with the observation data and other FPAR products, the uncertainty and accuracy of this model were clearly demonstrated. This improvement is very helpful in calculating GPP. Because the model formulas are easy to implement, they are highly suitable for integration into production systems to generate regional FPAR datasets.  
All of these analyses showed that the difference between direct and diffuse FPAR cannot be ignored. The inversion model used in this research was helpful in improving FPAR inversion accuracy. However, the DnD model has certain limitations. The input parameters include LAI, surface albedo, land cover and diffuse PAR proportion. The precision of these parameters directly affects the accuracy of FPAR inversion. In addition, many unanswered questions remain to be studied, such as multiple scattering of the soil background, acquisition of observed FPAR data and removal of the absorption PAR of soil from observed APAR.
## 2 Materials and Methods
### Sample Collection and Testing  
1154 surface sediment samples (Figure 1) were collected in Bohai Bay using a grab (Shuguang HNM12 0.05 m\({}^{2}\), CHN) in July 2010. Sediment was sampled at 3 km intervals in almost the entire bay, but at 5 km intervals in the northeastern part of the bay. The upper 2 cm of the sediment was collected and sealed for storage at room temperature. Sediment samples were tested in the Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Ocean University of China. Grain size tests were performed on samples from all stations, and geochemical tests were performed on samples from every other station. Data from several stations were excluded due to missing samples or poor data quality.  
The grain size of 1154 samples was measured by a laser diffraction grain size analyzer (Malvern Mastersizer 2000, Malvern, UK). After sample pre-treatment and preparation [15], the particle size was tested with a Malvern Mastersizer 2000. The relative error of repeated measurement was less than 3%. The grain size parameters were calculated using a moment method [16].  
The geochemical composition of 330 samples was measured by a SPECTRO XEPOS energy-dispersive X-ray fluorescence spectrometer (XRF, SPECTRO Analytical Instruments, Kleve, Germany). After sample pre-treatment and preparation [15], the content of the element was tested with the XRF. The relative error of repeated measurement was less than 2%.