esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bi-Objective Model for the Design and Management of RMSs
### Optimization Model Design: Assumptions, Notations, and Analytic Formulation  
The bi-objective model aims to best manage the trade-off among the inter-cell travel time among RMTs, the effort to install and disassemble the auxiliary modules on/from the RMT on which the part is located, i.e., RMT reconfiguration, and the ergonomics of the work activity in terms of repetitive movements, i.e., auxiliary module assembly/disassembly, performed during the work-shift. To reach this goal, two objective functions are considered: the former, i.e., technical, minimizes the sum of the inter-cell part and auxiliary module travel time and the RMT reconfiguration time in terms of auxiliary modules assembly and disassembly. The latter, i.e., ergonomics, minimizes the repetitive movements in terms of number of auxiliary module assembly/disassembly performed by the human operators within the working activities to reduce the risk of biomechanical overload. Relevant input data used to explore this trade-off are the information about the part work cycles and the compatibility among tasks, RMTs, and modules. Moreover, the following assumptions are adopted in the model formulation:  
* the part work cycles and compatibility data among tasks, RMTs, and auxiliary modules are known;
* auxiliary module units are available in a limited number per type, marking that they represent scarce resources existing in limited quantities;
* the layout of the reconfigurable production system is given;
* parts travel among RMTs according to their work cycles organized in batches;
* at the end of each period, all batches move to the next task of their work cycle;
* part travel, reconfiguration, and working times are known and deterministic. Such assumptions are derived by relevant existing studies proposing optimization model formulations for CRMS design and management [17; 24; 25]. The aim is to reproduce a realistic industrial setting having a defined layout (re-layout actions are usually time and cost consuming) and in which basic data about parts, auxiliary modules, and machines are collected and available. The following notations are used. **Indices**  
\(c\): RMCs \(c=1,\ldots,C\)  
\(m\): modules type m \(=1,\ldots,M\)  
\(n\): auxiliary modules \(n\)\(=1,\ldots,N\)  
\(o\): operations in part work cycle \(o=1,\ldots,O_{i}\)  
\(p\): parts \(p=1,\ldots,p\)  
\(r\): RMTs \(r=1,\ldots,R\)  
\(t\): time periods \(t=1,\ldots,T\)  
Figure 1: Schematic structure of a CRMS (AM: auxiliary module; BM: basic module).
# Detecting Bid-Rigging in Public Procurement. A Cluster Analysis Approach
## 3 Results
### Box-Plot Analysis  
The data discontinuity described above can be noted in Figure 2 (Appendix A). The cluster analysis in SPSS 23 shows the following distribution in the two clusters (see Figure 4).  
Normality tests for the two clusters can be seen in Table 6.  
The conclusion of these tests is that the data of the first cluster are normally distributed (\(\text{Sig}=0.000<0.05\)), while data for the second cluster do not have a normal distribution (\(\text{Sig}=0.061>0.05\)--Test Shapiro-Wilk, Sig. = 0.073 > 0.05--Kolmogorov-Smirnov test). This confirms that the difference between the two clusters is statistically significant.  
Figure 4: Box-plot cluster. Source: own processing data in SPSS 23.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Skewness**} & \multicolumn{2}{c}{**Kurtosis**} \\ \cline{2-5}  & **Statistic** & **Std. Error** & **Statistic** & **Std. Error** \\ \hline Cluster 1 & 2.365 & 0.597 & 6.655 & 1.154 \\ Cluster 2 & \(-1.95\) & 0.448 & 3.384 & 0.872 \\ \hline \hline \end{tabular} Source: own processing using SPSS 23 software.  
\end{table}
Table 5: Descriptive statistics.  
Now we focus on auctions for winter 2016-2017. The data discontinuity described above can be seen in Figure A3 (Appendix A). The cluster analysis in SPSS shows the next split between the two clusters (see Figure 5).  
The conclusion of these tests is that the data for the second cluster are normally distributed (Sig. = 0.60 > 0.05--Shapiro-Wilk test, Sig = 0.200 > 0.05--Kolmogorov-Smirnov test) of the first cluster did not have a normal distribution (Sig. = 0.033 < 0.05--Shapiro-Wilk test; Sig. = 0.025 < 0.05--Kolmogorov-Smirnov test). This statistically confirms the discontinuity in data from the two clusters.  
Now we perform a cluster analysis on the percentage increases between the price per contract kilometer in 2016-2017 compared to 2015-2016. The data discontinuity described above can be seen in Figure A4 (Appendix A). The cluster analysis in SPSS shows the next division in the two clusters (Figure 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Kolmogorov–Smirnov a**} & \multicolumn{3}{c}{**Shapiro–Wilk**} \\ \cline{2-7}  & **Statistic** & **df** & **Sig.** & **Statistic** & **df** & **Sig.** \\ \hline Cluster 1 & 0.15 & 40 & 0.025 & 0.939 & 40 & 0.033 \\ Cluster 2 & 0.192 & 10 & 0.200 & 0.945 & 10 & 0.606 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Test of Normality.  
Figure 5: Box-plot cluster. Source: own processing data in SPSS 23.  
The conclusion of these tests is that the data of the second cluster is normally distributed (Sig. = 0.096 > 0.05--Shapiro-Wilk test; Sig. = 0.062 > 0.05--Kolmogorov-Smirnov), while the of the first cluster do not have a normal distribution (Sig. = 0.000 < 0.05--Shapiro-Wilk test, Sig = 0.000 > 0.05--Kolmogorov-Smirnov test).  
The statistical tests performed in this section are proving the existence of two types of bid-rigging during the analyzed period: competitive and anticompetitive.
## 2 Designed Photovoltaic System
### Operation of the System
#### 2.5.3 Energy Storage Management  
The SOC manager monitors the state of charge of the battery and determines whether the battery should be charged or discharged. The following conditions are considered:  
* Charging: if \(P_{PV}>P_{load}\), the excess energy is used to charge the battery, provided the SOC is below the maximum limit.
* Discharging: if \(P_{PV}<P_{load}\), the battery supplies the deficit, provided the SOC is above the minimum limit. The SOC is updated by using Equation (2): \[SOCnew=SOCold+\left(Pnet.\frac{\eta battery}{Cattery}\right)\] (2)where \(Pnet\) is the net power flow, \(\eta battery\) is the battery efficiency, and \(Chattery\) is the battery capacity.
## 5 Conclusions  
The identification of pollution status is an essential step for assessing the potential impact of urban soil heavy metal pollution. However, assessed results were much different using different methods. Therefore, selecting a suitable method should be one of the most important aspects of heavy metal evaluation. Based on the mentioned results of the discussion, the following conclusions that can be used for further relevant research are acquired.  
The improved Nemerow index (\(I_{MN}\)) calculated based on the Geoaccumulation index is more suitable for heavy metal pollution assessment for soils within the vicinity of landfills and industrial areas of urban areas. Meanwhile, the degree of contamination (\(mC_{d}\)) is more suitable than \(I_{MN}\) for soil heavy metal pollution assessment for city parks.  
The pollution degree of each element in soils of city parks, the vicinity of the landfill, and the industrial area were decreased following the order: Cd \(>\) Hg \(>\) Zn \(>\) As \(>\) Pb \(>\) Cu; Cd \(>\) As \(>\) Zn \(>\) Ni \(>\) Pb \(>\) Cu; and Cd \(>\) Zn \(>\) Cu \(>\) Pb \(>\) Ag \(>\) Ni \(>\) Cr, respectively. Heavy metals Cd, Hg, Zn, and As were the main pollution elements in urban soils of Guangzhou.  
Considerable, high, to very high ecological risk was caused by heavy metal pollution of most samples collected from city parks and the industrial area. Meanwhile, low to moderate ecological risk was caused by heavy metal pollution of most samples collected from the vicinity of the landfill. Soil heavy metal potential ecological risks were mainly caused by Cd and Hg for the investigated three function areas. That the soil environment within the vicinity of the landfill was better than that within city parks differed from the public's traditional cognition.  
Figure 4: Percentage composition of different potential ecological risks caused by soil heavy metal based on calculation results of E for every element and RI for eight heavy metals: (**a**) city parks, (**b**) landfill, and (**c**) industrial area.  
**Author Contributions:** Conceptualization, T.O. and Y.G.; methodology, H.Z. and C.H.; investigation, Y.G., S.P. and Z.Z.; data curation, T.O. and Y.G.; writing--original draft preparation, H.Z. and T.O.; writing--review and editing, H.Z. and T.O.; supervision, Z.Z.; project administration, Z.Z. and S.P.; funding acquisition, T.O. and Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China (Grant No. 41977261) and the Guangzhou Land Resources and Planning Commission (Grant No. GSZB2C0025G).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data analyzed/generated are included and available in the manuscript.  
**Acknowledgments:** The authors are grateful to the anonymous reviewers for their constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 4 Modeling System Approach  
The satellite communication network serves as a facility that operates around the clock to receive calls. Incoming calls are regarded as customers awaiting service, and their wait times adhere to a specific distribution. To capture the call process, diverse topological arrangements of the network are created and implemented. Statistical analysis is subsequently performed to evaluate the network's capacity to handle and dispatch calls, thus enabling appropriate conclusions to be drawn regarding the queuing service process.
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 7 Discussion of the Approaches  
After presenting the three models for permeability prediction in porous media, i.e., Model (1): \([K^{S}-n^{F}]\)-CNN, Model (2): Informed \(K^{S}\)-CNN, and Model (3): Enriched \([K^{S}-n^{F}]\)-CNN model, this section presents a brief comparative discussion of these models. The first two models, Model (1) and Model (2), are directly comparable, as they consider exactly the same 3D samples of 150 \(\times\) 150 \(\times\) 150 voxels each. Model (3), however, considers 3D samples of 108 \(\times\) 108 \(\times\) 108 voxels each, as we need to ensure compatibility with the available synthetic data. Table 1 presents an overview and a comparison of the three CNN models.  
While Model (1) offers simplicity and efficiency with high accuracy, Model (2) incorporates additional physical parameters but does not significantly improve performance. Model (3) leverages transfer learning, enhancing generalization but increasing computa  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature** & **Model (1): \([K^{S}-n^{F}]\)-CNN** & **Model (2): Informed \(K^{S}\)-CNN** & **Model (3): Enriched \([K^{S}-n^{F}]\)-CNN** \\ \hline
**Input** & \(\mu\)-CT images & \(\mu\)-CT images & \(\mu\)-CT images \\  & & + physical parameters (\(n^{F}\), SSA) & (Real \& synthetic) \\
**Output** & \(\mathbf{K}^{S},n^{F}\) & \(\mathbf{K}^{S}\) & \(\mathbf{K}^{S},n^{F}\) \\
**Loss function** & Includes \(\mathbf{K}^{S},n^{F}\) & Includes only \(\mathbf{K}^{S}\) & Includes \(\mathbf{K}^{S},n^{F}\) \\
**Complexity** & Moderate & Higher (additional input branch & Highest (pre-training \& transfer learning) \\
**Training** & Some overfitting (\(\approx\)10\({}^{-3}\) loss) & Similar overfitting (\(\approx\)10\({}^{-3}\) loss) & Similar overfitting (\(\approx\)10\({}^{-3}\) loss) \\
**Final \(\mathbf{\mathbb{R}}^{2}\) score** & \(\approx\)0.985 & \(\approx\)0.983 & Similar to Model (1) (\(\approx\)0.985) \\
**Advantage** & Simple \& effective & Incorporates physical information & Faster training, \\  & & in the CNN architecture & good for small datasets \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the three CNN models.  
Figure 12: Model (3): Training and validation loss function values over the number of weight updates (Epoch) with no transfer learning (**left**) and with transfer learning (**right**). In both cases, the validation loss (blue curve) initially follows a similar trend to that of training loss (red curve) but starts to diverge after approximately 90 (**right**) and 100 (**left**) epochs, suggesting overfitting beyond this point.  
tional cost due to the initial training on the synthetic data. For the data considered in this study, Model (1) remains a practical choice for efficiency.
## 1 Introduction
Since the complicated structure of boiler systems translates into large inertia blocks, the power regulation speed of thermal power units is usually slow and; therefore, thermal power units face difficulty meeting the fast response speed requirement for rapidly changing fluctuations. As a special type of hydropower generator, hydro-pumped storage has two-way power adjustment capability, in terms of pumping water to consume exceeding electricity during off-peak hours and releasing water to generate electricity during peak hours. Generally speaking, hydro-pumped storage can effectively play the role of peak-shaving and valley-filling based on its large power regulation capacity and fast power regulation speeds. Battery units currently deployed in power systems are mainly the lead acid battery, advanced Zn/Br battery, advanced Na/S battery, and super capacitors, etc., and they have fast power regulation speeds, by rapidly charging or discharging, but relatively small power regulation capacity. Therefore, conventional batteries are suitable for alleviating the high-frequency fluctuations of wind-photovoltaic power generation. In summary, a thermal power generator has outstanding power regulation capacity but a slow power regulation speed at the hourly level, while a pumped storage unit has smaller power regulation capacity than that of a thermal power generator, but it has a faster regulation speed at the minute level. For a battery unit, its power regulation speed is excellent at second-level but its power regulation capacity is relatively low. Therefore, the coordination and complementary operation of hydro-pumped storage units, thermal power generators, and conventional batteries should be explored in different time-scales to counterbalance the power fluctuations of wind-photovoltaic power outputs for power system operation security.
###### Abstract  
Direct-drive permanent magnet synchronous generators (DD-PMSGs) have been widely adopted in wind power generation systems owing to their distinctive advantages, including direct-drive operation, high power density, and superior energy conversion efficiency. However, the high power density of the generator inevitably leads to heat generation issues, which affect the reliability of the generator. To address the thermal issues in the 4.5 MW direct-drive permanent magnet synchronous generator (DD-PMSG), this paper proposes a novel forced air-cooling ventilation system. Through comprehensive computational fluid dynamics (CFD) simulations and fundamental thermodynamic analysis, the cooling performance is systematically evaluated to determine the optimal width of the stator ventilation ducts. Furthermore, based on the temperature distribution of the stator and rotor, three optimization schemes for non-uniform core segments are proposed. By comparing the ventilation cooling performance under three structural schemes, the optimal structural scheme is provided for the generator. Finally, the feasibility of the heat dissipation scheme and the accuracy of the simulation calculations are verified by fabricating a prototype and setting up an experimental platform. The above conclusions and research results can provide some reference for the design of the core ventilation ducts structure of subsequent wind turbines.  
wind turbine generator; forced air cooling; ventilation structure optimization; non-uniform core segments 2025 15 8008
## 7 Discussion  
### Simulation Results Comparison of Different Controllers  
From the simulation results shown in Figure 5 and Table 2, all controllers can compensate the reactive power and current harmonics. Comparing four controllers, HCC shows the worst \(\text{THD}_{i\text{x}}\), the largest neutral current \(i_{sn}\) and the largest reactive power after compensation, the current after HCC compensation still exists as a large current ripple. The PCC shows a lower \(\text{THD}_{i\text{x}}\), \(i_{sn}\) and \(\text{Q}_{\text{Total}}\) than HCC with the fixed switching frequency, whereas the \(K_{p}\) gain of PCC is obtained by trial and error. LQRC shows a better harmonic, reactive power and neutral current compensation capability compared with PCC, as its control gain matrix K is optimized using the LQR method. LQRIC has the best harmonic and reactive power compensation capability with the lowest \(\text{THD}_{i\text{x}}\), \(\text{Q}_{\text{Total}}\) and \(i_{sn}\) because an integral action is added to perform the error correction and the control gain K is also optimized by the LQR method.
## 2 System Design
### Field Sensor Module  
The field sensor module, composed of the water-level sensor and the electrical level exchange chip, is mainly used to obtain the field water-level data. The module collects the water-level information as an analog signal, converts it to a digital signal by a 12 bits A/D of high accuracy, and then sends it to the MCU.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.6. Mexico Forest Biomass Map (250 m)  
[PERSON] et al. [66] generated a forest biomass map for Mexico at 250 m spatial resolution by combining remotely sensed predictors with forest inventory plots using a maximum entropy (MaxEnt) algorithm. The predictors were extracted from MODIS vegetation index products, ALOS PALSAR backscatter coefficient images, and the SRTM digital elevation model. The generated forest biomass map was validated with independent inventory plot data at the 50 m pixel level, municipality level, and state level. The validation results showed that the accuracy was improved along with the spatial scale from pixel level to municipality and state level, with the RMSE of 17.3 t C/ha, \(\pm\)4.4 t C/ha, and \(\pm\)2.1 t C/ha, and R\({}^{2}\) of 0.31, 0.75 and 0.94, respectively. At the pixel level, map uncertainty was quantified through an error propagation model which accounted for measurement, allometry, sampling, and remote sensing prediction errors, assuming that these source errors were random and independent. Although this study provided accurate spatial maps for national or regional REDD+ applications and MRV systems, uncertainties still cannot fully meet the requirements anticipated for the planned BIOMASS mission [67].
# Efficient Reachable Workspace Division under Concurrent Task for Human-Robot Collaboration Systems
###### Abstract  
Division of the reachable workspace of upper limbs under different visual and physical conditions, finding the efficient reachable area under concurrent task conditions, and using it as a basis to divide the incorporation boundaries that require robot assistance are the focus of this paper. These could be used to rationalize the allocation of human and robot workspaces to maximize the efficiency of multitask completion, which has significant applications in the enhancement of human-robot collaboration (HRC) capabilities. However, research on this has rarely been conducted due to the complexity and diversity of arm movements. In this paper, we considered the physical and visual restrictions of the human operator, extracted the movement data of 10 participants while completing the reaching task, and divided the workspace into five areas (their angles are 0\({}^{\circ}\)-44.761\({}^{\circ}\), 44.761\({}^{\circ}\)-67.578\({}^{\circ}\), 67.578\({}^{\circ}\)-81.108\({}^{\circ}\), 81.108\({}^{\circ}\)-153.173\({}^{\circ}\), and 153.173\({}^{\circ}\)-180\({}^{\circ}\)). Measuring the concurrent task completion times when the target object is in each area, respectively, we demonstrated that areas I-II are efficient, reachable workspaces for the human. In the non-efficient reachable workspaces, the average completion times for HRC were 86.7% for human operators (in area III) and 70.1% (in area IV), with the average number of warnings reduced from 2.5 to 0.4. The average completion time for HRC in area V was 59.3% for the human operator, and the average number of warnings was reduced from 3.5 to 0.5. Adding robotic assistance in this area could improve the efficiency of the HRC systems. This study provided a quantitative evaluation of human concurrent task completion capabilities and the incorporation boundaries of robots, which is a useful reference for achieving efficient HRC.  
Keywords: concurrent task; efficiently; human-robot collaboration; upper limb; workspace division +
Footnote †: journal: Applied