esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Selection of Commonality Indexes  
(1) Benefits  
Benefits are indexes that measure the effects and profits of a project. The benefits of various DSER are evaluated in terms of three aspects of economic benefits, environmental benefits, and social benefits. Taking WG as an example, the specific definitions are shown in Table 1.  
\(G_{wind}\)--The production value of WG in a certain region  
\(C_{wind}\)--The production cost of WG in the region  
\(E_{wind}\)--The amount of carbon dioxide emission reduction due to WG in a certain region  
\(S_{q}\)--The area of the region  
\(M_{wind}\)--The average satisfaction of residents in a certain area to WG, which is quantified by percentile system.  
(2) Resource Development  
The development of resources is another important index to evaluate DSER. The development of various DSER is measured by Equation (1).  
\[A_{2}=\frac{R_{use}}{R_{sum}} \tag{1}\]  
\(A_{2}\)--Resource development  
\(R_{use}\)--The amount of development of a DSER in a region  
\(R_{sum}\)--The total amount of the DSER in the region  
(3) Development Potential  
All kinds of DSER must be taken into consideration to settle the long-term development problem, so the development potential is also an essential index for evaluating all kinds of DSER.  
\begin{table}
\begin{tabular}{c c} \hline
**Subindex** & **Definition** \\ \hline Economic Benefits & \(A_{11}=C_{wind}/C_{wind}\) \\ Environmental Benefits & \(A_{12}=E_{wind}/S_{q}\) \\ Social Benefits & \(A_{13}=M_{wind}\) \\ \hline \end{tabular}
\end{table}
Table 1: Definition of benefits.  
Figure 2: The framework of the commonality indexes.  
The development potential of various DSER is evaluated in terms of four aspects upon policy support, sources of funds, benefits, and the amount of surplus resources. The specific definitions are shown in Table 2.  
\(A_{11}\)--The economic benefits of a certain DSER  
\(A_{12}\)--The environmental benefits of a certain DSER  
\(A_{13}\)--The social benefits of a certain DSER  
\(A_{2}\)--Resource development, which means proportion of resources currently developed in a certain region to the total resources of the region  
\(w_{1}\)--The weight of the economic benefits of the DSER  
\(w_{2}\)--The weight of the environmental benefits of the DSER  
\(w_{3}\)--The weight of the social benefits of the DSER
###### Abstract  
The original user relay (UR) selection scheme of non-orthogonal multiple access-based cooperative opportunistic multicast scheme, which realizes inter-group cooperation between two multicast groups, ignores the distribution trend of candidate UR in the cell and adopts fixed efficient relay selection range (ERSR) to select UR. It results in high UR selection ratio. Then the coverage efficiency, defined as the ratio of successfully received users to URs, is low. To tackle this problem, a range-division user relay (RDUR) selection scheme is proposed in this paper. Firstly, it divides the circular coverage range of base station into several continuous annular areas (AAs). Secondly, different ERSRs are assigned to unsuccessfully received users in different AAs. Under different ERSR assignments, the performances of UR selection ratio and coverage ratio are analyzed. Lastly, the radius set of ERSR that optimizes system coverage efficiency is used to perform UR selection. From simulation results, with different radius sets, analytical results of UR selection ratio and coverage ratio match well with their simulated ones. It is proved that ERSR allocation affects UR selection ratio and coverage ratio. With RDUR scheme, coverage efficiency increases by at least 14% and capacity efficiency has also been improved.  
coverage efficiency; cooperative multicast; NOMA; path loss; relay selection scheme +
Footnote †: journal: Accepted: 9 May 2019; Published: 15 May 2019
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
IoT Internet of thing  
MAC Media Access Control  
OFDMA  
Orthogonal Frequency-Division Multiple Access  
LPWAN  
Low-Power Wide-Area Network  
3 GPP  
3 rd Generation Partner Project  
BS Base Station  
LEACH  
Low-Energy Adaptive Clustering Hierarchy  
D2D  
Device-to-Device  
MTC  
Machine-Type Communications  
RAN
Radio Access Network  
CSMA/CA  
Carrier Sense Multiple Access/Collision Avoidance  
RRC  
Radio Resource Control  
PRACH  
Physical Random Access Channel  
C-RNTI  
Cell Radio Network Temporary ID  
PDSCH  
Physical Downlink Shared Channel  
PDCCH  
Physical Downlink Control Channel  
PHICH  
Physical HARQ Indication Channel  
RA
Random Access  
PUSCH  
Physical Uplink Shared Channel)  
CCCH  
Common Control Channel  
PDU  
Packet Data Unit  
LAN  
Local Area Networks  
RB  
Resource Block
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 3 Advanced Oxidation Processes (AOPs) for PFAS
### Heterogeneous Photocatalysis Materials
#### 3.1.2 Other Photocatalysts
Although implementing various photocatalytic materials and their different forms and compounds provides the solution for adequate removal of PFAS, maintaining a conducive environment during the photocatalytic reaction is equally vital. Factors such as aerobic or anaerobic conditions, type of light used, pH and temperature of the solution, and the presence of other impurities are crucial in deciding the photocatalytic degradation rate of PFAS. The effect of these factors is covered in other review articles [100; 101; 102; 103], and is out of scope for the present report. Figure 4: Charge separation and PFOA degradation (**a**) at the p-n heterojunction of BiOI@Bi5O7I (reprinted from [92], copyright (2020) with permission from Elsevier) and (**b**) in BOHP/CS composites (reprinted from [95], copyright (2020) with permission from Elsevier). Figure 5: PFOA degradation mechanism over (**a**) boron nitride (reprinted from [97], copyright (2020) with permission from ACS publications) and (**b**) the z-scheme based heterojunction of core–shell CeO2@NiAl-LDHs (reprinted from [99], copyright (2021) with permission from Elsevier). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Photocatalytic Performance**} & \multicolumn{6}{c}{**Photocatalytic Reaction Condition**} & \multicolumn{1}{c}{**Ref. **} \\ \hline
**Photocatalyst** & **Reaction** & **Rate Constant** & **\%** & **\%** & **Concentration** & **Catalyst** & **pH of** & **Light Source** & \\  & **Time (b)** & **(min-1)** & **Degradation** & **Defluorization** & **of FPO/AFPOS** & **D
# A Review of Thermal Error Modeling Methods for Machine Tools
## 2 Thermal Error Modeling Method for Machine Tools  
As early as the mid- to late-1980s, the research on thermal error modeling of machine tools has emerged. In the research of [PERSON] [16] in Japan, a tool-work piece relative displacement compensation control system was established to compensate the thermal deformation error of the spindle caused by the heat generated from the hydrostatic oil bearing during the rotation process. In the 1990s, Indonesian scholars [PERSON] and [PERSON] [17] proposed a time-variant volumetric error model to comprehensively compensate the geometric and thermal errors of the machining center. Based on this model, the accuracy of the machining center was improved by an order of magnitude. Since the 21 st century, a variety of mathematical modeling methods such as multiple regression, artificial neural network, time series modeling, etc., have been applied to develop the thermal error model of machine tools. According to the principles of the different mathematical methods, the modeling methods including the least square method, multivariable regression analysis, Grey system, neural network, support vector machine, hybrid model, etc., will be introduced in order.  
Figure 1: Thermal error compensation principle diagram.
## 6 Results and Discussions
### Performance Comparison on TSP Problem
Moreover, as the problem size scales up (e.g., \(n=100\) or \(n=200\)), the complexity of manual heuristic design grows substantially due to increased combinatorial interactions, whereas the automated generative mechanism remains tractable. These findings underscore both the early convergence efficiency and scalability of EoH-MR, making it particularly suitable for large-scale routing problems where high-quality solutions are needed under time constraints and without human-in-the-loop latency.
## 3 Results and Discussion
### Analysis of the Impact of Process Parameters on Combustion Reaction
#### 3.3.2 Effect of Process Parameters on Surface Reaction
When the inlet flow rate is below 1 \(\times\) 10\({}^{-7}\) m/s, the combustion rate increase is not obvious; however, when the inlet flow rate exceeds 1 \(\times\) 10\({}^{-7}\) m/s, the methane combustion rate changes considerably faster. These findings are more accurate compared with the uniform velocity concept used in previous studies. The pore size at the inlet should be controlled at 150 nm, and the optimal pore size at the wall should be larger than 300 nm and should not be too high. Otherwise, it will lead to too much internal space and lower temperature, resulting in a lower methane consumption rate. The pore size was measured more finely compared to 300 nm in previous studies. The results of this study are important for the adjustment and control of porosity and pore meshes in future simulations and experiments. The pore size length can be shortened, and the effective reaction interval can be increased in future studies. Conceptualization, B.S. ; methodology, T.Z. ; software, T.Z. ; validation, W.L., T.Z. and X.Q. ; formal analysis, W.L. and T.Z. ; investigation, X.L. ; data curation, X.L. ; writing--original draft preparation, B.S. and T.Z. ; writing--review and editing, B.S. ; supervision, X.Q. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant number: 52074111), the Natural Science Foundation of Heilongjiang province (grant number: YQ2020E034), post-doctoral funded projects in Heilongjiang Province (grant number: LBH-Z22248), and the development of key technologies for coal mine fire prevention and major disaster monitoring (grant number: 2020 ZX04A01). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the corresponding author. The authors declare that they have no conflict of interest.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
While rising temperatures may extend the growing season and promote the northward spread of crops, they may also increase pest and disease occurrences and the threat to food security posed by extreme weather [21]. Rainfall shortages and droughts directly damage food supplies and increase the instability of agricultural production [22]. Moreover, anthropogenic factors, such as land occupation and fragmentation, landscape pattern change, and increased human activity intensity, significantly impact cultivated land productivity [23; 24]. Fragmentation, boundary irregularity, and poor spatial connectivity threaten the stability of cultivated land productivity [25]. As a quantitative tool to reflect landscape characteristics, the landscape pattern index is closely related to the stability of cultivated land productivity [26]. However, most studies have focused on climatic or anthropogenic factors individually, and their combined impact remains unclear. China is the second-most populous country globally, with 1.42 billion people, and ensuring China's food security is imperative. Since the founding of the People's Republic of China, remarkable achievements have been made in protecting cultivated land. However, the cultivated land system still faces multiple challenges. Fragmentation caused by urban expansion has become a significant problem, particularly in the Bohai Rim Economic Zone and the Yangtze River Delta [27; 28], restricting the efficient use of cultivated land. The continuous reduction in cultivated land and intensification of fragmentation weaken the ecosystem service function and promote inefficient cultivated land use, threatening national food production [29; 30]. Simultaneously, the spatiotemporal pattern changes in cultivated land utilization driven by urban expansion threaten environmental sustainability [31]. In addition, non-grain cultivation trends have emerged in China under the influence of topography, irrigation, and drainage conditions; cultivation distance; rural labour shortages; and other factors [32]. Therefore, a long-term analysis of the relationship between cultivated land and other land-use types, focusing on cultivated land with stable long-term grain cultivation, is required to evaluate the stability of cultivated land productivity. The Poyang Lake region in Southern China is a key grain production area. However, rapid population growth and urbanization, coupled with the impact of extreme climate change, pose challenges to the productivity of cultivated land. Therefore, this study aimed to (1) analyze the spatiotemporal variation characteristics of cultivated land and NPP from 2001 to 2022; (2) explore the spatial distribution of cultivated land productivity stability from 2001 to 2022; and (3) identify the factors affecting the stability of cultivated land productivity. Evaluating the stability of cultivated land productivity in Poyang Lake has broad significance for ensuring national food security and the sustainable use of cultivated land resources [33].
# Measurement Uncertainty and Compliance Evaluation Applied to Natural Gas Moisture
## 2 Methodology
### Method Used by the Portable Moisture Analyzer--PM880  
The Panametrics PM880, as shown in Figure 1, is a portable hygrometer manufactured by Panametrics in Wilmington, NC, USA. It employs an aluminum oxide sensor to assess the moisture content in liquids and gases. This measurement is based on the temperature at which condensation occurs under specific barometric pressure conditions.  
The vapor pressure of water and ice, \(Pv\), depends on the dew point in \({}^{\circ}\)C, DP, and can be calculated from Equations (7) and (8):  
\[Pv_{water}=611.21(\frac{(17.502\cdot Pv)}{240.97\cdot Pv})\quad\ DP>0 \tag{7}\]  
\[Pv_{ice}=611.15(\frac{22.542\cdot DP}{27.38\cdot DP})\quad\ DP\leq 0 \tag{8}\]  
Figure 1: (i) Sampling system, and (ii) PM880 meter.  
The 1958 IGT8 (Institute of Gas Technology research bulletin #8) calculation based on the dew point value is expressed by Equation (9):  
\[IGT8=10^{(\frac{-1211}{0F+2/3.15}+6.687)} \tag{9}\]  
The non-ideality of natural gas, \(B\), depends on the dew point in \({}^{\circ}\)C, DP, and can be calculated by Equation (10):  
\[B=\frac{IGT8\times P_{v}}{Pv_{water}} \tag{10}\]  
The total pressure converted to Pascals, \(Pressure_{PA}\), is calculated by Equation (11):  
\[Pressure_{PA}=\frac{Pressure_{psig}+14.697}{14.697}\times 101325 \tag{11}\]  
The ratio of water vapor pressure to total pressure, \(X_{water}\), is calculated by Equation (12):  
\[X_{water}=\frac{P_{v}}{Pressure_{PA}} \tag{12}\]  
Finally, \(W\) is the water content (pounds water/million standard cubic feet natural gas) at reference conditions \(T_{b}\) (base gas measurement temperature, \(t_{b}+400\)), \(P_{b}\) (base gas measurement pressure, psia), Equation (13), considering the conversion of lbs/MMSCF to a fixed value, \(\frac{10^{6}\times 30.48^{3}}{\left(22414\times\frac{519.67}{491.67}\right)} \times\frac{18.0152}{453.59}=4743.05721\).  
\[W=X_{gua}\times\left[\frac{10^{6}\times 30.48^{3}}{\left(22414\times\frac{519.67 }{491.67}\right)}\times\frac{18.0152}{453.59}\right]+B \tag{13}\]  
For some industrial applications, the expression of moisture in parts per million by volume of water, \(ppm_{v}\)\(H_{2}O\), can be quite useful, as showcased in Equation (14):  
\[ppm_{v}\]  
\[H_{2}O=\frac{Pv_{ice}\times 10^{6}}{Pressure_{PA}} \tag{14}\]  
To highlight the innovations of this study, we emphasize the comparative advantages of the proposed method over existing approaches. The temperature range of the ASTM D1142 test method, approach 1, is limited from 0 \({}^{\circ}\)F to 100 \({}^{\circ}\)F, which is completely different from the Brazilian real samples of natural gas. The ASTM D1142 test method, approach 2, simplifies the final algorithm to calculate the water vapor content, in addition to not being appropriate where the dew point is measured in conditions close to the critical temperature of gaseous fuels. These limitations are not present in the method used by the Portable Moisture Analyzer--PM880.
## 5 Results and Discussion
### Discussion and Implications for Industry 5.0  
The results of this study position the MHMM as a powerful and practical tool for realizing the human-centric vision of Industry 5.0. Its high accuracy, robustness to imperfect data, and unique interpretability make it highly suitable for real-world deployment. By providing a continuous, context-aware understanding of human proficiency, the MHMM can power a new generation of adaptive systems:  
* Personalized Training: The system can tailor training curricula in real time, providing more support to a Novice and offering more challenging scenarios to an Expert.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
* Adaptive Human-AI Collaboration: A cobot could adjust its level of assistance based on the operator's inferred state, offering more explicit guidance during moments of fatigue or simplifying tasks when the operator is struggling.
* Proactive Well-being Management: By detecting early signs of fatigue-induced skill regression, the system can prompt interventions to prevent burnout and ensure workforce sustainability.  
The model's strong performance, grounded in realistic, empirically derived data, underscores its potential to bridge the gap between human operators and intelligent systems, creating safer, more efficient, and more supportive work environments.
## 9 Design and Manufacture of Robotic Prostheses with 3D Printing
### Selection of Printing Materials  
The choice of materials is crucial in the robotic prosthetic manufacturing process, as it impacts both the durability and comfort of the device. The most common materials used in prosthetic 3D printing include plastics such as PLA (polylactic acid) and ABS (acrylonitrile butadiene styrene), both of which are known for their strength, lightness, and ease of printing. In addition, for components that require flexibility, such as joints, softer materials such as TPU (thermoplastic polyurethane) or silicone can be used [9].  
The flexibility of 3D printing allows for the combination of different materials in the same prosthesis. For example, in a robotic prosthetic hand, rigid parts can be made from ABS to provide structure and support, while areas that require movement or skin contact can be printed with TPU for comfort and flexibility. The ability to integrate rigid and flexible materials in the same device has been one of the most significant advances in the design of 3D-printed prostheses [10].  
In addition, some researchers are exploring the use of bio-compatible materials and even recyclable materials for prosthetic printing, which not only reduces costs, but also makes prostheses more sustainable and globally accessible [1].
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 2 Related Work
### Robot Grasping Scenario in FaaS Platform  
Figure 3 depicts a sequence flow diagram for automated object feature extraction when the robot handler transfers the product to the buffer process after the product is manufactured in an additive manufacturing facility [7]. With regard to the robot grasping scenario of the previous work [7], we propose an HSV color-space-based automated object localization algorithm without prior knowledge.  
Figure 1: FaaS platform configuration: (**a**) FaaS platform architecture; (**b**) real manufacturing line; (**c**) octagonal modular factory.  
Figure 2: IoT device management and FaaS manager in the factory execution layer.  
The IoT-based robot handler collects an image of the product from the data aggregation module through a vision camera. The data conversion module converts the collected videos into images and converts them into an HSV.  
The object detection and analysis module works in HSV color-space-based automatic object localization and measurement. The data control module transmits the created recipe to the robot through the TCP socket and performs the grasping function.
## 6 Experimental Results
### Rotation Estimation  
Figure 12 shows the rotation test images, which are used to evaluate the rotation accuracy of the proposed rotation-estimation technique. To estimate the rotation accuracy, the simulated rotated images are generated by rotating the original image at rotation angles from \(0^{\circ}\) to \(359^{\circ}\) in increments of \(5^{\circ}\). For obtaining the rotation angle, the least second moment method is utilized to estimate the angle between the template and inspection image in [15]. Also, the pre-constructed rotated templates score and piecewise linear model are applied to estimate the rotation angle in [21]. Detailed comparison results are shown in Table 2. The error \(E_{r}\) is calculated using the following equation:  
\[E_{r}=|\theta_{a}-\theta_{e}| \tag{20}\]  
where \(\theta_{a}\) and \(\theta_{e}\) are the actual and estimated rotation angles, respectively.  
To provide an overall accuracy evaluation, three performance indices of error \(E_{r}\) are used to quantitatively show the performance: the mean of error \(E_{r,m}\), the standard deviation of error \(E_{r,std}\), and the maximum error \(E_{r,max}\).  
As seen in Table 2, the errors in the mean, standard deviation, and maximum in all test cases, which are derived by using the proposed ring-shifted technique, are less than 0.036\({}^{\circ}\), 0.027\({}^{\circ}\), and 0.102\({}^{\circ}\), respectively. In [15], the least second moment method cannot provide precise results because of the simple integer data representation. Besides, the pre-constructed method for calculating the rotation angle in [21], the accuracy of angle depends on the number of pre-constructed rotation template images and the location which are estimated by the normalized cross-correlation (NCC) method. If the NCC method cannot give a correct location of the template within the inspection image, the rotation estimation will have failed. The number of pre-constructed rotation template images is nine in this test. The proposed method is seen to be superior to the other methods. For the ring-shifted technique, the rotation-estimation technique uses the optimal shift value \(k_{opt}^{\ast}\left(r\right)\) by increasing the radius from \(R_{\text{min}}\) to \(R_{\text{max}}\). In this experiment, the proposed ring-shifted technique provides an accurate estimation when the template is rotated within the inspection image using arbitrary rotation angles.