esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## Case Study: Spatial Interpolation of Meteorological Data in Thailand  
### Data Description  
The study area is located in Thailand (\(5^{\circ}37^{\prime}\) N to \(20^{\circ}27^{\prime}\) N, \(97^{\circ}22^{\prime}\) E to \(105^{\circ}37^{\prime}\) E), which covers an area of 518,000 km\({}^{2}\). The data used in this study consist of annual averages of  
Figure 1: Example of the difference of window selection between the original K-means algorithm and the AMWKK approach with three clusters: (**a**) K-means clustering data with \(k=3\) and (**b**) Adaptive K-means clustering data with \(k=3\).  
pressure, relative humidity, and geographic locations (coordinates). The hourly data were collected from the National Hydroinformatics and Climate Data Center (NHC), developed by the Hydro-Informatics Institute (HII) over an observed period spanning from January 2018 to December 2018 [38]. Each dataset was attained from 930 meteorological stations. After identifying and eliminating the outlier samples, we used hourly averages of the remaining 318 pressure and 239 humidity data. The positions for each of the two datasets are shown in Figure 2.  
Figure 2: Positions of the study area for meteorological data in 2018: (**a**) pressure (blue points) and (**b**) humidity (green points).
## 4 Finite Element Model  
In this study, the seismic performance of the subway station structure is examined by using the quasi-static pushover analysis method. Considering the efficiency and the influence of calculation error, the length of the soil and structure model along the longitudinal direction of the station is 30 m in the soil-structure system. As shown in Figure 6, the total width of the soil-structure model is 140 m, which meets the requirement of more than seven times of the structural width. The model is fixed at the bottom, while the lateral boundaries are bundled, i.e., the soil nodes at the same height are bundled together for a consistent movement [35]. Both soil and structure are discretized by using C3D8R elements in ABAQUS, and the finite element mesh of soil is finer at the location near the station structure, in order to reduce the calculation error. Friction contact is set between the soil and the structure with kinetic friction coefficient taken as 0.4 [36; 37]. In order to simplify the numerical model, the partition plate unit is not established in this model. The adjacent sides of each unit column are directly set as friction contact as well, and the kinetic friction coefficient is taken as 0.8 [38]. When assessing the internal force response of the central column, the analysis is conducted on the middle section of the subway station. The center column and section numbers are shown in Figure 6, where C1 and C2 denote the upper column and lower column, and S1 and S2 denote the bottom column sections, respectively.  
Figure 5: Three-dimensional diagram of an underground station. (**a**) Original center column reinforcement. (**b**) Split column reinforcement.  
Figure 6: Finite element model for pushover analysis of the soil–structure system.  
Concrete is simulated by creating solid elements and using the Concrete Damage Plasticity model proposed by [PERSON] et al. (2018) and [PERSON] et al. (2018), which describes the change in concrete stiffness in tension and compression by introducing a tensile damage factor and a compressive damage factor. Its material properties are defined in ABAQUS software as shown in Tables 1-3 and Figure 7 (2018). In addition, the reinforcement rebar is simulated by creating truss elements with an ideal elasto-plastic intrinsic model. The initial elastic modulus, Poisson's ratio, and yield stress is taken as 200 GPa, 0.3, and 335 MPa, respectively. The reinforcement is placed in the station concrete by embedding method, and the slip between the reinforcement and concrete is not considered in the numerical calculation process (2018).  
In Equation (1), \(G\) and \(\gamma\) are the shear modulus and shear strain, respectively. \(G_{\text{max}}\) is the maximum shear modulus of the soil. \(A\), \(B\) and \(\gamma_{0}\) are the fitting parameters related to soil properties.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 6 Simulation Experiments and Results
### Access Delay and Packet Loss  
We also took measurements of access delay in the previously described simulation conditions. The corresponding results are shown in Figure 12.  
It is interesting to note that when the number of contending stations is greater than 150, the figures of access delay are better for DCF than both 2C-R\({}^{2}\) and 2 CA-R\({}^{2}\). However, this is due to the amount of packets that were dropped by DCF in these cases, when they exceeded the transmission Retry Limit. In contrast, for 2C-R\({}^{2}\) and 2 CA-R\({}^{2}\), no packets were dropped due to this reason since there was not a Retry Limit in effect for these protocols. For the same simulation scenarios, Figure 13 shows the percentage of dropped packets by DCF, which is the only pr  
Figure 11: Mean network throughput.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter Name** & **Value** \\ \hline Simulation area & 100 m \(\times\) 100 m \\ Number of stations & 1, 2, 3, 5, 10, 15, 20, 25, 50, 100, 150, 200 and 256 \\ No. of CRIs required to compute \(\hat{\text{N}}\) in 2 CA-R\({}^{2}\) & 1 \\ Maximum transmission range (\(d_{r,max}\)) & 71 m \\ Data transmission rate (\(R\)) & 1 Mbps \\ Data packet size & 65 bytes \\ RTS size/REQ size & 20 bytes \\ DCF Retry Limit & 7 \\ DTI slot duration & 520 μs \\ CRI minislot duration & 160 μs \\ Simulation length (\(t_{s}\)) & 60 μs \\ \hline \end{tabular}
\end{table}
Table 5: Simulation settings.  
We can observe that in the worst scenario, that is, when there were 256 stations simultaneously transmitting, around 16% of their packets were discarded. It is important to note that these packets did not contribute to the access delay statistic since they were never successfully delivered. In this way, the Retry Limit parameter in DCF helps to keep access delay under control. Therefore, the time delay will depend on the value chosen for such a parameter at the cost of discarding some data packets coming from the stations.  
The results obtained in this scenario in terms of network throughput and access delay show that the performance of 2C-R\({}^{2}\) was poor compared to DCF and 2 CA-R\({}^{2}\). For this reason, results from the 2C-R\({}^{2}\) mechanism are not included in the remaining test scenarios.
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
###### Abstract  
The development of hydrogen sensors with high detection accuracy, fast response times, long calibration periods, and good stability has become the focus of the space station environmental control and life support subsystem. We analyze the current research status of different types of hydrogen sensors, including catalyst combustion type, heat conduction type, semiconductor type, fiber optic type, etc. The response signals of most hydrogen sensors are affected by temperature and humidity, resulting in cross-sensitivity. Reducing the cross-sensitivity of temperature, humidity, and other interfering factors to achieve accurate hydrogen measurement in different environments is a challenge that limits the development of hydrogen sensors. Several hydrogen sensors that are currently commercially available have a narrow operating temperature range, most of them can only measure at room temperature, and high-temperature environments require a higher accuracy and lifetime of the sensor than required at room temperature. Many new hydrogen-sensitive materials were developed to improve the performance of the sensors. The excellent performance of fiber-optic hydrogen sensors is beneficial to temperature compensation and distributed multiparameter measurement, as well as to the research and development of intelligent sensing systems, in the context of the Internet of Things. The signal detection and demodulation techniques of fiber-optic sensors are the focus of future hydrogen sensor research.  
hydrogen energy; sensor; sensitive materials; detecting technology +
Footnote †: journal: Physics Letters A
## 6 Results
### Scenarios Without Load Balancing vs. Scenarios with Load Balancing
#### 6.1.3 Impact on Quality of Experience (QoE)  
The results underscore the substantial improvement in QoE achieved through load balancing:  
**Enhanced predictability**: Consistent response times contribute to a more predictable and satisfying user experience, reducing frustration caused by latency variability.  
**Reduced congestion**: By distributing loads evenly, the model minimizes critical congestion moments, resulting in 48% fewer latency spikes above 300 ms.  
**Improved scalability**: Activating fog nodes during peak loads highlights the model's scalability, enabling the system to accommodate high-demand scenarios without degrading performance.  
**Superior user retention**: Lower latency and consistent performance are likely to enhance user satisfaction, potentially improving retention rates by an estimated 20%.  
The consolidated analysis demonstrates that the proposed load balancing model significantly outperforms the scenario without load balancing. The improvements in latency (up to 69.5%), load distribution (42% variance reduction), and system stability (55% fewer overload instances) directly enhance QoE.  
Figure 4: Load behavior in relation to time in an environment without load balancing. **(a)** Server load behavior in relation to time in an environment without load balancing. **(b)** Fog nodes load behavior in relation to time in an environment without load balancing.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.2 Role of Segmentation Models
Examples of the contemporary literature on instance segmentation algorithms are provided in Table 11.  
Panoptic segmentation, which aims to provide a unified understanding of a scene by simultaneously segmenting both'stuff' and 'things', has seen notable advancements through various models. In 2019, semantic segmentation and instance segmentation were combined in [134] to cover both'stuff' and 'thing' classes, introducing a novel panoptic quality (PQ) metric to evaluate segmentation performance. The Occlusion-Aware Network (OANet) in [134] uses a feature pyramid network to extract feature maps from input images, applying two separate branches: one for semantic segmentation, and another using Mask R-CNN for instance segmentation. The outputs from both branches are processed by a novel spatial ranking module, yielding the final panoptic segmentation. The unified panoptic segmentation network (UPSNet) [134] addresses panoptic segmentation by employing a ResNet and FPN-based Mask R-CNN backbone to extract convolutional feature maps, which are then fed into three sub-networks for semantic segmentation, instance segmentation, and panoptic segmentation. Following the initial panoptic models, specific task-oriented models have also emerged. For example, PanoferoTeeth [80] was designed for automatic segmentation of dental images through the integration of a dual-path transformer block, while Hybrid-PA-Net [74] uses a graph convolutional neural network (GCNN) with ResNet50 and PANet to perform brain tumor segmentation. Examples from the contemporary literature on applications of panoptic segmentation in the healthcare field are described in Table 12.
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques
### Decoupling Network  
In [23], a decoupling network (DN) also termed decoupling and matching network (DMN) is used. It is a circuit-based approach to achieve good isolation in MIMO antennas. The basic principle of DMN is to reduce the coupling coefficients between the antennas while maintaining a good impedance matching for each antenna [24]. Figure 3 shows two L-shaped Inverted-F antennas which act as radiating elements. The diamond-shaped pattern etched in the ground known as the diamond-shaped pattern ground resonator (DSPRG), acts as a resonator which also works as a decoupling element [25]. This can control the surface current flow in the antenna elements and provides good isolation in the wideband.  
Figure 3: Diamond-shaped patterned ground resonator [23].  
Figure 2: Orthogonally polarized slot antenna DGS [16].  
In [26], another decoupling method known as a coupled resonator decoupling network (CDRN) is implemented between the two nearby antennas to achieve good isolation. CDRN consists of two similar open-loop square-ring resonators.
## 2 Research Conditions and Methods
### Optimization Methods
#### 2.3.2 Benefit Optimization Calculation  
Based on the combination of all road section schemes, the maintenance decision-making optimization work is carried out to determine the best maintenance benefit scheme. The research results define the maintenance benefit area as the area enclosed by the pavement performance curve \(\mathrm{y_{p}}\) after treatment measures and the natural attenuation curve \(\mathrm{y0}\) of pavement performance without measures, as shown in Figure 9. The Bene_cost ratio of benefit area to its cost is then taken as the final benefit evaluation value of rural road maintenance.  
Figure 8: Decision steps for an improved heuristic optimization method.  
Considering the performance decay characteristics and benefit influencing factors of rural road pavement, the _Bene_cost_ generated by RPCI and RRQI key performance indicators was calculated, respectively. According to the actual impact degree of each index, it is weighted and summed according to the given weights of 0.65 and 0.35, and the calculation formula of the comprehensive _Bene_cost_ based on moment t is constructed, as shown in Equations (4)-(6) below.  
\[\begin{split} COST=L\;*\;W\;*\;UC\end{split} \tag{4}\]  
\[\begin{split} Bene\_cost_{t}=\left\{\begin{array}{c}\sum \limits_{l=1}^{n_{l}}(\int_{t_{0}}^{t}y_{p}^{t}-y_{0}^{t})/\sum\limits_{l=1}^{ n_{l}}cost_{l}\;\sum\limits_{l=1}^{n_{l}}cost_{l}\;\
eq 0\\ 0\;\sum\limits_{l=1}^{n_{l}}cost_{l}=0\end{array}\right.\end{split} \tag{5}\]  
\[\begin{split} Bene\_cost_{t}=0.65\;*\;Bene_{costt}(RPCI)+0.35\;*\; Bene_{costt}(PRQI)\end{split} \tag{6}\]  
where \(COST\) is the cost of maintenance; \(L\) and \(W\) are length and width, respectively; \(UC\) is the unit area cost (\(\forall/\text{m}^{2}\)); \(Bene\_cost\) is the maintenance benefit calculated based on performance index \(i\) for the T year; \(l\) is the lane number; \(n_{l}\) is the number of lanes; \(y_{p}^{l}\) and \(y_{0}^{t}\) are the performance curves corresponding to the \(l\) lane with or without treatment measures taken, respectively.  
To improve the calculation efficiency in the process of solution optimization, the improved heuristic optimization method proposed in this paper introduces the allowable deviation variable parameter \(D\) based on a given budget, and appropriately \"relaxes\" the cost constraints, as shown in the following formulas (7) and (8), to quickly realize the scheme combination with the best benefit in the whole life cycle.  
\[\begin{split} B_{t}-D_{t}\leq Cost_{t}=\sum\limits_{i^{\prime}=1}^ {m}Cost_{fit}\leq B_{t}+D_{t}\;,\;t=1,\ldots,T\end{split} \tag{7}\]  
\[\begin{split} Bene\_cost=\sum\limits_{i^{\prime}=1}^{m}\sum \limits_{l=1}^{T}Bene\_cost_{fit}\end{split} \tag{8}\]  
where \(m\) is the number of road sections; \(B_{t}\) is the budget for each planning year; \(D_{t}\) is the maximum allowable deviation from the budget for each planning year.
# A Short S-Equol Exposure Has a Long-Term Inhibitory Effect on Adipogenesis in Mouse 3T3-L1 Cells
## 3 Results
### S-Equol Reduces ER\(\alpha\) Expression and Attenuates the Subexpression of ER\(\beta\)  
The results described above showed that the ER\(\beta\) agonist S-equol affects adipocyte differentiation and functions. It has been reported that the expression of ER\(\alpha\) is greater and constant during earlier stages of adipogenesis compared to ER\(\beta\), whose expression decreases during adipocyte differentiation, suggesting an association between the expression of ERs and adipogenesis progression [28; 29; 30]. Therefore, we analyzed the effect of S-equol on both ERs by real-time qRT-PCR assays (Figure 6). As previously reported, ER\(\alpha\) mRNA levels remained constant during the differentiation process in control cells; in contrast, they were reduced by about 48% and 62% in cells treated with S-equol on days 3 and 7, respectively, with respect to control cells (Figure 6A). Regarding ER\(\beta\), its expression was decreased by about 59% on day 7 in control cells; treatment with S-equol did not affect ER\(\beta\) mRNA levels on day 3, but it only produced a 43% reduction on day 7 (Figure 6B). These data indicated that S-equol exposure has an effect on ER expression in 3T3-L1 cells.  
Figure 5: Effect of S-equol on adipokine secretion in adipocyte differentiation. 3T3-L1 fibroblasts treated with S-equol (10 uM) for 72 h were induced to differentiation, and the secretion of Adiponectin (**A**), Leptin (**B**), Resistin (**C**), PAI-1 (**D**), MCP-1 (**E**), IL-6 (**F**), and TNF\(\alpha\) (**G**) was measured in the maintenance medium collected on days 7 and 9 of adipogenesis. Untreated cells, and cells treated with nosigltazone (Ros) and estradiol (E2) were used as controls. Data were obtained from three independent experiments in triplicate and expressed as mean \(\pm\) SD. Statistical analysis was performed by one-way ANOVA with [PERSON]’s multiple comparison post hoc test using the GraphPad Prism software. *** \(p<\) 0.0001; *** \(p<\) 0.001; *** \(p<\) 0.01; *** \(p<\) 0.05 vs. control; *** \(p<\) 0.0001; \(\#\)\(p<\) 0.05 S-equol vs. E2.
# A Method for Dynamic Insertion Order Scheduling in Flexible Job Shops Based on Digital Twins
## 5 Dynamic Scheduling Process of Rush Order Insertion  
In the actual production of enterprises, the insertion of emergency orders will lead to the abnormal execution of the decided scheduling scheme due to sudden disturbances in the actual production process. The production scheduling system of an enterprise must be able to respond to sudden disturbances in real-time, be fault tolerant and make effective adjustments. Therefore, in the actual production process, job scheduling cannot be scheduled according to the set scheduling strategy or scheduling model, but must follow the real-time scheduling and continuous optimization according to the actual situation of the job shop. For some sudden or random disturbances under real-time conditions, the production system can dynamically adjust the original scheduling scheme in time and effectively. According to the status of the current system, the system will arrange the processing sequence of workpieces and assign tasks to the machine.  
Based on the dynamic scheduling framework based on intelligent manufacturing in Section 2, a dynamic scheduling process for rush order insertion has been designed. When a new rush order appears, a new scheduling scheme is dynamically generated. The detailed process is shown in Figure 6. When the system receives the order, it generates the initial scheduling plan. In the process of executing the current plan, it determines whether the system receives the rush order on time. If there is no rush order insertion, it will continue to execute the current scheduling plan. If there is a rush order, the workshop's real-time monitoring system will check the idle equipment in the workshop. In the workshop with more idle equipment, it will check the serial number of current unprocessed workpieces and upload the data to the system. The system will regenerate a new scheduling scheme according to the number of unprocessed workpieces, the number of workpieces in the rush  
Figure 4: The diagram of workpiece string crossing.  
Figure 5: The diagram of optimization mutation.  
order, and the workshop equipment status, and release it to the workshop manufacturing execution system, and the new scheduling scheme will be implemented by the workshop as the current scheduling scheme until the workpiece processing is completed.  
The dynamic scheduling process effectively solves the impact of emergency order insertion on production. Moreover, in order to meet the constraints, the new scheduling scheme can be reasonably connected with the original scheduling scheme to optimize the scheduling objectives.
## 4 Methods
### Soil Moisture Estimation
#### 4.2.2 Soil Moisture to Dielectric Constant Inversion  
As the alpha approximation method requires dielectric constant values \(\left(\varepsilon\right)\), the initial in-situ volumetric soil moisture values \(\left(m_{v}\right)\) need to be converted as well as the derived dielectric constant values from alpha approximation must be converted back into soil moisture. Using the soil texture class data set, this is done by applying the following equation by [PERSON] et al. [53]:  
\[\varepsilon=(a_{0}+a_{1}S+a_{2}C)+(b_{0}+b_{1}S+b_{2}C)m_{v}+(c_{0}+c_{1}S+c_{2 }C)m_{v}^{2} \tag{17}\]with \(a_{i},b_{i},\ c_{i}\) being frequency dependent constants, while \(S\) and \(C\) are the sand and clay percentages. In this study, we used constants for 5.3 GHz, matching Sentinel-1 C-band frequency of 5.4 GHz, provided by [PERSON] et al. [84]. In the last step, the minimum and 95 th percentile of retrieved soil moisture values are rescaled between the observed minimum and maximum of the region retrieved from GLDAS dataset.