esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatiotemporal Changes in RE in Northern China's Grasslands
#### 3.2.2 Temporal Variation for RE in Northern China's Grasslands  
Seasonal variation in RE at the regional scale formed a unimodal curve peaking during summer (Figure 6a), which is consistent with the site-scale verification results (Figure S1). The RE for alpine grasslands in the TP was always higher than that of the temperate grasslands in the IM. The RE peak value for the TP was in the range of 2.5-3.0 g C m\({}^{-2}\) day\({}^{-1}\), and its trough value was around 0.5 g C m\({}^{-2}\) day\({}^{-1}\). The IM had a lower peak value of 2.0-2.5 g C m\({}^{-2}\) day\({}^{-1}\), and its trough value was significantly lower than 0.5 g C m\({}^{-2}\) day\({}^{-1}\). We analyzed seasonal dynamics for the mean 15-year seasonal RE in the seven grassland subtypes and found that vegetation heterogeneity had a significant impact on RE (Figure 6b). The seasonal change curves for RE in the alpine meadow steppe and desert steppe were relatively flat, and the differences in RE between the growing season and non-growing season were relatively small, which may have resulted from hydrothermal condition limitations. Compared with these two grassland subtypes, the other grassland subtypes had a higher RE peak value during the growing season.  
From 2001 to 2015, the RE of northern China's grasslands increased at a rate of 1.81 g C m\({}^{-2}\) yr\({}^{-1}\) (Figure 6c). The alpine grassland RE in TP increased at a rate of 2.36 g C m\({}^{-2}\) yr\({}^{-1}\), which was faster than the increase rate of 1.28 g C m\({}^{-2}\) yr\({}^{-1}\) in the IM temperate grasslands (Figure 6c). We further analyzed the increase rate of RE in the seven grassland subtypes from 2001 to 2015 and found that the maximum rate of increase occurred in the alpine shrub meadow, while the minimum increase rate was in the desert steppe (Figure 6d). The rate of increase for RE in the TP is greater than that of the IM, which shows that the TP has a stronger feedback response under the background of global climate change.  
Figure 5: **(a)** Spatial distribution for RE in northern China’s grasslands from 2001 to 2015; **(b)** the mean annual RE for grassland in northern China; **(c)** Mean annual RE magnitudes for the seven grassland subtypes and the area weighted percentages to total RE.
# Extracellular Matrix Tunes the Regenerative Potential of Fetal Stem Cells
## 1 Introduction  
Adult mesenchymal stem cells (MSCs) are a promising cell source for tissue engineering and regeneration [1]. However, MSCs experience cell senescence in clinical treatment, due to either donor age and/or ex vivo expansion [2, 3]. Compared to adult MSCs with median levels of human leukocyte antigen (HLA) class I and low levels of HLA class II, fetal MSCs have low levels of HLA class I and do not express HLA class II, indicating that fetal MSCs have greater immunomodulatory properties [4, 5, 6]. Moreover, fetal MSCs possess greater proliferation and differentiation capacities compared to adult MSCs, indicating that fetal MSCs may be advantageous for MSC-based tissue regeneration [7, 8, 9, 10].  
Despite adult MSCs being prone to aging during ex vivo expansion, the decellularized extracellular matrix (dECM) deposited by MSCs provides a solution to rejuvenating both proliferation and chondrogenic capacity in adult MSCs [11, 12]. There is an abundance of research supporting the use of cell-derived dECMs to maintain stemness, prevent cell senescence, and limit oxidative stress when expanding stem cells [13, 14, 15, 16, 17, 18]. Previously, we found that human adult synovium-derived MSCs (aSDSCs) were rejuvenated afterexpansion on dECM deposited by fetal SDSCs (fSDSCs), compared to that deposited by aSDSCs [19]. Furthermore, high-passage (senescent) infrapatellar fat pad-derived MSCs (IPFSCs) grown on dECM deposited by low-passage (young) IPFSCs exhibited greater proliferative and chondrogenic capacity, whereas high-passage IPFSCs expanded on dECM deposited by high-passage IPFSCs lost their proliferative and chondrogenic potential [20], indicating that the matrix microenvironment can greatly influence adult MSC regenerative potential. This evidence could also suggest that dECMs deposited by younger, more proliferative cells could have a greater ability to rejuvenate [19; 20].  
However, it remains unknown whether a fetal matrix microenvironment can influence the regenerative potential of fetal stem cells. In this study, a tissue-specific stem cell for chondrogenesis, SDSCs [21], and a chondrogenic progenitor cell, nucleus pulposus cells (NPCs) [22], were used to characterize fetal MSCs in terms of the expression of stemness genes and surface markers and proliferation capacity, as well as the chondrogenic, adipogenic, and osteogenic potential following expansion on dECMs deposited by fSDSCs (SECM) or fetal NPCs (fNPCs) (NECM). Major matrix proteins were also assessed for expression in dECMs that were deposited by fSDSCs and fNPCs, as well as in expanded cells and multi-differentiated fetal MSCs.
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 2 Related Works  
Single-image super-resolution has become a research hotspot in recent years. Deep-learning-based methods have shown great improvement compared with conventional methods such as interpolations, anchored neighborhood regression [2], self-exemplars [12], and methods based on sparse encoding [1]. SRCNN [3] firstly used convolutional neural networks to sample images and achieved significant improvements. The performance of SRCNN was limited by its shallow structure. To achieve higher performance, the networks tend to be increasingly deeper. [PERSON] et al. proposed the VDSR [13] model with a deeper structure. In order to make the deep model trainable, recursive supervision and residual models were introduced [8; 14; 15]. Recently, some very deep models have been proposed such as EDSRand MDSR [4], which achieves very pleasing performance on super-resolution tasks. In addition, super-resolution models integrated with dense connections are proposed, including SRDenseNet [9] and MemNet [8], which effectively use the hierarchical features. Later, RDN [7] was proposed, which effectively used hierarchical features and controlled parameter growth. RCAN [6] was proposed with channel attention and ultra-deep structure, which showed great improvements of quality. In terms of a reconstruction network, the model also gradually uses deconvolution and effective subpixel shuffle [5; 16] to replace the traditional pre-interpolation process, which simplified the computational complexity and preserved more image details. The above methods showed impressive performance; however, their structures were complex and very deep. To achieve higher performance, attention plays another key role in addition to the scale and complexity of network. RCAN [6] only introduced channel attention, which is not sufficient to make full use of the flow of information, and the ultra-large scale (over 400 layers) makes it consume much computation and memory. Moreover, the above methods cannot make full use of the residual and dense features. Although many methods [7; 8; 9] have used dense connections to create a contiguous memory, all of them construct the network with separated residual and dense links. We build the network with synchronous residual and dense connections, which enables linking the two connections together and gives a better fusion of residual and dense features. Also, feature extraction usually requires different receptive fields and effective recalibration or fusion for features from different subfields, which were neglected in previous super-resolution methods. In order to solve these problems, we proposed a triple-attention network with multi-kernel mixed-link structure for single-image super-resolution. We will introduce our proposed TAN in detail in Section 3.
# Deformation Analysis of Large-Scale Rock Slopes Considering the Effect of Microseismic Events
## 5 Conclusions  
Based on the damage-constitutive relationship considering the scale of rock failure, the deformation feedback analysis of the surrounding rock of the left bank slope of Baihetan hydropower station was carried out. The following conclusions can be drawn.  
Compared with the traditional constitutive relationship, the extended damage constitutive relationship, considering the rock fracture scale can directly reflect the damage-effect of the microcrack within the rock. The damage constitutive model can select the characterization parameters of the rock fracture scale according to the calculation requirements.  
The proposed method integrated MS data with the FLAC3D simulation. In particular, this method applied the source radius to characterize the rock mass fracture scale and analyze the surrounding rock deformation characteristics. Compared to the traditional method, this method can better invert the mechanical properties of the rock mass under construction disturbance, and realize the simulation analysis of the slope stability, considering the effect of MS damage. This method provided a new idea for the quantitative evaluation of surrounding rock failure and the analysis of surrounding rock feedback.  
The numerical results indicated that the displacement of the model, considering the impact of MS damage, is closer to the field monitoring data than the model without considering the influence of MS damage. The increment was not obvious; i.e., only a few millimeters. This finding was mainly due to various reasons and limitations. The selected research period was short; the MS event source radius was small; and the distribution was scattered. Moreover, the slope deformation in the simulation considering MS damage was slightly less than that of field monitoring. This reduction may be because the numerical simulation did not consider the influence of the columnar basalt with the obvious deformation characteristics in the area. Therefore, the feedback analysis of rock mass mechanical properties is critical for the stability evaluation of high rock slopes under complex conditions.  
[PERSON] proposed the idea of feedback analysis of rock slope considering the effect of MS events. [PERSON], [PERSON] and [PERSON] processed and analyzed the abundant of MS data; [PERSON] and [PERSON] wrote the paper.  
The National Key R&D Program of China (No. 2018 YFC1505004) and the National Natural Science Foundation of China (No. 51679158).  
Thanks are given to colleagues at the Baihetan hydropower station for their valuable contributions to the project. Finally, the authors would like to thank the editors and reviewers for their valuable comments and constructive suggestions.  
The authors declare no conflict of interest.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 3 Proposed Optimal Range Prediction Filter Design
### Filter Design Method  
The optimal filter gains is obtained by minimization of \(\rm RMSE_{\rm pred}\). Therefore, using Equations (14)-(16), the evaluating function for the design filter gains is defined as  
\[\mu(\alpha,\beta,c_{\rm RD},\Gamma_{\rm D}) \equiv\sigma_{\rm r}^{2}/\sigma_{\rm w}^{2}+(L_{\rm rp})^{2} \Gamma_{\rm D}^{2}\] \[=\frac{2\alpha^{2}+2\beta+\alpha\beta-\beta^{2}c_{\rm RD}}{\alpha (4-\beta-2\alpha)-c_{\rm RD}(4\beta(\alpha-1)+\beta^{2}(2c_{\rm RD}+1))}+ \left[\frac{1}{\beta}-\left(\frac{1}{2}+\frac{\alpha}{\beta}\right)c_{\rm RD }\right]^{2}\Gamma_{\rm D}^{2}. \tag{17}\]  
The optimal gains are calculated by solving the following optimization problem:  
\[\arg\min_{\alpha,\beta}\ \mu(\alpha,\beta,c_{\rm RD},\Gamma_{\rm D})\] \[\text{subject to}\ \ \ \beta c_{\rm RD}<\alpha\ \text{and}\ 0<\beta\ \text{and}\ \beta<4-2\alpha-2\beta c_{\rm RD}, \tag{18}\]  
for which the constraints are the stability conditions of the LFM \(\alpha\)-\(\beta\) filter [9].  
Different from the conventional methods, the proposed method directly determines \(\alpha\) and \(\beta\) by searching the minimum value of \(\mu\) without using the gain relations derived from the Kalman filter equations, for example, Equation (7). Moreover, the Kalman filter equations are also not used in deriving \(\mu\). This means that the proposed method does not require the assumption of the process noise that limits the gain relation of the \(\alpha\)-\(\beta\) filter. In addition, the proposed method minimizes not the errors in estimation (\(r_{\rm s,k}\)) but those in prediction (\(r_{\rm p,k}\)), which is different from the Kalman filter. Therefore, the proposed method determines optimal filter gains with respect to the steady-state range prediction. Table 1 summarizes the features of the conventional and proposed design methods.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods
### Construction of Point and Polygonal Sample Datasets  
The training, validation, and testing phases of deep learning-based tea mapping models required a substantial volume of labeled samples. This study employed two types of sample datasets. The first dataset comprised polygonal samples from representative regions, utilized for training and validation of semantic segmentation models. These polygonal samples were crucial for capturing the spatial context and semantic features inherent in remote sensing images, enabling accurate tea mapping based on semantic segmentation. However, due to the limited coverage area of polygonal samples, assessing the model's accuracy in mapping tea plantations across the entire county posed a challenge. Therefore, the second dataset consisted of randomly distributed sample points covering the entirety of the county. This dataset's utilization was primarily motivated by the need for a more comprehensive evaluation of model accuracy at a county-wide scale, compensating for the limitations imposed by the restricted coverage of polygonal samples.  
Both the polygonal and point samples were meticulously curated to ensure their representativeness and accuracy (Figure 7). The area of polygonal samples comprised  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Source** & **Feature Name** & **Feature Description** & **Total** \\ \hline \multirow{3}{*}{Sentinel-2} & Original bands & B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12 & \\  & Vegetation index & NDVI, LSWI & \\  & Red edge index & NDVIre1, NDVIre2, TCARI & \\ \multirow{2}{*}{Sentinel-1} & Radar bands & VV, VH & 2 \\  & Topographic indices & Elevation, Slope, Aspect & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The features, indices, and corresponding total number of features extracted from Sentinel-2, Sentinel-1, and SRTM DEM data used in the study.  
four sub-regions scattered across Anji, totaling approximately 76.13 km\({}^{2}\). Two of these sub-regions were located in the northeastern part of Anji's main tea-producing area, while the other two were situated in the southeastern and southwestern parts of Anji, where only a small number of tea gardens were distributed, thus ensuring the representativeness of the sample region. The polygonal samples were used as training and validation data. Additionally, the point samples consisted of 1294 sample points spanning the entirety of Anji County, generated randomly and uniformly distributed. The labeling of both types of samples was derived from various sources, including drone imagery, very high-resolution Google imagery, ground truth survey data, and others, ensuring the precision and reliability of the label data.
## 7 Methodology and Experimentation  
### Methodology  
The experimental design utilized in this study can be categorized into two main groups. The initial group focused solely on human-designed heuristics. It aimed to evaluate the effectiveness of the proposed heuristics, specifically ReleSEAS and RelBPA, in comparison to random device selection and previously examined heuristics, namely, eSEAS and BPA, as elaborated in Section 7.2. The second group was devoted to substantiating the capacity of a reinforcement learning agent to emulate the performance of human-designed heuristics. Furthermore, it sought to demonstrate the agent's adaptability to novel conditions by utilizing transfer learning techniques; this will be explained in depth in Section 7.3.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Depth Prediction
#### 2.1.3 Implementation Details  
All implementation and training were done in PyTorch [22], using densenet-169 [23] pre-trained on ImageNet [24] to initialize the encoder parameters, and the decoder used skip connection and up-sample structure. The Adma [25] optimizer with parameter values of \(\beta\)1 = 0.9 and \(\beta\)2 = 0.999 was used. The learning rate was set to 0.001, the training epoch was set to 50, the batch size was set to four, and the training input image size was 480 \(\times\) 640.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### Prerequisite
#### 3.1.2 Docker Container  
RabbitMQ  
Fundamental to the system has been the RabbitMQ broker, which has implemented the publish/subscribe mechanism, including Message Queuing Telemetry Transport (MQTT), enabling communication between components through message exchange. RabbitMQ has served as a reliable, scalable, and flexible message broker, capable of efficiently handling a high volume of real-time messages. With the utilization of MQTT, a lightweight messaging protocol designed for resource-constrained devices such as IoT devices, RabbitMQ has ensured efficient and effective communication among the system's components.  
Node-RED  
An important tool that has aided in building the dashboard for the CPS Wildfire Detection System has been Node-RED. It is a visual programming tool that has facilitated the creation of flow-based applications through a user-friendly drag-and-drop interface. Node-RED has offered a web-based platform for developing and deploying IoT applications. Furthermore, it has supported various input and output protocols, including MQTT, HTTP, and TCP/IP. The platform has come equipped with a vast library of pre-built nodes that have been utilized to construct intricate flows. Additionally, it has been employed to create a flow-based application that has established a communication socket, allowing for message exchange between ROS-based and MQTT-based devices.  
Nuclio Serverless  
Finally, another important aspect has been Nuclio Serverless, a cloud-native platform that has streamlined the development and deployment of serverless-based applications, including notification systems. It has offered several features, such as integration with services such as IFTTT ([[https://ifttt.com/](https://ifttt.com/)]([https://ifttt.com/](https://ifttt.com/)), accessed on 2 April 2023) and the Node-RED framework. This integration has provided an effortless way to create robust notification systems, connecting them with email and/or chatbot functionalities. These systems have been crucial for informing firefighter operators and establishing a connection between the CPS Wildfire Detection System and an external monitoring and control room.
## 2 Triterpenoids
### 8,9-ene-triterpenoids

Table 2: _Cont._Tridterpenoid Saponins  
The plant triterpenoids naturally exist in their glycosidic forms and are named as tr triterpenoid saponins [281]. More than 300 _Ganoderma_ triterpenoids have already been found, although very few _Ganoderma_ triterpenoid saponins have been identified [282]. _Ganoderma_ triterpenoid saponins (Figure 11) are a kind of rare constituent, and until now only three triterpenoid saponins were isolated from _Ganoderma_. Furthermore, their glycosyl groups were located at C-21. The first triterpenoid saponin, 3\(\alpha\)-acetoxy-5\(\alpha\)-lanosta-8,24-dien-21-oic acid ester \(\beta\)-D-glucose (**354**) from the fruiting bodies of _G. tsugae_, was reported in 1998. Its cytotoxicity analysis showed inhibition against Hep 3B cells through apoptosis [283]. Subsequently, Su et al. [206] isolated triterpenoid xylose and tsugavioside B (**355**) from this fungus. Liu et al. [198] identified another triterpenoid saponin, named ganosinoside A (**356**), during studying on the chemical constituents from _G. sinense._  
Due to the rare nature of _Ganoderma_ triterpenoid saponin, the modification (glycosylation) of natural _Ganoderma_ triterpenoids into triterpenoid saponins is a promising strategy for both creating new compounds and expanding the bioactivities of _Ganoderma_ triterpenoids.
## 3 Application Fields of Smart Manufacturing and Tactile Internet Powered by 5G
### Industry
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Use Case** & **Benefits** & **Why 5G?** \\ \hline Advanced Predictive Maintenance & Using dozens of sensors to give an accurate, real-time representation of the status of a machine to perform predictive and preventative maintenance & Reduce downtime speed on maintenance Reduce machine replacement rate & Reliability Device costs Device density \\ \hline Precision Monitoring \& Control & Real-time monitoring and control of robots/machine tools/end-product (e.g., change speed of process based on vibrations) & Reduce defects Increase throughput & Ultra-low latency Device density \\ \hline Augmented Reality \& Remote Expert & Using augmented reality headsets to guide worker via augmented display and/or remote expert when carrying out maintenance and repair tasks & Reduce spending (and time) on maintenance and repair maintenance and repair & Ultra-low latency Bandwidth \\ \hline Remote Robot Control & Controlling robotic machinery remotely (video link may be required) for tasks where human involvement may be hazardous & Health and safety Increase throughput & Ultra-low latency \\ \hline Manufacturing-as-a Service & Making manufacturing flexible and mobile by reducing time to set up a site (fixed infrastructure) and enabling multiple parties to use same facility & Increase rate of product Innovation Reduce overall costs Increase productivity & Flexibility Device costs Ubiquity \\ \hline Automated Guided Vehicle & Coordinating vehicle control of AGVs (e.g., forkflifts, pallet movers or tractors) in a more flexible way without needing to pre-define routes & Increase efficiency and productivity & Low latency Reliability Reliability Location-awareness \\ \hline Drone Inspections & Using drones to perform tasks that may be difficult, dangerous, or tedious for humans (e.g., inspecting hard to reach equipment, checking inventory) & Health and safety Reducing spending (and time) on inspections & Low latency Reliability Location-awareness \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Manufacturing use cases enabled by 5G [48].
## 2 Materials and Methods
### Method Validation
#### 2.5.5 Accuracy  
The accuracy of the method expresses the closeness of agreement between the value, which is accepted either as a conventional true value or an accepted reference value, and the value found. The accuracy of the method, which reflects the closeness of agreement between experimental and theoretical analysis results, was tested by calculating the percentage recovery of each vitamin at three different concentration levels, 150, 200, and 250 ng/band for vitamins D\({}_{2}\), D\({}_{3}\) and E, and 30, 40, and 50 ng/band for vitamin K\({}_{1}\). Each experiment was performed in triplicate, and the % recovery and %RSD of the vitamin standards were used to determine the accuracy of the method.