esa-sceva/satcom-chunk-collection huggingface dataset:
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 4 Experiment
### Ablation Study  
To validate the necessity of each stage and module in TTH-Net, we conduct corresponding ablation experiments in this subsection. Specifically, we add the second stage and CSE module on top of the first stage individually and record their performances in Table 2.  
Firstly, by adding a segmentation head on top of the first stage, we obtain segmentation results for the first stage with an IoU score of 83.83%, Spe score of 81.57%, Sen score of 86.03%, and Acc score of 87.67%. Then, the CNN module is added in the second stage, and TTH-Net achieves an improvement of 0.77%, 0.77%, 0.94%, and 0.79% in IoU, Spe, Sen, and Acc metrics, respectively. Based on the two stages, the CSE module can bring an increase of 0.77% in IoU, 0.6% in Spe, 0.91% in Sen, and 0.78% in Acc to the TTH-Net. As a result, each stage and the CSE module is necessary for achieving high-precision leaf vein segmentation.  
Additionally, we conduct corresponding ablation experiments on the loss function and the hyperparameter \(\lambda\). As shown in Table 3, we select the commonly used loss functions such as focal loss [51], cross-entropy (CE) loss [34], and dice loss (DL) [35]. As shown in Table 4, among all combinations of loss functions, TTH-Net achieves the best performance when the first stage utilizes the auxiliary loss function of the Cross-Entropy (CE) loss function while the second stage adopts the DL. For the hyperparameter \(\lambda\), we find that when \(\lambda=0.4\), the TTH-Net achieves the highest scores across all metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **Dice** & **Spe** & **Sen** & **Acc** \\ \hline with 1 st stage & 83.83 \(\pm\) 1.35 & 81.57 \(\pm\) 2.63 & 86.03 \(\pm\) 2.80 & 87.67 \(\pm\) 0.04 \\ with 1 st stage + 2 nd stage & 84.47 \(\pm\) 1.26 & 82.18 \(\pm\) 2.47 & 86.42 \(\pm\) 2.61 & 88.22 \(\pm\) 0.03 \\ with 1 st + 2 nd stage + CSE module & **85.24**\(\pm\) 1.28 & **82.95**\(\pm\) 2.46 & **87.36**\(\pm\) 2.55 & **89.01**\(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation studies of the proposed TTH-Net. Note that each metric is reported via the mean (standard deviation) of 5-fold cross-validation.  
Figure 6: Training performance plots. (**a**) Loss vs. iterations. (**b**) Accuracy vs. iterations.
# Flattening the Curve of Flexible Space Robotics [PERSON]
## 1 Introduction
### Broad Context and Highlighting Importance
#### 1.1.3 Modern Methods  
The term \"modern\" is often used to either indicate problem formulation in space-variable form, also called \"state space\" form [11; 12], or, alternatively, deterministic optimization (as opposed to stochastic optimization commonplace in classical methods) [13].  
Figure 1: (**a**) NASA’s remote-controlled robotic refueling mission on orbit [1]. (**b**) U.S. Naval Postgraduate School’s autonomous robotic laboratory free-floating simulator on a planar air-bearing table [2].  
Figure 2: Robotic assembly and manufacturing in space. (**a**) NASA and Maxar Technologies SPIDER (planned) robotic assembly in space [5]. (**b**) Autonomous robotic laboratory free-floating simulator on a planar air-bearing table [6]. This testbed is the subject of modeling and control efforts, leading to the discovery of whiplash compensation of flexible space robotics [6].  
Modern methods, even optimal instantiations, ubiquitously impose a specified limited form of the robotic control equation. Typically, the control is forced to be a negative feedback or errors, where gains associated with each error are discerned to optimize some specified performance function. One very common form of a modern optimal feedback controller is the linear quadratic regulator [14] (amongst others, including robust L1 adaptive control [15; 16]). An alternative modern method eliminated the mandatory use of feedback to discern a feedforward (open loop) approach to control flexible robotic systems where the deleterious responses of flexible, multi-body motion are codified in a method called input shaping [17; 18], where the anticipated strain energy is distributed with time to create a time-delayed control approach for flexible space robotics [19]. The methods (feedforward and feedback) have also been combined [20].
# Respiration Rate Extraction of Moving Subject Using Velocity Change in FMCW Radar
## 3 Results
### Respiratory Measurements of Subjects without Leaning against the Wall  
The respiration rate extraction accuracies of the conventional and proposed methods are compared for ten subjects who stood upright without leaning against the wall.  
As shown in Figure 9, the conventional method cannot accurately determine the respiration rate, owing to the added displacement pertaining to movement other than chest movement. However, because the proposed method can determine the specific velocity change in the chest due to respiration, a signal can be generated for each inhalation and exhalation cycle in one cycle, leading to accurate extraction of the respiration rate. We experimented with the motion conditions shown in Figure 5b. Table 3 shows the respiratory accuracy calculation values for all subjects obtained through the experiment.  
Figure 8: Respiration extraction results for the case shown in Figure 5a. (**a**) Subject #1, (**b**) Subject #2, (**c**) Subject #3, (**d**) Subject #4, (**e**) Subject #5, (**f**) Subject #6, (**g**) Subject #7, (**h**) Subject #8, (**i**) Subject #9, and (**j**) Subject #10.
## 2 Materials and Methods
### Nonthermal Plasma Processes  
In contrast to EB technology, which relies on electrons generated by an external source, NTP usually employs a corona discharge generated by high-voltage electrical pulses, exciting electrons present in ambient air above the liquid solution. The ionization induced by the electrical pulses produces singlet oxygen atoms which then generate ozone and hydroxyl radicals. Alternative NTP configurations may use dielectric barrier discharges, or create corona discharges in aerosols or within the water matrix itself, without substantial differences [14; 20; 21]. The generated low-intensity UV light wavelengths belong in the 250-1000 nm range, with spectral peaks in wavelengths associated with radical formation. NTP is considered a highly efficient technology since very little energy is lost in thermal form, most being used in the excitation of electrons [22].  
As with EB, one significant benefit of NTP is that oxidants are generated without the addition of chemicals, which is costly, or the need for UV lamps which, in addition to low energy conversion efficiency, require significant maintenance and which effect may be severely affected by turbidity or solution absorbance. Initially, NTP was extensively investigated to achieve degradation of volatile organic compounds (VOCs) [23] and gaseous effluents treatment [24]. Lately, NTP water and wastewater treatment application studies are gaining popularity due to the limitations associated with conventional AOPs.
## 3 Mean Field Game (MFG) Framework  
MFG is a framework designed to address scenarios in which a vast number of rational agents (nodes), interact with one another. The key assumptions underlying this framework are as follows:  
1. Nodes are rational and are motivated to optimize their individual objectives.
2. As the number of nodes tends to infinity, a mean field approximation is applied.
3. Nodes can only observe and react to the aggregate behavior of the population.
4. This aggregate behavior can be described through a distribution referred to as the mean field.
5. The actions taken by nodes have an impact on the system's overall dynamics.  
In MFG, at a specific moment in time, the mean field corresponds to the probability distribution describing the states across the entire group of nodes. It is represented by a mean field variable (\(m(t,s)\)) which is expressed as [49]  
\[m(t,s)=\lim_{K\to\infty}\frac{1}{K}\sum_{i=1}^{K}\mathbb{I}_{[s_{k}(t)=s]} \tag{13}\]  
where \(\lim_{K\to\infty}\) indicates that the operation is conducted in the limit as \(K\) approaches infinity. The term \(\sum_{i=1}^{K}\mathbb{I}_{[s_{k}(t)=s]}\) represents an average over \(K\) nodes, where \(\mathbb{I}_{[s_{k}(t)=s]}\) is an indicator function that takes the value \(1\) if \(s_{k}(t)=s\).  
The MFG framework is described by a system of two interconnected PDEs, namely the HJB Equation (11) and the Fokker-Planck-Kolmogorov (FPK) equation, denoted by  
\[\delta_{t}m(t,s_{k})+\
abla(m(t,s_{k})\cdot\delta_{t}s_{k}(t))=0 \tag{14}\]  
where \(\delta_{t}m(t,s_{k})\) represents how the variable \(m(t,s_{k})\) changes over time and \(\delta_{t}s_{k}(t)\) represents the rate of change of \(s_{k}\) with respect to \(t\). The equation describes how the probability density of a system's state evolves over time.  
The HJB equation describes the value function of each node which represents the expected cost that it seeks to minimize [48]. The FPK equation characterizes the evolutionof the distribution of the system's state over time. The mean field establishes the relationship between the value functions and the distribution of the states, connecting the HJB equation and the FPK Equation [50]. Thus, instead of solving \(K\) HJB equations, the strategy of a node, \(E_{k}\), is influenced by the collective state of all the other nodes. The interaction of these two equations is depicted in Figure 2.
## 4 Conceptualizing Blockchain Technology
### Blockchain Technologies  
The recent years have seen a lot of interest in blockchain technology across various industries [9; 18; 28] and sectors [3; 29; 30; 31]. Blockchain is a decentralized, secure, and tamper proof ledger that records transactions and tracks assets through P2P connections. It operates on the basis of a consensus protocol that ensures transaction validity and security, and its immutability is one of its key features [32].  
The term blockchain refers to the structure of the ledger. It consists of groups of transactions, organized and stored in blocks, which are chronologically ordered and linked by a block number (hash) and a proposer or validator. The participants in a blockchain network depend on the type of blockchain: public or permissionless blockchains allow anyone to participate, and all participants have equal roles, while private or permissioned blockchains are controlled by a limited number of entities that manage the chain, grant permissions, propose transactions, and validate blocks. There are also hybrid blockchain models. In private and hybrid blockchains, the roles and interactions of participants are defined by smart contracts, which are also stored on the blockchain and known to all participants.  
The rise of Bitcoin has led to the creation of various blockchain environments. Currently, Ethereum [33; 34; 35], Cardano [36], and Hyperledger [37; 38] are the dominant technologies in the market for decentralized application development based on smart contracts. Ethereum is a global, open-source, permissionless platform for decentralized applications that was launched in 2015, becoming the world's first blockchain for DApp development. Hyperledger, hosted by the Linux Foundation, is a growing open-source community that aims to advance blockchain technology for cross-industry use in business. With over 250 members worldwide, it is the fastest-growing project in Linux Foundation history. IBM has adopted Hyperledger as its blockchain technology, offering Hyperledger Fabric [37], which provides a framework for developing blockchain solutions with a modular architecture, pluggable implementations, and container technology. This allows for the development of applications as components that build a final product, while also offering confidentiality, scalability, and security for enterprise environments. This is important for companies who do not want their transactions to be made public.
## 4 Analysis of Bridge Deformation Characteristics
### Dynamic Deformation Characteristics of the Fenghuangshan Road Bridge Based on GB-RAR Technology
#### 4.1.1 Dynamic Deformation Characteristics of the Bridge with a 50 Hz Sampling Frequency
The maximum negative deflections at 40 m, 50 m, and 60 m are \(-8.48\) mm, 9.56 mm, and 10.86 mm, respectively. Judging from the maximum negative deflection deformation, the overall deformation of the bridge is a cosine curve. Therefore, during the green light period, the northern section of the bridge moves downward under the influence of the dynamic vehicle load. The dynamic load forms a stress wave inside the bridge and transmits an upward movement trend to the middle of the bridge, thus offsetting the influence of the gravity action of vehicles. The downward movement trend in the middle section of the bridge is weakened. When the dynamic load is transmitted to the southern end of the bridge with a downward movement trend, the influence of the vehicle gravity load on the bridge deformation is strengthened. To further verify the judgment pertaining to the overall bridge deformation, this study extracts the monitoring data of the seven characteristic positions of the bridge at six different times, namely, 152 s, 157 s, 162 s, 167 s, and 177 s, and obtains the overall  
Figure 14: Comparison of dynamic deformation trends of bridge at different positions. Figure 13: A 50 Hz sampling frequency: third monitoring results. (**a**) 48.65–48.95 s; (**b**) 308.8–309.0 s.  
deformation trend of the bridge. Meanwhile, the study extracts the monitoring data of the corresponding positions at 234 s, 239 s, 244 s, 249 s, 254 s, and 259 s, and obtains the corresponding overall bridge deformation trend. From the deformation curves depicted in Figures 15 and 16, it can be observed that the stress wave wavelength formed by the vehicle dynamic load during the green light is approximately 30 m, the transfer distance is approximately 50 m, and the local time is approximately 70 m, which is the furthest distance in the process of distance monitoring position between 20 m and 50 m. The deflection deformation attains 24.8 mm, the amplitude attains 32.9 mm, and the vibration period is approximately 0.006 s.
## 3 Results and Discussion
### Electrochemical Performance of CSLDH Electrode
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrode** & **Electrolyte** & **Current Density** & **Capacitance** & **References** \\
**Material** & & & & \\ \hline CoV\({}_{2}\)O\({}_{6}\) & 2 M KOH & 1 Ag\({}^{-1}\) & 223 Fg\({}^{-1}\) & 45 \\ Co\({}_{3}\)V\({}_{2}\)O\({}_{8}\) & 3 M KOH & 0.5 Ag\({}^{-1}\) & 739 Fg\({}^{-1}\) & 46 \\ Co\({}_{2}\)P & 6 M KOH & 1 Ag\({}^{-1}\) & 416 Fg\({}^{-1}\) & 47 \\ CoWO\({}_{4}\) & 2 M KOH & 1 Ag\({}^{-1}\) & 764 Fg\({}^{-1}\) & 48 \\ Co\({}_{11}\)(HPO\({}_{3}\))\({}_{6}\) & 3 M KOH & 1.25 Ag\({}^{-1}\) & 312 Fg\({}^{-1}\) & 49 \\ CSLDH-2 & 2 M KOH & 1 Ag\({}^{-1}\) & 658 Fg\({}^{-1}\) & Present Work \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performances including the different concentration of the electrolytes. The stability of the electrode is one of the most important issues in energy storage applications. All of the earlier electrochemical results based on CV, GCD and rate capability clearly indicate that the CSLDH-2, which has one dimensional nanofibrous morphology, produced the most improved capacitance. The cyclic stability of the optimized CSLDH-2 electrode was examined by the continuous CD analysis test at 4 Ag\({}^{-1}\) current density over the 2200 cycles. As shown in Figure 13b, the nanofibrous CSLDH-2 electrode showed excellent cyclic stability (4 Ag\({}^{-1}\)) up to the 2200 cycles and retained around 89% of the specific capacitance as compared to the CSHDS electrode. This better performance of the CSLDH-2 demonstrates that it is the presence of the cobalt filler in the CSLDH-2 electrode which helps to stabilize the overall structure of the nanocomposite during the charge-discharge process. The electrochemical performance of the CSLDH-2 electrode is comparatively higher than the other two electrodes (CSHDS and CSLDH-1). In addition, the retention of the CSLDH-2 electrode highlights its excellent cycling stability and suggests the high reversibility and excellent electrochemical stability of the electrode. However, the obtained capacitance retention is gradually decreased with the increase in the charge-discharge process, which might be attributed to the electrolyte ions blocking the pores of the electrode materials; in the present case, the electrolyte ions may be trapped in the layers of CSLDH-2 and barred from contributing to further electrochemical redox reaction processes in an aqueous electrolyte. Additionally, the Sn/Co might undergo structural degradation in the electrolyte during the charge-discharge process. Hence, the significant deficiency of capacitance that we noticed might be attributed to the unwanted electrochemical redox reaction and structural instability of the electrode in aqueous electrolyte. Efforts are still being made to upgrade the capacitance performance by introducing carbonaceous materials, such as graphene and other carbon-based material with different morphology, in work in the near future.
## 1 Introduction
### Problem Definition  
The energy consumption scenarios discussed in the previous section demonstrate that the communication of a node drains almost all of the energy. Unlike the sensing and computational task which consumes very little energy, the transmission and reception of data use tremendous amounts of energy. The motivation behind highlighting the energy efficiency of a WSN is the inevitable energy drainage of a resource-limited sensor node [26; 27]. The WSN is entirely application-specific; therefore, its design take energy consumption into account. Moreover, any system that provides precision in detecting any surrounding attribute with the same amount of energy consumption is termed energy efficient. Consequently, energy efficiency proves to be a trade-off between various performance parameters such as bandwidth, PDR, network longevity and energy utilization where a designer has to decide the specific requirements. So, it all depends on the application where the sensor nodes are to serve, and accordingly, the network longevity is achieved by keeping the network energy efficient. The sole objective of network longevity does matter for those applications where a delay is unacceptable, such as forest fire detection and other catastrophic events. In those applications, energy efficiency must be obtained in the form of reducing the delay in data delivery for each unit of energy consumption. Similarly, for some applications, the QoS (Quality of Service) parameters are of paramount importance; their network longevity is a secondary concern. Therefore, in this paper, the primary emphasis is on highlighting the stumbling blocks in acquiring energy efficiency and providing some measures to curb energy consumption as much as possible. There have been various surveys [6] criticizing the energy efficiency WSNs; however, the simulation analysis along with an optimum solution to attain smart energy efficiency is still unexamined. This motivates us to spotlight the energy efficiency aspects along with simulation analysis of the proposed strategies to achieve a smart energy-efficient WSN.  
The main gap in the existing literature is that the number of nodes are static for every round that does not ensure the number of acquired cluster heads will maximize the lifetime of network. In our proposed protocol, the number of nodes varies for every round which increases the efficiency and lifetime of the network.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology  
### Study Area  
The test site is located in Shanghai, southeast China. It covers the Shanghai central business district (CBD) and its immediate environs. The area under investigation is approximately 2437.7 km\({}^{2}\), and is centered at longitude 121\({}^{\circ}\)5\({}^{\prime}\)0.9\({}^{\prime\prime}\)E and latitude 31\({}^{\circ}\)8\({}^{\prime}\)5.8\({}^{\prime\prime}\)N. It is the most densely populated and most economically intensive part in the Shanghai metropolitan area. The location map of this area is given in Figure 1.  
Figure 1: Location map of study area in Landsat 8 OLI natural color composite image of RGB432; inset map on top right shows the approximate location of the study area in China in red dot.  
Shanghai is found in the subtropical zone and records annual mean temperatures and rainfall of 15.8 \({}^{\circ}\)C and 1149.7 mm, respectively [40]. To support year-round agricultural practices, irrigation schemes are often conducted to supplement insufficient rainfall and meet crop water requirements.  
Located in the western North Pacific at the mouth of the Yangtze River, Shanghai is a major commercial hub in China and is home to one of the largest seaports in the world. Shanghai has the highest population density in China, and with its vigorous economic growth, this municipality accounts for about 4.5% of China's gross domestic product (GDP) [40]. The population of Shanghai continues to soar with increasing migration and arable land for UPA is rapidly becoming scarce.  
Several years ago, southeast China including the Shanghai metropolis was one of the major rice producing areas in China. With rapid socio-economic development in this area, more land is required for housing, infrastructure and industry, and rice cultivation is increasingly becoming prominent in northeast China [33]. Even though rice fields have been shifted to the peri-urban, apart from competition from residential and industrial land-uses, they are also seriously contested by such lucrative farming activities as aquaculture, horticulture, and other high income earning and shorter duration crops. Rice fields are therefore becoming smaller and highly fragmented. Much of the paddy rice in Shanghai is now cultivated by subsistence and smallholder commercial farmers. As rice is one major food crop in southeast China, information on its spatial distribution is required for agricultural and food security planning, and in land, water and environmental management.  
A single-season rice cultivation system is practiced in Shanghai, which spans from late May to late November. After rice harvest, most of the paddy fields are cultivated with winter wheat, forming the most distinct crop rotation pattern in southeast China. Even though rice is cultivated in a single season in this area, inter-field variability due to different planting dates is evident. Hence, to accurately map rice fields in this area, multi-temporal data are imperative, and the use of multi-source datasets could bring substantial improvements in overall classification accuracies.