esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influencing Factors of CUE and WUE Variation in the NMR
In the NMR, which are dominated by subtropical forest ecosystems, water availability becomes a major limiting factor for grassland growth [45]. This may be because grasslands are significantly limited by insufficient water, which leads to reduced carbon absorption efficiency. In contrast, an overabundance of rainfall can lead to water saturation or oxygen depletion in the soil, inhibiting the growth and photosynthetic activity of shrubs, thereby reducing their carbon use efficiency [63]. Shrubland CUE exhibits a significant negative correlation with precipitation, likely due to the deeper root systems of shrubs, which can access groundwater during periods of low rainfall. However, with increased precipitation, excess water may lead to soil anoxia or water retention, adversely affecting root respiration and carbon conversion efficiency, resulting in a decrease in CUE [64]. Notably, temperature exhibits a strong direct relationship with shrubland CUE, whereas it is negatively correlated with CUE in other vegetation types. This could be because shrubs are more drought-tolerant and can utilize water and nutrients more effectively in high-temperature environments, whereas other vegetation types are more constrained by water evaporation, leading to a reduction in CUE [20]. Precipitation is negatively correlated with the WUE of forests, grasslands, shrublands, and croplands, with shrublands exhibiting the strongest negative correlation (\(-0.37\)) and forests the weakest (\(-0.20\)). On one hand, excessive precipitation leads to water retention, which limits the water use efficiency of shrublands [26]. On the other hand, forests have a strong water regulation capacity, and increases in precipitation do not significantly impact their water use efficiency [48]. Grasslands and croplands show a predominantly positive correlation with temperature in terms of WUE, whereas forests and shrublands primarily exhibit a negative correlation. Grasslands and croplands have high photosynthetic efficiency and short growing cycles, allowing them to rapidly enhance photosynthesis and transpiration in response to rising temperatures, thereby improving water use efficiency [65]. In contrast, forests and shrublands have larger biomass and deeper root systems. As temperatures rise, they experience increased transpiration, leading to greater water expenditure and a decline in effective water utilization efficiency [4]. Future research should focus on examining the interplay between soil characteristics, vegetation attributes, and climatic factors to gain a deeper understanding of the mechanisms driving these relationships.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 8 Conclusions and Future Directions  
In this paper, we formulate an innovative solution for WSN using modulated IRSs. Each sensor is attached to an IRS leading to an IRS-sensor node (ISN), which replaces sensors with active RF equipment. This mitigates the caveat of battery usage in the sensors, as the power for transmission is exported to the BS. First, we define the system model under LoS and Rayleigh channel conditions. We study BPSK transmission from the ISN and provide the system outage probability for different scheduling schemes, including opportunistic scheduling (OS), proportional fairness (PF), and round-robin (RR). Multi-user diversity is investigated and exploited in the OS scheme for different numbers of IRS elements \(N\) and numbers of sensors \(L\). The proposed scheduling schemes are further compared with different metrics, such as outage, minimum throughput, average throughput, achievable rate, and fairness. WSNs with ultra-low power sensors, as in the case of ISNs which do not depend on costly control circuitry, are investigated, and scheduling protocols under this setup are studied as well.  
Different scheduling schemes can be best used under different scenarios. For instance, OS is best used if the WSN has a large number of sensor nodes in order to attain multi-sensor diversity gains. On the other hand, in cases involving lack of feedback from the user, the BS can deploy RR scheduling, which is channel independent and fair. Moreover, to find a middle ground between fairness and performance, the PF scheduling scheme can be utilized. The system throughput is best improved with OS as opposed to other scheduling schemes in this system. Future directions include scheduling ISNs without assumed favorable propagation in the system and the subsequent effects on the system performance due to interference between the sensors. An interesting line for future work is to include environmental conditions (such as temperature, dust, weather conditions) in the model and observe their effects the performance in the system. Moreover, the choice of different fading channel distributions, such as Rician fading, can be included in future analysis.  
[PERSON] contributed to formal analysis, methodology, software, and writing--original draft; [PERSON] contributed to conceptualization, methodology, and supervision; [PERSON] and A.C. contributed to investigation, validation, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 14: Lifetime of WSN with IRS for different \(N\) and without an IRS against the number of sensor nodes \(L\).
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 4 Handwritten Chinese Text Recognition
### Line-Level Text Recognition  
Line-level text recognition methods are mainly divided into two categories: one is to perform pre-segmentation, use a single-character classifier for recognition, and combine it with the context to generate text lines. The other is to recognize the text directly without character segmentation. Segmentation-based approaches mostly use over-segmentation and depth detection networks. Since HCCs tend to form overlapping and consecutive strokes between characters in the unrestrained writing process, it will significantly impact the cutting results, thus limiting the final recognition accuracy. The over-segmentation-based methods also face the problem of data sparsity. Therefore, there has been more research on segmentation-free based methods in the past few years. Clearly, the segmentation-free approach loses the detection information of individual characters, so some scholars combine segmentation and segmentation-free approaches and use deep detection networks to achieve end-to-end recognition of text while preserving the location information of characters. We will present both types of approaches in the following.
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 4 Results and Discussion
### Implementation Results
It can be seen that the suggested decoder's throughput is 2.82 Gbps, which is almost equivalent to the throughput of the decoder in [29] and around 2.5 times higher than the decoder in [28]. Concerning hardware utilization efficiency, the proposed design provides a HUE value of 4.65 hardware resources/layer/Mbps. It shows a hardware utilization efficiency of approximately 4.5-5 times lower than the decoders in [29] or [21]. Although compared to [23], the proposed decoder has 1.7 times lower throughput and about 2.4 times higher HUE, it provides better decoding performance and memory efficiency. More precisely, the memory capacity of the proposed decoder is 4.4 times less than the decoder in [23] and at a BER of \(10^{-8}\), it shows a gain of 0.15 dB.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### Potential Scenarios
#### 6.6.1 Unipolar Structure  
For the past 35 years, the geopolitical landscape of space has been characterized by a unipolar structure, with the United States maintaining a dominant leadership position. The central inquiry in this study pertains to the duration of the United States' privileged position and the potential challenges from other states that could potentially replace it as the predominant space power. Both scenarios appear to be plausible, as set out below:  
1. United States Leadership: Since the implementation of Space Policy Directive 1 in 2017, the United States has made significant efforts to revitalize its space program. These efforts initially included the establishment of the ambitious civilian Artemis Program, which involves private and international partners. Subsequently, in 2019, the United States Space Force was established as a space service branch of the U.S. Armed Forces. The prevailing sentiment in the United States is that America must preserve its space power advantage [95].
2. A New Space Leader: It is evident that China is the only nation that has demonstrated both the ambition and capability to challenge the United States' dominance in space. Over the past few decades, China has made significant progress in reducing its technological gap with the United States, with the objective of achieving a balanced power balance by the year 2050. Experts such as [PERSON] have posited that the primary question is not whether China will replace the United States as the predominant superpower, but rather the timing of this transition [96].
# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 5 Fog Intelligence and Automation
### Automatic Orchestration and Collaboration
Figure 6 shows a schematic view of the proposed protocols for communication between each component. Communication among components in the first and second layers is mostly MQTT. As the devices and objects in these layers have limited power and resource, this Protocol can be a suitable communication protocol. For components that have a more computational resource, XMPP can be the right candidate as well. Moving upward to the Cloud, AMQP, and DDS can be chosen because they are more flexible with supporting different types of messages and routing options. The non-constrained resources in the Fog layer and Cloud layer can process the overhead of heavyweight protocols such as AMQP and DDS. [PERSON] et al. [59] discuss the Microservice software for automatic collaboration among various components. The Microservice is a distributed software structure contained different components in size and code types well suited for heterogeneous environments in the ecosystem. [PERSON] et al. [60] explains the structure of Microservice software and its preferences over a monolithic one. The comparison between Microservice software and monolithic  
Figure 6: The IoT–Fog-Cloud ecosystem with the preferred protocols. software shows that the former has the best suitability in the ecosystem. One challenge here is the conversion of a monolithic software to Microservice. Scalability, development, changeability, integration, communication, maintenance, interoperability, interconnection, and upgrades are the positive features of Microservice software. The interoperability and interconnection of different IoT components are discussed by [PERSON] [39]. The collaboration between IoT sensors, Fog nodes, and Cloud nodes is discussed in [41] in the healthcare domain, where providing the best timing services for patients is so vital. [PERSON] et al. [61] discusses the fuzzy rules and linguistic variables for defining communications. This paper shows that linguistic variables can interpret the Person-to-Person, Machine-to-Person, and M2M communications very well. [PERSON] [62] discusses the topic of value co-creation in collaboration of things with studying the waste management application as a use case in the smart city domain. [PERSON] [62] demonstrates that in an IoT application, a higher number of connected things proportions to higher economic value. In other words, more connected users, objects, and machines, the Internet means more economic value. The resources can expand or shrink to satisfy a particular task. The resources will be selected and released when the number of collaborators is changed, as noted by [PERSON] and [PERSON] [63]. Table 5 demonstrates the methods and techniques among the reviewed papers in this section, which are used for implementing the communication and collaboration in the ecosystem.
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation
## 3 Results and Discussion
### Cell Membrane Damage Analysis  
As osmotic stress perturbs the membrane, propidium iodide (PI) assay was used to assess any differences in membrane perturbation between BW25113 and EJW3 in the presence of hyperosmotic stress in different growth phases. Propidium iodide is a fluorescent molecule that normally cannot penetrate into cells with intact membrane [51; 52]; however, if the membrane integrity is compromised, the uptake of PI increases, and can be used to assess relative levels of membrane perturbation. In order to detect differences within a short-term challenge, a higher hyperosmotic stress with 0.7 M NaCl was used in this test. The membrane integrity of both strains was assessed after a 30-min exposure to (1) M9 only, (2) M9 supplemented with 0.7 M NaCl, (3) M9 supplemented with 10 mM acetic acid, and (4) M9 supplemented with 0.7 M NaCl and 10 mM acetic acid. Cells in either lag phase (OD\({}_{600}\) -0.1), exponential phase (OD\({}_{600}\) -0.8) or stationary phase (OD\({}_{600}\) -3.0) were tested. Low levels of PI staining were observed in both strains in lag phase and stationary phase cells with or without NaCl challenge (Figure S2), suggesting no major membrane perturbation by NaCl challenge during these non-growth phases. However, in exponential growing cells, BW25113 showed significantly higher PI staining than EJW3 (Figure 7) under 0.7 M NaCl challenge, suggesting a lower level of membrane perturbation in the _rpoC_ mutant under hyperosmotic stress. While our data showed that addition of 10 mM acetic acid improved growth of BW25113 in hyperosmotic stress (Figure 6), PI staining data revealed that the addition of acetic acid conferred no benefit to the hyperosmotic stress induced membrane perturbation in either strain, which suggests that the benefit of acetic acid to osmotic tolerance is not related to membrane integrity.  
Figure 6: Effect of acetic acid on osmotic tolerance. (**A**) M9 only. (**B**) M9 supplemented with 0.6 M NaCl. (**C**) M9 supplemented with 0.7 M NaCl. Error bars are standard deviations.  
Figure 7: Membrane integrity assay using PI staining of exponential growing cells. NaCl: 0.7 M NaCl. AA: 10 mM acetic acid. ** Statistically significantly different using two-tailed student’s \(t\)-test (_p_-value < 0.005). Error bars are standard deviations.
## 2 Generation of Mesostructures and Numerical Models  
### Size Distribution of Aggregates  
Concrete aggregate gradation, determined by the Fuller curve, can produce optimized structural density and strength [30]. According to the Varavan formula for 2D transformed from the Fuller curve [31], the probability of aggregates with diameters less than D\({}_{0}\) and the area of the aggregates within a grading segment [D\({}_{\text{s}}\), D\({}_{\text{s+1}}\)] are shown in Equations (1) and (2), respectively.  
\[\begin{split}\text{P}(\text{D}<\text{D}_{0})=\text{P}_{\text{k}}(1.065\text{D}_{0}^{0.5}\text{D}_{\text{max}}^{-0.5}-0.053\text{D}_{0}^{4}\text {D}_{\text{max}}^{-4}-0.012\text{D}_{0}^{6}\text{D}_{\text{max}}^{-6}\\ -0.0045\text{D}_{0}^{8}\text{D}_{\text{max}}^{-8}-0.0025\text{D}_{ 0}^{10}\text{D}_{\text{max}}^{-10})\end{split} \tag{1}\]  
\[\frac{\text{A}_{\text{agg}}[\text{D}_{\text{s}},\text{D}_{\text{s+1}}]}{\text {R}_{\text{agg}}\text{A}_{\text{con}}}=\frac{\text{P}(\text{D}_{\text{s+1}} )-\text{P}(\text{D}_{\text{s}})}{\text{P}(\text{D}_{\text{max}})-\text{P}( \text{D}_{\text{min}})} \tag{2}\]  
where P\({}_{\text{k}}\) is the volume percentage of aggregates, usually 0.75, P (D < D\({}_{0}\)) is the probability of aggregates with diameters less than D\({}_{0}\), D\({}_{0}\) is the sieve diameter, D is the actual size of the aggregate, D\({}_{\text{max}}\) and D\({}_{\text{min}}\) are the maximum and minimum size of aggregates, respectively, R\({}_{\text{agg}}\) is the area fraction of aggregates, A\({}_{\text{agg}}\) is the area of aggregates within a grading segment [D\({}_{\text{s}}\), D\({}_{\text{s+1}}\)], and A\({}_{\text{con}}\) is the cross-sectional area of the concrete specimen.  
Generally, the area of aggregates accounts for 60-80% of the area of concrete. Based on simplification ideas and previous research experience [2; 32; 33], only aggregates with diameters greater than 2.36 mm are considered in this study, and the smaller aggregates are assumed to mix with the cement to form a uniform mortar. Table 1 lists the aggregate gradations and the aggregate area of the corresponding gradation, which is the same as in [2].  
\begin{table}
\begin{tabular}{c c} \hline
**Diameter Range of Aggregate (mm)** & **Area of Aggregates (mm\({}^{2}\))** \\ \hline
19.5 \textless{} D \textless{} 12.7 & 423.6 \\
4.75 \textless{} D \textless{} 9.5 & 600.2 \\
2.36 \textless{} D \textless{} 4.75 & 101.2 \\ \hline \end{tabular}
\end{table}
Table 1: Size distribution of aggregates.
## 3 Experiment and Results
### Calculate Transmittance  
After obtaining the maximum and minimum target radiance values, we estimate the maximum and minimum transmittance using Equations (9) and (10). Since transmittance inherently ranges between 0 and 1, we further refine its bounds through Equation (11), as shown in Figure 12a,b. Regions occluded by dense fog exhibit relatively low transmittance, while unobscured regions show higher transmittance. Based on this principle, we assign minimum transmittance (\(t_{\text{min}}\)) to white regions and maximum transmittance (\(t_{\text{max}}\)) to black regions in the final segmentation map (Figure 12c). To further reduce errors, we apply mean filtering, yielding the refined transmittance map in Figure 12d.  
\[t_{\text{min}}=\text{min}(t_{\text{min}},1)\] \[t_{\text{max}}=\text{min}(t_{\text{max}},1) \tag{11}\]
# Mobility-Multihoming Duality
## 6 Results of Evaluation
### TCP Duplicate Packets  
Figure 9b on page 26 shows the TCP duplicate packets that were observed. As for the misordering ratio across the different delay configurations and directions of flow, duplicate packets were negligible.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 4 System Architecture
### Rural Healthcare Data and Communication Hub  
The fog layer needs to be connected to the Internet (TCP/IP network) to allow access to the cloud services, including data storage and analysis. Due to network delays and bandwidth limitations, a simple internet connectivity link is insufficient as any down-times would introduce congestion if the data is buffered on the e-healthcare gateways. Thus, we propose a centralized communication hub that can serve one or several adjacent rural communities depending on geography. This communication hub would have data storage and additional data processing capabilities. Such a centralized hub could benefit from having more expensive technologies to implement on the e-healthcare gateways, such as satellite telephones for emergency communication, dedicated microwave links, and high gain antennas for GSM where a signal too weak to be picked up by mobile phones exists.  
The connectivity must be made by one of the available options (cable, microwave, 3G/4G) or by another network access technology that may become available in the future, namely 5G, TVWS or LEO Satellites. In addition, several devices available on the market can work as a Gateway, with WIFI or 3G/4G connection. For example, the Dragino Gateway LG308, an open-source source LoRaWAN Pico Gateway allows bridging LoRa wireless network to an IP network via WiFi, Ethernet, Or 3G/4G cellular via optional Long Term Evolution (LTE) module that results from an application of the WiMAX (Worldwide Interoperability for Microwave Access) spectrum [40].  
With the emergence of 5G mobile telecommunications, we will see the development of devices supporting this technology, allowing a range of differentiated and possibly low-cost use, combined with a very efficient coverage. However, we will always be conditioned to mobile network coverage, which will not always be economically viable in remote and rural regions, making this not a technology of the first choice in some contexts, like other mobile communications based on 2G, 3G, and 4G.  
The future will involve using the Internet supported by LEO satellites, which is increasingly close to reality. The mass delivery of LEO satellites is intended to provide coverage of high-speed Internet services to all regions of the world [41]. Recently, major technology companies such as Google and SpaceX have entered into agreements to provide data, cloud services, and applications to domestic and business customers all over the world soon. Likewise, it was reported that SpaceX will also install ground stations within Google's data centers that will connect to Starlink satellites, allowing Internet services through the Google Cloud platform. Starlink's satellites are over 60 times closer to earth than geostationary satellites, resulting in lower latency and the ability to support Internet services typically not possible with traditional satellite [42]. This will undoubtedly be an unprecedented revolution that will democratize access to the Internet anywhere in the world.