esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanoscale Topographies for Corneal Endothelial Regeneration
###### Abstract  
The corneal endothelium is the innermost layer of the cornea that selectively pumps ions and metabolites and regulates the hydration level of the cornea, ensuring its transparency. Trauma or disease affecting human corneal endothelial cells (hCES) can result in major imbalances of such transport activity with consequent deterioration or loss of vision. Since tissue transplantation from deceased donors is only available to a fraction of patients worldwide, alternative solutions are urgently needed. Cell therapy approaches, in particular by attempting to expand primary culture of hCES in vitro, aim to tackle this issue. However, existing cell culture protocols result in limited expansion of this cell type. Recent studies in this field have shown that topographical features with specific dimensions and shapes could improve the efficacy of hCES expansion. Therefore, potential solutions to overcome the limitation of the conventional culture of hCES may include recreating nanometer scale topographies (nanotopographies) that mimic essential biophysical cues present in their native environment. In this review, we summarize the current knowledge and understanding of the effect of substrate topographies on the response of hCES. Moreover, we also review the latest developments for the nanofabrication of such bio-instructive cell substrates.  
Keywords:substrate topography; corneal endothelial cells; nanofabrication; cornea; corneal cell culture; topography; regenerative medicine; advanced cell culture substrates +
Footnote †: journal: Applied Sciences
## 4 Results and Analysis
### Analysis of the Impact of Various Factors on WUE
#### 4.3.4 Vapor Pressure Deficit Correlation Analysis  
In the context of current global warming, VPD is also gradually increasing. An excessively high VPD can lead to ecosystem water stress and drought, thereby threatening plant survival. Analyzing the relationship between VPD and WUE is conducive to the long-term sustainability of various ecosystems [38]. Figure 10 illustrates the spatial characteristics of the correlation between annual-scale Vapor Pressure Deficit (VPD) and water use efficiency (WUE) in the Li River Basin from 2001 to 2020. As indicated by this figure, the correlation coefficients between WUE and VPD range from \(-0.7092\) to \(0.7341\). The areas where VPD and WUE exhibit a positive correlation account for 49.58% of the total, with significant positive correlation and non-significant positive correlation constituting 2.55% and 47.03%, respectively; regions with a significant positive correlation are primarily concentrated in the central part of the basin. The proportion of areas showing a negative correlation is approximately 50.42%, with significant negative correlation slightly lower than significant positive correlation, accounting for about 2.17% of the basin's area; these regions are mostly located within Xing'an and Pingle counties. Non-significant negative correlation areas are distributed around the perimeter of the basin, predominantly on its northern side.  
Figure 10: Spatial distribution of correlation coefficients of WUE with VPD in Li River Basin from 2001 to 2020. (**(a)** Spatial distribution of correlation coefficients in the Analysis Results. (**b**) Spatial distribution of various significance Levels).  
Figure 9: Spatial distribution of correlation coefficients of WUE with NDVI and LAI in Li River Basin from 2001 to 2020.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 4 Results and Discussion
### Comparative Energy Consumption Analysis
The results validate that an adaptive, intelligent switching mechanism significantly improves energy efficiency in heterogeneous networks, ensuring optimal performance in jamming-prone environments. The implementation of the mitigation algorithm stabilized the hybrid approach by dynamically managing energy expenditure, mitigating inefficiencies in protocol transitions, and reducing fluctuations caused by jamming-induced overhead. These findings emphasize the importance of intelligent protocol management in maintaining energy efficiency and resilience in wireless sensor networks under interference conditions. Figure 11: Energy consumption analysis for BLE, LoRa, and hybrid BLE-LoRa switching under jamming conditions with and without mitigation.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Simple Adaptive Weight (SAW) Technique
#### 3.4.1 Scaling Stage  
Every involved metric has its value and unit. To harmonize all metrics, each metric needs to be scaled into non-dimensional values. The involved metrics can either possess negative or positive criteria depending on the nature of the metrics themselves. In this case, the \(RSS_{NV}\), \(AVC_{NV}\) and \(LLT_{IVNV}\) are metrics with positive criteria. The higher the values of these metrics, the better they optimize the gateway selection decision. However, in the case of the \(D_{IVNV}\), the increase in its value affects the optimality of the gateway decision, therefore this metric is categorized as a metric of negative criterion. In any case, the best score of any criterion ranges from zero to one, where one indicates the best value and zero indicates the worst value (see Table 3). Equations (9) and (10) were used to calculate the scaled metric value of the positive and negative criteria metrics.  
Figure 5: The link lifetime computation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Type of Criterion** & **Possible Values** \\ \hline \(RSS_{NV}\) & Positive & 0–1 \\ \(AVC_{NV}\) & Positive & 0–1 \\ \(LLT_{IVNV}\) & Positive & 0–1 \\ \(D_{IVNV}\) & Negative & 0–1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Categories of parameters according to its criterion.  
\[X_{i}=\frac{P_{i}-P_{min}}{P_{max}-P_{min}} \tag{9}\]  
\[X_{i}=\frac{P_{max}-P_{i}}{P_{max}-P_{min}} \tag{10}\]  
where \(X_{i}\) is the scaled metric value of the metric, \(P_{max}\) is the maximum value of the metric, \(P_{min}\) is the minimum value of the metric, and \(P_{i}\) is the value of the metric.
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
## 2 Coordinated Optimal Scheduling Model of Hybrid System
### Objective of Optimal Scheduling Model in Hybrid System
#### 2.2.4 The Model of BESS  
The battery energy storage component in power system not only can smooth the output fluctuation of some intermittent energy sources, such as wind power and photovoltaic power, but can also participate in demand side management and the schedule of the smoothing power system load curve to reduce security problems caused by the load fluctuation of power system. The principle of battery energy storage is the charging and discharging of the chemical reaction between across the electrodes inside the battery. The battery energy storage cost can be expressed as:  
\[C^{t,s}_{BESS}=\theta\times\left(P^{t,s}_{discharge}+P^{t,s}_{charge}\right) \tag{21}\]  
\[SoC^{t,s}_{BESS}=SoC^{t-1,s}_{BESS}+\eta^{c}_{BESS}P^{t,s}_{charge}-\frac{P^{t,s}_{discharge}}{\eta^{d}_{BESS}} \tag{22}\]  
\[SoC^{\min}_{BESS}\leq SoC^{t,s}_{BESS}\leq SoC^{\max}_{BESS} \tag{23}\]  
\[SoC^{0,s}_{BESS}=SoC^{begin}_{BESS} \tag{24}\]  
\[SoC^{\max,s}_{BESS}\geq SoC^{end}_{BESS} \tag{25}\]  
\[P^{t,s}_{BESS}=P^{t,s}_{discharge}-P^{t,s}_{charge} \tag{26}\]  
\[0\leq P^{t,s}_{discharge}\leq P^{\max}_{discharge} \tag{27}\]  
\[0\leq P^{t,s}_{charge}\leq P^{\max}_{charge} \tag{28}\]  
The cost of BESS in an objective function consists of the depreciable cost of discharge and charge shown in Equation (21), and the output power is calculated in Equation (26). Equation (22) represents the SoC constraint at time \(t\) and \(t-1\), and the right and left limits of the SoC in each hour are expressed in Equation (23). Meanwhile, Equation (25) fixes the SoC at the end of the period to be no less than the terminal limit. Equations (27) and (28) represent the right and left limits of discharge and charge power.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 6 Conclusions  
In this paper, we discussed the problem of node energy management in wireless sensor networks. In order to extend the service life of the network, we took three measures. Firstly, the solar panels and pressure ceramics were used to harvest solar energy and vibrating mechanical energy in nature to supply energy to the sensor nodes. Secondly, the neighbor discovery mechanism based on low-duty cycle was studied to reduce the idle listening energy consumption of nodes. Finally, the data fusion mechanism based on the integer wavelet transform was studied to reduce the transmission data energy consumption of the node. The experimental results show that these measures are effective in reducing the energy consumption of nodes and extending the life span of the network. However, there are numerous challenges and opportunities that still exist in the field of energy management in WSNs. Further research should focus on developing more efficient energy collection and conversion technologies, establishing dynamic models of bistable piezoelectric generation systems, and exploring the response characteristics of the latter under random excitation. Additionally, optimizing the design of bistable piezoelectric generation systems is crucial. Furthermore, the low power technology of nodes needs to be further studied in order to design and optimize the energy management system for the entire wireless sensor network. This includes node energy management, energy distribution strategies, and energy balance technologies. By effectively scheduling the energy usage of the nodes, the stable operation and long lifespan of the entire network can be ensured.  
Methodology, F.T.; Formal analysis, F.C.; Investigation, W.C.; Resources, W.C.; Data curation, F.C.; Writing--original draft, C.C.; Writing--review & editing, W.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under grants 61672540, 61379057, and 61309001, and the Natural Science Foundation of Hunan Province under grant 2017J]2057.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 3 System Model and Description
### System Models of Existing and Proposed Protocols
#### 3.3.2 Layering  
In the proposed schemes, network is divided into unequal layers (see Figure 7) due to high noise rate at sea surface as compared to sea depth. Therefore, layers are closer to each other as the data packets move near the surface. This happens to minimize the effect of noises in UWSN environment. The basic purpose of the proposed approach is to increase the reliability of the data packet and minimize the ratio of packet drop. The expression for the unequal size of layers distribution is given in Equation (12).  
\[C(x,y)=\frac{k_{1}x_{2}+k_{2}x_{1}}{K_{1}+k_{2}},\frac{k_{1}y_{2}+k_{2}y_{1}} {K_{1}+k_{2}}, \tag{12}\]  
here, the endpoint coordinates are represented by \(x_{1},y_{1},x_{2}\) and \(y_{2}\). Whereas, \(C(x,y)\) is the point of division as shown in Figure 8. In this figure, point \(C\) divides the line in \(K_{1}\):\(K_{2}\) that depends on the strength of noise. Cross nodes are uniformly distributed, so every packet passes through them without leaving any space.  
Figure 7: Forwarder Node Selection in SPF-Three and BFS-SPF-Three Layered Multi-path Routing Protocol.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Surface Quantities
#### 4.2.3 Distribution of Wall Heat Flux  
Figure 11 provides the aerodynamic heating results for different rotation parameters. When \(W=0\), the heat flux distributes symmetrically about the \(x\)-axis, with \(C_{h,\max}=0.8\) at the leftmost pole of the sphere. As the rotation speed increases, the high-heat-flux region moves upward and \(C_{h,\max}\) sharply grows to 2.1 when \(W=1\). The reason for this strong heat transfer is the high energy in the relative motion between the incident molecules and the fast-moving surface. For similar reasons, presented in Section 4.2.1, the intense heat transfer is concentrated on the upper half of the windward side of the sphere.
# Last-Mile Optimization Using Neural Networks
## 5 Validation and Robustness Testing: Applying Architectures to Predictive Maintenance Dataset  
The findings presented in this article are a result of a collaborative project with the city of Metz, focusing on the optimization of goods distribution. The dataset, consisting of 409 real, ungenerated records, was collected as part of this project. Recognizing the limitations of the relatively small dataset, we are exploring the incorporation of an additional database. To assess and validate the robustness of our proposed solutions, we initially introduced and studied the Predictive Maintenance Dataset from [[https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020](https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020)]([https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020](https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020)) (accessed on 30 November 2023). In addition, as the data in this database are very different in shape, size (10,000 records), and content from the database mentioned above, we are considering an alternative database to enhance the comprehensiveness of our analysis. Despite the  
Figure 5: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline KNN & 95.12\% & 94.81\% & 95.12\% & 94.86\% \\ RF & 93.9\% & 94.31\% & 93.9\% & 93.76\% \\ LR & 92.68\% & 88.18\% & 92.68\% & 90.36\% \\ SVM & 93.9\% & 89.48\% & 93.9\% & 91.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results analysis using various ML classifiers.  
challenges, the direct application of the employed architectures on the new database has shown promising results, prompting further investigation into larger datasets for a more comprehensive evaluation.  
In the course of our analysis, the ML models and the DL model tested have demonstrated commendable performance, underscoring their robustness, as shown in Table 6. The positive outcomes observed across various metrics highlight the efficacy of the employed algorithms, providing confidence in their ability to handle diverse datasets and real-world scenarios. These results encourage us to continue exploring and refining our models to further elevate their performance in the context of goods distribution optimization.  
Moreover, we are in the process of enhancing the efficiency of our database management by integrating automation. This initiative is designed to diminish the considerable time spent on manual data entry.
# Experimental Study of Vegetative Properties in
## 3 Results and Analysis
### Effect of Different Substrate Ratios on Seed Germination
#### 3.1.2 Effect on the Germination Characteristics of Cynodon Dactylon Seeds  
According to the findings presented in Figure 7, varying ratios of BC and ZL had notable impacts on the GE and GI of C.d. seeds. The average GE exhibited a general decrease with increasing BC content, while it initially increased and then decreased with increasing ZL content. Specifically, the average GE was 8.01 at 0% BC content and 5.28 at 7% BC content. A comparison with other ratios revealed that the average GE at 0% BC content was higher by 44.84%, 19.2%, 39.79%, and 51.7%, respectively. These results indicate that higher levels of BC may impede the germination of C.d. seeds. However, at 3% BC content, the GE was relatively high, suggesting that a moderate addition of BC could mitigate its  
Figure 6: Germination of substrate.  
inhibitory effects. Furthermore, at a ZL content of 3%, the average GE was 7.31, while at 12% ZL content, it was 5.52. A comparison with other ratios showed that a ZL content of 3% resulted in increases of 26.03%, 6.1%, 26.91%, and 32.43%, respectively. In conclusion, a ZL content of 3% demonstrated the most significant enhancement of GE for C.d. seeds.  
The average GI decreased with increasing BC content and initially increased before decreasing with increasing ZL content. The highest average GI of 813.85 was observed at 0% BC content, while the lowest of 700.77 was recorded at 7% BC content. Compared to other ratios, a BC content of 0% saw increases of 0.41%, 4.02%, 13.36%, and 16.14%, respectively, indicating that high BC content may negatively impact C.d. seed germination. However, at a BC content of 1%, the GI was similar to the no-addition state, suggesting that low BC content has minimal impact. For ZL content, an average GI of 872.15 was observed at 6% and 644.01 at 12%. A ZL content of 6% showed increases of 10.37%, 6.76%, 24.19%, and 35.42% compared to other ratios, indicating a significant effect on C.d. germination. Beyond 6% ZL content, there was a rapid decrease in GI with a noticeable inhibitory effect. Overall, the impact of BC content on C.d. seed GE was more pronounced, while the effect of ZL content on the GI was more prominent.
## 4 Proof-of-Concept Background and Setup  
We explored implementing such a SDDTN architecture through the development of a proof-of-concept SDDTN node in the form of a networking switch. Current BP implementations are software packages and there are no full hardware solutions, possibly due to the complexity of BP. We primarily investigated a hardware implementation due to its potential to provide increased bundle processing and forwarding rates compared to software implementations, which is in line with the goal of HDTN. In Section 4.1, we provide background information about P4, the language we chose to use to program the hardware. In Section 4.2, we provide details on the specific P4 program we aimed to implement. In Section 4.3, we provide information about the setup we used in the development of our hardware implementation.
## 2 Fronthaul Solutions for 4G/5G
### The 5G NR Dual Wireless and FSO-Based Fronthaul
In fact, our experimental results demonstrated a 1.61 Gbps 64-QAM throughput, which is within the 3 GPP EVM requirements, as long as the rain intensity was less than 36.4 mm/h (see Figure 2a) and the visibility exceeded 40 m.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Current Density Distributions  
The magnitudes of the current density distributions of the proposed FSS antenna at two resonating frequencies at 3.5 GHz, 3.62 GHz, 7.82 GHz, and 10.58 GHz are illustrated in Figure 17a-e. The blue color in the FSS corresponds to the lowest current density, and the green and red colors indicate the highest current density magnitude corresponding to resonance. It is noticed that when the antenna is not loaded with an FSS, its current density distribution has the highest magnitude in the Koch section, whereas the semi-circular  
Figure 16: Simulated vs. measured antenna gains.  
Figure 15: Simulated vs. measured radiation patterns in E-plane and H-plane at (**a**) 3.62 GHz, (**b**) 7.82 GHz, and (**c**) 10.58 GHz.  
section and ground layer have the minimum current magnitudes. This results from the lower overall gain at a frequency of 3.5 GHz (Figure 17a). When the antenna is loaded with an FSS, the FSS works as a reflector as per the Fabry cavity interference principle, and most radiation is reflected towards the radiating element. This will enhance the main lobe power of the radiating patch and result in the directional enrichment of the antenna gain (Figure 17b-e). At resonating frequencies of 3.5 GHz and 3.62 GHz, the magnitude of the peak current in the semi-circular patch section is reduced, and all four stubs have a good amount of mutually coupled current in all four metallic stubs. On the other hand, the magnitude of the current density improves in the semi-circular section and reduces in the Koch fractal section at 7.82 GHz and 10.58 GHz.  
Figure 17: _Cont._
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols
#### 4.1.7 Na Mac  
A novel neighbor association-based MAC is designed for reliable broadcast. The protocol is based on TDMA with the CSMA approach for disseminating basic safety messages (BSM), providing a short time for each slot. Duplicate slots allocated are avoided by the implementation of communication via a three-way handshake. The frame structure of NA MAC for collision avoidance is depicted in Figure 5. TDMA-based broadcast is provided by V2V and V2I communication. The protocol alleviated collision and hidden-terminal problems, providing reliable communication with less overhead and a good packet reception ratio. It also minimizes the latency of safety message transmission.  
This protocol's shortcoming is that it does not emphasize non-safety message transmission; single-channel access leads to improper resource utilization when considering both safety and non-safety messages. A broadcast storm problem causes performance degradation.  
Figure 5: Frame structure of NA MAC for collision avoidance [74].
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 3 TSN End-Side Equipment Design Scheme
### Overall Structure  
In order to achieve the scheduling requirements of sending different time-sensitive streams at regular intervals in substation-side devices, this paper proposes an egress scheduling scheme that integrates both software and hardware components. The software adopts the open-source Linux system, and the hardware adopts the Intel 210 network card. The overall architecture is depicted in Figure 3. For each packet that needs to be sent at regular intervals, the application program working in the user state generates two data structures: a buffer to store the packet payload and a socket buffer (SKB) to store the packet's sending time. The packet enters the Linux kernel state space through the socket subsystem, where the protocol stack wraps the packet into a complete Ethernet frame. Subsequently, the packet is processed by the packet scheduler of the corresponding NIC device I210, known as queuing disciplines (Qdisc), which is also referred to as the software TSN scheduler in this context. The Qdisc is configured using the TC tool provided by Linux, and it queues, stores, and forwards incoming packets according to the configured rules. When the packet is forwarded by the Qdisc to the corresponding NIC driver igb, the NIC driver notifies the I210 NIC driver Direct Memory Access (DMA) to transfer the packet to the NIC Random Access Memory, while also passing the packet sending time to the internal NIC. The I210 NIC then sends the packet from the internal NIC according to the predetermined sending time of the packet at regular intervals. In this case, the selection of ETF Qdisc determines the moment when the packet leaves the Linux kernel space, and the choice of the I210 NIC determines the time when the packet leaves the NIC. This integration of hardware and software components ensures that the data stream is dispatched from the end device at the specified time, thereby working with the time scheduling table of the TSN switch to achieve a defined end-to-end delay and jitter.