esa-sceva/satcom-chunk-collection huggingface dataset:
# Bounce: A High Performance Satellite-Based Blockchain System
## 6 Experiments, Results, and Analysis
### Slot Protocol Measurements
#### 6.3.2 Throughput and Response Time of the Multiple Roots per Slot Protocol  
The previous design assumes that all Merkle Trees contain one million transactions, with only a single multisigned Merkle Tree root sent per slot. However, this design can be significantly improved.  
* The first improvement is to allow \"premium\" transactions using 100,000-transaction Merkle Trees, while \"economy\" transactions are processed in 1-million-transaction Merkle Trees.
* The second improvement is to send many multisigned Merkle roots in an array, as illustrated in Figure 7, instead of just one.
* The third improvement is to allow the satellite to commit multiple Sending Station messages during a single slot.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Slot Event** & **Time Taken** \\ \hline The preparation of a Sending Station Message given the multisigned Merkle & 2 ms \\ Roots and the multisigned Commit Record & 375 ms \\ \hline Satellite validation of a Sending Station Message & 15 ms \\ \hline Satellite creation of a Commit Record & 10 ms \\ \hline One-way trip back to Earth & 375 ms \\ \hline The receiving Broadcast Ground Station gathers signatures from 19 other & 140 ms \\ Broadcast Ground Stations and multisigns the Commit Record & 4 ms \\ \hline Sending Stations receive, validate, and cache the multisigned Commit Record & 4 ms \\ \hline
**Total** & **921 ms** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Slot Event Timing.  
By integrating these enhancements, we achieve faster response times: two to three seconds for premium transactions and four to nine seconds for economy transactions, as shown in Figure 8. Regarding throughput, Figure 8 shows that starting with the commit at second 6, this deployment can achieve a throughput per slot of 5.2 million transactions per slot. (200,000 of which are premium transactions) and sometimes 6.2 million transactions per slot. In this configuration, one Sending station at Clemson CloudLab contributes two economy roots, as does Utah. Wisconsin CloudLab contributes one economy root from one Sending Station and two premium roots from another.  
Figure 6: The timeline for slot events with one slot per second, one million transactions per second, and \(f=19\). SS is Sending Station. GS is Broadcast Ground Station. A new Merkle Tree can be initiated each second. It takes 4 s to multisign it, so the multisignature for the Merkle Tree starting at second 2 will be ready at second 6 and the slot will take approximately one second.  
Figure 7: Experimental setup has Sending Stations at each of three sites in CloudLab: Clemson and Utah have one each and Wisconsin has two. During each slot, the Sending Stations at Utah sends two Merkle roots. Similarly for Clemson. During each slot, one Sending Station at Wisconsin sends two premium Merkle roots and another Sending Station sends one economy Merkle Root.
## 6 Conclusions and Future Work  
In this study, we revisited the relational triple-extraction task from a fresh bidirectional perspective and proposed a new joint relational triple-extraction framework bidirectional translating decoding model. The bidirectional framework of BTDM greatly mitigates error propagation; the translating decoding approach of BTDM handles the overlapping triple problem and efficiently aligns subject-object. The relation judgement of the bipartite graph of BTDM fully mines the relations between entity pairs to achieve accurate and efficient relation judgement. The experimental results show that BTDM exhibited SOTA performance on several public datasets and performed equally well in different complex scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**NYT\({}^{*}\)**} & \multicolumn{2}{c}{**WebNLG\({}^{*}\)**} \\ \cline{3-6}  & & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** \\ \hline \multirow{3}{*}{PRGC} & \((s,o)\) & **94.0** & 92.3 & **93.1** & **96.0** & 93.4 & 94.7 \\  & \(r\) & 95.3 & **96.3** & 95.8 & 92.8 & **96.2** & 94.5 \\  & \((s,r,o)\) & **93.3** & 91.9 & 92.6 & 94.0 & 92.1 & 93.0 \\ \hline \multirow{3}{*}{BTDM} & \((s,o)\) & 93.2 & **93.0** & **93.1** & 95.8 & **95.1** & **95.5** \\  & \(r\) & **96.9** & 95.4 & **96.1** & **96.2** & 95.1 & **95.7** \\ \cline{1-1}  & \((s,r,o)\) & 93.0 & **92.5** & **92.7** & **94.1** & **93.5** & **93.8** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on different subtasks. \((s,o)\) denotes the subtask of the entity pair, \(r\) means the subtask of relation, and \((s,r,o)\) means the task of relational triples.  
For future work, we hope to investigate more effective approaches to the error propagation problem. We would like to investigate the use of triple classification in additional types of information-extraction challenges, such as document-level relation extraction and event extraction.  
Conceptualization, Z.Z. and J.Y.; methodology, Z.Z.; software, Z.Z.; validation, Z.Z., J.Y. and H.L.; formal analysis, H.L. and P.H.; investigation, Z.Z.; resources, J.Y. and H.L.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z., H.L. and P.H.; visualization, Z.Z. and P.H.; supervision, J.Y.; project administration, Z.Z. and J.Y.; funding acquisition, J.Y. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Anhui Provincial Science Foundation (NO.1908085 MF202) and Independent Scientific Research Program of National University of Defense Science and Technology (NO.ZK18-03-14).  
Not applicable.  
This study did not report any data.  
The author declare no conflict of interest.
## 5 Stage-Wise Role of SiL and HiL Across Development Phases
### Strengths and Benefits of Each Approach  
Software-in-the-Loop procedures offer speed, flexibility, and cost-efficiency. They allow high-frequency regression testing, support automated verification workflows, and facilitate the rapid prototyping of control strategies. SiL is well-suited for algorithm validation, internal communication, fault injection (e.g., signal loss), and early behavioral tuning.  
Hardware-in-the-Loop procedures, on the other hand, provide critical realism. They validate signal integrity, actuator response, and the real-time performance of embedded systems under physical conditions. This is particularly important for tests where software behavior must react to sensor inputs or disturbances such as voltage spikes or CAN bus errors.  
Together, SiL and HiL create a complementary validation chain: SiL accelerates early design loops, while HiL ensures that physical integration meets safety, timing, and hardware constraints.
## 4 Discussion  
Studies on polysaccharides isolated from natural products with pharmacological activities, such as suppression of cancer metastasis through NK cells and macrophages, have been reported [17,18]. These polysaccharides are known to have low toxicity and side effects in animal experiments, suggesting a high possibility of their development as health functional food materials or pharmaceuticals in the future. However, detailed structural analysis of polysaccharides and studies investigating detailed mechanisms of their pharmacological activity are warranted. Recently, [PERSON] et al. reported that acidic polysaccharide (RGP-AP-I) purified from KRG mainly consists of rhamno (9.5%), galacturonic acid (18.4%), galactose (30.4%), and arabinose (35.0%), which showed macrophage stimulatory effects such as the production of IL-6, IL-12, and TNF-\(\alpha\) in RAW264.7 cells [27]. Further, [PERSON] et al. analyzed glycosyl linkages of RGP-AP-I, which comprised 21 different glycosyl linkages and possessed the rhamnogalacturonan-I structure. In addition, [PERSON] et al. purified a polysaccharide (RG-CW-EZ-CP) from KRG using \(\alpha\)-amylase and amyloglucidase, which mainly comprised arabinose (15.94%), glucose (5.06%), galactose (13.97%), and galacturonic acid (59.81) and increased NO, IL-6, and IL-12 production mediated by MAPK (ERK, p38, and JNK) phosphorylation in RAW264.7 cells [31]. Our previous study already reported that KRG-P comprised glucose (60.5%), galactose (11.0%), arabinose (6.8%), rhamno (1.9%), and galacturonic acid (19.7%) [22]. In this study, we demonstrated that KRG-P activates macrophages by increasing the production of cytokines, NO, and mRNAs of IL-6 and TNF-\(\alpha\), which is in turn mediated by the phosphorylation of p38, ERK, JNK, and NF-\(\kappa\)B p65. Based on these results, it is thought that the similarity in the composition of monosaccharides of the polysaccharide may result in similar pharmacological activity.  
Figure 4: Effect of KRG-P of mRNA expression of _IL-6_ (**a**), _TNF-\(\alpha\)_ (**b**) and _NOS2_ (**c**) in RAW264.7 macrophage cells using quantitative real-time PCR. RAW264.7 cells (3.0 \(\times\) 10\({}^{6}\) cells/well) were treated with KRG-P using a series of concentrations (10-50 \(\mu\)g/mL) or with LPS (500 ng/mL) in 6-well flat-bottom plates for 6 h. CTL; control, LPS; lipopolysaccharide. Data are presented as the means \(\pm\) SD of three independent experiments. **\(p\)\(<\) 0.01 or ***\(p\)\(<\) 0.001 vs. the control group.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### High-Power Thrusters
#### 4.3.6 Magnetic Nozzles  
Although magnetic nozzles are now being considered as an economical EP solution for small satellites (see Section 4.1.4), these devices can also be employed for high-power propulsion. Interestingly, we believe high-power magnetic nozzles are nearly as mature as their low-power equivalents, but the former's s-curve is much broader. The most prominent example of a high-power nozzlel EP thruster is the Variable Specific Impulse Magnetoplasma Rocket (VASIMR), an ion cyclotron resonance thruster outfitted with superconducting magnets to produce an exceedingly strong magnetic nozzle [128]. This device has been researched in earnest since the 1990s. Most recently, long-duration testing of VASIMR has led to the accumulation of roughly 100 h of operation at over 100 kW [129].  
High-power electron cyclotron resonance magnetic nozzle propulsion was also studied by [PERSON] both theoretically and experimentally in the early 1990s [130]. For a laboratory argon-fed accelerator with microwave power between 0.4 and 7 kW, he found experimental power efficiencies to be much poorer than anticipated, but the overall nozzle efficiency was still approximately 24% and could be as high as 90% in theory. Further, it was speculated in this work that using different propellants like deuterium could significantly reduce some loss mechanisms.  
These examples of past high-power magnetic nozzle studies highlight some of the current challenges to the advancement of this technology. Specifically, since magnetic nozzles require a strong magnetic field to guide electrons out of the device, high-power nozzled thrusters with dense plasmas will require magnetic fields of unprecedented intensity. In the case of VASIMR, considerable infrastructure is needed to produce the \(\sim\)2 T field strengths required for this device [131], and the other power processing requirements are equally challenging [132]. Moreover, the efficiency of magnetic nozzles at \(<\)1 kW is notoriously poor, as mentioned in Section 4.1.4 based on [36], and although VASIMR may reach efficiencies in excess of 60% [129], it is not clear if this success will hold for for devices in the gaping 1-100 kW span that is largely unexplored. For this reason, the investment of developing a high-power nozzlel thruster is unfavorable for most research institutions.
## 2 Electroosmotic Peristaltic Carreau Rheological Model
### Governing Equations and Non-Dimensionalization
Note that the terms on L.H.S of Equation (8) represent inertial forces and on R.H.S first term is due to pressure gradient, second and third terms are surface forces due to linear momentum and last term is body forces due to electrical force per unit volume. Moreover, L.H.S of energy equation characterizes heat transfer due to convection and first two terms on R.H.S represents heat transfer due to diffusion/conduction and the last two terms appearing on R.H.S of Equation (10) represents dissipation due to friction respectively. To shift from laboratory frame \(\Big{(}\overline{X},\overline{Y}\Big{)}\) to wave frame \(\Big{(}\overline{x},\overline{y}\Big{)}\), we define the translational transformation as [8]:  
\[ \begin{array}{l}\overline{x}=\widetilde{X}-c\overline{t}\overline{,y}= \widetilde{Y}\overline{,u}\Big{(}\overline{x},\overline{y}\Big{)}=\overline{U} \Big{(}\widetilde{X},\widetilde{Y}\overline{,t}\Big{)}-c\overline{,\overline{ v}}\Big{(}\overline{x},\overline{y}\Big{)}=\overline{V}\Big{(} \widetilde{X},\widetilde{Y}\overline{,t}\Big{)},\\ \overline{p}(\overline{x},\overline{y})=\overline{P}\Big{(}\widetilde{X}, \widetilde{Y}\overline{,t}\Big{)},T(\overline{x},\overline{y})=\overline{T} \Big{(}\widetilde{X},\widetilde{Y}\overline{,t}\Big{)}.\end{array}  \tag{15}\]  
For the simplification of governing equations, consider:  
\[\begin{array}{l}x=\frac{\overline{x}}{\Lambda},y=\frac{\overline{y}}{a},\ t=\frac{ \overline{u}}{\Lambda},\ b=\frac{\overline{u}}{a},\ \varphi=\frac{\overline{\varphi}}{a},\ p=\frac{\overline{p}\overline{u}^{2}  
\[\frac{\partial p}{\partial x}=\frac{\partial^{3}\Psi}{\partial y^{3}}+3\bigg{(} \frac{n_{i}-1}{2}\bigg{)}W_{e}\bigg{[}\frac{\partial^{2}\Psi}{\partial y^{2}} \bigg{]}^{2}\frac{\partial^{3}\Psi}{\partial y^{3}}+{m_{e}}^{2}U_{HS}\Phi, \tag{17}\]  
\[\frac{\partial p}{\partial y}=0, \tag{18}\]  
\[\frac{\partial^{2}\Theta}{\partial y^{2}}+B_{p}\Bigg{[}\bigg{(}\frac{\partial ^{2}\Psi}{\partial y^{2}}\bigg{)}^{2}+\bigg{(}\frac{n_{i}-1}{2}\bigg{)}W_{e} \bigg{[}\frac{\partial^{2}\Psi}{\partial y^{2}}\bigg{)}^{4}\Bigg{]}=0, \tag{19}\]  
After cross differentiation and pressure elimination from the non-dimensional Equations (17) and (18), we get  
\[\frac{\partial^{4}\Psi}{\partial y^{4}}+3\bigg{(}\frac{n_{i}-1}{2}\bigg{)}W_{e }\bigg{[}\frac{\partial^{2}\Psi}{\partial y^{2}}\bigg{]}^{2}\frac{\partial^{4 }\Psi}{\partial y^{4}}+3(n_{i}-1)W_{e}\bigg{[}\frac{\partial^{3}\Psi}{ \partial y^{3}}\bigg{]}^{2}\frac{\partial^{2}\Psi}{\partial y^{2}}+{m_{e}}^{2 }U_{HS}\frac{\partial\Phi}{\partial y}=0. \tag{20}\]
## 4 Discussion
### Drought Effects on GPP, ET, and WUE
Although ecosystem productivity and evapotranspiration responded to the same key climate drivers in our study, their responses differed during drought years. Productivity can be affected less during drought as stomatal closure triggers a stronger down-regulation of leaf water loss than of carbon assimilation [101]. Moreover, transpiration is closely related to stomatal conductance, while photosynthesis is affected by various other factors and does not respond linearly to variations in stomatal conductance [102]. Different responses of evapotranspiration and photosynthesis to drought suggests the complexity for ecosystem models to accurately simulate these processes.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 5 Conclusions  
This paper presented a deep convolutional network with the integration of local and global features with boundary refinement. The combination of these features helps in the accurate detection and segmentation of salient objects. The embedded global convolutional and boundary-refinement blocks help in better feature extraction and preserve spatial location present in initial layers, which are later refined by the refinement module, resulting in more distinct features and accurate detection. Moreover, we examined the capabilities of the proposed model on five large and widely used datasets. Experiment results indicated that the proposed method outperformed state-of-the-art methods and has high capability for many computer-vision tasks.  
**Author Contributions:** \"Conceptualisation\": W.S., N.A. and N.R.; \"Methodology\": W.S. and N.A.; \"Software\": W.S.; \"Validation\": W.S., N.A. and M.S.; \"Formal analysis\": W.S. and N.A.; \"Resources\": N.A., M.S. and N.R.; \"Initial draft\": W.S. and N.A.; \"Review and editing\": N.A., M.S. and N.R.; \"Supervision\": N.A.; \"Project administration\": N.A. and N.R.; \"Funding acquisition\": N.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partially supported by Under Erasmus\({}^{+}\) CBHE Project Number 619483.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Visual saliency maps generated by our method and four other methods. Ours achieved the best results, especially in recovering spatial details of salient objects.
## 4 Results and Validation
### Effect of Excitation Power on FCS Measurements
Previous deviations [39] from this theoretical behavior have been attributed to saturation or photobleaching effects; however, as discussed in the previous paragraph neither excitation saturation nor photobleaching was confirmed in the measurements. Instead, this deviation may be due to an alternative interpretation of the FCS measurement, as illustrated by Figure 9b. This interpretation is based on the fact that the molecules contributing to the fluorescence fluctuations are the ones at the rim of the detection volume rather than the core. The molecules confined by the detection volume will contribute to an overall increase in fluorescence signal, which agrees with the square behavior of the photon count observed in Figure 8c. Meanwhile, the molecules at the periphery of the observation volume diffuse in and out of the excitation volume and thus contribute to a faster correlation and diffusion time. At higher excitation levels, the ratio of effective surface area to size of the observation volume decreases. According to the dimensional analysis of [PERSON]'s diffusion law, diffusion time is proportional to area when diffusion constant remains the same [40]. Thus, the diffusion time should be inversely proportional to the surface area to size ratio, i.e., the square root of the radius of the detection volume, which is related to the normalized excitation power (for further theoretical reasoning, see Supplemental Material). This inverse relationship was confirmed in the data (Figure 9a) and implies that shorter diffusion times are obtained at higher excitation intensities. This effect is most likely also contributing to the discrepancy in acquired diffusion times comparing the 40\(\times\) and 63\(\times\) objective lenses. As discussed previously, the confinement of detection volume using 2 PE will be determined by the power distribution of excitation light in the focus. Furthermore, this effect is problematic when aiming for FCS studies in turbid media as excitation power inside the samples is expected to vary during measurements depending on the local variability of optical parameters of the sample. This spurs the need for development of protocols compensating for this effect when moving towards combination of imaging and MPM-FCS for biomedical studies of complex samples. Figure 8: Plot of the (**a**) autocorrelation amplitude function G(0) acquired using simple data fit (G(0) \(\sim\)(I/I\({}_{0}\))\({}^{3/2}\)) and (**b**) number of particles N using data fit (N–(I/I\({}_{0}\))\({}^{3/2}\)) on autocorrelated data (time binning 10,000 ns) from experimental measurements of a Rhodamine B solution (10 nM), the 63\(\times\) (NA 1.2) objective lens as a function of different laser powers. (**c**) the effect of excitation intensity on an average photon count. Data points in blue and red line is fitting.
## 3 Experiments and Results
### Performance Comparison with Other Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & \
\begin{tabular}{c} **Number of** \\ **Test Samples** \\ \end{tabular} &
\begin{tabular}{c} **SE-** \\ **Resnet50** \\ \end{tabular} & **RAN56** & **RAN92** & **DADRN** & **SESADRN** \\ \hline Danaus plexippus & 16 & 1 & 1 & 1 & 1 & 1 \\ Heliconius charitonius & 18 & 0.944 & 0.944 & 1 & 0.944 & 1 \\ Heliconius erato & 12 & 1 & 1 & 1 & 1 & 1 \\ Junonia coonia & 18 & 0.889 & 1 & 1 & 1 & 1 \\ Lyccana phleas & 17 & 0.941 & 1 & 1 & 1 & 1 \\ Nymphalis antiopa & 20 & 0.85 & 0.9 & 0.95 & 0.95 & 0.95 \\ Papilio creshontes & 17 & 1 & 1 & 1 & 1 & 1 \\ Pieris rapae & 11 & 0.818 & 0.909 & 0.818 & 1 & 1 \\ Vanessa atalanta & 18 & 0.944 & 1 & 1 & 1 & 1 \\ Vanessa cardui & 16 & 0.938 & 1 & 1 & 1 & 1 \\ Speyeria aglaja & 12 & 0.917 & 0.583 & 0.917 & 0.917 & 1 \\ Achillidesbianor & 8 & 0.5 & 0.625 & 0.5 & 0.625 & 0.75 \\ Cramer & & & & & & \\ Limentiis helmanni & 7 & 0.571 & 0.571 & 0.429 & 0.714 & 0.714 \\ Aphantopus & 7 & 1 & 0.714 & 0.857 & 0.714 & 1 \\ hyperanthus & 5 & 0.8 & 0.8 & 0.8 & 1 & 1 \\ Elymmias hypermnestra & 5 & 1 & 1 & 1 & 1 & 1 \\ Suastus gremius & 5 & 0.8 & 0.8 & 1 & 1 & 1 \\ Celastrina oreas & 4 & 0.5 & 0.25 & 0.5 & 0.75 & 0.5 \\ Papilio xuthus & 4 & 1 & 0.75 & 1 & 1 & 1 \\ Eurema heache & 4 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ Papilio alcmeror Felder & 4 & 0.75 & 0.75 & 0.75 & 0.75 & 0.75 \\ Jambrix salsala & 4 & 1 & 0.5 & 1 & 1 & 1 \\ Polygonia c-aureum & 4 & 1 & 1 & 1 & 1 & 1 \\ Pyrgusmaculatus & 3 & 0.667 & 0.667 & 0.333 & 1 & 1 \\ Abisara echerius & 2 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ Neptis themis Leech & 2 & 0 & 0 & 0 & 1 & 1 \\ Thecla betulae & 2 & 0.5 & 0.5 & 0.5 & 1 & 1 \\ Albulina orbitula & 2 & 1 & 1 & 1 & 1 & 1 \\ Menelaides protenor & 2 & 1 & 1 & 1 & 1 & 1 \\ Euploea midamus & 2 & 0.5 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of class-wise classification accuracy. Next, we compare the proposed method (SESADRN) with the method without a pre-trained model (SESADRNNoPretrained). The comparison results are shown in Figure 7. The results demonstrated that the use of pre-trained models can significantly improve the accuracy of recognition.