esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 5 Conclusions  
In this paper, the reliable semantic communication assisted by the knowledge graph was studied, which overcomes the problem that the meaning of data represented by the features of the deep learning model cannot be explainable [26; 28]. Specifically, we proposed a semantic extraction scheme that transforms the transmitted sentence into multiple triplets with semantic importance. Moreover, an adaptive transmission scheme is proposed, in which the important triplets are allocated more communication resources to combat channel distortion. Moreover, a semantic restoration scheme was designed to reconstruct the sentence and recover the whole semantic at the receiver. The simulation results show that the proposed system outperforms the traditional schemes in improving communication reliability, especially in the low SNR regime. However, the optimal number of triplets transmitted over a specific channel is still an 'open question'. In the future, more work is needed to analyze the relationship between the number of triplets and the channel quality.  
Conceptualization, S.J.; methodology, S.J. and Y.L.; formal analysis, Y.Z. and P.L.; investigation, K.C. and J.X.; supervision, H.Z.; writing--original draft preparation, S.J. and Y.L.; writing--review and editing, K.C., H.Z. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under grant nos. 61931020, U19B2024, and 62001483, and in part by the science and technology innovation Program of Hunan Province under grant no. 2021 JJ40690.  
Not applicable.  
Data Availability StatementData are available from the authors, on request.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Cd\({}^{2+}\) Adsorption with Biochar
#### 2.3.2 Effect of Various Initial Biochar pH Values on Cd\({}^{2+}\) Removal  
The pH of the solution not only affects the charge density on the surface of biochar, but also the state of the metal ions, thus affecting the removal of said metal ions by biochar [32]. From Figure 4, we can see that when the pH increased from 4 to 5, the Cd\({}^{2+}\) removal with the rice-husk biochar significantly increased (e.g., from 6.24% to 14.15% for sample RH300). However, between pH 5 and 7, no large changes were observed. Therefore, an initial pH of 5 is the best condition for Cd\({}^{2+}\) removal with rice-husk biochar. The Cd\({}^{2+}\) removal reached its maximum when a pH of 6 was used for cotton-straw biochar, with sample CS300 showing the smallest changes. The Cd\({}^{2+}\) removal increased from 28.57% (pH 4) to 35.92% (pH 6), while the removal of Cd\({}^{2+}\) by CS600 increased from 66.33% (pH 4) to 98.62% (pH 6). A pH change from 6 to 7 did not result in significant changes in the Cd\({}^{2+}\) removal. The isoelectric point of normal biochar is relatively low [33]. When the external pH is greater than the isoelectric points of the acidic functional groups (carboxyl-COOH, and phenolic) on the surface of biochar, these groups start dissociating, resulting in negative charges. Due to the electrostatic effects between Cd\({}^{2+}\) and the adsorbent, the higher pH results in more negative charges, and therefore, a greater amount of Cd\({}^{2+}\) adsorbed on the surface of biochar [34]. At the same time, due to the electron-shell characteristics of the heavy metal itself, as the pH increases, the degree  
Figure 3: Effect of the addition of various biochar amounts on Cd\({}^{2+}\) removal. Notes: RH300, RH400, RE500, RH600, and RH700 indicate that rice husk were pyrolysed at 300, 400, 500, 600 and 700 \({}^{\circ}\)C, respectively; CS300, CS400, CS500, CS600, and CS700 indicate that cotton straw were pyrolysed at 300, 400, 500, 600 and 700 \({}^{\circ}\)C, respectively.  
of hydrolysis increases, and more free hydroxyl ions exist in the solution. Biochar displays greater adsorption capability towards hydroxyl ions than towards free ions [35]. The experimental results when the pH was increased from 4 to 5 for the rice-husk biochar and from 4 to 6 for the cotton-straw biochar confirmed this line of reasoning. However, with increasing pH, the basicity increases and the biochar particles may aggregate [36]. This causes the adsorbed Cd\({}^{2+}\) to dissociate into the solution or to be trapped inside biochar aggregates, thus either reducing the removal or hampering the adsorption of further Cd\({}^{2+}\), respectively. Further research is required to examine the reasons behind these phenomena. Under our experimental conditions, biochar presented the maximum Cd\({}^{2+}\) adsorption at pH 5. When the pH was further increased, the Cd\({}^{2+}\) ions in solution existed in the form of CdOH\({}^{+}\) or Cd(OH)\({}_{2}\), which could precipitate, becoming a main influencing factor on the Cd\({}^{2+}\) removal [37].
## 4 Results and Analysis
### Performance Comparison between Septentrio and Ublox Receivers  
It is expected for the Septentrio receiver to perform better compared to the Ublox receiver. It is interesting to observe how the data for the Ublox fluctuates compared to the Septentrio. This gives us an opportunity to study and correct such fluctuations with reference to the stable output from Septentrio.  
Figure 6 shows the respective Refsys values for the receivers at NTU, at Stirling Road, and at FP2. The x-axis of all three plots shows number of days. Each boxplot of one day shows the distribution of Refsys values. The Refsys values here are for all the satellites visible for that particular day. We can note two important variations here. Firstly, the daily variations: as Refsys represents the difference between the receiver clock and the GPST, it must be the same irrespective of the time of the day and the visible satellites. However, we observe that there are variations for each day for both receivers. This might be because of the satellite clock errors. Different satellites have their own clock errors. Thus, the Refsys calculated from Sat 1, for example, might have a difference compared to when calculated from Sat 10. The boxplot shows such discrepancies.  
Secondly, a greater variation can be seen in the data when compared across the different days. It can be observed that the data vary quite distinctly with days for these two types of receivers. This is due to the fact that the receiver clock performance changes with time. The clocks have aging that affects their stability and performance. The Refsys data for Septentrio show a linear trend in the performance. Similar behavior is shown by the performance of the atomic clocks. Since the Septentrio receiver is disciplined by precision timing traceable to the cesium standards, it follows a similar pattern. No such linear trend is observed for the case of the Ublox receiver. The Refsys data for both the locations (NTU and Stirling Road) show that the performance is degrading with time but there is no fixed pattern. This was also observed for the case when Septentrio was freely running (without an external time source) in Figure 5. Moreover, the changing weather conditions for different days can add on to such variations.  
When the absolute amplitudes of these two types of receivers are compared, it shows that the performance of the Ublox receiver is very poor. The values for both the locations (NTU and Stirling Road) are poorer compared to the case when the Septentrio receiver was freely running. These higher errors can lead to greater uncertainty in the results produced by the Ublox receivers. This is one of the reasons that there is a greater variation in vertical height information when calculated using Ublox receivers [2]. This study shows that it is very important to build a method to correct such clock bias errors. The next section will propose the solution.
## 3 Results and Discussion
### Investigation on Classification Performance
The literature has investigated that some researchers employed C.T. and X-ray image-based combined models, which were observed with the lowest accuracy of 96.61% in a previous study [62] and the highest accuracy of 98% for another study [35]. It can be stated that radiological X-ray image-based models worked better than the C.T. image-based models by examining Table 2 because the highest accuracy of X-ray-based models is 99.7% by using D.C.N.N. [55] to classify COVID-19. Thus, the C.T. and X-ray images are optimal for detecting coronavirus, but medical experts are required for the RT-PCR test, C.T. scan, and X-ray techniques. It was observed that CNN and D.N.N. were the most considerable classification techniques for detecting COVID-19, followed by SVM, Random Forest followed by K-NN, and L.S.T.M. Moreover, compared to other ML/DL techniques, CNN was the most widely utilized classifier for the diagnosis of COVID-19. D.C.N.N. was the most accurate for the detection of COVID-19, but its usage was relatively short. Hence it needs to be explored further in future research for the detection and diagnosis of COVID-19. The study revealed that ML/DL-based approaches can significantly promote intelligent diagnosis systems, which are promising for healthcare professionals to make fast and reliable detection of the virus. It will also eliminate the manual flaws during the diagnosis by physicians and radiologists. Moreover, it will be a step towards time-efficient and accurate diagnoses to facilitate both hospitals and the patients.
## 4 Simulation Results, Theoretical Calculation, and Analysis
### Theoretical Calculation and Simulation of \(\alpha\) and \(\beta\)
After analyzing the optional \(\alpha\) and \(\beta\) values when the simulation settings were as displayed in Table 2, we calculated the optional \(\alpha\) and \(\beta\) values when the cooperation node was at different positions and different \(\gamma_{\text{th}}\). Tables 3 and 4 represent the best \(\alpha\) and \(\beta\) values when the cooperation node position was at \(R/2\) and \(R/3\), respectively.
## 3 Finite Element Simulation Study
### Finite Element Model Validation  
#### 3.2.1 Structural Modes  
The model is imported into the OptiStruct simulation solver module of HyperWorks software for free modal analysis. Figure 8 shows the cloud plot of the first six natural  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Density/kg/m\({}^{3}\)** & **Young’s Modulus/GPa** & **Poisson’s Ratio** & **Velocity/m/s** \\ \hline Steel & 7700 & 210 & 0.3 & / \\ Structural glue & 900 & 1.5 & 0.41 & / \\ Atmosphere & 1.225 & / & / & 340 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant structural material parameters.  
Figure 7: Finite element simulation model of the reinforced laminated plate.  
modes of the reinforced laminated plate. The simulation results of natural frequencies are compared with the experimental test results, as shown in Table 2. The results indicate a good agreement between simulation and experiment, with errors in the first six natural frequencies, all within 10%. The errors for the 4 th and 5 th natural frequencies are minimal, being 0.3%, while the error for the 2 nd natural frequency is the largest, being 9.1%. Through comparative analysis, the reliability of the finite element model is validated, laying the foundation for subsequent acoustic simulation studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Vibration Pattern** & **Experimental Value** & **Simulation Value** & **Inaccuracies** \\ \hline
1 st & (2, 2) & 126.2 Hz & 127.1 Hz & 0.7\% \\
2 nd & (1, 2) & 154.0 Hz & 168.0 Hz & 9.1\% \\
3 rd & (2, 3) & 259.9 Hz & 250.8 Hz & 3.5\% \\
4 th & (3, 2) & 354.2 Hz & 355.3 Hz & 0.3\% \\
5 th & (2, 4) & 378.8 Hz & 379.8 Hz & 0.3\% \\
6 th & (3, 3) & 445.2 Hz & 442.0 Hz & 0.7\% \\
7 th & (2, 5) & 516.1 Hz & 522.0 Hz & 1.1\% \\
8 th & (3, 4) & 564.0 Hz & 567.5 Hz & 0.6\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of modal experiment and simulation.  
Figure 8: Cloud plot of the first six natural frequency mode shapes of the reinforced laminated plate. (**a**) Cloud plot of the 1 st mode shape; (**b**) cloud plot of the 2 nd mode shape; (**c**) cloud plot of the 3 rd mode shape; (**d**) cloud plot of the 4 th mode shape; (**e**) cloud plot of the 5 th mode shape; (**f**) cloud plot of the 6 th mode shape.
## 4 Health Benefits of Probiotic Supplementation in Diseased Dogs
### Atopic Dermatitis
longum_ (5 \(\times\) 10\({}^{10}\) CFU/day) was orally supplemented in dogs with AD for 12 weeks, and the severity of the skin lesions was assessed using the CADESI score. Pruritus severity was also assessed using the pruritus visual analog scale (PVAS). The transepidermal water loss (TEWL) and the medication score was also recorded. The data were collected every four weeks of the study. The results revealed that the administration of _B. longum_ progressively reduced the CADESI score compared to the baseline, whereas probiotic supplementation was not effective in reducing the TEWL, PVAS, and medication scores. The study determined that _B. longum_ could improve skin lesions in dogs [66]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Model** & **Probiotic Used** & **Dose per Animal** & **Duration** & **Effect of Probiotic** \\  & & & & & & **Supplementation** \\ \hline \multirow{4}{*}{[19]} & Dog puppies & & & & Prenatal and postnatal & \\  & & & & (mother dog at third & \\  & Dog puppies & & _Lactobacillus_ & 20 \(\times\) 10\({}^{9}\) CFU per capsule; 10 & week of gestation and & \\  & (Probiotic group, n = 9; & _rhamnosus_ strain GG & capsules per day (mother); 5 & continuing throughout & \\  & Control group, n = 7) & & capsules per day (puppy) & lactation; postnatal & \\  & & & & (Starting at the age of 3 & \\  & & & & weeks until 6 months old) & \\ \hline \multirow{4}{*}{[65]} & Dogs with AD & & & & \\  & (Probiotic group, n = 32; & _Lactobacillus sakei_ & \\  & Placebo group, n = 10) & & probo-65 & 2 \(\times\) 10\({}^{9}\) CFU/day & \\ \hline \multirow{4}{*}{[66]} & Dogs with AD & & & & \\  & (Probiotic group, n = 7; & & & \\  & Placebo group, n = 4) & & & \\ \hline \hline \end{tabular} AD: Atopic dermatitis; CADESI: Canine atopic dermatitis extent and severity index.  
\end{table} 
Table 3: The effects of probiotic supplementation on the health status of dogs with atopic dermatitis.
# Human Appropriation of Net Primary Production: From a Planet to a Pixel
## 2 Background and Literature Review
### Limitations of HANPP  
While the advantages of HANPP as an ecological footprint measure are dispositive, there are important measurement and definitional issues [8], as is the case with all footprint analyses. First, the definition of HANPP (land use) is the difference between potential and actual NPP, yet the former is hypothetical and can only be simulated using ecosystem models such as Lund-Potsdam-Jena [17], or the Carnegie-Ames-Stanford Approach [18]. Different ecosystem models have been shown to generate quite different results, with none of them achieving accuracy within the bounds of flux tower measurement uncertainty [19]. While gross primary production can be directly measured using flux tower data, albeit on a course and unrepresentative spatial framework, net primary production can only be estimated indirectly from remote sensing data.  
Human \"appropriation\" of ecological energy flows also brings to bear definitional issues. For example, in a grain field, the yield of grain is clearly appropriated but the roots of annual crops and much of the straw or stover remain in place, providing ecological energy to detritus cycles. Are these then \"appropriated\" by humans? Partitioning HANPP into above and below-ground, and into economic yield and unused components, enables calculation of it under a variety of definitions [20,21].
## 3 Experimental Verification and Discussion  
### Acceleration Measurement  
One of the most important parameters for vibration analysis is the amplitude [19]. The amplitude describes the severity of the vibration, so machines with higher amplitudes are more prone to vibrations. The overall vibration measurements are usually expressed in terms of the root mean square amplitude (RMS). RMS amplitude represents the energy of vibration and is directly related to the root mean square abundance. For vibration measurements, it is necessary to select points close to the vibration source [20].  
Simulation and test results on flywheel housing correlation are shown in Figure 6. The largest relative difference occurs at 1600 rpm. In general, the simulation values tend to be slightly higher than the test results and the deviations become more pronounced at higher speeds, despite these differences, the variations remain within a reasonable range.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Rated power & 988 kW \\ Rated speed & 1942 rpm \\ Rated torque & 4856 Nm \\ Maximum speed \(n_{max}\) & 4480 rpm \\ Power at \(n_{max}\) & 777 kW \\ Moment of inertia & 16.75 kgm\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Horiba dynamometer specifications.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & \
\begin{tabular}{c} **Maximum** \\ **Measurement** \\ **Range** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Resolution** \\ \end{tabular} & **Frequency Range** \\ \hline Displacement & 3.999 mm & 1 \(\upmu\)m & 10 Hz–200 Hz \\ Peak-Peak value & 399.99 mm/s & 0.01 mm/s & 10 Hz–1 kHz \\ Velocity RMS value & 399.99 mm/s\({}^{2}\) & 0.1 m/s\({}^{2}\) & 10 Hz–10 kHz \\ Acceleration & 399.9 m/s\({}^{2}\) & 0.1 m/s\({}^{2}\) & 10 Hz–10 kHz \\ Peak value & & & \\ \hline \end{tabular}
\end{table}
Table 3: Specifications for accelerometer-PCE-VT 3700.  
The simulation and test results on the crankcase correlation are shown in Figure 7. The comparison between test and simulation results reveals that the simulation consistently predicts slightly higher values across all rpm levels. The increasing deviation at higher speeds suggests that damping factors and component flexibility models should be fine-tuned to improve simulation accuracy.  
The differences between experimental and simulation results can be attributed to several factors, including the modeling of components, experimental uncertainties, and model simplifications and assumptions [21]. In the context of this study, the divergence between test and simulation results is attributed to discrepancies in modeling assumptions and the parameters of experimental conditions.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 4 Engineering Analysis and Performance Comparison  
As a component of a space camera, the spaceborne connecting structure needs to have enough mechanical performances to withstand disturbance during ground transportation, rocket launching, and in-orbit operation. In order to verify the safety of the camera, it is necessary to conduct engineering analysis. First, we fixed the translation and rotation of screw hole element nodes at the bottom of the spaceborne connecting structure. Then, we analyzed the performances of the whole machine under static and dynamic loads, including the maximum displacement of the secondary mirror relative to the primary mirror, the wavefront error of the mirrors and the system, the mode information of the camera, and the acceleration response under random vibration load.
## 5 Analysis and Findings
### Exploratory Data Analysis
The IQR is relatively narrow for both groups, indicating consistency within each group regarding their intentions. However, there are a few outliers in the group that do not implement green logistics practices, suggesting that some companies in this group have a particularly low intention to adopt such practices. The whiskers extend further in the \"No\" category, especially towards the lower end, indicating a wider spread of intention levels among companies that do not currently implement green logistics practices. Figure 9: Boxplot of the perceived benefits of implementing green logistics practices. There is a general trend of higher perceived benefits and intentions to adopt green logistics among companies that already implement these practices, which could reflect a positive feedback loop where implementation leads to recognition of benefits, further fueling the intention to engage in green practices. The presence of outliers in the \"No\" category suggests that while some companies may not see immediate benefits or intend to implement green logistics, there are exceptions that might recognize long-term benefits or face barriers to implementation despite their intentions. The variability in the \"No\" category for both perceived benefits and intentions to adopt points to potential untapped opportunities for growth in the adoption of green logistics practices. Companies that do not yet implement green logistics practices but perceive high benefits or have a high intention to adopt may be in a transitional phase or influenced by external factors such as industry trends, customer demand, or regulatory pressures. The generally high intention to adopt green logistics across both groups indicates a recognition of the importance of these practices, although actual adoption may be influenced by other factors like financial capacity, operational feasibility, and organizational culture. For companies already implementing green logistics practices, maintaining and enhancing these practices could be a key strategy, as the perceived benefits reinforce their intentions to continue. Conversely, companies that have not yet adopted these practices may need additional support and incentives, along with success stories showcasing the benefits, to overcome barriers to adoption. While there is an overall positive attitude towards green logistics, companies that have already embraced these practices appear to have a more uniform and strong recognition of their benefits. For those that have not yet implemented green logistics, the greater variance suggests a need for targeted strategies to address the specific barriers and motivations influencing these companies.