esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Qualitative Evaluation and Open Discussion
### NW Interoperability and Identity Portability  
In the SSFXG model, the unique identifier eliminates the need for NW providers to constantly update their systems for roaming services when subscribers switch between MNOs. Meanwhile, blockchain's decentralized management system enables MNOs to integrate and share NW services and resources seamlessly. This approach simplifies the process of maintaining subscriber identities across different NWs and ensures that subscribers can effortlessly access services and maintain their identity regardless of the MNO they choose to use. Consequently, this enhances both interoperability among NWs and the portability of subscriber identities.
# Enhancing the Integration of Protein-Rich Oat Waste Material into Meat Formulations
## 3 Results
### Assessing the Possibility of Lsing OPH as a Meat Additive
#### 3.2.2 Changes in Colour Parameters of Meat Samples  
The samples containing OPH exhibited higher redness parameters, which increased during the storage time in all the samples with OPH, in contrast to the control sample (Table 4). All the samples with OPH were darker and slightly more yellow. The most significant differences were noted between the control and the 3H sample. The effect was visible, as presented in Figure 2, and confirmed by \(\Delta\)E calculations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variant** & **Storage Duration [Days]** & **L*** & & **a*** & & **b*** & \(\Delta\)E** \\ \hline \multirow{4}{*}{C} & 1 & 65.70 ab & \(\pm\)0.35 & 1.30 deg & \(\pm\)0.04 & 11.73 d & \(\pm\)0.17 & \\  & 7 & 65.90 a & \(\pm\)0.37 & 0.88 fgh & \(\pm\)0.06 & 12.00 cd & \(\pm\)0.13 & \\  & 14 & 65.43 ab & \(\pm\)0.70 & 0.66 h & \(\pm\)0.13 & 12.62 abc & \(\pm\)0.26 & \\  & 21 & 65.44 ab & \(\pm\)0.47 & 0.83 gh & \(\pm\)0.04 & 12.72 abc & \(\pm\)0.22 & \\ \hline \multirow{4}{*}{1H} & 1 & 64.68 abcd & \(\pm\)0.31 & 1.49 abcd & \(\pm\)0.04 & 11.98 cd & \(\pm\)0.15 & 1.04 \\  & 7 & 65.09 abc & \(\pm\)0.28 & 1.16 def & \(\pm\)0.03 & 12.17 bcd & \(\pm\)0.11 & 0.86 \\  & 14 & 63.94 abcd & \(\pm\)0.65 & 1.05 effg & \(\pm\)0.11 & 12.73 abc & \(\pm\)0.22 & 1.49 \\  & 21 & 64.39 abcd & \(\pm\)0.52 & 1.15 effg & \(\pm\)0.05 & 12.90 ab & \(\pm\)0.21 & 1.10 \\ \hline \multirow{4}{*}{2H} & 1 & 63.77 abcd & \(\pm\)0.26 & 1.66 ab & \(\pm\)0.05 & 12.32 abcd & \(\pm\)0.12 & 1.97 \\  & 7 & 64.26 abcd & \(\pm\)0.25 & 1.37 bcde & \(\pm\)0.04 & 12.39 abcd & \(\pm\)0.09 & 1.72 \\  & 14 & 63.11 deg & \(\pm\)0.55 & 1.29 cde & \(\pm\)0.07 & 13.00 a & \(\pm\)0.22 & 2.42 \\  & 21 & 63.65 bcde & \(\pm\)0.60 & 1.50 abc & \(\pm\)0.03 & 13.06 a & \(\pm\)0.16 & 1.91 \\ \hline \multirow{4}{*}{3H} & 1 & 62.92 cde & \(\pm\)0.27 & 1.79 a & \(\pm\)0.07 & 12.60 abc & \(\pm\)0.10 & 2.82 \\  & 7 & 63.61 bcde & \(\pm\)0.16 & 1.48 abcd & \(\pm\)0.06 & 12.64 abc & \(\pm\)0.04 & 2.38 \\  & 14 & 62.31 e & \(\pm\)0.53 & 1.59 abc & \(\pm\)0.09 & 13.15 a & \(\pm\)0.20 & 3.26 \\  & 21 & 62.56 de & \(\pm\)0.44 & 1.69 ab & \(\pm\)0.07 & 13.09 a & \(\pm\)0.12 & 3.00 \\ \hline \hline \end{tabular} Values in the same column with different letters indicate significant differences (\(p<0.05\)). Results are expressed as mean \(\pm\) standard error. The labels 1H, 2H, and 3H represent samples with 1, 2, and 3% of the oat protein hydrolysate added, respectively; C—Control sample.  
\end{table}
Table 4: Changes in colour parameters of meat samples during storage (mean values \(\pm\) standard errors); \(\Delta\)E value calculated using the Control as a standard.  
Figure 2: Cross-section of meat samples. The labels 1H, 2H, and 3H represent samples with 1, 2, and 3% of the oat protein hydrolysate added, respectively; C—Control sample.
## 6 Conclusions  
In this study, we describe a new algorithm for detecting forest disturbances using 8-day composite LAI data at 1000 m spatial resolution from the GLASS product, which uses the season-trend model and the time-stepping modeling (TSM) process, monitors the variation in the amplitude parameter of the dynamic model over a long period, and detects disturbance signals by capturing structural changes in the time series data. The resultant forest burn map had an overall accuracy greater than 96% and kappa coefficient greater than 0.77 based on the validation data derived from Landsat images. The results show that the TSM algorithm can improve the application of remote sensing data in forest disturbance detection and provide effective support for long-term monitoring of forest ecosystems. The results from this study also demonstrate that the TSM algorithm is automatic and robust and can be used to map forest disturbances in forest land. In the present study, by using the SWIR band as auxiliary data, the algorithm achieved good performance in Daxinganling and distinguished between burned and unburned areas. To distinguish other types of forest disturbances, future studies need to include more auxiliary information and more complicated scenarios.  
Figure 9: The time series curve of different products at the same location (\(3\times 3\) km); GLASS LAI (red), MODIS LAI (green) and MODIS normalized difference vegetation index (NDVI) (black).  
**Acknowledgments:** This research was supported by the National Basic Research Program of China under grant No. 2013 CB733403.  
**Author Contributions:** [PERSON] conceived and designed the study. [PERSON] contributed to the conception of the study, performed the data analysis, and wrote the paper. [PERSON] and [PERSON] aided with the discussion and the manuscript revision. [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Construction Sequencing and Deformation Control for Foundation Pit Groups
## 1 Introduction
Section 3 presents deformation results for segmented (Section 3.1) and adjacent (Section 3.2) pit configurations under different sequences, supplemented by parametric analysis of spacing effects (Section 3.3). Section 4 discusses optimization strategies and quantifies critical spacing thresholds. Conclusions and limitations are summarized in Section 5.  
Notable limitations include the following: (1) homogeneous sand used in physical models may not fully represent stratified natural soils; (2) scaling effects in model tests could influence deformation magnitudes; (3) groundwater-seepage coupling was not considered in simulations.
## 4 Conclusions
In terms of cabin temperature prediction, future work could incorporate a more sophisticated model that factors in environmental variables such as ambient weather conditions, wind speed, and water temperature, which influence the ship's temperature dynamics. This could improve the accuracy of the temperature prediction, especially under changing weather conditions. Additionally, integrating sensor-based data, such as from on-board temperature sensors, could allow for real-time adjustments to predictions, making the model more adaptable and responsive. Lastly, integrating this system into a real-time operational platform would allow for continuous, automatic ship temperature monitoring, enabling operators to detect temperature anomalies and perform predictive maintenance, ensuring operational safety and proactively identifying potential issues before they affect the vessel's performance. Conceptualization, X.H. and G.C. ; Data curation, G.C. and R.C. ; Formal analysis, X.H. and Y.C. ; Funding acquisition, X.H. ; Investigation, R.C. ; Methodology, X.H., G.C. and Y.C. ; Project administration, Y.C. ; Resources, Y.C. ; Software, G.C. ; Supervision, X.H. and Y.C. ; Validation, G.C. and R.C. ; Writing--original draft, X.H. and G.C. ; Writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the 2024 Guangdong Provincial Marine Economy Development Special Project under grant GDNRC [2024]20. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. **Conflicts of Interest:** Author [PERSON] was employed by the Guangzhou Shipyard International Company Limited. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Discussion
Due to equipment deficiency, unfortunately, we did not assess CH\({}_{4}\) emissions from this ecosystem. Nevertheless, [PERSON] et al. [59] estimated the emission of 151 g CH\({}_{4}\) animal\({}^{-1}\) day\({}^{-1}\) (beef cattle) in a natural pasture ecosystem of the Brazilian Pampa biome, 300 km away from our site, under very similar climate, soil, vegetation, and animal conditions. Thus, we estimated an average annual CH\({}_{4}\) emission in our system considering the average number of animals in each season (warm or cold) and the number of days of each season (Table 2), resulting in 208.1 kg CH\({}_{4}\) y\({}^{-1}\) ha\({}^{-1}\) (151 g CH\({}_{4}\) animal\({}^{-1}\) day\({}^{-1}\) \(\times\) 4.4 animal ha\({}^{-1}\)\(\times\) 158 days + 151 g CH\({}_{4}\) animal\({}^{-1}\) day\({}^{-1}\)\(\times\) 3.3 animal ha\({}^{-1}\)\(\times\) 207 days). Converting the CH\({}_{4}\) into CO\({}_{2}\)-eq (1 g CH\({}_{4}\) = 28 g CO\({}_{2}\)-eq, 100 years' time horizon not considering the climate-carbon feedbacks, as per IPCC, 2013), cattle were responsible for emitting 5826.8 kg CO\({}_{2}\)-eq ha\({}^{-1}\). With this methane emission rate, the global warming potential (GWP) of our study (GWP = NEE\({}_{\text{CO2}}\) + CH\({}_{\text{4 CO2-eq}}\)) can be estimated, and converting NEE into CO\({}_{2}\) (\(-\)207.6 g C m\({}^{-2}\) year\({}^{-1}\)) results in \(-\)7612.0 kg CO\({}_{2}\) ha\({}^{-1}\). Thus, the GWP of the system was \(-\)1785.2 kg CO\({}_{2}\)-eq ha\({}^{-1}\). These data imply that adequate management of the natural pastures of the Pampa biome would offset the methane emissions by cattle grazing, resulting in an absorption balance of roughly 1800 kg CO\({}_{2}\)-eq ha\({}^{-1}\) y\({}^{-1}\), which represents 1.8 carbon credits. The carbon credit is a new \"currency\" used to offset emissions by productive sectors, and the credits generated by beef production in the Pampa biome could be used as a form of financial compensation for environmental services and the environmental marketing of the meat produced in this system, which would help preserve the original vegetation of this biome. The results of this study represent a counterpoint to the common sense that Brazilian meat production comes from pastures in deforested areas. In the Amazon, located in northern Brazil, numerous studies have shown that converting forests into cultivated pastures produces CO\({}_{2}\) emissions [62; 63]. However, in the Pampa biome in southernmost Brazil, meat production occurs in natural pastures with European breeds and can absorb CO\({}_{2}\) when the pasture is well managed. The value of the secular activity of raising cattle in the Pampa should be properly acknowledged and not confused with other farming systems in Brazil as it is one of the few beef production systems in the world that has the potential to absorb C.
## 4 Conclusions and Future Works  
In this paper, the development of a wind-turbine-tower-climbing robot has been presented, along with the kinematic and dynamic analysis of each robot driving wheel, which was successfully tested through indoor experiments using the prototype with the wind turbine tower mock-up, proving the viability of the design. Based on the friction created by four rubber wheels, the robot could climb in a straight up-and-down movement at its various speeds. The winding mechanism was the significant advantage that can withstand the cable tension force to hold on to the tower surface and the high payload capacity to install additional equipment that is approximately above 50% of the robot total weight. The various wheel speeds of the robot to move along the tower mock-up were demonstrated by the indoor experimental results \"without load\" and \"with load of 3.540 kg\". Finally, the results indicate the effectiveness of the driving and winding mechanism to climb at the various speeds with and without the payload. The prototype needed to be further refined and tested, especially for use in an outdoor facility with varying wind strength. Later work included the use of wind sensors for wind gusts, a camera to observe external defects, and a robotic arm to transport inspection tools, etc. This prototype wind-turbine-tower-climbing robot could provide a testing ground for new developments.  
Conceptualization: J.-H.L. and K.E.P; data collection: K.E.P.; theoretical analysis: J.-H.L; experiment and test: K.E.P.; writing--original draft: J.-H.L. and K.E.P; writing--review and editing: J.-H.L. and K.E.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Specifications** & & **Values** & \\ \hline Robot Weight & _Body frame 1_ & 6.56 kg & 3.64 kg \\  & _Body frame 2_ & & 2.92 kg \\ Max payload weight & & & 3.540 kg \\  & _Low_ & 50 mm/s & \\ Average Speed & _Medium_ & 62.5 mm/s & \\  & _High_ & 125 mm/s & \\  & _Low_ & 45–74 rpm & \\ Wheel rpm & _Medium_ & 44–66 rpm & \\  & _High_ & 44–77 rpm & \\ Winding rpm & & 19–25 rpm & \\ Angle of inclination & & 1.67\({}^{\circ}\) & \\ Tower height & & 1200 mm & \\ Tower diameter for climbing & & 330–400 mm & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wind-turbine-tower-climbing robot specifications.  
Figure 25: RPM and time: **(a)** Fast Speed (FS) at 8 s, **(b)** rpm vs. time in FS and (c) rpm vs. time in FS with load.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 6 Evaluation Set-Up
### Recruitment and Participants  
The Rehabilitation Centre of the Ghent University Hospital selected four participants for this study. These 4 patients were undergoing rehabilitation therapy for USN at the moment of recruitment and all patients received their first VR therapy session on 19 th April 2021 and their last session on 30 th April 2021. The participants were all older than 12 years, suffered from an acquired brain injury with USN and were at least six months post-injury. Of the four participating patients, three had left-sided neglect, and one had right-sided neglect. Furthermore, every patient was assessed with standardised USN assessment tests prior to the study. These assessments showed that Patients 1 and 3 had significantly more severe neglect than Patients 2 and 4. Moreover, Patient 4 had Apxxia, i.e., a neurological disorder characterised by the inability to perform familiar movements on command, and a language disorder.  
The leading researchers recruited the non-diagnosed test users of this study themselves. In total, four test users participated. Each user was also assessed using the CBS to confirm that they did not have USN. The TAP neglect test was not performed on these test users.  
The information for each participant is summarised in Table 1. The first column is the pseudonym of the individual used in this paper. The second column indicates the participant's side of the neglect. The third and fourth columns present the CBS and TAP score. For Patient 4 and all test users, the results of the TAP assessment are not available.
## 2 Materials and Methods
### Spectral Preprocessing Methods  
In this study, in order to reduce the spectral noise, baseline drift, and the interference from other backgrounds as well as distinguish overlapping peaks, five preprocessing methods were applied to improve spectral resolution, sensitivity, and the signal-to-noise ratio of the spectra [25]. Among them, the Savitzky-Golay (S-G) smoothing algorithm uses a weighted average method to quantize the data in the moving window by polynomial least squares fitting as well as emphasizes the central role of the center point [26]. The basic idea of the multiplicative scatter correction (MSC) algorithm is to use an ideal spectrum to represent all spectra linearly regressed with the sample spectra, and the original spectra is corrected with the slope and intercept of the linear equation [27]. The principle of the standard normal variation (SNV) algorithm is that the absorbance value of each wavelength point satisfies a certain distribution in each spectrum, and the spectral correction is performed according to this assumption [28]. The idea of the detrending (DT) algorithm is that the spectral absorbance and wavelength are first fitted into a trend line \(d\) according to the polynomial, and then the trend line \(d\) is subtracted from the original spectra \(x\) to achieve the effect of the trend [29]. The 1 st-Derivation (1 st-Der) method can distinguish the overlapping peaks and eliminate interference from other backgrounds, which improves spectral resolution, sensitivity, and the signal-to-noise ratio of the spectra.
## 2 Unsaturated Subgrade Filler Triaxial Testing System
### Analysis of Experimental Results
#### 3.4.2 Analysis of Suction Variation
Although the moisture contents of the two samples are nearly identical, slight differences in suction were observed under the same isotropic load. For example, under a 50 kPa load, the suction of samples with moisture contents of 11.84% and 11.83% was 418.2 kPa and 430.5 kPa, respectively. This difference could be attributed to the stress history of the samples, suggesting that even with nearly identical moisture contents, prior loading conditions and structural differences can influence suction measurements. Figure 11: Responses of the tensiometer in a constant water content test for a sample in Set 2. Figure 12a,b show the variation in suction of the samples from Set 1 and Set 2 under 5 kPa and 200 kPa net confining pressures during the triaxial shear test. Figure 13 shows that moisture content is a key factor influencing suction response. For samples with moisture content greater than 13.41%, as shown in Figure 13a, suction variation is minimal under different net confining pressures. This suggests that samples with higher moisture content exhibit more stable suction changes under triaxial shear stress and are less affected by net confining pressure. For samples with moisture content less than 13.41%, as shown in Figure 13b, suction gradually decreases throughout the test, especially in the early shear stage, where it drops rapidly before slightly rebounding. This suggests that samples with low moisture content are more sensitive to suction variation under deviatoric stress. The data in the figure also show that applied net confining pressure significantly affects soil suction variation. Specifically, for samples with low moisture content, higher applied  
Figure 12: Comparison of suction variations under isotropic loading for samples with different moisture contents. Notes: (**a**) A load of 200 kPa (Set 2), (**b**) a load of 600 kPa (Set 3). net confining pressure leads to a more significant decrease in suction. This indicates that higher confining pressure compresses the pore water in the sample more easily, resulting in decreased suction. Therefore, as net confining pressure increases, the suction variation in the sample becomes more pronounced. Additionally, Figure 13b shows that failure occurs in Set 1 samples when the strain reaches 8%, limiting the axial strain to 8%. During shear testing, axial strain plays a critical role in soil stability. Higher axial strain indicates that the sample structure is more prone to damage, which is closely linked to suction variation. Particularly under high moisture content, the degree of structural damage is lower, further demonstrating the effect of moisture content on sample stability. Figure 13: Matrix suction–axial strain relationship during the shearing process for specimens with different moisture contents. Notes: (**a**) \(\sigma_{3}=5\) kPa (Set 1), (**b**) \(\sigma_{3}=200\) kPa (Set 2).
# Pattern Classification and Gear Design of Spatial Noncircular
## 4 Tooth Profile Classification Design of Spatial Noncircular Gear
### Tooth Surface Equation
\begin{array}{cccc}\cos\theta_{c}&\sin\theta_{c}&0&L_{c} \cos(\theta_{1}-\xi_{1})\\ -\sin\theta_{c}&\cos\theta_{c}&0&L_{c}\sin(\theta_{1}-\xi_{1})\\ 0&0&1&0\\ 0&0&0&1\end{array} \right], \tag{19}\]  
where \(\theta_{c}\) is the rotation angle of the generating gear around its own axis. Figure 14: The generating process from the generating gear to the curve face gear pair. same direction as the input shaft. The overall transmission ratio values of classes C and E are less than 0, which means that the output shaft turns in the opposite direction as the input shaft. Moreover, for classes A and C, the absolute values of \(i_{oi}\) gradually decrease with the increase of phase angle \(\theta_{p}\). For classes B, D, and E, the absolute values of \(i_{oi}\) gradually increase with the increase of phase angle \(\theta_{p}\). Further, for classes A, B, and C, the overall transmission ratio \(i_{oi}\) can be zero by selecting the appropriate parameters. At this time, no matter what the input rotation speed is, the output rotation speed is zero. However, for classes D and E, due to the limitation of transmission ratio principle, the overall transmission ratio \(i_{oi}\) cannot be zero. On the whole, the simulation results are in good agreement with the theoretical results, and the overall transmission ratio changes continuously with the continuous change of the phase angle, that is, the CVT was realized. Figure 16 shows the maximum relative error of overall transmission ratio simulation for each pattern. For classes A-E, the maximum relative errors are 6.2%, 6.9%, 7.6%, 4.9%, and 5.4%. The reason why the error of type I is greater than that of type II is that the structure of type I mechanism is more complex, and the transmission chain is longer, resulting in greater transmission ratio loss and transmission ratio error. In summary, the CVT mechanism characteristics of types I and II were shown in Table 5.  
The type I mechanism has the advantages of simple design, larger transmission ratio range, and less parameter restrictions, while the type II mechanism has the advantages of simple structure, small volume, and shorter transmission chain.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 5 Results and Discussion
### Limitations of Parallel Computation  
Based on the results discussed thus far, it can be observed that when the number of allocated threads and blocks increases, the performance improves, so that the GPCDA and the EPCDA can handle a large filter database. In this subsection, we discuss the limitations of parallel computation in a large-filter database in the GPCDA and the EPCDA. When the number of filters was large, even when the number of allocated threads for parallel computing increases several times, the performance did not increase accordingly (Figure 10). We have discussed the reason for this above. We observed that for parallel computing of threads under allocation 13,312 (1024, 13), the GPCDA and the EPCDA exhibited the best performance. However, the performance for threads under allocation 16,384 (1024, 16) decreased. GTX 970 supports 13 SM at most for parallel computing, and each block allows only up to 1024 threads. If more blocks are allocated, it causes some SMs to execute at least two computation blocks, and the execution time increases, due to hardware scheduling. In the 8192 (1024, 8) allocation, when threads executed the EPCDA, the difference was minimal compared to configuration 13,312 (1024, 13). Thus, when the workload approached a balance, we could use less hardware to achieve the same performance. This confirmed that the EPCDA can execute conflict detection using a GPU with a few cores to reduce hardware costs.