esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
## 6 MILP Model
\begin{array}{l}NPC=\sum\limits_{x\in N}\sum\limits_{y\in Nm}P_{RP}*\gamma_{ xy}+\sum\limits_{x\in N}\sum\limits_{y\in Nm}P_{Transponder}*\gamma_{xy}+\\ \sum\limits_{x\in N}\sum\limits_{y\in Nm}P_{EDFA}*F_{xy}*\rho_{xy}+\sum\limits _{x\in N}P_{OXC}\\ \hskip 113.811024 pt+\sum\limits_{x\in N}\sum\limits_{y\in N_{m}}P_{Reg}*RG_{ xy}*\gamma_{xy}+\sum\limits_{i\in N}P_{RP}*\beta_{i}\end{array} \] (11) Subject to: \[ \begin{array}{l}\sum\limits_{j\in N}i\
eq j\\ \end{array} \tau_{ij}^{sd}-\sum\limits_{j\in N}\tau_{ij}^{sd}= \begin{cases} \tau^{sd}\;i=s\\ -\tau^{sd}\;i=d\\ 0\;otherwise\end{cases} \] (12) \[ \begin{array}{l}\sum\limits_{s\in N}d\sum\limits_{s\
eq d}\tau_{ij}^{sd} \leq C_{ij}\\ \forall j,i\in N:i\
eq j,\end{array} \] (13) \[ \begin{array}{l}\sum\limits_{y\in Nm_{x}}\gamma_{xy}^{ij}-\sum\limits_{y\in Nm _{x}}\gamma_{xy}^{ij}=\begin{cases}C_{ij}\;x=i\\ -C_{ij}\;x=j\\ 0\;otherwise\end{cases}\\ \forall i,j,x\in N:i\
eq j,\end{array} \] (14) \[ \begin{array}{l}\sum\limits_{i\in N}\sum\limits_{j\in N}\gamma_{xy}^{ij} \leq\sum\limits_{i\in N}\sum\limits_{j\in N}\gamma_{xy}^{ij}\leq\;*F_{xy}\\ \forall x\in N,y\in Nm_{x},\end{array} \] (15) \[ \begin{array}{l}\sum\limits_{i\in N}\sum\limits_{j\in N}\gamma_{xy}^{ij} \leq\gamma_{xy}\\ \forall x\in N,y\in Nm_{x},\end{array} \] (16)  
The objective function given in (11) aims to minimize the total network power consumption in the IP and optical layers. The total network power is composed of the power consumed by router ports, transponders, EDFAs, optical switches, aggregation ports, and regenerators. Constraint (12) is the flow conservation constraint which ensures that incoming traffic is equal to outgoing traffic for every node, except when the node is a source or a destination. Constraint (13) is the link capacity constraint, where it is ensured that traversing traffic through any virtual link does not exceed its capacity. Constraint (14) is the flow conservation constraints for the optical layer. Constraint (14) ensures that the number of traversing wavelengths in a link should equal to the total incoming wavelengths except at the source and destinations nodes. Constraint (15) ensures that the sum of all wavelengths traversing a link should not exceed its capacity. Constraint (16) ensures that wavelengths in a physical link do not exceed the total number of wavelengths in the physical link.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 7 Interoperability Standards
### The Importance of Interoperability in the Real-Time Metaverse  
In a real-time metaverse, users and assets must be able to move seamlessly across different virtual environments and platforms [219]. For instance, a user should be able to switch from a VR meeting application to an AR-enhanced virtual shopping experience, all while maintaining consistent access to their avatar, digital assets, and interactive capabilities [220]. The same applies to the integration of XR (extended reality) hardware, including virtual reality (VR) headsets, augmented reality (AR) glasses, and mixed reality (MR) systems. Suppose these devices are built on incompatible standards. In that case, users are likely to experience significant barriers to interaction, including the need for different software or restrictions on which platforms they can access [161]. Two primary open standards that seek to solve these problems are OpenXR and WebXR [221]. These standards, developed and promoted by organizations, represent an important step toward achieving greater interoperability in the metaverse. However, the road to widespread adoption and true standardization is still long and fraught with challenges.  
Open standards play a crucial role in achieving interoperability. These publicly accessible specifications are the result of consensus-driven processes that frequently involve numerous stakeholders from business, academia, and government [222]. Open standards are designed to be vendor-neutral, ensuring that any organization can implement them without restrictive licensing terms. Open infrastructure promotes competition and innovation, as developers can build interoperable products without being locked into proprietary technologies. Examples of open standards include HTTP and HTML for web technologies, TCP/IP for internet communications [223], and Health Level 7 (HL7) for healthcare data exchange [224].  
The adoption of open standards offers several significant advantages. First, it enhances compatibility and integration, allowing different systems to communicate and share data more easily [225]. Data compatibility is particularly beneficial in complex environments such as smart cities, where various technologies must work together to deliver seamless services. Second, open standards support long-term sustainability and flexibility [226]. Since they are not tied to a single vendor, organizations can avoid vendor lock-in and more easily adapt to changing technological landscapes. Third, open standards foster a collaborative ecosystem where communities of developers and organizations can contribute to and benefit from shared advancements and improvements [227].
## 4 Results  
### Temporal Variation of RSEI from 2002-2022  
In the whole study area, we calculated the NDVI, WET, NDBSI, and LST using the GEE for the period of 2000-2022. Then, we applied PCA to obtain the RSEI, as shown in Figure 4.  
The EE has been consistently improving since 2002, with the fitted curve showing logarithmic growth. The RSEI curve had a steep slope before 2007, but the rate of increase slowed down in the later period. To study this trend more closely, we selected the RSEI values from June to October of 2002, 2007, 2012, 2017, and 2022, which were 0.500, 0.542, 0.556, 0.558, and 0.577, respectively, as shown in Figure 5.  
Over time, the RSEI has continued to increase, indicating an improvement in EE. The contribution of PC1 in the five images was 87.9%, 84.8%, 83.4%, 87.6%, and 85.4%, respectively, with a contribution over 80%. Among the four indicators of RSEI, NDVI and WET are positive impact indicators, while NDBSI and LST are negative impact indicators. The positive and negative indicators have opposite effects on the EE.  
Figure 4: A logarithmic growth in EE improvement since 2002.  
Figure 5: RSEI from June to October of 2002, 2007, 2012, 2017, and 2022.  
To more clearly demonstrate how the EE has changed over time, the RSEI of the five years was divided into five levels: [0,0.2), [0.2,0.4), [0.4-0.6), [0.6-0.8), and [0.8-1.0), which are expressed as poor, fair, moderate, good, and excellent, respectively. The spatial distribution of the EE levels in these five years is shown in Figure 6.  
Compared to 2002, the EE mainly improved in the GGP region, while it became worse in the regions around the Guanzhong plain urban agglomeration and Huanglong coal base, and other local regions. In the GGP region, the percentages of poor and fair decreased significantly, while the percentages of good and excellent increased significantly. The percentages of moderate rose and then fell, indicating that some regions changed from poor to excellent.  
On the other hand, the distribution characteristics of RSEI over time in different years were analyzed via CVA. The results are shown in Figure 7.  
The high values of the amplitude of ecological changes \(|\Delta V|\) are indicated in red, and the red areas are mostly continuously distributed from 2002 to 2007 and from 2007 to 2012,  
Figure 6: Spatial distribution of EE levels: (**a**) 2002, (**b**) 2007, (**c**) 2012, (**d**) 2017, and (**e**) 2022.  
Figure 7: Variation magnitude map of the RSEI in the study area: (**a**) variation in 2002–2007, (**b**) variation in 2007–2012, (**c**) variation in 2012–2017, (**d**) variation in 2017–2022, and (**e**) variation intensity map of RSEI.  
and are more obvious in the Huanglong coal base and the northern GGP region. In 2012 to 2017 and 2017 to 2022, the red regions are mostly discretely distributed, more obvious in the east of the Huanglong coal base and urban regions.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Reference Frames  
Quadcopters are characterized by two reference frames: the inertial or earth frame (E-frame) and the body frame (B-frame), as shown in Figure 11.  
They are described by their six Degrees of Freedom (6 DOF), which include their linear positions measured in meters (m) and angular positions measured in radians (rad), known as attitude. Their linear and angular positions are defined in the E-frame, and their translational and rotational velocities are defined in the B-frame, as shown in Table 4.  
With four rotors controlling 6 DOF, quadcopters are considered highly under-actuated non-linear models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference Frames** & **Linear** & **Angular** \\ \hline \multirow{2}{*}{E-frame positions} & \(x\) (forward/backward) & \(\phi\) (roll) \\ \cline{2-3}  & \(y\) (left/right motion) & \(\theta\) (pitch) \\ \cline{2-3}  & \(z\) (up/down) & \(\phi\) (yaw) \\ \hline \multirow{2}{*}{B-frame velocities} & \(u\) & \(p\) \\ \cline{2-3}  & \(p\) & \(q\) \\ \cline{2-3}  & \(w\) & \(r\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear and angular terms in reference frames.  
Figure 11: Quadcopter reference frames.  
Figure 10: Cross and plus configurations.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 7 Conclusions and Future Work  
Formal modeling and quantitative analysis of time behaviors is of vital importance for the SAS systems. However, few studies have systematically considered the time behaviors, especially the uncertainty delay time constraints within the self-adaptation loops. To this end, the Timed-SAS approach, which supports definition, formal modeling, quantitative analysis and optimization for the time behaviors of the SAS systems, was proposed in this paper. The Timed-SAS approach was created based on the commonly used MAPE-K self-adaptive software architecture, and its contribution is two-fold: the approach provides a systematical definition on the time constraints within each MAPE-K process and on the time-based evaluation metrics for the self-adaptation loops, and presents a set of formal modeling templates and quantitative analysis templates for the time behaviors within the SAS systems. The defined time constraints can be used to depict the deterministic time constraints, and the uncertainty time constraints within the self-adaptation processes; the time-based evaluation metrics can be used to evaluate the performance and response-time of the self-adaptation loops and the self-adaptation strategies; and the modeling and quantitative analysis templates can consolidate design knowledge for reuse, and can alleviate modeling difficulty and improve modeling efficiency of the SAS systems. Furthermore, the Timed-SAS approach can be used to optimize the self-adaptation strategies of the SAS systems. We evaluated the Timed-SAS approach with several example applications and a subject-based experiment. The results demonstrated that the Timed-SAS approach can satisfy modeling requirements of the SAS time behaviors, and effectively reduce the modeling and quantitative analysis difficulty of the SAS time behaviors.  
In future work, we will continue to study the formal analysis of self-adaptation properties, simplify the quantitative analysis complexity and enrich the verifiable self-adaptation properties.  
**Author Contributions:** Conceptualization, D.H. and Y.C.; methodology, D.H.; software, W.C.; validation, W.C.; formal analysis, D.H.; data curation, W.C.; writing--original draft preparation, D.H. and Z.C.; writing--review and editing, D.H. and Y.C.; visualization, Z.C.; supervision, A.L.; project administration, D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2021)Q-374).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Identification of Deformation Stage and Crack Initiation in TC11 Alloys Using Acoustic Emission
## 5 Conclusions  
In this study, the AE was used to monitor the deformation and damage of TC11 titanium alloy specimens. A characteristic parameter of AE signal energy ratio was used to determine the deforming stage of the specimen. The VMD was used to decompose the AE signals into different frequency ranges. The result showed that the AE signal energy ratio had a better performance compared with the traditional AE energy indicator. In addition, a method combining the partial energy ratio (PER) and the weighted peak frequency (WPF) was employed to determine the crack occurrence. The following conclusions can be drawn:  
1. The AE signals collected in the four stages of blade deformation had different characteristics in the time domain or the frequency domain. Thus, the AE can be used to obtain the deformation state of the specimen in time.
2. Preprocessing the AE signal by the VMD method can effectively filter out noises with frequencies less than 50 kHz, and it can decompose the AE signal in the frequency domain.
3. The AE signal energy ratio, the ratio of the AE signal energy generated by deformation to the signal energy generated by friction and hydraulic systems, can be used to identify the deformation stage of the test specimen, showing better robustness than the traditional AE characteristic parameters.
4. The combined use of the PER and WPF of the AE signal can determine the time of crack occurrence in the TC11 titanium alloy material, but with an earlier prediction time than the actual observation from the micro camera device.
5. The method we proposed in the paper will help to eliminate the need for a middle sensor by separating the noise based on frequency.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments under the supervision of [PERSON]; [PERSON], [PERSON] and [PERSON] analyzed the data under the supervision of [PERSON]; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 12: PE2-WPF during deformation and damage of titanium alloy.  
Funding: This work is partially supported by the National Natural Science Foundation of China (Grant No. 51575035). The authors declare no conflict of interest.
## 2 Background and Related Work
### Related Work
#### 2.3.4 SAST Tools Combinations Studies  
The work of [PERSON] et al. [60] proposes a framework for combining and ranking tool findings based on tool performance statistics. The initial result shows potential capabilities of the framework to improve tool effectiveness and usefulness. The framework weights the performance of SAST Tools per defect type and cross-validates the findings between different SAST tools reports. An initial validation shows the potential benefits of the proposed framework.  
The work of [PERSON] et al. [61], performed a comparison study of commercial (Fortify SCA and Checkmarx) and open source (Splint) SAST tools focusing on the Buffer Overflow vulnerability. In their work they describe the comparison of these tools separately, analyzing 63 open source projects including 100 bugs of this type. In their study, they include the combination of the tools in pairs (Fortify + Checkmarx, Checkmarx + Splint and Fortify + Splint).  
In another paper [7], the problem of combining diverse SAST tools is investigated to improve the overall detection of vulnerabilities in web applications, considering four development scenarios with different criticality. It tested five SAST tools against two benchmarks, one with real WordPress plugins and another with synthetic test cases. This work could be improved using test benchmarks with more vulnerability categories than SQLI and XSS and including leaders' commercial tools.
# Equsum Partitions of Sets of Positive Integers +
## 4 Divisor Sets
Continue with the construction used to prove Lemma 5, now adjusted by taking  
\[B_{1}:=D\big{(}2^{a-1}p^{m}\big{)},\ B_{2}:=\big{\{}2^{a}p^{i}:0\leq i\leq m \big{\}}.\]  
As \(m\) is odd, so \(\sigma(p^{m})\) is even. Let \(k:=\sigma(p^{m})/2\) and \(r:=2^{a}-k\), so \(0\leq r<2^{a}\). There is a subset \(R\subseteq\big{\{}2^{i}:0\leq i<a\big{\}}\subset B_{1}\) such that \(\Sigma R=r\), and the exchange  
\[\sigma(n)=\big{(}2^{a+1}-1\big{)}(p+1)\geq 2n\Longleftrightarrow 2^{a+1}\geq p+1\]0produces an equisum 2-partition of \(D(2^{n}p^{m})\). If \(r=0\), then \(R=\varnothing\). Hence, we have  
**Theorem 7**. : _Let \(a,\ m\in\mathbb{Z}^{+},\ 2\
mid m,\ p\in\mathbb{P},\ p\geq 3,\ 2^{a+1}\geq\sigma(p^{m})\). Then the divisor set \(D(2^{n}p^{m})\) has an equisum \(2\)-partition._  
It is worth noting that the equisum 2-partition explicitly constructed to prove Theorem 7 is not necessarily unique. For instance, when \(n=2^{5}3^{3}=864\), the constructed equisum 2-partition of \(D(n)\) has [8, 8, 96, 288, 864] as the block containing \(n\), while an alternative has [108, 288, 864] as the block containing \(n\). (This alternative essentially results from the identity \(2^{3}+1=3^{2}\).) We shall forego discussion of equisum \(b\)-partitions of divisor sets \(D(n)\) for cases when \(n\) has rank greater than 2. However, there is a nice question to note. Presumably for each \(b\in\mathbb{Z}^{+},\ b\geq 2\), there are divisor sets \(D(n)\) which have an equisum \(b\)-partition; if so, what is the smallest such \(n\)?
## 3 Case Analysis
### Prediction Accuracy with All Factors  
#### 3.1.1 Comparison of Model Performance with the Use of All Data  
In this analysis, we used the data from all flux sites to train the Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN models. According to the ten-fold cross-validation results of all the models, the relationship between predicted GPP and observed GPP is shown in Figure 4 and Table 2. It is obvious that there are different training effects between the four models. The Random Forest model has the lowest prediction accuracy, with a prediction RMSE of 0.954 g Cm\({}^{-2}\) d\({}^{-1}\), MAE of 0.553 g Cm\({}^{-2}\) d\({}^{-1}\), and R\({}^{2}\) of 0.810; the Deep Belief Network model achieves a certain improvement in prediction accuracy compared to the Random Forest model, with a prediction RMSE of 0.912 g Cm\({}^{-2}\) d\({}^{-1}\), MAE of 0.559 g Cm\({}^{-2}\) d\({}^{-1}\), and R\({}^{2}\) of 0.827; the Support Vector Machine model has similar prediction accuracy to the Deep Belief Network model, with a prediction RMSE of 0.910 g Cm\({}^{-2}\) d\({}^{-1}\), MAE of 0.571 g Cm\({}^{-2}\) d\({}^{-1}\), and R\({}^{2}\) of 0.827; and the GeoMAN model has the highest prediction accuracy with a prediction RMSE of 0.788 g Cm\({}^{-2}\) d\({}^{-1}\), MAE of 0.440 g Cm\({}^{-2}\) d\({}^{-1}\), and R\({}^{2}\) of 0.870, which indicates that the GeoMAN model could fit the GPP values of the nine flux sites in the Tibetan Plateau better than the other three models.
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 3 Results
### Color Stability
Separate one-way ANOVA tests were conducted for the two UV exposure conditions--RS 559-934 (360 nm) and QUV/Solar Eye (340 nm)--to evaluate the influence of pigment concentration and exposure duration on the total color difference (\(\Delta E^{*}\)ab). Both tests revealed statistically significant differences across the four exposure groups (\(p<0.05\)). Post hoc Tukey HSD comparisons confirmed that all pairwise differences between 2% and 8% pigment concentrations at 500 and 1000 h were statistically significant.
# Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
## 4 Discussion  
In this study, higher adsorption capacity values were achieved with aerogel capsules containing 5% cellulose than in the previous study by the authors. In previous studies, it was found that an increase in cellulose content from 3% to 5% contributed to the improvement in adsorption properties. The adsorption capacity values of 10.0 mg/L concentration Congo red after an adsorption process, which took 60 min, were 3.10 mg/g and 3.80 mg/g, respectively; the adsorption capacity values of Naphthol green B were 2.80 mg/g and 3.41 mg/g, respectively; the adsorption capacity values of Rhodamine B were 0.12 mg/g and 0.41 mg/g, respectively; and the adsorption capacity values of Methylene blue were 0.24 mg/g and 0.81 mg/g, respectively.  
In the future, we expect to extend the study by reusing capsules of aerogel which have 5% cellulose and apply the adsorption method together with the electrooagulation method.