esa-sceva/satcom-chunk-collection huggingface dataset:
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 2 What Is Interpretable Optimization?
### The Many Definitions of Interpretability  
Interpretability refers to the ability to understand and explain how models make decisions [24]. However, assessing this is not straightforward; we need to establish easily observable metrics to apply to the models we build. The main target is simplicity in the number of elements, their relationships, and their interpretation.  
One key related property is linearity, which enhances interpretability by allowing a direct understanding of coefficients and functional forms [25]. Linear models, such as linear regression, enable straightforward interpretation because their predictions are a direct sum of weighted features. This contrasts with highly nonlinear models, where interactions between variables can obscure interpretability.  
Another crucial factor is sparsity, which can improve interpretability by reducing the number of active features or interactions. By focusing on the most relevant predictors, sparse models help eliminate noise and provide clearer insights into the decision process [26; 27]. After all, our human capabilities are limited, and models with a high number of elements can be unmanageable to comprehend. Sparsity can be enforced through techniques like L1 regularization or integer constraints, which limit the number of nonzero coefficients in linear regression models [26].  
Monotonicity is another essential property, referring to constraints that ensure a consistent directional relationship between features and outputs. By enforcing monotonicity, models become more interpretable, as each feature's impact can be reasoned about independently [28]. Monotonic constraints are particularly valuable in domains such as healthcare and finance, where the expected effect of variables is well understood.  
Separability relates to a model's ability to clearly distinguish different classes or outcomes. In interpretable models, separability ensures that different categories are well defined, and that features contribute distinctly to the classification or regression task. This is especially useful in models that aim to provide explanations alongside predictions.  
Other properties, such as modularity and simulatability, further support interpretability. Modularity refers to structuring models in a way that allows components to be analyzed independently, making complex models more understandable [29]. Simulatability is the extent to which a human can follow the model's decision process, which is particularly relevant in rule-based models or decision trees.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 3 Results  
This section presents the operational flow of the SIBERIA system, illustrating how its components interact to provide secure and self-sovereign authentication and authorization within industrial environments. The complete process is detailed through the typical user lifecycle, including identity creation, credential issuance, service access, and ongoing monitoring. Each step highlights the role of the SSI, secure SASV, and behavioral biometric modules. Beyond the user-centric perspective, the system also includes preparatory mechanisms that establish the trust infrastructure supporting identity and credential verification. The overall operational flow of the SIBERIA system is outlined as follows:  
1. Initial System Configuration: The trust infrastructure is established by registering foundational elements necessary for credential issuance and verification.
2. User Identity Creation: Users generate decentralized identities via a mobile identity wallet, enabling secure key management and system interaction.
3. Issuer Onboarding: Credential issuers are integrated by registering their identities and setting up the components required to issue credentials in a trusted and verifiable manner.
4. Credential Issuance: Users request biometric credentials by providing requisite information or biometric data. The issuer evaluates these requests and issues VCs, optionally storing associated metadata or status information.
5. Credential Storage: The issued credential is securely stored in the holder's wallet for later use.
6. Service Access Request: The user presents the required credentials when attempting to access a protected service, along with fresh input to support authentication.
7. Verification and Access Grant: The verifier (the service owner) validates the authenticity of the presentation and compares it with the provided evidence to determine whether access should be granted.
8. Ongoing Monitoring: Once access is granted, SIBERIA performs CA to ensure that user behavior remains consistent with expectations, triggering actions if anomalies are detected.
## 3 Results and Discussion
### Building Consumption Vs. On-Site PV Generation Under NEM  
Solar availability varies daily with solar irradiation, cloud coverage as well as season. The farm is assumed to be located in an area with an annual average global horizontal irradiation of 4.53 kWh/m\({}^{2}\)/day. The effect of the size of the PV system with respect to the NEM and NEB pricing scenarios is examined in this analysis by varying the PV capacity from 35 to 70 and 105 kW\({}_{\rm P}\). The 70 kW\({}_{\rm P}\) PV system was assumed to generate about 85% of the farm's annual electricity consumption.  
Daily farm electricity consumption and PV generation from a system of 35 kW\({}_{\rm P}\) are compared over 12 months (Figure 8). The on-site PV electric generation of 35 kW\({}_{\rm P}\) fell short of supplying farm needs especially during the warmer season (Figure 8). On an hourly basis, a system of 35 kW\({}_{\rm P}\) exceeds real-time farm consumption from 9 a.m. to 3 p.m. on a December day but is lower than farm needs during every hour of a June day (Figure 9a,b). On the other hand, a 70 kW\({}_{\rm P}\) PV system significantly improves the renewable energy supply and offsets about 85% of the annual farm electricity consumption from the power grid and led to 85% electricity costs savings under an NEM scenario (Figure 10, Table 3). This would be a likely proposed system size for this four-house broiler farm if an NEM policy continued. Further increases to a 105 kW\({}_{\rm P}\) system resulted in annual negative consumption (overall credit) on the farm (Table 3).  
Figure 8: Monthly energy balance in the case of 35 kW\({}_{\rm P}\) PV installation with net metering billing arrangement.  
Figure 7: Seasonal trend of hourly electric power load of the broiler farm at midnight and mid-afternoon hours.
## 6 Conclusions  
The proposed QODELFA has been applied to solve the OPDG problem in RDSs by taking three objective functions under consideration, which are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those objective functions by using the weighted sum method have been studied while satisfying different operational  
Figure 18: Voltage profile for Case 3 of the IEEE 118-bus system using QODELFA.  
constraints. The effectiveness of the proposed QODELFA for solving the OPDG problem has been verified on the IEEE 33-bus, 69-bus, and 118-bus systems. For each system, three values of DGs' power factor have been included in the analysis. The performed comparisons between the proposed QODELFA and several existing methods from the literature for the three test systems have depicted that the proposed algorithm has better performance than many of the previous methods for most of the studied cases, particularly for the large-scale IEEE 118-bus system. Moreover, the results have signified that the proper selection of the DGs' power factor plays an important role in achieving the best solutions regarding the three studied objective functions. Additionally, the robustness and effectiveness of the proposed algorithm in solving the OPDG problem have been validated by depicting the fast convergence and small standard deviations for all cases. As a result, the QODELFA can be suggested as a powerful method for solving the OPDG problem.  
[PERSON] and N.F.A. did the methodology, simulation, and validation. [PERSON] did the analysis and wrote the paper. Conceptualization, R.J.M, Y.S., and N.F.A.; software, R.J.M., Y.S., and N.F.A.; investigation, R.J.M. and N.F.A.; resources, R.J.M., Y.S., and N.F.A.; data curation, R.J.M., Y.S., N.F.A. and H.H.A.; writing--original draft preparation, R.J.M.; writing--reviewing and editing, R.J.M., Y.S., N.F.A., H.H.A., P.S. and M.S.-k.; visualization, R.J.M., Y.S., N.F.A., H.H.A., P.S. and M.S.-k.; supervision, Y.S. and P.S.; project administration, Y.S.; funding acquisition, Y.S. and H.H.A.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61673161, and in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20161510.  
The authors declare no conflict of interest.
###### Abstract  
Link failures are the most common type of fault in software-defined networking (SDN), which is an extremely crucial aspect of SDN fault tolerance. Existing strategies include proactive and reactive approaches. Proactive schemes pre-deploy backup paths for fast recovery but may exhaust resources, while reactive schemes calculate paths upon failure, resulting in longer recovery but better outcomes. This paper proposes a single link failure recovery strategy that combines these two schemes, termed as flow-aware pro-reactive (FAPR), with the aim of achieving high-speed recovery while ensuring high-quality backup paths. Specifically, the controller adopts pro-VLAN to install backup paths for each link into switches, and precalculates multiple backup paths for each link in the controller before any link failures. In case of a link failure, pro-VLAN, i.e., a method based on the proactive approach, is initially utilized for swift recovery automatically without the involvement of the controller. Simultaneously, the controller analyzes types of affected flows based on the transport layer data, obtains several key network indicators of the backup paths, and then selects the most suitable path for different flows on the basis of the current network view. Simulation results and theoretical analysis show that the recovery time of the FAPR scheme reduces by over 65% compared with the reactive scheme. The interruption rate of flows after fault recovery is reduced by 20% and 50% compared with the reactive and proactive schemes, respectively. In addition, due to the principle of pro-VLAN, the number of backup flow rules required is at most 85% less than that required by the proactive scheme. In conclusion, FAPR promises the highest failure recovery speed and the lowest interruption rate among three methods, and helps to improve the quality of network services.  
SDN; failure recovery; protection; restoration; backup path; flow type +
Footnote †: journal: Applied sciences
## 6 Conclusions and Prospect  
This study is based on theoretical calculation and simulation analysis. A model of an automobile vibration-damping seat equipped with an adjustable eddy current damper is designed. The model is simplified into a theoretical model and a simulation model, and the theoretical and simulation analyses are performed separately with reasonable parameter settings based on response conditions. Observations and discussions of the results of the theoretical analysis and the simulation analysis lead to the following conclusions:  
1. Compared to light load conditions, heavy load conditions result in resonance peaks occurring at lower frequencies, with wider isolation bandwidths, making them more controllable.  
Figure 31: Comparison of amplitude response at 100 kg.  
Figure 29: Comparison of amplitude response at 80 kg.  
2. Increasing the magnetic potential value effectively suppresses resonance peaks, but it shifts the resonance peak to the right and narrows the isolation frequency band.
3. In the near-resonance band, insufficient or negligible damping can lead to strong beating phenomena in the response due to excitation. After leaving the resonance band, the response may exhibit a combination of excitation vibration and inherent vibration, resulting in frequent fluctuations that are challenging to stabilize. However, by using an eddy current damper to increase damping, effective suppression can be achieved.
4. Based on the differences in response across different frequency bands, a control strategy is proposed that balances effective damping and energy conservation.
5. The results of the theoretical model and the finite element simulation model are compared and the reasons for the errors are analyzed.  
Future work will focus on the construction of multi-degree-of-freedom vibration suppression models, the design of semi-active adaptive control systems, and the overall experimental verification of vibration suppression systems.  
Conceptualization, S.W.; Methodology, S.W. and X.Z.; Validation, H.X.; Formal analysis, H.X.; Investigation, X.Z.; Resources, P.M.; Data curation, H.X.; Writing--original draft, H.X.; Writing--review & editing, S.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company Ningbo Jifeng Auto Parts Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Discussion
### Impact of Load Variation
In the case of the DET metric, the largest drop in \(J_{\rm 1 min}\) occurs at +40%, decreasing by \(-\)0.14. The highest increase in \(J_{\rm 1 max}\) appears at +50%, increasing by +0.05. \(J_{\rm 2 min}\) deviates most at +30%, falling by \(-\)0.04, while \(J_{\rm 2 max}\) remains very stable. The sensitivity analysis reveals that system performance remains relatively robust near nominal load, but significant deviations occur beyond \(\pm\)30%. Metrics such as EV\({}_{\rm max}\) and TR show strong sensitivity under high load conditions, while DET highlights notable drops in \(J_{\rm 1 min}\) at +40%.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 4 Simulation and Result Analysis
### Evaluation Considering thePercentage of Malicious Nodes  
In this experiment, the three protocols were evaluated against the same adversary model and by varying the percentage of malicious nodes between 0 and 40% with other parameters being kept fixed. Especially, the mobility parameter is constant at 20 m/s.  
Packet delivery ratio (PDR): As shown in Figure 8, as the percentage of malicious nodes increases, there is an expansion in the number of packet drops in which PDR of AODV decays to about 30% under the adversary model while ETRS-PD provides some improvement to nearly 51.67%. TSQRS shows an improvement in PDR compared to AODV and ETRS-PD to achieve 60%. Trust mechanism that is used in TSQRS to judge the false behavior of neighboring nodes helps to eliminate the malicious nodes during routing, which in turn reduces the number of dropped packets.  
Routing overhead (RO): The RO of AODV fluctuates in the range 4.8 to 9.9 under the adversary model as shown in Figure 9. ETRS-PD again enhances RO to the range of 4.8 to 6.5 when contrasted to AODV. TSQRS achieves further improvement in RO. It is clear that the more the number of malicious nodes, the more easily they can cause damage. Since TSQRS selects only those nodes that are secure and have good link quality, there is a lower number of route failures, causing less number of control packets to be re-forwarded for route establishment.  
Figure 7: Energy consumption (EC) against mobility.  
Energy consumption (EC): As shown in Figure 10, under the adversary model, EC in ETRS-PD varies between 311.96 to 313.5 J while TSQRS provides an improvement to achieve 310.36 to 309 J, a difference of 1.6 and 4.5 J, respectively. Thus, TSQRS is more energy efficient compared to ETRS-PD in different percentages of malicious nodes due to fewer route failures.  
Figure 8: PDR against percentage of malicious nodes.  
Figure 9: RO against percentage of malicious nodes.
## 4 Fault Diagnosis Model  
### RFR-GA-BLS Fault Diagnosis Model  
The flow chart of the proposed RFR-GA-BLS fault diagnosis model is shown in Figure 4.  
The outermost side of the model is the RFR method. It sorts the 72 features by calculating the robustness of each column of features. The first loop selects the first feature at the top of the ranking. The second loop selects the second feature at the top of the ranking, and one is added in each loop until all 72 features are selected. In addition, a way to jump out of the loop is designed. Once the accuracy of a certain time exceeds 85% and the next four features are not as high as this one, it is determined that this is the best result found. All results are output at this point.  
The middle part is the GA optimized BLS model. The fault diagnosis accuracy of the BLS model is used as the fitness function. N1, N2 and N3 are used as the optimization parameters. After setting the parameter ranges of N1, N2 and N3 and generating the initial population, the initial population is first subjected to fitness calculation, and the dominant individuals are selected for retention. The left individuals are subjected to a series of genetic operations to generate offspring. In the continuous evolution, the remaining offspring have increasing fitness, and thus the optimal solution that meets the objective is selected.  
The remaining parameters were chosen as follows: for RFR, \(i\) from 1 to 72. For GA, the initial population N = 30; the spatial dimension d = 3; the maximum number of iterations per = 10; the crossover probability Pc = 0.8, and the variation probability Pm = 0.1. For BLS, the shrinkage factor of the augmented nodes was s = 0.8; the regularization parameter was C = 2-20, and the range of N1 was [10; 30]; the range of N2 is [10; 30]; the range of N3 is [500, 600], and the step length is 1.  
Figure 3: RoI in infrared images of each bearing fault type (**a**) HE; (**b**) HO; (**c**) IN05; (**d**) IN10; (**e**) IN15; (**f**) OU05; (**g**) OU10; (**h**) OU15; (**i**) RO.
# Elements of Hyperstructure Theory in UWSN Design and Data Aggregation
## 1 Introduction
### Mathematical Background of the Model  
In the UWSN topology, several aspects are important for successful data aggregation. First of all, there must exist a path linking every element of the network to the surface station. However, these paths need not be unique as there might be multiple possible paths which the data from a given element can use to reach the surface station. Second, there always exists a cetain kind of ordering of the set of the network elements. They can be ordered with respect to their physical depth, with respect to their importance, with respect to communication priority, remaining battery power, etc. Finally, as data are collected, they are combined in the \"upwards\" elements in order to be sent further on.  
Thus one may employ techniques of algebra or graph theory in the description of the data aggregation process as has been recently done by [PERSON] et al., [PERSON], or [PERSON] et al. [7; 8; 9]. However, given the multivalued nature of data aggregation (multiple paths, more than one possible links of elements, etc.), it seems relevant to make use of the elements of the algebraic hyperstructure theory. Notice that while in \"classical\" algebra, we regard operations, i.e., mappings \(f:H^{n}\to H\), in the algebraic hyperstructure theory we work with hyperoperations, i.e., mappings \(g:H^{n}\rightarrow\mathcal{P}^{*}(H)\), where \(\mathcal{P}^{*}(H)\) is the power set of \(H\) with \(\emptyset\) excluded (one need not consider this exclusion though). For  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **(Terrestrial) WSN** & **UWSN** \\ \hline Communication Media & RF Waves & Acoustic Waves \\ Frequency & High & Low \\ Node size & Small & Large \\ Deployment & Dense & Sparse \\ Power & Low & High \\ Energy consumption & Low & High \\ Propagation delay & Low & High \\ Bandwidth & High & Low \\ Path loss & Low & High \\ Cost & Inexpensive & Expensive \\ Memory & Sensor nodes have low capacity & Sensor nodes require large capacity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of some features of terrestrial and underwater wireless sensor networks (UWSN).  
the general introduction to the theory as well as definitions of concepts not explicitly defined further on, see [PERSON] and [PERSON] [10].  
In the algebraic hyperstructure theory, there are several concepts which make use of the aspect of ordering. A small selection includes [PERSON], [PERSON], [PERSON], [PERSON] et al. [11; 12; 13; 14; 15]. Further on we discuss three of these: \(EL\)-hyperstructures, quasi-order hypergroups, and ordered hyperstructures. Each of these concepts uses somewhat different background and assumptions:  
* [
## 3 Automated Calibration of Chaboche Parameter
### Parameter Generation Based on Physical Meaning  
In this subsection, a curve fitting process is adopted. The number of components that are needed for evaluation of the accuracy of the \(\alpha_{NLK}\) backstress curve are set as \(M=4\)[25]. The stress-plastic strain data obtained from Section 3.2, together with their corresponding Backstress \(\alpha\) obtained from Equation (8), are presented in Figures 12 and 13. Through trial and error, [PERSON] [25] first, roughly estimate the contribution of a set of three ratcheting parameters, \(\frac{C_{1}}{Y_{1}}\), \(\frac{C_{2}}{Y_{2}}\) and \(\frac{C_{4}}{Y_{4}}\), to be used as hysteresis curve of several stainless steels and carbon steels by the following equation:  
\[2*\left(\frac{C_{1}}{Y_{1}}\right)+2*\left(\frac{C_{2}}{Y_{2}}\right)+2*\left( \frac{C_{4}}{Y_{4}}\right)=\sigma_{x}^{+}-\sigma_{x}^{-}-2*\sigma_{0}-2*C_{3} \varepsilon_{L}^{p} \tag{16}\]  
Figure 12: Stress-plastic strain and Backstress for elastic limit = 167.2 MPa obtained from (**a**) post-stabilized monotonic curve, (**b**) hysteresis curve.  
Figure 13: Stress-plastic strain and Backstress for elastic limit = 244 MPa obtained from (**a**) post-stabilized monotonic curve (**b**) hysteresis curve.  
where, \(\sigma_{x}^{+}\) is the positive peak, \(\sigma_{x}^{-}\) is the negative peak of the hysteresis curve and \(\varepsilon_{L}^{p}\) is plastic strain range. Therefore, the right hand side of Equation 16 can be determined. The first rough contribution can be estimated then for \(2*\left(\frac{C_{1}}{Y_{1}}\right)\), \(2*\left(\frac{C_{2}}{Y_{2}}\right)\) and \(2*\left(\frac{C_{4}}{Y_{4}}\right)\). In the current study, this method is adopted for the post-stabilized monotonic curve by dividing both sides of Equation (16) by two. Hence in the first step, the initial estimates of Cs and \(\gamma\)s are allowed to evolve or adapt with generation, which is found to improve the convergence speed. In the second step, Equation (17) is adopted for all plastic strain data proportional to Backstress \(\alpha_{NLK}\), but \(\gamma_{3}\) is considered zero.  
\[\alpha_{NLK}=\alpha_{1}+\alpha_{2}+\alpha_{4}+\left(C_{3}*\varepsilon_{p} \right). \tag{17}\]  
The range used for each parameter for random generation of population, using PSO and GA intelligent methods, are as follows: \(\pm 5\%\) for \(C_{1}\) and \(C_{3}\) because these parameters can be estimated more accurately than others. The parameters \(C_{2}\), \(C_{4}\), \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{4}\) are varied \(\pm 10\%\) of the initial estimate. Therefore, the Chaboche stress \(\sigma_{NLK}\) is acquired by Equation (18):  
\[\sigma_{NLK}=\alpha_{NLK}+\sigma_{0}^{1}. \tag{18}\]  
Finally the value of \(\gamma_{3}\) is calibrated when other parameters are kept constant. Calibration is carried out within the range of 0-10 in uniaxial simulation, as described in Section 4.