esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modified Multi-Depot Vehicle Routing Problem
### Impact of the Modified Criterion  
The difference between MDVRP and M-MDVRP is illustrated on [PERSON]'s benchmark instance p03 [29; 30]. This instance is composed of 75 randomly distributed customers and a fleet of five vehicles.  
Table 1 presents the best known solution (BKS) for this task [31]--see value (a). This solution was found using the optimization criterion from Formula (1). Value (b) in the same column shows the same solution (set of routes); the cost, however, is recalculated according to the modified criterion specified in Formula (2). The third column shows the result found using the ACO algorithm proposed by the author (for details see Section 4). Value (d) presents the best solution found for this instance and modified criterion, whereas value (c) is the same solution recalculated in a similar way to the previous case, i.e., for the opposite criterion (1). Values (e) and (f) in the last column in Table 1 show the differences (gaps) between solutions (a) and (c), and (d) and (b) respectively. However, these values cannot be used to compare the quality of the algorithms, because different criteria were used for optimization.  
Figure 1 shows the situation graphically. On the left is the solution for the MDVRP optimization criterion, on the right the solution for M-MDVRP. Routes for individual vehicles are color coded. As can be seen, a vehicle may return to its depot several times on its route because the capacity and/or route length constraints exist. The lengths for all routes are also shown. The sum of these values serve as the solution for MDVRP, and the biggest value serves as the solution for M-MDVRP. Note the similar lengths in Figure 1b; the burden of serving customers is thus distributed to individual vehicles as equally as possible.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem** & **BKS** & **ACO** & **Gap** \\ \hline MDVRP & (a) 641.19 & (c) 670.82 & (e) 4.62\% \\ M-MDVRP & (b) 207.23 & (d) 136.05 & (f) 52.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Difference between MDVRP and M-MDVRP shown on instance p03.
## Materials and Methods
### Synthesis of Rh\({}_{2}\)O\({}_{3}\) Nanoparticles (Rh\({}_{2}\)O\({}_{3}\)-NPs)  
Rh\({}_{2}\)O\({}_{3}\) nanoparticles were prepared, according to the method in the literature [41] with slight modification. Typically, RhCl\({}_{3}\) (63 mg) and poly (allylamine hydrochloride) (200 mg) were dissolved in 100 mL of water. The pH of the solution was adjusted to 7.0 by KOH. After that, 10 mL of a HCHO solution was added to the solution. Then, the reaction solution was transferred to a teflon-lined high-pressure vessel and heated at 120 \({}^{\circ}\)C for 6 h. Then, 15 mL of NaClIO solution was added to the mixture and mechanically stirred for 72 h at room temperature. Finally, the suspension was centrifuged and then washed with water several times, to eliminate the residues and obtain Rh\({}_{2}\)O\({}_{3}\)-NPs.  
Graphene oxide (GO) was prepared, according to previous reports [42]. Briefly, graphite (0.5 g) and NaNO\({}_{3}\) (0.5 g) were added into concentrated H\({}_{2}\)SO\({}_{4}\) (23 mL) in an ice bath, followed by slow addition of 3 g KMnO\({}_{4}\). The mixture was heated at 35 \({}^{\circ}\)C for two hours, and then 40 mL of H\({}_{2}\)O was added. After that, the reaction temperature was raised to 95 \({}^{\circ}\)C, kept for 30 min, and then followed by the addition of 100 mL H\({}_{2}\)O and 20 mL of H\({}_{2}\)O\({}_{2}\). The final suspension was filtered, washed with 1.2 M of HCl and ultrapure water three times, and dried at 60 \({}^{\circ}\)C to obtain graphene oxide (GO).  
Preparation of Reduced Graphene Oxide-Rhodium Nanoparticle Electrode (Rh\({}_{2}\)O\({}_{3}\)-rGO/GCE)  
The schematic diagram of the preparation and action mechanism of the Rh\({}_{2}\)O\({}_{3}\)-GO/GCE is given as Scheme 1. The Rh\({}_{2}\)O\({}_{3}\)-NP solution was prepared by dispersing the above NPs into 10 mL water, then ultrasound treatment for 2 h. The GO suspension was dispersed into water to prepare 1 mg mL\({}^{-1}\) GO solution. Then, 100 \(\mu\)L of Rh\({}_{2}\)O\({}_{3}\) nanoparticle solution was dispersed into 10 mL GO solution (1 mg mL\({}^{-1}\)). After ultrasound treatment for 30 min, the Rh\({}_{2}\)O\({}_{3}\)-GO nanoparticle composites were synthesized successfully. Before modifying the electrode, the GCE was polished to a mirror-like finish using 0.3 mm alumina slurry, and sonicated in distilled water and ethanol. The Rh\({}_{2}\)O\({}_{3}\)-GO solution (5 \(\mu\)L) was coated on the GCE surface and left to dry in air. Reduction of GO/GCE to rGO/GCE and Rh\({}_{2}\)O\({}_{3}\)-GO/GCE to Rh\({}_{2}\)O\({}_{3}\)-rGO/GCE was performed by cyclic voltammetry (CV) at a reduced potential of \(-\)1.2 V for 120 s in phosphate buffer solution.
## 2 Materials and Methods
### Probe Configuration  
To investigate the effect of the different probe configurations, along with the CF length and CF contents, the electrical resistance of the cement composites was measured through both the two- and four-probe configurations. Figure 2 shows a graphical description of the test samples and their real pictures. Copper plate with \(75\times 20\times 0.3\) mm\({}^{3}\) was used as an electrode. The electrodes were inserted to the end of the cement composite, the remaining 25 mm part of the electrode was used for linking with the alligator clip. The electrode spacing in the two-probe configuration were set at 10, 25, and 40 mm (2E10, 2E25, and 2E40). In the four-probe configuration, all the electrodes were installed into the cement composites at spacing of 10 mm (4E10).
## 1 Introduction
Although losses in the rated mode increased by 4%, when underloaded, they decreased by 11%, which creates an additional advantage for applications that work most of the time with underload.
## 6 Conclusions  
This article has focused on the dynamic deformation monitoring of Tianjin Rainbow Bridge using GNSS-RTK and the corresponding modal parameters, which were analyzed via the employment of FFT and RDT. With a focus on the existence of noise in the signal, we proposed a combined denoising method, called CEEMDAN-WP. An FEM of the bridge was established in order to provide a reference with which to compare the field measurement results. Through an analysis of our field measurement and finite element simulation results, we were able to draw the following conclusions. (1) The GNSS-RTK technique can be used to monitor the dynamic response of long-span bridges  
Figure 16: The RDS of Signal R2 for different values of \(\tau\).  
Figure 14: Signal R2 and its PSD function.  
Figure 13: Signal R1 and its PSD function.  
Figure 15: Signal R3 and its PSD function.  
under ambient excitation. The sampling frequency and noise intensity of the GNSS-RTK receivers essentially meet the requirements of field measurement. (2) The proposed CEEMDAN-WP method is an efficient means with which to eliminate both low-frequency and high-frequency noise without distortion. (3) Through FFT and RDT analysis, a first natural frequency of Tianjin Rainbow Bridge of 0.1835 Hz and a corresponding damping ratio of 1.73% were obtained. (4) The identified natural frequency obtained from the processed signal was observed to be slightly greater than the FEM value and the difference between them was about 11%. This may have been caused by bridge damage or the inadequate accuracy of the finite element model.  
Conceptualization: Y.N.; Methodology: Y.N.; Experiments and data analysis: [PERSON] and C.X.; Investigation: [PERSON] and L.Y.; Supervision: [PERSON]; Writing--original draft: [PERSON]; Writing--review and editing: [PERSON], C.X., and L.Y. All authors contributed to the manuscript. This research received no external funding. This work was supported by Tianjin University and Tianjin Surveying and Hydrography Co., Ltd. The authors declare no conflict of interest.
## 3 Models  
### The PSO Algorithm  
PSO is an evolutionary algorithm, inspired by flocking birds [27]. We consider \(N\) trackers sampling \(f\) at their position \(\mathbf{p}_{i}(t)\). This value is given by \(f\big{(}\mathbf{p}_{i}(t),t\big{)}\) from (1). The trackers move to maximise \(f\big{(}\mathbf{p}_{i}(t),t\big{)}\). To do this, the PSO algorithm provides an update of the speed vector \(\mathbf{v}_{i}(t)\) as follows:  
\[\mathbf{v}_{i}(t+\Delta t)=\underbrace{c_{0}\mathbf{v}_{i}(t)}_{1}+\underbrace {c_{1}\alpha_{i1}(t)\big{(}\mathbf{p}_{i}^{b}(t)-\mathbf{p}_{i}(t)\big{)}}_{2 }+\underbrace{c_{2}\alpha_{i2}(t)\big{(}\mathbf{p}^{g}(t)-\mathbf{p}_{i}(t) \big{)}}_{3}. \tag{2}\]  
As described in (2), the speed vector at time \(t+\Delta t\) is a sum of three elements:  
1. The previous speed vector of tracker \(\mathbf{v}_{i}(t)\), weighted by a constant coefficient \(c_{0}\). For the convergence of the algorithm, we need to have \(c_{0}\in]-1;1[44]. \(c_{0}\) is homogeneous to a (pseudo) mass and is sometimes called \"inertia\" in the community.
2. The difference between the current position of tracker \(i\) and its best historical position noted \(\mathbf{p}_{i}^{b}(t)\) (_\"\(b\)\"_ for \"best\"_). The best historical position \(\mathbf{p}_{i}^{b}(t)\) is the position \(\mathbf{p}_{i}(t_{i})\), with \(t_{i}\) between time \(0\) and \(t\) where measure \(f\big{(}\mathbf{p}_{i},t_{i}\big{)}\) was the greatest. This component is weighted by a constant coefficient \(c_{1}\).
3. The difference between the position \(\mathbf{p}^{g}(t)\) (\"\(g\)\" for \"_global\"_) of the current swarm's best tracker and the current position of tracker \(i\). The position of the best tracker of swarm \(\mathbf{p}^{g}(t)\) is the tracker \(j\) measuring the greatest \(f(\mathbf{p}_{j},t)\) among the \(N\) trackers of the swarm. This component is weighted by a constant coefficient \(c_{2}\). The second and last components are _attractors_, weighted by a random number, respectively \(\alpha_{i1}(t)\) and \(\alpha_{i2}(t)\), uniformly distributed in \([0,1]\) and specific to agent \(i\). These random numbers provide diversity to the system and improve exploration, avoiding the swarm to be trapped in a local optimum [27; 45].  
Using the Euler integration scheme, the updated position of tracker \(i\) is computed as the sum of its previous position and the updated speed vector as follows [27]:  
\[\mathbf{p}_{i}(t+\Delta t)=\mathbf{p}_{i}(t)+\Delta t\cdot\mathbf{v}_{i}(t+ \Delta t). \tag{3}\]
## 5 Experimental Results and Analysis
### Parameter Setting of Morfeco-Ms
#### 5.1.2 Determining the Parameters \(p_{c}\) and \(p_{m}\)  
Table 7 provides a concise nomenclature for the MOFECO-MS algorithm corresponding to different combinations of \(p_{c}\) and \(p_{m}\). For instance, \(p_{cm1}\) denotes the algorithm with \(p_{c}=0.9\) and \(p_{m}=0.9\).  
Table 8 presents the experimental outcomes for various \(p_{c}\) and \(p_{m}\) values. We emphasize the algorithms exhibiting the best results for each performance metric. Notably, algorithm \(p_{cm1}\) almost demonstrates superior performance across all metrics. Consequently, we conclude that \(p_{c}=0.9\) and \(p_{m}=0.9\) yield optimal results. Furthermore, we conduct verification tests for specific \(p_{c}\) and \(p_{m}\) values, as shown in Tables 9 and 10. Once again, the results reaffirm the superiority of \(p_{c}=0.9\) and \(p_{m}=0.9\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(p_{c}=0.9\) & \(p_{c}=0.5\) & \(p_{c}=0.1\) \\ \hline \(p_{m}=0.9\) & \(p_{cm1}\) & \(p_{cm4}\) & \(p_{cm7}\) \\ \(p_{m}=0.5\) & \(p_{cm2}\) & \(p_{cm5}\) & \(p_{cm8}\) \\ \(p_{m}=0.1\) & \(p_{cm3}\) & \(p_{cm6}\) & \(p_{cm9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Algorithm names with different \(p_{c}\) and \(p_{m}\).  
Figure 5: Pareto fronts under different \(N\).  
Figure 6: Pareto fronts under different \(c_{max}\).
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 5 Discussion
### Predictive Maintenance  
None of the 26 selected primary studies talks about predictive maintenance. Having automatic support in carrying out the maintenance of LSs, especially those of public interest such as hospitals, schools, universities, malls, city downtown, and so on, is an emerging concern by the installers/maintainers of these systems since the effectiveness of this activity has a direct repercussion on the quality of service. A low-quality value can lead to the interruption of assistance contracts with serious economic and image damage for the manager. This deficiency has been highlighted in previous sector studies, as briefly reported on below.  
Figure 6: Open research challenges in EdgeML smart LSs.  
[PERSON] et al. [101] highlight the primary role of AIoT in implementing predictive maintenance of LSs in the context of future city management.  
[PERSON] et al. [102] state that public LSs are maintained via periodical inspections in situ of luminaires. During the inspection, the lamps that are likely to stop running are replaced, based on the only one parameter that is monitored, namely their running time. To overcome such an unsatisfactory practice, they propose the adoption of an imaging method suitable for analyzing the degradation in time of the LS parameters, on the basis of which predictive maintenance activities can be scheduled.  
[PERSON] et al. [82] cite six studies about highway LSs published between 2016 and 2020. None of them talk about the \"fault detection and diagnosis system\" that, in contrast, authors consider an imperative component in future applications for highways to prevent outages of the LS due to faults.  
The review reported in [4] does not even mention the words \"predictive maintenance\". However, authors talk about \"predictive control\" as the precondition to predict the proper maintenance time for street luminaires.  
In 2024, [PERSON] et al. [2] describe the general architecture and functionality of city lighting infrastructure. The latter leverages environmental sensors and a wireless communication network that connects a thousand smart, remotely controlled streetlights. In addition, the research discusses two lighting infrastructures deployed, respectively, in Italy and Vietnam. In the current version of the system, functional parameters (i.e., lamp temperature, electrical parameters, and energy consumption) of each luminaire are remotely controlled and stored. As future work, authors plan to expand the functionality of the infrastructure by implementing ML algorithms that learn from this huge data asset in order to offer an accurate predictive maintenance service to the managers of the infrastructure. In summary, predictive maintenance is a software functionality that should be featured by AIoT-based LSs in any application domain among those listed in Table 36.
## 5 Dynamic Response Characteristics and Safety Analysis
### Dynamic Responses of Secondary Lining Induced by Blasting the Lower Rock  
The dynamic responses of the secondary lining of the arch induced by lower rock blasts require analysis. The blasting of the lower rock mass in the Region 4 was taken as an example to obtain the vertical vibration velocities of the secondary lining. Figure 14 shows the PPVs of the arch crown, arch waist, and arch foot, respectively.  
Figure 13: Blasting the lower rock with and without secondary lining: (a) Existing pipeline 2; (b) Existing pipeline 3.  
As shown in Figure 14, the most adverse position of the secondary lining was the arch foot. The maximum peak velocity was 0.859 cm/s. The vibration velocity of the arch foot peaked at about 23 ms. The PPV was stable after 0.3 s. The maximum peak velocities of the arch crown and arch waist were 0.131 cm/s and 0.136 cm/s, respectively. The peak velocities of the arch crown and arch waist were reached in less than 50 ms and 36 ms. The time required for the velocity stabilization was about 0.6 s. The results indicate that the blast shock wave in the secondary lining mainly propagates through the surrounding rock from near to far. It is essential to take certain protective measures for the arch foot because the arch foot is close to the blasting source of the lower rock.  
Figure 14: Dynamic responses of secondary lining induced by blasting the lower rock: (**a**) Arch crown; (**b**) Arch waist; (**c**) Arch foot.
## 2 Variable Model for Mechanical Parameter of Phyllite with Confining Pressure Influence
### Variable Model for Poisson Ratio of Phyllite  
According to triaxial experimental results from previous references (the triaxial experimental information and reference source is listed in Table 2), the Poisson ratio variation of phyllite with variable confining pressure is shown in Figure 3.  
As shown in Figure 3, the Poisson ratio of soft rock has poor sensitivity to the confining pressure, and the value of the Poisson ratio under different confining pressure conditions is basically equal to that with 0 MPa confining pressure. In order to propose the nondimensional variable model to describe the Poisson ratio variation, the dimensionless process is carried out with the result data. In the nondimensional variable model, the stress-strength ratio (\(\sigma_{3}/\sigma_{\text{UCS}}\)) is regarded as the \(X\)-axis, and the Poisson ratio is regarded as the \(Y\)-axis. By the mathematical statistics method, the nondimensional variable model for the Poisson ratio is proposed in Figure 4, whose form is a constant function. Additionally, in the variable model for the Poisson ratio, \(v_{0}\) is the undetermined parameters, which equals the Poisson ratio under the 0 MPa confining pressure condition and can be obtained by experimental test.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Lithology** & **Rock Sample Source** & **Confining Pressure/MPa** & **Reference** \\ \hline
1 & quartz phyllite & Liutongzhai tunnel in China & 0, 5, 15, 20 & [PERSON], et al. [21] \\
2 & sercite phyllite & Maoxian tunnel in China & 0, 5, 15, 20 & [PERSON], [PERSON], et al. [21] \\
3 & sercite phyllite & Maoxian tunnel in China & 0, 5, 15, 20 & [PERSON], [PERSON], et al. [21] \\
4 & chlorite phyllite & Yangjiang tunnel in China & 0, 5, 15, 20 & [PERSON], [PERSON], et al. [21] \\
5 & quartz phyllite & Liutongzhai tunnel in China & 0, 5, 15, 20 & [PERSON], et al. [21] \\
6 & carbon phyllite & Yuelongmen tunnel in China & 0, 5, 15, 20 & [PERSON], [PERSON], et al. [21] \\
7 & carbon phyllite & Yuelongmen tunnel in China & 0, 5, 15, 20 & [PERSON], [PERSON], et al. [21] \\
8 & sercite phyllite & Tugonglting tunnel in China & 0, 5, 10, 15 & [PERSON] [23] \\ \hline \end{tabular} Note: (1) Reference [21] of group 1–7 provides the stress–strain curve results, and Poisson ratio variation of group 1–7 is obtained with the recommended method from the “standard for test methods of engineering rock mass in China (GB/T 50266-2013)” according to experimental results. (2) Reference [23] of group 8 directly provides the Poisson ratio variation result. (3) Although groups 1 and 5, 2 and 3 and 6 and 7 are from the same rock sample source, the angle between loading direction and bedding angle of the rock sample is different, so these groups are regarded as different groups.  
\end{table}
Table 2: Triaxial experimental result sources for Poisson ratio of phyllite.  
Figure 3: Poisson ratio variation with the confining pressure influence for phyllite.
## 5 Interoperability, Standardization and Security
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Key Features** & **Relevant Standards** & **Typical Applications** & **Example** \\ \hline \multirow{4}{*}{MQTT} & Lightweight, & \multirow{2}{*}{ISO/IEC 20922} & Sensor data & \multirow{2}{*}{[130,131]} \\  & publish-subscribe, & & transmission, device & \\  & suitable for & [129] & control, data & \\  & low-bandwidth & & integration & \\ \hline \multirow{4}{*}{CoAP} & RESTful, low overhead, & \multirow{2}{*}{RFC 7252 [80]} & Device management, & \multirow{2}{*}{[117,132]} \\  & designed for & & data exchange, service & \\  & resource-constrained & devices & & discovery & \\ \hline \multirow{4}{*}{OGC SensorThings API} & RESTful, & \multirow{2}{*}{OGC 15-078r6 [22]} & Sensor data & \multirow{2}{*}{[133,134]} \\  & geospatial-enabled, & & management, & \\  & facilitates IoT & & geospatial analysis, & \\  & integration & & data visualization & \\ \hline \multirow{4}{*}{AgGateway ADAPT} & Open-source, & \multirow{2}{*}{ANSI/ASABE S632 [135]} & Farm management & \multirow{2}{*}{[136]} \\  & agriculture-specific, & & information systems, & \\  & enables plug-and-play & & precision agriculture & \\  & compatibility & & data exchange & \\ \hline \multirow{4}{*}{agroXML} & XML-based, facilitates & \multirow{2}{*}{ISO 11783 [23]} & Farm management, & \multirow{2}{*}{[137]} \\  & data exchange between & & precision agriculture, & \\  & agricultural software & & traceability & \\ \hline \multirow{4}{*}{FIWARE} & Platform-agnostic, & \multirow{4}{*}{FIWARE [138]} & Smart farming, food & \multirow{4}{*}{[139]} \\  & facilitates app & & supply chain & \\  & development and & & management, & \\  & integration & & collaborative business & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Key data transfer and interoperability protocols used in irrigation systems.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Security and Privacy Enhancement  
One of the foremost research challenges for IoV in smart cities comes in strengthening security and privacy measures. Robust encryption techniques and authentication need to be developed to protect V2V and V2I communications from cyber threats. In addition, one of the challenges is to devise privacy-preserving mechanisms that allow for smart city applications to share data while maintaining individuals' private information safe. To solution for this challenge is a secure data exchange framework that secures the integrity of data exchange while preventing unauthorized access/manipulation and at the same time upholding users' privacy. Advancement in these areas can enable IoV to prosper as the trusted and secure part of urban mobility.
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 2 Theory
### TPE Optimization Method  
The Bayesian optimization algorithm uses a probabilistic proxy model to fit the objective function. It selects the next evaluation point based on the results of the prior sampling, thus quickly finding the optional value [24].  
\[p(f|H_{i})=\frac{p(H_{i}|f)p(f)}{p(H_{i})} \tag{3}\]  
\[H_{i}=\{(x_{1},f(x_{1})),\ldots,(x_{i},f(x_{i}))\}\]  
where \(p(f)\) and \(p(H_{i}\,|f)\) are the prior and likelihood distributions of \(f\), respectively, and \(p(f\,|\,H_{i})\) is the posterior probability distribution.  
In order to solve the problems of the large parameter space and the complexity of parameter tuning of the XGBoost model, the tree-structured Parzen estimator (TPE) algorithm is adopted as the probability estimation for sampling points of parameter intervals [25]. The probability distribution \(p(H_{i}\,|f)\) in Equation (3) is defined as  
\[p(x|y)=\left\{\begin{array}{l}l(x),\ y<y^{*}\\ g(x),\ y\geq y^{*}\end{array}\right.\]  
where \(y^{*}=\min\{(x_{1}f(x_{1})),\ldots,(x_{i}f(x_{i}))\}\), denotes the optimal sampling threshold; \(l(x)\) and \(g(x)\) are the probability estimates of \(p(x\,|\,y)\) in the loss function of observation \(x\). The expected value of the sampling function is  
\[E_{y^{*}}(x)=\int_{-\infty}^{y^{*}}(y^{*}-y)\,\frac{p(x|y)p(y)}{p(x)}dy\]  
If \(\gamma=p(y<y^{*})\), and \(p(x)\) is equal to  
\[p(x)=g(x)\gamma l(x)-(1-\gamma)\]\[\int_{-\infty}^{y^{*}}(y^{*}-y)p(x|y)p(y)dy=\gamma y^{*}l(x)-l(x)\int_{-\infty}^{y^ {*}}p(y)dy\]  
then the expected value is converted to  
\[E_{y^{*}}(x)=\frac{\gamma y^{*}l(x)-l(x)\int_{-\infty}^{y^{*}}p(y)dy}{\gamma l(x )-(1-\gamma)g(x)}\]  
When the conditions that \(l(x)\) is the maximum and \(g(x)\) is the minimum are satisfied at the same time, the expected value is maximum, and the corresponding sampling point \(x\) is obtained.