esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Method of Moving Load Identification Based on Strain Influence Line
## 2 Theoretical Background  
### Identification Theory of Influence Lines  
According to the strain influence line theory [28] and material mechanics for a simply supported beam, as shown in Figure 1, the strain of the mid-span point \(C\) can be expressed as below:  
\[\epsilon_{c}=\left\{\begin{array}{cc}\frac{p_{H}}{ET}&0<x<\frac{L}{2}\\ \frac{p_{H}}{2 EI}\left(1-\frac{x}{L}\right)&\frac{L}{2}<x<L\end{array}\right. \tag{1}\]  
where \(x\) is the distance between the moving load P and the beam end \(A\), \(L\) is the beam span, \(h\) is the height of the neutral axis, \(I\) is the section inertia moment of point \(C\), and \(E\) is the elastic modulus.  
Generally, the moving load on the bridge is a multi-axle vehicle load, so the measured strain response can be seen as the superposition of multiple concentrated loads [29]. Figure 2 shows a three-axle vehicle load as a sample. The strain equation of mid-span section under vehicle load can be expressed as below:  
\[\epsilon(x)=\epsilon_{1}(x-x_{1})+\epsilon_{2}(x-x_{2})+\epsilon_{3}(x-x_{3}) \tag{2}\]  
where \(x\) is the distance between the vehicle's first axis and the left end of the bridge, \(x_{1}\), \(x_{2}\), and \(x_{3}\) are the distances between each axle and the first axle, \(P_{1}\), \(P_{2}\), and \(P_{3}\) are the axle loads, respectively.  
Figure 1: Moving load acting on the simply supported beam.  
Figure 2: Mid-span strain influence line under moving load.  
From the above case of three-axle vehicle, the strain response of the mid-span beam under the \(n\)-axle vehicle load can be expressed as below:  
\[\varepsilon(x)=\sum_{i=1}^{n}\varepsilon_{n}(x-x_{n}) \tag{3}\]  
By introducing Equation (3) into Equation (1), the following equation can be obtained:  
\[\varepsilon_{n}(x-x_{n})=P_{n}f(x-x_{n}) \tag{4}\]  
in which:  
\[f(x)=\left\{\begin{array}{cc}\frac{hx}{2 EI}&0<x<\frac{L}{2}\\ \frac{hl}{2 EI}\left(1-\frac{L}{L}\right)&\frac{L}{2}<x<L\end{array}\right. \tag{5}\]  
When the multi-axle vehicle passes through the bridge, the area enclosed by the mid-span strain function and the \(x\) axis can be expressed as:  
\[A=\int_{-\infty}^{+\infty}\varepsilon(x)dx \tag{6}\]  
By introducing Equation (3) into Equation (6), the following equation can be obtained:  
\[A=\int_{-\infty}^{+\infty}\sum_{i=1}^{n}P_{n}f(x-x_{n})dx=\sum_{i=1}^{n}P_{n} \int_{-\infty}^{+\infty}f(x-x_{n})dx \tag{7}\]  
Consequently, the total weight \(P\) of the vehicle can be expressed as:  
\[P=\sum_{i=1}^{n}P_{n}=\frac{A}{\int_{-\infty}^{+\infty}f(x-x_{n})dx}=\frac{A}{\alpha} \tag{8}\]  
in which:  
\[\alpha=\int_{-\infty}^{+\infty}f(x-x_{n})dx \tag{9}\]  
where \(\alpha\) is the mid-span strain integral coefficient, it is related to the envelope area of the strain influence line. The \(\alpha\) can be calibrated by Equation (9) when a known vehicle load passes through the bridge. Then, the \(\alpha\) can be used to identify the vehicle load.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### Centralized Clustering  
The service-aware clustering proposed in this paper consists of mapping a physical network consisting of normal and hybrid nodes into a logical topology containing cluster heads and cluster slave/member nodes. It follows the five steps described below  
* Step 1: This step consists of applying the distributed provisioning described above to allow the gateway to get the necessary information from the sensor nodes in order to achieve global computation. The node discovery process is piggy backed on the signaling tree construction, which will produce a routing tree used later in the network provisioning phase.  
Figure 5: Signaling process: Steps 1, 2 and 3. (**a**) Initial network; (**b**) Step 2; (**c**) Step 3.  
Figure 6: Signaling process: Steps 4, 5 and 6. (**a**) Step 4; (**b**) Step 5; (**c**) Step 6.  
Figure 7: Signaling process: Steps 7 and 8; (**a**) Final tree; (**b**) Signaling tree.  
* Step 2: In this step, clusters are constructed based on the information received from the sensor nodes. This step is executed following three main sub-steps: (i) average residual network energy calculation used to achieve energy-aware pre-selection of cluster heads, while the sensor nodes' service types reported to the gateway are used to achieve service-aware pre-selection; (ii) cluster head election achieved by comparing the number of pre-selected cluster heads to the required number of cluster heads; (iii) cluster head-slave association performed using an optimization method that considers both the geographical location of nodes and their residual energy; and (iv) TDMA schedule generation to preclude an interference-free transmission of the information (sensor readings).
* Step 3: A node's role confirmation is achieved by using the signaling tree built by making use of the distributed provisioning process described earlier. It aims at informing the nodes of their roles, the next hop to the gateway and also their TDM slots.
* an approach that translates into increasing the probability of selecting the nearest neighbor's node and, thus, leading to the shortest delays on the backbone.
* Step 5: This step uses the TDMA schedule resulting from Step 2 to enable sensor readings' dissemination from the nodes to the gateway.
## 5 Case Studies  
This section presents simulation results based on two real-world size power system models. The first system is a detailed model of the All-Island Irish Transmission System (AIITS), which includes 1443 state variables and 7197 algebraic variables. The second system is a dynamic model of the European Network of Transmission System Operators for Electricity (ENTSO-E) system, which includes 49,396 state variables and 96,770 algebraic variables. Table 5 summarizes the versions and dependencies of the open-source libraries considered in this section. Note that we only consider the open-source libraries that we managed to compile and install on Linux and Mac OS X operating systems and that worked for relatively \"large\" eigenvalue problems.  
All of the simulations are obtained using the Python-based software tool Dome [70]. The Dome version utilized for this paper is based on Fedora Linux 28, Python 3.6.8, CVXOPT 1.1.9, and KLU 1.3.9. Regarding the computing times reported in both examples, we have two comments. First, all of the simulations were executed on a server mounting two quad-core Intel Xeon 3.50 GHz CPUs, 1 GB NVidia Quadro 2000 GPU, 12 GB of RAM, and running a 64-bit Linux OS. Second, since not all method implementations include two-sided versions and, in order to provide as a fair comparison as possible, all of the eigensolvers are called so as to return only the calculated eigenvalues and not eigenvectors.
## 4 Conclusions  
Thermochemical processes have proven to be highly efficient in transforming cocoa shells into energy products. Pyrosis, performed at temperatures between 500 and 700 \({}^{\circ}\)C, allowed the production of biochar a higher heating value (HHV) of up to 24.97 MJ/kg at 700 \({}^{\circ}\)C, as well as liquid (31.13-34.73%) and gaseous (24.92-32.92%) biofuels. On the other hand, gasification produced syngas with an HHV ranging from 0.89 to 3.36 MJ/m\({}^{3}\), with H\({}_{2}\) and CO contents reaching 9.98% and 11.77%, respectively, at 700 \({}^{\circ}\)C. These results position gasification as a viable technology for generating clean and high-quality fuels.  
The high potassium content in cocoa shell ashes (72.02% K\({}_{2}\)O) is a critical factor. While it can pose technical challenges, such as slag formation and deposits in equipment, it also presents opportunities for agricultural applications as potassium-rich fertilizers.  
Biomass derived from CCN51 cocoa shells is a promising energy source due to its high calorific value (up to 24.97 MJ/kg in pyrolysis), favorable elemental composition (45.50% C, 5.32% H, 0.91% N, 0.13% S), and significant availability as an agro-industrial waste. Given that up to 10 tons of cocoa shells are generated for every ton of dry cocoa beans produced, this material offers a sustainable approach to agricultural waste management in regions with high cocoa production, such as Ecuador.  
The statistical analysis confirmed the reproducibility of the pyrolysis and gasification results. The trends observed in gas yields, HHVs, and solid residues remained consistent across replicates, strengthening the conclusions drawn from this study. The low standard deviation in experimental measurements highlights the reliability of the results and supports the potential application of CCN51 cocoa shell biomass as a sustainable energy source. The increase in syngas production with higher temperatures further suggests that both pyrolysis and gasification can be optimized for improved energy recovery and industrial applications.  
In conclusion, this study demonstrates that thermochemical vaporization of cocoa shell biomass is not only feasible but also highly reliable and reproducible. The results align with previous literature on biomass thermochemical conversion, reinforcing the potential of cocoa shell biomass as a promising and sustainable energy resource.  
Conceptualization, J.F.G.; methodology, B.L., C.L.-A. and B.L.; writing--original draft preparation, C.L.-A.; writing--review and editing, S.R.-S. and B.L.; funding acquisition, S.R.-S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors thank CYTED (Programme for the Research Cooperation of Iberoamerican Countries), which through the net RIMSGES (Red para la Investigacion en Modelos de Sistemas de Gestion de Energia Sostenibles) has allowed [PERSON]'s Ph.D. research stay at the Universidad de Extremadura both in 2022 and 2024.  
The authors declare no conflicts of interest.
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 6 Conclusions  
In order to realize the scheduling and collaborative processing of power tasks from the edge equipment level to support the resource-constrained situation, a cloud-edge collaborative online scheduling strategy for power distribution station area tasks under the microservice architecture is proposed. The article analyzes the diversified, dynamic, and differentiated features of station tasks from the perspective of power distribution station tasks. At the device level, the edge devices are characterized by limited and fluctuating resources. Under the microservice architecture, the tasks are modeled using directed acyclic graphs and three processing cases of microservices are distinguished. Based on the cloud-edge scheduling mechanism of power tasks under the microservice architecture, a collaborative cloud-edge online scheduling algorithm for station tasks is proposed for the above characteristics. The priorities of different power tasks are calculated by fuzzy logic, and the tasks are scheduled online according to the different resources of the edge devices. The article simulates the impact of the number of concurrent containers supported by the edge devices and the cloud edge communication time on different strategies, and performs a comparative analysis. The results show that under the same scenario, the proposed strategy can handle more tasks in less time and has a higher task execution efficiency. By simulating the completion of important tasks when the edge computing system is resource-constrained, it is verified that the proposed strategy can improve the completion rate of important tasks and system security when the edge device is resource-constrained through resource replacement.  
Figure 8: Task processing time under strategy 1 and strategy 2.  
**Author Contributions:** Conceptualization, X.Z. and R.C.; methodology, R.C. and Q.C.; software, R.C.; validation, Q.C.; formal analysis, R.C. and Q.C.; investigation, R.C.; resources, X.Z.; writing--original draft preparation, R.C. and Q.C.; writing--review and editing, R.C.; visualization, Q.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available on request due to restrictions, e.g., privacy and ethical reasons.  
**Acknowledgments:** The authors are very grateful to the Shandong Kehui Power Automation Co., Ltd. for support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Stress-Softening Effect at Unloading
### Uniaxial Loading and Unloading Curves  
Figure 1 shows the stress-softening effect at unloading, i.e., the well known Mullins effect. When a sample is loaded to a certain point \((h^{*},\tau^{*})\) and then unloaded, there emerges the stress-softening effect as described below:  
Figure 1: Stress softening with unloading curves of different shapes.  
1. After the unloading is completed, a residual strain (permanent set) may be induced. The stress-strain curve in an unloading process may appreciably deviate from the monotone loading curve up to the unloading point \((h^{*},\tau^{*})\) at the onset of unloading;
2. for each strain \(h\in[0,h^{*})\), the stress on the unloading curve is always lower than the stress on the monotone loading curve, referred to as stress softening at unloading;
3. when reloading is introduced, the part of the reloading curve till the point \((h^{*},\tau^{*})\) is nearly coincident with the unloading curve in the foregoing, but the part of the reloading curve after the point \((h^{*},\tau^{*})\) follows the monotone loading curve after a transition at the point \((h^{*},\tau^{*})\).  
For a SMP sample of interest, test data are given for the loading curve and for a certain number of unloading curves. Consider \(M\) data sets for the unloading curves associated with \(M\) unloading stresses  
\[\tau_{1}^{*}<\tau_{2}^{*}<\cdots<\tau_{M}^{*}\,,\]  
which correspond to \(M\) values of the effective plastic work below:  
\[\bar{\kappa}_{1}<\bar{\kappa}_{2}<\cdots<\bar{\kappa}_{M}\,.\]  
Specifically, for the unloading curve associated with the unloading stress \(\tau_{s}^{*}\) or the effective plastic work \(\kappa_{s}\) for each \(s\in\{0,1,\cdots,M\}\), a set of strain-stress data is given below:  
\[(h_{0}^{(s)},\tau_{0}^{(s)}),(h_{1}^{(s)},\tau_{1}^{(s)}),\ \cdots,\ (h_{N_{s}}^{(s)},\tau_{N_{s}}^{(s)})\,. \tag{12}\]  
In the above, the first point \((h_{0}^{(s)},\tau_{0}^{(s)})\) is just given by the strain \(h_{s}^{*}\) and the stress \(\tau_{s}^{*}\) at the onset of unloading, and, moreover, the residual strain (permanent set) at vanishing stress, i.e., the point \((h_{s}^{p},0)\), should be incorporated in the above set of data.  
For any given data for the loading curve and any given sets of data for a certain number of unloading curves, as described in the above, it needs to find out such three quantities \(\overline{W}\) (cf., Equation (3)) and \(r\) (cf., Equation (7)) and \(c\) (cf., Equation (9)) that the predictions from the model established in Section 2 fit these data as closely as possible. In what follows, we are going to show that explicit procedures may be introduced to obtain these constitutive quantities in a sense of accurately matching test data for any given shapes of loading and unloading curves.
# State-Bust Feedback Control for Fault Recovery of Input/State Asynchronous Sequential Machines
## 6 Conclusions and Challenges  
We have shown that static corrective controllers can solve the problem of fault diagnosis and fault tolerant control for input/state ASMs subject to transient faults. The state burst is used as feedback to design a diagnoser that detects and isolates any transient fault. Since the static controller receives only the stable state and fault indicator signal from the diagnoser, the reachability condition for designing the controller is greatly enhanced compared with the previous result. We have addressed the existence conditions for the diagnoser and static controller and formal algorithms for their synthesis in the framework of corrective control. The case studies on the home security system and the asynchronous error counter validate the applicability of the proposed control scheme.  
Since the closed-loop system with the proposed static corrective controller preserves fundamental mode operations, we can code the system in very high speed integrated circuit hardware description language (VHDL) so as to implement it on configurable semiconductor devices such as field-programmable gate arrays (FPGAs); refer to [6; 7; 8] for the relevant prior work. We expect that the implementation of the closed-loop system will take significantly fewer resources than the case of dynamic corrective controllers, albeit the addition of the diagnoser \(G\). The design and implementation of the proposed static corrective control scheme on digital systems will be conducted as a further study.  
While the ASM in this study has the form of an input/state machine, many practical ASMs are modeled by input/output machines. Hence establishing a static corrective control scheme for input/output ASMs is an important future research topic. Further, although only transients faults were considered in this paper, fault tolerant control for other types of faults, e.g., permanent faults and intermittent ones, may be solved in input/output ASMs as addressed in Remark 1. Hence applying the proposed static control methodology to overcoming such faults in input/output ASMs is also an interesting future research topic.  
Conceptualization, J.-M.Y.; funding acquisition, J.-M.Y., S.-J.P., and S.W.K.; supervision, J.-M.Y.; methodology, S.-J.P. and S.W.K.; formal analysis, J.-M.Y.; software and hardware, S.W.K.; writing--original draft preparation, J.-M.Y.; writing--reviewing and editing, S.-J.P. and S.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2021R1I1A3040696), in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. NRF-2018R1A5A1025137), and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1D1A1B02012959).  
Not applicable.  
The authors declare no conflict of interest.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 6 Solving VRPTW with the Hyper-Heuristic Approach
### Evolving Heuristics for an Arbitrary Optimisation Criterion  
Since existing heuristics were usually designed to address certain criteria, they may not be appropriate for application under an arbitrary criteria defined in a concrete problem instance. Tackling an arbitrary optimisation criteria is possible with search-based metaheuristics such as evolutionary algorithms, but they exhibit already-described disadvantages in the dynamic scheduling scenario. As mentioned before, the proposed approach can produce routing heuristics for any conceivable objective measure, since the process of evolution is guided with an arbitrary fitness function. To apply this approach with an arbitrary optimisation criterion, the user must provide relevant parameters to calculate the objective value. These parameters may include information about the vehicles, their travel times and distances, or about servicing times at previous customers etc. In any case, we presume the schedule maker is able to collect information that is relevant to its designated criteria and provide that information to the learning algorithm.  
To illustrate the applicability of this scenario, we introduce another optimisation criteria that considers the deviation of travel time of individual vehicles; this may be the case of a given fleet size, where vehicles should complete their respective routes in approximately the same time. The choice of this particular objective is entirely arbitrary and serves only as a case study. The objective function in this experiment is outlined in Equation (4), where _distanceDeviation_ is calculated as mean squared deviation from the average travel time of all the vehicles in a single problem instance.  
\[\text{objectiveVRPTW}=(\text{nVehicles}\times 10,000)+\text{distanceDeviation}, \tag{4}\]  
The results of this experiment are shown in Table 10. The upper half of the table shows the results obtained with the initial objective criterion for VRPTW, while the bottom half presents results with the above objective (both sets of results correspond to GP heuristics). It can be seen from the bottom half of the table that the travel time deviations are indeed smaller than in the case of total distance minimisation. It is somewhat surprising that this objective contributes to the decrease of total travelled distance; however, this is accomplished with the increase in the number of used vehicles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measure** & **Best Known** & **Min** & **Median** & **Max** \\ \hline number of vehicles—hard TW & 100 & 109 & 112 & 117 \\ distance—hard TW & 26,715.4 & 46,401 & 50,141.8 & 56,897 \\ number of vehicles—soft TW & / & 107 & 109 & 111 \\ distance—soft TW & / & 45,959.3 & 49,505.7 & 55,662.3 \\ tardiness & / & 3278.7 & 8885.8 & 34,729.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: VRPTW test set, 10 instances with 200 customers each—soft time windows.
## 2 Theoretical Background and Related Works
### IoT-Based Occupational Safety and Health  
IoT has proved to be one of the leading exponents in communication and attracted considerable research attention in the twenty-first century. It allows vast amounts of items, including sensors, electric apparatus, or vehicles, to be connected to the Internet, offering helpful information, data, and resources [17; 19; 20; 34; 73]. IoT is not merely knowledge and communication technology but also technology in the safety and health sector. It plays a crucial role in healthcare and ambient monitoring applications. For instance, wireless sensors adopted in different spots observe surrounding environments, and sensors of wearables can be attached to workers' bodies to measure physiological conditions. The collected information will then be transmitted to the cloud infrastructure and delivered to target receivers [74]. In addition, construction is a challenging field from the viewpoint of OSH. The safety and health of workers are momentous considerations on a construction site. Mishaps of all kinds at the construction site will cause occupational injuries and illness to workers, hence decreasing their working effectiveness as well as changing their life permanently [75]. The emergence of IoT and its concomitant technologies such as wearables have raised interest in the OSH of health and safety monitoring applications and building work [74]. As the health status of workers and environmental parameters of the construction site are collected, proactive prevention can be taken after data analysis to lessen the occupational accident rate in the construction industry [41; 43; 76; 77; 78; 79; 80]. In short, IoT provides instant and consistent support in monitoring various human and environmental metrics. Therefore, IoT-based OSH can improve the safety and productivity of the industry and minimize workers' health impacts [18; 80; 81; 82; 83].