esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Magnetorheological Damper with Self-Powered Ability for Washing Machines
## 5 Experimental Validation
### Self-Adaptability of the Damping Force  
Experiments are implemented to verify the self-adaptability of the damping force when the EH part is close. Figure 16 shows the comparison between the damping force with the EH part and that under constant currents when an excitation of 2.4 Hz frequency and 30 mm amplitude is applied to the prototype self-powered MR damper. As shown in the figure, the damping force with the EH part varies between the levels of 0 and 0.34 A constant currents. The maximum damping force with the EH part is 75.1 N, which is almost the same as that under the 0.34 A constant current (76.2 N) since the difference is only about 1.4%. It is also observed from Figure 16a that the force-velocity loop of the MR damper powered by the EH part experiences a hysteresis in the preyield region, but not a linear relationship in the postyield region. The explanation can be easily found on the continuous variation of the voltages generated by the EH part. Besides, in Figure 16b, it is realized that the area of the force-displacement loop with the EH part is smaller than that under the 0.34 A constant current. The generated work of the MR damper in case of the 0.34 A constant current is about 8.7 J, while the MR damper with the EH part handles only about 7.4 J. In other words, the MR damper supplied by the external power source with the constant current dissipates more energy than that by the EH part.  
Figure 16: Experimental results of the MR damper with the energy-harvesting (EH) part and under constant external currents in the case when the excitation is 2.4 Hz frequency and 30 mm amplitude: (**a**) force vs. velocity and (**b**) force vs. displacement.  
The same conclusions could be drawn from Figures 17 and 18 when the prototype damper is tested under a 2 Hz frequency and a 21 mm amplitude and a 2.2 Hz frequency and a 26 mm amplitude, respectively. These are also the excitations that often appear when washing machines are entering the resonance region. Although the maximum damping forces produced by the MR damper with the EH part decrease due to the reduction of the excitation velocities, they are still high enough to attenuate the vibration of washing machines that has not reached to resonance peak yet. Consequently, the feasibility of the proposed self-powered shear-mode MR damper for washing machines has been demonstrated.  
Figure 17: Experimental results of the MR damper with the EH part and under constant external currents in the case when the excitation is 2 Hz frequency and 21 mm amplitude: (**a**) force vs. velocity and (**b**) force vs. displacement.
# Adaptive Energy Balanced Routing Strategy for Wireless Reschargeable Sensor Networks
## 2 System Model
In WRSN, node \(i\) generates sensing data and then transmits these data to the nodes in its forward region _NF(i)_[15]--nodes which are closer to sink node than node \(i\) and also are in node \(i\)'s transmitting area. Furthermore, energy consumption occurs when nodes transmit and receive data packets. According to energy loss model [30], the receiving energy consumption is much less than the transmitting energy consumption, thus, it is ignored here. To ensure the long-term stable operation of the network, it is necessary to optimize both charging and routing process to maximize the network lifetime, that is:  
\[\max\{\min_{i\in N}(\frac{\hat{E_{i}}}{\sum\limits_{j\in N\overline{r}(i)}C_{ ij}f_{ij}})\} \tag{4}\]  
Here, \(\min_{i\in N}(\frac{\hat{E_{i}}}{\sum\limits_{j\in N\overline{r}(i)}C_{ij}f_{ ij}})\) means the survival time of the first death node in the network, which is generally called network lifetime [31; 32; 33; 34]. The \(C_{ij}\) denotes the energy consumption per transmitting data rate from node \(i\) to \(j\), and \(f_{ij}\) represents the transmission data rate from node \(i\) to node \(j\). [35] From this equation, it can be concluded that providing more energy or reducing the energy loss is conducive to prolonging the network lifetime. Since the nodes are randomly deployed in the network, it is necessary to set a reasonable location planning of charging points, in which WCV can supply energy for nodes at any position. The energy consumption state of nodes at different positions differs; thus, it is absolutely essential to allocate the charging time at each charging point reasonably to provide enough energy for nodes. When WCV stays at certain charging point, the power received by the surrounding nodes are different, thus the energy obtained by nodes are also different. The nodes with low charging efficiency may not obtain enough energy to maintain its energy consumption, then their energy gradually decline faster. Therefore, adjusting the node charging efficiency and balancing the node energy can avoid some nodes' energy far less than the average level and lessening the network lifetime. As mentioned above, the charging efficiency, charging time and the energy after charging are different between nodes. If the routing algorithm only considers the current node energy and ignores the other charging characteristics, for some nodes with high residual energy and low charging efficiency, they will be selected as relay nodes with great probability and need to be recharged for a longer period of time to replenish the lost energy, which is unfavorable for the improvement of the overall network energy efficiency. Thus, it is important that selection of the next hop should consider the difference of charging characteristics of nodes.
###### Abstract  
Aimed at the problems of small object detection in high resolution remote sensing images, such as difficult detection, diverse scales, and dense distribution, this study proposes a new method, DCE_YOLOX, which is more focused on small objects. The method uses depthwise separable deconvolution for upsampling, which can effectively recover lost feature information and combines dilated convolution and CoTNet to extract local contextual features, which can make full use of the hidden semantic information. At the same time, EcaNet is added to the enhanced feature extraction network of the baseline model to make the model more focused on information-rich features; secondly, the network input resolution is optimized, which can avoid the impact of image scaling to a certain extent and improve the accuracy of small object detection. Finally, CSL is used to calculate the angular loss to achieve the rotated object detection of remote sensing images. The proposed method in this study achieves 83.9% accuracy and 76.7% accuracy for horizontal object detection and rotationally invariant object detection, respectively, in the DOTA remote sensing dataset; it even achieves 96% accuracy for rotationally invariant object detection in the HRSC2016 dataset. It can be concluded that our algorithm has a better focus on small objects, while it has an equally good focus on other objects and is well suited for applications in remote sensing, and it has certain reference significance for realizing the detection of small objects in remote sensing images.  
YOLOX; depthwise separable deconvolution; dilated convolution; small object detection; CSL
# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
## 3 Proposed Agent-Based Model
### Modeling of Interactions between Agents  
The interactions between different agents in the MiC supply chain are complex and require careful coordination to ensure efficiency and the timely completion of construction projects. The sequence diagram in Figure 5 illustrates a detailed view of these complex interactions, visualizing the flow of communication and activities between the various agents involved in the MiC supply chain, including the construction site, factories, suppliers, transporters \(T_{1}\) and transporters \(T_{2}\). It details the order and fabrication process, as well as the transport and assembly process, highlighting the communication and activities required for efficient logistics management.  
Figure 4: Multi-agent system use case diagram.  
The process begins with the construction site (CS) agent initiating the order for integrated construction modules from the factory (F) agent. The factory agent confirms the order and schedules the production process. To fabricate the modules, the factory agent places orders for prefabricated components with the supplier (S) agent.  
Upon receiving the component orders, the supplier agent produces the required components and requests transportation from the transporter (\(T_{1}\)) to deliver these components to the factory. The transporter confirms the transport request, loads the components, and delivers them to the factory. Once the components arrive, the factory agent proceeds to fabricate the integrated modules.  
After the modules are fabricated, the factory agent requests transportation from the transporter (\(T_{2}\)) to deliver the integrated modules to the construction site. The transporter confirms the transport request, loads the modules, and delivers them to the construction site. Upon delivery, the construction site agent assembles the modules into the final structure and notifies the factory of the completion.  
This sequence diagram effectively ensures that each step is clearly defined and that all agents work in coordination to achieve efficient supply chain management. The use of real-time data collected through various technologies enhances the accuracy and efficiency of the multi-agent simulation model, providing a robust framework for optimizing logistics and operational processes.  
Figure 5: Sequence diagram of multi-agent system for MiC supply chain.
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 1 Introduction
### Objectives  
While existing research has provided insights into the complex dynamics of airflow within urban street canyons, a significant knowledge gap persists. Although studies have concentrated on airflow magnitudes, such as velocity, turbulence, and temperature fields, they have frequently failed to provide concrete numerical results for air ventilation and pollutant dispersion. This leaves us with a restricted comprehension of the influence of the air velocity (and the associated Reynolds number) on the airflow pattern and the resulting number of vortices formed. Moreover, existing studies [21; 37] frequently focus on particular street configurations, which constrains their utility in developing effective pollution mitigation strategies in the real world, but the urban environment is characterised by a high degree of morphological diversity.  
This study aims to address this knowledge gap by providing a comprehensive analysis of pollutant dispersion and airflow patterns in real-world scenarios, including different urban layouts, street canyon geometry, and external wind conditions. By examining the relation between air velocity, the Reynolds number, and street canyon geometry, this works aims to quantify air ventilation within different street canyon configurations, assess the impact of air velocity on vortex formation and its implications for pollutant dispersion, and develop a framework for predicting airflow patterns and pollutant concentration in diverse urban environments.  
The primary objective is to generate detailed and applicable insights into street ventilation and air dynamics within urban canyons through simulations using a CFD model. The model employs a reference height of 10 m, representing a typical three-story urban building. The simulations encompass a range of parameters reflecting real-world street conditions. The AR varies from 0.75 to 4.5, encompassing different street morphologies (wide, regular, and narrow), while the air velocity ranges from 0.75 to 4 m/s. Additionally, the research findings are applied to a real-world scenario in Seville to predict pollutant concentrations, providing insights into air quality degradation risks based on street morphology and external conditions. This evidences the applicability of the findings in this work to identify hazardous scenarios in actual streets for city inhabitants.
## 3 Research Methodology
### Classification Algorithms  
The dataset used in this study was collected from the aquaculture industry and required extensive preprocessing to ensure suitability for ML model performance. Relevant attributes were selected based on their alignment with the study's research objectives, including environmental factors, such as salinity and water temperature, and operational variables, like gear types and fishing effort. The data underwent cleansing, adjustment, and normalization steps to handle missing values and normalize features before being processed by the selected ML models.  
For this study, we focused on three specific ML algorithms: Random Forest, DTs, and Linear Regression. These algorithms were chosen based on their compatibility with the characteristics of aquaculture data and their respective strengths in managing environmental and operational datasets. Random Forest was selected for its robustness in handling high-dimensional data and its ability to reduce overfitting, which is critical in datasets with noise and numerous environmental factors. DTs were chosen for their interpretability and versatility with both categorical and numerical data, making them ideal for classification tasks where explicit, interpretable models are needed. Linear Regression was included to address cases where the data suggested linear relationships, allowing us to analyze specific attributes' effects on outcomes straightforwardly and interpretably. These algorithms offer a complementary balance of robustness, interpretability, and efficiency, particularly well-suited for modeling in aquaculture, where both structured and environmental data types are common.  
Data were stored in the Apache Cassandra database, ensuring efficient management within the system, and were then loaded into the Apache Spark framework, where the Random Forest, DTs, and Linear Regression models were trained, validated, and evaluated. The dataset was split into a training set (70%) and a testing set (30%) to assess model effectiveness through the training and testing processes [28; 29; 30; 31; 32; 33]. Table 3 below summarizes each algorithm's strengths, weaknesses, and best use cases, clarifying the unique value each adds to this study's predictive analysis.
## 5 Experiment Data Analysis
### Bearing Vibration Signal Analysis and Bearing Fault Detection and Diagnosis
As seen in Figure 16d, in the autocorrelation envelope maximum entropy spectrum, the characteristic frequencies of bearing faults are directly extracted without interference frequencies. Through the above analysis, the method based on Kalman filter and spectrum analysis can effectively realize the detection and identification of the faulty bearing.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs
### Scenario Description
#### 2.1.3 Decision Variables  
\begin{tabular}{l l} \(\mu_{ij}^{w}\) & Binary variable related to wavelength assignment, which equals 1 if there is a lightpath \\  & from \(i\) to \(j\) on wavelength \(w\); otherwise, 0; \(\Theta_{mn}^{ij}=\sum_{w}\mu_{ij}^{w},\forall m,n\). \\ \(\xi_{f,p}\) & Binary variable equals to 1 if the \(f\)-th connection request is aggregated into the \(p\)-th \\  & sub-wavelength; otherwise, 0. \\ \(\theta_{p,w}\) & Binary variable equals to 1 if the \(p\)-th sub-wavelength is groomed onto wavelength \(w\); \\  & otherwise, 0; \(\delta=\sum_{p}\,\theta_{p,w}\), \(\forall w\). \\ \(Z_{ij}^{f,p,w}\) & The value is 1 when the \(f\)-th connection request is aggregated into the \(p\)-th \\  & sub-wavelength which traverses lightpath (\(i,j\)) on wavelength \(w\); otherwise, 0. \\ \(R_{mn}^{ij,w}\) & The value is 1 when the lightpath (\(i,j\)) traverses ISL (\(m\), \(n\)) on wavelength \(w\); \\  & otherwise, 0. \\ \end{tabular}
# Performance Evaluation of a Mesh-Topology LoRa Network
## 5 Proposed Approach: LoRaMesh
### Routing Information Dissemination  
In this research article, we only evaluated a basic routing topology, with limited optimisation regarding the routing of nodes in the network. Nodes are assumed to have a fixed routing at the network start time, based on the routing method discussed in Section 5.2. The following theoretical network routing process is proposed, which could potentially be deployed in a realistic scenario to set up the network's routing and transmission schedule:  
* All new nodes in the network shall start in reception mode, waiting for a beacon frame.
* The gateways in the network transmit a beacon frame (with maximum transmission power and SF to reach as many devices in a single hop as possible).
* All nodes within the reception range of the gateway range receive the beacon frame and log the Received Signal Strength Indication (RSSI) of the beacon frame.
* The nodes that received the beacon frame are assigned as single-hop nodes, with the possibility of serving as a routing node as well. All other nodes in the network will need to follow a multi-hop up-link route.
* Multi-hop nodes transmit their device address three times, with random offset times.
* All single-hop nodes receive these messages and compile a list of child nodes and their respective RSSIs between the child-parent node.
* Single-hop nodes transmit these compiled lists to the gateways in the network.
* The gateways forward the information to a network server, which assigns child nodes to parent nodes to evenly distribute the load on parent nodes to prevent bottleneck nodes (a more optimised solution is possible, but is beyond the scope of the current research).
* The gateways transmit the multi-hop device's parent nodes to them, along with their upload slot assignments, via the routing nodes.
* The process where multi-hop nodes without parent nodes transmit their device address, transmit the path-loss between each child/parent node to the network server, and distribute the routing information is repeated for a fixed number of cycles, depending on the network's maximum number of hops.  
Once all nodes have been assigned either to a gateway or a parent routing node, devices can start to upload data in their assigned upload slot.  
This proposed network routing setup procedure requires significant transmission overhead as routing cannot be solved on a child-parent node level, resulting in additional network congestion and power consumption. Furthermore, mobile sensor networks require a self-adaptive multi-hop structure rather than a fixed, precomputed network layout. Future work should focus on adaptive routing mechanisms to handle dynamic topologies with a solution that could resolve routing at the node level.
## 3 Preliminaries
### Trapezoidal Fuzzy Number and a-Cut  
#### 3.1.1 Definition of Trapezoidal Fuzzy Numbers  
To accurately reflect the complexity and uncertainty in real-world decision-making processes, fuzzy set theory has emerged as an effective approach. In this theory, trapezoidal fuzzy numbers are considered an important component. They are highly regarded for their ability to store a large amount of information, accurately characterize information features, and offer flexibility.  
**Definition 1**.: _For each trapezoidal fuzzy number \(\widetilde{a}=[a_{L},a_{M1},a_{M2},a_{R}]\), its membership function is given by the following:_  
\[\widetilde{a}(x)=\begin{cases}(x-a_{L})/(a_{M1}-a_{L}),&a_{L}\leq x\leq a_{M1} \\ 1,&a_{M1}<x\leq a_{M2}\\ (a_{R}-x)/(a_{R}-a_{M2}),&a_{M2}<x\leq a_{R}\\ 0,&x<a_{L}\ \&\ x>a_{R}\end{cases} \tag{1}\]  
In this context, \([a_{M1},a_{M2}]\) represents the mean interval of the trapezoidal fuzzy number \(\widetilde{a}\). The most likely values are taken within the interval \([a_{M1},a_{M2}]\), where \(a_{L}\) and \(a_{R}\) are the lower and upper bounds, respectively. The trapezoidal fuzzy number can only take values within the interval \([a_{L},a_{R}]\). Specifically, when \(a_{M1}=a_{M2}\), the trapezoidal fuzzy number degenerates into a triangular fuzzy number. When \(a_{L}=a_{M1}\) and \(a_{M2}=a_{R}\), it degenerates into an interval number, and when \(a_{L}=a_{M1}=a_{M2}=a_{R}\), it becomes a precise number. Thus, triangular fuzzy numbers, interval numbers, and precise numbers can all be considered special cases of trapezoidal fuzzy numbers.
## 5 Conclusions  
This study demonstrates that diosgenin effectively reduces persister cell formation in _Staphylococcus aureus_ through mechanisms involving metabolic suppression and membrane modulation. Diosgenin treatments significantly decreased persister cell survival under antibiotic stress, particularly during a relatively short period after exposure. An inhibition of _relP_ and _relQ_ gene expression and an associated reduction in intracellular ATP levels suggest that diosgenin disrupts key metabolic pathways that are essential for persister cell maintenance. Moreover, diosgenin selectively altered membrane fluidity without affecting membrane permeability or membrane potential, indicating a targeted effect on membrane-associated signaling rather than membrane integrity, which may further contribute to diosgenin's inhibitory effects on persister cells by influencing the dynamics of membrane proteins involved in stress response pathways. While our findings support diosgenin's potential as an anti-persister agent, its efficacy appears to diminish as cultures enter the stationary phase, highlighting the need for further studies assessing its long-term effectiveness and potential synergistic effects with conventional antibiotics. Future research should focus on in vivo validation and exploring combination therapies to optimize diosgenin'santi-persister activity. In summary, diosogenin demonstrates a dual mechanism of action against _S. aureus_ persister cells by targeting both metabolic and membrane-associated pathways, making it a promising compound for use in strategies to mitigate chronic and recurrent bacterial infections.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/ijms26136335/s1](https://www.mdpi.com/article/10.3390/ijms26136335/s1)]([https://www.mdpi.com/article/10.3390/ijms26136335/s1](https://www.mdpi.com/article/10.3390/ijms26136335/s1)).  
Conceptualization, M.K. and T.-J.K.; methodology, Y.S., M.K. and T.-J.K.; software, Y.S. and M.K.; validation, Y.S., M.K. and T.-J.K.; formal analysis, Y.S. and T.-J.K.; investigation, Y.S. and M.K.; resources, T.-J.K.; data curation, Y.S. and T.-J.K.; writing--original draft preparation, Y.S.; writing--review and editing, T.-J.K.; visualization, T.-J.K.; supervision, T.-J.K.; project administration, T.-J.K.; funding acquisition, T.-J.K. All authors have read and agreed to the published version of the manuscript.  
This study is financially supported by Korea Forest Service Government (KFSG) as Graduate School Specialized in Carbon Sink.  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.