esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Validation of the Proposed Method
### Validation of Fault Forecast Method and Inference  
The proposed method was trained with the ADAM optimizer and the initial learning rate was 0.005, which decreased by 0.0005 every 10 epochs. The batch size was set to 20. Three fault-prediction experiments were conducted, the training period was [900, 3700], the prediction period [2900, 4700] to predict 1000 points, [3000, 4800] to predict 1100 points, and [3100, 4800] to predict 1200 points. The results of the model are shown in Table 2. Considering the features of time-series sequencing, we distinguish the training set and testing set on the timeline, with the training set in the front and the testing set in the back. In addition, we set the input value (model input x) to be before the output value (model output y) on the timeline, which is in line with the natural law of prediction tasks. The natural law of prediction tasks states that prediction behavior generally involves analyzing historical events to infer future events.  
Several traditional time-series models and DL models suitable for prediction tasks were chosen as our comparison models, i.e., autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), deep autoregressive (DeepAR), Deepstate, and attention-based long short-term memory (AT-LSTM). During the experiments, the traditional time-series model could only predict a short time, and did not perform well in a long-series fault-prediction task. Compared with the traditional time-series model, the mean-square-error loss (MSELoss) of the Gaussian-linearized transformer was approximately 0.04. Regarding the DL comparison models, we found that AT-LSTM performed the best, with a MSEloss of 0.07. This is because the attention mechanism was added to the LSTM model, which made it easier to learn important relevant information during the training time.
## 2 Literature Review
### Big Data and Cloud Manufacturing  
Supply chains generate a lot of information that needs to be traced and undergo multiple authentications for product delivery. The manufacturer uses an electronic ERP system and uses paper job cards to record production work on the shop floor and trace assembly parts on the assembly line. A complex network is created in tracing parts, assembly processes, and suppliers within a railcar manufacturing enterprise, which is not spared from errors during data capture and the sharing of manufacturing specifications with purchase orders to suppliers. Therefore, information asymmetry in SCM needs to be eliminated as information flow is the core of operations in a supply chain network [4].  
Smart manufacturing is being managed by information systems and manufacturing data have become intelligent and valuable [36]. CC research has mainly focused on technological issues, whereas research on enterprise management has gained little traction [3]. The creation of new business models for cloud manufacturing has been identified as a research gap, as manufacturing has changed from being fixed in the same location to incorporating the new opportunities presented by digitization [13]. As such, DevOps, i.e., the integration of software development and operations powers industry 4.0 towards the synergy and interconnectedness of the value chain, is presenting new opportunities and infinite possibilities for value creation [52]. Based on findings by [PERSON], [PERSON], and [PERSON] [53], organizations are adopting MSA for different purposes such as agility (82%), performance improvement (57%), and scalability (78%), with DevOps influencing the implementation of MSA by (47%). Scalability is a crucial aspect for consideration in SCM, as the influx of orders is always fluctuating. The open serverless framework, OpenFaaS, allows developers to build serverless functions based on defined and created templates of different languages and is reliant on Kubernetes (K8s) to provide failover and high availability management [20].  
Microservices permit incremental modernization to achieve highly scalable systems, which are adaptable for both large enterprises and SMEs, whilst reducing the system redundancy of service instances at a low cost [54]. DDD offers guidelines for the domain decomposition of a business to identify principles, patterns, and practices [55]. The acquisition of boxed flat sheet metal parts is a subsystem of the assembling process, hence the cloud system is designed for flexibility as suppliers change with time. Scaling in cloud-native applications permits parallelism and concurrent use by multiple users seamlessly without disrupting production activities [56]. Microservices are ideal for cloud-native applications, as each microservice is independently deployed and encapsulated, making them failure-proof to failure in the system, a characteristic that improves the service availability of applications [57]. Communication between microservices is performed using the representational state transfer application (REST API), which is executed by an event [56].
## 3 Methodology
### Structured Model Reconstruction
#### 3.3.2 Room Structured Connection  
The above reconstructed models are expressing room semantic and geometric information, but still lacks room topological connection types. In the last step, the room's structural connection is recreated. For indoor scenes, the space created by a door is a type of connection space. Thus, the door's position is used to analyze the room connection types. In this study, the doors were extracted based on the segmented surfaces, and the model reconstruction is based on the horizontal slice of the segmented rooms. To correct some distance errors introduced during reconstruction, the detected door are attached to the walls if the following conditions must be satisfied: (a) the door must be parallel to a wall; (b) the distance along the normal should be less than the threshold of 0.2 m; and, (c) the door completely overlaps with the walls. The door connecting adjacent rooms is a subspace with thickness. Figure 12 shows the structured model with the reconstituted doors.  
Every room is associated with geometry elements that ceiling, floor, walls, doors and windows. In our work, openings between neighboring rooms are detected to obtain a room connectivity graph, in addition, for each wall of the room, we search a matching, approximately parallel surface with opposing normal orientation within user-defined distance and angle thresholds. Each matching pair of wall surfaces forms adjacent rooms. According to these rules, a building's room topology graph is constructed. If a space that is connected to more than three rooms and has many doors that is labeled a corridor. The rooms with topological relationship are shown in Figure 13, which can be applied to a service application in an indoor environment.  
Figure 11: The reconstructed room models.  
Figure 12: The structured model with openings.  
Figure 10: Line segments and point clouds as constraint conditions.
## 2 Materials and Methods
### Materials  
#### 2.2.1 Landslides Inventory  
The landslide inventory was elaborated in 2019, as a joint work between the Instituto de Estudios de Regimen Secional del Ecuador (IERSE), of Universidad del Azuay (Cuenca, Ecuador) and Universita degli Studi di Napoli Federico II (Naples, Italy), to characterize the landslides that have occurred in the area and to identify the areas susceptible to these events. The inventory covered an area of 380 km\({}^{2}\) divided into 36 quadrants of 13.5 km\({}^{2}\) to facilitate data collection. The methodology for the preparation of this inventory, in which 710 landslides were recorded (529 rotational landslides, about 75%), was based on field work for the collection of information using the Mobile Application for Regional Landslide Inventories (MARLI) [37]; photointerpretation with Planet satellite images, orthophotos of the area and differential interferometry processes (DInSAR) with COSMO-Skymed and Copernicus Sentinel-1 images to identify ground movements. In addition to rotational landslides, also falls and earthflows are described, with a landslide density per km\({}^{2}\) of 45.8 and an average landslide size of 2.2 ha [37]. Most rotational landslides were located in well-weathered andesitic conglomerates with sandstone intercalations, while the highest landslide density value corresponded to shales and sandy clays [7]. The landslide inventory applied in this study is currently being updated to improve the quality of its information and thus improve the accuracy of the models. The technical details of the inventory are described in the studies by [PERSON] et al. [36], [PERSON] et al. [12] and [PERSON] et al. [7].
# TMD Damping for Structures with Uncertain Modal Parameters
## 4 Results
### Discussion of Selected Results  
The maximum structural displacements due to increased TMD damping normalized by the maximum structural displacements due to nominal TMD damping (17) are depicted in Figures 4-7 for the selected TMD damping increase factors \(\gamma=1.05\), \(\gamma=1.1\), \(\gamma=1.15\), and \(\gamma=1.2\) and for the selected TMD mass ratios \(\mu=1\%\) and \(\mu=4\%\). The results for the other computed damping increase factors are not depicted in order to not overload the paper. Surface values with \(\max(X_{1}(\gamma\
eq 1))/\max(X_{1}(\gamma=1))<1\) describe regions where increased TMD damping is beneficial for the actual modal properties and vice versa. Please note that the criterion cannot take the following into consideration:  
* The duration for which the actual modal properties deviate from their nominal values, e.g., due to ambient temperature that changes during the day;
* If a severe vibration event occurs during the time when the actual modal properties deviate from their nominal values, e.g., due to a strong wind event.  
The results, due to the other considered values of \(\gamma\) and \(\mu\), are not depicted but are taken into consideration in Section 5 for the discussion of when increased TMD damping is advantageous. To better visualize the regions of reduced displacement amplitudes in the primary structure due to increased TMD damping, the horizontal surface at \(max(X_{1}(\gamma=1))/max(X_{1}(\gamma=1))=1\) is included in Figures 4-7 as well. The observation of Figures 4-7 points out the following:  
* The maximum structural displacement strongly depends on the difference between actual eigenfrequency \(f_{1}\) and nominal eigenfrequency \(f_{1,nom}\) that is used for TMD design. This agrees with the finding that the efficiency of TMDs is strongly frequency tuning sensitive [29; 30; 31; 32].
* Increased TMD damping improves the vibration reduction in the primary structure if the actual eigenfrequency \(f_{1}\) differs much from its nominal value \(f_{1,nom}\), and vice versa. The greater the value of the selected \(\gamma\) is, the greater the difference between \(f_{1}\) and \(f_{1,nom}\) must be so that increased TMD damping is beneficial.
* The difference between the actual modal mass \(m_{1}\) and the nominal modal mass \(m_{1,nom}\) that is used for the TMD design shows small influence on the maximum structural displacement. This is good news because, by far, the greatest uncertainty in the TMD design is the value of the modal mass.  
Figure 6: Maxima of structural displacement with \(\gamma=1.15\) normalized by maxima due to nominal TMD damping (\(\gamma=1\)) as a function of the considered \(f_{1}/f_{1,nom}\) and \(m_{1}/m_{1,nom}\) and for mass ratios (**a**) \(\mu=1\%\) and (**b**) \(\mu=4\%\).  
Figure 7: Maxima of structural displacement with \(\gamma=1.2\) normalized by maxima due to nominal TMD damping (\(\gamma=1\)) as a function of the considered \(f_{1}/f_{1,nom}\) and \(m_{1}/m_{1,nom}\) and for mass ratios (**a**) \(\mu=1\%\) and (**b**) \(\mu=4\%\).
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 3 Methodology
### Performance Evaluation Parameters
#### 3.3.1 Latency  
Latency can be defined as the time it takes for a fog server to send a message to a fog client in general. System failures can be detected by abnormal response times, such as high latency and irregularity. These can be measured through round-trip time (RTT) or time to first byte (TTFB). RTT is the time calculated when a fog node sends a packet request to the fog server and receives a response packet. TTFB calculates the time from the request sent to the fog server to the fog server and receives the first byte of the packet. This can be measured as a summation of processing delay (\(Pd\)), queuing delay (\(Qd\)), transmission delay (\(Td\)), and propagation delay (\(PrD\)):  
\[Latency:\ Pd+Qd+Td+PrD\]
# Investigation of the Mechanical Behavior of Polypropylene Fiber-Reinforced Red Clay
## 4 Conclusions  
Triaxial tests were conducted to study the mechanical properties of fiber-reinforced red clay and to investigate the effects of _FC_, _FL_, and cell pressure on its shear strength. Through fitting the experimental data, a hyperbolic constitutive model that considers the effects of _FC_, _FL_, and cell pressure was established. The major conclusions are summarized as follows:  
1. The stress-strain curve of the fiber-reinforced red clay exhibited a typical strain-hardening property, which became more notable as _FC_ and _FL_ increased. The shear strength of the fiber-reinforced red clay significantly increased as _FC_, _FL_, and cell pressure increased.
2. The fiber could provide tensile resistance at a small deformation, thereby confining the extension of the failure surface and improving the shear strength. With an increases in _FC_ and _FL_, the cohesion \(c\) of the fiber-reinforced red clay increased significantly, whereas the internal friction angle \(\varphi\) varied only slightly.
3. When considering the impact of cell pressure, _FL_, and _FC_, the results of the hyperbolic constitutive model coincide with test results well, confirming that the model can appropriately express the stress-strain relationship of the fiber-reinforced red clay.  
In this paper, the shear properties of fiber-reinforced red clay were investigated only under static loading. In future studies, the mechanical properties of fiber-reinforced red clay under dynamic loading, the stress path, and the reinforcement mechanism will be the focus points.  
Conceptualization, J.L. and X.L.; methodology, G.L.; validation, J.L., G.L. and J.Z.; formal analysis, J.L.; data curation, J.Z.; writing--original draft preparation, J.L. and X.L.; writing--review and editing, J.L. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (4187/225); the Fundamental Research Funds for the Central Universities, CHD (300102261719); the Natural Science Basic Research Program of Shaanxi Province (2021)M-535); and the Special Fund for Scientific Research by Xijing University(XJ18T01).  
Not applicable.  
The authors declare no conflict of interest.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 4 Overview of the Study Area
### Basic Characteristics of Landslides in the Study Area  
There are many transverse and oblique tensile cracks in the slope in the study area and they are mainly scattered highway cracks, up to dozens of which are distributed in the elevation of 360 m-470 m. They are perpendicular to the direction of the highway and intersect locally. The shape is zigzag, the nature is mainly subsidence-type tension, and locally it is shearing. The extension length is about 5 m-15 m, the opening is about 5 cm-15 cm,  
Figure 1: Location of study area.  
and the visible depth is 10 cm\(\sim\)20 cm. The rock mass near the air side near the local cracks also shows the characteristics of down-fault, with a height of about 10 cm\(\sim\)20 cm (Figures 2 and 3). The overall characteristics of the slope body are \"heavy rain big slippery, light rain small slippery, no rain not slippery\".
# Dynamic Simulation of Underground Cable Laying for Digital Three-Dimensional Transmission Lines
###### Abstract  
In light of the issues associated with the laying process of transmission line cables, including concealed security risks and contact collisions between pulleys and cables, which primarily stem from reliance on drawings, this paper introduces a simulation methodology for the cable laying construction process utilizing Building Information Modeling (BIM) technology. Initially, two-dimensional DWG graphic data are employed to develop a model of the target equipment and construction environment using BIM software (Solid works 2020). Subsequently, the cable is accurately modeled by applying ADAMS virtual prototype technology, the bushing force connection method, and the macro command language. This allows for the construction of a three-dimensional real cable laying system for transmission lines, enabling the simulation of the dynamic cable laying process in the field. Subsequently, an error analysis is conducted to compare the axial tension and laying speed of the cable with theoretical calculation values. The study then proceeds to analyze tension fluctuations during the cable laying process and assess the load-bearing capacity of the pulleys, thus facilitating effective control of the construction process and enhancing safety measures. The findings indicate that the proposed method can accurately and efficiently simulate the on-site cable laying construction process, with numerical errors maintained below 5%, thereby validating the integrity of the model. Furthermore, the traction overload safety protection amplification coefficient is determined to be \(\alpha=1.5\). It is highlighted that the bearing capacity of the block must exceed 60% of the load carried by the conductor at constant speed. This research provides a theoretical foundation for addressing safety hazards in cable laying engineering and holds certain engineering value.  
c 2025 15 979 +
Footnote †: journal: Applied sciences  
Article
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 4 Experiments and Analysis of Results
### Ablation Experiment  
Table 4 outlines the ablation studies we conducted, which were designed to thoroughly assess the improvements added to the YOLOv8 model. Specifically, these enhancements included the integration of the SimAM attention mechanism, the BiFPN architecture, and the Wise-IoU (WIoU) loss function. The results of these experiments offer clear, quantifiable evidence of the performance boosts each modification brings to the table.  
The YOLOv8n framework's application of the SimAM attention mechanism produced a 2.3% increase in mean average precision (mAP) over the baseline YOLOv8 model, indicating the mechanism's efficacy in enhancing the detection of minute and complex information. A comparable 2.3% gain in mAP over the baseline was obtained by integrating the BiFPN, which improved the feature fusion capabilities of the model. This emphasizes how the BiFPN helps enhance feature representation, which helps provide more precise object localization and recognition. With a 3.3% gain in mAP over the initial model, the switch from the conventional CIoU loss function to the WIoU loss function showed a noteworthy improvement. This modification shows how well the WIoU loss function optimizes the model for improved generalization [38]. The SimAM attention mechanism and the WIoU loss function resulted in a 2.7% increase in mAP over the baseline in the model. This combination suggests a synergistic impact that improves the overall performance of the model.  
Most impressively, the integration of SimAM, BiFPN, and WIoU into the YOLOv8n framework boosted the mAP by 9.5% over the baseline model. The combination of these three enhancements substantially elevated performance, demonstrating their collective impact in creating a robust model capable of detecting minute and low-contrast defects with high precision.
## 3 Results
### Environmental Analysis  
One of the main targets of this research is to reduce green house gas (GHG) emissions from transportation. This is based on overall CO\({}_{2}\) emissions. Transportation equipment produces direct greenhouse gas emissions of carbon dioxide (CO\({}_{2}\)), methane (CH4), and nitrous oxide (N2O) from the combustion of various fuel types, and several other pollutants, such as carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), sulfur dioxide (SO\({}_{2}\)), particulate matter (PM), and oxides of nitrate (NOx), which cause or contribute to local or regional air pollution. However, UNFCCC advises that N2O emissions from transportation are very limited, and CH4 is a major component in case of gas engines. Hence, this study included only CO\({}_{2}\) emissions [20].  
Table 8 summarizes the environmental impact of electrical consumption in the bassline scenario. Table 9 summarizes the overall environmental impact of the traditional baseline without the proposed upgrade. On the other hand, the proposed approach offers a reduction of emissions, by using electric buses instead of diesel buses and using PVs to produce electricity from solar energy to cover the required electric demand. As long as the scenario that is chosen for implementation provides all the required energy, all GHG emission from the baseline will be eliminated.  
Deploying the PV-supplied electric buses for the Amman BRT project will result in eliminating the release of 27,204 metric tons a year and 408,060 metric tons of CO\({}_{2}\) during the 15-year lifetime of the project, which has a tremendous influence on the project's carbon footprint.  
The social cost of carbon is a measure of the economic harm from those impacts, expressed as the dollar value of the total damages from emitting one ton of carbon dioxide into the atmosphere. The current estimate of the social cost of carbon is roughly $40 per ton [22].
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
###### Abstract
Reports have even mentioned the inhibition efficiency of sulfur atoms in cysteine (-SH) and methionine (-S-CH\({}_{3}\)) as major factors that increase the anticorrosive nature as compared with other amino acids without sulfur group [15]. Though many plants of tropical regions have been put forward as green inhibitors, mangroves are less known in the field of corrosion. Therefore, in this study, efforts were made to identify the capability of _Ceriops tagal_ (_C. tagal_), a salt-tolerant mangrove tree, for its antioxidant and anticorrosion properties. _C. tagal_ belongs to the family of Rhizohexae and is predominant in tropical or subtropical areas [16]; \(\alpha\)-amyrin, lupeol, triterpenoids, tannins, triterpenenes, phenylproponoids, dolabranes, and several other constituents isolated from _C. decandra_ and _C. tagal_ are widely accepted as antifungal, antitumor, anti-larval, antimalarial, antifedant, antidiabetic, anti-hemorrhage, antifouling, and antifeedant compounds [17; 18; 19]. In 2018, various new components, such as shown in Figure 1 (tagalphenylproponoidins A, tagalphenylproponoidins B, and 2,3,6-trimethoxy-5-(1-propenyl) phenol), from stems and twigs of _C. tagal_ were identified [20]. In addition to these, _Cladosporium_ sp. HNWSW-1 and _Cladosporium_ sp. JJM22, two endophytic fungi strains from _C. tagal_, have also been found to produce succinimide, ribofuranose phenol, and naphthalene derivatives [21,22]. In this study, efforts were made to find out the biological and anticorrosive effects of _C. tagal_. In the case of mild steel corrosion against 1 M HCl, weight loss measurements, UV-visible, atomic adsorption spectrometric (AAS), and scanning electron microscopy (SEM) energy dispersive spectroscopy (EDX) were performed. Additionally, the surface topography of mild steel was analyzed by atomic force microscopy (AFM) and scanning electron microscopy (SEM).