esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependency in the Corrosion Process of Reinforced Concrete Subjected to a Chloride Solution
## 3 Experiments  
### Experimental Setup  
Table 2 lists details of the materials and mix design used in this study. Ordinary Portland cement (OPC) CEM I 52.5N to BS EN 197-1:2000 was used [22]. The water-to-cement ratio (w/c) was set to 0.4 and 0.6. Three replicates of the concrete samples were fabricated. The sample dimensions were 250 mm \(\times\) 250 mm \(\times\) 170 mm with a dyke against a 250 mm \(\times\) 250 mm face to facilitate the wet-dry cyclic regime. Two mild steel bars with a diameter of 16 mm and length of 300 mm were placed into each concrete sample while being electrically isolated. A copper wire was attached to one end of each mild steel bar, and heat-shrinkable tubes were wrapped around both ends of the bar to create a specific area of exposure. All steel bars were degreased with acetone before casting. The installed steel bars were used as the working and counter electrodes for each sample. For the working electrodes, the steel was covered with heat-shrinkable tubes to determine the exposure area--this area covered 75.40 cm\({}^{2}\). To achieve uniform current distribution on the working electrode, steel counter electrodes were selected at a 10 cm depth and covered with heat-shrink insulation with a 75.40 cm\({}^{2}\) surface area. For concrete strength, the average values are shown with one standard deviation in Table 2. Figure 2 shows a schematic of the sample. After casting, the samples were wrapped in a polythene film and cured in a mould for 7 days. All faces of the samples, except for the dyke, were double coated with epoxy resin and then wrapped with a polythene film. The wrapped samples were cured in a curing chamber (20 \(\pm\) 2) \({}^{\circ}\)C.  
Figure 1: Data fitting for GP technique.  
To accelerate the corrosion process, a wet-dry cyclic regime was employed using a sodium chloride solution (chloride content = 19.5 g/L). Before initiating the cyclic regime, the sample surface was soaked in distilled water for an additional 7 days, followed by exposure to a wet-dry cyclic regime. This allowed the sample to become saturated and prevented any potential absorption effects induced by the drying process. The chloride solution was eliminated from the concrete surface during the drying period. The surface was subsequently exposed to laboratory conditions, with a temperature of (20 \(\pm\) 1) \({}^{\circ}\)C and relative humidity of (55 \(\pm\) 5)%. By contrast, a chloride solution was contained on the concrete surface using a dyke during the wetting period. The drying period in the cyclic regime gradually intensified, with 2 days of drying and 5 days of wetting during the first month, 5 days of drying and 2 days of wetting during the second month, and 8 days of drying and 6 days of wetting during the remaining periods. The corrosion tests were performed for up to 382 days and the tests are discussed below in detail.
## 6 Implications, Limitations, and Future Work
### Implications  
The findings from this research have significant implications for the development and application of LLMs in various fields. GPT-4o's high performance in tasks like medical exam question answering and financial analysis suggests its potential utility in educational and professional training environments. The model's ability to integrate vision and language data effectively positions it as a valuable tool in fields requiring multimodal analysis, such as healthcare, finance, and customer service. The demonstrated proficiency in few-shot learning highlights the model's potential for applications where data are scarce or expensive. This could lead to more accessible AI-driven solutions in underrepresented languages and domains, offering inclusivity and the broader application of AI technologies.  
Furthermore, enhancing the integration of language, vision, and speech capabilities in GPT-4o can significantly improve its performance in complex multimodal tasks and cross-domain interactions. Current methods of combining these modalities can be refined through the use of advanced multimodal fusion techniques. For instance, attention-based mechanisms [61] can dynamically weigh the importance of each modality depending on the task context, helping the model better respond to complex scenarios. Additionally, cross-modal training [62], where the model is exposed to tasks that require the simultaneous processing of text, images, and audio, can further enhance its multimodal interactions.  
Moreover, the need to evaluate newer models on comprehensive and diverse sets of data and tasks is underscored by this research. The gap in robust and extensive evaluations has been a notable limitation in understanding the full capabilities and potential weaknesses of advanced models like GPT-4o. This calls for the development and adoption of more comprehensive benchmarks that can rigorously test models across a wider array of real-world scenarios. The findings also suggest implications for policy and regulatory frameworks. As AI models become increasingly integrated into critical sectors, such as healthcare and finance, ensuring their reliability, transparency, and fairness becomes necessary [63]. This necessitates continuous monitoring, rigorous testing, and the establishment of standards to guide the ethical deployment of AI technologies.
## 3 Modeling and Analysis of Bare Pipe  
### Computational Domain and Boundary Conditions  
The dimensions of the unilateral restraint pipe were taken as follows: the pipe length was 17.942 m, the pipe diameter was 0.762 m, and the wall thickness was 0.019 m. The calculated first-order natural frequency was 2.355 Hz, and the estimated \(U_{min}\) was 7.18 m/s. The calculation domain was set as follows: the distance of the pipe was 10 D from the entrance, 30 D from the exit, and 10 D from the upper and lower boundaries. The length of the pipe was \(\pi\) D, and the polygonal unstructured mesh was divided. The polyhedral mesh technique is an advanced and mature mesh type that is used for the mesh topology of the fluid domain. Polyhedral mesh not only has the same calculation accuracy as hexahedral mesh, but also has better convergence and less mesh dependence than tetrahedral mesh [15; 16]. The boundary layer was divided into 15 layers near the wall of the pipe, as shown in Figures 1 and 2.  
Figure 1: Computational domain grid.  
In this study, the SST \(k\)-\(\omega\) turbulence model was calculated with Fluent software, and the boundary conditions are shown in Table 1.  
The calculation method adopted the SIMPLE method [17], the pressure discrete term adopted the second-order precision, the momentum equation adopted the second-order upwind term. The transient formulation adopted the second-order implicit, initialized with the inlet boundary.
## 4 Discussion and Results of Experiment
### Analysis of Experimental Results
#### 4.3.1 Comparison of Results with Other Models  
For the purpose of verifying the superiority of the GWO-gcForest model, we present fault diagnosis accuracies and standard deviations for GWO-gcForest, gcForest, PSO-SVM, SVM, GWO-Randomfroest (GWO-RF), and Randomforest (RF) methods in Figure 8. Based on Figure 8, it is evident that the approach introduced in this research achieves the highest average accuracy of 95.83%. This value surpasses those of other methods, including gcForest, PSO-SVM, SVM, GWO-RF, and RF, by 2.08%, 8.33%, 10.42%, 4.16%, and 6.25%, respectively. This indicates the superior performance and efficiency of the method proposed. There are certain advantages to the proposed methodology regarding accuracy and standard deviation. To analyze the model's performance further, the various statistical metrics of the method presented in this study are listed in comparison with those of other models in Table 2. Among the evaluation metrics, the GWO-gcForest model proposed in this study achieves the best results. In comparison to gcForest, the remaining three indicators including precision, recall, and F1 increased by 1.89%, 2.08%, and 2.09%, respectively, indicating the effectiveness of model optimization. Furthermore, in comparison to PSO-SVM, it is observed that the proposed model exhibits improvements in precision, recall, and F1 measures by 6.3%, 8.33%, and 8.53%, respectively. These results highlight that the proposed model achieves better overall performance in scenarios involving small sample sizes. Compared with GWO-RF, the three indicators are increased by 2.83%, 4.16%, and 4.32%, respectively, demonstrating the effectiveness of the cascade forest under the optimization algorithm.
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 3 The Proposed System Architecture
### The IoT Security Protection Scheme
#### 3.2.2 The Network Layer  
The network layer is for transmitting and processing data [38]. The network layer in this system includes the S-N. This node receives, processes, decrypts/encrypts data from/to the SA-N, and decrypts/encrypts data from/to the cloud-based database Firestore using the TLS protocol.  
The major security threat is related to the access to the Internet. The Uncomplicated Firewall (UFW) is used to protect the RPI3+ against cyber-attacks, monitor network traffic, and block unauthorized traffic according to a defined set of security rules. Fail2 Ban is used for intrusion prevention; this framework protects the S-N against brute force attacks and tracks the log files generated in order to detect malicious activity. Similarly, it can inform and update the firewall to avoid further suspicious IP login attempts. OpenSSH is used for providing a secured remote login connection to the RPI3+. This encrypts all traffic (including passwords) in order to prevent spying, hijacking, and other attacks, providing a secure network channel through a client-server architecture. The remote login is implemented using the key-based authentication standard where every authorized user must generate a key and encrypt the key with a passphrase to log on the computer. Finally, the passphrase is implemented in order to protect the user key in case a hacker takes control of the user computer and tries to log into the RPI3+ using the generated key.
## 3 Classification of Optical Waveguide Sensors
### Microring Structured Optical Waveguide Sensor
#### 3.1.4 Polymer-Based Microring Optical Waveguide Sensor
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Construction**} & S & \multirow{2}{*}{\
\begin{tabular}{c} _LOD_ \\ **(RIU)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} \(Q\) \\ \end{tabular} } \\  & & & & & \\ \hline
2008 & Cornell University [25] & Silicon slot MRRs & 490 & & 5000 \\
2009 & University of Ghent, Belgium & Slot waveguide SOI (silicon & 298 & & \\  & [27] & on insulator) ring resonator & & & \\  & & One-dimensional photonic & & & \\
2018 & Beijing University of Posts & & crystal slot waveguide & 97.47 & & \(10^{5}\) \\  & and Telecommunications [28] & microring & & & \\
2020 & Shanghai Jiaotong University & Few-mode SiN waveguide & & & \\
2016 & & ring sensor & & & \\
2017 & University of Southern & SOI coupled-ring resonators & 382 & \(10^{-3}\) & \\  & California [31] & Sensors based on cascaded FP & & & \\
2012 & Zhejiang University [32] & & cavity lasers and MRRs & & \\  & University of Paris-Saclay & Silicon slot waveguide ring & & & \\
2016 & [37] & resonator & & & \\
2019 & Columbia University [34] & Silicon photonic waveguide & & & \\  & & sensors & & & \\
2012 & University of Central Florida & chalcogenide glass compact & & & \\
2021 & [43] & microdisk resonance & & & \\
2021 & Ningbo University [40] & Ge-Sb-Se MRR & 123 & \(10^{-4}\) & \(7.7\times 10^{4}\) \\
2022 & Ningbo University [41] & ChGS slot MRR & 471 & \(10^{-4}\) & \(1\times 10^{4}\) \\
2016 & Khulnai university [64] & LNDI label-free optical MRR & 68 & \(10^{-2}\) & \\
2020 & Dalian University of & Polymer-based slot & & & \\  & Technology [68] & waveguide microring & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of microring structure optical waveguide sensors. Figure 13: Schematic diagram of an S-shaped bending resonator.
## 4 Conclusions  
The optimal selection of the MIMM was conducted to simulate a 3-d.o.f. model of an electric power plant by considering 16 cases with different combinations of the two sub-mounts. The frequency range of interest was established as 1-120 Hz, considering the operational requirements of the power plant and potential input from basement earthquakes. All system parameters within the original configuration were validated through experiments conducted in a previous study. To determine the optimal MIMM scenario, an extended 3-degree-of-freedom (3-d.o.f.) model was employed considering the variations in the mechanical parameters of the two sub-mounts. The mechanical properties of the MIMM were evaluated using a total performance index, which is a newly proposed criterion that combines two performance indices. The best case for the MIMM was determined by decreasing both the stiffness and damping coefficients in the upper sub-mount and increasing the stiffness coefficient in the lower sub-mount, which corresponds to Cases I-(c) and (d). By referring to an earlier study, the optimal condition for the lower bubble mount was changed to Case I from Case II, and a considerable reduction in the total performance index was achieved by increasing the stiffness in the lower sub-mount. The total performance index, which was 8.2 in a previous study, was significantly reduced to 0.2 using the proposed method. This outcome suggests that the optimal MIMM scenario can be achieved by implementing the proposed comprehensive variations in both sub-mounts simultaneously, rather than relying on partial variations in the lower sub-mount alone. Another novelty lies in the simultaneous improvement of the two performance indices, with both indices reflecting the performance of the MIMM under two different excitation conditions. Therefore, the optimal selection of mechanical properties aims to strike a balance in controlling vibration transfer for both excitation situations.  
Conceptualization, formal analysis, software, investigation, C.K.; validation, writing of the original draft, visualization, C.-J.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of SMEs and Startups of South Korea (Grant No. 1425172735).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
This research was supported by the Pukyong National University.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 5 Conclusions  
Aiming at the problems of existing gait recognition methods, such as high cost, easily affected by the environment, close sensing distance, and invasibility, this paper proposes a gait recognition system based on LoRa signals for the first time. Based on the amplitude of the received signal, Hampel, S-G filter, and Butterworth filter are used for preprocessing. After that, the autoencoder is used to automatically learn the features of the data samples, and it is stacked with a Softmax layer to generate a deep learning network. The gradient descent method is used for training, so as to recognize the target identity. In the case of 2\(\sim\)6 people, the recognition accuracy reached 99.7\(\sim\)95.6%. In the coal mine, even if the target is 20 m away from the transceiver, the recognition accuracy rate can reach 90.2%, which verifies the effectiveness of long-distance gait recognition based on LoRa and its applicability in the coal mine environment.  
Conceptualization, X.Z.; data curation, R.L.; formal analysis, K.W.; funding acquisition, T.M.; investigation, X.S.; methodology, Y.Y. and R.L.; project administration, Y.Y.; resources, Y.Y.; software, X.S.; supervision, T.M.; validation, K.W.; visualization, R.L.; writing--original draft, Y.Y., X.Z. and R.L.; writing--review and editing, T.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Anhui Provincial Key Research and Development Project (grant number 202104a07020005), the University Synergy Innovation Program of Anhui Province (grant number GXXT-2022-019), and the State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Open Fund (grant number SKLMRDPC22 KF20).  
Not applicable.  
The data presented in this study are available on request from the authors.  
The authors declare no conflict of interest.
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 4 Experiment and Results
### Comparison of Mask R-CNN, BlendMask, and SWIN  
For LR: 0.0001, the distribution of loss values for Mask R-CNN is shown in Figure 9.  
For Mask R-CNN, the results show that loss values started at 0.00002 or lower, even at early iteration stages. With additional iterations, the loss values continued with little variation, indicating only marginal improvement.  
Figure 10 presents the loss value results for BlendMask. While high loss values were occasionally observed until about 200,000 iterations, generally the loss values followed a descending curve. This indicates that with continued training, loss values decrease, enhancing the model's accuracy. However, with extremely small weight-update values, a vibration effect in model parameters was observed. This vibration phenomenon can undermine training stability and consistency, making it difficult to enhance performance.  
Figure 8: Loss values for SWIN (learning rate: 0.005).  
Figure 9: Loss values for Mask R-CNN (learning rate: 0.0001).  
Upon reviewing the loss value graph for the SWIN across training iterations (Figure 11), a consistently descending curve is apparent, distinguishing it from other models. The loss values for SWIN approached 0.00001 after 200,000 iterations; BlendMask attained similar values but only after exceeding 350,000 iterations. This result indicates that SWIN can produce accurate results faster. However, this observation applies when the learning rate is set to a very low level, specifically at 0.0001. In deep learning models, an excessively low LR can slow down the overall learning speed, with model-parameter updates becoming very small; this increases the likelihood of getting stuck in the local minimum, complicating convergence to the global minimum and degrading the model's performance. Furthermore, it may lead to issues such as overfitting and parameter vibrations. Therefore, it is necessary to determine the optimal learning rate by experiment and verification within an appropriate range.
## 2 Design of Experiment (DOE)
### Printing Setup  
The final part's quality and functionality are greatly influenced by the printing parameters. The surface smoothness, dimensional accuracy and mechanical characteristics of the printed product are directly impacted by variables such as layer height, nozzle temperature, printing speed, infill density and cooling parameters. For example, higher nozzle temperatures can improve layer adhesion at the expense of possible material deterioration, whereas lower layer heights produce smoother surfaces but increase the printing times. Optimizing the infill pattern and density according to its intended use is crucial because these factors also affect the part's weight and structural integrity. Adjusting these factors not only guarantees excellent part quality, but it also minimizes material waste and production time. The main printing parameters used in this study are presented in Table 3.  
To reduce weight, cost and printing time, slicer software offers an infill feature that hollows out the interior of the part and replaces it with a geometrically and mechanically stable structures, such as cubic grids, triangles, or honeycomb patterns. This technique significantly decreases the number of printed lines per layer, reducing printing time and, most importantly, minimizing structural weight. Additionally, the infill structure enhances the part's stiffness, ensuring that the part maintains its strength while using less material.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Property** & **Value** \\ \hline Layer height (mm) & 0.2 \\ Line width (mm) & 0.4 \\ Wall thickness (mm) & 2 \\ Top/bottom thickness (mm) & 2 \\ Infill density (\%) & 30 \\ Printing temperature (\({}^{\circ}\)C) & 200 \\ Build plate temperature (\({}^{\circ}\)C) & 60 \\ Print speed (mm/s) & 50 \\ Wall print speed (mm/s) & 15 \\ Build plate adhesion type & Brim \\ \hline \hline \end{tabular}
\end{table}
Table 3: Printing parameters.  
Figure 6: A batch of 3D-printed EMTs.  
In this study, a triangular infill pattern was used for the wing box, as shown in Figure 7. By optimizing the printing parameters, the printing process was completed in a total time of 21 days, and consumed approximately 2 kg of PLA+ material.
# An Indoor Space Model of Building Considering Multi-Type Segmentation
## 2 Conceptual Framework of Multi-Level Indoor Space Model
### Multi-Type Segmentation of Indoor Space
(4) Virtual segmentation of indoor space (Figure 2(4))  
When it comes to open spaces such as pavilions, canopies, and empty corridors, the boundary of indoor space cannot be completely defined only through line of sight barrier and guidance. This is mainly because there is a virtual segmentation of indoor space in the design process. Without blocking the line of sight, in order to meet people's spiritual feelings and aesthetic requirements, the space virtual segmentation based on visual perception has provisions on the material, height, and arrangement of components, so as to further divide the functions of indoor space. Spatial virtual segmentation mainly includes three categories (Figure 4): 1. Structural virtual segmentation is caused by different visual areas of corridor space. As shown in Figure 3(a), the visible area at the intersection of the corridor is larger, thus forming a virtual boundary segmentation of the corridor. 2. Virtual segmentation is formed by the different heights, materials, and textures of interface components (such as the floor, ceiling, etc.). 3. Column segmentation: the columns are set up to meet the load-bearing requirements of the structure in the vertical direction. However, in order to enrich the levels and changes of indoor space, columns are sometimes used to divide indoor space (Figure 3(c)). The closer the columns are to each other, the stronger the sense of segmentation. Figure 4: Spatial virtual segmentation. Figure 3: Vertical division components. Characteristics of virtual segmentation: the segmentation of indoor space is not clear, the boundary limits of components to indoor space are weak, and there is no tangible entity barrier in the line of sight. However, through symbolic segmentation, we can rely on the changes in some physical characteristics of components to give enlightenment, and divide a connected indoor space into two different indoor subspaces at the psychological level.