esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a Geotextile in the Treatment of Post-Subsidence in Karst Areas
## 4 Results and Discussion
### Effects of Anchorage Length of Geotextile
#### 4.1.1 Soil Pressure  
The soil pressure data beneath the geotextile for different anchorage ratios \(L/B\) at the final state of subsidence are shown in Figure 6.  
Figure 6: Soil pressure beneath the geotextile for different anchorage ratios \(L/B\) at the final state of subsidence: (**a**) stable area; (**b**) subsidence area.  
Figure 6a shows the soil pressure beneath the geotextile in the stable area for different \(L/B\) values. Each curve has a peak value of soil pressure when the horizontal distance from the center is about 0.8 m, corresponding to the limit between the stable area and subsidence area. There is a second-peak-value at the horizontal distance from the center of 2.25 m for each curve. With the increase of the anchorage ratio, the peak soil pressure increases, meaning that greater soil pressure transfers from the subsidence area to the stable area due to the tensioned membrane effect exerted by the geotextile.  
Figure 6b shows the soil pressure beneath the geotextile in the subsidence area for different anchorage ratios of \(L/B\). With sufficient anchorage length, the trends of the curves of soil pressure with the anchorage lengths of geotextile for \(L=1.0B\), \(L=0.75B\), and \(L=0.5B\) are similar, while, for the curve corresponding to an anchorage length of \(L=0.25B\), the soil pressure is similar to that of the curve without such an anchorage length, indicating that an anchorage length greater than 0.25\(B\) is required to ensure the safety and stability of the treated structure.  
Figure 7 shows a comparison for the soil pressure beneath the geotextile at the final state of subsidence between model tests T2, T3, T4, and T5 conducted by [PERSON] et al. (2018) (note that the values have been scaled based on the scaling factor \(N=5.0\) and similarity) and the numerical modeling of M1, M2, M4 and M6. The soil pressure in subsidence area for the anchorage length of \(L=0.5B\) and \(L=1.0B\) is close to zero. For the anchorage length of \(L=0.25B\), the soil pressure in the subsidence area in the model test is in contrast to that of the numerical simulations. For the anchorage length of \(L=0B\), the soil pressure in subsidence area decreases as the horizontal distance from the center of subsidence area increases for both numerical simulation and model test.  
In the stable area, the curves of the model test without using a geotextile have a similar tendency when comparing them to those of the numerical simulation. In general, the variation of soil pressure in both the model test and numerical modeling is similar. The friction of the interface between geotextile and soil and the size effect in the model test may contribute to the differences between the model test and numerical simulation data.
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 2 Project Characteristics
### Key Engineering Problems  
#### 2.2.1 The Particularity of Offshore Construction  
Offshore construction tools and monitoring have certain particularities. The wind, wave and flow on the sea are unpredictable and non-artificial. The change of weather has a great impact on the construction environment on the sea, and the change of the flow at the estuary is more drastic. Long and large pipelines are susceptible to wind, wave and flow during floating transportation. If the pipeline body deforms too much under the action of external forces, the damage is irreversible, and the pipeline quality and later service will have very adverse consequences. If the raw water pipeline is damaged, it is difficult to repair. [PERSON] et al. (2018) providing insights on the use of thermosetting liner for the  
Figure 1: Satellite image of immersed pipeline segment (taken from Google map).  
Figure 2: Aerial photo of floating transportation on site.  
repair of offshore pipelines exposed to corrosion and leakage. The seawater erodes the pipeline body and pollutes the inside of the pipeline and the airtightness of the pipeline cannot meet the requirements, resulting in a huge loss of social benefits and economic benefits. The flow of liquids from the pipes into the ocean may also cause pollution and affect its sustainable development. The impact of natural elements was quantified using the ICI index (Impact Quantification Index) proposed by [PERSON] et al. [46], which summarizes different sources of variation in a single value.  
Secondly, offshore construction operations are not as convenient and flexible as those on land. If there is no reliable construction monitoring, operators cannot make timely and correct responses, which is likely to cause irreparable damage to engineering structures. In addition, metal objects are prone to decay after immersion in seawater, so offshore construction also requires effective waterproofing and erosion protection of precision instruments and structures.
# A Compact Monopole Wideband Antenna Based on DGS
## 5 Conclusions  
The compact monopole wideband antenna utilizing DGS with a Taconic TLY dielectric substrate exhibits excellent wideband performance, high gain, and effective impedance matching. The integration of L-shaped and semi-circular slots in the ground plane significantly contributes to these attributes. Comparative analysis with existing designs highlights the superiority of the proposed structure. Experimental results validate the close consistency between simulated and measured outcomes. The antenna achieves a wide bandwidth of 4.08-18.92 GHz, with a 3D gain of 4.18 dBi and peak gain of 7.4 dB at 16.90 GHz and 15.5 GHz, respectively. These results indicate efficient radiation and signal transmission, making it a promising candidate for wireless applications, including satellite communication, radar systems, and UWB systems.  
Conceptualization, A.T. and X.-Q.L.; methodology, A.T., X.-Q.L. and H.L.; software, Abubakar H.S.A. and A.T.; validation, Abubakar H.S.A., A.T. and H.L.; formal analysis, A.T.; investigation, A.T.; resources, X.-Q.L. and H.L.; data curation, A.T. and H.S.A.; writing--original draft preparation, A.T. and X.-Q.L.; writing--review and editing, H.S.A. and H.L.; visualization, A.T. and H.L.; supervision, X.-Q.L. and H.L., project administration, X.-Q.L.; funding acquisition, X.-Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by in part by the National Natural Science Foundation of China (No. 62271122 and No. 62401105), the Zhejiang Key Laboratory of Information and Energy Integrated Microsystem (No. 2024 ZY01014), the Fundamental Research Funds for the Central Universities (No. ZYGX202 IJ008), and in part by Scientific Research Foundation for Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Huzhou, under grant Nos. U03210047, U05220004, and U032200112.  
All data have been included in the study.  
The authors declare no conflict of interest.
## 3 Overall System  
### Overall Service System Application  
The overall system description of the proposed solution, s-Guard, is depicted in Figure 3. First, patients who use s-Guard need to be diagnosed with OSA after polysomnography (PSG) testing. Although s-Guard itself provides sleep quality monitoring functions, it is \"treatment-centered\" rather than \"monitoring-\" or \"diagnosis-centered\".  
In our proposed sensible intraoral appliance s-Guard, six-axis motion sensors (composed of a three-axis gyroscope and a two-axis accelerometer) [20] are embedded to monitor the sleep state and bruxism state. In addition, temperature sensors and SpO\({}_{2}\) sensors were embedded to monitor compliance status (measurement of properly complying to device usage) and OSA status, respectively. Finally, Bluetooth low energy (BLE) [21; 22] communication module, microcontroller unit (MCU), and coin cell battery were used for the overall chip operation.  
Healthcare data sensed from s-Guard are then transmitted to the MAD cleaner (named Clium) via Bluetooth communication. This is because the Bluetooth protocol is a form of near-field communication (NFC) [23], which basically needs a form of gateway to fully communicate with a server for full-service launch.  
Raw data are then transmitted to the server from the cleaner communication hub through a Wi-Fi access point (AP). Other forms of fourth- (4G) or fifth-generation (5G) wireless communication can be applicable.  
Figure 3: Overall system architecture.  
In the server, a sleep health data analysis algorithm is loaded to analyze the SpO\({}_{2}\) data. A compliance monitoring analysis algorithm was conducted based on temperature sensing data. The sleep state monitoring algorithm analyzes data obtained from six-axis motion sensors (accelerometer and gyroscope). The bruxism status can be additionally analyzed using this function. Servers also manage connected IoT devices and users. The specific design and development features of the sensible intraoral appliance, s-Guard, are explained in Section 3.2.  
Statistical data analyzed from the server can be viewed by the patients through the mobile application. Only basic statistical data that could be easily perceived by no-professional patients are provided. More professional diagnosis-, treatment-, and monitoring-related data are provided to clinical professionals by the web application that is fully accessible through PC.
## 3 Proposed Approach
### Feature Extraction Using Pretrained DenseNet Model  
The deep pretrained model, DenseNet (a dense convolutional network) [21], is used in our proposed work to extract the features from the segmented image. Compared to all other CNN models, DenseNet [21] solves the vanishing gradient problem and improves the model's performance. This is achieved by the special architecture of the DenseNet, where each preceding layer concatenates its feature vectors to the future layers, eliminating the effect of reduction in the gradient value propagating through the entire path between the input layer and output layer. DenseNet [21] consists of a special block in its architecture known as a dense block. Each block consists of convolutional layers of sizes \(1\times 1\) and 3 \(\times\) 3. Each dense block is followed by a separate block, known as a transition block, that helps to down sample the feature from the preceding layer.  
The architecture of the pretrained DenseNet201 used in our work is shown in Figure 2. In our proposed work, we have removed the fully connected classifier layer from DensNet201, and the features are extracted from the average pool layer. Thus, the architecture of DenseNet201 used in our proposed work consists of a convolutional layer, a maximum pooling layer, a few dense block layers, transition layers, and an average pooling layer. The features are extracted from the average pooling layer of the pretrained DenseNet201. These layers learn the different features from our dataset with better performance, avoiding the vanishing gradient problem.  
Figure 2: Architecture of DenseNet201.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 6 Future Challenges and Research Scopes
### Limitations of DL Techniques in VANET  
* CNNs possess significant computational burdens, rendering them appropriate for equipment with limited resources [189; 190], such as cars.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Vehicular Applications** & **Issues** & **Techniques Used** \\ \hline \multirow{3}{*}{Integrated Architecture} & Specific Field: emerging technique computation - Latency in communication - Combination of emerging techniques & DRL [175], GAN [176], EDLN [177], CNN [180], SVM [181] \\ \hline \multirow{3}{*}{Clustering} & Specific Field: formation, maintenance - Modifications in mobility, network, and cluster topology, specifications for QoS, identification of neighbors, selection of cluster heads (CHs), membership, announcement, maintainance, reliability, and stability & NB [52], K-means [66], HC [68], GMM [7; 70; 181], fuzzy logic [184] \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._RNNs in VANETs encounter problems with overloading or overflowing while being trained, resulting in significant changes within the NN [191].
* Both LSTM and GRU models have limitations, particularly in VANET scenarios involving quick response times, and are affected by latencies. These negatives include heightened complexity and a significant demand for storage bandwidth.
* ANNs exhibit intensive training duration, and their development can be impeded if the experimental dataset is inaccurately portrayed.
* The lengthy learning period of RBMs makes it difficult for them to adjust to vehicle connections.
* DBNs have drawbacks since they require an extensive activation step and the necessity of embracing GAN learning [192].
* EDLNs in VANET can significantly enhance machine performance when integrating various mixed architectures [193].
* Distributed Reinforcement Learning, an approach in DL, involves multiple premises that are challenging to fulfill in practical scenarios, rendering it inappropriate for vehicular ad hoc network situations.
## 2 Materials and Methods
### Alamar Blue Assay for Cell Viability  
To determine cell viability, alamarBlue (Alamar Biosciences, Sacramento, CA, USA) was used. The assays were carried out according to the manufacturer's instructions. Briefly, cells were seeded in 24-well plates (1 \(\times\) 10\({}^{4}\) cells/well), and the desired Fe\({}_{3}\)O\({}_{4}\) NPs amounts and chemotherapeutic drug were added to the wells. Cells were then incubated for an additional 72 h at 37 \({}^{\circ}\)C (5% CO\({}_{2}\)). AlamarBlue was added to each well at 10% volume and incubated for 200 min. Metabolically active cells reduced the dye into a fluorescent form, and this fluorescent emission signal was measured using a plate reader (excitation/emission: 570/600 nm; Viento XS, DS Pharma Biomedical, Suita, Osaka, Japan). The emission signal was used to estimate cell viability by linear interpolation between the emission from cells treated with 0.1% saponin (0% viability) and that from untreated cells (100% viability).
# Structure and Sound Absorption Properties of Spiral Vane Electrospun PVA/PEO Nanofiber Membranes
## 4 Conclusions  
This study confirmed that the addition of PEO to PVA has an effect on the morphology, structure, and sound absorption properties. From the micro perspective, the fibers became uneven and curved, adhesion and interlacing between the fibers was more pronounced. As a result, the surface of the membranes behaved roughly and irregularly, the internal structure of membranes became fluffy with a large number of voids and cavities. Moreover, when the blend ratios were 80/20 and 70/30, a large amount of solid block structure appeared inside the films.  
Nanofiber membranes with a thickness of 2 mm showed better sound absorption property. Sample a showed maximum sound absorption coefficient 0.63 at the frequency of 4000 Hz, sample b reached up to 0.99 at the same frequency. The absorption peak sample c and d shifted to low-middle frequency, sample d behaved better sound absorption performance. The sound absorption performance of the nanofiber membranes were greatly improved after being compounded with needle-punched nonwoven. Sample a showed maximum sound absorption coefficient 0.78 at the frequency of 1580 Hz. Sample b-d showed consistent trend, absorption peaks reached up to 0.78, 0.82, 0.89 and 1, 0.79, 0.91 respectively at frequencies of 1000 Hz and 2000 Hz. With the addition of PEO, the absorption peak shifted to low-middle frequency, and the sound absorption property was improved in the whole frequency range. To sum up, the absorption band will be wider as the irregularity of the surface and internal fluff of the films increases.  
It is revealed that the different morphology and structure of nanofiber membranes have a great impact on the sound absorption performance. In this paper, we mainly discussed the effect of morphology and structure on sound absorption property. Flow resistance, areal density, and porosity are analyzed less. In the future, interaction of multiple factors should be considered on sound absorption properties.  
This work was supported primarily by the Second Phase of Jiangsu Universities, Distinctive Discipline Development Program for Textile Science and Engineering of Soochow University.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Results
### Construction of AGB Samples  
After obtaining the tree height and DBH of individual trees from LiDAR data, species-specific biomass equations listed in Table 1 were applied to calculate the aboveground biomass (AGB) of individual trees. Subsequently, a grid-based sampling method was employed to spatially partition the LiDAR data, forming an AGB sample dataset, from which a total of 720 valid samples were selected in this study (Figure 4). The sample set includes 300 samples of poplar, 130 samples of Mongolian Scots pine, and 290 samples of Chinese red pine. These samples were used for the subsequent development of the AGB estimation model. The biomass values in the sample set range from 22 to 13,053 g/m\({}^{2}\), with a mean of 1373 g/m\({}^{2}\). Notably, 75% of the samples are distributed below 5000 g/m\({}^{2}\), while biomass values between 5000 and 8000 g/m\({}^{2}\) are relatively scarce. This distribution is closely related to the characteristics of the study area, which is characterized by young forest stands and low canopy cover.  
In order to further prove the consistency of this sample set with respect to the field data, this study validates the sample set with the field sample points, with a total of 21 overlapping points. Moreover, the validation results are shown in Figure 5, which shows that the AGB sample set constructed in this study using LiDAR point cloud data has better consistency with respect to the field data. The validation points are mostly concentrated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tree Species** & **Model Number** & \(\mathbf{R^{2}}\) & **SSE** & **AIC** \\ \hline \multirow{4}{*}{Poplar} & 1 & 0.81 & 454.94 & 170.20 \\  & 2 & 0.82 & 443.96 & 168.58 \\  & 3 & 0.77 & 568.19 & 203.05 \\  & 4 & **0.82** & **432.91** & **162.85** \\ \hline \multirow{4}{*}{Mongolian Scots pine} & 1 & **0.80** & **17.29** & \(\mathbf{-118.55}\) \\  & 2 & 0.78 & 18.24 & \(\mathbf{-112.27}\) \\ \cline{1-1}  & 3 & 0.74 & 22.37 & \(\mathbf{-97.94}\) \\ \cline{1-1}  & 4 & 0.80 & 17.36 & \(\mathbf{-118.23}\) \\ \hline \multirow{4}{*}{Chinese red pine} & 1 & 0.88 & 19.29 & \(\mathbf{-134.62}\) \\ \cline{1-1}  & 2 & **0.88** & **19.23** & \(\mathbf{-134.90}\) \\ \cline{1-1}  & 3 & 0.84 & 25.41 & \(\mathbf{-109.82}\) \\ \cline{1-1}  & 4 & 0.88 & 19.86 & \(\mathbf{-132.00}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tree height–DBH models’ fitting accuracy.  
Figure 4: Example of AGB sample construction: (**a**) photos of measured sample plots; (**b**) LiDAR point cloud data; (**c**) individual tree segmentation. Different colors in (**c**) represent a separate tree.  
near the 1:1 line, and the validation accuracy is R\({}^{2}\) = 0.89 and RMSE = 781 g/m\({}^{2}\). This shows that the sample set constructed in this study can replace the field sample points as the ground truth to participate in the model's construction.
# Research on Optimization Design of Tunnel Blasting Scheme Adjacent to Buildings
## 6 Conclusions  
In this paper, the combination of numerical and in situ monitoring was applied. A study was carried out on the different geological conditions in the upper and lower sections of the tunnel between Jialingjiang Road Station and Xiangjiang Road Station of Qingdao Metro, in addition to exploring the problem of tunnels passing under buildings. A new composite construction method was proposed for the strata that were soft in the upper part and hard in the lower part. The construction parameters were also optimized, and the following conclusions were obtained:  
1. With the use of mechanical excavation combined with blasting construction methods for tunnel construction, the vibration velocity of buildings caused by blasting can be effectively reduced. This method can be used for projects with more stringent vibration velocity requirements on adjacent buildings, which is also used in conjunction with light-surface and micro-differential blasting.
2. For the construction section of tunnels adjacent to buildings where the geology is poor, the settlement of the buildings and the stability of their surrounding rocks should be considered, and the excavation progress should be reasonably controlled. The section where this project is located contains class V surrounding rocks, and it is suggested that the construction feed of cantilever boring machines in the upper section be 0.5 m.
3. Through mechanical excavation combined with blasting construction methods in the upper section of excavation, a free surface has been created, while also reducing the entrapment of rocks. It is possible to achieve the same good blasting effect without the creation of a trenching hole, which has been verified in this project.  
Figure 15: Comparison diagram of building vibration velocity.  
**Author Contributions:** Conceptualization, K.R. and A.J.; methodology, A.J.; software, X.G.; validation, K.R.; formal analysis, K.R.; investigation, Q.M. and K.R.; resources, A.J. and Q.M.; data curation, X.G. and Q.M.; writing--original draft preparation, K.R. and X.G.; writing--review and editing, A.J.; visualization, A.J.; supervision, A.J.; project administration, A.J.; funding acquisition, A.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, Grant No. 52078093; LiaoNing Revitalization Talents Program, Grant No. XLYC1905015; and Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University, Grant No. 2022 YBPY009.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.