esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Influence of File Installation Disturbance on the Settlement of Composite Foundation
## 5 Discussion  
In this manuscript, CPTU test was mainly applied to quantify the influence of CMP and PHC pile installation disturbance. The stress modified method was employed for analyzing settlement of soft soil foundation with low embankment (<2.5 m).  
The primary conclusion of this study is that pile installation disturbance weakens the compression modulus of the soil between the piles, and the weakening rapidly decreases with depth. The additional settlement generated accounts for an average of 6% (CMP) and 23% (PHC pile) of the total settlement, respectively.  
To mitigate the negative impacts of pile disturbance on settlement, several countermeasures will be discussed. Firstly, if the schedule allows, simple surcharge preloading method can be used to improve the consolidation of the foundation before pile installation.  
Secondly, in situations where there are cost and time constraints, improvements in installation techniques become quite important. For CMP, in shallow soil layer, reinforcement can be appropriately enhanced by injecting more slurry to strengthen the soil between piles. For PHC pile, it is advisable to reduce the hammering energy in shallow layers, gradually restoring it to the design value as the depth increases, thus minimizing disturbance to the shallow soil layers.  
Besides, the application of the settlement analyze method is almost not limited by soil type, because the disturbance can cause changes in soil compression characteristics. However, it should be noted that when installing displacement pile in sandy soil foundation, the soil compaction behavior is usually obvious. The settlement completed by vibration reduces the settlement experienced during the embankment filling process and the post-construction period, which is beneficial. A case [21] noted that at the moment of pile installation completion, the settlement of the sandy soil foundation was 42 mm, about ten  
Figure 13: Proportion of disturbance settlement to total settlement.  
times that of soft soil foundation. Thus, regarding the impact of pile installation disturbance on total settlement, the two conditions of sandy soil foundations and soft soil foundations may yield opposing conclusions.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 2 Audio Fingerprint Extraction Based on LLE  
A typical audio fingerprint is encoded by comparing the energy of bands or sub-regions in the frequency domain. This paper introduces LLE into audio fingerprint extraction which maps the energy vector to a lower dimension so that smaller fingerprint can be obtained. The newly-acquired fingerprint can represent the audio signal uniquely with fewer bits and it is more efficient in audio retrieval. The extraction process is as follows: first the data is pre-processed, then band division is implemented in the frequency domain and a set of energies is computed. Finally, after being processed by LLE, the audio fingerprint is encoded by comparing the adjacent energy.  
As seen in Figure 2 the dimensionality reduction consists of three steps. First, \(K\) frames with the nearest energy vector are selected for each frame according to the shortest Euclidean distance. Then, all the neighbors' reconstruction weights are computed. Finally, we obtain an energy vector mapping in a lower dimensional space. More details are described in the following sections.
## 2 Materials and Methods
### Clinical Measurements  
The participants were scheduled for postoperative evaluation after 1, 2, 3, 7, and 14 days. During those appointments, the evaluation of clinical measurements was performed by the same examiner masked to the study, including parameters such as soft tissue healing, pain, and the number of analgesics consumed. Postoperative pain was measured in all participants on days 1, 2, 3, and 7, while the healing index was measured on days 7 and 14 (even though the routine clinical examination was performed on the first days).  
The assessment of soft tissue healing around the sockets was performed using the healing index system described by [PERSON] et al. ([PERSON] et al., 2019). The following parameters were used to assess the level of healing: the color of tissues, epithelialization of wound margins, the presence of bleeding on palpation, granulation, and suppuration. The level of healing was scored as 1-very poor, 2-poor, 3-good, 4-very good, or 5-excellent. As an example, very poor healing was attributed to sites with tissue color that was more than 50% red, and the presence of bleeding, tissue granulation, and suppuration. On the other hand, excellent healing was attributed to sites with a healthy pink color and without bleeding, tissue granulation, or exposed connective tissue.  
Pain analysis was performed according to the VAS on a 0-10 scale, where 0 indicates the absence of pain and 10 was attributed to the most severe pain ever felt, along with the graphic classification scale [20]. The participants were previously oriented regarding the pain assessment and asked to report any difficulty in distinguishing between sites. The number of painkillers consumed was also recorded. In this study, a score \(\geq\) 5 was considered as an indicator of severe pain after the third molar extraction [21], resulting in the use of emergency medication for the patients. A derived time-weighted endpoint, the Sum of Pain Intensity Differences (SPID), was also calculated from the available times of data collection, considering the first measure (24 h) as a baseline, and its difference score calculated at each subsequent time point, in hours (SPID24-48, SPID24-72, and SPID24-196).
## 4 Experimental Results and Analysis
### Comparison Experiment  
#### 4.3.1 Comparison of Effects before and after Data Set Expansion  
The experimental dataset includes the UA-DETRAC dataset, the self-built dataset, and the processed CDnet dataset. To demonstrate the effectiveness of the expanded dataset, both the original YOLOv5 model and the improved YOLOv5 model were trained on the standard dataset and the expanded dataset. Three evaluation metrics were used to compare the performance of the two models, namely precision, recall, and mean average precision. Additionally, the training time was also recorded to examine the impact of dataset expansion. The experimental results are shown in Table 2.  
The experimental dataset consisted of the UA-DETRAC dataset, the self-built dataset, and the processed CDnet dataset. To demonstrate the benefits of expanding the dataset on network training, the original YOLOv5 and the improved YOLOv5 were trained on both the standard dataset and the expanded dataset. Precision, recall, average precision, and training time were used as evaluation metrics. From the results, it can be observed that the extended dataset showed significant advantages over the standard dataset when the number of epochs was increased. Specifically, when training on the extended dataset for 300 epochs, the precision increased by 0.0304, the recall increased by 0.0253, and the average precision increased by 0.037, compared to 100 epochs of training. When training for 500 epochs, the precision increased by 0.0022, the recall increased by 0.0004, and the average precision increased by 0.0038, compared to 100 epochs of training. It is noteworthy that the benefits of expanding the dataset are not significant when the number of epochs is small. Hence, we chose to train the improved YOLOv5-s model on the expanded dataset for 500 epochs to achieve optimal performance.
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 4 Results
### Characteristics of Roads Traversed by Micro-EVs  
The results of analyzing the road links around the areas traversed by the micro-EVs are presented in Table 9. Road links with a width of less than 5 m accounted for the largest proportion, making up approximately 57% of the total length. The micro-EVs used for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Section**} & \multicolumn{6}{c}{**Space Mean Speed (km/h) (n: \# of Data)**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Narrow Road (Width: 3 m)**} & \multicolumn{3}{c}{**Narrow Road (Width: 2 m)**} \\ \cline{2-7}  & **Conventional Vehicle (a)** & **Micro-EV (b)** & **Difference (b –a)** & **Conventional Vehicle (a)** & **Micro-EV (b)** & **Difference (b –a)** \\ \hline Section A & 20.6 (n = 22) & 24.7 (n = 8) & 4.1 & - & - & - \\ \hline Section B & 21.0 (n = 21) & 22.4 (n = 2) & 1.4 & 19.7 (n = 22) & 20.0 (n = 1) & 0.3 \\ \hline Section C & - & - & - & 15.5 (n = 22) & 23.8 (n = 7) & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Space mean speed of the micro-EVs and the conventional vehicle.  
the shared transport service were commonly located in areas where both narrow and wide roads coexisted, with a high concentration of points of interest (POIs). As a result, the distribution of road lengths across different road widths was relatively even. For the public service, the coexistence of large road-based public tasks and social welfare services targeting residential areas resulted in a higher proportion of travel on both narrow roads and wide roads (roads with a width of 20 m or more). Remarkably, in the case of the delivery service, the length of roads with a width of less than 5 m accounted for about 68% of the total length of roads that the micro-EVs traveled, suggesting that the micro-EVs were more frequently exposed to narrow roads compared to other service types. This is likely because the micro-EVs for the delivery service visited all detailed areas where deliveries could be made, leading to this observation.  
This study did not investigate which roads micro-EV users traveled on, or the reasons behind those choices. Therefore, the exact reasons for micro-EVs being exposed to narrow roads more frequently cannot be determined. For an accurate comparison, it would be necessary to compare them with conventional vehicles used for each service type. However, given the small size of micro-EVs, it is expected that users would feel less hesitance in driving on narrow roads compared to conventional vehicles. Thus, it can be concluded that micro-EVs were more frequently exposed to narrow roads.
## 4 Discussion
As imaging technology improves, there is a strong tendency toward virtual surgical planning relying upon 3D cone-beam computed tomography (CBCT) with intra-oral dental scans (e.g., Dolphin 3D; Dolphin Imaging 11.9 Premium, Chatsworth, CA, USA) for generating a surgical splint, or splint fewer surgeries using commercial software for example (3D Systems Healthcare, Littleton, CO, USA) and Maxilim (Mediciim, Mechelen, Belgium)that can help to facilitate the process of virtual surgical planning (VSP), rather than the conventional 2D planning using cephalometric analysis. Such preparations and planning are already frequently involved in orthodontic-surgical treatments, with promising, more accurate results [30]. Today much more orthodontists are combining the CBCT and intraoral scanners to collect and acquire as much accurate data as possible, which will help to facilitate the planning process; for example, IPS CaseDesigner(r) (KLS Martin Manufacturing, Jacksonville, FL, USA) and OrthoAnalyzer(tm) (3 Shape, Copenhagen, Denmark), which demonstrated high accuracy when compared to the gold standard. [31,32]. Lastly, there has been a significant surge in the number of studies exploring the potential applications of AI and ML in orthodontics. The research has primarily focused on diagnosis and treatment planning, growth and development assessment, and automated anatomic landmark detection and analysis, with more advanced AI (artificial intelligence) and ML (machine learning) [33].
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 2 Chitosan and its Properties  
### Source and Extraction  
Chitin is a linear homo-polysaccharide comprising of b-(1, 4)-linked N-acetyl-D-glucosamine units (Figure 2). Chitin is commonly present in invertebrates, such as crustacean shells or insect cuticles, as well as in the cell walls of fungi, some mushroom envelopes, green algae, and yeasts [10; 11; 12]. [PERSON] et al. reported that crustacean shells usually contain 20-30% chitin, 30-40% proteins, 30-50% calcium carbonate/phosphate, and some pigments (astaxanthin, canthaxanthin, lutein, or \(\beta\)-carotene), which varies depending on the sources, or species of sources, and harvesting seasons [13]. Chitosan has been extracted from shrimp shells [14; 15; 16; 17], fish scales of _Labeo rohita_[18], squid gladius (_Loligo vulgaris_) [19], locust waste [20], honey bees waste [21], fungus like _Aspergillus niger_[22], silkworm chrysalides [23], fishery waste [18], and blue crab (_Callinectes sapidus_) waste [24], amongst others.  
Figure 1: Wide ranging industrial applications of chitosan.  
Generally, chitin is commercially extracted from the exoskeleton of crustaceans (crab and shrimp shells) by acid treatments, followed by treatment with alkali to remove the calcium carbonates and proteins, respectively. The extraction process includes three major steps: demineralization, deproteinization, and depigmentation/discoloration (Figure 3). The demineralization step comprises of the elimination of calcium carbonate and calcium chloride, which are the primary inorganic compounds in a crustacean's exoskeleton. The digestion reaction is usually carried out in dilute hydrochloric acid (HCl) solution followed by filtration, washing, and drying. The emission of carbon dioxide (CO\({}_{2}\)) gas is a significant indicator of the removal of mineral contents from the materials. In the second step, deproteinization is performed using an alkaline solution, such as dilute sodium hydroxide (NaOH), followed by filtration, washing, and drying, similar to the first step, as described above. Proteins that are extracted from crustacean waste shells during this process have found use in animal feed [25]. The final step, depigmentation/discoloration, is a purification process during which colour pigments such as astaxanthin and \(\beta\)-carotene are removed using various organic and inorganic solvents, such as sodium hypochlorite, acetone, and hydrogen peroxide, to obtain a purified chitin [26]. The most common process for deacetylation of chitin is the treatment with concentrated sodium or potassium hydroxide solutions at elevated temperatures. The acetyl (-C\({}_{2}\)H\({}_{3}\)O) group is removed from the polymer chain of chitin resulting in the formation of an amino (-NH\({}_{2}\)) group, thus leading to the \(N\)-acetyl-glucosamine and D-glucosamine copolymer.  
Figure 3: Preparation of chitosan from crustacean wastes.  
Figure 2: Chemical structures of chitin and chitosan.
## 3 Methodology
### Data Preprocessing
#### 3.3.2 Sectorization Method  
In this study, alongside commonly used filtering methods, we introduce a sector division approach. By dividing the area into sectors, we can gather multiple RSSI measurements within each sector simultaneously. Thus, it was possible to minimize the influence of individual strongly deviated points with RSSI, due to which the regression model of ML achieves high accuracy. We propose and compare two configurations: dividing the area into 256 and 1024 sectors, corresponding to sector sizes of 25 cm and 12.5 cm, respectively (Figure 3). This segmentation is strategically selected, as the dimensions of the mobile node are comparable to the sector sizes, especially when dividing into 1024, which allows us to more accurately determine its location in space. In general, the number of sectors and the size of each sector depend on both the dimensions of the space and the size of the mobile node.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.1 Data Preparation and Feature Extraction  
Data preparation involved cleaning and standardizing raw FRA data. Key features were extracted to highlight patterns relevant to fault classification. Numerical and statistical indices were used to enhance model training and validation.
## Classifying the Topology of AHL-Driven Quorum Sensing Circuits in Proteobacteriaf Genomes  
**[PERSON] 1, [PERSON] 2, [PERSON] 2,3,**  
**[PERSON] 2, [PERSON] 1,4, [PERSON] 2,* and [PERSON] 2,* 1**  
1 Faculty of Information Technology, PazmanyPeter Catholic University, Prater u. 50/a, Budapest 1083, Hungary; E-Mails: [PERSON]DDRESS] (Z.G.); [PERSON]L_ADDRESS] (B.G.)  
2 International Centre for Genetic Engineering and Biotechnology (ICGEB), Padriciano 99, Trieste 32149, Italy; E-Mails: [EMAIL_ADDRESS] (K.S.C.); [EMAIL_ADDRESS] (B.G.C.); [EMAIL_ADDRESS] (S.H.)  
3 The Capes Foundation, Ministry of Education of Brazil, Cx postal 250, Brasilia, DF 70.040-020, Brazil  
4 Biological Research Center, Temesvari krt 62, Szeged 6726, Hungary  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (V.V.); [EMAIL_ADDRESS] (S.P.); Tel.: +39-040-3751; Fax: +39-040-226-555.  
_Received: 1 February 2012; in revised form: 24 March 2012 / Accepted: 26 April 2012 / Published: 27 April 2012_  
**Abstract:** Virulence and adaptability of many Gram-negative bacterial species are associated with an \(N\)-acylhomoserine lactone (AHL) gene regulation mechanism called quorum sensing (QS). The arrangement of quorum sensing genes is variable throughout bacterial genomes, although there are unifying themes that are common among the various topological arrangements. A bioinformatics survey of 1,403 complete bacterial genomes revealed characteristic gene topologies in 152 genomes that could be classified into 16 topological groups. We developed a concise notation for the patterns and show that the sequences of LuxR regulators and LuxI autoinducer synthase proteins cluster according to the topological patterns. The annotated topologies are deposited online with links to sequences and genome annotations at [[http://bacteria.itk.ppke.hu/QStopologies/](http://bacteria.itk.ppke.hu/QStopologies/)]([http://bacteria.itk.ppke.hu/QStopologies/](http://bacteria.itk.ppke.hu/QStopologies/)).  
**Keywords:** quorum sensing; N-AHL; topology; proteobacteria; bacteria
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the PLUGGY Platform
* Enhance the platform's user interface to support intuitive curation, annotation, and sharing of exhibitions. Iterative Development: Prototypes can be tested with diverse user groups, including cultural enthusiasts, educators, and institutional partners, to refine usability and engagement. Marketing Strategy: PLUGGY can be promoted through cultural heritage networks, emphasizing its user-centric tools and potential for community-driven storytelling. Stage 6: Testing and Refinement Functional Testing:
* Validate features like AR integration and NFT issuance across different devices. * Ensure user-created exhibitions are easy to navigate and share. User Feedback:
* Conduct surveys and interviews to evaluate user satisfaction, focusing on accessibility, ease of use, and engagement. * Address usability concerns, such as clarifying licensing options for uploaded content. Compliance Check: It should be ensured that the platform meets data privacy and intellectual property standards for user-generated cultural content. Stage 7: Deployment and Support Launch Strategy:
* Roll out enhanced PLUGGY features with targeted campaigns to cultural heritage communities and social media platforms. * Provide live support to assist users in creating and sharing their exhibitions. NFT Placement and Accessibility:
* Enable NFTs for digital collections, with intuitive interfaces for managing ownership and sharing. * Allow users to integrate their NFT content into physical exhibits or AR experiences. Compatibility and Maintenance: The platform should be regularly updated with new tools and features based on user feedback and emerging needs. Stage 8: Evaluation and Iteration Performance Metrics:
* Track engagement metrics, such as the number of exhibitions created, shared, and viewed. * Monitor user retention and satisfaction through surveys and analytics. Long-Term Improvements:
* Expand PLUGGY's API to support third-party applications and broader interoperability. * Use analytics to refine gamified tools and introduce new ways to engage users. Applying the framework to the PLUGGY platform enhances its role as a collaborative and user-driven cultural heritage tool. Integrating gamified features, AR storytelling, and blockchain technology ensures broader participation, secure data management, and long-term engagement. These enhancements not only empower individuals as creators and curators but also foster sustainable cultural preservation and a deeper connection to heritage.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### NOMA in Intelligent Reflecting Surfaces (IRS)  
IRSs' capacity to align the channel vectors of the user has been demonstrated to be a key facilitator of NOMA cluster formation. IRS phase shift gives an extra level of flexibility for NOMA cluster users, similar to BF techniques in MIMO-NOMA networks, and are employed to organise user clusters that are served by PD-NOMA techniques. The active BF at the BS and at the IRS passive BF have been optimised in two recent studies [134; 212] to obtain the greatest sum rate for the system's users and show an improvement over OMA-IRS systems. A new optimization method for improving spectral efficiency in a multi-cell RIS-NOMA network with signal decoding faults was presented in [213]. In particular, the BS's power budget and NOMA users' transmit power are optimised in each cell at the same time as the reflection matrix of the RIS. The joint problem is defined as non-convex because of the restrictions on objective function and quality of service, making it extremely difficult to determine the best overall solution.  
By combining NOMA and IRS to create IRSNOMA systems, it is possible to increase NOMA cluster formation potential via active and passive BF approaches. The more complex user orders and additional constraints from the passive reflectors that must be taken into account in rate optimization problems present new design challenges.