esa-sceva/satcom-chunk-collection huggingface dataset:
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 3 Results
### ESA-Induced Expansion Measurement Using Optical Fibers  
To determine if optical fibers could be used to measure ESA-induced expansion, the three cement paste prisms whose fabrication is described in Section 2.4 were fully immersed in sodium sulfate solution for 100 days. Figures 12 and 13 show the results of the expansion as measured by the extensometer and by the embedded OF, respectively. As an initial remark, it can be indicated that these figures illustrate the highly repeatable expansion behavior of the specimens.  
Figure 11: Penetration depth of sulfate ions measured by ICP-AES and drying method after 24 weeks of semi-immersion in the Na\({}_{2}\)SO\({}_{4}\) solution.  
Figure 12: Evolution of ESA-induced expansion measured with extensometer.  
The averaged measurement results for each sensor type are shown in Figure 14. This allows the validity of the results obtained with the OF sensors to be assessed, as extensometer measurement is considered to be a widely proven technique. As shown in Figure 14, the OF measurements were satisfactorily similar to those obtained with an extensometer, although the optical fibers measure slightly higher deformations. For example, the average final expansion recorded with the extensometers (before rupture of OF sensors) was 0.065%, while that recorded with the OF was 0.074%. Furthermore, the expansion paths were nearly identical regardless of the measurement method used.  
The first OF failure occurred after 57 days of immersion of the specimens (the OF of sample 1 in Figure 13). The two remaining OFs failed a few days after the last measurement (78 days of immersion) for unknown reasons (chemical attack on the coating and/or mechanical damage due to frequent handling of the extensometer during the measurement).  
Figure 14: Comparison of ESA-induced expansion measured with OF or with extensometer.  
Figure 13: Evolution of ESA-induced expansion measured with OF.
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 3 Experiment
### Experiments and Data Analysis
#### 3.2.3 Analysis of Mapping  
We also compared the proposed navigation system with LOAM, F-LOAM, and LIO-SAM in terms of mapping quality. In this subsection, we only show a comparison of the proposed system with LIO-SAM since F-LOAM and LOAM do not produce meaningful point cloud maps. The top view of the point cloud map is shown in Figure 12. The side view of the point cloud map is shown in Figure 13.  
In Figure 12, we zoom in on the outline of the lower left building in the top view. A lot of \"ghosting\" appeared in the outline of the building constructed by LIO-SAM. These \"ghost images\" reflect that LIO-SAM's estimation of the UAV's displacement significantly differs from the true displacement in the horizontal direction. In contrast, the proposed navigation system builds a clear outline of the lower left building. This reflects that the proposed navigation system can accurately estimate the displacement in the horizontal direction.  
In Figure 13, we zoom in on the bottom support and top of the space grid structure. Compared to LIO-SAM, the proposed navigation system has a more accurate representation of buildings in the constructed map. For example, the thickness of the point cloud on the surface of the shell structure at the top of the coal shed is smaller, and the point cloud at the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Direction** & **LIO-SAM** & **F-LOAM** & **DJI** & **Proposed System** \\ \hline
**X/m** & \(-8.6679\) & \(2.1266\) & \(-11.8198\) & **0.0550** \\
**Y/m** & \(-1.9806\) & \(-7.2650\) & \(-7.1752\) & \(-\)**0.0454** \\
**Z/m** & **0.0399** & \(1.4190\) & \(-0.5295\) & \(-0.0880\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landing point error comparison.  
Figure 11: The processing time of a single-scan lidar point cloud.  
bottom support better reflects the actual shape of the building structure and wall. This also reflects that the proposed navigation system is very accurate for UAV motion estimation.  
Figure 12: The top view of the point cloud map. (**a,b**) are the top views of the point cloud maps built by LIGO-SAM and the proposed navigation system, respectively.  
Figure 13: _Cont._
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
## 5 Research Challenges and Future Trends
### Application of Energy Methods  
Balancing thrust and drag at an aircraft's trailing edge could reduce energy loss, improve the L/D ratio, and shorten take-off distance. Other than reforming the propulsion system to achieve the purpose of thrust-drag balance. A choice of energy system could also indirectly change the propulsion configuration.  
So far, the energy types available for aircraft include mechanical, pneumatic, electrical, and hybrid power systems (see Table 3) [93]. The configuration and layout of the DP changes as the energy system changes. Mechanical and pneumatic systems are common energy systems that have more emission products. Despite that, new liquid hydrogen pneumatic energy systems are very eco-friendly and energy-saving. An advantage of using an electrical system is that there is no need to generate power through combustion. In principle, this propulsion system does not need to conform to the conventional configuration. It means a seamless elongated cross-flow fan could be constituted. However, the disadvantage of using an electrical system is having a complex configuration and the need to invert gravimetric energy from low to high density (DC to AC) [94]. This series of operations ends up providing only a small amount of power [95]. Furthermore, the emission products of the power system depend on the power generation resources. Chemical resources with low emissions have the potential to produce substances with significant environmental hazards.  
A novel and promising approach to energy is the electrohydrodynamic system, which offers advantages in terms of emissions and configuration. It is a method by which propulsion can be generated in a fluid. Specifically, its propulsion is generated by high-frequency, high-voltage initiation of electrons in the surrounding air (Corona discharge), ionizing the air and accelerating the ions by Coulomb force. This method presents a continuous propulsion system with no transfer components. Due to the thinness of the energy system, it is not complicated to configure different output powers in different portions of a \"body\" to achieve an effect of thrust-drag balance. At present, the thrust and flight speed of this system is relatively weak and low. It is now only available in a limited lightweight aircraft model [96]. The choice and implementation of a novel energy system on large  
Figure 7: Control methods of airflow distortion in BLI S-shaped inlet diffusers.  
aircraft is a research challenge that would lead to the future development trend of the propulsion system.
## 3 Results and Discussion
### Changes in VOCs in millet Huangjiu
#### 3.4.1. Esters  
Esters are the predominant class of compounds contributing to the fruity and floral characteristics of Huangjiu samples [23]. The formation of these compounds is chiefly attributed to esterase-catalyzed reactions of alcohols and acids, which occur due to the metabolism of glucose and amino acids by microorganisms [12]. Ester formation can also occur through non-enzymatic esterification reactions between alcohols and organic acids derived from the substrate during fermentation processes [24]. As indicated in Table 3, ethyl esters are the most abundant form of esters in Huangjiu samples, and they are known for their fruity, sweet, and creamy aroma notes [3]. Key ethyl esters identified in previous studies, including ethyl acetate (fruity, sweet), ethyl octanoate (fruity, mushroom), and ethyl butanoate (fruity, sweet) [25], were also detected in this study, albeit in varying amounts (Figure 3). Of the esters detected, the most abundant ones were ethyl acetate, ethyl butanoate, ethyl hexanoate, ethyl decanoate, and ethyl hexadecanoate, displaying the highest concentrations (Figure 4).  
Univariate statistical analysis showed that the concentration of the most predominant ester, ethyl hexadecanoate, was significantly increased from 46.5 mg/L (control group) to 62.9 mg/L (Alb-2 group), with similar increases observed in the Huangjiu samples from the Alb-1 and Glu + Alb groups, but not in the Glu-1 and Glu-2 samples. With the exception of a few minor esters, such as ethyl acetate, most of the major esters showed increased concentrations upon addition of albumin protein (Figure 4). Similar differentiations between the Glu and Alb groups were also observed in the heat map for minor esters, such as ethyl octanoate, \(\gamma\)-nonanolactone, ethyl oleate, and ethyl linoleate (Figure 3). Based on the results, it can be inferred that the inclusion of albumin protein causes a notable increase in the levels of esters, which can potentially influence the fruity and full-bodied aroma characteristics of millet Huangjiu.  
Figure 3. Heat map of the volatiles contributing to the metabolic discriminations among different millet Huangjiu samples.
## 4 Conclusions  
We synthesized highly monodispersed AgNCs-PMAA with a mean diameter of 1.84 nm using a simple photochemical approach. We embedded AgNCs-PMAA in a hydrogel matrix based on PEGDA. The photopolymerization of the matrix was easy and fast and the emission band of the NCs was still present. The hybrid hydrogel device preserves the sensitivity to Pb(II) ions thanks to the matrix porosity.  
We measured a fluorescence intensity enhancement as a function of the Pb(II) ions present in water in contact with the solid matrix. This behavior can be used for lead detection in water with a good linearity up to 8 uM. Despite the high detection limit, with respect to the bare colloidal solution of AgNCs-PMAA, such a solid-state device is hugely more exploitable as an optical sensor than the liquid colloidal ones. These preliminary results are encouraging to fabricate an economic solid-state optical device for water monitoring that could be used directly for detection of lead ions on the field.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11083470/s1](https://www.mdpi.com/article/10.3390/app11083470/s1)]([https://www.mdpi.com/article/10.3390/app11083470/s1](https://www.mdpi.com/article/10.3390/app11083470/s1)).  
Conceptualization, original draft preparation, and editing: L.B. and P.P.; review\" F.D.M., R.F. and P.P.; synthesis of materials and optical characterizations: L.B.; HRTEM investigations and analysis: J.L. and C.S. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the funding of Regione Lazio, through Progetto di ricerca 85-2017-15125, according to L.R.13/08 and the financial support of the University of Rome Tor Vergata through the project \"Beyond the borders\" n. E84I19002280005.  
Not applicable.  
All the supporting data are shown in the Supplementary Materials file.  
Figure 3: (**a**) PL spectra of the hydrogel matrix doped with AgNCs-PMAA as a function of Pb(II) concentration, the dotted grey line represents the emission of bare AgNCs-PMAA colloidal solution as reference; (**b**) the F/F\({}_{0}\) ratio as a function of Pb(II) concentration between 0–200 uM.
## 2 Materials and Methods
### Spectral Measurements and Analysis  
The FTIR spectra were carried out by means of a FTIR Microscope HYPERION 2000 connected to a Vertex 70 Bruker interferometer (Bruker Optik GmbH). The spectra were measured in the transmission mode for cells grown on CaF\({}_{2}\), microscopy glass, and coverslip substrate, and in the reflection mode for cells grown on low-e substrate, with a MCT (mercury cadmium telluride) detector (cooled in liquid N\({}_{2}\)) and a 15\(\times\) objective. Each spectrum was recorded in the high wavenumber range of 2500-4000 cm\({}^{-1}\), at the resolution of 4 cm\({}^{-1}\), with 64 scans. The sampling area was about 80 \(\times\) 80 \(\upmu\)m size, including 3-4 cells of each type. The background signal of each substrate was collected in a spatial region free of cells. For each experiment, about 30 cells were measured. Data acquisition was done by means of Opus 6.5 software (Bruker Optik).  
Each FTIR spectrum was firstly corrected by using RMie-EMSC algorithm of [PERSON] et al. [18] in the Matlab software; then the spectra were normalized to the intensity of Amide A peak at about 3300 cm\({}^{-1}\), by means of the Opus software, in order to minimize the contribution of scattering and the absorption from cells having different thickness, respectively.  
The Principal Components analysis was performed with ChemoSpec package of R software (version 3.4.1, R Core Team, Vienna, Austria, 2017) [19]. Radiometric analysis was performed by calculating the intensity ratio between two specific absorption peaks (at 2924 and 2956 cm\({}^{-1}\)) for each normalized single spectrum. Successively, the mean value and standard deviation were calculated for each set of values of the investigated intensity ratios, related to the two types of cells grown on each substrate. The statistical difference between the cancerous group and the normal group was assessed by the t-test method. All statistical analyses were performed by SigmaPlot software (version 12.5, Systat Software).
# Animal Sound Classification Using Dissimilarity Spaces
## 4 Clustering
### Spectral  
Spectral clustering partitions data into clusters starting from the adjacency matrix representing the undirected similarity graph of the patterns. Each pattern is a node of the graph, and two nodes are connected only if their similarity is larger than a threshold (typically set to 0).  
This clustering algorithm involves the following matrices:  
* The similarity matrix \(M\), whose cell \(m_{ij}\) is the similarity value of two patterns (i.e., two spectrograms \(s_{i}\), \(s_{j}\));
* The degree matrix \(D\), which is a diagonal matrix that is obtained by summing the rows of \(M\): \[D_{g}(i,i)=\sum_{j}m_{i,j};\] (10)
* The Laplacian matrix \(\mathrm{L}\), which is defined as \[L\;=\;D_{g}-M.\] (11) The algorithm for spectral clustering is a five-step process:  
1. Define a local neighborhood for each data point in the dataset (there are many ways to define a neighborhood; the nearest-neighbor method is the default setting in the MATLAB implementation of spectral clustering). Then compute the local similarity matrix of each pattern in the neighborhood.
2. Calculate the Laplacian matrix \(L\).
3. Create a matrix \(V\) containing columns \(v_{1}\),..., \(v_{k}\), where the columns are the \(k\) eigenvectors, i.e., the _spectrums_ (hence the name), corresponding to the \(k\) smallest eigenvalues of \(L\).
4. Perform k-means or k-medoids clustering by treating each row of \(V\) as a datapoint.
5. Cluster the original pattern according to the assignments of their corresponding rows.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 2 Current Technologies for Railway Track Maintenance
### Specialist Train  
The inspection and monitoring of vast railway networks require faster inspection methods and specialist trains. Equipped with a variety of sensors, these trains can finish inspection tasks quickly and carry repair crews and materials. The new measurement train (NMT) [41] and mobile maintenance train (MMT) [42] are two state-of-the-art specialist trains, used in the UK for track inspection and repair, as shown in Figure 4.  
NMT, shown in Figure 3(a), is fitted with multiple cameras, laser sensors, transducers, and accelerometers [41]. Plain line pattern recognition (PLPR) methods [43] can detect anomalies with the camera at a top speed of 125 mph, while the transducers and accelerometers can record the track geometry. This model generates and records almost 10 terabytes of data on an onboard computer for every 440 miles. On the other hand, MMT, shown in Figure 3(b), is a specially designed and equipped train for track maintenance tasks and has been described as a \"workshop on wheels,\" providing a workplace which is from the sun,  
Figure 4: Specially designed train in the UK for railway maintenance: (**a**) new measurement train (NMT) [41]; (**b**) mobile maintenance train (MMT) [42].  
rain, and trains in operation [42]. Based on their design, attached sensors, and actuation systems, specialist trains can perform inspections at a higher speed or can perform some repair tasks. However, these specialist trains require human involvement, are costlier to run, and reduce track possession while in operation.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 3 Proposed Method
### Meter Dial Location Network  
In order to achieve real-time meter dial location, we employ a lightweight version of the Darknet-19 framework, first presented in [19], as the backbone of our model. The main motivation is that Darknet-19 has been widely used in the YOLO family, and its effectiveness has been verified in many practical applications. Specifically, this lightweight network has 19 convolutional layers and 5 max pooling layers, the same as the original Darknet-19. The only difference between them is that the number of filters employed in each convolutional layer of our model is half that in Darknet-19. As a result, this lightweight network can locate the meter dial at a fast inference speed with satisfactory location accuracy.  
Figure 2 illustrates the architecture of the meter dial location network used in the first stage of our model. This network model first processes the input image via a series of convolution and pooling operations. Then, it applies an average pooling layer followed by a softmax layer to produce the output information about the confidence score, position, and size of each bounding box. At last, a non-maximum suppression operation is performed to further select the bounding box with the highest likelihood and output its coordinate information. After that, according to the coordinates of each bounding box, the input image is cropped to obtain the meter dial and feed it into the next stage to segment the pointer and reference points. Note that, in our model, k1 \(\times\) 1 and k3 \(\times\) 3 used in the Con2d layer represent 1 \(\times\) 1 and 3 \(\times\) 3 convolution kernels, respectively. In the Maxpool layer, k2 and s1 denote a pooling kernel of size 2 \(\times\) 2 and a pooling stride of size 1, respectively, and p1 means that the padding size is 1.  
Figure 1: The architecture of our proposed pointer meter reading model.
## 3 Results
### Performance of SpectraViT on Public Datasets  
To further validate the recognition and generalization performance of the SpectraViT model, we conducted a comparative analysis using the publicly available fruit puree dataset. The primary connection between this dataset and the jade dataset lies in their use for qualitative analysis, allowing us to apply similar analysis methods to process and compare the spectral features of different substances. Specifically, this dataset includes 351 strawberry samples, 159 raspberry samples, and 665 \"non-fruit\" samples (including various other fruits and \"contaminated\" strawberry and raspberry pureses, where the weight percentage of other fruits is greater than 10%). The dataset was sourced from reference [31]. The results are shown in Table 9. According to the performance comparison on the fruit puree spectral dataset, our SpectraViT model outperforms all other models across all evaluation metrics (accuracy, recall, precision, and F1 score), achieving 98.47%, 98.49%, 98.47%, and 98.48%, respectively. AlexNet and SpectraTr also performed well, with AlexNet achieving accuracy, recall, precision, and F1 scores of 97.96%, 97.95%, 97.99%, and 97.96%, respectively, and SpectraTr achieving 97.45%, 97.45%, 97.43%, and 97.44%, respectively. In contrast, DeepSpectra had noticeably lower metrics, with an accuracy of about 95.41%.  
Traditional machine learning methods did not perform as well on the fruit puree spectral dataset compared with the jade UV spectral dataset, with SVM and PLS-DA achieving accuracies of only 92.93% and 93.88%, respectively. Traditional machine learning methods struggle to achieve a strong performance across multiple datasets, whereas deep learning models can effectively classify multiple spectral datasets. This indicates that deep learning models have a significant advantage in handling spectral data. Furthermore, among deep learning models, our SpectraViT model demonstrates superior accuracy, recall, precision, and F1 score on both the jade UV spectral dataset and the fruit puree infrared spectral dataset compared with other deep learning models. This suggests that SpectraViT is a better-suited deep learning model for spectral data classification.
# Fabrication of Metal-Reinforced Complete Dentures Using the CAD-CAM Technique
## 1 Introduction  
With the wide applications of digital technology in dentistry, research to establish a completely digital workflow for the fabrication of complete dentures has been underway since 1994 [1]. Currently, complete dentures are being fabricated using computer-aided design and computer-aided manufacturing (CAD-CAM) via milling or rapid prototyping methods. The clinical feasibility of these dentures has been proven [2; 3; 4; 5]. Additionally, various advantages have been reported, such as better fit, retention, easy reproduction, and decreased manipulation [6; 7]. However, fabricating metal-reinforced complete dentures using the digital technique is different, as these dentures are fabricated using at least two other materials.  
Metal-reinforced complete dentures are associated with improved retention, reduced probability of fracture, and greater patient satisfaction in terms of comfort and pronunciation compared to conventional resin-based complete dentures [8]. Metal denture bases, fabricated using selective laser melting, milling, stereolithography apparatus, or digital light processing techniques, have been found to be feasible for clinical use [9; 10]. Digital fabrication of metal-reinforced complete denture bases using the current technology is difficult.  
To overcome this limitation, considerable research has been conducted. [PERSON] et al. [11] introduced a method to combine the digital technique and conventional flask procedure to fabricate a removable partial denture. Moreover, [PERSON] et al. [12] fabricated a digital denture by bonding the functional cusps of artificial teeth manufactured using a 3D metal printer with the resin denture base. Nevertheless, a fully digital workflow for the fabrication of metal-reinforced complete dentures, preserving the basic structure, has not been developed. Hence, in this technical article, we report a novel method to add a metal framework structure to the complete denture milling procedure. A geometry guide, an indentation for the complete denture, was designed to determine and replicate the correct positions of the artificial teeth and metal framework.