esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications and Outlook  
Gradation measurement is important for quality control of pavement construction. The gradation is obtained by digital image segmentation of the aggregates and transformed from 2D to 3D gradation. The visualization studies of the morphological characteristics of aggregates, as seen in the void fraction and gradation design of aggregates, include the development of 2D images and 3D digital reconstruction techniques for better application in engineering practice [153]. The mechanical properties and dimensional effects analysis of different aggregates are discussed through the establishment of real aggregate models and stochastic models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Pros** & **Cons** \\ \hline FEM & (1) Accurately model the micro geometry of aggregates and asphalt. (2) Investigate the internal strain distribution of asphalt mixes. & (1) Inability to solve the issue of aggregate-to-aggregate sliding in asphalt mixes, which makes the geometrical characteristics of aggregate contact not change dynamically. (2) Inherent defects in dealing with large deformation problems such as fracture behavior of microstructures. (3) The characteristics of voids in asphalt mixtures have not been fully considered. & (1) The time step in the calculation needs to be small, and the number of cells is large, and the computational efficiency needs to be improved (2) The linkage between aggregate distribution state and macroscopic mechanical properties has not been clearly established. (3) The mechanism of the influence of the aggregate distribution state on the properties of asphalt mixtures is not revealed. & (1) The time step in the calculation needs to be small, and the number of cells is large, and the computational efficiency needs to be improved (2) The linkage between aggregate distribution state and macroscopic mechanical properties has not been clearly established. \\ DEM & (1) Better characterization of the microstructure of asphalt mixes. & (2) The linkage between aggregate distribution state and macroscopic mechanical properties has not been clearly established. \\ DEM & (2) Convenient to simulate large deformation mechanical behavior such as cracking. & (3) The mechanism of the influence of the aggregate distribution state on the properties of asphalt mixtures is not revealed. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Pros and cons of FEM and DEM.
# Characteristics of Vibration Velocity Signal Using Liquid Carbon Dioxide Rock-Breaking Technology
## 2 Field Test
### Vibration Velocity Monitoring Results  
#### 3.4.1 Vibration Velocity Peak Analysis  
Blast design is intended to attenuate blasting vibrations [28; 29], and the blasting vibration attenuation law is closely related to the explosive type as well as the propagation site. This experiment uses [PERSON]'s formula to analyze the vibration attenuation law:  
\[PPV=K(Q^{1/3}R)^{\alpha} \tag{3}\]  
where \(PPV\) (peak particle velocity) is measured in cm/s; \(Q\) is the explosive quantity (kg), which is the total charge in the simultaneous blasting and the maximum section charge in the delay blasting; \(R\) is the distance of explosion source, which indicates the distance between the monitoring point and the blasting point (m); \(Q^{1/3}/R\) is the scaled charge; and \(K\) and \(\alpha\) are the coefficients and attenuation indices related to the topography and geological conditions from the blasting to the monitoring point, respectively. As \(\alpha\) increases, the blasting vibration is attenuated more rapidly.  
At present, there is no empirical formula for the vibration velocity attenuation law of L-CDRB. For a comparative analysis, the mass of a single charge of liquid CO\({}_{2}\) is converted into an equivalent emulsion explosive mass of equal energy, \(Q_{t}\), with reference to [PERSON]'s formula. Based on the data obtained from the field test monitoring, the nonlinear data were fitted, as shown in Figure 8.  
Figure 7: Fracturing tube’s internal p-t curve.  
The explosive blasting and L-CDRB vibration velocity attenuation laws are stated in Equations (4) and (5):  
\[PPV_{\rm ex}=450.419(Q^{1/3}/R)^{1.837} \tag{4}\]  
\[PPV_{\rm CO2}=9.450(Q_{t}{}^{1/3}/R)^{1.608} \tag{5}\]  
A comparison of these two test methods reveals that the attenuation index of the explosive blasting vibration is \(\alpha=1.837\), and that of L-CDRB is \(\alpha=1.608\), which means the vibration attenuation rate of L-CDRB under the same site conditions is less than that of explosive blasting.  
The \(PPV\) ratio produced by the two blasting methods can be expressed by Equation (6):  
\[PPV_{\rm ex}/PPV_{\rm CO2}=48.30(Q^{1/3}/R)^{0.229} \tag{6}\]  
Figure 9 shows that larger-scaled changes have larger ratios but gradually decreasing rates of increase. When the scaled charge was 0.1, the vibration velocity of explosive blasting was 28.5 times that of L-CDRB, and when the scaled charge was 0.2, the ratio was 33 times. This result shows that L-CDRB is obviously superior to explosive blasting in reducing the vibration velocity, especially in areas close to the blasting center.  
Figure 8: Vibration velocity attenuation law: (**a**) explosive blasting; (**b**) L-CDRB.  
Figure 9: Peak particle velocity (PPV) comparison.
## 2 Background  
Ideally, a holistic urban seismic risk assessment would investigate the behavior of every single building during seismic events via computational modeling. However, it is impractical to apply them at a regional scale because the computational models require detailed information of each building and it takes massive time to build models and execute structural analysis for hundreds of thousands of buildings. For vulnerability assessment to achieve adequate accuracy while avoiding exhaustive data collection and computational analysis, semi-empirical approaches have been developed: a few structural characteristics of buildings (e.g., the number and sizes of columns and walls, building height, and structural deficiencies) assessed by expert judgment are coupled with data-mining techniques to predict buildings' seismic vulnerabilities [1; 14; 15]. Therefore, collecting structural information is the first step to successfully apply the semi-empirical approaches for building seismic vulnerability assessment at a regional scale. Realizing deficiencies of traditional paper-based field surveys, vision-based technologies have been applied to structural information collection.  
A handful of prior studies on structural element detection methods can be generally classified into color/texture-based methods and shape-based methods: the former detects a structural element by judging if the color or texture of the object's material matches the predefined material databases, and the latter by judging if the shape of the object matches the predefined element shape templates. However, structural elements in images may fail to match these predefined databases or templatesbecause of their diversities in colors, textures, and shapes. Therefore, grayscale statistical feature-based object-detection methods, being able to detect objects without predefining databases or templates, are introduced in this section.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 1 Introduction
### Previous Work on Drone Swarming and Path Planning  
Routing drones safely and efficiently is a complex problem. Multi-robot navigation is a well-studied topic of research, with many constraints and requirements [19; 20]. Devising the path for each drone is an example of the Vehicle Routing Problem (VRP), a generalization of the Traveling Salesman Problem (TSP). In the VRP, a group of vehicles must visit each node of a graph with the minimal longest route. This ensures the evenest distribution of distance required to be traveled by each drone, which allows the fewest possible drones needed to adequately monitor the farm. Considerable research has been invested in TSP, VRP and its numerous variants [21; 22; 23], and as a result many solutions to this routing problem are available. More specifically, this environment is an example of Distance Constrained VRP (DVRP) due to the limiting factor being the range of the drones.  
However, most VRP research consists of highly idealized node graphs. While most studies focus on randomly generated node locations with 100 nodes or fewer, the reality of aquaculture farms is commercial farms typical have hundreds of ponds, and may have more than a thousand. Aquaculture farms also dig their ponds in a regular, gridlike manner, which would facilitate repetitive paths. There are relatively few VRP methods which are intended for calculating routes for more than 100 nodes [24; 25], and fewer methods for calculating paths in a grid space [26; 27]. Another factor to consider is the drones will be required to fly in inclement weather. The impact of wind on their path planning may be highly significant, yet there is little research regarding its impact on drone path planning [28; 29]. These unique conditions require special considerations that are not sufficiently studied in the literature.
## 1 Introduction
It was observed that, with increased ablation energy, (1) the expansion of PTFE augmented, and (2) the ellipticity and irregularity of the ablation zone intensified. These two observations were hypothesised to be correlatable and potentially influenced by the manufacturingprocess of the coaxial cable [25]. Therefore, the observed PTFE expansion could be alleviated by annealing the coaxial cable before antenna fabrication. The experiments described in this study aimed to address this hypothesis. The remainder of this paper is organised as follows. Section 2 begins with an introduction to PTFE as a polymer material and its essential characteristics. This is followed by a detailed description of the annealing process and the experimental methodologies employed to investigate thermal expansion of PTFE and its implications for ablation performance. Section 3 presents the results and discusses characterisation of the thermal expansion of PTFE, including a comprehensive comparison between annealed and non-annealed antennas, with specific emphasis on their ablation performance. Finally, Section 4 summarises the significant findings and their respective applications.
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion  
Generally, the experiment can be divided into two parts. The first part is to conduct the experiment using a single parameter, then analyze and investigate the effect of each parameter on corner error. The second part is optimization using the RSM and analyzing the effect of interaction between each parameter on corner error.  
Based on the Box-Behnken RSM method, 54 experiments were conducted for six parameters (as shown in Table 2). Each experiment was repeated three times for each parameter condition, and the average value was calculated for analysis. The corner error and machining speed were considered as response variables.
## 3 Challenges for Designing MAC Protocols in UWSNs
### Power Consumption  
The devices that are used for underwater sound propagation, have a higher order of signal magnitude transmission capacity, than the devices that are used in terrestrial communication, which are equipped with advanced measurement of power transmission/consumption. Thus we will be needing specially designed protocols for acoustical communication to make it more power efficient [59]. Since UWSNs has many special features that make it unique to traditional networks. Power limitations is one example of the special feature, because once the power source of a sensor nodes drains off. It will require an instant replacement, as a delay in replacement will cause the communication between communicating nodes to suffer [52; 55; 59; 60].
###### Abstract  
Chlorophyll-a (Chla) concentration, which serves as a phytoplankton substitute in inland waters, is one of the leading indicators for water quality. Generally, water samples are analyzed in professional laboratories, and Chla concentrations are measured regularly for the purpose of water quality monitoring. However, limited spatial water sampling and the labor-intensive nature of data collection make global and long-term monitoring difficult. The developments of remote-sensing optical sensors and technologies make the long-term monitoring of Chla concentrations for an entire water body more achievable. Many studies based on machine learning techniques, such as regression and artificial neural network (ANN) methods, have recently been proposed for Chla concentration estimation using optical satellite images. The methods based on machine learning can achieve accurate estimation. However, overfitting problems may arise because the in situ Chla dataset is generally insufficient to train a complicated machine learning model, which makes trained models inapplicable. In this study, an ANN model containing three convolutional and two fully connected layers with 4953 unknown parameters is designed. A transfer learning method, consisting of model pretraining, main-training, and fine-tuning stages, is proposed to ease the problem of insufficient in situ samples. In the model pretraining stage, the ANN model is pretrained and initialized using samples derived from an existing Chla concentration model. The pretrained ANN model is then fine-tuned using the proposed transfer learning technique with in situ samples collected in five different campaigns carried out during early 2019 from Laguna Lake, the Philippines. Before the transfer learning, data augmentation and rebalancing methods are conducted to enrich the variability and to near-uniformly distribute the in situ samples in Chla concentration space, respectively. To estimate the alleviation of model overfitting, the trained ANN model, using an in situ dataset from Laguna Lake, was tested using an in situ dataset from Lake Victoria, Uganda, obtained in 2019, which has a similar trophic state as Laguna Lake. The experimental results from Sentinel-3 imagery indicated that the overfitting problem was significantly alleviated and the trained ANN model outperformed related models in terms of the root-mean-squared error of the estimated Chla concentrations.  
ch chlorophyll-a concentration; artificial neural network; transfer learning; overfitting +
Footnote †: journal:  
00 2021
## Material and Methods
### Microarray cDNA Synthesis, Purification and Hybridization  
Before first-strand cDNA synthesis, RNA integrity was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA). Fluorescently labelled cDNA was obtained by using the SuperScript Indirect cDNA Labeling System (Invitrogen Carlsbad, CA, USA). After, the Cy3 and HyPer5 fluorescent dyes (Amersham Pharmacia Biotech, Buckinghamshire, UK) were coupled to the aminoallyl-modified first-strand cDNA, and purification of probes was carried out with the CyScribe GFX Purification Kit (GE Healthcare, Madrid, Spain). Labeling efficiency was assessed using a NanoDrop ND1000 spectrophotometer. Preparation of probes and hybridization at 65 \({}^{\circ}\)C during 17 h was performed as described on the Two-Color Microarray Based Gene Expression Analysis Manual (Quick Amp Labeling, Agilent Technologies, Madrid, Spain) with Tecan HS Pro Hybridization (V. 5.7/Agilent Technologies, Madrid, Spain). Slide _L. plantarum_ WCFS1 8x15 Microarray GE Agilent G2509F Oligo Microarrays (No. 026636) (Agilent Technologies, Madrid, Spain) was custom designed and contains 60-mer probes that were taken at the gene expression omnibus database (GEO Accession No.GPL5874). The oligo-microarray contained an average of three probes per transcript.  
Real-time quantitative RT-PCR assays (RT-qPCR). Real-time RT-qPCR was used to validate the microarray data. Amplification was carried out using a 7500 Fast System (Applied Biosystems, Inc., Foster City, CA, USA). RNA was reverse transcribed using High Capacity cDNA Reverse Transcription Kits (Applied Biosystems). The specific primers used for the RT-qPCR assays are listed in the Supplementary Table S1. The SYBR Green method was used, and each assay was performed in triplicate using SYBR Green real-time PCR Master Mix (Applied Biosystems). Amplification was initiated at 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 1 min. Control PCRs were included to confirm the absence of primer dimer formation (no-template control), and to verify that there was no DNA contamination (without RT enzyme negative control). All real-time PCR assays amplified a single product as determined by melting curve analysis and by electrophoresis. A standard curve was plotted with cycle threshold (Ct) values obtained from amplification of known quantities of cDNAs and used to determine the efficiency (E) as E = \(10^{-1/\mathrm{slope}}\). The expression levels of target genes were normalized. The Bestkeeper analysis [19] as applied and the geometric mean of the most stably expressed housekeeping genes (16S rRNA, _gapB_, _duG_, and _gyrA_) was used as a normalization factor. The expression ratios measured by microarrays and by RT-qPCR assay were plotted, and the linear correlation coefficient was calculated (y = 1.3163x-1.0264; R\({}^{2}\) = 0.94).
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 2 Development Stages of Pavement Distress Detection
### Non-Destructive Automated Detection Stage  
The third phase features fully automated, non-destructive detection. This represents a technological breakthrough by combining computer vision and deep learning in post  
Figure 1: Development stages of highway pavement distress detection.  
processing systems. The key advancement lies in integrating high-resolution imaging with intelligent distress classification. This approach reduces human involvement to essential validation tasks, significantly improving efficiency and cost-effectiveness compared to semi-automated methods.  
Throughout this process, the iterative advancement of pavement distress detection technology has consistently focused on two core elements: innovations in image acquisition devices and the optimization of image processing algorithms.
## 4 Distributed Multi-Hop Localization Based on Regularized Extreme Learning
### Location Estimation  
For any unknown node, it can obtain the hop-counts vector \(\mathbf{h}_{k}\subset\mathbf{H}_{t}\) and the mapping model \(\hat{\mathbf{\beta}}\) of the anchor node in the data collection stage to estimate the linear distance from \(S_{k}\) to the anchor node, that is:  
\[\mathbf{D}_{pred}=\mathbf{G}(\mathbf{H}_{t})\hat{\mathbf{\beta}}. \tag{9}\]  
Then, the unknown nodes combine the coordinates of the anchor nodes and the distance between them to obtain the final coordinates. That is solving the following equations group \(\text{fork}\in\{m+1,m+2,\ldots,n\}\):  
\[\left\{\begin{array}{c}\left(\hat{x}_{k}-x_{1}\right)^{2}+\left(\hat{y}_{k}- y_{1}\right)^{2}={d_{k1}}^{2}\\ \cdots\\ \left(\hat{x}_{k}-x_{m}\right)^{2}+\left(\hat{y}_{k}-y_{m}\right)^{2}={d_{km }}^{2}\end{array}\right., \tag{10}\]  
in which \({d_{kj}}=\mathbf{D}_{pred}(k,j)\) is the physical distance predicated by Equation (9). Above equations group is usually solved via the maximum likelihood estimation (MLE) of multiple-source location [25]. Specifically, the optimal solution to Equation (10) is expressed as:  
\[\hat{\mathbf{c}}_{k}=\left(\hat{x}_{k},\hat{y}_{k}\right)=\left(\mathbf{A}^{T}\mathbf{A} \right)^{-1}\mathbf{A}^{T}\mathbf{b}, \tag{11}\]  
where the two projection matrices are:  
\[\mathbf{A}=2\left[\begin{array}{cc}\left(x_{1}-x_{m}\right)^{T},&\left(y_{1}-y_ {m}\right)^{T}\\ \cdots\\ \left(x_{m-1}-x_{m}\right)^{T},&\left(y_{m-1}-y_{m}\right)^{T}\end{array} \right], \tag{12}\]and:  
\[\mathbf{b}=\left[\begin{array}{c}\left(x_{1}-x_{m}\right)^{2}+\left(y_{1}-y_{m} \right)^{2}+d_{km}^{2}-d_{k1}^{2}\\ \vdots\\ \left(x_{m-1}-x_{m}\right)^{2}+\left(y_{m-1}-y_{m}\right)^{2}+d_{km}^{2}-d_{k1 }^{2}\end{array}\right] \tag{13}\]  
respectively. Finally the details of location estimation are shown in Algorithm 2.  
```
1. Input: the shortest hop-counts for unknown nodes to anchor nodes \(\mathbf{H}_{t}=\left[\mathbf{h}_{m+1},\mathbf{h}_{m+2},\cdots,\mathbf{h}_{n}\right]_{m\times(n-m )}\); the coordinates of known nodes \(\{cor(S_{i})=\left(x_{i},y_{i}\right)\}_{i=1}^{m}\).
2. Employ the learned weights of Algorithm 1 to predicate the distances \(\mathbf{D}=G(H_{t})\hat{\mathbf{\beta}}\).
3. For\(k\) from \(m+1\) to \(n\)
4. calculate matrix \(\mathbf{A}\) using Equation (12);
5. calculate matrix \(\mathbf{b}\) using Equation (13);
6. calculate \(\ell_{k}=\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}\mathbf{A}^{T}\mathbf{b}\);
7. End.
8. Output: estimated coordinates of unknown nodes \(\{cor(S_{k})=\ell_{k}=\left(\hat{x}_{k},\hat{y}_{k}\right)\}_{k=m+1}^{n}\)
```  
**Algorithm 2**Location estimation.
# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
###### Abstract  
This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the air transport industry. The present paper focuses on the development of a model for medium-term flight demand estimation by flight destinations. This is based on the analysis of historical airline data on dates, departure times, and passenger demand, as well as the consideration of the influence of macroeconomic indicators, namely gross regional product (GRP), median per capita income, and population of departure and arrival points. This paper reviews international experience in the development of demand forecasting models and their use for resource planning in the industry. The developed model was evaluated using historical data on demand for a single turnaround flight operated by an airline. The developed model allows for the forecasting of the distribution of potential demand for airline flight destinations in the medium term, utilizing comprehensive historical data on departure times and flight demand by destination.  
Facelbook Prophet algorithm; passenger demand forecasting; passenger flow; prediction model; time-series forecasting
## 2 Materials and Methods  
### Chemicals and Reagents  
Bovine serum (BS), phosphate-buffered saline (PBS), fetal bovine serum (FBS), trypsin-ethylenediaminetetraacetic acid (EDTA), penicillin-streptomycin (P/S), and horse serum (HS) were obtained from Gibco (Gaithersburg, MD, USA). Quercetin, gallic acid, sodium carbonate, [PERSON]'s reagent, isopropanol, aluminum chloride, potassium acetate, potassium persulfate, 2,4,6-tripyridyl-s-triazine (TPTZ), FeCl\({}_{3}\) 6H\({}_{2}\)O (ferric chloride), 2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS), 1,1-diphenyl-2-picrylhydrazyl (DPPH), insulin, dexamethasone (DEX), 3-isobutyl-1-methylkanthine (IBMX), bisphenol A(BPA), oil red O (ORO), and N-acetylcysteine (NAC) were purchased from Sigma-Aldrich Co. (Saint Louis, MO, USA). N-methyl dibenzopyrazine methyl sulfate (PMS) reagent and 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazol-5-carboxanilide (XTT) assay kits were obtained from WelGene (Seoul, Republic of Korea). Antibodies targeting \(\beta\)-actin, peroxisome proliferatorator-activated receptor-\(\gamma\) (PPAR\(\gamma\)), and CCAAT/enhancer-binding protein-\(\alpha\) (C/EBP\(\alpha\)) were purchased from Cell Signaling Technology, Inc. (Beverly, MA, USA), and fatty acid synthase (FAS), peroxisome proliferatorator-activated receptor-\(\gamma\) coactivator 1\(\alpha\) (PGC-1\(\alpha\)), and carnitine palmitoyltransferase-1 (CPT-1) were purchased from Santa Cruz Biotechnology, Inc. (Dallas, TX, USA). Enhanced chemiluminescence (ECL) reagent was obtained from Bio-Rad, Inc. (Hercules, CA, USA).