esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mandibular Third Molar Detection Model  
This model extracts a region of interest (ROI) that is used as an input for classification of extraction difficulty and the likelihood of IAN injury from a panoramic radiographic image. Preprocessing was used to improve the mandibular third molar detection model's training efficiency and performance. First, large-sized panoramic radiographic images were resized to 1056 \(\times\) 512 and used as input to the model. Second, contrast limit adaptive histogram equalization (CLAHE) is applied to panoramic radiographic image to improve image contrast [43]. By using CLHAE, the deep learning model facilitates extraction features.  
The Retinanet was used for mandibular third molar detection [44]. The detection model's backbone was a ResNet-152, which was pre-trained on the ImageNet dataset [45; 46]. The mandibular third molar's bounding box was obtained through the detection model, which generated the ROI, which was cropped to 700 \(\times\) 700 to be suitable for inclusion of the mandibular third molar, the teeth adjacent to the third molar, and IAN in the panoramic radiographic image. According to the bounding box, the generated ROIs are set to the tooth number (#38 and #48). These are used as inputs to the classification model for predicting the extraction difficulty and the likelihood of IAN injury. The network was trained using the Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-4}\), a batch size of 2, and the focal loss [47]. The model was trained for 250 epochs, and each epoch was evaluated.
## 2 Monitoring Program and Result Analysis
### Monitoring Program  
This article focuses on the engineering monitoring of the deformation of the bridge structure and the subway track structure within the range of subway viaduct sections 3# to 16#. The corresponding mileage for this range is from K14 + 282 m to K14 + 693 m. The bridge structure and track structure within the range of 5#, 6#, 7#, 8#, 9#, 10#, 11#, 12#, and 13# are the primary areas of focus for monitoring. If necessary, appropriate protective measures should be implemented. The project can be divided into four monitoring stages based on the construction steps. These stages include the construction of the retaining pile and underpinned pile, the excavation of the foundation pit with underpinned abutment, the construction of the underpinned abutment, the pile foundation underpinning, and the construction of the deep foundation pit for the road. The first three monitoring stages pertain to the piling foundation underpinning phase of the construction project, whereas the fourth monitoring stage corresponds to the foundation pit excavation phase of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multirow{2}{*}{**Groundwater Type**} & \multicolumn{2}{c}{**Depth of Groundwater Level**} \\  & & \multicolumn{2}{c}{**(Pressure Head of Confined Water)**} \\ \cline{3-4}  & & **Buried Depth of Water Level (m)** & **Water Level Elevation (m)** \\ \hline
1 & \multicolumn{2}{c}{} & 6.40–12.30 & 10.57–13.79 \\ \hline
2 & \multicolumn{2}{c}{} & 13.20 & 9.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Groundwater level survey.  
Figure 3: The engineering geological conditions [25].  
building project. Pictures of the construction site are shown in Figure 4. Table 3 displays the deformation control values of the temporary retaining pile construction for a foundation pit with an underpinned abutment. Table 4 displays the deformation control parameters for the subway viaduct structure and track structure during the pile foundation underpinning. Table 5 displays the deformation control parameters for the subway viaduct structure and track structure during foundation pit construction. The allowable static geometric deviation of the track structure for the current subway route is displayed in Table 6.  
The track structure's vertical displacement measuring point is positioned on the track, as depicted in Figure 6's arrangement diagram. The current subway system in this project is comprised of two separate lines, namely the upper line and the lower line. Figure 7 displays the schematic diagram of the underground line.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Comprehensive Maintenance (mm)** \\ \hline Gauge & +4, \(-2\) \\ Horizontal & 4 \\ Vertical & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Allowable deviation of static geometric dimensions of track structure.  
Figure 5: Layout of measuring points for the existing subway bridge pier structure.  
Figure 6: Detailed layout of vertical displacement measuring points of track structure.  
Figure 7: Schematic diagram of existing subway lines.
## 4 Economic and Application Discussion  
### Economic Analysis  
Research on health monitoring sensors for short and medium-span bridges and the installation of a wireless sensor network can effectively reduce the hardware cost of the health monitoring system for these types of bridges in the road network and promote the application of the health monitoring system for these bridges. Based on the bridge monitoring data center, the monitoring data can be integrated on a platform for unified analysis and processing. It can provide better data support for transportation industry authorities, bridge owners, traffic management departments, the public and bridge design researchers.  
The authors have studied the displacement monitoring scheme of short and medium-span bridges and compared it to the common schemes on the market, such as the use of connecting pipes, inclinometers, satellite positioning systems, laser image methods, etc., to measure bridge displacement. Through the analysis of the hardware cost, construction cost, later maintenance cost, the reliability and performance of monitoring results, and market research a comparison of monitoring schemes was carried out.  
Comparative analysis of the hardware cost showed that the displacement monitoring scheme of the laser displacement monitoring equipment developed in this paper has a lower comprehensive cost than other common schemes on the market. Because of the intelligent data acquisition method with variable frequency, the monitoring results of the laser ranging scheme studied in this paper also have strong applicability and are suitable for large-scale promotion in the displacement monitoring of short and medium-span bridges.
## 3 Analysis
### Assumptions
#### 3.3.1 Fire Spread  
The two main fire scenarios considered are an apartment fire and a corridor fire; the first one is a 5 MW t-squared fire (\(\alpha\) = 0.0117 kW/s\({}^{2}\)), the second one is a 300 kW t-squared fire (\(\alpha\) = 0.0117 kW/s\({}^{2}\)). The heat release rate (HRR) of the fire [44] is most important in fire risk analysis as it impacts primarily on smoke and heat production. The peak heat release rate for the apartment fire was based on 100 Monte Carlo simulation runs using B-Risk Design Fire Generator (DFG) fire modelling software. The peak heat release rate for the corridor fire has been assumed to yield from the complete burning of three waste baskets and corresponds to the above value [43]. As soon as the peak heat release rate is reached, the fire burns at the maximum rate until the simulation ends. When the smoke management system is in operation, it offers tenability conditions for the entire simulation duration, hence no fatalities outside the apartment of fire origin are expected for similar scenarios. Similarly, when the sprinkler system is in operation, it offers tenability conditions for the entire simulation duration, hence no fatalities are expected for similar scenarios.  
Design fires are listed in Table 5 and are based on building use and the effects of the sprinkler system. For apartments, the design fire was developed for a typical dining/living compartment of 5.5 m width by 9 m length by 2.6 m height with a single window opening of 1.2 m height by 2 m wide. Flashover criterion was set at 500 \({}^{\circ}\)C for upper layer temperature. Fire load density range of 0-1000 MJ/m\({}^{2}\) and 80-percentile design value of 800 MJ/m\({}^{2}\) with triangular distribution and a mode of 400 MJ/m\({}^{2}\) were selected.
# Influence of Particulate Matter and Carbon Dioxide on Students' Emotions in a Smart Classroom
## 1 Introduction
### Particulate Matter and Carbon Dioxide  
PM refers to a mixture of solid particles and liquid droplets suspended in the air, including dust, dirt, soot, smoke, and other aerosol particles. These particles are categorized based on their diameter, with PM\({}_{\text{x}}\) (where x represents 10, 2.5, 1, or 0.1) indicating the mass of particles per unit volume of air with a diameter smaller than x micrometers. PM\({}_{10}\) particles, also known as inhalable particles, typically deposit in the thoracic or upper respiratory tract, while PM\({}_{2.5}\) particles, referred to as fine inhalable particles, can penetrate deeper into the lungs, reaching the alveoli. Ultrafine particles, or PM\({}_{0.1}\), are smaller than 0.1 um and pose unique health risks due to their ability to infiltrate both the lungs and the bloodstream, potentially leading to systemic health effects [5].  
CO\({}_{2}\), accounting for approximately 0.04% of the atmosphere, is a prevalent gas composed of carbon and oxygen. In indoor environments, CO\({}_{2}\) levels are often used as indicators of ventilation quality, as elevated concentrations can signify inadequate airflow relative to the number of occupants. Insufficient ventilation can result in higher CO\({}_{2}\) levels, which, in turn, impact both comfort and cognitive function [6].
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 2 Related Work
As shown in Figure 1b, if sensor \(D\) is dead, coverage hole is created in the network, and the target within that area remains undetected. As shown in Figure 1c, if the sensor \(D\) is dead, both coverage, as well as communication holes are created, and therefore, the purpose of deploying sensors to detect any target remains futile. In this paper, we propose a perimeter-based approach to find the coverage holes considering the points of intersection of the sensing discs of the wireless sensors deployed randomly over a monitoring region. The main contribution of this work over the existing hole detection protocols can be summarized as follows:  
Figure 1: Example of the consequences of coverage holes. (a) Loss of connectivity due to death of node \(D\); (b) Coverage hole due to death of node \(D\); (c) Both coverage and connectivity hole due to death of node \(D\). * A distributed coverage hole detection (DCHD) protocol is proposed to detect the coverage holes without the help of the sink by simply calculating the points of intersection of a node with its one-hop neighbors and checking them as covered or not. * Algorithms are designed to filter the points of intersection into covered and non-covered points and then use this filtering method to detect the coverage hole. Such a filtering method can be done dynamically in an autonomous manner by maintaining the network integrity. * Our proposed algorithms can detect the nature and shape of the coverage holes, which can be highly useful and make it easier to redeploy the nodes.
# RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analog Indicators
## 2 The State of the Art  
### Potential of Wireless Networks in the Industry  
Wireless network protocols as an IoT technology are used to enable physical objects to collect and exchange data nowadays of Industry 4.0 era [12]. Wireless networks are promising for current and future industrial aspects in terms of the digital transformation of companies, especially in automating the process of logistics, asset tracking digitization, monitoring, and controls [13]. The growing influence of IoT has created great potential for increasing the share of wireless communication networks in the industry. Compiling Hardware Meets Software (HMS) team annual reports for the last seven years (Figure 1) show that the majority of industrial networks' share (approximately 94%) is related to industrial networks with cable platforms such as Fieldbus and industrial Ethernet [14]. However, the wireless network graph has constantly been rising, from about zero percent in 2015 to 7% in 2021. This trend illustrates a clear picture of the future position of wireless networks in the industry.  
In [15], the authors merged the wireless network and RFID technology to manage the production line in a traditional manufacturing laboratory to avoid errors due to the manual recording, remotely monitoring the manufacturing process via the internet, and evaluating the workers' progress. In [16], authors proved that the transmission speed, low energy consumption, and a higher number of devices could be simultaneously operated using a  
Figure 1: Industrial networks’ market shares.  
wireless network. Furthermore, when discussing IoT applications in industry, we found the acronym IIoT that stands for Industrial IoT and that it is building a new domain with respect to IoT in everyday products. All the considerations mentioned above demonstrate that passive RFID could be a promising solution for digitizing analogical devices.
## 2 Materials and Methods
### Input Combinations and K-Fold Cross-Validation  
K-fold cross-validation is normally used for estimating the skill of a machine learning model on unseen data [22]. When using machine learning to develop the models, the data are usually divided into training set and testing set. Testing set is the independent data from training set. In the process of evaluation of the final model, over-fitting is the main problem. It means the model is well matched with the training set, but not well for the testing set. If the test data is used to adjust the model parameters, it is equivalent to the information of part of the test data is known during the training, which will affect the accuracy of the final evaluation result. A common practice is to separate the training data into validation data to evaluate the training effect of the model. Validation data is random selected from training set, but does not participate in training, so that the matching degree of the model to the data outside the training set can be evaluated objectively.  
In this research, five-folds cross-validation was used to improve the prediction accuracy of the SVMs models. As shown in Table 1, six input combinations were selected in this study to evaluate the performance in different meteorological parameters on frost prediction: (1) \(T_{max}\), \(T_{min}\) and \(T_{mean}\) with six heights; (2) \(RH\) with six heights (3) \(R_{n}\), \(R_{lu}\), \(R_{su}\), \(R_{ld}\) and \(R_{sd}\); (4) \(T_{soil}\) and \(G\); (5) the micro-meteorological data at the canopy height (\(T_{mean1.5}\), \(T_{min1.5}\), \(T_{max1.5}\), \(R_{H1.5}\), \(R_{n}\) and \(u\)) and (6) all the 32 micrometeorological parameters. Python (version 3.7.2) was used to write the program codes for developing SVM models.  
The number of training set and testing set was 408 and 175, respectively. The number of frost nights in the training set and testing set was 60 and 37, respectively.
# Innovative Sensor Approach to Follow _Campylobacter jejuni_ Development
## 3 Results and Discussion
### GC-MS Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RT** & **VOC** & \multicolumn{2}{c}{**Abundance**} \\ \hline  & & _Campylobacter_ & Control \\ \hline
1.540 & 3-Butynol & 8,834,835 & 2,135,579 \\
2.266 & Isovaleraldehyde & 221,889,809 & 3,852,176 \\
4.142 & Dimethyl Disulfide & 42,167,678 & 0 \\
8.377 & 1-Pentanol & 236,112,172 & 0 \\
9.229 & Isoamyl Alcohol & 0 & 737,186 \\
10.673 & Acetoin & 1,974,910 & 0 \\
11.125 & 2-Methylbutyl isovalerate & 1,684,776 & 0 \\
12.299 & 2,5-Dimethylpyrazine & 0 & 693,770 \\
14.400 & Nonanal & 0 & 123,487 \\
14.483 & Trimethylpyrazine & 0 & 258,896 \\
14.600 & 6-Methyloctadecane & 0 & 60,501 \\
15.275 & 2-Ethyl-3,6-dimethylpyrazine & 243,733 & 0 \\
15.392 & 4-Methyl-2-oxovaleric acid & 454,870 & 273,645 \\
15.702 & Ammonium acetate & 812,172 & 572,057 \\
15.903 & 2,7-Dimethyl-4,5-octanediol & 945,696 & 0 \\
16.186 & 1-(2-Methoxy-1-methylethoxy)-2-propanol & 0 & 79,930 \\
16.295 & 2-Propyl-1-pentanol & 308,880 & 0 \\
16.340 & Ethlyhexanol & 267,747 & 301,354 \\
16.845 & Benzaldehyde & 0 & 14,760,479 \\
17.366 & 1-Octanol & 972,272 & 256,675 \\
17.550 & Bicyclo[3.2.1]octan-6-ol & 164,638 & 0 \\
17.946 & 2-Undecanone & 103,647 & 0 \\
18.419 & 3-Hydroxycyclohexanone & 0 & 79,109 \\
18.581 & Benzeneacetaldehyde & 5,087,169 & 0 \\
18.668 & Acetophenone & 0 & 669,190 \\
18.740 & 1-Nonanol & 1,331,499 & 0 \\
18.914 & Methyl 4-hydroxybutanoate & 0 & 204,876 \\
19.446 & \(\gamma\)-Methylmercaptoppropyl alcohol & 690,884 & 0 \\
19.923 & E-11,13-Tetradecadien-1-ol & 2,393,731 & 532,815 \\
20.575 & \(\delta\)-Phenethyl acetate & 131,503 & 0 \\
20.844 & Heptanoic acid & 488,771 & 215,935 \\
21.187 & Benzyl alcohol & 342,619 & 146,035 \\
21.540 & Phenylethyl Alcohol & 26,577,900 & 1,429,647 \\
21.750 & m-Tolunitrile & 0 & 60,783 \\
21.996 & 1-Dodecanol & 492,208 & 270,053 \\
22.316 & Tropone & 165,612 & 56,068 \\
22.443 & 4-Hydroxybenzenepphosphonic acid & 0 & 77,574 \\
22.684 & Nerolidyl acetate & 0 & 116,357 \\
22.876 & Octanoic acid & 178,530 & 73,913 \\
23.555 & 1,3,2-Dioxaborlane, & & 2-ethyl-4-(3-oxiranylpropyl)- \\
23.822 & (9E)-9-Hexadecen-1-ol & 188,001 & 0 \\
25.047 & 2,4-Di-tert-butylphenol & 204,650 & 200,860 \\
26.632 & Pyrindan & 103,007,890 & 14,885,449 \\
27.555 & N,N-Dimethylformamide ethylene acetal & 40,116 & 40,546 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of VOCs for _C. jejuni_ and control samples with their retention times (RT) and abundance in arbitrary units at time T20.
## 4 Experiments
### Evaluation Task and Metrics  
#### 4.3.1 Link Prediction  
The main task of link prediction [45] was to deduce the new triplet of facts and predict the missing information. Here, for a positive triplet \((h,r,t)\), the head entity wasremoved, i.e., \((?,r,t)\). Then, the missing part of the triplet was replaced with all the entities in the entity set to create a set of candidate triplets with an equal number of entities [46]. The score of this group of candidate triplets was calculated according to the scoring function and sorted in order from low to high. In order to better evaluate the performance of the model, we needed to consider the issue of a candidate triplet being a false negative triplet, that is, the candidate triplet formed by replacing the triplet entity with another entity is itself a correct triplet. We could not treat this type of triplet as a negative triplet, if it ranked higher than the original triplet, as it would disturb the experimental results. In view of this, we evaluated the performance of link prediction in the filtered setting [47], i.e., all false negative triplets were removed from the candidate set. All experimental results in this paper are based on the results of the data after filtering out the false negative triplets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#Entity** & **\#Relation** & **\#Train** & **\#Valid** & **\#Test** \\ \hline WN18 RR & 40,943 & 11 & 86,835 & 3034 & 3134 \\ FB15K237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 \\ WN18 & 40,943 & 18 & 141,442 & 5000 & 5000 \\ FB15K & 14,951 & 1345 & 483,142 & 50,000 & 59,071 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of datasets. The symbols #entity and #relation denote the number of entities and relations, respectively. #train, #valid, and #test denote the size of train set, validation set, and test set, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FB15K237** & **WN18 RR** & **FB15K** & **WN18** \\ \hline \(d\) & 80 & 80 & 150 & 80 \\ \(\eta\) & 0.0001 & 0.0001 & 0.0005 & 0.0005 \\ \(\gamma\) & 5 & 5 & 4 & 4 \\ \(b\) & 1000 & 1000 & 2000 & 1000 \\ k & 50 & 50 & 50 & 50 \\ s & 30 & 30 & 30 & 30 \\ \(\alpha\) & 2 & 2 & 2 & 2 \\Eventually, the ranking of the triplet whose entity is \(h\) in this group of candidate triplets was recorded. In the experiment, emphasis was placed on the ranking of the correct entity rather than on finding the best result for the triplet. The performance of the model was evaluated by the ranking of the correct entity.
## 4 Experimental Results  
### Preliminary Considerations  
In this section, some preliminary considerations, useful for the analysis of the experimental data reported in the next paragraph, are summed up. The flow boiling phenomenon is controlled by nucleate boiling and convective boiling. At low vapor quality, nucleate boiling affects the two-phase heat transfer mechanism at the wall and, depending on the mass velocity and vapor quality, the flow pattern is typically bubbly, slug, or low vapor quality annular flow. In this region, the two mechanisms of flow boiling exist: nucleate boiling and two-phase forced convection, and up today it is not possible to separate these two contributes. As vapor quality increases, a transition from boiling to evaporation occurs, with consequent transition to an annular flow regime, with liquid flowing at the wall and vapor in the core. The vapor shear stress may cause entrainment of liquid droplets in the vapor phase. The liquid film thickness at the wall gets thinner and thinner as vapor quality increases, with consequent augmentation of the heat transfer coefficient, until the liquid at the wall evaporates. Depending on the working conditions of vapor quality and heat flux, there may be a local dryout phenomenon, i.e., the liquid at the wall partly evaporates, and these hot spots at the wall are immediately rewetted by the liquid phase. As dryout goes on, a fully dryout condition occurs, i.e., the liquid film at the wall completely evaporates with consequent increase of the wall temperature. For high heat fluxes, local dryout may occur at lower vapor qualities.  
The experimental results of heat transfer coefficient and frictional pressure drop will be presented in the next paragraph. Every single experimental datum, which will be later shown, comes from the average of 100 readings taken in steady state conditions at a frequency of 1 Hz. The tested mass velocities, \(G\), i.e., the ratio between mass flow rate and cross section of the tested tube, are: 200, 300, 400, 500, and 600 kg m\({}^{-2}\) s\({}^{-1}\). The tested heat flux, \(HF\), i.e., the ratio between the net heat flow rate and the inner surface of the tube, are: 12, 30, and 60 kW m\({}^{-2}\). Standard tests are carried out with a constant saturation temperature at the inlet of the test section of 30 \({}^{\circ}\)C, which corresponds to a pressure of 5.783 bar. Some additional tests were carried out, for some combinations of mass velocity and heat flux, at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C of saturation temperature, in order to catch its effect both on heat transfer coefficient and on frictional pressure drop.