esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nonlinear Simulation Analysis of a Single-Keyed Dry Joint
### Constitutive Relationship of Materials
#### 2.2.2 Plastic Damage Model of Concrete  
For nonlinear finite element (FE) analysis of concrete structure, plastic damage of the concrete should be considered. In ABAQUS, a concrete plastic damage model is provided. In this model, damage factor \(D\) is introduced to reduce the stiffness matrix so that the plastic damage of concrete could be reflected, shown as Equation (3a). The unloading stiffness would decrease as the damage in concrete increases. Thus, the one-dimensional constitutive relation of concrete considering plastic damage [35] could be expressed as Equation (3b).  
\[E_{D}=(1-D)E,0\leq D\leq 1 \tag{3a}\] \[\sigma=(1-D)E\Big{(}\varepsilon-\varepsilon^{pl}\Big{)},0\leq D\leq 1 \tag{3b}\]  
where, \(D\) is the damage factor, \(E\) is the elastic modulus with no damage, \(\sigma\) is the stress, \(\varepsilon\) is the total strain, and \(\varepsilon^{pl}\) is the inelastic strain. When \(D\) equals to zero, it indicates that the material is not damaged; when \(D\) equals to 1.0, it indicates that the material has no bearing capacity.  
According to the stress-strain curves in Figure 2, damage factor \(D\) could be obtained by Najar energy method [36; 37], shown as in Equation (4). Where, \(W_{0}=E_{0}\varepsilon^{2}/2\) and \(W_{e}=\int f(\varepsilon)d\varepsilon\) represent the strain energy of the material in the non-damaged and damaged state, respectively, \(f(\varepsilon)\) represents the uniaxial tensile and compressive stress-strain function of the concrete. The corresponding relationship between \(D\) and \(\overline{\varepsilon}^{pl}\) is illustrated in Figure 3a,b.  
\[D=\frac{W_{0}-W_{e}}{W_{0}} \tag{4}\]
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 4 Link Quality-Based Forwarding Strategies  
The DTN forwarding strategies here proposed aim to select the best path for a packet based on the quality of the different available links. For that, the network needs to assess the quality of each link, from every node to the mobile gateway. This section describes the estimation process of each communication link and the metrics involved.
## 1 Introduction  
Estimation of the age in forensic dentistry is of particular importance, especially in the case of unknown persons affected by war or natural disasters, criminals and undoccumented immigrants, and those injured in accidents. In forensic dentistry, to determine the age of individuals, application of dental indexes is better than those based on somatic development, as the development of teeth is less affected by hormonal and nutritional changes [1; 2].  
Mainly, dental age estimation is based on the growth, and biochemical and morphological alterations of teeth [3; 4]. In adults, morphological changes are preferable to the other two methods, since calcification of dental tissues is a continuous process that can be evaluated by radiography [5]. Also, methods based on biochemical changes in teeth, such as aspartic acid removal, are time-consuming, complex, and invasive, and thus are rarely used [6]. On the other hand, the root formation of all permanent teeth, except for the third molar, is completed around the age of 16 when their apices close [7; 8]. Therefore, age estimation using root development indexes such as [PERSON]'s and Willem's in adults is not applicable [9; 10].  
In the developmental process of teeth after maturation, secondary dentin deposition occurs on all walls of the dental pulp throughout a person's life, which continuously reduces the size of the pulp cavity. Based on this, the amount of reduction in pulp cavity size can be considered as an effective morphological criterion for estimating age in adults [11; 12; 13].  
Secondary dentin deposition can be assessed by tooth extraction and incision or by imaging techniques [14]. Panoramic and periapical radiographs have been used to assess secondary dentin deposition [15; 16]. However, these radiographic modalities have the disadvantages of being two-dimensional and providing a distorted view. Two-dimensional images also fail to provide accurate measurements due to the overlap of anatomical structures, radiographic geometry, and magnification [17; 18].  
To overcome the limitations of two-dimensional imaging for age estimation, studies have used three-dimensional modalities for this purpose. Cone beam computed tomography (CBCT) can be used as an accurate and ideal tool to assess dental pulp volume [19; 20].  
Several studies have used CBCT images for age estimation in different populations [21; 22; 23; 24]. However, limited studies have been performed in Iranian sub-population. Therefore, the present study aims to investigate the correlation between chronological age and pulp-tooth volume ratio in anterior teeth using cone beam computed tomographic (CBCT) images and provide equations for age estimation based on pulp-tooth volume ratio.
## 4 Results and Discussion
### Comparative Performance Between Datasets
#### 4.2.2 EAE  
The heat map for the EAE in Figure 11 for the five appliances measured with the three meters shows an excellent overall performance. A heat map visualizes the performance of algorithms in identifying energy consumption through the EAE metric. The color patterns reveal the efficacy and consistency of the algorithms: green columns indicate a better identification, while red columns signal difficulties. The uniform green color across all columns reflects the consistent performance for the devices.  
The television, incandescent lamp, and fan exhibit values very close to ideal, with the oZm v1 achieving a perfect performance in several cases. The vacuum cleaner shows more significant variability but maintains an excellent performance. The freezer, measured only with oZm models, presents optimal results. These findings highlight the effectiveness of these meters in accurately identifying the appliance energy consumption, which is crucial for optimizing energy efficiency in various settings. Overall, the oZm v1 stands out for its superior application performance. The oZm v3 and OMPM show small peaks in some appliances, although they insignificant given the scale used. The results reflect a high precision in the identification and monitoring of the household appliances studied, with values very close to the ideal of zero for the EAE metric.  
Figure 10: Mean F1-score by appliance and meter.
## 4 Discussion
### RAS-Regulatory Enzymes
#### 4.5.3 On the Regulatory Enzymes of the RAS in the Kidney
Given that significant differences in the response to beer consumption occur between WKY and SHRs, it is possible to investigate whether beer consumption, even in moderate amounts, might have a different impact on individuals with a genetic predisposition to hypertension. The impact of beer consumption on long-term kidney function may also be considered. Longitudinal studies in humans could assess whether regular beer consumption is associated with changes in kidney function or an increased risk of chronic kidney disease. 4.5.5. On the Regulatory Enzymes of the RAS in the Testis  
The RAS is also involved in the regulation of testicular function, as several of its components occur in this tissue. Thus, the testicular RAS is involved in the regulation of testosterone production through AngII, which inhibits the function of Leydig cells through their AT1 receptor, responsible for testosterone production [111]. This process inhibits adenylate cyclase activity and reduces cAMP production, which in turn decreases testosterone production. Some researchers suggest that Ang III, generated from AngII by the action of APA or ASAP, may be the main effector of RAS in the testis. However, very little is known about the effect of beer on testicular function. One study found that rats that consumed beer for 4 weeks showed a decrease in the plasma levels of follicle-stimulating hormone (FSH) and 17$-estradiol (E2) compared to rats that consumed a 10% ethanol solution or water. The authors suggest that this effect could be due to the intake of the phenolic compounds present in beer, such as phytoestrogens, which have hormonal activity [117]. The same study also observed the lower expression of cleaved caspase-3 in the Leydig cells of rats that consumed beer for 4 weeks compared to those that consumed the ethanol solution. Caspase-3 is a key enzyme in cell apoptosis, so this finding suggests that beer consumption may protect Leydig cells from ethanol-induced cell death [117]. Another study found that drinking beer with alcohol led to an increase in P21 protein and gene expression and a decrease in cyclin D1 expression, as well as a reduction in superoxide dismutase (SOD) and catalase (CAT) levels, an increase in malondialdehydehyde (MDA) levels and subsequent tissue damage in testicular tissue in a time-dependent manner [118]. The seminiferous tubule diameter and germinal epithelial thickness were also reduced in a time-dependent manner, and the percentage of seminiferous tubules with tubular differentiation, replacement and negative spermiogenesis coefficients increased significantly, which in turn reduced sperm production and quality [118]. Overall, beer consumption can have both positive and negative effects on testicular function. Further research is necessary to understand the effects of beer on testicular functions through the RAS.
## 4 KDN-Based Heavy-Hitter Identification Approach
### System Overview  
The current methods to identify HHs are threshold-based. However, such methods lack a smart system that efficiently identifies HHs according to the network behaviour. Hereinafter, we introduce an approach to overcome this lack and investigate the feasibility of using KDN to identify HHs. Figure 2 overviews our approach by a KDN control loop, including three modules: HH-DAM, HH-DANM, and HH-APM. Overall, HH-DAM sends packets captured to HH-DANM, which is responsible for storing and generating a network traffic state model. Finally, the Controller gets the awareness from HHs Flag sub-module in HH-APM and instructs the new network configurations to the forward devices. In a high-abstraction level, our approach operates as follows.  
1. _Forwarding Devices_\(\rightarrow\)_Packet Observation_. _Packet Observation_ performs packets capture from _Observation Points_ in the network devices, e.g., line card or interfaces of packet forwarding devices. Before starting to send the packets to the Data Collector, the packets can be pre-processed, trough sampling and filtering rules.
2. _Packet Observation_\(\rightarrow\)_Data Collector_. In the _Data Collector_, the packets provided by the HH-DAM are organised and stored into flows. This Collector aims at gathering enough information to offer a global view of the network behaviour.
3. _Heavy-Hitters Data Collector_\(\rightarrow\)_ML Techniques_. Data Collector feeds the ML Techniques with current and historical data. Thus, our approach can learn from the network behaviour and generate knowledge (e.g., a model of the network).
4. _ML Techniques_\(\rightarrow\)_HHs Flag_\(\rightarrow\)_SDN Controller_. The _E/M Flag_ eases the transition between the model-generated by the ML Technique sub-module and the control of specific actions. Based on KDN control loop, this step can be either open or closed. If the Administrator is responsible for deciding on the network, the control loop is open. In this case, HH-APM offers validations and recommendations, which the Administrator can consider when making decisions. For instance, in a control congestion case, the Administrator can query the model (e.g., HHs prediction model, HHs classification model, and link load model) to validate the tentative changes to the configuration before applying them to the network. In the closed control loop, the Administrator is not responsible for deciding on the network. In this case, the model obtained from ML Techniques sub-module can be used to automate tasks, since HH-APM can make decisions automatically on behalf of the Administrator. Furthermore, the model can be used to optimise the existing network configuration [34]. For instance, the model can learn adaptively according to the traffic change, and find the optimal configuration to routing HHs and, thus, avoid congestion.
## 2 Modified Topology and Control Scheme
\(V_{AB}\) is the voltage between the mid points of the two legs of the upper H-bridge inverter. \(V_{\text{inv}}\) is the output voltage across the load. The leakage current primarily depends upon the magnitude of the CMV. The CMV can be derived in (2) and (3) as follows:  
\[V_{\text{CM}}+V_{\text{AN}}-V_{\text{Lg}}-V_{\text{inv}}=0 \tag{2}\]  
\[V_{\text{CM}}+V_{\text{BN}}-V_{\text{Lg}}-V_{A^{\prime}B^{\prime}}=0 \tag{3}\]  
\(V_{\text{inv}}\) has much less effect on parasitic capacitance. Hence, it can be neglected. The filter inductance, \(I_{g}\), and the voltage drop caused by \(I_{g}\) are considered the same for the two H-bridges for ease of analysis. The mathematical expression of the CMV can be obtained by adding Equations (2) and (3) as follows:  
\[2V_{\text{CM}}+V_{\text{BN}}+V_{\text{AN}}-V_{A^{\prime}B^{\prime}}=0 \tag{4}\]  
From Equation (4), the CMV can be written as:  
\[V_{\text{CM}}=\frac{V_{A^{\prime}B^{\prime}}-V_{\text{AN}}-V_{\text{BN}}}{2} \tag{5}\]  
Equation (5) is utilized for defining the CMV in several intervals of referenced time.
## 3 Result and Discussion
### Head Loss Recovery Rate after Physical Backwashing in the Radial Fiber Media Filtration Process  
In this investigation, physical backwashing was commonly performed to remove adhesive contaminants from the surface of a fiber media filter. Figure 5 shows the head loss recovery in a radial-type multifiber media filtration process after physical backwashing. The head loss recovered by 40.5% after filtration. No improvement in the head loss recovery efficiency of the multimedia was achieved because the organic material in the CSOs formed a gel-like sticky layer on the surface of the media [33; 34].  
An average head loss of 8.5 cm occurred after every 2.5 h of the filtration process. The optimal backwashing period was determined at a head loss of 8.8 cm and a backwashing time of 15 min. These kinds of adhesive pollutants on the surface of the fiber media filter can increase the head loss of the RMF; additional force would be necessary to remove  
Figure 4: Particle size analysis of multifiber media filtration.  
Figure 5: Head loss recovery in radial-type multifiber media filtration process after physical backwashing every 2.5 h.  
adhesive pollutants from this surface. Therefore, physical backwashing showed a weakened backwashing effect, and thus, the head loss of the radial multimedia filtration process was only partly recovered after filter clogging by pollutants.
## 3 Experiments and Discussion
### Parameter Analysis  
The model parameter settings are analyzed. Based on a large number of experiments, several parameters that considerably affect the experimental results are selected for analysis: the size of the input patch; proportion of dropout probability values; and effectiveness of the morphological features, residual connections, and attention mechanism.
# A Video-Based Fire Detection Using Deep Learning Models
## 3 Proposed Method
### The Time Average over Weighted Areas of Suspected Regions of Fire (SRoFs)  
The fire judgment from LSTM is based on the temporally aggregated spatial features in the SRoFs and the non-fire objects. Here, we can consider additional temporal features related to the area of SRoFs. The multiple regions allow us to take the weighted sum of SRoFs, where the weights are given by the confidence score corresponding to the SRoF. In Equation (2), \(Z\) can be treated as the weighted area of objects in a frame. However, we separately calculate the weighted areas for flame and smoke objects to give a more precise interpretation. Therefore, the weighted areas are calculated as:  
\[Z_{flame}=\sum\limits_{\begin{array}{c}for\text{ {each bounding box }b\\ \text{{of flame SRoFs}}\end{array}}}\quad w_{b}\cdot\sum\
olimits_{(x,y)\in b}1 \tag{4}\]  
\[Z_{smoke}=\sum\limits_{\begin{array}{c}for\text{ {each bounding box }b\\ \text{{of smoke SRoFs}}\end{array}}}\quad w_{b}\cdot\sum\
olimits_{(x,y)\in b}1 \tag{5}\]  
After calculating the weighted areas in consecutive frames, we take averages over a period \(T_{\text{ave}}\), then for every \(T_{\text{rep}}\), the consecutive average areas are given separately from or with the final fire decision to obtain a better understanding of the current dynamic behavior of the fire. Figure 8 shows the process to generate information on the fire's dynamic behavior.  
Another fire decision can be made independently from the majority voting of the LSTM decisions. For example, the dynamically increasing or decreasing areas of flame and smoke are detected and accumulated by another type of decision-making algorithm. This other fire decision could be merged with the previous fire decision from the majority voting of the LSTM decisions for a more refined decision, even though it is not implemented. While the time for majority voting can vary, the accuracy of the model improves with longer durations for the majority voting.  
Figure 8: Calculation of weighted areas of SRoFs.
## 3 Mathematical Model for Optimal Trajectory Planning
### Dynamic Fitness Function Design  
In this section, we propose a multi-objective fitness function consisting of eight optimization indexes to assess the trajectories generated by the proposed multi-UAVs path planning algorithm. We divided optimization indexes into two groups and assign different priority levels. This is due to the different importance of optimization indexes during the optimization process. They are known as, (1) the optimization objectives, which need to maximize their value to derive an optimal trajectory, and (2) the constraints which must satisfy by UAVs due to their physical limitations. Table 1 shows these classifications and the equations to calculate them. Therefore, we formulate the dynamic fitness function as,  
\[F_{dynamic\_fitness}=\ F_{obj}+F_{const} \tag{3}\]  
where \(F_{obj}\) is the objective function, focusing to gain maximum values in terms of other parameters, \(F_{const}\) is the physical and environmental limitations, should be accomplished before trajectory planning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Objective Function**} \\ \hline Name & Energy Consumption & Flight Risk Estimation & Surveillance Area & Compliance \\ \hline Abbreviation & EC & FRE & SAI \\ \hline Equation & (5)–(10) & (11)–(14) & (15)–(17) \\ \hline Value/Range & \([0,1]\) & \([0,1]\) & \([0,1]\) \\ \hline \hline \multicolumn{5}{c}{**Constraint Function**} \\ \hline Name & Aerial & Restricted & Turning & Operational & Coverage \\  & Constraint & Area & Angle & Area \\  & & Constraint & Constraint & Constraint & Constraint \\ \hline Abbreviation & AC & RAC & TAC & OAC & CRC & CA \\ \hline Equation & (19) & (20) & (21) & (22) & (23) & (24) \\ \hline Value/Range & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization index Classification.
## 3 Results and Discussion
### Experiments of Actual Water Sample  
We have verified the accuracy of COD measurement using FDPE_PLS with or without turbidity interference in the laboratory. In this section, the accuracy of FDPE_PLS and DC method proposed in this paper will be verified by measuring actual water samples. Ten actual water samples collected in a city in Jilin Province were used for testing. The COD values of actual water samples were determined by chemical method in the laboratory, and the accuracy was \(\pm 5\%\). The turbidity values were determined by turbidimeter ZD-2A with an accuracy of \(\pm 8\%\). The COD and turbidity values of the ten water samples are shown in Table 10. After processing by DC and calculating by FDPE_PLS, the fitting result between actual COD values and predicted COD values is shown in Figure 18. The \(R^{2}\) is 0.99, and the average relative error is 0.07.  
Figure 17: The comparison among no turbidity compensation, MSC, and DC. No turbidity compensation: the COD measurement errors become large as the turbidity values increase. MSC: the COD measurement errors become small as the turbidity values increase. DC: the COD measurement errors fluctuate with the change of turbidity value.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\beta_{1}\) (\(0<x<25\)) & \(\beta_{1}\) (\(25<x<50\)) & \(\beta_{2}\) & \(\beta_{3}\) & \(\beta_{4}\) & \(\beta_{5}\) \\ \hline \(R^{2}\) & 0.98254 & 0.97795 & 0.98756 & 0.9916 & 0.98858 & 0.99209 \\ \hline \end{tabular}
\end{table}
Table 9: The correlation coefficients of five linear equations.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 3 Field Experiment Locations and Configurations
### Experiment 2: Intertidal Zone, Crosby  
The site on a wide intertidal beach at Blundellsands, Crosby (53 deg29'43.6'' N, 03 deg04'1.5'' W, WGS; British National Grid 329306, 400389), is 500 m from the sea defenses and 6 km from Gladstone Lock tide gauge (see Figure 5). The IoT tide gauge was attached to a navigation channel marker post, close to a Nortek acoustic wave and current profiler (AWAC).  
The AWAC was part of a local government coastal monitoring study and provided a direct comparison with the measurements from the IoT tide gauge. It was set up to measure currents and sea levels every ten minutes and provide hourly wave measurements based on burst sampling; these data were recovered five months later. The IoT tide gauge was deployed to capture data during Storm Ciara, which arrived in the UK during 8-9 February 2020, to determine the quality of the sea-level data in an exposed intertidal location with expected large waves.  
Configuration 2 (see Figure 2c) consisted of Blue Robotics waterproof case enclosing an Arduino micro-controller, MKR SD Proto Shield (with 2 Gb SD card) for storing data, a Blue Robotics pressure sensor with the electronics, two D-cell alkaline batteries and antenna. The Arduino's internal clock was used for timing, and it was set up to wake up every 15 min, record 40 s of 10-Hz data, calculate maximum, minimum and average values, store these data internally onto the SD card, store the 40-s averages into RAM memory, and send them back via the Sigfox network when the instrument was exposed at low tide (when underwater there was no data transmission). Each data package contained the month, day, hour, minute, and four Blue Robotics pressure values. This configuration allowed most of the sea-level data to be returned during the \"dried out\" period.  
Figure 4: Sensor installation at Sandon Half-tide dock, Liverpool.  
The IoT tide gauge (at the pressure sensor level) and AWAC were leveled to the local vertical datum using heights from GPS. In this experiment, the local atmospheric pressure was obtained from another device at the shoreline (it is envisaged that for an operational IoT tide network that these data would be provided by a weather station colocated with the X-band radar unit or using a cabled configuration as in Section 3.1). The IoT tide gauge was attached to the channel marker (see Figure 5) with cable ties and tape during low water on 6 February and then recovered immediately after the storm; last data were recorded at 00:50 on 10 February.