esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ANN Method for Maximum Power-Point Tracking (MPPT)
### Neural Network Analysis for MPPT  
In order to show the training result of neural network, according to the irradiance condition in Section 2.3, the regions with the same irradiance are taken as an integrated whole to simplify the structure of the neural network shown in Figure 4.  
According to Figure 5, it is noted that the output of the PV system varies over time and with the environmental conditions, so the periodic training of the ANN is crucial. A total of 361 sample data points (irradiance between 100 W/m\({}^{2}\) to 1000 W/m\({}^{2}\) of part A and B, starting at 100 W/m\({}^{2}\) in steps of 50 W/m\({}^{2}\) to 1000 W/m\({}^{2}\)) were generated to train the network. To perform the ANN, the number of layers, number of neurons of each layer, transmission function of each layer, and kind of training network needs to be assigned. The proposed ANN has three layers; the first and second layers have 10 neurons, while the third layer has one. The first and second layers of the transfer functions are Tansig and the third layer is Purelin. The training data are obtained by simulating the PV system in Matlab/Simulink using the Levenberg-Marquardt algorithm. Two hundred fifty training epochs and applying training as a training function are enough to get good results in Figure 5. The error computation is performed by algorithm of mean square error method (MSE) during the training process. The admissible of MSE for the ANN is assigned to be approximately \(10^{-4}\) in Figure 5.  
Figure 5: Testing data of ANN controller: (**a**) The output of the neural network training with the amount of target data; (**b**) the mean squared error (MSE) in converging process; (**c**) the absolute error of neural network training.  
Figure 5c shows the changing trend surfaces of the absolution and relative errors at the different irradiances between part A and B. Obviously, in most cases, the absolution errors of ANN output max power are within \(\pm 0.15\) V. Thus, the results of ANN training are better and are regarded as the reference voltage that is followed by DC/DC converter controller.
# Automated Schedule and Cost Control Using
###### Abstract  
Nowadays, many construction projects in KSA still struggle with cost overruns and delay in activities. Therefore, automatic monitoring approaches are needed in the construction progress monitoring domain (CPM) to address these concerns. Thus, this paper proposed a system integrating a BIM-planned model with site laser scans, as laser scanners showed massive potential in the CPM domain. The algorithms of the proposed system recognized 3D objects based on the intersection between models, alignment accuracy, and Lalonde features. The proposed system combined 3D object recognition technology with 5D information data into a 5D progress tracking system using earned value (EV) principles. The reason behind that is a lack of research regarding conducting a 5D assessment integrated BIM with 3D sensing technology in the CPM domain. The proposed system was verified using field data from a superstructure construction project where the object recognition indicators showed a 98% recall and 99% precision in recognizing 3D objects. The proposed system also used a color-coding system to address the condition of each element based on its recognition and scheduling state and address any occlusions while calculating the recognized objects. The results also revealed an automatically updated status of the project's progress in terms of schedule(4D) and cost(5D). The automated results were also validated with a manual calculation, where a slight variation (1.35%) was observed between those calculations. This system demonstrates a degree of accurate progress tracking, automatically exceeding manual performance with less computational time.  
automated progress tracking; 5D BIM; laser scanning; integration; EV principles +
Footnote †: journal: Applied sciences  
Then, integrating a 3D model with the project's information could produce an accurate 3D BIM-based model [5; 6]. The four-dimensional model was also recognized as the schedule model (4D). The scheduling model has been designated to establish the activities' sequence over time. Cost information on the project's activities is another dimension of BIM known as 5D BIM. Activities' completion and cost over time have been simulated in a virtual environment. There have been limitations to the current BIM-based cost model, such as the cash flow analysis [7]. Some researchers also identified the sixth dimension as the facility phase. However, other studies referred to the sixth dimension as sustainability and its implementation in smart cities [8].
# Intelligent Air Cutting Monitoring System for Milling Process
## 4 Verification Experiments  
### Experiment Design  
Experiments were designed to establish the monitoring threshold values by understanding the variation characteristics of the spindle load and cutting vibration during effective and ineffective cutting. With the function of NC code auto-identification and time calculation, the cutting state (effective cutting and air cutting) and its associated time can be monitored and recorded.  
There are two diagnosis methods for air cutting monitoring: (1) use spindle load current as the judgment basis, (2) use the cutting vibration value as the judgment basis. Due to different cutting conditions, the cutting amount will affect the clarity of the change in the judgment signal. Generally, with the appropriate vibration accelerometer, the cutting vibration signal can better show the presence or absence of micro-cutting. Therefore, two different monitoring methods are proposed in the study.  
The cutting parameter for the experiment based on spindle load judgment is shown in Table 2. An aluminum workpiece material with a dimension of 100 \(\times\) 80 \(\times\) 70 mm was used in this experiment. The Endmill tool with a diameter of 10 mm, three flutes, and tungsten carbide material was used. The straight-line cutting path along the Y-axis with 10 repetitions was carried out. The same cutting path (120 mm of moving along the Y-axis, cutting length is 100 mm) and same feed rate (1000 mm/min) were used to test the reproducibility and accuracy of the machining time monitoring result under the same cutting condition. Besides, it can be used to verify whether there is a problem in the real-time judgment and operation.  
The cutting parameters for the experiment, based on vibration value judgment, are shown in Table 2. The workpiece material to be cut is medium carbon steel, with a dimension of 100 \(\times\) 80 \(\times\) 70 mm. The Endmill tool with a diameter of 10 mm, three flutes, and tungsten carbide material was used. The straight-line cutting path along the X-axis with three repetitions was carried out. In this experiment, a width of cut of 0.1 mm was chosen to test whether the proposed monitoring system can correctly monitor the air cutting during micro-cutting. The same cutting path (200 mm of moving along the X-axis, cutting length is 100 mm) and same feed rate (600 mm/min) was used to test the reproducibility and accuracy of the machining time monitoring result under the same cutting condition, and also to verify the correctness and reliability of the real-time judgment of the monitoring system.
## 3 Proposed Work
### Message Flow Diagram during Intra-Domain Handover in PMIPv6 for VANET  
When a vehicle changes its attachment point from one MAG to another MAG in the same PMIPv6 domain, an intra-domain handover is the result. Figure 4 illustrates the sequence of messages that are swapped among various network entities, such as the LMA/MAG [19; 20; 21] and TTP, during an intra-domain handover. By sending a PBU signal to the LMA and requesting to be deregistered, the previous MAG starts the disengagement procedure in step 1. The LMA then starts a timer. The vehicle's entry is then deleted from the BCE after waiting for the threshold duration for a binding update request from the new  
Figure 2: Multicast-enabled architecture for handover for VANET.  
MAG. After receiving the request from the new MAG, it transmits the PBA message to the previous MAG, calling for the removal of the vehicle binding status in step 2. In step 3, when a vehicle sends a router solicitation message to a new MAG requesting attachment, for authentication, it then sends a request to a TTP already present in the local domain. It includes the vehicle ID and the MAG ID. If the vehicle is already authenticated, then its authentication information would have been communicated earlier, during the initial connection to the PMIPv6 domain. For this local TTP, it then multicasts the authentication request to the neighboring TTPs in step 5. The TTP having the authentication information then unicasts the authentication information back to the local TTP in step 6.  
Upon successful authentication, the TTP will send the authentication response consisting of the vehicle and MAG profiles to the MAG and LMA in step 7. In addition to this, the TTP also multicasts this profile information to the neighboring TTPs so that, later, during the inter-domain handover, this information can be used. The new MAG then transmits the PBU message to the LMA in step 8 and the vehicle's BCE is then updated by tying the same HNP to the new MAG in step 9. In step 10, the PBA message is delivered by the LMA to the new MAG [22; 23]. Considering this, it creates a new, bidirectional tunnel between the LMA and the new MAG during step 11. Following this, the new MAG sends the vehicle a router advisory message that contains the same HNP (step 12). The vehicle will not be aware of the above because the HNP will be preserved throughout the transfer.
## 2 Experiment Details
### Invasive Data Acquisition Methodology  
During the study, actual blood sugar data were simultaneously collected as a reference from the subject invasively using a commercially available medical standard glucometer. The meter can measure the sugar level from a small drop (1-2 \(\mu\)L) of blood sample collected from the fingertip of the subject. It can work in a range of 10-600 mg/dL. This technique also requires a one-time test strip and single-use safety lancets in addition to the meter. At first, the subject's hand was washed using a one-time hand sanitizer and alcohol solution on the finger from which the blood sample was taken. Then, a test strip was inserted into the glucometer to prepare the device for measuring the glucose level. The blood sample was collected by pricing the side of any finger using a lancet. Then, the picked finger was touched and held on the edge of the test strip to transfer the blood drop from the finger to the test strip. The pricked finger was treated with a band-aid. The glucometer usually takes 5-8 s to process the samples and displays the glucose level in mg/dL on the screen. At least one trial was collected from each subject to acquire the reference value.
## 2 Related Work  
This study focuses on leakage position estimation, which is one of the inspection tasks for removing fuel debris and decommissioning the FDNPP. [PERSON] et al. and [PERSON] et al. developed a remote leakage locating system using an ultrasonic array sensor to investigate the leakage locations in the FDNPP [9; 10]. The ultrasonic array sensor was placed in the water to detect the water flow for localization of the leakage positions; hence, for the leakage positions to be applicable to their methods, they needed to be submerged. However, the bottoms of the RPV of Units 1, 2, and 3 were not submerged; therefore, it was difficult to estimate the leakage position of the bottom of the RPV using their methods.  
Some studies utilized thermographic cameras to detect leakage positions. [PERSON] et al. and [PERSON] et al. employed thermographic cameras to detect the leakage positions of buried pipes [11; 12]. They detected the leakages by capturing the temperature changes of the soil surface, because water leakage from pipes decreases the temperature of the soil. However, it is difficult to detect water dripping from the bottom of the RPV using thermographic cameras because of the small volume of a waterdrop. Moreover, it is unclear how radiation affects the thermographic cameras.  
Compared to ultrasonic array sensors and thermographic cameras, conventional optical cameras are widely available. [PERSON] et al. proposed a method to estimate the leakage positions in tunnels from images using a deep learning-based approach [13]. They assumed that the environment was dry except for areas of the leakage. They used color change, because areas around the leakage positions had darker colors compared to the dry ones. In the FDNPP, most of the environment is wet; therefore, their methods cannot be applied.  
In this study, we utilized a stereo camera to detect waterdrops and triangulate the trajectories of the waterdrops to estimate the leakage positions on a two-dimensional map. In addition, the radiation noise was considered. There are affordable depth sensors available these days; however, it would be difficult for depth sensors to detect small and transparent objects such as waterdrops. For instance, the depth sensors would not measure a waterdrop itself but the wall of the pedestal, based on the study [14] on the performance in the rain of LiDAR (light detection and ranging). It is worth noting that some studies have focused on detecting waterdrops that adhere to the protection glass of cameras [15; 16]. We assume that the inspection system has wipers to remove the adherent waterdrop on the camera glass; therefore, we considered the adherent waterdrop as beyond the scope of this study.
## 3 Materials and Methods
### Optimal Solution Selection Using M-ABC Algorithm for IoMT Clients  
An algorithm based on swarm intelligence, known as the Modified Artificial Bee Colony (M-ABC), has been developed and proposed in [50]. The scout bee, onlooker bee, and employed bee all appear in the M-ABC algorithm. Scout bees are responsible for exploring new food sources, while the onlooker bee chooses a food source based on the dance of an employed bee. As a result, the bees employed are protected from exploitation because they are linked to their food source. Neither the scout bees nor the onlooker bees are associated with any particular food source. They are referred to as \"unemployed bees\" as a result. The main aim of the fitness function is the optimal selection of classification error and communication efficiency of the received models from the IoMT client sites. The objective of the fitness function is to minimize the classification error and number of rounds consumed to achieve higher accuracy. Algorithm 1 below presents the generalized working of the M-ABC optimizer.  
```
1:IoMT sites initialization phase using Equation (1)
2:Do Repeat
3: Employed bees for new solution using Equation (2)
4: Onlooker bees candidate solution using Equations (3) and (4)
5: Phase of Scout bees' candidate solution using Equation (5)
6: Memorize the best solution you came up with
7:until maximum number of cycles reached
```  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S\#** & **Risk Name** & **Description** & **Encoded Values** \\ \hline  & & & \(>\)240 mg/dL = High = 1 \\
4 & Serum cholesterol & In mg/dL & 200–239 mg/dL = Normal = 0 \\  & & & \(<\)200 mg/dL = Low = \(-\)1 \\ \hline
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 1 Introduction
The underlying goal of this study is to investigate the practical utility and optimum placement of FBG sensors for in situ thermal monitoring and thermal profiling of power electronics systems, extending beyond mere laboratory demonstrations. The detailed installation methods facilitate the easy and effective implementation of the FBG-based sensing technique for commercial applications.
## 4 Experimental Results and Analysis
### Comparison of Base Network Models  
The effects of different networks on pedestrian inertial navigation systems are investigated to validate the superiority of the proposed RBCN-Net. The four base models, CNN, LSTM, ResNet18 and ResNet50, are intended to be used in comparison experiments with the proposed RBCN-Net on the dataset VOIMU presented in this paper. The measured evaluation metrics are shown in Table 3. The RBCN-Net suggested in this research outperformed.  
The other four networks with the best assessment metrics, an average RMSE of \(2.079\) and an average RTE value of \(1.481\%\). ResNet50 and ResNet18 are second to RBCN-Net because the residual structure can effectively address the issue of network degradation. The LSTM network comes next. Although LSTM helps RNN's long-term dependence issue to some level, it can still be challenging for longer-sequence data. The experimental outcomes are the poorest because CNN is not very well suited for learning time series.  
A randomly selected set of experiments in the test set was used for visual trajectory comparison, which had a total trajectory length of \(176.42\) m, took \(160.2\) s, and contained long straight lines and seven right-angle turns. The trajectory comparison is shown in Figure 9.  
Figure 8: Distance error of coordinate points under simultaneous inscription.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Model** & **Average RMSE/m** & **Average RTE** \\ \hline CNN & 8.985 & 7.804\% \\ LSTM & 4.805 & 3.46\% \\ ResNet18 & 3.574 & 2.567\% \\ ResNet50 & 2.756 & 1.894\% \\ RBCN-Net & 2.079 & 1.481\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Position evaluation of five competing networks.  
The RBCN-Net predicted trajectory has the greatest overlap with the true trajectory. In the zoomed-in area, the pedestrian makes a second turn, and only this model predicts the route close to the true trajectory.  
The specific evaluation indicators are shown in Table 4. RBCN-Net obtained an RMSE of 0.761, RTE value of 0.56%, and the endpoint coordinates differed by only 1.14 m in this experiment. RBCN-Net's navigation accuracy is much better than other networks\({}^{\prime}\). The effect is only second to the RBCN-Net is ResNet50, whose endpoint coordinates differ from the real coordinates by 9.52 m, and the RMSE value is 2.313, which is somewhat different from the true trajectory. ResNet18 and LSTM do not differ much from each other in this experiment.
# Dynamic SDN Controller Load Balancing
## 3 Network Architecture
### Three Level Load Balancing Architecture  
In this paper, we consider the architecture, presented in [25; 26] as briefly presented in the previous section but we add a new, additional intermediate tier.  
Thus we consider a three-tier hierarchy, where the SC is in the top tier, some Master Controllers are in the intermediate tier and all other controllers are in the lower tier. In Figure 1 we show an illustration of our proposed architecture with an example of reclustering process where in green we see the master of each cluster and SC denotes the super controller. In this three-tier architecture, we observe two levels of load operations: \"_Clustering_\" and \"_Reassignments_\". In the top level of \"_Clustering_\", the SC organizes the controllers into clusters. In the lower level of \"_Reassignments_\", for each cluster, there is a Master Controller \((MC)\) responsible for the load balancing inside the cluster by reassigned switches to controllers dynamically. In order to define the problem of the \"_Clustering_\" for the high level of the load balancing operation, two aspects are considered: first, the minimal differences between clusters' loads are set as targets, and second, the minimal distances between controllers in each cluster.  
In this paper, we do not propose a new method for performing load balancing inside each cluster since many efficient algorithms already exist, for instance see [18; 20; 21]. However, the method employed, which is designed for the top level in which clusters are rearranged (as described in the next section), is sufficiently generic that it will operate with any of the existing load-balancing algorithms.  
At the start of each time cycle, the MC sends to the SC the Cluster Vector Loads (CVL), which includes the load of each controller inside the cluster. Then the SC may update the CV of each cluster in order to balance the load of some overloaded clusters, and may also update who is the new MC of some cluster. In parallel to the \"\(Clustering\)\" operation performed by the SC, the \"\(Reassignments\)\" operations of the load balancing are performed by the MS independently.  
Due to the three level DCF architecture, the load balancing runtime of both the SC and MC is very low, and enables a reduction in the TS duration accordingly. Thus the greater the reduction in the timeline, the greater the accuracy achieved [25].
# Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid
## 5 Simulation and Results
### Network Simulation
#### 5.2.1 Service Channel Failure Rate Comparison  
Figure 8a shows the service channel failure rates of _SCVRD-R_ algorithm, AAR-OS algorithm and RAP algorithm in 12 months under service intensity 1. Because _SCVRD-R_ algorithm finds route according to _SCVRD_, the service channel can adjust the route based on the distribution of failure arrival rate and \(TTR\) to avoid the fault paths, which can reduce the service channel failure rate. The results in Figure 8b,c are similar to the results in Figure 8a.
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Weld Metal Microstructures
#### 3.3.4 Hot-Pass Welding  
In the hot pass, the three materials are mixed (as visualized in Figure 11b), namely X60 (BM), Ni-based Alloy 59 filler wire, and clad material (316L SS). Some part of the previously deposited root pass will also be melted, forming a quaternary mixing system. The dilution rate will depend on both welding parameters and level geometry, particularly root gap and root face height. With excessive carbon diffusion from X60 or from the carbon-enriched interface between the clad and BM of the pipe, there is a risk of embrittlement.  
The chemical composition of single beads was determined by EPMA to include both root and hot pass, as shown in Figure 15. For a 309 SS wire, there is no significant change in the chemical composition. When using Inconel 625 wire (see Figure 16), Ni increases significantly in the root pass, while Fe decreases. This is related to the melting of a part of the pipe steel (X60) which has low Ni content. For the same reason, a slight increase in Cr, Mo, and Nb in the root pass is also observed, while the Fe concentration is substantially lower in the root pass, clearly showing very low dilution with the pipe. On the contrary, the hot pass contains 20-30 wt % Fe, which implies a certain dilution with the pipe.  
Figure 16: EPMA quantitative analysis of X60 steel pipe with Inconel 625 filler wire [19].  
Figure 15: Electron probe microanalyzer (EPMA) quantitative analysis of X60 steel pipe with AISI 309 SS filler wire [19].