esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Retention Curve of Biocemented Sands Using MIP Results
## 3 Materials and Methods
### Tests Performed
#### 3.4.2. Saturated Permeability  
A falling water head test was performed to measure the saturated permeability by adapting a triaxial cell to the size of oedometer samples [37]. The sample was placed in the base of a triaxial cell inside a metallic tube exactly with the same diameter (7 cm). All joints were sealed with silicone to avoid leakage and force water flow only through the soil. Then the tube was filled with 10 cm of water above the soil, and the downstream valve was opened. The time interval necessary for water drop 1 cm in the tube was recorded, and permeability was computed as usual. Three measurements were taken, and the average value was taken.
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
###### Abstract  
Heart failure (HF) is a devastating condition that impairs people's lives and health. Because of the high morbidity and mortality associated with HF, early detection is becoming increasingly critical. Many studies have focused on the field of heart disease diagnosis based on heart sound (HS), demonstrating the feasibility of sound signals in heart disease diagnosis. In this paper, we propose a non-invasive early diagnosis method for HF based on a deep learning (DL) network and the Korotkoff sound (KS). The accuracy of the KS-based HF prediagnosis was investigated utilizing continuous wavelet transform (CWT) features, Mel frequency cepstrum coefficient (MFCC) features, and signal segmentation. Fivefold cross-validation was applied to the four DL models: AlexNet, VGG19, ResNet50, and Xception, and the performance of each model was evaluated using accuracy (Acc), specificity (Sp), sensitivity (Se), area under curve (AUC), and time consumption (Tc). The results reveal that the performance of the four models on MFCC datasets is significantly improved when compared to CWT datasets, and each model performed considerably better on the non-segmented dataset than on the segmented dataset, indicating that KS signal segmentation and feature extraction had a significant impact on the KS-based CHF prediagnosis performance. Our method eventually achieves the prediagnosis results of Acc (96.0%), Se (97.5%), and Sp (93.8%) based on a comparative study of the model and the data set. The research demonstrates that the KS-based prediagnosis method proposed in this paper could accomplish accurate HF prediagnosis, which will offer new research approaches and a more convenient way to achieve early HF prevention.  
heart failure; Korotkoff sound; deep learning; MFCC; neural networks; prediagnosis Article
## 4 Performance Analysis and Simulation Outcomes
### Evaluation of the Proposed Solution Schemes
This is because as the Rician factor increases at first, the slowly varying LoS components between the users and the IRS are enhanced to boost the SE performance of the proposed RL-based solution schemes. However, when the Rician factor is relatively large, the LoS components are dominant and therefore have less effect on the channel strength. Figure 8: SE versus number of reflecting elements of IRS. Figure 7: SE versus minimum SINR requirement for D2D pairs.
## 5 Implementation and Simulations  
We designed and programmed the modules described above using C++ and implemented the various classes to work with existing NS-2.34 modules [45]. Several modifications were also carried out on existing NS-2.34 modules to incorporate the various required node behaviours and the overall functionality of the proposed reputation management model. Dynamic source routing was used as the routing protocol to verify the functionalities of the proposed model. Exhaustive simulations were also carried out, averaging 10 simulations for each specified scenario. Other general parameters used are mentioned in Table 4.
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## Materials and Methods
### Details of Satellite Maneuver  
The AIS receiver used in the LAPAN-A2 mission is a dual-channel VHF receiver, which can be tuned to any of the VHF channels in the maritime VHF band ranging from 156.025 to 162.025 MHz. When listening for AIS messages broadcasted on the two designated AIS channels, the radio is tuned to VHF channels 87B and 88B (161.975 and 162.025 MHz, respectively) [18].  
In this study, satellite maneuvering has been performed in order to orient the AIS antenna to the desired angle so the antenna will tilt \(135^{\circ}\) or \(180^{\circ}\) from the nadir axis. As depicted in Figure 4a, the flight configuration of nadir pointing is defined as a nominal condition. In this nadir pointing, the AIS antenna (that is placed on the \(y\)-axis) is oriented into south direction or \(90^{\circ}\) from its nadir axis. Based on Figure 4b, the spacecraft made a \(-90^{\circ}\) maneuver on the \(x\)-axis and then \(-45^{\circ}\) on the \(z\)-axis so the AIS antenna will tilt \(45^{\circ}\) from the flight direction or \(135^{\circ}\) from nadir. In the second setup (Figure 4c), the spacecraft only took a single \(-90^{\circ}\) maneuver on the \(x\)-axis in order to orient the antenna \(180^{\circ}\) from the nadir axis. This orientation was chosen by considering that the different angle of monopole antenna could give different reception AIS signals from the ground.  
For the consistency, in this paper, the flight configuration from the maneuver set up on Figure 4a-c respectively will be called as \"nadir\", \"45\({}^{\circ}\)-maneuvered\" and \"90\({}^{\circ}\)-maneuvered\" and then the dataset resultant from the maneuver will be labeled as \(D_{0}\), \(D_{45}\), and \(D_{90}\), respectively. The \"0\", \"45\", and \"90\" notation is coming from the tilting angle between AIS antenna and certain reference axes (south or flight direction).  
Figure 5 provides the typical pattern of monopole antenna in a different orientation toward the ground. It is shown that the antenna pattern of LAPAN-A2 during 90\({}^{\circ}\)-maneuver or the vertical antenna position has a broader area of detection compared to the nadir and 45\({}^{\circ}\)-maneuver configurations. The broader detection could lead to the increase of message reception; however, at the same time, it may have a collision problem of AIS signal [19]. By using the nadir, 45\({}^{\circ}\)-maneuvered, and 90\({}^{\circ}\)-maneuvered flight configurations, the AIS messages have been recorded and analyzed.
## 5 Conclusions and Outlook  
### Conclusions  
1. A welding defect warning and identification system based on multi-source heterogeneous sensing data of molten pool images, current signals, and vibration signals is constructed by simulating the operation mode of experienced welders in the process of welding thin plates.
2. In the field of identification of welding defects, vibration sensors were used for the first time, and the importance of vibration signals in the identification process was confirmed in experiments.
3. A support vector machine classification method based on neighborhood rough sets is introduced to reduce the size and complexity of the problem and increase the speed of diagnosis. The feature dimensions are reduced from 195 to 12, significantly lowering the complexity of the problem. The training time for a single model is reduced from 0.55 s to 0.11 s, greatly cutting resource consumption and improving diagnostic speed. Experimental results show that the method has high generalization potential.
4. This paper presents a new method that combines NRS with DOA-SVM to quickly and accurately distinguish between five types of defects and good welds in arc welding. The identification accuracy of this method is at least 98%, an improvement of approximately 4.97% compared to CART and 0.55% compared to standard SVM. These results confirm the research and application value of the method.
# Study on Filling Capacity of Optical Glass in a Novel Rapid Hot Embossing Process
###### Abstract  
This paper aims to present a novel rapid hot embossing approach and to study filling capacity of optical glass in the hot embossing process. Firstly, a novel rapid hot embossing device is developed, which consists of a rapid heating module and a precision loading module. Particularly, the rapid heating module allows a maximum temperature of 800 \({}^{\circ}\)C and a heating rate of 300 \({}^{\circ}\)C/min, with decent temperature control accuracy and uniform temperature distribution. In hot embossing process, by incompletely filling the microhole of silicon carbide mold, a microlens would be formed on the surface of glass disc, and the filling capacity of glass is quantified by the maximum height of the microlens. The tailor-made hot embossing device was exploited to conduct a series of experiments for evaluating effects of process parameters on the filling capacity of N-BK7 glass. Experimental results indicate that the filling capacity of glass could be enhanced by increasing the embossing force, the embossing temperature, the soaking time but decreasing the annealing rate. Furthermore, compared to soaking time and annealing rate, embossing force and embossing temperature have more significant influence on the filling capacity of N-BK7 glass. Therefore, the novel rapid hot embossing is a practical and promising technology for fabricating microstructures on glass materials with high softening points.  
hot embossing; optical glass; rapid heating; filling capacity; microlens Article
## 3 Results and Discussion

Table 2: Maximum and minimum values of tangential and normal MMM signals around the center and edges of the five V-shaped notches of an ASTM-A36 steel pipe. Figure 13: Maximum variation of tangential MMM signals versus depth of the V-shaped notch of the ASTM-A36 steel pipe. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**V-Notch Defect**} & **Maximum Variation (\(\Delta\)Bz) of the Tangential** & **Maximum Variation (\(\Delta\)By) of the Normal MMM** \\  & **MMM Signal around Notch Center** & **Signal between the Edges of Notch** \\  & **(\(\upmu\)T)** & **(\(\upmu\)T)** \\ \hline S1 & \(14.32\pm 1.62\) & \(27.95\pm 1.14\) \\ S2 & \(16.08\pm 1.86\) & \(26.4\pm 0.54\) \\ S3 & \(66.73\pm 1.13\) & \(62.63\pm 0.50\) \\ S4 & \(55.13\pm 1.33\) & \(51.98\pm 0.64\) \\ S5 & \(68.75\pm 1.10\) & \(71.37\pm 0.72\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variations of tangential and normal MMM signals around the center and edges of the five V-shaped notches of an ASTM-A36 steel pipe.
## 5 Results
### Analysis
#### 5.2.2 Fairness  
Figure 7a shows the results of [PERSON]'s fairness index (JFI) versus the number of stations for a three-channel configuration. The proposed protocol, whilst providing a higher throughput with the same number of available channels, proves to have a lesser [PERSON]'s fairness index than the original SA-MMAC. Therefore, we proposed an enhancement to the protocol, Max-E-SA-MMAC, which has a higher throughput than SA-MMAC whilst providing a similar JFI performance. Max-E-SA-MMAC assigns \(CW_{max}\) for nodes that have successfully transmitted data frames, allowing for those in the queue to reach the end of their backoff timer faster and gain access to the channel, improving fairness.  
In the 4-channel configuration, Figure 7b, we notice that the BiMMAC loses significant fairness performance after the number of stations passes 35. However, both the proposed protocols and SA-MMAC have a consistent fairness index even as the number of stations increases and remains fairer than the BiMMAC protocol. The queued nodes wait a longer period of time to access the channel in BiMMAC compared to our protocols due to the lack of control channel utilization for data transmission.  
In Figure 7c, all MMAC protocols apart from AMMAC remain consistent and relatively equal. The AMMAC protocol in all three configurations has the highest JFI due to the nature of the protocol only sending one data packet per handshake, allowing for more nodes to send single packets. This however comes at the cost of a poorer throughput performance in a saturated network with a high load of data packets to be sent. For other  
Figure 7: JFI comparison of the two MMAC protocols, E-SA-MMAC, and Max-E-SA-MMAC, versus others; (**a**) 3, (**b**) 4, and (**c**) 12 channels.  
protocols, except IEEE 802.11, the JFI remains equal due to the availability of a greater number of data channels; therefore, there are fewer stations queued for data transmission.
## 6 Experiment  
We carried out a sea experiment in November 2022 (Figure 7). We established two robots: AUV 0 was used for AUV charging and data interaction, and AUV 1 could move freely. AUV 0 and AUV 1 had the same control system, but AUV 0 did not carry a propeller, so it was set as an AUV with a sailing speed of zero. The two robots could be regarded as heterogeneous AUVs, and the entire experimental environment was 1500 m underwater. The experimental process was as outlined below.  
1. Positioning and communication devices were installed on the AUVs. Each AUV had the ability to exchange information with the other.
2. AUV 1 was placed into the water.
3. AUV 0 was placed into the water 300 m from AUV 1.
4. When the two devices reached the sea floor, the position of the two AUVs was determined by the ultra-short baseline equipment on board and the positions were transmitted to AUV 1 and AUV 0 via acoustic communication.  
Figure 5: (a) Consistent trajectories of five AUVs with three obstacles, where obstacles appear in the trajectories of AUV 1 and AUV 2. (b) Consistent trajectories of five AUVs with multiple obstacles, where obstacles appear in the trajectories of AUV 1 and AUV 2 in the Y–Z plane.  
Figure 6: Time histories of positions and controls of five AUVs with multiple obstacles.  
4. AUV 1 and AUV 0 exchanged information at 8 s intervals through their own acoustic communication devices.
5. AUV 1 moved towards AUV 0, and finally, AUV 1 appeared in front of AUV 0.  
It can be observed from Figure 8 that AUV 1 descended to the seabed in the form of a spiral wire, and AUV 0 was laid into the water during the process of AUV 1 diving. As indicated in Figure 9, The red sphere means the position information of AUV1 from AUV1; each two minutes the AUV0 obtains the information. The green sphere means the position information of AUV1 from AUV0 by the position system; each eight seconds the AUV0 obtains the information. The inset on the top right means the view from the above camera in the software. From the figure we obtain the trajectory of AUV1 moved toward AUV0. AUV 1 approached AUV 0 after exchanging information with AUV 0, and the entire process was completely automatic. The system realized the collection of heterogeneous AUVs through the algorithms.  
Figure 10 presents selected screenshots of a certain period to verify that AUV 1 appeared in front of AUV 0 and could proceed to the next step. It describes the final progress of the system reaching consensus, from the view of AUV0; AUV1 was moving towards AUV0 through their information and when the AUV1 moved, it lit the red light, the inset on the top left means time.  
Figure 7: Two AUVs in the experiment.
## 2 ARBF-MLPA Method
### Radial Basis Function (RBF) Neural Network  
In machine learning, neural network is a good model for forecast and analysis. There are several types of neural network. Among these, radial basis function neural network features optimal approximation, fast convergence speed, and the ability to overcome the problem of local minimal value. In addition, a radial basis network can approach any continuous function with any accuracy. Radial basis models mainly take the basis function as the sampling point symmetry center to build radial basis function model for discrete multivariate data interpolation. Radial basis function is usually expressed in expression (1):  
\[\hat{y}(\mathrm{x})=\sum_{j=1}^{m}c_{j}p_{j}(\mathrm{x})+\sum_{i=1}^{n_{s}} \lambda_{i}\varphi(r(\mathrm{x},\mathrm{x}_{i})) \tag{1}\]  
where \(m\) represents the number of items in polynomial expression, \(c_{j}\) represents the coefficient of polynomial basis function \(p_{j}(\mathrm{x})\), \(n_{s}\) represents the number of sampling points, \(\lambda_{i}\) represents the weighting coefficient of variable \(i\), \(r(\mathrm{x},\mathrm{x}_{i})\) represents Euclidean distance, and \(\varphi(r)\) represents radial basis function.  
In radial basis function models, the selection of basis function is critical. There are the following types of basis functions shown in Table 1, in which \(r\) represents Euclidean distance, and \(c\) represents shape parameter, which can take a random value you desire.  
\begin{table}
\begin{tabular}{c c} \hline
**Basis Function** & **Mathematical Expression** \\ \hline Linear & \(\varphi(r)=cr\) \\ Cubic & \(\varphi(r)=(c+r)^{3}\) \\ Thin plate & \(\varphi(r)=r^{2}\ln(cr^{2})\) \\ Gaussian & \(\varphi(r)=\exp(-cr^{2})\) \\ Multi-quadric & \(\varphi(r)=\sqrt{c^{2}+r^{2}}\) \\ Inverse multi-quadric & \(\varphi(r)=1/\sqrt{c^{2}+r^{2}}\) \\ \hline \end{tabular}
\end{table}
Table 1: RBF common basis functions.  
In view that vehicle crash is a complicated nonlinear function-changing process, the main advantage of RBF is that it can approximate nonlinear functions very well, can handle the laws that are difficult to analyze in the calculation process, and has good generalization capability and extremely fast convergence. For this reason, this paper selects RBF of Gaussian basis function to build the metamodel of whole vehicle-side crash optimization.
## 2 Previous Research  
### Fire Damage Inspection  
As shown in Table 1, the fire damage diagnosis methods presented in the KCI and AIJ specifications [1; 2] divide the fire damage levels into five grades. This classifies the structural members exposed to a fire below 300 \({}^{\circ}\)C, having no or marginal fire damage into the non-structural finishing materials, as Grades I or II, respectively. The case where the structure is exposed to an intermediate level of a fire ranging from 300 \({}^{\circ}\)C to 600 \({}^{\circ}\)C and micro cracks and small spalling damages are observed in column members can be classified into Grade III. When the structure is exposed to a relatively high temperature ranging from 600 \({}^{\circ}\)C to 950 \({}^{\circ}\)C, having many large cracks and partial exposure of steel bars, it is included in Grade IV. The case where the structure is exposed to a very high temperature exceeding 950 \({}^{\circ}\)C, having severe member deformation and large cracks, as well as heavy exposure of steel bars in a wide area, is classified as Grade V. Thus, as indicated by the fire damage diagnosis methods presented in the KCI and AIJ specifications, the heating temperatures of the members is the critical factor to determine the fire damage grades of RC structural members [1; 2; 3].  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Temperature** & **Damage Level** & **Grade** \\ \hline \multirow{3}{*}{\(\sim\)300 \({}^{\circ}\)C} & No damage & I \\ \cline{2-3}  & Finishing material damage & \\  & (soot, surface exfoliation) & II \\ \hline \multirow{3}{*}{300\(\sim\)600 \({}^{\circ}\)C} & Concrete damage without steel damage & \\  & (small cracks in concrete or spalling) & III \\ \hline \multirow{3}{*}{600\(\sim\)950 \({}^{\circ}\)C} & Bond damage of steel bars & \\  & (large cracks in concrete or exposure of steel bars) & IV \\ \hline \multirow{3}{*}{950\(\sim\)1200 \({}^{\circ}\)C} & Damage or buckling of steel bars & \\  & (large damage or deformation of structural members, & V \\ \cline{1-1}  & heavy exposure of steel bars in a wide area) & \\ \hline \(\sim\)1200 \({}^{\circ}\)C & Concrete melting & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fire damage grades [1; 2].  
As shown in Table 2, the fire damage inspections are conducted through the primary and secondary investigations. In the primary field inspection, concrete discoloration, cracks, spalling and peeling are visually observed, and it is then determined whether the fire damage level can be categorized as Grade I or not. If not, a secondary investigation needs to be done through non-destructive tests, mechanical tests, material sample examinations, analytical methods, etc. The conventional fire damage diagnosis method, however, has shortcomings in that it is difficult to consider the field inspection results in a comprehensive and synthetic manner, and consequently, the fire damage grade is determined strongly depending on the subjective judgment of the inspector. Therefore, this study proposed the FDDS based on fuzzy theory that can provide the fire damage grades in a reasonable and systematic manner.
## 3 System Design
### Tracking
#### 3.7.2 Tracking in Normal-Sensitivity Mode  
In normal-sensitivity mode, the receiver initiates tracking of detected signals without synchronization to the pilot's secondary code or the telemetry preambles. The tracking process commences with a CI time equivalent to the duration of the PRN primary code. Once synchronization with the pilot's secondary code or the telemetry preambles is achieved, the tracking process dynamically increases the CI time based on user configuration.
## 3 Relevant Research in Wind Farm Design and Optimization
### Solution Methods  
The solution methods adopted for the WFDO problem can be categorized in three different groups: (1) Mixed-Integer Nonlinear Programing (MINLP) models and Calculus-based methods; (2) Heuristic methods; and (3) Metaheuristic methods. Their usage to provide a solution to the different versions of the WFDO problem is illustrated in Figure 4. Descriptions of such methods are provided in Section 3.3.1. The most widely used solution methods are non-deterministic population metaheuristics based on the Genetic Algorithm. Nonetheless, is common to find combinations of methods in the literature, which are discussed in Section 3.3.4. In addition, the interested reader is directed to the review works [153, 154, 235, 322], which present a clear and comprehensive comparison between the most used metaheuristics for the solution of the WFDO problem.  
The evaluation of the performance metrics, discussed in Section 3.1, can be performed in a single-objective or in a multi-objective strategy. In the single-objective strategy, the first level of sophistication aims to include adverse effects (described in Section 2.2) as constraints and/or as penalties to the objective performance metric, which in turn affects the decision variables values (described in Section 2.4) during the optimization procedure. The second level of sophistication, adopted by most of the reviewed works, aims to include adverse effects during the performance metric quantification with the use of sub-models (as described in Section 3.2). However, as pointed out previously, the development of sub-models used to translate the implications of certain adverse factors (e.g., noise, EMI or visual impacts) into the performance metric (e.g., the cost of energy) is difficult, thus hindering extremization of each objective/factor simultaneously.  
In the multi-objective strategy, the inclusion and combination of performance metrics and adverse effects is easier and, therefore, both can be extremized simultaneously during the optimization procedure. Therefore, the main advantage of the multi-objective strategies is that there is no need to formulate complex sub-models that translate physical variables values into performance measure values, since both kind of variables can be considered as performance metrics of the problem. The WFDO problem is naturally categorized as a multi-objective problem, where individual design objectives are generally in conflict. An optimal solution to a multi-objective problem is one of a set of feasible solutions that satisfies the objectives to an acceptable level (_i.e._, where the trade-off between two or more objectives is the best). Only a few works in the WFDO problem [154, 159, 163, 202, 209, 250, 264] have implemented  
Figure 4: Type of solution methods used in the WFDO problem.  
multi-objective approaches. In addition, as pointed out in [153; 154], many different multi-objective formulations have not been implemented and/or tested for the solution of the WFDO problem.