esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 4 Proposition of Notch Equivalent Stress Correction Factor of Titanium Alloy Welded Structures
### Determination of Notch Equivalent Stress and Prediction of Fatigue Life  
It can be seen from Figure 6b that the thickness \(t\) of the loaded plate of the titanium alloy welded joint is 10 mm, the height \(H\) of the non-loaded plate is 40 mm, the thickness \(L\) of the non-loaded plate is 2 mm, and the size \(h\) of the welding leg is 2 mm. Through the process described in Sections 2.1 and 2.2 of this study, the notch equivalent stress at the weld position at each stress level can be calculated. Combined with the new three-parameter power function S-N curve model fitted in Figure 7b, the fatigue life predicted by the notch stress equivalent value method can be obtained. The calculated notch equivalent stress and predicted fatigue life are shown in Table 7. The average error between the test life in Table 6 and the predicted life in Table 7 is 9.01%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Stress Range** & **Test Fatigue Life** & & **Stress Range** & **Test Fatigue Life** \\
**Number** & **s\({}_{r}\)/MPa** & **lgN/Cycle** & **Group Number** & **S\({}_{r}\)/MPa** & **lgN/Cycle** \\ \hline
1 & 149.50 & 6.6247 & 11 & 207.00 & 5.8316 \\
2 & 151.05 & 6.6635 & 12 & 209.10 & 5.7289 \\
3 & 160.00 & 6.6069 & 13 & 217.00 & 5.7421 \\
4 & 176.00 & 6.0841 & 14 & 218.50 & 5.2760 \\
5 & 183.00 & 5.9511 & 15 & 223.00 & 5.3650 \\
6 & 190.00 & 6.1412 & 16 & 230.00 & 5.3374 \\
7 & 190.80 & 5.7544 & 17 & 231.00 & 5.3050 \\
8 & 195.50 & 5.6544 & 18 & 234.15 & 5.4236 \\
9 & 200.70 & 5.6988 & 19 & 241.50 & 5.1532 \\
10 & 204.00 & 5.9492 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test fatigue life.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Stress Range S\({}_{r}\)/MPa** & **Notch Equivalent Stress** & **Predicted Fatigue Life** \\
**Number** & & \(\sigma_{r}\)/MPa** & **lgN/Cycle** \\ \hline
1 & 149.50 & 202.07 & 6.1983 \\
2 & 151.05 & 204.17 & 6.1652 \\
3 & 160.00 & 216.27 & 5.9801 \\
4 & 176.00 & 278.9 & 5.6736 \\
5 & 183.00 & 247.36 & 5.5482 \\
6 & 190.0
## 6 Case Study
### Case Study--Literature Review
#### 6.2.1 Autonomous Systems  
Automation is slowly becoming part of nearly every aspect of our lives and plays an increasingly role in everyday life, including in our home and workplace. We can already see the influence in some sectors being replaced by automated systems, such as bank tellers, cashiers, and gradually drivers and deliveries. According to some studies, in the upcoming decades, 47% of the currently existing occupations in America are at high risk of decline [34]. One of the evolving sectors is the healthcare workforce. Studies have shown that automation will not simply replace the human part, but rather change its role [35]. New roles will be required for the control and supervision of autonomous systems, and contemporary structures of co-workers will be created to operate them: human-automation teaming (HAT). As with every team working together, trust between members is essential to collective functioning.  
Automation can be defined as technology that actively selects data, transforms information, makes decisions, or controls processes [36]. Automation implemented properly could increase efficiency, improve safety, lower operator workload, etc. [35]. Automation is a way of operating or controlling a process by automatic means. It refers to the full or partial replacement of a job that used to be completed by humans. Automation is not all or nothing but rather varies across a range of levels, from full manual performance to full automation. It is customary to use a scale of ten levels of automation (LOA) defined by [PERSON] and [PERSON] [37]. The scale divides the levels of automation from full control of the operator (Level 1) to full control of the system (Level 10).
# Updates on Impact Ionisation Triggering of Thyristors
## 3 Results
### Lifetime Test  
A total lifetime of \(2\times 10^{6}\) pulses (Figure 13a) is fulfilled for the driver. The test load for the driver consisted of a combination of \(180\,\mathrm{pF}\) (six \(30\,\mathrm{pF}\) coupling capacitors in parallel) in series with \(3.5\,\Omega\) (six \(20\,\Omega\) internal gate resistances of each SiC MOSFET in parallel). When connecting the driver to the six paralleled SiC MOSFETs, a total lifetime of at least \(1\times 10^{6}\) pulses (Figure 13b) is demonstrated on a \(4\,\Omega\) load.  
Figure 12: Output voltage of the FMMT413 (pink, TP1, Figure 6) and FMMT417 (green, TP2 Figure 6) avalanche transistors, charging voltages \(150\,\mathrm{V}\) and \(320\,\mathrm{V}\), respectively, and for the GS66508T GaN HEMT (blue, TP3, Figure 6), all measurements are on a \(50\,\Omega\) load. The final output has a maximum amplitude of \(620\,\mathrm{V}\) and a maximum slew rate of \(1\,\mathrm{kV/ns}\).  
Figure 13: Lifetime test. **(a)** Driver lifetime test, total \(2\times 10^{6}\) pulses. Loaded with \(180\,\mathrm{pF}\) and \(3.5\,\Omega\) to reproduce the six SiC MOSFET loading; charging voltage \(640\,\mathrm{V}\). **(b)**\(6\times\) SiC MOSFET lifetime test, total \(1\times 10^{6}\) pulses. Loaded with \(4\,\Omega\) to reproduce the condition of an output current \(>\)\(220\,\mathrm{A}\) (calculated output current \(>\)\(300\,\mathrm{A}\)); charging voltage \(1.3\,\mathrm{kV}\).  
Both lifetime tests conducted affirm that our pulse generator aligns with the commissioning criteria for a kicker magnet pulse generator in the beam dump section of the accelerator facility at CERN [27]. However, the results are preliminary, as no statistical lifetime test has been conducted.  
Given the negligible steady-state thermal dissipation in our setup and the extremely low duty cycle in our applications (<0.0001%), dynamic thermal dissipation can also be considered negligible. Consequently, we did not conduct any heat management studies on our devices.
# Search Space Reduction for Localization and Tracking of an Acoustic Source
###### Abstract  
Experimental data from the SACLANTCEN 1993 Mediterranean Experiment are reviewed to assess the reduction of the search space for the localization and tracking of an acoustic source in a three-dimensional environment. Key to this goal is the availability of an initial estimate of source range and depth (called the 2D initial guess); an ambiguous estimate of source bearing can be obtained from the 2D initial guess through Environmental Signal Processing, and the ambiguity can be removed by searching for the source only in the range/bearing regions where bearing estimates are higher. This search provides a new estimate of source range and a single bearing, which together with the estimate for source depth constitute the center of the reduced search space for source localization and tracking. The suggested approach is tested on experimental data from the SACLANTCEN experiment considering different frequencies, as well as a stationary and a moving source.  
matched field processing; normal mode models; environmental signal processing Article
## 4 Survey of Existing Works
### Ant Colony Optimization
#### ACO in Energy Efficient Clustering  
There are a number of works covering the area of energy efficient clustering in WSNs using ACO. [PERSON] _et al._ proposed an efficient routing algorithm for the cluster-based large scale WSNs using the ant colony optimization [89]. The technique uses two routing levels: intra-cluster and inter-cluster. In the first level cluster members send data directly to their cluster head. In the second level, the cluster heads use ACO to find a route to the base station. As only cluster heads participate in the inter-clusterrouting operation, the method can provide a smooth operation more effectively. As a result this method leads to a shorter convergence time and less routing overhead. To assess the efficiency of the proposed method it was compared with two other algorithms: a cluster-based routing without optimization [58] and an ACO-based routing algorithm without clustering. The results show lower power consumption and more load balancing for the proposed algorithm.  
In [90], the authors present a new energy aware clustering protocol, Ant Colony Optimization for Clustering (ACO-C). Using appropriate cost functions (at the base station), the protocol is said to minimize and distribute the cost of long distance transmissions and data aggregation among all sensor nodes evenly. The ACO-C protocol was successfully compared with other well known clustering algorithms like LEACH, LEACH-C and PSO-C over both network lifetime and data delivery to the base station. Their future work will deal with multi-hop routing schemes to improve the lifetime of the network.  
In order to improve the energy efficiency and achieve the network load balance, a novel energy efficient unequal clustering scheme for large scale WSNs is proposed in [91]. On the one hand, an improved fuzzy unequal clustering routing (IFUC) algorithm is used to determine one node's chance of becoming cluster head and estimate the cluster-head radius. On the other hand, the ACO is used in energy aware routing between cluster heads (CHs) and base station (BS). It reduces the ener gy consumption of CHs and solves the hot spots problem which occurs in multi-hop WSN routing in large scales. The experiment results have indicated that the proposed clustering scheme has more superior performance than other methods such as LEACH [58] and EEUC [92].  
Another work where an uneven clustering routing algorithm for WSNs based on ACO was proposed is [93]. It utilized the dynamic adaptability and optimization capabilities of the ACO to get the optimum route between the CH. Clusters closer to BS had smaller sizes than those far away from the BS, thus the closer CHs could preserve energy for the inter-cluster data forwarding. Simulation result indicates that the algorithm significantly improved in average energy consumption and survival rate, and extended the network lift cycle compared to LEACH.
## 4 Simulation and Performance Evaluation
### Comparison with VGDRA
#### 4.3.3 Only the Energy Consumption of Updating Routes Is Considered  
In this part, we study the cost of updating routes. We only deduct the energy consumption of updating routes. Here we adopt the initial energy in [24], and the initial energy is 1 mJ. As the distribution of source nodes has impact on results, so we talk separately. We use the length of sink moving as performance metric. That means we count how long sink has moved before the first node dies. Figure 22, shows the result of experiment. The blue line represents the length sink moves in VGDRA. The blue line is the lowest, and the length is almost unchanged with the increasing of nodes. The red line is the case of our GSDCS in an evenly distribution application scenario. The red line is higher than blue line, and the red line is increasing. The black line represents the lifetime of our GSDCS in the application scenario where source nodes are distributed in a small area of network. The black line is the highest, and it is increasing in mass. Hence, the cost of updating routes in our strategy is far below VGDRA's.
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 3 Medication Safety WSN Authentication Scheme
### Definition of the Proposed Medication Safety WSN Protocol
Step 7: PAD7 and PAD8 are produced by selecting UserID or PWD with the specific set of random numbers for Padgen function operation. Afterward, through XOR operation, CCMsgM3 \(\parallel\) CRC and CCMsgL3 \(\parallel\) CRC are obtained. Figure 2: Login/authentication phase. Step 8: UserID, CCPWD, CCMsgM3 \(\parallel\) CRC, and CCMsgL3 \(\parallel\) CRC are sent to HIS party from Pharmacy. Step 9: HIS checks UserID and CCPWD matching and verifies CRC code. PAD7 and PAD8 are produced by selecting UserID or PWD with the specific set of random numbers for Padgen function operation. Then, PWD, MsgM, and MsgL are computed through XOR operation. Steps 10 and 11: At last, the ACC_LOGIN signal is sent to User and Pharmacy end for completion of the phase. The third section is the password change phase, which includes six steps, as shown in Figure 3.  
The details of the operation are as follows:  
Step 1: UserID, PWD, and NPWD begin calculation with UserID or PWD selected along with specific random numbers given to Padgen function for obtaining PAD9 and PAD10. Then, NPWD is encrypted by XOR operation, yielding CCNPWD. Step 2: UserID, CCPWD, and CCNPWD are sent to HIS from PDA. Step 3: HIS verifies the received UserID and CCPWD for matching. Afterward, calculation is initiated with UserID or PWD selected along with specific random numbers given to Padgen function for obtaining PAD9 and PAD10. Hence, NPWD is gained through XOR operation between the PADs and CCNPWD, and then it is stored and updated. Step 4: UserID, CCPWD, and CCNPWD are sent to Pharmacy from HIS  
Step 5: Similarly, the Pharmacy will verify the received UserID and CCPWD for matching. Afterward, calculation is initiated with UserID or PWD selected along with specific random numbers given to Padgen function for obtaining PAD9 and PAD10. Hence, NPWD is gained through XOR operation between the PADs and CCNPWD, and then it is stored and updated. Step 6: Upon completion of updating NPWD, a signal successful change is sent to PDA.
## 2 LEA Detection and Tracking
### LEA Detection Using the Harris Corner Method  
The LEA configuration, as shown in Figure 3, has a rectangular shape and an intensity difference between the inside and the outside of the LEA. (This characteristic may be used to identify the LEA.) Because a quadrangle has four vertices, in this paper, we extract the vertices for LEA detection using the Harris corner method [12]. [PERSON] corner method has been improved upon [PERSON]'s corner detector by considering the differential of the corner score with respect to direction directly [12]. Here, the corner score is often referred to as the autocorrelation. For calculating the corner score, the sum of the squared differences is defined as in Equation (2):  
\[S\left(u,\ v\right)=\sum_{x,\ y}w\left(x,\ y\right)\left[I\left(x+u,\ y+v \right)-I\left(x,\ y\right)\right]^{2} \tag{2}\]  
where \(w\left(x,\ y\right)\) denotes the window at position \(\left(x,\ y\right)\), \(I\left(x,\ y\right)\) is the intensity at \(\left(x,\ y\right)\), and \(I\left(x+u,\ y+v\right)\) is the intensity at the moved window \(\left(x+u,\ y+v\right)\). Equation (1) can be expressed in matrix form as in Equation (3):  
\[S\left(u,\ v\right)=\left[u\ v\right]\left(\sum_{x,\ y}w\left(x,\ y\right) \left[\begin{array}{cc}I_{x}^{2}&I_{x}I_{y}\\ I_{x}I_{y}&I_{y}^{2}\end{array}\right]\right)\left[\begin{array}{c}u\\ v\end{array}\right]=\left[u\ v\right]\ M\left[\begin{array}{c}u\\ v\end{array}\right] \tag{3}\]  
where \(I_{x}\) and \(I_{y}\) are the partial derivatives of \(I\). Then, we can obtain the score for each window using Equation (4):  
\[S_{\mathrm{C}}=\lambda_{1}\lambda_{2}-k\left(\lambda_{1}+\lambda_{2}\right)^{2} \tag{4}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are the eigenvalues of the matrix \(M\) in Equation (3), and \(k\) is a tunable sensitivity parameter. If \(\lambda_{1}\) and \(\lambda_{2}\) have large positive values, then the position \(\left(x,\ y\right)\) can be identified as a corner.  
Figure 6: Examples of different locations of sliding search window and the corresponding color distribution of pixels within a window area in the CIE1931 color space.
## 3 Hardware Design
### 2.4 GHz Radio Front-End  
The current interchangeable top-layer module, named _Joshua_, is an analog radio front-end designed to operate in the 2.4 to 2.5 GHz industrial, scientific and medical (ISM) frequency band and to interface with the middle-layer mixed-signal module through a pair of analog baseband in-phase and quadrature-phase (I/Q) signals, both for transmission and reception. By interfacing with the analog baseband signals, the _Joshua_ board not only provides access to the lower PHY layers of 802.11b/g (WLAN) and 802.15.4 (e.g., ZigBee) protocols, but also allows experimentation with various types of channel access methods, such as frequency division multiple access (FDMA), TDMA and CDMA, and different modulation techniques.  
Figure 1: Photo (**left**) and block diagram (**right**) of the three-layer Marmote SDR platform comprising a _Joshua_ 2.4 GHz radio front-end (**top**), a _Teton_ mixed-signal processing (**middle**) and a _Yellowstone_ battery-operated power management (**bottom**) module.  
The _Joshua_ radio front-end is built primarily around the integrated Maxim MAX2830 RF transceiver, power amplifier, receive/transmit (Rx/Tx) and antenna diversity switch and, thus, supports both single and dual-antenna setups. While we looked at many alternative RF chips, we opted for the MAX2830, for it was one of the few models that makes both the Rx and Tx baseband I/Q signals accessible. The single die integration of most RF components saves board space and reduces the overall power consumption. However, these analog components are designed for higher linearity and dynamic range requirements than the commodity low-cost RF chips, and consequently, they draw significantly more current. The MAX2830 chip also incorporates a voltage controlled oscillator and a fast settling 20 Hz step adjustable RF synthesizer. While the original goal of the precise digital tuning capability is to allow use of low-cost crystals, we chose to use the integrated crystal oscillator as a buffer and to drive it by a precise 2.5 ppm, low-power temperature compensated crystal oscillator (TCXO). The stable and accurate TCXO along with the fine adjustable synthesizer are expected to give sufficiently precise control over the local oscillator frequency for applications, where formerly, we found this to be an issue [21].  
The direct conversion, zero-intermediate frequency RF-to-baseband Rx, baseband-to-RF Tx paths are also part of the RF chip, along with the programmable 7.5-18 MHz low-pass baseband filters. The analog Rx and Tx baseband signals are complex I/Q pairs digitized and processed by the ADCs/DACs and the FPGA, respectively, on the middle-layer mixed-signal processing module. While the _Joshua_ board hosts a single RF transceiver, future Marmote SDR radio front-ends may utilize a second transceiver for multiple-input and multiple-output (MIMO) and multi-band RF applications.
# Separable Gaussian Neural Networks: Structure, Analysis, and Function Approximations
## 5 Numerical Experiments
### Two-Dimensional Examples
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**2D**} & \multicolumn{2}{c}{**3D**} & \multicolumn{2}{c}{**4D**} & \multicolumn{2}{c}{**5D**} \\ \hline \multicolumn{2}{c}{**Seconds/Epoch**} & \multicolumn{1}{c}{**Loss**} & \multicolumn{1}{c}{**Seconds/Epoch**} & \multicolumn{1}{c}{**Loss**} & \multicolumn{1}{c}{**Seconds/Epoch**} & \multicolumn{1}{c}{**Loss**} & \multicolumn{1}{c}{**Seconds/Epoch**} & \multicolumn{1}{c}{**Loss**} \\ \hline \(f_{1}\) & 0.065 & \(2.31\times 10^{-4}\) & 0.081 & \(4.43\times 10^{-4}\) & 0.099 & \(1.41\times 10^{-3}\) & 0.113 & \(4.43\times 10^{-4}\) \\ \(f_{2}\) & 0.063 & \(7.34\times 10^{-5}\) & 0.081 & \(8.31\times 10^{-4}\) & 0.098 & \(2.50\times 10^{-3}\) & 0.114 & \(8.31\times 10^{-4}\) \\ \(f_{3}\) & 0.064 & \(4.26\times 10^{-6}\) & 0.083 & \(1.50\times 10^{-5}\) & 0.106 & \(4.13\times 10^{-5}\) & 0.110 & \(1.50\times 10^{-5}\) \\ \(f_{4}\) & 0.065 & \(2.80\times 10^{-6}\) & 0.084 & \(2.91\times 10^{-5}\) & 0.100 & \(9.12\times 10^{-5}\) & 0.108 & \(2.91\times 10^{-5}\) \\ \(f_{5}\) & 0.063 & \(7.40\times 10^{-5}\) & 0.083 & \(7.53\times 10^{-4}\) & 0.099 & \(1.00\times 10^{-1}\) & 0.107 & \(7.53\times 10^{-4}\) \\ \(f_{6}\) & 0.063 & \(1.39\times 10^{-6}\) & 0.083 & \(1.27\times 10^{-5}\) & 0.101 & \(2.11\times 10^{-5}\) & 0.115 & \(1.27\times 10^{-5}\) \\ \(f_{7}\) & 0.063 & \(4.44\times 10^{-5}\) & 0.083 & \(4.08\times 10^{-4}\) & 0.099 & \(1.89\times 10^{-3}\) & 0.111 & \(4.08\times 10^{-4}\) \\ \(f_{8}\) & 0.063 & \(1.97\times 10^{-5}\) & 0.083 & \(4.36\times 10^{-5}\) & 0.100 & \(7.76\times 10^{-5}\) & 0.113 & \(4.36\times 10^{-5}\) \\ \(f_{9}\) & 0.063 & \(2.27\times 10^{-4}\) & 0.079 & \(1.76\times 10^{-3}\) & 0.099 & \(9.93\times 10^{-3}\) & 0.111 & \(1.76\times 10^{-3}\) \\ \(f_{10}\) & 0.064 & \(3.51\times 10^{-6}\) & 0.082 & \(6.32\times 10^{-6}\) & 0.101 & \(9.84\times 10^{-6}\) & 0.113 & \(6.32\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The computation time of SGNN per epoch scales linearly with the increase in dimensions. Data are generated by averaging the results of 30 runs. Data Size: 16,384, Mini-batch size: 256. Neurons per layer: 20. Figure 3: Approximating the two-dimensional \(f_{4}\) by SGNN. (**a**) Training history; (**b**) absolute error; (**c**) prediction vs. exactness; (**d**) prediction; and (**e**) ground truth. Size of training dataset: 2048. Figure 2: Approximating the two-dimensional function \(f_{3}\) by SGNN. (**a**) Training history; (**b**) absolute error; (**c**) prediction vs. exact value; (**d**) prediction; and (**e**) ground truth. Size of the training dataset: 2048.
## 8 Challenges and Future Directions
### Challenges
#### 8.1.4 Ethical Considerations  
HAR models trained on biased data will give discriminatory results. When the training data do not represent the diverse population they are intended to serve, the resulting HAR system becomes inherently biased against certain demographics, such as race, gender or age. This could further reinforce existing healthcare disparities [125] and systematic discrimination. HAR can also be used as a discriminatory tool against individuals based on their activities or behaviours such as in granting or denying access to services like insurance.  
Most recent HAR systems use AI models. These AI models are black boxes, and so it is difficult to ascertain how they arrive at the results, and this makes it difficult to determine their clinical validity. When users can understand how these models work, they are more likely to adopt these HAR systems. This also raises the ethical question of accountability. It is not always clear who is to be held accountable in cases where AI-based systems are deployed.  
There are also the ethical concerns of reduced human autonomy, where HAR systems could dictate what the user has to do, thus taking away the user's autonomy [126]. It is important that users can continue to maintain their autonomy and can choose to use or not use these HAR systems.