esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 5 Results and Discussion
### Network Lifetime  
Experimental results of network lifetime have been discussed, for the same protocols. Figure 10a shows the comparison among the network lifetime for the aforementioned protocols. Figure 10b, represents exhausted nodes increases with increasing the packet size. The AOMDV-ER performs better than AOMR-LM because it employs a proficient recoiled node technique.  
Figures 10c and 11b show the exhausted node increase with simulation time. The Figure 11a,b show the energy consumption with varying simulation time in protocols. The AOMDV-ER performs best because of the recoil node technique, thereby conserving the energy level in nodes while reducing the number of transmission.  
Figure 10: (**a**) N Lifetime vs. Node Speed; (**b**) N. Lifetime vs. Packet Size; (**c**) N. Lifetime vs. Sim. time.  
Figure 9: (**a**) Overhead vs. Node Speed; (**b**) Overhead vs. Packet Size; (**c**) Overhead vs. Sim. time.
# High-Functioning Autism and Virtual Reality Applications: A Scoping Review
## 3 Results
### Investigated Dimensions
#### 3.2.4 Job Training  
Finding job opportunities and maintaining an occupation in the long term is a challenge for ASD individuals, whatever their functioning level. In the present study, only two papers met the inclusion criteria and discussed the use of immersive VR as a tool for supporting employment for people with HF ASD.  
[PERSON] and colleagues developed an immersive VR system to train vocational skills in a group of HF ASD individuals: cleaning, loading the back of a truck, money management, shelving, environmental awareness, and social skills. The results were promising both for the improvements in the targeted skills and for the possibility of transferring what the ASD individuals learned [55].  
The same group in 2018 explored the user experience of 15 HF ASD individuals undergoing immersive VR tasks; scenarios simulating three warehouse tasks were evaluated based on five design attributes: instruction methods, visual fidelity, view zoom, clutter, and motion [64]. The results indicated that avoiding verbal instructions is preferable, as well as low visual fidelity, normal view zoom, no clutter, and no motion.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 5 Summary, Conclusions, and Future Work
### Conclusions  
This method can be used to detect various types of composite damage, including simulated delamination and actual impact damage. Although the ABT excitation consisted of pressure waves, the SH0 mode was successfully generated due to the anisotropy of the quasi-isotropic composite layup. The use of pure S0 and SH0 modes allowed for damage separation that would not have been possible otherwise. It was found that the S0 mode was only sensitive to the impact damage, while the SH0 mode was sensitive to both delamination and impact damage. The present method of simply measuring the change in the amplitude and frequency spectrum can detect various composite damage types, which is simple, reliable, and suitable for a quick inspection of a large area. All of the received signals, even in the presence of delamination and impact damage, maintained a high signal-to-noise ratio despite considerable attenuation of the composite plates. A higher amplitude was maintained after damage and long-distance propagation in the composite plates, which is important for practical applications. The developed method is based on guided wave detection over large areas, which can be highly effective and reliable in the detection of manufacturing flaws and operational damage in composites, such as fiber waviness, delamination, and impact damage. The proposed method is most effective for a full inspection of composite structures before assembly in order to detect manufacturing flaws. It can also be used for the in-service inspection of composite structures after an unusual event (e.g., hail and runway debris) or for the detection of hot-spot damage appearing during normal operation. It could be a very useful technique during periodic maintenance of composite structures based on flight cycles or flight hours.
###### Abstract  
Phased array antennas play an indispensable role in millimeter-wave (mmWave) communications. The Antenna-in-package (AiP) system combines advanced antenna and packaging technology, making it highly valuable for various cellular, radar and automative applications. The benefits it brings in terms of small size, low development costs, low power consumption and fast beam-steering capability further drive the vast deployment of phased array antennas in mmWave systems. In this paper, an 8 \(\times\) 8 AiP experimental platform is presented and its operating performance is measured and analyzed. Further, two application examples of the AiP are presented, namely, a platform for investigating the phased array calibration performance of different methods, and an AiP-based channel sounder for channel characterization. The performance of the channel sounder is verified by analysing the angle of arrival (AoA), angle of departure (AoD) and propagation delay of the measured dominant propagation components (DPCs).  
Keywords:phased array antennas; millimeter-wave (mmWave) communications; antenna-in-package (AiP); phased array calibration; channel sounder +
Footnote †: journal: _electronics_
# Design and Analysis of the Composite Stability Control of the Reflective Optoelectronic Platform
## 3 Design and Analysis of Composite Stabilization Control System
### The Design Process  
The composite stabilization control system for reflectors is designed and derived in Sections 3.1 and 3.2. The design specifications are as follows:  
* The two-axis fast reflector is developed based on a high bandwidth position closed-loop control system that receives position instructions. Its motion range is five times greater than the coarse-stable zone and covers the maximum angle error of coarse stabilization. The angle control error is less than one tenth of the maximum angle error of the coarse stabilization.
* Two-axis velocity gyros are installed on the active axes of the coarse-stable framework and used as feedback signals to design the two-axis inertial stabilization controller.
* The gyro signals are adjusted using Equation (14) and treated as the position instruction input for the fast reflector.
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 3 Deep Reinforcement Learning Model with Dual-Agent
### Reward Function  
For both types of traffic signal controllers in dual-agent architecture, a reward provides a feedback to an intersection agent after executing a given action. Thus, an appropriate reward definition will accurately guide the agent to learn an optimal policy that can effectively ease traffic congestion.  
The commonly used reward function is based on the difference of previous and current cumulative delay [5,14]. However, it is hard to acquire cumulative delay of vehicles in a real traffic system because this needs to identify each vehicle and precisely calculate its waiting time. Therefore, for simplicity, we propose two new reward functions for two agents according to the difference between the number of vehicles that pass through an intersection during the last time interval and those that are staying. Specifically, the agent \(P\) obtains the number of staying vehicles twice between two time steps to calculate the difference. Agent \(P\) reward function at time step \(t\) is defined as  
\[r_{pt}=O_{pt}-PS_{t} \tag{8}\]  
where \(O_{pt}\) is the number of going straight vehicles passing through the stop line during \(\tau_{p}\) time, \(PS_{t}\) is the number of staying vehicles in the other lanes with traffic signal state 'r'. \(PS_{t}\) is regarded as a penalty for agent \(P\) to avoid vehicles in other control lanes of the agent \(P\) waiting too long. Similarly, the agent \(A\) reward function is defined as  
\[r_{at}=O_{at}-AS_{t} \tag{9}\]  
where these parameters are similar to the agent \(P\). Unlike the agent \(P\), the agent \(A\) controls the left-turn green light of each road. It is noted that the information of rewards and agent states is all extracted from incoming lanes.
## 2 Materials and Methods
### Detection of Osteogenic Gene Markers  
The alteration of osteoblastic mRNA marker levels was analyzed using quantitative PCR (qPCR). Total RNA was extracted from WSM-treated groups using the Direct-Zol RNA MiniPrep Kit (Zymo Research, Tustin, CA, USA) according to the manufacturer's protocol. Approximately 500 ng of total RNA was reverse transcribed into complementary DNA (cDNA) using the Superscript III RT Kit (Invitrogen, Carlsbad, CA, USA). The qPCR was performed using SYBR Green PCR Mastermix (Applied Biosystems, Warrington, UK) with the following primer sequences: GAPDH (internal control), 5\({}^{\prime}\)-CATGTTCCAGTATGACTCCACT-3\({}^{\prime}\) and 5\({}^{\prime}\)-GGCCTCACCC CCATTTGATGT-3\({}^{\prime}\); Runx2 (Runt-related transcription factor 2), 5\({}^{\prime}\)-CGCCCCCTCCCA CTCT-3\({}^{\prime}\) and 5\({}^{\prime}\)-TGCCTGCCTGGGATCTGTA-3\({}^{\prime}\); alkaline phosphatase (ALP), 5\({}^{\prime}\)GGGAT CAAACCTTCGTT-3\({}^{\prime}\) and 5\({}^{\prime}\)-CCGCAGGTTCACCTAGG-3\({}^{\prime}\); Osteocalcin (OCN), 5\({}^{\prime}\)-CAGCGGCCCTGAGTCTGA-3\({}^{\prime}\) and 5\({}^{\prime}\)-GCCGGAGT CTGITCACT ACCTTA-3\({}^{\prime}\). Amplifications were performed in a program of 45 PCR cycles as follows: The first cycle consisted of 10 min at 95 \({}^{\circ}\)C, and the next 44 cycles consisted of 15 s at 95 \({}^{\circ}\)C, 30 s at 57 \({}^{\circ}\)C for RUNX2 and ALP, and 64 \({}^{\circ}\)C for OCN. The 2\({}^{-\triangle\triangle\)CT method was applied for relative quantification of mRNA levels, allowing comparison between WSM-treated samples and the negative control group. Data were collected from triplicate experiments and presented as fold-changes. This approach ensures robust and reproducible quantification of gene expression changes in osteoblastic differentiation markers [24].
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 4 Simulation Results
### Energy and Network Model  
The energy consumed to transmit k bits between the transmitter and a receiver separated by a distance d using a free space channel is calculated by Equation (1) [122]:  
\[\mathrm{E_{Tx}(k,d)=k*(E_{elec}+\varepsilon_{fs}*d^{2}),d<=Td} \tag{1}\]  
Equation (1) Transmission energy though a free space channel  
Or through multipath channel model using Equation (2):  
\[\mathrm{E_{Tx}\ (k,d)=k*(E_{elec}+\varepsilon_{ms}*d^{4}),d>Td} \tag{2}\]  
Equation (2) Transmission energy through a multipath channel model  
While the reception of k bits requires the energy calculated by Equation (3):  
\[\mathrm{E_{Rx}(k)=E_{elec}*k} \tag{3}\]  
Equation (3) Reception energy of k bits  
Knowing that threshold distance is calculated by Equation (4):  
\[\mathrm{Td=SQRT\ (\epsilon_{fs}/\varepsilon_{ms})} \tag{4}\]  
Equation (4) Threshold distance value  
Note that \(\varepsilon\)fs presents the amplifier energy consumption used to maintain an acceptable signal in a free space channel model while \(\varepsilon\)mp is used in multipath fading channel model depending on a chosen acceptable bit-error-rate.  
As indicated in Table 12, a packet of 4000 bits is used during this comparison, and the confidence interval is taken to get precise plots.
# Variable Selection Using Deep Variational Information Bottleneck with Drop-Out-One Loss
## Appendix A  
In this appendix, we will provide the proofs of the estimation of \(KL(p(z|x)|r(z))\) in VIB. The parameters in \(p(z|x)\) are learned by the encoder, and \(r(z)\) is assumed as the standard distribution.  
**Case 1**: \(r(z)\) and \(p(z|x)\) obey the Gaussian distribution  
\[r(z)\sim\mathcal{N}(0,I),\quad p(z|x)\sim\mathcal{N}(\mu,\sigma^{2}I).\]  
In [35], [PERSON] et al. assumed that the \(K\) dimensions of the true posterior \(Z|X\) are approximately independent. Hence, the resulting estimator is  
\[KL(p(z|x)|r(z))=-\frac{1}{2}\sum_{j=1}^{K}(1+\log\sigma_{j}^{2}-\mu_{j}^{2}- \sigma_{j}^{2}), \tag{1}\]  
where \(\mu_{j}\) and \(\sigma_{j}^{2}\) are the expectation and variance of the \(j\)-th dimension of \(Z|X\), respectively.  
**Case 2**: \(r(z)\) and \(p(z|x)\) obey the exponential distribution  
\[r(z)=\exp{(-z)}\mathbf{1}(z\geq 0),\quad p(z|x)=\frac{1}{\sigma}\exp{(-\frac{z- \mu}{\sigma})}\mathbf{1}(z\geq\mu), \tag{2}\]  
where \(\mathbf{1}\) is an indicator function. For convenience, we first discuss the case \(K=1\). The KL-divergence between \(p(z|x)\) and \(r(z)\) can be written as:\[KL(p(z|x)|r(z))= \int\frac{1}{\sigma}\exp{(-\frac{z-\mu}{\sigma})}\log\frac{\frac{1}{ \sigma}\exp{(-\frac{z-\mu}{\sigma})}}{\exp{(-z)}}dz\] \[= -\log\sigma+\int\frac{\sigma-1}{\sigma}z\frac{1}{\sigma}\exp{(- \frac{z-\mu}{\sigma})}dz+\int\frac{\mu}{\sigma}\frac{1}{\sigma}\exp{(-\frac{z -\mu}{\sigma})}dz\] \[= -\log\sigma+\sigma-1+\mu.\]  
According to the assumption that the \(K\) dimensions of \(Z|X\) are approximately independent [35], the equation above can approximately transform into following:  
\[KL(p(z|x)|r(z))=\sum_{j=1}^{K}(-\log\sigma_{j}+\sigma_{j}-1+\mu_{j}), \tag{10}\]  
where \(\mu_{j}\) and \(\sigma_{j}\) are the location parameter and scale parameter, respectively, of the \(j\)-th dimension of \(Z|X\).
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
###### Abstract  
In this paper, a method based on deep learning is proposed to predict the parameters of bonded metal wires, which solves the problem that the transmission characteristics of S-parameters cannot be predicted. In an X-band microwave chip circuit, gold wire bonding technology is often used to realize bonding interconnection, and the arch height and span of the bonded metal wire will have a great influence on the microwave transmission characteristics. By predicting the S-parameters of the bonded metal wire, the relationship between the structure parameters of the single wire and the transmission performance of the microwave device can be deduced. First, the gold wire bonding model is established in HFSS simulation software. After parameter optimization, the simulation results meet the requirements of establishing data sets. Then the sampling range of S parameters is set, and the parameters are scanned to establish data sets. Second, the artificial neural network model is built. The model adds a dropout mechanism to the hidden layer to enhance the generalization of the neural network, prevent overfitting phenomenon, and significantly improve the model's prediction performance. Finally, the model predicts the corresponding relationship between the arch height and span of the bonding wire and the insertion loss, return loss and standing wave ratio. The mean square error of the test set is less than 0.8. The experimental results show that compared with the traditional process measurement method, this method can quickly and accurately infer whether the microwave characteristics of the bonded product are qualified, which greatly reduces the time and economic cost of the engineer and improves the work efficiency.  
microwave chip; wire bonding: artificial neural network; parameter prediction +
Footnote †: journal: Applied  
doi:10.3390/app13179631  
Academic Editor [PERSON]  
Received: 13 July 2023  
Revised: 16 August 2023  
Accepted: 23 August 2023  
Published: 25 August 2023
# Preliminary Study of Recycled Aggregate Mortar for Electric Arc Furnace Dust Encapsulation
## 3 Results
### Environmental Risk from Heavy Metal Leaching  
Table 7 shows the release levels of elements obtained in the tank leaching test XP X31-211:2012 of the hardened mortars. The conditions during the test are also indicated conductivity, temperature and pH.  
All elements were kept below the inert limit in the reference mortar of each family: NA-REF and RCA-REF. In the case of RCA-REF, this is of special interest since it is an inert waste.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element (mg/kg)** & **NA-REF** & **NA-EAFD/2:1** & **RCA-REF** & **RCA-EAFD/2:1** \\ \hline Cr & 0.013 & 0.029 & 0.110 & 0.131 \\ Ni & 0.010 & 0.009 & 0.023 & 0.022 \\ Cu & 0.003 & 0.012 & 0.013 & 0.029 \\ Zn & 0.070 & 7.630  \\ As & \textless{}0.05 & \textless{}0.05 & 0.012 & \textless{}0.05 \\ Se & 0.003 & \textless{}0.1 & 0.027 & \textless{}0.353  \\ Mo & 0.022 & \textless{}0.5 & 0.020 & \textless{}0.5 \\ Cd & 0.000 & 0.001 & 0.001 & 0.003 \\ Sb & 0.001 & 0.003 & 0.010 & 0.016 \\ Ba & 2.010 & 2.829 & 2.240 & 5.284 \\ Hg & 0.001 & 0.001 & 0.000 & 0.004 \\ Pb & 0.023 & 14.745  & 0.073 & 35.939  \\ Fluoride & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 \\ Chloride & 7.463 & 3250  & \textless{}10 & 4072.700  \\ Sulphate & 19.790 & 754.525 & \textless{}50 & 105.650 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Leaching behavior of monolithic samples of mortar (XP X31-211:2012).  
Figure 2: Results (mean values and standard deviation) of the mechanical properties.  
Mortars made with EAFD, NA-EAFD/2:1 and RCA-EAFD/2:1, show similar element release results, which shows that the substitution of NA for RCA does not significantly impair leaching. In the case of NA-EAFD/2:1, Zn and chlorides exceeded the inert waste limit; however, Pb exceeded the limit of 10 mg/kg (14,745 mg/kg) established to classify the material as hazardous. In the RCA-EAFD mortar, in addition to Zn and chlorides, the inert limit was exceeded. Pb also exceeded the limit of 10 mg/kg (35,993 mg/kg). To reduce the amount of Pb in the leachate, further studies with less EAFD should be carried out until the optimal ratio of EAFD:mortar is achieved.  
The leaching of Pb in the starting EAFD (Table 3) was 5483.866 mg/kg, an amount that exceeds the limits of the EU Council Decision 2003/33/EC [19] for its deposit in landfill. With the encapsulation of the EAFD in cement-based mortars, using both NA and RCA it is possible to reduce this amount by 99.73% and 99.34%, keeping the release of Pb within the limits of the EU Council Decision 2003/33/EC [19]. With the incorporation of EAFD, the resulting matrix is more porous, which favors the mobility of Pb [16; 45]. The higher conductivity obtained in mortars with EAFD is in accordance with the higher presence of metals in the leachate.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 4 Experimental Results
### Results Analysis
#### 4.2.3 Analysis Results of Preemption Ability  
Different TCP connections have different bandwidth preemption levels in a real-world transmission network because they use different congestion control protocols. The bandwidth preemption level shows the ability to preempt bandwidth in terms of transmission performance. The greater the preemption capability is, the more efficiently network resources are used. The preemptive results of the BBR, Cubic, and BoostTCP algorithms are presented in the following figures.  
Figures 13 and 14 illustrate the preemptive test curves of the congestion algorithms for the zero network delay and packet loss rate. The first test was conducted with the Cubic algorithm, followed by the BBR algorithm 10 s later. At the moment, the Cubic and BBR algorithms coexisted, and the Cubic algorithm severely preempted the BBR's bandwidth, resulting in no significant increase in the BBR algorithm's throughput. BoostTCP was restarted after 20 s, after which congestion occurred. The BoostTCP algorithm's throughput rate reached a stable value quickly, within 3 s, while the BBR algorithm's throughput rate gradually increased. After 35 s, the three algorithms' throughput rates converged to a steady-state. The BoostTCP algorithm had a higher throughput rate than the Cubic and BBR algorithms. The second test started with the BBR algorithm, and was followed by the Cubic algorithm 10 s later. Thus, the Cubic and BBR algorithms coexisted 10 s after the test began. The Cubic algorithm severely restricted the BBR's bandwidth, resulting in a throughput rate of nearly zero. After 20 s, the BoostTCP algorithm was invoked and congestion occurred. The BoostTCP algorithm's throughput rate reached a stable value quickly, within 3 s, while the BBR algorithm's throughput rate gradually increased. After 35 s, the three algorithms' throughput rates stabilized. The BoostTCP algorithm had a higher throughput rate than the Cubic and BBR algorithms.  
Figure 12: The fairness test curves of the BoostTCP congestion control algorithm at a network delay of 100 ms and a packet loss rate of zero.  
the Cubic and BBR algorithms coexisted, and their throughput rates were essentially identical. After 20 s, the BoostTCP algorithm was started. Congestion occurred during this period. The BoostTCP algorithm's throughput rate rapidly stabilized after 3 s, whereas the BBR and Cubic algorithms' throughput rates decreased slightly. Finally, the three algorithms' throughput rates reached their steady states. The BoostTCP algorithm had a higher smoothed throughput rate than the Cubic and BBR algorithms.  
The results of the three tests indicated that the BoostTCP algorithm had a better ability to preempt bandwidth than the BBR and Cubic algorithms under different delay conditions. Additionally, the results demonstrated that the BoostTCP algorithm was beneficial to the BBR and Cubic algorithms by assisting suppressed algorithms in resuming their normal throughput rates, demonstrating the BoostTCP algorithm's correctness.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Finance
#### 6.2.1 Example 1: Illuminating Credit Risk Assessment  
The power of XAI models in finance is exemplified by a groundbreaking study conducted by [PERSON] et al. [75], which introduces an XAI-based model tailored for credit risk assessment. This innovative model serves as a potent tool for financial institutions, significantly enhancing their risk management capabilities. Through transparent explanations of the factors influencing credit risk, financial professionals can make more informed decisions, reducing potential financial losses and fostering greater trust in their lending practices.