esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Surface Micro-Hardness Change in Multistep
## 4 Conclusions  
Multistep cylindrical turning experiments of titanium alloy were performed to acquire the finishing machined surfaces. The friction and wear tests were carried out by UMT-2 friction and wear testing under dry sliding conditions along the feed direction. The effect of final finishing machined surface micro-hardness on the friction and wear properties of titanium alloy was studied. The main conclusions were as follows:  
1. The local area of micro-hardness at 100 \(\upmu\)m below the surface along the feed direction after roughing was obviously affected by the cutting parameters. In the multistep cutting process, affected by the roughing work hardening, the local area of the finishing surface micro-hardness will change versus the roughing cutting parameters. During the three-step machining, semi-finishing reduced the influence of roughing on work hardening of the surface layer, thereby reducing the local variation of finishing surface microhardness.
2. Through observing the surface wear microtopography and subsurface microstructure were observed and analyzed by scanning electron microscope, the micro-hardness variation in the local area of the finishing surface, mainly reflected by the change in standard deviation of micro-hardness measurement results along the feed direction, will cause the extension of unstable friction time stage while withstanding the cyclic and alternating contact stresses.
3. The soft-hard alternating area should be the sources of friction and wear defects, for instance cracks, peeling pits and even the wear fracture zone to crack propagation and peeling off. The friction and wear of the finishing surface was positively correlated with the local change of the machined surface micro-hardness. Adjusting and controlling the machined surface quality in multistep machining will thereby help to improve the friction and wear resistance of the finishing titanium alloy components.  
Figure 14: The cross-sectional schematic diagram of the difference of the surface layer micro-hardness in the local area along the feed direction.  
**Author Contributions:** Conceptualization, G.H. and A.L.; methodology, G.H.; software, G.H.; validation, G.H. and A.L.; formal analysis, G.H.; investigation, G.H. and A.L.; data curation, G.H.; writing--original draft preparation, G.H.; writing--review and editing, G.H. and A.L.; visualization, G.H.; supervision, A.L.; project administration, A.L.; funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (51605260), the Key Research and Development Program of Shandong Province (2019)ZZY010114), and the Young Scholars Program of Shandong University (2018 WLJH57).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
In the initial stage of GaN epitaxial layer growth, the hydrogen in the reaction chamber would etch the graphene film on the substrate surface, which increased the defects of the surface for graphene and promoted the nucleation of the GaN film. Therefore, we could analyze the information between the characteristic peaks of graphene in the Raman spectrum to reflect the defects of graphene. The Raman intensity ratio of the 2D/G mode indicated that the graphene transferred on the sputtered AlN and MOCVD AlN layer was the monolayer, which showed that the single layer graphene was stable. Because graphene exhibited similar in-plane lattice arrangements as wurtzite structure GaN, the single-layer graphene was more conducive to remote epitaxy and guided the nucleation of the GaN epilayer. Meanwhile, weak van der Waals forces on both sides of the graphene shielded the stress resulting from lattice mismatch and thermal expansion coefficient mismatch between the GaN epilayer and sapphire substrate, thus improving the crystalline quality of the GaN epilayer. Through analyzing the E\({}_{2}\) (high) and A\({}_{1}\) (LO) phonons in GaN epitaxial layer grown by MOCVD with four different types of buffer layers, the peak shift of the E\({}_{2}\) (high) phonon peak was found to be the lightest shift, which suggested that the stress that the GaN films grown on the graphene/sputtered AlN buffer layer suffered was lowest. In addition, the phonon lifetimes reflected through the A\({}_{1}\) (LO) phonon peak were shortest when GaN film was directly deposited on the graphene/sputtered AlN buffer layer. In this case, it is beneficial to the carrier migration in the GaN with the graphene/sputtered AlN buffer layer. Through Raman analysis of GaN and graphene, it is concluded that the GaN obtained on the graphene/sputtered AlN buffer layer was subject to stress-free conditions, and the single-layer graphene obtained by the wet-transfer was also more conducive to nucleation in the growing process. This work will play an important role in the subsequent manufacture of GaN-based optoelectronic devices dominated by optical-phonon and carrier interaction.
###### Abstract  
Global Navigation Satellite System performance can be significantly enhanced by introducing inter-satellite links (ISLs) in navigation constellation. The improvement in position, velocity, and time accuracy as well as the realization of autonomous functions requires ISL distance measurement data as the original input. To build a high-performance ISL, the ranging consistency among navigation satellites is an urgent problem to be solved. In this study, we focus on the variation in the ranging delay caused by the sensitivity of the ISL payload equipment to the ambient temperature in space and propose a simple and low-power temperature-sensing ranging compensation sensor suitable for onboard equipment. The experimental results show that, after the temperature-sensing ranging compensation of the ISL payload equipment, the ranging consistency becomes less than 0.2 ns when the temperature change is 90 \({}^{\circ}\)C.  
ranging consistency; temperature-sensing sensor; ranging compensation; inter-satellite link; navigation constellation +
Footnote †: journal: Sensors
## 4 Advanced Identification of Expansion Gap Using Machine Learning
### Expansion Joint Gap Segmentation  
Even if the image patch of the expansion joint is extracted from the line-scan image, image segmentation is required within the detected image to accurately measure the expansion gap. Image segmentation is a pixel-level classification that deduces the class each pixel belongs to (i.e., expansion joint device or background). A masking image  
Figure 11: Recognition accuracy test results for each CNN structure.  
representing the pixel corresponding to the expansion joint device can be generated by the image segmentation algorithm.  
Figure 12 shows the image patch (left) and correct mask (right) of the expansion joint device. The neural network structure receives image patches as an input and performs binary classification of the individual pixels. The deep learning model receives image patches as the input and learns to predict a masking that is close to the correct answer. In this case, the class of a pixel should be determined by considering the global and local characteristics of the image rather than individual pixel values.  
This study uses a U-Net model, which was previously developed for sophisticated analyses of organ lesions in the field of biomedical science [36]. U-Net is suitable for the problem of precisely detecting the shape of the analysis target by simultaneously learning the global and local information of the image. The structure of U-Net was modified to develop EfficientNet, a neural network that extracts image information.  
U-Net infers the probability that an arbitrary pixel is an expansion joint device and learns the correct answer for each pixel from the correct answer masking. Therefore, the prediction error for all pixels \(M\) of the image patch expressed as BCE is as follows:  
\[L_{patch}=\sum L_{pixel}=\sum_{i=1}^{M}(-y_{i}\log P_{i}-(1-y_{i})\log(1-P_{ i})),\;\;i=1,2,3,\;\ldots,\;N \tag{5}\]  
The final cost function \(J\) is expressed by summing the mean error for \(N\) image patches and the L2 regularization term:  
\[J=\frac{1}{N}\sum_{j=1}^{N}\left(L_{patch}^{j}+\lambda|w|^{2}\right) \tag{6}\]  
where \(\lambda=10^{-4}\).  
The gradient descent method updates model parameters in the direction of reducing the cost function \(J\) as follows:  
\[w\;\Leftarrow\;w\;-\;\alpha\cdot g \tag{7}\]  
where \(\alpha\;=10^{-4}\), \(g\;=\;\frac{\partial J}{\partial w}\).  
We compared the performance between U-Net's masking image and the correct masking image in the test dataset. Table 7 shows the pixel-level classification performance for the test set; the pixel precision of the expansion joint device was 96.61%, recall rate was 94.38%, and f1-score was 95.49%. The f1-score of the expansion joint device pixel detection was within 5%. In the post-processing process, by correcting the error of the  
Figure 12: Image of flexible expansion joint and mask of correct answer: (**a**) original image, (**b**) masked image.  
predicted masking image of U-Net, the minimum gap point was detected, and the distance was measured.
# Can AI Automatically Assess Scan Quality of Hip Ultrasound?
### Related Work on Deep Learning in Hip Ultrasound  
With the recent success of Convolutional Neural Networks (CNNs) in medical image processing, data-driven approaches have been proposed for automatic image segmentation and classifications in hip ultrasound. [PERSON] et al. [22] developed a CNN for segmenting the acetabulum that used multi-scale super pixels as inputs to an AlexNet model.  
Figure 1: Examples of ultrasound hip images of different scan quality. (**A**) Example of a high-quality image in which all landmarks such as ilium, os ischium, labrum and femoral head are clearly visible. The alpha angle is measured as the inner angle between the iliac line and acetabular roof. (**B**) Moderate-quality scan containing all landmarks, but with the blurring of the os ischium and blurring and slight tilt of the iliac line. (**C**) Poor-quality image with none of the landmarks visible.  
The 3D convolutional networks (3 DconvNet) such as C3D and I3D have been used for video classification. The C3D network uses a homogeneous architecture with 3 \(\times\) 3 \(\times\) 3 convolution kernels in all layers and gave 52.8% accuracy on the UCF101 dataset [23]. The Inflated 3D ConvNet (I3D) used a new two-stream approach to combine 2D and 3D convolutions. With pretraining on Kinetics, the I3D model gave accuracies of 80.9% on the HMDB-51 dataset and 98.0% on the UCF-101 dataset [24]. Similarly, 2D segmentation models such as the 3D U-Net [25], 3D V-Net [26] and the 3D Deeply Supervised network [27] have also been used for image segmentation in videos. Extending these models to 3 DUS volumes of the hip is non-trivial, mainly due to the lack of large datasets.  
Generally, deep learning models are trained on hip ultrasound images acquired by well-trained sonographers in research settings. Such images contain all necessary imaging landmarks such as straight and horizontal acetabulum and round femoral head. The scan quality of the images used in these datasets was assessed subjectively by the sonographer. In this paper, we develop a deep learning technique to automatically assess the image quality of hip ultrasound images and categorize it as adequate vs. inadequate for DDH diagnosis. Similar deep learning techniques have been developed using CNNs [28; 29] and Recurrent Neural Networks (RNNs) [30] for 2 DUS scan quality assessment. Along these lines, we propose a new 3D CNN model to predict the adequacy of hip scans for DDH diagnosis in 3D ultrasound images and validate it on two large clinical datasets.  
This is the first study to validate an automatic hip quality assessment technique on a large dataset, of 2187 hip scans performed by 3 DUS (Dataset 1). Any systematic error in prediction is also examined by evaluating the algorithm on subgroups of images based on age {0-3 months, >3 months} and sex {Male, Female}. On a separate dataset of 107 images (Dataset 2), we conducted a multi-reader study with four readers who semi-quantitatively evaluated scan quality using a 10-point scoring system [17] and compared it to predictions from our AI-based approach.
## 6 Discussion and Conclusions
In our experiments only one fixed setting that covers the full range of light intensity was used but a more flexible approach could possibly further improve sensitivity for PPFD and RLQI at conditions with very low or high light intensity. The AS7341 is an ideal candidate that can be deployed in applications with a high number of sensors. The integrated optical filters and ADC provide a simple digital interface that facilitates the development of a complete PAR and RLQI sensor due to minimal needed additional periphery. Its cost, size, weight, and power consumption make it perfect to be used in a wireless sensor node that can be massively deployed in the field. We have already built a solar powered wireless sensor node that uses the AS7341 including additional sensors for temperature and humidity. For a field and long-term study, a larger and improved number of sensor devices are currently prepared and will be tested in a real field scenario to gain experience and data how well such a device can be used. We are confident that the demonstrated device can improve the data quality recorded in the field. With numerous sensor nodes deployed at different canopy levels and large areas, not only a higher granularity is possible, but the system itself can either become an alternative or supplement method for manual or satellite-based recording. The data can expedite a deeper understanding of biological and environmental processes in a forest helping to reduce the effort needed to measure common indexes, such as the GPP. Conceptualization, E.B. ; Data curation, D.Z. ; Formal analysis, E.B. and S.S.; Investigation, D.Z. and S.S.; Methodology, S.S.; Project administration, E.B. ; Software, D.Z. ; Supervision, E.B. and P.W. ; Visualization, S.S.; Writing--original draft, E.B. and S.S.; Writing--review & editing, E.B. and P.W. All authors have read and agreed to the published version of the manuscript. The article processing charge was funded by the Baden-Wuertemberg Ministry of Science, Research and Art and the University of Freiburg in the funding program Open Access Publishing. The authors declare no conflict of interest.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 5 An Overview of Attack-Resilient Control and Communication Techniques
### Resilient Communication Techniques
#### 5.2.2 Fake Acknowledgement Messages in State Estimation  
In a networked state estimation problem, an interesting communication technique was considered by [PERSON] et al. [65]. In the problem formulation, [PERSON] et al. [65] considered an estimator that attempts to transmit state estimates to a remote receiver over an insecure wireless channel. The decision to attempt transmission or not is made by the estimator in a stochastic fashion by setting a probability value \(\theta_{\mathrm{S}}(t)\in[0,1]\) such that a transmission attempt at time \(t\) is made with probability \(\theta_{\mathrm{S}}(t)\).  
When a transmission attempt is made at time \(t\), it is not guaranteed that it will be successful due to the jamming attacks on the channel. The likelihood of a transmission failure is determined based on the power level of the jamming interference signal emitted to the wireless channel by the attacker at that time instant.  
The goal in [65] is to minimize the time-averaged variance of the estimation error. To this end, [PERSON] et al. [65] proposed the idea of generating a _fake acknowledgement message sequence_\(\{\phi(t)\in\{0,1\}\}_{t\in\mathbb{N}}\). Instead of sending real acknowledgements, the receiving node follows the fake acknowledgement sequence and sends back to the sensor acknowledgement messages that are possibly misleading for the attacker. For instance, if \(\phi(t)=1\), the receiving node sends a positive acknowledgement indicating that a packet is successfully received, even if no packet is received at time \(t\). Under certain assumptions on the jamming interference powers, the work [65] derives the optimal transmission attempt probabilities \(\{\theta_{\mathrm{S}}(t)\}_{t\in\mathbb{N}}\) as well as the optimal fake acknowledgement  
Figure 9: Illustration of the randomized communication protocol, where each agent attempts communicating with its neighbors at random time instants. At time instants denoted inside rectangles with green dashed borders, the communication attempts can avoid DoS attacks.  
message sequence \(\{\phi(t)\}_{t\in\mathbb{N}}\) that minimizes the time-averaged variance of the estimation error. It is shown that fake acknowledgements improve the estimation performance.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Correlation Analysis
#### 3.3.5 The Speed Performance of the Proposed Chaos-Based Cryptosystem  
Computing performance is an important issue for practical applications and largely depends on the programming languages used. The Matlab language is not a good candidate to achieve high computing performance compared to C language, VHDL description language, etc. However, with Matlab, we can design and realize cryptosystems faster than the other languages mentioned.  
We evaluated the computational performance: average encryption time, average encryption throughput (\(ET\)), and average number of needed cycles per bytes (\(NCpB\)) of the proposed cryptosystem and we compared its performance (\(NCpB\)) with other cryptosystems designed in the Matlab language.  
The \(ET\) and \(NCpB\) are given by the following equation:  
\[ET=\frac{Image\ Size\ (Bytes)}{Average\ Encryption\ Time\ (second)} \tag{37}\]  
\[NCpB=\ \frac{CPU\ Speed\ (Hertz)}{ET\ (Bytes/s)} \tag{38}\]  
In Table 12, we give the computing performance obtained by the proposed encryption system using 100 different secret keys, for four plain images.  
In Table 13, we compare the \(NCpB\) of our algorithm for the Lena image of size \(512\times 512\times 1\) with the \(NCpBs\) of other chaos-based encryption algorithms.  
As Table 13 shows, the proposed encryption system had higher computational performance efficiency than the others.
# Protection Circuit Design for Ultrasound Transducers
###### Abstract  
In ultrasound systems, a protection circuit must be used to protect the receiver electronics from the high-voltage pulses generated by the transmitter and to minimize the signal loss and distortion of the low-voltage echoes generated by the transducer. Especially for certain ultrasound applications, such as intravascular ultrasound, particle manipulation, and cell stimulation, proper performance of the ultrasound transducers is desirable due to their low sensitivity. As the operating frequency of the ultrasound transducer increases, the size of the transducer decreases, increasing the amplitude of the transmitted signals to achieve proper acoustic performance. In such environments, a protection circuit can be used to protect the receiver electronics in ultrasound systems. To design suitable protection circuits, transistors, resistors, capacitors, and inductors are used, and the parameters of insertion loss, noise, total harmonic distortion, and recovery time of the protection circuits must be carefully considered. Various approaches have been developed to protect circuits such as transmission lines, transformers, bridge diodes, and metal-oxide-semiconductor field-effect transistor devices. Certain protection circuits are beneficial for impedance matching and area reduction. Other protection circuits have been designed to increase bandwidth, reduce insertion loss, or improve the signal-to-noise ratio for different ultrasound applications. Therefore, this review article may be useful for academic ultrasound researchers or circuit designers in selecting appropriate protection circuit types for specific ultrasound transducer applications.  
protection circuit; ultrasound transducer; ultrasound system +
Footnote †: journal: Applied sciences
## Nomenclature  
### Abbreviations  
AC  
Alternating current  
ANM  
Active network management  
BESS  
Battery energy storage system  
BTB  
Back-to-back  
DC  
Direct current  
DG  
Distributed generation  
D-SCOPF  
Distribution security constrained optimal power flow  
DSO  
Distribution system operator  
DSSR  
Distribution system security region  
EV  
Electric vehicle  
HVDC  
High voltage direct current  
LTC  
Load tap changer  
MV  
Medium voltage  
MVDC  
Medium voltage direct current  
NPV  
Net present value  
OPF  
Optimal power flow  
P2P  
Point-to-point  
PWM  
Pulse width modulation  
PV  
Photovoltaic  
SCADA  
Supervisory control and data acquisition  
TSC  
Total supply capability  
VSC  
Voltage source converter  
Notations  
\(x\)  
state variables (i.e., voltage magnitudes and angles)  
\(u\)  
conventional control variables (i.e., tap changer)  
\(w\)  
fast control variables (i.e., modulation index and angle of VSC)  
\(\pi_{ds}\)  
payment price for demand shifting  
\(\pi_{dg}\)  
payment price for DG curtailment  
\(\pi_{em}\)  
an electricity market price  
\(\xi_{b}\)  
demand shifting factor at bus \(b\)  
\(m_{a}\)  
modulation index  
\(E_{dc}\)  
voltage at DC side of VSC terminal  
\(V_{cr}\)  
voltage at AC side of VSC terminal  
\(V_{g}\)  
AC voltage at the connected grid of VSC  
\begin{tabular}{l l} \(Y_{cr}\) & equivalent VSC interfaced admittance \\ \(B_{sw}\) & equivalent VSC switching shunt susceptance \\ \(G_{sw}\) & equivalent VSC switching conductance \\ \(P_{loss}\) & active power loss \\ \(P_{w}\) & active power output of DG unit \\ \(P_{curt}\) & curtailed active power of DG output \\ \(P_{shift}\) & shifted active power of demand \\ \(P_{d}\), \(Q_{d}\) & active and reactive power consumption \\ \(P_{g}\), \(Q_{g}\) & active and reactive power injections at AC connected bus of VSC \\ \(P_{0}\), \(Q_{0}\) & active and reactive power injections at DC connected bus of VSC \\ \end{tabular}