esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 5 Conclusions  
With the improvement of multifunctionality and reliability requirements for microwave products in modern aerospace fields, such as satellite communication navigation, relay network data transmission, microwave remote sensing, manned flight, deep space exploration, etc., it is necessary to develop a spaceborne microwave SIP based on a high reliability microwave interconnection. In this paper, the microwave transmission and stress characteristics of several common interconnection structures are simulated, and a novel coaxial structure combined with microstrip impedance transformation and gold ribbon interconnection is proposed to realize microwave signal interconnection. This interconnection mode can simultaneously achieve high reliability, high transmission characteristics, and excellent assembly. The design formula of this microwave interconnection is derived, and the design and simulation are carried out through an example. Based on these high-performance microwave interconnection structures, the 3D metallic microwave SIP is realized. A SiP of a Ka band receiver is packaged, tested and verified. Compared with the traditional transition form of the same Ka circuits, its amplitude flatness is optimized by about 0.8 dB, and its noise coefficient is optimized by about 0.1 dB. It has been subjected to a long-term temperature cycling test and a structural analysis, and its metallographic analysis results have also proved its good characteristics of resistance to thermal stress failure. The airtight metallic packaging design based on this interconnection structure can flexibly realize the horizontal and vertical transmission of Ka band microwave signals, and has temperature change and mechanical adaptability, which can also be applied in other spaceborne microwave systems.  
Methodology, B.L. and G.W.; design and analysis, B.L., Y.L. and G.C.; simulation and calculation, B.L., Z.S. and F.R.; manufacture and verification, G.C., Y.L. and J.S.; measurement and data curation, J.S., F.R. and Z.S.; writing--original draft preparation, B.L.; writing--review and editing, Y.L. and G.W. All authors have read and agreed to the published version of the manuscript.  
Figure 25: The metallographic analysis of the bonding joints in the Ka band receiving SIP.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant Nos. 12175176 and 61901360), the National Key Laboratory Foundation 2022-JCJQ-LB-006 (Grant No. 6142411552214), the National Key R&D Program of China (Grant No. 2021 YFB3900300) and the Civil Aerospace Program (Grant No. D010103).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the detailed data or results can be found in this paper, as shown in Figures 6, 8, 13-15 and 19-21 and Tables 1-7.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 3 Results
### System Validation
Holding the phantom in his hand in a way that would not interfere with phantom contour detection, the participant inspected the internal structures as they were projected onto the phantom's surface. Phantom position and participant's head were tracked with PhTM and HdTM, respectively. 3. When the participant decided to trick the structure, he had to put the phantom on the desk (for stability) and pause PhTM tracking by depressing a foot pedal. Otherwise, his hands or the syringe occluding the phantom contour would cause PhTM to provide false data about phantom position. Only then pricing could be performed. HdTM and PrM were working continuously so the projection parameters of the internal structures depended on the current position of the participant's head. The duration time of the pricing procedure was measured as in the control experiment. 4. The test experiment was performed with the first phantom. 5. The control experiment was performed with the second phantom. The tests showed that the failure rate (i.e., participants not being able to hit the desired structure) dropped from 50% without projection to 30% with projection
# Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
## 2 Background and Related Work
When comparing these results, it can be stated that the accuracy of machine learning can be increased by changing the dataset or by adding additional samples and information. In the studies the kinds of attack which can be carried out in a given network were also addressed. This data affects the accuracy of the KNN network, which can lead to false detection, as described in the research conducted by the given authors [31; 33; 34; 46; 47]. These most common attacks are described in Figure 2, which shows the share of DDoS attacks on the three layers of the OSI model. According to an Arbor Networks report, the most commonly used DDoS attack methods include SYN flood, DNS flood, and Smurf attack techniques [34]. This report also shows that the frequency of attacks has increased by more than 83% in the last three years. Attackers looking to maximize bandwidth usage launch a UDP flood attack, which can quickly and efficiently overwhelm the system resources of the target network [48]. Figure 3 shows the percentages of the most common network attacks. The most common attack type is UDP flooding, accounting for up to 56% of all detected attacks. The second most common type of attack is TCP flooding. This type of attack is mostly directed against web and application servers [49; 50]. Of the attack techniques described above, TCP flood and UDP flood can be used to attack HTTP services. These attacks can be used to exploit the vulnerabilities of the particular web server or to consume all of the allocated bandwidth. New HTTP protocols allow communication using UDP to speed up data transfer. One such protocol is the new HTTP V3 QUICK protocol, which allows a portion of the data to be sent over UDP, i.e., without verification of the delivery of the affected packets [51]. As shown in Figure 3, the proportion of such attacks can be as high as 74%. This service is the most vulnerable  
Figure 3: Most common DDoS attacks. Figure 2: Multi-vector DDoS network attacks. because it allows the execution of a large number of applications with a web interface with which users interact [52; 53]. From the point of view of machine learning, KNN and RNN methods are based on already available works. According to [PERSON]'s work [54], which used KNN methods, algorithms, Naive bayes, K-means clustering and Random Forest achieved almost absolute success. Authors [PERSON], [PERSON] and [PERSON] described, in their work, the possibilities of detecting DoS and DDoS attacks using Deep Learning and IDS [14]. The authors managed to achieve attack detection success of more than 99% based on RNN. From the available research, it can be said that the use of RNN and KNN are appropriate in both cases, and it is possible to achieve high accuracy in the detection of DDoS attacks on various components of computer networks, based on a suitably developed and balanced dataset, as well as on the parameters set in deep learning.
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 4 Firmware Remote Upgrade Design
### Reliability Design  
The program upgrade files are transmitted in segments during the transmission process. Consequently, packet loss [31], equipment disconnection, garbled coding, and other communication issues may occur, resulting in an incomplete delivery of the upgrade file. Furthermore, the transmission of program files differs from that of generic parameter files. It is vital to pay close attention to the reliability and real-time [32] of the transmission process; otherwise, it will interfere with the engine room simulator's typical teaching function.  
Figure 5: Remote upgrade configuration.
# Blind Image Deblurring Based on Local Edges Selection
## 1 Introduction
In addition, when image become blurred, it is even harder to extract image edges well by these methods. Recently, image restoration methods based on deep learning [22; 23] and super resolution [24; 25; 26] are proposed, which can obtain good results when applied to image deblurring. However, a large number of computations are needed, or a lot of images are required for training, which add to the complexity of the algorithm. Considering the limitation of global edges of images, we propose a new blind image deblurring method based on local edges selection. The contributions of the proposed method are summarized as follows:  
1. The proposed image deblurring model is built based on MAP, but different from traditional MAP based methods, in the deblurring model, we add creative local image edges, the local edges are selected from the bright and dark channels of the image. 2. In most blind image deblurring methods, the blurring kernel is estimated first and the clear image is obtained by non-blind deblurring methods. Different from these methods, the clear image and blurring kernel are obtained based on alternating iteration in the proposed method. 3. Tests are carried out based on the dataset of gray value images, color images, and natural color images. The experimental results show that the proposed method can effectively deblur different kinds of images. By comparing with other state-of-the art methods in visual results and quantitative matrices, the clear image and blurring kernel results verify the effectiveness of the proposed method. The rest of this paper is organized as follows. Section 2 consists of five parts. In Section 2.1, the proposed local edges selection method is introduced. Then the image deblurring model and blind deblurring process are introduced in Section 2.2. In Sections 2.3 and 2.4, we present the estimation of the blurring kernel and clear image, respectively. Section 2.5 introduces the stopping criterion. Section 3 provides image deblurring results and discussions, consisting of four parts. The results of image edges selection are shown in Section 3.1. In Sections 3.2 and 3.3, we discuss the parameters in the deblurring model and the convergence of the proposed algorithm. In Section 3.4, we provide the results of the image deblurring we carried out, and compare the images deblurring results with other state-of-the-art methods to verify the effectiveness of the proposed method. Finally, Section 4 is the conclusion of this paper.
## 2 Circuit Model and Theoretical Analysis
### Analysis of CVO Characteristics and ZPA Operation  
As in the analysis in the previous section, the output voltage \(\mathbf{U_{\text{s}}}\) can be obtained as  
\[\mathbf{U_{\text{s}}}=-\frac{j\omega MZ_{\text{m}}R_{\text{ac}}}{AR_{\text{ac}}+B}= -\frac{j\omega MZ_{\text{m}}\mathbf{U_{\text{in}}}}{A+\frac{B}{R_{\text{ac}}}} \tag{13}\]  
According to Formula (13), when the condition \(B\) = 0 is established, the output voltage \(\mathbf{U_{\text{s}}}\) is independent of \(R_{\text{ac}}\), and CVO characteristics can be realized, namely  
\[Z_{1}Z_{\text{m}}Z_{\text{s}}+Z_{1}Z_{\text{p}}Z_{\text{s}}+Z_{\text{m}}Z_{ \text{p}}Z_{\text{s}}+\omega^{2}M^{2}Z_{1}+\omega^{2}M^{2}Z_{\text{m}}=0 \tag{14}\]  
Furthermore, the input impedance \(Z_{\text{in}}\) of the proposed system can be expressed as  
\[Z_{\text{in}}=\frac{\mathbf{U_{\text{in}}}}{\mathbf{I_{1}}}=\frac{(Z_{1}Z_{\text{m}}+ Z_{1}Z_{\text{p}}+Z_{\text{p}}Z_{\text{m}})R_{\text{ac}}}{\omega^{2}M^{2}+Z_{ \text{m}}R_{\text{ac}}+Z_{\text{p}}R_{\text{ac}}+Z_{\text{m}}Z_{\text{s}}+Z_ {\text{p}}Z_{\text{s}}} \tag{15}\]  
According to Formula (15), if Formula (16) holds, it can be guaranteed that the input impedance \(Z_{\text{in}}\) has no imaginary part and is related only to \(R_{\text{ac}}\), namely, the realization of the ZPA operation.  
\[Z_{\text{m}}+Z_{\text{p}}=0 \tag{16}\]  
The conditions for satisfying CVO and ZPA input can then be summed up as  
\[\left\{\begin{array}{l}Z_{1}Z_{\text{m}}Z_{\text{s}}+Z_{1}Z_{\text{p}}Z_{ \text{s}}+Z_{\text{m}}Z_{\text{p}}Z_{\text{s}}+\omega^{2}M^{2}Z_{1}+\omega^{ 2}M^{2}Z_{\text{m}}=0\\ Z_{\text{m}}+Z_{\text{p}}=0\end{array}\right. \tag{17}\]  
According to the above conditional equations, the input impedance \(Z_{\text{in}}\) and the voltage gain \(E\) can be simplified as  
\[Z_{\text{in}}=\frac{Z_{\text{p}}Z_{\text{m}}R_{\text{ac}}}{\omega^{2}M^{2}} \tag{18}\]  
\[E=\frac{\mathbf{U_{\text{s}}}}{\mathbf{U_{\text{in}}}}=\left|\frac{\omega M}{Z_{\text{ p}}}\right| \tag{19}\]  
Obviously, according to Formula (18), the input impedance \(Z_{\text{in}}\) is related only to the equivalent load resistance \(R_{\text{ac}}\), so the system can achieve ZPA input under different loads. According to Formula (19), the voltage gain \(E\) is not affected by the equivalent load resistance \(R_{\text{ac}}\), and therefore the system can realize CVO at the full load.
## 5 Results
### Structural Model Evaluation  
To estimate the significance of path coefficients and test the hypotheses, a bootstrapping using 5000 bootstrap subsamples is conducted in SmartPLS. The path coefficients, the significance of path coefficients, the coefficient of determination (R2) and its effect size f2, cross-validated redundancy (Q2) are given in Table 10.  
All the path coefficients are significant (\(p\) < 0.001) with t-values larger than 2.58 (see Figure 3). The R2 value of CSCI is nearly 1.00 because it is a higher order construct, and other constructs' R2 values are acceptable according to the rule of thumb with a minimum value of 0.50. The effect size f2 is also given to assess how strongly one exogenous construct contributes to explaining a certain endogenous construct regarding R2 (T
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 3 Proposed Method
### Information Entropy  
Information entropy serves as an indicator and technique of unpredictability in information theory. The quantity of entropy can always be utilized to characterize the outcome of plaintext and ciphertext confidentiality. The greater information entropy shows better security. For example, a value of 8 for the information entropy of the cipher image suggests that pictures are exceptionally near to random distribution, and the security is more remarkable [24]. Mathematically it is expressed as shown in (7).  
\[IE=\sum_{i=1}^{n}P(Y=y_{i})\,\log P(Y=y_{i}) \tag{7}\]  
where \(Y\) is a discrete random variable with values \(\{y_{1},y_{2},\cdots,y_{n}\}\), and \(i\) is an integer variable with values ranging from \(1\) to \(n\).  
Figure 7: Results of encrypted and decrypted test images.  
In the equation, \(P(Y=y_{i})\) is the probability of \(Y\) taking the value of \(y_{i}\), this denotes the fraction of all \(Y\) occurrences with the value \(y_{i}\).
## 4 Extension to Constrained Designs
### Minimum False Target SNR and Matching Gain Ratio  
To improve the detection performance in multi-target environments, two types of constant false alarm detection methods are most commonly used: the mean constant false alarm detection proposed for noisy clutter environments and the ordered statistical constant false alarm rate (OS-CFAR) detection proposed for multi-target environments [43]. In general, the mean constant false alarm performs well in the background environment with constant noise and clutter boundaries, but its detection performance degrades significantly when multiple false targets are distributed on both sides of the target detection cell [44].  
In this paper, we assume that the false alarm probability and the detection probability of OS-CFAR are known based on a priori detection information.  
Assume that the joint communication jamming system does follow-team support jamming and is placed at a distance \(R_{j}\) from the radar signal emission direction. After the delayed partial forwarding by the joint system and the processing of the radar pulse compression and coherent accumulation, the signal-to-noise ratio (SNR) of the jamming signal be expressed as  
\[JNR=\eta_{i}^{2}\frac{P_{jl}G_{jl}^{2}\lambda^{2}\sigma}{(4\pi)^{2}R_{j}^{2} kT_{0}BFL_{j}} \tag{17}\]  
Figure 8: Ambiguity function comparison of the Hermite (**a**) and PHYDYAS (**b**) prototype filters.  
where \(P_{jl}\) is the jamming signal transmit power of the joint system; \(G_{jl}\) is the jamming antenna gain of the joint system; \(\lambda\) is the radar signal wavelength; \(\sigma\) is the radar scattering cross-sectional area of the target; \(k\) is the Boltzmann constant; \(F\) is the receiver noise factor; \(B\) is the signal bandwidth; \(T_{0}\) is the effective noise temperature; \(L_{j}\) is the atmospheric loss and joint communication jamming system feed line loss.  
Based on Equation (10), the ratio of the frequency shift (\(\xi^{\prime}=n(n-1)\tau k_{r}\)) to the modulation frequency \(k_{r}\) depends on the \(n\) and \(\tau\). The ratio of the \(i\)th frequency-shifted jamming to the target echo matching filtering gain loss, \(\eta_{i}\), is defined as follows:  
\[\eta_{i}=1-n((n-i+1)!-(n-i)!)\tau/T_{r} \tag{18}\]  
where \(n!\) is the factorial function; \(n\) are positive integers, s.t. \(n\geq 2\).  
Using the known false alarm probability (\(P_{fa}\)), the minimum detectable probability (\(P_{d\text{min}}\)), the radar-related parameters, and the minimum SNR required for the joint communication jamming system (\(JNR_{\text{min}}\)), the corresponding matching gain ratio \(\eta_{\text{imin}}\) of the joint system can be found by substituting Equation (18).
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 2 Literature Review
### Increasing the Tolerable Safety Limits of Touch and Step Voltage
#### 2.2.3 Diversion of the Fault Current  
The fault current diversion method is adapted from the concepts of the fault current path and its distribution in power systems, especially in transmission substations with overhead transmission lines. From the maximum earth grid current \(I_{\mathrm{G}}\) Equation (3) defined in Section 3: Earth Grid Design Criteria and existing literature [15; 16; 1], it is acknowledged that not all the fault current generated by the power system returns to the system through the earth grid: various elements, such as the fault type, fault location, and impedance properties of the power system's neutral conductors, as well as the overhead ground lines, dissipate a margin of the generated fault current [17]. This is supported by a study conducted by [PERSON] [15] that suggests that the fault current division factor is greatly influenced by the overall earth grid resistance and tower footing.  
Considering the proportional relationship between resistance and current, it can be deduced that, by diverting a portion of the fault current away from the substation's earth grid, the total grid current will be marginally reduced. From the IEEE Std 80 specification, it is evident that this will directly reduce the ground potential rise of the earth grid. In another study conducted by [PERSON] et. Al. [18], it is stated that, with a lower ground potential rise, the odds of the tolerable touch voltage exceeding the ground potential rise and making the earth grid design compliant are greatly increased. With that in mind, determining the correct and practical current diversion factor is of paramount importance.  
Generally, computer simulations are conducted to determine the accurate amount of fault current diverted from the earth grid, as computers are capable of accurately using numerical algorithms that consider a variety of data, and to apply first principle equivalent circuit components for the conductors connected to the earth grid [17]. Additionally, other factors, such as the location of the fault and the total earth grid resistance, are also considered by the computer simulation tools [2; 17]. The commonly used simulation tools in the industry for substation earth grid design and analysis include ETAP, CDEGS, and SafeGrid.
# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## 3 Calibration of a Population
### Efficient Re-Sampling of Households  
The slow convergence rate of the SA can be improved by selecting and deselecting individuals in a more targeted way. Instead of a purely random sample of individuals, the improved version of the SA selects individuals with a certain probability. For individuals belonging to margins where the current person or household count is higher (lower) than specified by the target margins, they should be deselected (selected) with a higher probability. Thus, the improved sampling aims to calculate sampling probabilities for eachperson based on their impact on the objective function. Let us denote \(e_{m,r}\) as the differences between the target population counts, \(t_{m,r}\), and the synthetic counts \(\hat{t}_{m,r}\)  
\[e_{m,r}=t_{m,r}-\hat{t}_{m,r},\quad r=1,\dots,R;\ t=1,\dots,M_{r}. \tag{18}\]  
For each individual \(i\), we calculate the values \(e_{1,r(1,i)},\dots,e_{M,r(M,i)}\), which correspond to all the differences between the target and the synthetic counts for the categories the individual \(i\) is part of. When adding persons and subsequently households, e.g., changing \(\delta_{i}\) from 0 to 1, we propose a sampling probability \(p_{i}\) for individual \(i\) with  
\[p_{i}=\begin{cases}\frac{v_{1}}{N_{i}}\quad\text{if}\ v_{i}>0\\ \exp(-\sum\limits_{u:v_{u}\leq 0}\frac{v_{u}}{N_{u}})\quad\text{otherwise}\end{cases} \tag{19}\]  
with  
\[v_{i}=\frac{1}{M}\sum\limits_{m=1}^{M}\left|e_{m,r(m,i)}\right|\cdot\text{sgn }\left(\sum\limits_{m=1}^{M}e_{m,r(m,i)}\right) \tag{20}\]  
and \(N_{i}\) as the number of people in the synthetic population that belong to the same set of margins \(t_{1,r(1,i)},\dots,t_{M,r(M,i)}\) as individual \(i\). If \(v_{i}<0\), individuals belonging to the same set of margins as individual \(i\) are over-represented in the synthetic data, e.g., individual \(i\) should not be additionally included to the synthetic population. In this case, \(i\) has a diminishing small sampling probability \(\exp(-\sum\limits_{u:v_{u}\leq 0}\frac{v_{u}}{N_{u}})\). In the case \(p=1\), the sampling probability reduces to  
\[\frac{e_{1,r(i)}}{N_{i}}=\frac{t_{1,r(i)}-\hat{t}_{1,r(i)}}{N_{i}}, \tag{21}\]  
with the objective function \(\sum\limits_{i=r}^{R_{1}}|t_{1,r}-\hat{t}_{1,r}|\). That is, the sampling probability for the individual \(i\) is proportional to \(t_{1,r(i)}-\hat{t}_{1,r(i)}\), in other words, its contribution to the objective function. The sampling probability for removing individuals from the synthetic population, e.g., changing \(\delta_{i}\) from 1 to 0, is calculated in the same way as described before with the only difference that \(e_{m,r}\) is calculated by  
\[e_{m,r}=\hat{t}_{m,r}-t_{m,r},\quad m=1,\dots,M,\ r=1,\dots,R_{m}. \tag{22}\]  
Since sampling with probability can be very computationally intensive if the set to be drawn from is very large, e.g., exceeds the order of \(10^{4}\), we use the efficient implementation for weighted sampling from the R package wrswoR (see [31]).