esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 4 Experimental Results
### Experimental Measurement on Edge Spinning Motion of PMSM Rotor  
Figure 20 shows that the PMSM rotor shaft spinning at a tilted angle. When the rotor shaft was in motion in three-DOF, the spherical rotor span around the tilted z axis at the same time.  
Figure 21 shows the experimental measurement on the edge spinning motion of PMSM rotor.  
Figure 19: Experimental results on spinning motion of PMSM rotor at center point: (**a**) Experimental and analytical results of \(\theta\); (**b**) difference between experimental and analytical results of \(\theta\); (**c**) experimental and analytical results of \(\varphi\); (**d**) difference between experimental and analytical of \(\varphi\); (**e**) experimental and analytical results of \(\omega\); (**f**) difference between experimental and analytical of \(\omega\).  
Figure 20: Edge spinning motion of PMSM rotor.  
Figure 22a shows the tilting angle_\(\theta\) when the PMSM rotor made an edge spinning motion, its average value was about 24\({}^{\circ}\). The variation of the tilting angle might be caused by the PD control algorithm and the time delay of position detection used in the PMSM. The maximum difference of the tilting angle between the experimental and analytical results was about 0.22\({}^{\circ}\), which is shown in Figure 22b. Figure 22c shows the yaw angle_\(\varphi\). It can be seen that the PMSM rotor is rotating anti-clockwise at the approximate 4.5 turns around the z axis in the SCF. The maximum difference of the yaw angle between the experimental and analytical results was about 0.3\({}^{\circ}\), as shown in Figure 22d. Figure 22e shows the spinning angle_\(\omega\), we can see that the PMSM rotor was rotating clockwise at the approximate 4 turns around the z axis in the RCF. The maximum difference of the spinning angle between the experimental and analytical results was about 0.45\({}^{\circ}\), as shown in Figure 22f, this difference was mainly caused by the three LEDs in the LSGM which were not at the same level when the rotor was tilted.  
Figure 21: Experimental measurement on the edge spinning motion: (**a**) Image captured by high-speed camera and coordinate values of optical feature points; (**b**) Position of rotor shaft tip.  
Figure 22: _Cont._
## 2 Literature Review
### Neural Networks  
Artificial neural networks (ANNs)--or, simply, neural networks (NNs)--represent a specialized subset within the domain of machine learning (ML), forming the fundamental core of deep learning (DL) algorithms [17]. NNs are constructed from information processing units, referred to as neurons. Each neuron within a network is interconnected and possesses associated weights and thresholds, which play a pivotal role during the back-propagation process during training. This mechanism activates neurons and transmits output data to adjacent layers [18]. The weight assigned to each connection signifies the relative importance of a variable in contributing to the output, thereby influencing the data before they are passed to the activation function. Subsequently, the output is compared to a pre-defined threshold; if it surpasses this threshold, the neuron is activated, and the data are relayed to the next layer. In a broader sense, NNs consist of one or more hidden layers in conjunction with input and output layers. Achieving high accuracy and performance hinges on several key factors, including the number of layers, the choice of optimizer, and the quantity of nodes or neurons, all of which require careful consideration when designing a neural network [19].  
Various types of NNs exist, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), MLP, LSTM networks, etc. [20]. Each NN type is meticulously engineered to address particular types of tasks and data. Consequently, the judicious selection of an appropriate neural network type for a given task represents a critical consideration in the design and implementation of an artificial intelligence (AI) system. In this research, the objective is to detect spoofed signals and estimate the ToA of the spoofer. To achieve this, the performance of two distinct types of neural networks (NNs) are evaluated and compared, with the aim of achieving high accuracy and improved results. Specifically, we detail the MLP and LSTM models in the following subsection.
###### Abstract  
Maritime Autonomous Surface Ships (MASSs) are expected to become vital participants in future maritime commerce and ocean development activities. This paper investigates a channel capacity-based scheme for estimating the persistence of ship-to-ship communication links in MASS communication scenarios. Specifically, this study presents a relative motion model for nodes within the network and estimates link persistence based on the dynamic characteristics of the links. Additionally, transmission modes tailored to maritime communication scenarios are proposed to optimize link capacity and reduce interference. Simulation results demonstrate that the proposed method can accurately estimate the duration and capacity of the links, thereby achieving higher network capacity. When used as a metric for routing protocols, the proposed link-persistence measure outperforms traditional metrics in terms of packet loss ratio, end-to-end delay, and throughput. Comparisons with other mobility models show that the proposed mobility model offers greater accuracy and reliability in describing the relative mobility of nodes.  
Keywords:MASS; link persistence; mobility model; channel capacity +
Footnote †: journal: Electronics
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 6 Analysis and Experimentation
### Empirical Analysis
#### 6.2.4 Binary Analysis  
The analysis of the corpus and the information retrieved by the remaining job attributes resulted in seven more rules: (a) opportunistic career pages usually do not have a corporate logo; (b) scammers omit adding screening questions; (c) usually mention salary information even in their title to lure candidates; (d) skip designated job attributes (i.e. industry, function, candidate's education level, and experience level) used for job board categorization; (e) prompt defrauded candidates to apply at external websites, bypassing the ATS pipeline; (f) or force them send their resumes to their personal email addresses directly and (g) address lower educational level. The results acquired for each of the aforementioned rules are displayed in Figure 5. As shown in Figure 5a, the vast majority of fraudulent job postings (392 out of 450 included in the sample) do not have a corporate logo. On the other hand, only a mere of 16% of the legitimate job postings neglect to include a logo. A similar trend applies to screening questions presence displayed in Figure 5b. Moreover, according to Figure 5c,e,f, 176 fraudulent job postings mention salary, 115 redirect applicants to apply to other websites bypassing the ATS, and 96 prompt them to forward their CV to untrustworthy email addresses. Lastly, 30% of fraudulent ads state that higher education is not mandatory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Aggregated \%** & **Normalized \%** \\ \hline \multicolumn{3}{c}{(**a**) Legitimate job ads.} \\ \hline Description & 4.9 & 9.8 \\ Requirements & 2.46 & 8.71 \\ Benefits & 2.95 & 20.42 \\ \hline \multicolumn{3}{c}{(**b**) Fraudulent job ads.} \\ \hline Description & 8.64 & 40.51 \\ Requirements & 3.75 & 26.38 \\ Benefits & 3.35 & 15.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Emphasized text averages. Aggregated percentages are computed on every record of the dataset, whereas normalized percentages exclude job ads missing the corresponding field.
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
###### Abstract  
Progress in microwave and millimeter-wave technologies has enabled advanced diagnostics for industrial applications. The transmission, reflection, scattering and radiation processes of electromagnetic waves are utilized as diagnostic principles. Specifically, the reflectometric method has gained importance in various applications due to the possibility of the high localization and accessibility of measurements, as well as the non-destructive nature of the systems. In this paper, radar reflectometers were applied to the measurement of the inside of a building wall, that is, the inspection of tile materials attached to a concrete wall. The measurement principle utilizes the phase interference effect of the reflected wave due to the multiple reflections between the two layers (Fabry-Perot effect). The results show the imaging inside the surface related to the peering condition between the tile and concrete wall, and the quantitative evaluation of the condition with non-destructive inspection.  
millieter-wave; reflectometer; non-destructive inspection; imaging; industrial application 2022
## 3 GaN HEMT Device on MMIC Die  
Monolithic Microwave Integrated Circuits (MMICs) based on AlGaN/GaN high electron mobility transistors (HEMTs) are usually used in aircraft, land vehicles, satellite application and other multifunctional RF systems. Thermal analysis of HEMT structures for MMICs is important due to increased circuit packing densities and RF output powers. Solid state power amplifier SSPA may consist of one or more MMIC chips. In illustration [39], an example of a RF 50 W SSPA is shown which consists of four (4) MMIC chips, each of which develops 12.5 W with 50% PAE (_power added efficiency_), and are combined by a lossless circuit in the output. Each chip has 8 multi-gate amplifiers and each amplifier includes ALGaN/GaN HEMT with eight (8) gate fingers of 1.56 W power amplifier (i.e., \(8\times 1.56\) W). Thanks to a lossless combiner the 4 MMIC chips produce nearly a 50 W output power, and for cooling and efficiency thermal dissipation, these 4 chips are assumed to be soldered onto a heat sink. Figure 3 below shows a concept described of a GaN MMIC chip.  
Figure 2: TLM Boundary conditions principle.
Entropy_**2015**, _17_, 4064-4082; doi:10.3390/e17064064  
_Entropy_**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
_Article_  
**Passive Decoy-State Quantum Key Distribution with Coherent Light**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 2,3 and [PERSON] 2**  
\({}^{1}\) Escuela de Ingenieria de Telecomunicacion, Department of Signal Theory and Communications, University of Vigo, Campus Universitario, Vigo 36310, Pontevedra, Spain  
\({}^{2}\) ICFO-Institut de Ciencies Fotoniques, Mediterranean Technology Park, Castelldefels 08860, Barcelona, Spain; E-Mails: [PERSON]RESS] (M.J.); [PERSON] (V.P.); [EMAIL_ADDRESS] (M.W.M.)  
\({}^{3}\) ICREA-Institucio Catalana de Recerca i Estudis Avancats, Barcelona 08010, Spain  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-986-818615.  
_Received: 31 March 2015 / Accepted: 9 June 2015 / Published: 12 June 2015_  
**Abstract:** Signal state preparation in quantum key distribution schemes can be realized using either an active or a passive source. Passive sources might be valuable in some scenarios; for instance, in those experimental setups operating at high transmission rates, since no externally driven element is required. Typical passive transmitters involve parametric down-conversion. More recently, it has been shown that phase-randomized coherent pulses also allow passive generation of decoy states and Bennett-Brassard 1984 (BB84) polarization signals, though the combination of both setups in a single passive source is cumbersome. In this paper, we present a complete passive transmitter that prepares decoy-state BB84 signals using coherent light. Our method employs sum-frequency generation together with linear optical components and classical photodetectors. In the asymptotic limit of an infinite long experiment, the resulting secret key rate (per pulse) is comparable to the one delivered by an active decoy-state BB84 setup with an infinite number of decoy settings.  
**Keywords:** quantum cryptography; quantum key distribution; quantum communication
## 5 Performance Evaluation and Analysis
field migration aware routing and spectrum assignment (BMA-RSA)_ algorithm we proposed can achieve migration-aware dynamic connection provisioning before network migration occurs, which can greatly improve the overall network performance because during the brown-field migration process, node upgrade probability is an important factor affecting service connection. In order to provide service reliability guarantee, we must provide it with more attention.
# Botnet Detection Technology Based on DNS
## 2 Botnet Evading Technology
### Fast Flux
Single flux targets are involved in the second step of the above-mentioned method. Bots do not have direct communication with the C and C server. In fact, an intermediate layer of machines act as proxies and relay the communication between bots and the C and C server. Infected by the botnet, these machines are called 'proxy bots'. These resolved IP addresses represent the proxy bots. At a short time interval, a different subset of the entire pool of proxy bot IP addresses is correlated to the domain name. A collection of proxy bot IP addresses is employed by the botmaster in a circular manner; therefore, the same IP addresses may reappear in the DNS A record regularly. To order that all bots see such frequent change in the domain's record, the TTL of a name server's response is set to be very short, this is usually a few seconds. After being employed by botnets for a short time, a domain will be replaced by a new one, and the same set of proxy bots will be correlated with a new domain name by the botnets. By extending the concept of flux to the name server responsible for resolving the C and C domain name, double fluxing brings the fast fluxing to a new level. The name server under the botnet's control (referred to in step (1)) will resolve a query request for the C and C domain name. The IP addresses correlated to the name server change rapidly. The name server's reply will contain the IP addresses correlated to the queried domain name. The resolved IP addresses change very rapidly and correspond to the proxy bots that transmit information to and from bots and the C and C servers (step (2)).
## 3 Convergence-Performance Analysis and Re-Design of PLDPC Channel Codes
### M-JPEXIT Algorithm
Assuming that the sequence \(\left\{L_{\rm BCJR\_OUT}\right\}\) follows a symmetric Gaussian distribution, we evaluate the MI \(I_{\rm BCJR\_OUT}\) of the sequence \(\left\{L_{\rm BCJR\_OUT}\right\}\) using (12). \[L_{\rm BCJR\_OUT}=F(L_{\rm ch},L_{\rm BCJR\_IN}), \tag{15}\]  
where \(F(\cdot)\) represents the LLR processor of the BCJR detector. The threshold is the lowest value of \(E_{s}/N_{0}\) (\(\left(E_{s}/N_{0}\right)_{min}\)) for which \(I_{\rm s}^{\rm APP}(j)=1\) for \(j=1\cdots n_{s}\). Note also that:  
* The conventional JPEXIT algorithm tailored for JSCC systems is extended to a three-stage parallel concatenated system, which examines the evolution of the input/output MI exchanging among the BCJR detector, the channel decoder, and the source decoder. * Different from the JPEXIT algorithm for AWGN channels, the modified algorithm is a hybrid performance analysis tool and it incorporates Monte Carlo simulation into information theoretical derivation.
## 3 Results
### Effect of Classifiers in the Case of Single-Date Autumn Images  
The 10-fold cross-validation accuracies obtained from the two categories of features extraction suites (very high-resolution versus ultra-resolution) proposed in this research have been shown in Tables 3 and 4. In all three sites (Hakkoda, Zao and Shiranuka) studied in this research, the SoftVoting classifier showed slightly better performance than the other two classifiers (XGBoost and Random Forests) without a substantial difference among the three classifiers (XGBoost, Random Forests, and SoftVoting).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Model** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} & **F1-Score** & **Recall** & **Precision** \\ \hline Hakkoda & XGBoost & 0.653 & 0.622 & 0.653 & 0.653 & 0.653 \\  & RF & 0.659 & 0.629 & 0.659 & 0.659 & 0.659 \\  & SoftVoting & 0.663 & 0.634 & 0.663 & 0.663 & 0.663 \\ \hline Zao & XGBoost & 0.590 & 0.566 & 0.590 & 0.590 & 0.590 \\  & RF & 0.601 & 0.577 & 0.601 & 0.601 & 0.601 \\  & SoftVoting & 0.606 & 0.582 & 0.606 & 0.606 & 0.606 \\ \hline Shiranuka & XGBoost & 0.673 & 0.627 & 0.673 & 0.673 & 0.673 \\  & RF & 0.665 & 0.619 & 0.665 & 0.665 & 0.665 \\  & SoftVoting & 0.676 & 0.631 & 0.676 & 0.676 & 0.676 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the very high-resolution suite using single-date autumn images.  
Figure 6: True color composite (Red: Band 5, Green: Band 3, Blue: Band 2) of a pan-sharpened image (**a**) and a multi-spectral image (**b**).  
The variation of the classification accuracy in terms of the F1-score with the classifier is summarized and shown in Figure 7. There was not a substantial difference among the three classifiers (XGBoost, Random Forests, and SoftVoting) employed for the classification of land cover and vegetation types using both categories of features extraction suite. In all sites, as described in Figure 7, the ultra-resolution suite showed superior performance over the very high-resolution suite using any machine learning classifier employed in the research. It should be noted that both suites utilized the same size of ground truth data.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 2 Methodology
### Rail Track Extraction  
The rail images captured by UAVs involves redundant areas, as shown in Figure 3. Besides the areas of rail tracks, the rest areas are excluded for the next step. Therefore, Hough Transform and the method based on cumulative gray value of each pixel column are used to extract the area of rail track from rail images. Hough transform is a graphic detection algorithm based on the duality of point and line, and can be applied in the extraction of rail tracks [41].  
Considering an image as a \(M\times N\) matrix, the \(N\)-dimensional matrix \(Cg\) that consists of cumulative gray value of each pixel column of the image matrix is determined by:  
\[\begin{array}{c}Cg=[\sum\limits_{i=0}^{M-1}D_{i0},\;\sum\limits_{i=0}^{M-1}D_ {i1},\;\ldots\ldots\;\sum\limits_{i=0}^{M-1}D_{i(N-1)}\;],\\ Cg(n)=\sum\limits_{i=0}^{M-1}D_{in},\;\;n\in[0,N-1]\end{array} \tag{1}\]  
where, \(D_{xy}\) is the pixel value of the coordinate (\(x\), \(y\)). The matrix \(Cg\) of the vertical rail image (\(M\) = 550, \(N\) = 350) is shown in Figure 3. It should be noted that \(Cg(n)\) is mapped to a small range. From this figure, it can be observed that the value \(Cg(n)\) of area of the rail track is higher than the rest. This method is based on two factors: (1) Area of rail track has a higher value of \(Cg(n)\). (2) The width \(w_{d}\) of the rail track is fixed in the rail image, as shown in Figure 3. The detail procedure about the method for rail track extraction is shown in Appendix A.  
Figure 2: Flow diagram of the inspection method for the rail surface defects based on the UAV images.
## 4 Review of [PERSON] et al.'s Protocol
### Authentication Process  
The main goal of this process is to negotiate a session key between \(SD_{i}\) and \(UC_{j}\). Therefore, the \(SD_{i}\) and \(UC_{j}\) must authenticate each other. Figure 5 describes the authentication process of [PERSON] et al.'s protocol. The detailed process is described below.  
**Step 1:**: \(SD_{i}\) chooses a random number \(u\in Z_{p}^{*}\) and generates a current timestamp \(T_{1}\). After that, \(SD_{i}\) computes \(U_{i}=u.G\) and \(C_{i}=h(TC_{i}||T_{1})\oplus h(RID_{i}||U_{i}||T_{1})\) and sends authentication request message \(\{U_{i},C_{i},T_{1}\}\) to the \(UC_{j}\) over insecure channel.
**Step 2:**: After receiving the message, \(UC_{j}\) checks \(|T_{1}-T_{1}^{*}|\leq\Delta T\), where \(\Delta T\) is maximum transmission delay bound and \(T_{1}\) is current timestamp. If the condition is valid, \(UC_{j}\) computes \(D_{j}=C_{i}\oplus h(RID_{i}||U_{i}||T_{1})\) utilizing the corresponding \(RID_{i}\) of \(SD_{i}\) stored in the database.
**Step 3:**: \(UC_{j}\) then generates timestamp \(T_{2}\) and a random number \(v\in Z_{p}^{*}\), and calculates \(V_{j}=v.G\), \(W_{j}=v.U_{i}=(uv).G\), the session key shared with \(SD_{i}\) as \(SK_{ij}=h(W_{j}||D_{j}||h(RID_{j}||TC_{j}||T_{2}))\), \(SKV_{ij}=h(SK_{ij}||RID_{i}||T_{2})\) and \(Z_{j}=h(RID_{j}||TC_{j}||T_{2})\oplus h(RID_{i}||U_{j}||T_{2})\). After that, \(UC_{j}\) sends the authentication message \(\{V_{j},Z_{j},SKV_{ij},T_{2}\}\) to the \(SD_{i}\) over insecure channel.
**Step 4:**: After receiving the message, \(SD_{i}\) checks condition \(|T_{2}-T_{2}^{*}|\leq\Delta T\). If it is correct, \(SD_{i}\) further calculates \(E_{i}=Z_{j}\oplus h(RID_{i}||U_{i}||V_{j}||T_{2})=h(RID_{j}||TC_{j}||T_{2})\), \(W_{i}^{\prime}=u.V_{j}=(uv).G\), and session key shared with \(UC_{j}\) as \(SK_{ij}^{\prime}=h(W_{i}^{\prime}||h(TC_{i}||T_{1}||E_{i}))(=SK_{ij})\), \(SKV_{ij}^{\prime}=h(SK_{ij}^{\prime}||RID_{i}||T_{2})\). If the condition \(SKV_{ij}^{\prime}\
eq SKV_{ij},\ SD_{i}\) aborts communication. Otherwise, \(SD_{i}\) generates a timestamp \(T_{3}\) and calculates \(SK_{ij}^{*}=h(SK_{ij}^{\prime}||RID_{i}||V_{j}||T_{3})\). After that, \(SC_{i}\) sends acknowledgment message \(\{SKV_{ij}^{*},T_{3}\}\) to the \(UC_{j}\) over insecure channel.
**Step 5:**: After receiving the message, \(UC_{j}\) checks the condition \(|T_{3}-T_{3}^{*}|\leq\Delta T\). If the condition is valid, \(UC_{j}\) computes \(SK_{ij}^{**}=h(SK_{ij}||RID_{i}||V_{j}||T_{3})\) and checks if \(SK_{ij}^{**}=SKV_{ij}^{*}\) holds. If the condition is valid, \(SD_{i}\) and \(UC_{j}\) store the common session key \(SK_{ij}(=SK_{ij}^{\prime})\).  
Figure 4: Utility center registration process of [PERSON] et al.’s scheme.
## 5 Experimental Results
### Experimental Settings
#### 5.2.3 Step 3: Training the First GAN Network  
We began by training the first GAN network using the set of labeled data we made in Step 2. We trained the GAN network many times using different sizes of sampled data to study the effect of the number of sampled images on increasing the accuracy of the segmentation. We tried 7 sizes of sampled images from the target domain (Vaihingen): 1, 3, 12, 23, 47, 94, 188. All the selected images are of size \(512*512\). The GAN network learns during the training how to translate images from the target domain to the semantic label domain. The GAN architecture was implemented using Tensorflow [53]. We set the value 0.2 for the slope \(\alpha\) of Leaky ReLu. We used ADAM optimizer with an initial learning rate of 0.0002 and momentum term \(\beta\) 0.5. The weight for the \(L_{1}\) loss is set to 100 and the weight for the GAN weight is set to 1.