esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 2 Preliminaries
### Fuzzy Extractors  
This section discusses the basic concepts of a fuzzy extractor. According to [22], this mechanism involves two procedures, such as \(Gen\) and \(Rep\). The detailed description for \(Gen\) and \(Rep\) are below:  
* \(Gen\): After a user imprints the biometric input \(Bio\), the probabilistic function \(Gen\) selects a consistent random string \(\rho\in\{0,1\}^{l}\) and a random auxiliary string \(\sigma\in\{0,1\}^{*}\).
* \(Rep\) : After a new user imprints the biometric input \(Bio_{new}\) and the string value \(\sigma\) in a session, \(Rep\) successfully recovers the value \(\rho\).
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 3 Problem Definition  
In this paper, machine learning is used to identify the modulation of radio and to classify the input radio signals by constructing a multi-class neural network model. Inputting signal data \(x\) with a dimension size of \(1\times M\times N\), an output of \(1\times M\) is finally obtained, where the size of \(M\) is equal to the number of modulation types that can be classified through the network. The neural network is a reverse propagation algorithm that fits function \(f\) in \(y=f(x)\) through rounds of learning and saves the final model weight. For the newly input signal, \(x\), the type of signal that can be obtained by this operation, and each neuron, can be represented by Equation (1):  
\[z(x)=f\mathbf{w}^{T}x+b \tag{1}\]  
where \(\mathbf{w}^{T}\) is a \(T\)-dimensional vector representing the weight, \(b\) is a biased value, and the result \(z\) is obtained after multiple transformations by inputting the signal or the data \(x\) from the upper layer. It is then added to the SoftMax function with Equation (2):  
\[p(z_{i})=\frac{e^{z_{i}}}{\sum_{j}e^{z_{j}}} \tag{2}\]  
where \(p\) represents the probability of the category. The loss function is of cross-entropy, which is shown in Equation (3):  
\[L=-\sum_{i=1}^{M}y_{i}\log(p_{i}) \tag{3}\]  
where \(M\) represents the number of categories, \(y_{i}\) represents the indicative variable (i.e., categories), and \(p_{i}\) represents the probability that the first category belongs to the \(i\) category.  
After calculating the loss, \(L\), the weight \(w\) and bias values \(b\) can be updated through the reverse propagation algorithm. The bias indicators of \(w\) and \(b\) can be calculated as:  
\[w^{\prime}=\frac{\partial L}{\partial w}=\frac{1}{n}{\sum\limits^{x}}x(\sigma(z) -y) \tag{4}\]  
\[b^{\prime}=\frac{\partial L}{\partial b}=\frac{1}{n}{\sum\limits^{x}}x(\sigma( z)-y) \tag{5}\]  
where \(\sigma\) represents the sigmoid function and \(n\) represents the amount of training data. Therefore, a new weight and bias can be obtained, with \(\alpha\) as the learning rate:  
\[w=w-\alpha*w^{\prime} \tag{6}\]  
\[b=b-\alpha*b^{\prime} \tag{7}\]  
Finally, we repeat the above steps until the loss \(L\) converges and reaches a minimum value. Then, training can be stopped; the final weight is saved and can be used in the method to classify the radio modulation.
# Analysis and Optimization of Driveline Bushing for Lateral Ride Vibration under Shock Excitation
## 5 Optimization of Rear Drive Unit Bushing Parameters
### Bushing Optimization  
In this paper, the PSO was executed on a 2.90 GHz i7-4910 MQ Intel Core. In the implementation, six optimization parameters, \(k_{\text{x}\_\text{FL}}\), \(k_{\text{y}\_\text{FL}}\), \(k_{\text{x}\_\text{FR}}\), \(k_{\text{y}\_\text{FR}}\), \(k_{\text{x}\_\text{RR}}\) and \(k_{\text{y}\_\text{RR}}\), were contained in the problem. The upper and lower boundaries of the optimization parameters were set as 50% and 150% of their baseline values. The overall VDV at the driver seat rail was considered as the objective function given as following (ISO 2631-1: 1997):  
\[\text{VDV}=\left[\left(k_{\text{x}}\text{VDV}_{x}\right)^{4}+\left(k_{y}\text {VDV}_{y}\right)^{4}+\left(k_{z}\text{VDV}_{z}\right)^{4}\right]^{1/4} \tag{8}\]  
where the subscripts \(x\), \(y\) and \(z\) represent the fore-and-aft, lateral and vertical directions, and \(k_{x}\), \(k_{y}\) and \(k_{z}\) were the multiplying factors in the corresponding directions. In this paper, the VDVs were calculated at the feet positions so that \(k_{x}=0.25\), \(k_{y}=0.25\) and \(k_{z}=0.4\) were used according to ISO 2631-1:1997.  
To improve the ride quality of the vehicle, the objective of the optimization was set as minimizing the overall VDV at the driver seat rail. For the algorithm parameters, the initial inertia weight was chosen as 0.9, the population size was 40, the number of particles was 5 and \(c_{1}=c_{2}=2\). The iteration will be terminated if the average cumulative change of the overall VDV over 50 generations was less than 1 \(\times\) 10\({}^{-6}\) or the number of iterations reached 500.  
The six optimized stiffness of the four bushings were obtained as: \(\left[k_{\text{x}\_\text{FL}},k_{\text{y}\_\text{FL}},k_{\text{x}\_\text{FR}}, k_{\text{y}\_\text{FR}},k_{\text{x}\_\text{RR}},k_{\text{y}\_\text{RR}} \right]=\left\{1.50,\,0.99,\,0.94,\,1.3,\,1.04,\,1.05\right\}\) times the baseline values. It means to achieve the best ride under a shock input at 35 km/h, the longitudinal and lateral stiffness of the front left differential bushing should increase by 50% and decrease by 1% respectively, those of the front right rear drive unit bushing should decrease by 6% and increase by 13% respectively, and those of the rear drive unit bushings should increase 4% and 5% respectively.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 4 Performance of In-Network Links
### PRR of In-Network Links  
Figure 14 demonstrates the temporal variation of two nodes' neighborhoods over four sequential time points. For clarity, in Figure 14, we present only two sensor nodes, S2 and S12, and retain only at most five incident links (neighbors) for each of S2 and S12. Although in our experiment, a sensor node only chooses at most five PRR best neighboring nodes as its neighbors, the variation of the PPR of incident links shown in Figure 14 demonstrates that the topology of the low-power network obviously varies with time increasing.  
Figure 13: Overview of a ten-node wireless sensor network deployed in the Jiufeng forest (only a part of the wireless links is shown here for clarity).  
Figure 15a shows the average PRR of each directional link in the Jiufeng experiment. We can see that most link PRRs range between 50% and 80%; good links whose PRR is greater than 90% account for only 14%; such an observation shows that in comparison with the single link experiment in the Bajia and the Campus forests, the network contains more probabilistic links in the mountain forest environment with irregularly-distributed, denser shrubs and trees, even though the transmit power is set to the highest level. Figure 15b plots the variation of the average link PRR as the time increases. Recall that the network updates its link quality (PRR) every two minutes; Figure 15b includes some missing PRR records due the the failed CTP paths, which cannot correctly deliver the link information to the sink. Clearly, during the experiment, the link quality stays dynamic, though being to a relatively small extent. The mean of PRR ranges from 70% to 80% and the standard variance of PRR from 16% to 25%.  
Figure 16 examines the time-varying characteristic of link asymmetry. According to the average PRR in the whole experiment, we divide all of the two-way links into three categories: the poor link (PRR \(\leq\) 55%), the middle link (55% \(<\) PRR \(<\) 90%) and the good link (PRR \(\geq\) 90%). The standard variance of good links' asymmetry is at most 15% and is often less than 10%, similar to the results of the Campus forest experiment; and this observation is also true for poor links. The asymmetry of middle links varies more frequently, leading to a relatively higher standard variance of at least 10%. These results demonstrate the possible relation between the link quality and the link asymmetry, yet with low confidence.  
Figure 14: Temporal variation of the topology of the Jiufeng wireless sensor network (shown here are only links incident to nodes S2 and S12). (**a**) The 10 th minute; (**b**) The 20 th minute; (**c**) The 30 th minute; (**d**) The 40 th minute.  
Figure 15: PRR performance of directional links. (**a**) Over each valid directional link; (**b**) Over all valid direction links against time.
## 1 Introduction
Some studies suggest increasing the cost of construction for wireless communications to reduce spacing of base stations in order to obtain favorable indicators of communications; few studies involve the optimization of the overall performances with the fast handover algorithm as the pointcut under the condition of the existing size of base stations. This paper takes the wireless communications of 5G for HSRs as an application scenario to analyze system architecture, planning of the handover zone, synchronization signal reference signal receiving power (SS-RSRP), and quality (SS-RSRQ) measurement of the serving cell (SCell) and the neighboring cell (NCell). The collection of the measured data with location information can be acquired by the portable control unit (PCU). It is composed of five ports to connect external devices, as shown in Figure 1. An alternating current (220 V) can be obtained from the train or from a portable power source. The terminals HUAWEI P40 Pro, installed with developed software to measure the wireless communications, are connected to port 3 and port 4. The software SPark can be used to display some of the test results in time, including the path loss, SS-RSRP, SS-RSRQ. A fast handover algorithm in HSRs is realized through the process of the speed decision, the location recognition, and the weight calculation. The results of this paper have important implications for designing wireless networks, constructing base stations, and improving performances of communications for 5G-R. The rest of the article is organized as follows: In Section 2, the architecture of 5G-R in HSRs is presented through the measured data, with the factors for handling the handover zone analyzed. Section 3 develops the fast handover algorithm in detail. The experimental results are presented to verify the effectiveness of the proposed algorithm in Section 4. Finally, Section 5 concludes with some concluding remarks and discussions.
# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
## 2 Literature Review
### Log Parsing
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Model** & **Dataset** & **Online Mode** & **Log-Parser** & **Anomaly Identifier** & **Anomaly Localizer** & **Explainable AI** \\ \hline [3] & PCA & Logs Stream & ✗ & ✓ & ✓ & ✗ & ✗ \\ \hline [5] & Custom & Distributed DB & ✗ & ✗ & ✗ & ✗ \\ \hline [8] & LSTM & HDFS, BGL & ✗ & ✓ & ✓ & ✗ & ✗ \\ \hline [9] & BiLSTM & HDFS & ✗ & ✓ & ✗ & ✗ & ✗ \\ \hline [10] & Att. GRU & HDFS, BGL & ✗ & ✓ & ✗ & ✗ & ✗ \\ \hline [11] & NB, HMM & - & ✗ & ✗ & ✓ & ✗ & ✗ \\ \hline [12] & RF, Cox & Custom & ✗ & ✓ & ✗ & ✗ & ✗ \\ \hline [13] & Fail-Stutter & - & ✗ & ✗ & ✓ & ✓ & ✗ \\ \hline ADAL-NN & Att. BiLSTM & HDFS, Custom & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Documents concerning anomaly detection and localization. performance issues with related system resource use. BIRCH is used to identify anomalies, and root cause analysis (RCA) is performed by employing a graph that represents anomaly transmission across systems. Additionally, there is recent research in this field as discussed in [44; 45; 46; 47; 48; 49; 50; 51; 52; 53].
## 4 Discussion
### Applicability for Physiological Pump Control
The nonlinear relationship between blood flow rate and power consumption demonstrates that the minimum power consumption cannot be obtained at the point of zero flow in the pump [37]. The described relationship shows that estimation of the blood flow rate in axial pumps using indirect methods does not reliably determine the lower range values. Furthermore, some physiological control methods lack consideration of errors in measurement or estimation of pulsatility measure, pump flow rate, or pressure head. The represented pulsatility index is simply and readily available from an LVAD controller without any complex calculations and/or invasive measurements of hemodynamic parameters. _CPO_ or _CO_ parameters are commonly used by clinicians for evaluation of myocardial function and can be verified, if necessary, by noninvasive tools in clinics. And there is no need to manually select any reference parameters for the represented evaluation method. Therefore, the represented method can potentially overcome some of the common shortcomings of physiological control methods. However, this opportunity should be addressed in future work.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 2 IEEE 802.11 ax Use Cases in Fifth-Generation Radio Access Network on Unlicensed Bands (5G-U)
### Gigabit Ethernet Connection Replacement  
The most upfront use case of the latest gigabit Wi-Fi amendment IEEE 802.11 ax HEW is the opportunity to replace ancient gigabit ethernet connections, such as a connection to a server (e.g., server A in Figure 1a), with HEW radio links. With this use case, it is likely to serve either more WEs (servers) with the same throughput as before or the same number of WEs with improved throughput. The former benefit is particularly significant from the perspective of densely deployed networks, and the latter from the perspective of backhaul radio links supporting dense networks.
## 2 Materials and Methods
### Investigated Datasets
#### 2.1.1 Satellite Datasets  
In this paper, we analyze the ionospheric disturbances retrieved from satellite in situ measurements possibly related to earthquakes. In particular, we analyze data from the _Swarm_ three-identical satellite constellation launched by the European Space Agency (ESA) on 22 November 2013. At the present time (April 2022), all three satellites are still in orbit, with most instruments in good condition. The _Swarm_ constellation is the \"state of the art\" to monitor with the best accuracy the spatial structure and the temporal evolution of the Earth's magnetic field from space [31]. All three _Swarm_ satellites include identical payloads onboard. In particular, in this work, the Y-East component of the magnetic field and electron density (_Ne_) are analyzed as originally measured by fluxgate magnetometers (Vector Field Magnetometer, VFM) and Langmuir probes (Electric Field Instrument, EFI), respectively. The Y-East magnetic field component has been selected, as it is expected to be more sensitive to the internal perturbations [32] and, in the case of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) coupling, to the Field Aligned Currents (FAC) induced by air ionization from radon release or p-holes. At the ionospheric heights, these currents are expected to be almost horizontal, while at the ground/atmospheric level they are expected to be almost vertical [21]. Among the two X-North and Y-East horizontal components, the Y-East component should be more sensitive to such kinds of perturbations, as it is quasi-orthogonalto the main geomagnetic field generated by the Earth's outer core, so we expect that the same absolute perturbation is more evident on the Y-East component. The used magnetic field data are at \"Low-Resolution\" and are provided at a sample rate of 1 Hz in a daily file that contains 86,400 samples associated with 31 or 32 tracks (i.e., half orbits) per day. _Swarm Ne_ is sampled at 2 Hz and is provided in the Electric Field Instrument (EFI) product. There is an open debate on the discrepancies in the _Ne_ absolute value calibration measured with another satellite in a similar orbit, the China Seismo-Electromagnetic Satellite (CSES). For instance, it is known that the absolute value of electron density measured by CSES-01 (i.e., the first satellite of the CSES series) is lower than the ones estimated by _Swarm_[33] and by the IRI-2016 ionospheric model (e.g., comparison in the Indonesia area in [34]). However, such an absolute difference (bias) does not affect our results because our algorithms work on residuals, i.e., they are based on variations measured along the track, which are well consistent between the two satellite missions [33; 34].  
This paper analyzes the nighttime and daytime tracks for both the magnetic field (B) and electron density (_Ne_) datasets. In fact, as the investigation approach is track-by-track, we do not expect that the solar-driven daytime activity can affect our result in terms of \"false anomalies\", even if some potential pre-earthquake anomalies could be lost inside the higher day noise (especially for _Ne_).
## 3 Method
### Cavity Profile Design  
The diaphragm cavity profile of the diaphragm compressor needs to meet the following requirements:  
\[\left\{\begin{array}{l}h(0)=0\\ h(R)=H\\ \frac{dh(r)}{dr}\Big{|}_{r=0}=\frac{dh(r)}{dr}\Big{|}_{r=R}=0\\ \frac{d^{2}h(r)}{dr^{2}}\Big{|}_{r=0}\leq 0\end{array}\right. \tag{16}\]  
where \(h\) is the deflection curve with respect to the radius \(r\).  
The double exponential diaphragm cavity profile (DEP) is described as [10; 11]  
\[h_{b}(r)=H+H\frac{(N_{b}+1)(M_{b}+1)}{(N_{b}-M_{b})}\left[\frac{(\frac{r}{R})N_{b }+1}{N_{b}+1}-\frac{(\frac{r}{R})M_{b}+1}{M_{b}+1}\right] \tag{17}\]  
where \(h_{b}\) is the DEP cavity profile function. \(M_{b}\) and \(N_{b}\) are the deflection exponents.  
A novel double-arc diaphragm cavity profile (DAP) is proposed and described as  
\[h_{N}(r)=\left\{\begin{array}{ll}h_{Na}(r)=H-R_{Na}+\sqrt{R_{Na}^{2}-r^{2}}& r\in[0,\lambda_{N}R]\\ h_{Nb}(r)=R_{Nb}-\sqrt{R_{Nb}^{2}-(r-R)^{2}}&r\in[\lambda_{N}R,R]\end{array} \right.\quad(0<\lambda_{N}<1) \tag{18}\]  
where \(h_{N}\) is the DAP cavity profile function. \(h_{Na}\) and \(h_{Nb}\) are the first and second arc segments, respectively. \(R_{Na}\) and \(R_{Nb}\) are the first and second arc segments' radii, respectively. \(\lambda_{N}\) is the dividing ratio of these two arc segments.  
To meet the design requirements of Equation (16), the radii of the first and second arc segments (\(R_{Na}\) and \(R_{Nb}\)) can be determined by the following equations:  
\[\begin{split}&\left[\frac{\lambda_{N}^{2}}{(1-\lambda_{N})^{2}}-2 \right]R_{Nb}^{2}+\left\{2\sqrt{R_{Nb}^{2}-R^{2}(1-\lambda_{N})^{2}}+\frac{2 \lambda_{N}\left[H+\sqrt{\frac{R_{Nb}^{2}\lambda_{N}^{2}}{(1-\lambda_{N})^{2} }-R^{2}\lambda_{N}^{2}}\right]}{1-\lambda_{N}}\right\}R_{Nb}\\ &+\left[H+\sqrt{\frac{R_{Nb}^{2}\lambda_{N}^{2}}{(1-\lambda_{N})^{2 }}-R^{2}\lambda_{N}^{2}}\right]^{2}+R^{2}(1-\lambda_{N})^{2}=0\\ &\frac{R_{Na}}{R_{Nb}}=\frac{\lambda_{N}}{1-\lambda_{N}}\end{split} \tag{19}\]  
Consequently, in addition to the geometric parameters \(H\) (maximum deflection) and \(R\) (maximum radius), the SEP is characterized by a single adjustable design parameter (\(Z_{a}\)), the DEP has two adjustable design parameters (\(N_{b}\) and \(M_{b}\)), while the DAP has one adjustable design parameter (\(\lambda_{N}\)).
## 4 Simulation and Results
### Static Simulation  
To test the effectiveness of proposed indoor positioning system, a static test is performed first. The distribution of the RA and four TAs is illustrated in Figure 7. RA is at the centre of the four TAs and at a height of 26.32 m, 1.32-m higher than the TAs' heights since it is outside. The four TAs are on the same plane and located on the vertexes of a 100 m \(\times\) 100 m square. Assuming the RA and each TA are connected using cables, the route between the TAs and the RA is \(1.32+50\sqrt{2}\) m \(\approx\) 72.03 m. The user terminal is on the lowest layer and its real position in the local coordinates is (70, 40, 22).  
Figure 6: Sky plot.  
Figure 7: Trace of static test.  
Figure 8 displays the 2-D positioning results of the static test with a fixed vertical value of 22 m. The results show that the proposed system is able to provide an indoor positioning solution as accurate as \(\sim\)1 m. The average positioning error is close to zero, as small as 0.01 m in X axis and 0.08 m in Y axis. The maximum error is 2.89 m in the X axis and 2.70 m in the Y axis. The standard deviation (STD) of the X-axis error is 1.13 m and of the Y-axis error is 1.07 m. The results also suggest that the accuracy of the proposed system is not affected by the bad satellite geometry.  
Additionally, the high accuracy of the proposed system results from the fact that the propagation errors, such as ionospheric delay, tropospheric delay and orbit error, are cancelled in calculating \(\Delta\rho_{i}\) through \(\rho_{i}^{RGPS}\) minus \(\rho_{i}^{GPS}\) which has been shown in Equation (3). Thus, the distance from the user terminal to each TA can be accurately estimated according to Equation (5). Figure 9 shows that the estimated distances are similar to the real value. For instance, the real value of \(d_{2}\) is 92.24 m. Its estimated result is 92.20 m with 1.42 m STD.  
Figure 8: Static test results: (**a**) positioning results and (**b**) positioning error.  
Figure 9: Estimated distance between the user terminal and each TA.  
Figure 10 shows the antenna distribution for the 3-D positioning test with four transmitting antennas (a) and its positioning errors in three axes (b). In the figure, the positioning error is 0.04 m with 1.37 m STD in the X axis and 0.06 m with 1.13 m STD in the Y axis, which is similar to the 2-D positioning accuracy. Compared to the horizontal error, the Z-axis error is much larger, reaching 0.23 m with STD = 10.56 m. It should be noticed that the low accuracy in the Z-axis has little effect on the horizontal accuracy, as mentioned above. Additionally, the average error in the Z axis is 0.23 m which is acceptable to detect floors, although the Z-axis error changes in a large range.
## 3 Results and Discussions
### Snap Loads  
If slack occurs to the wire rope during the payload lowering, snap loads will be induced in the rope, bringing great threat to its structural integrity. In order to trigger slack conditions, a rapid downward displacement is imposed at the crane tip. The displacement is defined with a sigmoid function in Equation (18), making the crane tip move 1 m downwards in about 4 s. As mentioned previously, if slack occurs to a segment in the cable-payload system, the traction in the segment is set to zero. Once the segment is tensioned, the traction is restored.  
\[S(t)=\frac{1}{1+\exp[-10*(t-2)]}\, \tag{18}\]  
Figure 8: Influence of the lowering velocity on the dynamic force on the top of the cable.  
Figure 7: Traction at the top of the cable in the lowering process, considering different lowering velocities (0.1, 0.3, or 0.7 m/s).  
To investigate the snap loads due to rapid movement of the crane tip, several cases with lowered depths of 100 m, 300 m, and 1500 m are simulated. In these cases, the payout speed is set as zero, and the sinusoidal vessel motion is neglected. The payload motion and the corresponding traction on the payload are plotted in Figure 9, in which all the cases are studied at the depth when the rapid movement starts. For the case with a 100 m lowered depth, the payload sinks independently from the crane tip when it moves rapidly. In this process, slack occurs to the rope, resulting in zero traction on the payload. After the crane tip motion stops, the payload keeps sinking, and then induces a large traction in the rope. Such large forces are very dangerous for the payload itself and the cable. It is also shown that the longer the suspended cable length is, the less slack is likely to occur, and the lower the snap load is.