esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.6 System Time  
Sender APP packet contains the playout time that refers to the corresponding SR packet. RTCP-SR packet's NTP timestamp field contains the time when this packet was sent in NTP timestamp format. System time parameter refers to RTCP-SR NTP timestamp field (according to the common reference clock). \(t_{sr}\) is the time when RTCP-SR is prepared and is contained in SR packet's NTP timestamp field. APP packet is prepared right after SR packet at time \(t_{app}\). \(t_{sr}\) and \(t_{app}\) are not same as shown in the Figure 7. App packet contains the sender's calculated playout time. As we know this playout time corresponds to the NTP timestamp of the SR packet so \(t_{sr}\) is used as System time for the calculation of playout time as stated in Equation (4).
# A Comprehensive Survey for Deep-Learning-Based
## 5 Methodologies
### Abnormality Detection with Deep Learning Methods
#### 5.3.3 Unsupervised Learning  
Unsupervised learning is essential in abnormality detection since the labeled data is not always available. It is possible to find the inherent boundary that separates the normalities and abnormalities. AE is the fundamental architecture for unsupervised learning. A relational AE was proposed that shows a strong ability to extract features in high-dimensional data, and a low classification error rate was obtained with well-known datasets [83]. [PERSON] et al. designed a deep-AE-based method, together with a parametric density estimator, to learn the probability distribution [22]. The experiments were performed with three data types: images, videos, and cognitive data. The performance showed the method is transferable to diverse contexts. A mixture method was proposed in [81]. Deep AE first reduces the dimensions and feeds the extracted features into a Gaussian mixture model (GMM). Unlike other cascade architectures, the parameters of AE and GMM are shared and optimized in a joint way, which solves the inconsistent optimization problem in decoupled models.  
To process time-series data, RNNs are also widely used in an unsupervised way. In [84], RNN was adopted for early detection of cyber attacks, and it had a better performance than dynamic PCA. [PERSON] proposed an LSTM method for abnormality detection [85]. The trade-off between prediction accuracy and abnormality detection ability was discussed. A better setting for prediction may not be a good one for abnormality detection. The evaluation was done with three real-world datasets and shows that the method works well for both time series modeling and abnormality detection. [PERSON] et al. proposed the first GAN-based method to find anomalies for a patrolbot [87]. In each monitoring environment, a separate GAN is set up to learn normal objects in an unsupervised way. To make real-time application possible, a sliding window with a small fixed length is used. The evaluation is carried out with a mobile robot that projects circular images, and a low false-positive rate is achieved.  
In this subsection, we discussed some deep learning techniques used in abnormality detection. Based on the availability of labeled data, supervised learning, semi-supervised learning, and unsupervised learning were discussed and presented. Table 5 summarizes the related work in terms of the techniques used. Various approaches have been proved to be effective in different detection scenarios. The goal of this subsection was to give a comprehensive view of deep learning model selection for a particular domain or dataset from power grids. It is noted that we also include some state-of-the-art deep-learning-based solutions that go beyond the data collected in smart grids, which may help interested readers build their own network models for their complex issues.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ3: What Methods and Techniques Are Utilized to Detect IoT Botnets?
#### 5.3.4 Specification-Based Methods  
The following methods did not use AI algorithms, and instead used specification-based methods for detection. Generally, a specification detection method depends on specifications that describe the intended behavior. It can detect non-previously-encountered attacks and has a low false positive rate, but it has the disadvantages of being less effective than anomaly detection methods and more time consuming.  
The authors in [76] introduced an agile detection system termed ConnSpoiler that can identify IoT botnets accurately in a resource-restricted way. ConnSpoiler operates via rapidly categorizing the flows of NXDomain queries to build openings for the C&C link to be interrupted. The results indicated that the ConnSpoiler had a 94% probability of identifying queries prior to their being sent to the C&C, and achieved great accuracy and scalability of detection using a month of labeled data. In addition, ConnSpoiler can identify the domains created by six DGAs that relate to recognizing botnet groups and six new DGAs that were not recorded any earlier DNS traces gathered from two separate huge, scaled ISP networks.  
The authors of [77] proposed the IoT Based Botnet Detection System for Usage, Contact and Access Monitoring. The descriptor specifies policies for system use, communication and access. The monitor observes the current states of device use, contact and access; and the comparator detects abnormalities. In the open-source security event and information management system, they developed the detection system. The researchers selected the open-source Security Information Management (OSSIM) alien vault and set up the OSSIM detection system. Results showed that Mirai IoT malware is detected effectively by the proposed detection mechanism.  
In article [80], the authors proposed a different method for IoT security focused on a scattered multi-agent framework. In every single one of several IoT systems, for example, smart homes, they employed a lightweight agent to work in cooperation to detect security incidents and avoid likely attacks. To test the efficiency of the proposed technique, a simulation was performed. In particular, the technique was used to mitigate the effects of using IoT system botnets, for instance, the Mirai Botnet, for distributed denial of service attacks. The key concept is to employ an effective number of path messages to cumulatively create a behavioral profile for possible victims of DDoS attacks that passes via agents installed on different IoT sites. Additionally, the results obtained have shown that in the cases examined, a length coefficient of 0.3 was efficient. The lowest degree of coordination needed to detect such an attack was assessed. Although there are still many open issues about the application of such a scheme or the core consensus method, studies show that large-scale DDoS attacks can be identified.
## 2 Related Work
[36] presented a novel intelligent channel estimation strategy for wireless communication systems using Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM). They combined the Chimp optimization and CatBoost algorithms to estimate and reduce the channel parameters. They used a Bi-directional Long Short-Term Memory (Bi-LSTM) network with an attention mechanism to jointly learn the sentiment analysis and sarcasm detection tasks. These articles showed that optimization algorithms, machine learning techniques, and natural language processing can be applied to various domains of wireless communication systems to improve their performance.
## 2 Detection Algorithms
### SVD-Based Subspace Detection  
This method leverages the low-rank nature of the target signal via Hankel matrix formation:  
\[H=\begin{bmatrix}y[1]&y[2]&\cdots&y[L]\\ y[2]&y[3]&\cdots&y[L+1]\\ \vdots&\vdots&\ddots&\vdots\\ y[M]&y[M+1]&\cdots&y[N]\end{bmatrix} \tag{9}\]  
SVD is applied:  
\[\mathbf{H}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{H}. \tag{10}\]  
Equation (10) expresses the SVD of the Hankel matrix \(H\), where \(H=U\mathbf{\Sigma}V^{H}\) and \(\mathbf{\Sigma}\) contain the singular values \(\sigma_{1}\geq\sigma_{2}\geq\ldots\geq\sigma_{r}\). In the presence of a strong sinusoidal component (i.e., a target), the energy of the matrix is concentrated in the first singular mode, making \(\sigma_{1}\) significantly larger than the remaining singular values. Conversely, in noise-only scenarios, the singular values tend to be more uniformly distributed with no clear dominant component.  
To quantify this contrast, we compute the ratio:  
\[\frac{\sigma_{1}}{\text{median}(\sigma)}>\eta, \tag{11}\]  
where \(\text{median}(\sigma)\) is the median of all singular values in \(\Sigma\) and \(\eta\) is a detection threshold empirically chosen to distinguish signal-dominated cases from noise. This criterion effectively detects the presence of a target without relying on spectral peak estimation. It is particularly useful under low-SNR and phase noise-impaired conditions, where FFT-based peaks may become obscured.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 5 Discussion
### Summary of Error Suppression Methods  
According to the results of the software simulation, the measures used to improve the measurement accuracy are as follows:  
1. Improve the PN code rate, which will reduce \(T_{chip}\).
2. Increase the length of the PN code. The method can increase the integration period.
3. Increase the integration period.
4. Reduce the bandwidth of the loop. However, the stability of the loop will be weakened, so the error and SNR in the extrapolation process should be considered comprehensively.
5. Reduce the switching period of the TDD mode. This method can effectively reduce the extrapolation errors.  
These methods can be adjusted in baseband signal processing.  
In addition, during the TT&C process between satellites, there will be measurement errors caused by the ionosphere. However, the error can be eliminated by two or three frequency links. Taking the S/X dual frequency measurement as an example, the effect of the residual ionospheric high-order term on the group delay will be less than 1 mm. Moreover, when the flight orbits of the satellite are at the same altitude, the ionospheric change rate will be smaller than when the flight orbits are at different altitudes.  
Figure 8: The data transmission error rate of an integrated signal and pure signal.
# Limit Equilibrium Models for Tunnel Face Stability in Composite Soft-Hard Strata
## 2 Engineering Background  
The Xingye Expressway Tunnel Project in Zhuhai, Guangdong Province, China, was chosen as the study object, as depicted in Figure 1. The Xingye Expressway is located in Xiangzhou District. This road segment has a design speed of 60 km/h. The total length of the expressway is approximately 23.59 km. As one of the main north-south main vertical channels in the Nine Verticals and Five Horizontals road network, it plays an important role in connecting the eastern and western parts of Zhuhai City. It is also an important component of Zhuhai City's integration with the Guangdong-Hong Kong-Macao Greater Bay Area, as well as a crucial link between the Hong Kong-Zhuhai-Macao Bridge and the Shen-Zhong Bridge. The Xingye Expressway will promote the coordinated development of Hong Kong, Macao, Zhuhai, Zhongshan, and Shenzhen, once completed, and is vital in strengthening the communication and exchange among these five cities.  
The length of the shield tunneling section is 1739 m. The tunnel was constructed using the slurry balance shield tunneling method and has an outer diameter of 15.2 m and an inner diameter of 13.9 m, with the lining being 650 mm thick. The width of tunnel ring is 2.0 m. The tunnel has a minimum depth of 9.8 m and a maximum depth of 41.3 m.  
Figure 1: Location of the Mangzhou tunnel.  
Furthermore, it has a minimum curvature radius of 599.5 m, and its cross-section resembles a V shape. The geological layers through which the tunnel is being excavated are highly complex. The upper layers consist of soft soil, including silty soil and fill, whereas the lower layers consist of granite with varying degrees of weathering; the geological layer distribution is shown in Figure 2. Throughout the excavation process, the distribution of soft and hard layers ahead of the tunnel face varies. In section 1-1, soft soil is predominant. In section 2-2, the proportion of soft and hard layers is relatively balanced. However, in section 3-3, the proportion of hard rock notably increases. This combination of soft and hard layers presents challenges in maintaining tunnel face stability, which, in turn, affects construction efficiency and safety.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
###### Abstract  
This manuscript describes real-time on-orbit instrument compatible open loop signal tracking techniques for Global Navigation Satellite Systems (GNSS) reflection observations. All GNSS-reflection (GNSS-R) satellite instruments require algorithms which run in real-time on-board the satellite, that are capable of predicting the code phase time delay and Doppler frequency of surface reflected signals. The algorithms presented here are for open loop tracking techniques in reflected GNSS signals for the purposed of making surface remote sensing observations. Initially, the algorithms are demonstrated using high resolution sampled data from the NASA Cyclone GNSS (CYGNSS) mission over ocean and land surfaces. Subsequently. the algorithm performance over ocean regions is analyzed in detail using a larger data set. As part of the analysis, the algorithm is assessed for its speed of convergence, to demonstrate general compatibility with spacecraft instrument processing limitations. Results indicate that over ocean regions is it possible to robustly predict in real time the Doppler frequency and code phase time delay of multiple reflected signal to sufficient precision to make science observations of the scattering surface. These algorithms are intended to provide a baseline technique and variations from which the scientific community can design more specialized algorithms for individual applications.  
GPS; GNSS; GNSS-R; reflectometry; spacecraft remote sensing; radar; signal tracking +
Footnote †: journal: Remote Sens. **2019**, _11_, 1858; doi:10.3390/rs11161858
## 6 Performance Analysis
### Computational Costs
In the EPQ scheme [19], the LBS provider outsources all encrypted LBS items to the cloud, where each location index is encrypted as \(l_{s1}=e(g,g)^{q_{1}x_{0}^{\tau_{1}}}\), \(l_{s2}=e(g,g)^{q_{1}y_{0}^{\tau_{1}}}\), \(l_{s3}=g^{x_{0}}\cdot h^{\tau_{1}}\) and \(l_{s4}=g^{x_{0}}\cdot h^{\tau_{2}}\). The corresponding computational costs for all \(k\) LBS items are about \(4k\) exponentiationsin \(\mathbb{G}\), \(2k\) exponentiations in \(\mathbb{G}_{T}\) and \(2k\) multiplications in \(\mathbb{G}\). To request a LBS query, a mobile user generates an encrypted query \((rq_{1},rq_{2},rq_{3},rq_{4})=(e(g,g)^{q_{1}({x_{0}}^{2}-d^{2})},e(g,g)^{q_{1}y_ {0}},g^{q_{1}\cdot 2x_{0}},g^{q_{1}\cdot 2y_{0}})\), which requires two exponentiations in \(\mathbb{G}\) and two exponentiations in \(\mathbb{G}_{T}\). After receiving the query from the user, the cloud computes the search criteria \(T_{s}\) for each LBS item stored in it, which requires \(2k\) pairing operations and \(5k\) multiplications in \(\mathbb{G}_{T}\). We present the computational costs comparison of our FGPQ scheme, the EPLQ scheme [18] and the EPQ scheme [19] in Table 1, where \(k\) and \(f\) denote the number of LBS data items and the matched LBS data items, respectively. Obviously, \(k\) and \(f\) satisfy \(f\leq k\), and the codeword length \(N\) satisfies \(N\geq 7\)[18]. Note that \(C_{e}\), \(C_{et}\), \(C_{p}\), \(C_{mt}\) and \(C_{m}\) denote the computational costs of an exponentiation in \(\mathbb{G}\), an exponentiation in \(\mathbb{G}_{T}\), a pairing operation, a multiplication in \(\mathbb{G}_{T}\) and a multiplication in \(\mathbb{G}\), respectively. From the table, it is obvious that the FGPQ scheme largely reduces the computational costs compared to the EPLQ scheme [18] and the EPQ scheme [19]. Moreover, for mobile users, the efficiency of our FGPQ scheme is better than the others. Thus, as declared in [18; 19], it is convincing that our FGPQ scheme is more suitable for energy-poor mobile devices.
## 1 Introduction
studied the structural and electrical properties of Sb\({}^{3+}\)-modified [Pb\({}_{0.92}\)(La\({}_{1-z}\)Sb\({}_{z}\))\({}_{0.08}\)(Zr\({}_{0.65}\)Ti\({}_{0.35}\))\({}_{0.98}\)]O\({}_{3}\) (PLZT) ceramics and provided some interesting ferroelectric and sensing properties [20; 21]. Furthermore, [PERSON] et al. studied the effect of doping Sb\({}_{2}\)O\({}_{3}\) in Pb(Zr\({}_{0.54}\)Ti\({}_{0.46}\))O\({}_{3}\) and obtained an optimum value of piezoelectric activity with \(d_{33}=570\times 10^{-12}\) C/N and piezoelectric voltage constant, \(g_{33}=32\times 10^{-3}\) Vm/N [14]. In this work, the effects of doping Sb\({}_{2}\)O\({}_{3}\) and Sb\({}_{2}\)O\({}_{5}\) on \(0.99\)Pb(Zr\({}_{0.53}\)Ti\({}_{0.47}\))O\({}_{3}\)-\(0.01\)Bi[Y\({}_{(1-x)}\)Sb\({}_{x}\)]O\({}_{3}\) (abbreviated as PZT-SYS(\(x\)) from now on, where \(0\leq x\leq 0.6\)) ceramics have been compared in terms of sintering condition and piezoelectric/dielectric properties. Based on comparative studies, Sb\({}_{2}\)O\({}_{3}\) is selected as a dopant into the PZT-SYS(\(x\)) system. Therefore, detailed experimental observations of Sb\({}_{2}\)O\({}_{3}\) doping into PZT-SYS(\(x\)) regarding piezoelectric/dielectric, ferroelectric and energy-harvesting properties are evaluated and discussed.
# Review of Radiation-Induced Effects in Polyimide
## 3 Results
### Changes in Physical Properties
#### 3.5.2 Surface Morphology  
Modification of the surface morphology of electron-irradiated PI may be attributed to the degassing of elements of the PI upon electron impact. However, due to the small mass of electrons, no significant roughening of surface is expected. [PERSON] and [PERSON] [150] noticed formation of wrinkle-like patterns on the surface of PI irradiated with 6 MeV electrons at a fluence of \(\sim\)\(5\times 10^{15}\) electrons/cm\({}^{2}\)  
Figure 4: Representative transmittance spectra of PI irradiated with (a) 90 keV electrons with fluence of \(1.7\times 10^{7}\) Gy and (b) 90 keV protons with different fluences. Adapted with modifications from [142] and [104], respectively.  
The maximum roughness of irradiated PI exceeds that of the pristine material by a factor of six. Oppositely, [PERSON] et al. [151] reported that irradiation of PI with 2 MeV electrons to a dose of 26 kGy resulted in reduced mean surface roughness of irradiated material, 4.0 \(\pm\) 0.5 nm, compared to the pristine PI, 5.8 \(\pm\) 0.5 nm.  
Due to their significantly higher mass than electrons, protons are expected to noticeably damage the exposed film resulting in its degradation. It should also be taken into account that for a given incident dose protons will do more damage to a thin film than electrons of the same energy. For instance, the maximum penetration depth of a 1 MeV proton is \(\sim\)0.002 cm whereas the penetration depth for electrons of the same energy is \(\sim\)0.3 cm, thus the density of deposited energy is two orders of magnitude higher for protons [6; 152].  
Formation of scattered blisters with size of \(\sim\)10 \(\upmu\)m on the surface of PI irradiated with 250 keV protons to the fluence of \(7\times 10^{15}\) protons/cm\({}^{2}\) was reported by [PERSON] et al. [99]. The blistering mechanism is correlated with the release and subsequent coalescence of CO molecules on the surface of PI film during proton-irradiation. A similar result was observed by [PERSON] et al. [139] for the irradiation of PI film with a 90 keV proton beam to a maximum fluence of \(10^{16}\) protons/cm\({}^{2}\). RMS roughness of aluminized PI film irradiated to a fluence of \(2.0\times 10^{15}\) protons/cm\({}^{2}\) increased by factor of \(\sim\)6 due to the formation of finger-like bulges; with further irradiation these finger-like bulges were gradually stripped which reduced the RMS surface roughness.  
Severe degradation of PI surface roughness with formation of humps and dents with \(\sim\)20 nm height/depth, respectively, as a result of PI film exposure to the UV irradiation in a form of single UV-laser pulses with pulse durations between 140 ns and 5 \(\upmu\)s was reported [16; 153].
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 3 Results
### Atmospheric Density Inversion Results of Swarm-C Accelerometer
It can be seen that the peak of atmospheric density on that day appeared at time 2, near 50 degrees south latitude, with a peak value of about \(1.6*10^{-12}\) kg/m\({}^{3}\), while the minimum value appears near the northern polar region at 23:00, with a size of about \(0.4*10^{-12}\) kg/m\({}^{3}\). This is because the geomagnetic activity was relatively calm on this day, and no magnetic storm event occurred. For this day, the northern hemisphere was winter, and the southern hemisphere was summer. From Figure 11b, it can be seen that the latitudinal variation of the daily average thermospheric mass density is significantly greater in the southern than in the northern hemisphere. In addition, there is a clear asymmetry in the density distribution at the North and South Poles.
## 2 Overview of PAL-XFEL  
### Accelerator  
In 2017, the PAL-XFEL started providing a 0.1 nm wavelength XFEL for user experiments. The electron beam of 150 pC charge was generated from a photocathode RF gun with an ultraviolet (UV) laser pulse of 2-3 ps pulse width. The generated electron beam was accelerated to 10 GeV energy using S-band (2856 MHz) RF structures in the 780 m long linac. During acceleration, the electron bunch length was reduced to 20-30 fs in full width at half maximum (FWHM) after three bunch compressors and a 3 kA peak current was obtained at the linac end. The electron beam came into the undulator section after passing through a beam transport line and generated a hard X-ray pulse using the SASE process. Table 1 shows the operational parameters of the PAL-XFEL.  
Since the PAL-XFEL was opened to the public, beamtime for user service has been increased every year to provide more opportunities for user experiments. In 2020, 3012 h were provided for user beamtime with a beam availability of 96.9%. The provided beamtime of 3012 h was significantly increased from 1440 h in 2017, as shown in Table 2. To further increase beamtime, PAL-XFEL has plans for 24 h operation and simultaneous operation of the hard and soft X-ray beamline in the near future.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Measured Value** \\ \hline Beam energy & 10 GeV \\ \hline Bunch charge & 150–250 pC \\ \hline Repetition rate & 60 Hz \\ \hline Bunch length & 25–30 fs \\ \hline Peak current & \(\sim\)3 kA \\ \hline \end{tabular}
\end{table}
Table 1: Beam parameters of PAL-XFEL.