esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C  
In this appendix we derive (26). Let us start from the definition of the covariance matrix. Each element of the covariance matrix is then given by  
\[\left[\mathbf{C_{T}}\right]_{ij} =\text{cov}\left(T_{2i},T_{2j}\right) \tag{111}\] \[=E\left[\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2i}\frac{1}{K}\sum_{k=0 }^{K-1}y_{k}^{2j}\right]-E\left[\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2i}\right]E \left[\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2j}\right]. \tag{112}\]Now, consider the first term of the right-hand side. After some manipulation we get  
\[E\left[\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2i}\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2j} \right]=\frac{1}{K^{2}}\sum_{k=0}^{K-1}E\left[y_{k}^{2i+2j}\right], \tag{101}\]  
where we have used the independence assumption of the Gaussian noise. Similarly, we obtain that the second term on the right-hand side of (101) is  
\[E\left[\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2i}\right]E\left[\frac{1}{K}\sum_{k=0} ^{K-1}y_{k}^{2j}\right]=\frac{1}{K^{2}}\sum_{k=0}^{K-1}E\left[y_{k}^{2i}\right] E\left[y_{k}^{2j}\right]. \tag{102}\]  
Putting all together, the covariance (100) can written as  
\[\text{cov}\left(T_{2i},T_{2j}\right)=\frac{1}{K}\sum_{k=0}^{K-1}\left(\frac{1 }{K}E\left[y_{k}^{2(i+j)}\right]-\frac{1}{K}E\left[y_{k}^{2i}\right]E\left[y_{ k}^{2j}\right]\right). \tag{103}\]  
By using (3) we can explicitly write the first term as  
\[\frac{1}{K}\sum_{k=0}^{K-1}E\left[y_{k}^{2(i+j)}\right]=\sum_{p=0}^{i+j} \binom{2\left(i+j\right)}{2p}A^{2p}\left(\frac{1}{K}\sum_{k=0}^{K-1}x_{k}^{2p} \right)(2(i+j)-2p-1)!!\sigma^{2(j+j-p)}. \tag{104}\]  
Since the term  
\[\frac{1}{K}\sum_{k=0}^{K-1}x_{k}^{2p}, \tag{105}\]  
as \(K\) grows to infinity, it is asymptotically equal to the moment of order \(2p\) of \(x_{k}\) when the initial phase \(\phi\) is random. Then, using (100) we have that asymptotically  
\[\frac{1}{K}\sum_{k=0}^{K-1}E\left[y_{k}^{2(i+j)}\right]\simeq M_{i+j}=\sum_{p =0}^{i+j}\binom{2\left(i+j\right)}{2p}A^{2p}\frac{(2p-1)!!}{(2p)!!}\left(2(i+ j)-2p-1\right)!!\sigma^{2(j+j-p)}. \tag{106}\]  
The remaining term is written as  
\[\frac{1}{K}\sum_{k=0}^{K-1}E\left[y_{k}^{2i}\right]E\left[y_{k}^{ 2j}\right] =\frac{1}{K}\sum_{k=0}^{K-1}\sum_{p=0}^{i}\binom{2i}{2p}A^{2(i-p) }x_{k}^{2(i-p)}E\left[w_{k}^{2p}\right]\sum_{q=0}^{j}\binom{2j}{2q}A^{2(j-q)} x_{k}^{2(j-q)}E\left[w_{k}^{2q}\right] \tag{107}\] \[=\sum_{p=0}^{i}\sum_{q=0}^{j}\binom{2i}{2p}\binom{2j}{2q}A^{2(i+j -p-q)}\left(\frac{1}{K}\sum_{k=0}^{K-1}x_{k}^{2(i+j-p-q)}\right)E\left[w_{k}^{ 2p}\right]E\left[w_{k}^{2q}\right].\]  
Now, asymptotically we have  
\[\left(\frac{1}{K}\sum_{k=0}^{K-1}x_{k}^{2(i+j-p-q)}\right)\simeq E\left[x_{k}^ {2(i+j-p-q)}\right]=\frac{(2\left(i+j-p-q\right)-1)!!}{(2\left(i+j-p-q\right) )!!}. \tag{108}\]  
Hence, the term \(D_{i,j}\) is  
\[\sum_{p=0}^{i}\sum_{q=0}^{j}\binom{2i}{2p}\binom{2j}{2q}A^{2(i+j-p-q)}\frac{ (2\left(i+j-p-q\right)-1)!!}{(2\left(i+j-p-q\right))!!}\left(2p-1\right)!! \left(2q-1\right)!!\sigma^{2(p+q)}. \tag{109}\]
## 5 Conclusions  
A real-time full-state EKF vertical protection level (VPL) method against IMU sensor faults to assure navigation integrity is proposed in [18]. Based on the method, the final error overbounding equation against the IMU faults for the error-state EKF estimation can be expressed in an identical manner. The IMU faults propagation process was analyzed in this paper for the error-state EKF of tightly coupled IMU/GNSS integrated architecture. Accordingly, the error overboundings against the IMU faults was obtained. The error overboundings for the full-state and the error-state EKFs presented the same form in theoretical derivation, consisting of terms relating to the EKF innovation, the estimated bias, and the remaining position error. The simulation result shows that the derived error overboundings can fit the position error, reflecting the position error change caused by the IMU faults. Moreover, the error overboundings of the error-state EKF were the same as that of the full-state EKF in mathematical simulation. As the error-state EKF is more practical and its error overboundings calculation time was lower, the error-state EKF is recommended to assure the integrity of the IMU/GNSS integrated architecture for an UAV. The proposed error overbounding method with real collected data will be carried out in future work.  
[PERSON] and [PERSON] conceived of and designed the research. [PERSON] and [PERSON] performed the theoretical derivation, experiments, and analyzed the results. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] were responsible for data acquisitions and data processing. [PERSON] was responsible for project management. [PERSON], [PERSON], [PERSON], and [PERSON] finalized the manuscript version to be published.  
The work was carried out with financial support from the National Natural Science Foundation of China (Grant Nos. 61871012 and U1833125), the major project on the second satellite navigation system of China (Grant No. GFZX0305030106) and the Shaanxi Key Laboratory of Integrated and Intelligent Navigation (SKLIIN-20190205).  
The authors would like to thank many people in the National Key Laboratory of CNS/ATM for their advice and interest.  
The authors declare no conflict of interest.
## 2 Project Overview
### Construction Safety Control Technology
#### 3.6.2 Optimization and Control of Shield Tunneling Parameters  
The setting of excavation parameters during shield tunneling construction is a prerequisite for ensuring the progress of shield tunneling construction and ground settlement control. To minimize the impact of shield tunneling passing through the elevated bridge, it is recommended to pass through the disturbance range of the bridge continuously and quickly. Before the shield tunneling passes through the Beijing-Shanghai high-speed railway, the section below the Beijing-Taiwan expressway was selected as the shield tunneling simulation test section. At the same time, based on the measured data of the experimental section, we optimized various propulsion parameters and strictly controlled the propulsion speed of the shield machine. We ensured synchronous grouting volume and secondary grouting and tried to reduce the ground settlement caused by shield tunneling. To ensure the accuracy of excavation parameters, combined with domestic and foreign shield tunneling construction experience and our own construction examples, the excavation parameters of shield tunneling were calculated and selected. The specific details are shown in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline Parameter & Trial excavation standards \\ \hline Soil pressure & Upper soil pressure 1.5–2.0 bar \\ \hline Total thrust & 800–2000 T \\ \hline Knife disc torque & 1000–2500 KN-m \\ \hline Advance speed & 30–50 mm/min \\ \hline \end{tabular}
\end{table}
Table 4: Adjustment of shield tunneling parameters.  
Figure 7: Isolation piles and sleeve valve pipes.
## 2 Literature Review
### Machine Learning-Based Approaches for the Identification of Malicious Nodes
Typically, the evaluation of routing efficiency hinges upon key metrics such as packet delivery ratio, end-to-end delay, hop count, and packet drop ratio. However, a sophisticated, security-oriented infrastructure-based routing approach focusing on robust network throughput remains absent. Consequently, our proposed method offers a productive and secure routing paradigm for VANETs. By fusing a fitness function and integrating forward and backward control packets for short path prediction alongside a supervised deep learning model for malicious node detection, this approach significantly enhances routing efficiency and reliability regarding average end-to-end delay, hop count, and network throughput [38]. Despite the prominence of VANET research, particularly in data delivery and routing, the field remains relatively underexplored regarding security. While the literature encompasses many techniques, including AODV protocols, TAD-HOC routing protocols, cluster-based routing, Dyte algorithms for optimizing packet delivery efficiency, fuzzy logic-based routing decisions, and similar approaches, these predominantly focus on routing solutions. These approaches often lack a comprehensive consideration of potential attacks on the vehicles involved in the routing process, instead concentrating on statistical and mathematical results [39]. Moreover, they overlook the integration of machine learning (ML) techniques, such as ANNs. In light of these limitations, we seek to address the gaps that persist within the existing approaches. Our results endeavor to present the ANN-based intelligent secure routing protocol for vehicular ad hoc networks. This protocol delivers substantial enhancements across various facets of network performance, particularly in detecting black hole attacks, end-to-end delay for packet transmission, transmission efficiency, hop count, and network throughput. The findings presented in the concluding section, Section 5, underscore the comprehensive efficiency and enhancements of our proposed network solution.
# Temporal Multimodal Data-Processing Algorithms Based on Algebraic System of Aggregates
## 7 Discussion  
Let us consider a use case in the area of healthcare that demonstrates the practical use of the proposed approach of multimodal data processing (Figure 21). The healthcare use case assumes that the data describing the patient's health status can be obtained from multiple sources, including medical investigation devices (MRI scanners, CT scanners, ECG machines, etc. ), manual medical measurement tools and devices (pulse oximeters, thermometers, sphygmomanometers, etc. ), test systems (e.g., blood testing), and medical documentation records (treatment events). Data sets collected from these sources are temporal multimodal and semantically interconnected as they describe different features of the same object that is a patient's organism. The multi-image concept and ASA operations can be employed to consolidate such a compound data collection and implement the logic of its processing. Let us consider an example which demonstrates the formation of multi-image from several timewise data sequences according to the algorithm proposed in Section 6 and the algorithms of the ASA operations presented in Section 5. Suppose that the patient's blood pressure readings (systolic and diastolic), pulse rate, and oxygen saturation level are being measured several times a day. A pair of blood pressure values \(\left(a_{i}^{press(s)},a_{i}^{press(d)}\right)\) are to be received from the sphygmomanometer, and the values of pulse rate \(a_{i}^{pulse}\) and oxygen saturation level \(a_{i}^{start}\) are to be received from a pulse oximeter. The number of measurements conducted by the sphygmomanometer is \(n_{1}\); the number of measurements conducted by the pulse oximeter is \(n_{2}\). As a result of patient health parameters monitoring, the following multi-image must be obtained:  
\[I=\llbracket T,M_{press(s,d)},M_{pulse},M_{start}\mid\langle t_{i}\rangle_{i=1}^ {n_{1}+n_{2}},\left\langle\left(a_{i}^{press(s)},a_{i}^{press(d)}\right)_{i=1} ^{n_{1}}\right\rangle,\ \left\langle a_{i}^{pulse}\right\rangle_{i=1}^{n_{2}}, \langle a_{i}^{start}\rangle_{i=1}^{n_{2}}\rrbracket.\]  
Figure 21: An example of temporal multimodal data processing context. According to the algorithm of multi-image formation, the received data are represented as two partial multi-images--a partial multi-image \(I_{1}\) containing values received from the first device (sphygmomanometer) and a partial multi-image \(I_{2}\) containing values received from the second device (pulse oximeter):  
\[I_{1}=\llbracket T,M_{press(s,d)}\ \mid\ \langle\$:30,\ 12:05,16:10,\ 21:00 \rangle,\Big{\langle}(160,78),(172,81),(155,76),(158,75)\rangle\llbracket\]  
\[I_{2}=\llbracket T,M_{pulse},M_{satur}\ \mid\ \langle\$:30,\ 15:08,\ 21:00\rangle, \langle\$3,75,66\rangle,\langle\$,95,98,96\rangle\llbracket\]  
To obtain the multi-image, these partial multi-images must be properly merged.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ANN Artificial Neural Network  
AWGN Additive White Gaussian Noise  
BER Bit Error Rate  
BPSK Binary Phase Shift Keying  
CDP Constructed Data Pilots  
CFR Channel Frequency Response  
CP Cyclic Prefix  
CPU Central Process Unit  
C-ELM Complex Extreme Learning Machine  
C-V2X Cellular Vehicular to Anything  
DC Direct Current  
DL Deep Learning  
ELM Extreme Learning Machine  
ETSI European Telecommunication Standards Institute  
FFT Fast Fourier Transform  
FPGA Field-Programmable Gate Array  
GPU Graphics Processing Unit  
IFFT Inverse Fast Fourier Transform  
LS Least Squares  
ML Machine Learning  
MMSE Minimum Mean-Square Error  
OFDM Orthogonal Frequency Division Multiplexing  
PHY Physical Layer  
RAM Random Access Memory  
SS Semi-Supervised  
SS-ELM Semi Supervised Extreme Learning Machine  
STA Spectral Temporal Averaging  
SNR Signal to Noise Ratio  
VCS Vehicular Communication Systems  
V2V Vehicle to Vehicle  
V2I Vehicle to Infrastructure  
WiFi Wireless Fidelity
# Robust Optimization of Transport Organization for China Railway Express
## 4 Example Analysis
### Summary of This Section  
In this section, the [PERSON] robust optimization theory is used to transform the China-Europe freight transport organization optimization model, in which the freight transport demand is subject to the box-type uncertainty set distribution, into a mixed integer linear programming model with the minimum train operating cost and container operating cost as the objective function and with robust horizontal control parameters. And the above section of the establishment of the China-Europe freight train transport organization network case is analyzed and solved.  
Through the model solution and parameter sensitivity analysis, it can be seen that when the robustness level \(\Gamma\) gradually increases from 0, that is, the number of nodes in the transport network where transport demand fluctuations occur also increases, the conservatism of the model will increase, and the model will maintain the stability of the original transport scheme by increasing the uncertain demand cost. When the robustness level is \(\Gamma=16\), all freight demand nodes in the transport network will produce uncertain fluctuations, and the uncertainty cost in the network reaches the highest point. When the robustness level is \(\Gamma>16\), since there are only 16 freight demand nodes at most in the example, the uncertainty cost will no longer be increased.  
In the case of a robustness level \(\Gamma=5\), the freight disturbance range \(\epsilon_{i}\) of each node is analyzed. When the disturbance range \(0.0\leq\epsilon_{i}\leq 0.6\), the model will increase the uncertainty cost to maintain the stability of the original transport scheme. When the disturbance range is \(0.7\leq\epsilon_{i}\leq 1.0\), the increase in the uncertainty cost will destroy the optimality of the original transportation scheme. At this time, the model will re-search for another optimal transportation organization scheme under the condition of the current freight disturbance range.
## 3 Methodology
### Occlusion-Aware Attention Module  
When the target partially overlaps with its adjacent area, it results in the disappearance of local characteristics, thus making the detection, identification, and pinpointing of the target more complex. In response to this problem, an OAAM is created to tackle the difficulties of local overlap and loss of target features amid intense background noise and partial blockage. As shown in Figure 3, the first part of the OAAM integrates output features from patches of different sizes to adapt to varying degrees of local overlap. Specifically, this is achieved by serially applying three 3 \(\times\) 3 convolutions to integrate network output features across patch sizes of 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7. Motivated by the citation [36], our method utilizes depthwise separable convolutions [37], integrating residual links, to determine the importance of different channels. While depthwise separable convolutions can understand the significance of various channels and lessen parameters, they fail to consider the informational interconnections among channels. As a remedy for this deficit,  
Figure 2: Schematic diagram of the iAFF. Application considerations for the MS-CAM block: the solid arrow path on the left logically represents the application of the weight M(.), while the dashed arrow path on the right logically represents the application of the weight (1\(-\)M(.)).  
the results from various depthwise convolutions are then merged using pointwise (1 \(\times\) 1) convolution, as depicted in Figure 3. Subsequently, a dual-layer fully integrated network is utilized to amalgamate data across various channels, thereby improving the inter-channel connections within the network. The fully connected layers process outputs through an exponential function, expanding the value spectrum from [0, 1] to [1, e]. Exponential normalization creates a steady correlation in mapping, thereby boosting the detection output's robustness against positional inaccuracies caused by local overlaps. In the end, the performance of the OEAM module improves when the primary features are integrated as the focal point, thereby enhancing the model's capacity to handle local overlaps and manage the disappearance of target features in the presence of considerable background interference and partial occlusion.
## 3 Results and Discussion  
### Relationship between Radial Load and Skidding Damage  
The radial load will directly affect the performance of a rolling bearing. When the radial load is too small, and the roller speed is high, the oil film bearing area does not form enough oil film traction force, thus causing strong skidding between the key contact pairs of the bearing. However, when the radial load is too large, the oil film in the bearing area will rupture, resulting in dry friction between the key contact pairs of the bearing, in turn causing skidding damage [25]. From Figure 8, we can analyze that with the increase in the radial load, when the load fluctuates widely in the test, the impact component induced by roller skidding becomes more obvious in the vibration response of the rolling bearing and the impact energy increases. At this time, the rolling bearing was undergoing skidding damage. The value of the radial load which from 3 s to 12 s fluctuates heavily, which is seen from Figure 10. At this time, the contact part between the inner ring and the roller fluctuates heavily in the radial load, resulting in skidding damage. The motorized spindle has been closed before 15 s, the roller and inner ring gradually reduces, and the radial load gradually tends to be stable.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
###### Abstract  
5G is expected to deal with high data rates for different types of wireless traffic. To enable high data rates, 5G employs beam searching operation to align the best beam pairs. Beam searching operation along with high order modulation techniques in 5G, exhausts the battery power of user equipment (UE). LTE network uses discontinuous reception (DRX) with fixed sleep cycles to save UE energy. LTE-DRX in current form cannot work in 5G network, as it does not consider multiple beam communication and the length of sleep cycle is fixed. On the other hand, artificial intelligence (AI) has a tendency to learn and predict the packet arrival-time values from real wireless traffic traces. In this paper, we present AI based DRX (AI-DRX) mechanism for energy efficiency in 5G enabled devices. We propose AI-DRX algorithm for multiple beam communications, to enable dynamic short and long sleep cycles in DRX. AI-DRX saves the energy of UE while considering delay requirements of different services. We train a recurrent neural network (RNN) on two real wireless traces with minimum root mean square error (RMSE) of 5 ms for trace 1 and 6 ms for trace 2. Then, we utilize the trained RNN model in AI-DRX algorithm to make dynamic short or long sleep cycles. As compared to LTE-DRX, AI-DRX achieves 69% higher energy efficiency on trace 1 and 55% more energy efficiency on trace 2, respectively. The AI-DRX attains 70% improvement in energy efficiency for trace 2 compared with Poisson packet arrival model for \(\lambda=1/20\).  
discontinuous Reception; multiple beam communications; artificial intelligence; energy efficiency; 5G; wireless communications +
Footnote †: journal: Accepted: 9 July 2019; Published: 11 July 2019
## 1 Introduction
Formation control of multiple UAVs based on DDS middleware was introduced in [24; 25; 26]. DDS middleware was developed to solve data loss issues between multiple UAVs as well as the interoperability issues that occur with different UAV platforms. As a result, using the DDS middleware approach improved portability, flexibility, and reliability significantly. Researchers are becoming more interested in the secure consensus of multi-agent systems, as evidenced by the preceding literature review. However, most of the multi-agent systems' secure consensus literature available has focused solely on attack resilient control or detecting attacks. There is a scarcity of studies on the secure formation of multi-agent systems. The following are the major contributions of this study:  
* A robust navigation framework for multiple UAVs' secure formation control has been designed. * A robust control security level is built to handle the effect of cyberattacks. * Reliable QoS middleware for extra cyber security and sharing information has been built. The rest of the paper is arranged as follows. The preliminaries of DDS middleware, graph theory techniques, DoS attacks, and quadrotor dynamics are addressed in Section 2. The secure formation control of multiple UAVs utilizing linear matrix inequalities (LMIs) and L1 approach are discussed in Section 3. Section 4 presents and discusses the simulation results. Finally, the conclusions, as well as the final recommendations, have been noted.
## 4 Experiments and Results  
### Data Collection  
A short baseline experiment was conducted from day of year (DOY) 296 to 311, 2019, on the roof of the School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, China. The observation data of the BDS B1 signal were collected using two continuously operating reference stations. The sampling frequency of the observation data was 1 Hz, and the satellite cut-off elevation angle was 15\({}^{\circ}\). Figure 4 shows the observation environment around the stations. The two stations were close and had no elevation difference. It can be considered that the double-differenced technique eliminates atmospheric residuals. Therefore, the stations are only affected by the multipath and the noise.  
For the ASF method, the ORT time shift of each satellite is used to model the multipath. For the SD-MHM method, the DOY 296-302 residuals are used to model the multipath, the full constellation period. Finally, the coordinates calculated by the long-term observation data of the stations are used to evaluate the accuracy.