esa-sceva/satcom-chunk-collection huggingface dataset:
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout  
Base coverage urban, micro-cell, and rural/high-speed scenarios do not specifically take topographical data into account. On a regular grid, base stations are organized in a hexagonal arrangement. At Samabula Campus of Fiji National University, a straightforward hexagonal design shown in Figure 5 is considered. This includes three cells per site, together with definitions of the fundamental geometry of the antenna. There will be a wraparound simulation with 19 sites and three cells per site. All users are distributed equally across the system. The channel bandwidth required in a link direction that is employed in the simulation is defined when the traditional frequency reuse method is considered.  
Consider the urban macro-cell scenario column in Table 2 for full buffer services as an example. The simulation bandwidths for FDD-based systems are \(10+10\) MHz, and they are 20 MHz for TDD-based systems. The spectrum produced by an FDD proposal with a frequency reuse ratio of 3 is \(3\times 10=30\) MHz for each link, or 60 MHz considering both up and downlink.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Deployment** & **Carrier** & **Bandwidth (BW) and** **Numerology** & **Split Option 2 Fronthaul Rate** & **Split Option 7 Fronthaul** \\ \hline
5G: macro; \(4\times 4\) MIMO, 3 sector, 1 carrier & Sub-6 GHz & SCS = 60 kHz & \(\sim\)4 Gbps & \(\sim\)34 Gbps (\(\sim\)12 Gbps/sector) \\  & & (u = 2) & & (\(\sim\)12 Gbps/sector) \\
5G: macro; \(8\times 8\) MIMO, 3 sector, 1 carrier & Sub-6 GHz & BV = 100 MHz; & \(\sim\)8 Gbps & \(\sim\)67 Gbps \\  & & (u = 2) & & (\(\sim\)23 Gbps/sector) \\
5G: small cell; \(4\times 4\) MIMO, 1 sector, 1 carrier & Sub-6 GHz & SCS = 60 kHz & \(\sim\)2 Gbps & \(\sim\)12 Gbps (\(\sim\)89 Gbps/switch \\  & & (u = 2) & & for 8 antenna sites) \\
5G: small cell; \(16\times 16\) MIMO, 1 sector, 1 carrier & Sub-6 GHz & BW = 100 MHz; & \(\sim\)6 Gbps & \(\sim\)45 Gbps (\(\sim\)355 Gbps/switch \\  & 10 carrier & 10 carrier & SCS = 60 kHz & \(\sim\)6 Gbps & for 8 antenna sites) \\  & & (u = 2) & & \\
5G: small cell; \(64\times 64\) array & & BW = 100 MHz; & \(\sim\)39 Gbps (\(\sim\)309 Gbps/switch for & - \\  & & (u = 3) & & 8 antenna sites) \\
5G: small cell; \(128\times 128\) array & & BW = 100 MHz; & \(\sim\)78 Gbps (\(\sim\)618 Gbps/switch for & - \\  & & (u = 3) & & 8 antenna sites) \\ \hline \hline \end{tabular}
\end{table}
Table 1: 5G fronthaul rate summary for certain simple use cases.
## 3 Acsa-Olta  
It is known that the positioning accuracy of GNSS is directly related to the satellite pseudo range accuracy. Since pseudo range is calculated from the code phase measurement, the code phase measuring accuracy of open loop tracking is concerned in this section. Firstly, we study the code phase measurement errors of open loop tracking with different code search grid sizes and the equivalent weight pseudo range error (EWPRE) is proposed to obtain the optimal grid size. Then, the code phase measuring error of different measurement calculation methods are assessed and compared with each other. Finally, based on the analysis, the adaptive correlation space adjusted open loop tracking Algorithm (ACSA-OLTA) is proposed to improve the pseudo range measuring accuracy. Since carrier frequency is much larger than code rate, the effect of carrier Doppler frequency error on the code phase measurement accuracy is little. Moreover, considering the carrier Doppler frequency estimation error is really small, it is ignored and assumed to be zero in the following.
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 3 Proposed Method  
Figure 1 depicts the structure of the proposed method. First, we linearly interpolate a different length video \(x\) into a fixed length video \(x_{\text{fix}}\). Next, we calculate optical flows \(x_{\text{opt}}\) from \(x_{\text{fix}}\) and then throw away the last frame of \(x_{\text{fix}}\) to match the length of \(x_{\text{fix}}\) and \(x_{\text{opt}}\):  
\[x=[i_{1},i_{2},i_{3},\dots,i_{M}],\ \ \text{image}\ i\in\mathbb{R}^{H \times W\times C}, \tag{1}\] \[x_{\text{fix}}=[i_{1},i_{2},i_{3},\dots,i_{N}],\ \ \text{throw away}\ i_{N+1}\ \text{for matching},\] (2) \[x_{\text{opt}}=[o_{1},o_{2},o_{3},\dots,o_{N}],\ \ \text{optical flow}\ o\in\mathbb{R}^{H \times W\times C}, \tag{3}\]  
where the sequence length \(M\) depends on the data sample, \(N\) is the desired number of lengths, \((H,W)\) is the resolution of the video, and \(C\) is the number of channels. We explain this preprocessing in detail later.  
Afterward, grayscale or color images \(x_{\text{fix}}\) and optical flows \(x_{\text{opt}}\) are independently passed through two transformer backbones, face backbone \(f\) and motion backbone \(g\). These backbones have the same structure but do not share parameters to extract \(k\)-dimensional feature vectors, \(z_{\text{face}}\) and \(z_{\text{motion}}\). These vectors are then transformed to \(z_{\text{fusion}}\) via concatenation:  
\[z_{\text{face}}=f(x_{\text{fix}}),\ \ z_{\text{face}}\in \mathbb{R}^{k}, \tag{4}\] \[z_{\text{motion}}=g(x_{\text{opt}}),\ \ z_{\text{motion}}\in \mathbb{R}^{k},\] (5) \[z_{\text{fusion}}=\text{concat}([z_{\text{face}},z_{\text{motion} }]),\ \ z_{\text{fusion}}\in\mathbb{R}^{2k}. \tag{6}\]  
Finally, we push \(z_{\text{fusion}}\) into the classifier \(h\) composed of the fully connected layer, followed by the SoftMax layer, to obtain a class score \(s\) and use cross entropy loss using \(t\in\mathbb{R}^{c}\) for training:  
\[s=h(z_{\text{fusion}}),\ \ s\in\mathbb{R}^{c}, \tag{7}\] \[\mathcal{L}_{\text{ce}}(s,t)=-\sum_{i=1}^{c}t_{i}\log(s_{i}),\ \ t\in \mathbb{R}^{c}, \tag{8}\]  
where \(c\) is the number of target classes and subscript \(i\) means the position of the elements in the vector.  
Figure 1: Structure of proposed method.
## 2 3-D Numerical Simulations  
### Model Size and Parameters  
The numerical models used for the simulation were constructed in ADINA. The analytical model shown in Figure 1 refers to the specific dimensions of a typical section of a Chinese railway structure. Figure 2 shows the specific dimensions of the calculated model, including the rail, concrete track slab, roadbed, subgrade, and foundation, from top to bottom, which are 400 m, 94 m, and 40 m, respectively. The input parameters for numerical simulations are listed in Table 1, referring to Zeng [27]. In the transient dynamic analysis, the damping coefficient of the structural system was given as the input parameter of the model, by means of modal analysis. In this study, it was assumed that the damping force of the structure was equal to the equivalent viscous damping and proportional to the displacement velocity and strain velocity. In this case, the Rayleigh linear combination method was used for the numerical analysis, to calculate the damping coefficient. It should be noted that the corresponding parameters for the train speed and embankment stiffness (i.e., roadbed and subgrade) should be changed in the parametric analysis. Moreover, to evaluate the effects of train speed and embankment stiffness on the dynamic response of the high-speed railway formation, three different train speeds (\(v\) = 56 m/s (200 km/h), 83 m/s (300 km/h), and 112 m/s (400 km/h)) were analyzed, since the current operating speed of Chinese high-speed railways has reached 350 km/h. In addition, three different embankment stiffnesses (roadbed stiffness of 105 MPa and subgrade stiffness of 35 MPa (\(E_{1}\)); roadbed stiffness of 150 MPa and subgrade stiffness of 50 MPa (\(E_{2}\)); and roadbed stiffness of 225 MPa and subgrade stiffness of 75 MPa (\(E_{3}\))) were selected, according to previous research by [PERSON] [27].
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 4 The Joint Positioning Method of Angle and Distance  
In complex environments, occlusion has a significant impact on the performance of wireless signal measurement, and it is necessary to identify non-line of sight effects. In this paper, an NLOS recognition method based on comprehensive feature dynamic hierarchical optimization is adopted to effectively identify the propagation state of wireless signals on each path, and finely measure the observations under line-of-sight paths [24]. This article uses an efficient MUSIC algorithm based on fourth-order cumulants for angle search, and performs joint positioning calculation after obtaining angle information [25].
# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 4 Experimental Analysis
### Final Configuration
Scenarios in S1 were constant, thus its results were the best ones in terms of EER. Using two-days in training decreased the metrics slightly on the first day of acquisition  
Figure 8: Performance results using data from S1. Figure 7: Performance results using data from S1 and S2. Figure 9: Performance results using data from S2. but affected more positively when considering visits on a different day (i.e., from 2.70% to 0.24% for D2V2). When dealing with S2, results are always improved to those obtained under one-day enrollment. Results for D1V1 barely changed as they went from 0% to 0.03%. However, in the most dramatic change of scenario (D2V2 in S2), the EER decreased 64%. In S2 the scenario was the same but varying the day, and the EER got decreased by 91%. This result was a dramatic change in performance considering how the ECGs vary between days and physical activity. Using S1 and S2 jointly for training provided results that ranged between those in S1 and S2 independently, proving that two-days data enrollments could result in a great generalization even when dealing with heterogeneous data in recognition. Comparison with Previous Works for the Same Database  
Works related to the applied database were limited due to its privacy. However, a similar analysis was provided in a previous work [36], where subset S2 with MLP classifiers was employed. The enrollment was longer, with 187 QRS complexes for the best results, and it required the first differentiation of the QRS. The performance for S1 and S2 was also obtained in [26], and it required the Stationary Wavelet Transform (SWT) for the heartbeat classification and an extra Infinite Feature Selection (IFS). The differences and results are summarized in Table 9, including the best ones for the final configuration chosen in this work. Results for S2 improved noticeably when using the proposed two-days enrollment and classification compared to MLP, as the minimum EER goes from 2.69% in MLP to 0.03% in BioECG. Analogously, the worst-case goes from 4.71% to 3.93%. In the case of using a joint dataset with S1 and S2, results were improved using both types of enrollment. However, when using two-days enrollment, the lowest EER improved from 1.74% to 0% and the highest EER from 5.47% to 1.35%.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 4 Experiments and Analysis
### Simulation Analysis
Figure 7: Handover failure (HOF) rate according to velocity. The confidence interval is 95%. utilization of the assets is because of the rationality of our handover decision-making, which makes it conceivable to modify the handover trigger quickly. Handover failure has a fundamental role in the discontinuance of services, and it affects the packet loss rate. Since the handover is not achieved successfully, the association with another network consequently requires increased time. The packet loss rate, which denotes the ratio of packet loss value and the total number of sent packets, will be evaluated and presented over velocity in Figure 8. This demonstrates that our proposed system beats the RSS-MIH-PMIPv6 and RSS-MIH systems. We can presume that the development of packet loss rate begins from a velocity corresponding to 15 m/s, and the rate starts to develop and achieves an estimation of 10% for a velocity equivalent to 19 m/s. However, for the other system, the average rate of packet loss can reach 20%. The motivation behind why the packet loss rate of the proposed scheme is consistently lower than in the other systems is because of the intervention of the anticipating handover triggering technique, which allows the UE to make a decision before degradation of the QoS. From that point forward, for the proposed scheme the packet loss rate keeps on expanding similarly until reaching 12.5% and 13% for velocities equivalent to 23 m/s and 24 m/s, respectively, where it begins to increment in a passive manner. For other schemes, these rates increase to 19% and 25%, respectively. The delay includes the full period of execution of the process; this period begins at the time of sending a packet from the source, continuing until it is admitted by the destination. For our situation, handover delay is calculated from the time the UE sends a link-going-down message to begin the handover procedure, until the connection with the new serving BS is built. We will concentrate on the changes brought about by PMIPv6 to MIH for handover delay. Figure 9 trick the normal handover delay of the UE moving at various speeds. It demonstrates that our proposed policy in RSS-MIH-PMIPv6 scheme outperforms the RSS-MIH scheme, and shows that handover delay diminishes proportionately as UE velocity grows. As an interpretation, we note that the quicker the UE travels, the shorter the time expected to surpass the distance in order to recognize the occurrence of new competitor BS. This helps to lessen the time-scanning interval. In addition, according to Table 3, at a velocity higher than 21 m/s the favored populace is the LTE network, in which the delay was enhanced by 0.15 ms since the lifetime condition encouraged the client to keep LTE as the only option. Along these lines, access to LTE is made specifically without passing through the resource availability stage. Figure 8: Packet loss rate according to velocity. The confidence interval is 95%.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 7 Discussions  
It has to be clarified that the use of the RF pilot tone for the PNC in this work has occupied one polarization state in the fiber, since here we aim to investigate and discuss the efficiency of the use of an RF pilot tone for PNC in a simplified transceiver structure. In fact, the RF pilot tone can also be transmitted using the same polarization state as the optical data signals, which requires a more complicated implementation to perform the generation and recovery of the RF pilot tone [7,30,31]. In such systems, a pilot-carrier vector modulation (PCVM) scheme has been applied, where the transverse magnetic (TM) component is loaded with the signal data in X-polarization and the transverse electric (TE) component is employed to carry both the signal data in Y-polarization and the RF pilot tone [7]. It was reported that the PNC in such PCVM transmission schemes can also provide a good performance for an effective linewidth of up to 30 MHz [7,31]. The performance comparison between such PCVM transmission scheme and our proposed RF pilot tone-PNC scheme will be investigated in future work. In addition, for orthogonal frequency division multiplexing (OFDM) optical transmission systems it will be straightforward to employ one subcarrier as the RF pilot tone within the OFDM spectrum to remove the influence of laser phase noise and EEPN [32].  
To study the impact of PMD on the proposed RF pilot tone PNC scheme, numerical simulations using the same 28-Gsym/s QPSK coherent transmission setup have been implemented with a PMD coefficient of \(0.1\ ps/\sqrt{km}\). The differential group delay (DGD) and the random rotations between the transmitted data and the RF pilot tone (due to effect of PMD) are mitigated using a constant modulus algorithm CMA equalizer [5,33,34]. The CPR is performed using the one-tap NLMS approach. Figure 8 shows the PNC results of the same 2000 km coherent system, with and without including the impact of PMD. Similarly, the linewidth of the Tx laser is 170 MHz and the linewidth of the LO laser is 0 Hz in Figure 8a, and the linewidths of both the Tx and the LO lasers are 5 MHz in Figure 8b. Both scenarios indicate an effective linewidth of 170 MHz, and there is no EEPN in Figure 8a. It can be clearly found that, for both transmission scenarios, the impact of PMD can be fully suppressed using the CMA equalizer and the performance of the RF pilot tone-based PNC (both with and without EEPN) will not be affected due to the introduction of PMD.  
Figure 7: PNC results of the 2000 km coherent transmission system (DPD CPR). Ideal: linewidth of both Tx and LO lasers are 0 Hz. (**a**) Transmitter: 170 MHz, local oscillator: 0 Hz; (**b**) Transmitter = local oscillator: 5 MHz.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 3 Improvement of A* Algorithm
### Path Smoothing Method  
From Figure 4c, we can see that a predominant portion of the planned path is within the safe regions; however, there are many turning points on the path which are detrimental to the robot's movement. Accordingly, it is necessary to smooth the path.  
The planning result of A* algorithm is a series of adjacent states. By connecting these adjacent states with straight lines, we can obtain the planned path. To reduce the turning points of the path, we may traverse the path from the initial state to the goal state. For every state \(X\) except \(S\) and \(G\) on the path, we connect the two adjacent states of \(X\) by a straight line. If there is no obstacle on the line, then \(X\) is deleted and the path is renewed. However, this method does not consider the safety cost of the new path. The number of turning points on the path is reduced but the new path may be closer to obstacles, reducing its safety. As shown in Figure 5, the path \(AC\) has fewer turning points than the path \(ABC\), but it is closer to the obstacle.  
Figure 5: Diagram of path smoothing.  
To keep the new smooth path away from obstacles, a path evaluation mechanism is adopted in the path smoothing process which compute the safety cost of the new path and the old path. The new path can be adopted only when the safety cost is not increased. Concurrently, to improve the flexibility of the path smoothing process, the intersection points between the original path and the map grid lines are added into the state sequence of the original path to generate a new sequence. The flow diagram of the path smoothing method is shown in Figure 6.  
The obtained smooth path is shown in Figure 7 which uses the proposed path smoothing method to process the planned path of Figure 4.  
Figure 6: Flow diagram of the path smoothing method.  
Figure 7: Smooth path of the improved A* algorithm.  
Obviously, the smooth path has fewer turning points and smaller turning angles than the path shown in Figure 4. As such, it is more suitable for the robot's movement. The parameter comparison of the two paths is shown in Table 2.  
It can be seen from Table 2 that the smoothing process reduces the path length by 5.4%, reduces the turning times by 25.0%, and reduces the sum of turning angles by 68.7%. At the same time, the length of a risky path is decreased by 3.2% and the length of a dangerous path is decreased by 1.5%. This demonstrates that the path smoothing method is able to smooth the path without decreasing the safety of the path.
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 5 Adaptive Technology of Fiber Optic Current Sensor
### Optical System Structure  
The adaptive method is based on the polarization-type fiber optic current sensor. Magneto-optical glasses are added on both sides of the optical fiber [18]. The specific structure of the optical system is shown in Figure 8. A small driver works at a constant current or constant power mode to drive a laser diode to output linearly polarization light. The incident light is split into three beams with factors \(K_{1}\), \(K_{2}\), and \((1-K_{1}-K_{2})\) with an optical splitter, where \(0.001<K_{1}<0.999\), \(0.001<K_{2}<0.999\), and \(0.001<K_{1}+K_{2}<0.999\). Each beam of light passes through a collimator, polarizer, sensing material, analyzer, and collimator. Finally, the light signal is converted to an electrical signal with a photodetector for further signal selection and processing.  
The fiber signal \(u_{F}\) can be expressed as follows:  
\[u_{F}=K_{2}P_{0}(1-2\theta_{1}) \tag{15}\]  
Two pieces of magneto-optical glass with the same size and material are used as sensing units for signal correction. They are placed symmetrically on both sides of the conductor. The arrangement is shown in Figure 9.  
The first and second correction signal can be expressed as follows:  
\[u_{G1}=K_{1}P_{0}(1-2\sin\theta_{2})\approx K_{1}P_{0}(1-2\theta_{2}) \tag{16}\]  
\[u_{G2}\approx(1-K_{1}-K_{2})P_{0}(1+2\theta_{2}) \tag{17}\]  
where \(P_{0}\) is the power of the light source, \(K\) is the factor, \(\theta\) is the Faraday rotation angle.
## 3 Preliminaries and Definition
### Cryptographic Protocol
#### 3.2.2 Aggregate Signatures  
Aggregate signatures [29; 32; 33; 34] allow multiple signers to sign different messages while keeping the total signature size constant. Each signer forwarding a message adds its signature to the label of the next signer on the advertised route, so that route authenticity can be verified upon receipt of the aggregate. Thus, aggregate signatures can be used to prevent unauthorized parties from extending the path and enables signers to sign their own messages.  
However, solutions based on public key infrastructure (PKI) [32; 33; 34] incur overhead in the distribution of the public key and certificate of each user to all other users. [PERSON] _et al._[29; 35] proposed identity-based sequential aggregate signature schemes for routing-based applications (Recently, [PERSON] _et al._[36] has proven that the assumption on which the aggregate signature scheme proposed in [35] was based is false and that the scheme is universally forgeable, _i.e._, anyone can generate forged signatures on any message of its choice.). In identity-based cryptography, an arbitrary identity string acts as a user's public key and verifying a signature only requires knowledge of a sender's identity. The overhead associated with obtaining and storing identity-based keys is typically much smaller than that of obtaining traditional public keys and certificates for the signers, since the identity-based setting eliminates it. Thus, an identity-based solution can offer a superior alternative to previous PKI-based approaches.  
An identity-based sequential aggregate signature suits the verification of metering data aggregates in smart grid networks because signatures are aggregated one-by-one as the aggregate-so-far moves alongthe path, which is natural in the routing-based applications we consider. It simplifies key management and reduces storage overhead for smart meters, while providing a way for the concentrator to verify the integrity of the aggregate and authenticate the participating meters on the routing path. Thus, we will adopt an identity-based sequential aggregate signature scheme [29] in our construction.