esa-sceva/satcom-chunk-collection huggingface dataset:
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 2 Advanced Television System Committee (ATSC) 3.0 System Overview  
Figure 1 shows the basic transceiver structure of _LDM_ in the ATSC 3.0 system. To highlight the core idea, additional information such as signaling and pre-synchronization are not shown in this figure. At the transmitter, the ATSC 3.0 system provides many different configuration modes according to different requirements for robustness and capacity by different services. Besides, every layer can share the same orthogonal frequency division multiplexing (OFDM) numerologies, including fast Fourier transform (FFT) size, pilot structure, and cyclic prefix (CP) etc. From the perspective of low implementation complexity, only a two-layer _LDM_ scheme is adopted in the ATSC 3.0 physical layer standard where the transmission power can be allocated to the two layers by adjusting the power injection level. In a typical two-layer _LDM_ system, _CL_ signals usually use a more robust coding and modulation configuration than _EL_ signals.  
At the receiver, the optimal maximum likelihood (ML) detection can be employed to detect the signals of all layers. However, the complexity of the ML detector can be extremely high. The existing detection scheme of two-layer _LDM_ is based on the SIC detector, as illustrated in Figure 1b. Firstly, this part demodulates the most robust _CL_ signal during which the _EL_ signal is disposed as noise. Secondly, it decodes the _CL_ signal to obtain the bit information for mobile services. Thirdly, it carries out BICM reconstruction of the bit information to generate _CL_ symbols, which are then subtracted from the received _LDM_ signal. Finally, obtains the bit data for fixed service by decoding the _EL_.  
Layered Division Multiplexing Extension Frequency-Division Multiplexing (LDM-Ex-FDM) Scheme for the ATSC 3.0 System  
The traditional _LDM_ scheme cannot improve the performance of _EL_ while maintaining the bit error rate (BER) performance of _CL_. In this section, the reasons why the proposed LDM-Ex-FDM scheme can solve this problem will be explained, and the LDM-Ex-FDM multi-service transmission scheme and the low-complexity DIC scheme for fixed service reception will be described in detail.
## 4 Discussion
### Future Work  
The battery management implemented will experience limitations in-flight applications. Current works have shown that more development will need to ensure the aircraft's proper battery protection and continuous power provision during prolonged use.  
The assembly and experimentation for this article were simple in order to validate the control theory. Therefore, there are more areas for future testing to help optimize a turboelectric powerplant. Current research with this turboelectric system has used flat-pack, pouch-style Li-Po batteries. Future endeavors could experiment with cylinder-style, Li-Po batteries. Additionally, future tests could compare Graphene Li-Po batteries to take advantage of potential higher charge and discharge rates. This initial system utilized one 12S battery in the system. Further development could be performed by placing multiple 12S batteries in parallel to reduce the current requirements of each battery and corresponding individual cells. Using multiple batteries in parallel could also increase the potential overall power output of the system.  
The integration of this actively controlled turboelectric system in a VTOL UAS was conducted successfully [21]. Oklahoma State University has flown the featured turboelectric system on two platforms testing and proving flight worthiness. Ongoing and future flight testing will help to make the system more reliable for future applications.  
Another way this turboelectric system could be further refined is with the manufacturing of the system. Currently, this turboelectric system is built with commercial-off-the-shelf (COTS) parts. Less manufacturing is required for parts such as the housing of the turboelectric system. There are many possibilities to optimize the manufacturing for this system. The main area that could be improved is the housing for the system, which could be made significantly lighter.  
Additionally, the design of the overall system could be optimized by using parts other than COTS parts. Potential future turboelectric research could investigate applications where the generator is fully integrated with the turbine. An integrated system would reduce weight and offer the potential for further optimization with the electrical generator of the system. It would be advantageous for a future production turbogenerator to design the system from the \"ground-up\".
## 3 Results and Discussion
### The Influence of Train Running Position on the Initial Compression Wave
#### 3.4.2 Comparison of the Waveforms  
To investigate the effect of the train's running position on the one-dimensional initial compression wave, Figure 23 compares the pressure distribution at the roof measurement point located 7_Di_ away from the tunnel entrance for both cases. By comparing the wavefront  
Figure 22: Comparison of the three-dimensional distribution of pressure iso-surfaces at (**a**) 2400 Pa, (**b**) 1800 Pa.  
pressure distribution at the measurement point, the differences in segment AB between the two cases are minimal, indicating that offset running has little impact on the pressure disturbances generated during open-air operation. However, there are differences in the disturbances generated within the tunnel; the amplitude of the initial compression wave in the center running case is 3.66% smaller than in the offset running case. In terms of pressure gradient, the time at which the maximum pressure gradient occurs is the same for both operating cases. However, the amplitude of the pressure gradient for the center running case is 6.87% smaller than that for the offset running case.  
Based on the general equations of steady gas dynamics, [PERSON] [21] developed a comprehensive formula for the overall pressure rise \(\Delta p_{\text{Hara}}\) across the compression wave:  
\[\Delta p_{\text{Hara}}=\frac{1}{2}\theta_{0}V_{tr}^{2}\frac{1-(1-\beta)^{2}}{(1 -M_{tr})\Big{(}M_{tr}+(1-\beta)^{2}\Big{)}}, \tag{3}\]  
where \(M_{tr}\), \(\beta\) represent the train's Mach number and blockage ratio, respectively.  
Table 2 compares the initial wavefront amplitudes between the formula prediction and the simulation results. The theoretical values align well with the numerical simulation results for both operating cases, with errors of \(-0.53\%\) and \(3.25\%\) for offset and center operations, respectively.
# _remote sensing_
## 4 Calibration Results
### Hy-2c  
The HY-2C was launched on 21 September 2020, which is the third ocean dynamic environment monitoring satellite after HY-2A and HY-2B and the second satellite in the marine dynamic satellite series established as part of China's national civil space infrastructure [40].  
The ascending Pass No. 170 and descending Pass No. 185 fly over the Dan'gan island in WSCS (Figure 2). The two passes of 80 and 81 cycles were shown as an example of the SSH measurements (Figure 10a). In the two cycles, the SSH of the altimeter was good when the footprint was 5 km from the southeast of Dan'gan island. Considering the effect of contamination of the island to the microwave radiometers [10], the Cal/Val campaign has been conducted in the area from 10 to 25 km using the ATGs of Dan'gan and Zhi'wan island (on the southeast of Dan'gan island). There was also an open sea area for HY-2C after the overflight of Dan'gan island. The SSH of such an area was less contaminated by the land/island. Therefore, the ATG of Wai'ling'ding island was also used for the Cal/Val in both passes in a distance of about 20 km from the footprint of the altimeter (Figure 2).  
Figure 9: The SSH of HY-2B when it flies over the Zhi’wan island and the calibration results. Image (**a**) represents the SSH of HY-2B. The dark green lines represent the Zhi’wan island. The x dots represent the HY-2B SSH of 20 Hz data, and the circles represent the 1 Hz data. Image (**b**) shows the calibration results using the ATGs of WSCS. The red dotted line in image (**b**) represents the drift of the SSH.  
The CLS 2015 MSS model and HAMTIDE12 tide model were used for MSS and tidal correction as described in Sections 3.3 and 3.4. The mean biases have been determined to be \(-\)18.9 \(\pm\) 48.0 mm for Pass No. 170 and \(-\)5.6 \(\pm\) 49.3 mm for No. Pass 185, with drifts of 0.0 mm/year and \(-\)0.3 mm/year, respectively. Such results are consistent with each other for different passes.
## 3 Results and Discussion  
### Failure Modes  
During double-shear tests, special attention was paid to failure modes. Based on the experimental results of bond property between carbon fiber sheets and concrete surface after room temperature curing or elevated temperature exposure, several failure modes were found in this study as follows (as shown in Figure 4): (a) delamination of the carbon fiber sheets, which is the dominate failure mode under both room temperature and elevated temperature exposure lower than 200 \({}^{\circ}\)C in this study; (b) complete debonding failure between the adhesive and the concrete substrate, which was only occurred for several MPC specimens when the concrete has a low strength grade or the epoxy resin specimens [23]; (c) partial debonding failure between the adhesive and the concrete substrate (this failure can be attributed to minor eccentricity of loading or nonhomogeneity of adhesive matrix, or the poorer quality of concrete surface preparation) [24]; (d) failure in the adhesive layer, due to the adhesive cracking at high temperature; (e) failure at the end anchorage zones, due to minor eccentricity of loading; and (f) peeling off of the carbon fiber sheets, due to the decrease of adhesive bond strength at high temperature. The failure modes (d) and (e) have been reported in literature [25]. For the specimens failed through failure mode (a), the real bond strength cannot be obtained; therefore, the following reported bond strength for such failure specimens is lower than the real bond strength.  
Figure 4: _Cont._
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 3 Hardware Circuit Design Scheme
### Detection Circuit Design
The specific parameters of the original signal and the waveform information obtained after signal conditioning are shown in Table 1.  
The simulation results show that the rising time of the modulated signals is fast, and the attenuation time of the signals after reaching the peak value is slow. The time when the detection signals reach the peak value is basically the same as that of the original signal. The results show that the simulated detection circuit can complete the amplification of the signal and retain the envelope information of the original signal. The modulated signal can \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & \(A\) **(V)** & \(f_{\epsilon}\) **(MHz)** & \(\tau\) **(ns)** & **Amplitude (V)** & **Extend (\(\mu\)s)** \\ \hline
1 & 1 & 300 & 80 & 2.10 & 4.47 \\
2 & 1 & 900 & 80 & 1.52 & 3.14 \\
3 & 1 & 900 & 120 & 1.49 & 3.13 \\
4 & 0.5 & 900 & 120 & 1.37 & 2.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation signal conditioning results. Figure 7: Simulation signals 1 **(a)**, signal 2 **(b)**, signal 3 **(c)**, and signal 4 **(d)** conditioning results. be extended to more than 3 us, which can satisfy the requirement of data collector for its sampling rate.
## 3 Numerical Model  
### Model Outline  
In this research the seismic response of a RC bridge pier is studied through a numerical model based on a three-component approach, in which the following coexisting behavioral mechanismsare accounted for: flexure, shear and bonding. As schematically depicted in Figure 1, the lateral displacement of a cantilever bridge pier can, indeed, be idealized as the sum of those three components.  
Flexure is by far the most relevant aspect in determining the seismic response of a bridge pier and it is also the most investigated.  
The bonding is essentially responsible for the additional displacement due to the slippage of the longitudinal reinforcing bars in the anchoring concrete. In most cases this phenomenon can be represented as a fixed-end rotation of the pier due to the strain penetration carried by the steel bars anchoring into the foundation concrete. This additional displacement can also account for the possible extra effects due to reinforcement interruption (due to limitations in steel bar lengths or to concrete casting performed in stages) and consequent need to splicing the reinforcement by overlapping the bars.  
Finally, regarding shear deformation, in general it is admitted that in slender columns the contribution of the shear flexibility on the total displacement can be neglected: it starts to have a significant effect only on squat elements with a low height-to-depth ratio. However, it becomes relevant in any case, if the element is expected to be damaged in shear.  
The proposed computational model has been developed using the finite element method within the OpenSEES framework for seismic analysis [18]. Specifically, a two dimensional (2D) nonlinear finite element model has been employed, by means of a three-component model, in which the three aforementioned deformability contributions (flexure, shear, and slip) are separately considered and modelled.  
As illustrated in Figure 2, a fiber-based, nonlinear beam-column element has been connected at each end of the column together with a zero-length rotational spring to account for the bond-slip behavior and a zero-length translational spring to account for the shear behavior. Since the element is loaded only at the ends (no distributed loads are applied along the element length and no competent mass is assigned to the element during a dynamic analysis) the shear internal force is constant, so that just one translational spring is sufficient for the shear behavior. In the case of a typical bridge pier cantilever schematization, the rotational and translational springs should be added just to the fixed end.  
Figure 1: Idealized components of horizontal displacement: (**a**) Original undeformed configuration as a cantilever column; (**b**) bending deformation; (**c**) shear deformation and (**d**) bonding deformation.
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 1 Introduction
research [11; 12; 13]. Traditional FSL approaches often overlook such hierarchical structures due to the limited scale of novel classes they are designed to handle. To address these challenges in FSL-MNC, we propose the novel Simple Hierarchy-Aware Pipeline (SHA-Pipeline). Our SHA-Pipeline introduces two primary strategies. * Efficiency Enhancement. Our extensive experiments reveal that the efficiency of EML in FSL-MNC remains nearly constant across different numbers of ways when utilizing various backbones. By maintaining the number of ways in episodes at five, we significantly reduce the complexity associated with the number of ways in EML. Additionally, to expedite the training process, we minimize the communication overhead of support samples and avoid GPU memory overflow by efficiently distributing the support and partial query sets, as shown in Section 5.3. * Performance Improvement. We enhance classification performance by employing a fast, non-parametric hierarchy clustering strategy to capture the class hierarchy, and then we leverage such class hierarchy by structured representation learning. This approach enhances representation learning on two levels: prototype-level and sample-level. At the prototype level, we strive to maintain the hierarchical structure of class prototypes by maximizing the Cophenetic Correlation Coefficient (CPCC). At the sample level, we use hierarchical triplet loss to ensure that similar samples from different parent classes remain distinctly separate. The contributions of our paper are as follows. We propose the \"few-shot learning with many novel classes (FSL-MNC)\" setting to bridge the gap between traditional few-shot learning and realistic open-world scenarios, presenting a practical yet underexplored challenge. Our SHA-Pipeline efficiently addresses both computational and performance challenges by reducing meta-training overhead to a constant level and utilizing class hierarchy during meta-testing. Experimental results demonstrate that the SHA-Pipeline efficiently outperforms state-of-the-art alternatives across various novel class sizes. The extension of our journal paper from our previous conference publication [4] is outlined as follows. In this journal article, we provide a more detailed description and analysis of our proposed problem setting and method in Section 3 and Section 4, respectively, to ensure the completeness of the model. To strengthen our experimental results, we extend our experiments and include a more detailed analysis of the meta-training strategy and computation overhead in Section 5.2 and Section 5.3, respectively. Additionally, we evaluate our methodology on traditional FSL benchmarks in Section 5.5 and conduct visualization experiments in Section 5.7. The remainder of our work is organized as follows: We present related work in Section 2. Section 3 formulates our problem setting. Section 4 describes our models in detail. Section 5 provides experimental analysis, followed by the conclusion and future work in Section 6.
# Estimate of Passive Time Reversal Communication Performance in Shallow Water
## 4 Results  
Figure 2 shows a block diagram of the four-channel single-input multiple-output(SIMO) system for passive time reversal communication. First, the CIR \(h_{i}(t)\) at each receiver position was estimated by matched filtering of a replica of the LFM probe signal with its measurement by each receiver. The communication data stream \(p_{i}(t)\) and the \(h_{i}(t)\) received at each receiver were matched filtered with the \(h_{i}(-t)\) and were coherently summed to make a focused communication stream \(y(t)\) and \(q\)-function. The \(y(t)\) was passed through the PLL to compensate for the phase distortion of the signal. It was multiplied by \(e^{-i\omega t}\) (\(\omega\) is an angular frequency) to recover the baseband waveform and was then low-pass filtered. Finally, symbol decision was performed, and the BER and the output SNR were estimated. The goal of this paper was to investigate the correlation between the performance of passive time reversal communication and the parameter \(E_{q}\), therefore no additional equalization process was used to eliminate the residual ISI in the receiver system.  
Figure 1: (**a**) Sites of the passive time reversal communication experiments; (**b**) The experimental geometry and the sound speed profile at site 1 (having a relatively slow bottom) and (**c**) those at site 2 (having a relatively fast bottom).
## 8 Application and Discussion of the Results
### Summary  
We identified several threats (RQ1) and some countermeasures (RQ2). Based on the discussion in the previous section, we notice that not every threat and attack can be applied to SSI, whereas some descriptions are of a generic nature (RQ3). Although the human layer can be applied to SSI, it has to be regarded in more detail. The threats of the application layer are most likely not applicable to SSI. Nonetheless, SSI has applications, such as wallets and agents, and VC that are sent between entities. The first results can be found in the literature for SSI, but more work is required. The consensus algorithms of cryptocurrencies outlined in the literature and SSI diverge. Hence, those applied for SSI, such as RBFT, and their implementations have to be evaluated. Although the threats of the data layer can be applied, we found only a few threats in the literature. Thus, this might require future work. Lastly, the threats and attacks concerning the network layer can be applied to SSI. Some might be mitigated by governance, which is added as a layer in the following section. Here, we also found the most known threats to SSI. One issue with the analysis of threats to SSI is that the area is still progressing. Hence, the threats are rather generic and not specific for one or a few implementations. If SSI should be enrolled in a certain use case, the threats have to be evaluated for the setting, including the chosen implementations. For example, threats to Hyperledger Indy might be different from those to Hyperledger Fabric, and the type of wallet (smartphone app, cloud, or hybrid wallet) also plays a role. Sinceseveral threats cannot be applied to SSI directly, the countermeasures have to be regarded specifically for SSI.
## 3 The Proposed Method for Operators' Workload Assessment
Similarly, _pupil activity parameter (PA)_ is calculated as follows:  
\[PA=\frac{PD\ mean-PD\ baseline}{PD\ max-PD\ baseline} \tag{4}\]  
where _PD user_ is defined as the mean value of the specific user's _PD_ as recorded during the task execution, _PD baseline_ is the mean _PD_ value as recorded during the user's baseline phase, and _PD max_ is the maximum _PD_ value as recorded for each user during the task performance. Figure 1: The proposed framework for the operators’ workload assessment. With the same approach, electrodermal activity (_EDA_) is calculated as follows:  
\[EDA=\frac{EDA\ mean-EDA\ baseline}{EDA\ max-EDA\ baseline} \tag{5}\]  
where _EDA mean_ and _EDA max_ are calculated in the same manner as the previous parameters during task performance while _EDA baseline_ is the mean _EDA_ value as recorded during the user's baseline phase. Performance analysis is mainly based on the time taken to accomplish the task, collecting data from video analysis. With regard to performance analysis, the _User Time_ (_UT_) parameter is calculated considering the user time performance. The time to accomplish the task is clocked for each user (_T_) and compared with the time employed by the user who took less time (_T min_) and the time employed by the user who took the longest time (_T max_), as shown in the following equation:  
\[UT=\frac{T-T\ min}{T\ max-T\ min} \tag{6}\]  
The subjective assessment is used to weight the calculated physiological parameters, to include the operator's perceptions. The NASA-TLX assesses the perceived workload according to six questions for each user on a 100-graduations scale: for this study we considered the Mental Demand judgment to weight the _PD_ parameter, the Frustration Level judgment to weight the _RR_ parameter, the Effort Level judgment to weight the HA parameter, and finally the Overall Performance judgment to weight the _UT_ parameter. Each judgment is then normalized to a 5-point scale. According to this, a set of weights ranging from 1 to 5 can be defined to consider the user's subjective experience. Similarly, from NAS questionnaires, this score (structured on a 1 to 10 scale and normalized to a 5-point scale) has been considered to weight the _EDA_ parameter. The above-mentioned parameters, properly weighted as described, are used to generate three workload metrics: the Postural Workload (_PW_), the Mental Workload (_MW_) and Stress (_S_). The _PW_ is calculated as follows, considering the mean value of _RUILA_ score during the task:  
\[PW=mean\ (RUILA\ subtask) \tag{7}\]  
while the _MW_ one is computed as:  
\[MW{=}\ \omega_{1}*PA+\omega_{3}*HA+\omega_{5}*UT \tag{8}\]  
where \(\omega_{1}\) is the Mental Demand weight, \(\omega_{3}\) is the Effort weight (since it also considers the mental effort) and \(\omega_{5}\) is the Performance weight (since the scientific literature relates it to the mental demand), and finally the \(S\) is defined as follows:  
\[S{=}\ -\omega_{2}*RR+\omega_{4}*EDA \tag{9}\]  
where \(\omega_{2}\) is Frustration weight and \(\omega_{4}\) is _NAS_ derived weight.