esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
extended a hybrid attention-based connectionist temporal classification system with view-temporal attention to perform multi-angle lipreading. [PERSON] et al. trained an end-to-end CNN-LSTM model [14]. Many studies have been conducted focusing on AVSR. In this paper, we would like to introduce a couple of state-of-the-art works. An AVSR system based on a recurrent neural network transducer architecture was built in [15]. The authors evaluated the system using the LRS3-TED data set, achieving high performance. In [16], the authors proposed a multimodal attention-based method for AVSR, which could automatically learn fused representations from both modalities based on their importance. They employed sequence-to-sequence architectures, and confirmed high recognition performance under both acoustically clean and noisy conditions. Another AVSR system using a transformer-based architecture was proposed in [17]. The experimental results show that on the How2 data set, the system improved word error rate relatively over sub-word prediction models. In [18], we proposed an AVSR method based on deep canonical correlation analysis (DCCA). DCCA consequently generates projections from two modalities into one common space, so that the correlation of projected vectors could be maximized. We thus employed DCCA techniques with audio and visual modalities to enhance the robustness of ASR. As a result, we confirmed that DCCA features of each modality can be improved compared to the original features, and better ASR results in various noisy environments can be obtained. Although we can find a lot of VSR and AVSR methods, there are only a few works combining ASR and multi-angle VSR to accomplish angle-invariant AVSR. One of them is [19], where the authors proposed an early fusion-based AVSR method using bidirectional LSTMs. Similar to their past work [11], the authors put lip images at various angles and corresponding audio signals into the bidirectional LSTM models.
# Optimal Order of Time-Domain Adaptive Filter for Anti-Jamming Navigation Receiver
## 4 Optimal Filter Order
### Design of Optimal Filter  
The anti-jamming filter of navigation receivers is usually a band-stop filter, as shown in Figure 6, and its minimum stop-band attenuation can be expressed as the interference signal ratio.  
\[As=JSR \tag{24}\]  
The transition band of the filter obtained from Formula (22) can be expressed as.  
\[\Delta f=\frac{D\cdot f_{s}}{N-1}=\begin{cases}\frac{(As-7.95)f_{s}}{14.36(N-1 )},&As>21\\ \frac{0.922\gamma}{N-1},&else\end{cases} \tag{25}\]  
Suppose that the center frequency of interference is \(f_{J}\), then the normalized upper and lower cutoff frequencies of the filter can be defined as.  
\[\omega_{a1}=2\pi\bigg{(}f_{J}-\frac{1}{2}B_{J}-\frac{1}{2}\Delta f\bigg{)}/f_ {s}\,, \tag{26}\]  
\[\omega_{a2}=2\pi\bigg{(}f_{J}+\frac{1}{2}B_{J}+\frac{1}{2}\Delta f\bigg{)}/f_ {s} \tag{27}\]The ideal anti-jamming filter frequency response can be defined as.  
\[H_{d}(\omega)=\left\{\begin{array}{ll}e^{-j\omega\frac{N-1}{2}},&\{0\leq|\omega| \leq\omega_{a1}\}\cup\{\omega_{a2}\leq|\omega|\leq\pi\}\\ 0&,&\omega_{a1}<|\omega|<\omega_{a2}\end{array}\right., \tag{28}\]  
The frequency response of the anti-jamming filter is.  
\[H(f)\overset{\omega=2\pi f}{=}\frac{1}{2\pi}[H_{d}(\omega)*W(\omega)], \tag{29}\]  
When the filter length increases, the estimated CNR will get close to the ideal CNR, which means the shortest filter can be designed to meet the demand for anti-jamming. The anti-jamming requirement is the maximum acceptable CNR loss, represented by 3 dB-Hz in simulations and 8 dB-Hz in practical tests. When the difference between the C/N ratio under non-interference conditions and the estimated C/N ratio after anti-jamming is less than 3 dB-Hz, the anti-interference requirements are met.  
\[\Delta CNR=\left[C/N\right]_{0}-\left[C/N\right]_{\text{ajm}}=10\lg\left\{ \frac{B_{\text{zz}}\cdot\int_{\infty}^{\infty}S_{\text{z}}(f)df\cdot\left( \int_{\infty}^{\infty}S_{\text{z}}(f)df-\int_{\infty}^{\infty}|H(f)|^{2}S_{ \text{z}}(f)df\right)}{\int_{\infty}^{\infty}S_{\text{z}}(f)df\cdot\int_{ \infty}^{\infty}|H(f)|^{2}S_{\text{z}}(f)df}\right\} \tag{30}\]  
\[M_{\text{min}}=\min\{M|\Delta CNR\leq 3\:\text{dB}\}, \tag{31}\]  
The optimal order of the time-domain adaptive filter is.  
\[M_{\text{min}}=\min\{M|\Delta CNR\leq 3\:\text{dB}\}+\Delta M, \tag{32}\]  
where \(\Delta M\) is the correction value between the optimal order obtained by theoretical derivation and design and the actual optimal filter order.
# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
###### Abstract  
An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% \(\pm\) 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots.  
exoskeleton robot; locomotion pattern recognition; feature extraction; SA-SVM; finite state machine +
Footnote †: journal: Applied Sciences
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
###### Abstract  
Synthetic Aperture Radar (SAR) is a well-established and powerful imaging technique for acquiring high-spatial-resolution images of the Earth's surface. With the development of beam steering techniques, sliding spotlight and staring spotlight modes have been employed to support high-spatial-resolution applications. In addition to this strengthened high-spatial-resolution and wide-swath capability, high-temporal-resolution (short repeat-observation interval) represents a key capability for numerous applications. However, conventional SAR systems are limited in that the same patch can only be illuminated for several seconds within a single pass. This paper considers a novel high-squint-angle system intended to acquire high-spatial-resolution spaceborne SAR images with repeat-observation intervals varying from tens of seconds to several minutes within a single pass. However, an exponentially increased range cell migration would arise and lead to a conflict between the receive window and 'blind ranges'. An efficient data acquisition technique for high-temporal-resolution, high-spatial-resolution and high-squint-angle spaceborne SAR, in which the pulse repetition frequency (PRF) is continuously varied according to the changing slant range, is presented in this paper. This technique allows echo data to remain in the receive window instead of conflicting with the transmitted pulse or nadir echo. Considering the precision of hardware, a compromise and practical strategy is also proposed. Furthermore, a detailed performance analysis of range ambiguities is provided with respect to parameters of TerraSAR-X. For strong point-like targets, the range ambiguity of this technique would be better than that of uniform PRF technique. For this innovative technique, a resampling strategy and modified imaging algorithm have been developed to handle the non-uniformly sampled echo data. Simulations are performed to validate the efficiency of the proposed technique and the associated imaging algorithm.  
high-temporal-resolution; high-spatial-resolution; high-squint-angle; synthetic aperture radar (SAR); continuously varying PRF (CVPRF); high-order imaging algorithm +
Footnote †: journal: Sensors
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Aperture Antenna
#### 3.8.2 Vivaldi Antenna  
The low cost, simplicity, and high band operation of Vivaldi antenna has brought about its wide application and usage in many devices [76]. These antennas are used in radar application and are also used in microwave imaging [77]. The antenna has received attention as a suitable candidate for emerging technologies, such as 5G [78], wideband communication using image theory technique [79] and high-gain antenna using optical lens [80]. Figure 10 illustrate a design example of Vivaldi antenna [81].  
Figure 9: Log-periodic dipole array antenna [72].
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 4 Experimental Results and Performance Analysis
### Statistical Analysis  
To validate the classification results, a paired \(t\)-test for the nonequivalence is performed on the EEG frequency bands of the delta, theta, alpha, and beta in stress and relax conditions as they tend to follow a normal distribution. \(p\)-values are calculated for the average of each frequency from all electrodes using a significance level of 0.05. The population size is considered 67 + 25 = 92 samples. The null hypothesis is that the mean band power is that the same in stressed and relaxed individuals. At the same time, an alternative hypothesis is that the mean band power is different in stressed and relaxed individuals. \(p\)-values for each frequency band are given in Table 5. The results show that videos have a statistically significant effect on the average alpha band as its value is less than 0.05. The related studies [18; 19; 20; 21; 22; 34] also show that EEG alpha band frequencies are highly affected and are sensitive to stress.  
Figure 4 shows the average and standard deviation of alpha band power from all EEG electrodes for stressed and relaxed participants. The y-axis shows the average alpha band power for all electrodes, and the x-axis labels show average and either stressed or relaxed conditions. The average alpha band power is higher for relaxed people than for stressed people. Meanwhile, the standard deviation in alpha waves is lower for relaxed conditions than for stressed conditions. When someone is stressed, their brain waves tend to be faster and more irregular. However, it is important to note that EEG results can vary depending on several factors.  
Figure 3: The number of videos labeled as stressed and relaxed is based on the values of valence and arousal.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Band** & \(p\)**-Value** \\ \hline Delta & 0.2169 \\ Theta & 0.8131 \\ Alpha & 0.0006 \\ Beta & 0.2443 \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(p\)-values of EEG frequency bands in stressed and relaxed individuals for all electrodes.
# Analysis and Optimization of Driveline Bushing for Lateral Ride Vibration under Shock Excitation
## 4 Relationship between Seat Rail Vibration and Bushing Parameters
### Effect of the Design Parameter on Seat Rail Vibration  
Based on Equations (3)-(5), the main effects of each parameter on the responses are found out and given in. In the longitudinal direction, the \(\text{VDV}_{\text{DR}}\) decreased with increasing  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \(k_{x\_FL}\) & \(k_{y\_FL}\) & \(k_{x\_FR}\) & \(k_{y\_FR}\) & \(k_{x\_RR}\) & \(k_{y\_RR}\) & & \multicolumn{4}{c}{**VDV (m/s\({}^{-1.75}\))**} & \multicolumn{4}{c}{**RSM**} \\  & & & & & & & & & & **x** & **y** & **z** & **x** & **y** & **z** \\ \hline
1 & 1.2 & 1.3 & 0.9 & 1.0 & 0.8 & 1.0 & 0.82 & 0.10 & 0.70 & 0.82 & 0.10 & 0.70 \\
2 & 0.5 & 1.2 & 0.9 & 0.9 & 1.2 & 1.5 & 0.83 & 0.19 & 0.7\(k_{x\_FL}\), \(k_{y\_FL}\) and \(k_{y\_FR}\), but increased with increasing \(k_{x\_FR}\), and \(k_{x\_BR}\). The effect of \(k_{y\_RR}\) on the \(\mathrm{VDV_{DR}}\) was not monotonous--the \(\mathrm{VDV_{DR}}\) increased when the \(k_{y\_RR}\) increased from 50% to 110% of its baseline and then decreases when the \(k_{y\_RR}\) further increased. In the lateral direction, the \(\mathrm{VDV_{DR}}\) decreased with increasing \(k_{x\_FL}\), \(k_{y\_FL}\), \(k_{y\_FR}\) and \(k_{x\_RR}\), but increased with increasing \(k_{x\_FR}\) and \(k_{y\_RR}\). Again, the effect of \(k_{y\_RR}\) on the \(\mathrm{VDV_{DR}}\) varied according to the parameter range. In the range of 50%-100% of the baseline, the \(\mathrm{VDV_{DR}}\) decreased with increasing \(k_{y\_RR}\). In the range of 100%-150% of the baseline, the \(\mathrm{VDV_{DR}}\) increased with increasing \(k_{y\_RR}\). In the vertical direction, more parameters including \(k_{y\_RR}\), \(k_{y\_FL}\), \(k_{x\_FR}\), \(k_{y\_FR}\) and \(k_{x\_RR}\) show non-monotonous effects on the response.  
As can be seen from the vertical scales of Figure 7, the relative changes of the \(\mathrm{VDV_{DR}}\) were considerable (from 12% to 63%) in the lateral direction but marginal in the fore-and-aft and vertical directions (less than 5%).  
The longitudinal \(\mathrm{VDV_{DR}}\) will reach its minimum value when \(k_{x\_FL}\) was minimized and \(k_{y\_FL}\) was maximized. In the lateral direction, the optimal \(\mathrm{VDV_{DR}}\) could be achieved with a combination of the maximum value of \(k_{x\_FL}\) and the maximum value of \(k_{y\_FL}\). In the vertical direction, the situation was more complicated as the effect of \(k_{y\_FL}\) on the \(\mathrm{VDV_{DR}}\) was not monotonous. The lowest \(\mathrm{VDV_{DR}}\) was obtained when \(k_{x\_FL}\) was maximized and \(k_{y\_FL}\) was taken a value between 1.0 and 1.5 times of the baseline values. It can be found that when several parameters were considered together, the VDVs in different directions required different solutions to achieve their lowest values.
# Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
## 5 Conclusions  
In this paper, we contribute to the few-shot NOTA relation classification with a concise and effective prompt tuning baseline named commonsense knowledge-aware prompt tuning. We propose a commonsense knowledge-enhanced method for prompt tuning that injects commonsense knowledge into the prompt label construction in order to express the NOTA relation more comprehensively. We design a learned scoring strategy on top of the embedding of our model, which is specially designed for the NOTA task combined with the prompt-tuning method to more accurately identify the NOTA class. Experiments show that our method achieves a new state of the art in the field of few-shot NOTA classification, indicating that the use of the prompt tuning method to classify samples is a promising direction for future research.  
Our approach can also be applied in areas such as cultural heritage [40] and labor market analysis [41]. However, commonsense knowledge-aware prompt tuning methods are handcrafted and somewhat straightforward. A natural direction for improving it is training an additional convolutional neural network end to end to measure the transductive similarity.  
In our experiments, we found that different templates have a great impact on the accuracy of the pre-training model, and the training method of the pre-training model during the pre-training process also has a great impact on the experimental results. Therefore, there are two important directions for future work: (1) design a unified task format and corresponding pre-training objectives for other types of tasks, such as language generation and relation extraction, and (2) build an automatic template generation tool to generate different templates for different tasks.  
Conceptualization, B.L. and L.J.; methodology, B.L. and L.J.; validation, B.L., Y.Z. and H.W.; formal analysis, B.L. and X.L.; investigation, B.L.; resources, H.W.; data curation, L.J.; writing, B.L.; visualization, Y.Z.; funding acquisition, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences grant no. Y835120378.  
Not applicable.  
The authors declare that they do not have any conflict of interest. This research does not involve any human or animal participation. All authors have checked and agreed with the submission.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline NAV & 0.52 s & 2.62 s \\ MNAV & 0.68 s & 3.12 s \\ \hline CKPT & **0.47 s** & **2.28 s** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The run-time of the model prediction.
## 2 Materials and Methods
### Design of the Device
#### 2.1.6 Cost of the Device  
The trade-off between cost and linearity [36] affected the design. The thermistor manufacturer suggests a circuit including a differential amplifier that improves the linearity of the thermistor. Since the ADS1115 includes 4 single channels or 2 differentials, we decided to use the single ones because the sensors were 3, leaving one channel unused. By using 2 differential channels, only 2 thermistors can be supported.  
Other devices, like HOBO MX2303 which is weatherproof, power efficient, has high accuracy (\(\pm\)0.2 \({}^{\circ}\)C) and has an additional Bluetooth connection, could be a good fit to the experiments demand but it is only equipped with two probe sensors and not three, the diameter of the sensors (0.53 cm) is wider than the proposed system's internal sensors (0.165 cm) and the housing of the sensor is a cylindrical stainless steel compared to the proposed system's internal sensor, which is a point epoxy material which senses a significantly smaller area than the HOBO MX2303. Overall, the HOBO MX2303 is inappropriate because it provides only two sensors, compared to our device, which provides three, and the sensors of the HOBO MX2303 are much more intrusive comparing to the proposed systems sensors. Lastly, the Hengko temperature and relative humidity data recorder sensor is specifically made for fruits and vegetables, but it only provides one sensor, has an a non-acceptable for the experiment accuracy (\(\pm\)0.5 \({}^{\circ}\)C) and its probe is highly intrusive comparing to the proposed system.  
The total cost was around EUR 110 (end 2020 market costs) which can be reduced substantially by not using breakout and prototype boards (for example adafruit 32u4 proto), instead create a PCB that include only the vital parts for the system that will also decrease the size of the device.
## 4 Performance Evaluation  
The performance of the implemented protocol has been evaluated in a network simulator (NS-2.34) by creating vehicular scenarios and establishing wireless IVC links. NS2 is an ideal open-source simulator for the implementation of new protocols, where the network traffic can be visualized graphically [57, 58], and these results will become the foundation for developing the comparative analysis of simulation in NS-3 [59] along with the simulation of urban mobility (SUMO) [60] in future work. The object tool command language (OTCL) interpreter is used to create various scenarios according to the requirements of the proposed protocol to analyze its performance. Similarly, multicast communication, packet handling, channel configuration, event scheduling, timer adjustment, etc. are handled in C++ language. Awk scripts are also written in C++ to extract the required information from the trace file to compute packet delivery ratio, throughput, delay, etc.  
The performance of the proposed protocol has also been compared to the multicast ad hoc on-demand distance vector (MAODV) [61] and dynamic source routing (DSR) [62] protocols. The performance evaluation is based on the computation of the packet delivery ratio, routing overhead, throughput, cluster stability, and delay with the confidence interval of 95%. The simulation parameters for performance evaluation within NS2 are given in Table 2. The total vehicles considered within a kilometer are 5-20 per kilometer during simulation.  
The MAODV has been selected to analyze and compare the multicasting feature of MAODV with the proposed protocol because the multicast tree is maintained by the group leader in MAODV and can quickly adapt.  
Similarly, the node's joining and leaving features can be analyzed by making a comparison with the DSR, which is based upon source routing that establishes the routes on demand. The routes are updated upon the successful delivery of messages at the destination.  
In MAODV, the multicast tree is maintained by the group's leader through the transmission of \"Hello\" messages at regular intervals. The node replies are lost in MAODV because of the movement of intermediate nodes to new positions, increasing the routing overhead.  
The DSR is based upon source routing that establishes the routes on demand. The routes are updated upon successful delivery of a message at the destination. DSR floods the packets to all the network nodes and can transmit them to a single node using the address filtration technique, which causes high overhead and delay during dynamic mobility and dense traffic [13; 62].
Despite optimizing energy efficiency and exploiting D2D communications are two key topics in the forthcoming 5G network, significant work is still needed in order to increase the global energy efficiency while integrating the D2D paradigm in the traditional cellular communication. According to the classification proposed in [7], the approaches for increasing the energy efficiency of wireless networks can be grouped under four categories: (i) resource allocation, (ii) network planning and deployment, (iii) energy harvesting and transfer, and (iv) hardware solutions. In this paper, we opt for the first category and propose Energy Efficient D2D over Single Frequency Network (hereinafter referred to as EED2D-SF), a resource allocation algorithm for multicast service delivery that couples conventional multicast with D2D communication and takes advantage from the Single Frequency paradigm to increase the network energy efficiency. The proposed scheme aims to face the challenging energy consumption problem of the current machine-type cellular communication, i.e., the LTE-M standard. The study considers all foreseen 5G frame configurations [8] that differ from current cellular frame configurations in the number and position of downlink and uplink slots. An overall simulation analysis is carried out with the aim to assess the performance of the proposed solution when focusing on file downloading (e.g., software upgrade/update), as one of the key use cases for the mMTC scenario. The remaining of the paper is organized as follows. In Section 2, a brief summary of the main related work in the field is presented. Section 3 shows the reference system model. The proposed EED2D-SF scheme is described in Section 4, while performance evaluation results are discussed in Section 5. Conclusive remarks are reported in Section 6.