esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Gait Recognition in Wild Nighttime Scenes
## 5 Results and Discussion
### Ablation Study
#### 5.2.3 Analysis of Multi-Scale Temporal Rhythm Perception Module  
To verify the effectiveness of the proposed multi-scale temporal rhythm perception module, we designed ablation experiments for validation. The comparative network structure remained unchanged, but each branch adopted the same temporal stride. The experimental results are shown in Table 5. When we employed the multi-scale temporal rhythm perception module, setting the temporal strides of the three branches to 1, 2, and 3, respectively, optimal results were achieved across all three datasets. In contrast, when all branches used the same temporal stride, recognition accuracy declined to varying degrees.  
This experimental result further validates the efficacy of the multi-scale temporal rhythm perception module. In setting different temporal strides, the model can better capture gait feature variations across different time scales, thereby enhancing the overall recognition performance. When all branches use the same temporal stride, the model's temporal perception ability is limited, leading to decreased recognition accuracy. This indicates that the multi-scale temporal rhythm perception module can effectively improve the model's ability to capture gait information, demonstrating significant advantages in processing complex temporal data.  
Furthermore, to validate the applicability of our proposed method to other temporal aggregation approaches, we substituted the MS-TCN with two different temporal feature extraction modules [8; 11]. The experimental results show that our MS-TCN module achieves optimal performance under various temporal stride settings, further confirming the effectiveness of our chosen module.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Block** & **CASIA-B** & **Gait3D** & **GaitDN** \\ \hline ResGCN [27] & 63.74 & 15.33 & 65.83 \\ \hline ST-GCN [35] & 73.60 & 19.89 & 74.33 \\ \hline Shift-GCN [36] & 71.26 & 18.44 & 74.49 \\ \hline PAGCN [29] & 74.10 & 22.56 & 84.77 \\ \hline SA-GC & **76.32** & **22.90** & **86.24** \\ \hline \end{tabular}
\end{table}
Table 4: Analysis of the spatial feature extraction module, in which we keep the network architecture consistent.
## 3 Petri Net Representation
### Green Extension Strategy  
The conventional green extension strategy begins with an initial fixed green time; each time a transit vehicle is detected by the magnetic loops or cameras, it will add an extra green period until the maximum green time is reached [6]. In the proposed strategy, an extra green time will be appended to the green time, while the following two conditions are met simultaneously. The first one is that an approaching transit vehicle is detected by the RSU sensor; the second one is that the time when the transit vehicle is detected is at the specific green period. Different from the conventional strategy, this strategy conditionally decides whether an extra green time is appended based on the residual green time when the transit vehicle arrives at the stop bar.  
The new green extension strategy that we propose is the following:  
1. The residual green time when a transit vehicle is detected is expressed as \(T_{r\_gt}\). The additional green time is set to \(T_{\delta\_gt}\);
2. The distance between the RSU sensor installed on the road side and the stop bar is expressed as \(D_{RSU}\). The nominal speed of the transit vehicle is expressed as \(V_{tv}\), when it is detected by the RSU sensor. The estimated time to travel to the stop bar is expressed as \(T_{e}=D_{RSU}/V_{tv}\);
3. When a TSP request is received by the controller in the current phase, it checks whether the detected transit vehicle has enough green time to travel to the stop bar or not, there are four cases should be considered: 1. If the condition \((T_{e}<T_{r\_gt})\) holds, it means that the transit vehicle will travel through the intersection without delay. 2. If the condition \((T_{r\_gt}\leq T_{e})\) holds, it means that the yellow light will turn on and the transit vehicle has to wait for the green light in next cycle before the stop bar. 3. For Case 2, if the formula \((T_{e}-T_{\delta\_gt}<T_{r\_gt})\) is also satisfied, then an additional green time \((T_{\delta\_gt})\) can be appended to the residual green time such that the new remaining green time \((T_{r\_gt}+T_{\delta\_gt})\) is long enough to serve the transit vehicle during this phase. 4. If the condition \(((0<T_{r\_gt})\) and \((T_{r\_gt}\leq(T_{e}-T_{\delta\_gt})))\) holds, it means that the original residual green is between one to \((T_{e}-T_{\delta\_gt})\) and is not long enough to serve this vehicle during this phase either. In this situation, even if an additional green time is appended, the transit vehicle has to wait until the next cycle.
## 4 Pilot Protection Based on Jensen-Shannon Distance
### Jensen-Shannon Distance Algorithm  
The Jensen-Shannon distance is a statistical measure quantifying the divergence between two probability distributions. Widely applied in information theory and statistics, it provides a symmetric and bounded method to compare the similarity between probability distributions. In machine learning, the Jensen-Shannon distance can be used for similarity measurement in clustering algorithms or for evaluating the quality of generated samples in generative adversarial networks.  
When measuring the similarity between two discrete probability distributions \(P\) and \(Q\) using the Jensen-Shannon distance, an intermediate distribution \(M\) between \(P\) and \(Q\) is defined, typically as the arithmetic mean of \(P\) and \(Q\):  
\[M\ =\frac{1}{2}(P\ +\ Q) \tag{9}\]  
Then the KL divergences of \(P\) and \(Q\) are calculated with respect to the intermediate distribution \(M\):  
\[KL(P||M)\ =\ \sum_{i=1}^{n}p_{i}log(\frac{p_{i}}{M_{i}}) \tag{10}\]  
\[KL(Q||M)\ =\ \sum_{i=1}^{n}q_{i}log(\frac{q_{i}}{M_{i}}) \tag{11}\]  
Then the weighted average of the two KL divergences is calculated, namely the Jensen-Shannon divergence:  
\[JSD(P||Q)\ =\ \frac{1}{2}KL(P||M)+\frac{1}{2}KL(Q||M) \tag{12}\]Finally, half the square root of the Jensen-Shannon divergence is taken as JSD:  
\[JSD(\,P||Q\,)\ =\ \sqrt{\frac{1}{2}JS(\,P||Q)} \tag{13}\]  
The Jensen-Shannon distance (\(D_{\rm JS}\)) has many practical properties. Firstly, \(D_{\rm JS}\) is symmetric, that is, \(D_{\rm JS}(P\,|\,1\,Q)=D_{\rm JS}(Q\,|\,1\,P)\); secondly, the range of \(D_{\rm JS}\) is fixed between \([0,1]\), where 0 means that the two distributions are exactly the same, and 1 means that the two distributions are completely different; and thirdly, \(D_{\rm JS}\) is always non-negative and takes zero value only when \(P=Q\). The above features of the Jensen-Shannon distance mean that when an external fault occurs on the transmission line, the two current waveforms are almost the same, and the distribution of their current samples also tends to be consistent. At this time, the calculated \(D_{\rm JS}\) tends to 0; conversely, when the two current waveforms are very different, the distribution of the current samples is also very different, and the calculated \(D_{\rm JS}\) will be much greater than 0, and the greater the difference, the closer the result is to 1. Therefore, by obtaining \(D_{\rm JS}\), the similarity of the distribution of current samples at both line ends can be quantified, and the corresponding fault identification criteria can be constructed.
## 9 Conclusions  
Recent research on sixth-generation wireless communication networks especially concerning IoT applications, machine learning, and energy has been reviewed in this paper. Future breakthroughs in these domains are expected to significantly impact many aspects of our life over the next several years, regardless of industry. The next-generation networks will likely be highly complicated. They will require energy that can power billions of connected devices simultaneously, making it impractical to change or recharge the batteries of so many devices regularly. As a result, energy-efficient networks and devices will be required in the future. Because of the importance of energy efficiency in future 6G networks, this paper provided a survey of recent works in 5G and 6G energy efficiency for IoT networks. This paper also reviewed the primary challenges and limitations that must be addressed to achieve energy efficiency-based huge IoT enabled by 6G. This study aims to put forward a set of guidelines to researchers working in energy efficiency, IoT, and AI for the next-generation mobile communication networks.  
Conceptualization, M.N.M., M.M. and A.R.A.; methodology, M.N.M. and A.R.A.; validation, M.N.M. and H.N. and A.R.A.; formal analysis, H.N. and M.A.S.; investigation, M.N.M., M.M. and Q.S.Q.; resources, M.N.M., Q.S.Q. and A.R.A.; data curation, M.N.M., H.N., M.M. and A.R.A.; writing--original draft preparation, M.N.M., M.M. and A.R.A.; writing--review and editing, M.N.M., H.N., and A.R.A.; visualization, M.N.M., M.M. and H.N.; supervision, M.N.M. and Q.S.Q.; project administration, A.R.A.; funding acquisition, A.R.A. All authors have read and agreed to the published version of the manuscript.  
Universiti Tenaga National (UNITEN), Innovation and Research Management Center (iRMC): RJ010517844/005 (2020).  
Not applicable.  
This research was sponsored and supported under the Universiti Tenaga National (UNITEN) internal grant no RJ010517844/005 (2020). Many thanks to the Innovation and Research Management Center (iRMC), UNITEN who provided their assistance and expertise during the research.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3 GPP & Third Generation Partnership Project \\ mMTC & Massive Machine Type Communications \\ AI & Artificial Intelligence \\ mURLLC & Massive Ultrareliable Low Latency Communication \\ AR & Augmented reality \\ ML & Machine learning \\ B5G & Beyond 5G \\ m2m & machine to machine \\ BL & Bayesian learning \\ MR & Mixed reality \\ CU & Cellular mode \\ NB & Naive Bayes \\ EDoS & Economic Denial of Sustainability \\ NR & New Radio \\ E2E & end to end \\ NFV & Network function virtualization \\ eMBB & Enhanced Mobile Broadband \\ NMA & Network management automation \\ EHD & Extremely high definition \\ \end{tabular}
# Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument
###### Abstract  
A new electro-hydraulic servo point load instrument was designed to address the problem that the existing point load instrument cannot be loaded continuously and uniformly; different loading rates (using three loading rates: 0.1, 0.5, 1.0 kN/s) were conducted on fine-crystalline granite, coarse-crystalline granite, and siltstone (each rock sample contains four sizes: \(20^{3}\), \(30^{3}\), \(40^{3}\), \(50^{3}\) mm\({}^{3}\)) for point load tests. Firstly, the influence of loading rate on the axial stress distribution of rock sample loading was investigated in conjunction with the rock strength damage theory. Next, the influence of rock sample size and loading rate on different standard point load strength evaluation methods was analyzed to find a reasonable evaluation method and loading rate and range of rock sample size. Finally, the relationship between standard point load strength and uniaxial compressive strength was analyzed on this basis to obtain its empirical conversion formula. The results show that: (1) With the increase in the loading rate of point load, the tensile and compressive stresses in the loading axis increase, and the compressive stresses near the center of the loading axis of the rock sample are more influenced by the loading rate; the standard point load strength increases with the increase in the loading rate, but the increase in the standard point load strength decreases when the loading rate increases to a certain range. (2) With the increase in size, the standard point load strength solved by method I, method III, and method IV has an obvious size effect, while the size effect of standard point load strength solved by method II is not obvious. (3) The conversion factors of fine-crystalline granite, coarse-crystalline granite, and siltstone were obtained by zero-intercept linear regression analysis as 16.80, 15.32, and 14.60, respectively, which indicated that the conversion factors of rocks with high strength were higher than those of rocks with low strength. The present research results can provide theoretical support for revising the existing point load strength calculation equations.  
a _applied_ sciences
# Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane
## 3 Result and Discussion
Therefore, unmasking in this region could be greatly beneficial for the differentiation between words, thus benefiting SRT. In addition, the \(-90^{\circ}\) condition displays the largest amount of attenuation against \(0^{\circ}\) above 1 kHz. This is likely to be due to a significant magnitude of shadowing effect by the human body when the source is placed directly below. In addition, it is worth noting that similar studies on spatial unmasking in the median plane [3,4] reported SRTs around \(-6.4\) dB and \(-9\) dB respectively at \(0^{\circ}\) masker elevation angle. On the other hand, the current result demonstrates SRTs up to around \(+9\) dB on average at \(0^{\circ}\). This difference is likely due to the fact that the current study used a speech-shaped noise masker specific to each speech target sentence, rather than a noise masker averaged with respect to all sentences, thereby vastly increasing the difficulty of each trial. However, as mentioned earlier, this method ensures greater consistency for comparison between each vertical noise masker position, which was the main focus of the study.
## 7 Numerical Results
### Isolator Fragility Curves  
The fragility curves of storage tanks isolated via SFPB and TFPB with either standard design or optimized parameters are presented in Figure 9. They have been calculated in terms of isolator displacements for various PGA levels. As mentioned earlier, the maximum displacement of SFPB and TFPB is related to the Maximum Credible Earthquake (MCE) hazard level, with probability of exceedance of 2% in 50 years. In particular, the results for the three approaches are illustrated for each isolator type (i.e., SFPB1, SFPB2, SFPB3 and TFFB1, TFPB2, TFFB3, respectively). It can be observed from Figure 9a that in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SFPB2** & **SFPB3** & **TFFB2** & **TFFB3** \\ \hline Number of isolators & 60 & 39 & 60 & 36 \\ Effective Period & 2.55 s & 3.11 s & 2.29 s & 2.73 s \\ Effective Damping & 19.8\% & 18\% & 21.9\% & 16.9\% \\ Effective Stiffness & 53,805.6 kN/m & 30,810 kN/m & 97,802.7 kN/m & 68,096 kN/m \\ Displacement capacity & 0.28 m & 0.28 m & 0.72 m & 0.72 m \\ Eccentricity & 0.0007 m & 0.0007 m & 0.0001 m & 0.0007 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization results for all optimized isolations configurations.  
Figure 8: Convergence histories.  
case of single bearings, the fragility curves of the conservative SFPB1 design are identical to those of SFPB2 and SFPB3 single- and dual-optimized- schemes although the SFPB3 scheme contains significantly fewer isolators. On the other hand, the fragility curves of the tank isolated with triple bearings shown in Figure 9b illustrate that TFPB1 presents slightly better results compared to the other two more cost-efficient approaches, TFPB2 and TFPB3, which result in almost identical results.
# Comparative Analysis of Volatile Compounds in Flowers of Different _Actinidia_ Species
## 1 Introduction
An analysis carried out on solvent and headspace samples of cut flowers showed a presence of 107 and 59 compounds, respectively. Most of them were carbonyls, alcohols, hydrocarbons and esters [20]. Another headspace analysis of cut flowers identified 27 mostly terpenoid compounds [21]. [PERSON] et al. [22] analysed the headspace of unpollinated flowers and identified 45 volatile compounds, mostly straight chain hydrocarbons and terpen. To date, little attention has been paid to volatile compounds of flowers of other actinidia species. The chemical analysis (headspace and solvent extracts GC-MS (gas chromatography--mass spectrometry)) of seven female and two male genotypes of _A. arguta_ helped to identify some 60-70 compounds in flowers and 40-80 in fruits. The most numerous chemical groups in flowers were represented by terpen (including lilac derivatives coming from linalool) and benzenoid compounds. A GC analysis showed differentiation in the composition of flower volatile compounds between the tested genotypes [23]. A study on a different species has shown that the flower aroma of even closely related species, such as _Brunfelsia australis_ and _B. pauciflora_, may differ significantly [24]. Considering the above, understanding the composition of volatile compounds in flowers of less known species of actinidia, may help to optimize the commercial production of their fruit. The aim of the research was to compare the composition of volatile compounds of flowers from male genotypes of _A. chinensis_ var. _delicious_, _A. arguta_, _A. kolomikta_ and _A. polygamma_, as well as male and female genotypes of _A. arguta_ and _A. kolomikta_, the two most winter-hardy actinidia species commercially cultivated in colder regions.
## 4 Simulation and Discussion
### Composite Scattering of Canyon/Valley Environment with or without Target  
In this subsection, the monostatic composite scattering characteristics of one target or none above the canyon environment are investigated. The RMS height \(h_{\text{rms}}=0.2\lambda\), the slope angles \(\alpha_{1}=30^{\circ}\), \(\alpha_{3}=45^{\circ}\). The incident angles are \((\theta_{i},\varphi_{i})=(-90^{\circ}\sim 90^{\circ},90^{\circ})\). The illustration of simulation geometry is shown in Figure 19.  
As shown in Figure 20, each curve has three peaks at \(\theta_{s}=-45^{\circ}\), \(\theta_{s}=0^{\circ}\), \(\theta_{s}=30^{\circ}\), respectively. The main reason the peaks appear in these three angles is the structure of the canyon. When incident waves hit the slope or the bottom vertically, the peak appears, which means the vertical reflection field from the environment dominates the scattering field. What's more, comparing the black/red curve and the blue/green curve, we can draw out that the target scattering fields play a more critical role when the incident waves hit the slopes or bottom non-vertically than vertically.  
This discipline is also suitable for Figure 20b, which displays the results for a valley model with wind speed \(v_{19.5}=0.2\) m/s. Additionally, the peak value at \(\theta_{s}=0^{\circ}\) in Figure 20b is much higher than in Figure 20a, because the calm water can reflect more power vertically than the rough terrestrial bottom.  
These results enlighten us that when we detect the targets above the canyon/valley environment, the incident EM waves should best not illuminate the slopes or bottom vertically.  
Figure 17: The geometry of a missile-like target above composite rough surface for monostatic scattering.  
Figure 19: The geometry of a missile-like target above composite rough surface for monostatic scattering.  
Figure 18: Monostatic RCS simulation results: (**a**) canyon, HH-polarization; (**b**) canyon, VV-polarization; (**c**) valley, HH-polarization; (**d**) valley, VV-polarization.
## 3 2 DOF IMC Strategy for SRMs Based on ADO
### Design of the ADO  
Considering the parameter variation and unknown uncertainties, the system model can be further described by the following equation based on (13).  
\[\begin{bmatrix}u_{d}\\ u_{q}\\ u_{0}\end{bmatrix}=R\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}+\omega_{e}\begin{bmatrix}0&-L_{dc}&0\\ L_{dc}&0&\frac{\sqrt{2}}{2}L_{ac}\\ 0&0&0\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}+\begin{bmatrix}l_{d}\\ f_{q}\\ f_{0}\\ f_{0}\end{bmatrix} \tag{20}\]  
where the disturbances \(f_{d}\), \(f_{q}\) and \(f_{0}\) are given by:  
\[\begin{cases}f_{d}=\Delta R+\Delta L_{dc}p(i_{d})+\Delta L_{ac}p(i_{0})+\epsilon _{d}\\ f_{q}=\Delta R+\Delta L_{ac}\omega_{e}i_{0}+\Delta L_{dc}p(i_{q})+\epsilon_{q} \\ f_{0}=\Delta R+\frac{1}{2}\Delta L_{ac}i_{0}+\frac{1}{2}\Delta L_{ac}p(i_{d})+ \Delta L_{dc}p(i_{0})+\epsilon_{0}\end{cases} \tag{21}\]  
In this equation, the \(\Delta R\), \(\Delta L_{ac}\) and \(\Delta L_{dc}\) represent the uncertainties of \(R\), \(L_{ac}\) and \(L_{dc}\), \(\epsilon_{d},\epsilon_{q},\epsilon_{0}\) are the \(d\)-, \(q\)- and \(0\)-axis uncertainties caused by the unmodeled dynamics.  
Based on (20), an ADO is designed to estimate the uncertain disturbances \(f_{d}\), \(f_{q}\), \(f_{0}\). Firstly, the discrete-time state-space model of ADO is given:  
\[\textbf{x}(k+1)=\textbf{A}\textbf{x}(k)+\textbf{B}(\textbf{u}(k)-\textbf{f}(k)) \tag{22}\]  
where:  
\[\textbf{x}(k)=\begin{bmatrix}i_{d}(k)&i_  
where the \(\mathbf{\hat{x}}\), \(\mathbf{\hat{f}}\) are the observed values of \(\mathbf{x},\mathbf{f}\). The errors \(\mathbf{e}\) between the actual and observer values of the state variable \(\mathbf{x}\) can be defined as:  
\[\mathbf{e}(k)=\begin{bmatrix}e_{d}(k)\\ e_{q}(k)\\ e_{0}(k)\end{bmatrix}=\begin{bmatrix}i_{d}(k)-i_{d}^{*}(k)\\ i_{q}(k)-i_{q}(k)\\ i_{0}(k)-i_{0}(k)\end{bmatrix} \tag{24}\]  
Moreover, a quadratic error function \(\mathbf{E}(k)=\frac{1}{2}\mathbf{e}^{2}(k)\) is used to assess the performance of ADO. Hence, in this paper, to minimize the \(\mathbf{E}(k)\), the steepest descent method is adopted [27], and the Jacobian matrix \(\mathbf{J}\) can be given as:  
\[\mathbf{J}=\frac{\partial\mathbf{E}(k)}{\partial\mathbf{\hat{f}}(k)}=\mathbf{B}\mathbf{e}(k) \tag{25}\]  
Then, the estimated value at the next control period can be obtained:  
\[\mathbf{\hat{f}}(k+1)=\mathbf{\hat{f}}(k)+\triangle\mathbf{\hat{f}}(k)=\mathbf{\hat{f}}(k)- \mu\mathbf{B}\mathbf{e}(k) \tag{26}\]  
where \(\mu\) is the adaption gain, and \(\triangle\mathbf{\hat{f}}=\mathbf{\hat{f}}(k)-\mathbf{\hat{f}}(k-1)\). As the SRM system has a natural dynamic characteristic of bounded-input bounded-output stability, the output \(\mathbf{\hat{f}}(k)\) of ADO should also be bounded within certain limits.
# Progress on Single-Feed Quality Wideband Linear Wire Array
Additionally, the QWL antenna's compact and lightweight design holds significant potential for integration into space-constrained systems such as UAVs and portable devices, paving the way for promising future technological advancements. Section 2 provides a comprehensive overview of the QWL antenna. In Section 3, the impedance characteristics and radiation features of the QWL antenna's driven element are discussed. Section 4 presents the latest advancements in the research on the QWL antenna's directors and reflectors. Section 5 details the design approach for the QWL antenna's low-common-mode-current rugged structure. Section 6 introduces the series-fed array configuration of the QWL antenna. Section 7 outlines the design methodology for the QWL antenna. Finally, Section 8 offers a discussion and conclusions, along with future research directions for the QWL antenna. The main contributions of this paper are as follows:1. A systematic review of the development history of the QWL antenna is presented for the first time, covering advances in its driven element, director, reflector, low-common-mode-current interference connector, and array series-fed feed design. 2. The analytical expression and a quick design formula for the input impedance of the QWL antenna's driven element, the linear WHEMS antenna, are provided for the first time.