esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Performance Evaluation
Therefore, when extracting more than 30 frames, LSTM-based models could recognize action with higher accuracy than weighted mean-based models. In addition, \"Bi-ConvLSTM\" utilizes information when sequentially listed \(FM_{K}^{N}\) is inputted in the reverse direction; \"Bi-ConvLSTM\" has higher accuracy than \"ConvLSTM\" when extracting frames number (\(N\)) increases. This shows that the LSTM-based model shows higher performance as the large number of frames is extracted from the videos. However, as the results of comparing data testing time increase, the proposed weighted mean-based HAR model showed faster data processing speed than LSTM-based models when extracting more than 30 frames. This is because the proposed model recognizes action using MLP, which is lighter than LSTM as a prediction network. LSTM receives time series data sequentially listed over time and controls the amount of information stored while repeating the operation of hidden layers by the length of data. Therefore, when extracting many frames from videos, LSTM has a structure that receives a relatively long time of data input, and the data processing speed is slowed. The data processing speeds of \"ConvLSTM\" and \"Bi-ConvLSTM\" in Figure 5 show that the data processing speed is slower as \(N\) increases. However, MLP predicts action at a faster rate than LSTM by conducting forward propagation operations regardless of data length. LSTM receives data sequentially to identify the temporal features of spatial feature maps, which are inputted, and repeats the computation of hidden layers as the frame number (\(N\)). However, the proposed model receives the spatiotemporal features generated by the weighted mean of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Method} & \multicolumn{2}{c}{GT: ApplyEyeMakeup} & \multicolumn{2}{c}{GT: Longlump} & \multicolumn{2}{c}{GT: Kayaking} \\ \hline \multirow{2}{*}{Method} & Accuracy score & Testing time & Accuracy score & Testing time & Accuracy score & Testing time \\  & (\%) & (s) & (\%) & (s) & (\%) & (s) \\ \hline Element-wise Mean & 65.23 & 0.28 & 68.72 & 0.34 & 67.34 & 0.31 \\ \hline Weighted Mean & 88.64 & 0.59 & 89.02 & 0.63 & 88.46 & 0.61 \\ \hline ConvLSTM & 69.25 & 0.89 & 71.87 & 1.05 & 68.53 & 1.26 \\ \hline Bi-ConvLSTM & 71.24 & 0.91 & 72.53 & 1.08 & 67.71 & 1.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of action prediction accuracy using four methods, when \(N=20\). \(FM_{K}^{N}\) to \(FD^{N}\) representing time information at a time as data of the entire time. Jetson NANO, with low-cost GPUs, can recognize action at a faster speed than LSTM. In addition, the weighted mean-based model recognizes action with a high accuracy of 30% or more even when extracting \(\sim\)10 frames less than LSTM-based models. This shows that the proposed method, which generates spatiotemporal features through weighted mean efficiently, extracts motion representations for action recognition. Therefore, the HAR model can be constructed by extracting relatively few frames and the instantaneous action can be recognized quickly.
## 4 Preclinical Studies
Although the outcomes of both in vitro and in vivo research concerning immunogen electrotransfer were promising, many of them were performed with the use of plasmids containing an antibiotic resistance gene, which decreases the safety profile of GET therapies because of its risk of spreading and providing bacteria with the resistance to antibiotics used in clinical practice. Moreover, antibiotic resistance genes increase the plasmid size, which diminishes the effectiveness of plasmid expression due to their decreased capability of reaching the cell nucleus [69]. Considering the above, the most challenging aspect of the latest studies is to design a plasmid devoid of the antibiotic resistance gene. The first evaluations in vitro of antibiotic resistance gene-free plasmid encoding human IL-12 were already performed, and the results encourage further in vivo testing and human clinical trials. It was proven that GET of these plasmids outcomes with the production of biologically active IL-12 in the human squamous carcinoma cell line FaDu and the murine colon carcinoma cell line CT26, and its transcription level showed a time-dependent diminution. Furthermore, combining it with another stimulus, such as radiation, could increase the IL-12 expression [70]. Another research also resulted in the efficient expression of IL-12 after transfection. Additionally, the survival of B16-F10 and the human malignant melanoma cell line SK-MEL-28 was substantially decreased after using antibiotic resistance gene-free plasmids compared to commercial ones. Nonetheless, the plasmid production ought to be optimized before in vivo transfection to progress the quality of the plasmid by reducing the number of dimers and other destabilizing topoisomerase emerging due to the appliance of the recombinant strains of bacteria required in this process [4]. The additional determining aspect of IL-12 plasmid production is to opt for the appropriate promoter controlling IL-12 expression. Studies have shown that p21 is an efficient promoter used in these plasmids. The p21 is the promoter of the cyclin-dependent kinase inhibitor 1 (CDKN1a) gene. It determines the low expression in normal cells and high expression in tumor cells. Furthermore, it is stress-induced, which implies that the expression can be enlarged by either radiotherapy or chemotherapy [70; 71]. It allows combining the IL-12 GET with other therapies, which may elicit the ameliorated response of the tumor cells. IL-12 GET combined with radiation was proven to induce a significant antitumor effect with the tumor growth delay, although it was similar to the therapy with a constitutive promoter. Regardless, the p21 is a prospective promoter in cancer treatment due to its endogenous provenance because, as a result, it is not susceptible to transcriptional inactivation [72].
## 3 Channel Estimation in Different Frequency Bands
### Channel Estimation for RIS Systems in Low Frequency Bands
#### 3.2.2 Channel Estimation for Semi-Passive RIS  
The estimation of separate channels can be easily accomplished by setting active elements with transceiver signal capability installed on the RIS. However, in semi-passive RIS, it is necessary to consider not only the method of channel estimation, but also to set the number and location of active RIS elements in a reasonable way. The goal is to be able to improve the accuracy or reduce the complexity of channel estimation while ensuring that the hardware complexity and power consumption are acceptable.  
An architecture consisting of a passive RIS and an active RF chain for baseband reception was proposed in [74]. Using this architecture and assuming sparse wireless channels in the beamspace domain, an alternating optimization method was proposed to achieve explicit channel estimation on the RIS side. This method saves pilot overhead and training time.  
[PERSON] et al. [75] proposed a two-stage channel estimation scheme based on ANM. A new two-stage channel estimation approach was developed in a hybrid RIS architecture. The proposed channel estimation required fewer active RIS elements and only one-way training, which has better estimation performance.  
[PERSON] et al. [76] proposed a semi-passive RIS using a grouping structure. Based on this structure, a two-stage channel estimation method was proposed. The first stage estimated the BS-RIS channel signal parameters by the estimation of signal parameters via rotational invariance technique (ESPRIT). The second stage estimated the channel using the total LS (TLS) ESPRIT and multi-signal classification (MUSIC) methods. This method has better performance and lower training overhead, and the performance can be further improved by flexibly allocating the training time between the two phases. The best performance is achieved when the ratio of first stage to total time is 0.1. However, the grouping structure made the beamforming performance degraded.  
Compared with the high frequency bands, the low frequency bands lack channel sparsity characteristics. Sparse matrices can be created by assuming a partial opening of the RIS or other assumptions. High-dimensional matrices can be reduced in complexity by matrix decomposition or by using tensor methods. Further, deep learning methods can be used to improve the estimation performance and reduce the pilot overhead. The channel estimation algorithms and performance comparison in low frequency bands are described in Table 4. Table 4 describes the methods from four aspects: system setup, problem, method, and results analysis.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
## 4 Discussion
### Acceleration Problem  
A segmental analysis in Figure 8 shows that the acceleration values decrease as one moves up from the foot to the thigh, which is explained by analysing the equivalent mechanism. The foot performs a rotational movement which generates a centrifugal acceleration that is directly dependent on the angular velocity squared and the radius of rotation, which is approximately equal to the crank length. The pedalling speed was defined as constant. It was experimentally proven that this is true on average, although participants produce slight oscillations in angular velocity during the pedalling cycle [26]. Therefore, the tangential acceleration is approximately zero. In the case of the thigh, it is performing an alternating rotational movement with an angular velocity of the order of one third of the pedalling cadence and a rotation radius that is of the order or higher than the length of the crank. This result implies that both tangential and centrifugal accelerations are lower than those of the foot.  
A component analysis shows that the largest accelerations occur in the sagittal plane (except for the pelvis), with the CC component being slightly greater than the AP. This is explained by the fact that in the CC direction, the inertial forces have to balance the gravity action. Usually, kinematics outside the sagittal plane are not considered in cycling practice because they present an ROM orders of magnitude lower than that in the sagittal plane, being these assumptions generally extended to the dynamic analysis. However, in the results obtained in this work (Figure 8), it can be observed that the acceleration in the LM direction of the thigh can reach a value between 20% and 30% of the acceleration in the sagittal plane. Therefore, the authors of this paper argue that the values obtained in the LM direction, although small compared to the other two components, should not be underestimated. The accelerations obtained imply the appearance of inertia forces, which cannot be negligible in the overall balance of the system, especially in the estimation of the stresses to which the joints are subjected. Joint reaction force calculations are key to assessing performance and possible injury in cyclists. No results have been found in the literature that develop this hypothesis. Preliminary results obtained by the authors indicate that accelerations can cause forces of inertia in the LM direction of the same order of magnitude of the total resultant force in this direction that the joint is subjected to, especially in positions around the TDC or the BDC.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 3 Comparison of Maximum Diameter and Particle Size Parameters  
### Correlation between Coarseness Index and Particle Size Parameters  
In addition to the \(CI\), the most widely used methods for measuring and analyzing particle size include the average particle size and absolute particle size. These were obtained by mathematical calculations using the particle size distribution acquired by sieve analysis.  
The median particle size (\(d_{50}\)) corresponds to the midpoint of the particle size distribution. Thus, \(d_{50}\) is smaller than half of the rock fragments and larger than the other half. [PERSON] [26] reported a strong power-function relationship between \(d_{50}\) and \(CI\). [PERSON], et al. [18] reported that \(d_{50}\) has an exponential relationship with \(CI\).  
Figure 5: Sieving machine and example of rock chips after sieving: (**a**) number and opening size of sieves; (**b**) example of rock chips classified by size range (\(p\) = 9 mm and \(s\) = 48 mm at UCS = 40 MPa).  
The mean particle size (\(d_{\text{MPS}}\)) is obtained from the arithmetic average of all phi sizes of the particles in the sample. The phi size is a logarithmic scale that [PERSON] [27] modified from the Udden-Wentworth scale and is primarily used to determine the particle size of rocks in sedimentology; it is calculated using Equation (3):  
\[\Phi=-\log_{2}\frac{d}{d_{0}} \tag{3}\]  
where \(\Phi\) denotes the phi size, \(d\) denotes the diameter of the particle in millimeters, and \(d_{0}\) denotes the reference diameter, which is equal to 1 mm.  
Because it is difficult to determine the value of all rock fragment particles, \(d_{\text{MPS}}\) is usually calculated using percentile values, as shown in Equation (4). [PERSON], et al. [18] and [PERSON], et al. [28] reported that \(d_{\text{MPS}}\) has a power or exponential function with \(CI\).  
\[d_{\text{MPS}}=d_{0}\times 2^{-\frac{\Phi_{\text{Life}}+\Phi_{\text{Life}}+\Phi_{ \text{Life}}}{3}} \tag{4}\]  
where \(d_{\text{MPS}}\) denotes the mean particle size and \(\Phi_{16}\), \(\Phi_{50}\), and \(\Phi_{84}\) denote the phi sizes corresponding to 16%, 50%, and 80% in the cumulative curve, respectively.  
The absolute particle size (\(d^{\prime}\)) is obtained from the Rosin-Rammler distribution function, which is one of the most widely used particle size distribution curves. The Rosin-Rammler relationship is given by Equations (5) and (6):  
\[R=100 exp\left[-\left(\frac{d}{d^{\prime}}\right)^{n}\right] \tag{5}\]  
\[\ln\left[\ln\left(\frac{100}{R}\right)\right]=n\,\ln\,d-n\,\ln\,d^{\prime} \tag{6}\]  
where \(R\) denotes the cumulative mass (volume) as a percentage retained on the sieve of size \(d\); \(d^{\prime}\) denotes the absolute particle size defined as the size at \(R=36.79\%\); and \(n\) denotes the distribution parameter.  
[PERSON] and [PERSON] [15] and [PERSON], et al. [18] reported that d has a very strong exponential relationship with \(CI\). [PERSON], et al. [19] and [PERSON], et al. [28] found that \(d^{\prime}\) and \(CI\) have a strong power-function relationship.
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## 5 Conclusions  
This paper proposed a novel FDSR algorithm based on the feature extraction of DBN for fault diagnosis. Deep learning was introduced to solve the multiparameters problem of complex systems, extracting features layer by layer with dimension reduction. FDSR is an improved sparse representation method that takes advantage of sparse representation and Fisher discriminant analysis. Then, the FDSR algorithm was proposed to learn a discriminative structured dictionary that is cascaded by subdictionaries of specified classes. The discrimination ability of the FDSR algorithm is demonstrated both in the dictionary and coefficients. First, each subdictionary of a specified class has great discrimination ability for extracting features from the corresponding class but has poor representation power for the features from other classes. In addition, by minimizing the between-class scatter and maximizing the within-class scatter, the sparse coefficients solved were sufficiently discriminative. The computational complexity is shown in this paper. Consequently, the inputs were reconstructed with the coefficients and the dictionary acquired, and the classification results were acquired from the reconstructive error. In the end, we introduced t-SNE to visualize the performance of the proposed algorithm.  
The learning performance was compared with DBN, SRC, SVM, and BP neural networks. The fault diagnosis experiment on the TE process verified the effects of the proposed algorithm. In terms of the algorithm, studying the iteration rule to achieve faster convergence rate in the future would be beneficial. In terms of application, further research should focus on applying this algorithm to other complex systems for fault diagnosis.  
[PERSON] proposed the original idea. [PERSON] designed the overall framework of this paper, built the simulation model and complete the manuscript. [PERSON] modified and refined the manuscript. [PERSON] conducted data analysis.  
This work was supported by the National Natural Science Foundation of China No.61273080 and No.61633005, and this work is also funded by the Natural Science Foundation of Chongqing City, China (cstc2016 jcyjA0504).  
The authors declare no conflict of interest.
## 5 Numerical Evaluation Results
### Evaluation Setup
#### 5.1.3 Comparison Benchmark  
The baseline in our evaluation is the performance of a no-SDN wireless scheduling framework, i.e., the absence of the LayBack orchestrator to coordinate the scheduling. As a result, each operator \(o\) can only occupy its own backhaul bandwidth \(Z_{o}\), i.e., there is no inter-operator bandwidth sharing. More specifically, in our simulations, the no-SDN benchmark solves only the optimization up to the subproblem Equation (4) with the dynamic operator allocation \(x_{o}\) replaced by the static operator capacity \(Z_{o}\), and the subproblem Equation (5) [but not the subproblem Equation (3)]. That is, the no-SDN benchmark only optimizes the allocations within each given operator individually, i.e., performs essentially only \"intra-operator\" optimization. The no-SDN benchmark follows the same multi-timescale behavior with \(K_{1}=10\), \(K_{2}=5\), and \(K_{3}=10\) as the SDN-based optimization. We report the aggregate of the gateway allocations \(\sum_{g\in G_{o}}y_{g}\) for operators \(o=1\) and \(2\) as the actual allocated operator upstream bitrates of the no-SDN benchmark; whereas, for the SDN-based optimization, we report the operator rate allocations \(x_{o}\).  
We note that an alternate benchmark without any optimization could consider a static allocation of backhaul capacity portions to individual eNBs. Such a static allocation would perform poorly for dynamic bursty traffic models, as specified in Section 5.1.4. The static allocation would incur substantially longer queue lengths than the considered \"intra-operator\" optimization, which individually independently optimizes the allocations within each operator. Another alternative benchmark could employ conventional two-layer NUM between the eNBs and the operators (with the gateways subsumed by the operators). Such a two-layer benchmark would still perform the intra-operator optimization, but with only two layers compared to the three layers in the considered benchmark. These two benchmarks would generally perform similarly, with differences being influenced by convergence characteristics [79]. For the present study, we focus on the impact of the sharing of the backhaul resource across operators as quantified by comparing the considered no-SDN \"intra-operator\" optimization with the full SDN-based optimization involving the central SDN orchestrator.
## 3 Application Examples  
In all the application examples that will be presented, two types of calculation will be carried out in order to compare the results. The first type corresponds to the electrode considered as ideal or PEC. For this purpose, the general calculation method proposed in this paper will be used, assigning a very small internal resistivity. The second type of calculation considers electrodes with appreciable internal resistance. In practice, it is about comparing electrodes made with copper, which is the most used material and is considered to be an almost perfect conductor, with electrodes made of less conductive materials such as stainless steel, aluminum, or others. In the following examples, PEC or the ideal electrode stand for an electrode made of copper of resistivity \(\rho=1.78\times 10^{-8}\)\(\Omega\)m.
# Analysis of the Impact of Small Group Behavior on Cruise Ship Emergency Evacuation
## 4 Cruise Ship Evacuation Simulation Model Construction
### Comparison of Bypassing Distance with and without Groups  
Influenced by the behavior of small groups, detouring behavior occurs during the evacuation of passengers. The detour distance, which is the difference between the actual distance people move from the starting position to the destination and the straight-line distance, is used to measure the detour phenomenon in the evacuation process. The detour distances for evacuation methods I and II with and without groups are shown in Figures 10 and 11, respectively.  
From Figure 12, it can be seen that in Evacuation mode I, the average detour distance for individuals without groups is 14.38 m, while for individuals with groups, it is 17.91 m. This small group behavior results in a 24.54% increase in the average detour distance for passengers. In Evacuation mode II, the average detour distance without groups is 18.70 m  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Congestion Duration/s** & **I-a** & **I-b** & **I-c** & **II-a** & **II-b** \\ \hline Without group & 365–615 & 482–671 & 382–805 & 372–641 & 410–724 \\ Without group & 408–801 & 508–798 & 491–1050 & 510–765 & 495–830 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of congestion durations with and without group.  
Figure 11: The rate of change in sustained congestion time under the effect of more no clusters.  
and with groups is 22.05 m. The small group behavior increases the average detour distance for passengers by 17.89%.  
From the simulation results, under the effect of small group behavior during evacuation, the personnel behaved as looking for companions, resulting in inconsistent or even opposite movements compared to others. This behavior hinders the evacuation of other personnel and increases the overall average detour distance during evacuation, thus making the evacuation behavior of cruise passengers more disorderly, increasing the overall evacuation time and decreasing the evacuation efficiency.
## 4 Implementation of Proposed Architecture  
This section describes the implementation of the MEC/Cloud Orchestrator for deploying applications on MEC or the cloud. The Orchestrator plays an essential role in acquiring end-user requests and deploying the application based on the collected information and algorithm. The architecture and implementation sequence are included separately for the centralized and distributed types explained in the previous section.
## 2 Materials and Methods
### Materials Properties and Selection
#### 2.1.1 AA 6061 Alloy  
AA 6061, as a general-purpose alloy, exhibits good weldability and is usually manufactured by extrusion. It has found acceptance in the automotive, aeronautic, and food industries to produce aircraft wings and fuselages, automotive flashlights, canned foods, and beverages.