esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Convergence Speed Analysis  
Apart from the FER performance, the convergence speed is another critical factor to assess the decoding latency. Define the average number of iterations required for decoding one codeword as \(I_{\text{avg}}\). In Table 3, we compared the convergence speed of different quantized decoders with \(q_{m}=4\) for the 802.11n LDPC codes in perspective of \(I_{\text{avg}}\). It can be seen that the proposed RC-MIM-QMS decoder outperforms both the RC-FAID decoder and the RC-MIM-QMS decoder for all simulated code rates by reducing \(I_{\text{avg}}\) up to 45.67%. The RC-MIM-QMSMS decoder also achieves up to 34.07% less \(I_{\text{avg}}\) than the conventional QSMS decoder in the low-to-moderate SNR region. We observe that \(I_{\text{avg}}\) of the proposed RC-MIM-QMSMS decoder is up to 7.67% less than the RD-MIM-QMSMS decoder for the rate-2/3 802.11n LDPC code, and it slightly increases compared to the RD-MIM-QMSMS decoder for the simulated LDPC codes with code rates 3/4 and 4/5. This is because the proposed RC-MIM-QMSMS decoder is designed based on the joint degree distributions, which considers a larger portion of high-degree VNs with respect to the individual degree distributions of the rate-2/3 802.11n LDPC code. However, compared to the individual degree distributions of both rate-3/4 and rate-5/6 802.11n LDPC codes, there are a large portion of low-degree VNs considered by the joint degree distributions. The high-degree VNs lead to a faster convergence speed and the low-degree VNs have a slower convergence speed. Therefore, the proposed RC-MIM-QMSMS decoder requires less \(I_{\text{avg}}\) for lower code rates and more \(I_{\text{avg}}\) for higher code rates compared to its rate-dependent counterparts. Figure 3: The FER performance of different decoders for the 5G LDPC codes with lifting size 26 and code rates 3/4, 5/6, and 8/9. (**a**) \(R_{c}=3/4\). (**b**) \(R_{c}=5/6\). (**c**) \(R_{c}=8/9\). Table 4 demonstrates the \(I_{\text{avg}}\) of different quantized decoders with \(q_{m}=4\) for the 5G LDPC codes. We can see that the proposed RC-MIM-QSMS decoder achieves less \(I_{\text{avg}}\) for all simulated code rates by at least 37.71% in the moderate-to-high SNR region. Compared to the conventional QSMS decoder, the RC-MIM-QSMS decoder can reduce \(I_{\text{avg}}\) by up to 42.04% for the rate-3/4 5G LDPC code. Similar to the case of the 802.11n LDPC codes, we also observe the phenomenon that \(I_{\text{avg}}\) of the proposed RC-MIM-QSMS decoder has a minor reduction compared to the RD-MIM-QSMS decoder for the rate-3/4 5G LDPC code while it increases slightly for the code rates 5/6 and 8/9.
## 6 Experiment
### Using the IXMAS Dataset
#### 6.1.1 Many-to-One Cross-View Action Recognition  
A single view was used in our experiment as a test view, while all other views were used for training. The experiment enabled us to evaluate our method of learning shared and private features.  
The renowned methods in [25; 28; 29; 5] were compared with our method and it was found that approximately 99.9% was achieved by our method, as shown in Table 3. Our method Seb1 achieved a better performance compared to the other approaches, which illustrates the benefit of using our private and shared feature approach in our paper. To determine the resemblance among video samples  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **TEST VIEW 0** & **TEST VIEW 1** & **TEST VIEW 2** & **TEST VIEW 3** & **TEST VIEW 4** \\ \hline TRAINING VIEW 0 & (71, 99.4, 99.1, 100, **97.6**) & (82, 96.4, 99.3, 100, **86.8**) & NA & (76, 97.3, 100, **94.2**) & (72, 90.0, 96.4, 100, **88.1**) \\ \hline TRAINING VIEW 1 & (80, 85.8, 99.7, 100, **94.3**) & (77, 781.5, 98.3, 100, **88.2**) & (73, 93.3, 97.0, 100, **92.1**) & (72, 83.9, 98.9, 100, **96.3**) & NA \\ \hline TRAINING VIEW 2 & (75, 98.2, 90.0, 100, **94.8**) & (75, 97.6, 99.7, 100, **91.3**) & (73, 99.7, 98.2, 100, **91.3**) & NA & (76, 90.0, 96.4, 100, **89.5**) \\ \hline TRAINING VIEW 3 & (72, 98.8, 100, 100, **94.2**) & NA & (74, 99.7, 97.0, 100, **97.9**) & (70, 92.7, 89.7, 100, **87.9**) & (66, 90.6, 100, 99.7, 89.7) \\ \hline TRAINING VIEW 4 & NA & (79, 98.8, 98.5, 100, **93.2**) & (79, 99.1, 99.7, 100, **97.2**) & (68, 99.4, 99.7, 100, **97.2**) & (76, 92.7, 99.7, 100, **84.9**) \\ \hline \multirow{2}{*}{Average} & (74, 95.5, 97.2, 100, **95.2**) & (77, 93.6, 98.3, 100, **89.4**) & (76, 98.0, 09.87, 100, **93.9**) & (73, 93.3, 97.0, 100, **93.9**) & (72, 92.4, 98.9, 100, **99.8**) \\ \cline{1-1} \cline{2-2} \cline{4-4}  & & (74, 95.5, 97.2, 100, **95.2**) & (77, 93.6, 98.3, 100, **89.4**) & (76, 98.0, 09.87, 100, **93.9**) & (72, 92.4, 98.9, 100, **95.2**) \\ \hline \hline \end{tabular}
\end{table}
Table 2: One-to-one cross-view recognition outcome for different controlled methods on the IXMAS dataset. The values enclosed in brackets are the recognition accuracies for [8; 29; 41; 42] and our supervised method (with and without the \(p\) pause).  
Figure 7: Three-dimensional dense trajectories of the target view obtained by following each feature point during the interval of time \(t\). This can be further interpreted as the set of positions occupied by each feature point of the target view [16].  
across camera views, our method implements the sample-affinity matrix with the direct consequence of accurate characterization of the similitude across views by the learned shared features. Furthermore, the learned private features become more edifying for categorization, as the redundancy between private and shared features is reduced.
## 4 Experiments and Results  
### Description of Experimental Environment and Conditions  
The experiments were conducted with real sorting equipment to assess the performance of the plastic waste classification system (Figure 10). The experimental setup consisted of the conveyor belt system equipped with the multi-camera module and the integrated YOLO-based object detection algorithm (Figure 11). The conveyor belt was set to operate at a constant speed of 2 m per second, simulating a typical industrial scenario. Plastic waste samples, specifically PET (polyethylene terephthalate) and PET-G (polyethylene terephthalate glycol), were used for the experiments. The learning algorithm was trained using a dataset specifically created for these plastic types.  
Figure 10: Installation site to verify the proposed garbage-sorting system: (**a**) Input side of the waste. (**b**) Conveyor with the lights and machine vision.
## 3 Proposed Method
### MTAD-GAN Network  
#### 3.2.1 Network Structure  
To achieve anomaly detection of multivariate time series data, we designed a multi-level crossover GAN network (MTAD-GAN), as shown in Figure 3. During the MTAD-GAN model training phase, we input random noise with the same dimension as the training sample into the knowledge graph attention and Hawkes attention long short-term memory (KH-LSTM) to obtain the generated sample. Then the generated samples and real samples are simultaneously transmitted to the discriminator for iterative training until the network reaches Nash equilibrium. In the anomaly detection phase, the test samples are mapped to the random latent space, and the reconstruction samples are generated through iterative training by calculating the reconstruction loss. In order to strengthen the feature difference between normal samples and abnormal samples, a scoring network is introduced here to calculate the score loss of the reconstructed samples. Combining the Gaussian joint distribution, the adaptive dynamic Gaussian score (ADGS) is constructed by fusing the scoring loss, reconstruction loss and discrimination loss to detect and judge the anomaly of the data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Meaning of Place** & & \\ \hline P1 data collection & P2 abnormal data & P3 low level warning \\ P4 normal data & P5 advanced warning & P6 detection feedback information \\ P7 On-site monitoring notice & P8 Alert information & P9 On-site warning information \\ P10 Historical information data & P11 Pre-plan library & P12 accident data \\ P13 Plan confirmed & P14 loss estimate & P15 Plan optimization \\ P16 resume operation & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The meaning of place in Petri net.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 3 Results and Discussion  
The outcomes of the analysis are described in this section and summarized as follows. First, the results of the irrigated/rainfied classification (Section 3.1) show that maize can be uniformly considered irrigated on the pilot area; in fact, the analysis performed on the fields in and outside the irrigation districts did not point to any relevant difference in the satellite time series. The results from the analysis performed throughout the growing season are discussed in Section 3.2, comparing the potential of optical and SAR data to retrieve information for different growing phases. The maize sowing periods (SP) are reported and discussed in terms of emergence periods of maize plants detected from SAR information, and the derived distribution of actual SP over the pilot area (Section 3.3).
# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
## 2 Related Work
### Graph Convolutional Neural Networks  
With regard to graph structure data, the present model of transferring the graph into a set of vectors in the data preprocessing stage cannot guarantee the integrity of graph structure information, and the obtained results heavily depend on the results of graph preprocessing. [PERSON] [21] et al. first proposed the graph neural network (GNN) model, constructed the learning process on graph data directly, and mapped nodes and edges of a graph to a low-dimensional space through the model. The spectral network [22] was the first method used to introduce convolution into the GNN, which defined convolution operation in the Fourier domain, but this method leads to potential intensive operation, and the convolution kernel has no characteristic of locality. [PERSON] [23] et al. introduced parameters with smoothing factors, which afforded locality to the convolution kernel of the spectral network. The graph convolution kernel defined by spectral network depends on the Laplacian matrix of the graph; thus, parameters cannot be shared in other graphs, and the complexity of network calculation is high. [PERSON] [24] et al. proposed ChebNet based on the polynomial convolution kernel, which greatly improved computational efficiency. Adaptive graph convolutional neural networks (AGCNs) [25] learn not only the relationships between the nodes of the original graph structure but also the possible potential relationships between the nodes. However, the convolution kernels of the abovementioned models depend on the basis vector of the Laplacian matrix's features, which depend on the structure of the graph. That means that this kind of model is trained for specific structures, cannot be applied to different structure graphs directly, and has poor generalization capability. At the same time, methods based on spectral decomposition need to process the complete graph in the calculation. There is a high time complexity in matrix decomposition, which makes it difficult to extend to large-scale graph data to learn. [PERSON] [26] et al. proposed neural FP, which uses different weight matrices for nodes with different amounts of degree. However, when the scales of nodes are large and the degrees of nodes are various, this model cannot be applied because there are too many parameters. [PERSON] [27] et al. tried to sort the nodes, selected a fixed amount of neighbor nodes, and convolved them, imitating the method of the convolutional network, which transferred the learning problem of graph structure into the traditional Euclidean data learning problem. [PERSON] [28] proposed MoNet, which used pseudo-coordinates to transform GNN models into Gaussian kernel hybrid models. Geodesic convolutional neural networks (GCNN) [29], anisotropic CNN (ACNN) [30], GCN [31], diffusion-convolutional neural networks (DCNN) [32], etc., can be regarded as MoNet models. [PERSON] [33] et al. proposed a universal inductive reasoning framework that sampled and aggregated neighbor node features to generate a representation of a node.
## 5 Conclusions  
In this paper, a compact and low-cost SIW LWA for 60 GHz band operation, and it was used to support multiple users in a 60 GHz hot-spot.  
PCB-based SIW LWAs providing 1D beam steering and multibeam operation in the V-band have been designed and fabricated. The antennas showed a peak gain of 15.4 dBi and over 40\({}^{\circ}\) beam steering in the H-plane. To overcome the well-known broadside problem of LWAs, arrayed microstrip radiating elements and unit cell asymmetry were applied to the antenna design. This way, a flat gain with a variation of \(+/-1.5\) dB was achieved, even around broadside.  
Furthermore, a novel approach for generating multiple steerable beams with the LWA is presented. By using DWDM and remote optical heterodyning, multiple RF carriers were generated at the feeding point of the LWA. Because of the different carrier frequencies of the RF signals, they were inherently radiated into different directions by the LWA. By changing the wavelength of an optical carrier, and thus the radio frequency fed to the LWA, the direction of the individual beam could be steered. By using multiple optical DWDM carriers, multiple steerable beams were generated simultaneously.  
This approach was then used to support single and multiple users in a 60 GHz hot-spot. For a single user, a maximum data rate up to 2.5 Gbit/s was achieved using OOK modulation. For three users, each supported by an individual beam, a maximum cell capacity of 3 Gbit/s was experimentally demonstrated.
# Study on the Machinability of Ni-Based Superalloy by
## 3 Result and Discussion
### Effects of Cooling Mode
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cooling Mode**} & \multicolumn{2}{c}{**Cutting Speed \(v\)**} & **Surface Roughness \(Sa\)** & **Reduction Rate** \\  & **(m/min)** & **(\(\mu\)m)** & **(\%)** \\ \hline \multirow{4}{*}{Dry milling} & 37.68 & 2.59 & \\  & 56.52 & 2.38 & \\  & 75.36 & 2.16 & \\  & 37.68 & 2.47 & 4.63 \\ \multirow{4}{*}{Air cooling} & 56.52 & 2.23 & 20.85 \\  & 75.36 & 1.99 & 27.41 \\ \multirow{4}{*}{MQL} & 37.68 & 2.05 & 6.30 \\  & 56.52 & 1.75 & 26.47 \\ \multirow{4}{*}{Nanofluid MQL} & 75.36 & 1.46 & 28.99 \\  & 37.68 & 1.88 & 7.87 \\ \multirow{4}{*}{Nanofluid MQL} & 56.52 & 1.69 & 32.40 \\  & 75.36 & 1.25 & 42.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Surface roughness with different cooling modes and cutting speeds.
## 1 Introduction
For this purpose, a coastal study area in southern Italy (Figure 1) was selected to include the city of Crotone and its province, the Capo Colonna promontory and the namesake archaeological site in Calabria Region, where ground instability, including both natural geological processes and human-induced hazards, have been recorded (e.g., [26; 27; 28]). Instances of surface deformation matching with known landslides, long-term subsidence, and modern coastal protection infrastructure are used to showcase the type and precision of deformation products that can be generated with P-SBAS processing of Sentinel-1 big data using the GEP, and also the key information retrievable to support the geological interpretation and analysis of the process under investigation. This includes value-adding through the combination of multiple P-SBAS datasets for the retrieval of the vertical and east-west deformation fields (Section 5.1), and discussion of the geo-information and knowledge enhancement through ground deformation datasets outputted from a SAR big data analysis (Section 5.2).
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
###### Abstract
The concept has been paraphrased: \"sufficiently diluted toxicants should have a beneficial effect on the organism\" [33]. In general, many studies have claimed that all toxic compounds may have hormetic effects, where low doses are stimulatory and large doses are harmful. However, the concept of LDIR-induced hormesis was not well received initially, and provoked considerable discussion since it contradicted the concept of the LNT model as shown in Figure 1b. The concept of LNT proposed that all radiation is harmful, with no threshold, indicating zero dose to be safest. This has been contradicted by the LDIR phenomena (described below). The ICRP has defined LNT as _\"a dose-response model which is  
Figure 1: Controversy of extrapolating cancer risks from high doses to low doses characteristic of radiation protection principles (adapted from [PERSON] and Giaccia 8 th edition [27]). Legend: Line (**a**) assumes that risks are greater at low doses than would be predicted from a linear extrapolation; Line (**b**) illustrates the LNT hypothesis, adopted as the model for radiological protection by BEIR, UNSCEAR, ICRP, and NCRP; Line (**c**) assumes that there is a threshold in dose, below which there are no deleterious biologic effects; Line (**d**) illustrates the hypothesis that low levels of radiation are beneficial, activating repair mechanisms that protect against disease, known as hormesis. based on the assumption that, in the low dose range, radiation doses greater than zero will increase the risk of excess cancer and/or heritable disease in a simple proportionate manner_\" [3]. The dose limits for occupational and public exposures have been guided to be 20 mSv/year and 1 mSv/year, respectively, and national regulatory agencies generally adopt these control levels. The LNT theory has also been endorsed by the BEIR, UNSCEAR, ICRP, and the NCRP [3; 4; 6; 7; 26]. To date, LDIR hormesis is controversial and is a much-debated subject even though the therapeutic implications are potentially beneficial to society as depicted in Figure 2. Insufficient clinical findings have resulted in prevailing controversy and scepticism of the scientific evidence of hormetic effects. Hence, there is a need for supportive evidence of LDIR-induced hormesis to underpin claims of benefits in clinical indications. In this review of the literature, we discuss the asserted beneficial and detrimental effects of LDIR-induced hormesis, seeking to provide an insight into any potential therapeutic implications for human health. The LDIR-induced detrimental and hormetic effects are firstly briefly discussed, followed by a review of LDIR phenomena.
## 2 Traffic Management and Ramp Metering
The first RM was implemented in Chicago, Illinois on the Eisenpower Expressway in 1963 [14]. In 1967, the first two fixed-time RMs were installed in Los Angeles on the northbound Hollywood Freeway (Route 101). Two years later, they were upgraded to a traffic-responsive system [8]. In the United States, more than 2200 ramp meters have been deployed by 1995 [14]. According to a federal report in 2014, various types of RM have been deployed across the US [15]. In Europe, the implementation and utilization of RM have been much more recent. In 1986, England installed its first RM devices on the M6 freeway. In 1989, the Netherlands began experiments with the RM concept near Amsterdam. France installed fixed-time traffic lights onto the thirty-five ramps of the Paris freeway network in 1970 [16; 17]. Regarding the evaluation of RM, the Kansas Department of Transportation and Missouri Department of Transportation proved that RM is beneficial to traffic flow by decreasing overall accidents on the freeway by 64% in their 2011 study conducted on I-435 Road between Metcalf Avenue and the Three Trails Memorial Crossing [18]. Washington State Department of Transportation, Georgia Department of Transportation, and the California Department of Transportation also experienced a reduced risk of merging accidents due to RM on their highways and freeways. Furthermore, the case of Minnesota's Twin Cities was very interesting. After turning off the active 430 RMs during the fall of 2000, they decreased through-traffic by 14%, doubled travel time unpredictability, and increased crashes by 26% [19]. [PERSON] and [PERSON] [20] determined that RM increased the capacity of freeway bottlenecks, after observing traffic flow characteristics at twenty-seven active bottlenecks in the Twin Cities for seven weeks with RM and seven weeks without RM. Before the observation, they tested the effectiveness of RM for several representative freeways in the Twin Cities. They then concluded that RM is more helpful for long-distance trips than for short trips [21]. [PERSON] et al. [22] applied a real-time crash prediction model (CPM) to investigate the safety benefits of a local traffic-responsive ALINEA RM control on a freeway. From their simulation study, they concluded that ALINEA RM control could reduce crash potentials by 5-37%, compared with the no-control case. In Saudi Arabia, especially in Riyadh, the concept of RM is utilized by dispatching police officers to freeway on-ramps. Relying on his own judgment, the police officer manually controls the entrance flow rate, based on the congestion on the freeway and the service road. Implementation of RM, with the help of sensors and traffic lights, will also relieve the police officers from the hard work they are doing.