esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Three-Dimensional Modeling of the Human Body
Equation (1) represents the objective function of SMPLify, which implies camera parameter \((K)\), scalar weights \(\big{(}\lambda_{\theta},\lambda_{a},\lambda_{sp},\lambda_{\beta}\big{)}\), and the 2D distance between estimated joints \((\mathit{J}_{est})\):  
\[E(\beta,\theta)=E_{j}(\beta,\theta;K,\mathit{J}_{est})+\lambda_{\theta}E_{ \theta}(\theta)+\lambda_{a}E_{\theta}(\theta)+\lambda_{sp}E_{sp}(\theta;\beta)+ \lambda_{\beta}E_{\beta}(\beta) \tag{1}\]  
However, in terms of estimating the human body shape, the SMPLify framework only uses the connection length between two joints to fit the 3D model, so that the derived result can have insufficient accuracy. Thus, [PERSON] et al. [36] added an image silhouette (\(S\)) and a model silhouette (\(\hat{S}\)) to the SMPLify framework. Thus, Equation (1) is modified with the bi-directional distance between \(S\) and \(\hat{S}\), which is shown in Equation (2):  
\[E_{S}(\overset{\rightarrow}{\theta},\overset{\rightarrow}{\beta},\overset{ \rightarrow}{\gamma};S,K)=\sum_{\overset{\rightarrow}{x}\in S(\overset{ \rightarrow}{\theta},\overset{\rightarrow}{\beta},\overset{\rightarrow}{ \gamma})}dist(\overset{\rightarrow}{x},S)^{2}+\sum_{\overset{\rightarrow}{ x}\in S}dist(\overset{\rightarrow}{x},\overset{\rightarrow}{\beta},\overset{ \rightarrow}{\beta},\overset{\rightarrow}{\gamma}))^{2} \tag{2}\]  
In contrast to the other techniques, the framework called HMR [37] has advantages in terms of training process and output results. First, the HMR framework directly derives 3D mesh parameters from image pixels, while the aforementioned frameworks only estimate 2D joint locations and 3D model parameters. Next, compared to outputting only the 3D skeleton, it is more appropriate in terms of applied shapes, as well as poses for outputting 3D meshes. To make this possible, the HMR framework consists of two steps: (1) the image is encoded through the CNN process; then, (2) the encoded latent vector \(\Theta=\{\theta:pose,\beta:shape,R,t,s:camera\}\) is sent to the 3D regression module to learn to represent the 3D human shape. At this time, for the 3D shapes representing the vector, the latent vector \(\Theta\) is inferred by minimizing the re-projection error.
# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
## 2 Materials and Methods
In Figure 2, the attention mechanism is composed of three main components: Query (Q), Key (K), and Value (V). These components are used to compute the contribution of each input element to the output. By calculating the similarity between the query vector Q and the key vector K, the weights for the value vector V are determined, resulting in a weighted sum of the values that contributes to the output. The first step involves performing a matrix multiplication between the query vector Q and the transpose of the key vector K to obtain a score matrix representing the similarity between the query and the keys. This is computed as follows:  
Figure 1.1To avoid excessively large dot product values, which could lead to vanishing gradients, the score matrix is scaled by dividing it by the square root of the dimensionality of the key vector, \(\sqrt{\mathrm{d_{k}}}\). The formula is given by the following equation:  
Figure 1.2  
Next, the scaled score matrix is passed through the SoftMax function to convert it into a weight matrix, where the elements of each row sum to 1. This process represents the relative importance of each key in relation to the query:  
Figure 1.3  
Once the weight matrix is obtained, it is multiplied by the value vector \(\mathrm{V}\) to generate the final output. The formula for this operation is as follows:  
Figure 1.4  
In the context of drilling pump energy consumption prediction, the attention mechanism can identify the impact of different time points or features on energy consumption variations in time series data. Its advantage lies in the dynamic allocation of importance weights to different features, overcoming the limitations of fixed weights in traditional methods and improving the accuracy of the model's predictions.
## 2 Methodology
### LSTM Neural Network  
LSTM outperforms simple RNN by addressing the issue of gradient explosion, resulting in enhanced ability to learn long-term dependencies, as described in [23; 24]. An LSTM consists of three types of gate structures, the forget gate, input gate, and output gate, which serve to protect and regulate cell states. The network structure's makeup is as follows:  
Figure 2: Block diagram of adaptive CKF algorithm.  
(1) The forget gate determines which information to remove from the cell state:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)}=\sigma\Big{(}W_{fh}h_{t- 1}+W_{fx}h_{t-1}+b_{f}\Big{)} \tag{15}\]  
where \(W_{f}\) represents a weight that is not shared; \(f_{t}\) is the output vector with each dimension in the range of [0,1], where 1 represents full retention and 0 represents full discarding; \(h_{t-1}\) denotes the previous moment output and \(x_{t}\) denotes the current input; \(\sigma\) represents the activation function that regulates the values flowing through the network to be within the range of [0,1]; and \(b\) denotes the bias parameter of the node learning bias.  
(2) The input gate decides which new information is stored in the cell state:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})=\sigma(W_{ih}h_{t-1}+W_{ix}x_{t} ]+b_{i}) \tag{16}\]  
\[\widetilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C})=\tanh(W_{Ch}h_{t-1}+W _{Cx}x_{t}+b_{C}) \tag{17}\]  
where \(\widetilde{C}_{t}\) generates new candidate vectors for layer \(\tanh\) and the \(\tanh\) activation function constrains the value within the range of [-1,1].  
(3) Update the previous cell status to the new candidate values:  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t}. \tag{18}\]  
(4) The output gate decides the output correlation value:  
\[\begin{split} o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})=\sigma(W_ {oh}h_{t-1}+W_{ox}x_{t}+b_{o})\\ h_{t}=o_{t}*\tanh(C_{t})\end{split} \tag{19}\]
## 4 Experiments
### Ablation Study
#### 4.3.5 Analysis of Time Complexity and Inference Time Cost  
Our model includes both spatial feature extraction using convolutional neural networks (CNNs) and temporal modeling using our multi-scaled feature fusion-Euclidean Transformer (MSF-ET) and involves several computational stages, each contributing to the overall complexity. The stage of spatial feature extraction employs a stack of convolutional layers. The time complexity for each convolution operation is generally \(O(d^{2}k^{2})\), where \(d\) is the dimension of the feature map, and \(k\) is the kernel size. In the feature fusion module, we utilize a self-attention mechanism across different feature maps generated by the spatial encoder. The complexity of the self-attention mechanism is \(O(l^{2}\cdot d)\), where \(l\) is the sequence length, and \(d\) is the dimensionality of each token. During the temporal stage, the Euclidean Transformer involves calculating distances between all pairs of input sequence elements, resulting in a complexity of \(O(t^{2}\cdot d)\).  
We conduct additional analyses to measure the processing time. Our MSF-ET model processes each frame through several stages, including spatial feature extraction, feature fusion, and temporal encoding, as shown in Figure 8 In our current implementation on an Nvidia RTX 3090 GPU, the average processing time per 25 frames is approximately 96\(\sim\)134 milliseconds (ms). This includes convolution operations, feature fusion using self-attention, temporal encoding with our Euclidean Transformer, and the CTC decoding. Due to the parallel processing capabilities of attention mechanisms, inference time remains below 250 ms even when the number of video frames exceeds 200 (as shown in the following figure), enabling real-time inference for sign language videos that are under 200 FPS.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 7 Summary of Experimental Results
### Findings  
In this study, we have identified three significant findings:  
* NCCL's latency spike in cross-docker virtualization environments: We observed a notable increase in latency when using the NCCL library in cross-docker virtualization environments. For instance, when comparing the latency of broadcast operations in the Linux shell with four GPUs, we found a 213% increase when using cross docker compared to the single-docker environment. Similarly, in PyTorch, the latency of allgather operations increased by 54% in the cross-docker setup.
* Improved latency in single docker for GLOO gather operations: Our research revealed that GLOO gather operations exhibited 36% lower latency in cross-docker environments compared to single-docker configurations. This finding underscores the importance of considering the choice of virtualization environment when optimizing latency for specific collective communication tasks.
* NCCL dominance in all-reduce operations: NCCL demonstrated superior performance in all-reduce operations compared to MPI and GLOO. For example, in the bare metal server environment within a Linux shell, NCCL achieved a remarkable 78% reduction in latency compared to MPICH during all-reduce operations. In PyTorch, NCCL showcased a substantial performance advantage, with a latency difference of 345% when compared to MPI.  
These findings provide valuable insights for practitioners seeking to optimize collective communication library selection and configuration in distributed deep learning environments, particularly when dealing with virtualization variations impacting performance.
## 4 Experimental Results
### Ablation Experiments  
The ablation experiments are typically used to remove the main proposed components of the developed method gradually to evaluate the effectiveness of each component on detection performance. To verify the effectiveness of the encoder-decoder module and attention module, ablation experiments have been done using the KMU dataset. The initial simulation is the original SSD [16], which is the baseline detector. Then the decoder module is added to make an improved version, which is the encoder-decoder module. Finally, the attention module is added to form our AED-CNN. The average precision value of each module is given in Table 2. The baseline detector SSD [16] achieved 92.04% AP, while the encoder-decoder module and attention module have significantly improved the detection accuracies, of 95.36% and 97.50%, respectively.
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 3 SNR Calculation
### Signal Electron Number  
The number of signal electrons generated per unit wavelength can be calculated by:  
\[S_{e}(\lambda)=\frac{\pi\lambda\text{A}_{d}L(\lambda)\tau_{o}(\lambda)DE_{\text{ int}}\eta(\lambda)\Delta\lambda}{4F^{2}hc} \tag{5}\]  
where \(\lambda\) is the wavelength value; \(A_{d}\) is the pixel area of the sensor; \(L(\lambda)\) is the spectral radiance of the entrance pupil; \(\tau_{o}(\lambda)\) is the transmittance of the optical system; \(DE\) is the diffraction efficiency of the grating; \(t_{\text{int}}\) is the integral time, which is estimated to be 80% of the integral time calculated from Equation (1); \(\eta(\lambda)\) is the quantum efficiency (\(QE\)) of the sensor; \(\Delta\lambda\) is the interval of spectral sampling; F is the \(f\)- number of the optical system; \(h\) is the Planck constant (6.626176 \(\times\) 10\({}^{-34}\) J-s); and \(c\) is the speed of light in a vacuum [18; 19].  
The spectral radiance of the entrance pupil was calculated by Modtran software, and the calculation conditions were:  
1. Altitude: 1 km;
2. Solar zenith angle: 30\({}^{\circ}\);
3. Ground albedo: 30% (a commonly used remote sensing condition);
4. Atmospheric model: Mid-latitude summer;
5. Boundary temperature (T): 300 K; and
6. Aerosol model: Rural aerosol, visibility 23 km.  
The spectral radiance curve of the entrance pupil for 0.4-1.0 \(\upmu\)m calculated by Modtran is shown in Figure 1.  
The diffraction efficiency (\(DE\)) of the grating is shown in Figure 2, which is the measured data from grating manufacturers. The quantum efficiency (\(QE\)) of the sensor is shown in Figure 3.  
The other values of Equation (5) are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline \(A_{d}\) & 4.225 \(\times\) 10–11 m\({}^{2}\) \\ \(\tau_{o}(\lambda)\) & 0.85 \\ \(t_{\text{int}}\) & 0.014 s \\ \(\Delta\lambda\) & 15 nm \\ F & 4.5 \\ \hline \end{tabular}
\end{table}
Table 3: Relative parameter values for Equation (5).  
Figure 1: The spectral radiance curve of the entrance pupil.
## 2 Materials and Methods
### Study Data
#### 2.2.2 Ndvi  
The NDVI time-series data from SPOT/VGT and MODIS satellite remote-sensing imagery are often used to track changes in the patterns of vegetation, land use/cover, categorize macro-vegetation cover, and estimate net primary productivity [44]. The NDVI data used in this study were obtained from the \"China Monthly 1 km Vegetation Index (NDVI) Spatial Distribution dataset\" provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))). The NDVI dataset available was based on the remote-sensing data of the SPOT-VEGFATION NDVI satellite. Monthly data were synthesized using the maximum synthesis method on the basis of data every 10 days, with a spatial resolution of 1 km \(\times\) 1 km [45]. From the downloaded monthly data, we determined the average NDVI of the Northeast Forest growing season  
Figure 1: Map of the study area (green area indicates the forest area of northeastern China).  
(April-October) from 2000 to 2020 to see the NDVI values at different locations where flash drought events occurred.
# Controlling Traffic Congestion in a Residential Area via GLOSA Development
### Traffic Management System  
Simulating a real smart city environment for testing the effects of navigation applications on traffic can provide valuable insights into how such technologies can improve traffic flow and reduce congestion in urban areas, which has important implications for urban planning and transportation policy. The assumption is that drivers assigned routes via navigation apps will strictly adhere to the instructions. The navigation applications give preference in the options for the shortcut route to shorten the time, and this feature is very useful. Drivers may choose to deviate from the route for various reasons, such as avoiding heavy traffic, taking a more scenic route, or stopping at a particular location. But this is at the expense of pass-through residential areas and traffic congestion, which can have negative impacts on the quality of life for residents in those areas.  
This could include incorporating data on residential areas and their potential sensitivity to increased traffic flow into the algorithm used to generate route suggestions or providing alternative routes that prioritize residential areas and work towards finding a balance between efficiency and quality of air. We are mainly interested in diverting traffic away from residential roads, effectively reducing GHG emissions in such locations.  
One potential solution to the problem of vehicles using shortcuts through residential areas is to implement dynamic road infrastructure and adjust traffic light timings to discourage this behavior during rush traffic periods (adaptive or intelligent traffic lights). By changing the duration of red lights at traffic lights that lead to shortcuts passing through residential areas, drivers may be deterred from taking these routes during rush hours. In Table 1, a comparison is made between routing applications and GLOSA. Note: This (*) means that this technology supports.
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 3 Experiment
### Filtering Effect and Comparison
#### 3.2.3 Medium-Density Random Value Impulse Noise Experiment
From the objective evaluation criteria, the peak signal-to-noise ratio (_PSNR_) and structural similarity (_SSIM_) calculation for each filtering method are shown in Table 3, and the comparison figure is shown in Figure 17.  
From the perspective of _PSNR_, the _PSNR_ of the proposed filtering method is always the highest, followed by median filtering and mean filtering. With the increase in noise density, the _PSNR_ of the switched median filter and the adaptive median filter decreases rapidly, and the median filter is more stable than the proposed filtering method. From the perspective of _SSIM_, the structural similarity of the filtering method in this paper is still the highest, with median filtering second. With the increase in noise density, the _SSIM_ of the proposed filtering method decreases to a certain extent, but it is still higher than median filtering. The _SSIM_ of the median filter decreases faster with increases in noise density, and the _SSIM_ is lower than 0.7 at 40% density. With the increase in noise density, the _SSIM_ of switching median filtering, adaptive filtering, and mean filtering all significantly decrease by different degrees and their structural similarity is lower than 50%, which shows that filtering quality is poor. To sum up, under the subjective and objective evaluation criteria, the filtering method proposed in this paper has a better denoising effect than other filtering methods for medium-density random value impulse noise.
## 3 Methodology and the Model
### Inhomogeneous Shale Model and User-Defined Material Failure Criterion  
#### 3.3.1 Inhomogeneous Mechanism Parameters  
Unlike the homogeneous rock, shale rock exhibits distinct heterogeneity, especially in the form of elastic modulus, tangent modulus, and Poisson's ratio. To investigate the inhomogeneous characteristic of shale rock, four specimens were used to conduct experiments using a rock mechanics servo-controlled testing system (Instron 1346 (Illinois Tool Works Inc., High Wycombe, United Kingdom) (Figure 6). The results showed that the specimens exhibited different stress-strain curves (Figure 7). This is due to the random probability distribution of solid skeleton and pore structure in a microcosmic scale.  
Figure 6: Rock mechanics servo-controlled testing system (Instron 1346).  
Figure 7: Uniaxial compression experiment of four specimens. (**a**) Shale rock specimen; (**b**) The stress–strain curves of the specimen.  
Table 1 presents a comparison of five different parameters. It can be seen that the material properties were different for different specimens. When the specimen was magnified 500 times using Scanning Electron Microscope (SEM) (Figure 8), many fractures and pores were observed. Due to different distributions of fractures and pores in the shale rock, the stress-strain curves obtained on the macroscopic scale were also different. This means that the characteristic of inhomogeneous mechanism should be considered while constructing the simulation model.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 2 Overview of NB-IoT Downlink Technology
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Channels** & **Usage** \\ \hline \multirow{6}{*}{DL} & Narrowband Physical Downlink & Uplink and downlink \\  & Control channel (NPDCCH) & scheduling information \\  & Narrowband Physical Downlink & Downlink dedicated \\  & Shared Channel (NPDSCH) & and common data \\  & Narrowband Physical & Master information \\  & Broadcast Channel(NPBCH) & for system access \\  & Narrowband Synchronization & Time and frequency \\  & Signal (NPSS/NSSS) & synchronization \\  & Narrowband reference & Channel estimation \\  & signal (NRS) & and equalization \\ \hline \multirow{3}{*}{UL} & Narrowband Physical Uplink & \multirow{3}{*}{Uplink dedicated data} \\  & Shared Channel (NPUSCH) & \\ \cline{1-1}  & Narrowband Physical Random & \\ \cline{1-1}  & Access Channel (NPRACH) & Random access \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NB-IoT channels and signals. Figure 1: Deployment modes of NB-IoT: (**a**) in-band operation; (**b**) guard-band operation; and (**c**) stand-alone operation mode. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{I_{TBS}}\)} & \multicolumn{8}{c}{**Number of Subframes**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **8** & **10** \\ \hline
0 & 16 & 32 & 56 & 88 & 120 & 152 & 208 & 256 \\ \hline
1 & 24 & 56 & 88 & 144 & 176 & 208 & 256 & 344 \\ \hline
2 & 32 & 72 & 144 & 176 & 208 & 256 & 328 & 424 \\ \hline
3 & 40 & 104 & 176 & 208 & 256 & 328 & 440 & 568 \\ \hline
4 & 56 & 120 & 208 & 256 & 328 & 408 & 552 & 680 \\ \hline
5 & 72 & 144 & 224 & 328 & 424 & 504 & 680 & \\ \hline
6 & 88 & 176 & 256 & 392 & 504 & 600 & & \\ \hline
7 & 104 & 224 & 328 & 472 & 584 & 680 & & \\ \hline
8 & 120 & 256 & 392 & 536 & 680 & & & \\ \hline
9 & 136 & 296 & 456 & 616 & & & & \\ \hline
10 & 144 & 328 & 504 & 680 & & & \\ \hline
11 & 176 & 376 & 584 & & & & \\ \hline
12 & 208 & 440 & 680 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NPDSCH TBS table. Figure 2: Frequency time grid of NB-IoT downlink systems.
# ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems
## 3 Convergence Analysis for Second Order P-Type ILC
: _For fractional order linear distributed parameter System (1) and a given reference \(y_{d}(x,t)\), if:_  
\[\rho\{(I+G(0)\Gamma)^{-1}(I-C(0)L)\}<1, \tag{55}\]  
_and:_  
\[\rho\{(I+G(t)\Gamma)^{-1}\}<1, \tag{56}\]  
_then for all \((x,t)\in\Omega\times[0,T]\), the close-loop P-type ILC updating law:_  
\[\left\{ \begin{array}{ll}u_{k+1}(x,t)=u_{k}(x,t)+\Gamma e_{k+1}(x,t),\\ q_{k+1}(x,0)=q_{k}(x,0)+Le_{k}(x,0),\end{array} \right. \tag{57}\]  
_guarantees that:_  
\[\lim_{k\to\infty}\|e_{k}(x,t)\|_{L^{2}}=0. \tag{58}\]  
From the proof of Theorem 1, it is not difficult to show the following Corollary. **Corollary 3**. : _For fractional order linear distributed parameter System (1) and a given reference \(y_{d}(x,t)\), suppose that in the learning process, the system states start from the same initial value, or more generally,_  
\[q_{k}(x,0)=\varphi_{k}(x),\;x\in\Omega,\;k=0,\;1,\;2,\;\cdots, \tag{59}\] \[\|\varphi_{k+1}(x)-\varphi_{k}(x)\;\|_{L^{2}}^{2}\leq lr^{k},\;r \in[0,1),\;l>0, \tag{60}\]  
_if:_  
\[\rho\{(I+G(t)\Gamma)^{-1}\}<1, \tag{61}\]  
_then for all \((x,t)\in\Omega\times[0,T]\), the close-loop P-type ILC updating law:_  
\[u_{k+1}(x,t)=u_{k}(x,t)+\Gamma e_{k+1}(x,t), \tag{62}\]  
_guarantees that:_  
\[\lim_{k\to\infty}\|e_{k}(x,t)\|_{L^{2}}=0. \tag{63}\]  
**Remark 3**. : _It should be pointed out that the derived result in Corollary 3 is still true for the integral order (\(\alpha=1\)) case. Note that the convergence analysis of ILC updating law (62) for integer order linear distributed parameter System (1) has been investigated in [19], in which the convergence condition is:_  
\[\|(I+G(t)\Gamma)^{-1}\|^{2}\leq\rho_{0}<\frac{1}{2}. \tag{64}\]  
_Since \(\rho\{(I+G(t)\Gamma)^{-1}\}\leq\|(I+G(t)\Gamma)^{-1}\|\), the convergence Condition (61) is less conservative than Condition (64) (see, e.g., the example in Section 4)._