esa-sceva/satcom-chunk-collection huggingface dataset:
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 4 Empirical Study
### Experimental Results
#### 4.3.3 Comparisons
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Running Time (s)**} & \multicolumn{2}{c}{**Fab-Wide**} & \multicolumn{2}{c}{**Equipment-Related**} & \multicolumn{2}{c}{**Wafer-Related**} \\ \cline{3-8}  & & **MOV** & **PR** & **EU\_P** & **EU\_O** & **PCSR** & **ODR** \\  & & **(Steps)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline CDR & 4.37 & 90,238 & 37.57 & 69.56 & 68.24 & 91.08 & 84.23 \\ Driven by & & & & & & & \\ Equipment-related performances & & & & & & & \\ Driven by Fab-wide performances & 748.44 & 98,216 & 40.69 & 72.57 & 70.88 & 95.66 & 88.64 \\ Driven by Wafer-related performances & 875.37 & 97,051 & 39.12 & 74.59 & 73.17 & 97.79 & 91.19 \\ Driven by 6 performances & 1038.27 & 97,326 & 39.49 & 75.65 & 73.80 & 97.09 & 90.40 \\ (this research) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Simulation results of the scheduling system with different performance-driving. * The proposed scheduling method with fab-wide performance driving could maximally improve fab-wide performances;
* The proposed scheduling method with equipment-related performance driving could maximally improve equipment-related performances;
* The proposed scheduling method with wafer-related performance driving could maximally improve wafer-related performance;
* The simulation findings demonstrate that the performance-driving scheduling method could significantly enhance performance. From all the experimental results mentioned above, it can be seen that this method can improve the CT, MOV, ODR, and other indicators of the production line, but it also has the disadvantage of long running time. Compared with other algorithms, although the calculation time of this algorithm is relatively long, the response time is kept within 17 min, which can fully meet the requirements of intelligent manufacturing systems for the response time of scheduling schemes. The simplified version of this algorithm has been successfully applied in a semiconductor enterprise in Shanghai. The challenges in practical application mainly focus on the quality of company data. If the quality of enterprise data is high, this method system is theoretically feasible and excellent.
# Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems
### Contribution  
Even though there are quite a few works on model compression for deep neural networks, we found that they may not be well suited for learning and inference with DNNs in small embedded systems:  
* Full model training is required prior to model compression: in network pruning [23; 24], including the state-of-the-art method [33], and low-rank approximation [16; 17; 18; 19], full models have to be trained first, which would be too burdensome to run on small computing devices. In addition, compression must be followed by retraining or fine-tuning steps with a substantial number of training iterations, since, otherwise, the compressed models tend to show impractically low prediction accuracy, as we show in our experiments.
* Platforms are restricted: existing approaches [23; 24; 25] have been implemented and evaluated mainly on CPUs because sparse weight matrices produced by model compression typically have irregular nonzero patterns and therefore are not suitable for GPU computation. In [PERSON] et al. [25], some evaluations have been performed on GPUs, but it was implemented with a proprietary sparse matrix library (cuSPARSE) only available on the NVIDIA platforms (Santa Clara, CA, USA). [PERSON] et al. [39] considered auto-tuning of model compression experimented on NVIDIA GPU and a mobile CPU on Google Pixel-1 (Mountain View, CA, USA), yet without discussion on how to implement them efficiently on embedded GPUs.  
In this paper, we provide a model compression method for deep neural networks based on \(\ell_{1}\)-regularized optimization, often referred to as sparse coding [41; 42]. Application of \(\ell_{1}\)-based regularization for DNNs is not entirely new; however, we discuss that it can be far more effective than previously reported. In particular, we apply \(\ell_{1}\)-based sparse coding with (i) _proximal operators_, which induce explicit sparsity in learning weights during training. In addition, it is followed by a (ii) _debiasing_ step, which tries to reduce estimation bias due to sparse coding: we discuss that this technique allows us to compress DNNs further without sacrificing prediction accuracy much. While inducing sparsity on learning weights, we also store sparse weights in (iii) _compressed data structures_, being able to compute forward and backward passes with compressed weights on any (iv) _OpenCL-capable devices_, e.g., mobile GPUs such as ARM Mali-T880 (Cambridge, UK).
## 5 Simulation and Experimental Verification  
### Numerical Simulation Results  
First of all, the wide range of no backflow power operation under an appropriate outer phase shift ratio is verified. For a fixed \(d\) which is set to 0.22, the value of \(d_{1}\) is changed to observe the open-loop results in the Matlab/Simulink model. The main simulation parameters are shown in Table 1 and the simulation waveforms are shown in Figure 10. According to the open-loop simulation results, the backflow power is basically eliminated for both bridges of the converter, for various values of \(k\). Therefore, it can be summarized that an appropriate \(d\) ensures a wide range of no backflow power operation.
###### Abstract  
Investigating river bars and their vegetation dynamics in response to upstream damming is important for riverine flood management and ecological assessment. However, our mechanical understanding of the damming-induced changes in river bar and vegetation, such as bar area, morphology, and leaf area index (LAI), remains limited for large river systems. Leveraging satellite images and in situ observed hydrogeomorphic data from, we improve a machine learning-based LAI inversion model to quantify variations in river bar morphology, vegetation distribution, and LAI in the Middle Yangtze River (MYR) following the operation of the Three Gorges Dam (TGD). Then we analyze the mechanisms controlling the bar and vegetation dynamics based on high-resolution river cross-sectional profiles as well as daily discharge, water levels, and sediment in both the pre- and post-TGD periods. Our results indicate that the river bar area decreased by approximately 10% from 2003 to 2020, while the vegetation area and average LAI of these bars increased by >50% and >20%, respectively. Moreover, the plant community on most river bars tended to expand from the bar tail to the bar head and from the edge to the center. The main factor driving vegetation expansion in the MYR after the TGD's operation was the reduction in bar submergence frequency (by 55%), along with a slight bar erosion. Further analysis revealed that the standard deviation of annual discharge decreased by approximately 37%, and the frequency of vegetation-erosive flow decreased by approximately 74%. Our data highlight the potential impact of large dams downstream flow regimes and vegetation encroachement. Such findings further the understanding of the biogeomorphological impacts of large dams on the river bar vegetation and have important implications for riverine plant flux estimation, flood management and ecological restoration in dammed river systems.  
bar; vegetation; leaf area index; flood; Yangtze River 2023  
28 March 2023  
2023
## 4 Results and Discussion
### Hoods at Tunnel Exit
In addition, a reflected expansion wave is emitted backward when the front of the compression wave passes through the opening holes, which can further relieve the pressure gradient of the compressed wave. The variation of the gradient pressure at RH point with different number of holes on the hood is shown in Figure 16a. With the increase in the number of holes, the opening area enlarges and approaches the monitoring point gradually, which leads to a stronger reflected expansion wave arriving earlier; then, the pressure gradient at RH point decreases correspondingly. The MPW at RM point with a varied number of holes on the hood is shown in Figure 16b. It is found that the amplitude of the MPW deceases rapidly with the increasing hole number due to the reflected expansion wave and the relief effect of the holes. The amplitude of the MPW can be reduced to 27.02 Pa when 22 holes is arranged on the hood symmetrically, which is 79.23% less than that without hood. Figure 14: Pressure contours with different hood exits. Figure 15: The effects of oblique hood exit on the MPW.
## 4 Numerical Examples
### Waveform Parameters Impacts  
First, we evaluate the PSLR and the ISLR of the velocity ambiguity functions of the designed waveforms in terms of different sparsity \(K_{s}\) and scaling \(\eta\) factors. The results are shown in Figure 7. It can be seen from the figure that the PSLR and the ISLR of the velocity ambiguity function of the designed waveform gradually decrease as the sparsity increases, which illustrates that the sidelobe leakage decreases as the number of OFDM symbols increases. In Figure 7a, we can see that the PSLR decreases as \(\eta\) increases. Moreover, when \(K_{s}\geq 4.28\%\) and \(\eta\geq 0.5\), the PSLR remains relatively stable. In addition, when \(\eta\leq 0.2\), the PSLR appears to fluctuate as \(K_{s}\) increases. The reason for this is that the weight of PSLR in the cost function is too small, which makes the PSLR not well-optimized. In Figure 7b, the ISLR of the velocity ambiguity function of the designed waveform does not fluctuate significantly as \(\eta\) increases, except for \(\eta=1\). The reason for this is that the optimization of the ISLR is not considered in the optimization algorithm when \(\eta=1\). Next, we compare the performance of the velocity ambiguity function for the designed waveform between \(K_{s}=4.28\%\), \(\eta=0.6\) and that of the uniform waveform, which has a total of 80 OFDM symbols, each equally spaced within a wireless frame. The timings of the designed waveform and the uniform waveform are shown in Table 1. The row where the designed waveform and the uniform waveform are located indicates that the \(i\)th OFDM symbol within a slot is extracted as the sensing symbol. There are 80 slots within a wireless frame when the 5G system parameter \(\mu=3\). And 'null' indicates that there is no sensing symbol in the corresponding slot. We note that the uniform waveform does not satisfy the constraint in (9). The velocity ambiguity functions are shown in Figure 8. It can be seen from the figure that the velocity resolution is 2.08 km/h for both the designed waveform and uniform waveform, and the maximum unambiguous velocity is \(\pm 83.09\) km/h. Moreover, the PSLR of the designed waveform's velocity ambiguity function is \(-13.37\) dB, which is close to the PSLR of the uniform waveform. However, it should be noted that the ISLR is \(-0.35\) dB, which is much higher than the ISLR of \(-9.92\) dB for the uniform waveform. This means that the designed waveform has a sidelobe leakage problem, but the peaks of the sidelobe are all low. Figure 7: Sidelobe intensity vs. sparsity. **(a)** PSLR vs. sparsity for different \(\eta\) factors. **(b)** ISLR vs. sparsity for different \(\eta\) factors. In the next experiment, we analyze the sparse recovery performance of the designed waveform for different target numbers. We set \(\text{SCNR}=-10\) dB, the number of Doppler cells is set to 320, and the target numbers are set to \(N_{t}=4\), 8, and 12, respectively. And the signal-to-clutter-plus-noise ratio (SCNR) is expressed by (24), according to (14).
## 1 Introduction
### The Realistic Implementation-Oriented Model  
Conventionally, the well-known memoryless linear discrete-time multiple-input multiple-output (MIMO) fading channel model has been indistinctively adopted for modeling the received samples for single-receiver, multi-sensor and for multiple-receiver, single-sensor cognitive devices in data-fusion cooperative spectrum sensing. However, this model, henceforth called conventional model (_C-model_),is not well suited to the case of multiple CR receivers cooperating, at least not without appropriate modifications in the receiver model. These modifications are needed because, in the conventional model, the samples collected by each CR are considered forwarded to the FC exactly as they are, as if no signal processing task is performed in advance. Here, supported by the results in [8], we consider a more realistic implementation-oriented MIMO (_R-model_) approach in which typical signal processing operations realized within a direct-conversion CR receiver architecture are taken into account, such as filtering, automatic gain control (AGC) and quantization.
# Algorithmic Matching Attacks on Optimally Suppressed Tabular Data
## 4 Evaluation of the Matching Attacks
### Safety of Primary Suppressed Cells  
We first evaluate how many primary suppressed cells in the test tables are unsafe; that is, the widths of their effective feasibility intervals are smaller than the minimum width of 8 in the case of our experiments. Table 2 shows the ratios of unsafe primary suppressed cells whose effective feasibility intervals become shorter than the threshold for the minimum width. The ratio for each table size is computed by dividing the sum of unsafe primary suppressed cells by the sum of all primary suppressed cells in 50 test tables of that size. The bar chart in Figure 6 displays the same results graphically. The results show that about 46% to 83% of primary suppressed cells violate the safety requirement in Definition 3. We observe that, as the size of a table becomes larger, the ratio of unsafe cells increases.  
We next examine how the ratio of unsafe cells depends on the dimension of the null space \(N(A)\) of the coefficient matrix \(A\) of a suppressed table in Section 3.2. Table 3 shows the ratios of unsafe cells by crossing tables with the dimensions of the null spaces and the number of cells in those tables. The results show that the lower the dimension of the null space is, the higher the risk of having unsafe cells in the table.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**\#Cells in a Table**} & **\#Unsafe Primary** & **\#Primary** & **Ratio of** \\  & **Suppressed Cells** & **Suppressed Cells** & **Unsafe Cells** \\ \hline
16 & 48 & 104 & 0.46 \\
25 & 117 & 170 & 0.69 \\
36 & 190 & 230 & 0.83 \\
49 & 226 & 271 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ratio of unsafe primary suppressed cells attacked by the matching algorithm. We count the total sums of unsafe primary suppressed cells and primary suppressed cells in 50 different tables of each size.  
Figure 6: The number of unsafe primary suppressed cells compromised by the matching algorithm. The left bar and the right bar of each table size show the number of unsafe primary suppressed cells and that of primary suppressed cells, respectively.  
Figure 7 shows the number of candidate tables in dependence to the dimension of the null space of a suppressed table in box plot graph. We show the results for each size of tables. We see that when the dimension of the null space of a suppressed table is low, there are only a small number of possible combinations of cell values, and, therefore, the widths of the effective feasibility intervals of the suppressed cells become very slim.
## 3 Simulation Results
### Time-Domain Current Density and Magnetic Flux Density Field  
Figure 3 shows the soil current density and the magnetic field lines resulting from the application of a Dirac voltage impulse \(\delta(t)\) to Phase Conductor \(a\). The results in Figure 3a,b refer to two different instants of the same simulation and were obtained through the described numerical inversion of the solution in the Laplace domain.  
Figure 3a shows the results after \(1\times 10^{-3}\) s from the application of the impulse. Comparing the obtained magnitudes of the phase conductor (\(I_{phase\;a}\)) and soil (\(I_{soil}\)) currents, \(I_{soil}\) exhibits a faster decay. The soil features a lower time constant \(\tau\) compared to the phase conductor, due to its lower electrical conductivity.  
Finally, Figure 3b refers to \(t=0.138\) s, i.e., the moment corresponding to the pipeline current sign inversion. This feature is highlighted by the change in magnetic field topology compared to Figure 3a.  
Figure 3: Current density distribution and magnetic field lines at \(t=1\times 10^{-3}\) s (**a**) and \(t=0.138\) s (**b**) obtained from the impulse response in the Laplace domain.
# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 2 Using Genetic Algorithm to Optimize Spatiotemporal Correlation Forecast Model
### Spatiotemporal Correlation Prediction Model
#### 2.1.1 The First Feature Extraction Based on DSCNN  
The classic Convolutional Neural Network (CNN) is a deep learning model proposed by [PERSON] in 1998 and applied to the field of computer vision. It is to achieve dimensionality reduction of high-dimensional data while performing feature extraction through convolution and pooling operations. Due to the excessive number of parameters and the increase of computational cost in the process of feature extraction by classical CNN, to solve this problem, Xception [17] separates spatial convolution and channel convolution, and separates the correlation in two directions. MobileNet [18] adopted DSCNN to optimize the model complexity while improving performance. In the model of this paper, the classical convolution is replaced by the DSCNN operation, which aims to achieve a shallow global feature extraction from the two dimensions of space and channel.  
Figure 2 shows the structure of the DSCNN, the input feature map size is C \(\times\) H. It is mainly optimized on the network structure and consists of two processes: Depthwise  
Figure 1: Using Genetic Algorithm (GA) to optimize Spatiotemporal correlation forecast model.  
Convolution (DWC) and Pointwise Convolution (PWC). This approach not only conforms to the law of multi-dimensional feature extraction, but also can freely adjust the model representation ability compared with standard convolution.
## 2 Materials and Methods
### Data Collection and Analysis  
All patients underwent CBCT and intraoral scanning on the same day using the same device for both the preoperative (T\({}_{0}\)) and postoperative (T\({}_{1}\)) images (the latter of which were taken at least 4 months after surgery). The CBCTs were obtained using a Veraview X800 ([PERSON], Kyoto, Japan) and saved as.dcm files, whereas the dental scans were obtained using a 3 shape TRIOS intraoral scanner and saved as.STL files. Both sets of CBCT data were separately imported into the planning software, Simplant O&O (Materialise Dental NV, Leuven, Belgium). The 3D virtual models and soft tissue volumes were reconstructed and oriented using a Frankfort horizontal plane (FHP; orbital--middle point of portion) parallel to the floor. Since all patients had fixed appliances in place at the time the CBCTs were taken, metal artifacts from the metal brackets were scattered all over the tooth surfaces, obscuring the details of occlusion. An intraoral scan was then imported and superimposed over the original blurred CT dentition using occlusal landmarks and voxel-based matching methods to obtain a clear vision of the dentition (Figure 1). For the T\({}_{0}\) images, virtual skulls were separated into independent segments according to the procedures that the patient underwent, and these were prepared for surgical movement. The software's automatic segmentation will distinguish a specific component and eliminate all other structures for better visualization and analysis, and this is less time-consuming compared to manual segmentation [8]. The preoperatively virtually osteotomized bone segments were moved the same amount as the actual surgical movement in both the anteroposterior and vertical directions. The actual surgical movements for each patient were obtained using the voxel-based matching method after importing and superimposing the virtual skull and soft tissues from the T\({}_{1}\) onto the T\({}_{0}\) project (Figure 2). The native Simplant algorithm was used to generate a 3D soft tissue prediction image (T\({}_{P}\)) (Figure 3). The distances between seven landmarks (Table 1) of the T1 and the TP were measured. All of these points were standard anthropometric or cephalometric landmarks [9; 10; 11]. All distance testing was completed by a single investigator to reduce inaccuracies between viewers. All measurements were taken twice, two weeks apart.  
Figure 1: A lateral view of a CT virtual skull from a patient showing the imported intraoral scan superimposed over the original blurred CT dentition.  
Figure 3: Lateral views of the soft tissue volumes created from the CBCTs: (**a**) pre-treatment; (**b**) actual post-treatment; and (**c**) prediction.  
Figure 2: A virtual skull showing the T1 superimposed on the T0 followed by using the voxel-based matching method to generate the actual surgery movements.
## 5 System Implementation and Configuration
### Technical and Economic Criteria of the SPV/WTG System
#### 5.4.4 Battery  
The _\"Trojan L16P\"_ battery model is considered in this study, which provides good characteristics combined with low cost. Figure 12 summarises the technical specifications to _\"Trojan L16P\"_ battery model. More details can be found in [34]. The technical specifications and costs used in the HOMER simulation based on the proposed battery model are listed below:  
1. Economic issues: the initial installation costs of a battery, the replacement costs, and the annual O&M costs per unit are $300, $300, and $10, respectively.
2. Technical issues: the lifetime of the _\"Trojan L16P\"_ battery and the efficiency are set as 5 years and 85%, respectively.
3. Battery size: the sizes of the simulation-values of the applied inverter are 32, 40, 48, 56, 64 and 72 units.  
Figure 11: The output power over a different wind speed values.  
Figure 12: _“Trojan L16P\"_ battery model characteristics.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 3 Results and Discussion
### Results of Homogeneous FBG Modeling
It should be noted that the deviation peaks of the LS and TM methods did not coincide besides the central wavelength, which is the evidence of differences in the mathematical modeling approaches. Nevertheless, the deviations of the modeled FBG spectra across the whole relevant wavelength range can be considered negligibly small for most applications, since the deviations do not exceed \(-40\) dB, while the typical signal-to-noise ratio of the FBG interrogation device is between 30 to 40 dB [24]. Therefore, the results confirm the correctness of the modeling methods implementation. In addition, the RMSD values of the LS method were obtained for various partition intervals \(\Delta\)z, and the corresponding dependence is presented in Figure 6.  
The results shown in Figure 6 indicate that the RMSD\({}_{\text{LS}}\) values in the case of homogeneous FBG modeling decrease monotonically with the decrease of partition interval \(\Delta\)z. The data points were approximated by the second order polynomial (blue line in Figure 6) with the following coefficients: 1.184, 1.311 \(\times\) 10\({}^{-3}\), 2.366 \(\times\) 10\({}^{-3}\) and the norm of residuals equal to 3.087 \(\times\) 10\({}^{-5}\). Thus, with the vanishing partition interval of the homogeneous FBG model, the RMSD\({}_{\text{LS}}\) converges to the value slightly lower than the RMSD\({}_{\text{TM}}\).