esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Vehicle State Observer
### Interacting Multiple Model Fusion Algorithm  
Traditional Kalman filter usually sets the noise matrix as a constant, which does not give accurate estimation results when the model changes [27]. The IMM algorithm designed in this paper consists of a total of three sub-filters, whose models are all based on the above brush tire model as a 3-DOF vehicle dynamics model, respectively. All sub-models set the same state vector and measurement vector, all using the same input, using the SCKF algorithm to build the observer. Due to the uncertainty of the a priori statistical information of the noise, in order to keep the model of the minimum tracking error as the output of the model, multiple sub-models with different state noise and measurement noise are set separately:  
\[p=\left[\begin{array}{ccc}p_{11}&\cdots&p_{1r}\\ \cdots&\cdots&\cdots\\ p_{r1}&\cdots&p_{rr}\end{array}\right] \tag{39}\]  
where \(p_{ij}\) denotes the state transfer probability from model \(i\) to model \(j\).  
The IMM algorithm's entire iterative process is detailed below:  
Step 1. Input interacting
## 2 Configuration and Mechanism
According to the theory of space harmonics [25], the phase constant of the nth spatial harmonics can be calculated as:  
\[\beta_{\mathrm{n}}=\beta_{0}+\frac{2\pi\mathrm{m}}{\mathrm{P}} \tag{1}\]  
where \(\beta_{0}\) is the phase constant of the transmission \(\mathrm{TE}_{01}\) mode. Then the beam angle \(\theta_{\mathrm{n}}\) can be calculated from:  
\[\sin\theta_{\mathrm{n}}=\frac{\beta_{\mathrm{n}}}{\mathrm{k}_{0}}=\frac{\beta _{0}+2\pi\mathrm{m}/\mathrm{P}}{\mathrm{k}_{0}} \tag{2}\]  
where \(k_{0}\) is the wavenumber in free space. Thus, if \(|\beta_{\mathrm{n}}|<\mathrm{k}_{0}\) is satisfied as the radiation condition of harmonics, a fixed-frequency beam scanning can be achieved with a reconfigurable period length \(P\).
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 3 Materials and Methods
### Sources of Data
#### 3.1.4 Info about the Weather and Runoff  
The observed meteorological data were entered into the SAWT model in the first stage, and the weather generator was then constructed based on the input model, which comprised the meteorological database component of the SWAT model. The primary daily meteorological data that were used were those related to temperature, relative humidity, solar radiation, wind speed, and precipitation. The National Tibetan Plateau Scientific Data Center provided the CMADSV1.1 meteorological data that were used in this work [19]. The time length was from 2008 to 2016, the spatial resolution was 0.25, and the time resolution was day by day. These parameters essentially satisfied the requirements of the experimental time scale. One of the most popular databases for SWAT model meteorological data is this one, and if the measured meteorological data are modified, the simulation accuracy will increase dramatically. The precipitation temperature, relative humidity, sun radiation, wind speed, and 60 meteorological stations in the study area were chosen as experimental meteorological data in this work based on the Jiamusi DEM data range. From 2008 to 2016, monthly runoff data from the Fuyuan Hydrology Station, which is located at the complete outlet of the No. 2 watershed, and the Tongjiang Hydrology Station, which is located at the total outlet of the No. 1 watershed, were chosen.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Random Telegraph Noise (RTN)
#### 3.2.2 Main Experimental Data
Such dependences prompted several works and studies on the optimization of the cell process/architecture in order to reduce the extent of the RTN [124; 126; 127; 128; 129; 130; 131; 132; 133; 134]. In the NAND array, moreover, the RTN amplitude was shown to depend on the cell position and state along the string [135; 136] (because of the different transconductances, depending on the source and drain series resistances) and on the state of cells on adjacent BLs [137] (because of the modification in the electron density profile induced by electrostatic interference). The cycling dependence was also investigated in [138], in connection with the temperature dependence of RTN. Two cycling schemes were adopted, involving different temperatures and cycling times in accordance with the activation energy of damage recovery which will be discussed in Section 3.4. Then, RTN was read at room temperature. Figure 11 (left) shows that no significant  
Figure 10: Experimental data for the RTN distribution in NAND arrays as a function of \(N_{\text{C}}\) (**left**) and RTN tail height at a fixed \(|\Delta V_{T}|\) value and slope as a function of \(N_{\text{C}}\) for a 60 nm NAND (**right**) Flash array [123], © 2008 IEEE. Figure 9: Experimental results for the \(\Delta V_{T}\) statistical distribution on a 65 nm NOR (**left**) and a 60 nm NAND (**right**) Flash array, obtained following the procedure highlighted in Figure 8. Both the cumulative distribution function (cdf) and its complement are displayed, to highlight the exponential tails. Results for different numbers of reads are also shown for the NOR case. difference can be detected in the RTN cdfs, meaning that the high-temperature phase applied during the 500 h scheme is not annealing any defect responsible for RTN. The right-hand side of Figure 11 shows instead the dependence upon the read temperature. Again, no dependence appears when the temperature is changed from room temperature to \(55^{\circ}\)C. This result may seem surprising, as it is well known that trap capture and emission times are temperature-dependent [136; 139; 140], but comes from the distribution of such times among the cells, as discussed in the next section.
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
###### Abstract  
Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural networks at a bit-width above 6 bits. Different from previous studies that address this problem by clipping the outliers, this paper proposes a two-stage quantization method. Before converting the weights into fixed-point numbers, this paper first prunes the network by unstructured pruning and then uses the K-means algorithm to cluster the weights in advance to protect the distribution of the weights. To solve the instability problem of the K-means results, the PSO (particle swarm optimization) algorithm is exploited to obtain the initial cluster centroids. The experimental results on baseline deep networks such as ResNet-50, Inception-v3, and DenseNet-121 show the proposed optimized quantization method can generate a 5-bit network with an accuracy loss of less than 5% and a 4-bit network with only 10% accuracy loss as compared to 8-bit quantization. By quantization and pruning, this method reduces the model bit-width from 32 to 4 and the number of neurons by 80%. Additionally, it can be easily integrated into frameworks such as TensorRt and TensorFlow-Lite for low bit-width network quantization.  
deep neural network; quantized neural networks; particle swarm optimization; clustering +
Footnote †: journal:  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Simulation Analysis and Measurement Verification
### Simulation Analysis and Verification of Mutual Inductance
When the vertical distance \(h\) is 220 mm, horizontal offsets occur in 25 mm increments, with the offset \(\Delta y\) increasing from 0 mm to 250 mm. When h is 340 mm, Y-axis offsets occur in 30 mm increments, with \(\Delta y\) increasing from 0 mm to 300 mm. When \(h\) is 460 mm, Y-axis offsets occur in 35 mm increments, with \(\Delta y\) increasing from 0 mm to 350 mm. Similar to the X-axis direction, under all three height  
Figure 8: Mutual inductance variation curve versus X-axis horizontal displacement; (**a**) Free space (air, h = 220 mm); (**b**) Water debris (water, h = 220 mm); (**c**) Free space (air, h = 340 mm); (**d**) Water debris (water, h = 340 mm); (**e**) Free space (air, h = 460 mm); (**f**) Water debris (water, h = 460 mm). conditions, the mutual inductance decreases as the horizontal offset \(\Delta y\) increases. This is primarily due to the reduction in the overlapping area between the OAU and the balise as \(\Delta y\) increases, leading to reduced magnetic flux between the antennas. However, compared to the X-axis direction, the decrease in mutual inductance is more rapid along the Y-axis, as Y-axis changes result in a greater reduction of the relative overlapping area. As the conductivity \(\sigma\) increases, the mutual inductance between the two antennas decreases. The measured mutual inductance values closely align with the theoretical calculations and simulations, exhibiting only minor deviations. As the reference loops' central positions diverge, the relative deviation increases slightly, primarily due to the inverse relationship between the mutual inductance and distance. However, the model's results remain valid within the balise's operational range. Figure 9: Mutual inductance variation curve versus Y-axis horizontal displacement; (**a**) Free space (air, h = 220 mm); (**b**) Water debris (water, h = 220 mm); (**c**) Free space (air, h = 340 mm); (**d**) Water debris (water, h = 340 mm); (**e**) Free space (air, h = 460 mm); (**f**) Water debris (water, h = 460 mm).
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
## 5 Results
### Generation of Flow Data  
We generated simulated flow data to represent network communications over a _three-path_, \(\gamma\), defined by:  
\[\gamma=\{(A,B),(B,C),(C,D)\}\]  
and represented by the Figure 6:  
Figure 6: Graphical representation of \(\gamma\).  
Each data record includes information such as the date and time of communication, communication duration, source and destination addresses, the protocol used, the number of packets exchanged, etc. These data were randomly generated using appropriate distributions for each attribute.
# FADIT: Fast Document Image Thresholding
## 4 Experimental Results
### FADIT Compared with Classic Algorithms  
In this section, experiments are conducted to demonstrate the effectiveness of FADIA by comparing FADIT method with two classic thresholding methods: [PERSON]'s method and [PERSON] et al.'s method, and the experimental results are shown in Figure 2.  
In [PERSON]'s method, the between-class variance can also be written as,  
\[\sigma_{b}^{2}(\tau)=P_{i}(\tau)P_{j}(\tau)[\mu_{i}(\tau)-\mu_{j}(\tau)]^{2}. \tag{28}\]  
The criterion function Equation (28) maximizes both the term \(P_{i}(\tau)P_{j}(\tau)\) and the term \([\mu_{i}(\tau)-\mu_{j}(\tau)]^{2}\) simultaneously. The term \(P_{i}(\tau)P_{j}(\tau)\) produces the maximum value when the object and background has same pixels. The other term \([\mu_{i}(\tau)-\mu_{j}(\tau)]^{2}\) determines the criterion function to  
Figure 2: The input images are shown in the first column: (**a**) image 1, (**f**) image 2, (**k**) image 3, (**p**) image 4 and (**u**) image 5. The second (**b,g,l,q,v**), third (**c,h,m,r,w**), and forth (**d,i,n,s,x**) columns are results obtained by [PERSON]’s, [PERSON] et al.’s, and FADIT methods, respectively. Images in the last column (**e,j,o,t,y**) are the corresponding ground-truth images.  
be similar to the liner discriminant analysis. Although [PERSON]'s method is popular in image thresholding, it is not suitable for degraded document image. [PERSON] et al.'s method selects the global threshold corresponding to minimum thresholding error based on the assumption that each class is normally distributed, which limits its application because local regions do not always obey a normal distribution. FADIT method is based on two reasonable assumptions directly according to the inherent characteristics of degraded document image, so it obtains good thresholding results.  
For image 1, the result of FADIT remains the least shadow in the right side and the difference can be easily seen in Figure 2b-d, so our result displays all the characters. After the comparison of the results of image 2, image 4 and image 5, we find that: (1) results of [PERSON]'s method render the handwriting thin and even discontinuous, (2) results of [PERSON] et al.'s method has many black points in the background, and (3) results of FADIT are quite close to the ground-truth images. Especially, the result of image 5 obtained by [PERSON] et al.'s method (see Figure 2w) preserves background texture and some characters of the back side. For image 3, [PERSON]'s and [PERSON] et al.'s methods produce the worst results (see Figure 2l,m), and in them dark blocks totally cover the text.  
We quantitatively evaluate different methods with the five images and list the result in Table 2. For any image, FADIT method obtains the largest PSNR and the smallest ME, which means that the objective quantitative evaluation is consistent with the subjective visual effect of the thresholding results. Meanwhile, FADIT algorithm runs much faster than [PERSON]'s and [PERSON] et al.'s algorithm.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 2 Materials and Methods
### Angular-Domain Recurrence-Plot Analysis
#### 2.1.1 Phase-Space Reconstruction in Angular Domain  
The possible states of the dynamic system can be represented by phase-space reconstruction that reconstructs the one-dimensional time series into a high-dimensional phase space to unfold the attractor and reflect characteristics of a dynamic system. The main methods of phase-space reconstruction were proposed, and the time-delay method was employed in this paper. In the same way, for resampled angular-domain observations, angle-delay vector \(\mathbf{y}(\theta,\delta)\) in \(d\)-dimensional space could be reconstructed as in [14] by:  
\[\mathbf{y}(\theta,\delta)=\{x(\theta),x(\theta+\delta),x(\theta+2\delta), \cdots,x(\theta+(d-1)\delta)\} \tag{1}\]  
where \(\delta\) is the angle delay and \(d\) represents the embedding dimension. It is easy to see from Equation (1) that either determining angle delay \(\delta\) or calculating embedding dimension \(d\) is critical to obtain a better reconstruction result.
## 2 Materials and Methods
### Evaluation of Olfactory and Gustatory Functions  
The Sniffin' Sticks test (Burghart Messtechnik, Wedel, Germany), consisting of pen-like odor-dispensing devices for the determination of odor threshold, discrimination, and identification, was used for olfactory function assessment [24; 32] (Figure 1a).  
Sniffin' Sticks test is a validated and commonly used tool for routine clinical assessment of olfactory function and a previous study established its test-retest reliability and validity in subjects with normal sense of smell and in individuals with smell loss [32]. All  
Figure 1: Sniffin’ Sticks test for the assessment of olfactory function (**a**), “Taste Strips” test for gustatory function (**b**), the aromatic myrtle herbal-liqueur (Mitrtamaro) (**c**), and the hedonic Likert-type scale (7-points, from 0 to 6) to assess pleasantness, intensity, and familiarity dimensions of Mittamaro odor and taste (**d**).  
participants could not smoke or use scented products before the test [24; 26]. The odor threshold (OThr) was assessed using 16 stepwise dilutions of n-butanol as previously reported [24; 26; 35; 36]. OThr scores range from 16 (perception of the lowest n-butanol concentration) to 1 (no perception of the highest concentration). Three different pens were used for the odor discrimination (ODi) test, two containing the same odor and the third containing the odor target. The ODi scores ranged from 0 to 16 and were the sum of correct answers [35; 36]. The odor identification (OId) test was assessed by 16 common odors [32]. The total olfactory score (TDI) was calculated as the sum of OThr + ODi + OId, and scores as \(\leq\)16, between 16.25 and 30.5, between 30.75 and 41.25, and \(>\)41.5 were indicated functional anosmia, hyposmia, normosmia, and super smellers, respectively [37].  
The \"Taste Strips\" test (Burghart Messtechnik, Wedel, Germany) was used to assess the gustatory function as previously reported [24; 26; 33]. This test consists of filter paper strips impregnated with 4 concentrations of basic taste qualities: sweet (sucrose: 0.4, 0.2, 0.1, 0.05 g/mL), salty (sodium chloride: 0.25, 0.1, 0.04, 0.016 g/mL), sour (citric acid: 0.3, 0.165, 0.09, 0.05 g/mL), and bitter (quinine hydrochloride: 0.006, 0.0024, 0.0009, 0.0004 g/mL) [24; 26; 33] (Figure 1b). Drinking water was used to prepare all taste stimuli and for participant mouth-washing before the experiment. The total taste score (TT) was calculated as the sum of correct answers for each taste modality (values ranged from 0 to 16, and scores \(\geq\) 9 were considered normoguesia [33]). The \"Taste Strips\" test is a validated method to evaluate gustatory sensitivity, amply used for clinical assessment of gustatory function not only in healthy subjects [33], but also in patients with chemosensory deficits [24; 38].
## 3 Results and Discussions
### Cell Pulse Electrical Breakdown Experiment  
In order to study the changes in the protein under the action of the electric field in the real environment, the lightning surge generator (SUG61005 TB, Prima, Shanghai, China) was used as the pulse power supply in this project, as shown in Figure 10. The myeloma cells (SP/20) were removed from the liquid nitrogen tank and melted in a water bath at 37 \({}^{\circ}\)C, then centrifuged at 1000 r/min for 5 min before the removed supernatant; then, they were re-suspended with an SP/20 cell culture medium and spread in a 10 mL cell culture dish, and then cultured in a cell incubator. After the cells grew normally, they multiplied for 2 to 3 generations and multiplied the cells once the day before the experiment, controlling the cell density by 80% to 90%. An an appropriate amount of myeloma cell suspension was dropped into the electric cup, and the pulse electric field voltage was set as 0.2 kV, 0.4 kV, 0.6 kV, 0.8 kV, and 1 kV, respectively. The pulse number was 50, and the pulse width was fixed as 12.8 \(\upmu\)s. The electrode plate spacing of the rotating cup was 0.4 cm. The control group was cells in the natural environment, and the experiment times in the pulsed electric field environment were five times, respectively. After the completion of each experiment, cell samples were taken and placed on a slide of a biological microscope (Olympus CX43, Olympus Corporation, Tokyo, Japan) to observe the changes in cell structure.  
Figure 9: (**a–d**) Represents the SASA numerical curves of protein No. 4H9W, residues 61–88, residues 139–149, and residues 169–180 under different amounts and directional electric fields.  
In this paper, the structure of myeloma cells was observed many times under different levels of electric field intensity. The control group did not apply the pulsed electric field, and the five experimental groups applied a pulsed electric field of 0.5 kV/cm, 1 kV/cm, 1.5 kV/cm, 2 kV/cm, and 2.5 kV/cm. Under the electric field intensity of 0.5 kV/cm, 1 kV/cm, and 1.5 kV/cm, the structure of myeloma cells did not change significantly, as shown in Figure 11. When the pulsed electric field intensity was 2 kV/cm and 2.5 kV/cm, the cell membrane structure of some myeloma cells was broken. The results show that when the pulse electric field intensity reaches 2 kV/cm, the electric field has a clear effect on the phospholipid membrane structure.
## 3 Results
### Displacement Vectors and Contour Diagram
Figure 11 reflects that the soil of the MN-15 model was mainly distributed on both sides of the bottom of the grid structure (area 4 and area 5), except for area 3, and both area 4 and area 5 were oblique upward displacements. In the X-direction contour diagram (Figure 12a), a large area of lateral displacement appeared only at the bottom of the outer grid structure, and the maximum displacement was about 3 mm. This uncovered that the grid structure possessed a large lateral deformation. In the Y-direction contour diagram (Figure 12b), the main displacement changes occurred around the outer grid structure with an upward direction. The maximum was about 2.8 mm, implying that the bottom of the grid structure was damaged and deformed upwards by a downward load. As a result, the surrounding soil moved. The maximum downward displacement at the bottom of the hard crust was only 0.2 mm. Thus, the soil bore less load when it was damaged. As h increased, the larger soil displacement only occurred at area 4 and area 5 (Figures 13 and 14). It can be observed in Figure 15 that the soil displacement law of other structures similar to the former was obtained. With the increase in cement content, the flexural capacity of the upper hard crust was enhanced, enabling the structure to keep intact before the whole destruction. With the increasing load, the pressure on the grid structure gradually increased. The material destruction emerged when the shear strength of the cement soil was reached. Therefore, under the premise of meeting the bending resistance achieved by the hard crust, its cement content can be appropriately reduced and the cement content of the grid structure can be increased to enhance the overall bearing capacity of the structure while making reasonable use of resources. In Figure 15c, the hard crust was also destroyed because the modulus of the structure and soil was relatively large, and the gridded hard crust bore most of the load. Nevertheless, the increase in s made the bending moment of the hard crust increase, and the hard crust with smaller t was destroyed by bending when the structure reached the limit state. Therefore, when this type of structure is chosen for construction, the flexural resistance of the hard crust needs to be improved by, for example, upgrading the cement content or adding fibers.