esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 4 3D NAND Reliability  
In this Section, we will mainly focus our attention on the most promising 3D NAND architecture, i.e., the one based on vertical strings of cells and parallel planes connecting the control gates [23; 24; 25].  
Figure 33: (**Left**) \(V_{T}\) shift due to program and pass disturbs as a function of the pass voltage [36], © 1996 IEEE; (**Right**) BER due to program disturb as a function of the number of P/E cycles [257], © 2009 ACM.  
A schematic view of the memory array organization is shown in Figure 34 (left), but we will leave the details about the array operation to the specific literature [28]. The individual cell is a vertical-channel transistor with polysilicon body, usually shaped as a hollow cylinder filled with dielectric [286], whose cross-section is depicted on the right of Figure 34. Such a device was shown to exhibit superior performance with respect to a conventional vertical nanowire transistor, because of the high defectivity in the central region that plagued the performance of the latter structure. The gate stack of today's 3D NAND can be based on either a floating gate [23; 287; 288; 289; 290; 291], similar to planar NAND devices, or a charge-trap stack similar to an oxide/nitride/oxide (ONO) layer, where the charge is stored in traps within the nitride layer [10; 11; 22; 292].  
As far as reliability is concerned, all mechanisms effective in planar NAND cells are also present in 3D devices, though their impact is affected by the new cell geometry and design and array operation. Unfortunately, the very limited amount of data publicly available makes it hard to conduct an exhaustive assessment of the role played by each of them. On a general standpoint, the 3D cylindrical cell footprint and channel length can be made larger than their planar counterparts [22; 291], as the 3D architecture relieves the burden of scaling the cell area by placing it onto the increase in the number of stacked layers. As a consequence, a better reliability is achieved in 3D NAND cells, which resulted in the development of QLCs. However, the vertical polysilicon channel and the layer stacking give rise to new and unexpected issues, providing ever newer ground for research. In the following, we will review the reliability issues in 3D NAND within the same framework adopted so far: we will only discuss issues that become relevant with P/E cycling or repetitive data read, neglecting those associated, for example, with \(V_{T}\) placement, string resistance and time-0 variability.
## 3 Results
### Sorption Properties
The results presented (Table 3) showed that the highest values of sorption capacity (C = 0.0018 mg/g) and PC against ammonia are characterized by MCM-41 (PC = 5.76 \(\times\) 10\({}^{-5}\) mol kg\({}^{-1}\) Pa\({}^{-1}\)), whose BET surface area is the highest (310.70 g/m\({}^{2}\)) in comparison with all solvents used. In the case of VOCs, i.e., acetone and cyclohexane, the highest PC indicator is characterized by the zeolite material SM, for which the PC value is 2.98 \(\times\) 10\({}^{-3}\) mol kg\({}^{-1}\) Pa\({}^{-1}\) and 2.73 \(\times\) 10\({}^{-4}\) mol kg\({}^{-1}\) Pa\({}^{-1}\), respectively. For capacity, these values were: Acetone--4.0716 mg/g; cyclohexane--0.1860 mg/g), respectively. It is worth emphasizing that this material showed the second highest BET surface area (111.73 g/m\({}^{2}\)) in relation to all adsorbents used in the above work. Based on the obtained data, it can be concluded that it has the best adsorption properties to VOCs compared to the rest of the modifiers. It was noted that the large structure of pores and the BET surface area of the adsorbents used have a large impact on the breakthrough time, capacity, and partition coefficient when saturated with test chemical substance vapors. Due to the small size of the specific surface  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Tested Gas** & **Type of** & **Sorbent** & **Concetation,** & **Partial** & **Capacity** & **Partition Coefficient (PC),** \\  & **Sorbent** & **Code** & **ppm** & **Pressure, Pa (C), mg g\({}^{-1}\)** & **mol kg\({}^{-1}\) Pa\({}^{-1}\)** \\ \hline \multirow{8}{*}{AMMONIA} & & Na-A & 18.7 & 1.87 & 0.0003 & 8.77 \(\times\) 10\({}^{-6}\) \\  & & Na-P1 & 1.87 & 0.0002 & 5.21 \(\times\) 10\({}^{-6}\) \\  & & SM & 1.87 & 0.0008 & 2.48 \(\times\) 10\({}^{-5}\) \\  & & SM & 1.87 & 0.0013 & 4.17 \(\times\) 10\({}^{-5}\) \\  & & Na-A H & 1.87 & 0.0002 & 5.8 area and pore volume, the remaining group of modifiers used did not give the highest ammonia and VOCs adsorption capacity.
# Shear Mechanical Properties of Bolt-Grout Interface under Different Bolt Surface Profiles
## 3 Test Results
### Shear Strength Characteristics
#### 3.2.1 Effect of Rib Spacing on Shear Strength  
Figure 10 illustrates the variation of peak strength with rib spacing under different normal stresses. The change rule of peak strength is the same, i.e, the increase in rib spacing shows the change trend of decreasing and then increasing. Meanwhile, the smaller the rib spacing, the more obvious the change. For example, when the normal stress was 0.5 MPa, the shear strength decreased by 1.33 MPa and 0.15 MPa when the rib spacing was increased from 12 mm to 20 mm and 28 mm to 36 mm, respectively. The figure also shows that, under all typical stress conditions, the shear strength at 44 mm rib spacing surpasses 36 mm, which is due to the change in the failure mode.  
Figure 11 illustrates the variation of residual strength with rib spacing under different normal stresses. As the rib spacing rises, the residual shear strength generally exhibits a trend of dropping and then increasing. It is significant to observe that, at 0.5 MPa, the residual shear strengths for all five rib spacings are comparable. The rib spacing has a minimal effect on the residual shear strength. This is because the residual shear stress is mainly generated by the friction of the interface. The interface friction is weaker at 0.5 MPa, and changing the rib spacing no longer affects the interface friction.  
Both peak and residual shear strength increase with increasing normal stress in a roughly linear correlation, which was exhibited by the five sets of data with different rib spacings. This is because the meshing effect and frictional resistance at the Bolt-Grout interface rise with an increase in normal stress, which in turn causes an increase in shear stress.
## 4 Results  
### Defect Detection Experiment  
#### 4.1.1 Defect Detection Evaluation Index  
For defect detection, this experiment uses precision (\(P\)), recall (\(R\)), and \(mAP\) (mean of Average Precision) to evaluate the effect of YOLOv6 on surface defect detection. The calculation formulas of \(P\), \(R\), and \(mAP\) can be defined by Formulas (16) to (18), respectively.  
\[P=\frac{TP}{TP+FP} \tag{16}\]  
Figure 9: Fitting circle effect.  
\[R=\frac{TP}{TP+FN} \tag{17}\]  
\[mAP=\frac{\sum_{i=1}^{K}AP_{i}}{K} \tag{18}\]  
Among them, \(TP\) is the number of positive samples that are correctly detected as positive by the algorithm, \(FP\) is the number of negative samples that are correctly detected as positive by the algorithm, and \(K\) is the number of defect types. \(AP_{i}\) is the average precision of various classes, and its definition is shown in Equation (19).  
\[AP=\int_{0}^{1}P(R)d(R) \tag{19}\]
## 4 Experiments
### Results
#### 4.3.1 SORT and Deep-SORT Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cost Matrix**} & \multicolumn{8}{c}{**Evaluation Metrics**} \\ \cline{2-11}  & **\% MOTA\(\uparrow\)** & **\% MOTP\(\uparrow\)** & **TP\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** & **\% MT\(\uparrow\)** & **\% ML\(\downarrow\)** & **FM\(\downarrow\)** & **FPS\(\uparrow\)** \\ \hline \multicolumn{11}{c}{SORT} \\ \hline \(IoU\) & 90.57 & 92.21 & 29,589 & 31 & 2932 & 114 & 60.5 & 1.5 & **556** & 1368 \\ \(D_{E}\) & 88.85 & 91.48 & 29,307 & 310 & 3031 & 297 & 59.0 & **0.9** & 618 & **1389** \\ \(R\) & 68.23 & 88.45 & 25,015 & 2748 & 5550 & 2070 & 30.7 & 3.6 & 1179 & 1380 \\ \(E_{D}^{IoU}\) & 90.44 & 92.27 & 29,538 & 22 & 2974 & 123 & 59.0 & 1.5 & 561 & 1317 \\ \(R^{IoU}\) & 90.34 & 92.30 & 29,491 & 9 & 3008 & 136 & 58.7 & 1.8 & 566 & 1377 \\ \(R^{D_{E}}\) & 90.77 & 92.00 & 29,713 & 91 & 2827 & 95 & 65.0 & **0.9** & 562 & 1368 \\ \(M\) & 90.21 & **92.35** & 29,445 & **5** & 3053 & 137 & 57.4 & 1.8 & 564 & 1311 \\ \(A\) & 90.87 & 91.96 & **29,756** & 101 & **2799** & **80** & **67.5** & 1.2 & 558 & 1288 \\ \(W_{M}\) & **90.90** & 92.10 & 29,715 & 50 & 2832 & 88 & 64.4 & 1.2 & 558 & 1298 \\ \hline \multicolumn{11}{c}{SORT with Class Gate Metric} \\ \hline \(IoU\) & 90.63 & 92.20 & 29,611 & 33 & 2926 & 98 & 61.7 & 1.5 & **550** & 1404 \\ \(D_{E}\) & 90.82 & 92.00 & 29,739 & 100 & 2830 & 66 & 66.9 & 1.2 & 566 & 1408 \\ \(R\) & 87.74 & 91.56 & 29,134 & 500 & 3216 & 285 & 63.2 & 1.2 & 642 & **1425** \\ \(E_{D}^{IoU}\) & 90.49 & 92.27 & 29,553 & 22 & 2969 & 113 & 59.6 & 1.5 & 556 & 1337 \\ \(R^{IoU}\) & 90.36 & 92.32 & 29,497 & 8 & 3015 & 123 & 59.3 & 1.8 & 559 & 1392 \\ \(R^{D_{E}}\) & 90.98 & 92.05 & 29,767 & 77 & 2813 & 55 & 68.1 & **0.9** & 555 & 1375 \\ \(M\) & 90.24 & **92.36** & 29,456 & **5** & 3050 & 129 & 58.1 & 1.8 & 559 & 1307 \\ \(A\) & **91.02** & 92.02 & **29,785** & 81 & **2799** & **51** & **69.3** & 1.2 & 554 & 1292 \\ \(W_{M}\) & 90.93 & 92.10 & 29,727 & 53 & 2837 & 71 & 65.3 & 1.2 & 552 & 1305 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of the SORT, Deep-SORT, and proposed data association cost matrices on the ISR Tracking dataset. Figure 4: Tracking MOTA variation according to different data association thresholds on the MOT17 dataset.
## 3 Multi-Sensors Spatio-Temporal Multi-Correlation Fusion Analysis
### Signal Fusion Based on Spatio-Temporal Multiple Cross-Correlation and Correlation Function  
Multiple sensors with the same accuracy are used to measure different parts of the same object, and the effectiveness of the information collected may be inconsistent. It is obviously inappropriate to distribute the weight of the sensor according to a precision characteristic when an unexpected situation occurs in it, such as a large difference between the collected data and the actual situation, or damage leading to a complete failure. Since the correlation function of vibration signals is a measure of waveform similarity, it can also be seen as the support of one signal to another. Therefore, this support is used as the basis for weight assignment, that is, data fusion based on the starting point of the correlation function weighting method [37]. The implementation steps of the method in this section are shown in Figure 5.  
In the fusion of traditional weighting algorithms, the most difficult thing is to determine the weight. Generally, the weighted average method is used to directly fuse the data of various sensors, which is not strict enough. On the contrary, the weighted value adjustment based on multiple cross-correlation functions is more accurate.  
In this method, the accuracy of the sensor and the influence of random factors are taken into account to connect any signal with other signals. The greater the correlation, the greater the weight assigned, and vice versa. In general, the more accurate the information reflecting the target state, the higher the degree of support. The energy of the correlation signal is used to represent the correlation degree; that is, the higher the energy, the higher the correlation degree. The energy of discrete signals \(x(t)\) and \(y(t+\tau_{1})\) is calculated by the following formula:  
\[E_{xy}=\sum_{i=1}^{n}\bigl{[}R_{xy}(i)\bigr{]}^{2} \tag{14}\]  
Assuming that the energy of the signal obtained by performing the cross-correlation operation of each signal is \(E_{ij}\), the total correlation energy of the signal collected by the sensor \(i\) and the signal collected by other sensors can be expressed as:  
\[E_{i}=\sum_{j=1,j\
eq i}^{n}E_{ij} \tag{15}\]  
When the weight \(p_{i}\) is proportional to the energy of the correlation function, there were:  
\[\begin{array}{c}p_{1}:p_{2}:\cdots:p_{n}=E_{1}:E_{2}:\cdots:E_{n}\\ p_{1}+p_{2}+\cdots+p_{n}=1\end{array} \tag{16}\]  
Signals \(x_{1}\), \(x_{2}\),..., \(x_{i}\) from the same fault source acquired by different sensors, the different weights of each signal can be obtained from the above formula, so the final fusion result \(X\) can be obtained as:  
\[X=p_{1}x_{1}+p_{2}x_{2}+\cdots+p_{i}x_{i} \tag{17}\]
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 5 Proposed Solution
### Relaxing Objective Function  
To cut the optimization time of the proposed scheme, an upper bound function of the objective function is offered. The upper bound function is to minimize the sum of all link utilization instead of minimization of maximum link utilization. The function is as follows:  
\[\sum_{i=1}^{n}\sum_{j=1}^{n}\frac{\sum_{f=1}^{p}\mathbf{A}_{ij}^{f}\cdot\max( PF_{f},TM_{f})}{\tau\cdot\mathbf{B}_{ij}}. \tag{9}\]  
To prevent violation of the links' bandwidth constraints, Equation (10) is subjoined. Although it decreased the optimization time, the \(\gamma\) must be selected wisely. It should grow up to more than 1, down to the fact that packet loss is unavoidable as the user's traffic exceeds the network capacity in peak times:  
\[\sum_{f=1}^{p}\mathbf{A}_{ij}^{f}\cdot MS_{f}\leq\gamma\mathbf{B}_{ij}. \tag{10}\]
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 5 Simulation Experiment
### Dynamic Task Planning Simulation
#### 5.2.1 UAV Fails and Cannot Complete Its Mission  
Randomly delete one UAV and assign its remaining reconnaissance targets to other UAVs. In this simulation, the randomly deleted UAV is selected as No. 3, and the contingency occurs at the time of 3.6 h, when the No. 3 UAV has completed the reconnaissance of task area 11. The remaining reconnaissance tasks of task areas 12 and 10 need to be reassigned.  
The results of the traditional dynamic task planning strategy solution are shown in Table 4, and the corresponding path planning is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline
**UAV No.** & **New Sequence of Reconnaissance Task Areas** & **Reconnaissance Time (h)** \\ \hline \(1\) & [4, 5, 10, 2, 3, 6] & [2.159, 1.024, 1.585, 1.590, 1.590, 1.590] \\ \(2\) & [8, 7, 1, 12, 9] & [1.558, 2.157, 1.159, 1.159, 1.159] \\ \(3\) & [11] & [3.238] \\ \hline \end{tabular}
\end{table}
Table 4: Task planning results of traditional assignment model with reduced UAV.  
Figure 9: Diagram of task planning for the traditional assignment model with reduced UAV. (Red dot—UAV positions when contingency, red-cross—deleted UAV, dashed line—cancelled initial flight path, dotted line—added new flight path, same below).  
The results of the dynamic task planning strategy solution proposed in this paper are shown in Table 5, and the corresponding path planning is shown in Figure 10.  
In the case of UAV reduction, the traditional dynamic assignment model solves the dynamic task planning to obtain a UAV's total reconnaissance gain of 4.588 and a total flight distance of 1479.981 km. The dynamic assignment mechanism proposed in this paper solves the dynamic task planning to obtain a UAV's total reconnaissance gain of 4.619 and a total flight distance of 1442.816 km. The time consumed by the algorithm is 0.079 s. We can see that the proposed method is more advantageous and can effectively improve the UAV's reconnaissance gain and reduce the flight distance.
# Model-Based Design of Aircraft Landing Gear System
## 3 Dynamic Verification Model  
Once all of the previously presented computations have been carried out, a preliminary configuration of the landing gear in terms of the size and characteristics is available. The next step would be to test if the newly designed components are actually capable of carrying out their function. One of the most useful methods to reach such a goal is the use of time-dependent dynamic models. These kind of models make it possible to simulate the behavior of complex systems, including their reciprocal interactions, during a defined mission. Such a dynamic is true also for aircraft systems, which are highly integrated and interdependent, and, as seen in Equation (2), each is in charge of a specific function.  
When considering the entire flight envelope that usually characterizes a typical mission of an aircraft, the landing phase is a very minor one in terms of the time and distance covered. This is especially true when larger-sized aircraft, designed for longer missions, are considered. However, it is actually described as the most problematic phase on the entire mission due to the difficulty of the maneuver and the overall criticality in terms of safety [23]. For this reason, a highly granular dynamic model for the prediction of the landing behavior of an aircraft has been developed. The software chosen for this task is the Simulink(Version 10.7; MathWorks, 2023) software for its attitude to model even highly complex systems and complete integration with the MATLAB(r) environment. Such interconnection allows for seamless transfer of the parameters computed by the preliminary sizing script into the dynamic model. As such, the latter may be employed to test the landing performance of any kind of aircraft, regardless of its size or intended use. This model is comprised of multiple sections, witheach dedicated to a different aspect of the landing phase of the aircraft and the landing gear subsystems. More specifically, in the order with which each will be discussed, it includes:  
* The longitudinal dynamics block;
* The vertical dynamics block;
* The landing gear block:
* The main wheels:
* The brake pack;
* The tire/road interface;
* The braking system control.
## 3 The Relationship between Waist Radius and Working Distance of the Probe  
Figure 2 shows that the smaller the waist radius \(\omega_{f}\) of the GRIN fiber probe is, the higher the coupling efficiency is. However, Equation (8) is based on the premise that the coupling distance between the probe and HC-PCF is equal to the working distance \(z_{\omega}\) of the GRIN fiber probe. Because of this, it is necessary to analyze the relationship between the waist radius and working distance. According to Equation (1) and Equation (2) and previous studies [18; 19; 20], the nonlinear relationship between the waist radius \(\omega_{f}\) and working distance \(z_{\omega}\) is complex. Both of them are related to the structural parameters of the GRIN fiber probe, such as the length \(L_{0}\) of the no-core fiber and the length \(L\) of the GRIN fiber.  
With the length of GRIN fiber increasing, the working distance and waist radius of the probe change periodically, while both of them become larger with the increase of the length of the no-core fiber. On one hand, to obtain a longer working distance and smaller waist radius, the length of the no-core fiber should be weighed. Reference [21] pointed out that the length of the no-core fiber should not be too long, otherwise the light beam may exceed the inner diameter of the GRIN fiber, which makes the beam enter GRIN fiber cladding and reduces the spot quality. The maximum length \(L_{0\text{max}}\) of the no-core fiber can be calculated when the parameters of the fibers and beam are known. On the other hand, due to the periodicity pitch characteristics of the GRIN fiber, analysis of the first cycle is enough, so the length of the GRIN fiber can be limited to the first cycle \(L_{\text{max}}\). However, previous works have not studied how to design the length of fibers to ensure the longest working distance of the probe at a specified waist radius. In view of this, an optimization solution was proposed to guide the design of the probe.  
For a probe with a specified waist radius \(\omega_{f}\), more than one set of no-core fibers and GRIN fibers (\(L_{0}\), \(L\)) is satisfactory because of the periodic property of the function. The set that can generate the largest working distance needs to be identified. The problem above can be summarized as finding the optimal solution (\(L_{0}\), \(L_{\text{opt}}\) from all sets (\(L_{0}\), \(L\))\({}_{\text{m}}\) that satisfy a specified waist radius in Equation (2), where the  
Figure 2: Theoretical coupling efficiency \(\eta\) versus \(\gamma\).  
optimal solution can acquire the longest working distance \(\text{Max}(z_{\omega})\) in Equation (1). The problem can be converted to an optimization problem:  
\[\left\{\begin{array}{l}z_{\omega}=\frac{S_{1}\cos(2 gL)+S_{2}\sin(2 gL)}{S_{0}-S _{3}\cos(2 gL)-S_{4}\sin(2 gL)}\\ s.t.\ \omega_{f}=\omega_{0}\sqrt{P_{0}\cos^{2}(gL)+P_{1}\sin^{2}(gL)-P_{2}\sin(2 gL)} \\ 0<\text{L}<\text{L}_{\text{max}}\\ 0<\text{L}_{0}\leq\text{L}_{0\text{max}}\\ \text{Max}(z_{\omega})\end{array}\right. \tag{9}\]
###### Abstract  
Mechanical damage and bruising of fruit is a critical problem in the food industry. Minimizing brusing and damage can be achieved by designing energy-absorbing structures and packaging systems in order to ensure the long-term quality of fresh produce. The aim of this study is to investigate the response and bruise susceptibility of pears under impact loading conditions through finite element analysis (FEA) methods. In this paper, three impact heights (0.25 m, 0.5 m, and 1.0 m), four impact material surfaces (poplar wood, rubber, cardboard, and acrylonitrile butadiene styrene (ABS) plastic), two packaging sizes (standard 0.22\({}^{\prime\prime}\) and sandwich lattice 2.1\({}^{\prime\prime}\)), and three impact design structures (rigid, corrugated, and honeycomb) are considered. Based on mesh sensitivity analysis, a mesh element of 1.5 mm was adopted for all simulations, assuring the accuracy of results and considering the trade-off between mesh size and computational time. The response surface analysis approach was utilized in order to develop predictive empirical models related to pear bruising. Results revealed that the rubber-based impact platform yielded minimal bruise susceptibility at all heights, while standard-sized corrugated cardboard performed best at a height of 0.25 m. Furthermore, single, double, and triple layers of packaging cardboard were tested. We observed that adding a second soft layer of corrugated cardboard reduced the stress on the pear by around 33%. However, adding a third layer only reduced stress by 5%. The 3D-printed honeycomb ABS has potential as protective packaging but would require further investigations and parameter optimization. Stacking multiple layers of cardboard on top of each other is a cost-effective solution that could improve damping and, therefore, ensure good quality and increase the shelf life of the fresh produce. This study will help decision-makers select the optimal energy-absorbing material for cushioning and packaging designs in order to improve the handling and post-harvesting logistics of fresh produce.  
honeycomb; corrugated packaging; bruise susceptibility; energy absorption; explicit dynamics simulation; finite element analysis; postharvest transport technology +
Footnote †: journal: Applied Sciences
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
###### Abstract  
We propose a dual-view integral imaging 3D display based on a multiplexed lens-array holographic optical element (MHOE). A MHOE is a volume holographic optical element obtained by multiplexing technology, which can be used for dual-view integral imaging 3D display due to the angle selectivity of the volume HOE. In the fabrication of the MHOE, two spherical wavefront arrays with different incident angles are recorded using photopolymer material. In the reconstruction, two projectors are used to project the elemental image arrays (EIA) with corresponding angles for two viewing zones. We have developed a prototype of the dual-view integral imaging display. The experimental results demonstrate the correctness of the theory.  
Keywords:dual-view display; 3D display; integral imaging; lens-array holographic optical element +
Footnote †: journal: Applied Physics Letters