esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Aneurysm without Treatment: Control Case  
In Figure 4 the streamlines colored with the local velocity magnitude are shown in the aneurysmic sac before treatment. Coinciding with other studies [22; 57], the blood flow within the aneurysm enters from the distal portion of the aneurysm neck and swirls around the periphery of the aneurysm sac with a slower velocity. Finally, the flow leaves the sac in the proximal region around the inflow zone (see Figure 4a). The pressure within the sac is almost constant and similar between the control case and the stented cases (see Figure 5). Also, in agreement with the previous study [24], the pressure drop is very small despite the presence of the aneurysm. The WSS at the peak flow of the cardiac cycle is almost constant along the parent vessel upstream of the aneurysm except at the entrance of the sac, where the changes in velocity direction are registered. In the sac, much smaller WSS values were found in comparison with the vessel, as reflected by the TAWSS (see Figure 6a). As an average computed variable, the TAWSS attenuates and smooths the spatial differences. Larger values are depicted at the junction of the artery with the neck. The maximum TAWSS is found at the bottom of the proximal neck region, with a maximum value around 6 Pa, almost two times of the value observed in the parent vessel just upstream of the aneurysm. The OSI for the untreated artery had large values in the proximal region of the sac near to the vessel. This finding agrees with previous studies, indicating that high OSI is of an aneurysm risk factor [50]. Finally the RRT shows higher values and hence higher residence of flow particles at the top of the sac. From the theoretical point of view, it is believed that for an aneurysm flow with vortical structures, the flow near the wall tends to be recirculating, slow, and oscillatory. Thus, the average TAWSS tends to be low while the OSI tends to be high. The area exposed to low shear stress tends to be large and the time spent by the fluid particles near the wall tends to increase provoking a high RRT.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Flow (Kg/s) (mm)** & **Max TAWSS (mm)** & **Max OSI (mm)** \\ \hline
**Control case** & \(1.28\times 10^{-6}\) & 3.43 & 0.49 & 41.09 \\
**STENT BS** & \(6.75\times 10^{-8}\) & 5.09 & 0.48 & 54.25 \\
**STENT 1** & \(4.47\times 10^{-8}\) & 4.29 & 0.48 & 48.42 \\
**STENT 2** & \(3.82\times 10^{-8}\) & 3.56 & 0.47 & 42.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the simulation results.  
Figure 5: Pressure contours for the cases of (**a**) control case, (**b**) STENT BS, (**c**) STENT 1, and (**d**) STENT 2.
## 4 MAMZI with Phase Noise Stabilization
### MAMZI Simulation with Phase Noise  
To evaluate the controllability described above, we have simulated MAMZIs for dimensions \(2\times 2\) up to \(8\times 8\) using the phased array model, MBSs obtained from Equation (1), and the phase matrix from Equation (2). Our simulation is a discrete system, where the phase \(\phi^{noise}[n]\) is provided by a matrix of \(N\times l_{max}\times M\), where \(l_{max}=40.000\) represents the time length of the simulation, and each noise sample is a cumulative random sequence with a variability of \(0.01\pi\), allowing us to mimic the behavior of a fiber MAMZI [22]. We performed \(M=100\) experiments for each simulated control process. The generated phase noise is introduced into the plant \"sample by sample\", perturbing the intensity Equation (5).  
Figure 3 shows the open-loop intensity response of applied phase noise over diverse MAMZIs. Figure 3a shows one of the \(100\) matrices of \(40.000\times 8\) for the accumulated phase noise simulation, while Figure 3b-g shows the open-loop response of applying N vectors of the noise matrix to the \(N\times N\) system. Note, for example, that in Figure 3g, eight vectors are used, resulting in a random intensity response for all MAMZI outputs. Each noise matrix was generated, ensuring output intensity with a symmetrical temporal distribution.  
Figure 3: Open-loop response in an arbitrary time unit (AU). (**a**) Phase noise matrix used on MAMZI systems. Open loop noise intensity response for (**b**) \(N=3\), (**c**) \(N=4\), (**d**) \(N=5\), (**e**) \(N=6\), (**f**) \(N=7\), and (**g**) \(N=8\).  
Figure 2: A feedback control scheme for a plant from a MAMZI whose input is the phases and its output is the optical intensity.
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 4 Erase Algorithm
### Reliability Improvement  
As described above, the block size of 3D NAND is continuously enlarging to further increase the bit density. However, since erase operations are also performed in units of blocks, these operations burden system-level reliability. To improve this problem, a block-by-deck (BBD) concept was proposed [72]. In this method, the entire WL is divided into three decks and when the erase operation is performed, individual bias is applied to the WL of each deck so that the erase operation is performed separately for each deck. In this way, the reliability of the chip can be improved because one physical block is composed of three logical sub-blocks. However, in this method, since the erase operation and the erase inhibition must be performed at the same time, a strong transverse electric field is applied between the decks, which is likely to cause hot carrier injection disturbance. Therefore, arranging optimal dummy WLs between the decks is an important procedure.  
[PERSON] et al. reported that, because of repetitive erase operation, program disturbance may occur due to cycling-induced Vt shift of the top select transistor (TST) [87]. During the erase operation, when high voltage is applied to SL and BL and 0 V is applied to the TST gate, a hot hole is generated by the strong transverse electric field applied under the channel between the dummy WLs and the TST. Then, hot holes break the Si-H bonds at the poly-Si/SiO2 interface, leading to TST Vt shift. The transverse electric field is alleviated by pushing a positive voltage to the TST after a certain point when SL and BL are rising. However, if the voltage gap between TST and BL and SL is too small, GIDL current generation is reduced and the erase speed may be degraded. Therefore, the trade-off relationship between reliability and erase performance must be carefully adjusted.  
[PERSON] proposed a method of applying a small positive pulse immediately after the erase operation to overcome the Vt transient phenomenon [88]. In this study, vertical redistribution of holes at interface between CTL and blocking oxide toward the tunnel oxide direction continuously decreases the erased Vt, and increases the error bit in the erase verification operation. By applying a small positive pulse immediately after the erase pulse, increment in error bit can be reduced because the hole redistribution rapidly settled down.  
Figure 13: (**a**) Flowchart of erase operation using the two-step annealing pulse scheme. Reprinted/adapted with permission from Ref. [40], 2021, IEEE. (**b**) conceptual behavioral model of annealing pulse scheme. Reprinted/adapted with permission from Ref. [40], 2021, IEEE.
## 3 Results and Discussion  
### Sensitivity Analysis  
In numerical modelling studies, evaluating the simulations with the available data supports the performance of the selected model configuration [32]. Therefore, based on this principle, this section presents the results of the WRF sensitivity analysis due to the use of five different PBL schemes. The first step is to determine which set of physical options (parameterizations) leads to the best results (lower error and uncertainty) for a winter and a summer month. Here, only the weighted mean values (i.e., the mean for all mast stations weighted by the number of data records) of each statistical parameter were presented at each height. The RMSE, Bias and STDE values between measured and simulated wind data at the heights of 10, 40 and 80 m for January 2013 are presented in Table 4. These statistical indicators were calculated for pairs of simultaneous and valid records of simulations and observations. According to this table, the QNSE PBL scheme is that with the lowest errors for January 2013, with Bias, RMSE and STDE values equal to 0.78, 4.14 and 4.05 (m/s), respectively. Given the higher-ranking criterion of the STDE over the RMSE and Bias, it is concluded that after the QNSE, the ACM2 configuration presented the best performance (STDE = 4.07 m/s). Overall, a positive bias was determined, indicating that the WRF model consistently overestimates the wind speed regardless of the PBL scheme. This observation highlights the presence of systematic biases in model simulations over complex terrain regions such as deep valleys and mountain tops. However, the simulation biases may vary highly between the stations and some of them may also indicate a model underestimation, as was seen for the case of Fadeshk meteorological tower (Supplementary Material). According to Table 5, the statistical results of the comparison obtained for July 2013 were relatively different from those of January 2013. The best performance was presented for the ACM2 configuration with Bias, RMSE and STDE equal to 0.6, 3.4 and 3.22, respectively. The QNSE PBL scheme, which showed the best performance in the cold season, revealed the highest errors in the warm season (with STDE = 3.65). Similar to what was seen in the cold season, all simulations showed a tendency to overestimate the wind speed in summer as well. In general, the error values were lower in July than in January, indicating a better model performance in summer. However, all PBL schemes indicate rather small differences in the statistical indicator values (Bias, RMSE, STDE) in both cold and warm months, resulting in a challenge in detecting the best scheme for model simulations of wind climatology in Iran. It should be noted that in these tables, we present the general picture about biases from the use of different PBL schemes, while the results are site-specific. In general, smaller domains in WRF simulations tend to have smaller wind speed biases and a higher RMSE when compared to tall mast observations. However, it is unclear whether this is truly a result of domain size or rather the location of domain boundaries with respect to larger-scale flow [32].
# Railway Wireless Communications Channel Characterization
## 6 Deployment Example
The results of the different tests are customizable in terms of visualizing the average, the number of measurements, and the maximum or the minimum value (see Figure 9), and it can also decide the size of the area where the previous options have to be applied. Figure 6: Round Trip Time test. Figure 8: Comparison of the same measurement depending on the KSR of each application (right), max RTT 200 ms, left max RTT 7000 ms). Figure 7: Technology coverage on the track. Figure 10 shows the average value from every RTT test performed along the track. In this way, the user can see which average RTT was measured in the selected sized areas. The user knows the average of the RTT, which has been obtained from the performance of the same impairment in different moments in each location of the track. This information can be stated as the summary of the whole communication channel on the track. Based on these results, a decision can be made on which application would fit on the track or which technology would be sufficient to communicate on this track. Apart from the visualization in a map, different charts are available, as shown in Figure 11. This type of chart compares the RTT values with the RF values and the disconnections (CCT Agent had no connection) chart. The RF values in LTE technology are split into different quality indicators: RSSI, SINR, RSRQ, and RSRP. The charts bring some interesting information for analysis. For example, the bad quality of the signal in the 22 h 54 m time could be the cause of the last disconnection; the SINR is negative and the RSRP is close to 100 dBm, which is probably because of being in the cell edge of the cell. Moreover, if the user has some KSR from an application, it can insert them into the system, and as a result of the application, it will behave correctly or not in the track, as Figure 12 shows. CBTC or ETCS are the applications that are compared in this paper vs. the RTT values along the track. Finally, as said throughout the whole paper, the user can test the communication channel to know if the application would behave correctly or not. Regarding Figure 12, the specific applications chosen are ETCS and CBTC. In this case, ETCS would work correctly along this track because the KSR regarding the delay (3.5 s) is higher than the measured RTT values. In contrast, the CBTC application would not behave properly due to the peaks of RTT that the track has. Figure 11: Charts RTT vs. RF values.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 3 Method Description
### Moving Average and Normalization  
Since the ZEROES method intends to identify the zeros of the first-order derivative based on the positions of peaks and valleys, noisy variations of the extracted signal \(x\) result in short-term fluctuations that therefore interfere with a proper zeros' detection (Figure 5). The extracted signal \(x\) shown in Figure 5 was obtained after the decimation of columns (\(\Delta_{col}=4\)) and rows (\(\Delta_{row}=100\)) of a 3040 \(\times\) 4056 (N \(\times\) M) resolution image in order to reduce the whole amount of data to process while reducing the noise. Then, a moving average was applied to smooth out the signal noise by computing an average value for each point based on the neighboring points with a sliding window \(W=\lfloor P_{b}\rceil\), with \(\lfloor.\rceil\) being the round operator.  
\[\overline{x}(i)=\frac{1}{2W+1}\sum_{j=-W}^{W}x(i+j) \tag{2}\]  
Thus, the moving average computed \(\forall i\in\left[\lfloor W/2\rfloor,\ \ \lfloor N/\Delta_{col}\rfloor-\lfloor W/2 \rfloor\right]\), with \(\lfloor.\rfloor\) being the floor operator, acts as a low-pass filter by attenuating the noisy high frequencies of the signal while preserving the low frequencies containing the transmitted information. Then, the moving averaged signal \(\overline{x}\) is normalized in \(x_{n}\) between 0 and 100 to ensure a  
Figure 4: Flowchart diagram of ZEROES method.  
proper upcoming zeros' detection of the first-order derivative regardless of the transmission conditions.  
\[x_{n}(i)=\frac{\overline{x}(i)-\overline{x}_{min}}{\overline{x}_{max}-\overline{ x}_{min}}\times 100 \tag{3}\]  
with \(\overline{x}_{max}\) and \(\overline{x}_{min}\) being the maximum and minimum values of \(\overline{x}\), respectively.  
As shown in Figure 6, the extracted signal after moving average filtering presents less noisy fluctuations while retaining sharp edges for the good of the demodulation. However, signal improvement is achieved only at the cost of a reduced extinction ratio, which affects the correct demodulation.
# Potential Metal Contamination in Foods of Animal Origin--Food Safety Aspects
## 2 Major Toxic Metals in Food
### Mercury (Hg)
By altering intracellular thiol levels, mercury promotes oxidative stress and lipid peroxidation, alters mitochondrial processes and heme metabolism, and disrupts cellular calcium homeostasis. Its cytotoxic effects are threshold-dependent, presumably due to the buffering effect of endogenous ligands (e.g., metallothioneein, glutathione). Up to a certain dose level, there is no cell death, but if the buffer system is saturated, then at higher doses, cell death develops rapidly, often as an all-or-none response. The main target organ of inorganic mercury compounds is the kidney. Damage to the proximal tubules and glomeruli is primarily observed (tubular dilation, degeneration and atrophy of tubular epithelial cells, thickening of the basal membrane). Kidney damage is indicated by proteinuria, oliguria, dilution of urine, and elevated plasma creatinine levels. Neurological symptoms (muscle tremors, inactivity, abnormal posture) may also occur, despite the fact that inorganic compounds only partially cross the blood-brain barrier. Salivation, gastrointestinal symptoms, and anemia may also be observed. The lethal dose of mercuric chloride (HgCl2) in an adult is 10-42 mg/kg [123]. The target organ of organomercury compounds is primarily the nervous system. The severity of damage and the symptoms that develop are influenced by the duration and amount of exposure, as well as the developmental stage of the nervous system. Young organisms with an immature nervous system are much more sensitive to damaging effects than adults. Due to damage to the central and peripheral nervous systems, ataxia, incoordination, muscle convulsions, paralysis, and visual disturbances can be observed. Behavioral, learning, and memory disorders, as well as reduced activity, may also develop. Methylmercury reduces spermatogenesis and sperm motility, can cause abortion, and increases fetal resorption and developmental abnormalities. It crosses the placenta and is involved in causing fetal Minamata disease, which is characterized by microcephaly, degeneration and atrophy of cortical structures, dilation of the brain ventricles, gliosis, loss of myelin, and lack of cellularity. Convulsions, muscle rigidity, blindness, and severe learning disabilities are observed in such newborns [120]. The concentrations of total mercury are the highest in fish and other seafood, mainly in the flesh of fish (especially tuna, cod and whiting, swordfish, pike, hake, and shark). The average exposure to inorganic mercury via foods was estimated as 0.13-0.25 ug/kg b.w./week in adults and 2.16-4.06 ug/kg b.w./week in children. Due to the consumption of methylmercury-containing fish meat, the estimated methylmercury dietary exposure was 0.06-0.14 ug/kg b.w./week in elderly adults; it was higher in infants (1.57 ug/kg b.w./week), children (7.48 ug/kg b.w./week), and adolescents (5.05 ug/kg b.w./week) [131].
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 3 The Proposed Beamforming Methods: Max-SR-SLNR and MRT-NSP-PA
### The Proposed Max-SR-SLNR
Therefore, the objective function of (28) can be expressed as  
\[\text{such that }\mathbf{v}_{CM}^{H}\mathbf{v}_{CM}=1,\mathbf{ \theta}^{H}\mathbf{\theta}=N_{s}. \tag{19b}\]7  
where \(\mathbf{A}=\text{diag}(\mathbf{H}_{ai}\mathbf{v}_{CM})\mathbf{h}_{ib}\mathbf{ h}_{ib}^{H}\text{diag}(\mathbf{H}_{ai}\mathbf{v}_{CM})\), and \(\mathbf{B}=\text{diag}(\mathbf{H}_{ai}\mathbf{v}_{CM})\mathbf{h}_{ie}\mathbf{ h}_{ie}^{H}\text{diag}(\mathbf{H}_{ai}\mathbf{v}_{CM})\)\(+\frac{\sigma_{z}^{2}}{N_{s}}\mathbf{I}_{N_{s}}\). Accordingly, the Rayleigh-Ritz ratio theorem can be used, and \(\theta\) can be obtained by the following formula  
\[\text{such that }\mathbf{v}_{CM}^{H}\mathbf{v}_{CM}=1,\mathbf{ \theta}^{H}\mathbf{\theta}=N_{s}. \tag{19b}\]8  
Let us define the eigenvector corresponding to the largest eigenvalue of (32) as \(\mathbf{u}\). Since \(\mathbf{\Theta}\) has a constant mode constraint, \(\mathbf{\Theta}\) can be given by  
\[\text{such that }\mathbf{v}_{CM}^{H}\mathbf{v}_{CM}=1,\mathbf{ \theta}^{H}\mathbf{\theta}=N_{s}. \tag{19b}\]9  
We designed the \(\mathbf{v}_{CM}\), \(\mathbf{v}_{AN}\), and PSM of the IRS. It is particularly noted that the \(\mathbf{v}_{AN}\) is independent of the \(\mathbf{v}_{CM}\) and PSM of the IRS, while \(\mathbf{v}_{CM}\) and the PSM of the IRS are mutually coupled. Therefore, it is necessary to alternately optimize the \(\mathbf{v}_{CM}\) and \(\mathbf{\Theta}\) until \(R_{s}^{(p)}-R_{s}^{(p-1)}\leq\epsilon\), where \(p\) represents the total number of iterations, and the optimal \(\mathbf{v}_{CM}\) and \(\mathbf{\Theta}\) can be iterated. The whole iterative process is listed in Algorithm 1. ```
1:Set initial solution \(\mathbf{\Theta}^{(0)}\), \(\mathbf{v}_{CM}^{(0)}\), and \(\mathbf{v}_{AN}\). Random multiple phases of \(\theta\), and calculate the initial \(R_{s}^{(0)}\). 2:Set \(p\) = 0, threshold \(\epsilon\). 3:repeat
4: Given (\(\mathbf{\Theta}^{(p)}\),\(\mathbf{v}_{AN}\)), according to (27) to obtain \(\mathbf{v}_{CM}^{(p+1)}\). 5: Given (\(\mathbf{v}_{CM}^{(p+1)}\), \(\mathbf{v}_{AN}\)), according to (33) to obtain \(\mathbf{\Theta}^{(p+1)}\). 6: Compute \(R_{s}^{(p+1)}\) using \(\mathbf{v}_{CM}^{(p+1)}\), \(\mathbf{v}_{AN}\) and \(\mathbf{\Theta}^{(p+1)}\). 7:\(p\) = \(p+1\);
8:until\(R_{s}^{p}-R_{s}^{p-1}\leq\epsilon\), and record the maximum SR value. ```  
**Algorithm 1** Proposed Max-SR-SLNR method  
The computational complexity of the Max-SR-SLNR method is  
\[\gamma_{b}=\frac{\mathbf{v}_{CM}^{H}\mathbf{h}_{b1}\mathbf{h}_{ b1}^{H}\mathbf{v}_{CM}}{\mathbf{v}_{AN}^{H}\mathbf{h}_{b2}\mathbf{h}_{b2}^{H} \mathbf{v}_{AN}+\sigma_{b}^{2}}, \tag{20}\]0 \[\gamma_{b}=\frac{\mathbf{v}_{CM}^{H}\mathbf{h}_{b1}\mathbf{h}_{ b1}^{H}\mathbf{v}_{CM}}{\mathbf{v}_{AN}^{H}\mathbf{h}_{b2}\mathbf{h}_{b2}^{H} \mathbf{v}_{AN}+\sigma_{b}^{2}}, \tag{20}\]1float-point operations (FLOPs), where \(D_{1}\) and \(D_{2}\) represent the iterative numbers of the optimization variables \(\mathbf{v}_{CM}\) and \(\mathbf{\Theta}\).
# Edge-Event-Triggered Synchronization for Multi-Agent Systems with Nonlinear Controller Outputs
###### Abstract
Although there have been some results on the coordination of nonlinear multi-agent systems based on event-triggered control [45; 54; 55], the influences of data quantization and actuator nonlinearities on systems have not been considered. Our work evaluates the applicability of event-triggered mechanisms in such scenarios. The novelties of this study are summarized in the following. 1. The proposed event- and self-triggered policies take the effects of controller output nonlinearities into account, not only including controllers' inherent nonlinear properties, but also the signal distortion caused by network data transmission. In comparison with edge-based distributed event-triggered control in [39; 45; 46; 47; 54; 55; 43; 47; 48; 49; 54; 43; 44; 45], our policies consider non-ideal digital signal transmission in actual application scenarios, paving the way for digital applications of distributed sampled-data control. 2. The proposed event- and self-triggered schemes have trade-offs in the scheduling of system resources. The event-triggered policy does not require digital signal transmission between neighboring individuals but requires all sensors to continuously obtain edge state information to evaluate triggering conditions and implement intermittent controller updates. The proposed self-triggered policy avoids continuous acquisition of edge state information and requires a small amount of information transmission between neighboring individuals. The trade-off between these two policies is also suitable for scenarios with different levels of resource schedulability. Furthermore, in comparison with the previous distributed self-triggered approaches [34; 39; 41; 42; 46; 47; 52], the interactive control inputs in our algorithm are quantized information, which is a more efficient solution for saving communication bandwidth. The rest of this article is arranged as follows. Section 2 gives preliminaries on graph theory and problem formulation. Section 3 provides convergence analysis of the proposed edge-event-triggered policy. Section 4 further designs the edge self-triggered control algorithm. Section 5 shows the results on numerical simulations. Finally, Section 6 concludes this paper.