esa-sceva/satcom-chunk-collection huggingface dataset:
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 4 Experimental Results
### Time Complexity  
Tables 8 and 9, respectively, present the average number of iterations of each algorithm and the average calculation time per run (each algorithm was executed 50 times). The number in parentheses indicates the ranking of the algorithm, and Arank indicates the average ranking of the algorithm. Note that there is no iterative process in the HDCCI algorithm, so its relevant statistical data is not needed here.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Sets** & **Algorithms** & **V-FCM** & **DVPFCM** & **SCAD1** & **ESSC** & **FRPCM** & **VSFCM** \\ \hline \multirow{4}{*}{face} & CH(+) & 0.2527 & **0.2664** & 0.2524 & 0.2520 & 0.2512 & 0.2570 \\  & NMI(+) & 0.9573 & 0.9700 & 0.9516 & 0.9828 & 0.9591 & **0.9956** \\  & EARI(+) & 0.9292 & 0.9654 & 0.9418 & **0.9975** & 0.9667 & 0.9964 \\  & CR(+) & 0.6950 & 0.8350 & 0.7500 & 0.9450 & 0.8300 & **0.9850** \\  & XB(\(-\)) & 0.9769 & 2.3237 & 1.2936 & 0.8954 & 0.7964 & **0.5651** \\ \hline \hline \end{tabular}  
* The bold represents the best value among all algorithms, and the underlined represents the second best.  
\end{table}
Table 7: Clustering results of the Olivetti face database.  
Figure 8: Histogram of the Olivetti face database running results.  
It can be seen from the following two tables that the VSFCM algorithm ranks third in terms of the average number of iterations and second in terms of average iteration time. Compared with other clustering algorithms, the number of iterations and iteration time of the VSFCM algorithm required are generally less. This shows that the convergence speed of our proposed algorithm is relatively fast.
## 4 Discussion  
### Impact Tests  
The results of the impact tests provide key insights into the dynamic response of energy-absorbing countermeasures (EACs) and their influence on headform acceleration. The data presented in Figure 9 demonstrate that the foam EAC exhibits a lower amplitude but a longer-duration acceleration pulse compared to the covered EAC. The peak acceleration of all EACs increases with the impact velocity; however, the rate of increase is most pronounced in the foam EAC. Notably, at an impact velocity of 11 m/s, the foam EAC causes a peak acceleration of the headform that exceeds that of the covered variants. This may be attributed to the increasing stiffness of the foam as deformation increases, as well as the faster initial deceleration caused by the shells.  
During quasi-static Instron testing, there was a notable rise in stress beyond 60% strain. However, those tests did not give rise to the viscoelastic effects observed in the dynamic tests. As the headform penetrates into the foam in the latter, air is expelled through the open cell pores within the foam block. It is hypothesised that the rate at which the air is expelled from the foam could alter the contact stiffness. Figure 17 shows how the contact stiffness changes with both penetration and velocity. This effect may explain why, for the 10 m/s tests, the aluminium and ABS shells experience similar acceleration pulses (Figure 9) despite differences in the penetration time history (Figure 11). The total penetration of the headform into the foam EAC during this test reached 0.27 m, equivalent to 90% of the total length, positioning it within a higher stress region from an elastic perspective. However, in contrast, the velocity of the headform within this region is very small, tending towards zero. This places the headform in a lower stiffness region from a viscous perspective. Both the aluminium- and ABS-shelled EACs demonstrated an initially higher stiffness compared to the foam EAC but a lower penetration. This reduced penetration into the foam effectively limits exposure to the foam's higher stiffness region, thereby providing a protective advantage at higher impact velocities. For lower impact velocities, where both the penetration into the foam and velocity are low, the initial stiffness of the shells creates a higher peak acceleration.  
Figure 16: Overall safety effect of each countermeasure design.  
Figure 13 demonstrates that the MADYMO model faithfully replicates the acceleration-time history during both the impact loading and unloading phases using the derived contact characteristics. The loading curve was derived based on the velocity change matching the initial impact velocity, determined via integration. However, due to the strong velocity dependency of the contact stiffness, the simulations were limited to this specific impact speed.
###### Abstract  
Wastewater treatment plants (WWTPs) in the City of New York, United States, are particularly vulnerable to frequent extreme weather events, including storm surges, high-intensity rainfall, and sea level rise, and are also affected by the cascade of these events. The complex structural configuration of WWTPs requires very fine-scale flood risk assessment, which current research has not pursued. We propose a robust technique to quantify the risk of inundations for the fourteen WWTPs through an automated sub-basin creation tool; 889 sub-basins were generated and merged with high-resolution building footprint data to create a comprehensive database for flood inundation analysis. The inundation depths and extents for the WWTPs and flood-prone regions were identified from hydrodynamic modeling of storm surge and sea level rise. The economic damage due to flooding for the WWTPs was also quantified using the HAZUS-MH model. Results indicated that the storm surges from various categories of hurricanes have the dominant impacts on flood depths around WWTPs, followed by high-intensity rainfall. Sea level rise was shown to have a relatively minor impact on flood depths. Results from economic damage analysis showed that the WWTPs are subjected to damage ranging from USD 60,000 to 720,000, depending on the size of the WWTP and the extremity of storm surge. The method of analyzing the inundation status of the research object through the sub-basin enables more accurate data to be obtained when calculating the runoff. It allows for a clearer view of the inundation status of the WWTPs when combined with the actual buildings. Using this database, predicting flood conditions of any extreme event or a cascade of extreme events can be conducted quickly and accurately.  
wastewater treatment plant; storm surge; high-intensity rainfall; sea level rise; sub-basins; SLOSH; runoff model; NOAA; HAZUS-MH +
Footnote †: journal: Applied Sciences
# Architecture and Process Integration Overview of 3D NAND Flash Technologies
## 1 Introduction  
Recently, the demand for mobile electronic devices has been increasing owing to a no-contact lifestyle. This is leading to an enormous market growth in storage memory, particularly NAND flash, because the technology transformation from hard-disk drives to solid-state drives has already been established, owing to the requirements for a faster operation speed and a lower power consumption. Since NAND flash was commercialized for mass production in the early 1990s, reducing the cost per bit has been one of the most important approaches for NAND flash memory, and there have been efforts to scale down the effective cell size. Interestingly, NAND flash cells have been integrated into three-dimensional (3D) NAND flash architectures for further scaling down, unlike logic devices.  
The 3D NAND flash architectures have been realized in various structures and can be categorized into two types based on the stacking direction: channel and gate stacks. Figure 1 shows the structure-year classification of 3D NAND flash. Toshiba announced the first 3D stack flash based on a gate stacked structure, such as bit cost scalable (BiCS) [1], pipe-shaped BiCS (PBiCS) [2] with charge trapping layers and a horizontal-channel-type floating gate (HC-FG) [3] with a floating gate. In addition, Samsung has developed various 3D structures based on both stack methods. Examples are the terabit cell array transistor (TCAT) flash [4], vertical recess array transistor (VRAT) flash, vertical stacked array transistor (VSAT) flash [5] structure for efficient connection of peripheral circuits, vertical gate (VG) NAND [6] with a vertical gate and channel stacked structure, and V-NAND, which was the first 3D NAND product commercialized by Samsung [7]. SK Hynix has also developed several 3D architectures based on both methods: a dual control gate with a surrounding floating-gate (DC-SF) [8] structure, a hybrid 3D NAND structure [9] with mixed gate and channel stacks, a stacked memory array transistor (SMArT) [10] using an oxide-nitride-oxide (ONO) film to minimize the stack height, and a metal control gate past process (MCGL process) [11] using a metal gate based on the DC-SF structure. Channel-stacked 3D architectures have been investigated, including the dual-channel 3 DIn this study, all 3D NAND architectures are analyzed and compared from a structural perspective. First, in Section 2, the 3D NAND flash architectures are discussed based on the stack method and their operational characteristics. Section 3 compares and analyzes the fabrication methods of all structures based on the gate fabrication method.
## 3 Design of CP-ABE
### Preliminaries Works
#### 3.1.4. XOR-Based Logical Connective Policy Hiding  
The proposed policy hiding algorithm uses an XOR-based logical connective to hide the access policy in the CP-ABE scheme. We utilized XOR to modify the entire access policy to form a reliable proposition. According to [PERSON] [38], logical connective is defined by the truth table, which declares that a fact could be either true or false, but not both. [PERSON] described proposition logic as: 'Let \(p\) and q be propositions. The exclusive-or of \(p\) and q, denoted by \(p\) XOR q, is the proposition that is true when exactly one of \(p\) and q is true  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Explanations** \\ \hline \(MSK\) & Master Key \\ \(SK\) & Secret Key \\ \(M\) & Message (data sharing by the data owner) \\ \(CT\) & Ciphertext \\ \(Att(x,\ y)\) & location of the Attribute in attribute value involved in \(\mathbb{A}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._and is false otherwise. According to [39], XOR is a lightweight operation that does not incur much computational overhead. Therefore, it is very appropriate to be adopted in this scheme as a second layer of protection.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 3 Notations and Preliminaries  
### Notations  
In this section, we introduce the basic notations used throughout this paper. A scalar is denoted as \(x\), a vector as \(\mathbf{x}\), and a matrix as \(\mathbf{X}\), where these represent zero-, first-, and second-order tensors, respectively. A general tensor is represented by \(\mathcal{X}\). The element at position \((i,j,k)\) in the tensor \(\mathcal{X}\) is denoted as \(x_{i,j,k}\). The notation \(\mathcal{X}(:,:,k)\) or \(\mathbf{X}_{k}\) refers to the \(k\)-th frontal slice of the tensor \(\mathcal{X}\).  
The inner product between two tensors \(\mathcal{X}\) and \(\mathcal{Y}\) is represented as \(\langle\mathcal{X},\mathcal{Y}\rangle\), which is computed as the sum of the element-wise products of their corresponding entries. The Frobenius norm of a tensor is denoted as \(\|\mathcal{X}\|_{F}\), and the tensor L\(\gamma\)-norm is represented as \(\|\mathcal{X}\|_{\gamma}\). The trace of a matrix \(\mathbf{X}\) is written as \(\mathrm{Tr}(\mathbf{X})\), which is the sum of the diagonal elements of the matrix. Lastly, the mode-\(k\) folding operation is represented as \(\mathrm{fold}_{k}(\cdot)\), which maps a matrix back to a tensor in the appropriate mode.
## 2 Materials and Methods  
### Methodology  
#### 2.1.1 Preprocessing  
In the initial preprocessing stage, the raw recording \(X=\{x_{1},x_{2},\dots,\ x_{N}\}\) undergoes min-max normalization, where \(1\dots N\) represents the datapoints. This technique rescales the values to a standardized range between 0 and 1. After normalization, raw signals are segmented into shorter times series, called epochs (e). In this study, each epoch is fixed at 56 data points (N) as \(e_{i}=\{x_{1},x_{2},\dots,\ x_{56}\}\).
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 3 Proposed Approach  
This section presents the approach proposed to achieve the outlined objectives. First, a high-level overview of the approach is given. Section 3.1 shows greater detail of its architecture. Finally, Section 3.2 provides insight into the implementation.  
This study aims to build and assess a proof-of-concept system that employs a security protocol in a C-ITS hybrid environment. Thus, the system should enable G5-connected ITS stations to send protected messages that can be received and verified by other ITS stations and mobile applications (apps) and vice versa. As for security, DLAPP [13] and MFSPV [14] protocols are implemented, evaluated and compared using real equipment. To illustrate the proposed approach to achieve these goals, a simplified depiction is provided in Figure 6.  
A soft-mobility user message (e.g., CAM) is transmitted through a mobile app to the ITS centre, which then relays it to road infrastructures, such as semaphores equipped with RSUs. These RSUs disseminate the messages over the G5 network to vehicles equipped with OBUs as they pass by. Conversely, messages initially sent via G5 are routed through  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Efficiency Metric** & **[PERSON] et al. [13]** & **[PERSON] et al. [14]** \\
**DLAPP** & **MFSVP** \\ \hline Overhead (bytes) & 40 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the communication overhead introduced by each protocol when sending a message (e.g., CAM or DENM).  
Figure 6: Simplified representation of the proposed approach.  
RSUs to the ITS centre, which distributes these messages to mobile apps. This approach establishes a bi-directional communication channel, bridging the G5 and cellular networks. Furthermore, within the hybrid C-ITS ecosystem, every type of node--smartphones, OBUs, and RSUs--is required to implement the security protocols MFSPV [14] and DLAPP [13], so one of them can be used, thus enabling information sharing among road users with security guarantees.
## 2 Modeling
### Simulation Conditions and Scope of Study  
Before the simulation, the geometric parameters and operational conditions were established, with Table 2 detailing the basic geometric parameters for each component involved in the simulation.  
During the investigation, the refrigerant mass flow was kept at 0.05 kg/s, with the default cooling water inlet temperature and flow rate set at 45 \({}^{\circ}\)C and 0.15 kg/s, respectively. The default refrigerant used was R-113, with both the first and second CPUs power set to 80 W, matching the thermal design power of the Intel Xeon E5520 CPU. The study explored the PCTCS's characteristics on default configuration and the influence of refrigerant type, condenser height position, and varying cooling water temperature on the PCTCS performance as detailed below:  
* Default configuration;
* Three different types of refrigerants, namely, R-113, R-134a, and R-32, were selected to facilitate an analysis of their impacts on the performance of the PCTCS. These refrigerants have atmospheric boiling points of 47.6 \({}^{\circ}\)C, \(-\)26.1 \({}^{\circ}\)C, and \(-\)51.7 \({}^{\circ}\)C, respectively, thus representing low-, medium-, and high-pressure refrigerants. Their widespread use in refrigeration and data center heat dissipation, coupled with their distinctly varying physical properties, make them ideal for evaluating the diverse influences they can exert on the performance of the PCTCS;
* The impact of the condenser height on the PCTCS performance was assessed. The condenser height was altered relative to the reservoir and the evaporator by varying the heights of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Categories** & & **Connecting Pipes** & & **Mini-Channel Evaporator** & **Plate-Type Condenser** \\ \hline Label & 2-3 & 4-5 & 6-7 & 8-1 & 3-4, 5-6 & 7-8 \\ Length (m) & 10 & 0.5 & 10 & 3 & 0.035 & 0.25 \\ Width/Diameter (m) & 0.008 & 0.003 & 0.012 & 0.008 & 0.035 & 0.111 \\ Height (m) & 0 & 0 & Variable & Variable & 0.002 & 0.25 \\ Heat transfer area (m\({}^{2}\)) & - & - & - & - & 0.00303 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic geometric parameters of simulation components.  
the connecting pipes in sections 6-7 and 8-1. The configurations included 0 m, 0 m (same height); 1 m, \(-\)1 m (condenser is 1 m higher); and \(-\)1 m, 1 m (condenser is 1 m lower);
* The cooling water temperature range was established between 15 and 60 \({}^{\circ}\)C, with increments of 5 \({}^{\circ}\)C.
# GC- and UHPLC-MS Profiles as a Tool to Valorize the Red Alga Asparagopsis armata+
## 3 Results and Discussion  
### Aspargopsis armata Polar Extracts Yield and Chemical Diversity  
Extracts from fresh _A. armata_ were prepared using polar solvents (water, ethanol) and different energy sources according to the data present in Table 1. The yields of extractions are presented in Table 2. Preparing polar extracts from fresh instead of dry seaweed has the additional advantage of circumventing the difficulties of drying seaweed and grinding after drying. The alga has a high-water content (92.9%), and the drying process is prolonged. However, no signs of chemical degradation were evident (no changes in color or observed microorganism development). After drying, the seaweed becomes extremely hard, requiring very robust equipment for its grinding.  
The different methods and solvents tested reveal identical performance in terms of extraction yield.  
Considering the yields obtained and the more sustainable experimental conditions (economically and environmentally \"greener\"), the profiles of the most interesting extracts were qualitatively analyzed by UHPLC-MS, with the most relevant results shown in Figure 1.  
The chemical profiles of the ethanol extracts (extracts 7 and 8) are very similar, being both ultrasound and microwave the most selective extraction method. The use of ethanol under microwave energy extracted almost exclusively one compound. Although it was not our aim to identify this compound, its isotopic pattern and _m/z_ in the mass spectra suggests that it is a 2,3-diphenylpropanoic acid derivative, having two hydroxyl groups and four bromine atoms distributed in the aromatic rings ([M-H]\({}^{-}\)_m/z_ 568/570/572/574/576). Due to the fragment with _m/z_ 319/321/323, we suggest that each aromatic ring possesses one hydroxyl group and two bromine atoms.  
The chemical profile of the water extracts (extracts **1** and **2**) showed greater chemical diversity. Thus, the extraction method must be chosen according to the intended application to the extracted compounds that are different according to the method used. If the extraction time is the most relevant, it should choose ultrasound extraction since it is cheaper than microwave extraction, which requires a more expensive apparatus. Using water extraction by maceration at room temperature will be cheaper and allow chemically richer extracts.  
As this work intends to study the chemical profile of the polar extract of _A. armata_, the characterization of the chemical composition of the aqueous extract obtained by maceration at room temperature (extract **2**) constitutes a significant scientific contribution to knowledge about new polar secondary metabolites in this alga.
## 7 Numerical Examples
### Problem 2 (Mixture Model)  
We consider here the IVP proposed in [15], which was a model of a storage tank in an oil refinery that holds 2000 gal of gasoline with 100 lb of an additive mixed within it. To prepare for winter weather, 40 gal/min of gasoline that contains 2 lb of additive per gallon is pumped into the storage tank. The well-mixed solution is pumped out at a rate of 45 gal/min. Let \(y\) be the amount of additive (in pounds) in the tank at time \(x\). When \(x=0\), we know that \(y=100\). The mixture process is modeled by the IVP, \(y^{\prime}=80-\frac{45}{(2000-5x)}\)\(y\), \(y(0)=100\), and the analytic solution, \(y(x)=(2000-5x)\{2-\frac{3000}{(2000)^{3}}(2000-5x)^{8}\}\), \(0\leq x\leq 1\). Tables 4-6 show the absolute errors among third-order methods and the VRK3 scheme, with different step sizes of \(h=0.1\), \(h=0.05\), and \(h=0.025\). Figures 5-7 depict the graphical analysis used to support the numerical results in Tables 4-6. A comparison of CPU time between the new method and other third-order schemes is shown in Table 8.  
Figure 4: The absolute errors for numerical results in Table 3.  
Figure 3: The absolute errors for numerical results in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(x_{i}\) & **Ralston’s Scheme** & **RK3 Scheme** & **ARK3 Scheme** & **Heun’s Scheme** & **VRK3 Scheme** \\ \hline zero & zero & zero & zero & zero & zero \\
0.1 & \(4.6501\times 10^{-10}\) & \(5.1354\times 10^{-10}\) & \(4.8308\times 10^{-10}\) & \(4.1457\times 10^{-10}\) & \(3.9459\times 10^{-10}\) \\
0.2 & \(9.2889\times 10^{-10}\) & \(1.0258\times 10^{-9}\) & \(9.6499\times 10^{-10}\) & \(8.2821\times 10^{-10}\) & \(7.8832\times 10^{-10}\) \\
0.3 & \(1.3910\times 10^{-9}\) & \(1.5361\times 10^{-9}\) & \(1.4451\times 10^{-9}\) & \(1.2403\times 10^{-9}\) & \(1.1805\times 10^{-9}\) \\
0.4 & \(1.8506\times 10^{-9}\) & \(2.0437\times 10^{-9}\) & \(1.9225\times 10^{-9}\) & \(1.6499\times 10^{-9}\) & \(1.5704\times 10^{-9}\) \\
0.5 & \(2.3096\times 10^{-9}\) & \(2.5506\times 10^{-9}\) & \(2.3994\times 10^{-9}\) & \(2.0952\times 10^{-9}\) & \(1.9600\times 10^{-9}\) \\
0.6 & \(2.7670\times 10^{-9}\) & \(3.0556\times 10^{-9}\) & \(2.8745\times 10^{-9}\) & \(2.4671\times 10^{-9}\) & \(2.3482\times 10^{-9}\) \\
0.7 & \(3.2222\times 10^{-9}\) & \(3.5584\times 10^{-9}\) & \(3.3474\times 10^{-9}\) & \(2.8729\times 10^{-9}\) & \(2.7345\times 10^{-9}\) \\
0.8 & \(3.6763\times 10^{-9}\) & \(4.0598\times 10^{-9}\) & \(3.8191\times 10^{-9}\) & \(3.2778\times 10^{-9}\) & \(3.1199\times 10^{-9}\) \\
0.9 & \(4.1288\times 10^{-9}\) & \(4.5595\times 10^{-9}\) & \(4.2892\times 10^{-9}\) & \(3.6813\times 10^{-9}\) & \(3.5040\times 10^{-9}\) \\
1 & \(4.5789\times 10^{-9}\) & \(5.0566\times 10^{-9}\) & \(4.7568\times 10^{-9}\) & \(4.0826\times 10^{-9}\) & \(3.8859\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the absolute errors among third-order schemes in Problem 2, for \(h=25\times 10^{-3}\).