esa-sceva/satcom-chunk-collection huggingface dataset:
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 4 Experimental Results and Analysis
### Retention Error in Fresh DRAM Modules
#### 4.2.4 Impact of True Cells and Anti-Cells  
We observe the impact of previously discussed architectural variation as true cell and anti-cell for Micron and Hynix DRAM modules in Figure 5a,b. For the Micron 1 GB module, we observe two different architectural variations inside a bank. Each bank consists of 16,000 rows or pages, and each page holds 1000 columns, each with a 64 bit length. We observe a spatial locality of 512 pages in the first half of the bank. In this portion, the first 512 pages induce error for pattern 0x00 and are characterized as anti-cells, and the next 512 consecutive pages are characterized as true cells. On the second half of the pages, we could not find any large-scale granularity, which means that either one or very few consecutive pages are true cells or the next page or a few are anti-cells. On a different note, one or more individual bits in a column could be true cells, and the next one or more could be anti-cells, which is the most likely case to maintain uniformity across the 3D stack of a DDR3 memory module. The Hynix module follows the same architecture as the second half of the Micron module across the whole module (Figure 5b). Figure 5c illustrates the retention error across rows in the case of the Samsung modules. It shows that all the cells are designed with true cells, which explains the errors found only for data pattern 0 xFF.
## 4 4-Chain Based Routing
### Network Model and Assumptions  
We assume a cylindrical network for an acoustic environment in order to design application-oriented network. We divide the network into four regions and also create four groups of sensor nodes on the basis of these regions. Figure 3 shows the formation of regions in the assumed network on the basis of ranges of \(\theta\).  
In our scheme, we assume \(\aleph\) number of nodes with the same amount of initial energy which are randomly deployed in the region of 1000 \(m^{2}\). The transmission range of each sensor node is \(R\). Four ranges of \(\theta\) defining the basis for regions are as follows:  
Figure 3: Regions formation in 4-chains based scheme.  
\[\theta_{1}\in[0,\pi/2),\quad\theta_{2}\in[\pi/2,\pi)\] \[\theta_{3}\in[\pi,3\pi/2),\quad\theta_{4}\in[3\pi/2,0]\] \[G_{1}(r,\theta_{1},z),\quad\ G_{2}(r,\theta_{2},z)\] \[G_{3}(r,\theta_{3},z),\quad\ G_{4}(r,\theta_{4},z)\]  
Here, \(G_{1}\), \(G_{2}\), \(G_{3}\) and \(G_{4}\) show the groups of nodes, randomly located in the four regions, and their ranges in terms of \(\theta\). Nodes in the all four groups have the same ranges for \(r\) and \(z\). Creation of chains starts in all the regions through token passing approach. Four separate chains are created as the token passes from the farthest node from sink to the nearest node to the sink. We compute the lengths of four chains as follows:  
\[l_{i,i+1}^{j}=(n_{i+1}^{j}-n_{i}^{j}) \tag{28}\]  
In the above equation, \(n_{i}^{j}\) denotes the _ith_ node present in the _jth_ chain of the network, whereas, \(l_{i,i+1}^{j}\) is the length of chain between any two consecutive nodes in _jth_ chain.  
\[L_{G}^{j}=\sum_{i=1}^{N^{j}-1}(n_{i+1}^{j}-n_{i}^{j}) \tag{29}\]  
In Equation (29), \(L_{G}^{j}\) shows the total length of the _jth_ chain which consists of \(N^{j}\) nodes.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 2 Cellular-Based Positioning Methods
### Database Lookup Methods  
Database lookup methods are used to determine the location of a mobile device quickly through static database queries. These methods can be classified into three categories: a cell-ID-based method [19], a handover-based method [20; 21; 22], and a fingerprint positioning method [23; 24; 25; 26; 27; 28]. However, the lengths of cells and handoff zones are approximately 2 km and 200 m, respectively, and the location error depends on the cell size [12; 19; 29]. Although precise location estimation can be obtained by the fingerprint positioning method, higher computation power and an establishment fee are required [30].  
To resolve these disadvantages, this study proposes a high-efficiency mobile positioning method based on the database lookup method to determine the location of mobile device quickly. Additionally, this method considers the RSSI of the connected cell to increase the accuracy of the estimated location.
## 4 Simulation and Physical Experiments
### Physical Verification Experiments  
To demonstrate the applicability of the SA-PSO method, experiments are carried on the Stewart-Gough platform H-850 with six laser displacement sensors calculating the length change of six outriggers. The experimental platform is shown in Figure 10. The measurement accuracy of the laser displacement sensor is 10 \(\upmu\)m, and its measuring range is \(\pm\)5 mm. The hexapod platform can be controlled by a moving signal from the controller, and the parameters of it are illustrated in Table 9.  
Figure 10: The experimental platform to verify the SA–PSO forward solution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Success Rate (\%)**} & \multicolumn{2}{c}{**Average Time Consuming (s)**} \\ \cline{2-5}  & **NR** & **SA–PSO** & **NR** & **SA–PSO** \\ \hline
**1** & 96.2 & 99.9 & 0.3708 & 0.0372 \\
**2** & 97.4 & 100 & 0.3468 & 0.0356 \\
**3** & 97.7 & 99.8 & 0.3725 & 0.0377 \\
**4** & 97.3 & 100 & 0.4706 & 0.0348 \\
**5** & 97 & 100 & 0.3638 & 0.0350 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The random comparison experiments between NR and SA–PSO.  
Let the H-850 Stewart-Gough platform move in a single degree of freedom, record the reading of the laser displacement sensor, pass it to the SA-PSO forward kinematic algorithm, and finally compare the errors between the algorithm results and the given motion postures of the platform.  
The experimental results show that the SA-PSO algorithm can accurately calculate the corresponding platform posture value through the input outrigger length. In Figure 11, the errors between the solution results and the given posture of the controller are no more than 40 \(\upmu\)m. The main sources of these errors are as follows: 1, the measurement error of the laser displacement sensor; and 2, the positioning error of the platform itself. The measurement accuracy of the laser displacement sensor is not more than 10 \(\upmu\)m, and the structure error caused by the installation will also reduce the accuracy of outrigger rod length measurement. There will be a small positioning deviation when the platform moves. In the future, precise optical means can be used to calibrate the attitude of the platform. Based on the efficiency of the SA-PSO algorithm in forward kinematics, we will next consider applying the algorithm to the model calibration of the SGP structure.
## 3 Methods
### Baseline Model: HiSup  
This paper adopts HiSup as the baseline model for extracting building polygons from remote sensing images. The model structure is illustrated in Figure 5. HiSup consists of three feature learning branches, including the Mask Branch, Line Branch, and Vertex Branch. Additionally, HiSup incorporates modules for multi-scale feature extraction, channel attention, boundary enhancement, and polygon construction and simplification. Specifically, for an input remote sensing image \(I\in R^{3\times H\times W}\), HRNet is employed as the multi-scale feature extractor to obtain the feature map \(F\in R^{3\times H_{S}\times W_{S}}\). Here, \(H_{S}=H/S\) represents the length of feature map \(F\), and \(W_{S}=W/S\) represents its width, with \(S\) being the downsampling factor. After obtaining the image feature map \(F\) through the multi-scale feature extractor, \(F\) is fed into three distinct branches to learn the vertex feature \(F_{ver}\), line feature \(F_{line}\), and semantic mask feature \(F_{seg}\) of buildings. Each branch contains three consecutive processing units, including \(3\times 3\) convolutional layers, a batch normalization layer [52], and a ReLU [53] layer in each processing unit. Specifically, the vertex feature \(F_{ver}\) and line feature \(F_{line}\) are derived from mid-level image features, while the semantic mask feature \(F_{seg}\) is obtained from high-level image features. Subsequently, the channel attention module integrates these three features to generate the vertex prediction heat map \(M_{H}\), vertex distance offset field \(M_{O}\), line attraction field [29] prediction map \(M_{A}\), and building a semantic mask prediction map \(M_{S}\). Next, by utilizing the line attraction field \(M_{A}\), and image feature \(F\) to impose boundary constraints on \(M_{S}\), a more regular segmentation result is obtained. Finally, the vertex, line, and semantic prediction results are utilized to construct vector polygons, which are further simplified to obtain the final building polygon extraction results.  
To further reduce the influence of complex backgrounds on building boundaries, we improved HiSup. In the boundary enhancement module, Dice Loss was incorporated to alleviate the issue of building adhesion. This modification aims to improve the segmentation performance by effectively separating buildings from their surrounding environment, as shown in Figure 5.  
Figure 5: The architecture of the improved HiSup. \(F\) denotes the image features from HRNet. \(F_{seg}\) denotes the semantic features. \(F_{line}\) denotes the line features. \(F_{ver}\) denotes the vertex features. \(\delta\) represents the sigmoid activation function. GAP represents the global average pooling. \(L_{seg}\), \(L_{line}\), and \(L_{cor}\) represent the loss function in the Mask branch, Line branch, and Vertex branch, respectively.
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
## 3 Results and Discussion
### Impact of Load Variation on Friction and Wear Properties
#### 3.2.4 Effect on Wear Morphology
When compared with the wear morphology of Ti-6 Al-4V alloy under corresponding loads, it was obvious that the wear resistance ability of alloying coating was better than that of Ti6 Al4V alloy at room temperature, because TiN, TiB, Ti\({}_{5}\)Si\({}_{3}\), and other hard particles existed on the surface of the alloying coating, in the same condition, serious plastic deformation was relatively difficult to form and the metal of the coating was not easy to break away from the surface. Consequently, the amount of debris was less and the wear was not serious. Figure 11 shows the wear cross-section trajectory curve of the alloying coating under different loads. It can be found that the wear depth and width of the alloying coating increased with the increasing load. As the load increases, the friction coefficient of the Ti-6 Al-4V alloy surface decreases during the friction and wear process, the amount of wear and the specific wear rate increases, the depth of the furrow of the wear scar surface  
Figure 10: Wear morphology of alloying coating under different load. (**a,b**): 3 N; (**c,d**): 6 N; (**e,f**): 9 N.  
increases, and the amount of wear debris increases, indicating that, with the load, the increase of titanium alloy surface wear is serious. With the increase of the load, the friction coefficient of the laser alloy coating remains in the range of 0.39-0.42, but the amount of wear and wear rate increase, and the depth of the furrow of the wear scar surface increases. The increase in the amount of wear debris indicates that the wear of the alloyed coating increases as the load increases. Comparing Ti-6 Al-4V alloy and alloyed coating, it can be seen that the wear resistance of laser-alloyed coating is higher than that of Ti-6 Al-4V alloy at room temperature.
## 8 Results Analysis
### Multi-Objective Optimization Based on PSO Algorithm  
The PSO algorithm is an optimization method based on particle analysis. It simulates the social behavior of bird flocks or fish schools to achieve global search, thereby avoiding the randomness of adjusting control parameters and reducing the likelihood of getting trapped in a local optimum. The iterative update of particles can be represented as follows:  
\[\left\{\begin{aligned} & v_{i}^{sc}=\omega\cdot v_{i}^{sc}+c_{1}rand(1) \cdot(Pbest^{sc}-x_{i}^{sc})+c_{2}rand(2)\cdot(Gbest^{sc}-x_{i}^{sc})\\ & x_{i}^{sc}=x_{i}^{sc}+0.5v_{i}^{sc}\end{aligned}\right. \tag{16}\]  
where \(sc=1\), \(2\), \(\ldots\), i = \(1\), \(2\)\(\ldots\), \(\omega\) are the inertia weight; \(c1\), \(c2\) are acceleration constants; and _rand(1)_, _rand(2)_ are random value functions.  
To quickly optimize the above variables by combining the PSO algorithm with response analysis, the following multi-objective optimization function and constraints must be satisfied:  
\[Constraint\ function:\ \cdot\  
When \(c_{1}\) equals 0, the particle loses its cognitive ability and behaves according to a purely social model, which is referred to as the global PSO algorithm. Conversely, when \(c_{2}\) equals 0, there is no social information exchange between particles, resulting in a purely cognitive model, known as the local PSO algorithm [40; 41]. These two parameters significantly influence the extent to which particles tend to move towards their personal best and the swarm's historical best positions, thereby affecting the algorithm's convergence speed and the quality of the solution.  
To better optimize the model, five different parameters were selected for \(\omega\), \(c_{1}\), and \(c_{2}\) as shown in Table 11. The control variable method was used to separately discuss the impact of the five different parameters of \(\omega\), \(c_{1}\), and \(c_{2}\) on the optimization results.  
From Figure 20a-c, when \(\omega\) equals 10, c\({}_{1}\) equals 2, and c\({}_{2}\) equals 0.5, the optimization effect is the best. Using these optimal parameters, the wall thickness uniformity was optimized, resulting in the outcome shown in Figure 20d.  
From Figure 20d, it can be observed that the algorithm converges rapidly in the initial stage, with convergence gradually stabilizing after approximately 35 iterations. The overall  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Number** & \(\omega\) & \(c_{1}\) & \(c_{2}\) \\ \hline
1 & 10 & 0.5 & 0.5 \\
2 & 1 & 0.6 & 0.6 \\
3 & 0.1 & 0.8 & 0.8 \\
4 & 0.01 & 1.2 & 1.2 \\
5 & 0.001 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Parameter selection.  
Figure 20: The relationship between fitness function and iteration times of the particle swarm optimization algorithm. (**a**) \(\omega\) optimal value, (**b**) \(c_{1}\) optimal value, (**c**) \(c_{2}\) optimal value, and (**d**) optimal results.  
results indicate that using the particle swarm optimization (PSO) algorithm to explore the optimal parameter combination for achieving wall thickness uniformity is feasible. The optimal parameter combination is shown in Table 12
## 3 Results and Discussion
### Effect of Contact Time and Temperature  
Time is also a crucial factor in designing an appropriate treatment process. The effect of contact time at different temperatures on the adsorption of CV and Cr (VI) is shown in Figure 10. It is clear from the figure that for all temperatures studied the adsorbed amount increased rapidly during the first minutes, then gradually slowed down, and finally became constant after an equilibrium time of 45 min and 80 min for CV and Cr (VI), respectively. After equilibrium, the adsorption was insignificant as a function of contact time under the conditions employed. Therefore, these two times were selected for further studies.  
Figure 9: Effect of adsorbent dose on Cr (VI) and CV removal.  
The rapid adsorption at the beginning is due to a large concentration gradient between the solution and the adsorbate concentration on the solid surface, as well as the large number of important vacant adsorption active sites. The slow increase in the later stage is attributed to the low number of free sites. Regarding the effect of temperature, the decrease in the adsorbed amount of both pollutants with increasing temperature indicates that the adsorption is exothermic and is therefore more favourable at low temperatures. This shows that increasing the temperature leads to a decrease in the binding forces between the adsorbent and the adsorbate. These observations are consistent with those of [PERSON] et al. [82], concerning adsorption of crystal violet dye using biochar from cocoa leaves, and those of [PERSON] et al. [83], concerning the adsorption of Cr (VI) on activated carbon from alo vera leaf powder waste.
# A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
## 5 Experimental Results
### Image Hashing
#### 5.2.1 Geometric Manipulation  
As shown in Table 2, the identification accuracies are measured according to IoUs between the original photos and randomly cropped images with various aspect ratios and sizes. The accuracies of all methods increase as an IoU and a descriptor size increase. The proposed image hashing achieves better accuracies than those of HoG and binaryGAN. These results show that the proposed image hashing network that learns various geometric manipulation is effective. HoG, a low-level handcrafted feature, shows a rapid decrease in accuracy as an IoU decreased, and BinaryGAN based on autoencoder network seems difficult to handle the geometric manipulation compared to the proposed image hashing.
## 3 ARIMA-ML Framework
### ARIMA-ML in Structural Damage Identification  
Integrating the above-mentioned four modules describes the two-stage framework of the ARIMA-ML. Stage 1 is used to determine a set of TS models with multiple candidate \((p,q)\) pairs, and stage 2 is used to train the classification models. Repeating stage 2 for each candidate pair, the best TS model with its corresponding trained classification model is obtained.  
While applying the ARIMA-ML framework to the structural damage identification, its input is the acceleration response collected **after** a possible damaging loading scenario, e.g., due to an earthquake. This response can be excited from small white noise signals applied to the structure from ambient vibration or from a small external force, e.g., a hammer impact excitation as used in [31]. The underlying assumption is that the structural damage state or pattern is determined from the damaging loading stage, and the white noise excitation with low amplitude from ambient vibration or external force does not cause further damage, i.e., the structure responds linearly with a degraded stiffness compared to its undamaged state. Based on damage criteria, these TS data are labeled accordingly.  
From the ARIMA-ML framework with labeled data, the best TS model, i.e., ARIMA(\(p\), \(l\), \(q\)), can be obtained, and the corresponding classification models are trained. From the physical interpretation in Section 2, the order of the TS model is more related to the number of DOFs and varies according to the TS properties. Even though TS data with different damage states may differ in properties leading to different \((p,q)\) selection, it is suggested to use the higher order among multiple model choices [16].  
The well-trained ARIMA-ML framework can be applied automatically for real-time SHM. Through inputting newly received TS signal from the sensors, the framework automatically processes the TS data through pre-processing, feature extraction, and classification modules once, and outputs the corresponding damage state. This workflow (refer to Figure 3 for the pseudo-code of the trained ARIMA-ML framework) indicates an end-to-end efficient run-time performance, which is suitable for developing embedded intelligent devices for real-time automatic damage detection. The code implementation is slightly different from Figure 1 where the SSND is followed by feature extraction segment by segment to form a stacked feature matrix, which is then fed as vectorized data into the ML classification to accelerate the computation and increase its efficiency.
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
###### Abstract  
Reversible data hiding has attracted significant attention from researchers because it can extract an embedded secret message correctly and recover a cover image without distortion. In this paper, a novel, efficient reversible data hiding scheme is proposed for absolute moment block truncation code (AMBTC) compressed images. The proposed scheme is based on the high correlation of neighboring values in two mean tables of AMBTC-compressed images to further losslessly encode these values and create free space for containing a secret message. Experimental results demonstrated that the proposed scheme obtained a high embedding capacity and guaranteed the same PSNRs as the traditional AMBTC algorithm. In addition, the proposed scheme achieved a higher embedding capacity and higher efficiency rate than those of some previous schemes while maintaining an acceptable bit rate.  
data hiding; high efficiency; AMBTC; privacy protection; reversibility +
Footnote †: journal: Applied Sciences
# Properties of a Lightweight Fly Ash-Slag Alkali-Activated Concrete with Three Strength Grades
## 3 Experimental Results  
### Slump and Density  
Table 5 shows the slump, density, and UPV test results and other mechanical properties of the different concrete mixtures. Each value in the table represent the average of the test results of at least three samples. The workability of the different mixtures measured by the slump test shows that the slump values of the blended fly ash-slag-based AAC (200-270 mm) are much larger than those of OPC concrete (60-90 mm) of the same grade. The reason for the increased slump of the AAC mixtures was that the fly ash replaces the OPC and the spherical-shaped fly ash particles increases the fluidity, thus improving the workability of the concrete [8]. [PERSON] et al. [36] concluded that the rheology of the AAC mixtures was different from that of the OPC mixtures because the liquids used in the AAC had a higher viscosity. Therefore, the traditional measurement standard for the workability level in the OPC mixture was not suitable given the slump and flow test results of the AAC. Even with the same measurement value of slump as the OPC concrete, the workability of AAC was worse than that of its cement concrete counterpart. The literature [37,38] concluded that AAC with a slump value greater than 200 mm had reasonable workability and can be used for casting structural components (such as beams and columns). According to this point of view, the NAAC and LAAC mixtures developed in this study can be used in the fabrication of structural concrete members. As shown in Table 5, the slump value of the LAACs containing sintered fly ash aggregates is in the range of 237-268 mm, which is about 16-20% higher than that of the NAACs (205-223 mm) containing limestone. It may be that the relatively smoother and rounder surface of the lightweight aggregates enhanced the workability of the fresh AAC, compared with the traditional coarse aggregate containing more edges and corners.  
The results show that the average dry density of the NAAC mixture ranged from 2351 to 2400 kg/m\({}^{3}\) at 28 days, which was completely within the normal-weight concrete scope of 2155-2560 kg/m\({}^{3}\), specified in the ACI building code [39]. According to JGJ12-2006 [40], the concrete with an oven dry density of less than 2000 kg/m\({}^{3}\) and 28 days compressive strength greater than 20 MPa can be used as structural lightweight concrete. All the LOPC and LAAC mixtures list in Table 5 meet the requirements for structural lightweight concrete.