esa-sceva/satcom-chunk-collection huggingface dataset:
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 6 Conclusions  
SRAM based PUFs are popular for key/ID generation. Such keys may be unreliable due to noise. Since the reliability of keys is of paramount importance, various ECC and fuzzy extraction based techniques have been proposed previously. Unfortunately, they are also expensive. We present an alternative design based on a new voter scheme which reduces the error rate significantly. Our integrative solution, encompassing analysis, design and test techniques, ensures low error rate, high yield and simplicity of design. The proposed technique can be viewed as both alternative to ECC schemes in low cost systems and complementary to ECC in high reliability design requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Implementation** & **Area (\(\upmu\)m\({}^{2}\))** \\ \hline repetition[9;1;9]; [PERSON][23;12;7] [32] & 7648 \\ \hline repetition[9;1;9]; [PERSON][16;5;8] [32] & 7945 \\ \hline \multirow{3}{*}{UP/DOWN counter scheme} & \(4:1\) Mux & 4538 \\ \cline{2-3}  & \(8:1\) Mux & 3266 \\ \cline{1-1} \cline{2-3}  & \(16:1\) Mux & 2630 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Area estimates using Nangate Cell Library [29].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Implementation** & **\# Bits generated** & **\# Cycles** \\ \hline [PERSON] et al. [30] & 171 & 286 \\ \hline \multirow{3}{*}{Our Approach} & 4:1 & 248 \\ \cline{2-3}  & 8:1 & 192 \\ \cline{1-1} \cline{2-3}  & 16:1 & 848 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Generation time results.  
The future extension of this work includes analysis of the impact of aging on reliability improvement of the proposed scheme. We also plan to fabricate this design to conduct silicon experiments in order to validate our results experimentally.  
**Acknowledgments:** This work is supported in part by grants from the National Science Foundation (NSF) (Grant No. 1421352) and Intel Corporation, Hillsboro, OR, USA.  
**Author Contributions:** The primary idea for this work was developed by [PERSON] and [PERSON]. [PERSON] was responsible for the mathematical analysis showcased in this work. The simulation setup, generation of results and comparison with previous works were performed, jointly, by [PERSON] and [PERSON]. The manuscript was written by [PERSON] and [PERSON], with technical and formatting aspects handled by [PERSON]. [PERSON] supervised the entire research and provided valuable guidance to help with making crucial observations. He also directed the writing of this paper and its technical proofing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 3 Preliminaries
### Contributions  
In this paper, we present a multi-user security system that facilitates the controlled sharing of facial data on online photo platforms. The proposed system extends SnapSafe to support multi-user scenarios, enabling the selective encryption of facial regions in images while preserving the visibility of non-sensitive content. By incorporating dynamic access controls, the system ensures that facial data remain secure and accessible only to authorized users. The key contributions of this work are as follows:  
* Key Splitting for Access Control: The system introduces key splitting as a mechanism to enable access control over shared facial data. This approach allows the image owner to selectively share access to facial regions in shared images. This key splitting operation enables the secure sharing of facial data while keeping the image owner exclusive access to the original key.
* Owner-Centric Permission System: The system implements an owner-centric permission system, designating the image owner as the primary authority for granting access to facial data. This system ensures that the owner retains full control over the shared data, enabling the management of access permissions for multiple users. In addition to granting access, the owner can revoke previously granted permissions at any time, ensuring that facial data remain protected under the owner's supervision.
* Request-Based Access Mechanism: The system incorporates a request-based access mechanism that allows users to request permission to access protected facial regions. By default, this mechanism ensures that facial regions in images shared on online platforms remain protected. Only the image owner has access to these regions, while other users must submit access requests. The owner retains full authority to approve or reject such requests, thereby maintaining control over the visibility of facial data.
## 3 Cross-Layer Controller Tasking Scheme
### Case Analysis  
This case analysis is performed for \(Task_{pi}\) and \(TAE_{r}\) under different \(TM\) and machines. This outcome does not fit the \(TM_{group}\), as the processing tasks are similar and are evenly allocated. Therefore, the analysis is considered as the post-conclusive outcome of the learning process below.  
In Figure 6, the outcomes for \(Task_{pi}\) and \(TAE_{r}\) are represented by assimilating the data source and proposed concept. The \(Task_{vol}\) per interval increases if \(TM_{group}\) increases and \(\overline{\mathsf{C}}_{\text{Iyc}}\) is operative. This requires sequential validation using \(\mathcal{E}_{oi}\) and \(v_{oi}\) assessments using the learning process provided. Based on the increasing \(Task_{vol}\), the information (of the controller) sharing increases for cross-layer operations. Hence, in this case, the learning network operates concurrently for \(\exists\) and \(TAE_{i}\) reduction as inferred from \(\in_{oi}=\textit{fal}_{\text{Iyc}}\).  
This further recalls the \((\in_{o},\ n\in N)\) initialization such that the \(Task_{vol}\) in \(TM_{group}\) selects controllers with \(R_{n}=1\). The external edge nodes are responsible for ensuring the controller assignment is swapped between \(R_{n}=1\) for increasing tasks. End of Case Analysis.  
In the IIoT platform, the task acceptance error is prevented to reduce the complexity in production, maintenance, outcome, and prolonged time delay. However, the cross-layer controllers perform an information-sharing analysis as in Equation (14). This analysis ensures better productivity as follows:  
\[\begin{split} Analyze(\mu_{G},TM)=&\mathbb{R}\times \exists\times n\times TM_{group}=\frac{\mathbb{P}}{\mathbb{R}}*\hat{iv}\Big{[} \mathbb{P}_{R_{G}}\bigoplus Task_{pi}\bigoplus Task_{vol}\bigoplus TM\Big{]} \\ =&\Big{[}\mathbb{P}_{\mathbb{C}_{I\!,\!key}} \bigoplus Task_{pi}\bigoplus Task_{vol}\bigoplus TM\Big{]}\end{split} \tag{14}\]  
In Equation (14), the task grouping conditions as in Equations (1)-(3) and (11) are consolidated to provide a linear output. Based on the mediate outputs and learning network, task migration and information sharing are performed. Hence, if the task prolonging time and synchronized completion time vary, then the above task volume is used. Both the concurrent tasking and completion are carried out, whereas the cross-layer controllers are migrated alone in the IIoT along with the reliable reputation of the edge devices. The IIoT information is disseminated between the interconnected layers and centralized processing units to improve the productivity rate. Therefore, synchronized completion time ensures additional network training and high-time-delay systems in both the sender and receiver ends. Edge computing and graph learning are used to reduce the TAE and time delay systems in the IIoT platform.  
Figure 6: \(Task_{pi}\) and \(TAE_{r}\) outcomes.
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 2 State of the Art Overview
In the first step, performance of three different approaches is investigated in order to design and develop sink mobility models for energy efficient data gathering and delay-tolerant static wireless sensor networks. In the second step, a sensor node is randomly chosen based on the maximum number of exposed neighbor nodes. Finally, the authors recommend to use random node selection-based strategy for sink mobility models instead of keeping the track of the number of exposed neighbor nodes per node and available residual energy of the sensor node. These existing mobility models either focus on maintaining the connectivity or throughput performance. Whereas, our proposed lattice location-based mobility model concurrently determines the node and sink mobility for improving the energy efficiency and QoS provisioning. It reduces the pause time, number of control packets and node dependency. Furthermore, it also detects the node's moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination.
## 3 Results
### GPP Simulation Results and Temporal and Spatial Evolution
#### 3.1.2 Temporal and Spatial Evolution Trend of GPP  
Analysis of the monthly scale RF_GPP data revealed an annual average GPP of \(508.35\) gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\) during the growing season, with a growth rate of \(1.784\) gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-2}\), indicating a significant interannual increase (Figure 3d). Spatially, the annual average GPP shows a pronounced gradient, decreasing from southeast to northwest. The high-value GPP areas in the southeast are dominated by forests and shrubs, while the low-value areas in the northwest mainly consist of alpine and desert vegetation (Figure 3a). The spatial distribution of the annual mean and standard deviation generally follows a pattern of decreasing from southeast to northwest, indicating greater annual variability in GPP in the southeast (Figure 3b). Examining the spatial distribution of the interannual change trend, most regions exhibit a significant increase in GPP, with the fastest growth observed in the southeastern plateau and a declining trend in some arid areas of northern Tibet. The spatial distribution of the interannual change trend shows a general pattern of decreasing from southeast to northwest (Figure 3c).  
\begin{table}
\begin{tabular}{c c c} \hline  & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline GLASS\_GPP & 0.36 & 91.764 \\ regGPP & 0.429 & 86.578 \\ RF\_GPP & 0.611 & 69.162 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy comparison between observation data and model results.  
Figure 3: Spatial pattern of GPP and its interannual trend (**(a)** shows the spatial distribution of the annual average value of GPP, with the histogram in the upper left corner displaying the area proportion  
under different annual average values of GPP; (**b**) shows the standard deviation spatial distribution of the annual average GPP; (**c**) shows the spatial distribution of the interannual change trend, with the upper left corner indicating the significance of the growth trend at \(p<0.1\), where the green area shows significant change and the red area shows no significant change; (**d**) shows the scatter plot of the annual GPP change and the fitting curve of the annual growth trend).
## 3 Results
### Algorithms Retrieval
For the organic fraction (POM) of TSM (Table 3), the best algorithms using only data from 0.0035 mg/L to 6 mg/L were the simple band R\({}_{783}\) and the R\({}_{665}\)/R\({}_{560}\) ratio, with errors of 0.84 mg/L and 0.86 mg/L between the measured and estimated data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band Comb. (x)** & \multicolumn{1}{c}{**Formula**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**RMSE (mg/L)**} & \multicolumn{1}{c}{**NRMSE (\%)**} \\ \hline
705 & y = 153.57x + 1.2252 & 0.6302 & 1.56 & 12.17 \\ \hline
783 & y = 210.75x + 1.9694 & 0.3472 & 2.08 & 16.17 \\ \hline
740 & y = 208.06x + 1.9144 & 0.3424 & 2.08 & 16.23 \\ \hline
665 & y = 5.7616x + 0.9984 & 0.2918 & 2.16 & 16.84 \\ \hline
560 & y = -4.905x + 5.9536 & 0.2717 & 2.19 & 17.08 \\ \hline
443 & y = -5.4007x + 7.2749 & 0.2561 & 2.22 & 17.26 \\ \hline
490 & y = -5.0836x + 6.8363 & 0.247 & 2.23 & 17.36 \\ \hline
205 & y = 4.029x + 1.7254 & 0.2269 & 2.26 & 17.59 \\ \hline
865 & y = 5.6585x + 0.2233 & 0.2233 & 2.26 & 17.64 \\ \hline
560 & y = -5.1331x + 7.1515 & 0.18 & 2.32 & 18.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithm results for PIM. Only the best 10 results are shown. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band Comb. (x)** & \multicolumn{1}{c}{**Formula**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**RMSE (mg/L)**} & \multicolumn{1}{c}{**NRMSE (\%)**} \\ \hline
783 & y = 133.02x + 0.7976 & 0.5785 & 0.84 & 14.75 \\ \hline
665 & y = 4.0442x + 0.0715 & 0.5619 & 0.86 & 15.04 \\ \hline
443 & y = -4.0683x + 4.716 & 0.5611 & 0.86 & 15.05 \\ \hline
865 & y = -4.5634x + 0.7954 & 0.5563 & 0.87 & 15.13 \\ \hline
740 & y = -129.97x + 0.7707 & 0.5543 & 0.87 & 15.17 \\ \hline
705 & y = 3.1611x + 0.5007 & 0.5395 & 0.88 & 15.42 \\ \hline
783 & y = 3.3831x + 0.6307 & 0.5333 & 0.89 & 15.52 \\ \hline
560 & y = -3.4773x + 3.5726 & 0.5328 & 0.89 & 15.53 \\ \hline
560 & y = -4.491x + 5.1542 & 0.5317 & 0.89 & 15.55 \\ \hline
490 & y = 4.3313x + 5.1168 & 0.5309 & 0.89 & 15.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm results for POM. Only the best 10 results are shown. Figure 10 shows the correlation between R\({}_{783}\) and the in situ data. In this case it has been adjusted by the method of least squares to a linear fitting function, to obtain the best adjustment with the following equation:  
\[\text{POM (mg/L)}=133.02\text{ R}_{783}+0.7976 \tag{4}\]  
Figure 10: POM in situ data in function of R\({}_{783}\) using all data under 6 mg/L. Figure 9: TSM in situ data in function of R\({}_{705}\) using all data under 30 mg/L. For PIM, it emerged that R\({}_{705}\) is the one that presents the best correlation (shown in Figure 11) with an error of 1.56 mg/L between measured and estimated data. Adjusting by the method of least squares to a linear fitting function, the best adjustments are obtained with the equation:  
\[\text{PIM (mg/L)}=153.57\text{ R}_{705}+1.2252 \tag{5}\]  
From Figures 10 and 11, can be seen that the Ebro and Jucar watershed reservoirs have inorganic sediment predominance, whereas in the marshes the organic component dominates.
# Research on Optimization Design of Tunnel Blasting Scheme Adjacent to Buildings
## 3 Numerical Simulation to Verify the Feasibility of the Solution
### Comparison Analysis of the Vibration Velocity of Different Building Story Heights  
The vibration velocity at different building layers under the full-section blasting scheme is analyzed and plotted on a graph, as shown in Figure 7. It can be seen from the figure that the vertical direction of vibration velocity with the rise of floors first decays after the phenomenon increases. Taking seven floors as the demarcation, floors 1 to 7 can be taken as the vibration velocity decay area, and floors 7 to 18 can be taken as the vibration velocity amplification area. According to the variation curves of vibration velocity at different floors in Figure 7, it can be seen that the vibration rate of high-rise buildings under the influence of blasting vibration is divided into two processes: decay and amplification. Regarding floors 1 to 7, there is no high-rise amplification effect. The peak value of vibration velocity attenuates with the increase in detonation distance, which is consistent with the relationship between blasting seismic wave strength, as the increase in burst center distance and attenuation increase, with a peak attenuation of 42.3%. The ppv attenuation rate is calculated by dividing the attenuation value by the vibration velocity value at floor 1. The main reason for the appearance of the attenuation zone is that with the increase in burst distance, when the vibration waves come from a small area of the column section to a relatively large cross-sectional area of the floor section, there will be a reflection of the wave transmission; then, the wave energy's loss of part of the peak particle velocity will become smaller. Elevation amplification occurs from floor 7 to 18. The main reason for the appearance of the amplification zone is that the top layer is less constrained and that the amplification of vibration velocity occurs relative to the bottom layer, which is more constrained. With the increase in floor, the decaying trend of blast vibration velocity is gradually less obvious than the amplifying trend, which happens when the high-level amplification effect dominates. Therefore, the elevation amplification effect should become a non-negligible consideration during the construction of tunnels passing under high-rise buildings.  
Figure 6: The relationship between the tunnel structure and building measurement points in the numerical mode: (**a**) building and tunnel structure diagram; (**b**) schematic diagram of the building monitoring site.
## Nomenclature  
**Abbreviations**  
CFD Computational Fluid Dynamics  
KDE kernel density estimation  
KD-Tree k-Dimensional Tree  
KL Kullback-Leibler Divergence  
KNN k-Nearest Neighbors  
ML Machine Learning  
MSE Mean Squared Error  
OOB Out-of-bag  
**Variables**  
\(a_{k}\) The boundary between the (k-1)-th and k-th bins for a feature  
\(b_{y}\) Number of intervals (bins) used for feature partitioning  
\(c\) A sample point drawn  
\(c_{i}\) The i-th feature value of a sample in the current sample set  
\(C\) The current set of selected (or accepted) samples  
\(d\) The dimensionality of the feature space (i.e., number of features)  
\(D_{i}^{\text{OOB}}\) The OBB sample set for the i-th tree  
\(\delta\) Gaussian random variable (zero-mean unless otherwise specified)  
h Bandwidth parameter used in kernel density estimation (KDE)  
h\({}_{i}\) The prediction of i-th tree  
K(-) Kernel function  
\(l_{i}\) The region corresponding to the i-th multidimensional feature  
combination  
\(MSE_{i}^{\text{OOB}}\) MSE for the i-th tree in the OBB sample set  
\(\mu(f)\) Mean of the feature \(f\)  
Total count; used to denote the number of layers, datasets, candidate  
points, or invalid points depending on the context  
Nbatch The total number of points sampled in the current batch  
Nrejected The number of points that fall within the dead zone  
\(q_{k}\) k-th quantile of a feature distribution  
r Current rejection rate during sampling  
r\({}_{t}\) The target rejection rate  
R The radial distance from the substrate center  
Rmax The maximum radial distance
# The Architecture of the Access Protocols of the Global Infocommunication Resources +
## 2 Materials and Methods
### Step II. Data Transfer from the Terminal Device to the Base Station
#### 2.2.2 Expressions for Calculating Probability-Time Characteristics (PTC)  
The probability that the user data blocks being transmitted will be delivered in a timely manner.  
The study assumes the situation of using a stochastic restriction on the allowed time for transmitting user data blocks in the 1 st and 2 nd phases with the parameter \(Q_{aj}\):  
\[Q_{aj}=1-\frac{T_{j}}{T_{aj}},j=\overline{1,2}. \tag{7}\]  
In this situation, you can use the following formula to find the probability that the transmitted user data blocks will be delivered in a timely manner:  
\[\overline{\Pi}=\overline{\Pi_{1}}\,\overline{\Pi_{2}},\overline{\Pi_{j}}=g_{j }(z)\Big{|}_{z=Q_{aj}^{-1}},j=\overline{1,2}, \tag{8}\]  
where \(g_{j}(z)\) is set by Equations (2) and (3).  
The average delay time for transmitting user data blocks from the terminal device to the base station can be found from the expression:  
\[\overline{t}=\overline{t_{1}}+\overline{t_{2}}, \tag{9}\]  
where \(\overline{t_{1}}\) and \(\overline{t_{2}}\) are mathematical expectations for the transfer times of user data blocks in phase 1 and 2 are based on the Khinchin-Polachek formula  
\[\overline{t_{j}}=\overline{n_{j}}\,T_{j},\overline{n_{j}}=f_{j}^{\prime}(1)+ \frac{q_{1}\,f_{j}^{\prime\prime}(1)}{2(1-\theta_{j})},j=\overline{1,2}, \tag{10}\]  
\[f_{j}^{\prime}(1)=(d/dz^{-1})f_{j}(z)\Big{|}_{z=1},f_{j}^{\prime\prime}(1)=(d /dz^{-2})f_{j}(z)\Big{|}_{z=1},j=\overline{1,2}, \tag{11}\]  
where \(f_{j}(z)\) is set by Equation (5).  
The information speed of the real time access radio network at the last mile is determined from the expression  
\[R^{RT}=ID\mu Q\overline{\Pi}, \tag{12}\]  
where \(\overline{\Pi}\) is set by Equation (8). \(R^{RT}\) shows how much [bit] traffic is successfully transmitted in the access radio network per unit of time [s]. The \(R^{RT}\) calculation takes into account all losses that occur when transmitting user data blocks on the last mile section. The losses are caused, first, by possible service failures at the first step (see Figure 3), and, secondly, losses arising from delayed delivery of user data blocks in the 1 st and 2 nd phases of the second step-directly in the transfer process.
## 3 Results
### Temporal Analysis of Forest Recovery  
The temporal changes in the study areas were displayed using RGB composite images from Landsat-5/TM and Landsat-8/OLI, which were created from visible red (Band 3 or Band 4), green (Band 2 or Band 3), and blue (Band 1 or Band 2) data (Figure 4). August is the month when the most active plant growth occurs in South Korea. In the 1999 image of the pre-fire forest, the RGB composite image had pixels with a similar green color in the forest areas (Figure 4a). However, brown colored pixels increased immediately after the forest fire (Figure 4b). In images from 2004, four years after the East Coast fire, there were still a large number of brown colored pixels (Figure 4c). Over time, as seen in the 2007 and 2010 images, the brown colored pixels gradually decreased, while the green colored pixels in the burned areas increased. The RGB composite image in 2016 showed that most of the burned area pixels changed from brown to green (Figure 4f).  
The values of NBR\({}_{2.1}\), NBR\({}_{1.6}\), NDVI, and GPP immediately dropped after the forest fire. The extent of the change depended on the severity of the forest fire. However, the initial differences compared with the reference values decreased over time (Figure 5). NBR\({}_{2.1}\) and NBR\({}_{1.6}\) showed a similar time series patterns, but the NBR\({}_{2.1}\) and NBR\({}_{1.6}\) values in the areas of all damage severity reached the averaged reference value in different years (Figure 5a,b). The recovery-time points were statistically evaluated using an independent two-sample \(t\)-test (Table 2). In low (high) fire se  
Figure 4: The RGB composite images of Landsat-5 and Landsat-8: (**a**) 8 August 1999; (**b**) 13 August 2000; (**c**) 8 August 2004; (**d**) 17 August 2007; (**e**) 9 August 2010; (**f**) 25 August 2016.  
The NDVI was nearly constant in the reference area (Figure 5c). NDVI trends increased continually for 18 years in the burned area, and the rate of increase was greater in areas with high fire severity levels. SD in the NDVI between areas with low and moderate fire severity and the reference area was shown until 2009. There was also no SD between these values in 2012, 2013, 2015, 2016 (moderate), and 2017 (low). However, in areas with high fire severity levels, SD was shown until 2017.  
In the first July-August period after the forest fire, the GPP differences of the areas with low, moderate, and high severity levels existed as NBR\({}_{2.1}\), NBR\({}_{1.6}\), and NDVI (Figure 5d). However, the SD between these values ceased earlier than for other indices. In areas with low and moderate fire severity, there was no SD (0.848, 0.134; \(p<0.05\)) between GPP in those areas and in the reference area in 2001, which was one year after the forest fire. GPP in areas with high fire severity reached GPP levels in the reference area (0.614; \(p<0.05\)) in 2004. Recent GPP in the burned areas was higher than GPP in the reference area, and a significant difference appeared earlier in areas with high forest fire severity.