esa-sceva/satcom-chunk-collection huggingface dataset:
# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 5 Conclusions  
This study presents a facial recognition-based voter authentication system that effectively integrates modern biometric technology into the electoral process, addressing significant concerns about multiple voting and electoral fraud. By employing an enhanced ResNet50 neural network architecture combined with the Adam optimisation algorithm and utilising flattened layers alongside a sequence of dense layers, we have improved classification accuracy compared to traditional methods that rely on global average pooling (GAP).  
Key innovations in our system include the adoption of ArcFace loss, which minimizes false positives, and the implementation of MTCNN for precise facial detection. Furthermore, we used the CutMix data augmentation technique to increase the diversity of the training dataset, contributing to the overall enhancement of facial recognition capabilities. Our rigorous testing on LFW dataset yielded an impressive average validation accuracy of 99.56%, demonstrating the efficacy of the proposed solution.  
Looking ahead, there are opportunities to further refine this system. Future work could explore continual learning approaches to enable rapid adaptation to new datasets or changing conditions without extensive retraining. Furthermore, investigating adaptive lighting techniques, such as contrast-limited Adaptive Histogram Equalization (CLAHE), could enhance the system's robustness against varying lighting scenarios, thereby improving recognition accuracy in real-world environments. In general, our proposed system not only improves the integrity of the electoral process but also provides a foundation for the future application of biometric technologies in voting systems around the world.  
Conceptualization: A.H. and T.N.G.T.; Methodology: A.H., D.G.O.O. and A.N.F.; Formal analysis: A.H., D.G.O.O. and D.V.V.K.; Investigation: A.H., D.G.O.O., A.N.F. andD.V.V.K.; Data Curation: A.H., T.N.G.T., D.V.V.K. and A.N.F.; Writing--original draft preparation: A.H. and T.N.G.T.; Writing--Review and Editing: A.H., T.N.G.T., D.G.O.O., A.N.F. and D.V.V.K.; Supervision: A.H. and T.N.G.T. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by University of Johannesburg.  
The authors declare that they have no competing interests.
# A Manycore Vision Processor for Real-Time Smart Cameras +
## 4 Application Domain Analysis
### Motion Estimation  
The ARM processor is responsible for the motion estimation. It receives the manycore's processed images (HCD results) through the DMA interface. For each image, the center of mass (CoM) of the detected corners is computed in a software routine. A CoM's displacement vector is then determined containing the CoM's trajectory. The motion estimation described here is a global operation, in the sense that it requires the corners positions of the complete image. This level of abstraction is better implemented in the ARM processor than in the manycore.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**OpenVX Function** & **Classification** \\ \hline Canny Edge Detector, Fast Corners, Gaussian Image Pyramid, & \\ Histogram of Oriented Gradients, Harris Corners, Hough Lines Probabilistic, & \\ Laplacian Image Pyramid, Optical Flow Pyramid, Reconstruction from & composition of different types \\ a Laplacian Image Pyramid, Equalize Histogram & \\ \hline Mean and Standard Deviation, Min, Max Location, Tensor Add, & \\ Tensor Convert Bit-Depth, Tensor Matrix Multiply, Tensor Multiply, & \\ Tensor Subtract, Tensor Table LookUp, Tensor Transpose, Control Flow, & \\ Data Object Copy, Histogram of Oriented Gradients, LBP descriptors & \\ \hline Absolute Difference, Arithmetic Addition, Arithmetic Subtraction, & \\ Bitwise AND, Bitwise Exclusive OR, Bitwise Inclusive OR, Bitwise NOT, & \\ Channel Combine, Channel Extract, Color Convert, Convert Bit Depth, & \\ Magnitude, Phase, Pixel-wise Multiplication, Remap, Table Lookup, & \\ Thresholding, Weighted Average & \\ \hline Bilateral Filter, Box Filter, Custom Convolution, Dilate Image, Erode Image, & \\ Gaussian Filter, Integral Image, Match Template, Max, Median Filter, Min, & Region to Pixel \\ Non Linear Filter, Non-Maxima Suppression, Sobel \(3\times 3\) & \\ \hline Scale Image, Warp Affine, Warp Perspective & Region to Region \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of the 58 OpenVX functions by operation structure.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 2 Preliminaries
Due to the presence of confounding variables, which are the variables that affect both \(X\) assignment and \(Y\), \(P(Y=y\mid X=x)\
eq P(Y=y\mid do(X=x))\) is usually true [22]. To examine the causal effect of each \(X\) on \(Y\), it is essential to eliminate the influence of all confounding variables on the causal model. The effect of confounding variables can be eliminated by means of backdoor adjustment [21] and frontdoor adjustment [21]. For a pair of ordering variables (\(X\),\(Y\)), backdoor adjustment can be performed using the set \(Z\) of confounding variables if the set \(Z\) satisfies certain conditions. These conditions are as follows: there are no descendants of \(X\) in the set \(Z\), and all backdoor paths, that is, all paths between \(X\) and \(Y\) starting with the arrow of \(Z\) pointing to \(X\), are blocked by the confounding  
Figure 1: Cause diagram of external factors. variables \(Z\). With the backdoor adjustment, the causal effect can be calculated using the following formula:  
\[P(Y|do(X))=\sum_{z}\left[P(Y|X,Z=z)P(Z=z)\right] \tag{1}\]  
If all of the factors on the blocking path are not observable, then the backdoor path cannot be computed. In this case, frontdoor adjustment can be used to confirm causality. Two conditions must be satisfied for frontdoor adjustment: first, all paths from \(X\) to \(Y\) have element \(z\); second, all paths from element \(z\) to \(Y\) are single paths. In this case, \(P(Y|do(X))\) can be computed using the set \(Z\) of \(z\) to complete the computation. By making the set \(Z\) of confusing variables on the front door path conditional, Equation (2) can be used to convert a formula with do-calculus into one without do-calculus. \[P(Y|do(X))=\sum_{z}\left[P(Z=z|X){\sum_{x}P(Y|X,Z=z)P(X=x)}\right] \tag{2}\]  
By intervening in the causality graph using the frontdoor adjustment and the backdoor adjustment, causal relationships can be derived from observed data and distributions alone without do-calculus. After determining the causal relationships between each node on the constructed structural causal diagram, historical data is used to assess specific causal effects and thus calculate the impact of each external variable on load.
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
###### Abstract  
IP fragmentation is still prevalent on the Internet. Defragmented traffic is a prerequisite for many network processing algorithms. This work focuses on the size and organization of a flow table, which is an essential ingredient of the hardware IP defragmentation block. Previous research suggests that fragmented IP traffic is highly local, and a relatively small flow table (on the order of a thousand entries) can process most of the traffic. Samples of IP traffic were obtained from public data sources and used for a statistical analysis, revealing the key factors in achieving design goals. The findings were backed by an extensive design space exploration of the software defragmentation model, which resulted in the efficiency estimates. To provide a robust score of the simulation model, a new validation technique is employed that helps to overcome the limitations of the samples.  
IP fragmentation; network traffic analysis; network hardware design; statistical estimation +
Footnote †: journal: _electronics_  
0000-0002-0001-9500-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-0000  
Given the task of designing a protocol acceleration block in FPGA or silicon technology, an engineer faces the challenge of predicting the utilization of hardware resources required for flawless operation in a particular deployment scenario. The resources in question mostly consist of memory blocks that could be static or dynamic RAM. Memory consumption is the main factor determining the cost of the design up to the point of rendering it completely infeasible when predicted RAM consumption exceeds platform capacity. It is worth noting that contemporary high-end FPGA silicon typically has an order of hundreds of megabits of internal Synchronous Random Access Memory (SRAM) memory--a magnitude that is not so impressive given the fact that those cards are designed for interfacing with 100 Gb/s Ethernet links. One of the ways to escape the limitations of static RAM arrays is to use external dynamic memory banks (DRAM). Some of the cards have separate external DRAM dies (DDR4) or internal memory slices (HBM2) [13], but relying on a memory controller has certain consequences. First and foremost, it mandates a completely different hardware logic as opposed to the fast, synchronous SRAM. Secondly, it greatly increases the overall hardware cost and energy consumption, especially for SOC platforms.  
Taking all these factors into consideration, any attempt to design a hardware protocol stack should be preceded by a careful estimation of memory consumption for a given design. Such estimation should help to make a business decision (influencing cost) as well as a design choice for the target platform.
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 1 Introduction  
Weak interlayers in rock slopes tend to develop into clay interbeds and cause slope deformation due to their poor physical properties and argillization characteristics. Moreover, the presence of clay interbeds in the rock slopes was a significant factor in several major landslides, such as the Vajont landslide [1] in Italy, the Qingjiang landslide [2] and the Shanshucao landslide [3] in the Three Gorges area of China. However, there is no unified consensus on the formation mechanism, stage division, and evolutionary characteristics of clay interbeds. In this paper, a comprehensive review of clay interbeds in rockslides is presented. The review includes a discussion on the formation conditions, the governing factors of the argillization process, and the investigation methods.
## 2 Methodology of SSI-COV
### Hankel Matrix Parameters: Dimension and Order  
The dimension of the Hankel matrix is directly influenced by the amount of sampled data and the desired resolution for the analysis [31]. The selection of the number of blocks \(i\) and \(j\) is determined by the following conditions:  
\[j\ \geq\ m_{i} \tag{8}\]  
\[i\ \geq\ \beta, \tag{9}\]  
where  
\[\beta=f\mathrm{s}\ /\ f_{0} \tag{10}\]  
\(f_{0}\) represents the system's lowest frequency, \(f\mathrm{s}\) is the sampling frequency, and \(i\) can be chosen to be greater than \(\beta\) to ensure complete coverage of the system's dynamic range.  
To guarantee that the Hankel matrix sufficiently captures the system dynamics, the following conditions must also be met:  
\[m_{i}\cdot\ g\ \geq\ n \tag{11}\]  
\[m_{i}\cdot\ h\ \geq\ n \tag{12}\]  
where \(g\) and \(h\) are the numbers of rows (past blocks) and columns (future blocks) of the Hankel matrix, respectively, and \(n\) is the system order. Additionally, m corresponds to the number of sensors [32].  
Through the construction of the Hankel matrix, the extended observability and stochastic controllability matrices can be extracted, enabling the determination of the state-spacematrices \(A\) and \(C\). This approach provides a robust foundation for accurately identifying the system's dynamic and modal characteristics, which are crucial for structural engineering and vibration analyses [33].
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 3 Results
### Shear Wave Velocity  
The data of shear wave velocity for the three experimental groups at different maintenance periods are shown in Figure 8. Inspections show the highest increase in shear wave velocity for T2, followed by T1, and finally, T3. Specifically, the shear wave velocity shows a continuous increase until 60 h, while the increase rate decreases gradually. The increase rates of T1 and T2 are significantly higher than those of T3, and the increase in shear wave velocity ranges from 100 m\(\cdot\)s\({}^{-1}\) to 150 m\(\cdot\)s\({}^{-1}\). After 60 h, the shear wave velocity gradually stabilized and remained constant. At 96 h, the shear wave velocities of T1 and T3 were close, but that of T2 were significantly higher than those of T1 and T3, exceeding 100 m\(\cdot\)s\({}^{-1}\). Meanwhile, the increasing trend of shear wave velocity is consistent with the changing trend of calcium carbonate content in Figure 5.
# Comparison of Algorithms to Compute Relaxation Time Maps in Magnetic Resonance Imaging
###### Abstract  
Magnetic resonance imaging (MRI) is a valuable diagnostic tool that provides detailed information about the structure and function of tissues in the human body. In particular, measuring relaxation times, such as T1 and T2, can provide important insights into the composition and properties of different tissues. Accurate relaxation time mapping is therefore critical for clinical diagnosis and treatment planning, as it can help to identify and characterize pathological conditions, monitor disease progression, and guide interventions. However, the computation of relaxation time maps in MRI is a complex and challenging task that requires sophisticated mathematical algorithms. Thus, there is a need for robust and accurate algorithms that can reliably extract the desired information from MRI data. This article compares the performance of the Reduced Dimension Nonlinear Least Squares (RD-NLS) algorithm versus several widely used algorithms to compute relaxation times in MRI, such as Levenberg-Marquardt and Nelder-Mead. RD-NLS simplifies the search space for the optimum fit by leveraging the partial linear relationship between signal intensity and model parameters. The comparison was performed on several datasets and signal models, resulting in T1 and T2 maps. The algorithms were evaluated based on their fit error, with the RD-NLS algorithm showing a lower error than other fit-ting algorithms. The improvement was particularly notable in T1 maps, with less of a difference in T2 maps. Additionally, the average T1 values computed with different algorithms differed by up to 14 ms, indicating the importance of algorithm selection. These results suggest that the RD-NLS algorithm outperforms other commonly used algorithms for computing relaxation times in MRI.  
Keywords:relaxation time; T1 mapping; T2 mapping; MRI; nonlinear fit; RD-NLS +
Footnote †: journal: Applied Sciences
## 2 Description of Prototype Seismically Isolated Buildings
### Design of Seismically Isolated Buildings
A value of \(\Delta F_{R,req}\) for the restoring force criterion of isolation systems is 2.5% of the seismic weight and thus, it is similar for the prototype buildings with the same number of stories. The lower bound \(\Delta F_{R}\) values of isolation systems at the high seismicity regions resulting in the larger deformation demands are larger than those at the low seismicity regions. Figure 4 illustrates the monotonic properties of the isolation systems that were designed to satisfy the required lateral restoring forces differentiating according to design conditions. Table 3 compares the seismic design values of the prototype buildings and the mean, lower, and upper properties of the isolation system. With the increase in the \(\Delta F_{R,req}\) and a number of stories of the prototype building, the required effective stiffness is increased, which results in the decrease of the fundamental effective periods, \(T_{M}\). It is clearly shown from values of \(T_{1}\)/\(T_{M}\) in the table that the \(T_{M}\) values of all prototype isolated buildings are lengthened compared to the 1 st modal periods, \(T_{1}\), of the corresponding fixed structures. Note that the largest values of \(T_{1}\)/\(T_{M}\) are found in the prototype buildings designed with smallest seismic design load and largest design wind loads. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a) Wind Load Conditions**} \\ \hline - & \multicolumn{2}{c}{**Exposure Category**} & \multicolumn{2}{c}{**Basic Wind Speed, m/s**} \\ \hline Strong windy load & C & 30 \\ Weak windy load & B & 40 \\ \hline \multicolumn{4}{c}{**(b) Seismic Design Parameters**} \\ \hline \(R\) & \(\Omega_{0}\) & \(C_{d}\) & \(I\) & \(R_{I}\) & **Site Class** \\ \(5\) & \(3\) & \(4.5\) & \(1.0\) & \(1.875\) & C \\ \hline \hline \end{tabular} Note: \(R\) is the response modification factor, \(\Omega_{0}\) is the over-strength factor, \(C_{d}\) is the displacement amplification factor, and \(I\) is the important factor.  
\end{table} 
Table 1: Design parameters of prototype isolated buildings. Figure 4: Comparison of force–displacement relations of isolation systems (10-story isolated buildings): (**a**) High seismicity regions; (**b**) Low-seismicity regions. Figure 3: Configurations and experimental results of typical isolations (after [PERSON] and [PERSON] [13]; [PERSON] and [PERSON] [25]): (**a**) Lead rubber bearing (LRB); (**b**) Natural rubber bearing (NRB); (**c**) Friction sliding bearing (FSB).
## 4 Analysis and Requirements
### Provide Alternative, Reliable Means of Communication  
The excerpts in the previous section clearly show that the classical trunked radio communication can be severely impaired for various reasons. Problems regarding the range, _i.e._, due to interference or/and strong attenuation, could be approached using lower carrier frequencies and higher transmit power or by employing repeaters instead of point-to-point connections. Of these options the latter is probably the most feasible. Higher transmit power typically means larger batteries and lower frequencies typicallymean larger antennae. Also, with a repeater-based approach, the topology of the radio link can be adapted to the requirements on site. In fact, the repeater approach is supported to some extent (only one additional hop) by the digital radio communication standard TETRA-25. The downside, however, is that the throughput and latency of data are affected by the number of hops. Thus, a multi-hop approach needs to carefully consider the type of service _i.e._, speech or data that can be provided over the link.
## 3 Experimental Validation of the Hydroelasticity Theory
### Comparison of Numerical and Experimental Time Series  
The comparison of numerical and experimental time series of full-scale ship vertical motion and loads in the selected Condition 1 (\(H_{1/3}\) = 15 m, \(T_{z}\) = 12.25 s, \(U\) = 5 knots) and Condition 2 (\(H_{1/3}\) = 9.2 m, \(T_{z}\) = 11.4 s, \(U\) = 24 knots) are shown in Figures 5 and 6, respectively. It is noted that the total VBM amidships were separated into low-frequency (LF) wave loads and high-frequency (HF) whipping loads by Fourier filter. As seen from the comparison, it is clear that the curves show similar trends and magnitudes between numerical simulation and experimental measurement results. It should be noted that the numerical and experimental time series will not agree in detail since the exact elevation of incident waves acting on the model was not measured during the experiment. Only an in situ wave probe that located in front of the wave maker was used to measure the experimental waves. Therefore, the numerical irregular waves are generated by using the random initial phase.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Empirical-Model-Based Predictive Modeling
#### 2.1.2 Two-Ray Path Loss Model  
The two-ray path loss model is often used in scenarios where there is a clear line-of-sight (LOS) between the (Tx) and (Rx), and it includes both the direct path and a ground-reflected path. This model provides a more accurate prediction of the path loss over long distances compared to the simple free-space model [3]. In the two-ray model, the total electric field \(E_{\text{two-ray}}(d,t)\) at the receiver is the sum of the electric fields of the direct and ground-reflected rays and is given by the following formula:  
\[E_{\text{two-ray}}(d,t)=\frac{E_{0}d_{0}}{d^{\prime}}\cos\biggl{(}\omega_{c} \biggl{(}t-\frac{d^{\prime}}{c}\biggr{)}\biggr{)}+\Gamma\frac{E_{0}d_{0}}{d^{ \prime\prime}}\cos\biggl{(}\omega_{c}\biggl{(}t-\frac{d^{\prime\prime}}{c} \biggr{)}\biggr{)} \tag{2}\]  
where \(E_{0}\) depicts the electric field in free space at the reference distance \(d_{0}\), \(d^{\prime}\) represents the distance of the direct path between the Tx and Rx, \(d^{\prime\prime}\) shows the distance of the reflected path between the Tx and Rx, \(\omega_{c}\) is the angular frequency of the carrier, \(c\) is the speed of light, and \(\Gamma\) is the ground reflection coefficient. The received power \(P_{\text{two-ray}}(d)\) can then be calculated from the total electric field using the following formula:  
\[P_{\text{two-ray}}(d)=\frac{|E_{\text{two-ray}}|^{2}G_{r}\lambda^{2}}{480\pi^{ 2}} \tag{3}\]  
where \(G_{r}\) is the gain of the receiving antenna, and \(\lambda\) is the wavelength of the signal. This model takes into account both the direct line-of-sight signal and the ground-reflected signal, making it suitable for environments where such reflections are significant.
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
## 4 Discussion
Warm springs trigger earlier vegetation activity and may contribute to reducing the soil water availability in the summer [20; 68] and [69] showed that warm springs can amplify summer temperatures. Our results in EM in 2007 are consistent with this effect: vegetation activity until April was higher than normal, associated with high spring temperatures and dry conditions which started in late spring, which, along with extreme hot summer conditions, may have exacerbated the reduction in soil water availability and driven the vegetation to critical ecological stress in several areas of the Eastern Mediterranean. Our results have highlighted the important role of hot and dry events in vegetation productivity. Nevertheless, some key points were not addressed here, related to the high heterogeneity of the Mediterranean basin in terms of land use changes, soil properties, topography and land management policies [70], which can partly affect the results of different land cover responses to heat and drought. Another aspect that was not addressed was the vegetation memory effect, as we used only remote sensing data and our analysis showed the vegetation productivity recovery before the next ecological event. This can be accommodated through the application of approaches that are able to incorporate vegetation memory (e.g., through the use of ARx models [71; 72]). These models allowed for a better quantification and understanding of hot and dry effects on vegetation productivity [73]. Finally, our study period comprised 19 years (2001-2019), relying on the availability of MODIS satellite data, which can constitute a limitation to the assessment of extreme events on vegetation since a climatic long-term dataset calls for an extended period of 30 years. Although this study presents the referred limitations, we firmly believe that the proposed approach is highly valuable and can be easily replicated to other areas. Moreover, this approach will surely be useful, supplying responsible authorities and managers with a spatially comprehensive tool aiming for the development of mitigation plans that will be crucial in the case of the occurrence of more frequent hot and dry episodes.