esa-sceva/satcom-chunk-collection huggingface dataset:
# Silicon Integrated Nanophotonic Devices for On-Chip Multi-Mode Interconnets
## 2 Silicon Multi-Dimensional (De)Multiplexers
### Silicon Hybrid (De)Multiplexers
Here, we propose a general configuration for the scalable WDM/PDM/MDM hybrid systems, as illustrated in Figure 9. As an example, we show a 32-channel hybrid (de)multiplexer with four wavelengths, two polarizations and four modes. At the transmitter terminal, the 32-channel carriers with TE\({}_{0}\) are firstly coupled into eight single-mode waveguides in term of the wavelengths. Four of them are cascaded with the broadband polarization rotators [130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141] that convert TE\({}_{0}\) to TM\({}_{0}\). The PBSs [142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153] are then utilized to combine the orthogonal polarizations, so that the 32-channel signals can be carried by four single-mode waveguides. Finally, all the carriers are coupled into a multi-mode bus waveguide by using a polarization/mode hybrid multiplexer working on TE\({}_{0-3}\) and TM\({}_{0-3}\)[100]. At the receiver terminal, the 32-channel carriers are decoupled from the multi-mode bus waveguide by cascading MDM, PDM and WDM in sequence. This configuration can be used as a general architecture to build any user-defined WDM/PDM/MDM hybrid system. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Size (\(\mu\)m)**} & \multirow{2}{*}{**Capacity (TE + TM)**} & \multicolumn{2}{c}{**IL (dB)**} & \multicolumn{2}{c}{**XT (dB)**} & \multicolumn{2}{c}{**BW (nm)**} \\ \cline{5-10}  & & & & **Sim.** & **Exp.** & **Sim.** & **Exp.** & **Sim.** & **Exp.** \\ \hline
[96] & 2014 & \(\approx\)300 & 4 + 4 & 2 & 2 & \(-\)10 & \(-\)10 & 100 & 30 \\
[99] & 2014 & \(\approx\)600 & 4 + 4 & / & 0.2\(\sim\)3.5 & / & \(-\)20 & / & 100 \\
[100] & 2018 & \(\approx\)500 & 6 + 4 & \(<\)0.1 & 0.2\(\sim\)1.8 & \(-\)20 & \(-\)25\(\sim\)\(-\)15 & 130 & 90 \\
[97] & 2019 & 74.75 & 6 + 4 & \(<\)1 & / & \(-\)11.4 & / & 85 & / \\ \hline \hline \end{tabular}  
* IL, insertion loss. XT, crosstalk. BW, working bandwidth. Sim., simulation results. Exp., experimental results.  
\end{table} 
Table 3: Summary of the reported silicon polarization/mode hybrid (de)multiplexers. Figure 9: A proposal for the scalable WDM/polarization-division multiplexing (PDM)/MDM hybrid systems.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Makespan for DRFTSA Using Regular Dataset by Varying Number of VMs  
In this subsection, we present the generated makespan for DRFTSA using the regular dataset by varying the number of VMs and using 100 to 600 tasks. In this scenario, we have varied VMs by assigning 10 VMs to 100 tasks, 20 VMs to 200 tasks, 30 VMs to 300 tasks, 40 VMs to 400 tasks, 50 VMs to 500 tasks, and 60 VMs to 600 tasks to check efficacy of the scheduler in terms of minimization of makespan. The generated makespans for PSO with 100, 200, 300, 400, 500, 600 tasks are 586.71, 676.47, 687.87, 712.36, 757.86, 809.27, respectively. Generated makespans for GA with 100, 200, 300, 400, 500, 600 tasks are 603.39, 673.18, 706.88, 759.52, 784.18, 828.88, respectively. Generated makespans for ACO with 100, 200, 300, 400, 500, 600 tasks are 525.88, 712.74, 802.18, 769.17, 813.87, 798.54, respectively. Generated makespans for DRFTSA with 100, 200, 300, 400, 500, 600 tasks are 389.19, 432.17, 447.91, 478.23, 483.63, 587.41, as shown in Table 4, respectively.  
From Table 5 and Figure 5, it is evident that DRFTSA minimized makespan compared to state-of-the-art approaches while running with a regular dataset and using variable numbers of VMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Tasks** & **PSO** & **GA** & **ACO** & **DRFTSA** \\ \hline
100 & 684.3 & 725.62 & 545.17 & 421.22 \\
200 & 712.54 & 756.09 & 759.97 & 503.12 \\
300 & 732.57 & 791.36 & 834.18 & 519.96 \\
400 & 812.78 & 854.03 & 871.22 & 485.78 \\
500 & 836.9 & 912.36 & 891.22 & 505.88 \\
600 & 895.87 & 953.47 & 932.18 & 456.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Makespan for DRFTSA using regular dataset with fixed number of VMs.  
Figure 4: Calculation of makespan for regular dataset using fixed VMs.
## 3 Dynamic Time Slots Allocation Based on Genetic Algorithm  
### Beam Hopping Time Slots Allocation  
As stated above, the motivation of beam hopping technology is utilizing system resources efficiently to meet the user's traffic demand as much as possible. Thus, this paper intends to build an N-order difference objective function [36] as follows.  
For K beams, supposing \(\hat{R}_{i}\) and \(R_{i}\) are the request traffic capacity and system offered capacity in one beam hopping period, respectively, (measured by bit), W is the beam hopping period (time window) and \(N_{i}\) is the number of time slots allocated to beam \(i\). \(T_{ij}\in[0,1]\) is the element of beam hopping time slot allocation matrix \(\mathbf{T}\). \(T_{ij}=1\) means \(j\)-th time slot is allocated to \(i\)-th beam, which means \(i\)-th beam is working in \(j\)-th time slot. Thus, \(\sum_{j=1}^{N_{i}}T_{ij}=N_{i}\).  
N-order difference objective function:  
\[\min\sum_{i=1}^{K}|R_{i}-\hat{R}_{i}|^{n} \tag{4}\]  
\[s.t\ R_{i}\leq\hat{R}_{i},\ \forall i \tag{5}\]  
Usually, \(n=2\), which makes it a second order objective function. Supposing Gaussian coding is used, the offered capacity in one beam hopping period \(W\) of the \(i\)-th beam is:  
\[R_{i}=N_{i}B_{w}log_{2}(1+SNR_{i}) \tag{6}\]  
\(SNR_{i}\) is signal-to-noise ration the \(i\)-th beam. It can be represented by  
\[SNR_{i}=\frac{P_{i}}{N_{0}}\frac{G_{T}G_{R}}{L_{SL}L_{RA}} \tag{7}\]  
\(P_{i}\) is the transmitter power. \(G_{T}\) and \(G_{R}\) are the transmitter and receiver antenna gain, respectively. \(L_{SL}\) is the propagation loss. \(L_{RA}\) is the rain attenuation of communication link. Different from previous works, in this paper, rain attenuation is not constant but time-varying and different among beams.  
Moreover, as known, DVB standard is the most widely used protocol for high-throughput broadcast satellites. As stated above, ETSI published DVB-52X standards [16]to support beam hopping. DVB-S2X offers smaller roll-off options, and a finer gradation and extension number of MOCODs, which provide foundation for potentially employing ACM to improve spectral efficiency. Note that this paper is focused on beam hopping under rainfall environment referring to DVB-S2X, rather than studying ACM schemes.  
Thus, Equation (4) can be transformed as:  
\[\min\sum_{i}^{K}|R_{i}-\hat{R}_{i}|^{2} \tag{8}\]  
\[s.t.\ R_{i}\leq\hat{R}_{i},\ \forall i \tag{9}\]  
\[R_{i}=\sum_{j}^{W}B_{w}\cdot\eta_{ij}\cdot T_{ij} \tag{10}\]  
\[\hat{R}_{i}=\hat{r}_{i}\cdot W \tag{11}\]  
where \(\eta_{ij}\) is the spectrum efficiency of \(i\)-th beam at \(j\)-th time slot. The spectrum efficiency \(\eta_{ij}=f_{\text{DVB-S2X}}\left(SNR_{ij}\right)\) is a piecewise function in DVB-S2X, where \(SNR_{ij}\) represents signal-to noise ratio of \(i\)-th beam at \(j\)-th time slot. \(\hat{r}_{i}\) represents the request traffic rate (measured by bps) of \(i\)-th beam.
## 4 Discussion
### Active Lateral Earth Pressure  
The effects of particle size, density, and confining pressure on the active lateral earth pressure within a uniform type of sand, sorted into different mean particle sizes, are explored in Figure 16. The active lateral earth pressure increases as the mean particulate size increases. This result is because larger particles will have a higher dry density than smaller particles, which will increase the lateral earth pressure. Also, the active lateral earth pressure increases equally with increasing sample density; this increase is most likely due to an increase in particle content, as the number of particles in compact samples is greater than in loose samples. Therefore, the active lateral earth pressure is greater for denser samples. A similar correlation exists between an increase in normal stress and an increase in active lateral earth pressure. This phenomenon is related to the increased force applied perpendicular to the soil particles, which increase the active lateral earth pressure. In conclusion, the study highlights the importance of mean particle size, density, and normal stress on the active lateral earth pressure, where the active lateral earth pressure increases as the particle size, density, and confining pressure increase.  
Figure 16: Comparison of active lateral earth pressure and dry density for different particle sizes of various sands under different normal stresses.  
Figure 15: Shear strength versus normal stress for two different mean particle sizes of glass beads.
## 3 Materials and Methods
### Self-Attention and Inter-Sample Attention Network (SAINT)  
The self-attention and inter-sample attention network (SAINT) model, introduced by [PERSON] et al. [7], is designed for tabular data classification by integrating feature-level and inter-sample attention mechanisms. This dual-attention approach enables SAINT to capture both intra-sample feature relationships and inter-sample contextual patterns, making it particularly effective for datasets such as the water potability dataset used in this study. SAINT [7] implements a dual-attention mechanism that simultaneously captures both feature interactions and sample-to-sample interactions.  
\[E(x)=\text{GELU}(\text{LN}(W_{e}x+b_{e})) \tag{17}\]  
This embedding aligns with the preprocessing steps applied to the dataset's attributes (e.g., pH, hardness, and total dissolved solids). The embedded features are then processed through feature-level attention, which models relationships within each sample's features using multi-head attention:  
\[FeatureAttn_{l}(x)=\mathcal{T}_{\ell}(x). \tag{18}\]  
For \(l\in 1,2,\ldots,L\) where \(\mathcal{T}_{\ell}\) represents the \(l\)-th transformer encoder layer, and \(L\) denotes the total number of feature attention layers. To capture contextual patterns across samples in a batch, SAINT employs inter-sample attention:  
\[\text{InterAttn}(X_{batch})=\text{MultiheadAttention}(X_{batch},X_{batch},X_{ batch})=X_{batch}=[x_{1},x_{2},\ldots,x_{B}]^{T}. \tag{19}\]  
The dual-attention structure in Equations (17)-(19) is directly inspired by the SAINT model, which integrates feature-level and sample-level attention for tabular data modeling [7].  
\[\text{InterOut}(X_{batch})=\text{LN}(X_{batch}+\text{Dropout}(\text{InterAttn}( X_{batch}))) \tag{20}\]\[\text{InterFinal}(X_{batch})=\text{LN}(\text{InterOut}(X_{batch})+\text{Dropout}( \text{FFN}(\text{InterOut}(X_{batch})))) \tag{21}\]  
where \(B\) represents the batch size, and the inter-sample attention enables the model to leverage contextual information from other samples in the batch, potentially capturing population-level patterns in water quality data.  
Integrated classification pathway:  
\[h_{1}=\text{GELU}(W_{1}\cdot\text{InterFinal}(\text{FeatureAttn}(E(x)))+b_{1}) \tag{22}\]  
\[h_{2}=\text{Dropout}(h_{1}) \tag{23}\]  
\[y=\sigma(W_{2}\cdot h_{2}+b_{2}). \tag{24}\]  
The SAINT architecture offers a unique advantage in capturing both featured interactions within individual water samples and patterns across different samples, potentially identifying subtle quality relationships that affect potability.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 5 The Proposed VLSI Architecture and Implementation
### Update State Module  
As shown in Figure 7, using the current estimated state and the current measurement state with a weight value, to complete the update of the current state. Use the current estimated state. Among them, the location information with noise will enter this module through X-Table and Y-Table. If it has entered the decision mode, KG will be replaced by \(\alpha\) and \(\beta\) as input. Finally, obtaining \(\alpha_{k}\) and \(\beta_{k}\) in the update state module.  
Figure 6: VLSI architecture of the Kalman gain and \(\alpha\),\(\beta\) module.  
Figure 7: VLSI architecture of the update state module.
# Aerodynamic Drag Study of the Heat Exchange Equipment with Different Fin Geometries
###### Abstract
The study of [PERSON] aims to investigate the impact of both passive control (in the form of a fin) and active control (via suction) on the aerodynamic characteristics of a modified Ahmed's body vehicle model [15]. The primary focus is on analyzing the flow characteristics, pressure field, and aerodynamic drag. The goal is to identify effective strategies for reducing aerodynamic drag, delaying flow separation, and increasing pressure at the rear of the vehicle to enhance fuel efficiency. [PERSON] and others in their work investigate flow and heat transfer around a slender bluff body with a rectangular cross-section at low Reynolds numbers. Various parameters such as Reynolds number, Prandtl number, aspect ratio, and angle of incidence are considered. Simulation is conducted using a finite volume code employing the SIMPLEC algorithm. Spatial resolution, grid independence, and instantaneous flow parameter variations are analyzed. The study also computes global quantities such as pressure, viscous drag, lift coefficients, RMS of drag and lift, and Strouhal number. The results show the angle of incidence relocates the stagnation point, with verification against the existing literature [16]. All studies have been conducted for a variety of surface structures that create aerodynamic resistance, but there are no research studies for the pipes used in heat exchange equipment, which would allow for optimization of the structures of heat exchangers not only by the coefficient of heat transfer, but also by hydraulic resistance. If there are studies for the specific industry, they are made for the pipe bundle, not a single nozzle, which narrows the possibilities of optimizing structures based on the use of specific fins. The scientific novelty of the article lies in the combination of a novel fin design, numerical modelling techniques, and the exploration of drag coefficients under different conditions, with potential applications in heat exchange equipment and beyond. The findings contribute to the understanding of aerodynamic behavior in specific fin configurations and provide insights for optimizing designs in various engineering applications.
## 2 Materials and Methods
### Pixel Normalization Method  
The pixel normalization method, based on the fundamental principles of radiometric normalization in remote sensing imagery [23; 24], is used to further process images after dynamic polarization adaptive suppression. The radiometric normalization method uses the image's own pixel values as parameters to establish a radiometric transformation equation, converting the image's pixel values into radiometric pixels with physical significance, thereby restoring radiometric pixels without the need for any additional parameters.  
This proposed method, guided by the principle of radiometric normalization, establishes a further suppression technique for images following dynamic polarization adaptive suppression. The pixel normalization method establishes a transformation relationship between the pixel values after dynamic polarization suppression and the glare pixel values, mapping the post-suppression pixel values to glare pixels to effectively restore glare pixels. As shown in Figure 5, the composition of the image after dynamic polarization suppression includes three parts: part A represents the non-glare portion; part B represents the dynamic polarization suppression portion; and part C represents the unsuppressed glare portion. In the proposed pixel normalization glare suppression method, part B is taken as the reference pixels, and then pixel normalization processing is applied to part C.  
Figure 4: Flowchart of the sea surface glare adaptive polarization suppression method based on the geographic polarization suppression model.  
Figure 5: Glare distribution map.  
Firstly, select the maximum and minimum pixel values of part B, denoted as \(B_{\text{max}}\) and \(B_{\text{min}}\), which will be the target range for pixel normalization. Secondly, select the maximum and minimum pixel values required for part C, denoted as \(C_{\text{max}}\) and \(C_{\text{min}}\). Lastly, set a threshold value \(T\) and search for pixels greater than the threshold \(T\) pixel by pixel, using the transformation relationship in Equation (20) to calculate the normalized pixel value.  
\[normalize(I)=\frac{(B_{\text{max}}-B_{\text{min}})*I}{(C_{\text{max}}-C_{\text{ min}})}+B_{\text{min}} \tag{20}\]  
In the equation, \(I\) represents the pixel points greater than the threshold \(T\), and \(normalize(I)\) represents the normalized pixel value.  
The overall process of the sea surface glare adaptive suppression method is shown in Figure 6.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Semi-Automatic Mapping of the Flooded Area
#### 4.2.1 SAR Data Analysis
In all of our study areas, the capability to map flooded areas by SAR data is also limited by the presence of dense arboreal vegetation (small areas in the Ebro valley) or dense urban settlements (area of Moncalieri [57]). This limitation is already known in the literature [26]; more-complex SAR processing was developed to detect flooded areas in forested or urban areas [88; 89]; moreover commercial L-Band satellites (e.g., ALOS-2) have better performance on forested areas [90].
## 3 Sensor Node Position Estimation
### The Position Packet Validation Algorithm  
Since the mobile sink is more powerful, it can carry a GPS module. Thus, it can provide position packets to sensor nodes in order to aid them in estimating their own positions. Furthermore, in our scenario, the random mobility adopted by the sink implies that some position packets might not be good enough to be used in the trilateration algorithm, as can be seen in Figure 5. Figure 5a shows a sensor node receiving two position packets at times \(t1\) and \(t2\) from opposite directions. In this scenario, the intersection between these packets generates a single point. However, a GPS error may occur and it may shift \(t1\) slightly to the left and/or \(t2\) slightly to the right. In this case, the circles would not intersect, making it impossible to estimate the sensor node position. Thus, upon receiving a new position packet, the sensor node will first validate if this packet position circle intersects with the  
Figure 4: Intersection Points among the Position Packets.  
positions previously validated. If so, the next phase of the validation will be performed. Otherwise, the packet is discarded.  
In Figure 5b, it can be observed that all position packets shown belong to the same quadrant when the network area is divided into four quadrants with the sensor node positioned at the center. This results in poor position estimation due to the GPS error, which produces a small deviation that leads to a large discrepancy of intersection points when one circumference is contained within another. Therefore, we propose three algorithms to validate the position packet and avoid the scenario described above: position distance, circle limit, and a hybrid solution. Once the position packet is validated and accepted, i.e., the position is a good candidate to be used in the trilateration algorithm, the sensor node stores its information for future estimations.  
Even after receiving three good candidate position packets, the sensor node continues to receive such packets in an attempt to improve its position estimation. Specifically, it uses the received signal strength of a newly received packet to calculate the distance between its estimated position and the source of the packet. If these values do not match (within a certain threshold), the sensor node validates the packet and checks whether replacing one of its current position packets with the new one will result in a more accurate estimation. If this is the case, the replacement is made permanently.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 4 Consensus Mechanism Weaknesses
### Delegated Proof of stake (DPoS)  
Delegated proof of stake (DPoS) is a consensus mechanism that permits shareholders to vote for witnesses [35]. The main idea of DPoS is to reduce the wastage of energy and enhance the speed of transactions. The overall block generation process makes this consensus mechanism many times faster than the PoW consensus. DPoS comprises a one vote per share policy, which gives the stakeholders the option to cast more votes while they possess more coins. The witnesses are rewarded for generating blocks, but they are also penalized when they fail to perform the required task, resulting in them being unpaid and voted out. Witnesses must acquire the largest number of votes from random stakeholders in order to perform the instructed task. The stakeholders also vote for the delegates to reform and make changes in the network, which are reviewed for an ultimate decision.  
DPoS was developed to bring up efficiency in transactions and overcome the limitations introduced by various other consensus mechanisms; however, it includes severe flaws. It fails to achieve adequate decentralization, and the network slows down due to the vast number of validators. Due to the centralized aspects, it can be a focal point of random attackers. DPoS is vulnerable to the 51% attack. The attacker can convince the stakeholders to obtain 51% voting power to execute a 51% attack [36]. This consensus mechanism is also vulnerable to other primary attacks, such as a long-range attack, DDoS attack, P + epsilon attack, Sybil attack, and the balance attack.  
In the presented study, we pointed out that the three major consensus algorithms have significant limitations and are vulnerable to various attacks. The weakness in the consensus makes the digital transactions at a high risk of potential attacks. Table 2 presents our evaluation summary. It is worth noting that all three consensus mechanisms can be exploited by the 51% attack, making this attack very attractive for attackers, especially for the PoW, where obtaining the necessary hashing power is less costly.
# Noises in Double-Differenced GNSS Observations
###### Abstract
Currently, it is also used to analyze various types of phenomena in astrometry or geodynamics [40; 41] in addition to tools such as the autocorrelation function or spectral densities [42]. The most common geodetic usage is the analysis of the geodetic time series. This has been used to determine the uncertainties associated with site positions [43]. Moreover, AVAR is resistant to data gaps up to 50% [44]. The greatest advantage, apart from simplicity and speed, of AVAR is that it also allows for the noise type and level to be characterized without any a priori model assumptions [45]. An Allan graph in a log-log scale shows the relationship between the Allan variance and the sampling time in terms of its error spectrum, where slopes of the graphs represent noise, e.g., slope \(-1\) is white noise, while a slope of 0 is flicker noise [46]. The paper is organized as follows: In Section 2, we present the materials data analysis methods used; in particular, the zero-baseline double-differenced GNSS observables are characterized, and the mathematical foundations of the ACF, the spectral analysis using the L-S method, and AVAR are outlined. In Section 3, the data set and statistical analysis of the GPS, GLONASS, and Galileo codes and the carrier phase observations are presented. In Section 4, the results of the noise analysis that was performed based on different statistical tools are described in detail. Finally, in Section 5, a discussion is conducted, and concluding remarks are made.