esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Managing Scalability
#### 5.3.2 Identifying Bottlenecks  
Congested network links manifest as high betweenness centrality edges in transaction graphs. Alleviating such bottlenecks using solutions like off-chain payment channels improves throughput [82].  
Figure 16 visualization demonstrates how graph analysis, particularly edge betweenness centrality, can be used to identify critical links in a blockchain network that leads to bottlenecks. Addressing congestion on these links, perhaps through off-chain payment channels or other scaling solutions, can potentially improve overall network throughput and efficiency. Nodes represent entities in the blockchain network. Edges represent transaction links between these entities. Edges with high betweenness centrality are highlighted in red. These edges are potential network bottlenecks, as they participate in a large number of shortest paths that experience high traffic and congestion. Other edges are shown in grey and dashed, indicating normal transaction links with less centrality and, presumably, less congestion.  
**Assumption 15**.:
* _The blockchain network is modeled as a transaction graph_ \(G(V,E)\) _with edges_ \(e_{i}\) _having capacities_ \(c_{i}\)_._
* _Transaction throughput is limited by congested edges._  
**Application Statement 15**.:
* _Edges with high betweenness centrality in the blockchain transaction graph correspond to network bottlenecks limiting throughput. Offloading transactions along these edges to payment channels can improve capacity._  
**Proof.**  
1. Compute edge betweenness centrality \(BC(e_{i})\) in graph \(G\).
2. Edges with the highest \(BC(e_{i})\) participate in the largest number of shortest paths.  
Figure 16: Identifying bottlenecks.  
3. Congestion manifests as transaction backlogs along these top betweenness edges, reducing throughput.
4. Off-chain payment channels ease congestion along top bottleneck edges, improving network capacity.  
Graph centrality metrics identify network bottlenecks while payment channels help mitigate congestion and transaction delays on blockchain platforms. This demonstrates a graph analysis-based approach to pinpoint and alleviate congestion bottlenecks limiting blockchain transaction throughput using concepts like betweenness centrality and off-chain scaling solutions.
## 4 RAM analysis of EPBM
### Reliability and Maintainability Analysis  
A basic methodology for the reliability and maintainability analysis was provided by [26]. Firstly, trend and correlation tests should be conducted to choose the most suitable method for M(t) and R(t) analysis. These tests and analyses are performed on each subsystem, and reliability and maintainability diagrams of each subsystem are created. These methods, and how to apply them, are shown in Figure 4. Due to the series relationship between subsystems, R(t) and M(t) equations will be as presented below [25; 27]:  
\[R(t)_{i}=e^{-\int\lambda(t)dt} \tag{20}\]  
\[M(t)_{i}=1-e^{-\int\mu(t)dt} \tag{21}\]  
\[R_{EPBM}=\prod_{i=1}^{n}R_{i}=R_{E}\times R_{M}\times R_{C} \tag{22}\]  
\[M_{EPBM}=\prod_{i=1}^{n}M_{i}=M_{E}\times M_{M}\times M_{C} \tag{23}\]  
These equations can be changed to Markov equations with constant \(\lambda(t)\) and \(\mu(t)\). In other words, the probability density function of each subsystem is exponential.  
As seen in Tables 3 and 4, the EPBM availability value using the Markov model equals 45%, while it is equal to 39% using the network modeling. The real availability values should be calculated to validate the estimated availability values and find the most appropriate analysis method. This can be conducted using the complete database of Line 1 construction, based on all the TBF and TTR values for the whole system, as shown in Figure 5. Table 5 presents the mean values of TBFs and TTRs. The real availability of the EPBM can then be calculated based on these values.  
According to the calculations, the real availability value for EPBM in Line 1 is 45%, the same as the result of the Markov method. Thus, the Markov method is considered a more appropriate method to calculate the availability of the EPBM. It can be seen that the network modeling gives inaccurate results to this factor, primarily by increasing the subsystems of a system. The Markov method can also provide the unavailability value of each subsystem in addition to the availability value of the whole system, which is unavailable in the network modeling method. The Markov method indicates about 3%, 15%, and 37% unavailability in the electrical, mechanical, and cutterhead subsystems of the EPBM in Line 1.
# Targeting KRAS in PDAC: A New Way to Cure It?
## 5 Drug Resistance Mechanisms of KRAS Inhibitors  
To date, MRTX1133 has been tested in preclinical studies as a KRAS\({}^{\rm G12D}\) inhibitor but has not entered clinical trials. Therefore, the analysis of the resistance mechanism of KRAS\({}^{\rm G12D}\) inhibitors needs to be carried out on the resistance status of KRAS\({}^{\rm G12C}\) inhibition that has been applied in the clinic (Figure 3). Studies have been conducted to analyze tissue or fluid samples from 43 patients treated with the KRAS\({}^{\rm G12C}\) inhibitor sotorasib [94]. A comparison of pre-treatment and post-treatment samples revealed that 27 patients developed multiple gene mutations after treatment, including KRAS, NRAS, BRAF, EGFR, FGFR2, and MYC. As mutations occurring in a single patient are insufficient to suggest an association with drug resistance, the investigators constructed corresponding cellular models and xenograft models for further exploration. Their studies showed that the induction of KRAS\({}^{\rm G12V}\), NRAS\({}^{\rm Q61K}\) or MRAS\({}^{\rm Q71R}\) (a small GTPase involved in regulating the dimerization and activation of CRAF, a signaling molecule of the MAPK-ERK pathway downstream of KRAS [95]) mutations in tumor cell lines containing KRAS\({}^{\rm G12C}\) mutations reduced the inhibitory effect of KRAS\({}^{\rm G12C}\) inhibitor sotorasib on downstream signaling and failed to significantly alter the level of endogenous KRAS activation. As PDAC is highly heterogeneous, the further application of KRAS\({}^{\rm G12D}\) inhibitors to PDAC has a high potential to increase the frequency of the mutated genes mentioned above. \begin{table}
\begin{tabular}{c c c c c} \hline
**ClinicalTrials.Gov Identifier** & **Title** & **Phase** & **Drugs** & **Targets** \\ \hline \multirow{4}{*}{NCT05374538} & VIC-1911 Monotherapy in Combination & & & \\  & With Sotorasib for the Treatment of & & & Sotorasib & KRAS\({}^{\rm G12C}\) \\  & KRAS G12C-Mutant Non-Small Cell & & & VIC-1911 & Aurora Kinase A \\  & Lung Cancer & & & \\ \hline \multirow{4}{*}{NCT05067283} & A Study of MK-1084 as Monotherapy & & & \\  & and in Combination With & & & \\ \cline{1-1}  & Pembrolizumab (MK-3475) in & 1 & MK-1084 & KRAS\({}^{\rm G12C}\) \\ \cline{1-1}  & Participants With KRASG12C Mutant & & & Pembrolizumab & PD-1 \\ \cline{1-1}  & Advanced Solid Tumors (MK-1084-001) & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Registered trials of KRAS\({}^{\rm G12C}\) inhibitor combination therapy on clinicaltrials.gov. MAPK-ERK and PI3K-AKT-mTOR pathways are two important RAS downstream signaling pathways, and their signaling is regulated by RAS proteins [96]. Notably, the PI3K-AKT-mTOR pathway is not only regulated by RAS proteins, but also by various signaling molecules, including PDK-1 and IGF1 [97]. It has been demonstrated that after silencing the KRAS gene in pancreatic cancer cell lines containing KRAS mutations using short hairpin RNAs (shRNAs), some of the cell lines still survive, exhibiting dependence on PI3K [98]. Other studies have identified that YAP1 overexpression regulated by PI3K is a
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 4 Experiment
### Evaluation Parameters of the Test  
To assess the performance of the prediction model, objective metric data was utilized. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. Performance metrics, including mean percentage error (_MAPE_), accuracy, root mean square error (_RMSE_), and mean error (_MAE_), were employed. The validation set was used to evaluate the in-model prediction performance, while the training set was utilized to measure the out-of-model prediction performance. The working rates of quantum CNNS and existing CNNS were determined by recording the time required to achieve the minimum verification loss. Due to the increased conversion between quantum architectures caused by quantum information coding, the original _RMSE_ and _MAE_ values underwent changes in scale. Therefore, the _RMSE_% and _MAE_% error indicators were adopted to assess the accuracy between the two architectures. Here, \(e_{t}\), \(f_{t}\), and \(d_{t}\) are the error, predicted value, and expected value at time \(t\).  
\[e_{t}=f_{t}-d_{t},\]\[MAPE=\frac{1}{n}\sum\frac{|\epsilon_{t}|}{d_{t}}, \tag{9}\]  
\[Accuracy\%=100(1-MAPE), \tag{10}\]  
\[MAE\%=\frac{\sum|e_{t}|}{\sum d_{t}}, \tag{11}\]  
\[RMSE\%=\frac{\sqrt{\frac{1}{n}\sum e_{t}^{2}}}{\frac{\sum d_{t}}{n}}, \tag{12}\]
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 6 Experimental Results and Discussion
### Clutter Model Fitting and Performance Evaluation
In contrast in Figures 27c and 28c (far range), the CCDF from the K-Distribution using NLLSQ for parameter estimation, the chi-square and the 3 MD model fit very well with the data CCDF. The reason why other models don't fit in far range can again be explained in terms of low clutter-to-noise ratio (CNR) and Rayleigh distributed characteristics. In the following sections, the methods to evaluate the accuracy of the clutter models (cf. Section 4) are presented. For this paper, we have used two different error matrices: threshold error for a fixed CCDF and false alarm rate error for a fixed PFA = 10\({}^{-6}\).
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work
### Resilient Overlay Networks  
RON [10] is a remedy for potential problems, such as BGP's fault or path outages, because BGP hides information about traffic conditions and topological details in the interests of scalability and policy enforcement. Distributed applications layer a \"resilient overlay network\" over the underlying Internet routing substrate. The nodes comprising a RON reside in a variety of routing domains and cooperate with each other to forward data on behalf of any pair of communicating nodes in the RON. Because ASes are independently administrated and configured, and routing domains rarely share interior links, they generally fail independently of each other. As a result, if the underlying topology has physical path redundancy, RON can often find paths between its nodes, even when wide-area routing Internet protocols, such as BGP, cannot. This RON approach is an overlay network which selects different way-points, but it cannot select the actual network path.
## 3 Results
### Results of Training and Prediction (Testing) with the LSTM Neural Network  
For the designed LSTM neural network, the data are divided into two parts as recommended in [18]. One part is 90% of the entire data and is used for training the network, while the remaining part (10%) is used to test the trained LSTM network for its accuracy in predicting the intended output [TT121], as follows:  
* **Training data size**: time step 500:2300 (1800 data points = 90% of data);
* **Test data size**: time step 2301:2500 (200 data points = 10% of data).  
There are several parameters that can influence the accuracy of the LSTM network such as the learning rate (LR), number of layers, epoch, gradient threshold, and dropout factor as described in [18]. Parametric analysis is performed to study these as shown in Figure 19.  
MATLAB programming environment was used to study the behaviour of the selected parameters by varying each one during training iteration while others were kept constant to determine the corresponding influence on RMSE. The RMSE obtained in this case is called training RMSE.  
Figure 19 shows that a learning rate of 0.1 gives the lowest RMSE for all training iterations. Also, as the dropout factor approaches 0, the RMSE reduces significantly. In other cases, for the same learning rate of 0.1, a gradual increase in the number of layers and epoch value further reduces the RMSE. Finally, a gradient threshold of approximately 1 was seen to produce a much lower RMSE. From these observations, an optimum parameter set was obtained which resulted in a satisfactory RMSE of 0.09 during training. A plot of the training process is shown in Figure 20 while the paremeter set used for training are shown in Table 4.  
Figure 19: Optimisation of parameters to reduce RMSE during training of the LSTM for each selected learning rate (LR). The effect of variation in (**a**) dropout factor; (**b**) number of layers; (**c**) number of epochs; and (**d**) gradient threshold on the RMSE, are shown.  
The trained LSTM network was tested by using it to predict the electrolyser hydrogen temperature ([TT121]). Satisfactory prediction was obtained as shown in Figure 21 with a testing RMSE of 0.1351.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Training Parameter**} & \multirow{2}{*}{**Value**} & **Training RMSE** & \multirow{2}{*}{
\begin{tabular}{} **Testing RMSE** \\ **(LSTM)** \\ \end{tabular} } & **(Predicted Variable** \\  & & & **TT121)** \\ \hline Learning rate & 0.1 & & \\ Number of layers & 40 & & \\ Epoch & 400 & 0.09 & 0.1351 \\ Gradient threshold & 1 & & \\ Dropout & none (0) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Final set of parameters used to reduce RMSE of the LSTM neural network.  
Figure 21: Target variable (hydrogen temperature TT121) predicted by developed LSTM neural network, and adjustment with experimental sensor data.  
Figure 20: Final training result with training RMSE of 0.09.
###### Abstract  
The coseismic slip on the main fault related to the 2017 Iran-Iraq Mw7.3 earthquake has been investigated by previous studies using DInSAR (differential interferometric synthetic aperture radar) ground deformation measurements. However, DInSAR observation is not sensitive to the ground deformation in the along-track (AT) direction. Therefore, only the one-dimensional (1D) DInSAR coseismic deformation field measurements, derived in the LOS (line-of-sight) direction of radar, was applied in source parameters estimation. To further improve the accuracy of the fault slip inversion, the 3D (three-dimensional) coseismic deformation fields were reconstructed in the first place, by a combined use of the DInSAR and MAI (multiple aperture InSAR) measurements. Subsequently, the LOS and 3D deformation data sets were used as the constraint respectively, to perform a two-step inversion scheme to find an optimal geometry and slip distribution on the main fault. The comparative analysis indicated that the 3D coseismic deformation data sets improved the inversion-accuracy by 30%. Besides, the fault invention results revealed a deep dislocation on a NNW trending fault (the strike is \(352.63^{\circ}\)) extending about 60 km, along the fault dips \(14.76^{\circ}\) to the ENE. The estimated seismic moment is \(8.44\times 10^{19}\) Nm (Mw7.3), which is close to the solution provided by USGS (United States Geological Survey). The slip distributed at the depth between 12 and 18 km, and the peak slip of 6.53 m appears at the depth of 14.5 km left near the epicenter. Considering the geological structure in the earthquake region and fault source-parameters, it comes to a preliminary conclusion that the ZMFF (the Zagros Mountain Front fault) should be responsible for the earthquake. In general, this paper demonstrated the superiority of using the 3D coseismic deformation fields on source parameters estimation.  
The 2017 Iran-Iraq Mw7.3 earthquake; DInSAR; MAI; 3D coseismic deformation fields; source parameters +
Footnote †: journal: _Article_  
1
## 3 Results
### Sea-Ice Concentration Uncertainties
#### 3.3.1 MODIS Ice Tie-Point Threshold
In summary, Figure 12 shows that a threshold of 266.5 K or lower needs to be chosen to ensure that the mean uncertainty is below our sanity threshold of 0.3 for all granules. At the same time, thresholds lower than 266.5 K would lead to discarding a high number (15-30%) of pixels in May. We thus adopt a threshold of 266.5 K. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Threshold** & **265.0** & **265.5** & **266.0** & **266.5** & **267.0** & **267.5** & **268.0** & **268.5** & **269.0** & **269.5** & **270** \\ \hline \(n_{feb}\) & 0.003 & 0.003 & 0.003 & 0.002 & 0.002 & 0.002 & 0.001 & 0.001 & 0.001 & 0.000 & 0.772 \\ \(n_{mar}\) & 0.013 & 0.011 & 0.009 & 0.007 & 0.006 & 0.004 & 0.003 & 0.002 & 0.001 & 0.001 & 0.772 \\ \(n_{apr}\) & 0.003 & 0.002 & 0.002 & 0.001 & 0.001 & 0.001 & 0.000 & 0.000 & 0.000 & 0.000 & 0.738 \\ \(n_{may}\) & 0.030 & 0.026 & 0.022 & 0.018 & 0.014 & 0.010 & 0.007 & 0.005 & 0.003 & 0.001 & 0.826 \\ \(n_{all}\) & 0.012 & 0.010 & 0.009 & 0.007 & 0.006 & 0.004 & 0.003 & 0.002 & 0.001 & 0.001 & 0.776 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of additionally discarded pixels for each threshold, normalised to the number of pixels in one MODIS granule. “Additionally discarded” means the increase in the number of discarded pixels compared to the number of pixels which the highest (i.e., most conservative) threshold, 270 K, would have discarded. The numbers are absolute values, i.e., an additionally discarded fraction of 0.003 for the lowest threshold, 265 K, in February means an increase from 0.772 (77.2% of all pixels) to 0.775 (77.5% of all pixels). For the highest threshold of 270 K, the absolute of discarded pixels are given. Figure 12: Uncertainty of MODIS SIC per granule. The panels show the mean (**a**) and the 99 th percentile (**b**) for each granule. Panel (**c**) shows the “additionally discarded values”, i.e., the absolute difference between the fraction of values which would be discarded by the respective threshold and the fraction of values would be discarded by the highest threshold. The markers in the panels show the respective parameter for different thresholds between 265 and 270 K, where ice tie-point values above the respective threshold are discarded. The x-axis shows the date, the vertical lines mark the days with Sentinel-2 scenes.
## 2 Problem Formulation
### Problem Constraints  
_Power balance constraint._ Generation should cover the total demand and the active power losses that occur in the transmission system.  
\[\sum_{j=1}^{Ng}P_{i}=P_{d}+P_{loss} \tag{2}\]  
where \(P_{d}\) is the total demand load and \(P_{loss}\) is the total transmission losses computed using quadratic approximation.  
\[P_{loss}=\ \sum_{i=1}^{Ng}\sum_{j=1}^{Ng}P_{i}B_{ij}P_{j} \tag{3}\]  
where \(B_{ij}\) is the loss coefficient matrix. In this paper it is assumed constant.  
_Power generation limits_. Each unit should generate power within its minimum and maximum limits.  
\[P_{i}^{min}\leq\ P_{i}\leq P_{i}^{max} \tag{4}\]  
The objective and constraints can be mathematically formulated as follows.  
\[\min\ [F_{c}(P_{i})] \tag{5}\] \[\textit{subject\ to:}\left\{\begin{array}{l}g_{j}(P_{i})\leq\ 0\ j=1,...,J\\ h_{k}(P_{i})=\ 0\ k=1,...,K\end{array}\right. \tag{6}\]  
where \(J\) and \(K\) are the number of inequality and equality constraints respectively. In this paper, the power balance constraint has been handled using the penalty function. This paper uses a penalty function that adds a penalty to the fitness function if the constrains are violated. This turns a constrained optimization problem into an unconstrained optimization problem. Mathematically it can be denoted as  
\[\min[\varphi(P_{i})]=\ F_{c}(P_{i})+p*G[\ h_{k}(P_{i})] \tag{7}\] \[\textit{Subject\ to:}\ g_{j}(P_{i})\leq\ 0\ j=1,...,J \tag{8}\]\[G[\;h_{k}(x)]=\varepsilon^{2}+\;\omega^{2} \tag{9}\]  
where \(p\) is the penalty factor for the violation of the constraints. \(\varepsilon\) and \(\omega\) are equality and inequality constraint violation penalties respectively and are calculated using the following formulas.  
\[\varepsilon=\left|P_{d}+P_{loss}-\sum_{j=1}^{Ng}P_{i}\right| \tag{10}\]  
And  
\[\omega=\left\{\begin{array}{c|c}|P_{i}^{min}-P_{i}|&P_{i}^{min}>P_{i}\\ 0&P_{i}^{min}<P_{i}<P_{i}^{max}\\ |P_{i}-P_{i}^{max}|&P_{i}^{max}<P_{i}\end{array}\right. \tag{11}\]
## 4 Simulation Results and Analysis
### Free-Space Electric Dipole Radiation  
The computational space is discretized into \(1000\Delta x\times 300\Delta y\times 300\Delta z\), and the total number of grids is 90 million. The excitation source is an electric dipole in the z-direction located in the center of the calculation region. It is a time-harmonic field with a frequency of 300 MHz. In this example, the FDTD algorithm based on a CPU and GPU is used to calculate the snapshot of the z-component of the electric field (Ez) for the xoy cross-section at the 3000 th time step, as shown in Figure 7. The results of the CPU and GPU at a monitoring point are compared in Figure 8, which proves that the FDTD calculation results based on a GPU are accurate.  
To verify the parallel efficiency of the GPU, Table 1 shows the calculation time, speedup, and parallel efficiency of the CPU, single GPU, dual GPUs at a dual node, and four GPUs at a dual node. It can be seen from Table 1 that the calculation acceleration ratio of a single GPU is about eight times. With the increase in GPU cards, the acceleration ratio shows a linear growth trend. In addition, the NCCL technology shortens the communication time between the GPUs, thereby improving the computational efficiency.  
Figure 8: The waveform of Ez at the monitoring point.  
Figure 7: The snapshot of Ez for the xoy cross-section at the 3000 th time step: (**a**) CPU and (**b**) GPU.