esa-sceva/satcom-chunk-collection huggingface dataset:
# FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
## 5 Ablation Study
### Estimation of Computational Cost for Each Algorithm  
In the actual experimental environment, using non-IID data makes it challenging to estimate computational costs. Therefore, this work estimates the computational costs using the number of FLOPs. The number of parameters in each layer is mentioned in Table 5.  
To compare the computational costs of FedAvg, FedBABU, and our scheduling algorithms, this work considers the data processed by each client in an IID (independent and identically distributed) environment. Costs are measured in FLOPs, and for simplicity, all algorithms are assumed to use datasets with an equal pixel count per image. In datasets like MNIST, CIFAR-10, and CIFAR-100, each with a total of 50,000 training data points and 100 clients, each client processes 500 data points. In the setting of our experiments, a batch size of 10 is used, with each client handling 50 batches per epoch.  
For FedAvg, as seen in Table 5, the total model parameters are 582,026, and the computational cost per round is 582,026 \(\times\) 50. Thus, the total computational cost for all clients and all rounds is 873.039 billion FLOPs. In the same environment, the computational cost for FedBABU, considering that the head (fc2) layer parameters are not computed during the training rounds, results in 576,896 learning parameters. Hence, the total computational cost for FedBABU is 865.344 billion FLOPs. Using the same method for our scheduling  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Number of Parameters** \\ \hline conv1.weight & 800 \\ conv1.bias & 32 \\ conv2.weight & 51,200 \\ conv2.bias & 64 \\ fc1.weight & 524,288 \\ fc1.bias & 512 \\ fc2.weight & 5120 \\ fc2.bias & 10 \\ \hline Total & 582,026 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of parameters per layer.  
Figure 7: Comparison of client-specific accuracy in the Tiny-ImageNet dataset. This figure visualizes the accuracy of each client in ascending order.  
algorithms, the computed parameters result in 314.912 billion FLOPs for Vanilla Scheduling and 838.880 billion FLOPs for Anti Scheduling, as detailed in Table 6.  
Figure 8 visually compares the computational costs between FedAvg, FedBABU, and our scheduling algorithms. It illustrates how Vanilla Scheduling significantly reduces computational costs in the earlier rounds by updating only the earlier unfreezed layers. This visualization supports the data presented in Table 6, emphasizing the efficiency of Vanilla Scheduling in managing computational resources.
# Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar
###### Abstract  
This paper proposes two modified space time adaptive processing (STAP) methods based on piecewise sub-apertures and data constraints for non-stationary interference cancellation in synthetic aperture radar (SAR) applications. In these methods, the entire synthetic aperture time is divided into several sub-apertures so that the interference can be considered as stationary in each sub-aperture. At the same time, the consistency of the echo phase in the slow time domain is preserved by the data constraint to ensure the null depth of the antenna pattern for non-stationary interference cancellation and the performance of azimuth focusing in SAR. The proposed algorithms are validated through the model simulation and measured data.  
synthetic aperture radar (SAR); space time adaptive processing (STAP); generalized sidelobe canceller (GSC); interference cancellation +
Footnote †: journal: Applied Sciences
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Testbed Description
#### 3.1.3 Benchmarking Process  
The test suite process begins with starting a middleware server, then 3 requests are sent to \"warm-up\" the middleware (to allow JVM load all classes used in the process)--register client request, notification insert request and client deletion request. These messages are not taken into account in result analysis. Afterwards the devices send requests in a predefined time frame periodically. All devices work in parallel and are started at the same time. After the time frame has elapsed device simulators end its application execution, the middleware is stopped and results are collected.  
One test suite provides us with one set of results which is composed of entries containing the following details:  
* request send and related response received timestamps--client (device) side,
* request processing begin timestamp--server side,
* request processing end timestamp--server side,
* request processed with or without errors--server side.  
Each test suite was repeated 7 times and took 10 min per suite. These provided us with 7 result data sets--including processing time (calculated as a difference between timestamps of request and response events on the client side). In order to minimize any external factors (eq. OS cron or update services) we rejected the highest and the lowest values over these 7 sets of data. The remaining results were merged together to calculate a median, first quartile and third quartile values of processing times. Such data was used for calculating all metrics. Restricting cores used by the server was achieved using _taskset_ command. The following configurations were tested:  
* taskset -c 0-1--two physical cores of CPU 0,
* taskset -c 0-3--four physical cores of CPU 0,
* taskset -c 0-19--10 physical cores of CPU 0 and 10 physical cores of CPU 1,
* taskset -c 0-39--all logical cores in the system (including HyperThreading technology).
## 3 Computational Methods
### Computational Domain and Boundary Conditions  
The computation domain is depicted in Figure 3. The flow field is tested to confirm that it is large enough to eliminate boundary effects. The computational domain size is \(96.5H\times 45H\times 20H\), where \(H\) is the height of the train and \(H\) = 3.55 m. The origin of the Cartesian reference system locates at the distance x = 29 m from the train geometry. The inflow velocity is 350 km/h. The Reynolds number, \(Re\), is \(2.25\times 10^{7}\) based on inflow velocity and the train's height \(H\). A three-car train model with the pantograph is used to study the effects of the pantograph with different configurations of the installation base on the aerodynamic noise characteristics. The effects of other complicated components, such as bogies and windshields, are neglected to save computational cost.  
The physical time step of the calculation is \(10^{-4}\) s and 20 sub-steps are used within each time step. The total physical calculation time is 0.4 s. The Courant-Friedrichs-Lewy number \((\mathrm{CFL}=\mathrm{u}\,\Delta\mathrm{t}/\Delta\mathrm{x})\) is less than 1 so that the calculation is converged within each time step. Boundary conditions are set as follows: (1) The surface of the train is a fixed wall with non-slip and penetration conditions. (2) Both lateral sides and the top of the computational domain are given as the far field condition with the characteristic line method, and the air flows in the positive x-direction. (3) The ground is set to be a moving wall, and its velocity is the same as the incoming velocity of the air. Figure 4 shows the observer locations used to evaluate the acoustic pressure spectrum and directivity in the near and far field of the pantograph, namely, two positions at y = 5 and 25 m for the spectrum characteristics, and planes y = 0, 10 and 20 m for the distributions of aerodynamic noise, respectively. Furthermore, five positions in the x-direction i.e. x = 0, 5, 10, 15 and 20 m are monitored to investigate the overall sound pressure level (OASPL) caused by the pantograph with the different base configurations. It should be noted that these five positions are 3.5 m above the ground.  
Figure 4: Locations of aerodynamic noise observers.  
Figure 3: Computational domain for flow field and aerodynamic noise.
## 4 Concept Stability Calculation Based on Maximal Non-Generator
### Mng-Sc Algorithm  
Based on the previous discussion, the MNG-SC algorithm for the accurate computation of concept stability is proposed. The guiding idea is the fact that the maximal non-generators of a given formal concept is equivalent to its lower neighbor concepts.  
The pseudo-code of the MNG-SC algorithm is described by Algorithm 1. The algorithm takes a formal concept \((A,B)\) as input and starts by initializing the stability value \(\sigma\) to 0, the total number of non-generator \(Nb\) to 0, the \(flag\) of the empty maximal non-generator intersection to false (c.f. Line 1). In Lines 3-4, the maximal non-generator objects are assigned to the lower neighbor concepts of \((A,B)\) (c.f. Theorem 2). The goal of the for loop (c.f. Lines 5-13) is to compute the total number of non-generator. To do so, if the \(flag\) is false, the getINongenNum function will be invoked. The goal of the function is to calculate the number of non-generator obtained by \(i\) maximal non-generator objects. The flag is true means that the intersection of i or more maximal non-generator is empty. Therefore, \(Nb\) is accumulated according to Equation (3). In Lines 14-15, the stability \(\sigma\) of the concept is calculated (c.f. Equation (3)) and returned.  
As shown in Algorithm 2, the getINongenNum function inputs the maximal non-generator objects, the number of maximal non-generator objects to be intersected and outputs the number of non-generator obtained by \(i\) maximal non-generators. It starts by initializing \(INb\) to 0 (c.f. Line 2). In the for loop on the \(i\) elements of the objects \(M\) (c.f. Line 3), the intersection of any \(i\) maximal non-generator objects is calculated and the number of non-generators is accumulated and assigned to \(INb\). In Lines 7-9, if the \(INb\) is equal to 0, the \(flag\) represented the empty set of \(IM\) is assigned to true. At last, the number of non-generator obtained by \(i\) maximal non-generators \(INb\) is returned.  
```
Input: A formal concept \(C=(A,B)\) Output: Stability \(\sigma\) of concept \(C\)
1 Initialize \(\sigma\) =0, \(Nb\) = 0, \(flag\) = false
2 begin
3 MaxNonGen = C.lowerNeighbors
4N = \(|\)MaxNonGen\(|\)
5 for(i = 1; i \(\leq\) N; i++) do
6 if\(flag\) == false then
7\(Nb\) = \(Nb\) + getINongenNum(MaxNonGen, i)
8 elseif\(flag\) == true && i%2 == 0 then
9\(Nb\) = \(Nb\) + \(\binom{N}{i}\)
10 elseif flag == true && i%2!= 0 then
11\(Nb\) = \(Nb\) - \(\binom{N}{i}\)
12 endif
13 endfor
14\(\sigma=1-Nb/2^{|A|}\)
15 return\(\sigma\)
16 end if
```  
**Algorithm 1**Maximal Non-generator-based Stability Calculation Algorithm.  
```
Input: \(M\): The maximal non-generator objects of A

\(i\): The number of maximal non-generator objects to be intersected

**Output:**\(INb\) : The number of non-generator obtained by \(i\) maximal non-generators
1 begin
2\(INb\) = 0
3 for\(M_{1}\), \(M_{2}\), \(\cdots M_{i}\in M\)do
4\(IM=M_{1}\cap M_{2}\cap\cdots\cap M_{i}\)
5\(INb=INb+|IM|\)
6 endfor
7 if\(INb\) == 0 then
8\(flag\) = true
9 endif
10 return\(INb\)
11 end
```  
**Algorithm 2**getINongenNum Function.
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Models and Predictions
#### 3.4.8 LSTM Model  
Long short-term memory (LSTM) models [32] are recurrent neural networks used for time-series data and natural language processing. For an application of LSTM in energy consumption forecasting, see [33]. This model successfully addresses the vanishing or exploding gradient problem that other recurrent networks face, thus understanding long-range dependencies in sequence data. Instead of regular nodes, such as in MLP, LSTM networks have cells (arranged in layers), which are more complex structures that can update the information they store by using mechanisms such as the forget gate or the cell state. In our model, we also added fully connected layers after the LSTM layers. The considered hyperparameters were the following:  
* n_LSTM. The number of LSTM layers. We tested one and two layers;
* n_dense. The number of fully connected layers besides the output layer. The values studied are one, two, three, and four;
* unit. The number of cells per LSTM layer. The assessed values are 5, 10, 15, 20, and 50;
* neuron. The number of neurons in each fully connected layer. We tried 10, 40, 50, and 100 neurons;
* dropout. The random deactivation of some input units to reduce overfitting. The studied values are 0, 0.1, and 0.2.  
The selected hyperparameters for the LSTM model are available in Table A3 in Appendix A.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 1 Introduction
### Trend  
Currently, traditional public key encryption algorithms may have some potential issues, such as the threat of quantum computing, increasing key length requirements, and having specific algorithmic characteristics that are vulnerable to targeted attacks. Although the capability of chaotic cryptography to resist quantum computing is still under research, it offers a new approach that is different from traditional public key encryption. Chaotic encryption algorithms can provide more efficient computational performance than traditional public key algorithms, especially in resource-constrained environments such as IoT devices. Certain characteristics of chaotic systems allow for the design of lightweight encryption schemes, thereby saving computational and energy resources. Therefore, it is necessary to find new practical public key cryptographic algorithms as a supplement or replacement to current public key cryptographic algorithms. Table 1 lists the development of chaotic public key cryptography in chronological order, with people attempting to design public key cryptographic algorithms using cellular automata, chaotic iterative structures, distributed dynamical systems, multi-chaos systems, Chebyshev polynomials, torus automorphisms, etc. However, there are security flaws or low efficiency problems in all of them, which is why chaotic public key cryptography cannot match traditional public key cryptography. As a new direction in the field of public key cryptography research, chaotic public key cryptography is still in its early stages and has very important exploratory value. According to Table 1, the development of chaotic public key cryptography, especially the Chebyshev-based public key cryptography discussed in this paper, can be roughly divided into three time periods, as shown in Figure 1.  
1. In the embryonic stage, researchers started to explore the design of public key cryptography algorithms using different chaotic systems, such as cellular automata, distributed dynamical system models, Chebyshev chaotic mappings, chaotic synchronization systems, etc. 2. In the development stage, more attention was paid to the security of chaotic public key cryptography algorithms, especially the analysis and improvement of Chebyshev-based public key cryptography algorithms, to achieve higher security and efficiency.
## 3 Methods of Slope Stability
### Parametric Study  
Prior to the slope stability analysis for the chosen cross-section, a parametric study was carried out to evaluate the change in the factor of safety as a function of slope angle and groundwater table. Table 2 shows the changes in the factor of safety as a function of varying slope angles from 22\({}^{\circ}\) to 45\({}^{\circ}\) and changing groundwater tables from 1 m to 3 m below the slope surface. The lowest factor of safety is for the slope where the slope angle is steepest, and the groundwater table is closest to the terrain.  
The factors of safety were calculated, assuming the temperature in the test site for 2 years, 2017 to 2018. A large deviation between the highest and lowest temperature is observed in Figure 10a. The average temperature was implemented to the surface of the ground. The temperature at the surface is gradually transmitted under the ground, depending on the soil conductivity. If a temperature decreases under zero, for instance during December and January, the low temperature is transmitted to a certain depth below the surface, where ice lenses can lead to a rise in groundwater level. The increased groundwater level decreases the factor of safety of slope stability. Figure 10b presents the changing factor of safety with temperature for a slope angle of 37\({}^{\circ}\) for the last 2 years period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Slop ID**} & \multirow{2}{*}{**Slope Angle, deg**} & \multirow{2}{*}{**Slope Height, m**} & \multicolumn{4}{c}{**Factor of Safety**} \\ \cline{4-7}  & & & & **Matetrable** & **Watertable** & **Watertable** & **Watertable** \\  & & & **GL-10 m** & **GL-3 m** & **GL-2 m** & **GL-1 m** \\ \hline S-22 & 22 & 20 & 1.96 & 1.15 & 1.10 & 1.05 \\ \hline S-24 & 25 & 20 & 1.81 & 1.12 & 1.07 & 1.03 \\ \hline S-27 & 27 & 20 & 1.64 & 1.09 & 1.04 & 1.00 \\ \hline S-30 & 30 & 20 & 1.49 & 1.06 & 1.01 & 0.97 \\ \hline S-34 & 35 & 20 & 1.32 & 1.02 & 0.98 & 0.93 \\ \hline S-39 & 40 & 20 & 1.16 & 0.96 & 0.93 & 0.90 \\ \hline S-45 & 45 & 20 & 1.00 & 0.89 & 0.85 & 0.84 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of factors of safety depending on slope angles and groundwater tables.  
Figure 8: Thermal conductivity vs. temperature and unfrozen water content vs. temperature.  
Figure 9: Water conductivity vs. matrix suction and water content vs. matrix suction.  
When the temperature drops below zero, the factor of safety decreases below one. When the temperature is high (summer months), the factor of safety is not influenced. It was confirmed that the factor of safety of slope stability was mainly affected by changing (1) temperature, (2) slope angle, and (3) level of the groundwater table.
## 3 Results and Discussion
### Co2 SnO4 and Co2 SnO4/Co3O4 Sensing Results and Discussion under Ethanol
#### 3.2.2 Response Stability under Ethanol  
After mechanism deduction, an investigation of the annealing process effect on the response stability under ethanol was also conducted. Figure 8 presents the response stability of each sample under an ethanol vapor concentration of 500 ppm at the optimum temperature, for a duration of three months. As presented in Figure 7a, we note that for CTO\({}_{\text{as}}\) the stability deteriorated after the first month, and the layer response drift increased  
Figure 7: The energy band structure diagram and schematic diagram of the sensing mechanism of Co\({}_{3}\)O\({}_{4}\)/Co\({}_{2}\)SnO\({}_{4}\) p–n heterojunctions (CTO\({}_{\text{ann}}\)): (**a**) no contact under vacuum; (**b**) contact under dry air and (**c**) contact under ethanol vapor.  
to reach 35% at the third month, whereas \(\text{CTO}_{\text{ann}}\) response drift did not exceed 5%. The result shows an improvement in stability following the annealing process, as well as sensibility toward ethanol vapor. In addition, in our previous work [37] we demonstrated that humidity has a neglected effect on the response stability of \(\text{Co}_{2}\text{SnO}_{4}\). Thus, we think that the \(\text{Co}_{2}\text{SnO}_{4}/\text{Co}_{3}\text{O}_{4}\) composite which presented better stability will be not affected by humidity; this prediction could be investigated in future research. Moreover, a comparative study of \(\text{CTO}_{\text{ann}}\) sensing parameters with those of other sensing materials under the concentration range 500-600 ppm of ethanol vapor, which is situated in our concentration range, are presented in Table 2. These further reveal the good performance of our \(\text{Co}_{2}\text{SnO}_{4}/\text{Co}_{3}\text{O}_{4}\) composite (\(\text{CTO}_{\text{ann}}\)).
## 3 Case Study
### Collapse Risk Assessment Based on Multi-Source Data Fusion Method
The multi-source information fusion method proposed in this paper simultaneously considers information from three sources: advance geological forecast data, site inspection data and instrument monitoring data, which provide a more comprehensive understanding of tunnel collapse risk, thus reducing data uncertainty and improving the accuracy of assessment. Compared with the single information source risk assessment method, the multi-source information fusion assessment has higher accuracy. 3. When the evaluation results of three single information sources has high conflict (e.g., Tunnel section No. 5), the fusion result of the improved D-S theory is better than the traditional D-S theory. The traditional D-S theory accumulates consensus support only and rejects a proposition completely if it is opposed by any evidence, no matter what support it may acquire from any other evidence. As a result, when three kinds of single information evaluation give high conflict results, the traditional D-S theory will give a fusion result contrary to common sense. The improved method has high accuracy when merging high conflict information sources because of considering the importance rating and credibility.
# An Effective AUSM-Type Scheme for Both Cases of Low Mach Number and High Mach Number
## 3 Higher Order Reconstruction and the AUSM-Type Schemes
### Brief Introduction to Higher Order Reconstruction Methods
#### 3.2.2 The AUSMPW+ Scheme  
Studies show that AUSM+ is inclined to show oscillations near the region with small pressure gradient or convection and numerical overshoots behind strong shock waves. Therefore, [PERSON] proposed the AUSMPW+ by constructing the mass flux with pressure-based functions \(f\) and \(w\).  
\[\hat{m}_{i+1/2}=\hat{f}_{M}^{+}c_{i+1/2}\rho_{i+1/2}^{+}+\hat{f}_{M}^{-}c_{i+1/ 2}\rho_{i+1/2}^{-} \tag{25}\]  
\[M_{i+1/2}=f_{M}^{+}+f_{M}^{-} \tag{26}\]  
\[\begin{array}{l}\text{If }\ M_{i+1/2}\geq 0,\\ \hat{f}_{M}^{2+}=f_{M}^{+}+f_{M}^{-}\cdot\big{[}(1-w)\cdot(1+f_{R})-f_{L}\big{]} \\ \hat{f}_{M}^{-}=f_{M}^{-}\cdot w\cdot(1+f_{R})\end{array} \tag{27}\]  
\[\begin{array}{l}\text{else}\\ \hat{f}_{M}^{+}=f_{M}^{+}\cdot w\cdot(1+f_{R})\\ \hat{f}_{M}^{-}=f_{M}^{-}+f_{M}^{+}\cdot\big{[}(1-w)\cdot(1+f_{R})-f_{L}\big{]} \end{array}\]  
The terms \(f_{M}^{+},f_{M}^{-}\), and the pressure flux in AUSMPW+ are the same as AUSM+'s. More details about\(f_{L,R},w\), and \(c_{i+1/2}\) are available in Ref. [6].
## 3 Results and Analysis
### Contact Angle Measurement Test and Analysis
#### 3.2.2 Analysis of the Contact Angle Measurement Results  
The surface free energy parameters obtained through the contact angle measurement test were listed in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\gamma\) & \(\gamma^{d}\) & \(\gamma^{p}\) & \(\gamma^{+}\) & \(\gamma^{-}\) \\ \hline CR & 22.80 & 21.94 & 0.86 & 0.17 & 1.10 \\ Basalt & 32.39 & 24.64 & 6.75 & 1.08 & 10.56 \\ Limestone & 37.74 & 31.77 & 5.97 & 0.75 & 11.89 \\ Distilled water & 72.8 & 21.8 & 51.0 & 25.5 & 25.5 \\ \hline \end{tabular}
\end{table}
Table 11: Surface free energy parameters of different materials (mJ/m2).  
Figure 13: Schematic diagram of asphalt–aggregate adhesion-cracking model: (**a**) Dry condition; (**b**) Wet Condition.  
The surface free energy parameters listed in Table 11 were substituted into Equation (3) to obtain the adhesion work \(W_{as}\) and \(W_{asw}\) between CR and basalt and limestone in dry and wet conditions, respectively. The water stability indices of the adhesion work of different aggregates and CR were further calculated, and the results are plotted in Figures 14 and 15.  
It can be seen from Figure 14 that the wet surface reduces the adhesion work between the aggregate and the CR. Although the adhesion work between the CR and the basalt and limestone aggregates decreases, it is obvious that water has a greater impact on the limestone. Under the wet condition, the adhesion work between the limestone and CR decreases more than that between the basalt and CR on the wet surface. It can be seen from Figure 15 that the \(W^{\prime}\) between CR and basalt is greater than that between CR and limestone, indicating that the adhesion between limestone and CR decays faster under wet conditions. Although limestone has better adhesion with CR under dry conditions, limestone does not have good water stability.