esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies
### Case Study One: Khan Academy
#### 5.1.2 Application of DFDLOF in Khan Academy  
The application of DFDLOF in Khan Academy involves several key steps, starting with integrating advanced machine learning algorithms to analyze learner data more deeply. These algorithms allow a more sophisticated understanding of students' learning patterns, preferences, and challenges. The DFDLOF model enhances Khan Academy's existing adaptive learning system, enabling it to adjust the content and teaching methodologies based on real-time feedback and learner analytics.  
One of the core functionalities of DFDLOF in this context is the dynamic adjustment of learning paths. For instance, if students excel in certain topics, the system can introduce more challenging content or explore related subjects, maintaining an engaging and stimulating learning experience. Conversely, if a student struggles, the system can provide additional resources, simplify concepts, or revise foundational material.  
Another significant aspect is the personalization of feedback and assessments. The DFDLOF model enables the generation of personalized feedback that is more specific and actionable, thus providing students with clear guidance on how to improve their learning process. Additionally, the assessment methods become more adaptive, aligning with each student's current level of understanding and learning style.  
The integration of DFDLOF into Khan Academy demonstrates the potential of machine learning to transform traditional online education platforms into more dynamic, responsive, and personalized learning environments. This application serves as a model for how advanced data analytics and machine learning can be utilized to enhance the efficacy and personalization of online education.
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 4 Experiments
### Quantitative Evaluations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & **Backstep** & **Cylinder** & **DNS Turbulence** & **JHTDB** & **SQG** & **Uniform** & **Cavity** & **Dipole** & **Sink** & **Source** & **Vortex** \\ \hline RAFT256-PIV [26] & 3.52 & 2.92 & 11.9 & 14.5 & 3.97 & 14.8 & 6.40 & 2.11 & 1.09 & 1.21 & 3.03 \\ \hline FTGM-PIV & 2.13 & 1.90 & 12.2 & 8.11 & 6.03 & 2.66 & 8.11 & 0.60 & **0.18** & **0.18** & **0.64** \\ FTGM-PIV-Ref & **1.73*** & **1.57** & **10.4** & **6.50** & **3.85** & **1.67** & **5.88** & **0.44** & 0.25 & 0.26 & 0.94 \\ \hline \hline \end{tabular}  
* All methods listed are trained and tested based on PIV Dataset II. The error unit is set to pixels per 100 pixels for easier comparison. The best results are highlighted in bold. For these metrics, lower is better.  
\end{table} 
Table 2: Average endpoint error for all test cases of PIV Dataset II.
## 3 Numerical Experiments
### Simulation Research of PE  
In order to verify the validity of applying PE to the detection of nonlinear signals, the FM signal is adopted to conduct simulation researches and to calculate the PE. The following FM signal in Equation (23) is applied in the simulation.  
\[x(t)=\sin(20\pi t+1.5\cos(8\pi t)) \tag{23}\]  
where the sampling frequency \(f_{s}\) is of 1024 Hz, and the sampling time \(t\) is 3 s. A Gaussian white noise signal is superimposed on the FM signal between 1 to 2 s. It aims at simulating the abnormal condition of mechanical parts. Through analyzing the noisy FM signal, the validity of PE applying to gear and bearing classification can be verified. The time domain plot of FM signal locally superimposed by Gaussian white noise is shown in Figure 21.  
The above long time series is divided into several subsequences of length \(l\), and the cutoff point of subsequences corresponds to the third second moment of sampling time. These subsequences employ overlapping case, namely that each subsequence moves backward 20 data points to get the next subsequence. Then the PE of each subsequence is calculated, and its value is assigned to the middle data point of the subsequence. If the value of \(l\) is too small, the calculated results will lose statistical significance. Meanwhile, in order to detect the change of signal accurately, the value of \(l\) also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SNR of Added Noise**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{2}{c}{**Cm**} & \multicolumn{2}{c}{**MSE**} \\ \cline{2-7}  & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline \(-1\) & \(-1\) & 7.216 & 0.681 & 0.718 & 0.289 & 0.179 \\ \(-0.1\) & \(-0.1\) & 9.725 & 0.750 & 0.807 & 0.243 & 0.127  
cannot be too large. After comparison analysis of several times simulation experiments, here \(m=5\), \(\tau=3\), \(l=300\) are adopted in the simulation experiment. The calculated results are shown in Figure 22.  
It can be seen from the above figure that the PE values increase after the noise signal is superimposed on the FM signal, which results in less volatility and smooth variation of PE values, and the results indicate that the FM signal superimposed with noise is more random than the original FM signal, resulting in the bigger PE value. When noise disappears, this FM signal becomes regular again, and then PE values decrease to the previous level. From the simulation experiments, it can be concluded that, as for a given signal of random time series, mutation and status changes of signal will lead to a great change of PE value. Therefore, PE can be applied to mutation detection and state recognition, and thus can be adopted in gear and bearing fault classification.
# Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?
## 6 Threats to Validity  
Potential threats to the validity of our research method are shown as follows.  
**Threats to internal validity**: we use the SDP technique as the basic method for coarse-level tasks. The datasets we used in the experiment are cleaned, which means 18 common metrics are used and similar instances with software MC from cross-projects are selected. Besides, we have checked our experiments four times by all of the authors, there may still be some errors. Moreover, to the best of our knowledge, it is the first time to apply SDP for coarse-level tasks. Therefore, we did not execute control experiments. However, we explain the advantage of our proposed approach as compared with the uncertainty of evenly allocating resources.  
**Threats to external validity**: we have verified the effectiveness of DPAHM via applying it to a specific software. Moreover, we only use LRM to make defect prediction for Alternatives-layer. Because our goal is to provide a method about allocating test resource for coarse-level tasks instead of finding the best model for the tasks. In order to generalize our proposed approach, we analyze the different types SDP methods (i.e., classification, ranking, numeric types) of DPAHM. In the future, more software for coarse-level tasks should be considered to reduce external validity.  
**Threats to construct validity**: DPAHM relates to AHP and PIM. Therefore, the framework and relationships are important for the final result. We carefully check and draw the structure via the specifications and codes. In addition, our proposed DPAHM is based on SDP. According to previous SDP studies [14; 38; 63], different performance measures are used. In the paper, we just follow paper [38] and also use PoD to assess the performance of SDP in the Alternatives-layer.
# Service for Deploying Digital Twins of QKD Networks
## 2 Background
### Quantum Network Simulators
By emulating quantum and classical channels, it enables the execution of quantum applications between remote devices, managing the operations and state of the qubits [39]. It implements its own protocol, the CQC protocol [39], to achieve the communication between applications and physical quantum equipment (either real or emulated by SimulaQron itself). Although it has been mainly developed in Python, it can be programmed using any language capable of connecting to the CQC server backend using a TCP connection and sending packets in the required form [39]. Table 1 presents a comparison of the considered quantum network simulators and emulators. Each column takes into account one of the sought features discussed previously: the possibility of distributing the nodes of the QKD network in order to resemble a real network as much as possible, and the modularity to facilitate the independent development of features in different components of the network. The last column, called \"Simulation environment\", refers to the programming language used to write the quantum applications that can be run on each solution. N/A in this last column means that the authors did not specify the programming language of their tool. Although each of them has unique strengths and constraints, the publicly available emulator SimulaQron was chosen for our implementation since, to the authors' knowledge, it is the only one that allows implementing a quantum application on separate network nodes, emulating realistic quantum communications between these nodes. In addition, SimulaQron presents a modular design that eases implementation aspects and offers comprehensive Python implementation of quantum operations that facilitates the development of quantum applications.
# Improving Decodability of Polar Codes by Adding Noise
###### Abstract  
This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain.  
Keywords:fifth generation; channel coding; polar code; perturbation noise; neuro-evolution +
Footnote †: journal:
## 2 Main Result
### Overview of Updating the Reference Model  
The following sections will derive a control law to minimize the state error between the true (Equation (2)) and model (Equation (3)) systems given the presence of stochasticities in the true system. Note that both the true and model systems match in dimensions but may differ in internal interactions and constitutive constants. The eigenvalues of the initial model plant \((A_{m})\) are stable (\(\text{Re}(\sigma\{A_{m}\})<0\)) and the weak stochasticities \((v_{x})\) are unknown, bounded, and not accounted for in the reference model. Accounting for stochasticities in the true system adds an additional layer of complexity, potentially making the reference model dynamics less reliable.  
\[\text{Reference Model}\begin{cases}\dot{x}_{m}=A_{m}x_{m}+Bu\\ y_{m}=Cx_{m}\end{cases} \tag{3}\]  
A board overview, the reference model considers both the input and output of the true system to modify its dynamics, aiming to minimize the estimated state errors \(\{e_{x},e_{y}\}\)  
Figure 1: Control diagram for adaptive plant and state estimation accounting stochastic variations and environmental/health changes in the true system using known inputs and outputs.  
To update the reference model, the true system's plant is assumed to be decomposable into two components: the initial plant model \((A_{m})\) and the plant correction term \((BL_{*}C)\):  
\[A=A_{m}+BL_{*}C. \tag{4}\]  
The decomposition of the true plant is structured so that the initial model can be updated and corrected in the estimator via a correction term \((L)\):  
\[L(t)=\Delta L(t)+L_{*}, \tag{5}\]  
where \(\Delta L\) is the variability-uncertainty term. Derived later, the correction term \((L)\) depends on the output state \((y)\) and the output error \((\hat{e}_{y})\) between the model and true systems to adaptively update. By adjusting the reference dynamics, both internal and external errors can be guaranteed to converge to a neighborhood around zero asymptotically. Alternatively, for the derived control scheme to be feasible, the triplet of \((A,B,C)\) must be ASD \(\
i A=A_{m}+BL_{*}C\) and \((A_{m},B,C)\) is SPR.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 3 Energy Management of an HRES Based on Deep Q-Network
### Markov Decision Process Model for the EMS
#### 3.2.4 Rewards  
Reward function is used to calculate the reward from environment in response to a given state and action. It describes how the agent ought to behave. A good reward function can accelerate convergence during the training process. It can also affect the controller performance. For a simple approach, our designed reward function is the consumption of the reward from each system component as follows:  
\[r_{t}(s_{t},a_{t})=r_{t,Bat}+r_{t,FC}+r_{t,EL}+r_{t,DG} \tag{18}\]  
where \(r_{t,Bat}\), \(r_{t,FC}\), \(r_{t,EL}\), and \(r_{t,DG}\) are the rewards from the subsystems: battery, fuel cell, electrolyzer, and diesel generator.  
The component rewards are essentially defined as follows:  
\[r_{t,Bat}=\left\{\begin{array}{ll}\frac{P_{Bat}}{P_{discharge,max}}&if\ \left(P_{PV}+P_{WT}+P_{DG}-\frac{P_{Load}}{P_{motor}}\right)\geq 0\\ -\frac{P_{Bat}}{P_{discharge,max}}&otherwise\end{array}\right. \tag{19}\]  
\[r_{t,FC}=\left\{\begin{array}{ll}\frac{2^{n}P_{FC}}{P_{Cmax}}&if\ \left(P_{PV}+P_{WT}-P_{Bat}-\frac{P_{Load}}{P_{motor}}\right)\leq 0 \ and\ SOC\leq 0.5\\ \frac{P_{FC}}{P_{Cmax}}&if\ \left(P_{PV}+P_{WT}-P_{Bat}-\frac{P_{Load}}{P_{motor}} \right)\leq 0\\ -\frac{P_{FC}}{P_{Cmax}}&otherwise\end{array}\right. \tag{20}\]\[r_{t,EL}=\left\{\begin{array}{l}\frac{2\pi_{t}P_{t,L}}{P_{EL,max}}\quad if\ \left(P_{PV}+P_{WT}+P_{DG}-P_{Bat}-\frac{P_{load}}{\eta_{imeter}}\right)\geq 0 \ and\ SOC\geq 0.9\\ \frac{P_{t,L}}{P_{EL,max}}\quad\quad\quad\quad if\ \left(P_{PV}+P_{WT}+P_{DG}-P_{Bat}- \frac{P_{load}}{\eta_{imeter}}\right)\geq 0\\ -\frac{P_{t,L}}{P_{EL,max}}\quad\quad\quad\quad\quad\quad\quad\quad\quad otherwise \\ \\ r_{t,DG}=-\frac{Fuel_{t}}{Fuel_{max}}\end{array}\right. \tag{21}\]  
\[r_{t,DE}=-\frac{Fuel_{t}}{Fuel_{max}} \tag{22}\]  
where \(\eta_{imeter}\) is the inverter efficiency, \(Fuel_{t}\) is the fuel consumption of the diesel generator based on the actual operating power at time step \(t\), and \(Fuel_{max}\) is the fuel consumption at the maximum capacity.  
As shown in Equations (19)-(22), the component reward functions are defined based on the result of the power balance function. For example, the battery will get negative reward when the sum of \(PV\), \(WT\), and \(DG\) powers is smaller than 0. Thus, the agent will learn to avoid choosing the negative-reward actions. The reward functions of \(FC\) and \(EL\) are similar to that of battery. For \(DG\) reward function, more fuel consumption means more negative rewards. Thus, it helps the agent to stop the operation time of the \(DG\) as much as possible.  
In addition, the agent receives a big penalty if these parameters are out of their boundaries as shown below:  
\[SOC_{min}\leq SOC\leq SOC_{max} \tag{23}\]  
\[P_{Bat,discharge}\leq P_{Bat}\leq P_{Bat,charge} \tag{24}\]  
\[L_{H2,min}\leq L_{H2}\leq L_{H2,max} \tag{25}\]  
\[0\leq P_{FC}\leq P_{FC,max} \tag{26}\]  
\[0\leq P_{EL}\leq P_{EL,max} \tag{27}\]
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 4 Results and Discussion
### Number of Parameters for Different Algorithms  
In order to further confirm the superiority of the DHRE-Net algorithm, a comparison was made regarding the model's parameter quantity (Params), computational load (floating point operations per second, GFLOPS), and the inference speed (frames per second, FPS) in detecting the number of images. The results of this analysis are presented in Table 6.  
Figure 10: Comparison of the recognition effect of the improved network model with the other models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **MIOU/\%** & **MPA/\%** & **Precision/\%** & **Recall/\%** & **F1-Score/\%** \\ \hline U-Net & 82.72 & 91.19 & 89.26 & 91.19 & 90.18 \\ Deeplabv3+ & 83.65 & 90.68 & 90.86 & 90.65 & 90.75 \\ PspNet & 84.09 & 91.79 & 90.36 & 91.79 & 91.07 \\ DHRECA-Net & **88.37** & **94.55** & **93.17** & **94.55** & **93.85** \\ \hline \end{tabular} The best results are in bold.  
\end{table}
Table 5: Performance comparison between the DHRECA-Net and other models.  
The data listed in Table 6 reveal that the DHRECA-Net exhibited the lowest parameter count and computational load among the four algorithms (U-Net, Deeplabv3+, PspNet, and DHRECA-Net). This implies that DHRECA-Net is more easily deployable and operable in resource-constrained environments, such as mobile devices or embedded systems. Additionally, the DHRECA-Net exhibited the highest FPS, demonstrating high real-time performance in applications.
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 2 Materials and Methods
### Adsorption Studies  
Adsorption experiments were conducted using 50 mL conical flasks, each containing 0.1 g of biochar mixed with 10 mL of a silver ion solution. These mixtures were then agitated on a rotary shaker for different periods to investigate the adsorption process. After shaking, the solid particles were separated from the solution by filtration. The concentrations of silver ions in the filtrate were measured by inductively coupled plasma optical emission spectrometry (ICP-OES) using a Prodigy 7 ICP-OES spectrometer (Teledyne Leeman Labs, Mason, OH, USA).  
A stock solution of silver ions at 1000 mg L\({}^{-1}\) was prepared using Titrisol (Merck, Darmstadt, Germany), and was diluted to the required initial concentrations for the experiments. All reagents used were of analytical grade. Batch experiments were performed to determine the influence of variables such as contact time, pH, initial silver ion concentration, and temperature on the adsorption process. The equilibrium adsorption capacity (Q\({}_{e}\), expressed in mg/g) was calculated using the following formula:  
\[\mathrm{Q_{e}=(C_{0}-C_{e})\times V/m} \tag{1}\]  
where C\({}_{0}\) is the initial concentration of silver ions, C\({}_{e}\) is the equilibrium concentration, V is the volume of the solution, and m is the mass of the biosorbent used.  
The adsorption behaviour of silver ions (Ag\({}^{+}\)) was explored under various conditions to understand how different factors influence the process. First, the effect of contact time was studied at a pH of 3.5 with a silver ion concentration of 50 mg L\({}^{-1}\). Next, to determine the impact of acidity on silver ion removal, experiments were conducted over a pH range of 1.9 to 5.5 using a silver ion concentration of 20 mg L\({}^{-1}\). The influence of initial silver ion concentration on adsorption capacity was examined by testing concentrations from 10 to 200 mg L\({}^{-1}\) at a pH of 3.5. Additionally, the effect of temperature on the adsorption process was investigated at three different temperatures, 293 K, 303 K, and 333 K, with a silver ion concentration maintained at 200 mg L\({}^{-1}\). For all experiments, the pH of the initial solutions was precisely adjusted to the required level using 0.1 M nitric acid and 0.1 M sodium hydroxide solutions before mixing the suspensions.
## 6 Discussion  
It can be seen that the proposed model is able to reproduce the pore pressure development in a qualitative manner, but shows some discrepancies compared with the experiment data. During vibratory pile driving, the maximum excess pore pressure is usually one of the key indices to predict the construction influence in geotechnical engineering. In Figure 8, the percent error of 2-22% can be seen when the maximum excess pore pressure recorded at different penetration depths are compared between the simulation and the experiment. This error range could be accepted in most geotechnical engineering practices. It should also be pointed out that value differences between simulations and experiments can also be found in the literature [16; 17; 18; 19]. For example in the literature [16], there are significant discrepancies of maximum excess pore pressure between the simulation and the experiment (Figure 11). In Figure 11, the approximate errors are 68% for the PWD1 and 4% for the PWD2, respectively. Compared with the results in Figure 11, the proposed model seems to have a better ability to predict the maximum excess pore pressure from the measurement.  
Figure 11: Evolution of pore pressures at two observation points. Adapted with permission from Ref. [16]. 2022, Elsevier.  
Figure 10: Fields of excess pore pressure with varying penetration depth.  
In the presented paper, the underlying reasons for the discrepancies between the simulation and the experiment may include two points. Firstly, there are fixed boundary conditions with small model sizes both in the experiment and the simulation. The reflection of waves at boundaries can make the measured values difficult to be replicated in the simulation. Secondly, as reported in [16], the soil mechanical characteristics are complicated to be modelled by most soil constitutive models during vibratory penetration. For example, there are large numbers of load or deformation cycles on the soil. Furthermore, the large monotonic deformation and the cyclic deformation concurrently exist on the vibratory pile penetration.