esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Process of Runoff Formation  
As shown in Figure 2, a higher intensity of precipitation results in a more pronounced surge in the runoff rate. Taking a slope gradient of 10\({}^{\circ}\) as an instance, the peak runoff rates achieve at precipitation intensities of 50, 80, and 110 mm/h are 16.3, 31.2, and 38.5 mL/s, respectively, with corresponding average runoff rates of 13.6, 27.7, and 32.7 mL/s. Notably, the augmentation of both peak and average runoff rates is substantial when the precipitation intensity increases from 50 to 80 mm/h. However, this enhancement is not linear, and the peak runoff rate experiences only a 23% surge, while the average runoff rate increases by less than 20% as the precipitation intensity elevates from 80 to 110 mm/h.  
It is also worth mentioning that, for a fixed precipitation intensity, the impact of slope gradient on the runoff rate is relatively minor. Specifically, when the precipitation intensity is 110 mm/h, the average runoff rate undergoes a modest 19% escalation as the slope  
Figure 2: Variation in runoff rate with time under different rainfall intensities for the same slope gradients. (**a**) Slope is 10\({}^{\circ}\). (**b**) Slope is 20\({}^{\circ}\). (**c**) Slope is 30\({}^{\circ}\). (**d**) Slope is 40\({}^{\circ}\).  
gradient rises from 20\({}^{\circ}\) to 30\({}^{\circ}\). Subsequently, as the slope gradient increases from 30\({}^{\circ}\) to 40\({}^{\circ}\), the minimum increase in average runoff rate (8%) is observed. Consequently, it can be inferred that the rise in runoff rate with increasing slope gradient diminishes substantially once the slope gradient exceeds 30\({}^{\circ}\).  
For a given slope gradient, the runoff rates associated with distinct rainfall intensities exhibit a descending order of 110 mm/h \(>\) 80 mm/h \(>\) 50 mm/h. Specifically, the runoff rate initially rises as rainfall intensity grows, followed by a stabilization of the runoff rate. This phenomenon is attributed to the low soil water content during the initial rainfall stage, leading to the transformation of rainfall into seepage. As the soil water content gradually increases, the slope water content approaches saturation, and the rainfall is converted into slope runoff.  
Compared to the slope gradient, rainfall intensity has a more significant effect on the runoff rate. Specifically, the escalation of rainfall intensity leads to an augmented amount of precipitation on the slope, leading to a corresponding increase in the runoff volume. In contrast, a rise in the slope gradient alters the slope's shape, resulting in greater downward acceleration and flow velocity of the water traveling down the slope.
## 6 Evaluation
### Ciphertext Inference  
In this subsection, we present the results of our trained model's ability to reason over ciphertexts. We implemented our proposed model using the Helayers library [34], released by IBM Research, which connects to the three underlying homomorphic cryptographic libraries--HELib, HEAAN, and SEAL. Our three models all use the HEAAN ciphertext, and our simulation environment is a cloud server with an Intel(R) Xeon(R) Gold 5218 CPU @ 2.30 GHz processor (48 cores) and 100 GB of RAM.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 4 Real-Time Experiments and Analysis
### Evaluation of ADRC3  
In evaluation of ADRC\({}_{3}(\overline{T}_{d})\) controllers with the ESO delay \(\overline{T}_{d}=T_{d}\) we will focus on choice of the ESO parameters \(T_{c}=T_{d}\), \(T_{c}=T_{d}/2\) and \(T_{c}=T_{d}/3\). Thereby, we will analyze, how these values can be combined with the tuning parameter \(T_{2}\). By doing so we will explore, if the ESO parameter \(T_{c}\) should be decreased with respect to the faster dynamics of the loop with stabilizing PDA controller expressed by the time constant (23).  
For the basic choice \(T_{c}=T_{d}/2\), the vector of ESO time constants has been specified according to  
\[T_{2}/T_{c}\in\{1/4,\ 1/2,\ 3/4\},\text{or}\ T_{2}/T_{d}\in\{1/8,\ 1/4,\ 3/8\} \tag{44}\]  
These values \(T_{2}/T_{d}\) have then also been applied for the alternative tuning with \(T_{c}=T_{d}\) and \(T_{c}=T_{d}/3\), when the fractions \(T_{2}/T_{c}\) no longer correspond to the values (44). Therefore, for simplicity, we prefer to keep the numbers \(T_{2}/T_{d}\) in legends in other situations as well.  
Figure 9 shows that with \(T_{c}=T_{d}\) the transients can change abruptly depending on the selected value of \(T_{2}\). With \(T_{2}=T_{d}/8<<T_{c}\), ESO can oscillate continuously. However, increasing the value to \(T_{2}=T_{d}/4\) here can already lead to high-quality transients. For \(T_{2}=3T_{d}/8\) the transients would yet be smoother.  
Because we are mainly interested in the question of whether it is possible to obtain faster and at the same time sufficiently smooth transients by using a higher order controller and ESO, we will focus on shorter values \(T_{c}=T_{d}/2\) and \(T_{c}=T_{d}/3\).  
Sample of experimentally measured transients in Figure 10 and evaluation of a complete set of achieved performance measures in Figure 11 confirm intuitive expectations that, in general, decreasing \(T_{c}\) increases the effect of noise.  
Figure 8: ADRC\({}_{2}\): Performance measures corresponding to the controller in Figure 3 and transients in Figure 7, \(T_{dm}=\overline{T}_{d}=T_{d}-T_{1}\).  
However, for small values of \(T_{2}\), shortening \(T_{c}\) leads to a reduction in the effect of noise, and for large values of \(T_{2}\), on the contrary, to an increase in their impact. It shows that the choice of these two parameters needs to be harmonized.  
The smallest used time constant \(T_{2}=T_{d}/8=0.0375\) s was only slightly larger than the sampling period \(T_{s}=0.02\) s. At \(T_{c}=T_{d}/2\), this was reflected in high excessive control effort and excessive output wobbling (Figure 11). At \(T_{c}=T_{d}\), auto-oscillations even occurred in the circuit. In general, when designing controllers, it is necessary to check the condition that the used time constants are significantly larger than \(T_{s}\) and to adapt the design of the controller or the selection of the corresponding hardware.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
###### Abstract  
The time synchronization of LTE-R train-to-ground communication systems plays an important role in ensuring the safety of high-speed railways. In the LTE-R time synchronization process, existing problems, such as the time synchronization message broadcast address and LTE-R all-IP architecture, are vulnerable to attack. In order to analyze the impact of these problems, we propose a new vulnerability analysis method of LTE-R time synchronization based on stochastic Petri nets. Firstly, we construct a stochastic Petri net model of an LTE-R time synchronization process under attack. Secondly, steady-state probability expressions are obtained using the model isomorphism Markov chain. Finally, bychanging the firing rate of several key vulnerable nodes, the relationship curve between the firing rate and the steady-state probability of the clock node is obtained. Simulations show that the vulnerability of LTE-R time synchronization is most affected by the attack on eNodeB of the LTE-R base station. The results can provide a certain theoretical basis for the evolution of high-speed railway GSM-R communication systems to LTE-R.  
high-speed railway; time synchronization; LTE-R; stochastic Petri nets; vulnerability analysis +
Footnote †: journal: Applied Sciences
## 5 Electrical Characterization
### PUF Metrics
#### 5.3.3 Entropy  
Entropy is the measurement of randomness in a system. In cybersecurity applications, entropy quantifies the number of possible key combinations from a PUF's CRPs, as shown in Equation (1). In this equation, \(E\) is the number of unique CRPs from a PUF, and \(r\) is the size of the key.  
\[log_{2}\binom{E}{r} \tag{4}\]  
While a PUF may have a large number of CRPs, the responses of a PUF should not be correlated to ensure a high level of security and randomness. Entropy density measures the amount of uncertainty or randomness in a sequence. It is a function of the distribution of the random variable. For PUFs, it represents a generalized and unconditional upper bound on the average predictability of an unobserved response. Ideally, an entropy value as close as possible to 1.00 is desired. To calculate the entropy, we used the Shannon entropy density function shown in Equation (2) and described in [40; 41]:  
\[H(X)=-\sum_{x}P(x)\log_{2}(P(x)) \tag{5}\]  
In this equation, \(P(x)\) represents the probability of the random variable X taking on the value x. The logarithm is usually taken to base 2 (\(\log_{2}\)) to measure the entropy in bits. The negative sign ensures that entropy is always non-negative. The Shannon entropy measures the average amount of information needed to encode the outcomes of a random variable. It is highest when all outcomes are equally likely, indicating maximum uncertainty, and lowest when there is only one possible outcome, indicating no uncertainty.  
To quantify the entropy of our MRAM PUF design, we generated a one million-bit-long key in both low- and high-resistance states. Additionally, we also generated a key filtered for ternary states and an unfiltered key. The results of these tests gave us **0.9994**  
Figure 5: BER vs. challenge-pair voltage difference vs. settling time vs. resistive state.  
(low resistance) and **0.995** (high resistance) for unfiltered keys, while yielding **0.99999** (low resistance) and **0.99999** (high resistance) for filtered keys.
# Lifetime Prediction of Single Crystal Nickel-Based Superalloys
## 2 Experimental Procedure and Methodology
### Lifetime Assessment
#### 2.3.3 Artificial Neural Networks (ANNs)  
ANNs are machine learning tools designed to establish the nonlinear relation between the given input and the desired output by imitating the human brain's neurological structure [28]. ANNs are composed of three main parts, as demonstrated in Figure 4.  
Figure 4: Structure of artificial neural networks designed for regression tasks, demonstrating input, hidden, and output layers.  
The input vector, \(x_{j}\), represents the independent variables affecting the output vector, \(y_{i}\). Hidden layers are used for establishing the nonlinear relationship between the input layer and the output layer. Hidden layers receive different independent variables from the input layer and modify this information by adjusting weights, \(w_{ij}\) and biases \(b_{i}\). The mathematical notation of the i-th neuron in a hidden layer as follows  
\[y_{i}=g_{i}\left(\sum_{j}w_{ij}x_{j}+b_{i}\right) \tag{18}\]  
The activation function, \(g_{i}\), introduces the nonlinearity to the system by processing the established linear relation by weights and biases. These functions are one of the main parameters used to control the output range and learning ability of artificial neurons. Since ANNs are built up in a feed-forward structure, the output produced from each neuron feeds into the next neuron until the prediction in the output neuron is established. The adjustment of weights and biases is performed by monitoring the output difference between the predicted and actual outcome using the backward propagation algorithm: gradient descent. ReLUs, demonstrated in Equation (19), are preferred in many cases as activation functions since they reduce the vanishing gradient problem and its derivatives are easier to compute. In the vanishing gradient problem, gradients tend to get smaller as the training progresses, slowing down the learning significantly or completely terminating it.  
\[ReLU(x)=max(0,x)\ i.e.,\ \ \left\{\begin{array}{ll}x,&\text{if }x>0\\ 0,&\text{if }x\leq 0\end{array}\right\} \tag{19}\]  
The difference between the actual output and predicted values in the output layers is computed by establishing a loss function, \(L\), between the two, such as mean square error. The loss function is minimized by taking its derivatives with respect to weights and biases in each iteration, \(k\), until an acceptable error between predicted and actual data is achieved. The mathematical expression of the loss function optimization, i.e., gradient descent as follows:  
\[b_{i}^{k+1}=b_{i}^{k}-\alpha\frac{\partial L}{\partial b_{i}^{k}} \tag{20}\]  
\[w_{ij}^{k+1}=w_{ij}^{k}-\alpha\frac{\partial L}{\partial w_{ij}^{k}} \tag{21}\]  
Convergence to the minimum value of the loss function is controlled by the learning rate value, \(\alpha\), which acts like the step size between each iteration.
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 3 Results
### Performance of the Hybrid RED-ED System
However, for longer experimental durations, it is anticipated that the reduction in the salinity gradient could negatively affect the hybrid system's performance, potentially diminishing the energy available to drive the ED process effectively. The desalination rate for each scenario was calculated as the difference between the initial and final conductivity of the desalinated water, and the results are presented in Table 5. In our study, the RED/ED system operated with an initial salt concentration of 10 g/L, achieving the highest efficiency of a 15% reduction in salt concentration within one hour at a flow rate of 80 mL/min in batch mode. This process had a specific energy consumption of 0.1388 Wh/m\({}^{3}\) and a recovery rate of 50%. In comparison, ref. [22] used a 50-cell RED/ED stack with initial concentrations of 30 g/L and 0.5 g/L in the high- and low-salinity compartments, respectively. Their system achieved over 90% desalination with an energy consumption of 0.004-0.065 kWh/m\({}^{3}\) by leveraging salinity gradients. Ref. [16] employed a PFED configuration with 20 RED cell pairs, reaching potable water standards at 72 uS/cm without external power. Similarly, ref. [15] used a hybrid RED/ED approach, achieving over 90% desalination for high-salinity wastewater. Unlike multi-cell setups, our single-cell configuration offers a simplified, effective method for moderate desalination targets, with practical energy efficiency at a 10 g/L salt concentration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ED Flow Rate (mL/min)** & **RED Mode** & **Salt Removal (\%)** \\ \hline
40 & Continuous & 12.28 \\
40 & Batch & 13.73 \\
80 & Continuous & 12.22 \\
80 & Batch & 15.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Salt removal efficiency under different operational conditions. Figure 10: Conductivity variation during ED desalination powered by RED under different operational modes: (**a**) conductivity behavior with ED operated at 40 mL/min under batch and continuous modes for the RED reactor; (**b**) conductivity response with ED operated at 80 mL/min under both batch and continuous RED modes.
## 5 Securing CPS
### Approach
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sr\#** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline
1 st-Fold & 99.5\% & 98.6\% & 99.1\% & 99.1\% \\
2 nd-Fold & 99.2\% & 98.7\% & 99.2\% & 98.6\% \\
3 rd-Fold & 99.1\% & 98.3\% & 99.3\% & 98.4\% \\
4 th-Fold & 99.8\% & 98.7\% & 99.9\% & 99.5\% \\
5 th-Fold & 100.0\% & 99.1\% & 99.8\% & 99.3\% \\
6 th-Fold & 99.6\% & 98.6\% & 99.7\% & 99.2\% \\
7 th-Fold & 99.4\% & 98.7\% & 99.6\% & 99.1\% \\
8 th-Fold & 100.0\% & 99.4\% & 99.5\% & 99.7\% \\
9 th-Fold & 99.2\% & 98.4\% & 99.4\% & 99.8\% \\
10 th-Fold & 99.7\% & 98.5\% & 99.7\% & 99.9\% \\ \hline Average & 99.60\% & 98.81\% & 99.16\% & 99.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 10-fold cross-validation results using MLP. Figure 5: Performance of the deep learning model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Proposed & Malicious & 99.62 & 98.89 & 99.24 & 99.08 \\ \hline SVM [40] & URL & 90.70 & 93.43 & 88.45 & - \\ \hline RF [40] & Detection & 96.28 & 91.44 & 94.42 & - \\ \hline Proposed & & 99.87 & 99.14 & 99.02 & 99.08 \\ \hline Deep Learning [41] & \multirow{2}{*}{KDD Cup 99} & 92.00 & - & - & - \\ \cline{3-5} Rule Based Model [41] & & 89.00 & - & - & - \\ \hline DT-RF [34] & & 99.21 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification result of all learning models.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
###### Abstract  
Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality's temporal and spatial distribution characteristics. In this study, Xi'an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi'an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model's hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model's accuracy and its promising application prospects.  
Bi-LSTM; CNN; PSO algorithm; air quality prediction; AQI +
Footnote †: journal: Applied Sciences  
0
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# Feature Interaction in Terms of Prediction Performance
## 1 Introduction
The H-statistics used to measure the interaction between feature \(A\) and \(B\) are defined by  
\[H_{AB}^{2}=\sum_{i=1}^{n}\left[PD_{AB}(x_{A}^{(i)},x_{B}^{(i)})-PD_{A}(x_{A}^{( i)})-PD_{B}(x_{B}^{(i)})\right]^{2}/\sum_{i=1}^{n}PD_{AB}^{2}(x_{A}^{(i)},x_{B}^{( i)}) \tag{1}\]  
where \(PD()\) is a partial dependence function. The variable interaction network method, introduced by [PERSON], decomposes the prediction function into the main effects and feature interactions [PERSON] et al. introduced the partial dependence-based feature importance and interaction measures [PERSON] et al. (2000), which facilitates the measurement between two features. This measures feature interaction between feature \(A\) and \(B\) with the following expressions:  
\[\mathrm{S}_{A}=sd(\mathrm{i}(A)|B_{\mathrm{j}}) \tag{2}\]  
\[\mathrm{S}_{B}=sd(\mathrm{i}(B)|A_{\mathrm{j}}) \tag{3}\]  
\[\mathrm{Interact}(A,B)=(\mathrm{S}_{A}+\mathrm{S}_{B})/2 \tag{4}\]  
where \(sd\) is a standard deviation; \(\mathrm{i}(A)\) is a feature importance of \(A\); \(\mathrm{j}=1\), \(2\), \(3\),..., \(n\); and \(A_{i}\) is an \(i\)-th feature value of \(A\). Figure 1: Example of a partial dependence plot (PDP). (**a**) The data values in the range [0, 1] of a feature “Petal.Width” show high prediction performance, while those in the range [1.5, 2.0] show low prediction performance. (**b**) The light yellow region indicates the data range of two features that produces a high prediction performance, whereas the dark blue region represents a low prediction performance data range. The Shapley value, which was introduced by [PERSON], shows the contribution of each feature toward the interpretation of a single prediction result [5]. Each feature in the Shapley value is considered to be a \"player\" in a game. The Shapley value ensures a fair distribution of the \"payout\" among all features. Figure 2 shows an example of a Shapley value plot. [PERSON] has presented an in-depth discussion of model interpretability that covers various methods discussed above [18] as well as implementing the iml package [19] for R and Python that contains various functions related to model interpretability. In this paper, a new measure for feature interaction based on prediction performance is proposed. This study tries to understand feature interaction as a variation of prediction performance. In recent studies, feature interaction has been measured using partial dependency, wherein feature combination is used to measure the partial dependency variance [7; 8; 17]. Although the concepts of partial dependency are clearly known, it is difficult to intuitively understand how it impacts prediction performance. This necessitates the need for more lucid feature interaction measures to explain prediction performance. Prediction performance has emerged as a new candidate in feature interaction measurement. While various combinations of feature interactions exist in a model, this study will focus on the interaction between any two features.
# On the Subrange and Its Application to the R-Chart
## 2 The Distribution of the Subrange
### The Unbiasing Factors for the Subrange  
Using the pdf of the range in (2), we can obtain the \(l\)-th moment of the range by calculating the expectation as follows:  
\[E\Big{(}Y_{2}^{l}\Big{)}\ =\ \int_{0}^{\infty}y_{2}^{l}g_{[k]}(y_{2})dy_{2}\]  
We denote  
\[d_{2}(n,k)\ =\ E(Y_{2}). \tag{3}\]  
Let \(X_{i}\) be a sample from the normal distribution \(N\big{(}\mu,\sigma^{2}\big{)}\). Since \(\sigma\) is a scale parameter, we have \(X_{i}\ =\ \sigma Z_{i}+\mu\), where \(Z_{i}\) is a sample from the standard normal distribution \(Then we have \(E\Big{(}R_{[k]}\Big{)}\ =\ E\Big{(}X_{(n-k)}-X_{(k+1)}\Big{)}\ =\ d_{2}(n,k)\sigma\). Thus, we can obtain the unbiased scale estimator of \(\sigma\) using the subrange which is given by  
\[\widehat{\sigma}_{[k]}\ =\ \frac{R_{[k]}}{d_{2}(n,k)}. \tag{4}\]  
Note that the unbiasing factor, \(d_{2}\) in the quality control literature is obtained by \(d_{2}\ =\ d_{2}(n,0)\), so that \(\widehat{\sigma}_{0}\ =\ R/d_{2}\). Next, we denote  
\[d_{3}(n,k)\ =\ \text{SD}(\gamma_{2})\ =\ \sqrt{E\big{(}\gamma_{2}^{2}\big{)}-E \big{(}\gamma_{2}\big{)}^{2}}. \tag{5}\]  
Then, we have \(\text{SD}\Big{(}R_{[k]}\Big{)}\ =\ d_{3}(n,k)\sigma\). Also, the unbiasing factor \(d_{3}\) in the quality control literature is also given by \(d_{3}\ =\ d_{3}(n,0)\).  
In Tables 1 and 2, we provide the values of \(d_{2}(n,k)\) and \(d_{3}(n,k)\), respectively. For brevity, we provide the values for \(n\ =\ 2,3,\cdots 50\) and \(k\ =\ 0,1,\cdots,9\) in the tables. The R language program is provided in the online supplement. Subrange. Available online:  
[[https://github.com/jin-yuyu/subrange.git](https://github.com/jin-yuyu/subrange.git)]([https://github.com/jin-yuyu/subrange.git](https://github.com/jin-yuyu/subrange.git)) (accessed on 1 December 2021).
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods  
Known from experience of the length measurement, as for a small space of 0.1 mm of length, the measurement repeatability component is the main part of measurement uncertainty. Thus, the measurement to improve the measurement repeatability is important. The two aspects of optical and mechanical factors and electronic factors to improve measurement repeatability are mainly studied in this paper.