esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Combined with the rainfall and ponding monitoring data in the study area, a multi-step and multivariable ponding prediction model is established based on the LSTM method. The performance of three loss functions (MSE, MAE and MSLE) in the rainfall-ponding prediction model was compared, and the influence of rolling step on the prediction result of water accumulation in the next hour was also explored.
## 3 Results and Discussion
### Comparison of Training-Time between IFOA-LS-SVM Model and MR, SVM, LS-SVM Models  
In order to further demonstrate the advantage of IFOA-LS-SVM model in terms of training-time, comparisons of the values of training-time for the MR, SVM, LS-SVM and IFOA-LS-SVM models were listed in Table 5.  
As can be seen, the training-time value of IFOA-LS-SVM model was \(2.298\) s, which was much shorter than that obtained by MR and SVM models (which were \(16.863\) and \(27  
Figure 14: Residual error of the values predicted by IFOA-LS-SVM and MR models from the experimental values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **MR** & **SVM** & **LS-SVM** & **IFOA-LS-SVM** \\ \hline Training-time (s) & 16.863 & 27.652 & 3.247 & 2.298 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training-time comparison between SVR, LS-SVM and IFOA-LS-SVM models.  
Figure 13: Cross-correlation of predicted and experimental values of quercetin yield for: IFOA-LS-SVM model (**a**); and multiple regression (MR) model (**b**).  
as well as shorter than that obtained by LS-SVM model (which was 3.247 s). It indicated that the improved fruit fly optimization algorithm by optimizing the model parameters can speed up the training speed of LS-SVM model and therefore can shorten the training-time of model.  
In conclusion, compared to MR, SVM and LS-SVM models, the IFOA-LS-SVM model has highest prediction accuracy with very narrow deviations between the predictive values and experimental values, as well as a shortest training-time, which outperforms the MR, SVM and LS-SVM models in the UAE process forecasting. Furthermore, the proposed model can enormously save resources and manpower by reducing the number of essential experiments. Despite the advantages of the proposed model, few studies have reported to modeling for the UAE process by reducing the number of experiments. Therefore, the proposed model is a hopeful and useful approach to accelerate and enhance the UAE process for different nature products.
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
## 1 Introduction
However, use of fluoride in water-borne preservative formulations has steadily declined. One of the limitations of fluoride compounds such as sodium fluoride (NaF) and sodium fluorosilicate (Na\({}_{2}\)SiF\({}_{6}\)) is their solubility which makes them highly susceptible to leaching. Recent research on NaF treatment in wood with ground contact reported that more than 90% of fluoride was lost which resulted in more than 10% mass losses against termites, _Reticulitermes flaviceps_[19]. Also, the reported higher leachability of NaF was in agreement with results on NaF-treated particleboard [20]. Loss of fluoride from treated wood reduces the formulations protective capacity against fungal decay in outdoor applications where exposure to precipitation cannot be avoided. It is known that fluorides that are not fixed in wood with chromium compounds are highly leachable [18]. However, restrictions were introduced on the use of chromium by the European Commission that governs the use of biocides [21]. These regulations have limited the use of chromium and thereby resulted in the decline of fluoride-based preservatives. An alternative to conventional metal fluorides used in preservative formulations would be low-water soluble metal fluorides such as MgF\({}_{2}\) and CaF\({}_{2}\). Their biocidal efficacy potential was reported in 1926 [22]. However, until recently no results were reported on durability of wood treated with these fluorides. A major limitation of treating wood with crystalline, low- water soluble fluorides is their size; they are too large to penetrate in wood cavities. This limitation was circumvented by accessing their nanoscopic form via fluoride sol-gel synthesis [23]. The nanoparticles of metal fluorides also known as nano metal fluorides (NMFs) were characterized by XRD and TEM which confirmed their composition and nano-size [23]. The resultant NMF treated wood was characterized by back-scattered electron images that showed aggregates of NMFs homogeneously distributed in the wood matrix. In addition, nano metal fluorides showed efficacy against brown-rot fungi, _Contiophora putteana_ and termites, _Cototermes formosanus_ with mass losses below 3% [24]. However, the NMF treatment did not provide sufficient protection from Rhodonia placenta_[23]. The results presented here were obtained from optimized NMF formulations that fully protected treated wood against _R. placenta_. In addition, the efficacy of nano metal fluorides was tested against white-rot fungus, _Trametes versicolor_. Therefore, the following presents the durability of NMF treated wood against brown-rot fungus, _R. placenta_ and white-rot fungus, _T. versicolor_.
# Motor Vector Control Based on Speed-Torque-Current Map
## 1 Introduction
The dynamic model and control of the motor can fully respond to the whole process of mode operation and mode switching, which is in good agreement with the actual operation process of the motor. The combination of dynamic control and the multi-power vehicle model can reflect the characteristics of speed-torque and energy consumption. Exploring the influence of dynamic control on the performance of multi-power vehicles can be used to guide the formulation of the control strategy of a multi-power coupled electric drive system. The rest of the paper is organized as follows. Section 2 introduces the electric drive system configuration and the main parameters that are used in the subsequent strategy. In Section 3, a novel vector control strategy of PMSM based on the speed-torque-current map is proposed. Then, the proposed control strategy is validated in Section 4 to evaluate the results of the running of the motor controlled by the new strategy.
###### Abstract  
Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.  
COVID-19; social mobility patterns; socioeconomic and demographic characteristics; smart device tracking; California +
Footnote †: journal: Medical Informatics
## 4 Discussion
### Limitations and Strengths  
The most important limitation of our study was that PALs were not directly measured but instead self-reported by participants. Next, physical fitness and anthropometric variables were collected almost three months before the baseline measurement of PAL. Additionally, this study involved participants from one region in Croatia, and therefore the results are only generalizable to similar samples. The last limitation is particularly important given the climate in the studied region (Mediterranean region), and that during the observed time, the lowest temperature was rarely below 10 \({}^{\circ}\)C; as such, the weather was ideal for outdoor activities (that were not strictly prohibited if social-distancing was respected).  
Our study is one of the rare studies where PAL and changes in PAL in the period during the COVID-19 pandemic are shown specifically for urban and rural adolescents. The physical fitness variables were tested as part of a well-organized project by experienced evaluators, and the results may be observed as plausible and objective. Therefore, we think that our results will contribute to the knowledge in our field and encourage further research.
# SMC-CPHD Filter with Adaptive Survival Probability for Multiple Frequency Tracking
## 2 Processing Steps of the SMC-CPHD Filter  
The CPHD filter is used for tracking multiple frequencies and is designed with an SMC structure [10; 23]. This section presents a brief review of the SMC implementation in the CPHD filter with the target birth density driven by measurements [23]. Suppose at time \(k\) there are \(n_{k}\) targets with states \(\mathbf{x}_{k,1},\ldots,\mathbf{x}_{k,n_{k}}\) in a state space, \(X\subseteq\mathbb{R}^{n_{x}}\), and \(m_{k}\) measurements, \(\mathbf{z}_{k,1},\ldots,\mathbf{z}_{k,m_{k}}\), in an observation space, \(Z\subseteq\mathbb{R}^{n_{x}}\). Then, a multiple target state and a multiple target observation are, respectively, defined as the finite sets: \(\mathbf{X}_{k}=\{\mathbf{x}_{k,1},\ldots,\mathbf{x}_{k,n_{k}}\}\in F(X)\) and \(\mathbf{Z}_{k}=\{\mathbf{z}_{k,1},\ldots,\mathbf{z}_{k,m_{k}}\}\in F(Z)\). The state vector, \(\mathbf{x}=(\mathbf{y},\beta)\), consists of the usual kinematic components denoted by \(\mathbf{y}\) and a label \(\beta\), which distinguishes between a newborn target (\(\beta=1\)) and a persistent target (\(\beta=0\)) [23]. The PHD intensity function, \(D_{k|k}(\mathbf{x}|\mathbf{Z}_{1:k})\triangleq D_{k|k}(\mathbf{x})\), is referred to as \(\int\delta_{\mathbf{x}}(\mathbf{x})f_{k|k}(\mathbf{x}|\mathbf{Z}_{1:k}) \delta\mathbf{x}\). \(\rho_{k|k}(n|\mathbf{Z}_{1:n})\triangleq\rho_{k|k}(n)\) is the cardinality distribution at time \(k\) and \(\rho_{\Gamma,k|k-1}(n)\triangleq\rho_{\Gamma}(n)\) is the cardinality distribution of new targets at time \(k\). The CPHD filter jointly propagates \(D_{k|k}(\mathbf{x})\) and \(\rho_{k|k}(n)\). In this Algorithm 1, as the adaptive survival probability is not applied, the assumption is that the survival probability is constant, \(p_{S}(\mathbf{x})=p_{S}\). At time \(k=0\), it is assumed that \(\rho_{0|0}(n)=1\) if \(n=0\), and \(\rho_{0|0}(n)=0\) if \(n=1,2,\ldots,n_{\max}\) (\(n_{\max}\) denotes the targets' maximum anticipated number), and \(D_{0|0}(\mathbf{y},0)=D_{0|0}(\mathbf{y},1)=0\). The intensity function, \(D_{k|k}(\mathbf{x})\), is approximated by using the random particles. At time \(k-1\):  
\[D_{k-1|k-1}(\mathbf{y},0)\approx\sum_{n=1}^{N_{k-1}^{p}}w_{k-1,p}^{(n)}\delta _{\mathbf{y}_{k-1,p}^{(n)}}^{(n)}(\mathbf{y},1) \tag{1}\]\[D_{k-1|k-1}(\mathbf{y},1)\approx\sum_{n=1}^{N_{k-1}^{b}}w_{k-1,b}^{(n)}\delta_{ \mathbf{y}_{k-1,b}^{(n)}}(\mathbf{y}) \tag{2}\]  
where \(\delta_{\mathbf{y}_{0}}(\mathbf{y})\) represents the Dirac delta function [8]\(\left\{\left(w_{k-1,p^{\prime}}^{(n)}\mathbf{y}_{k-1,p}^{(n)}\right)\right\}_{n =1}^{N_{k-1}^{p}}\) and \(\left\{\left(w_{k-1,p^{\prime}}^{(n)}\mathbf{y}_{k-1,b}^{(n)}\right)\right\}_{ n=1}^{N_{k-1}^{b}}\) denote the weighted particle sets for persistent targets and newborn targets. \(N_{k-1}^{p}\) and \(N_{k-1}^{b}\) define the number of persistent target particles and newborn target particles.
# SSNet: Learning Mid-Level Image Representation Using Salient Superpixel Network
## 3 The Proposed Method for Image Representation
### Image Representation with the Weighted Second-Order Pooling
#### 3.2.2 Weighted Second-Order Pooling  
In order to introduce the second-order information, two novel feature pooling methods will be presented, which are based on a collection of independent superpixels.  
Given an image, we segment it into a set of \(M\) superpixel regions \(R=(R_{1},\cdots R_{M})\) by the simple linear iterative cluster (SLIC) method [54]. Assuming that there exist \(N_{i}\) feature descriptorsin the region \(R_{i}\), we encode them with the method \(\varphi(\cdot)\) and obtain the corresponding coding matrix \(\Phi_{i}=\left[\varphi_{1},\cdots,\varphi_{N_{i}}\right]\). According to Equation (25), the related weighting vector \(w_{i}\) can be determined.  
Based on Equation (19), the weighted first-order pooling \(\
u_{i}\) for the region \(R_{i}\) is defined as:  
\[\
u_{i}=\Phi_{i}w_{i} \tag{26}\]  
which can be considered as a representation vector for the region \(R_{i}\).  
For an image, we define the weighted second-order average pooling (_WSOAP_) as:  
\[WSOAP=\frac{1}{M}\sum\
olimits_{i=1}^{M}\
u_{i}\
u_{i}^{T}. \tag{27}\]  
Here, the result of \(\
u_{i}\cdot\
u_{i}^{T}\) is a matrix, which contains the second-order statistical information about the features inside the region \(R_{i}\). The \(WSOAP\) captures the second-order information included in the \(M\) regions of an image.  
Correspondingly, we define the weighted second-order max pooling (_WSOMP_) as  
\[WSOMP=\underset{i=1:M}{max}\left\{\
u_{i}\cdot\
u_{i}^{T}\right\} \tag{28}\]  
where the max operation acts on the corresponding elements with the same position of the matrices resulting from the outer products of the weighted first-order pooling vectors.
## 4 Pairwise Clock Synchronization under Exponential Delays
### Clock Offset Estimation under Exponential Delays
#### 4.1.1 Maximum Likelihood Estimator
Towards this end, the density functions of \(U_{i}\) and \(V_{i}\) are expressed as:  
\[p(U_{i}|\xi) = \lambda_{1}^{N}\exp\left(-\lambda_{1}\sum_{i=1}^{N}(U_{i}-\xi) \right)I(U_{(1)}-\xi) \tag{11}\] \[p(V_{i}|\psi) = \lambda_{2}^{N}\exp\left(-\lambda_{2}\sum_{i=1}^{N}(V_{i}-\psi) \right)I(V_{(1)}-\psi) \tag{12}\]  
The resulting MLE of \(\xi\) can be formulated as a convex optimization problem:  
\[\hat{\xi}_{\text{ML}}= \arg\max_{\xi}\exp(N\lambda_{1}\xi)\] \[\text{s.t.}\ \ \xi\leq U_{(1)}\]  
and it follows that \(\hat{\xi}_{\text{ML}}=U_{(1)}.\) The analysis of \(\psi\) is analogous to the case of \(\xi\), and it yields that \(\hat{\psi}_{\text{ML}}=V_{(1)}.\) Thus, the MLE of \(\theta\) follows from Equation (10) and admits the form:  
\[\hat{\theta}_{\text{ML}}=\frac{U_{(1)}-V_{(1)}}{2}\]  
which coincides with the result in Equation (8). The advantage of this analytical approach is that it provides a more general derivation of MLEs, which can by applied to obtain the MLEs for the delays under other types of distributions, which include Gaussian and log-normal as special cases [29].
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods  
### Study Design  
Twelve male students with science and engineering backgrounds were recruited for this experiment. All of them passed the personality psychological test and signed the informed consent forms. The mean age of the participants was 23.250 \(\pm\) 2.314 years, and their mean body mass index (BMI) was 22.57 \(\pm\) 1.95 \(\text{Kg}/\text{m}^{2}\). The subjects were healthy, right-handed, with normal or corrected vision, normal hearing, and normal electrocardiograph results (ECG). Before the experiments, participants received sufficient training on the tasks. The subjects had adequate sleep and a good mental state, abstaining from drinking any stimulant substances such as caffeine and alcohol and did not take any drugs before and during the experiments. All participants completed the tests without dropping out, and accepted financial compensation after the experiments.  
The twelve subjects were divided into three groups: there were four subjects in each group. Each subject was exposed to one noise condition every day and completed all test assignments at the same time for three consecutive days. The three noise exposure sequences were counterbalanced. In addition, the MATB task with three different mental workload levels was also counterbalanced by traversing all possible sequences at three noise conditions. The research protocol has been approved by the Institute Review Board (IRB) of Beihang University.
# Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace
### Vigenere Cipher  
The Vigenere cipher is a poly-alphabetic substitution method developed by [PERSON] in the 16 th century [20]. Cipher substitution algorithms are divided into two categories, where one is mono-alphabetic, and the other is poly-alphabetic [21]. Various mono-alphabetic and poly-alphabetic substitution algorithms are shown in Figure 2. The difference between mono-alphabetic and poly-alphabetic algorithms is that when the data are encrypted with plaintext and key, a fixed cipher value is obtained in a mono-alphabetic algorithm. In a poly-alphabetic algorithm, the resulting cipher value is not fixed when the plaintext and key are mapped to the algorithm [22]. When the plaintext is encrypted using the Vigenere cipher algorithm, the Vigenere table is used to obtain the ciphertext. A Vigenere table consists of special symbols, numeric keys, and alphabets. In this table, the number of rows and columns should be equal. Whenever data are encrypted using the Vigenere cipher, key and plaintext are used, and the value obtained from both is considered the cipher value.  
Several compatible algorithms are available for converting data from plaintext to ciphertext that can be used to broadcast the data in a secure form, but with the help of such an algorithm, data can be encrypted and decrypted with a single key. If an attacker steals the key through any mechanism, the attacker can access all kinds of data, which is a huge problem. Many algorithms can be used for data encryption, but such algorithms are unreliable for data broadcasting. One of them is the Vigenere cipher algorithm. In the Vigenere cipher algorithm, plaintext is written in a vertical position (column) while the key is written in a horizontal position (row). The main problem of the Vigenere cipher algorithm is that when column (plaintext) and row (key) intersect, a value is obtained called  
Figure 1: Data Transmission between clients.  
Figure 2: Cryptography Algorithms.  
the Vigenere cipher value. If these algorithms are made efficient, these algorithms can provide better transmission than all other efficient algorithms. An algorithm was developed in this paper to make the Vigenere cipher algorithm, in which one static key is used to encrypt the data, efficient. In contrast, two different keys are used to decrypt the data in the proposed algorithm. If the proposed algorithm is used for encryption, the data can be protected from an attacker and can also be protected from key-snatching mechanisms such as the Kasiski test.
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 6 Conclusions  
The experimental results obtained in the SDR environment show that a cognitive protocol can be implemented to protect the primary user in MCRN by combining GNURadio, Python, and the DNN libraries. The CSS is used to detect the _PUE attack_, and according to the results, it works better with low SNR values. In combination with an entropy detector, it shows a higher probability of detection than all the systems, including the SVM. The implemented PUE detection solution increases the PD by about 10% for an SNR value of \(-18\) dB and more than 20% at \(-21\) dB, using the three SUs as detectors in the CSS scheme. If more than 90% PD is needed, this can be achieved with the DNN algorithm for an SNR value of \(-21\) dB or higher.  
The learning process takes several minutes to measure the variables at several distances and SNR values. Still, once it is performed, the detection system can be conducted significantly faster, which prevents the interference of the PU, which is one of the objectives of the MCRN. The system can release the channel in milliseconds, using the energy detector as the first step. At the same time, it is analyzed to determine if there is a PUE present in the radio environment. The use of the DNN to detect the PUE works better than other methods found in the literature [9].  
Using the CSS gives us more coverage in the MCRN and allows us to obtain higher probability values for detection. Thanks to the DNN algorithm, high detection results are achieved even with slow SNR values. These results are similar to the values found in the literature. The system successfully detects the _PUE attack_ signals individually and in a CSS scheme. In this case, the OR rule is better to have a better knowledge of the radio environment, separating the _PUE attack_ signal and the MU efficiently because the FC has more data from the attacks detected on each SU, avoiding PUE attacks and errors in the network. A blacklist helps the system detect a previous PUE attacker without all the processes.  
The methodology, algorithms, and testbed proposed in this paper have been conceived by E.C.M.; the experiment scenarios by E.C.M., G.C.P. and R.C.-S.; testing by E.C.M., G.C.P., R.C.-S., A.A.-M. and M.E.B.; results and conclusion, review, and edition by E.C.M. and R.C.-S. and A.A.-M. All authors participated in the discussion and proofreading work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad Santo Tomas, Proyecto 2253501-AI, 2022.  
Not applicable.  
We express our gratitude to Universidad Santo Tomas for the founding of this project.  
The authors declare no conflict of interest.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
###### Abstract  
The distribution network is extended throughout cities, towns, and villages. Because of the increase in loads and the decrease in power passageways and public corridor reduction, double circuit lines are increasingly being used instead of single circuit lines. Fault location in double circuit power networks is very important because it decreases the repair time and consequently the power outage time. In this paper, a new improved method for fault location in double circuit medium power distribution lines is proposed. The suggested impedance-based fault location method takes into account the mutual effect of double circuit lines on each other. To the best of our knowledge, the proposed method is the first of its kind which supports double circuit distribution networks. In the proposed method, a new quadratic equation for locating fault in power distribution networks is obtained using recorded voltage and current at the beginning of feeder. In this method, the \(\pi\) line model is used for improving the accuracy of the suggested method. The proposed method is supported by mathematical proofs and derivation. To evaluate the accuracy of the proposed method, the proposed method is tested on a thirteen-node network in different conditions, such as instrument error, various fault resistances, and different fault inception angles in various distances and fault types. The numerical results confirm the high accuracy and validity of the proposed method.  
fault location; double circuit line; distribution networks; impedance-based method +
Footnote †: journal: Applied Sciences