esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
The RAKE processing output includes the residual Doppler shift, which may be expressed as Equation (4):  
\[z_{k,i}=\sum_{i\in C}\widetilde{c}_{i}exp(jf_{d,k}),\ 1<i<N,\ 1<k<N_{b} \tag{4}\]  
where \(z_{k,i}\) denotes the \(i\)-th RAKE output at the \(k\)-th band. \(\widetilde{c}_{i}\) denotes the \(i\)-th de-spreading bit, and \(f_{d,k}\) denotes the residual Doppler shift at the \(k\)-th band. After RAKE processing, a decision feedback equalizer (DFE) removes multi-path interference from each band [19]. The output of the DFE at time \(t\) for \(k\)-th band, \(z_{k}(t)\), is given by:  
\[z_{k}(t)=\sum_{i=0}^{m_{c}-1}c_{i}[t]z_{k,t-i}-\sum_{j=0}^{m_{b}-1}b_{j}[t]z_{k }[t-j] \tag{5}\]  
where \(c_{i}[t](i=0,1,\cdots,m_{c}-1)\) are the forward equalizer tps at time t, \(b_{j}[t](j=0,1,\cdots,m_{b}-1)\) are the feedback tps at time \(t\). The LMS updates the algorithm for the feedforward and feedback filter taps [19]. In the multi-band configuration, the output signal of each band is combined, and they are input into the decoder. In combining process, the multi-band configuration may have worse performance than a single-band one. This is because the performance degradation in a particular band affects the output from all bands [20]. This problem can be solved by the proposed EC-BER algorithm, which analyzes the error rates of each band, sets threshold values, and allocates lower weighting values to inferior bands. EC-BER algorithm is a method of analyzing the reliability of the received data based on the performance difference between demodulated and decoded data. Here, the input symbol of the decoder can be expressed by Equation (6):  
\[L_{e}^{D}=\sum_{k=1}^{N_{b}}W_{k}z_{k}(t) \tag{6}\]  
After threshold detection, a weighting value \(W_{k}\) for the \(k\)-th band is assigned. The extrinsic value \(L_{c}^{D}\) that calculates the post-probability is the error-correction term. The re-interleaving of the computed value as \(L_{c}^{D}-L_{c}^{D}\) is input to DFE. Then, \(L_{c}^{I}\) is updated to compensate for the errors. As the number of iterations in turbo equalization increases, the updated information may approach the original signal to be transmitted, thereby improving the BER performance [14].
# Settlement Prediction of Footings Using \(V_{s}\)
## 3 Centrifuge Study
### Results and Discussion
#### 3.2.3 Comparisons of Load-Settlement Curves with Predictions
As the plastic deformation of soil was not considered in the developed settlement prediction method, the predictions and load-settlement curves from centrifuge tests showed greater discrepancies, like in the case of the 1 st loading stage. On the other hand, predictions using \(I_{z}\) from [PERSON] et al. [6] gave similar global load-settlement behavior with the centrifuge test results up to the normalized settlement of 3% even though there were still slight differences before and after the maximum previous loading. Given that the settlement prediction method developed by [PERSON] et al. [6] is a semi-empirical method based on a combination of elasticity theory, laboratory tests and numerical analysis, there is a possibility that [PERSON]'s varying triangular \(I_{z}\) dependent on applied pressure had been developed to consider the nonlinear deformation behavior of soil over the elastic limit empirically for simple use in engineering, rather than using the relatively complicated plasticity theory. Therefore, the use of the \(I_{z}\) from [PERSON] et al. [6] generally results in an approximate solution throughout the entire strain region despite the fact that the modulus was converted from \(V_{S}\) not \(q_{c}\) in this study. Figure 7: Comparisons of load-settlement curves with predictions (\(L/B\) = 1.0): (a) 1 st loading; (b) 2 nd loading.
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 3 Results
### Sensitivity of the Model to Changes in Demand  
To analyze how sensitive the management model is to different changes in the energy demand of the microgrid, Figure 2 shows the values of the objective function and the percentage of renewable energy used for different levels of demand as long as the installed capacity of renewable production is constant. It shows that, at different levels of demand, the management algorithm uses renewable energy resources in order to satisfy as much demand as possible through them.  
Additionally, it is evident that since there is a relative increase in demand of 25% for each one of the three experimental levels, the decrease in the use of renewable sources to cover the demand is proportional. This shows that the percentage of demand coverage by renewable sources is determined by factors other than the level of demand, which are probably associated with the installed capacity of renewable sources.  
For the special case in which within one hour period the demand is as its maximum peak during the day, the model handles demand coverage in a similar way as it does with medium load because it is capable of reconfiguring the battery system's usage processes to meet peak demand.  
For its part, the operating cost of the microgrid by making use of renewable sources is lower compared to the use of diesel to fully satisfy the demand. This represents a saving in economic terms that is presented in the second part of Figure 2. In this case, a concordance is obtained in terms of demand coverage and savings generated by the use of renewable energies to satisfy different levels of demand from the same installed production capacity.  
Figure 1: Microgrid load management over the time horizon (24 h).
## 4 Simulation
### Link Load  
Our third set of experiments measured the link load. We calculated the number of flows traversing through each link. Due to encapsulation, a given link may be traversed multiple times by a single flow, and these events were counted. We used the load of the link with the largest load among all links to evaluate the performance in terms of link load. We used a topology with 100 routers and set the load threshold to five in this set of experiments. In the experiment shown in Figure 8a, the number of groups and number of receivers were set to 100 and 20. In the experiment shown in Figure 8b, the number of groups and the number of sources per group were set to 100 and 20. In the experiment shown in Figure 8c, the number of sources and the number of receivers were set to 20 and 20.  
Figure 7: The comparison of the maximum number of join requests. (**a**) The variation trends of the maximum number of join requests with a different number of receivers; (**b**) The variation trends of the maximum number of join requests with a different number of groups.  
From Figure 8a-c, we can find that as the number of sources, receivers, or groups increased, the maximum link load in all multicast methods increased. However, the maximum link load in ILDM is less than that in other multicast methods. First, in ILDM, we use a dynamic core selection mechanism to dynamically allocate a low-load core to each multicast group, thereby distributing the traffic sent by the multicast sources to different cores. Then, we used a path state-aware multicast tree node selection mechanism to select the MTN with the best path state for each receiver to join. In this way, the multicast traffic is distributed to low-load links. However, other multicast methods always use the shortest path between the source or core and each receiver to construct the multicast tree without considering the link state. This results in many multicast flows passing through the same link and underutilizing the low-load links. In addition, in PIM-SM and MCT, all multicast traffic needs to be aggregated to one or several cores, which will cause traffic aggregation on these cores. Although the maximum link load in SBT was close to that in ILDM, from the results of other experiments, we can find that ILDM has advantages over SBT in terms of the number of join requests, traffic concentration, and routing state.
## 5 Simulations, Results, and Discussions
### Suspicious Scenario  
This scenario represents a high average arrival rate that might be either normal (e.g., dense areas) or Suspicious (e.g., the launching of an attack).  
In this scenario,  
* We keep using the same resources for the LTE system (\(C=3\)).
* We increase the average arrival rate of \(R_{R}\) to be (\(\lambda_{1}=2\)).
* We also increase the average arrival rate of \(M_{R}\) to be (\(\lambda_{2}=2\)).
* We decrease the service rate of \(R_{R}\) to be (\(\mu_{1}=2\)).
* We also decrease the service rate of \(M_{R}\) to be (\(\mu_{2}=2\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Steady-State Probability** & **Probability Value** & **Phase** \\ \hline S(0,0) & \(\pi\)(0,0) & \(162/314=51.6\%\) & Initial \\ S(0,1) & \(\pi\)(0,1) & \(54/314=17.2\%\) & Authentic \\ S(0,2) & \(\pi\)(0,2) & \(9/314=2.86\%\) & Authentic \\ S(0,3) & \(\pi\)(0,3) & \(1/314=0.3\%\) & Suspicious \\ S(1,0) & \(\pi\)(1,0) & \(54/314=17.2\%\) & Authentic \\ S(1,1) & \(\pi\)(1,1) & \(18/314=5.73\%\) & Authentic \\ S(1,2) & \(\pi\)(1,2) & \(3/314=0.95\%\) & Suspicious \\ S(2,0) & \(\pi\)(2,0) & \(9/314=2.86\%\) & Authentic \\ S(2,1) & \(\pi\)(2,1) & \(3/314=0.95\%\) & Suspicious \\ S(3,0) & \(\pi\)(3,0) & \(1/314=0.3\%\) & Suspicious \\ \hline \hline \end{tabular}
\end{table}
Table 2: The probability values for each state S(\(i\),\(j\)) in the normal cycle, where “S(\(i\),\(j\))” represents different states, \(\pi\)(\(i\),\(j\)) is the Steady-state probability, “\(i\)” is the number of ongoing services for Read Requests (\(R_{R}\)), and “\(j\)” is the number of ongoing services for Modify Requests (\(M_{R}\)).  
Figure 10 shows the results and percentages of different phases in the Suspicious scenario:  
* Initial phase probability \(=\pi_{(0,0)}=16\%\).
* Authentic phase probability \(=\pi_{(0,1)}+\pi_{(1,0)}+\pi_{(0,2)}+\pi_{(1,1)}+\pi_{(2,0)}=63\%\).
* Suspicious phase probability \(=\pi_{(0,3)}+\pi_{(1,2)}+\pi_{(2,1)}+\pi_{(3,0)}=21\%\).
# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 7 Conclusions and Future Work  
The paper proposes a meta-learning-based Mandarin-Tibetan cross-lingual TTS to realize both Mandarin and Tibetan speech synthesis under a unique framework. We first build two kinds of Tacotron2-based Mandarin-Tibetan cross-lingual baseline TTS, including the shared encoder Mandarin-Tibetan cross-lingual TTS and the separate encoder Mandarin-Tibetan cross-lingual TTS. Then, to further improve the synthesized speech quality of Tacotron2-based Mandarin-Tibetan cross-lingual TTS, we propose the meta-learning-based Mandarin-Tibetan cross-lingual TTS using an additional dynamic network to predict the parameters of the language-dependent text encoder. Finally, we synthesize Mandarin speech or Tibetan speech through the unique acoustic model. The baseline experimental results show that the separate encoder Mandarin-Tibetan cross-lingual TTS can handle the input of different languages better than the shared encoder cross-lingual Mandarin-Tibetan TTS. Furthermore, the experimental results also show that the proposed meta-learning-based Mandarin-Tibetan cross-lingual speech synthesis method could effectively improve the voice quality of synthesized speech in terms of naturalness and speaker similarity.  
Future work will investigate how to use the least corpus for synthesizing Mandarin-Tibetan cross-lingual speech with satisfactory voice quality. Future work will also improve prosody with other linguistic features (e.g., BERT-derived features).  
Conceptualization, W.Z. and H.Y.; methodology, W.Z.; software, W.Z.; validation, W.Z., H.Y.; formal analysis, W.Z.; resources, H.Y.; data curation, W.Z. and H.Y.; writing--original draft preparation, W.Z.; writing--review and editing, H.Y.; visualization, W.Z.; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript. The research is supported by the research fund from the National Natural Science Foundation of China (Grant No. 62067008, No. 62267008). Additionally, part of this work is also supported by the Science and Technology Program of Gansu Province (Grant No. 20 JR10 RA095, No. 21 JR7 RA117) and the University Innovation Foundation of Gansu Province (Grant No. 2022B-091). Not applicable. Not applicable. Not applicable. Data Availability Statement: The open-source Mandarin speech synthesis dataset of DataBaker can be found at [[https://www.data-baker.com/data/index/TNtts](https://www.data-baker.com/data/index/TNtts)]([https://www.data-baker.com/data/index/TNtts](https://www.data-baker.com/data/index/TNtts)), (accessed on 26 October 2022). Access to Tibetan_Lasa_1 can be contacted with the corresponding author. The authors declare no conflict of interest.
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 2 Methods
### Convolutional Neural Network  
The convolutional neural network (CNN) has become popular for solving problems that contain features such as image recognition and is considered the best algorithm for visual recognition problems [36]. CNN contains a convolutional layer before the main neural network that is made up of multi-channel filters that extract unique features of each class. CNN thus breaks problems into smaller tasks, making the classification task for the next layers much easier [26]. The convolutional layer functions as a feature extractor, and the neural network (also called the fully connected layer) classifies based on features instead of the raw data. The CNN we used for this study contained four convolutional layers with 50 filters (sized 1 \(\times\) 5) in each layer. We used MaxPool as a downsampling layer with a dimension of (1 \(\times\) 3) to keep the maximum value of each of the 3 samples. So, the output of the MaxPool layer is one-third of the original data (1247/3 = 415 samples). There are 4 convolutional layers, each followed by a MaxPool layer. The final output of the convolutional layer is 50 channels signal, and each channel contains 13 features. In other words, the output is (13 \(\times\) 50) the features map. We used a flatten layer to convert this map to a list with 650 variables to introduce it into the fully connected layer.  
The fully connected layer contains four hidden layers and a final output layer (Figure 3). The details of the CNN model architecture used in this study are listed in Table S2. We chose four convolutional layers after testing different numbers of layers and considering the trade-offs between accuracy and computational time.
# An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network
## 2 Robot Kinematics
 \tag{4}\]  
where \(\mathrm{Atan2}(y,x)\) is the arctangent function of a two-parameter variable. These equations can provide the pose of the robot relative to the world coordinate system. The forward kinematics equation of the robot can be expressed as:  
\[\mathrm{F}_{\mathrm{forward kinematics}}\big{(}\theta_{1},\theta_{2},\theta_{3} \big{)}\ =\ (p_{x},p_{y},p_{z},\alpha,\beta,\gamma) \tag{5}\]  
As shown in Equation (5), the end pose of the robot can be obtained through the joint variables of the robot. However, in practical applications, it is often necessary to obtain the joint angles of the robot through the end pose of the robot, so the robot inverse kinematics is solved by the formula:  
\[\mathrm{F}_{\mathrm{inverse kinematics}}(p_{x},p_{y},p_{z},\alpha,\beta,\gamma)= \ (\theta_{1},\theta_{2},\theta_{3}) \tag{6}\]  
In order to avoid the complicated process of solving robot inverse kinematics, this paper proposes an FOA optimized BP algorithm. The BP neural network is used to fit Equation (5). The fruit fly optimization algorithm is used to optimize the initial weights and thresholds of the BP neural network to improve the global optimization ability of the neural network. The data set is used to continuously train the FOA optimized BP neural network to find the optimal neural network. Finally, the optimal network is used to solve the robot inverse kinematics. The model of the algorithm is shown in Figure 3.
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 3 State-of-the-Art of the Class Imbalance Approaches
### Over-Sampling Approaches  
**ADASYN** is an extension of SMOTE, creating more samples in the vicinity of the boundary among the two classes than in the interior of the minority class [13].  
**ADOMS** The Adjusting the Direction Of the synthetic Minority claS method, setting the direction of the synthetic minority class samples, this works like SMOTE, but it generates synthetic examples along the first component of the main axis of the local data distribution [45].  
**ROS** The Random Over-Sampling duplicates samples randomly from the minority class, biasing the discrimination process to compensate for the class imbalance.  
**SMOTE**[8] generates artificial samples of the minority class by interpolating existing instances that lie close together. It finds the \(k\) intra-class nearest neighbors for each minority sample, and then, synthetic samples are generated in the direction of some or all of those nearest neighbors.  
**B-SMOTE** Borderline-SMOTE [11] selects samples from the minority class that are on the borderline (of the minority decision region, in the feature space) and only performs SMOTE on those samples, instead of over-sampling all or taking a random subset.  
**SMOTE-ENN** This technique consists of applying the SMOTE and then applying the ENN rule [9].  
**SMOTE-TL** is the combination of SMOTE and TL [9].  
**SL -SMOTE** Safe-Level SMOTE is based on the SMOTE, but it generates synthetic minority class samples positioned closer to the largest safe level; then, all synthetic samples are only generated in safe regions [12].  
**SPIDER-1** is an approach that combines a local over-sampling of those minority class samples that are difficult to learn with removing or relabeling noisy samples from the majority class [14].  
**SPIDER-2** The major difference between this method and SPIDER-1 is that it divides into two stages the pre-processing of the majority and minority class samples, i.e., first pre-processing the majority class samples and next the minority class samples (considering the changes introduced in the first stage) [15].
## 2 Materials and Methods  
### Sample Selection  
The present study analyzed IFs available at various commercial stores and pharmacies in Olsztyn (Poland). Each IF was packaged in a cardboard box or can and took the form of a powder. The selection of IFs in this study was conducted by reviewing the market in Olsztyn stores and pharmacies. In selecting IFs, the authors were primarily guided by the easy availability of specialist formulas, which may be difficult to access. Regarding their composition and purpose, the study material was divided into three categories: (1) initial IFs (intended for infants aged 0-6 months), (2) follow-on IFs (intended for infants aged 6-12 months), and (3) specialist IFs (intended for feeding children with special nutritional needs). A total of fourteen types of IFs were analyzed--four each of initial and follow-on and six of specialists. The samples were coded accordingly, while the manufacturers of each brand were not disclosed. Samples C-A1-C-D1 represent conventional initial IFs, C-A2-C-D2--conventional follow-on IFs and S1-S6--specialist IFs. The selected conventional formulas are designed for feeding healthy infants and are nutritionally complete up to the age of 6 months (initial IFs) or can be consumed with appropriate complementary formulas after 6 months of age (follow-on IFs). Among the specialist IFs, six formulas are designed to feed infants with special nutritional needs. Formulas S1 and S2 are products prepared using partial enzymatic hydrolysis dedicated to infants with gastrointestinal disorders or lactose intolerance. Formula S4 is an extensive hydrolyzed product for infants diagnosed with cow's milk protein allergy. Formula S3 is based on soy protein and is intended mainly for lactose intolerance, galactosemia, and during elimination of cow's milk protein from the diet. The products S5 and S6 belong to the group of amino acid-based formulas, which contain only individual amino acids and none of the protein and peptide fragments. They are used in severe forms of food allergy to milk proteins when the use of extensively hydrolyzed formulas is not effective [23]. Table 1 presents descriptions of the tested formulas, including composition and nutritional purpose.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Descriptors** & **Site 1** & **Site 2** \\ \hline Groundwater depth (m) & \(-18\) & \(-2\) \\ Groundwater fluctuation (m) & \(0.15\)\(-\)\(0.20\) & \(0.70\) \\ Groundwater flow direction & constant & variable \\ Plume geometry & single plume & two plume areas \\ Depth of residual NAPLs (m) & \(-6/-10\); \(-11/-14\) & \(-1/-2\); \(-2/-3\) \\ Age of the spill (years) & \(20\) & \(3\) \\ Radon saturation & up to \(2.4\%\) & up to \(54\%\) \\ Aerobic/Anaerobic condition & Mostly aerobic & Mostly anaerobic \\ Mitigation & P and T; SVE; Biosurfactants & P and S \\ \hline
**Driving Factors** & **Site 1** & **Site 2** \\
**(\% Variance)** & **Site 1** & **Site 2** \\ \hline Radon deficit promoted by rainfall & 50 & 37 \\ Radon deficit promoted & negligible & 23 \\ by groundwater fluctuation & & \\ Degradation in aerobic condition & 20 & negligible \\ Degradation in anaerobic condition & negligible & 20 \\ Volatilization/degassing & 15 & negligible \\ \hline \hline P and T is Pump-and-Treat; SVE is Soil Vapor Extraction; P and S is Pump-and-Storage. & \\ \end{tabular}
\end{table} 
Table 6: Comparison table with descriptors and driving factors (see factorial analysis) for site 1 and 2. Degradation in aerobic conditions, rather than in anaerobic environment, occurs in site 1, coupled with stronger volatilization and degassing phenomena. These processes are coherent with a thick vadose zone. At site 2, the shallow depth and the dynamics of the water table makes the soil where the residual NAPLS are located more frequently anaerobic. Under these contrasting environmental conditions, the biodegradation in sites 1 and 2 proceed with different rates and mechanisms. The different loadings and correlations of NAPL concentrations, strengthened by appropriate references, help to outline these scenarios. Finally, multivariate regression was applied to develop a model for radon estimation. Following, the equations were obtained for sites 1 and 2:  
\[\text{Site1: Radon (Bq L}^{-1})=8.2\ -0.000485\text{ Total Hydrocarbon (\(\mu\text{g L}^{-1}\)) + 0.00440\text{ MTBE (\(\mu\text{g L}^{-1}\)) }-0.0495\text{ ETBE (\(\mu\text{g L}^{-1}\)) }+5.28\text{ Groundwater depth (m)}\]  
\[\text{with R}^{2}=22.48\%\text{, R}^{2}\ (\text{adjusted})=20.25\%,\]  
\[\text{Site 2: Radon (Bq L}^{-1})=-6.85\ -0.000101\text{ Total Hydrocarbon (\(\mu\text{g L}^{-1}\)) + 0.01366\text{ MTBE (\(\mu\text{g L}^{-1}\)) }+0.4514\text{ ETBE (\(\mu\text{g L}^{-1}\)) }+5.15\text{ Groundwater depth (m)}\]  
\[\text{with R}^{2}=68.41\%\text{, R}^{2}\ (\text{adjusted})=66.23\%,\]  
\[\text{Even if the regression coefficients, R}^{2}\text{, of both equations (22.48\% for site 1 and 68.41\% for site 2) are not very high, we want to stress the importance of this approach to estimate the value of an unknown variable starting from others or to establish relationships among factors. With a stronger dataset, the regression coefficients would be more robust and could help in predicting unavailable data and managing mitigation actions. To our knowledge, this is the first application of such a statistical approach to this type of dataset.