esa-sceva/satcom-chunk-collection huggingface dataset:
Research has shown that not all data augmentation techniques work for all types of diverse speech [23]; we therefore, use these commonly used augmentation approach in E2E systems, such as SP, rather than any specific pitch or frequency modification to investigate the effect of a common augmentation approach on diverse speech to further understand the limitations and possibilities of existing data augmentation techniques on diverse speech. We study the effect of augmentations (SP and SpecAug) and normalization (VTLN) separately and together. The augmentation and normalization techniques are evaluated on both children's and adults' speech, with the goal of improving children's speech recognition performance while maintaining performance on adults' speech when adapting the model to children's speech. Summarizing, in this work our contributions are: (1) We assess the effectiveness and language independence of the data augmentation and VTLN approaches within E2E systems for three distinct languages: Dutch and German, two closely related Germanic languages, and Mandarin, an unrelated Asian tone language. (2) We analyze the effects of data augmentation and VTLN for different child age groups and gender categories. (3) Previous work on E2E models of adults' speech recognition showed that a VTLN filter-bank front-end provides better performance than the original filter-bank features [24]. VTLN's potential benefits in the context of E2E children's speech recognition are explored here for the first time. (4) Where typically VTLN models are trained on the same adult or children's speech data as the ASR model is trained on, we explore various types of training data for VTLN model training. Moreover, we assess the warping factors and effectiveness of using adult and/or children's speech as VTLN training data, as well as monolingual versus multilingual, multi-speaker speech data for VTLN training. (5) VTLN can be applied during training and testing or during testing alone, with potentially different results [25]. We investigate the effect of applying VTLN during training and testing and only during testing for E2E children's speech recognition across different languages and different speech styles. Our work can thus be considered a baseline or benchmark in E2E modeling using VTLN for children speech recognition in the absence of children's speech data for acoustic model and language model training as it provides comprehensive results and comparisons across different languages and age groups.
## 5 Conclusions  
In conclusion, this study designs a laser automatic frequency stabilization system based on SAS with the DAQ card as the core. A friendly interactive interface was designed through LabVIEW. In terms of program design, a consumer-producer structure was adopted to save the experimental results, which is convenient for analysis and processing. The functions of automatic peak seeking, digital feedback, and automatic relocking are realized through the FSM structure. Experiments showed that the system can arbitrarily select the locking point for frequency locking and automatically relock within 3.5 s, which improves the ability of the laser to operate stably for a long period. The short-term frequency stability is 1.68 \(\times\) 10\({}^{-10}\)@1 s, and the long-term stability is up to 4.77 \(\times\) 10\({}^{-12}\)@1000 s. Compared to current commercial servers, we have achieved similar performance with a smaller volume. This design provides a low-cost, versatile, and rapidly realizable method of automatic frequency stabilization for the fields of atomic cooling and trapping, and laser spectroscopy.  
Conceptualization, Z.D.; Data curation, R.X.; Writing--original draft, Y.W.; Writing--review & editing, Y.L.; Funding acquisition, F.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number [61873275, 42274013].  
Not applicable.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. The data are not publicly available due to privacy.  
Figure 14: Automatic locking process of the frequency stabilization system.
# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 2 Related Work
### Evidence Classification  
In the field of pattern classification, evidence classification algorithms have significant advantages over traditional classification algorithms in representing uncertain information and can effectively improve classification accuracy. The key to implementing evidence classification lies in the construction and combination of decision evidence, and the basic principles of its implementation are as follows.  
Assuming an object \(O\) needs to be divided into a certain class in the category set of \(\Omega=\{\omega_{1},\cdots,\omega_{c}\}\). Here, \(C\) represents the total number of pattern categories. Let \(\{(X_{i},C_{i}),i=1,\cdots,N\}\) be the training set with \(N\) samples. \(C_{i}\) is the class label of sample \(X_{i}\). It is necessary to select a certain number of samples from this training dataset to construct evidence, which can be used to assist decision making. There are many ways to select samples, but random selection and selecting \(K\) nearest neighbors based on feature distance are the two most commonly used methods.  
Figure 1: The relationship between \(Bel\) and \(Pl\) in belief functions.  
Suppose that \(X_{j}(j=1,\cdots,K)\) is the \(j\)th nearest neighbor sample of the object \(O\), with a class label of \(C_{j}=\omega_{q}\). Here, \(\omega_{q}\in\{\omega_{1},\cdots,\omega_{c}\}\), \(q\) is a specific value that can be obtained from training samples. The decision evidence constructed through \(X_{j}\) can be described by Equation (7).  
\[\left\{\begin{aligned} m_{j}(\omega_{q})&=\varphi(d_{ j})\\ m_{j}(\Theta)&=1-\varphi(d_{j})\end{aligned}\right. \tag{7}\]  
where \(m_{j}(\omega_{q})\) is the confidence that \(O\) belonging to class \(\omega_{q}\), and \(m_{j}(\Theta)\) represents the uncertainty about the class of \(O\). \(d_{j}\) is the feature distance between \(X_{j}\) and \(O\). \(\varphi\) is a mapping function that can convert the feature distance \(d_{j}\) to the interval \([0,1]\). In [21], it was proposed toto be set as:  
\[\varphi(d_{j})=\alpha\exp(1/d_{j}^{\beta}) \tag{8}\]  
The parameter \(\alpha\) can be heuristically set as 0.9 to 0.95, and \(\beta\) is usually selected as 1 or 2. After obtaining the evidences of \(K\) nearest neighbors in sequence, the next step is to combine them using the Dempster-Shafer rule.  
\[m=m_{1}\oplus m_{2}\oplus\cdots\oplus m_{K}. \tag{9}\]  
The class label of \(O\) can be determined by the global mass function \(m\). We can then assign object \(O\) to the class \(\omega_{o}\) with the highest confidence.  
\[\omega_{o}=\underset{\omega_{b}\in\ \Omega_{b}=1,\cdots,c}{\text{arg }\max}m(\omega_{b}) \tag{10}\]
# A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
## 2 Data and Preliminary Process
### Data Preparation  
To prepare the dataset that was used for the selected forecast models, the following steps were performed (see Figure 4):  
1. **Data imputation**. The raw data gathered for each building presented missing values. To solve this issue, the raw data were preprocessed. As the missing data were less than 0.3% of the total data, the linear interpolation method was used to solve the issue.
2. **Calendar data creation**. To obtain a better result in the forecast, the following calendar variables were created: hour, weekday, month, and holiday.
3. **Weather data preparation**. To avoid adding error to the forecasting models by not knowing exactly the future values of the climatic variables, the values of the previous day were used.
4. **Past series values creation**. From the analysis of the ACF and PACF, the decision to use 25 time gaps was reached because after 25 lags the partial correlation decreases significantly.
5. **Dataset formation**. After creating all the variables that would be used, the final dataset was built with the aforementioned steps.  
Figure 3: (a) ACF and PACF graphs for the time series of building 1; (b) ACF and PACF graphs for the time series of building 2.
###### Abstract  
This study addresses the challenges of grid stability posed by the integration of renewable energy sources, focusing on regions, like Jeju Island, where overgeneration complicates energy management. We introduce a novel sensitivity-based control algorithm for Virtual Power Plants (VPPs) that optimizes the deployment of distributed energy resources (DERs). Unlike conventional curtailment methods, such as Last In, First Out (LIFO) and Pro-rata, which often overlook the nuanced impacts of individual DERs on grid stability, our approach leverages electrical sensitivity analysis. This analysis quantifies the effect of each DER on voltage stability, allowing resources to be grouped and managed based on their specific sensitivity. By deriving the sensitivity matrix from the Jacobian matrix of power flow equations, the algorithm identifies critical DERs that significantly influence grid conditions, enabling more strategic curtailment decisions. The proposed method was validated using real-world data from Jeju Island, demonstrating improved efficiency and reduced curtailment compared to existing techniques. These findings underscore the potential of sensitivity-based control to enhance the efficiency of renewable energy systems, supporting sustainable energy transitions.  
curtailment; distributed energy resources; distribution system; electrical sensitivity; fuel cell(gas); renewable energy; virtual power plant; grouping Article
## 2 Materials and Methods
### Clinical Evaluation  
Clinical evaluations of patients were performed at the time of recruitment (T0) and 1 month after the end of the proposed therapy (T1). During the clinical evaluations, the presence and frequency of FM-like symptoms was assessed. The following rating scales were also administered: NRS [19], to assess the extent of pain; Barthel Dyspnea Index [20], for the evaluation of dyspnea; SF-12 [21], to evaluate quality of life; FIQ [22], to assess the disability degree in relation to the clinical picture; and PHQ-9 [23], for the evaluation of depression status.  
Primary endpoints analyzed were: perceived musculoskeletal pain and degree of dyspnea. Secondary endpoints analyzed were: quality of life, disability degree and depression status.  
The NRS scale is a quantitative rating scale by which patients are asked to rate their pain on a defined scale, from 0 to 10 [19].  
The NPI evaluates dyspnea in relation to 10 ADLs such as: grooming, bathing, feeding, toilet use, stair use, dressing, bowel movement, bladder discharge, mobility, wheelchair use and transfers (from bed to chair and back). The overall score ranges from 0 (no dyspnea) to 100 (maximum level of dyspnea) [20].  
The SF-12 scale is the abbreviated version of the Short Form 36 items Health Survey (SF36) questionnaire and serves as a generic indicator of the quality of life. It consists of 12 questions that investigate 8 different health aspects: physical activity, role limitations due to physical health, emotional state, physical pain, perception of general health, vitality, social activities, and mental health. The lower the score, the greater the degree of disability [21].  
The FIQ consists of 10 questions. The first question contains 11 items relating to the ability in the last week to carry out activities of daily life, with a variable score between 0 (always) and 3 (never). The second and third questions ask the number of days in the last week that the patient felt well and was unable to carry out their work (including housework) due to symptoms of FM. Questions from 4 to 10 are horizontal linear scales on which the patient evaluates the difficulty of work, pain, fatigue, morning fatigue, stiffness, anxiety, and depression. The maximum score of the FIQ is 100; for in-patients with FM, the average values are around 50, while only patients with severe clinical pictures have results above 70 [22].  
The PHQ-9 is a nine-item questionnaire designed for depression screening. Each element of the questionnaire is evaluated on a 4-point scale, ranging from 0 (not at all) to 3 (almost every day), with a total score from 0 to 27 with scores \(\geq\)10. Higher scores indicated greater symptom severity and a greater probability of major depressive disorder [23].
## 2 Algorithm
### Other Systematic Errors
#### 2.3.1 Satellite Orbit Determination Error  
In the algorithm equations, the pseudorange \(\rho\) is calculated on the basis of pre-estimated satellite positions \(X^{s}=\left(x^{S},y^{S},z^{S}\right)\). In an actual situation, satellite orbit determination is not perfect, and pre-estimation of the satellite position entails some error relative to true positions (\(\Delta X^{S}_{sat\ OD}\) ). According to a general satellite orbit determination process, the error is decomposed along with the satellite velocity direction (Along), satellite zenith direction (Radial), and cross-track direction (Cross). In this simulation, orbit determination error is defined along with the Along, Radial, and Cross directions and then converted into a user frame:  
\[\Delta X^{S}_{sat\ OD}(t_{i})=T\times\left(\Delta Along(t_{i}),\Delta Radial(t _{i}),\Delta Cross(t_{i})\right) \tag{39}\]  
where \(T\) is a coordinate transformation matrix from Along, Radial, and Cross to a topocentric frame. The definition of the topocentric frame is explained in the previous chapter. In multilateration theory, only satellite orbit determination error in the line-of-sight direction (rover to satellite) matters, and other directions have almost no impact on rover position error. In the MDPO algorithm, line-of-sight direction error is eliminated by the Double Difference method, along with the satellite, the rover, and the lander clock biases. Hence, basically, satellite orbit determination error has no impact on the rover position error in the MDPO algorithm.
## 2 Analysis of Related Work
### Modeling Approaches and Techniques for Security Analysis
#### 2.2.4. SysML Sec Methodology  
SysML Sec is a model-driven engineering approach for creating secure embedded systems. This methodology presents semi-formal specifications of both security and safety features and properties at various development cycle phases [19].  
The SysML Sec methodology has three stages: analysis, design, and validation. The analysis stage covers security requirements and attack scenarios; it also serves as an identification of the main functions and candidate system architecture. In the system design phase, security requirements are refined with security properties, and security-related functions are defined. The validation stage gives users a formal assessment of whether security properties are valid and verified [19].  
In the SysML Sec methodology, security requirements are based on an extended SysML Requirement diagram. A new security requirement stereotype with the property of Kind (e.g., confidentiality, access control, integrity, freshness) allows users to distinguish security requirements from functional and non-functional requirements. Attack trees can be specified with a customized SysML Parametric diagram. A Formal Dolev-Yao attacker model (for describing attacks on the protocols deployed between the components of the embedded system model) can be modeled with extended SysML Block and State Machine diagrams [20].  
Key techniques for defining security aspects: Requirement diagrams, attack scenarios, Dolev-Yao attacker model.  
Integration to MBSE process: SysML Sec was created to support all methodological stages of the design and development of embedded real-time systems. As SysML Sec uses extended SysML diagrams for capturing security concerns, the principles of it can be adapted to the MBSE process.
# Anaerobic Digestion of Duckweed Used to Remediate Water Contaminated with Zinc and Ammonium
## 2 Materials and Methods
### Duckweed Pretreatment  
The acid pretreatment of solid substrates is a critical step in enhancing the efficiency of anaerobic digestion, particularly for lignocellulosic biomass [36; 37; 38]. Sulfuric acid is predominantly used for this purpose due to its effectiveness in hydrolyzing complex carbohydrates, thereby converting cellulose and hemicellulose into simpler sugars that are more amenable to fermentation by anaerobic microorganisms [39; 40; 41; 42]. In contrast, nitric acid is less favored because its oxidative properties can lead to the formation of toxic byproducts, such as nitrogen oxides, which may inhibit microbial activity and compromise the anaerobic digestion process. Moreover, nitric acid can degrade organic matter, ultimately reducing biogas yields. Hydrochloric acid, while effective in hydrolysis, can lead to the formation of chloro-organic compounds that may inhibit microbial activity or result in corrosive chloride ions, which may corrode the equipment [38]. The cost-effectiveness and ease of handling of sulfuric acid further solidify its position as the preferred choice for large-scale operations. Additionally, research indicates that sulfuric acid not only enhances biogas production, but also minimizes operational challenges and the environmental impacts associated with the pretreatment process [42]. Thus, the application of sulfuric acid in acid pretreatment aligns with the overarching goal of maximizing biogas production while ensuring the sustainability of anaerobic digestion systems.  
In this study, duckweed underwent acid pretreatment using the following optimized protocol described by [PERSON] et al. [35]: sulfuric acid (1% H2 SO4) at a solid/liquid ratio (SLR) of 1:20 (\(w/v\)) for 60 min at 100 \({}^{\circ}\)C. The pretreated liquid and solid fraction of the duckweed suspension were separated by using a 100 \(\upmu\)m pore size sieve.
# A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
## 3 General Framework of MOUTP Based on TSCEA
### Algorithmic Time and Space Complexity Analysis of TSCEA  
In this section, we provide an analysis of the time and space complexity of the TSCEA algorithm. The main components of the TSCEA algorithm are the exploration phase, the exploitation phase, and the optimization process of the key trajectory points.  
(1) Time complexity analysis Exploration phase: In this phase, the algorithm performs an extensive search of the search space, assuming that the population size is \(N\) and each individual evolves within T generations; the time complexity of the exploration phase is \(O(N\times T)\).  
Exploitation phase: In this phase, the algorithm uses the population information stored in the exploration phase to perform fine-grained optimization. The population size is \(N\) and each individual evolves within \(\Gamma^{\prime}\) generations; the time complexity of the exploitation phase is \(O(N\times T^{\prime})\).  
The optimization of key trajectory points includes B-spline fitting curves and other optimization steps, assuming that the optimization of key trajectory points requires K iterations and the complexity of each iteration is \(O(M)\), where M is the number of trajectory points, the time complexity of this step is \(O(K\times M)\).  
Combining the above parts, the overall time complexity of the TSCEA algorithm is \(O(N\times T)+O(N\times T^{\prime})+O(K\times M)\).  
(2) Space complexity analysis The space complexity of the TSCEA algorithm depends mainly on the population size and the stored population information.  
Population storage: In the exploration and exploitation phase, \(N\) individuals need to be stored, each containing \(d\)-dimensional decision variables; then, the space complexity of population storage is \(O(N\times d)\).  
Auxiliary storage: Including key trajectory point information and other intermediate variables, assuming that an additional S units of space are required, the space complexity of auxiliary storage is \(O(S)\).  
Combining the above parts, the overall space complexity of the TSCEA algorithm is \(O(N\times d)+O(S)\).
# Finite Element Analysis on Wall Fluid Shear Stress on Cells under Oscillatory Flow
## 2 Materials and Methods
### Data Analysis  
A group of cells placed on the bottom plate were arranged in a hexagonal closely-packed mode (Figures 1C and 3A). The wall FSS on the dorsal surface of the central cell in each group was divided into four sectors. The sector with the high global FSS (SHFSS) and the opposite one with the low global FSS (SLFSS) in the direction of gradient FSS were selected to reveal the distribution of local wall FSS on the cell surface (Figure 1G,H). The average relative value (ARV) of local wall FSS in SHFSS relative to that in SLFSS at three y locations with the same x location were calculated, i.e., ARV = (average FSS in SHFSS - average FSS in SLFSS)/average FSS in SHFSS. A positive ARV indicates the higher number of activated mechanosensitive ion channels in the SHFSS region than in the SLFSS region. This phenomenon may further lead to the polarized distribution of intracellular calcium ions and the migration toward low FSS region. By contrast, a negative ARV reveals that the cells may move along the gradient direction of global FSS. An ARV close to zero suggests that the cells tend to migrate along the flow direction.  
Figure 2: Global wall FSS on the bottom plate in the modified plate flow chamber under the oscillatory flow with different frequencies. (**A**) Inlet and outlet pressures in an oscillatory flow cycle with a frequency of 0.25 Hz. The streamline and FSS distribution are shown in the enlarged graph. (**B**) Inlet and outlet pressures in an oscillatory flow cycle with a frequency of 1 Hz. (**C**) Inlet and outlet pressure in two oscillatory flow cycles with a frequency of 4 Hz. (**D**) Fluid velocity at the locations with the same height of 0.4 mm and two different x positions. (**E**) Wall FSS distribution along the y axis, in which the values of all nodes at different FSS gradients are shown and linearly fitted by the dotted line. (**F**) Mesh sensitivity test; the maximum or minimum value of wall FSS on the bottom plate for the total mesh number of this FE model is presented. The coordinate origin is at the down-right corner of the plate flow chamber in Figure 2A, and z = 0 is the bottom plate.