esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ground-Based Calibrations
### Energy Calibration
#### 5.1.2 Calibration Results  
The calibration experiments were conducted on the electron and proton accelerator platforms, and the experimental scheme is shown in Figure 14. The instrument was installed in the vacuum target chamber of the accelerator, the sensor center was at the same height as the particle beam, and the incident beam was within the detector opening angle. The output of the high-energy particle detection was transmitted to the data acquisition equipment after the ground test system.  
In the calibration experiments, multiple particle beams with specific energy were selected to irradiate the detector. Data analysis was performed on the response of different energy beams, thereby determining the deviation between the actual measurement value and the design value of each energy channel in the instrument.  
The high-energy proton calibration experiment was conducted in the proton accelerator of the China Institute of Atomic Energy. During the experiment, the energy of the proton beam was adjusted to calibrate the threshold energy (15 MeV, 25 MeV, 40 MeV, and 80 MeV) designed by the instrument. Figure 15 shows the calibration results output by the three probes when the proton energy channel was 40 MeV. Through this analysis [41], it can be seen that the errors of all energy channel divisions in the three directions of high-energy protons were less than 10%, which meets the index requirements of less than 15%.  
Figure 14: Experimental scheme of energy channel calibration.  
Figure 13: The principle of energy range calibration.  
Figure 15: Calibration results of proton energy channel in three directions (40 MeV).  
The high-energy electron calibration experiment was performed in the electron accelerator of the National Space Science Center of the Chinese Academy of Sciences. During the experiment, the threshold energy (150 keV, 350 keV, 650 keV, and 1.2 MeV) designed by the instrument was calibrated by adjusting the energy of the electron beam. Figure 16 shows the calibration results output by the three probes when the electronic energy channel was 650 keV. Through analysis, it can be seen that the errors of all electronic energy channel divisions in the three directions of high-energy electrons were less than 5%, which meets the index requirements of less than 15%.  
For the high energy range that cannot be covered by the accelerator energy, the test results of the coverage range were used to perform linear extrapolation for calculation [42].
## 2 Fixed Tilt Angle System vs. Horizontal Single-Axis Tracker
### Technical Considerations for \(PV\) Module Mounting Systems
#### 2.1.2 Horizontal Single-Axis Tracker  
A horizontal single-axis tracker has its axis of rotation aligned with the north-south axis. Therefore, the \(PV\) field rotates from east to west, following the daily movement of the  
Figure 2: Schematic of fixed tilt angle systems.  
Sun. Furthermore, this east-west rotational movement has a limited tilt angle range (\(\beta_{\text{max}}\)). This value is usually \(\pm\)60(\({}^{\circ}\)) [19]. The structural system of a horizontal single-axis tracker is comprised of several elements: columns (1), beams (2), torsion tube (3), drive device (\(DC\) motor and drivers) (4), and spherical bearings (5) (see Figure 3):
## 5 Results and Discussion
### Comparison of Scenarios
Figure 5: Comparison of the overall costs in all scenarios without reprocessing. Source: own representation. in order to reduce storage costs before transportation to a final repository. Also the number of CISFs that can be shut down within the model horizon varied depending on the timing of final repository availability and capacity. It should be emphasized that the providing of solely one long-term repository is not sufficient for the amount of waste and thus, some CISFs would never be shut down. Even in the case that Yucca Mountain is operational by 2030 or 2060, three CISFs would have to store HLW forever. All CISFs were built following a similar scheme (Figure 6). The CISFs at Barstow, California was built in every scenario, as it is close to the final repository Yucca Mountain and provided storage capacity for the few NPPs at the West coast of the U.S. All other interim storages are built close to the East cost, where the vast amount of NPPs and ISFSis is located. The location of Newville, Pennsylvania had been chosen in every scenario, and also Barnesville, Georgia and Lasalle, Illinois and Old Chatham, New York seem to be efficient locations for CISFs from a logistic point of view. Furthermore, the percentage of casts that are refilled in every scenario can be compared. Without a final repository or reprocessing, the entire SNF has to be filled into new casks by 2100 because of the decay of the old ones. The availability of final repositories reduces this portion up to only 59% assuming that no recasking is necessary in the final repository. When reprocessing waste in the Yuc_reproc, the portion of SNF filled into new casks can even be reduced to only 50%. Although this reduced costs and risks linked to the recasking, the total cost and risk increased due to the reprocessing. An additional sensitivity analysis on the discount factor for the overall costs, which we have assumed to be 1%, was performed to evaluate its impact on the overall results. It shows that a variation of this factor between 0% and 5% only slightly changed the overall costs, but did not effect the timing of processes nor the relative cost differences between the scenarios. For values higher than 5% major investments, mainly the CISF construction, were postponed to later periods, but no change in the number of CISF to be built nor in the economic feasibility of reprocessing (see below) can be observed.
###### Abstract  
Wireless Power Transfer (WPT) technology is gaining global popularity. However, in some applications, data transmission is also required to monitor the load states. This paper presents an alternative wireless power and data transmission method via the shared inductive link. With the method, the system presents three characteristics: (1) controllability and stability of the output voltage; (2) miniaturization in volume of the system; (3) decoupled transmission of power and data. The output voltage control is realized by a non-inductive hysteresis control method. In particular, data is transferred when the power transmission is blocked (i.e., the hysteresis switch is off). The interference between power and data transmission is very small. The signal to noise ratio (SNR) performance which is relevant to the interference from power transfer to data transfer and data transfer capacity, is studied and optimized. Both simulation and experimental results have verified the proposed method.  
wireless power transfer; data transmission; hysteresis control; signal to noise ratio (SNR) Article
## 3 Problem Formulation
### Formulation of Traditional Service Restoration Problems  
The top priority in the emergency condition is to immediately resume service to as many outage consumers as possible [2; 3; 4; 5; 6; 7; 12; 23; 24; 25]. Therefore, the service restoration problem is formulated as an optimization problem in terms of minimizing the weighted sum of the total amount of outage loads and the number of switching operations. The objective function is represented as  
\[\min_{x}f(x), \tag{2}\]  
\[f(x)=\sum_{j\in S^{\prime}(x)}\big{|}I_{j}\big{|}+\alpha\sum_{h\in W}\big{|}x_ {h}-x_{h}^{*}\big{|}, \tag{3}\]  
where \(I_{j}\) is the net load of section \(j\), \(x_{h}^{*}\) is the switch state in the initial configuration, \(S^{\prime}(x)\) is the set of unrestored section numbers and \(\alpha\) is the weighting factor (\(\sum_{j\in S^{\prime}(x)}\big{|}I_{j}\big{|}\gg\alpha\sum_{h\in W}\big{|}x_{h} -x_{h}^{*}\big{|}\)).  
The load sections must, unless they are unrestorable, satisfy the following operational constraints:  
\[\sum_{i\in F}y_{i,j}(x)=1,\text{ for }j\in\text{$\text{$\text{$\text{$ \text{$S$}}$}}$}\text{$\text{$\text{$S$}}$}^{\prime}(x), \tag{4}\]  
\[\Bigg{|}\sum_{j^{\prime}\in L_{ij}}\Big{(}I_{j^{\prime}}y_{i,j^{\prime}}(x) \Big{)}\Bigg{|}\leq C_{j},\text{ for }i\in F,\text{ }j\in\text{$\text{$\text{$S$}}$}\text{$\text{$ \text{$S$}}$}^{\prime}(x), \tag{5}\]  
\[\underline{V_{j}}\leq V_{i}-\sum_{j^{\prime}\in H_{ij}}\Bigg{[}Z_{j^{\prime \prime}}\sum_{j^{\prime}\in L_{ij^{\prime\prime}}}\Big{(}I_{j^{\prime}}y_{i,j ^{\prime}}(x)\Big{)}\Bigg{]}\leq\overline{V_{j}},\text{ for }i\in F,\text{ }j\in\text{$ \text{$\text{$S$}}$}\text{$\text{$\text{$S$}}$}^{\prime}(x), \tag{6}\]  
where \(y_{i,j}(x)\) is the 0-1 variable that indicates whether section \(j\) receives power from feeding point (the root of feeder) \(i\) under the switch status \(x\), \(V_{i}\) is the sending voltage of feeder \(i\), \(Z_{j}\) is the line impedance of section \(j\), \(C_{j}\) is the line capacity of section \(j\), \(\overline{V_{j}}\) and \(\underline{V_{j}}\) are the upper and lower voltage limits in section \(j\), \(F\) is the set of feeder numbers, \(S\) is the set of section numbers, \(L_{ij}\) is the set of section numbers starting from section \(j\) toward the end of feeder \(i\) and \(H_{ij}\) is the set of section numbers from the root of the feeder toward section \(j\).  
Equation (4) restricts the distribution network configuration radially, and the others respectively represent acceptable ranges of the line current and voltage.
## 3 Results
The SOC content ranged from 8.63 to 9.51 g kg\({}^{-1}\) and it increased with increasing N application rate; however, the low-N, high-N, and medium-N treatments had statistically similar SOC throughout the study period. Compared to the control, the medium-N treatment significantly increased the SOC content by 6.71% in 2018. Soil pH was also significantly varied by N enrichment from 2016 to 2018, and it decreased over time (Table 2). Soil pH ranged from 8.01 to 8.31 and it decreased with an increase in the N application rate. Compared to the control, soil pH significantly decreased by 1.71% and 2.32% under the medium-N and high-N treatments, respectively, in 2018. Moreover, total soil nitrogen significantly varied by N enrichment from 2016 to 2018 (Table 3). The total N content in the soil ranged from 0.95 to 1.28 g kg\({}^{-1}\). All the treatments increased total N content over time, but it increased significantly with an increase in N application rate. However, the medium-N and the high-N treatments had statistically similar total N content throughout the experiment. The medium-N treatment significantly increased total N content by 11.11%, 14.04%, and 17.89% in the 2016, 2017, and 2018 seasons, respectively, compared to the control. Soil available nitrogen (NH\({}_{4}\)\({}^{+}\)-N and NO\({}_{3}\)\({}^{-}\)-N) concentration was significantly increased by N enrichment from 2014 to 2018 (Table 3). The NH\({}_{4}\)\({}^{+}\)-N concentration ranged from 0.76 to 1.25 mg kg\({}^{-1}\), whereas NO\({}_{3}\)\({}^{-}\)-N concentration ranged from 4.68 to 19.18 mg kg\({}^{-1}\). The concentrations of NH\({}_{4}\)\({}^{+}\)-N and NO\({}_{3}\)\({}^{-}\)-N in the soil were ranked as high-N > medium-N > low-N > no-N throughout the experiment. In 2018, the high-N treatment increased NH\({}_{4}\)\({}^{+}\)-N and NO\({}_{3}\)\({}^{-}\)-N concentration by 38.40% and 75.50%, respectively, compared to the control. Figure 4: Effects of nitrogen enrichment on dissolved organic nitrogen (**a**), dissolved organic carbon (**b**), dissolved organic carbon and soil organic carbon ratio (**c**), dissolved organic nitrogen and dissolved organic nitrogen ratio (**d**). DON = dissolved organic nitrogen; DOC = dissolved organic carbon; SOC = soil organic carbon. Bars represent \(\pm\) standard error of means, with four replications. Figure 3: Effects of nitrogen enrichment on microbial biomass nitrogen (**a**), microbial biomass carbon (**b**), microbial biomass carbon and soil organic carbon ratio (**c**), microbial biomass carbon and microbial biomass nitrogen ratio (**d**). MBN = microbial biomass nitrogen; MBC = microbial biomass carbon; SOC = soil organic carbon. Bars represent \(\pm\) standard error of means, with four replications. The functional relationships between the N rate and aboveground plant biomass (APB), SOC and APB, and total N and APB are showed in Figure 5. There was a significant relationship between the N rate and APB (R\({}^{2}\) = 0.99). Similarly, APB significantly related to SOC (R\({}^{2}\) = 0.97) and total N (R\({}^{2}\) = 0.96).
# Aging-Friendly Design Research: Knowledge Graph Construction for Elderly Advantage Applications
## 4 \"Elderly Advantages Knowledge Graph\" Construction
### Ontology Construction for Elderly Advantages Knowledge Graph
#### 4.2.1 Core Classes and Their Attribute Relationship Definitions  
Definition 1 (user): Unlike the traditional concept of a user in data-related fields, the user in design refers to a group characteristic, representing a class of individuals with similar traits or attributes. In this study, the user's attributes include three fundamental pieces of information, namely, age, gender, and activity ability, which form the basis for identifying elderly advantages. Associations are established between these attributes and pain point behaviors, user needs, and elderly advantages.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline Software Teaching & 3 & 0 & 0 & 0 & 0 & 0 & 2 & 2 & 0 & 3 & 3 & 0 \\ Software Popularization & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 2 & 0 & 0 & 2 & 1 \\ Aging-Friendly Interaction & 2 & 0 & 2 & 0 & 2 & 2 & 0 & 0 & 3 & 3 & 0 & 1 \\ Community Activities & 3 & 1 & 0 & 3 & 1 & 1 & 0 & 0 & 0 & 0 & 3 & 0 \\ Metro Trip Alert & 0 & 0 & 0 & 0 & 3 & 1 & 2 & 0 & 2 & 0 & 3 & 0 & 2 \\ Cognitive Tools and Resources & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 2 & 0 & 2 & 3 \\ Reduced walking & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 0 & 3 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlation-scoring table for needs and advantages (partial).  
Definition 2 (pain point behavior): This refers to behaviors that result in poor user experience during the use of a product or service. It reflects the situational information when a user need arises. These pain point behaviors are associated with user needs.  
Definition 3 (user needs): This refers to the unmet functional requirements and experiences of users when using a product or service, typically generating specific associations with pain point behaviors. In this case study, based on the AHP hierarchical analysis method, the subcategories of user needs are defined as N-criteria layer and N-subcriteria layer.  
Definition 4 (elderly advantages): Elderly advantages fall under two categories: capabilities and resources. In this case study, based on the AHP hierarchical analysis method, both capabilities and resource advantages are further divided into subcategories, namely, C-criteria layer, C-subcriteria layer, R-criteria layer, and R-subcriteria layer.  
The final constructed knowledge graph ontology is shown in Figure 10.
## Theory
### Sound Radiation Efficiency of Structural Modes  
According to the theory of modal superposition, both the displacement of the cylindrical shell and the radiated sound field can be expressed in the form of modal superposition, as shown in Equations (1) and (4). To analyze the contribution of each structural mode to the radiated sound field, the sound radiation efficiency of each order mode needs to be analyzed.  
According to the definition, radiated sound power \(w\) and mean square velocity \(<v^{2}>\) are easily calculated. The sound radiation efficiency \(\sigma\) is related to radiated sound power \(w\) and mean square velocity \(<v^{2}>\)[28].  
After the modes of the cylindrical shell are acquired, velocity and radiated sound pressure can be simulated in COMSOL Multiphysics by taking the modal displacement as the boundary condition. Then, the radiated sound power and mean square velocity of the corresponding mode can be calculated. Finally, the radiated sound efficiency of each mode can be obtained.  
The sound radiation efficiency of several modes in this case is presented in Figure 3.  
Figure 3 shows that the sound radiation efficiency of some modes is much higher than that of other modes, such as mode (0,1), mode (1,1), and mode (2,1). In addition, many calculations related to modal sound radiation efficiency show that the sound radiation efficiency of these modes is always the highest of all modes (in the low frequency band). The high sound radiation efficiency is due to the characteristic of overall vibration in a large area, as shown in Figure 2. These modes may contribute greatly to the total radiated sound power when the structure is vibrating and radiating energy outward; these modes are the focus in the latter part of this paper.  
Figure 2: Partial low-order modes. (**a**) mode (3,1): 397.88 Hz, (**b**) mode (2,1): 553.18 Hz, (**c**) mode (1,1): 1029.9 Hz, (**d**) mode (0,1): 1593.7 Hz.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Algorithm Development
#### 4.3.1 Initialization  
The algorithm starts its local searching from initialization, which generates \(N_{gen}\) individuals for the first generation of the GA. Each individual encodes the sequence and timing of train passages through the local junction, and the passing time for train \(\tau_{m}\) is randomly chosen from \(\left[{t_{\overline{t}_{\overline{t}_{\overline{t}_{\overline{t}}}}^{O}},{t_{ \overline{t}_{\overline{t}_{\overline{t}}}}^{O}}+t_{slot}}\right]\), \(\tau_{m}\in\mathbb{T}\), where \(t_{slot}\) is the delay to train \(\tau_{m}\) in minutes against its original passing time in its operational timetable, \(t_{\overline{t}_{\overline{t}_{\overline{t}}}}^{m}\). Furthermore, the generated individual is stated as \(\mathbf{X}_{k}^{lm}\), where \(k\) is an index variable, \(k\in\left\{1,2,3\ldots,N_{gen}\right\}\), and \(N_{gen}\) stands for the number of individuals in the initial generation. A generation in the searching algorithm is defined as \(\left\{\mathbf{X}_{1}^{lm},\mathbf{X}_{2}^{lm},\mathbf{X}_{3}^{lm},\ldots,\mathbf{X}_{N_{gen}} ^{lm}\right\}\), \(J_{i}\in\mathbb{J}\). To ensure that the final solution is not worse than FCFS in terms of objective function, FCFS is also added as a seed in the first generation.
## 3 Results and Discussion  
### General Parameters  
#### 3.1.1 pH and Total Acidity  
As illustrated in Table 1, all general parameters were influenced by the presence of chitosan when compare to the control (C). pH was increased after the treatment with chitosan (KT) or with dissolved chitosan (KTS) (0.05 and 0.06 units respectively). As already reported by others [20; 21], this trend may be due to a reduction in the content of organic acids absorbed by chitosan through an electrostatic interaction between its charged amino groups (-NH3+) and the anionic form of organic acids present in wine (Reactions 1-3):  
\[\text{CHIT-}\mathbf{NH_{2}}\leftrightarrow\text{CHIT-}\mathbf{NH_{3}}^{+} \tag{1}\]  
\[\mathbf{AH}+\text{H}_{2}\mathbf{O}\leftrightarrow\mathbf{A}^{-}+\text{H}_{3} \mathbf{O} \tag{2}\]  
\[\text{CHIT-}\mathbf{NH_{2}}+\mathbf{AH}\leftrightarrow\text{CHIT-}\mathbf{NH_{3 }}^{+}\mathbf{A}^{-} \tag{3}\]  
\[\text{CHIT-}\mathbf{NH_{2}}+\text{H}_{2}\mathbf{O}\leftrightarrow\text{CHIT-} \mathbf{NH_{3}}^{+}+\mathbf{OH}^{-} \tag{4}\]Accordingly, samples added of solid chitosan also resulted lower in tittatable acidity (Table 1). However, when dissolved, chitosan tended to behave somehow differently, suggesting that acidity could be influenced by a further mechanism. For example, as already proposed by [PERSON] and co-workers [22], chitosan amino groups could get positively charged once in aqueous solution by hydrogen abstraction from water, leading to the generation of hydroxyl anions (OH-) and the consequent rise in pH (reaction 4). The role of these two mechanisms as affected by the addition mode of chitosan (powder or dissolved) remains to be investigated.  
However, if the weaker impact of dissolved chitosan on tittatable acidity is confirmed by further studies, this addition mode could be an interesting alternative to solid form in order to minimize the effect of this polysaccharide on the acidic parameters of white wines.
# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 7 Conclusions  
The motivation behind the research study presented in this paper is the increasing necessity to enhance human-machine speech communication, including variations in vocal effort. Given the substantial acoustic mismatch between normal and whispered speech, achieving reliable and accurate speaker-independent recognition of whispered speech in real-world scenarios poses a significant challenge for researchers.  
Recent research studies have demonstrated the viability of utilizing data augmentation strategies to enhance the robustness of ASR systems, particularly those utilizing deep learning algorithms. The efficacy of traditional augmentation techniques such as pitch shifting, time stretching, volume control, and their combinations is assessed. Furthermore, the potential of generating pseudo-whispers through inverse filtering algorithms is explored.  
The findings of this study suggest that data augmentation can be successfully utilized to enhance the performance of isolated-word whispered speech recognition systems. The conducted experiments have shown that the success of the recognizer, besides traditional augmentation methods, can be additionally improved by an inverse filtering strategy for both HMM and CNN recognizers. Conducted statistical tests confirmed the significance of improvement in performance.  
The direction of future works includes enlarging the dataset of whispered speech used for training along with testing evaluated algorithms on different whisper datasets that are currently available.  
The supplementary documents can be downloaded at: [[https://github.com/jovan81](https://github.com/jovan81) etf/whisper]([https://github.com/jovan81](https://github.com/jovan81) etf/whisper) (accessed on 26 July 2024).  
Data curation, J.G., B.M. and S.S.; Funding acquisition, B.P.; Methodology, J.G. and D.G.; Resources, J.G. and S.S.; Software, J.G.; Visualization, J.G. and D.G.; Writing--original draft, J.G., D.G. and B.P.; Writing--review and editing, J.G., B.M., D.G., B.P. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science Fund of the Republic of Serbia, #7449, Multimodal Multilingual Human-Machine Speech Communication--AI-SPEAK.  
The dataset Whi-Spe is available on request from the authors. The speech dataset used for testing can be downloaded at: [[https://github.com/jovan81](https://github.com/jovan81) etf/whisper]([https://github.com/jovan81](https://github.com/jovan81) etf/whisper) (accessed on 26 July 2024).  
The authors would like to thank the students for agreeing to participate in the creation and recording of the whispered speech database used for testing purposes (DBtest).  
Conflicts of Interest: Author [PERSON] was employed by the company Grid Dynamics, Belgrade, Serbia. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.