esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### MADRL with Actors and Critics Applied to Distributed Entities in a Multi-Agent Environment
#### 6.5.2 [PERSON]'s MADRL Approach with Actors and Critics  
In [80], [PERSON] et al. embedded MADRL with actors and critics in both centralized agent, which is the data center, and distributed agents, which are VNFs. Both centralized and distributed agents select actions to maximize their individual rewards, and hence resources, particularly transmission opportunities, are allocated in a competitive (X.2.1) manner. The centralized agent selects Internet of thing (IoT) devices to be served, and subsequently the distributed agents allocate resources to the IoT devices (A.1). The proposed approach addresses the challenge of high dynamicity (C.2) due to the dynamic condition of the network.  
The data center \(i\) updates the system state and performs action \(a_{t}^{i}\), which is selecting the IoT devices to be served. Each VNF \(n\) represents state \(s_{t}^{n}\) with a three-tuple information: (a) the capacity of IoT devices; (b) the number of service requests; and (c) the number of unserved service requests in the previous time instant \(t-1\). The action \(a_{t}^{n}\) represents a transmission opportunity and it is part of the joint action \(a_{t}\) decided by the centralized data center \(i\) and the distributed VNF \(n\). The reward \(r_{t+1}^{n}(s_{t+1}^{n})\) of a distributed VNF \(n\) represents throughput. The proposed scheme has been shown to increase the overall throughput (P3).
# Effects of Melatonin Treatment of Postharvest Pear Fruit on Aromatic Volatile Biosynthesis
## 3 Discussion
ADH is a different class of green-note volatiles to C\({}_{6}\) aldehydes and alcohols and is considered a pivotal enzyme in the reduction reaction that transforms C\({}_{6}\) aldehydes into C\({}_{6}\) alcohols [25]. With the exception of C\({}_{6}\) aldehydes and alcohols, esters and lactones are both recognized as fruit-note volatiles that impact on pear fruit aroma. Synthesis of esters derives from the esterification reaction between alcohol and acyl-CoA catalyzed by AAT [26; 27], and genes encoding LOX and AAT are dependent on, and regulated by ethylene in apple fruit [28]. [PERSON] et al. [29] found that volatile esters dominate in 'Nanguoli' pear (_Pyrus ussuriensis_ Maxim. ), while higher ester content has been found in fruit treated with intermittent warming compared with 1-MCP treated fruit. Ethylene is positively related to ester content; however, in our study, while MT inhibited ethylene production, there was no effect on esters. The reason was that although melatonin inhibited ethylene production, it regulated the enzyme activity associated with aroma synthesis, activated activity of ADH and AAT, and promoted the conversion of alcohols to esters which ultimately increases content of esters in fruit. This is the main reason why the amount of aldehydes decreased after melatonin treatment, but the content of esters increased. In summary, we found the principal aromatic components in 'Korla' and 'Abbe Fetel' pear varieties were the C\({}_{6}\) aldehydes, alcohols, and esters. In 'Korla', MT inhibited HPL activity and downregulated _PbHPL_ expression to reduce content of hexanal and (_E_)-hex-2-enal, whereas in 'Abbe Fetel', MT inhibited LOX enzyme activity, downregulated expression of _PbLOX1_ and _PbLOX2_, and promoted activity of ADH and AAT that similarly reduced content of hexanal and (_E_)-hex-2-enal, and increased the content of propyl acetate, and hexyl acetate (Figure 6). This study lays a foundation for the application of MT in the effective storage and preservation of postharvest fruit.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 4 Experiment and Analysis
### Fusion of Improved A* and DWA Algorithm Experiment
#### 4.2.1 Static Obstacle Avoidance Experiment  
Firstly, the obstacle avoidance experiment of static obstacles is carried out, and the results are shown in Figure 13. In the figure, the blue solid line represents the globally  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Calculation Time** & **Search Nodes** & **Extended Nodes** & **Number of Inflection Points** \\ \hline _Original A*_ & 0.0215 & 741 & 580 & 57 \\ _A*_ with & 0.04 & 735 & 568 & 67 \\ Diagonal Collision & & & & \\ A* with Modified & 0.03 & 817 & 326 & 56 \\ Heuristic Function & & & & \\ A* after & & & & \\ Adaptive B-Spline & 0.018 & 817 & 326 & 11 \\ Curve Smoothing & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Comparison table after improvement of each step.  
planned path, the red dashed line, its smoothed counterpart, and the orange solid line, the DWA-based obstacle-avoidance trajectory. Labels 1, 2, and 3 denote the sequential avoidance maneuvers for static obstacles introduced after the global planning phase. As shown in the figure, the fused algorithm can safely and collision-freely avoid the new static obstacles, and basically travel along the smoothed global path outside the obstacle avoidance part. In the process of obstacle avoidance, the algorithm will not deviate significantly from the original path, nor will it fall into the local optimum, which will lead to the situation that the original path cannot be found by avoiding the obstacle around the big bend, whereby the expected effect is achieved.
## 4 Incorporation of Synthetic Data
### Data Fitting with Two Parameters
#### 4.2.1 \(N=1\)  
When we simultaneously adjust both \(b\) and \(\eta\) to match the model, our focus lies in the ratio of these parameters. This emphasis arises from the fact that the specific environment used induces territory aggregation as \(b\) increases, whereas territory dispersion occurs as \(\eta\) increases. This behavior can be seen mathematically through a scaling argument applied to the energy function (3). As a consequence, overestimating or underestimating both parameters tends to offset each other and results in a comparable territory outcome. In Figure 12, we fit the model with data generated using \(\eta=5\) and \(b=0.25\). Figure 12a illustrates that the algorithm converges to approximately \(\eta=4.25\) and \(b=0.19\), with both parameters being underestimated. However, while a lower \(b\) leads to less aggregated territory, a lower \(\eta\) also results in a less spread-out territory. These effects can counterbalance each other, resulting in a territory resembling the one used to generate the data. The equilibrium solutions for System (2) with the parameters used for data generation and the estimated parameter set are depicted in Figure 12b,c. These parameter sets yield similar territory boundaries, highlighting this phenomenon.  
Figure 10: Iterations of SGD minimizing the negative log-likelihood function to estimate \(b\) using data generated with \(M=150\), \(N=2\), \(b=0.25\), \(a=1\), and \(\eta=5\) and the equilibrium solutions to System (2) using the parameter used to generate the data and the estimated parameter. (**a**) Iterations of SGD and corresponding estimated \(b\) (**top**) and negative log-likelihood value (**bottom**). (**b**) PDF used to generate data. (**c**) PDF from estimated parameters.
# Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification
## 4 Experiments
### Evaluation on the UCR Archive  
The first experiment compares the EM-FCN, EM-ResNet, and EM-Inception with FCN, ResNet, and Inception to demonstrate the effectiveness of the elastic matching mechanism for the CNN. Table 1 and Figure 7 indicate that CNN architectures with the elastic matching mechanism exhibit better performance than lock-step matching. Compared with other methods, EM-Inception obtains the best rank in all the metrics. The second experiment is to validate that the elastic matching mechanism is suitable to address the temporal distortion. The compared methods surveyed in this experiment consist of the following: DTW [28], shapeDTW [10] and DTW feature (DTW-F) [29], Edit distance (Subsequence (LCSS) distance [30], Edit Distance with Real Penalty (ERP) [31], Time warp edit (TWE) distance [32] and Move-Split-Merge (MSM) [33]), Ensembles of elastic distance measures (EE) [34], Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) [35], Time warping invariant Echo State Networks (TWIESN) [36], MMF-CNN [8] and EM-Inception. The results in Table 2 and Figure 8 indicate that EM-Inception achieves a comparable performance with HIVE-COTE (the state-of-the-art method on the UCR archive). Moreover, HIVE-COTE is an ensemble method based on 35 different classifiers, including DTW-1 NN, MSM-1 NN, and others. It has a robust ability to address temporal distortion. Hence, the experimental results also reflect the effectiveness of the proposed mechanism. Compared with other methods, such as MMF-CNN and shapeDTW, the superiority of EM-Inception demonstrates that an end-to-end learning architecture with an elastic matching mechanism is preferred. Figure 7: Critical difference diagram of a pairwise statistical difference comparison of FCN, ResNet, Inception, EM-FCN, EM-ResNet and EM-Inception on the UCR archive.
## 1 _Article_
### Overtopping: Small-Scale Physical Modelling  
Small-scale physical modelling has traditionally been one of the most used techniques for studying wave overtopping. It involves a small-scale port model, where the overtopping will be studied. These models allow the most significant physical phenomena that intervene in overtopping to be reproduced.  
In this type of modelling, the tests are usually conducted using a wave energy spectrum, which is then used to generate a free surface wave time series at the wave paddle. When the water overpasses the model's breakwater due to overtopping, it is channelled and collected using structures built to measure the overtopping water volume.  
Small-scale modelling is used, for instance, to determine the influence of certain factors in wave overtopping. For example, in [8], the authors studied the influence of wind waves on overtopping.  
The methods involved in this type of modelling continue to evolve. The tests usually use a wave energy spectrum free surface wave time series at the wave paddle. Although this method could generate an infinite number of time series, only one is usually generated due to the expense of running multiple tests in a physical model. [PERSON] et al. proposed an improvement to this method in [9], where the authors used several time series generated from the same wave energy spectrum to study the variation in the main overtoppingmeasures. As a result, they showed that using different time series gives different results for some of the overtopping measures, indicating that this is the correct approach.  
This method continues to generate new knowledge. For instance, [PERSON] et al. [10] proposed a new formula for calculating wave overtopping at vertical and sloping structures with shallow foreshores using deep-water wave characteristics. Another example is the work of [PERSON] et al. [11], where the authors conducted an experimental study focusing on overtopping characteristics of coastal seawalls under random bimodal waves and, based on the experimental results, proposed a modification to the EurOtop formula to better capture the overtopping discharge under bimodal conditions.  
An example of a different technique for detecting overtopping events is the one used by [PERSON] and [PERSON] [12]. In this work, the authors used a semi-automatic and customisable method based on a threshold-down-crossing analysis of the sea surface elevation signals obtained using consecutive gauges. They applied their method to new and past data and compared the results with well-established formulae, obtaining accurate and reliable results.  
Although new and clever techniques exist to measure overtopping on a physical model, the downside is that some are effective in a laboratory but not practical in a real environment due to the high number of elements required or the need to construct a gigantic structure to collect the water of an overtopping event, and the high economic cost and time consumption.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 4 Multi-Frequency RTK Positioning Accuracy Analysis
### Time to First Fix  
Time to first fix is the time taken to initially fix the ambiguities, which is another indicator to evaluate the performance of RTK. Factors affecting the time to first fix of ambiguity include: the number of observation frequencies, PDOP, how fast the relative geometric configuration changes between the satellite and the receiver, observation accuracy, correlation between observations, data sampling rate, and the magnitude of atmospheric error delay [21; 22]. To analyze the influence of the baseline length on TTFF, the data on 218 baselines of TGBU-TGCU (30 km) and TGCU-TGDA (96 km) were selected, and the daily observation data were divided into a total of 24 segments per 1-h period for testing. (When performing a positioning solution, each period is reinitialized.) In this research, the following three conditions are used as the index of ambiguity fixing:  
1. Coordinate deviation in horizontal direction <0.1 m; coordinate deviation in vertical direction <0.2 m.
2. \(R\_best\geq 3\).
3. The current epoch and the following nine consecutive epochs all satisfy (1), (2).  
As shown in Figure 12, the zero fixing time on some frequencies means that it can be fixed on the first epoch. At the short-baseline RTK positioning mode, the TTFF is short, and the first epoch ambiguity can be fixed correctly in multiple time periods. When the baseline length increases, the TTFF increases due to the need to separate atmospheric errors. Since the ionosphere is more active due to the higher temperature at noon, the TTFF is longer during that time. BDS-3 has fewer visible satellites and poorer geometry distribution, resulting in longer TTFFs for BDS-3 (B1C/B2a), BDS-3 (B1I/B3I), and BDS-3 (B1C/B2a/B3I) than for BDS-2+BDS-3 (B1I/B3I) and GPS (L1L2). BDS-3 (B1C/B2a) and BDS-3 (B1C/B2a/B3I) were better than BDS-3 (B1I/B3I) in the case of a BDS-3 only system.  
Figure 11: Ratio value variation of TGCU-TGD2 (128 km).  
Figure 12: Time to first fix of baseline TGCU-TGBU (**left**); time to first fix of baseline TGCU-TGDA (**right**).
## 3 Intelligent Jamming
### Radio Sensitivity of a Mobile Phone  
To determine the effectiveness of the jamming device, it is very important that when the mobile phone tries to register in the network, it is aware of its position in the cell, whether it is within the cell's core coverage area, whether there are neighbouring cells or whether it is at the cell's coverage edge. Several measurements must be undertaken to verify the signal strength of the received signal. The radio sensitivity of a mobile phone refers to its ability to detect radio signals from mobile communication networks (such as signals from cell towers) in conditions of weak reception or when the signal is faint. The sensitivity of a smartphone can vary depending on several factors, including the model, manufacturer and technology used. In general, the signal sensitivity level at which a smartphone can operate efficiently falls within the range of approximately \(-90\) dBm to \(-120\) dBm.
## 4 Materials and Methods
### Data Collection Instruments and Analysis  
An online questionnaire prepared as a Google form was the main data collection instrument. The questionnaire was based on the MEEGA+ model and the corresponding questionnaire for evaluating educational games proposed by [PERSON] et al. [18]. The reliability and validity of the model and the questionnaire have been confirmed in the literature and utilized in several studies [30]. Based on the MEEGA+ model, the questionnaire included questions for two quality factors, namely player experience and perceived short-term learning. The player experience quality factor comprises seven dimensions: usability; confidence; challenge; satisfaction; fun; focused attention; and relevance. Usability is further divided into five subdimensions: aesthetics; learnability; operability; accessibility; error prevention and recovery. The MEEGA+ model was considered ideal for the goals of the study, while it is an instrument that is both complete since it was designed based on an extended literature and validated for its reliability and validity. The questions of the MEEGA+ model are actually 5-point Likert-scale statements, and the respondent expresses his/her level of agreement for each one of them (1 = strongly disagree, 5 = strongly agree). The questionnaire also included demographic questions, questions about the participants' game-play habits, difficulties and knowledge on algorithms and programming, and finally the scenario of the game and the inclusion of the educational material in it. The data collected were analyzed using descriptive statistics.
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 5 Conclusions  
In this work, we developed a 3D avatar-based sign language learning system that converts the English speech or text into corresponding ISL movements. Initially, the input speech is converted into an equivalent English sentence using the IBM-Watson service. The converted English text is further translated into the corresponding ISL sentence using regular expression and script generator. Finally, each word of the ISL sentence is transformed into an equivalent sign movement, represented by a 3D avatar. The translation method (English-ISL and ISL-sign movement) has been evaluated by the SER, BLEU, and NIST metrics. The proposed model achieved an 82.30% BLEU score that represents  
Figure 6: Sign language representations of English sentences: (**A**) Come to my home, (**B**) Hello, good morning, (**C**) Bye baby, (**D**) Please come.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SER** & **BLEU** & **NIST** \\ \hline
10.50 & 82.30 & 86.80 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance analysis of proposed translation system. SER: Sign Error Rate; BLEU: Bilingual Evaluation Understudy; MIST: National Institute of Standards and Technology.  
the translation accuracy from English to ISL sentences. The text-to-sign translation model (from ISL sentence to sign movement) achieved a 10.50 SER score, which signifies that 89.50% of signs were correctly generated by the 3D avatar model for the respective ISL sentence. It may be noted that the proposed system has been developed for a limited corpus, and no facial expressions were included, which can be considered as an important part of any sign language system. The transition between the signs while performing a sign sentence can be improved further by learning specific transitions based on hand positions while signing. Moreover, the sign language recognition system that converts a sign to text/speech is significantly more difficult to develop. Such a system can be added within the proposed framework to build a complete sign language interpretation system.  
Conceptualization, D.D.C. and P.K.; methodology, D.D.C., P.K. and S.M.; software, S.M.; validation, D.D.C. and P.K.; formal analysis, D.D.C. and P.K.; investigation, P.P.R. and M.I.; data curation, D.D.C. and P.K.; writing--D.D.C., P.K.; writing--review and editing, P.P.R. and M.I.; visualization, M.I.; supervision, P.P.R. and B.-G.K.; project administration, B-G.K. All authors have read and agreed to the published version of the manuscript.  
No research funding has been received for this work.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Studies
### Results Analysis and Discussion
#### 4.3.3 Statistical Results of Solutions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Algorithm** & _Dim_ & **N+** & **N=** & **_N\(-\)_ & _CP_ & **Algorithm** & **Dim** & **N+** & **_N=_ & **_N\(-\)_ & _CP_ \\ \hline MLDMS-PSO & 10-D & 28 & 0 & 1 & 27 & MLDMS-PSO & 10-D & 11 & 14 & 4 & 7 \\ vs. & 30-D & 28 & 1 & 0 & 28 & vs. & 30-D & 17 & 4 & 8 & 9 \\ GPSO & 50-D & 27 & 2 & 0 & 27 & TSLPSO & 50-D & 18 & 5 & 6 & 12 \\ MLDMS-PSO & 10-D & 29 & 0 & 0 & 29 & MLDMS-PSO & 10-D & 22 & 3 & 4 & 18 \\ vs. & 30-D & 29 & 0 & 0 & 29 & vs. & 30-D & 20 & 4 & 5 & 15 \\ FIPS & 50-D & 29 & 0 & 0 & 29 & DMS-PSO & 50-D & 16 & 8 & 5 & 11 \\ MLDMS-PSO & 10-D & 15 & 9 & 5 & 10 & MLDMS-PSO & 10-D & 22 & 3 & 4 & 18 \\ vs. & 30-D & 19 & 7 & 3 & 16 & vs. & 30-D & 15 & 2 & 12 & 3 \\ CLPSO & 50-D & 18 & 8 & 3 & 15 & PSO-DLS & 50-D & 15 & 5 & 9 & 6 \\ MLDMS-PSO & 10-D & 18 & 3 & 8 & 10 & MLDMS-PSO & 10-D & 21 & 6 & 2 & 19 \\ vs. & 30-D & 19 & 1 & 9 & 10 & vs. & 30-D & 12 & 8 & 9 & 3 \\ CCPSO-ISM & 50-D & 18 & 6 & 5 & 13 & HCLDMS-PSO & 50-D & 12 & 7 & 10 & 2 \\ MLDMS-PSO & 10-D & 26 & 1 & 2 & 24 & & & & & & \\ vs. & 30-D & 23 & 4 & 2 & 21 & & & & & & \\ XPSO & 50-D & 20 & 7 & 2 & 18 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical results of the Wilcoxon rank sum test for 10-D, 30-D, and 50-D problems. the exploitation ability of the algorithm to a certain extent. The MLDMS-PSO made the algorithm more compatible with exploration and exploitation through population contraction and an adaptive learning strategy adjustment, so it had a better performance on the 10-D, 30-D, and 50-D problems.
# FrameAugment: A Simple Data Augmentation Method for Encoder-Decoder Speech Recognition
## 2 Related Works  
### Network Architecture  
As speech recognizers change from a hybrid network architecture to a deep learning network architecture, many deep learning network architectures have been proposed for speech recognition [14; 15]. Speech recognizers generally adopt an encoder-decoder network architecture. In the encoder-decoder architecture, the encoder receives the entire utterance and outputs the features of the utterance, and the decoder outputs the result by using the feature vector input from the encoder and the previous result. As shown in Figure 1, the Transformer [16] model is used as the baseline encoder-decoder network architecture except that the input embedding layer is replaced by a subsampling layer to compress the input spectrogram. The encoder of the Transformer learns the relationship among spectrogram data in different positions through self-attention and outputs feature vectors. Because self-attention does not perform regression as LSTM (long short-term memory) [17] does, parallel processing is possible. The decoder of the Transformer uses self-attention like an encoder. However, whereas the encoder's self-attention learns the positional relationship of the spectrogram, the decoder's self-attention learns the relationship among the output units. Because the rear part of the currently predicted unit cannot be known, the decoder uses masked-self-attention in the learning process, which learns by masking the rear part of the prediction unit. In the subsequent multi-head attention sub-layer, the decoder learns the relationship between input and output.  
In the encoder-decoder network architecture, it was difficult to align at the beginning of the learning process. To solve this problem, CTC (connectionist temporal classifica  
Figure 1: Baseline encoder-decoder network architecture.  
tion) [15] was combined with the encoder from the MTL (multi-task learning) [18] method. The combined architecture was shown to improve learning speed and speech recognition performance compared to the single model by alleviating the alignment problem [19].  
The total loss of the combined architecture is calculated as follows:  
\[L_{MTL}=\tau L_{CTC}+(1-\tau)L_{Attention} \tag{1}\]  
where \(\tau\) is a constant greater than or equal to 0 and less than or equal to 1, \(L_{CTC}\) is CTC loss and \(L_{Attention}\) is attention loss obtained from the Transformer, respectively.