esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Performance Decay Prediction of the DEA Mode
#### 3.1.2 Comparison of Hidden Layer Numbers and Activation Functions  
Comparison of one-hidden-layer ANN and that of two-hidden-layer ANN predictive values and true values of current density change evolution for the PEM fuel cell with the DEA mode are shown in Figures 2 and 4. The mean and maximum relative errors by one-hidden-layer ANN are 0.725% and 1.563% that are much larger than that by two-hidden-layer ANN of 0.341% and 0.932%. Comparison of one-hidden-layer M-ANN and that of two-hidden-layer M-ANN predictive values and true values of current density change evolution for the PEM fuel cell with the DEA mode are shown in Figures 3 and 5. The mean and maximum relative errors by one-hidden-layer M-ANN are 0.198% and 0.803% that are slightly larger than that by two-hidden-layer M-ANN of 0.158% and 0.534%. From the above two groups of comparison, it is obvious that the performance of two-hidden-layer is better than that of one-hidden-layer, which is a predictable result. Because  
Figure 6: MPR-MPR. Comparison of predictive values and true values of current density change evolution for the PEM fuel cell with the DEA mode. (**a**–**l**) represent the 12 transient processes. (**a**–**h**), (**i**,**j**) and (**k**,**l**) are the train set, validation set, and test set, respectively.  
the two-hidden-layer neural network is more complex than the one-hidden-layer, it can learn deeper knowledge from the data, which leads to superior prediction results.  
Figure 7 shows the comparison between the predictive values of two-hidden-layer M-ANN with sigmoid activation function and the true values of current density evolution in DEA mode. The mean and maximum relative errors by two-hidden-layer M-ANN with sigmoid activation function are 0.157% and 0.617%. Comparing Figures 5 and 7, it can be seen that the prediction curves obtained by using sigmoid activation function are smoother than those obtained by using rectified linear unit (ReLU) activation function.
# Assessment of the Resource Potential of Baltic Sea Macroalgae
## 2 Materials and Methods
### Research Methodology for Assessing the Antioxidant Properties of Macraalgae  
To assess the resource potential of algae biomass as natural antioxidants, the antioxidant activity of their alcohol extracts was examined, as well as the antioxidant activity of the major groups of substances separately (fats, carbohydrates, and proteins).  
For antioxidant extraction, 0.1 g of crushed dry algae was mixed with 10 mL of 96% ethanol (diluted with distilled water). Extraction was carried out at room temperature for 24 h. The mixture was then filtered through a paper filter. The filtrate was collected and used as the crude extract.  
The activity of the extract was evaluated by scavenging 2-2-Diphenyl-1-picrylhydrazyl (DPPH) free radicals. A 0.2 mM solution of DPPH (Sigma, Tokyo, Japan) in ethanol was prepared and added to 2 mL of the extracted samples at various concentrations (0.5, 1.0, and 2.0 mg/mL). After a 30 min incubation, the optical density was measured in a UV-3600 spectrophotometer (Shimadzu, Kyoto, Japan) at a wavelength of 517 nm. Ascorbic acid at various concentrations (from 0.00125 to 0.008 mg/mL) was used as a control for antioxidant activity. The percentage of DPPH inhibition was calculated using the following equation:  
\[\text{absorption effect (\%)}=\text{(1 -- absorbance sample/absorbance control) }\times 100\%. \tag{1}\]  
A plot of absorption activity versus sample concentration was created based on the measurement results.  
The IC50, or effective concentration at which 50% of the DPPH radicals are absorbed, was determined using linear regression. The lower the IC50 value, the higher the antioxidant activity.
# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 2 Research Method  
### Data Selection Protocol  
The bibliographic research was conducted on the Scopus database. For the query definition, the following conceptual roadmap was identified: (a) the paper title must include, in exact form or variations, the words _diagnostics_ or _condition monitoring_, and _vibrations_, whereas (b) the title, abstract, or keywords must appear in at least one of the variations of the substrings _rotating machinery_, _wind turbine_, _bearings_, _electric motors_ or _actuators_, _gearboxes_. The search string designed to combine possible variations of the desired keywords was therefore: \"(TITLE (diagnos* OR (condition AND monitor*) AND vibrat*) AND TTLE-ABS-KEY ((rotat* AND machin*) OR (wind AND turbine*) OR bearings OR (electric AND (motor* OR (actuat*))) OR gearbox*))\".  
The following inclusion criteria were applied to filter the results: (i) English language, (ii) document classified among at least one of the subject areas, _Engineering_, _Computer Science_, _Decision Sciences_, or _Multidisciplinary_.  
The query, updated for the last time on 4 November, 2021, provided 957 documents. Figure 1 describes the results by the type of document, whereas Figure 2 depicts, in a black line, the evolution of the products by year, detailing in stacked form the trend of the two main subsets: conference paper plus book chapters on one side, and articles plus reviews on the other.  
Since data revealed that the higher number of published papers occurred in the last decades, and given the purpose of providing the reader with an up-to-date review, suitable for everyday practice, the research string was further refined. In more detail, two additional criteria were included: (iii) publication year from 2000, and (iv) document type, detected as _article_ or _review_.  
Among the 422 identified products, papers still not consistent with the inclusion criteria (e.g., presenting only the abstract in the English language or a misclassified type of document), as well as those works considered unrelated to the topic of interest, were excluded by the set. In conclusion, 401 products passed the selection process and they were assembled in the final dataset for further analysis.
## 5 Conclusions  
In this study, the YOLOv7-tiny algorithm and the DeepSORT algorithm are used to detect and track citrus in natural environments, while a citrus fruit counting strategy based on modern citrus plantations is proposed. The YOLOv7-tiny network model was trained using the acquired images of different citrus varieties in different locations, and its average detection precision (ADP) and video average detection accuracy (VDA) values reached 97.23% and 95.12%, and it takes only 0.011s to detect an image. Using this model, the citrus fruits are detected frame by frame in the video dataset, and the pixel coordinates of the citrus are obtained. Based on the Kalman filter to predict the citrus position, and successively based on the Euclidean distance and the intersection over union to measure the similarity between predicted and detected citrus. The Hungarian algorithm is used to match the same fruit inconsecutive video frames, and the prediction frames that failed to be matched are updated to a new trajectory which is re-tracked and matched again after two stages life filter. The multiple object tracking accuracy (MOTA) and multiple object tracking precision (MOTP) are 67.14% and 74.15%, respectively. Finally, fruit counting was achieved by a drawing lines counting strategy, combining the identity ID assigned to each citrus, and with an ACP (average counting precision) of 81.02%. Based on the algorithm and counting strategy of this paper, we obtained the number of citruses in 10 citrus videos, which was processed in combination with the number of manual counts to obtain a coefficient of determination of 0.9982. And the RMSE (root mean square error) is 32.300, which indicates the effectiveness of the drawing lines counting strategy.  
In conclusion, the presented approach of dynamic tracking and counting for obscured citrus holds immense potential for smart orchard information monitoring. Its applications include yield estimates for different periods, efficiency enhancement, and inventory management. It will contribute to the development of smart orchards and provide support for the establishment of unmanned standard orchards. At the same time, relevant technologies should also continue to be studied to promote sustainable development and smart agriculture.  
Conceptualization, Y.F., W.M. and Y.T.; Software, Y.F. and A.G.; Validation, Z.T.; Investigation, J.Q. and H.Y.; Resources, Y.F.; Data curation, Y.F.; Writing--original draft preparation, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Institute of Urban Agriculture, Chinese Academy of Agriculture Sciences (grant number: ASTIP2023-34-1 UA-10); key projects of the Sichuan Provincial Science and Technology Plan (grant number 2022 YFG0147); the Chengdu Local Finance Special Fund Project for NASC (grant number: NASC2022 KR08), (grant number: NASC2021 KR02) and (grant number: NASC2021 KR07).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 5 Conclusions and Prospects  
In the context of cross-domain collaborative task allocation, and acknowledging interdependencies among subtasks, this study divides the task into two components: target allocation and control allocation. We propose the MSIDBO algorithm, which integrates parallel solving, adaptive adjustment mechanisms, opposition-based learning, and an optimal value guidance strategy to effectively address the model. Through sufficient simulations and a comparative analysis, it was observed that the multi-agent cross-domain cooperative task allocation method based on the multi-strategy improved dung beetle optimization algorithm proposed in this research can be used to obtain the optimal task allocation scheme while meeting timeline requirements. The superior performance of the MSIDBO algorithm, as compared to classical and other improved DBO algorithms, underscores its potential for practical applications.  
Moreover, this research not only validates the feasibility of the MSIDBO algorithm in solving a single-wave cross-domain cooperative task allocation problem but also opens up new avenues for future exploration. Recognizing the complexity and dynamic nature of real-world multi-wave scenarios, we aim to extend our work by investigating the effectiveness of the MSIDBO algorithm in handling such problems. This includes exploring strategies to adapt the algorithm for scenarios with varying task waves, optimizing the parameter tuning process, and integrating additional mechanisms to handle uncertainties and disruptions. Additionally, we plan to evaluate the algorithm's scalability and robustness in larger-scale systems to further solidify its practical significance.  
In conclusion, this research presents a significant advancement in the field of cross-domain collaborative task allocation, and MSIDBO algorithm offers a promising solution. On average, our optimization performance was 32.5% higher than traditional algorithms and their enhanced versions. The identified gaps and future directions outlined above provide a roadmap for continuing this important work and pushing the boundaries of what is possible in this challenging domain.  
Conceptualization, Y.Z. and F.L.; methodology, Y.Z.; software, Y.Z. and L.W.; validation, F.L., Y.Z. and J.X.; resources, J.X.; writing--original draft preparation, Y.Z.; writing--review and editing, L.W.; project administration, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technical Area Fund of the 173 Program of the Military Science and Technology Commission, grant number 2023-JCJCQ-JJ-0388.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 6 Conclusions  
In this study, we explored the application of OTFS modulation in UWA communications, particularly for overspread UWA channels. At the transmitter of the proposed UWA OTFS system, we introduced a frame structure design which is tailored with respect to the characteristics of UWA channels, in order to avoid inter-block interference and mitigate the impact of overspread channels. At the receiver, we introduced a dual-domain joint channel estimation method and an iterative decoding feedback-based detector to enhance the robustness of UWA OTFS communication systems.  
Our simulation results demonstrated the feasibility of the proposed OTFS system in overspread UWA channels. Through meticulous frame design, the OTFS system exhibits significant performance improvements in the case of strong overspread. Moreover, leveraging the proposed channel estimation method and detector, our OTFS system achieved enhanced communication performance when compared to conventional systems such as the OFDM, IE-IDF-MRC, and 2D-PTR-DEF methods.  
Our future work will involve deploying the proposed OTFS communication system in real-world UWA environments in order to further analyze its operational performance and effectiveness.  
Conceptualization, L.L.; methodology, L.L.; software, L.L.; validation, C.M.; formal analysis, L.L.; writing--original draft preparation, L.L.; writing--review and editing, C.M. and Y.D.; visualization, C.M. and X.Q.; supervision, Y.D. and X.L.; project administration, X.L. and X.Q. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Stable Supporting Fund of Acoustic Science and Technology Laboratory (Grant No. JCKYS2024604 SSJ5012).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the anonymous reviewers for their careful assessment of our work.  
The authors declare no conflicts of interest.  
Figure 10: BER comparison of the IDF-MP, OFDM, IE-IDF-MRC, and 2D-PTR-DEF methods.
# Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
## 2 Related Work  
### Controlled Text Generation  
Recently, there has been many studies that aim to generate text conditioned on input attributes with neural networks. Some of the earlier efforts have studied this controlled text generation by training a conditional generative model [10; 11] while fine-tuning pre-trained models with Reinforcement Learning (RL) [3] and training a Generative Adversarial Network [12] have also shown inspiring results. The Conditional-Transformer-Language (CTRL) model [2] is a recent approach that trains a language model conditioned on a variety of control codes (e.g., \"Reviews\" and \"Legal\" control the model to generate reviews and legal texts, respectively), which prepended meta-data to the text during generation. Although it uses a GPT-2-like architecture to generate high-quality text, the result is at the cost of fixing the control codes and training a very large model. PPLM [4] composed a pre-trained LM with attribute controllers guiding text generation toward the desired attribute. At the same time, its flexible design allows it to control the generating process through relatively small \"pluggable\" attribute models while keeping parameters in the LM fixed. [PERSON] et al. [13] incorporated a pre-trained GPT-2 model with a Content-Conditioner (CoCon) to control the generated text under the guidance of target text content. [PERSON] et al. [14] proposed a simple and flexible method, infusing attribute representations into a pre-trained unconditional LM without changing the LM parameters to achieve sentiment- and topic-controlled generation. Different from our fine-grained sentiment-controlled text-generation (FSCTG) task, these works focus on sentence-based sentiment and topic control in text generation. In the FSCTG task, the text-generation process is controlled by a series of fine-grained sentiments (e.g., aspect-opinion or aspect-sentiment).
# Review of Automatic Estimation of Emotions in Speech
## 9 Conclusions  
Recent SERs tend to use large, pre-trained Transformer models [129], but their ability to generalize and be robust in training a single model using diverse sets of databases is less clear. They tend to need significant domain and corpus adaptation. How to generalize fine-tuned self-supervised SER representations across different domains is important, especially when testing in different conditions.  
Most recent SER work has ignored earlier research about ways speakers show emotion in speech, as well as how listeners perceive emotion. Those noted the importance of intonational cues, particularly F0 and speaking rate, which were commonly exploited in earlier SER. However, fitting intonation into neural SER models has been difficult, as current SER models prefer general use of spectrograms or SSL analysis. Since it is clear that emotional aspects of speech use intonation, although in complex ways, it may be better to have ANN inputs from acoustic features that are carefully crafted, rather than with actual speech waveform samples, or with elementary spectral measures (FBEs or MFCCs). These were used with the LLDs noted earlier, but have been little used for SER in the last few years.  
At the least, one should likely estimate simple intonation measures, which are known to correlate well with emotions, such as F0 statistics (frame-based mean and variance) and speaking rate, to include explicitly with other speech spectral features. Current SERs overly rely on very basic acoustic measures such as waveforms or mel-spectrograms, or from implicit SSL of features from audio via models like HuBERT and Wav2 Vec2. Such general methods have been attractive for speaker-characteristic tasks such as ASV and SER, where direct phonetic links that are useful for ASR (VT shapes to identify phonemes) are not readily known. However, the direct links of emotion to intonation, which distribute in complex ways across time, are difficult to abstract via general neural methods.  
This research was funded by NSERC grant number 142610.  
Not applicable.  
The author declare no conflicts of interest.  
List of Acronyms  
ANN artificial neural network  
AR autoregressive  
ASR automatic speech recognition  
ASV automatic speaker verification  
FBEs filter bank energies  
CNN Convolutional Neural Network  
DFT discrete Fourier transform  
E2E end-to-end machine learning system  
F0 fundamental frequency of vocal cord vibration  
GMM Gaussian Mixture Model
## 3 Voltage-Sharing Driving Method for RMVSC
### Design Principles for Voltage-Sharing Driving Method
Taking the RMVSC dc current as the reference value, the per-unit currents on both sides of the main bridge within one voltage cycle are shown in Figure 3.  
For an m-level RMVSC, the switching functions of the \(i\)th submodule (\(i=1\), \(2\ldots\rm m-1\)) in the Y reinjection circuit and the D reinjection circuit are denoted as \(f_{\rm syi}\) and \(f_{\rm sidi}\), respectively. Here, a value of 1 signifies that the submodule is connected (S\({}_{1}\) off, S\({}_{2}\) on), while a value of 0 indicates that the submodule is bypassed (S\({}_{1}\) on, S\({}_{2}\) off). The current through the submodule is given by  
Figure 3: The dc current of the Y and D main bridge. \[\left\{ \begin{array}{l}I_{\text{SMy}i}(\omega t)=f_{\text{sy}i}(\omega t)I_{ \text{dCr}}(\omega t)\\ I_{\text{SMd}i}(\omega t)=f_{\text{sydi}}(\omega t)I_{\text{dCr}}(\omega t)\end{array} \right. \tag{14}\]  
The period of the reinjection voltage corresponds to the pulsation period of the dc current in the RMVSC. According to the principle of submodule voltage balancing previously discussed, the current flowing through each submodule during an integration period must be equal. The current through each submodule is determined by its switching function. Therefore, by designing appropriate on and off times for the submodules or by generating suitable drive pulses, the current through each submodule can be equalized within each integration period, ensuring both the balance of submodule capacitor voltages and the stability of the system.
## 3 Result Analysis
### Comparison Analysis
To validate the performance of the proposed method, some recent deep learning approaches, including raw signal-based TL approach [33], hierarchical CNN-based approach [29], and ensemble deep autoencoders approach [27], are compared with the proposed method. In addition, the proposed TL-based network approach is compared with some of the classical machine learning approaches (e.g., SVM and ANN). These network architectures are analyzed with the same test scenarios to validate the performance improvement using the proposed TL-based CNN approach, as shown in Table 2. Table 7 shows the comparison results in detail for each scenario of the experiment, and our proposed approach outperforms conventional methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Health Types** & **WTL (\%)** & **NTL (\%)** & **Improvements (\%)** \\ \hline HC & 100 & 100 & 0 \\ IRF & 95.72 & 97.92 & \(-\)2.2 \\ BF & 96.22 & 95.2 & 1.02 \\ ORF@12 & 96.79 & 93.41 & 3.38 \\ ORF@3 & 97.21 & 94.90 & 2.31 \\ ORF@6 & 93.11 & 91.39 & 1.72 \\ Overall & 96.51 & 95.47 & 1.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison analysis of the classification accuracies of transfer learning-based model (WTL) vs. without transfer learning (NTL). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Classification Accuracy (\%)**} & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Improvement (\%)**} \\ \cline{3-8}  & & **HC** & **IRF** & **BF** & **ORF@12** & **ORF@3** & **ORF@6** & **Accuracy (\%)** & **[Proposed Method]** \\ \hline \multirow{4}{*}{\(1\)} & ANN & 70.29 & 68.22 & 67.45 & 64.92 & 65.79 & 67.39 & 67.34 & 3
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 4 Construction of UPF Model
### Registration and Encryption  
4.2.1. KGen(\(\lambda\)) Construction  
* Input: \(\lambda\).
* Output: \(pk\), \(sk\), \(k\).  
Key materials are generated. \(pk\) and \(sk\) are the public key and private key for ElGamal encryption and \(k\) is the secret key shared with CC in advance for pseudorandom function f.