esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 2 Related Work  
The Chain of Thought (CoT) is a prompting method that significantly enhances the capabilities of large language models in complex reasoning tasks [13]. It achieves this by presenting a small number of examples to the model, explaining the reasoning process in these examples, and guiding the model to generate intermediate reasoning steps. The introduction of the Chain of Thought has led to substantial progress in large language models. Scholars have also applied the chain-of-thought approach to sentiment analysis. [PERSON] [16] utilized the CoT framework to simulate human-like reasoning processes in implicit sentiment analysis, step-by-step extracting implicit aspects, opinions, and sentiment polarity, achieving outstanding results in implicit sentiment analysis.  
Currently, the main modeling paradigms for ABSA tasks are Sequence-level Classification (SeqClass), Token-level Classification (TokenClass), Machine Reading Comprehension (MRC), and Sequence-to-Sequence Modeling (Seq2 Seq) [17]. SeqClass and TokenClass paradigms are mostly used for single ABSA tasks and cannot meet the current demand for extracting multiple sentiment elements. The MRC paradigm extracts sentiment elements by constructing relevant questions, with the model predicting the start position of words in the original text. This method requires the extracted elements to appear in the original text, making it ineffective for texts containing implicit aspects or implicit opinions. In contrast, the generative paradigm of Seq2 Seq can be widely applied to various ABSA tasks, offering high flexibility and providing a unified framework for ABSA task modeling. [PERSON] [10] transformed the ASQP task into a paraphrase generation process, demonstrating for the first time the excellent capabilities of generative paradigms in handling ABSA tasks. [PERSON] [12] using a generative model combined with contrastive learning, achieved optimal performance in quadruplet extraction on ACOS datasets containing implicit language. This approach also significantly improved the extraction of implicit aspects and opinions. This indicates that the generative paradigm has the potential for datasets containing implicit terms and requiring strong reasoning abilities.  
Inspired by the generative paradigm and the Chain of Thought approach, we propose a Self-Inference Template based on generative aspect sentiment quadruplet prediction. By guiding the model to generate the reasoning process for aspect sentiment elements, our approach helps the model better comprehend the text and improves the results of aspect sentiment quadruplet prediction.
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 1 Introduction
For event-driven sensors, the CEM-RM scheme is also applicable. However, the maximum delay of packet transmission is the length of a superframe. Since the time that events occur cannot be predicted accurately, the time of waiting on communication resources increases the total end-to-end delay. The scheduling of CEM-RM aims to deal with the problem of schedulability for multipath retransmission scheduling. Therefore, first, we assume that the multipath routing graph of all the nodes can be successfully constructed. Second, the IEEE 802.15.4-based radio transceivers support 16 channels on 2.4 GHz. The scheduling of CEM-RM needs multiple channels; thus, we assume that at least eight channels can be used for scheduling without interference. Finally the last assumption is that multiple sampling periods exist on scheduled industrial flows and that the minimum period should not be less than the transmission delay of nodes, which are at the greatest distance from the gateway. The main contributions of this paper are listed as follows:  
1. We analyze the resource scheduling principle for the multipath retransmission scheme based on the WirelessHART standard, and we design a link release algorithm to cater the principle of routing order in data flow. 2. We use the approaches of multiple sinks, CCA-embedded timeslot, and rate monotonic scheme to respectively decrease the negative effects of collision, path crossing, and diverse period during resources scheduling. Based on the above approaches, we propose an algorithm (CEM-RM) to improve the schedulability of resources scheduling. 3. We finally simulate our proposed algorithm to prove the improvement of schedulability. Additionally, we implement a WSAN system with our proposed scheduling in a real factory. The practical experiments are conducted to prove the reliability and real-time performance. The rest of the paper is organized as follows. Section 2 presents the related works, and Section 3 describes the system model and the principle of multipath retransmission scheme. Moreover, we present the constraints during resources allocation. In Section 4, we present the proposed Link Releasealgorithm and CEM-RM algorithm in more detail. The simulation and experimental results are illustrated in Section 5, and finally, we conclude our work in Section 6.
## 4 Experiments and Results
### Verification of the Correction Algorithm  
In order to verify the proposed method, the following experiment was performed. First, in the case where the hot plate was not turned on, and the specimen was not loaded in displacement, an image was taken as the reference image of the DIC and BOS technique. Next, the hot plate was turned on, the temperature of the hot plate adjusted to 300 \({}^{\circ}\)C, and 200 images taken after the environment was stable. Using the particle image velocimetry (PIV) algorithm in the BOS technique, the distortion vector field of each image was obtained by calculating the amount of distortion of every particle in the 200 images compared with the corresponding particle in the reference image. By time-averaging 200 distortion vector fields, the vector field of the main distortion was obtained, as shown in Figure 17.  
The micrometer screw was gently turned to rotate the disc by 0.2 degrees. The in-plane displacement to the disc was loaded, and then 100 images of the specimen were taken through the heat waves. The main distortion displacement field was utilized to remap the 100 images to remove the main distortion on them. Then, using the time-average method, 100 images with the main distortion removed were averaged to remove the random distortion. The image with the heat wave disturbance removed was finally obtained, as shown in Figure 18. The corrected image was correlated to the reference image to complete the DIC measurement. The measurement result is shown in Figure 19, and the components of the displacement field in the U and V directions are shown in Figure 20. It can be  
Figure 16: Displacement field after time average: (**a**) Component in U direction; (**b**) Component in V direction.  
Figure 17: Main distortion measured by the BOS technique: (**a**) Displacement vector distribution (the box represents local amplification results); (**b**) resultant displacement vector distribution.  
The root means square error (RMSE) is used to evaluate the displacement measurement results before and after the correction. The RMSE reflects the degree to which the measured value deviates from the real value, and can reflect the accuracy of the measurement. The smaller the root mean square error, the higher the measurement accuracy. The calculation formula of the RMSE is shown in Equation (5) [35].  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(X_{i}-X_{ture,i})^{2}}{n}} \tag{5}\]  
where \(X_{i}\) is the measured value, the \(X_{ture,i}\) is the real value and the n is the number of measurements. The \(RMSE\) of the measurement of the displacement component in the U direction before correction was 0.0732 pixels, while it was 0.0126 pixels after correction. The \(RMSE\) of the measurement of the displacement component in V direction before correction was 0.0711 pixels, while it was 0.0102 pixels after correction.
## 2 Materials and Methods  
### Isolation and Identification of PNSB  
The PNSB was isolated from the sediment of a fish pond in Dongtai City, Jiangsu Province, China. Enrichment culture was carried out in 120 mL serum bottles containing 60 mL of enrichment medium (C\({}_{2}\)H\({}_{3}\)O\({}_{2}\)Na 3 g/L, MgSO\({}_{4}\cdot\)7H\({}_{2}\)O 0.5 g/L, NH\({}_{4}\)Cl 0.5 g/L, NaCl 0.5 g/L, K\({}_{2}\)HPO\({}_{4}\) 0.5 g/L, NaHCO\({}_{3}\) 1 g/L, yeast extract 0.1 g/L, pH 7.0) under anaerobic light conditions (30 \({}^{\circ}\)C, 3000 lux) provided by an incandescent lamp. When the culture turned brownish red, the double-layer plate method was employed for bacterial isolation and purification [20]. The morphological characteristics of the isolated strain were observed using a transmission electron microscope Hitachi H7650 (Hitachi, Tokyo, Japan).  
The isolated PNSB strain was cultured in a shake flask under a temperature of 30 \({}^{\circ}\)C containing 100 mL yeast extract-peptone (YP) medium (yeast extract 3 g/L, peptone 3 g/L, MgSO\({}_{4}\cdot\)7H\({}_{2}\)O 0.5 g/L, NH\({}_{4}\)Cl 0.5 g/L, NaCl 0.5 g/L, K\({}_{2}\)HPO\({}_{4}\) 0.5 g/L, pH 7.0) until the logarithmic growth was reached (\(OD_{660}=1.0\)). A 50 mL sample was centrifuged (12,000 rpm, 5 min) to collect the bacterial cells, which were immediately frozen in liquid nitrogen and sent with sufficient dry ice to Wuhan BGI Biotechnology Co., Ltd. (Wuhan, China). DNA extraction and library construction were performed using the Illumina TruSeq\({}^{\text{TM}}\) Nano DNA Kit, followed by whole-genome sequencing using PacBio technology. The full-length 16S rRNA gene sequence was obtained from the bacterial genome and submitted to NCBI (GenBank number: PQ416756). It was then compared with those of related type species, and a phylogenetic tree was constructed using the neighbor-joiningmethod. The whole-genome sequence was uploaded to the JSpeciesWS website and compared with the type strain _Cereibacter spineroides_ ATH 2.4.1 to calculate the average nucleotide identity (ANI) value.
## 4 Applications of WSN in Urban Areas
### Power System Applications
By employing Root Mean Square (RMS) voltage and RMS current sensors in a centralized network with a mesh network topology, the methodology utilizes optimization procedures to compute insertion nodes for monitoring power quality disturbances. This approach enhances fault location and mitigation strategies, resulting in improved reliability and efficiency in power quality monitoring. Table 3 compares various power quality monitoring methods, focusing on their objectives, sensor types, network configurations, results, and limitations. The methods use various sensors in power system applications such as power meters, RMS voltage and current, light and motion sensors, and ammeters and cameras within centralized or decentralized networks employing mesh, star, P2P, or tree topologies. While each method effectively addresses specific aspects such as EC, NL, E, Delay, R, and T. These approaches exhibit certain limitations, such as the cost is not minimized, the LR during monitoring is not reduced, reliability for observability is not increased, and power quality disturbances are not reduced. Despite their varied approaches, optimizing the placement and utilization of PQ monitors remains a common challenge across these methods. The \(\swarrow\) in Table 3 shows that the method increases the performance of the particular metric, whereas the blank space indicates that the method does not consider the performance of the particular metric. The summary conclusions of power system applications are listed below. * The studies provide cost-effective solutions for power quality monitoring in urban areas, addressing voltage sags, harmonic resonance, and power quality issues. * Network topologies optimize power quality monitor deployment, enhancing data collection, resource optimization, redundancy, and cost-effectiveness. * Mesh topology is most efficient for its reliability, scalability, and data collection in urban power quality monitoring. * Star topology is effective for targeted monitoring and resource optimization, while P2P topology excels in cost-efficiency and observability. * Decentralized architectures offer valuable insights into urban power system challenges, improving power quality management and reliability.
### Results  
The initial search of information sources yielded 875 articles from various databases, all of which were deemed relevant to the research question by the AI tool. In the second step, 483 duplicate articles were removed, and the abstracts of the remaining 392 articles were manually reviewed for relevance to the research question. However, 300 of these articles were found to be outside the scope of the review. In the third step, the remaining 92 articles were thoroughly reviewed. Out of these, 65 were excluded for various reasons: some were not related to healthcare, some did not discuss AI, some did not address privacy challenges, some did not discuss large-scale data processing, Differential Privacy, or anonymization schemes, some did not discuss patient perceptions, some were published before the specified date range, some were not published in English, and some had low quality or a high risk of bias. After this rigorous screening, only 27 of the original 875 articles were deemed suitable for review using the PRISMA template, as shown in Figure 1. This methodical approach ensured that only the most relevant and high-quality articles were included in the review, enhancing the strength and validity of the research findings.  
Figure 1: Process map of research methodology.
## 3 Results and Discussion
### Effect of KOH Modification  
As reported above, differences between raw and modified hydrochars are notable for all the HTC treatment temperatures. In general, the KOH treatment significantly enhanced the adsorption capacity of MB onto hydrochars, showing also that MB in high concentrations in the range of 200-300 mg L\({}^{-1}\) could be easily adsorbed. These results are consistent with the FTIR analysis which has shown that the KOH treatment causes a change in the hydrochar surface making it more homogeneous, which contributes significantly to an increase in the adsorption capacity of the modified hydrochars. However, the KOH treatment does not have the same effect on all of the hydrochars. In particular, the alkali modification on the 250 MHC sample provided a sort of \"soft cleaning effect\" on the hydrochar surface, as reported by SEM analysis, which is consistent with the slight increase in the surface area proved by the BET model. This means an increased number of surface active sites available for the adsorption corresponding to an increase in the MB adsorption capacity of MHCs. Conversely, the KOH treatment applied at hydrochars obtained at the lower temperatures was detrimental in terms of surface area, which dramatically decreased for the hydrochar obtained at 190 \({}^{\circ}\)C, passing from 31 for the raw hydrochar to 0.29 m\({}^{2}\) g\({}^{-1}\)for the KOH-treated sample--a similar but less pronounced effect was observed for the 220 \({}^{\circ}\)C hydrochars. Considering adsorption tests, consistently the 190 MHC performed the worst when compared to the other MHCs.  
Conclusively, results reveal that the alkali treatment on hydrochars generates different effects depending on the HTC treatment temperature, to which different hydrochars characteristics and chemical stability correspond. A common feature is that for all the hydrochars, the KOH post-treatment increased the MB adsorption rate and, more generally, increased their performances as adsorbents.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Pseudo First-Order**} & \multicolumn{3}{c}{**Pseudo Second-Order**} \\ \cline{2-7}  & \(\mathbf{q_{e}}\) & \(\mathbf{k_{1}\,(min^{-1})}\) & \(\mathbf{R^{2}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{k_{2}}\) & \\  & \(\mathbf{(mg\,g^{-1})}\) & & & \(\mathbf{(mg\,g^{-1})}\) & \(\mathbf{(g\,mg^{-1}\,min^{-1})}\) & \\ \hline
190 HC & 22.46 & 0.0125 & 0.8897 & 42.15 & 0.0041 & 0.9997 \\
220 HC & 21.18 & 0.0053 & 0.9378 & 41.56 & 0.0008 & 0.9990 \\
250 HC & 24.94 & 0.0021 & 0.8783 & 36.65 & 0.0003 & 0.9631 \\
190 MHC & 4.79 & 0.0390 & 0.3351 & 49.54 & \(-\)0.0080 & 0.9999 \\
220 MHC & 13.03 & 0.0740 & 0.7815 & 52.56 & 0.0190 & 0.9999 \\
250 MHC & 37.95 & 0.0310 & 0.9501 & 52.01 & 0.0030 & 0.9998 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kinetics parameters for the adsorption of MB onto raw and modified hydrochars.
## 4 Experimental Research
### Ablation Experiment
#### 4.5.2 Discrete Strategy Ablation Experiments  
The DE algorithm is mostly used for solving continuous optimization problems, and a common method for dealing with discrete variables is the rounding method, where the main idea is to keep the algorithm searching in the real number domain, but rounding down the values of the real variables before solving for the values of the corresponding objective function of the individual [33]. In order to verify the effectiveness of the probabilistic discretization strategy, this subsection applies the rounding discretization strategy and the probabilistic discretization strategy to the traditional DE algorithm, respectively. The experimental results are shown in the Table 8 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mutation Strategy** & _RT Max(s)_ & _RT Min(s)_ & _RT Average(s)_ \\ \hline DE/rand/1 & 19.05 & 17.02 & 17.96 \\ DE/rand/2 & 20.76 & 18.22 & 19.36 \\ DE/best/1 & 17.12 & 15.53 & 16.27 \\ DE/best/2 & 16.06 & 14.90 & 15.59 \\ mutation strategies pool & 14.91 & 14.00 & 14.53 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The rendering time of the Qshan community case under the optimization results of various mutation strategies.  
Figure 4: Box plot of rendering time for Qshan Park case under optimization results of various mutation strategies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Clusters** & _p-Value_ \\ \hline mutation strategies pool vs. DE/rand/1 & \(4.6958\times 10^{-4}\) \\ \hline mutation strategies pool vs. DE/rand/2 & \(2.9365\times 10^{-4}\) \\ \hline mutation strategies pool vs. DE/best/1 & \(4.2497\times 10^{-4}\) \\ \hline mutation strategies pool vs. DE/best/2 & \(1.5873\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The \(p\)-value of the Mann–Whitney U test of the mutation strategy pool and the other four mutation strategies.  
The rendering time (RT) obtained by the probabilistic discretization strategy is 11.36% lower than the rounding discretization strategy, and the \(p\)-value of the RT obtained by the two discretization strategies is 4.9537 \(\times\)\(10^{-4}\) lower than 0.05, which indicates that the use of the probabilistic discretization strategy is better able to find the optimal values of the rendering parameters of the 3 DTiles model.
# Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss
## 4 Experimental Setup and Simulation Analysis
### Analysis of Training Results
In the late stage of training, the loss value of Dynamic Huber loss can also be kept at a lower level, showing its stability and efficiency throughout the training process. Table 3 lists the UAV obstacle avoidance training durations based on MSE, Smooth L1 loss, Huber loss, and Dynamic Huber loss under different environments and update methods. When the Soft update is used in a static environment, the training duration  
Figure 6: UAV obstacle avoidance training loss maps with different loss functions: (**a**) represents the loss map in a static environment with Soft update; (**b**) shows the loss map in a dynamic environment with Hard update; (**c**) is the loss map in a dynamic environment with Soft update; (**d**) depicts the loss map in a dynamic environment with Soft update and wind interference. of Dynamic Huber loss is 339.57, which is 76.3%, 16.4%, and 11.9% shorter than MSE, Smooth L1 loss, and Huber loss, respectively; when the Hard update is used in a dynamic environment, the training duration of Dynamic Huber loss is 389.47, which is shorter than MSE, Smooth L1 loss, and Huber loss, which are shortened by 39.8%, 34.2%, and 19.2%, respectively; when the Soft update is used in a dynamic environment, the training duration of Dynamic Huber loss is 354.91, which is shortened by 30.8%, 27.1%, and 18.1% compared with that of MSE, Smooth L1 loss, and Huber loss, respectively. In the case of wind interference and using the Soft update, the training time of Dynamic Huber loss is 406.67, which is 30.2%, 22.7%, and 11.3% shorter than that of MSE, Smooth L1 loss, and Huber loss, respectively. Taken together, the Dynamic Huber loss function has the shortest training time under various environments and updating methods, which significantly improves the convergence speed of the model and has a significant advantage over other loss functions in improving the training efficiency.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 4 Short-Term Frequency Support of a DFIG
### Conventional Short-Term Frequency Support Method of a DFIG
#### 4.1.2 Power Reference in the Acceleration Period of Short-Term Frequency Support Method  
At Point C in Figure 3, the deceleration period ends and the acceleration period begins. To recover the rotor speed to \(\omega_{0}\), this method decreases \(P_{STFS}\) from \(P_{STFS}(\omega_{c})\) to \(P_{STFS}(\omega_{c})-0.03\) p.u. and then maintains this value until \(P_{STFS}\) intersects \(P_{MPT}\) at Point D. Thereafter, the rotor speed is restored to \(\omega_{0}\) with the MPT curve. Even though this method can guarantee a lesser SFD, it delays the rotor speed restoration, thereby capturing less mechanical energy from the wind because \(P_{m}\) varies with the rotor speed.