esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Interaction between Factors
#### 3.5.1 Interaction of Three Factors in Iron Ion Removal  
It can be seen from Figure 7a-c that when the pH was 7.1-7.3, the dosage of manganese sand was 3.4-3.6 g/L; that is, the initial concentration ratio of iron and manganese ions was between 3.5 and 4, the 3D surface had the deepest chromaticity, and the iron ion removal effect was better. It can be seen from the figure that the removal rate of iron ions could be significantly improved by increasing pH, which indicated that the change in pH significantly affected the whole adsorption system during the process of the adsorption of iron ions by manganese sand. The figure shows that with the increase in pH, the dosage of manganese sand, or the concentration of iron and manganese ions, the degradation rate of iron ions first increased and then decreased. Therefore, there was an optimal matching value between the different influencing factors, and appropriate experimental conditions can maximize the economic benefits of the model and effectively improve the iron ion removal rate.  
Figure 7a shows the interaction between the pH value and the manganese sand dosage and their effect on the iron ion removal rate when the initial concentration ratio of iron to  
Figure 6: Residual plots of Fe and Mn ions (**a**) and plots of predicted vs. actual values (**b**).  
manganese was 4. It can be seen from the figure that the two-dimensional contour line was oval, indicating that the interaction between the pH and the dosage was significant. Figure 7b shows the interaction between the pH and the initial concentration ratio of iron and manganese when the dosage was fixed at 3.5 g/L. The steepness of the response surface in the figure was not as significant as the above combination, indicating that the interaction between the pH value and the initial concentration ratio of iron and manganese was not as significant as the interaction between the above pH value and the dosage of manganese sand. The slope of the pH in the response surface graph was steep, indicating that the pH value had a greater impact on the iron removal rate than these two factors. The interaction between the manganese sand dosage and the initial concentration ratio of iron and manganese is shown in Figure 7c. The steepness of the response surface in the figure was not obvious, indicating that the interaction between the dosage of manganese sand and the initial concentration ratio of iron and manganese was not significant.
## 2 Materials and Methods
### Task Design and Procedures  
The experiment utilized consonant-vowel-consonant monosyllabic English words [36] embedded in stationary speech-shaped noise. The SNR level of the experiment was set at 0 dB, which aimed for a medium level of difficulty at around 75% accuracy based on our preliminary data. The composite stimuli, normalized to 80 dB A through root mean square (RMS) amplitude adjustments, were presented monoaurally to the better ear through ER-2 insert earphones (Elymotic Research, Elk Grove, IL, USA). The better ear was chosen based on pure-tone air conduction thresholds averaged across 0.5, 1, 2, and 4 kHz.  
Participants were seated in a chair in a single-walled sound booth. A computer monitor was placed in front of the participants 0.5 m away from them at eye level. Every trial started with the indication of the trial number on the screen in silence for a half second, followed by another half second of the \"+\" sign centered on the screen. This plus sign continued throughout the trial, and participants were instructed to maintain focus on the sign to minimize the ocular artifacts. After the silent fixation to the plus sign for a half second, the stationary noise began 0.5 s before the onset of a target word and continued for 1.5 s (Figure 1 top panel). After the noise stimuli ended, participants were given four options on the screen and answered using a keyboard; for example, four words with a different initial consonant, in this case \"sat\", \"pat\", fat\", and \"that\", were given for the target word \"sat\". No feedback (i.e., correct or incorrect) was provided. Behavioral response accuracy and electroencephalographic (EEG) responses were recorded for each trial. Three sets of 55 target words (i.e., 165 different target words overall), balanced across initial phonemes regarding voicing, manner, and place of articulation, were randomly assigned to each of three experimental conditions: NR off, NR 1, and NR 2. The details regarding the implementation of NR algorithms and calculations of the neural SNR are described in later sections. The experiment was implemented in MATLAB (R2022b, MathWorks, Natick, MA, USA) with the Psychtoolbox-3 [37].  
Further, participants were asked to complete the abbreviated version of the Weinstein Noise Sensitivity Questionnaire [38; 39], with ten items to evaluate subjective ratings of noise tolerance in daily life. The sample questions included the statement, 'I get used to most noises without much difficulty'. using a Likert scale from 1 (strongly disagree) to 6 (strongly agree). Scales for the questions with opposite directions (i.e., negative statements) were reversed during the calculation process; the greater the score, the greater the noise tolerance they may have. [PERSON], [PERSON] [18] suggested that the Weinstein Questionnaire scoreswere highly correlated with the _noise annoyance_ criterion rather than other noise-tolerance domains of loudness, distraction, or speech interference.
## 2 Literature Review
### Single Prediction Models  
Individual price forecasting models can be classified into two general types: statistical and machine learning approaches. Statistical forecasting techniques mainly depend on the correlation between historical and future prices, presenting this relationship in a functional form. Common methods for price forecasting in this area comprise quadratic exponential smoothing, cubic exponential smoothing, the autoregressive integrated moving average (ARIMA) model, and grey model (GM) forecasting techniques [4; 5; 26; 27; 28].  
These techniques project future price trends linearly using past price data, and their predictive accuracy tends to deteriorate with expanding time horizons. Given the inherent volatility and non-linearity of price series, traditional statistical forecasting methods struggle to capture these non-linear attributes [4], resulting in their limited effectiveness in producing reliable forecasts. Due to their robust non-linear adaptability and capacity to model the non-linear dynamics of price series effectively [29], machine learning models are gaining traction in price forecasting. Examples of such models include the BPNN, GRNN, ELM, SVM, recurrent neural network (RNN) [30], and long short-term memory (LSTM) [4; 23]. Basically, machine learning models utilize a predictive framework to encompass the non-linear relationship between historical and future prices in a rather opaque \"black box\" way, instead of explicitly as a functional representation. While individual machine learning models can offer strong predictive performance, they are still subject to the influence of their own parameters [31; 32; 33; 34].
## 4 Conclusions  
This paper proposed a cost-effective concurrent direct inter-ONU and upstream communication IMDD PON with an advanced passive remote node. In comparison with existing PONs, the advanced PON considerably improves the overall network throughput, flexibility, scalability, and adaptability, thus giving rise to a significantly enhanced network resource utilization efficiency. In addition, it also significantly reduces the latency and power loss for inter-ONU communications. Furthermore, as each ONU and OLT utilize a single pair of the cascaded IFFT/FFT-based P2 MP flexible optical transceivers, multiple ONU-to-ONU and ONU-to-OLT connections can be established simultaneously and dynamically, each of which is tailored to meet the requirements of a specific direct inter-ONU or upstream communication service.  
The proposed network has been experimentally explored in a 27 km IMDD upstream PON with two ONUs, where the low-frequency and high-frequency ONUs, respectively, utilize a conventional intensity modulation-produced ODSB signal and an optical filter-produced OSSB signal. The experimental results have shown that for both upstream and direct inter-ONU communications, the impacts of the transmission system impairments including chromatic dispersion, the Rayleigh and Brillouin backscattering effects, and the channel interference effects are negligible. Moreover, for each ONU, dynamic channel allocation can be made according to actual end-user communication requirements without considerably compromising its transmission capacity.  
Conceptualization, W.J., L.C. and Y.H.; methodology, J.T. and T.W.; software, R.P.G. and J.H.; validation, L.C. and W.J.; formal analysis, M.S.F. and X.Y.; investigation, M.H.; data curation, L.C.; writing--original draft, W.J.; writing--review and editing, L.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially funded by the Sichuan Science and Technology Program (SSTP) (2023 YFH0067) and China Scholarship Council (CSC) (202008310010), partially funded by the European Regional Development Fund through Welsh Government, partially funded by the North Wales Growth Deal through Ambition North Wales, Welsh Government and UK Government, partially funded by the Science and Technology Commission of Shanghai Municipality Project Grant (SKLSFO2021-02, SKLSFO2022-04), partially funded by the National Natural Science Foundation of China (NSFC) (62475144), and partially funded by the Natural Science Foundation of Shanghai (22 ZR1423000).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Oxidative Stability Assessment
#### 3.3.2 \(p\)-Anisidine Value (\(p\)-AV)
Evaluating the results obtained for control samples with the addition of sodium nitrite (DCN and SCN) and samples with the addition of the DYBPB and DRBPB, we can conclude that the DRBPB in doses that provide a polyphenic compound content of at least 180 mg GAE/kg processed meat can replace sodium nitrite in meat products both for dry samples and scaled samples. Regarding the DYBPB replacement in sausage formulations, promising results were recorded at a dose ensuring a polyphenic compounds level of 270 mg GAE/kg of processed meat. A similar trend was observed in the previous study published by the authors regarding the supplementation of pork sausages with different doses of tomatoes processing byproducts [12]. In this case, the \(p\)-AV decreased with increasing doses of added by-products, and lower values were obtained for smoked and scaled sausages (0.202-2.241) than for smoked and dried sausages (0.260-2.443) [12]. [PERSON] and [PERSON] [54] also reported a reduction in the \(p\)-AV value in their study evaluating the effect of grape seed powder on the oxidative stability of precooked chicken nuggets during five months of storage; the values were higher than in the present study. After one month of storage for the control sample, the \(p\)-AV increased from 5.99 to 7.48 and for the sample with the highest amount of grape seed powder, the \(p\)-AV increased from 4.69-5.77. [PERSON] et al. [55] analyzed the evolution of the degree of lipid oxidation in meat subjected to different heat treatments and observed that cooked samples showed significantly lower values than dried samples.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
### Emergence of Large Language Models  
Large Language Models (LLMs) [42; 43; 44], a subset of AI, have emerged as powerful tools capable of processing and generating human-like text based on deep learning algorithms [45]. Developed through training on vast datasets comprising diverse linguistic content, LLMs such as OpenAI's GPT series have demonstrated remarkable proficiency in understanding context, generating coherent narratives, and performing complex language-related tasks [46; 47; 48; 49].  
The development of LLMs represents a significant milestone in AI research. Their ability to interpret and generate natural language allows for applications beyond simple communication; they can analyze unstructured data [50], summarize reports, translate languages [51; 52], and even provide insights based on textual information. In the context of maritime operations [53; 54], the significance of LLMs lies in their potential to process large volumes of textual data--ranging from maintenance logs and safety reports to regulatory documents--and extract actionable intelligence that can enhance safety protocols and risk management practices.
## 4 Performance evaluation  
This section presents a description of the dataset and conducts a statistical analysis of the RSSI, the CCA, and the PDR values, which are defined as:  
* RSSI (Received Signal Strength Indicator): average power (dBm) of a packet that has been received by the gateway;
* CCA (Clear Channel Assessment): average power (dBm) that a transmitter has sampled before transmitting a packet;
* PDR (Packet Delivery Ratio): percentage of packets that have been successfully received by the gateway within a given period of time.
# Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data
###### Abstract  
This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN-SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN-SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller-actuator (C-A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.  
unmanned surface vehicles (USVs); event-sampled mechanism; adaptive neural state observer; output feedback control; adaptive neural control +
Footnote †: journal: Applied sciences
## 4 Dataset Description and Preprocessing  
### Description  
In the initial phase, data collection involved gathering historical data from reliable cryptocurrency exchanges, such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB),  
Figure 1: Proposed methodology.  
and Litecoin (LTC). We included these cryptocurrencies to capture a broad spectrum of market conditions, volatility, and different use cases. Bitcoin serves as the market benchmark with the largest market capitalization, and Ethereum serves as that with the second market capitalization that supports smart contracts technology. Binance Coin illustrates an exchange-based utility token, and Litecoin offers fast and lightweight transactions [38]. These varied digital assets provide robust historical data and diverse market dynamics that can enable comprehensive evaluations of price forecasting and anomaly detection. The dataset encompasses critical features, including closing prices, opening prices, high and low prices, trading volume, and market capitalization. The data collection time frame spanned 2015 until 2021 to ensure a comprehensive analysis of price trends [39]. The following are brief descriptions of the dataset's features:  
* Date: Represents the observation date and serves as the dataset's temporal index.
* Open: Captures the opening price of the cryptocurrency, reflecting the first transaction at the start of the trading day.
* High: Denotes the highest price achieved within the selected time frame, providing insight into market volatility on the date.
* Low: Represents the lowest price traded during the interval, helping assess the minimum demand and price fluctuation range compared to the high feature.
* Close (Target): Records the final price at the end of the day.
* Volume: Indicates the total of cryptocurrency units traded. Higher volumes suggest greater market interest, while lower volumes may indicate uncertainty.
* Market Cap: Calculated as the closing price times circulating supply, it measures the cryptocurrency's overall market value, used for ranking. A higher market cap suggests stability; a lower one may indicate riskier investments.
## 2 Materials and Methods
### Experimental Conditions  
Four culture media were employed: Provasoli's enriched seawater medium (PESI) [60], enriched artificial seawater medium (ESAW) [61], artificial enriched seawater medium (ASP-2) [62], and [PERSON]'s enriched seawater medium (VS) [63]. Meristem and stipe were used for callus induction. Each section was cut into 1 \(\times\) 1 cm\({}^{2}\) (L \(\times\) W) pieces, treated with a 10\(\times\) antibiotics mixture for 30 min at 12 \({}^{\circ}\)C, and placed on agar media in a growth chamber (Multi-Room Incubator; LMI-3004 PL, Daihan Labtech Co., Ltd., Namyangju, Republic of Korea) for callus development. Six to eight explants were inoculated on each agar plate, and callus formation was confirmed under a microscope (Routine Microscopes; CX33; Evidient Co., Ltd., Shinjuku-ku, Tokyo, Japan). Various treatments to optimize callus induction were performed as described in Table 1. The plant growth regulators, carbon sources, and polyamines were purchased from Sigma-Aldrich, St. Louis, MO, USA.  
\begin{table}
\begin{tabular}{l c l} \hline
**Parameter** & & **Experimental Conditions** \\ \hline \hline Effect of culture medium & \(\bullet\) & Explants were cultured on PESI, ESAW, ASP2, or VS solid medium supplemented with 1.5% agar. \\  & \(\bullet\) & Growth was monitored at 12 \({}^{\circ}\)C with a light period of 12 h for eight weeks. \\ \hline Effect of agar & \(\bullet\) & Explants were cultured on PESI solid medium containing 1.2\% or 1.5\% agar. \\ concentration & \(\bullet\) & Growth was monitored at 12 \({}^{\circ}\)C for eight weeks. \\ \hline  & \(\bullet\) & Explants were cultured on PESI solid medium containing 1.5\% agar. Varying photoperiods (0 h or 12 h light period at a light intensity of 160 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) using a fluorescent lamp; 36 W; \\  & & FPL36 EX-D/C, Ilshin Vitson Co., Ltd., Namyangju, Republic of Korea) and temperatures (12 \({}^{\circ}\)C or 18 \({}^{\circ}\)C) were tested. \\  & \(\bullet\) & Growth was monitored for eight weeks. \\ \hline \end{tabular}
\end{table}
Table 1: Experimental conditions for callus induction in explants from _E. cava_ meristem and stipe.
## 3 Controller Design
### PSO-Fuzzy PID Controller
#### 3.3.2 System Simulation for PSO-FPID Tuning of Frequency Control  
This section presents the simulation results for frequency control of stand-alone synchronous generators. The simulation was performed using a mathematical model of the proposed method, with MATLAB/Simulink as the platform. The primary objective of the simulation was to optimize the Fuzzy PID controller using the PSO algorithm to determine appropriate values for _Kp_, _Ki_, and _Kd_ and to implement the PSO-Fuzzy PID controller in real industrial systems for real-time operations.  
The parameters of the PSO algorithm, including the number of agents (Np), iterations (Ni), cognitive, social coefficient, and inertia weight, are summarized in Table 3. The hyperparameters used for the PSO algorithm were selected through rigorous experimentation with the mathematical model in Simulink (MATLAB). Specifically, the PSO algorithm was executed with 50 agents (Np = 50) for 100 iterations (Ni = 100). These values were chosen because they provided a balance between computational efficiency and convergence speed, yielding optimal values for _Kp_, _Ki_, and _Kd_, as presented in Table 4.  
Figure 18 illustrates the Simulink block diagram, where the controller block consists of the Fuzzy PID controller and the system model. A step trajectory was used as the reference input signal to evaluate the synchronous generator speed control system.  
In this study, PSO was used to adjust the controller parameters, balancing optimization efficiency with low computational cost. The most significant PSO parameters--number of particles, cognitive and social coefficients, and inertia weight--were carefully chosen based on established optimization guidelines and validated through empirical tests. To evaluate the performance of the controller, the ITAE function was employed as the objective function. Figure 19 illustrates the effectiveness of the PSO-based optimization, as the ITAE values gradually decrease over successive generations, indicating improved control accuracy. A summary of optimal parameter values obtained through PSO for Fuzzy PID controllers is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & _Kp_ & _Ki_ & _Kd_ \\ \hline PSO-Fuzzy PID & 4.858 & 9.751 & 0.849 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal parameter values found via PSO for Fuzzy PID controllers.  
Figure 18: Simulink block diagram.
## 2 The Wustenrot Pilot Site  
The Plus-Energy settlement \"Vordere Viehweide\" (see Figure 1) consists of 23 recently built residential buildings with high-energy standards. All buildings are equipped with decentralized heat pumps, thermal buffer storages, PV systems between 6 and 13 kW\({}_{\text{p}}\), and in some cases with battery storages. All heat pumps are connected to a low-temperature DHC network. A novel large-scale, very shallow horizontal geothermal collector also called an \"agrothermal collector\" works as the heating and cooling source. The name \"agrothermal collector\" defines the simultaneous use of very shallow geothermal energy from the soil together with agricultural usability on top of the surface.  
The settlement was originally planned within the national research project \"EnVisaGe\" [15] in Wustenrot, Germany. There, it was part of a larger strategy for the whole municipality of Wustenrot with the aim to produce more thermal and electrical energy by its annual balance than its demand to become energy independent. Investigations showed that this was reasonable on the electricity side, but not on the thermal side, underlining the importance of sector coupling. Further information is described in the final report of the specific project [16].  
Within the research projects EnVisaGe, Envisage Plus [17], and the Horizon2020 project Sim4 Blocks [18], a settlement monitoring was installed in 11 out of 23 buildings. The monitoring of six buildings is more detailed, comprising all thermal and electrical energy flows together with the temperatures in detail (intensive monitoring). By doing so, it is possible to make a statement of the buildings' performance. The other five buildings' monitoring includes as well thermal and electrical monitoring. However, here the energy flows are measured at the buildings' boundaries (extensive monitoring). All monitoring data are accessible via a cloud-based monitoring and control system. The pilot site also contributed to work in the Horizon 2020 projects FLEXYNETS [19] and REFLEX [20]. The results of the innovative low-temperature DHC system together with the agrothermal use are supporting the energy transition in Germany.