esa-sceva/satcom-chunk-collection huggingface dataset:
### Environmental and Technical Challenges in Mariculture Production
Cage farming in the Adriatic Sea and the Mediterranean is still based on floating round cages, while submerged cage structures are increasingly being used in ocean farming. The location of the fish farm greatly affects the quality of production. Peaceful locations with high water quality far from the tourist spots and ports are more suitable. Differences between offshore and coastal farming are presented in Table 1, according to [26]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Coastal Farming** & **Offshore Farming** \\ \hline Distance & \textless{}500 m from shore & \textgreater{}2 km from the shore \\ Depth & \textless{}10 m & \textgreater{}50 m \\ Waves & \textless{}1 m & Up to 5 m \\ Accessibility & 100\% & 80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between coastal and offshore farming. Figure 2: Technical challenges in mariculture. Based on Figure 2, another important factor for viable mariculture production is fish feed, and most of the production costs are related to the feeding process. Rethinking mariculture production with an integrated mentality will be required to address the simultaneous issues of feed and energy demands, land and water requirements, and customer preferences [27]. Feeding can be operated manually or automatically, but it is more efficient to use automatic feeders. The feeding procedure accounts for around 50-75% of total expenses [6]. Some of the research that dealt with feeding technologies are conducted in [28; 29; 30]. A major problem that occurs in mariculture production is biofouling. Biofouling accumulation invading fish farms is thought to have largely negative consequences on the fish inside the cages, resulting in financial loss [31; 32]. [PERSON] and [PERSON] [33] and [PERSON] et al. [34] studied the direct economic consequences of biofouling control. Biofouling growth is relatively fast because the waters surrounding mariculture operations are enhanced by organic and inorganic wastes (uneaten food, and fecal and excretory material) produced by high-density fish populations [35]. [PERSON] et al. [36] studied the resistant biofouling cage nettings. Thus, the key focus in fish culture is on net fouling reduction, as this leads to damaged cage structure [37], and affects the fish's health, primarily due to limited flow-through of water, resulting in inadequate dissolved oxygen availability. Mariculture development requires a number of natural resource inputs, such as high-quality water or space, food, or fingering sources, as well as disease and predator control systems [38]. Proper planning and placement of the production program will increase the likelihood of return on investment for the individual mariculture project and contribute to the improvement of economic effects [39]. [PERSON] et al. [40] and [PERSON] [41] studied the production costs and innovative, intensive manufacturing technologies.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 5 Discussion
### Interpretation of Simulation Results  
The numerical simulations demonstrate that heuristic methods--particularly Cultural Algorithms (CAs), Genetic Algorithms (GAs), and Artificial Immune Systems (AISs)--effectively reduce discrepancies between the detailed and simplified thermal models. The observed convergence and temperature differences below 3 \({}^{\circ}\)C in transient simulations confirm their ability to handle complex, nonlinear parameter spaces. These findings suggest that heuristic approaches can serve as a robust alternative to traditional, manually tuned methods in thermal correlation analysis.  
A key aspect of this discussion concerns parameter tuning in heuristic methods. In this study, a Genetic Algorithm (GA) optimization process was employed to fine-tune parameters across different heuristic techniques. The goal was to establish a more systematic and comparable optimization loop for each method, enabling a more natural convergence of the parameter tuning and then a better performance comparison based on fitness function (\(FF\)) values.  
Some heuristic methods, e.g., the CA, GA, and AIS, share common parameters, such as: \"Population Size\", \"Mutation Rate\", and \"Mutation Magnitude\".  
However, others have unique parameters that are not directly comparable across methods, such as Cultural Population Size (specific to CAs) and Clone Factor (specific to AISs).  
If we compare the common parameters, we find the following:  
* Population Size: 49 (CA), 43 (GA), and 21 (AIS). While the CA and GA are similar, the AIS has a significantly smaller value.
* Mutation Rate: 0.271 (CA), 0.299 (GA), and 0.268 (AIS), indicating close similarity.
* Mutation Magnitude: 0.557 (CA), 0.582 (GA), and 0.667 (AIS), with the AIS showing a difference.  
Despite similarities in some tuning parameters, significant differences emerge in others, such as Population Size. Additionally, unique parameters specific to each method make direct comparisons challenging. To address this, we compiled a comprehensive statistical dataset for each heuristic method. This dataset serves as a reference for researchers and practitioners looking to apply heuristic methods to thermal model correlation, providing insights into acceptable parameter ranges and expected optimization behaviours.
## 2 Materials and Methods
### SERS Characterization  
For SERS characterization, the model molecule 1-naphtalenethiol (1-NAT) was used. Sample solutions were prepared at concentrations 10\({}^{-2}\) M, 10\({}^{-4}\) M, 10\({}^{-6}\) M, 10\({}^{-8}\) M, 10\({}^{-10}\) M, and 10\({}^{-12}\) M. These different solutions were injected in the PDMS-paper device containing the assembled NPs, and allowed to dry. After drying, the PDMS part was dissembled from the paper part for measurement. Raman measurements were carried out in a 300R Confocal Raman microscope (WITec) using the 785 nm laser line grating (600 gr cm\({}^{-1}\)) as the excitation source and a 100\(\times\) objective. Acquisition was performed for 1 s at five different points of the NP-assembled area. A map of 625 spectra was also acquired from the GNSs paper substrate to analyze the homogeneity of the sample. Each individual spectrum of the map was acquired for 1 s with a step size of 4 \(\upmu\)m using the 10\(\times\) objective. SERS spectra were processed with the Project4 WITec software for background and cosmic ray removal corrections and GRAMS/AI Spectroscopy software for figure preparation.
## 6 Discussions  
Based on the comparison results in Section 5, we use nonparametric tests under \(\alpha=0.05\) confidence level to examine the superiority of the proposed TISA; it was verified that, when the size of the problem is small, the immigrant strategy does not induce significantly the quality of the final solution, since the hypothesis Basic GA = GARI and GARI = TISA cannot be rejected with significance 0.317 and 1.000, respectively. It is reasonable that, when the problem is small, solution space can almost be searched by basic GA without adding any improved scheme. For large-size problems, the hypothesis Basic GA = GARI and GARI = TISA are rejected with the significance of 0.002 and 0.000, respectively, which showed that the proposed immigrant strategy induces significantly the solution quality of GA.  
Overall, the proposal TISA has the following features and advantages when compared with GAs:  
* With the problem being NP-hard, it is tough to search for all solutions when the size of the problem increases. In this case, a good search scheme, such as the proposed trajectory-based immigration strategy to direct GA to obtain a better or optimal solution, is important.
* The possibility to converge to solutions with good quality more quickly due to the trajectory-based immigration strategy.
## 1 Introduction
### Practice of the Power System Utilization  
From an operational point of view, N-1 contingency criterion (the occurrence of one contingency, i.e., one fault of any major unit) is usually used. An electrical power system can be described as N-1 safe when it can maintain normal operation after a single contingency, such as an unplanned loss of a transmission line, generator, or transformer. This standard is approved/used by system operators worldwide. It can be used to plan contingency operations, manage system operations, and perform immediate actions aimed at restoring a safe and stable operating state of systems within a reasonable time (usually within 15-30 min) after a single unforeseen event.  
System operators monitor power systems in real time to ensure safe operating conditions and their ability to respond to emergency events in a timely and effective manner.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Field of Application/Scope** & **Method/Index** & **Year** & **Reference** \\ \hline This approach provides a better balance between accuracy and efficiency in identifying vulnerable lines. The proposed generator-to-load power percentage is used as an indicator to assess the stability of the power grid. & Electrical betweenness & 2022 & [21] \\ \hline The classification strategy identifies the most important transmission elements in a 500 kV transmission network. The power system network is analyzed using a node breaker model and steady-state analysis. & Ranking strategy & 2019 & [22] \\ \hline The composite severity index allows an accurate assessment of the voltage on each line in terms of megawatts of overload and voltage instability. The line with the highest probability of severity for different faults shall be equipped with an interline power flow controller (IPFC). & Composite Severity Index (CS  
Operational control typically relies on real-time or near-real-time information provided by supervisory control and data acquisition (SCADA) systems. The results are used to assess actual operating conditions subject to key technical constraints and feed into network simulations that are used to update contingency assessments.  
In the event of an emergency or N-1 contingency, system operators must be able to intervene in a timely and effective manner to stabilize the power system and return it to an N-1 safe state within the time set by the reliability standards. In the event of a power outage, system operators typically have recovery plans and procedures that are immediately activated to restore the power system to a stable and safe operating condition as quickly as possible [28]
## 4 Discussion
### Performance Variation between Levels of Fatigue Grouping Methods  
Outcomes of our study showed that the developed models performed better in predicting medium vs. low fatigue for binary classification, as depicted in the confusion matrices in Figure 5. For instance, the model for the FL2G2 and FB2G2 groupings correctly predicted 82 and 61 more samples at medium fatigue vs. low fatigue, respectively. One reason for this difference could be the lower proportion of datapoints at low fatigue compared to medium fatigue levels in our dataset. We expected lower low fatigue level samples, as the selected binary grouping method consisted of categorizing RPE levels <2 as low fatigue and levels of 3-6 as medium fatigue, leading to a higher number of task cycles. While our current model showed better performance for predicting medium fatigue levels, prediction for low fatigue levels can be further improved by adding a greater number of datapoints for low fatigue levels.  
After implementing SMOTE, which uses existing observations to generate synthetic data, performance did not substantially improve as variation across features did not increase [61]. Nonetheless, the practical implications of our study imply higher importance in detecting medium fatigue levels, as interventions would be needed for fatigue levels exceeding RPE >7. Considering this, we selected our models based on recall performance for medium fatigue levels in binary classification. Meanwhile, considering tertiary grouping methods, both FL3G2 and FB3G2 were able to predict low fatigue better than medium and high levels. However, looking at the confusion matrices, the samples at medium fatigue were much lower than the other two levels, potentially affecting the classification of the data into three groups. Specifically, due to lower samples of medium fatigue level, the model was not able to perform classification between low vs. high fatigue level correctly, as seen from the high values in the bottom-left boxes. One practical deficiency in our experiment was that an equal number of task cycles were not performed for each fatigue level, as study participants demonstrated varying levels of capacity. To resolve this, we recommend that studies include separate data collection sessions for each fatigue grouping to ensure equal datapoints in each grouping method (such as 100: low level (RPE of 1-2) and 100: medium level (RPE of 3-6)).
## 4 Network Comparative Analysis
### Testing Set Analysis  
To verify the accuracy and generalization ability of the proposed hybrid network's prediction, 1600 pieces data from 5518 to 5735 feet, Y3 and Y1 logging data is used as the training and testing set, and the MAE and R\({}^{2}\) of the prediction results from Inception, BiLSTM, and the proposed networks are analyzed, as presented in Figure 10 and Table 2. The prediction results of the Inception network show a significant deviation from the actual values. The BiLSTM network's predictions are closer to the actual values than those of the Inception network, but there are still errors. The proposed network outperforms the Inception network, which is the reason for the Inception network, which is the reason for  
tion and BiLSTM networks, while the peak predictions are much closer to the actual values. The proposed network's MAE is lower than that of the other two networks, demonstrating smaller errors, while its R\({}^{2}\) value is higher than both, demonstrating greater accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Networks** & **MAE** & **R\({}^{2}\)** \\ \hline Inception & 0.671 & 0.981 \\ BiLSTM & 0.215 & 0.991 \\ Inception–attention–BiLSTM & 0.211 & 0.994 \\ \hline \end{tabular} All models were trained on Y3 and tested on Y1.  
\end{table}
Table 2: Comparison of the predicted effects of the Inception, BiLSTM, and Inception–attention–BiLSTM.  
Figure 10: _Cont._  
The performance analysis reveals that the prediction performance of the proposed network is better than that of the individual Inception and BiLSTM networks.  
The correlation between true data and the prediction result of the proposed network is shown in Figure 11. The predicted values from the proposed network are evenly distributed around the actual values with small errors, indicating a high correlation and a greater reliability of the data.  
Figure 11: The correlation between the true Vs and the Inception–attention–BiLSTM hybrid network predicted.  
Figure 10: The prediction results of the three networks. (**a**) Inception network, (**b**) BiLSTM network, (**c**) Inception–attention–BiLSTM hybrid network.  
The prediction results demonstrate the superior performance of the proposed network compared with standalone Inception and BiLSTM. While the predictions from both standalone Inception and BiLSTM exhibit trends that align with data changes, the proposed network's predictions closely match the actual data. This suggests that the proposed network compensates for limitations in the standalone Inception network's consideration of data dependencies and addresses the BiLSTM network's challenges with multi-scale feature extraction.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Methods  
#### 2.2.1 Hazardous Weather Testbed Design  
The HWT takes place between April and June of each year. During this period, there is often significant convective activity over portions of the continental United States (CONUS), giving HWT forecasters daily opportunities to evaluate products in real time. The HWT consists of two coordinated programs, one that focuses on improving prediction (Experimental Forecast Program (EFP)) and another on the warning systems (Experimental Warning Program (EWP)). While NUCAPS was evaluated in the EWP, we describe both because the programs are coordinated. In the morning, the EFP produces convective-centric forecasts, which provide spatial and temporal focus regions and strategy for the EWP to utilize later in the day. The EWP typically consists of six forecasters, who apply to the HWT and are selected by the facilitators, most from the NWS but often includes military and broadcast meteorologists. To get up to speed quickly, forecasters in the EWP receive digital training videos for each product demonstrated in the HWT, and on arrival, \"quick guides\" to reference during forecast and warning exercises. Four days of the week included live forecasting for eight hours, and the last day was used as a debrief session and presentations (Tales from the Testbed, [[https://hwt.nssl.noaa.gov/tales/](https://hwt.nssl.noaa.gov/tales/)]([https://hwt.nssl.noaa.gov/tales/](https://hwt.nssl.noaa.gov/tales/))).  
The forecasters in the EWP begin start their day with a morning briefing. During this briefing discussion, forecasters can give verbal product feedback and ask questions that they had from the day before. Forecasters were paired into groups and assigned a region based on the EFP forecast discussion and the Storm Prediction Center (SPC) convective outlook. The forecaster can issue warnings on potential severe storms using the AWIPS workstation, which were internal to the HWT. If the hazard risk dissipates early in the shift, a backup region is used.  
To document feedback, we use three methods to collect data from participating forecasters during the HWT, (1) written descriptions and screenshots, (2) survey responses, and (3) a debrief at the end of the week. Aside from the in-person meetings and debriefs, the responses are anonymous. As developers, we are present in the room but do not interfere with forecasts as our primary goal is to observe, respond to questions, engage in discussion, and to resolve technical glitches if they occur.
## 5 BER Estimation for eSSK  
In order to examine how the use of the optical filter affects the performance of the VLC system, its BER performance is investigated. The modulation technique of SSK will be implemented in this VLC system. This technique is a kind of spatial modulation where every LED lamp will transmit a different symbol when it is on. For \(N_{b}\) bit per symbol, the number of symbols \(N_{s}\) that can be created is [48]  
\[N_{s}=2^{N_{b}} \tag{9}\]  
Therefore, for 2 bit per symbol, four transmitters are required, one for each symbol. In order to reduce the probability of error, different pulse durations for each symbol can be used by deploying the technique of eSSK. More specifically, the time duration of each symbol will be \(\tau_{i}T\), with \(T\) being the duration of symbol of the SSK technique and \(\tau_{i}\) being the factor that corresponds to the specific pulse width in eSSK.  
The BER for the eSSK scheme is given as [21; 22]  
\[BER\leq\frac{4}{N(\text{N}-1)\log_{2}(\text{M})}\sum_{j=1}^{N-1}\sum_{i=j+1}^{N }Q\left(\sqrt{\frac{TN}{4S}SNR\left(\left\|\tau_{i}h_{i1}-\tau_{j}h_{j1}\right\| _{F}\right)^{2}}\right) \tag{10}\]  
with \(N\) being the number of transmitters; \(M\) representing the number of symbols of the system; \(S=\sum\limits_{i=1}^{N}\tau_{i}\), \(\left\|\mathbf{l}\right\|\)**-**\(\left\|\mathbf{r}\right\|\)** standing for the Frobenius norm for matrices; and \(h_{ik}\) being the channel gain between the _i-th_ transmitter and the _k-th_ receiver, where, for our system, \(k=1\), consisting of the channel's gain matrix \(H(t)=\left[\begin{array}{cccc}h_{11}&h_{12}&h_{13}&\ldots&h_{1N}\\ \end{array}\right]^{T}\)[21; 22]. The channel gain matrix, \(H(\mathbf{t})\), for every transmitter can be calculated as the ratio of the total received optical power on the receiver to the transmitted optical power [49]:  
\[h_{i1}=\frac{P_{r_{i}}}{P_{t_{i}}} \tag{11}\]
## 2 System Model of Aircraft
### Channel Models and Requirements in the Aircraft  
The requirements of aircraft wireless communication systems are determined as four kinds of systems, which are categorized according to locations and types of data rate as Low Inside (LI), Low Outside (LO), High Inside (HI), and High Outside (HO) subsystems. Where L and H mean low data rate and high data rate, respectively. In this paper, we focus on the low data rate system because the bandwidth of the low data rate system is different from the high data rate system. In other words, when the data rate is below 10 kbit/s it is the low data rate system. In contrast, when the data rate is over 10 kbit/s it is the high data rate system.  
A general channel model for aircraft wireless sensor networks is applied. The channel model has total channel response that consists of large-scale fading and small-scale fading. The key parameter of large-scale fading is path loss and shadowing fading, and the key parameter of small-scale fading is multi-path fading. According to the channel response, the total channel response can be represented by Equation (1), which is defined by ITU-R for a aircraft [6].  
\[L=\gamma_{ij}\times Y\times X. \tag{1}\]  
where \(\gamma_{ij}\) is path loss, \(Y\) is shadowing fading, and \(X\) is multi-path fading. The path loss \((\gamma_{ij})\)[6] can be obtained by:  
\[\gamma_{ij}=C_{1}d^{-n}f^{-k}. \tag{2}\]  
where \(C_{1}\) is a constant offset, \(d\) is the distance between transmitter and receiver, \(n\) is exponent of distance, \(f\) is the center frequency for aircraft wireless communication, and \(k\) is exponent of the center frequency. In addition, the components \(i\) and \(j\) of path loss \(\gamma_{ij}\) mean transmitter (\(i\)) and receiver (\(j\)) node, respectively. Thus, the gamma \(\gamma_{ij}\) means path loss between transmitter(\(i\)) and receiver (\(j\)). Furthermore, there are three path losses between source node (\(i\)) and relay node (\(j\)), source node (\(i\)) and destination node (\(j\)), and relay node (\(i\)) and destination node (\(j\)). In this paper, according to the intra-communication network in the aircraft group B parameter (inter-cabin) and the group D parameter (inter-cabin-to-exterior) from Table 1 are applied for \(C_{1}\), \(n\), and \(k\) parameters of Equation (2).  
Figure 1: Examples of the system model.  
In the case of the shadowing fading (\(Y\)) the inter-cabin and inter-cabin-to-exterior parameters are referred. Thus, the maximum shadowing fading can be applied by 4.66 dB. Finally, the multi-path Rayleigh fading is applied for small-scale fading (\(X\)). We assume that the Rayleigh distribution characteristic is the same as Nakagami distribution. Thus, the Nakagami-m fading channel model is applied, which experiences frequency flat fading.