esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Discussion of Results  
Differences in the phase of the wavefront measured under different environmental conditions are mainly attributed to differences in the properties of different interfering materials in the atmosphere. On rainy days, for example, water droplets in the air lead to higher _RMS_ values and more irregular phase fluctuations through enhanced scattering and refraction effects. This phenomenon is consistent with the Mie scattering theory that larger particulate matter causes more severe wavefront aberrations. In contrast, in dusty and snowy weather, particulate matter causes increased irregularities in the light field, mainly through scattering and partial absorption. In contrast, under clear-sky conditions, thermal turbulence due to temperature gradients is the main source of wavefront aberrations, with a large number of small-scale turbulent eddies causing random phase ups and downs. In addition, the statistical analyses show that the _RMS_ values are positively correlated with humidity, particle density, and wind speed, which further indicates the complex effect of environmental conditions on wavefront aberrations. Through these analyses, we reveal the physical nature of wavefront aberrations and their environmental relevance, providing insight into the propagation properties of light waves in atmospheric turbulence.  
Differences in wavefront aberration assessment metrics reveal the complex effects of turbulent conditions on optical wavefronts. For example, peaks and valleys (\(PV\)) reflect extreme differences in the phase of the wavefront, which are significantly higher in rainy, dusty, and snowy weather due to localized scattering and refraction by water droplets or particulate matter. Meanwhile, stable conditions such as cloudy skies have a lower \(PV\) and a more homogeneous wavefront. The root mean square (_RMS_) value is more concerned with the overall quality of the wavefront. On rainy days, the large number of water droplets in the air leads to an increase in the overall aberration of the wavefront and a significant increase in the _RMS_ value. Under cloudy conditions, the _RMS_ value is lower, and the overall deviation of the wavefront is smaller. Gaussian curvature provides an analysis of the degree of local curvature of the wavefront, showing the effect of turbulence strength on the local aberrations of the wavefront. For example, the local curvature fluctuates significantly  
Figure 18: Wavefront analysis at different wavelengths.  
in severe weather, such as sandy and dusty conditions, whereas under conditions such as sunny or cloudy days, the curvature distribution of the wavefront is smoother.
## 3 Results and Discussion
### System Identification
#### 3.1.2 Data-Based Mechanistic Modelling of CIVD
The results showed that the average amount of dissipated heat from the skin of the finger before the CIVD kick-off (termed here as _latent heat for CIVD_ or LH-CIVD) is 102.10 (\(\pm\)22.36) joules. We found that there was a significant difference between the test subjects (interpersonal) that exhibited the hunting effect with respect to their latent heat for CIVD (LH-CIVD), and this at a significance level of 1%. In other words, the LH-CIVD can be considered an individual-dependent characteristic. The proposed LH-CIVD term combined three main factors that are believed to affect the thermostatic responses, namely, the rate of cooling [35], the temperature to which the skin is cooled [36], and the duration of exposure [37]. The second-order TF (6) describing the CIVD reaction was decomposed into two first-order TFs with a parallel configuration (the same as described in Figure 5) as the most mathematically suitable configuration (average \(R_{2}^{T}\) = 0.93 \(\pm\) 0.01). The resulting time constants \(\tau_{1}\) and \(\tau_{2}\) of \(TF_{1}\) and \(TF_{2}\), respectively, were calculated and compared for all test subjects exhibiting the CIVD reaction, as shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\tau_{1}\) (**min**) & \(\tau_{2}\) (**min**) \\ \hline Mean & 7.96 & 0.81 \\ Standard deviation & 4.35 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean and standard deviation of the calculated time constants, of the decomposed two first-order TF, obtained from all test subjects with CIVD reaction during the two sets of experiments (Exp.1 and Exp.2, Phase I). Figure 8: Measured and simulated finger skin temperature during the CIVD (upper graph), the dissipated heat rate from the finger and the area-under-the-curve represents the amount of dissipated heat in joules (lower graph). In the same fashion as with the CIVC reaction, the calculated time constants suggested two parallel responses with clear and different dynamics. The first response, represented by the first-order model \(TF_{1}\), was slow (\(\tau_{1}\approx 7.96\) min) in comparison to that represented by the first-order \(TF_{2}\) (\(\tau_{1}\approx 0.81\) min). The fast response can be attributed to the gradual opening of the AVAs during the CIVD. The time constants, \(\tau_{1}\) and \(\tau_{2}\), for the CIVC and CIVD responses were compared by the Kruskal-Wallis test. It was found that there was no significant difference, at a significance level of 1%, in the first time constant (\(\tau_{1}\)) for both CIVC and CIVD. The same results were noticed for the second time constant (\(\tau_{2}\)).
# A Few-Shot Object Detection Method for Endangered Species
## 2 Fine-Tuning-Based Few-Shot Object Detection Algorithm  
The goal of FSOD is to rapidly detect new objects from a novel small number of samples. The FSOD method in this paper follows a two-stage process shown in Figure 2, where the classes of the training dataset can be categorized into the base class (\(C_{base}\)) and the new class (\(C_{novel}\)). The whole process can be divided into two phases: first, learning transferable knowledge on the dataset \(D_{base}\) with abundant samples; then, fast adaptation on the new class dataset \(D_{novel}\) with only a small number of samples, usually only K-shot (\(K-shot,~{}K\leq 30\)). Note that the base class \(C_{base}\) and the new class \(C_{novel}\) are non-overlapping, \(C_{base}\cap C_{novel}=\phi\), so that the generalization ability of the few-shot detector can be effectively evaluated. In this paper, we make improvements on the basis of the fine-tuning-based few-shot object detection method (FSCE), which not only has strong generalization ability but also outperforms the meta-learning-based and metric-learning-based few-shot object detection methods in terms of detection accuracy.  
The FSCE method uses Faster R-CNN [25] as the base detection model. The Faster R-CNN network consists of a backbone ResNet, a Feature Pyramid Network (FPN), RPN, and a two-layer fully connected sub-network as the feature extractor. First, the Faster R CNN is trained using rich base class samples (\(D_{train}=D_{base}\)). Then, the base detector is transferred to new instances of the balanced dataset and randomly sampled base instances (\(D_{train}=D_{base}\cup D_{novel}\)) by fine-tuning the base detector to the new samples. The backbone feature extractor is frozen during fine-tuning, randomly initialized weights are assigned to the box prediction network for the new classes, and only the classification and regression network, the last layer of the detection model, is fine-tuned.  
Fine-tuning-based few-shot object detection methods have shown remarkable results in coping with few-shot object detection problems. However, when dealing with endangered species, which are full of diversity and complexity, the feature details between some classes are highly similar, which makes the detection often misclassified, and the detection accuracy is not high. This paper addresses the above problems by firstly improving the feature extraction network backbone, secondly proposing the Attention-RPN, and finally introducing a weighted prototype comparison branch at the head of RoI to increase the inter-class differences so that the detection of endangered species can still show a high  
Figure 1: The training architecture consists of two phases: base training and a small amount of fine-tuning. Base training is performed using the Pascal VOC, while a small amount of fine-tuning is performed using a limited number of samples (K-shots) trained on the endangered species dataset.  
detection accuracy with only a small number of samples. The improved detection network is shown in Figure 2.
## 4 Experimental Results
### Two-Stream UDN
#### 4.2.4 Computational Complexity Analysis  
For comparing the computation efficiency of different models, we have computed the model scale and evaluated the running time, which are shown in Table 4. Table 4 also shows the corresponding hardware for achieving the running time. All the deep networks were executed on computers with GPU, the shallow net SDN is also executed on computer with GPU. UDN and the proposed two-stream UDN are executed on the computer with CPU. Although executed on the computer without GPU, the proposed method is much faster than SDN [22] executed on the computer with GPU, which has a competitive detection miss rate with our model. Very deep models such as CSP (Center and Scale Prediction) [33] back boned with ResNet-50 run on GPU obtain approximately the same speed with our model run on CPU and have much larger model parameters. Even the backbone network is changed to squeezed models such as MobileNetV1; their model is still very large and the speed-up execution time highly relies on expensive GPU hardware.  
Compared with the baseline model improved UDN (which has the same model size with UDN), our two-stream UDN model adds a subnet (Local network), which brings approximately 46,000 parameters increase and 0.025 s running time per image. Although the improvement of LAMR is only 1.07%, the Local network mainly reduces false positive samples, which is what the subnet designed for. As for the detection results of Caltech, the two-stream UDN reduces false positives by 8.6% compared with the improved UDN.
## 4 Modeling of the System Components
### Evaporator and Condenser  
Several geometry-independent models for single-phase, boiling, and condensing flows were used to determine the most accurate combination for the thermal modeling of the evaporator and the condenser of the ORC unit. Hence, hydraulic modeling of the PHEs was firstly elaborated, and then the developed hydraulic models were used in the thermal modeling.  
Figure 15: Measured and calculated pump electric power (**left**)and electromechanical efficiency (**right**).
## 1 Introduction
### Contributions of Article  
* In this paper, we propose a novel framework that utilizes NCMs to understand how business strategies and objectives interact with each other and determine which nodes are most impactful. To the best of our knowledge, it is the first time in related literature that a soft computing method such as NCMs has been used to evaluate smart port performance.
* The combination of static and dynamic analysis allows decision-makers to predict possible obstacles and facilitates what-if scenarios, thus prioritizing development areas with more confidence. In this way, stakeholders gain insights into the immediate impacts (static analysis) and evolving outcomes (dynamic analysis) of strategies (e.g., operational, financial, environmental). This dual approach not only solves current gaps in performance evaluation approaches but also delivers actionable insights for long-term competitiveness and innovation in the marine sector.
* In contrast to relying exclusively on FCMs, our model utilizes NCMs due to the increased capabilities of neutrosophic theory in efficiently handling the inherent uncertainty associated with expert judgments. By using neutrosophic logic, our suggested model offers a comprehensive approach for navigating the intricacies of smart port management, ensuring that the analysis accurately represents the various and sometimes contradictory viewpoints of stakeholders.
* To validate the usefulness, reliability, and objectivity of the proposed method, an illustrative example of smart port performance evaluation under group decision support environment is introduced. This example serves as a proof-of-concept to demonstrate the practicality of the suggested method. The findings show that NCMs may successfully model complex interrelationships between performance factors, giving useful insights for decision-making in real-world smart port management. This enhanced framework, which can increase the reliability of an NCM by incorporating the opinions of more than one domain experts, has the potential to reform decision-making processes in the smart port management field, offering stakeholders a stronger and more informed basis for strategic planning and operational optimization.
* Unlike traditional MCDM models, which often overlook the numerous linkages between strategies, our work introduces a new method that delves deeper into understanding these interrelationships. Traditional MCDM models typically evaluate factors affecting port performance using set criteria, but our proposed technique conducts a more in-depth analysis by considering the dynamic interactions between strategies.  
By adopting this innovative approach, we aim to illuminate the subtle interactions and dependencies between the various techniques, offering valuable insights into their overall impact on port performance.
## 2 Materials and Methods
### Neural Network Modelling of Specific Energy Requirements
The model underwent rigorous trial and error adjustments to fine-tune the network connection weights, minimizing the mean squared error (MSE) between the predicted and target values of SPE. ANN has been known to perform reliably and robustly even with smaller datasets; for this reason, it was selected for SPE estimations in this study. To assess the performance of the ANN model, the coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), and mean absolute error (\(MAE\)) were used as evaluation metrics [41; 42]. These parameters were calculated using Equations (11)-(13):  
\[R^{2}=\frac{\sum_{i=1}^{N}\left(\mathbf{O}_{i}-\overline{\mathbf{O}}\right)\times \left(\mathbf{P}_{i}-\overline{\mathbf{P}}\right)}{\sqrt{\sum_{i=1}^{N}\left(\mathbf{O}_{ i}-\overline{\mathbf{O}}\right)^{2}\times\sum_{i=1}^{N}\left(\mathbf{P}_{i}-\overline{ \mathbf{P}}\right)^{2}}}, \tag{11}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(\mathbf{O}_{i}-\mathbf{P}_{i}\right)^{2}}{N}}, \tag{12}\]  
\[MAE=\frac{\mathbf{1}}{N}\sum_{i=1}^{N}\left|\mathbf{O}_{i}-\mathbf{P}_{i}\right|, \tag{13}\]  
where \(N\) is the number of observations, \(\mathbf{O}_{i}\) is the observed values, \(\mathbf{P}_{i}\) is the predicted values, and the overhead bar (e.g., \(\overline{\mathbf{O}}\)) denotes the mean of the variable. Figure 5: Topology of developed neural network model (\(x_{1}\), \(x_{2}\) and \(x_{3}\) are input variables, and \(y\) is estimated output; \(b_{h}\) and \(b_{o}\) are the bias for the hidden layer and the output layer, respectively; \(W_{ij}\) and \(W_{jk}\) are the connection weights of input hidden layer and hidden output layer, respectively).
## 2 Experimental
### Methodology
#### 2.2.3 Batch Adsorption Test  
The adsorption process was conducted in the binary metal ion system which contains the mixture of Cu and lead (Pb) ion to validate the selectivity of the amine immobilized GMA-\(g\)-NWF and IRP samples towards Cu ion. Approximately 0.05 g of the prepared absorbent samples were tested by immersed into 50 mL of the solution with continuous stirring at a pH of 4 with 0.5 M HNO\({}_{3}\). The reaction time was allocated up to 8 h to ensure equilibrium adsorption for amine immobilizedGMA-\(g\)-NWF. The reaction time for IRP was set accordingly for comparison purpose. The residual metal in the solution was determined by Inductively Coupled Plasma-Optical Emission Spectrometry, ICP-OES (Thermo Fisher Scientific, iCAP 7200) (Waltham, MA, USA). The adsorption capacity (Q) was calculated via Equation (3).  
\[Q=\left(\frac{C_{0}-C_{e}}{1000m}\right)V \tag{3}\]  
where C\({}_{0}\) and C\({}_{e}\) are the initial and equilibrium concentrations (mg/L) of metal ion solution and V (mL) is the volume of the metal ion solution, and \(m\) (g) is the mass of the amine immobilized GMA-\(g\)-NWF.  
Moreover, the selectivity (\(K_{\mathrm{s}}\)) of the samples towards Cu was also appraised in the binary solution. was calculated by Equations (4) and (5). However, the Equation (4) requires the calculation of the distribution coefficient (\(K_{\mathrm{d}}\)), which define as Equation (5);  
\[K_{s}=\frac{K_{d}(\mathit{Copper})}{K_{d}(\mathit{Lead})} \tag{4}\]  
\[K_{d}=\left(\frac{C_{i}-C_{f}}{C_{f}}\right)\frac{V}{m} \tag{5}\]  
where, C\({}_{i}\) and \(C_{f}\) are the initial and final concentrations of metal ions (mg.L\({}^{-1}\)), V is the volume of solution (mL), and m is the mass of amine immobilized GMA-\(g\)-NWF (mg). \(K_{\mathrm{d}}\)(Copper) and K\({}_{\mathrm{d}}\)(Lead) are the distribution coefficients of Cu and Pb in dimensionless units, respectively [21].  
All experiments were performed in duplicate, and all measurements were done using the same instruments for consistency.
# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 3 Results and Discussion
### Participant Demographics and Sampling Approaches  
Another important aspect concerns the types of participants and users examined in the analyzed studies. A significant majority of the research (\(n=21\)) focused on higher education students, including bachelors, masters, and doctoral students (e.g., [37; 38; 39; 40; 41]), with data primarily collected through web-based surveys conducted in university settings (e.g., [31]). Among these, several studies specifically targeted business students (e.g., [34; 42; 43]) and English as a Foreign Language (EFL) students (e.g., [29; 44; 45]). Additionally, individual studies included healthcare students [46] and art and design college students [47], while others did not specify the students' study programs. Only one study focused on elementary education students [48].  
Beyond students, a smaller number of studies examined educators, particularly higher education teachers [32; 49], pre-service teachers [50], or a mix of educators and higher education students [35; 51]. Figure 2 illustrates the distribution of participant types in studies on GenAI adoption in education.  
Sample sizes varied significantly, ranging from the smallest group of 16 healthcare students [46] to the largest group of 1389 higher education students in a study by [PERSON] et al. [28]. Notably, smaller sample sizes--typically up to 400 participants--were more common, highlighting a tendency toward moderate-sized participant groups in these studies.
## 4 Experiment and Results
### Graph-Based Experiment
Scenario 2: Interest Topics  
This scenario investigated the second drop-down list and was able to choose any interest topics from it, as shown in Figure 13(a). After selecting one of the topics, it will show all users interested in this topic specifically (see Figure 13(b)). An example of this might be that the selection of racism as the topic for consideration would provide a figure portraying three users with an interest in this subject. Figure 11: The sample of Heterogeneous Graph in Plotly Library. Figure 12: Graph with Functionality. Figure 13: Graph-Types Scenario. Scenario 3: User Interests  
This scenario investigated the third drop-down list and was able to choose any users from the drop-down list, as shown in Figure 15a. Selecting one of the users will show all topics that this user was interested in. An example of this can be seen in Figure 15b. Scenario 4: Relationships between Users  
This scenario investigated the fourth drop-down list and, as shown in Figure 16a, was able to choose any users from a drop-down list. After selecting one of the users from the user list and selecting another from the third drop-down, it will produce a graphic representation of their collective interests, which enables one to study the inter-user relationships with interest topics, as shown in Figure 16a. In this example, these two users were similar and, hence, there was a link between them, while in Figure 16b, the users were dissimilar and, therefore, there was no correlation between them. Figure 14: Interest Topic Scenario. Figure 16: Relationships between Users Scenarios. Figure 15: User Interests Scenario.
# Optimization of Vacuum Freeze-Drying Process and Quality Evaluation of _Stropharia rugosanmulata_
## 2 Materials and Methods
### Vacuum Freeze-Drying Process of Stropharia rugosaannulata  
#### 2.3.1 Preprocessing  
Cleaning: First, all mushrooms with bent stipes or damaged surfaces were removed. Then, the roots of the remaining fresh _Stropharia rugosaannulata_ were trimmed, ensuring a 100% qualification rate. The materials were separated and processed according to the preset size standards. Then, the _Stropharia rugosaannulata_ were thoroughly washed, ensuring that no dirt or other foreign substances remained. Using running water, any surface dirt was washed away, and the surface water was drained.  
Bacteriostasis: The _Stropharia rugosaannulata_ were soaked in a 0.05% nisin solution for 30 min and then the solution was drained. This process inhibited bacterial growth during subsequent processing. The inhibitory action of nisin against spores of Bacillus and Clostridium pathogens has been recognized for well over 50 years [10]. Nisin specifically binds to lipid II, inhibiting the proper formation of the bacterial cell wall and increasing membrane permeability, ultimately causing the leakage of essential cellular contents and bacterial death. Additionally, by targeting lipid II, nisin's targeting of lipid II indirectly impairs the activity of enzymes involved in cell wall restructuring, such as transglycosylationes, thereby exacerbating the degradation of the bacterial cell wall [11]. After nisin treatment, the safety of the _Stropharia rugosaannulata_ slices is significantly enhanced, with minimal bacterial contamination during processing, meeting modern standards for food safety, health, and environmental friendliness.  
Color protection: After nisin treatment, to prevent browning, the _Stropharia rugosaannulata_ slices were immersed in a 0.45% sodium metabisulfite solution for 10 min and then rinsed with running water for 5 min, and the surface water was drained.  
Blanching, pre-cooking, and slicing: at a temperature above 98 \({}^{\circ}\)C, 0.3% saline was used to blanche the mushrooms for 4 min. This step ensured that the _Stropharia rugosaannulata_ was thoroughly cooked without becoming mushy, and it inactivated any enzymes to prevent browning. The addition of 0.3% edible salt to the blanche water provided some color protection and sterilization. Blanching and pre-cooking _Stropharia rugosaannulata_ make the mushroom's structure more compact, facilitating slicing. Pre-slicing increases the drying surface area and reduces the freeze-drying time [12]. When slicing, the mushrooms were laid flat and sliced diagonally at a 45-degree angle into round slices, with a thickness of 2-4 mm. The slices of the mushroom cap were wedge-shaped, with a width of 2-4 mm.  
Pre-cooling: The _Stropharia rugosaannulata_ slices were pre-frozen to shorten the freeze-drying time in the vacuum freeze-dryer. After removing the surface moisture, they were placed in a freezer at \(-20\)\({}^{\circ}\)C to \(-80\)\({}^{\circ}\)C for 48 to 72 h.