esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach for Environmental Risk Assessments of Living Modified Organisms in South Korea
## 3 Results and Discussion
### Approach to New Tools  
The approach proposed by the EPA for conducting LMO risk assessment for target organisms is based on the traditional steps, namely laboratory assessment (toxicity assessment) and field trials (exposure assessment) (Figure 2a) [23]. Among laboratory-based risk assessment methods, the artificial feeding bioassay is one of the most common approaches used for toxicity assessment; it uses a recombinant LMO protein diluted to the correct concentration and pipetted onto the surface of a solidified artificial diet [29; 30; 31]. Its advantages are that it provides rapid and easy application and uses significantly less LMO protein [29]. However, with artificial diets, there are disadvantages in that there can be an uneven distribution of LMO protein on the artificial diet surface, leading to non-uniform treatment and the inconsistent exposure of larvae [30]. Another major drawback of artificial diets is the instability of LMO proteins in artificial diets [30]. On the other hand, field trials have the advantage of using LMOs directly to evaluate putative ecological risks in the food chain. Furthermore, field trials have been used to collect a vast amount of otherwise anonymous information on how LMOs affect environmental changes in the field and natural ecosystems [31]. Nevertheless, there are three reasons for this lack of consideration in domestic field trials. (1) The disadvantage of field testing is that it is time-consuming and costly [8; 18]. (2) Human resources and legal forces are inadequate to strictly manage all the confined fields for LMO risk assessment [7; 18; 32]. (3) There is a lack of long-term field trial data for LMOs that modify the domestic state; therefore, it is difficult to set the isolation distance [33]. Eventually, an approved LMO cannot be guaranteed upon adventitious release into the domestic environment.  
We propose a new LMO risk assessment tool that compensates for limitations associated with laboratory assessments and field trials. Agroinfiltrated plants with transiently expressed LMO proteins were used in new trials that indirectly reflect the domestic natural ecosystem. The risk assessment of the scenario of the target species inhabiting South Korea using a mimic plant can overcome the disadvantages of present environmental risk assessment methods. The use of Vip3 Aa-agroinfiltrated plants can be regarded as a semi-field trial at an intermediate stage between laboratory and field trials (Figure 2b). The application of this tool in LMO risk assessment and review may have several advantages: (1) self-production would be possible without security disputes with LMO developers; (2) transient expression plants have a short production period; (3) a semi-field trial would be possible as an evaluation method using plants; (4) domestic target insects could be used; and (5) the risk to the natural ecosystem would be low, even if the agroinfiltrated plants were unintentionally released from the isolated field.
## 6 Example Analysis
### Comparison and Analysis of Prediction Results  
To assess the accuracy of the model proposed in this study, a comparative analysis is conducted between the proposed model and three other prediction models: Transformer, Transformer-BiLSTM, and VMD-Transformer-BiLSTM. The same dataset is utilized for both model training and testing, and error analysis is performed on the results from both phases.
## 2 Materials and Methods
### Discretized Network Model  
In this study, we employed a gene-wise discretized network model to investigate the complex dynamics of gene regulatory networks. A discretized network is represented by a directed graph \(G(V,A)\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) is a set of nodes, \(A=\{(v_{i},v_{j})\}\subseteq V\times V\) is a set of interactions, and the state value of gene \(v\) at time \(t\), \(v(t)\) is represented by \(l\) discrete values \(\{0,1,2,\ldots,l-1\}\). We note that this is called a Boolean network if \(l=2\) for all genes in \(V\). Consider a target node \(v\in V\) regulated by \(k\) genes \(u_{1},u_{2},\ldots,u_{k}(u_{i}\in V)\). Let \(E\) and \(E_{i}\) be the sets of discretized expression values of genes \(v\) and \(u_{i}\), respectively. The value \(v(t+1)\) is updated by a discrete function \(f:E_{1}\times E_{2}\times\cdots\times E_{k}\to E\) of the values of \(k\) regulatory genes \(u_{1},u_{2},\ldots,u_{k}\) at time \(t\). Hence, the update scheme of \(v\) can be described by the following formula:  
\[v(t+1)=f(u_{1}(t),u_{2}(t),\ldots,u_{k}(t)) \tag{1}\]  
We note that the update time lag used in this study is one and that the number of all possible functions with respect to \(f\) is \(l^{\Pi_{k}l_{i}}\), where \(l\) and \(l_{i}\) are the cardinalities of \(E\) and \(E_{i}\), respectively.
## 6 Discussion
### Comparative Evaluation with Existing Models  
To contextualize these results, the proposed LoRA-BiomedBERT classifier was compared with two recent state-of-the-art approaches: **SynerGNet** and **HAN-DDI**. While SynerGNet delivers strong performance, it depends on cell-line omics data, which limits its applicability in real-time clinical settings. HAN-DDI, despite its high F1 score and strong generalization for both known and novel drugs, requires structured biomedical triples and cannot process unstructured narrative data directly. In contrast, our approach operates entirely on raw DrugBank sentences, requires no experimental or graph-based inputs, and achieves over 70% accuracy with just approx. 12K trainable parameters. It offers a practical trade-off between interpretability, deployment simplicity, and data accessibility (see Table 5).  
Apart from independent implementation, the suggested polarity-aware classifier is also a contender for inclusion into more general hybrid diagnostic systems. Especially, we view it in line with [PERSON] et al.'s \"Hybrid Intelligent Medical Diagnosis System\" [20], which combines symbolic thinking and connectionist models for enhanced clinical inference. The technique we propose might improve such models by providing sophisticated DDI classifi  
Figure 4: Comprehensive validation analysis comparing pretrained and LoRA-tuned BiomedBERT models. (**a**) Confusion matrix for the base model, showing poor classification of synergistic samples. (**b**) LoRA-enhanced model confusion matrix, with major improvements in both classes. (**c**) Performance comparison by metric (accuracy, F1, precision, recall) between both models. (**d**) ROC curves showing near-identical AUCs for synergistic and antagonistic prediction. (**e**) Per-class AUC scores confirm strong separability. (**f**) Precision–recall curves further highlight LoRA’s improvements across confidence thresholds.  
cation from unstructured text, hence strengthening the system's capacity to understand challenging pharmacological environments within multimodal diagnostic pipelines.
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 4 Conclusions  
This study systematically analyzed sedimentary parameters of eight global braided river deltas using Google Earth, integrating advanced data processing and statistical methods, and drew the following key conclusions:  
1. Google Earth proves to be an effective tool for quantitative multi-parameter extraction. Through sensitivity analysis and listwise deletion for missing data, reliable morphological parameters were obtained. The quadratic relationships between channel wavelength and amplitude (R\({}^{2}\) = 0.537-0.913) reflect nonlinear energy distribution during sedimentation. Positive quadratic coefficients, as seen in the Yukon-Kuskokwim delta, indicate high sediment supply, while negative ones, such as in the Poyang Lake delta, are associated with energy dissipation due to channel avulsion.
2. Parameter distributions show significant correlations with depositional environments. Statistical tests, including Kruskal-Wallis (H = 12.73, \(p\) = 0.005) and Mann-Whitney U tests, reveal that short wavelengths (<4000 m) mainly occur in proximal settings, where the sediment supply is abundant. High amplitudes (average > 800 m) are typical of rapid deposition areas, often found in the upper plain zones of deltas. For example, 65% of bifurcation angles cluster at 30-60\({}^{\circ}\), reflecting balanced hydrodynamics, which is distinct from fan deltas.
3. The nonlinear coupling of wavelength and amplitude provides a solid quantitative basis for sedimentary dynamics analysis. This relationship not only helps explain the formation mechanisms of different deltaic facies but also enables the prediction of sedimentary patterns. For instance, the negative correlation between bifurcation spacing and sediment supply is demonstrated by the fact that 54% of Poyang Lakedelta's spacings are <2000 m, indicating dense channel networks, while 80% of Lena River delta's spacings are <15,000 m, suggesting stable main channels.
* 4. A three-zone geological knowledge base model was established, integrating statistically validated thresholds. The model categorizes deltas into upper plain, middle front, and lower prodelta zones, each characterized by specific parameter ranges. By parameterizing sedimentary characteristics, this model effectively guides the exploration of subtle hydrocarbon reservoirs. For example, the model can predict reservoir connectivity based on channel bifurcation angles and wavelength-amplitude relationships, providing critical inputs for reservoir prediction and enhancing the accuracy of hydrocarbon exploration in complex basins.  
Conceptualization, X.Y. and M.D.; writing--original draft preparation, X.Y.; writing--review and editing, S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Performance Metrics
### Capacity Analysis  
The capacities of CP-OFDM, TDS-OFDM, and eOFDM are compared based on  
\[C=\max_{p(x)}I(X;Y) \tag{9}\]  
where \(I(X;Y)\) represents the mutual information between channel input \(X\) and channel output \(Y\) over all input probability distributions of the channel. It should be pointed out that for the given transmission schemes, binary phase shift keying (BPSK) modulation and hard decision decoding (9) can be simplified as  
\[C=1-H_{b}(\epsilon) \tag{10}\]  
where \(H_{b}(.)\) represents a binary entropy function, and \(\epsilon\) denotes the probability of the bit error.
# End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution
###### Abstract
A dense connection structure is also used between each convolutional layer to achieve feature reuse in the network learning process. The simulation results show that the BER performance and robustness of the proposed scheme are better than those of conventional pilot-based channel estimation methods.
## 4 Results and Discussion
### Survey  
#### 4.3.1 Cool Roofs  
We interviewed 64 residents living in the cool roof installation zone in Gimhae. According to the survey in Table 5, most residents spent an average of \(\leq\)6-12 h (50.0%) in cool roof buildings, followed by 12-18 h (34.4%), 18-24 h (7.8%), and \(<\)6 h (7.8%). The results showed that 28.5% of residents spent the hours from 18:00 to 24:00 or from 6:00 to 12:00 in cool roof buildings, followed by 23.5% from 15:00 to 18:00 and 19.5% from 12:00 to 15:00. Detached houses constituted 70.3% of cool roof buildings, followed by apartments (9.4%), row houses (4.7%), multi-family houses (7.8%), and others (5%). Most residential roofs were composed of slabs (81.3%); only 10.9% of roofs were composed of panels. Climate change mitigation activities were perceived as \"very important\" by 31.3% of respondents, followed by \"important\" (60.9%), \"not important\" (1.5%), and \"do not know\" (6.3%), suggesting that most respondents recognize the need to mitigate climate change. When respondents were asked if they were aware of cool roof technology, 15.6% indicated that they were well aware of it, 50.0% indicated that they had heard of it, and 34.4% indicated that they were not aware of it. We also found that 9.4% of respondents were very interested in cool roof technology, whereas 18.8% were interested, and 64.1% were neutral. After cool roof installation, 4.7% of residents perceived a \"very likely\" decrease in indoor temperature, followed by 34.4% \"likely\", 48.4% \"neutral\", 12.5% \"unlikely\", and 0% \"very unlikely\"; thus, most respondents felt a reduction in temperature as a result of cool roof installation. When asked whether air conditioner use would decrease after cool roof installation, 3.1% said it was \"very likely\", followed by 29.7% \"likely\", 46.9% \"neutral\", 20.3% \"unlikely\", and 0.0% \"very unlikely\". When asked whether cool roofs would reduce the occurrence of sleep disturbance related to heatwave, 3.1% of respondents said it was \"very likely\", followed by 21.9% \"likely\", 54.7% \"neutral\", 17.2% \"unlikely\", and 3.1% \"very unlikely\". After cool roof installation, 3.1% of respondents reported that a decrease in health issues such as stress or loss of stamina related to heatwaves was \"very likely\", followed by 20.3% \"likely\", 57.8% \"neutral\", 18.8% \"not likely\", and 0.0% \"very unlikely\". The large proportion of \"neutral\" responses suggests that many respondents did not experience a significant reduction in stress. Residents reported their satisfaction with indoor temperature reduction in relation to cool roof installation as \"very satisfied\" (3.1%), \"satisfied\" (28.1%), \"neutral\" (51.6%), \"dissatisfied\" (15.6%), or \"very dissatisfied\" (1.6%). These results indicated a generally satisfactory response. When asked to express their overall satisfaction levels, 1.6% of
# Nutritional Analysis of Red-Purple and White-Fleshed Pitaya (_Hylocereus_) Species
## 3 Results
### The Difference of Volatile Compounds between Red- and White-Fleshed Pitaya  
The aroma in fruits is an important parameter to attract consumers and commercial market competition in pitaya fruit [67]. In addition to fruit flavor, volatile compounds have also been reported in some studies to play important roles in pitaya fruit aging, biotic-abiotic stress factors, and responses to plant disease [29; 51; 67; 73; 74]. In the present study, 39 volatile compounds were detected in pitaya fruit flesh by HS-SPME/GC-MS techniques (Table 3). Regarding their chemical composition in the samples tested, they were shown to be complex mixtures of many components. Table 3 shows the compounds identified, retention time and percentage obtained by GC-MS. Two different assayed SPME fibers (PDMS/DVB, 85 um, blue) and (PDMS, 100 um, red) were used for the detection of volatile compounds. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenolic Contents** & **Red-Fleshed** & **White-Fleshed** \\ \hline Gallic Acid & 0.17 \(\pm\) 0.02 & 0.16 \(\pm\) 0.03 \\ Caffeic Acid & 0.13 \(\pm\) 0.02 & 0.12 \(\pm\) 0.02 \\ \(p\)-Coumaric Acid & 0.16 \(\pm\) 0.07 & 0.16 \(\pm\) 0.04 \\ Ellagic Acid & 0.10 \(\pm\) 0.03 & 0.15 \(\pm\) 0.02 \\ Myricetin & 0.33 \(\pm\) 0.02 & 0.47 \(\pm\) 0.03 \\ Quercetin & 3.43 \(\pm\) 0.16 & 3.09 \(\pm\) 0.10 \\ Kaempfer & 0.21 \(\pm\) 0.04 & 0.26 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Phenolic contents of two pitaya species (_Hylocereus_ spp.) (mg/100 g). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RI** & **R.T.** & **Compound Name** & \multicolumn{3}{c}{**Red SPME (PDMS)**} & **Blue SPME (PDMS/DVB)** \\ \cline{3-6}  & & & **Red Pitaya Pulp** & **White Pitaya Pulp** & **Red Pitaya Pulp** & **White Pitaya Pulp** \\ \hline \multicolumn{6}{c}{Alcohols} \\
1777 & 40.861 & 1-Hexadecal & 6.54 & 4.17 & - & - \\
1587 & 36.229 & 1-Tetadecamol & - & 6.69 & - & - \\
1027 & 19.111 & 2-ethyl-hexanol & - & - & 2.6 & - \\
1472 & 33.177 & Lauryl alcohol & - & - & - & 37.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile profiles of red and White-fleshed pulp pitaya species using red and blue SPME fibers by HS-SPME/GC/MS techniques. Retention Time (R.T) percent (%). A total of 39 volatile compounds and six chemical groups including the alcohols, terpenenes, ketones, aldehydes, esters, acids and other compounds were identified (Table 3). Similarly, [PERSON] et al. [74] and [PERSON] et al. [75] identified a total of 84 and 49 volatile compounds in pitaya fruit by using DVB/CAR/PDMS fiber (Divinylbenzene/Carboxen/Polydimethlysiloxane; Gray). These compounds also mainly include aldehydes, esters, alkanes, ketones, terpenenes and alcohols.
## 2 Materials and Methods
### Autoencoder Model  
The concept of an autoencoder was originally proposed by [PERSON] in 1987 [46]. It is an artificial neural network designed to acquire efficient representations of input data, primarily for dimensionality reduction or feature extraction. It comprises two components, namely an encoder and a decoder. The encoder compresses the input data into a reduced-dimensional representation, referred to as the \"latent space\" or \"code\", whereas the decoder utilizes the compressed latent representation to recreate the original input data with maximal fidelity. Since it was invented, numerous autoencoder variations have been effectively used in a variety of domains, including natural language processing [47; 48; 49], computer vision [50; 51; 52], and speech recognition [53; 54]. In our study, we employed a conventional autoencoder with a reasonable number of hidden layers to achieve a compromise between efficiency and training duration [55; 56]. Unlike traditional autoencoders that aim to reconstruct the input data itself, the proposed model is trained to predict the next state \(v(t+1)\) given the current state \(v(t)\) as input. This architecture retains the encoder-decoder structure, but the training objective shifts from input reconstruction to temporal state prediction. It seeks to reduce the discrepancy between the actual and predicted values by minimizing the loss function. By learning a compressed latent representation of \(v(t)\) that captures temporal dynamics, the decoder is optimized to generate the corresponding \(v(t+1)\) rather than reproducing \(v(t)\). While the model may produce diverse gene expression profiles due to the limited dataset, this variation is consistent with natural biological variability. Instead of evaluating the biological plausibility of the synthetic data directly, we assess its utility through downstream network inference, interpreting the successful reconstruction of gene regulatory networks as an indirect yet meaningful indicator of data quality.
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
## 3 Nanomaterial Swelling Oil Displacement Mechanism  
Traditional methods such as water flooding mainly rely on osmotic pressure to drive water molecules into crude oil, but the effect is limited. In addition, polymer flooding reduces the flow ratio by increasing fluid viscosity, but its effect is affected by high-temperature and high-salt environments [66]. Nanoparticles show advantages in the field of chemical flooding due to their special physical and chemical properties. Nanoparticles usually have smaller particle sizes and larger specific surface areas [67]. In addition, the hydroxyl groups on the surface can adsorb metal cations and organic matter. Based on this principle, in the oil-water system, nanoparticles can adhere to the oil-water interface, prompting the surface active substances originally present on the oil film to enter the oil phase in the form of reverse micelles, and the nanoparticles themselves will be adsorbed on the oil-water interface [68]. According to this principle, the volume of crude oil will be expanded to a certain extent.
## 4 Results
This suggests that interference-based logic operations, such as NOR and XNOR, are more affected by ASE due to phase noise accumulation and loss of contrast in constructive and destructive interference mechanisms. At P\({}_{\text{ASE}}\) = 4 \(\mu\)W, NOR and XNOR operations approach near-failure states, with QF values plunging to 0.493 and 0.007, respectively. Meanwhile, the AND gate retains some level of performance with a QF of 6.78, demonstrating relatively better tolerance to ASE. The rapid deterioration of XNOR performance at high ASE levels suggests that its dependence on both AND and NOR operations compounds its susceptibility to noise. At the highest P\({}_{\text{ASE}}\) levels (4.5 \(\mu\)W and 5 \(\mu\)W), QF values for all logic gates drop drastically, with the AND gate falling to 2.45, while \(\overline{\text{AB}}\) and \(\overline{\text{AB}}\) decline to 1.88 and 1.21, respectively. The NOR and XNOR functions become completely non-functional, highlighting their extreme sensitivity to ASE. These results, as shown in Figure 6, emphasize the significant impact of ASE on CR-SOA-MZI-based logic operations at ultra-high data rates. The AND operation demonstrates the highest resilience, while XNOR is the most vulnerable. The rapid decline in the QF with increasing ASE suggests that mitigating ASE is crucial for maintaining the performance of AOLGs. Future improvements could focus on optimizing SOA gain dynamics, employing ASE suppression techniques, and refining phase stabilization methods to enhance the robustness of these logic gates in high-speed optical computing and communication applications.
## 3 Results and Discussion
### Outer Surface Temperature  
Figure 4 shows the visible light photo of the compressor during thermal imaging and Figure 5 shows partial infrared photos at 1.5 bar from the same perspective as Figure 4. The temperature span of the color palette for all infrared photos was uniformly set to 14-70 \({}^{\circ}\)C in order to visually present the temperature level through color. In addition, temperature displays for ten points from a to j were added, and their temperature values are listed on the right side of the infrared photo.  
It can be seen that the temperature distribution at the motor and compressor was relatively uniform and the average temperature was low. However, the outer surface temperature of the intercooler and the exhaust pipe connected to it was significantly higher than elsewhere. Due to the fact that the surface temperature at the intercooler was closest to the compressor discharge temperature, its variation best reflected the impact of rotating speed and cooling water flow rate on compressor performance, which was also the reason for setting the greatest number of points at this location.  
Comparing the color and temperature value of the intercooler in the photos, it can be observed that the outer surface temperature increased with the increase in the rotating speed. This is because a higher rotating speed causes the compressor to generate more heat per unit time. Meanwhile, the outer surface temperature decreased with the increase in the cooling water flow rate, which indicated that the increase in the cooling water flow rate had, to some extent, reduced the discharge temperature.  
Figure 4: Visible light photo of the compressor.  
Figure 5: Infrared photos of the compressor at 1.5 bar. (**a**) Cooling water flow rate of 3 L/min and rotating speeds of 6000 rpm, 8000 rpm, and 10,000 rpm, respectively. (**b**) Cooling water flow rate of 10 L/min and rotating speeds of 6000 rpm, 8000 rpm, and 10,000 rpm, respectively.