esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Differential Unigenes Related to Sesquiterpene Synthesis  
Sesquiterpenes are characteristic components of agarwood. Differentially expressed uniggenes were annotated to sesquiterpenoid/triterpenoid biosynthesis [ko00909] and terpenoid backbone biosynthesis [ko0900] pathways. Key terpenoid biosynthetic enzymes showed significant expression changes. Based on RPKM values, we predicted expression levels of MVA and MEP pathway enzymes in sesquiterpene biosynthesis. Terpenoid backbone pathway DEGs were predominantly upregulated versus BMZ controls, encoding 14 key enzymes--ACAT, HMGCS, HMGCR, MVK, PMVK, MVD, FDPS, DXS, DXR, MCT, MDS, HDS, HDR, and IDI (Figure 11)--critical for IPP precursor formation. Sesquiterpene/triterpenoid pathway DEGs were also mainly upregulated, encoding seven terpenen synthases: germacrene D synthase, germacrole synthase, germacrole synthase, squalene monooxygenase, farnesyl-diphosphate farnesyltransferase, premnaspirodiene oxygenase, costunolide synthase, and lupeol synthase (Figure 12). Heatmaps revealed treatment-specific expression patterns for enzymes in the MVA pathway (Figure 13), with cjYB1Z4 showing the highest expression for ACAT, HMGCS, and HMGCR. In the MEP pathway (Figure 14), expression variation was insignificant versus controls. This  
Figure 8: Functional GO enrichment.  
suggests coordinated enzymatic regulation and MVA pathway dominance in _A. sinensis_ induction. Sesquirerpenes are primarily synthesized via the cytoplasmic MVA pathway, whereas the MEP pathway operates in plastids, explaining MVA's pivotal role in agarwood formation. SS (sesquiiterpene synthase) expression was lowest in BMZ but elevated in CZ4 and cjYB1Z4 (Figure 15), indicating that fungal-insect co-induction potentially enhances SS expression to accelerate agarwood formation.  
Figure 10: KEGG pathway enrichment of downregulated genes. Note: A—LZZ4 vs. BMZ; B—JJB1Z4 vs. BMZ; C—CZ4 vs. BMZ; D—cjYB1Z4 vs. BMZ.  
Figure 9: KEGG pathway enrichment of upregulated genes. Note: A—LZZ4 vs. BMZ; B—JJB1Z4 vs. BMZ; C—CZ4 vs. BMZ; D—cjYB1Z4 vs. BMZ.  
Figure 11: Terpenoid backbone biosynthesis pathway. Note: Green indicates that the expression level of the enzyme involved in sesquiterpene biosynthesis in the sample is downregulated compared with the BMZ control, red indicates upregulation, and blue indicates both upregulation and downregulation.  
Figure 12: Sesquirereponoid and triterpenoid biosynthesis pathway. Note: Green indicates that the expression level of the enzyme involved in sesquiterpene biosynthesis in the sample is downregulated compared with the BMZ control, red indicates upregulation, and blue indicates both upregulation and downregulation.  
Figure 14: Heatmap of differential unigenes involved in the MEP pathway.  
Figure 13: Heatmap of differential unigenes involved in the MVA pathway.
## 3 Theoretical Background and Hypothesis Development  
### Elaboration Likelihood Model Theory  
The elaboration likelihood model (ELM) was proposed by [PERSON] and [PERSON] [30], which supplied a dual process theory to understand the effectiveness of persuasion and attitude changes [31]. ELM is based on the two-process model of human cognition in cognitive psychology, namely, human attitudinal changes may be caused by two influencing \"routes\"-- the central route and peripheral route [30]. The central route requires individuals to critically think over opinions on the information related to the issue. On the other hand, the peripheral route usually relies on environmental clues connected to the information, not catering to any grand thinking or cognitive efforts [32; 33]. Concretely speaking, the central route refers to the process requiring a high level of cognitive efforts, while the peripheral route indicates the process needing less cognitive efforts and may also be influenced by other factors [34].  
ELM has already been employed by many researchers to study factors from central and peripheral routes influencing user attitudes and decisions in information systems. For example, [PERSON] applied ELM to study the influence from the quality and number ofconsumer comments on attitudes towards consumer goods [32]. [PERSON] (2007) explained how the degree of participation in products (sensing personal relevance of the product) modified influences from central and peripheral clues on consumer purchasing intention [35]. [PERSON] (2016) demonstrated that self-efficacy significantly softened the influence on initial trust from quality of opinions [36].  
[PERSON] et al. believed that, although privacy decisions in the online environment were dissimilar to those under advertising or sales, the method for handling external information was similar. They expanded the range of the ELM and validated the dual route of the ELM in the mHealth privacy context by combining privacy fatigue and privacy calculus.  
We suggest that the process of users' privacy decisions in the social media context follows both a central route and a peripheral route. Specifically, the central route handles evidence related to the information, requiring time and effort to understand and investigate, before arriving at the ultimate judgment and attitudinal response. On the other hand, the peripheral route makes use of sample clues such as source factors and individual heterogeneity characteristics to affect the efficacy of privacy decisions.
# Axial Bearing Mechanism of Post-Grouted Piles in Calcareous Sand
## 4 Analysis of Grout Distribution and Bearing Mechanism of a Post-Grouted Single Pile
### Bond Strength between the Cement Grout and the Pile  
To evaluate the bond strength between the cement grout pressed into the side of the pile and the pile shaft, the two ends of the cement-stabilized mass of the ideal-geometry grouted pile were ground flat, and a pull-out test was conducted to detect the shear strength between the cement-stabilized mass and the pile shaft. The pull-out force-slip curves, between the cement-stabilized mass and the pile shaft in calcareous sand and silicone sand, obtained in this test are shown in Figure 10.  
Figure 9: Cement grout material of a single pile with grouting: (**a**) MP2; (**b**) MP3; (**c**) MP5; (**d**) MP6.  
Figure 10 shows that the relationship between the pull-out load and slip is approximately linear during the initial loading stage. When the slip amount is small, the pull-out force increases rapidly, and the initial slope is large. After reaching the peak point, the drawing load required by the specimen decreases gradually with increasing slip, and the pull-out force-slip curve shows a downward trend. After being reduced to a certain value, the required pull-out load of the specimen gradually stabilizes. Splitting failure of the siliceous sand specimen occurs during the pull-out process, which results in failure to measure the residual section of the pull-out force-slip curve. Figure 10 also shows that the maximum pull-out loads of the calcareous sand and siliceous sand specimens are 803 N and 971 N, respectively. To obtain the shear strength between the cement-stabilized mass and the pile, the pull-out force is divided by the surface area of the cement-stabilized mass that covers the length of the model pile. The maximum shear strengths of the calcareous sand and siliceous sand specimens are 142 kPa and 172 kPa, respectively. Since both ends of the cement-stabilized mass are ground flat, the cover length of the model pile is taken as 90 mm when calculating the shear strength. The shear strength obtained in this test is similar to the result obtained in the static load test. This may be due to the excessively long time that the specimens were tested in the pull-out test and drying shrinkage, which affects the bonding strength between the cement-stabilized mass and the pile shaft.
# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 2 Measurement and Analysis of Grain Inventory Quantity
### Steps for Checking the Quantity of Grain Stock
According to the above criteria, the formula for calculating the amount of natural loss in grain depots is as follows:  
\[W_{3}=\mu W \tag{5}\]  
where \(W_{3}\) is the natural loss mass of the grain, \(\mu\) is the natural loss coefficient, and \(W_{1}\) is the actual book mass of the grain put into storage. Accordingly, the formula for calculating the original quality of the grain depot can be calculated as follows:  
\[W_{4}=W_{1}+W_{2}+W_{3} \tag{6}\]  
The formula for the measured grain pile mass, also known as the empirical formula, is computed as follows:  
\[W_{4}=W(\sigma_{1}-\sigma_{2})/(100-\sigma_{2})+k\rho_{1}(V_{1}-V_{2})+\mu W \tag{7}\]  
The grain quality error rate for grain depots is calculated as follows:  
\[\gamma=(W-W_{4})/W \tag{8}\]  
where \(\gamma\) is the difference rate under percentage conversion. In the process of the actual grain quantity inspection, according to China's \"Food and Material Reserve Law Enforcement Supervision Work Regulations\", if the standard bulk grain difference rate is within \(\pm\)2% and the non-standard grain difference rate is within \(\pm\)3%, it is determined that the account is consistent. Otherwise, it is determined that there is a grain deviation, but it is also necessary to analyze the storage quality, incomplete grain, storage month, and other aspects of the stored grain in order to determine if there is a grain quantity problem. Thus, the regular grain count calculation is the basis for the grain count check.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Discussion and Analysis
#### 8.6.3 Comparison with Existing Systems  
Compared to centralized systems, our NFT-based solution offers enhanced security, transparency, and decentralization at the cost of lower raw performance. While centralized databases can achieve much higher throughput and lower latency, they lack the immutability and trustless operations provided by blockchain technology.  
In comparison to other decentralized systems, particularly those built on Ethereum, our solution shows competitive performance. The throughput of 33.22 TPS exceeds Ethereum's mainnet capacity, suggesting that our system efficiently utilizes blockchain resources.
# Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
## 3 Experimental Details
### Experimental and Measurement Equipment  
A copper-nickel sheet single-sided double-point resistance welding test schematic is shown in Figure 4a. The welding power supply uses a homemade variable polarity transistor-type resistance welding power supply with constant current, constant voltage, constant power, and mixed control mode. The power supply has a rated output current of 4 kA, a minimum control cycle of 10 us, a variable polarity pulse output, and an output accuracy of \(\pm\)1%. The power supply output is calibrated using a MIYACHI (Tokyo, Japan M,) MM410 resistance welding monitor. In this paper, the welding power supply using constant voltage control mode output and welding process power supply output voltage waveform are shown in Figure 4b, and the meaning of each segment is shown in Table 1. The welding head comprised a homemade parallel-gap pneumatic pressurised head, a pressure control range of 5 N-50 N, and a maximum stroke of 30 mm pneumatic pressurised head, with a 3 mm diameter chromium-zirconium-copper electrode.  
Figure 4: Resistance spot welding test. (**a**) Parallel gap single-sided double spot\(-\)welding schematic; (**b**) welding voltage waveform.  
Figure 3: Geometry of the welded specimens.  
In this paper, an AGX-10 kN electronic universal testing machine produced by Shimadzu Corporation of Japan was used to test the tensile shear load of the welded joints. The tester's effective tensile load range was 20 N-10 kN, with a loading speed of 5 mm/min. A WEIMI-322 ES micrometer was used to measure the welded joints nugget diameter; its maximum optical magnification was 255 times the measuring instrument and its minimum linear accuracy was less than or equal to 6 \(\upmu\)m.
## 3 Results and Discussion
### Hierarchical Cluster Analysis and Principal Component Analysis
The remaining CO\({}_{2}\)-NADES systems were placed in a separate cluster (red and purple horizontal cluster). Figure 3: Clustered heat map (double dendrogram) of the NADES and the group of compounds (OM—Oxygenated Monoterpenes, MH—Monoterpene Hydrocarbons, NT—Non-Terpenes, OS—Oxygenated Sesquiterpenes, SH—Sesquiterpene Hydrocarbons) detected in the extracts. The blue–red scale presents the percentage distribution of a group of compounds in the samples. The PCA modeling, based on the chemical composition in terms of the detected group of compounds, resulted in a model that took into account 76.5% of total variance based on two PCs described by Eigenvalues (Ei) higher than 1: PC1 (Ei = 2.51) explained 50.29%, and PC2 (Ei = 1.31) explained 26.20% of the total variance. In the score plot, presented in Figure 4a, it is evident that there was a clear separation (red line) between the samples monitored at the start of the experiment and the samples monitored after 3 and 6 months. The distribution of the samples along the PC1 axis was mostly influenced by SH, MH, and OM content, whereas the OS content had a significant influence on the PC2 axis (the score plot, Figure 4b). The content of NT had a moderate influence on the distribution of the compounds along both the PC1 and PC2 axes. The PCA results were in agreement with the results obtained by HCA presented in Figure 3. The main observations arising from the score graph indicated the distinction of the Bet/EG samples and significant differences between the control samples (significant distance of the sample Cont0 from the other control samples is evident). Additionally, the score plot indicated a noticeable change in the content of certain groups of compounds (SH, MH, and OM) from 0 to 3 months for certain samples (e.g., control samples, Bet/EG, Gly/Glu, Bet/Suc/Pro/W, Bet/Sor/W).
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 2 Bonding of Embedded CFRP Rod in Concrete G40 and UHPFRC
### Considered Coupled Concrete Cube Samples Linked with CFRP Rod  
Material property and behaviour analyses indicate that the CFRP rod and conventional concrete exhibit limited flexibility under cyclic loading. Conventional concrete demonstrates brittle behaviour, leading to reduced performance and often premature fatigue failure in structures such as bridges and parking facilities. These structures frequently require significant rehabilitation to prevent catastrophic failure.  
In contrast, Ultra-High-Performance Fibre-Reinforced Concrete (UHPFRC) not only offers a high strength-to-weight ratio but also increased ductility, mitigating the risk of fatigue failure under cyclic loading. Although CFRP is a brittle composite, its combination with UHPFRC allows for better absorption of dynamic loads, potentially enhancing bond strength compared to its performance with conventional concrete.  
In this context, the present study analyses two cubic samples: one comprising conventional Grade 40 concrete and the other with a CFRP rod embedded in Ultra-High-Performance Fibre-Reinforced Concrete (UHPFRC). Finite element simulations were conducted to determine key parameters including stiffness, bond strength, and displacement. These parameters are crucial to refining the analytical model for more accurately simulating CFRP-rod-reinforced concrete under cyclic loads.  
The geometric modelling for the double pull-out test specimen was conducted in accordance with specific dimensions to fit the laboratory limitations for the testing setup. Table 1 displays the geometric dimensions of all components. The specified embedment length of 300 mm for both the top and bottom sections of the concrete has been configured to adhere to the conservative guidelines of ACI 440.3R, which tend to overestimate the values [14]. This specification is in line with the findings of [PERSON]. [PERSON]'s research, which suggests the optimal bonding length to be 25 times the CFRP rod's diameter, denoted as \"25d\" [15,16]. Figure 1 illustrates the test specimen's dimensions, while Figure 2 depicts the complete assembly of the required components.
# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## 4 Experiments and Analysis
### Experiment and Analysis Based on Deep Learning
Iteration means the maximum number of times for learning, and nUnit indicates the number of cells in the LSTM network. The 1 st decay LR (Learning Rate) means the value set at the corresponding number of learning times reduced to 1/10 from the initial learning rate. The 2 nd decay LR is a parameter that is set to prevent overfitting by reducing the learning rate reduced in the 1 st decay LR to 1/10 again. Finally, the number of HN (Hidden Nodes) means the number of hidden nodes contained in a single cell. For example, in the first experiment presented in Table 6 below, there are 500 repetitions, 64 cells inside the LSTM network, and the initial learning rate value is 0.01. At this time, the learning rate is 0.001 at 250 th, the 1 st decay LR, and 0.0001 at 375 th, the second decay LR. Further, the number of neurons inside the LSTM cells means that the number of neurons is set to 128 when creating the prediction model. A summary table (Table 6) of the results of the experiment is presented below. The sixth experiment showed a stroke prediction accuracy of 98.958% when it was predicted that 70% of the total data were randomly extracted, and the remaining 30% were not involved in learning. Consequently, the prediction of stroke based on the bio-signals of EMG showed stable predictions when setting the number of neurons inside LSTM Cells to twice or three times that of nUnit, with 2000 learning sessions and a learning rate of 0.001. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **LSTM** \\ **Number** \\ \end{tabular} & **Iteration** & **nUnit** & \
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **1 st Decay** & **2 nd Decay** & **Number** &
\begin{tabular}{c} **Accuracy (\%)** \\ **LR** \\ \end{tabular} \\ \hline
1 & 500 & 64 & 0.01 & 250 & 375 & 128 & 92.70 & 92.66 \\
2 & 500 & 256 & 0.001 & 250 & 375 & 768 & 94.77 & 94.80 \\
3 & 1000 & 128 & 0.01 & 500 & 750 & 384 & 95.82 & 95.86 \\
4 & 1000 & 256 & 0.001 & 500 & 750 & 768 & 96.44 & 96.48 \\
5 & 2000 & 64 & 0.01 & 1000 & 1500 & 128 & 96.870 & 96.924 \\
6 & 2000 & 128 & 0.001 & 1000 & 1500 & 384 & 98.958 & 98.952 \\
7 & 3000 & 128 & 0.01 & 1500 & 2250 & 384 & 97.78 & 97.74 \\
8 & 3000 & 256 & 0.001 & 1500 & 2250 & 768 & 98.56 & 98.62 \\
9 & 5000 & 128 & 0.01 & 2500 & 3750 & 384 & 97.86 & 97.88 \\
10 & 5000 & 256 & 0.001 & 2500 & 3750 & 768 & 98.74 & 98.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Estimation accuracy of stroke disease by LSTM network structure (%). Figure 5(a) below shows the accuracy of the prediction when the learning data is 70% and the test data is 30% of the sixth experiment described in Table 6. Figure 5(b) shows an EMG-based stroke disease prediction model that reduces errors and ensures optimal performance with repeated learning.
## 4 Data Analysis
In contrast to ID 1, ID 4 has only one fully observed viral load, as the other is left-censored at around 4.8 months (0.4 years). Based on this information, M1 predicts that the  
Figure 2: Trace plots for all parameters in M2. viral trajectory dipped under the detection limit around 1.2 months (0.1 years), while M2 predicts the time of viral suppression at a time around 2.4 months (0.2 years). Nevertheless, both models predict ART initiation at around the time when the observed viral load is recorded, which is reasonable due to the occurrence of viral suppression. For IDs 5 and 6, there are two fully observed viral loads before viral suppression. The values and the positions of the two viral loads influenced the shape of the entire trajectory. For example, compared to the slope of ID 6, the slope of the two viral loads for ID 5 is relatively flat; therefore, both M1 and M2 predict a flat pre-change point line. We present the example for IDs 7, 8, and 9, where more fully observed viral loads are available. Here again, our model fits those data points quite well, and the predicted ART initiation times are reasonable. Importantly, ID 7 represents cases where ART initiation time is estimated to be zero, the time of HIV diagnosis. In such cases, the random change point model effectively degenerates to an NLME model. Similarly, ID 3 represents a case in which the change point model degenerates to an LME model, where the change occurs beyond the last observed viral load. Visual inspection of Figure 3 reveals that the ART initiation times estimated by the \(log1 plus^{*}\) method generally align with the model-based predictions (M1 and M2) when viral load measurements show a sharp and well-timed decline (e.g., IDs 4, 5, and 7). Figure 3: Individual trajectory prediction plots. Open circles (\(\circ\)) connected by a solid line represent the observed viral loads. Dashed lines and the dash-dot lines indicate the predicted trajectory from M1 and M2, respectively. The detection limit (for IDs 4–9) is shown by dotted line. A triangle (\(\blacktriangle\)) marks the predicted ART initiation time and corresponding viral load under M1, while a solid circle (\(\bullet\)) does so for M2. The vertical dash-dot line represents the ART initiation selected by the \(log1 plus^{*}\) method. Visual comparison shows that this method often yields similar estimates to model-based predictions when there is a clear viral load decline, but may differ in cases with sparse or censored data (e.g., ID 6 or ID 8), highlighting the value of a model-based approach. However, notable discrepancies occur in cases with sparse measurements or censoring. For instance, in ID 6 and ID 8, the \(log1 plus^{*}\) method identifies ART initiation earlier than both M1 and M2, likely due to its reliance on observed declines rather than inferred trajectories. These differences underscore the advantage of the model-based approach in accommodating censoring, nonlinear post-ART dynamics, and between-subject variability in estimating ART initiation timing.
## 5 Discussion  
In this paper, we devised an enhancement to receiver-initiated MAC protocols for duty-cycle-based wireless sensor networks. Our enhancement addresses the perpetual collisions for heavy traffic loads in which many devices are trying to transmit to the same entity (sink or relay). Such a collision storm is inherent in the conventional receiver-initiated MAC protocols for such a setup, as the receiver's invitation to transmit a beacon initiates a transmission from multiple devices, resulting in a tedious contention resolution procedure.  
Our enhancement, termed MAR-RiMAC, relies on a reservation-based mechanism in which the reservations are short signals that can be transmitted simultaneously. After the designated receiver decodes the identity of the multiple devices, it sends a transmission request and it polls them consecutively, one after the other, with no idle intervals. We implemented both MAR-RiMAC and RI-MAC (the original receiver-initiated MAC protocol) and thoroughly examined their behaviors under diverse traffic loads. We showed that, under heavy traffic loads, MAR-RiMAC can resolve contention very efficiently and much faster than RI-MAC at the expense of a small overhead of short signaling messages, which is negligible even under light loads.  
[PERSON] devised the concept, designed the protocol, analyzed the simulation results, wrote and edited the manuscript. [PERSON] implemented both protocols (MAR-RiMAC and RI-MAC), conducted the simulations, and obtained all results. Both authors have read and agreed to the published version of the manuscript.  
Figure 18: Hidden-terminal topology: The performance attained by a four-device topology under very heavy to light traffic loads. (**a**) Sojourn time. (**b**) Duty cycle.