esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
## 3 Mechanism of Action of PGIs  
Major PGIs and their allelopathic mechanisms and targets are described in Table 1. Accordingly, PGIs can regulate recipient plant growth via various biological pathways including oxidative stress induction and the inhibition of cell division, cell permeability, photosynthesis, respiration, and transpiration. In addition, these substances can impede phytohormone and enzyme activities through the suppression of relevant gene expression. PGIs can also interact with soil microorganisms that affect recipient plant growth.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Nodes of Action** & **Allelochemicals** & **Recipient Plant** & **Rice Organs** & **References** \\ \hline Inhibition of root and hypocotyl growth & \begin{tabular}{l} Momilactone A (MA) and B (MB) \\ \end{tabular} & \begin{tabular}{l} **Growth inhibition** \\ _Lepidium sativum seedlings, Lactus sativum seedlings, Echinolica crus-galli_, and Monochoria vaginalis_ \\ \end{tabular} & \begin{tabular}{l} Husks, leaves, \\ brans, roots, and \\ root exudates \\ \end{tabular} & \begin{tabular}{l} [5,38] \\ \end{tabular} \\ Root growth suppression & \begin{tabular}{l} Gallic acid, protocatechic acid, _p_-hydroxybenzoic, \\ vanillic acid, syringe acid, _p_-cumaric acid, \\ _m_-cumaric acid, ferulic acid, _o_-cumaric acid \\ \end{tabular} & \begin{tabular}{l} _Brassica rapa_ and _O. sativa_ \\ seeds. \\ \end{tabular} & \begin{tabular}{l} _Brasica rapa_ and _O. sativa_ \\ seeds. \\ \end{tabular} & Leaves and stems & [29] \\ Inhibition of root and shoot growth & \begin{tabular}{l} Blumenol A and grasshopper ketone \\ \end{tabular} & \begin{tabular}{l} _L. sativum, L. sativa, Phleum_ \\ _pratense, Digitaria_ \\ _sanguinalis, Lolium_ \\ _multiforum_, and _E._ \\ _crus-galli._ \\ \end{tabular} & Whole plant & [31] \\ Inhibition of the radicle growth & \begin{tabular}{l} _p_-Hydroxybenzoic, \\ _p_-cumaric, vanillic, ferulic and _o_-hydroxyphenylacetic \\ acids, and several \\ unknowns \\ \end{tabular} & \begin{tabular}{l} _L. sativa_ seeds, _O. sativa_ \\ seeds and seedlings. \\ \end{tabular} & Residues in soil & [39] \\ Inhibition of seed germination, seedling length, and dry weight & \begin{tabular}{l} _E. crus-galli_ \\ _p_-hydroxybenzoic acid \\ \end{tabular} & \
\begin{tabular}{l} _E. crus-galli_ \\ \end{tabular} & Straws & [40] \\ Inhibition of plant height, root length, monocotyledon and fresh weight &
\begin{tabular}{l} _E. crus-galli_ \\ \end{tabular} & Whole plant & [41] \\ \end{tabular}
\end{table}
Table 1: Major PGIs and their allelopathic mechanisms and targets.
## 4 Performance and Economic Feasibility of a Retaining Wall Displacement Measurement System
### Economic Feasibility of System
#### 4.2.1 Definition of Lifecycle Cost Analysis Scope  
The LCC analysis scope was defined by analyzing the conventional measurement process (Figure 1) and automated measurement process (Figure 2), as shown in Figure 13. The conventional measurement method has labor, material, and equipment costs owing to the manpower, equipment, and material inputs necessary for boring, inclinometer casing installation, and measurements. Regarding the automated measurement method, the labor, material, and equipment costs are associated with the installation of the displacement measurement hardware and measurement. Since hardware installation is easier than that in conventional methods, and measurements are performed by a single worker, economic benefits can be seen when the input of personnel and equipment are reduced. The automated measurement method has the following additional costs: (1) system construction, (2) system repair, and (3) replacement costs for components that have expired.  
Figure 12: Economic feasibility analysis process.
## 3 Analysis of SR Stability and Excavation Scheme of a Large Underground Cavern Group  
### Project Profile  
There is a hydropower station with a total installed capacity of 1800 MW located in Kangding County, Sichuan Province, China, on the upper reaches of Dadu River. The underground cavern group of the hydropower station is composed of three cavern groups: the main powerhouse cavern (MP cavern), the main transformer chamber cavern (MTC cavern) and the tailrace surge tank cavern (TST cavern). The buried depth of the underground cavern group is about 180-420 m, and the SR mass is mainly composed of Yanshanian porphyritic black cloud K-feldspar granite interspersed with granitic pegmatite dikes less than 1m thick. According to the statistics, pegmatite dikes accounts for about 10.67%, the rock mass is fresh, compact and hard, and the rock strata occurrence is N50\({}^{\circ}\)-70\({}^{\circ}\)E/NW \(<\) 32-51\({}^{\circ}\); two faults, f1 and f2, are mainly considered.  
The section forms of the three caverns are round arch and straight wall, and the three caverns are arranged in parallel; the model can be simplified to a two-dimensional plane model. The section size of the MP cavern is 25 \(\times\) 50 m (width \(\times\) height), the section size of the MTC cavern is 18 \(\times\) 24 m (width \(\times\) height), and the section size of the TST cavern is 24 \(\times\) 60 m (width \(\times\) height).
# Robust Template-Based Watermarking for DIBR 3D Images
## 2 Background
### Curvelet Transform  
The curvelet transform is a multi-scale decomposition-like wavelet transform, and the curvelet represents the curve shape for the various directions in the spatial domain [38; 39; 40; 41]. The curvelet transform is developed to improve the limitation of wavelet-based transforms and can represent edges more efficiently than conventional wavelet-based transforms. Moreover, curvelet bases cover all frequencies in contrast to other directional multi-scale transforms, such as the Gabor and ridgelet transforms [42]. The curvelet transform is expressed as follows,  
\[\begin{split} C(g,l,k):=&\langle f,\varphi_{g,l,k} \rangle=\int_{\mathbf{R}^{2}}f(x)\overline{\varphi_{g,l,k}}dx\\ =&\frac{1}{(2\pi)^{2}}\int\hat{f}(\omega)U_{g}(R_{ \theta_{l}}\omega)e^{i\langle x_{k}^{(g,l)},\omega\rangle}d\omega,\end{split} \tag{3}\]  
\[U_{g}(r,\theta)=2^{-3g/4}W(2^{-g}r)V\Big{(}\frac{2\left\lvert g/2\right\rvert \theta}{2\pi}\Big{)}, \tag{4}\]  
In (3), \(C\) is the curvelet coefficient, \(g=0,1,2,...\) is the scale parameter, \(l\) is the rotation parameter and \(k=(k_{1},k_{2})\) is the translation parameter. \(U_{g}\) is a \"wedge\"-shaped frequency window represented in (4). \(R_{\theta}\) is the rotation operator, and \(\theta_{l}=2\pi\cdot 2^{-\left\lvert g/2\right\rvert}\cdot l\). In (4), \(W\) and \(V\) are the radial and angular windows, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **2D DCT** & **2D DFT (Mag)** & **2D DWT (Haar)** \\ \hline Average & Unmatched & 209\% & 39\% & 215\% \\ Energy change & Matched & 207\% & 40\% & **4\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average energy change between coefficients of center and synthesized images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Block Size** & & \\ \cline{2-5}  & \(\mathbf{5\times 5}\) & \(\mathbf{10\times 10}\) & \(\mathbf{20\times 20}\) & \(\mathbf{40\times 40}\) \\ \hline Undistorted blocks (\%) & 94.32 & 85.68 & 70.67 & 48.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average percentage of undistorted blocks after DIBR.  
The curvelet is illustrated in Figure 3. Figure 3a illustrates the tiling of the curvelet in the frequency domain, and the curvelet shape in several directions and scales in the spatial domain is shown in Figure 3b-d.  
As shown in Equation (3) and Figure 3, the curvelet represents frequency information of a small spatial block similarly, so the curvelet is also not distorted by DIBR. In addition, energy conservation is better with the curvelet transform than with conventional Haar DWT when image rotation occurs [43]. For example, when the image rotates 10 degrees, the energy inserted into Haar DWT is reduced to 50%, but that inserted into the curvelet is maintained up to 85%. In the case of scaling attack, energy is well maintained in most DWT.  
In summary, the curvelet transform is suitable for use as a template due to its robustness to DIBR and geometric transform.
###### Abstract  
The aim of this study was to assess the impact of soil amendments, characterized by different sorption properties, on the effectiveness of trace elements' (Cu, Zn, Pb, Cd, Ni, and Cr) stabilization and bioavailability to earthworms. The study was conducted as a microcosm experiment using soil derived from a heavily contaminated post-industrial area. The _Eisenia veneta_ earthworm was cultured for 4 weeks in soils amended with materials characterized by different properties, origins, and potential effects on limiting the availability of metals in soils: two type of compost (Zabrze compost-ZC; GWDA compost-GC), two types of biosolid (Belchatow biosolids-BB, Grabow biosolids-GB), calcium phosphate (CP), iron oxide (IO), bentonite (BE), rock waste (RW), and limestone (CC). After the incubation, the biomass and survival numbers of the earthworm species decreased significantly (_p_ < 0.05). The accumulation of metals in the earthworm tissues expressed by the bioaccumulation factor value (BSAF) were dependent on the type of amendment applied to the soil. The highest decrease in the earthworms' weight and survival rate was caused by compost (72%) and bentonite (33%), while the lowest was caused by the rock waste (10%) and iron oxide (11%). The biosolids exhibited the greatest toxicity, causing the mortality of all the earthworms. The accumulation of metals in earthworm tissues and the BSAF value were dependent on the type of amendment applied to the soil. The BSAF for the contaminated soil by Cd decreased to the greatest extent after the addition of ZC (by 57%), GC (55%), CP (41%), and IO (37%). A similarly positive effect was noted for Pb after IO addition (45% decrease). The Zn, Cr, and Ni concentration in earthworms, contrary to other elements, increased, regardless of the amendment. The results showed that the applied soil amendments were characterized by varying potential for the reduction in the metal bioavailability in the soil, depending on their composition and physicochemical properties. Moreover, earthworms may exhibit a diversified response to soil amendments as a result of the impact of amendment on the metal forms in soils and their direct impact on organisms. Generally, the Cd was easily transferred from the soil into and accumulated in the earthworm tissues. Our study confirms that this element creates the highest risk for the trophic chain in soils affected by the Zn and Pb smelting industry. Moreover, greater Zn supply reduces the accumulation of Cd in animal bodies. This study provides valuable practical knowledge on the short-term biological effects of a range of soil amendments in metal-contaminated soils.  
metal immobilization; cadmium; zinc; lead; copper; nickel; chrome; bioassay; BSAF; compost; biosolids +
Footnote †: journal: Applied Sciences
# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
###### Abstract  
Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin-destination pairs within EAA's range limitations. This research introduces a three-phase data-mining and geographic information system (GIS) algorithm to support data-driven decision-making under uncertainty. Analysts can modify and expand this workflow to scrutinize origin-destination commodity flow datasets representing various locations. The algorithm identifies four commodity categories contributing to more than one-third of the value transported by aircraft across the contiguous United States, yet only 5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20% of the weight of those four commodity categories. A distance band of 400 miles among these eight locations accounts for more than 80% of the transported weight. This study addresses a literature gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can guide planners and investors in identifying prime target markets for emerging EAA technologies using regional datasets.  
Electric and autonomous aircraft; cargo shipping disruption; data-mining techniques; sustainable supply chain; supply chain redesign +
Footnote †: journal:  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-00000-0000-00000-0000-00000-00000  
to EAA utilization, as they can help reduce the adverse effects of traffic congestion [10] and protect the environment from dangerous cargo spills [11]. However, commercial EAA services face uncertainties due to current federal regulations restricting their use [12]. Nevertheless, the goal of this research is to provide data-driven answers to the following research questions:  
1. Which commodities are best suited for air transport?
2. What should EAA deployments focus on to meet the highest demand?
3. What is the distribution of origin-destination pairs that fall within EAA flight range capabilities?  
This research contributes a practical data-mining workflow with algorithms that logistical planners and investors can leverage to analyze available commodity flow data for any region of interest. Using comprehensive commodity flow data from the United States, the authors illustrate the developed workflow, highlighting its utility in answering the research questions.  
The organization of the rest of this paper is as follows: Section 2 reviews literature on middle-mile deliveries using EAAs. Section 3 outlines the developed data-mining algorithms to address the research questions. Section 4 discusses the analysis results and their implications for stakeholders like supply chain managers, shippers, and urban planners. Section 5 concludes the research and proposes future work to evaluate the sensitivity of mode shifts relative to adoption time horizons for EAAs and EATs.
## 3 Results
### Control Specim Analysis and Experimental Data Processing
#### 3.2.1 Type 1 Test Specimens
These variations are consistent with their fluctuating performance across aging cycles and reflect intrinsic differences in formulation and response. reduced cohesion (25% in Cycle 4). In contrast, M4 displayed more heterogeneous behavior, with fluctuating adhesion values throughout the cycles. Despite an initial drop (4.02 MPa in Cycle 1), its adhesion strength recovered in later stages, reaching 8.43 MPa in Cycle 4, suggesting improved adhesion to the substrate over time. **Shore C hardness. ** Within the study of the stability and compatibility of materials, Shore hardness is a measure of the resistance of materials. The method for measuring this parameter consists of pressing the material with a hardened steel penetrator, designed with a specific shape and force according to the selected measurement scale [47]. Ten measurements were taken for each putty, and the average values obtained are presented in Figure 7.  
**Results**. M1, M4, M5, M7, and M9 exhibited the highest Shore C hardness values (90-95 HC), whereas the M2 and M6 fillers showed the lowest values (80-82 HC). These results are directly related to the formulation of each material, particularly the type of binder/agglomerate, fillers, and plasticizers present. In this regard, the M4 and M8 fillers \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
* **Ch**: **C**V%: Coefficient of Variation calculated from the adhesion values across cycles. Values > 15% reflect heterogeneous behavior or sensitivity to degradation. Source: Authors’ own elaboration, DorART Project, 2025.  
\end{table} 
Table 3: Analysis of the adhesion evolution over the test cycles. Figure 7: Chart of Shore C hardness measurements of tested fillers. Source: Authors’ own elaboration, DorART Project, 2025.  
contain vermiculite and small wood fragments, respectively, bound with resin, which significantly influences their mechanical behavior. M3 and M8 presented intermediate hardness values of around 87 HC. Regarding their behavior after the aging cycles (Figure 8), putties M2, M3, and M8 exhibited an increase in hardness, which may be attributed to crosslinking processes in the resins that compose these materials. In contrast, M4, M5, and M6 showed a significant decrease in hardness, suggesting the presence of degradation processes likely related to the breakdown of polymer chains. M1, M7, and M9 showed minor fluctuations (ranging between 0.7 and 4 Shore C units), indicating greater resistance to aging.
# Extracellular Vesicles for Regenerative Medicine Applications
## 2 Extracellular Vesicles (EVs)
### Exosomes  
Exosomes are a subclass of EVs secreted by all cell types; they have been identified in plasma, urine, saliva, semen, bronchial lavage fluid, cerebral spinal fluid, amniotic fluid, synovial fluid, tears, lymph, bile, breast milk, and gastric acid [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Exosomes participate in cell-to-cell communication can induce physiological changes in recipient cells by transferring their molecular cargo including proteins, lipids, and nucleic acids [28]. Exosomes, which range in size from 30-150 nm in diameter, have a biogenesis that occurs via the inward budding of intracellular endosomes [29; 30; 31; 32; 33]. Repeated inward budding of the endosomal membrane results in the formation of a multivesicular body. The multivesicular body then fuses with the cell membrane, and the exosomes within are released into the extracellular fluid [29; 30; 34; 35; 36]. Although the molecular cargo of exosomes can vary depending on the cell source, exosome-specific marker proteins have been identified. These include proteins involved in the formation of multivesicular bodies such as ESCRT proteins, ESCRT-accessory proteins (TSG101, Alix, HSC70, and HSP90 beta), and select tetraspanins (CD63, CD9, and CD81) [37; 38; 39; 40; 41; 42; 43; 44].
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 3 Implementation of Modeling Methods
### Convergence Analysis  
In order to ensure proper operation in industrial applications, this section gives the convergence analysis of SOFNN-CA, which is mainly divided into a structure fixation phase and a structure dynamic-adjustment phase.  
Assume that at moment \(t\), the current error \(e(t)\) is:  
\[e(t)=\hat{y}_{t}-y_{t}. \tag{49}\]  
Define the Lyapunov function as:  
\[V(\Theta(t))=\frac{1}{2}e^{T}(t)e(t), \tag{50}\]  
**Theorem 1**.: _There are fixed \(r\) neurons in the rule layer of SOFNN-CA. The parameters of SOFNN-CA are updated by (38), if_  
\[\|\Delta\Theta(t)\|\leq\min\biggl{\{}\|\Delta\Theta(t-1)\|,\frac{\|\Omega( \Delta\Theta(t-1))\|}{\|\Psi(\Delta\Theta(t-1))\|}\biggr{\}}, \tag{51}\]  
_then the convergence of SOFNN-CA can be maintained, and there is e(t)\(\to\)0 as \(t\to+\infty\)._
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 3 IANT Scheme Design
### BER and Security Performance Analysis  
In most scenarios, we can assume that the probabilities of the data symbols '0' and '1' of the tag backscatter are equal. Therefore, the bit error rates of the reader end and the eavesdropper end can be expressed as:  
\[\begin{array}{ll}P_{b,r}&=\Pr(\hat{B}_{i,r}(q)\
eq B_{i}(q))\\ &=\Pr(B_{i}(q)=1)\Pr(\hat{B}_{i,r}(q)=0\big{|}B_{i}(q)=1)\\ &+\Pr(B_{i}(q)=0)\Pr(\hat{B}_{i,r}(q)=1\big{|}B_{i}(q)=0)\\ &=\frac{1}{2}\Pr(\hat{B}_{i,r}(q)=0\big{|}B_{i}(q)=1)+\frac{1}{2}\Pr(\hat{B}_{ i,r}(q)=1\big{|}B_{i}(q)=0)\end{array} \tag{18}\]\[P_{b,c}=\Pr(\hat{B}_{i,e}(q)\
eq B_{i}(q)) \tag{19}\]  
Formula (18) is the same as Formula (19), so it is not expanded, and the achievable secrecy rate at reader side and eavesdropper side can be expressed as:  
\[R_{r}=R_{s}Q(1-P_{b,r})\,/N \tag{20}\]  
\[R_{e}=R_{s}Q(1-P_{b,e})\,/N \tag{21}\]  
where \(R_{s}\) represents the data rate of RF signal source, \(Q\) is the number of symbols of the tag backscatter signal B(\(n\)), and \(N\) represents the number of symbols of the ambient RF signal \(s(n)\) in a time slot. In this paper, the achievable secrecy rate is the difference in the rate of the received signal between the reader and the eavesdropper. It represents the difference of received signal-to-noise ratio between reader and eavesdropper, so it can be used as an important indicator to reflect the security performance of the scheme proposed in this paper, as follows:  
\[R_{d}=R_{r}-R_{e}=R_{s}Q(P_{b,e}-P_{b,r})\,/N \tag{22}\]
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
## 4 Preliminary Validation
### Calibration of WIM Sensor  
The weight of the vehicle is simulated by the applied load on the testing machine platform. A total of eight groups of experiments are conducted. The applied loads are 5 kN, 10 kN, 15 kN, 20 kN, 25 kN, 30 kN, 35 kN, and 40 kN, respectively. Each group of the applied loads is conducted several times in the experiment, and a linear relation is built between voltage and force. Hence the calibrated value can be obtained by converting the collected voltage. The experimental data and processed data are listed in Table 3 (the calibrated value is obtained by converting the collected voltage). In these eight experiments, the maximum relative error of the WIM sensor is less than 7%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Applied Load (kN)** & **Measured Load (kN)** & **Absolute Error (kN)** & **Relative Error (\%)** \\ \hline
5 & 5.342 & 0.342 & 6.83 \\
10 & 10.345 & 0.345 & 3.45 \\
15 & 14.637 & \(-\)0.363 & 2.42 \\
20 & 20.180 & 0.180 & 0.90 \\
2
# An End-to-End Mutually Interactive Emotion-Cause Pair Extractor via Soft Sharing
## 5 Ablation Study
### Clause-Level Encoder  
The excellent performance of ECPE-2D(BERT) [14] and ECPE-MLL(ISML-6) [15] with a larger number of parameters in the ECPE task cannot be ignored. Inspired by their work, we replaced the clause-level encoder in Emiece from the stacked Bi-LSTM [48] to BERT [49] as the new Emiece-BERT model. Nonetheless, due to the server storage space limitation, training Emiece-BERT can only set the batch size to 16. To control the variables, we keep the hyperparameters of Emiece-LSTM in line with Emiece-BERT and retrain it.  
As can be seen from Table 4, under the end-to-end soft-sharing emotion-cause pair extraction framework, simply replacing a more complex encoder, i.e., BERT [49], makes the model surprisingly effective in improving each metric on each of the three tasks of ECPE. However, this undoubtedly introduces a much larger number of parameters for training. In total, 69 hours are consumed by the BERT model in one training, while 16 hours are consumed by Emiece-LSTM training; such a replacement causes a considerable overhead and makes the model redundant and unwieldy.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clause-Level Encoder**} & \multicolumn{3}{c}{**Emotion Extraction**} & \multicolumn{3}{c}{**Cause Extraction**} & \multicolumn{3}{c}{**Pair Extraction**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline stacked Bi-LSTM [47] & 0.7425 & 0.6665 & 0.7014 & 0.6570 & 0.4762 & 0.5473 & 0.5398 & 0.4973 & 0.5153 \\ BERT [49] & **0.8263** & **0.7441** & **0.7830** & **0.7135** & **0.5522** & **0.6225** & **0.6833** & **0.5325** & **0.5985** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the model effects of Emiece composed of two different clause-level encoders. The results are under the same hyper-parameter setting.  
Figure 5: A case study of our method. Our method also works well in extracting emotion–cause pairs when the input text contains multiple emotions and multiple causes that match each other.