esa-sceva/satcom-chunk-collection huggingface dataset:
# Multifunctional Integrated Underwater Sound Absorption Materials: A Review
## 2 Underwater Acoustic Absorption Mechanisms and Research Advancements in This Area
### Research Advancements in Underwater Sound Absorption Materials  
Figure 3 shows the research history of USAMs, which can be traced back to the early 20 th century. Early studies focused on natural materials such as sponges and corals, but these materials had an unstable sound-absorbing performance and poor durability. In the 1960s and 1970s, synthetic materials such as polyimides and polystyrene gradually became a popular research topic. These materials have a good sound-absorbing performance but are expensive and require complex preparation processes. After the 1980s, researchers began to use new porous, fiber, and multi-layer structure materials, and explored new preparation methods and technologies, such as polymer foaming and ultrasonic preparation. These materials have a good sound-absorbing performance, low cost, and simple preparation, and thus have been widely used and promoted. In recent years, with the emergence of new materials and technologies, such as nanomaterials, metamaterials, and biomimetic materials, research on underwater sound-absorbing materials has continued to advance. Researchers have not only made breakthroughs in sound-absorbing performance using these new materials and technologies, but have also made vast improvements to the durability and stability of such materials, opening up wider opportunities for the application of underwater sound-absorbing materials.  
In general, USAMs can be classified into four main categories based on their composition: polymer-based, metal-based, ceramic-based, and acoustic metamaterials.  
Figure 3: The schematic of research history of USAMs.
## 7 Simulation Results and Performance Analyses  
In this section, some existing underwater clustering routing protocols: EGRC [30], LEACH-ERE [28], LEACH [27], FCMMFO [31], and FBCPSO [32] were selected as the references to verify the proposed EECRP. The used metrics for evaluating the performance were the network lifetime, the energy consumption, and the packet loss rate. We used MATLAB to conduct the experiments. MATLAB is a simulation software, which can be applied to sensor networks, data analysis, deep learning, image processing, computer vision, risk management, control systems, communications, signal processing and so on. It is an abbreviation of matrix and laboratory and it is developed by MathWorks. The MATLAB settles the high-tech computing problems such as scientific computing, visualization, and interactive programming. It integrates many powerful functions like numerical analysis, matrix calculation, scientific data visualization, and nonlinear dynamic system modeling and simulation in an easy-to-use software environment. It provides a comprehensive solution for scientific research, engineering design, and many scientific problems that require effective numerical calculations. Moreover, it gets rid of the editing mode of traditional non-interactive programming languages, such as C and Fortran, to a large extent, and it provides many feature-rich practical toolboxes such as signal processing toolboxes and communication toolboxes. Figure 7 displays the MATLAB workspace and Figure 8 illustrates some code of calculating the nodes alive.  
The simulation parameters are shown in Table 1:  
Figure 8: The code of calculating the nodes alive.  
Figure 7: The MATLAB workspace.
## 2 Theory and Methodology
### Leja Interpolation Nodes
#### 2.3.1 Unweighted Leja Nodes  
A sequence of standard, unweighted Leja nodes [41]\(\left\{y_{j}\right\}_{j\geq 0^{\prime}},y_{j}\in[-1,1]\), is defined by solving the optimization problem:  
\[y_{j}=\operatorname*{argmax}_{y\in[-1,1]}\prod_{k=0}^{j-1}\left|y-y_{k}\right|, \tag{9}\]  
for each node \(y_{j}\), \(j\geq 1\). The initial node \(y_{0}\in[-1,1]\) can be chosen arbitrarily, therefore Leja sequences are not unique. Using simple scaling rules, the unweighted Leja sequences defined in Equation (9) are directly applicable to Lagrange interpolation involving constant weight functions, equivalently, uniform PDFs in the UQ context. The extension to non-uniform PDFs is discussed in Section 2.3.2. With respect to the remaining node requirements, it has been shown that the Lebesgue constant associated with unweighted Leja nodes increases subexponentially [42]. Regarding Leja-based quadrature rules, an extensive discussion can be found in [30]. Considering uniform PDFs, a number of works [30; 36; 37] show that Leja-based quadrature schemes are sufficiently accurate, albeit suboptimal compared to more standard choices, e.g., based on Clenshaw-Curtis nodes. Finally, the optimization problem of Equation (9) results in nested node sequences irrespective of the employed level-to-nodes function \(m(i)\). This is an additional advantage of using Leja nodes, since the nested node sequences can be as granular as the user wishes. In this work, we use \(m(i)=i+1\) to get the minimum of one extra node per interpolation level, i.e., \(\#\left(Z_{i}\setminus Z_{i-1}\right)=1\). Another popular choice is to use \(m(i)=2i+1\)[36], which is typically employed for the construction of symmetric Leja sequences, where \(y_{0}=0\), \(y_{1}=1\), \(y_{j}\) is given as in Equation (9) for odd \(j>1\), and \(y_{j}=-y_{j-1}\) for even \(j\). In all cases, Leja sequences are more granular compared to other nested node sequences, e.g., the level-to-nodes function \(m(i)=2^{i}+1\) must be used for nested Clenshaw-Curtis nodes.
## 2 Materials and Methods
### Total Phenolic Content (TPC) and Antioxidant Capacity Analysis  
Prior to analysis, the different formulations of raw and cooked burgers, as well as the arithoke extracts, were homogenized in a methanol/water solution (80%, _v/v_). The mixture was incubated for 24 h at 4 \({}^{\circ}\)C and centrifuged (4500 rpm, 25 min). Supernatants were filtered (0.45 \(\upmu\)m) and kept at -18 \({}^{\circ}\)C until analysis.  
The total phenolic content (TPC) was determined following the method described by Singleton et al. [34] using Folin-Ciocalteau reagent and gallic acid as the standard. The analysis was carried out in triplicate, and the TPC was expressed as mg of gallic acid equivalents (GAE) per g of sample.  
The ferric-reducing antioxidant power (FRAP) assay was determined following the protocol described by Benzie and Strain [35]. The FRAP reagent was prepared with a solution of 300 mmol/L acetate buffer, pH = 3.6, 20 mmol/L FeCl\({}_{3}\)\(\cdot\)6 H2O, and 10 mmol/L TPTZ (2,4,6-tripyridyl-s-triazine) in 40 mmol/L HCl (10:1:1, _v/v/v_). A total of 100 \(\upmu\)L of sample was mixed with 1 mL of FRAP reagent in plastic cuvettes, and absorbance was measured at 593 nm after 4 min of incubation. The same procedure was carried out with a standard solution of Trolox. Analyses were carried out in triplicate, and the antioxidant capacity was expressed as \(\upmu\)M Trolox equivalents (TEs) per g of sample.  
Antioxidant activity related to chelating capacity was measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging method described by Brand-Williams et al. [36]. A total of 100 \(\upmu\)L of sample was mixed with 3.9 mL DPPH reagent in plastic cuvettes, and absorbance was measured at 515 nm after 30 min of incubation in the dark. The same procedure was carried out with a standard solution of Trolox. Analyses were  
Figure 1: Appearance of the four different beef burger formulations. CB: control burger; FRB: fat-replaced burger; FRB-AE: fat-replaced burger with arithoke extract; FRB-TAE: fat-replaced burger with treated arithoke extract.  
carried out in triplicate, and antioxidant activity related to chelating capacity was expressed as \(\upmu\)M Trolox equivalents (TEs) per g of sample.  
The radical cation scavenging capacity against 2,2-azinobis (3-ethylbenzothizolin)-6-sulphonic acid (ABTS) was carried out following the method described by Re et al. (2017). The ABTS reagent (7 mM) was prepared in 2.45 mM potassium persulphate (1:1, \(v/v\)), pH = 7.4, and adjusted to an absorbance of 0.7000 at 734 nm. A total of 1 mL of ABTS reagent was mixed with 100 \(\upmu\)L of sample, and absorbance was measured at 734 nm after 2 min of incubation. Analyses were carried out in triplicate, and antioxidant activity related to radical scavenging capacity was expressed as \(\upmu\)M Trolox equivalents (TEs) per g of sample.
## 5 Simulations
### Simulation Results  
Table 4 shows a comparison between the scheduling obtained by using: (1) the first-fit probabilistic rule (commonly used in hospitals), (2) the batch scheduling approach in [19], (3) the constructive algorithm proposed in [20], and (4) the SHA approach presented in this paper. The scheduling is obtained following the simulation methodology explained in the previous subsection for a minimum confidence level \(Cl=70\%\). The average value of probability of not exceeding the total time, occupation rate, and the order of the patients (\(\Omega\)) are analyzed. Moreover, the total overtime and the total number of treated patients per year are shown.  
The three approaches analyzed in Table 4 improve the occupation rates obtained by using the first-fit probabilistic rule. However, the improvement in the occupation rate of the time block implies a decrement in the confidence level. For example, the Batch Scheduling approach [19] achieves the highest occupation rate (79.06%) and the highest number of treated patients (447.78), and, consequently, the lower confidence level (75%) and the highest total overtime (1183 [min]) are obtained. Taking into account the pairs of values occupation rate and confidence level, the Batch Scheduling approach obtains the better solution with: (1) the highest occupation rate and (2) a confidence level within the allowed.  
The SHA approach presented in this paper obtains a little worse occupation rate (78.28%) than the Batch Scheduling (79.06%). However, considering the order of the patients given by the indicator \(\Omega\), it can be checked that the SHA approach by far obtains the best scheduling (\(\Omega=395\)). Medical doctors of the OSD consider that the scheduling obtained using the SHA approach is the best one since it maintains the order of the waiting list better. Due to patients having more orders with SHA scheduling, it is possible to provide an estimation period for the patients in the list and this is a possible extension.  
**6. Case Study Results**  
This section presents a comparison between a scheduling obtained manually in the OSD and the scheduling obtained by using CIPLAN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Avg. Confidence** & **OVERTIME** & **Avg. Occupation** & **Total \# of** & **Average** \\  & **Level** & **(Total) [min]** & **Rate (\%)** & **Surgeries** & \(\Omega\) \\ \hline first-fit rule & 81.98 & 806.41 & 76.12 & 429.9 & 1935.9 \\ Constructive Alg. [20] & 80.43 & 922 & 76.69 & 432.02 & 2840 \\ Batch Scheduling [19] & 75 & 1183 & 79.06 & 447.78 & 3993.1 \\ SHA (this paper) & 77.31 & 1059 & 78.28 & 438.3 & 395.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the one and the half years scheduling using different chain-constrained approaches with a minimum confidence level of 70%.
## 4 Discussion and Future Perspective
The high cost of such conventional technologies, such as activated carbon filtration and chemical stabilization, often limits their application at scale, with some estimates exceeding USD 1000 per gram of Hg removed depending on site conditions and treatment complexity [65]. In contrast, NbSs, including constructedwetlands and biochar-amended systems, show promise as cost-effective alternatives due to lower operational demands and co-benefits like ecosystem enhancement [66; 67]. However, their large-scale adoption requires supportive regulatory frameworks that incentivize low-cost, decentralized technologies. A comparative analysis of mercury removal efficiency, cost, and operational advantages is summarized in Table 5. While information on installation and production costs was obtained from external literature sources beyond the primary studies reviewed, the analysis of benefits and drawbacks was synthesized from the findings of this review. Constructed wetlands were found to exhibit high removal efficiencies (30-98%), with installation costs ranging from 50 to 120 USD/m\({}^{2}\)[68]. These systems have been recognized for their applicability in large-scale, passive treatment scenarios. However, risks of MeHg formation under anaerobic conditions were also identified, particularly in sulfate- or iron-rich environments. Bioretention systems demonstrated removal efficiencies between 24% and 78%, with installation costs between 100 and 200 USD/m\({}^{2}\)[69]. Their compact footprint and design flexibility have supported widespread use in urban contexts, though baseflow conditions have been associated with MeHg export due to the formation of localized anoxic microsites. Moreover, biochar amendments were observed to offer high removal potential (57-95%) and improvements in soil chemical properties, including pH and cation exchange capacity. Production costs were reported in the range of 300-500 USD per ton, depending on feedstock and processing method [70; 71]. While Hg immobilization has generally been enhanced through sorption processes, increased MeHg formation has also been reported under reducing conditions, likely a result of elevated dissolved organic carbon and altered microbial dynamics. These findings underscore the need for careful evaluation of each NbS option in terms of cost-effectiveness, site conditions, and environmental risk. Integrating economic data with system-specific benefits and limitations, as summarized in this study, is essential for guiding sustainable and context-sensitive NbS implementation. In terms of policy, initiatives such as performance-based subsidies, flexible permitting, and integration into national Hg reduction plans could accelerate NbS deployment while maintaining environmental safeguards. As such, aligning economic assessments with policy development is critical to mainstream NbSs for mercury-contaminated sites. Furthermore, the implementation of NbSs aligns with the goals of the Minamata
## 2 Methodology
### Antennas Under Test  
Figure 2 shows the structures of the antennas, which are attached to the RF modules in different scenarios to carry out the IoT performance evaluation. A modified shape-optimized Landstorfer Yagi-Uda antenna (LaYUA) and a turnstile antenna composed of two printed dipole radiators (TuSA) are presented as custom-designed radiators for the experiment. Additionally, the commercial dipole antenna included in the RF modules is utilized for in this experimentation.  
The LaYUA radiator is a scaled version, at 2.4 GHz, of the antenna reported in [11] and serves as the radiator for transmission. The analysis of a turnstile antenna is detailed in [12; 13], with the key difference being that the 90\({}^{\circ}\)-phased dipole is achieved through a coaxial line. Due to its low gain, this antenna operates at the reception side. Lastly, the commercial dipole corresponds to a 2.4 GHz antenna with an RP-SMA connector that is to be attached to the RF module.
## 3 Model Structure
### Model Framework  
Our model consists of variational mode decomposition, auto-correlation mechanism and correction module, and its model structure is shown in Figure 4. By variational mode decomposition, multiple intrinsic mode functions with different frequencies and the same length can be obtained. Each intrinsic mode function corresponds to an encoder and decoder, respectively, and the detailed internal structure of the encoder and decoder is shown in Figure 5. The encoding and decoding operations for data of different frequencies can not only reduce the data noise, but also analyze the patterns of data changes more carefully and comprehensively, and then extract more useful data information. The calculation results of the encoder for each intrinsic mode function are added together as the final input for encoding, and the output of the decoder for each Intrinsic mode function is added together as the final input for decoding so that the extracted data information from different frequencies can be fully integrated. Finally, the desired predicted output length is obtained through linear mapping.  
Figure 4: Structure of VMD–auto-correlation model.  
Figure 3: Schematic diagram of the correction module (blue dashed part is the correction module).  
In the encoder and decoder, we compute auto correlation for all the sequence data on the input, increasing the efficiency of using all the data. The data integrity are further enhanced by the correction module after the output of the auto-correlation mechanism.  
Our encoder consists of N stacks of identical layers. Each layer contains only one auto-correlation mechanism and correction module; the data are computed by the auto-correlation mechanism and correction module; and then, the layer normalization operation is performed, and this cycle is carried out until the key and value are output. Our decoder is composed of M stacks of identical layers. Each layer also contains only an auto-correlation mechanism and a correction module. The key and value from the encoder operate with the auto-correlation mechanism with the new input query, and then use the correction module to correct the data, and finally also perform the layer normalization operation, and so on until the auto correlation value is output.
# NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
## 1 Introduction
Surfactin can potentially influence the activity of enzymes engaged in metabolic processes. All of these impacts may interact to influence a plant metabolome. This systemic impact is a series of molecular processes that spread beyond the treated region, influencing other areas of the plant [16; 17; 18]. Therefore, this work aims to study how these metabolic changes manifest in flax plants when exposed to surfactin treatment. Metabolomics research on flax has been proven to be a powerful approach to elucidate its metabolic functions [19]. Flax is known to contain numerous metabolites in its aerial parts and roots, often serving as biomarkers of plant health [19; 20]. Employing a combination of analytical methods is interesting for studying flax metabolism, with gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC--MS), and nuclear magnetic resonance (NMR) allowing for the detection of differently accumulated metabolites in the plant [21; 22]. In our investigation into the systemic and local effects of surfactin on flax, advanced metabolomics analyses using both \({}^{1}\)H NMR and LC-MS were employed on different extracts of the three vegetative parts of flax. The aim of this study was to evaluate the changes observed in terms of primary and secondary (semi-)polar metabolites, which can be affected by surfactin treatment and are involved in plant resistance mechanisms.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 2 Related Works
### Disaster Management  
Disaster management is a systematic approach that involves planning, mitigation, rescue, response, and recovery, requiring collaboration among federal, state, local, and private sector entities. This coordinated effort is essential for minimizing the impact of disasters and ensuring an efficient and effective response to emergencies. [PERSON] et al. [16] applied the Standard Deviation Ellipse (SDE) method to examine the spatial patterns of urban floods and determine Areas of Interest (AOI) using social media data from Chengdu, China. Social media data served as the response variable, while ten urban flood-influencing factors were chosen as independent variables to evaluate their impact on flood occurrences. This analysis highlighted how urban floods can result in severe property damage, disrupt transportation networks, interrupt daily activities, and pose significant threats to public safety. Expanding the scope of social media data analysis, [PERSON] et al. [17] introduced a novel approach for identifying and classifying emergency situations using a large language model. The study explored two primary applications: enhancing 911 dispatch operations by supporting telecommunators in processing emergency calls and providing personalized protective action guidance to the public based on real-time social media data. This approach demonstrates the potential of advanced language models in improving emergency communication systems and supporting timely interventions during crises.  
Integrating diverse data sources, including social media streams and IoT-generated data, with advanced big data analytics tools such as Apache Spark and Apache Kafka can greatly enhance the efficiency and effectiveness of emergency management processes [18]. These technologies enable real-time collection, processing, and analysis of large-scale data streams, allowing for faster detection of crisis events and more coordinated emergency responses. Additionally, [PERSON] et al. [19] utilized a visualization of similarities to analyze and interpret crisis datasets, supporting more effective crisis management and decision-making. By applying advanced data visualization techniques, the study enhanced the understanding of patterns, relationships, and trends within disaster-related data. This improved visualization enables emergency response teams to allocate resources more effectively, identify critical areas in need of intervention, and develop more efficient response strategies based on the evolving nature of disaster events.