esa-sceva/satcom-chunk-collection huggingface dataset:
# A New CO\({}_{2}\)-EOR Methods Screening Model Based on Interdependency Parameters
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **[PERSON] et al.** & **[PERSON]** & & & & **[PERSON] et al. [5]** & & \\  & [10] & **and [PERSON] [11]** & **Minimum** & **Mean** & **Median** & **Maximum** & **Standard** \\  & & & & & & & & **Deviation** \\ \hline Porosity, \% & - & 3–37 & 3 & 16.3 & 14.55 & 37 & 7.3 \\ Permeability, mD & NC & 1.5–4500 & 0.1 & 290.1 & 30 & 9244 & 1070.6 \\ Depth, ft & \textgreater{}2500 & 1500–13,365 & 1150 & 6404.2 & 5600 & 15,600 & 2700.2 \\ Oil Gravity, \({}^{\circ}\)API & \textgreater{}22 & 22–45 & 25 (special & & & & \\  & & & & & & & & \\ Oil Viscosity, cp & \textgreater{}10 & 0–35 & 0.15 & 3.8 & 1.2 & 4 (special case: & 5–188) & 15.6 \\ Temperature, \({}^{\circ}\)F & NC & 82–257 & 70 & 141 & 122.5 & 260 & 50.2 \\ Oil Saturation, \% & \textgreater{}20 & 15–89 & 15 & 52.2 & 50.4 & 98 & 16.7 \\ Net Thickness, ft & Wide Range & Wide Range & 15 & 105.6 & 71 & 824 & 124.5 \\ MMP, psi & - & - & 1020 & 2231.5 & 2075 & 3600 (special & & \\  & & & & & & & case: 4000–4200) & 790.3 \\ Compositions &
\begin{tabular}{c} High percent \\ of C2 to C12 \\ Sandstone or \\ carbonate \\ \end{tabular} & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing screening criteria for miscible CO\({}_{2}\)-EOR. The screening model in the previously mentioned research analyzed the parameters independently. Therefore, they did not affect one another. As a result, each parameter has its respective criteria limit in this individual screening model. For example, porosity has its screening criteria limit, as do permeability and other parameters. However, in reality, these parameters are interdependent because they influence and relate to one another. Therefore, a screening model capable of considering these parameters simultaneously is needed. A new screening model utilizing the interdependency among the parameters called the simultaneous screening model was developed in this research. Essentially, the present research aims to develop a combined screening model that is between simultaneous and individual models for the CO\({}_{2}\)-EOR methods. Two approaches were simultaneously applied to achieve this purpose, namely statistics and expert domain. The statistical approach is used to model physical phenomena. The expert domain approach is used to correct the results of statistical correlations adjusted to the real physical phenomena. The updated data on the miscible and immiscible CO\({}_{2}\)-EOR was the basis for supporting this goal. Nine significant screening parameters were used, including porosity, permeability, oil viscosity, oil gravity, depth, temperature, oil saturation, net thickness, and MMP.
## 2 Description of the Mathematical Model
### Design Procedures  
Figure 4 shows the overall scheme for a quarter-car with a PM-ASS that is controlled by the GP-FSB-ASMC+\(H_{\infty}\), where the control law is shown in (33), the adaptive laws are Equations (34) and (35), and the sliding surface is Equation (23). The block \"Road generator\" simulates a reference road condition \(y_{m}\) from a given terrain \(R_{r}\) for the system. A Fuzzy logic controller (FLC) is used to track given terrain trajectories \(R_{r}\). To compensate for the delay in the control input, a \"Grey Model First-Order One Variable\" state-predictor \(GM(1,1)\) predicts the future error \(\hat{\varrho}\) for the FSB-ASMC+\(H_{\infty}\), where \(GM(1,1)\) is a time-series-forecasting model that predicts the possible error for the next sampling time. The steps for \(GM(1,1)\) are:  
Step 1: Define the original data set:  
\[e^{(0)}(k)=\left\{e^{(0)}(1),e^{(0)}(2),\ldots,e^{(0)}(n)\right\},\ n>4 \tag{39}\]  
Step 2: Do Accumulating Generation Operator (AGO) to generate an accumulated data set:  
\[e^{(1)}(k)=\left\{e^{(1)}(1),e^{(1)}(2),\ldots,e^{(1)}(m)\right\} \tag{40}\]  
where \(e^{(1)}(k)=\sum\limits_{i=1}^{k}e^{(0)}(i)\), \(k=1,2,\ldots,m\).  
Step 3: Construct a differential equation for \(GM(1,1)\):  
\[\frac{de^{(0)}}{dt}+ae^{(1)}=b \tag{41}\]  
Step 4: Determine coefficients \(a\) and \(b\) using a Least Square Method (LSM):  
\[\hat{a}=\left[\begin{array}{c}a\\ b\end{array}\right]=\left(B^{\mathrm{T}}B\right)^{-1}B^{T}\gamma \tag{42}\]where \(B=\left[\begin{array}{ccc}-\frac{1}{2}(e^{(0)}(1)+e^{(0)}(2))&1\\ -\frac{1}{2}(e^{(0)}(2)+e^{(0)}(3))&1\\ \vdots&\vdots\\ -\frac{1}{2}(e^{(0)}(n-1)+e^{(0)}(n))&1\end{array}\right]\) and \(Y=\left[\begin{array}{ccc}e^{(0)}(2)&e^{(0)}(3)&\cdots&e^{(0)}(n)\end{array} \right]^{T}\).  
Step 5: Predict the next state as:  
\[\hat{e}^{(0)}(k+1)=\hat{e}^{(1)}(k+1)-\hat{e}^{(1)}(k) \tag{43}\]  
where \(\hat{e}^{(1)}(k+1)\) is calculated using a whitening equation:  
\[\hat{e}^{(1)}(k+1)=(e^{(1)}(1)-b/a)\times\exp(-ak)+b/a \tag{44}\]
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 1 Introduction
In [19], the authors explored the relationship between longitudinal profile parameters and track irregularity detection through simulation, subsequently recommending lengths for incline segments of high-speed rail designs with a target speed of 300 km/h. Lastly, the authors of [20] utilized extensive dynamic inspection data to investigate the influence of vertical curve parameters on track dynamic irregularities and proposed optimization strategies for these parameters. In summary, existing research often integrates track design and maintenance experiences through theoretical and empirical analyses, as well as simulation calculations, to adjust longitudinal profile parameters, thereby enhancing passenger comfort and improving track smoothness. However, research focused on improving the detection accuracy of long-wavelength irregularities near vertical curves and preventing abnormal exceedances in inspection data remains insufficient, which is critical for informed decision-making in track maintenance operations. The structure of the remainder of this paper is outlined as follows: In Section 2, a multibody dynamics model is established, focusing on the design principles for vertical curves and the inertial reference method, followed by validation of the constructed model. Section 3 investigates the relationships between key parameters--such as train speed, vertical curve radius, length, and gradient--and the detection accuracy of track irregularities, identifying the primary factors influencing detection accuracy. Section 4 analyzes the reasons for significant errors in detection data from vertical curve segments and introduces the reference cancellation method to mitigate the impact of the inertial reference (track alignment) on detection accuracy, with simulation experiments conducted to validate the proposed method's effectiveness. Finally, Section 5 presents the conclusions.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 2 EMS Measurement and Prediction
### EMI Effects  
After the EMS experiment, it was found that there is a complex nonlinear relationship between the jamming-to-signal ratio (JSR) and the center frequency or the bandwidth of the EMI with different types. As shown in Figure 2, the JSR of three typical frequencies of each EMI type are analyzed when the data link can't keep communication which is called losing lock. Figure 2a shows the variation of JSR with EMI frequency offset. Figure 2b shows the JSR of the data link operating signal varies with different interference bandwidths at three typical frequencies.  
By comparing the effects of the three types of interference on the data link, it can be concluded that under the same frequency offset, the data link is more sensitive to CW or BPSK interference. Besides, the sensitive frequencies of CW interference are concentrated near the center frequency of the operating signal, while BPSK and WGN interference are  
Figure 2: Variation of JSR when the UAV’s data link loses lock in presence of EMI signal with typical parameters: (**a**) JSR varies with frequency offset of EMI when the bandwidths of WGN and BPSK are 1 MHz and 8 MHz and (**b**) JSR varies with the EMI bandwidth when the center frequencies of WGN and BPSK are \(f_{0}\), \(f_{0}-4\) MHz, \(f_{0}+2\) MHz.  
still effective when the frequency offset is high due to the distribution of energy within a certain bandwidth.  
According to the practice of UAV's data link in the application scenarios [18; 19], a unified method is adopted to evaluate the risk to the data link in the presence of different types of EMI signal. It is determined that the data link is threatened by EMI when the power difference between the losing lock threshold and the actual EMI signal is less than 6 dB [10]. On the basis, the EMI risk is divided into four levels: stable, minor disturbance, risky, and losing lock, of which the corresponding power difference is 6 dB, 3 dB, 1 dB, and 0 dB, respectively.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Dynamic Cluster Construction  
In order to efficiently deliver content and reduce the load of network traffic, this paper divides all nodes in the edge into several clusters. In order to reduce the communication interference within the cluster, the cluster members considered in this paper cannot communicate directly, and any communication can only be carried out through CH. Each cluster consists of cluster heads and cluster members (CM). Each cluster can have multiple CM but only one CH, depending on the coverage of CH and the size limit of the cluster. Each type of node in the cluster has different responsibilities, and CH is mainly responsible for caching all CM updated cache content information. Then, all nodes in the cluster except CH are CM. CM can be cached, and all CM participate in the CH selection process.
# SPME-GC/MS Analysis of Methanol in Biospecimen by Derivatization with Pyran Compound
## 3 Materials and Methods
### Sample Preparation  
The GC-FID analysis of methanol was performed with 200 \(\upmu\)L of blood in a 10 mL glass vial containing 200 \(\upmu\)L of saturated aqueous sodium chloride (NaCl) solution and 100 \(\upmu\)L of 0.05% t-butyl alcohol aqueous solution (internal standard). The homogenized sample was analyzed by warming at 65 \({}^{\circ}\)C for 5 min in the oven of an autosampler and then injecting 500 \(\upmu\)L of the headspace of the sample vial into the GC inlet. GC/MS analysis for the blood was performed by the standard addition method. For GC/MS analysis with derivatization, 100 \(\upmu\)L of blood sample and 100 \(\upmu\)L of 0.1% D5-EtOH aqueous solution (internal standard) were added sequentially into a 10 mL glass vial containing 200 uL of saturated aqueous NaCl solution, then 100 \(\upmu\)L of methanol standard solution of varying concentrations (250 \(\upmu\)g/mL, 500 \(\upmu\)g/mL, 1000 \(\upmu\)g/mL, 1500 \(\upmu\)g/mL, and 2000 \(\upmu\)g/mL) were added to each vial followed by a 35 \(\upmu\)L of derivatizing reagent (DHP). Then 25 \(\upmu\)L of concentrated hydrochloric acid was added to the homogenized vial to derivatize. The 2-methoxytetrahydrofuran-hydropyryran at the headspace was extracted using SPME. Extraction was subjected to exposure for 10 sec using 75 \(\upmu\)m CAR/PDMS SPME fiber and then directly analyzed using GC/MS (Figure 12).
## 3 Results
### Local TEC Responses from MGEX Observations  
The STEC was represented in Figure 5 to distinguish the impact of the geomagnetic storm. Tremendous TEC enhancements were observed at the low latitudes of the southern hemisphere, compared with the northern hemisphere, indicating the asymmetrical reaction of the ionosphere during this storm. CUT0, NNOR, MOR1, and KARR had great STEC enhancements before 10:00 UT on the 23 rd at local day sides within the same time zone. NRMG and CKIS were located east in another time zone and the TEC enhancements were also dominant until local midnight. The STEC from the BDS measurements were at the same level of those from the GPS and GLONASS, and it sometimes became the most dominant.  
Figure 4: The global total electron content (TEC) responses to the HSS ((**a**) TEC responses; (**b**) TEC difference responses).  
The calibrated VTEC was also considered. Figure 6 shows the VTEC enhancements at the Asian-Pacific, European-African, and American regions during the storm phase. In Figure 6a, the peak value occurred at ~05:20 UT in CUT0 and NNOR, at ~05:50 UT in MRO1 and KARR, and at ~06:50 UT in CKIS. For the European-African region, the TEC enhancements achieved a maximum value at ~13:20 UT in HARB and WIND and at 05:58 UT in REUN. However, the TEC depletions were also found in the northern hemisphere in Europe (CPVG and VILL), continuing for most of the day after the second sub-phase of the storm. Similar TEC depletions were also noticed at AREG, KOUR, and ABMF in the American region. The different times of the peak value in similar longitudes indicated the propagation of the TEC variations due to the storm. In Figure 6b, the maximum variation percentage reached up to 417% in the HARB station. The anomalies fit well with Figure 4b. It seems that the anomalies mainly occurred in the EIA regions, pushing EIA southward (Asian, Europe) or greatly inhibiting the EIA after the main phase of the storm (America). As consequences, the obvious positive storm effect was discovered in CUTO, NNOR, MRO1, and KARR in the Asian-Pacific region; the enhancements were also observed in HARB and WINDREUN in the southern hemisphere of the European-African region; depletions were found in CPVGand VILL in the northern hemisphere in the European-African region; and the inhibition effect was also observed in AREG, KOUR, and ABMF in the American region.  
Figure 5: The slant TEC (STEC) responses to the storm on 23 June (Multi-GNSS observations from MGEX data). (blue: GPS; red: GLONASS; green: BeiDou Navigation Satellite System (BDS); pink: GALILEO).
## 3 Results
### Construction of Flood Disaster Knowledge Graph  
(1). Construction of data organization model  
The study constructed a data model to reveal the data organization structure and integrated the extracted literature and flood disaster information. Figure 6 shows the flood disaster data organization model. Article_ID is a unique identifier that integrates the title, theme classification, flood disaster subcategory, research institution, author, keyword, discipline category, publication journal, publication year, full-text link, DOI, communication email, and other data for each article.  
Figure 5: Spatial distribution of flood disaster research institutions (only the top 20 countries are labeled).  
Figure 6: Organization model of flood disaster literature data.  
(2). Flood disaster knowledge graph construction  
Using the study's data organization model, the extracted flood disaster information and the crawled literature information were integrated in the form of triples, stored in the Neo4j native database, and visually displayed. The results are shown in Figure 7, which includes 39,679 nodes and 64,908 edges. Node labels include UID, theme, title, subcategory, institutions, keywords, doi, authors, link, journal, year published, and email. The relationship types include belong_to, subclass_is, theme_is, etc. Nodes with different colors represent different categories of entities. The larger the node, the higher the level of the entity's category. The red bubble indicates the root node of the flood disaster, the blue bubble represents the research theme, the orange bubbles represent the studies, and the other color bubbles represent the relevant information on each item in the literature. The left side of Figure 7 shows the overall distribution of 5181 flood disaster studies, which were divided into seven themes; the numbers of each theme were relatively evenly distributed. The right side of Figure 7 provides three literature items as an example, showing the details of each literature item in the flood disaster knowledge graph. The three articles can be connected with each other using \"year\" and \"2019\" as the same filed value. The knowledge graph can establish links between articles, and ultimately form an interconnected literature network.  
Figure 7: Visualization of flood disaster knowledge graph.
## 4 Proposed DQN-Based Offloading Decision Algorithm
### DQN-Based Offloading and Compression Decision  
For the DQN-based offloading decision algorithm, the states, actions, and rewards are defined as mentioned in Section 3.1. This proposed algorithm is formally described in Algorithm 1. In this subsection, we explain the details of the proposed algorithm. Each state is the observation that the agent gets after taking an action from the environment. We set the capacity of TD, StandBy Q, and ES as state, and it represents the capacities' changes over time. Likewise, the agent will get a reward (\(R_{comb}\)) in each step. The reward we set is the combination of three aspects of the reward, i.e., the reward of execution delay, the reward of energy consumption, and the reward of terminal device's capacity. The agent uses the state information and reward to make a better action decision:  
\[R_{combi} = (\gamma\times R_{delay}+(1-\gamma)\times R_{energy})\times\beta+R _{capacity}\times(1-\beta) \tag{6}\]  
where \(R_{combi}\) stands for the reward function definition which is the combination of \(R_{delay}\) (reward under the consideration of execution delays), \(R_{energy}\) (reward under the consideration of energy consumption), and \(R_{capacity}\) (reward under the consideration of capacity) where the three reward components are defined as follows:  
\[R_{delay} = 1-D_{dyn}/D_{local}, \tag{7}\] \[R_{energy} = 1-E_{dyn}/E_{local},\] (8) \[R_{capacity} = -1\times tasktdo\times 0.01, \tag{9}\]  
where \(D_{dyn}\) and \(E_{dyn}\) are  
\[D_{dyn} = \sum_{n=1}^{N}\alpha_{n}*D_{n}^{offload}+(1-\alpha_{n})*D_{n}^{ local}, \tag{10}\] \[E_{dyn} = \sum_{n=1}^{N}\alpha_{n}*E_{n}^{offload}+(1-\alpha_{n})*E_{n}^{ local}, \tag{11}\]When offloading computing is decided in each step based on DQN, \(D_{local}\) and \(E_{local}\) stand for the execution delay and energy consumption when the tasks are processed in the local at time step \(t\). Similarly, \(D_{offload}\) and \(E_{offload}\) are the values when the tasks are processed in the edge server. Finally, the total execution delay and energy consumption for all the tasks are represented as (10) and (11). Thus, semantically, the (10) and (11) are the linear combinations of execution delay and energy consumption depending on offloading decisions. Based on the definitions of (10) and (11), the rewards \(R_{delay}\) in (7) and \(R_{energy}\) in (8) are defined as the difference between the value when the tasks are performed locally and the value by the decision algorithm based on DQN. As a result, the learning is carried out to minimize \(D_{dqn}\) and \(E_{dqn}\) while it maximizes \(R_{delay}\) and \(R_{energy}\). Furthermore, the reward \(R_{capacity}\) in (9) is a value to represent an overflow the terminal's capacity. Note that \(R_{capacity}\) is meaningful only under certain conditions according to Algorithm 2. If local computing (i.e., no offloading) is decided by the algorithm and the capacity of terminal device is not sufficient to process task at time step \(t\), the negative reward, \(R_{capacity}\), is applied.
## 4 Challenges and Opportunities in Marine Biotechnology
### Sustainability and Alignment with Global Goals  
Marine biotechnology is also aligning with global sustainability goals. Microalgae use less water and land than terrestrial crops, sequester CO2 during photosynthesis, and offer bio-based alternatives to petroleum-derived products. Their applications in bioplastics, biofuels, and carbon capture underscore their role in the transition to a circular and green economy. Importantly, microalgae-derived technologies support several United Nations Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger) through protein-rich biomass for food and feed, SDG 3 (Good Health and Well-being) via nutraceuticals and therapeutic compounds, SDG 6 (Clean Water and Sanitation) through integration with wastewater treatment, SDG 12 (Responsible Consumption and Production) via biorefinerymodels and resource recovery, SDG 13 (Climate Action) by capturing CO\({}_{2}\) and offsetting emissions, and SDG 14 (Life Below Water) by offering marine-friendly production systems. These connections highlight the potential of marine microalgae to contribute to sustainable development at the environmental, economic, and social levels [15].  
Moreover, the adoption of responsible research frameworks is gaining traction in microalgal biotechnology. For example, Life Cycle Assessment (LCA) is being employed to quantify the environmental impacts of different cultivation and extraction strategies across their entire production chain. In parallel, Techno-Economic Analysis (TEA) is used to evaluate the financial feasibility and scalability of these innovations. As AI becomes increasingly embedded in bioprocess optimisation, there is growing emphasis on ethical AI principles, such as transparency in algorithm design, data privacy, and equitable access to digital tools. These governance models are critical for ensuring that technological advancements align with sustainability, equity, and long-term societal benefit [15].  
In conclusion, while marine biotechnology faces big challenges in strain engineering, cultivation optimisation, downstream processing, and regulatory compliance, it also presents transformative opportunities for sustainable bioproduct development. Strategic investment in research, infrastructure, and international collaboration will be essential to unlock the full potential of marine microalgae in addressing pressing global needs across health, food, energy, and environmental sectors [16; 129].