esa-sceva/satcom-chunk-collection huggingface dataset:
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Droplet Size Analysis
For instance, [PERSON] et al. [107] demonstrated that, despite DLS data collected over months of emulsion monitoring indicating excellent emulsion stability, a microscopic analysis of the same sample showed substantial changes in its internal structure. The authors observed that, while the average size of the droplets determined by DLS was 150 nm, droplet size established based on freeze-fracture TEM and confirmed by AFM varied between 50 and 500 nm, with the majority of the droplets measuring around 100 nm. These and similar findings from other studies indicate that DLS data needs to be confirmed by additional analytical methods, including microscopic analysis, field-flow fractionation [108], nuclear magnetic resonance spectroscopy [109], or Fourier transform infrared spectroscopy [110]. Another group of particle size analyzers frequently used for emulsion characterization are devices that utilize electrical pulse counting. The electrical pulse counting is performed in setups comprising a glass tube with two electrodes and a small hole, through which the emulsion sample is drawn. Electrical pulse counting methods rely on the measurement of variations in the electrical conductivity of the sample caused by the passing of the emulsion droplets between two electrodes [111]. Since oils have much lower electrical conductivity than water, droplets passing between the electrodes affect the electrical current flowing through the emulsion, creating electrical pulses. Droplet size is determined based on the assumption that larger particles move more slowly and create larger electrical pulses. This technique is appropriate for measuring droplets ranging in diameter from 0.4 um to 1200 um [28]. The measurement is constrained by the size of the hole in the glass tube, which requires adjustments to accommodate a wide range of droplet sizes. Additionally, similarly to the methods using light scattering, emulsion samples need to be diluted before measurement. High concentrations of droplets in the sample hinder the smooth passage of single droplets into the glass tube. Therefore, while suitable for droplet size analysis, this method is not ideal for studying the flocculation process, as dilution can disrupt the gathered droplets and result in misinterpretation of data on emulsion stability. A method enabling the determination of droplet size in emulsions with high droplet concentrations (up to 50%) without dilution is ultrasonic spectrometry [112]. This technique estimates emulsion droplet size and concentration based on the scattering of the ultrasound waves transmitted through the emulsion. Scattering of the ultrasound waves by the droplets leads to a decrease in velocity and an increase in attenuation of the frequency of ultrasonic waves. Ultrasonic spectroscopy can be used to determine droplet sizes ranging from 10 nmto 1000 \(\upmu\)m. Its major advantage over other particle size analyzers lies in its ability to characterize not only concentrated but also optically opaque emulsions.
## 5 Conclusions  
This paper presents an improved Grover's search algorithm-based quantum key agreement (GSA-QKA) protocol tailored for secure communication in Internet of Medical Things (IoMT) environments. The proposed scheme ensures fairness, correctness, and security among participants without relying on authenticated classical channels or quantum memory. By enhancing existing GSA-QKA frameworks with a symmetric architecture and integrating decoy photon techniques, the protocol guarantees equal user contribution to the final key while defending against both external and internal attacks. Rigorous analysis confirms resilience to intercept-resend, entanglement-based, and hardware-level threats, as well as insider attacks involving parameter manipulation. The decoy photon mechanism further provides quantifiable assurance against unauthorized key extraction. Although the protocol achieves a theoretical efficiency of 25%, which is moderate compared to some existing GSA-based schemes, it provides significant improvements in security, fairness, and practicality, making it well suited for secure applications in IoMT environments. These characteristics offer a robust and scalable solution for future quantum-secure medical communication networks. While the current work focuses on theoretical analysis and security proofs, we acknowledge the importance of empirical validation. We have added a discussion outlining our planned future work.  
This research received no external funding.  
The original contributions presented in this study are included in the article material. Further inquiries can be directed to the corresponding author.  
The author would like to thank the Department of Computer Science and Information Engineering, National Chiayi University for providing the academic environment conducive to this research. AI-assisted tools were used solely for English grammar improvement in the preparation of this manuscript, while all research content, analysis, and conclusions remain the original work of the author.  
The author declares no conflicts of interest.
## 3 Non-Bracket Construction Method
### Analysis Method
#### 3.1.2 Model Updating Iteration  
Model updating includes configuration, internal force, and original length updating. After the node coordinates are updated by the deformation of nonlinear dynamic finite element analysis, the member length in the model also changes, and the updated member is in a stress-free state. However, theoretically stress and strain exist in the member before the model is updated, so the stress and strain should be applied again after the model is updated. Different element types need different methods.  
1. Cable strut element  
In cable system structures, the equivalent initial strain or temperature difference is often used to simulate the tension cable or control the original length. For a cable with an original length that needs to be controlled, the equivalent initial strain or temperature difference should be adjusted according to the updated length based on the principle that the original length should remain unchanged before and after updating. For cables that need to control internal forces (such as lifting traction and tension), the original length is updated directly. The equivalent initial strain (\(\epsilon_{\text{p}}\)) or equivalent temperature difference (\(\Delta T_{\text{p}}\)) can be determined according to the following formulae:  
\[\epsilon_{\text{p}}=S/S_{0}-1 \tag{8}\]  
\[\Delta T_{\text{p}}=-\epsilon_{\text{p}}/\alpha=(1-S/S_{0})/\alpha \tag{9}\]  
\[\epsilon_{\text{p}}=F/(E\cdot A) \tag{10}\]  
\[\Delta T_{\text{p}}=-\epsilon_{\text{p}}/\alpha=-F/(E\cdot A\cdot\alpha) \tag{11}\]  
where S is the unit length in the model; S\({}_{0}\) is the original length of the unit; E, A, and \(\alpha\) are elastic modulus, cross-sectional area, and coefficient of temperature expansion, respectively; and F is the internal force of the cable strut.  
2. Beam element  
The instate method is used considering that beam element contains axial, bending, and tangential strain.
## 3 Materials and Methods
### Methods
#### 3.2.2 Yolo  
YOLO is a state-of-the-art object detection algorithm developed by [PERSON] in 2016. It handles object detection as a classification and regression problem. YOLO's one-stage detection relies on deep learning. YOLO uses features extracted from an entire image to predict each bounding box. Moreover, it simultaneously predicts all bounding boxes of all classes for a given image [32].  
The YOLO model utilizes a methodology in which the input image is divided into a grid comprising of cells(C), thereby generating square blocks with dimensions of \(\mathbb{S}\times\mathbb{S}\). Each prediction of a bounding box is composed of five essential elements: x, y, w, h, and confidence. The x-coordinate and y-coordinate signify the position of the center of the bounding box about the grid cell it belongs to. The width and height of the bounding box are represented as a proportion to the entire image. The confidence score denotes the likelihood that the bounding box encompasses an object and the accuracy of its prediction. Its estimates in the YOLO algorithm are based on the intersection over Union (IoU) between the predicted bounding box and the ground truth bounding box. The intersection refers to the area where the two boxes overlap, while the union refers to the combined area of both boxes. IoU is calculated by dividing the area of the intersection by the area of the union as shown in Equation (7) [33].  
\[\text{IoU}\ =\ (\text{Area of Intersection})/(\text{Area of Union}) \tag{7}\]  
Another metric for evaluating the effectiveness of models in object detection is average precision, which is given in Equation (8). In this equation, \(\text{AP}_{\text{K}}\) represents the average precision for class k, while n represents the total number of classes. The mAP metric, in turn, compares the identified bounding box with the ground truth.  
\[\text{mAP}\ =\ \frac{1}{\text{n}}\sum_{\text{k}\ =\ 1}^{\text{k}\ =\ \text{n}} \text{AP}_{\text{K}} \tag{8}\]
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 5 Conclusions  
This study presents a techno-economic analysis of blue hydrogen production in Nigeria using the lean and green approach. By integrating process simulation in Aspen HYSYS with Data Envelopment Analysis (DEA) and inverse DEA, the study not only demonstrates the technical feasibility of this method but also assesses its economic viability of SMR with CCS as a sustainable pathway for hydrogen production from flare gas.  
The CBR results gave 2.15 (payback 4.11 years with carbon credit) and 1.96 (payback 4.5 years without carbon credit). This highlights the financial advantage of this approach. These results demonstrate the dual benefits of reducing environmental impact while achieving economic benefits, establishing flare gas management technology as a crucial element in Nigeria's transition toward a circular and low-carbon economy.  
Another key finding from the study is that the LCOH from SMR with CCS is lower compared to similar studies by other researchers. This suggests that establishing an SMR plant in Nigeria is more cost-effective due to the country's abundant natural gas, which serves as a feedstock for hydrogen production. Nigeria's cost advantage stems from its abundant natural gas reserves, making it a readily available feedstock for hydrogen production. Sensitivity analysis shows that the LCOH becomes highly competitive at lower gas prices. In other words, an increase in natural gas price usually leads to an increase in LCOH. This makes Nigeria's blue hydrogen highly competitive compared to other countries with higher gas prices.  
Therefore, the project presents a promising and economic opportunity for the government, as this hydrogen can be utilized and transported locally and internationally, positioning Nigeria as a central hub for hydrogen production as demand continues to rise. Overall, this integrated approach demonstrates the potential of combining lean practices with green goals to enhance sustainability in the petroleum industry.  
Finally, future techno-economic research should be carried out on an alternate route of hydrogen production using the Autothermal reforming (ATR) and pyrolysis process to determine the best technology selection to adopt in Nigeria.  
Conceptualization, F.D., O.O., J.R., H.N.D. and C.D.; methodology, F.D.; formal analysis, F.D., O.O., J.R., C.D. and H.N.D.; original draft preparation, F.D.; writing--review and editing, F.D., C.D., O.O., J.R. and H.N.D.; supervision, O.O., H.N.D. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Data Silos and Fragmentation:
### Mathematical Model
* Sybil Attacks: Attackers may create multiple malicious identities within the network, undermining the trust and consensus mechanisms of the blockchain. * Insider Threats:
* Malicious Insider: An insider with authorized access may abuse their privileges to manipulate data, compromise the integrity of the federated learning process, or intentionally leak sensitive information [42]. * Malware and Cyberattacks:
* Ransomware: Malicious software may infect the system, encrypting EMRs or disrupting the operation of the blockchain network, demanding a ransom for their release or recovery. * Distributed Denial of Service (DDoS) Attacks: Attackers may attempt to overwhelm the system with a high volume of traffic, rendering it inaccessible or causing disruption to normal operations. * Regulatory and Compliance Risks:
* Non-Compliance: Failure to adhere to applicable regulations and compliance standards, such as HIPAA (Health Insurance Portability and Accountability Act), may result in legal consequences or reputational damage. To mitigate these threats, the framework should incorporate appropriate security measures and practices, including:  
* Strong encryption techniques to protect data during storage and transmission. * Access control mechanisms to ensure that only authorized entities have access to sensitive information. * Robust authentication and authorization protocols to prevent unauthorized access. * Regular security audits and vulnerability assessments to identify and address any weaknesses in the system. * Implementation of consensus mechanisms in the blockchain network to prevent tampering and maintain the integrity of the stored data. * Continuous monitoring and intrusion detection systems to identify and respond to potential security incidents promptly. It is essential to note that this threat security model serves as a starting point, and a comprehensive security analysis should be conducted to identify and address specific threats and vulnerabilities relevant to the deployment environment of the proposed framework [25]. Let us introduce the algorithms designed to ensure security and privacy in the proposed approach.
## 3 Multi-Searcher Optimization
### Execution Procedure  
In order to solve a specific optimization problem, the number of optimization variables, constraints, and the objective function should be given to the proposed MSO. In fact, the number of optimization variables will determine the parameters setting of the MSO, while the fitness function is completely dependent on both of the objective function and constraints. Therefore, the proposed MSO can be used for addressing different benchmark test functions or the proposed OED.  
At last, the overall execution procedure of the MSO for OED is given in the Figure 5.  
* Step 1: Input system parameters and unit parameters, including the operating cost coefficients of the thermal units, CHP units and heat-only units, the feasible operating region of the CHP units, and the main parameters of renewable energy resources such as wind speed and solar irradiance. Set the algorithm parameters, including the number of GS, the number of LS, the maximum search radius, minimum search radius, and the maximum iteration number.
* Step 2: Initialize the double-layer searcher based on Equations (35)-(36) in chaos theory; and set the current iteration number T = 1.
* Step 3: Determine the global searcher level and searcher radius with Equation (37). Particularly, the level 0 is assigned to the GS corresponding to the least cost, and the highest level is assigned to the GS corresponding to the highest cost.
* Step 4: Implement the global searcher and local searcher separately, where the local searcher is surrounded by each global searcher and centered around the searcher within a specified radius.
* Step 5: Calculate the objective function with Equation (15), which is equal to the total operating cost.
* Step 6: Evaluate the objective function using the penalty function method from Equation (38), based on the given M.  
* Step 7: Determine the distance and direction based on random walk theory and guide global searcher migration, in which both \(\lambda\) and \(\theta\) are the random values with a distribution of uniform probability.
* Step 8: Judge the termination of MSO. If the termination condition is met, then output the economic scheduling result, else turn to step 3 and enter the next iteration.
# Biosynthesis Progress of High-Energy-Density Liquid Fuels Derived from Terpenes
## 4 Strategies for Constructing Efficient Microbial Cell Factories for Terepenes
### Optimizing Metabolic Pathways
#### 4.2.2 Optimization of the Upstream and Downstream Pathways
Moreover, the synthesis of terpenes is also influenced by bypass and competing pathways. The inhibition or knock-out of genes in the competing or bypass pathways is an effective method to increase terpene production. The activity of GPPS is an important factor influencing the production of monoterpenes. By optimizing the ribosome binding site of GPPS or introducing GPPS from plants, the production of geraniol was increased by 6 times in _E. coli_, reaching 2.0 g/L [143; 144]. Researchers have also developed a strategy based on the N-degor-dependent protein to regulate ERG20 activity in yeast, and successfully increased the production of farnesol and limonene [145]. Additionally, by knocking out the endogenous NADPH-consuming enzymes OYE2 and ATF1, the production of farnesol was increased by 1.7 and 1.6 times, respectively, and the further dynamic regulation of ERG20 increased the farnesol production to 1.69 g/L in _S. cerevisiae_[73]. FPP is the precursor for sesquiterpenes synthesis, which is also a precursor of squalene required for cell membrane synthesis. Therefore, balancing the FPP supply for the synthesis of sesquiterpenes and squalene is crucial for ensuring normal cell growth and a high production of sesquiterpenes. Current methods used to regulate FPP metabolism include both the degradation of squalene synthase (ERG9) and dynamic regulation of ERG9 [44]. [PERSON] et al. effectively increased the production of valencene by 86% by establishing an endoplasmic-reticulum-associated protein degradation (ERAD) method to reduce intracellular ERG9 protein levels [146]. Additionally, regulating the expression of _ERG_9 using the methionine promoter \(\mathrm{P}_{\mathrm{MET3}}\) or the glucose-regulated promoters \(\mathrm{P}_{\mathrm{HXT1/2}}\) could effectively increase the production of sesquiterpenes [147].
## 3 Query Optimization to Obtain Bounded Congestion at a Fixed Target Time  
Suppose our goalfor a given entity set \(\mathcal{E}\), is to optimize queries to guarantee low congestion potential at some fixed target time \(\tau>0\) in the future. If granularity is not an issue, \(O(|\mathcal{E}|)\) queries suffice, provided they are made sufficiently close to the target time.  
Figure 2: A configuration of five unit radius entities. The 3-ball \(B_{2}(3)\) of entity \(e_{2}\) is shown shaded.  
Maximizing the minimum query granularity is less straightforward. In fact, it is NP-hard, even in the case where all entities are stationary (this follows directly from [1] [Theorem 17]). Nevertheless, it is clear that any reasonable query scheme using minimum query granularity \(\gamma\), that guarantees a given measure of congestion potential at most \(x\) at the target time \(\tau\), will not query any entity more than once within the final \(n\) queries. Thus, any such optimal query scheme determines a radius \(1+k_{i}\gamma\) for each entity \(e_{i}\in\mathcal{E}\), where (i) \(k_{1},k_{2},\ldots,k_{n}\) is a permutation of \(1,2,\ldots,n\), and (ii) the uncertainty configuration, in which entity \(e_{i}\) has an uncertainty region \(u_{i}\) with center \(\zeta_{i}(\tau-k_{i}\gamma)\) and radius \(1+k_{i}\gamma\), has the given congestion measure at most \(x\). For any measure, we associate with \(\mathcal{E}\) an _intrinsic fixed-target granularity_, defined to be the largest \(\gamma\) for which these conditions are satisfiable.  
It is not hard to see that, by projecting the current uncertainty regions to the target time (assuming no further queries), some entities can be declared \"safe\" (meaning their projected uncertainty regions cannot possibly contribute to making a congestion measure, for itself or any other entity, exceed \(x\) at the target time). This idea is exploited in query schemes that query entities in rounds of geometrically decreasing duration, following each of which a subset of such \"safe\" entities are set aside with no further attention, until no \"unsafe\" entities remain.
###### Abstract  
We demonstrate measurements of the absorption coefficient \(\alpha\approx\) 2.5 \(\times\) 10\({}^{-7}\) cm\({}^{-1}\) in synthetic crystalline quartz at a wavelength of 1071 nm with a signal-to-noise ratio of 10/1 using the Time-resolved photothermal common-path interferometry (TPCI) scheme. It utilized cells filled with flowing argon and eliminated the influence of ambient air absorption. The scheme elements limiting the sensitivity of measurements at the level of \(\approx\)7.8 \(\times\) 10\({}^{-8}\) cm\({}^{-1}\) were revealed. When these elements are replaced by better ones in terms of their thermal influence, the sensitivity of absorption coefficient measurements in crystalline quartz is \(\sim\)10\({}^{-8}\) cm\({}^{-1}\). The calculation of the correction due to these optical elements of the values of the measured absorption coefficients is also described, which makes it possible to achieve the same sensitivity without replacing the elements. The improved scheme confirms the presence of the spatial inhomogeneity of absorption with a minimum coefficient of 2.5 \(\times\) 10\({}^{-7}\) cm\({}^{-1}\) in synthetic crystalline quartz. The discrepancy of the absorption coefficient values in different regions of the crystal in the presented series of experiments was 2.5 \(\times\) 10\({}^{-7}\) cm\({}^{-1}\) to 4 \(\times\) 10\({}^{-6}\) cm\({}^{-1}\). Taking into account the ratio of thermo-optical parameters and the heat diffusion effect, the calculation shows that for quartz glasses the corresponding sensitivity of the absorption coefficient measurements equals \(\approx\)1.5 \(\times\) 10\({}^{-9}\) cm\({}^{-1}\).  
Keywords:time-resolved photothermal common-path interferometry; ultrapure quartz materials; absorption measurement; argon +
Footnote †: journal: Applied Physics Letters