esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Additionally, their structure lacks the capacity to incorporate changing data sources and real-time updates [28], making them ineffective in highly dynamic contexts such as smart ports. By utilizing neutrosophic cognitive maps (NCMs), this study introduces a methodological framework capable of handling the uncertainty and complexity inherent in smart port performance evaluations [29]. They combine FCMs and neutrosophic sets, providing a more comprehensive and flexible approach to modeling complex systems. \"Indeterminacy\" is a fundamental factor in real-life situations, as demonstrated by previous research [30; 31]. This inherent indeterminacy creates obstacles for decision-makers as it renders outcomes uncertain and difficult to forecast with certainty. In this context, NCMs have been successfully applied in various research disciplines, such as analyzing criminal behavior, traffic congestion, organic farming, and healthcare (e.g., regarding COVID-19) [32; 33; 34; 35]. Interested readers are referred to [36] which offers a comprehensive literature review of previous studies that have utilized NCMs, highlighting the diverse purposes and application areas in which they have been employed. By utilizing NCMs, our framework enables a more comprehensive examination of multiple variables and their interactions, ultimately enhancing decision-making processes in smart port management. While NCMs have been applied in fields such as healthcare and environmental management, their potential in evaluating smart port performance remains largely unexplored. This study aims to fill that gap by introducing an NCM framework for assessing smart port metrics in a collaborative decision-making setting, tackling the sector's unique challenges of unpredictability and interdependency. Our methodology introduces a new computational modeling tool to navigate the intricate dynamics of smart port metrics. NCMs effectively manage the complexity and uncertainty of port operations through neutrosophic logic, providing a more realistic representation of causal relationships. This approach is highly adaptable, allowing for the integration of diverse stakeholder perspectives and inputs. By encouraging active participation and facilitating the exploration of various scenarios, our strategy empowers decision-makers to develop a deeper understanding of the factors influencing smart port performance.
## 3 Experimental Results and Discussions
### The Impact of Transmission Distance  
To further understand the impact of transmission distance and prove that our proposed system can be applied to more scenarios, the measurement results at different distances are discussed in this section. Except for the change of communication distance, the system parameters are consistent with Table 1.  
First, to have a better understanding of the distance impact to the received signal based on the micro-LED, we can measure the corresponding received optical power and Vpp in the free-space transmission of 2 to 4 m. As we can see from Figure 9, the received optical power is 16, 10, 8 uW at 2, 3, 4 m. The Vpp of the received signal is proportional to the received optical power. At the data rate of 1.5 Gbps, the eye diagrams of 2 m and 4 m are given, respectively. It can be seen that the communication performance of a single-hop system will deteriorate with the increase in transmission distance.  
Then, the relationship between distance and data rate is analyzed. The comparison between Hop 1 and Hop 2 at different communication distances is given, which is shown in Figure 10. In the single-hop system, the data rates within FEC are 2 Gbps in the cases of 2 m and 3 m transmission distance, and the BER performance of 3 m is slightly increased with the increase in distance. When the distance increases to 4 m, the maximum data rate of the single-hop system decreases to 1.6 Gbps. In the dual-hop system, the data rates within FEC are 1.1 Gbps, 1.1 Gbps, and 1 Gbps at the length of 2 m to 4 m. It can be seen that when the single-hop communication performance is not limited by distance, the dual-hop communication performance also remains relatively stable with the increase in distance, which can further prove the potential application indoors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & **Distance** & **Vpp** & **Bandwidth** & **Data Rate (FEC)** \\ \hline Hop 1 & 2.0 m & 137.6 mV & 880 MHz & 2.0 Gbps \\ Hop 2 & 4.0 m & 112.0 mV & 715 MHz & 1.1 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication-performance comparison from Hop 1 and Hop 2.  
Figure 9: Measured received power and Vpp under the different free-space transmission lengths of 2 m to 4 m (Inset: the eye diagram at the data rate of 1.5 Gbps at 2 m and 4 m).
# Nanomaterials for Healthcare Biosensing Applications
## 2 Carbon Allotrope-Based Nanomaterial Applications in Healthcare Biosensing
### Nanodiamonds
In addition to electrochemical biosensors, carbon allotrope-based nanomaterials find abundant applications in optical, piezoelectric, and other types of biosensors, as shown in Table 2. Excluding the aforementioned carbonaceous nanomaterials, nanoonions [125; 126], nanonorms [127; 128], and many other carbon allotropes have started to emerge as interesting materials for biosensing applications [2; 18; 22]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Sensor Platform/Label** & **Analyte** & **Detection Mechanism** & **Detection Range** & **LOD** & **Reference** \\ \hline LFS-Strr/CMWCNT-DNA & Hg\({}^{2+}\) & VE, IJS & 0.05–1 ppb & 0.05 ppb & [129] \\ \hline LFS-MWCNTs-DNA/biotitylated DNA & DNA & VE, IJS & 0.1–20 nM & 0.004 nM & [130] \\ \hline Gelatine stabilised RGO-AuNP & Cys & UV-vis, FS & 0.51–0.4 \(\mu\)M & 0.51 nM & [99] \\ \hline Eu–GQD & Cu\({}^{+2}\) & UV-vis, FS & 0.1–10 \(\mu\)M & 0.056 \(\mu\)M & \\  & Cys & UV-vis, FS & 0.5–50 \(\mu\)M & 0.31 \(\mu\)M & [100] \\ \hline CD–PAMAM-RGDS & Cu\({}^{+2}\) & UV-vis, FS & 0.01–2 nm & 0.01 \(\mu\)M & [131] \\ \hline CD–anti-Desmin & Desmin & PL & 0.714–4.286 ng mL\({}^{-1}\) & \(<\)1 ng mL\({}^{-1}\) & [41] \\ \hline  & _E. coli_ & & 10–10\({}^{3}\) cfu mL\({}^{-1}\) & 10 cfu mL\({}^{-1}\) & \\ IDE-SWCNT-pleurocdin & _E. faecalis_ & MSPQC & 100–10\({}^{5}\) cfu mL\({}^{-1}\) & 100 cfu mL\({}^{-1}\) & [82] \\  & C. _aliobians_ & & 100–10\({}^{5}\) cfu mL\({}^{-1}\) & 100 cfu mL\({}^{-1}\) & [84] \\ \hline  & & & & & \\ \hline  & & & & & \\ \end{tabular}
\end{table} 
Table 2: Carbon allotrope-based biomedical diagnostics using optical, piezoelectric, and other types of sensing techniques.
## 5 Lactic Acid Bacteria
### Mechanism of LAB  
There are two fermentation pathways: homolactic fermentation and heterolactic fermentation. In homolactic fermentation, glucose is the carbon source for producing pyruvic acid, which is converted to lactic acid by the lactate dehydrogenase enzyme. Energy is generated through reduced nicotinamide adenine dinucleotide (NADH). Consequently, from one glucose molecule, two lactic acid and two adenosine triphosphate (ATP) molecules are produced through the glycolytic pathway. On the other hand, heterolactic fermentation produces lactic acid, acetic acid or alcohol, and carbon dioxide (CO\({}_{2}\)) from one glucose molecule through the phosphoketolase pathway [63]. Glucose 6-phosphate is converted into CO\({}_{2}\), ribulose 5-phosphate, and nicotinamide adenine dinucleotide phosphate (NADPH). The enzyme lactate dehydrogenase plays an essential role in the production of lactic acid because it transforms pyruvate into lactic acid. Furthermore, its stereospecificity determines the configuration of lactic acid. L-lactic acid is catalyzed by L-lactate dehydrogenase, whereas D-lactate is catalyzed by D-lactase dehydrogenase [68]. Additionally, during fermentation, many other metabolites are produced in this pathway, such as aromatic compounds (e.g., diacetyl and acetaldehyde) and different organic acids. LAB can also metabolize other hexoses, such as fructose, mannose, or galactose, as alternative carbon sources [63].  
LAB can be categorized as homofermentative or heteroferrantative organisms based on their ability to ferment sugars. For example, _Streptococcus thermophilus_ and _Lactobacillus delbrueckii_ subsp. _bulgarcius_ are homofermentative bacteria [69]. On the other hand, bifidobacteria are heteroferrantative bacteria because they produce acetic and lactic acids; however, generally, they metabolize hexoses using the \"bifidus pathway\", where the fructose-6-phosphate phosphoketolase is the key enzyme and CO\({}_{2}\) is not produced [66].
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 3 Experiment
### The Experiment for Comparison
In summary, the CNN-GRU method demonstrates clear advantages in geophysical process analysis, offering more precise and dependable outcomes for geological structure analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MSE (mm)** & **RMSE (mm)** & **MAE (mm)** & **R\({}^{2}\)** \\ \hline EKF & 0.1646 & 0.4057 & 0.3236 & 0.9037 \\ CNN-GRU & 0.0125 & 0.1118 & 0.0767 & 0.9920 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of the CNN-GRU and EKF. Figure 14: The error histogram (the left is the CNN model, the middle is the GRU model, and the right is the CNN-GRU model). Figure 15: Another monitoring site used for comparative experiment. Figure 16 illustrates a comparative analysis between the CNN-GRU and EKF methodologies, focusing on both the test dataset and a 24 h extrapolation scenario. The figure comprises a time series plot and an error scatter plot. In the time series plot, the CNN-GRU method demonstrates closer adherence to the original observations from the test dataset, while the EKF method exhibits more frequent fluctuations throughout the process. This observation is further supported by the error scatter plot, which indicates the superior performance of the CNN-GRU approach over EKF. Specifically, around the 240 th hour, EKF presents prediction errors of approximately 1 mm, whereas CNN-GRU maintains a more consistent behavior with fewer and less pronounced deviations over the entire interval, reaching a maximum of about 0.8 mm. Overall, the CNN-GRU methodology outperforms EKF, delivering predictions characterized by higher accuracy and stability.
## 2 Materials and Methodology  
In this study, evaluation of the efficiency of leakage management and the leakage management model formulation were derived using data related to revenue water ratio improvements according to the leakage of small- and medium-sized WDSs. The evaluation was performed by MRA analysis and DNN, which followed the procedure shown in Figure 1. There are three steps in developing the approach for the evaluation of the water loss reduction factors, which are (1) data collection, (2) pre-processing of data, and (3) evaluation of water loss management efficiency and determination of the formulation of water loss.
# Software Project Risk Identification Based on Scrum Artifacts
## 4 Experimental Evaluation of the Risk Identification Framework
### Results of the Risk Analysis Stage in Experimental Projects  
A summary of the risk detection and registration statistics observed during the risk analysis stage is presented in Figure 12. The number of detected risks for each project is calculated by summing the unique risks identified per sprint for each artifact. The variation in the number of attributes that reached the threshold was influenced by several factors, including the project lifecycle stage during which the framework was applied, the team's expertise in selecting artifacts, attributes, and risks for monitoring, and the choice of threshold values for attributes.  
In Project 1, the team opted to track fewer artifacts, yet a significant proportion of their attributes reached the threshold in each sprint. As a result, 399 occurrences of attributes reaching the threshold were identified, leading to the detection of 2364 potential risks per Sprint per artifact, corresponding to an average of 118 risks per sprint. While the team considered increasing the threshold values, they ultimately decided to adhere to the predefined thresholds as a guideline. Despite the substantial number of potential risks identified, the team successfully filtered and registered only those risks that related to  
Figure 12: Results of the risk analysis stage in experimental projects: (**a**) Number of attributes that triggered risk detection, because they reached the upper or lower limit of their threshold value; (**b**) Numbers of detected risks compared to registered risks, which were deemed significant by the project team.  
attributes with the largest deviations from the thresholds. In total, 36% of the detected risks were registered in Project 1.  
In Project 2, a larger number of artifacts and their attributes were tracked, which increased the likelihood of occurrences reaching the threshold. However, this number of occurrences was also influenced by the team's choice of threshold values, as a higher tolerance for failure resulted in fewer attributes exceeding the threshold. Compared to Project 1, Project 2 emphasized the risk categories RC6 (Coding) and RC7 (Testing). Ultimately, 33% of the detected risks were registered.  
In Project 3, the framework was introduced mid-project. To minimize the probability of deviations in attributes, the team selected a smaller subset of attributes to track. This approach, combined with effective collaboration within the team and with the client, resulted in minimal deviations in attribute values and a lower number of potential risks. As in Project 1, the team registered only those detected risks that were associated with the largest deviations from the thresholds in attribute values.  
The following section provides a more detailed analysis of the framework application's results. Project 1 was selected for an in-depth examination of the risk analysis process, focusing on statistics from the perspectives of artifacts, risks, and their categories.
# Closed-Form Solution of the Bending Two-Phase Integral Model of Euler-Bernoulli Nanobeams
## 3 Closed-Form Solution of [PERSON]'s Two-Phase Integral Model Equations
### Algorithm  
The method for solving the above three boundary value problems can be easily programmed in any computer algebra system. For this, we provide the following algorithm in Listing 1.  
```
input\(L,I,E,\tau,\xi_{1},q(x)\) compute\(g(x)=-\frac{\xi_{2}}{2\pi\xi_{2}}e^{\frac{\pi}{x}}\) \(Q(s)=\frac{\xi_{1}(\tau^{2}s^{2}-1)}{s^{2}(\xi_{1}^{2}x^{2}-1)}\) \(G(s)=\mathcal{L}\{g(x)\}\) \(\hat{g}(x)=\mathcal{L}^{-}\{G(s)Q(s)\}\) \(D^{-1}g(x)=\hat{g}(x)\) \(AD^{-1}g(x)=\frac{d^{2}}{dx^{2}}\big{(}D^{-1}g(x)\big{)}\) \(\Psi\big{(}AD^{-1}g(x)\big{)}=\int_{0}^{L}e^{-\frac{1}{t}}AD^{-1}g(t)dt\) \(W=1-\Psi\big{(}AD^{-1}g(x)\big{)}\) if\(\det W\
eq 0\) compute in case: \(\text{CF: }f(x)=\frac{1}{E\xi_{1}}\big{[}\int_{0}^{x}(x-t)q(t)dt-x\int_{0}^{L}q(t)dt+ \int_{0}^{L}tq(t)dt\big{]}\) \(\text{CP: }f(x)=\frac{1}{E\xi_{1}}\big{[}\int_{0}^{x}(x-t)q(t)dt-\int_{0}^{L}(L-t)q(t) dt+C_{1}(x-L)\big{]}\) \(\text{CC: }f(x)=\frac{1}{E\xi_{1}}\big{[}\int_{0}^{x}(x-t)q(t)dt+C_{1}x+C_{2}\big{]}\)  end if \(F(s)=\mathcal{L}\{f(x)\}\) \(f(x)=\mathcal{L}^{-1}\{F(s)Q(s)\}\) \(D^{-1}f(x)=\hat{f}(x)\) \(AD^{-1}f(x)=\frac{d^{2}}{dx^{2}}\big{(}D^{-1}f(x)\big{)}\) \(\Psi\big{(}AD^{-1}f(x)\big{)}=\int_{0}^{L}e^{-\frac{1}{t}}AD^{-1}f(t)dt\) \(w(x)=D^{-1}f(x)+D^{-1}g(x)W^{-1}\Psi\big{(}AD^{-1}f(x)\big{)}\) in case: \(\text{CP: }\text{\bf solve }w(L)=0\text{ wrt }C_{1}\) \(\text{CC: }\text{\bf solve }w(L)=0\text{, }w^{\prime}(L)=0\text{ wrt }C_{1}\), \(C_{2}\)  end if print\(w(x)\) else print'There is no unique solution'  end
```  
**Algorithm 1** Algorithm for solving the BVP: CF: (16), (20), CP: (16), (34) and CC: (16), (42).
## 3 Results and Discussions
### Chemical Images of MC, pH and SSC in Intact Tomato  
Figure 6 showed a sequence of representative processed images, illustrating the application of the hyperspectral image processing, single-band and threshold methods for the prediction of the MC, pH, and SSC in intact tomato. The 1082 nm waveband image was used as a representative image for visualization purposes because it showed the higher contrast among the other bands. The background regions of the non-fluorescence black cup were eliminated from the image by using a 0.1 value of a threshold. The resultant image reveals the major area of the tomato from the background. It shows that the HSI technique is allowed to acquire multiple samples at a time that contains a complete spectrum for every pixel in each sample. It also allows for the visualization of the different chemical constituents in each sample based on their spectral signatures because regions of similar spectral properties have similar chemical composition.  
Figure 7 shows the chemical images/prediction map of the MC, pH and SSC of the intact tomato. The images were constructed by multiplying the obtained beta coefficient (regression coefficient) from the best preprocessed PLS regression model with the spectra of each pixel in the image. The power of these distribution maps resides in the rapid and easy access they afford to the spatial distribution of MC, pH and SSC in the tomato and their relative concentrations.  
By including all the pixels, this approach has the advantage of displaying more detailed and accurate information. The difference in MC, pH and SSC within the same sample was very interesting and easily visualized in the concentration maps. The MC of the tomato showed uniform distribution along the fruits, while the pH was almost doubled in certain areas of the tomato. In the case of pH,  
Figure 6: (**a**) Tomato samples; (**b**) 3-D hypercube of tomato image; (**c1**) Corrected grayscale image at 1082 nm; (**c2**) Binary image at 0.1 threshold; (**c3**) Resultant grayscale image after masking with the previous image; (**d1–d3**) Chemical image of MC, pH and SSC respectively.  
the tomato shows pH variation (red-yellow-blue color variation) with lower pH (more blue) in the central areas of the fruit compared to the peripheral areas of the fruit. In the case of SSC content, lower SSC showed uniform distribution in their peripheries and, to a greater extent, SSC towards their central parts in tomato. The whole-tomato differences in internal qualities such as MC, pH, and SSC may be caused by differences in sunlight exposure for the fruit surface during cultivation.
## 4 Proposed Scheme
### Integration into Distributed Applications
#### 4.7.1 Blockchain and the Role of the Scheme  
A blockchain is a distributed ledger that stores transactions or messages in an immutable manner. The proposed scheme can be used to authenticate data transmitted to the blockchain, ensuring their integrity and confidentiality. Each signature \(\sigma\) becomes cryptographic proof that the message \(M\) comes from an authentic signatory and that the data has not been altered.  
Signature validation: Blockchain nodes use the public key \(K_{pub}\) to validate the signature \(\sigma\) by checking the equation:  
\[\sigma^{e}(mod\ n)=M.\]  
This validation can be performed simultaneously by multiple nodes and in this way a way of system scalability can be built.  
Data storage: After validation, messages \(M\) and signatures \(\sigma\) are recorded in blockchain blocks. The anonymity provided by \(T\) allows sensitive data to be kept confidential, while providing traceability and auditability.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Ceramic Industry  
The industry of ceramics encompasses a wide range of products, including dinnerware, sanitaryware, tile, electrical porcelain, pottery, and refractories, all of which utilize kaolin [82]. Both kaolin and ball clay (kaolinite clay) are primary ingredients in many ceramic products. The specific ceramic product and the conditions of the manufacturing plant determine the parameters of the kaolin clay used. Key variables include the proportions of kaolin, other clays, silica, and flux in the ceramic body. The consistent chemical composition of kaolin is essential to ensure stable firing and vitrifying properties [83].  
Ceramics, which are made by applying high temperatures to earthen materials, date back to prehistoric times when early humans used clay for cooking and found that heat could solidify shapes made from plastic clays [84]. Over time, ceramics evolved into an engineering profession. The properties of ceramic materials vary based on their clay  
Figure 8: Schematic representation of industrial applications of kaolin-based materials.  
mineral composition, particle size, organic content, and non-clay minerals, with kaolinite being the most crucial due to its beneficial physical and chemical properties.  
Key properties provided by kaolin and ball clay include plasticity, green strength, dry strength, fired strength and color, refractoriness, thermal conductivity, water absorption, and controlled shrinkage [7; 32; 85; 86]. Plasticity refers to a material's ability to deform under stress without breaking and to retain its shape once the stress is removed. Measuring plasticity can be performed by assessing water content, penetration resistance, or deformation under stress. Green strength, which is measured by the breaking strength of a clay bar, must be sufficient for handling without damage. Drying shrinkage occurs as clay dries, and firing shrinkage happens during firing; both are influenced by water content and particle size. Dry strength, which is higher than green strength, relates to particle size and is essential for the final product's durability [87]. During firing, kaolinite transforms, losing its structure and forming new phases at high temperatures, such as mullite [88; 89; 90]. The fired color of kaolinite is white, while ball clays are light cream. The modulus of rupture (MOR) of fired pieces, which indicates strength, is significantly higher than that of dried pieces.  
In refractories, kaolin and ball clay have been used since the early 1800s for products like firebricks and insulating bricks [6]. Refractory clays must withstand high temperatures and are measured using pyrometric cones that indicate heat resistance. Flint clays, while refractory, lack plasticity and are mixed with plastic clays for shaping. Companies like _Villeroy & Boch_ and _Royal Doulton_ rely on kaolin to achieve the desired aesthetic and functional properties of their ceramic products.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures
### Safety Devices within Battery
#### 5.1.2 Current Interrupt Device  
Another common safety device is called the CID, and it can be activated by high temperature. Under the conditions such as overheating, overcharging and short-circuiting, an increase of temperature can be seen inside the battery causing an increase in the vapor pressure of the electrolyte solvent; therefore, the solder joints welded to the aluminum sheet and the pressure relief sheet will fall off and the pressure relief sheet will turn over leading to the broken circuit inside battery [193]. Compared to the mechanism of safety vent releasing the internal pressure, the CID is used to interrupt battery current, which subsequently prevents the occurrence of hazard.