esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The HTST and UHT methods are promising methods in the fruit and vegetable industry. The ability to modify the processing parameters can allow for the selection of adequate preservation parameters for individual products and better results than other unconventional methods, such as HPP or PEF. HTST and UHT are methods characterized by the ability to inactivate unwanted microorganisms, allowing for product shelf-lives that last for many weeks.  
In the case of basic physicochemical characteristics, such as total soluble solids, pH or acidity, the HTST and UHT methods most often do not determine significant changes that negatively affect the quality of the final product. In the case of color, contents of bioactive components and the antioxidant and enzymatic activities it is significantly important to choose the right temperature and duration. Excessively high temperatures or exposure times can lead to the degradation of key bioactive components, such as vitamins, carotenoids or phenolic compounds, while the right choice of parameters can allow these compounds to be extracted from the food matrix, transformed or, at least, to remain at similar levels as in raw samples. Accordingly, the transformation of natural colorants due to heat leads to changes in the absolute color, which can be important in consumer evaluations. The HTST and UHT methods show highly effective inactivation of enzymes such as POD, PPO or PME, allowing for better quality of products to be preserved including during storage. In the case of volatile compounds, these thermal methods most often lead to changes in their profiles, leading to flavor and aroma degradation to some extent.  
It is presumed that in the near future the HTST and UHT methods will be dynamically improved upon on an industrial scale in the fruit and vegetable industry because of their numerous benefits compared to the traditional pasteurization process commonly used in the industry. Better retention of bioactive components, less sensory degradation and equally or even more effective inactivation of microorganisms, as well as lower financial costs of these processes are the most important advantages in the rapid heating of food. A challenge for the described preservation methods is to investigate their application in terms of adapting process parameters to products with varying raw material particle sizes, such as beverages enriched with different raw materials or multivegetable soups with solid pieces. A little-developed area of research is the application of HTST and UHT in a combined method, for example with HPP.  
Conceptualization, N.P., S.K. and B.K.; methodology, N.P.; software, N.P.;
## 2 Literature Review and Background
### Aging Index and Aging Condition Index  
Deriving an ACI through the application of an aging coefficient is essential, as an accurate evaluation of aging allows for predicting the remaining lifetime of cables, optimizing maintenance schedules, and reducing operational costs. This metric enables an objective assessment of the insulation's condition, and the resulting aging index serves as a foundational tool for maintenance strategies.  
To calculate the lifetime of a cable, as illustrated in Figure 1, the aging coefficient must first be determined in order to establish the aging model. Based on this model, the Aging Condition Index (ACI) and the remaining lifetime can then be derived.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \begin{tabular}{c} **XLPE (Cross Linked** \\ **Polyethylene)** \\ \end{tabular} & **PP (Polypropylene)** \\ \hline Material Type & \begin{tabular}{c} Thermost \\ (cross-linked structure) \\ \end{tabular} & \begin{tabular}{c} Thermoplastic \\ (non-cross-linked structure) \\ \end{tabular} \\ \hline Thermal Properties & \begin{tabular}{c} Continuous operating \\ temperature 90 \({}^{\circ}\)C \\ Melting point 110 \({}^{\circ}\)C \\ \end{tabular} & \begin{tabular}{c} Continuous operating \\ temperature 110 \({}^{\circ}\)C \\ Melting point 160 \({}^{\circ}\)C \\ \end{tabular} \\ \hline Electrical Properties & \begin{tabular}{c} Excellent performance \\ with low dielectric loss \\ \end{tabular} & \begin{tabular}{c} Excellent performance with \\ low moisture absorption \\ \end{tabular} \\ \hline Mechanical Properties & \begin{tabular}{c} Excellent flexibility \\ and durability \\ \end{tabular} & \begin{tabular}{c} Inherently hard and rigid \\ (flexibility can be improved \\ with additives) \\ \end{tabular} \\ \hline Recyclability & Difficult to recycle & \begin{tabular}{c} Recyclable and \\ environmentally friendly \\ \end{tabular} \\ \hline Application & \
\begin{tabular}{c} Used in various insulation \\ application such as power \\ cables \\ \end{tabular} &
\begin{tabular}{c} Suitable for high \\ temperature cables, as well \\ as advanced application \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of XLPE and PP Materials.  
Figure 1: Conceptual diagram for deriving the Aging Condition Index of cable using the aging coefficient.  
* Aging coefficient: A numerical parameter that quantifies the rate of insulation property aging under specific conditions, such as temperature and time.
* Aging condition index: A normalized index ranging from 0 to 1 that represents the current degree of insulation aging relative to its initial condition.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 4 Evaluation Metrics of MOEA  
### Evaluation Settings  
In the design and implementation process of MOEA, efforts should not only be put into determining appropriate tests and designing appropriate experiments but also selecting reliable test tools, as well as performing corresponding statistical evaluation and comparative judgment. To assess the capability of MOEA algorithms and, at some level, facilitate the search for optimal solutions, researchers often use certain standard test functions. Comprehensive evaluation indices, such as HV and IGD, are commonly employed to gauge the convergence, uniformity, and diversity of the Pareto solutions generated by an EMO algorithm. These indices provide a scalar value that reflects both the convergence and distribution of the solutions simultaneously. The number and variety of Pareto solutions resulting from the algorithm are also important factors to consider.
## 7 Results and Discussion
### Average Heliostat Efficiency (AHE)  
The DHE average of the 366 days of 2024 (leap year) for each representative heliostat permits defining a yearly heliostat efficiency (YHE):  
\[\text{YHE}=\frac{\sum_{i=1}^{366}\text{DHE}_{i}}{366} \tag{13}\]Finally, the average of the six representative heliostats allows an average heliostat efficiency (AHE) to be defined for all heliostats, a single figure of merit that is a quantitative, representative, measurement of the efficiency of the geometric layout of the whole heliostat field. As the previous discussions noted, the front-row heliostats have different capabilities (no blocking and little shading) from the other heliostats, so it makes sense to reflect this difference in the average formula used to calculate AHE (see Figure 9):  
\[\mathrm{AHE}=\frac{9\times\frac{\mathrm{YHE_{SHF}+YHE_{5}+YHE_{SF}}}{3}+(66-9) \times\frac{\mathrm{YHE_{SHF}+YHE_{5}+YHE_{5}}}{3}}{66} \tag{14}\]  
This single number AHE is convenient because it allows a quick comparison of the relative merits of the various layout configurations. The YHE for the six representative heliostats, plus the corresponding AHE, is reported in Table 2 for the eight configurations studied.  
As discussed above, YHE is close to 1.0 for the three front-row heliostats (SW, S, and SE) for all eight configurations (no blocking and residual lateral shading). For the back-row heliostats (NW, N, and NE), YHE decreases substantially, from 0.1 to 0.4, due to significant shading (early morning and late afternoon) and some blocking (midday). As expected, YHE increases 0.05-0.08 when going from Julich (latitude = 50.9133\({}^{\circ}\)) to Protaras (latitude = 35.0125\({}^{\circ}\)), as shading decreases. As expected, replacing the flat ground by a 10\({}^{\circ}\) slope increases YHE, about 0.07 in Julich and 0.04 in Protaras. This effect is larger in Julich, where shading is more significant.  
However, the most striking result is the clear superiority of the rectangular heliostats over the square ones, as discussed above. For example, Julich with flat ground and rectangular heliostats shows a better AHE (0.8213) than Protaras with square heliostats and a 10\({}^{\circ}\) slope (0.8121). Replacing square heliostats with rectangular ones (and increasing the north-south distance between heliostats from 1.0 m to 1.9 m to avoid mechanical collisions, as discussed above) increases AHE about 0.13 in Protaras and 0.12-0.14 in Julich. These improvements are 2-3 times larger than the gains obtained, for example, with the 10\({}^{\circ}\) slope.
## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Systemic Deficiencies in Current Traceability Systems
#### 3.1.3 Identification and Traceability Issues  
* Unique identification for traceability: For effective traceability, each product unit must be uniquely identified [28]. The sources highlight that current systems struggle with this due to inconsistent or lost lot identities [29]. This inability to uniquely identify products leads to a significant gap in product identity, hindering the ability to trace products [29].
* Bulk product challenges: Bulk agricultural products are especially hard to trace due to the mixing of product lots during transportation and trade. This makes it difficult to maintain a unique identity for these types of products [12].
* Identification consistency: Inconsistent product identification is a major cause of traceability failures and disconnects. This is because a robust digital identification model is often lacking and it is difficult to track products effectively when they lose their unique identifiers [12].
# Effect of Methane Adsorption on Mechanical Performance of Coal
## 5 Conclusions  
Trixai compression tests were carried out under different methane adsorption equilibrium pressures and confining pressures. The main conclusions are as follows:  
1. The triaxial stress-strain curve of a adsorbed methane coal body is similar to that of a non-adsorbed coal body, that is, before reaching the peak strength, it has gone through four typical stages: microfracture compression and closure, elastic deformation, stable fracture expansion, and rapid fracture expansion stage. However, the compressive strength of coal decreases after methane adsorption. The greater the adsorption equilibrium pressure of methane, the smaller the compressive strength of coal.
2. When the adsorption equilibrium pressure is constant, the compressive strength, elastic modulus, and maximum strain of coal samples increase with the increase in the confining pressure; when the confining pressure is constant, the compressive strength, elastic modulus, and maximum strain of coal samples decrease with the increase in the confining pressure. The adsorbed methane reduces the surface energy of coal and then reduces the overall strength of coal. The adsorption of methane leads to the plastic effect of coal and the decrease in the elastic modulus of coal.
3. The change in the mechanical properties (compressive strength and elastic modulus) of coal caused by methane adsorption can be described by the Langmuir curve and the correlation coefficient is more than 0.99. Under any stress environment, high-rank coal shows greater strength and lower elastic modulus than low-rank coal, which is mainly due to the existence of a developed cleat system in high-rank coal that provides more conditions for methane adsorption.  
Conceptualization, F.C. and J.Y.; methodology, F.C.; formal analysis, J.F.; investigation, F.C. and J.F.; data curation, F.C.; writing--original draft preparation, F.C.; writing--review and editing, J.Y.; visualization, F.C., J.Y. and J.F.; supervision, F.C., J.Y. and J.F.; project administration, F.C., J.Y. and J.F.; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the scientific research project of \"Study on key technology of coal and gas CO mining based on 110 working method retaining roadway along goat in eastern Yunnan mining area (Grant No. HNKJ21-HF07)\", sponsored by China Huaneng Group Co., Ltd., and by the Collaborative Innovation Project of Anhui Universities (Grant No. GXXT-2020-057).  
The authors confirm that the data supporting the findings of this study are available within the article.  
We appreciate the support from the State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, and China Huaneng Group Co., Ltd.  
The authors declare no conflict of interest.
## 3 Parametric Modeling of GSPCW Walls
### Axial Load Ratio  
The axial load ratio is was varied between 0.3 and 0.7. Figure 7 shows the hysteresis and skeleton curves for the parametric wall models with different axial load ratios. It can be seen from the figure that the lateral load capacity calculated by numerical modeling varies little, but the ductility for the GSPCW walls exhibits some changes influenced by variations in the axial load ratio, i.e., as the axial load ratio increases, the ductility decreases. As can be seen from the figure, when the axial load ratio is greater than 0.5, the ductility appears to evidently decrease as compared with axial load ratios below 0.5. Consequently, it is recommended in this study that the axial load ratio of GSPCW walls be controlled to below 0.5 in practice in order to ensure good ductility performance.  
\[n=\frac{N}{f_{c}^{\prime}A} \tag{8}\]  
where, \(n\) is axial load ratio, \(N\) is the vertical compressive force, \(f_{c}^{\prime}\) is the compressive strength of concrete, \(A\) is the entire horizontal cross-section.
# The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins
## 3 The Relationship of Chemical Structure and the Antioxidant Ability of Peptides
### The Relationship of Peptide Structure and Its Antioxidant Activity
#### 3.2.5 Antioxidant Peptides' Stability and Their Synergistic Effects  
Peptides exert their antioxidant activity as an integrated molecule. However, they are probably digested into amino acids by endogenous digestive enzymes before reaching the target organs.  
It is important for peptides to resist proteolysis by gastrointestinal, brush border and serum peptidases, in humans. The antioxidant peptides from whole thermolysin extract was found to resist gastrointestinal digestion with little change in structures and antioxidant activity [4]. Some special molecular structures remaining intact have been found after passage through the gastrointestinal tract. [PERSON] and the coworkers reported that peptides above 3000 Da were more inclined to be cut up during gastric-intestinal digestion than those below 3000 Da [41]. The antioxidant activity of the product mainly consisted of five oligopeptides (molecular weight: 202.1, 294.1, 382.1, 426.3, and 514.4 Da) from duck egg white kept stable after _in vitro_ digestive simulation [38]. Moreover, _in vitro_ gastrointestinal digestion has been applied to produce hydrophobic peptides of 0.75 kDa with soybean lipoxygenase [58]. Obviously, low molecular weight (<1 kDa) was the useful characteristic for the antioxidant activity of peptides. On the other hand, like the precusor proteins, some compact structural features may resist the _in vitro_ gastrointestinal digestion, such as the \(\beta\)-sheet structures or disulfide-rich ones with head-to-tail cyclic peptides like lunasin. According to research results, lunasin can exert its bioefficacy by maintaining its native structure during heating (100 \({}^{\circ}\)C, 10 min), protease degradation or gastrointestinal digestion [8; 59; 60].  
In addition, significantly enhanced antioxidant activity after fractionation has also found [38; 61]. However, a decreasing trend of antioxidant capacity of hydrolysates after fractionation has been found [19]. The results suggest a considerably wide presence of synergistic influence of antioxidant activity among peptides [4; 7; 11]. Understanding the underlying structural mechanism of the synergistic effect will be useful for exploring novel antioxidant peptides.
## 3 Underwater Channel Characterization  
In order to design and deploy any UWSN applications, we need to decide the carrier frequency and, more importantly, the factors that influence the carrier frequency. To illustrate the factors, the 2.4 GHz ISM license free frequency band is considered for underwater wireless communication. There are a number of factors that influence the EM wave propagation underwater. These factors are medium and frequency specific. As these factors are analyzed, a relationship between the UW wireless channel and free space practical models can be built. This section demonstrates channel characteristics and explains the influence over the carrier frequency in the UW medium.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 5 Experiment and Analysis of Experimental Results
### Experimental Analysis
The scenes on the left and right are the same, with vehicles and pedestrians appearing on the street at different scales. Faster R-CNN detected most targets in the image well but failed to detect pedestrians appearing at a small scale in the middle of the image. CMF Net can adapt to the multiscale characteristics of the target because it adopts two multiscale feature extraction mechanisms. Therefore, the pedestrians on a small scale can be successfully identified and positioned correctly. 1. _Experiment IV_: CMF Net, a target detection model based on multiscale feature fusion, has a problem: it is easy to cause misjudgment in the complex situation of multi-target detection. In particular, it is challenging to detect CMF Net effectively when multiple targets are close to or even overlapping each other. It is imperative to use the contextual information around the target effectively. This paper proposes an infrared image target detection model CMF-3 DLSTM based on multiscale feature fusion and context analysis. CMF-3 DLSTM is inherited from CMF Net. The difference between CMF-3 DLSTM and CMF Net is that it replaces the complete connection layer of the classification regression network with a 3D long- and short-term memory network. Context information can be extracted based on multiscale feature fusion. CMF-3 DLSTM improved target detection performance by about 2.9% on the infrared image dataset FLIR compared to CMF Net's mAP. The experimental results are shown in Figure 11. The target detection model CMF-3 DLSTM and other target detection models are evaluated on the test set of FLIR. Using the above methods, CMF-3 DLSTM obtained about 73.3% mAP, while the mAP on CMF Net was about 70.4%, the mAP on Faster R-CNN was about 68.7%, the mAP on YOLO3 was approximately 64.8%, and the mAP on SSD was about 60.8%. Although the CMF-3 DLSTM model does not achieve optimal detection results for car, person, and bicycle alone, the average detection accuracy is more important in complex situations where multiple targets are nearby or even overlap or obscure each other. The target detection results of CMF-3 DLSTM on the infrared image dataset FLIR are illustrated in Figure 12.  
Figure 10: Comparison of target detection results between CMF Net and Faster R-CNN.
## 4 Discussion  
### The Mechanism of Spectral Information at Flowering Stage on Improving Yield Estimation Accuracy  
The traditional agronomic methods for rice yield estimation mainly use rice yield components to calculate rice yield per unit area, which is also known as theoretical yield. This theoretical yield has a high correlation with actual yield [43]. Generally, rice yield per unit area can be calculated by the following four yield components [28]:  
Yield per unit area = Total grain number per unit area \(\times\) Seed setting rate \(\times\) 1000-grain weight  
Since the total number of grains per unit area is grossly equal to the number of florets per unit area, the agronomic yield can also be expressed as:  
Yield per unit area = Floret number per unit area \(\times\) Seed setting rate \(\times\) 1000-grain weight  
The contribution rates of these three yield components to yield from high to low are as follows: floret number per unit area > seed setting rate > 1000-grain weight. It can be seen that floret number per unit area plays a major role in the formation of the final yield regardless of types and yield levels of rice. Therefore, the monitoring of the rice canopy at the flowering stage by remote sensing implies the monitoring of florets per unit area, which is the most important component of rice yield. Compared to the traditional empirical models which indirectly estimate rice yield by monitoring rice biomass, leaf area index and other growth parameters [44], this method is essentially a direct monitoring of yield component, that is floret number per unit area. Therefore, introducing spectral information at the flowering stage into traditional VIs-based models can improve the estimation accuracy of rice yields.  
However, rice florets are white and small, and it is difficult to obtain pure information about florets. The signals of rice florets received by sensors are weaker than rice canopies, indicating the vulnerability of these signals to environmental factors [45]. Therefore, in our study, the method of constructing florescence difference or ratio reflectance using canopy spectral data at the very day before flowering and these days during the flowering stage are proposed to characterize the florescence spectral information, and further to express the direct monitoring of floret number per unit area.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 4 Simulation
### Simulation Conclusion  
By comparing the number of control messages and delays, it can be proved that the method proposed in this article has the advantages of low cost (using fewer control messages) and low latency (almost real-time response). In addition, by comparing the traffic congestion level, the forwarding status congestion level, and multicast capacity, we can find that the proposed method can reduce load congestion and improve multicast capacity.  
Among existing methods, Near and SPT are two MJN selection methods that have low processing delays and minimal control messages. RSPT has more than twice the multicast capacity improvement compared to Near and SPT, so it can be chosen as the MJN selection method when a large multicast capacity is required. WAVE has the largest multicast capacity, but it has a large amount of control messages and extremely high processing delays. If deployed in practical engineering environments, it will greatly reduce the quality of user services and bring a heavy burden to the network. Based on this, in practical  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Multicast Capacity** & **200** & **2 k** & **200 k** & **2000 k** \\ \hline SPT & 2000 & 5421 & 21,000 & 29,784 \\ WAVE & 3877 & 11,773 & 51,747 & 78,378 \\ RSPT & 3541 & 10,243 & 42,433 & 69,765 \\ Near & 2720 & 8310 & 32,241 & 43,320 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multicast capacity of each algorithm.  
engineering environments, RSPT should be chosen as the MJN selection method instead of WAVE.