esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation
### Performance Measure of Latency  
Latency is determined based on the amount of time taken in order to reliably deliver the data packets between the legitimate users in the satellite network. Thus, the latency is mathematically measured using the following formula:  
\[L=\sum_{i=1}^{n}\delta_{i}\cdot\tau \tag{15}\]  
In Equation (15), '\(\delta_{i}\)' refers to a data packet and '\(\tau\)' describes the time utilized for efficiently transmitting the single data packets in the network, whereas '\(n\)' depicts the total number of data packets assumed as input. The latency is calculated in terms of milliseconds (ms).  
Table 9 and Figure 4 display the comparative testing results of latency for various numbers of input data packets using the developed MRFCO-ZTS approach and the existing ZTAP-HECC [1]. During the experimental evaluation, different numbers of data packets in the range of 50 to 250 are gathered as input to test the performance of the developed work. As depicted in the above tabulation and graphical diagram, the developed MRFCO-ZTS approach acquired minimum latency during the secure satellite communication process via continuous authentication. Also, the developed MRFCO-ZTS approach gives better latency output while considering the greater number of data packets as input when compared to conventional ZTAP-HECC [1]. This is owing to the application of cost-optimized ZTS for seamless user authentication in our proposed work, contrary to conventional ZTS-secured communication systems. For this reason, the developed MRFCO-ZTS model reduces the time required for reliably delivering the data packets among the authentic users in the satellite network. Thus, the developed MRFCO-ZTS approach achieved a 1.15 ms latency while assuming 250 users as input, whereas existing ZTAP-HECC [1] obtained a 1.80 ms latency.  
Figure 3: Graphical performance of security level.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Data** & \multicolumn{2}{c}{**Security Level (\%)**} \\ \hline
**Packets** & **Existing ZTAP-HECC** & **Proposed MRFCO-ZTS Mode** \\ \hline
50 & 90.11 & 96 \\ \hline
100 & 90.73 & 96.55 \\ \hline
150 & 91.41 & 96.79 \\ \hline
200 & 92.80 & 97.04 \\ \hline
250 & 93.32 & 97.88 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Simulation performance of security level.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 5 Evaluation Methodology
### Network Simulation Tool
Figure 10: OMNet++ framework for handover simulation in VLC networks. The information from the MMC is then used to assess the DC channel gain and the RMS delay spread which are associated with the received power and bandwidth restrictions, respectively. This channel evaluation requires the current node's position which is obtained through this submodule. After that, the bit error rate and frame error rate are computed using the frame length, the received power, and the modulation scheme information. With these probability models, the PHY layer submodule decides when a frame transmission was successful. Finally, the submodule retains the packet to assure that the communication is not interrupted by another transmission. If two transmissions happen during the signal propagation time, a collision occurs. On the other hand, the MAC layer submodule executes the logical decisions. When a frame arrives at this submodule, it verifies the OWPAN ID or destination address to be sure that it needs to process this frame. Also, it checks that the transmission was done in the appropriate communication window inside the superframe structure. Then, it executes the corresponding action for the frame control information in the header. Additionally, this submodule performs the channel access scheme and other protocols of the MAC layer from the standard IEEE 802.15.7 as described in Section 3.1. A deeper description of this simulator structure can be found in [60]. Meanwhile, the network controller is divided internally into two submodules: MAP and HME. MAP registers any user in the network, and HME checks if the user needs to perform a handover. Furthermore, the network controller sub-module also generates some objects that represent the data frames on the network. These frames are sent to the user using the transmission protocols simulating in the V2I communication. These packet representations help to evaluate the network performance and other metrics as those described in Section 5.3.
# Physicochemical, Functional, and Antioxidative Characteristics of Oyster Mushrooms
## 4 Oyster Mushrooms as Functional Food Ingredients  
Due to their diverse nutritional profile, oyster mushrooms are increasingly integrated into functional food applications to enhance the nutritional content of common foods. Recent research demonstrates their use in fortifying products like bread, soups, and flour to boost fibre, protein, and essential minerals while enriching antioxidant and polysaccharide contents [36,42]. Studies highlight the effectiveness of oyster mushroom flour as a protein-rich supplement in wheat-based products, providing amino acids and essential nutrients while maintaining a favourable calorie-to-nutrient ratio. Additionally, soups and broths enhanced with oyster mushrooms improve the flavour profile and contribute compounds like ergothioneine and selenium, known for their antioxidant properties [8]. This type of fortification not only meets consumer demand for healthier alternatives but aligns with sustainable food production practices by using bioavailable and easily cultivated mushrooms. Further studies emphasize the potential for these mushrooms to improve both the nutritional and sensory properties of functional foods, demonstrating their broad applicability across food categories [43].  
Oyster mushrooms show significant potential for use in food processing due to their unique textural properties, taste, and functional components that contribute to preservation and sensory enhancement in food products. Studies have shown that drying methods, such as microwave drying, influence their texture and moisture-retention properties, with microwave-dried mushrooms generally maintaining higher rehydration and water solubility indexes [44,45]. These properties make them effective in soups, sauces, and various rehydrated food products, where a stable texture and rich taste profile are valued [46]. Regarding taste and consumer appeal, oyster mushrooms contain a range of flavours, from mild umami to sweet and nutty notes, making them suitable for enhancing the taste of processed foods. Research involving sensory analysis has identified that their umami-rich profile can improve flavour in products that rely on savoury depth, such as broths and meat substitutes [47]. Oyster mushrooms also support functional applications by improving nutritional density in fortified food items like mushroom-flavoured flours used in baked goods and ready-to-eat meals [48,49].  
These applications illustrate the versatility of oyster mushrooms in food processing, both as an ingredient and a functional food component, helping enhance taste, texture, and preservation across diverse food categories.
## 7 Discussion and Conclusions  
Short-term scheduling for crude oil operations is a complex problem since a schedule needs to provide detailed activities such that all resource and process constraints are satisfied. Because of its NP-hard nature, there are no efficient techniques and software tools, and the scheduling job is still done manually. While scheduling a refinery without sufficient charging tanks, the limited number of charging tanks further complicates the problem. It is more challenging than a scheduling problem with sufficient charging tanks as the operational constraints and costs must be considered.  
Instead of mathematical programming models, this paper studies the scheduling problem of crude oil operations by following a two-level architecture that solves the problem based on control theory. By this method, a detailed schedule is provided at the lower level to realize a refining schedule that is generated at the upper level. With this idea in mind, [PERSON] et al. [21,22,24,25,26,27,28] built a hybrid PN model for the system to obtain a feasible schedule for crude oil operations. In these studies, generally, three charging tanks are needed for each distiller to find a schedule with the maximal production rate. Although their charging tank requirement can be satisfied for many refineries, there are refineries for which such a requirement is impossible. This is often the case when some charging tanks have to be maintained, or more distillers are installed without enough charging tanks being built. Hence, this work focuses on scheduling a refinery without sufficient charging tanks to solve such a practical problem. We build a new PN model for the system to describe the process accurately. Based on this model, a method that can schedule the system is proposed, and a schedule with the minimal usage of the SCF mode can be efficiently obtained via simple calculation.  
This paper focuses on finding a schedule to realize a given refining schedule. When there are not sufficient charging tanks, it is better to create a refining schedule with the conditions given in this paper as constraints such that the obtained refining schedule is easy to realize. Our future work will take up this challenge.  
This work was supported in part by the Science and Technology development fund (FDCT) of Macau under Grants 106/2016/A3, 065/2013/A2, and 078/2015/A3.  
This work was conducted by PhD student [PERSON] under the supervision of [PERSON] and [PERSON]. It was mainly drafted by [PERSON] and checked and revised by [PERSON] and [PERSON]. [PERSON] collected and provided the data from a refinery, and conducted the case study and drafted the report for the case study.  
The authors declare no conflict of interest.
## 7 Limitations and Threats to Validity  
We recognise some limitations and threats to the validity of the research presented in this paper and its outcomes, and we advise readers to consider these aspects when interpreting our findings.  
Our research began by inviting participants to take the survey through a social network group, targeting candidates with experience in software development. However, there exists a possibility that not all respondents genuinely have experience with software development using intelligent assistants, even though we inquired about their experience in this domain.  
A notable limitation is the availability of many of the intelligent assistants. Many of those identified through our systematic literature review are still under development and not widely accessible. As a result, our comparison was limited to specific tools, namely, GitHub Copilot, Tabnine, Kite, and IntelliCode.  
The domain of our research is evolving rapidly, especially with tools like GitHub Copilot introducing advanced functionalities. This rapid evolution might lead to changes in the landscape of intelligent assistants, which could impact the relevance of our findings over time.  
While we categorised respondents based on their years of programming experience, it is essential to highlight that the validity of our statistical analysis might be influenced by how participants self-assessed their expertise. Their perception of their own experience might differ from the actual years of experience they possess.  
In our survey, we focused on structured questions and did not include free-text responses. While this approach streamlined the data analysis, it limited our ability to gather in-depth qualitative feedback from participants about their experiences with intelligent code assistants.  
Lastly, given the agile nature of our research domain, there is a potential threat to the credibility of our findings. The research stages spanned an extended period, during which the field might have undergone changes, affecting the current relevance of our findings.
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 3 Methodology
### Cluster Hierarchy Construction  
The minimum spanning tree is then converted into a hierarchy of connected components. In reverse order, sort the tree's edges by distance (in ascending order); then, cycle through each edge, yielding a new merged cluster. The two clusters are then compared to see if they may merge. This is accomplished through the use of a union-find data structure. A union-find data structure is also known as a disjoint-set data structure. It is a data structure for storing a group of non-overlapping (disjoint) sets. It records a partition of a set into disjoint subsets in the same way. It includes functions for creating new sets, merging sets (replacing them by their union), and locating a set's representative member. The final operation quickly determines if two components belong to the same or distinct sets. The result is a dendrogram of cluster hierarchy [21].
## 3 Results
### Effects of Land Cover Change on Vegetation Carbon Source/Sink
#### 3.4.2 Results of Modeling Scenario Design  
According to the results of scenario L1 (dynamic land cover type while other factors remained fixed), 94.04% of the ARNWC expressed an increase in NEP under the effects of land cover type conversion. mainly within the areas of cropland expansion along the economic belt on the northern slope of the Tianshan Mountains and the edge of the Tarm Basin, as well as regions where grassland converted to forestland in the southern part of the IIi region (Figure 8a). The effects of land cover type conversion promoted an increase in total NEP at a rate of 0.10 Tg C/a (Figure 8c), among which cropland, forestland, grassland, urban, and bare areas contributed 0.08 Tg C/a, 0.05 Tg C/a, \(-\)0.03 Tg C/a, 0.005 Tg C/a, and \(-\)0.01 Tg C/a, respectively. The net increase in total NEP under the effects of land cover type conversion was 1.77 Tg C until 2018, which explains approximately 15.55% of the change in original NEP (Table 8). Furthermore, the effects of land cover type conversion increased the total NEP by 1.40 Tg C, 0.92 Tg C, and 0.08 Tg C for cropland, forestland, and urban areas, respectively, and decreased the total NEP by 0.44 Tg C and 0.19 Tg C for grassland and bare areas, respectively.  
Figure 8: Linear trends of the results from two modeling scenarios for land cover change affecting vegetation carbon sources/sinks in the ARNWC from 2001–2018. (**a**) L1 trend (dynamic land cover type, other factors remain fixed); (**b**) L2 trend (dynamic land cover type, NDVI, EVI, other factors remain fixed); (**c**) annual total NEP for L1; (**d**) annual total NEP for L2.  
Figure 7: Changes in NEP in the context of expansion of different land cover types. The codes for the different land cover type conversions represented in the legend can be found in Table 6.  
In addition to the effects of land cover type conversion, it is equally important to consider the effects of vegetation growth enhancement. Therefore, we designed scenario L2 (dynamic land cover type, NDVI, EVI, other factors kept fixed). Compared with the results of scenario L1, the area showing an increasing trend in NEP was reduced, which accounted for approximately 60.02%, mainly distributed in the regions where cropland and grassland existed (Figure 8b). However, the net increase in total NEP reached 14.75 Tg C (Table 8), which exceeded the results of scenario LC1 (reaching 129.61% for its results). This indicated that land cover change has an obvious positive influence on the carbon sink of vegetation, especially the growth enhancement of fresh vegetation. The mean rate of increase was approximately 0.87 Tg C/a (Figure 8d), among which cropland, forestland, grassland, urban, and bare areas contributed 0.51 Tg C/a, 0.06 Tg C/a, 0.21 Tg C/a, 0.01 Tg C/a, and 0.08 Tg C/a, respectively. Meanwhile, the contribution to total NEP was the largest in cropland and grassland, reaching 8.66 Tg C and 3.49 Tg C, respectively, and the net increase in NEP in forestland, urban, and bare areas was 1.10 Tg C, 0.09 Tg C, and 1.41 Tg C, respectively.
## 3 MEC Network Model
### Queuing Model
\[q_{k,n} =\lim_{\Delta t\to 0}\left\{\frac{\left[v_{k}\Delta t+o(\Delta t) \right]\pi_{k,n}}{\sum_{n=0}^{c_{k}+r_{k}-1}\left[v_{k}\Delta t+o(\Delta t) \right]\pi_{k,n}}\right\}\] (8) \[=\lim_{\Delta t\to 0}\left\{\frac{\left[v_{k}+o(\Delta t)/ \Delta t\right]\  
When the CPU core of the \(k\)th-tier MEC server is not fully occupied, tasks are processed immediately upon arrival with a waiting time of 0. The probability distribution is given by Equation (9)  
\[ \begin{split} F_{T_{w,k}}(0)&=q_{k,0}+q_{k,1}+q_{k,2}+ \cdots+q_{k,c_{k}-1}\\ &=\sum_{n=0}^{c_{k}-1}\frac{\pi_{k,n}}{1-\pi_{c_{k}+r_{k}}}\end{split}  \tag{9}\]  
If there are already x requests queued up when the task arrives, the task must wait for the completion of the previous \(x+1\) tasks. The departure flow of the \(c_{k}\) CPU cores follows an exponential distribution with parameter \(c_{k}u_{k}\). Therefore, the waiting time for the task is the sum of the time for \(x+1\) tasks to complete, which follows the Erlang distribution. \[F_{T_{w,k}}(t)=F_{T_{w,k}}(0)+\sum_{n=c_{k}}^{c_{k}+r_{k}-1}q_{k,n}\int_{0}^{t }\frac{c_{k}u_{k}(c_{k}u_{k}x)^{n-c_{k}}}{(n-c_{k})!}e^{-c_{k}u_{k}x}dx \tag{10}\]  
By substituting Equation (9) into Equation (10), we can obtain the cumulative distribution function (cdf) of the waiting delay, which is given by Equation (11). \[F_{T_{w,k}}(t)= \begin{cases}0,&t\leq 0\\ \sum_{n=0}^{c_{k}-1}\frac{\pi_{k,n}}{1-\pi_{c_{k}+r_{k}}}+\sum_{t=c_{k}}^{c_{k }+r_{k}-1}q_{k,n}\int_{0}^{t}\frac{c_{k}u_{k}(c_{k}u_{k}x)^{n-c_{k}}}{(n-c_{k} )!}e^{-c_{k}u_{k}x}dx,&t>0\end{cases}  \tag{11}\]  
The probability distribution function of the waiting delay can be obtained from the cdf of waiting delay with respect to \(t\), which is given in Equation (12). And \(\delta(t)\) is the impulse function. \[f_{T_{w,k}}(t)= \begin{cases}0,&t\leq 0\\ \sum_{n=0}^{c_{k}-1}\frac{\pi_{k,n}}{1-\pi_{c_{k}+r_{k}}}\delta(t)+\sum_{n=c_{ k}}^{c_{k}+r_{k}-1}q_{k,n}\frac{c_{k}u_{k}(c_{k}u_{k}t)^{n-c_{k}}}{(n-c_{k} )!}e^{-c_{k}u_{k}t},&t>0\end{cases}  \tag{12}\]
## 4 Conclusions  
The results of the conducted study show that the utilization of the PEF applied before convective (CD) and infrared-convective (IR-CD) drying allows dried yellow mealworm larvae to be obtained with a high content of valuable compounds. The utilization of the PEF treatment before drying accounts for a higher fat extraction yield and total polyphenol content, as well as reduced microbiological contamination as compared to an untreated sample.  
The PEF-treated and convective dried yellow mealworms exhibited a higher fat extraction yield and antioxidant properties measured against ABTS radicals. For yellow mealworms PEF-treated and dried by means of the IR-CD method, a higher total polyphenol content was observed. Moreover, larvae subjected to the highest energy input of the PEF (40 kJ/kg) before IR-CD drying exhibited significantly higher antioxidant activity (both ABTS and DPPH assay) compared to those dried by means of the CD method.  
The protein content across the tested samples was comparable; however, a significantly lower content for larvae subjected to higher PEF energy input (20 and 40 kJ/kg) and dried by means of the CD method was recorded.  
This study has also shown that PEF-treated and IR-CD yellow mealworms complied with the microbiological quality requirements for insects intended for food production. In the case of allergens, the crustacean content was up to 10 times higher than the mollusk content. It was also observed that insects treated by means of a lower PEF energy (0 and 5 kJ/kg) and CD method exhibited lower mollusk content, whereas a higher PEF energy (20 and 40 kJ/kg) and the CD method resulted in a higher mollusk content as compared to the content detected in the IR-CD samples. Thus, in order to explain all the changes occurring in protein and bioactive compound molecules throughout the yellow mealworm larvae processing by means of the convective and infrared-convective drying methods, further studies in detail are needed.  
Conceptualization, R.B. and M.N.; methodology, R.B., P.L.K. and K.P.; software, R.B.; validation, R.B., P.L.K., K.P., A.O. and K.R.; formal analysis, R.B.; investigation, R.B., P.L.K., K.P., K.R. and A.O.; resources, M.N. and A.W.; data curation, R.B. and K.S.; writing--original draft preparation, R.B., K.P., P.L.K., A.O., K.S. and M.N.; writing--review and editing, M.N. and A.W.; visualization, R.B. and M.N.; supervision, M.N. and A.W. All authors have read and agreed to the published version of the manuscript.  
The research for this publication was carried out with the use of research equipment purchased as part of the \"Food and Nutrition Centre--modernisation of the WULS campus to create a Food and Nutrition Research and Development Centre (CZiZ)\" co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (project no. RPMA.01.01.00-14-8276/17).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.