esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Adaptive Core Distribution Optimization Algorithm
### ACTR Algorithm
```
1:An \(m\times n\) physical array H with defect locations D
2:Optimized target array \(T_{best}\) of size \(p\times q\)
3:Initialize \(T_{best}\) to an empty array;
4:Precompute non-defective PEs positions in H and store in a list N;
5:Initialize a sliding window F of size \(p\times q\) at the top-left corner of H;
6:Call Sub-SlideWindow(H, F, \(T_{best}\), D);
7:Procedure Sub-SlideWindow(H, F, \(T_{best}\), D);
8:for each row i from 0 to m-p do
9:for each column j from 0 to n-q do
10: Move the sliding window F to position (i, j) in H;
11: Initialize currentUtility = 0; //Store utility for the current window configuration
12:for each defect in D do
13: Find the nearest non-defective PEs using precomputed positions in N;
14: Replace the defective PEs with the nearest non-defective PEs;
15: Update currentUtility based on the configuration of F;
16:endfor;
17:if currentUtility > \(u(T_{best})\)then // Update condition corrected for maximization
18: Update \(T_{best}\) to the current configuration of F;
19:endif;
20:endfor;
21:if p = q then
22://Optional: If the target array is square, try rotating the window;
23:Call Sub-SlideWindow(H, F rotated, \(T_{best}\), D);
24://Choose the better configuration between the current \(T_{best}\) and the rotated one;
25: Update \(T_{best}\) to the better configuration;
26:endif;
27:return\(T_{best}\);
```  
**Algorithm 3** SDW Procedure. In order to analyze the time complexity of the SDW procedure, we have taken a physical topology of size. In this algorithm, the sliding window traversal requires a double loop, while for each window, the algorithm needs to find the nearest non-defective PEs for each defect and replace them. It also needs to calculate the effectiveness value in order to update the value of Tbest. In summary, the total time complexity of this SDW procedure is approximated as \(O(r\times s\times t)\), where \(r=p\times q\), \(s=(m-p+1)\), \(t=(n-q+1)\).
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 3 Problem Formulation  
### System Model  
We consider a standard asynchronous message-passing system with a trusted setup, as follows:  
Trusted settings: Assume that there are \(n\) nodes, \(P_{1-n}\), in the system. These \(n\) nodes know each other and the public keys of other nodes, and they all have correct cryptography settings so that hashing, encryption and decryption, and signature operations in the protocol are all correct. This can be considered as having a trusted third party during the system setup phase, or through distributed key generation [38].  
Adversary limitations: The adversary's computing resources are limited and cannot find a pair of colliding hashes or crack asymmetric encryption in polynomial time.  
Static Byzantine failure: The adversary is given full control over up to \(f\) failed nodes before the protocol begins, where \(f\) is a protocol parameter. In our protocol, the number of faulty nodes is limited to \(n\geqslant 3f+1\), and the faulty nodes controlled by the adversary are not allowed to change during the execution of the protocol.  
Fully asynchronous network: Assume that each pair of nodes is connected by a reliable P2P channel, and the channel will not drop messages. Delivery time is determined by the adversary, but messages between the correct nodes must eventually be delivered.  
Timeout mechanism: In the context of asynchronous networks, the conventional determination of timeouts through the \"global stabilization time,\" often employed in synchronous or semisynchronous protocols, is not feasible. Consequently, akin to the work of [PERSON] [12], nodes dispatch messages addressed to themselves within the network. Upon receiving such a message, the timer increments, followed by the message being sent again. This process continues until the counter maintained by the timer reaches the threshold \(\tau\), signifying a timeout.
## Application of Raman Spectroscopy for Characterizing Synthetic Non-Metallic Inclusions  
Consisting of Calcium Sulphide and Oxides  
[PERSON] 1,*, [PERSON] 1, [PERSON] 1(c), [PERSON] 1, [PERSON] 1(c),  
**[PERSON] 2 and [PERSON] 1**  
Process Metallurgy Research Unit, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland;  
[PERSON]RESS] (M.A.); [PERSON]T.A.); [PERSON]ESS] (T.V.);  
[PERSON]RESS] (Q.S.); [EMAIL_ADDRESS] (T.F.)  
Nano and Molecular Systems Research Unit (NANOMO), University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland; [PERSON]S]  
Correspondence: [EMAIL_ADDRESS]  
Received: 25 February 2020; Accepted: 12 March 2020; Published: 20 March 2020  
**Abstract:** The presence of non-metallic inclusions (NMI) such as sulphides and oxides may be detrimental to the control of the steel casting process and product quality. The need for their identification and characterization is, therefore, urgent. This study uses time-gated Raman spectroscopy for the characterization of synthetic duplex oxide-sulphide phases that contain CaS and the oxide phases of Al\({}_{2}\)O\({}_{3}\), CA, C12A7, C3A, and MgO\(\cdot\)Al\({}_{2}\)O\({}_{3}\) (MA). Binary phase samples of CaS-MA, C3A-CaS, C12A7-CaS, Al\({}_{2}\)O\({}_{3}\)-CaS, and MA-CaS were prepared with varying phase contents. The relative intensities of the Raman peaks were used to estimate the samples' phase content. For a quantitative estimation, linear regression calibration models were used to evaluate the change in phase content in the samples. The most suitable Raman peak ratios had mean absolute error (MAE) values ranging from 3 to 7 wt. % for the external validation error, and coefficients of determination (R\({}^{2}\)) values between 0.94 and 0.98. This study demonstrated the use of Raman spectroscopy for the characterization of the calcium sulphide, magnesium aluminate spinel, Al\({}_{2}\)O\({}_{3}\), and calcium aluminate phases of CA, C3A, and C12A7 in a duplex oxide-sulphide system, and it offers potential for inclusion characterization in steel.  
**Keywords:** duplex oxide-sulphide; inclusions; Raman spectroscopy; characterization; steel
## 1 Introduction  
The consumption of meat alternative products from plant-based sources is increasing due to the environmental and health concerns related to meat production [1]. The global market size of meat analogues was USD 6.6 billion in 2023 and is projected to reach USD 18 billion by 2035, with a compound annual growth rate of 8.9% (from 2024 to 2035) [2]. Meat production has a significant environmental impact due to the intensive use of land, water, and energy, as well as the increase in greenhouse gas emissions. Another concern is animal welfare, which is affected by the breeding and slaughtering practices involved in meat production. Additionally, meat products raise public health concerns due to the risk of contamination by foodborne pathogens [3]. Also, red meat consumption can increase the risk of societally relevant diseases such as ischemic heart disease, the obesity epidemic [4], and colorectal cancer [5]. For these reasons, an increasing number of people are choosing to reduce their meat consumption or eliminate meat from their diets altogether, leading to the creation of alternatives to conventional animal products [6]. The production of meat analogues is intended to help consumers reduce their meat consumption [7]. Various terms are commonly used to describe alternatives to animal products, including \"meat analogues\", \"imitation meat\", \"meat substitutes\", \"meat replacements\", \"mimic animal meat\", \"meatless meat\", \"mock meat\", and \"faux meat\". Meat-free food products that closely resemble traditional meat in appearance, texture, taste, and nutritional composition are referred to as \"meat analogues\" [8, 9]. These plant protein-based products are designed to mimic the sensory properties of meat and meat products [7]. The primary functions of meat analogue products are to replace meat, provide similar sensory attributes, and be prepared and consumed as if they were meat. The resemblance of sensory properties--such as colour, texture, appearance, taste, and smell--to those of meat is particularly important for consumers who are accustomed to choosing meat [10]. The development of plant-based meat analogues is focused on the modifications of physical properties (through proper sensory attributes), on the one hand, and the provision of similar nutritional values, on the other [11]. Most characteristics of meat analogues (such as sensory, physicochemical, and functional properties) are designed to mimic conventional meat products by modifying their texture, appearance, flavour, mouthfeel, and nutrient bioavailability [12].  
The aim of the study was to determine the degree of similarity between selected meat analogue products and conventional meat products by comparing their physical, sensorial, and microstructural properties.
## 4 Network Layer: The Main Building Block of Fixed Networks
### Five Generations of Access: From PSTN to 50G EPON
#### 4.1.1 F1g--1970's  
**PSTN:** The first generation of fixed-access (F1G) network architecture comprises the use of the Public Switched Telephone Network (PSTN, which appeared in 1875). Thanks to call setup and the fixed bandwidth PSTN requirements, a certain QoS can be guaranteed end-to-end during the entire duration of a connection. Another special characteristic of a circuit switching network as an access technology is the equal share of the transmission link bandwidth among its users. Although circuit switching has the advantage of QoS, it has the disadvantage of ineffective utilization of the link bandwidth (bandwidth is occupied even if it is not used). Of course, the F1G network belongs to the narrowband era, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Use Case** & **Annual Growth Rate (\%)** & **Market Size (USD Billions)** \\ \hline Cloud VR & 66.7 & 292 (2025) \\ Smart Home & 129 (in total between 2019–2023) & 154 (2023) \\ Gaming & 9.3 & 174 (2021) \\ Social Networking & 45 (in total between 2017–2022) & 57 (2022) \\ Cloud Desktop & 39.4 (compound annual rate between 2016–2022) & 2.58 (2022 in China) \\ Safe City & 7.5 & 24.1 (2022) \\ Enterprise Cloudification & 30 & 103.5 (2022) \\ Online Education & 9.5 (compound annual rate between 2017–2026) & 400 (2026) \\ E-health & 22 & 39.2 (2023) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Economic predictions for critical F5G services [3].  
thus it is unable to support broadband services. An advantage of this F1G network is the access distance of tens of km (typical reach). Needless to say, fiber optic cables (which are incorporated into the contemporary PSTN) were not included in the PSTN of the 1970s.
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 3 Database Creation  
When testing the performance of a NILM technique, it is important to consider real data. Due to the challenges in the creation of such data sets, mainly related to the required time and the high costs involved, researchers typically refer to data sets that have been shared online and can be used as common reference. This choice also allows comparing the performance of different approaches.
## 5 Data Set  
For the investigation of validity of CNMs, a commercial data set from Tutela Ltd. (Victoria, Canada) is used. Tutela collects data and conducts network tests through software embedded in a variety of over 3000 consumer applications. Although started at random times, measurements are performed in the background in regular intervals if the user is inactive, and information about the status of the device and the activity of the network and the operating system are collected. The data is correlated, grouped, and evaluated according to device and network status (power saving mode, 2G/3G/4G connectivity). Tests are conducted against the same content delivery network. Tutela measures the network quality based on the real performance of the actual network user, including situations when a network is congested, or users are throttled because they exceeded the data volume of their contract. The results in this paper are based on throughput testing in which 2 MB files are downloaded via Hypertext Transfer Protocol Secure (HTTPS). The chosen size reflects the median of the web page size on the internet. The data used were collected over six months from July 2019 to December 2019 in France and in its overseas departments of the French territorial collectivity. Within the used data set, 20,486,257 CNMs are included. Figure 1 shows the location of the measurements in France. The color within the plot represents the number of measurements per square kilometer. The more crowdsourced measurements were made at a location, the brighter the point. The differences are particularly noticeable for the Paris region in the inner city, which becomes clear in the subfigure at the bottom-left. The measurements for the region around Lyon are shown at the bottom-right. Overall, these figures show where most of the measurements are carried out, namely in cities or in busy places such as main roads and highways. The mean number of measurements per square kilometer is 48.29. In addition to meta information like date and geo-coordinates, the data set includes information on current network performance, including, amongst other variables, download throughput. The question now arises as to whether the number of measurements in a region of interest is sufficient to be able to make a valid statement. This is examined in the following sections.
# Effects of Various Extraction Factors on Protein Yield of Haliotis discus hannai (Abalone)
## 4 Conclusions  
_Haliotis discus hannai_ (Abalone) is protein-rich seafood that is widely consumed in Asian countries. To diversify our food sources with processed abalone, accurate protein content determination is critical. The present study examined the effects of various factors on abalone protein extraction, including the extraction buffer (PB or Tris-HCl buffer), use of sonication, use of salt (NaCl), and type of detergent (SDS or Triton-X). PB showed greater protein extraction compared with Tris-HCl. With 60 s of sonication, 84.44 \(\mathrm{\SIUnitSymbolMicro g}\)/mg dw protein was obtained. The inclusion of 0.6 M NaCl or 16 mM SDS increased the protein yield to 141.9 \(\mathrm{\SIUnitSymbolMicro g}\)/mg dw and 253.15 \(\mathrm{\SIUnitSymbolMicro g}\)/mg dw, respectively. However, combining these factors did not show a synergistic effect. Cooked abalone showed lower protein extraction efficiency compared with raw abalone. Myosin heavy chain, paramyosin, actin tropomyosin, and myosin light chain were detected by electrophoresis assays. The application of extraction factors altered the protein profile of abalone extract. Our findings demonstrate the effects of various extraction conditions on the protein extraction yield of abalone. The present study supports that the extraction method can affect the protein yield, which is important for the quality of protein in processed food. Furthermore, the present study can provide comprehensive information to researchers who explore methods to increase protein yield and specific protein amounts of shellfish by modulating various factors.  
Conceptualization, B.K. and S.L.; validation, B.K. and S.L.; formal analysis, S.L.; investigation, H.-J.Y. and S.-J.J.; methodology, Y.L.; project administration, S.L.; resources, Y.L.; writing--original draft, H.-J.Y.; writing--review and editing, H.-R.K., B.K. and S.L.; supervision, H.-R.K. and B.K.; project administration, S.L.; funding acquisition, B.K. and S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Future Fisheries Food Research Center, the Ministry of Oceans and Fisheries, Republic of Korea under Grant number 201803932 and National Institute of Fisheries Science (NIFS), Republic of Korea under Grant number R2022065.  
Not applicable.  
The are no relevant financial or non-financial competing interest to report.
# Biocompatible Dispersants for Carbon Nanomaterials
## 5 Factors Affecting Dispersion Efficiency
Due to the thermodynamic stability of the polymer coating around the CNT, polymer wrapping tends to result in irreversible, non-covalent adsorption, with the added benefit of enabling the removal of excess free-polymer molecules from solution [33]. The surfaces of CNMs are highly conjugated, meaning \(\pi\)-\(\pi\) interactions with aromatic groups are fundamental in stabilising hybrid systems [34]. It is known that polycyclic aromatic hydrocarbons can share their \(\pi\)-orbitals with graphene's sp\({}^{2}\) lattice, lowering the free energy of the surface and forming a strong non-covalent bond in the process [14]. Hydrogen bonding also plays a vital role, particularly in dispersant-solvent interactions [35]. Biocompatible dispersants are non-toxic molecules capable of solubilising materials in aqueous solutions. These are crucial in the deployment of carbon nanomaterials in biomedical applications. The different biocompatible dispersants classes are described in this review, along with relevant examples of their use as CNM dispersants.
# Predicting the Evolution of Syntenies--An Algorithmic Review
## 2 Syntenies Defined as Gene Orders
### Trees  
If not specified differently, all trees are considered rooted and binary, where a _binary tree_ is a tree with all internal (i.e., non-leaf) nodes being binary. We denote by \(r(T)\) the root, by \(V(T)\) the node set, by \(\mathcal{L}(T)\subset V(T)\) the leafset and by \(E(T)\) the edge set of \(T\). An edge of \(E(T)\) is written as a pair \((x,y)\) of two adjacent nodes, where \(x\), the closest to the root, is called _the parent_ of \(y\) and \(y\) is called _the child_ of \(x\). In a binary tree, each internal node has two children. For an internal node \(x\) of a tree \(T\), we denote by \(T_{x}\) the subtree of \(T\) rooted at \(x\).  
The _lowest common ancestor_ (LCA) in \(T\) of a subset \(L^{\prime}\) of \(\mathcal{L}(T)\), denoted by \(lca_{T}(L^{\prime})\), is the ancestor common to all nodes in \(L^{\prime}\) that is the most distant from the root.  
Given a binary tree \(T\), an _extension of \(T\)_ is a tree \(T^{\prime}\) obtained from \(T\) by grafting edges to \(T\), where _grafting_ consists of subdividing an edge \(xy\) of \(T\), therefore creating a new node \(z\) between \(x\) and \(y\), then adding a leaf \(w\) with parent \(z\).  
A tree \(S\) is a _species tree_ for a set \(\Sigma\) of species if its leafset is in bijection with \(\Sigma\). A species tree represents an ordered set of speciation events that have led to \(\Sigma\).  
A _gene family_ is a set \(\Gamma\) of genes where each gene \(g\) belongs to a given species \(S=s(g)\) of \(\Sigma\). A tree \(T\) is a _gene tree_ for a gene family \(\Gamma\) if its leafset is in bijection with \(\Gamma\).
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 3 Architecture of IoD
### Architecture of IoD
Throughout the application, the UAV is programmed to follow the specified routes in the inter-cluster or intra-cluster coverage area. Consider Figure 5, which shows a drone-aided data gathering system. There are two clusters, each with a cluster head and number of sensor nodes. Data from a cluster is routed to a UAV and a UAV beacon is established. In the network structure, the angles formed by nodes to the BS are equally important in addition to the distance to the BS. Therefore, the clusters are organised based on different angles and distances between BS and sensor nodes. In these structures, a process called layering can be performed. The idea is to make use of multi-hop transmission of data, which makes the network more efficient in terms of consumption of resources. Intra-cluster routing can be achieved by breaking a long cluster-head to node journey into shorter paths, reducing the energy consumption. However this can cause delay, a QoS issue,but connectivity is achieved through every member of the cluster. The authors in [52] have made a framework for industrial environments where IoT based system is necessary. Development and testing time is minimised, abiding by the needs and standards. Efficient adoption of IoT in industry must be treated as a interdisciplinary issue. Steps must be taken to minimise disruption and risks. We should approach this problem holistically with end-to-end verification from sensor nodes for the interfacing with end user. Considering the hardware and software requirements, a method should be devised for the overall optimisation of the WSN. This study searches for UAV-compatible routing protocols and defines them in the context of IoDs. In the same manner, the challenges faced by IoDs are explained in detail. Figure 4: Drone path in a WSN cluster. Figure 5: Drone data gathering system.
## 3 Results
### Responses of Ecosystem Carbon Fluxes to Drought  
As illustrated in Figures 7 and 8, in this study, we compared the resistance and resilience of two different ecosystems in the face of two extreme drought events. In comparison with the grassland ecosystem, the forest ecosystem presented a higher GPP-derived Rt of about 21.4 but a lower Re-derived Rt of about 5.1, whereas a lower GPP-derived Rt of about 5.63 but a higher Re-derived Rt of about 10.1 were observed in the grassland ecosystem during the drought 2003 (Figure 7). A similar phenomenon was found during the drought of 2018 (Figure 8). However, the GPP-derived Rs of forest was relatively weaker than that of the grassland with 2.65 vs. 16.74 during the 2003 drought, and 2.73 vs. 13.43 during the 2018 drought, respectively. The Re-derived Rs of grassland was apparently lower than its Rt for both drought periods. Meanwhile, this study revealed that, although the NEE-derived Rt of the forest was weak, the NEE-derived Rs values were strong with 0.05 vs. 3.54 during the 2003 drought and 0.92 vs. 41.25 during the 2018 drought, respectively. Similar to the GPP-derived Rt and Rs, the NEE-derived Rt and Rs of the grassland sites exhibited weak resistance but strong resilience for both droughs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Variables** & \multicolumn{3}{c}{**Forest Site**} & \multicolumn{3}{c}{**Grassland Site**} \\ \cline{3-6}  & & **Multi-Year Means** & **Drought 2018** & **Multi-Year Means** & **Drought 2018** \\ \hline \multirow{10}{*}{Climate factors} & Rg & 150.53 & 155.16 & 141.05 & 149.66 \\  & (W m\({}^{-2}\)) & & 3.1\% & & 6.1\% & \\ \cline{2-6}  & Ta & 4.22 & 5.23 & 9.81 & 11.10 \\  & (\({}^{\circ}\)C) & & 23.9\% & & 13.1\% & \\ \cline{2-6}  & VPD & 3.13 & 3.60 & 2.76 & 3.83 \\  & (hPa) & & 15.0\% & & 38.8\% & \\ \cline{2-6}  & Pre & 862.1 & 818.3 & 1158.5 & 872.8 \\  & (mm y\({}^{-1}\)) & & \(-\)5.1\% & & \(-\)24.7\% & \\ \hline \multirow{10}{*}{Carbon fluxes} & GPP & 1238.32 & 1405.86 & 3313.06 & 2752.37 \\  & (g C m\({}^{-2}\) y\({}^{-1}\)) & & 13.5\% & & \(-\)16.9\% & \\ \cline{2-6}  & Re & 1133.3 & 1416.49 & 3564.1 & 3290.6 \\ \cline{2-6}  & (g C m\({}^{-2}\) y\({}^{-1}\)) & & 24.9\% & & \(-\)7.7\% & \\ \cline{2-6}  & NEE & \(-\)108.62 & 9.17 & 217.07 & 464.6 \\ \cline{2-6}  & (g C m\({}^{-2}\) y\({}^{-1}\)) & & \(-\)108.4\% & & 114.0\% & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relative deviations of changes in climate and ecosystem carbon fluxes between the multi-year means during adjacent normal years and the drought 2018 at the forest (CH-Dav) and grassland sites (CH-Cha), respectively.