esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
## 4 Discussion
Keratin substances, such as feathers, are exceptionally strong due to their richness in disulfide bonds, hydrogen bonds and hydrophobic interactions, and most purified keratinases are unable to complete degradation. In this study, purified DgoKerA did not degrade intact feathers quickly enough without DTT, and it is possible that the enzyme does not have a strong reductive capacity. It has been shown that the reductants DTT [36], Na\({}_{2}\)SO\({}_{3}\)[37] and L-cysteine [38] can all work synergistically with keratinase to accelerate the degradation of feather keratin. In this paper, we found that DgoKerA can degrade intact feathers in the presence of 5 mmol/L DTT and has good potential for industrial applications. We also modified the keratinase DgoKerA using a combination of homology comparisons and site-directed mutagenesis. Mutant A350S and V299F have degrading activity against soluble keratin and the optimum temperature of reaction is consistent with that of WT DgoKerA. In addition, both mutant keratinases showed better degradation of intact feathers at 60 \({}^{\circ}\)C. The amino acid residue Val 299 is lying at the entrance of the S1 pocket where the substrate binds. [PERSON] et al. mutated amino acid residues associated with the keratinase FDD substrate binding site and found that Y215F increased the enzyme activity by altering the keratinase substrate specificity [29]. The mutation of the valine residue at position 299 to phenylalanine in this study alters the spatial structure of the pocket channel, and its increased feather degradation ability is likely caused by altering the substrate binding specificity. The helix \(\alpha\)5 is the region where the catalytic center Ser357 is located and is closely related to the stability of the substrate channel and the active pocket [2; 39]. Position Ala350 is located in the Loop region, connecting the helix \(\alpha\)5. Mutation of the alanine residue at this position to a serine may lead to the extensive formation of new hydrogen bonds, altering the flexibility of the loop and thus affecting catalytic activity [40]. Analysis showed that the thermal stability of A350S was reduced, with a half-life of 17.33 min at 60 \({}^{\circ}\)C, less than two-fifths of that of WT DgoKerA (46.52 min). The high proportion of hydrophobic amino acids in the mature region of thermotolerant keratinase was found to be the molecular basis for its thermal stability [41; 42]. The alanine residues are more hydrophobic than the serine residues, and therefore the reduced thermal stability of mutant A350S may be related to the hydrophobicity of residues in the mature region. As A350S is significantly more active against soluble keratinase than DgoKerA, subsequent modifications can be carried out on the basis of mutant A350S to improve its thermal stability.
## 2 Methods and Calculations Details
 } & Bi\({}_{12}\)Te\({}_{15}\) & Bi\({}_{18}\)Te\({}_{21}\) & Bi\({}_{24}\)Te\({}_{27}\) & Bi\({}_{6}\)Te\({}_{8}\) & Bi\({}_{12}\)Te\({}_{3}\) & Bi\({}_{6}\)Te\({}_{3}\) & Bi\({}_{24}\)Te\({}_{18}\) & Bi\({}_{6}\) \\  & & N\({}_{\text{BL}}\): N\({}_{\text{QL}}\) & 0.3 & 1.5 & 2.7 & 3.9 & 1.2 & 3.3 & 2.1 & 15.6 & 3.0 \\  & N\({}_{\text{BL}}\)/(N\({}_{\text{BL}}\) + N\({}_{\text{QL}}\)) & 0 & 0.167 & 0.222 & 0.250 & 0.333 & 0.500 & 0.667 & 0.714 & 1 \\ \hline \multirow{3}{*}{Structural} & Volume/N\({}_{\text{Atom}}\) (Å\({}^{3}\)) & 33.892 & 33.974 & 33.986 & 34.015 & 33.887 & 34.224 & 35.093 & 34.352 & 35.383 \\  & & a\({}_{\text{Hex}}\) (Å) & 4.388 & 4.415 & 4.424 & 4.410 & 4.423 & 4.451 & 4.490 & 4.472 & 4.546 \\  & & c\({}_{\text{Hex}}\) (Å) & 30.488 & 54.330 & 78.200 & 103.000 & 24.002 & 41.890 & 18.090 & 119.000 & 118.662 \\  & & c\({}_{\text{Hex}}\)/N\({}_{\text{Atom}}\) (Å) & 2.033 & 2.012 & 2.005 & 2.020 & 2.000 & 1.995 & 2.010 & 1.983 & 1.977 \\ \hline \multirow{3}{*}{PBE} & Intralayer & BL & - & 1.697 & 1.693 & 1.701 & 1.692 & 1.681 & 1.671 & 1.674 & 1.977 \\  & w/o & thickness (Å) & QL & 7.529 & 7.472 & 7.453 & 7.492 & 7.449 & 7.415 & 7.390 & 7.358 & - \\  & SOI & Interlayer & BL-BL & - & - & - & - & - & - & 2.464 & 2.375 & 1.977 \\  & & BL-QL & - & 2.436 & 2.428 & 2.453 & 2.421 & 2.434 & 2.447 & 2.363 & - \\  & distance (Å) & QL-QL & 2.634 & 2.600 & 2.586 & 2.626 & 2.570 & - & - & - \\ \cline{2-11}  & & Intralayer & BL & - & 1.758 & 1.753 & 1.762 & 1.748 & 1.729 & 1.704 & 1.702 & 1.977 \\  & w/ & thickness (Å) & QL & 7.623 & 7.560 & 7.538 & 7.579 & 7.525 & 7.480 & 7.454 & 7.409 & - \\  & SOI & Interlayer & BL-BL & - & - & - & - & - & - & 2.468 & 2.375 & 1.977 \\  & distance (Å) & BL-QL & - & 2.412 & 2.400 & 2.423 & 2.386 & 2.377 & 2.380 & 2.303 & - \\  & & QL-QL & 2.539 & 2.488 & 2.465 & 2.494 & 2.433 & - & - & - \\ \hline \multirow{3}{*}{LDA} & Intralayer & BL & - & 1.661 & 1.657 & 1.66 & 1.657 & 1.645 & 1.632 & 1.638 & 1.977 \\  & & thickness (Å) & QL & 7.454 & 7.403 & 7.384 & 7.419 & 7.383 & 7.346 & 7.310 & 7.291 & - \\ \cline{1-1}  
our electronic band structures were projected onto atomic elements (Bi: blue, Te: red), except for Bi\({}_{2}\), and our DOS is projected onto electronic orbitals (s: blue, p: red, d: green, f: violet). For the electronic band topology of (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) phases, the direct bandgap was defined as E\({}_{\rm gap}^{\rm d}\) (the minimum of the difference between CBM and VBM at the same k-point) and the indirect bandgap as E\({}_{\rm gap}^{\rm i}\) (the difference between CBM and VBM). Electric types were defined to determine the electric transport characteristics of (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\): semiconductors if both E\({}_{\rm gap}^{\rm d}\) and E\({}_{\rm gap}^{\rm i}\) were positive, semimetals if E\({}_{\rm gap}^{\rm d}\) was positive and E\({}_{\rm gap}^{\rm i}\) negative, and metals if both E\({}_{\rm gap}^{\rm d}\) and E\({}_{\rm gap}^{\rm i}\) were negative.
## 5 Conclusions  
Networks are expanding in scale as a result of the development of SAGINs and UAV technology, and user requirements are diversifying. It is vital to develop a routing algorithm that is safe, dependable, and effective and can be used on large-scale networks. In this paper, we present the latest communication architecture for UAV networks under SAGINs and introduce the applicable routing protocols for FANETs. In addition, we review the latest routing algorithms for FANETs and analyze the limitations of existing routing algorithms, discussing and analyzing the optimization directions for each type of approach. In addition, we summarize the experimental scenarios of existing research in terms of network size, routing algorithm research targets, and simulation platforms, and we visualize the trends and shortcomings of the designed experimental scenarios in the form of statistical charts. Finally, we make plans for future research on routing algorithms for FANETs under SAGINs. We propose a routing algorithm that is based on the current state of research on routing algorithms and takes into account the highly dynamic and other network characteristics of FANETs while incorporating space-based and ground-based networks in SAGINs.  
Conceptualization, Y.L. and W.W.; methodology, K.K.I. and P.R.; writing--original draft preparation, Y.L.; writing--review and editing, H.Z. ([PERSON]) and Y.D.; supervision, H.Z. ([PERSON]) and P.Z.; funding acquisition, W.W. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Shandong Provincial Natural Science Foundation, China, under Grant ZR2020 MF006 and ZR2022 LZH015; partially supported by the Industry-University Research Innovation Foundation of the Ministry of Education of China under Grant 2021 FNA01001 and 2021 FNA01005; partially supported by the Major Scientific and Technological Projects of CNFC under Grant ZZD2019-183-006; partially supported by the Open Foundation of State Key Laboratory of Integrated Services Networks (Xidian University) under Grant ISN23-09; and partially supported by the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant SKLNST-2021-1-17.  
Data sharing not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodological Strategy for Comparative GNSS Processing  
In this study, station coordinates were computed for approximately 3164 unified geodetic control points (UGCPs) distributed across the Republic of Korea using both the GAMIT/GLOBK and Bernese GNSS software. To ensure comparability, both software packages were configured to follow a consistent processing strategy. For all observations, the same precise satellite orbits and Earth rotation parameters provided by the International GNSS Service (IGS) were applied. GPS L1/L2 observations were processed using a double-differencing technique, and ionospheric delay was removed through ionosphere-free linear combinations.  
Tropospheric delay estimation was also harmonized between the two software. The Bernese software used Vienna Mapping Function 1 (VMF1), while GAMIT/GLOBK used the Global Mapping Function (GMF), both applied at fixed intervals. Antenna phase center corrections based on the IGS14 ANTEX model were uniformly applied in both solutions. Integer ambiguity resolution was handled using the Quasi-Ionosphere-Free (QIF) method in Bernese [19] and the wide-lane followed by phase-update strategy in GAMIT/GLOBK [16; 17].  
While the baseline processing and network adjustment approaches differ--Bernese uses GPSEST for combined least-squares estimation and GAMIT uses a GLOBK-based adjustment--both solutions initially employed a minimally constrained network. Final station positions were realized in the KGD2002 reference frame (epoch 2002.0) through a 7-parameter Helmert transformation using a fixed set of 17 nationwide continuously operating reference stations (CORSs) as control points [26; 27; 28].  
This approach ensures that coordinates from both software systems are directly comparable within the same static geodetic datum. Previous studies have shown that when such consistent processing parameters and frame realization strategies are applied, coordinate discrepancies between GAMIT/GLOBK and Bernese remain at the sub-centimeter or even millimeter level. The stepwise methodology of this study is summarized in Figure 2, including data processing, coordinate comparisons, residual analysis, and the proposed transition to ITRF2020.  
Figure 2: Workflow diagram of the study.
## 5 Discussion
### Effect of Sample Length of Sliding-Window  
Previous SWA-based GLM often used single-trial or longer length sliding-windows to avoid lacking samples of the mathematical model of CBF in a design matrix corresponding to the task section [5; 16]. However, the influence of changing the fixed length of a sliding-window on brain activity estimation accuracy is still unknown. The result in Section 4.2 gives us some possible answers for the question. Interestingly, even though the sample size was restricted by SWA, the length of the sliding-window was not significant for improving estimation accuracy. For example, with a sliding-window set at a two-trials-sample length, the weight for CBF, which represents the amplitude of cerebral-hemodynamics at time \(k\), would be estimated as a summarized value across two-trials of CBF though the amplitudes between current and previous trial are different. This leads to lower tracking capabilities for the signal fluctuations during trials and retains the estimation errors, even though a sufficient length to explain task-related CBF evocation was employed. In contrast, with a shorter than 350-sample-window length, the RMSEs dramatically increased because the CHM element lacked some samples by sliding-window cannot explain whole of task-related evocation of CBF. Thus, our results indicate that by using a one-trial sample length (350 samples in this simulation setting), SBF artifacts can be sufficiently removed. Additionally, sample length has an effect on computational complexity. A calculation complexity using ordinary least squares is \(O(N_{T}^{3})\) depended on time samples in the sliding-window [34]. This means we should avoid using longer-sample length sliding-windows than necessary for fast computation in real-time. It should be noted that the averaged process time for each time sample using a 350-sample-wide sliding-window of our proposed method was approximately 26 ms, sufficiently shorter than the sampling period of the simulation setting (130 ms) when we processed it using the MATLAB software (version 9.2.0.556344 (R2017a), Mathworks Inc., Natick, Massachusetts, United States) on a general desktop personal computer (Intel\({}^{\circledR}\) CoreTMi7-4770K, 3.50 GHz, Intel Corp., Santa Clara, CA, USA).
## 3 Results and Discussion
### HS-SPME/GC-MS Analysis Results
#### 3.2.2 Alcohols  
Alcohols are formed either by anabolism or catabolism (Ehrlich pathway) of amino acids [30]. These aroma compounds have both positive and negative effects. There are more alcohols in fresh coconut water than in coconut jam. [PERSON] et al. reported that fresh coconut water contained 13 types of alcohols, of which 3-methyl-1-butanol, hexanol, 2-ethyl-1-hexanol, 1-octanol and 1-decanol were present in high amounts, accounting for 72% of all the alcohols [23]. The GC-MS results are shown in Table 1. Five alcohols present in the coconut jam samples were identified as isopentyl alcohol, 2-methyl-1-butanol, pentyl alcohol, 2-octanol and furfuryl alcohol. The content of 2-octanol was the highest among the alcohols from 0 to 20 min. However, furfuryl alcohol became the dominant aroma compound after sterilization and was present at a significantly higher content than the other alcohols (\(p<0.05\)). The compound 2-octanol has aromatic odor characteristics, and furfuryl alcohol has caramel aroma characteristics [34]. The formation of furfuryl alcohol and the interaction of this compound with other the aroma compounds imparted a special flavor to the sterilized coconut jam.  
Figure 3: Relative contents of aldehydes, acids, alkenes, furfurrans and pyrazines for the coconut jam obtained by GC-MS. Values identified with different letters represent significant differences for each class of compound.
# Addressing ZSM Security Issues with Blockchain Technology
## 7 Assessment of Blockchain and DAG Platforms to Be Used for the ZSM-Solution Implementation
### Candidate Blockchain and DAG Solutions for Our Approach
#### 7.1.2 Ethereum Quorum  
Quorum is a permitted implementation of Ethereum and it was initially developed by JP Morgan. The goal of this blockchain is to cover the needs of scenarios designed to operate in a controlled network where the identity of the members is known and access to the public is restricted. Therefore, it is considered an ideal solution for the implementation of private and consortium networks.  
In contrast to the traditional Ethereum network, Quorum supports two different types of consensus mechanisms: the Raft and the IBFT. This feature allows developers to use a mechanism that suits better to their use case. For example, the Raft which belongs to the Crash Fault Tolerance (CFT) consensus family is preferred in cases where the existence of a malicious participant is unlikely and the need for fault tolerance is high. Nevertheless, in cases where many different entities are participating in the Quorum network and the likelihood of a malicious member is high, the IBFT mechanism is preferred since it introduces byzantine fault tolerance.  
Similar to Ethereum, the network of Quorum supports the use of tokens and SCs that allow the creation of distributed secure applications. This feature broadens the application area of Quorum as many DApps can be developed to implement various scenarios. However, a major difference between these two blockchains is that Quorum supports privacy which was one of its main design goals. More specifically, it allows subsets of parties in a consortium to transact with one another without making the transactions public to members of the larger consortium. Quorum practically splits the ledger into a public and a private ledger. All nodes of the network can observe the public ledger, while the private ledger is visible only to the transacting parties. Only a hash of the private transaction appears on the public ledger and is visible to other nodes that are not counterparties to the transactions. This process can be conducted also for the deployment of private smart contracts which would be visible only to the transacting parties [47].  
Moreover, another significant difference between Quorum and Ethereum is the fact that Quorum does not adopt the concept of adding cost to a transaction using gas. Although it is a fork of Ethereum and supports the use of gas, it sets this value to zero to run transactions without gas fees. Since Quorum is usually deployed in a consortium or private blockchain, the use of gas in Ethereum terms is not mandatory [47]. Additionally, the Quorum platform can be combined with the oracle mechanism in order to become part of a solution that is not limited only to the blockchain world.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Description of the Protocol Phases  
Figure 2 shows the protocol sequence diagram. It shows the messages to be sent between the entities and the functions to be performed. The protocol is divided into four main phases.  
The five phases that make the protocol can be described in terms of their function and their inputs and outputs:  
* Phase 0--Setup and sensors authentication: Generates the movement data of \(B_{(Acc,Gyro)}\), \(C_{(Acc,Gyro)}\) and \(D_{(Acc,Gyro)}\). The output is the acceptance or denial of access to the WBAN.
* Phase 1--Key generation: Generates a key pair \((P_{KA},S_{KA})\) and sends \(P_{KA}\) to B and D.
* Phase 2--Sensing, encrypting, and sending information: C sensors register and send the encrypted information to B.
* Phase 3--Decryption of the information, signing, and sending instructions: B receives the encrypted information, decrypts it, and generates instructions and a digital signature that is sent to D.
* Phase 4--Verification of the signature and application: A verification function determines if it is safe to apply the instructions received based on the signature that came with the instructions.  
Figure 2: Interaction between participant entities and functions of the protocol.
## 3 Design of Approximate QAM Demodulation Circuits
### Exploration Results
Considering the power consumption (Table 1) of the presented circuits, we observe that for each algorithm group, there is an increase in the power consumption of the accurate FXP implementation compared to the FLP implementations, either accurate or approximate. Moreover, the approximate FXP implementations consume less power than the accurate FXP, but in the same degree as the FLP ones. This behavior can be justified taking into account that the target platform for our evaluation is an FPGA device, which has static and dynamic power factors. For example, considering the PLLR implementations, a significant reduction in resource utilization is observed moving from the accurate FLP to FXP-T11, thus the static power of the circuit, which relates to the number of used resources, is reduced. However, the operating frequency increases, which leads to higher switching activity of the utilized resources and thus, the dynamic power consumption of the circuits also increases and \"compensates\" for the lower static power consumed. Additionally, another factor that we must take into account is that the synthesis tool, after placement and routing may spread the utilized resources of a circuit across the FPGA fabric leading to increase usage of routing resources that also consume power. That power consumption (static and dynamic) is also included in the one reported by the tool. When considering higher-order QAMs, i.e., 256-QAM, as explained before, ELLR does not fit in the FPGA. Regarding ALLR, it is possible to fit only the FXP implementations, as the 256-QAM ALLR utilizes \(\sim\)4\(\times\) the resources of the 64-QAM demodulator. As a result, the LUT utilization reaches 97% of the total chip's resources for the accurate FXP implementation with datapath of 16 bits. However, when inserting approximations it is significantly decreased: the designs with bit truncation i.e., FXP-T8 and FXP-T11, utilize 38% and 21% of the LUT resources, while the use of the approximate multiplier (FXP-K6) reduces the utilization to 62%. Similar scaling is observed for the other two algorithms, which enable the implementation of even higher QAM orders, e.g., 1024, as they impose low computational complexity. Regarding the BER performance we have observed that the approximations introduced perform equally well for higher-order QAM modulation formats, e.g., 256-QAM. Finally, in real-world scenarios, the FPGA is used to implement additional computational-intensive functions of the baseband processing chain; thus, it is important not to preserve all the resources for the demodulation. In this direction, as shown, methodical approximations can provide significant resource gains at the expense of small accuracy loss. In addition, when considering more platform-specific implementations, e.g., to exploit the hardwired DSP primitives of the FPGA, we can achieve additional resource savings as well as increased throughput.
In addition to machine monitoring, WSNs can be deployed to measure basic physical quantities such as pressure, temperature, flow or more complex events such as process quality or automotive performance in industrial environments [4]. Although wired sensor networks can also be deployed for such monitoring scenarios, WSNs present additional advantages. In fact, their wireless capability allows deployments in hostile environments, where vibrations or moving parts may prevent the use of cables that would be damaged or even broken. In addition to reduce cabling costs, the WSNs provide network flexibility, as the sensor nodes may be relocated quickly without necessitating time-consuming cable installation and maintenance. However, the nature of the wireless medium opens up security and QoS issues. For example, potential attackers may easily eavesdrop or manipulate wireless communication in absence of security mechanisms and the wireless channel has to be efficiently allocated between the different devices to provide the required QoS. Our contributions are as follows:  
1. We first select relevant WSN technologies dedicated to industrial automation and we provide an exhaustive survey of their characteristics. 2. We then analyze industrial automation applications to determine QoS requirements and evaluate the selected standards according to each identified QoS requirement. Open issues are discussed based on the results of this evaluation. 3. We carry out a threat analysis to identify pertinent security requirements and we investigate if and how the selected standards fulfill the previously identified security requirements. Related open issues are finally highlighted and discussed. The paper is structured as follows. Section 2 provides a detailed overview of the following WSN technologies: Wireless Interface for Sensor and Actuators (WISA), WirelessHART, ISA100.11a, ZigBee, ZigBee PRO, and 802.15.4e Factory Automation MAC Layer. Section 3 and Section 4 focus on QoS and security respectively. Both sections are composed of an analysis of the respective requirements and an evaluation of the selected specifications. Open issues are listed and discussed at the end of both sections. Section 5 concludes our work.