esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Case No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Number of cloud nodes & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 3 \\ Number of fog nodes & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 \\ Number of tasks & 10 & 15 & 20 & 25 & 30 & 35 & 40 & 45 & 50 \\ Number of 0-1 variables & 130 & 195 & 260 & 325 & 390 & 455 & 520 & 585 & 650 \\ Number of continuous variables & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 \\ Number of constraints & 37 & 42 & 47 & 52 & 57 & 62 & 67 & 72 & 77 \\ Average _Timespan_ (sec) & 40.4057 & 63.6957 & 89.3140 & 99.3824 & 124.0032 & 107.3536 & 186.0245 & 160.2466 & 195.3863 \\ Average _Total\_Cost_ & 145.7769 & 228.4093 & 266.5487 & 386.4935 & 498.4282 & 561.9562 & 607.3436 & 673.5105 & 764.3977 \\ Average CPU time & 0.2191 & 1.1957 & 12.0210 & 46.0756 & 102.5049 & 414.2663 & 580.9763 & 590.1869 & 677.3602 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the proposed method for 3 cloud nodes and 10 fog nodes (dataset 1).
## 3 Results and Discussion  
### NIR Spectra Interpretation  
The second derivative of the NIR spectra of the BSFL samples analysed is displayed in Figure 1. It can be observed that the main variations in the NIR spectra are around 4320 and 4336 cm\({}^{-1}\), 4624 cm\({}^{-1}\), 5200 cm\({}^{-1}\), 5792 cm\({}^{-1}\), and 7296 and 7008 cm\({}^{-1}\). These bands are related at 5200 cm\({}^{-1}\) with O-H combination bands and O-H stretching in the 1 st overtone region of water bands [52; 53]. At 7008 cm\({}^{-1}\), this band is linked with CH\({}_{2}\) combinations associated either with proteins or carbohydrates, while at around 5792 cm\({}^{-1}\), this bandis associated with amine groups and proteins [54]. At around 4624 cm\({}^{-1}\) and 4336 cm\({}^{-1}\), these wavelengths are associated with both C-H aromatic groups and C-H combinations associated with lipids and compounds containing aromatic rings. Overall, it can be observed that common constituents present in BSFL, such as protein and lipids, contributed to explaining the observed variations in the NIR spectra of the samples analysed.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
###### Abstract  
Free increase has limitations for its use in industrial applications that require laccase immobilization on proper support, to improve its catalytic activity. Herein, the nanoparticles of magnetic iron oxide (Fe\({}_{3}\)O\({}_{4}\)) and copper ferrite (CuFe\({}_{2}\)O\({}_{4}\)) were successfully used as support for the immobilization of free laccase, using glutaraldehyde as a cross-linker. The immobilization conditions of laccase on the surface of nanoparticles were optimized to reach the maximum activity of the immobilized enzyme. The synthesized free nanoparticles and the nanoparticle-immobilized laccase were characterized using different techniques, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), vibrating sample magnetometer (VSM), and thermogravimetric analysis (TGA). CuFe\({}_{2}\)O\({}_{4}\) nanoparticles, as support, enhanced laccase activity compared to free laccase and Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase that appeared during the study of pH, temperature, and storage stability on free and immobilized laccase. The CuFe\({}_{2}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase showed superior activity in a wide pH range, temperature range, and storage period, up to 20 days at 4.0 \({}^{\circ}\)C, when compared to free laccase. Additionally, the synthesized nanobiocatalysts were examined and optimized for the biodegradation of the anionic dye Direct Red 23 (DR23). HPLC analysis was used to confirm the dye degradation. The reusability of immobilized laccases for the biodegradation of DR23 dye was investigated for up to six successive cycles, with a decolorization efficiency over 70.0%, which indicated good reusability and excellent stability.  
activity enhancement; immobilization; magnetic nanoparticles; laccase; biodegradation +
Footnote †: journal: Physics Letters B
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 1 Introduction
This abrupt profile limited the end-fire gain and bandwidth. Therefore, a slot array was implemented in the proposed design to resolve the issue. The slot array improved the gain and reduced the antenna resonant frequency, implying the size miniaturization. As a result, the antenna gain is enhanced approximately 2 dB and 3 dB by introducing the new Vivaldi antenna and by implanting the antenna vertically in the skull. The rest of the paper is organized as follows. Section 2 describes the antenna design. Furthermore, it provides details of the brain environment and slot array structure. Section 3 presents the antenna prototype fabrication process and measurement results. Section 4 discusses the specific absorption rate (SAR) simulation and measurement results along with the link budget analysis to estimate the performance of the communication system equipped with the proposed antenna. Section 5 concludes the paper.
## 3 Results  
### Supply Voltages and Current Consumption  
To guarantee the optimal functioning of the bidirectional system with visible and IR capabilities, the LiFi router is powered with a 12 V supply, consuming 920 mA of current. The supply voltage of the USB dongle is 5 V, and the current consumption is 1.12 A.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 4 Dijkstra Algorithm for Type V Fuzzy Graph  
In order to work with the fuzzy costs associated to the links in a Type-V fuzzy graph, we propose a modified version of the classical Dijkstra algorithm referred to as fuzzy Dijkstra algorithm (FDA). In a similar way to the classical version, the proposed algorithm is able to identify the shortest path between a generic source vertex \(r\) and another vertex on the graph \(\tilde{G}\), even when the costs are fuzzy numbers.  
This algorithm is supported by the arithmetic operations and the ranking method of triangular fuzzy numbers.  
Figure 2: The cost of each link can be either a triangular or trapezoidal fuzzy number.
# Numerical Simulation Study of Cavity Formation in Soil under Blast Load
## 4 Conclusions  
The effect of non-ideal explosives in the soils was researched by simulation with the Ansys 2020 R1 Autodyn 2D computer program, and the results of the simulations were compared with the results of test blasts. In order to research the influence of the mechanical soil parameters, simulations were carried out for ten soil types. As input parameters for the soil properties, data from laboratory tests in an oedometer were used, which provided the necessary data for the linear model of the load on the observed soils.  
Figure 7: Summary correlations for ANFO and powdered explosives: (**a**) dependence of water content and volume of cavity and (**b**) dependence of plasticity index and volume of cavity.  
According to the performed research, it can be concluded that the volume of the cavity in the soil depends on the mass of the explosive, or in other words, the volume of the cavity increases with the mass of the charge, which corresponds to the amount of energy released.  
The volume of the cavity increases with decreasing relative humidity and a lower plasticity index.  
From this, it can be concluded that the application of the model integrated in Ansys 2020 R1 Autodyn 2D is possible, but with a considerable correction of the ideal explosive parameters and the JWL parameters. This correction is possible by combining the values obtained by applying a computer code in the EXPL05 V7.01.01 program, which takes into account the non-ideality of the explosives used. These values are determined on the basis of the experimentally measured detonation velocities.  
The applicability of the research presented lies in the planning and design of blasting work in soils. Using known parameters, such as water content and plasticity index, for the soil and detonation velocity for the explosive, the volume of the cavity can be calculated. In this way, the modeled volume can be used for geotechnical design without test blasts with acceptable uncertainty.  
Conceptualization, M.D. and I.D.; methodology, M.D.; software, M.S. and M.D.; validation, M.D., I.D. and D.T.; formal analysis, M.D.; investigation, M.D.; resources, D.T. and I.D.; writing--original draft preparation, M.D. and I.D.; writing--review and editing, M.D. and I.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData and information developed in this research are available by emailing the authors upon any reasonable request.  
The authors declare no conflicts of interest.
# Predicting the Colouration between Binaural Signals
## 2 Method
### Iterative Dataset Normalisation  
There are two options for dataset normalisation. First, a single value in dB can be specified, which is applied to the test dataset, whereby a value of 0 dB would result in no change to the dataset amplitude. Second, an iterative normalisation stage is possible, in which the amplitude of the test dataset is adjusted to produce the lowest average PBC values. This is akin to adjusting the loudness of a test system such that it matches the loudness of a reference system. The iterative normalisation of the test dataset is as follows. First, values of PBC are calculated between the signals in the two datasets with no normalisation. In the second iteration, an initial value of normalisation is applied to the test dataset, calculated as the difference between the mean values of each dataset (after the model weights), and the PBC values are calculated again. The normalisation value is then iteratively altered until a minimum value of average PBC of the datasets is found. The process is illustrated in Figure 2, where marker colours become lighter with each iteration, showing the initial normalisation value of 0 dB, followed by a second value of \(-0.54\) dB, which increases until the optimal normalisation is found at \(-0.19\) dB. Typically this normalisation procedure will offset the test dataset by approximately 1-2 dB compared to, for example, simply equating the average RMS values of the signals directly.  
Solid angle weighting is available when calculating average PBC values between the two datasets. This should be used when the signals to be compared correspond to  
Figure 1: Block diagram of the predicted binaural colouration method. ERB denotes equivalent rectangular bandwidth.  
Figure 2: Illustration of the PBC dataset normalisation; marker colours get lighter with iterations. The normalisation gain applied to the test dataset is adjusted until the average PBC between the two datasets is a minimum.  
measurements on a sphere with a non-uniform distribution, for example. It attributes a weighting to each angle within a group of angles based on its relative position. Simply, it assigns a value to each angle such that it is proportional to the area on a sphere within which it is the closest angle [32]. The result is that signals corresponding to positions that are grouped closely together on the sphere will each be assigned a lower value, and signals corresponding to positions that are spaced far apart will each be assigned a higher value, and that an overall summation of results (average PBC values) will therefore be a more even representation of the sphere.
# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 4 Simulation Experiment and Analysis
### System Closed-Loop Control
The identification process occurs at position 2 in Figure 14a, with the corresponding three-phase output voltage waveform under the Classical FCSMPC control shown in Figure 14c. This waveform data is fed into CNN for load-type identification. Following successful identification, the neural network controller is switched at \(t=2\) s, and the corresponding current waveform is shown in Figure 14f. The inverter control performance is significantly improved, and the curve of tracking control error is shown at position 3 in Figure 14a, and the corresponding output voltage waveform is shown in Figure 14d. In Figure 15, the inverter system is initially operated by the Classical FCSMPC algorithm. After connecting HPFRIL, the control strategy is switched to the NNC (HPFRIL--Optimal FCSMPC). At \(t=1\) s, the load type is changed to NLL, thereby triggering control strategy switching and load-type identification. After completion of load-type identification, the control strategy switches to the NNC (NLL--Optimal FCSMPC). Based on the tracking control error curve shown in Figure 15a and the output voltage waveforms corresponding to the positions on the curve, it is evident that changes in load type affect the control performance of the current NNC. However, by identifying the load type and  
Figure 14: Switching from NL to NLL. (**a**) Tracking control error variation curve. (**b**) Output voltage waveform corresponding to position 1. (**c**) Output voltage waveform corresponding to position 2. (**d**) Output voltage waveform corresponding to position 3. (**e**) Output current waveform corresponding to position 1. (**f**) Output current waveform corresponding to \(t=2\) s.  
then switching to the NNC, the adaptability of the neural network controller is enhanced, thereby ensuring stable system control performance. The above closed-loop control experiments demonstrate that a single neural network controller struggles to adapt to inverter load-type variation. However, through the designed switching strategy and load-type identification method, the system can precisely determine the inverter load type and achieve neural network controller switching, thereby significantly enhancing the adaptability and reliability of the inverter FCSMPC algorithm based on a neural network.
## 3 Duckweed as an Alternative Source of Nutrients and Bioactive Compounds
### Protein  
The primary nutritional benefit of duckweed consumption lies in its protein content, which varies [13] depending on the harvest location, variety, and weather conditions.  
Furthermore, the biological value of the protein in duckweed is enhanced by its well-balanced amino acid profile, with a good representation of essential amino acids [44]. Duckweed proteins have similarities to whey proteins in terms of amino acid composition, making them easily digestible and assimilable by both humans and animals [24]. Duckweed protein contains 2.7% methionine and cysteine, 7.7% phenylalanine and threonine,and 4.8% lysine [44], while whey proteins contain 2.6% methionine and cysteine, 3.5% phenylalanine and threonine, and 9.4% lysine, and both are rich in leucine, threonine, valine, and isoleucine [45]). These similarities suggest that duckweed could serve as an ideal alternative to traditional protein sources, particularly in regions where access to more conventional protein-rich foods may be limited. This property has led to the exploration of duckweed as a viable animal feed supplement as well.  
Studies have shown that the contents of lysine, isoleucine, leucine, valine, and phenylalanine in duckweed are 5.9%, 4.29%, 8.42%, 5.46%, and 7.77%, respectively, which are higher than in most cereal crops [46]. The content of threonine is 4.46%, which is higher than the human consumption standard recommended by the FAO (Food and Agriculture Organisation) [46; 47].  
_Wolfia globose_ and _Wolfia columbiana_ are exceptionally rich sources of proteins, while _Wolfia arrhiza_ mainly contains carbohydrates. The amino acid compositions of _Wolfia microscopica_ and _Wolfiella hyalina_ are comparable with the daily requirements for adults recommended by the WHO (World Health Organisation), and they contain sufficient proportions of lysine, sulphur-containing amino acids, threonine, and tryptophan, so they are desirable as human foods [44]. On the other hand, a nutraceutical powder of _Lemma minor_ showed an imbalanced amino acid composition as it was deficient in certain amino acids, mainly lysine and methionine [48]. The digestibility of proteins and the bioavailability of essential amino acids are also species-dependent [49] and affected primarily by the amount of antinutritional factors, such as oxalates, phytic acids, and tannins [50].  
The duckweed protein produced by RuBisCO is accepted by EU as a good source of essential amino acids, and it has high nutritional value, high in vitro digestibility, and low allergenicity, making it a good candidate for functional foods [51].
# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 3 The Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2  
Convolutional neural networks (CNNs) have exhibited huge success in image recognition. A number of detection networks have been adapted from image recognition networks and further advanced. While such improvements have been remarkable, there is not much consideration of the real-time performance of Chinese traffic sign detection. In this paper, we build on YOLOv2 to construct a single convolutional neural network for Chinese traffic sign detection.
# Biological Importance of Cotton By-Products Relative to Chemical Constituents of the Cotton Plant
## 5 Pharmacological Properties of Compounds in Cotton
### Cytotoxic and Contraceptive Properties  
Cytotoxic activities associated with compounds isolated from cotton are mostly reported in relation to cancer cell lines. \(\alpha\)-Bisabolol, a common compound present in cotton possesses the ability to induce apoptosis in malignant carcinoma cell lines without affecting the viability of healthy cells [184]. A dose of 2 uM of \(\alpha\)-bisabolol is reported to be effective against cancer cell lines, but an increase in dosage from 50 to 250 uM can induce cytotoxicity in normal cells. Another sesquiterpene, carryophyllene oxide, also exhibits cytotoxic properties against cancer cell lines with a minimum dose of 3.125 uM resulting in reduction in viabilities of the target cells, with this effect more pronounced as the dosage increased [185]. Gossypol is a major compound present in cottonseed oil and other parts of the cotton plant and has been found to have contraceptive properties in mammals. In human males, a concentration of 0.3 mg/kg of body weight can induce azoospermia in a time-dependent manner, whereas in male rats, a concentration of 30 mg/kg will induce the equivalent effect [192]. The contraceptive property of gossypol is not restricted to males alone as a study by [PERSON], [PERSON], and [PERSON] [193] indicated that this compound, if administered at a dose of 40 mg/kg body weight of female mammals, induces abnormal oestrous cycles and reduced pregnancy rates.
_Appl. Sci._**2015**, \(5\), 1570-1589; doi:10.3390/app5041570  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
Irregular Homogeneity Domains in Ternary  
Intermetallic Systems  
**[PERSON] *, [PERSON] and [PERSON]**  
Chimie Metallurgique des Terres Rares (CMTR), Institut de Chimie et des Materiaux Paris-Est  
(ICMPE), Centre National de la Recherche Scientifique (CNRS) Universite Paris-Est Creteil, 2-8 rue  
[PERSON] 94320 Thiais Cedex, France; E-Mails: [EMAIL_ADDRESS] (M.A.);  
[EMAIL_ADDRESS] (J.-C.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +33-149-78-13-44; Fax: +33-149-78-12-03.  
Academic Editor: [PERSON]  
_Received: 19 October 2015 / Accepted: 25 November 2015 / Published: 4 December 2015_  
**Abstract:** Ternary intermetallic \(A\)-\(B\)-\(C\) systems sometimes have unexpected behaviors. The present paper examines situations in which there is a tendency to simultaneously form the compounds \(AB_{x}\), \(AC_{x}\) and \(BC_{x}\) with the same crystal structure. This causes irregular shapes of the phase homogeneity domains and, from a structural point of view, a complete reversal of site occupancies for the \(B\) atom when crossing the homogeneity domain. This work reviews previous studies done in the systems Fe-Nb-Zr, Hf-Mo-Re, Hf-Re-W, Mo-Re-Zr, Re-W-Zr, Cr-Mn-Si, Cr-Mo-Re, and Mo-Ni-Re, and involving the topologically close-packed Laves, \(\chi\) and \(\sigma\) phases. These systems have been studied using ternary isothermal section determination, DFT calculations, site occupancy measurement using joint X-ray, and neutron diffraction Rietveld refinement. Conclusions are drawn concerning this phenomenon. The paper also reports new experimental or calculated data on Co-Cr-Re and Fe-Nb-Zr systems.  
**Keywords:** phase diagrams; DFT; Rietveld; site occupancies; homogeneity domains
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 6 Discussion
### Enhancing Security, Integrity, and Resilience  
Blockchain technology offers a robust framework for securing Industrial Internet of Things (IIoT) systems, addressing vulnerabilities inherent in traditional centralized architectures. As IIoT networks grow in complexity, they face critical challenges, including data manipulation, unauthorized access, and single points of failure. Blockchain mitigates these risks through its decentralized, immutable, and transparent ledger system, ensuring secure and trustworthy operations.  
The immutable ledger is a cornerstone of blockchain's contribution to the Industrial Internet of Things (IIoT). By recording every transaction in an unalterable chain, blockchain guarantees data integrity--a crucial requirement for systems generating vast amounts of high-speed sensor data. This immutability prevents tampering and supports real-time auditing, enabling rapid detection and resolution of security breaches [43]. Furthermore, the decentralized nature of blockchain eliminates single points of failure, thereby enhancing resilience against cyberattacks, such as Distributed Denial of Service (DDoS) attacks. Even if one or more nodes are compromised, the distributed network ensures continuous operation, maintaining system reliability in industrial environments.  
Smart contracts further strengthen security by automating device authentication and access control processes. These programmable agreements reduce human error, enforce consistent policies, and streamline operations. Additionally, blockchain's transparent ledger facilitates real-time auditing and traceability, which is essential for regulatory compliance and accountability in industries with stringent standards [43, 44].  
Despite these strengths, scalability remains a challenge for blockchain in IIoT applications due to the high volume of real-time transactions. To address this, the proposed framework utilizes edge computing to offload computational tasks to edge nodes, thereby reducing latency and enhancing efficiency. Furthermore, adopting a Proof-of-Stake (PoS) consensus mechanism reduces energy consumption while enhancing transaction processing speed. These innovations ensure that blockchain can meet the demanding performance requirements of IIoT systems without compromising security or operational efficiency [45].