esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Methods
#### 2.3.4 Color Parameters Measurement and Browning Index (BI)  
The color parameters of the apple cubes were measured using a CR-400 colorimeter (Konica Minolta, Tokyo, Japan) using the CIELAB color space. The measurement was performed by directly applying the colorimeter's measuring window to the sample. The color was expressed in CIE L*a*b* system coordinates where L* is the lightness, and a and b* are chromatic parameters that indicate the color directions: from red to green (a*) and from yellow to blue (b*). Measurements were repeated ten times for each sample. The data was used to calculate the total color change (\(\Delta\)E) between fresh apple cubes and vacuum-impregnated apple cubes according to the following equation:  
\[\Delta E=\sqrt{\left(L_{0}^{*}-L_{VI}^{*}\right)+\left(a_{0}^{*}-a_{VI}^{*} \right)+\left(b_{0}^{*}-b_{VI}^{*}\right)} \tag{2}\]  
\(L_{0}^{*}\); \(a_{0}^{*}\); \(b_{0}^{*}\)--values of color parameters for fresh apple cubes, \(L_{VI}^{*}\); \(a_{VI}^{*}\); \(b_{VI}^{*}\)--values of color parameters for apple cubes after VI.  
The browning index (BI) was calculated according to [22] as follows:  
\[BI=100\cdot\frac{x-0.31}{0.172}\text{ where }x=\frac{a^{*}+1.75\cdot L^{*}}{5.645 \cdot L^{*}+a^{*}-3.012\cdot b^{*}} \tag{3}\]
# Analysis of Short Texts Using Intelligent Clustering Methods
## 4 Conclusions  
This study presents a comparative analysis of short text clustering methods, including TF-IDF, BERT, and novel hybrid approach LDA + BERT + AE. The experimental results demonstrate that while BERT effectively captures contextual relationships and TF-IDF identifies keyword importance, their performance remains limited when used independently. The proposed LDA + BERT + AE model combines topic modeling, contextual embeddings, and dimensionality reduction, resulting in significantly improved clustering accuracy and F1-score. Training and validation loss curves show good convergence and generalization, confirming the model's robustness. The method has practical implications for optimizing NLP systems across various domains, including marketing, content moderation, and social media analytics. However, its reliance on computational resources and preprocessing quality may present challenges in certain applications. Future work will focus on expanding validation to multiple datasets, reducing computational complexity, and comparing performance with recent deep learning models such as SBERT and SimCSE. The findings highlight the potential of hybrid embedding techniques in developing accurate and adaptive NLP solutions for short text analysis. Thus, the proposed hybrid approach is not just a combination of existing methods, but a synergetic architecture that takes into account thematic and contextual features of the text. This provides more accurate clustering of short texts than traditional methods.  
Conceptualization, J.T., A.K. and M.Y.; methodology, J.T., A.K., A.M. and Z.A. ([PERSON]); software, J.T. and A.M.; validation, A.K., Z.A. ([PERSON]) and Z.A. ([PERSON]); formal analysis, J.T., M.Y. and Z.A. ([PERSON]); investigation, A.K., A.B. and Z.A. ([PERSON]); resources, A.K. and A.B.; data curation, A.M. and M.Y.; writing--original draft preparation, J.T., A.K. and M.Y.; writing--review and editing, Z.A. ([PERSON]), Z.A. ([PERSON]) and A.B.; visualization, A.M. and M.Y.; supervision, A.K. and Z.A. ([PERSON]); project administration, A.K. and Z.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19677451).
## 3 The Quintic NURBS Interpolation
### Matrix Expression of Quintic NURBS Curve  
Based on the derivative formula of k-th NURBS curves, the angular velocity, angular acceleration, and angular jerk of the manipulator at the moment \(u\) can be obtained by Equation (9):  
\[P^{(k)}(u)=\frac{A(u)-\sum\limits_{i=1}^{k}C_{k}^{i}\omega(u)P^{(k-i)}(u)}{ \omega(u)} \tag{9}\]  
where \(A(u)=\sum\limits_{i=0}^{n}N_{i,k}(u)d_{i}\omega_{i}\), \(\omega(u)=\sum\limits_{i=0}^{n}N_{i,k}(u)\omega_{i}\).  
According to Equation (10):  
\[Q_{m}=\sum\limits_{i=0}^{n+2}N_{i,k}(u)d_{i}\;\;i=0,1,\cdots,n\quad m=2,3, \cdots,n-1 \tag{10}\]  
To solve the NURBS curve with \(n+1\) unknowns, four endpoint constraints need to be added to make the system of equations have a unique solution [19]. Therefore, four constraint equations are introduced as Equation (11):  
\[\left\{\begin{array}{ll}d_{0}=Q_{0}\\ d_{n+2}=Q_{n}\\ d_{0}=d_{1}\\ d_{n+1}=d_{n+2}\end{array}\right. \tag{11}\]  
This constraint equation constrains the angular velocity and angular acceleration at the start and final positions of the manipulator. Substituting it into Equation (5) yields a matrix equation for solving all the control points.  
\[\left[\begin{array}{ccc}M_{0}\\ M_{1}&\cdots&0\\ M_{2}\\ &\ddots\\ 0&M_{n-2}\\ 0&\cdots&M_{n-1}\\ &&M_{n}\end{array}\right]\left[\begin{array}{c}d_{0}\\ d_{1}\\ d_{2}\\ \vdots\\ d_{n-2}\\ d_{n-1}\\ d_{n}\end{array}\right]=\left[\begin{array}{c}Q_{0}\\ Q_{1}\\ Q_{2}\\ \vdots\\ Q_{n-2}\\ Q_{n-1}\\ Q_{n}\end{array}\right] \tag{12}\]  
where \(M_{i}=\left[N_{i,5(u)},N_{i+1,5(u)},N_{i+2,5(u)},N_{i+3,5(u)},N_{i+4,5(u)} \right]\), \(i=2,3,\cdots,n-2\), \(u\) is the time corresponding to the \(U_{i}\), \(M_{1}=\left[-1\quad 1\quad 0\quad\cdots\quad 0\quad 0\quad 0\right]\), \(M_{n-1}=\left[0\quad 0\quad 0\quad\cdots\quad 0\quad-1\quad 1\right]\), \(M_{0}=\left[1\quad 0\quad 0\quad\cdots\quad 0\quad 0\quad 0\right]\), \(M_{n}=\left[0\quad 0\quad 0\quad\cdots\quad 0\quad 0\quad 1\right]\), \(Q_{1}=Q_{n-1}=0\). The remaining points are the known numerical values.  
The displacement equation of the manipulator can be obtained from Equation (5), and the angular velocity, angular acceleration, and angular jerk equations of each joint of the manipulator can be obtained from Equation (9).
# Dual aptamer-Functionalized 3D Plasmonic Metamolecule for Thrombin Sensing
###### Abstract  
DNA nanotechnology offers the possibility to rationally design structures with emergent properties by precisely controlling their geometry and functionality. Here, we demonstrate a DNA-based plasmonic metamolecule that is capable of sensing human thrombin proteins. The chiral reconfigurability of a DNA origami structure carrying two gold nanorods was used to provide optical read-out of thrombin binding through changes in the displayed plasmonic circular dichroism. In our experiments, each arm of the structure was modified with one of two different thrombin-binding aptamers--thrombin-binding aptamer (TBA) and HD22--in such a way that a thrombin molecule could be sandwiched by the aptamers to lock the metamolecule in a state of defined chirality. Our structure exhibited a K\({}_{\text{d}}\) of 1.4 nM, which was an order of magnitude lower than those of the individual aptamers. The increased sensitivity arose from the avidity gained by the cooperative binding of the two aptamers, which was also reflected by a Hill coefficient of \(1.3\pm 0.3\). As we further exploited the strong plasmonic circular dichroism (CD) signals of the metamolecule, our method allowed one-step, high sensitivity optical detection of human thrombin proteins in solution.  
Keywords:DNA origami; chirality; plasmonics; thrombin; aptamer; sensing +
Footnote †: journal: Applied Physics Letters
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 2 Control of VPPs
### Frequency Control of Wind Power Plants  
The frequency controller of wind turbines is shown in Figure 4[30]. This controller is coupled with the MPPT output and includes a droop control and a Rate of Change of Frequency (RoCoF) control, where the LPF is used to filter out noises. The dead-band is used to enable the frequency control only if its output signal is above a given threshold. The output quantity \(p_{\text{wind}}\) shown in Figure 4 represents the active power reference signal that is used in the converter of the wind turbine and that imposes the actual active power generation of the WG.
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 1 Introduction
This naturally calls into question whether any biologically meaningful information may be inferred from electrical activity alone [37; 38; 39]. Functional overlap between parameters is, however, highly conditional to the nature of the stimulation applied to circuits. Parameters which may be redundant under tonic stimulation often reveal their functional significance under complex stimulation protocols. [PERSON] et al. [40] and [PERSON] et al. [41] have demonstrated this by perturbing central pattern generators with changes in temperature and pH level. They observed switching into a new mode of electrical oscillations, revealing the role of certain parameters in setting the onset of the new oscillation regime. Similarly, the rebound and adaptation dynamics of neurons are the signature of calcium currents, or the hyperpolarization-activated \(h\) current which is only revealed under time dependent current stimulation. The answer to the third challenge may therefore lie in the design of appropriate stimulation protocols since the process of natural evolution would have filtered out most redundant functions in neurons and biocircuits. From a modelling viewpoint, there are two reasons to suggest that the complement of ion channels might eventually be inferred. Firstly, conductance models are known to be sufficiently close approximations of actual neurons and to predict neuronal oscillations with great accuracy [7; 8; 32]. Therefore, algorithms that are capable of correcting residual model error will retain the parameter estimation accuracy demonstrated in well-posed models. Secondly, some of the estimated parameters consistently fall in a narrow range that matches known biological values. We first discuss well-posed problems, reviewing the criteria that warrant the systematic recovery of correct parameters in twin experiments. We then estimate the parameters of rodent and avian neurons with guessed conductance models. We describe three strategies that have obtained sensible biological parameters while accounting for residual model error.
## 2 Materials and Methods  
### Raw Materials  
Commercially available pure Al (\(\sim\)99.5%) was chosen as the matrix material because of its poor mechanical properties. Two aluminum ingots purchased from Kazakhstan Metal Industrial Company LLP, Astana, Kazakhstan, had dimensions of approximately 80 \(\times\) 170 \(\times\) 750 mm. The ingots were cut into blocks with a band saw and placed in the cavity of a graphite melting crucible with an inner diameter of 84 mm. A coolant aqueous emulsion was continuously fed into the cutting zone to avoid undesirable overheating of the ingots during cutting. B4C powder (\(\sim\)94% purity) was purchased from Dongguan Sat Nano Technology Material Co., Ltd., Dongguan, China. It is a gray-black powder with low density (\(\sim\)2.5 g/cm\({}^{3}\)), high hardness (3000 kg/mm\({}^{2}\)), and melting point (2450 \({}^{\circ}\)C). Potassium hexafluorotitanate K2 TiF6, purchased from Gongyi City Meiqi Industry & Trade Co., Ltd., Gongyi, China, was used as an additive to improve the wettability of the B4C particles in the aluminum matrix [13].  
Table 1 presents the designations of the specimens and the corresponding proportions of minor reinforcements with B4C particles. Specimens S1 and S1-HT (HT--heat-treated) are the initial cast specimens without reinforcement. Before preparing the AMCs, the desired proportions of matrix Al and B4C were weighed on a precision laboratory-scale. K2 TiF6 was added following the recommendations in [13].  
To improve mixing, wetting, and to reduce thermal shock, proportionally weighted blocks of pure aluminum with crucible and B4C powders loaded in steel cups were preheated to a temperature of 450 \({}^{\circ}\)C in a PM-5 muffle furnace (Plavka.Pro, Korolev, Russia) at a relatively low heating rate of 3.75 \({}^{\circ}\)C/min for two hours. The graphite crucibles were preheated in a muffle furnace to a relatively low temperature of 200 \({}^{\circ}\)C to vaporize and remove moisture or volatiles from the interior of the crucible. The K2 TiF6 used in the commercial form was not subjected to any treatment before being added to the melt by the steel spoon. The steel casting mold had a box shape and a split design to easily remove AMCs after cooling to room temperature. Note that the aforementioned low heating rate in the muffle furnace is because, at rapid heating, B4C powders begin to sinter, forming \(\sim\)2-8 mm fractions of satellite particles.  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen** & **Pure Al** & **Boron Carbide B4C** \\
**Designation** & **(wt. \%)** & **(wt. \%)** \\ \hline S1 & 100 & 0 \\ S1-HT & 100 & 0 \\ S2 & 99.64 & 0.36 \\ S2-HT & 99.64 & 0.36 \\ \hline \end{tabular}
\end{table}
Table 1: Designation of stir cast specimens.
## 3 Results and Discussion
### Analysis of FTIR Peak for FAME  
In the present study, the mid-FTIR region was selected to identify the functional groups in the biodiesel sample. The mid-IR range between the 4000 and 400 cm\({}^{-1}\) wavenumbers. A summary of the IR spectroscopy band assignments for both microalgae species after ultrasonically assisted lipase-catalyzed transesterification is presented in Table 3. The bands were assigned to specific molecular groups based on biochemical standards and previous studies [63; 64]. There were three significant peaks identified for characteristic bonds in the FAMEs for _Chlorella_ sp. and _Spirulina_ sp., which were the functional groups for alkanes, esters, and alkyls. The patterns of band distribution and peaks obtained were found to be similar for both species with closely matching peaks.  
The region in the spectra from 3000 to 2800 cm\({}^{-1}\) indicates the presence of lipids in the sample and is due to the symmetrical and asymmetrical stretching vibrations of -CH\({}_{2}\)- groups [65]. These -CH\({}_{2}\)-groups form the backbone of the lipids. Both _Chlorella_ sp. and _Spirulina_ sp. showed a transmittance peak at 2923 cm\({}^{-1}\), indicating the presence of the lipid backbone. An absorbance peak for _Spirulina_ sp. was shown at a similar wavenumber by [PERSON] et al. [66]. The bands of interest to locate ester bonds in the compounds can be found in two regions: 1750-1735 cm\({}^{-1}\) and 1320-1000 cm\({}^{-1}\). In particular, the peaks between 1750 and 1735 cm\({}^{-1}\) were identified as C=O bonds of ester groups primarily from lipids and fatty acids, which indicates the conversion of oil to biodiesel [67]. Ester bond peaks were found at 1735, 1740, and 1741 cm\({}^{-1}\) for these biodiesel, and show the presence of esters in the FAME analyzed. On the other hand, asymmetric deformation vibration of C-H [68] from proteins is usually found at \(\sim\)1455 cm\({}^{-1}\), which represents the presence of alkanes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multicolumn{3}{c}{**Acid Value(mg KOH/g)**} \\  & **Soxhlet Extraction** & **Ultrasonication** & **Solvent Extraction** \\ \hline _Chlorella_ sp. & 20.38 & 10.82 & 30.58 \\ _Spirulina_ sp. & 22.05 & 16.24 & 38.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison study for the acid value of crude microalgae oil from three different methods of extraction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Functional Group**} & \multirow{2}{*}{**Assignment**} & _Chlorella_ sp. & _Spirulina_ sp. \\ \cline{3-4}  & & \multicolumn{2}{c}{**Wavenumber, cm\({}^{-1}\)**} \\ \hline
**Alkanes** & C-H Stretching vibration & 2922 & 2923 \\
**Esters** & C=O stretching vibration & 1741 & 1740 \\
**Alkanes** & C-H bending vibration & 1462 & 1462 \\
**Esters, alcohols** & C-O stretching vibration & 1166 & 1027 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bands assignment of FTIR spectra for both species of microalgae.
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 2 Network Steganography
### Steganography for OSI RM Model
For this purpose, authors use a protocol feature that has a dual nature, meaning the same feature can be obtained in more than one way, the feature is not mandatory, there exists a random value field, or there is no strict rule on how to obtain new values for some fields. Another interesting approach is called lost audio packets steganography (LACK), which uses the fact that excessively delayed real-time protocol (RTP) packets are always discarded and not used to reconstruct transmitted data. An even higher capacity named transcoding steganography (TranSteg) has been proposed. The key innovation of TranSteg lies in its ability to select a codec for a chosen voice stream that maintains similar voice quality but results in a smaller voice payload size than the original codec. The transcoding process occurs in a manner that intentionally preserves the original voice payload size, and the codec change is concealed. Subsequently, the transcoded voice payload is embedded, and any remaining free space is utilized for hiding data. An SSH-based method was also suggested, involving concealment by creating a MAC-like message and incorporating supplementary encrypted content into the packet. There are undoubtedly other network steganography approaches, such as using protocols from many layers at once or modifying packet headers along with packet ordering, that can accomplish different bandwidths in addition to the ones mentioned above.
## Detecting Unknown Attacks in Wireless Sensor Networks  
That Contain Mobile Nodes  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Departamento de Ingenieria Electronica, ETSI Telecomunicacion, Universidad Politecnica de Madrid,  
Av. Complutense, 30, 28040 Madrid, Spain; E-Mails: [EMAIL_ADDRESS] (D.F.);  
[EMAIL_ADDRESS] (J.M.M.); [EMAIL_ADDRESS] (J.C.V.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-91-5495-700 (ext. 4204); Fax: +34-91-3367-323.  
_Received: 10 June 2012; in revised form: 28 July 2012 / Accepted: 31 July 2012 / Published: 7 August 2012_  
**Abstract:** As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.  
**Keywords:** wireless sensor networks; mobility; unknown attacks; clustering algorithms; reputation systems
# The Evaluation of Software Security through Quantum Computing Techniques: A Durability Perspective
###### Abstract  
The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The accurate assessment of software's durability and security is a dynamic aspect in assessing, administrating, and controlling security for strengthening the features of security. This paper essentially emphasises the demarcation and depiction of quantum computing from a software security perspective. At present, different symmetrical-based cryptography approaches or algorithms are being used to protect different government and non-government sectors, such as banks, healthcare sectors, defense, transport, automobiles, navigators, weather forecasting, etc., to ensure software durability and security. However, many crypto schemes are likely to collapse when a large qubit-based quantum computer is developed. In such a scenario, it is necessary to pay attention to the security alternatives based on quantum computing. Presently, the different factors of software durability are usability, dependability, trustworthiness, and human trust. In this study, we have also classified the durability level in the second stage. The intention of the evaluation of the impact on security over quantum duration is to estimate and assess the security durability of software. In this research investigation, we have followed the symmetrical hybrid technique of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The obtained results, and the method used in this estimation, would make a significant contribution to future research for organising software security and durability (SSD) in the presence of a quantum computer.  
software security; software durability; quantum computing; symmetrical technique; fuzzy AHP; fuzzy TOPSIS +
Footnote †: journal: Applied Sciences
## Materials and Methods
### OCT System  
The spectral domain optical coherence tomography (SDOCT) system was built into a customized upright laser scanning microscope (Eclipse E400, Nikon, Tokyo, Japan) equipped with a 10\(\times\) air objective (10\(\times\), NA 0.25, Nikon CFI Achro). Light source for the OCT system was a broadband Ti:Sapphire laser (800 nm central wavelength, 150 nm bandwidth at full width half maximum) and 75 MHz repetition rate. The maximum power on the sample was 2.5 mW. Galvanometric mirrors were implemented for scanning the sample plane (6220H, Cambridge Technology, Bedford, MA, USA). OCT signal detection was achieved with a spectrometer consisting of 1200 lines per mm grating (Wasatch Photonics, Logan, UT, USA) and 840 nm central wavelength and 12 bit CCD-based line scan camera with a maximum line rate of 70 kHz and 2048 pixels (AViiVA Atmel EM4 CL 2014, Essex, UK). The signal-to-noise ratio was determined to be 97 dB. The SDOCT system had an axial resolution of 1.5 \(\upmu\)m and lateral of 2.2 \(\upmu\)m, respectively. The field of view (FOV) for all the measurements was ~1.2 \(\times\) 1.2 mm and the depth range covers 1.3 mm. Collection of OCT data were controlled via an in-house written Matlab software. The live view OCT operated at 20 B-scans per second, including real-time processing allowing for navigation and screening without noticeable time lag. The acquisition time for an OCT volume was 66 s. Each raw OCT data consists of \(2048\times 800\times 4000\) voxels, which includes five times repetition at every B-Scan position. After performing Fourier transformation and five times averaging, the processed OCT data set (1024 \(\times\) 800 \(\times\) 800 voxels) were further analyzed, as described in Section 2.4 and illustrated in Figure 1.