esa-sceva/satcom-chunk-collection huggingface dataset:
Type II terpene synthases have a DxDD motif near the N-terminus. The motif's second aspartate is crucial for the protonation-initiated cyclization of GGPP. Advancements in genome and transcriptome sequencing have facilitated the identification of additional functions of TPS enzymes in plants. For instance, the gene clusters responsible for sesterterpene production in _Arabidopsis thaliana_ have an impact on the plant's root microbiota [20]. In rice, several _TPS_ genes are insect- or fungus-induced, and are expressed for the formation of volatile terpenes [21]. In maize, most _TPS_ genes are responsible for producing volatile terpeniod induced by herbivory [22; 23]. Despite the rich secondary metabolites produced by _Z. candida_, little is known about the profile of VOCs and the molecular mechanisms underlying terpene biosynthesis in this plant. This study has two primary objectives: to comprehensively determine the volatile compounds present in _Z. candida_ flowers, and to elucidate the terpene biosynthesis in _Z. candida_. These volatile compounds, particularly terpenoids, play a crucial role in the plant's ecological interactions with its environment and greatly contribute to distinctive floral fragrances that have captivated human fascination for centuries. Using advanced chemical analysis techniques, including Solid Phase Microextraction Gas Chromatography-Mass Spectrometry (SPME-GC-MS), the complex combinations of terpenoids and other volatile compounds produced by _Z. candida_ flowers can be revealed. This study's secondary aim was to explore the genetic basis of monoterpene production in _Z. candida_ by identifying the _TPS_ genes responsible for this process. Through the extensive selection of potential genes, a particular _TPS_ gene, named \(\beta\)-ocimene synthase, is responsible for the production of \(\beta\)-ocimene, which is a crucial monoterpene present in _Z. candida_ blooms.
## 3 Methodology
### Dataset Description and Analysis
#### 3.2.4 Data Quality and Preprocessing  
The data preparation process addressed several quality aspects. Missing value treatment handled 0.8% of gaps in occupancy data through interpolation for gaps under 15 min and moving averages for larger gaps. Outlier detection employed the IQR method for occupancy data, Z-score analysis for usage durations, and domain-specific rules for fixture counts.  
Data validation combined cross-validation with security logs, weekly manual spot checks, and automated consistency checks. Feature engineering produced temporal features, including hour, day, week, and month indicators; rolling statistics across various time windows; occupancy derivatives measuring rate of change and acceleration; and environmental feature aggregation.  
Figure 2 illustrates the 24-h temporal distribution of building occupancy and sanitary facility usage, separated by gender. The graph demonstrates distinct peak periods during working hours (9:00-17:00) and shows the relationship between total occupancy (purple line), male usage (green line), and female usage patterns (orange line). The data points are plotted at hourly intervals, revealing clear usage trends throughout the day.  
Figure 3 presents the gender-wise occupant distribution across all five floors of the Singtel building. Each floor's occupancy is represented by paired bars showing male (green) and female (orange) populations. The visualization reveals varying gender ratios across floors, with Level 6 showing the highest male concentration and Level 2 showing a female majority, information vital for sanitary facility planning.  
Figure 2: Daily Occupancy and Usage Patterns in the Singtel Building.  
These figures provide quantitative visualization of both temporal patterns and spatial distribution of the building's occupants, addressing the reviewer's request for better dataset explanation through visual means.
# Application-Oriented Input Spectrum Design in Closed-Loop Identification
## 2 Robust Estimation of Model Parameters
The spectrum \(\Phi_{r}\) of the external excitation signal \(r(t)\) in the closed-loop system design is related to the spectra \(\Phi_{u}\) and \(\Phi_{ue}\) described by the following formulas [13]:  
\[\Phi_{u}=\lambda_{0}\Big{|}(1+T(\omega)G_{0}(\omega,\theta_{0}))^{-1}T(\omega )H_{0}(\omega,\theta_{0})\Big{|}^{2}+|1+T(\omega)G_{0}(\omega,\theta_{0})|^{- 2}\Phi_{r}, \tag{14}\]  
\[\Phi_{ue}=-\lambda_{0}(1+T(\omega)G_{0}(\omega,\theta_{0}))^{-1}T(\omega)H_{0 }(\omega,\theta_{0}). \tag{15}\]  
In this paper, the simpler optimization problem is solved concerning the fixed operating compensator and the design variable \(\[PERSON]_{u}\). The optimal external excitation spectrum \(\[PERSON]_{r}^{opt}\) can be reconstructed from the optimal \(\[PERSON]_{u}^{opt}\) and the optimal \(\Phi_{ue}^{opt}\) by inverting the following formulas [13]:  
\[T(\omega)=-\Phi_{ue}^{opt}\Big{(}\lambda_{0}H_{0}(\omega,\theta_{0})+G_{0}( \omega,\theta_{0})\[PERSON]_{ue}^{opt}\Big{)}^{-1}, \tag{16}\]  
Figure 1:0The objective of the optimization is to obtain the minimal energy \(\alpha\) of the spectrum of the reference signal. Based on expressions (8)-(13) and applying the frequency sampling range, it is possible to solve the input spectrum design problem expressed as the minimization task:  
\[ \begin{array}{l}\underset{\Phi_{u}}{minimize}\ \alpha\\ \text{\emph{subjectto}}\ \left|T(\omega)\frac{G_{0}(\omega,\theta_{0})-G( \omega,\theta)}{G(\omega,\theta)}\right|^{2}\leq\gamma^{2},\ \ \forall\omega,\\ N(\theta-\theta_{0})^{T}P^{-1}(\Phi_{u})(\theta-\theta_{0})\leq\chi,\\ \frac{1}{2\pi}\int_{-\pi}^{\pi}\Phi_{u}(\omega)d\omega\leq\alpha,\\ \Phi_{u}(\omega)\geq 0,\ \ \forall\omega.\end{array}  \tag{18}\]  
Based on the worst-case quality definition of \(\Delta\) (i.e., the expression in the first constraint of the set (18)) over all models in the uncertainty region (12), the relative error should be smaller than the predefined value of \(\gamma\) for all models inside the uncertainty set [10]. Thus, the objective of such an optimization task is to obtain the input signal, which spectrum has minimal energy. As has been noted before, the spectrum of the excitation signal used in the identification experiment disrupts the parameter estimates. Thus, the input spectrum can be obtained using the frequency grid method or by implementing the KYP technique [32].
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 5 Conclusions and Future Research Directions  
Network lifetime is one of the primary constraints considered for implementing a successful WSN. Several techniques like routing, clustering, load balancing, and duty cycling are the most widely used methods for WSN lifetime improvement. The proposed work utilizes a data aggregation method for improving the network lifetime, where the variational autoencoders are considered. To prove the efficiency of the variational autoencoders, its performance on data compression and reconstruction was compared to that of several existing methods. This work utilizes the Intel Berkeley Research Lab dataset for its analysis, which was found to be satisfied with VAE on both compression and reconstruction. Reconstruction plays a significant role in the satisfaction of any compression technique to reach the actual sensor data at the destination. The VAE-based method was found to be satisfactory, with a better network lifetime than all the comparative methods. However, the delay and energy consumption for data transmission seem to be slightly higher than that of AE. AE attains a minimum delay and a better energy consumption due to its lesser computational cost. Although AE is better on delay and energy consumption, it cannot overtake VAE, as its reconstruction rate is marginally higher than that of AE. In the  
Figure 11: Network lifetime using different methods.  
future, the VAE network will be finetuned with customized neuron layers for reducing its computational complexity, to obtain a better efficiency on delay and energy consumption.  
Conceptualization, methodology, validation, analysis, writing, editing, supervision: B.C.S., P.M.S., S.S.A., D.K.S., D.D., P.C., J.D.K.H. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original data presented in the study are openly available at [22].  
The authors declare no conflicts of interest.
# Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications
## 3 Selected Sample Agent Platforms
### SPADE Agent Platform  
In contrast to the Java-based JADE and Jadex, SPADE is implemented in Python and uses XMPP/Jabber as its foundation. SPADE utilises communication channels with message templates for agent communication. While SPADE offers an XML-based communication protocol that supports FIPA-ACL metadata, the platform is not fully FIPA compliant, meaning that communication with other agent platforms may require significant adaptation. SPADE agents are represented as users with unique Jabber identifiers, and the agent platforms (understood as agent's environments, defined by FIPA [51]) correspond to XMPP servers. It is important to note that mapping agent platforms to XMPP should be seen as a conceptual simplification, as SPADE does not adhere to the FIPA-based concept of \"platforms\".  
Currently, SPADE does not offer built-in mobility features. Communication security is handled using XMPP mechanisms, such as certificates and encryption of client-to-server and server-to-server communications. New agent discovery is also supported within the XMPP protocol. SPADE provides a web-based interface for graphical control over the agent system, allowing for insights into agent operations. Although SPADE remains supported and developed, its updates are irregular due to the limited development resources, such as the number of individuals involved in its implementation. This results in SPADE having a narrower feature set compared to JADE or Jadex. Nevertheless, it remains the most widely used general-purpose Python-based platform.  
To conclude this section, it should be noted that despite all three platforms remaining under maintenance, they were originally developed several years ago: JADE in 1999, Jadex in 2003, and SPADE in 2006 (with SPADE being re-implemented in 2018 to version 3). Since their inception, approaches to software design and maintenance, including the complete SDLC, have evolved significantly. These changes should have necessitated revisions and adaptations of these platforms to meet modern development standards, which will be outlined in the next section.
## 5 Numerical Results and Discussions
Thus, the \(\kappa-\mu\) shadowed distribution can also approximate the Nakagami-\(q\) distribution, and the equivalence notion between the parameters can be written as:  
\[\mu=\frac{(1+q^{2})^{2}}{2(1+2q^{4})},\text{ for }\ \kappa\to 0,\,\mu\leq 1, \text{ and }\ m\to\infty. \tag{23}\]  
In Figure 8, we investigate the equivalence notion in the capacity performance of the considered system. We set the parameter values as \(\mu=0.75\) and \(q=0.707\) for the \(\kappa-\mu\) shadowed links and Nakagami-\(q\) fading links, respectively, which satisfies Equation (23). Figure 8 shows the capacity performance of the ORA technique against the \(\kappa-\mu\) shadowed fading links (when \(\mu_{1}=\mu_{2}=0.75\)) and its analogous counterpart Nakagami-\(q\) fading links (when \(q_{1}=q_{2}=0.707\)). The channel capacity for \(\kappa-\mu\) shadowed fading is obtained from Equation (16), whereas, for the Nakagami-\(q\) fading, it is obtained from the simulation. It is observed that the performance difference is greater when each node is equipped with a single antenna, whereas this performance gap reduces when the number of antennas increase. For instance, when the system is operating over \(\kappa-\mu\) shadowed fading links and its equivalent Nakagami-\(q\) fading links for \(N_{1}=N_{2}=1\), at an average SNR per hop of 14 dB, a difference in the capacity of 0.083 dB is obtained, while a smaller difference in the capacity of 0.033 dB is obtained when \(N_{1}=N_{2}=3\). Figure 7: Average channel capacity of ORA technique with respect to the fading parameter \(\kappa\) (\(\kappa_{1}\), \(\kappa_{2}\)) for different shadowing parameter \(m\) when \(\overline{\gamma}=8\) dB, \(\mu_{1}=\mu_{2}=1\), and \(N_{1}=N_{2}=2\).
## 2 Materials and Methods
### Procedures and Analytics
#### 3.1.5 Ln Stripping Batch Tests  
During previous countercurrent ALSEP tests, the Ln stripping was found to be difficult with using just dilute HNO\({}_{3}\), and it was concluded that the Ln stripping needed to be improved [27]. The use of TEDGA (Figure 1) had previously been found to be effective in Ln stripping from a loaded ALSEP solvent [23]. As the chemical conditions for the Ln stripping section were not fixed before, batch stripping tests were conducted aimed at finding appropriate conditions to be tested in a single centrifugal contactor test and finally to be tested in a full countercurrent process demonstration. The batch tests were conducted using the loaded organic phase from the An stripping section single centrifugal contactor test. Different HNO\({}_{3}\) and TEDGA concentrations were tested in the range of  
Figure 5: Am, Cm, and Eu distribution ratios as a function of the experimental run time (data points) and equilibrium values (horizontal lines) in the Ln re-extraction section single centrifugal contactor test. “g”, “a”, and “I” indicate values measured by gamma and alpha spectrometry as well as ICP-MS, respectively.  
0.3-1.0 mol L\({}^{-1}\) HNO\({}_{3}\) and 0-0.5 mol L\({}^{-1}\) TEDGA. The results (Table S5) show that, without addition of TEDGA at any of the HNO\({}_{3}\) concentrations tested, distribution ratios for several heavier Ln(III) were above one and distribution ratios increased with increasing HNO\({}_{3}\) concentration. With the addition of TEDGA, the An(III) and Ln(III) distribution ratios were decreased considerably and the Ln(III) ions were stripped nearly quantitatively within one contact (\(D\) values ca. 0.01). Based on these results, it was decided to test 0.5 mol L\({}^{-1}\) TEDGA in 0.5 mol L\({}^{-1}\) HNO\({}_{3}\) in the single centrifugal contactor test.
## 2 Principle of Subcarrier Index Modulation OFDM (SIM OFDM)  
Subcarrier Index-Modulated OFDM (SIM-OFDM) is a relatively recent addition to the family of OFDM variants [14; 15]. In SIM-OFDM, the information is not only modulated onto the subcarrier, but is also carried by the index of the active subcarriers. It provides a tradeoff between spectral efficiency and power efficiency by changing the number of active subcarriers. Figure 1 shows a conventional SIM OFDM transmitter. The data bit stream is divided into P parallel substeams and the total available subcarriers are also divided into P subgroups of size n. The splitter could be simple Time Division Demultiplexer that separates even and odd bits. Each substeam goes to a subgroup creator, where it is further split into two streams. One of these (B1) goes to the index selector and is used to select the subcarriers that are turned on in a particular subgroup. The second stream (B2) is modulated onto the selected subcarriers using conventional modulation techniques (e.g., QAM). The same process happens for each subgroup.  
Figure 2 shows a closer view of individual sub-block creator. The available subcarriers are divided into several subgroups. The input stream of bits for each  
Figure 1: System block diagram of SIM OFDM transmitter.  
two substeams. One substeam (B2) is modulated conventionally (e.g., QAM), while the other (B1) is used for index selection.  
Table 1 shows examples of SIM-OFDM implementation, where S1, S2, and S3 are the activated subcarriers corresponding to the index bit.  
At the receiver end, the incoming OFDM signal is divided into groups of subcarriers of size n. B1 is obtained from the locations of the ON subcarriers. B2 is then obtained by demodulating those subcarriers, and thus the data stream is reconstructed. A functional block diagram of SIM OFDM receiver is given in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**2-Active Subcarriers**} & \multicolumn{2}{c}{**3-Active Subcarriers**} \\ \hline
**Index Bits (B1)** & **Indices** & **Sub-Blocks** & **Indices** & **Sub-Blocks** \\ \hline
00 & 1,2 & S1,S2,0,0 & 1,2,3 & S1,S2,S3,0 \\ \hline
01 & 2,3 & 0,S1,S2,0 & 1,3,A & S1,0,S2,S3 \\ \hline
10 & 3,4 & 0,0,S1,S2 & 1,2,A & S1, S2,0,S3 \\ \hline
11 & 1,4 & S1,0,0,S2 & 2,3,A & 0,S1,S2,S3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subcarrier selection for a 2-bit index data word on B1 using conventional SIM OFDM.  
Figure 3: System block diagram of SIM OFDM receiver.  
Figure 2: Block diagram of SIM OFDM sub-block creator.
## 4 Simulation Platform and Algorithm Performance
### Initial Value Estimation Performance
The changes in the relative error \(\bar{e}_{r}\) at different \(SNR\) are shown in Figure 11. The changes in \(\bar{e}_{r}\) are the same as those in \(\bar{d}\) in Figure 9, which indicate that \(\bar{e}_{r}\) is related mainly to the depth  
Figure 7:0 Depth estimation error of a cylindrical target at different depths. Figure 7:1 Cylindrical target depth estimation error at different SNR values. estimation performance. This result occurs because the horizontal estimation error is much less than the depth estimation error. However, the depth estimation can be kept within an order of magnitude from the true parameter, which is accurate enough for subsequent filter algorithms.
# Machine-Learning-Based Password-Strength-Estimation Approach for Passwords of Lithuanian Context
## 2 Related Works
This is superior to the 80.06% accuracy shown by the existing model. [PERSON] et al. [23] proposed to predict the strength of new passwords using machine learning models. Therefore, several machine learning algorithms were chosen and experimentally investigated, such as logistic regression, gradient-boosted trees, random forest, multilayer perceptron, and Naive Bayes. Approximately 80,000 passwords were collected [24] and used in the experimental investigation, which was divided into training and testing datasets, respectively, 80/20%. All passwords of the datasets were assigned to three classes: 0-weak, 1-medium, and 2-strong. The research results showed that the highest accuracy was obtained using the gradient-boosted tree algorithm (99%). A slightly smaller accuracy (95%) was obtained by multilayer perceptron, and the smallest accuracy was obtained by Naive Bayes (only 75% was achieved). [PERSON] et al., in their research [25], used the same dataset as [PERSON] et al., but only the largest number of passwords was included. The authors proposed a password-strength-estimation model based on deep learning algorithms and multiclass classification, which solves the existing problem that leaked frequency is not considered during the evaluation. To evaluate the performance of the proposed model, an experiment was conducted that compared the password leaked frequency stored in a database using a password list. As a result, the proposed model correctly evaluated 99% of the 345 leaked passwords. The analysis performed of related works has shown that there is no research focused on Lithuanian-language password analysis, neither using mathematical approaches nor models based on machine learning. The most suitable solution for Lithuanian password-strength estimation is to use existing password meters, such as zxcvbn, and modify them for the language by adding dictionaries. However, in the case of the Lithuanian language, the language is more specific, and common words have many different forms, so it is not enough to use traditional dictionaries such as in English password detection. For this reason, the new approach based on machine learning, similarity measures, and the modification of the password-strength meter zxcvbn has been proposed and is described in the next section.
# From Self-Cleaning to Self-Aware Solar Mirror Skin
###### Abstract  
Self-cleaning coatings for solar mirrors aim to reduce water usage for cleaning, cut down on maintenance costs for solar fields, and lower the overall electricity production costs in concentrated solar power (CSP) systems. Various approaches have been developed for mirrors with back surface (BSM) and front surface (FSM) architectures, all sharing the characteristic that the self-cleaning coating serves as the outermost layer, acting as a \"skin\" that protects against fouling. A recent trend in this field is to enhance this \"skin\" with sensing capabilities, allowing it to self-monitor its performance in terms of soiling or failure, contributing to the digitalization of solar fields and CSP technology. Building on previous work with auxetic aluminum nitrides and ZnO transparent composites, which were developed to replace alumina as the self-cleaning layer in BSMs, this study explores the potential of adding sensing properties to these coatings. The approach leverages the piezoelectric properties of the materials, which can be linked to dust accumulation and surface soiling, as well as their electrical resistive behavior, which can help monitor potential failures. The promising d33 values of sputtered piezoelectric AlN and the tailored electrical properties of ZnO composites, combined with their self-cleaning effects and optical clarity across the full solar spectrum, suggest that these coatings could serve as an intelligent, self-aware skin for solar mirrors.  
self-cleaning; solar mirrors; piezoelectric; aluminum nitride; zinc oxide; composites Article
## 5 Conclusions  
The objective of this study, which is to theoretically calculate the watermarking algorithmic robustness against image cropping attack for remote sensing images, has been realized. The proposedmodel comprises analysis principles deduced from well-designed watermarking algorithms, probability-based evaluation index and robustness computation method. The robustness results calculated by the proposed model have been compared to experimental ones, and the fact that errors are very small, demonstrate the effectiveness and accuracy of the proposed model. Furthermore, based on the proposed model, the relationships of the robustness, watermark information length and watermark repeat times have been deduced as long as the watermarking algorithms conforming to the proposed principles. The deductions can be used to optimize the watermarking algorithm parameters and enhance the robustness against image cropping. In summary, the proposed robustness computation model provides an effective method to analyze and calculate the watermarking algorithmic robustness, and it can be adopted to facilitate the improvement of watermark robustness. Further work will attempt to analyze other types of attacks on remote sensing images and extend the applicability of robustness computation model for them.  
[PERSON] and [PERSON] proposed the idea, fundamental structure, theoretical analysis and conducted the experiments. [PERSON] advised the research background, principles, results discussions, manuscript writing and revision. [PERSON] improved the computation method and advised on the design of the experiments.  
This work was supported by the National Natural Science Foundation of China under grant No. 41301413, the Priority Academic Program Development of Jiangsu Higher Education Institutions and Six Talent Peaks Project in Jiangsu Province.  
The authors declare no conflict of interest.