esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conventional Frequency-Offset Estimation Method
### Conventional Scheme A  
The L LISE of the CFO and SFO is a well-known solution widely used for the case of unknown CFR [23; 24; 25], which was proposed originally by using two successive pilots. Here, we straightforwardly extend the LMSE to \(N_{b}>2\) symbols by averaging statistically independent samples. Based on an observation of \(N_{b}\) successive OFDM symbols, the phase-difference dependent signal \(Y_{l}(k)\) at pilot subcarrier \(k\) can be written by:  
\[Y_{l}(k)=\sum_{m=l}^{N_{b}+l-2}R_{m}^{*}(k)R_{m+1}(k),\;k\in\mathcal{S}_{f} \tag{6}\]  
which is further derived as:  
\[Y_{l}(k)=\sum_{m=l}^{N_{b}+l-2}|\hat{H}_{m}(k)|^{2}Exe^{i2\pi\varphi(k)\beta}+ \sum_{m=l}^{N_{b}+l-2}\hat{\mathcal{C}}_{m}(k)+\sum_{m=l}^{N_{b}+l-2}\hat{Z}_{m }(k),\;k\in\mathcal{S}_{f} \tag{7}\]  
where \(\mathcal{S}_{f}\) is the set of subcarrier indices for CPs, \(\hat{H}_{m}(k)=H_{m}(k)e^{i\pi(N-1)\varphi(k)/N}\) means an equivalent CFR, \(E_{X}=|X_{m}(k)|^{2}\) is the pilot symbol energy with a boosted power level of \(7/3\), \(\beta=N_{u}/N\), \(\hat{C}_{m}(k)\) represents the combined ICI expressed as:  
\[\begin{split}\hat{C}_{m}(k)&=\hat{H}_{m}^{*}(k)X_{ m}^{*}(k)C_{m+1}(k)e^{-j2\pi\varphi(k)((m+1)N_{u}+N_{g})/N}+\hat{H}_{m+1}(k)X_{ m+1}(k)C_{m}^{*}(k)e^{j2\pi\varphi(k)(mN_{u}+N_{g})/N}\\ &+C_{m+1}(k)Z_{m}^{*}(k)+C_{m}^{*}(k)Z_{m+1}(k)+C_{m+1}(k)C_{m}^ {*}(k)\end{split} \tag{8}\]  
and \(\hat{Z}_{m}(k)\) is the AWGN given by:  
\[\begin{split}\hat{Z}_{m}(k)&=\hat{H}_{m}^{*}(k)X_{ m}^{*}(k)Z_{m+1}(k)e^{-j2\pi\varphi(k)(mN_{u}+N_{g})/N}\\ &+\hat{H}_{m+1}(k)X_{m+1}(k)Z_{m}^{*}(k)e^{j2\pi\varphi(k)((m+1) N_{u}+N_{g})/N}+Z_{m}^{*}(k)Z_{m+1}(k).\end{split} \tag{9}\]  
For notational convenience, let us consider from (7) that \(\angle Y_{l}(k)=2\pi\beta\varphi(k)+I_{l}(k)\), where \(\angle x\) means taking the argument of \(x\) and \(I_{l}(k)\) denotes the combined interference contribution after taking an argument. With this notation, LMSE of CFO and SFO can then be represented as:  
\[\begin{split}\hat{\varepsilon}&=\frac{1}{2\pi \beta N_{f}}\sum_{k\in\mathcal{S}_{f}}\angle Y_{l}(k)\\ &=\varepsilon+\frac{1}{2\pi\beta N_{f}}\sum_{k\in\mathcal{S}_{f}} I_{l}(k)+\frac{\xi}{N_{f}}\sum_{k\in\mathcal{S}_{f}}k\end{split} \tag{10}\]  
and:  
\[\begin{split}\hat{\xi}&=\frac{1}{2\pi\beta\sum_{k\in \mathcal{S}_{f}}k^{2}}\sum_{k\in\mathcal{S}_{f}}k\angle Y_{l}(k)\\ &=\xi+\frac{1}{2\pi\beta\sum_{k\in\mathcal{S}_{f}}k^{2}}\sum_{k \in\mathcal{S}_{f}}kI_{l}(k)+\frac{\varepsilon}{\sum_{k\in\mathcal{S}_{f}}k^{ 2}}\sum_{k\in\mathcal{S}_{f}}k\end{split} \tag{11}\]  
where \(N_{f}\) stands for the total number of CPs in \(\mathcal{S}_{f}\). In DVB-C2, CPs are not distributed symmetrically around DC, so that \(\sum_{\mathcal{S}_{f}}k\
eq 0\).
###### Abstract  
A comprehensive evaluation of the recently developed Fraunhofer line depth (FLD) and laser-induced saturation pulse (FLD-LISP) method was conducted to measure chlorophyll fluorescence (ChlF) parameters of the quantum yield of photosystem II (\(\Phi_{\text{PSII}}\)), non-photochemical quenching (NPQ), and the photosystem II-based electron transport rate (ETR) in three plant species including paprika (C3 plant), maize (C4 plant), and pachira (C3 plant). First, the relationships between photosynthetic photon flux density (PPFD) and ChlF parameters retrieved using FLD-LISP and the pulse amplitude-modulated (PAM) methods were analyzed for all three species. Then the relationships between ChlF parameters measured using FLD-LISP and PAM were evaluated for the plants in different growth stages of leaves from mature to aging conditions. The relationships of ChlF parameters/PPFD were similar in both FLD-LISP and PAM methods in all plant species. \(\Phi_{\text{PSII}}\) showed a linear relationship with PPFD in all three species whereas NPQ was found to be linearly related to PPFD in paprika and maize, but not for paprika. The ETR/PPFD relationship was nonlinear with increasing values observed for PPFDs lower than about 800 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) for paprika, lower than about 1200 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) for maize, and lower than about 800 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) for paprika. The \(\Phi_{\text{PSII}}\), NPQ, and ETR of both the FLD-LISP and PAM methods were very well correlated (R\({}^{2}\) = 0.89, RMSE = 0.05), (R\({}^{2}\) = 0.86, RMSE = 0.44), and (R\({}^{2}\) = 0.88, RMSE = 24.69), respectively, for all plants. Therefore, the FLD-LISP method can be recommended as a robust technique for the estimation of ChlF parameters.  
C3 plant; C4 plant; Fraunhofer line depth and laser-induced saturation pulse (FLD-LISP) method; non-photochemical quenching (NPQ); photochemical yield of photosystem II (\(\Phi_{\text{PSII}}\)); photosystem II-based electron transport rate (ETR); solar-induced chlorophyll fluorescence (SIF) +
Footnote †: journal: Remote Sensing
# Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
## 5 Security Evaluation and Experimental Results  
The security evaluation of the hiding countermeasure proposal has been conducted experimentally. As an experimental setup, the scheme shown in Figure 8 is proposed, where the following equipment is used: (1) computer, (2) oscilloscope Keysight InfiniVision DSOX3054T, with 4 G/samples and a bandwidth of 500 MHz, (3) power supply Keysight E36312A, and (4) SAKURA-G FPGA board (a specific board designed for SCA attacks). The power supply supplies the SAKURA-G board precisely and the oscilloscope makes the trace acquisition. The computer controls the instruments, communicates with the AES implementation in the Spartan-6 Xilinx FPGA of the SAKURA-G board, and processes the data to carry out the CPA attacks.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Implementation** & **LUTs** & **Slices** & **Freq. (MHz)** \\ \hline Unprotected AES & 3616 & 1518 & 66.20 \\ \hline Protected CPNG AES & 4666 & 1630 & 66.20 \\ \hline Overhead/Degradation & 29.04\% & 7.38\% & 0\% \\ \hline \end{tabular}
\end{table}
Table 2: Area overhead and frequency degradation comparison between unprotected and protected AES with CPNG in SAKURA-G Spartan-6 Xilinx FPGA.  
Figure 7: The connection of a simple PRNG based on a 128-bit linear-feedback shift register.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Future Aspects
#### 4.2.4 Standardization and Collaboration  
The widespread adoption of AIoT in solar energy systems will require coordinated global efforts to establish standardized protocols, interoperability frameworks, and collaborative research initiatives. Currently, the absence of uniform data formats, communication protocols, and integration standards across various AIoT platforms creates challenges for seamless deployment and scalability. Developing global interoperability standards for AI and IoT will ensure compatibility among diverse PV systems, sensors, and grid infrastructures, thus fostering a more unified energy ecosystem. Collaboration among academia, industry, and policymakers will be crucial to driving innovation, enhancing cybersecurity, and ensuring regulatory compliance for AI-driven solar energy management. Open-source initiatives and shared datasets can significantly accelerate research, allowing AI models to be trained on diverse and representative data. Furthermore, cross-sector partnerships--including energy providers, AI researchers, and IoT manufacturers--will facilitate the creation of more efficient,transparent, and secure AIoT-based solar energy solutions. By prioritizing standardization and fostering collaboration, the industry can overcome existing challenges, ensuring a more sustainable and globally integrated future for AI-powered solar energy systems.
# Fault Tolerant Spectral/Spatial Optical Code Division Multiple Access Passive Optical Network
## 4 Failure Recovery Analysis  
This section analyzes the performance of the ring topology in terms of system recovery in the case of failure across the feeder level. Figure 9 represents the arrangement of the simulation setup, where a failure, a fiber cut, is introduced at a certain point of failure (PoF) between \(RN_{2}\) and \(RN_{4}\) in order to disrupt the conventional flow of clockwise traffic towards all ONT modules of remote nodes from \(RN_{1}\) to \(RN_{4}\). The switch, \(SW_{SA}\), employed at the switching arrangement is then flipped from port 1 towards port 2 in order to allow for the flow of traffic in both clockwise and counterclockwise directions as per the system's description in Section 2.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**RN** & \(\mathbf{RN_{1}}\) & \(\mathbf{RN_{2}}\) & \(\mathbf{RN_{3}}\) & \(\mathbf{RN_{4}}\) \\ \hline \hline \multirow{2}{*}{Eye Diagram} & \multirow{2}{*}{\(5.11\times 10^{-18}\)} & \multirow{2}{*}{\(3.2\times 110^{-16}\)} & \multirow{2}{*}{\(1.6\times 110^{-14}\)} & \multirow{2}{*}{\(8.3\times 110^{-11}\)} \\  & & & \\ \hline \hline Received Power & \(-9.6\) dBm & \(-13\) dBm & \(-16.2\) dBm & \(-19.4\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 3: BER, eye diagrams, and received power at each RN for clockwise flow of traffic.  
The BER is observed at ONT 1 of each RN while performing simulation analysis as per the Table 2 performance parameters. It can be observed in Table 4 that the proposed system is able to handle the flow of traffic in both clockwise and counterclockwise directions, as acceptable values of BER are obtained at each ONT module after fiber failure across the feeder level. A degradation in the BER and eye height values is observed as the signal progresses from RN1 to RN2 while receiving clockwise traffic and from RN4 to RN3 for counterclockwise traffic. This behavior aligns with the performance analysis presented earlier, confirming the robustness of the implemented model. Additionally, the results demonstrate that the proposed 2D DW-ZCC system, incorporating spectral-spatial and temporal decoding, can effectively support 128 subscribers in both normal and failure scenarios.
## 3 Results
### Behavioral Experiments  
Nine of the 20 tested females exhibited searching and oviposition behaviors. Out of these responding females, no preference was detected between plants covered with honeybee originating from aCO\({}_{2}\) or eCO\({}_{2}\). The duration of the flight near each plant was similar (Student \(t\)-test: \(t\) = 2.020, \(p\) = 0.078), as was the time spent on each plant (Student \(t\)-test: \(t\) = 0.221, \(p\) = 0.831) and the time spent laying eggs (Student \(t\)-test: \(t\) = 0.439, \(p\) = 0.672) (Figure 2). These hourly females laid 22 \(\pm\) 4 eggs on plants covered with aCO\({}_{2}\) honeydew and 25 \(\pm\) 5 eggs on plants covered with eCO\({}_{2}\) honeydew (Student \(t\)-test: \(t\) = \(-\)1.557, \(p\) = 0.158) (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **CAS \#** &
\begin{tabular}{c} **Retention** \\ **Time (min)** \\ \end{tabular} & **aCO\({}_{2}\)** & **eCO\({}_{2}\)** & **P** \\ \hline Acetaldehyde & 75-07-0 & 3.406 & D & - & - \\ Ethanol & 64-17-5 & 3.592 & D & D & - \\
2-propanone & 407-25-0 & 3.756 & D & D & - \\
2-thipropane & 75-18-3 & 3.939 & D & D & - \\ Isobutanol & 78-83-1 & 4.958 & **0.504 \(\pm\) 0.147** & - & - \\
3-methylbutanal & 590-86-3 & 5.391 & 2.371 \(\pm\) 0.306 & 1.456 \(\pm\) 0.277 & 0.197 \\
2-methylbutanal & 96-17-3 & 5.557 & 2.357 \(\pm\) 0.328 & 1.272 \(\pm\) 0.219 & 0.081 \\
3-methyl-1-butanol & 123-51-3 & 7.038 & **2.108 \(\pm\) 0.301** & 0.682 \(\pm\) 0.164 & 0.009 \\
2-methyl-1-butanol & 137-32-6 & 7.077 & **2.458 \(\pm\) 0.203** & - & - \\
3-methyl-2-butten-1-ol & 556-82-1 & 8.201 & **0.916 \(\pm\) 0.264** & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Volatile organic compounds emitted from the honeydew of aphids reared under aCO\({}_{2}\) and elevated CO\({}_{2}\) concentrations. Values in bold indicate significant differences between CO\({}_{2}\) conditions.  
Figure 1: Amount (ng \(\pm\) standard error (SE)) of the different volatile organic compounds (VOCs) emitted by the honeydew of _A. pisum_ reared under aCO\({}_{2}\) (**white bars**) and eCO\({}_{2}\) (**grey bars**) conditions.
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 4 Modern Pigments and Dyes in Restoration
### Applications of Modern Pigments and Dyes  
Modern pigments and dyes find numerous applications within the field of restoration, mainly in two major directions:  
* Restoration of paintings and frescoes:
* Modern pigments are extensively used in retouching paintings and frescoes. Conservators utilize these pigments to fill in losses while ensuring that the retouched areas are visually indistinguishable from the original work [60]. The precision offered by modern formulations allows for detailed work on intricate designs.
* Some modern varnishes incorporate synthetic resins combined with stable pigments to provide protective coatings that enhance color depth while safeguarding against environmental damage [61]. These varnishes can be reversible, allowing future conservators to remove them without harming the underlying artwork.
* Advanced gelled materials based on synthetic polymers have been developed for cleaning artworks effectively. These gels can control solvent release rates while minimizing residue left on treated surfaces [62]. Such innovations significantly improve cleaning methods compared to traditional approaches.
* Decorative arts:
* In textile conservation, modern dyes are used to restore color to faded fabrics while maintaining compatibility with original materials. The ability to create color-matched dyes ensures that repairs blend seamlessly with existing textiles. For example, natural dyes derived from plants have been successfully applied in restoring historical garments [63].
* Modern pigments are also employed in restoring ceramics and glassware. They can withstand the high firing temperatures required during glazing processes while retaining their color stability [64]. This application is particularly important for historical ceramics where authenticity is paramount.
## 2 LFM Signal Detection Based on FrFT
### Problem Statement  
In this section, we consider the FrFT detection problem for LFM signal in the underwater multi-path channel. The schematic of FrFT detection is shown in Figure 1. Considering the typical ray model [40], the underwater acoustic channel associated with the target can be expressed as  
\[h(t)=\sum_{i=1}^{M}a_{i}\delta(t-\tau_{i}), \tag{14}\]  
where \(a_{i}\) and \(\tau_{i}\) are the amplitude and the delay of the \(i\)th path, respectively. \(M\) is the total number of paths.  
The traditional FrFT detection method has four steps.  
Step 1: the transceiver emits LFM signal \(s(t)\) to illuminate the target.  
Step 2: the transceiver receives the echo signal \(y(t)\).  
\[\begin{split} y(t)&=s(t)*h(t)+n_{1}(t)\\ &=\sum_{i=1}^{M}a_{i}s(t-\tau_{i})+n_{1}(t),\end{split} \tag{15}\]  
where the symbol \(*\) denotes convolution and \(n_{1}(t)\) is the additive noise.  
Step 3: the processor searches for the optimal transformation order and transforms \(y(t)\)to the optimal fractional Fourier domain.  
Step 4: the processor decides whether there is a target.  
Figure 1: The schematic of Fractional Fourier transform (FrFT) detection.  
Substituting (15) into (1) with \(x(t)\) set to \(y(t)\), it can be verified that the FrFT of the received signal \(Y_{p}(u)\) is given by  
\[Y_{p}(u)=\sum_{i=1}^{M}A_{i}S_{p}(u-u_{i})+N_{1p}(u), \tag{16}\]  
with  
\[A_{i}=a_{t}e^{\mathrm{j}\pi\tau_{i}^{2}\sin\alpha\cos\alpha- \mathrm{j}2\pi\pi u_{i}\sin\alpha},i=1,2,\ldots,M,\] \[u_{i}=\tau_{i}\cos\alpha,i=1,2,\ldots,M, \tag{17}\]  
and \(S_{p}(u)\) is the FrFT form of the emitted signal \(s(t)\) with transform order \(p\). \(N_{1p}(u)\) is the FrFT form of the additive noise \(n_{1}(t)\) with transform order \(p\).  
The echo signal \(y(t)\) is extended in the fractional Fourier domain under the influence of multipath. When the signal-to-noise ratio (SNR) is high, the main lobe (corresponding to the direct path) and side lobes (corresponding to other paths) are clearly distinguishable, it is then easy to be detected. When the SNR is low, in the fractional Fourier domain, the noise will be disturbed and coupled with the spectrum of echo signal \(y(t)\), and then the detection performance of the FrFT detector will be seriously deteriorated.
# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### 2 What Do the Proposed IoT Maturity Models Consider the Main Dimensions?
These dimensions, factors, or elements can be grouped into different categories as follows:  
Strategy, vision, and objectives: These dimensions assess the alignment and clarity of the organization's objectives [70] and strategies for the adoption, implementation, and exploitation of IoT in the organization [71], as well as the support and commitment of top management [52] and stakeholders. It also involves the assessment of the organization's value proposition [56], its market position, and its potential for innovation in IoT technology [59]. Examples of the elements in these categories are technology vision [63] and roadmap [22], decision-making, management support, willingness to adapt business processes, alignment, and methods [18]:  
Culture and organization: This category reflects the organization's values, beliefs, and norms that influence the adoption and implementation of IoT in the organization [52; 55]; it also considers the organization's willingness and readiness to embrace change, innovation, and data-driven decision-making, as well as collaboration and communication between different departments and functions involved in IoT projects [46]. This category includes organizational culture and governance [73], customer integration, strategic orientation, innovation cooperation, and digital culture. It is essential to indicate that these types of dimensions are established in maturity models to a lesser extent, but there has been an apparent increase in the frequency of appearance of this type of categorization in recent years for these measurement mechanisms [59]:  
Process and performance: This category evaluates the impact and improvement in IoT technologies on the optimization and efficiency of organizational resources and processes and the quality and reliability of products and services [61]. It also refers to business processes and workflows impacted by or supporting IoT [45], which involves process optimization, integration, standardization, and metrics and indicators to monitor and evaluate process performance [42]. This category includes resources/processes, asset utilization, labor, quality, performance monitoring, and process documentation [22; 43];  
Technology and data: Dimensions used to a lesser extent and are more technical categories, where the availability and reliability of data sources and technologies used by the organization [18], both within and across domains and companies, as well as the standardization and interoperability of data and technologies, are evaluated [13]. This category includes domain knowledge, data quality, data analytics, data failures, security, risk assessment, and reference architectures [47; 48];  
Equipment and competencies: This category assesses the existence and competence of a dedicated, cross-functional team that manages and executes IoT projects [52] and the recruitment and development of people with digitization skills.
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 2 Materials and Methods
### Autonomous Driving Setup  
The robot was operated using waypoint data generated from the GPS coordinates of the test field. The setup process for autonomous driving is outlined in Figure 2 and described sequentially. (A) Setup: The robot loaded the waypoint data and located the start point. The PTO shaft began to rotate in idle mode. (B) Initialization: After loading the data, the robot moved to the start point and leveled itself both horizontally and vertically for the operation. (C) Set off: The robot performed ridge forming along a straight line as per the imported data. Upon reaching the end point, the implement was raised, and the RPM of the PTO shaft and the number of ridges formed were calculated to ensure zero errors. After completing these calculations, the system shut down.  
For autonomous driving, maintaining linearity in driving was a crucial parameter. The vehicle's steering needed to respond promptly to real-time data, and precise control would enhance linearity. This concept can be described using the kinematic model below [29,30]:  
\[\dot{c}=\frac{v\cdot(\cos\theta)}{1-p(c)y}, \tag{1}\]  
\[\dot{y}=v\cdot\sin\theta, \tag{2}\]  
\[\dot{\theta}=v\cdot\left[\frac{\tan\delta}{L}-\frac{p(c)\cdot\cos\theta}{1-p(c )\cdot y}\right], \tag{3}\]  
where \(c\) is the coordinate on the path, \(v\) is the robot's velocity, \(y\) and \(\dot{\theta}\) are the lateral and angular deviations of the robot along the path, respectively, and \(L\) is the wheelbase of the robot.  
Based on this kinematic model, the equation for controlling the robot, without incorporating additional factors except for the attachment of electro-hydraulic cylinders for adjusting the robot's behavior, is given as follows [31,32]:  
\[\begin{split}\delta(y,\theta)=\tan^{-1}(L\cdot\frac{\cos^{3} \theta}{(1-y\cdot p(c))^{2}}\cdot(-K_{d}(1-y\cdot p(c))\cdot\tan\theta)-K_{p} \cdot y\\ +p(c)\cdot(1-y\cdot p(c))\cdot\tan^{2}\theta)+\frac{p(c)\cdot \cos\theta}{1-y\cdot p(c)},\end{split} \tag{4}\]  
Figure 2: The operation process using the developed robot.  
where \(K_{d}\) and \(K_{p}\) are the constants for tuning the robot's driving behavior with a controller.
## 4 Security and Privacy of IoNT
### Security and Privacy Attacks  
Having discussed the security and privacy challenges pertaining to the IoNT, in this section, we provide a brief discussion on security and privacy attacks that target the IoNT ecosystem. In general, security and privacy attacks that target the IoNT ecosystem can be apportioned to four categories, as shown in Figure 5.  
The security and privacy attack categories can be summarized as follows:  
* Disruption The availability of IoNT services and their dependability would both suffer as a result of disruption attacks [1; 4];
* Disclosure Malicious actors involved in disclosure attacks are able to gain access to sensitive information. When it comes to the IoNT, these kinds of attacks are difficult to launch when  
Figure 5: Categorization of security and privacy attacks.  
employing mechanical or molecular communication, whereas via electromagnetism and acoustics throw open the door for potential adversaries due to the comparatively broad region that is covered [9; 17];
* Deception Attackers' ability to misrepresent or modify information is key in deception attacks [9]. Integrity checks are one method of putting a stop to these kinds of attacks;
* Usurpation Beyond causing disruption to the system, unauthorized entities here have the ability to influence system services or entities [38; 39; 40]. Attackers can even gain complete control of the system in these types of attacks.