esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Robust Low-Complexity CFO Estimator
### Performance Analysis  
Since \(f(\epsilon)\) is a function of the normalized frequency \(\epsilon\) and the proposed criterion for determining the CFO estimate is based on the minimization, which is expressed by Equation (16), the proposed criterion is similar to that of the minimum variance distortionless response (MVDR) spectrum estimator [20]. Specifically, the CFO estimate \(\hat{\epsilon}_{d}\) satisfies the condition:  
\[\dot{f}(\hat{\epsilon}_{d})=\left.\frac{d}{d\epsilon}f(\epsilon)\right|_{ \epsilon=\hat{\epsilon}_{d}}=0 \tag{17}\]  
Since the noise vector \(\mathbf{v}\) is statistically equivalent to \(\mathbf{w}\), it follows that \(E\{\mathbf{v}\}=\mathbf{0}\) and \(E\{\mathbf{v}\mathbf{v}^{H}\}=\sigma_{n}^{2}\mathbf{I}_{N}\). Hence, Equation (24) can be rewritten as:  
\[E\{\tilde{f}(\epsilon_{d})\} =\mathbf{s}^{H}\mathbf{\Gamma}\mathbf{s}+\sigma_{n}^{2}\text{Tr} \left\{\mathbf{\Gamma}\right\} \tag{25}\] \[=\mathbf{s}^{H}\mathbf{\Gamma}\mathbf{s}\] \[=\mu\]  
where, by definition, \(\mu=\mathbf{s}^{H}\mathbf{\Gamma}\mathbf{s}\). According to Equations (23) and (25), the estimation error (bias) is easily shown as:  
\[\Delta\epsilon_{d} \approx\tfrac{\tilde{f}(\epsilon_{d})}{E\{\tilde{f}(\epsilon_{d} )\}} \tag{26}\] \[=\mu^{-1}(\mathbf{s}+\mathbf{v})^{H}\mathbf{A}(\mathbf{s}+\mathbf{ v})\]  
By taking expectation on the estimation error shown in Equation (26), we obtain the result:  
\[E\{\Delta\epsilon_{d}\} \approx\mu^{-1}\mathbf{s}^{H}\mathbf{A}\mathbf{s}+\mu^{-1}E\left\{ \mathbf{v}^{H}\mathbf{A}\mathbf{v}\right\} \tag{27}\] \[=\mu^{-1}\mathbf{s}^{H}\mathbf{A}\mathbf{s}+\mu^{-1}\sigma_{n}^{ 2}\text{Tr}\left\{\mathbf{A}\right\}\] \[=\alpha\mu^{-1}\]  
where \(\alpha=\mathbf{s}^{H}\mathbf{A}\mathbf{s}\). Using the fact of the Gaussian noise \(\mathbf{v}\) and discarding the self-product term of the noise, the mean square error is further expressed as:  
\[E\{(\Delta\epsilon_{d})^{2}\} \approx\mu^{-2}\left\{\alpha^{2}+2\alpha E\{\mathbf{v}^{H} \mathbf{A}\mathbf{v}\}+E\{(\mathbf{s}^{H}\mathbf{A}\mathbf{v}+\mathbf{v}^{H} \mathbf{A}\mathbf{s})^{2}\}\right\} \tag{28}\] \[=\mu^{-2}\left(\alpha^{2}+2\alpha\sigma_{n}^{2}\text{Tr}\{ \mathbf{A}\}+\sigma_{n}^{2}\mathbf{s}^{H}\mathbf{A}\mathbf{A}\mathbf{s}\right)\]  
For high SNR, Equation (28) reduces to:  
\[E\{(\Delta\epsilon_{d})^{2}\}\approx\mu^{-2}\alpha^{2} \tag{29}\]
## 4 Experiments and Results
### Discussion of Results
This suggests that the SLF-RPL possesses a more robust and responsive mechanism for detecting potential security threats within the network. The increasing trend in both protocols' attack detection rates as the time interval grows reflects their improving adaptability over longer monitoring periods. Moving on to the Average Residual Energy (J), a critical metric indicating the energy consumption efficiency of the routing protocols, the SLF-RPL maintains a notable advantage over the PCC-RPL. The decreasing values of average residual energy for both protocols over the time intervals indicate a potential energy conservation effect. However, the SLF-RPL consistently maintains lower energy consumption levels compared with the PCC-RPL, indicating its proficiency in optimizing energy usage, which is particularly advantageous for prolonging network lifetime and supporting resource-constrained devices. The Packet Loss Ratio (%) reveals the reliability of data transmission within the network. The SLF-RPL consistently exhibits lower packet loss ratios compared with the PCC-RPL across various time intervals. This suggests that the SLF-RPL offers enhanced data transmission stability and integrity, which is crucial for maintaining a seamless communication environment. The diminishing trend in packet loss ratios for both protocols as the time interval increases indicates their improved data transmission reliability over extended periods. In summary, Tables 5 and 6 detail evaluation parameters that provide a comprehensive insight into the comparative performance of the PCC-RPL and SLF-RPL routing protocols. The SLF-RPL demonstrates superiority in terms of attack detection rate, energy efficiency, and packet loss ratio across all evaluated time intervals. These findings underscore the potential benefits of SLF-RPL in enhancing network security, optimizing energy consumption, and ensuring reliable data transmission, making it a promising candidate for various real-world applications. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Key Evaluation Parameters** & \multicolumn{2}{c}{**10 Min**} & \multicolumn{2}{c}{**20 Min**} & \multicolumn{2}{c}{**30 Min**} & \multicolumn{2}{c}{**40 Min**} & \multicolumn{2}{c}{**50 Min**} & \multicolumn{2}{c}{**60 Min**} \\ \hline  & **PCC-** & **SLF-** & **PCC-** & **SLF-** & **PCC-** & **SLF-** & **PCC-** & **SLF-** & **PCC-** & **SLF-** & **PCC-** & **SLF-** & **PCC-** & **SLF-** \\  & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** & **RPL** \\ \hline Attack Detection Rate (\%) & 0.45 & 0.56 & 0.54 & 0.66 & 0.65 & 0.78 & 0.74 & 0.84 & 0.81 & 0.89 & 0.92 & 0.99 \\ \hline Average Residual Energy (J) & 0.73 & 0.77 & 0.62 & 0.65 & 0.48 & 0.52 & 0.42 & 0.45 & 0.35 & 0.40 & 0.31 & 0.35 \\ \hline Packet Loss Ratio (\%) & 0.51 & 0.47 & 0.49 & 0.44 & 0.46 & 0.39 & 0.39 & 0.33 & 0.28 & 0.23 & 0.15 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results Comparison of PCC-RPL vs. SLF-RPL at the Network Level.
# Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm
## 3 Results and Discussion  
Table 3 summarizes the results of all eight trainings. The F1 measures and GIoU demonstrated the best values during the second training, after using the trained anchor boxes (anchors = 24,26; 29,46; 31,80; 38,60; 42,45; 45,66; 46,81; 64,75; 66,42.). These anchor boxes were chosen and trained using the k-means algorithm on the original training set. The best values of mAP = 0.996 and test loss = 0.138 were obtained during the fifth training, after the batch size was augmented to 128. Some training employed the dimensions of anchor boxes trained on the COCO dataset [37] (anchors = 10,13; 16,30; 33,23; 30,61; 62,45; 59,119; 116,90; 156,198; 373,326).  
Figure 8 shows examples of the results for detection, localization, and classification by YOLOv3 algorithm implementation.  
To assess the effects of each technique applied for training set augmentation on the model performances, a model was designed (training 9) with new combinations of hyperparameters based on previous trainings: initial learning rate was 0.001; with first reduction, the learning rate was 0.0001 after 3200 iterations, and the learning rate after the second reduction was 0.00001 after 3600 iterations, with a batch size of 128, and with trained anchor boxes. The results are displayed in a graphical (Figure 9) and tabular (Table 4) form in the next section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Training** & **GIoU** & **Train Loss** & **Precision** & **Recall** & **mAP** & **F1** & **Test Loss** \\ \hline
1 & 0.0732 & 0.142 & 0.866 & 0.995 & 0.992 & 0.923 & 0.157 \\
2 & 0.0577 & 0.122 & 0.941 & 0.995 & 0.993 & 0.967 & 0.142 \\
3 & 0.0733 & 0.136 & 0.871 & 0.997  
Based on the table of obtained values (Table 4), it can be concluded that elimination of data produced by the rotation technique yields better results for test loss = 0.125,  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Training** & **Detection Time (in Hours)** & **GIoU** & **Train Loss** & **mAP** & **F1** & **Test Loss** \\ \hline Original set—training 9 & 9.061 & 0.0579 & 0.125 & 0.995 & 0.965 & 0.146 \\ Without brightness set & 6.474 & 0.0692 & 0.156 & 0.992 & 0.953 & 0.183 \\ Without gamma set & 7.074 & 0.0728 & 0.16 & 0.991 & 0.945 & 0.192 \\ Without rotation set & 8.175 & 0.047 & 0.092 & 0.995 & 0.981 & 0.125 \\ Without mirror effect set & 8.685 & 0.057 & 0.109 & 0.994 & 0.975 & 0.143 \\ Without blur set & 7.607 & 0.069 & 0.161 & 0.991 & 0.933 & 0.206 \\ Without noise set & 8.094 & 0.065 & 0.142 & 0.994 & 0.97 & 0.156 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summarized results for effects of other applied augmentation techniques.  
Figure 8: Example of the results obtained in training 3: (**a**) compliant raspberry, (**b**) class of noncompliant raspberry and stalk, (**c**) defective material.  
Figure 9: Graphical representation of the results for training 9.  
F1 = 0.981, and GIoU = 0.047, whereas the highest positive effect was achieved by the blurring technique, and better results were also produced by other techniques.
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 3 Methodology
### Modeling of a Battery Cell
#### 3.1.1 Open Circuit Voltage Calculation  
Open circuit voltage (\(OCV\)) corresponds to the battery voltage during a stable state when there is no perturbation. This is one of the critical parameters to be modeled. Because the \(OCV\) value depends on the \(SOC\), a mathematical equation can express the link between them. The block diagram that is shown in Figure 4, based on [33], models this relationship and the following equation shows the mathematical relationship:  
\[OCV=-a\times\mathrm{e}^{-b\times SOC}+c\times SOC-d\times SOC^{2}+e\times SOC ^{3} \tag{31}\]  
where \(a\) = 1.031, \(b\) = 3.685, \(c\) = 0.2156, \(d\) = 0.1178, and \(e\) = 0.3201.
# Time-Resolved Imaging of Femtosecond Laser-Induced Plasma Expansion in a Nitrogen Microjet
## 2 Materials and Methods  
### Glass De Laval Nozzle Fabrication  
The fused silica micronozzles were fabricated by the FLICE technique, which allows the fabrication of 3D microfluidic devices inside a glass substrate with a maskless approach [9; 10]. A femtosecond laser beam is focused inside the glass substrate and, due to nonlinear processes that occur at the focal spot, the material is permanently modified, presenting a localized higher etching rate. By exposing the irradiated substrate to an etchant solution, it is possible to fabricate microfluidic networks with the desired geometry.  
We used the second harmonic of a femtosecond laser beam (Satsuma HP, Amplitude). It provided pulses with a 270-fs duration at a 1-MHz repetition rate, and we used pulse energies ranging from 250 nJ to 380 nJ. We focused these pulses inside the fused silica substrate by a 63\(\times\) microscope objective (0.75 numerical aperture (N.A.)) equipped with a corrective collar for spherical aberrations. The fused silica slab was mounted onto a precision 3D movement system (ANT130-035-L-ZS-PLUS and ANT95-50-XY-CMS-ULTRA from AEROTECH Inc.) that permits the movement of the sample with respect to the laser beam, with an overall 275 nm accuracy. The sample was irradiated in a bottom to top direction.  
The fused silica glass chip had a trapezoidal external shape with an embedded micronozzle, as represented in the illustration in Figure 1. The external trapezoidal shape allowed the interaction of the gas jet with ultrafast laser pulses near the nozzle exit without damaging the device.  
To define the gas tube reservoir and the external trapezoidal shape, the energy of the pulses used for the fabrication was fixed at 300 nJ and 380 nJ, respectively, with sample translation speeds of 2 mm/s and 5 mm/s. To shape the typical convergent-divergent tube geometry of a De Laval nozzle (see Figure 1a), the sample was irradiated following a 3D helical trajectory with a variable radius [11]. The translation speed was 0.2 mm/s and the helix pitches (\(\Lambda\)) were 8 um and 2 um in the convergent and divergent cones, respectively. The pulse energy was reduced to 250 nJ to avoid over-exposure due to the low translation speed. These irradiation conditions guaranteed a good superposition of the laser tracks.  
After the irradiation, the sample was immersed in an ultrasonic bath of a 20% HF aqueous solution for 80 min to completely reveal the desired empty inner volumes. For the present work, we designed a De Laval micronozzle with the final geometric characteristics reported in the blow-up of Figure 1a,b. This device represents an example of the FLICE capabilities for the fabrication of micronozzles. Indeed, we can choose any arbitrary diameter, shape, and length for the different sections of the nozzle, considering a minimum lateral dimension of 10 um due to HF etching selectivity [9; 10; 11].
# Review on Fast Pyrolysis of Biomass for Biofuel Production from [PERSON]
## 5 Conclusions  
This review consists of recovering date palm waste through the process of fast pyrolysis to improve the liquid yield produced using this technology. There are several technologies that can be used to produce biogas: combustion, gasification, and pyrolysis.  
According to a technology watch on biomass patents, there are 1296 patent families and 2210 articles on the subject of fast biomass pyrolysis of palm residues. Fast pyrolysis of date palms represents one of the most innovative and promising methods for biofuel production. Several experiments have shown that this technique provides a high yield of biofuel from date palm residues, which are considered an abundant and renewable source of biomass. However, it is important to take into account several parameters that have a direct influence on biofuel yield and the refining process. The chemical composition of date palm residues plays an important role in biofuel yield.  
As explained in the article, the efficiency and yield of biofuel produced via fast pyrolysis depend on several key parameters, such as temperature, which directly influences the yield and quality of the biofuel produced. High temperatures can increase gas production, while low temperatures can favor biochar production. It is therefore important to strike a balance between these two aspects to maximize biofuel production while minimizing the formation of undesirable by-products. This process stabilizes the product, eliminates impurities and adjusts its properties to meet market requirements and standards. Various refining techniques can be used to purify and improve biofuel quality and performance, such as hydrogenation, distillation and deoxygenation.  
Conceptualization, B.K. and M.B.; methodology, B.K., A.H. and F.K.-S.; validation, M.B. and F.K.-S.; data curation, B.K., A.H. and M.T.; writing--original draft preparation, B.K. and A.H.; writing--review and editing, B.K., A.H., F.K.-S. and M.B.; resources, M.B.; supervision, F.K.-S. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Impact of Individual Posture  
The identification results for different individual postures are shown in Table 5. The high overall precision showed in the results represents the excellent performance of our model for various individual postures. For the identification of proper hard hat use, the recall rate for the squatting position is lower than the others because of failures of the body parts detection via OpenPose. However, the recall rate is still above 81%. The identification results indicate that the impact of individual posture has little effect on the identification of proper full-face mask use performance, as the precision and recall rate remaining robust in different individual postures.  
The overall precision and recall rates are 97.64% and 93.11%, respectively, which demonstrates the robustness of the proposed approach in the identification of proper PPE use at different distances and individual postures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Categories** & **Distance** & **TP** & **FP** & **FN** & **Precision (\%)** & **Recall (\%)** \\ \hline \multirow{4}{*}{Hard hat} & 3 m & 666 & 27 & 18 & 96.10 & 97.37 \\  & 5 m & 695 & 36 & 75 & 95.08 & 90.26 \\  & 7 m & 665 & 15 & 153 & 97.79 & 81.30 \\ \multirow{4}{*}{Full-face mask} & 3 m & 499 & 0 & 1 & 100 & 99.80 \\  & 5 m & 500 & 7 & 0 & 98.62 & 100 \\ \multirow{4}{*}{Full-face mask} & 7 m & 487 & 0 & 13 & 100 & 97.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification results under different distance.  
Figure 6: Identification examples on the testing dataset.
# Analysis of a Novel Automatic Control Approach for the Free Forging Hammer
## 2 Methodology
If the position detection device is installed on the hammer body, the sensor is easily damaged due to the large instantaneous acceleration, and the measured data contain a lot of noise, which poses a great challenge to the subsequent processing. Therefore, this paper proposes a non-contact displacement measurement system based on the photoelectric effect to measure the position of the hammerhead. The measuring sensor is installed on the bracket drawn from the top of the operating room, which uses the movement of the forging hammer to block the light, and converts the light signal into an electrical signal output so as to obtain the real-time position of the forging hammer. In this detecting system, the measuring sensor is isolated from the hammer body and the foundation, and the impact and vibration during striking do not affect the measuring accuracy of the sensor. Meanwhile, the sensor installation does not affect the on-site operation, and the on-site operation does not damage the sensor. On the premise of not affecting the measurement accuracy, it is beneficial to protect the measurement sensor and prolong its service life.
The analysis is intended to uncover research gaps and provide recommendations for the responsible implementation of AI in regional ecosystems, with a view to ensuring a fair and sustainable technological transition. The specific objectives of this review are fourfold. First, it aims to identify the main research areas addressing the impact of AI on the sustainability of regional ecosystems. Second, it seeks to analyze the current state of academic knowledge concerning the role of AI in agri-industrial regions and to reveal existing knowledge gaps. Third, the review focuses on identifying potential opportunities and challenges associated with AI integration into regional processes, including its effects on environmental, economic, and social stability. Finally, it proposes directions for future research aimed at developing strategies for the responsible and context-sensitive application of AI in regional ecosystems. This analysis contributes to a deeper scientific understanding of the role of artificial intelligence in supporting sustainable development. It also provides a foundation for formulating recommendations on the responsible integration of digital technologies into regional strategies, particularly in the context of agricultural ecosystems that require integrated resource management and mitigation of socio-economic inequality. This review covers a broad spectrum of technologies--including machine learning algorithms, remote sensing, digital twins, and the IoT--and is structured according to the functional domains of AI implementation. It reflects both ecological and socio-economic implications of AI adoption. By integrating interdisciplinary approaches, the article offers a comprehensive perspective on the role of AI in transforming local ecosystems and lays a scientific basis for further research and practical applications in the field of regional sustainability. Unlike most existing studies, this review pays particular attention to the regional and sectoral specifics of AI implementation and identifies key social, economic, and technological barriers to its adoption. The findings are intended to expand knowledge for both academic researchers and practitioners working in the field of sustainable development, especially within a regional context. This article is organized into four main sections. The first outlines the relevance, objectives, and research questions. The second presents the methodology for selecting and analyzing peer-reviewed publications, ensuring a structured and replicable approach. The third section explores key research domains, including the impact of AI on water, forest, agricultural, industrial, urban, and social ecosystems. The final section summarizes the main findings and proposes directions for future research focused on achieving balanced AI integration and minimizing the asymmetry of its impacts.
# Bioassays with _Allium cepa_ for the Monitoring of Toxicity in the Groundwater of Yucatan, Mexico
## 2 Methods  
### Study Site  
Seven deep wells were selected in Yucatan, Mexico (Figure 1). All the wells used in this study are part of the Coastal Aquifer Monitoring Network in the Northwest of the State of Yucatan (RNWY), established by UNAM Coastal Engineering and Processes Laboratory (LIPC) in conjunction with the National Coastal Resilience Laboratory (LANRESC). Sites are located in two transects to the coast; one transect runs from Sierra Papacal in Merida to Chuburna in Progreso municipality (sites P1-P3), and the other transect runs from Nohuaium in Tetiz to Sisal in Hunucma municipality (sites P4-P7). General information on the monitored wells is presented in Supplementary Table S1.
## 2 Methodology
### General Framework for Image Deconvolution
#### 2.1.1 The Richardson-Lucy Algorithm: Motivation  
In this work, we use the Richardson-Lucy (RL) algorithm, the details of which are described in Section 2.1.2 below. The RL algorithm is one of the most widely used methods in image deconvolution, especially in astronomical observations because of its ability to enhance image resolution all the while preserving the observed total intensity. The procedure is particularly effective in restoring images blurred by the telescope's PSF, making it useful for disentangling closely spaced point sources and enhancing faint structures in extended sources [14].  
One of the key advantages of RL deconvolution is that it is well-suited for Poisson noise, which is quite common in low photon-count observations, such as those carried out by X-ray and optical telescopes [33]. Furthermore, the iterative nature of the algorithm allows for gradual refinement of the image, but the technique requires careful tuning of the number of iterations to avoid noise amplification noise or introducing numerical artifacts.  
While RL deconvolution is computationally efficient and does not assume a specific noise model beyond standard Poisson statistics, it can struggle in cases of extremely low signal-to-noise (S/N) ratios where the noise dominates over the deconvolution process.  
Despite these limitations, the ability of the RL algorithm to recover spatial details in low photon-count observations makes it a valuable tool for analyzing astronomical images.