esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Synthesis
### Nanofilers Deposition  
The ZnO nanorod synthesis essentially requires two procedures: seeding, and growth for them to bond to the carbon fabric substrate. Dip-coating method was employed for seeding purposes. A seeding solution was prepared with 658.4 mg of zinc acetate dehydrate (Alfa Aesar, Tewksbury, MA, USA), 600 mL of deionized (DI) water, and 80 mL of ethanol, sonicated together for 10 min. The carbon fiber substrate was dipped in this solution for five iterative cycles. Each cycle includes dipping, drying, and rinsing with ethanol. The substrate was further dried at 100 \({}^{\circ}\)C for 30 min after the completion of the seeding procedure.  
The growth procedure involves preparing a bath of DI water, in which zinc acetate dehydrate and hexamethylenetetramine (HMTA, Alfa Aesar, Tewksbury, MA, USA) were added in adequate amounts. A homogenized mixture of zinc acetate dehydrate (15.72 g) and HMTA (10.09 g) with DI water (3600 mL) was achieved via sonication. Based on a parametric study for the time and temperature dependence of the length and diameter of nanorods, 90 \({}^{\circ}\)C and 8 h growth time was chosen for this experiment.  
The carbon fiber fabrics with surface-grown ZnO nanorods were dried further in preparation of the deposition of CNTs. Air brush spraying technique was employed for this process. Ethanol was utilized as a solvent, while cyclohexyl-2-pyrrolidone was used a surfactant. The multiwalled CNTs (1.5% wt.) were sonicated for 30 min with ethanol to cyclohexyl-2-pyrrolidone (CHP) ratio of 80 to 20 volume%. This final suspension was sprayed over the carbon fiber substrate five times, with appropriate drying time between each iteration.  
With hydrothermal and spraying techniques to grow nanorods and nanotubes, respectively, the growth is randomly scattered over the carbon fabric, as seen in Figure 1. However, the adhesion to the fabric is ensured by drying the samples for 24 h at 90 \({}^{\circ}\)C before making the composite. Tensile tests and fracture analysis performed for various configurations by the authors previously [48] clearly indicated an increase in the interfacial shear strength due to the addition of nanofillers.
## Intercomparison of Seven NDVI Products over the United States and Mexico  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department of Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721, USA;  
E-Mails: [PERSON]L_ADDRESS] (X.Z.); [PERSON]DDRESS] (P.B.);  
[PERSON]_ADDRESS] (M.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-520-954-7764; Fax: +1-520-621-6833.  
_Received: 4 December 2013; in revised form: 15 January 2014 / Accepted: 16 January 2014 / Published: 27 January 2014_  
**Abstract:** Satellites have provided large-scale monitoring of vegetation for over three decades, and several satellite-based Normalized Difference Vegetation Index (NDVI) datasets have been produced. Here we intercompare four long-term NDVI datasets based largely on the AVHRR sensor (NDVIg, NDVIg, STAR, VIP) and three datasets based on newer sensors (SPOT, Terra, Aqua) and evaluate the effectiveness of homogenizing the datasets using the green vegetation fraction (GVF) and the impact it has on phenology trends. Results show that all NDVI datasets are highly correlated with each other. However, there are significant differences in the regression slopes that vary spatially and temporally. There is a general trend towards higher maximum annual NDVI over much of the temperate forests of the US and a longer greening period due mostly to a delayed end of the season. These trends are less well-defined over rainfall dependent ecosystems in Mexico and the southwest US Compared with the NDVI datasets, the derived GVF datasets show more one-to-one relationships, have reduced interannual variation, preserve their relationships better over the entire time period and are characterized by weaker trends. Finally, weak agreement between the trends in the datasets stresses the importance of using multiple datasets to evaluate changes in vegetation and its phenology.  
**Keywords:** vegetation fraction; NDVI; phenology; trends; satellite; MODIS; AVHRR; SPOT
## 2 Materials and Methods
### Determination of Antioxidant Activity  
The antioxidant activity was determined by measuring the ferric reducing antioxidant power (FRAP) and the free radical scavenging activity of the enzyme extracts in microdilution tests.  
For FRAP analysis, first, a reagent solution was prepared containing 80 mL of 300 mM acetate buffer (pH 3.6), 8 mL of 10 mM 2,4,6-tris(2-pyridyl)-s-triazine (TPTZ; Sigma-Aldrich, Munich, Germany) diluted in 40 mM HCl, 8 mL of 20 mM iron(III) chloride, and 4.8 mL of distilled water. Then, 6 uL of extract diluted in 50% (\(v/v\)) ethanol (10-fold dilution) was transferred to 200 uL FRAP reagent. After vortexing, the reaction mixture was incubated at 37 degC for 30 min, and the absorbance was determined at 593 nm (SPECTROstar Nano, BMG Labtech, Offenburg, Germany). The calibration curve was established using a 1 mM iron(II) sulfate solution in the concentration range of 0.1-1.0 mM. The FRAP of the hawthorn extracts was expressed as uM Fe(II)/g DM.  
The free radical scavenging capacity was determined using the 2,2-diphenyl-1-1-picrylhydrazaryl (DPPH; Sigma-Aldrich, Munich, Germany) reagent. For the reaction, 150 uL of 100 uM DPPH solution prepared freshly in 80% (\(v/v\)) ethanol was mixed with 25 uL of extract diluted in 50% (\(v/v\)) ethanol (10-fold dilution). The control sample contained 150 uL of 100 uM DPPH and 25 uL of 50% (\(v/v\)) ethanol. After a 25-min incubation of  
Figure 2: Flowchart diagram of the production process of hawthorn extracts.  
both the reaction mixture and the control sample in a dark place at room temperature, the absorbance was measured at a wavelength of 517 nm (SPECTROstar Nano, BMG Labtech, Offenburg, Germany). The results were plotted as percentage DPPH radical scavenging capacity against the control. The following equation was used to calculate the free radical scavenging activity:  
\[\text{DPPH (\%)}=[(\text{A}_{\text{control}}-\text{A}_{\text{extract}})/\text{A}_{ \text{control}}]\times 100\]  
where A\({}_{\text{control}}\) is the absorbance of the control sample, and A\({}_{\text{extract}}\) is the absorbance of the reaction mixture containing the diluted hawthorn extract.
###### Abstract  
Quality inspection is critical for ensuring efficiency and compliance in assembly lines. The increasing adoption of AI-driven technologies, such as machine vision systems, offers significant potential to enhance detection performance and reduce inspection costs. However, these technologies are often integrated with human operators in hybrid inspection systems, posing complex design challenges. Motivated by gaps in the existing research, this paper proposes a novel MILP model that introduces several previously unaddressed capabilities in inspection planning. Specifically, it simultaneously optimizes the inspection method selection, sampling rate, and detection rate across multi-product systems featuring multiple inspection technologies with varying costs and accuracies. This unified formulation represents a substantive advancement over existing models, which typically address only isolated aspects of the problem. The model minimizes the total quality-related costs--comprising investment, inspection, penalty, and rework costs--while considering operational constraints such as workforce availability, inspection and rework time limits, and equipment capacity. Key modeling assumptions include heterogeneous inspection accuracies, product-specific defect probabilities, and the feasibility of partial inspections. The approach is validated on both synthetic datasets and a real-world automotive case study, demonstrating its ability to significantly reduce costs and to highlight the benefits of effectively combining human and machine-based inspections.  
Keywords:inspection allocation; quality control; machine vision; multi-stage manufacturing systems +
Footnote †: journal: Applied sciences
# Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform
## 4 Results and Discussion
### Verification of Numerical Results
The results indicate that the mean drift forces calculated using the middle-field method agree with those obtained using the far-field method, demonstrating that the middle-field method is a reliable approach to calculating the full quadratic transfer functions in the analysis. The maximum value of the mean drift force in the sway direction is significantly larger than that in the surge direction. This can be attributed to the larger area subjected to forces in the sway direction compared with the surge area. The peak values of the mean drift force in the sway direction occur at approximately 1.6 rad/s (corresponding to a wave period of 3.93 s), 1.15 rad/s (corresponding to a wave period of 5.46 s), and 0.35 rad/s (corresponding to a wave period of 17.95 s, with a value of 14,085 N/m). In addition, the peak value of the mean drift force in the sway direction is achieved at a wave frequency of approximately 1.75 rad/s (corresponding to a wave period of 3.59 s) and 0.35 rad/s (corresponding to a wave period of 17.95 s, with a value of 18,436 N/m). Furthermore, the peak value of the mean drift force in the yaw direction occurs at a wave frequency of around 1.55 rad/s (corresponding to a wave period of 4.05 s) and 1.85 rad/s (corresponding to a wave period of 3.39 s). Figure 14: The mean drift force of surge at the 0\({}^{\circ}\) heading angle. Figure 13: The mesh model. For the platform without nets in a free-floating state, natural periods of heave, roll, and pitch motions are calculated using the software HydroStar. The calculated natural periods are as follows: 16.908 s for heave, 16.801 s for roll, and 18.208 s for pitch. Notably, these periods are far from the wave periods typically existing in the East and South China seas. This indicates that the designed platform is capable of effectively mitigating structural resonance caused by waves.
## 4 Optimization Verification  
### Comparison of Internal Flow Field  
In this section, the flow characteristics of the cylindrical unfolding surface at different spans of the impeller are compared between the original scheme 1 and optimal scheme 3. The dimensionless distance of the impeller blade from hub to shroud is defined as:  
\[\text{span}=\frac{r-r_{h}}{r_{t}-r_{h}}\times 100\%, \tag{21}\]  
where \(r_{t}\) is the radius of shroud; \(r_{h}\) is the radius of hub; \(r\) is the radius of the cylindrical surface.  
Figure 19 shows the comparison of velocity streamline at different spans under the design flowrate. It is obvious that a large area of flow separation occurs in the flow passages for scheme 1, large-scale separation vortices appear in the suction side, and the flow state becomes disordered, which seriously affects the performance of the pump. After optimization, the flow in the impeller passage shows a uniform state. The large-scale separation vortex disappears in the flow passages. Thus, the flow loss is reduced, which improves the performance of the pump.  
Figure 19: _Cont._  
Figure 18: Comparison of external characteristic curves.  
Figure 20 shows the comparison of the pressure distribution at span = 50% under three flowrates. From the graph, the pressure distribution of the blade is lower at the wheel hub, the pressure at trailing edge location is higher. As the flow rate increases, the static pressure decreases. Under the same flow rate, the optimized pressure distribution diagram becomes more uniform than that before optimization. The pressure is gradually increased from the wheel hub to the rim, and the pressure at the front edge of the blade is effectively reduced.  
From the above analysis, the optimization scheme is superior to the original scheme in terms of internal flow.  
Figure 19: Comparison of streamline at different spans under \(1.0Q_{d}\): (**a**) Origin scheme 1, span = 10%; (**b**) Origin scheme 1, span = 50%; (**c**) Origin scheme 1, span = 90%; (**d**) Optimal scheme 3, span = 10%; (**e**) Optimal scheme 3, span = 50%; (**f**) Optimal scheme 3, span = 90%.  
Figure 20: Pressure comparison at span = 90% under different working conditions: (**a**) Origin, \(0.8Q_{d}\); (**b**) Origin,\(1.0Q_{d}\); (**c**) Origin, \(1.25Q_{d}\); (**d**) Optimal, \(0.8Q_{d}\); (**e**) Optimal, \(1.0Q_{d}\); (**f**) Optimal, \(1.25Q_{d}\).
## 6 Conclusions  
This paper proposes a hybrid beamforming and relay selection scheme in a non-regenerative multi-relay MIMO network for capacity maximization. The constant modulus constraint associated with the RF processing component makes the problem non-convex, and hence, mathematically intractable. Therefore, it is quite challenging to find the global optimal solution. To address this problem, the expression for end-to-end SNR is derived by converting the composite channel into the equivalent SISO channels. Then, the original complicated problem, based on SNR maximization, is transformed into two independent sub-problems. Furthermore, each sub-problem is tackled by following the decoupled approach to reduce the complexity for deriving the hybrid processing components at different communicating nodes. In particular, the source analog beamformer and relay RF combiner are obtained by maximizing SNR at the receiving end of the relay station. While the phase-only precoder at the relay node and the destination analog combiner are derived by maximizing SNR at the destination. Finally, digital baseband processing components are designed to minimize interference among different sub-channels. Computer simulations were conducted by changing system parameters and propagation environments (i.e., Rayleigh fading channel and mm-Wave channel) to show the effectiveness of the proposed methodology. It is obvious from the obtained results that the proposed algorithm achieves performance close to its fully digital counterpart, and significantly better than conventional relay selection techniques such as BHM and SVD. In addition, the proposed scheme outperforms when compared with several relay-based hybrid beamforming techniques. The extension of this work considering frequency-selective channels is a promising future direction.  
Conceptualization, H.M.T.M.; Validation, Y.-H.Y.; Formal analysis, J.-I.B.; Investigation, H.M.T.M.; Resources, H.-K.S.; Data curation, J.-I.B., Y.-H.Y. and Z.A.; Writing--original draft, H.M.T.M.; Supervision, H.-K.S.; Project administration, H.-K.S.; Funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2020R1A6A1A03038540).  
Not applicable.  
The authors declare no conflict of interest.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 4 Results and Discussion
### Two Tones Plus the Environmental Noise  
Starting from the recorded environmental noise in the case of a car traveling along a straight with asphalt, two distinct tones were added in order to simulate the presence of an inner and outer race defect. More precisely, a tapered roller bearing [31] was considered and its dimensions exploited to evaluate the \(BPFI\) and \(BPFO\) fundamental frequencies, resulting in \(BPFO=1115\) Hz and \(BPFI=197.6\) Hz. The amplitude of such two tones was not constant, but different values were tested. In a such a way, it was possible to emulate different defect sizes. The average of the amplitude spectrum of the environmental noise in the range of 100 Hz to 1 kHz, named \(A_{aver}\), was evaluated, and the amplitude of the two tones was defined according to  
\[A_{tones}=A_{aver}\left(1+10^{\frac{A_{dB}}{20}}\right). \tag{11}\]  
Thus, the environmental noise containing the two tones, which emulate a bearing with two defects, was reproduced closed to the proposed module, as shown in Figure 9, by means of a speaker. The module was configured so that \(N_{s}=16\),\(384\), \(f_{s}=6\) kHz and the top flat window was exploited. Two ranges, centered around the \(BPFO\) and \(BPFI\) were considered, in particular \(TH_{BPFO}=TH_{BPFI}\) = 10 mV and \(f_{BPFO,e}-f_{BPFO,s}=f_{BPFI,e}-f_{BPFI,s}\) = 2 Hz. The frequency spectra evaluated by the modules itself are shown in Figure 10a-d for \(A_{dB}\) and are equal to 40, 35, 30, and 25, respectively. As far as such a configuration is concerned, the proposed module is able to detect the tone at \(BPFI\) if \(A_{dB}\geq 30\) dB, and the one at \(BPFO\) if \(A_{dB}\geq 35\) dB. Indeed, the detection capability is strongly affected by the noise floor in the correspondence of the frequency ranges to be monitored.  
Figure 9: Test setup including the developed module and the speaker that reproduces the synthesized sound.
## 2 Proposed Framework and Experimental Methodology
### Proposed Encryption Workflow with Hybrid CPU-GPU Strategies
#### 2.5.3 Adaptive Workload Distribution  
Algorithm 3 explains the adaptive workload distribution, where tasks are dynamically allocated based on data size. Smaller data are encrypted by the CPU, while larger datasets are processed by the GPU, with the hybrid encryption time minimized as \(T_{hybrid}=min(T_{CPU},\ T_{GPU})\), optimizing resource usage. The threshold value (\(N_{threshold}\)) was set at 32 MB, determined through iterative benchmarking, indicating optimal CPU-GPU workload distribution. Lower \(N_{threshold}\) values increase CPU utilization but might reduce overall throughput, while higher values effectively utilize GPU resources but might increase initial CPU overhead. The adaptive workload distribution strategy is further supported by recent research, highlighting substantial performance improvements when dynamically adjusting CPU and GPU tasks based on data size and resource availability in heterogeneous computing environments [23].
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Physical Methods
#### 2.1.5 Gas-Phase Synthesis  
The gas-phase synthesis method is part of the bottom-up methods and was implemented for developing multifunctional nanoparticles [15]. The procedure involves the formation of nanoparticles directly from gaseous precursors providing control over size, composition, and crystallinity. There are two common gas-phase synthesis methods: chemical vapor condensation (CVC) and inert gas condensation (IGV).  
CVC is based on the reduction of the metal precursors in the gas phase. In this regard, volatile metal precursors such as metal halides or carbonyls are used as chemical precursors. They are reduced at high temperatures, leading to the formation of MNPs that are cooled down and collected.  
During the IGV process, the nanoparticles are produced as a condensation result after the inert gas-based cooling of the vapor produced by the vacuum evaporation of metal atoms or clusters.  
The main advantage of the gas-phase synthesis procedures refers to their good control over particle size [16].
## 3 Data and Preprocessing
### Text Cleaning, Segmentation, and Structuring  
After collecting multi-source texts and labels, we applied a unified cleaning pipeline to all statements and speeches. This included removing HTML tags, unifying Unicode encoding and full/half-width symbols, correcting common OCR errors, and standardizing punctuation. Subsequently, we used regular expression-based sentence splitting algorithms to segment documents into paragraph units delimited by periods or paragraph symbols. Meta-tags such as \"Publication Date\", \"Speaker/Body\", and \"Crisis Period Indicator (Dummy)\" were embedded before each segment to ensure that the model input could utilize temporal, source, and contextual prior information simultaneously. These steps referenced the preprocessing script details from the official example notebook for \"FOMC Meeting Statements and Minutes\" on Kaggle to ensure consistency with community practices and reproducibility.  
Figure 1 illustrates the comprehensive text preprocessing pipeline employed in our study. The process begins with raw document ingestion from multiple sources, followed by systematic cleaning and standardization steps. HTML tags and metadata markers are stripped, Unicode characters are normalized, and common OCR errors are corrected using pattern matching and contextual replacement rules. The cleaned text undergoes sophisticated segmentation using linguistically-informed algorithms that preserve semantic boundaries while maintaining compatibility with our tokenization approach. Finally, domain-specific tags and temporal markers are embedded to enable the model to leverage contextual information during training and inference.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Source** & **Period** & **Scale** & **Label Type** & **Source** \\ \hline FOMC Statements/Minutes & 1977–2024 & \(\approx\)900 documents & Date & Kaggle [35] \\ FOMC Annotations & 1994–2023 & \(\approx\)30k paragraphs & Hawkish/dovish & Trillion Dollar Words [38] \\ ECB Speeches Dataset & 1999–2024 & \(\approx\)1000 speeches & Date & ECB Website [39] \\ EA-CED Event Study & 1999–2024 & \(\approx\)4400 events & Market reaction & Banque de France [37] \\ FRED Interest Rates & 1954–2025 & Daily/monthly & Benchmark rates & St Louis Fed [36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of data sources used.
# Polymer@gold Nanoparticles Prepared via RAFT Polymerization for Opto-Biodetection
###### Abstract  
Colloidal gold nanoparticles (Au NPs) have been used in several biological applications, which include the exploitation of size- and shape-dependent Localized Surface Plasmon Resonance (LSPR) in biosensing devices. In order to obtain functional and stable Au NPs in a physiological medium, surface modification and functionalization are crucial steps in these endeavors. Reversible addition-fragmentation chain transfer (RAFT) polymerization meets this need offering the possibility of control over the composition and architecture of polymeric shells coating Au NPs. Furthermore, playing with a careful choice of monomers, RAFT polymerization allows the possibility to design a polymer shell with the desired functional groups aiming at Au based nanocomposites suitable for biorecognition and biotargeting. This review provides important aspects concerning the synthesis and optical properties of Au NPs as well as concepts of RAFT polymerization. Understanding these concepts is crucial to appreciate the chemical strategies available towards RAFT-polymer coated Au core-shell nanostructures, which are here reviewed. Finally, examples of applications in opto-biodetection devices are provided and the potential of responsive \"smart\" nanomaterials based on such structures can be applied to other biological applications.  
R RFT; nanocomposites; gold nanoparticles; plasmonic nanostructures; bioapplications +
Footnote †: journal: Polymers