esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 3 Performance Characteristics of Power QKD System
### Simulation Environment Test
Combined with the test results under power overhead cable environment, a fast polarization feedback algorithm has been proposed to improve the performance of QKD [29; 30]. This algorithm utilizes wavelength division multiplexing technology to achieve both strong and weak light fiber transmission. At the receiving end, the light is broken down to detect polarization changes in the photon state of the intense light. Then, by establishing a corresponding change model, the polarization state of the weak light is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Case** & **Environment** & **Average Secret Key Rate (Kbps)** \\ \hline
1 st day & & 21.22 \\
2 nd day & & 22.39 \\
3 rd day & 16.58 km & 22.28 \\
4 th day & 8.72 dB & 23.46 \\
5 th day & Light Breeze & 20.28 \\
6 th day & & 20.28 \\
7 th day & & 22.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Stability test. Figure 5: Comparison of unidirectional transmission throughputs among three algorithms. corrected using an electric polarization controller. In addition, it solves the problem that it is difficult to encode due to frequent interference of photon polarization states. For the practical application of power QKD technology, the accuracy of fiber optic splicing needs to be improved. It is also necessary to reduce the number of fiber channel connections. Taking into account the different operating environments of various business systems, selected equipment and business requirements, encryption algorithms should be selected according to the needs of the actual business, and an efficient power QKD network with multiple encryption algorithms co-existing should be constructed.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
###### Abstract  
Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution which makes these estimations useless for vegetation monitoring at plot scale. Therefore, the aim of this study is to provide the first approach to estimate VOD at plot scale for non-irrigated plots from C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The proposed approach was tested on a study site of 50 km \(\times\) 50 km located in Catalonia, Spain. VOD estimates were provided for two crop growth cycles of non-irrigated crop types (barley, fallow, oat, wheat, and rapeseed). The relevance of VOD estimates was investigated for both growth cycles using temporal profiles of the Normalized Difference Vegetation Index (NDVI). It is shown that the temporal dynamics of VOD values computed from VV polarization fits that of NDVI with a medium to good coefficient of determination (\(R^{2}\) ranging from 0.39 to 0.61 for barley, fallow, oat, and wheat respectively). However, during the beginning of the senescence period in both cycles (mainly in May for winter crops), VOD decreases with the decrease in Vegetation Water Content (VWC) while NDVI keeps increasing as photosynthetic activity continues developing. This illustrates the importance of VOD in crop water loss (stress and/or transpiration) monitoring. The potential of VOD to spot water loss in vegetation is also demonstrated as the evening (18h00) VOD values are lower than those of morning (06h00) due to high daytime temperature that reduces water content in vegetation. Finally, it is shown that VOD values computed from VH polarization are not correlated with NDVI.  
Keywords:Vegetation optical depth; Sentinel-1; Sentinel-2; Crops +
Footnote †: journal: _Article_
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 4 Discussion
### Response of WUE to Gradient Warming  
The significant relationship between WUE and ET (Figure 5) indicates that the responses of both WUEc and WUEe to warming were predominantly driven by water fluxes in Zoige peatland. The lowered WUEc and WUEe in the TL treatment could be attributed to the increased T and ET, which were driven by the slightly elevated TS. These changes reflect the direct impact of warming on the ecosystem's water dynamics, and increased T and ET effectively reduced WUE. The non-significant changes in T and ET in the TH treatment might be explained by the decline in plant stomatal conductance due to water deficiency, which stemmed from the excessively elevated TS. This phenomenon revealed the threshold nature of plant responses to warming, where moderate increases in temperature can enhance water fluxes, while extreme temperatures may impair plant water uptake and utilization. Additionally, an anomalous increase in WUEc and WUEe was observed in the TH treatment in August (Figure 4), which can be explained by the anomalous decrease in T (Figure 3). The GPP was not significantly correlated with WUE (Figure 5), although it decreased with the gradient warming treatments. Our results indicated that T and ET, rather than GPP, determined WUE when the SWC was reduced in Zoige peatland; this is consistent with the results of a previous study in Hongyuan County, which is adjacent to Zoige peatland [40]. Similarly, studies by [PERSON] et al. in alpine meadows in Naqu showed that warming increased ET in alpine meadows without affecting the GPP, resulting in decreased WUE [12].
# Event-Based Probabilistic Embedding for POI Recommendation
###### Abstract
So, we use a hidden variable topic to explore the joint impact of POI category and time factors on the user behavior and use a hidden variable region to explore the joint impact of geographic location and time factors on user behavior. As region and topic are interdependent, TARE combines the time factor, geographic factor, POI category, and user preference into a unified model. The main contributions of this work are summarized as follows:  
* We propose a novel embedding method called TARE which takes into account a variety of factors including geographical effect, temporal effect, user preference and POI categories. In addition, TARE is also able to explore the intrinsic link between time and geographic factors and the intrinsic link between time factors and POI categories to improve the accuracy of recommendations. * The objects embedded in TARE are events. Compared with the existing embedding methods, TARE not only considers user preference and POI information but also time and location of the event. To the best of our knowledge, this is the first attempt to explore event-based embedding in POI recommendation. * We conduct extensive experiments on three real-world datasets to evaluate the performance enhancement in recommendation accuracy of TARE, and our approach achieves up to 12.39% performance gains in recommendation accuracy compared to existing state-of-the-art methods.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
However, the solutions proposed for convectional systems--i.e., for fully digital architectures--cannot be directly used in hybrid architectures where the processing is distributed by the analog and digital domains. Consequently, the design of new iterative analog-digital beamforming schemes for hybrid SC-FDMA based systems is of paramount importance. Therefore, in this paper we adopt a hybrid analog-digital architecture, to achieve a good tradeoff between performance and complexity and we design a fully iterative analog-digital multiuser equalizer for wideband mmWave mMIMO SC-FDMA systems.
## 2 Research Area and Datasets  
### Research Area  
The geographical focus of this study encompasses two provinces in southern China: Fujian and Jiangxi. As shown in Figure 1. These regions are distinguished by their extensive mountainous and hilly landscapes, which exhibit significant forest coverage, particularly in the elevated areas where forest resources are plentiful [31]. However, the occurrence of wildfires in these forests presents considerable challenges for containment due to the complex topography and the inherent difficulties associated with firefighting efforts [32]. This often results in rapid fire propagation and the potential for extensive damage. Furthermore, the region is situated within a subtropical climate zone, characterized by elevated summer temperatures and irregular precipitation patterns, which heighten the risk of wildfires during the dry season [33]. Consequently, the wildfire-prone period in these provinces predominantly occurs during the summer and autumn months, when high temperatures and strong winds create optimal conditions for the ignition and spread of forest fires.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 5 Results
\begin{tabular}{l l l l l l l l l} \hline \hline
**Ref.** & \begin{tabular}{} **Freq.** \\ **[GHz]** \\ \end{tabular} & **Lens Type** & **Tech.** & **Mat.** & **Feed Type** & \begin{tabular}{} **Diameter/Thickness** \\ **(F+T) mm.** \\ \end{tabular} & **Dir. (Sim./Meas.)** & \begin{tabular}{} **Ap. eff.** \\ **(Sim./Meas.)** \\ \end{tabular} \\ \hline lens A & 10 & Flat heterogenous & SLA & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & 160/25 & 21/19.5 & 49.1\%/32.17\% \\ \hline lens B & 10 & Airy disk & SLA & \begin{tabular}{} \end{tabular} & 160/25 & 20/22.1 & 41.22\%/55.80\% \\ \hline lens C & 10 & Ring & SLA & \
\begin{tabular}{} \end{tabular} & 10K &
\begin{tabular}{} \end{tabular} & 160/25 & 20/22 & 42.37\%/49.21\% \\ \hline
[10] & 12, 15, 18 & Flat finite Focal & FFF & PLA &
\begin{tabular}{} \end{tabular} & 120/18.5 & 18\% at 12 GHz/21.4 at 15 GHz/24 at 15 GHz 2 & 12 GHz/33\% at 15 GHz 2 & 15 GHz/41\% at 18 GHz 2 \\ \hline
[36] & 12-22 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & 150/90 & 22.6/22.9 & 38\%/40\% \\ \hline
[11, 37] & 34 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & 88/40.5 & 26.5/25.7 & 45\%/37\% \\ \hline
[38] & 12-40 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & 120/18.5 & 15.1 at 12 GHz/23.6 at 40 GHz 2 & 82\% 2 \\ \hline
[39] & 13.4 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & 180/223 & 26.3/25.1 & 66.7\% 1 \\ \hline
[40] & 15 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & -/59 & 21.3/19.1 & 30\% 1 \\ \hline
[41] & 27 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & 77/77 & 21.3\({}^{1}\) & 30\% 1 \\ \hline
[42] & 33 & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} & 97.6/39.3 & 25.2/25.2 & -/31.1\% \\ \hline \hline \end{tabular}   
\({}^{\dagger}\) Simulated data. 2 Measured data.
## 6 Results, Implementation, and Testing
### Software Implementation  
From the study of the machine learning and deep learning classifiers, we chose the Random Forest model for embedded software implementation. To implement the trained machine learning model in a microcontroller environment, the next step is to convert the model to an executable C code file. This conversion was performed using the m2 cgen Python library [51], which transforms a trained machine learning model into a format compatible with microcontrollers. By generating the C code file, the model can be uploaded to the microcontroller, enabling it to perform classification tasks directly on the device. The generated code file, totaling 1.2 MB in size (approximately 25,500 lines of code), is fully compatible with microcontrollers without causing any concerns regarding available flash memory capacity.  
The input for the trained model consists of 160 samples, each capturing data from six dimensions: accelerometer and gyroscope data in X, Y, and Z. These samples are directly obtained from the built-in IMU within the microcontroller board. Combining the six dimensions results in a total of 960 features, which are used as input for the trained model.  
Once the model is uploaded to the microcontroller, it can process the input data received from the IMU and execute the machine learning C code to obtain an output. This output corresponds to the classification of the sampled movement as idle, motion, step, or fall, providing real-time fall detection capabilities directly from the microcontroller. The fall detection system can operate autonomously without relying on external computational resources. This integration enables efficient and prompt classification of movement data, enhancing the overall performance and responsiveness of the fall detection system.
# Enhanced Beacons Dynamic Transmission over TSCH
## 5 Experimental Results and Discussion
### A Synchronizer-Join-Seeker Pair Scenario  
The first scenario is composed of a pair of nodes (a synchronizer and a join-seeker), as shown in Figure 6. The green circle depicts the transmission range of node 1 and the grey circle the interference range. The values of the total number of available channels \(m\) and the threshold \(u=\lfloor\beta m\rfloor\) that delimits the intensive and the predetermined phases are varied through the simulations.  
In this scenario, theoretical results obtained using expression (7) are compared with the simulation results as a function of the number of available channels and \(\beta\) values. Figure 7 shows that the theoretical results are inside the confidence interval of the simulation results, thus validating the analytic model developed in Section 4.1. Regardless of the number of available channels, the association time decreases as the \(\beta\) factor increases, which implies more transmissions of EBs during the intensive phase. Note that for \(\beta=0\) there is no intensive phase, and its results correspond to the behavior of the TSCH with the default minimal 6 TiSCH configuration.  
There is a trade-off between the number of available network channels, the increase in \(\beta\), and the corresponding improvement in the average association time. When there are 16 available network channels, the improvement in the average association time for \(\beta=1.8\) versus \(\beta=1.5\) is 3.2 s, but this is only 0.4 s when 4 channels are compared, for the same values of the \(\beta\) factor.  
Figure 6: Pair synchronizer–join-seeker network scenario. Node 1 is the network coordinator and node 2 is a join-seeker. The green circle depicts the transmission range of node 1, while the grey circle depicts the interference range.  
Figure 7: EBDT-TSCH association time. Pair synchronizer–join-seeker scenario.
## 6 Discussion  
### Comparison of Different Weight Calculation Methods  
The AHP [9] and the entropy weight method [10] were used to calculate the index weights to demonstrate the superiority of the PP for the weight calculation. The results are listed in Table 7. The weights of the AHP were calculated using the Yaahp software.  
The ranking of the first-level indicators is the same for the PP and AHP, but the ranking order of \(R_{1}\) and \(R_{4}\) is different, i.e., 69.95% of the secondary indicators have the same order, but only seven indicators (e.g., \(R_{54}\)) have different orders of importance. The index weights calculated by the AHP exhibit large differences, unlike those obtained from the PP. When the evaluation matrix of the AHP does not match the test standard, the evaluation and scoring must be repeated. This process is not required in the PP; thus, it has better applicability.  
The weight of the environment (\(R_{5}\)) ranks second for the entropy weight method. However, the geological survey data and field survey showed no unfavorable geological landforms and no flood threat. Therefore, there is a substantial difference between the weight ranking obtained from the entropy weight method and engineering practice. The entropy weight method uses the entropy as an indicator of the dispersion of an index. The greater the degree of dispersion, the greater the influence of the index is on the overall evaluation results. The greater the difference in the experts' opinions, the greater the weight of the index is, rather than the more important weight.
# Functional Characterization of Mung Bean Meal Protein-Derived Antioxidant Peptides
## 2 Results and Discussion
### Sequencing and Antioxidant Activities of Peptides
#### 2.3.2 Hydroxyl Radical Scavenging Activity (HRSA)  
The hydroxyl radical is one of the most reactive oxygen radicals, causing radical chain reactions and inducing severe damage to cells in the human body, including destruction of carbohydrates, nucleic acids, lipids, and proteins, which makes it an important free radical and a target of antioxidants in the human body [29; 33]. Peptides LAF, HC, CTN, CGN, and CSGD were the most potent hydroxyl radical scavengers with EC\({}_{50}\) values lower than 1 mg/mL and similar to that of GSH (Table 1). Other peptides, ERF, QFAAD, MMGW, and LAN, showed poor hydroxyl scavenging radical activity with higher EC\({}_{50}\) values while EYW, FLQL, and QFAW had no detectable activities. A comparison of LAN with LAF suggests that the presence of phenylalanine (\(F\)) at the C-terminal provides better HRSA than asparagine in the same position. The observed difference may be due to the reported ability of the aromatic ring in phenylalanine to donate hydrogen atom to the hydroxyl radical and thus terminate the free radical chain reaction [32; 33]. HC, CTN, CGN, and CSGD exhibited strong hydroxyl radical scavenging activity that is similar to their DRSA, which could be attributed to the presence of cysteine at the N- or C-terminal. The sulfhydryl group in cysteine can also donate a proton to neutralize the hydroxyl radical. Therefore, the results suggest that the presence of cysteine or phenylalanine at the N- or C-terminal could make a significant contribution to the HRSA of peptides [32]. The EC\({}_{50}\) values for LAF, HC, CTN, CGN, and CSGD are lower (stronger HRSA) than the 2.358 mg/mL reported for Bluefin leatherjacket peptide GPP [30] while the LAF had similar value as VLYEE (EC\({}_{50}\) 0.353 mg/mL), a peptide obtained from croceine muscle protein hydrolysate [29] and FY (EC\({}_{50}\) 0.41 mg/mL), a peptide derived from perilla seed protein [34]. In the case of phenylalanine,which is present in LAF and FY (perilla seed peptide), both of these possessed strong HRSA. On the other hand, leucine, which is found in LAF and YL (perilla seed peptide) lacked HRSA. The results suggest that the presence of phenylalanine at the N- or C-terminal could be largely related effective HRSA. In contrast, peptides that contained leucine at C-terminal had low HRSA. Therefore, the results support the principle that type of amino acid and its position has significant effect on antioxidant activity.
## 3 Results
### Volatile Compounds of the Parmigiano Reggiano Cheese (SPME-GC-MS Results)  
The results of the headspace SPME-GC-MS measurements showed the presence of several volatile components in the Parmigiano Reggiano cheese contributing to the typical aroma of this cheese. About 150 key aroma compounds were identified in the cheese samples under study and the main compounds are listed in Table 3. The major categories of volatile compounds identified were aldehydes, ketones, free fatty acids, esters, hydrocarbons, alcohols, esters, lactones, aromatic compounds, amines, and pyrazines. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **Retention Time (min)** & **Relative Abundance** \\ \hline \hline
**Alcohols** & & \\ \hline Ethanol & 2.100 & 1,271,909 \\ (R)-(\(-\))-2-Pentanol & 5.140 & 1,041,682 \\ 2-Pentanol & 8.666 & 437,882 \\ 3-Buten-1-ol, 3-methyl- & 14.758 & 79,385 \\ 2-Hexanol, 5-methyl- & 17.856 & 322,043 \\ (\(\pm\))-5-Methyl-2-hexanol & 17.866 & 518,829 \\ 1-Pentanol, 5-methoxy- & 17.890 & 52,461 \\ 1-Hexanol & 19.094 & 92,232 \\ Ethanol, 2-butoxy- & 20.814 & 7,805 \\ 7-Octen-2-ol, 2,6-dimethyl- & 22.632 & 74,098 \\ 2-Propyl-1-pentanol & 24.067 & 39,338 \\ 2-Nonanol & 25.065 & 53,071 \\ 2,3-Butenediol & 27.010 & 405,842 \\ Cyclohexanol, 1-methyl-4-(1-methylethyl)-, _trans_- & 27.722 & 105,611 \\ Ethanol, 2-(2-ethoxy)- & 28.209 & 31,675 \\ 2-Furanmethanol & 29.655 & 136,185 \\ Phenylethyl Alcohol & 36.585 & 55,055 \\ 2-Butanol, 1-benzyloxy-3-methyl- & 36.634 & 32,202 \\ 1-Dodecanol & 38.108 & 125,734 \\ n-Tridecan-1-ol & 38.128 & 19,231 \\ \hline \hline
**Aldehydes** & & \\ \hline Butanal, 3-methyl- & 2.760 & 53,795 \\ Furfural & 23.145 & 12,006 \\ Benzaldehyde & 24.230 & 206,435 \\ Benzenzoacetaldehyde & 28.627 & 179,525 \\
2-Decenal, (E)- & 28.716 & 16,977 \\
2-Propenal, 3-phenyl- & 34.250 & 27,352 \\ \hline \hline
**Ketones** & & \\ \hline
2-Hydroxy-3-pentanone & 3.290 & 4595 \\
2-Heptanone & 11.283 & 6,431,421 \\ Acetoin & 16.108 & 197,040 \\
2-Nonanone & 18.291 & 3,015,823 \\
8-Nonen-2-one & 22.119 & 311,544 \\
4\({}^{\prime}\)_,6\({}^{\prime}\)_-Dimethoxy-2\({}^{\prime}\)_,3\({}^{\prime}\)_-dimethylacetophenone & 25.136 & 28,165 \\
2-Undecanone & 27.311 & 367,514 \\
3-Buten-2-one, 4-phenyl- & 37.516 & 22,744 \\
2H-Pyran-2-one, tetrahydro-6-propyl- & 43.545 & 66,023 \\
2H-Pyran-2-one, tetrahydro-6-pentyl- & 43.555 & 46,553 \\ Ethanone, 1-(3,4-dimethylphenyl)- & 44.520 & 19,259 \\ \hline \hline
**Esters** & & \\ \hline Butanoic acid, ethyl ester & 5.054 & 8,522,322 \\
1-Butanol, 3-methyl-, formate & 12.956 & 71,080 \\ Hexanoic acid, ethyl ester & 13.897 & 13,149,658 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of volatile organic compounds (VOCs) identified in Parmigiano Reggiano cheese by SPME-GC-MS analysis. Figure 7 represents a histogram obtained from the SPME-GC-MS analysis showing the aroma compounds of representative undegraded and degraded Parmigiano Reggiano cheese samples.
## 2 Materials and Methods
### Evaluation  
Daily observations were conducted to monitor specific symptoms and check for mortality, with any abnormalities recorded separately. Photographs of the application area were taken and documented on days 1, 2, 3, 7, and 14 after the procedure. Two experienced practitioners used the Early Healing Score (EHS) to visually assess the initial healing process of the APF (Figure 5) [21; 22]. The EHS consists of three parameters: clinical signs of re-epithelialization (CSR), clinical signs of hemostasis (CSH), and clinical signs of inflammation (CSI). For tongue ulcers, the Healing index of Landry, Turnbull, and Howley (LTH) was used for assessment [23]. Moreover, OCT scans were performed on days 0, 1, 2, 3, 7, and 14 after the procedure. The OCT images were captured using Swept Source-OCT equipment (Thorlabs, Newton, NJ), USA) with a central wavelength of 1300 nm, A-scan Rate of 100 kHz, depth resolution of 7 \(\upmu\)m, and lateral resolution of 14 \(\upmu\)m. The region of interest (ROI) was 8 mm \(\times\) 8 mm.