esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 4 Discussion  
### Computational Complexity of MED and USD  
MED and USD use quantum measurement methods for demodulation, the complexity of which is determined by the construction step of the POVM operator. Without loss of generality, with \(n\) users using quantum detection methods, \(2n\) POVM operators are required for measurement with MED, and \(2n+1\) POVM operators are required for USD. The implementation steps for MED and USD are \(2n\) and \(2n+1\), respectively. So, their computational complexity is \(O(n)\).
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Advantages of C-RAN
#### 2.5.2 Load Balancing  
In a C-RAN, load balancing can be performed on the BBU as well as RRH sides. This is because BBUs from a large area are co-located in the same BBU pool [5]. Load balancing can be performed on the BBU side by assigning appropriate BBU resources within a pool. Load balancing can be achieved on the RRH side according to the capacity of the BBUs within a BBU pool, when users move from one cell to another. C-RAN provides the facility to switch off a BBU or an RRH depending on the traffic load in the network. BBU can be switched off by an appropriate association of BBU-RRH. When the traffic load in the RRH is low, the associated BBU can be switched off by connecting that RRH to  
Figure 3: Types of C-RAN: (**a**) fully centralized C-RAN, (**b**) partially centralized C-RAN.  
another BBU within that BBU pool. The RRH can also be turned on/off dynamically according to the user demand on the cell site and by associating the users with another RRH.
## 2 Materials and Methods
### Sampling and Chemical Analyses  
The substrates of different treatments were sampled and stored at -20 \({}^{\circ}\)C until analyzed for chemical composition. The dry matter (DM; methods 934.01) and ash (method 942.05) of substrate samples were determined according to AOAC [17]. The content of organic matter (OM) was calculated as the difference between 100 and the ash content. The crude protein (CP; method 990.03) was analyzed according to AOAC [17] by a Kjeldahl nitrogen testing instrument (KjelteCM 2100, Foss Technology Corp., Hoganas, Sweden). The ether extract (EE; method 920.39) was determined in line with the AOAC by an ANKOMXT15 Extractor (Ankom Technology Corp., Macedon, NY, USA). Analysis of the neutral detergent fiber (aNDF), and acid detergent fiber (ADF) were carried out using Ankom Fibre Analyser (Ankom Technology, Fairport, NY, USA) in accordance with the method described by [PERSON] et al. [18], and \(\alpha\)-amylase and sodium sulfite were applied in the procedure of aNDF.  
At the end of incubation, the total gas production was recorded. Then gas was sampled from each syringe into a glass tube for determining the gas composition. The value of total gas production is that the total gas production (mL) divided by mass (g). The CH\({}_{4}\) and CO\({}_{2}\) of the gas samples were analyzed using the gas chromatography (Model TP-2060T, Beijing Tianpu Instrument, Beijing, China). The conditions for the analysis were TCD detector, TDX-01 column, size 1 m \(\times\) 2 mm \(\times\) 3 mm, column temperature 70 \({}^{\circ}\)C and detector temperature 100 \({}^{\circ}\)C. The carrying gas was argon with the flowing rate of 30 mL/min.  
The pH of the incubation residues was measured immediately by a portable pH meter (HJ-90B, Aerospace Computer Corporation, Haidian District, Beijing, China). Next, three aliquots of 1-mL culture were obtained from each glass syringe into 2 mL centrifuge tube and stored in liquid N for microbial analysis. The remaining culture was mixed and strained with filter bags (Ankom Technology, Macedon, NY, USA) to measure the DM and aNDF degradability of the added substrates gravimetrically according to the method of [PERSON] et al. [19].  
Six aliquots of 1 mL culture filtrates, which was added 0.25 mL of metaphosphoric acid (25 g/100 mL), were sampled into centrifuge tubes to analyze NH\({}_{3}\)-N concentration and VFA respectively. The concentration of NH\({}_{3}\)-N in the culture medium was determined in accordance to Bremner and Keeney [20] by a spectrophotometer (UV-1700, Shimadzu Corporation, Kyoto, Japan). The VFA concentrations were measured by a high-performance gas chromatograph (GC-2014;Shimadzu Company, Japan), which was equipped with a hydrogen flame detector and a Rtx-Wax capillary column (30 m \(\times\) 0.25 mm ID \(\times\) 0.25 \(\upmu\)m, polyethylene glycol; Restek, Evry, France).
# Fatigue Crack Growth in Managing Steel Obtained by Selective Laser Melting
## 2 Fatigue Crack Growth Analysis Based on \(\Delta\)K
### da/dN-\(\Delta\)K Curves  
Fatigue crack growth tests were carried out according to the recommendations outlined in the ASTM E647 standard. Figure 1 shows the geometry of the C(T) specimens, obtained with thicknesses of 3 or 6 mm. The surface finishing of the C(T) specimens was achieved by high-speed mechanical polishing. All fatigue crack growth (FCG) experiments were carried out using a DARTEC servo-hydraulic testing  
\begin{table}
\begin{tabular}{c c} \hline
**Element** & **Percentage** \\ \hline C & 0.01 \\ Ni & 18.2 \\ Co & 9.0 \\ Mo & 5.0 \\ Ti & 0.6 \\ Al & 0.05 \\ Cr & 0.3 \\ P & 0.01 \\ Si & 0.1 \\ Mn & 0.04 \\ Fe & Balance \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the 18 Ni300 steel (weight percentage).  
machine, equipped with a 100 kN load cell. The tests were performed at room temperature in ambient air, under load control mode, at a frequency of 25 Hz, using a PC-based data acquisition system. The load cases studied are presented in Table 2. Three stress ratios were considered: R = 0.05, 0.3 and 0.6. The crack length was measured with a travelling microscope, with magnification of 45\(\times\) and an accuracy of 10 \(\upmu\)m. The data was collected and recorded for crack lengths, a, greater than 7 mm, as indicated in Table 2. The fatigue crack growth rates (FCGR) were obtained from the five-point incremental polynomial method.  
Figure 2a shows da/dN-\(\Delta\)K plots for the 18 Ni300 steel, in log-log scales. The increase of \(\Delta\)K increases the FCG rate significantly, as is well known. Paris law regime is evident, and for relatively low values of da/dN, there is a significant reduction of da/dN, typical of regime I of FCGR. There is also a trend for the increase of da/dN with stress ratio, as could be expected. Anyway, the influence of stress ratio is relatively small, which indicates a reduced level of crack closure. The results for thicknesses of 3 and 6 mm are nearly coincident, which also indicates a small level of crack closure. Figure 2b compares FCGR for the 18 Ni300 steel with results for other materials. The 7050-T6 aluminum alloy (AA) has the highest crack growth rate, followed by the AA2050-T8. The 304L stainless steel has the lowest values. Finally, the 18 Ni300 is between the SS304L and the AA2050-T8. The slopes of da/dN-AK curves in the Paris law regime are similar. Please note that only the 18 Ni300 steel was obtained by SLM.
## 2 Materials and Methods
### Data
#### 2.4.2 Geometrical Properties  
Contact parameters are shown in Table 2. The front contact is the positive pole, while back contact is the negative one, as shown in Figure 5. The metal-semiconductor interface is determined by the MS-Schottky numerical model, and the interface between the conductors is given by the drift-diffusion model, with the spacing between the semiconductor wafers being 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Input Parameters** & **a-Si:H(n+)** & **a-Si:H(i)** & **a-Si:H(p+)** & **c-Si:H(n)** \\ \hline Thickness (nm) & 3–12 & 4 & 3–12 & \((10-20)\times 10^{4}\) \\ Dielectric constant \(dk\) & 11.9 & 11.9 & 11.9 & 11.9 \\ Electron affinity \(\chi\) (eV) & 3.8 & 3.8 & 3.8 & 4.05 \\ Band gap \(E_{g}\) (eV) & 1.74 & 1.74 & 1.7 & 1.12 \\ Effective conduction band density \(N_{c}\) (cm\({}^{-3}\)) & 1\(\times 10^{20}\) & 1\(\times 10^{20}\) & 1\(\times 10^{20}\) & 2.8\(\times 10^{19}\) \\ Effective valence band density \(N_{p}\) (cm\({}^{-3}\)) & 1\(\times 10^{20}\) & 1\(\times 10^{20}\) & 1\(\times 10^{20}\) & 1.04\(\times 10^{19}\) \\ Electron mobility \(\mu_{n}\) (cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\)) & 10 & 20 & 10 & 1040 \\ Hole mobility \(\mu_{p}\) (cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\)) & 1 & 2 & 1 & 421 \\ Doping concentration of acceptors \(N_{a}\) (cm\({}^{-3}\)) & 0 & 0 & \((6.05-6.30)\times 10^{17}\) & 0 \\ Doping concentration of donors \(N_{d}\) (cm\({}^{-3}\)) & \((1-3)\times 10^{18}\) & 0 & 0 & \((4-14)\times 10^{16}\) \\ Thermal velocity of electrons \(v_{e}\) (cm s\({}^{-1}\)) & 1\(\times 10^{7}\) & 1\(\times 10^{7}\) & 1\(\times 10^{7}\) & 1\(\times 10^{7}\) \\ Thermal velocity of holes \(v_{h}\) (cm s\({}^{-1}\)) & 1\(\times 10^{7}\) & 1\(\times 10^{7}\) & 1\(\times 10^{7}\) & 1\(\times 10^{7}\) \\ Layer density \(\varrho\) (gcm\({}^{-3}\)) & 2.328 & 2.328 & 2.328 & 2.328 \\ Auger recombination coefficient for electron (cm\({}^{6}\) s\({}^{-1}\)) & 0 & 0 & 0 & 2.2\(\times 10^{-31}\) \\ Auger recombination coefficient for hole (cm\({}^{6}\) s\({}^{-1}\)) & 0 & 0 & 0 & 9.9\(\times 10^{-32}\) \\ Direct band-to-band recombination coefficient (cm\({}^{3}\) s\({}^{-1}\)) & 0 & 0 & 0 & 9.9\(\times 10^{-32}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameter range of crystalline and amorphous graphene silicon materials.  
Solar cell modeling also considers the metallization of the front and back parts. The geometrical characteristics of the busbars and the fingers are shown in Table 3[60; 61].  
As reference, this study considers the approximate experimental values of the heterojunction solar cell a \(-\) Si : H/c \(-\) Si: open-circuit voltage \(V_{oc}=713\) mV, short-circuit current density \(J_{sc}=37.3\) mA/cm\({}^{2}\), fill factor \(FF=75.9\%\), and energy conversion efficiency \(\eta=20.2\%\), as measured by [PERSON] et al. in their study [62; 63].
# Application of Improved LightGBM Model in Blood Glucose Prediction
## 3 Experimental Results and Discussion
### Comparative Experiments
#### 3.2.1 Comparison between the HY_LightGBM Model and LightGBM Model  
This experiment verified the effectiveness of the Bayesian hyper-parameter optimization algorithm for improving the LightGBM model by comparing the LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm with the LightGBM model without parameter optimization. The optimal parameters found by the Bayesian hyper-parameter optimization algorithm were input into the LightGBM model, and the prediction result of the improved HY_LightGBM model was compared with that of the LightGBM model. The specific experimental results are shown in Table 4 and Figure 11.  
The experiment demonstrated that although the LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm takes a little more time than the LightGBM model without parameter optimization, it significantly outperforms the LightGBM model without any parameter optimization in terms of prediction accuracy.
## 4 Results and Discussion
### Total Cost  
Figure 8 presents the total costs, comprising economic costs and durability costs, under varying battery capacities and weighting coefficients, with the maximum fuel cell power predefined. The results show that, as the weighting coefficient increases, the total cost first decreases and then increases. Similarly, with an increasing battery capacity, the total cost first decreases and then increases. The total system cost exhibits a significantly higher sensitivity to variations in battery capacity compared to changes in the weighting factor. The analysis reveals that the minimum total cost is achieved when the weighting coefficient is set to 0.5 and the battery capacity is 44 Ah.  
To demonstrate that using the weighting factor to normalize fuel economy and system durability into a single objective function effectively reduces total costs, a comparison is made between scenarios considering only fuel economy, only system durability, and the use of Pareto front analysis. The comparison evaluates the selection of the minimum economic cost with an appropriate durability cost, as well as the minimum economic cost with a suitable durability cost, as shown in Table 5.  
Figure 8: Total costs under different battery capacities and weighting factors.  
Figure 7: Per kilometer performance degeneration percentage of fuel cell and battery under different weighting factors: (**a**) fuel cell and (**b**) battery.  
It can be found that using the weighting factor minimizes the total cost and reduces the economic cost by 1%, the FC decay level by 1%, and the BAT decay level by 13%, but reduces the total cost by 1%, as compared to using the Pareto frontier to consider the optimal system durability.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 2 Basic Concepts and Related Information  
### Climatological Data in Agricultural Applications  
Climate is one of the most important factors that controls crop growth. Climate condition heavily influences every process of crop production starting from planting until harvesting. Climatological data are used for not only simulating crop growth, but also for agricultural management and farmer decision making [2]. Simulation models for crop are often based on climatological data in the form of daily intervals for estimating crop growth and yield [3]. The key climate variables that impact crops are temperature, solar radiation and precipitation [4]. Temperature substantially affects photosynthesis and respiration, plant growth and phenological development, while solar radiation significantly affects plant photosynthesis [5]. Precipitation represents water availability in an area. Meteorological variables are used as input for weather generator models and crop modeling. Commonly-required variables are maximum temperature, minimum temperature, solar radiation and precipitation in the form of daily intervals.
## 4 Relative Uncertainty Analysis for Scattering-Rate Measurement System
### Relative Uncertainty Analysis
Due to the influences of the laser power fluctuation, the laser incident angle, the sample gap distance and the ambient light, the ambient light angle and the off-center position of the incident laser beam are independent of each other, and according to the error propagation theory, the total measuring relative uncertainty of the scattering rate on the coating surface should meet the following equation:  
\[u_{\text{total}}=\pm\sqrt{u_{1}^{2}+u_{1}^{2}+u_{\text{g}}^{2}+u_{\text{a}}^{2 }+u_{0}^{2}} \tag{19}\]  
where \(u_{\text{total}}\) is the total measurement relative uncertainty of the coating to be tested; \(u_{1}\) is the relative uncertainty caused by the instability of laser power; \(u_{i}\) is the measuring relative uncertainty of the surface scattering rate due to the deviation of the incident laser angle; \(u_{\text{g}}\) is the measuring relative uncertainty caused by the gap distance between the sample and the integrating sphere due to the surface roughness and the installation deviation of the sample; \(u_{\text{a}}\) represents the relative uncertainty due to the influence of ambient light; \(u_{\text{a}}\) represents the relative uncertainty due to the off-center position of the incident laser beam  
By substituting the simulated and experimental relative uncertainties from the above mentioned five factors into Equation (19), the total relative measurement relative uncertainties for simulation and experiment were found to be \(\pm 2.28\%\) and \(\pm 2.22\%\), respectively. Compared with the 3-5% measuring relative uncertainty for the scattering rate reported in the current works of the literature, the experimental measurement accuracy of the high-precision scattering-rate measurement system based on the three-hole integrating sphere with wide infrared band range proposed in this paper was found to be improved by 26-56%, which had achieved the high-precision measurement for the specific region on the coating surface.
## 3 Temperature Sensor Clustering Method
### Hierarchical Clustering Method  
The main concept of clustering is dividing temperature sensors into several clusters according to the distance matrix. Temperatures measured by the sensors that are clustered into the same cluster have smaller differences. Thus, one representative temperature sensor is selected from each cluster to form the final selection of temperature sensors. There are many kinds of clustering methods, but since the number of the temperature sensors is not large, hierarchical clustering method was used. The clustering process is shown as follows:  
1. Find the minimum element of the upper triangular part of the distance matrix \(\mathbf{D^{a}}\), named as \(d_{pq}^{a}\) (\(p=1\sim n\), \(q=1\sim n\), \(p<q\)), which means temperature sensors \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\) have the smallest distance, and clustering \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\) into one cluster, named as \(\mathbf{x_{(pq)}}=\left[\begin{array}{cc}\mathbf{x_{p}}&\mathbf{x_{q}}\end{array}\right]\);
2. Since \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\) are clustered, the distance matrix is re-calculated: 1. Delete the \(q\)th row and the \(q\)th column of the original distance matrix \(\mathbf{D^{a}}\); 2. Calculate the average temperature of \(\mathbf{x_{(pq)}}\) by rows, named as \(\overline{\mathbf{x_{(pq)}}}\); 2. Calculate the new \(d_{pq}^{e}\) and \(d_{pq}^{r}\) between \(\overline{\mathbf{x_{(pq)}}}\) and other temperature sensors (clusters) according to Equations (4), (5), (7), and (10); 2.4 Derive the new \(d_{pq}^{a}\) based on new \(d_{pq}^{e}\) and \(d_{pq}^{r}\) according to Equation (13); 2.5 There is one new \(d_{pq}^{a}\) between \(\overline{\mathbf{x_{(pq)}}}\) and every other temperature sensor (cluster). Thus, \((n-1)\) different \(d_{pq}^{a}\) can be obtained. Arrange them into the corresponding place of the \(p\)th row and the \(p\)th column of the distance matrix to derive a new distance matrix \(\mathbf{D^{a}}\) with \((n-1)\) rows and \((n-1)\) columns;
3. Loop step 1 and step 2 until \(n\), which is the size of the distance matrix \(\mathbf{D^{a}}\), reduced to an ideal number \(\lambda(\lambda<n)\). \(\lambda\) is the number of the clusters we would like to cluster to. The temperature sensors in each cluster can also be derived according to step 1.  
The flow chart of the hierarchical clustering is shown in Figure 4:
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 3 Solution
### Detection of ROIs  
The ROIs detection step is the initial step of two alternative streams for set similarity scoring, based on deep neural networks or on classic key-point descriptors. We use the Selective Search [4] algorithm to find ROIs in images. Selective Search uses color, texture, size, and shape measures to characterize image regions and iteratively to perform hierarchical region grouping into ROIs. The list of obtained ROIs is ordered by area. We prefer to skip the one with the biggest size and to select the next five (2 nd-6 th) for further analysis. We chose this selection to avoid the whole car ROI and to concentrate on a fixed number of meaningful ROIs. To obtain ROI similarity scores, their feature vectors are obtained by two alternative methods: DeepRanking and the SIFT-descriptor.
## 5 Conclusions  
To sum up, in terms of numerical results, we could guarantee almost perfect detection by selecting a threshold in the range of 25%-30% from \(P_{zc}\). On the other hand, employing a ZC preamble with 6 dB power higher than the TIBWB-OFDM data block resulted in close BER performance using both MMSE and IB-DFE equalization. Hence, the developed work constitutes a new step to provide an insight on channel estimation and frame detection algorithms that could perform well in disperse channel conditions for the TIBWB-OFDM scheme.  
Our work shows that the developed estimators by means of block-type preamble-assisted frames can provide close BER system performance in comparision with the true CSI performance due to the careful choice of the preamble sequences. So, it is useful to implement the pilot design based on ZC sequences due to their good correlation characteristics, and it was proven that they could jointly effectively execute channel and synchronization estimation in our hybrid modulation architecture. Nevertheless, it is necessary to guarantee that the preamble power block is amplified.  
Therefore, proof of concept of how the studied modulation scheme should perform in a real-world environment was established.  
Considering the obtained results, future developments should involve the study of these estimators applied to TIBWB-OFDM with windowing-time overlap [8], and the implementation of the proposed synchronization and channel estimators in a practical real environment, i.e., using a software-defined radio (SDR)-based testbed.  
A future study should also compare the performance of TIBWB-OFDM with other beyond-5G waveform contenders (such as generalized frequency division multiplexing (GFDM), filter-bank multicarrier (FBMC), and filtered OFDM) under imperfect CSI.  
Conceptualization, funding acquisition, project administration, and supervision, M.G. and V.S.; investigation and methodology, all authors; software, J.M. and F.C.; formal analysis, J.M., M.G., and V.S.; writing--original-draft preparation, J.M.; writing--review and editing, M.G., V.S., F.C., and R.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by FCT/MCTES under projects MASSIVE5G (POCI-01-0145-FEDER-030588), UIDB/50008/2020-UIDP/50008/2020 and in part by the FCT Ph.D. under Grant SFRH/BD/08124/2020.  
Figure 8: BER results for TIBWB-OFDM with IB–DFE over a dispersive channel employing Algorithm C for a ZC preamble sequence with 6 dB higher average power than the data symbols.