esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### The Static Safety Domain of the System at Different Penetration Rates  
Through extensive simulation calculations, it was found that the boundary of the static security region (SSR) can be fitted using hyperplanes. Then, several operating points near the boundary are selected for time domain simulation to verify the constructed boundary, ensuring that the error remains within the permissible range for engineering applications. The figure below shows the cases where the penetration rate ranges from 5% to 50%.  
The SSR constructed in three-dimensional space visually reveals the range of static security in the three-dimensional space, delineated by the coordinated active power output of synchronous units G103 and G66, along with the active power output of DFIG wind turbine D27. The SSR constructed from these three entities exhibits a smooth characteristic. As the penetration rate of new energy increases, the determining factors of the SSR boundary tend to converge towards new energy sources. The static security domain between the three entities was fitted by critical points searched using an improved gradient descent method.  
In Figure 10, from the top left to the bottom right, one can observe significant changes in certain areas of the graph as the wind power penetration rate increases. At lower penetration rates, the data appear more concentrated and coherent. As the penetration rate increases, the data begin to exhibit greater fluctuations, especially in certain areas in Figure 10 at penetration rates of 45% and 50% (such as the transition area from yellow to red) where abrupt changes occur, indicating that the system behaves more dynamically or unstably at higher penetration rates.  
Figure 10: Cont.  
At lower penetration rates, the system exhibits more stable behavior with a relatively even distribution of active power. As the penetration rate increases, the graph becomes more variable, particularly in areas with higher power levels. This means that as more wind power is integrated, the system becomes more dynamic and requires more regulation to maintain stability. At high penetration rates (such as 45% and 50%), the system experiences significant fluctuations, indicating potential stability issues at these nodes, which need to be addressed by adjusting the outputs of other nodes or by adding energy storage and other frequency regulation measures.  
From the perspective of the safety domain, at low penetration rates, the safety domain is larger, meaning that the power system can operate safely within a larger state space. At high penetration rates, the safety domain irregularly shrinks, indicating that the system is more sensitive to power changes, with stricter operational restrictions. Particular attention is given to the static safety domain of the system with a wind power penetration rate of 40%. Although its stability is not as good as that at about 10% penetration in Figure 10, it generally meets the stability requirements.
## 3 Machine Learning (ML)
### Conventional ML Methods (Features Engineered Approaches)
#### 3.2.4 Regression  
Regression refers to a class of supervised ML models that predict continuous values by identifying the optimal-fitting curve between input and output data (Figure 5d) [81]. Typically, in regression models, the objective is to minimize the mean squared error (MSE) between the fitted curve and the data points. Various regression methods include linear, polynomial, logistic, LASSO, and ridge methods. Regression proves to be a valuable tool for quantifying outcomes, particularly for biomedical applications. A notable challenge in regression lies in extrapolation for values beyond the range covered by the training dataset.  
These models find diverse applications, notably in predicting disease progression, drug responses, and patient outcomes. For instance, in neurodegenerative diseases, regression models leverage biomarkers and clinical data to estimate the rate of disease advancement, guiding personalized treatment strategies. Additionally, they contribute to the realm of personalized medicine by predicting individual responses to specific drugs based on genetic and molecular data. Furthermore, regression models serve as valuable tools in experimental biology, aiding in the estimation of biological parameters and facilitating a deeper understanding of intricate cellular processes. Whether predicting survival rates or aiding in the development of diagnostic tools, the versatility of regression ML models underscores their significance in advancing biomedical research, fostering more precise clinical decision making, and ultimately contributing to improved patient outcomes.
###### Abstract  
Campylobacter on poultry meat needs to be controlled to reduce the risk of infection caused by the consumption of chicken meat. Pulsed light (PL) application on poultry meat was studied to control _Campylobacter_ spp. The effect of this technology was evaluated regarding poultry meat colour and volatile compound changes. Two breast sample groups were prepared: inoculated with _Campylobacter_ (10\({}^{7}\) bacteria of _Campylobacter jejuni_ strains) and not inoculated. Samples were submitted to PL, five pulses/s of 300 ms, 1 Hz, and 1 J/cm\({}^{2}\) in the apparatus, PL Tecum unit (Claranor). A response surface experimental design was applied regarding the factors of voltage (1828 to 3000 W) and distance to the source UV lamp (2.6 to 5.4 cm). The binomial factorial treatment (voltage and distance) with PL induced different energy doses (fluence J/cm\({}^{2}\)) received by samples, 2.82 to 9.67 J/cm\({}^{2}\). Poultry meat pulsed light treated had a significant decrease of _Enterobacteriaceae_ counts. The treatments applied were unable to reduce 1 log _Campylobacter_ cfu/g of poultry meat. The poultry meat PL treated became slightly light, redder, and yellower than those not treated. PL can decrease the proportion of aldehydes on total volatiles in meat, particularly on those associated with chicken-like, chicken skin-like, and sweet odour notes in fresh poultry meat. Further studies of PL with higher energy doses will be necessary to confirm if there are _Campylobacter_ reductions and about poultry meat treated under storage to evaluate if volatile compounds can affect the flavour of PL-treated meat samples.  
Campylobacter; pulsed light; emergent technologies; poultry meat; safety Article
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 5 Conclusions  
In this paper, a diffuse and direct APAR-based (DDA) method is proposed to explore the effects of partitioning diffuse and direct APAR on half-hourly GPP estimations. The main conclusions are as follows:  
1. LUE increased with increasing diffuse fraction for all vegetation types, which showed the enhancement effect of diffuse radiation on LUE. Moreover, a diurnal co-variation of LUE and diffuse fraction was observed for all vegetation types at FLUXNET sites, which further demonstrated the necessity of partitioning diffuse and direct APAR in half-hourly GPP estimations.
2. Half-hourly GPP increased with increasing diffuse and direct APAR but at different rates. Half-hourly GPP presented higher growth rates under diffuse conditions, with obvious increase of slope values (the variation coefficient of slope is up to 203.125%), which indicated the significant contribution of diffuse radiation to the process of vegetation photosynthesis.
3. Half-hourly GPP estimated using the DDA-based method showed higher R\({}^{2}\), lower RMSE and RMSE* values (R\({}^{2}\) varied from 0.565 to 0.682, RMSE ranged from 3.219 to 12.405 and RMSE* were within the range of 2.785 to 8.395) against the FLUXENET GPP than the GPP_TA (R\({}^{2}\) varied from 0.558 to 0.653, RMSE ranged from 3.407 to 13.081 and RMSE* were within the range of 3.321 to 9.625), which suggested a better performance by partitioning the diffuse and direct APAR in half-hourly GPP estimations when using big leaf models.  
Conceptualization, S.C. and L.L.; methodology, S.C.; software, S.C.; validation, S.C. and X.L.; formal analysis, L.S.; investigation, S.C. and X.L.; resources, S.C. and L.L.; writing--original draft preparation, S.C.; writing--review and editing, S.C. and L.L.; supervision, L.L. and L.S.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFA0603001 and National Natural Science Foundation of China, grant number 41825002.  
Not applicable.  
The authors gratefully acknowledge the CI products provided by [PERSON] at Beijing Normal University, the MODIS products obtained from the Land Products Land Processes Distributed Active Archive Center (LP DAAC), observations from FLUXNET2015 network and EC-LUE model proposed by [PERSON] at Sun Yat-sen University.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Fractional Vegetation Cover  
In this study, we used pixel dichotomy to calculate the FVC of Shaanxi Province from 2000 to 2018. Under the assumption that each pixel contains only two parts of information--soil and vegetation--the NDVI value represented by each pixel was calculated by a weighted average sum based on the proportion of soil and vegetation in the pixel [43].  
Figure 1: Elevation and topographical regions of Shaanxi Province.  
The formula of NDVI converted to FVC is as follows:  
\[FVC=(NDVI-NDVI_{s})/(NDVI_{v}-NDVI_{s}) \tag{1}\]  
where \(NDVI_{s}\) represents pixels that contain only soil information (approximately 0), and \(NDVI_{v}\) represents pixels that contain only vegetation information (approximately 1). \(NDVI\) values with a confidence of 5% and 95% were used as \(NDVI_{s}\) and \(NDVI_{v}\), respectively, to exclude the influence of individual extreme values on the calculation results. In this paper, we divided FVC into six grades: very low FVC (FVC \(\leq\) 10%), lower FVC (10% \(\times\) FVC \(\leq\) 30%), low FVC (30% \(\times\) FVC \(\leq\) 45%), moderate FVC (45% \(\times\) FVC \(\leq\) 60%), high FVC (60% \(\times\) FVC \(\leq\) 75%), and higher FVC (75% \(\times\) FVC \(\leq\) 100%).
## 5 Field Test
### Verification of Slotting Depth Model  
In the slotting experiments, the jet pressure, borehole diameter, outer slot diameter, and nozzle diameter were set as 25 MPa, 75 mm, 50 mm, and 3 mm, respectively. The slotting borehole depth was 15 m. As shown in Figure 14, the number 1, number 3, and number 5 inspection holes were arranged at the right of slotting borehole with a distance of 0.5 m, 1.5 m, and 2.5 m from the slotting borehole, respectively. On the left side, the number 2, number 4, and number 6 inspection holes were arranged at distances of 1, 2, and 3m, respectively. The depths and diameters of all inspection holes were 15 m and 42 mm, respectively. The inclination dip of all boreholes was 15\({}^{\circ}\). The slotting position of the high-pressure water jet was located 14 m away from the slotting borehole. Four groups of slotting tests were conducted. The rotation speed and slotting time in the high-pressure water jet slotting were n = 60 r/min and t = 5 min, n = 50 r/min and t = 10 min, n = 40 r/min and t = 15 min, and n = 30 r/min and t = 20 min, respectively.  
Figure 13: Site of water jet slotting field test: Shoushan Number 1 coal mine affiliated with Pingdingshan Coal Group Co. Ltd.  
The slotting depth was estimated by observing whether water flowed out from the inspection hole. The results are presented in Table 1. Under the test conditions of n = 60 r/min and t = 5 min, water flowed out from the number 1 and 2 inspection holes but not out of the number 3-6 holes. This indicates that the slotting depth was more than 1.0 m but less than 1.5 m, which is consistent with the depth predicted based on Equation (14). The results of the other three test groups were similar, which indicates that the proposed model for calculating the slotting depth is reliable and can be used in field applications.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 2 Method
### Network Model  
We illustrate in Figure 2 the network model of the proposed UAV-based olive irrigation system. As represented in Figure 2, we separate the olive farm into \(N\) cellular networks. We use M-MIMO technology, where every cellular network has a single ground base station (GBS). Equipped with very large number of antennas \(M_{n}\), (\(1\leq n\leq N\)), the \(n\)-th GBS simultaneously serves \(K_{U}\) UAVs with onboard sensors, \(K_{A}\) actuators nodes (ANs), and \(K_{F}\) fog nodes. The actuators devices are placed directly in the soil beside each olive tree. We assume in the present work that all these devices are geographically distributed in the cellular network and are equipped with a single antenna.  
The \(n\)-th GBS receives data from the UAVs and also from the fog devices in uplink communication. The received signal at the \(n\)-th GBS is denoted as \(\mathbf{r}^{UL}\in\mathbb{C}^{M_{n}}\). We denote by \(\mathbf{h}_{nk}\) the channel between the UAV (or the fog device) and the \(n\)-th GBS. We assume that the channel vectors are independent and identically distributed (i.i.d.) complex Gaussian samples of a random variable with zero mean and unit variance. We also denote by \(\mathbf{x}_{nk}\sim\mathcal{N}_{\mathbb{C}}(0,p_{nk})\) the signal from the UAV (or the fog device) to the \(n\)-th GBS, where \(p_{nk}=E\big{(}\abs{\mathbf{x}_{nk}}^{2}\big{)}\) represents the power of the transmitted signal. The noise, \(\mathbf{b}\sim\mathcal{N}_{\mathbb{C}}\big{(}0,\sigma^{2}\mathbf{1}_{M_{n}}\big{)}\), is assumed to be independent additive white noise. The received signal in the uplink at the \(n\)-th GBS is modeled as [29]:  
\[\mathbf{r}^{UL}=\sum_{n=1}^{N}\sum_{k=1}^{K_{U}+K_{F}}\mathbf{h}_{nk}\mathbf{x}_{nk}+\mathbf{b}. \tag{1}\]  
Correspondingly, in downlink communication, the \(n\)-th GBS sends data to different IoT devices (i.e., the UAVs, the fog nodes, and the actuators nodes). We denote by \(K_{I}=K_{U}+K_{F}+K_{A}\) the total number of connected devices in the cell. The data signal intended for the \(k\)-th connected node in the \(n\)-th cell is \(\mathbf{x}_{nk}\), (\(1\leq k\leq K_{I}\)) and (\(1\leq n\leq N\)). This signal is precoded using the beamforming vector \(\mathbf{w}_{nl}\in\mathbb{C}^{M_{n}}\). In this work, we deploy the TDD (time division duplex) mode and we assume that the CSI (perfect channel state information) of all channels is available at the GBS. In this case, the GBS performs channel estimation using only the uplink pilot transmission from each IoT device. Consequently, the received signal in the downlink at an IoT device is modeled as [29]:  
\[\mathbf{r}_{k}^{DL}=\sum_{n=1}^{N}\sum_{l=1}^{K_{l}}(\mathbf{h}_{nk})^{H}\mathbf{w}_{nl} \mathbf{x}_{nl}+\mathbf{b}_{k}. \tag{2}\]
###### Abstract  
A digital front-end decimation chain based on both Farrow interpolator for fractional sample-rate conversion and a digital mixer is proposed in order to comply with the long-term evolution standards in radio receivers with ten frequency modes. Design requirement specifications with adjacent channel selectivity, inband blockers, and narrowband blockers are all satisfied so that the proposed digital front-end is 3 GPP-compliant. Furthermore, the proposed digital front-end addresses carrier aggregation in the standards via appropriate frequency translations. The digital front-end has a cascaded integrator comb filter prior to Farrow interpolator and also has a per-carrier carrier aggregation filter and channel selection filter following the digital mixer. A Farrow interpolator with an integrate-and-dump circuitry controlled by a condition signal is proposed and also a digital mixer with periodic reset to prevent phase error accumulation is proposed. From the standpoint of design methodology, three models are all developed for the overall digital front-end, namely, functional models, cycle-accurate models, and bit-accurate models. Performance is verified by means of the cycle-accurate model and subsequently, by means of a special C++ class, the bitwidths are minimized in a methodic manner for area minimization. For system-level performance verification, the orthogonal frequency division multiplexing receiver is also modeled. The critical path delay of each building block is analyzed and the spectral-domain view is obtained for each building block of the digital front-end circuitry. The proposed digital front-end circuitry is simulated, designed, and both synthesized in a 180 nm CMOS application-specific integrated circuit technology and implemented in the Xilinx XC6 VLX550T field-programmable gate array (Xilinx, San Jose, CA, USA).  
ASIC; carrier aggregation; CIC; decimation; digital front-end; digital mixer; Farrow interpolator; FIR filter; FPGA; fractional sample-rate conversion; LTE +
Footnote †: journal: 2021  
_Acticle Design and Implementation of a Farrow-Interpolator-Based Digital Front-End in LTE Receivers for Carrier Aggregation._Electronics_  
Footnote †: journal: 2021
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### System Setup, Simulation Parameters, and Assumptions
#### 6.1.3 Baseline  
The performance evaluation of the optimization strategies implemented as hierarchical agents was carried out in [22]. In that evaluation, we measured the energy savings obtained by comparing it against the baseline scenario with all resources activated at each site, i.e., from an operator perspective, the maximum bandwidth of 10/20/100 MHz and the maximum antenna configuration, 4/4/64 on the three bands 700/2100/3500 MHz, respectively. Furthermore, we assumed that the sleep modes had been activated, and the base station could switch down to one of the three levels depending on the duration of the idle period and the activation-deactivation time of each sleep mode. The three sleep levels, \(SM_{1}\), \(SM_{2}\), and \(SM_{3}\), had sleep deltas of 0.84, 0.69, and 0.5, respectively. Similar to [22], in this work, the energy-savings obtained were additional to those obtained through advanced sleep modes. A few RL hyperparameters are listed in Table 5.
## 2 Materials and Methods
### Water-Use Efficiency (WUE) Method  
SIF and GPP can both be represented in the form of light use efficiency (LUE) models [36]:  
\[\text{SIF}=\text{APAR}\times\Phi_{\text{F}}\times\Omega_{\text{c}} \tag{1}\]  
\[\text{GPP}=\text{APAR}\times\text{LUE} \tag{2}\]  
where APAR stands for the photosynthetically active radiation absorbed by photosynthetic pigments; \(\Phi_{\text{F}}\) is the fluorescence quantum yield; and \(\Omega_{\text{c}}\) is the probability of SIF photons escaping from the canopy. Combining Equation (1) with Equation (2), a linear model between SIF and GPP can be expressed as:  
\[\text{GPP}=\text{SIF}\frac{\text{LUE}}{\Phi_{\text{F}}\times\Omega_{\text{c} }}. \tag{3}\]  
Based on previous works [37; 38], the factor \(\frac{\text{LUE}}{\Phi_{\text{F}}\times\Omega_{\text{c}}}\) can be set as a constant for a specific plant type, and GPP can be calculated by SIF directly. For the C\({}_{3}\) and C\({}_{4}\) plants, WUE was relatively stable. Under this assumption, T can be calculated by SIF using simple linear regression (SLR):  
\[\text{GPP}=\text{k1}\times\text{SIF} \tag{4}\]  
\[\text{T}_{\text{SLR}}=\text{k2}\times\text{GPP} \tag{5}\]  
where k1 and k2 are two parameters denoting \(\frac{\text{LUE}}{\Phi_{\text{F}}\times\Omega_{\text{c}}}\) and WUE, respectively. Equation (5) is the theory base of the empirical linear relationship between SIF and T.  
However, k2 is also affected by environmental factors [39], especially the dryness of air. Previous work indicated that WUE became more stable by incorporating the effects of VPD from diurnal to annual time scales [40; 41]. Moreover, [PERSON] et al. [8] pointed out that the atmospheric demand for water helps to explain a lot of variability in the SIF-T relationship at the ecosystem scale. Here, we proposed a WUE method as:  
\[\text{T}_{\text{WUE}}=\text{k3}\times\text{VPD}^{\text{k4}}\times\text{GPP} \tag{6}\]  
k3 is a parameter concluding information on water-use efficiency. k4 quantifies the non-linear effect of VPD on k3. In this work, VPD is calculated from the air temperature and relative humidity of the air.
###### Abstract  
Monitoring the stability and safety of climbing scaffolds in super-high-rise construction is critical to ensuring construction quality and worker safety. This study proposes a Global Navigation Satellite System (GNSS)-based real-time monitoring method to track scaffold displacement and assess structural performance. A multi-level data optimization framework integrating gross error elimination, data interpolation, robust Kalman filtering, and a Cumulative Sum Control Chart (CUSUM)-based early warning system is developed to enhance monitoring accuracy. The key objectives of this research are to improve real-time displacement tracking, suppress measurement noise, and establish an automated anomaly detection mechanism for climbing scaffolds under complex construction conditions. The proposed method was validated in a super-high-rise construction project in Tianjin, China. Experimental results demonstrated that the system effectively reduced high-frequency noise and gross errors, achieving root mean square error (RMSE) reductions of 51.4% in the E direction, 45.5% in the N direction, and 49.6% in the U direction. The system successfully tracked vertical climbing displacements of 4.4 m per ascent and horizontal deviations of 4 cm (E direction) and 2 cm (N direction). Additionally, the multi-level warning mechanism identified displacement anomalies based on predefined thresholds, providing an early warning function to enhance scaffold safety management. Compared to conventional monitoring methods, the proposed BeiDou/GNSS-based system provides higher precision, real-time adaptability, and enhanced automation, offering a scalable solution for intelligent construction safety management. The findings contribute to structural health monitoring (SHM) applications and can serve as a reference for future high-rise construction safety assessments.  
BeiDou/GNSS-based monitoring; super-high-rise buildings construction; external climbing scaffold monitoring; robust Kalman filter; CUSUM-based anomaly detection; structural health monitoring (SHM)