esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance of Multi-Row Variety 'Meridian' in 2023  
In 2023, the yield optima for the six-row barley variety Meridian confirmed the trends observed in previous years (Table 6). The theoretical optimal yield (Y\({}_{\rm opt}\)) was 11.0 t ha\({}^{-1}\), achieved with an optimal N application (N\({}_{\rm opt}\)) of 217 kg ha\({}^{-1}\) (Figure 4). Over the three years of the study, Meridian consistently reached very high yields, ranging from 10.6 t ha\({}^{-1}\) in 2021 to 11.6 t ha\({}^{-1}\) in 2022, with N requirements increasing from 173 kg ha\({}^{-1}\) in 2021 to 217 kg ha\({}^{-1}\) in 2023. The unfertilized control plot (N 1) showed very low performance, with a yield of only 3.1 t ha\({}^{-1}\) and an N uptake of 33 kg ha\({}^{-1}\). The highest yield in 2023, 11.1 t ha\({}^{-1}\), was recorded in treatment N 10 with 200 kg ha\({}^{-1}\). The GFO treatment (N 12) achieved a statistically similar yield but with 29 kg ha\({}^{-1}\) less N applied. The Sensor  
Figure 3: Relationships between N fertilization and yield and between N fertilization and N surplus for the year 2022 (Meridian). Yield is expressed as fresh matter, standardized to a dry matter content of 86%. The upper lines depict the relationship between N fertilization and yield, whereas the lower lines represent the relationship between N fertilization and N surplus. 10 t ha\({}^{-1}\) treatment (N 18) also delivered a comparable yield of 10.1 t ha\({}^{-1}\) with significantly less N input, requiring only 145 kg ha\({}^{-1}\), which is 55 kg ha\({}^{-1}\) less than N 10. However, sensor-based treatments with further reduced N input (N 19 to N 21) resulted in significant reductions in yield, protein content, and N uptake. These treatments also slightly increased N surpluses compared to N 18, although the differences were not statistically significant. Similarly, GFO treatments with reduced N application (N 13 to N 15) led to significant reductions in yield, protein content, and N uptake. Higher N applications in GFO and sensor-based treatments (e.g., N 16 and N 22) did not increase yields but had a positive effect on protein content.
## 2 Results and Discussion
Probably, one of them forms as a result of the local substitution of the Y\({}^{3+}\) cation by an Nd\({}^{3+}\) ion, and another one is a result of the local substitution of the Sc\({}^{3+}\) cation by an Nd\({}^{3+}\) ion in the YScO\({}_{3}\) host. The Tm\({}^{3+}\) ion has the same behavior in Tm\({}^{3+}\):YScO\({}_{3}\). Luminescence excitation spectra of Tm\({}^{3+}\):YScO\({}_{3}\) have been obtained for of \({}^{3}H_{6}\rightarrow{}^{3}H_{5}\) magneto-dipole transitions in the Tm\({}^{3+}\) ion at selective detection of 1945 nm (\({}^{3}F_{4}\rightarrow{}^{3}H_{6}\) transition) at a temperature of 77 K (Figure 2c). Two intensive peaks are observed at 1202.4 and 1209.4 nm. Ratio of line intensities depends on delay in registration in time-resolved spectra. This indicates the presence of two optical centers of Tm\({}^{3+}\) ion with two different lifetimes. Figure 2: (**a**) Luminescence excitation spectra of Nd\({}^{3+}\):YScO\({}_{3}\) being obtained at a time delay of: t\({}_{1}\) = 20 \(\upmu\)s; t\({}_{2}\) = 254 \(\upmu\)s. (**b**) Luminescence spectra of Nd\({}^{3+}\):YScO\({}_{3}\) being obtained at excitation wavelength of 823.8 and 821.4 nm. (**c**) Luminescence excitation spectra of Tm\({}^{3+}\):YScO\({}_{3}\) being obtained at a time delay of: t\({}_{1}\) = 0.6 ms; t\({}_{2}\) = 6 ms; t\({}_{3}\) = 10 ms; t\({}_{4}\) = 12 ms. (**d**) Luminescence spectra of Tm\({}^{3+}\):YScO\({}_{3}\) being obtained at excitation wavelength of 1202 and 1209 nm. All measurements were accomplished at 77 K.  
Figure 2d shows luminescence spectra of Tm\({}^{3+}\) ions being measured in the \({}^{3}\)F\({}_{4}\)\(\rightarrow\)\({}^{3}\)H\({}_{6}\) electron transition at selective excitation of 1202.4 and 1209.4 nm. The luminescence kinetics have been recorded on the \({}^{3}\)F\({}_{4}\)\(\rightarrow\)\({}^{4}\)H\({}_{6}\) electron transition at two different excitations of 1202.4 and 1209.4 nm. In both cases, decay curves are described by exponential law with lifetimes of \(\tau_{1}\)(77 K) = 4.25 ms (at 1202.4 nm excitation) and \(\tau_{2}\)(77 K) = 3.7 ms (at 1209.4 nm excitation). The close lifetimes and relatively equal intensities of the spectral lines obtained from two different centers indicate the same \(C_{2}\) symmetry in both cases. One of Tm\({}^{3+}\) optical center forms as a result of the local substitution of the Y\({}^{3+}\) cation by a Tm\({}^{3+}\) ion, and another one is a result of the local substitution of the Sc\({}^{3+}\) cation by a Tm\({}^{3+}\) ion in the YScO\({}_{3}\) host.
## 4 Array Method
### Task Scheduling Phase  
#### 4.1.1 The Resource Matrix  
**Definition 15**.: _The Resource Matrix, is a symmetrical matrix that stores the characteristics of the resources._  
Consider an HDCS, as shown in Figure 1, similar to the system previously proposed [19]. In our case, the system is constituted by three clusters joined by a high-speed network; each cluster is formed by different commodity processors and different number of processing cores in each commodity processor.  
The cluster is characterized in the Table 1 resource matrix according to Definition 1. For our sample case, Table 1 shows the resource matrix of the HDCS in Figure 1 using three clusters. Then, using Definition 1, \(C_{k,m,n}\) represents cluster \(k\), processor \(m\), and processing core \(n\). Thus, \(C_{1,1,2}\) represents cluster 1, processor 1, and processing cores 2. The positions of the matrix that contain 0 represent the processors that remain in the same network. Although the distance of the processors is 0, the communication time must be considered at the start time of all the tasks. A list of the main steps of the algorithm to create resource matrix is provided in Algorithm A1.  
The columns that constitute the resource matrix include the following.  
* Resource ID number: an incremental number generated according to its integration to HDCS.
* Number of processing cores of the resource: identified by an integer that represents the number of processing cores of each processor according to Definition 1.
* Distance from one resource to another: a value obtained from the distance (the number of jumps) from one processor to each of the processors in external clusters.  
When a new resource is added to the HDCS, its features are extracted and placed into this matrix using the feature extraction and resource sorting algorithm (the algorithm for verification of new resources in the system is provided in Algorithm A1).  
Figure 1: Heterogeneous distributed computer system (HDCS) with 3 clusters joined by a high-speed network.  
The resource matrix aims to solve the problem of changes in resource availability [10], which occurs during the release and allocation of resources.  
The procedure for filling and updating values in this matrix is performed with the algorithm for verification of new resources in the system, as shown in Appendix A. Processor speeds were not considered in this study.
## 4 Simulations Results
### Design Simulations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oscillator** & **Technology Corners** & **Frequency Variations** & **Standard Deviation \(\sigma\) (Hz)** \\ \hline \multirow{3}{*}{RO} & slow-slow & 31.46\% & 0.63 \\ \cline{2-4}  & typical & 28.34\% & 0.89 \\ \cline{2-4}  & fast-fast & 15.72\% & 3.34 \\ \hline \multirow{3}{*}{LC} & slow-slow & 9.01\% & 99.69 \\ \cline{2-4}  & typical & 9.07\% & 578.5 \\ \cline{2-4}  & fast-fast & 7.04\% & 132.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frequency variations and standard frequency deviation, respectively, for PVT (Process-Voltage-Temperature) and Monte Carlo simulations. Figure 10: Phase noise simulated for RO-VCO (**red line**) and LC-VCO (**blue line**) at post-layout in typical conditions at the same working frequency of 6.25 GHz. These simulations were performed using the Cadence environment. the RO was more sensitive to temperature variations than the LC-VCO. The time-domain VCO stability was made with the use of the Allan variance [27; 28], or two-sample variance, defined as  
\[\sigma_{x}^{2}(\tau) = \frac{1}{2}E\Big{[}(\overline{x_{2}}-\overline{x_{1}})^{2}\Big{]} \tag{5}\]  
Figure 11 shows the Allan deviation, or \(\sigma\)-\(\tau\) plot, calculated as the square root of Equation (5) when the VCO was in the steady-state oscillation. In particular, Figure 11a,b show the comparison between the Allan deviation in frequency and in amplitude, respectively, for both architectures. LC-VCO exhibited lower variations in frequency and amplitude than the RO-VCO.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 3 Analytical Calculations of Sheath Currents under Fault Conditions
### Flooding in Link Box  
In some areas of China, a large amount of standing water will soak into the cable tunnels during the rainy season every year, and when the link box is damaged or poorly sealed, it causes the protector inside the whole box to be flooded and the sheath is directly  
Figure 6: Equivalent circuit diagram of breakdown fault of cable joint JA1.  
connected to the earth, which seriously affects the safe operation of the whole system [18]. Taking the flooding in link box J1 fault as an example, the equivalent circuit diagram is shown in Figure 7 (the leakage current components are not marked in the diagram). \(I_{\text{XL}n}\) and \(I_{\text{XR}n}\) (\(n=1\), \(2\), \(3\)) represent the sheath induced currents on the left and right sides of the nth loop fault point. As the resistance formed by the water inside the box is small, the water body inside and outside the box is connected, and in general, the area of the water body is much larger than its depth, therefore, the water resistance is negligible [19].  
When flooding in link box J1 fault occurs, the sheath circuit is changed, then the capacitive current component flowing into the ends of the sheath is also changed, to A1 minor section as an example, the capacitive current component is calculated as shown in the Equation (18).  
\[\begin{cases}I_{\text{CLA1}}=\frac{Z_{\text{mB21}}}{Z_{\text{mL1}}+Z_{\text{mR 1}}+R_{\text{e}}}I_{\text{CA1}}\\ I_{\text{CRA1}}=\frac{Z_{\text{mL1}}+R_{\text{e}}}{Z_{\text{mL1}}+Z_{\text{mR 1}}+R_{\text{e}}}I_{\text{CA1}}\end{cases} \tag{18}\]  
According to the equivalent circuit of Figure 7, the sheath induced current is calculated as shown in Equation (19).  
\[\begin{cases}&I_{\text{XL1}}=\frac{U_{\text{mA1}}}{Z_{\text{mA1}}+R_{\text{e} }}\,\ \text{XR1}=\frac{U_{\text{mB2}}+U_{\text{mC3}}}{Z_{\text{mB3}}+Z_{\text{mC3}}+ R_{\text{g}}}\\ &I_{\text{XL2}}=\frac{U_{\text{mB1}}+U_{\text{mB3}}}{Z_{\text{mB1}}+R_{\text{e }}}\,\ \text{XR2}=\frac{U_{\text{mC2}}+U_{\text{mB3}}}{Z_{\text{mC2}}+Z_{\text{mB3}}+ R_{\text{g}}}\\ &I_{\text{XL3}}=\frac{U_{\text{mC1}}}{Z_{\text{mC1}}+R_{\text{e}}}\,\ \text{XR3}=\frac{U_{\text{mB2}}+U_{\text{mB3}}}{Z_{\text{mA2}}+Z_{\text{mB3}}+ R_{\text{g}}}\end{cases} \tag{19}\]  
Therefore, the first end of the grounding box sheath current as shown in Equation (20).  
\[\begin{cases}&I_{\text{sa1}}=I_{\text{CLA1}}-I_{\text{XL1}}\\ &I_{\text{sa2}}=I_{\text{CLB1}}-I_{\text{XL2}}\\ &I_{\text{sa3}}=I_{\text{CLB1}}-I_{\text{XL3}}\\ &I_{\text{sa4}}=I_{\text{XR2}}+(I_{\text{CRC2}}+I_{\text{CRA3}})-(I_{\text{CLC2 }}+I_{\text{CLA3}})\\ &I_{\text{sa5}}=I_{\text{XR3}}+(I_{\text{CRA2}}+I_{\text{CRB3}})-(I_{\text{ CLA2}}+I_{\text{CLB3}})\\ &I_{\text{sa6}}=I_{\text{XR1}}+(I_{\text{CRB2}}+I_{\text{CRC3}})-(I_{\text{ CLB2}}+I_{\text{CLC3}})\end{cases} \tag{20}\]
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 5 Experiment Setup and Results
With this input, the controller performance can be seen in Figure 16. In the upper part of Figure 16, the angle error between command (50 deg) and the measurement is shown. In this figure, it can be seen that, at the beginning, the angle error is relatively large, but the error quickly converges and reaches a stable state after around 10 s. The angle error in the stable state (after 20 s) is shown in the bottom part of Figure 16 to show more details, from which it can be seen that the angle error is less than 0.1 deg.
## 4 Results and Discussion
### Mechanical Analysis
Figure 5: Evolution of the damage parameter \(D\) during static fatigue experiments for various \(\sigma/\sigma_{\mathrm{R}}\) ratios 0.44, 0.56, 0.62, 0.71, 0.79 and 0.95 at 500 \({}^{\circ}\)C on SiC\({}_{f}\)/[Si-B-C] composites. The evolution of strain and acoustic emission events is shown in Figure 6 for a specimen loaded at \(\sigma/\sigma_{\text{R}}=0.44\). Under the steady loading the strain rate is important at the beginning then decreases before reaching a constant value. At this time, strain rises monotonically up to the final failure for the majority of the specimens (Figure 6b). The AE activity recorded during the constant load hold is also plotted in Figure 6b. The acoustic emission activity evolves with the same trend. Figure 6c shows the evolution of the cumulated acoustic energy. The main activity occurred at the beginning of the test and then reached a plateau. The renewal of AE activity (Figure 6c) just before failure seems to be an interesting way to anticipate the fracture but was not always noticeable on the AE energy _vs_. time plot. The main activity occurs at the beginning of the static fatigue. Then the AE rate decreases without stopping. During the initial loading up to the nominal load, the number of events increases with the applied stress, which is consistent with the increasing number of cracks in the matrix (the main source of acoustic emission) as the applied stress increases. Then, during the static loading, the number of  
Figure 6: (a) Stress-strain curve and cumulated number of acoustic emission (AE) events recorded during initial loading up to the nominal load, and during the static loading at a constant load (\(\sigma/\sigma_{\text{R}}=0.44\) and \(T=500\) °C); (b) Strain and cumulated number of AE events _versus_ time; (c) Cumulated acoustic energy _versus_ time. events before failure is lower under high stress than under low stress (Figure 7): damage needs to progress more under low stress to cause the final failure of the composite, than under high stress where the matrix already contains a lot of cracks.
# Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data +
## 4 Discussion
One of the advantages of deep learning is it does not require elaborate feature engineering, which is why we skipped the temporal feature extraction. At the same time, the prevalence of individual ICD or CPT codes tends to be very low, and thousands of ICD and CPT codes exist. If we do not aggregate diagnoses and procedures, the temporal image will be very large and mostly blank, which is not suitable for either any human inspection or deep learning. For example, for the same reason, given a large number of words, word2 vec is often used for a reduction in dimensionality before applying a deep learning algorithm to a text. One limitation of the dataset we used is that it included predominantly male patients. Another is that we did not have the lifelong record of each patient. While we did require a minimum of 1 year of history, the incident bipolar diagnoses in our dataset may not be the true onset of the disease. This study represents a first step in applying deep learning and temporal images to mental health risk prediction with promising results. There are many alternatives that we have yet to explore. For example, the choice of a 1-year prodromal period is somewhat arbitrary, and we could examine 2-year and 3-year prodromal periods in the future. We focused on the 1-year outcome in this study. The prediction of longer term (e.g., 3-, 5-, 10-year) outcomes would be of interest in future studies. There are several ways to represent temporal information. We only aggregated the data by week. In some prior research, data closer to an event (in our case, the event would be the BD diagnosis) could be shown with finer granularity (e.g., day or hour), and data further from an event could be shown with coarse granularity (e.g., week or month).
# Graphene Oxide Adsorption Enhanced by Attapulgite to Remove Pb (II) from Aqueous Solution
## 2 Materials and Methods
### Methods
#### 2.2.3 Adsorption Experiment  
In the adsorption experiments, the absorbent solution and Pb\({}^{2+}\) stock solution were mixed, and then DI water was added to obtain 0.3 mg/mL adsorbent and 150 mg/L Pb\({}^{2+}\).The pH value was 5, which was adjusted by 0.1 mol/L HCl and 0.1 mol/L NaOH. The reaction mixture was stroked 2 h at 298 K followed by centrifuged at 6000 r/min for 10 min. The concentration of Pb\({}^{2+}\) in the supernatant was measured using an atomic absorption spectrophotometer (Spectra AA110/220, Varian Inc., Palo Alto, PA, USA). All experimental data were the average of three parallel experiments. The removal efficiency of Pb\({}^{2+}\) was calculated using the following equation:  
\[\eta=\frac{C_{0}-C_{\mathrm{e}}}{C_{0}}\times 100\% \tag{1}\]  
where \(C_{0}\) and \(C_{\mathrm{e}}\) are the initial and equilibrium concentrations of Pb\({}^{2+}\) in mg/L; and \(\eta\) is the removal efficiency in %.  
To compare the adsorption capacity, the initial concentration of Pb\({}^{2+}\) was 100 mg/L. To investigate the pH effects on Pb removal, the pH was adjusted from 3-9. To examine the contact time effects on Pb removal, the solution was stroked for 5 to 120 min. To evaluate the initial concentration on Pb\({}^{2+}\) removal, Pb\({}^{2+}\) dosage varied from 20 mg/L to 160 mg/L. The experiments ware conducted at 298 K, 308 K, and 318 K to investigate the thermodynamic models. To investigate the regeneration and recycling properties of the as-prepared GO, HCl solution was used as the regenerate; the concentration was varied from 0.2 to 0.3, 0.5, 0.8, and 1.2 mol/L.
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 2 Past Related Work
An adaptive probabilistic mechanism based on the wave propagation is proposed in [50], while an energy efficient routing algorithm using probabilistic flooding is proposed in [51]. Probabilistic flooding has also been proposed for the area of nanonetworks by [52, 53], and underwater environments by [54], whereas vehicular networks have also been the focus by [55, 56]. An adaptive version for multi-path routing purposes is proposed by [57]. Authors in [58] propose an advanced probabilistic flooding mechanism considering both the popularity of resources and the distance from the initiator node. A method for evaluation of network-wide broadcast protocols is proposed in [59], whereas, in [60], the use of probabilistic flooding in their broadcast protocol for wireless body area networks is proposed. The work presented here is an extended version of a previous work in [5] that has been significantly enhanced to provide for further evaluation using traces from a realistic environment (i.e., solar radiation dataset obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science/Applied Science Program [6]) and realistic network node specifications (i.e., based on the datasheet of MICA2-MPR400 CB [61]).
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 5 Conclusions and Implications
### Implications
Through institutional reform and policy integration, cross-regional barriers can be dismantled, governance efficiency elevated, and resource allocation optimized--paving the way for Guangdong, Hong Kong, and Macao to jointly develop a high-functioning, integrated economic system within the Greater Bay Area. Despite its contributions, this study presents several limitations. First, the analysis relies on static data from discrete time nodes, which constrains the ability to capture long-term dynamic shifts and forecast future development trajectories. Second, while the adopted methodology effectively delineates network structures and synergistic effects, it lacks the capacity to uncover causal linkages and dynamic interactions between the logistics and capital flow networks. In particular, the absence of dynamic modeling and mechanism-based analysis restricts accurate assessment of policy adjustments or external shocks on system performance and evolution. Moreover, the study primarily focuses on macro-level structures--such as regional flow networks and intercity dynamics--without delving into micro-level factors like enterprise behavior and individual resource flows. This analytical gap hampers a holistic understanding of how micro-behaviors interact with and shape macro-network configurations. Future research will incorporate spatio-temporal modeling, micro-level analysis, and interdisciplinary approaches--leveraging innovative data sources and analytical techniques--to deepen insight into the complex interplay of logistics and capital flows within the Greater Bay Area. These enhancements will improve the comprehensiveness, diagnostic precision, and explanatory power of subsequent investigations. Conceptualization, S.W. and Y.Q. ; Methodology, Y.Q. and X.L. ; Software, X.L. ; Validation, S.W. ; Formal analysis, Y.Q. ; Investigation, Z.W. ; Data curation, Y.L. ; Writing--original draft, S.W. and Y.Q. All authors have read and agreed to the published version of the manuscript. This research was supported by the Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources (No. 2024 NRMK08). Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. We are grateful to Ministry of Natural Resources, Lanzhou University, Lands and Resource Department of Guangdong Province and Dalian University of Technology for the assistance provided. The authors declare no conflicts of interest.