esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Missions and Spacecraft Payload
Supplementary, the K-Band ranging system enables measuring the separation changes between the two satellites to micrometer precision in order to map the Earth's gravity field [47]. Equipped with a similar BlackJack GPS receiver as CHAMP [48], signals received with the zenith antenna are used for POD and the aft-looking antenna provides RO measurements for sensing physical properties of the atmosphere [34; 49]. As secondary mission goal RO instrumentation was tested on both GRACE satellites for shorter periods before continuous activation of RO measurements on 22 May, 2006 on GRACE-A [27]. The majority of measurements were obtained by GRACE-A intermittted by shorter periods of occultations by GRACE-B (Jul-Dec 2014, Jun-Oct 2015, Apr-Sep 2016) when swapping maneuvers took place, making GRACE-B the trailing satellite [16]. Therefore, we limit the evaluations in this paper to the quality assessment of GRACE-A, seen as representative for both flight models. After decommissioning and atmospheric reentry of GRACE on 24 December, 2017 (GRACE-B) and 10 March, 2018 (GRACE-A) the success of the mission was continued with the launch of GRACE follow-on in May 2018 [50; 51]. However, as of early 2020, RO data from this follow-on mission are not yet available to the community. As part of the EUMETSAT Polar System the Meteorological Operational (Metop) satellite series consists of three flight models Metop-A/B/C, which were placed sequentially in time (see above) in a sun-synchronous polar orbit of about 98.7\({}^{\circ}\) inclination and an altitude between 796 and 884 km [52]. The centerpiece for GPS inter-satellite tracking is the onboard GNSS Receiver for Atmospheric Sounding (GRAS) developed by Saab Ericsson Space [53; 54]. The GRAS unit provides dual-frequency navigation and occultation tracking at 12 \(\times\) 3 channels for L1 C/A and L1/L2 P(Y). A zenith-looking antenna observes GPS satellite signals at 8 channels with a sampling rate of 1 Hz for POD [28; 29]. Furthermore, 4 channels are shared by two high-gain beam forming antennas looking in flight velocity and anti-velocity direction for rising and setting occultations respectively, supporting closed loop (50 Hz) and open loop (1 kHz) measurements [55]. For orientation of the satellite a nominal alignment is applied for the POD. In contrast to CHAMP and GRACE, the Metop satellites are not equipped with an LRR for orbit verification from the terrestrial laser tracking network. Up to this day, the Metop satellite series serves with an almost constant number of occultations per day and thereby underscores its importance as a reliable long-term backbone mission for numerical weather prediction and climate research [56].
# Phenotypic, Genetic, and Metabolite Variability among Genotypes of _Vicia sativa_ L.
## 3 Results
### SCoT Markers Associated with Metabolites and Yield Characteristics through MRA
Three bands were associated with tyrosine, one of them (SCoT30_900) showed a very strong positive association (standardized beta coefficient = 0.875, \(p\) value < 0.001) and another one of them (SCoT13_350) showed a strong negative association (standardized beta coefficient = \(-\)0.794, \(p\) value < 0.001). Seven bands were associated with \(\beta\)-alanine, eight bands were associated with \(\gamma\)-tocopherol, and one of them (SCoT30_900) showed a very strong positive association (standardized beta coefficient = \(-\)0.879, \(p\) value < 0.001). Four bands were associated with seed yield and one of them (SCoT30_1300) showed a strong negative association (standardized beta coefficient = \(-\)0.651, \(p\) value < 0.001). Finally, four bands were associated with biomass yield and one of them (SCoT30_1300) showed a strong positive association (standardized beta coefficient = 0.695, \(p\) value < 0.001). According to the results of the SCOT marker analysis, there was a statistically significant correlation with several metabolites of _V. sativa_, as determined by MRA. Additional information on alleles per trait can be found in Supplementary Materials Table S1, and information on the intensity of the association can be found in Table 9. According to all of the above, 119 bands were associated with the examined traits. From those, 20 bands have strong or very strong positive or negative associations. The most common bands with strong or very strong positive or negative associations were SCOT13_350 and SCOT30_900, associated with four traits, SCOT13_1600 and SCOT30_1300, associated with two traits, and SCOT13_350, SCOT30_1500, SCOT30_1300, SCOT15_600, SCOT30_1500, SCOT33_600, SCOT6_2000, and SCOT66_3000, associated with one trait.
# Application of Artificial Intelligence in Wind Power Systems
## 4 Artificial Intelligence Algorithms
### Algorithms for Wind Turbine Operation  
Time series forecasting models (LSTM, GRU, CNN-LSTM and CNN-RNN) are crucial for short-term forecasting of wind energy, typically minutes to hours in advance, as they capture temporal dependencies in wind speed and power data [38; 49; 50; 52; 55]. LSTM networks can reduce the mean absolute percentage error, and hybrid CNN-GRU or CNN-LSTM architectures often improve accuracy for high-frequency wind data.  
Ensemble methods such as random forest, XGBoost and LightGBM combine predictions from multiple sub models to increase accuracy in power generation forecasting [49; 50; 54; 79]. XGBoost is often used for predicting the electrical output of wind turbines and for temperature modelling, while random forest helps to identify features that are strongly correlated with system failures or power fluctuations.  
Adaptive neuro-fuzzy inference systems (ANFIS) and fuzzy logic approaches enable the control of wind turbines (e.g., dynamic pitch or yaw adjustments), stabilize voltages in doubly fed induction generators and improve consumption prediction under uncertainty [50; 56; 58].  
Computer vision and object recognition techniques based on deep learning enable real-time monitoring of birds and bats, collision avoidance and leaf icing detection [44; 59; 60; 61; 62].
# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
## 1 Introduction
### Literature Review  
The decision-making process based on collaborative models allows different strategies to be used. In [20], secondary users are classified into two levels: users in real time (RT) and users in non-real time (NRT). This allows the conflict and cooperation between both levels that make use of game theory to be studied. An auction game-based model that analyzes the decision-making process is proposed. A high number of secondary users is catered for, along with the reduction of user blockage probability, user handoff and channel saturation, as well as an increase in the probability of user acceptance.  
To resolve the hidden terminal problem that an SU might encounter, [21] proposes a reputation-based incentives strategy where the collaborative simulation problem is modeled as an indirect reciprocity game. The results obtained with this strategy show that the proposed scheme achieves proper performance. Another type of strategy uses techniques based on logical statements. In [22], a collaborative decision-making process is proposed that uses fuzzy logic. The decision-making criterion works with relative link quality (RLQ) and SINR. The model uses fuzzy logic rules based on the fuzzy interference system (FIS).  
Algorithms based on multi-criteria decision-making (MCDM) are widely used for the decision-making processes based on collaborative models. They operate using weight allocation, which is adjusted according to certain requirements. [23] compares the performance of four algorithms for spectral occupancy of the PU during the communication with an SU. The performance of the algorithms is assessed using five metrics: handoffs, failed handoffs, bandwidth, delay, and throughput.  
The process of decision making requires working with spectral occupation data. This can be generated randomly or obtained through measurements. Then, the collected data is used to train and validate the models. Too little of this data can lead to inefficient decisions, while too much can result in significant computational costs and delays.
## 3 Results and Discussion
### Evaluation of Monthly Precipitation  
Overall, the selected SPPs performed better on the monthly time scale compared to the daily one, a known effect where the performance of SPPs improves substantially as the time scale increases. [PERSON] _et al._[51] found, however, that the performance of SPPs tends to increase from one to five days and to remain stable at higher temporal aggregation. Estimations from all SPPs were significantly different from observations, at \(p\) \(<\) 0.05 (Table 3). A two-dimensional histogram of mean monthly precipitation between rain gauges and each precipitation product is shown in Figure 3. The best monthly precipitation estimates were obtained by using 3B42V7, with an R\({}^{2}\) value of 0.56. This was consistent with the findings of [PERSON] _et al._[52] using 3B42V6 over New Mexico over a period of four years. APHRODITE had the second highest R\({}^{2}\) value (0.52), followed by the 3B42 RT (R\({}^{2}\) = 0.45), PERSIANN-CDR (R\({}^{2}\) = 0.43), CMORPH (R\({}^{2}\) = 0.4), and GPCP-1 DD (R\({}^{2}\) = 0.39) (Table 4).  
CMORPH and GPCP-1 DD showed poor performance, which was consistent with the findings of [PERSON] and [PERSON] [48] in the Philippines. Such a poor accuracy of CMORPH and GPCP-1 DD might come from the inability of these techniques to differentiate between radiative properties of the ocean and the land [53]. Most of the MW techniques rely indeed on high frequencies (\(\geq\)85 GHz), which are more adapted to ice particle detection than to liquid water over a land area, thus explaining why MW satellites miss most of the warm and light precipitation events [54]. Moreover, the poor performance of GPCP-1 DD might also be due to the inherently large spatial resolution (1\({}^{\circ}\) degree) of the product. In the case of CMORPH, high estimation errors can come from the absence of gauge calibration.  
When looking at estimation errors by the best SPPs, we find that a slight overestimation of the observed monthly precipitations was observed for the 3B42 RT (2%), 3B42V7 (4.7%), and PERSIANN-CDR (2.1%), while an underestimation occurred for the GPCP-1 DD (2.8%). By contrast, dramatic underestimations occurred for CMORPH and APHRODITE: 13.2 and 19.7%, respectively. The significant underestimation by CMORPH might be due to a poor ability in estimating heavy rainfall(\(>\)10 mm/day), as shown in Section 3.7, which confirms previous observations [55]. With a RMSE of 111.53 mm/month, 3B42V7 was the best performing SPP for monthly precipitations over Malaysia, a finding agreeing with other previous studies reporting better performances of TRMM products [22].
## 6 Development Trends and Prospects
### Real Time  
Real time refers to intelligent edge computing and real-time monitoring. With the development of Internet of Things technology, fault detection is gradually developing to edge computing and real-time monitoring. Intelligent sensors and edge devices can directly collect and process data on-site, achieving real-time fault detection and prediction. This trend reduces the latency and cost of data transmission, supporting the immediate application of fault detection in industrial production. For example, deploying lightweight models using low-power edge devices can enable real-time monitoring of bearing status and early warning of faults on the production line.  
The Siemens SIPLUS CMS status monitoring system, combined with the cloud-based open IoT operating system MindSphere, can be used to analyze large quantitites of data and monitor machines and equipment located around the world in real time.
## 4 Experimental Results
### Inception-ResNet v2  
Based on the model of prediction and classification described in Section 3.1, Figure 7 shows the results of nutritional deficiencies in tomato plants determined using the Inception-ResNet v2 architecture. As can be seen from Figure 7, we compare the forecast results based on the nutritional deficiency observed in both the leaves and the fruits of tomato. For Potassium, the lack of this macronutrient is expressed in leaves and fruits, while for Calcium, the phenomenon can only be observed in the fruits of tomato, and for Nitrogen, shortage is expressed only in the tomato leaves.  
The results of Calcium deficiency are presented in Figure 7a-c. It can be seen that the forecasting rate of Figure 7a is 0.998, while this value is 0.977 for Figure 7b, and 1.0 for Figure 7c. For Potassium, the prediction results are shown in Figure 7d-f, whose maximum values are 0.989, 0.999, and 1.0, respectively. For the prediction of Nitrogen deficiency, the identification results are shown in Figure 7g (0.962) and Figure 7h (0.747). In addition, the accuracy of the prediction results was 87.27% when using the Inception-ResNet v2 model applied in our study.  
We use a confusion matrix with three classes to calculate actual and predicted classifications for both of the models in this paper. The actual and predicted precision will be calculated for each nutrient based on the confusion matrix results in Section 5. The precision of Inception-ResNet v2 is based on the proportion of correct predictions and the total number of data used for testing.
## 4 Analysis, Results, and Discussion
### Plastic Failure Analysis  
During tunnel excavation and support, the stress path of the surrounding rock is influenced by the combination of its mechanical properties and support means. By studying the distribution features of the surrounding rock's plastic zone, the interaction between the surrounding rock and support and the stress features of the tunnel can be intuitively presented, which can reflect the weak links in the construction process and provide a reference for the tunnel support. For revealing the surrounding rock disaster mechanism of the shallow-buried bias tunnel, the plastic zone distribution of the surrounding rock in the middle of the model (Y = 25 m) was selected for analysis after the model excavation, as shown in Figure 14.  
As shown in Figure 14, there were two types of failure in the surrounding rock, i.e., shear and tension failure (mainly, it is shear failure). The distribution of the plastic zone in the surrounding rock of the left tunnel was asymmetric. The damage was mainly concentrated in the larger range of the arch crown and the right arch foot, and the deepest part was about 6 m away from the tunnel outline line. The distribution of the plastic zone in the surrounding rock of the right tunnel was symmetrical. The damage was mainly concentrated in the right arch shoulder and bottom, and the deepest part was about 6 m away from the tunnel outline line. The tension-type failure occurred mainly in the shallow part of the surrounding rocks at the bottom of the arch of the double tunnel, and the affected area was small. During the construction, the arch foot and bottom positions should be focused.
## 3 Simulation and Analysis
### Analysis of the Influence of Extended Step Size  
The setting of the extended step size will affect the efficiency of the improved RRT algorithm in planning the initial path. In this section, experiments are carried out by selecting different values of the step size, such as Table 1, to analyze its impact on the performance of the improved RRT algorithm. In order to test the best combination of step coefficients, the above three different experimental maps were tested. In order to avoid the contingency of the experiment, each coefficient combination was tested 30 times independently, and the time average of 30 experiments was recorded. Table 2 is the specific statistical data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combination** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Non- & & & & & & \\ narrow & 2 & 3 & 4 & 4 & 5 & 6 \\ channel & & & & & \\ Narrow & 4 & 5 & 4 & 6 & 7 & 8 \\ channel & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Combination of different step coefficients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (s)** & **Map 1** & **Map 2** & **Map 3** \\ \hline Combination 1 & 12.06 & 10.53 & 9.14 \\ Combination 2 & 9.84 & 7.64 & 8.23 \\ Combination 3 & 9.76 & 7.51 & 7.30 \\ Combination 4 & 6.76 & 4.69 & 7.05 \\ Combination 5 & 11.56 & 10.52 & 13.52 \\ Combination 6 & 15.95 & 14.86 & 17.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The influence of different step coefficient combinations on the efficiency of the RRT algorithm.  
Figure 5: Simulated maps. (**a**) Map 1; (**b**) Map 2; (**c**) Map 3.  
From the comparison results in Table 2 and Figure 6, it can be seen that the combination 4 has the shortest average time in the three maps. In Map 1, Map 2, and Map 3, too short or too long a step size will lead to a decrease in the efficiency of generating the initial path. This is because the random tree expansion speed will slow down, and the efficiency will be low when the step size is too short. When the step size is too long, it is difficult to select the appropriate collision-free sampling point, resulting in an increase in search time. By comparing combinations 3 and 4, increasing the step size in the narrow channel makes the algorithm more efficient. Therefore, after many experiments, combination 4 is the best parameter in the experiment.
# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 3 Results and Discussion
### Discussion of Challenges and Recommendations
#### 3.3.5 Project Structure and Support  
Complicated ecosystem: Successfully implementing an agriculture-IoT project involves coordination of a large, and sometimes complicated, ecosystem of actors over a long timescale.  
_Project managers_: Try to organize a team of collaborators that includes data scientists, sensor experts, and agriculture experts so that each is contributing to solving a specific part of the problem in which he/she is an expert.  
IoT business model: Most people we connected with during this project agreed that data from on-farm sensors is valuable. However, identifying a specific customer, or buyer, of the device or service is less straightforward, especially when the beneficiary is unlikely to have the means to afford the technology.  
_Project managers_: Identifying and understanding whom the end customer is and who will be paying for the data is important. A good example is the Kenya RAPID project, described in Section 3.2, in which the county government includes the IoT services in their annual budget.  
Short funding timelines: Two- and three-year grants from donor agencies are short timelines; when it comes to farming seasons, this is a relatively short time period because you get data from only one or two cropping cycles, so collecting quality data and making actionable recommendations is a challenge.  
_Funders_: Increase funding timelines to five to seven years to allow for data collection over multiple cropping periods. A larger dataset also enables better pattern recognition and predictive analytics, leading to suitable actionable recommendations.  
Vertical integration: Many organizations are providing an end-to-end solution in which a single organization takes on the responsibility of farmer recruitment, IoT technology development, implementation, data analysis and recommendations, and monitoring and evaluation.  
_Project managers_: Some of the key stakeholders the team spoke with indicated that, while they were experts in a few aspects of the IoT and agriculture, trying to work on the entire end-to-end process of an agriculture IoT ecosystem was stretching them thin; thus, incorporating a horizontal structure in which each organization offers specific expertise to solve the overall problem in a piecemeal fashion is favorable for project success.  
_Funders_: Funding agencies can play a facilitation role to connect diverse groups with specific expertise to solve the overall problem. Organizations forming new partnerships could particularly benefit from guidance in negotiating agreements and contracts.
## 5 Maximize Energy Production of Wind Farm
### Optimization Method  
To maximize the energy production of the wind farm across its lifespan, the objective function can be expressed by:  
\[Max(P_{1}(v_{1},\beta_{1},\lambda_{1})L_{1}(v_{1},\beta_{1},\lambda_{1})+P_{2 }(v_{2},\beta_{2},\lambda_{2})L_{2}(v_{2},\beta_{2},\lambda_{2})) \tag{4}\]  
where \(P\) and \(L\) are the active power and lifetime of each turbine, respectively, with both relating to the wind speed \(v\), pitch angle \(\beta\), and tip speed ratio \(\lambda\). Subscripts \(I\) and \(2\) denote the parameters of WT\({}_{1}\) and WT\({}_{2}\), respectively.  
In the case of constant wind direction and constant ambient wind speed, the process to obtain the optimal pitch angle and tip speed ratio of WT\({}_{1}\) and WT\({}_{2}\) is shown in Figure 14. Overall, the optimal pitch angle and tip speed ratio of WT\({}_{1}\) can be selected by comparing the maximum total energy production of the wind farm at each set of pitch angle and tip speed ratio of WT\({}_{1}\). At each set of pitch angle and tip speed ratio of WT\({}_{1}\), due to the fixed active power and the lifetime of WT\({}_{1}\), as well as the wind speed of WT\({}_{2}\), the maximum energy production of the wind farm and the maximum energy production of WT\({}_{2}\) are obtained at the same pitch angle and tip speed ratio of WT\({}_{2}\). Thus, the optimal pitch angle and tip speed ratio of WT\({}_{2}\) can be selected first by maximizing the energy production of WT\({}_{2}\) at each wind speed of WT\({}_{2}\). Then, the optimal pitch angle and tip speed ratio of WT\({}_{1}\) can be selected with the optimized pitch angle and tip speed ratio of WT\({}_{2}\). Consequently, the optimal pitch angle and tip speed ratio of each wind turbine can be selected separately from the downstream wind turbine to the upstream wind turbine.  
Figure 13: Energy production comparison between the MPPT method and the MPG method considering the field wind direction: (**a**) Wind direction distribution; (**b**) Energy production of the wind farm at typical wind directions of \(270^{\circ},276^{\circ}\) and \(282^{\circ}\); (**c**) Total energy production of the wind farm.  
For example, at the first set of pitch angle and tip speed ratio of WT\({}_{1}\) which are \(\beta_{1\_1}\) and \(\lambda_{1\_1}\), as the \(P_{1\_1}\), \(L_{1\_1}\), and \(v_{2\_1}\) are fixed values, the maximum \(P_{1}L_{1}+P_{2}L_{2}\) is obtained at the maximum \(P_{2}L_{2}\). The maximum \(P_{2}L_{2}\) can be obtained by selecting the optimal pitch angle and tip speed ratio of WT\({}_{2}\) at \(v_{2\_1}\), which are \(\beta_{2\_1\_OPT}\) and \(\lambda_{2\_1\_OPT}\). If the optimal pitch angle and tip speed ratio of WT\({}_{2}\) to maximize the energy production of WT\({}_{2}\) is selected previously at each wind speed of WT\({}_{2}\), the maximum energy production of the wind farm at each set of pitch angle and tip speed ratio of WT\({}_{1}\) can be obtained. By comparing the maximum energy production of the wind farm at each set of pitch angle and tip speed ratio of WT\({}_{1}\), the optimal pitch angle and tip speed ratio of WT\({}_{1}\) can be obtained.