esa-sceva/satcom-chunk-collection huggingface dataset:
# Recoil Control of Deepwater-Drilling Riser with Optimal Guaranteed Cost \(H_{\infty}\) Control
## 3 Design of Optimal Guaranteed Cost \(H_{\infty}\) Recoil Controller
### Computation of the Gain Matrix \(K\) of the Controller  
To solve the gain matrix \(K\) of the optimal guaranteed cost \(H_{\infty}\) recoil controller, let  
\[\Phi=\text{diag}\Big{\{}P^{-1},\ I,\ I,\ I,I\Big{\}}\]  
and pre- and post-multiply both sides of (33) by \(\Phi\) and its transpose, respectively; denote \(\bar{P}=P^{-1}\) and \(\bar{K}=KP^{-1}\). Then, from (33) and (35), one obtains the following linear matrix inequalities as:  
\[\left[\begin{array}{cccc}\bar{\Pi}&D&\bar{K}^{T}R&\bar{P}Q&\bar{P}C_{1}^{T}\\ *&-\gamma^{2}I&0&0&E_{1}^{T}\\ *&*&-R&0&0\\ *&*&*&-Q&0\\ *&*&*&*&-I\end{array}\right]<0 \tag{36}\]  
\[\left[\begin{array}{cccc}-\mu&\hat{z}^{T}(0)\\ *&-\hat{P}\end{array}\right]<0 \tag{37}\]  
where \(\bar{\Pi}=\bar{P}A^{T}+\bar{K}^{T}B^{T}+A\bar{P}+B\bar{K}\).  
Further, the computation of the gain matrix \(K\) can be formulated as the following optimization problem:  
Minimize \[\mu\] s.t. (36) _and (37)._  
Now, we have the following proposition.  
Proposition 2: _For a given scalar \(\gamma>0\), matrices \(Q\geq 0\), \(R>0\), and the initial state value \(\hat{z}(0)\), if there exist \(6\times 6\) matrix \(\bar{P}>0\) and \(1\times 6\) matrix \(\bar{K}\) such that there exists the feasible solution to the optimization problem (38), then the gain matrix \(K\) of the optimal guaranteed cost \(H_{\infty}\) recoil controller can be solved by \(K=\bar{K}\bar{P}^{-1}\)._  
Remark 1: _Proposition 2 provides a method to solve the optimal guaranteed cost \(H_{\infty}\) recoil controller for the riser system. Compared with the classic linear quadratic optimal controller designed in [23], where the external disturbance, i.e., the characteristics of friction force of discharging fluid on the riser are not used to design the controller, while in this paper, based on the introduced \(H_{\infty}\) performance index, the characteristics the friction force are used to design the recoil controller. In this sense, the designed optimal guaranteed cost \(H_{\infty}\) controller may be better than the optimal controller to reduce the recoil response of the riser, which was verified by simulation results, and presented in the next section._  
**Remark 2**: _Note that there always exists physical limitations of the controller from the application point of view. By adjusting the opening \(\lambda(t)\) of the recoil control valve, the flow of hydraulic oil in the tensioner can be controlled. The opening of recoil control valve \(\lambda(t)\) can be written as [23]:_  
\[\lambda(t)=\sqrt{\frac{\rho_{d}\bar{q}A_{r}^{3}\tilde{x}_{p}^{2}(t)}{2\tilde{s}^ {2}|u(t)|}},\;u(t)\
eq 0 \tag{39}\]  
_where the density of the hydraulic oil is represented by \(\rho_{d}\), kg/m\({}^{3}\); \(\bar{q}\) is the flow resistance coefficient; \(S\) is the maximum flow area of recoil control valve port, \(m^{2}\). In the next section, during the recoil controller design and application, the above relation of the control valve opening \(\lambda(t)\) and the control input \(u(t)\) is considered._
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
###### Abstract  
During the in-orbit operation of spacecraft, permanent magnet synchronous motors are commonly used as power sources in the drive mechanisms of solar panel arrays and the high-precision servo control systems based on satellites. Apart from the performance of the motors themselves and the software control algorithms, the accuracy of the entire control system is also influenced by angle sensors used to detect the rotor position of the motors. As a high-precision angular measuring instrument, the inductsyn possesses excellent environmental adaptability and long service life. Effectively utilizing the inductsyn can greatly enhance the performance of servo control systems. To address the complexity of the decoding process for dual-channel round inductsyn-to-digital converters, this paper proposes a design of the decoding circuit for dual-channel round inductsyn based on the parallel-synchronization decoding method of two AD2S1210 Resolver-to-Digital Converter (RDC) decoding chips. The decoding circuit amplifies the excitation signal outputted by the AD2S1210 for driving the round inductsyn, and processes the sine and cosine induction signals outputted by the round inductsyn through filtering, amplification, and other methods; by using analog circuitry, the output signals of the dual-channel round inductsyn are processed to meet the input requirements of the AD2S1210. Finally, through both the Multisim (circuit simulation software Version 14.1) simulation and physical experiments, it was verified that the decoding circuit designed in this paper could process the input/output signals of the dual-channel round inductsyn and AD2S1210, and successfully decoded the analog induction signal of the round inductsyn. This greatly simplifies the signal decoding process for the dual-channel round inductsyn.  
Keywords:dual-channel round inductsyn; AD2S1210; signal decoding; analog signal processing
###### Abstract  
This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions.  
5G networks; network slicing; network slice orchestration; resource management; resource allocation; machine learning; supervised learning; reinforcement learning; traffic prediction
## 4 Global Distribution of PhenoCams  
PhenoCam is a viable alternative to the present method of ecosystem phenological monitoring that combines ground-based data with satellite observations [49]. Time-lapse and fixed digital cameras have demonstrated remarkable potential at capturing phenological metrics across a diverse range of biomes. These include high Arctic vegetation [50], North American grasslands [51], Amazon rainforests [52], grasslands in Japan [53], Australian grasslands [33], deciduous and evergreen forests of North America [54], tropical forests of Malaysia [55], and croplands [56]. In this review, we looked at only peer-reviewed articles that had the word \"PhenoCam\" in their titles or keywords. We carefully read each article and collected important information from them (see Table S1). From this list of 72 studies, we observed that a significant proportion of studies using PhenoCam were conducted in deciduous forests (31.5%), followed by grasslands (19.8%), evergreen forests (17.1%), and shrublands (8.1%) (Figure 4). This diverse distribution of research sites highlights the versatility and broad applicability of PhenoCam in different ecosystem types.  
The recognition of the significance of PhenoCam systems in monitoring plant phenology has led to the establishment of several phenological networks worldwide. These networks include the PhenoCam Network--USA [32]--the Phenological Eyes Network--Japan [57]--the National Ecological Observatory Network--USA [58]--the EURO-Pheno Network--Europe [59]--the European Integrated Carbon Observation System (ICOS) Network--Europe [59]--and the Australian PhenoCam Network--Australia [60] (Table S2). When considering the spatial distribution of PhenoCam studies, it is notable that 53 percent of the studies were from North America, followed by 28 percent from Europe and 11 percent from East Asia. This highlights a significant gap in PhenoCam-based monitoring in regions such as Africa, South America, South Asia, Central Asia, and Australia. There is a need to expand the PhenoCam network in these geographies to better understand their global forest dynamics and the impact of global change on forest ecosystems [61].  
Here, we spotlight India as a case study and discuss the need for a PhenoCam network in India and its potential role in improving forest management in this country. The experiences from the Indian PhenoCam network could be useful in the expansion of PhenoCam networks in geographies with similar socio-economic circumstances, such as the other countries of South Asia, Africa, etc. By expanding the reach of PhenoCam networks to currently underrepresented regions, we can enhance the comprehensive monitoring of vegetation phenology worldwide. This could enable a more holistic perspective on the impact of climate change and contribute to a deeper understanding of ecological processes on a global scale.
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 3 Collision-Free Path Planning
### Model Predictive Artificial Potential Field
In the manipulator used in this paper, each joint has different limits. To ensure that all the joints stay within lower and upper limits during the path planning process, \(\mathbf{\theta}_{l},\mathbf{\theta}_{u}\in\mathbb{R}^{m}\), the following constraints were added to the optimization problem:  
\[\mathbf{C}\cdot\mathbf{\theta}_{l}\leq\mathbf{\hat{\theta}}_{k+1}\leq\mathbf{C}\cdot\mathbf{ \theta}_{u} \tag{7}\]  
Equation (7) can be expressed with the decision variable \(\mathbf{\hat{u}}_{k}\), by using relation (3)-(5):  
\[\mathbf{\phi}_{l}\leq\mathbf{\hat{u}}_{k}\leq\mathbf{\phi}_{u}\] \[\mathbf{\phi}_{l}=\mathbf{C}\cdot\frac{1}{T_{s}}(\mathbf{\theta}_{l}-\mathbf{ \theta}_{k})\] \[\mathbf{\phi}_{u}=\mathbf{C}\cdot\frac{1}{T_{s}}(\mathbf{\theta}_{u}-\mathbf{ \theta}_{k})\]  
The nonlinear optimization problem formulated in Equation (6) was implemented in Python 3.7.6 using the SciPy 1.4.1 optimization library [33]. The computational time increases significantly as the number of obstacles increases due to collision checking for all joints and the nonlinearity of the objective function. To mitigate this, feasible groups of obstacles are represented as clusters. For example, in the case of a collision check problem with joints and obstacles, shown in Figure 8, collision checks are conducted between the joints of the manipulator, which are represented by red circles while the obstacles are represented by blue circles. In a problem with three joints and obstacles, Figure 8a requires nine collision checks between all the joints and obstacles. In contrast, it is sufficient to perform three collision checks only between the end-effector and the obstacles located within the clustered range, as shown in Figure 8b. The range of clusters is defined so that the repulsive potential is generated only when a joint is within this range, thereby reducing the amount of computations and thus minimizing the computational time. In this paper, the cluster range is defined by extending the minimum and maximum obstacle coordinates along each axis by the safety distance \(D_{min}\) used in the repulsive potential. The path planning method using MPAPF was validated through simulation in Section 4 and experiments in Section 5.
## 2 Materials and Methods
### Data  
Continuous records of half-hourly GPP were calculated based on the net ecosystem exchange (NEE) and ecosystem respiration (Re), with \(R\)e determined with nocturnal thermal sensitivity curves [34]. The data gaps of NEE associated with equipment failures and unsuitable micrometeorological conditions were filled with the temperature response approach or light response approach [34]. A continuous one-day GPP and ET were summed to obtain daily values. The daily values were then averaged to 8-day intervals in accordance with the temporal resolution of the MODIS data. These EC-derived GPP and ET were then used to calibrate and test the model. Meteorological data (i.e., air temperature, precipitation, relative humidity, wind speed, \(R_{\mathrm{n}}\) and PAR) were the input driving variables for the modified SWH model. In situ measurements of these variables at the flux tower sites were used when deriving the site-specific parameter sets. In addition to the flux data from FLUXNET towers, we also collected data of annual values of GPP and ET at 43 non-Chinaflux sites in China, which was extracted from the published literature (site information is available in [PERSON] et al. [35]). Since this dataset included only the annual values, it was not used for parameterization, but only for testing the model performance.  
To estimate regional ET in China based on in situ measurements at 750 nationwide meteorological stations, air temperature, precipitation, relative humidity and wind speed were spatially interpolated at a resolution of 10 km with the method of a bi-variate thin plate with elevation as a covariate using the ANUSPLIN software package [36]. \(R_{\mathrm{n}}\) was calculated as the balance of incident downward and upward shortwave and longwave radiation, in which the MODIS albedo product was used to estimate the net downward shortwave radiation, and the model of [PERSON] et al. [37] was used to estimate the net upward radiation. Incident shortwave radiation and PAR were estimated with the Angstrom equation [38]. Detailed information of this method is available in [PERSON] et al. [39].  
NDVI is another key driving variable in the SWH model. MODIS NDVI (MOD13Q1) data are satellite products acquired from the website of ORNL DAAC (1 km, [[http://daac.ornl.gov](http://daac.ornl.gov)]([http://daac.ornl.gov](http://daac.ornl.gov))). These MODIS products contain some cloud-contaminated or missing data. Therefore, before inputting the data into the model, these products were processed using the software package TIMESAT3.0 (an asymmetric Gaussian method) to exclude noise and fill gaps [40].
Federated multi-task learning (FMTL) further allows each center to jointly train shared feature representations while retaining personalized task-specific parameters, thereby facilitating robust cross-institution SDB monitoring and sleep staging [27; 28]. Although various FL methods have been proposed in the broader machine learning literature, few have focused on combining FL with multi-task learning in the specific context of sleep health. For example, [PERSON] et al. [29] employed a federated learning framework to analyze ECG data to detect sleep apnea, achieving an accuracy of 79.77%. [PERSON] et al. [30] applied clinical federated learning to classify ICD-10 using electronic health records from multiple Spanish hospitals. However, to the best of our knowledge, the integration of multi-task learning with federated learning for comprehensive and privacy-preserving sleep monitoring remains largely unexplored. Motivated by this gap, this study aims to achieve two key objectives:  
1. To develop a unified multi-task framework that leverages multimodal PSG signals, including EEG, respiration, ECG, snoring, and SpO2, for joint sleep staging and SDB severity classification, thereby enhancing diagnostic accuracy and efficiency. 2. To address the non-IID challenge inherent in real-world sleep data through federated learning, enabling decentralized model training across clinical centers while protecting patient privacy and improving cross-site generalization. To this end, we propose a multimodal, multicenter federated multi-task learning framework that integrates a hybrid deep neural network architecture with a parameter-averaging federated optimization strategy. This approach provides a scalable, privacy-preserving solution for automated sleep disorder assessment and lays the groundwork for a fully digitalized, generalized sleep health monitoring system.
## 3 Results and Discussion
### Spectroscopic Analysis
#### 3.4.3 XRD Analysis  
Figure 13 shows the XRD analysis of the selected samples. The XRD results of the UMU and CZT2 samples are similar. There are peaks from the same phases: Si\({}_{2}\)O, Fe\({}_{2}\)O\({}_{3}\), and CaSi\({}_{2}\)O\({}_{6}\). They differ only in the intensity of the peaks. In the sample SZY, in addition to peaks from Si\({}_{2}\)O, CaSi\({}_{2}\)O\({}_{6}\), and Fe\({}_{2}\)O\({}_{3}\), there is also a reflection from SiC. Similar results were presented in the work of [PERSON] et al. [47], which analyzed tire chips with other additives. The XRD analysis presented in the work of [PERSON] et al. shows that the tires contain the same compounds as in the results presented in this paper. Moreover, in the work of [PERSON] et al. [48] XRD of rubber from recycled tires was also examined. In the results  
Figure 12: FT-IR spectroscopy of extracts obtained from fractions extracted with DCM and iPrOH.  
of [PERSON] et al. the occurrence of CaCO\({}_{3}\) and CaSi\({}_{2}\)O\({}_{6}\) compounds in the tested tires was shown; these compounds also appear in the XRD reflections in this work.
## 5 Discussion
It should also be noted that the structure observed in the heat map (Figure 9a) confirms that the longitudinal structure of the RLS signal is not of noise nature. If it was related to noise, the structure of the reflectogram would change for different sections of the pulses used in the processing, which is not the case. This structure contains information about local scattering centers in the LUT. Moreover, the amplitude in the reflectogram at each point of the fiber is not simply the value of the scattering amplitude, but the result of the interference of signals scattered within a small area of size \(\delta l\) determined using the Expression (3). The result of the interference depends on both the fluctuations of the scatterer density withinthe region \(\delta l\) (fluctuations of the refractive index in the LUT core) and the optical frequency of the radiation. The random nature of the refractive index fluctuations causes an irregular pattern in the part of the reflectogram corresponding to the RLS. This effect is of the same nature as the fading effect observed in C-OTDR systems, where the interference can result in zero integral scattering in some regions of the LUT. The above-mentioned dependence of the interference result on the optical frequency can be observed in Figure 10a. As the center frequency is tuned, the interference result changes for each LUT segment. Effectively, the scattering spectrum is scanned for each LUT segment. The vertical slice in Figure 10a corresponds to the spectrum at a particular LUT coordinate and contains information about the spectral dependence of the interference of the scattered signals. As a result, the fading effect can be eliminated in the case of the frequency domain averaging. The averaging makes fast oscillations in the reflectograms smoother, but leads to the loss of information about the spectral characteristics of the LUT. In other words, the red line in Figure 10b with averaging reflectograms over the spectrum makes the measurements of the local integral scattering absolute values more accurate at the expense of spectral effects. Thus, averaging in the frequency domain affects not only the NFL, but also the shape of the useful signal. The choice of averaging approach should be determined based on the task to be solved via the OFDR system under development. For distributed temperature and strain measurements, it is necessary to fix the reference reflection spectrum from the frozen inhomogeneities and study its evolution over time using correlation analysis methods. In this case, only averaging in the time domain or over different sections of the mode pulses can be used to increase the contrast between the useful signal and the noise floor and reduce the fluctuations. In turn, frequency domain averaging can be used to measure the absolute value of the scatter/reflectance, thereby reducing the useful signal fluctuations.
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 4 Swarming Approach Description
### Minimum Average One-Hop Delay  
In Section 4.1, a swarming approach is proposed to find the minimum average one-hop delay of inter-platoon communications iteratively. In each iteration, the minimum contention window sizes of backbone vehicles are adjusted to enable the one-hop delays of backbone vehicles to get close to an optimization objective. The optimization objective is denoted as \(D_{avg}\) that is usually set as a positive small value. In this paper, in order to obtain the minimum average one-hop delay, the value of \(D_{avg}\) should be set as a value that the one-hop delay of each backbone vehicle cannot reach by adjusting the minimum contention window sizes. For simplicity, the value of \(D_{avg}\) is set to be 0. After the optimization objective is determined, the minimum contention window sizes of backbone vehicles are adjusted to enable the one-hop delays of backbone vehicles to approach the optimal objective. The closeness between the one-hop delays of backbone vehicles and the optimization objective is evaluated by an objective function. The variance between the one-hop delays of backbone vehicles and \(D_{avg}\) can be used to evaluate the closeness between them. Hence, the objective function is defined as the variance between the one-hop delays of backbone vehicles and \(D_{avg}\).  
Next, we will introduce the swarming approach in detail. the swarming approach is divided into three sub-procedures, i.e., the initialization procedure, iteration procedure and output procedure. The pseudocode of the swarming approach is described as Algorithm 1.