esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 4 Resource Allocation
### Remarks  
Tables 2 and 3 present approaches using RL and DRL, respectively, to perform resource allocation mainly and separately in RAN and fog/edge network. RL-based approaches [48; 49; 50; 51; 52; 53; 54; 55; 56; 57] used Q-learning, SARSA, expected SARSA, Monte Carlo, and actor-critic mainly to allocate radio or edge/fog resources to network slices efficiently, regarding operator revenue maximization, QoS satisfaction, and resource (computing and networking) utilization. Similar to Q-learning and SARSA (including variations), the worst-case complexity of Monte Carlo is \(O(|S|*|A|)\) showing a dependence on the size of spaces of states and actions [49]. DRL-based approaches [58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 71; 72; 74; 75; 76; 77; 78] employed algorithms  
such as DQN with one or two NNs, DDQN, dueling DQN, and LSTM-based A2C to allocate resources to RAN and (fog) edge network slices while optimizing the operator revenue and network utilization, and deal with scalability issues faced by RL-based approaches. Particularly, in [58; 59] the worst-case complexity of DDQN and dueling DQN is \(O(|S|^{2})\), where \(S\) is the size of the space of states. Furthermore, as mentioned earlier, the worst-case complexity of DQN is \(O(|H|*|N|)\), which depends on the number of hidden layers and their neurons [60].  
Regrettably, few works (e.g., [52; 61; 63; 70]) have been developed to allocate resources to end-to-end network slices. Indeed, the investigations primarily focused on proposing resource allocation approaches for RAN and (fog) edge networks, disregarding the importance of TN and CN when accomplishing NSL. As NSL is an end-to-end concept, novel allocation solutions in multitenant ecosystems should involve elements from RAN, CN, edge networks, and TN (like [61; 63] did by considering the availability of optical resources as wavelengths and links). They need to optimize revenue and utilization across the entire network and meet the QoS/QoE performance requirements demanded by 5G/6G use cases. Furthermore, the solutions should operate jointly with admission control approaches. Since almost all cited works in Tables 2 and 3 use centralized training and synthetic datasets, it is needed to investigate the limitations and advantages of using multiagent (like [79] did) and online RL/DRL techniques to allocate resources to slices of multiple tenants dynamically. In addition, it is crucial to test the existing approaches with real datasets (such as in [61; 72]) and networks (no work uses an actual deployment) to corroborate their practicability.
###### Abstract  
This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications.  
s +
Footnote †: journal: Applied Sciences  
article components; multiparametric health assessment; Wasserstein distance; spectral clustering; seasonal decomposition
# Adjustable Capacity Evaluation Method Based on Step-by-Step Power Mapping of Offshore Wind Farms
## 1 Introduction
This leads to a large error in calculating the grid-connected adjustable capacity of offshore wind farms using the maximum theoretical power of wind turbines. It is difficult to accurately control and dispatch wind farms, which will further affect the consumption of offshore wind power. Compared with the physical model, the data-driven model is more rapid and simple, and the accuracy of the calculation results is higher. The adjustable capacity can be calculated by inputting the power of the wind turbine. In order to improve the calculation accuracy of the adjustable capacity of offshore wind farms, this paper establishes power mappings for each power transmission process in offshore wind farms. The losses of each process is analyzed and modeled separately, and the step-by-step mapping from the power of the wind turbines to the power of the grid connection point of the wind farm is realized. Firstly, spectral clustering is used to classify wind turbines to reduce the dimension of input data and reduce the difficulty of model fitting. Then, the neural network is used to calculate the power of the collector system and the loss of the submarine cable. Finally, other losses of the offshore wind farm are statistically analyzed and the adjustable capacity is calculated. In this way, the adjustable capacity of wind farm can be evaluated based on the maximum theoretical generating power of wind turbines.
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 3 Study Area and Data
### Reference Forest Observations  
Spanish forests' field data were derived from the third Spanish Forest Inventory (NFI3), performed during 1997-2007 [38]. A total of 92,340 plots distributed in a regular 1 km \(\times\) 1 km grid were surveyed in the study area. Each plot consists of four concentric circular subplots with 5 m, 10 m, 15 m, and 25 m radii. Trees with 75 mm \(\leq\) DBH \(<\) 125 mm, 125 mm \(\leq\) DBH \(<\) 225 mm, 225 mm \(\leq\) DBH \(<\) 425 mm, and DBH \(\geq\) 425 mm were measured respectively in each subplot at a breast height of 130 cm. For each of the five forest types considered in the present study, a subset of NFI3 plots was selected considering the spatial and species homogeneity and the availability of site information. Specifically, only NFI3 plots with _Quercus _ilex_, _Quercus robur_, _Fagus sylvatica_, _Pinus pinea_, and _Pinus nigra_ were selected for EBF, LDBF, HDBF, LENF, and HENF, respectively. In addition, it was imposed that the 9 pixels in a \(3\times 3\)-pixel window of a forest type map (see Section 3.3) centered in each plot presented the same forest type (except for LDBF--at least 3 of 9--and LENF--at least 8 of 9--due to plot availability). For each plot, GSV and current annual increment (CAI) observations of the four subplots were added to obtain values representative of the whole plot. Table 1 summarizes the data used per forest type.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 2 GNSS and IMU Integrated Filter Design  
The overall sensor fusion framework integrating the GNSS and IMU sensor data with significant GNSS signal errors is illustrated in Figure 1. It mainly consists of four procedures, including data analysis, prediction process, update process and reverse smoothing, contributing to the developed ESKF \(-\)RTS smoothing localization algorithm. In particular, the algorithm defines a nominal state without considering the measurement noise of IMU and system disturbance, and an error state containing the noise and disturbance information is used for state estimation. The nominal state condition and error state prediction are updated simultaneously, and the error state is corrected using GNSS signal measurement and injected into the nominal state. The error state and its covariance matrix are subsequently reset. The acquisition frequency for GNSS data is 1 Hz, while the IMU data are acquired at a frequency of 100 Hz. The reverse time update and reverse segmentation smoothing are parallelly performed, which will be detailed below.
## 3 Improved Active Disturbance Rejection Control Strategy Design  
The article presents a novel approach to position control for PMSMs using a combined control method that integrates ADRC with RBF neural networks. Initially, ADRC is employed to control the PMSM servo system. Furthermore, leveraging the fast convergence and optimal approximation properties of RBF neural networks, the parameters of the extended state observer (ESO) in ADRC are adjusted and fine-tuned. Additionally, to enhance the system's dynamic tracking performance, a feedforward link is incorporated. The overall control block diagram is illustrated in Figure 4 to demonstrate these integrated control strategies.  
The improved active disturbance rejection control structure still adopts PI control in the current loop and uses the vector control of \(i_{d}=0\) for the control object, the PMSM. The control principle can be roughly described as measuring the stator currents \(i_{u}\), \(i_{b}\), and \(i_{c}\) of the three-phase motor or only measuring any two phases and calculating the remaining one phase. The voltage components \(u_{d}\) and \(u_{q}\) of the \(d\), \(q\) axis are obtained by Clarke and Park transformation and then adjusted by the ADRC + RBF neural network + feedforward control. Finally, the \(u_{a}\) and \(u_{\beta}\) components in the stationary coordinate system are obtained by inverse Park transformation. Then, the three-phase sinusoidal voltage is generated by SVPWM (Space Vector Pulse Width Modulation) to control the operation of the motor.
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 3 The Novel AVS/RS/ML System
### Description of the Operation of the System  
The main motivation of the newly proposed system is to apply technically simple and economically affordable portable lifting devices (i.e., MLs) together with higher number of vehicles (i.e., AGVs) to balance utilization of those both devices in the system and to provide higher throughput with less expenses. Here, AGVs can travel inside the warehouse aisles as well as on the factory floor outside of the proposed AVS/RS/ML. When AGV receives a storage transaction request, first it picks up the tote at the factory floor or at the I/O point and travels to the storage address aisle. After it enters the aisle, it travels to the target column. If the target storage compartment is at an upper level, an available ML is directed to move to the regarding column simultaneously with the AGV. When both the ML and the AGV arrive at the target column location, the AGV clamps itself on the ML's lifting chains and it is lifted to the target storage compartment by the ML. After the storage process is completed, the ML lifts down the AGV to the ground level. After the lift reaches at the ground floor, the empty AGV releases itself from the ML's lifting chains, it leaves the warehouse aisle, and waits for further instructions. The retrieval transaction is performed in an identical manner. However, the difference is that the AGV is empty when it enters the aisle. However, it leaves the aisle with a tote.  
The horizontal movement of MLs is relatively slow. To prevent excess waiting times of AGVs, as well as for further busting of the throughput performance of the AVS/RS/ML, ability of the ML to lift up two AGVs at the same time can be implemented. For that, AGVs should be equipped with retractable stoppers (see Figure 3) by which the AGV can be anchored to the rack construction. For successful implementation of this solution, the system logic must ensure that the first AGV attached should manipulate the tote in higher storage compartment than the second one. Hence, AGVs do not collide during their lifting process. Appropriate anti-collision algorithms must also be considered while AGVs' descending. In the present research, these features have not yet been included however proposed as future works. Here, simpler anti-collision principles were implemented, as explained in Section 4.  
Figure 5: Lifted AGV at the target level, with telescopic attachment in extended position.
## 3 Results and Discussion
### Characterization of the EGS@APTES-GT Hybrid Adsorbent
#### 3.1.2 Morphological Characterization  
Scanning electron micrographs for EGS@APTES-GT in Figure 2a-c show the irregular spherical morphology of the EGS@APTES-GT adsorbent with a diameter up to 2 mm and a highly porous surface and interior structure. The EGS@APTES-GT particles exhibit different morphologies ranging from spherical to a slightly irregular spherical shape. Figure 2c indicates that the surface contains connected open pores that make the material suitable for effective diffusional mass transport. The surface of the material consists of a mesoporous and microporous structure. It can be seen that goethite particles are homogeneously distributed, often clustered or aggregated on the external and internal porous surface (Figure 2c,e,f). It is visible that the formation of oxides in the pores, without material clogging or a diameter reduction, maintain material porosity with no interference to the diffusional processes, which are a good condition for pollutant adsorption onto the outer and interior surface.  
Figure 2: _Cont._  
The elemental content and its distribution on the adsorbent surface, the distribution through the inner structure of the porous material, were investigated by the EDS. Figure 3 (outer sphere surface) and Figure 4 (cross-section of the inner sphere surface) show a porous surface with an appropriate quantity of deposited iron.  
Figure 3: ECS@APTES-GT sphere outer surface EDS mapping with Fe, Si, and O concentration distribution.  
Figure 2: SEM images of EGS@APTES-GT (**a**), external surface at magnifications of 200 and 1000\(\times\) (**b**,**c**), cross-section, (**d**) and internal surface in cross-section of EGS@APTES-GT at different magnifications of 1000 and 5000\(\times\) (**e**,**f**).  
The mapping results from Figure 4a,b indicate that iron is dispersed along the cross-sectional area and it is obvious that the GT deposit entered in the pores and formed a lower quantity of GT in the region of the sphere's center. The iron concentration continually decreases from the external surface to the center of the spherical particle. The mapping results showed the presence of the GT deposit with a large number of active sites for As(V) and fungicides adsorption.  
The EDS results, shown in Figures S3-S5 in SM S3.1.2, indicate that EGS@APTES-GT contains mainly Si, O, and Fe with a trace amount of Na, Ca, Mg, Mn, K, and Al since the EGS material was obtained in the recycling process.
# Study on Mechanical Properties of Modified Polyurethane Concrete at Different Temperatures
## 3 Experimental Results and Discussion  
### Failure Mode  
#### 3.1.1 Cube Specimens  
For the cube compression test, the crack development process of all cube specimens is similar before failure. With the load increasing, many micro-cracks, white, distributed in a grid shape, appeared in the middle of specimens. Afterward, the micro-cracks gradually extended to the top corners of the specimens. With the load continuing to increase, the shape of the specimens changed significantly, but there was no spalling concrete on the surface. When approaching failure, a higher number of cracks appeared on the surface of the specimens, and the crack width increased. The crack was caused by the interface debonding between modified polyurethane binder and aggregate and the damage of aggregate. At this time, a few micro-cracks disappeared, and this was due to the viscosity of the modified polyurethane binder.  
The failure modes of specimens are different at different temperatures. At the temperature of 0 \({}^{\circ}\)C, the specimens made a loud bang when it failed. A large area of concrete fell off on the side of the specimens, and the damaged specimens were conical, as shown in Figure 9a. The failure mode was similar to that of ordinary concrete. At 15, 40 and 60 \({}^{\circ}\)C, the specimens also made sounds during damage, but the sounds decreased with the increase of temperature. This phenomenon might be caused by the softening of modified polyurethane at high temperatures. Although the specimens had oblique cracks, there was no spalling concrete, as shown in Figure 9b-d.  
Figure 10 shows the failure modes of the specimens of the splitting tensile test at different temperatures (0, 15, 40 and 60 \({}^{\circ}\)C). The failure modes of the specimens at different temperatures were almost similar. The main cracks appeared between the upper and lower loading points and destroyed the specimens. When the temperature was 0 and 15 \({}^{\circ}\)C, the main cracks were straight lines. When the temperature was 40 and 60 \({}^{\circ}\)C, the main cracks were curves. Finally, the specimens were split into roughly equal halves, which was a typical brittle failure. At failure, the specimens all made sounds which decreased with the increase of temperature. Figure 11 showed the fracture surfaces of one specimen. The polyurethane mortar, coarse aggregate and binder were damaged, as shown in Figure 11. This phenomenon showed that the cohesive force between aggregate and binder is strong.  
Figure 8: Loading diagram of cube splitting tensile experiment. (**a**) Test setup. (**b**) Schematic diagram.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 3 Phenology of Croplands  
Analysis of cropland phenology is different from that of forests in several ways. Although for both it is important to differentiate background signals (e.g., from bare soil) from vegetation, analysis of time series curves for croplands can be particularly challenging due to multi-cropping, management practices, and standing water for rice crops. Additionally, the multi-modal vegetation index time series curves observed in areas with high cropping intensity, or more than one cropping season annually, are in sharp contrast to the unimodal time series curves of forests. Therefore, processing of time series data must be tuned accordingly and allow for decreases in vegetation index values indicating events such as harvest or slurry spreading [31]. While these issues present a challenge for any remotely sensed approach to crop phenology, the higher spatial, temporal and spectral resolutions of Sentinel-2 offer potential improvements.
# GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
## 2 Data and Methods
### GNSS ZTD/PWV Retrieval Based on BE-PPP
PWV retrieval based on radiosondes: The University of Wyoming Atmospheric Science Radiosonde Archive (uwyo.edu) provides radiosonde data for global international exchange stations. The data files include atmospheric pressure, altitude, temperature, dew point temperature, relative humidity, wind direction, and water vapor mixing ratio for each layer. Using the water vapor mixing ratio \(m_{x}\), the specific humidity \(q\) can be calculated as follows:  
\[q=\frac{m_{x}}{1+m_{x}}. \tag{9}\]  
The discrete form of Equation (9) can be used:  
\[\mathrm{PWV}=\frac{1}{\rho_{w}}\sum_{i=1}^{n}\frac{\overline{q}_{i}}{\overline {s}_{i}}\Delta p_{i} \tag{10}\]  
In the equation, \(i=1,2,\ldots,n\) represents the \(i\)th atmospheric layer, \(\overline{q}_{i}\) represents the average specific humidity of the \(i\)th atmospheric layer, \(\overline{g}_{i}\) represents the average acceleration due to gravity of the \(i\)th atmospheric layer (m/s\({}^{2}\)), and \(\Delta p_{i}\) denotes the difference in pressure between the bottom and top of the \(i\)th atmospheric layer (hPa).
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 3 Materials and Methods
### Network Parameters Learning  
We train the constructed DualCT-Net in a supervised way. We denote \(\Omega\) as the training dataset. \(\Omega\) is composed of N data pairs \((x_{i},y_{i})\), where \(y_{i}\) denotes the compressive snapshot measurements corresponding to the original hyperspectral images, \(x_{i}\). In order to better guide the parameter learning, we design a composite loss function composed of root mean square error (RMSE) loss \(L_{RMSE}\) and spectrum constancy loss [44]\(L_{\text{{SCL}}}\). This composite loss function is defined as follows:  
\[L_{mix}(\theta)=L_{RMSE}(\theta,\Omega)+\gamma_{1}L_{\text{{SCL}}}(\theta, \Omega)\,, \tag{16}\]  
where \(\theta\) represents the network parameters, and \(\gamma_{1}\) denotes the weight parameters that coordinate the importance of the root mean square error and the spectral constant loss.  
Figure 5: Residual mask attention.  
Specifically, the \(L_{RMSE}\) loss calculates the root mean square error (RMSE) between the reconstructed images and the original images; the formula is defined as follows:  
\[L_{RMSE}(\theta,\Omega)=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-F_{Net}(y_{i}, \theta))^{2}}, \tag{17}\]  
where \(F_{Net}(y_{i},\theta)\) is the hyperspectral image reconstructed from \(y_{i}\) by DualCT-Net. Considering the spatial-spectral correlation of hyperspectral images, the spectrum constancy loss \(L_{SCL}\) is introduced and is defined as follows:  
\[L_{SCL}(\theta,\Omega)=\frac{1}{N}\sum_{i=1}^{N}\lVert\bigtriangledown_{ \lambda}\Omega_{i}-\bigtriangledown_{\lambda}x_{i}\rVert^{2}, \tag{18}\]  
where \(\bigtriangledown_{\lambda}\) represents the gradient along the spectrum across different spectral bands. This composite loss function can constrain the reconstructed hyperspectral image to approximate in the original domain and gradient domain, resulting in better reconstruction quality.
## 5 Result and Discussion
### Practicality Comparison  
The practicality of IMs can be represented by the regression parameter \(b\), slope of the PSDM in Equation (2). A larger value of \(b\) refers to a more practical IM. The practicality comparison of different IMs considering different Demand Parameters (max acceleration and max displacement of the structure) is illustrated in Figure 7. The figure shows that A\({}_{\text{rms}}\) tend to be the most practical IM while considering max acceleration as demand parameter, with the five most practical IMs being A\({}_{\text{rms}}\) > EDA > PGA > ASI > A95. The corresponding values \(b\) are 1.021, 0.971, 0.97, 0.968, and 0.967 respectively. These five most practical IMs are acceleration-related IMs.  
Figure 7: Practically comparison.  
On the contrary, VSI turns out to be the most practical IM while considering max displacement as demand parameter, followed by HI, SMV, V\({}_{\rm rms}\), and PGV. Interestingly, all of these five IMs are velocity-type IMs. Moreover, it needs to be mentioned that whether max acceleration or displacement is considered EDP, D\({}_{\rm rms}\), and PGD are the two least practical IMs that are displacement-type IMs.