esa-sceva/satcom-chunk-collection huggingface dataset:
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 6 Technical/Critical Analysis and Recommendations for Deployment
### Technical Analysis  
Our study aimed to improve the performance of the RPL routing protocol by integrating the Q-learning algorithm into it. Throughout the development phase, we encountered several technical challenges, the most notable being the construction and size management of the Q-table. Initially, we considered including metrics directly in the Q-table, which posed significant scalability issues. We opted for a simplified approach to overcome this challenge, maintaining the Q-table size proportional to the number of neighbors of the sending/forwarding node and integrating metric values into reward computations. This  
Figure 11: PDR progression in Montreal and Barcelona scenarios for different Q-RPL variants.  
decision facilitated more manageable Q-tables while preserving essential information for decision-making within the protocol.  
Additionally, we focused on fine-tuning the learning parameters. Special attention was given to the learning rate and the discount factor to balance the speed of convergence against the stability of the learning process. The learning rate was calibrated to control how quickly new information affected the Q-values, while the discount factor was adjusted to weigh the importance of future rewards. These parameters were optimized based on empirical results gathered from extensive simulations. This approach ensured that the learning process was neither too fast nor too slow, which could hinder timely convergence and adaptability in routing decisions. The selected settings demonstrated robust performance across various network conditions, significantly enhancing routing efficiency, as detailed in the results section.
## 6 Discussion
### Analysis of Precision  
The observation data of two ground GPS monitoring stations in the study area were compared with the ground vertical displacement obtained by the SBAS-InSAR technology, and the results are shown in Figure 12a,b. It can be found by comparison that the displacement curves obtained by the two monitoring methods were basically the same in trend. The maximum errors were 4.40 mm and 4.21 mm respectively, with a high fit, demonstrating the reliability of the SBAS-InSAR deformation monitoring results.
## 2 Versatile Video Coding (VVC)
### Intra Prediction  
VVC introduced 65 directional intra prediction modes along with the planar and DC modes, whereas HEVC supported 33 directional intra prediction modes. In addition, a wide-angle intra prediction mode was adopted for non-square blocks by substituting some traditional angular modes, and the candidate with the most probable modes list was increased to 6. Other intra prediction coding tools were included in VVC: (1) The multiple reference line tool was used for angular prediction by referencing neighbouring lines 1 and 3 of the prediction block [29]. (2) The mode-dependent intra-smoothing tool was used to enhance the prediction efficiency using a four-tap intra filter. (3) A cross-component linear model was used in chroma samples for prediction to build reconstructed luma samples in the same CU.
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 3 Message Exchange in Mixed-Reality Simulation  
In MR simulations where all entities can communicate with each other (V2V) and with GCS (similar to V2I), there are principally two networks--one among virtual entities and one among real entities (SWE and HWE) and GCS, where a simulation platform serves as a message relay between the two networks. Three types of connections are possible (SWE-SWE, HWE-HWE, HWE-SWE), and each of them employs different tools and methods for the message exchange and different processing of communication visibility of neighbors, see Table 1.  
An HWE-HWE connection uses real communication devices, such as radio-frequency (RF) modems, to exchange messages directly, and the visibility of neighbors is given by a physical RF signal propagation. In the respective SWE-SWE connection, messages are exchanged directly among virtual entities using a TCP/UDP socket connection, and their mutual communication visibility needs to be modeled. The HWE-SWE connection enables the MR interaction among entities that exist in the physical world and those that are being modeled. For this type of connection, both RF modems and TCP/UDP socket communication are required, as well as a message relay node that serves as a bridge between two different networks. In this case, SWEs send and receive their messages using the message relay node that is equipped with the RF modem of its own. The communication visibility in this type of connection is then partially determined using software models, and it is partially limited by the signal attenuation between the RF modems used for the actual message exchange.
## 3 Results and Discussion
### Identification of the Optimal Model  
The CNN system learns from the empirical set of data features automatically with multiple levels of abstraction. Hence, it allows learning the complex functions of the input data with features accessed automatically. The results of the different CNN layers and activation function scenarios, which were used for experimentations, are compared in Tables 4 and 5, considering accuracy, precision, validation loss, F1 score, and recall.  
Figure 14: Confusion matrix for the 12-lead ECG.  
Figure 13: Confusion matrix for the single-lead ECG.  
Network-8 exhibited good accuracy during the training and testing phases (99% and 100%), with less validation loss (0.00022 and 0.00075) for both single-lead and 12-lead ECGs. The results are acceptable, owing to the good precision and recall rates. The model by [PERSON]; [PERSON] et al. [10], of the long short-term memory (LSTM) network with a convolutional neural network (CNN), provided good results in automatically diagnosing CAD ECG signals with the inclusion of lead II and ECG segments. This limitation was easily overcome with the application of the present method by retaining the same accuracy. The prediction method of single-lead and twelve-lead includes data extraction and data classification. Since it is a complex methodology, the processing time might be higher compared to a normal repository dataset analysis. This model can further be tested for patients located in geographically-far locations, so that generality can be ensured for a wider diaspora. A technically experienced person can effectively deal with these types of prediction systems. The three-layer CNN network model (Network-8) (Figure 15) is preferred for the classification of CAD due to its superiority in performance.  
The results of Tables 4 and 5 indicate that a three-layered network with ReLU, ReLu, and LeakyReLU activation functions assigned to the first, second, and third hidden layers, was the best-suited architecture for the model, which was not attempted earlier. The developed three-layer CNN model worked equally well for both the single-lead and 12-lead ECG signal data, exhibiting generality. The performances of the developed model in terms of training and validation accuracies are plotted in Figure 16a,b for the single-lead. Figure 17a,b show good convergence of validation loss; it is an ideal fit for twelve-lead ECG/TMT-ECG data, which were extracted from the ECG/TMT-ECG graph. The model achieved an accuracy of 99% with a very small validation loss of less than 0.0008% on the 25 th epoch.  
Figure 15: The best CNN model for the single-lead ECG and the multi (12)-lead ECG.
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
## 6 Conclusions  
The MODIS Dark Target (DT) algorithm has been applied to retrieve AOD over land and ocean since early 2000. Although there have been significant improvements to the algorithm in the past decade, it has not yet been able to retrieve AOD over turbid coastal water due to its high water-leaving radiance from the ocean bottom or water color. We designed the Coastal Water AOD retrieval algorithm (CW algorithm) for these regions to supplement the current DT algorithm. The AOD retrieval algorithm for turbid coastal water takes advantage of the fact that water-leaving radiance is negligible at 2.1 um, hence this band is only used to retrieve while other auxiliary information such as aerosol single scattering properties are obtained from DT retrievals over the nearby non-turbid water surfaces. In other words, the aerosol modes and reflectance weighting parameter of the pixel to be retrieved is substituted by the closest counterpart from the MxD04 ocean AOD product.  
The CW algorithm not only fills the gaps of MxD04 AOD retrievals over turbid coastal water but also improves their comparison with AERONET measurements. CW AOD retrievals are validated against measurements of six AERONET sites that are located at coastal regions. The new algorithm yields \(\sim\)18% more of MODIS-AERONET collocated pairs, and CW AOD retrievals are comparable or better than MxD04 in quality; the RMSE of MxD04 and CW product is 0.154 and 0.147, respectively. In the situation that both AOD retrievals from MxD04 and CW are available within a 50 km diameter circular regions centered at AERONET sites, the merged AOD retrievals show better agreement with AERONET AOD either of them alone, in terms of the percentage of collocated pairs in the EE (49.2%, 40.6%, and 53.1% for MxD04, CW, and merged product, respectively) and RMSE (0.114, 0.148, 0.110 for MxD04, CW, and merged product, respectively). In addition, 0.05 + 0.15(MODIS AOD) can be considered as prognostic error of the merged product.  
Funding for this study was provided by the NASA Earth Science Division as part of NASA's Terra/Aqua and VIIRS science team program (grant numbers: NNX16 AT63G and NNX17 AC94A) and NASA's GEO-CAPE and KORUS-AQ program (grant number: NNX16 AT82G), and was also supported by Office of Naval Research (ONR's) Multidisciplinary University Research Initiatives (MURI) Program under the award N00014-16-1-2040. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON] provided guidance to the project, reviewed and edited the paper.  
The authors declare no conflict of interest.
## 3 Experimental Procedures and Results
### Feature Distribution and Results  
The FFT analysis was conducted using 283 U-phase stator current data obtained under various bearing conditions. First, the FFT analysis was performed for healthy motor where the bearing condition was healthy, followed by the other bearing conditions of SS and FS, respectively. Figure 4 compares the frequency spectra of the basic scratch faults (SS and FS) with that of the healthy case (H) at a rotation speed of 1765 min\({}^{-1}\). Similarly, the frequency spectra of multiple faults at a rotation speed of 1780 min\({}^{-1}\) were calculated for concentrated and distributed scratch faults. The amplitude was normalized so that the maximum was 0 dB. At frequencies of 30 and 90 Hz, large amplitude differences were observed depending on the bearing conditions. A similar difference was also seen in spectra obtained at other rotation speeds.  
A two-dimensional display of the features was provided for fault classification; the amplitudes of the 30 and 90 Hz frequency components were plotted along the \(x\) and \(y\) axes, respectively. The feature distributions of SS and FS at 1765 min\({}^{-1}\) are illustrated in Figure 5. Features under a given bearing condition gathered in a certain region; a small amplitude was observed in the cases of SS and FS compared with H. This case had no overlapping of distribution regions, and the fault condition was easily identified. The feature distributions of the concentrated scratch faults (C2S and C4S) at 1765 min\({}^{-1}\) are shown in Figure 6 for a multiple scratch analysis with H and SS. The features were localized depending on the faults, and a slight overlapping was evident. For the concentrated scratch faults, the amplitude increased with the number of scratches.  
In factories, motor rotation speeds are not constant, and feature distribution is discussed without considering the rotation speed. The features of H, SS, and FS at four rotation speeds are merged and illustrated in Figure 7. SS was diagnosed properly, even in this situation. However, H and FS showed overlapping features despite being distributed to their own classes. Since the feature distribution results of SS were extended along the results of H, differentiating the bearing conditions may be difficult. Thus, to predict the bearing condition effectively, further diagnosis was conducted using a deep learning model as a diagnostic tool. A similar trend and overlapping were observed in the case of the concentrated scratches, as shown in Figure 8, and a diagnosis was performed using the CNN.  
Figure 4: Spectral analysis of H, SS, and FS at 1765 min\({}^{-1}\).  
Figure 5: Feature distribution of H, SS, and FS at 1765 min\({}^{-1}\).  
Figure 6: Feature distribution of concentrated scratches at 1765 min\({}^{-1}\).  
Figure 7: Feature distribution without considering the rotation speed (basic scratch).
## 5 Proposed Contour Tracking Control Systems
### Contour Tracking Using 2 PID Control System  
In the position feedback loop of each axis LM, the contour tracking control system that composes of three 2 PID controllers is firstly designed, as shown in Figure 3. This controller for each LM comprises a main PID controller \(G_{b}(s)\) and a feedforward PD controller \(G_{f}(s)\) as follows [23]:  
\[G_{bx}(s)=K_{px}(1+\frac{1}{T_{lx}s}+T_{Dx}s), \tag{26}\]  
\[G_{byi}(s)=K_{pyi}(1+\frac{1}{T_{lyi}s}+T_{Dyi}s), \tag{27}\]  
\[G_{fx}(s)=-K_{px}(\alpha_{x}+\beta_{x}T_{Dx}s), \tag{28}\]  
\[G_{fyi}(s)=-K_{pyi}(\alpha_{yi}+\beta_{yi}T_{Dyi}s), \tag{29}\]  
where \(K_{px}\), \(K_{pyi}\), \(T_{lx}\), \(T_{lyi}\), \(T_{Dx}\), and \(T_{Dyi}\) are the P gain, I time, and D time control parameters; \(\alpha_{x}\), \(\alpha_{yi}\), \(\beta_{x}\), and \(\beta_{yi}\) are the feedforward parameters namely the P and D constants. As such, from (26)-(29), the control laws of the contour tracking control system in time-domain can be represented as follows:  
\[u_{bx}(t)=K_{px}\bigg{[}e_{x}(t)+\frac{1}{T_{lx}}\int_{0}^{t}e_{x}(\tau)d\tau +T_{Dx}e_{x}(t)\bigg{]}, \tag{30}\]  
\[u_{byi}(t)=K_{pyi}\bigg{[}e_{yi}(t)+\frac{1}{T_{lyi}}\int_{0}^{t}e_{yi}(\tau) d\tau+T_{Dyi}e_{yi}(t)\bigg{]}, \tag{31}\]  
\[u_{fx}(t)=-K_{px}\bigg{[}a_{x}x_{d}(t)+\beta_{x}T_{Dx}\dot{x}_{d}(t)\bigg{]}, \tag{32}\]  
\[u_{fyi}(t)=-K_{pyi}\bigg{[}a_{y}y_{i}(t)+\beta_{yi}T_{Dyi}\dot{y}_{d}(t) \bigg{]}. \tag{33}\]  
Therefore, the final control signal of the contour tracking control system can be formulated as:  
\[u_{x}(t)=K_{px}[(1-\alpha_{x})x_{d}(t)-x(t)+\frac{1}{T_{lx}}\int_{0}^{t}e_{x }(\tau)d\tau+T_{Dx}\bigg{[}(1-\beta_{x})\dot{x}_{d}(t)-\dot{x}(t)\bigg{]}], \tag{34}\]  
\[u_{yi}(t)=K_{pyi}\bigg{\{}(1-\alpha_{yi})y_{id}(t)-y_{i}(t)+\frac{1}{T_{lyi} }\int_{0}^{t}e_{yi}(\tau)d\tau+T_{Dyi}\bigg{[}(1-\beta_{yi})\dot{y}_{i_{d}}(t )-\dot{y}_{i}(t)\bigg{]}\bigg{\}}. \tag{35}\]  
Figure 3: The contour tracking control configuration of the LMs-driven X-Y-Y stage using two-degree-of-freedom proportional-integral-derivative (2 PID) controllers.  
In contrast with the conventional PID control with only one set of tuning parameters (\(K_{p}\), \(T_{I}\), and \(T_{D}\)), the 2 PID control scheme can simultaneously facilitate perfect tracking and disturbance rejection through the individual control loops by tuning its two sets of independent controller parameters. However, since there are totally fifteen control parameters required for the \(x\), \(y_{1}\), and \(y_{2}\) axes of the 2 PID contour tracking control system, it raises the severe difficulty in carefully tuning these control parameters for the LMs-driven X-Y-Y stage to achieve superior positioning performances. Besides, fixed control parameters also cannot perform favorable control performance levels against the unknown system uncertainties and external disturbances.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 3 Methods
### The Optimization Stages  
In the first stage (Section 4.1), reference measurements of the energy consumption of the oxygen separation process were performed. The measurements were made at the oxygen generator factory  
Figure 4: The test stand view.  
settings. The measurements aimed to determine the average energy consumption of oxygen separation in a standard low-capacity PSA generator. The determined energy consumption served as a reference point for optimizing the generator's operation.  
In the second stage (Section 4.2), the dependence of the separated oxygen purity on the difference between the adsorption pressure and the pressure in the oxygen tank was tested. To obtain this relationship, the adsorption pressure was set constant. Next, the outflow from the oxygen tank was controlled to achieve the desired pressure difference between the adsorption pressure and the pressure in the oxygen tank. After establishing the conditions, the purity of the separated oxygen was read.  
In the third stage (Section 4.3), the transfer time and the optimal oxygen separation capacity for different adsorption pressures were determined. The transfer time should be selected to take full advantage of the adsorption capacity at a given pressure. The second factor is the need to adjust the volume flow of oxygen received from the generator. It should ensure continuous and uninterrupted operation of the installation, but without excessively reduced oxygen pressure in the tank downstream of the generator (determined in stage two). During the measurements, the required operating pressure was set, and the purity of the separated oxygen was observed for different transfer times. The goal was to find the maximum transfer time with an oxygen purity of 95%. The measurements were carried out for adsorption pressures ranging from 2.5 to 6.0 bar(g) in 0.5 bar increments. The optimal oxygen flux for a given adsorption pressure was determined such that the pressure difference between the product reservoir and the adsorption bed did not drop below the value specified in the second stage.  
In the fourth stage (Section 4.4), the energy consumption of oxygen separation was measured at optimal generator settings. These settings were made as a result of the second and third steps. These measurements were performed in the adsorption pressure range from 2.5 to 6.0 bar(g), in 0.5 bar steps. After the oxygen generator was started, and the required pressure and oxygen purity in the tank were achieved, the appropriate oxygen stream from the tank was set, and the electricity meter readings were measured. After completing the measurement, the final reading of the electricity meter was made. The amount of separated oxygen was calculated based on the measured flow rate and the measurement time. These measurements were aimed at determining the energy consumption of the oxygen separation process with optimal generator settings.
## 5 Earth System Models
### Allometry  
ESMs rarely include dynamic responses to changes in resource availability. The most common allocation approach assigns C to each plant component (usually leaf, stem, and root) via fixed ratios that vary with plant functional type (PFT), but not spatially or temporally [38; 85; 88; 95; 107; 113; 114; 115]. For models that include N (and less often P), N uptake plays a strong role in governing C assimilation and drives competition between plants and decomposers. In these cases, C allocation requires an additional constraint of fulfilling C:N:P ratios. Nitrogen uptake is controlled by plant demand and decomposition requirements determined from fixed C partitioning and C:N stoichiometry. When N and P demands to maintain stoichiometric C:N:P are unmet, photosynthesis is downscaled [116; 117]. This simple approach works well for regions that have relatively stable environments, but it does not permit plant plasticity responses to changing nutrients. This method of C allocation has been highlighted as a weakness of these models [118; 119] and has resulted in some models swapping to more dynamic allocation schemes that allow responses to stress, as in [PERSON] et al. [120]. Carbon allocation in two ESMs (CTEM [86; 87] and ORCHIDEE [94]) is governed by the most limiting resource; light limitation results in more biomass being allocated to the stem, and water and N limitations result in more biomass being allocated to the roots. However, because N is not explicit in these models, the N limitation is parameterized.  
The simple representation of plant allometry in ESMs can have a significant influence on biogeochemistry since the allocation of biomass in the form of C to woody versus non-woody (or photosynthetic versus non-photosynthetic) plant components drives biogeochemical cycling, the quality and quantity of litter, and the duration of C storage. Our ability to model C uptake and subsequent storage therefore depends on including C allocation relationships and their responses under changes in resource availability.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 2 Peer-To-Peer (P2P) and Ad-Hoc Networks
### Ad Hoc Networks  
In many cases, network connections cannot be preserved in a disaster situation, and ad hoc networks can allow the rapid deployment of connectivity. There are multiple wireless mobile nodes dynamically forming a temporary network without using the existing network infrastructure such as cellular or circuit switched networks. As this ad hoc network has the limitations of wireless network interfaces, multiple hops are required, which also require routing protocols [17]. Since the protocol should consider transmission range, limited buffer space for message storage and hardware constraints, various mobility models have been developed [18]. However, disaster sensors are mostly located in fixed locations despite being able to be moved by disasters. This is a unique trait, as disaster sensors are not mobile. Therefore, it is required to consider the mobility when designing networks for disaster recovery networks. If disaster sensors are moved by natural causes such as heavy rain, storms, or strong winds, the location ID of those sensors shall be adjusted accordingly and updated in the DRA system. If required, additional sensors may be added to fill gaps in areas during scheduled or regular maintenance works of LPWAN networks and devices. Some disaster sensors may be out of service due to an empty battery and may need to be replaced in a timely manner; for example, every year, 10% of sensors are replaced when the battery lifetime is 10 years.