esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 3 Results  
### EVI Changes in Time  
The study area contained 241,378 forest pixels at 250 m resolution. Around 4% (\(n=10\),472) of these could not be processed by BFAST's algorithms mainly due to data gaps, leaving 230,806 pixels (12,416.83 km\({}^{2}\), 1,241,683 ha). A total of 15,904 breaks (11,969 negative and 3935 positive, 75.3% and 24.7%, respectively) were found by the bfast algorithm (Table 2). Figure 10 shows the temporal distribution of negative and positive EVI breaks. A peak of negative breaks (\(n=2487\); 21% of all negative breaks) occurred in the year 2013 while no clear peak was seen among positive breaks over the two decades analyzed. The median magnitude of change for negative breaks was \(-755\) while that for positive breaks was 723. The strongest median negative break (\(-1141\)) occurred in 2004 and the highest median positive break (1277) occurred in 2018. Comparing the 20-year median with the annual median, five consecutive years (2018 to 2022) were more negative than the overall negative median. Among positive breaks, four consecutive years (2008 to 2011) showed consecutive medians that were lower than the 20-year positive median.
# Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints
###### Abstract  
Supply chain management requires regular updates of the location of assets, which can be enabled by low power wide area networks, such as Sigfox. While it is useful to localize a device simply by its communication signals, this is very difficult to do with Sigfox because of wide area and ultra narrowband nature. On the other hand, installing a satellite localization element on the device greatly increases its power consumption. We investigated using information about nearby Wi-Fi access points as a way to localize the asset over the Sigfox network, so without connecting to those Wi-Fi networks. This paper reports the location error that can be achieved by this type of outdoor localization. By using a combination of two databases, we could localize the device on all 36 test locations with a median location error of 39 m. This shows that the localization accuracy of this method is promising enough to warrant further study, most specifically the minimal power consumption.  
localization; Internet of Things; low power wide area networks; Wi-Fi; Sigfox; fingerprinting +
Footnote †: journal: Applied
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Simulation Study
#### 4.2.1 Settings  
The simulation was implemented in C++ under Windows 10 O.S on a computer with Intel i5-3570 K 3.4 GHz processor with 8G RAM. The technical specification of the drone is followed by DJI Phantom and the results from [4]. The drone had a 15.2 V with a 4480 mAh battery. Instead of using _Max. Speed_ discussed in Equations (4) and (5), the drone flying speed was set to a more reasonable speed of 18 km/h with 20 min of flight time. The spatial granularity of the moving distance of the drone was one second. The effective sensing range for drones was set to 10 m (i.e., \(r\) = 10) according to our field experiments. The sensing frequency was 20 samples (i.e., 20 RSSIs) per second (0.2 Hz). A _median filter_ with \(n\) = 15 was applied to the collected samples according to our field experiments.  
The field configuration was an area of 1000 m by 1000 m. The number of drones varied in different settings. Initial starting points of drones were randomly placed at the edge of the field. The initial starting time (e.g., joining the SAR operation) of drones was also random. Multiple simulation runs (100 runs per setup on average) were conducted for each scenario and collected data were averaged over those runs with a 95% confidence interval.
## 3 Elicitors and Their Classifications
### Influence of Elicitors on Grape Aroma Biosynthesis  
Aroma biosynthesis is a complex process that involves different metabolic pathways synchronized during development by several enzymes (Figure 4) [146; 147; 148; 149]. External conditions such as elicitation influence the synthesis of volatile compounds [147]. The application of elicitors mainly impacts the composition of grape aroma in two ways. Elicitors modify the biosynthetic pathways, thus, influencing the accumulation and distribution of metabolites. On the other hand, elicitors enhance the absorption of water molecules into the berry cells, causing the size to expand and diluting soluble sugars and other primary metabolites, altering the volatile concentrations [4; 150]. Accordingly, the aroma concentrations either increase or decrease depending on the skin-to-pul ratio of the berry as influenced by the berry size because the grape skin is the essential site for the synthesis and storage of volatile aromas in large quantities [3; 151].  
The volatile compounds of most grape varieties are similar but differ after elicitation due to modifications in the pathways. The differences are most likely associated with the genes and enzymes in the biosynthesis pathways. Elicitors may have altered the aromated genes, and the associated enzymes could either be activated or deactivated [136; 150]. Moreover, as secondary metabolites, volatile compounds also play vital physiological roles such as resisting pests, and attracting pollinators, among others [152; 153]. For instance, terpenoids take part in the defense against herbivores, pathogens, and biotic and abiotic stressors [153; 154]. Since the primary function of elicitors is to induce defense mechanisms, the concentration of terpen increases specifically in most elicitor-treated grapes, as shown in numerous studies.
# Fragility Assessment of Traffic Embarkments Exposed to Earthquake-Induced Liquefaction
## 4 Results and Discussion
### Displacements, Accelerations and Pore Pressure Ratio Time Histories  
A representative deformed shape of the embankment, along with the location of the recording point of the vertical displacement used for the definition of the embankment damage state, is presented in Figure 5, for the case of a 6 m high embankment, underlain by soil profile S1 and subjected to a PGA at bedrock of 0.6 g. In the literature [37; 38], a similar failure mechanism was observed as was seen in the embankments' earthfill material considered in this study. In general, ground liquefaction causes lateral movements that result in rotational slope failure and crest settling. The amount and magnitude of such displacements depend on many factors, including GM intensity, embankment geometry and soil parameters. The majority of the deformations of the embankment occurred during a strong part of the GM, when the shaking had the greatest impact and liquefaction was fully developed. Later, the displacement increments gradually decreased and the displacements settled at their end values.  
For the same example presented in Figure 5, the displacement time history at the crest's midpoint, the pore pressure ratio (\(R_{u}\)) distribution in the middle of the liquefiable layer under the embankment's longitudinal axis and the acceleration time history under the embankment and at the base of the model are shown in Figure 6.  
From Figure 6b, it can be seen that the pore pressure ratio rose smoothly until it rapidly (in a second or two) reached unity during the strong part of the ground shaking. Afterwards, it gradually decreased with the weakening of the dynamic load. Combining these observations with the embankment deformations, it can be seen that during the same part of the ground motion, the majority of the displacement at the crest midpoint occurred (Figure 6a). When examining the results of all the performed analyses, it was apparent that the dissipation of excess pore water pressure was highly dependent on the soil parameters (e.g., permeability) and other drainage conditions. For example, when comparing the results obtained for soil profiles S1 and S2, both with the same thickness of liquefiable layer, it was noted that a faster dissipation and thus lower \(R_{u}\) at the end of shaking were obtained for soil profile S2 due to the absence of the clayev crust layer with its low permeability.  
Furthermore, a possible de-amplification effect on the acceleration time histories can be observed under the embankment (Figure 6c), since sandy soils that turn into a liquid mixture during liquefaction are incapable of transmitting shear stresses to the upper layers.  
Figure 5: Example of a deformed embankment shape (u\({}_{y}\)—vertical displacement at the crest midpoint used for derivation of fragility curves).
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 3 Proposed Deep Learning-Based MIMO Receiver
### Threshold for Receiver-Type Decision
 \tag{12}\]  
To ensure the proposed receiver guarantees a certain level of error performance compared to the SER of the ML receiver, we set the threshold \(\eta\) as follows:  
\[\eta=\gamma\mathbb{E}_{\mathbf{H}}\Big{[}\bar{P}_{S}^{ML}(\rho,\mathbf{H}) \Big{]}, \tag{13}\]where \(\gamma\) denotes the scaling factor for the threshold \(\eta\). In (13), \(\mathbb{E}_{\mathbf{H}}[\cdot]\) represents an expectation over \(\mathbf{H}\), so \(\mathbb{E}_{\mathbf{H}}\big{[}\bar{P}_{S}^{ML}(\rho,\mathbf{H})\big{]}\) can be calculated by the average of \(\bar{P}_{S}^{ML}(\rho,\mathbf{H})\) over a number of channel realizations. Substituting (13) into (12), \(f_{R}\) is rewritten as  
\[f_{R}= \begin{cases}0;&\dfrac{\bar{P}_{S}^{ZF}(\rho,\mathbf{H})}{\mathbb{E}_{ \mathbf{H}}\big{[}\bar{P}_{S}^{ML}(\rho,\mathbf{H})\big{]}}>\gamma,\\ 1;&\dfrac{\bar{P}_{S}^{ZF}(\rho,\mathbf{H})}{\mathbb{E}_{\mathbf{H}}\big{[} \bar{P}_{S}^{ML}(\rho,\mathbf{H})\big{]}}\leq\gamma.\end{cases}  \tag{14}\]  
As explained in Section 2, the ZF receiver and ML receiver have \(N_{R}-N_{T}+1\) and \(N_{R}\) of diversity order, respectively, and this implies that the gap in SER performance between the two receivers widens as the SNR increases. To reflect the SER gap according to SNR, we set the scaling factor \(\gamma\) as follows:  
\[\gamma=\alpha\Bigg{(}\dfrac{\mathbb{E}_{\mathbf{H}}\big{[}\bar{P}_{S}^{ML} \big{]}}{\mathbb{E}_{\mathbf{H}}\big{[}\bar{P}_{S}^{ZF}\big{]}}\Bigg{)}^{\beta}, \tag{15}\]  
where \(\alpha\) and \(\beta\) represent a coefficient and exponent depending on the system parameters such as antenna configurations and modulation order, respectively.
# Desertification Mitigation in Northern China Was Promoted by Climate Drivers after 2000
Over the arid region of NC, except for human agricultural activities and ecological water conveyance, precipitation is the main factor driving the spatial distribution of desertification [33]. Rapid global warming has led to increases in the frequency and intensity of extreme precipitation and droughs, further increasing the risk of desertification [34; 35]. Climate driver (such as precipitation, temperature) variations are linked to the changes in atmospheric circulation. Studies have demonstrated that Northeast China experienced a noticeable increase in temperature starting in the mid-1990s, accompanied by interdecadal changes in large-scale circulation marked by a high-pressure anomaly [36; 37; 38]. Along with the temperature rising, observations revealed a significant interdecadal transition in Northwest China's summer precipitation after the late 1980s, with increases in the amount and intensity of precipitation [39; 40]. Studies also indicated that desertification increased in NC prior to the 1990s and subsequently underwent a reversal [41; 42]. However, how atmospheric circulation is linked to the impacts of climate drivers on desertification remains unclear. Additionally, various ecological restoration projects were implemented, including the Three-North Shelter Forest Program and Grain-for-Green. These major ecological projects were also implemented in different regions and at different times [43; 44]. Therefore, the impacts of climate drivers and human activity on desertification may have varied during different periods. However, under the background of climate change, the contributions of climate drivers and human activity to desertification in NC in different periods should be further explored. This study focused on two aspects: What were the contributions of climate drivers and human activity to the desertification process in NC over the past decades? What were the dominant factors that drove desertification during different periods and the mechanism behind desertification? To answer these questions, this study utilized remote sensing data and meteorological datasets to analyze desertification. The methodology involved a quantitative analysis using statistical models and remote sensing techniques to assess contributions to desertification changes. Then, this study identified the primary drivers of desertification evolution, separating natural and anthropogenic factors, which was aimed at deepening our understanding of the mechanism of desertification evolution. This paper concludes with a discussion of the mechanisms underlying these drivers based on the attribution results.
## 2 Experimental Methods  
### Electrode Preparation  
All purchased commercial materials in this study were used as received. Three types of commercially available carbon blacks (acetylene black, Vulcan XC-72R, and Ketjenblack ECP) were obtained from Xiamen Top New Energy Technology Company (Xiamen, China). Each type of carbon was mixed with polytetrafluoroethylene (PTFE) in different ratios (10%, 20%, 30%, 40%, and 50% by weight of PTFE for each carbon) in isopropanol solution. The corresponding carbon slurries were ultrasonically oscillated for 1 h to ensure thorough mixing of carbon and binder. The slurries were then filtered through a 20-mesh sieve to remove larger particles that could adversely affect the electrode preparation process. The prepared carbon slurries were uniformly coated onto W051011 carbon cloth using an LTD model automatic coating machine bought from Xiamen Top New Energy Technology Company(Xiamen, China). The total coating thickness was 300 \(\upmu\)m. It was divided into two layers of 150 \(\upmu\)m each to prevent cracking after electrode drying. After each coating, the electrodes were dried in ambient air for 6 h, followed by a heat treatment at 350 \({}^{\circ}\)C for 30 min to complete the electrode fabrication. The carbon content of different carbon electrodes is shown in Table 1.
## 4 Conclusions  
The enzymatically hydrolysed AP demonstrated different technological properties depending on the composition of the used commercial enzymes and the duration of the enzymatic hydrolysis. Hydrolysis with Pectinex(r) Ultra Tropical and Viscozyme(r) L significantly decreased the DF content by increasing the RS content, water-swelling capacity and water solubility index, and emulsion stability. Meanwhile, by using Celluclast(r) 1.5 L, the highest amount of SDF was produced. The probiotic _Bifidobacterium animalis_ DSM 20105 viability in the jelly candies during 56 days of storage remained above the recommended level for probiotic food (more than 6 log10 CFU/g). A sensory evaluation indicated that the jelly candies enriched with hydrolysed AP and probiotics were acceptable to the assessors. The obtained results demonstrate that Celluclast(r) 1.5 L can be used for increasing the soluble dietary fibre in apple pomace, which can be further used in conjunction with _B. animalis_ for added-value jelly candy production or can applied in other functional foods. These findings highlight the potential for pomace valorisation and its application in functional foods, not only by increasing its nutritional value and functionality as a probiotic carrier but also by showcasing its promise in the development of innovative food products. Further studies should focus on assessing the broader functional properties of this product and exploring the adaptability of such formulations using different types of pomaces. Additionally, studies should assess the viability of different probiotic strains, including their survival during storage and digestion. This could provide valuable insights into the potential applications and benefits of these products in various contexts.  
Conceptualization, J.J. and D.C.; methodology, J.J. and D.C.; software, J.J.; validation, J.J.; formal analysis, J.J.; investigation, J.J.; resources, D.C. and L.B.; data curation, L.B.; writing--original draft preparation, J.J. and D.C.; writing--review and editing, J.J., L.B. and D.C.; visualization, J.J.; supervision, D.C. and L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 4 Results and Discussion
### Implementation of DQN-Based EMS in MATLAB/Simulink  
We carried out the simulation of the designed HRES in the Reinforcement Learning Toolbox of the MATLAB/Simulink environment. The time interval between two time-steps was one hour. There was a total of 5000 episodes during the training process where each episode ran for a randomly selected 48-h period. At the beginning of each episode, random initial conditions were generated including the initial state of charge and the initial amount of hydrogen in the tank.  
Based on the experiences from previous publication [34] as well as trial-and-error during the training process, the structure of the network and its training parameters were determined. This is a usable reference in this area because there are not many publications that discuss details of the implementation of DRL for an HRES. In this study, the structure of the critic network applied for the DQN method is depicted in Figure 8, while the initial setting parameters for the simulation are displayed in Table 1. The amount that the network weights are updated during training is referred to as the step size or the learning rate (\(\alpha\)). A large learning rate helps the agent to learn faster, and it could obtain the local optimal solution. On the other hand, a smaller learning rate may allow the agent to learn a global solution but may take significantly longer to train. In this study, the learning rate of the critic network is set to 0.001. It would mean that weights in the Q network are updated 0.1% of the estimated weight error each updating time. The action space of DQN comprises the combination of the actions of the four system components: battery, fuel cell, electrolyzer, and diesel generator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Value** \\ \hline Memory capacity & \\ Batch size & 64 \\ Discount factor (\(\gamma\)) & 0.9 \\ Exploration rate (\(\varepsilon\)) & 1 \\ Decay of exploration rate & 0.001 \\ Minimum exploration rate (\(\varepsilon_{min}\)) & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the simulation of the DQN-based EMS.  
Figure 8: The structure of the critic network for the DQN-based EMS.  
The discount factor (\(\gamma\)) affects how much weight it gives to future rewards in the value function. \(\gamma=0\) means that the agent will be completely myopic and only studies actions that produce an immediate reward. \(\gamma=1\) means that the agent will assess each of its actions based on the sum total of all of its future rewards. Exploration rate (\(\varepsilon\)) is the probability that our agent will explore the environment rather than exploit it. It is set to 1 at the beginning and reduced gradually over the training time. This ensures that the agent has enough time to explore and learn all about the environment.
## 6 Conclusions  
In order to control and avoid the stick-slip vibration in deep hole drilling, this paper simplified the drilling system to a two-degree-of-freedom discrete model and studied the non-linear dynamic stability without considering the bit bounce. There are two kinds of delay models (CD model and SDD model) in stick-slip vibration that are caused by regenerative cutting. The linear stability solved by the characteristic equations and Hopf bifurcation characteristics solved by the MMS are compared with the two delay models; our conclusions are as follows:  
1. The stability interval of the drilling system increases with rotational speed. In the linear stability analysis, the stability difference between CD model and SDD model is very small when the control parameter \(\rho\) is small. However, when the control parameter \(\rho\) become large, the stable intervals of the SDD model are bigger than those of the CD model, which means the SDD model can be applied to a wider operational range.
2. It is found that there are only subcritical Hopf bifurcation regions in the CD model through the study of Hopf bifurcation properties. The Hopf bifurcation properties of the SDD model depend on the control parameter \(\rho\). The subcritical Hopf bifurcation point on the right of the stability lobe is transformed into the supercritical Hopf bifurcation point with the increase of \(\rho\).
3. In the subcritical Hopf bifurcation point, the stable cutting points that co-exist with the limit cycle (stable periodic motion) in the system stability interval may also generate the stick-slip vibration. In the supercritical Hopf bifurcation point, the stable periodic motion co-exists with the cutting points of the linear unstable region, while the linear stable region does not have stable periodic motion. Therefore, the system will not produce stick-slip vibration in the stability interval.  
In conclusion, the SDD model can be applied to a wider operational range than the CD model, and can better reflect the non-linear nature of the drilling system. On the one hand, the stability bounds of the SDD model are higher than for the CD model. On the other hand, the supercritical Hopf bifurcation point of the SDD model is more conductive to the stability of the drilling system. The method and results can be adopted for deep hole drilling stability prediction and provide a reference for the dynamic optimization design.  
[PERSON] and [PERSON] conceived and designed the study; [PERSON] and [PERSON] performed the simulation; [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 51675091), the Joint Funds of the National Natural Science Foundation of China (Grant No. U1708257), and the Collaborative Innovation Center of Major Machine Manufacturing in Liaoning.  
The authors declare no conflict of interest.  
Figure 21: The variation of the amplitude \(a\) with the dimensionless cutting depth \(\psi_{SDD}\) (\(\Omega=4\), \(\psi_{c}=0.116\), \(\rho=30\)).