esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Burned Area Map  
#### 3.2.1 Results of Traditional Empirical Formula Method  
For comparison with the proposed method, we utilized the traditional empirical formula method [18] on the Hot Pot Fire and Range 12 Fire to obtain the burned area maps; the results are shown in Figure 9a,b. The empirical formula used to analyze the two wildfire cases is listed in Table 5. The red pixels in the figures are the burned area identified by the empirical formula method.  
The results demonstrate that the traditional empirical formula method can detect the main burned area in these two cases. However, there are too many misclassifications and miss-classifications in the burned area maps. Figure 9a shows that a few burned area pixels are not identified in the middle of the image, and a few unburned pixels are misclassified as burned at the lower right and lower left quadrants of the image. Figure 9b shows the poor detection results for the Range 12 Fire, the evidence being the large number of misclassifications. Although the main burned area in the middle of the image is identified, many pixels of unburned types are misclassified as burned. This is mainly due to the limited sample sets extracted from this wildfire case; in other words, the misclassified pixels were not extracted in the separability analysis, so the established empirical formula cannot separate them from the burned pixels. In comparison to the OLI-based burned area map, the traditional empirical  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Burned** & **Unburned** & **Average OE** & **Average CE** \\ \hline Burned & 120.90 \(\pm\) 1.92 & 0.10 \(\pm\) 0.30 & 2.72\(\pm\) 1.96\% & 0.08\(\pm\) 0.25\% \\ Unburned & 4.10 \(\pm\) 1.92 & 250.90 \(\pm\) 0.30 & 0.04\% \(\pm\) 0.12\% & 1.33\% \(\pm\) 0.95\% \\ \hline average OA & \multicolumn{3}{c}{98.68\% \(\pm\) 0.94\%} \\ average K & \multicolumn{3}{c}{97.46\% \(\pm\) 1.27\%} \\ \hline \hline \end{tabular}
\begin{tabular}{c} OE: omission error, CE: commission error, OA: overall accuracy, K: Kappa coefficient.  
\end{table}
Table 6: Average error matrix of accuracy assessment by a 10-fold cross-validation method. (The values are the mean and its standard deviations; the columns of the error matrix represent the actual types, and the rows represent the predicted types of the proposed method.).  
Figure 9: Results of burned area detection by the traditional empirical formula method. (**a**) Detected burned area in Hot Pot Fire; (**b**) detected burned area in Range 12 Fire.  
formula method achieves a CE of 20.16% and an OE of 20.00% in Hot Pot Fire, and it performed worse with a CE of 66.73% and an OE of 20.32% in Range 12 Fire. This weakness proves that the traditional empirical formula method has low applicability for burned area detection in varying regions.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 3 Design and Control of the Drive Circuit
### DC/DC Voltage Conversion Stage  
The boost stage of the circuit is implemented by the flyback converter, which evolves from the buck-boost circuit. Unlike a forward converter, the flyback converter has no secondary output inductance, thereby considerably improving its cost and volume advantages. The flyback converter is suitable for applications where low power and high boost ratio are required. In addition, the flyback converter minimizes reduction in energy efficiency and difficulty in manufacturability, because of the miniaturization of the circuit topology.  
When the power MOSFET (Q) is turned on, the primary current \(I_{p}\) starts to build up under the action of the power supply and energy is stored in the magnetizing inductance. When Q is turned off, the energy stored in the transformer starts to transmit to the secondary side through magnetic coupling and the rectifier diodes (D), thereby outputting a high DC voltage.  
The voltage and current waveforms of the flyback converter operating in a steady state are shown in Figure 5a. The converter operates in a discontinuous mode (DCM), in which the flyback transformer current must return to zero before a new switching cycle begins. This operation mode generally results in high efficiency at low output power levels [29]; it also simplifies the design of the control loop while achieving a high step-up ratio. The voltage step-up gain in DCM mode is given by  
\[\frac{V_{c}}{V_{bat}}=D\cdot n\cdot\sqrt{\frac{R}{2L_{m}f_{s}}}, \tag{4}\]  
where \(V_{c}\) and \(V_{bat}\) are the input and output voltages, respectively; \(D\) is the controllable on-time duty cycle; \(n\) is the transformer turn ratio; \(R\) is the equivalent load impedance, which represents the impedance of the half-bridge converter and the capacitive loads; \(L_{m}\) is the magnetizing inductance of the transformer; and \(f_{s}\) denotes switching frequency. As mentioned in [12], the voltage step-up gain of the flyback converter in DCM operation is considerably higher than that in continuous conduction mode operation.  
The output voltage is stabilized by a resistive feedback divider and an analog comparator. When the output voltage is higher or lower than the reference voltage, the microcontroller unit adjusts the switching duty ratio accordingly to monitor it in real time, as shown in Figure 5b. In this manner, the flyback converter operates at the desired level over a range of load currents.  
Figure 4: Bidirectional converter topology and control architecture.
## 4 Experimental Results and Performance Analysis
### Accuracy Analysis: Working with Optimum Number of Parameters  
Apart from the complexity and sensitivity, the performance of the four CD algorithms in terms of accuracy has to be analyzed. To illustrate the benefit of using best satellites instead of all satellites in view (complexity vs. sensitivity), let's compare the cumulative horizontal positioning error (HPE) of each CD algorithm, as shown in Figure 10.  
By observing these curves, we can see that the fact of combining two reference stations in the CD process has no impact on accuracy. We can see that the CD algorithm using all visible satellites has less positioning error than the others. Despite this, there is no significant difference between the HPE using only six best satellites and nine satellites (all satellites in view). This can be explained by the low influence of the weak signals exploitation, which come to collaborate to the collective detection metrics. We can deduce from this that we can limit the processing to only 6 satellites in order to minimize the computational load, with the goal being to use the minimum possible number of satellites while retaining a precise positional solution. The motivation idea is to carry out the minimum of calculations while retaining a good margin of precision of position.  
Figure 9: Compromise Sensitivity-Complexity of all 4 CD algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite**RS** & **Best RS** & **All RS** \\ \hline Best Satellites & less sensitive less complex & less sensitive more complex \\ \hline All Satellites & more sensitive less complex & more sensitive more complex \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of CD process.
## 2 Materials and Methods
### Controller Node Failure-Recovery  
Once a controller node failure is detected by the actuator node a recovery procedure is initiated. The aim of the recovery procedure is to intelligently identify the most suitable candidate among all available inter- and intra-satellite computing nodes to take over the control task of the failed controller node. As mentioned in the introduction the recovery procedure should meet certain design criteria:  
\begin{tabular}{l l} DC1: & It should scale with the number of nodes in the network \\ DC2: & Candidate selection should put a low processing burden on the computing power \\  & limited initiator node \\ DC3: & Candidate selection should not take longer than \(N_{m}in\,T_{s}\) to avoid an empty command buffer at the actuator node. \\ DC4: & Required communication should be kept minimal \\ DC5: & The possibility to include multiple objectives in the candidate selection \\  & While meeting the primary objectives of the new control assignment: \\ AO1: & High Quality-of-Service (QoS) of the new controller for the control-loop \\ AO2: & Avoidance of unnecessary burden on the network during control execution \\ AO3: & Avoidance of single-point of failures and unbalanced load on the computing nodes \\  & in the network \\ \end{tabular}  
The problem of re-allocation of the control task to a new controller node falls under the domain of task allocation in a multi-agent cooperative setting and is a problem in the field of classical Artificial Intelligence (AI). In general allocating the control task while also taking the m-tasks already running on the n-available computing nodes into account leads to the optimal distribution of (m+1) tasks among the n nodes, which is a combinatorial General-Assignment-Problem (GAP) and is NP hard [49].
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 2 Datasets
### The GF-6 Images Dataset  
#### 2.1.1 Study Area  
Tai'an is a prefecture-level city covering ~7761 km\({}^{2}\) in Shandong Province, eastern China (116\({}^{\circ}\)20-117\({}^{\circ}\)59\({}^{\circ}\)E, 35\({}^{\circ}\)38\({}^{\circ}\)-36\({}^{\circ}\)28\({}^{\circ}\)N; Figure 1); it has jurisdiction over the districts of Taishan and Daiyue, the cities of Feicheng and Xintai, and the counties of Ningyang and Dongping. Its terrain is highly variable, including mountains, hills, plains, basins, and lakes (Figure 1c). The mountains are concentrated in the east and north, including Mount Tai (one of the Five Great Mountains of China). The hills are mainly distributed in southwestern Xintai, eastern Ningyang, the northwestern city suburbs, southern Feicheng, and northern Dongping. The major basin lies within Dongping and contains Dongping Lake, Daohu Lake, and Zhoucheng Lake.  
In the study area, crops are divided into summer and autumn crops, according to the growing season. Summer crops mainly refers to winter wheat, and its growth period begins in the autumn and reaches the early summer of the second year. Autumn crops mainly refer to corn, millet, and potato. The growth period generally ranges from early summer to early winter. Therefore, crop planting areas are usually divided into winter wheat and farmland.  
There are eight main land-use types in the study area: developed land, water bodies, agricultural buildings, roads, farmland, winter wheat, woodland, and others. Developed land includes residential and factory areas, agricultural buildings refer to buildings in crop planting areas, and others refer to areas not used. These diverse landforms and land-uses render the study area representative of many different regions and suitable as an experimental area for this study.
# Place Recognition: An Overview of Vision Perspective
## 3 Convolutional Neural Networks
change, and seasonal and weather changes, while features extracted from top layers are more robust to viewpoint changes. Features learnt by CNNs are proved to be versatile and transferable, i.e., even though they were trained on a specific target task, they can be successfully deployed for other problems, and often outperform traditional hand-engineered features [18]. However, their usage as black-box descriptors in place recognition has so far yielded limited improvements. For example, visual cues that are relevant for object classification may not benefit the place-recognition task. Reference [19] designed a new CNN architecture based on VGG-16 and the VLAD representation; they removed all the fully connected layers and plugged the VLAD layers into it by making it differentiable. The loss function used in this paper is triplet loss, which can be seen in many other recognition tasks. Reference [14] gathered a huge scene-centric dataset called \"Places\" containing more than 7 million images from 476 scene categories. However, scene recognition is fundamentally different from place recognition. Reference [40] creates, for the first time, a large-scale place-centric dataset called SPED, containing over 2.5 million images. Though CNNs have the power to extract high-level features, we are far from making full use of them. How to gather a sufficient amount of data for place recognition, and how to train a CNN model in an end-to-end manner to automatically choose the optimal features to represent the image, are still underlying problems to be solved.
## 3 Materials and Methods
### Scenario Selection  
In order to assess the robustness of different algorithms and their general ability to forecast different price scenarios in the following analysis section, three representative periods with different characteristics are selected. The three scenarios are determined by the day-ahead electricity prices' mean and the standard deviation. The researchers of this study visually assess the prices. The goal is to have scenarios with high, moderate, and low mean electricity prices and a high, moderate, and low standard deviation. The approximated mean prices are 395, 250, and 110/MWh, and the corresponding standard deviations are 80, 150, and 306/MWh. Figure 3 gives an overview of the three selected scenarios:  
* Scenario 1 comprises the period from Friday, 9 September-Friday, 16 September 2022. This shows a day-dependent, cyclical behavior with normal price volatility.
* Scenario 2 ranges from Wednesday, 28 September-Wednesday, 5 October 2022. It is characterized by high volatility and an acyclical price fall towards 06/MWh.  
Figure 3: Representation of the scenarios based on the history of electricity prices on the German spot-market [(8)]. Scenario 1 in the period 9 September–16 September 2022. Scenario 2 in the period 28 September–5 October 2022 and Scenario 3 in the period 24 October–31 November 2022.  
* Scenario 3 covers the period from Monday, 24 October-Monday, 31 October 2022 and shows low volatility and periodic prices with an offset of around \(-250\)C/MWh.
# Thermometric Characterization of Fluorescent Nanodiamonds Suitable for Biomedical Applications
## 3 Results and Discussion
### Nanothermic Scale of High-Pressure High-Temperature Nanodiamonds
This  
Figure 2: Fluorescence spectra of NV\({}^{0}\) and NV\({}^{-}\) centres in HPHT nanodiamonds of 35 nm (**a**,**b**), 70 nm (**d**,**e**) and 100 nm (**g**,**h**) for temperatures from 25 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. The dependence of the maximum fluorescence intensity on temperature (**c**,**f**,**i**) is obtained through normalization and using a Gaussian model. A linear fit gives R\({}^{2}\) values are 0.97825, 0.82308 and 0.98147 for the NV\({}^{0}\) centre and 0.90851, 0.93936 and 0.98033 for the NV\({}^{-}\) centre, for nanodiamonds of 35 nm, 70 nm and 100 nm, respectively. fact may be attributed to sp\({}^{2}\) carbon contamination of the surface during the growth process [71] and the proximity of NV centres to her [72]. Furthermore, the nanothermic scales for 35 nm-sized nanodiamonds performed nearly as good as the 100 nm-sized nanodiamonds, differing in the overall spectrum broadening due to a smaller particle size [23]. As an example of thermal bioimaging at room temperature, a fluorescence image of the HeLa cells incubated with 100 nm nanodiamonds is shown in Figure 3a. Moreover, their original shape remains unaltered because of a non-cytotoxic interaction. The HPHT FNDs rendered biocompatible (>90%) for concentrations up to 0.1%, as depicted in Figure 3b. We observe that most nanodiamonds are accumulated preferentially on the protuberance generated by the nucleus' volume (Figure 3a). This feature has been used to indirectly measure the temperature on the malignant cell's nucleus, which presents a higher gene expression rate and protein production and demands higher energy levels of ATP consumption [73]. The thermal bioimaging for measuring cell temperature of protuberances may be an efficient technique to differentiate between healthy and infected cells [74]. Improvements to thermal bioimage could be achieved with the implantation of selected ions to the surface of FNDs to slow NV\({}^{-}\) to NV\({}^{0}\) conversion energy. Being H, an efficient fluorescent termination as demonstrated by [PERSON] et al. [75].
###### Abstract  
This study investigated compositional changes in red wines resulting from wine alcohol removal by reverse osmosis-evaporative perstration (RO-EP) and provides insight into the physical and chemical changes in reduced alcohol wine (RAW). Trial 1 involved RO-EP treatment of three wines that were analyzed pre-treatment, post-treatment, and post-treatment with alcohol adjustment (i.e., addition of ethanol to achieve the original alcohol content). Trial 2 involved partial dealcoholization of two wines and analysis of samples collected during RO-EP treatment, i.e., wine in, wine out, retenate, permeate (pre- and post-EP treatment) and strip water. Wine color was analyzed by spectrophotometric methods, while other compositional changes were determined by WineScan, high performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) analyses. In general, RAWs were slightly more concentrated than pre-treatment wines, which resulted in greater color intensity and increased phenolics and organic acids. However, partial dealcoholization resulted in lower concentrations of some fermentation volatiles, particularly ethyl esters, which may reflect ester hydrolysis following ethanol removal.  
alcohol; evaporative perstration; dealcoholization; reduced alcohol wine; reverse osmosis Article
## 5 Performance Evaluation
### Benchmark and Metrics  
In this work, \(k\)-means algorithm is employed as a benchmark method, since it was widely used in similar problems [16]. \(k\)-means is an unsupervised clustering algorithm, where the data points are clustered according to certain features. In \(k\)-means, a centroid is assigned to each cluster and the objective is to place the centroids to the position which yields minimum cumulative distance to the data points. In particular, in the initialization of the algorithm, the centroids are placed randomly and the data points are assigned to each centroid to form a cluster, and the assignment is done in a way that each data point is assigned to the cluster that is closest to it in terms of Euclidian distance. Then, the centroids are moved towards to the center of their clusters, and this process iteratively continues until the convergence, where the centroids cannot be moved anymore.  
Therefore, as this algorithm finds the position of the centroid, where the cumulative distance between the centroid and the data points is the minimum, it serves as a strong benchmark for this problem. In particular, if the UAV BS is considered as the centroid, while the ground users are the data points, the \(k\)-means algorithm positions the UAV BS at a point where it is closest to the ground users in terms of distance. Given that the distance is the primary parameter affecting the link quality between the transmitter and receiver, \(k\)-means algorithm becomes a appropriate benchmark. With this algorithm, we compute the centroid position related to the actual ground users' positions. Consequently, the centroid corresponds to the best 2D position for UAV, in terms of respective distances between UAV and ground users.  
Two different phases, one for training and one for testing, respectively, are performed to demonstrate the efficiency of the proposed \(Q\)-learning algorithm in terms of coverage and energy consumption. Regarding the coverage, we count the number of ground users, which are normally out of service from the terrestrial network, connected to the UAV BS, and the more users covered means a better performance in terms of the coverage. In the energy consumption, we measure the total energy consumption, \(ET\), of the UAV BS while it is providing service to the users, and the less energy consumption refers to abetter performance in terms of the energy consumption as the flight time of the UAV BS is prolonged.  
During the training phase of the developed \(Q\)-learning algorithm, the simulation is conducted through a certain number of episodes to populate the related \(Q\)-table and consequently achieve the needed learning. A trade-off between coverage and energy consumption prioritization is considered both in training and testing phases. Specifically, five different experiments are performed with different values of the weights (e.g., \(w_{1}\) and \(w_{2}\)) that are responsible of prioritizing the coverage or the energy consumption.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughs, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughs can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughs and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760 toc (SIFtoc at 760 nm) from a radiir view that was downscaled to F760 tot (SIFtot at 760 nm), NIR\({}_{v}\), and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760 tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760 tot and NIR\({}_{v}\) were able to distinguish the differences between different drought levels, and F760 tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760 tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought.  
solar-induced chlorophyll fluorescence (SIF); canopy level; leaf level; NIR\({}_{v}\); drought monitoring; soil moisture; winter wheat +
Footnote †: journal: Remote Sensing  
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0000-0002-2891-9999][PERSON] Assessing the Potential of Downscaled Far Red Solar-Induced Chlorophylll Fluorescence from the Canopy to Leaf Level for Drought Monitoring in Winter Wheat. _Remote Sens._**2022**, 14, 1357. [[https://doi.org/10.3390/rs14061357](https://doi.org/10.3390/rs14061357)]([https://doi.org/10.3390/rs14061357](https://doi.org/10.3390/rs14061357))