esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 3 Descriptive Analysis
### Sensor Type  
In the study, we also analysed how often particular sensor types and satellite systems were used in the estimations. The reviewed studies were based on various applications of Earth observation data received from 30 different remote sensing sensor systems. The dominant applications were using optical sensors (264 cases, 80%), followed by thermal (Modis) (21%), LiDAR (7%), synthetic aperture radar (SAR) (5%), and aerial photo data (2%) (Figure 6a).  
Figure 6b shows the distribution of cases that use single or multiple remote sensing data sources. Almost half of the reviewed studies (154, 47%) used a single data source in their studies. In order to enhance the accuracy of forest burned area, another 41% of the studies used multiple optical data sources. About 12% of the studies used a combination of optical data and data from active sensors such as ALOS PALSAR and LiDAR.  
In order to assess satellite sensors, we discussed three classes of spatial resolution: low (larger than 30 m), medium (5-30 m), and high (under 5 m). The most frequently used optical sensor type (185 cases, 40% of all studies) were multispectral Landsat systems of medium resolution, such as the Multispectral Scanner System (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the Operational Land Imager (OLI) (Figure 6c). The next image sources of medium resolution for burnt area studies mainly came from multispectral sensors including the Sentinel Multispectral Instrument (MSI) (41 cases or 8.9%), multiple sensors of SPOT satellite (25 cases, 5.2%), Linear Imaging Self-Scanning Sensors (LISS) of Indian ResourceSat family satellites (10 cases, 2.1%), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) of Terra satellite (5 cases, 1.1%), and the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) (5 cases, 1.1%). In terms of remote sensing imagery with low resolutions, the commonly used data sources were the Moderate Resolution Imaging Spectroradiometer (MODIS) (94 cases, 20.2%) on board of Terra and Aqua satellites, the Advanced Very-High-Resolution Radiometer (AVHRR) (27 cases, 5.2%), the Visible In  
Figure 6: Remote sensing sensor types and sensor systems used in selected publications: (**a**) number of sensor types applied; (**b**) sensors combination; (**c**) distribution of optical sensor system used in the studies.  
-frared Imaging Radiometer Suite (VIIRS) (4 cases, 0.9%), and the Envisat Medium Resolution Imaging Spectrometer (MERIS) (4 cases, 0.9%). Few studies were conducted with the use of high spatial resolution systems such as WorldView (6 cases, 1.3%), Ikonos (5 cases, 1.1%), Quickbird (4 cases, 0.9%), and Resurs (2 cases, 0.4%).
## 3 Simulation and Discussions
### Specific Process to Improve Wavelet Energy Entropy  
Since this simulation is a discrete simulation, the set step size is 5 \(\times\) 10\({}^{-6}\), thus 4000 data points in one work frequency cycle are taken for analysis. The 4000 points are divided into 40 groups of 50 points each, and the time series composed of them are wavelet-transformed, and the energy entropy is obtained. In this way, a set of current data is turned into a 40-point energy entropy sequence. Since six sets of voltage and current data are obtained from each simulation group, a 6 \(\times\) 40 feature matrix is composed, representing 40 six-dimensional column feature vectors, as the training object of the neural network. The specific flow is shown in the following Figure 4.  
The 500 sets of training data were divided into five categories of levels with new energy percentages of 0-10%, 10-20%, 20-30%, 30-40%, and 40-50%, respectively, and were recorded as R1, R2, R3, R4, and R5. Each set of data from each category was analyzed for its energy entropy data, as shown in the following Figure 5. (R1-R5 is conducted in the same simulation model.)  
Figure 3: Current signal of low-voltage feeder under different proportions.  
From the figure, it can be found that the peak of the improved energy entropy is concentrated in the starting phase as well as the midpoint phase, and the other phases are relatively smooth, indicating that the new energy fluctuation amplitude is more obvious in these three phases. Therefore, if we want to improve the operation speed of the algorithm and reduce the storage burden, we can segment the waveform and intercept the key segments for feature extraction, which can effectively improve the subsequent training speed.  
At the same time, observing the waveform information at different levels under the same stage, it can be found that in the start-stop stage, the improved energy entropy shows an increasing trend as the share of new energy increases, with the most drastic change in the end stage. In the intermediate stage, the energy entropy shows a small range of sinusoidal characteristics and changes in a similar way as the start-stop stage. Therefore, it indicates that the improved energy entropy taps the information of signal characteristics under various types of levels.
## 3 Results and Discussion
### Quantitative Analysis and Normalized bOCT Contrast
#### 3.2.2 Comparison of Depths  
The average contrast was obtained across all subjects, irrespective of gender and age differences, for the shallow (left) and deeper (right) regions, respectively, and is shown in Figure 9. A histogram of the average biospeckle contrast was calculated from ROIs of different depths for all of the participants, irrespective of gender and age. * and ** denote respectively, the average biospeckle contrast before and soon are statistically significant with respect to that of at rest after five minutes of heating. From the histogram results for the shallow region close to the epidermis-dermis border, the effects of heat stress are significant, and this could possibly be due to the surface capillaries crisscrossing the surface along the papillary dermis layer at the border region of the epidermis and dermis. However, as shown in the red columns for ROI2 for the deeper region within the dermis, there is no significant difference in the heat stress dependence of the whole of the population with respect to heat stress.  
Figure 8: Average speckle contrast of ROI1 calculated with rest data by dividing the subjects into two groups indicated respectively, in blue and red. A \(t\)-test carried out with the groups revealed no significant differences between them, thus demonstrating the long time stability of the experiment itself.  
Figure 9: Average speckle contrast as a function of three different conditions obtained for all participants for two different depths: one shallow, close to the epidermis–dermis border, indicated as ROI1, and the other within the dermis, denoted as ROI2. Comparing the shallow and deeper results, there is no significant difference between any of the three conditions. In the shallower regions, the difference between 5 min rest after heating is statistically different from before heating, as indicated by *, and also from soon after heating, as denoted by **.
## 4 Results and Discussion  
### PPMCC Analysis of Water Quality Indicators  
After standardization and normalization of the experimentally measured data of the six water qualities, the correlation analysis of PH, temperature, conductivity, salinity, total dissolved solids, and DO were performed, and the confusion matrix of the corresponding PPMCC values was obtained as shown in Figure 3. It can be seen from the results that the PPMCC values of DO and PH were only 0.244, and the PPMCC values with temperature, conductivity, salinity, and total organic salts were between \(-0.7\) and \(-0.8\). According to the range classification criteria of \(|r|\), it can be assumed that there is little correlation between PH and DO, while the other characteristics are moderately correlated with DO. Therefore, according to the PPMCC value, the water quality data of temperature, conductivity, salinity and total dissolved solids that have a certain correlation with DO were initially screened.  
Figure 4 further shows a two-dimensional scatter plot of the linear fit correlation between DO and Temp, CON, Salt, and TDS, respectively. The results depict that the proportion of dissolved oxygen can be explained by temperature is the highest, and there is an inverse ratio between the two, indicating that the higher the temperature in the pond, the lower the dissolved oxygen content in the water. Similarly, the electrical conductivity, salinity, and total organic salt content are inversely proportional to dissolved oxygen, indicating that the dissolved oxygen content in the water will decrease significantly when  
the water temperature, water salinity, and solid dissolved matter content are higher, which is not suitable for perch growth. It can also be concluded that the relationship between the dissolved oxygen and other parameters in water is partially linear based on Figures 3 and 4.
# An Energy Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks
## 3 Clustering Routing Framework  
### Network Layering  
Layering the network helps nano-nodes find the layers they belong to. Assuming that the monitoring area is circular, the entire network is divided into several virtual layers as the NC is set to be center. The width of each layer is \(\tau\)/2, the radius of the \(l_{\text{th}}\) layer is \((l+1)r/2\), where \(l\) is the number of layers, and \(r\) is the communication range of each nanomode. This way of dividing layers ensures that the transmission range of each nanomode can cover its adjacent layers. When a broadcast message is received from the NC, all nano-nodes calculate their distance from the NC, compare their distance to the layer radius, and register themselves to the appropriate layer. The algorithm of Nanonode Distribution over Layers (NDL) is given in Algorithm 1.  
```
Notations: \(v_{k}\) = No. of deployed nanosensors \(d_{k}\) = Distance of nanosensor \(v_{k}\) with respect to NC \(L(v_{k})\) = Layer number to which node \(v_{k}\) is assigned \(r\) = Transmission range of each nano-node Data: \(r\), \(v_{k}\) Result:\(L(v_{k})\) NC broadcasts \"Hello packets\" to all \(v_{k}\); foreach\(v_{k}\)do \(L(v_{k})\) = floor(\(2\frac{d_{k}}{r}\)); \(v_{k}\) registers itself into layer \(L(v_{k})\); end foreach
```  
**Algorithm 1**NDL Algorithm  
The number of clusters of the \(l_{\text{th}}\) layer is  
\[\frac{\pi\Big{(}\Big{(}\frac{l+1}{2}\Big{)}^{2}r^{2}-\Big{(}\frac{l_{\text{p}}} {2}\Big{)}^{2}\Big{]}}{\pi\Big{(}\frac{r}{2}\Big{)}^{2}}=2l+1 \tag{12}\]
## 4 Feature Extraction of RC Valve Fault
### Fault Characteristics Analysis of Excitation Signal
#### 4.1.3 Extraction of Excitation CIs  
The excitation CIs used in this paper are as follows:  
The root mean square (RMS) is the square root of the mean value of the sum of squares of the sample signal, which reflects the amplitude and energy of the signal in the time domain.  
\[RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i})^{2}}, \tag{11}\]  
Kurtosis (K) is the normalized fourth-order center distance of the sample data, which reflects the distribution characteristics of the data and it is particularly sensitive to shock signals. The formula is defined, as follows:  
\[K=\frac{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{4}}{\left(\frac{1}{N}\sum_{i=1}^ {N}(x_{i}-\mu)^{2}\right)^{2}}, \tag{12}\]  
The correlation coefficient (CC) is used to characterize the degree of correlation between two data sets. The value range of CC is \(-1\) to \(1\). The closer to \(1\) or \(-1\), the stronger the correlation. The formula is defined, as follows:  
\[CC=\frac{\sum_{i=1}^{N}(x_{i}-\mu_{x})\Big{(}y_{i}-\mu_{y}\Big{)}}{\sqrt{\sum_{ i=1}^{N}(x_{i}-\mu_{x})^{2}}\sqrt{\sum_{i=1}^{N}\big{(}y_{i}-\mu_{y}\big{)}^{2}}}, \tag{13}\]  
where, \(x_{i}\), \(N\), \(\mu\) represent sample data, number of samples, and mean value, respectively.  
In this paper, CC is calculated by leaking valve data and normal valve data, and CC is processed by taking absolute value (\(CC\in[0,\ 1]\)).  
From each group of data, 50,000 data were taken and divided into 50 parts, and EEMD decomposition was performed, respectively. The K, RMS, and CC are calculated while using the signals reconstructed by the IMF8 and IMF9 of each group. Table 1 shows the calculated excitation CIs of each group, and the number of each CI for each set of data is 50. The LL denotes the leakage level.  
Figure 10: Frequency-domain waveform of IMF8 and IMF9; (**a–d**) represent normal valve, 2 mm leakage, 6 mm leakage, and 8 mm leakage valve, respectively.  
From the CIs in Table 1, it can be roughly seen that, as the degree of valve leakage increases, the K and CC show a decreasing trend. The trend of the CI that is calculated by the experiment is consistent with the trend of the CI of the theoretical analysis.
## 4 Anti-Cancer Activities of Tanshinones
### Interaction with DNA Minor Groove and P53 Activation  
[PERSON] _et al._[250; 251] reported that TIIA could interact with DNA minor groove, and in turn activate P53 and induce apoptosis by interrupting RNA polymerase II (RNAPII)-dependent transcription in cancer cells. It has been documented that groove binding agents (such as distamysin A) can arrest RNAPII-dependent transcription via altering the conformation of the transcribed DNA [252; 253; 254]. The transcription arrest will trigger phosphorylation and degradation of RNAPII and in turn activate P53-mediated apoptosis, a process known as RNAPII response [255; 256; 257]. TIIA could interact with duplex DNA by binding to the minor groove (preferentially to A/T-rich sequences) [250]. Similar to other DNA groove binders, TIIA treatment induced RNAPII response in H22 liver cancer and K562 leukemia cells [251]. TIIA at 0.4-4 \(\upmu\)M increased RNAPII phosphorylation in a concentration-dependent manner. When TIIA concentration rose above 4 \(\upmu\)M, RNAPII was fully phosphorylated and began to degrade. P53 was activated by TIIA exactly at the same concentration where the RNAPII level started to be down-regulated. P53 activation and apoptosis were observed in the concentration range of 4-20 \(\upmu\)M of TIIA treatments [251]. Moreover, activation of P53 was independent of ataxia-telangiectasia mutated (ATM) activation, suggesting that TIIA-induced P53 activation in these cells was not through ATM-mediated DNA damage-response pathway [251]. Furthermore, _in vivo_ administration of TIIA (40 mg/kg of TIIA in 0.5% carboxymethyl cellulose, _i.p._ injection, every other day for 10 days) induced RNAPII down-regulation and apoptosis in H22 xenograft tumors. Taken together, the results provide a possible mechanism of TIIA activating the P53 pathway and inducing apoptosis in some cancer cells.
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 5 Experiment and Numerical Results
### Results  
#### 5.2.1 Convergence Evaluation  
We first evaluate the convergence of our proposed method to illustrate its performance. As shown in Figure 4a, we show the reward values, which are significant because they impact the convergence, using different settings (e.g., datasets of 30, 50, and 70 drugs). With the smallest setting, we obtain the fastest convergence, in close to 2800 iterations, because this setting occupies the smallest state space. The system meets the stop condition at around 3200 and 4000 iterations for the larger settings of 50 and 70 drugs, respectively. This evaluation also illustrates a promising aspect of our proposed method: when we increase the number of drugs in the system, the number of iterations does not exponentially increase.  
In the next evaluation, we show the mean error rate of the DNN in Figure 4b. This figure shows how the convergence of the DNN is affected by varying the learning rate. The DNN can reach a fast convergence when the learning rate is 0.01 (less than 5000 s), but the error rate is still high. After some experiments, we selected a learning rate of 0.005, which resulted in a low error rate where the system reached convergence after 10,000 s. Although it requires a long training phase, with this rate, the DNN has a better decision-making performance. The convergence status allows us to demonstrate the total cost in the system compared to the optimal results calculated by the Ipopt solver. In Figure 4c, a small gap is visible between the optimal value and the DRLD result for all settings. However, the optimal gap depends on the training phase and the training data. With our limited data set, we present a simple, but promising result in this work. We will investigate this aspect in future work to find a real public dataset with which to obtain a practical training model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Settings** \\ \hline Storage demand of drugs & [0.001–0.005] ft\({}^{3}\) \\ Total inventory capacity & 40 ft\({}^{3}\) \\ Refilling cost of drugs & [5–100] USD/unit \\ Storing cost & [2–5] USD/ft\({}^{3}\) \\ Drug demand & [5–20] unit \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation settings.  
Figure 4: Convergence evaluation. DRLD, Deep Reinforcement Learning model for Drug inventory.
## Data and Methods
### Wetland Probability Mapping Algorithm
#### 2.3.3 Tasseled Cap Transformation  
The Tasseled Cap (TC) transformation separates spectral features in MODIS NBAR reflectance data that relate to different land surface properties. The transformation consists of a principal component analysis performed on the MODIS NBAR data to define initial axes, and then subsequent rotations to separate non-vegetated, vegetated, and water and snow features [63]. The rotated components are expressed as a linear combination of the seven reflectance bands with weights as defined in Table 1:  
\[\text{TC}_{x}=w_{x1}(\text{red})+w_{x2}(\text{NIR}_{1})+w_{x3}(\text{blue})+w _{x4}(\text{green})+w_{x5}(\text{NIR}_{2})+w_{x6}(\text{MIR}_{1})+w_{x7}( \text{MIR}_{2}), \tag{3}\]  
where \(x\) corresponds to either brightness (\(b\)), greenness (\(g\)) or wetness (\(w\)).  
The first set of coefficients developed from the TC transformation define the TC Brightness index and are sensitive to changes in total reflectance, as well as physical properties influencing total reflectance [63; 64]. Based on a sensitivity analysis, soil variability is unambiguous, but differences in vegetation density do not greatly alter TC Brightness [64]. Index values are positive, ranging from 0 to around 0.75. Values closer to 0 correspond to water and/or barren areas, while values in the mid- to upper range characterize savanna regions; forests and cropland are represented throughout the entire range of values [63].  
The second set of coefficients resulting from the TC transformation corresponds to the TC Greenness index (Table 1). The TC Greenness index is the difference between the sum of visible bands and near-infrared bands. Similarly to NDVI, TC Greenness is responsive to both high absorption in the visible bands and high reflectance in the near-infrared bands because of plant pigments and cellular leaf structure, respectively [64]. Higher greenness values represent greater densities of green vegetation, while lower greenness values generally correspond to water and soils.  
The third rotated component is the TC Wetness index calculated as the difference between the sum of visible and near-infrared bands and longer infrared bands. The third set of coefficients derived from the TC transformation represents this wetness index (Table 1). In this calculation, longer infrared bands are suggested to be most sensitive to soil and plant moisture, with soil moisture status as the primary characteristic. Variation in plant moisture has limited representation in TC wetness [64]. The TC Wetness index yields negative values; those closest to 0 indicate the presence of bodies of water, while negative values farther away from 0 characterize a moisture gradient in which an increase in the magnitude of the index value generally corresponds to a decrease in moisture [63].
## 2 Related Literature
### Related Empirical Studies  
There are many empirical studies that provided empirical evidence on the impact of customization over SaaS adoption in the context of SMEs [49; 50; 51; 52]. For example, a cross-sectional field study conducted by [49] revealed that there are convincing grounds for software vendors to perform customizations to SaaS ERP systems. However, these studies did not provide any empirical evidence on the impact of software customization on software quality. Therefore, an extensive search in SpringerLink, IEEE Xplore, ScienceDirect, ACM, and Google Scholar digital libraries was conducted [17]. Though the primary objective of extensive search was not restricted to any type of software product, the overwhelming majority of the related-empirical studies were executed on Enterprise Resource Planning (ERP) software (e.g., [19; 20; 53; 54]).  
Some statistical techniques were used to empirically investigate the relationship between software customization and software quality. For example, the results of the ordinary least square regression analysis showed that database and source code customization significantly influence the ERP quality, whereas the module customization does not [19]. A correlation analysis found that there was a strong negative relationship between ERP efficiency and customization [20]. Conversely, software customization did not a significant impact on ERP usability [54]. It should be noted that the results given by some these studies referred to the customization effect without specifying the type of customization [20; 54].  
Nevertheless, such studies provide empirical insight into the effect of software customization on software quality, there has been no evidence specifically addressing the effect of customization on SaaS quality, where more attention is being given to customization. Furthermore, all of these studies had subjectively assessed the effect of software customization on the quality attributes which supports the fact that most software quality attributes are conceptually subjective and are experienced by users when the system is in operation [55; 56; 57].