esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Research Strengths and Limitations  
The performed calculations are done on the basis of the real clothing insulation of both the surgeons and patient. The study is not spread among other players in the OR (anaesthetists, nurses, assistants), concentrating on the thermophysiological comfort of the subjects that share one and the same thermal environment. The calculations of the PMV/PPD indexes for different clothing insulation, metabolism, temperature, and relative humidity are fast, reliable, and confirm once again the need for special HVAC design or other measures that could guarantee the thermophysiological comfort of all persons in the OR.  
The limitations of the study are related to the fact that the PMV/PPD model is developed for healthy people and its application for a patient under anaesthesia might not be correct, as the thermophysiological reactions could be different. Another limitation of the predictions is that the OR is taken as a standard one, while different OR exists (orthopedic, cardiac, etc.).
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 3 Results
### Selection of Lakes by SPR Approach

Table 1: Contingency matrix of 2024 lakes selected via clustering of land cover burned (2011) and total area burned (2001–2020). Potential geographical and land cover combinations with burn temporal pattern (frequency/intensity). Number of lakes included in the selected clusters are highlighted in bold (n = 314). Figure 1: Global map of the six vegetation clusters burned in 2011. Land cover indicator values of each cluster are reported in Table S1. among the most vulnerable [52]. We used as a proxy for precipitation the SPI calculated for the period 1980-2019. Positive SPI values identify moderately to very wet conditions in the catchment. The other factor considered was the catchment to lake area ratio, with low values indicating lakes potentially having a long residence time while high values would typically have a short residence time and be more strongly influenced by their drainage area. The ratio catchment area/lake area was calculated and plotted against the SPI for the 314 lakes selected from the previous cluster analysis (contingency matrix in Table 1), allowing the identification of lakes that were potentially more prone to pollution (burned materials and released nutrients) from wildfire occurrence in their catchments (Figure 2). In particular, lakes having a 95 th percentile of SPI \(>\) 1.5 and a catchment area to lake area ratio \(>\) 30 (close to the limit identified by other authors [53; 54; 55] located in the upper right part of the plot in Figure 2) have been identified as lakes more influenced by catchment dynamics and terrestrial transport. These lakes should thus potentially be more prone to be receptors of fire-derived substances and would eventually have more affected water quality. These criteria used in lake selection identified a total of 153 lakes selected for further consideration in our analysis. The method of data extraction utilized five regions of interest (ROI) within the Lakes_cci product layers for Chl-a and turbidity and was applied for the subset of the 153 lakes to ensure lake pixels had a 96% presence of water. Following these criteria, 47 lakes were excluded from the analysis, leaving a final number of 106 lakes with reliable data to be analyzed. The final list of the 106 lakes selected is presented in Table S2 in the Supplementary Materials.
# Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data
###### Abstract  
Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky-Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.  
hyperspectral data; Kalman filter; speckle noise +
Footnote †: journal: Applied sciences
# Biomedical Applications of Polymeric Cryogels
## 1 Introduction  
In recent years, there has been an increase in the number of studies using macroporous polymeric structures for the separation of biological molecules. High and rapid purification abilities of supermacroporous polymeric materials make them applicable in separation of different biological macromolecules. Cryogels have a wide range of applications in the biotechnology, bioseparation and biomedicine fields, taking part in many applications for the purification and immobilization of proteins, nucleic acids, and polysaccharides [1].  
Cryogels are gel matrices formed by polymerization at temperatures below zero. While the polymerization process takes place in the unfrozen part, ice crystals formed act as pore forming agents. At the end of the polymerization process, after cryogels are thawed, supermacroopores are formed within cryogel structure [2]. Since the interconnected macro pores provide a great advantage when working with viscous biological fluids, there has been a large increase in the number of studies using cryogels for the purification of natural source proteins over the last decade. Enzymes, nucleic acidsand carbohydrates can be separated from their source without back pressure problems and diffusion restriction due to the interconnected supermacroopores. Large, interconnected supermacroopores provide unique spongy structure to the cryogels. Elastic and spongy structure of cryogels makes them different from rigid monoliths [3].  
Supermacroporous cryogels have a great deal of interest in biomedical, biotechnology and pharmacy. Supermacroporosity of cryogels allows them to be used without any diffusion problems when working with biological macromolecules. Cryogels can be used in the purification of macromolecules such as proteins and nucleic acids, as well as in the separation of cell organelles, viruses, plasmids, microorganisms and mammalian cells. In addition, since the porous structures are suitable for cell adhesion and mechanical stability of cryogels offers applicability in in vivo processes, cryogels are also used in tissue engineering studies [4]. Cryogels have a three-dimensional structure suitable for the proliferation of cells. In particular, the surface properties of the support materials used in tissue engineering greatly affect cell affinity. Hydrophilic and porous structure of cryogels make them suitable for cell affinity. This review describes the specific features of cryogels and refers to recent applications of cryogels in biomedicine and related area [5].
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 4 Performance Evaluation
### Objective Evaluation
Compared to the reference method, our proposed method utilizes a dereverberation model and a DNN for non-sparse component point recovery, which benefits the separation performance in reverberant conditions. The fourth test was conducted in Room1 with \(T_{60}=300\) ms and Room2 with \(T_{60}=450\) ms. Source separation was \(50^{\circ}\) and \(70^{\circ}\) for the case of \(N=2\) and \(N=3\), respectively. Statistical results with 95% confidence intervals are shown in Figure 11. Condition \"ICA\" and \"EM\" mean the two reference methods proposed in [10] and [35], respectively. Condition \"LCMV\" is the reference method in [9]. It is obvious in Figure 11 that the proposed method achieves the best PESQ scores in total test conditions, which proves that the performance of the proposed method performs better than the reference methods. To compare the average SDR and SIR of the signals separated by the proposed method and the reference methods, the fifth test was conducted in an echoic room, Room1 and Room2. The sound source number was three, and the source separation was \(60^{\circ}\). The BSS  
Figure 11: Average PESQ in different reverberant rooms. Figure 10: Average STOI of signals separated by the proposed method with or without dereverberation. EVAL toolbox was employed in this test to measure the SDR and the SIR of the separated signals obtained by different methods [26]. Statistical results of average SDR and SIR are shown in Figure 12. A high SDR or SIR score indicates a high quality of the separated signal. Condition \"Mix\" refers to the mixed signal (i.e., W-channel signal recorded by sound-field microphone in this test). It can be observed from Figure 12 that both average SDR and SIR of the separated signal obtained by the proposed approach are the highest, which indicates the lowest distortion in the separated signal. Consequently, the proposed separation approach can maintain satisfying performance and is robust in different environments.
## 2 Engineering Geological Conditions
### Advance Rate Conditions of the 12308 Working Face  
During the coal extraction process, the mining operations progressed through both the initial mining phase and the regular extraction phase, with the advance rate variations throughout these stages being quantitatively illustrated in Figure 3.  
As shown in Figure 3, during the routine mining phase of the longwall face, the inadequate pre-mining detection of geological discontinuities (including faults, folds, and collapse columns) resulted in a constrained initial advance rate. The face advance rate during the low-speed mining stage ranged from 2-4 m/d, with an average value of 3.7 m/d, whereas the high-speed mining stage exhibited an advance rate of 8-10 m/d, averaging 8.34 m/d.
## 2 Materials and Methods
### Loss Function  
The cross-entropy loss function in RS semantic segmentation is a commonly used loss function for multi-class classification problems. In the task of the semantic segmentation of RS images, the goal is to classify each pixel in the image into different categories (for example, different types of land cover). The cross-entropy loss function is very effective in these types of problems because it measures the difference between the predicted probability distribution and the true label distribution. The formula for this is as follows:  
\[L=-\sum_{i=1}^{N}\sum_{c=1}^{C}y_{ic}log(\hat{g}_{ic}) \tag{15}\]  
In the formula: \(N\) represents the total number of pixels in the image; \(C\) is the total number of categories; \(y_{ic}\) is a binary indicator, where a value of 0 indicates that the pixel does not belong to the target category (for example, 'winter wheat'), and a value of 1 indicates that the pixel belongs to the target category; \(\hat{g}_{ic}\) is the probability predicted by the model that pixel \(i\) belongs to category \(c\).  
The objective of the cross-entropy loss function is to minimize the difference between the predicted probability distribution and the actual label distribution. During the training process, by adjusting the model parameters to reduce this loss value, the accuracy of the model in segmenting RS images is improved. This function is particularly suitable for handling situations where there is an imbalance in class categories.
## 2 Materials and Methods
### Frequency Domain Thermorelectance Set Up  
The FD-TR setup is presented in Figure 3.  
The 100 mW modulated pump laser (488 nm Cobolt model 06-MLD) was intensity modulated using the sinusoidal signal from the 30 MHz waveform generator (Agilent 33522A). While the 2 mW probe beam (638 nm Cobolt model 06-MLD) was used to monitor the changes of the sample reflectivity due to changes of the temperature induced by the pump beam. The beam spots (1/e2) have been measured (Beam Profiler Slit 2 um S-BR2-Si) to be 3 \(\upmu\)m in diameter. Laser beams were focused on the sample by an objective Edmund Optics, 100X model 46-401. The signal was processed by a lock-in amplifier (LIA, SR865 from Stanford Research able to work in the frequency range between DC and 2 MHz). The amplitude A and phase (\(\varphi\)) were acquired, but only the phase was analyzed by a computer for each modulation frequency. The temperature-dependent measurements were performed using a Linkam System able to work between \(-200\)\({}^{\circ}\)C and \(420\)\({}^{\circ}\)C. The reference phase was measured by placing the detector instead of the sample. The reference phase was then extracted from the measured phase of the thermoreflectance signal.
## 2 Materials and Methods
### Data Frame  
Each of the OFDM data frames starts with a synchronisation preamble, which is a Pseudo-Random Binary Sequence (PRBS). It is based on an _m-sequence_ of rank 8, which modulates the carrier frequency \(f_{c}\) equal to 30 kHz. The sequence is repeated 20 times and its duration \(T_{synch}\) is equal to 1.02 s. The PRBS sequence is followed by the OFDM signal of a duration of 2.5 s. Subsequent data frames contain the synchronisation sequence and the OFDM signal. The duration of each frame is equal to 3.52 s. In each frame, a constant amount of information \(N_{i}\) is sent, which is equal to 5 kbits. Thus, the data transmission rate in the case of ECC not being used, but with the pilot tones technique, is equal to 1.42 kbps. The Bit Error Rate is calculated as \(BER=N_{e}/N_{i}\), where \(N_{e}\) is the number of incorrectly detected bits in a single transmission frame, and \(N_{i}\) is the number of all transmitted bits.
## 4 Reference Scenario
### Analysis of Simulation Results  
To assess the veracity and consistency of the simulation results, a statistical analysis was conducted through a detailed comparison between the process data collected in the same operational phase and the results obtained from the digital model simulation. This approach was chosen to verify the model's ability to accurately replicate the behavior of the real system, allowing accurate validation of its performance. Process data were collected during specific operational phases of the real system, ensuring that operating conditions were well documented and reproducible. In parallel, simulation results were generated under the same operating conditions, ensuring a direct and meaningful basis for comparison. After a warm-up phase of the digital model of about 25 days, the system simulated an entire year's production of the actual process, recording outputs monthly. Table 6 compares the outputs obtained from the simulated model with those from the real process.  
To test whether the digital model estimate is indeed in line with the results observed in reality, the paired \(t\)-test was performed. A statistical tool used to compare two closely related or paired data sets to determine whether there is a significant difference between their averages [105]. The goal is to determine whether discrepancies between the two data sets are due to chance or represent a systematic difference [106].  
In this case, the actual production data of a company is compared with the output of the digital model over the 12-month period of 2022 to test the accuracy of the model in predicting actual outcomes. Paired t test for samples produced the following results:  
* Observed t value: \(-0.454\)
* \(p\)-value: 0.65
* Mean difference (d): \(-4.33\)
* Standard deviation of differences: 33.03
* Degrees of freedom: 11  
The comparison of the observed t-value (\(-0.454\)) with the theoretical t-value (\(\pm 2.201\)) enhances the reliability of the simulated model, demonstrating the absence of significant differences from the real output data with respect to the digital model outputs. This is also confirmed by the \(p\)-value of 0.658, which is much higher than the commonly used significance level (\(\alpha\) = 0.05), thus being unable to reject the null hypothesis. The direct comparison approach between the collected process data and the simulation results proved effective in validating the digital model. This allowed the analysis of the simulation results to proceed with an awareness of their validity, enabling well-founded conclusions to be drawn.
###### Abstract  
In existing traffic simulation software or studies related to traffic flow at signalized intersections, the treatment of yellow lights is often simplified or overlooked. However, driving behavior during the yellow phase is characterized by significant uncertainty, which can lead to discrepancies between simulation results and real-world conditions. To address this issue, this paper develops a driving behavior model based on risk perception and fuzzy decision-making and integrates it into a simulation framework to replicate continuous driving behaviors at isolated signalized intersections. The performance of intersections under varying yellow light durations is analyzed, yielding some key findings. For instance, when vehicles strictly adhere to the designed speed, increasing the yellow light duration from 3 s to 5 s results in higher traffic volumes under high traffic density. Furthermore, real-time traffic speed fluctuations stabilize, and the occurrence of unsafe driving behaviors decreases. The concept of risk perception is employed to explain the underlying mechanisms behind these phenomena. This paper provides both a theoretical foundation and a simulation framework for more detailed representations of driving behaviors and for explaining the fundamental principles governing intersection performance.  
traffic simulation; risk perception; fuzzy decision making; signalized intersection; behavior mechanism Article
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
## 3 Results
### Performance of the Two-Compartment Model for All Designs
#### 3.3.1 Specific Absorption Rate (SAR) of the Two-Compartment Model for All Designs  
Figure 7 presents the SAR distribution of a two-compartment model across all designs.  
Figure 7: _Cont._  
All simulations were conducted using the same tumor type with consistent properties. It is obvious that only two applicators restrict the electromagnetic energy within the tumor. These are the balun-free base-fed monopole (Figure 7g) and the helical open waveguide (Figure 7). Explicitly, one may see that the 7.5 W/kg contour lies entirely inside the tumor only for these two cases.
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 4 Problem Modelling as a Static Game
### Extension to \(N_{p}>2\)  
The payoff functions derived in the previous section for the case that there are only two players can be extended to the general case when there are more than two players. In this case, again, there is one server which can choose between two pure actions (\(d\), \(nd\)) and there will \(n_{2}>1\) clients, each client being able to choose between two pure actions (\(s\), \(ns\)). In the general case, the payoff function of each player will be a multidimensional array of dimensions \(na_{1}\times na_{2}\times...\times na_{p}\), where \(na_{i}\) denotes the number of pure actions available to player \(i\in\{1,N_{p}\}\). Observe that when \(N_{p}=2\), each player payoff function is a matrix.  
We define a vector of pure actions as \(a_{p}=(a_{p,1},a_{p,2},...,a_{p,N_{p}})\). Observe that the payoff multidimensional array contains a payoff value for each possible vector \(a_{p}\). In order to obtain the payoff function of each player, for each \(a_{p}\), we will define \(n_{2}^{s}\) as the number of clients that play pure action \(s\) and \(n_{2}^{ns}=n_{2}-n_{2}^{s}\) as the number of clients that play pure action \(ns\). The payoff each player receives will be coupled with the actions of the rest of the players: in general, it will be a function \(f_{i}(a_{p})\), where \(i\) denotes a concrete player. There will be a payoff function for each \(a_{p}\).  
The payoff function for the server will depends on \(a_{p}\) as follows. If the server plays \(d\), the payoff function of the server will be \(k_{s}n_{1}(S^{ns}-S^{s}_{n_{1}})-k_{d}\). Remark that \(S^{ns}\) is obtained considering that there are \(n=n_{1}+n_{2}\) stations. Also, there will be different possible values of \(n_{2}^{s}\), thus \(S^{s}_{n_{1}}\) will depend on the number of clients playing \(s\). Finally, observe that if all clients played \(ns\), \(n_{2}^{s}=0\) and hence, \(S^{ns}=S^{s}_{n_{1}}\); thus, the payoff of the server in this case is \(-k_{d}\).  
If the server plays \(nd\), the payoff value for each \(a_{p}\) is \(k_{s}n_{1}(S^{s}_{n_{1}}-S^{ns})\). It is the same as when the server played \(d\), but now there is no cost \(k_{d}\) and the sign is reversed.  
The payoff for client \(j,j\in\{1,...,n_{2}\}\) if it plays \(ns\) will be \(0\). If client \(j\) plays \(s\) and the server plays \(d\), the payoff for client \(j\) will be \(-k_{j}S^{ns}\). If client \(j\) plays \(s\) and the server plays \(nd\), the payoff for client \(j\) will be \(k_{j}(S^{s}_{j}-S^{ns})\). Observe that \(S^{s}_{j}\) will depend on \(n_{2}^{s}\).  
We follow this procedure for each \(a_{p}\) value in order to obtain the payoff values. Observe that if \(n_{2}=1\), all the expressions in this section reduce to the ones given in the previous section.
## 3 Spiral Rail Spike Positioning Method
### Improved Canny Algorithm for Image Edge Detection
#### 3.3.3 Adaptive Threshold Setting  
The selection of double thresholds directly affects the result of edge detection. The traditional Canny algorithm has a fixed double threshold and poor adaptability. In the complex environment of railway sites, its detection effect is inadequate, and it is prone to edge discontinuity, which leads to difficulties in subsequent positioning. Therefore, this paper combines the OTSU algorithm [17] to achieve the adaptive setting of Canny thresholds.  
Figure 4: Four-direction gradient template.  
The OTSU algorithm uses the one-dimensional gray histogram of the image to perform threshold segmentation. Assuming that there is a threshold value T, the pixels with a gray value less than or equal to T belong to the foreground and the rest belong to the background, their occurrence probability and average gray value are calculated, and the maximum between-cluster variance in the foreground and the background is calculated, as shown in formula (8):  
\[\sigma^{2}=p_{1}\big{(}m_{1}-m_{g}\big{)}^{2}+p_{2}\big{(}m_{2}-m_{g}\big{)}^{2} \tag{8}\]  
In the formula, \(m_{1}\) is the mean grayscale value of the foreground pixels, \(m_{2}\) is the mean grayscale value of the background pixels, and \(m_{g}\) is the overall mean grayscale value of the image. \(P_{1}\) is the proportion of foreground pixels, and \(P_{2}\) is the proportion of background pixels. By iterating through the grayscale levels of the image, the threshold \(T\) that maximizes the inter-class variance \(\sigma^{2}\) is considered the optimal threshold. Let the high threshold \(T_{H}=T\) and the low threshold \(T_{L}=T/2\), thereby enhancing the adaptability of the entire algorithm.