esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physicochemical Parameters
#### 3.1.2 Peroxide Value (PV)  
Analyzing the results of A, it was observed that the PV values for O04 and O1 generally showed an increase with treatments compared to the control oil value (Table 5). Regarding the evolution of PV, it is noted that O04 reached a maximum value in the T5 treatment, while O1 reached a maximum in the T2 treatment. In B, a decrease in PV was observed in O04 for the 40 \({}^{\circ}\)C treatments of T7 y T8, followed by an increase with a maximum at T10. In O1, an increase in PV was observed across all treatments. Additionally, there was a maximum increase of 11.95% and 25.92%, respectively, for the T10 treatment. In this case, no clear trend of peroxide value reduction was observed in the 60 \({}^{\circ}\)C treatment.  
There is no consensus in the literature on the evaluation of PV after thermal treatments. Some authors observed a significant decrease in PV with treatments similar to those established in this research [37; 38]. Others observed a slight increase in the PV of extra virgin olive oil after 120 min of microwave heating at 170 \({}^{\circ}\)C, while a decrease in parameters was observed in virgin olive oil under the same conditions [39]. They also reported an increase in the PV of extra virgin olive oil after 8 min of microwave heating and suggested that the increase in PV is caused by the formation of hydroperoxides and the appearance of secondary oxidation products, which reduce the PV value [40]. In long-term tests, authors exposed samples to room temperature, both in the absence and presence of light, observing a slight increase in this index in samples treated in different containers [41].  
Furthermore, no trend associated with storage was observed in any of the analyzed oils. However, a clearly upward trend of the thermal treatment effect at both 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C on PV in O04 and O1 of A can be observed. This indicates that the thermal effect increased the PV of the samples, as observed by other authors [42]. Finally, both O04 and O1 complied with current legislation in A and B, keeping values below the established limits of \(\leq\)15 [24; 30] after the treatments.
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
## 3 Results and Analysis
### BER Improvement with Matched Filter
Figure 9b compares the maximum error-free transmission distances with and without matched filter as well as with those achieved when employing a regular PIN photodiode as photodetector. The bar chart demonstrates that applying the matched filter technique increases the error-free transmission distances for the 10  kbps transmission data rate case from around 3 cm to almost 7 cm. This matches the performance of the LED photodetector to that of a PIN photodiode. At 200 kbps the LED-to-LED link with matched filter exceeds the error-free transmission distance achieved by the LED-to-PIN photodiode link at photovoltaic mode. The power consumption of the whole system is measured at 110 mW in the worst-case scenario when the transmitter LED is turned on for emitting (which consumes almost 100 mW) and the photodetection circuit is detecting (consuming the remaining 10 mW). The average power consumption is expected to be half of this value assuming equal probabilities of '1' and '0' bits because the transmitter LED is then switched off half of the transmission time. The power consumed by the photodetection circuit can also be slightly reduced by programming an event-driven turning on of the detector circuit: With the help of low-power comparators, the detection circuit is turned on only in response to system interrupts and stays off otherwise [25].
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 3 Results
### Remote Sensing Model of LAI in Sailanba Area
#### 3.1.3 PCA Model  
Using the PCA method, the principal components were extracted for coniferous forests, and the contribution of each principal component was analyzed (Table 6), leading to the development of a principal component regression model for LAI estimation based on each vegetation index. The components in the table are the principal components \(C_{i}\) (\(i=1,\,2,\,3,\,4\)) derived by conducting PCA.  
As can be seen from Table 6, the cumulative contribution of the first two principal components was 98.35%, which can retain as much information about the original variables as possible. Accordingly, the principal component regression model of the vegetation index and LAI is shown in Equation (7), with \(R^{2}=0.554\) and \(RMSE=0.384\).  
\[LAI=3.592 NDVI+0.033 SR-0.00032 DVI+4.687 ARVI-3.553 \tag{7}\]  
As seen above, the \(R^{2}\) of the principal component regression model for coniferous forests decreased instead. Therefore, the multiple regression model of four VIs was used in the prediction of the LAI of coniferous forests in the study area.  
Using the above results to estimate the LAI of the study area on 21 August 2019, the distribution is shown in Figure 4a, and using 29 validation data for accuracy verification, the \(R^{2}\) of the regression equation between the estimated LAI and the measured LAI was 0.85 (Figure 4c), which can be used as a source of LAI\({}_{\text{max}}\) data. At the same time, the LAI of the study area on 25 August 2020 was estimated. The distribution is shown in Figure 4b. Overall, 47 validation data were used for accuracy verification. The \(R^{2}\) of the regression equation between the estimated LAI and the measured LAI was 0.72 (Figure 4d).
## 5 Specific Absorption Rate  
The specific absorption rate (\(SAR\)) shows how much radiation is absorbed by human tissues when irradiated by an electromagnetic field. The \(SAR\) is a measure of the rate at which the radio-frequency energy from a mobile phone is absorbed by the human body [17].  
The local \(SAR\) is calculated as the power loss \(dP_{1}\) absorbed in an infinitesimal mass \(dm\) in the following way:  
\[SAR=\frac{dP_{1}}{dm}=\frac{\sigma_{eff}E_{rms}^{2}}{\rho}=\frac{J_{rms}^{2}} {\rho\sigma_{eff}} \tag{5}\]  
where \(E_{rms}\) is the root mean square value of the electric field, \(J_{rms}\) is the current density. \(\sigma_{eff}\) is the effective conductivity of human brain tissue, and \(\rho\) is the tissue density. Therefore, the \(SAR\) unit of measurement is W/kg. Energy from electromagnetic fields is absorbed into the human tissues and warms them. This leads to another definition of \(SAR\), namely:  
\[SAR=c_{p}\frac{\Delta T}{\Delta t} \tag{6}\]  
where \(c_{p}\) is the specific heat capacity of the tissue and \(\Delta T\) is the change in temperature over a period of time \(\Delta t\).  
A distinction should be made between the instantaneous \(SAR\) and the permissible SARs, where an average value is measured for a given mass of tissue and a specified period of time. It is best to use a computer model to study the \(SAR\) and the thermal effects. The benefit of the model is that it visualizes the processes in depth.
# An Efficient Deep Learning Approach for Colon Cancer Detection
## 2 Related Work  
Recent literature mainly focused on using deep learning and transfer learning to detect colon cancer from the analysis of histopathological images [5; 6; 7; 8; 9; 10; 11; 12]. [PERSON] [5] showed how to categorize lung and colon cancer using an AI-supported model and optimization methods using histopathological images. The researcher analyzed a set of five histopathological image classes, two for colon cancer and three for lung cancer. To train the image classes from scratch, the DarkNet-19 model was employed and then a support vector machine (SVM) was used to classify the features. Overall, the approach achieved a 99.69% accuracy rate. [PERSON] et al. [6] conducted a comparative investigation of two feature extraction approaches for categorizing of lung and colon cancers. They extracted six handcrafted traits based on color, texture, shape, and structure. They used multiple classifiers with handcrafted features for colon cancer classification. The extracted deep features classify lung and colon cancers using traditional classifiers. They obtained the best accuracy of 98.60%, recall of 98.60%, precision of 98.63% and F1 score of 0.985 using DenseNet-121 for feature extraction with RF classifier. [PERSON] and [PERSON] [7], presented a method based on CNN to detect of colon cancer images. They used a 45-layer model in a model called MA_ColonNET to classify colon cancer. They attained a 99.75% accuracy rate, demonstrating that the presented approach can detect colon cancer earlier and facilitate treatment. [PERSON] et al. [8] developed a categorization scheme for histopathological differentiating two benign and three cancerous lung and colon tissues. The researchers discovered that their framework is capable of accurately identifying cancer tissues to a maximum of 96.33%. They claimed that by implementing their methodology, medical experts would be able to establish an automated and reliable system for detecting various forms of lung and colon cancers.
# Genetic Programming to Remove Impulse Noise in Color Images
## 5 Experimental Results
### Benchmarks  
Thirty benchmarking color images were used as test images for the proposed adaptive filter (GP). Each of these images was perturbed by the three impulse noise models with four different densities (5%, 10%, 15%, and 20%), i.e., a total of 360 test color images. These noise densities were selected to ensure a more equitable comparison among the different comparison filters. This decision stems from the recognition that certain filters have been specifically designed for higher noise densities, whereas others may perform more effectively in scenarios with lower noise levels. By evaluating the filters across an intersecting range of noise densities, we aim to conduct a comprehensive and balanced assessment. In this vein, the comparison of filters encompassed a diversity of robust and adaptive filters, some of which are widely used in digital image processing, whereas others use advanced adaptive methods. Filters using fuzzy logic tools to provide adaptiveness to local features were used, such as the fuzzy metric peer-group filter (FMPGF) and the noise adaptive fuzzy switching median (NASFM). Additionally, the decision-based algorithm for removing impulse noise (DBAIN) was also used, a filter designed for highly corrupted color images. Given their performance, the vector median filter (VMF) and the generalized synthesis and analysis prior algorithm (GSAPA) were also considered. Finally, a deep learning approach, the impulse detection convolutional neural network (IDCNN), was also used. The performance metrics used to compare the quality of the resulting images of these filters were the MAE, PSNR, SSIM, and FSIM (described in Section 3.2).
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 3 GIMAD Prototype Description
### Signal Processing Module (SPM)
#### 3.1.2 Characterization Modules/Algorithms  
The characterization of the time-frequency of the monitored signal can be undertaken by means of (i) a spectrogram, (ii) the Wigner-Ville distribution, and (iii) a cyclostationary spectrum.  
Further details on algorithms and decision keys for selection can be found in [1].
## 5 Proposed Channel Model
### Proposed Model Analysis  
The analysis includes the simulation of proposed path loss in four different environments at varying frequencies and altitudes, considering elevation angles as depicted in Figures 8 and 9. The findings provide a detailed analysis of path loss across various environments at a frequency of 5.8 GHz, specifically at a UAV altitude of 200 m. As shown in Figure 8a, the results reveal that the suburban environment experiences the lowest level of path loss, indicative of more favorable communication conditions. In contrast, the denseurban environment is characterized by significantly higher path loss, attributed to the greater density of buildings and obstacles that contribute to increased signal attenuation and shadowing.  
This trend is consistent across additional frequencies of 1.8 GHz and 2.1 GHz, observed at varying altitudes. To further understand the behavior of path loss in the dense urban setting, simulations were conducted for 1.8 GHz, 2.1 GHz, and 5.8 GHz, focusing on both vertical and horizontal polarization of the signals. Figure 8b illustrates that as frequency increases, there is a corresponding rise in path loss for both types of polarization, highlighting the challenges posed by higher frequency signals in urban landscapes.  
In addition, this study examines the relationship between the altitude of the UAV and its impact on the path loss. Figure 9a,b depict a strong correlation: as the UAV's altitude increases, resulting in a notable decrease in overall path loss due to increase in line-of-sight communication between transmitter and receiver. This finding provides valuable insight into how variations in altitude can significantly influence the effectiveness of communication systems, particularly in urban environments where obstructions are prevalent. Overall, this comprehensive analysis deepens our understanding of the dynamics affecting path loss in various scenarios.  
Figure 8: Proposed model path loss for (**a**) different environments at frequency 5.8 GHz and altitude 200 m and (**b**) dense urban environments at different frequencies and polarization at altitude 200 m.  
Figure 9: (**a,b**) Proposed model path loss for dense urban environment at UAV altitude 100–500 m at frequency 5.8 GHz for vertical and horizontal polarization, respectively.
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Comparative Radiography
#### 3.3.1 2D-2D Approaches for Comparative Radiography  
The comparison of AM and PM radiographs is the most extended approach in forensic literature. In order to highlight the applicability of CR-based forensic ID, it is important to remark that X-ray images represent the most commonly employed medical imaging modality [125]. In particular, chest X-rays are the most commonly performed radiology examination world-wide [126] because they are able to produce images of the heart, lungs, airways, blood, vessels, spine, and chest [127], and because of their diagnosis and treatment potential [126; 128].  
There are several works that semi-automatically compare different skeletal structures between AM and PM radiographs. These skeletal structures include frontal sinus [129; 130], cranial vault [131], and teeth [132; 133; 134]. These methods are based on the comparison of the silhouettes of skeletal structures in radiographs using geometric morphometric techniques. The elliptical Fourier analysis [135] is used to compare AM and PM silhouettes obtaining a shortlist of the most probable PM matches for each AM case. The segmentation of the skeletal structures in AM and PM radiographs is required in all these methods. Related to that, there are a few computational approaches that automate the manual segmentations using ad-hoc rule-based segmentation methods, as the automated dental identification system (ADIS) [136; 137] for teeth comparison [138], or [139] for frontal sinuses segmentation, or via the direct comparison of the intensities as the computer-assisted decedent identification (CADI) [138] for vertebrae comparison. However, the latter approach suffers from the elapsed time between the AM and PM radiographs, and the consequent change in the intensities of the skeletal structures. CADI reduces its impact via the manual selection of a region of interest around each vertebra, the equalization of the pixels within these areas (e.g., with a histogram equalization filter), and lastly the comparison of AM and PM vertebrae using the Jaccard similarity metric.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 3 Methodology  
### Flowchart  
The research methodologies applied in the present study are as shown in Figure 6. The flowchart consists of four major steps, as follows:  
1. Data preparation. The multitemporal DEM data, influencing factors, and historical landslide events are prepared;
2. Data preprocessing. The multitemporal DEM data is compared to eliminate the noise based on coverage probabilities for confidence intervals;
3. Susceptibility assessment of landslides. The assessment results for 1992, 2000, and 2009 are obtained using a LRM;
4. Comparison and analysis. The assessment results for 1992, 2000, and 2009 are compared to obtain the impacts of the elevation difference and the historical landslide events on the susceptibility assessment.
## 3 Results
### Analysis on Driving Factors of TSS Change
#### 3.3.3 The Influence of Human Activities on TSS Concentration
There is no doubt that the great changes in water quality in the study are inseparable from the extensive planting and frequent telling of eucalyptus trees, in addition to the cultivation of fruit trees, which are very common in western Guangdong and Guangxi. Considering the specific influence factors on water quality, the TSS in Hedi Reservoir shows obvious differences under different seasons and precipitations. At the upstream reservoir area in Shijiao Town, the TSS is higher in the dry season from upstream mankind activity, especially in 2015 with an average TSS of 42.85 mg/L, and presents a negative correlation with rainfall intensity; on the contrary, the TSS is lower during the flood period, especially in 2015 with an average of 25.06 mg/L, and the TSS trend is positively correlated with rainfall intensity. As a typical trans-provincial area of Hedi Reservoir, there is the largest difference in the average TSS concentration of 17.794 mg/L between the flood and dry seasons in 2015 in Shijiao Town, while the smallest difference of TSS in 2018 was 1.831 mg/L (Figure 8). The concentration above 40 mg/L was farthest away at 2.1 km toward the south of Shijiao Town. At the same time, we also found that, in the dry seasons of 2016 and 2017, the downstream reservoir area (southeast) near Xin'an Town also experienced deterioration in water quality with an average TSS of 24.50 mg/L due to the cultivation of orchards. Besides, since the implementation of  
Figure 10: Historical view of the Google Earth image showing the polluted water environment in Hedi Reservoir caused by different anthropogenic activities: (**a**) shows the status of large-scale illegal sand mining activities before the river chief system was implemented, (**b**) indicates that sand mining activities are effectively restricted after the execution of the river chief system policy and (**c**,**d**) depict the extensive area of eucalyptus planting with frequent cutting. All the red circled areas in Figures (**a**,**b**) indicate changes in sand mining vessels in the same area of the reservoir before and after implementation of the river chief system policy; (**c**,**d**) reflect the changed progress in eucalyptus planting and cutting. the river chief system from September 2017, the water quality has been significantly improved in the upstream reservoir area (north) of Shijiao Town, where the average TSS concentration in Shijiao has been reduced from 30.98 in 2017 to 13.84 mg/L in 2018 (Figure 5b). At the same time, the average TSS concentrations in Hechun Town, Xin'an Town and the entire reservoir all decreased from 16.28, 31.84 and 19.47 in 2017 to 14.35, 13.29 and 14.01 mg/L in 2018, respectively, which indicates the regional effectiveness of implementing the river chief system of China.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing
### Data Management  
In the Inform@Risk project the AlpGeorisk ONLINE (www.alpgeorisk.com) (accessed on 1 March 2021) data management platform is used to manage the large amounts of data (several GB per month) which are generated by the geosensor network. This platform is responsible for data quality management (identification of data errors), calibration (conversion of raw data to measurement data), storage (structured storage in a relational database), analysis (see below) and visualization via web-portal as well as system management (sensor status checks) and control (e.g., change acquisition interval) and notifications (alarms and early warnings via email, SMS and/or sirens etc.). As part of the Inform@Risk project, a mobile app is being developed to complement the AlpGeorisk ONLINE data management system and make the data more accessible and understandable for the residents in informal settlements. The presented monitoring system can of course also be used in combination with a different data management platform (e.g., if one is already available), but the combination with the AlpGeorisk system is very convenient since it is already adapted to the specific requirements.
## 4 Model Validation
### Model Result and Analysis
#### 4.3.5 Resilience Assessment  
For the forecasted project performance results obtained, we assess the resilience of the project to the disruption \(\mathrm{E_{1}}\) by means of the project resilience assessment model. Using sample 1 as an example, its project resilience level is calculated through Equation (3). It can be seen that the SPI is 1 before project 1 encounters a disruptive event, and at the moment of \(\mathrm{t_{2}}\), the SPI drops to a minimum value of 0.7597, and finally, relying on the project's own resilience and assessment, such as the reallocation of project resources, the SPI is restored to 1. According to Equation (3), the real resilience of project 1 is obtained as \(\varphi_{\text{real-1}}=1\). The forecast of project performance through the constructed network model yields the SPI (\(\mathrm{t_{2}}\)) forecasted result is 0.769 and the SPI (\(\mathrm{t_{3}}\)) forecasted result is 0.988. Calculated according to Equation (3), the project resilience forecasted result \(\varphi_{\text{pre-1}}=0.948\). By analogy, the true resilience value \(\varphi_{\text{real-2}}=1.604\) and the forecasted resilience value \(\varphi_{\text{pre-2}}=1.456\) can be calculated for Project 2. The relative error between the forecast and the true value is less than 10%, which indicates that the model is able to effectively forecast the resilience level of the past projects in the completed portfolio. This forecast indicates that project sample 1 has a high level of resilience and that the project schedule has a strong ability to cope with disruption and to take recovery assessments and return to the initial level after a disruption. The following Figure 9 shows the comparison between the forecast and true value of project resilience for the datasets.  
A comparison of the forecasted and actual value of project resilience (Figures 9 and 10) reveals that the majority of project resilience values fluctuate above and below 1, and the forecasted accuracy is high, with error values below 10%. By analyzing the project resilience, the project performance level can be determined, thereby assisting the project manager in evaluating the acceptability of the recovery schedule. If the project schedule is slower than the planned schedule, corrective actions are required to reduce the recovery time. Based on the RCF technique, the proposed model forecasts project disruption and recovery profiles based on previously observed trends in the portfolio. In our sample, the accuracy of the forecasts is improved by taking into account the actual performance  
Figure 9: The project resilience of testing sample.  
knowledge of a reference class of comparable projects rather than relying on expert estimates. In other words, it allows managers of data-driven organizations to understand the behavior of similar projects in the face of disruption based on what has already happened in the portfolio.