esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 3 Results
### Comparative Assessment  
An assessment evaluation between the proposed algorithm and some implemented methods in the literature was conducted to analyze the obtained outputs and efficiency and measure robustness. This comparative study included the number of used datasets, applied technology, obtained accuracy, and DSC. Table 8 shows the performed comparative evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline
**FLOPS** & **Number of Parameters** & **Execution Time** \\ \hline
57.71 & 63.82 & 8.45 s \\ \hline \end{tabular}
\end{table}
Table 7: The complexity of the proposed model.  
Figure 5: Classification results of the testing group.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Works** &
\begin{tabular}{c} **The Number of** \\ **Utilized Datasets** \\ \end{tabular} & **Applied Technology** & **Accuracy** & **DSC** \\ \hline
[1], 2023 & 1 & GAN, GAS, and PCA & 99.8\% & 98.5\% \\ \hline
[3], 2023 & 1 & Histogram threshold segmentation classifier & 98\% & N/A \\ \hline
[6], 2022 & 1 & VGG-16 CNN & 98\% & N/A \\ \hline
[8], 2022 & 1 & Region-based CNN & 97.3\% & N/A \\ \hline
[9], 2022 & 1 &
\begin{tabular}{c} Hybrid deep learning tools: CNNs and \\ inception v2 and support vector machine \\ \end{tabular} & 100\% & N/A \\ \hline
[10], 2022 & 2 & Squeeze and Excitation Learning & 98.3\% & N/A \\ \hline
[12], 2021 & 1 & Weighted ensemble of different CNNs & 86.2\% & N/A \\ \hline The Proposed algorithm & 4 & Image preprocessing and UNET & 98.76\% & 98.89\% \\ \hline \end{tabular}
\end{table}
Table 8: The comparative evaluation results.  
The presented approach performed better accuracy than the implemented methods in [3; 6; 8; 12], while the approaches in [1; 9] gave better results. However, both methods used a single dataset in each, while the proposed algorithm utilized four datasets. In addition, the number of used images in the proposed system was bigger than both models [1; 9]. Nevertheless, the presented algorithm attained over 99% accuracy when the number of iterations exceeded 15000 and the value of learning changed from 0.001 to 0.01. Furthermore, the accuracy was enhanced by nearly 1.8% when the patch size decreased by half.  
Figure 6 contains four subgraphs: (a) illustrates a confusion matrix for a sample of 700 images, (b) depicts an error histogram, (c) depicts a chart of Mean Squared Error (MSE), and (d) depicts a curve of Receiver Operating Characteristic (ROC). In Figure 6a, class 1 denotes the ALL type and class 2 refers to the AML type. Figures 7 and 8 display samples of segmentation outcomes for two inputs. Both Figures 7 and 8 contain three subgraphs in each. These subgraphs are original inputs, identified and outlined potentially infected cells, and binary segmented masks.  
In Figure 6, the proposed system categorized nearly 97.3% of the given sample accurately. The best value of MSE occurred at epoch 4 with a minimum value of 0.519.  
Figure 8: Samples of the segmented image.  
Figure 7: Samples of the segmented image.
## 3 Results and Discussion
### Impact on Central Carbon Metabolites
In a surprising fashion, acetic acid displayed significant (\(p<0.05\)) and opposed responses to both vitamins (Tables 4 and 5); as such, while it appears to increase concurrently with the initial thiamine concentration in the medium, its contents seem to drop when increasing the initial biotin. Interestingly, however, the statistical interaction between both vitamins, by itself, does not hold any significant influence (\(p>0.05\)) on the acetic acid contents formed during alcoholic fermentation. The positive effect held by the initial thiamine on acetic acid here appears consistent with previous conclusions, that found higher concentrations of acetic acid to be associated with higher thiamine [20]. Although the negative effect held by the initial biotin on acetic acid contents appears to be more surprising, evidence show that there is a lesser incorporation of acetic acid into fatty acids in biotin-deficient yeast cells [45], suggesting that, in the absence of the vitamin, there might be an accumulation of acetic acid in the medium, leading to the higher contents that were observed here. While biotin did not have any significant influence (\(p>0.05\)) on the D-lactic acid contents of the produced wines, a clear effect of thiamine (\(p<0.05\)) on those contents appeared (Tables 4 and 5). Interestingly, D-lactic acid concentrations seemed to be decreasing against the initial thiamine contents. This negative effect most likely results from increases in lactic acid production favored by thiamine depletions due to the defective thiamine pyrophosphate (TPP)-dependent pyruvate decarboxylase (Pdc) activity [46], that therefore redirects pyruvate towards lactic acid synthesis. Finally, glycerol contents in the final wines displayed a significant dependency (\(p<0.05\)) towards both the initial biotin and thiamine concentrations in the growth medium (Tables 4 and 5); as such, the amounts of glycerol produced noticeably increased when increasing both vitamins. This appears consistent with previous findings, that concluded towards drops in the glycerol contents when fermenting in thiamine-deficient media [47], while the effect of biotin on glycerol production has not been assessed yet. The statistical interaction between both vitamins, however, did not display any significant influence (\(p>0.05\)) on glycerol production. All in all, the consequent involvement of thiamine in the yeast central carbon metabolism (CCM) is potently highlighted here, as a reflection of its cofactor role towards several CCM-associated enzymes [42]. While the effect held by biotin on these metabolites appeared slightly less consequent, its noticeable influence on both acetaldehyde and acetic acid emerged here, overall concluding towards the clear existence of an effect of both vitamins on the yeast metabolism during alcoholic fermentation.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Unmanned Maritime Vehicles in UAV-Aided NTN Communication  
Maritime network traffic has grown rapidly in the past few years due to the significant expansion development of manned and unmanned platforms utilized for national defense, scientific research, oceanic surveys, and international shipments [158]. In the new era of 6G communications, the vision of ubiquitous wireless coverage has brought new insight into the full-dimensional network architecture that integrates space, air, ground, and sea networks. Compared to terrestrial networks, maritime communication is far from satisfactory and requires thorough investigation.  
Non-terrestrial networks have created new opportunities for maritime applications, particularly unmanned maritime vehicles, which have critical communication needs due to the challenges of deploying terrestrial communication infrastructure in ocean areas. Unmanned Maritime Vehicles encompass unmanned surface vehicles (USVs) and unmanned underwater vehicles (UUVs) [158; 159; 160; 161]. An unmanned surface vessel is a promising platform capable of performing hazardous and labor-intensive tasks typically carried out by manned vessels. With advanced control, perception, and communication systems, Unmanned Maritime Vehicles (UMVs) are capable of performing various military and civil tasks, such as search and rescue, reconnaissance, transportation, waste clearance, and resource survey. With the advancements in integrating high-capacity and ultra-reliable non-terrestrial network technologies, ubiquitous maritime connectivity can be achieved to support UMV applications.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 3 Methodology
### Near Real-Time Fire Center Extraction  
In this study, the centroid of the burned area was defined as the fire center. The centroid of a target is one of the most useful measurements for describing the spatial distribution of a target [53; 54; 55; 56]. Therefore, the centroid of the burned area can represent the average location of a fire at any time stamp. After obtaining the burned area data (see Section 3.2), Equation (4) was applied together with R software [57] to extract the geographical coordinates of each fire center.  
\[X_{c}=\frac{\sum\limits_{i=1}^{n}m_{i}x_{i}}{\sum\limits_{i=1}^{n}m_{i}},Y_{c }=\frac{\sum\limits_{i=1}^{n}m_{i}y_{i}}{\sum\limits_{i=1}^{n}m_{i}} \tag{4}\]  
Figure 2: A flowchart that demonstrates the methodology used in this study.  
where \(X_{\text{c}}\) and \(Y_{\text{c}}\) are the geographical coordinates of the fire center, \(n\) is the number of pixels flagged burned, \(x_{i}\) and \(y_{i}\) are the geographical coordinates of the \(i\)-th pixel, and \(m_{i}\) is the area of the \(i\)-th pixel.
## 3 Experiments
### Experimental Setting
#### 3.1.3 Operation  
The operation phase of the experiment is composed of three main tasks: preparation, execution, and data validation. The preparation of the experiment includes all the actions required to have the material and participants ready. This comprised the instructions for the participants (including the wording of the exercises), the guideline models, the answering forms, and the tools (spreadsheets) for gathering and grading the answers provided.  
Among the preparation tasks, we can consider the development of software tools to compute the values of the metrics for all the PROforma models used in the experiment. For this purpose, we have implemented a Java program that takes as input a PROforma file in XML format, transforms it into a series of graphs, and uses these graphs to compute the values of all the metrics proposed in this paper. Notice that, originally, the format of PROforma CIGs is plain text. To facilitate the processing of PROforma files, we have opted for building an appropriate metamodel using the Eclipse Modeling Framework (EMF) [35]. This solution has the advantage of providing code generation facilities, such as editingtools adapted to the metamodel. We have used such an editor to produce the PROforma XML files required to compute the metric values.  
Regarding the execution of the experiment, as mentioned before, all participants played the subject role, solving the exercises proposed by the rest of participants. The models were displayed and handled using the PROforma graphical editor Tallis Composer [36]. The solutions to the exercises were recorded in the provided answering forms and, if required, in modified PROforma models. Moreover, the subjects screen-recorded themselves while they were solving the exercises, describing the steps followed talking aloud. After reviewing the answers recorded in the forms, the modified models, and the screen-recorded videos, each participating researcher graded the exercises she/he had proposed as Correct (C) or Incorrect (I) using as tool the spreadsheets provided. Note that due to the inherent difficulty of establishing objective criteria to grade an exercise as partially incorrect, we finally opted for a binary rating of the exercises as Correct (C) or Incorrect (I).  
The final task consists in checking that the data collected in the above spreadsheets is valid. First, we have removed invalid and void answers, e.g., answers stating that the exercise had no solution or that the wording made no sense. Second, and related to the latter, we have removed those exercises with severe ambiguities in their wording. To do this, we have considered a wording as ambiguous when three or more subjects had concerns about it. After this validation, we obtained a total of 368 observations, each one corresponding to an exercise solution by one subject, and including the rating of the solution, as well as the exercise and subject details.
## 2 Materials and Methods
### The PROSAIL Radiative Transfer Model  
The widely used PROSAIL radiative transfer model [51] was selected to retrieve LAI in the wetland of Schiermonnikoog. The PROSAIL model is a compound of the SAILH canopy reflectance model [52; 53; 92] and the PROSPECT leaf optical properties model [25; 50; 93]. PROSAIL requires a limited number of input parameters, which facilitates the model inversion. It has been used for retrieval of biophysical parameters in a wide range of vegetation canopies [27; 48; 51; 57; 58; 66; 69; 72; 94; 95] with various degrees of success. The overall suitability of PROSAIL for grassland studies has been demonstrated by [48; 96], and the capacity of the model to simulate spectra of heterogeneous grassland in forward mode has been demonstrated in our earlier studies [27; 30; 95]. In the current study, we performed local sensitivity analysis [97] to understand the impact of each model input parameter, in particular, LAI, on the simulated canopy reflectance of RapidEye and Sentinel-2. We examined the effect of each model input parameter on the corresponding spectral reflectance of RapidEye and Sentinel-2 by systematically varying one parameter value at the time and keeping the rest of the parameters at their constant mean values. The model parameters that did not affect either RapidEye or Sentinel-2 simulated reflectance spectra were further fixed to constant values in the subsequent generation of LUTs.  
Figure 3: Reflectance of the measured plots in Schiermonnikoog island, The Netherlands. Extracted from, RapidEye in 2015 (n = 30) and Sentinel-2 in 2016 (n = 20), respectively. The broken lines show the centre positions of the spectral bands of the sensors.  
The PROSPECT leaf model simulates the leaf hemispherical reflectance and transmittance as a function of four input parameters: the leaf chlorophyll \(a+b\) content \(C_{ab}\) (\(\mu\)g cm\({}^{-2}\)); the dry matter content \(C_{m}\) (g cm\({}^{-2}\)); the equivalent water thickness \(C_{w}\) (cm) and the leaf structural parameter \(N\) (unitless). The simulated leaf reflectance and transmittance by PROSPECT are inputs into the SAILH which is a one-dimensional bidirectional turbid medium radiative transfer model and has been modified for the hot spot effect in plant canopy reflectance [92]. The SAILH model entails eight input parameters for simulation of the top-of-canopy bidirectional reflectance. These parameters are solar zenith angle, sensor viewing angle, relative azimuth angle, the fraction of diffuse incoming solar radiation, the hot spot size parameter, LAI, average leaf inclination angle, and background soil reflectance. In order to address the soil brightness variation triggered by moisture and roughness, a multiplicative soil brightness parameter was used [27; 98].
# Study of Discharge Characteristics on Ignition Performance via High-Speed Imaging in a CVCC
## 2 Experimental Setup
### Ignition Coils and Test Conditions  
Figure 6 shows the discharge characteristics of three types of ignition coils. This research takes coil A as the standard coil, coil B as the high current coil, and coil C as the high energy coil. The detailed specifications of the coils are shown in Table 1. As described in Table 1, coil A and coil B have almost the same ignition energy, and coil C is the highest energy one. However, coil C has almost the same discharge duration as coil A, and coil B is much shorter than coil A and coil C. Coil B and coil C had almost the same discharge current, which is much higher than coil A.  
The experiments were carried out on three conditions: basic condition, high dilute condition, and high flow condition to determine the suitable discharge strategy under different conditions. Experimental conditions are as listed in Table 2. In order to control the composition of the mixture, we used the O\({}_{2}\) and N\({}_{2}\) to imitate the air and added more N\({}_{2}\) to imitate the dilute condition, the air-propane mixture with an equivalence ratio of one is used in the experiment. The combustion chamber temperature was fixed at 358 K and initial pressure of four bar for ensuring the same initial conditions of each test. Steady flow velocities were calculated by 3-D numerical simulations of CONVERGE software. Each condition of each coil was tested ten times to eliminate the experimental error.  
Figure 5: Direct and CO radiation images on static fields with/without fuel conditions (top: CO radiation images (aim to detect the initial flame kernel)/lower: direct images (aim to capture the discharge channel). (**a**) With no fuel condition; (**b**) With fuel condition.
# Effects of Climate Conditions before Harvest Date on Edamame Metabolome
## 2 Results
### Metabolome of Harvested Edamame  
Edamame of the same variety were harvested at the same time (7 to 8 a.m.) every year using the same method in the same field. Boiling the edamame immediately after harvest prevented the loss of freshness and made the edamame ready for consumption. After each sample of these boiled edamame was extracted and pre-treated using conventional methods, metabolome analysis was performed using CE-MS. Fifty-eight types of ionic compounds were quantified in edamame produced between 2013 and 2018 (Supplemental Table S1). These included amino acids, such as alanine and glutamic acid; organic acids, such as citric acid and fumaric acid; and nucleotides, such as AMP and GDP. Alanine, the amino acid that is most abundant in boiled edamame, showed a more than double amount difference depending on the harvest date, even in the same year (Figure 2). Similarly, the citric acid concentration more than doubled within the same year; however, the diurnal variation pattern was different from that of alanine (Supplementary Figure S2). These results indicate  
Figure 1: Climate conditions before harvest day: (**A**) sunshine duration, (**B**) average temperature, (**C**) rain fall, (**D**) average wind speed, (**E**) temperature difference. Each color represents data for each year. The dotted line shows the average values of each climate condition for 6 years.  
that the amount of compounds contained in edamame differed depending on the harvest date. Moreover, the effect was different depending on the variety of compounds.
## 2 Materials and Methods
### Software Architecture and App  
Spoke is the proprietary software platform, specially designed for fulfilling the needs of medical staff for activating and monitoring the delivery. The Spoke platform includes the Spoke backend, the Spoke app, and Spoke AI (i.e., the software running on the Smart Capsule). The software was designed to interact with commercial flight control systems (Pixhawk, DJI) and is easy to use thanks to the Spoke app, which can be directly used by medical or non-expert staff to activate autonomous flights on pre-authorized routes. Thanks to the intuitive software, the whole delivery process can be managed directly by the healthcare facility, without the need of third-party couriers, thus improving efficiency and reducing costs and waiting times. It is enough to select the desired route from the app menu in order to be guided to activate the autonomous flight. In the following figure, the overall software architecture is shown, together with the app page for mission activation and live monitoring. The Spoke backend allows further monitoring of different missions performed by different systems running in parallel in a real working scenario.  
The detailed software architecture of Spoke includes:  
WEB PORTAL (i.e., Spoke backend): a tool for using the administration functions of the platform, monitoring and management of processes (back end user)  
MOBILE APP (i.e., Spoke app): a tool for using functionalities of request, authorization and reception of transport in mobility (end user).  
API LAYER: architectural component responsible for displaying services necessary for the implementation of user functions.  
APPLICATION LAYER: application logics for the management of function systems in compliance with the designed logics.  
PUSH NOTIFICATION: form for sending alerts and notes on mobile devices (end user).  
INTEGRATION LAYER: module for the development of integration logics with external systems and the connectivity module.  
CONNECTIVITY: sensor management logics (measurement acquisition and transmission of the same) and control of the Smart Capsule and the drone.  
SMART CAPSULE (i.e., Spoke AI): the software running at the Smart Capsule level implementing AI, flight activation and supervision  
DRONE: the software running on the commercial flight controller of the drone.  
In addition to the software connection with the drone for commands related to flight and transport, the Spoke AI also manages the hardware components of the Smart Capsule, including sensors and, in the future, actuators that could be useful (i.e., cooling systems). The sensors integrated on Raspberry Pi(r) include internal temperature sensors and external sensors as well as humidity sensors. The hardware-software architecture guarantees that the sensors installed on board are constantly monitored and allows to detect anomalies in real time, both during the flight phase of drones and during the start-up of the activities related to the configuration phases.  
The overall software architecture and the detail of the Spoke app are shown in Figure 2.
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 5 Results
### Known Behavioral Anomalies  
In most of the plots showing number of retransmissions, node 19 (the one at the end of the convoy) is shown to consistently transmit more than the others (see Figures 8, 10 and 12). This is because node 19 is effectively unaware that it is the end of the convoy. Because there is no rearward peer to retransmit the SM to (which would cause node 19 to cancel its pending retransmissions), it simply continues retransmitting until the retransmission counter hits zero. This issue was deemed a low priority as it is isolated to a single node and predictable due to its position. Nevertheless, it is still mentioned in the Future Work section (Section 7). The similarly high transmission numbers of node 0 are discussed in Section 5.3.
## 4 Results and Discussion
### Flexural Test Results
#### 4.4.3. Cracking Patterns of RC Beams  
The cracking pattern of each RC beam tested in this study is shown in Figure 20, while the crack spacing in each beam is shown in Figure 21. It was observed that cracks in the beams containing fine RBAs and either of the coarse aggregates exhibit more damage as compared to the other specimens, with the applied load approaching the yielding point.  
Figure 21. Crack spacing in all tested beams (in mm).  
Figure 20. Cracking behavior of RC beams prepared with 100% RAs and 100% NAs.  
As a minimum area of reinforcement was provided in the beam, making them tension-controlled sections, initially, the flexural cracks originated from the center of the beam, and with the increase in load, cracks propagated towards the compression face. As the load was further increased, a few inclined flexure-shear cracks were formed, but no obvious cracks appeared in the vicinity of the shear zone. Finally, the flexural crack in the central region of the span expanded, and the beams failed because of the crushing of concrete in the compression zone. It was also observed that in the presence of recycled fine and coarse aggregates, the propagation of the cracks was quick as compared to the one that appeared in the specimens containing coarse NAs. Additionally, it was observed that the opening of the cracks in beams with recycled aggregates was higher compared to the control specimen because of their lower tensile strength and bond stress, as evaluated in the current research study, which was found to be consistent with the findings of past research [71; 72].