esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Experimental and Full Simulated Results  
Regarding whether results were obtained experimentally or via simulation, we found seven papers that presented results from full simulations (with artificially generated data), 40 using public datasets, and 114 that presented empirical results (note that there were articles that perform several experiments and/or simulations).  
Four of the papers reporting results based on simulations performed real-world experiments as well [29; 45; 69; 70]. While the authors in [69; 70] implemented a simple Log-Distance Path Loss (LDPL) model to generate the RSSI values, [PERSON] et al. [45] included a wall attenuation factor to better model the radio propagation with the LDPL model under Non-Line-of-Sight (NLOS) conditions. In contrast to these, [PERSON] et al. [29] used a more sophisticated Ray Tracing model to generate the RSSI values.  
In any event, the results reported in simulations tend to be better than the ones reported in the real-world experiments performed in those papers that performed experiments in both scenarios. Those simulations assuming that Line-of-Sight (LOS) conditions are always met, included a low Gaussian noise, or implemented a simple model, represent an optimistic view of real-world evaluation in one way or another, and therefore the results may be much better in terms of positioning error. This is the case, for instance, in [PERSON] et al. [70], where the errors in the simulation are 30%-50% better than those reported in the real-world experiment.  
The empirical results reported are usually better than those obtained with public datasets. There are several reasons for this behavior. In general, researchers have much more knowledge about their own testing areas than those external areas included in public datasets, which impact the selection of the algorithm and its hyperparameters. Performing the experiments in their own facilities enables researchers to select an optimal sub-area for evaluation (e.g., the one with better Wi-Fi coverage or higher density of APs), have custom deployment of APs, or even add additional supporting infrastructure in the operational area. Therefore, public datasets are a more challenging testing scenario for algorithms;  
Figure 6: Metrics used.  
in addition, they allow for comparing different algorithms, as they are tested in the same area. Thus, it is important to note the increase in the number of papers that used public datasets in recent years, as can be seen in Figure 7.
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
## 6 An Emulation Framework for Fault Injection
### Nexys 4 DDR Based on Artix-7 FPGA  
Among all the compatible FPGAs, the supported device chosen for this tutorial has been the Nexys 4 DDR based on an Artix-7 (15,850 logic slices, consisting of four 6-input LUTs and 8 flip-flops).  
For the implementation of the fault injection platform based on the SEM IP core presented in this tutorial, the next resources are used to automatize the injection functionality and its monitoring services:  
* Peripheral module (Pmod) ports, for the serial communication tasks between the FPGA and the computer, supporting monitoring functions like send and receive data.  
Figure 6: SEM IP entity.  
The Pmod is an input/output interface board, developed by Digilent, that enables a simple connection between the FPGA and other standardized sockets from other external devices or even computers. Pmod ports avoid welding wires to the FPGA as they are convenient and easy to plug modules.
* FPGA configuration reset button, allows to reset the FPGA after each fault injection. Note that because of an emulated SEU which permanently alters the configuration layer, the FPGA requires to be reset. After these reset conditions, the FPGA must be configured again loading the original bitstream. For this purpose, a flash memory included in the board contains a copy of the stored design, in order to automatize the configuration after each fault is injected, avoiding a manually load through the Vivado tool.
## 2 Model Test
### Result Analysis  
2.2.1. Sectional Temperature Variation  
Figure 5 presents the temperature changes of eighteen different measuring points with time at Section A-A, which are used to analyze unsteady heat conduction.  
As shown in Figure 5, the initial temperature of each measuring point is inconsistent. Because the thickness of the filled soil in the test is 1.2 meters and each measuring point is buried in different positions and depths, there will be a certain temperature difference. It can clearly be seen in Figure 5a that in the heating stage, the temperature of each measuring point from \(A_{S1}\) to \(A_{S6}\) presents a linear increase. After 1000 min of loading, the temperature of each measuring point tends to be stable, and the temperature difference remains at approximately 5 \({}^{\circ}\)C. The measuring point \(A_{S6}\) is close to the heating pipe, and the final temperature reaches 66.5 \({}^{\circ}\)C, which is basically consistent with the inlet temperature monitored in Section 2.1.3. Measuring points \(A_{N1}\)-\(A_{N6}\) are buried above the tunnel. The temperature of each measuring point increases slowly at first and then linearly, and the  
Figure 5: Temperature curve of the measuring points at Section A-A with time: (**a**) temperature at points \(A_{S1}\)-\(A_{S6}\); (**b**) temperature at points \(A_{N1}\)-\(A_{N6}\); and (**c**) temperature at points \(A_{E1}\)-\(A_{E6}\).  
temperature difference between each measuring point is kept at approximately 1 \({}^{\circ}\)C, as shown in Figure 5b. It can be obviously seen from Figure 5b that the temperature of each measuring point has a relatively obvious decline stage in the initial heating process. This is caused by the influence of the periodic temperature boundary conditions on the upper surface of the model. According to Equation (2), it can be seen that the ambient temperature presents a sinusoidal distribution, with an average daily temperature of 2 \({}^{\circ}\)C and the lowest temperature of \(-1.8\)\({}^{\circ}\)C. Since point \(A_{N6}\) is only 30 cm from the upper surface, its temperature drops most significantly. Figure 5c shows the temperature variation of measuring points \(A_{E1}\)-\(A_{E6}\) along the horizontal direction with time, and the temperature difference of each measuring point is 0.5 \({}^{\circ}\)C. The temperature of measuring point \(A_{E6}\) near the boundary reaches 35.5 \({}^{\circ}\)C, indicating that the adiabatic boundary set is reliable. However, the temperature at point \(A_{E1}\) is the lowest, which is near the tunnel. In the test, the tunnel is filled with soil, which may not be dense and cause heat loss.
## 3 Results and Discussion
### Effects of Derivatization Time and Displacement of 2-Furfural  
Summary of the effects of derivatization time is shown in Table 3. Extraction of aliphatic aldehydes (ethanal, propanal, butanal, pentanal, hexanal, and octanal), and aliphatic ketones (acetone, and heptanone) with PFBHA at 17 mg/mL, increased linearly with time with R\({}^{2}>\) 0.93 up to the 60 min reaction time tested (Table 3). An exception was observed with 2-furfural, whose derivative (oxime) decreased with progressing derivatization reaction as early as 5 min. This compound is of special interest. According to information provided in Material Safety Data Sheets of corresponding byproducts from ozonated FL solution/suspension, the oral LD\({}_{50}\) in rats of 2-furfural is the lowest among the detected byproducts [39]. Thus, a low LOD of 2-furfural was required in this study. The highest average RSD of 29.1% during 5 to 60 min derivatization was also observed with 2-furfural. The lowest Henry's law constant of 2-furfural among other aldehydes indicates it is the slowest to diffuse from aqueous phase to headspace (Table 3). This diffusion process from sample to headspace was found to be decisive to derivatization efficiency of on-fiber derivatization SPME [33]. A 1 cm magnetic bar, stirring at 800 rpm, was used here to facilitate the sample-to-headspace diffusion of analytes. The carbonyl carbon of 2-furfural is adjacent to the conjugate ring with an electrophilic oxygen, and has a relatively low affinity to the nitrogen nucleus of PFBHA. This, and the high steric hindrance of the furan ring, suggest a low reaction kinetic between 2-furfural and PFBHA. Desorption of carbonyl derivatives and PFBHA doped onto SPME fiber associated with longer derivatization time [33] is expected to be another factor affecting the uptake of 2-furfural. It is hypothesized that the apparent decrease of 2-furfural derivative uptake with time, and the high RSD resulted from the low affinity to PFBHA and desorption of 2-furfural-PFBHA
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 4 Preventive and Corrective Lost Circulation Measures
### Preventive Measures to Avoid Lost Circulation during Drilling
#### 4.1.3. Casing While Drilling (CwD)  
Casing while drilling (CwD) (also known as Casing Drilling--CD) is a relatively novel method where casing is used instead of drill pipes and drill collars. This type of well drilling is recognized as one of the most effective and cost-saving ways to drill and complete a well [68], as it provides the simultaneous drilling and casing of a well. Casing drilling eliminates costs associated with drill pipes, with a much greater benefit of eliminatingpotential problems that can occur during drill pipe tripping operations. These include circulation loss, wellbore stability problems and well control incidents [69], which can lead to complex and expensive operations. Also, this method enables continuous mud circulation which is a safer option than phases of static conditions without circulation, which is the case in conventional drilling.  
The main advantages of this method are a reduction of drilling non-productive time, a reduction of lost circulation in problematic zones and overcoming wellbore stability problems [70; 71; 72; 73]. The diameter of the casing is considerably larger than the diameter of drill pipes used in conventional drilling, which results in a smaller area for mud flow in the annulus. In these conditions, an effect known as plastering occurs and it enhances wellbore stability. The ratio of the outer diameter casing to hole size is certainly the primary factor for the occurrence of the plastering effect which is believed to be more than 0.75 [70]. Drilled cuttings are crushed to finer particles between the casing and the wellbore during contact due to limited annular clearance [72], which results in a larger particle size distribution profile than during conventional drilling [74]. A higher mud particle concentration and size distribution helps in bridging and fracture closure through the plastering effect [75], because finer sized cuttings are smeared into the wellbore face by the mechanical contact of the large diameter casing with the borehole walls [74]. This creates a thin, but almost impermeable mud cake that sets up a barrier and successfully prevents fluid loss, which has  
Figure 3: Comparison of pressure gradients and safe drilling window.  
been proven in field trials [72]. Consequently, the plastering effect that occurs during casing drilling increases the fracture gradient and improves the wellbore strengthening [76].
# YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
## 4 Results
### Conclusions  
To summarize the performance of the compared models, it is important to note that two versions of the network were leaders in terms of the achieved results. YOLOv5 and YOLOv8 obtained the best values in the training process, as shown in Table 24 and Figure 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Selected YOLO Model** & **Precision** & **Recall** & **AP\({}_{\mathbf{50}}\)** \\ \hline YOLOv5m 90/10 data split & 0.652 & 0.556 & 0.568 \\ YOLOv6L 90/10 data split & 0.605 & 0.437 & 0.413 \\ YOLOv7x 90/10 data split & 0.594 & 0.547 & 0.480 \\ YOLOv8x 90/10 data split & 0.667 & 0.493 & 0.524 \\ YOLOv9e 90/10 data split & 0.623 & 0.497 & 0.472 \\ YOLOv10m 90/10 data split & 0.601 & 0.473 & 0.493 \\ YOLOv11x 90/10 data split & 0.594 & 0.487 & 0.463 \\ \hline \hline \end{tabular}
\end{table}
Table 24: Comparison of the results obtained for the best model in each YOLO version.  
Figure 13: Example results from the conducted real-world scenario experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Selected YOLO Model** & **Correctly Classified (\%)** \\ \hline YOLOv5m 90/10 data split & 82.3 \\ YOLOv6L 90/10 data split & 63.2 \\ YOLOv7x 90/10 data split & 71.4 \\ YOLOv8x 90/10 data split & 76.6 \\ YOLOv9e 90/10 data split & 64.2 \\ YOLOv10m 90/10 data split & 74.0 \\ YOLOv11x 90/10 data split & 70.1 \\ \hline \hline \end{tabular}
\end{table}
Table 23: The results of a simulated real-world scenario for 100 generated images with 1562 objects in them.  
Although the results are good for the two mentioned versions, some key points need to be corrected before they can be implemented in a tool for teachers to adapt materials to the needs of the blind. First and foremost, the problematic classes such as parabolas and hyperbolas, which were visible in the confusion matrix for each version, need to be adjusted. Secondly, more time needs to be spent on training for both versions. Thirdly, the dataset needs to be revised and enriched with new data or adapted to the current images to make it a better fit for the proposed models. Figure 14 shows that although YOLOv5 has higher AP\({}_{50}\) values, YOLOv8 has slightly higher precision values. The theory of how high these values can go needs to be studied further.  
It is also important to note that although two versions achieved the highest results, the others should also be corrected. Their scores were lower, but by analyzing the causes of the problems, they can also provide valuable clues as to how to achieve better results in the future, or even surpass the current top performers. This can be done by the means already mentioned, such as looking more closely at the datasets and making changes, analyzing the optimization parameters more closely, and extending the training process.
## 4 Results  
### DCP Operation  
The functionality of the interacting state machines can be demonstrated in a simulated setup to validate state transitions. In Figure 7, the DCP state IDs for the master and slave are shown, as well as the vehicle velocity, the gas and brake pedal values, and the master's coolant temperature difference status.  
Within the first 100 s, both state machines change their states twice. The slave ID (brown dash-dotted) changes a third time shortly before 100 s. However, the master ID (black dashed) does not change before until \(\approx\)110 s, when the status for the coolant temperature difference \(\Delta T_{status}\) (green) is set to 0. The master can only leave this state if the slave transmits a CONFIGURED state, and in addition if the master's conditioning systems are within 1 \({}^{\circ}\)C of the desired coolant temperature.  
As soon as the master state 5 (RUN) is reached, the test run starts. The virtual driver then releases the brake pedal (blue dash-dotted) and starts controlling the vehicle velocity via the pedals. The control behavior of the virtual driver is not of central importance for the DCP operation, therefore the trend of the gas (orange dashed) and brake pedal (blue dash-dotted) as well as the vehicle velocity (red) will not be discussed further.  
A simulated test abort is achieved by setting the master state to 7 (STOP). As a result, the slave also leaves state 11 (RUNNING) and changes to state 15 (STOPING). The virtual driver sets the gas pedal to zero and applies the brake pedal, resulting in a vehicle standstill. If desired, this transition can be optimized by a suitable communication between state machine and hybrid controller.  
For a new test run, the master and slave have to start again at state 0 (REGISTER or ALIVE). A detailed allocation of the state ID and state name is given in Table 7.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**State ID** & **Master State** & **Slave State** \\ \hline
0 & REGISTER & ALIVE \\
1 & CONFIGSURE & CONFIGaturation \\
2 & APPLY & PREPARING \\
3 & INITIALIZE & PREPARED \\
4 & SEND\_I & CONFIGURING \\
5 & RUN & CONFIGURED \\
6 & SEND\_D & INITIALIZING \\
7 & STOP & INITIALIZED \\
8 & STOPPED & SENDING\_I \\
9 & & SYNCHRONIZING \\
10 & & SYNCHRONIZED \\
11 & & RUNNING \\
12 & & COMPUTING \\
13 & & COMPUTED \\
14 & & SENDING\_D \\
15 & & STOPPING \\
16 & & STOPPED \\
17 & & ERRORHANDLING \\
18 & & ERRORRESOLVED \\ \hline \hline \end{tabular}
\end{table}
Table 7: DCP state IDs and related states for master and slave.  
Figure 7: Example of DCP state machine functionality.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 3 Agricultural Applications of Quadcopters
### Plant Health Assessment  
By offering a bird's eye view, quadcopters can provide detailed images and information on plant health. This enables the early detection of diseases, pests, and nutrient deficiencies, leading to healthier plants and better agricultural productivity.  
Various quadcopters were used to execute plant health assessment in many research works, as follows:  
* DJI Phantom 4:
* It was used in [76] to take multispectral images and assess the health status of olive trees by evaluating different parameters such as nutritional values, biometric features, and vegetative status.
* It was employed in [77] to assess soil health by counting rice plants in an agricultural area in Tando Adam, Sindh, Pakistan.
* It was selected in [78] to capture multispectral images and identify banana areas infected with Panama disease, specifically \"Fusarium wilt\". This monitoring enables early treatment and improvements in planting methods.
* Its Pro version was implemented in [79] to capture high-resolution images to monitor the health of Eucalyptus pellita, detecting any infestations from pests and viruses.
* The authors in [80] used it to demonstrate that aerial images can serve as a cost-effective alternative to traditional field measurements for assessing citrus diseases such as Huanglongbing and Phytophthora, leading to improved crop production management.
* Co-Axial Quadcopter: it was implemented in [113] to show how multispectral images can assist satellite data in detecting stress in trees and monitoring forest health.
* DJI Matrice 100: it was paired in [62] with a camera to capture multi-band images for detecting orchard apple trees and evaluating vegetation parameters for better health assessment.
* DJI Inspire 2: it was selected in [114] to evaluate plant vegetation and rice crop damage, resulting in improved crop monitoring.
* DJI Phantom 3 Standard: two of these drones were used in [115] to take multispectral images to assess the health of Capsicum annuum crops for better field productivity.
* UX4: it was implemented in [116] to capture hyperspectral images to identify trees affected by citrus gummosis, the predominant fungal disease in Brazil, facilitating more effective management of plant health.  
After exploring the various quadcopters employed in different agricultural missions, we selected several examples and presented them in Table 1, given in Appendix A, where we highlight the limitations identified in each study. After evaluating the challenges faced, we proposed specific recommendations, such as employing more advanced techniques to address and overcome these issues. These suggestions can open the door for future work and provide a basis for future advancements in the field.  
Having established a comprehensive understanding of the diverse applications of quadcopters in agriculture, the discussion now transitions to quadcopters' modelling in the next section, examining various aspects essential for ensuring a reliable design.
## 6 Conclusions and Future Research  
Number of Clinical Decision Support Systems have been increasing in recent years, and they have proven to be a valuable help in the diagnosis process. Most of these systems are focused on specific areas of diseases in order to improve accuracy. However, our system focuses on a wider set of diseases so that it could help clinicians in primary health care. Our system's advice could avoid misinterpretation of laboratory tests' results, which leads to high waste of resources and longer turnaround times for diagnosis. In addition, our application could become a tool for information sharing between diverse health care levels, since it allows storing and retrieving of patient data. Our Expert System is not intended to replace clinicians or medical staff, which also must consider signs and symptoms to achieve a conclusion but can imply a useful tool for the process of diagnosis. Furthermore, our tool is easy to use and the rule set is also easy to maintain, without any need of advanced computer skills. Thus, we conclude that use of this system in primary health care can lead to increased efficiency and quality of the health service.  
As future research, we consider extending the framework by means of the application of a neural network on analysis data to make a model which allows us to compare current outputs of system with outputs from machine learning methods and combine both of them synergistically, taking advantage of pattern recognition, ability of artificial neural networks as well as the development of a module, which enables the rule set to grow and to correct itself taking into account previous cases. The rules could be improved taking into account age group, gender or ethnicity. In this way, it is possible to generate diverse rulesets, individualized for each of these groups, which could lead to an improvement of the accuracy of the system. We intend to test system outputs with a broader set of cases, taking into consideration several areas of diseases, in order to check accuracy and relevance of system's suggestions. In addition, the system could incorporate symptoms and inquiries of physical examinations in order to have a more comprehensive framework and improve quality results. As the ease of use is very important for the exploitation of the system, a research on the usability of the system is intended to be performed. This will allow us to check how much time it takes to use and familiarize oneself with the system and inquire about difficulties shown by the doctors, in order to improve the system interface.
# Design and Experimental Verification of a PZT Pump with Streamlined Flow Tubes
###### Abstract  
In this paper, a streamlined flow tube valveless piezoelectric pump (SLFT PZT pump) is proposed to modify the single flow trend and improve the fluid flow stability. Firstly, the structural and working principle of the streamlined flow tube, which accounts for changing the flow trend and improving the flow stability, were analyzed. The flow resistance and flow rate equations were established. Secondly, the pressure and velocity fields of the tube were simulated. These simulated results were consistent with the theoretical results. Thirdly, the flow resistance of the flow tube was tested with pressure differences of 1000 Pa, 1200 Pa, 1400 Pa and 1600 Pa respectively. The trend of the result curves was consistent with the simulated results. The amplitude-frequency relationship and the flow-rate-frequency relationship were also tested, both result curves highly corelate. The maximum amplitude was 0.228 mm (10 Hz, 120 V), and the maximum flow rate was 17.01 mL/min (10 Hz, 100 V). Finally, the theoretical flow rate of the SLFT PZT pump was calculated at 100 V and 120 V. These results roughly fitted with the experimental results. The streamlined flow tube could change the internal flow trend that remarkably improved the flow stability. Therefore, it promoted the application of the valveless PZT pump in living cells, biomedical and polymer delivery.  
Keywords:valveless piezoelectric pump; streamline structure; flow trend; flow stability +
Footnote †: journal: Applied Sciences