esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Diabetes Complications Using Computational Intelligence Techniques
## 3 Solution Design and Implementation
### Validation Prototype  
The proposed system utilises a dataset to predict the likelihood of an individual developing diabetes. The system utilises the Iterative Dichotomiser 3 algorithm to create decision trees and to aid in monitoring the patient's health by providing results from various checkups for fasting and postprandial blood sugar levels. The system generates awareness messages based on the patient's blood sugar levels, as shown in Table 1. This table displays the normal blood sugar levels and the type of diabetes associated with them.  
Incomplete or missing data is a common issue in medical datasets and can cause problems for machine learning models, leading to biased or inaccurate results [27]. Handling missing values in medical datasets before training a model is an important step in the data preprocessing stage. There are several approaches to handling missing values in medical datasets, including:  
**Imputation:** This involves replacing missing values with a substituted value, such as the mean or median value of the corresponding feature, or using more complex techniques, like regression models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Blood Glucose Level**} \\ \hline  & **Minimum Value** & **Maximum Value** & **Value after Eating Food** \\ \hline Normal range & 70 & 100 & \textless{}140 \\ Diabetes at early stage & 101 & 125 & 141–200 \\ Diabetes Established & \textgreater{}126 & - & \textgreater{}200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Blood glucose level chart.  
**Deletion:** This involves removing the samples or features with missing values. However, this approach can result in a significant loss of data.  
**Using specialised algorithms:** Some machine learning algorithms, such as decision trees or random forests, can handle missing values directly during the training process.  
It is important to choose an appropriate strategy based on the specific characteristics of the dataset and the requirements of the problem at hand. Proper handling of missing values can help to improve the accuracy and reliability of the machine learning model and reduce the potential for biases or inaccuracies in the results.
# Identifying Growth Patterns in Arid-Zone Onion Crops (Alium Cepa) Using Digital Image Processing
## 2 Materials and Methods  
### Development of Experimentation  
Crop development for this work occurs in the municipality of Fresnillo, Zacatecas, Mexico, with Cartesian coordinates \(23^{\circ}06^{\prime}30.6^{\prime\prime}\) N10\({}^{\circ}\)38\({}^{\prime}\)26.3\({}^{\prime\prime}\) W, as shown on the maps in Figure 1. A UAV Phantom 4 Pro with an RGB camera, a GPS/GLONASS system, and a 20-megapixel camera 4K resolution flew over onion crops (Allium Cepa) to capture photographs throughout the crop cycle. It presented a flight every week from March to July 2019 for 16 weeks, obtaining an average of 758 images in a land area of approximately three hectares at 20 m in height and a series of 5m. These series verified the optimal height, starting at 2 m and continuing at 5, 10, 15, 20, and successively up to 120 m.
# A Pervasive Collaborative Architectural Model at the Network's Periphery
###### Abstract
Note that collaborative computing can be defined as \"a fertile blend of technologies and techniques which facilitate people working together via computer-assisted means\" [19]. Pervasive computing is an emerging field of distributed systems, where objects are small, mobile and connected by wireless and radio P2P technologies. It can be divided into three sub-classes: systems of ubiquitous computing, mobile systems and sensor networks [20]. Our research combines collaborative and pervasive computing to (i) embrace a pervasive collaborative model for IoT and (ii) facilitate and assist cooperation between people, especially in harsh environments, to exploit the capabilities of each object (device). Indeed, we think the interesting applications come from the collaboration of objects with minimal human collective intervention rather than from individual ones. Other technologies are used in this paper. Let us introduce them briefly. LoRa is a wireless technology that enables communications over long distances, but at a low data rate. It uses sensors and actuators for P2P and IoT applications. It can use the radio spectrum to enable low power broadband communication between remote devices and network gateways. LoRa is the de facto platform for low power wide area network (LPWAN) technology for IoT. The LoRaWAN(r) specification, designed by Pycom (Guildford, UK), is a networking protocol designed to enable rapid establishment of public or private IoT networks anywhere using bidirectional communication, mobility and localization services with end-to-end security [21]. Wi-Fi-direct is a Wi-Fi ad hoc standard for peer-to-peer wireless connections that allows two devices to communicate without an access point, router, or Internet connection. The remainder of this paper is organized as follows. Section 2 describes related work, and discusses the problem of pervasive architectural models at the periphery. Section 3 describes the proposed pervasive collaborative architectural model. Section 4 presents the experimental study we conducted. Finally, concluding remarks are presented in Section 5.
# Collapse Analysis of a Two-Span Reinforced Concrete Bridge Model
## 4 Analysis of Collapse Mode
### Influence of Structural Systems  
#### 4.5.1 Isolation System  
The traditional seismic design method is to improve the strength and stiffness of the structure to enhance the resistance of the structure, which will undoubtedly greatly increase the construction cost. The structural control theory developed in the past 30 years can be used to improve the vibration performance of the structure under catastrophic earthquake excitation. The structural vibration control technology can be divided into four categories according to whether the control measures adopted need external energy: passive control, active control, semi-active control, and hybrid control. The vibration control method of the continuous beam bridge basically is passive control method. Passive control of the continuous beam bridge includes bearing isolation, energy dissipation, and restraint device, etc. Bearing isolation is the most common method.  
The HDR bearing is one kind of isolation bearing. HDR increases the damping of the bearing by improving the formula of rubber, and its damping ratio can reach about 0.15, which is less affected by temperature. It integrates vertical bearing capacity, horizontal restoring force, and damping. The initial elastic stiffness can play a braking effect on wind load, while the HDR bearing plays a role of damping energy dissipation under large displacement. On the one hand, it maintains the good mechanical properties of the plate rubber bearing; on the other hand, the HDR has high viscosity and damping, so it can absorb seismic energy and reduce seismic response in the earthquake. In addition, the cost of HDR bearings is not high and environmental impact is small.  
After the shaking table test of the bridge model of ordinary rubber bearings, the ordinary rubber bearings were replaced with HDR bearings and the isolation system was investigated by the shaking table test. The results showed that the seismic response was significantly reduced [28]. The collapse modes before and after isolation were then obtained by numerical analysis. Taking the El Centro wave case as an example, under the longitudinal input of a 1.2 g seismic wave, the failure of the middle pier caused serious damage to the main beam, which exhibited a pronounced deflection above the same middle pier. In the HDR system, the collapse occurred under the longitudinal input of 2 g PGA, and the failure mode was the same as that of the ordinary system [32]. It could be seen from the above that the isolation bearings played a role in delaying the collapse of the structure but maintained the basic characteristics of the original structure.  
Figure 19: Collapse mode of bridge model under Chi–chi wave input: (**a**) column length of 1.8 m; (**b**) column length of 2.4 m; (**c**) column length of 3 m; (**d**) column length of 3.6 m.
## 3 Simulation Studies and Results
### Data Preprocessing  
The data cleaning of the original data is mainly to eliminate the garbled records in the original data sets and standardize the data format. During the data transmission process, the noise monitoring records of the monitoring points are missing in some seconds, and the time series processed by the LSTM network requires sequences of the same time interval due to obstacles and signal interference. The average of the noise detected before and after the second of the missing record was thereby employed to fill the lost records in the noise time series. The raw data was divided into eight data sets in accordance with the monitoring points (01, 02, 03, and 04) and the monitoring periods (a and b). Period a refers to August 15-21 in 2015 and period b refers to September 15-21 in 2015. The statistical indicators of the filled noise time series data are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Average** & **Maximum** & **Minimum** & **Standard Deviation** & **Quartile 1** & **Quartile 2** & **Quartile 3** \\ \hline
01\_a & 60.38 & 95.2 & 38.7 & 6.68 & 55.8 & 61.7 & 64.6 \\
02\_a & 52.07 & 95.1 & 38.7 & 4.8 & 49.9 & 52.1 & 54.5 \\
03\_a & 56.63 & 91 & 38.6 & 6.48 & 51.8 & 55.1 & 61.9 \\
04\_a & 61.04 & 102 & 40 & 5.39 & 58.4 & 61.8 & 64 \\
01\_b & 61.77 & 131.9 & 38.2 & 7.47 & 58.4 & 62.4 & 65.3 \\
02\_b & 52.2 & 88.8 & 39.6 & 4.74 & 50 & 52.2 & 54.6 \\
03\_b & 57.65 & 95.3 & 40 & 6.78 & 52.6 & 57.6 & 62.5 \\
04\_b & 61.63 & 97 & 40.2 & 5.07 & 59.8 & 62.1 & 64.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics for noise time series of different data sets/dB(A).  
Figure 5: Distribution of monitoring points.  
The variation of statistical indicators for the two monitoring periods at the same monitoring point is presented in Table 5. As shown in Table 5, for the same monitoring point, the mean change in the different monitoring weeks is \(-0.1\)-\(1.39\) dB(A), with the maximum value and minimum value of \(-6.5\)-\(33.5\) dB(A) and \(-1.2\)-\(1.4\) dB(A) respectively. The standard deviation obtained ranges from \(-0.32\)-\(0.79\) dB(A), and the quartile varies from \(-0.2\) to \(2.6\) dB(A). The weekly variation of other statistical indicators is slight except for the maximum value, revealing that although the environmental noise is random, there is determinism in the randomness. The overall noise level is stable in the absence of significant changes in the ambient noise source at the monitoring point, implying that the ambient noise time series can theoretically be predicted.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 3 System Development
### Sensor Package  
The sensor package consisted of three components: (i) electronics for data gathering, storage, and wireless control, (ii) a docking mechanism based on an electropermanent magnet, and (iii) the power supply (battery). These components are presented in Figures 2 and 3, while Table 1 summarizes the main features of the sensor package. A compact 3D-printed frame was built to house these components, forming the standalone sensor package shown in Figure 4 (black box).  
The sensor package features the ARM Cortex-M7 processor onboard the Teensy 4.0 high-performance microcontroller. The system utilizes the 600 MHz clock speed of the Teensy 4.0 and the Serial Communication Interface (SPI) to communicate between the modules and achieve a data collection rate up to 28 kHz. The sensor package also includes three control options (Figure 2): a PWM port used for wireless control, a digital 5 V as a hard-wired trigger, and manual controls for testing and calibration purposes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Size** & **Rate of Data Collection** & **Resolution** & **Sensitivity** & **Steady-State Power** & **Package Weight** \\ \hline
74,000 Samples & 28 kHz & 16-bit & 5400 LSB/g & 0.296 W & 380 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the sensor package features.  
Figure 2: Block diagram of the sensor package circuit schematic.  
Power is supplied using a DC-DC buck converter (MP1584 EN manufactured by Monolithic Power Systems) and a lithium polymer battery (Lumenier 7.4V 1000 mAh), as shown in Figure 3. This battery currently runs the sensor package for about 11 h on a single charge. The deployment period can be prolonged by utilizing standby mode where unused modules are turned off to conserve energy. In cold weather though, testing has shown that batteries tend to discharge relatively faster, in turn requiring a more restrictive power conservation schedule, limiting the number of tests conducted per deployment.  
The docking mechanism consists of an electropermanent magnet (NicaDrone EPM V3 R5C) able to mount the sensor package onto metal surfaces (Electropermanent Magnet #1 in Figure 4). The package collects acceleration data using a MEMS accelerometer (Murata SCA3300-d01) mounted to the metal frame of the electropermanent magnet to ensure contact with the structure being examined. The system can collect up to 74,000 samples as one continuous dataset, before having to transfer the data onto the onboard nonvolatile memory (SD card), a process that takes approximately 3.288 s.
# A Review Study on Total Ankle Replacement
## 2 TAR System Study
### Component Number  
Currently, there are both two and three component systems on the market for TAR, both of which have advantages and disadvantages. Neither has been shown to be definitively superior.  
The only three-component system that is currently approved by the Food and Drug Administration (FDA) is the STAR implant from Stryker. The mobile polyethylene component allows for rotation and translation to theoretically reduce the stress at the junction of the implant and the bone interface [33]. Initial outcomes with STAR showed promising functional results, but with concern for peri-implant osteolysis [34]. Later studies confirmed positive functional outcomes and consistent pain relief, but noted a high rate of revision surgery at 10 years post operatively [35] is another mobile bearing implant, but is currently used in Europe and Canada and not approved for use in the US.  
Two-component systems have a conforming saddle geometry and large surface area to distribute stress, theoretically leading to decreased wear [36].  
A study that followed 20 patients with a two-component system and 20 patients with a three-component system found that both types of systems significantly improved qualityof life for patients. However, when compared to a control group that did not undergo TAR, all 40 patients scored lower on spatial-temporal and functional parameters and did not achieve the normal locomotion patterns of the control group. A clinical difference in outcomes between two or three component systems has not been shown [37; 38].
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Maximum Range for Minimum Reliability  
In the previous subsections, we presented the effect of reliability on the SNR and the transmitter power. Another aspect that we can examine includes the impact of reliability on the maximum range between two vehicles. For this purpose, we studied the maximum distance two vehicles can have in order to achieve the minimum required reliability, as defined by each QoS scenario. For the calculations, we assume that the transmitter power is 23 dBm and the noise figure at the receiver is 9 dB [9] (p. 9). Additionally, we assume that the vehicles transmit at the highest possible rate for each QoS scenario and that the \(E_{b}/N_{0}\) at the receiver is the minimum required to achieve the minimum tolerable reliability of each QoS scenario. Figure 6 presents the maximum range per QoS scenario, simulation scenario and coding scheme where we have limited our scope to a 1500 m maximum range, which is three times bigger than the maximum typical range of Table 2.  
QoS scenarios Q1 and Q4 are the most range-effective scenarios. QoS scenarios Q1 and Q4 have a maximum typical range at 350 and 200 m, respectively, and from Figure 6a,d, we can observe that the minimum reliability requirements are fulfilled for all simulation  
Figure 5: Minimum transmitter power requirements for each system model simulation scenario and coding scheme that achieves the minimum reliability requirements. (a) QoS scenario Q1; (b) QoS scenario Q2; (c) QoS scenario Q3; (d) QoS scenario Q4; (e) QoS scenario Q5; (f) QoS scenario Q6.  
scenarios and coding schemes. This fact is true not only for the maximum typical range scenario but also for much longer distances.  
QoS scenarios Q2, Q3 and Q5 achieve the 200, 350 and 500 m thresholds, respectively, in all simulation scenarios and for all coding schemes, except scenario S4 (urban-NLOS). The urban-NLOS scenario in these QoS scenarios indicates that the coding schemes achieve reliable communication at 42 to 48% of the maximum typical range depending on the coding scheme. Nevertheless, from the urban-NLOS cases, the superiority of the BCJR-based algorithms becomes apparent, since they have the maximum range among all the coding schemes.  
Figure 6: _Cont._  
The QoS scenario that has the worst range in all system simulation scenarios and coding schemes is Q5. Although only simulation scenario S4 fails to reach the maximum typical range fulfilling the reliability requirements, it has become obvious that the dynamics of scenario Q5 are smaller. The distances for Q5 in all system simulation scenarios have been shortened, with a characteristic example being the highway-NLOSv scenario, where the specifications of the distance are marginally met.  
In conclusion, we observe that all simulation scenarios, except urban-NLOS, achieve the range required by each QoS scenario. For cases where this range is not reached, BCJR-based coding schemes have the widest possible range, polar has a range comparable to SOVA and LDPC has the worst range.
# Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle
###### Abstract  
Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time simulations (MLTSs) has been proposed. The main purpose of this work is to demonstrate how significantly different results from a minimum lap time optimal control problem can be obtained when using inappropriate elevation data sources in the track reconstruction problem, and how the road model reconstructed using poor input data can lead to misleading conclusions when analyzing real vehicle and driver performances. Two road models derived from high- and low-resolution digital elevation models (DEMs) are compared and their impact on the optimal maneuver of a racing motorcycle is examined. The essentials of track identification are presented, as well as vehicle positioning on the 3D road and the generalized QSS motorcycle model. Obtained 3D and 2D road models are analyzed in detail, on a case example of the Road Atlanta racetrack, and used in minimum lap time simulations, which are validated by the experimental data recorded on the Superport motorcycle. The comparative analysis showed that great care should be taken when selecting the elevation dataset in the track reconstruction process, and that the 1 m resolution local DEMs seem to be sufficient to obtain MLTS results close to the measured ones. The example of using the 3D free-trajectory QSS minimum lap time problem to localize the track segments where real driver actions can be improved is also presented. The differences between simulation results on different road models of the same racetrack can be large and influence the interpretation of optimal maneuver.  
optimal maneuver; road modeling; three-dimensional road; minimum lap time simulation; optimal control; motorcycles; experimental data; trajectory optimization; free-trajectory
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.3 Advancing Sea Ice Monitoring and Prediction with DL
[159] proposed a global-local Transformer network for recognizing sea ice types in optical remote sensing imagery. This network combines a global attention head to capture structural information with fine local details, resulting in improved classification accuracy. Similarly, [PERSON] et al. [178] explored multiscale feature extraction from SAR data by fusing CNN and ViT architectures, enhancing feature representation and classification performance. It should be noted that the performance of DL-based sea ice classification algorithms may vary across different seasons due to changes in sea ice conditions. Specifically, in winter, ice cover is stable, and DL models often achieve higher classification accuracy. However, during transitional seasons such as spring and fall, melting and refreezing processes generate complex surface features in satellite imagery, making it more challenging to distinguish between sea ice and open water. As a result, classification accuracy tends to decrease in these periods. Future research could incorporate season-specific training and validation strategies that account for seasonal variability to improve models' robustness and generalization. Detecting and classifying sea ice, along with retrieving parameters such as concentration and thickness, are essential for understanding its current state. Beyond these static analyses, dynamically predicting sea ice changes over time is another important task and DL methods have proven to be useful for dynamic sea ice analysis [179; 180]. For example, [PERSON] et al. [181] combined CNN and LSTM architectures for spatiotemporal sea ice forecasting and introduced a perceptual loss function that leveraged pretrained feature representations to enhance model performance. [PERSON] et al. [182] developed IceNet, a probabilistic prediction model based on the U-Net architecture. Trained on climate simulations from CMIP6 and observational data from satellite sensors, IceNet provides reliable six-month forecasts of sea ice concentration at a resolution of 25 km. [PERSON] et al. [160] proposed SICFormer, utilizing a 3D-Swin Transformer for hierarchical feature extraction and PixelShuffle for high-resolution image reconstruction. Trained on data spanning from 2006 to 2022, SICFormer obtained great performance in 8-day sea ice concentration forecasts. [PERSON] et al. [183] introduced a Transformer-based model for Arctic sea ice prediction, providing daily forecasts of concentration and thickness for up to 45 days. DL has powerful capabilities for sea ice detection, classification, and prediction. Using diverse data sources and advanced models, researchers have improved the accuracy and reliability of sea ice monitoring, essential for understanding the ongoing impacts of climate change on the cryosphere.