esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Classical Digital Signal Processing  
For all subjects, all reflection and transmission measurements were analyzed for all permutations of two transducer elements of the arrays. It is not possible to classify a single US wave traveling through an open growth plate in single A-scans with only small changes in amplitude, frequency content, or phase shifts caused by wave cancellation. On the other hand, the signal changes over time during sled movement. Both the intensity value and the signal change in terms of the difference of consequential A-scans were used for processing (see Figure 9).  
Intensity fluctuations were seen best in the difference image, which removed the static background signal. The integral sum over all amplitude intensities moving over a possible growth plate provided us with a relevant feature. While subjects with a fully ossified growth plate showed only slight signal changes during transducer movement along the forearm, subjects with an open growth plate showed a stronger signal change, caused by diffraction, scattering, and reflection of the US wave through the open growth plate. Although a difference in integrated signal change is seen for younger (<15 years of bone  
Figure 9: Analysis of measurement data plotted as A-scans (color coded) over motor movement (x-axis) and measurement time (y-axis) of a person with an open growth plate (**a**) and an ossified growth plate (**b**); transmission signal envelope amplitude (left-hand plot in panels **a** and **b**) and transmission signal change (difference signal change to previous A-Scan, right-hand plot in panels **a** and **b**).  
age) and older (\(\geq\)15 years of bone age) women, a linear clustering based on such a feature extraction does not look very promising (see Figure 10).  
Using a combination of multiple additional features extracted by signal processing could lead to a more robust classifier but optimizing such a higher dimensional nonlinear classification is also challenging.  
With this result, we proceeded with ML approaches, as those proved most promising in creating a robust classifier for our purposes.
## 5 On-Site Monitoring and Analysis of Surface Deformation Law
### On-Site Monitoring Data
#### 5.1.2 Monitoring Data Analysis  
Monitoring commenced simultaneously with the start of shield tunneling. At the CDBC719 monitoring section (comprising three monitoring points), data were recorded twice daily. When the shield machine was at a greater distance (approximately 40 m) from this section, the monitoring frequency was reduced to once per week. The CDBC709 monitoring section (comprising four monitoring points) is located at the end of the jet grouting reinforcement zone. As noted in Section 3.2, this section experiences relatively  
Figure 8: Layout of monitoring points.  
Figure 9: Schematic diagram and site picture of automatic monitoring instrument.  
large settlements during construction and is more prone to accidents. Therefore, the monitoring frequency at the CDBC709 section was maintained at twice daily until the end of construction. Monitoring data from each point are illustrated in Figure 10.  
As illustrated in Figure 10, with increasing excavation distance, the three monitoring points at the CDBC719 section generally exhibit ground settlement. The trend is characterized by alternating increases and decreases, forming a fluctuating pattern. The values primarily fluctuate around \(-2\) mm, with settlement at the tunnel centerline greater than at the sides (SCDBC719-2 > SCDBC719-1 > SCDBC719-3). Settlement values at the three monitoring points generally range from \(-4\) mm to \(0.5\) mm, well below the settlement control threshold, indicating that the reinforcement scheme has effectively stabilized the ground at the tunnel launching section.  
Figure 10 shows that at the CDBC709 section, settlement trends across the four monitoring points initially decrease, then increase, and finally decrease again toward stabilization. The settlement values are ranked as follows: SCDBC709-2 > SCDBC709-3 > SCDBC709-1 > SCDBC709-4. On the 20 th day of construction, settlement reached \(-10.1\) mm. Field investigations revealed two main reasons: first, the tunnel portal had not been sealed, and the slurry chamber pressure was set too low at \(0.6\) bar; second, the replacement layer at the crown had poor self-supporting capacity. After grouting treatment, the settlement values were brought under control.
# An Overview of CubeSat Missions and Applications
## 4 The Use of CubeSats as Telecommunication and Asset-Monitoring Platforms  
A significant portion of the CubeSats that are currently orbiting the Earth serve the mission of enabling and facilitating communication services and applications or demonstrating state-of-the-art communication technologies. Originally, CubeSat platforms were only used for the demonstration of communication technologies and their adoption for the provision of commercial communication services was hampered by the low downlink-bandwidth that stems from the small size of antennas and electronics that can fit inside the CubeSat volume. However, the hardware developed for said technology demonstration missions and the subsequent miniaturization of telecommunications electronics and antennas that provide adequate downlink data-rates, along with the emergence and adoption of laser-optical communication technologies, have enabled CubeSats to undertake significant roles in global non-terrestrial communication. Moreover, with humanity's increasing need for even greater data transfer capacity and with the number of proposed or under development constellations of communication CubeSats rising, the number of communications-related CubeSats in orbit is expected to increase significantly in the following years.  
Communication CubeSats (and satellites in general), depending on their application and onboard equipment, may transmit in a variety of different areas of the electromagnetic spectrum which can be divided into two principal areas, the radio spectrum ranging from 3 kHz to 300 GHz and the optical spectrum which encompasses the frequencies from 300 GHz up to \(10^{12}\) GHz. The examples of communication CubeSat missions discussed in the present section are primarily divided based on the region of the electromagnetic spectrum their main mission payloads are designed to operate in, namely radio telecommunication CubeSats and optical telecommunication CubeSats.
## 2 Setup and Simulation Environment  
To compare the performance of the three selected networking technologies--i.e., DSRC, LTE-infrastructural, and LTE Device-to-Device mode 3--in a vehicular environment, we prepared a federated telco-traffic simulation experiment. To realistically simulate the vehicular mobility, a traffic microsimulation was run in the Simulation of Urban Mobility (SUMO) traffic simulator [13] with a realistic model of the road infrastructure in the city of Zilina (Slovakia). The SUMO simulation served as a mobility generator for communication nodes in the OMNeT++ discrete event network simulator [14], where the corresponding communication protocols were simulated. The positions of all vehicles were synchronized between the two simulators during the simulation runtime via a bi-directional Traffic Control Interface (TraCl) [13], a part of the SUMO traffic simulator. The communication protocols of the DSRC network were modeled using the Veins simulation framework [15]. The protocol stack of the LTE network was modeled using the SimuLTE simulation framework [16]. The higher communication layers were modeled using the INET simulation framework [17].
# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
## 2 Materials and Methods
### Cell Settings  
RBCs were represented by a surface mesh consisting of \(374\) nodes. In the relaxed state, they had dimensions of \(7.82\times 2.58\) um, and their volume and surface area were \(92.7\) um\({}^{3}\) and \(132.9\) um\({}^{2}\), respectively. In this study, four levels of RBC deformability were used, and each level was indicated by its respective value of the stretching coefficient, which has the greatest influence on deformability. All other coefficients had the same values at all levels of deformability. The values of the elastic coefficients of a healthy well-deformable RBC (denoted as \(ks=0.05\)) were determined by calibration based on a biological experiment with optical tweezers [22]. More details about RBC validation can be found in [23]. The values of all the elastic coefficients of the healthy RBC are shown in Table 1. The least deformable RBCs showed rigidity approximately at the level of cells infected with malaria in the schizont stage (denoted as \(ks=0.03\)). The elastic coefficients were chosen by calibration based on the results of experiments in [24]. The remaining two levels of RBC elasticity were equally distributed between healthy and infected RBCs with \(ks\) values of \(0.0133\) and \(0.0216\), respectively.  
Each simulation involved \(36\) RBCs, representing \(9\) cells from each level of deformability. Mutual interactions between cells were modeled using the membrane_collision potential  
Figure 2: On the left, microfluidic channel topology is shown. Only the basic part with five obstacles (depicted with blue colour) was simulated. The figure on the right shows the scheme of the simulation box with the dimensions of the individual parts.  
with the parameters mc_K = 0.01, mc_n = 1.0, mc_cut = 0.4. The interactions between the cells and the walls and obstacles were modeled using the soft_sphere potential with the parameters soft_K = 0.001, soft_n = 1.2, soft_cut = 0.5.
## 3 Results
### Description of Developed Software Tool: \"PetriNet editor + PetriNet engine\"
#### 3.1.4 Memory Optimisation  
ATmega328 has 32 KB of flash memory, 2 KB of SRAM memory and 1 KB of EEPROM memory. From the numbers, it can be seen that the SRAM memory is small compared to the flash memory. When the SRAM memory is full, the program may behave unexpectedly. The memory had to be optimised for the PetriNet engine to run stably, as a simple Petri net consisting of one place and two transitions took up 89% of the SRAM memory. To optimise memory usage, the ability to move all strings from SRAM memory to flash memory was used. This is done using the approach provided by PROGMEM. PROGMEM is a variable modifier that tells the compiler to store the marked variable in flash memory.  
In this way, the optimised memory showed high efficiency in the described application. Table 2 shows the memory usage before and after optimisation.  
Without optimization, the relatively simple Petri net, consisting of 10 places and 10 transitions, could not be compiled. After optimisation, it was possible to compile a Petri net consisting of 40 places and 40 transitions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Places**} & **Number of Transitions** & \multicolumn{2}{c}{**Use of Memory without Optimisation**} & \multicolumn{2}{c}{**Use of Memory with Optimisation**} \\ \cline{3-6}  & & **FLASH** & **SRAM** & **FLASH** & **SRAM** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{0} & 12 308 B & 1 812 B & 13 328 B & 444 B \\  & & (38 \%) & (88 \%) & (41 \%) & (21 \%) \\ \hline \multirow{2}{*}{0} & \multirow{2}{*}{1} & 12 178 B & 1 832 B & 13 622 B & 444 B \\  & & (37 \%) & (89 \%) & (42 \%) & (21 \%) \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{1} & 12 822 B & 1 842 B & 14 240 B & 452 B \\  & & (39 \%) & (89 \%) & (44 \%) & (22 \%) \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{2} & 13 204 B & 1 856 B & 14 672 B & 468 B \\  & & (40 \%) & (90 \%) & (45 \%) & (22 \%) \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{3} & 13 528 B & 1 872 B & 15 100 B & 484 B \\  & & (41 \%) & (91 \%) & (46 \%) & (23 \%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The use of memory without optimisation and with optimisation.
## 3 Results
#### SVM Classifier vs. 1D-CNN Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Parameter** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Wavelet Transform & Min & 46.00\% & 46.00\% & 100.00\% & 63.01\% \\ + HOS & (\(\gamma=0.0001\); C = 10.0) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & Max & 85.33\% & 83.09\% & 85.51\% & 84.29\% \\  & (\(\gamma=10.0\); C = 1.0 \(\times\) 10\({}^{9}\)) & (0.00\%) & (\(-\)3.06\%) & (+4.35\%) & (+0.71\%) \\  & Mean & 68.00\% + 13.80\% & 54.31\% + 27.92\% & 73.30\% + 35.71\% & 61.31\% + 29.79\% \\  & (4.13\%) & (\(-\)0.35\%) & (\(-\)0.35\%) & (+4.88\%) & (+1.71\%) \\ \hline Wavelet Transform & Min & 46.00\% & 46.00\% & 100.00\% & 63.01\% \\ + ULBP & (\(\gamma=0.0001\); C = 10.0) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & Max & 68.67\% & 66.18\% & 65.22\% & 65.69\% \\  & (\(\gamma=0.0001\); C = 1.0 \(\times\) 10\({}^{9}\)) & (\(-\)16.67\%) & (\(-\)19.97\%) & (\(-\)15.94\%) & (\(-\)17.89\%) \\  & Mean & 53.62\% + 4.88\% & 24.57\% + 26.75\% & 27.69\% + 41.16\% & 26.65\% + 28.38\% \\ \hline Wavelet Transform & Min & 46.00\% & 46.00\% & 100\% & 63.00\% \\ + RR & (\(\gamma=0.0001\); C = 10.0) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & Max & 92.00\% & 89.04\% & 94.20\% & 91.54\% \\  & (\(\gamma=10.0\); C = 1.0 \(\times\) 10\({}^{9}\)) & (\(+\)6.67\%) & (\(+\)2.89\%) & (+13.04\%) & (\(+\)7.97\%) \\  & Mean & 73.78\% + 16.98\% & 60.97\% + 31.17\% & 75.42\% + 36.66\% & 66.21\% + 32.38\% \\  & (\(-\)14.25\%) & (\(-\)30.04\%) & (\(-\)40.73\%) & (\(-\)38.95\%) \\ \hline Wavelet Transform & Min & 46.00\% & 46.00\% & 100.00\% & 63.00\% \\ + Wavelet Dec. + PSD & (\(\gamma=0.0001\); C = 10.0) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & Max & 85.33\% & 86.15\% & 81.15\% & 83.58\% \\  & (\(\gamma=10.0\); C = 1.0 \(\times\) 10\({}^{9}\)) & (0.00\%) & (0.00\%) & (\(-\)0.01\%) & (+ 5.00\%) \\  & Mean & 67.87\% + 14.18\% & 54.61\% + 29.86\% & 68.42\% + 36.00\% & 59.59\% + 30.55\% \\  & & (0.00\%) & (0.00\%) & (\(-\)38.56\%) & (+8.82\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results obtained with combined handcrafted features for the SVM.
## 3 Experimental Set-Up
### Optical Transmitters  
We consider phosphor-coated blue LEDs because of their high energy efficiency [23] and easy implementation in hardware testbeds. In this sense, these LEDs are useful for applications that require illumination for a long time. Specifically, the optical transmitter corresponds to the LED NSPW570 DS of manufacturer NICHIA. It provides a white color and a wide radiation semi-angle equal to 60\({}^{\circ}\), which emulates the illumination in an indoor scenario. The design of the driver, which modulates the information, is based on the well-known common emitter transistor configuration. For this experiment, the transistor used was the BJT 2N2222. Notice that this approach provides greater stability in comparison with more complex polarization architectures due to the negative feedback and the independence of the polarization point at the base and collector inputs of the transistors. This approach provides more accurate control of the amplifier's sensitivity to signal variations [24].  
After detailing the hardware configuration of the optical transmitters, the software that manages the proposed positioning system is presented. It is important to note that the signals from the four LEDs must be synchronized in time. To achieve this, a single Arduino microcontroller controls the optical transmitters, ensuring that transmission follows a continuous loop, preventing collisions, and allowing only one LED to transmit information in each time slot.  
The frames transmitted by each LED are composed of 12 bits each. The first six bits represent the frame header, mainly used for synchronization. The other six bits contain information for each lamp, such as the lamp ID. In addition, due to the uniform power of these headers, it is possible to measure the intensity of the illumination at the receiver side, which allows us to determine the distance to each optical transmitter. Specifically, the information is transmitted following an FSK modulation, which provides data transmission that is resistant to interference, attenuation, or noise compared to other modulations, such as on-off keying (OOK) or pulse amplitude modulation (PAM) [25].  
The signal assigned to the transmission of the '1' bit corresponds to a frequency \(f_{0}\) equal to 1 kHz, while the '0' bit corresponds to a frequency \(f_{1}\) equal to 2 kHz. The bit period is equal to 5 ms for each bit, which leads to a frame comprising 60 ms of duration. The frames transmitted by each of the four LEDs are depicted in Figure 4.  
Figure 3: Experimental system diagram.
# A Decentralized Framework for Managing Task Allocation in Distributed Software Engineering
## 3 The Proposed Task_Auc_ Framework
### Task Categorization  
Tasks can be complex, domain-specific, or dependent on other tasks. The dependency is represented over time and it indicates that a latter task cannot be processed until its dependencies are fulfilled. The proposed approach captures all possible task dependencies to create clusters of equal sizes and proposes the three categorizations presented below. This will help project managers to create task clusters of equal sizes based on their difficulty,  
Figure 1: The proposed _TaskAuc_ architecture.  
dependency, cost, and time to leverage the well-known advantages of parallel workflows to support rapid development. The _TaskAuc_ implementation has three main phases: auction, bidding, and winner announcement. Registered developers will be allowed to place their valuations as bids for particular tasks. All placed bids are decrypted, analyzed, or evaluated to announce the winner. The winning bids are pushed back to the auction process with the bidder key. We assume that there will be only one auctioneer and multiple bidders. The lowest-cost bids from the range specified in the block structure will be selected and notified. The lowest-cost bid strategy is most optimal for getting the lowest possible cost value results for better optimization of results. In the end, the tasks which are not selected or allocated will be discussed with potential developers until the tasks are allocated to all developers. This will help project managers to create task clusters of equal sizes based on their difficulty, dependency, cost, and time to leverage the well-known advantages of parallel workflows to support rapid development.  
* **Definition 1:** Type 1 task dependency--if it is an independent task and cannot be further decomposed into sub-tasks.
* **Definition 2:** Type 2 task dependency--if a task has a possible set of constraints (input, output, any other constraint) that will be accomplished either by decomposing the task into an sub-independent task (similar to type 1 dependency) or into decomposable sub-tasks of type:
* **Definition 2.1:** Direct task dependency: t1 cannot be completed until t2 is completed.
* **Definition 2.2:** Indirect task dependency: t2 cannot be completed until t3 is completed, which imposes a transitive condition for t1 to complete.
* **Definition 3:** Type 3 task dependency--if a task can be broken down into sub-tasks of type 1 dependency with no constraints and which (1) can be allocated to different teams, i.e., \(n\) tasks to \(n\) teams, or (2) one or more such sub-tasks can be allocated to the same team, i.e., \(m\) tasks to \(n\) teams.
## 2 Materials and Methods
### Spectral Data Analysis  
The line-scan hyperspectral images could contain noises due to the electromagnetic spectrum characteristics, and these are generally caused by environmental interferences, such as defects in the camera or the spectrograph itself or due to mechanical attributes. To remove redundant noise and suitably perform HSI on the data, a noise detection and removal algorithm was implemented. In this study, the authors used several preprocessing methods, such as normal scattering correction (MSC), normalization (between \(-1\) and \(1\)), first and second derivative algorithm (D1 and D2), and moving average (MA) [20]. MSC is based on the distance between ideal and measured spectra, where the mean spectrum is usually used as the ideal spectrum. The D1 and D2 methods based on the Savitzky-Golay algorithm use the window size of \(11\) and a quadratic filter. MA is a standardization method that uses the average value of the mean and left and right values. Normalization performs data normalization based on (Xi-M)/sqrt(sum(Xi-M)\({}^{2}\)), where, Xi means input data and M is mean of the data. More details about the preprocessing methods and their application can be referred from the article by [21]. Furthermore, multivariate analysis methods, such as LDA, PLS-DA, SVM, and classification and regression tree (CART), along with several of the preprocessing methods were used to develop a classification model, and their classification results were compared. The accuracy of the developed prediction model was measured and compared with that of the confusion matrix method based on accuracy (\(A\)) and kappa coefficient (\(K\)). The whole image-processing procedure was performed in Matlab (R2016a,  
Figure 3: Schematic flowchart of data processing.  
Figure 2: SWIR range mask making schematic based on principal component analysis (PCA) loading vector. Three PC images from PC1 to PC3 (**a**), loading vectors of PC1 and PC2 (**b**), peak images of 1016 nm (10 th), 1167 nm (57 th) and 1535 nm (172 th), respectively (**c**). The threshold value of 5000 was selected based on 1016 nm image (**d**) and mask (**e**) from 1016 nm image (**f**) and boundary as red line (**g**).  
The MathWorks Inc.; Natick, MS, USA) and spectral data analysis was performed using free software for statistics, R (ver. 3.6.2, R Foundation for Statistical Computing, Vienna, Austria)
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 2 Materials and Methods
### Proposed Methods  
A three-axis accelerometer provides a vector that comprises components of gravity on the local coordinate frame. In the case of the HAR dataset, activity was recorded with an accelerometer in a smartphone (Samsung Galaxy S II). As the smartphone had different initial orientations from person to person for data estimation, normalizing of all the measurements was carried out by keeping the laying data as the reference orientation. This was accomplished by calculating the initial orientation of the z-axis of the accelerometer frame with respect to the gravity vector. After initializing the orientation of the device, the angle (degree) was derived between the gravity and accelerometer vector for every sample. Poincare plot parameters SD1, SD2, and SDRR were calculated for angle values, and parameters were fitted to a support vector machine classifier.  
Multi-classification was implemented in three steps. The step by step classification process is shown in Figure 1.  
At first, the classification between static and dynamic activities was done. After that, variants of static and dynamic activities were classified with mean angle and variance analysis. The Actigraph count-based activity classification method was studied for all cases, and all the analyses were accomplished with 10-fold cross-validation with an SVM classifier. In sedentary body posture estimation, more specifically sitting and standing classification, count-based classification is not satisfactory, where an angle-based method can be a better solution.  
A comparative study between angle- and count-based activity classification and modification (sitting and standing classification) with the angle-based over count-based method were shown with novel angle variance analysis for human activity recognition.