esa-sceva/satcom-chunk-collection huggingface dataset:
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy
### Slice Management through Resource Transfer (SMART)
#### 4.3.4 VNF for Priority Provisioning  
In the next provisioning cycle, the Alien Nodes in each slice are given the highest priority because they are the most difficult to reach. When new NSRs seek resource provisioning at physical resources, priority service is given to Alien Nodes in each slice when the request is received. As illustrated in Figure 3d, when VNF-M detects that there are Alien Nodes in the mMTC slice, it first searches for the release of Alien Nodes (i.e., Ae1 and Ae2) supplied at the Alien Slice before receiving the new NSR when it goes to the next time cycle. If the resources required by the Alien Nodes are not available, the nodes are either kept in the same slice or are differently assigned to the appropriate slice, depending on the situation. Essentially, the goal of this endeavour is to provide priority service to Alien Nodes while simultaneously maintaining the Alien Slice in order to preserve resources for their linked NSR slice nodes for the following cycle.
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 1 Introduction
### Eye-Tracking  
ET technology is a valuable tool for user testing as it enables the precise assessment of a subject's perception and behavior during task execution. ET is widely used in both real-world and simulated settings for diverse measurements, proving especially beneficial in examining human behavior within the contexts of aviation and vehicular driving [17]. ET is underscored as a crucial method for determining driver distraction through the classification of glance targets. Three primary approaches are present to interpreting ET data in driver attention research [18]: 1. Direction-Based Approach: Evaluates gaze direction (e.g., forward, up, down) to calculate indicators like Eyes-Off-Road. Its limitation lies in not considering the context of the driver's gaze; 2. Target-Based Approach: Identifies objects intersected by the driver's gaze through manual video coding or deep learning. While it distinguishes glance targets, it may overlook the context; 3. Purpose-Based Approach: Focuses on areas deemed essential for driving attentiveness, integrating traffic rules and situational contexts to evaluate gaze relevance and adequacy. These approaches offer varying perspectives on analyzing eye-tracking data for driver attention, each with its strengths and limitations in context sensitivity and specificity. ET systems can record detailed user interactions and provide accurate measurements of gaze shifts, AOI times and pupillometry, even for cognitive abilities. Previous CDS measurements were based on a wearable eye-tracker that provides fixation points of the driver's gaze and detects changes in pupil diameter to monitor distraction and estimate higher cognitive load in comparison tests [19]. Others introduced the Index of Pupillary Activity, a novel ET measure assessing cognitive load via pupil oscillation frequency [20]. The replicable method helps differentiating task difficulty and cognitive load.
## 5 Results and Discussion
### Predictive Models Developed Using ANN
Figure 2: Artificial Neural Network (ANN) architecture of the predictive models developed using R.  
The activation function is frequently a bounded nondecreasing, differentiable, and nonlinear function such as the hyperbolic tangent or the logistic function [26]. We tried to used logistic and hyperbolic tangent as the activation functions. First, we developed an ANN model with logistic activation function. Then, we developed another ANN model with hyperbolic tangent activation function. Logistic function may have different forms, but the one used in R programming--the one we used in this research--is the standard logistic function, which is also known as sigmoid function. The formula of the logistic function is as follows  
\[f(x_{i})=\frac{1}{1+e^{-x_{i}}}=\frac{e^{x_{i}}}{e^{x_{i}}+1}\]  
where \(x_{i}\) is the value of the \(i\)th independent variable [32]. Meanwhile, hyperbolic tangent function, which is also known as tanh function, has the formula as follows  
\[\tan\text{h}(x_{i})=\frac{\sin\text{h}(x_{i})}{\cos\text{h}(x_{i})}=\frac{e^{x _{i}}-e^{-x_{i}}}{e^{x_{i}}+e^{-x_{i}}} \tag{4}\]  
where \(x_{i}\) is the value of the \(i\)th independent variable [33]. We provided the biases and weights of our ANN models, so our developed ANN models can be useful for other researchers who want to conduct similar researches. We displayed them in tables so it would be easier to read. Below is the annotation of the notations in the tables below:  
Bi-Hj: weight from Bi to Hj, where i = 1, 2 and j = 1,..., 6. Ik-Hj: weight from Ik to Hj, where k = 1,..., 4. Bi-OI: weight from Bi to OI, where l = 1, 2, 3. Hj-OI: weight from Hj to OI. Tables 5 and 6 were the tables of the biases and weights of our ANN models. 1. ANN model developed using logistic activation function \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Data Training Percentage**} \\ \cline{2-4}  & **70\%** & **80\%** & **90\%** \\ \hline B1–H1 & 5.4039439 & \(-\)4.4910300 & 4.5469032 \\ \hline I1–H1 & \(-\)15.0766425 & 11.3254070 & \(-\)11.0499554 \\ \hline I2–H1 & 1.5627953 & \(-\)1.7490682 & 2.7245021 \\ \hline I3–H1 & 2.7587930 & \(-\)0.9163399 & 0.9683699 \\ \hline I4–H1 & \(-\)0.1562694 & \(-\)0.6655746 & \(-\)1.9540237 \\ \hline B1–H2 & \(-\)13.793552 & \(-\)1.4021008 & \(-\)3.2660749 \\ \hline I1–H2 & 6.031370 & 9.1146927 & 0.7803872 \\ \hline I2–H2 & 6.958133 & \(-\)0.3967136 & 6.6604275 \\ \hline I3–H2 & 4.942339 & 3.8428082 & \(-\)0.7337680 \\ \hline I4–H2 & 7.859338 & \(-\)7.9523955 & 5.1178065 \\ \hline B1–H3 & \(-\)4.721011 & 4.965760 & \(-\)8.241048 \\ \hline I1–H3 & 4.605214 & \(-\)6.577878 & 1.584072 \\ \hline I2–H3 & \(-\)5.536911 & 5.634194 & 6.583553 \\ \hline I3–H3 & \(-\)13.412774 & 7.212313 & \(-\)2.312179 \\ \hline I4–H3 & 10.487666 & \(-\)6.157306 & 6.642880 \\ \hline B1–H4 & \(-\)2.739654 & \(-\)5.661526 & \(-\)3.080241 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The biases and weights of the ANN model developed using logistic activation function.
## 3 SADIE II Binaural Database
### Head Related Transfer Functions
#### 3.2.2 Low Frequency Compensation  
Due to the size of the loudspeakers' diaphragms and the low-frequency limit of the anechoic chamber, frequencies below 200 Hz could not be reliably measured and were modelled instead. It is well established that the effects of a listener's ear and pinnae are only influential at mid to high frequencies (\(>\)4000 Hz) [53]. At low frequencies (\(<\)400 Hz), analytical simulations such as those in [44] show that even a listener's head barely effects (\(<\)1 dB) the frequency content of a signal either. It is therefore reasonable to adopt a low frequency model, similar to that in [54], which extends a flat frequency response and linear phase response below approximately 400 Hz.  
Low frequency compensation was performed independently for each channel of each HRTF. A crossover frequency of 275 Hz was chosen. This balanced the preservation of natural higher-frequency content with the need to accommodate the crossover filter's low frequency roll off when applied to the HRTF signals which only included data down to 200 Hz.  
A Dirac pulse was generated with an amplitude and delay equal to the average amplitude and group delay of the signal to be extended between 250-300 Hz. This would typically position the Dirac _after_ the peak of a HRTF. A phase response calculation was made at the crossover frequency and the Dirac shifted _forward_ in time until the signals' phases aligned precisely at 275 Hz. A forward shift ensured that the low frequency model remained well within any future amplitude windows applied to the HRTF around its peak. A pair of 4096 tap Finite Impulse Response (FIR) low/high pass filters were utilized to crossover the low-frequency model with the valid portion of the input signal. High order filters ensured that neighbouring frequencies were sufficiently attenuated to avoid de-constructive interference caused by slight phase misalignments in these regions. An example of the crossover between a measured HRTF and a corresponding low frequency model is shown in Figure 3.  
A smooth transition between the High-Pass filtered HRTF and Low-Pass filtered low frequency model is shown in the output signal. Note the small amplitude variation (\(<\)1 dB) below 400 Hz in the measured signal.
# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 5 Human Activity Recognition Module Architecture  
From the HAR system's point of view, we have a n-Tier architecture: Zed Camera & Nvidia Jetson, Gateway, System Expert. From the point of view of the application that runs on Nvidia Jetson, we have a multi-level architecture. The hardware architecture is presented in Figure 6. The HAR data flow is presented in Figure 7: the video stream is processed on the Jetson platform, after which an alert is sent when activities of interest are recognized.  
The ambient images of the patient are acquired by the ZED 2 stereo camera.  
At the software level, the application level runs a multi-layer architecture consisting: the image acquisition and preprocessing layer, the logic layer and the communication layer.  
* The first layer acquires images and preprocesses them in order to be fed to the logic layer.
* In the logic layer, two neural networks are used: one for human and object detection and another one for human-object interaction detection. Based on the interactions, the activities which are performed by the monitored patient are determined.
* The communication layer prepares the data and transmits it to the server. The communication between the HAR system and the Gateway is carried out over the MQTT  
Figure 6: Hardware and software architecture.  
Figure 7: HAR module communication diagram.  
protocol. Based on a voting procedure that takes into consideration other sensor results as well, the Gateway decides when notification alerts are sent to the family or the medical doctor.  
Figure 8 illustrates the proposed pipeline architecture, consisting of two distinct and modular parts: the patient and the environmental objects detection and the detection of the human-object interactions. Based on the interaction between the person and the objects of interest, we can draw conclusions regarding the monitored activities and conditions: falling/fainting, immobility, serving the meal, drinking liquids, etc.
# Analysis of the Guide Vane Jet-Vortex Flow and the Induced Noise in a Prototype Pump-Turbine
## 5 Results and Analysis
### Instaneous Turbulent-Flow-Induced Noise Field
The dimensionless eddy dissipation rate coefficient \(C_{\varepsilon}\) is defined as:  
\[C_{\varepsilon}\ =\ \frac{\varepsilon^{2}}{g^{3}R_{hi}} \tag{15}\]  
The square value of \(\varepsilon\) is used because \(k\) and \(\varepsilon^{2}\) contribute to the same extent in predicting the turbulence-induced-noise using Equation (11). Figure 11 shows the comparison of \(L_{sp}\), \(C_{k}\), and \(C_{\varepsilon}\) contours. Figure 10: Local high \(L_{sp}\) regions on the blade leading-edges. LE: Leading-edge. (**a**) Runner leading-edge; (**b**) Guide vane leading-edge and trailing-edge; (**c**) Stay vane leading-edge. Based on Figure 11, it can be found that the \(C_{k}\) distribution is similar to the \(L_{sp}\) distribution. This means that the turbulence kinetic energy strongly influences the near-field noise in pump-turbine. A high \(C_{\varepsilon}\) region can be also found in the guide vane jet flow region. However, the high \(C_{\varepsilon}\) region is very small in area. Figure 12 shows the \(C_{\varepsilon}\) contours on the runner, guide vane, and stay vane leading-edges in a reduced range of \(C_{\varepsilon}\) = 0\(\sim\)1\(\times\)10\({}^{6}\). In the reduced \(C_{\varepsilon}\) range, high \(C_{\varepsilon}\) regions can be observed on the leading-edges and almost overlap the high \(L_{sp}\) regions in Figure 10.  
Generally, the point of view that high flow noise is strongly related to the flow regime has been proven in this study. The turbulence-induced-noise in vortex is mainly affected by turbulence kinetic energy \(k\). For the vortex core region on the number III, V and VII sites, noise increases in the vortex core. For the vortex core region on number IV site, noise decreases in the vortex core. The turbulence-induced-noise in leading-edge separation regions is jointly affected by turbulence kinetic energy \(k\) and eddy dissipation rate \(\varepsilon\). Hence, the turbulence kinetic energy \(k\), which is physically the RMS value of fluctuating velocity, plays the most important role in inducing noise. Further analysis on the transient characteristic of \(k\) field is necessary.
## 3 Results: Numerical Analysis
### Comparison of Bilinear Spring versus Cubic Spring  
There are many choices regarding which bilinear spring or cubic spring to use below the bottom piston. It is important to mention that the displacement of the bottom piston will be many times larger than the input displacement given to the engine mount. Figure 9 compares the bilinear spring versus the cubic spring used in this paper. As Figure 9 indicates, for the bilinear spring, there is a change in stiffness at 0.5 mm (let us refer to this point as the switch point \(S\), \(S\) = 0.5 mm). To find the optimum switch point for a given application, one will need to conduct many numerical simulations at different switch points to determine the optimum location of \(S\). For the bilinear spring, in this paper, for piston deflections less than 0.5 mm, \(K_{1}\) was chosen as 33,183 N/m, matching the nonlinear spring stiffness at small motions, and \(K_{2}\) was chosen as 230,000 N/m. For the bilinear spring, the \(K_{2}\) value of 230,000 N/m was chosen to match the force value of the cubic nonlinear spring at a piston deflection of \(\pm\)3 mm, namely 600 N. Notice that both bilinear and cubic springs share the same slope for deflections less than \(\pm\)0.5 mm.  
For the bilinear spring, choosing \(K_{1}\) is straightforward. \(K_{1}\) is found by the desired location of the notch frequency. But, \(K_{2}\) requires simulations. Just like the optimum location of the switch point \(S\) for the bilinear spring, to find the optimum value of \(K_{2}\), many simulations are necessary for a given application. Our mathematical model can be used to determine the optimum value for \(K_{2}\). For the cubic spring, we have \(F_{cubic}=33,183\ x+1.88\times 10^{10}\ x^{3}\). The details of the cubic spring design will be provided later.
###### Abstract  
Background: The study aimed to evaluate the immediate effect of Heat-Compression-Ternary with Game Ready equipment (GR-HCT) on biomechanical properties at different treatment times. Reducing the duration of stimulation through HCT can help optimize recovery processes in sports. Methods: Twenty male MMA fighters (26.5 \(\pm\) 4.5 years, BMI 24.75 \(\pm\) 3.0, training experience: 10.3 \(\pm\) 5.0 years) performed two experimental sessions: (a) 5 min heat-compression therapy (HCT) stimulation (GR-HCT-5); and (b) 10 min heat-compression therapy (GR-HCT-10). All registered parameters, tissue perfusion (PU), tissue temperature (\({}^{\circ}\)C), muscle tone (T-Hz) stiffness (S--N/m), elasticity (E--arb), pressure pain threshold (PPT--N/cm) and isometric muscle strength (Franz--kgf), were measured at rest (r--2 min before GR-HCT stimulation) and 1 min after GR-HCT (p-post) for the flexor carpi radialis muscle (MFCR) and the brachioradialis muscle (MBR). Results: The [PERSON]'s test for MBR revealed significant differences between GR-HCT5 min vs. GR-HCT10 min for PU (\(p<0.0001\)), S (\(p=0.008\)), and for MBR and MFCR for \({}^{\circ}\)C (\(p<0.0001\)). The pairwise comparisons for PU, S, and \({}^{\circ}\)C showed significant differences between rest vs. post values for 5 min as well as between rest vs. post values for 10 min. Further, there was a significant interaction effect (5 min-10 min \(\times\) Rest-Post \(p<0.0001\)) for PU for MFCR. The post hoc comparison for the interaction effect showed significantly higher PU for post- compared to premeasurement in 5 min PU and 10 min PU (\(p<0.001\)). Further, there was a significant main effect of condition for Franz both for MFCR and MBR (5 min vs. 10 min \(p<0.001\)); The post hoc comparison for the main effect of condition showed a significantly higher Franz for post- compared to premeasurement in 5 min and 10 min (\(p<0.001\)). Further, there was a significant main effect of condition for pressure pain threshold [N/cm] both for MFCR and MBR for Tor for MBR time of measurement rest vs. post (\(p<0.001\); \(p=0.006\); \(p<0.001\), respectively). The post hoc comparison for the interaction effect showed a significantly lower PPT for post vs. rest in 5 min (\(p<0.001\) only for MFCR) and in 10 min (\(p<0.001\)). The delta values for PU (post-rest) and \(\Delta\)Temp \({}^{\circ}\)C showed significant differences between the 5 min and 10 min conditions (MFCR; \(p<0.0001\)); (MBR \(p<0.001\)) and (MFCR \(p<0.0001\)); (MBR \(p<0.001\)). Conclusion: Our results show that GR-HCT significantly affects recovery of muscle biomechanical parameters, pain threshold, strength, tissue perfusion, and temperature. Our findings show that a 5 min effect can be sufficient when compared to a 10 min one which is a clue for designing effective recovery protocols.  
**Keywords: microcirculation; combat sports; Game Ready; muscle stiffness and tension; sports optimization**
# Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand
## 3 Experimental Setup and Results
### Tuning of Controllers  
The controller parameters derived in our previous work [9] had to be newly tuned because the quadrotor showed unstable behavior. This was caused because the dynamics of the test stand and its lack of rigidness had considerable impact on the quadrotor performance. Moreover, some parameters used in the mathematical model was not accurately computed. The example of such parameters can be the motor constants, because bearings of the motors are probably worn in the moment of the data reading. In addition, the dynamics only of one motor were identified. Further when the identification was made only with one motor connected to the battery at that time, this was completely different situation compared to four motors connected. Therefore, the used battery cannot supply stable power to all motors.  
Figure 8: Application used to log data and send commands to the real platform.  
The implemented attitude controllers were evaluated by tracking desired sequence of orientation. Parameters for each axis were tuned separately. The first tuned parameters were parameters related to \(z\) axis, followed by the parameters of \(x\) axis. The last tuned parameters were those of \(y\) axis.  
Firstly, proportional parameters of the PD controller were identified. The derivative parameter was set to 0 and the proportional part of the controller was increased step by step until the quadrotor starts to oscillate around the particular axis. Then the 80% of the value was set to be the proportional part of the PD controller. The derivative part of the PD controller was increased until the settling time of the quadrotor was around 1-2 s. Table 3 shows the list of the chosen corresponding gains for proportional and derivative components of the attitude PD controller.  
The LQR controller was tuned in the similar way as the PD controller. Firstly, the elements of gain matrix \(\mathrm{K_{A}}\) that correspond to the quaternion were tuned. Subsequently, the elements of the gain matrix \(\mathrm{K_{A}}\) corresponding to angular velocity of the quadrotor were identified. The regulator gain matrix \(\mathrm{K_{A}}\) of LQR controller is given by equation:  
\[\mathrm{K_{A}}=\left[\begin{array}{cccccc}1.34&0&0&0.3&0&0\\ 0&1.34&0&0&0.3&0\\ 0&0&0.6&0&0&0.3\end{array}\right] \tag{2}\]  
The parameters of the backstepping controller were tuned as followed: The value of the parameter \(\mathrm{c_{1A}}\) is 10 and \(\mathrm{c_{2A}}\) is a diagonal matrix with vector \(\left[\begin{array}{cccccc}11&11&3\end{array}\right]\) on the main diagonal. The parameters of backstepping controllers were obtained in the similar way as the parameters of the PD and LQR controller. That means the first tuned parameter was \(\mathrm{c_{1A}}\), which corresponds to the attitude of the system. Then the vector \(\mathrm{c_{2A}}\) related to angular velocity of quadrotor was identified.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Travelling Antenna
#### 3.5.2 Helical Antenna  
A helical antenna has one or more conducting wires wound in such a way that the shape looks like a helix and hence the name. Helical antenna has been extensively used in practice for many years because of the practical emission and how easy it is to use [56; 57]. The properties of this type of antenna is extraordinary and exceptional and as such, they are widely used in getting microwaves from VHF [58]. The helical antenna is also used in satellite communication due to the high gain requirement. Figure 6 demonstrates the structure of the helical antenna [59].
## 3 Results and Discussion  
### Materials and Physical Properties  
Two DPP-based small molecules, _o_- and _p_-DPP-PhCNs, were selected as the third component to be incorporated into the P3 HT:phenyl-C61-butyric acid methyl ester (PCBM) system, resulting in P3 HT:DPP-PhCN:PCBM ternary films. In our previous report, two DPP-PhCNs were synthesized using Pd-catalyzed Suzuki coupling reactions [21], in which the two small molecules had the same molecular backbone but differed in the position of the cyanide groups at the phenyl ends. The chemical structures of all the materials used in the active layer of the OSCs are shown in Figure 1.  
Figure 2a shows the normalized UV-vis absorption spectra of films of the two DPP-PhCNs and P3 HT prepared from CF solutions. Although the two DPP-PhCNs had the same molecular backbone, their packing behaviors in the film state appeared to differ according to the position of the CN groups. The para-substitution resulted in more red-shifted absorption and in stronger molecular aggregation compared with the ortho-substitution. In the spectrum of the _p_-DPP-PhCN film, the absorption maximum (\(\lambda_{\text{max}}\)) of the low-energy band, which is assigned as the 0-0 transition, appeared at 670 nm, indicating a more effective \(\pi\)-\(\pi\) stacking between molecular backbones [25; 26; 27; 28]. The optical bandgaps (\(E_{\text{g}}\)s) were obtained from the absorption onset (\(\lambda_{\text{onset}}\)) on the long-wavelength side (\(E_{\text{g}}=1240/\lambda_{\text{onset}}\)). The _p_-DPP-PhCN exhibited a lower \(E_{\text{g}}\) (1.75 eV) compared with that of the _o_-DPP-PhCN (1.80 eV), consistent with the relatively stronger molecular packing of the _p_-DPP-PhCN molecules. The energy diagrams of the materials used in OSC fabrication are shown in Figure 2c. The HOMO level of the donor (P3 HT) and the LUMO levels of the acceptors (DPP-PhCNs and PCBM) were measured by CV, and the LUMO of the donor and HOMO of the acceptors were calculated from the \(E_{\text{g}}\) values. The HOMO energy levels of the two DPP-PhCNs were found to be similar; however, for the LUMO energy levels, the para-substitution resulted in an energy level slightly lower than that of the ortho-substitution. The incorporation of DPP-PhCNs resulted in a cascade  
Figure 1: The chemical structures of the _o_- and _p_-diketopyrrolopyrrole (DPP)-PhCNs together with poly(3-hexylthiophene) (P3 HT) and phenyl-C61-butyric acid methyl ester (PCBM).  
energy-level alignment from P3 HT, via DPP-PhCN, to PCBM in the P3 HT:DPP-PhCN:PCBM ternary system.