esa-sceva/satcom-chunk-collection huggingface dataset:
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Average End to End-to-End Delay (AE2E)  
As shown in Figure 9, AE2E latency increases as the number of rounds rises. This is due to the time required to identify the node positions and the path construction time if the nodes move. Our suggested method (ABBSOA-MSSOA) decreases AE2E node latency compared to conventional clustering and routing protocols. For example, in round 1000, the proposed minimum ETE latency is 25 ms, while the maximum ETE latency for M-GWO, ALOC, DHCO, and MT-MAC frames is 30 ms, 40 ms, 60 ms, and 65 ms, respectively.  
Distinct performance developments across several rounds are revealed by comparing the proposed ABBSOA and MSSOA techniques with traditional techniques. Proposed measurements tend to become more effective each round, demonstrating its modest refining. Proposed system has considerable improved performance when compared to MT-MAC, DHCO, GWO and ALOC. The patterns vary throughout the rounds, and all things considered, the ABBSOA and MSSOAGWO technique shows a more promising approach.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 5 Simulation Result
### Result Analysis
#### 5.2.3 Summary  
From the data above, we can see that the total power loss and voltage offset decline remarkably in Case 2 (17.49% and 21.12%) and Case 3 (28.82% and 28.58%) compared with Case 1. To satisfy the power demand of the MGs, the dispatch approach in Case1 will consume more fluctuations of power interaction, which causes the result of more power loss and voltage offset. Moreover, the dispatch method in Case 1 could not accommodate the power from the PVs and WTs in the DN well, which can be solved in the Case 2 and Case 3. Moreover, the curve of the power interaction in Case 3 (shown in Figure 9c) is more similar to the forecast power of the renewable power sources, which reflects the effectiveness of the DN reconfiguration.  
The total cost of the MGs increases by 23.31% and 13.23%, respectively, in Case 2 and Case 3. Under the requirement of the DN, the devices in the MGs will adjust their working conditions. Although the operation cost of the MGs increase, the coordinate optimization method can effectively reduce the power loss, voltage offset, improve the power flow distribution and obtain a better global economic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** \\ \hline
**MG1** & 28,173.95 & 33,338.42 & 31,646.77 \\
**MG2** & \(-\)2773.21 & \(-\)1986.90 & \(-\)3012.89 \\
**MG3** & 7558.35 & 9433.32 & 8802.69 \\
**Total** & 33,059.09 & 40,784.84 & 37,436.03 \\ \hline \hline \end{tabular}
\end{table}
Table 6: MGs in different cases.  
optimal value of the whole DN system. In addition, the network reconfiguration of the DN can realize further optimization.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 2 Background
### Vehicle Tracking
#### 2.2.2 Kalman Filter for Tracking  
Kalman filtering also known as linear quadratic estimation (LQE) is a series of measurements observed over time (in our case, frame by frame). The Kalman filter is a powerful tracking method, for extracting motion from video sequence surfaces over collections of points. It is an optimal estimator that infers the parameters of interest from indirect, inaccurate, and uncertain observations. The Kalman filter consists of two steps: prediction and update. The first step uses previous states to predict the current state. The second step uses the current measurement, such as detection of bounding box location, to correct the state. The Kalman filter has the following important features that tracking can benefit from:  
* predict the object's upcoming location;
* correct the prediction based on new measurements;
* reduce noise introduced by inaccurate detection;
* facilitate the process of associating multiple objects with their tracks.  
The Kalman filter tracker is fast, which is an advantage over other tracking algorithms. Therefore, we updated the original algorithm and used it in our project as the main tracking method.  
Despite quite the variety of methods, we focus on improving the time speed of detection, robustness in different lighting conditions, and accuracy. Therefore, we combined the YOLOv3 method for detection with the Kalman filter for tracking estimation.
## 2 M&S Software Design for Multi-Radio Integrated Navigation System  
### Multi-Radio Integrated Navigation System M&S Software  
Multi-radio integrated navigation systems consist of signal sources, receivers, and integrated navigation algorithm [3; 5]. The signal sources include navigation satellites and ground-based radio navigation stations. The receivers process the signals from the signal source to generate measurementsand navigation outputs. The integrated navigation algorithm gives the output of the system from measurements and navigation outputs.  
Measurements are generated by the receivers from received signals. The signals in some frequency bands can be blocked by obstacles in the signal path. Errors in the measurements are caused by the signal source, signal distortion in the channel, and the receiver [3; 5]. The position, velocity, and clock of the signal source contain uncertainties and the signal distortion depends on the channel characteristics. Receiver errors are caused by the antenna and the RF (radio frequency) front-end [3; 5]. When the receiver is under attack of a jammer, no measurements or measurements with a large error are available [4].  
For a given navigation performance and distribution of the signal sources, coverage analysis can be performed to check the area at which the performance is satisfied. Signal sources can be additionally placed to have better coverage analysis results. Optimal placement of the additional navigation signal sources can be determined to maximize the results [22; 23]. This optimal placement of the signal sources is called gap filling [14; 23].  
The multi-radio integrated navigation system M&S software should provide various navigation environments and operation scenarios. Measurements for various navigation environments and operation scenarios should be generated to test a multi-radio integrated navigation algorithm. The display for navigation results and internal variables of the M&S software should be provided to developers and operators. If the tested navigation results are not satisfied, new signal sources can be placed through the coverage analysis. Figure 1 is the schematic diagram of the multi-radio integrated navigation M&S software.
## 3 Methods and Materials
### Statistical Hypothesis Tests
Thus, placing \(x_{bk}\) corresponds to the value of the classification accuracy metric acquired from the predictor model generated with a given random seed value. Thus, the algorithm with the highest metric value gets the first position in the ranking, the second highest gets the second position, and so on [32]. Equation (1) presents the mathematical calculation of the ranking. From the calculation of Equation (1), the value of the critical difference is the most important, because it indicates whether there is a statistical difference between the summation values of two algorithms in the ranking. This difference is discovered by subtracting these values. Thus, if the result of the subtraction obtained is greater than the critical distance, then it corresponds that the two algorithms are statistically different and that one of them is better in the task adhered to them, that is, in the dataset in which they were applied [33]. Therefore, with the Friedman test, it is possible to identify if there is a statistical difference between classification algorithms in the face of a given dataset when there is such a difference. \[\left|R_{i}-R_{j}\right|\geq Z\left(\frac{\alpha}{k\left(k-1\right)}\right) \sqrt{\frac{N\times k\left(k+1\right)}{6}} \tag{1}\]  
where: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Categorization** & **Variable** & **Group** & **Pairing** \\ \hline Z-test & Parametric & Quantitative & Individual & - \\ \hline T-test & Parametric & Quantitative & Individual & - \\ \hline Wilcoxon for 1 sample & No parametric & Quantitative, ordinal qualitative & Individual & - \\ \hline T-test for 2 samples & Parametric & Quantitative, nominal & Pairs & No paired \\ \hline T-test for 2 samples & Parametric & Quantitative, nominal & Pairs & No paired \\ with different variances & Parametric & Quantitative, nominal & Pairs & Paired \\ \hline T-test & Parametric & Quantitative, nominal & Multiple & No paired \\ \hline ANOVA & Parametric & Quantitative, nominal & Multiple & No paired \\ \hline ANOVA for repeated measures & Parametric & Quantitative, nominal & Multiple & Paired \\ \hline Mann-Whitney & No parametric & Quantitative, ordinal qualitative, nominal & Pairs & No paired \\ \hline Wilcoxon Paired & No parametric & Quantitative, ordinal qualitative, nominal & Pairs & Paired \\ \hline Kruskal-Wallis & No parametric & Quantitative, ordinal qualitative, nominal & Multiple & No paired \\ \hline Friedman & No parametric & Quantitative, ordinal qualitative, nominal & Multiple & Paired \\ \hline Test for 1 proportion & Parametric & Nominal & Individual & - \\ \hline Test for 2 proportion & Parametric & Nominal & Pairs & No paired \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of central tendency tests. Figure 3: [PERSON] test ranking. * \(R_{i}\) and \(R_{j}\) is the sum of the positions of the algorithms \(i\) e \(j\) in the ranking;
* \(|R_{i}-R_{j}|\) is the difference between the sum of the algorithms;
* \(Z\left(\frac{\alpha}{k(k-1)}\right)\sqrt{\frac{N\times k(k+1)}{6}}\) is the critical difference.
## 1 Introduction
The temperature characteristics of the proposed THz fiber are analyzed and verified by the finite element method. The confinement loss and the low-frequency side of the dip near 0.5 THz is temperature controlled. The temperature sensitivity of the THz fiber is obtained at 0.09614 dB\(\cdot\)m\({}^{-1}\)/K at 0.45 THz with a high core power fraction up to 98%.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 1 Introduction
### Article Structure  
The paper is structured as follows. Section 2 derives theoretical expressions for the channel phase and capacity of n-order elements. Section 3 analyzes the optimal placement of \(2\times 2\) and larger transceiver antennas based on the maximum capacity theory. Section 4 provides a comparative analysis of the performance of different antenna configurations. Section 5 provides physical insight and applications of the MIMO system in coupling train communication and inter-satellite communication. Section 6 draws the conclusion of this paper.
## 3 Results and Discussion
### Electrooxidation of Glucose
#### 3.2.2 Common Interferences Study  
Another important aspect of a good glucose sensor is the performance in the presence of common interferences. Most of the nonenzymatic glucose sensors based on precious metals and alloys present poison problems in presence of chloride ions [39]. Other molecules such as ibuprofen (IB), ascorbic (AA), and uric acid (UA) were also investigated since they are glucose coexisting species in common biological fluids. Concentration values for the analysis have been selected according to the maximum biological values in the bloodstream [40; 41; 42].  
Figure 11 represents the electrode signal for 4 mM of glucose with and without the interferences of 0.41 mM UA and 0.05 mM of IB. At 300 s, the bulk solution--4 mM of glucose and the corresponding interference concentration--was changed to the 4 mM glucose solution. As it can be seen in Figure 11, the presence of 0.41 mM UA decreases the electrode signal roughly by 16%, which according to the reported sensitivity, this means a variation of 0.46 mM. It is hypothesized that uric acid may be adsorbed onto the electrode surface blocking glucose to be oxidized. However, the signal reduction meets the commercial error requirements.  
Figure 10: Stability of copper oxide electrode. Pulsed amperometric detection (+0.55V) of 4 mM glucose in 0.1 M NaOH during 1200 measurements each 10 s.  
On the other side, ibuprofen does not interfere the glucose oxidation on copper oxide electrodes.  
Figure 12 shows the signal with chlorine and ascorbic acid interferences. This time the experiment started with 4 mM glucose solution and after 340 s, the bulk solution was replaced by a solution of glucose and the respective interference.  
Figure 11: Pulsed Amperometric response of the copper oxide electrode in the presence of common interfering substances and 4 mM of glucose at +0.55 V (vs. Ag/AgCl) in 0.1M NaOH (pH 13). 0.41 mM uric acid and 0.05 mM ibuprofen.  
Figure 12: Pulsed Amperometric response of the copper oxide electrode in the presence of common interfering substances and 4 mM of glucose at +0.55 V (vs. Ag/AgCl) in 0.1 M NaOH (pH 13). 0.1 mM ascorbic acid and 0.1 M chlorine.  
Chlorine reduced the electrode signal up to 15% while ascorbic acid reduced it by nearly 14%. This result indicates that CuO/GCE relatively prevented the poisoning of the chloride ion, which made some nonenzymatic glucose sensors lose their activity.  
All interferences, except ibuprofen, decrease the glucose oxidation signal by less than 18%. It means that the interfering molecule is adsorbed on the active sites of the electrode blocking the area for the glucose to be oxidized. However, the signal reduction fits the standard error provided by ISO 15197.
# Improving Stability Prediction in Peripheral
## 3 Results  
### Experimental Validation of Stability Prediction  
In order to validate the proposed approach, down-milling tests were conducted in a conventional milling center. A monolithic artifact of AI7075T6 was designed to reproduce the dynamic response of a single degree of freedom system in the \(y\)-axis, which is orthogonal to the feedrate direction. The workpiece displacements were recorded and analyzed via Fast Fourier Transform, to observe the frequency content and to compare it with tool cutting frequencies (see Figure 3).  
The modal parameter values of the end-milling cutting processes were found by impact testing: \(\omega_{n}\) = 499 rad/s, \(\xi\) = 0.04, and \(m_{m}\) = 10.1 kg. The transfer function obtained through the impact test fit well with the theoretical results. By using these values and computing the eigenvalues of the transition matrix, the stability lobes of the end-milling cutting operation were calculated. Figure 5 shows the stability charts for down-milling with 25% radial immersion, feed rate 0.05 mm/teeth, and a measured apparent radius of 17 \(\upmu\)m. The discontinued lines correspond to the typical stability lobes when the cutting speed, runout, and helix angle are neglected, while the solid lines plot the stability boundaries, taking these effects into account.  
Figure 4: Experimental and fitted curves of cutting shear coefficients in the (**a**) tangential and (**b**) radial directions.  
Figure 5: Experimental data from the down-milling operation, comparison between typical and improved stability analysis.  
The stable cutting data are denoted by circles, unstable quasi-periodic by square symbols, and unstable double periodic by diamonds. Down triangles denote one period chatter respectively, and up triangles denote quasi-one-period data. When it was difficult to distinguish between experiments with stable and unstable data due to there not being a dominant frequency, the vibration amplitude was considered as a second criterion. The experimental data demonstrated the high accuracy of the stability boundary prediction, even to predict the transition between quasi-periodic and double-periodic unstable zones. For comparison, the effects of the runout and cutting speed were also introduced in the CH, SD, FD methods. In this case, the boundary limits obtained were almost identical when \(N=30\), however, the convergence rate and computation times differed depending on the method.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 3 Preliminary Studies
### Different Types of UAVs in Space
#### 3.2.3 Glider  
A glider is an aircraft of a unique nature and has no engine. In-flight, in contrast to the four forces operating on a powered aircraft, a glider has three forces. The forces of lift, drag, and weight are revealed to all aircraft types. The powered aircraft has a thrust-producing motor, although there is no thrust in the glider [79]. In the 1960s to 1970s, NASA thought of using another concept for space exploration: the paraglider. Invented in the early 1960s by [PERSON], the [PERSON] wing used lightweight fabric airfoils arranged in a V-shape. More accurate than a parachute, the lightweight wing could be guided. Plus, instead of a splash landing in the water, the astronauts could operate the wing to glide to a stable landing on the ground. First, [PERSON] thought about using the versatile recreational wing. After deciding that there was nowhere to market his proposal for public use, he presented it to the space industry. It may not be used for space landings, but it has become part of a common sport. Kitty Hawk, North Carolina, where the Wright Brothers discovered another famous way to fly, is one of the most popular places in America for hang gliding [24]. Today, NASA hopes to deploy a small glider fleet to explore parts of Mars that other spacecraft cannot reach. [PERSON], [PERSON], and colleagues at the University of Arizona claimed to use gliders for Mars exploration. Their concept is to propel an unpowered glider into the Mars atmosphere and hold it aloft to achieve altitude by using thermal updrafts to lift columns of warm air. For a larger mission to Mars, the glider would be inflatable and packed in a volume compact enough to be transported as a secondary payload [5].
# Realistic Simulation for Body Area and Body-To-Body Networks
## 4 Performance Evaluation  
To compare the two approaches presented above fairly, there are a number of constraints and limitations. For example, with the semi-deterministic approach, there are limitations to the number of nodes per BAN and mobility patterns. Whereas, the deterministic mobility approach is not limited by the number of nodes, but mainly by the mobility patterns and duration, which are imported from motion capture systems. With regards to channel models, in particular to incorporate the body shadowing and fading, the semi-deterministic approach characterizes these factors and includes their impacts separately, whereas the deterministic approach is inclusive, but does not characterize these factors separately. It should be noted that a common application scenario and the setup for the evaluation of the two approaches are therefore limited to the above-mentioned constraints.
# Off-Axis Diffractive Optics for Compact Terahertz Detection Setup
## 3 Structures Manufacturing  
The iteratively optimized diffractive elements have been fabricated from Polyamide 12 (PA12) using additive manufacturing (3D printing) by powder-based selective laser sintering (SLS) and extrusion-based method using filament. So far, the main problem with these methods has been the smallest thickness of the high wall equal to around 0.8 mm and printing resolution of around 0.05 mm (depending on a printing method and a device). Such a resolution is insufficient for the details of the sophisticated DOEs. In the case of the designed structure, the narrowest zone is around 0.82 mm thick and 0.94 mm high (with the details of the optimized structure being even smaller). It is not possible to manufacture these details using the most of 3D printing techniques. Therefore, the usage of higher-order kinoforms (HOKs) is proposed here. They are designed as classical diffractive lenses for small integer multiples of basic wavelength. As a result, these structures introduce a phase delay ranging from 0 to the multiple of \(2\pi\), instead of \(0-2\pi\) phase shift. Nevertheless, the optimization has always been performed for the basic wavelength which in this case has been a harmonic of the design wavelength. HOKs are thicker than classical diffractive lens by a factor which is equal to the order of the kinoform \(p\) but their zones are also respectively wider. Several HOKs of low order (1 st, 2 nd and 4 th order) with respectively bigger details of the structures have been designed.
# RF Energy Harvesting
## 6 RF Energy Harvester Simulation
### Circuit Model  
In all three environments, the same circuits were modelled using the conditions found in Section 5. At first, a simple resonator as proposed in Figure 2 was tested. It is a simple RF energy harvester that converts the energy received in the antenna into the current in the load. The antenna receiving the signal is modelled as a Thevenin's equivalent voltage source with a resistance of 50 \(\Omega\). The voltage source models a receiving antenna in which voltage is excited by RF waves. It is followed by an impedance-matching network that makes sure the maximum possible amount of energy is transferred from the antenna to the harvester module. The circuit is closed with a 5 k\(\Omega\) load. This can represent the end device, such as a battery charging module. This circuit is presented in Figure 3 as designed for KiCad EDA, in Figure 4 for LT Spice, and in Figure 5 for Simscape Electrical. In this circuit, all elements are ideal ones taken from the default SPICE libraries. All the figures just mentioned are actual screenshots from the programs and show the differences in the Graphical User Interfaces and representations of particular elements. It was decided to present the screenshots here for ease of understanding the schemes, and as manipulating the models may be important for further research.  
In the next step, the circuitry was extended by adding a simple rectifier part. This design does not present the ideal harvester but gives the possibility to verify the impact of a particular diode's parameters on the performance. In the designs below (Figures 6-8), a particular diode model was used: HSMS_2851 [27]. It is a Schottky diode that has a particularly low switch time. Hence, it can be used in high-frequency applications of up to 1 GHz, as checked in [28] for a model up to 900 MHz. For other electronic parts, the default ideal models available in the SPICE databases were used.  
Figure 4: Energy resonator model designed in LT Spice.  
Figure 5: Energy resonator model designed in Simscape Electrical.  
Figure 3: Energy resonator model designed in KiCad EDA.  
Figure 2: Transfer energy with increasing voltage.