esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Product Flow Rate on Oxygen Generation Performance of CP-\(\pi\) Adsorber  
The oxygen product flow rate in the outlet end can greatly impact the oxygen generation performance, that is, the product purity and the recovery [34]. Figure 12a shows the variations in the oxygen product purity and the oxygen recovery with product flow rate for the CP-\(\pi\) PSA oxygen generation. The oxygen purity decreases (from 98.6% to 78.5%) and the recovery of the production increases (from 38.72% to 70.18%) with increasing product flow rate (from 0.16 to 0.36 m\({}^{3}\) h\({}^{-1}\)). Under the same conditions, improving the product flow rate requires additional feed gas flow, which further increases the gas velocity and faster movement of the mass transfer area in bed. Meanwhile, due to the unchanged mass transfer coefficient of the adsorbent, the higher product flow rate prolongs the mass transfer area and leads to the increased recovery as well as the lowered maximum oxygen purity. Figure 12b gives the non-recovered oxygen contributions by the Cb and Pu steps. The contribution of the non-recovered oxygen mainly comes from the Cb step; this is because an amount of oxygen with high pressure and concentration remains in the outlet of the bed after the Ad step (as shown on Figure 11). With increases in the product flow rate, the high oxygen purity area moves towards the outlet of the adsorber, and the non-recovered oxygen contributions by Cb decrease.  
Figure 11: Oxygen purity profiles at the axial center position at the end of each step in steady state (after the 8 th cycle) for the CP-\(\pi\) adsorber.  
The recovery and product flow rate simultaneously grow and are very important for selection of the oxygen generator for industrial application. By lowering the requirement for oxygen purity, generating more oxygen in the same equipment can decrease operational costs, lower the scale of the equipment, and thus save the initial investment. For instance, oxygen purity of 80% can meet a lot of industrial requirements [25].  
The modeling results show that when the oxygen purity is lowered from 98% to 80% at a product flow rate from 0.16 m\({}^{3}\) h\({}^{-1}\) to 0.35 m\({}^{3}\) h\({}^{-1}\), the total amount of generated oxygen increases by 2.19 times due to the significant rise in recovery from 38.7% to 67%. Therefore, based on a constant demand, e.g., 100 m\({}^{3}\)/h, for the total amount of generated oxygen in a certain application, the feed flow rate derived from Equation (13) could be reduced by \(\sim\)40% from 1230 m\({}^{3}\) h\({}^{-1}\) to 711 m\({}^{3}\) h\({}^{-1}\). Finally, \(\sim\)40% of the energy consumption proportional to the feed flow rate of the compressor could be saved, which is a benefit of the process adjustment according to the simulation results.
## 2 Theory  
### Sound Propagation through a Partition  
When a sound wave having a certain power \(W_{0}\) impinges a wall (Figure 1), some of the power is reflected back (\(W_{r}\)), and some of it propagates beyond the surface and is partially dissipated into heat (internal losses \(W_{d}\)) and partially distributed across the junctions to the adjacent structures (losses due to transmission to the adjacent structures \(W_{t^{\prime}}\)). This part is related to flanking transmissions. Finally, a portion of the power crosses the partition and is transmitted along a direct path across the wall (\(W_{t}\)).  
The two terms \(W_{t}\) and \(W_{t^{\prime}}\) represent the portion of the acoustic power transmitted to the adjacent structures and can be put together into a single term \(W_{t,tot}\). Considering a small element of the wall, it is possible to write an equation representing the power balance over the element:  
\[W_{0}=W_{r}+W_{d}+W_{t,tot}. \tag{1}\]  
Dividing both terms by \(W_{0}\), Equation (1) can be rewritten as:  
\[1=r+\delta+\tau, \tag{2}\]  
where \(r\) is the reflection coefficient, \(\delta\) is the dissipation coefficient and \(\tau\) is the transmission coefficient. The 'apparent' sound absorption coefficient, \(\alpha\), is defined as the sum of the dissipated and transmitted power fractions. Since both of them depend on the angle of incidence of the sound on the surface,different \(\alpha\)-values can be obtained for the same type of material according to the adopted measurement technique [19]. The transmission coefficient \(\tau\) is also related to the sound transmission loss \(R\):  
\[R=10\log_{10}\left(\frac{1}{\tau}\right), \tag{3}\]  
and its determination is therefore the key to describe the sound insulation properties of a partition. Coefficient \(\tau\) is a function of the angle of incidence, the mass per unit area, the bending stiffness and the total losses. The next sections will provide a background theory for the propagation of bending waves through partitions, which forms the basis for the calculation method of the transmission coefficient in composite structures.
## 4 The WBANs Communication Stack
### Mobility and Radio Channel Modeling for WBANs  
In order to realize optimized body centered communication between wearable sensors, understanding the behavior of wireless channels is very important. The channel model generally characterizes the path loss of WBAN devices that takes into account shadowing due to the human body mobility or obstacles near the human body and postures of human body. According to the latest development in channel modeling for WBANs, IEEE 802.15.6 proposed number of channel models in [31] and explains the following main differences from typical wireless channel [32]:  
Figure 16: Security Hierarchy [9].  
* _Type of antennas_: Electric type antennas such as dipoles and magnetic type antennas such as loops interact with the human body differently. In general, the magnetic type antennas are known to generate weaker electric fields in human tissue so that the specific absorption rate (SAR) is smaller. As well, the radiation pattern and the polarization are different for these two types.
* _Setup of body area devices/antennas_: Position of the body influences the orientation of antennas and shadowing due to the different parts of the body. Rotation of the devices/antennas causes the change of the directions of radiation pattern and polarization. Small spacing between the body and the antenna causes the impedance mismatch and the distortion of the radiation pattern.
* _Posture and movement of body_: Depending on the posture and the positions of the devices/antennas, part of the body may shadow the line-of-sight (LOS) path. In particular, the shadowing by the trunk causes very large attenuation [33]. Movement of the body changes the orientation of the antennas, distances between antennas, and the shadowing conditions.
* _Variation of human body_: The channel response varies with different human samples. Many factors such as the difference in the shape and the dielectric constant may influence the channel response.  
The next section summarizes the most important characteristics and key features of the IEEE 802.15.6 proposed channel models.
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## 3 Gamma Flux Solution for Simple Source Geometries
### Planar Gamma Source  
The presented concept of PK method is now applied to a uniform gamma disc with specific intensity of \(S_{a}\) (phot/cm\({}^{2}\)/s), radius \(a\) (cm), and a point detector located on the symmetrical line at distance \(z\) (cm). Figure 1 shows a narrow annulus area \(2\pi\rho\cdot d\rho\) for arbitrary radius \(\rho\), with source points distant \(R\) from the detector, so the differential response is  
\[d\phi=2\pi\rho\cdot d\rho\cdot S_{a}\cdot G(R). \tag{6}\]  
Proceeding with integration, one would obtain the following general solution for a flux emerging from the gamma disc or infinite plane:  
\[\phi=2\pi S_{a}\int\limits_{R=z}^{R=\sqrt{z^{2}+d^{2}}}R\cdot G(R)\cdot dR\ \text{( finite disc)} \tag{7}\]  
\[\phi=2\pi S_{a}\int\limits_{R=z}^{R\rightarrow\infty}R\cdot G(R)\cdot dR\ \text{( infinite plane)} \tag{8}\]  
If the slab shield of thickness \(t\) (cm) is now put between disc and the point detector, using the \(G(R)\) from Equation (3) with \(B=1.0\), one can easily derive the analytical solution of the uncollided gamma flux:  
\[\phi=\frac{S_{a}}{2}\bigg{[}E_{1}(\mu t)-E_{1}(\mu t\sqrt{1+(a/z)^{2}})\bigg{]} \ \text{(finite disc)} \tag{9}\]  
\[\phi=\frac{S_{a}}{2}E_{1}(\mu t)\ \text{(infinite plane)} \tag{10}\]  
The exponential integral function \(E_{1}(x)\) of order 1 is a special case of a more general definition (order \(n\)) given as  
\[E_{n}(x)=x^{n-1}\int\limits_{x}^{\infty}\frac{e^{-t}}{t^{n}}dt, \tag{11}\]  
Figure 1: Gamma disc uncollided flux as a superposition of point sources [2].  
which can be found in standard shielding handbooks [1; 3]. However, this solution is not conservative since it is neglecting the process of gamma-ray scattering or buildup inside the shield, so the \(B\)-factor of some form must be included in the analytical integration step. Repeating the mathematical derivation using Equation (4), one would arrive at the following results:  
\[\phi=\frac{S_{t}}{2}\left\{\begin{array}{l}A\bigg{[}E_{1}(\mu t(1+\alpha_{1}))- E_{1}(\mu t(1+\alpha_{1})\sqrt{1+(a/z)^{2}})\bigg{]}+\\ (1-A)\bigg{[}E_{1}(\mu t(1+\alpha_{2}))-E_{1}(\mu t(1+\alpha_{2})\sqrt{1+(a/z) ^{2}})\bigg{]}\end{array}\right\} \tag{12}\]  
(gamma buildup flux from a finite disc)  
\[\phi=\frac{S_{t}}{2}\{A\cdot E_{1}(\mu t(1+\alpha_{1}))+(1-A)\cdot E_{1}(\mu t (1+\alpha_{2}))\} \tag{13}\]  
(gamma buildup flux from an infinite plane)  
The derived equations form the basis for the programming of the PK code with following variants: \"easy solution\"--shield thickness is a known input parameter, while buildup flux is calculated; \"hard solution\"--point detector buildup flux is a known input parameter, while shield thickness is being searched iteratively.
###### Abstract  
A critical requirement for unmanned aerial vehicles (UAV) is the collision avoidance (CA) capability to meet safety and flexibility issues in an environment of increasing air traffic densities. This paper proposes two efficient algorithms: conflict detection (CD) algorithm and conflict resolution (CR) algorithm. These two algorithms are the key components of the cooperative multi-UAV CA system. The CD sub-module analyzes the spatial-temporal information of four dimensional (4D) trajectory to detect potential collisions. The CR sub-module calculates the minimum deviation of the planned trajectory by an objective function integrated with track adjustment, distance, and time costs, taking into account the vehicle performance, state and separation constraints. Additionally, we extend the CR sub-module with causal analysis to generate all possible solution states in order to select the optimal strategy for a multi-threat scenario, considering the potential interactions among neighboring UAVs with a global scope of a cluster. Quantitative simulation experiments are conducted to validate the feasibility and scalability of the proposed CA system, as well as to test its efficiency with variable parameters.  
collision avoidance; UAV; 4D trajectory; distributed dynamic optimization; casual analysis Article
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 3 Methodology
### Algorithm 1  
Algorithm 1 is designed to perform the load balancing in the control plane. The load among the controllers is balanced through clustering and AHP to achieve the highest throughput and minimum delay.  
```
1:Flows classified into AT or PT through AHP.
2:if AT (VoIP, Live Streaming, Video conferencing then
3: PC == 1 and lowest L\({}_{i}\) then
4: Priority level == Highest
5:else
6:if PT (file transfer, email, web browsing etc) then
7: PC == 0 and L\({}_{i}\) is greater
8: Priority level == Lowest
9:endif
10: RCs report load status to UC
11: RCs are classified into OUC, UUC and NUC by UC
12: Load of RC is divided on the basis of 5 parameters X\(\eta\), Y\(\eta\), Z\(\eta\), Pp\({}_{\eta}\) and Pb\({}_{\eta}\).
13: Check the \({}_{\eta}\) at every 5 s.
14: Compute Equation (2) for the the load status
15: Compute Equations (3) and (4) to calculate the processor used, memory used, hard disk used, packets in processing and Packets in the buffer.
16: Load is evenly distributed among the RCs by the UC.
17:endif
```  
**Algorithm 1** Control Plane Load Balancing
## 3 Results and Discussion
### Photoalignment of PAA and P(Az-CB) Films
#### 3.2.1 In-Plane Photoalignment of PAz Coated with Polymer Layers  
A PAz polymer (thickness: 100 nm) was prepared, and then the top surface was coated with a polymer layer of PMBA and PVP as described in Section 2.2. The laminate film was first annealed at 120 \({}^{\circ}\)C for 10 min. After cooling the film to room temperature, the  
Figure 6: (**a**) UV-visible absorption spectra of PAz film (thickness: 100 nm) in repeated cycles of top-coating and removal of P4 VP with annealing. The processes are indicated in colors on the right side, and the colored spectra on the left side correspond to the colored steps. (**b**) Schematic illustration of the mesogen orientation by repeated cycles of coverage and removal of the P4 VP top layer.  
sample was heated to 90 \({}^{\circ}\)C, and the LPL at 436 nm was irradiated at 1.0 mW cm\({}^{-2}\). During these processes, the Az mesogens kept the planar orientation, as confirmed by UV-visible absorption spectroscopy.  
Figure 7 shows the polarized UV-visible absorption spectra of the resulting films coated with PBMA and PVP. In both cases, a strong dichroic characteristic appeared, as indicated in Figure 6a,b. The order parameter (5) was calculated with the following equation  
\[S=\frac{A_{\perp}-A_{//}}{A_{\perp}+2A_{//}}\]  
and reached 0.67 and 0.77 for the PBMA and PVP, respectively. Figure 7c,d displays the GI-SAXS data for the PBMA-coated PAz film. Here, the incidence for the X-ray beam was selected parallel (//) and perpendicular (\(\perp\)) to the irradiated LPL, as illustrated below in Figure 7d. The X-ray scattering spot (\(d\) = 3.5 nm) was observed only in the in-plane direction of the parallel X-ray incidence (Figure 7c). Essentially, no scattering signal was obtained for the perpendicular incidence (Figure 7d). These facts show that highly homogeneous in-plane structural anisotropy was attained, which coincided with the polarized spectral data.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results
### Signal Classification Accuracy
#### 3.2.2 Impact of the Wavelet Parameters on the Signal Processing Outcomes  
The selection and fine tuning of the wavelet parameters were pivotal in optimizing the CWT for underwater acoustic signal analysis. This subsection discusses the impacts of these parameters on the results and their significance in the feature extraction and classification processes.  
* **VPO**: As above mentioned, this parameter dictates the resolution of the scale analysis within each octave. This parameter was adjusted to ensure that the CWT could discern subtle features within the acoustic signals, which could be critical for accurate classification and demodulation. A lower number of VPOs might gloss over these features, leading to a potential loss of critical information, while a higher value offers more granularity. To illustrate the impact of this parameter on the results, Figure 7 demonstrates how different settings affect the feature extraction and subsequent classification accuracy. A minimum of 35 voices per octave was determined as essential to capture the subtle nuances of the signals, underscoring the importance of high-resolution analysis in effective feature extraction.
* **Sample rate**: The sample rate, set at 2 MHz, was another significant parameter. It ensured that the detailed signal characteristics were preserved, allowing for an exhaustive analysis across the desired frequency ranges. This choice was instrumental in accommodating the wide bandwidth of underwater acoustic signals.
* **Frequency limits**: Setting frequency limits to focus on the 150 kHz to 250 kHz band allowed for concentrating the analysis on the most relevant parts of the signal. This targeted approach facilitated the extraction of pertinent features while excluding extraneous data, thereby enhancing the efficiency of the signal-processing pipeline.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 4 Performance and Evaluation  
This section evaluates the performance of our parallel implementation of the quantum circuit for Gauss-Jordan elimination. We used the quantum programming tool ProjectQ to verify and evaluate the required quantum resources for our implementation. Table 1 shows the estimated quantum resources based on the matrix size \(n\). As mentioned in Section 2.6, for the decomposition of Toffoli gates, we use one of the methods presented in [19], which involves seven \(T\) gates and eight Clifford gates with a \(T\) depth of four and a total depth of eight for each Toffoli gate.  
In Table 1, we could not report specific quantum resources of the quantum circuit presented in [9] because they did not provide details on decomposed resources (such as Clifford and T gates, or full depth). However, it is intuitively clear that our method uses significantly more qubits compared to [9], but provides a much lower Toffoli depth. We report conservative estimates of Toffoli depth. It appears that unexpected, non-trivial parallelization of Toffoli gates occurs in the quantum circuit. Thus, the actual Toffoli depth is likely lower than the reported result in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Matrix** \\ **Size** \\ \end{tabular} } & \multirow{2}{*}{**\#CNOT**} & \multirow{2}{*}{**\#1 qCliff**} & \multirow{2}{*}{**\#T**} & \multicolumn{2}{c}{**Toffoli**} & \multirow{2}{*}{**\(T\)-Depth** \(\pi\)} & \multirow{2}{*}{**\#Qubit**} & \multirow{2}{*}{**Full**} & \multirow{2}{*}{\(TD\times M\)} & \multirow{2}{*}{\(FD\times M\)} \\  & & & & & **(TD)** & & & & \\ \hline \(n=4\) & 455 & 78 & 392 & 9 & 36 & 53 & 112 & 477 & 5936 \\ \(n=8\) & 3861 & 492 & 3136 & 35 & 140 & 247 & 407 & 8645 & 100,529 \\ \(n=16\) & 31,329 & 3352 & 24,640 & 135 & 540 & 1067 & 1880 & 144,045 & 2,005,960 \\ \(n=32\) & 251,321 & 24,368 & 194,432 & 527 & 2108 & 4435 & 12,260 & 2,337,245 & 54,373,100 \\ \(n=48\) & 848,401 & 79,432 & 652,736 & 1175 & 4700 & 10,107 & 39,488 & 11,875,725 & 399,105,216 \\ \hline \hline \end{tabular}  
*: Toffoli depth one has a \(T\)-depth of four.  
\end{table}
Table 1: Quantum resources required for quantum Gauss–Jordan elimination; # indicates the number of circuits.  
Our quantum circuit for Gauss-Jordan elimination is designed for quantum Information Set Decoding using [PERSON]'s algorithm. Since the parallelization performance of [PERSON]'s algorithm is poor (see Section 2.5), minimizing depth is more effective than reducing qubit count. In this context, our depth-optimized quantum circuit for Gauss-Jordan elimination proves to be effective for quantum Information Set Decoding.
## 2 Dataset and Methodology
### Methodology
#### 2.2.3 Feature Segmentation and Extraction  
Segmentation using windowing is important for extracting meaningful information from real-time data, as it facilitates the recognition of patterns. It also improves computational efficiency, as it provides a structured input format for ML models and processes smaller segments, improving computational efficiency [25]. For this study, a sliding window size of 200 was applied with an overlap of 100. This represents 2 s of sensor reading, as it was collected at 100 Hz.  
Some experiments utilize only accelerometer readings [26]. Gyroscopic data were included in this study, as they clearly showed the most variation in activities (Figure 4). For each x, y, and z-axis, hand-crafted (manually engineered) time and frequency domain features were extracted. The time-domain features were derived by performing statistical calculations that offer simple and low computational complexity, while the frequency-domain features capture inherent patterns and trends by decomposing the data into real and imaginary values, which represent wave characteristics. This decomposition is achieved using methods such as Fourier or Wavelet transforms [27]. From this, 70 hand-crafted features were extracted. Table 1 provides a list of features extracted from the sensors.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 2 Related Work
### FHMP Method  
Currently, there is a scarcity of study on the FHMP approach. [PERSON] et al. [34] introduced a method called Coupled Non Negative Matrix Factorization (CNMF) for the fusion of HSI, MSI, and PAN images. [PERSON] et al. [35] introduced an Unconstrained Least Square Unmixing (ULSU) method that combines HSI, MSI, and PAN images. The suggested method outperforms the CNMF method in terms of fusion performance in both spectral and spatial domains, while also minimizing spectral distortion. Nevertheless, the efficacy of these conventional techniques for fusion is heavily reliant on manually set parameters, hence significantly constraining the fusion performance of these approaches.
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node
### Hardware Node: UCODALI Board
#### 4.1.2 CC2530 OEM Module  
Figure 11 shows a photograph of the CC2530 OEM module. This module is the main component of the board, as it acts as a processor, controls the node and implements both the DALI communication and IEEE 802.15.4 protocols as well as the necessary network and application layers for the remote management of lighting ballasts. Our research group selected this module because we have broad experience using Texas Instruments wireless modules and because its characteristics and features meet the requirements for our node.  
It has reduced power consumption, although this is not an essential requirement here, as the node will be powered directly by the 230 VAC network. The module contains a CC2530F256 chip [41]; two quartz crystals, one 32 MHz and the other 32 KHz; an SMA antenna connector; and a radiofrequency impedance adaptation circuit. The CC2530F256 has, in just one chip, an IEEE 802.15.4-compliant RF transceiver, an 8051 microcontroller, an AES Security Coprocessor, flash memory and RAM.  
The CPU 8051 enables the execution of an instruction period, as it has three different buses with which to access the SFR (special function register) memories, DATA (data register) and SRAM. On the other hand, having three general-purpose timers is very important for our objectives, for us to be able to implement the Manchester coding-decoding used in the DALI interface.
## 10 HIV and Atrial Fibrillation  
Early studies have established a strong link between HIV infection severity and the risk of developing atrial fibrillation (AF). Studies suggest that inflammatory cytokines contribute to atrial remodeling and electrophysiological changes, increasing the likelihood of AF development. Echocardiography and cardiac MRI derived data have demonstrated that HIV-infected individuals often exhibit left atrial enlargement and fibrosis, which creates an environment favoring the abnormal electrical conduction and AF development.  
Interestingly, some studies suggest that traditional stroke risk scores, such as CHA\({}_{2}\)DS\({}_{2}\)-VASc, may underestimate the thromboembolic risk in HIV patients, indicating the need for HIV-specific risk stratification models [75]. Moreover, AF seems to have a higher recurrence in HIV patients after catheter ablation compared to controls, requiring a personalized treatment approach [76].