esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 4 Proposed Algorithms
### User-Scheduling Algorithms  
In the proposed communication scenario, in a given coverage area, a large number of APs \(A\) are deployed, and each AP serves a subset of users \(U_{a}\) from the total number of \(U\) users distributed randomly in the network. Let \(\{U_{1},U_{2},.....U_{A}\}\) be the subsets of users assigned to the APs \(\{1,2,.....A\}\). \(l_{p}\) denotes the length of the mutually orthogonal pilot sequences. Initially, the pilots are assigned to the first \(l_{p}\) users. For the remaining users, pilot assignment involves, first, choosing an AP \(a^{*}\). In the MDS algorithm (Algo Algorithm 1), the distance between each UE-AP pair is calculated, and for a particular user \(u\), the AP with the minimum distance to user \(u\) is selected as \(a^{*}\). In the MCS algorithm (Algo Algorithm 2), the AP with the maximum channel gain to user \(u\) is selected as \(a^{*}\). After that, the pilot \(l\) with minimum interference between the selected AP \(a^{*}\) and the user \(u\) is chosen and assigned to it. In the end, for all deployed APs, users are selected. For each AP \(a\), \(l_{p}\) users are selected, each assigned with a different orthogonal pilot. From the users assigned with the same pilot \(l\), the user \(u\) nearest to the AP \(a\) is selected, that is, the one with the minimum distance in the case of the MDS algorithm. In the case of the MCS algorithm, however, a user \(u\) with the maximum channel gain between \(u\) and AP \(a\) is selected. The process is repeated till the user subsets for all the APs are selected.  
```
Input : \(A\), \(l_{p}\), \(U\) Output : \(U_{a}\), \(a\in\{1,.....A\}\) Initialization: \(\{U_{1}=U_{2}=........=U_{A}=\phi\}\) while\(u<l_{p}\) \(l_{u}\gets u\) while\(u>l_{p}\)  find \(a^{*}\) \(a^{*}\gets arg\ min\ a_{\in\{1,.....A\}}d_{ua}\)  find \(l^{\prime}\) \(l^{\prime}\gets arg\min_{l\in\{1,.....,l_{p}\}}\sum_{i=1,l_{i}=l}^{u-1} \beta_{ia^{*}}\) \(l_{u}\gets l^{\prime}\) for\(a\)=1:\(A\) for\(l\)=1:\(l_{p}\) \(i\gets arg\ min\ u_{\in\{1,.....,l\},l_{u}=l}d_{ua}\) \(U_{i}=U_{i}\cup\{u\}\) return\(U_{i}\)
```  
**Algorithm 1** MDS algorithm.  
```
Input : \(A\), \(l_{p}\), \(U\) Output : \(U_{a}\), \(a\in\{1,......A\}\) Initialization: \(\{U_{1}=U_{2}=........=U_{A}=\phi\}\) while\(u<l_{p}\) \(l_{u}\gets u\) while\(u>l_{p}\)  find \(a^{*}\gets arg\ max\ a_{\in\{1,.....A\}}\ \ \beta_{ua}\)  find \(l^{\prime}\) \(l^{\prime}\gets arg\min_{l\in\{1,....,l_{p}\}}\sum_{i=1,l_{i}=l}^{u-1} \beta_{ia^{*}}\) \(l_{u}\gets l^{\prime}\) for\(a\)=1:\(A\) for\(l\)=1:\(l_{p}\) \(i\gets arg\ max\ k_{\in\{1,....,K\},l_{u}=l}\ \beta_{ua}\) \(U_{i}=U_{i}\cup\{u\}\) return\(U_{i}\)
```  
**Algorithm 2** MCS algorithm.
###### Abstract  
Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies' efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson's CC = 0.9904 for 120 sps in the case of the transformer patent data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.  
Electrocardiogram (ECG); non-uniform sampling; neural networks; transformer model; temporal convolutional network (TCN); signal fidelity; samples per second (sps) +
Footnote †: journal: Medical Imaging  
1 Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30-059 Krakow, Poland; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 5 Conclusions  
### Key Outcomes  
This paper highlights the problem of inadequate real training sets for training DNN-based SE models that limit the model's effectiveness and robustness. Hence, we propose a noise modeling method that creates realistic paired training sets. The main advantages of our approach are that of NM-GAN:  
* Offers high precision, with the generated samples being close to the real noise manifold.
* Achieves high recall by generating \"diverse\" noise that does not exist in limited captured noisy speech.
* Provides synthetic training sets to train SE models that enhance effectively similar types of noisy speech, even if the noise contained in other noisy speech recordings is a little different.
* Operates end to end, not requiring hand-crafted features and not making explicit assumptions about the raw noise.
* Can learn complex noise distributions by combining U-Net and LSTM to provide the generator with powerful modeling capabilities.
* Can operate directly in the time domain, permitting integrated modeling of the phase information and considering context information.  
In this work, we conduct two comparative \"GAN-train\" and \"GAN-test\" experiments to assess the proposed method's viability. In the first \"GAN-train\" experimental group, we verify that an SE model trained using synthetic training sets generated by the NM-GAN architecture achieves good noise reduction for real noisy speech signals. In the second \"GAN-train\" experiment, we find that an SE model trained using synthetic training sets generated by the NM-GAN architecture achieves an improved performance on real noisy speech samples than trained on synthesized \"limited\" real data with a small number of noise samples. Finally, we prove that the proposed method for generating noise and preparing training sets to train DNN-based SE models is feasible.  
The proposed method increases the robustness of the SE model training involving actual speech and can also be used for blind speech denoising when the noise type is not labeled, and the clean speech signal is unavailable. Beyond this, the proposed method is effective for noise calibration of sound equipment and in special environments.
# DNA Barcoding as a Plant Identification Method
## 6 Super Barcode  
With the development of NGS technology, the use of the chloroplast (cp) genome for phylogenetic analysis has rapidly developed. Numerous analyses using the entire sequence of the cp genome have been conducted over the past ten years to solve phylogenetic problems at deep nodes [60, 61]. The complete cp genome analysis generates the same amount of data as the _COI_ gene utilized in animals and it can provide molecular identification even between closely related species [3, 62]. The cp genome, ranging from 110 to 160 kb, offers more diversity, enabling the discrimination between closely related plants [63]. Additionally, PCR efficiency, potential problems with gene deletions, and difficulties in sequence retrieval are avoided by using the cp genome as a marker. Researchers have recommended the complete cp genome as a super barcode to differentiate closely related species, even though sequences from a single or many nuclear and chloroplast genes have proved useful for doing so [3, 62].  
The creation of a complete cp genome database, the reduction in sequencing cost, and obtaining better DNA quality and quantity are the key to super barcoding [64]. The first cp genome was sequenced in 1986 [65]; since then, the number of cp genomes sequenced has significantly expanded due to the development of next-generation sequencing (NGS) technology [3].
# Nano Aerial Vehicles for Tree Pollination
## 3 Proposed Solution
### Hardware  
As seen in the state-of-the-art literature, projects developed on the topic of Nano Aerial Vehicles (NAVs) use the Crazyflie 2.0, which was developed by Bitcraze. However, Bitcraze has discontinued the Crazyflie 2.0 and replaced it with a new version known as the Crazyflie 2.1 ([[https://www.bitcraze.io/products/crazyflie-2-1/](https://www.bitcraze.io/products/crazyflie-2-1/)]([https://www.bitcraze.io/products/crazyflie-2-1/](https://www.bitcraze.io/products/crazyflie-2-1/)), accessed on 27 March 2023). This new version has a new radio and Inertial Measurement Unit (IMU), support for dual antennas (onboard antenna and external antenna), and a new radio power amplifier that improves the quality of the radio link. To improve the flight performance of the Crazyflie 2.1, the IMU has been changed to a combination of BMI088 and BMP388 sensors.  
Figure 1: Categories of developed works—NAB.  
The Crazyflie 2.1 is a 27 g commercial NAV that is limited to a 15 g payload. The NAV has a LiPo battery that provides power for up to 7 min of continuous flight and charges for 40 min. The Crazyflie 2.1 has four DC motors and plastic propellers to attach the motors to the NAV. The two most essential components of the Crazyflie are the microprocessors, the STM32F4 and the nRF51822. The main microcontroller (STM32F4) is a 32-bit ARM Cortex-M4 embedded processor that handles Crazyflie's main firmware with all low-level and high-level controls. The nRF51822 handles all radio communications and power management and communicates with the ground station. The IMU described above is onboard the NAV. Sensors provide measurements to stabilise the NAV's flight. The expansion ports directly access buses such as UART, I2C, and SPI.  
Expansion decks that are designed for different purposes can be added to the Crazyflie. One of these decks is the AI Deck 1.1 ([[https://www.bitcraze.io/products/ai-deck/](https://www.bitcraze.io/products/ai-deck/)]([https://www.bitcraze.io/products/ai-deck/](https://www.bitcraze.io/products/ai-deck/)), accessed on 27 March 2023), which includes a GAP8, an ultra-low-power monochrome camera, and a NINA-W106 as its core components. Together, these characteristics form a platform that enables the implementation of low-power AI from the edge of a drone. The AI Deck 1.1 extends the computational capabilities and allows for complex artificial intelligence-based workloads to run onboard, possibly achieving fully autonomous navigation capabilities. The NINA-W106 adds Wi-Fi connectivity, which could potentially be used for image transmission and manipulation control.
# Time-Variant Seismic Fragility of Offshore Continuous Beam Bridges Based on Collapse Analysis
## 4 Case Study  
### Bridge Description  
A four-span offshore continuous beam bridge with rigid piers was selected as a case study to study the effect of the material deterioration on the seismic capacity of reinforced concrete bridge. The main girder section area is 9.52 m\({}^{2}\) with a single box and a single chamber, and C50 concrete is adopted. The torsional inertia moment of deck section \(J\) is 25.76 m\({}^{4}\). The second moment of area \(I_{z}\) is 14.29 m\({}^{4}\), and \(I_{y}\) is 82.42 m\({}^{4}\). 2\({}^{\#}\), 3\({}^{\#}\) piers and 1\({}^{\#}\), 4\({}^{\#}\), 5\({}^{\#}\) piers are made of C50 and C40 concrete, respectively. Longitudinal reinforcement and stirrup are HRB335 rebar that the diameter is 32 mm and R235 rebar that the diameter is 16 mm. The concrete cover thickness is 0.09 m. The bottom of the pier is a spread foundation, with C25 concrete. Seismic fortification intensity is 7 degrees, and the site class is in II. Further, the foundation is mostly weakly weathered rocks and the breeze rocks.  
Taking the 2\({}^{\#}\) rigid frame pier as an example, the pier section is hexagonal and its top is fixed with the main girder. The bottom is a 1.5 m thick spread footing foundation, and the concrete cover thickness is 9 cm, considering the confining effect of the core concrete by the stirrup, using [PERSON] model cross section can be divided into confined and unconfined concrete, which is located in the core concrete and the concrete cover, respectively, as shown in Figure 3. In the finite element model, the pier body is divided into 37 nonlinear beam-column elements, and each element is provided with three integration points, except that the length of element 37 is 0.44 m. The other 36 elements are all 1 m. Fix the spread footing foundation at the bottom of the rigid frame pier without considering the interaction between pile and soil. The corner and displacement deformations caused by bond-slip are simulated by the zero length section element, the consolidation of the pier top and beam is simplified to a zero length element. The detailed data of box girder, bridge pier and support are shown in Figure 3[34].  
Figure 3: Finite element model and schematic diagram of the bridge.
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
## 4 Results
### HSCAT Maximum Wind Speed versus Best-Track MSW  
Figure 4 shows the best-track MSW versus the maximum wind speed of HSCAT retrievals (a) and ECMWF forecasts (b). Since the best track, scatterometer and ECMWF winds are with different spatial scales, they show a different high wind scaling, similar to the results in [6]. Note that the objective of this paper is to seek for a parameter well representing TC intensity, rather than to quantify the high wind scaling factor. Consequently, a linear regression model (i.e., \(y=b+a\)x) is simply used to assess the correspondence between the MSW and HSCAT/ECMWF maximum wind speed. The bias factor \(b\) and the scaling coefficient \(a\) are estimated using the linear least square method. Moreover, the [PERSON]'s  
Figure 3: Illustration of exception handing in the estimation of R17: (**a**) more than one 17-m/s intersection in a wind profile; (**b**) isogram of 17-m/s wind speed constructed from the radial extent of 17 m/s winds closest to TC center; (**c**) corrected isogram of 17-m/s wind speed.  
correlation coefficients between the MSW and HSCAT/ECMWF maximum wind speed are calculated and shown in the lower-right corner of each panel. With the linear regression model, the SD error of TC intensity predicted by the HSCAT and ECMWF maximum wind speed are also calculated, respectively. As expected, the relationship between the HSCAT maximum wind speed and best-track MSW is weak: the Pearson's correlation is 0.32 and the SD error is 11.6 m/s, so it may be useless in quantifying the TC intensity. In contrast, the maximum wind speed forecasted by ECMWF shows a remarkable correlation with the best-track MSW (r = 0.6), and the SD error (SD = 8.88 m/s) is better than HSCAT.
## Appendix A Parameters of \(\gamma_{D}\)
### Expected Value of \(\gamma_{D}\)  
All the fading coefficients are independent and identically distributed and \(\mathbb{E}[\gamma_{D}]=\mathbb{E}[\|\mathbf{h}\|^{2}]\). Therefore,  
\[\mathbb{E}[\gamma_{D}]=\mathbb{E}[\sum_{k=1}^{K}|h_{k}|^{2}]=K\times\mathbb{E }[|h_{k}|^{2}]. \tag{101}\]  
Considering that \(\mathbb{E}[|h_{k}|^{2}]=\mathbb{E}[c_{k}^{2}+s_{k}^{2}]=\mathbb{E}[c_{k}^{2}] +\mathbb{E}[s_{k}^{2}]\), then  
\[\mathbb{E}[c_{k}^{2}]=\text{var}(c_{k})+(\mathbb{E}[c_{k}])^{2},\]  
and, by using (100) and (100),  
\[\mathbb{E}[c_{k}^{2}]=\frac{\sigma_{SD}^{2}}{2}+N^{2}\times\mu_{LD}^{2}\;\mu_ {SL}^{2}\;\alpha_{1}^{2}+\frac{N}{2}\times[(1+\alpha_{2})\times\mu_{SL}^{2}\mu _{LD}^{2}+\ldots(\sigma_{LD}^{2}\sigma_{SL}^{2}+\sigma_{LD}^{2}\mu_{SL}^{2}+ \sigma_{SL}^{2}\mu_{LD}^{2})(1+\alpha_{2}+2\alpha_{1}^{2})]. \tag{102}\]  
For the quadrature component, consider that \(\mathbb{E}[s_{k}]=0\), and, consequently, \(\mathbb{E}[s_{k}^{2}]=\text{var}(s_{k})\). Then,  
\[\mathbb{E}[s_{k}^{2}]=\frac{\sigma_{SD}^{2}}{2}+\frac{N(1-\alpha_{2})(\sigma_ {LD}^{2}\sigma_{SL}^{2}+\sigma_{LD}^{2}\mu_{SL}^{2}+\sigma_{SL}^{2}\mu_{LD}^{ 2}+\mu_{SL}^{2}\mu_{LD}^{2})}{2}. \tag{103}\]  
and the mean value of the overall fading coefficient magnitude is given by  
\[\begin{array}{c}\mathbb{E}[\gamma_{D}]=\frac{K}{2}\times(2\sigma_{SD}^{2}+ 2N^{2}\times\mu_{LD}^{2}\;\mu_{SL}^{2}\;\alpha_{1}^{2}N[(1+\alpha_{2})\times \mu_{SL}^{2}\mu_{LD}^{2}+\ldots\\ (\sigma_{LD}^{2}\sigma_{SL}^{2}+\sigma_{LD}^{2}\mu_{SL}^{2}+\sigma_{SL}^{2}\mu _{LD}^{2})(1+\alpha_{2}+2\alpha_{1}^{2})]+\ldots\\ N(1-\alpha_{2})(\sigma_{LD}^{2}\sigma_{SL}^{2}+\sigma_{LD}^{2}\mu_{SL}^{2}+ \sigma_{SL}^{2}\mu_{LD}^{2}+\mu_{SL}^{2}\mu_{LD}^{2})).\end{array} \tag{104}\]
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 5 Aviation Big Data Multi-Layer Security Sharing Framework
### Blockchain Optimization Deployment and Consensus Mechanism for ATM
#### 5.1.1 Blockchain Deployment Optimization Strategy  
Blockchain provides secure protection for data storage and sharing, but as the chain grows, its storage demands increase, posing challenges for resource-constrained participants.  
Time-based chain segmentation: The blockchain is divided into a real-time chain (containing real-time/near-real-time data, such as aircraft health status, flight trajectories, and weather updates) and a traceability chain (storing historical data).  
Geographical network partitioning: The aviation network is divided into multiple consensus domains connected via heterogeneous networks. Each domain includes ATM participants such as air traffic control stations, weather stations, airports, airlines, and aircraft (both ground and airborne nodes). Airborne nodes within a domain jointly maintain a real-time chain instead of each storing a full blockchain (see Figure 3), which resolves the conflict between high storage demands and limited airborne node resources. Ground nodes, with sufficient computing and storage capacity, maintain full chains to ensure data security and traceability. If an airborne node lacks certain data, it sends a query to other nodes in its domain. Nodes holding the requested block return it, and the requester verifies data authenticity and integrity by comparing multiple copies.  
As ATM operations continue, blocks accumulate. When a node's storage exceeds a threshold, block redeployment optimization is triggered. By defining an objective function, the storage occupancy ratio, response latency, and block availability are balanced while ensuring security. A multi-objective optimization algorithm computes the optimal deployment scheme:  
\[\min_{\mathbf{x}}\ \Big{\{}f_{AvgOccStRatio}(\mathbf{x}),f_{AccRespLatency}(\mathbf{x} ),f_{AvgUnAvailProb}(\mathbf{x})\Big{\}}\] \[\text{s.t.}\ \ g_{SecLevel}(\mathbf{x})\leq 0\] \[\ \ g_{StotThres}(\mathbf{x})\leq 0\]  
where \(\mathbf{x}\) is the block allocation scheme, \(f_{AvgOccStRatio}(\mathbf{x})\) denotes the average storage occupancy ratio, \(f_{AccRespLatency}(\mathbf{x})\) is the access response latency, and \(f_{AvgUnAvailProb}(\mathbf{x})\) is the average unavailability probability. And \(g_{SecLevel}(\mathbf{x})\leq 0\) and \(g_{StotThres}(\mathbf{x})\leq 0\) mean security level and storage threshold constraints, respectively.  
The optimization process follows \(\Pi_{opt}=\text{(Init, FactorizePop, SubPopEA, UpdateFullPop)}\) to derive the Pareto-optimal solution for block deployment.
###### Abstract
Moreover, Table 1 highlights that measurement campaigns in low-energy houses include mostly measurements of two points only (living room and one bedroom) using passive sampling techniques over several days as proposed for instance by the protocol used for the French national survey [17]; this makes it difficult to estimate the dynamic occupant exposure and to study the transport of pollutants between rooms. Finally, the accuracy of the concentration measurements remains to be studied. Data on measurement accuracy, measurement devices, and measurement calibration are often missing in the reviewed literature. Uncertainty in measurements includes the precision and bias of the measurement device, as well as the impact of the user, and is well documented in the ISO guide to the expression of uncertainty in measurement [18]. The accuracy of measurements of pollutant concentrations, notably sensor accuracy [19; 20; 21; 22; 23; 24], has been studied extensively, both in situ and in laboratory research. Within the context of these findings, the present work aims to contribute toward understanding the temporal and spatial variation of IAQ in a low-energy house combined with outdoor air ventilation airflow measurements, in order to show that such detailed data allow for a better-quality health impacts assessment and for a better understanding of the transport of pollutants between rooms. For this purpose, after an inter-comparison phase in the laboratory using laboratory measuring devices, a detailed IAQ campaigns in an occupied low-energy house was conducted, including ventilation airflow, the particle numbers and PM\({}_{2.5}\), CO\({}_{2}\), TVOC, formaldehyde, temperature, relative humidity, and occupant questionnaires. Then, a description of the methodology is chosen for the in situ measurement. Next, the temporal evolution of the concentrations for each pollutant and for the two campaigns is achieved. Last, several IAQ performance indicators are calculated, compared with thresholds available in the literature and discussed. This work was conducted as part of efforts led by the IEA-EBC Annex 68 \"Indoor Air Quality Design and Control in Low-Energy Residential Buildings\". \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## 2 Materials and Methods
### Dual Network Inference  
During inference, the two networks work in series (Figure 4), where the first network receives the images sequentially with a size of 224 \(\times\) 244 pixels and designates the region in which the landmark has the highest probability of existing. It should be noted that if this first network predicts incorrectly, the second network will not be able to select the correct catheter tip. The selected region's resolution is updated to match the VGG-16 network architecture and entered as the 224 \(\times\) 224 \(\times\) 3 input to the network. This provides a connection between the localizer network and the predicted landmark's coordinates of the region. A transformation occurs to convert the network output to the local landmark coordinates (marked as the center of the marker and indicated with a red arrow) and, consequently, the global coordinates of the original image.  
Both networks' designs were implemented in Google Colab utilizing the Python programming language. Google hosted Colab for the artificial intelligent applications with many inbuilt libraries and free GPU and TPU accelerators. Concerning hardware acceleration, we ran the model on Colab with a GPU which proved to have the highest efficiency. Colab also offers TPU computing; however, the TPU takes up a considerably more training time compared to the GPU mode typically in small batch sizes.