esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments  
### Datasets  
One synthetic dataset and two public available real datasets were used in this work to evaluate the proposed method.  
1. **Synthetic data:** To quantitatively evaluate the performance of the proposed denoising method, we synthesized noise-free DW images using Phantomas ([[http://www.emmanuelcaruyer.com/phantomas.php](http://www.emmanuelcaruyer.com/phantomas.php)]([http://www.emmanuelcaruyer.com/phantomas.php](http://www.emmanuelcaruyer.com/phantomas.php)), accessed on 12 December 2021). with a b-value of 1000 s/mm\({}^{2}\) and six diffusion gradient directions. The fiber structure setting is the same as that used in the ISBI 2013 HARDI challenge. The DW image size is \(55\times 55\times 55\), and the spatial resolution is \(1.5\times 1.5\times 1.5\) mm\({}^{3}\). To obtain the noisy images, random Gaussian noise with level of 10% was added to the clean image five times, resulting in a total of 1650 noisy DW images.  
2. **Ex vivo porcine cardiac DTI data:** This dataset was provided by the cardiac MRI research (CMR) group at Stanford University ([[https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html](https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html)]([https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html](https://med.stanford.edu/cmrgroup/data/ex_vivo_dt_mri.html)), accessed on 22 November 2022). It comprises seven ex vivo porcine hearts that were imaged using a SIEMENS Prisma_fit scanner with a diffusion sequence, TE = 58 ms, TR = 16,670 ms, and 30 diffusion gradient directions with b-value of 1000 s/mm\({}^{2}\), conducted five times. The image spatial resolution is \(1\times 1\times 1\) mm\({}^{3}\), and the image size is \(128\times 128\times 120\). That means that there are a total of 126,000 DW images in this dataset.
3. **In vivo human cardiac DTI data at multiple cardiac phases:** This in vivo human cardiac DTI dataset was also provided by Standard University ([[https://med.stanford.edu/cmrgroup/data/myofiber_data.html](https://med.stanford.edu/cmrgroup/data/myofiber_data.html)]([https://med.stanford.edu/cmrgroup/data/myofiber_data.html](https://med.stanford.edu/cmrgroup/data/myofiber_data.html)), accessed on 30 June 2023). It contains the cardiac DW images of nine healthy volunteers acquired using a 3T MRI scanner (Prisma, Siemens) and a single-shot spin EPI sequence incorporated with second-order (M1-M2) motion-compensated gradient. For each subject, only one mid-ventricular short-axis slice was imaged at early systole, end systole, and end diastole phases, respectively. The acquisition parameters are: TE = 61 ms, matrix size = 128 \(\times\) 104, in-plane resolution = 1.6 \(\times\) 1.6 mm\({}^{2}\), slice thickness = 8 mm, b-value = 350 s/mm\({}^{2}\), and number of diffusion gradient directions = 12. Each subject was scanned eight times, meaning a total of 96 DW images were acquired per cardiac phase. In total, there are 864 images in this dataset.
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 2 Materials and Methods
### Quantified Return Map Analysis  
In this subsection, we describe the software tool for analyzing the secrecy of a chaotic communication system. The considered tool is based on a return map analysis technique. The standard amplitude return map assumes a simple transformation of the local minima (\(Y_{m}\)) and maxima (\(X_{m}\)) values of the signal [30]. Let us define the following variables:  
\[A_{m}=\frac{X_{m}+Y_{m}}{2};\;\;B_{m}=X_{m}-Y_{m};\;\;C_{m}=\frac{X_{m+1}+Y_{m }}{2};\;\;D_{m}=Y_{m}-X_{m+1}. \tag{7}\]  
These are the average values of a consecutive maximum-minimum pair and the distance between them. They have the exact inversion symmetry so that the \(A\) vs. \(B\) section is identical to the \(-C\) vs. \(-D\) section [30]. There are several ways to numerically estimate the difference between return maps. [PERSON] et al. [31; 32] proposed a cost function for estimating the similarity of the attractors topology. The quantified return map analysis based on 2D histograms analysis [13] is another possible approach. We chose the latest one as it is more relevant to the considered problem. Two-dimensional histogram analysis can be performed as follows. Let us split the return map images into cells of the specified sizes.  
To calculate the difference between two return maps, one can use the following formulae:  
\[\theta(x)=\begin{cases}x&x\geq\epsilon;\\ 0&x<\epsilon.\end{cases}\;\;\epsilon\in\mathbb{N}_{1}; \tag{8}\]  
\[\Delta_{i,j}=|X_{i,j}-Y_{i,j}|\cdot|\Theta(X_{i,j})-\Theta(Y_{i,j})|,\;i,j\in[ 1,N]; \tag{9}\]  
\[D=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{\theta(\Delta_{i,j})}{\theta (X_{i,j})+\theta(Y_{i,j})}, \tag{10}\]  
where \(\Theta\) is the Heaviside step function, \(N\) (\(N\times N\)) is 2D histogram resolution, and \(\epsilon\) is a threshold for cutting off rare points that affect the calculation of the difference between two return maps [13]. In all our calculations, we define the resolution of 2D-histogram \(N=120\) and \(\epsilon=3\).  
Figure 6: Synchronization error on the receiver side for (**a**) PM and (**b**) SCM technique.
###### Abstract  
This paper proposes a generalized space time block coded (GSTBC) enhanced fully optical generalized spatial modulation (EFOGSM) system based on Malaga (M) turbulent channel. GSTBC-EFOGSM adopts the hybrid concept of generalized space time block coded and optical spatial modulation to further utilize the high transmission rate of EFOGSM and the diversity advantage of GSTBC in free space optical (FSO) communication systems. Considering the combined effects of path loss, pointing error and atmospheric turbulence, the Meijer G function is used to derive the closed-form expression for the average bit error rate (ABER) of GSTBC-EFOGSM. Then, the ABER performance, data transmission rate, energy efficiency and computational complexity at the receiver of GSTBC-EFOGSM are compared with other optical spatial modulation schemes by simulation. In addition, the effects of key factors, such as data transmission rate, encoding ratio, number of photodetectors and modulation order, on the ABER performance of the system are also analyzed via simulation. Monte Carlo (MC) simulation is used to verify the correctness of the numerical simulation. The simulation results show that the GSTBC-EFOGSM system has better ABER performance and good performance gain.  
FSO; optical generalized spatial modulation; generalized space time block coded; ABER
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
###### Abstract
Channel modeling was also studied by the authors of [17] for mountainous terrain. They applied the ray-tracing method to reconstruct the propagation scenario and then acquired the wireless channel data at 3.5 GHz, 4.9 GHz, 28 GHz, and 38 GHz bands in mountainous scenarios. Important statistical properties, such as the power delay profile, Rician K-factor, path loss, and root mean square delay spread, were captured and analyzed. Channel characteristics were also analyzed. A noticeable gap is the lack of studies that comprehensively analyze the most effective weather conditions for signal attenuation and bandwidth availability. Most studies have concentrated primarily on one weather aspect, such as rain, and its effect on the attenuation of frequency bands at 28 GHz, 30 GHz, and 60 GHz. Additionally, no prior studies have conducted comprehensive analyses of the effects of the most important weather conditions on various UAV communication frequencies, power transmissions, modulation schemes, and distances. These research gaps highlight the need for comprehensive studies to evaluate the effects of various combined meteorological conditions on signal attenuation and bandwidth availability. Therefore, this study aims to fill this gap by investigating the impact of the most critical weather conditions, such as atmospheric gases, cloud/fog, and rain, on UAV-to-GCS communication, covering a wide range of modulation schemes, distances, and other important variables. The main contributions of this paper are as follows:  
* A simplified uniformly distributed multi-segment model of the link to compute weather-specific attenuation and its comparison with non-linear model of the link. * The impact of weather-related factors affecting bandwidth availability. * A methodology for modeling the impact of adverse weather conditions on bandwidth availability and determining the new maximum operating frequency under a specified channel model and set of operating parameters. * A methodology to determine permissible relative speed to allow UAV operation under a given bit error rate (BER) constraint. The remainder of this paper is organized as follows: Section 2 describes the methodology in terms of the mathematical and system models used in this study. Section 3 details the results and comprehensively analyzes the various results obtained from the mathematical models. Section 4 provides this study's conclusions and future work.
## 3 Proposed Method
### Test-Time Augmentation-Based Parallel Ensemble Processing Technique
For each corner \(j\), when the predicted position of model \(i\) is \(C_{i,j}=\left(x_{i,j},y_{i,j}\right)\) and the confidence is \(w_{i,j}\), the final x-coordinate is expressed by Equation (8), and the y-coordinate by Equation (9). \[x_{final,j}=\frac{\sum_{i-1}^{3}w_{i,j}\cdot x_{i,j}}{\sum_{i-1}^{3}w_{i,j}}= \frac{w_{1,j}\cdot x_{1,j}+w_{2,j}\cdot x_{2,j}+w_{3,j}\cdot x_{3,j}}{w_{1,j}+w _{2,j}+w_{3,j}} \tag{8}\]  
\[y_{final,j}=\frac{\sum_{i-1}^{3}w_{i,j}\cdot y_{i,j}}{\sum_{i-1}^{3}w_{i,j}}= \frac{w_{1,j}\cdot y_{1,j}+w_{2,j}\cdot y_{2,j}+w_{3,j}\cdot y_{3,j}}{w_{1,j}+w _{2,j}+w_{3,j}} \tag{9}\]  
The final edge coordinates \(C_{final,j}=\left(x_{final,j},y_{final,j}\right)\) are transformed into the original image coordinate system. This parallel ensemble processing algorithm can effectively respond to distortions at various angles without additional model training. Since each transformed image is  
Figure 4: Aspect ratio adjustment parallel ensemble processing pipeline. optimized for distortions at different angles, stable corner detection is possible under various environmental conditions.
###### Abstract  
Ensuring the safety and stability of buildings during earthquakes is of utmost importance. This can be achieved by assessing the seismic performance of reinforced concrete structures with consideration of design details. This study focused on the seismic behavior of reinforced concrete buildings by comparing the effects of two different types of stirrups, namely those with a 135\({}^{\circ}\) angled end-hook shape and straight hooks, with variation of concrete strength. Pushover analysis of a sample building was performed to determine the effect of hook shape on stirrup reinforcement with a constant volumetric ratio for various concrete strength classes. The results of the analysis indicated significant differences in concrete strength and seismic behavior between the two stirrup configurations. The hooked stirrups demonstrated superior energy dissipation capability and ductility, which led to better seismic performance compared to unhooked stirrups across varying levels of concrete strength. To extend the investigation, the study compared the [PERSON] et al., Kent-Scott-Park, and Kappos-Konstantinidis concrete models with different concrete classes (C50-C25-C20-C16-C10). The findings emphasized the importance of stirrup configuration in the design of earthquake-resistant structures. The study concluded that RC structural performance with the 135-degree hooked concrete members exhibited much better behavior of the 90-degree members for the various concrete strength. In this way, it has been revealed the arrangement and detailing of reinforcement in the construction beams and columns improves the governing effect on seismic structural performance.  
concrete structures; stirrups; seismic behavior; concrete models; earthquake resistance; compressive strength +
Footnote †: journal: Applied Sciences
## 2 Risk Modeling Techniques
### Statistical Risk Models
#### 2.1.2 Statistical Reliability Models for Power System  
The LOLP is the probability that the system's demand will exceed the available generation capacity at a given time, which results in a loss of load event [8]. The LOLP of a system can be generally expressed as follows [9]:  
\[LOLP=\frac{Number\ of\ hours\ where\ D_{t}>G_{t}}{T} \tag{10}\]  
where \(D_{t}\) and \(G_{t}\) are the demand and generation at time \(t\), respectively. \(T\) is the total studied time period.  
Also, the LOLP of a system including spinning reserve (SR) at any time can be calculated as follows:  
\[LOLP_{t}=\sum_{i\in\Omega}p_{i,t}\cdot b_{i,t} \tag{11}\]  
where, \(i\) is the index of state, \(t\) is the index of time, \(\Omega\) is the set of scenarios, \(p_{i,t}\) is the outage probability, and \(b_{i,t}\) can be expressed as follows:  
\[b_{i,t}=\begin{cases}1&if\ \Delta OC_{i,t}-SSR_{t}>0\\ 0&if\ \Delta OC_{i,t}-\ SSR_{t}\ \leq\ 0\end{cases} \tag{12}\]  
where \(\Delta OC_{i,t}\) is the outage committed capacity for state \(i\) at time \(t\) and \(SSR_{t}\) is the total system SR at time \(t\).  
The LOLE is the expected number of hours (or days) within a time period during which the system will experience a loss of load event. It is calculated by summing the probabilities over time intervals. The LOLE can be calculated as follows [10; 11; 12]:  
\[OLE=\sum_{t=1}^{T}p_{t}(C_{t}-L_{t})\ =\sum_{t=1}^{T}LOLP_{t}\,\quad\forall:\ C_{t}- \ L_{t}<0 \tag{13}\]where \(T\) is generally 8760 h or 365 days, \(C_{t}\) is the available capacity at hour \(t\), \(L_{t}\) is the forecasted peak load at hour \(t\), and \(p_{t}(C_{t}-L_{t})\) is the outage capacity cumulative probability, which is equivalent to the probability of loss of load at hour \(t\).  
The EENS represents the expected amount of energy that cannot be supplied due to insufficient generation capacity over a specific time period [13].  
\[EENS=\sum_{t=1}^{T}LOLP_{t}\cdot[D_{t}-G_{t}]\cdot\Delta t=\sum_{t\in\Omega}p _{i,t}\cdot b_{i,t}\cdot[\Delta OC_{i,t}-SSR_{t}] \tag{14}\]  
where \(D_{t}-G_{t}\) is the energy shortfall when demand exceeds generation capacity at time \(t\) and \(\Delta t\) represents the time step.  
The index of reliability of a system can be expressed as follows [14]:  
\[Reliability=1-\frac{ENS}{D_{total}} \tag{15}\]  
where ENS and \(D_{total}\) are the energy not supplied and the total demand of the system in a specific time period (typically one year), respectively.  
The ENS refers to the actual amount of energy demand that is unmet in a specific scenario or time step due to system constraints. On the other hand, the EENS is the statistical expectation of ENS over multiple scenarios, representing a reliability metric that averages the impacts of uncertainties.
## 3 Results
#### Model Evaluation Results  
Table 2 presents the diagnosis performance of the system when these three parameters were treated separately. The neural network was trained with each parameter one time. The maximum accuracy (82%) was obtained using the angles parameter when hidden layer size is eight. The considered type of neural network is feed-forward.  
Table 3 presents the diagnosis performance of the system when two parameters were treated together. The neural network was trained with area and curly level. The best result is for the eight hidden layers with a performance of 75%.  
Table 4 presents the diagnosis performance of the system when the neural network was trained with all three input parameters. The performance increased from 75% to 95%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vascularization Report Area** & **Curly Level** & **Angles Deviation** & **Result** \\ \hline
33 & 10.3 & 50 & 0 \\
54 & 13 & 75 & 0 \\
56 & 13 & 75 & 0 \\
26 & 6 & 55 & 1 \\
16 & 10 & 89 & 1 \\
15 & 1 & 100 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Doppler mode US analysis parameter values.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Hidden Layer Sizes** & **Performance** \\ \hline Area & 8 & 52\% \\ Curly & 8 & 53\% \\ Angles & 8 & 82\% \\ Area & 7 & 48\% \\ Curly & 7 & 37\% \\ Angles & 7 & 77\% \\ \hline \end{tabular}
\end{table}
Table 2: Diagnosis module accuracy for each parameter and variable hidden layer size.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Hidden Layer Sizes** & **Performance** \\ \hline Area & 8 & 75\% \\ Curly & 7 & 60\% \\ \hline Area & 9 & 62\% \\ \hline \end{tabular}
\end{table}
Table 3: Diagnosis module accuracy for the combination of 2 parameters and variable hidden layer size.  
Table 5 presents the diagnosis performance of the system when using various training methods like Levenberg-Marquardt and scale conjugate gradient.  
To provide a more detailed evaluation of model performance, additional classification metrics were computed. The model achieved a sensitivity of 96.5%, specificity of 97.2%, precision of 97.1%, F1-score of 96.8%, and AUC score of 0.985, demonstrating strong diagnostic capability. Table 6 presents the confusion matrix for the classification results, showing the distribution of true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs).
## 5 Applications and Services
### Services
#### 5.1.1 IoTs Sensor Networks  
UAVs are effective for enabling timely and reliable data delivery in remote IoT sensor networks. RISeUAV has been used to minimize the average age of information (AoI) of the IoT data received by the BS over time [103]. A time-constrained model-predictive-control-based algorithm, with a mathematically rigorous proof of the optimality, has been proposed for 3D UAV navigation for RIS-assisted data collection from sensor networks on uneven terrain [139].
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.6 Emissivity and Absorptivity Coefficients  
It is assumed that the satellite's body is made with aluminum alloy Al 6061-T6. It is also assumed that the 60% of the total surface area of the CubeSat is uniformly covered by solar panels. With the material properties of the panels defined as absorptivity coefficient \(a_{panel}=0.9\) and emissivity coefficient \(\varepsilon_{panel}=0.85\) and the aluminum surface properties as \(a_{Al}=0.16\) and \(\varepsilon_{Al}=0.03\), the CubeSat's coefficients are as follows: \(a\ =0.6\) and \(\varepsilon\ =0.52\), resulting from a weighted average [19].
## 2 Materials and Methods
### Samples Preparation and Measurements
Uranium is concentrated using UTEVA resin. The eluate produced by each chromatographic process (U and Pu) is precipitated with Fe(OH)\({}_{3}\), centrifuged, and freed of the supernatant. After at least three rinses and centrifugations with Milli-Q water, the precipitate is dried at 80 \({}^{\circ}\)C for at least one night. Each sample baring U and Pu separately is then converted into U and Pu oxides and iron oxides (U\({}_{x}\)O\({}_{y}\) + Fe\({}_{2}\)O\({}_{3}\) and Pu\({}_{x}\)O\({}_{y}\) + Fe\({}_{2}\)O\({}_{3}\)) by calcination in a muffle furnace at 550 \({}^{\circ}\)C for at least 6 h, before transferring the samples in aluminum cathodes for the AMS measurements. Chemical yields are not reported here because they are the subject of a detailed optimization procedure that will be discussed in a future study. Measurements were performed at the CIRCE AMS facility based on an NEC 9 SDH-2 Pelletron accelerator operated at 3 MV, according to the method in [25]. Thirty-nine cathodes form each measurement batch, including cathodes of U, Pu, chemical blanks, chemical standard blanks, and measuring standards. Some samples, along chosen transects (T0, T1, and T5), were measured with a high-resolution germanium hyperpure \(\gamma\)-ray detector (1.9 keV resolution at 1.332 MeV and 70% efficiency) properly shielded. The scope was to measure \({}^{137}\)Cs, produced in human activities connected to power plant operations and nuclear accidents and experiments, and \({}^{40}\)K, a naturally occurring radioisotope chemically related to Caesium, widespread in the environment. For this purpose, samples were dehydrated, sieved (2 mm), sealed in Marinelli vessels, and analyzed with a high-resolution germanium hyperpure \(\gamma\)-ray detector (the details of the preparation and measurement are the same as [15]). On some marine sediment samples, the particle size distribution was determined by the sieve method. Sieves with mesh sizes of 2, 1, 0.5, 0.250, 0.125, and 0.063 mm were used to determine the percentage of the various types of sand and silt.
## 2 Graph Based Critical Sensor Assessment
### Algorithm 1--Identifying the Most Disruptive Fire Outbreak Locations  
This algorithm is inspired by the definition of _critical nodes_ as nodes that, if removed, will cause the greatest increase in the shortest distance between two other nodes [27]. Our approach differs from this standard definition and actually ranks all nodes according to how many shortest exit paths from nodes occupied by evacuees become disconnected when the examined node becomes inaccessible. The ranking metric is linked to the \"disruptiveness\" of a location, since the evacuee's instinctive behaviour is to follow the shortest path to the nearest exit. For instance, if a fire breaks out at a highly ranked node, then more evacuees would need re-routing and advice. The algorithm first creates a reference map [Figure 1(a)] that records the shortest exit path from every node. Then it iteratively removes each node from the building graph--to simulate an area blocked by fire--and finds a new set of shortest paths and compares them with the initial one [Figure 1(b), 1(c)]. Each change in the updated path corresponds to a disrupted shortest path and the corresponding ranking metric is incremented. This algorithm is modified so that only nodes whose elimination actually changes the exit point or staircase are considered.