esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Analysis of Optimal Cache Deployments
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \(h_{metro}\leq 4.5\) & \(4.5<h_{metro}<6.5\) & \(h_{metro}\geq 6.5\) \\ \hline \(\alpha<1\) & & AMENs: Low number, medium capacity & AMENs: Low number, large capacity \\ \hline \(\alpha=1\) & AMENs: High number, small capacity & MCEN: Medium capacity & MCEN: no cache \\ \hline \(\alpha>1\) & & AMENs: High number, large capacity & AMENs: High number, small capacity \\  & & MCEN: Small capacity & MCEN: no cache \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best cache deployment (number of caches and their storage capacities) among different network levels based on location of MCEN cache (\(h_{metro}\)) and value of the popularity distribution skew parameter \(\alpha\). Figure 7: Average resource occupation \(RO_{avg}/req\) for increasing value of total allowed storage capacity for \(\alpha\) = 0.9 and 1.  
the amount of storage capacity granted. On the contrary, for \(h_{metro}=7.5\), the results confirm that for a relatively lower value of \(\alpha\) (Figure 7a,b), the utilization of small number of large-capacity caches yields the minimum \(RO_{avg}/req\) per VoD request. For a larger value of \(\alpha\) (Figure 7b), it becomes more beneficial to utilize many small-capacity caches, since in such a popularity distribution, the popular items gain more popularity, making it decisive to deliver them from the nearest locations possible. In conclusion, the ideal amount of storage capacity to deploy in the network heavily depends on both the dimensions of the metro network and the popularity distribution.
# Trusted Electronic Contract for Enabling Peer-to-Peer HPC Resource Sharing
## 3 Proposed Design
### Smart Contract Design and Communication Flow  
Smart Contract is a business logic that is deployed on blockchain nodes to govern network activities. In this study, our smart contract composes of the main logic (i.e, ResourceSharingContract) and four types of objects (i.e., User, HPC System, Job, Cluster State), in which the functions, attributes, and the relationship between components are illustrated in Figure 3.  
Figure 2: Computing Resources Sharing System Architecture. (**a**) Trusted System Model; (**b**) Untrustworthy System Model. Displayed in Figure 4, communication flow can be segregated into main functional tasks as follow:  
1. **Identity enrolment** For the first time entering the system, clients (users and HPC systems) are required to go through a registration process which could be carried out manually or automatically depending on the level of strictness of the onboarding process. Once the consideration process is passed, clients will be assigned to a specific role and cryptographic credentials corresponding to identity will be generated. The private key will be stored locally on client machines, while public credentials will be sent to the blockchain. These cryptographic materials will be used in signing transactions produced by clients, allowing the blockchain to validate if the transaction's  
Figure 4: Communication Flow. Figure 3: Class Diagram of Resource Sharing Smart Contract. owner is permitted before executing the requested functions. Displayed in Table 1, role-based permission is defined on network resources and business logic execution. 2. **Resource Matching** The system needs to ensure users are matched to the suitable resources at satisfactory rental prices and providers can benefit from renting computing resources. On user requests for job submission, the blockchain will perform searching through the HPC system information (obtained during the HPC registration process (Figure 5a) to find a set of candidates based on the user's requirements (e.g., resource spec, programs, etc.). The result set will be returned along with the latest cluster state (Figure 5b) and rental price, which is slightly adjusted according to the distance between user location and the HPC system to encourage utilizing local resources. Users can select the options based solely on system information (without revealing provider names) to promote competitiveness and encourage new providers to compete in the ecosystem.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 2 System Model
### Performance Metrics
#### 2.3.1 Positioning Accuracy  
Positioning accuracy for each user \(k\) can be determined by the position-error-bound (PEB), computed using the Fisher-information-matrix (FIM) [9]. Assuming the full-cooperation scenario and receive beam \(\mathbf{w}_{b}\in\mathcal{W}_{b}\) of each coordinating BS \(b\) is used in a time-sequential way. Then, the PEB for \(k\)th user, \(Q_{k}\) is computed as  
\[Q_{k}=\sqrt{\sigma_{p_{k}^{k}}^{2}+\sigma_{p_{g}^{k}}^{2}}\, \tag{4}\]  
where \(\sigma_{p_{k}^{k}}\) and \(\sigma_{p_{g}^{k}}\) are obtained from the inverse of FIM \(\mathbf{J}_{k}\) for \(k\)th user [9; 12], which includes the Fisher information on the user's location and corresponding channel coefficients, as given in Equation (28). The construction of FIM matrices \(\mathbf{J}_{k}\)\(\forall k\) and detailed derivation for the PEB calculation is provided in Section 3.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 4 Domain Model for Data Sources
### Data Sources Which Can Be Transported to Certain Locations  
**Microphone, carbon dioxide meter, and microwave radar:** These data sources are generally used after earthquakes by transporting them to the location of the disaster. Microphones are brought under the rubble to detect any meaningful sounds. Carbon dioxide meters measure the breathing of living creatures [54]. Similarly, microwave radars [7] detect movements of persons and breathing of lungs. All these data sources are relevant for estimating the location of persons. Microwave radars are quite accurate and effective; however, they are much more expensive than the other two sensors.  
**Other data sources:**For brevity, this article mainly focuses on data sources that can be used for earthquake detection. There are of course many more data sources which can be used for various purposes. For example, barometers and compasses are mainly used by the flight control systems of UAVs; such data sources are considered out of the scope of this article. The adoption of a mobile analysis laboratory in situ is a promising approach which is likely to be more often used in detecting the effects of disasters including earthquakes [33].
## 2 Modulation Transfer Function (MTF) Calculation Method
### The MTF for the Mixed Image Motion of Linear Motion and Sinusoidal Motion
The expressions are presented as follows:  
\[ \begin{array}{l}\text{A}=\sum\limits_{n=1}^{\infty}{(-1)^{n}}{\text{J}_{2n}(2 \pi\text{fD})\left[\frac{\sin{2(n\omega+\pi\text{fv})}{\text{t}_{\text{e}}}}{2n \omega+2\pi\text{fv}}+\frac{\sin{2(n\omega-\pi\text{fv})}{\text{t}_{\text{e}}} }{2n\omega-2\pi\text{fv}}\right]}\\ \text{B}=\sum\limits_{n=1}^{\infty}{(-1)^{n}}{\text{J}_{2n-1}(2\pi\text{fD}) \left\{\frac{1-\cos{(2n-1)\omega+2\pi\text{fv}}{\text{t}_{\text{e}}}}{(2n-1) \omega+2\pi\text{fv}}{\text{t}_{\text{e}}}-\frac{1-\cos{(|2n-1)\omega-2\pi \text{fv}|}{\text{t}_{\text{e}}}}{(2n-1)\omega-2\pi\text{fv}}\right\}}\\ \text{C}=\sum\limits_{n=1}^{\infty}{(-1)^{n}}{\text{J}_{2n}(2\pi\text{fD}) \left[\frac{1-\cos{2(n\omega+\pi\text{fv})}{\text{t}_{\text{e}}}}{2n\omega+2 \pi\text{fv}}-\frac{1-\cos{2(n\omega-\pi\text{fv})}{\text{t}_{\text{e}}}}{2n \omega-2\pi\text{fv}}\right]}\\ \text{D}=\sum\limits_{n=1}^{\infty}{(-1)^{n}}{\text{J}_{2n-1}(2\pi\text{fD}) \left\{\frac{\sin{(|2n-1)\omega+2\pi\text{fv}|}{\text{t}_{\text{e}}}}{(2n-1) \omega+2\pi\text{fv}}{\text{t}_{\text{e}}}+\frac{\sin{(|2n-1)\omega-2\pi\text{ fv}|}{\text{t}_{\text{e}}}}{(2n-1)\omega-2\pi\text{fv}}\right\}}.\end{array} \]  
Thus, the analytical expression of MTF for the mixed image motion of linear motion and sinusoidal motion is described by Equation (14). As can be seen in Equation (14), the MTF is the function of the blur radius when the vibration amplitude of the sinusoidal motion is zero, which means the image motion becomes a simple form of linear motion. Equation (14) becomes identical to the expression of linear motion derived by other MTF calculation methods [19].
## 7 Discussion
### Future Work  
The assessment of the SPD aspects for a system is usable for figuring out if it meets the desired SPD requirements and mitigates attacks under a specific threat model, and comparing various configurations and reasoning which is the best.  
Nevertheless, from the engineering and business points of view, it also becomes fruitful to deduce which of the settings that achieve the targeted SPD level is the most cost-effective/profitable. Therefore, CompoSecReasoner can be extended to assess the implementation cost of the examined settings. The SPD and cost measurements could be incorporated in business frameworks for cost-benefit analyzes and investment evaluation for IoT or other information systems.  
In the long-term, technology ageing also raises as a significant parameter [87; 88]. In the smart home environment, the deployed equipment (e.g., surveillance cameras, fridge, etc.) is in use for several years. The long lifetime influences protection, and after decades of technology evolution, the currently deployed and secure mechanisms could have become inadequate. In the cryptography domain for instance, the outdated cipher DES was replaced by the newer cipher AES some years ago, while now crypto-systems which are based on elliptic curves are considering as the substitution of the RSA ones. Thus, an adequate assessment methodology has to consider the ageing problem of the system as well. The proposed method can tackle several of these issues. The analysis which evaluates the provided defense of the deployed controls based on the known limitations could be periodically revisited to reflect the current situation. Thereupon, vendors could keep up-to-date the SPD of their products with the system operator (or the automated management framework) re-calculating a system's SPD, when changes are reported.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 4 Interpretation of Rf Stray Currents in Spider
### CVD at Secondary Side  
With the CVD connected at the secondary side, we can further separate the analysis of the RF stray problem in two cases.  
CVD outer leg: in SPIDER, the outer conductor of the RF coaxial line has only one physical potential reference, namely the connection to the plasma source. Inside ISRF, the CVD leg connected to the screen of the RF coaxial line (CVD outer leg) provides a high impedance reference to the HVD potential. Figure 4a represents one of the most direct connections between the reference potentials and the ensuing RF stray current circulation.The common mode current magnitude is defined by the voltage drop across the longitudinal elements of the transmission line and the impedance of the common mode path.  
CVD inner leg: the CVD leg connected to the inner conductor of the RF coaxial line (CVD inner leg) allows the circulation of a differential mode current between one of conductors in the transmission line and the outer screen of the RF coaxial line. One of these possible connections is represented in Figure 4b, where the bias conductor and the outer part of the RF coaxial line screen assume the role of a transmission line with characteristic impedance \(Z0_{2}\), supporting the circulation of a differential current \(I_{D2}\). In this case, the stray currents magnitude depends on the differential voltage at the secondary side of the RF transformer.  
In Figure 4, the same circuit loop is represented; thus, we can compare these cases to qualitatively understand the relevance of one mode over the other, for the RF stray currents issue. Without considering the resonance phenomenon, we can make the following observationsBoth common mode and differential mode currents are mainly limited by the impedance defined by the CVD leg capacitance. This impedance has the same value for both CVD legs.
* The differential mode voltage is significantly larger than the voltage drop across the longitudinal elements of the transmission lines, which defines the common mode voltage.  
Thus, when the CVD is connected at the secondary side of the transformer, the main component of RF stray currents is the differential mode current, arising from the connection of the CVD inner leg.
## 2 Materials and Methods
### Eligibility Criteria  
The PICOS (population, intervention, comparators, outcomes, study design) criteria were used to delimit the inclusion and exclusion criteria.  
_Participants:_ No restrictions for gender, socio-economic status, ethnicity, or geographical area were set.  
_Intervention:_ RT programs (no duration restriction) based on dynamic resistance exercises performed using a measurable external load were included. Thus, interventions in which the VL was incurred through other types of exercises (e.g., endurance or sled training) were excluded.  
_Comparators:_ The VL threshold was considered as the main independent variable. Eligible investigations should have described the VL threshold used by each experimental group throughout the training program (e.g., Group A = 10% VL vs. Group B = 30% VL). Studies in which the VL threshold was not the only training variable differencing the experimental groups were excluded. This review only considered those investigations in which the velocity of all repetitions performed throughout the RT program was monitored. Moreover, studies reporting the effect of only one VL threshold were excluded (e.g., 10% VL vs. Control group).  
_Outcomes:_ Two main outcomes were considered: (i) changes in muscle strength measured by dynamic, isometric, or isokinetic tests, and (ii) changes in athletic performance evaluated by jump, sprint, agility, or other specific tests.  
_Study design:_ The present review considered experimental investigations based on a pre-post design.
## 3 Results and Discussion
### Effect of Lignin Concentration on Nanoparticle Synthesis  
#### 3.2.1 Particle Size and Shape  
In this study, nanoparticles were produced at different lignin concentrations by a solvent exchange between THF-SL solution and water. Table 3 shows the size and PDI of the lignin nanoparticle identified by measuring the TEM image or dynamic light scattering (DLS). It is generally known that the size of the polymeric nanoparticle is affected by several parameters such as stirring rate, polymer concentration, and polymer molecular weight [12]. When the polymer concentration increased, organic solution viscosity also increased; therefore, the diameter size of the polymeric nanoparticle became greater. The average particle size was at a minimum at 1 mg/mL concentration. The size then increased with increasing concentration, from 128 nm for NP-C1 to 559 nm for NP-C4 (peak size). It is known that the smaller particle sizes easily move in the blood vessels, yet evades phagocytosis by macrophages [19; 20; 21]. The shape of the particle also affects the margination in blood vessels, besides the margination tendency for a spherical nanoparticle depends on the size; thus demonstrating extended circulation time in the living organism [22; 23]. The particle produced at the condition of 1 mg/mL concentration was the only sample that presented a diameter below 200 nm. PDI of the size increased with increasing concentration, indicates that a relatively more heterogeneous size distribution of lignin nanoparticles could be obtained with higher pre-dialysis concentration of lignin. There was quite a large gap between the peak size and the size from the TEM image, assuming the particles were formed in different and varying diameters. Note that the TEM image represents only a mere fraction of the whole sample.  
The diameter and structure of lignin nanoparticles were observed by TEM imaging (Figure 2A-C). It is presumed that exchanging THF and water in this method involves the nanoprecipitation process [24]. Supersaturation occurs when the solvent and non-solvent are mixed and solubility of the solute decreased. Then the changes in the concentration lead to the nucleation process followed by growth by condensation of particles. We could observe the particles by TEM image that clearly shows uniformly spherical lignin nanoparticle formation with nanoprecipitation of SL. THF is much less polar than water (relative polarity 0.207), which makes it a good solvent for the solvent exchange process with water [25]. Rapid solvent exchange using THF and a dialysis tube produces the symmetric lignin nanoparticle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Size from Image (nm)** & **Peak Size (nm)** & **PD c** \\ \hline NP-C1 1  & 404 & 128.4 & 0.3 \\ NP-C2 & 468 & 466.7 & 1.0 \\ NP-C4 & 590 & 559.7 & 1.0 \\ NP-F1 2  & 474 & 444.8 & 0.8 \\ NP-F2 & 508 & 592.9 & 1.0 \\ NP-F3 & 1024 & 850.3 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SL nanoparticle sizes by pre-dialysis concentration and fractions.
# Visualizing Small Objects Using Amplitude-Modulated Laser Light at Microwave Frequencies
## 4 Experimental Results and Comparison between Two Experiments
### Results of the SAIL Experiment  
The objects that were employed on the synthetic aperture measurements were chosen to be the optical corner cubes because both incident and reflected light travel along an identical path-length. Thus, an optical corner cube can be utilized as a point target, resulting in the reconstructed image of a point target becoming a point spread function (PSF). This is necessary to determine the spatial resolution. The interference data were obtained by turning the stage from \(-\pi/4\) to \(\pi/4\). The fan-shaped blue area in Figure 7 shows the wavenumber space where the interference data were taken. To obtain the image, Fourier transform was carried-out in the red area [15].  
In Figure 8a, the positions of two corner cubes are separated by 110 mm. The photograph in Figure 8b corresponds to the image in Figure 8a. Both corner cubes were located at the same radius. In Figure 8c, the corner cubes positions are diagonal, separated by a cross-range of 53 mm in and by 40 mm in the range directions, as shown in the photograph Figure 8d. The observed separation between the two targets were in agreement with the set positions. The image width at half height in the cross-range direction was found to be about 9 mm and about 24 mm in the range direction. The observed wid  
Figure 7: The wavenumber k space showing the area where the interference data were taken (blue) and where 2D FFT was performed (red). This polar graph was plotted as a function of the azimuth look angle in degree and wavenumber of the FM microwave in rad/m. The azimuth look angle corresponds to the angle of the rotation stage.  
agreement with \(\pi/K_{x}\) and \(\pi/K_{y}\), as shown in Equation (3); that is, the expected image resolutions in cross-range and range directions are about 8 mm and 37.5 mm, respectively.
## 2 Related Work
### Atlas-Based Approaches  
The solutions in this category are generated by comparing the anatomical atlas generated by manually segmented data [12; 14; 15; 16; 17]. The approaches usually require a high number of instances in the atlas to guarantee generalizability to different anatomical features and acquisition protocols. The size of the atlas, however, is directly linked to an increased computational cost. To counteract such limitations, the solutions are often restricted to a specific anatomical part. An example of such an application is provided by [PERSON] et al. [18], in which the authors use an atlas-based approach to segment the pectoral muscle, relying on a simpler pixel-based approach to segment the chest wall edge.
## 4 Discussion
### Comparison Across Datasets  
The PViT variants consistently outperform the traditional models, such as RED-CNN, TED-Net, and DSC-GAN, on all datasets. The GLAM-PViT model with the proposed loss function delivers the best denoising results, achieving high SSIM and PSNR values with low uncertainty. The performance variations across datasets reflect the differences in noise complexity and structural features, with the piglet and abdomen datasets showing better results compared to the thoracic and head datasets. This demonstrates the adaptability of GLAM-PViT to diverse noise patterns and anatomical structures.
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 2 Materials and Methods
### Other Features Extraction
#### 2.3.1 Box-Counting Features  
In the box-counting method, the embedding dimension \(m\) is usually assigned a value of 2, allowing the inscription of a two-dimensional phase-space image describing the trajectory of the system [17; 34]. Box-counting method can characterize the signal complexity and identify potential ECG signal desynchronization phenomena [20]. After obtaining the phase space image, it is exported as a \(1024\times 1024\) high-resolution grayscale image. The pixels in the image are counted as \"black box\" if the pixel value is 0 and as \"white box\" if the pixel value is 1. The black-box pixels represent the measure of trajectory spreading, so it will be counted to calculate statistical parameters named as box number \(n_{b}\). In this study, two parameters, \(CV\) and \(\kappa\), were calculated with sliding windows. \(CV\) is the distribution of the trajectory normalized by the mean, which is the ratio of the standard deviation (\(\sigma\)) to the mean (\(\mu\)).  
\[\mu=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}}n_{b}(i),\sigma=\sqrt{\frac{1} {\mathrm{N}}\sum_{i=1}^{\mathrm{N}}(n_{b}(i)-\mu)^{2}}_{,}(i=1,\ 2,\ 3,\ 4,\ \ldots,\ \mathrm{N}) \tag{7}\]  
\[CV=\frac{\sigma}{\mu}, \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **TDA 0** & **TDA 1** & **TDA 2** \\ \hline Sum & sum 0 & sum 1 & sum 2 \\ Variance & var 0 & var 1 & var 2 \\ Mean & mean 0 & mean 1 & mean 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Defined nine TDA features.  
\(\kappa\) is the fourth-order statistical moment of the box number, measuring whether the data distribution is steeper or flatter relative to the normal distribution [18].  
\[\kappa=\sqrt{\frac{1}{\text{N}}\sum\limits_{i=1}^{\text{N}}[(n_{b}(i)-\mu)/\sigma ]^{4}},(i=1,\ 2,\ 3,\ 4,\ \ldots,\ \text{N}), \tag{9}\]  
where N is the number of sliding windows.