esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 4 Discussion
The observed discrepancies in frequency predictions between the TPMS and stochastic RVE models primarily stem from inherent differences in their microstructural representations, which influence stiffness and mass distributions. TPMS models exhibit idealized, smooth, and periodic geometries that distribute material uniformly, thereby simplifying dynamic behavior but potentially overlooking local heterogeneities. In contrast, stochastic RVE models reflect the random particle distributions and irregularities found in real metallic foams, leading to local variations in stiffness and mass that significantly affect resonance characteristics. As a result, the two approaches capture different aspects of foam complexity, yielding frequency deviations of approximately 24% for the TPMS model and 37% for the stochastic model relative to experimental data. However, both models show strong agreement in amplitude responses, suggesting their capability to replicate key dynamic features despite frequency shifts. To address these limitations, an optimization framework was developed based on the initial models and calibrated using representative specimens. This framework enables the prediction of dynamic responses--including resonance frequency, amplitude, damping, and air volume fraction--for foams of varying densities, without requiring additional experimental testing. While it does not predict exact local distributions, it accurately captures global behavior, making the models suitable for industrial applications. Metallic foams characterized in this way are widely employed in the automotive and aerospace industries for lightweight structural components, vibration damping, and energy absorption, where overall dynamic trends are more critical than precise frequency matching. The work also highlights the importance of considering geometric constraints in modal analysis. Specifically, accounting for the fixed portion of the sample during testing (e.g., 10 mm embedded in the vibration shaker) is crucial for accurate frequency prediction using the Euler-Bernoulli beam theory. In conclusion, the methodology presented can serve as a solid foundation for rapid material characterization of metallic foams or similar cellular materials subjected to vibration loading.
## 4 Conclusions  
The use of magnetostriction synergistically combines with variable reluctance energy harvesters by making use of the intrinsic magnetic field bias and pre-stress and producing a larger output voltage than would otherwise have been possible. We compare two equivalent variable reluctance VEHs, with and without significant magnetostriction, and find comparable tunability but potentially larger RMS voltages by a factor of two for the system utilizing magnetostriction.  
The simulation results in this work show a moderate mechanical tuning sensitivity (140 Hz/mm) and relatively high-power output (5.1 mW) achieved at a low base acceleration (0.5 g). Coupled with a relatively small size, the commonly used metric mW/cm\({}^{3}\)g\({}^{2}\) is at a reasonable level of 1.1. The short beam length and light proof mass lead to relatively large resonance frequencies and thus low tunability relative to the natural frequency. Optimization of the geometry, coils and material choice can likely improve performance.  
The increase in RMS voltage at \(\beta\) = 4.6 mm and 4.7 mm is indicative of a local optimum, likely regarding magnetic field bias and pre-stress, although this requires further investigation.  
Although the geometry and design are not optimized regarding power, the achieved performance is promising, and merits continued development using magnetostriction in unison with variable reluctance energy harvesters.  
The next step in this research is to develop a more efficient numerical solver based on a lumped mechanical and equivalent electric circuit model. This would enable a rigorous search of a large parameter space and provide insight into the key factors for an optimal design.  
J.B.: Conceptualization, methodology, software, validation, visualization, investigation, data curation, writing--original draft, formal analysis. C.R.: conceptualization, methodology, supervision, resources, project administration, funding acquisition. C.J.: conceptualization, methodology, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work received funding from Swedish Foundation for Strategic Research in the program for 'Research Institute PhD' (grant no. FID16-0055) and from ECSEL Joint Undertaking (QU) project 'Energy ECS' (grant no. 101007247). The APC was funded by RISE Research Institutes of Sweden AB.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 5 Conclusions  
In conclusion, this study provides a summary of the results obtained in the last ten years of research efforts focusing on the assessment of RF-EMF exposure and proving information specific to the levels of exposure in indoor environments. The results included a wide range of sources, both deployed outdoor or indoor the environment in which the exposure level was evaluated, a wide number of different environments, from offices and transportation, to private houses, and were obtained by different approaches, including spot and long-term measurements, personal measurements and computational methods. All the results showed that in all considered environments the levels of exposure were well below the ICNIRP guidelines for general public exposure [39], with the maximum mean levels of the exposure considering the whole RF-EMF frequency band in offices (1.14 V/m) and in public transports (0.97 V/m), and the lowest levels of exposure in homes and apartments, with mean values in the range 0.13-0.43 V/m.  
[PERSON] conceived the key idea of the paper and the methodology, interpreted the results, and drafted the final version of the manuscript. [PERSON] and [PERSON] conceptualized the study and supervised the work. [PERSON], [PERSON], [PERSON] and [PERSON] helped reviewing and editing the paper.  
This research was supported by The French National Research Program for Environmental and Occupational Health of Anses (EST-2016-2 RF-04) Project AMPERE: Advanced MaPpingof residential ExposuRE to Rf-Emf sources.  
The authors declare no conflict of interest.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 4 Proposed Congestion-Aware RPL
### Parent Selection Scheme  
When a node wants to change its parent due to some inconvenience, for instance, buffer overflow at the parent node, or wants to join the network first time, CoA selects the most suitable one between the candidate parent set (\(C_{purs}\)) based on the routing score. Moreover, it checks the node's neighbor density through \(NI\) metric during the selection of a parent when the routing score is equal. Algorithm 1 summaries the proposed parent selection mechanism, which makes use of CoA-OF and \(NI\) metrics to choose the best parent. The algorithm first creates the set of candidate parents as a list. After that, it finds the routing score of all the elements in the list and sorts them in descending order. For each node in the candidate parent set, it checks the routing score as the initial criteria (line 4). If the score of the candidate parent that currently selected is maximum, then the candidate parent node is selected as the preferred parent. However, if the candidate parent in the list that has equal routing score, it checks the neighbor density (line 9) and the one with the smallest _NI_ value is selected as the preferred parent. In this way after checking all the nodes in the list of candidate parent, the algorithm returns the preferred parent as the selected parent.  
```
Input: DIO Message from nodes Output: Preferred parent (P\({}_{\text{P}}\)) selection of node \(k\)
1:\(state\)\(\leftarrow\) Candidate
2: Find the maximum routing score \(max(\eta(C_{\text{pars}}))\)
3:for each \(p\in C_{\text{pars}}\)do
4:if\((\eta(p)==max(\eta(C_{\text{pars}})))\)then
5:\(C_{\text{pars}}[i]\)\(\leftarrow\)\(P_{p}\)
6:end
7:if\((\#(\eta(p)==max(\eta(C_{\text{pars}})))>1)\)do
8: Find minimum neighborhood index \(min(Nl(C_{\text{pars}}))\)
9:if\((Nl(p)==min(Nl(C_{\text{pars}})))\)then
10:\(C_{\text{pars}}[i]\)\(\leftarrow\)\(P_{p}\)
11:end
12:end
13:end
14:return\(P_{p}\)
```  
**Algorithm 1**Preferred parent selection mechanism
## 2 Methods
### Anti-Cancer Activity of Aw-AgNPs
#### 2.7.2 Cell Viability Assay  
The cytotoxic effect of Aw-AgNPs against breast cancer cell lines was determined by an MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolzolium bromide) assay. The Aw-AgNPs were evaluated against HCC-712 cell lines (1 \(\times\) 10\({}^{6}\) cells/mL) and the culture was seeded in sterile 96-flat-bottom-well plates. Before the examination, the cells were attached to the 96-well plates for 72 h in 200 uL of DMEM with 10% fetal bovine serum (FBS). The stock solutions of nanoparticles (5 mg/mL) and seed extract (5 mg/mL) were prepared in sterile distilled water and diluted to the required concentrations (25, 50, 100, 150, 200, and 500 ug/mL) using the cell culture medium and incubated for 24 h in a 5% CO\({}_{2}\) atmosphere. After 24 h, the cells were washed with phosphate-buffered saline (PBS) followed by addition of 100 uL of MTT, and the cells were again incubated for 3-4 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\). Thereafter, the formazan crystals were dissolved in 200 uL of dimethyl sulfoxide (DMSO) and the optical density of each well was observed. The quantity of formazan product, as measured in a calorimetric assay at 570 nm, and the growth inhibition rates were calculated as follows: percentage of growth inhibition = A570 of treated cells/A570 of control cells \(\times\) 100.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## 2 System Model  
This section considers an FTN-OTFS transmission scheme with \(N\) symbols and \(M\) subcarriers that was designed for high-mobility V2X communication scenarios, such as those commonly encountered on highways. In this system, the subcarrier spacing is denoted by \(\Delta f\) and the symbol duration is given by \(T=1/\Delta f\). Consequently, each OTFS frame spans a total duration of \(NT\) and occupies a bandwidth of \(M\Delta f\). The corresponding system model (As shown in Figure 1, V2X communication links are manifested in multiple modes, including but not limited to vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), and vehicle-to-roadside unit (V2R). These links are typically characterized by highly time-varying channels, significant Doppler shifts, and pronounced delay spreads) is illustrated in Figure 1.  
Figure 1: Block diagram of FTN-OTFS systems for V2X communications.
## 3 Advantages of the Proposed Configuration
### Multiple Tunnel Layers for Lower-Frequency Seismic Noise Suppression  
The multiple level topology provides effectively unlimited vertical space and pendulum lengths. The start of the \(1/f^{2}\) roll off at lower frequency shifts the seismic noise farther from the Einstein Telescope detection frequency band. Consider, for example, filters housed in 4 m-tall excavations vertically separated by 8 m of rock. The resulting \(\sim\)12 m separation between filters is four times that in the current Einstein Telescope design and produces pendulum resonances below 0.15 Hz. This arrangement, assuming the same number of filters, would cut in half the starting frequency of the seismic attenuation roll off. This of course works only if that frequency can be matched by vertical attenuation filters [28; 29] also tuned at or below 0.15 Hz (see Section 5 for detail).  
The attenuation performance of a 60 m long chain is compared in Figure 2 with that of the 17 m chain of the Einstein Telescope baseline design. The expected start of the attenuation curve at half the frequency is visible in the simulation at \(\sim\)0.55 Hz. The structure above 0.55 Hz is contributed by suspensions that are not yet re-optimized. The displacement noise is compared in Figure 3 by folding the attenuation transfer function of Figure 2 with a typical underground seismic spectral noise (data from [30]). Additional simulations show that with longer suspensions, made also possible by the proposed topology, the full gain from the longer wires can be recovered.
# Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid
## 3 Proposed Algorithm  
The proposed algorithm will be given in sequence as the objective function, energy management strategy for grid-connected and island modes and the proposed PSO algorithm. First, the PSO algorithm generates random PV and BESS sizes. The proposed energy management algorithm, which also will be explained later, uses these sizing values and generates PV and BESS power output according to the inputs and constraints.
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
## 3 Public Camera Parameters Estimation  
Model parameters (3), (7)-(9) define the transformation of the 3D point, visible by camera, to pixel coordinates in the image. These parameters are: rotation matrix of the camera orientation \(R\) (2) and (3); camera position coordinates (point \(O\)) in \(F_{emu}\) (3); intrinsic camera parameters \(f_{u},f_{v},c_{u}\), and \(c_{v}\), (1) and (4); and radial distortion coefficients \(k_{1}\) and \(k_{2}\), (8) and (9).  
The camera calibration process estimates the parameters using a set of 3D point coordinates \(\{P_{emu}^{i}\}\) and a set of the point image coordinates \(\{P_{im}^{i}\}\). The large set \(\{P_{emu}^{i}\}\) is called a point cloud. There are software libraries that work with point clouds. Long range 3D lidars or geodesic instruments measure 3D coordinates values for a set \(\{P^{i}_{emu}\}\). Another camera depth map can help obtain a set \(\{P^{i}_{emu}\}\). Stereo/multi-view cameras can build depth maps, but it is hard to obtain high accuracy for long distances. When the tools listed above are unavailable, it is possible to obtain global coordinates of points in the camera field of view by GNSS sensors or from online maps. The latter variants are easier to access but less accurate. There are many tools [8] to translate global coordinates to an ENU.  
Camera calibration procedure is well studied ([7] (pp. 178-194), [9] (pp. 22-28), [10], [6] (pp. 685-692)). It looks for the parameter values that minimize the difference between pixels \(\{P^{i}_{im}\}\) and pixels generated by the model (3), (7)-(9) from \(\{P^{i}_{emu}\}\). Computer vision software libraries include calibration functions for a suitable set of images from a camera [5]. The OpenCV function calibrateCamera [5; 11] needs a set of points and pixels of a special pattern (e.g., \"chesboard\") rotated before the camera. The function returns an estimation of intrinsic camera parameters and camera placements concerning the pattern positions. These relative camera placements are usually useless after outdoor camera installation. If the camera uses a zoom lens and the zoom changes on the street, the camera focal length changes too, and new calibration is needed. If we use a public camera installed on a wall or tower, it is hard to collect appropriate pattern images from the camera to apply a function such as calibrateCamera. Camera operators usually do not publish camera parameters, but this information is indispensable for many computer vision algorithms. We need a calibration procedure applicable to the available online data.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Particulate Matter Sensor
#### 3.2.1 Tapered Element Oscillating Micro-Balance (TEOM) Analyzers [88]  
The TEOM analyzers are widely used in the conventional air pollution monitoring systems. The operation principle of TEOM is that the oscillation frequency of the tapered glass tube is proportional to the mass of the tube. The PMs deposited onto the tube will change the mass and oscillation frequency of the tube. By measuring the oscillation frequency change of the tube and the volume of air sampled, researchers are able to deduce the mass concentration (\(\mu\)g/m\({}^{3}\)) of PM in ambient air.  
Note that the air is sampled through a size selective inlet. For example, a PM\({}_{10}\) size selective inlet rejects 50% (no design can reject 100%) of the particles with diameter more than 10 \(\mu\)m and let through particles with diameter of 10 \(\mu\)m and less. In order to eliminate the effect of humidity change, a heating element or a dryer is used.
## 3 Results
### Evaluation Metrics  
The NEU-dataset in our experiment is evaluated basically. It mainly includes Average accuracy, Positive sample recognition rate (\(PRR\)), Negative sample recognition rate (\(NRR\)) and so on for each attribute on the dataset. In addition, we use one label-based metric (\(mA\)) and four example-based metrics: _Acc_ (Accuracy), _Prec_ (Precision), _Rec_ (Recall), and \(F1\). These five indicators evaluate the algorithm of attribute recognition [4; 23; 24], and the calculation of these five indicators is as follows:  
\[mA=\frac{1}{2K}\sum_{i=1}^{K}\left(\frac{|TP_{i}|}{|P_{i}|}+\frac{|TN_{i}|}{|N _{i}|}\right) \tag{14}\]  
\[Acc=\frac{1}{N}\sum_{i=1}^{N}\left(\frac{\left|Y_{i}\cap f(x_{i})\right|}{|Y_{i }\cup f(x_{i})|}\right) \tag{15}\]  
\[Prec=\frac{1}{N}\sum_{i=1}^{N}\left(\frac{\left|Y_{i}\cap f(x_{i})\right|}{|f( x_{i})|}\right) \tag{16}\]  
\[Rec=\frac{1}{N}\sum_{i=1}^{N}\left(\frac{\left|Y_{i}\cap f(x_{i})\right|}{|Y_{ i}|}\right) \tag{17}\]  
\[F1=\frac{2\cdot\text{Prec}\cdot\text{Rec}}{\text{Prec}+\text{Rec}} \tag{18}\]  
where \(K\) is the number of attributes, \(|TP_{i}|\) and \(|TN_{i}|\) are the number of positive samples and number of negative samples correctly predicted respectively for the \(i\)th attribute. \(|P_{i}|\) and \(|N_{i}|\) are the number of positive samples and negative samples in the \(i\)th attribute of ground truth. N is the number of samples. \(Y_{i}\) is the positive sample of the \(i\)th attribute in ground truth, and \(f(x_{i})\) is the positive sample label of the \(i\)th attribute in the predicted result.
# Friction Factor Correlation for Regenerator Working
## 5 Results and Discussion
### Friction Factor Correlation
\begin{table}
\begin{tabular}{l c c c c} \hline
**Regenerator Mesh Screen Numbers** & **\#200** & **\#180** & **\#94** & **\#30** \\ \hline Permeability, \(K\) (m\({}^{2}\)) & \(1.1505\times 10^{-10}\) & \(0.99012\times 10^{-10}\) & \(10.5715\times 10^{-10}\) & \(41.29\times 10^{-10}\) \\ \hline \end{tabular}
\end{table} 
Table 3: CFD-predicted permeability for regenerator investigated. In Figure 7, there are 17 square, nine triangle, six circle and 11 diamond symbols that represent tests of samples #180, #200, #94 and #30, respectively. A fit can be obtained for all the cases as shown by the line in the figure. The friction data obtained from the numerical model fitted the following correlation:  
\[f=\frac{d_{h}}{2\sqrt{K}}\frac{1.48}{Re_{d}}. \tag{35}\]  
The correlation was obtained based on the least-square fit with a confidence level of 95%. The correlation represents a friction loss of all the four regenerator mesh screens tested. The correlation best represents the hydrodynamic losses of high regenerator mesh numbers and slightly under-predicts the losses occurring at a regenerator build with low mesh screen numbers. The maximum difference between the correlation and the friction value of regenerator with low mesh screen number are 30% and 50% for #94 and #30, respectively. The diamond symbols for \(Re_{d}\) larger than 60 are clearly deviating from the line. This may be due to the fact that for large Reynolds number and a very coarse size of regenerator mesh screens the Forchheimer inertial coefficient may become significant and Equation (30) may apply. The constant 2 in the denominator of Equation (35) is the result of the conversion from Darcy-based correlation to Fanning-based correlation and left on purpose for clarity. Figure 8 shows that the numerical results represented by correlation (35) and the results calculated using [PERSON] and [PERSON] [7] correlation (cf. Equations (3)-(5)) both qualitatively agree with experimental measurement. [PERSON] and [PERSON] [7] defined their friction correlation using Darcy-based correlation, and therefore the results calculated through Equation (3) through to Equation (5) need to be divided by 4 for comparison. The results indicate that the assumption used by [PERSON] and [PERSON] [7] is acceptable. The assumption states that pressure drop and velocity relate in a similar way as in steady flows [7]. There are arguments found in the literature that claimed that a pressure drop condition in oscillatory flow deviated from predictions of steady flow. An explanation to that situation could relate to the influence of phase-shifting between pressure and velocity. Further discussion of this is provided in Section 5.2 for low mesh number regenerators. Current results have shown that a pressure drop  
Figure 7: Correlation of friction factor in terms of \(d_{h}\), \(K\) and \(Re_{d}\). condition for an oscillatory flow with travelling-wave time-phasing does not deviate much from the [PERSON] and [PERSON]'s correlation, especially when the mesh number is high.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Heterogeneous Paths
#### 5.4.3 Observation 6  
This experiment can be summarized as follows. The proposed MPTCP algorithm was robust and outperformed the others under adverse channel conditions.  
More importantly, we observed that, in the case of heterogeneous paths, the target loss and delay were mostly satisfied by the proposed RSF MPTCP where possible (i.e., bounded by Equation (27)).