esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### \({}^{222}\)Rn Exhalation Rate  
To determine the radon exhalation rate from each aliquot of the analyzed stones, measurements of the \({}^{222}\)Rn activity that accumulated in a vessel after a specified build-up time were carried out through E-PERM electre ion chambers. During each run, temperature and humidity factors were constantly monitored and controlled. This method is referred to as the \"accumulator method\" [56] (see Figure 5).  
The variation in \({}^{222}\)Rn specific activity, C\({}_{Rn}\) (Bq m\({}^{-3}\)), over time, in a sealed accumulation vessel, can be expressed as follows:  
\[C_{Rn}=\frac{E(1-e^{-\lambda_{Rn}t})m}{V_{A}\lambda_{Rn}}+C_{Rn}^{0}e^{- \lambda_{Rn}t}, \tag{7}\]  
where E is the specific exhalation rate (Bq kg\({}^{-1}\) h\({}^{-1}\)) from the sample, \(\lambda_{Rn}\) is the decay constant for \({}^{222}\)Rn (h\({}^{-1}\)), V\({}_{A}\) is the volume of the accumulation vessel (m\({}^{3}\)), m is the sample mass (kg), and C\({}^{0}_{Rn}\) (Bq m\({}^{-3}\)) is the \({}^{222}\)Rn activity concentration at the start of the accumulation time (t = 0) [57].  
The E-PERM system operates by leveraging the ions generated during the radon decay process, thereby decreasing the electrical potential of a Teflon disk integrated in an  
Figure 5: A schematic drawing of the experimental setup.  
electrostatically charged chamber. The measurement of radon concentration was accomplished through a combination of techniques, employing a 210 mL chamber configured with a short-term electret. Specifically, the radon activity concentration, C\({}_{Rn}\) (Bq m\({}^{-3}\)), was determined by following the established relation:  
\[C_{Rn}=\frac{(V_{i}-V_{f})}{C_{F}T}-BG, \tag{8}\]  
with V\({}_{i}\) and V\({}_{f}\) equal to the initial and final electret voltages, respectively, C\({}_{F}\) the calibration factor (V per Bq m\({}^{3}\) d), T the exposure interval (days), and BG the background-related equivalent radon concentration of natural gamma radiation. In particular, C\({}_{F}\) was determined as follows:  
\[C_{F}=0.04589+0.0000155\frac{(V_{i}+V_{f})}{2}. \tag{9}\]  
According to [58], the total volume for the E-PERM chamber, after subtracting the excluded volume, was found to be 3.8 L. Accordingly, the \"back diffusion\" effect had no impact on the measured radon exhalation rates, since the sample volume was more than ten times smaller than the chamber's volume.  
According to [55], the specific exhalation rate, E (Bq h\({}^{-1}\) kg\({}^{-1}\)), is given by  
\[E=\frac{(C_{Rn}V_{A}\lambda_{Rn})/m}{1-\left(1-e^{-\lambda_{Rn}t}\right)/ \lambda_{Rn}T} \tag{10}\]  
In this study, the net \({}^{222}\)Rn concentration was determined by subtracting the blank reading, which was obtained by placing E-PERM chambers into the empty jar utilized for the experiments and exposing it for the same duration as the experimental setup.
## 2 Proposed Method
### Experiment
#### 2.2.2 Performance Metrics  
Because the reference image does not exist, it is hard to quantitatively evaluate a fused image's quality. In recent years, many fusion metrics have been proposed to evaluate fusion methods' performance, but none of them are widely believed. Therefore, it is necessary to apply several fusion metrics to evaluate fusion methods. To better evaluate the fusion performance of the cell region, we manually segment the source image of the source image into cell, and background and all the evaluation metrics measure the fusion performance in the areas of cells. Segmentation results are shown in Figure 5. The quantitatively evaluate metrics, we chose the following five metrics, including standard deviation (SD) [16], mutual information (MI) [17], Xydeas and Petrovic metric (\(Q^{AB/F}\)) [18], feature mutual information (FMI) [19], and the visual information fidelity fusion (VIFF) [20]. Here are the definitions of the evaluation metrics.  
1. SD is a value of the degree of a set of image data averages, reflecting the fused image's contract. Mathematically, SD is defined as follows: \[SD=\sqrt{\sum_{i=1}^{M}\sum_{j=1}^{N}(I_{F}(i,j)-\mu)^{2}}\enspace,\] (15) where \(I_{F}\) is the fused image, and \(\mu\) is the mean value of the fused image.
2. MI measures the degree of dependency between two events \(A\) and \(B\). It is defined as follows: \[I_{UV}(U,V)=\sum_{v\in V}\sum_{u\in U}p(u,v)log2\frac{p(u,v)}{p(u)p(v)}\enspace,\] (16) where \(p(u,v)\) is the joint distribution and \(p(u)\) and \(p(v)\) are the marginal distribution. Considering two source images \(A\) and \(B\), and the fused image F, MI is defined as follow: \[MI=I_{FA}(f,a)+I_{FB}(f,b)\enspace.\] (17)
3. \(Q^{AB/F}\) is an objective performance metric which measures the relative amount of edge information that is transferred from the source images to the fused image. It is defined as follows: \[Q^{\frac{AB}{F}}=\frac{\sum_{i=1}^{N}\sum_{j=1}^{M}(Q^{AF}(i,j)w^{A}(i,j)+Q^{ BF}(i,j)w^{B}(i,j))}{\sum_{i}^{N}\sum_{j}^{M}(w^{A}(i,j)+w^{B}(i,j))}\enspace,\] (18) \[Q^{AF}(i,j)=Q^{AF}_{g}(i,j)Q^{AF}_{o}(i,j)\enspace,\] (19) \[Q^{BF}(i,j)=Q^{BF}_{g}(i,j)Q^{BF}_{o}(i,j)\enspace,\] (20) where \(Q^{XF}_{g}(i,j)\) and \(Q^{XF}_{o}(i,j)\) are the edge strength and orientation preservation value at the pixel \((i,j)\). \(w^{A}(i,j)\) and \(w^{B}(i,j)\) reflect the weight of \(Q^{AF}(i,j)\) and \(Q^{BF}(i,j)\).
4. FMI computes the amount of feature transfer from source images to the fused image. A gradient map of an image provides information of texture, edge strength, and contrast included.
5. VIFF is a recently proposed metric that measures the visual information fidelity between the source images and fused image. It measures the visual information of every block in each sub-band of the fused image based on the Gaussian scale mixture model, distortion model, and human visual system (HVS) model.
# Reducing Railway Track Vibrations by Applying Particle-Damping Systems
## 3 Results
### Equal Mass Comparison  
This section will provide results and comparisons regarding all four particle materials, where the total particle mass (1.1 kg) was kept fixed for comparison purposes. A standard filling of 80% was used for the comparison, and it was used in all cases but one. Once the rubber band reservoir was completely filled (100%) for the reference mass, it was not possible to test this material in the partially filled condition. Figure 7a presents the results for the entire frequency range (from 50 Hz to 4000 Hz). The results containing lower frequencies are presented in Figure 7b. For this range, it can be seen that at very low frequencies, up to 250 Hz approximately, FIMO and rubber band systems yielded better results. This might be associated with the smaller individual particle mass of these cases, due to their low density, which some authors correlate with better damping at lower frequencies (and higher individual particle mass is also correlated with better efficiency at higher frequencies), as stated by [PERSON] et al. [26]. After that range, natural rubber results are not very promising when compared with the other particles. Regarding the peak vibration for lower frequencies, around approximately 450 Hz, magnetite powder presented the greatest vibration reduction (from 100 m/s\({}^{2}\) to 45 m/s\({}^{2}\)), closely followed by FIMO and lead particles. After approximately 1100 Hz, lead spheres presented the best vibration damping.  
Regarding the opposite frequency spectrum, the higher ones, Figure 7c presents an overview of the behaviour of the four particle types analysed in this work. Once more, lead particles proved to be more efficient for the higher frequency spectrum, extending from 1100 Hz up to 4000 Hz (the maximum analysed frequency). After lead, FIMO particles were the ones that presented the best vibration damping (except for the peak amplitude at approximately 2600 Hz).
## 3 Results
### Muscle Activity (EMG)
#### 3.1.2 Effect of Working Height on EMG  
The differences in muscle activity according to working height were significant for the upper trapezius (UT), biceps brachii (BB), biceps femoris (BF), and tibialis anterior (TA), as shown in Table 2. While the UT and TA showed significantly greater muscle activity at the lowest working height of 60 cm, the BB and BF showed the highest muscle activity at the 85 cm working height.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 2 Theoretical Background
### Kalman Filter  
The estimate of a system state can be improved by fusing noisy data obtained from different types of sensors. A typical example is the Kalman Filter (KF), extensively described in [20], and usually applied in the linear dynamics model. The main groups of equations were \"Time Update Equations\" and \"Measurements Update Equations\"; the filter operates according to a prediction/correction model. In particular, the first set of equations corresponds to the state prediction while the second one allows to correct state through available measures [21].  
Navigation applications are characterized by non-linear dynamic models; this way, a linearization approach is applied to the KF to obtain the so-called Extended Kalman Filter (EKF) [22]. Assuming that noise can be modeled as Gaussian white noise, the EKF provides a suboptimal technique for non-linear model and estimates system state by means of a least square error approach. A description of the different data fusion techniques is presented in [23; 24; 25; 26].
## 3 Results and Discussion
Based on the experimental data and theoretical calculation results, the photocatalytic mechanism of ZTO, Co-ZTO, and Ni-ZTO can be explained as follows: under irradiation, only a small number of electrons can be excited from the valence band to the conduction band of ZTO. Hence, the pure ZTO samples exhibit poor photocatalytic activity in the CO\({}_{2}\) reduction. After the introduction of Co and Ni ions in the substitutional mode, new doping energy levels occurred above the valence band of ZTO, and the light response from the UV to the visible region was also enhanced. More electrons can be excited from the doping energy levels of Co or Ni ions to the conduction band of ZTO and take part in the photocatalytic reaction. The introduction of Co\({}^{3+}\) or Ni\({}^{3+}\) ions would also benefit the separation of the charge carriers. The holes would react with H\({}_{2}\)O to yield H\({}^{+}\). The electrons would react with CO\({}_{2}\) to generate CO as the immediate product, which would further react with H\({}^{+}\) and electrons to generate CH\({}_{4}\) as the final product. As a result, more CH\({}_{4}\) molecules would be produced for the Co-ZTO and Ni-ZTO samples. It is expected  
Figure 7: (**A**) UV-vis diffuse reflectance absorption spectra of the pure ZTO, Co-ZTO, and Ni-ZTO samples; (**B**) plots of (_altr_)\({}^{1/2}\) versus photon energy for the pure ZTO, Co-ZTO, and Ni-ZTO samples; (**C**) photocurrent response versus time for the pure ZTO, Co-ZTO, and Ni-ZTO samples. that the photocatalytic activity can be further improved by optimizing the amount of doped Co and Ni ions.
# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 2 Related Work
Despite these advancements, most of these techniques have been tested in controlled settings on image and text datasets, leaving a significant gap in addressing heterogeneity in FL for real-world biomedical data, such as ECG signals. Given the computational constraints of wearable and mobile ECG devices, there remains an urgent need for resource-aware FL approaches that ensure the effective participation of low-power devices without compromising model performance. Our research builds on these advancements by addressing device heterogeneity in FL-based ECG classification. We leveraged HeteroFL's model slimming technique, which adjusts the neural network width to accommodate resource-limited devices. Unlike existing FL-based ECG studies, which primarily focus on data privacy and accuracy, our work explicitly tackled the challenge of deploying FL in a heterogeneous device ecosystem. By integrating model slimming, we enabled efficient, scalable, and resource-adaptive ECG classification, ensuring that even low-power devices could meaningfully contribute to the global model while maintaining robust performance.
## 5 Results and Discussion
### Confidence Regions and Cluster Probabilities
Finally, we should point out that the reliability of the results regarding confidence ellipses and probabilities of clusters of samples depends upon the reliability of the simulated distances. For this reason, the _MAPsov()_ function provides calculated sample distances (D), population distances (Dp), and simulated distances (Dsim) using Monte-Carlo or bootstrapping and averaged simulation distances (Dav). It was found that in all cases studied, the calculated sample distances (D) converged very satisfactorily with the simulated distances (Dsim) when using the Monte-Carlo method. Moreover, we found that in all corrected distances for small sample sizes (cED, cMD1, cMD2, MMD, UMD, and cBMD1), the population distances (Dp) converged satisfactorily with the averaged simulation distances (Dav). This property is not related to confidence ellipses and the estimation of reliable cluster probabilities but indicates that these distances are unbiased estimators of population divergence, which is a useful piece of information in biodistance studies.
# A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems
## 5 Conclusions  
In this paper, a new pilot design scheme is proposed for the LS channel estimation method utilized in OFDM systems. The scheme implements the channel estimation and communication ranging functions on the original OFDM system framework. Moreover, it improves the spectrum utilization of the LS channel estimation. In addition, the paper designs the receiver and transmitter system based on the scheme. The simulation selects three different ranging codes as pilots, and the maximum unambiguous distance is 3,277,500 m. When the code sampling frequency is 6 MHz, the ranging accuracy can reach 50 m. The simulation results show that the system can correctly perform ranging when the SNR is above 0 dB. Moreover, this new pilot design scheme also improves the estimation performance of the LS channel estimation method. With the same BER, the requirement for SNR is reduced by about 1 dB when using the new scheme.  
The scheme proposed in this paper can be applied to communication systems based on OFDM technology. Moreover, it provides a new choice regarding how to integrate ranging and communication. However, this study still has some limitations. We preliminarily listed some possible problems and research directions in the follow-up research.  
1. In the complex UAV communication environment, the inter-carrier interference is inevitable when OFDM technology is used for transmission. However, this method is sensitive to inter-carrier interference and requires more accurate frequency offset estimation and equalization. In this paper, we mainly conducted research under the  
Figure 8: Composite code phase error.  
condition of perfect frequency offset equalization. Therefore, research on the way to reduce the inter-carrier interference based on the proposed scheme is needed in the subsequent research.
2. In OFDM systems, there are many pilot-based channel estimation methods. This paper only considers the design scheme based on LS channel estimation. Although LS channel estimation has low complexity and is relatively easy to implement, in different communication environments, it may be necessary to consider using other channel estimation methods. The innovative ideas proposed in this paper can continue to be applied to other pilot-based channel estimation methods for research.  
B.H.: Conceptualization, Formal analysis, Methodology, Software, Validation, Writing---original draft, Writing---review & editing; X.L., L.X.: Conceptualization, Formal analysis, Methodology, Writing---review & editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (No. 2021 YFB3900300) and the Fundamental Research Funds for the Central Universities (No. 2022 CDJKYJH036).  
Not applicable.  
The authors declare no conflict of interest.
## 6 The Case Study and Simulation Results
### The Case Study
Starting from the initial chosen value of 40 \({}^{\circ}\)C, at first, the temperature decreases by about 10 \({}^{\circ}\)C. As described in [7], it is due to the Joule-Thompson cooling effect that undergoes the gas in an isenthalpic expansion through an orifice. When it enters in a nearly empty cylinder, this cold gas mixes with and compresses the gas originally in the tank. The result is that the combined mixed gas temperature initially reduces. When the compression and conversion of supply enthalpy energy to cylinder internal energy overcomes the Joule-Thompson cooling effect, which becomes smaller as the cylinder pressure increases, the mixed gas temperature in the cylinder begins to increase [13]. It reaches 78 \({}^{\circ}\)C at the end of the process in the considered case, with an increment of 38 \({}^{\circ}\)C. This result is consistent with those found in [6], and it corresponds to a fill efficiency of 85%, in comparison with an ideal loading at the initial temperature of 40 \({}^{\circ}\)C.  
The results obtained with reference to the adiabatic unloading process are summarized in Figures 12-16. They show the trend as a function of time of pressure and temperature in the pressure vessels (Figure 12), the mass flow rate (Figure 13), the mass of CNG in the pressure vessels (Figure 14), the compression ratio (Figure 15), and the compressor power (Figure 16), respectively. The entire unloading process needs about 46 h, divided into 12 h for the initial free phase and 34 h for the subsequent forced phase, as clearly shown by the compressor power diagram. The 10 h more than the loading phase are due to the compressor working at higher compression ratio values. As in the previous case, the singularities in the flow rate, compression ratio, and compressor power curves during the forced phase are due to the speed regulation of the compressor. Figure 11: Compressor power (adiabatic loading). Figure 14: Mass of CNG in the pressure vessels (adiabatic unloading). Figure 12: Pressure vessels CNG conditions (adiabatic unloading). Figure 13: CNG flow rate (adiabatic unloading). The unloaded mass is about 6150 ton. The residual mass left in the cylinders is 600 ton higher than that at the start of the loading process, at the same pressure of 30 bar: this is due to the very low temperature, about 60 \({}^{\circ}\)C below zero, reached at the unloading end. In fact, such a low temperature cannot be tolerated because it could lead to the condensation of propane and butane, compromising the normal functionality of the compressor. To avoid this, the heater (Figure 1) must be able to guarantee a gas temperature of at least 4 \({}^{\circ}\)C.  
The mean flow rate is about 135 ton/h. The fuel consumption is equal to 51 ton (\(\simeq\)0.7% of the total cargo), and the specific energy consumption is 151 kJ/kg. Taking into account both the loading and unloading processes, the adiabatic overall fuel consumption is \(\simeq\)1.14% of the total cargo, corresponding to about three pressure vessels.
## 3 Methodology
### Loss Function  
With the encoder and decoder architecture defined, we now formulate a comprehensive loss function that balances multiple critical objectives for effective trajectory prediction in general aviation.  
Our training objective combines multiple terms to address the multifaceted requirements of trajectory prediction.  
\[\mathcal{L}=\mathcal{L}_{\text{NLL}}+\lambda_{\text{Lyap}}\mathcal{L}_{\text{ Lyap}} \tag{15}\]  
The negative log-likelihood term measures the fit between predicted distributions and actual trajectories:  
\[\mathcal{L}_{\text{NLL}}=-\frac{1}{H}\sum_{t=T+1}^{T+H}\log p(y_{t}|\mu_{t}, \sigma_{t}^{2}) \tag{16}\]  
\[=\frac{1}{2H}\sum_{t=T+1}^{T+H}\left(\log(2\pi\sigma_{t}^{2})+\frac{(y_{t}-\mu _{t})^{2}}{\sigma_{t}^{2}}\right) \tag{17}\]  
The Lyapunov stability term penalizes violations of the stability condition:  
\[\mathcal{L}_{\text{Lyap}}=\frac{1}{H-1}\sum_{t=T+2}^{T+H}\max(0,\Delta V_{t}) \tag{18}\]  
This comprehensive loss function balances accurate prediction (NLL) and physical plausibility (Lyapunov), resulting in trajectory predictions that are both precise and realistic.
## 2 Materials and Methods
### PV Facilities under Study  
As explained above, this study focused on the SOLTEN I phase (13 MW) that, in total, has 130 PV production units of 100 kW each. In this phase, 81.560 PV modules, made with mono or polycrystalline cells and provided by four different manufacturers, ranging from 175 W to 155 W of nominal power, were installed. All the installed modules with monocrystalline technology were formed by 72 125 \(\times\) 125 mm cells connected in series, while the majority of the installed modules that carry polycrystalline cells had \(48\,156\times 156\) mm cells, also associated in series. In both cases, the module fabrication process applied modern lamination techniques, with encapsulations consisting of solar glass, interlayer sheets of ethyl vinyl acetate (EVA), and three-layer backing film, made of two polyvinyl fluoride films (PVF) that sandwich a polyethyleneerephthalate (PET) layer, usually called TPT (tedlar(r)-polyester-tedlar(r)). It is important to notice that these photovoltaic modules were not mixed, that is, each of the 130 PV production units was installed using a single variety of those that were available. Thus, one way to identify one PV production unit from another could be by considering the manufacturer of the modules used for its installation, the type of cells that were used for the modules' lamination (which we have referred to as \"technology\" here), and their corresponding nominal power (see Table 1).  
Moreover, the PV production units were installed in straight rows, placed from east to west, called streets. The number of units per street varied in order to adapt to the available plot of land. Thus, another way to identify one PV production unit from another could be by considering its belonging to a determined street, as much as its particular location within that street. Regarding this topic, it is important to notice that from street 16 to 19, the eastern side of each street had PV production units that belong to SOLTEN II and, therefore, the units belonging to SOLTEN I start somewhere in the middle of these streets (see Table 2).  
Figure 3: Aerial view of the SOLTEN 20 MW PV installation (**left**) and a diagram highlighting SOLTEN I (13 MW), as well as its distribution in streets and PV units, as a function of the type of module installed (**right**). Own elaboration.