esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Gravitating Bose-Einstein Condensates and the Thomas-Fermi Approximation
###### Abstract  
Self-gravitating Bose-Einstein condensates (BEC) have been proposed in various astrophysical contexts, including Bose-stars and BEC dark matter halos. These systems are described by a combination of the Gross-Pitaevskii and Poisson equations (the GPP system). In the analysis of these hypothetical objects, the Thomas-Fermi (TF) approximation is widely used. This approximation is based on the assumption that in the presence of a large number of particles, the kinetic term in the Gross-Pitaevskii energy functional can be neglected, yet it is well known that this assumption is violated near the condensate surface. We also show that the total energy of the self-gravitating condensate in the TF-approximation is positive. The stability of a self-gravitating system is dependent on the total energy being negative. Therefore, the TF-approximation is ill suited to formulate initial conditions in numerical simulations. As an alternative, we offer an approximate solution of the full GPP system.  
Bose-Einstein condensates; dark matter halos Article
## 5 Automatic Signal Recognition
### Validation
#### 5.2.2 Analysis of Variable \(N\)  
The previous analysis details the performance of the algorithm in the form of a confusion matrix. Ideally, confusion matrices for each case of \(N\) (i.e., the number of IQ samples used to derive an RSSI) should be presented for a full-scale analysis. However, that would introduce too many details and make it hard to perform an effective comparison. Hence, only the true positive rate of each technology  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ActualPrediction** & **Wi-Fi** & **LTE** & **DVB-T** & **Noise** & **Unknown** \\ \hline Wi-Fi & 92.6\% & 1.85 \% & 0\% & 3.70\% & 1.85\% \\ LTE & 0\% & 100\% & 0\% & 0\% & 0\% \\ DVB-T & 0\% & 1.85\% & 98.15\% & 0\% & 0\% \\ Noise & 0\% & 0\% & 0\% & 0\% & 0\% \\ Unknown & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of the measurement results, for \(N=20\), \(T=1\) s.  
is plotted in Figure 11. All results are obtained when \(T=1\) s. Given the 1-MHz ADC sample rate, the average interval \(\frac{N}{\text{TMHz}}\) has the same numerical value as \(N\) in \(\mu\)s.  
Despite some local fluctuations, there is a clear trend of performance degradation due the increment of \(N\). This is because the larger average interval filters out the fast changes of RSSI in the time domain. As a result, the standard deviation is reduced, which is the main feature used to identify LTE and Wi-Fi. When the averaging interval reaches 160 \(\mu\)s (approximately two-times the LTE symbol time) the recognition accuracy for LTE starts to drop drastically, due to the incapability to distinguish signal variations between symbols. The detection rate for Wi-Fi remains above 90% until the interval reaches 280 \(\mu\)s, which is approaching the duration of a Wi-Fi beacon packet. The large average window blurs the boundary between noise and signal, which makes the noise peak in the histogram less pronounced, and consequently, the recognition of Wi-Fi becomes more difficult. As DVB-T's signal is stable in the time domain, its recognition performance is least affected by the averaging length. However, for a range of IQ samples collected in a given time frame, the larger average windows reduce the quantity of RSSI. From the statistical point of view, training processes based on a smaller population are less reliable. Hence, the recognition rate of DVB-T also decreases eventually.
## 3 Results
### Comparison between the Adsorbers  
A comparison between the adsorbers has been realized to highlight the benefits arising from the use of a composite technology based on an aluminum, porous structure and a SAPO-34 coating. In Figure 14, the power trend after the beginning of adsorption process is shown for the two types of adsorbers. This is a qualitative measure of the adsorption dynamics in the two different cases. As expected, the peak power of the composite adsorber based on an aluminum, porous structure and a SAPO-34 coating is double with respect to the pellet one, and the whole duration of adsorption is significantly lower (900 s against 4500 s); meanwhile, the pellet adsorber is able to accomplish a more constant discharge.  
Figure 12: Comparison of 3T and 4T operation on discharge power—composite adsorber.  
Figure 13: Comparison of 3T and 4T operation on stored energy—composite adsorber.  
A second step in the comparison between the two systems is reported in Figure 15a,b, wherein the discharge power and recovered energy for the two adsorbers are shown. The boundary conditions used are: 85 \({}^{\circ}\)C driving temperature; 15 \({}^{\circ}\)C evaporation temperature; and 3600 s charge/discharge time for the pellets adsorber and 900 s for the composite adsorber. Results are displayed as a function of condensation temperature. The results for the energy comparison make use of the specific energy; they are normalized for the masses of the adsorbent materials in the two exchangers. There is a relevant difference in the discharge power achievable with the two technologies: as previously stated, average power during each cycle is much higher in the case of the composite adsorber, about double that of the pellets heat exchanger. The results in terms of specific energy stored do not show the same marked difference; there is only a low increase for the composite technology. However, the results regarding composite heat exchanger probably should take into account the effect of the mass of aluminum composite inside the exchanger.  
Finally, Figures 16 and 17 show the comparison as a function of evaporation temperature, by considering the operation of the composite adsorber as well, in the best performing configuration, with four temperature levels. In addition, in this case, the results in terms of stored energy are normalized for the mass of adsorbent inside the storage. By considering these conditions, the effect of composite technology is to increase the achievable discharge power by about 60%, especially for higher evaporation temperatures. The maximum specific energy measured in both the pellet and composite adsorber was around 90 Wh/kg, but in the case of the composite adsorber, this result was achieved for an evaporation temperature of 15 \({}^{\circ}\)C, whereas for the pellet adsorber this was obtained for an evaporation temperature of 22 \({}^{\circ}\)C.  
Figure 14: Dynamic curve of power during discharge for the two adsorbers tested.  
Figure 15: (a) Comparison of the adsorbers—power; (b) comparison of the adsorbers—energy.
## 4 Discussions
### Limitations and Future Works
#### 4.5.6 Future Research Directions  
To overcome these limitations, future research should focus on key advancements in scalability, security, and novel blockchain applications.  
Scalability Enhancements with Layer-2 Solutions:  
* Optimizing Rollups and Plasma to reduce transaction costs and improve on-chain efficiency [19; 20; 97].
* Developing data compression techniques to minimize blockchain state size [52; 95]. Improving Security in Hybrid Blockchain Models:
* Designing cryptographic proof mechanisms (e.g., Zero-Knowledge Proofs, Merkle trees) for off-chain data verification without fully storing it on-chain [109; 113].
* Developing automated auditing frameworks to detect unauthorized data modifications [36; 47]. Continuous Authentication and IoT-Blockchain Integration:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feature** & **Layer-1 Blockchain** & **Layer-2 Solutions** \\ \hline Security & Maximum security, but slow transaction & Dependent on Layer-1, vulnerable to data availability attacks [96] \\ \hline Transaction Speed & Low (\(\sim\)15–30 TPS on Ethereum) [97] & High (\(\sim\)2000 TPS with ZK-Rollups, near-instant with State Channels) [97] \\ \hline Decentralization & Fully decentralized [52] & Partial decentralization (some Layer-2 solutions require trust assumptions) [96] \\ \hline Scalability & Limited, high transaction fees [52; 97] & High scalability, but off-chain storage risks [96] \\ \hline Implementation Complexity & Directly operates on smart contracts [52] & Requires additional protocols and integration layers [95] \\ \hline Examples & Ethereum, Bitcoin [97] & ZK-Rollups, Optimistic Rollups, Plasma, State Channels [95; 96] \\ \hline \end{tabular}
\end{table}
Table 16: Comparison of Layer-1 and Layer-2 Blockchain Solutions.  
* Future research should explore blockchain-based Continuous Authentication (CA) for IoT systems to enhance security and reduce trust dependencies. The integration of authentication mechanisms such as behavioral biometrics and real-time identity verification can strengthen IoT security models [197]
* Integrating biometric authentication with smart contracts for real-time device identity validation [12; 36; 42]. Custom Blockchain Models for Satellite Communications:
* Blockchain can be leveraged for secure and decentralized communication networks in satellite-based systems. Customized blockchain frameworks can facilitate secure inter-satellite communications while ensuring data integrity in space networks [196].
* Developing Proof-of-Authority (PoA) consensus models for low-latency satellite networks [15; 47; 93]. Leveraging AI and Blockchain for Smart Data Management:
* AI-driven storage optimization techniques to enhance data partitioning and retrieval [109; 129].
* Machine learning-based anomaly detection to identify fraudulent data transactions in off-chain storage [113; 115].
## 4 Discussion  
The goal for this work was to find an optimal method for mapping land-cover change that could approach the high level of accuracy achieved through manual photo-interpretation but conducted at regional extents that usually necessitate an automated remote sensing approach. The threshold setting task in our process provides the optimization between these two approaches. At a threshold of 100%, no polygons would be reviewed and the result is the RF50 map, an entirely automated approach. If the threshold is set at 0%, all polygons would be reviewed and the result would be a photo-interpreted  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Bins** & **Polygons** & **Hectares** & **Observed Polygons** & **Observed Hectares** & **Observed Change** & **Observed Change** & **Polygon** & **Area Percent Percent Change** & **Polygon** & **Hectares** & **Hours To** \\
**(0\%–5\%)** & 343210 & 237400 & 4037 & 2705 & 9 & 1.1 & 0.2\% & 0.04\% & _765_ & _94_ & _1373_ \\
**(5\%–10\%)** & 61783 & 15708 & 757 & 193 & 12 & 1.3 & 1.6\% & 0.68\% & _979_ & _107_ & _247_ \\
**(10\%–15\%)** & 23090 & 5754 & 290 & 69 & 12 & 1.3 & 4.1\% & 1.89\% & _955_ & _108_ & _92_ \\
**(15\%–20\%)** & 10645 & 2906 & 106 & 26 & 13 & 2.8 & 12.3\% & 11.10\% & _1306_ & _322_ & _43_ \\
**(20\%–25\%)** & 5297 & 968 & 62 & 8 & 12 & 1.6 & 19.4\% & 21.50\% & _1025_ & _208_ & _21_ \\ \hline \end{tabular}
\end{table}
Table 3: Omission analysis extent, effort, observations and predictions for different predicted change probabilities in sample-only bins. The observed polygons consisted of the Omission samples. Columns show the areal extent and polygon count for each bin, the polygons observed and the number of polygons observed as omitted change. The last three columns in italics are predictions based on observed proportions. Predictions can be made by using either the proportion of observed polygons that changed or the proportion of area observed that changed. Review hours are based on a rate of 250 polygons per hour.  
map. At 25%, we observed roughly 4.7% of the total population of polygons with an estimated success rate of 86.6% as measured by the Adjusted Producer's Accuracy.
# Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
## 3 Models
### Classic Models  
The first (although unsatisfactory model) that can be used to perform the reconstruction of an MR image with an under-sampled k-space, is to simply use the inverse Fourier transform with the unknown Fourier coefficients replaced by zeros (zero-filled inverse Fourier transform). This method is called zero-filled reconstruction and we have:  
\[\mathbf{\hat{x}}_{zf}=\mathbf{F}^{-1}\mathbf{y} \tag{2}\]  
The second model we want to introduce makes use of the fact that MR images can be represented in a wavelet basis with only a few non-zero coefficients [4] according to the sparsity principle. The reconstruction is, therefore, done by solving the following optimization problem:  
\[\mathbf{\hat{x}}_{z\texttt{M}\texttt{P}}=\operatorname*{arg\,min}_{\mathbf{x}\in \mathbb{C}^{n\times n}}\frac{1}{2}\|\mathbf{y}-\mathbf{F}_{\Omega}\mathbf{x}\|_{2}^{2}+ \lambda\|\mathbf{\Psi}\mathbf{x}\|_{1} \tag{3}\]  
where the notations are the same as in Equation (1), and \(\lambda\) is a hyper-parameter to be tuned, and \(\mathbf{\Psi}\) is a chosen wavelet transform. This problem can be solved iteratively using a primal-dualoptimization algorithm like Condat-Vu [22] or Primal Dual Hybrid Gradient (PDHG) [23] (also known as Chambolle-Pock algorithm) or, if the wavelet transform is invertible (i.e., non-redundant or decimated), using a proximal algorithm like the Fast Iterative Shrinkage Algorithm (FISTA) [24] or the Proximal Optimal Gradient Method (POGM) [25]. Since the problem is convex, all these algorithms converge to the same solution, only at different speeds.  
The last model we choose to introduce is the dictionary learning model [26; 27]. Its assumption is that an MR image is only composed of a few patches, and can therefore be expressed sparsely in a corresponding dictionary. This dictionary can be learned per-image, leading to the following optimization problem:  
\[\begin{split}\hat{\mathbf{x}}_{dl}=\operatorname*{arg\,min}_{\mathbf{x}, D,\{\alpha_{ij}\}_{(i,j)\in I}}&\frac{1}{2}\|\mathbf{y}-\mathbf{F}_{\Omega} \mathbf{x}\|_{2}^{2}+\lambda\sum_{(i,j)\in I}\|R_{ij}\mathbf{x}-D\alpha_{ij}\|_{2}^{2} \\ \text{subject to}&\forall(i,j)\in I,\,\|\alpha_{ij}\| _{0}\leq T_{0}\end{split} \tag{4}\]  
where the notations are the same as in Equation (3), and \(I\) is the fixed set of patches locations, \(D\) is the dictionary, \(\lambda\) and \(T_{0}\) are hyper-parameters to be set, and \(R_{ij}\) is the linear operator extracting the patch at location \((i,j)\). This problem is solved in two steps:  
1. The dictionary learning step, where both the dictionary \(D\) and the sparse codes \(\alpha_{ij}\) are updated alternatively.
2. The reconstruction step, where \(\mathbf{x}\) is updated. Since this subproblem is quadratic, it admits an analytical solution, which amounts to averaging patches and then performing a data consistency in which the sampled frequencies are replaced in the patch-average result.
## 3 Testing Results and Analysis
### Triaxial Permeability Test  
The indoor permeability test was based on the ASTM D2434 test and performed using a triaxial method. Infiltration was calculated using [PERSON]'s law, that is,  
\[\mathrm{q}=\mathrm{kiA}, \tag{2}\]  
where i is the hydraulic gradient and A is the circulation area. The specimen was approximately 10 cm in length, and its upper and lower hydraulic head gap was 50 kPa, indicating that the hydraulic gradient i = 500/10 = 50. Each test required approximately 1 week for flow rate to stabilize before measurements were taken. Studies have explored the hydraulic conductivity of mudstone in Taiwan. They include a 1998 study by [PERSON] et al. [40], who reported that excessively high or low moisture content on both the dry and wet sides of compacted OMC resulted in high hydraulic gradient k values, and a 2004 study by [PERSON] and [PERSON] [34], who reported that reduced compaction energy and overly large grain sizes led to a substantial increase in the hydraulic gradient. In the present study, the triaxial permeability test was conducted to examine three clad grain sizes and two dry density levels, with the test revealing that the k values of the soil specimens ranged between \(10^{-4}\) and \(10^{-7}\) cm/s. Excluding the clods with grain sizes larger than 3/4\", other specimens exhibited low permeability or impermeability. These results corresponded to the impermeability characteristics of mudstone. A specimen with added cement paste (C = 4%) was used for testing. After 1 week, no notable water effluence was observed from the specimen, which was then regarded to be almost impermeable. Figure 17 presents the relationships between dry density and hydraulic conductivity for each specimen. When the size of clad grains increased, the hydraulic gradient k increased. Grain size was not directly associated with dry density. Although the soil was compacted, a considerable number of large-sized grains still existed in the clods, thereby leading to the formation of a substantial void among grains in the clods and greater hydraulic conductivity. [PERSON] and [PERSON] [31] reported similar test results in their 1989 study.  
Figure 16: Comparison of shear strength of the samples for triaxial Consolidated Undrained and Unsaturated Unconsolidated Undrain test.  
Figure 17: Permeability coefficient vs. dry density for different clods samples.
## 4 New Propagation Model
### Shadowing Loss
The implications of these findings are expected to be significant, particularly in the context of evaluating additional shadowing path loss. Our work takes into account the complex interactions associated with various frequencies, altitudes, polarizations, and environmental contexts. Ultimately, this extensive experimentation has culminated in the development of a comprehensive and refined solution for the UAV channel model, specifically attuned for the unique challenges of an Indian metropolitan area. This model is particularly focused on radio frequency (RF) planning at low altitudes, effectively addressing the intricate nuances introduced by differing frequencies and polarizations, thereby enhancing the planning capabilities vital for urban environments.
## 4 Results--Case Study  
### Company Overview  
Company A is a small logistic operator working as an express courier. The company began its activities approx. 20 years ago, with six drivers, ensuring daily deliveries in its area of activity. Soon, the activity grew, including also parcel deliveries with high service requirements, such as delivery within a day or within three days. At present, Company A performs thousands of shipments per day, collaborates with more than 30 drivers (which can double in peak periods or on request) and has enrolled more than 10 employees.
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 3 Data Preparation  
### Data Collection  
The LTPP database contains thousands of measured faulting data in concrete pavements. To eliminate the influence of irrelevant factors (such as pavement maintenance activities and different pavement types) on faulting data, it is necessary to preprocess the data in this study. Therefore, the following data filtering criteria were used to select the faulting data:  
1. In the faulting data, the measured values are recorded as positive or negative depending on the condition of the pavement sections. To remove the influence of negative values, this study only used the most common positive measured faulting data.
2. The LTPP database includes two types of concrete pavements: Jointed Plain Concrete Pavement (JPCP) and Jointed Reinforced Concrete Pavement (JRCP). This study focuses primarily on the most frequent type of concrete pavement, which is JPCP.
3. Maintenance and rehabilitation (M&R) activities are conducted in response to the deterioration of pavement conditions, resulting in a decline in driving quality. After appropriate maintenance, the pavement condition is usually restored. The faulting data measured after repair are often much lower than the data before repair. Therefore, in the faulting prediction analysis, this study does not consider the faulting data after repair to avoid negative impacts on the prediction results.  
Considering the feature variables used in previous faulting prediction studies, this study took 17 feature variables from the LTPP database as input variables for the model (as shown in Table 1). Due to mismatched monitoring dates for different types of monitoring data, the collected faulting dataset often contains many missing values. In this study, to avoid data distortion, we adopted a deletion strategy for samples with missing values instead of filling them in. Ultimately, we obtained an initial fault dataset for ordinary concrete pavements with 160 samples and 17 features.
# Insight into the Role of Cerium (III) Addition to a MgAl-LDH Coating on AA6082
## 2 Experimental Section
### Characterization  
The electrochemical impedance spectroscopy measurements (EIS) were collected over a 1 cm\({}^{2}\) exposed area. The impedance curves were collected over 216 h into 0.1 M Na\({}_{2}\)SO\({}_{4}\) + 0.05 M NaCl solution (23 \(\pm\) 2 \({}^{\circ}\)C, pH 5.8). The reduced chloride content (0.05 M NaCl) allows to minimize the EIS data scattering in the low-frequency domain (<0.1 Hz). A Metrohm AutoLab PG Stat 302 N (Utrecht, The Netherlands) was employed. An amplitude of 10 mV (rms) and a frequency range from 10 mHz to 100 kHz were selected. The experimental EIS datasets were fitted using ZSimpWin software. A three-electrode cell  
Figure 2: Equilibrium curves for Ce\({}^{3+}\)/Ce(OH)\({}_{3}\) as a function of the solution pH. The image is reproduced from [55].  
arrangement consisting of an Ag/AgCl reference electrode (3.5 M KCl, + 0.205 V vs. SHE), a platinum ring counter electrode, and the analyzed samples as a working electrode was employed. The morphology of the surface and the cross-section of the selected MgCe-LDHs were observed by an SEM (JEOL JSM-IT300, Tokyo, Japan) equipped with an energy dispersive spectrometer (EDS). The crystallographic structure was analyzed by XRD (X'Pert High Score diffractometer--Rigaku, Tokyo, Japan) with Cu emission source (\(\lambda\) = 1.54056 A), the monochromator operating at 30 kV-10 mA and the scanning rate set at 4 min\({}^{-1}\). The LDH characteristic peaks reflections (hkl) were used to measure the basal space \(d_{hkl}\) and to evaluate the integral breath \(\beta\) needed to calculate the crystallite size \(D\) by the Scherrer formula, Equation (1) [56]. The Scherrer constant \(K\) is assumed equal to 0.9 [57]. The lattice parameters \(a\), \(b\), and \(c\) were calculated by using Equation (2) related to the hexagonal crystal system, where \(d\) is the lattice spacing calculated by the Braggs law. The functional groups and the chemical bonding were investigated by FTIR-ATR (Varian 4100 FTIR Excalibur Series, Santa Clara, CA, USA) in the wavenumber range from 500 to 4000 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\) and acquiring 32 scans for each spectrum. All the analyses were performed on the as-synthesized materials and after 400 h of immersion in 0.1 NaCl solution. SEM images were also taken after the EIS measurement over 216 h.  
\[D=\frac{K\lambda}{\beta\cos\theta} \tag{1}\]  
\[\frac{1}{d^{2}}=\frac{4}{3}\left(h^{2}+hk+\frac{k^{2}}{a^{2}}\right)+\frac{l^ {2}}{c^{2}} \tag{2}\]