esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap
## 4 Our Proposal: Bayesian Nonparametric Learning Applied to Ensemble Tree Modeling  
In the context of ensemble tree models, the statistic of interest \(\phi(F,\mathbf{X})\) is a decision tree model. As explained in Section 3, an estimation of the posterior distribution of \(\phi\) can be derived through bootstrap procedures. In [13] [PERSON]'s bootstrap is applied to estimate the posterior distribution for \(\phi(F,\mathbf{X})\). Each tree is obtained fitting the model on the weighted dataset (whose weights are drawn from a Dirichlet distribution) and the obtained predictions are an approximation of the posterior mean.  
This paper extends the contribution of [13] introducing the Proper Bayesian bootstrap proposed in [19]. As described in Section 3, the Proper Bayesian bootstrap needs a definition of a prior distribution \(D(kF_{0})\) for \(F\). In our case \(F\) represents the distribution of the data at hand and \(D(kF_{0})\) is a Dirichlet process with parameters \(k\) and \(F_{0}\). In order to explain a response variable \(\psi\) given a vector of \(P\) covariates \(x_{1},\ldots,x_{P}\), the parameter \(F_{0}\) of the Dirichlet process is a joint distribution depending both on \((\mathbf{x},y)\).  
Following [19] we sample from the posterior of \(\phi(F,\mathbf{X})\) using Algorithm 2.  
```
Input: Training set \(T\) for\(b\) in 1:\(B\)do  Sample \((\mathbf{x}_{1}^{*},y_{1}^{*}),\ldots,(\mathbf{x}_{m}^{*},y_{m}^{*})\) from \((k+n)^{-1}(kF_{0}+nF_{n})\);  Draw \(\mathbf{w^{b}}\) from \(D(\frac{n+k}{m},\ldots,\frac{n+k}{m})\);  Get \(\phi^{b}=\phi(\mathbf{w^{b}},\mathbf{x^{*}})\) running weighted tree on the new sample \(\mathbf{x}_{1}^{*},\ldots,\mathbf{x}_{m}^{*}\)
```  
**Algorithm 2**  
The bootstrap resample \((\mathbf{x}_{1}^{*},y_{1}^{*})\ldots,(\mathbf{x}_{m}^{*},y_{m}^{*})\) is generated by a mixture of distributions of the prior guess \(F_{0}\) and the empirical distribution \(F_{n}\). When a new observation of bootstrap resample is generated, a new vector of covariates \(\mathbf{x}\) is generated from the prior distributions \(F_{0}\) defined for the covariates, then the new value of the response variable is associated on the basis of the prior distribution chosen to model the relation between the target variable and the covariates. As in the other ensemble procedure based on tree models, the proposed method can be applied both for regression and classification problems, just considering different prior distribution based on the nature of the data at hand.  
The main difference, with respect to classical ensemble procedure (i.e., Bagging trees and Bayesian Forest), is that the prior \(F_{0}\) allows to generate new observations, not contained in the training set, which can enrich our prediction model. The obtained bootstrap samples are less dependent one to each other, thus obtaining a model which is less sensitive to changes in the learning dataset reducing overfitting. For this reason the obtained model is more stable and the error prediction benefits from a lower variance, as shown in Section 5.
## 5 Experiments with Tree Compatibility
### Worst-Case Time versus Empirically-Observed Time  
By Theorem 1, the worst-case time of FCT(0) depends on the number of edge scans performed when searching for replacement edges. A naive estimate yields a worst-case bound of \(O(M_{\mathcal{P}}^{2})\) for this number: \(O(M_{\mathcal{P}})\) edge deletions, each requiring \(O(M_{\mathcal{P}})\) scans. This implies a time bound of \(O(M_{\mathcal{P}}^{2}\log M_{\mathcal{P}})\) for the entire execution of FCT(0). In contrast, we now present evidence that FCT(0)'s performance in practice may be closer to \(O(M_{\mathcal{P}}\log M_{\mathcal{P}})\) than to its worst-case bound.  
Performance on Rooted Triples  
Figure 7a shows the running time of FCT(0) on complete sets of triples. The number of taxa, \(n\), varied from 5-60 with increments of 5, and \(M_{\mathcal{P}}\) varied from 65-205,380. Also plotted in that figure are the functions \(c_{1}\cdot M_{\mathcal{P}}\log M_{\mathcal{P}}+c_{2}\) and \(c_{1}^{\prime}\cdot M_{\mathcal{P}}\log^{2}M_{\mathcal{P}}+c_{2}^{\prime}\), where \(c_{1}\), \(c_{2}\), \(c_{1}^{\prime}\), and \(c_{2}^{\prime}\) are appropriate constants. The plot suggests that, in practice, the running time of FCT(0) was far better than the above-mentioned worst case and may be close to \(O(M_{\mathcal{P}}\log^{2}M_{\mathcal{P}})\).  
We also explored the effect of altering the _balance factor_ of the binary phylogenetic \(X\)-tree \(\mathcal{T}\) from which the triples wee derived (the balance factor is the ratio \(\alpha\) of the size of the smaller subtree to that of the larger subtree: a binary phylogenetic tree is _balanced_ when \(\alpha=0.5\)). Figure 7b shows the results of running FCT(0) on the profile of triples on \(n=40\) labels for three balance factors: 10%, 30%, and 50%. The \(x\)-axis indicates the percentage of the maximum possible number of triples, \(\binom{n}{3}\), included in a profile. The percentage varied from 10%-100%, with increments of 10%. The running time of FCT(0) appeared to be close to linear in the number of triples; the balance factor has a negligible impact. Thus, in the rest of this section, we use starting trees \(\mathcal{T}\) that are, on average, balanced.  
Figure 6: Performance of FCT for varying degrees of level truncation on complete sets of triples.
## 4 Results and Discussion
### Characterization of the Developed Substrates
(**b**) shows the nanocone image at 100,000\(\times\) magnification for easier to observe the morphology of nanocone. Figure 4: The relationship between the sputtering duration and the size of the obtained nanospheres and the gaps between the nanospheres. Figure 3: The SEM images show silver nanospheres uniformly distributed on the nanocone array substrate. (**a–f**) show different sputtering duration of 50, 100, 150, 200, 250 and 300 s, respectively. Top right corner of each image shows the silver nanospheres at higher magnification for easier to observe their morphology. It can be seen that the longer the duration, the larger the diameter of these nanospheres. If the sputtering duration is too long, the nanospheres will start to merge. By carefully adjusting the sputtering duration, we can fine-tune the diameter of the nanosphere so that we can control the gaps between the nanospheres in the range of a few nanometers. Figure 5: (**a**) Shows the SEM image of the SERS active substrate used for EDS analysis (**b**) Shows the EDS spectrum, it shows that the SERS active substrate consisted of 77.5% Ag and 11.48% C. (**c**) shows the EDS mapping of Ag of the SERS active substrate. (**d**) Shows the EDS mapping of C of the SERS active substrate. Figure 6: UV-vis absorption spectra of SERS active substrate with different sputtering durations of 50, 100, 150, 200, 250 and 300 s, respectively.
## 2 Materials and Methods  
### Data  
#### 2.1.1 Study Site  
The study area is the Charted Community of Navarre in Northern Spain. Navarre's sub-regions are divided into agarian zones, as shown in Figure 1. Montanaa is divided into two zones, as well as Media, while Ribera Alta is one zone by itself. The region of study is very diverse and incorporates several of the most common Spanish agro-climates, with rainfalls averaging from 800 to 1000 mmin the northern areas, between 800 to 300 mm in the middle and under 300 mm in the southern areas. The two most cultivated rain-fed winter crops are bread wheat (_Triticum aestivum_ L.) and barley (_Hordeum vulgare_ L.), which occupy the 83% of winter croplands in Navarre [34]. The cultivation of bread wheat is ubiquitous throughout Navarre, except for the Northern Pyrenean and Atlantic areas with more rugged topography, lower temperatures, and excess precipitation, as well as in southern bottom-end semi-arid areas, which are too arid and thus unsuitable for growing commercially rain-fed wheat. The majority of crop lands are middle to small holdings, which has made them previously difficult to map adequately with satellite data.  
The region has an advanced agricultural intensification with high agronomic standards in terms of cultivation (e.g., certified seeds and widespread access to fertilizers). The regional three-season crop rotation guidelines for bread wheat are fallow, followed by fava beans, oat or sunflower, and bread wheat again in the following third season. Rotation practices are widely encouraged by agrarian institutions. Wheat is sown between the middle to the end of October depending on the agrarian region within Navarre, harvesting happens between the end of June and the middle of July. Grains are mainly commercialized to markets through various agrarian cooperatives distributed throughout the region.
## 2 Fatigue Strength Assessment of Real Components
### Fatigue Assessment of the Compressor Mounting Bracket
#### 2.1.4 FEM Calculations  
Following the process in Figure 2, FEM random vibration analysis was performed using the PSD profile from Table 1. Vibration isolators were modelled using 1D CBUSH elements, defining a constant stiffness value of 4 N/mm for the vertical direction. This value corresponds to the single spring constant. Transverse stiffnesses were omitted here. The purpose of such definition is to achieve a single resonance peak for the structure, as shown in Figure 7, which in further analysis can be easily controlled and modified, for example, by adjusting the critical damping ratio \(\zeta\).  
Knowing the PSD and RMS response at the collision location from experimental studies (Table 2), the critical damping ratio \(\zeta\) for the FEM model can be adjusted so that the RMS acceleration results for the reference point are in line with the measurements. In this case, a final critical damping ratio was set as \(\zeta\) = 0.0315. For this value, the RMS accelerations of the linear FEM model at the reference point match the experimental RMS accelerations recorded at the output signal location, as shown in Figure 8.  
Figure 6: PSD functions for the input and output signal.  
Figure 7: The FEM frequency response function of the compressor suspension system close to the collision region.  
The von Mises stress results from the random vibration analysis using the equivalent effective acceleration method are presented in Figure 9. Points A-F serve as reference points for further comparison with the experimental data.  
From the notch area, stress PSD function was extracted and is presented in Figure 10. It serves as the basis for fatigue life calculations using the spectral method, e.g., Dirlik or Tovo-Benasciutii [20; 21; 22].  
Figure 8: The RMS response of the compressor suspension system—adjusting the critical damping ratio for the FEM model.  
Figure 10: Von Mises stress PSD function in the notch region.  
Figure 9: RMS von Mises stress distribution determined by the equivalent RMS acceleration method.  
First, from the stress PSD function, the spectral moments \(m_{0}\), \(m_{1},m_{2}\) and \(m_{4}\) were determined. Next, using the relations between the spectral moments, the statistical stress parameters were determined: the expected number of zero-crossings \(E[0]\), the expected number of positive peaks \(E[P]\), the irregularity factor \(\gamma_{2}\), and the equivalent RMS stress value \(\sigma_{RMS}\). The bracket material is specified as fine-grained steel suitable for cold-working. The S-N curve (Figure 11) was derived based on the fatigue limit \(\sigma_{af}=185\) MPa and the corresponding number of cycles \(N_{0}=10^{6}\). The curve slope \(k\) is determined to be 5.  
Following that, fatigue calculations were conducted employing the methods proposed by [PERSON] and [PERSON]. The outcomes, with calculated lifetime in hours \(T_{cal}\) and the corresponding damage parameter \(D\), are presented in Table 3.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 7 Results
### Energy Consumption  
The effect of this interdependence can also be observed in total energy consumption (Figure 18), computed after 20 transmitted beacons (i.e., a 1-h test). Important savings (up to 15%) can be achieved when using multi-hop schemes with respect to single-hop ones in not extremely high-error configurations (\(E_{0/0}\)-\(F_{20/10}\)) and similar or slightly worse values (less than 4% of extra consumption) in \(E_{30/15}\). From previous studies [38], we believe that in larger networks, these gains will be much higher.  
Time percentage of STAs' microprocessors in low power mode is, in all studied cases, above 97% for X-MAC and 99% for NullRDC, due to the higher number of operations involved in the first case. However, the impact of radio module sleeping periods introduced by X-MAC reduces total energy consumption in up to 50% with respect to NullRDC. Consequently, values of energy consumed per bit of payload delivered are confined between 50-65 mJ/bit for X-MAC, and 105-140 mJ/bit for NullRDC.  
As for the battery lifetime, Table 9 compiles the duration in days of the 800 mAh battery included in the Zolertia(tm) RE-Mote with \(T_{p}=3\) min, as well as two estimations with \(T_{p}=1\) h and \(T_{p}=4\) h. The temporal flexibility of the TDMA-based system employed in HARE allows this kind of extrapolations, by assuming that, in non-active time periods, both the microprocessor and the radio module remain asleep.  
Figure 16: PDR (in %) for different RDC sublayers, network topologies, and error probabilities.  
Figure 17: Number of transmissions per packet received.
###### Abstract  
Honeycomb cordierite-based Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalysts were prepared by the impregnation method and used for low-temperature selective catalytic reduction (SCR) of NO\({}_{\rm x}\) with NH\({}_{3}\), with and without SO\({}_{2}\) and/or H\({}_{2}\)O in a homemade fixed-bed tubular reactor. The catalyst reached nearly 80% NOx conversion at 100 \({}^{\circ}\)C in the absence of SO\({}_{2}\). However, SO\({}_{2}\) reduces the catalytic activity (80% to 72%) of the honeycomb cordierite-based Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalysts under identical conditions. This finding demonstrated that the catalyst exhibited high activity at low temperature and excellent SO\({}_{2}\) resistance in the presence of 50 ppm SO\({}_{2}\). The fresh and sulfated honeycomb cordierite-based Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalysts were characterized by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), N\({}_{2}\) adsorption-desorption, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetry and differential thermal analysis (TG-DTA), and Fourier transform infrared (FT-IR) spectroscopy. Characterization results indicated that the deactivation by SO\({}_{2}\) was primarily the result of the deposition of ammonium hydrogen sulfate and sulfated CeO\({}_{2}\) on the catalyst surface during the SCR process. The formed sulfates depressed the catalytic activity via the blocking of pores and the occupation of active sites. Additionally, the competitive adsorption between SO\({}_{2}\) and NH\({}_{3}\) always decreased the catalytic activity.  
Mn-Ce/Al\({}_{2}\)O\({}_{3}\) catalyst; low temperature selective catalytic reduction; honeycomb cordierite; SO\({}_{2}\) resistance
## 3 Measurement of System Performance
The calculated BER is \(3.16\times 10^{-10}\) which is fairly low. This is expected because of the larger gap between the signal levels of the two symbols.
## 2 Proposed Approach to Modeling POS Combinations in MUPSA
### Step 1: Review and Modification of PSA Models for Each Unit  
In the first step, each unit's FP and LPSD SUPSA models are reviewed from the MUPSA perspective and revised as necessary. The main tasks usually performed here are as follows.  
* Considering the scope of the MUPSA and the risk metrics, the FP and LPSD SUPSA models for each unit are reviewed to check whether they contain all the information necessary to develop a MUPSA model, and if necessary, the SUPSA models are revised.
* The SUPSA models for different units are thoroughly reviewed to check for lack of consistency in terms of key factors, such as hazards or IE grouping, event tree/fault tree models, data used, and major assumptions. If it is judged that the effects that the lack of consistency between the units may have on the MUPSA results are not negligible, the SUPSA models are revised to ensure inter-unit consistency.
* For both FP and LPSD models for each unit, event tree and fault tree logics unrelated to the IE being analyzed (e.g., multi-unit loss of offsite power) are removed or set to FALSE, leaving only the logics related to the IE. This can reduce the size of the models for each unit.
* \"Unit identifiers\" (e.g., \"U1-XXX\") are added to the names of all gates and basic events in order to distinguish between units. An identifier involving the corresponding units (e.g., \"U12-XXX\") is attached for those related to a system or component shared between two or more units. For those treated as completely dependent between units (e.g., multi-unit IE), the same gate or basic event is applied equally to all corresponding unit models with no unit identifiers.
## 2 Broadband System
### Wireless Broadband System  
Note that most of the existing broadband networks are largely based on optical fiber cables for different network elements connections. Nevertheless, with the envisaged massive machine-type communications (mMTC), provision of eMBB services using fiber might be time-consuming and cost-intensive. In view of this, wireless solutions have been gaining significant attention. The growing recognition is due to the salient advantages such as higher flexibility, lower cost, and ease of deployment [47].  
In general, wireless broadband solutions are technologies in which PtP or PtMP microwave in various frequencies are employed for transmitting signals between the hub sites and the end user equipment. Furthermore, licensed and unlicensed bands can be employed for wireless broadband technologies. Within the wide range of applicable frequencies, higher frequencies offer a set of benefits such as more spectrum availability, smaller antenna dimensions, and better directivity. The advantages result in ease of installation and performance improvement. On the other hand, high-frequency systems are susceptible to attenuation caused by poor weather conditions. This may constraint the system operation to the LoS and consequently results in distance/coverage limitations [16]. Besides, the density and height of the obstacle are other limiting factors [27,150].  
Mainly, wireless solutions can be grouped into the LoS and non-LoS (NLOS) technologies. For instance, PtP microwave, Free Space Optics (PSO), Local Multipoint Delivery Service (LMDS), and Broadband Satellite are LoS technologies. This is due to the fact that LoS is required for effective signal transmission when they are implemented. In contrast, cellular technologies such as CDMA, GSM, WiMax, Wireless Fidelity (Wi-Fi), 3G, as well as fixed wireless broadband technologies like Multichannel Multipoint Distribution Service (MMDS) do not require LoS for reliable signal transmission. Therefore, unlike LoS solutions, NLOS technologies offer better benefits regarding ease of system deployment and broader network coverage [16,18,151]. In the subsequent subsections, we present an overview of some viable wireless technologies.
## 5 Performance Analysis
### DSP-Based Approach  
Figure 8 shows the process of sending a unicast frame from the source node to the destination node under the DSP-based approach. Unlike the standard HSR protocol, which duplicates and floods the frame to the whole network, the DSP-based approach forwards the frame through two pre-established paths between the source and the destination.  
The network traffic when a source node sends a unicast frame to a destination node under the DSP-based approach, denoted by \(nt_{disp}^{1}\), is calculated by:  
\[nt_{disp}^{1}=n_{link}^{p1}\ +\ n_{link}^{p2}, \tag{7}\]  
where \(n_{link}^{p1}\) and \(n_{link}^{p2}\) are the number of links in these two paths.  
Generally, when a source node sends \(N\) unicast frames to a destination node under the DSP-based approach, the network traffic, denoted by \(nt_{disp}\), is determined as follows:  
\[nt_{disp}=N\Big{(}n_{link}^{p1}\ +\ n_{link}^{p2}\Big{)}. \tag{8}\]  
For the sample network in Figure 5, \(nt_{disp}=22N\ (frames)\).  
Figure 7: Network traffic under the standard HSR protocol.
# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 3 Proposed Method
### Feature Extraction
#### 3.2.2 Continuous Wavelet Transform  
The existing Fourier Transform does not consider the time domain of the signal, can only analyze the frequency domain, and has the limitation of being able to analyze only a fixed domain. To complement this, the wavelet transform can analyze signals that change over time, and because it enables a multi-resolution analysis through various scales, it can effectively extract changes over time and features from PCG signals that occur at various frequencies [27]. CWT is expressed as Equation (6), where \(a\) is the input signal, and \(\alpha\) is the transformation parameter, which means that the signal is analyzed according to the wavelet function position or time change. \(\psi\) is the wavelet function, and \(p\) is the scale parameter, which controls the compression and expansion of the wavelet function. In the case of a low scale, narrow resolution in the time domain and high resolution in the frequency domain  
Figure 4: WST for classes from PASCAL Classifying Heart Sounds Challenge Dataset A.  
are possible; therefore, detailed feature analysis of PCG signal problems, such as diseases and abnormalities, can be analyzed. Additionally, in the case of a high scale, the overall state of the signal can be analyzed with a wide resolution in the time domain and a low resolution in the frequency domain.  
\[CWT_{d}^{\phi}(p,\alpha)=\frac{1}{\sqrt{p}}\int_{-\infty}^{+\infty}a(t)\psi \bigg{(}\frac{t-\alpha}{p}\bigg{)}\mathrm{d}t \tag{6}\]  
Figure 5 shows some signals from which features were extracted using CWT for the Normal, Murmur, Extrahls, and Artifact classes of the PASCAL Classifying Heart Sounds Challenge Dataset A.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 7 Open Challenges and Future Directions
### Unavailability of Some Communication Interfaces  
In our approach, we have assumed that all devices are equipped with all same communication interfaces but for different reasons, this could not be the case. Some interface may be unavailable because the device has not been equipped with it, or it is damaged or the environment does not affect all interfaces similarly. This is thus worth integrating in our scheme the fact that all devices can represent the clique for a given layer.