esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Fault Feature Extraction Methodology for the Fault Diagnosis of Rolling Bearings  
Inspired by the advantages of ICEITDAN and CQMA, an ICEITDAN-CQMA technology for bearing fault detection is proposed. The steps are as follows:  
1. Apply ICEITDAN to fault signals and obtain a series of goal proper rotation components (GPRCs).
2. Calculate the periodic modulation components to the generalized interference (PMGI) value of each GPRC.
3. Select the first three components with the highest PMGI value among the GPRCs and reconstruct them for further analysis.
4. Determine the scale range of CQMA and perform the CQMA operation for the main components that were obtained in step 3.
5. Use the envelope spectrum of the result of the CQMA analysis to extract the fault feature frequency.  
The scheme that is proposed in this paper includes two steps: ICEITDAN noise reduction and demodulation analysis that is based on the composite quantum morphology analysis. The process of the ICEITDAN method will be introduced in detail in Section 2.2. For more detailed information on composite quantum morphological analysis-related theories and fault feature extraction methods for rolling bearing fault diagnosis, please refer to Sections 2.3 and 2.4.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 5 Discussion
### Industrial Strength Data
#### 1.1.2 Large-scale testing  
In most of the large testing projects reviewed here, Weibull analysis was not included. The simple estimation method improved in this study can easily add Weibull modulus data to elaborate data collection and analysis conducted in metal and construction industries and elsewhere. The m values will be beneficial in subsequent analyses, such as those conducted in the structural health monitoring of structures [140].
## 3 Results and Discussion
The first is used to stress the bandwidth limitations of the system components and the second to test linearity of the MZM drivers and the system's SNR. Figure 4: Plots of BER vs. ROP and post-equalizer structure for the (**a**) 14 Gbaud PAM-8 and (**b**) 28 Gbaud PAM-4 signals accompanied by the corresponding eye diagrams captured in the ONU before and after digital post-equalization employing 43-FF&21-FB taps. Figure 3: Plots of BER vs. ROP and post-equalizer structure for the (**a**) 28 Gbaud OOK, (**b**) 28 Gbaud EDB, and (**c**) 14 Gbaud PAM-4 signals accompanied by the corresponding eye diagrams captured in the ONU before and after digital post-equalization employing 43-FF&21-FB taps. Figure 3 shows that equalizer with just 9-FF&5-FB taps significantly improves the performance of the 28 Gbaud OOK system. The optical power budget is 29 dB, which is by 2 dB smaller as compared to the case with 43-FF&21-FB taps. Unfortunately, only FF taps are not able to provide the required improvement to meet the pre-FEC BER requirement. At \(-18\) dBm of the received optical power, the BER approaches but not crosses the defined threshold. As for the 14 Gbaud PAM-4 and 28 Gbaud EDB, an equalizer with just 4-FF taps is enough to improve the performance and leads to a higher sensitivity--by 1 dB and by 3 dB, respectively. The use for a more complex structure with more taps does not enhance the performance, and the optical power budget remains unchanged. Further, we explore two scenarios--14 Gbaud PAM-8 and 28 Gbaud PAM-4--ensuring a higher line rate (42 Gbps and 56 Gbps, respectively). Figure 4a shows results for the 8-level PAM signals, 14 Gbaud PAM-8, which was used to test the MZM driver linearity. The obtained eye diagrams evidence their good linearity as signal levels are equally distributed, but we see that the system is SNR limited as the upper and lower eyes are noisy. Therefore, applying even strong linear post-equalization (i.e., using 43-FF&21-FB taps) does not improve the quality significantly to fulfill the performance requirement although the BER approaches the threshold of \(3.8\times 10^{-3}\). Finally, Figure 4b shows that the linear equalization with 9-FF&5-FB taps is sufficient to fulfill the HD-FEC BER requirements and reach the optical power budget of 22 dB for the 28 Gbaud PAM-4 alternative. A more complex structure of the equalizer does not provide significant improvements neither in terms of the signal quality nor in the optical power budget. The system is a partially SNR limited. Insets in Figure 4b show the best eye diagrams for the configuration without the equalizer and with 43-FF&21-FB tap equalizer. The latter has a relatively large eye opening. The fact that the BER threshold of \(2\times 10^{-2}\) is met for all considered modulation format alternatives proves the viability of IM/DD formats to be used in HS-PON solutions delivering 25+ Gbps or even 50+ Gbps in commercial PONs if SNR improvement and modulator chirp management are achieved.
## 4 Discussion
### Comparison between Evergreen Forest SiF and Other Terrestrial Ecosystems
The correction of SiF to _SiF_\({L_{650}}^{-1}\) improved the strength of the relationship with LUE but not with GPP (Table 1). This is logical since both SiF and GPP are strongly controlled by incoming PAR (indeed APAR), in contrast, _SiF_\({L_{650}}^{-1}\) is intrinsically normalized by PAR via \({L_{650}}^{-1}\). In fact, because _SiF_\({L_{650}}^{-1}\) is normalized by PAR, it becomes a relative measure of fluorescence yield and therefore should be expected to reflect the seasonal variations in photosynthetic LUE much better than SiF. Similarly long-term SiF (and SiF yield) measurements in a deciduous forest site at Harvard Forest (2016) highlighted stronger relationships between SiF, SiF Yield, GPP and LUE, with similarly strong results between SiF and GPP reported in a mixed forest site ([PERSON] and others, 2017). Interestingly, the slight increase in apparent quantum yields of photochemistry (Figure 4G) and LUE (Figure 4H) observed here during early May, was registered by the PRI (Figure 4F) but not by _SiF_\({L_{650}}^{-1}\). Although this observation will require further validation and studies with even broader temporal coverage it preliminary points to potential limitations of SiF to track LUE in evergreens. The nature of the relationship between SiF and GPP, continues to be questioned. Is it solely driven by the dependence on APAR or a combination of APAR and the photosynthetic light and dark-reactions? ([PERSON] and others, 2017; [PERSON] and others, 2017; [PERSON], 2017; [PERSON], 2017). Long term and in situ measurement will prove particularly attractive in answering this question. Previous studies have highlighted that heat dissipation (NPQ) is the main driver of variations in fluorescence and photosystem yields, and the correlation between SiF yield and LUE is consistent with field studies and model-based assessments ([PERSON] and others, 2017; [PERSON], 2017). In the present study however, we found only a very weak correlation between SiF and GPP, and _SiF_\({L_{650}}^{-1}\) and LUE for the canopy component of an evergreen Scots pine forest.
## 3 Results  
### Multi-Incidence Estimates in X-Band  
The statistical performances obtained by comparing the backscattering coefficients modeled and the values derived from the multi-incidence TerraSAR-X images for buffers ranging from 5 to 30 m are presented in Figure 4. A notable increase in accuracy is first observed when considering the three first sizes of buffer (from 5 to 15 m), highlighted by the rise in the correlation from 0.74 to 0.89, and the decrease in RMSE from 1.56 to 0.94 dB. Then, performances saturate and the gain in accuracy only slightly increases with the size of the buffer. With a correlation close or up to 0.9 and RMSE lower than 1 dB, the performances overpass the results presented in several studies, using semi-empirical or physical models, with validation being performed at the plot spatial scale [1, 2, 3, 4, 5, 6, 7]. In these studies, model performances are first established, showing limited levels of accuracy (with correlation ranging between 0.06 and 0.59 and an error of 2.3-10.6 dB). The low performances obtained at the plot scale can be explained by the fact that parameters measured locally are not fully representative of the surface state at the plot scale. Then, some studies propose modifications of the formalism to correct certain biases (particularly with respect to roughness). The proposed methodology consists of identifying and correcting the residues of the models, depending on the surface properties (roughness, moisture, texture) and/or sensor characteristics (frequency, incidence angle). They obtained an improvement in accuracy, with a correlation of 0.69 and error of 1.41 dB, for the best performance obtained with a modified version of the model proposed by [PERSON] [5, 10].  
Figure 5 presents an example of comparison between modeled and satellite-derived backscattering coefficients for the buffer of 30 m. At this spatial scale, the proposed approach is associated with a high level of performance (during training and validation steps), as evidenced by the correlation of 0.91 (0.91) and the low RMSE of 0.85 (0.83) dB for the training and (validation), respectively. Such a level of performance exceeds the previous results obtained at the spatial scale of the plot (correlation of 0.86 and error of 1.01 dB [24]). Beyond the good overall performance, one phenomenon is particularly interesting to analyze: the overestimation of simulations for low measured values of the backscattering coefficient. These conditions apply to smooth, bare, dry soils, for which too little field data are available to train the statistical model. More measurements are needed to make up for this deficiency.  
Figure 4: Evolution of the statistical performances (coefficients of correlation and root mean square errors, bars and dots, respectively) with the buffer’s size and at the plot spatial scale (PS, [24]) for the training (gray) or validation (black) subsets of samples.
###### Abstract  
The prevalence of the Internet of Things (IoT) paradigm in more and more applications associated with our daily lives has induced a dense network in which numerous wireless devices, many of which have limited capabilities (e.g., power, memory, computation), need to communicate with the internet. One of the main bottlenecks of this setup is the wireless channel. Numerous medium access control (MAC) protocols have been devised to coordinate between devices that share the wireless channel. One prominent approach that is highly suitable for IoT and wireless sensor networks (WSNs), which rely on duty cycling, is the receiver-initiated approach, in which, rather than the transmitter, the receiver initiates the transaction. The problem with this approach is that when many devices are trying to respond to the receiver's transmission invitation and transmit simultaneously, a collision occurs. When the network is highly loaded, resolving such collisions is quite tedious. In this paper, we devise an enhancement to the receiver-initiated approach that aims at preventing this inherent collision scenario. Our modification relies on multiple devices sending a short predefined signal, informing their intended receiver of their intention to transmit simultaneously. The data transaction is done via a four-way handshake in which, after all backlogged devices have informed their designated receiver of their desire to transmit simultaneously, the receiver identifies them and polls them one by one, avoiding the collision. We compare the performance of Receiver-Initiated-MAC protocol (RI-MAC), which is one of the prevalent receiver-initiated protocols, with and without the suggested enhancement, and show superior air-time utilization under high traffic loads, especially in the presence of hidden terminals.  
wireless sensor networks (WSNs); IoT; medium access control (MAC); asynchronous duty cycling; receiver-initiated +
Footnote †: journal: Sensors  
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## 5 Results and Discussion
The complexity arises from eddy currents and associated secondary fields generated within conductive (even paramagnetic) or residual ferromagnetic materials due to temporal variations in the primary magnetic field experienced by the vessel (e.g., attitude or azimuth changes) [8; 32]. While methods exist to mitigate VM on ferromagnetic vessels using a towed scalar magnetometer [3; 9], robust correction using only shipborne fluxgate data remains difficult. Our results demonstrate that the paramagnetic hull significantly reduces the magnitude and duration of VM compared to ferromagnetic vessels (Figure S6), alleviating the problem without relying solely on signal processing. The observed short-wavelength noise during rapid yaw changes is consistent with weak, temporary paramagnetic magnetization. Although low-pass filtering removed localized high-frequency noise, software-based VM correction remains challenging, suggesting that a combined approach using appropriate platform materials and advanced correction techniques is optimal. This approach will be even more critical for future applications, as the recent development of higher-sensitivity sensors, such as those using Giant Magneto-Resistance or Magneto-Impedance, also makes them more susceptible to platform-induced magnetic noise. Therefore, the correction methodology validated in this study is considered essential to keep pace with these new sensor technologies. Future research applying and optimizing these algorithms for MI sensor data is expected to contribute significantly to the advancement of marine geophysical exploration technology.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 2 Materials and Methods
### Design of the Grid Array Antenna for Radar Sensing  
In a GAA, the directivity increases as the number of loops rises, but this reduces the bandwidth. Therefore, it is necessary to adopt trade-offs between directivity and bandwidth, which has implications for the number of loops. High directivity is required for long distance detection, whereas wider beamwidth is preferable for detection at shorter distance when illuminating the whole scene at a time. As mentioned in the introduction, for the target application aiming to detect obstacles up to 2 m, pattern beamwidths of around 30-40 degrees are pursued. The GAA comprises a metallic radiating grid on a dielectric slab backed by a metallic ground plane. The radiating grid of the GAA (see Figure 4) comprises four rectangular loops of conductors on the top of a dielectric substrate backed by a metallic ground plane. The short side of the loops with length W = \(\lambda\)g/2 (being \(\lambda\)g the guided wavelength at the center frequency of operation [13]) acts as a radiator. This yields nine radiating elements in total. The other requirement for the GAA resonance is that the length of the long sides should be approximately l = \(\lambda\)g. The losses in the long side of the loops (acting as transmission line) and the radiation level of cross-polar components are controlled by WI. The radiation coefficients of the array and hence the width of the aperture and the side lobe level (SLL) depend on Ws. To fix and flatten the antenna for measurements, there are four holes of 2 mm diameter at the four corners of the substrate. The radiating grid is fed near its center by a coaxial probe with 0.38 mm, 0.5 mm and 2 mm diameters, respectively, for the probe, inner and outer conductor, and with Teflon filling.  
PP dielectric is considered as the substrate and aluminum (Al) as the conductor for the radiating grid and the ground plane. For the antenna optimization, starting from the calculation of the guided wavelength \(\lambda\)g at the central frequency of the band (24.15 GHz) on the PP material, which results in 9.85 mm, the initial values of W and l are established for the antenna resonance at that frequency, while Ws and WI take initial values that consider manufacturing constraints. Through iterative parametric sweeps with FEM-based electromagnetic simulation software, W and l are adjusted to set the band, and once this is achieved, Ws and WI are modified to optimize the radiation pattern. The resulting optimized dimensions are indicated in Figure 4.  
Figure 3: Angular stability of the metaradome in the target frequency band.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 4 Radio Channel Models  
In this section, we introduce a simple but effective propagation model based on power measurements, designed following the widely used methodology in indoor modeling. This model consists of making a linear regression, using the least squares error method, of the power values expressed on a logarithmic scale with the value of the distance from the measured point. In this way, a straight line is achieved with a specific slope that relates the logarithmic power to the distance. This slope expresses the physical way in which the transmission power decays logarithmically with distance. This modeling method is known as the Log-distance Path Loss Model [58], and it is used to derive the path loss exponent, also known as the decay factor, which is specific for each environment and frequency. This model is also known as the Close-In free space reference distance (CI) path loss fitting model [57]. The equation of this model is as follows:  
\[PL^{CI}(d)[dB]=L_{0}+10n\text{log}_{10}\left(\frac{d}{d_{0}}\right)+X_{\sigma}^ {CI} \tag{2}\]  
where \(L_{0}\) is the free space path loss at 1 m in dB according to Equation (3), \(n\) is the decay factor, \(d\) is the distance expressed in meters, \(d_{0}=1\) m, and \(X_{\sigma}^{CI}\) is a lognormal variable with the standard deviation of \(\sigma\) in dB.  
\[L_{0}(dB)=10\text{log}_{10}\left(\frac{4\pi f_{c}}{c}\right)^{2} \tag{3}\]  
where \(f_{c}\) is the frequency used (\(868\ \times\ 10^{6}\) Hz) and \(c\) is the speed of light (using \(3\ \times\ 10^{8}\) m/s).  
This fitting model represents the physical behavior of the propagation attenuation by means of the parameter \(n\), but it has the disadvantage of not being suitable for measurement fits under NLOS conditions [57], so an alternative model is proposed for greater precision in this circumstance.  
The proposed alternative option is the Floating-Intercept (FI) path loss fitting model used in the WINNER II and 3 GPP channel models [59]. In order to know the correct slopeof the NLOS zone, the FI model uses two parameters, \(\alpha\) and \(\beta\), to provide the best minimum error fit. This model is given in Equation (4). The modeling will be applied by separating the NLOS-1 zone measurements from those of the NLOS-2 zone in order to know the specific path loss exponents for these two scenarios  
\[PL^{FI}(d)[dB]=\alpha+10\beta\text{log}_{10}(d)+X_{\sigma}^{FI} \tag{4}\]  
where \(\alpha\) is a Floating-Intercept coefficient in dB, \(\beta\) is a distance-dependent coefficient, and \(X_{\sigma}^{FI}\) is a lognormal variable with the standard deviation of \(\sigma\) in dB.
## 5 Conclusions  
To address the complexity of instability modes in soil-rock binary structure slopes and the limitations of traditional methods in balancing accuracy and efficiency, this study proposes a coupled numerical approach that integrates the finite element method with the Partitioned Rigid Element-Interface Element-Finite Element (PRE-IE-FE) method. The key findings are summarized as follows:  
1. Type I slopes (loose soil overlying stable bedrock) exhibit composite failure, involving rotational sliding within the soil and linear sliding along the soil-rock interface. Type II slopes (rock masses containing weak structural surfaces) destabilize via planar sliding along these weak structural surfaces, which drives rotational sliding of the overlying soil and forms composite slip surfaces.
2. The PRE-IE-FE method couples the overlying soil (modeled as a continuous medium using the FEM) and the underlying rock (simulated as discontinuous rigid elements) through interface elements. This framework simultaneously captures soil elastic deformation and rock contact nonlinearity while maintaining computational efficiency.
3. Leveraging ANSYS secondary development and a Python-based hybrid programming framework, a partitioned dynamic stiffness matrix update strategy significantly enhances solving efficiency. Integration with the strength reduction method and a numerical non-convergence criterion enables the rapid determination of safety factors and the visualization of sliding surfaces.
4. The safety factors derived from PRE-IE-FE demonstrate close agreement with results obtained from established methods, such as limit equilibrium analysis and FLAC, confirming the method's reliability in the stability assessment of soil-rock binary slopes.
## 5 Experiment Results
### Results of Sensitivity Experiments  
To validate the generalization capability of DRL-HA under different task distributions, we trained it at the scheduling scenarios with scales of 50 and 100 in this paper, and 100 scheduling instances were generated for the other seven orbits (distinguished by the  
Figure 8: Performance of HCNN at different sizes. (**a**) Average profits for HCNN at different sizes. (**b**) Total time for HCNN at different sizes.  
Figure 9: Performance of the algorithms for scenarios with different scales. (**a**) Average profits of the algorithms for scenarios with different scales. (**b**) Total time of the algorithms for scenarios with different scales.  
number of orbits, where O-1 is the test/training orbit). These scales of samples are representative from the small to large scale. Since this test was designed mainly for comparing competitors instead of performance on different scales, these samples are appropriate. The random algorithm, WF, PF, PDF, and PWF were compared with the HCNN.  
In order to study the performance in a single-orbit with a large-scale task scenario, this experiment used a scale of 100 single orbit scheduling instances and generated 100 sets of scheduling instances for different orbits. The average task total profits and the total running time of different orbits in different scenarios are shown in Tables 8 and 9, while Figure 10 displays the line trend charts. From these tables and figures, at every orbit and different scenario sizes, the HCNN obviously accomplishes more average profits than other algorithms. In addition, the HCNN works better with the increasing scenario sizes. For example, the HCNN achieves 24.74
# Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids
## 5 Conclusions  
This work analysed a novel method for detecting integrity attacks in cyber-physical infrastructures. The proposed framework exploits the correlations existing among the datastreams of the physical part of the network by means of LTI and GMM models. Each normal state comprises a point in the probabilistic space and the algorithm computes the affinity of novel data with the ones encountered during training by computing the respective KL divergences. One of the most important characteristics of the proposed algorithm is its flexibility, which is expressed in a twofold way: a) it can be applied practically unaltered to diverse types of ICT-controlled CIs, and b) it can be updated to deal with unseen types of nominal states by training new GMMs. The present framework is quite modular since new normal system states can be incorporated just by collecting the associated data, training a GMM and inserting it in the probabilistic space.  
However, there are several issues which remain to be addressed in our future works. The main two research directions we wish to follow are:  
* investigating how a data reconstruction technique might facilitate the controller of the network and substitute the node(s) under attack, and
* cooperating with an operator for applying the overall attack diagnosis framework on real-world data and understanding its limitations.  
**Acknowledgments: This work is supported by the Politecnico di Milano International Fellowship Program.**  
**Author Contributions: S.N. conceived and designed the experiments; S.N. performed the experiments, analyzed the data, and contributed reagents/materials/analysis tools; S.N., and [PERSON] wrote the paper.**  
**Conflicts of Interest: The authors declare no conflicts of interest.**