esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 4 A Joint Denoising Learning Model for Weight Update STD Method  
In this section, firstly, the structure of the proposed method is presented. Secondly, the characteristics of UWA data are analyzed in detail, and a complex-type orthogonal compression pre-processing structure is designed. Then, a noise cancellation learning (NCL) model based on the complex-CGAN is constructed. Finally, an STD model based on a weight update strategy is proposed to suppress MAI. As Figure 4 shows the general structure of the proposed method, it can be considered staged processing.  
In the first stage, the UWA data are firstly received and collated in airspace. Next, the received signal \(\mathbf{y}(t)=[\mathbf{y}_{1}(t),\mathbf{y}_{2}(t),\ldots,\mathbf{y}_{M}(t)]\) and the reference noise \(\mathbf{\tilde{n}}(t)\) are pre-processed to obtain the complex-type features that can be processed by the noise-reduction learning model. Then, the features are fed into the model, which mainly consists of complex-CGAN, ResNet, and TDNN, responsible for adaptive learning, noise cancellation, and time-series restoration, respectively. Finally, the output \(\mathbf{\hat{y}}(t)=[\mathbf{\hat{y}}_{1}(t),\mathbf{\hat{y}}_{2}(t),\ldots, \mathbf{\hat{y}}_{M}(t)]\) of the noise-reduction estimation is obtained.  
In the second stage, the noise-reduction estimate \(\mathbf{\hat{y}}(t)=[\mathbf{\hat{y}}_{1}(t),\mathbf{\hat{y}}_{2}(t),\ldots, \mathbf{\hat{y}}_{M}(t)]\) is fed into the improved weight update model. The model collates the results of beamforming into a matrix of corresponding weights while maximizing the weighting of the main path signal and suppressing signals on other paths. In this way, it is possible to suppress the MAI caused by spread spectrum signals for transmitting signals. Finally, the output of the STD is obtained by the combining strategy, which has a lower BER.
# Applications of Probabilistic Forecasting in Demand Response
## 2 Materials and Methods
### Lineal Quantile Regression  
Quantile regression involves the estimation of parameters of functions that establish the relationship between the quantiles of the dependent variable and the corresponding explanatory variables. These functions can take various forms, and if they are linear, the model is referred to as a Linear Quantile Regression (LQR) model. In [34], the authors present a method for fitting the LQR model. This method allows the model to estimate the \(\theta\)th quantile of the dependent variable, with \(0<\theta<1\), based on a linear relationship. Such a relationship is expressed in (9), where \(L_{\theta}(t)\) is the \(\theta\)th conditional quantile of the load variable for the hour \(t\); \(\beta_{\theta}\) is the vector of parameters (coefficients); \(\mathbf{x}(t)\) is the vector of explanatory variables for such a moment; and \(\varepsilon\) is the error term.  
\[L_{\theta}(t)=\beta_{\theta}\mathbf{x}(t)+\varepsilon \tag{9}\]  
The parameter vector \(\beta_{\theta}\) can be estimated by minimizing the loss function \(\rho_{\theta}(z)\), known as the pinball loss function, which is defined by Equation (10). This estimation process yields the expected values of the parameter vector, \(\hat{\beta}_{\theta}\), for the \(\theta\)th quantile, as shown in Equation (11), where \(i\) represents any hour in the fitting period (training dataset) and \(N_{f}\) is the number of data used to fit the model. The expected value of the \(\theta t\) conditional quantile of the hourly load at hour \(t\), \(\hat{L}_{\theta}(t)\), can be obtained using Equation (12).  
\[\rho_{\theta}(z)=\begin{cases}\theta z&if\;z\geq 0\\ (1-\theta)z&if\;z<0\end{cases} \tag{10}\]  
\[\hat{\beta}_{\theta}=\underset{\beta}{\text{argmin}}\Big{[}\sum_{i  
possibility of encountering crossing quantiles arises. This occurs when the estimated quantiles at different levels do not maintain the expected ordering, i.e., when a lower quantile exhibits higher values than a higher quantile at specific points, thus violating the expected relationship between quantiles. This can lead to inconsistencies in the quantile regression model. The issue of quantile crossing and its solution have been addressed in the literature [35], involving the rearrangement of quantiles for each hour to prevent crossing.
# An Approach Based on VR to Design Industrial Human-Robot Collaborative Workstations
## 2 The Proposed Approach
### VR-Based Interaction Design
#### 2.2.1 Human-Machine Interaction Mapping  
Interaction mapping is the initial stage of the interface design process. In this phase, designers need to consider all the aspects that influence human-machine interactions, such as work conditions and user's needs. To this end, the VR scenario allows designers taking into consideration all the requirements concurrently. Starting from the virtual scene created according to the described steps in Section 2.1, designers can see all the necessary information at a glance to begin the interaction mapping. In particular, VR helps understanding interactions by showing scene components, actors (e.g., operators, robots) and working conditions (e.g., presence of noise or personal protective equipment worn by the operator/s). The vision of these elements allows designers to understand the following key aspects:  
1. _where_: Where interactions with the interface/s take place;
2. _which_: Which information must be exchanged through the interface/s;
3. _direction_: Which communication direction, from/to the user/s and other actors (e.g., robot, system);
4. _when_: When interactions occur.  
These four aspects are essential to define the most suitable interface type (e.g., graphics, voice, gesture) and supporting device according to the specific context of use. Such aspects can be easily mapped and graphically represented in the VR scenario to provide a clear view of the context of use and a deeper understanding of the user experience. To this end, a set of graphic elements have been identified to map the four key aspects (an example is depicted in Figure 9):  
1. _where_: Represented with a balloon where the interactions with the interface will take place;
2. _which_: Represented with a text label about the information to be exchanged, distinguishing those from user/s to other actors and vice versa;
3. _direction_: Represented with an arrow pointing to or from the identified interaction point;
4. _when_: Represented through a colour change of the interaction balloon and the information label to be exchanged in the specific moment.  
Figure 9: Example of VR-based interaction mapping.  
The unified view of all the interaction information facilitates designers to understand the UX and to define the best interface typology. For example, the identification of multiple interaction points may indicate the need to have a multiple and/or mobile interface, whereas a high communication complexity can guide the definition of a graphic, voice or gesture interface. Generally, for quite complex and variable communications, graphic interfaces are the clearest ones, though they could be redundant for just a few repeated communications (e.g., ok, start, stop).
# A Palm-Jacobaeus Loss Formula for Multi-Service Systems with Separated Resources
## 5 Conclusions  
In this paper, a new method for calculating the probability of strictly determined multi-service resources in a group of resources has been proposed. The method allows us to elaborate a new formula for calculating the blocking probability of strictly determined resources in systems servicing multi-service traffic, e.g., in a group of cells in 4G and 5G systems. The new formula is an extension of the well-known Palm-Jacobaeus loss formula elaborated for systems with single-ratetraffic. The new multi-service Palm-Jacobaeus formula opens new possibilities in the analytical modeling of communication systems with separated resources, such as multi-service switching networks [38; 40; 41; 56; 57; 58], data centers [59; 60; 61], and systems with traffic overflow [62; 63; 64; 65].  
Conceptualization, M.G. and M.S.; methodology, M.G. and M.S.; software, A.K.; validation, A.K. and M.G.; formal analysis, M.G. and M.S.;investigation, A.K.; resources, A.K., M.G., and M.S.; data curation, A.K.; writing--original draft preparation, M.G. and M.S.; writing--review and editing, M.G. and M.S.; visualization, A.K. and M.G.; supervision, M.G.; project administration, M.G.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Polish Ministry of Science and Higher Education, Grant 08/82/SBAD/8229.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Compressive and Flexural Strength
#### 3.4.2 Flexural Strength  
The flexural strength results for different UHPC mixtures are presented in Figure 11. When compared with the compressive strength, the addition of 1% steel fiber had a limited increase in flexural strength. In addition, the addition of 2% and 3% steel fiber provided a remarkable increase in flexural strength of about 24.9%-58.2% and 48.8%-88.7%, respectively. This is attributed to the mechanical interlock and friction at the fiber-matrix interface, which suppressed the propagation and development of cracks. Furthermore, the higher addition of fiber provides more crack-bridging effects, thus, increasing the flexural load carrying capacity [24]. The variation in water-to-binder ratio and superplasticizer dosage had similar influences on flexural strength as that on compressive strength. For most of mixtures, less entrapped air content in the fresh state leads to an increased flexural strength of UHPC specimens, as expected.  
Figure 11: Flexural strength of UHPC mixtures (column is strength and line is air content). (**a**) \(w/b=0.18\); (**b**) \(w/b=0.20\); (**c**) \(w/b=0.22\); (**d**) \(w/b=0.24\).  
Figure 10: Failure appearance for UHPC specimen under uniaxial compression. (**a**) Plain specimen; (**b**) steel fiber reinforced specimen.
## 2 Materials and Methods
### LUE Model
#### 2.2.2 PAR Estimation  
The PAR estimate is based on the daily incoming shortwave radiation data using a conversion factor from shortwave (400-2500 nm) to PAR (400-700 nm) of 0.48. This coefficient was determined by [42] from measurements at several points around the globe. The daily incoming shortwave radiation data were obtained from the ERA5 land reanalysis at a spatial resolution of 11,132 m and were produced by replaying the land component of the ECMWF ERA5 climate reanalysis [43]. A total of 1278 daily aggregated periods corresponding to the surface_solar_radiation_downwards_sum dataset were extracted using the Google Earth Engine platform [44].
# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 3 Three Versions of Running Skewness and Efficient Calculation Methods
### Performance Comparison among the Three CSS Calculation Methods
#### 4.3.3 Correctness Verification  
Driven by the discussions in [8; 9], we decided to use modern software systems of statistics to empirically validate the correctness of our method. For our convenience, we employ Microsoft Excel's native function SKEW() to return different data samples' skewness as reference, to verify and compare the calculation results from the GSL method, the GoL method, and our method. In particular, we choose the top \(N\) prices records (ranging from the first three records to the first one million records) as data samples from the aforementioned benchmark dataset. The selected validation results are listed in Table 2.  
By referring to the output from Microsoft Excel, we claim that both our method and the GoL method can accurately calculate CSS. In contrast, the GSL method delivers different sample skewness values, although the difference becomes smaller and smaller as the sample size grows. This further confirms that due to the employment of Fisher-Pearson skewness coefficient, the GSL method may not be suitable for modern applications. More importantly, considering that online learning only uses a small set of data for its initial training, the big error of skewness from the GSL method may lead to a huge threat to the implementations of skewness-aware online edge learning. Thus, from the perspective of correctness, we suggest avoiding the GSL method when implementing skewness monitoring in production. Note that we have double-confirmed the GSL method's calculation results by running a C program that directly utilizes the relevant libraries.
## 3 Results
### Relationships between SOSD and Baseline Socio-Demographic, Questionnaire, and Sleep Data  
Given the increased likelihood of type 1 error due to the large number of exploratory analyses, the handful of statistically significant associations presented below should be interpreted with caution, and in the context of weak-to-moderate effect sizes. DBAS (expectations subscale) and wake after sleep onset SOSD were negatively correlated (r = -0.216, \(p\) = 0.010), indicating that wake after sleep onset overestimation was associated with lower DBAS (expectations). However, none of the SOSD measures were related to any socio-demographic, PSG, insomnia severity or other daytime impairment scales (see Supplementary Materials Tables S3-S7).  
There was no relationship between overall the apnoea-hypopnoea index (AHI) and any SOSD parameter at baseline. Stage one sleep (N1) AHI and wake after sleep onset SOSD were negatively correlated (r = -0.195, \(p\) = 0.021), indicating that greater wake after sleep onset overestimation was associated with lower AHI in N1 sleep. N2 AHI and sleep onset latency SOSD were positively correlated (r = 0.190, \(p\) = 0.025), indicating that greater sleep onset latency underestimation was associated with a lower AHI in N2 sleep. REM AHI and total sleep time SOSD were positively correlated (r = 0.198. \(p\) = 0.022), indicating that greater overestimation of total sleep time was associated with a higher AHI in REM sleep. Arousal index scores and wake after sleep onset SOSD were negatively correlated (r = -0.243, \(p\) = 0.01), indicating that greater wake after sleep onset overestimation was associated with lower arousal index scores. Minutes in N2 sleep was positively correlated with wake after sleep onset SOSD (r = 0.229, \(p\) = 0.006), and negatively correlated with both total sleep time SOSD (r = -0.362, \(p\) < 0.001) and sleep efficiency SOSD (r = -0.304,  
Figure 3: Between-group change in mean (\(\pm\)standard deviation) total sleep time (TST) subjective-objective discrepancy from baseline to 6-week follow-up.  
\(p\) < 0.001), indicating that greater underestimation of sleep time was associated with more N2 sleep. Minutes in REM sleep was negatively correlated with both total sleep time SOSD (r = \(-\)0.295, \(p\) \(\leq\) 0.001) and sleep efficiency SOSD (r = \(-\)0.209, \(p\) = 0.013), indicating that greater total sleep time and sleep efficiency underestimation is associated with more minutes in REM sleep. Removal of extreme outliers (Figure 2) did not substantially change the pattern of results described above.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Online Detection  
After offline training experiments and analysis, the CNAT model showed excellent detection performance. We have further illustrated that this model's performance is optimal for online detection. In this section, experiments compare the CNLS, LSAT, and CNAT performance in terms of malicious traffic detection capability and response time. Finally, we selected and deployed the best model at the ingress gateway to achieve a fine-grained classification of DDoS attacks.
# Multi-Modal X-ray Imaging and Analysis for Characterization of Urinary Stones
## 3 Results
### MicroCT Volumetric Analysis  
Figure 3 shows reconstructed slices for each stone sample. A 3D rendering of the samples is also depicted in Figure 3.  
The stone sample from the cat exhibits two main intensity ranges in the slice's histogram: a relatively medium intensity, uniformly distributed inside the stone, and some hyperintense regions mostly in central slices close to the border in one view. The lower-intensity area is detected to be polycrystalline whewellite, and the hyperintense signal at the core of the stone is related to the highly absorbing hydroxyapatite (Figure 3a). Some porosity and cracks can be visualized inside the stone. The stone surface shows considerable overall roughness and irregularities, seen in the edges of the reconstructed slices and well rendered in the 3D volume (Figure 3d).  
The dog's stone appears very smooth in intensity, with some inner cracks and porosity, depicted in the image volume in Figure 3b,e. It is expected to see such a high degree of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} Hydroxplapatite \\ (main) \\ \end{tabular} & Ca\({}_{5}\)(PO\({}_{4}\))\({}_{3}\)(OH) & Hexagonal & P6\({}_{3}/m\) & 3.8 \\ \hline \hline \end{tabular}  
* SC: single-crystal; 2 poly: polycrystalline; 3 nano-poly: nano-polycrystalline.  
\end{table}
Table 1: Composition and phase analysis of urinary stones by the XRD method.  
Figure 3: Reconstructed vertical slice of X-ray microtomography through three stone samples from (**a**) cat, (**b**) dog, and (**c**) human urinary stones. (**d–f**) Corresponding 3D rendering of the full stones in (**a–c**). In (**e**), the porosity size distribution is depicted by the side colorbar. The effective voxel sizes for the (**a–c**) microtomographs are 5.15, 12.86, and 39.9 μm, respectively.  
intensity uniformity due to the single crystalline phase of this stone, detected by XRD analysis. The laminar structures of the human urinary stone can be well observed from the microCT slices, implying whitelockite as the repeated hyperintense layers grown around the relatively hypotointense hydroxylapatite as the core (Figure 3c). There are granular heterogeneities from grown nano-polychrystalline whitelockite also distributed close and around the core. The hydroxyapatite layers are also observed between the whitelockite layers, creating high-contrast wavy patterns in the stone's cross-sections. It should be noted that the advantage of microCT is that it can provide high-resolution, full volumetric information of the stones.
## 3 Numerical Experiment and Discussion
### Computational Complexity Analysis  
Here, based on run-time, we conducted a complexity analysis of the PV, SPV, PWD-SRP, and DPD-MUSIC. The processing of the implementation of all the methods was not parallel. Matlab software installed on a personal computer (core i7, Intel processor, 8 th Generation, 16 GB RAM, 64-bits and 1T hard-drive) was used for the implementation and execution of the algorithms. Measured data were used to estimate the elapse time for all the methods. Note that both the DPD-MUSIC and PWD-SRP methods employ pre-calculated steering vectors, but the PV and SPV algorithms employ pre-calculated elements of the dictionary. The average elapse time comparison for grid resolution \(\{10^{0},5^{0},2^{0},1^{0}\}\) for varying \(N\) and DoA search grids is depicted in Table 1. An increment in the DoA search grid resolution affects the PWD-SRP, and DPD-MUSIC, more than the proposed PV and SPV methods. The developed methods lead to a significant reduction in computational complexity. Hence, the PV and SPV exhibit reduced computational complexity and better performance.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Hydroxyapatite Nanoparticles  
Hydroxyapatite (HAp, Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)), a mineral occurring in mature bones, is highly crystalline and the most stable compound of calcium and phosphate in a Ca/P ratio of 5:3. HAp has favorable mechanical properties that are essentially influenced by the size of its particles, porosity, and density. HAp after implantation in the body may remain integrated into the regenerated bone due to its slow resorption, unlike other calcium and phosphate-containing compounds, e.g., \(\beta\)-tricalcium phosphate (\(\beta\)-TCP). For these reasons, it is often used in the form of nanoparticles (nHAp) as a filler in polymers to improve their mechanical properties [217] and to coat metal implants to give them better bioactivity [218], or in combination with \(\beta\)-TCP to improve bone regeneration [219]. The physico-chemical and biological properties of nHAPs are very similar to natural bone apatite, and therefore, the effort is to mineralize the organic matrix with calcium phosphate, especially with HAp nanoparticles, which are the most stable and similar to in vivo mineral phase. There are exceptions where nHAp did not improve the mechanical properties of composites (e.g., nHAp/polyester scaffolds), but the slowing down of the degradation rate of polymers occurs without exception for all nHAp-containing composites. The addition of nHAp into natural and even synthetic polymers was shown to significantly improve mechanical properties, such as Young's modulus, strength, or elastic modulus, and also to increase ALP activity and mineralization of human osteoblasts. It means that nHAp supports osteogenesis of MSCs in vitro. It is further stated that a mixture of HAp and polymers is easy to use and is much better handled than HAp alone [20]. With the advent of 3D printing technologies, there has been a significant shift in bone tissue engineering, as this technology can address the poor biodegradability of scaffolds consisting just or predominantly of nHAp. This is because a 3D-printed internal structure can also enhance the reparation effect [216].