esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case Study
#### 3.1.5 Results of Proposed Day-Ahead Load Forecasting Methodology with Real Data
By segmenting the data according to demand patterns, identifying statistical seasonality, and differentiating typical weekdays from holidays, the training process of the LSTM model can be significantly enhanced, leading to improved forecast accuracy, even in challenging situations like low or moderate consumption. Furthermore, to illustrate the effectiveness of this method, load forecasts were obtained over an extended period, and three significant weeks of the year were identified. These weeks, along with the main reasons for their selection, are as follows:  
* The Easter week. During this time, load profiles exhibit different patterns compared to typical weekdays due to the holidays. * The week of maximum load demand occurs annually. Among these weekdays, 21 May 2022 recorded the highest load demand value of the year. * The week of the annual minimum load demand featured one weekday that recorded the lowest load demand value of the entire year, specifically 25 December 2022. Figure 17 illustrates the registered energy load demand alongside the forecasted values for Easter week. Both curves align closely during most hours of the day, with slight differences during peak load periods where the forecasted values exceed the actual daily peaks. Figure 18 illustrates the registered energy load demand alongside the forecasted values for the annual maximum load demand week. Both curves align for most hours of the day, revealing slight differences in the minima and maxima of the valleys. As mentioned  
Figure 16: Historical and forecasted load demand data for a holiday corresponding to 25 December 2022. Figure 17: Historical and forecasted load demand data for the Easter week from 11 April 2022 to 18 April 2022.  
earlier, the maximum yearly load demand was recorded on 21 May at 10 PM, reaching 8208 MWh/h. Figure 19 shows the measured energy load demand along with the forecasted values for the weekly minimum load demand throughout the year. Both curves align for most hours of the day, with slight differences noted on the night of 24 December 2022. On 25 December 2022, between 7 AM and 9 AM, the absolute minimum of the yearly load demand occurs, reaching 3597 MWh/h at 8 AM. The next subsection quantifies the accuracy of these results by calculating the corresponding MAPE values and comparing them to those derived from the forecasted values calculated by CENACE.
## 2 Methodology
### Theoretical Basis
#### 2.1.2 Land Surface Albedo Definition  
The directional hemispherical albedo \(\alpha_{BSA}\) (also known as black-sky albedo, BSA) and the double hemispherical albedo \(\alpha_{WSA}\) (also known as white-sky albedo, WSA) can be obtained by integrating the bidirectional reflectivity on the angle [30]:  
\[\alpha_{BSA}(\theta_{s},\lambda)=\sum_{k}[f_{k}(\lambda)\times h_{k}(\theta_{ s})] \tag{2}\]  
\[\alpha_{WSA}(\lambda)=2\int_{0}^{\pi/2}\alpha_{BSA}(\theta_{s},\lambda)cos \theta_{s}sin\theta_{s}d\theta_{s}=\sum_{k}f_{k}(\lambda)\cdot H_{k} \tag{3}\]  
\[h_{k}(\theta_{s})=\frac{1}{\pi}\int_{0}^{2\pi}\int_{0}^{\pi/2}k_{k}(\theta_{v },\theta_{s},\varphi)cos\theta_{v}sin\theta_{v}d\theta_{v}d\varphi_{v} \tag{4}\]  
\[H_{k}=2\int_{0}^{\pi/2}h_{k}(\theta_{s})cos\theta_{s}sin\theta_{s}d\theta_{s} \tag{5}\]  
where \(f_{k}(\lambda)\) is the kernel coefficient of the BRDF model, \(k_{k}(\lambda)\) is the kernel of the BRDF model, and \(h_{k}(\theta_{s})\) is the integral of the zenith angle and azimuth angle checked by the BRDF model. \(H_{k}\) is the integral of each kernel of the BRDF over all angles. The subscript \(k\) here means isotropic scattering, volume scattering, and geometric optical scattering.  
Figure 1: Three-dimensional shapes of the RossThick volume scattering kernel and LiSparseR geometric optics scattering kernel with solar zenith angles of 15\({}^{\circ}\) and 45\({}^{\circ}\).  
The actual surface albedo is the weighted sum of the black-sky albedo and the white-sky albedo according to the sky diffuse light scattering factor \(S\):  
\[\alpha=(1-S)\times\alpha_{BSA}+S\times\alpha_{WSA}. \tag{6}\]  
\(S\) is defined as the ratio of incident sky radiance due to the scattering of solar radiation in the atmosphere to total incident radiation [31]. In this study, the \(S\) at noon of the day is calculated according to the day in a year and latitude information. The empirical formula are expressed as:  
\[\theta_{sun}=2\pi(day-1)/365 \tag{7}\]  
\[lats_{sun} =(0.006894-0.399512 cos(\theta_{sun})+0.072075 sin(\theta_{sun})\] \[-0.006799 cos(2\theta_{sun})+0.000896 sin(2\theta_{sun}) \tag{8}\] \[-0.002689 cos(3\theta_{sun})+0.001516 sin(3\theta_{sun}))\times 180/\pi\]  
\[\phi_{sun}=arcsin(sin(lat/180\times\pi)sin(lat_{sun}/180\times\pi)+cos(lat/180 \times\pi)cos(lat_{sun}/180\times\pi)) \tag{9}\]  
\[\theta_{bs}=\pi/2-\phi_{sun} \tag{10}\]  
\[S=0.122+0.85\times e^{-4.8 cos(\theta_{bs})} \tag{11}\]  
where \(\theta_{sun}\) is the noon sun angle, \(day\) represents the order of each day in the year, \(lat_{sun}\) represents the noon sun latitude, \(\phi_{sun}\) represents the local noon sun elevation angle, \(\theta_{bs}\) represents the local noon sun zenith angle, and \(lat\) represents the latitude data of the observation point.
## 2 System Model
### Modeling 3D Clusters of Millimeter-Wave Multiband Small Cells  
#### 2.2.1 Floor Attenuation Loss  
Even though indoor radio signal propagation modeling has been studied for a long time, no standard methodology has yet been developed to measure the penetration loss of walls and floors for different building materials, making it difficult to compare results [13]. Moreover, studies at high frequencies are also limited in the literature, particularly in millimeter frequency bands. Hence, following [14], we consider a typical reinforced concrete floor for modeling purposes. According to [14], the penetration loss of a typical reinforced concrete floor with a suspended false ceiling is 20 dB at 5.2 GHz. Since the floor penetration loss is frequency-dependent and increases rapidly with an increase in frequency, using the floor penetration loss data at 24 GHz in [15] and fitting the curve for a concrete block in [13], we assume that the floor penetration loss in the 28 GHz mmWave spectrum is 55 dB for the first floor as a worst case analysis. Note that the floor penetration loss is not fixed for all floors between transmitters and receivers. Instead, the impact of floor penetration loss is nonlinear, whereby it decreases with an increase in the number of floors.  
Moreover, according to [16], an internal wall exhibits a penetration loss of 6.84 dB at 28 GHz. Furthermore, outdoor external tilted glass causes 40.1 dB penetration loss, whereas brick walls show penetration loss of 28 dB at 28 GHz (Table 1). This large penetration loss at 28 GHz for both intra-floor and inter-floor levels, along with high external wall penetration loss, causes the radio frequency to be confined within a building, resulting in no or insignificant interference with outdoor signals of the same frequency. Due to the higher frequency, the loss also increases at 60 GHz as compared to 28 GHz, such that the 3D cluster model of small-cell base stations (SBSs) with the 28 GHz spectrum is also applicable for the 60 GHz unlicensed spectrum.
## 2 Materials and Methods
### Feature Extraction
#### 2.4.1 Statistical Time-Domain Features  
* Kurtosis--Calculates whether the data is heavy-tailed or light-tailed relative to a normal distribution.
* Skewness--Computes the asymmetry of the data. When the data points are skewed to the left, it is called a negative skew, and data points skewed to the right are called a positive skew.
* Peak value--Gives the maximum absolute value of the signal.
* Impulse factor--Compares the height of a peak to the mean level of the signal.
* Crest factor--Defined as the peak value divided by the root mean square value.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP - Third-Generation Partnership Project CCSDS Consultative Committee for Space Data Systems COTS commercial off the shelf CVES common vulnerabilities and exposures CVSS vulnerability scoring system DDoS distributed denial of service DoS denial of service GCS ground control system GEO geostationary-Earth orbit GNSS global navigation satellite system IoT internet of things ISLs inter-satellite links ITU International Telecommunication Union LEO low-Earth orbit LLM large language model LPWAN low-power wide-area network MEO medium-Earth orbit NMEA National Marine Electronics Association NTN non-terrestrial network RF radio frequency RTOS real-time operating system SATCOM satellite communication SCS satellite communication system SDN software-defined network UAV unmanned aerial vehicle
## 3 Results and Discussion
### Influence of Laser Treatment on Mechanical Properties
#### 3.2.3 SEM and Cruciform Tests Analyses  
Cruciform specimens shown in Figure 2b were obtained from laser textured foil and then subjected to an increasing load until the failure was reached (hereinafter 100%). This test was carried out in three different conditions, i.e., applying the load along a single axis (uniaxial, UNI), along two perpendicular axes (biaxial, BI) and with a plane strain loading (PS). To validate the resilience of LIPSS and consequently of the surface functionality after a forming process, SEM analyses were carried out on a textured specimen before and after the tests.  
Although a slight reduction in the LIPSS height might be expected [31], the results shown in Figure 7 indicate that the LIPSS morphology was not affected by the mechanical load, regardless of the direction along which the load is applied. The morphology observed before the tests (Figure 7 left) was kept unchanged not only at 100% but also for specimens subjected to a load (pre-strain) value, enabling an elongation of, respectively, 75%, 50% and 25% of the failure (micrographs not shown).
## 3 Fundamental Equations
### Equations of Internal Equilibrium in the Cross-Section  
The bar's cross-section was discretized to produce a computational model. The cross-section of the concrete was divided into layers that were \(\Delta l\)--thick and, within this, the areas of the two steel layers were defined as \(A_{s1}\) and \(A_{s2}\), which is shown in Figure 5.  
Figure 4: Equilibrium system of the internal forces.  
The functioning of the computational cross-section model is conditioned by models of deformation of concrete and steel, as well as by kinematic hypothesis. The basis of this hypothesis is the assumption of plane cross-section that is not perpendicular to the central axis of the deformed element. Kinematic hypothesis determines the state of deformation of all the layers of the cross-section, as well as the principle of active layers in joint action, such as steel layers and concrete layers being able to carry the stresses.  
Depending on the ability of concrete for compressive and tensile deformation, the concrete layers carrying the stresses and layers not carrying the stresses, such as cracked layers (in tension) or crushed layers (in compression), were distinguished. Cracked layers after the closing of cracks can again carry the compressive stress, but the crushed layers become permanently passive layers and cannot carry any stresses.  
The strain states in the different cross-sectional layers, for a given time step, are defined in Equation (11).  
\[\left\{\begin{array}{cc}\varepsilon_{11r}^{n}=e^{n}+z_{r}\cdot\kappa^{n},&z_{ r}=(z_{k},\,z_{s1},\,z_{s2}),\quad k=1,\,2,\,\ldots,\,K\\ &\varepsilon_{12r}^{n}=\frac{1}{2}\gamma^{n}\end{array}\right. \tag{11}\]  
If the longitudinal strain of the central axis (\(e^{n}\)), the change in the average rotation angle of the cross-section (\(\kappa^{n}\)), and the average angle of the non-dilatational strain (\(\gamma^{n}\)) were known, then, after using the material models, the longitudinal force (\(N^{n}\)), the bending moment (\(M^{n}\)), and the transverse force (\(Q^{n}\)) can be determined from the equilibrium equations of the cross-section.  
\[\left\{\begin{array}{cc}N^{n}=\sum\limits_{k=1}^{K}\sigma_{11,k}^{n}\cdot A _{c,k}+\sigma_{11,s1}^{n}\cdot A_{s1}+\sigma_{11,s2}^{n}\cdot A_{s2}\\ M^{n}=\sum\limits_{k=1}^{K}\sigma_{11k}^{n}\cdot A_{c,k}\cdot z_{k}+\sigma_{11,s1}^{n}\cdot A_{s1}\cdot z_{s1}+\sigma_{11,s2}^{n}\cdot A_{s2}\cdot z_{s2}\\ Q^{n}=\sum\limits_{k=1}^{K}\sigma_{12,k}^{n}\cdot A_{c,k}+\sigma_{12,s1}^{n} \cdot A_{s1}+\sigma_{12,s2}^{n}\cdot A_{s2}\end{array}\right. \tag{12}\]  
where \(A_{c,k}\) is the cross-section area of the concrete layer and \(A_{s1,2}\) is the cross-section area of the tensile/compressive reinforcing steel.
# Phase Transition and Controlled Zirconia Implant Patterning Using Laser-Induced Shockwaves
## 3 Results  
### Phase Transitions in Zirconia  
The XRD analysis of zirconia at different sintering stages provides insights into the phase transitions and structural changes. Zirconia exhibits polymorphism, meaning it has multiple phases that are stable within specific temperature ranges: cubic zirconia (c-ZrO\({}_{2}\)) is stable above 2370 \({}^{\circ}\)C, tetragonal zirconia (t-ZrO\({}_{2}\)) between 1170 \({}^{\circ}\)C and 2370 \({}^{\circ}\)C, and monoclinic zirconia (m-ZrO\({}_{2}\)) below 1170 \({}^{\circ}\)C [42]. To stabilize the tetragonal and cubic phases of zirconia at room temperature, it is doped with trace amounts of yttria.  
Figure 1 displays the XRD patterns of zirconia in three forms: green, pre-sintered, and sintered. The hydrophobicity measurements for the green body, pre-sintered, and sintered samples, using a drop shape analyzer and young modulus, had shown 0\({}^{\circ}\), 60\({}^{\circ}\), and 82\({}^{\circ}\) contact angles, respectively, giving insight into the porosity and surface modifications happening. As expected, the XRD pattern of green body zirconia shows a pure monoclinic phase, with no distinct peaks at other phases. The pre-sintered sample shows a mixture of monoclinic and tetragonal zirconia. Figure 1 shows that the phase transition was observed, as evidenced by the change in peak intensities in the X-ray diffraction (XRD) patterns, with a decrease in the intensity of the monoclinic phase peaks and a corresponding increase in the intensity of the tetragonal phase peaks. Such transitions are significant in zirconia ceramics because the balance between monoclinic and tetragonal phases strongly influences the material's mechanical properties, including fracture toughness and strength. Repeating sintering with a faster cooling rate increases tetragonal phase retention, indicating that  
the cooling rate influences the phase transition, and rapid cooling favors phase retention. Other factors affecting phase transition and retention are the uneven distribution of yttria and yttria evaporation. The XRD pattern of fully sintered zirconia shows a pure tetragonal phase, confirming the total phase transition from monoclinic zirconia. This transformation is consistent with zirconia's phase stability, where the tetragonal phase is the most stable between 1170 \({}^{\circ}\)C and 2370 \({}^{\circ}\)C. The full phase transition in the sintered sample shows that the sintering temperature of 1450 \({}^{\circ}\)C provides enough energy for zirconia to completely convert from the monoclinic to the tetragonal phase. The sharp, high-intensity tetragonal peaks in the XRD pattern confirm the crystallinity and purity of the tetragonal phase. These observations are consistent with the theory of zirconia polymorphism. When monoclinic zirconia is heated above 1170 \({}^{\circ}\)C, it transitions to the tetragonal phase, where it undergoes a martensitic transformation involving significant volume change and shear strain.
## 2 Materials and Methods
### Soil DNA Extraction, Amplification, and Illumina MiSeq High-Throughput Sequencing  
(1) Extraction of soil bacterial and fungal DNA: Soil microbial DNA was extracted using the Power Soil(r) DNA kit (MOBIO Laboratories, Carlsbad, CA, USA), DNA was detected on 1% agarose gel, and DNA was purified using the Gel Recovery Kit (Tian Gen Biotech Co. Ltd., Beijing, China). The DNA was purified by Gum Recovery Kit (Tian Gen Biotech Co. Ltd.) and stored in the refrigerator at \(-\)40 \({}^{\circ}\)C after purification, with three replicates for each soil sample [6; 7].  
(2) qRT-PCR quantitative analysis: TransStartFastPfu DNA polymerase was used in the PCR process, and the PCR instrument used ABI GeneAmp(r)99700 (Foster City, CA, USA). PCR reaction steps and conditions are as follows: denaturation at 95 \({}^{\circ}\)C for 30 s, to ensure DNA double-helix hydrogen-bond breakage, and the formation of a single-stranded DNA as a template for the reaction; 55 \({}^{\circ}\)C-annealed for 30 s, at the complementary region of primer and template binding to form a template-primer complex. After annealing at 55 \({}^{\circ}\)C  
Figure 1: Schematic map of the research area.  
for 30 s, the primer and the complementary region of the template form a template-primer complex; a 72 \({}^{\circ}\)C extension in implemented for 45 s, with the primer as a fixed starting point, under the action of DNA polymerase, to synthesize a new DNA chain [6; 7]. The above three steps were repeated as one cycle a total of 35 times. The PCR products were quantified by QuantiFluor TM-ST blue fluorescence system, and then mixed according to the sequencing amount of each sample. Finally, the MiSeq library was constructed and sequenced.  
(3) Illumina MiSeq High-Throughput Sequencing. Raw sequences were trimmed, merged, and assigned in QTIME software (v1.8.0, [[http://www.qitime.org/](http://www.qitime.org/)]([http://www.qitime.org/](http://www.qitime.org/)), accessed on January 2022-November 2024). Low-quality sequences (length \(<\) 150 bp) were removed. High-quality sequences were clustered into operational taxonomic units (OTUs) with 97% similarity. The number of OTUs, the \(\alpha\)-diversity index, Chaol index, Shannon, and Simpson indices were calculated in QTIME. Differences between treatments were also analyzed using ANOSIM analysis.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 1 Introduction
### Paper Organization  
The rest of the paper is organized as follows: We introduce the signal and system model in Section 2. The outage probability analysis is given in Section 3. Section 4 presents the performance evaluations for the ergodic capacity. Numerical results are discussed in Section 5. Finally, Section 6 concludes the paper.
## 1 Introduction
We have developed a framework that can be used universally to interpret 3D PC data captured from different sources, including airborne laser scanning (ALS), mobile laser scanning (MLS), and terrestrial laser scanning (TLS). This is because it only relies on the spatial 3D geometry, represented as appropriate object surface measurements, as input from the real world. Some applications, such as smart cities, agriculture, autonomous vehicles (AVs), and mobile rover navigation, require a framework to tackle environmental obstacles. Examples of automatic systems that observe the environment include reverse engineering, AVs and robots, modeling, concurrent positioning, and navigation vision. In automated systems, sensors play a crucial role, and LiDAR is widely used in research projects for remote sensing of the earth's surface. This active sensor has improved object detection accuracy and provides a real-time, error-free environment, resulting in the generation of a local map of the area. Despite the widespread use of camera sensors in surveillance cameras, traffic signals, and similar devices, they are not appropriate for applications that require distance and lighting-based measurements, and their accuracy is limited. In the medical field, robots utilize 3D scanners for body scans, which require a high resolution. An accurate laser scanner, based on the triangulation principle, is used for this purpose. To prevent harm to human eyes during face scanning, these precise scanners use laser emitters classified as Class 1 or Class 1M. The motivation of this study is to make the different componentsuseable for end users who do not have specialist expertise by taking into consideration their ease of use, their efficiency, and their ability to be reproduced. The paper's primary contributions include,  
* The creation of a connection among 3D LiDAR PCs and cameras data for road recognition by using a unique approach. * The enhancement of autonomous navigation by choosing the most important geometric elements for route planning. * The use of four steps to pick neighborhoods, obtain classifying characteristics, and select features for an automated architecture in SS. The other phases of the studies are as follows: Section 2 offers a related review; Section 3 offers a proposed technique; Section 4 offers findings and perspectives; and Section 5 offers the conclusions.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 3 Results and Discussion
### Fine Particulate (PM\({}_{2.5}\))
#### 3.3.2 Model Performance  
To evaluate PM\({}_{2.5}\) modelling performance for each chemical mechanism, various statistical performance benchmarks were used. Figure 10 shows NMB, NME, FB, and FE at three monitoring sites during summer and winter.  
The model exhibits an underestimation bias in both summer and winter. In the summer, none of the chemical mechanisms meet the NMB, NME, FB, or FE criteria, though RACM2 performed slightly better than both SAPRC07 and CB6R3 in terms of lower bias and error. At the Grande Prairie site, all mechanisms operate better in the winter, and the RACM2 mechanism outperforms the other two chemical mechanisms.  
Beaverlodge  
At Beaverlodge as well, the model exhibits underestimation bias, like Grande Prairie. All three mechanisms miss the NMB, NME, FB, and FE goals and criteria and have similar performance, though RACM2 appears to outperform the other mechanisms slightly in both bias and error. The three mechanisms perform similarly during summer and winter.  
Figure 7: (a) Predicted average hourly PM\({}_{25}\) concentrations for (a) CH63 (summer), (b) CR63 vs. SAPPC07 \({}^{2}\) (summer), (c) CH63 vs. RACM2 2 (summer), (d) CR63 vs. SAPRC07 \({}^{2}\) (winner), (f) CR63 vs. RACM2 \({}^{2}\) (winner). \({}^{2}\) (liorP(SARPC07 - CR63/ CR63K)) \({}^{2}\) [liorP(RACM2 - CR63/ CR63K)].  
Figure 8: Comparison of CheR3, SAPRC07, and RACM2 predicted monthly mean values for (**a**) Nitrate, (**b**) Sulphate (**c**) Ammonium, (**d**) SOA-Anthropogenic, (**e**) SOA-Biogenic, (**f**) Total fine particulate (PM\({}_{\rm c}\), s).  
Figure 10: PM\({}_{2.5}\) model performance as measured by NMB, NME, FB, and FE at three ambient monitoring stations in summer (**left**) and winter (**right**).  
Figure 9: Percentage relative compositions of key species for PM\({}_{2.5}\) predicted during summer (**left**) and winter (**right**).  
Edson  
In the summer, all three mechanisms missed the NMB, NME, and FB criteria, but met the FE criteria. Winter performance is better. The NMB and NME criteria are met by all three mechanisms, with SAPRC07 outperforming the other mechanisms slightly. All three mechanisms meet the FB and FE goal, and again SAPRC07 performed the best, followed by RACM2 and CB6R3.