esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Monitoring System and Optimized Building Operation
### Data Preprocessing and Analysis
#### 3.1.3 Experimental Validation and Prediction Inaccuracy
The prediction models with the kNN and DTR methods each have strengths and weaknesses, but the kNN model represents the data in the baseline and peak range more strongly. The problem with both models is that both can often not correctly position the demand peaks, which suggests that the models could achieve better predictive performance through better orientation. Figure 21: Decision tree regression (DTR): Hourly demand and prediction over ca. 612 h.  
Figure 20: kNN: Daily demand and prediction over approximately 25 days. The MAPE (see Table 3) of the respective models roughly represents the results achieved in other studies with prediction models from the machine learning field [37]. With the results obtained so far, further methods from the field of machine learning are now being used to test their suitability as prediction models for the electrical energy demand of residential buildings. Furthermore, an analysis is carried out on how the dataset can be upgraded so that the procedures can better allocate peak demand. Here, for example, calendar information, such as school holidays, is implemented.
## 2 Properties of Fine Granular Area
### Influences Regarding the Formation Process of FGA
#### Influence of the Stress Ratio  
Stress ratio is known to influence the fatigue behavior in the HCF-regime, with higher mean stress reducing the fatigue limit as well as the fatigue lives. This influence is also observed for the fatigue in the VHCF-regime independent of loading type (see Figure 1) [54].  
The threshold value for long crack propagation \(\Delta\)K\({}_{\text{th}}\) is known to decrease with increasing load ratios [58; 59]. Furthermore, the load ratio is reported to additionally influence the FGA-formation process. It is often stated in literature that only negative load ratios lead to the formation of FGA and that the FGA vanishes with increasing stress ratios [14; 60; 61]. However, this statement does not sufficiently describe the different existing observations in regards to FGA-formation at different load ratios. [PERSON] et al. recently observed FGA-formation for a load ratio of R = 0 [62], while for a high-strength-bearing steel FGA-formation has also been observed for R = 0.1 [27; 63]. Other authors observed FGA-formation even for higher stress ratios of R = 0.3 [64] and even for R = 0.5 [65]. The FGA tends to be increasingly obscure when observed via secondary electron microscope (SEM) at higher  
Figure 1: S-N data for shot pened spring steel [54] (**A**) for torsion, (**B**) for tension.  
stress ratios, while the localized higher concentration of C (see Section 2.2.6) as well as the typical rough fracture surface morphology can still be observed in those cases [65].  
[PERSON] et al. [60] observed an increase in the highest number of cycles leading to failure and also a more pronounced fish-eye formation with increasing load ratios. They attribute both to the growing influence of cyclic creep. Additionally, it was shown that the \(\sqrt{area}-\text{Model}\) (see Section 2.2.1) is applicable for the load ratios R = -1, 0.1, and 0.5 within their test series, but is not valid for the load ratio of R = 0.7, because of the increasing influence of cyclic creep. The fracture surface for R = 0.7 corresponds to those of quasi-static fractures with high deformation and elongation of the specimen due to highly pronounced cyclic creep. The increasing influence of cyclic creep was also observed by [PERSON] and [PERSON] [66] at a load ratio of R = 0.5.
applied sciences  
The Influence of Flight Direction and Camera Orientation on the Quality Products of UAV-Based SfM-Photogrammetry  
[PERSON] 1,2C, [PERSON] 1, [PERSON] 3,4 and [PERSON] 1,*  
Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4 Dcapture software (Ver. 4.11.0)). The different combinations of these two main parameters (camera orientation and flight direction) resulted in 11 different flight cases for which individual planimetric and vertical accuracies were evaluated. Eleven flight sets of dense point clouds, DEMs, and ortho-imagery were created in this way to compare the achieved positional accuracies. One set was created using the direct georeferencing method (without using GCPs), while the other ten sets were created using the indirect georeferencing approach based on ground truth measurements of five artificially created GCPs. Positional accuracy was found to vary depending on the user-defined flight plan settings, despite an approximately constant flight altitude. However, it was found that the horizontal accuracy achieved was better than the vertical accuracy for all flight sets. This study revealed that combining multiple sets of images with different flight directions and camera orientations can significantly improve the overall positional accuracy to reach several centimeters.  
unmanned aerial vehicle (UAV); flight parameters; positional accuracy; indirect georeferencing; evaluation; accuracy analysis
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 5 Future Investigation  
While this paper provides encouraging results for a Transformer-based architecture (TransIQ), there are many possible future research directions. We introduced a new approach to process the signal data that come in IQ samples. This opens the door to further research on how to represent signals differently. In the future, we plan to combine various methods of signal representation, including amplitude/phase and time-frequency. This combination could give us more detailed information and help improve how well we can recognize and understand these signals.  
A key area for future research will also be to explore strategies for maintaining high recognition accuracy even with limited data samples. Moving forward, we aim to delve into the development of a Transformer-encoder designed to achieve high accuracy in scenarios characterized by small sample sizes. This direction not only addresses the challenge of data scarcity, but also enhances the method's applicability across various contexts where collecting large datasets is impractical. Additionally, the model's robustness may diminish due to the absence of crucial labels. Therefore, exploring semi-supervised or unsupervised learning methods presents a promising avenue for future research. These approaches could potentially enhance the model's ability to learn from limited or unlabeled data, thereby improving its overall effectiveness and reliability.  
In future work, we plan to explore the application of Transformer-based architectures within distributed environments, especially in scenarios where IoT devices are widely dispersed. Investigating the development and implementation of Transformers in such environments represents a significant avenue for advancing the application of Transformer techniques in IoT networks. An important aspect of this research will focus on handling multi-receiver datasets, where each receiver is subject to its unique channel effects. This direction aims to address the challenges of integrating sophisticated deep learning models like Transformers with the complex dynamics of distributed IoT systems, enhancing their ability to efficiently process and analyze data across various network conditions.
# A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands
## 1 Introduction
The S-band patch can be used as a TT&C antenna, while the X-band patch can be used to downlink the payload data collected by a CubeSat. This paper is organized as follows: Section 2 presents the detailed antenna geometry along with the design parameters. Simulated and measured results from the fabricated antenna are presented in Section 3. The performance of the proposed antenna is compared with other related work in Section 4. Section 5 has some concluding remarks.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 4 MROR System and Network Model
### Mobility-Enhanced Channel Availability Modeling  
The infrastructure-based primary network is assumed to be fixed with each cell comprising a base station and multiple receivers referred to as PUs. The PU network operates on a set of licensed channels C with similar temporal channel-usage statistics. Since the PUs are fixed, it is assumed that, both the (i) average density of PUs on each channel and (ii) temporal channel usage characteristics are known to the SUs. However, at any specific time, the SUs can only detect the availability of a channel via spectrum sensing. As highlighted, the proposed model considers, PU protection, channelestimation for mobile SU nodes and ensures the selection of stable routes. These entities are all jointly related as presented in the analytic formulations presented below.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 4 Experimental Results
### Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{\(n=2\)} & \multicolumn{2}{c}{\(n=4\)} \\ \cline{2-7}  & **BD-Rate** & **BD-Rate** & **Decoding and** & **BD-Rate** & **BD-Rate** & **Decoding and** \\  & **Y-PSNR** & **IV-PSNR** & **Runtime** & **Y-PSNR** & **IV-PSNR** & **Rentering** \\ \hline Chess & \(-\)9.3\% & \(-\)13.0\% & 59.0\% & \(-\)7.9\% & \(-\)4.2\% & 77.8\% \\ \hline Guitarist & \(-\)0.4\% & 1.3\% & 53.2\% & 26.5\% & 34.0\% & 86.3\% \\ \hline Hijack & \(45.3\%\) & 40.2\% & 65.3\% & 26.4\% & \(-\)43.0\% & 78.7\% \\ \hline Cyberpunk & \(-\)7.3\% & \(-\)5.0\% & 57.7\% & \(-\)6.1\% & \(-\)2.8\% & 71.2\% \\ \hline Kitchen & \(1.1\%\) & \(0.3\%\) & 56.5\% & 1.9\% & 1.4\% & 51.8\% \\ \hline Cadillac & \(0.3\%\) & 1.1\% & 54.8\% & \(-\)0.4\% & 1.1\% & 54.4\% \\ \hline Mirror & \(1.2\%\) & \(1.8\%\) & 56.7\% & 2.6\% & 2.6\% & 55.8\% \\ \hline Fan & \(0.1\%\) & \(0.7\%\) & 53.8\% & \(-\)2.2\% & \(-\)0.9\% & 54.9\% \\ \hline Group & \(40.4\%\) & 31.0\% & 58.2\% & 31.1\% & 124.6\% & 77.6\% \\ \hline Dancing & \(3.7\%\) & 2.4\% & 58.1\% & 5.5\% & 3.5\% & 55.1\% \\ \hline Painter & \(0.5\%\) & \(0.6\%\) & 57.9\% & 0.7\% & 0.0\% & 45.4\% \\ \hline Breakfast & \(-\)8.2\% & \(-\)5.2\% & 57.9\% & \(-\)12.9\% & \(-\)7.1\% & 60.7\% \\ \hline Barn & \(3.3\%\) & \(0.3\%\) & 59.6\% & 1.4\% & \(-\)1.1\% & 55.5\% \\ \hline Frog & \(0.2\%\) & \(0.2\%\) & 50.3\% & 0.2\% & 0.1\% & 33.7\% \\ \hline Carpark & \(12.0\%\) & \(7.8\%\) & 56.7\% & 15.7\% & 10.4\% & 53.8\% \\ \hline Street & \(-\)1.3\% & \(-\)1.3\% & 57.2\% & 3.3\% & 0.7\% & 49.9\% \\ \hline Fencing & \(-\)1.1\% & \(-\)3.3\% & 56.9\% & 45.9\% & 18.3\% & 41.0\% \\ \hline CBA Basketball & \(-\)25.4\% & \(-\)27.0\% & 57.2\% & \(-\)4.3\% & \(-\)9.3\% & 59.7\% \\ \hline MartialArts & \(-\)7.6\% & \(-\)10.8\% & 54.3\% & 32.9\% & 4.4\% & 64.2\% \\ \hline Average & \(1.6\%\) & \(0.7\%\) & 57.4\% & 7.2\% & 6.1\% & 60.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Figure 3: The comparison of depth maps estimated for the MartialArts sequence. Depth maps estimated using graph-cut optimization with: (**A**) alpha-expansion, (**B**) hierarchical alpha-expansion (\(n=2\)), (**C**) hierarchical alpha-expansion (\(n=4\)), (**D**) hierarchical alpha-expansion with second-cycle label shift (\(n=2\)), (**E**) hierarchical alpha-expansion with second-cycle label shift (\(n=4\)), (**F**) hierarchical alpha-expansion with second-cycle label shift and neighboring segments label examination.
## 2 Materials and Methods
### Antimicrobial Activity  
The antimicrobial efficacy of CNC-CTAB-encapsulated CBG, free CBG, and the carrier was examined against _Escherichia coli_ ATCC 25922, _Salmonella enteritidis_ ATCC 13076, _Listeria innocua_ ATCC 33091, and _Campylobacter jejuni_ ATCC 33560 following the Clinical and Laboratory Standards Institute (CLSI-M07-A9) guidelines. Mueller-Hinton agar was prepared with biocompatible concentrations of 0.12 g/L for the delivery system, 0.01 g/L (30 uM) for CBG, and 0.09 g/L for CNC-CTAB. The agar was inoculated with the microorganisms at 10\({}^{4}\) CFU/drop and incubated at 37 \({}^{\circ}\)C for 48 h, with _Campylobacter jejuni_ cultured in 5% O\({}_{2}\) conditions. After incubation, microbial growth inhibition was assessed visually by comparing the plates against a dark matte surface, where no microbial growth indicates antimicrobial activity against the tested microorganisms.
## 4 Performance Evaluation of the Proposed Framework  
The effectiveness of the proposed approach is validated through extensive simulations and a case study using real-time sentiment analysis on Twitter data of several institutions. The performance evaluation is done in terms of scalability and capacity of the Hadoop cluster and the average time  
Figure 2: Alarm threshold setting based on CPU usage.  
taken for all the real-time workloads to complete on the computing resources. A comparison is provided between the designed static and auto-scaling cluster of the relaying Hadoop schemes, such as MapReduce and Ganglia. The four Hadoop cluster setups as previously described are considered: (1) three-slave cluster, (2) seven-slave cluster, (3) fourteen-slave cluster, and (4) auto-scaling cluster. The time consumed by each map reducing jobs on the created Hadoop clusters are recorder over one-hour and three-hour time frames.
## 3 On-Site Monitoring Arrangement
### Transverse Ground Settlement  
According to the site monitoring arrangement, the 6#-11# measuring points at monitoring section II and is 75 m away from the operation shaft. The monitoring results of transverse ground settlement in the process of the two-layer pipe jacking are shown in Figure 4. In the Figure 4, \(L_{2}=-10\) m and \(L_{2}=10\) m mean the cutterhead of jacking machine is 10 m for reaching or passing through the monitoring section II, respectively, and the \(L_{2}=0\) m represents that the cutterhead is located on the right in section II.  
Figure 4: Transverse ground settlement.  
Figure 3: Layout profile of on-site monitoring points.  
It can be seen from Figure 4 that: (1) The ground settlement of lower pipe jacking in the soil-rock stratum is about 0.8 mm, and the ground settlement of the upper pipe is about 2.3 mm, which is almost 3.0 times that of the lower pipe; (2) the curve of the transverse ground settlement caused by the lower pipe jacking is approximately horizontal, while the transverse ground settlement decreases significantly from the middle to both sides of the upper pipe, and the horizontal influence range exceeds 2D (D is the outer diameter of the jacking tube).
## 5 Test Systems and Simulation Cases
### IEEE 69-Bus System  
This test system works at 12.66 kV, which is composed of 69 nodes, 68 branches, and a unique slack node located at node 1. The configuration of this feeder is shown in Figure 3b, and its parameters can be consulted in [3]. Its initial active power losses are equal to 224.9520 kW. The total active and reactive power demands of this system are 3890.7 kW and 2693.6 kVA, respectively. The voltage and power base values chosen are 12.66 kV and 1 MW, respectively.  
Figure 3: Electrical configuration of the test systems: (**a**) IEEE 33-bus system and (**b**) IEEE 69-bus system.
###### Abstract  
In this article, a second-order iterated Circular Minkowski fractal antenna (CMFA) tailored for ultra-wideband (UWB) applications is designed and developed. Leveraging the power of Minkowski fractal geometry, this antenna design achieves a high gain across the UWB frequency spectrum. The design utilizes a circular groove on the ground plane and an arc slot on the radiating element for improving the antenna performance. The proposed antenna is fabricated using cost-effective material, an FR-4 substrate. The antenna is simulated and optimized. The fabricated optimized antenna undergoes real-world testing. Measured results reveal an impressive 120.6% impedance bandwidth spanning from 3.37 GHz to 13.6 GHz, with resonant frequencies at 4.43 GHz, 6.07 GHz, and 9.3 GHz. Meanwhile, the simulated results indicate an impedance bandwidth of 118% ranging from 3.17 GHz to 12.44 GHz. Real-world measurements validate the anticipated UWB traits, closely aligning with the simulation data, and confirming efficient impedance matching with a VSWR of less than 2 across the 3.37 GHz to 13.6 GHz frequency range. The radiation pattern analysis demonstrates a robust bidirectional E-plane pattern and a nearly omnidirectional H-plane pattern. This research introduces a highly promising circular Minkowski fractal antenna for UWB applications, offering exceptional bandwidth and resonance characteristics. This antenna design holds excellent potential for multi-functional wireless systems and opens avenues for enhanced UWB communication and sensing capabilities in diverse applications.  
Ultra-wideband; high gain; reflection coefficient; circular Minkowski fractal; partial ground; microstrip feedline +
Footnote †: journal: Medical Imaging  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Citation**: [PERSON]; [PERSON]; [PERSON] Design and Performance Evaluation of a Second-Order Iterated Circular Minkowski Fractal Antenna for Ultra-Wideband Applications. _Fractal_ Part. **2023**, 7, 806. [[https://doi.org/10.3390/fractal/?T10806](https://doi.org/10.3390/fractal/?T10806)]([https://doi.org/10.3390/fractal/?T10806](https://doi.org/10.3390/fractal/?T10806))
# Buckling Analysis of CNTRC Curved Sandwich Nanobeams in Thermal Environment
[47] exploited the strain-driven nonlocal integral theory to study the bending response of isotropic doubly curved high-order shear deformation nanoshell under a combined assumption of exponential and trigonometric shape functions. Furthermore, [PERSON] and [PERSON] [48] exploited the nonlinear stability and vibration of imperfect CNTs modeled as Euler-Bernoulli beams with a mid-plane stretching, while in [49; 50; 51], the authors studied the free and forced vibration and the dispersion behavior of elastic waves of doubly-curved nonlocal strain gradient theory nanoshells in conjunction with a higher-order shear deformation shell theory. Based on the available literature, however, the influence of a material scale, size scale, and graduation distribution functions on the thermal static stability of curved sandwich nanobeams with temperature-dependent material seems to be generally lacking. To this end, the present paper aims at providing a closed-form solution to the problem, for different boundary conditions, that could be useful as theoretical benchmark for different computational studies and engineering design applications. The paper is organized as follows. In Section 2, the theoretical formulation of curved sandwich CNTRC nanobeams is reviewed, including the kinematic field, relations and constitutive equations. Section 3 illustrates the governing equilibrium equation of curved sandwich beams in a classical and nonclassical domain, while discussing about different thermal field distributions and temperature-dependent properties of materials. Section 4 presents the analytical solutions of the problem for different boundary conditions, whose comparative study is performed systematically and discussed in Section 5. Finally, in Section 6, conclusions are drawn together with possible future research directions.
## 2 Results
### The Impact of NMIBC and MIBC on Urinary Volatile Profile  
Concerning a possible distinction of the two NMIBC stages based on the urinary volatile, the PLS-DA model of stage T1 vs. Ta/Tis (43 observations \(\times\) 495 variables, Figure S3) unveiled some differences, with an AUC of 0.837, 71% sensitivity, 73% specificity and 72% accuracy. Patients diagnosed with stage T1 showed higher urinary levels of decane, octanal and one unknown metabolite (unknown 7), and lower levels of levomenthol and another unknown (unknown 8) (Table S3). However, none of these metabolites retained statistically significant differences after FDR correction.  
To evaluate the differences between MIBC vs. NMIBC, two comparisons were performed after variable selection, namely stages \(\geq\)T2 vs. stage Ta/Tis (36 observations \(\times\) 486 variables, Figure 2a) and stages \(\geq\)T2 vs. stage T1 (27 observations \(\times\) 856 variables, Figure 2c), due to the discrepancy in the sample size of NMIBC and MIBC cohorts. Similar classification performances were found for both comparisons with AUC of 0.938, 80% sensitivity, 92% specificity and 89% accuracy for distinction of stages \(\geq\)T2 from stage Ta/Tis (Figure 2b), and AUC of 0.894, 70% sensitivity, 94% specificity and 85% accuracy for distinction of stages \(\geq\)T2 from stage T1 (Figure 2d).  
Figure 2: **(a)** PLS-DA scores scatter plot and **(b)** ROC curve obtained for urinary volatile profile (VOCs and VCCs concatenated matrix), after variable selection (36 observations \(\times\) 486 variables), of patients diagnosed with MIBC (stages \(\geq\) T2, green squares, \(n\) = 10) and NMIBC (stage Ta/Tis, blue circles, \(n\) = 26). **(c)** PLS-DA scores scatter plot and **(d)** ROC curve obtained for urinary volatile profile (VOCs and VCCs concatenated matrix), after variable selection (27 observations \(\times\) 856 variables), of patients diagnosed with MIBC (stages \(\geq\) T2, green squares, \(n\) = 10) and NMIBC (stage T1, red circles, \(n\) = 17).  
The heatmap depicted in Figure 3 represents the urinary metabolites differing between MIBC and the two NMIBC groups, which are listed in detail in Tables S4 and S5. These results unveiled that urines of patients diagnosed with MIBC were characterized by higher levels of alkanes (2,4-dimethylheptane, 4-methyloctane and decane), aldehydes (formaldehyde and methylglyoxal), aromatic compounds (1,2,3-trimethylbenzene, 1,2,4-trimethylbenzene and 1,2,4,5-tetramethylbenzene) and two unknowns (unknown 8 and 9), and lower levels of 2,5-dimethylbenzaldehyde. These findings suggest that NMIBC and MIBC have distinct urinary volatile profiles, requiring further investigation in larger cohorts.