esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying the Training and Match-Play External and Internal Load of Elite Gaelic Football Players
## 3 Results
### Analysis of External Load by Phase  
The external load of elite Gaelic footballers across specific variables is presented within Table 2. Analysis of variance revealed significant main effects for weekly distance covered across phases (F = 389.3, \(p\) < 0.05). Total distance in training was significantly greater during pre-season 1 when compared to all other phases of the season (_p_ < 0.001; ES: 2.95-7.22; _very large_). Within the in-season period training based total distance was higher during provincial 1 when compared to other phases (_p_ < 0.05; ES: 0.16-2.32; _trivial-large_). When match-play total distance was considered championship 2 had the highest total distance (_p_ < 0.05; ES: 0.10-0.67; _trivial-moderate_) when compared to other phases followed by championship 1 and in-season 2 periods. Similar trends were observed for high-speed running and very high-speed running. When sprint distance was considered the pre-season 1 phase (_p_ < 0.05; ES: 0.33-1.69; _small-large_) showed the highest sprint distance completed with this significantly reduced across phases for the duration of the season (Table 2). The distribution of high-power meters (m; \(\geq\) 25 W\(\cdot\)Kg\({}^{-1}\)) and average metabolic power (P\({}_{\text{meti}}\), W\(\cdot\)Kg\({}^{-1}\)) across seasonal phases is shown in Figure 2. Increases in loading were seen between pre-season and in-season 1 (_p_ = 0.038; ES: 0.32; _small_). Additionally, increases in loading were noted between club 1 1 and provincial 1 (_p_ = 0.021; ES: 0.98; _moderate_) and pre-season 1, in-season 1 (_p_ = 0.021; ES: 0.89; _moderate_) and in-season 2 (_p_ = 0.004; ES: 1.45; _large_). When average metabolic power was considered this variable was seen to increase from preseason phase to in-season 1 (_p_ = 0.045; ES: 0.78; _moderate_) with further increases noted during the provincial 1 phase (_p_ < 0.05; ES: 0.58-1.44; _moderate-large_) when compared to the rest of the seasonal phases.
## 2 Materials and Methods  
### General Information  
The synthetic products in this article were confirmed by Bruker Avance 300 for proton NMR spectroscopy(Bruker, Billerica, MA, USA), and JEOL JMS-AX505 WA (JEOL Ltd., Tokyo, Japan), HP5890 series II (Hewlett Packard, Wokingham, UK)for fast atom bombardment (FAB) mass spectroscopy. The optical properties were evaluated using HP 8453 UV-VISNIR spectrometer (Agilent, Santa Clara, California, USA) for UV-Vis absorption, and Perkin-Elmer luminescence spectrometer LS50 (Perkin-Elmer, Waltham, MA, USA) (Xenon flash tube) for PL and EL. TGA offered degradation temperatures (T\({}_{\text{d}}\)). Non-doped OLED devices for blue emission were built in the structure of indium tin oxide (ITO)/2-TNATA (60 nm)/NPB (15 nm)/synthesized material (35 nm)/Alq\({}_{3}\) (20 nm)/LiF (1 nm)/Al (200 nm): 2-TNATA stands for 4,4',4''-tris(N-(2-naphthyl)-N-phenylamino)-triphenylamine, and constituted the hole injection layer (HIL); N,N'-bis(naphthalene-1-yl)-N,N'-bis(phenyl)benzamide (NPB) made up the hole transporting layer (HTL); Tris(8-hydroxyquinolinao)aluminium (Alq\({}_{3}\)) formed the electron transporting layer (ETL); the  
Figure 1: Chemical structures of fused new core and the synthesized materials.  
electron injection layer (EIL) comprised lithium fluoride (LiF); and ITO served as the anode, while aluminum (Al) was employed as the cathode. Organic molecules were thermally evaporated at a vacuum base pressure of 10\({}^{-6}\) Torr, and formed an emitting layer of 4 mm\({}^{2}\) with a deposition rate of 0.1 nm/s. The Al layer was also formed in pressure of 10\({}^{-6}\) Torr. A white OLED device by solution processing in the structure of ITO/PEDOT:PSS (40 nm)/TP-ASF: 0.2 or 0.4 wt% rubrene (50 nm)/TPBi (30 nm)/LiF/Al. PEDOT:PSS was applied as HTL, and TP-ASF was used as a blue dopant as well as a host for rubrene. The role of a red dopant was played by (5,6,11,12)-Tetraphenylnaphthalene (rubrene). Chlorobenzene was chosen for a solvent. PEDOT/PSS mixtures (Baytron PVP CH8000, H. C. Starck GmbH) in a water dispersion were spin-coated over ITO anodes in the atmosphere. The spin-coated films were subjected to baking in ambient conditions on a hot plate at 110 \({}^{\circ}\)C for 5 min, followed by baking at 200 \({}^{\circ}\)C for 5 min in a nitrogen (N\({}_{2}\)) environment. After spin-coating to achieve a thickness of 50 nm under a nitrogen atmosphere, the emitting layer was subjected to baking on a hot plate at 80 \({}^{\circ}\)C for 30 min. Electron-transporting layers made of TPBi, the deposition of LiF and Al layers was carried out under the same condition as applied to the nondoped OLED devices. The current-voltage (I-V) characteristics of the fabricated OLED devices were assessed using a Keithley 2400 Source Meter (Keithley, Cleveland, OH, USA). The EL spectrum of the devices was measured by a Minolta CS-1000A spectroradiometer (Konica Minolta, Toyko, Japan).
###### Abstract  
Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations. To overcome these challenges, we propose an intelligent detection system that can accurately identify instances of illegal wearing of power construction workers in real-time. Firstly, we integrated the squeeze-and-excitation (SE) module into our convolutional neural network to enhance detection accuracy. This module effectively prioritizes informative features while suppressing less relevant ones, resulting in improved overall performance. Secondly, we present an embedded real-time detection system that utilizes Jetson Xavier NX and Edge-YOLO. This system promptly detects and alerts power construction workers of instances of illegal wearing behavior. To ensure a lightweight implementation, we design appropriate detection heads based on target size and distribution, reducing model parameters while enhancing detection speed and minimizing accuracy loss. Additionally, we employed data augmentation to enhance the system's robustness. Our experimental results demonstrate that our improved Edge-YOLO model achieves high detection precision and recall rates of 0.964 and 0.966, respectively, with a frame rate of 35.36 frames per second when deployed on Jetson Xavier NX. Therefore, Edge-YOLO proves to be an ideal choice for intelligent real-time detection systems, providing superior accuracy and speed performance compared to the original YOLOv5s model and other models in the YOLO series for safety monitoring at construction sites.  
Edge-YOLO; YOLOv5s; edge device; power construction +
Footnote †: journal: Applied Sciences  
1
## 3 Analysis of Calculation Results
### Wind Turbine Wake Flow Field Characterization  
#### 3.2.1 Wake Turbulence Intensity  
Turbulence intensity reflects the relative strength of pulsating wind speed and can characterize the degree of chaos in the flow field. Figure 7 shows the distribution of turbulence intensity in the wind turbine wake flow field for different numbers of lay-up layers. The turbulence intensity in the wind turbine wake flow field shows a trend of increasing, then decreasing, and then increasing, which is due to the interaction between the tower and the hub in the range of 0-2\(D\). The tower shadow effect can strengthen the turbulence intensity in the wind turbine near the wake flow field, while the tip disturbance in the range of 2-5\(D\) is the main reason for the turbulence intensity in this section to increase.  
Figure 5: Stress values along blade spread.  
Figure 6: Strain along blade spread.  
Figure 7: Turbulence intensity curves of different sections.  
The spatial distribution trend of turbulence intensity in the flow field of wind turbine blades with different numbers of layers is the same. However, comparing with the same relative radius position, with the increase in the number of layers, due to the uneven change in blade deformation, the trend of the turbulence intensity change is more gentle, and the degree of the flow field disorder after blade winding is smaller. Overall, the turbulence intensity in different cross-sections of the wake of the blade with 44 layers is always lower than that of the blade with 16 layers. This indicates that the larger blade deformation disturbs the flow field around the wind turbine more, leading to flow field chaos.
## 1 Introduction  
### Background  
Flood disasters are a persistent problem globally, affecting millions of people and causing extensive economic damage every year. One of the primary reasons for these flood disasters is the inadequacy of urban drainage systems, particularly imperfect or inefficient drainage networks. As urbanization continues to intensify, cities are becoming increasingly vulnerable to heavy rainfall events, which often overwhelm existing drainage infrastructure. The drainage network, as a core component of urban stormwater management, plays a vital role in mitigating the impacts of extreme weather conditions. However, many of these networks suffer from outdated designs, aging infrastructure, and insufficient capacity, leading to frequent urban flooding [1]. Optimizing the layout and operational parameters of urban drainage networks has thus become a critical measure for flood prevention. Effective parameter optimization can significantly enhance the performance of existing networks, allowing cities to control excess surface runoff and reduce flooding incidents effectively [2, 3].  
Common optimization techniques include dynamic programming, simulated annealing, and genetic algorithms. Dynamic programming is well suited for problems with overlapping subproblems and optimal substructure properties, but it is computationally intensive [4]. Simulated annealing avoids local optima by mimicking the physical annealing process; however, its convergence is relatively slow, requiring extended computation time [5]. In contrast, genetic algorithms simulate natural selection and genetic mechanisms (e.g., selection, crossover, and mutation) to explore the solution space effectively, avoiding local optima and exhibiting strong global search capabilities. Furthermore, genetic algorithms have been widely applied to multi-objective optimization problems in urban drainage system optimization, demonstrating excellent performance. Therefore, the adoption of genetic algorithms is more suitable for addressing complex urban drainage network optimization problems, improving both optimization efficiency and the reliability of the results [6].
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
## 5 Experiment
### Scalability of HMP-GMR  
The first experiment examined improvement in scalability of HMP-GMR over HMP and Hybrid-JT.  
The initial setting of this experiment consists of (1) maximum of 4 components output by GMR and (2) 100 iteration limit for each of HMP and HMP-GMR. Eight CG BNs (i.e., conditional linear/nonlinear cases for CG BNs 1, 2, 3 and 4) were run with HMP, HMP-GMR and Hybrid-JT using the following inputs and outputs. The input value of \(n\) for both BNs was varied from 1 to 10. The output value is the execution time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **CG BN 1** & **CG BN 2** & **CG BN 3** & **CG BN 4** \\ \hline Cycles & 21 & 501 & 0 & 21 \\ Combinations of discrete states & 4 & 4 & 16,384 & 16,384 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of four CG BNs with \(n=7\).  
Figure 3: Additional Conditional Gaussian BNs.  
Figures 4 and 5 show the results of this experiment summarizing the execution times (milliseconds) for the CLG case as the number of nodes \(n\) is varied. The solid line denotes the HMP-GMR results. The dashed line denotes the HMP results. The dotted line denotes the Hybrid-JT results.  
Results for CG BNs 1 (Figure 3(a)) and 2 (Figure 3(b)) show a similar pattern. The HMP algorithm with no GMR exceeded the time limit at \(n=7\) and \(n=4\), respectively. Execution times for HMP-GMR were higher than those for Hybrid-JT in both cases. The increase in execution time for both HMP-GMR and Hybrid-JT was linear in \(n\).  
In Figure 5, results from HMP and Hybrid-JT show exponential growth in execution time, while execution time of HMP-GMR increased linearly. For HMP, the execution time limit for CG BNs 3 (Figure 4(a)) and 4 (Figure 4(b)) was exceeded at \(n=7\) and \(n=4\), respectively. For Hybrid-JT, the execution time limit for CG BNs 3 and 4 was exceeded at \(n=9\).  
Results for the CNG networks showed similar patterns and are not shown here for brevity. These experiments showed that HMP-GMR is scalable to large BNs for both linear and nonlinear CG networks. However, scalability alone is not sufficient. Accuracy and good operational performance are also essential.
# An Overview of GEO Satellite Communication Simulation Systems
## 3 Resource Management and Scheduling
### User Fairness  
As service types have evolved from traditional broadcasting to encompass data links, Internet of Things (IoT) backhaul, and cross-domain relaying, service requirements have become increasingly diverse and demanding. Therefore, another important objective is ensuring user fairness and QoS, where scheduling algorithms strive to balance resource allocation across users while meeting their respective service-level requirements. To fulfill the service requirements of different users in the GEO-LEO communication network, a multi-dimensional resource allocation with incomplete information was investigated to maximize the revenue of the access satellite under constraints of data transmission and energy consumption, which is the sum of priorities of the successfully accessed users [22]. Especially, a model-free reinforcement learning framework was employed to pre-train the historical network data to compensate for the shortcomings caused by incomplete information. In [23], a fairness-aware flow scheduling problem for network utility maximization was investigated with joint consideration of resource allocation and rate control. The tradeoff between throughput and fairness was analyzed. Furthermore, a flexible satellite resource assignment design was proposed in [24], whose goal is to satisfy the heterogeneous traffic demand while maximizing the satellite spectrum utilization by jointly optimizing the transmit power and bandwidth allocation. In [25], a novel user scheduling and power allocation design capable of providing guarantees in terms of the individual QoSrequirements while maximizing the system throughput under a limited power budget was proposed. Results demonstrated that this mechanism can provide better QoS satisfaction combined with outstanding overall system throughput.
## 4 Waveguide Characterization
The longer waveguides also allowed us to assess the waveguide's group index more accurately: with an FSR of 0.4 nm +/ -0.006 nm for the 1.928 mm long waveguide, we obtained an estimated value for \(\mathrm{n_{g}}\) of 1.55. Figure 11: (**a**) Measured insertion loss variation of five identical waveguide samples of equal total length (1728 \(\upmu\)m). (**b**) Loss regression analysis for estimation of waveguide propagation loss. Figure 10: (**a**) HRSEM image of the fabricated spiral waveguide, consisting of four fabricated waveguide structures in parallel. (**b**) HRSEM image of the spiral-shaped waveguide, supported with a narrow fin. We can now estimate the waveguide propagation loss more accurately, by fitting a trend line relating the reduction in fringe visibility (due to increased cavity losses attributed to propagation losses) to the waveguide length (Figure 11b). The relationship between propagation loss, \(a\), waveguide length, L, end-facet reflection, R, and fringe visibility, v, is given by (detailed derivation in the Appendix A):  
\[\alpha\cdot\mathrm{L}-\ln(2\mathrm{R})=-\ln(\mathrm{v}) \tag{3}\]  
Equation (3) indicates that if we plot the measured term \(-\ln(\mathrm{v})\) for the different waveguide lengths, then the slope of the regression line will measure the propagation loss and the intercept of the curve at zero length will provide the end reflection. Using this technique, we found propagation loss values of 2.9 dB/mm and 3.4 dB/mm for the LP01x and LP01y polarizations, respectively (the OVA presents the principal modes without distinguishing them. We ascribe the higher loss polarization of the LP01y mode to the impact of the nano-fin). The end-facet reflection values were 0.055 and 0.08 for the LP01x and LP01y polarizations, respectively. These estimated values of Fresnel reflections were slightly high, but within the right range. We attribute the high propagation losses to the roughness of side wall of the waveguide, as shown in Figure 8c. Propagation losses due to surface roughness scale with core-cladding index contrast and mode intensity at the core-cladding interface [28]. For air-cladded nano-fabricated waveguides, these two contributing factors are high, which is the likely cause of our relatively high propagation loss values. However, since the overall length of these waveguides are envisaged to be short, the main loss contributor will originate from external coupling, which can be better optimized.
## 2 Data and Methods
### Reference Data
#### 2.1.3 Geophysical Model Function  
The GMF, CMOD5.n, provides the C-band VV NRCS as a function of the incidence angle, wind direction (relative to the radar look direction), and 10 m neutral wind speed [21]. It takes the following general form:  
\[\sigma^{0}(\theta,U_{10},\varphi)=B_{0}(\theta,U_{10})(1+B_{1}(\theta,U_{10}) \cos\varphi+B_{2}(\theta,U_{10})\cos 2\varphi)^{1.6} \tag{1}\]  
where \(\sigma^{0}\) is the NRCS in natural units (not in decibels), \(\theta\) is the incidence angle, \(U_{10}\) is the 10 m neutral wind speed, and \(\varphi\) is the azimuthal wind direction angle. \(B_{0}\), \(B_{1}\), and \(B_{2}\) are functions of \(\theta\) and \(U_{10}\) and were obtained by fitting (1) to the European Remote Sensing Satellite (ERS) scatterometer data. The mean absolute difference between CMOD5.n and the ASCAT measurements was about 0.3 dB, and the root-mean-square difference was less than 2 dB [22].  
When such a GMF is applied to HH-polarized scattering, polarization ratio (PR) models have to be used for converting VV- into HH-NRCS. A prevalent C-band PR model provided by [PERSON] et al. [23,24] was used in this paper:  
\[\mathrm{PR}=\frac{\left(1+2\tan^{2}\theta\right)^{2}}{\left(1+\alpha\tan^{2} \theta\right)^{2}} \tag{2}\]  
where \(\alpha=0.6\).
## 1 Space-Bore X-ray and Gamma-ray Observational Research  
The recent progress in CubeSats [1, 2] technologies allows for novel scientific payloads and fast-track scientific missions to be considered and developed. Progress includes small spacecraft steering capabilities and attitude control which bring additional possibilities for CubeSat missions such as tandem flights or CubeSat constellation fleets [3, 4]. The tandem flight arrangement can, for example, allow for the deployment of payloads with focal lengths exceeding the typical dimensions of nanosatellites.  
In this direction there is a recent effort to develop innovative high-sensitivity but miniature instruments to exploit these new possibilities. The payloads should ideally be of small size with highly integrated electronics and low power consumption [5, 6].  
The X-ray and Gamma-ray sky is rich in many types of variable, transient, and flaring stellar sources. In addition to the space sources, there are also high-energy triggers in and above Earth's atmosphere such as terrestrial Gamma-ray flashes (TGF) and auroral emissions [7]. For purposes of imaging of such sources on space-borne deployments, the focusing of hard X-rays above 100 keV as well as of gamma rays is particularly challenging. Most space imaging experiments in hard X-rays and soft Gamma-rays in the past make use of coded masks which are heavy and ineffective.
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 2 Materials and Methods  
Work in this study is divided in three main tasks; hardware development, programming and software development, and system evaluation (laboratory and in-field performance evaluation).
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
###### Abstract  
The Internet of Medical Things (IoMT) overcomes the flaws in the traditional healthcare system by enabling remote administration, more effective use of resources, and the mobility of medical devices to fulfil the patient's needs. The IoMT makes it simple to review the patient's cloud-based medical history in addition to allowing the doctor to keep a close eye on the patient's condition. However, any communication must be secure and dependable due to the private nature of patient medical records. In this paper, we proposed an authentication method for the IoMT based on hyperelliptic curves and featuring dual signatures. The decreased key size of hyperelliptic curves makes the proposed scheme efficient. Furthermore, security validation analysis is performed with the help of the formal verification tool called Scyther, which shows that the proposed scheme is secure against several types of attacks. A comparison of the proposed scheme's computational and communication expenses with those of existing schemes reveals its efficiency.  
dual signature; internet of medical things; authentication; scyther tool; hyperelliptic curve +
Footnote †: journal:  
00 2023 by the authors. Licensee 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/))).
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.1 Rate Controlling Performance
The correlation that exists between the three parameters is, however, ignored when performing the rate control, which results in large average rate-controlling errors, as illustrated in the Table 1. The rate-controlling algorithm in HM 16.0 which follows a R-\(\lambda\)-based bit allocation and coding parameter selection approach also shows some deficiency in achieving the allocated bit budget for each frame. However, as illustrated in the Table 1, the HM 16.0 encoder still demonstrated a 1.75% error in its rate-controlling function. In contrast, the proposed algorithm enables the encoder to effectively utilize the correlation between the three parameters to perform rate and decoding-complexity allocation, and appropriate coding mode selection, resulting in a smaller percentage bit error (illustrated in the bottom row of Figure 2 and Table 1). Moreover, the parameter update process in Section 4.3 keeps the algorithm content-adaptive, further minimizing the rate control error.