esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
## 3 Proposed Empirical Models
### The Logistic-Bell Empirical Model  
The logistic-bell empirical model (LBEM) is an alternative rheological model that has few parameters that can be easily linked to typifying quantities of the DMA data, but is still fast to compute and can describe the responses of real viscoelastic materials over a wide frequency range. From data gathered in the literature, it can be observed that in the logarithmic scale the storage modulus reassembles the shape of a logistic function and the loss tangent approximates a bell-shaped function. Therefore, a variation of the logistic function is proposed for modelling the storage modulus, as follows:  
\[\log_{10}\left(E^{\prime}(\omega)\right)=ry_{o}+\frac{L}{1+e^{-k\left(\log_{10 }(\omega)-r\omega_{0}\right)}} \tag{10}\]  
where:  
* \(E^{\prime}\) is the real or storage modulus in (Pa) based on a logistic function.
* \(ry_{o}\) is a vertical shift.
* L is the curve's maximum value.
* k the logistic rate of increase or steepness of the curve.
* \(\omega\) is the excitation frequency in rad/s.
* \(r\omega_{0}\) is the logarithm in base 10 of the frequency value at the sigmoid midpoint.  
The loss tangent is modelled using a piecewise bell-shaped function:  
\[\tan\delta(\omega)=\begin{cases}sy_{1}+(p-sy_{1})sech\frac{\log_{10}(\omega)- t\omega_{0}}{r_{1}}\;\;if\;(\log_{10}(\omega)-t\omega_{0})\leq 0\\ sy_{2}+(p-sy_{2})sech\frac{\log_{10}(\omega)-t\omega_{0}}{r_{2}}\;if\;(\log_{10 }(\omega)-t\omega_{0})>0\end{cases} \tag{11}\]  
Figure 4: Demonstration of linear (**left**) and higher degree (**right**) polynomials with the critical boundary points that initiate curvature, for the storage modulus of SBR with no phr carbon black [20].  
where:  
* \(\tan\delta\) is the loss tangent.
* \(t\omega_{0}\) is the logarithm in base 10 of the frequency at the bell's midpoint or peak.
* \(sy_{1}\) is the bell function vertical shift at low frequencies before the peak.
* \(sy_{2}\) is the bell function vertical shift at high frequencies after the peak.
* \(p\) is the bell function peak value.
* \(\omega\) is the excitation frequency in \(rad/s\).
* \(r_{1}\) is the bell's rate of increase at low frequencies before the peak.
* \(r_{2}\) is the bell's rate of increase at high frequencies after the peak.  
Finally, the loss modulus can be derived from Equation (8).
## 1 Introduction
Moreover, the process of forming crystals in bioceramic sealers such as carbonate apatite (CO\({}_{3}\)Ap) is crucial for their bioactivity and their sealing ability in endodontic procedures. Recently, there has been a development in the field of root canal therapy with the advent of premixed flowable sealers. Unlike other formulations, these materials are pre-prepared and do not require blending, as their solidification process occurs when moisture is exposed [14]. Scientists have documented that those materials containing calcium silicate have a beneficial interaction with the tissues surrounding the apex of a tooth without causing any inflammation or adverse reactions from the body [12; 13; 14]. The chemical composition of hydraulic calcium silicate-based sealers is broadly consistent among various brands. Tricalcium silicate is the main component responsible for the cementitious properties [15]. Root canal sealers must comply with the specifications stated in ISO 6976:2012 [16]. AH Plus(r) Bioceramic Sealer (Dentsply, Konstanz, Germany) is a pre-made sealer primarily consisting of zirconium dioxide (50-70%) as a radiopacifier and tricalcium silicate (10-15%) as a bioactive ingredient. In addition, the producer states that the product contains dimethyl sulfoxide, small amounts of lithium carbonate, and substances used to thicken it. This study aimed to evaluate the bioactivity of AH Plus(r) Bioceramic Sealer (Dentsply, Konstanz, Germany) by analyzing the concentrations of bioactive elements in dentin at the interface with the bioceramic sealer. Furthermore, this study can highlight several aspects associated with these sealers that pertain to their performance, efficiency, and bioactive interactions with dentin. Even though bioceramic materials have proven to be biocompatible and capable of forming hydroxyapatite at the dentin-sealer interface, more studies are needed to evaluate their long-term efficacy, particularly regarding their porosity and chemical stability [17; 18; 19; 20; 21]. The objective of this study was to evaluate the elemental composition of dentin at the interface with a bioceramic sealer using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) and to assess the bioactive potential of the constituent elements at the sealer-dentin interface.
## 2 Study Area and Datasets
### Ground Observations  
In the study area, there are 17 stations (Figure 1) equipped with Automatic Meteorological Station Systems and EC Systems that are both automatic data acquisition systems. The Automatic Meteorological Station System can acquire meteorological variables, including air temperature, humidity, air pressure, solar radiation, wind speed, wind direction and so on, at sample intervals of 10 min and one minute. The EC System can monitor surface fluxes such as sensible heat flux, latent heat flux and so on, at a sample interval of 30 min.  
All data mentioned above were provided by the HiWATER experiment which is an watershed-scale and eco-hydrological experiment designed from an interdisciplinary perspective to address problems that include heterogeneity, scaling, uncertainty and closing of the water cycle at the watershed scale [33]. The experiment was performed in the Heihe River Basin. Users can attain the data by submitting an application to the data center, Cold and Arid Regions Science Data, at Lanzhou ([[http://westdc.westgis.ac.cn/](http://westdc.westgis.ac.cn/)]([http://westdc.westgis.ac.cn/](http://westdc.westgis.ac.cn/))).
## 2 Methods  
### Subjects and Data Collection  
Five healthy subjects (4 males, 1 females, age 25.2 \(\pm\) 2.5 years, height 1.75 \(\pm\) 0.11 m, mass 66.8 \(\pm\) 12.2 kg) and five individuals with early stage PD (2 males, 3 females, age 62.8 \(\pm\) 3.9 years, height 1.72 \(\pm\) 0.03 m, mass 77.5 \(\pm\) 17.88 kg, Hoehn and Yahr stage 1 or 2) participated in the study after providing written informed consent to participate in the protocol approved by the Institutional Review Board at The University of Texas Southwestern Medical Center. Patients did not have a deep brain stimulator implanted and were medicated with their prescribed dosage at the time of experiments. Sixty-six reflective markers were attached to anatomical body locations to track 12 body segments of upper-limb, lower-limb, and trunk (Figure 1B). Upper-limb segments include left and right forearms and upper arms. Lower-limb segments include left and right thighs, shanks and feet. Trunk segments includes torso and pelvis [19; 20]. A 10-camera optical motion capture system (Vicon, Motion Systems Ltd., Oxford, UK) was used to capture marker trajectories at 100 Hz in three-dimensional space. Experimental setup consisted of a \"terrain park\" circuit including an over-ground walkway, a four-step staircase with step height of 0.15 m and depth of 0.30 m, a 2.5 m ramp inclined at 10\({}^{\circ}\), and elevated platforms to connect the stairs and ramp (Figure 1A). Participants were asked to perform sets of four walking trials where they alternated starting point (A and B) as well as beginning the trial with their left leading and right leading leg (Figure 1A). Healthy subjects performed five sets while using the handrails, and five sets without using the handrails (total of 40 trials per person). Individuals with PD were asked to walk at their comfortable speed and perform five trials of the circuit for both left leading and right leading legs in the following orders: stair ascent-ramp descent and ramp ascent-stair descent (total of 20 trials per person). They were instructed to walk at a comfortable speed, and to use handrails when desired to ensure safety.
## 3 Results and Discussion
### Cases Design
#### 3.1.1 Parameters Setting for Normal Orbital Operation  
The external heat flux (solar radiation heat flux, earth albedo heat flux and earth infrared radiation heat flux) of the satellite in each direction (\(\pm\) X, \(\pm\) Y, \(\pm\) Z) varies periodically during the normal orbit cycle. The satellite orbit parameters used in the simulation analysis are shown in Table 2. The simulation time is one orbital period (1.63 h), in which, the initial moment of the entire simulation cycle is the time when the satellite-Z direction is subjected to the maximum solar radiation intensity. The satellite entered the earth's shadow at 1884.97 s and left the earth's shadow at 3968.28 s.  
The external heat flow is calculated using the software THERMAL DESKTOP based on the orbital parameters of the satellite. Then, by importing the calculated external heat flow results into THIEM, the dynamic temperature of each component node of the satellite can be solved, and the health index of the satellite in normal orbit can be obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Solar incident angle & 17.23 °C \\ Orbit altitude & 641.65 km \\ Average of solar radiation & 1354 W/m\({}^{2}\) \\ Albedo & 0.35 \\ Earth infrared radiation & 221.484 W/m\({}^{2}\) \\ Space temperature & 4 K \\ \hline \hline \end{tabular}
\end{table}
Table 2: Satellite orbit parameters.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
###### Abstract  
The emergence of low-power wide area networks (LPWANs) as a new agent in the Internet of Things (IoT) will result in the incorporation into the digital world of low-automated processes from a wide variety of sectors. The single-hop conception of typical LPWAN deployments, though simple and robust, overlooks the self-organization capabilities of network devices, suffers from lack of scalability in crowded scenarios, and pays little attention to energy consumption. Aimed to take the most out of devices' capabilities, the HARE protocol stack is proposed in this paper as a new LPWAN technology flexible enough to adopt uplink multi-hop communications when proving energetically more efficient. In this way, results from a real testbed show energy savings of up to 15% when using a multi-hop approach while keeping the same network reliability. System's self-organizing capability and resilience have been also validated after performing numerous iterations of the association mechanism and deliberately switching off network devices.  
LPWAN; IoT; M2M; multi-hop; power saving mechanisms Article
## 3 Experiments and Results Analysis
### Comparative Analysis of Experimental Results
#### 3.3.4 Comparison with State-of-the-Art Models  
It is necessary to compare the proposed model with other state-of-the-art hybrid models, all of which are TCN-based or data decomposition models published in recent years. Table 8 lists the comparative results of the proposed model and other baseline models. It is obvious that the proposed model significantly outperforms state-of-the-art models  
Figure 14: Boxplots of MAPE for each model based on (**a**) ISO-NE dataset and (**b**) GEFCom2012 dataset.  
Figure 13: Maximum, minimum and average values of RMSE for each model based on GEFCom2012 dataset.  
in terms of the MAPE. Even if the same datasets are used in [14; 54] for one-hour-ahead forecasting, the MAPE values of the proposed model are significantly reduced by 37% and 36%, respectively. It should be emphasized that [14] used an EMD decomposition method, and achieved high prediction accuracy. However, compared with the results of [14], the proposed model in this research reduces the MAPE by 37% and the RMSE by 39%. This also demonstrates that VMD decomposition outperforms EMD decomposition in terms of data smoothing and linearization. Furthermore, the hybrid models with data decomposition all achieved high accuracies in load forecasting, which proves that the smoothing and stabilizing of load data is a very important process for load forecasting. It should be noted that the RMSE values of some comparative models are lower than that of the proposed model due to their simpler structures and fewer hyperparameters. Overall, the proposed model can extract in-depth spatiotemporal features, and then globally enhance key features to achieve reliable load forecasting with greater accuracy.
## 5 Real-Time PPP Performance of Smartphone  
In this experiment, because of the lack of carrier observations for GLONASS satellites in the received observation data, only the real-time PPP performance of the smartphone of the GPS, BDS, GPS/BDS combination, GPS/BDS/Galileo combination and GPS L1/L5 combination were evaluated.
## 4 Results
### Geotechnical Analysis  
The water absorption values of each location, ranging between 0.03% and 0.2%, are presented in Table 3. The maximum water absorption was 0.2% in the GMA1 Ayun area, whereas the minimum water absorption value was 0.03% for rock samples GMKB1, GMKB2, GMK2, GMGT2, GMGG2, and GMD2 in the Khairabad, Kesu, Gahirat, Gahirat Gol, and Domun localities, respectively. The values of water absorption for Gahirat Marble were lower than the values for Nauseri Marble Muzzaffarabad, Nikani Ghar Marble, and the Nowshera Formation (Figure 7; Table 3) [1; 26].  
The specific gravity of rock samples from Chitral's various areas ranges between 2.46 and 2.71 gcm\({}^{-3}\). The highest specific gravity value was 2.71 for GMKB1 in the Khairabad area, while the lowest specific gravity value was 2.46 for rock samples GMC1 and GMA1 from Chinar and Ayun, respectively.  
Figure 6: CaO-MgO-SiO\({}_{2}\) ternary diagram classification system for marbles and plots of studied samples. 1: pure calcite, 2: pure dolomitic calcite, 3: pure calcite dolomite, 4: pure dolomite, 5: siliceous calcite, 6: siliceous dolomitic calcite, 7: siliceous calcite dolomite, 8: siliceous dolomite, 9: calc–silicate calcite, 10: calc–silicate dolomitic calcite, 11: calc–silicate calcite dolomite, 12: calc–silicate dolomite, 13: calcite marble, 14: dolomitic calcite marble, 15: calcite dolomite marble, 16: dolomite marble non-marble and MgO \(>\) pure marble.  
Figure 7: Comparison between the results of geotechnical tests of current study with Nauseri Marble [1] and Nikani Ghar Marble [26].(**a**) comparison of different values of water absorption, (**b**) comparison of different values of specific gravity, (**c**) comparison of different value of soundness test, (**d**) comparison of different values of UCS, (**e**) comparison of different values of BTS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multirow{2}{*}{**Location**} & **Water** & **Specific** & **Soundness Test** & **UCS** & **BTS** \\  & & **Absorption (\%)** & **Gravity** & **(\%)** & **(MPa)** & **(MPa)** \\  & & **( ASTM C97)** & **(ASTM C97)** & **(ASTM C88)** & **(ASTM C170)** & **(ASTM D-3967)** \\ \hline GMC1 & Chinar & 0.14 & 2.46 & 0.65 & 73 & 7.79 \\ GMC2 & Chinar & 0.11 & 2.50 & 0.52 & 80 & 8.02 \\ GMGL1 & Goja Lasht & 0.07 & 2.47 & 0.32 & 86 & 8.57 \\ GMGL2 & Goja Lasht & 0.04 & 2.68 & 0.23 & 92 & 10.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Laboratory tests results from the study area.
## 3 Materials and Methods
### Architectural Enhancements over Original StarGAN
#### 3.2.3 Module Integration into StarGAN  
The Perspective Transformation Module and Viewpoint Attention Module are strategically inserted in the middle of the Generator's processing pipeline, between the down-sampling and residual blocks of the network.  
After the input image and domain information are processed through the initial convolution and the two down-sampling layers, the resulting feature maps are passed through the Perspective Transformation Module. This module applies spatial transformations to the features, helping the model adapt to different viewpoints.  
Immediately following this, the transformed features are fed into the Viewpoint Attention Module, which applies attention mechanisms to focus on the most relevant regions of the feature maps.  
Only after these two attention-based modules have processed the features do they proceed to the residual blocks, which maintain the transformed and attention-weighted representation while adding identity connections for better gradient flow.  
This architecture places the attention mechanisms exactly where they can have the most impact after initial feature extraction but before the deeper processing of residual blocks, allowing the network to focus on relevant spatial information early in the processing pipeline. The Enhanced Generator architecture is described in Figure 3.  
Figure 3: Generator comparison: original vs. model with attention.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
###### Abstract  
Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP.  
diffusion model; oriented object detection; aerial images +
Footnote †: journal: Applied Sciences
## Article
###### Abstract  
Herein, spectrally selective single-layered CuO nanocoatings were successfully demonstrated via green synthesis and deposited on stainless steel (SS) substrates using a spin coater at 700, 800, 900, and 1000 rpm. The morphological, structural, and compositional analyses of the obtained nanocoatings were studied using SEM, XRD, EDX, and Raman spectroscopy. The SEM images show nanorod-like structure surfaces with dense surface morphology. The XRD patterns confirmed the presence of peaks indexed to a monoclinic structural phase of CuO. The EDX spectra clearly revealed the presence of Cu and O elements, and XPS spectra showed peaks of Cu2p and O1S core levels, which are typical characteristics of Cu (II) and O(II), respectively, in CuO. The Raman spectra showed peaks at 305, 344, and 642 cm\({}^{-1}\) attributed to Raman active (A\({}_{\mathrm{g}}\)+2B\({}_{\mathrm{g}}\)) modes for Cu-O stretching. Rutherford backscattering spectrometry (RBS) determined the content of the elements and the changes in the thicknesses of the coatings with the rotational speed (RS) of the spin coater. The elemental content of Cu and O atoms were, respectively, 54 and 46%. The thicknesses were calculated to be 1.406 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (296.3 nm), 1.286 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (271.0 nm), 1.138 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (239.8 nm), and 0.985 \(\times\) 10\({}^{15}\) atoms/cm\({}^{2}\) (207.5 nm) at 700, 800, 900 and 1000 rpm, respectively. The optical properties of the CuO nanocoatings were characterized using UV-Vis-NIR and FTIR spectrometers; its vital solar selectivity parameters of solar absorptance (\(\alpha\)) and emissivity (\(\epsilon\)) were evaluated in the ranges of 0.3-2.5 and 2.5-20 \(\upmu\)m wavelengths, respectively. The obtained coatings exhibited solar parameters (\(\alpha\) = 0.90, and \(\epsilon\) = 0.31) associated with 700 rpm due to an intrinsic and interference-induced absorption as well as higher attenuation of light.  
Keywords:green synthesis; solar energy; optical property; copper oxide; nanocoatings +
Footnote †: journal:
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 4 Results and Discussion
### Performance Analysis on Benchmark Functions  
A simulation analysis for 30 benchmark functions (refer to Tables 4 and 5) using AOA, MFO, SCA, GWO, WOA, HHO, and the proposed HHAOA was conducted. The selected benchmark functions were independent of each other in multiple dimensions and had various local minima, global minima (\(G_{m}\)), different ranges, and diverse boundary values to ensure a reliable comparative analysis. Furthermore, the algorithms were comprehensively tested using multimodal and hybrid composition functions in single and multiple dimensions. Figure 5: FOPPI controller tuning using HHAOA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cat.** & \(G_{m}\) & **Func.** & **Description** & **Range** \\ \hline \multirow{6}{*}{**Category**} & 0 & F1 & \(F(x)=\sum_{i=1}^{n}x_{i}^{2}\) & [100,100] \\ \cline{2-4}  & 0 & F2 & \(F(x)=\sum_{i=1}^{n}\left|x_{i}\right|+\prod_{i=1}^{n}\left|x_{i}\right|\) & [10,10] \\ \cline{2-4}  & 0 & F3 & \(F(x)=\sum_{i=1}^{d}\left(\sum_{j=1}^{i}x_{j}\right)^{2}\) & [100,100] \\ \cline{2-4}  & 0 & F4 & \(F(x)=\max_{i}\left|\ X_{i}\right|,1\leq i\leq n\) & [100,100] \\ \cline{2-4}  & 0 & F5 & \(F(x)=\sum_{i=1}^{n-1}\left[100\left(x_{i+1}^{2}-2x_{i+1}x_{i}^{2}+x_{i}^{4} \right)+\left(x_{i}^{2}-2x_{i}+1\right)\right]\) & [30,30] \\ \cline{2-4}  & 0 & F6 & \(F(x)=\sum_{i=1}^{n}\left(x_{i}^{2}+0.25+x_{i}\right)\) & [100,100] \\ \cline{2-4}  & 0 & F7 & \(F(x)=\sum_{i=1}^{n}x_{i}^{4}+\text{random}\ \left(0,1\right)\) & [128,128] \\ \hline \multirow{6}{*}{**Category**} & \(-148.9829\times\text{n}\) & F8 & \(F(x)=\sum_{i=1}^{n}-x_{i}\sin\left(\sqrt{\left|x_{i}\right|}\right)\) & [500,500] \\ \cline{2-4}  & 0 & F9 & \(f_{9}(x)=\sum_{i=1}^{n}\left[x_{i}^{2}-10\cos(2\pi x_{i})+10\right]\) & [5.12,5.12] \\ \cline{2-4}  & 0 & F10 & \(F(x)=e+20\left(1+\exp\left(-0.2\sqrt{\frac{1}{n}\sum_{i=1}^{n}x_{i}^{2}- \left(\frac{1}{n}\sum_{i=1}^{n}\cos(2\pi x_{i})\right)\right)}\right)\) & [32,32] \\ \cline{2-4}  & 0 & F11 & \(F(x)=\frac{1}{4000}\sum_{i=1}^{n}x_{i}^{2}-\prod_{i=1}^{n}\cos\left(\frac{x_ {i}}{\sqrt{i}}\right)+1\) & [600,600] \\ \cline{2-4}  & 0 & F12 & \(F(x)=\frac{\pi}{n}\{10\sin(\pi y_{1})\}+\sum_{i=1}^{n-1}(y_{i}-1)^{2}[1+10 \sin^{2}(\pi y_{i+1})+\sum_{i=1}^{n}u(x_{i},10,100,4)\), \\ \cline{2-4}  & & & where \(y_{i}=1+\frac{x_{i}+1}{4},u(x_{i},a,k,m)\begin{cases}K(x_{i}-a)^{m}&x_{i}>a\\ 0&-a\leq x_{i}\geq a\\ K(-x_{i}-a)^{m}&-a\leq x_{i}\end{cases}\) \\ \hline \multirow{6}{*}{**Category**} & 0 & F13 & \(F(x)=\)0.1\(\left[\sin^{2}(3\pi x_{1})+\sum_{i=1}^{n}(x_{i}-1)^{2}\left[1+\sin^{2}(3\pi x_{i}+1) \right]+\left(x_{n}^{2}-2x_{n}+1\right)\right.\) & [50,50] \\ \cline{2-4}  & & & \(\left[1+\sin^{2}(2\pi x_{n})\right]\right]+\sum_{i=1}^{n}u(x_{i},5,100,4)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimization test functions—Unimodal and Multimodal benchmark functions.