esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 4 Theoretical Analysis
### QR\(-\)LoRa Chirp Energy  
Intuitively, changing the envelope will have an influence on the energy. Thus, we evaluate the energy of the QR\(-\)LoRa chirp.  
Let the slope of the envelope of a QR\(-\)LoRa chirp be \(\mu\), and \(A(t)\) can be represented as \(A(t)=\frac{\mu t}{T}\), \(0\leq t<T\). Then, the energy can be calculated as  
\[E_{QR,\mu} =\int\limits_{0}^{T}{\left(\frac{\mu}{T}t\right)^{2}}\mathrm{d}t= \frac{\mu^{2}}{3}T \tag{18}\]  
Likely, the energy of a LoRa chirp is given as  
\[E_{L} =T \tag{19}\]  
Obviously, the energy of a QR\(-\)LoRa chirp is \(\frac{\mu^{2}}{3}\) times that of a LoRa chirp. If the envelope slope is set to \(\mu\leq\sqrt{3}\), the energy of a QR\(-\)LoRa chirp will be the same as the LoRa chirp. We define the energy efficiency of the symbol as  
\[\rho=\frac{h}{E} \tag{20}\]  
where \(h\) is the height of the peak of the amplitude spectrum. Accordingly, the energy efficiency of LoRa chirp is defined as  
\[\rho_{L}=\frac{2^{SF}}{T} \tag{21}\]  
The energy efficiency of the QR\(-\)LoRa chirp with envelope slope \(\mu\) can be derived by  
\[\rho_{QR,\mu}=\frac{h_{QR,\mu}}{E_{QR,\mu}}=\frac{3}{2\mu}\cdot\frac{2^{SF}-1 }{T} \tag{22}\]  
where \(h_{QR,\mu}=\mu\cdot\frac{2^{SF}-1}{2}\). We can see that when the slope \(\mu\leq\frac{3}{2}\cdot\frac{2^{SF}-1}{2^{SF}}\approx\frac{3}{2}\), the energy efficiency of a QR\(-\)LoRa chirp will not be lower than that of a LoRa chirp.  
Let \(\mu=1\), we have  
\[E_{QR}=\frac{T}{3}=\frac{1}{3}E_{L} \tag{23}\]  
\[\rho_{QR}=\frac{3}{2}\cdot\frac{2^{SF}-1}{T}\approx\frac{3}{2}\rho_{L} \tag{24}\]  
In summary, QR\(-\)LoRa can **reduce energy consumption** and **improve energy efficiency** compared with the conventional LoRa.
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 5 Discussion
### Parametric Analysis
These values were chosen to achieve optimal performance across the datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(\iota\) & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline Los Angeles & 0.9949 & 0.9954 & 0.9961 & 0.9963 & 0.9965 & 0.9966 & 0.9966 & **0.9967** & 0.9967 \\ Gulfport & 0.9991 & 0.9993 & 0.9992 & 0.9993 & 0.9992 & **0.9994** & 0.9992 & 0.9987 & 0.9985 \\ San Deigo & 0.9956 & 0.9959 & 0.9961 & **0.9969** & 0.9958 & 0.9956 & 0.9952 & 0.9945 & 0.9933 \\ \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 6: Sensitivity analysis of \(\iota\) was conducted on three datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **1\(\times\)10\({}^{-4}\)** & **1\(\times\)10\({}^{-3}\)** & **1\(\times\)10\({}^{-2}\)** & **1\(\times\)10\({}^{-1}\)** & **1** \\ \hline Los Angeles & 0.9960 & 0.9960 & 0.9962 & **0.9965** & 0.9964 \\ Gulfport & **0.9994** & **0.9994** & **0.9994** & **0.9994** & 0.9994 & 0.9993 \\ San Deigo & 0.9964 & 0.9964 & 0.9963 & **0.9969** & 0.9966 \\ \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 7: Sensitivity analysis of \(\gamma_{\text{te}}\) was conducted on three datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **1\(\times\)10\({}^{-4}\)** & **1\(\times\)10\({}^{-3}\)** & **1\(\times\)10\({}^{-2}\)** & **1\(\times\)10\({}^{-1}\)** & **1** \\ \hline Los Angeles & 0.9960 & 0.9960 & 0.9962 & **0.9965** & 0.9964 \\ Gulfport & **0.9994** & **0.9994** & **0.9994** & **0.9994** & 0.9994 & 0.9993 \\ San Deigo & 0.9964 & 0.9964 & 0.9963 & **0.9969** & 0.9966 \\ \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 8: Sensitivity analysis of \(\delta\) was conducted on three datasets. To further validate the impact of the diffusion denoising model on hyperspectral target detection, we selected CNN-based Unet network [53] and Vision Transformer [58] as denoising diffusion models, respectively, for comparison with our DiffTransformer. \(AUC_{(P_{D},P_{F})}\) is primarily used to measure detection performance accuracy, while Params is used to measure the number of parameters in deep learning models, and FLOPs are used to assess the model's computational efficiency and speed. As shown in the Table 10, our method achieves the best results in terms of \(AUC_{(P_{D},P_{F})}\), Params, and FLOPs, demonstrating the effectiveness of the method we employed.
# MIMO Relaying UAVs Operating in Public Safety Scenarios
## 3 Results
These results may be explained by distance and channel modeling between both links: eNB--relay and relay--user.
# Applied Research of the UAV Illumination Measurement System in Sports Stadiums
## 2 Materials and Methods
### Illumination Acquisition System Architecture
The structure of the illuminance acquisition module is as Figure 1.  
As shown in Figure 1, the system is structured in three main layers, including UAV, illumination acquisition module, and cloud platform. The UAV is used as a working  
Figure 1: Structure of the illuminance acquisition module. platform for the entire illuminance acquisition system and plays an important role in illuminance acquisition as well as in the positioning of measurement points. The illumination acquisition module is used as the data input and control command-execution part of the system, consisting of an illumination sensor (BH1750 FVI), a WiFi transmission section (ESP8266), a laser distance measurement module (ToF Sense), and a Li-ion battery power supply. The illuminance acquisition module can get illuminance data every 180 ms. The individual parts of the module transmit data to each other via the different communication protocols, where data transfer between the illuminance sensor and the STM32F103 RCT6 is based on the I2C communication protocol, and command delivery and data transfer between ESP8266 and STM32F103 RCT6 via UART serial communication protocol, and STM32 RCT6F103 also can control the laser distance-measurement module to complete the height-measurement judgment. When the UAV illumination acquisition system is used to acquire illumination in the sports venue, the distribution of illumination varies at different heights, as proved by the inverse squared ratio. Therefore, a distance measurement module is required to be carried in the system. The ToF Sense can perform distance measurement with a refresh frequency of 30 HZ, and the measuring distance is from 0.03-8 M with an accuracy of \(\pm\)0.03 M. In addition, the ToF Sense's small size and low power consumption make it very suitable for use as a sensor on the UAV. The communication between the various parts of the system is shown in Figure 2.
## 2 Materials and Methods  
### Equipment and Participants  
The experiment was divided into two parts: (1) evaluating products using ET through the visualisation of AD models and (2) evaluating the same ADs by physically manipulating and using them.  
The Tobii Pro X2-60 device (Tobii AB, Danderyd, Sweden) was used for the ET experiment (gaze accuracy of 0.4\({}^{\circ}\) and gaze precision of 0.32\({}^{\circ}\)). It was positioned beneath a 24-inch monitor, recording data at a 60 Hz, with participants seated approximately 60 cm from the screen. Calibration was performed for all participants at the beginning of the test using a 9-point method, according to manufacturer's guidelines. The ET experiment lasted approximately 10 min and the physical evaluation 30 min.  
The Ethics Committee of Universitat Jaume I approved the experiment (approval number CD/79/2021), and all participants provided informed consent. Initially, 64 subjects (aged 18 to 66) participated, all with normal or corrected vision. Subjects with hand pathology were excluded due to their higher likelihood of prior experience with ADs, as the study aimed to assess users' first impressions based solely on visual perception. For data analysis, participants with an ET registration percentage below 75% (as recorded by the Tobii Pro Lab software, version 1.145.28180 [\(\times\)64]) were excluded. This resulted in the exclusion of three participants, with registration percentages of 28%, 46%, and 59%. One additional participant was excluded due to a power outage during the session. The final dataset included 60 participants (28 females and 32 males) with an average age of 40.5 \(\pm\) 10.7 years.
## 3 Numerical Results and Discussion
### Models for Verification
#### 3.1.1 The Duffing Oscillator  
A Duffing oscillator is utilized in this section, as shown in Figure 1, with stiffness coefficient \(k=40\) and damping coefficient \(c=0.1\sqrt{40}\) respectively. The nonlinear force is expressed in the form of cubic function, and the friction coefficient \(k_{\tau}\) is the investigated parameter in sensitivity analysis.  
Based on the model mentioned above, the equation of motion for this system takes the form:  
\[m\ddot{x}+c\dot{x}+kx+f_{\tau}(x)+f_{e}=0 \tag{33}\]  
where the excitation force \(f_{e}\) has the form \(f_{e}=-100\sin(\omega t)\). Although Equation (33) can be easily solved using time integral method such as New-Mark time integral methods, for the purpose of verification, it is still solved by the HBM mentioned in Section 2.1. The first 9 Harmonic components are kept and the number of time sampling points in Fourier transformation is 4096.  
Firstly, the responses of the system with different friction coefficients are obtained. It can be seen in Figure 2 that multi-solution regions exist in the response curve. With increase of friction coefficients, the multi-solution regions grow larger and maximum amplitudes decrease. To verify the accuracy of HBM, time-domain displacement results obtained by the HBM and the time marching method (TMM) when \(k_{\tau}=100\) N/m\({}^{3}\) are illustrated in Figures 3 and 4. The results show that high accuracy is achieved.  
The sensitivity coefficients are specifically defined as the variation of maximum displacements when the parameters of interfaces change by one percent, as shown in Figure 5. Results reveal that with increase of friction coefficient, the sensitivity of the response will decrease. An abrupt change of first-order sensitivity happens at around \(\omega=6.5\) rad/s. In this case, the first-order sensitivity is extremely sensitive to the variation of friction coefficient thus the values of second-order sensitivity rise, as shown in Figure 5b.  
Figure 1: Sketch of the Duffing oscillator used for verification.  
Figure 2: Response curves of the Duffing oscillator with different friction coefficients.  
Figure 4: Response curve and hysteresis loop of the Duffing oscillator obtained by HBM and TMM at \(\omega\) = 3.0672 rad/s and \(k_{\tau}\) = 10 N/m3: (**a**) The response curve; (**b**) The hysteresis loop.  
Figure 5: Sensitivities of the response of the Duffing oscillator to friction coefficient with different nominal values of friction coefficients: (**a**) First-order sensitivity; (**b**) Second-order sensitivity.  
Figure 3: Response curve and hysteresis loop of the Duffing oscillator obtained by HBM and TMM at \(\omega\) = 30.1612 rad/s and \(k_{\tau}\) = 10 N/m3: (**a**) The response curve; (**b**) The hysteresis loop.
## 3 Modeling Procedures of Extended Finite Element Method (XFEM)-Based Multiscale Analysis
### Material Properties of XFEM-Based Solid Elements  
In the lower part of the two-scale model, the classic bilinear elastic-plastic model is adopted as the constitutive law of steel bars connected with solid elements. The elastic modulus, density and yield stress of the steel bars are detailed in Table 2. In order to consider the Bauschinger effect of steel under cyclic loading, kinematic hardening is adopted for material definition. The maximum principal stress criterion is used for homogeneous concrete, mortar, aggregate and the ITZ as the cracking initiation criterion of XFEM. The linear softening and B-K criterion are employed to define the damage evolution [1; 7]. Since the compressive crush of concrete cannot be simulated using XFEM, the compressive stress-strain curve is defined as per the CDP model to consider the softening of concrete under compression [1; 7]. The material parameters of both concrete and steel reinforcements involved in this study are shown in Table 2.  
In conventional numerical simulation on RC structures, the concrete is usually assumed to be a homogeneous material without considering its mesostructures. It is relatively easier to define the material parameters for homogeneous concrete. However, the concrete at meso-level has to be modeled as composite component consisting of aggregates, ITZ and mortar, which makes it challenging to directly obtain all material properties for each phase from material tests. Similar issues also exist in the mesoscale modeling of concrete using the XFEM method. The determination and calibration of fracture energy and the maximum principal stress of ITZ are also critical issues. Therefore, the material parameters which cannot be easily obtained from material strength tests were determined as per the definition rules in existing mesoscale simu  
Figure 7: Verification of material subroutines and simulation results with different modeling techniques.
# Chemical, Nutritional and Antioxidant Characteristics of Different Food Seeds
## 3 Results and Discussion
### FAs Composition  
The FAs obtained from seeds after extraction were converted to corresponding FAMEs to determine their profiles by GC-FID. The main problem identified in analysis of FAMEs standards is the overlapping of isomers. MUFAs and PUFAs present two configurations (cis and trans) based on the position of the first double bond from the FAs. The peaks are overlapping for oleic acid (C18:1 (n-9)) and linoleic acid (C18:2 (n-6). C20:3(n-6) overlap with C21:0, C20:3 (n-6) and C21:0 coelute and C22:6(n-6) and C24:1(n-9) coelute. Similar results were reported by [PERSON] et al. for FAs from milk samples [31]. The peak overlapping is caused by oleic acid (C18:1) present in olive and rapeseed oils, by linoleic acid (C18:2) present in sunflower and soya bean oils and by linelenic acid (C18:3) present in flaxseed and hemp seed oils [1]. Figure 1 shows the representative chromatograms of the 37 components FAME standard mixture (A) and analyzed hemp seed oil (B). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **Lipid Yield (\%)** \\ **(This Work)*** \\ \end{tabular} & \begin{tabular}{c} **Lipid Yield (\%)** \\ **(Related Studies)** \\ \end{tabular} & \begin{tabular}{c} **Extraction Method** \\ \end{tabular} & **Reference** \\ \hline Hemp seed & 30.8 \(\pm\) 0.21 & 29.6 & extraction with n-hexane & [26] \\ \hline Sunflower seed & 54.1 \(\pm\) 0.50 & 49.1 & \begin{tabular}{c} extraction with petroleum \\ ether in Soxhlet apparatus \\ \end{tabular} & [27] \\ \hline Poppy seed & 38.2 \(\pm\) 0.31 & 46.8 & cold press extraction & [28] \\ \hline Flax seed & 31.2 \(\pm\) 0.20 & 35.0 & \begin{tabular}{c} extraction with \\ chloroform:methanol \\ (2:1(_v_/_v_)) \\ \end{tabular} & [29] \\ \cline{2-4}  & & 41.0 & \
\begin{tabular}{c} supercritical extraction \\ with n-propane \\ \end{tabular} & \\ \hline Sesame seed & 40.0 \(\pm\) 0.40 & 41.6 &
\begin{tabular}{c} extraction with petroleum \\ ether in Soxhlet apparatus \\ \end{tabular} & [30] \\ \hline \hline \end{tabular}  
* Data represents mean \(\pm\) standard deviation SD, n = 10.  
\end{table} 
Table 1: A comparison between the obtained lipid yield and those reported in other studies. The obtained oilseeds analyzed for FFAs (as acid value) presented an acid value of 0.70 \(\pm\) 0.1 mg KOH/g for sunflower oil, 0.72 \(\pm\) 0.2 mg KOH/g for sesame oil, 1.43 \(\pm\) 0.2 mg KOH/g for flaxseed oil, 1.68 \(\pm\) 0.3 mg KOH/g for hempseed oil and 1.86 \(\pm\) 0.3 mg KOH/g for poppy seed oil. The FAs, SFAs, MUFAs and PUFAs composition in the oilseeds and PUFAs/SFAs ratio are presented in Table 2.  
Figure 1: Of (**A**) the 37 components fatty acid methyl esters (FAME) standard mixture; (**B**) hemp seeds oil sample.
# Immuno-PET for Glioma Imaging: An Update
are highly malignant and carry a significantly worse prognosis. GBM, in particular, is notorious for its aggressiveness and resistance to treatment, with a median survival of around 14-16 months after diagnosis, despite the use of aggressive therapies like surgery, radiation, and chemotherapy [4]. The scarcity of viable therapeutic options for gliomas is one of the biggest obstacles to their management. When possible, surgical excision attempts to remove the majority of the tumor. However, total tumor excision is sometimes unattainable due to the infiltrative nature of high-grade gliomas, and tumor persistence/recurrence is frequent. Although they are often used, chemotherapy and radiation therapy, including temozolomide, only slightly improve patient survival [5]. There is optimism for better results derived from research into cutting-edge therapeutic approaches, including targeted molecular medicines and immunotherapy. These strategies seek to target particular genetic alterations inside the tumor cells or to activate the body's immune system [6; 7]. However, challenges related to the blood-brain barrier (BBB) and tumor heterogeneity continue to pose obstacles in the development of effective glioma treatments. In this context, therapeutics, an approach that integrates diagnosis and therapy in a unified process, might advance the field [8]. There are two main stages in the field of theranostics: (1) the use of a molecule (radiopharmaceutical) labeled with a radionuclide emitting energy suitable for imaging to identify tumor-associated biomarkers; (2) the administration of the same molecule, or a very similar one, conjugated with radionuclide-emitting particles (beta or alpha) to exert anti-tumoral effects. In this case, there is an unfulfilled need to take into account glioma-associated biomarkers as potential theranostic targets. Through the novel technique known as immuno-PET, cancer-associated biomarkers can be found by combining the high diagnostic performance of positron emission computed tomography (PET/CT) with the increased specificity of antibodies. This may prove beneficial for therapies that target certain molecules. In addition to attempting to describe the necessary steps for its future use in clinical practice, the present research aims to give an updated summary of the literature on the applications of immuno-PET in gliomas.
## 2 Numerical Model of Solidification
### Mixed Time Partitioning Methods
#### 2.1.1 Sub-Cycles and Total-Cycles  
The separation of all domains into two groups: (i) a mold and cores (domains with similar properties as mold which help in heat carrying out from the cast) and (ii) a casting, so-called sub-domains, offers the possibility to use different time stepping schemes in every group. Moreover, the mixed time partitioning method allows selecting the size of time steps in various sub-domains of the computational mesh. In particular, firstly a small and fixed time step \(\Delta t\) in casting domain \(B\) is determined. Then, in mold/cores sub-domain \(A\) it is beneficial to choose a time step \(m\Delta t\), where \(m\) is a stability coefficient which is a positive integer value resulting from stability requirements. The local time step in the given sub-domain is always chosen on the basis of the analysis (necessary conditions are described in [25]).  
It is important to note that the stability analysis is local and the stability restriction of one sub-domain does not enforce the size of the time step for the entire mesh.  
Using sub-cycles requires that two systems of equations are being constructed: (i) one for casting when only sub-cycles are considered and (ii) one for all domains (mold, core, and casting) when a total cycle is considered. In practice, global matrices and vectors are assembled according to the elements partitioning (see Figure 3). Calculations connected to casting domain are made in sub-cycles with time step \(\Delta t\). Temperature vector is being updated only for elements belonging to casting domain. All elements in the mesh take part in calculations made in total-cycles with a time step \(m\Delta t\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(m\) & \(\mathbf{\Theta}_{cast}\) & \(\begin{array}{c}\mathbf{\Theta}_{mold}\\ \mathbf{\Theta}_{core}\end{array}\) & \(\mathbf{\Delta t}_{cast}\) & \(\mathbf{\Delta t}_{mold}\) & \(\begin{array}{c}\mathbf{\text{Symbol of sub-}}\\ \mathbf{\text{\bf{4}}}\mathbf{\text{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{ \bf{ \bf{ }}}}}}}}}}}}}}}\) \\ \end{tabular} &
\begin{tabular}[]{c}\mathbf{\text{Description of Symbols sub-}}\\ \mathbf{\text{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{\bf{ }}}}}}}}}}}}}}}\) \\ \end{tabular} } \\ \hline \(\geq\)2 & 1 & 1 & \(\Delta t\) & \(\text{m}\Delta t\) & \(I-I^{m}\) & Implicit – Implicit \\ \(\geq\)2 & 0 & 0 & \(\Delta t\) & \(m\Delta t\) & \(E-E^{m}\) & Explicit – Explicit \\ \(\geq\)2 & 0 & 1 & \(\Delta t\) & \(m\Delta t\) & \(I-E^{m}\) & Implicit – Explicit \\ \hline \hline \end{tabular}
\end{table}
Table 1: The combination of applied integration schemes. (\(m\) is a numerical stability coefficient, \(\Theta\) is a parameter of one-step time integration scheme—assumes value 0 or 1 in the examined case, \(I\) is a symbol indicating the implicit scheme, and \(E\) a symbol indicating the explicit scheme).
# The Wettability and Numerical Model of Different Silicon Microstructural Surfaces
## 1 Introduction
[22] fabricated a micro-pillar array with a re-entrant structure on stainless steel by laser ablation and electrodeposition, the results of which indicated that the presence of the re-entrant structure and the increase in the surface roughness generated an increase in the surface contact angle and created a superhydrophobic surface. [PERSON] et al. [23] measured the contact angles of five different surfaces. The measurement results indicated a decrease in the contact angles of the hydrophilic silicon wafer and the polished silicon wafer, whereas the contact angles of the other three surfaces were relatively larger. For micro-grooved surfaces, [PERSON] et al. [24] observed a larger apparent contact angle parallel to the grooves as compared to that of the smooth surface, and the microstructures exhibited a minimal effect on the contact angle perpendicular to the grooves. An increase in the grooved area and the surface energy resulted in a more anisotropic droplet. [PERSON] et al. [25] coated the metal surface with tiny nanoparticles, and [PERSON] et al. [26] fabricated microstructures on the silicon surface by plasma immersion ion implantation (PIII) technology, both of which resulted in the contact angles of the surfaces exhibiting no obvious change. [PERSON] et al. [27] presented a two-dimensional (2-D) thermodynamic model to describe the wetting on hierarchically grooved surfaces by droplets, of which the results indicated that the  
Figure 1: Wetting models of (**a**) [PERSON]’s theory, (**b**) [PERSON] theory, and (**c**) Cassie–Baxter theory. influence of gravity was negligible. [PERSON] et al. 's results indicated that the nanoscale roughness increased the effective Young's angle of the microscale features, thereby generating smaller required aspect ratios (height to width) for the surface structures. In our study, we first used plasma immersion ion implantation (PIII) technology [28] to prepare microstructures on the surface of monocrystalline silicon, and studied their wetting characteristics. There was a great difference between the experimental results and the theoretical results. It was seen that the Cassie-Baxter theory had great limitations in predicting the static contact angle of complex structures. In order to analyze the reasons for the observed differences, the contact angle numerical model was set up on the basis of the volume-of-fluid (VOF) model, and a simplified waveform of the microstructure surfaces was obtained.