esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
In the following, a summary of studies that included georeferencing without the use of GCP points (RTK, PPK, and NRTK) in their work is provided. The presented results are only from studies that used georeferencing (GEO) without the additional use of GCPs, and verification points (VPs) as a validation method. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors** & **UAV** & **Study Area** & **GEO** & **Flight Configuration** & \multicolumn{3}{c}{**Bundle Block Adjustment**} & **Orthomosaic** & **DSM** \\ \cline{5-10}  & & & & & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** \\  & & & & & **Accuracy (m)** & **Accuracy (m)** & **Accuracy (m)** & **Accuracy (m)** \\ \hline \multirow{3}{*}{[PERSON] et al., 2015} & microdrone & & - & Lines flown in opposite & & & & \\  & mdt-1000 & flat agricultural & & & direction and perpendicular & & & \\  & quadcopter with & area & & cross line. & & & & \\  & & & & -60-40\% overlap. & & & & \\  & RTK GNSS & & & - & Station 16 km away. & & & & \\ \hline \multirow{3}{*}{[PERSON] et al., 2016} & UAV RTK & agricultural, & & - & 80-60\% overlap, & & & \\  & platform & semi-industrial & RTK & - & 120 m flying height & & & \\  & platform & area & & lines flown in opposite & & & & \\  & & area & & direction. & & & & \\ \hline \multirow{3}{*}{[PERSON] et al., 2016} & microdrone & & & - & Adjacent lines flown in & & & \\  & mdt-1000 & railway corridor & & & opposite directions and one & & & \\  & quadcopter with & & & & perpendicular cross line. & & & \\  & RTK GNSS & & & - & 80 m flying height. & & & \\  & & & & - & 50-80\% overlap. & & & \\ \hline \multirow{3}{*}{[PERSON] et al., 2019} & october DI & & & & & & & \\  & S100 with RGB & & & PFK1: single & & & & \\  & camera (PFK2) & agricultural area & & freq. & & & & \\  & and DI 900 with & (crops, abandoned & & & & \\  & multispectral & vineyards, & & & & & \\  & camera & vegetation) & & & & & \\  & (Miscellaneous & & & & & \\  & RedEdge)—PFK1 & & & & freq. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The review of studies that included georeferencing without the use of CCP points (RTK, PFK, and NRTK) in their work. Sign “/” is indicating that information is not available.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Analysis of Results
#### 4.3.5 End-to-End Delay (ms)  
End-to-end delay is one of the primary key metrics to measure the efficiency of routing protocol, which describes the time the packet takes to arrive at the destination node from the source. From Figure 10, we can observe that our proposed scheme offers an 18% delay at the initial point. While other schemes' delay at the initial point starts from higher values as the number of nodes increases, the delay also increases due to a high number of packets generated across the network, which causes the packet collision as well as packets lifetime expires before reaching the destination place. In IoMT networks due to the presence of black holes or rough devices, there is a chance these devices not only try to back the data but also generate fake packets as a result, the collision occurs, which increases the delay across the network. Figure 10 shows that as the number of nodes increases, the delay for all routing schemes increases despite the increment in delay values. Our proposed SBAODV schemes maintain lower values up to 30% delay (ms), which we obtain at the highest number of nodes. On the other hand, benchmark schemes delay increases quickly as we can see from the results, the values increase in higher order. The proposed schemes take advantage of the Stackelberg game theory model to mitigate the rough devices efficiently; therefore, the information on reliable routes is available from the source to the destination node. Hence, the destination UAV finds the routes efficiently to decrease the delay while other schemes suffer, increasing their delay.
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 3 Results
### Experimental Setup
#### 3.1.2 Dictionary Learning  
We used PAN images from the Nihonmatsu and Yokohama datasets as training images for dictionary learning. For training image \(X\) and dictionary, the number of training times was 40, the number of atoms of the dictionary \(D\) was 1024, and the size of each atom was \(p=4\). The number of patches was 6433 (Nihonmatsu) and 10,000 (Yokohama). The training dataset was a set of 1000 patches randomly selected from these patches. As the training image \(X\), a training PAN image for the high-resolution dictionary was used as the high-resolution data \(X^{high}\), and a training PAN image for the low-resolution dictionary was used as the low-resolution data \(X^{low}\). The low-resolution image was generated by downsampling and upsampling via bicubic interpolation. The sparsity regularization parameter was \(\lambda=0.1\). The sparse representation \(\beta\) was calculated using Equation (10) by solving the L1 norm regularized least-squares problem, and the dictionary \(D\) was obtained using Equation (11) by solving the least-squares problem with quadratic constraints. We used the code provided by [PERSON] et al. [40]. The learned high-resolution dictionaries are shown in Figure 3. For Nihonmatsu images, the dictionary created using Nihonmatsu images was used. For Yokohama images, the dictionary created using Yokohama images was used.
## 3 Results and Discussion
### Reducing Sugar and Lignin Content of CS after Digesting  
The reducing sugar and lignin content of CS residue after co-digestion were measured to evaluate the effect of the CS pretreatment on the performance of the co-digestion of swine manure and CS. Reducing sugar, which is converted from cellulose and hemicelluloses by a chemical method, can represent the content of cellulose and hemicelluloses in CS. [33] In fact, the loss of reducing sugars can be correlated with biogas generation because sugars are easily converted to biogas by anaerobic microorganisms [52].  
The reducing sugar content of RCS was reduced from 50.7% (Table 1) to 30.3% of RCS after the anaerobic co-digestion (Figure 5). However, it decreased about 3.6%-13.9% for the pretreated CSs after anaerobic digestion. The results indicated that more cellulose and hemicelluloses in the pretreated CS were converted to biogas than those of RCS [37]. Corn stover pretreated with NaOH only (Treat-CSA) showed the highest effectiveness (Figure 5).  
The lignin content of CS was 37.12%-49.92% in the residue after digestion (Figure 6). RCS showed the maximum reducing sugars (30.28%) and lower lignin content (37.12%) in the residue because of incomplete digestion of CS. However, Treat-CSA and Treat-CSB showed lower reducing sugar (21.19%) and higher lignin content (47.49%) compared with others.  
D-value, the difference of lignin content of feedstock before and after digestion, is used for evaluating the extent of digestion [53]. A larger D-value means that more cellulose and hemicelluloses  
Figure 5: Comparison of reduced sugar and lignin contents of CS residue after digestion.  
Figure 6: Comparison of lignin content of CS before and after digesting.  
were converted to biogas in anaerobic digestion [54]. Compared to 23.7% lignin content of RCS, alkaline-pretreated CS showed lignin content of 13.14%-16.68%. However, after digestion, Treat-CSA showed 49.92% lignin content and the largest D-value (36.75%). The D-value for the RCS was only 13.40% with the lowest biogas production. This indicates that the loss of cellulose and hemicelluloses of CS had been converted to biogas, and a larger D-value means more biogas production [55]. This also indicated that addition of NaOH and CaO can help more CS to be converted to reducing sugars for biogas production [9; 32]. It also means that CaO can effectively replace NaOH and provide supplementary alkalinity [34; 56].
## 3 Hybrid Weight Assignment Model
### Hybrid Model
#### 3.3.3 Time Weighted Postposition Method  
The time weighted postposition method (TW\({}_{\text{post}}\)) is a weighting method that considers the influence of indicators dimension, firstly, and then reflects the influence of time dimension, just the opposite of the time weighted preposition method. The processing steps are shown concretely below.  
(1) Combined with the evaluation indicators and the traditional evaluation mathematical model \(G(w,a)\), the UUSR is evaluated using the original standardized data \(a_{ij}(t_{k})\) and the weight of time dimension \(w_{j}^{e}\) to obtain the static evaluation results \(y_{i}(t_{k})\) of the evaluation object \(i\) at different moments. It reflects the different data characteristics of the evaluation indicators at different moments. The mathematical calculation model is:  
\[y_{i}(t_{k})=G\big{(}w_{j}^{e},a_{ij}(t_{k})\big{)} \tag{11}\]  
where \(k=1,2,\cdots,s\), \(i=1,2,\cdots,m\), and \(j=1,2,\cdots,n\).  
(2) The weight of time dimension \(w_{k}^{t}\) is utilized to perform weighted average processing on the static evaluation results \(y_{i}(t_{k})\) of the UUSR evaluation object \(i\) at different moments based on the TOWA operator. Thus, the final evaluation result \(V_{i}\) of the evaluation object \(i\) can be obtained naturally. The mathematical calculation model is:  
\[V_{i}=F(\big{\langle}t_{1},y_{i}(t_{1})\big{\rangle},\big{\langle}t_{2},y_{i }(t_{2})\big{\rangle},\cdots,\big{\langle}t_{k},y_{i}(t_{k})\big{\rangle}, \cdots,\big{\langle}t_{s},y_{i}(t_{s})\big{\rangle})=\sum_{k=1}^{s}w_{k}^{t}y_ {i}(t_{k}) \tag{12}\]
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 3 Endomet Cancer
### Deep Myometrial Invasion  
Due to its clinical and prognostic significance, approximately 10 studies investigated the role of radiomics in assessing DMI. [PERSON] et al. [38] extracted 11 features from MRI of a small cohort of patients with EC using a random forest model. Areas under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to assess DMI were estimated at 0.84, 79.3%, 82.3%, 81.0%, 76.7%, and 84.4%. [PERSON] et al. analysed an MRI radiomics-powered machine learning (ML) model to increase radiologists' performance in assessing DMI. After a three-dimensional segmentation of the EC, the authors extracted several radiomic features from a small number of patients (\(n=54\)), concluding that the radiologist's performance increases from 82% to 100% with the radiomics support [39]. In another prospective study, the authors extracted radiomics features from two-dimensional ROIs in 180 patients. Their results proved that high tumour entropy on apparent diffusion coefficient (ADC) maps independently predicted DMI (odds ratio [OR] 3.2, \(p<0.001\)) [40]. Another radiomic model, based on whole-tumour features, showed a good performance for DMI prediction, with an AUC of 0.84 and 0.76 in the training and test cohorts, respectively [41]. [PERSON] and co-workers defined a geometric feature to describe the irregularity of tissue structure inside the corpus uteri caused by EC and built a model combining it with texture features extracted by the whole uterus region. Compared with models in other studies, this model showed better performance in predicting DMI in terms of sensitivity (94.7%) and specificity (93.3%) [42]. [PERSON] et al. built three radiomics models on T2 WI and DWI. The models based only on T2 WI or DWI, and the model which combined T2 WI and DWI, which included 24, features obtained in the validation set AUCs of 0.76, 0.80, and 0.85, respectively. Nevertheless, the authors did not observe a statistically significant difference between diagnostic values obtained by the model based on both T2 WI and DWI and those obtained by two radiologists' subjective assessments [43]. More recently, three other studies evaluated the performance of MRI radiomics-based machine learning models to predict DMI, obtaining comparable AUC values, including between 0.79 and 0.83 [44; 45; 46]. However, in the research performed by [PERSON] and colleagues, the diagnostic performance for DMI of four radiologists did not show statistically significant improvement with the support of radiomic classifiers [46], contrary to a previous investigation [39].
# Improved Sound Radiation of Flat Panel Loudspeakers Using the Local Air Spring Effect
## 3 Numerical Analysis of the Pressure Deviations
### Introduction of Modal Related Decomposition and Sound Pressure Related Decomposition  
The distributed parameter model of flat panel loudspeakers represents every single mode as a separate network. [PERSON] [22] introduced an electromechanical circuit, which illustrates the coupling of the mechanical modes to the electrical and acoustical domains. The contributions of each mode can be represented as an analogous electrical circuit, which is summed up to the complex movement of the whole panel. Each mode has its own circuit, and the overall results are a superposition of all modes. With the simulation tool used, it is possible to decompose each mode's ratio to the overall performance. The result of all the modes \(SPL_{Total}\) compared with the individual parts of Mode 1 \(SPL_{Mode1}\) to Mode 11 \(SPL_{Mode11}\) is shown in Figure 7.  
Figure 6: Simulated pressure response of the standard prototype with the visualization of the front displacement at discrete frequencies in the upper part. In the lower part, an overview of the relevant modes is presented. All even modes are not excited by the central excitation.  
Another evaluation method was presented by [PERSON] [23]. The sound-pressure-related decomposition is an efficient postprocessing method to identify regions on the loudspeaker or panel that effectively contribute to the total radiated pressure level at a certain observation point. The structural vibration is decomposed by using the AAL (accumulated acceleration level). The separation contains the following components: in-phase components \(AAL_{In}\), anti-phase components \(AAL_{Anti}\) and quadrature components \(AAL_{Quad}\). The in-phase components have a constructive contribution to sound radiation. In contrast, the anti-phase components counteract sound radiation. The quadrature components have no contribution to the radiated sound at all. The \(AAL_{Total}\) is the superposition of all components and represents the total movement of the panel as shown in Equation (1):  
\[AAL_{total}=AAL_{In}+AAL_{Anti}+AAL_{Quad} \tag{1}\]  
The total pressure \(\bar{p}_{Total}\) is the result of the in-phase \(\bar{p}_{In}\) and anti-phase pressure components \(\bar{p}_{Anti}\) presented in Equation (2). The \(SPL_{Total}\) is twenty times the base-10 logarithm of the ratio of the total pressure to reference pressure value.  
\[\bar{p}_{Total}=\bar{p}_{In}+\bar{p}_{Anti} \tag{2}\]  
Furthermore, the following relationships are valid. The in-phase component \(AAL_{In}\) can be larger than the total \(SPL_{Total}\) but never exceeds the \(AAL_{Total}\). However, a small difference between the in-phase \(AAL_{In}\) and anti-phase component \(AAL_{Anti}\) causes acoustical cancellation and dips in the \(SPL_{Total}\).  
Figure 7: Simulated modal related and sound pressure related decomposition of the panel with an exciter (standard construction). The contribution of Modes 1–11 is extracted. All symmetrical eigenmodes are not excited due to the centered exciter position.
# Experimental Study on Torsional Shear Testing of
## 3 The Mechanical Theory of Torsional Shear Tests
### The Analysis of Torsional Stress for a Prism
#### 3.1.1 Torque in Unconfined Compression  
The prismatic bar torque theory, originally proposed by [PERSON] in 1853, was used to solve the boundary conditions of the torsion function and [PERSON] applied a conjugate function to simplify the boundary conditions. Under the rectangular coordinates of Figure 3, and according to [PERSON]'s torsion function, the stress of any point of the cross section is expressed as follows:  
\[\left\{\begin{array}{l}\tau_{xx}=G\omega\Big{(}\frac{\partial\varphi}{ \partial x}-y\Big{)}\\ \tau_{zy}=G\omega\Big{(}\frac{\partial\varphi}{\partial x}+x\Big{)}\\ \sigma_{x}=\sigma_{y}=\sigma_{z}=\tau_{xy}=0\end{array}\right. \tag{1}\]  
where \(\omega\) is the angle of twist per unit length, \(\varphi(x,y)\) denotes the torsion function, and \(G\) denotes the shear modulus. In general, \(\varphi(x,y)\) is rather complicated but as a plane harmonic function, accordingly, it may be written in the form of conjugate function \(\Psi(x,y)=\psi(x,y)-\big{(}x^{2}+y^{2}\big{)}/2\), a rectangular cross section; \(\Psi\) is an even function of \((x,y)\); hence, it is assumed in the form of Equation (2):  
\[\Psi=\left[\frac{1}{4}b^{2}+\frac{1}{2}\Big{[}x^{2}-y^{2}\Big{]}+\sum_{n=1,3, \ldots}^{\infty}A_{n}\text{cosh}\,\frac{n\pi x}{b}\text{cos}\,\frac{n\pi y}{ b}\right]-\frac{1}{2}\Big{(}x^{2}+y^{2}\Big{)} \tag{2}\]  
Figure 2: Application of the differential load on a prismatic specimen.  
The conjugate function may be represented either in terms of stress or torque (see Equations (3)-(6)).  
\[\frac{\tau_{xx}}{G\omega}=-2y-\frac{\pi}{b}\sum_{n=1,3,...}^{\infty}nA_{n} \mathrm{cosh}\frac{n\pi x}{b}\mathrm{sin}\frac{n\pi y}{b} \tag{3}\]  
\[\frac{\tau_{xy}}{G\omega}=-\frac{\pi}{b}\sum_{n=1,3,...}^{\infty}nA_{n} \mathrm{sinh}\frac{n\pi x}{b}\mathrm{cos}\frac{n\pi y}{b} \tag{4}\]  
\[\frac{T}{G\omega}=\frac{ab^{3}}{3}\left[1-\frac{192}{\pi^{5}}\frac{b}{a}\sum _{n=1,3,...}^{\infty}\frac{1}{n^{5}}\mathrm{tanh}\frac{n\pi a}{2b}\right] \tag{5}\]  
\[A_{n}=(-1)^{(n+1)/2}\frac{8b^{2}}{\pi^{3}n^{3}}sech\frac{n\pi a}{2b} \tag{6}\]  
The shearing stress vector is tangent to the boundary of the rectangular cross section. The shear stress is zero at external corners, and the maximum shear stress occurs in the middle portion of the bigger side of the rectangle in Figure 3. The shear stresses at points \(A\), \(A^{\prime}\), \(B\), and \(B^{\prime}\) are expressed as follows:  
\[\left(\tau_{xx}\right)_{x=0,y=b/2}=\alpha_{A}\frac{M_{T}}{ab^{2}} \tag{7}\]  
\[\left(\tau_{xy}\right)_{x=a/2,y=0}=\alpha_{B}\frac{M_{T}}{ab^{2}} \tag{8}\]  
For \(a=b=0.04\) m, it yields \(\alpha_{A}=\alpha_{B}=4.804\). With Equations (7) and (8), the maximum shear stress of a straight bar with rectangular cross section is equal to \(\tau_{zxmax}=\tau_{zymax}=7.50625\times 10^{-2}M_{T}\), where the unit of moment \(M_{T}\) is the newton-meter (N-m) and the unit of shear stress \(\tau\) is the megapascal (MPa).
## 3 System Model and Security Goals
### IoT-Based System Model
#### 3.1.1 Remote User (Physician)  
The physician connects to the server remotely through a recourse-constraint mobile device and wants to access the sensor nodes to see the patient's reports. It is necessary to verify the remote user's legitimacy before granting access. The physician updates the patient's history online.
## 3 Materials and Methods
### Parameters of Dual-Beam Laser Polishing  
Based on the preliminary research of our team [43], a dual-laser-beam five-axis polishing test platform (Figure 5) consisting of a dual-laser 3D galvanometer and a two-axis CNC rotary table was used. Using the polishing of the MS additive manufacturing part shown in Figure 1 as an example, the infrared continuous laser and pulsed laser with top-hat characteristics entered the 3D galvanometer of their respective front-focusing system. The movement of the focused laser spot on the surface of the three-dimensional cavity of the mold can be controlled by the Z-axis moving lens and the X-axis and Y-axis rotating lens. On the two axes, the rotation of the NC rotary table and mold cavity laser processing blind area, in the range of the 3D vibrating mirror machining, can be exposed. In this way, the galvanometer of the three movements in the X, Y, Z, and NC rotary table two axes linkage can be used to realize laser spot scanning in the mold cavity arbitrary area of the polishing processing. Parameters of the dual-beam laser polishing are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Features of the Category** & **Numerical Value** & **Parameters of the Category** & **Numerical Value** \\ \hline Maximum molding size & 250 mm \(\times\) 250 mm \(\times\) 325 mm & Optical scanning system & F-theta \\ Type of laser & Yb-fiber & Scanning speed & Highest 7.0 m/s \\ Laser power & 400 W & Power & Maximum 8.5 kW \\
3D printing speed & 5-20 cm\({}^{3}\)/h & Thickness & 20-100 \(\mu\)m \\ Laser spot diameter & 100-500 \(\mu\)m & Overall dimensions & 2500 \(\times\) 1300 \(\times\) 2190 mm \\ Scanning system & SCANLAB galvanometer & Spread direction of powder & Horizontal, one-way powder \\ XY-axes laser accuracy & \(\leq\)\(\pm\)6 \(\mu\)m & Z-axis powder accuracy & \(\leq\)\(\pm\)10 \(\mu\)m \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the EOS M290 metal additive manufacturing system.  
Figure 4: SEM morphology of EOS managing steel MS1: (a) 100\(\times\), (b) 1000\(\times\).
# Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
## 3 Results
Figure 7:7 Deposited vs. undeposited particles for each test case (high-pressure case). The particle deposition efficiency (DE) is shown in Figure 22 for the medium-pressure case. The DE analyzed here is defined as the percentage of particles that deposit on the substrate to the total number of particles that impact the substrate. It is worth noting that the DE for the high-pressure case is greater than 99.9% for all test cases, with almost all the particles depositing. This exceptionally high deposition efficiency aligns with the high-pressure scenario observed in the experimental and numerical study by [PERSON] et al. (2017), where cold spray was applied to both on-axis and off-axis nozzle/substrate hole geometries. However, for the medium-pressure case, there appears to be a strong linear relationship between the DE and the mask SOD. As the mask distance from the nozzle exit is increased, the DE of the particles appears to linearly increase. This behavior can be seen for all four masks and both 10 mm and 20 mm substrate SODs. This increase in deposition as the mask SOD is increased can be associated with the particle velocity, which increases when the mask is moved further away. The mask properties, such as the wire size and opening area, also affect the DE, with Mask 1 resulting in the highest DE. As discussed, several researchers, including [PERSON] et al. (2017), have experimentally investigated the manufacturability of pyramidal fin arrays using the masked cold spray process. Similar to their work, we considered commercially available masks. However, while their research focused on aluminum and stainless-steel powders for the development of fins in compact heat exchangers ([PERSON] et al., 2017), our study emphasizes nickel particles due to their relevance in the HER. Consequently, a direct one-to-one comparison is not feasible, but the observed trends can be evaluated. In their work ([PERSON] et al., 2017), the standoff distances between the substrate, mask, and nozzle were empirically optimized for an inlet pressure of 3 MPa. The cold spray system employed was Plasma Giken PCS-1000, featuring a nozzle with a 3.0 mm throat diameter and a 6.5 mm exit diameter. Through multiple trials, they examined the influence of the standoff distances between the mask and the nozzle, as well as the mask and substrate ([PERSON] et al., 2017). While differences existed in nozzle type, powder material, and inlet pressure, both the current study and the work of [PERSON] et al. (2017) demonstrate that maintaining a longer distance between the substrate and the nozzle (approximately 20-25 mm), along with positioning the mask closer to the substrate, is critical for achieving optimal conditions. Figure 7:8 Deposition efficiency for medium-pressure cases.