esa-sceva/satcom-chunk-collection huggingface dataset:
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 2 Materials and Methods
### Pipeline Design
#### 2.1.3 Image Fusion (IF)  
In the final stage of the pipeline, the intermediate results are merged to produce the final, corrected image. The module takes the original image (\(X\)) and the intermediate correction results, namely \(X_{OE}\) for the \(OE_{b}\) correction and \(X_{UE}\) for the \(UE_{b}\) correction, along  
Figure 5: Segmentation masks. The figure represents some examples of our segmentation.  
with their respective non-overlapping masks (\(M_{CE}\), \(M_{OE}\), and \(M_{UE}\)) as input. The merging process, as introduced in [33], relies upon a Hadamard product (denoted by the symbol \(\circ\)) to combine the input images and their corresponding masks:  
\[Y=X\circ M_{CE}+X_{UE}\circ M_{UE}+X_{OE}\circ M_{OE} \tag{6}\]  
The formula ensures that the final image is a combination of the correctly exposed regions of the original image and the regions corrected by the \(UE_{b}\) and \(OE_{b}\) branches, as defined by their respective masks. This property is guaranteed by the fact that the three masks (\(M_{CE}\), \(M_{OE}\), and \(M_{UE}\)) are mutually exclusive and together form a matrix of ones, except for the edges.  
Although Equation (6) ensures that the resulting image covers all the desired regions, it may produce abrupt transitions and visible artefacts. Therefore, to produce a final image with smooth transitions between regions and a natural-looking appearance, we utilise the Laplacian Pyramid Blending [34], which involves the following steps:  
Pyramid Creation: Construction of the Laplacian Pyramids of \(X\), \(X_{UE}\) and \(X_{OE}\), as well as the Gaussian Pyramids of \(M_{CE}\), \(M_{UE}\) and \(M_{OE}\).  
Layer Fusion: Apply Equation (6) layer-wise to the pyramids generated in the previous step to obtain the Pyramid of Blended images (\(Pyr_{b}\)).  
Reconstruction: Reconstruct the final image from \(Pyr_{b}\) by performing the following steps: at each level, expand the current layer with lower resolution to match the size of the next level in the pyramid. Add the expanded layer to the corresponding layer in the pyramid to form a higher-resolution blended image. Repeat this process until the final level is reached, yielding the final blended image with the original resolution.
## 3 Results and Discussion
### Treatment
[PERSON] conducted research showing that periprocedural administration of pentoxifylline both pre- and posttreatment has a positive impact in regards to the healing process of a postextraction wound in MRONJ patients [48]. Such treatment is beneficial to the bone tissue healing process and inflammatory reaction reduction [48]. It has been proven that bisphosphonate intake is linked to the increase in the vascular endothelial growth factor (VEGF) which leads to healing impairment [48]. Pentoxifylline is a powerful vasodilator that is able to reduce VEGF levels in the bones reversing the effect previously induced by bisphosphonates [48]. [PERSON] et al. presented research concerning a link between vitamin D3 deficiency and a higher risk of MRONJ in oncological patients that are currently in the active stage of the disease [49]. According to [PERSON] et al., vitamin D3 deficiency is not an MRONJ risk factor [49]. Other research on vitamin D has shown that this secosteoroid can play a potentially significant role in various etiological paths of MRONJ [50; 51]. There are a number of comorbid diseases that correlate between MRONJ occurrence and deficit of this vitamin [50]. The recurrence rate of MRONJ is high, however, it can be reduced with proper procedures [52; 53; 54; 55]. According to an analysis made in eight Japanese units in the years 2006-2016, in 361 patients that were observed for approximately 15 months each, a complete recovery was obtained in 25.2% of patients that underwent conservative treatment. On the other hand, 76.7% of patients who had surgical treatment experienced a complete recovery [56]. [PERSON] et al. carried out a study on 49 patients that required surgical treatment in the oral cavity area both before and during BP and denosumab therapy [57]. In the course of the described study, there were no signs of MRONJ during a 2 year observation period [57]. Proper healing of the soft tissue and absence of pain were noted by [PERSON] et al. [57]. Despite growing knowledge about the risk of MRONJ occurrence and ability to properly treat patients, it is still a highly relapsing disease, and according to various studies, it can affect 11.8% to 45% of the risk group patients [58].
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Cryptography Authentication
#### 2.5.2 Geo-Encryption  
Geo-encryption is an encryption method that uses the user's geographical location as the user's identity itself. It enables data encryption for a specific place that may be identified spatially and temporally. Without the position information received using an anti-spoof GPS device, it is impossible to decode the data with the particular availability spoofing module (SAASM). The GPS signal includes encrypted binary Y codes. SAASM receivers can track Y codes only when loaded with the correct decryption key [13]. This type of encryption is useful when there is a requirement for disabling decryption outside a specified geo-location.
## 2 Antenna Design
### Single Communication Mode
#### 3.3.3 Communication Mode #3  
NB Antenna #3 operates from 8.48 GHz to 11.02 GHz (fractional bandwidth of Mode #3: 26.1%) with a resonant frequency at 9.5 GHz, as shown in Figure 10. The isolation between NB Antenna #3 and the UWB antenna is higher than 19 dB, with a peak value of 26.9 dB over the high-frequency band (8 GHz-11 GHz).  
To investigate the operating principle of NB Antenna #3, the surface current distribution at the resonant frequency of 9.5 GHz is shown in Figure 11. NB Antenna #3 operates in a loop current mode. Due to the large separation between the UWB antenna and NB Antenna #3, high isolation performance can be obtained in the high-frequency band.  
Figure 10: Simulated S-parameters of the proposed antenna for communication mode #3.  
Figure 9: Simulated surface current distributions of NB Antenna #2 at three resonant frequencies (**a**) at 5.5 GHz, (**b**) at 6.52 GHz, and (**c**) at 7.65 GHz.
# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
## 3 Experimental Results
### Model Training  
The structure of the Faster R-CNN model was adjusted according to the unique features of the ingot image, the model was developed with PyTorch. To achieve higher accuracy, instead of training a model from scratch, we adopted a transfer learning technique and used a pretrained Faster-RCNN model on ImageNet as our chosen model to customize and initialize weights. The model was trained on an Intel core i7-7700 CPU, NVIDIA RTX 2070 super GPU, 16 GB memory PC. The stochastic gradient descent (SGD) optimization algorithm was adopted with a starting rate of 0.0025, a momentum of 0.9, and a weight decay of 0.0001. The learning rate decay mode was warm up multistep learning rate ([PERSON] et al., 2015) with a factor of 0.001, iterators of 1000, and linear method. The network was trained over 120 epochs using a batch size of 6 images and a step of 580 images per epoch. The loss curve is shown in Figure 8, as the number of iteration increased, the loss maintained a steadily decreasing trend, and showed a steady convergence state at a later period of training.  
To test the robustness of our model, an inference test was conducted on the test set. Inference results of a sample example of different types of habit lines are shown in Figure 9. The first row is the original images, second row shows the result images after preprocessing, third row is the annotations, and fourth row is the inference results. There is occlusion in columns (a)-(f), there is no occlusion in columns (g)-(j). According to the actual task requirements, to early warn of the DHL phenomenon in the production process. For a single image, the detection accuracy of comprehensive judgement result was 97.33%, the model had good detection effect whether there was occlusion or not. In the hardware experimental environment, the inference speed was about 14.7 FPS, which could meet the needs of the actual production process.  
Figure 8: The loss curves.  
Figure 9: Model inference results. (**a–f**) results of images with occlusion; (**g–j**) results of images without occlusion.
# C Band 360\({}^{\circ}\) Triangular Phase Shift Detector for Precise Vertical Landing RF System
In order to solve some of the limitations shown by previous designs for vertical landing of drones, we propose the design of a novel triangular phase shift detector with a range of \(\pm\)180\({}^{\circ}\), based on switched dual-product phase detectors with AGC. The design procedure increases the phase detection range to \(\pm\)180\({}^{\circ}\) and, therefore, the tracking area. This allows the frequency to be increased and size of antennas to be reduced, while maintaining the tracking area similar to solutions operating at \(\pm\)90\({}^{\circ}\). The inclusion of the AGC simplifies the modeling of the detector curves and the circuit calibration procedure. In addition, it significantly reduces the calculations required to obtain phase shifts, allowing the use of low computing power platforms. Furthermore, a C band prototype has been manufactured to explain the details of the design procedure, and a simple landing program has been developed to show the drone guidance algorithm and detection outside the tracking area. This paper is organized as follows: Section 2 reviews the fundamentals of precision landing by triangular phase shift detector, describes the limitations of the precision landing system with \(\pm\)90\({}^{\circ}\) detector, and justifies the choice of using a 360\({}^{\circ}\) phase detector of double multiplication, in phase and phase-shifted. All the keys to the design of the C frequency band-fabricated circuit are detailed in Section 3, which includes the experimental characterization of the prototype and quantification of the phase errors. Section 4 develops the algorithm to calculate the phase and the guidance program during the landing maneuver. Next, the landing algorithm is evaluated by means of a small simulation program to assess its theoretical behavior when the drone is inside and outside the tracking range of \(\pm\)180\({}^{\circ}\). Finally, the results are discussed in Section 5.
## 2 Numerical Model
### Lubrication Model  
It is assumed that the hydrodynamic pressure of the ATF film is axisymmetric and the boundary film pressure is equal to zero. Based on the uniform laminar flow assumption, considering the surface roughness effect and the permeability of friction material, the average oil film pressure is indicated as [4]:  
\[\overline{p}=\frac{B}{4A}\left(r^{2}-R_{o}^{2}\right)+\frac{3\eta}{A}\frac{ \partial\overline{h}_{T}}{\partial t}\left(r^{2}-R_{o}^{2}\right)+\ln\frac{r }{R_{o}}\left(\frac{B}{4A}+\frac{3\eta}{A}\frac{\partial\overline{h}_{T}}{ \partial t}\right)\frac{R_{o}^{2}-R_{i}^{2}}{\ln R_{i}-\ln R_{o}} \tag{3}\]  
Figure 2: Force analysis of the wet multi-disc clutch.  
where \(r\) is the radius; \(R_{i}\) and \(R_{o}\) are the inner and outer radii of the friction pair, respectively; \(\eta\) is the dynamic viscosity of ATF; \(\overline{h}_{T}\) is the average oil film thickness. The coefficient \(A\) and \(B\) are noted as:  
\[A=\phi_{r}h^{3}+12\Psi d_{m} \tag{4}\]  
\[B=\phi_{r}\rho h^{3}\Big{(}3\omega_{f1}^{2}+4\omega_{f1}\omega_{f2}+3\omega_{f2 }^{2}\Big{)}/5 \tag{5}\]  
where \(\phi_{r}\) is the pressure-flow factor [26], \(h\) is the nominal oil film thickness, and \(\rho\) is the density of ATF; \(\Psi\) and \(d_{m}\) are the permeability and thickness of friction material, respectively; \(\omega_{f1}\) and \(\omega_{f2}\) are the angular velocities of the separator disc and friction disc, respectively.  
With the Gaussian surface assumption [5], the change rate of average oil film thickness was deduced by [PERSON] et al. [27]. By integrating the average oil film pressure in the ATF lubrication area \(A_{v}\), the hydrodynamic force of friction pairs can be deduced as:  
\[F_{v}=\pi\bigg{\{}\frac{B}{2A}+\frac{3\eta}{A}\frac{dh}{dt}\bigg{[}1+\text{ erf}\bigg{(}\frac{h}{\sqrt{2}\sigma}\bigg{)}\bigg{]}\bigg{\}}\bigg{[}\frac{R_{i}^ {4}-R_{o}^{4}}{4}-\frac{\big{(}R_{o}^{2}-R_{i}^{2}\big{)}^{2}}{4\big{(}\ln R_{ i}-\ln R_{o}\big{)}}\bigg{]}(1-A_{\text{red}}C) \tag{6}\]  
where \(\sigma\) is the standard deviation of roughness, and \(A_{\text{red}}\) is the non-groove area ratio.  
The ATF film also exists between the piston and the first separator disc, contributing to the generation of a hydrodynamic force. As both the piston and the first separator disc are made of steel and rotate at the same speed, the hydrodynamic force between them can be expressed as follows by simplifying Equation (6).  
\[F_{v}=\pi\bigg{\{}\rho\omega_{f1}^{2}+\frac{3\eta}{\phi_{r}h^{3}}\frac{dh}{dt }\bigg{[}1+\text{erf}\bigg{(}\frac{h}{\sqrt{2}\sigma}\bigg{)}\bigg{]}\bigg{\}} \bigg{[}\frac{R_{i}^{4}-R_{o}^{4}}{4}-\frac{\big{(}R_{o}^{2}-R_{i}^{2}\big{)} ^{2}}{4\big{(}\ln R_{i}-\ln R_{o}\big{)}}\bigg{]}(1-C) \tag{7}\]
## 3 Methodology  
As illustrated in Figure 1, the methodology in this study is structured around a comprehensive DTM framework for managing evolving topics over time and exploring emerging technological topics. This framework consists of three main modules: a data acquisition and pre-processing module, a BERTopic-based DTM module, and a weak signal analysismodule. Initially, the data acquisition module collected patent records from the incoPat [52] database, extracted titles and abstracts, and performed the data pre-processing process with an LLM-based summarization technique to obtain more relevant information as input without removing the technical content. Then, we deployed specifically selected pre-trained transformer-based model to generate document-level embeddings after a comparative performance analysis. Consequently, we performed a hyperparameter search to enhance the performance of the BERTopic-based framework. Finally, a weak signal analysis process was applied to interpret the weak signals in the evolving technological field and identify the emerging technological trends over time. Our automatic framework can provide valuable insights with interpretable emerging technological trends analysis for strategical decision-making in the 6G industry. The following subsections elaborate on each module's specific process and contributions in detail.
## 2 Problem Formulation
### Correlation and Detection Process  
The baseband signal \(r(t_{k})\) multiplies a locally generated replica and the result is processed through correlation operation. The resultant output is expressed as:  
\[\begin{split} I_{k}=&\ \frac{1}{N_{k,c}}\sum_{t_{ 0}=0}^{N_{k,c}}\mathrm{s}(t_{k})\tilde{G}(t_{k}-v)\cos(2\pi(f_{c}-u)t_{k})\\ Q_{k}=&\ \frac{1}{N_{k,c}}\sum_{t_{k}=0}^{N_{k,c}} \mathrm{s}(t_{k})\tilde{G}(t_{k}-v)\sin(2\pi(f_{c}-u)t_{k})\end{split} \tag{2}\]where \(N_{{}_{kc}}\) is number of samples per coherent accumulation segment, equal to \(T_{c}/T_{s}\), where \(T_{s}\) is the sampling period and \(T_{c}\) is code chip rate. \(u\) and \(v\) are the guessed value of the Doppler frequency and code delay, respectively, in frequency search space \(\left[f_{c}-u_{max,lo},f_{c}+u_{max,up}\right]\) and code search space \(\left[v_{max,lo},v_{max,up}\right]\). The values \(u_{max,up}\) and \(u_{max,lo}\) are the upper and lower boundaries of the Doppler frequency, respectively. Similarly, the values \(v_{max,up}\) and \(v_{max,lo}\) are the upper boundary and lower boundary of code delay, respectively. \(\tilde{G}(\cdot)\) is an unfiltered local replica. The in-phase \(I_{k}\) and quadrature-phase \(Q_{k}\) are squared respectively and then summed altogether. The result is accumulated after \(K\) non-coherent integration time. The correlation output is shown as follows:  
\[\begin{array}{l}y(u,v)\\ =\sum\limits_{k=1}^{K}(I_{k}^{2}+Q_{k}^{2})\\ =\sum\limits_{k=  
achieve a certain probability of false alarm in noise alone by employing the well-known Neyman-Pearson criterion [26].  
The above depiction shows that what concerns the designer is to rapidly find the required \(u\) and \(v\) in a short time so as to have the correlation exceed the set threshold. The following chapters demonstrate that the proposed acquisition method can effectively speed up signal acquisition speed and maintain high estimated parameter precision.
## 2 Materials and Methods
### Experimental Procedure  
#### 2.2.1 Preparation of Simulated Fallout Material (SFM) Containing Radiostrontim (Sr-85)  
Arizona test dust with a particle size ranging from a few micrometers (\(\upmu\)m) to a few hundred \(\upmu\)m (Powder Technology Inc., Arden Hills, MN, USA) was selected to be used in the evaluation testing since the particles in selected particle size ranges were susceptible to resuspension and tracking. To prepare the mixture of particles, equal amounts of ultrafine test dust (ISO 12103-1, A1) and coarse test dust (ISO 12103-1, A4) were mixed. To make radioactive Sr-85 contaminated particles, an aqueous solution that contained 500 microcuries (\(\upmu\)Ci) Sr-85 (strontium chloride in 0.5 M HCl, Eckert & Ziegler Isotope Products, Valencia, CA) was added to \(\sim\)250 g (g) of substrate particles using a sprayer (BS-3 3 oz Locking Personal Sprayer, Sprayco, Livnia, MI, USA). The particles were well-mixed by manually shaking the container side to side for approximately 10 s and then manually rotating the container for approximately 30 s. This process was repeated until the required volume of aqueous Sr-85 was added to the particles. After being allowed to dry overnight in a well-ventilated exhausted box in a fume hood (T: 22.0 \(\pm\) 0.7 \({}^{\circ}\)C; RH: 24.9 \(\pm\) 0.5%), the spiked substrate particles were mixed again for approximately 10 min to ensure a homogenous mixture. Then, the dry particles were transferred to saltshakers (Tablecraft Products Company, 3-oz glass with stainless steel top, UNSPC# 52152013), used for application. For simulated vehicle experiments and walking experiments on concrete and asphalt surfaces, \(\sim\)3 g of particles were transferred from each saltshaker to contaminate one testing surface. For walking experiments on the soil walking path surface, \(\sim\)40 g of particles were transferred from each saltshaker for one experiment.  
To characterize the particle size of the contaminated particles and confirm the lack of particle clumping during contamination, an identical particle mixture was prepared, and one aliquot was wetted with non-radiological aqueous solvent and then dried using a procedure similar to the procedure for the preparation of radiological particles. The particle size was determined for both aliquots using a standard test method for particle-size analysis of soils, ASTM D422-63. The analytical results of the particle sizes are listed in Table 1, and the wetted and nonwetted samples produced similar results.
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 4 Results and Discussion
### Evaluation Metrics  
There are several metrics used to evaluate the model, including precision (\(P\)), recall (\(R\)), F-measure (\(F_{\beta}\)), and mean absolute error (MAE). The precision calculates the proportion of correctly predicted positive samples among the predicted positive samples. The recall calculates the proportion of correctly predicted positive samples among the true positive samples. The F-measure calculates the weighted harmonic mean considering the precision and recall, while the absolute mean error measures the average pixel absolute error between the predicted saliency map and the true value. \(M\) is the binary mask image transformed from the saliency map, \(G\) is the true value, \(S\) is the predicted value, and \(W\) and \(H\) represent the width and height of the feature map, respectively. The introduction of hyperparameters \(\alpha,\beta\) results in a set of maximum or average \(F_{\beta}\) measures.  
\[P=\frac{|M\cap G|}{|M|} \tag{17}\]  
\[R=\frac{|M\cap G|}{|G|} \tag{18}\]  
\[F_{\beta}=\left(1+\beta^{2}\right)\frac{P\times R}{\beta^{2}(P+R)} \tag{19}\]  
\[MAE=\frac{1}{W\times H}\sum_{i=1}^{W}\sum_{j=1}^{H}\left|S_{i,j}-G_{i,j}\right| \tag{20}\]
## 2 Materials and Methods
### Calculation of Dynamic Characteristics
#### 2.3.3 Tidal Prism Calculation  
Tidal prism is an important indicator for studying the hydraulic characteristics of the bay environment. Tidal capacity refers to the volume of tidal water that can be accommodated between adjacent high tides and low tides in the sea area during a tidal cycle, that is, the total amount of tidal water flowing through the bay section during a high-tide or low-tide period. The tidal prism during high tide or low tide is obtained by setting a bay section (Figure 2), calculating the volume of tidal water passing through the bay section per unit time, and then accumulating the cross-sectional flow according to the high-tide or low-tide period to obtain the tidal prism. The calculation formula [33] is:  
\[TP=\int_{t_{1}}^{t_{2}}(Q_{u}+Q_{v})dt \tag{5}\]  
\[Q_{u}=\sum_{i=1}^{n}u_{i}\cdot l_{v_{i}}\cdot h_{i} \tag{6}\]  
\[Q_{v}=\sum_{i=1}^{n}v_{i}\cdot l_{u_{i}}\cdot h_{i} \tag{7}\]  
where \(TP\) is the tidal prism; \(Q_{u}\) and \(Q_{V}\) are the volume of tidal water passing through the bay section in the east-west and north-south directions per unit time; \(t_{1}\) and \(t_{2}\) are the start and end times of the high-tide or low-tide period, respectively; \(n\) is the number of grids in the section; \(u_{i}\) and \(v_{i}\) are the velocity components of the \(i\)-th grid in the east-west and north-south directions, respectively; \(l_{u_{i}}\) and \(l_{v_{i}}\) are the widths of the \(i\)-th grid in the east-west and north-south directions, respectively; \(h_{i}=\eta+d\) is the total water depth of the \(i\)-th grid in the section; \(\eta\) is the water level; and \(d\) is the still water depth.  
Figure 2: _Cont._
## 1 Introduction
### Notation  
Let \(\mathbb{R}\) and \(\mathbb{R}^{n}\) be the set of real numbers and the \(n\)-dimensional real vector space, respectively. Let \(B^{n}(y_{0},r):=\{y\in\mathbb{R}^{n}\mid\|y-y_{0}\|<r\}\subset\mathbb{R}^{n}\). For a set \(C\subset\mathbb{R}^{n}\), its closure, boundary, and interior are denoted by \(\hat{C}\), \(\partial\hat{C}\), and \(\hat{C}\), respectively. Let \(2^{C}\) be the power set of \(C\), i.e., the set of all subsets of \(C\). The set \(C\) is said to be as follows:  
* Convex, if for any \(y_{1},y_{2}\in C\) and \(\lambda\in[0,1]\), \((1-\lambda)y_{1}+\lambda y_{2}\in C\) holds;
* Strictly convex, if for any \(y_{1},y_{2}\in C\) and \(\lambda\in(0,1)\), \((1-\lambda)y_{1}+\lambda y_{2}\in\hat{C}\) holds [35].  
A function \(f:\mathbb{R}^{n}\to\mathbb{R}\) is said to be convex if for any \(y_{1},y_{2}\in\mathbb{R}^{n}\) and \(\lambda\in[0,1]\), the inequality \(f((1-\lambda)y_{1}+\lambda y_{2})\leq(1-\lambda)f(y_{1})+\lambda f(y_{2})\) holds. Moreover, \(f\) is said to be strictly convex if the above inequality is strict when \(a\
eq b\) and \(\lambda\in(0,1)\).