esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Network Model and the Problem Formulation
### Inter Cluster Communication and the Mobile Sink  
The data processing and the communication distribution between the CH and the sink node is included in the intra cluster communication [33]. As we take into account a mobile sink in this work, the CH must be updated with the sink node position. Figure 2 indicates the process flowchart of initiating the ring node and broadcasting of the frequent sink node position advertisement throughout the network. The criteria for the mobility of the sink and the implementation of the ring routing protocol is discussed in the next section.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 2 Methods
### GNSS/5G Integrated Three-Dimensional Positioning Scheme
#### 2.3.3 GNSS/5G Integrated Three-Dimensional Positioning Algorithm  
Based on the above analysis, the GNSS/5G integrated three-dimensional positioning algorithm is summarized in Algorithm 3 as GITPA. Specifically, there are three steps. Firstly, based on the GNSS and TOA/RSS measurements, we obtain the positions of all the MUs according to DATM, including the requesting nodes and the cooperative nodes, as shown in lines 1 and 2. Then, the clustering results based on the TOA and RSS measurements are weighted, as lines 3 to 7 indicate. In final, GITPAPF is conducted to further improve the accuracy of the requesting nodes, as line 8 indicates.
## 2 Methods  
The consecutive series of patients diagnosed with BRVO or CRVO and consulted at the University Hospital in Bydgoszcz, Poland and at the Outpatient Clinic in Bydgoszcz between January and May 2019 were taken into consideration in the study. Patients with significant other ocular disturbances in the affected eye, such as ocular trauma, retrobulbar external compression from Graves' disease or orbital tumor, and optic nerve drusen, as well as some systemic diseases which may influence vessel condition, such as coagulation disorders or inflammatory systemic disorders, were excluded. These conditions were especially important in young individuals (less than 50 years old), who were considered for participation only if secondary pathologies, such as thrombophilia and uveitis, were excluded. To avoid possible false measurements of the SAF, participants with skin pigmentation, using skin sun creams, or with extensive scarring of the skin, were also excluded from the study. A small number of patients failed to arrive for the clinical examination. Ultimately, 23 subjects were enrolled in the study. The control group consisted of 14 healthy volunteers with no diagnosed general or ocular diseases and with normal values in laboratory tests.  
Basic demographic and clinical information were obtained all patients, including the date of the first occurrence of CRVO or BRVO, incidents of cystoid macular edema (CME), previous treatment of RVO (anticoagulants, intravitreal anti-VEGF, or steroid injections, laser therapy), sex, and eye laterality. Detailed information about the patients' smoking status; sunlight exposure; concurrent diagnoses of systemic diseases, such as HTN, DM, hyperlipidemia (HL), heart diseases; and any ocular disturbances, such as glaucoma, were obtained. Retrospectively, the best corrected visual acuity (BCVA) before the episode of RVO and shortly after treatment was noted.  
At the final follow-up, a clinical examination of all patients was performed at the same center (University Hospital in Bydgoszcz, Poland) between June and September 2019. The examination included a BCVA assessment (using Snellen charts, decimal fraction), intraocular pressure (IOP), slit-lamp biomicroscopy (with an evaluation of the lens status, presence of abnormal pupil reactions, neovascularization of the iris, and iridocorneal angle), indirect ophthalmoscopy (due to possible neovascularization of the optic disk or elsewhere and/or vitreous hemorrhage), and optical coherence tomography (OCT, Nidek RS-3000) for CME detection.  
The metabolic status of each individual was assessed. For this purpose, laboratory examinations were performed, and blood glucose levels, HbA1c, and lipid profiles were assessed. The diagnosis of diabetes mellitus was based on the patient's medical history with the use of diabetic medication. The metabolic tests were carried out to better assess the metabolic status of each individual, apart from each patient's medical history with antidiabetic treatment. By assessing metabolic status, we were able to ensure that our study groups (with DM and without DM) were variable.
# A Deeper Insight on the Stability of Water-Induced Reconstruction of Anatase (001) Surface
## 5 Conclusions  
In summary we have revised the stability hierarchy of water induced reconstruction periodicities of anatase (001) surfaces on the basis of the newly found (2\(x\)_n_) geometry. Using the dispersion energy correction, we have shown that in bulk water the stability hierarchy found in vacuum is changed with the (2 \(\times\) 4) surface reconstruction being more stable than the (2 \(\times\) 2) one. The (2 \(\times\) 3) surface appears to be the most stable one also in bulk water but the surface energy difference between (2 \(\times\) 3) and (2 \(\times\) 4) decreases with respect to the vacuum case. The molecular adsorption energy values on both the terrace and the ridge sites are larger (in absolute values) on the (2 \(\times\) 4) than on the (2 \(\times\) 3) in accordance with the surface energy values and the residual surface stress. This means that the molecular adsorption induces a larger relaxation pattern onto the (2 \(\times\) 4) than onto the (2 \(\times\) 3) surfaces. Thus we can argue that, if one considers the dispersion energy involved in the molecular adsorption, the same surface energy correction behavior could occur for molecular adsorption (to a lesser extent perhaps) as the one found for dissociative adsorption. Another point is that the water adsorption onto the (2 \(\times\) 6) surface is not spontaneously dissociative, meaning that some activation energy must exist. For all these reasons, we can state that the real stability hierarchy of WIR anatase (001) surface in bulk water should not be considered as a closed question yet. Free energy measurement in the context of ab-initio molecular dynamics simulations could help to clarify this important issue.  
[PERSON] has contributed with supervision, conceptualization, investigation, data curation, writing and editing. [PERSON] has contributed with conceptualization and data curation.  
The authors declare no conflict of interest.  
We kindly acknowledge the CRESCO/ENEAGHD High Performance Computing infrastructure and its staff [40]. CRESCO/ENEAGHD High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes, see [[http://www.cresco.enea.it/english](http://www.cresco.enea.it/english)]([http://www.cresco.enea.it/english](http://www.cresco.enea.it/english)) for information. We also acknowledge [PERSON] for providing the BOMD outputs.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
DFTDensity Functional Theory  
GGAGeneralized Gradient Approximation  
PBEPerdew-Burke-Ernzerhof  
WIRWater Induced Reconstruction  
BOMDBorn-Oppenheimer Molecular Dynamics
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 1 Introduction
### Approach and Contributions  
In this paper, motivated by the BS cooperation scheme in [23], we address this shortcoming by proposing a novel location-aware cross-tier cooperation (LA-CTC) scheme for downlink transmission in two-tier HetNets. To tackle the poor offloading effect of FBSs in the vicinity of their MBSs, the FBSs located at a prescribed distance away from any MBS are deactivated, and thereby the whole area is divided into an inner region and an outer region. The non-uniform deployment of FBSs is similar to that in [31; 32; 33; 34]. Hence, the users within the inner region are only served by the MBSs. Meanwhile, a pairwise BS cooperation scheme based on each user's location information is proposed to use only CoMP transmissions to eliminate the cross-tier interference for the offloaded users within the outer regions, whereas the other users within the outer regions are served directly by either a MBS or a FBS. Subsequently, we derived the mathematical coverage probability and average rate expressions for the proposed LA-CTC scheme, and the analytical results were verified via Monte Carlo simulations. The numerical results show that the proposed LA-CTC scheme can improve the overall coverage probability and average rate significantly. Furthermore, the proposed scheme is compared with the conventional MRP scheme, the CRE scheme and the location-aware cross-tier CoMP transmission (LA-CTCT) scheme. Numerical comparisons show that the proposed cooperation scheme outperforms the other three schemes.  
The rest of the paper is organized as follows: The system model and the proposed LA-CTC scheme are presented in Section 2. The mathematical coverage probability and average rate expressions are derived in Section 3. In Section 4, the numerical results on the performance evaluation for the proposed LA-CTC scheme are analyzed. Finally, the paper is concluded in Section 5.
## 2 Materials and Methods
### Data Pre-Processing
#### 2.1.1 CT Scan Reconstruction  
The marching cubes algorithm was applied in the present research study; it consists in a computational method utilized to create 3D surface representations of complex data, such as medical images [18; 19]. In the first phase of the algorithm, the space that is specified by the DICOM data is converted into a grid of voxels, which consists of 3D pixels representing the values of grayscale at each location. It must be noted that the proper selection of the intensity range of HU is essential since diverse tissues and structures in the human body possess different densities and therefore different ranges of HU. The core objective of the methodology is the determination of a potential surface that belongs to the 3D model (bone, organ, etc.) in each of these investigated voxels, taking into account an intensity range of values that are related to the gray tone of the examined voxel's vertices. This stage could be illustrated via a marching of a theoretical cube into a scalar field, in which the absence or presence of a boundary surface in each vertex of the cube is categorized in one of the potential generated surface polygons [18]. This process is terminated after the investigation of the whole examined scalar field (all the voxels of the scene); hence, a 3D model is constructed with a polygonal approximation of the generated surfaces through connecting the vertices of all the traversed cubes. The employed methodology is schematically illustrated in Figure 2a, where the main functions of the automatic 3D reconstruction module considering DICOM data as input are displayed.  
In the next phase, filtering methods are utilized in the generated 3D mesh, enabling the removal of parts that do not belong to the investigated heart model. In the present case, several bones that are not directly attached to the heart, like the scapula, clavicle, sternum, as well as spine bones, were removed from the scene using the open3d library [20]. In particular, a function that clusters connected triangles was applied, i.e., triangles that are connected via edges are assigned the same cluster index in order to remove small parts that are produced and should be removed from the scene. Moreover, a reference 3D model of a heart was aligned with the examined one using the Iterative Closest Point (ICP) algorithm, which can be applied in order to minimize the differences between two point clouds [21]. Hereupon, parts of the reconstructed model that possessed a certain distance to the reference were removed from the 3D model of the heart. In addition, algorithms concerning the removal of outliers and noise were also applied in order to isolate the  
Figure 1: Workflow of the research study.  
examined heart. Finally, the extracted mesh was further edited with processes like hole-filling, smoothing, etc., in order to secure the correctness and continuity of the meshes. The generation of the 3D mesh as well as the influence of the filtering methods in the captured scene, coupled with the final outcome of the employed methodology, are demonstrated in Figure 2b.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods
### Patch-Wise Diffusion Model  
#### 3.5.1 Patch Selection Strategy  
The algorithm presented in Algorithm 1 provides a method to randomly select non-overlapping patches from an input image. The main objective is to extract six patches of different sizes from the image while ensuring that none of these patches overlap. This method is particularly useful in computer vision tasks such as data augmentation, feature extraction, or training region-based models, where diverse and independent sampling of image regions is essential for robust model performance.  
```
Input:\(I\): Image, \(m_{s}\): Minimum Patch Size, \(M_{s}\): Maximum Patch Size Output:\(P\): Set of non-overlapping patches \(h,w\leftarrow\operatorname{GetDims}(I)\) \(P\leftarrow\emptyset\) \(c\gets 0\) while\(c<6\)do \(h_{p}\leftarrow\operatorname{Rand}(m_{s},M_{s})\) \(w_{p}\leftarrow\operatorname{Rand}(m_{s},M_{s})\) \(x\leftarrow\operatorname{Rand}(0,w-w_{p})\) \(y\leftarrow\operatorname{Rand}(0,h-h_{p})\) foreach\((x_{i},y_{i},w_{i},h_{i})\in P\)do if\((x+w_{p}\leq x_{i})\vee(x\geq x_{i}+w_{i})\vee(y+h_{p}\leq y_{i})\vee(y\geq y_{i}+h_{i})\)then \(P\gets P\cup\{(x,y,w_{p},h_{p})\}\) \(c\gets c+1\) break
```  
**Algorithm 1**Randomly select non-overlapping structure patches  
To achieve this goal, the algorithm takes as input an image \(I\), along with the minimum and maximum sizes for the patches (\(m_{s}\) and \(M_{s}\)). The output is a set of selected patches, denoted as \(P\). Initially, the dimensions of the input image are obtained using the function extGetDims, which returns the height (\(h\)) and width (\(w\)) of the image. An empty set \(P\) is then initialized to store the selected patches, and a counter variable \(c\) is set to zero to track the number of patches selected. The algorithm continues to select patches until six non-overlapping patches have been chosen. In each iteration, the height (\(h_{p}\)) and width (\(w_{p}\)) of the patch are randomly selected within the range specified by \(m_{s}\) and \(M_{s}\). The top-left coordinates (\(x,y\)) of the patch are also randomly determined within the boundaries of the image, ensuring that the patch lies completely within the image dimensions. To ensure that the patches do not overlap, the algorithm checks each newly generated patch against all previously selected patches in the set \(P\). Specifically, for each patch \((x_{i},y_{i},w_{i},h_{i})\) in \(P\), the algorithm verifies if the new patch does not intersect with any of the existing patches. This is done by ensuring that the new patch lies entirely to the left, right, above, or below each previously selected patch. If the new patch does not overlap with any existing patches, it is added to the set \(P\), and the counter \(c\) is incremented by one. The process repeats until six non-overlapping patches are successfully selected. The final output is the set \(P\), which contains the coordinates and dimensions of the selected patches.
## 4 Flat-Panel Beam-Steering Antennas with Low-Profile Phase Shifters with Fast Response
### Low-Profile Planar Inverted Microstrip Line and Grounded Coplanar Waveguide Phase Shifter
By this, a cavity similar to a  
Figure 21: Measured IP\({}_{3}\) versus the frequency difference \(\Delta f\) of the two tones at room temperature and maximum input power for three device configurations: empty (air) and filled with K15 and MDA-03-2844 [184; 185; 186]. metallic waveguide can be built up inside the LTCC, thus combining high performance with low profile. After processing the LTCC structure, microwave LC mixtures are filled into these cavities, thus enabling compact and tunable components such as filters and phase shifters [27; 103; 165; 166; 167; 183; 264; 265; 266]. These phase shifters can easily be integrated into an LTCC module together with biasing lines and electronics for tuning as well as with individual antenna elements to form an array. In [74], a LTCC delay line phase shifter based on an IMSL topology was investigated, using different LTCC CT707 layers with \(\varepsilon_{r,CT707}=6.13\) at 30 GHz. For a proof-of-concept and a comparison, the realized LTCC-IMSL phase shifter was filled with TUD-188 (having similar performance than GT3) and TUD-566. Matching \(|S_{11}|\) is better than \(-13\) dB over the frequency range from 25 to 35 GHz, while the insertion losses are below 3.5 dB and 4.5 dB, respectively. Measurements of the empty phase shifter indicated that the insertion losses are dominated by metallic losses, leaving, e.g., for TUD-566 a proportion of just around 1.3 dB. The maximum differential phase shift rises from about 220, 250\({}^{\circ}\) to around 300, 350\({}^{\circ}\) over the above frequency range, while the FoM keeps around 45, 70\({}^{\circ}\)/dB for TUD-188 and TUD-566. The measured switch-off response times \(7_{10}^{90}\) are about 33 s and 150 s, respectively, which might be feasible for portable applications, just to reconfigure the antenna beam, or for slow-moving applications, but definitely not for on-the-move applications, requiring high beam-scanning rates. To speed up tuning, the LC layer height \(h_{LC}\) has to be decreased significantly, but this means increasing insertion losses [74; 175], i.e., a lower FoM, according to Figure 18. Hence, with the IMSL topology, high FoM and fast switch-off response times, i.e., fast beam-scanning rates, are not achievable at the same time.
## 3 Description of the Simulation Model
### Model Structure and Working Principle
Using DES, the simulated period is divided into equal time steps. In this study, the length of the applied time step is one hour. This means that a change in the state of the system is occurring every hour. Another important time unit is the number of days since the start of the simulation, based on which date is defined. The date is important as it, as explained above, defines the prevailing ice conditions along a specific leg and whether a brash ice channel is present. As per Figure 1, IBs are assumed to operate within a limited IB operating area. In the simulation, this means that once an IB resource has assisted a ship entity to the border of its operating area, it will leave the ship. If further assistance is needed, a ship entity will request an IB resource from within the IB operating area that it is entering. Thus, in line with the available maritime traffic data, a ship entity might be assisted by several different IB resources on its way towards its destination. In this case, the total waiting time for IB assistance is the accumulated sum of the waiting times related to each instance of IB assistance.
## 2 System Model and Problem Formulation
### Formulation of the Energy Harvesting Optimization Problem
By introducing an auxiliary variable as \(t\triangleq\max(\tau_{1},...,\tau_{U})\), the problem \(\mathbf{P}_{1}\) is equivalently reformulated into the problem \(\mathbf{P}_{2}\), as follows:  
\[\mathbf{P}_{2}:\min_{h,\mathbf{X},t} t\] (20) s.t. \[\tau_{u}=\dfrac{Q_{u}^{\text{Req}}}{\operatorname{Tr}(\mathbb{E}[\mathbf{h}_{ u}\mathbf{h}_{u}^{H}]\mathbf{X})},\ \forall u\in\mathcal{U}. \tag{14}\]0 \[\tau_{u}=\dfrac{Q_{u}^{\text{Req}}}{\operatorname{Tr}(\mathbb{E}[\mathbf{h}_{ u}\mathbf{h}_{u}^{H}]\mathbf{X})},\ \forall u\in\mathcal{U}. \tag{14}\]1 \[\tau_{u}=\dfrac{Q_{u}^{\text{Req}}}{\operatorname{Tr}(\mathbb{E}[\mathbf{h}_{ u}\mathbf{h}_{u}^{H}]\mathbf{X})},\ \forall u\in\mathcal{U}. \tag{14}\]2 (22) \[\tau_{u}=\dfrac{Q_{u}^{\text{Req}}}{\operatorname{Tr}(\mathbb{E}[\mathbf{h}_{ u}\mathbf{h}_{u}^{H}]\mathbf{X})},\ \forall u\in\mathcal{U}. \tag{14}\]3  
\(\mathbf{P}_{2}\) is a non-convex optimization problem, since the constraints of (20) are non-convex with respect to the UAV's altitude \(h\). More specifically, in constraint (20), \(\mathbf{G}_{u}(h)\) is non-linear as well as very complex with respect to \(h\) for all \(u\in\mathcal{U}\), because \(d_{u}\), \(\alpha_{u}\), and \(\mathcal{K}_{u}\) in \(\mathbf{G}_{u}(h)\) are non-linear and complex functions of \(h\). Therefore, obtaining an optimal solution is, generally, extremely difficult. In order to tackle the problem \(\mathbf{P}_{2}\), we first solve the problem in a single-EHD scenario, to draw insights, and then establish efficient methods for obtaining an optimal solution in a multiple-EHD scenario.
## 3 Materials and Methods
### Interpretability Methodology  
This section briefly describes the four stages and five steps of the proposed methodology for the interpretability implementation. In Figure 2, we graphically depict the proposed architecture of the process. This methodology is sequentially described step by step, as follows.  
* Step 1: IVI feature selection. Common informative features are extracted with the IVI algorithm.
* Step 2: Application of the MIFF filter [1].
* Step 3: Latent representation. Compress high-dimensional to a latent space in order to isolate fraud transactions.
* Step 4: STE interpretability. Feature weight evaluation for individual transactions.
* Step 5: Clustering through ITR.  
For the first step of our methodology, we focused on FS. To achieve this, we applied a recently proposed FS technique called IVI [13], capable of identifying the informative, redundant, and noisy features. The IVI algorithm introduced in the original work was implemented with CME as a weight generation method designed to be competitive with the standard linear algorithms. In our methodology, we expanded the weight generator using different classification algorithms, SVM, LDA, LR, and GB (see the companion paper [1]).  
It is common for FS to fall into two biases, on the one hand due to biases in the training data and on the other hand the biases are due to the intrinsic characteristics in the ML algorithms used. In this sense, the second step of our methodology focused on feature selection extension, reducing the bias and obtaining a global view of the problem. Using the IVI algorithm, we resampled the data to train ML algorithms, and in this setting, we minimized the training data bias. In the case of the bias in other ML algorithms, we used and combined different ML algorithms, aiming to discover which of these features were truly informative in all cases. For that purpose, the features needed to be subjected to a filtration process, at the end of which only the features that appeared as consistent were included in our model. In the companion paper [1], we had established two kinds of filters over the relevant features extracted by using IVI and leaving aside the redundant and noisy features: maximally informative features filter (MIFF) and the recurrent features filter (RFF). In this work, we focused on MIFF because we obtained the best result using this filter in the companion paper. The MIFF filter consists of selecting those features that at least have been retrieved in two of the ML algorithms used. This filter is less restrictive and provides moderate feature reduction compared with the RFF. In contrast, this filter is able to identify relationships among features achieving higher prediction accuracy.