esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Generation of a phase-locked Raman frequency comb has been demonstrated recently in a simple setup consisting of a microchip laser as pump source and two hydrogen-filled hollow-core photonic crystal fibers [15]. Molecular modulation in gas produces high repetition rate, low-energy pulse trains [16]. However, high energy, isolated pulses are generally more useful in studies of ultrafast phenomena. To this end, we have extended the molecular modulation technique to a qualitatively different time regime and to a different state of medium--Raman-active crystals driven by femtosecond pulses. We have demonstrated the mutual coherence of the spectral sidebands generated through molecular modulation in diamond and shown the capability to control spectral phases in a precise and stable manner in a setup that combined manual course adjustment of individual sideband phases with programmable pulse shaping and fine phase (and amplitude) tuning across the full spectrum [17,18]. Recently, we studied coherent transfer of optical orbital angular momentum in multi-order Raman sideband generation [19,20]. The energy of the ultrafast waveform produced in the scheme of References [17,18] is limited by the damage threshold of the pulse shaper. In order to obtain high energy ultrafast waveforms, we design a reflection scheme using spherical mirrors to combine the Raman sidebands [21]. The sidebands and the driving pulses are refocused back to the Raman crystal and the relative spectral phases are retrieved from an interferogram based on nonlinear Raman interaction. Furthermore, using a deformable mirror (DM) to adjust the spectral phases, we demonstrate that the setup is capable of synthesizing ultrafast waveforms using coherent Raman sidebands. In this paper, we review and expand on our recent work [21]. We start by describing the algorithm used to retrieve the spectral phases from a two-sideband-interferogram, giving the details of our theoretical simulation of Reference [21]. The method is then extended into the scenario of the interferogram composed of more sidebands; we show the standard deviation of the simulation _vs._ original interferogram.
## 4 Data Preparation and Results
### Data Preparation and Categorization  
We have considered all kidney cancer cases in which miRNA information was provided. These cases represent the samples taken from patients who had kidney cancer which belonged to one of five different cancer sub-types. Some individual cases had more than one miRNA sequence data file, which were represented by both isoform expression quantification and miRNA expression quantification. In our study, we only considered the miRNA expression qualification data because it tabulated in a balanced way, i.e., all cases had the same number of miRNAs.  
Figure 3 shows the schematic diagram for the data preparation procedure. The data from the TCGA server was downloaded, and then it was categorized using a MATLAB program. First, the information related to each case was matched with its corresponding miRNA quantification files using the file ID. The information of miRNA read per million for each miRNA was considered in our experiment. The miRNA files were then matched with those in clinical data, stored in javascript file, using the case ID. This clinical data provides the record of cancer sub-types and other patient clinical information such as age, sex, and demographics. The above procedure of preparing the cancer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Disease Type** & **Project Name** & **No. of Cases** & **No. of Files** \\ \hline Kidney Renal Clear Cell Carcinoma & TCGA-KIRC & 516 & 616 \\ Kidney Renal Papillary Cell Carcinoma & TCGA-KIRP & 291 & 323 \\ High-Risk Wilms Tumor & TARGET-WT & 127 & 138 \\ Kidney Chromophobe & TCGA-KICH & 66 & 91 \\ Rhabdoid Tumor & TARGET-RT & 44 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table Type Styles.  
Figure 2: Kidney diseases projects available in the The Cancer Genome Atlas (TCGA) data repository.  
data facilitated automatic classification of kidney cancer sub-types based on the miRNA quantification expression information of the patients.  
With the procedure given above, we obtained all of the miRNAs provided. However, we noticed that many miRNAs had null readings. By removing those fields, we ended up with a total of 1627 out of 1881 miRNA and 1221 cases, which were grouped into the five subtypes as mentioned in Figure 2 and Table 1.
## 3 System Model and D2D Use Cases for Combined LTE/5G and WiFi
The base station sends the data to the cluster head that relays the data to other users over WiFi. The data (such as recently popular YouTube videos) is cached in the cluster head for some time in order to serve requesting nodes directly. Figure 5: Simplified model for analysis. LB1: Link between the base station and the UE 1; LB2: Link between the base station and the UE 2; LB3: Link between the base station and the UE 3; LBn: Link between the base station and the UE n; L12: D2D link between UE 1 and UE 2; L13: D2D link between UE 1 and UE 3; L23: D2D link between UE 2 and UE 3; L3n: D2D link between UE 3 and UE n.  
3. **Case 3**: The base station sends the data to the cluster head that relays the data to requesting nodes over LTE. 4. **Case 4**: The base station sends \(1/N\) of the required packets to \(N\) different nodes requesting the same data (e.g., certain content in Facebook shared among friends). Different parts are sent to different users and the missing parts are shared using D2D connections among nodes over WiFi. 5. **Case 5**: Same as case 4, but the sharing is done using an LTE interface.
## 1 Introduction
In [16], the authors present a technique to generate (logically and mathematically) a tree-like fractal structure without considering its intersection with other physical structures (inside the CAD part) or the physicality of the fractal structure. The fractal models in [16] were two dimensional (i.e., the fractals were defined by points and line segments), and the CAD parts were modeled manually (using Siemens NX) based on the fractal geometric parameters. The mathematical model from [16] represents the basis for the computational model presented in this paper. A complete mathematical definition of the proposed tree-like fractal with all of the required properties is presented (as an extension of [16]). A tabular computational method to preserve the fractal structure geometrical data is proposed, which is highly compatible with vector programming languages (e.g., Matlab, Python numpy). Furthermore, this paper addresses the intersection of the fractal structures with other components given in CAD (which are defined as surface meshes). A generalization of the Moller and Trumbore [19] algorithm is used to intersect the fractal structure with a surface mesh. In addition, volume is applied to the fractal structures to provide a physical three-dimensional representation. Lastly, the paper presents methods to unite the fractal structures with the CAD component (the input shell that requires fractal fillings). Three case studies are presented to demonstrate the functionality of the computational model. The output of the proposed computational method, namely the CAD parts with internal fractal structures, will help to validate the use of such structures in two major ways. On the one hand, the CAD will be used to manufacture test samples for mechanical properties testing, and on the other hand, the CAD will be used in finite element analysis (FEA) tests to study the correlation between real laboratory experiments and computer simulations (to create numerical predictable models). Following the introduction, the paper is structured as follows: Section 2 presents the general algorithm proposed to obtain the final CAD parts, where clear links are established between the inputs and the outputs of each step; Section 3 presents a complete, comprehensive, mathematical model for the tree-like fractal structures (all the necessary mathematical properties are derived); Section 4 presents the intersection of the tree-like fractals with relevant structures and the trimming (cutting the fractal where required)procedure; Section 5 presents the procedure of adding volume to the fractals and how to unite the fractals with the CAD component shell; Section 6 shows three cases studies; the conclusions are presented in Section 7.
## 2 Experiment Description
### Experimental Equipment and Test Design
#### 2.2.2 Test Design  
* Expansion Force Test  
In order to ensure good sealing effect, the connecting bolts between flange blind plate and grouting barrel should be uniformly loaded when assembling the test device. In addition, in order to demold the sample smoothly after the test, butter was applied on the inner wall of the grouting container. At the beginning of grouting, the exhaust holes are opened first. Once the slurry is discharged from the vent cap, the vent cap is quickly closed. During the test, the law of expansion force change at different time is tested by pressure sensor until the pressure is stable or begins to decrease. After the test, the specimen was demolded by jacking out method, and they are marked in turn.  
* Elastic Modulus of Polymer Test  
In view of the fact that the compression test of polymer grouting material referred to in this study has no current specifications to follow. In order to facilitate quantitative research, the compression test method of rigid foam plastics (GB 8813-88) [24] is used as a reference standard in this study. Three sets of cylindrical polymer specimens with diameter of 50 mm and height of 100 mm were designed in the test, and their densities were 0.246 g/cm\({}^{3}\), 0.284 g/cm\({}^{3}\), and 0.323 g/cm\({}^{3}\), respectively. Considering the convenience of taking the mold, a separable high polymer sample mold was made (Figure 2). The central cylinder was designed as a pair of open structures with an inner diameter of 50 mm, a height of 600 mm, and a wall thickness of 10 mm.  
The production process of the specimen was as follows: (1) A layer of grease was applied on the inner wall, bottom, and top of the mold; (2) different amount of slurry was injected into the mold to form different density samples; (3) after standing for 15-20 minutes, the mold was removed and with the specimens removed, the head and tail of the specimens are transversely cut into five specimens with a height of 100 mm on a precision lathe; (4) The density of the cut sample is calculated by weighing, and numbering them in turn, as shown in Figure 2b-d; and (5) WAW-1000 micro-computer controlled electro-hydraulic servo universal testing machine was used to load the specimen. The strain control mode was adopted, and the loading rate was set to 0.3 mm/min to simulate the quasi-static process.  
Figure 1: Schematic diagram of the expansion force test. (**a**) Placement of the pressure cell; (**b**) Molds for testing.
## 3 Data Assimilation
### Data Assimilation Algorithm  
At each time step, the flow model (Delft3D FM) receives real velocity data from the drifters and the shallow water model equations produce a collection of states (ensembles) representing the evolution of the processed inputs. The EnKF compares these real data to the estimated outputs to provide the best estimation of the flow state in the field.  
Below, we explain the fundamentals behind the EnKF algorithm in Equations (1)-(7). The true model state based on the physical state of the lake at time t is defined as \(A\)t (in our case, velocity for the entire model grid), \(M\) is the nonlinear lake system operator, \(\omega\) is the noise, and U is the hydrodynamic forcing vector (here discharge and water level) for time \(t\):  
\[A_{t}=M_{t}\ (S_{t-1},\ U_{t-1})\ +\ \omega_{t-1}. \tag{1}\]  
The state-space vector, noted \(\hat{A}\) is an approximation by the hydrodynamic model Delft3D-FM of the true state \(S\). The noise is added to the forcing term \(\omega\).  
The forecast state is specified by \(\hat{S}^{f}\) and the analysis state acquired after DA is \(\hat{S}^{a}\):  
\[\hat{A}^{f}_{t}=H_{t}\ \big{(}\hat{A}^{a}_{t-1}\,\ U_{t-1}). \tag{2}\]The observation (\(y\)) equation is described as:  
\[y_{t} =H_{t}\left(A_{t}\right)+\xi_{t}. \tag{3}\]  
where \(H\) is an operator connecting the state of the system to the observation and \(\xi\) is measurement noise.  
The observation prediction equation is:  
\[\hat{y}_{t} =H_{t}\left(\hat{A}_{t}^{f}\right). \tag{4}\]  
and the state estimation of the system obtained from DA which will be used in the next sequence as an initial condition is:  
\[\hat{A}_{t}^{a} =\hat{A}_{t}^{f}+K_{t}\left(y_{t}-\hat{y}_{t}\right) \tag{5}\]  
\(K\) is a weighting factor called Kalman gain and is considered as a balance between model and observation uncertainties [41]. \(K\) tends to decrease the error covariance of the state estimate during the analysis time:  
\[K_{t} =P_{t}^{f}H_{t}^{T}\left(H_{t}P_{t}^{f}H_{t}^{T}+R_{t}\right)^{-1}. \tag{6}\]  
where \(R_{t}\) and \(P^{f}\) are the measurement error covariance matrix and a priori state error covariance matrix, respectively.  
The EnKF is able to compute a time-varying covariance error based on the dynamics of the system. For an ensemble of forecasts (\(j=1,\,\ldots,\,N\)), each prone to some level of uncertainty in model processes, forcing, or initial conditions, \(P\) is:  
\[P_{t}^{f} =\frac{1}{N-1}\sum_{j=1}^{N}(A_{t,j}^{f}-A_{t}^{f})\left(A_{t,j}^ {f}-A_{t}^{f}\right)^{T}. \tag{7}\]  
where \(P_{t}^{f}\) is the forecast error covariance.
## 2 Related Work and Theory  
### Wavelet Transform  
Wavelet transform (WT) simultaneously analyzes both time (or spatial) and frequency domains, offering localization and multi-resolution properties [19]. It separates high-frequency rain streaks from low-frequency backgrounds, enabling targeted rain removal without affecting the image structure.
# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 2 Materials and Methods
### Shallow Feature-Extraction Module
#### 2.2.2 Paired Asymmetric Transformer Block  
In recent years, Transformer architectures have achieved remarkable success in computer vision tasks, most notably image super-resolution, thanks to their exceptional ability to model long-range dependencies. Standard Vision Transformers (ViTs) and their variants employ the self attention (SA) mechanism to capture global contextual information, which is essential for reconstructing the intricate textures and structures in HR images [21; 22]. This global modeling capability directly addresses the limitations of conventional convolutional neural networks (CNNs), whose restricted receptive fields often struggle with complex details [23].  
However, SR simultaneously demands deep feature transformation and precise detail refinement--two objectives that a single Transformer block may find hard to balance. To meet this challenge, we propose a two-stage process-and-refine strategy embodied in our core module, the PATB. As illustrated in Figure 5, each PATB contains two cascaded Transformer submodules. The first submodule focuses on initial, broad-scope context modeling and feature transformation; the second submodule builds on these results to perform targeted, fine-grained refinement. This paired design enables progressive, in-depth feature processing within a single block.  
In the conventional SA paradigm, the query, key, and value vectors are all derived from the same input feature map, effectively modeling intra-feature dependencies [24]. However, this homogeneous perspective may suppress or distort important low-level cues that are crucial for fine-grained detail reconstruction. Therefore, in the second submodule we replace self attention with a cross attention mechanism. Specifically, we generate the query vector \(q_{c}\) not from the intermediate feature \(X_{mid}\) but via a dedicated context-query generator applied to the block's original input \(X_{in}\).  
Figure 5: Illustration of the Global Feature-Extraction Module (GFEM). (**a**) The structure of GFEM. (**b**) The Paired Asymmetric Transformer Block (PATB), which consists of two cascaded Transformer submodules. The Contextual Query Generator (CQG) generates the query for the second submodule based on the original input features of the PATB.  
The core motivation of this design is to preserve and leverage original feature cues that might otherwise be degraded by the first submodule's complex nonlinear transformations. By supplying the second Transformer with a query \(q_{c}\) drawn directly from \(X_{in}\)--while still using \(X_{mid}\) to form the keys and values--we guide the attention mechanism to emphasize aspects most relevant to the original image structure and texture, thereby enhancing the recovery of fine details. The cross attention operation is defined as follows:  
\[\text{Attention}(q_{c},k,v)=\text{Softmax}\bigg{(}\frac{q_{c}k^{T}}{\sqrt{d_{ k}}}+b\bigg{)}v \tag{6}\]  
where \(d_{k}\) denotes the dimensionality of the key vector K, and b is a learnable bias term.
## 2 Materials and Methods
### Universal GNSS Channel Validation  
The resulting architecture of the proposed GNSS Universal Channel is presented in Figure 9, where different colors help highlight added feature sets:  
* red dotted and dashed lines for L2C TMBPSK of a L2 CM memory code with a locally generated L2 CL code,
* blue dashed lines for MBOC sub-carriers replicas generation and feedback,
* green dotted lines for dual-component overhead (extra correlators and sub-carriers combining not shown),
* purple solid lines for the optional Variable Spacing Correlator (VSC) used to plot Auto-Correlation Function (ACF) plots.  
The following sections present the different test scenarios conducted to validate the proposed architecture in terms of constellations and signals on different frequency bands, with different spreading codes and modulations. The reader should be advised that this paper focuses on available civil signals, although its architecture also applies to restricted access signals.
# Thermoresponsive Behavior of Magnetic Nanoparticle Complexed pNIPAm-_co_-AAc Microgels
## Materials and Methods
### Dynamic Light Scattering (DLS) Analysis  
The microgel was titrated by adding aliquots of 0.6 ug of aMNP in solution to a dilute solution of microgels (10 \(\upmu\)L of purified microgels diluted to 1 mL) at pH 6.5 (\(\sim\)0.001 ionic strength) contained in a plastic cuvette. Each microgel solution was prepared before each measurement. The radius and thermoresponsive behavior of the microgels following titration was measured using Dynamic Light Scattering instrument (DLS, Zetasizer Nano ZSP, Malvern Panalytical, Malvern, UK). The radius value was an average of 12 individual radius measurements using a 5 s integration time for each measurement. For volume phase transition temperature (VPTT) measurements, the microgels were either first titrated with a given amount of aMNP or not. The solution was heated at an interval of 1 \({}^{\circ}\)C and the microgel size was determined by DLS. At each temperature, 5 consecutive runs of a sample were performed where each run was composed of 12 individual radius measurements using a 5 s integration time for each measurement. The error bars were estimated by the 5 consecutive runs of the same sample.
## 3 Results
### Total Phosphorus Reduction Efficiency Analysis Result  
Figure 14 shows the total phosphorus reduction efficiency based on the water quality simulation results for each of the previous water quality improvement scenarios. In this study Figure 14 shows when only the soil improvement operation scenario was applied to Zone A (yellow column), when both the soil improvement scenarios were applied to Zone A and Zone B (green column) and the PSTWs' effluents were treated (blue column), and when all scenarios were considered together (red column). In addition, Figure 15 shows the changes in water quality during a specific period for analyzing the total phosphorus reduction efficiency.  
Figure 12: Simulated results of T-P concentration (Zone B, S0, S5–S6).  
Figure 13: Simulated results of T-P concentration (Zone B, S0, S7–S8).  
Regarding the T-P reduction efficiency of Zone A, the results are summarized as follows: S1, 4.6 %; S2, 6.4 %; S3, 4.6 %; S4, 7.0 %; S5, 8.3 %; S6, 17.3 %; S7, 17.3 %; and S8, 28.9 % mg/L. Regarding the T-P reduction efficiency of Zone B, the results are summarized as follows: S1, 13.5 %; S2, 17.4 %; S3, 13.5 %; S4, 17.4 %; S5, 7.4 %; S6, 13.5 %; S7, 26.3 % and S8, 36.5 % mg/L.  
In both Zone A and Zone B, S1 and S3 show similar results, as do S2 and S4. This is because the level of soil contamination in Zone B is not relatively higher than that in Zone A (PO4-P benthic flux parameter in model (PO4D): 0.002 g/m\({}^{2}\)/d in Zone A, 0.001 g/m\({}^{2}\)/d in Zone B, and 0.0001 g/m\({}^{2}\)/d in Zone C). When comparing the results of Zones A and B, the effect of the water quality improvement by the dredging operation was greater in Zone B than in Zone A. This effect is significant because of some seawater circulation and the overall water quality improvement of the reservoir in Zone B (Figure 15a).  
When the scenarios (S5, S6) to improve the quality of water discharged from the PSTWs were applied, there was a marked difference between Zones A and B. In the case of Zone A, the results of the discharged water improvement from the PSTW scenario were better than the improvement of the soil contamination scenario, indicating that the PSTWs significantly impacted the water quality in Zone A. In Figure 15b, it can also be seen that the water quality deteriorates rapidly because of the inflow of upstream water in Zone B.  
Figure 14: T-P removal rate results (Sn/S0, \(n=\) nth scenario).  
Figure 15: T-P concentration changes in the estuarine reservoir.  
Zone A. However, unlike the dredging scenario in Zone B, it can be seen that the effect of improving water quality is less than that of Zone A. Although improving water quality of the discharged water from the PSTWs reduces the external inflow load and decreases the system's impact, its effect can be considered insignificant if the bottom soil of the estuarine reservoir is not improved. Therefore, these results reveal that reducing the estuarine reservoir's internal load can increase its internal resilience by improving the bottom soil and have a significant effect on the overall water quality improvement.