esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Enhancing the EPR Effect by Targeting the Tumor microenvironment (TME)
#### 5.1.1 Targeting Molecular Markers in the TME  
One promising approach to overcoming tumor heterogeneity and enhancing the EPR effect involves equipping nanodrugs with ligands that specifically target components of the TME. For instance, targeting extracellular matrix (ECM) components and specific molecular markers such as \(\alpha\
u\beta 3\) integrin has shown potential in improving nanodrug delivery. Arguyl-glycylaspartic acid (RGD)-modified nanoparticles, which bind to \(\alpha\
u\beta 3\) integrin, have demonstrated enhanced drug accumulation and therapeutic efficacy in tumors. However, while RGD-modified nanoparticles show promise, they may also carry risks of immune activation, which must be carefully managed to avoid adverse effects [168; 169].
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Algorithm
#### 3.3.3 Step 3--Doppler Rate Estimation  
After performing the time synchronization, the receiver has to estimate the DR to remove the impact of the time varying frequency shift \(f_{d}(t)\) on symbol estimation. To this end, we propose an algorithm based on the estimation of the peak position in each \(T\)-long sequence of the preamble up-chirps. The peak frequency values are processed in order to find the linear regression, which represents the frequency slope due to the DR. The proposed algorithm, using the same principle as in [22], is summarized by the following three points:  
* Estimate the \(argmax\) of the FFT module in each symbol interval of the preamble. If we note \(i_{p}\), the \(argmax\) of the FFT module in the \(p^{th}\)\(T\)-long sequence, we have: \[\hat{i}_{p}=\underset{k\in[0,M-1]}{argmax}\ (|R[k,p]|)\] (28) with \(R[k,p]=\frac{1}{\sqrt{M}}\sum_{n=0}^{M-1}\left(r(n,p)x_{ref}^{*}(n)\right)e^{ -j2\pi\frac{m}{M}}\) and \(r(n,p)=r(n+(p-1)M)\), \(\forall n\in[0,M-1]\). It should be noted here that the interpolation method, as presented in Section 3.1.2, is used to increase the accuracy of the estimate \(\hat{i}_{p}\), while in [22], a classical \(argmax\) function, is performed.
* The FFT \(argmax\) is used in pairs to compute different DR estimates noted \(\hat{\mathcal{E}}_{d}^{p,l}\). These estimations are obtained using the couple \(\{\hat{i}_{p},\hat{i}_{p+l}\}\), with \(p\in\{1,N_{p}-1\}\) and \(l\in\{p+1,N_{p}\}\). Thus, by considering (17), we have: \[\hat{\mathcal{E}}_{d}^{p,l}=\frac{2}{T^{2}}\frac{\hat{i}_{p+l}-\hat{i}_{p}}{l}\] (29)
* An estimation of the DR is obtained by averaging \(\hat{\mathcal{E}}_{d}^{p,l}\), as follows: \[\hat{\mathcal{E}}_{d}=\frac{1}{N_{p}-1}\sum_{p=1}^{N_{p}-1}\frac{1}{N_{p}-p} \sum_{l=p+1}^{N_{p}}\hat{\mathcal{E}}_{d}^{p,l}\] (30)  
We note that the estimation of the DR is done at the sampling rate \(T_{s}=\frac{1}{f_{min}}\), while in [22], the sampling rate is equal to \(\frac{1}{2f_{min}}\). Furthermore, this estimation is needed only if the frequency separation between two adjacent bins, \(\Delta_{b}=\frac{1}{T}=\frac{B}{M}\) is greater than a specific value. In this case, the compensation of the DR is mandatory before starting the estimation of the fractional offsets. In the payload, the compensation of the DR is done after the compensation of the fractional STO and perform the downsampling at the frequency rate \(f_{min}\). In the next section, the robustness of our proposed receiver is tested with different separation \(\Delta_{b}\), i.e., different values of \(B\) and \(SF\).
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 4 System Model
### Routing Model

Table 2: Node/time slot allocation for transmission of messages. overhearing transmissions [59]. However, in the underwater scenario it is very difficult to determine whether a path has been corrupted or the node is dead. In the previous example, node \(b\) knows that node \(d\) has received a message from \(a\), and therefore, it has to forward the message. Node \(b\) knows this by _overhearing_ the transmission, therefore, it now waits for node \(d\) to forward the message. If this does not happen, it has no way to know if node \(d\) is dead or the paths \(a-d\) or \(d-b\) have been blocked. It may also happen that node \(d\) has a long queue of messages waiting to be sent, by delaying the one from \(a\). In this context, epidemic routing provides the highest probability of transmission. In [53] the authors analyze the performance of some opportunistic routing protocols considering average time to arrival and energy consumption in the network. In that paper it is shown that the energy consumption of the epidemic routing is not always worse than other approaches, as the time needed to deliver a message is shorter. The other aspect that should be considered is the amount of data to be transmitted. This kind of network has a low channel utilization. It is not used for transferring large amounts of data or provide Internet connectivity.
## 5 Conclusions  
The analysis of the initial and final force values from the three types of NOXI aligners, each characterized by different thicknesses and cutting margins, alongside the thermoformed aligners with varying cutting configurations, led to the following findings:  
* The NOXI aligner with both horizontal and vertical gradients and a high margin exerted the greatest overall force, particularly at the gingival point where force dissipation was most evident. It was followed by the NOXI with a horizontal gradient and a high margin.
* No statistically significant differences in stress relaxation were observed among the five aligners tested.
* For aligners of the same thickness, the height of the margin significantly influenced the force exerted.
* The force applied was greater at the upper first molar than at the upper central incisor.  
These results demonstrate the effectiveness of the different aligners in generating orthodontic forces on both the upper central incisor and first molar. The comparison between various NOXI models reveals the importance of structural features such as thickness gradients and margin types in optimizing force application for specific areas. The NOXI aligner exhibits a different force application profile, which may influence mechanical performance compared to conventional aligners worn for 20-22 h per day.  
Conceptualization, F.C., C.P. and L.B.; methodology, C.P. and L.B.; software, C.P.; validation, F.C., C.P. and L.L.; formal analysis, C.P.; investigation, C.P.; resources, C.P.; data curation, C.P.; writing--original draft preparation, A.K.S.; writing--review and editing, A.K.S.; visualization, C.P.; supervision, L.L.; project administration, F.C.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 5 Analysis of Results
### Analysis of Reinforcement Forces at the Damage Site  
For reinforced concrete structures, when the protective layer of concrete fails due to tension or compression, the reinforcing bars may further bear the structural loads. Consequently, it was imperative to delineate the stress variations experienced by these reinforcing bars throughout structural analysis. The solid line and dashed line in Figure 21a represent the damage to the arch foot bottom slab and top slab, respectively, while the solid line and dashed line in Figure 21b represent the damage to the arch hance bottom slab and top slab, respectively. These figures capture the loss in stiffness when the material in the damaged sections has effectively reached its failure point.  
From the stress curves in Figure 21, the following points were observed:  
1. Once the concrete underwent comprehensive failure throughout the cross-section, the stress within the primary reinforcement predominantly remained beneath the yield strength of 465 MPa [16]. This signified that the reinforcing bars largely sustained an elastic stress state, with the predominant structural failure stemming from the concrete's collapse.
2. Compared to the reinforcing bars in the top slab, the reinforcing bars in the arch foot bottom slab experienced higher stress. In the reinforcement design, the specifications of the reinforcing bars in the top and bottom slabs can be adjusted based on the most  
Figure 21: Stress curves of main reinforcement at damage location: (**a**) damage to the foot of the arch; (**b**) arch lumbar injuries.  
Figure 20: Stiffness failure distribution in the arch rib unit.  
unfavorable conditions, aiming to save materials while ensuring the load-bearing requirements are met.
3. Compared to damage at the bottom slab of the arch hance, damages sustained by the top slab resulted in the reinforcing bars undergoing augmented stress levels. This escalation in stress was witnessed earlier. Nevertheless, the change in stress for both the top and bottom plate reinforcements followed a similar trend, indicating that the damage location exhibited no significant impact on the development of reinforcement stress.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
###### Abstract
It was coded as a plug-in to GIS software and compared the results of three methods: OK and SVM that used the attribute itself interpolated by Inverse Distance Weighing as a covariate (SVM1) and with the use of covariates (SVM2). [PERSON] and [PERSON] [21] compared the digital soil mapping techniques Universal Kriging, Sequential Gaussian Simulation, Random Forest (RF) with Kriging, and Quantile Regression Forest to quantify uncertainty in surveying soil organic carbon stock. [PERSON] et al. [22] conducted OK, Geographically Weighted Regression, SVM, ANN, and a hybrid approach, geographically weighted regression Kriging and Artificial Neural Networks Kriging, to predict soil organic carbon and concluded that the hybrid approach was promising. [PERSON] et al. [23] combined RF, OK, and Co-Kriging to estimate aboveground biomass. The validation result indicated that the combined model showed better performance than the single RF model. [PERSON] et al. [24] presented a hybrid geostatistical method (Extreme Learning Machine-OK) for estimating soil organic matter and predicting the spatial variability of contents. For this purpose, Simple Kriging, OK, Regression-OK, ANN-OK, and the results confirmed that the proposed hybrid method showed superior performance. [PERSON] et al. [25] enhanced the Land Use Regression model with machine learning to estimate the spatial-temporal variations in benzene, toluene, ethylbenzene, and xylenes concentrations. They compared Hybrid Kriging-Land Use Regression, Geographically Weighted Regression, RF, and Extreme Gradient Boosting and indicated that the models incorporating machine learning showed improved performance. However, studies combining machine learning and geostatistics for interpolating and predicting the concentrations of heavy metals in the soil of abandoned mine areas are scarcely found. The objective of this study was to create a soil contamination map in abandoned mine sites that is improved in terms of the prediction error from field survey (sampling) data using an interpolation model that combines geostatistics and machine learning. To achieve this, three techniques were applied to interpolate sampling values of heavy metal contamination concentrations in soil and compare their prediction errors: machine learning, geostatistics, and combined models. Previous studies on estimating the distribution of soil contamination in abandoned mine areas typically relied entirely on geostatistical techniques, and there are clear limitations to a single approach. To improve the prediction accuracy, the number of sampling data is usually increased, but in this study, from a methodological point of view, a combined machine learning and geostatistical model is used to generate a map with improved prediction accuracy.
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 3 Experiments
### Software Implementation of the Geometric Calibration for X-ray Imaging  
C++ and the MFC library were used to create the software application for geometric calibration for X-ray imaging. Figure 7 shows the graphical user interface (GUI) for the application. VTK 8.2.0, GDCM 2.8.9, and OpenCV 4.0.1 open-source libraries were used to implement the core functions. Each function, as well as the order in which it was used, is described below. The 'Load DICOM' button reads the sinogram data and X-ray images, as well as their metadata, such as the angle of each slice and pixel spacing. A selected slice of data of the sinogram data is rendered in 'Window 1'. The rendered slice in 'Window 1' can be changed to a different slice with a different angle using either the mouse or keyboard. The 'Calibration' button enables the calculation of the geometric parameters of the X-ray imaging using the loaded sinogram data belonging to the predefined range of angles (in degrees) and angle interval. The angle range and interval can also be defined using the application GUI's edit boxes. The calculated geometric parameters are displayed in the annotation at the top right of 'Window 1' and saved as an XML file. The 'Projection Error' check box allows the user to specify whether the error of 'Window 2' is displayed. The '2D Reflection Button' allows the operator to overlay two images in the same view. In 'Window 2', the computational result of the projected markers based on the geometric parameters and the detected markers in the corresponding slice of sinogram data can be superimposed. Additionally, the 'Error' button calculates the root mean square error between the superimposed data in 'Window 2', and the error is also written to the file in CSV format. For comparison purposes, previously computed results are also loadable using the 'Read Points' button, which enables the reading of the computed positions of the projected markers written in the XML file format.  
Figure 6: C-arm angio imaging device was applied to the experiment, which has the functions of 2D projectional X-ray imaging and CBCT imaging.
## 4 Analysis of the Experimental Results  
In this section, the performance of the system is evaluated by evaluating the results of the fronthaul latency, jitter, and probability of error. How these quality metrics meet the requirements of the 3 GPP are also evaluated. The 3 GPP specifies that the maximum allowed one-way latency for the MAC-PHY split (Option 6 according to 3 GPP terminology) should be 250 \(\upmu\)s (Table A-1 in [14]). The maximum allowed average RTT latency, therefore, is 500 \(\upmu\)s (250 \(\times\) 2). Since RTT in this experiment is measured as stated in Section 3.2, the analysis considers 500 \(\upmu\)s as the maximum acceptable latency on the fronthaul.
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 2 Materials and Methods  
### Methodology  
The deep learning network known as U-Net is utilized for picture segmentation and has the potential to be applied for counterfeit detection as well. Based on the behavior of grasshoppers, GOA is a metaheuristic optimization algorithm. The proposed method utilizes U-Net architecture to detect image forgery and forgery regions. Figure 1 provides an illustration of the format of this procedure. The proposed method is carried out in several steps:The pre-processing of data, which includes the separation of data into a training and test set, as well as the scaling of photographs to a consistent size.
2. Develop the architecture of the U-Net network, with an encoder and a decoder, in addition to many convolutional layers and max-pooling/upsampling layers.
3. Train the U-Net network for a predetermined number of epochs utilizing the Dice coefficient loss function and the Adam optimizer.
4. Apply the GOA algorithm to optimize the U-Net network by utilizing the network parameters as decision variables and the mean squared error as the fitness function.
5. Evaluate the performance of the U-Net network based on measures such as precision, recall, and the F1 score by testing it with the test set.  
The proposed method should predict the masks of the given image. Therefore, this method needs to have an encoder- and decoder-type of architecture because we are trying to highlight the manipulated regions in the given image. The method should split the image into two parts. One class is for the manipulated region (light color), and the other is for the entire region (dark color).  
In the proposed method, we consider U-Net as a base model because it already has proven results for similar image segmentation types and satisfies the above conditions. The initial U-Net architecture for biomedical image segmentation consisted of two paths, one for the encoder, which comprised a stack of convolution, activation, and pooling layers, and one for the decoder. The other is for decoding, which is symmetrical with the encoder path. It should be mentioned that we have a mask for fake images, but we do not have a mask for intact images. Therefore, for all intact images, the default mask is considered, which only contains black pixels, and for all intact images, the mask generated by the U-Net is the same.
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 4 Results and Analysis
### Scalability  
This section discusses the scalability of our decoder using the MEMAP criterion. Since our proposed criterion MEMAP can measure how good the seq2 seq decoder is, the scalability of our decoder is discussed in Figure 7, which illustrates the variation of the MEMAP as the number of RLL-coded frames fed into the decoder increases. One can see that the MEMAP increases exponentially as the number of input frames increases. This is because, for a larger number of frames \(M\), the decoder needs to distinguish more classes (\(2^{M\cdot k}\), where \(k\) is the length of information bits for original 4B6B RLL codes), thereby degrading the generalization ability of the decoder due to the restricted learning abilities. Therefore, the number of transmitted frames is crucial and there exists a _saturation frame_ for the RNN-based seq2 seq decoder.  
In Figure 7, we can further observe that \(M=8\) is a point that requires our utmost attention. If the number of parallel frames exceeds 8, the MEMAP value will rise sharply. After a lot of trials, one fact that was proven is that our decoder was no longer capable of decoding properly and was thoroughly underfitting with more than eight frames, which indicates the _saturation frame_ of our proposed decoder is frame 8 (assuming having a certain tolerance of performance loss). Thus, our proposed decoder is suitable for other RLL codes and shows strong prospects for practical applications of the VLC, and has ability to distinguish \(2^{32}\) different codewords.  
In addition, for a code with information bits \(k=32\), the regular table look-up method with a codebook of \(2^{32}\) source word-to-codeword mappings has been considered impractical, whereas our decoder can easily handle this task. Thereafter, our design of seq2 seq decoder provides a reference to replace the look-up table decoding method for a code with fewer information bits.
## Materials and Methods  
### Hydrological Model  
The hydrological model used in this study is Sejong University Rainfall Runoff (SURR) model, which was developed as an open-source package by the Water Resource and GIS Laboratory, Sejong University [25]. The model is a continuous hydrological model that is based on the event-oriented storage function [26]. The SURR model is a semi distributed rainfall runoff model that is able to estimate hydrological components such as soil moisture, actual evapotranspiration, as well as basin and channel discharge based on the connection of channel and basin storage function with the physical-based hydrological component calculation technique (Equations (1) and (2)) as follows:  
\[\frac{dS(t)}{dt}=\left[Q_{sur}(t-\tau)+Q_{lat}(t-\tau)+Q_{gw}(t-\tau)\right]-Q(t) \tag{1}\]  
\[Q(t)=\left(\frac{S(t)}{K_{sb}}\right)^{1/P_{sb}} \tag{2}\]  
where \(S(t)\) is water storage in the basin (m\({}^{3}\)) at time \(t\); \(Q(t)\) is outflow of the storage basin (m\({}^{3}\)/s); \(Q_{sur}\) is surface flow(m\({}^{3}\)/s); \(Q_{lat}\) is lateral flow (m\({}^{3}\)/s); \(Q_{gw}\) is groundwater flow (m\({}^{3}\)/s); \(Q_{sur}(t-\tau)+Q_{lat}(t-\tau)+Q_{gw}(t-\tau)\) is inflow of storage; \(K_{sb}\) is \(K\) coefficient and \(P_{sb}\) is \(P\) coefficient, both are the storage function constants for the basin, in which \(P_{sb}\) ranges from 0.5 to 1; \(K_{sb}\) is proposed by [PERSON] et al. [27] with \(K_{sb}=\beta\)A0.14 where A is watershed area (km\({}^{2}\)), and \(\beta\) is coefficient depending on the land conditions.  
Hydroologic simulation in SURR involves topographic, soil, land use, and meteorological data. GIS data sets for soil type, vegetation cover, and land use were obtained from the food and agriculture organization (FAO), while orographic data (digital elevation model, DEM) were obtained from the United States Geological Survey (USGS) at a grid resolution of 30 m. Daily precipitation from 68 automated weather stations (AWS) in South Korea within the study catchment was obtained from the water resources management information system (WAMIS) to compute mean Areal precipitation (MAP), while air temperature, humidity, wind speed, and solar radiation from only 3 automated surface observing system (ASOS) stations obtained from the Korea Meteorological Administration (KMA) were used to calculate mean areal evapotranspiration (MAE) using the Penman-Monteithmethod for each subbasin. The rainfall and evapotranspiration data have hourly temporal resolution and are interpolated spatially by the Thiessen polygon method using GIS (Figure 1b).  
To evaluate the impacts of upstream hydraulic structures on the downstream basin, in this study, the observed meteorological and hydrological data for 6 events, 2007, 2008, 2009, 2010, 2011, and 2012, were collected. Of these, the events in 2007 (23 July 2007-2 September 2007) and 2008 (1 July 2008-23 August 2008) were used for calibration, while the events in 2009 (21 June 2009-4 August 2009), 2010 (9 July 2010-17 September 2010), 2011 (15 June 2011-24 August 2011), and 2012 (1 July 2012-9 September 2012) were used for validating and evaluating dam impacts.
# Globally Optimizing QAOA Circuit Depth for Constrained Optimization Problems
## 2 Materials and Methods
### Example: Linear and Product Formulations  
Consider the 3-SAT problem  
**Example 1**.: \[\begin{split}& x_{1}\lor x_{2}\lor\backsim x_{3}\\ & x_{1}\lor x_{3}\lor x_{4}\\ &\backsim x_{2}\lor x_{4}\lor x_{5}\\ & x_{1}\lor\backsim x_{2}\lor x_{5}.\end{split}\]  
Labeling the indicator variable for the first clause \(z_{1}\), the second \(z_{2}\), and so on, we can write the objective function for this problem as  
\[\max\ \sum_{c=1}^{4}z_{c}\]  
subject to the constraints  
\[\begin{split}& x_{1}+x_{2}+(1-x_{3})\geq z_{1},\\ & x_{1}+x_{3}+x_{4}\geq z_{2},\\ &(1-x_{2})+x_{4}+x_{5}\geq z_{3},\\ & x_{1}+(1-x_{2})+x_{5}\geq z_{4},\\ & x_{i},\,z_{c}\in\{0,1\}.\end{split}\]  
We consider the unitary circuit depth for one layer of QAOA to solve this problem. First, we look at the linear characterization found in Section 2.3.1. Then, we look at the product formulation and compare the degrees of the resulting derived graphs to determine the unitary circuit depth for one layer of QAOA from each method, assuming three qubit gates are possible. We next devise a variable-substitution method to use the product formulation in Section 2.5, and analyze the unitary circuit depth for a two-qubit gate implementation of the product formulation in Section 2.5.3.