esa-sceva/satcom-chunk-collection huggingface dataset:
# PolySorbate-Based Drug Formulations for Brain-Targeted Drug Delivery and Anticancer Therapy
## 3 PS-Based Formulations for Various Routes of Drug Administration
### PS-Based Formulations for Intranasal Delivery  
Intranasal delivery is a noninvasive administration that can bypass the BBB and thus facilitate the transport of drugs to the brain. However, intrinsic drug accumulation in the brain after intranasal delivery may not be sufficient to achieve clinical efficacy. PS 80 was used to increase the drug accumulation in the brain. In vivo rat model studies showed that the intranasal delivery of 2,3,5,6-tetramethylpyrazine (TMPP) along with PS 80 increased the concentration of TMPP in the brain [98]. The nasal mucosa is known to be the entry site of respiratory syncytial virus (RSV) into the body. The results showed that intranasal administration of a formulation containing virucidal lipids along with PS 20 significantly lowered the RSV load in the nasal mucosa of infected rat models compared to saline-treated groups [99].  
Neurotoxin-II (NT-II) is an analgesic peptide, which has limited permeability across the BBB via intravenous injection. Intranasal delivery of NT-II into brain using PS 80-coated poly(lactic acid) (PLA) NPs was demonstrated. In vivo mouse model studies revealed PS 80-coated PLA NPs promoted the biodistribution of NT-II into brain via intranasal delivery [100].
# Development of a Wideband Slotted Antenna Array with Low Profile and Low Sidelobe
## 2 Design of the Antenna Array
### Power Feeding Network  
#### 2.2.1 Unequal Power Divider  
In order to reduce the SLL, an unequal power dividing method is necessarily used to replace the amplitude-uniform feeding network [27]. The unequal power division is achieved by adding an offset metal wall and another two identical small metal windows in a T-junction. The offset metal wall is designed \(m\) away from the middle of the input port (Port1), and the two windows are located at the end of the input port, as shown in Figure 6. By properly adjusting the location of the offset wall, a power division ratio as large as 7.31 dB between the two output ports (Port2 and Port3) can be achieved. Although a desired power ratio can be readily obtained by only adjusting the distance \(m\) of the offset metal wall, there will also be an obvious phase difference between the two output waves. The phase difference can dramatically affect the final radiation performance of the antenna array. Therefore, the lengths \(d_{1}\) and \(d_{2}\) of the two outputs are changed to make the transmission paths of the two ports different, so that the phase difference between the two output ports can be compensated. This method can significantly improve the design flexibility and simplicity.  
Figure 4: Simulated reflection coefficient and gain for the improved (red line) and primary (blue line), \(2\times 2\) subarrays.  
Figure 5: Radiation patterns for the improved (solid line) and primary (dashed line) \(2\times 2\) subarrays.  
The 6 dB unequal power divider shown in Figure 6 is simulated and the analytical results are plotted in Figure 7. It is seen that the reflection coefficient of the input port is always smaller than \(-25\,\mathrm{dB}\), which indicates that a good impedance matching is achieved. Notably, an unequal power division at the two ports can be observed, and, as expected, the power ratio between Port2 and Port3 is 6 \(\pm\) 0.9 dB within the frequency range from 17.5 GHz to 22.5 GHz. Additionally, the phase difference of the two outputs stays within \(\pm\)0.8\({}^{\circ}\) over the entire operating bandwidth. These analytical results demonstrate that the proposed power division method can be used to freely design the power distribution in the complete feeding network, while keeping the output phase error within a minimum range.
# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
## 3 Results  
### Patient Characteristics and Overall Study Design  
We selected 65 cases from the well-curated OCCAMS consortium multi-center dataset [24; 25] that had available Mandard Tumour Regression Grading (TRG) information and WGS data from pre-treatment biopsies. These were classified into two groups based on TRG: 27 responders (TRG1 (\(n=18\)) and TRG2 (\(n=9\))) and 38 non-responders (TRG4 (\(n=28\)) and TRG5 (\(n=10\))) (Figure 1A). We excluded TRG3 classified cases because of their prognostically heterogeneous behavior [43]. A summary of the clinicopathological data for the cohort is shown in Table 1 with full details available in Table S1. Median follow-up in the cohort was 56.7 months (1.5-78.8 months). In line with our previous multi-center cohort study [7], TRG defined responders had favorable prognosis compared to non-responders with a significantly longer overall survival (78.5 vs. 33.8 months, \(p<0.001\), Figure 1B). As expected, following NAC, non-responders had higher pathological TNM stage (ypT and ypN) compared to responders (\(\chi^{2}\) test, \(p=0.001\), Table 1) but there was no difference in pre-NAC TNM stage [44]. All 65 patients had WGS data generated from endoscopic biopsies and matched germline DNA taken at the time of cancer diagnosis and before any treatment. In total, 9 responders and 21 non-responders had matched RNA-seq data to complement the WGS dataset according to the availability of tissue.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results
### Results Comparison
#### 5.4.2 Model-Parameters  
The results achieved from model parameters are shown in Figure 14 where (a) shows the achieved SNR values and (b) shows the amount of enhancement. The following conclusions can be extracted from these tests:  
* In defects above 5% CSA, the algorithm enhances the results using all frequencies. For defects with CSA size of lower than 4%, the algorithm can enhance the SNR of most frequencies except for 30, 32, 34, and 38 kHz.
* Except from the case of 3% CSA error, both maximum SNR and maximum enhancements are achieved using 34 and 38 kHz in the tests. In the 3% CSA test, the maximum enhancement is for 30 kHz; nonetheless, the final SNRs of 34 and 38 kHz are greater.
* The variations in the case of 30 kHz are less in comparison to all other cases, as shown in Figure 14b.
###### Abstract  
This investigation aimed to assess, through a systematic review, the effect of non-thermal plasma treatments on root canal sealers' adhesion to dentin. This study followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was undertaken without limits on time or language, until May 2023, in PubMed-MEDLINE, Scopus, Web of Science, OpenGrey, and three endodontic journals. The included studies underwent quality assessment and data extraction. Out of an initial 188 articles, 4 studies were included. Three of these studies based the adhesion ability on the push-out test in human extracted teeth, while the other used bovine dentin samples to measure the contact angle with the sealer (wettability). While there was no consensus about the effect of non-thermal plasma (NTP) on the AH Plus sealer's adhesion to radicular dentin, NTP seemed to positively influence the adhesion ability of BioRoot RCS and Endosequence BC. The findings of the present review should be interpreted cautiously due to the scarcity of studies on the topic. The NTP parameters should be optimized to obtain a stronger evidence base in endodontics on its role as an adjuvant tool to increase sealers' adhesion to dentin.  
adhesion; gutta-percha; plasma treatments; root canal filling; root canal obturation; root canal therapy; root canal sealers +
Footnote †: journal: Applied  
0023
## 2 Materials and Methods
### Data Processing and Analysis Methods
#### 2.3.2 Feature Wavelength Selection Methods  
To reduce the dimensionality of spectral data and eliminate redundant or irrelevant variables, three widely adopted wavelength selection techniques were employed: successive projections algorithm (SPA), UVE, and competitive adaptive reweighted sampling (CARS). These methods aim to preserve informative spectral features while enhancing model performance and computational efficiency.  
SPA is a forward selection algorithm designed to minimize collinearity among selected variables. It works by iteratively projecting candidate variables onto the residual subspace of previously selected ones, identifying the variable that contributes the most independent information at each step. SPA is particularly suited for spectral data, where adjacent wavelengths often exhibit high redundancy, and effectively reduces multicollinearity while preserving critical features for modeling [30].  
UVE is a variable selection method based on the partial least-squares (PLS) regression framework. It introduces artificial noise variables into the dataset and evaluates the stability of each spectral variable through cross validation. Variables with low stability values--comparable to or worse than the artificial noise--are eliminated, resulting in a refined set of relevant wavelengths [31].  
CARS employs a Darwinian \"survival of the fittest\" strategy that iteratively samples subsets of variables using Monte Carlo sampling and adaptive reweighting. At each iteration, variables with the highest importance (based on the regression coefficients) are retained, while less relevant ones are removed. This approach has proven highly effective in spectroscopy-based modeling [32].  
Each feature selection method was integrated with the classification models following CWT preprocessing, and their performance was evaluated in the Results section to determine the optimal strategy for detecting cumulative damage in prunes.
## 3 Results
### Land Use and Vegetation Information in Beijing Sponge City  
By analyzing the Sentinel-2-derived 10m resolution LCC map in 2017, we find that the Beijing Sponge City project (Figure 3) covers \(\sim\)1265 km\({}^{2}\) over the central Beijing city, China, including impervious surface buildings (59.27%), grasslands (26.08%), mixed forest (7.34%), croplands (5.10%), and wetlands and water bodies (\(\sim\)2%). Figure 3 presents fine spatial details of the urban ecosystem, such as clear patterns of lakes, rivers and streets. Most grasslands are parks, and fixed forests are mainly concentrated in northwestern Beijing Sponge City, while the eastern parts are croplands.  
We further analyze the NDVI and LAI for June 2018, which was composited from 10-day Sentinel-2 images in June 2018 in clear-sky conditions. We can see high spatial details in the NDVI from Figure 4a. The NDVI for lakes and rivers is \(\leq\)0.0, the impervious surfaces (e.g., large mansions and main streets) are 0.0-0.25, and grasslands and croplands are 0.1-0.5, while mixed forests and some parks with forest reserve have NDVI values of 0.5-0.7 (Figure 4a). A high NDVI indicates a high LAI in this study. Figure 4b shows the detailed pattern of LAI for different land cover classes for the sponge city area. As expected, the lakes and rivers have no LAI, and the impervious surfaces (e.g., large mansions and main streets) have only \(<\)1.0 m\({}^{2}\) m\({}^{-2}\) of LAI, but 1\(\sim\)2 m\({}^{2}\) m\({}^{-2}\) of LAI can be seen in many avenues with greenbelts. The mixed forests in northwestern Beijing Sponge City have LAI values ranging from 1 to 3 m\({}^{2}\) m\({}^{-2}\). Most grasslands and croplands have 1\(\sim\)2 m\({}^{2}\) m\({}^{-2}\) of LAI, but some parks with forest reserves show the highest values (3-8 m\({}^{2}\) m\({}^{-2}\)) of LAI (Figure 4b).  
Figure 3: Land cover classification at 10m resolution for Beijing Sponge City. Land cover map for 2017 derived from the FROM-GLC10-based Sentinel-2.
## 2 Materials and Methods
### Stand Level Measurements  
At the stand level we quantified the monthly litterfall and branch fall rates. Five traps were made and installed in each plot. Every trap had a square frame (0.5 m \(\times\) 0.5 m) and four legs (0.3 m height) made by PVC pipes (\(\up\) 16). Legs mounted the structure 0.3 m above the ground to prevent needle sepsis during the wet season. A fiberglass window net was installed inside each frame so as to create an inner curve with a maximum depth of 0.2 m, in order to prevent wind from removing needles and needle sepsis during the wet season. Litterfall accumulated in the traps was collected monthly and then oven-dried for 48 h at 70 \({}^{\circ}\)C. Needles were separated from other forms of litter, such as cones, twigs, and bark specimens. The mass of the needles was recorded and divided by the total area of the litterfall traps, for estimation of litterfall mass per m\({}^{2}\).  
The LAI of each plot was estimated using three different methods. In the first approach, namely the Litterfall Method (LFM), we used data from the litterfall traps [60,61] to estimate LAI by dividing the annual sum of litterfall mass per m\({}^{2}\) with the annual average LMA and multiplying by the average needle lifespan (1.5 years, [62], [PERSON] observations). In the second method, namely the Hemispherical Photography Method (HPM), we divided each plot into nine subplots where we took two fisheye photographs with a Canon EOS-60D and a fisheye lens (sigma 4.5 mm circular fisheye), mounted on a tripod and placed 1 m above the forest floor. The camera was always levelled, and its top side was oriented northward. All pictures were taken under overcast conditions, early in the morning during the spring of 2020, with a fixed aperture (f = 7.9) and were slightly underexposed (2/3 f-stops) [63]. Images were analysed using the HemiView Software (HemiView version 2.1 Delta-T Devices Ltd. Burwell, UK) and an average LAI per plot was estimated. In the third approach, namely the Ceptometer Method (CMM), an average plot-level LAI was estimated by systematically measuring incoming PAR within the plot using the AccuPAR linear PAR ecptometer (Decagon Devices, Inc., Pullman, WA, USA) and comparing with measurements outside the canopy at full light conditions. CMM LAI estimation was made with measurements taken around solar noon and the leaf distribution parameter set to X = 1. In each plot, six parallel transects with 5 m distance from each other were identified, and PAR measurements were taken every 5 m, at 1 m above the forest floor, counting a total of 36 LAI values for each plot, which were averaged to estimate a mean plot-level LAI.
# Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures
## 4 Case Study: Cret De I'Anneau Viaduct
### Reliability Results  
The cumulative reliability index along the service life of the structure is presented in Figure 8 for the case where uncertainty in vehicle number \(X_{n}\) is 1% and CoV for \(\log K\) is as 0.39 (MLM) and 0.2 (Bayesian).  
Corresponding annual reliability index at 120 years is presented in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**CoV of log\(k\)** & **Annual Reliability Index at 120 Years** \\ \hline
0.39 (MLM) & 3.90 \\
0.20 (Bayesian) & 4.25 \\ \hline \end{tabular}
\end{table}
Table 4: Annual reliability index as function of CoV of \(\log k\)  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Distribution** & **Mean** & **Standard Deviation** & **Remark** \\ \hline \(\Delta\) & Lognormal & 1 & 0.30 & Model uncertainty related to PM Rule 1 \\ \(X_{w}\) & Lognormal & 1 & 0.05 & Uncertainty in strain measurements \\ \(X_{n}\) & Lognormal & 1 & 0.01 & Uncertainty in number of vehicles \\ \(logk\) & Normal & 18.77 & 0.07 & Location parameter in Wöhler curve \\ \(m\) & Fixed & 5 & — & Slope of Wöhler curve fixed to 5 2 \\ \(\epsilon\) & Normal & 0 & \(\sigma_{c}\) & Error term taken from Table 2 \\ \(\sigma_{c}\) & Normal & 0.39/0.21  & 0.06/0.004  & & from Table 2 \\ \(\rho_{logkc}\) & Deterministic & 0.06/0.003  & & — & Correlation coefficient between location and standard deviation of error taken from Table 2 \\ \hline \end{tabular}
\end{table}
Table 3: Stochastic model for Wöhler curve.  
The actual stress in slab of viaduct is very low, thus exhibiting a very high fatigue reliability. Current results are shown for the case of scaled stresses. Even after the scaling of the stresses annual reliability index is within acceptable levels, which is more than 3.7 (for the case of very high consequence and low efficiency of intervention, [48]). Furthermore, it can be seen from the results that CoV for \(logK\) has a very high influence on reliability index. Thus, estimating the CoV with great accuracy is very important in order to estimate the safety of the structures reasonably.
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 4 Error Probability Analysis  
In this section, we have analytically investigated the error performance when the \(k\)th HEMS obtains the signal from the \(\ell\)th HEMS. Similarly to the previous section, we'll consider the cases \(k\) odd, \(\ell\) odd and \(k\) odd, \(\ell\) even. The case \(k\) even, \(\ell\) odd will have similar expressions for error performance as the second case (\(k\) odd, \(\ell\) even). On the other hand, case \(k\) even, \(\ell\) even will yield similar results to the first case (\(k\) odd, \(\ell\) odd). This analysis is based on binary phase shift keying (BPSK) modulation at the HEMSs, but this can be similarly extended to quadrature phase shift keying (QPSK) modulation, since QPSK transmissions can be considered as two orthogonal BPSK transmissions.
## 3 Results and Discussion
### Electrical Conductivity and Electrical Percolation Theory  
The electrical conductivity of the composite mainly depends on the content of the conductive filler, the aspect ratio, and dimensions. Because the electrical pathway is formed by the contact points between conductive fillers and the tunneling effect [32; 33], 1D fillers are more effective in constructing an electrical network than 0D and 2D fillers [2]. Therefore, 1D fillers can allow the insulating polymer to be conductive, even with small  
Figure 2: SEM image of cryo-fracture surface of 5 wt.% MWNT/NR composite: (**a**) low magnification, and (**b**) high magnification. Cross-section view of hybrid structure: (**c**) low magnification, and (**d**) middle magnification.  
contents, which 0D and 2D fillers cannot. This is referred to as the electrical percolation phenomenon [34; 35]. In this study, we calculated the electrical conductivity and electrical percolation threshold of MWNTs in the NR matrix. The electrical conductivity of the composite was calculated using the following equation:  
\[\sigma_{conductivity}=\frac{l}{RA} \tag{1}\]  
where \(\sigma_{conductivity}\) (\(\sigma_{c}\)) is the electrical conductivity of the composite, \(l\) is the distance between the metal electrodes, \(R\) is the resistance of the composite, and \(A\) is the cross-sectional area of the sample. Based on Equation (1), we measured the electrical conductivity of the MWNT/NR composite at each concentration. As the number of MWNTs increases, the contact points between MWNTs increase, which leads to higher electrical conductivity, as shown in Figure 3. Owing to percolation, the MWNT/NR composite became electrically conductive at certain points. This is referred to as the electrical percolation threshold, measured at 0.1 wt.% in this study. According to the electrical percolation equation, the relationship between filler content and electrical conductivity can be determined as follows:  
\[\sigma_{c}=\sigma_{0}(P-P_{c})^{t}\textit{ where }P>P_{c} \tag{2}\]  
where \(\sigma_{0}\) is a constant, \(P\) is the filler content, \(P_{c}\) is the percolation threshold, and \(t\) is the critical index. Using Equation (2), we calculated \(t\) to be 2.51, similar to previous studies [34]. However, when the MWNT content exceeds 3 wt.%, the electrical conductivity gradient decreased. This implies that 3 wt.% is sufficient to form an electrical network.  
Therefore, even if more MWNTs are present in the NR matrix, the electrical conductivity curve saturates at a certain value.  
Figure 3: Electrical conductivity of MWNT/NR composites as function of contents (wt.%) and electrical percolation threshold of MWNT/NR composite. Inset image is a loglog plot: relationship between electrical conductivity of the composites and (P\(-\)Pc).
# 4D Building Reconstruction with Machine Learning and Historical Maps
## 1 Introduction
The replicability of the proposed methodology was also tested on the historic city centre of Bologna, Italy (Section 3.2). The innovative aspects of the work are:  
* evaluation of multiple regressors to infer building heights from historical maps;
* introduction of the geometric, neighbourhood, and categorical features usable when only digitised building footprints are available;
* testing and evaluation of the proposed method on two different locations and multi-temporal historical maps; and
* the realisation of 4D level of detail 1 (LoD1) building models for the geo-visualisation enhancement, creating a 4D cadastre, and spatial analysis purposes (e.g., volumetric density studies.
## 4 Discussion  
The underground space in cities can generally be categorized into two main types: utility tunnels used for various lines (such as gas, electricity, and communication), and water pipe networks for draining rainwater or domestic sewage. The presence of water can serve as a distinguishing factor to differentiate the purposes of these underground networks. Manhole covers are convenient identifiers for the type of underground space, but radar wave genetic detection is a relatively effective method for old damaged areas and unmarked areas.  
When the ground-penetrating radar passes near a manhole cover, electromagnetic waves oscillate downward from the surface. The presence of air in a line pipe gallery results in regular oscillations on the waveforms or profiles, with slow decay of the frequency components compared to dense soil. If the space is filled with water, the waveforms appear even looser and the frequency components decay more quickly [31]. As the radar passes through areas where underground spaces may exist (not passing the manhole cover), it scatters in the surface soil and oscillates within any cavities present. This results in complex waveforms with a regular hierarchy, visible in both radar profiles and instantaneous amplitude slopes [32; 33]. The presence of water also affects instantaneous linear frequency profiles [34; 35]. It is important to compare these findings with relatively dense soil without anomalies in all cases.