esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 4 Bridge Response Results  
Bridge response parameters of (1) girder axial force, (2) girder bending moment, (3) pile moment, and (4) pile head displacement for all 72 studied cases were investigated with respect to each selected parameter for steel (STL, black line) and PSC (light blue line) girder IABs, as shown in Figure 11. The following factors: (1) AB (abstument to backwall) joint stiffness, (2) backfill height (\(H\)), and (3) bridge length (\(L\)) were used to evaluate the bridge response parameters. The averaged bridge response parameters of corresponding cases were represented as solid lines and the envelopes of the upper and lower bounds were represented as dashed lines. Although much IAB research has been performed, bridge length and backfill height effects considering AB joint stiffness have not been investigated. Thus, this study provides extensive parametric results to identify IAB behavior with respect to the four considered parameters.  
Typical IAB behaviors need to be understood to correctly interpret the analysis results. As presented in Figure 12, IABs expand and contract owing to thermal loading. During this thermal movement, the AB joint allows a differential rotation and crack opens if it is not fully rigid. However, it should be noted that the abstument rotation is related to the resistance of the substructure (backfill and piles) and the capacity of the superstructure movement. If a typical bridge with short girders and short (stub-type) abutments is considered, construction joints on the rear face of the abstument open during bridge expansion; see Figure 12a,d. However, the opposite IAB behaviors can happen if the superstructure moment is large enough owing to thermal movement or temperature gradient, such as for longer and deeper girders with a higher abutment, as presented in Figure 12c,f. If the concrete girder bridge is long enough, initial time-dependent effects pulling the abutment toward the bridge are significant and the crack on the rear face may not open (Figure 12b,e).  
Figure 10: Linear temperature gradient model equivalent to AASHTO LRFD.  
Figure 11: Sensitivity of engineering response to variation of bridge parameters: (**a–c**) girder axial force, (**d–f**) girder moment, (**g–i**) pile moment, and (**j–l**) pile displacement (solid line: averages, dashed line: envelopes).
## 2 Methodologies
### Experimental Validation  
As explained in this section, the experimental results were built as a reference to compare the consistency with numerical models. A laboratory experiment was carried out at the Dalian University of Technology & Bridge Research Base. To avoid the natural environment's influence, an artificial heat source was used instead of a solar heat source. First, CFST specimens with different voids were heated in the laboratory and then the temperature data of the specimens were collected. Differences in the temperature distribution between the void and the solid CFRT were compared.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases  
### Guanine Nucleotide Dissociation Inhibitors (GDIs)  
Despite the vast number of RHOGEFs and RHOGAPs, only three GDIs exist in the human genome. The RHOGDI family includes ubiquitously expressed GDI1 (or RHOGDI\(\alpha\)) [62]; GDI2 (GDI\(\beta\), LY-GDI or D4-GDI), mainly in hematopoietic tissue [63]; and GDI3 (or GDI\(\gamma\)), which is usually expressed in human cerebral, lung, and pancreatic tissue [64]. An N-terminal extension that anchors GDI3 to the membrane of Golgi vesicles distinguishes this isoform from the others [65].  
Several studies in recent decades have provided information about the structure and function of GDIs and proposed that they act as shuttles for RHO GTPase [8; 66; 67; 68]. The shuttling process is initiated by the release of RHO GTPases from donor membranes, the formation of inhibitory cytosolic GDI-RHO GTPase complexes, and the delivery of RHO GTPases to the membranes of subcellular compartments [66; 67].  
It has been demonstrated that the isoprenylation process in cells can be regulated by GDIs [69]. GDI mediates the release of RHO GTPases from the membrane, maintains them in an inactivated state, and safeguards them against degradation or nonspecific activation by RHOGEFs [25; 29; 30]. Different structural studies have revealed two sites of GDI and RHO GTPase interaction [70; 71; 72; 73; 74]. First, an N-terminal regulatory arm of GDI binds to the switch region of RHO GTPases and inhibits GDP dissociation and GTP hydrolysis. Second, the N-terminus of GDI attracts the positively charged RHO hypervariable region, which is engaged with negatively charged phospholipids of the membrane and initiates the insertion of the geranylgeranyl moiety on the RHO GTPases into a hydrophobic pocket in the GDI molecule, leading to membrane release [75].
## 3 Localization Algorithm
### Proximity Sensors Based Localization  
The second proposed localization method consists of locating the user based on ranging results only. The basic idea behind the algorithm is illustrated with Figure 2.  
When the result from only one sensor is available (Figure 2a) it is difficult to precisely localize the user--his location can only be expressed as an arc located at the measured distance. Since the system requires results as exact \(x\)-\(y\) coordinates, the user is roughly localized in the center of the sensor beam at the measured distance.  
In this situation, when the user is in the range of two or more proximity sensors, his/her location can be inferred more precisely. One of the most popular choices for localization in case of ranging measurements is the trilateration algorithm [14]. It consists of finding an intersection of two arcs corresponding to distances measured by the sensors. Here, since the user, from whom the signal is reflected is not very small, a situation in which those arcs intersect would be extremely rare and the trilateration algorithm would suffer. Here, an alternative, simplified method is used. In this method, the user is located by each of the sensors separately at the sensors's main axes as presented in Figure 2b. Then, the obtained localizations are fused by calculating the weighted average:  
\[x=\frac{\sum_{i}\frac{1}{\sigma_{i}^{2}}x_{i}}{\sum_{i}\frac{1}{\sigma_{i}^{2 }}}\ \ \ \ y=\frac{\sum_{i}\frac{1}{\sigma_{i}^{2}}y_{i}}{\sum_{i}\frac{1}{\sigma_{i}^{ 2}}} \tag{11}\]  
where \(\sigma_{i}^{2}\) is the variance of the distance measurement performed by the sensor. Applying weights, which are inversely proportional to measurement variance allows to take into account the higher uncertainty of locations at bigger distances, which result from a lower level of the reflected signal received by the sensor and a longer arc due to a wide covered area. Assuming that all of the sensors are independent of each other, the variance corresponding to the computed localization can be approximated as:  
\[\sigma^{2}=\frac{1}{\sum_{i}\frac{1}{\sigma_{i}^{2}}} \tag{12}\]  
The obtained value would be the final result in case of localizing a user without the tag. In case of a presented hybrid solution, it will be an input to loosely coupled version of the hybrid algorithm.
# Polymers and Polymer Nanocomposites for Cancer Therapy +
## 7 Conclusions  
The results of the studies done and presented in this review could lead to new ideas for the improvement of cancer drug carriers: they are a strong impulse for the continuation of research in this area.  
Many of the polymer systems mentioned above are replacing conventional drug delivery platforms due to their main characteristics, such as the lack of side effects and minimized damage to healthy cells. The application of external or internal stimuli is highly recommended for cancer treatments.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The author declares no conflicts of interest.  
**Abbreviations**  
AA & Algae, alginate ART & Artenisimin drug AS & Alphastatin drug b Block (for copolymer) BPLP & Biodegradable photoluminescent polymer co & (For copolymer) CA & Candesartan drug CPP & _Cyclocaru paliurus_ polysaccharide CS & Chitosan CSC & Cancer stem cell CUR & Curcumin DGMNs & Dextran goldmag nanoparticles DMTK & Dimethylthioetkal DNA & Deoxyribonucleic acid DOX & Doxorubicin or Addryamicine drug DPA & 3,3' didithiodipropionic acid DTX & Docetaxel drug DVA & 9 divinyl-2,4,6,8,10-tetraoxispiol-(5,5') undecane ELP & Elastin-like polypeptide FA & Folic acid FU & Fluorouracil drug GM & Goldmag (Au/Fe\({}_{3}\)O\({}_{4}\)) GRP & Gastrin-releasing peptide  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Macromolecular Carrier** & **Carrier Structure** & \begin{tabular}{c} **Carrier** \\ **Morphology** \\ \end{tabular} & \
\begin{tabular}{c} **Experimemented** \\ **Drug** \\ \end{tabular} &
\begin{tabular}{c} **Reference** \\ **Reference** \\ \end{tabular} \\ \hline mPoly(ethylene glycol)- & Core cross-linked & & & & \\ Starch-lithiopropionicacid & polymers & Micelles & DOX & [71] \\ Poly(ethylene glycol)-copoly(lactic & Triblock copolymer & Micelles & DOX & [72] \\ acid)-co-Polyethylene glycol & & & & \\ Polyethylene glycol-copoly(d,l,lactic) & Diblock copolymer & Micelles & DTX & [76] \\ Sodium/chitosan/hydroxyl apatite & Nanocomposite & Hydrogel & DOX & [93] \\ Hyaluronic acid-calcium-Algae & Nanocomposite & Hydrogel & Cyclosporine A & [98] \\ Chitosan & Biopolymer & Nanogel & HCPT & [111] \\ N-isopropylacrylamide-co-itaconic & & & & \\ acid/Fe\({}_{3}\)O\({}_{4}\) & & & & \\ Lectime-cholesterol(ethylene glycol) & Polymer blend & Liposome & Carboplatin & [130] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Carriers and drugs used in different studies.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 2 Construction and Preprocessing of Insulator Image Dataset  
### Image Augmentation Based on GraphCut Segmentation  
The images acquired by transmission line inspection are usually with different shooting angle, uneven illumination, and complex backgrounds. In order to obtain a more robust, stable, and generalizable deep learning model, the GraphCut segmentation algorithm was used to extract defective insulator targets, which were blended with the background image of practical power grid environment. The constructed defective insulator images based on this image blending method can not only expand the sample set, but also balance the sample numbers of normal insulators and defective insulators.  
The GraphCut segmentation algorithm [31] uses the energy function \(E(x)\) to determine each pixel in the image is whether foreground or background. An image is defined as \(G=(X,Y)\), where \(X\) represents all pixel nodes in the image and \(Y\) represents the connection edge between nodes. For anyone node \(i\in X\) in the image, \(x_{i}=1\) if \(i\) belongs to the foreground target; otherwise, \(x_{i}=0\) if \(i\) belongs to the background. Thus, the image segmentation problem can be described as an extreme value solution of energy function. The energy function of image segmentation is defined as  
\[E(x)=\sum_{i\in X}E_{1}(x_{i})+\lambda\sum_{i,j\in Y}E_{2}(x_{i},x_{j}) \tag{1}\]  
where \(E_{1}\) is the energy consumed by defining the node \(i\) as the foreground or background; \(E_{2}\) is the energy cost of two adjacent and different pixel nodes, which depends on the color similarity of two pixels; and \(\lambda\) is the energy balance parameter and it is generally taken as \(\lambda\) = 50, which is a compromise between model-based classification and smooth-based classification. The calculation of energy \(E_{1}\) and \(E_{2}\) can refer to literature [32], and then the minimum value of \(E(x)\) is obtained by MaxFlow [33], so as to realize the segmentation of insulators in the image. Finally, the segmented insulator target was fused with the real and complex background image of the transmission line to form a virtual insulator defect image sample, as shown in Figure 1. This method can generate a large number of image samples containing real backgrounds and defective insulator targets, thereby avoiding the imbalance numbers between normal insulators and defective insulators, improving the robustness and generalization ability of the model.
# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 4 Method
### Overall Architecture  
The overall architecture of our proposed network is detailed below. Table 3 shows the parameter configurations for each layer of the network in this paper. Since the number of channels in the MobileNetV2 network increases significantly starting from the eighth layer, to reduce inference time and achieve more efficient segmentation, we only selected the first seven layers of MobileNetV2 as the baseline for our model. The fundamental building block of this baseline is a bottleneck depthwise separable convolution with residual connections, which enhances the model's representational capacity while maintaining low computational cost and latency, as depicted in Figure 5.  
To further enhance the network's ability to capture both global and local information, we introduce the MobileHDC (MobileNetV2 Hybrid Dilated Convolutions) backbone, which incorporates hybrid dilated convolution layers and standard convolution blocks. By concatenating these two types of convolutional layers, we can obtain global information while preserving the precise capture of detailed features. Specifically, the hybrid dilated convolution extends the receptive field of the convolutional kernel, capturing broader contextual information without increasing the computational burden. In contrast, the standard convolutional block excels in processing local features, effectively extracting edges, textures, and other detailed information from the image. This ensures that the model can effectively tackle the challenges posed by the jet segmentation task, particularly demonstrating strong performance when dealing with small and dispersed target regions. Additionally, a decoder is included to fuse the extracted global and local features, enhancing overall segmentation performance.  
Figure 5: An overview of the basic architecture of our proposed model. Here, we set the parameters \(N_{1}\), \(N_{2}\), \(N_{3}\) for the repeated times as \(N_{1}=6\), \(N_{2}=4\) and \(N_{3}=2\). The operation \(\oplus\) represents the concatenation operation.
## 6 Results and Discussion  
This section discusses the Monte Carlo simulated results of turbo coded OFDM with MIMO antennas scheme in cooperation over fast Rayleigh fading channels. The performance of CMI has been evaluated under turbo coded OFDM with MIMO (2 \(\times\) 2) scheme over fast Rayleigh fading channels. Moreover, the performance of CMI has also been evaluated in turbo coded OFDM scheme over AWGN channel. For a fair comparison among the proposed schemes, the code rate is kept constant as 1/3. The generator matrix is taken as \(\mathrm{G}(1,5/7)_{8}\). The turbo decoding algorithm is chosen as Log-MAP for all the simulations. The length of CMI is taken as 1024 bits, which are used, in coded-cooperative schemes. For all coded-cooperative simulations, the S-R channel is assumed to be perfect (\(\mathrm{SNR}_{SR}=\infty\)). Moreover, the relay-R node is placed near to the destination node therefore it has been given 1 dB additional gain in SNR over source-S node. It has assumed that channel response is already known at the destination-D. The delay spread of fast Rayleigh fading channels is taken as 9 bits. Furthermore, the channel remains constant for two consecutive OFDM frames. The size N of OFDM block is chosen as 64, and 15 percent of the N samples are used as the cyclic prefix. The modulation schemes chosen in all the simulation are BPSK, QPSK, and 16 QAM. Moreover, signal constellation with gray mapping in employed in all the simulations.
## 2 Materials and Methods
### Experimental Design
#### 2.3.2 Impacts of Scale on LAI Retrievals  
LAI was estimated from the UAV data through the calibration of a Gaussian processes regression (GPR) machine learning [50] algorithm using the ground measurements. GPR provides a non-linear, non-parametric and probabilistic approach to establishing relationships between inputs (i.e., reflectance values) and outputs (i.e., LAI ground measurements), allowing for both the predictive mean and variance to be estimated [51; 52]. Research in [PERSON] et al. [27] demonstrated that GPR performed favourably for the estimation of wheat yields when compared to alternative machine learning algorithms. Furthermore, when compared to ground measurements, research in [PERSON] et al. [26] achieved high agreement with GPR-modelled wheat LAI estimates retrieved using the same UAV multispectral sensor used in this present study.  
The GPR modelling approach was implemented using the Machine Learning Regression Algorithms toolbox (MLRA v.1.24; [53]) available in the ARTMO (Automated Radiative Transfer Models Operator v.3.26; [54]) software package. In order to quantify the impact of spatial scale on the LAI retrieval accuracy, two GPR model calibration approaches were applied using the combined LAI dataset consisting of all 80 measurements that were recorded across the farms and measurement dates. [PERSON] et al. [26] identified the 705-nm and 865-nm spectral bands as being the most informative for estimating LAI; we, therefore, only include these two Sentinel-2 bands within the GPR model calibration. The first calibration approach involved training the GPR model based on the LAI measurements using the average UAV 705-nm and 865-nm reflectance data that was observed within a one-metre radius of each ground measurement point location. This radius accounted for the locations of the three LAI replicate measurements. The second GPR calibration involved re-training the retrieval algorithm with the average of the 705-nm and 865-nm reflectance within a 20 x 20 m square with the position of the ground measurement point at the centroid. Through the spatial aggregation involved in this second calibration (i.e., varying from 2 to 20 m), we, thus, mimic LAI that could be retrieved from 20-m scale  
Figure 2: Sample CIred-edge image over a field on Farm 2 shown for the high-resolution (5 cm) UAV CIred-edge imagery (acquired on 23 May 2018) with an overlapping sampling grid generated from the Sentinel-2 20-m pixels.  
Sentinel-2 observations. Both calibration approaches were applied to a combined dataset consisting of all multi-date measurements acquired from both farms directly compared to the corresponding LAI point ground measurements.
# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 1 Introduction
This network effectively improves the model's prediction accuracy while maintaining a small computational cost. The inter-frame consistency module, called the inter-frame consistency module, is introduced as an information measurement and comparison method based on the consistency of information between adjacent frames. It is used to induce the model to extract more accurate road information from the images. In the multi-scale feature space, the adapter network is applied to effectively identify scene objects of different scales. The proposed approach is validated on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo. Experimental results demonstrate that, compared to the baseline models, the proposed model Swin-APT achieves an improvement of up to 13.1% mIoU. Additionally, in the road detection branch of the CeyMo dataset, experiments on road marking detection show an improvement of 1.85% mAP compared to the baseline model. The main contributions and innovations of this work are as follows:  
* A lightweight network called Swin-APT, based on the Swin-Transformer, is introduced, and an adapter network suitable for image semantic segmentation and object detection tasks is proposed. The prediction accuracy of the model is improved while maintaining a small computational cost;
* A module based on the inter-frame consistency of image frames is proposed, which induces the model to extract more accurate road information from the images;
* The adapter network is applied in the multi-scale feature space to effectively improve the recognition rate of scene objects in downstream tasks;
* Extensive experiments are conducted on four public road semantic segmentation datasets and a road mark detection dataset. The experimental results aim to find a balance between accuracy and computational cost. The effectiveness of the proposed approach is demonstrated through these experiments.
## Abbreviations  
ANSTO  
Australian nuclear science and technology organization  
AS  
Australian synchrotron  
BB  
Broad beam  
CCL2/MCP1  
Chemokine ligand 2/monocyte chemoattractant protein-1  
CCL22  
Chemokine ligand 22  
CCR2  
Chemokine ligand 2 receptor  
CERN  
European organization for nuclear research  
CNS  
central nervous system  
COX2  
Cyclooxygenase-2  
CSFIR  
Colony-stimulating factor-1 receptor  
CT  
Computer tomography  
DC  
Dendritic cell  
DLD  
Dihydrolipoyl dehydrogenase  
DSB  
Double-strand break  
DNA  
Deoxyribonucleic acid  
ESRF  
European synchrotron radiation facility  
FBA  
Fructose bisphosphate aldolase  
FLASH  
Ultra-high dose rate radiotherapy  
FT-IR  
Fourier-transform infrared microscopy  
Gamma (\(\gamma\))-H2 AX  
phosphorylated histone H2 AX  
GRID RT  
Type of SRT  
Gy  
Gray (unit of ionising radiation dose)  
HRS  
Hyper radiosensitivity  
HSP-71  
Heat shock protein 71  
ID17  
ESRF biomedical beamline  
IL-8  
Interleukin-8  
IL-10  
Interleukin-10  
IMBL  
Imaging and medical beamline  
IR  
Ionising radiation  
IRR  
Increased radioresistance  
KeV, MeV, GeV  
kiloelectron volts, megaelectron volts, gigaelectron volts (a unit of energy)  
LHC  
Large hadron collider  
LINAC  
Linear accelerator  
MeJA  
Methyl jasmonate  
MeSA  
Methyl salicate  
MDM2  
mouse double minute 2 homolog  
MISTRAL  
Full-field transmission X-ray microscopy beamline  
MRT  
Microbeam radiotherapy  
NAD (P)H oxidase  
nicotinamide adenine dinucleotide phosphate oxidase  
NK  
natural killer cells  
NSG  
NOD SCID gamma  
NSLS  
National synchrotron light source  
OCCL  
Oxidative clustered DNA lesion  
PB  
Pencil beam  
PSICHE  
Pressure, structure and imaging by contrast at high energy beamline  
Ptch1  
Patched 1  
RIAE  
Radiation induced abscopal effect  
RIBE  
Radiation induced bystander effect  
ROS  
Reactive oxygen species  
RT
Radiotherapy  
SCC  
Squamous cell carcinoma  
SFRT  
spatially fractionated radiotherapy  
SYRMEP  
Synchrotron radiation for medical physics beamline  
TAM  
Tissue-associated macrophages