esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Geological exploration plays a fundamental and crucial role in geological engineering. The most frequently used method is to obtain borehole videos using an axial view borehole camera system (AVBCS) in a pre-drilled borehole. This approach to surveying the internal structure of a borehole is based on the video playback and video screenshot analysis. One of the drawbacks of AVBCS is that it provides only a qualitative description of borehole information with a forward-looking borehole video, but quantitative analysis of the borehole data, such as the width and dip angle of fracture, are unavailable. In this paper, we proposed a new approach to create a whole borehole-wall cylindrical panorama from the borehole video acquired by AVBCS, which provides a possibility for further analysis of borehole information. Firstly, based on the Otsu and region labeling algorithms, a borehole center location algorithm is proposed to extract the borehole center of each video image automatically. Afterwards, based on coordinate mapping (CM), a virtual coordinate graph (VCG) is designed in the unwrapping process of the front view borehole-wall image sequence, generating the corresponding unfolded image sequence and reducing the computational cost. Subsequently, based on the sum of absolute difference (SAD), a projection transformation SAD (PTSAD), which considers the gray level similarity of candidate images, is proposed to achieve the matching of the unfolded image sequence. Finally, an image filtering module is introduced to filter the invalid frames and the remaining frames are stitched into a complete cylindrical panorama. Experiments on two real-world borehole videos demonstrate that the proposed method can generate panoramic borehole-wall unfolded images from videos with satisfying visual effect for follow up geological condition analysis. From the resulting image, borehole information, including the rock mechanical properties, distribution and width of fracture, fault distribution and seam thickness, can be further obtained and analyzed.  
borehole video; video image mosaic; SAD; gray projection transformation +
Footnote †: journal: Applied Soft. 2019, 9, 3437; doi:10.3390/app9163437
## 4 Ischemic Heart Disease
### Mechanisms of IHD
\begin{table}
\begin{tabular}{c c c} \hline \hline
**HIV Medication Class** & **Drug Examples** & **Adverse Cardiovascular Effects** \\ \hline Nucleoside Reverse Transcriptase & abacavir, didanosine, lamivudine, & Higher risk for myocardial infarct \\ Inhibitors (NRTIs) [33; 34; 35] & emtricitabine, zidovudine, & controverted in studies \\  & stavudine & Dyslipidemia \\  & & Insulin resistance \\ \hline Non-nucleoside reverse transcriptase & efavirenz, nevirapine, etavirine, & Higher risk for myocardial infarct \\ inhibitors (NNRTIs) [36; 37] & rilpivirine, doravirine & Accelerated atherosclerosis \\  & & Rare QT prolongation. \\  & & Hypercholesterol \\  & & hypertriglyceridemia \\ \hline Protease inhibitors (PIs) [38; 39] & ataxanavir, darunavir, & Increased mortality in subjects with HF \\  & lopinavir/ritonavir, ritonavir, & Hypercholesterol \\  & saquinavir, tipranavir & Hypertriglyceridemia \\  & & Accelerated atheromatosisis \\ \hline \multirow{3}{*}{\begin{tabular}{c} Integrase strand transfer inhibitors (INSTIs) [40] \\ \end{tabular} } & dolutegravir, bictegravir, & \begin{tabular}{c} QT prolongation \\ Hypercholesterol \\ \end{tabular} \\  & raltegravir, elvitegravir & \
\begin{tabular}{c} Weight gain \\ Arterial hypertension \\ \end{tabular} \\ \hline Entry inhibitors [41] & maraviroc, enfuvirtide, &
\begin{tabular}{c} Hypotension \\ Rare QT prolongation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common cardiovascular adverse effects of HIV medications. Kearns and colleagues proposed potential mechanisms for HIV-associated atherosclerosis, emphasizing the role of endothelial dysfunction as the initial step. Endothelial dysfunction promotes the release of orthorhombic and proinflammatory cytokines (IL-6, TNF) and adhesion molecules (VCAM-1, P-selectin), which amplify inflammation and attract monocytes (pro-atherogenic CD-14+, CD-16+) and T cells. Once inside the intima, macrophages engulf oxidized low-density lipoproteins (oxLDL), forming foam cells and leading to plaque development--the hallmark of atherosclerosis. Since they remain chronically infected, monocytes and macrophages seem to be some of the leading cellular players in HIV-atherosclerosis, where the virus, although in a latent state due to HAART, can affect their function through potent viral regulatory proteins [48, 49, 50, 51]. Regarding molecular mechanisms, several factors, such as increased oxidative stress, inflammasome formation and dysregulation of autophagy, are included, but they are beyond the scope of this review.
# Polymeric Nanoparticles--Tools in a Drug Delivery System in Selected Cancer Therapies
## 3 Nanoparticles in Cancer Therapy--Passive and Active Mechanisms  
In the treatment of cancer, the main problem is that a medicinal substance does not target only cancer cells. Current therapy has a dualistic effect on the patient's body. On the one hand, it attacks cancer cells, causing inhibition of tumor growth or development, while on the other hand, it also destroys healthy cells [18]. In addition, the use of chemotherapy often does not completely eliminate tumor cells and contributes to their recurrence [18]. A significant advantage of polymeric nanoparticles is the possibility of freely modifying nanoparticles at the stage of their manufacture, as a result of which it is possible to obtain biodegradable PNPs formulated from D,L-PLGA or PLAs characterized by different sizes, rates of degradation and thus rates of release of the medicinal substance [19; 20]. By increasing the size of the nanoparticle, the half-life of the drug at the target site, resulting from impaired elimination by the kidneys, can be prolonged [21]. In addition, by manipulating the surface area, the entire mechanism of action of a specific nanoparticle can undoubtedly also be altered [6]. For cancer, PNPs are used, which usually have a targeted mechanism of action [6; 22]. Depending on the ability of nanoparticles to penetrate blood vessels, as well as the presence of a specific ligand on their surface, an active mechanism and a passive mechanism can be distinguished (Figure 3) [6; 21; 22; 23].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods of** & **Description** & **Advantages** & **Disadvantages** & **Example** & **Cancer** & **Ref.** \\ \hline Emulsion & Two subgroups may be & & PNPs with a high & For the & & & \\  & distinguished: using a & molar mass & continuous & & & \\  & continuous organic phase & Often used & organic phase, it & & & \\  & and using an aqueous phase & Does not require & requires the use & Cur-loaded & Human lung & \\  & Dispersion of the monomer & surfactants when & of surfactants and & PMMA NPs & cancer lines & [7; 10; 11; 15; 16] \\  & into an emulsion occurs, or & using a & toxic solvents & & & \\  & the monomer is dissolved in & continuous & High cost & & & \\  & an aqueous solution without & aqueous phase & Time-consuming & & & \\  & surfactants, respectively & & & & & \\ \hline  & The latest techniques, based & & & & & \\  & on the use of living & & & & & \\  & organisms, e.g., _Escherichia_ & Efficient method & Necessary use of & & & \\  & _coli_, to produce a specific & Formulates & living organisms & K\({}_{8}\)-ELP & Human & \\  & biopolymer by altering the & small sizes & Method under & /pDNA & breast cancer & [11; 17] \\  & expression of genes, result & For gene delivery & development & & & lines \\  & in various amino acid & compositions and & & & & \\  & particle properties & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bottom-up techniques for formulation of polymeric nanoparticles.
## 3 Modeling Approach
### Calculation Results and Analysis of Bridge Dynamic Response  
When the train traversed the 10 \(\times\) 25 m simply supported beam bridge at speeds ranging from 60 to 120 km/h, the dynamic response characteristics of the bridge were analyzed. The results are summarized in Table 4 and further visualized as a line chart in Figure 13.  
Figure 12: Flowchart of vehicle–bridge coupled interaction analysis.  
Figure 13: _Cont._  
Based on the dynamic response analysis of the 10 \(\times\) 25 m simply supported beam bridge under varying operational conditions, the maximum lateral displacement of the main girder reached 1.55 mm under the single/double-track 110 km/h scenario, while the peak vertical displacement of 3.27 mm occurred under the double-track 90 km/h scenario. The transverse displacements showed negligible correlations with train speed and no discernible trend, whereas vertical displacements were significantly influenced by track configuration, with double-track conditions producing markedly higher values than single-track scenarios. Train speed demonstrated minimal impact on vertical displacements, similarly lacking a clear trend. Both the lateral and vertical accelerations of the main girder were highly sensitive to train speed, though no consistent pattern emerged. For pier top lateral displacements, pier top lateral accelerations, and the angle of beam end (lateral/vertical), the parameters were largely insensitive to train speed and showed no observable regularity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Working** \\ **Condition** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Speed** \\ **of Ve-** \\ **hicle** \\ **(km/h)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Impact** \\ **Coeffi-** \\ **c** **client** \\ \end{tabular} } & \multicolumn{3}{c}{**Mid-Span**} & \multirow{3}{*}{\begin{tabular}{c} **Main Girder Span** \\ **Main Girder** \\ **(m/s2)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Lateral** \\ **Displace-** \\ **ment of** \\ **Pier Top** \\ **(mm)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Angle of Beam End** \\ **ment of** \\ **Pier Top** \\ **(mm)** \\ \end{tabular} } \\ \cline{4-10}  & & & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** & **(mm)** & **Horizontal** & **Vertical** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Single \\ track \\ \end{tabular} } & 60 & 1.03 & 1.47 & 2.01 & 0.05 & 0.18 & 1.65 & 0.03 & 0.53 & 2.43 \\  & 70 & 1.04 & 1.50 & 2.03 & 0.11 & 0.21 & 1.65 & 0.07 & 0.53 & 2.41 \\  & 80 & 1.06 & 1.4
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 3 Results
### Photoluminescence Analysis  
Photoluminescence (PL) analysis illustrated the electronic structure of the undoped and Cr-doped ZnO samples, further to the energy states of the impurities and defects. PL measurements were obtained using an He-Cd laser at 325 nm excitation wavelength. A sharp PL spectrum was located at the limited domain of UV, which covered the entire visible range for the different investigated samples, i.e., undoped and Cr-doped ZnO samples (Figure 5). The PL band located in the UV-domain could be attributed to both the free exciton recombination affecting the near band emission [43] and the donor-acceptor transition [14,44,45,46,47]. Such a band is kept at the same energy position, which is in good agreement with the weak change of the band gap energy Eg with Cr doping as was reported in our previous work [11]. Eg was found to be equal to 3.238, 3.221, 3.206 and 3.202 (eV) respectively for ZnO-Cr\({}_{0}\), ZnO-Cr\({}_{1}\), ZnO-Cr\({}_{3}\) and ZnO-Cr\({}_{5}\)[11]. A large decrease in the PL band intensity was observed by enhancing Cr doping, which is related to the degradation of the crystalline quality as revealed by XRD analysis. Various sharp peaks were also found in each spectrum. An intense violet band appeared at 419 nm, originating from the stacking  
Figure 4: (a) XPS survey of Cr-doped ZnO nanorods. The XPS spectra of the: (b) Zn-2p, (c) O\({}_{18}\); (d) Cr-2p\({}_{3/2}\) and Cr-2p\({}_{1/2}\) core level regions.  
faults as well as the transition occurring from the conduction band to deep energy levels in the band gap related to Zn vacancies [39; 48]. A blue emission at 469 nm was observed due to interstitial Zn (Zni) defects [47]. Large and relatively intense green bands are related to different ionized oxygen vacancies, i.e., VO\({}^{+}\), VO\({}^{++}\), and anti-site oxygen (O\({}_{\mathrm{Zn}}\)) [47]. A typical yellow emission is also observed at 561 nm. This is related to the interstitial oxygen sites Oi, where its energy level is located at approximately 2.28 eV from the conduction band [47]. Finally, the red emission observed here is induced by oxygen vacancies (VO) [47]. These peaks demonstrate the Cr doping effects on the emergence of various origins of defects in the ZnO host for undoped and Cr doped samples. From these emissions, any compound with a long PL lifetime will be interesting for photocatalysis application since the separation of electrons and holes will be easy.
# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
###### Abstract  
The discipline of automatic image captioning represents an integration of two pivotal branches of artificial intelligence, namely computer vision (CV) and natural language processing (NLP). The principal functionality of this technology lies in transmuting the extracted visual features into semantic information of a higher order. The bidirectional long short-term memory (Bi-LSTM) has garnered wide acceptance in executing image captioning tasks. Of late, scholarly attention has been focused on modifying suitable models for innovative and precise subtitle captions, although tuning the parameters of the model does not invariably yield optimal outcomes. Given this, the current research proposes a model that effectively employs the bidirectional LSTM and attention mechanism (Bi-LS-AttM) for image captioning endeavors. This model exploits the contextual comprehension from both anterior and posterior aspects of the input data, synergistically with the attention mechanism, thereby augmenting the precision of visual language interpretation. The distinctiveness of this research is embodied in its incorporation of Bi-LSTM and the attention mechanism to engender sentences that are both structurally innovative and accurately reflective of the image content. To enhance temporal efficiency and accuracy, this study substitutes convolutional neural networks (CNNs) with fast region-based convolutional networks (Fast RCNNs). Additionally, it refines the process of generation and evaluation of common space, thus fostering improved efficiency. Our model was tested for its performance on Flickr30k and MSCOCO datasets (80 object categories). Comparative analyses of performance metrics reveal that our model, leveraging the Bi-LS-AttM, surpasses unidirectional and Bi-LSTM models. When applied to caption generation and image-sentence retrieval tasks, our model manifests time economies of approximately 36.5% and 26.3% vis-a-vis the Bi-LSTM model and the deep Bi-LSTM model, respectively.  
image captioning; bidirectional long short-term memory; attention mechanism; fast region-based convolutional network; common space
# Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization
## 1 Introduction
### Review of PSO Algorithm
[PERSON] studied the influence of control randomness on the particle swarm search scheme by introducing three different pseudo-random number (PRN)allocation strategies. The results show that we can systematically select the appropriate PRN strategy and corresponding parameters to make the PSO algorithm more powerful and efficient [45]. According to the above discussion, this paper uses the improved particle swarm optimization algorithm (MPSO) to solve the optimal parameters of the Bayesian network model. The algorithm combines the ideas of [37; 40; 41], introduces the adaptive decision mechanism, and introduces the nonlinear inertia weight. The experimental results show that the algorithm is better than the traditional PSO algorithm. This research accomplishes the following three things:  
* Constructs a multilayer Bayesian network demand prediction model that considers demand volatility and time series correlation. * Introduces a modified PSO algorithm (MPSO) for solving the maximum a posteriori estimates of parameters. * Assesses the proposed approach's performance via a thorough experimental evaluation. Based on the above discussion, the motivation behind this research work is based on two arguments. First, Bayesian methods have been successful in many fields and predictive environments and are particularly promising and appealing when there are considerable changes in the data. Second, it is assumed that the demand and parameters obey the normal distribution, which has a conjugate prior. When the posterior distribution is derived, the maximum posterior is used to estimate the relevant parameters. Therefore, combining the BN with the PSO algorithm, we use the improved PSO algorithm to optimize the function form of the posterior distribution. This paper proposes a more general Bayesian network demand-forecasting model and designs a more novel solution method, which provides a new idea for demand forecasting in the supply chain. The remainder of the paper is organized as follows. Section 2 introduces the proposed Bayesian network prediction model. In Section 3, we describe the solution algorithm of the model. Section 4 is dedicated to the experiment and the discussion of results. Conclusions and future work are put forward in Section 5.
# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 4 Experiments and Results
### Datasets
#### 4.1.1 HKPU Dataset  
The HKPU vein database [3] was acquired by using a contactless imaging device on Hong Kong Polytechnic University campus from April 2009 to March 2010. The database consists of 3132 images at 513 \(\times\) 256 pixels from 156 subjects, where the first 105 subjects were captured in two different sessions. The minimum, maximum, and average intervals between sessions were one month, more than six months, and 66.8 days, respectively. Each of the 105 subjects provided six samples from each of their index and middle fingers. Images from the remaining 51 subjects were captured in a single session.  
In our research, all 156 subjects were used: a single session from the first 105 subjects and all sessions from the remaining 51 subjects, which gave us a total of 1872 images (156 subjects \(\times\) 2 fingers \(\times\) 6 images per finger). Each finger was treated as a different subject, and therefore, we obtained a total of 312 subjects, out of which 66 subjects were designated as good quality.
# SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
## 3 Problem Formulation  
### Graph-Based Sybil Detection  
Graph-based Sybil detection uses social graph data for detection. We model the social graph as \(G(V,E)\), where we take each user as a node \(u\in V\), and the directed relationship between users \(u\) and \(v\) as a directed edge \((u,v)\). For example, \"following\" and \"retweeting\" on Twitter or sending friend requests on Facebook can be viewed as forming a directed relationship from one user to another. We call a one-way directed edge unidirectional and a two-way edge bidirectional. Note that we follow the convention of [20] that incoming neighbors, outgoing neighbors, and bidirectional neighbors are separately treated.  
Each node in \(G\) should be either labeled Sybil or benign, while we only have a part of the whole picture, i.e., a labeled training set \(T\) consisting of the labeled Sybil \(L_{s}\) and labeled benign nodes \(L_{b}\). The goal of the graph-based Sybil detection is to predict those remaining unlabeled nodes with the training set \(T\).
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 3 Materials and Methods
### Generator Networks  
As shown in Figure 2, the UDSN is first modified to generate intermediate HR images, and then the dense subnet branch of the EESN is also modified. Regarding the UDSN, we replace the original dense block [32] with two different blocks (i.e., residual dense  
Figure 1: Proposed network architecture.  
block (RDB) and RRDB), and use two different convolution topologies (i.e., original one-dimensional and new two-dimensional (2D) topology [5] in the convolutional layers, which is shown in Figure 3) as the choice. Through the extensive experiments, we list the best results for each of the above four combinations, as shown in Table 1. We can see it will have the best PSNR and FSIM results when the block type is RRDB with 2D-topology and the number of blocks is 3, while the number of convolutional layers in each block is 5. This means to modify the generator network architecture will output SR images that are more close to real HR images.  
Then we use convolutional layers with 2D topology to replace the original convolution layers in the adopted RDB for the dense network branch of the EESN, as shown in Figure 4. We hope that the EESN can generate useful edge features through this network architecture. In Table 2, we test four different combinations for block types (RDB, RRDB) and convolution topologies (1D, 2D). We can find the model will have the best PSNR and SSIM values when the block type is RRDB with 2D-topology and the number of blocks is 2, while the number of convolutional layers in each block is 3.  
Figure 3: The convolutional layers with 2D topology.  
Figure 2: Proposed generator architecture.