esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Stability of Dendrimer Modified CdTe/CdS QDs  
As seen in Figure 3a, the photoluminescence (PL) lifetimes of freshly prepared CdTe QDs and dendrimer modified CdTe/CdS QDs is about 20.1 ns and 58.3 ns, respectively. The great increase of the decay lifetime of dendrimer modified CdTe/CdS QDs is ascribed to the spatial separation of electron and hole in type-II QDs [13]. Additionally, the PL quantum yields (QYs) of CdTe QDs and dendrimer modified CdTe/CdS QDs are 25% and 55%, respectively.  
Figure 3: _Cont._  
In order to develop a stable PL signal for the biological applications, the fluorescence stability of dendrimer modified CdTe/CdS QDs was accessed under various tested conditions such as pH, buffer solution and incubation time, respectively. As shown in Figure 3b, the PL signal of dendrimer-CdTe/CdS QDs is more stable when they were stored in Dulbecco's modified eagle medium (DMEM) and stimulate body fluid (SBF) buffer compared with they were stored in PBS buffer. Particularly, the fluorescence intensity of dendrimer modified CdTe/CdS QDs remains at 88% of the initial value after storage for 144 h in the PBS buffer. However, exposure of the QDs solutions to the acidic condition resulted in a decreased PL intensity, but it was basically stable under either neutral condition or alkaline condition (Figure 3c). This phenomenon is attributed to the surface charge characteristics of dendrimer modified QDs [14].  
As shown in Figure 3d, the cytotoxicity of as-prepared dendrimer modified CdTe/CdS QDs was measured by MTT analysis. The cell viability of 293T cells treated with dendrimer modified CdTe/CdS QDs (50 nM) was about 95.2% and 93.2% after 24 h and 48 h incubation, while the cell viability was only about 36.7% after incubation 293T cells directly exposed with bulk CdTe QDs for 24 h, which proved that the dendrimer modified CdTe/CdS QDs had lower cytotoxicity than bulk QDs. Furthermore, none of cell lines including 293T, U251 and GES-1 cells shows a significant decrease in cell viability after exposing dendrimer modified CdTe/CdS QDs in the concentrations arrange from 0.5 to 50 mM for 24 h (Figure 3e). In a word, our prepared dendrimer modified CdTe/CdS QDs demonstrates an impressive biocompatibility with ultra-low toxicity, which is benefited to our following research.  
Figure 3: **(a)** Photoluminescence (PL) decay profiles of CdTe QDs and dendrimer modified CdTe/CdS QDs; **(b)** Effect of different buffer on the fluorescence intensity of dendrimer modified CdTe/CdS QDs at different time intervals; **(c)** Effect of different buffers on the fluorescence intensity of dendrimer modified CdTe/CdS QDs at different time intervals; **(d)** Cell viability of 293T cells after incubation with PBS buffer, bulk CdTe QDs and dendrimer modified CdTe/CdS QDs at 24 h and 48 h, respectively; **(e)** Cell viabilities of 293T cells, U251 cells and gastric epithelial cell line (GES-1) cells after incubating with various concentrations (0.5, 5, and 50 nM) of freshly prepared QDs for 24 h. The 0 nM concentration of QDs is taken as a control.
## 2 Results  
### Isolation and Identification of Compounds **1-6**  
Dried _G. biloba_ leaves were subjected to extraction using 80% aqueous methanol (MeOH). Following the extraction process, the extract was filtered to remove impurities and the resulting extract was then concentrated using a rotary evaporator, yielding a green crude extract. Solvent partitioning and continuous chromatography were employed to isolate bioactive compounds from the crude MeOH extract, guided by LC/MS analysis. Initially, we observed that a substantial portion of the hexane-soluble fraction comprised flavonoids, lipids, and ginkgolic acids. This observation was verified by comparing their mass and UV absorption spectra with data from our in-house libraries and previously reported information [23].  
Our primary objective was to isolate characteristic compounds of _G. biloba_, known as ginkgolic acids, which possess various bioactivities, including antimicrobial, antitumor, anti-inflammatory, antiparasitic, and enzyme inhibitory properties [24]. With the guidance of LC/MS, we successfully isolated six ginkgolic acid derivatives (**1-6**) using high-performance liquid chromatography (HPLC) (Figure 1).  
The identification of these isolated compounds was accomplished, and their structures were determined as follows: 2-hydroxy-6-(10\({}^{\prime}\)-hydroxypentadec-11\({}^{\prime}\)(\(E\))-en-1-yl) benzoic acid (**1**) [25], 2-hydroxy-6-(11\({}^{\prime}\)-hydroxypentadec-9\({}^{\prime}\)(\(E\))-en-1-yl) benzoic acid (**2**) [25], 2-hydroxy-6-tricedylbenzoic acid (**3**) [26], 2-hydroxy-6-pentadecylbenzoic acid (**4**) [27], 2-hydroxy-6-(12\({}^{\prime}\)-hydroxyheptadec-13\({}^{\prime}\)(\(E\))-en-1-yl) benzoic acid (**5**) [25], and 2-hydroxy-6-(11-hydroxymolecyl) benzoic acid (**6**) [28] (Figure 2). All of these compounds were elucidated through a combination of NMR spectra comparison with reported data and LC/MS analysis (Figures S1-S18). Notably, compound 6 was isolated from _G. biloba_ leaves for the first time. In the present study, compounds **1-6** were evaluated for their inhibitory activity against PTPs to assess their potential efficacy in the context of T2 DM treatment.
# Battery Management System Hardware Concepts: An Overview
## 3 Topologies of BMS
### Real-World Examples
In the i-MiEV battery, the cell monitoring ICs are also mounted on PCBs on top of the battery modules, taking a bit more space. The BMS-master that also controls the contactors is not contained in the pack's housing, which is why a certain amount of cabling is necessary to connect the components to each other. A part of the cables found in the i-MiEV battery could be reduced by putting the BMS-master into the pack and by organizing the power supply cabling more reasonably. The e-Up pack finally is an example for a central monitoring unit. Every cell voltage is acquired using long voltage measurement cables, which connect cell modules to the monitoring unit in a star layout. Apart from these differences, the BMS components are very similar in terms of their functionality. All mentioned examples use specialized monitoring ICs to implement the basic functions, which are supervised by a central ECU (BMS-master) for higher functions, or central functions like contactor control.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 4 Learning to Optimize An RIS  
In addition to mathematical optimization methods, AI-based methods have recently emerged as a promising direction for solving resource allocation problems. Problem (5) can be regarded as a regression problem (or classification problem for discrete phase shifts), which can be tackled by deep-learning (DL) methods. When DL is employed, a deep neural network (DNN) is adopted to learn the mapping from the channel state information (CSI) to the optimized phase shift coefficients. Once the AI model is trained, the computation of phase shift coefficients is extremely fast, and it can be readily implemented in various operating systems, such as Linux and Android, via model loading. In the following, three learning-based methods are discussed.
# Captioning Transformer with Stacked Attention Modules
## 3 Model Architecture
### Attention in Transformer  
#### 3.4.1 Scaled Dot-Product Attention  
The Transformer uses the Scaled Dot-Product Attention. The input consists of three input, i.e., keys \(K\), values \(V\) and queries \(Q\). This attention is shown as follows:  
\[Attention(Q,K,V)\ =\ softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{1}\]  
where \(Q\in R^{L\times d_{k}}\) is the query, \(K\in R^{L\times d_{k}}\) is the key, \(V\in R^{L\times d_{p}}\) is the value and the \(L\) is the length of the sequence. We can find that in Equation (1), \(QK^{T}\in R^{L\times L}\) is the product operation, this could make the result become too large or small. This will influence the precision of the variable. So \(\sqrt{d_{k}}\) is used to scale \(QK^{T}\). At last, we get \(Attention(Q,K,V)\in R^{L\times d_{p}}\). The dot-product attention is faster and space-efficient, and it can be implemented by the optimized matrix multiplication method. \(K\) and \(V\) is the key-value pair. If this pair at the first sub-layer of the Transformer, it will be the text embedding matrix. If this pair at the second sub-layer, it will be the image embedding matrix.
###### Abstract  
Steel risers are widely used in offshore oil and gas industry. However, the production capacity and depths are limited due to their extreme weight and poor fatigue and corrosion resistance. Nowadays, it is confirmed that fiber reinforced polymer (FRP) composite risers have apparent advantages over steel risers. However, the study of vortex induced vibration (VIV) for composite risers is rarely involved. Three different risers (one steel riser and two composite risers) were compared for their VIV characteristics. The effects of 2D and 3D models and fluid-structure interaction (FSI) were considered. The models of composite risers are established by effective modulus method (EMM) and layered-structure method (LSM). It is found that 2D model are only suitable for ideal condition, while, for real situation, 3D model with FSI has to be considered. The results show that the displacements of the FRP composite risers are significantly larger than those of the steel riser, while the stresses are reversed. In addition, the distributions of the displacements and stresses depend on the geometries, material properties, top-tension force, constraints, etc. In addition, it is obvious that EMM are suitable to study the global working condition while LSM can be utilized to obtain the results in every single composite layer.  
FRP composite riser; vortex-induced vibration; coupled fluid-structure interaction; CFD simulation 2018 8 684 (); 3390/app8050684
## 3 Results and Discussion
### GWO-WWOA Learning Process
The optimized values of the sliding surfaces, command filters, and controllers of each joint through the GWO-WWOA technique, are listed in Table 1. The membership function of the IT2 fuzzy system was selected as the Gaussian function as shown in Figure 1b. Table 2 shows the optimized IT2 fuzzy parameters through the proposed GWO-WWOA technique, where the Gaussian function parameters are presented; \(m_{ij}\) denotes the mean value in \(\Delta m_{ij}=\overline{m}_{ij}-\underline{m}_{ij}\) and \(\sigma_{ij}\) denotes the standard deviation. \begin{table}
\begin{tabular}{l l} \hline \hline
**Joint** & **Optimized Values** \\ \hline \multirow{4}{*}{Joint 1} & \(\gamma_{1,1,1}=0.19,~{}\gamma_{1,1,2}=0.73,~{}\gamma_{1,2,1}=0.05,~{}\gamma_{1,2,2 }=0.74,\) \\  & \(\lambda_{1,1,1}=0.09,~{}\lambda_{1,1,2}=0.15,c_{1,1}=2,~{}c_{1,2}=2,~{}c_{1, 3}=1.0,~{}c_{1,A}=1.0,\) \\  & \(k_{1,1,1}=1,~{}k_{12}=0.9,~{}v_{1,1}=0.001\) \\  & \(\gamma_{2,1,1}=0.2,~{}\gamma_{2,1,2}=0.7,~{}\gamma_{2,2,1}=0.19,~{}\gamma_{2,2,2}=0.86,\) \\  & \(\lambda_{2,1,1}=0.29,~{}\lambda_{2,1,2}=0.25,c_{2,1}=2,~{}c_{2,2}=2,~{}c_{2, 3}=0.96,~{}c_{2,A}=0.51,\) \\  & \(k_{2,1,1}=5.2,~{}k_{22}=5.6,~{}v_{2,1}=0.001\) \\  & \(\gamma_{3,1,1}=0.2,~{}\gamma_{3,1,2}=0.71,~{}\gamma_{3,2,1}=0.14,~{}\gamma_{3, 2,2}=0.80,\) \\  & \(\lambda_{3,1,1}=0.07,~{}\lambda_{3,1,2}=0.53,c_{3,1}=2,~{}c_{3,2}=2,~{}c_{3,3}= 1.4,~{}c_{3,4}=1.6,\) \\  & \(k_{3,1,1}=2.7,~{}k_{32}=2.2,~{}v_{3,1}=0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimized values of the parameters of the controller via GWO-WWOA. Figure 3: The configuration of the self-designed articulated manipulator system.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
###### Abstract  
The Global Positioning System (GPS) has been widely applied in outdoor positioning, but it cannot meet the accuracy requirements of indoor positioning. Comprising an important part of the Internet of Things perception layer, Radio Frequency Identification (RFID) plays an important role in indoor positioning. We propose a novel localization scheme aiming at the defects of existing RFID localization technology in localization accuracy and deployment cost, called ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna, which introduces a rotary table in the experiment. The reader antenna is fixed on the rotary table to continuously collect dynamic data. When compared with static acquisition, there is more information for localization. After that, the relative incident angle and distance between tags and the antenna can be analyzed for localization with characteristics of Received Signal Strength Indication (RSSI) data. We implement ANTSpin using COTS RFID devices and the experimental results show that it achieves a mean accuracy of 9.34 cm in 2D and mean accuracy of 13.01 cm in three-dimensions (3D) with high efficiency and low deployment cost.  
Keywords:absolute localization; RFID; spinning antenna; RSSI +
Footnote †: journal: Sensors
## 3 Questionnaire for the Relationship between Facial Expressions and Fonts
### Questionnaire Results  
Figure 3 shows the results of the questionnaire for emotions, and we can see that the results below show it is distinguishable for this questionnaire. It is found that font 3 is the most selected with 43.6% having chosen it, followed by font 7, which has been chosen by 19.9% of the participants for the emotion of anger. For the emotion of contempt, font 7 is the most selected with 45.4% having chosen it, followed by font 4, which has been chosen by 23.3% of the participants. For the emotion of happiness, font 6 is the most selected with 56.2% having chosen it, followed by font 10 with up to 13.5%. For the emotion of neutral, font 5 is the most selected with 76.1% having chosen it, followed by font 6, which has been chosen by 12.8% of the participants. For the emotion of surprise, font 8 is the most selected with 30.1% having chosen it, followed by font 1, which has been chosen by 13.5% of respondents. These results are relatively well distinguished. However, the choice of font for disgust, sadness, and fear is relatively unclear because the results' difference in the questionnaire is small. Therefore, we choose font 9 because it is the highest option for all three emotions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Question Sequence** & **Question Content** \\ \hline Question 1 & What’s your name? \\ Question 2 & What’s your gender? \\ Question 3 & How about your experience on calligraphy? \\ Question 4 & Talking about fonts reflecting emotions \\ Question 5 & Choose a font best corresponds to “anger”. \\ Question 6 & Choose a font best corresponds to “contempt”. \\ Question 7 & Choose a font best corresponds to “disgust”. \\ Question 8 & Choose a font best corresponds to “fear”. \\ Question 9 & Choose a font best corresponds to “happiness”. \\ Question 10 & Choose a font best corresponds to “neutral”. \\ Question 11 & Choose a font best corresponds to “sadness”. \\ Question 12 & Choose a font best corresponds to “surprise”. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Questions 1–4 aim to acquire basic information of respondents, which is useful to improve the credibility of questionnaire. Questions 5–12 are main content of questionnaire.  
Figure 2: A typical example of the questionnaire, which asking respondents to choose a font that corresponds to the emotion shown in the facial image (The emotion “anger” is shown here).  
According to the results of the questionnaire, we get some correspondence as shown in Table 2. For example, we find \"Anger\" best corresponds to font 3, as well as \"Contempt\" to font 7, etc.
## 3 Results and Discussion
### Zeta (\(\zeta\)) Potential Characterization  
In order to find out how well MCC and CNCs samples would remain suspended in water, zeta (\(\zeta\)) potential measurements were performed. As can be seen in Figure 10, both the MCC and CNCs samples had negative zeta potentials of around 17.4 \(\pm\) 5.32 mV and 25.6 \(\pm\) 8.24 mV, respectively.  
Figure 9: TGA and DTG curves of CNCs (**b**) with reference of standard MCC (**a**).  
Dispersion in water was aided by the negative electrostatic layers formed on the nanocrystals due to the grafting of sulfate groups during sulfuric acid hydrolysis [54]. The sulfate groups that became linked to the CNCs chains during hydrolysis may have been responsible for the relatively larger negative zeta potential observed in the CNCs sample. They can be kept in solution because their zeta potential is less than \(-30\), which is negative [55]. Furthermore, nanoparticles with a negative zeta potential are preferable because they can resist aggregation in a colloidal environment, leading to a wider distribution [9]. When the zeta potential of nano-cellulose is between \(-15\) and \(15\) mV [56], agglomeration forms because the particles lack the necessary charges to repel each other and instead clump together. Nano-cellulose with zeta potentials between \(-30\) mV and \(30\) mV [57] or \(25\) mV (other reports) has been reported to be a stable suspension [58].  
Based on our research, we know that the CNCs sample has very negative surface charges, which indicates that there are strong electrostatic repulsive forces between the nanocrystals, which is what ultimately leads to a uniform nano-cellulose suspension. As a result of these findings, nano-cellulose has been proposed as a possible reinforcement.
_applied sciences_  
_Article_  
Prediction of Landslide Deformation Region Based on the Improved S-Growth Curve Model  
**[PERSON] 1, [PERSON] 1,2,*, [PERSON] 3, [PERSON] 4, [PERSON] 1, [PERSON] 2 and [PERSON] 5**  
1 School of Resources and Environmental Engineering, Jiangxi University of Science and Technology,  
Ganzhou 341000, China  
2 State Key Laboratory of Safety and Health for Metal Mines, Maanshan 243000, China  
3 School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China  
4 School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China  
5 Fujian Yonking Geotechnical Co., Ltd., Longyan 361000, China  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract: Quantitative research on and the prediction of a landslide deformation area is an important point to accurately and comprehensively understand the failure mechanism of landslides and the degree of slope failure. This study uses image processing techniques to quantitatively identify the area and volume of deformation regions during rainfall-type landslide destabilization under multifactor conditions. The findings revealed that (1) an increase in rainfall intensity and slope angle, as well as the existence of slope crest, will accelerate the process of slope instability. In our study, when the rainfall intensity was 140 mm/h and the landslide volume reached 35.68%, the landslide failure was the most serious. (2) Slopes with high compaction of subsoil as well as those without perimeter pressure are relatively more damaged. (3) The higher the density of vegetation cover, the stronger the protection ability of the slope, and the higher the wind speed, the greater the failure to the slope. Furthermore, an improved S-growth curve model was proposed to predict landslide volumes in 16 sets of experiments. In detail, the proposed S-growth curve model predicted landslide volumes with an average absolute percentage error of 4.34-16.77%. Compared with the time series analysis moving-average method (average absolute percentage error of 6.39-68.89%), the S-growth curve model not only has higher prediction accuracy but also can describe the three stages of deformation region development from a physical perspective and can be applied to the volume during landslide change prediction.**  
**Keywords: deformation area; landslide volume; type of space damage; S-growth curve model**
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 1 Introduction
### ImageCLEF Caption Detection Task
#### 1.8.3 ImageCLEF Caption Detection Task 2019  
The ImageCLEF 2019 Caption Detection task [27], third in the series after 2017 and 2018, focused solely on radiology images mined from PubMed Central to reduce label noise. A single subtask required detecting UMLS concepts from images. The training set included 56,629 images, with 14,157 for validation. Teams mainly applied deep learning approaches such as CNNs, RNNs (especially LSTM), adversarial autoencoders, and transfer learning models. The evaluation used F1-scores averaged across 10,000 test images (Table 9).  
AUEB NLP's top-ranked systems combined CNN image encoders with retrieval and multilabel classification approaches. Their best model adapted CheXNet [3] using DenseNet-121, extending it to predict 5528 concepts by replacing the original output layer, applying sigmoidal activation, and optimizing thresholding at 0.16 [28]. Another system combined CheXNet probabilities with k-NN retrieval scores, and a VGG-19 based model ranked fifth.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **F1-Score** \\ \hline U A.PT Bioinformatics & 0.1102 \\ U A.PT Bioinformatics & 0.1082 \\ U A.PT Bioinformatics & 0.0978 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Concepts detection performance in terms of F1-score.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **F1-Score** \\ \hline ImageSem & 0.2501 \\ ImageSem & 0.2343 \\ ImageSem & 0.2278 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Caption detection performance in terms of BLEU score.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **F1-Score** \\ \hline AUEB NLP & 0.2823 \\ Damo & 0.2655 \\ ImageSem & 0.2235 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Caption detection performance in terms of F1-score.  
[PERSON] et al. [29] proposed a hybrid CNN-LSTM framework with attention mechanisms. Features were extracted using a fine-tuned ResNet-101 encoder, followed by an LSTM decoder using attention to dynamically focus on image regions during concept prediction. Their method ranked second overall.  
ImageCLEF 2019 confirmed the effectiveness of combining transfer learning, retrieval, and attention-based models for biomedical concept detection.