esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of CFRP and Steel Vibration Characteristics of Machine Tools Components
## 1 Introduction
[21] conducted a study on the fatigue and mechanical behavior of woven and nonwoven continuous CFRP composites manufactured using 3D printers. More studies on the vibration characteristics of CFRP are urgently needed to produce mechanical parts that are stable in terms of vibration and have excellent vibration reduction characteristics. In this study, modal and vibration experiments were conducted on a CFRP chuck adapter. The results were compared with the vibration of a steel chuck adapter and a CFRP with steel chuck adapter. This has important applicable value for reducing the displacement and vibration of machine tools. This method is illustrated in Figure 1.  
Figure 1: Schematic illustration of vibration experiment for different chuck adapters (CFRP, steel, and CFRP with steel).
## 3 Methods
### K-Means for Anchor Boxes  
We use the k-means algorithm to generate anchors for the four detection layers. The k-means algorithm generates anchors that have large IOUs with the ground truth, which is more conducive to network convergence. The specific method is as follows:  
1. Randomly select some points as centroids of cluster for the initial aggregation, with the centroid of the cluster corresponding to the center of the sample that we will approach;
2. For each sample in the datasets, calculate the ground truth to the centroid of each cluster, and classify the sample into the cluster with the smallest distance, as shown in Equations (2) and (3), where bbox represents the bounding box, and d(bbox, centriod) represents the distance between the centroid of the cluster and the center of the bbox; \[\text{d}(\text{bbox,centriod}):=1-\text{IOU}(\text{bbox,centriod})\] (2) \[\text{IOU}:=\frac{\text{S}_{\text{overlap}}}{\text{S}_{\text{union}}}\] (3)
3. Recalculate the cluster center for each cluster;
4. Repeat steps 2 and 3 until the clusters converge.  
For resolution 416 \(\times\) 416 input images, with the k-means algorithm, we generated 12 anchor boxes for the four detection layers: (21, 25), (25, 31), (33, 39), (44, 51), (59, 81), (84, 95), (104, 116), (119, 148), (161, 184), (221, 201), (246, 213), and (259 278). The anchor boxes (21, 25), (25, 31), and (33, 39) were designed for the added 104 \(\times\) 104 detection layer, and they can be used to detect small objects, which are usually only a few pixels in size, in remote sensing images. For medium-sized objects, a slightly larger anchor can be used on a 52 \(\times\) 52 or 26 \(\times\) 26 feature map. The anchor boxes (221, 201), (246, 213), and (259 278) were designed for the big objects on 13 \(\times\) 13 feature map. Therefore, even if an image contains objects of different sizes, as shown in Figure 7, the anchor of hierarchical designed can match the objects.  
Figure 6: The structure of Dense-FPN.
# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 3 Modeling a Hybrid Model
### Model Functional Principle  
This hybrid text processing model provides sophisticated analysis and generation of responses. The model processes complex textual data containing different information, writing styles, and contextual nuances. Through meticulous tokenization, it transforms the text into a comprehensible form for further analysis. Text processing includes lemmatization, stop word removal, and keyword extraction. In addition, the model utilizes word vector representation techniques to create semantically rich representations of the input data.  
Advanced forms of syntactic tagging, such as complex neural networks, are used to identify and label grammatical relationships between words. Through in-depth syntax analysis, the model recognizes grammatical structures, including subjects, objects, and other elements, enabling a better understanding of structural and semantic relationships in the text. By applying pattern recognition algorithms to extract relevant information from complex sentences, the model also uses its own database for an additional contextual layer.  
Through decision-making model ensembles, the model evaluates multi-criteria factors to select the optimal response. These factors include semantic relevance, grammatical correctness, and the history of previous responses. The result is a high-quality, contextually coherent response generated by the model adaptable to various scenarios and contexts (Figure 3).  
Figure 2: Transformer architecture.  
The initial phase of text preprocessing involves transforming raw text data into a format that can be analyzed. This includes measures such as lowercasing and eliminating punctuation. After this step, the sentence structures are examined through syntax labeling and syntax analysis, in which the grammatical roles of each word are determined. The next step is pattern recognition, in which the model identifies recurring phrases or common themes in the text. Using these patterns, the model selects the most appropriate response based on the input it receives. To formulate its responses, the model relies on a database that contains a wealth of information. Finally, the output represents the model's final response, which is generated through a meticulous analysis of the input and interacting with the database.  
The integration of this approach not only promotes a deep understanding of language, but also efficiently incorporates various aspects of linguistic analysis, providing responses of exceptional precision and contextual relevance. Contemporary methods of natural language processing form the basic structure of this model, which focuses on the subtleties of linguistic dynamics.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Gata3-As1  
The involvement of GATA binding protein 3 antisense RNA 1 (GATA3-AS1) in BC highlights its potential as a regulatory lncRNA. GATA3-AS1 is a divergent lncRNA gene neighboring GATA3, a regulator of TH2 lineage commitment that enables TH2 cells to transcribe genes encoding cytokines IL-4, IL-5, and IL-13 [74]. GATA3 seems to suppress the breast tumor progression [75]. [PERSON] et al. demonstrated that GATA3-AS1 was significantly overexpressed in TNBC tissues. Elevated levels of GATA3-AS1 were associated with a larger tumor size, LNM, and advanced TNM stage. The research showed that this lncRNA could inhibit PD-L1 ubiquitination by upregulating COPS5 and thus stabilize the PD-L1 protein. Knowing the upstream molecular mechanism of PD-L1 in TNBC and exploiting the fact that both PD-L1 and GATA3-AS1 are overexpressed in this BC type, this combination remains not only a novel biomarker but also a possible strategy for the immunotherapy of TNBC [76].
## 2 Intelligence Artificial (AI)-Based Resource Allocation Algorithms in Dynamic Traffic Scenario
### Prediction-Based NFV Reconfiguration Algorithm
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sets and Parameters** & **Definition** \\ \hline \(N\) & Number of VNF-FGs \\ \(b_{j}(i)\) & Bandwidth of the \(i\)-th VNF-FG in the \(j\)-th TI \\ \(V\) & Set of nodes of the VNFI graph \\ \(L\) & Set of links of the VNFI graph \\ \(\tilde{V}\) & Set of nodes of the physical graph \\ \(L\) & Set of links of the physical graph \\ \(f_{v}^{(j)}\) & Processing capacity required by the node \(v\in V\) in the \(j\)-th TI \\ \(f_{e}^{(j)}\) & Bandwidth of the link \(e\in L\) in the \(j\)-th TI \\ \(C_{RA}\) & Cloud Resource Allocation Cost \\ \(C_{QoS}\) & QoS degradation cost \\ \(h\) & Prediction step \\ \(b_{n,h}\) & Vector of the \(h\) bandwidth values \(b_{(n+j)}\) (\(j=1,\cdots,h\)) for the generic VNG-FG \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sets and Parameters. \begin{table}
\begin{tabular}{l l} \hline \hline \(\widehat{b}_{n+j}(i)\) & Predicted bandwidth of the \(i\)-th VNF-FG in \((n+j)\)-th TI \\ \(\widehat{f}_{v}^{(n+j)}\) & Predicted Processing capacity required by the node \(v\in V\) in \((n+j)\)-th TI \\ \(\widehat{f}_{e}^{(n+j)}\) & Predicted bandwidth of the link \(e\in L\) in \((n+j)\)-th TI \\ \(\widehat{b}_{n,h}\) & Vector of the \(h\) predicted bandwidth values \(\widehat{b}_{n+j}(i)\) (\(j=1,\cdots,h\)) for the generic VNG-FG \\ \(e_{n+j}\) & Bandwidth prediction error for a generic VNF-FG in \((n+j)\)-th TI \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variables. \(b_{j}(i)\) (\(i=1,\cdots,N,j=1,\cdots,n\)) known up to TI \(n\) and the VNFI graph \(G=(V,L)\). Next a multi-step ahead prediction of the VNF-FG bandwidth is performed in step 2 by predicting the next \(h\) VNF-FG bandwidth values \(\widehat{b}_{n+j}(i)\) (\(i=1,\cdots,N\), \(j=1,\cdots,h\)). That allows for the evaluation in step 3 of an estimate of the link bandwidths \(\widehat{f}_{e}^{(n+j)}\) and the nodes processing capacities \(\widehat{f}_{v}^{(n+j)}\) of the VNFI graph in the TIs \(n+1,\cdots,n+h\). The knowledge of these estimated values and the application of cloud and bandwidth resource reconfiguration algorithms allow in step 4 for the determination of \(h\) new embeddings \(\Gamma_{n+j}(\tilde{G},G)\) (\(j=1,\cdots,h\)) to be applied in the TIs \(n+1,\cdots,n+h\). We apply the reconfiguration algorithms proposed in [8] referred to as Least Cloud Resource and Bandwidth (LCBC) and Deployment Costs Aware (DCA). The new embeddings are evaluated from the current embedding \(\Gamma_{c}(\tilde{G},G)\), which is the one applied in TI \(n\). Finally the framework returns the evaluated embeddings \(\Gamma_{n+j}(\tilde{G},G)\) (\(j=1,\cdots,h\)). ```
1:Input:
2:Input: Cloud Infrastructure and Network Graph \(\tilde{G}=(\tilde{V},L)\) VNF-FG bandwidths: \(b_{j}(i)\) (\(i=1,\cdots,N\), \(j=1,\cdots,n\)) VNFI graph: \(G=(V,L)\) Current Embedding \(\Gamma_{c}(\tilde{G},G)\)
1:Input:
2:Output:\(\Gamma_{n+j}(\tilde{G},G)\) (\(j=1,\cdots,h\))
```  
**Algorithm 1** Prediction-Based NFV Reconfiguration Algorithm
## 3 Model Comparisons
### Data Analysis and Model Comparison with Real Application
#### 3.2.3. Tandem Computer Data  
Here we examine models using another software's testing data collected from Tandem Computers Release #1 (Data Set 3, DS-3) [5]. The failure data are recorded by week and are shown in Table 7. We use all data points to fit the models and estimate the parameters in the models. The fault data set also only has the detected fault number. The model parameters, MSE values, \(R^{2}\) values and Adjusted \(R^{2}\) values for goodness-of-fit are listed in Table 8.  
From Table 8, we can see that the new model provides the best descriptive power. The fitting of the models (existing and proposed) to DS-3 is graphically illustrated in Figure 7. Figure 8 is three dimensional and the coordinates X, Y and Z illustrate the values of MSE, \(R^{2}\) and Adjusted \(R^{2}\) respectively. Figure 6: (X, Y, Z) represents (MSE, \(R^{2}\), Adjusted \(R^{2}\) ) values for DS-2. Figure 7: The fitting results of comparison SRGMs compared with actual data for DS-3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Testing Time (Weeks)** & **CPU (h)** & **Faults** & **Cumulative Faults** & **Testing Time (Weeks)** & **CPU (h)** & **Faults** & **Cumulative Faults** \\ \hline
1 & \(519\) & \(16\) & \(16\) & \(11\) & \(6539\) & \(6\) & \(81\) \\
2 & \(968\) & \(8\) & \(24\) & \(12\) & \(7083\) & \(5\) & \(86\) \\
3 & \(1430\) & \(3\) & \(27\) & \(13\) & \(7487\) & \(4\) & \(90\) \\
4 & \(1893\) & \(6\) & \(33\) & \(14\) & \(7846\) & \(3\) & \(93\) \\
5 & \(2490\) & \(8\) & \(41\) & \(15\) & \(8205\) & \(3\) & \(96\) \\
6 & \(3058\) & \(8\) & \(49\) & \(16\) & \(8564\) & \(2\) & \(98\) \\
7 & \(3625\) & \(5\) & \(54\) & \(17\) & \(8923\) & \(1\) & \(99\) \\
8 & \(4422\) & \(4\) & \(58\) & \(18\) & \(9282\) & \(1\) & \(100\) \\
9 & \(5218\) & \(11\) & \(69\) & \(19\) & \(9641\) & \(0\) & \(100\) \\
10 & \(5823\) & \(6\) & \(75\) & \(20\) & \(10,000\) & \(0\) & \(100\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Failures per week and cumulative failures for DS-3.
## Results and Discussion
### Photophysical Properties  
Figure 4 shows the absorbance and phosphorescence spectra of PtTFPP at a concentration of 2.2 \(\upmu\)M in micelles, THF, and DCM, respectively. The results demonstrated that at the same PtTFPP  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Micelles-**} & **Concentation** & **Concentrations** & **Quantum Yield** & **Encapsulation** & **PITFP** & **Loading Ratio** & **Size** & **PDI*** \\  & **of Polymers** & **of PtTFPP** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(nm)*** \\ \hline
**M1** & 1.4 & 0.219 & 15.9 & 65.7 & 15.4 & 203.8 & 0.221 \\
**M2** & 2.1 & 0.226 & 11.4 & 79.1 & 9.7 & 89.3 & 0.176 \\
**M3** & 2.7 & 0.195 & 6.0 & 82.9 & 6.7 & 40.0 & 0.213 \\ \hline \hline \end{tabular}  
* Polymer concentration in micellar solutions. b PTTFPP in micellar solutions. c The weight ratio of PtTFPP encapsulated in micelles to the fed PtTFPP for micelles’ preparation. d The weight percentage of PtTFPP to the block copolymers. e Determined by DLS.  
\end{table}
Table 2: Properties of micelles **M1**–**M3**. DLS: dynamic light scattering.  
Figure 3: Dynamic light scattering (DLS) results of micelles **M1**–**M3** with PtTFPP.  
Figure 2: Preparation of platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-penta-fluorophenyl)-porphyrin (PtTFPP) encapsulated micelles.  
concentration, the PtTFPP's emission peak in micelles is significantly higher than that in tetrahydrofuran and dichloromethane, indicating that PtTFPP in micelles has a higher quantum yield than that in organic solvents THF and DCM. The quantum yield of PtTFPP in micelle **M1** under nitrogen conditions was measured to be 0.159 (Table 2), which is much higher than its quantum yield in dichloromethane (0.088 [38]). This situation may be explained by PCL providing a suitable micro-environment for PtTFPP because of its polarity compatibility [38]. Therefore, the use of micelles not only enabled the use of PtTFPP in aqueous solution, but also preserved the quantum yield of PtTFPP. Owing to the different ratios of hydrophobic chains of PCL to hydrophilic segments of PEG, the quantum yields of PtTFPP in the three micelles were also affected by the polymer structures. Among them, the **M1** micelles with the longest hydrophobic segment PCL can alleviate more significant interactions between PtTFPP and water, so that PtTFPP in **M1** showed the highest quantum yield among the three micelles.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 3 Materials and Methods
### Three DOF Positioning Tests  
#### 3.4.1 Experimental Setup: Three DOF  
The two-DOF positioning method was used with a small number of modifications to add a third DOF, as shown in Figure 4G. These modifications were: a spherical coupling to attach the cables to the shaft; a telescopic shaft; and a rotary joint, or equivalent, that constrains the proximal end of the shaft. Three DOFs of the end effector are controllable: yaw, \(\psi\); pitch, \(\phi\); and extension in the \(x\) direction.  
As a result of the spherical coupling's design, the direction vectors of the three cables are assumed to have a single point of intersection in the plane of the cable entry points of the structure. Therefore, controlling the position of the point of intersection can be achieved in the same way as in the two DOF tests.  
A prismatic joint was added by incorporating an inner telescopic shaft to the existing shaft. To produce linear motion of the distal tip of the telescopic shaft along its axis, a motorised linear stage moves the instrument axially in relation to a Bowden tube.  
By constraining the end of the shaft assembly with a rotary joint, the two prismatic DOFs of the shaft became rotational DOFs. A continuum joint was used, as both the size was small and friction with the telescoping instrument was low. The curvature was assumed to be constant along the length of the continuum joint. Furthermore, the length of the continuum joint was assumed to be constant.
## 3 Experimental Results and Analysis
### Comparative Experiment of Different Methods
Compared to the Transformer-based methods included in the comparison, the accuracy of the BDiT model is only slightly lower than TransFG. This can be attributed to the fact that TransFG uses overlapping patches as input, resulting in a 3.0% higher accuracy than ViT, making it more suitable for the classification task on the CUB-200-2011 dataset. However, it should be noted that the BDiT model is primarily designed for analyzing blast furnace tuyere images, while the CUB-200-2011 dataset consists of bird images that may exhibit variations in pose, viewpoint, and background. Additionally, the dataset may have imbalanced sample distribution among different bird species, with some categories having more or fewer samples than others. These factors contribute to the challenge of classification due to limited training samples, making it difficult for the model to fully learn the distinguishing features of these specific categories. Figure 10: Different distillation parameters and accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{2-3}  & **CUB** & **Dogs** \\ \hline ESERNet [31] & 60.72 & 73.20 \\ LSERNet [32] & 63.57 & 74.39 \\ ResNet101 [26] & 72.93 & 84.35 \\ ResNet50 [27; 28] & 74.58 & 87.16 \\ repVGG [31] & 69.17 & 85.81 \\ ViT [13] & 77.42 & 88.27 \\ DeiT [18] & 76.06 & 91.32 \\ TransFG [15] & 80.43 & 90.24 \\ BDiT & 77.65 & 90.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy comparison of different models on the CUB-200-2011 dataset and the Stanford Dogs dataset. The Stanford Dogs dataset has a relatively larger number of images and a smaller number of categories compared to the CUB-200-2011 dataset, which provides favorable conditions for model training. As shown in Table 3, our proposed BDiT model achieved comparable accuracy to other methods on the Stanford Dogs dataset, indicating that the BDiT model proposed in this paper exhibits a certain level of generalization ability.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 4 Measuring the Deformation Trough Differential Interferometry
### Deformation Estimation Results  
The estimated deformations are represented in Figure 6. The deformations of the Eiffel Tower show a very high variability as presented in Figure 6a. On the contrary, Figure 6d shows the annual periodicity of the deformations of the Mirabeau Tower. Except for some date, an annual periodicity can be seen in Figure 6b, for the deformations of the Cristal Tower for the years 2008 and 2009. This periodicity is unclear for the other years even if the deformation are smaller in winter than in summer. The deformations of the Keller Tower are also minimal in winter and maximal in summer but the variability of the measured deformation that can be observed in Figure 6c lead to an unclear annual cycle.  
The annual periodicity of the deformation measured on the Mirabeau Towers would indicate thermal expansion due to the temperature as the major cause of the deformation. The more important dispersion observed in the deformation of the other towers underline the numerous deformation sources For example, under the sun light action, the buildings bend in direction the opposite to the sun. The sunshine can change the amplitude and the direction of the deformation on a daily basis. Moreover, even a small rain shower reset the buildings back in their original position, changing their deformation state at the time of the acquisition.  
On top of the several sources of deformation that can co-exist, the measurement can still be impacted by noise. The low backscattering on the Cristal Tower required some averaging and to increase the threshold value on the PS selection. This is why a special effort has been made to validate and cross-check our measurements with other data, the first of which are weather parameters.  
Figure 6: Measured deformation given the number of days separating the acquisitions. (a) \(\bullet\) Eiffel Tower; (b) \(\blacktriangledown\) Cristal Tower; (c) \(\blacklozenge\) Keller Tower; (d) \(\blacksquare\) Mirabeau Tower. The vertical dashed lines represent the years from the master image acquired the 24 January 2009.
## Materials and Methods
### Methods  
The test apparatus was designed to simulate cavity expansion and to observe surface collapse (Figure 1). There are three chambers in the test apparatus: a ground (or soil) chamber in the middle section, containing the soil samples, and groundwater level control chambers (water chambers), which are located on both sides of the ground chamber (Figure 1a). The dimensions of the tank are \(300\times 200\times 50\) mm. Each water chamber was 50 mm long and 200 mm high, and included permeable porous ceramics. The permeable porous ceramic filter plates are located between the ground and water chambers (Figure 1a). Thus, water is able to move only through the porous filter. The tank model was made of a transparent acrylic plate for visual observation of the cavity expansion during the inflow and outflow cycles of the soil and water. The cavity can form at the bottom of the soil chamber by opening a drainage valve which is 5 mm in diameter (Figure 1b). A constant amount of water (i.e., 200 cm\({}^{3}\)was supplied from the water tank. The opening valve was opened after 60 min, and a cycle of water supply and soil and water drainage is considered due to the breakage of sewer pipes in urban areas. The inflow and outflow cycles were repeated until surface collapse was observed. The same procedure was applied for different groundwater levels.  
The soil samples filled the ground chamber, and the soil was added in layers. Each soil layer was compacted manually to a thickness of 1 cm. Colored soil layers with a thickness of 5 cm were placed in each tank model, in order to allow easier observation of the ground deformation and wetting in the front and rear of the tank model. The water level in the tank model is regulated to be 5, 10 and 15 cm high at a fixed relative density of the soil sample (i.e., 90%). The water for the saturation and adjusted groundwater levels was supplied into the ground model from the bottom of the plate using the minimum inflow rate (\(\leq\)0.1 mm/s), without any sample disturbance. Then, the drainage valve was opened. The weight of the soil and water flowing out of the tank was measured. According to the previous research of [PERSON] et al. [20], the overburden load in the ground tank is not a significant influencing factor in the process of cavity and sinkhole formation. Therefore, in this study, the overburden load is not considered. A high-quality and easy-to-use camera was used to record images of the ground deformation during the test. Variations in pore water pressure, volumetric water content, electrical conductivity and temperature are considered as sinkhole indicators. Sensors were placed in the ground model, at 8 cm from the surface (Figure 1).  
Figure 1: Tank model apparatus: (**a**) model diagram with sensors; (**b**) saturation; (**c**) setup; (**d**) sensor (called 5 TE) designed to measure electrical conductivity, temperature and water content; (**e**) pore water pressure sensor; (**f**) sand; (**g**) sand-clay mixture. Figure 1f,g show significant punching failure type for water levels of 10 and 15 cm high.