esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Temperature Field Fitting and Simulation of CRTS III Slab Track
### Simulation Analysis of CRTS III Slab Track Deformation
The displacement variation at the edge of the slab is greater than that at the middle of the slab. When the overall temperature is lowered, the slab track shrinks due to the lowering of the temperature, and the displacement is all negative. The displacement in the middle of the slab is slightly larger than at the edge of the slab. Table 5 and Figure 9 are the simulation results of vertical displacement of the slab track under a positive temperature gradient and a negative temperature gradient. In Table 5, the center position is the geometric center of the upper surface of the slab track. The edge position is the quadrilateral vertex on the upper surface of the slab track in the model. It can be seen that under the effect of a positive temperature gradient, the middle part of the slab deforms upward, and the edge of the slab deforms downward. The downward displacement of the middle of the slab is the largest under the action of the negative temperature gradient. An elliptical area is formed in the middle. The more outward the displacement, it gradually changes from downward to upward. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Displacement Variation in** & **Displacement Variation of** \\  & & **the Middle of Slab/mm** & **the Edge of the Slab/mm** \\ \hline \multirow{3}{*}{Heating} & \(10\,^{\circ}\)C & \(0.032\) & \(0.035\) \\  & \(20\,^{\circ}\)C & \(0.083\) & \(0.086\) \\  & \(30\,^{\circ}\)C & \(0.134\) & \(0.137\) \\  & \(40\,^{\circ}\)C & \(0.185\) & \(0.188\) \\ \hline \multirow{3}{*}{Cooling} & \(10\,^{\circ}\)C & \(-0.07\) & \(-0.067\) \\  & \(20\,^{\circ}\)C & \(-0.121\) & \(-0.118\) \\ \cline{1-1}  & \(30\,^{\circ}\)C & \(-0.172\) & \(-0.169\) \\ \cline{1-1}  & \(40\,^{\circ}\)C & \(-0.223\) & \(-0.22\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Displacement changes in the middle of the slab track and the edge of the slab during the overall heating and cooling.
## 5 Experiment and Results
### Evaluation and Analysis  
In order to train the model, the parameters are set as follows. First, we choose cross-entropy as the loss function. This loss function applies when the labels are multi-class patterns. Then we choose Adam as the optimizer. It uses the first-order moment estimation and second-order moment estimation of the gradient to dynamically adjust the learning  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Start Frequency (GHz) & 77 \\ Scene Classifier & best\_range\_resolution \\ Azimuth Resolution (deg) & 15 + Elevation \\ Range Resolution (m) & 0.044 \\ Maximum unambiguous Range (m) & 9.06 \\ Maximum Radial Velocity (m/s) & 1 \\ Radial velocity resolution (m/s) & 0.13 \\ Frame Duration (ms) & 125 \\  & 1 (List of detected objects) \\ Output Data Types & and \\  & 5 (Range/Doppler heat-map) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radar configuration.  
Figure 9: Output frame structure sent to the serial port.  
rate of each parameter. The advantage is that each iteration of the learning rate has a clear range, which makes the parameter change very smooth. Accuracy is selected as the metrics method. Batch_size is set to 20.  
Five kinds of networks are trained below. We first train the two networks of only inputting point cloud data and the two networks of only inputting range-Doppler data mentioned above, and then pick out the networks with the highest accuracy and the smallest loss, and then connect them in parallel. Then we input both point cloud data and range-Doppler data into the new network for training.
# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 2 Related Works
### Generative Adversarial Networks  
GAN is a powerful generative model based on deep learning. Since it was proposed by [PERSON] et al. in 2014 [27], GAN has been successfully applied in many fields, such as computer vision and natural language processing. In recent years, GANs are also unitized to tackle object detection problems. The main approaches are divided into two categories. The first is to create a spatial deformation by occluding certain regions on a feature map or manipulating feature maps to generate an adversary network of hard samples [28; 29], as shown in Figure 4. The key idea is to create adversarial samples in the convolutional feature space instead of directly generating pixel-level data. The other is to generate super-resolution feature maps to reduce the difference in representation between the small and large objects for improving small object detection performance. Specifically, the generator learns to transform small object representations into super the resolution representations that are sufficiently similar to real large objects to deceive against the discriminator. Perceptual GAN [30], the model discriminator, and the generator are confronted to identify the generated representation and impose conditional requirements on the generator; the generated small object representation must be conducive to the detection of the target. The discriminator of the multitask generative adversarial network (MTGAN) [31] is a multitask network that uses true/false scores, object category scores, and bounding box regressors to describe each super-resolution image patch. Compared with these methods of directly generating pixel-level images and directly expanding the dataset, it is easier to generate samples in the convolutional feature space.  
Figure 4: Overview of GANs-based object detection.  
Therefore, inspired by the methods proposed by [PERSON] [29] and [PERSON] [30], we used GAN networks to generate deformation samples. Unlike their methods, our method does not use masks or generate similar training samples. We generate deformation samples to achieve the detection of objects with dramatic deformation. For the problem of the accuracy of the one-stage method not being as good as that of the two-stage method, we used focal loss [13] to solve it. The Vit [23] and YOLO [12] algorithms achieve the establishment of object contextual relations by dividing the feature map, thus improving the accuracy of small target detection. Vit is a transformer-based detection network as it requires a large amount of training data, while YOLO is not as accurate as the two-stage network, although its prediction is fast. In this paper, we used object contextual relations to improve the accuracy of small object detection, then propose the ODEM-GAN module.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 2 Distinctive Characteristics of Tumor Microenvironment and Heterogeneity of EPR
### Ligand-Receptor Pairs  
Intercellular communication through signaling requires direct physical contact between neighboring cells, with the ligand of one cell binding to homologous membrane receptors on the neighboring cell, creating specific signaling and response pathways in the target cell [44,56]. Depending on the ligand, a different adhesion molecule, such as cadherins, selectins, and members of the immunoglobulin superfamily, binds and becomes part of an adhesion network [44,57].  
At epithelial migration barriers, intercellular adhesion complexes such as tight junctions (TJs), adherens junctions (AJs), and desmosomes are formed. Tight junctions regulate barrier permeability, facilitating the transport of ions and molecules, while contributing to osmosis, cell differentiation, and the regulation of cell polarity [44,58,59,60].  
Structural and genetic changes or the altered expression of receptors and adhesion molecules contribute to the development of diseases such as breast cancer, for example, through the dysfunction of the estrogen receptor [56]. In cancer, the loss of cellular connections due to the altered regulation of tight junctions (TJs) or E-cadherin affects disease development, metastasis, and prognosis [44; 60]. However, during metastasis, the loss of cell-cell contact alone is insufficient; the absence of this protein is also required [61]. Alterations in the expression of both E-cadherin and other components of desmosomes have been observed in various types of cancer, such as gastric and prostate cancer [44].  
Tunnel Nanotubes (TNT) and Tumor Microtubules (TM)  
The transport of chemical or biological materials as well as electrical coupling between cells over long distances is facilitated by the formation of tunnel nanotubes (TNTs) and tumor microtubules (TMs).  
Tunnel nanotubes (TNTs) are long and thin (50-200 nm) cytoplasmic extensions formed by F-actin filaments [44; 62; 63]. The open end of the channel connects the cytoplasm of cells, transporting cellular vesicles, mitochondria, miRNAs, viral particles, and proteins, and is involved in cellular homeostasis, tissue repair, cancer invasion, and resistance to therapies [44; 62; 63; 64].  
Tumor microtubules (TMs) are larger, richer in F-actin, and have a longer lifespan. They contain microcapsules and mitochondria, aiding in vesicle transport and local ATP production. Tumor microtubules contribute to the aggression, invasion, and therapeutic resistance of tumor cells, as observed in glioma cells [44; 65].
## 3 Research Methods
### Analysis 2: Ordinal Regression Analysis  
Ordinal logistic regression analysis was conducted to determine the influence of demographic and work-related variables on mobile security practice to supplement the path analysis results and provide more insight into employees' mobile security practices. Demographic and work-related variables are categorical in nature and could not be adequately examined using path analysis. The dependent variable, mobile security practice, was first transformed into six ordinal categories in order to facilitate the analysis. The transformation involved calculating the percentiles of the responses and reclassification of the responses into six categories, namely, 1--Never, 2--Seldom, 3--Sometimes, 4--Quite frequent, 5--Frequent, and 6--Very frequent. The continuous score was used for calculating the percentiles in the 17 th, 30 th, 50 th, 66 th, 83 th, and 100 th percentiles. Corresponding values received from the percentiles were used in categorizing the responses. Table 5 presents the data distribution of mobile security practice after the transformation. Average values of each respondent for work-related independent variables, past security experience, and perceived risk were calculated to be used in the analysis. The data distribution of work-related independent variables is depicted in Table 6.  
Ordinal logistic regression analysis that included likelihood ratio tests, parameter estimates, and tests of parallel lines were performed on the data. The likelihood ratio tests in Table 7 show the significance of each of the independent variables. Four of the independent variables have \(p\)-values that are less than 0.05, indicating there is a significant relationship between the variables and the dependent variables, namely, \"Gender, \"Job function\", \"Past security experience\", and \"Perceived risk\". Looking further into parameter estimates for these four independent variables in Table 8, the odds of mobile security practice for females (gender1) are 0.616 times lower than the odds of mobile security practice for males (reference group: gender0). Next, the odds of mobile security practice for someone whose work function was security and risk management (jobfunction7) are 4.246 times higher than the odds of mobile security practice for someone who works in sales and marketing (reference group: jobfunction1). Moreover, the odds of mobile security practice for someone who has experienced prior security incidents (incident1) are 0.665 times lower than the odds of mobile security practice for someone who has no known past security incident (reference group: incident0). Additionally, the odds of mobile security practice for someone who perceived that the risk of information security is quite
## 1 Introduction
[15] developed a fuzzy control method to drive RBPs based on speed modulations. This technique ensured an adequate perfusion by maintaining the mean aortic pressure whilst increasing the pulse pressure. In a different study, a pulsatility index algorithm was designed for the pump flow based on a proportional-integral (PI) FLC to drive RBPs. The study was numerically evaluated in vitro and in vivo, and the results showed that the controller was able to adequately maintain the body perfusion and prevent suction [16]. Although several modern and classical control methods have been developed and implemented for IRBPs, sensorless control methods for IRBPs are still emerging as essential in offering a long-term alternative treatment for patients with HF. Furthermore, estimating the flow and pressure presents additional features for implementing a physiological controller for these devices [17]. Therefore, this work aimed to design an intelligent physiological controller for IRBPs using a sensorless estimator. The control method was developed and implemented using a proportional-integral (PI) controller associated with an FLC system. The design method assumed that the aortic valve was totally closed to maintain the physiological perfusion in a safe operation mode for the IRBP. Therefore, the controller was utilized as a flow estimator to automatically adjust the physiological demand of the patient by tracking the desired reference flow.
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
## 3 A Gain for Query Reformulations
### A Similarity Matrix for Recall Prediction  
In the proposed context, we can think of an 'optimal' query as the one capable of combining all the diverse facets of the information need it represents. In order to estimate which query reformulation \(q\) is the closest to the unknown optimal one, we propose the following procedure:  
1. we define \(D_{q}\) as the set of documents retrieved by \(q\);
2. \(D_{i}=\bigcup_{q\in V_{i}}D_{q}\) as the set of all documents retrieved by _at least_ one reformulation \(q\);
3. \(\textbf{R}\in\mathbb{R}^{|V_{i}|\times|D_{i}|}\) as the matrix of rankings for the information need \(i\) where each row corresponds to a specific reformulation and each column to a document. The value of an element \(r_{k,d}\) of **R** is defined as \(|D_{q}|-\rho_{q,d}\) where \(\rho_{q,d}\) is the rank of document \(d\) retrieved by \(q\). **R** is at the end normalized with norm \(l2\).  
At this point, we want to build a similarity matrix to predict the impact in terms of recall that each reformulation will have on the retrieval. We compute the cosine similarity between each pair of rows in **R**, obtaining a symmetric matrix **S** where each row (or column) represents how a reformulation is similar to the others. We use the sum the \(k\)-th row (or column) of **S** to predict the importance of the k-th query; then, we order the query reformulations in decreasing order where greater values indicate a higher probability of retrieving more relevant documents. This measure describes how close each query is to the ideal \"centroid\" query that perfectly describes the topic.
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 3 Numerical Investigation
### Damage Detection with Modal Rotational Damage Indicators  
In order to have a comparison between the displacement mode shape and the rotational mode shape for damage detection, the changes of displacement mode shapes between the damaged and the undamaged states are firstly used to identify the crack. The color plots of these changes of the first four mode shapes are shown in Figure 4. For the first mode, the identified results contain not only the approximate locations of the two cracks but also the free edge area of the right side of the plate. The change of the second mode shape indicates the large damage areas between the two cracks along the x direction and the free edge area of the right side of the plate although the heavier damage location is obvious. For the third and fourth modes, both changes can indicate multiple possible damage areas including the real damage places, but still fail to flag out the exact damage locations. Figure 4 also indicates that the displacement mode shapes of lower modes are not sensitive to the damage locations.  
The color plot of the first four modal rotational damage indicators of both the x and y directions are listed in Figure 5, with the normalization by the maximum value of the damage indicators of both directions. It clearly shows that the modal rotational indicators of both directions can correctly flag out the damaged areas of the plate. The modal rotational indicators of the y direction are significantly larger than the results of the x direction with the detected damage areas more accurate. The reason can be attributed to the fact that the crack span direction is along the y axis, and the crack will cause vibration separation perpendicular to the crack span direction and the modal rotation will be increased significantly at the crack location. Therefore, the second and fourth torsional modes are the most sensitive to the damage locations. Their modal rotational indicators of the y direction exactly localized the crack positions and the span directions.  
Figure 4: The colored plot of changes of the first four displacement mode shapes. (**a**) The first mode. (**b**) The second mode. (**c**) The third mode. (**d**) The fourth mode.  
Figure 5: The rotational damage indicators (RDIs) colored plot of the first four modes. (**a**) RDIs of the first mode of the x direction. (**b**) RDIs of the first mode of the y direction. (**c**) RDIs of the second mode of the x direction. (**d**) RDIs of the second mode of the y direction. (**e**) RDIs of the third mode of the x direction. (**f**) RDIs of the third mode of the y direction. (**g**) RDIs of the fourth mode of the x direction. (**h**) RDIs of the fourth mode of the y direction.
# Brake Light Detection Algorithm for Predictive Braking
## 4 Results
### Accuracies  
The random forest algorithm was trained based on the images in the dataset. To show the benefits of the preprocessing stage, detection accuracies were compared with preprocessed images and raw images as the input for the random forest algorithm. All the reported accuracies are the average results of 15 full training iterations, meaning that the algorithm was fully trained 15 times and the average accuracy was calculated over the individual classification results on the test set. On average, the algorithm classified an image in 6.6 ms, whereas the YOLOv3 bounding box extraction required 28 ms. The result is from the dataset test set including 152 pictures. Evaluating the algorithm with the preprocessing stage led to an accuracy of 81.8%. The corresponding accuracy without preprocessing was 73.4%. These average accuracies can be seen in Table 5 as well as minimum and maximum over 15 training iterations. Min and max values were reported to show that the algorithm behavior is robust and consistent. Accuracies with preprocessing were found superior to those achieved with raw images.  
Figure 6: Visualization of the learned weights in image format (white color important, black less important).  
The acquired confusion matrices are shown in Figure 7, with the confusion matrix from the raw image classifier shown on the left, and the confusion matrix from the classifier with preprocessing shown on the right. In the reported confusion matrices, top left and lower right mean correct perception. The lower left and top right show false negative and false positive perceptions, respectively. The result shows that the algorithm with preprocessing performs well, although indicating a small bias towards false negatives. Nevertheless, the overall accuracy achieved with preprocessing is still higher. Direct comparison of the presented algorithm to other state-of-the-art algorithms would be beneficial, yet unfortunately the source codes of relevant algorithms are not available. However, Table 6 presents main operation conditions and key parameters of each study respectfully. Results are presented from each study and compared to the result achieved in this study. Unambiguous comparison of the accuracies is difficult, as each study has utilized different datasets, specific hardware, different weather conditions, and distances to the vehicles in front.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 3 Comparison of the Solutions
### Criteria for Comparing Solutions
#### 3.1.3 The Range of Coverage of the Telecommunication System  
The third parameter concerns the coverage area of the telecommunication system. It depends on the propagation environment (geography and constitution of the atmosphere on the installation site), the carrier's frequency, and the sensitivity of the receiver. The lower the sensitivity (minimum power of the received signal for proper restitution of information), the higher the transmitter-receiver distance. With the same aim of increasing the transmis  
Figure 12: The spindles to make captive balloons.  
sion distances and the covered area, the UHF waves (915 MHz) will be more suitable than a carrier frequency of 2.4 GHz or 5 GHz corresponding to Wi-Fi transmission [74]. On the other hand, the UHF frequencies do not make it possible to reach large data rates. For the sensor data transmission, limited in volume, UHF frequencies are very suitable. However, for video transmission, the Wi-Fi [75] is inevitably more convenient. In this specific case, the distance is limited. An alternative would be satellite transmission, the cost of which is excessive, especially in large volumes.  
In comparing the different solutions according to the coverage criterion, it is necessary, as indicated above, to consider the height at which the antennas will be placed for telecommunication systems operating in UHF. The aerial solution with Loon or Stratobus balloons has an advantage as the balloons evolve in the stratosphere at altitudes of 20,000 km. The area covered [76] on Earth is, therefore, quite large. In any case, it is much higher than that achievable with telecommunication towers. According to legislation, drones can fly up to 90 m without special authorization, giving them an advantage compared to towers. Optical fiber is apart. Network coverage will depend on the kilometers of fiber deployed and the openings made in certain places to access the signal or send a signal. In any case, significant lengths of optical fibers will be required to cover the same surface as the balloons or telecommunication towers.