esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study
## 3 Materials and Methods
### BERT Models  
This paper employs different BERT models tailored to the system design within the domain of English-to-Chinese and Chinese-to-English translation.  
For C-E translations, BERT models were tested in several variations, including bert-base-uncased, bert-large-uncased, bert-base-cased, and bert-large-cased, which were originally released for cased and uncased input text. The differences between these models primarily lie in their size (number of parameters) and whether they are case-sensitive or case-insensitive during tokenization.  
For E-C translation, the bert-base-chinese model was used for E-C translations. The model is pre-trained on a massive amount of Chinese text data, learning to predict masked words within sentences and understand the relationships between words in context.  
As the input length of BERT is limited to 512 tokens, it becomes a challenge to apply it to long texts in this research, where the texts to be translated are significantly longer than 512 tokens. Therefore, there is a need to preprocess the input text in a way that it can  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Number of Expert Translations** & **Number of Valid Translations** \\ \hline C–E Translations & 4 & 3822 \\ E–C Translations & 4 & 6825 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The corpus.  
be truncated or split into smaller segments of 512 tokens or less, while still preserving its semantic meaning.  
There are two common pre-processing strategies that can be used to limit the input text to 512 tokens. The first approach is text truncation, which involves cutting off the text after the first 512 tokens and dropping the rest. However, this can result in the loss of valuable information and the resulting vector may not fully represent the global information of the entire text. The second strategy is to use summarization to condense the translated text into a summary of no more than 512 tokens. This limits the length of the text to what BERT can handle. The summary text captures the main information of the original text, and only the information that is not critical to the text is lost. Therefore, the vector of the summary text can generally be seen as representing the semantic content of the entire text. However, this also fails to capture the global information of the entire text. Thus, we propose to apply the method of segmentation in this study, which involves comparing the mean embeddings of all the paragraphs in a text as a whole, where all the paragraphs are within 512 tokens. After pre-processing, the BERT model, acting as a text encoder, outputs a vector that captures semantic information from the input text.
## 2 Related Work
### Vision Transformer (ViT) and Attention Mechanism Module  
The attention mechanism provides the ability to focus on information clutter and filter information. It can find small object features in big data, thus analyzing and understanding complex detection tasks more effectively.  
Researchers have introduced different attention mechanisms into convolutional neural networks to improve their performance in vision tasks by learning attention weights to highlight important features and minimize noise effects. The compression and excitation [46] module is a local attention mechanism based on channel attention, which can calculate channel weights through the global average pooling layer and two fully connected layers. The active channel attention (ECA) [47] module is a lightweight local attention mechanism that focuses only on the channel size and learns the interaction between channels and feature representation capabilities through one-dimensional convolution operations. In addition, the coordinate attention (CA) [10] module is a local attention mechanism based on location coordinates and can be implemented by performing a spatial transformation network on the input feature map. It can learn importance weights for each location and capture location information and spatial relationships. However, while the convolutional block attention module (CBAM) [48] takes into account channel attention, it also considers spatial attention and performs attention operations in both channel and spatial dimensions. Different from these local attention mechanisms, the global attention module (GAM) [49] is a global attention mechanism that generates a global context vector by weighted aggregation over the entire input sequence to capture the key information of the entire sequence.  
As a result, the implementation of an attention mechanism in convolutional neural networks can provide greater representation ability and adaptability to the model. However, selecting an appropriate attention mechanism requires taking into account many factors, such as the application scenario and the computational load of the model.
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 2 Materials and Methods
### Pipeline Design
#### 2.1.2 Exposure Segmentation (ES)
We experimentally tested various threshold values to determine the optimal ones and visually evaluated the resulting masks to assess their accuracy in identifying over- and underexposed regions. Based on this evaluation, we found that a threshold value of 0.9 for brightness and 0.1 for saturation was effective in detecting overexposed regions, while a threshold value of 0.1 for brightness was effective in identifying underexposed regions. Examples of masks generated using different thresholds are presented in Appendix A. Figure 5 highlights some results of the ES module.
## 4 Implementation of the LHM-N Model
### Simulation Experimental Setup  
Table 2 presents the parameters for the simulation of the LHM-N model implementation and PSFE algorithm. The model is experimented with and compared with the conventional QAM protocol. The performance efficiency of the implemented model is evaluated based on the following instances:  
1. Evaluation with respect to BER and SNR over a Rayleigh fading channel.
2. Evaluation with respect to throughput and SNR over a Rayleigh fading channel.
3. Evaluation with respect to bit error rate (BER) and latency over a Rayleigh fading channel.  
Further, the PSFE algorithm is experimented with and compared with TCP Reno and TCP Cubic algorithms for packet forwarding. TCP Reno uses slow start and fast retransmit, including packet loss, as criteria to detect network congestion during packet transmission. TCP Cubic uses a cubic function of time from the inception of the last packet loss with the inflection point set to the window prior to the congestion occurrence during packet transmission. These algorithms are compared with the PSFE algorithm in terms of packet forwarding to the device interface.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Trellis State** & \(g_{o}\) & \(g_{1}\) & \(g_{2}\) & \(g_{m}\) & \(d_{free}^{2}\) & **Asymptotic Gain (dB) for 16-QAM** \\ \hline
4 & 5 & 2 & - & - & 4 & 4.4 \\
8 & 11 & 2 & 4 & - & 5 & 5.3 \\
16 & 23 & 4 & 16 & - & 6 & 6.1 \\
32 & 41 & 6 & 10 & - & 6 & 6.1 \\
64 & 101 & 16 & 64 & - & 7 & 6.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal polynomial generators for a good TCM encoder.
###### Abstract
Deep learning techniques have shown promising results in automating the segmentation of pulmonary nodules, but the availability of annotated data is often limited in the medical domain. In this context, the combination of generative adversarial networks (GAN) and SegNet has emerged as a potential solution for generating additional training data and improving the segmentation accuracy of pulmonary nodules in CT images. In this article, we present a study that investigates the performance of a GAN-enhanced SegNet model for the automated detection and segmentation of pulmonary nodules in CT images. The proposed model was evaluated on a publicly available dataset and compared with other deep learning models in terms of accuracy and AUC. The results demonstrate the effectiveness of the proposed model in improving the segmentation accuracy and reducing the need for large amounts of manually annotated data. The study provides valuable insights into the potential of GAN-enhanced SegNet for improving the accuracy and efficiency of lung nodule segmentation in CT images, with potential applications in clinical practice and research. The performance comparison was tabulated. Finally, the receiver operating characteristic (ROC) curve of SegNet while training the model with GAN and without GAN was also investigated. The area under the curve (AUC) for SegNet with GAN was found to be 0.851 while that of SegNet was 0.76. The proposed method stood ahead in all respects considered. The proposed work, with the help of available data sets, managed to generate sufficient amounts of data with the help of a GAN. Perfect samples were considered here for the generation of data. Hence, the data generated assured the quality that was used for training the network. For training the GAN, 440 images and augmented images were used. A total of 1,001,000 images were generated and used for training the SegNet architecture. The segmentation showed improved accuracy in the case of benign samples, and it had a perfect shape compared to malignant samples. Through the evaluation of the parameters, it is clear that the GAN along with image augmentation shows better performance.
## 2 Materials and Methods
### Experimental Setup for Sensor Characterization  
The experimental setup employed for the sensor characterization is presented in Figure 2. It primarily includes an appropriate source meter and a mini-CNC device. Specifically, a commercial CNC Engraver (Woodpecker CNC 3018,TOOLOTS, CA, USA) was modified to provide a suitable platform for sensor support and strain application. This was achieved by replacing the engraving spindle with a bracket of appropriate geometry  
Figure 1: (**a**) Strain gauge design; (**b**) 3D printed devices.  
and attaching a second one fixed to the frame of the engraver, both 3D printed; the specific modification is presented in Figure 2. The sensor was placed between the two supports; during characterization, consecutive steps of displacement with an accuracy of 0.1 mm were applied to the movable bracket, by appropriately programming the engraver (G-code). Thus, one edge of the sensor was fixed while the other edge was displaced on the \(x\)-axis, as shown in Figure 3, causing the device to bend along the z-axis; note that movement is not permitted on the \(y\)-axis. The change in the initial resistance of the bent sensing element due to the piezoresistive properties of the carbon nanotubes-enriched filament was recorded by applying a constant current to the sensing element and measuring the corresponding voltage through an appropriate source meter (Kethley 2401, KEITHLEY, Solon, OH, USA).  
The investigation of the properties and performance of the developed sensor included the measurement of key characteristics that are considered to be significant [27] and Scanning Electron Microscope (SEM) characterization of appropriate printed samples to investigate the morphology of the structure, homogeneity, and chemical composition of the resulting piezoresistor. In more detail, the characteristics investigated were the following:  
* Gauge Factor  
Note that the gauge factor was calculated separately for each step of displacement, as well as for the total (maximum) range of displacement applied, employing the equation \(\mathrm{GF}=\frac{\frac{\mathrm{d}\theta}{\mathrm{d}\tau}}{\varepsilon}\), where  
\(\mathrm{R_{0}}\)  
the initial gauge resistance in non-strain condition;  
\(\Delta\mathrm{R}=\mathrm{R_{F}}-\mathrm{R_  
* Hysteresis The change of the initial resistance (R\({}_{\text{o}}\)) was calculated for every cycle of compression and decompression; the average and maximum hysteresis were recorded.
* Repeatability  
The changes in all of the above properties were recorded after applying a series of successive strain cycles to the same sample.
# EXOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
## 3 Proposed Method
### EYOLOv3: Optimized YOLOv3 Model
#### 3.3.3 Improved NMS Algorithm  
The traditional NMS only selects the prediction box with the highest confidence in an area, so the prediction boxes whose overlapping area with the prediction box with the highest confidence is greater than the threshold will be directly suppressed, then rise the missed detection. We set the confidence for each prediction box based on the overlapping area, which can reduce false positive samples and effectively detect the floating objects with high overlapping simultaneously. The improved NMS algorithm is as follows:  
* First, sort the prediction boxes according to their confidence scores, and select the detection box \(M\) with the highest score;
* Traverse the remaining prediction boxes of the object. If the overlapping of the prediction box \(b_{i}\) and \(M\) is greater than the IOU threshold, the attenuation function is used to adjust the score of the prediction box. In order to prevent score faulting,  
\begin{table}
\begin{tabular}{c} \hline
**Anchor Boxes** \\ \hline Anchor boxes = (14,8) (18,19) (28,21) (37,10) (48,25) (57,31) (91,33) (148,51) (262,109) \\ \hline \end{tabular}
\end{table}
Table 2: Anchor boxes.  
Figure 6: Aspect ratio distribution and the clustering process of the ground truth boxes of floating. (**a**) Aspect ratio distribution, (**b**) clustering result of 10 iterations, (**c**) clustering result of 30 iterations, (**d**) final clustering result.  
the Gaussian weighted attenuation function is used here. The calculation formula is shown in Formula (4): \[S_{i}=S_{i}e^{-\frac{i\omega(M_{i})^{2}}{\sigma}},\ \forall b_{i}\
otin D\] (4) where \(D\) represents the set of high score detection boxes that is reserved.
* Finally, select the box with the highest score again from the rest and set it to \(M\), repeat the above, and successively reduce the scores of the prediction boxes with high overlapping, sort the final scores from high to low in sequence, and select all the prediction boxes with higher score than the threshold as the final.  
The above process not only completes the non-maximum suppression to filter the redundant boxes, but also improves the detection accuracy of the model while there are multiple floating objects with higher overlapping area.
## 1 Introduction  
Corneal tomography is a validated technology for evaluating the changes occurring in the corneal morphology of keratoconus (KC) disease [1], which allows for the control of the geometric decompensation driven by the asymmetry present while this disease progresses [2, 3]. However, no agreement has been reached about the relative importance of the indices and technologies to be used to detect which patients may suffer post-surgical corneal iatrogenic ectasia when evaluating patients' suitability for refractive surgery [4, 5, 6, 7, 8, 9, 10].  
Some studies have evaluated the objective efficiency of different indices for early stages of this disease in an individualized way. These studies are generally related to values of curvature, elevation and pachymetry [11], and to patterns based on the combination of indices that come from the same [12; 13; 14; 15; 16] or different technologies [7; 9; 17; 18; 19; 20]. The evaluation of these patterns is based on subjective analysis [7; 21], which is critical to gain a better discrimination capability according to clinical experience. Nevertheless, there are still significant discrepancies in terms of the relative value of the results obtained in their evaluation of the risk probability associated with disease development.  
In KC, geometric decompensation occurs and causes disease progression [11], which means increased high-order optical aberrations and high irregular astigmatism values [22], whose main refractive sign is the impossible complete optical compensation of their anteropia by spherical-cylindrical lenses. Consequently, corrected visual acuity will diminish compared to individuals with no corneal pathology [23]. The scientific literature contains many classifications for the degree of KC severity [24; 25; 26; 27; 28; 29; 30; 31]. However in clinical practice, it is difficult to handle the many indices on which these classifications are based for proper optical-geometrical evaluations of disease progression. From an optical point of view, patients show a deteriorated spectacle-corrected visual acuity during disease development, insofar as their visual performance worsens as the degree of KC severity progresses. Following this criterion, a classification of disease stages has been developed [23; 32]: the so-called RETICS grading. This grading takes into account four geometrical parameters (Internal Astigmatism, RMS Coma-Like, Q8 mm and Pachimetry) and a functional one (Corrected Distance Visual Acuity (CDVA)) to establish five KC degrees: I to V.  
This study develops and validates a Graphics User Interface (GUI) that combines two automatic learning models based on a set of independent variables with two aims: defining a new concept of risk probability associated with the development of early-stage KC and classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale to assist ophthalmology professionals in disease management.
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 4 Implementation
### Defect Geometry Generation and Alignment  
For semantic segmentation of inspection images, a retrained TernausNet16 [56] was used. This CNN is based on the UNet architecture with the 16 layers of the Visual Geometry Group (VGG) network architecture as encoder. Similar to the original TernausNet16, K-Folding cross-validation was used, albeit for 5 folds with 6 batches and 15 epochs per fold for transfer learning on the concrete structure spalling and crack (CSSC) database [45; 46], from which 715 relevant annotated images of 768 \(\times\) 768 pixels were selected. This allowed the learning process to be performed with the limited number of images available. The mean performance metrics of the retrained TernausNet16 model are detailed in Table 1.  
The camera calibration, which consists of the intrinsic parameters and distortion coefficients, is calculated by processing a set of photos with checkerboard patterns through OpenCV [57]. SfM is used to generate the point clouds based on the inspection photos containing defects. The OpenSfM library in Python [58] has the advantage of generating noticeably better quality of point clouds in comparison with other alternatives. Additionally, its commands for 3D reconstruction can be run seamlessly in a background process. Backwards projection of an image additionally requires the estimated pose of the camera, the undistorted image synthetically generated based on the camera calibration parameters previously estimated, and the depth map of said image, that could be retrieved from the generated outputs of the 3D reconstruction.  
Aligning the defect and generating the defect geometry require the point cloud of the undamaged building or object. This is achieved by modeling the structure or object in Revit [14], export this model into IFC, import the IFC into Blender [59] for triangulation, and final generation of the dense point cloud. Next, the point cloud from the SfM algorithm and the point cloud generated based on the BIM model are aligned using ICP [50; 51].
## 2 Materials and Methods  
### Theoretical Analysis of the Magnetoelasticity-Based (MB) Sensor  
The MB sensor [23; 24] (Metglas 2826 MB, Hong Kong, China) was purchased from Hong Kong, China, as a continuous ribbon of 11.7 mm width and 28 \(\upmu\)m thickness containing 40% Fe, 38% Ni, 18% B, and 4% Mo (Fe\({}_{40}\)Ni\({}_{38}\)Mo\({}_{4}\)B\({}_{18}\)). The magnetoelastic coupling factor reached 0.98 and a magnetostriction of 12 \(\times\) 10\({}^{-6}\)[25; 26]. The physical and magnetic properties of the material are presented in Table 1. This material has special characteristics such as low resistivity, high permeability, and a high magnetoelastic coefficient, and is much thinner and cheaper than Terfenol-D and galfenol, which gives it a wide range of applications [27]. The principle is based on the inverse magnetoelastic effect (Villari effect)--when an MB sensor is subjected to tensile or compressive stress under an external magnetic field, its permeability changes accordingly. The variation in the permeability can cause a change in spatial magnetic field.  
Magnetoelasticity refers to the coupling of magnetic and elastic/mechanical energies within a material. For the MB sensor, the reverse situation (i.e., Villari effect) also holds true in this study because an applied stress changes the susceptibility \(\chi\)[26; 28; 29]:  
\[\sigma=\frac{F}{A}, \tag{1}\]  
\[\chi=\frac{{M_{s}}^{2}}{2 KU-3\lambda_{s}\sigma}. \tag{2}\]Here, \(\sigma\) is the stress, \(F\) is the external force (50 and 100 N in this study), \(A\) is the force area of PAB, \(M_{\mathrm{s}}\) is the magnetization density, \(K\) is the surface current density (current per unit length), \(U\) is the internal energy, \(\lambda_{s}\) is the saturation magnetostriction, and \(\chi\) is the susceptibility.  
The susceptibility of the material is directly related to its magnetic permeability by [26]  
\[\mu=\mu_{0}(1+\chi), \tag{3}\]  
where \(\mu\) is the permeability, and \(\mu_{0}\) is the vacuum permeability.  
In this study, the stress (\(\sigma\)) of compressive force working on the MB sensor causes a change in the permeability (\(\mu\)). Moreover, the degradation of PAB can cause a change in the force area of PAB (\(A\)), and then lead to the change in stress (\(\sigma\)). Hence, the relationship between degradation behavior of the PAB and the output power of the MB sensor was obtained to express the degradation behavior of the PAB in vitro. The Villari effect describes the magnetization change induced by a mechanical load. The response of the flux density with respect to the input stress is the principle of the MB sensor, as shown in Figure 1. Importantly, the stress range between phase M and phase N in Figure 1 was allocated to monitor the degradation behavior of PAB in this study.
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
###### Abstract  
Exosomes are a subpopulation of extravascular vesicles with a diameter of 30-150 nm. They are cellular-communication mediators, often reaching very distant organism tissues. Information is transferred by exosomal cargo, composed of a wide variety of macromolecules such as nucleic acids, proteins, and lipids. Exosomes possess natural specific cell targeting properties that are desirable in designing targeted macromolecules (DNA and RNA) and drug delivery systems (doxorubicin, paclitaxel, and taxol). In this context, exosomes can be defined as bio-derived drug transporting and protecting devices for the treatment of bacterial (toxicplasmosis and salmonellosis), viral (AIDS and hepatitis B), and cancer (lung, pancreatic, colon, brain, and breast) diseases. Extensive research proves that exosomes' natural cargo can double-act, both increasing and decreasing the disease severity. In this case, the exosomes need to be prepared, namely, their origin and their cargo need to be screened and known. Thus, appropriate methods for intact and price-effective exosome isolation are needed with further exosome properties description. Among many utilized isolation methods, the most common are ultracentrifugation, polymer-based precipitation, and affinity precipitation-isolation systems, but novel microfluidic methods compromising high efficacy and purity are being developed. In this review, we state the current knowledge and trends in exosome-based drug delivery systems.  
exosomes; drug delivery systems; targeted drug delivery; exosomes properties; exosomes composition; exosomes loading; exosomes isolation methods; exosomes cancer treatment