esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A new reliability estimation method based on partial multiplicative dimensional reduction is proposed for probabilistic and non-probabilistic hybrid structural systems. The proposed method is characterized by decorrelating interval input variables from random input variables using the partial multiplicative dimensional reduction method in conjunction with the weakest-link theory. In this method, the failure statistics of the original performance function are equivalent to a statical chain of two elements, in which one of the two elements represents the failures due to random input variables and the other represents the failures due to interval variables  
practical problems [15, 16]. At this point, it is noted that the hybridization of two types of variables is one of the most challenging problems. For the treatment of interval variables, scholars have proposed methods such as the interval angular vector [17] and the Taylor expansion method [18]. Recent studies have suggested using decorrelation methods for the solution of such a difficult problem. [PERSON] et al. [19] utilized the multiplicative dimensional reduction method (M-DRM, Toronto, ON, Canada) [20, 21, 22] for the decorrelation of each variable. [PERSON] et al. [23] employed the Taylor series expansion in combination with M-DRM in order to obtain better decorrelation results. However, a total decorrelation of each input variable through M-DRM would be necessary since there exist many available methods that can deal with cases of uniformly random variables or interval variables [24]. Further, the reliability estimation obtained from a total decorrelation of each variable is usually too conservative.  
Aiming at providing accurate reliability assessment for satisfactory probabilistic and non-probabilistic hybrid structural systems, this paper proposes a novel method for probabilistic-interval hybrid structural reliability estimation based on a partial M-DRM and the Weakest-link theory. In this method, the original performance function is statistically equivalent to a statical chain of two elements, in which one of the two elements represents the failures due to random input variables and the other represents the failures due to interval variables. Evidently, since the failure of any element in the chain causes the failure of the entire system, the system's reliability is computed as the product of each element's reliability. The rest of this paper is structured as follows: Section 2 presents the theoretical background; Section 3 proposes and validates the method; Section 4 compares the proposed method to other methods; Section 5 studies the error-related properties of the proposed method; and Section 6 demonstrates the engineering applications of the proposed method.
## 2 Methods
### Convolution
#### 2.3.2 Dilated Convolution  
Dilated convolution, alternatively referred to as convolution with dilation rates, represents a pivotal augmentation to traditional convolutional operations [36]. Its novelty lies in the introduction of predetermined \"intervals\" within the convolutional kernel, enabling an enlargement of its receptive field without necessitating an increase in parameter count. Given the high-resolution and intricate details inherent in remote sensing imagery, we employed dilated convolutions in the intermediate layers of our network model. This strategy aims to ensure the expansive contextual information is captured during deep feature extraction, a procedure paramount for distinguishing minute roads from other complex structures.  
Figure 2: Schematic diagram of depth-separable convolution operation. The red dotted box in the figure represents the schematic diagram of depth convolution, and the green dotted box represents the schematic diagram of the point-by-point convolution structure.  
Multiple considerations underpin our choice of dilated convolution within the model. Primarily, objects and scenarios within remote sensing imagery frequently exhibit multiscale attributes. Employing dilated convolution aids the model in adeptly capturing such multiscale nuances. Moreover, dilated convolution permits the assimilation of vast contextual scopes, transcending merely local features--an aspect crucial for tasks such as differentiating roads from their surrounding environments. Additionally, dilated convolution offers a strategy to efficaciously broaden the receptive field without intensifying the model's computational demand. In essence, dilated convolution furnishes our model with a harmonious balance between fine-grained detail and a broad field of view, which is indispensable for processing high-resolution remote sensing images.
## 6 Experiments Results and Discussion
### Discussion  
#### 6.4.1 Compare the Impact of the Features in Our Model  
In this research, we conducted a final experiment where we represented the system using the chosen features with different combinations of all considered features. To establish a baseline for comparison, we utilized the BLSTM model and its associated hyper-parameters. The outcomes of this experiment are presented in Table 10. The best features experiment percentage is bold, while the worst experiment result is underlined, as shown in the table.  
As shown in Table 10, different combination features were used separately in the first three experiments, and as clearly shown, MFCCs are provided high accuracy compared to others. However, there is not much difference if we compare the third and fifth experiments, where pitch and energy information were added to MFCCs. The reason is that MFCCs contain enough information.  
From those experiments, we came up with the following observations:  
1. The primary outcome is pitch cannot be associated with MFCCs because this will degrade the accuracy dramatically. See Exp_3, 4, 5, and 7.
2. MFCCs are superior in accuracy compared to combined or alone pitch and energy. See Exp_1 and Exp_2.
3. The pitch will cause MFCC accuracy degradation, but on the other hand, energy will enhance MFCC accuracy. See Exp_5 and Exp_6.  
From Table 10 and the above explanation, we can see that if unnecessary features are added to the feature vector, then the automatic system output is degraded because of the burden of redundant (and not useful) input data. For example, what we can see from the table is from Experiment 5 and Experiment 6. In Experiment 6, the accuracy dropped when only pitch information was added.
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 3 Methodology
### Process of Training and Predicting  
Traditional semantic Deep Learning methods come from definition of computer science serval years ago and then adopted into engineering area until then. Input images of these methods tend to be lower resolutions because of the hardware limitations. However, with the development of hardware especially cameras, GPUs and monitors, 4k or even higher resolution images are possibly taken and more conveniently and fluently shown to people. Due to Damage Segmentation is pixel-level damage detection, some improvement for input data should be taken into consideration to use these Deep Learning methods. Here are two methods for Damage Segmentation and the processes of training is shown as Figure 6. One method is called Squashing Segmentation and to input full images which are squashed into 224 x 224 pixels before training process, whatever original sizes are. This is traditional semantic segmentation for object. Notably, this method loses a lot of characteristics information of small objects during process of squashing but remain relationship information between objects. Since cracks are not large enough objects in high resolution images, and just accounts less than 2% of pixels in one image, another method called Cropping Segmentation is adopted to input cropped images with size of 224 x 224 pixels for training. During cropping processes, width and height of small pieces of images are same as stride as 224 pixels, and the left parts less than 224 pixels in width and height of original images are discarded. The schematic of cropping is shown as Figure 7. This method keeps original size of damage pixels while training, avoiding the loss of characteristic information of damages during squashing process. To compare the results of different training methods or input data size, each data set runs 50 epochs. All the described works is based on workstation installed with GPU (NVIDIA Geforce GTX 1080). All the training processes are performed on Ubuntu system and coded by Python 3.6. Virtual environment was established by TensorFlow 1.2 and Keras 2.0.  
After training different VGG-Unet models under different methods, the next step to do is predicting damages from images of test data set. To compare the performance of different VGG-Unet models, here each data set chooses the best FCN model from 50 epochs to evaluate. The testing process for VGG-Unet model training from Squashing Segmentation is similar as training process, and it inputs squashed images before prediction. After predicting and get outcomes, these predicted images are resized as original images before squashing with same size. And testing process of the other method, Cropping Segmentation, is to input cropped images, whose size is 224 x 224 pixels, for prediction. The outcomes after predicting are stitched back to full images. The processes of prediction are shown as Figure 8.  
Figure 6: The training process of Damage Segmentation.
## 3 Model Problems and Numerical Results
### Model Problem 1
#### 3.1.3 Mesh Discretization  
The beam model is discretized by a succession of structured nested quadrilateral meshes. We consider two different types of mesh discretization: h-refinement and p-refinement. MLMC and MLQMC will be applied to both refinement schemes. These combinations result in h-ML(Q)MC and p-ML(Q)MC. For h-ML(Q)MC, the coarsest mesh is chosen such that the beam is discretized by at least four elements over the height and twenty elements over the length. Similarly, the discretization for p-ML(Q)MC consists of four elements over the height and twenty elements over the length. This discretization remains the same for all levels. Table 1 lists the number of elements (Nel), degrees of freedom (DOF), the element order per level (Order), and the number of quadrature points per element (Nquad) for h-ML(Q)MC and p-ML(Q)MC. The mesh hierarchies are shown in Figures 1 and 3.
## 2 Materials and Methods
### Synthesis of the Electrode Materials and Their Standard Characterization
Chemical Electrode/Electrolyte Compatibility Studies  
The electrolyte materials, chosen for the study in contact with the developed electrode materials, were obtained and characterized in the framework of our previous studies using the following synthesis techniques, described elsewhere: Ce\({}_{0.8}\)Sm\({}_{0.2}\)O\({}_{1.9}\) (SDC) by a solid-state reaction method [53]; BaCe\({}_{0.80}\)Sm\({}_{0.19}\)Cu\({}_{0.10}\)O\({}_{3-\delta}\) (BCSCuO) [54], BaCe\({}_{0.80}\)Gd\({}_{0.19}\)Cu\({}_{0.10}\) O\({}_{3-\delta}\) (BCGCuO) [55], and BaCe\({}_{0.52}\)Zr\({}_{0.3}\)Y\({}_{0.1}\)Yb\({}_{0.1}\)O\({}_{3-\delta}\) (BCZYYbO) [56] by a citrate-nitrate combustion method. The electrolytes' unit cell parameters are given in Table S2. The obtained powders were shaped into disks using semi-dry pressing at 10 MPa with the addition of a polyvinyl butyl binder and then sintered at 1600 \({}^{\circ}\)C for 5 h (SDC) and 1450 \({}^{\circ}\)C (BCSCuO, BCGCuO, BCZYYbO) for 5 h in air using a Nabertherm LHTCT 01/16 furnace (Lilienthal, Germany) with a heating/cooling rate of 150 \({}^{\circ}\)C h\({}^{-1}\). The sintered substrates were polished using a diamond 60/40 instrument down to 1 mm in thickness. The relative density of the sintered electrolyte substrates, calculated from their weight and dimensions, was no less than 95%. To study the compatibility of the potential cathode materials NCNCO02 with the electrolyte materials of SDC, BCSCuO, BCGCuO, and BCZYYbO, the related electrode/electrolyte powders were mixed in a weight ratio of 1:1 and thoroughly mixed in an agate mortar with the addition of ethanol. The resulting mixtures were sequentially annealed at temperatures of 900, 1000, and 1100 \({}^{\circ}\)C for 15 h at each stage. After each annealing step, the phase composition of the mixtures was analyzed using XRD.
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
###### Abstract  
Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved by formulating language modeling of low-resource languages as a domain adaptation task using transformer-based language models pretrained on corpora of high-resource languages. Our novel cross-lingual post-training approach selectively reuses parameters of the language model trained on a high-resource language and post-trains them while learning language-specific parameters in the low-resource language. We also propose implicit translation layers that can learn linguistic differences between languages at a sequence level. To evaluate our method, we post-train a RoBERTa model pretrained in English and conduct a case study for the Korean language. Quantitative results from intrinsic and extrinsic evaluations show that our method outperforms several massively multilingual and monolingual pretrained language models in most settings and improves the data efficiency by a factor of up to 32 compared to monolingual training.  
cross-lingual; pretraining; language model; transfer learning; deep learning; RoBERTa 2021 11 1974 10.3390/app11051974
## 2 Materials and Methods  
### Study Area and Sampling Design  
In this study, an extensive survey was conducted of the Bhatt al-Arab River and its tributaries in Basra, Iraq. Illustrating an area spanning approximately a 300 km distance, Figure 1 shows the twelve different canals and rivers that were covered. To obtain a detailed understanding of the region of the survey stations, aerial survey flights were carried out using two DJI series drones from November 2022 to May 2023 (see Appendix A). The data collected from these flights consist of approximately 1000 images and videos, allowing for a detailed examination of the riverbanks and the presence of various pollutants.  
The twelve canals and rivers were chosen based on factors such as geographic distribution, accessibility, variety of environmental conditions, and known pollution hotspots (see Figure 2 for the pollution density distribution in Basra canals), along with the importance for ecosystem and human activities. This selection aimed to provide a representative sample of the pollution scenario across the Shatt al-Arab River system, allowing for a comprehensive understanding of pollution sources and their impacts on the region.  
Figure 1: Basra’s main canals and rivers covered in this study.  
Figure 2: Trash density and oil spills in Basra’s canals. The dark lines represent highly polluted parts of Basra’s canals.  
In the context of Iraq's climate, particularly during periods of sunny, clear skies and partly cloudy conditions, the UAV data collection was effective. Iraq experiences a dry and arid climate, especially in regions like Basra where the Shatt al-Arab River is located. During periods of sunny weather with clear skies, the visibility for UAV flights was optimal, allowing for high-resolution imaging and video capture. These conditions provided excellent lighting for capturing detailed images of the survey area, enhancing the quality and accuracy of the collected data. Partly cloudy conditions, depending on the extent of cloud cover, introduce some variability in the lighting conditions. However, during data collection, the visibility remained adequate, with partly cloudy conditions being still suitable for UAV operations as there was no rain. Overall, sunny and clear weather conditions are preferred for UAV data collection in Iraq, ensuring optimal visibility and high-quality data capture for the study of the Shatt al-Arab River.  
The findings of the survey revealed the presence of wastewater, oil spills, debris, and other forms of pollution in the Shatt al-Arab River and its tributaries. These pollutants pose a significant threat to the ecosystem of the river and its surrounding areas. This information is crucial for the development of effective environmental management strategies in Basra, as it provides an excellent understanding of the pollution sources and other environmental challenges faced by the region. These observations can lay the groundwork for additional research and planning aimed at preserving and restoring the ecological health of the river and its surrounding areas.
## 3 Results and Discussion
### Swelling Studies  
Swelling studies of all samples were performed by immersing the scaffolds in normal saline solution at 37 \({}^{\circ}\)C under stagnant conditions. Four dry samples were studied for each formulation in a period of 3 days. The results are shown in Figure 4a. In scaffolds containing alginates and methyl cellulose, the maximum equilibrium swelling (S%) occurred at 150 min and was 850%, but immediately, the scaffolds began to disintegrate. Scaffolds containing TMC reached a higher degree of swelling (1000%) at 2 days and remained stable until 3 days. Furthermore, macroscopically, the scaffolds remained stable for at least 20 days. Therefore, it is clear that the incorporation of TMC in the alginate/MC ink significantly increased the stability of the printed scaffolds in normal saline solution. This can be attributed to the complexation between the negatively charged alginate and the positive charged TMC [11]. The results are very interesting, and the presence of TMC should be further evaluated since calcium alginate hydrogels are very sensitive to degradation, especially in solutions containing phosphate ions [32].  
Swelling studies were also conducted on composite samples. The results are shown in Figure 4b. It was observed that scaffolds loaded with glass also remained stable after 3 days. The maximum swelling degree was equal or lower in comparison with the Alg/MC/TMC sample. It should also be noted that the samples maintained their structural integrity. The addition of fillers in alginate hydrogels enhanced their structural stability. The 3D-printed alginate hydrogels containing 0.5% and 1% ZnO improved the structural stability in comparison with pure alginate gels until 28 days after immersion in PBS solution [33]. Furthermore, the addition of graphene oxide at concentrations lower than 1 mg/mL  
Figure 4: Swelling behavior of pure polysaccharide scaffolds (**a**) and composite scaffolds together with the sample containing TMC for comparison (**b**).  
improved the structural stability of 3D-printed alginate constructs after incubation in culture medium for 10 days [34].
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 6 Discussion  
The main objective of this study was to compare the performance of controllers through graphs and performance indices associated with trajectory planning for the robot, how to avoid obstacles, and ensure that the path does not exceed the voltage and torque limitations of the actuators. Computed Torque Control is an effective technique that provided good results, achieving smoothness in movement.  
The PI-PD fuzzy and MPC controllers handled abrupt changes at vertices during trajectory tracking acceptably, exhibiting moderate vibrations. All controllers showed good precision in controlling the desired trajectory.  
Additionally, a Scrobot robot model was developed in Simscape to visualize its movements through animations and verify its displacement in the XYZ plane using the study trajectories. This work contributes to the visualization of the behavior of real trajectories in the robot's space-time in the presence of external disturbances and abrupt changes according to the nature of the selected trajectories.  
In the performance graphs of the three controllers, high precision in trajectory tracking is observed. This is confirmed by their RMS indices, which yield RSDs of approximately 1 cm in the XYZ coordinate axes, while the IAs reach values close to one.  
The PID controller remains widely used globally, and as we can see from the obtained results, using it with Gain Scheduling achieves excellent trajectory tracking.  
Figure 34: 3D graph of trajectory A, evading from above.  
In real-world situations, trajectory planning is carried out to minimize accidents and avoid obstacles. In this regard, the proposed trajectories are presented in three-dimensional graphs, through which it is verified that the performance of the Computed Torque PD controller is excellent.  
Table 14 summarizes the weaknesses, strengths, and complementary characteristics of the controllers, extracted from this study.