esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Design  
The sample consisted of 140 patients grouped into 2 study groups. The first group (BG) consisted of 70 patients in orthodontic treatment with fixed multibracket appliancesusing the MBT technique and a 0.022\" slot (Diamond Plus(r), Cimbis Orthodontics, Madrid, Spain), and the second group (IG) consisted of 70 patients in orthodontic treatment with removable aligners (Invaslign). All patients were treated by the same specialist during the 2019-2020 period. The study participants were selected from a non-probability convenience sampling with efforts made to balance the sample in age and sex between each orthodontic treatment group.  
Previous research was considered when calculating the sample size. With an error rate of 5% and a confidence level of 95%, the minimum sample size required was considered to be 67 patients for each treatment group. Therefore, the final sample consisted of 140 participants.  
The inclusion criteria were the following: adult patients over 18 years of age; patients with permanent dentition; patients without previous orthodontic treatment; patients with a negative bone-dental discrepancy between \(-6\) and \(-2\) mm in both arches (TSALD) [21]; without missing teeth except for the third molars; skeletal class I or slight skeletal classes II and III (ANB between 0 and 5 degrees) [22]; and patients for whom an IPR between 0.1 and 0.5 mm per tooth was predicted in the treatment plan.  
The exclusion criteria were as follows: patients with physical or mental disabilities; patients diagnosed with psychological problems such as anxiety or stress; patients with orthodontic-surgical treatment; patients being treated with analgesics, anti-inflammatory, anxiolvtics and/or antidepressants; patients with dental caries; patients with gingival or periodontal pathology; pregnant patients; and those with auxiliary orthodontic appliances.  
Before beginning orthodontic treatment, the treatment protocol was explained to the patient, both verbally and via written literature, as well as their voluntary and impartial inclusion in this study. Once a patient gave their written consent, orthodontic treatment began. Due to the design of this study, blinding of either patient or operator was not possible.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 1 Introduction  
Device-to-device (D2D) communication is poised as the perfect candidate for 5G and beyond (B5G) communication in the mmWave band due to its inherent advantages over other contemporary technologies. This is due to the larger bandwidth, increased data rate, ultra-low latency, and lesser interference in the unused spectrum [1]. Furthermore, it enhances the spectral (SE) and energy efficiency (EE) of the network by setting up a direct information transfer among the devices without directly controlling the base station (BS). However, setting up D2D communication in the mmWave band of 28 GHz has some challenges associated with it [2]. Signal propagation in the mmWave band undergoes extensive degradation due to interference and blockages such as atmospheric attenuation, attenuation due to precipitation, blockage by humans, blockage by foliage, etc. This significantly reduces the strength of the received signal at the user, which subsequently reduces the signal-to-interference-plus-noise ratio (SINR). In addition to this, this decrease in the SINR lowers the data rate of the overall network, which results in a decrease in the SE and EE [3].  
Relaying plays a pivotal role in extending the coverage area of D2D communication in mmWave networks. As the distance between the D2D users increases or if a D2D user is located at a far edge of the cell, the signal strength also drops extensively, which further diminishes the throughput of the D2D user. To solve this problem, the relaying technique is applied effectively to maintain the SINR above a certain predefined threshold level and to enhance the quality of service (QoS) of the communication network. In comparison to direct communication, relaying has more advantages in the case of mmWave networks such as increased throughput, increased network coverage, better capacity, etc. [4]. Among all relaytypes, decode-and-forward (DF) relaying is widely used in communication systems. The DF relay node receives a signal transmitted from a D2D transmitter, decodes the transmitted signal, then forwards it to the receiving D2D user after the self-interference cancellation procedure, which shows an advantage over amplify-and-forward (AF) relaying. It also helps enhance the security of the network. Although the computational time increases when implementing DF relaying, the huge benefit it provides balances these minor issues. In the next section, we present a literature survey of the state-of-the-art works to provide a glimpse at the current trends in D2D communication using DF relaying.
# Ghost Imaging by a Proportional Parameter to Filter Bucket Data
## 3 Experimental Results
Respectively, we sampled a total of 1200 sets of data with and without filtering process for the two different images, \"GI\" and \"ZHONG\", which were processed by the PGI and SMGI algorithms to further test the effectiveness of our method. PGI and SMGI using the data filtered by the two thresholds were recorded as T-PGI and T-SMGI, respectively, for short, and all the results are shown in Figures 5-8, where 300, 600, 900, and 1200 in Figures 5 and 7, respectively, represent the amount of data used from the total 1200 sets of data, and Figures 6 and 8 are the PSNR curves corresponding to Figures 5 and 7 respectively. Figure 5: Experimental results for the “GI” and “ZHONG” by the pseudo-inverse ghost imaging (PGI) and T-PGI methods with 300, 600, 900, and 1200 sets of data. In Figure 5, the visual effects of reconstructed images of T-PGI and PGI enhanced along with the increase in data sets, and under the same amount of data, the visual effect of  
Figure 8: PSNR curves of the reconstruction result “GI” and “ZHONG” of SMGI and T-SMGI; (**a**) the PSNR curves of the “GI” of SMGI and T-SMGI; (**b**) the PSNR curves of SMGI and T-SMGI of “ZHONG”. Figure 6: PSNR curves of the reconstruction result “GI” and “ZHONG” of pseudo-inverse ghost imaging (PGI) and T-PGI; (**a**) the PSNR curves of the “GI” of PGI and T-PGI; (**b**) the PSNR curves of “ZHONG” of PGI and T-PGI. Figure 7: Experimental results for the “GI” and “ZHONG” were obtained by the scalar-matrix-structured ghost imaging (SMGI) and T-SMGI methods with 300, 600,900, and 1200 measurements. T-PGI was also better than that of PGI. From Figure 6, we can see that the PSNR increased along with the data sets when the PSNR of T-PGI was much higher than that of PGI all the time, which were consistent with the visual effects in Figure 5.  
It can be seen from Figures 7 and 8 that SMGI and T-SMGI had similar results. These results confirm that our data screening method is suitable for PGI and SMGI algorithm enhancement. In addition, the PSNR and visual effect of SMGI reconstruction results were better than those of PGI, which is consistent with our previously published experimental results [19]. In fact, the threshold effect of counting hundreds of sets of data was similar to that of 5000 groups of data, because the values of Bn were basically concentrated near the mean value of Bn. Because the process of data screening was directly completed in the sampling process, and the filtering process only involved simple addition or subtraction calculation, the filtering process hardly increased the sampling process time, but also saved the storage space occupied by the original redundant data and greatly reduced the time of the following reconstruction computation. The experimental results above were all based on the reconstruction of binary images, so this method is suitable for GI, PGI, and SMGI when processing binary images.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Implementation Details  
We train our model on a server that is equipped with an Intel Xeon Gold 6325 CPU (up to 2.90 GHz) and an NVIDIA A40 GPU with 48 GB of memory. For implementation, we use the PyTorch framework (v2.2.1) [54] with MONAI library (v0.9.0) [55]. We employ the AdamW [56] with its default configurations to optimize the LG UNETR. The batch size, initial learning rate, total number of epochs, dropout rate, and hyperparameter \(P\) are set to 1, 1 \(\times\) 10\({}^{-4}\), 500, 0.25, and 128, respectively. For model training and testing, we randomly divide each dataset into three parts: the training set, validation set, and test set, with a ratio of 8:1:1. For each model architecture, we train separate optimal models on both datasets. The input image is cropped to a fixed size of 128 \(\times\) 128 \(\times\) 128. A data augmentation approach, including random scaling (with a scaling factor of 0.1 and a probability of 0.1), flipping (along the x, y, and z axes with a probability of 0.5), and shifting (with an offset of 0.1 and a probability of 0.1), is applied to the training set to further improve the model's generalization ability.
## 2 Interdependent Network Construction  
Interdependent networks, as a type of complex network, have multiple layers of inter-network interdependence within them. This interdependence can be reflected in the real world in terms of physical dependencies, information exchange, etc.  
In the actual operation of the ATC system, the pilots of the aircraft in the air can communicate with each other by radio, and there may be conflicts between aircraft in close proximity. For controllers, they have the absolute control of aircraft, and aircraft pilots need to strictly comply with the controller's commands to ensure the safety of flight. Moreover, when the aircraft exceeds its control range, the controller needs to transfer the control right to the controller in the adjacent control sector. Therefore, we can build a flight conflict network and a control network according to the flight situation and control situation in the air. The two constructed networks are dependent on each other, and the nodes and edges of the two networks are constructed under different conditions. Therefore, the two networks can be coupled and form an interdependent network.  
In order to clarify the research methodology, the network model is simplified and assumed accordingly here.  
1. The flight conflict network model is a complex network model constructed using the speed barrier method. The network constructed is not specified or specific to a flight field or control phase, and is universal in nature. The conflicts identified and discussed in the network are derived from potential conflicts.
2. In the actual operation, the size, shape and height of the sector have less influence on the controller's workload and the transfer of control rights. Therefore, a square area is used for the delineation of sectors in this paper.
###### Abstract
(2019) proposed the Temporal Fusion Transformer to enhance interpretability of multi-horizontal time series forecasts, effectively capturing temporal patterns through learnable positional encodings and improving the accuracy of forecasts ([PERSON] et al., 2019). Additionally, [PERSON] et al. (2019) introduced a method to strengthen locality to solve the memory bottleneck of the Transformer model, and used learnable location encoding to better reflect the characteristics of time series data and improve prediction performance ([PERSON] et al., 2019). This learnable location encoding enables the model to learn the optimal representation of temporal information during training, which can reflect the location information in the context of a specific task or dataset. This allows the model to learn to capture temporal structure more precisely, further improving the model's predictive performance, and enabling the model to appropriately reflect different temporal dynamics. The proposed framework uses a deep learning model based on transformer-encoders to more accurately predict the error time of bus arrival time. The model integrates current bus locations, transportation conditions, past arrival times, and other data and processes them simultaneously through a multi-head attention mechanism. The results include a significant improvement in prediction accuracy with a substantial reduction in the average arrival time errors compared with existing methods. Specifically, the model achieved a Root Mean Squared Error (RMSE) of 92 s, demonstrating considerable improvement over traditional machine learning systems. We also used k-fold cross-validation to ensure the generalization performance of the model and prevent overfitting. The major contributions of this study can be summarized as follows:  
* We develop a preprocessing pipeline that provides optimized data for the prediction model through preprocessing processes such as data filtering, feature extraction, and resampling. * We propose a methodology that can effectively learn temporal and spatial information by combining learnable positional encoding and linear projection and improve prediction accuracy by utilizing a fully connected layer. * The experimental results show that the proposed model can estimate the actual bus arrival time more accurately, and the prediction error is 30 s lower than that of the existing model, and the generalization performance is secured by k-fold cross-validation. The remainder of this paper is organized as follows. Section 2 provides a detailed background and related research, and discusses existing methods and their limitations. Section 3 explains the research methodology, including data collection, preprocessing, and the transformer-encoder architecture. Section 4 introduces the experimental set-up, datasets, training procedures, and evaluation metrics. Section 5 presents the experimental results, highlights the performance improvements of the proposed model, summarizes key findings, and suggests future research directions.
## 3 Experiments and Results
### Experimental Setup  
The proposed NR-UNet was implemented using Python and Tensorflow, with experiments carried out on a system equipped with an Nvidia GeForce RTX 3090 GPU, with 24 GB memory, and an Intel CPU i9-10900K. In this experimental configuration, we utilized an input feature map of size \((256,256,1)\), with the number of CNNs on each layer of decoders set to 64, 128, 256, 512 for all U-shape encoder-decoder networks, incorporating two successive CNN layers within each encoder/decoder. With regards to the ViT-related design elements, we assigned an image patch-embedding dimension of 768, an MLP count of 1024, and 12 heads for the MSA. The bottleneck of the NR-UNet contained six ViT layers. Given that the downstream task was 2D image semantic segmentation (i.e., a per-pixel binary classification), the final layer was a \(1\times 1\) CNN layer. Runtimes for 50 epochs, including data transfer, ranged between 500 and 800 min. We opted for a training batch size of 4, and utilized the Adam optimizer, with a learning rate of \(10^{-5}\). The chosen loss function relied on the Dice coefficient (Equation (12)), a frequently used metric for assessing overlap in semantic segmentation, particularly suited to managing the imbalance between a region of interest (ROI) and the background.  
\[\mathcal{L}_{\text{Dice}}=1-\frac{2\cdot\sum_{i=1}^{N}y_{i}\hat{y}i}{\sum_{i=1} ^{N}y_{i}+\sum_{i=1}^{N}\hat{y}_{i}+\epsilon} \tag{12}\]  
where \(\epsilon\) is a small positive constant (e.g., \(10^{-8}\)) added to the denominator to prevent division by zero, \(y_{i}\) denotes the ground truth mask, \(\hat{y}_{i}\) represents the predicted segmentation mask, and \(N\) is the total number of pixels in the image. This experimental setup allowed us to thoroughly investigate the contribution of the proposed adaptive denoising learning strategy to the overall performance of the NR-UNet in the context of medical image segmentation.
## 7 QSAR: Benefits, Challenges, and Limitations  
QSAR serves as an essential tool for streamlining the time-intensive and resource-demanding process of drug design and discovery by identifying promising hits from extensive compound libraries and supporting hit-to-lead optimization. By reducing the need for synthesizing and testing a large number of compounds, QSAR significantly lowers both time and financial costs. Additionally, QSAR allows for the prediction of biological activity for newly designed compounds before chemical synthesis, facilitating the development of innovative analogs. It also provides critical insights into molecular mechanisms by revealing interactions between specific functional groups in designed molecules and their target enzymes or proteins. Despite its many benefits, QSAR has faced criticism due to the poor statistical quality of many studies in the literature, which has contributed to its undeserved negative reputation. This can be attributed to the complexity and specificity of QSAR modeling. To ensure the reliability of QSAR models, scientists must meticulously follow best practices, including the careful selection and preparation of biological data, appropriate selection and reduction of descriptors based on their relevance and quantity, thorough model validation, and clear definition of the applicability domain. Neglecting any of these steps can result in flawed or unreliable QSAR models. While QSAR offers numerous advantages, it also has some limitations, including its (i) dependence on biological data: the accuracy of QSAR depends on the quality of biological data, which can be affected by experimental errors, potentially leading to false correlations; (ii) limited training datasets: small training sets may fail to adequately represent the studied properties, diminishing the predictive power of the QSAR model; (iii) descriptor challenges: selecting and reducing descriptors can be challenging, especially when they lack clear physical or chemical significance, leading to QSAR models being perceived as \"black boxes\"; and (iv) validation and applicability domain: ensuring robust model validation and defining the applicability domain are crucial for regulatory applications, but these processes cannot fully guarantee the model's reliability for all scenarios. By addressing these challenges and adopting rigorous modeling practices, QSAR can continue to be a cornerstone in the advancement of drug discovery and development.
# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 2 Related Work
### Estimation Using 3D Images  
Recent advancements in muckpile fragmentation estimation have shifted from traditional 2D imaging systems to more advanced 3D model-based techniques [16; 17]. Our proposed approach harnesses the power of Structure-from-Motion (SfM), a state-of-the-art 3D photogrammetry method [2; 18], combined with Patch-based Multi-View Stereo (PMVS) [4; 19]. This methodology allows for the detailed reconstruction of muckpile's 3D shape, subsequently facilitating the estimation of its particle size distribution.  
The resulting 3D structure is portrayed as a point cloud, where each point represents a specific position in space. To discern individual rocks within the muckpile, we employed the Supervoxel clustering technique. This method clusters these points based on the k-means objective function [20; 21], taking into account parameters like Euclidean distance, color differentiation, and normal vectors. By applying the Laplacian of Gaussian filter to multi-view images of the muckpile, we can effectively detect rock edges [22].  
The process further categorizes clusters based on specific thresholds. When a point surpasses a predetermined boundary threshold between two clusters, these clusters are distinguished as separate rocks. Conversely, if no points exceed this threshold, clusters are identified as the same rock, merging them. A significant challenge arises here, as the number of clusters doesn't always equate to the actual number of rocks. This disparity complicates the precise measurement of particle size distribution, especially when aiming to consolidate multiple clusters representing a single rock.  
One approach to addressing the challenge of accurately measuring the particle size distribution is to use a combination of machine learning techniques, such as deep learning and convolutional neural networks (CNNs), to improve the detection and segmentation of rocks within the 3D point cloud [23; 24]. These methods have been successfully applied in various fields, including object recognition, image segmentation, and 3D point cloud processing. By training a model with a large dataset of labeled 3D point clouds, the deep learning algorithms can learn to identify and segment rocks more accurately, potentially improving the estimation of the particle size distribution.  
Another possibility is to integrate other sources of information, such as LiDAR data, to enhance the accuracy of the 3D models and the subsequent particle size distribution estimation [25; 26]. By combining different types of data, more accurate and robust models can be generated, leading to improved estimations of the particle size distribution and better optimization of downstream processes in the mining industry. In an effort to provide a comprehensive evaluation of our method, a comparative analysis with conventional estimation techniques has been included. This juxtaposition not only validates the performance of our proposed method but also elucidates its relative strengths.
# A Power Planning Algorithm Based on RPL for AMI Wireless Sensor Networks
## 5 Evaluation
The DODAG method consistently outperforms the other methods with respect to the parent set size and also offers solutions with improved path cost for small values of \(k\), as can be observed in Figures 4 and 5. The actual parent set size obtained with RPL depends on the density of the network, and increasing \(k\) above a certain value has no effect. An important feature of the proposed algorithm is that the power assigned to the nodes is not increased if it does not improve the connectivity of the network. Figure 8: Parent set size \(X\) target parent set size in a rural area scenario. Figure 7: Preferred parent path cost in an urban area scenario. Figure 9: Preferred parent path cost \(X\) target parent set size in a rural area scenario.