esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction
## 2 Background and Related Works
### Automated Machine Learning (AutoML)  
The ultimate goal of AutoML is to determine the best-performing learning algorithm without human intervention [48]. To exclude human intervention in the auto-design process, there are several challenges, including hyper-parameter optimization (HPO), meta-learning, and NAS [49].  
HPO aims to find the optimal hyper-parameters in a specific DNN. Every machine learning system has several hyper-parameters, and they can represent a complex neural network architecture [18; 49]. HPO can define a neural network architecture. Bayesian optimization (BO) is a well-known method for HPO.  
Meta-learning can improve the performance of an ANN by using meta-data. However, it cannot search for neural architecture significantly faster than HPO or NAS. The challenge for meta-learning is to learn from prior experience in a systematic, data-driven way [49]. Collecting meta-data is required to form prior learning tasks, and the system can then learn from this meta-data.  
NAS techniques provide the automated generating of DNN architecture. Recent DNNs have increased in complexity and variety. However, currently employed neural network architectures have mostly been designed manually [49]. This includes a time-consuming and error-prone process [12]. NAS can design a neural architecture automatically. This includes the HPO process. However, it is not aimed at HPO. To generate the DNN architecture topology, NAS usually uses EAs.
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 3 Development of CBR Key Technologies
### Case Adaptation
These methods focus on the extraction efficiency of knowledge and rules to enhance adaptation performance. 3. Adaptation methods based on machine learning (ML) or DL [51; 83; 84]. [PERSON] et al. [84] proposed a feature re-use case adaptation (FR-CA) method based on an SVR machine, which can automatically and intelligently solve product experience features and achieve the integration of expert comprehensive decisions with the least expert participation. These methods simplify the compilation and extraction of adapted knowledge through an end-to-end learning process, but the design of network structures often affects the learning effect. These learning-based case adaptation methods often use different evaluation criteria to evaluate the performance of the adaptation model in different tasks. According to task requirements such as classification and diagnosis tasks, accuracy, F1 score, and recall rate are often used as evaluation criteria for the model. In tasks such as timing or demand forecasting, various error formulas and R\({}^{2}\) are used as evaluation criteria. In addition, the next step in case adaptation in CBR is case revise, i.e., a further evaluation of the adaptation results; however, this step often needs to be implemented after the model is actually applied to practice.
## 4 Experiment and Results
### Statistical BertGCN with a Shorter Input Sequence  
Table 1 shows that the baseline model attained a validation accuracy of 85.53% under the clued condition, which is lower than all other \(\lambda\) cases. In contrast, the BertGCN model exhibited higher performance than the baseline across all \(\lambda\) conditions ranging from 0.1 to 1.0. The highest validation accuracies for the clued cases were achieved at \(\lambda\) conditions of 0.6 and 0.8, with values of 87.11% and 87.20%, respectively. The test accuracy of the best model was 81.77% for the clued case.  
The results also revealed that the baseline model achieved a validation accuracy of 86.13% for the un-clued dataset, where no keyword is given to some documents. While this performance is respectable, it falls short of most other \(\lambda\) cases, except for an outlier of 0.1. The BertGCN model achieved better performance than the baseline across all \(\lambda\) conditions ranging from 0.2 to 1.0, and the highest validation accuracy was achieved at a \(\lambda\) condition of 0.4 with a value of 87.11%. The test accuracy of the best model was 82.34% for the un-clued case.  
In summary, Table 1 shows that when training a model with a fixed input length of 128 tokens, increasing the composition parameter \(\lambda\) led to better overall model performance. This result is consistent with BertGCN's findings that the gap between the highest and lowest \(\lambda\) values was about 2%. However, no significant gap was revealed between the clued and un-clued datasets.
## 4 Model Optimization Based on Stepwise Regression
### Experiment Results
Thirdly, the optimized parameter model performs as well as the mount model, despite having two correction terms for the elevation error. A probable explanation is that it contains multiple sources of information and no multicollinearity. Finally, the elevation residual curves in the three tests generally showed a regular trend, suggesting that a certain factor, i.e., astronomical refraction, was missed during modeling. Therefore, choosing the appropriate empirical term to compensate for this trend is another area of research interest.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 4 Result and Discussion  
### Regression Performance  
Prediction of MTF is plotted in Figure 5. Since the test set is split into five subsets, the regression plot for each test set are shown from top to bottom.  
Figure 5: Regression results of 5 test sets on RMSE, MAE and \(R^{2}\); (**a**) Val 1 (**b**) Val 2 (**c**) Val 3 (**d**) Val 4 (**e**) Val 5.  
Even though the best performing data set for all performance metrics tends to vary as the test set is changed, the overall regression result improves as the number of samples is increased. This is shown by the prediction curve in the red dotted line that is much more generalized than the blue dotted line. Figure 6 shows the average values of validation results presented in Figure 5 in terms of RSME, MAE, and \(R^{2}\). It shows that it is best to use 768 samples for training to get the least RMSE, MAE and the highest \(R^{2}\), which in this case are 5.89, 4.78 and 0.65 respectively.  
The result implies that the performance increases by 18%, 15% and 25% for each performance metric when more training data is used. However, the plots also show that the performance begins to degrade at the number of samples of 768. This is because the first model for pseudo-labeling of the synthesized data is not the best, thus the pseudo labels should bear an error indispensably. In addition, through SRS for generating synthetic data, infeasible samples may have been generated. This implies that additional training data with error should be used only in a limited amount. To demonstrate that the data augmentation through pseudo labeling brings improvement in the general regression performance, the evaluation metrics are also measured for several other promising machine learning algorithms as shown in Table 5. Though it does not excel in every aspect, our proposed model shows the best result overall. The hyper-parameters for each machine learning algorithms have been heuristically optimized as follows: Support Vector Machine (kernel = 'rbf' and regularization parameter C = 5.0), Random Forest (number of trees = 100 and max depth = 2), Gradient Boosting (number of boosting stages = 100, learning rate = 0.1 and max depth = 5), XGBoost (learning rate = 0.3, max depth = 6 and L2 regularization term on weights = 1).
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 6 Conclusions  
This research introduces the GLFUnet model for medical image segmentation, which builds upon the U-Net framework. It integrates features derived from both Swin Transformer and ConvNeXt encoders in parallel, utilizing an attentional feature fusion module. The model effectively captures spatial and semantic information through skip connections. GLFUnet is adept at extracting both global and local detailed information crucial for pathology segmentation. Evaluations on datasets of stomach and liver cancer demonstrate GLFUnet's superior performance and adaptability against other segmentation models. Ablation tests confirm the efficacy of each introduced component. However, the integration of the attention mechanism increases computational demands and prolongs processing time. Future work will focus on refining the model's architecture to reduce complexity and streamline operations. Additionally, experimenting with various CNN and Transformer combinations will be pursued to enhance segmentation efficiency and precision.  
Conceptualization, J.L., S.M. and L.P.; methodology, J.L. and S.M.; software, S.M.; validation, S.M.; formal analysis, J.L. and S.M.; investigation, J.L. and S.M.; resources, S.M. and L.P.; data curation, J.L., S.M. and L.P.; writing--original draft preparation, S.M.; writing--review and editing, S.M. and L.P.; visualization, S.M.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Taiyuan Institute of Technology scientific research initial funding (2022 KJ092), and the Opening Foundation of Shanxi Key Laboratory of Signal Capturing & Processing (2022-01).  
Not applicable.  
Two datasets were used in this study. One was a public dataset (BOT gastric slice dataset [42]), and the other was supported by data from the Third Xiangya Hospital of Central South University.  
The authors declare no conflict of interest. The funder had no role in the design of the study, in the writing of the manuscript or in the decision to publish the results.
## 4 Problem Formulation
### Quality of Service (QoS) Constraints  
Here, we explain the two QoS constraints that are considered in this work i.e., PU interference and Bit Error Rate (BER) constraints. Both constraints follow the set up developed in [4].
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Mission Requirements  
The study requirements are as follows:  
1. The mission should deliver a 25 kg small satellite as a secondary payload on a Mars orbiter mission to a 200 \(\times\) 2000 km near-equatorial Mars orbit.
2. The concept must allow for at least 1.0 deg. of Theoretical Corridor Width (TCW) and accommodate a +/\(-\)0.2 deg. 3\(\sigma\) EFPA error at the entry interface.
3. The maximum peak deceleration during aerocapture is not to exceed 5 g.
4. The maximum peak stagnation-point heat rate is not to exceed 40 W/cm\({}^{2}\), which is well within the 240 W/cm\({}^{2}\) tested limit of the carbon cloth TPS.
5. The stagnation-point total heat load is not to exceed 5 kJ/cm\({}^{2}\).
6. The periapsis raise maneuver propulsive \(\Delta V\) to be performed by the small spacecraft after orbit insertion is not to exceed 40 m/s.
7. The deflection maneuver to be performed by the orbiter to target its insertion into a 200 \(\times\) 70,000 km orbit must not exceed 30 m/s.
8. The deflection maneuver must be performed no earlier than 4 days prior to entry (to keep the small satellite EFPA error low) and no later than 2 days to allow the primary spacecraft to prepare for its orbit insertion maneuver.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 3 Results
### Gravity  
The Bouguer anomaly for the study area is between \(-\)85.6 and \(-\)223.1 mGal (Figure 4). The left side of the Chaman Fault shows relative high gravity (\(--\)110 mGal), which may indicate that the depth to basement is shallow and/or thinner than crustal thickness. There are many metamorphic and volcanic rocks exposed along the Chaman Fault that may further support shallow basement depth [(35)]. Two trends and anomalies are clearly observable on the Bouguer map: (1) Between the Chaman Fault  
Figure 8: **(A)** SBAS results generated from 15 ascending PALSAR scenes from 2006 to 2011 show a surface deformation velocity ranging from \(-\)10–40 mm/year. **(B)** The profile BB’ shows changes in gravity across the Karahi and Harnai Faults. The gravity data indicate the presence of three different fault blocks that cross faults. **(C)** The SBAS multi-temporal analysis shows a change in pre-, co-, and post-seismic velocity rates for zones 1, 2 and 3 at the epicenters of the 2008 earthquake swarms.  
and the Ghazaband Fault, there are many linear patterns striking northeast-southwest with a small gravity gradient of \(\sim\)1.5 mGal/km; (2) east of the Quetta Valley, there are nearly circular patterns with a high gravity gradient of \(\sim\)8 mGal/km. The linear patterns may indicate folded structures in the Katawaz basin, and a small gravity gradient may indicate low-angle fold structures. The circular patterns with a relatively higher gradient of the Bouguer anomaly can be interpreted as a combination of high-angle basement faults and high-angle fold structures.  
Two-dimensional Bouguer anomaly curves, AA' and BB', crossing the Quetta Valley, and the 2008 earthquake's location were selected and analyzed for integration with the InSAR results (Figure 4). The Bouguer anomaly profile of AA' shows highs and lows forming a trend likely related to mountain ranges and basins (Figure 7). Gravity profile BB' across the Katahi and Harnai Faults shows a density change in the three blocks, which may indicate that they are each a different rock type (Figure 8B).
# Experimental Analysis of the Behaviour of Piled Raft Foundations in Loose Sand
## 1 Introduction
### Introduction
Since the raft and the piles each contribute to the behaviour of the raft with piles system, the key component for the design is the portion of load carried by each element [36; 43]. The piled raft foundation structure is a foundation structure that combines the effective bearing capacity of raft and piles while taking into account different interactions: pile-soil, pile-pile, raft-soil, pile-raft. The interaction between soil and structure has an impact on soil behaviour as well as the behaviour of piles subjected to load [44; 45; 46]. The behaviour of the pile-soil system is mostly nonlinear. The load in the horizontally loaded pile is resisted by the pile-ground interaction, which also depends on pile diameter and pile material [47]. [PERSON] [48] investigated the influence of the pile-soil interaction in a vertically loaded group of piles. Some previous studies have specified the ratio of load distribution as a function of the geometry of the foundation, stiffness, and compressibility of the soil [49; 50; 51]. For sandy soils, the pile-raft interaction effect is significant for the overall behaviour of the piled raft system [52; 53; 54]. Based on experimental results, [PERSON] and other [55] authors proposed a simplified method that can be used in design. The experiment shows that, at the beginning of the loading of the piled raft foundation, piles take over most of the load, and, after reaching the bearing capacity of the pile, the load is transferred to the raft. This method can provide effective results if used with the finite element method for estimating settlement of piled raft foundations. Some of the analytical and numerical studies conducted on this topic are described in [56; 57; 58; 59; 60; 61]; however, experimental studies on piled raft systems are few, while verification of the analytical and numerical results can occur by conducting tests on physical models in the laboratory. In this paper, the results of experimental analysis of the 1g model of the raft foundations, conducted for the purpose of doctoral thesis research of the first author, were used. The aim of the research was to point out that, when designing these types of foundation structures on loose sand, the load bearing capacity of the raft foundation should not be neglected. This will reduce the number of piles required to receive vertical force and thus lead to more efficient foundation structure design. In addition to raft and piles interaction, pile group effect was also investigated.
## 2 Computational Details  
### Quantum Chemical Calculations  
Firstly, aflat B1, B2, G1, and G2 were studied using density functional theory (DFT) methods. These calculations involved two approximations of the quantum potential--1) [PERSON]'s three-parameter hybrid functional with Lee-Yang-Parr's correlation B3 LYP [12] and 2) the meta GGA functional M06-2X [13]--both in combination with a 6-311G(d,p) polarized basis set. Geometrical optimizations were performed for isolated molecules in the gas phase and in water solution. The later was simulated by employing the IEFPCM solvation method [14,15]. After selecting the structures which corresponded to a minimum of the total energy hypersurface, they were compared with X-ray diffraction data. Later on, this comparison was used for the selection of the best methodology for the analysis of the aflatoxin B1 molecular structure [16]. All the computational studies were performed using the Gaussian 09 suite of programs [17].  
Figure 1: Aflatoxins’ chemical structure.
## 4 Result and Discussion
### Result in One Machine Test  
The result of using and without using algorithms for only one machine is shown in Table 4. From Table 4, it is seen that more orders could be arranged after using the proposed algorithms.  
The performance with and without algorithms in a working day using one machine is shown in Figure 6. The bar chart shows that before using the proposed algorithm, the machine can handle 20 orders per day in the working hours. After applying the proposed  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type of Machine** & **Number of Orders in Schedule** & **Total Setup Time (min)** \\ \hline one machine with using algorithms & 37 & 150 \\ \hline one machine without using algorithms & 20 & 285 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The overall performance of the total number of orders and total setup time used in the schedule.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(i\) & Job (\(i\), \(i+1\in\) I) \\ \(l\) & Total of jobs \\ \(k\) & Machines (\(k\in M\)) \\ \(M\) & The total number of machines \\ \(o\) & Order (\(o\),\(o+1\in\) PO) \\ \(PO\) & Total production orders \\ _Ami_ & The set of alternative machines on which job \(i\) can be processed (\(AMi\subseteq M\)) \\ _Oitc_ & Demand of job that cannot be produced on time in day \(tc\) \\ \(T_{i}\) & Set of total items in the orders \\ \(A_{o}\) & Arranged orders \\ \(G_{o}\) & Grouped orders \\ \(M_{c}\) & Machine capacity \\ \(C_{oc}\) & Current order color \\ \(C_{ot}\) & Current order thickness \\ \(P_{oc}\) & Previous order color \\ \(P_{ot}\) & Previous order thickness \\ \(ST_{i}\) & Setup time of job \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of production-line notation.  
algorithms, the machine can increase the number of orders to 37 orders. There were 17 orders more than without using the proposed algorithms. Overall, the number of orders has increased by 20% after using the proposed algorithms.  
Figure 7 shows the performance of the total setup time before and after applying the proposed algorithm in the scheduling system. From the chart itself, the total setup time used in a day has reached 285 min, which is 4 h and 45 min in a day of working hours. After applying the proposed algorithms, the total setup time was reduced from 285 min to 150 min, which was 47.3% reduction from original setup time. It means the material changing lead time was also reduced. With lower material changing lead time, the machine can settle more orders.
## 1 Introduction  
Prostate cancer (PCa) [1] is the second leading cause of cancer-related mortality among men and remains one of the most frequently diagnosed malignancies worldwide. In the United States alone, 2023 saw 609,820 cancer-related deaths [2] and 1,958,310 new cancer cases [3]. Approximately 15% of patients with newly diagnosed PCa present with pelvic lymph node metastases (PLNM), a major prognostic factor linked to distant metastases and biochemical recurrence after curative treatment [4]. Therefore, accurate pre-treatment identification of PLNM is crucial, as it significantly influences clinical decision-making and treatment strategies [5,6].  
Radiomics is a rapidly developing field that uses advanced computational techniques and artificial intelligence to extract quantitative imaging features, providing non-invasive biomarkers of tumor phenotype [7,8]. In prostate cancer, radiomics has shown potential in improving diagnosis, risk stratification, and treatment planning [9]. Among imaging modalities, magnetic resonance imaging (MRI) stands out for its high soft-tissue contrast and multiparametric capabilities, making it ideal for radiomic analysis [10]. Sequences such as T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps allow for the extraction of relevant features related to tumor texture, shape, and intensity [11].  
In prostate MRI, two anatomical regions--the peripheral zone (PZ) and transition zone (TZ)--are typically evaluated, with suspicious lesions often appearing hyperintense on DWI and ADC sequences [12,13].  
Beyond tumor tissue, recent evidence suggests that peritumoral environments, such as periprostatic adipose tissue (PPAT), may play a critical role in cancer progression [14]. PPAT and adipose stromal cells (ASCs) have been shown to promote tumor growth and invasiveness through neovascularization, extracellular matrix remodeling, immune modulation, and paracrine signaling. In particular, PPAT-derived soluble factors and lipid transfer mechanisms have been implicated in enhancing PCa aggressiveness and local spread [15,16].  
This study aims to investigate whether radiomic features extracted from the tumor nodule, the entire prostate gland, and the surrounding PPAT can predict lymph node invasion in patients with prostate cancer.