esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Role of AI in Dentistry
### AI-Assisted Orthodontic Treatment Planning  
Orthodontic treatment planning is a complex process that involves the careful consideration of various factors, including tooth alignment, jaw structure, and facial aesthetics. Traditionally, orthodontic treatment planning has relied heavily on the expertise of clinicians, who use a combination of clinical examination, radiographic analysis, and experience to develop individualized treatment plans. However, the integration of artificial neural networks (ANNs) into orthodontic practice is revolutionizing this process by providing data-driven insights that enhance decision making. ANNs are capable of analyzing vast amounts of patient data, including dental records, images, and biometric measurements, to predict the most effective treatment strategies. One of the key applications of AI in orthodontics is in determining whether a patient requires tooth extraction as part of their treatment plan. Tooth extraction decisions are often challenging, as they depend on various factors such as the severity of crowding, the position of the teeth, and the patient's overall facial profile. ANNs can analyze these factors in conjunction with other patient-specific data to provide recommendations that are both accurate and personalized. Additionally, AI can assist in monitoring the progress of orthodontic treatment, identifying any deviations from the planned tooth movement, and suggesting adjustments to the treatment plan as needed. This level of precision and adaptability is particularly beneficial in complex cases where traditional methods might fall short. As AI continues to advance, its role in orthodontic treatment planning is expected to grow, leading to more efficient, effective, and personalized orthodontic care.
## 3 Experiments  
For verification of the proposed method, four point cloud datasets were captured with a leveled Trimble SX 10 scanner mounted on a tripod at the School of Geography and Planning Building at the East Campus of the Sun Yat-sen University, Guangzhou, China, during mid-October 2021, as shown in Figure 11. For Datasets A, B, and C, a die with a volume of 0.125 m\({}^{3}\) was used as the target, whereas a smaller die (volume of 0.027 m\({}^{3}\)) was installed for Dataset D as shown in Figure 11. It is worth noting that the dice are very affordable as a registration target: the 0.125 m\({}^{3}\) die costs RMB 150 (USD \(\sim\)22), while the 0.027 m\({}^{3}\) die costs RMB 20 (USD \(\sim\)3). A set of spherical targets traditionally used for registration was also placed in the scene for reference and for comparison of results. A set of six spherical targets is considerably more expensive, costing about RMB 2000 (USD \(\sim\)300). Details of the collected datasets are shown in Table 1.  
Four check planes (two vertical and two horizontal) were selected at different ranges for each registration, and the RMSE of the planar residuals was used for quantification of the registration accuracy. Before the registration, the check planes at both stations have their own stand-alone RMSEs after the fitting. If the registration is perfect (possesses zero errors), the registered check plane will succeed the larger RMSE from one of the check planes. In other words, any errors accumulated on the top of this larger RMSE from one of the check planes are attributed to the registration errors. In addition, each registration was also independently carried out with four to six spherical targets for comparison.
# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 2 Related Works
Therefore, this paper uses the Perspective-n-Point (PNP) method to solve the camera pose and then solves the Euler angle of the head according to the camera pose. Given a general 3D head model and more than four 2D points, the Euler angle of the head on the image can be estimated by direct linear transform (DLT) or the Levenberg-Marquardt (LM) algorithm. The goal of DLT is not to minimize the projection error, which leads to inaccurate results. The results of the LM algorithm solved by iteration are not necessarily a positive solution. Lepetit et al. [22] proposed EPnP to estimate the pose of a calibrated camera from 3D-to-2D point correspondences, where time complexity is O(n). Therefore, this paper uses EPnP to estimate the camera pose and then transforms it into the Euler angle of the head. The existing fatigue assessment methods include PERCLOS [23] and POM. According to research, when PERCLOS exceeds 0.8 or POM is greater than 0.5, the driver will enter the fatigue state. Compared with other fatigue detection methods, head pose estimation is added to the method proposed in this study. Therefore, the head posture change is also included in the fatigue detection parameter.
# Grasp Analysis for the Robot-Based Manipulation of Pre-Assembled Cables with Electrical Connectors
## 5 Discussion
### Key Findings  
The presented methodology allows us to quantify the mechanical cable influence on the grasping process of ECs. The results demonstrate that the required grasp force significantly depends on the current deformation state of a cable. Thus, a grasp force estimation solely based on the mating force is generally not permissible for pre-assembled cables with ECs. In addition to the cable deformation, the selection of surface pairs affects the required grasp force. For the examples shown, the required grasp force can differ by up to a factor of approximately 1.8; compare Figure 9a. Moreover, in general, the required grasp force should be evaluated individually for both grippers. Although there are qualitative similarities between the courses for both grippers (see Figure 9), their maxima may differ significantly for asymmetrical cable deformations; compare Figure 9e,f. Furthermore, for most WPs of the considered assembly task, the required grasp force is lower for a non-planar surface pair compared to its planar alternative; see surface pairs 1 and 2, as well as surface pairs 3 and 4. In general, each combination of a surface pair and a cable wrench results in an individual contact situation. The cable wrench, in turn, significantly depends on the trajectories and the cable parameters. In conclusion, it can be stated that each of the assembly tasks considered should be evaluated individually due to the large number of parameters affecting the required grasp force. Transferring already estimated courses from one surface pair to another or from one cable to another is generally not possible without further effort. The results also indicate that the proposed automated scenario generation in a physics simulation is suitable for simulating the dynamic cable wrenches with little user input. However, the initially generated trajectories can cause unfavorable cable deformations. Thus, in most cases, manual adjustments are required.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 2 Research Content and Method
### Current Status of Wastewater Treatment Plants
#### 2.1.2 Analysis of PFCs  
The PFCs analysis was conducted on five compounds: perfluorooctanoic acid (PFOA), perfluoro-n-pentanoic acid (PFPeA), perfluorohexane sulfonate (PFHxS), perfluorohexanoic acid (PFHxA), and perfluoronanoic acid (PFNA). The pre-treatment and analysis conditions are provided in Table 1. The pre-treatment for the analysis of PFCs involves the use of solid-phase extraction (SPE) to complete a four-stage solid phase extraction, and the conditioning stage uses methanol as the solvent. The loading and washing stages used 10.0 mL of de-ionized water, and the elution stage used 2.0 mL of methanol. After the solid phase extraction stage, the concentration stage used 40 \({}^{\circ}\)C nitrogen gas to concentrate the PFCs so that their final volume was reduced to 500 \(\mu\)L. After the concentration stage, an analysis was conducted with LC-MS [11].  
Figure 1: The process flowchart of the wastewater treatment plants in city K.
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 4 Methodology
### Per-Face Popularity with \(\beta\)-Parameterization  
The typical workflow with ndnSIM involves creating scenarios, which are implemented in typical C++ source files. The WAF build automation utility [24] is then used to build and run the scenarios and support files. The scenarios are also often accompanied by the use of extensions, which are simply modifications (usually using inheritance from base classes of the simulator) or additional classes needed for the scenarios to function. The scenario file itself is where the entry point for the application lives.  
One extension that we created was based on the Policy class. The Policy class in ndnSIM is the actual decision maker as far as what items remain and what items are removed from the content store's cache. We created a PopularityPolicy class as a subclass of Policy to define our cache replacement policy. In ndnSIM, data are automatically added to the cache, which is physically slightly bigger than the logical limit of the cache size. In Policy's doAfterInsert method, a pure virtual function (method) is called immediately after a content object is added to the cache. Then, in this method, the decision can be made to remove any content object according to the cache replacement algorithm.  
The PopularityPolicy class that we created maintains a pointer to the popularity manager (PopMan), which is also maintained in the ndnSIM's NFD Forwarder class, which we modified. Therefore, the PopMan acts as an essential bridge from the forwarding strategy and fabric to the cache replacement policy. The PopMan records when interestsarrive and over which face, so that ranking can be accomplished. This interest and face data are ultimately maintained in FaceContentFrequency objects, which maintain, as the name suggests, the frequency of requests for content organized by specific faces. Each FaceContentFrequency object maintains all content requests and a ranked list for its face. This is the class responsible for determining an individual face's contribution to overall popularity. The PopMan, in turn, utilizes the contributions from its list of FaceContentFrequency objects to determine what should be removed when it must decide on the next item to remove from the cache.  
So, to summarize, the Policy (PopularityPolicy), in its doAfterInsert method, determines when the cache is full after an insert of a content object obtained from a returned data message. If the cache is full, the PopularityPolicy asks the popularity manager what content object must be removed from the cache. As described earlier, this decision is based on the overall contributions from all faces, normalized such that no single face has more influence than any other face. When this information is obtained, the PopularityPolicy sends an eviction signal to the content store, which evicts the corresponding object from the cache.
# Experimental Study on the Permeability of Microbial-Solidified Calcareous Sand Based on MICP
## 4 Conclusions  
This study investigates the effects of grain gradation on the permeability coefficient (\(K\)) for calcareous sand improved by microbial-induced calcite precipitation (MICP) technology. Permeability tests, CT scans and SEM analyses were conducted to reveal the law and mechanism of permeability variations in MICP-improved sand samples with different grain gradations. Finally, the correlation of the permeability coefficient with the uniformity coefficient (\(C_{u}\)) and the curvature coefficient (\(C_{c}\)) was analyzed. The main conclusions from this study are as follows:  
Figure 12: The reacquired curves for a wider range of Cu and Cc: (**a**) 2 \(<\) Cu \(<\) 10; (**b**) 0.5 \(<\) Cc \(<\) 8.  
The grain gradation of cemented sand influences its permeability. In the case of the same \(D_{r}\), the decreased amount of the permeability coefficient of each MICP treatment cycle increased with the rise in \(C_{u}\) and \(C_{c}\).
2. All sand samples showed a certain increase in induced calcium carbonate and a decrease in porosity after the MICP treatments. In the case of the same \(D_{r}\), the decrease amount of the porosity and increase amount of the cementation content of each MICP treatment cycle increased with the rise in \(C_{u}\) and \(C_{c}\).
3. From the SEM analyses, after MICP treatment, the microstructures of the sand samples with different gradations were similar. The calcium carbonate crystals not only coat on the particle surfaces but also fill the particle pores, thus reducing permeability and decreasing porosity.
4. The permeability coefficient (\(k\)) is proportional to \(C_{u}\) and \(C_{c}\). When the MICP treatment cycle is less than five, the larger \(C_{u}\) (\(C_{u}\geq 6.0\)) and smaller \(C_{c}\) (\(2.0>C_{c}>0.5\)) will lead to a more significant reduction in permeability, and permeability was fitted well with \(C_{u}\) and \(C_{c}\). However, the permeability coefficient does not correlate well with \(C_{u}\) or \(C_{c}\) after a number of cycles of MICP treatment (MT \(\geq 5\)) because the interparticle pores are basically filled by calcium carbonate crystals.
5. This work provides a reference for the freshwater storage of calcareous sand foundations and the antiseepage of calcareous sand structures on island reefs. The targeted application of MICP according to soil gradation makes permeability a controlled variable, which not only saves the cost of construction but also avoids damaging the local ecosystem due to a permeability which is too low.  
Formal analysis, Y.L.; Funding acquisition, J.C.; Resources, J.C.; Writing--original draft, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51004087).  
Some or all data, models, or codes generated or used during this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 3 Proposed Method
### Loss Function: Geometry-Aware Loss
To resolve this issue, we normalized the Hausdorff Distance Loss by dividing it by the maximum possible diagonal distance of the image. This allows the loss values to be on a comparable scale, enabling more stable and balanced joint optimization with the Dice Loss. The final loss function, which combines the Dice Loss with the normalized Hausdorff Distance Loss, is designed to simultaneously improve both region-level segmentation and boundary accuracy. \[\mathcal{L}_{total}=\lambda_{1}\mathcal{L}_{Dice}+\lambda_{2}\mathcal{L}_{HD}^{ norm} \tag{9}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are weighting factors for the Dice Loss and the Hausdorff Distance Loss, respectively. Through empirical evaluation, we found that setting \(\lambda_{1}=1.0\) and \(\lambda_{2}=0.3\) yielded the most stable convergence and superior segmentation performance. During the early stages of training, the \(\mathcal{L}_{HD}^{\text{norm}}\) term exhibited relatively high values, reflecting initial boundary discrepancies. However, as training progressed, this term gradually decreased, demonstrating its positive contribution to boundary recognition performance over time.
# A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters
## 5 Conclusions  
A bivariate proposal for PA modeling has been presented in this work. The approach considers an internally generated variable which operates as the second input to a bivariate Volterra system, giving a PA output expressed as a double Volterra series. In the present exposition, the beneficial terms with the envelope raised to an odd power respond to a bivariate Volterra model structure and are formally obtained following a procedure based on previous circuit model knowledge. Starting from a general representation, the particular case of the GMP model seen as a bivariate Volterra model is demonstrated.  
Experimental data show that the modeling capabilities of a reduced-order bivariate Volterra model applied to both a class AB and a class J PAs outperform those of the univariate Volterra model. Finally, the linearization results for a 30 MHz 5G-NR signal and the class AB PA operating with an average output power of 31.2 dBm (gain compression of 2.8 dB) indicate that the NMSE and ACPR for the upper and lower adjacent channels achieved by the conventional CKV model are \(-41.9\) dBc and \(-48.5/-48.1\) dBc, respectively, compared to the values of \(-47.0\) dB and \(-54.8/-54.2\) dBc for the GMP model, and values of \(-47.4\) dB and \(-55.7/-55.5\) dBc for the bivariate CKV model.  
Further development of the present approach, constructed with more complex models (e.g., the FV model), can provide bivariate models with a richer set of regressors and a reasonably improved performance.  
Conceptualization, C.C.-C., M.J.M.-A. and J.A.B.; methodology, C.C.-C., M.J.M.-A. and J.A.B.; software, C.C.-C., M.J.M.-A. and J.A.B.; validation, C.C.-C., M.J.M.-A. and J.A.B.; formal analysis, C.C.-C., M.J.M.-A. and J.A.B.; investigation, C.C.-C., M.J.M.-A. and J.A.B.; writing--original draft preparation, C.C.-C.; writing--review and editing, C.C.-C., M.J.M.-A. and J.A.B.; visualization, J.A.B.; project administration, M.J.M.-A.; funding acquisition, C.C.-C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministerio de Ciencia e Innovacion--Agencia Estatal de Investigacion of the Government of Spain under Grant TEC2017-82807-P, and by the European Regional Development Fund (ERDF) of the European Commission.  
Not applicable.  
Figure 10: Normalized power spectral densities (PSDs) of the output signal without DPD and with the models in comparison for class AB PA operated at 3.6 GHz with an average output power of 31.2 dBm.  
Data Availability Statement: The data presented in this study are available on reasonable request from the corresponding author.  
The authors would like to thank the team of [PERSON] at Universidad de Cantabria for providing the class J power amplifier used in the experimental part of this work.  
The authors declare no conflict of interest.
# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 3 Materials and Methods--The Proposed Framework
### Stage 2: Ensemble Model Integration  
To address the inherent uncertainty in cold-start prediction, an ensemble methodology that combines predictions from multiple transformer-enhanced models was developed. DCN-V2, DIFM, FiBiNET, and MMOE were used [29; 30]. This ensemble approach:  
* Leverages diverse modeling perspectives to improve prediction robustness;
* Employs a learnable weighted aggregation strategy optimized through fine-tuning;
* Outputs calibrated probability scores for candidate advertisements;
* Selects top-performing candidates for further refinement.  
Below, we provide a mathematical formulation of the ensemble process:  
Let \((M=M_{1},M_{2},M_{3},M_{4})\) denote the four pretrained transformer-enhanced models, where \((M_{1}=DCN\)-\(V2,M_{2}=DIFM,M_{3}=FiBiNET,M_{4}=MMOE)\). Each model \(M_{i}\) takes a user-ad pair \((u,a)\) as input and outputs a probability score \((p_{i}(u,a)\in[0,1])\), representing the predicted likelihood of user \(u\) clicking on advertisement \(a\). For a set of candidate advertisements \(A=a_{1},a_{2},\ldots,a_{N}\) the predictions form a vector: \(\left[P_{i}=[p_{i}(u,a_{1}),p_{i}(u,a_{2}),\ldots,p_{i}(u,a_{N})]\in[0,1]^{N}\right]\) for each model \((i=1,2,3,4)\).  
The ensemble combines these predictions using a linear weighted aggregation with weights \(w=[w_{1},w_{2},w_{3},w_{4}]\). To ensure \(w_{i}\geq 0\) and \(\sum_{t=1}^{4}w_{i}=1\), the weights are derived from learnable parameters \(z=[z_{1},z_{2},z_{3},z_{4}]\in R^{4}\) using the softmax function: \(w_{i}=\frac{exp(z_{i})}{\sum_{t=1}^{4}exp(z_{k})}\). The ensemble's predicted score for advertisement \(a_{j}\) is \(s_{ensemble}\big{(}u,a_{j}\big{)}=\sum_{i=1}^{4}w_{i}\cdot p_{i}\big{(}u,a_ {j}\big{)}=\sum_{i=1}^{4}\bigg{(}\frac{exp(z_{i})}{\sum_{t=1}^{4}exp(z_{k})} \bigg{)}\cdot p_{i}\big{(}u,a_{j}\big{)}\). The parameters \(z\) are optimized by minimizing the binary cross-entropy loss: \(L=-\frac{1}{N}\sum_{j=1}^{N}\big{[}y_{j}log\big{(}s_{ensemble}\big{(}u,a_ {j}\big{)}\big{)}+\big{(}1-y_{j}\big{)}log\big{(}1-s_{ensemble}\big{(}u,a_ {j}\big{)}\big{)}\big{]}\) where \(y_{j}\in 0,1\) is the ground-truth click label. Optimization is performed using gradient descent to balance the contributions of each model, enhancing robustness in cold-start scenarios.  
The ensemble ranks all advertisements in set A based on their calibrated probabilities. The top 5 candidates are passed to the LLM for message refinement and explanation generation (Section 3.3). This ensemble approach ensures robust predictions by leveraging diverse transformer-enhanced models, with fine-tuned weights optimizing performance for cold-start advertising scenarios
## 4 Results and Discussion
### ANFIS-GA Model
The criterion for the accuracy of hybrid model was the root mean square error (\(RMSE\)), correlation coefficient (\(R^{2}\)), and Variance account (\(VA\)) within the measured and predicted advance rate of the shield machine [58; 59; 60]. According to the testing sets, the hybrid ANFIS-GA model had \(RMSE\) = 0.11, \(R^{2}\) = 0.85, and \(VA\) = 0.77, while the ANFIS model had \(RMSE\) = 0.15, \(R^{2}\) = 0.79, and \(VA\) = 0.74. Because of smaller \(RMSE\) and greater values of \(R^{2}\) and \(VA\), the ANFIS-GA model performed better than the ANFIS model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Data** & **Relative Deviation** \\ \hline Training data & 160 & \(\pm\)15\% \\ Testing data & 40 & \(\pm\)15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The relative deviation of the hybrid ANFIS-GA model. Figure 9: Comparison between measured and predicted AR from the ANFIS-GA model: (**a**) training set and (**b**) testing set.
# Satellite Image Compression Guided by Regions of Interest
## 3 Results
Figure 7:4 Results for image 21: (**a**) RGB image (truncated at 2000); (**b**) binary image; (**c**) labeled tiles (truncated at 4000).
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 6 Challenges and Perspectives
### Further Improvement of Detection Performance and Robustness at a Low SNR  
From the literature reviewed in the previous section, it can be seen that DL-based SS algorithms all have high perceptual performance under high-SNR conditions, but their performance under low-SNR conditions often falls short of expectations, although some scholars, such as [57; 58], have achieved higher detection performance under low SNRs by extracting statistical features through a CNN. However, it can be found that most of the schemes in a certain range of low SNR have a sharp decline in detection performance, such as in [53], in the range of \(-13\) dB to \(-20\) dB, from 98% to about 55%, which shows that the robustness of the performance under low SNRs needs to be improved. Current research is focused on improving robustness at low SNRs via data augmentation methods or joint reinforcement learning. Improving perceptual performance under low SNRs has always been a hot research topic in this field, and usually models that perform better under low SNRs also perform well under high-SNR conditions [113]. However, focusing on improving detection performance while ensuring robust performance at low SNRs is also a focus of future deep-learning-based SS research.