esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
The unaesthetic appearance of the color tone might be due to the low transparency of the coloring coating that requires masking the tooth color of the underlying material. Moreover, the characteristics of the material coating on the printed prosthesis could have low longevity. As computer imaging, designing, and 3D printing technologies develop, digital manufacturing of removable dentures is being attempted. Facial scanning can also be adapted to increase the predictability of treating patients with maxillofacial trauma [21]. By incorporating facial scans or virtual simulations into the digital workflow, clinicians can analyze facial features like lip support and midline to create overall facial harmony in creating personalized prostheses. This approach ensures accurate customization and improved communication between patient and dentist, ultimately leading to more predictable and aesthetically pleasing outcomes that align with the patient's unique facial characteristics [22]. The use of digital techniques has certain advantages; however, considering that 3D printing materials generally have lower mechanical properties than heat polymerization materials that are used for conventional methods, dentures must be designed carefully, considering the thickness of the denture base to reduce fracture risk [23]. Moreover, proficiency in computer design is essential for the digital denture manufacturing method. The color stability of 3D-printed prostheses is lower than that of the milled conventional methods. This is because the layered structure creates a less smooth surface finish that can be corrected by additional post-processing steps such as polishing or coating to achieve desired surface properties [18]. Additionally, the range of materials used in digital fabrication is limited compared to traditional methods, which can impact the final prosthesis's durability. Although digital workflows offer high precision, errors in scanning or design processes can lead to inaccurate results [12]. Even though the cost of 3D printers has decreased, the high-quality 3D printers capable of producing durable medical-grade prosthetics can still be expensive, impacting the overall cost of prostheses [18]. Flipper fabrication requires design accuracy, retention, and better adaptation to the underlying tissues [14]. In the present case, the flipper fabrication protocols were described in detail, and the designed flippers showed good adaptation on evaluation of fit accuracy. Due to the nature of the clinical case report study, there are constraints in quantitative analysis and in-depth statistical analysis. In the future, more comprehensive clinical studies, such as randomized controlled trials, are needed to examine various outcome variables of the flippers fabricated using the three different approaches and to generalize the findings of this present study. To increase the clinical use of digital technology, research not only on the development of materials but also on clinical factors such as long-term prognosis and patient satisfaction is needed.
## 1 Introduction
Thus, extracting the joint time-frequency entropy features of power samples can balance both local and global features of the signal. EO has superior performance in terms of feature dimensionality and classification accuracy, and has also been confirmed to generalize well over different datasets and classifiers. Displaying feature distribution with box and whisker plots can effectively represent feature information [37; 38]. Having taken these factors into account, this paper put forward a novel electric load feature selection method for the low-frequency data collected in industrial power consumption scenarios. This method introduces entropy value features based on extracted time-frequency domain features and then uses the EO to screen in the joint feature, which helps achieve accurate classification of industrial power loads. The structure of this paper is as follows. In Section 2, the flowchart for the procedure proposed in this paper is first described succinctly. Then, the data sources for this experiment, the time-frequency entropy features of various electrical loads, the principle of the algorithm of EO, and the application of the EO to the joint feature selection of electric loads are presented. Section 3 presents the specific experiments. First, the power curve and cleaning effect are shown, then the joint features are extracted according to the feature principle, and next, the joint feature set is screened using the EO. Based on this, the authors verify the validity of this study with five classifiers. Section 4 demonstrates the necessity and effectiveness of the experimental steps by comparison. The classification effects before and after feature extraction are first compared, and then the performance of different feature combinations is compared to demonstrate the superiority of the joint feature set. To assess the dependability of the EO, the authors select four metaheuristic algorithms for the comparison of feature selection effects. Finally, the authors conclude by summarizing their study and anticipating future research.
## 5 Comprehensive Optimization  
### Optimization Process  
The detailed optimization flowchart is summarized in Figure 14, which mainly includes the following four steps:  
Step 1: Initial design of the IPM motor. Firstly, the rotor structure of the three-segment PM is adopted by analysis. Then, five optimization variables are selected. Finally, the influence of five variables on motor efficiency and rotor stress is analyzed to determine the variation range.  
Step 2: Comprehensive sensitivity analysis. Firstly, the sensitivity of rotor variables to motor characteristics is calculated based on the Pearson coefficient. Then, the effect of each parameter on multi-physics performance is analyzed and evaluated with sensitivity coefficients.  
Step 3: Multi-objective optimization. Firstly, the surrogate model for rotor design parameters and motor performance is established. Secondly, the optimization objectives  
Figure 13: Sensitivity values for optimization parameters.  
and constraints are reasonably selected. Then, the multi-objective genetic algorithm is used to achieve the optimal solutions. Finally, select the final optimization parameters.  
Step 4: Performance verification. In this step, the electromagnetic performance, mechanical stress, and temperature distribution of the initial scheme and the optimized design are compared. And the reliability and safety of the optimization design are tested and verified by experiments.
## 3 Results
### Inverse Dynamics Results
The second scenario is closest to the average, while the third scenario shows reduced cumulative moments in the shoulder area. Typically, the moment  
Figure 9: Average neck and arm moment values, with ergonomic limit and identified motion phases: (**A**)—patient lift motion phase; (**B**)—patient move-while-lifting motion phase; (**C**)—patient put-down motion phase; (**D**)—neck moment; (**E**)—shoulder moment; (**F**)—elbow moment. Gray colored area shows \(\pm\)MAD. values are 9.74% higher compared to the whole sample average, while shoulder moments are, on average, 9.67% lower than the mean value. Another data cross-section presented in Table 1 is age. Shoulder and elbow groups of test subjects up to 25 years and 25-35 years do not show significant differences, but a 10.35% positive effect is observed in the left shoulder's moment for those older than 50 years, along with a 6.88% positive effect for the right elbow. In the neck area, individuals under 25 years generated an 8.39% higher accumulated moment, while those in the 25-35 and over 50-year-old age groups exerted a 13.65% lower moment on average. Significant deviations in the physical readiness group are seen only in the neck and right elbow areas among individuals with medium and high physical readiness. In the neck area, the group with high physical readiness generated a remarkable 22.99% higher cumulative moment, while those with medium physical readiness generated 15.98% less. In the right elbow, a 5.15% better accumulated moment is observed in the group with medium physical readiness and a 9.74% higher composite moment among those with high physical readiness. When the data are sorted by work experience, individuals with the most experience accumulate, on average, 7.57% less moment across all joints. Interestingly, individuals with 1-2 years of work experience accumulate, on average, 8.98% more moment across all joints. A distinction between genders is only noticeable in the neck area. Women generate 8.93% less moment, while men generate 15.19% more compared to the total sample size average. Comparing these groups, men's cumulative moment is 26.48% higher than women's.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 1 Introduction
We demonstrate that 3D spatial feature information captured from depth images can be used as information on occlusion, enabling accurate prediction even in body-to-body scenarios. Additionally, the proposed HPE method adopts a bottom-up approach to maintain stable low computational complexity regardless of the number of people in the image. As a result, the proposed method mitigates the ambiguity caused by overlapping inter-personal body parts by exploiting the correlation between color and depth information and shows improved accuracy compared to previous bottom-up methods in body-to-body occlusion scenarios. The main contributions of this study are as follows:  
* We propose a novel architecture that integrates depth image features into a bottom-up HPE model. In previous HPE models based on RGB-D images [26; 27; 28], depth images have been used primarily to augment the base features extracted by the feature extractor. In contrast, our approach progressively fuses depth features with those from color images, thereby substantially improving HPE performances in occlusion scenes. We demonstrate that progressively fusing depth features at each stage of the HPE process leads to improved performance. * We specifically selected images containing occlusion scenarios and experimentally demonstrated that leveraging features extracted from depth images leads to clear improvements in pose estimation performance for these cases. Furthermore, we conducted an analysis of how RGB and depth images contribute to pose estimation at each stage of feature fusion. The remainder of this paper is organized as follows. Section 2 reviews related works. Section 3 presents the proposed HPE method using RGB-D images. Section 4 describes the experimental results for the proposed method. Finally, Section 5 provides a conclusion.
## 2 Related Work
### ConvNeXt  
As discussed in Section 2.1, Convolutional Neural Networks (CNN) have been employed as the feature extraction component in existing watermarking methods. However, it is noteworthy that the performance of CNN has become outdated in various tasks. Hence, [PERSON] et al. introduced ConvNeXt, a nomenclature devised to distinguish it from traditional Convolutional Networks (ConvNets) while signifying the next evolution in ConvNets [32]. Rather than presenting an entirely new architectural paradigm, ConvNeXt draws inspiration from the ideas and optimizations put forth in the Swin Transformer [33] and applies similar strategies to enhance a standard ResNet [8]. These optimization strategies can be summarized as follows:  
(1) Modification of stage compute ratio: ConvNeXt adjusts the number of blocks within each stage from (3, 4, 6, 3) to (3, 3, 9, 3).  
(2) Replacement of the stem cell: The introduction of a patchify layer achieved through non-overlapping 4 \(\times\) 4 convolutions.  
(3) Utilization of grouped and depthwise convolutions.  
(4) Inverted Bottleneck design: This approach involves having the hidden layer dimension significantly larger than that of the input.  
(5) Incorporation of large convolutional kernels (\(7\times 7\)) and depthwise convolution layers within each block.  
(6) Micro-level optimizations: These include the replacement of ReLU with GELU, fewer activation functions, reduced use of normalization layers, the substitution of Batch Normalization with Layer Normalization, and the implementation of separate downsampling layers.  
Remarkably, the amalgamation of these strategies results in ConvNeXt achieving a state-of-the-art level of performance in image classification, all without requiring substantial changes to the network's underlying structure. Furthermore, a key feature of this paper lies in its detailed presentation of how each optimization incrementally enhances performance, effectively encapsulated in Figure 2.  
From Figure 2, it is evident that employing the strategy of stage ratio modification and patchify stem leads to an improvement in accuracy, increasing from 78.8% to 79.5%. Further enhancements are observed with the introduction of depth convolution and larger width, resulting in an accuracy improvement of 80.5%. The utilization of an inverted bottleneck and larger kernel size contributes to a higher accuracy of 80.6%. Finally, with micro-optimizations, the accuracy of ConvNeXt reaches 82.0%, surpassing that of Swin.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 3 Results and Discussion  
The training was performed using the PyTorch programming library and the Python v.3 environment. As an optimizer, Stochastic Gradient Descent was used. The training was performed for 10 epochs with a randomly selected batch of training images for each epoch.  
Hyperparameters for the output model were tuned using the Grid Search cross-validation method. All of the results presented in this section were obtained for the best set of hyperparameters (learning rate, batch size, number of learning epochs). The same approach was applied to PyTorch's v2.5.0 ViTMAE model. However, the Grid Search method does not guarantee that the set of output parameters and hyperparameters is optimal. It provides a method for exploring the search space that yields satisfactory results.
## 2 The Proposed Model Algorithm
### Predictive Scoring Module  
After obtaining the final interest representation vector, \(H_{s}\), of session S by the calculation in the previous section, we can use it to make predictive recommendation. Based on the final interest representation vector of session S and the initial vectors of all items, we obtain the predicted score \(y\) of the next item \(v_{i}\in V\), which can be obtained by the following equation:  
\[\hat{y}_{i}=Softmax(H_{s}\tau h_{v_{i}}) \tag{27}\]  
\(\hat{y}_{i}\) represents the probability distribution of all recommended items. The recommendation problem is actually a classification problem, and the input predicted scores are also calculated by the Softmax function, so here we choose the cross-entropy loss function as the loss function. For a single sample, the cross-entropy loss function is calculated as follows:  
\[L=-[ylog\hat{y}+(1-y)\log(1-\hat{y})] \tag{28}\]  
\(y\) is the actual score of the item and \(\hat{y}\) is the predicted score of the item. When \(y=1\), \(L=-log\hat{y}\), the predicted score also tends to \(1\), and the trend of the loss function is consistent with the actual situation. When the actual score \(y=0\), \(L=-\log(1-g)\), the predicted score also approaches the actual score 0, and the loss function L is smaller; the predicted score tends to 1, and the loss function L is also larger. It can be seen that the loss function perfectly replicates the gap between the predicted score and the actual score. Moreover, the growth of the loss function is similar to the nature of the log function, which increases exponentially, which makes the predicted score output by the model after training closer to the actual score data. Therefore, the final loss function is as follows:  
\[L=-\sum_{i=1}^{n}y_{i}\log(\hat{y}_{i})+(1-y_{i})\log(1-\hat{y}_{i})+\lambda|| \theta||^{2} \tag{29}\]  
\(y_{i}\) is the actual score of the item, \(v_{i}\), \(\hat{y}\) is the predicted score obtained, and the set of all learnable parameters \(\theta\) is used as a regularization term to prevent overfitting.
## 4 Implementation and Experiments
### Limitations  
Despite the promising performance of several experiments, our system still has notable limitations. The accuracy of volume estimation tends to decline when processing foods with highly diverse shapes and appearances, which suggests that more advanced modeling approaches are needed to handle such complexity. Although NutriLLM shows strong capabilities in generating nutrition-related content, it remains limited in addressing more complex or nuanced user queries, highlighting the need for further fine tuning and domain  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **BLEU-4** & **ROUGH-1** & **ROUGH-2** & **ROUGH-L** & **METEROR** \\ \hline Qwen2-7B-Instruct & 30.18 & 38.36 & 16.93 & 25.80 & 35.01 \\ BLOOMZ-7b1-mt & 2.61 & 11.63 & 3.32 & 8.04 & 10.56 \\ Llama3-8B-Instruct & 24.67 & 29.77 & 12.80 & 18.59 & 30.09 \\ Llama3-8B-Chinese-Chat & 31.25 & 39.95 & 18.19 & 27.21 & 35.71 \\ NutriLLM & 45.13 & 53.73 & 34.79 & 43.82 & 42.46 \\ \hline \end{tabular}
\end{table}
Table 13: Text generation capabilities of different models.  
specific enhancement. The current processing speed may also pose challenges for real-time use, potentially affecting the overall user experience; future improvements will focus on optimizing computational efficiency while maintaining predictive accuracy. Additionally, the scope of the datasets used for training and evaluation remains confined to a limited range of food types. Broader and more diverse datasets are essential to improve the system's generalizability and robustness. Future work will also explore clinical validation in collaboration with certified nutrition experts or dietitians to strengthen the system's real-world applicability, enabling a more rigorous assessment of its practical effectiveness and reliability.
## 2 Materials and Methods
### Synthesis of AgNPs-FITC and AgNPs-RTTC  
The synthesis procedure was similar to that reported in our previous works for RITC-functionalized AuNPs [57] and was performed using different metal precursor/dye molar ratios, (Ag/FITC = 1/2, 1/1, and 1/0.1, and Ag/RITC = 1/2, 1/1, and 1/0.1, see Supplementary Materials, Table S1a,b). For example, in a typical procedure, Ag/FITC = 1/1 (mol/mol) (AgNPs-FITC\({}_{-}\)1), a water solution of FITC (5.1 \(\times\) 10\({}^{-4}\) mol, in 10 mL of deionized water) was mixed with a water solution of the silver precursor (AgNO\({}_{3}\), 5.1 \(\times\) 10\({}^{-4}\) mol, in 10 mL of deionized water), and the solution was bubbled under nitrogen at room temperature for 10 min. Subsequently, 0.096 g of NaBH\({}_{4}\) (2.5 \(\times\) 10\({}^{-3}\) mol) dissolved in 10 mL of deionized water was added. After 2 h at room temperature, under magnetic stirring, the product was purified (by centrifugation by mini-spin centrifuge at 13,000 rpm, 20 min, 3 times). The main parameters of the characterizations for the samples are as follows:AgNPs-FITC _1: UV-Vis (0.2 mg/mL in H\({}_{2}\)O), \(\lambda_{\text{max}}\) (nm): 398, 474 nm; dynamic light scattering (DLS) in H\({}_{2}\)O, <2R\({}_{\text{H}}\)> (nm): 78 \(\pm\) 12 nm; \(\zeta\)-potential in H\({}_{2}\)O (mV): \(-17\)\(\pm\) 3 mV. AgNPs-FITC _1: UV-Vis (0.2 mg/mL in H\({}_{2}\)O), \(\lambda_{\text{max}}\) (nm): 414, 560 nm; DLS in H\({}_{2}\)O, <2R\({}_{\text{H}}\)> (nm): 68 \(\pm\) 20 nm; \(\zeta\)-potential in H\({}_{2}\)O (mV): \(-22\)\(\pm\) 3 mV.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 4 Approach, LPWAN Communication Technology, and Energy Consumption Analysis
### Device and Measurement Approach  
To evaluate the energy consumption for LPWAN technology, firstly we adhere to Section 3 and secondly, to make the measurement consistent, we baselined the parameters and configurations across all measurements. The energy consumption in the end-device predominantly happens in sensor unit, processing unit and a communication unit. Following are the assumptions used in the experiment to evaluate the energy consumption.  
* ensure constant MCU state among all end-devices;
* no external hardware such as RTC were connected with the modules;
* same battery type and capacity are used for all end-devices;
* fixed packet length of 5 bytes (no sensing is performed) with the provision of only uplink messages;
* techniques such as fog computing, edge computing, the local database are not considered but only real-time measurement and transmission is considered;
* end-devices are powered at the same voltage level equal to 3.7 V.  
For the energy measurements we have used two different power analyzers (to validate the results)-Keysight N6705B DC Power Analyzer [26] and Joulescope Precision DC Energy Analyzer [27]. The experimental setup is shown in Figure 4. It shows the setup of LPWAN end-devices connected to the power analyzer. The energy consumption data logs were further analyzed through the application software from the Keysight Technologies. We have used ABZ-078-860-930 MHz LPWAN MCU Module [28] with Semtech (SX1276) and STM (STM32L) micro-controller for measuring LoRaWAN  
Figure 3: Generic representation for different states of energy consumption in an LPWAN end-device.  
and DASH7, TD1207 module [29] for SigFox, and ultra-low power SARA-N2 module for NB-IoT [30] energy consumption as shown in Figure 5.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 2 Related Work
### Semi-Supervised Anomaly Detection  
DeepLog models the error between the predicted vector and the actual vector in the validation set as a Gaussian distribution. During testing, if the error falls within the high-confidence interval of the Gaussian distribution, the parameter value vector of the log entry is considered normal. Otherwise, it is deemed anomalous. LogBert establishes a candidate set consisting of \(g\) normal log keys. If the log key is in the candidate set, it is considered normal. Otherwise, it is deemed an anomalous log key. Subsequently, when a log sequence consists of more than \(r\) anomalous log keys, the sequence is labeled as anomalous. LAnoBERT utilizes computed loss errors and prediction probabilities for anomaly detection. When the probability of predicting a log occurrence is low, it is difficult to find the corresponding context in the normal log context. Thus, it is identified as an anomalous log sequence. LogFiT is trained only on normal log data using masked sentence prediction. When new log data are used, the \(top-k\) prediction accuracy of the model can serve as a threshold to judge whether the new log data deviate from the normal log data. The aforementioned methods distinguish anomalous log sequences based on data distribution or empirical thresholds. Enabling the neural networks to recognize log sequences belonging to anomalous data distributions is achieved by making the network autonomously determine whether a log sequence is anomalous. Placing a Dirichlet distribution on class probabilities, we treat the neural network predictions as subjective opinions and learn a function from the data that collects evidence leading to these opinions of deterministic neural networks. Leveraging evidential neural networks, the network can autonomously identify normal log sequences and anomalous log sequences.