esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Criteria of Vital Pulp Therapy
### Diagnosis and Treatment Planning  
To determine appropriate treatment options for pulp vitality and necrosis, a comprehensive diagnosis, including pain history, clinical examination, radiographic imaging, three-dimensional cone-beam computed tomography (CBCT), and pulp vitality tests, is of utmost importance [24]. Clinical examination should be done after dental plaque has been removed, the surface is dry and is examined under proper lighting. Additional caries detection methods like Quantitative Light-induced Fluorescence, DIAGNOdent pen, Fiber-optic Transillumination, and Electrical Conductance can also be used. These methods classify cavities based on severity and activity, often determined by reflection and texture [25; 26]. A radiographic examination should also be rendered to see the extent of the caries spread and determine the stage of the carious spread [25; 26; 27]. Another important aspect of the diagnosis includes the differentiation between the affected and infected dentin. The infected dentin is the part of the tooth that will feel soft and is demineralized; on the other hand, the affected dentin is the part of the tooth that is stained, but still has retained hardness and integrity [28]. Recent studies have highlighted the importance of advanced diagnostic tools such as CBCT in providing detailed three-dimensional images, which help in precise diagnosis and treatment planning [29]. CBCT has been shown to improve the accuracy of detecting periapical lesions and assessing the extent of pulp involvement. Additionally, sensitivity tests, including electric pulp testing and thermal testing, are crucial in evaluating the responsiveness of the pulp to stimuli, aiding in the diagnosis of pulp vitality.  
The treatment options for pulpal disease vary based on factors like the tooth's strategic importance, restorative prognosis, patient preference, and overall oral health [30]. The appropriate treatment options can range from VPT to tooth extraction and extensive prosthetic restorations. Learning from consequential evidence, maintaining tooth vitality in the early stages is crucial for tooth longevity and oral health. Early and minimal interventions using pulp capping procedures would deliver optimal disinfection and facilitate the innate healing response of the root canal system of primary teeth, which can be succeeded by the permanent tooth without introducing a periapical or periodontal pathology. The various methods in vital pulp treatment include IPT, DPC, partial pulmonary, and full pupotomy. A pulpectomy is used in non-vital cases, but surgical interventions like extraction may be necessary in beyond-restorable cases [31]. Among these intervention approaches, DPC could maintain pulpal vitality while promoting natural reparative processes at the beginning of the caries progression, making it a key approach in modern minimally invasive dentistry.
## 5 Research on Optimization Design of ILVT Structure  
### Scheme Optimization Design  
The quest to accommodate larger axle load train drive through the ILVT in heavy-haul railway, based on the calculation results on the mechanical performance and full-scale test of the two track structures under the axle load of 30t and above, coupled with the quests to accommodate larger axle load train through LVT in tunnel, the optimization was suggested for the weakest position of the track structure. It can be seen from Section 4 that under the lateral and vertical design loads of the 30t and 35t axle heavy-haul train, the maximum stress of the track bed slabs and bearing blocks of the two track structures are within the ultimate tensile strength of concrete. Therefore, we took the 40t axle heavy-haul train as the research object, and used the double-axis load to apply the lateral and vertical design load  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**TLVT**} & \multicolumn{3}{c}{**ILVT**} \\  & **30** & **35** & **40** & **30** & **35** & **40** \\ \hline T1 & 2.31 & 2.69 & 3.06 & 2.03 & 2.34 & 2.65 \\ T2 & 1.15 & 1.32 & 1.50 & 1.13 & 1.26 & 1.40 \\ T3 & 1.66 & 1.93 & 2.20 & 1.59 & 1.84 & 2.09 \\ T4 & 2.63 & 3.10 & 3.58 & 2.60 & 3.07 & 3.53 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Stress value at No. 11 loading point under lateral and vertical coupled load (MPa).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**TLVT**} & \multicolumn{3}{c}{**ILVT**} \\  & **30** & **35** & **40** & **30** & **35** & **40** \\ \hline T1 & 1.03 & 1.21 & 1.37 & 1.45 & 1.63 & 1.82 \\ T2 & 1.03 & 1.07 & 1.14 & 1.07 & 1.22 & 1.36 \\ T3 & 0.21 & 0.25 & 0.28 & 0.76 & 0.85 & 0.95 \\ T4 & 2.12 & 2.51 & 2.83 & 2.33 & 2.79 & 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Stress value at No. 8 loading point under vertical load (MPa).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**TLVT**} & \multicolumn{3}{c}{**ILVT**} \\  & **30** & **35** & **40** & **30** & **35** & **40** \\ \hline T1 & 1.82 & 2.08 & 2.40 & 1.58 & 1.80 & 2.08 \\ T2 & 1.73 & 2.02 & 2.22 & 1.28 & 1.48 & 1.68 \\ T3 & 1.15 & 1.34 & 1.50 & 1.33 & 1.50 & 1.67 \\ T4 & 2.09 & 2.45 & 2.80 & 2.28 & 2.74 & 3.07 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Stress value at No. 11 loading point under vertical load (MPa).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**TLVT**} & \multicolumn{3}{c}{**ILVT**} \\  & **30** & **35** & **40** & **30** & **35** & **40** \\ \hline T1 & 1.90 & 2.21 & 2.53 & 1.93 & 2.22 & 2.52 \\ T2 & 0.64 & 0.74 & 0.84 & 0.88 & 0.99 & 1.09 \\ T3 & 0.41 & 0.48 & 0.54 & 0.86 & 1.01 & 1.15 \\ T4 & 2.71 & 3.20 & 3.69 & 2.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Cse-Cic-Ids2017 Results
#### 4.3.2 Overall Accuracy and Macro-F1  
Table 5 presents the overall classification accuracy and macro-F1 scores on the CSE-CIC-IDS2017 dataset. All methods achieve high overall accuracy (\(\geq\)99.5%), with minimal variation across traditional and GAN-based augmentation techniques. This confirms that the proposed enhancements do not compromise general classification performance.  
Macro-F1 provides a more balanced assessment of performance under class imbalance by treating each class equally. Across all the configurations, integrating SNNL yields slight but consistent improvements in macro-F1 compared to the base models. For instance,  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Bot**} & \multicolumn{3}{c}{**Infiltration**} \\  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SMOTE & 0.333 & **0.957** & 0.473 & 0.723 & **0.670** & 0.630 \\ ADASYN & 0.347 & **0.957** & 0.480 & 0.723 & **0.670** & 0.630 \\ ROS & 0.440 & 0.923 & 0.560 & 0.693 & 0.640 & **0.643** \\ WGAN & 0.838 & 0.534 & 0.645 & **0.833** & 0.346 & 0.478 \\ WGAN+SNNL & 0.844 & 0.546 & 0.656 & **0.833** & 0.442 & 0.558 \\ CWGAN & **0.863** & 0.534 & 0.648 & **0.833** & 0.315 & 0.446 \\ CWGAN+SNNL & 0.857 & 0.560 & **0.677** & **0.833** & 0.363 & 0.487 \\ WGAN-GP & 0.848 & 0.545 & 0.658 & **0.833** & 0.363 & 0.493 \\ WGAN-GP+SNNL & 0.847 & 0.547 & 0.664 & **0.833** & 0.442 & 0.559 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minority-class performance comparison on CSE-CIC-IDS2017 dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Accuracy (\%)** & **Macro-F1 (\%)** \\ \hline SMOTE & 99.5 & 85.9 \\ ADASYN & 99.5 & 85.1 \\ ROS & 99.6 & 87.7 \\ WGAN & 99.7 & 86.6 \\ WGAN+SNNL & 99.7 & 87.9 \\ CWGAN & 99.7 & 86.1 \\ CWGAN+SNNL & 99.7 & 87.1 \\ WGAN-GP & 99.7 & 87.0 \\ WGAN-GP+SNNL & 99.7 & 88.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall accuracy and macro-F1 comparison on CSE-CIC-IDS2017 dataset.  
WGAN-GP+SNNL achieves the highest macro-F1 of 88.0%, up from 87.0% in WGAN-GP. These results suggest that SNNL improves class-wise balance without adversely affecting majority-class performance or overall accuracy.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 7 Result Comparison and Performance Evaluation
### Protocol Evaluation
#### 7.2.3 Scenario 3  
Figure 11 shows the PDF, throughput and overhead vs covered area. In 30 \(\times\) 30 m\({}^{2}\) terrain dimension with PUMA, ERASCA and EERASCA protocol, EERASCA demonstrates superiority over PUMA, ERASCA within a small, covered area because of the fewer possibilities of packet drop due to link failure. Hence, throughput and packet delivery fraction improve and overhead depreciates. In contrast, with the increase in the covered area, throughput and packet delivery fraction decrease, but its graph is still higher than the PUMA and ERASCA, as shown in Figure 11a,b. The performance further deteriorates when flooding is used. EERASCA shows improved PDF, overhead, and throughput in 11c than PUMA and ERASCA because both PUMA and ERASCA flooding are used. In  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Protocol** & **Mean Interval** & **Confidence Interval** \\ \hline EERASCA PDF & 90.56\% & 93.08–88.04\% \\ \hline ERASCA PDF & 84.88\% & 86.08–83.68\% \\ \hline PUMA PDF & 79.94\% & 81.45–78.43\% \\ \hline EERASCA Overhead & 2.41 byte/s & 2.47–2.36 bit/s \\ \hline ERASCA Overhead & 2.45 byte/s & 2.50–2.40 bit/s \\ \hline PUMA Overhead & 2.49 byte/s & 2.54–2.44 bit/s \\ \hline EERASCA Throughput & 88.54\% & 89.70–87.38\% \\ \hline ERASCA Throughput & 86.53\% & 87.83–85.23\% \\ \hline PUMA Throughput & 82.55 & 83.65–81.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confidence Interval for PDF, Overhead and Throughput.  
Figure 10: Comparison of Number of Receivers with PDF, Overhead and Throughput.  
EERASCA, multicasting is used in a large, covered area, and unicasting is used in a small covered area with a smaller number of nodes. The confidence interval for PDF, overhead and throughput is shown in Table 5 in the presence of covered area (0-30 m\({}^{2}\)), which shows the mean value, highest and lowest value.
## 6 Results
### Effectiveness of Spectrogram Enhancement  
One of the main contributions of this work is the spectrogram enhancement presented in Section 2.3.3. It is evident qualitatively from Figure 9 that the proposed approach enhanced the features of the spectrogram. In order to compare the performance quantitatively, we employed the SSIM index. Here, we considered the spectrogram generated just after the ADC as the ground truth before any digital-domain processing and denoising. The achieved SSIM indices for the spectrograms enhanced using the proposed algorithm and that in [8] are presented in Table 8. We note that a higher SSIM means more similarity for the original noisy spectrogram and vice versa. Accordingly, the low SSIM index achieved with the proposed algorithm indicated that the proposed algorithm provided a better enhancement compared to the algorithm proposed in [8].
## 5 Experiment
### Position of Attention Module  
We conducted an ablation study to evaluate the impact of the position of attention module. To evaluate the performance, we included the performance of the attention mechanism that achieved the highest performance at each specified position and each sub-dataset and analyzed the tendency based on positional differences. Through this study, we can validate that the model performs best when the attention module is placed on the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Attention**} & **Relative** & **FLOPs** & \multirow{2}{*}{**Params (K)**} \\  & **Performance** & **(GFLOPS)** & \\ \hline STDenseNet & 1.000 & **30.04** & **47.48** \\ MVSTGN & 0.416 & 33.72 & 284.19 \\ \hline ADNet w. 3 \(\times\) 3 Spatial & 1.013 & 30.06 & 47.52 \\ ADNet w. 5 \(\times\) 5 Spatial & 1.051 & 30.10 & 47.59 \\ ADNet w. 7 \(\times\) 7 Spatial & 1.038 & 30.17 & 47.68 \\ \hline ADNet w. Channel & 0.961 & 30.13 & 48.72 \\ \hline ADNet w. 3 \(\times\) 3 CBAM & 0.989 & 30.15 & 52.44 \\ ADNet w. 5 \(\times\) 5 CBAM & 0.989 & 30.19 & 52.70 \\ ADNet w. 7 \(\times\) 7 CBAM & 0.960 & 30.25 & 53.08 \\ \hline ADNet w. 3 \(\times\) 3 Triplet & 1.068 & 30.10 & 47.61 \\ ADNet w. 5 \(\times\) 5 Triplet & 1.088 & 30.20 & 47.80 \\ ADNet w. 7 \(\times\) 7 Triplet & 1.082 & 30.34 & 48.09 \\ \hline ADNet w. Non-local Softmax & **1.113** & 145.23 & 56.97 \\ ADNet w. Non-local Gaussian & 1.086 & 41.99 & 66.32 \\ \hline ADNet w. Triplet Non-local Softmax & 0.958 & 311.13 & 97.67 \\ ADNet w. Triplet Non-local Gaussian & 1.066 & 77.02 & 147.32 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of FLOPs, MACs, and parameters for different attentions in 100 \(\times\) 100 grid. The bold numbers indicate the best performances, while the underlined numbers indicate the second-best in the table.  
Figure 4: The visualization of comparison between FLOPs and parameters for different attention in 100 \(\times\) 100 grid.  
last of dense block structure (Table 8). We attribute this result to the DenseNet architecture used for feature extraction. When the attention module is applied after feature extraction, it computes attention scores based on the final feature map, which is computed with the input and hidden layers' outputs. In contrast, when the attention layer is placed before or during feature extraction, the feature map used for attention score computation contains relatively less information, potentially leading to lower performance. Also, we can observe that inserting the attention layer between dense blocks in feature extraction resulted in lower performance compared to placing it before feature extraction. This phenomenon may be explained by the DenseNet architecture in feature extraction. When the dense blocks go deeper, the number of feature maps that the block receives also increases. These inputs consist of both feature maps processed by the attention module and feature maps bypassing it via skip connections. The entanglement of feature maps from different levels may harm the effectiveness of the attention mechanism.
## 4 Design of the POM-PSASIDEA Algorithm
### Progressive Optimization Mechanism  
The existing MSIMPLTS problem is stuck using the overall optimization mechanism to perform satellite and target task sequence allocation, resulting in the existence of a local optimum rather than a global optimum, as well as introducing a task conflict problem. This paper proposes a progressive optimization mechanism (POM) based on the MSIMPLTS-MLOO model. The framework of the progressive optimization mechanism is shown in Figure 3, which consists of three phases: allocation optimization, time window optimization, and global optimization.  
Figure 3: Progressive optimization mechanism.
## 3 Results
### Analysis of the Microbial Community Composition and Diversity
#### 3.3.2 Analysis of the Microbial Community Composition and Diversity
Primarily, the bacteria in the YQ sample included _Virgibacillus_ (23.80%), _Kroppenstedtia_ (20.55%), _Oceanobacillus_ (18.88%), _Saccharomyces_ (8.10%), _unclassified_f_Bacillus_ (5.46%), _Scopulibacillus_ (4.16%), _Bacillus_ (3.79%), _norank_f_Pseudonocardiaceae_ (2.38%), _Pseudogracilibacillus_ (2.11%), _Staphylococcus_ (1.71%), and _unclassified_C_Bacillus_ (1.42%). Figure S1a shows the results at the species level; _Bacillus_smithii_ within the genus _Bacillus_ accounted for 34.78% and 27.20% bacterial abundance in the BQ and YQ samples, much higher than WQ (0.06%). These results revealed that the compositions of the bacterial communities in different _Daqu_ types were significantly different. Figure 3: Microbial community composition of each sample. (**a**) Diagram for the bacteria according to the phylum level. (**b**) Diagram for the bacteria according to the genus level. (**c**) Diagram for the fungi according to the phylum level. (**d**) Diagram for the fungi according to the genus level. At the fungal phylum level, Ascomycota was the predominant fungal phylum, and the Ascomycetes level was nearly 100% in the BQ (98.26%), WQ (99.29%), and YQ (99.85%) samples (Figure 3c). Figure 3d displays the differences in the three samples at the level of the fungal genus. The genera of _Thermoascus, Thermomyces, Byssochlamys_, and _Aspergillus_ were dominant in all samples. The abundances of _Thermoscus_ were 38.15%, 40.68%, and 31.95% in the BQ, WQ, and YQ samples, respectively. _Thermos_ was also present in the BQ sample (18.03%) and in WQ (34.86%) and YQ (37.10%). The abundances of _Byssochlamys_ were 31.74%, 17.31%, and 24.78% in BQ, WQ, and YQ, respectively. _Aspergillus_ in BQ, YQ, and WQ accounted for 7.77%, 3.29%, and 5.45%, and _Saccharomyces_ in BQ, YQ, and WQ accounted for 0.84%, 1.32%, and 0.30%, while _Monascus_ in BQ, YQ, and WQ accounted for 1.22%, 0.85%, and 0.15%. It should be emphasized that high-temperature types of _Dauq_ were also found to include _Saccharomyces_ and _Monascus_, both of which had low relative abundances (1%) but were essential for the fermentation of baijiu. At the fungal species level, _Thermomyces_Januginosa_ within the genus _Thermomyces_ accounted for 37.09% and 34.83% fungal abundances in YQ and WQ. _Thermascus_crusta_ within the genus _Thermascus_ had the highest proportion in YQ (31.86%). Lastly, _Byssochlamys_spectabilis_ within the genus _Byssochlamys_ occupied the highest proportion of the BQ sample (31.71%) (Figure S1b).
## 3 State of the Art
### Graph-Based Design Languages Implemented with UML and DC43
#### 3.2.3 Implementation of Graph-Based Design Languages with UML and DC43
This compiler uses this information to generate the design graph. From this central meta-model of the system, additional models (e.g., geometry models in generic  
Figure 9: Entities and associations in the UML class diagram of a graph-based design languages. CAD formats, simulation models such as models for finite element analyses (FEA)) for multibody system (MBS) analyses or for computer-aided planning (CAP) or reports in office tools can be generated in a fully automated manner. In the multicopter project, it was possible to create several generic models (Figure 10--adapted from [49]). The design compiler is able to process all abstraction levels of a design language in a manner that is detailed enough to create sufficient information for elaborate simulation models. This capability may lead to an automated product development process within this unified framework. The main difference between this framework based on GBDLs and conventional product development can be described based on the characteristic that a single product manifestation is not designed, but that a large \"family\" of product variants is designed automatically. Thus, it is important to note that the automated design of a product family instead of a single product necessitates a novel mindset on the part of the involved designer and may, therefore, be understood as a paradigm shift. The implementation of the design language is the engineer's skill and depends on his/her experience; the creation of the different engineering models is \"mechanistic\" work for the compiler. In this process, the design compiler executes design rules formulated in UML activity diagrams and can automatically derive the analysis models needed for respective design domains (CAD, MBS, FEA, etc.). The information content of both the graph and the engineering models is not limited to geometry. The graph and the engineering models can also contain information concerning functions, material(s), physical loads, etc.
# Extending Appearance Based Gait Recognition with Depth Data
## 3 Image Processing and Feature Extraction
### Determination of Human Height from the Silhouette  
Once the silhouette has been extracted, the height of the corresponding person can be estimated using the information regarding the distance of the person from the camera. This information can be derived since the distance of each point of the silhouette is encoded in the depth image (Figure 3).  
The height feature is constructed as the average of the estimated silhouette heights in the gait cycle:  
\[H(t)=\frac{1}{N}\sum_{t=1}^{N}h(t) \tag{2}\]  
The height estimation accuracy depends on various factors, including the type and technology of depth sensor used, the environment conditions, the current motion state of the subject, etc. An analysis of the estimation accuracy obtainable from depth images in indoor environments and estimation results of different methods is presented in [22]. In particular, the analysis reports height estimation results for humans in the state of walking, as opposed to humans standing still in front of the camera, which is of interest for this work.  
Figure 3: The distance from the camera to the silhouette is encoded in the depth image (**a**) using different color/gray levels for the pixels depending on the distance of the corresponding object from the camera plane. The relation with the actual distance is shown on the right (**b**).  
Figure 2: Example GEI images from: (**a**) CASIA B dataset, with silhouettes extracted from RGB images; (**b**) TUM GAID dataset, with silhouettes extracted from depth images; and (**c**) custom dataset, with silhouettes extracted from RGB images in challenging lighting conditions.
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 3 Methods
### Hyperparameter Optimization  
As mentioned, both ESNs and DDNs are randomly initialized networks, which can be seen as samples from a hyperparameter-defined distribution. Although conventional ESNs are usually defined as a'single, homogeneous reservoir' (i.e., all neurons are sampled from the same hyperparameter distribution), in [17] we compared both single-reservoir and multi-reservoir ESNs to equivalent DDN counterparts. In the case of multi-reservoir ESNs, this means that neurons are sampled from a multimodal hyperparameter distribution. We showed that multi-reservoir systems outperform single reservoirs, both in the case of ESNs and DDNs. As such, in this work, both ESN and DDN systems were optimized as a multi-reservoir system, where hyperparameters were defined for multiple sub-reservoirs (referred to as clusters), and connection-related hyperparameters were defined between and within each cluster. ESN hyperparameters were the means and standard deviations of the reservoir weights \(\mathbf{W_{\text{res}}}\) defined per cluster pair, the leak parameters \(\alpha\), and the mean and standard deviation of the bias weights \(\mathbf{b_{\text{res}}}\) defined per cluster. In addition to the standard ESN hyperparameters, DDNs also specified a distribution of neuron spatial coordinates. These were sampled from a multi-modal Gaussian distribution, whose parameters were optimized along with the standard ESN hyperparameters.  
To optimize these hyperparameters for specific tasks, we made use of the covariance matrix adaptation evolutionary strategy (CMA-ES) [31], which is an evolutionary optimization technique suitable for highly irregular error landscapes. The CMA-ES is commonly used for RC hyperparameter tuning [32; 33]. We sampled 20 candidate hyperparameter sets for each generation. From each candidate, we randomly sampled five reservoirs. We optimized DDNs and ESNs for the delayed bitwise XOR task and the NARMA-30 task, which are further explained in Section 3.6. We trained and validated each reservoir, and used the normalized root mean squared error (NRMSE) on the validation set, averaged over the five reservoirs as a fitness measure for the candidates. Each model type (i.e., the ESN and DDN) was optimized over 200 generations. In the remainder of this paper, unless otherwise specified, we analyze the properties of reservoirs sampled from the best hyperparameter candidate throughout the evolution.