esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Aspect Ratio  
As described in Section 2.3, the shape of the vortex is analyzed by means of an \"aspect ratio\", meaning the distance between the vortex's velocity profile peaks in the horizontal and vertical directions. Figure 17 shows an example of a strong asymmetrical vortex, how the vortex velocity profile looks in both directions, and how this is visible on the vector plot, as shown in Figure 3. The plots of Figure 12 reveal a trend where the absolute value of the aspect ratio decreases from above 1 to below 1 as the MVG height increases, indicating a change in the vortex shape with increasing MVG height. The aspect ratio is defined as the distance between the peaks in the Y direction over the distance between the peaks in the Z direction. This means that a low aspect ratio corresponds to a flatter elliptical shape of the maximum velocity contours, while a higher aspect ratio indicates a more elongated (vertical) shape. Thus, increasing the MVG height results in a \"flattening\" of the vortex. The main reason for this can also be found in the velocity profiles themselves as we see that the influence of the wall is far greater for lower MVG pairs.  
As can be seen in Figure 8, for a given MVG height, a greater spread between the MVG pairs (higher periodicity) generally leads to lower aspect ratios, likely due to the reduced influence of the adjacent MVG pairs. Placing single MVGs closer together tends to create flatter vortices as the aspect ratio decreases with a higher inner distance over the periodicity values. There does not appear to be a strong correlation between the aspect ratio and drag reduction, as shown in Figure 12, again indicating that this is not a crucial factor in optimal streak creation. Here, a correlation coefficient of 0.23 is only achieved. However, a clear trend emerges when comparing the aspect ratios to the circulation values, shown in Figure 13, where a stronger global correlation coefficient of 0.49 is found. A loweraspect ratio, indicating a flatter vortex, corresponds to a higher circulation value. This observation aligns with the previously described analysis of the circulation trends.
## Applications
### Categories Based on System Architecture
#### 3.2.2 Decentralized Federated Learning  
Decentralized FL eliminates the central server, with clients directly communicating and sharing model updates with each other [46; 47]. This approach enhances resilience and privacy as there is no single point of failure or central data repository [48; 49]. Decentralized FL requires more complex coordination protocols to manage peer-to-peer communications [15; 50; 51].  
**Applications**  
* Peer-to-peer networks: Applications in blockchain or decentralized networks where trust is distributed.  
* Edge computing: IoT devices collaborating directly to update models without a central server.
## 5 Experimental Results and Analysis
### Comparison with DE, PSO, and Three State-of-the-Art DE Variants
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Function** & **Metrics** & **HE-DEPSO** & **DEPSO** & **SHADE** & **CoDE** & **DE** & **PSO** \\ \hline \(F_{28}\) & Std & **3.0463 \(\times\)10\({}^{-2}\)** & 1.8112\(\times\)10\({}^{0}\) & 2.7252\(\times\)10\({}^{0}\) & 6.1120\(\times\)10\({}^{0}\) & 1.6134 \(\times\)10\({}^{-1}\) & 1.5566 \(\times\)10\({}^{2}\) \\  & Rank & 2 & **1** & 4 & 5 & 3 & 6 \\  & Significance & & \(+\) & \(+\) & \(+\) & \(\approx\) & \(+\) \\ \hline \multirow{3}{*}{\(F_{29}\)} & Mean & 2.3616 \(\times\)10\({}^{2}\) & **2.3508 \(\times\)10\({}^{2}\)** & 2.4658 \(\times\)10\({}^{2}\) & 2.5569 \(\times\)10\({}^{2}\) & 2.8284 \(\times\)10\({}^{2}\) & 3.4235 \(\times\)10\({}^{2}\) \\  & Std & 6.1991\(\times\)10\({}^{0}\) & 8.2538 \(\times\)10\({}^{0}\) & **6.1477 \(\times\)10\({}^{0}\)** & 9.0010 \(\times\)10\({}^{0}\) & 1.4685 \(\times\)10\({}^{1}\) & 9.4838 \(\times\)10\({}^{1}\) \\  & Rank & 2 & **1** & 3 & 4 & 5 & 6 \\  & Significance & & \(\approx\) & \(+\) & \(+\) & \(+\) & \(+\) \\ \hline \multirow{3}{*}{\(F_{30}\)} & Mean & **2.0061 \(\times\)10\({}^{2}\)** & 2.0113 \(\times\)10\({}^{2}\) & 2.0227 \(\times\)10\({}^{2}\) & 2.0313 \(\times\)10\({}^{2}\) & 2.0320 \(\times\)10\({}^{2}\) & 1.6044\(\times\)10\({}^{6}\) \\  & Std & **1.5718 \(\times\)10\({}^{-1}\)** & 5.2187 \(\times\)10\({}^{-1}\) & 3.8772 \(\times\)10\({}^{-1}\) & 4.7938 \(\times\)10\({}^{-1}\) & 8.5716 \(\times\)10\({}^{-1}\) & 2.1981\(\times\)10\({}^{6}\) \\  & Rank & **1** & 2 & 3 & 4 & 5 & 6 \\  & Significance & & \(+\) & \(+\) & \(+\) & \(+\) & \(+\) \\ \hline Average rank & **1.68** & 2.27 & 2.75 & 3.24 & 4.48 & 5.48 \\ Final rank & **1** & 2 & 3 & 4 & 5 & 6 \\ W/T/L & \(-/-/-\) & 17/9/3 & 20/4/5 & 24/3/2 & 25/4/0 & 29/0/0 \\ \hline \hline \end{tabular}
\end{table}
# Structure-Aware Trail Bundling for Large DTI Datasets
## 3 Method
### Structure-Emphasizing Rendering  
The additions to the classical CUBu pipeline discussed in Section 3.2--constrained advection, anisotropic advection, and reseeding--generate a bundled trail set \(B(T)\) that has less occlusion than the original set \(T\) but captures well the underlying structures created by fibers. As outlined in Section 2.1, rendering \(B(T)\) using classical methods such as illuminated tubes (Stein
## 3 Results and Discussion
### Optimization of SPME
#### 3.4.2 Effect of Extraction Time  
Due to the equilibrium distribution of organics during SPME process, the extracted organics would reach their maximum amount at the equilibrium time [16]. Stirring of water sample could improve the extraction of analytes due to the faster diffusion of organics to the solid fiber. Analytes, with low distribution constants, need excessive time to reach their equilibrium state [32]. Hence, extraction time is an important parameter during preparation process related to the extraction efficiency [33].  
The influence of extraction times on the extraction efficiency in SPME process was significant (Figure 5). In order to ensure the accuracy of the detection, three replicate water samples were extracted in SPME system and detected in GC-QqQ-MS/MS system at each extraction time. The peak area ratios obtained in different extraction time indicated that the signal intensity increased sharply as the extraction time increased from 10 to 30 min. After 30 min of extraction, further extension of extraction time had no impact on extraction efficiency. The above discussion indicated that 30 min sample extraction time was optimum processing time for the detection of selected analytes.
# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
###### Abstract  
Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising.  
wireless capsule endoscopy; feature selection; color space selection; statistical analysis; support vector machine; grid search; overlapping area; classification +
Footnote †: journal: Applied Sciences
# Analysis of Vertical Stiffness Characteristics Based on Spoke Shape of Non-Pneumatic Tire
#### 2.1.2 Modeling of Spoke Deformation of Non-Pneumatic Tire  
The \"SolidWorks', as an analytic program tool, was used to modify the spoke shape of the non-pneumatic tire model. To analyze the vertical stiffness characteristics according to the deformation of the spoke shape, the outer and inner diameters, the thicknesses of the shear beam and inner band, and the width of the tire were not modified. Only the shape of the spoke was modified.  
The tree branch structure of the spoke model was maintained, and the overall shape was modified into three types: the fillet-applied model, asymmetric-spoke division model, and symmetric-spoke division model.  
1. Fillet-applied model of spoke  
Figure 4 shows the overall shape of the fillet-applied model, in which a fillet is applied to the part of the spoke that experiences concentrated stress and tensile stress. A detailed view of the spokes is also presented in the figure.  
Figure 3: Detailed dimensions of non-pneumatic tire.  
The filter radii were set as 10 mm and 3 mm for the shear beam and inner band connections, respectively. Excessive application of the fillet can increase the weight of the tire and lead to a large stress. Therefore, the model was modified by applying the minimum dimensions that preserved the design characteristics of the tire developed by Kumho Tire Co., Inc.  
(2) Asymmetric-spoke division model  
Figure 4(a) shows the front view and 3D sectional view of the asymmetric-spoke division model, where the width of original (Kumho Tire) model and the shape of the spoke branches were maintained. The model was designed to improve the vertical stiffness by dividing the spoke length in the width direction in half and placing it asymmetrically in both directions. Figure 4(b) shows a detailed cross-sectional view of the asymmetric-spoke division model, and the length of the spokes in the width direction were designed to be 64.9 mm each. To minimize interference when the spoke deforms and to prevent a decrease in rigidity due to the reduction in size, a minimum clearance value of 0.2 mm was applied as the spoke spacing.  
(3) Symmetric-spoke division model  
Figure 5(a) shows a three-dimensional view and a 3D cross-sectional view of a symmetric-spoke division model, where the width of the Kumho Tire model and the basic shape of the spokes were maintained. The model was segmented into three parts and arranged symmetrically by dividing the length of the spokes in the width direction to prevent distortion during loading. Figure 5(b) presents a detailed cross-sectional view of the symmetric-spoke division model, and the lengths of the two side spokes and the central spoke were designed to be 43.27 mm, respectively, in the width direction. To minimize interference when the spokes deform and to prevent a decrease in rigidity due to size reduction, a clearance of 0.1 mm was applied to the spokes, i.e., the minimum margin value for the gap.  
Figure 4: Fillet-applied model.  
Figure 5: Asymmetric-spoke division model.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 3 Simulation Results
### Effect of Distance of Thermal via to the Center on Heat Dissipation of High-Lead QFN Package  
Due to the relatively low thermal conductivity of the PCB, the heat generated by the die often accumulates between the die and the PCB, leading to an elevation in temperature. In order to address this concern, this section investigates the impact of thermal vias on the thermal dissipation performance of the high-lead QFN package by introducing thermal vias into the PCB. We explore this issue by analyzing the influence of thermal vias at different positions on the temperature distribution and conducting a comprehensive comparison against the results under baseline conditions. To mitigate the influence of multiple thermal vias, we have limited the number of thermal vias to one (Figure 6). Simultaneously, we have controlled the diameter and the copper plating thickness of the thermal vias. In this section, the diameter of the thermal vias is set at 0.3 mm, with a copper plating thickness of 0.02 mm, ensuring a fair comparison between thermal vias positioned at various locations.  
Figure 5: Temperature distribution of (**a**) conventional QFN and (**b**) dual-row QFN packages under reference conditions.  
Figure 6: Distribution of a thermal via in a PCB.  
Figure 7 illustrates the maximum temperature scenarios for the high-lead QFN package at different distances from the thermal via to the center of the heat source. It is observable that as the thermal via distance increases, the maximum temperature progressively rises. When the thermal via is located in the central position, the maximum temperature of the high-lead QFN package is 57.4 \({}^{\circ}\)C, indicating optimal heat dissipation performance. Compared to the baseline scenario, the maximum temperature has been reduced by approximately 1.0 \({}^{\circ}\)C. This observation indicates that the closer the thermal via is to the heat source, the more pronounced its effect on the thermal dissipation of the high-lead QFN package. It is worth noting that the high-lead QFN package shows a significant temperature change occurring within the range of 2 mm to 2.5 mm for thermal via distances. This is attributed to the thermal via positioned at 2 mm, located beneath the thermal pad and solder joint, facilitating efficient heat transfer and consequently better heat dissipation. Conversely, the heat generated by the die is not effectively transferred to the thermal via positioned at 2.5 mm, resulting in inferior heat dissipation performance.
## 5 Evaluation
### Machine Learning Framework  
For the effectiveness study, the proposed methodology is evaluated in terms of accuracy, precision, recall, F1 score, root mean square error, and R-squared score as evaluation metrics. The generated dataset is provided to the intrusion detection system that utilizes XGBoost random forest (RF), decision tree (DT), extra tree (ET), stacking, and \(k\)-means algorithms. The detailed results of the intrusion detection effectiveness evaluation are given in Figure 4. It can be seen that all algorithms perform well due to hyperparameter optimization with XGBoost showing the best results with 99.77% accuracy, 0.1055 root mean square error, and 99.81% R-Squared score. These results indicate that algorithms perform well and fit the data model. Further, XGBoost can handle large datasets. Therefore, performing classification or regression using XGBoost typically begins with an estimate, determines the similarities value, and obtains a tree for each potential threshold [68].  
A comparison of the effectiveness with respect to related works is shown in Table 2. The proposed model has improved accuracy, precision, recall, and F1 score as compared to other state-of-the-art related works.  
Figure 4: Effectiveness of the Intrusion detection system using the selected machine learning algorithms.
## 5 The Proposed Routing Method  
Here, the tree-cluster-based secure routing protocol using the dragonfly algorithm (CTSRD) is described for IoT networks in detail. CTSRD consists of three main phases:  
* Distributed and lightweight trust mechanism (W-trust);
* Trust-based clustering process (T-clustering);
* Routing tree based on dragonfly algorithm (DA-tree);
## 2 Background
### HVAC Modeling Techniques
#### 2.1.3 Hybrid Models  
Hybrid models represent an advanced synthesis of data-driven and physics-based methodologies. These models are grounded in physical laws, while their parameters are estimated using data-driven techniques derived from experimentally obtained training data. Such models excel in scenarios where the studied system encompasses physical interactions that are not explicitly defined and where the training dataset is relatively sparse. This dual approach enables hybrid models to effectively capture the complexities of the system, thereby enhancing the accuracy of predictions and overall system optimization. [PERSON] and [PERSON] [24] derived the energy balance equations for each HVAC subsystem and estimated the parameters utilizing the nonlinear least squares optimization technique, successfully capturing the impact of ON/OFF controllers on energy consumption with high accuracy. Similarly, [PERSON] and [PERSON] [15] employed numerically simulated temperature data to estimate the parameters of a semi-nonlinear thermal model designed to approximate the temperature dynamics of a room, using FloVENT software version 9.3. Additionally, [PERSON] and [PERSON] [16] developed a physics-based Auto-Regressive Moving Average with Exogenous Inputs (pbARMAX) model to predict room temperature, demonstrating a root mean squared error of less than 0.10 over a four-week period.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 4 Proposed DEOR Algorithm
### Recovery Mechanism
By utilizing the updated routing quality assessment, void nodes can select appropriate recovery relay nodes downward in the planar accident mine, thereby effectively restoring the transmission path. The pseudocode of the recovery mechanism is shown in Algorithm 3. ```
Input:\(N_{i}\) Output: The candidate recovery forwarding set \(C_{\mathrm{down}}(i)\)
1:for each node \(n_{j}\in N_{i}\)do
2: Get the subset \(N(d_{j})\) using Equation (20)
3: Get the subset \(N(\varepsilon_{j})\) using Equation (21)
4:if\(n_{j}\in N(d_{j})\)&& \(n_{j}\in N(\varepsilon_{j})\)
5: then add \(n_{j}\to C_{\mathrm{down}}(i)\)
6:\(C(i)=C_{\mathrm{down}}(i)\)
7:endif
8:endfor
9:switch to Algorithm 2
```  
**Algorithm 3** Recovery Mechanism of Void Nodes
## 2 Materials and Methods
### Evaluation Criteria and Baseline Model  
To compare the performance of each model, four evaluation metrics commonly used in text classification tasks are used in this paper, namely _Accuracy_, _Precision_, _Recall_ and \(F1\) (F-Score). Since the positive and negative portions of the dataset used in this paper are not balanced, there will be gaps in _Precision_ and _Recall_. Therefore, \(F1\) is used as the most significant metric to assess the predictive ability of the model. \(F1\) is the harmonic mean of  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **Number** \\ \hline training set & 235,132 \\ validation set & 29,392 \\ test set & 29,392 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset division.  
Figure 5: Bipolar data distribution.  
Precision_ and _Recall_, and it can be used to reconcile _Precision_ and _Recall_ to make _Precision_ and _Recall_ compatible. The calculation formula of \(F1\) is as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{5}\]  
To test the performance of the models proposed in this paper, some relevant baseline models are introduced for comparison experiments based on the evaluation criteria. The following are the models involved in this comparative experiment:  
1. CNN: The model uses the pre-trained model of PKUseg [35], an open-source word separation tool of Peking University on the Weibo corpus, for text word separation after data pre-processing. Then, Tencent word vectors are used to construct the word embedding matrix, and each word is converted into a 200-dimensional word vector, which is input into the two-layer convolutional neural network. After the convolution layer and the pooling layer, the output layer is finally passed through the fully connected layer, and the Softmax function is used to complete the sentiment polarity two-classification task.
2. BiLSTM: After converting text data into word vectors according to the above method, the word vectors are input into the BiLSTM model. Finally, the output layer is passed through the fully connected layer, and the Softmax function is used for text sentiment classification.
3. GRU: Similar to the method used above, the model uses GRU for text sentiment classification.
4. ET_P: Sentence vectors representing the semantic features of whole sentences are obtained using Ernie-Tiny processing and passed into the output layer through the fully connected layer. The Softmax function is used for text sentiment classification.
5. ET_s: The model calculates the mean value of the output sequence vector of the last layer of Ernie-Tiny, and then transmits it to the output layer through the fully connected layer. The Softmax function is used for text sentiment classification.
6. ET_s_BiGRU: The sequence vector obtained through Ernie-Tiny processing is input into BiGRU, and then the output of BiGRU is passed into the output layer through the fully connected layer. Finally, text sentiment classification is performed using the Softmax function.
7. ET_s_BG+p: The model proposed in this paper.
## 5 Transgenic Rabbit as a Bioreactor  
Lastly, we will briefly discuss transgenic rabbits as bioreactors. In a bioreactor, recombinant proteins are expressed in mammary gland epithelial cells and produced in the milk of transgenic animals. By purifying the recombinant proteins from the milk, useful substances can be obtained at lower costs than conventional production methods such as cell culture systems [10; 118]. For this purpose, large animals are believed to be suitable as bioreactors because they produce a large amount of milk. In contrast, rabbits require a simple means of farming compared with larger livestock. Breeding rabbits in a laboratory is also possible and they mature early (5-6 months of age) [119]. Their reproductive capacity is high and they deliver six to eight pups per birth. Additionally, rabbits do not have seasonal reproductive cycles and the amount of milk per lactation period is 1-1.5 L [119]. Therefore, rabbits are quite suitable for small scale production and allow rapid development of recombinant proteins. Many researchers have reported the production of valuable physiologically active substances using transgenic rabbits [3; 7; 8].  
The first therapeutic protein produced by a bioreactor for a human disease was antithrombin III (Atrin(r)) from goat milk, which was approved in 2006 in the EU and 2010 the USA. Therapeutic protein from rabbit milk have also been approved, such as C1-esterase inhibitor (Ruconest(r)) for hereditary angioedema in 2010 (EU) and 2014 (USA), and Factor VIIa (Sevenfact(r)) for hemophilia A or B and inhibitors in 2020 (USA).