esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Managing Uncertainty in Input Parameters: A Probabilistic Approach  
To enhance the ability of the IBKA-CNN-Transformer model to handle uncertainties in input parameters, this study introduces a probabilistic approach designed to capture the variability of key input parameters. Landslide dam lifespan prediction is influenced by numerous uncertain factors, including hydrological conditions, dam geometry, and material properties, which can fluctuate due to environmental changes, measurement errors, and natural variability. As such, properly accounting for these uncertainties in the model is crucial for improving prediction reliability and generalization.  
To model these uncertainties, appropriate probability distributions are assigned to each key input parameter, such as rainfall intensity, upstream catchment area, dam height, dam width, and material strength. These distributions are selected based on historical data analysis and expert domain knowledge. By adopting this approach, the model avoids treating parameters as fixed values, allowing them to vary within a defined range, thereby providing a more accurate representation of the uncertainty inherent in real-world scenarios.  
Figure 3: Intelligent prediction model frame diagram.  
The Monte Carlo simulation method [34] is employed to implement this probabilistic approach. Each input parameter is sampled randomly, and the model is iteratively run to generate multiple lifespan predictions. This process produces uncertainty intervals for each prediction, thus overcoming the limitations of traditional methods, which typically provide only a single deterministic forecast. By adopting this approach, we obtain a distribution of predictions, each associated with a confidence level, and can further assess the risks involved.  
Ultimately, this probabilistic modeling approach enables a more comprehensive risk assessment. It not only quantifies the uncertainty in the predictions but also assists decision makers in evaluating potential risks. This methodology significantly improves the model's applicability and reliability in practical engineering contexts, particularly for managing uncertainties in complex systems like landslide dam lifespan prediction.
## 3 Results
### Vertical Ground Reaction Forces  
A two-way repeated-measures ANOVA revealed a significant main effect of the plantar foot region on vGRFs for walking condition A (F = 109.035, \(p\) < 0.001, \(\eta^{2}\) = 0.732), B (F = 144.729, \(p\) < 0.001, \(\eta^{2}\) = 0.783), C (F = 150.539, \(p\) < 0.001, \(\eta^{2}\) = 0.790), D (F = 157.323, \(p\) < 0.001, \(\eta^{2}\) = 0.797), and E (F = 101.093, \(p\) < 0.001, \(\eta^{2}\) = 0.716). A significant interaction between the plantar foot region and foot type on vGRFs was also observed for walking condition A (F = 10.903, \(p\) < 0.001, \(\eta^{2}\) = 0.214), B (F = 12.830, \(p\) < 0.001, \(\eta^{2}\) = 0.243), C (F = 7.958, \(p\) < 0.001, \(\eta^{2}\) = 0.166), D (F = 9.134, \(p\) < 0.001, \(\eta^{2}\) = 0.186), and E (F = 6.158, \(p\) < 0.001, \(\eta^{2}\) = 0.133). Post hoc analyses using Bonferroni corrections showed that vGRFs exerted on the midfoot were significantly greater for individuals with hyperpronation compared to their counterparts with a neutral foot type in all walking conditions. Additionally, vGRFs were significantly lower on the medial and lateral forefoot in the hyperpronated foot type group compared to the neutral foot type group (see Figure 6 for pairwise comparisons). There was no significant main effect of foot type on vGRFs across all walking conditions.  
Figure 5: Means and standard deviations (error bars) of the (**a**) anteroposterior and (**b**) mediolateral center of pressure (COP) location in individuals with hyperpronated and neutral foot type during downhill (A and B), level (C) and uphill (D and E) walking. Negative numbers on the vertical axis of the graphs indicate the posterior and left locations of the COP in the anteroposterior and mediolateral axes, respectively. Significantly different compared to \({}^{\rm a}\) A (\(p\) < 0.05); \({}^{\rm b}\) B and C (\(p\) < 0.001); \({}^{\rm c}\) A, B, C and D (\(p\) < 0.001); \({}^{\rm d}\) A and B (\(p\) < 0.001); \({}^{\rm e}\) A (\(p\) < 0.05) and B (\(p\) < 0.001) walking conditions. \({}^{\star}\)\(p\) < 0.05; \({}^{\star\star\star}\)\(p\) < 0.01.  
(F = 116.274, \(p\) \(<\) 0.001, partial \(\eta^{2}\) = 0.744), midfoot (F = 42.535, \(p\) \(<\) 0.001, partial \(\eta^{2}\) = 0.515), lateral forefoot (F = 50.956, \(p\) \(<\) 0.001, partial \(\eta^{2}\) = 0.560), inner forefoot (F = 329.992, \(p\) \(<\) 0.001, partial \(\eta^{2}\) = 0.892), medial forefoot (F = 58.094, \(p\) \(<\) 0.001, partial \(\eta^{2}\) = 0.592), and toes (F = 71.764, \(p\) \(<\) 0.001, partial \(\eta^{2}\) = 0.642), regardless of foot type.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.4 IoT and Sensors  
The aim is to utilize _smart sensors_, and in general the _IoT paradigm_, for multiple fields of application. In particular, an IoT data management platform will be developed on which to graft control algorithms and represent current and forecast values on dashboards, including notifications and alerts in real time. The 5G network will enable the direct communication of objects (sensors, cameras, etc.), thus speeding up and optimizing the implementation processes. Specific objectives are as follows:  
* real-time remote control of the operating conditions of processes or plants;
* remote control of heavy machinery in hazardous environments;
* logistics optimization;
* tracking of products.  
One of the most important applications is the collection of environmental data, including those related to the health of the workplace, data for the management of a smart city, and data deriving from remote reading systems of gas meters, water, and electricity.
# Image Watermarking Scheme Using LSB and Image Gradient
## 3 Methodology  
### Watermarking Scheme  
In this paper, an image watermarking based on a chaotic map using LSB and image gradient is presented. The goal of this technique is to provide high capacity, imperceptibility, and robustness against attacks. The performance of our suggested approach is tested by considering well-known parameters, i.e., peak signal-to-noise ratio (PSNR) and normalized correlation (NC). The appropriate embedding positions are chosen using the gradient magnitude and direction of the individual image block. The embedding used in this technique is LSB because of its low computation cost and high perceptual capacity. This proposed framework gives high robustness against common image processing and geometrical attacks because of its embedding in edge surface areas.
## 3 Results
### Antitussive Assay
#### 3.2.3 Histopathological Findings of the Trachea and Lung
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Diameter of Lumen** & \multicolumn{3}{c}{**Thickness (\(\mu\)m)**} & **Cells (Numbers/mm\({}^{2}\))** \\
**Groups** & **(\(\mu\)m)** & **Total Wall** & **Epithelium** & **Submucosa** & **Inflammatory** & **Mast** \\ \hline Controls & & & & & \\ Intact & 1177.86 \(\pm\) 130.95 & 161.47 \(\pm\) 15.34 & 14.38 \(\pm\) 2.91 & 26.68 \(\pm\) 4.85 & 21.40 \(\pm\) 12.21 & 1.20 \(\pm\) 0.79 \\ NH4 OH & 658.57 \(\pm\) 106.44 \({}^{\rm a}\) & 220.38 \(\pm\) 13.45 \({}^{\rm h}\) & 37.53 \(\pm\) 9.48 \({}^{\rm h}\) & 89.73 \(\pm\) 10.46 \({}^{\rm h}\) & 449.30 \(\pm\) 102.75 \({}^{\rm h}\) & 38.70 \(\pm\) 11.66 \({}^{\rm h}\) \\ Reference & & & & & \\ TB 50 mg/kg & 860.11 \(\pm\) 100.75 \({}^{\rm ac}\) & 191.23 \(\pm\) 10.24 \({}^{\rm h}\) & 24.21 \(\pm\) 4.36 \({}^{\rm h}\) & 47.31 \(\pm\) 11.39 \({}^{\rm h}\) & 167.40 \(\pm\) 40.26 \({}^{\rm h}\) & 14.30 \(\pm\) 4.60 \({}^{\rm h}\) \\ AR 400 mg/kg & 934.14 \(\pm\) 104.43 \({}^{\rm ac}\) & 185.59 \(\pm\) 10.94 \({}^{\rm h}\) & 23.07 \(\pm\) 3.32 \({}^{\rm h}\) & 44.99 \(\pm\) 6.09 \({}^{\rm h}\) & 62.70 \(\pm\) 37.57 \({}^{\rm h}\) & 8.60 \(\pm\) 2.27 \({}^{\rm h}\) \\ KOG 400 mg/kg & 934.89 \(\pm\) 83.13 \({}^{\rm ac}\) & 186.21 \(\pm\) 11.10 \({}^{\rm h}\) & 23.87 \(\pm\) 2.38 \({}^{\rm h}\) & 40.31 \(\pm\) 6.64 \({}^{\rm h}\) & 184.10 \(\pm\) 20.32 \({}^{\rm h}\) & 8.40 \(\pm\) 1.51 \({}^{\rm h}\) \\ SKOG & & & & & \\
400 mg/kg & 1087.76 \(\pm\) 118.72 \({}^{\rm bcdf}\) & 171.56 \(\pm\) 7.07 \({}^{\rm dam}\) & 18.92 \(\pm\) 2.14 \({}^{\rm h}\) & 30.30 \(\pm\) 3.24 \({}^{\rm h}\) & 85.50 \(\pm\) 19.82 \({}^{\rm h}\) & 1.80 \(\pm\) 1.03 \({}^{\rm h}\) \\
200 mg/kg & 1036.55 \(\pm\) 76.84 \({}^{\rm acg}\) & 175.45 \(\pm\) 5.03 \({}^{\rm h}\) & 19.74 \(\pm\) 1.37 \({}^{\rm h}\) & 32.71 \(\pm\) 3.79 \({}^{\rm h}\) & 120.70 \(\pm\) 20.94 \({}^{\rm h}\) & 3.70 \(\pm\) 1.34 \({}^{\rm h}\) \\
100 mg/kg & 936.74 \(\pm\) 70.89 \({}^{\rm ac}\) & 185.19 \(\pm\) 7.57 \({}^{\rm h}\) & 23.76 \(\pm\) 3.34 \({}^{\rm h}\) & 42.41 \(\pm\) 8.60 \({}^{\rm h}\) & 177.60 \(\pm\) 26.02 \({}^{\rm h}\) & 8.70 \(\pm\) 2.16 \({}^{\rm h}\) \\ \hline \end{tabular}  Values are presented as the mean \(\pm\) SD of 10 mice. Different letters indicate statistical significance. Treatment groups expressed as \({}^{\rm a}\)\(p\)\(<\) 0.01 and \({}^{\rm b}\)\(p\)\(<\) 0.05, \({}^{\rm c}\)\(p\)\(<\) 0.01, \({}^{\rm d}\)\(p\)\(<\) 0.01 and \({}^{\rm e}\)\(p\)\(<\) 0.05, and \({}^{\rm f}\)\(p\)\(<\) 0.01 and \({}^{\rm g}\)\(p\)\(<\) 0 Figure 2: Changes in the histological features of trachea in mice. Different treatment groups as followed by the (**A**) intact vehicle control, (**B**) NH4 OH control, (**C**) TB 50 mg/kg + NH4 OH, (**D**) AR 400 mg/kg + NH4 OH, (**E**) KOG 400 mg/kg + NH4 OH, (**F**) SKOG 400 mg/kg + NH4 OH, (**G**) SKOG 200 mg/kg + NH4 OH, and (**H**) SKOG 100 mg/kg + NH4 OH. Scale bar = 120 μm. LU: Lumen; CA: Cartilages; SM: Submucosa; EP: Epithelium.
## 4 Results
### Comparative Experiments  
In order to comprehensively evaluate the performance of the YOLOv5-G model, this paper selected the two-stage Faster-RCNN algorithm and the one-stage SDD YOLOv4 and YOLOv4-tiny algorithms and used the same training techniques and parameter settings to conduct experiments on a self-made dataset. The comparative experiments are shown in Table 7.  
Table 7 shows that the mAP of the YOLOv5-G algorithm is 92.8%, which is 8.5%, 10.7%, 4.1%, and 14.2% greater than the mAPs of the Faster-RCNN, SSD, YOLOv4, and YOLOv4-tiny algorithms, respectively. YOLOv5-G has a higher average accuracy than other algorithms. YOLOv5-G has a strong generalization ability and can learn target generalization information. It has certain universality. When it is migrated, the model has high robustness, so YOLO is much more accurate than other target detection methods. The detection rate of YOLOv5-G is higher than that of the Faster-RCNN, SSD, and YOLOv4 algorithms in terms of detection speed FPS. YOLOv5-G is 3.8 times and 3.1 times quicker than the SSD and YOLOv4 algorithms, respectively, and has a detection rate that is 17.2 times faster than that of Faster-RCNN. Compared to the YOLOv4-tiny algorithm, YOLOv5-G  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Ghost** & **CA** & **ECIOU** & **mAP** & **Params** & **GFLIOP** & **FPS** & **Size** \\ \hline YOLOv5 & - & - & - & 91.1\% & 7,015,519 & 15.8 & 55 & 14.5 \\
1 & \(\surd\)1 & - & - & 87.5\% & 3,678,423 & 8.1 & 123 & 7.9 \\
2 & - & \(\surd\) & - & 95.2\% & 7,028,855 & 15.9 & 51 & 14.6 \\
3 & - & - & \(\surd\) & 91.6\% & 7,015,519 & 15.8 & 61 & 14.5 \\
4 & \(\surd\) & \(\surd\) & - & 92.4\% & 3,746,583 & 8.2 & 98 & 8.0 \\ YOLOv5-G & \(\surd\) & \(\surd\) & \(\surd\) & 92.8\% & 3,746,583 & 8.2 & 103 & 8.0 \\ \hline \hline \end{tabular}  
* stands for importing module.  
\end{table}
Table 6: Ablation experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **mAP** & **Size** & **FPS** \\ \hline Faster-RCNN & ResNet & 84.3\% & 108.3 & 6 \\ SSD & VGG16 & 82.1\% & 92 & 27 \\ YOLOv4 & DarkNet53 & 88.7\% & 244 & 34 \\ YOLOv4-tiny & DarkNet53 & 78.6\% & 22.6 & 118 \\ YOLOv5-G & GhostNet & 92.8\% & 8.0 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative experiments.  
is 1.2 ms slower, but in return, the mAP is greatly improved by 14.2%. In terms of model size, YOLOv5-G is only 8 MB, which is the lightest among the above models and is more suitable for porting to embedded devices.
# Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Process
## 1 Introduction
This method does not allow online monitoring of the clamping force. To measure the clamping force using ultrasonic waves, the tone burst signal is transmitted through the bolt to measure the time of flight (ToF) [24]. When the bolt is stretched by the tensile force, the path through which the wave is transmitted becomes larger [25]. The clamping force is measured from the time difference of arrival (TDoA). Measurement of the clamping force using ultrasonic waves is robust and widely used. The cutting process for both bolt ends to induce normal incidence and reflection of the ultrasonic waves is essential and makes the application to actual manufacturing processes difficult [26]. Dynamometers precisely inserted directly between bolts and assembled parts were used to measure bolt clamping force [27]. This method is not applicable to the actual production process due to difficulties in continuous monitoring of the clamping force using load cells. In this study, we propose a clamping force estimation method that can be applied to the actual bolt assembly process. The accuracy of the estimation process was compared with that of the conventional torque method. The vibrations generated during bolt rotations showed fastening process characteristics. Accelerometers were used in the actual production process to provide advantages in sensing and installation. This allowed quality evaluation for the bolting process. In the speaker recognition algorithm, features were extracted using linear frequency cepstral coefficients (LFCC) [28]. Quantification was performed by applying the vector quantization algorithm to the extracted speaker features. Each speaker was classified by clustering the quantized speaker voice information. In this study, the speaker recognition algorithm was applied to the bolt vibration responses. The responses for different clamping forces were classified as the corresponding speakers in the recognition algorithm. The feature with the highest clamping force was used as a reference. The method of similarity estimation was applied. This provided a highly efficient, nondestructive method to estimate the clamping force.
## 4 Strengthening Mechanism with Activated Fe-SMA  
### Structural Reinforcement Mechanism  
When used for structural reinforcement, it is required to connect the pretensioned Fe-SMA members to parent structures first, and then activate Fe-SMA to apply precompression to structures. Figure 7 illustrates the repair process of concrete beams and cracked steel plates with activated Fe-SMA. During reinforcement, the Fe-SMA members need to be stretched to a predetermined strain (Step 1) and then unloaded freely to the residual deformation (Step 2). Thereafter, the Fe-SMA members are installed on the reinforced structures, and the Fe-SMA is activated to cause the reverse martensite transformation, to realize the prestressed reinforcement of structures (Step 3). After completing the aforementioned process, the reinforced structure enters the stage under service load (Step 4).  
After reinforcement, the concrete beam can achieve a reverse arch effect, inhibit the tensile cracks at the lower flange of the beam under load, and improve the bearing capacity and energy dissipation capacity [21; 72; 91]. When Fe-SMA is used to repair local cracks, the stress response can be reduced by increasing stiffness of the cracked region. Moreover, the activated Fe-SMA can introduce precompression stress, in order to reduce the average stress and stress peak in the structure under cyclic load, and to effectively decrease the positive stress amplitude of the structure when the stress valley is less than zero (as shown in Figure 8) [92; 93; 94]. The reinforcement process does not involve jack tension or steel strand cutting, and the Fe-SMA members shrink uniformly on the overall scale, so there is no prestress loss caused by friction and anchorage. However, the elastic shortening and initial free deformation during the activation process under non-ideal constraints will slightly reduce the recovery stress [95].
# Long Length Document Classification by Local Convolutional Feature Aggregation
## 3 Model
### Model_2: CNN Feature with LSTM Aggregation  
In the first model, we extracted and aggregated local CNN features from the randomly sampled blocks or sentences of the document for classification. This kind of feature aggregation is simple and effective, though the correlation among those sampled blocks is lost. This is because there is no order information between the randomly sub-sampled blocks, which results in less semantic association between the feature vectors of these blocks. Therefore, we consider using a recurrent neural network (RNN) to explicitly model the semantic order of the sampled blocks. RNNs have shown great success in many NLP tasks, such as non-segmented continuous handwriting recognition [21] and autonomous speech recognition [22]. Specifically, in this model we use LSTM [15] nodes as RNN computational nodes, shown in Figure 2. The advantage of the LSTM node is that it well tackles the difficulty of \"gradient vanishing\" and \"gradient exploding\" by its carefully designed gates. Therefore, it is possible to design a deeper LSTM network and unfold this network into many steps, which is suitable for practical sequential data modeling.  
The LSTM node is calculated as follows [15]:  
\[\begin{split}& i=\sigma(U^{i}x_{t}+W^{i}h_{t-1})\\ & f=\sigma(U^{f}x_{t}+W^{f}h_{t-1})\\ & o=\sigma(U^{g}x_{t}+W^{g}h_{t-1})\\ & g=\tanh(U^{g}x_{t}+W^{g}h_{t-1})\\ & c_{t}=c_{t-1}\otimes f+g\otimes i\\ & h_{t}=\tanh c_{t}\otimes o\end{split} \tag{4}\]  
In our second model, CNN_LSTM_Agg, in short, we sorted each group of words in the order of text to obtain the semantic order, so as to form a set of ordered document data. The first steps are the same as the model of CNN_Random_Agg, but the key difference to the model of CNN_LSTM_Agg is  
Figure 2: Long–short term memory (LSTM) node.  
that we use an LSTM layer to aggregate the extracted local CNN features for classification. The specific model structure is shown in Figure 3.
###### Abstract  
We show a successful numerical study of lid-driven square cavity flow with embedded circular obstacles based on the spectral/hp element methods. Various diameters of embedded two-dimensional circular obstacles inside the cavity and Reynolds numbers \(Re\) (from 100 to 5000) are considered. In order to verify the effectiveness and accuracy of the current methods, numerical results are investigated by comparing with those available in the literature obtained by the moving immersed boundary method (MIBM)  
domain center using the moving immersed boundary method, and compared results with the body-conforming mesh method. [PERSON] et al. [13] applied the lattice Boltzmann method to study the lid-driven square cavity flow inside cavities with internal circular obstacles for various Reynolds numbers \(Re\) (\(Re\) is between 100 to 5000).  
In the field of industrial applications, [PERSON] et al. [14] studied T91 steel specimens in liquid lead. They described the various specimens and treatment conditions either giving rise to the cavitation effect or not. [PERSON] et al. [15; 16] investigated numerically laminar free convection flow in a square enclosure driven by magnetic forces and MHD natural convection in a laterally and volumetrically heated square cavity. In this paper, we only consider the classical lid-driven square cavity flow with embedded circular obstacles instead of considering heat exchanges.  
The spectral/hp element methods incorporate both the spectral method and the hp-version finite element method, and combine the flexibility of the classical finite element solution domain with the faster convergence and higher accuracy of the spectral method. Theoretically, the spectral/hp element methods can provide an arbitrary order of accuracy in spatial discretization requiring relatively lower computational cost. Hence, in recent years, the spectral/hp element methods have attracted more and more attention and have been widely used in academia and engineering, especially in computational fluid dynamics (CFD), see [17; 18; 19; 20; 21; 22; 23]. Although the spectral/hp element methods have been extended to various engineering fields, as far as we know, few research of lid-driven square cavity flow with embedded obstacles using the high-order method can be found in the existing literature.  
This study aims to apply the spectral/hp element methods of high accuracy and efficiency to simulate the flow inside a square cavity with embedded two-dimensional circular obstacles. The computational tool used in the simulation is an open source spectral/hp element framework: Nektar++5.0.1 [24]. In order to show the less preprocess requirements and high-resolution characteristics of the present methods, we compare current results with [PERSON] et al. [8] and [PERSON] et al. [13] and adopt the same parameters and the boundary conditions (various diameters and different Reynolds numbers \(Re\) (\(Re\) is between 100 to 5000)) in numerical models. The effectiveness and accuracy of current methods are demonstrated by numerical models.