esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Q2: What Are the Challenges of the Current UIoT System
### Technical Challenges of UioT
#### 3.2.1 Limited Resources  
In the UIoT environment, the battery and storage capacity of sensing devices are very limited.  
_Limited battery:_ The optical and acoustic communication channel in the UIoT environment consumes more power than RF communication. Furthermore, energy harvesting is impossible due to the unavailability of solar power creation in the UIoT environment. This causes data loss and reduces battery lifetime [33]. In addition, the existing low energy consumption or optimization methods used in the terrestrial environment, for example, the methods used in references [34; 35], cannot be applied to UIoT networks.  
_Limited storage capacity:_ The memory size of devices in the UIoT environment is limited. Moreover, memory formatting is impossible in the UIoT environment. This causes failure in data gathering and data transmitting [2].
## 2 Related Works
### Filter Pruning  
The main importance of filter pruning is to define some criteria to select unimportant filters. [PERSON] et al. [24] calculate filter magnitude in terms of \(l_{1}\)-norm is used to remove low-norm filters. Again, [PERSON] et al. [13] used \(l_{2}\)-norm to remove the filters in a soft manner and suggest that \(l_{2}\)-norm criteria work slightly better than \(l_{1}\)-norm criteria. Another soft filter pruning, FPGM [15], prunes redundant filters using filter Euclidean similarity criteria rather than pruning less important filters using magnitude criteria. A similar soft pruning technique presented by [PERSON] et al. [16] adaptively selects the most suitable criteria for pruning via a meta-attribute of the network at the current state. The global filter ranking algorithm, LeGR [37], can remove the required amount of bottom-ranked filters to achieve a target pruning rate. [PERSON] [26] formulated pruning as an optimization problem, which prunes filters in the current layer and minimizes the reconstruction error in the next layer. In contrast to ThiNet [26], NISP [27] focus on minimizing the reconstruction error in the final response layer while pruning filters in the current layer. [PERSON] et al. [14] trained the differentiable criteria sampler to learn the most appropriate pruning criteria for different layers. Also, Bayesian optimization-based variable rate one-shot filter pruning is recently presented by [PERSON] et al. [29], in which optimal pruning rates for each layer are automatically identified. Recently, [PERSON] [25] extensively studies the filter importance criteria for each layer and proposes that variable rate pruning across convolution layers outperforms fixed rate filter pruning. Instead of selecting pruning filters using magnitude or similarity criteria, [PERSON] et al. [38] formulated the information preserving of pretrained CNN filters as a matrix sketch problem, but they still use the fixed rate pruning for all pruning layers in the network.  
Filter pruning or structured pruning does not have limitations on specialized hardware or software as entire filters are pruned and the original structure of the network still remains intact. Therefore filter pruning is more favorable as compared to weight pruning.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 3 Results
### Data Transmission
#### 3.1.3 Comparison of Echo Cancellation Performance
\begin{table}
\begin{tabular}{c c c c c} \hline
**Evaluation Index** & \(\mathbf{S_{LMS}(t)}\) & \(\mathbf{S_{PNLMS}(t)}\) & \(\mathbf{S_{RLS}(t)}\) & \(\mathbf{S_{DPRNN}(t)}\) \\ \hline SNR (dB) & 9.331 & 14.112 & 5.974 & 29.437 \\ SISNR (dB) & 22.911 & 43.688 & 16.243 & 113.826 \\ NCC & 0.7533 & 0.8952 & 0.6391 & 0.9829 \\ BER & \(5.593\times 10^{-2}\) & \(2.182\times 10^{-3}\) & \(3.742\times 10^{-2}\) & \(3.502\times 10^{-4}\) \\ ERLE & 6.47 & 17.82 & 4.22 & 32.53 \\ \hline \end{tabular}
\end{table} 
Table 2: Signal quality with different methods of echo cancellation. Figure 11: Signals before and after echo cancellation. (**a**) Signals \(S_{T}(t)\) on the transmitting transducer. (**b**) Signals \(S_{R}(t)\) on the receiving transducer. (**c**) Signals \(S_{LMS}(t)\) after LMS adaptive filtering. (**d**) Signals \(S_{RLS}(t)\) after RLS adaptive filtering. (**e**) Signals \(S_{PNLMS}(t)\) after PNLMS adaptive filtering. (**f**) Signals \(S_{DPRNN}(t)\) after DPRNN treating.
## 5 Results and Analysis
### Comparison Experiment of Different Models  
The improved YOLOv8n model is experimentally compared with other mainstream two-stage detection models such as Faster-RCNN and single-stage detection models including SSD, YOLOv5, YOLOv7, and YOLOv8n. The experimental results are presented in Table 2.  
From the table, it can be observed that Faster-RCNN has the lowest average precision, slowest inference speed, and largest weight, making it unsuitable for seedling planting status detection. The original YOLOv8n model exhibits the highest overall performance, with an inference speed of 120, slightly lower than the YOLOv5 model, but still meets the real-time detection requirements, demonstrating the feasibility of using this model for seedling planting status detection.  
The average precision of the improved YOLOv8 model surpasses other models, with improvements of 11.3%, 27.6%, 4.9%, and 3.3%, respectively. Due to the increased complexity of the improved model, the inference speed decreases from 120 to 113, and the weight  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **mAP/\%** & **FPS** & **Model Size/MB** \\ \hline Faster-RCNN & 83.9 & 15 & 124.2 \\ \hline SSD & 67.6 & 34 & 92.1 \\ \hline YOLOv5 & 90.3 & 125 & 13.8 \\ \hline YOLOv7 & 91.6 & 103 & 74.8 \\ \hline YOLOv8n & 91.9 & 120 & 6.3 \\ \hline Improved YOLOv8n & 95.2 & 113 & 8.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results of different network models.  
Figure 14: Performance parameter variation curve based on different improved algorithms for YOLOv8n. (**a**) Precision; (**b**) recall; (**c**) mAP.  
size increases from 6.3 MB to 8.8 MB. However, the focus on improving the recognition accuracy of qualified seedlings and floating seedlings detection in the improved model helps avoid issues such as missed detections and false alarms. Moreover, the slight decrease in frame rate and minor increase in weight do not affect the real-time detection requirements or the practical application of the model in real-world environments.
## 3 Results
### Partial Least Squares--Path Modelling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Independent Variables** & **Dependent Variables** & **Mean Estimate** & \(t\)**Value** & \(p\)**Value** \\ \hline Family support for autonomy & BPNS for autonomy & 0.27 & 2.59 & 0.01 \\ Family support for competence & & 0.32 & 3.14 & 0.002 \\ \hline Family support for competence & BPNS for competence & 0.21 & 2.06 & 0.04 \\ \hline Family support for autonomy & BPNS for relatedness & 0.24 & 2.48 & 0.01 \\ Family support for competence & & 0.22 & 2.65 & 0.009 \\ \hline Family support for competence & BPNF for autonomy & \(-\)0.31 & \(-\)3.20 & \(<\)0.001 \\ \hline Family support for competence & BPNF for competence & \(-\)0.21 & \(-\)2.15 & 0.03 \\ Satisfaction of social support & & \(-\)0.41 & \(-\)3.95 & \(<\)0.001 \\ \hline Family support for competence & BPNF for relatedness & \(-\)0.41 & \(-\)4.44 & \(<\)0.001 \\ Satisfaction of social support & & \(-\)0.21 & \(-\)1.99 & 0.05 \\ \hline BPNF for competence & Depression & 0.26 & 2.64 & 0.01 \\ BPNF for relatedness & & 0.29 & 2.44 & 0.02 \\ \hline BPNS for competence & Purpose of life & 0.19 & 1.82 & 0.07 \\ BPNF for competence & & \(-\)0.36 & \(-\)3.76 & \(<\)0.001 \\ BPNF for relatedness & & \(-\)0.22 & \(-\)2.17 & 0.03 \\ \hline \hline \end{tabular} Note. BPNS = basic psychological need satisfaction; BPNF = basic psychological need frustration; For simplicity, the nonsignificant paths are omitted from Table 3 (all the structural results are available upon request from the first author).  
\end{table} 
Table 3: Inner model of the partial least square path modelling.
## 2 Materials and Methods
### YOLOv10 Network Improvement
#### 2.2.1 Bounding Box Regression Loss Optimization  
Bounding Box Regression Loss is one of the core parts of the YOLOv10 network, which is mainly used to evaluate the difference between the predicted bounding box and the real bounding box. However, in the existing YOLOv10 network, the original bounding box regression method uses the relative position and relative shape between the bounding boxes to calculate the loss. It only considers the geometric relationship between the predicted bounding box and the real bounding box, but ignores the influence of the shape and size of the bounding box itself. When using unmanned aerial vehicles for disease detection in Yunnan large-leaf tea trees, diseases often exhibit overlap, occlusion, and diverse morphological variations, which result in suboptimal recognition accuracy in the original YOLOv10 network [18].  
To address this issue, this study considers the scale factor of the target in the dataset using Shape-IoU, and introduces the weight coefficient related to the shape of the real bounding box to optimize the loss function of the YOLOv10 network [19]. The improved Bounding Box Loss is expressed in Equation (1); in Equations (2)-(7), B denotes the predicted bounding box, \(B^{st}\) denotes the ground-truth bounding box, \(w^{gt}\) denotes the width of the ground-truth bounding box, \(h^{gt}\) denotes the height of the ground-truth bounding box, w denotes the width of the predicted bounding box, and h denotes the height of the predicted bounding box. Scale represents the aspect ratio factor related to the disease, while \(W_{w}\) and \(H_{h}\), which are related to the shape of the ground-truth bounding box, are the weight coefficients in the horizontal and vertical directions, respectively. \(x_{c}\) and \(y_{c}\) denote the x and y coordinates of the center of the predicted bounding box, and \(x_{c}^{gt}\) and \(y_{c}^{gt}\) denote the x and y coordinates of the center of the ground-truth bounding box.  
\[L_{S-IoU}=1-IoU+distance^{s}+0.5\times\Omega^{s} \tag{1}\]  
\[IoU=\frac{\left|B\bigcap B^{gt}\right|}{\left|B\bigcup B^{gt}\right|} \tag{2}\]  
\[W_{w}=\frac{2\times\left(w^{gt}\right)^{scale}}{\left(w^{gt}\right)^{scale}+ \left(h^{gt}\right)^{scale}} \tag{3}\]  
\[H_{h}=\frac{2\times\left(h^{gt}\right)^{scale}}{\left(w^{gt}\right)^{scale}+ \left(h^{gt}\right)^{scale}} \tag{4}\]  
\[distance^{s}=\frac{H_{h}\times\left(x_{c}-x_{c}^{gt}\right)^{2}}{c^{2}}+ \frac{W_{w}\times\left(y_{c}-y_{c}^{gt}\right)^{2}}{c^{2}} \tag{5}\]  
\[\Omega^{s}=\sum_{t=w,h}\left(1-e^{-\omega_{t}}\right)^{\theta},\theta=4 \tag{6}\]  
\[\left\{\begin{aligned} \omega_{w}&=H_{h}\times \frac{\left|w-w^{gt}\right|}{\max\left(w,w^{gt}\right)}\\ \omega_{h}&=W_{w}\times\frac{\left|h-h^{gt}\right|}{ \max\left(h,h^{gt}\right)}\end{aligned}\right. \tag{7}\]
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 4 Experimental Results
### Results on Rendered Synthetic Samples  
Three textured synthetic samples are rendered using the _Blender add-on_ setup from [PERSON] et al. [26] as in [PERSON] et al. [14]. These samples are designed by rendering 3D models of photorealistic scenes containing various properties. The evaluation metrics include RMSE (root mean square error), Bad Pixel 1.0 (\(|d-d_{-}gt|>1.0\)) and Bad Pixel 2.0 (\(|d-d_{-}gt|>2.0\)) metrics. The focus distance for the 'Room' samples is 1.0 m, and for the 'Basket' sample, it is 1.5 m. The focal length is set to 50 mm. All synthetic samples feature complex shapes and textures.  
With the ground truth depth map and imaging parameters known, the light field disparity and depth values can be inter-transformed for evaluation based on the formulas in [26]. It can be seen from the statistical results in Table 2 and Figure 4 that the proposed method is more accurate, and the post-refinement is effective. For 'Basket', depth ambiguities are observed over the floor and background, but the depth accuracy for thin shapes is good.
## 4 Results and Discussions
### Experimental Findings
#### 4.2.1 Binary Classification  
The compact ConvNet-18 is trained and tested with images from both original and augmented MEAT2C database. The suggested model correctly predicts 183 among 190 fresh meat images, resulting in a sensitivity of 96.31%. Among 190 spoiled images, 180 are correctly classified by the model. Thus, an overall accuracy of 95.52% is obtained for original database. The model may fail to capture the distinct features from meat images. To enhance the overall performance of model, the amount of images is increased. This is achieved by the oversampling data level technique. The images in the original database are rotated with appropriate step size to generate the augmented database. It includes 10,428 fresh and 10,428 spoiled images.  
ConvNet-18 is trained and tested with the augmented database in three different configurations. Table 3 indicates the metrics obtained by ConvNet-18. It is inferred that Conf1 yields an accuracy of 99.6% by correctly predicting 2086 fresh images and 2068 spoiled images. In Conf2, a total of 4149 out of 4172 images are identified by the model. This results in an accuracy of 99.4%. When the model parameters are updated using RmsProp optimizer in Conf3, 2080 fresh and 2065 spoiled meat images are correctly predicted. This yields an accuracy of 99.3%. The robustness of the model may be improved with the ensemble technique. This work employs the average ensemble technique, where the metrics obtained by the three configurations are averaged. It is evident from experimental findings that the final ensemble model yields an accuracy of 99.43%, sensitivity of 99.8%, specificity of 99%, precision of 99%, and F1-score of 99.4%.  
Figure 5: Confusion matrix for binary classification task.
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 4 Experiment Settings
### Datasets  
Cityscapes: Cityscapes is a large-scale dataset containing multiple cities that supports different vision tasks such as semantic segmentation and instance segmentation. We only use the semantic segmentation dataset part of Cityscapes, which contains a total of 2985 image materials from 18 cities for the training set and 500 images from 3 cities for the validation set. Every image in Cityscapes has a native size of 2048 \(\times\) 1024 pixels, which is cropped to 513 \(\times\) 513 pixels in experiments. There are 34 classes in the original semantic segmentation dataset, which are redundant in road segmentation. Therefore, only the road class is retained, and the remaining classes are merged into one non-road class.  
CamVid: CamVid, short for The Cambridge-driving Labeled Video Database, is a lightweight semantic segmentation dataset. Compared with the Cityscapes dataset, the images in CamVid have more complex roads, more vehicles, and smaller dimensions, making prediction relatively more difficult. The CamVid dataset contains 701 images, of which 367 images are in the training set, 101 images belong to the validation set, and 223 images are used in the test set. Each image in CamVid is 480 \(\times\) 360 pixels in size, and it is cropped to 360 \(\times\) 360 pixels as the input. The CamVid dataset provides 32 ground-truth semantic labels, which are merged into 11 broad categories when used in a semantic segmentation task. In our experiment, only the road class is preserved and the remaining class are integrated to produce road and non-road class labels applicable to drivable area segmentation.  
Whether the Cityscapes dataset or the CamVid dataset is used, their data are all labeled data. Thus, in unsupervised training, a certain percentage of images are randomly selected from the original training set, considering them as unlabeled data by ignoring their corresponding labels, and the rest of the data are kept intact for supervised training. Figure 5 illustrates the above operations.
## 2 Method
### Numerical Model Validation  
To validate the CFD model, the measurement data obtained from an aircraft cabin mockup [20] were used. Figure 4 shows the geometry of the cabin mock-up, which was similar to that used in Case 1 as the reference case. However, only the front six rows of seats were seated with the thermal manikins. The total air-supply rate into the cabin was 9.5 L/s per passenger, of which 60% was from the side air supply and 40% from the middle ceiling air supply. The air distribution in the cross-section across the fourth row and the steady-state concentration fields in the cross-section across the fourth row and the longitudinal section across the Cth column were measured. The polydisperse Di-ethyl-hexyl-sebacate (DEHS) particles were released on the head of manikin 4B by a perforated plastic ball at a fixed rate. The test instruments used to measure airflow were three ultrasonic anemometers mounted on a guide rail. The resolution of air velocity was 0.005 m/s with an accuracy of 1%. The particle concentration was measured with an aerodynamic particle size spectrometer (type: 3321; TSI, USA). The resolution of particle counting was 0.001 particles/cm\({}^{3}\), with an accuracy of \(\pm\)10% plus the statistical deviation. Only the particles at 1 \(\upmu\)m were analyzed. More details on the boundary conditions and the measurement can be found in [20].  
Again, the RNG \(k\)-\(\epsilon\) model together with the standard wall function was adopted for turbulence modeling. The steady-state flow field was solved first and then the transient solution to both flow and particle concentration was switched on. Both the flow and temporal particle concentrations for a total of 700 s were solved.
## 4 Results and Discussion
### Calculation of Thermal Activation Energy (\(\Delta\)H)  
From  
As shown in Figure 5, linear approximation was used to estimate the slope of the curves for all configurations. Equation (5) was used to calculate \(\Delta H\) for each sample using both methods. These results are displayed in Table 4, where values for \(\Delta H\) vary from 0.83-0.96 kJ/mol. In general, the  
Figure 5: Variation of \(T_{g}\) as measured by tan delta peak curves (left column) and loss modulus curves (right column) for the different composites’ configurations. (**a**,**b**) Just CF composite configuration. (**c**,**d**) CF and ZnO composite configuration. (**e**,**f**) CF and CNTs composite configuration. (**g**,**h**) The CNT and ZnO composite configuration.  
samples with nanofillers seem to show an improvement in \(\Delta H\) when compared to raw composite sample. The highest value of \(\Delta H\) was seen in the samples that have just CNTs as nanofillers, reporting a value of 0.96 kJ/mol. The difference between both the methods is not profound, as seen in Table 4. The creep rate is related to the thermal activation energy (i.e.; change in activation enthalpy) by the following relation:  
\[\dot{\varepsilon}_{s}=K\sigma^{n}\exp\left(-\frac{\Delta H}{RT}\right) \tag{6}\]  
where \(K\) is the Arrhenius constant, \(\sigma\) is the applied stress, \(n\) is the stress \(\Delta H\) is the activation enthalpy (also known as activation energy), \(R\) is the gas constant, and \(T\) is the absolute temperature [46]. This equation implies that the creep rate is exponentially and inversely related to the activation energy. Hence, as seen in Table 4, the improvement in the activation energy significantly decreases the creep rate of the hierarchical composites. For example, assuming all the parameters are the same in Equation (6) and varying \(\Delta H\) from 0.85 for CF sample to 0.96 for the CF and CNTs sample will drop the creep strain rate by 20%.
###### Abstract
Although some of these produced a high classification accuracy, they did so at the expense of longer computational times and in the end a more complex model. Other studies did not consider the impact of different machine learning techniques on the classification accuracy. The method presented in this paper addresses these limitations by not only finding the optimum machine learning model for tumor classification accuracy, but also by minimizing the number of gene RNA sequence features required to achieve that high accuracy. We used different reduction techniques, including a factor analysis, for feature reduction. To top it off, the tumor classification accuracy achieved by the optimum method outlined in this paper beats all similar previous studies. In particular, this paper presents an effective method to classify cancer tissue samples by tumor type (breast cancer, lung cancer, colon cancer, etc.) using machine learning techniques. Over 60,000 gene RNA-Seq features were analyzed and six different machine learning classification algorithms were explored, individually and as an ensemble. The classification algorithms evaluated were _logistic regression_, \(k\)-_Nearest Neighbor_, _decision tree_, _random forest_, _neural network_, _and support vector machine_. To train, test, and validate these techniques, a dataset of 5400 tumor samples was used, representing 18 different cancer tumor types. RNA-Seq gene datasets involve enormous amounts of genetic data, which make analysis and predictive modeling both computationally intensive and time-consuming. This paper is organized as follows. Section 2 provides the details of the composition of the dataset. Challenges with the very large feature set are tackled through the preprocessing stages detailed in Section 3. Classification using individual and ensemble models, along with results validation, is examined in Section 4. Model accuracy comparisons with related studies are explored in Section 5. The conclusions of the paper are offered in Section 6.
## 3 The Principle of CEEMDAN-WP and RDT  
This section introduces a novel algorithm named CEEMDAN-WP, which integrates the CEEMDAN and wavelet packet decomposition techniques for signal denoising. In addition, RDT and a related technique are proposed for modal identification. A flow chart of signal denoising and modal identification procedures is shown in Figure 3. The specific algorithms of mentioned are introduced in the following subsection.