esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Instability Level and Sensory Information on Movement Characteristics  
3.2.1. Chest Movement Velocity  
This analysis presents a comparison of chest movement velocities under different ILs and SIS. The data from the study is summarized in Table 2.  
The velocity of chest movement shows an increasing trend moving from the Basic towards the Vestibular strategy at both lower and higher instability levels. At the 0 IL level, the chest velocity in the Basic strategy is approximately 0.78 cm/s, which increases to around 2.0 cm/s in the Vestibular strategy. Comparing chest velocity between Basic 3 IL and Vestibular 3 IL, we observe a larger increase of 462%.  
The trend is even more noticeable when both the instability level and the amount of sensory information are considered together. For example, the increase in chest velocity from Basic 0 IL to Vestibular 3 IL is approximately 580%. Figure 2 represents the comparison of velocities at different instability levels under the different sensory integration strategies, while Figure 3 shows the general trend in how the chest movement velocity changes with different combinations of ILs and SIS. The data demonstrate a direct correlation between the level of instability and the decrease of sensory information and the velocity of chest movements.  
\begin{table}
\begin{tabular}{|c|c|c|c||c|c|c|c|} \hline \multirow{2}{*}{**IL**} & \multirow{2}{*}{**SIS**} & \multicolumn{2}{c}{**0 IL**} & \multicolumn{2}{c}{**1 IL**} & \multicolumn{2}{c}{**2 IL**} & \multicolumn{2}{c}{**3 IL**} \\ \cline{2-9}  & & **Median** & **IQR** & **Median** & **IQR** & **Median** & **IQR** \\ \hline
**Basic** & 0.78 & 0.21 & 0.88 & 0.18 & 1.07 & 0.32 & 0.98 & 0.28 \\ \hline
**Visual** & 0.86 & 0.23 & 1.08 & 0.46 & 2.20 & 2.33 & 1.93 & 0.95 \\ \hline
**Proprioception** & 1.20 & 0.18 & 1.22 & 0.36 & 1.46 & 0.44 & 1.54 & 0.35 \\ \hline
**Vestibular** & 2.00 & 0.50 & 2.95 & 0.93 & 4.19 & 1.77 & 4.53 & 1.87 \\ \hline \end{tabular}
\end{table}
Table 2: A comparison of chest velocity (cm/s) of different instability levels (ILs) under different sensory integration strategies (SIS) with median values and color scale (green: lower, yellow: moderate, red: higher).
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 5 Experimental Results and Analysis
### Comparison Experiments of Different Loss Functions  
To showcase the superior performance of the SloU loss function in comparison to other loss functions, we enhanced the YOLOv5 algorithm by incorporating GloU, CloU, and SloU. The improved models were trained and tested using identical parameters, and the outcomes of the three loss functions were assessed. The convergence speed and training outcomes associated with each loss function are depicted in Figure 14 and summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Loss Function** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** \\ \hline GloU & 92.7 & 86.0 & 90.1 \\ CloU & 92.4 & 85.6 & 89.3 \\ SloU & 92.8 & 85.2 & 90.5 \\ \hline \end{tabular}
\end{table}
Table 4: Results of different loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{8}{c}{**Improvement Methods**} & \multirow{2}{*}{**P/\%**} \\ \cline{2-2} \cline{4-9}  & \multirow{2}{*}{**AMFDH**} & \multirow{2}{*}{**AWDMSM**} & \multicolumn{2}{c}{**Att-CSP**} & \multirow{2}{*}{**GIoU**} & \multirow{2}{*}{**SIoU**} & \multirow{2}{*}{**P/\%**} & \multirow{2}{*}{**R/\%**} & \multirow{2}{*}{**F1/\%**} & \multirow{2}{*}{**[EMAIL_ADDRESS]\%**} \\  & & & & & & & & & & \\ \hline
1 & - & - & - & \(\surd\) & - & 92.5 & 81.2 & 86.3 & 88.3 \\
2 & - & \(\surd\) & - & \(\surd\) & - & 91.8 & 86.6 & 89.0 & 89.8 \\
3 & - & - & \(\surd\) & \(\surd\) & - & 92.7 & 85.2 & 88.3 & 89.2 \\
4 & - & - & - & \(\surd\) & 92.8 & 85.2 & 88.3 & 90.5 \\
5 & \(\surd\) & - & - & \(\surd\) & - & 91.6 & 88.0 & 90.0 & 91.9 \\
6 & \(\surd\) & \(\surd\) & - & \(\surd\) & - & 92.7 & 90.3 & 91.7 & 92.0 \\
7 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & - & 92.7 & 88.1 & 90.3 & 92.4 \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & - & \(\surd\) & 93.8 & 89.2 & 91.3 & 93.4 \\ \hline \end{tabular}
\end{table}
Table 3: Ablation experiment of AC-YOLO network model.  
The results presented in Table 4 demonstrate the superiority of the SIoU loss function compared to GIoU and CIoU in terms of the \(p\)-value and mAP. The accuracy rates for SIoU were 92.8%, showing a slight improvement of 0.1% and 0.4% compared to GIoU and CIoU. Similarly, the mAP for SIoU showed an improvement of 0.4% and 1.2% over GIoU and CIoU. However, the R-value obtained with SIoU was 0.8% and 0.4% lower than that of GIoU and CIoU, respectively. This discrepancy can be attributed to SIoU's emphasis on estimating smaller bounding boxes. Additionally, as depicted in Figure 15, SIoU exhibited faster convergence than the other two loss functions within the initial 70 training rounds, while CIoU showed the slowest convergence, aligning with the findings in Table 4. Therefore, based on this analysis, it is evident that the SIoU loss function offers comprehensive advantages over the other two alternatives and demonstrates excellent performance in detecting insulator defects.
# Single-Shot Object Detection with Split and Combine Blocks
## 3 Method
### Training and Testing  
Our detector SCNet is built on a Pytorch v1.5.1 framework with CUDA 10.2 and cuDNN v8.0.1. Adaptive Moment Estimation (Adam) [39] with the default setting is exploited to optimize the entire network. A learning rate is initialized with \(10^{-5}\) which increases in the first five warmup epochs from \(10^{-5}\) to \(10^{-3}\), and then is reduced by a smoother cosine learning rate adjustment from \(10^{-3}\) to \(10^{-6}\)[40; 41]. The detector is trained on two 2080 ti GPUs with total batch size of 24 for 300 epochs.  
Figure 6: Positive samples selection for training. Ground truth boxes (in red) still have high IoU with prediction boxes (in blue), although their centers are positive (in red mask) and negative (in blue mask), respectively.  
All the convolutional layers are initialized with an He weight initialization method [42], and the backbone is first fine-tuned at the 608 \(\times\) 608 resolution for 50 epochs on the ImageNet dataset [43] to speed up the convergence. We use a convolutional layer, Cross mini-Batch Normalization (CmBN) [44], and Mish [45] activation to construct a conv block. Use the multi-scale training method to boost the robustness of the model. More specifically, after each iteration, the training resolution is randomly picked in \(\{320,352,384,416,448,480,512,544,576,608\}\) with the aspect ratio as 1. We design a new data augmentation pattern called Admix which mixes five different images at a time; by comparison, CutMix [46] mixes two images and Mosaic mixes four images. Some examples of Admix are shown in Figure 7. We combine the Admix with several usual methods of data augmentation in training to alleviate the problem of overfitting and improve the generalization ability of the network, such as random horizontal flipping, random scaling, random cropping, random shifting RGB and random changing hue, saturation, and value. Undersized bounding box annotations (area < 8) are also filtered out after cropping. We use DropBlock [47] as the regularization technique and increase the block size at 90, 120, and 150 epochs, respectively. SPP [48] is integrated into our SC-PANet to further improve the discriminability and robustness of features. During inference, soft non-maximum suppression (Soft-NMS) [49] is used to filter out suboptimal predictions.
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### Leaf Temperature Variability
(2004) studied the effect of the canopy architecture on the crop temperatures of two grapevine cultivars grown under two irrigation regimes (fully and not irrigated). They concluded that it was desirable to calculate an average over several leaves per canopy in order to reduce the effect of variation in leaf angles. The simple CWSI, without further data acquisition, was sufficient for detecting the relative stress required for scheduling irrigation.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## Materials and Methods
### Computer Aided Surgery Planning: Screws' Optimal Length, Diameter and Insertion Direction  
The target of the arthrodisis surgery is to locate the screws. The standard procedure expects a barycentric insertion of the screw with respect to the cross sections of the pedicle and tendentially parallel to the vertebral plate (that is the transverse plane) [14]. The standard procedure is a freehand empirical method where anatomical landmarks suggest the ideal insertion direction. The target is to find an optimized insertion of the screw from a structural and patient safety point of view, avoiding the \"pull out\" phenomenon. The trajectories are chosen in order to be properly convergent.  
These specifications reduce the probability of damage to spinal, nerve and circulatory structures (especially in the upper part of the vertebral column).  
In the proposed procedure, the search for optimal directions is carried out, as previously mentioned, through an algorithm based on a rigorous mathematical method that avoids to define a minimal section of the isthmus, unlike other research works mentioned in the introduction. In this case, in fact, the algorithm defines a free 3D space (whose volume can be safely crossed by a screw without touching the outer pedicle's surfaces) whose visibility is guaranteed because it coincides with the entry point (visible) chosen by the surgeon. The previous sections describe the innovativeness of this procedure, which includes the innovative trajectory of insertion design in [8; 12; 15].  
Figure 1: Computer-aided-design (CAD) reconstruction of a lumbar vertebra.  
The algorithm identifies the available bone space building a solid angle from the entry point located on the surface of the bone. From this point, the algorithm estimates the barycentric trajectories and the acceptable maximum screws' diameter. Moreover, it calculates the length of the screws based on the distance between the screws' tip, the verteb external surface, and the sagittal plane, considering an opportune safety factor (for further details, see [8; 11; 12]).
## 3 Materials and Methods
### Data Set
#### 3.1.4 Image Preprocessing  
Pixel intensities can have large variations between different images; quite often, they span a few orders of magnitude. In addition, there are a significant number of outliers, especially in the PU data set. If left unaddressed, these data could potentially dominate the classification procedure (due to their larger scale) [45] or hinder the modeling/learning process [46]. Thus, we introduced a couple of carefully chosen pre-processing techniques to address this issue while ensuring data validity and accurate NN performance.  
Feature normalization is applied to improve the numerical stability of model(s) and speed up the training process. In regard to outliers in a dataset, the straightforward procedure for handling outliers is to remove them from a dataset. But in our case study, outliers are the highest energy points, and deleting them would mean losing vital information. Some standard approaches in the presence of outliers are to use (quantile-based) capping [47] or robust scaler normalization [45].  
Tests were performed with both techniques, and they showed similar results in terms of accuracy performance. However, capping proved simpler to implement and demonstrated better performance in terms of stability and resource utilization when applied to FPGAs. Thus, it was selected and applied only in some test cases, as is presented in more detail in Section 3.2.  
The selection and quantization of input data are also considered to reduce the model's memory footprint, enabling its implementation on FPGAs and the execution of event selection in the set time frame (\(<\)12 us). Two types of data quantizers are created: linear, where the quantization levels are evenly distributed, and non-linear, where the relationship between the quantization levels is logarithmic.
## 3 Numerical Model: Environment Region
### Turbulence Model  
Turbulence is modeled through the Large Eddy Simulation (LES) method. The one-equation eddy viscosity model has been used to model the sub-grid scale stress tensor. The following conservation equation for the turbulent kinetic energy is solved  
\[\frac{\partial(\overline{\rho k})}{\partial t}+\
abla\cdot(\overline{p}\bar{ \mathbf{u}}k)=\
abla\cdot(\mu_{eff}\
abla k)-\overline{\rho}\underline{\tau} _{\underline{\Sigma}gg}:\underline{\underline{S}}\underline{-}C_{B,1}\ \overline{\rho}\ \frac{k^{1.5}}{\Delta} \tag{27}\]  
where \(\Delta\) is the local filter cutoff width, calculated by taking the cubic root of the grid cell volume. \(\underline{\underline{S}}\) is the strain rate tensor and \(C_{B,1}\) is a constant which value is usually set equal to 1.048 [29]. The Boussinesq approximation is considered to model the sub-grid scale stress tensor and dynamic viscosity  
\[\underline{\underline{\tau}}_{ggs}=-2\mu_{ggs}\frac{1}{2}[\
abla \bar{\mathbf{u}}+(\
abla\bar{\mathbf{u}})^{T}]+\frac{2}{3}\overline{\rho}k \underline{\underline{I}} \tag{28}\] \[\mu_{ggs}=\rho C_{B,2}\Delta k^{0.5} \tag{29}\]  
where the constant \(C_{B,2}\) is usually set equal to 0.094 [29].
## 3 Results and Discussion  
### Effects of Shift in Aeration Rate on Production of CMCase  
The effects of the shift in the aeration rate of bioreactors on the cell growth of _E. coli_ KACC 91335P and its production of CMCase were examined in 7 L bioreactors. The optimal aeration rate of a 7 L bioreactor for the cell growth of _E. coli_ KACC 91335P was 0.50 vvm, whereas that for the production of CMCase was 0.95 vvm [17]. The optimal conditions of the aeration rate and agitation speed of the 7 L bioreactor for the cell growth of _E. coli_ KACCP 91335P and its production of CMCase were optimized using the response surface methodology based on the cell growth measured as DCW and produced CMCase [17]. Carbon source was 132.3 g/L rice bran and nitrogen source for the production of CMCase was 4.68 g/L ammonium chloride, which had been optimized in a previous study [16]. The aerationrates of the 7 L bioreactors for experiments were (1) constant at 0.50 vvm, (2) a shift in the aeration rate from 0.50 to 0.95 vvm at 24 h, (3) a shift in the aeration rate from 0.50 to 0.95 vvm at 36 h, (4) a shift in the aeration rate from 0.50 to 0.95 vvm at 48 h and (5) constant at 0.95 vvm. The agitation speed of 7 L bioreactors was fixed at 416 rpm, which was the optimal aeration rate for the production of CMCase by _E. coli_ KACC 91335P (16). As shown in Figure 1, the maximal cell growth was obtained at a constant aeration rate of 0.50 vvm, whereas the highest production of CMCase was obtained with the shift in the aeration rate from 0.50 to 0.95 rpm at 36 h, at which the production of CMCase was 891.4 U/mL.  
The cell growth of microorganisms in a batch culture can be divided into four or five different phases--lag phase, log phase, stationary phase, deceleration phase and death phase (22). Based on the cell growth of _E. coli_ KACC 91335P, the times at 24, 36 and 48 h can be considered the early, mid- and late-log phase of the microbial cells growth (23). The shift in the optimal agitation speed from the cell growth to its production of CMCase at the mid-log phase of cell growth resulted in a relatively higher cell growth of _E. coli_ KACC 91335P to participate in the production of CMCase, which in turn enhanced the production of CMCase (24). The shift in the optimal aeration rate from the cell growth of _E. coli_ KACC 91335P to its production of CMCase at the mid-log phase of cell growth also resulted in a relatively higher cell growth of _E. coli_ KACC 91335P, which in turn enhanced the production of CMCase. The production of CMCase with a shift in the optimal aeration rate from the cell growth of _E. coli_ KACC 91335P to its production of CMCase at the mid-log phase of cell growth was 1.09 times higher than that with a fixed optimal aeration rate for the production of CMCase and even 1.40 times higher than that with a fixed optimal aeration rate for cell growth.
## 2 Methodology  
In this section, we describe the methodology used to estimate Chl-\(a\) from satellite data in the IMEOCAL region. We initiate with the application of a global algorithm to the remotely sensed reflectance, \(\text{R}_{\text{ss}}(\lambda)\), in order to obtain the modeled Chlorophyll concentration, mChl-\(a\). Then, mChl-\(a\) is compared with the in situ Chlorophylll concentration data (Chl-\(a\)) to ascertain the algorithm's suitability for the region. Should discrepancies arise, we propose a regional adaptation of the algorithm.  
The methodology unfolds in three phases. First, we describe the data, both R\({}_{\mathrm{rs}}\)(\(\lambda\)) and in situ Chl-\(a\). Subsequently, we describe the algorithms that transform R\({}_{\mathrm{rs}}\)(\(\lambda\)) mChl-\(a\). Lastly, we make an assessment of the algorithm using a comparison between the mChl-\(a\) and the in situ Chl-\(a\) data.
## Materials and Methods
### Satellite Data
#### 2.6.3 Time Series Analysis  
To further eliminate noisy data in the time series, smoothing techniques are usually applied [32]. However, these techniques need the time series to be complete or at least have a predefined seasonal signal [51]. Because we aim at predicting \(\Psi_{\text{stem}}\) from VIs without having prior information on the seasonal behavior of the vineyard, conventional smoothing techniques could not be used. Instead,we adopted a simple weekly average approach similar to a moving average on a complete time series to eliminate noisy data. We calculated the weekly average VI value (VI avg) for each vineyard. In addition, for the time series analysis the maximum and minimum VI values in each week were extracted (VI max and VI min, respectively). To further eliminate noise of background signal related to soil and/or understory vegetation, the VI avg was scaled by dividing it by the VI avg value at POS.  
Another important variable used in the time series analysis is \(\Delta\)VI, which is defined as the difference between the VI avg at POS and that at Pre, when green vegetation is absent in the vineyard. This \(\Delta\)VI represents the difference in fractional vegetation cover between the two periods due to leaf expansion and canopy development of the vines. This means that vineyards with vines of less developed canopies will display a small \(\Delta\)VI compared to those with more developed canopies. Because \(\Psi_{\text{stem}}\) is from the vines and VI is a mixed signal from vines and background signal, we would expect a negative correlation between \(\Delta\)VI and the slope of the VI-\(\Psi_{\text{stem}}\) relationship, meaning a weak VI response (steeper slope) to seasonal variations in \(\Psi_{\text{stem}}\), in vineyards with small \(\Delta\)VI (less developed canopies).
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Mode Selection
#### 7.5.2 Mode Selection Overhead  
A high amount of overhead can be incurred by the mode selection process. The overhead includes control signaling and channel estimation. Channel estimation can be generated based on the CSI of the links. However, when the CSI becomes outdated, it can greatly affect the network performance. To be exact, it is important to minimize the selection mode overhead to increase the device's lifetime.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 4 Methodology
### Step 3: Evaluation  
In the third step, the final classification layer consists of 26 neurons (matching the number of classes), with a softmax activation to output probability distributions for multi-class classification. Two fully connected (Dense) layers with 256 neurons each, ReLU activation, and L2 regularization (to prevent overfitting) refine the learned features.  
The evaluation process is conducted in three steps: compiling the model, training the model, and assessing the results. Initially, the compile() method is invoked to compile the model. Metric selection is crucial for evaluating the optimization algorithm, learning rate, loss function, and overall performance of the model in this method.  
Evaluation metrics are employed to assess the performance of a predictive statistical or machine learning model. Numerous metrics exist for testing a model, making metric selection a crucial consideration to accurately gauge the model's quality.  
The loss function primarily assesses the correspondence between predicted and actual values. Additionally, the loss function plays a crucial role in neural network performance. It also referred to as cost or error function, its purpose is to enhance optimization and minimize the cost during training.  
Various types of loss functions exist: Mean Squared Error, Absolute Mean Error, Binary Cross-Entropy, Categorical Cross-Entropy, and Logarithmic Loss. In this study, a multi-class classification problem involves, namely \"categorical cross-entropy\".  
The \"accuracy\" metric is chosen for evaluating neural network performance. Accuracy is the ratio of the total negative and positive observation results predicted by the classification result to the correct estimate results. Recall is correctly classified as a positive sample (TP) number, total positive sample count (TP + FN) ratio and it is also called TPR. Precision is the ratio of positive values to the sum of positive and negative values.