esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 2 Materials and Methods
### Detection of Feature Points with SIFT
#### 2.2.1 Detection of Scale-Space Extrema  
For the two-dimensional image \(I(x,y)\), its scale space \(L(x,y,\sigma)\) can be constituted by the convolution of the image \(I(x,y)\) and the two-dimensional Gaussian kernel function \(G(x,y,\sigma)\). The Gaussian difference pyramid \(D(x,y,\sigma)\) can be obtained by performing difference operations on images of different scales. The calculation formula is as follows:  
\[\begin{array}{l}D(x,y,\sigma)=L(x,y,k\sigma)-L(x,y,\sigma)\\ =I(x,y)*(G(x,y,k\sigma)-G(x,y,\sigma)).\end{array} \tag{8}\]  
In Equation (8), \(G(x,y,\sigma)=\frac{1}{2\pi\sigma^{2}}e^{-(x^{2}+y^{2})/2\sigma^{2}}\) is the two-dimensional Gaussian kernel function, \(\sigma\) is the variance of the two-dimensional Gaussian kernel function, \(k\) embodies different scales, and \(*\) is the convolution. After construction of the pyramid \(D(x,y,\sigma)\), the extremum point on a certain scale can be defined as the maximum or minimum point of a total of 27 pixels points in a \(3\times 3\times 3\) template centered on that point.
## 2 Materials and Methods
### Analytical Methods  
Gaseous samples for the inlet and outlet concentrations were withdrawn from the gas sampling points. The concentration of gaseous H\({}_{2}\)S was analyzed using a portable analyzer (GFM-140, Gasdata, United Kingdom), with an electrochemical sensor. The analyzer had a 10-2000 ppm measurement range and its detection limit was 10 ppm. Concentrations of less than 10 ppm were measured, using a gas detection tube (GASTEC, Japan). The concentration of gaseous NH\({}_{3}\) was quantified, using a portable detector (Gas Alert Micro 5, Honeywell, Charlotte, NC, USA) with an electrochemical sensor. The detector had a 1-1000 ppm measurement range.  
Liquid samples were collected from the sampling port installed on the electrochemical oxidation reactor, using a syringe. The total volume of sample collected during the experiment did not exceed 5% of the total aqueous solution. The concentration of aqueous H\({}_{2}\)S was measured, using an analyzer (H\({}_{2}\)S analyzer, ECH, Halle, Saxony-Anhalt, Germany), which can analyze concentrations from 0.1 to 10,000 ppm. The concentration of aqueous NH\({}_{3}\) was quantified using a dedicated analysis kit (Hach, Loveland, Colorado, USA), applying the indophenol colorimetric method.
## 4 Results and Discussions
### Limitations of the Study  
Although the simulated grocery environment proved valuable in the experimental phase of this study, the inherent limitations of replicating the real-world shopping experience pose a challenge in fully validating ML-DProSA's assistive capabilities. Despite the simulator's ability to capture various aspects of shopper behavior, it falls short of accurately representing the unpredictable and multifaceted nature of actual grocery trips. This shortcoming prevents the validation phase from comprehensively evaluating the algorithm's ability to handle the nuances and complexities of real-world grocery shopping scenarios.  
Two limitations of this study remain to be addressed. First, instances of the same item appearing in multiple, widely separated locations can distort the accuracy of cluster formation. Second, while a basic mechanism was implemented to handle changes in item placement, removal, and other instances of item movement, the implemented fix resets important data for that item, requiring the model to relearn the data to properly assign the item to a new cluster.  
Furthermore, this study did not incorporate multiple grocery store layouts into the simulation. This limited scope was intentional, as the researchers initially focused on demonstrating the algorithm's potential effectiveness. While the study successfully demonstrated that the algorithm reduces shopper dwell time, there was a possibility of a negative outcome. Including multiple grocery store layouts without first validating ML-DProSA's performance would have unnecessarily complicated the study. However, now that there is conclusive evidence of ML-DProSA's positive impact on the grocery shopping experience, future studies could explore the aforementioned aspects of the grocery shopping experience.
# Precipitation Polymerization: A Powerful Tool for Preparation of Uniform Polymer Particles
## 2 Categories of Precipitation Polymerization
### Controlled/\"Living\" Radical Precipitation Polymerization
#### 2.4.1 Atom Transfer Radical Precipitation Polymerization (ATRPP)  
ATRPP is the incorporation of an ATRP system with a PP procedure, which is simply a substitute for the conventional initiator (e.g., AIBN) with an ATRP initiator. After activation under appropriate reaction conditions, all chains are quickly initiated and grow simultaneously, leading to soluble branched oligomers at the beginning of the polymerization. The controlled characteristics of ATRPP have major roles in uniform particle growth and size [98; 99]. Efficient strategies have been continuously conducted to prepare high-quality polymer microspheres. [PERSON] et al. first introduced the ATRP mechanism into PP for the generation of uniformly \"living\" polymer microspheres with number-average diameters ranging from 0.73 to 3.25 \(\upmu\)m and polydispersity indices being typically lower than 1.01 (Figure 6) [98]. [PERSON] et al. combined the reverse ATRP mechanism with PP and obtained surface-functionalized \"living\" polymer microspheres via two-stage PP with better-controlled chain growth and size distribution, where the first stage is a conventional PP (for nucleation) and the second stage is a reverse ATRP (for particle growth) [100]. [PERSON] et al. first reported a new ATRP-based methodology, electron transfer atom transfer radical precipitation polymerization (ARGET ATRPP), in which the products are of high quality (in a size range of 1-3 \(\upmu\)m) and can be used directly in grafting-from experiments without any need for the installation of initiator moieties [101].
## 4 Experiments and Results
### Simulation Results
#### 4.2.3 Efficiency of the Leading Autonomous Vehicles Regarding Mobility and Energy  
To consider the efficiency of leading autonomous vehicles regarding the MOE evaluation--namely, average speed, fuel consumption, and emissions--the results of the MOE evaluation are used for the urban network over AV penetration rates, as shown in Figure 9. The MOE evaluation's results express the fact that the efficiency of the leading autonomous vehicles was significantly more obvious with a higher AV penetration rate. Concerning mobility, the average speed increased when the AV penetration rate was higher. As seen in Figure 9a, comparing the  
Figure 8: The spatial–temporal diagram over autonomous vehicle (AV) penetration rates.  
20% AV penetration rate case, full automation traffic increased the average speed 1.07 times. Concerning energy, fuel consumption and emissions were gradually reduced when the AV penetration rate was higher. As seen in Figure 9b,c comparing the 20% AV penetration rate case, full automation traffic decreased fuel consumption 1.09 times and emissions 1.23 times. Therefore, the efficiency of leading autonomous vehicles with respect to the MOE evaluations became much better with a higher AV penetration rate.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 3 Formulation
### Prm
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Definition** \\ \hline \(P_{start}\) & Starting position of UAV \((\text{X}_{\text{s}},\text{Y}_{\text{s}},\text{Z}_{\text{s}})\) \\ \(P_{end}\) & Ending position of UAV \((\text{X}_{\text{e}},\text{Y}_{\text{e}},\text{Z}_{\text{e}})\) \\ \(G\) & Graph \\ \(V\) & UAV’s configuration in free space (vertex) \\ \(E\) & Connection between vertices \\ \(\gamma\) & Shortest path between nodes in V \\ \(P_{l}\) & Path length \\ \(N_{P_{start}}\) & Close neighbors of Pstart \\ \(R\) & Nearest node from Pstart \\ \(R^{\prime}\) & Nearest node from R \\ \(D_{max}\) & Maximum distance \\ \(D\)(\(R\),\(R\)) & Euclidean distance between R and R’ \\ \(w(R)\) & Path complexity of R \\ \(f(R)\) & Count of failure, attempts\({}_{(\text{R})}\) \\ \(T_{1}\) & Time for free sample generation \\ \(T_{2}\) & Time to obtain a path between nodes in G \\ \(n^{\prime}\) & Number of iterations \\ \(N\) & Number of sensor nodes \\ \(S\) & Optimization parameters \\ \(h_{j}\) & Higher limit of parameters \\ \(l_{j}\) & Lower limit of parameters \\ \(r\) & Randomly selected candidate solution \\ \(N_{s}\) & Number of feasible solutions \\ \(p_{n}\) & Position of sensor nodes \\ \(H\) & Height of UAV \\ \(p\) & Velocity of UAV \\ \(T\) & Flight duration \\ \(M\) & Time slots \\ \(E_{trans}\) & Transmission energy \\ \(F_{(l)}\) & Flying duration \\ \(H_{(l)}\) & Hovering time \\ \(E_{T}\) & Total energy \\ \(W\) & Bandwidth \\ \(\beta\) & Reference signal-to-noise ratio at 1 m \\ \(\infty\) & Path loss exponent \\ \(W_{p}\) & Number of waypoints \\ \(v_{w}\) & Wind speed \\ \(r\) & Coverage radius of UAV \\ \(Min_{h}\) & Minimum possible height of UAV \\ \(\rho\) & Density of air \\ \(D\_{size_{c_{i}}}\) & Cluster data size \\ \hline \end{tabular}
\end{table} 
Table 2: List of notations. \(R\) is avoided. Subsequently, a new node from \(V\) is selected using the probabilistic scheme, as follows  
\[Prob(R)=\omega(R), \tag{4}\]  
with \(\omega(R)\) expressed as  
\[\omega(R)=\frac{\frac{f(R)}{attempts(R)}}{\sum\
olimits_{i\in V}f(i)}, \tag{5}\]  
where \(f(R)\) is the count of failure, \(attempts_{(R)}\) the number of attempts made to connect \(R\) with other nodes, and \(\sum\limits_{i\in V}f(i)\) the total failure ratio while connecting all the nodes. The PRM has a fast sampling rate and generates the shortest path within a short time. Two time-consuming phases of the PRM are during free sample generation and during the test, where the local method can yield a path between the new sample and the configuration in the graph. If \(T_{1}\) and \(T_{2}\) denote the tentative time required in two corresponding phases, then the total time required by the algorithm can be calculated as follows  
\[T_{TOT}=\omega(R)\times n\times(T1+T2),\;\;T_{1}\ll T_{2}. \tag{6}\]
# Decorrelation-Based Deep Learning for Bias Mitigation
## 3 Materials and Methods  
Our proposed method focuses on using distance correlation in the objective function to decorrelate bias from features learned by CNN and ANN architectures. To generalize our proposed method across different domains, we used different datasets with various biases and also implemented different architectures. This opens up new opportunities to utilize this proposed approach across different deep learning or neural network architecture to mitigate different types of biases.
# A Bounded Scheduling Method for Adaptive Gradient Methods
## 4 Experiments
### Experimental Setup  
We implemented these experiments on a server configured as 2 NVIDIA TITAN XP GPUs, 1 Intel I7-6800K CPU, 16G*8 DDR4, 240G SSD and 1T SATA. These experiments were coded in PyTorch, each experiment was run three times and we chose the best one.  
The algorithms under consideration have many hyper-parameters and the setting of hyper-parameters has a great influence on the performance of the optimization algorithm. Here we will describe how we adjust hyper-parameters. We use a logarithmical grid search on a large space of learning rate and then fine-tune it, the results are shown in Table 1. Specifically, the learning rate of each algorithm is adjusted as follows:  
* **SGD(M)** We used SGDM for image classification tasks and SGD for language modeling tasks. When using SGDM, we set the momentum parameter to the default value 0.9. we roughly tuned the learning rate for SGD(M) on a logarithmic scale from \(10^{-3}\) to \(10^{2}\) first and then fine-tune the learning rate.
* **Adagrad** The general learning rates used for Adagrad are chosen from {0.0005, 0.001, 0.005, 0.01, 0.1}.
* **Adam** The general learning rates used for Adam were chosen from {0.0005, 0.001, 0.005, 0.01, 0.05}. We set {\(\beta_{1}\), \(\beta_{2}\)} as the recommended default value {0.9, 0.999}. The perturbation value \(\epsilon\) is set to \(10^{-8}\).
* **Bsadam** We used the same hyper-parameter settings for Adam. The upper and lower bounds were determined by learning rate range test and the rise and decrease of bounds are linear.  
Other hyper-parameters such as batch size and weight decay use the default values recommended by the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Set** & **Model** & **Network Type** & **SGD(M)** & **Adagrad** & **Adam** & **Bsadam** \\ \hline MNIST & 1-Layer Perceptron & Feedforward & 0.1 & 0.001 & 0.001 & (0.01,0.5) \\ MNIST & 1-Layer Convolutional & Convolutional & 0.1 & 0.001 & 0.001 & (0.01,0.5) \\ CIFAR-10 & ResNet & Deep Convolutional & 0.1 & 0.001 & 0.001 & (0.01,0.5) \\ CIFAR-10 & DenseNet & Deep Convolutional & 0.1 & 0.001 & 0.001 & (0.01,0.5) \\ CIFAR-10 & SENet & Deep Convolutional & 0.1 & 0.001 & 0.001 & (0.01,0.5) \\ CIFAR-100 & ResNet & Deep Convolutional & 0.3 & 0.001 & 0.001 & (0.05,1) \\ CIFAR-100 & DenseNet & Deep Convolutional & 0.1 & 0.001 & 0.001 & (0.05,1) \\ CIFAR-100 & SENet & Deep Convolutional & 0.1 & 0.001 & 0.001 & (0.01,0.5) \\ Penn Treebank & 1-Layer LSTM & Recurrent & 50 & 0.001 & 0.001 & (5,100) \\ Penn Treebank & 2-Layer LSTM & Recurrent & 50 & 0.001 & 0.001 & (5,100) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarizing the models and the data sets utilized for our experiments. The optimal hyperparameters for stochastic gradient descent (SGD) with momentum (M), Adagrad, Adam and Bsadam for all experiments are also listed.
## 3 Results
### HSI Camera  
When using hyperspectral cameras, an additional dimension (wavelength) is added to the processing. Just as with UV lamps, uneven illumination presents a challenge in hyperspectral imaging. To address this, we can apply the same approach using our innovative algorithm, allowing us to concentrate on a specific line. This technique effectively mitigates the issue of uneven lighting, enhancing the accuracy and quality of hyperspectral imaging results. In Figure 11, the HS camera displays three distinct bands or wavelengths, illustrating the presence of uneven illumination across the image. This is particularly noticeable as the bottom part of the image appears darker. This variation in brightness across different wavelengths not only highlights the challenges in maintaining consistent illumination in hyperspectral imaging but also underscores the importance of advanced processing techniques to compensate for these inconsistencies, ensuring that the final image accurately represents the subject under observation. The object shown is a piece of fabric with weaving imperfections and a black and white woven pattern. This pattern makes it difficult to examine with an RGB camera, as shown in Figure 12. When selecting a specific band, the woven black and white pattern will not be visible and the weaving will be clearer.  
Figure 13 shows the temporal matrix for a single hyperspectral band. It can be seen that not only the choice of wavelength is important but also the temporal position to obtain a high contrast between the weaving defects and the normal tissue. In Figure 13c, for example, the illumination of this spatial position is too large, causing the object to be over-saturated.  
Figure 11: Three separate bands from the HS camera for a single frame in the sequence, before applying the novel algorithm. The identification of weaving errors is complicated through the heterogeneous illumination. (**a**) Wavelength: 677 nm; (**b**) wavelength: 776 nm; (**c**) wavelength: 959 nm.  
Figure 10: Three frames from the temporal matrix: (**a**) frame 1; (**b**) frame 50; (**c**) frame 100. Notice that due to heterogeneous illumination from the UV light source, the different frames of the temporal matrix show a different brightness value.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 4 Eviction Set Construction Algorithms
### Eviction Set Construction Algorithms
#### 4.2.2 Group Testing Algorithm  
The _group testing_ (GT) algorithm [48] is an optimization of the single address elimination method (see Algorithm 2). In the group testing algorithm, the initial candidate set is split into \(W+1\) groups (line two). The algorithm iterates over all groups and tests whether \(E\) without the group still evicts \(x\) (lines three and four). When this is true, the group is removed from \(E\) (line five). In the other case, the algorithm tests other groups until it finds one that satisfies this requirement. Then, \(E\) is divided into \(W+1\) groups again and the same process starts over. This is repeated until there are \(W+1\) elements left in \(E\) (line one). In this way, an entire group of addresses can be eliminated per iteration instead of just one element, as in the single address elimination algorithm. The group testing method requires \(\mathcal{O}(W^{2}n)\) cache accesses.  
```
Input:\(E\)=candidate set, \(x\)=target address Output:\(E\)=minimal eviction set
1 while\(|E|>W\)do
2\(\{\Upsilon_{0}\ldots\Upsilon_{W+1}\}\leftarrow\) split \(E\) into \(W+1\) groups for\(i\gets 1\)to\(W+1\)do
3 if\(E\backslash\{T_{i}\}\) evict \(x\)then
4\(E\gets E\backslash\{T_{i}\}\)
5 end if
6
7 end if
8 return\(E\)
```  
**Algorithm 2**The group testing algorithm
## 2 Materials and Methods
### Materials
#### 2.1.2 6061-T6 Aluminum Alloy  
Aluminum alloys are widely utilized across various industries due to their lightweight nature, high strength, and excellent corrosion resistance. However, in the context of sawing mechanisms, particularly those subjected to significant stress and operating in cold climates, aluminum alloys are not the optimal choice:  
1. Strength and Rigidity: While aluminum alloys exhibit a high specific strength (ratio of strength to weight), their absolute strength, such as yield strength and tensile strength, is generally lower than that of Q345. During sawing processes, particularly when dealing with hard materials or requiring high stability, aluminum alloys may not meet the necessary requirements.
2. Low-Temperature Performance: Although aluminum alloys do not become extremely brittle at low temperatures like some metals, they still exhibit a notable decrease in strength as the temperature drops. In contrast, Q345 maintains relatively high strength and toughness even at low temperatures, making it more suitable for operational demands in cold climates.  
6. Wear Resistance: Sawing mechanisms frequently come into contact with materials during operation, leading to wear and tear. Aluminum alloys possess relatively poor wear resistance, which may result in faster wear and more frequent maintenance requirements. In contrast, Q345 exhibits better wear resistance, contributing to the extended service life of sawing mechanisms.  
In summary, Q345, as a material for sawing mechanism, exhibits notable advantages. Its superior mechanical properties, including high yield strength and tensile strength, ensure the stability and safety of the saw-cutting mechanism under considerable stress. Moreover, Q345 retains and adapts to various working conditions. Additionally, its excellent corrosion resistance and processability enable Q345 to withstand harsh working environments and be easily fabricated into various shapes and structures of saw-cutting mechanism components, thereby, enhancing the overall structural stability and reliability. These advantages render Q345 an ideal material for the manufacturing of high-performance saw-cutting mechanisms.  
The definition of material properties is presented in Table 1.
## 4 Limitations  
A few limitations of this review must be acknowledged. First, we used only three electronic scientific databases (PubMed, Scopus, and Web of Science) and obtained reference lists to search for systematic reviews and/or meta-analyses, whereas we did not search any other electronic or nonelectronic databases. Second, only four studies were specifically investigating the outcomes after executing conservative nonpharmacological treatments for treating IAT. Third, only 25 articles included all the components of PICO so the authors were able to report the difference between the experimental and control group(-s). Fourth, the risk of biases of the included studies in peaked articles was not assessed in 16 articles, while it was just partially assessed in 7 out of 50 reviews. Fifth, meta-analysis was conducted in just 14 out of 50 included articles. Sixth, when the authors of the included articles did not conduct a meta-analysis, we had to overlook two out of seven critical domains (item 11 and item 15) when measuring the methodological quality of the included studies (that happened in 36 out of 50 articles). Seventh, publication biases were sufficiently investigated and discussed in just three included articles, while when assessing the methodological quality of the other 11 articles with meta-analysis, 7 of them had fewer than 10 included studies in the meta-analysis, which is the minimum to perform the test for publication biases, so the articles were positively assessed when the authors investigated and discussed the publication biases. Finally, because of the lack of similar research with the same outcome measures, and due to different exercises or exercise protocols, we could not carry out the meta-analysis to find out the heterogeneity of the results with which we would conclusively confirm the obtained data.
## 3 Results
### ET Simulation Results and Temporal and Spatial Evolution
#### 3.2.2 Temporal and Spatial Evolution Trend of ET  
Analysis of the monthly scale REG_ET data revealed that the annual average ET during the growing season of the QTP from 1982 to 2018 was 355.44 mm\(\cdot\)yr\({}^{-1}\), with a growth rate of 0.817 mm\(\cdot\)yr\({}^{-2}\), indicating a significant upward trend consistent with findings by [PERSON] [68]. Spatially, the annual mean ET gradually decreases from southeast to northwest (Figure 4a). The standard deviation of ET was highest in the west, reflecting greater variability, while the central region exhibited the least variability (Figure 4b). The spatial distribution of the growth trend shows a significant increase across the plateau, with rapid growth in some southeastern areas of Tibet and a smaller area in western Tibet; however, the spatial distribution pattern of growth is not distinct (Figure 4c).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **B** & **Beta** & **T** & **Sig** & **VIF** & **F** & **Adjusted R\({}^{2}\)** \\ \hline (constant term) & 7.779 & & 2.449 & 0.015 & & 91.518 & 0.601 \\ GLASS & 0.249 & 0.178 & 1.738 & 0.083 & 7.907 & & \\ PTJPL & 0.062 & 0.038 & 0.545 & 0.586 & 3.678 & & \\ PEW & 0.12 & 0.075 & 0.905 & 0.366 & 5.22 & & \\ S & 0.125 & 0.129 & 1.601 & 0.111 & 4.932 & & \\ SiTHv2 & 0.475 & 0.406 & 4.514 & 0 & 6.11 & & \\ \hline \end{tabular}
\end{table}
Table 4: Multiple linear regression model.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **ET mean** & **ET trend** & **RMSE** & **R\({}^{2}\)** \\  & **(mm\(\cdot\)yr\({}^{-1}\))** & **(mm\(\cdot\)yr\({}^{-2}\))** & **RMSE** & **R\({}^{2}\)** \\ \hline GLASS & 212.66 & 0.518 *** & 30.6 & 0.54 \\ PEW & 273.33 & 0.744 *** & 29.05 & 0.48 \\ PTJPL & 198.80 & 0.164 ** & 36.11 & 0.44 \\ SET & 335.99 & 0.526 *** & 26.35 & 0.5 \\ SiTHv2 & 345.09 & 1.069 *** & 22.26 & 0.57 \\ \hline \end{tabular} Notes: \(p<0.01\)***, \(p<0.05\)**.  
\end{table}
Table 3: Differences in the annual average and precision of different ET datasets.