esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 5 Computational Results
### Analysis of Different Robustness Measures  
In this section, the performance of different robustness measures is analyzed. The Monte Carlo simulation is adopted to approximate the realized schedules. Robustness was indicated by the average value of 5000 samples, as follows:  
\[QR_{MC}=\frac{\sum_{s=1}^{5000}\big{(}C_{max}(\delta_{r})-C_{max}\big{(}\delta_ {pm}\big{)}\big{)}}{5000} \tag{14}\]  
\[SR_{MC}=\frac{\sum_{s=1}^{5000}\sum_{i=1}^{n}\sum_{j=1}^{m}\big{(}C_{ij}(\delta _{r})-C_{ij}\big{(}\delta_{pm}\big{)}\big{)}}{5000} \tag{15}\]  
To verify the accuracy of analytical approximation calculation, \(SRD\) (\(SR_{AC},SR_{MC}\)) and \(QRD\) (\(QR_{AC},QR_{MC}\)) are defined to investigate the difference between the two measures. The smaller the value of \(SRD\) (\(SR_{AC},SR_{MC}\)), the more accurate the measurement result of the \(M_{AC}\) for the solution's robustness. The same principle applies to \(QRD\) (\(QR_{AC},QR_{MC}\)).  
\[SRD(SR_{AC},SR_{MC})=\frac{|SR_{AC}-SR_{MC}|}{SR_{MC}}\times 100\% \tag{16}\]  
\[QRD(QR_{AC},QR_{MC})=\frac{|QR_{AC}-QR_{MC}|}{E[C_{max}(\delta_{r})]}\times 100\% \tag{17}\]  
Table 2 summarizes the experimental results. From Table 2, it is clear that all Mean(\(SRD\))s of \(SR_{AC}\) are close to that of \(SR_{MC}\) for all tested benchmarks. For the case of \(tc=20\) and \(\theta=0.5\), the means of \(SRD(SR_{AC},SR_{MC})\) have a maximum value of 7.75%. In addition, all the standard deviations of \(SRD(SR_{AC},SR_{MC})\) are less than 4.14%. Similar results can also be found for \(QRD(QR_{AC},QR_{MC})\), where the values are less than 2.27%. The standard deviations of \(QRD(QR_{AC},QR_{MC})\) is within [0.29%, 1.42%]. The experimental results show that \(SR_{AC},QR_{AC}\) can be valid substitution for \(SR_{MC}\),\(PR_{MC}\). Table 2 also provides the results of CPU time in seconds for measuring the robustness based on \(SR_{AC}\) and \(SR_{MC}\). It shows that the mean CPU time based on \(SR_{AC}\) is much smaller than that based on \(SR_{MC}\); the time ratio \(\eta\) between \(T_{AC}\) and \(T_{MC}\) is within [0.35%, 0.74%]. Meanwhile, as the machine failure rate and CM time increase, the computing time required by \(M_{MC}\) increases more significantly than that required by \(M_{AC}\). Therefore, \(M_{AC}\) has higher computing efficiency than \(M_{MC}\).  
The distributions of \(SRD(SR_{AC},SR_{MC})\) are available in Figure 7a by a quartile graph. Figure 7a shows that the largest \(SRD(SR_{AC},SR_{MC})\) of all tested benchmark problems is below 14.40%. In addition, it can also be found that \(SRD(SR_{AC},SR_{MC})\) will decrease significantly with the reduction of failure probability. Figure 7b shows that \(QRD(QR_{AC},QR_{MC})\) is stable, but with the increase of \(tc\), the fluctuation of \(QRD(QR_{AC},QR_{MC})\) becomes larger. The experimental results show that the performance of \(M_{AC}\) is better when the failure probability and CM time are small.
## 3 Optimization Agent Model and Sensitivity Analysis
### Sensitivity Analysis
#### 3.3.1 Ejection Speed  
During the ejection process of the unloader, the oil inlet pressure was the main power source, and the rigidity of the reset spring was the main resistance source. As shown in Figure 6a, when the oil inlet pressure was constant, the increase in spring stiffness caused a decrease in the ejection speed. The ejection speed at high inlet pressure was higher than that at low pressure, but the change rate of the ejection speed was almost the same, which indicated that there was no interaction between the spring stiffness and oil inlet pressure. As shown in Figure 6b, when the spring stiffness was constant, the return pressure increased slightly, which resulted in a slight increase in the ejection speed. This was because the inlet flow increased, owing to the increase in return pressure, which indirectly resulted in a slight increase in pressure (Figure 8), but the high and low curves  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Evaluation Index**} & \multicolumn{4}{c}{**Objective Function**} \\ \cline{3-6}  & & _Error\_V_ & _Error\_W_ & \(\mathbf{v_{min}^{\mathbf{\pi}}}\) & \(\mathbf{v_{min}^{\mathbf{\ell}}}\) \\ \hline
90 & \(R^{2}\) & 0.933 & 0.999 & 0.997 & 0.999 \\  & Adjusted \(R^{2}\) & 0.918  
were almost parallel. Therefore, there was no interaction between the spring stiffness and return pressure on the ejection speed. The return pressure was constant, and the increase in inlet pressure caused an increase in ejection speed (Figure 6c), and the two also had no interaction with the ejection speed. Under different loads, the design variables had the same effect on the ejection speed, which meant that the ejection of the unloader was not affected by the load setting.
## 3 The Proposed Evaluation Method
### Preprocessing Evaluation Data  
Since differences in dimensions and value ranges among performance indicators may affect evaluation results, preprocessing the data is essential. A common method is standardization, which transforms diverse data types into a uniform [0; 1; 1] scale.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 4 Challenges Faced in the Development of AM Metal Mirror
### Dimensional Stability
#### 4.2.1 Material Characteristics  
The material characteristics have a vital influence on the dimensional stability of mirrors. The characteristic parameters include the characteristic thermal parameters: CTE, specific heat, thermal conductivity, and thermal diffusion coefficient; force characteristics include the parameters: stiffness, Young's modulus, specific stiffness, micro yield strength, and Poisson's ratio. Among them, the micro yield strength and CTE are essential factors for dimensional stability. The materials can be selected from those introduced in Section 2.2.1 to ensure dimensional stability. Moreover, the same material of optical and mechanical structure can realize the optimal internalization of optical systems.
###### Abstract  
Purpose: Perioperative pregabalin administration has been found to reduce the risk of persistent pain after a variety of surgical procedures. However, this approach has not been tested in relation to eye surgery. As such, the purpose of this study was to evaluate whether perioperative pregabalin can reduce the presence of dry eye (DE) symptoms, including eye pain, six months after laser-assisted in situ keratomileusis (LASIK). Methods: Prospective, masked, randomized single-center pilot study. Patients were treated with either pregabalin (oral solution of pregabalin 150 mg twice daily, first dose prior to surgery, continued for a total of 28 doses over 14 days) or placebo solution. The primary outcome was dry eye symptoms as measured by the Dry Eye Questionnaire 5 (DEQ-5). Secondary outcome measures included pain-related eye symptoms. Results: In total, 43 individuals were enrolled in the study and randomized to pregabalin (\(n=21\)) or placebo (\(n=22\)). Of those, 42 individuals completed the final visit after six months of follow-up. Some differences were noted between the two groups at baseline, including a higher frequency of females in the pregabalin group. At 6-months, there were no significant differences in the percentage of patients with DE symptoms (DEQ5 \(\geq\) 6, 57% vs. 33%, \(p=0.14\)), DE symptom severity (DEQ5, 6.6 \(\pm\) 5.0 vs. 4.5 \(\pm\) 4.2, \(p=0.14\)), ocular pain intensity (numerical rating scale, 1.10 \(\pm\) 1.48 vs. 0.38 \(\pm\) 0.97, \(p=0.08\)), or neuropathic pain complaints (Neuropathic Pain Symptom Inventory-Eye, 2.81 \(\pm\) 4.07 vs. 3.14 \(\pm\) 5.85, \(p=0.83\)) between the pregabalin and control groups. Ocular signs were likewise similar between the groups, and of note, did not correlate with DE symptoms. The strongest predictor of DE symptoms six months post-surgery was the presence of DE symptoms prior to surgery. Conclusions: Perioperative pregabalin did not reduce the frequency or severity of DE symptoms at a six month follow-up after LASIK in this small pilot study.  
dry eye symptoms; dry eye; ocular pain; pregabalin; refractive surgery; LASIK
## 2 Materials and Methods
### Twitter Data Extraction and Sentiment Analysis  
The keywords on which this study has focused its interest are related to the following vaccines and diseases:  
- Invasive meningococcal disease (\"EMI\" in Spanish): Bexsero, Trumenba, Nimernrix.  
- Invasive pneumococcal disease (\"ENI\" in Spanish).  
- Influenza.  
- Hepatitis.  
- Rotavirus: Rotarix, Rotateq.  
- Measles (\"Sarampion\" in Spanish) and MMR (\"Triple virica\" in Spanish).  
- Sepsis.  
- Whooping cough (\"Tosferina\" in Spanish).  
- Chickenpox (\"Variecia\" in Spanish): Varivax, Varilrix; and Shingles (\"Zoster\" in Spanish).  
- Human papillomavirus infection (\"VPH\" in Spanish): Cervarix, Gardasil.  
Although Instagram data were also considered for the current methodology, the amount of data compared to Twitter was very low so for the sake of quality models generation, Instagram data were discarded.  
Twitter data were obtained by using the official API (Application Programming Interface), from which all the required information for the current study could be extracted. The execution of the extraction process obtained a total of 1,028,742 tweets, from which 318,302 were different/original tweets. The number of retweets was 10,440 and the number of quotes of the original tweets was 65,806. The keywords used in the search were mentioned 1,187,046 times. After extracting all the tweets, they were all submitted to a cleaning process in order to get a consistent and understandable version of the texts. Hashtags (#), user mentions (@), URLs, email addresses, retweet markers (RT:) and emojs and other non-representable characters were removed.  
Sentiment analysis examines the content of free-text natural language to identify opinions and emotions. One of the principals and most important sources of text comes from the Internet and it is social media. Sentiment analysis from social media has been a common topic on research and diverse software tools have been developed to automatize its processes, enabling the classification of large numbers of texts [51]. Methods may focus on the polarity of texts (\"positive\", \"negative\", \"neutral\") but also can be centered on feelings and emotions (\"angry\", \"happy\", \"sad\") or intentions (\"interested\", \"not interested\"). Sentiment analysis approaches have been categorized in three main groups: knowledge-based, statistical and hybrid methods [55]. Efforts have been made to extract sentiments associated with polarities of positive or negative for specific subject of a text, instead of classifying the whole text as positive or negative [56].
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 3 Proposed Methodology
### Feature Extraction CNN-Based Model
```
0: Input mammogram image \(f(x,y)\)
0: Binary class label (1 = Malignant, 0 = Benign)
1: Step 1: Preprocessing:
2: Crop 5% from the image borders to remove outer artifacts
3: Apply intensity filtering using Equation (1) and enhance contrast using CLAHE
4: Compute the center of mass and extract a \(256\times 256\) ROI centered at this location
5: Step 2: Phase-Based Feature Representation:
6: Compute the Fourier transform \(F(u,v)\) of the ROI
7: Apply Poisson band-pass filter using Equation (3)
8: Compute the monogenic signal using Equation (2)
9: Obtain inverse transforms \(f_{bp1}(x,y)\), \(f_{bp2}(x,y)\), \(f_{bp}(x,y)\) using Equation (6)
10: Compute: * Local phase \(\varphi(x,y)\) with Equation (7): * Local orientation \(\theta(x,y)\) with Equation (8)
11: Phase congruency \(PC(x)\) using Equation (9)
12: Construct a 3-channel image using \(\varphi(x,y)\), \(\theta(x,y)\), and \(PC(x)\)
13: Step 3: Feature Extraction with CNN:
14: Input the 3-channel image into a pre-trained EfficientNetV2 variant (S, M, L, or XL)
15: Remove the final classification layers
16: Extract a 1280-dimensional vector from the last fully connected layer
17: Step 4: Dimensionality Reduction:
18: Construct the reduced feature descriptor \(S_{FC}=[\mu,\sigma^{2},\sigma,\text{max},\text{min}]\) from the original 1280-dimensional vector by computing the statistics defined in Equations (10)-(14)
19: Step 5: Classification:
20: Classify \(S_{FC}\) using ensemble learning methods: Voting, Stacking, Bagging, and Boosting
21: Return predicted class label
```  
**Algorithm 1** Proposed method for breast cancer detection
## 3 Results
### Geographical Distribution of Natural and Man-made (Technological) Disasters
Similarly, Africa's significant number of man-made (technological) disasters indicates a need for improved technological safety and infrastructure development (Table 2 and Figure 3). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Disaster Type**} & \multicolumn{2}{c}{**North**} & \multirow{2}{*}{**Asia**} & \multirow{2}{*}{**Africa**} & \multirow{2}{*}{**Europe**} & \multicolumn{2}{c}{**South**} & \multirow{2}{*}{**Oceania**} & \multicolumn{2}{c}{**Total**} \\  & & & & & & & & & & & & & & \\  & **(n)** & **(\%)** & **(n)** & **(\%)** & **(n)** & **(\%)** & **(n)** & **(\%)** & **(n)** & **(\%)** & **(n)** & **(\%)** \\ \hline Chemical spill & 47 & 0.18 & 20 & 0.08 & 4 & 0.02 & 29 & 0.11 & 3 & 0.01 & 1 & 0.00 & 104 & 0.40 \\ Collapse (industrial) & 3 & 0.01 & 88 & 0.34 & 70 & 0.27 & 7 & 0.03 & 15 & 0.06 & 0 & 0.00 & 183 & 0.71 \\ Collapse (miscellaneous) & 29 & 0.11 & 156 & 0.60 & 61 & 0.24 & 30 & 0.12 & 25 & 0.10 & 2 & 0.01 & 303 & 1.18 \\ Explosion (industrial) & 57 & 0.22 & 509 & 1.97 & 59 & 0.23 & 108 & 0.42 & 32 & 0.12 & 4 & 0.02 & 769 & 2.98 \\ Explosion (miscellaneous) & 17 & 0.07 & 118 & 0.46 & 40 & 0.15 & 35 & 0.14 & 10 & 0.04 & 0 & 0.00 & 220 & 0.86 \\ Fire (industrial) & 22 & 0.09 & 137 & 0.53 & 15 & 0.06 & 35 & 0.14 & 7 & 0.03 & 0 & 0.00 & 216 & 0.85 \\ Fire (miscellaneous) & 111 & 0.43 & 395 & 1.53 & 117 & 0.45 & 117 & 0.45 & 42 & 0.16 & 7 & 0.03 & 789 & 3.05 \\ Fog & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 \\ Gas leak & 13 & 0.05 & 38 & 0.15 & 0 & 0.00 & 10 & 0.04 & 0 & 0.00 & 1 & 0.00 & 62 & 0.24 \\ Industrial accident & 7 & 0.03 & 89 & 0.34 & 16 & 0.06 & 5 & 0.02 & 8 & 0.03 & 1 & 0.00 & 126 & 0.48 \\ Infestation & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 \\ Mass movement (dry) & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 & 0 & 0.00 \\ Miscellaneous accident & 23 & 0.09 & 129 & 0.50 & 68 & 0.26 & 31 & 0.12 & 19 & 0.07 & 1 & 0.00 & 813 & 2.10 \\ Oil spill & 3 & 0.01 & 2 & 0.01 & 0 & 0.00 & 2 & 0.01 & 1 & 0.00 & 0 & 0.00 & 24 & 0.03 \\ Poisoning & 6 & 0.02 & 50 & 0.19 & 6 & 0.02 & 10 & 0.04 & 4 & 0.02 & 0 & 0.00 & 228 & 0.29 \\ Radiation & 1 & 0.00 & 4 & 0.02 & 0 & 0.00 & 0 & 0.00 & 1 & 0.00 & 0 & 0.00 & 18 & 0.01 \\ Rail & 74 & 0.29 & 288 & 1.11 & 111 & 0.43 & 122 & 0.47 & 15 & 0.06 & 5 & 0.02 & 1845 & 2.38 \\ Road & 168 & 0.65 & 1073 & 4.15 & 1129 & 4.37 & 159 & 0.62 & 338 & 1.31 & 5 & 0.02 & 8616 & 11.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Measurement of Head-Related Transfer Functions: A Review
## 2 Principle of HRTF Measurements
### Microphone Position and In-Ear Microphones
Artificial heads (or dummy heads, head-and-torso systems) such as KEMAR (GRAS Sound & Vibration A/S, Holte, Denmark), Neumann KU-100 (Georg Neumann GmbH, Berlin, Germany), HMS IV (HEAD acoustics GmbH, Herzogenrath, Germany), and B & K 4128 (Bruel & Kjaer, Naerum, Denmark), are widely employed for measuring non-individual acoustic impulse responses and recording binaural sounds. Compared to human subjects, dummy heads show some advantages in the measurement, e.g., measurement errors due to unconscious movements or breathing can be avoided, measurement results are highly repeatable, and a long-term measurement is possible. Dummy heads are therefore suitable for comparing different HRTF measurement systems or methods.
# Mean Estimation on the Diagonal of Product Manifolds
## 2 Background
### Weighted Diffusion Mean  
The diffusion mean [4,5] was introduced as a geometric mean satisfying the probabilistic property of the Euclidean expected value, specifically as the starting point of a Brownian motion that is most likely given observed data. This results in the diffusion \(t\)-mean definition  
\[\mu_{t}=\operatorname*{arg\,min}_{p\in M}\mathbb{E}[-\log p_{t}(p,X)], \tag{3}\]where \(p_{t}(\cdot,\cdot)\) denotes the transition density of a Brownian motion on \(M\). Equivalently, \(p_{t}\) denotes the solution to the heat equation \(\partial u/\partial t=\frac{1}{2}\Delta u\), where \(\Delta\) denotes the Laplace-Beltrami operator associated with the Riemannian metric. The definition allows for an interpretation of the mean as an extension of the Frechet mean due to [PERSON]'s result stating that \(\lim_{t\to 0}-2t\log p_{t}(x,y)=d(x,y)^{2}\) uniformly on compact sets disjoint from the cut-locus of either \(x\) or \(y\)[11].  
Just as the Frechet mean, the diffusion mean has a weighted version, and the corresponding estimator of the weighted diffusion \(t\)-mean is given as  
\[\hat{\mu}_{t}=\operatorname*{arg\,min}_{p\in M}\sum_{i=1}^{n}-\log p_{t/w_{i}} (p,x_{i}). \tag{4}\]  
Please note that the evaluation time is here scaled by the weights. This is equivalent to scaling the variance of the steps of the Brownian motion [12].  
As closed-form expressions for the heat kernel are only available on specific manifolds, evaluating the diffusion \(t\)-mean often rely on numerical methods. One example of this is using bridge sampling to numerically estimate the transition density [9; 13]. If a global coordinate chart is available, the transition density can be written in the form (see [14; 15])  
\[p_{T}(z,v)=\sqrt{\frac{\det g(v)}{(2\pi T)^{2}}}e^{-\frac{\left[\beta(z)(z-v) \right]^{2}}{2T}}\mathbb{E}[\varphi], \tag{5}\]  
where \(g\) is the metric matrix, \(a\) a square root of \(g\), and \(\varphi\) denotes the correction factor between the law of the true diffusion bridge and the law of the simulation scheme. The expectation over the correction factor can be numerically approximated using Monte Carlo sampling. The correction factor will appear again when we discuss guided bridge proposals below.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 1 Introduction
* Design SCoRe, an \"on-demand scheme for joint Scheduling of Command and Responses\", on multihop low-power wireless networks to improve both reliability and latency simultaneously. * Implement SCoRe on a real low-power embedded platform, and evaluate it through simulations and testbed experiments on 31 devices. The remainder of this paper is organized as follows. Section 2 discusses the related work in the literature, and Section 3 motivates our work by showing why it is necessary to consider both command dissemination and response collection phases \"jointly\". Then, Section 4 describes our proposed design of SCoRe, and Section 5 presents the evaluation results. We discuss the potentials of SCoRe in Section 6, and conclude the paper in Section 7.
# Voting-Based Document Image Skew Detection
## 2 Skew Detection Algorithms
### Projection Profiling Row Align  
#### 2.2.1 Mechanism  
The projection profiling method [26] computes histograms on the vertical axis of the document, accumulating horizontal scanline black pixel count, assuming the text is black on white background. The algorithm swipes a scanline from the top to the bottom of the document, which writes the number of black pixels it contains in a buffer, at an index proportional to its current vertical position in the document.  
We refer to this buffer as the histogram or the scanline pixel count buffer. Its size is fixed, is set by the user and is referred to as the histogram resolution.  
The histogram will present the maximum variance when the text is skew-free, because of scanlines containing complete horizontal slices of lines of text (above the mean), and scanlines completely containing whitespace (completely below the mean). In other words, the skew angle is computed as the angle in the SASS which produces maximal scanline variance. The process is illustrated in Figure 4.  
Figure 3: Two examples of frequency domain projection profiling, deskewing by variance maximization for each axis, i.e., finding the angle \(\Theta_{\text{deskew}}=\text{argmax}_{\Theta}(\sigma^{2}(\text{H}(\Theta))+ \sigma^{2}(\text{H}^{-}(\Theta))\).  
Variance \(\sigma^{2}\) of the histogram \(H\) of size \(N\) is given by the equation:  
\[\sigma^{2}(H(\theta))=\frac{1}{N}\sum_{k=1}^{N}\bigl{(}H_{k}-\overline{H}\bigr{)} ^{2} \tag{2}\]  
where \(H_{k}\) is cycling through all connected components axis-aligned bounding boxes (AABBs), the number of top or bottom points that lie on the same horizontal scanline at a document height correspondent to position k in the histogram. As the resolution of H is fixed, k is not equal to the y coordinate of the points in document space. \(\overline{H}\) is the mean of \(H\) and \(\theta\) is the angle by which the points are rotated.